diff --git a/.circleci/config.yml b/.circleci/config.yml index 35b9bdee375..5e939431ab5 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -68,14 +68,42 @@ setup_env: &setup_env name: "Setup environment" command: ./build-system/scripts/setup_env "$CIRCLE_SHA1" "$CIRCLE_TAG" "$CIRCLE_JOB" "$CIRCLE_REPOSITORY_URL" "$CIRCLE_BRANCH" "$CIRCLE_PULL_REQUEST" -# This step is used to save logs from various barretenberg test to the workspace so that they can be used later to parse benchmark values out of them -save_logs: &save_logs - persist_to_workspace: - root: /tmp/test-logs - paths: - - ./* - jobs: + # Noir + noir-x86_64: + docker: + - image: aztecprotocol/alpine-build-image + resource_class: small + steps: + - *checkout + - *setup_env + - run: + name: "Build" + command: cond_spot_run_build noir 32 + + noir-arm64: + docker: + - image: aztecprotocol/alpine-build-image + resource_class: small + steps: + - *checkout + - *setup_env + - run: + name: "Build" + command: cond_spot_run_build noir 32 arm64 + + noir-ecr-manifest: + machine: + image: ubuntu-2204:2023.07.2 + resource_class: medium + steps: + - *checkout + - *setup_env + - run: + name: "Create ECR manifest" + command: create_ecr_manifest noir x86_64,arm64 + + # Barretenberg barretenberg-wasm-linux-clang: docker: - image: aztecprotocol/alpine-build-image @@ -152,7 +180,6 @@ jobs: - run: name: "Test" command: cond_spot_run_test barretenberg-x86_64-linux-clang-assert 32 ./scripts/run_tests 1 stdlib-tests - - *save_logs barretenberg-dsl-tests: docker: @@ -164,7 +191,6 @@ jobs: - run: name: "Test" command: cond_spot_run_test barretenberg-x86_64-linux-clang-assert 32 ./scripts/run_tests 1 dsl_tests - - *save_logs barretenberg-tests: docker: @@ -176,7 +202,6 @@ jobs: - run: name: "Test" command: cond_spot_run_test barretenberg-x86_64-linux-clang-assert 32 ./scripts/bb-tests.sh - - *save_logs barretenberg-honk-tests: docker: @@ -188,7 +213,6 @@ jobs: - run: name: "Test" command: cond_spot_run_test barretenberg-x86_64-linux-clang-assert 32 ./scripts/run_tests 1 honk_tests - - *save_logs barretenberg-proof-system-tests: docker: @@ -200,7 +224,6 @@ jobs: - run: name: "Test" command: cond_spot_run_test barretenberg-x86_64-linux-clang-assert 32 ./scripts/run_tests 1 proof_system_tests - - *save_logs barretenberg-stdlib-recursion-ultra-tests: docker: @@ -212,7 +235,6 @@ jobs: - run: name: "Test" command: cond_spot_run_test barretenberg-x86_64-linux-clang-assert 32 ./scripts/run_tests 3 stdlib_recursion_tests --gtest_filter=-*turbo* - - *save_logs barretenberg-join-split-tests: docker: @@ -224,20 +246,6 @@ jobs: - run: name: "Test" command: cond_spot_run_test barretenberg-x86_64-linux-clang-assert 32 ./scripts/run_tests 3 join_split_example_proofs_join_split_tests --gtest_filter=-*full_proof* - - *save_logs - - # barretenberg-benchmark-aggregator: - # docker: - # - image: aztecprotocol/alpine-build-image - # resource_class: small - # steps: - # - attach_workspace: - # at: /tmp/test-logs - # - *checkout - # - *setup_env - # - run: - # name: "Test" - # command: ./scripts/ci/store_test_benchmark_logs $AZTEC_GITHUB_TOKEN barretenberg-acir-tests-bb: docker: @@ -315,7 +323,6 @@ jobs: - run: name: "Test" command: cond_spot_run_test circuits-x86_64-linux-clang-assert 32 ./scripts/run_tests 1 x86_64 scripts/a3-tests -*.skip* - - *save_logs l1-contracts: machine: @@ -381,7 +388,7 @@ jobs: - *setup_env - run: name: "Build and test" - command: build aztec-sandbox false x86_64 + command: build aztec-sandbox aztec-sandbox-arm64: machine: @@ -396,7 +403,7 @@ jobs: # out to eu-west2 despite the image being locally tagged, resulting in unauthorized 401. Weird docker bug? command: | echo "export DOCKER_BUILDKIT=" > $BASH_ENV - build aztec-sandbox false arm64 + build aztec-sandbox cli-x86_64: machine: @@ -407,7 +414,7 @@ jobs: - *setup_env - run: name: "Build and test" - command: build cli false x86_64 + command: build cli cli-arm64: machine: @@ -422,29 +429,7 @@ jobs: # out to eu-west2 despite the image being locally tagged, resulting in unauthorized 401. Weird docker bug? command: | echo "export DOCKER_BUILDKIT=" > $BASH_ENV - build cli false arm64 - - aztec-p2p-bootstrap: - machine: - image: ubuntu-2204:2023.07.2 - resource_class: large - steps: - - *checkout - - *setup_env - - run: - name: "Build and test" - command: build p2p-bootstrap | add_timestamps - - aztec-node: - machine: - image: ubuntu-2204:2023.07.2 - resource_class: large - steps: - - *checkout - - *setup_env - - run: - name: "Build and test" - command: build aztec-node | add_timestamps + build cli mainnet-fork: machine: @@ -457,17 +442,6 @@ jobs: name: "Build" command: build mainnet-fork | add_timestamps - deploy-mainnet-fork: - machine: - image: ubuntu-2204:2023.07.2 - resource_class: medium - steps: - - *checkout - - *setup_env - - run: - name: "Deploy mainnet fork" - command: deploy_ecr mainnet-fork - aztec-faucet: machine: image: ubuntu-2204:2023.07.2 @@ -479,32 +453,6 @@ jobs: name: "Build and test" command: build aztec-faucet | add_timestamps - pxe-x86_64: - machine: - image: ubuntu-2204:2023.07.2 - resource_class: large - steps: - - *checkout - - *setup_env - - run: - name: "Build and test" - command: build pxe false x86_64 - - pxe-arm64: - machine: - image: ubuntu-2204:2023.07.2 - resource_class: arm.large - steps: - - *checkout - - *setup_env - - run: - name: "Build and test" - # We need to force not to use docker buildkit because for some reason on arm only, it ends up making a call - # out to eu-west2 despite the image being locally tagged, resulting in unauthorized 401. Weird docker bug? - command: | - echo "export DOCKER_BUILDKIT=" > $BASH_ENV - build pxe false arm64 - ecr-manifest: machine: image: ubuntu-2204:2023.07.2 @@ -516,7 +464,6 @@ jobs: name: "Create ECR manifest" command: | create_ecr_manifest aztec-sandbox x86_64,arm64 - create_ecr_manifest pxe x86_64,arm64 create_ecr_manifest cli x86_64,arm64 boxes-blank-react: @@ -552,17 +499,6 @@ jobs: name: "Test via adhoc script" command: ./yarn-project/boxes/run_tests token - canary: - machine: - image: ubuntu-2204:2023.07.2 - resource_class: large - steps: - - *checkout - - *setup_env - - run: - name: "Build" - command: build canary-build - end-to-end: machine: image: ubuntu-2204:2023.07.2 @@ -629,6 +565,28 @@ jobs: name: "Test" command: cond_spot_run_compose end-to-end 4 ./scripts/docker-compose.yml TEST=e2e_token_contract.test.ts + e2e-blacklist-token-contract: + docker: + - image: aztecprotocol/alpine-build-image + resource_class: small + steps: + - *checkout + - *setup_env + - run: + name: "Test" + command: cond_spot_run_compose end-to-end 4 ./scripts/docker-compose.yml TEST=e2e_blacklist_token_contract.test.ts + + e2e-slow-tree: + docker: + - image: aztecprotocol/alpine-build-image + resource_class: small + steps: + - *checkout + - *setup_env + - run: + name: "Test" + command: cond_spot_run_compose end-to-end 4 ./scripts/docker-compose.yml TEST=e2e_slow_tree.test.ts + e2e-sandbox-example: docker: - image: aztecprotocol/alpine-build-image @@ -928,6 +886,15 @@ jobs: - run: name: "Build docs" command: build docs + - run: + name: "Deploy docs" + command: | + if [ "$CIRCLE_BRANCH" == "master" ]; then + echo "Deploying docs (on master)." + docs/deploy_netlify.sh + else + echo "Skipping doc deploy (not on master)." + fi e2e-join: docker: @@ -938,7 +905,7 @@ jobs: name: "Noop" command: echo Noop - e2e-end: + end: docker: - image: cimg/base:2023.09 resource_class: small @@ -947,15 +914,6 @@ jobs: name: "Noop" command: echo Noop - # canary-end: - # docker: - # - image: cimg/base:2023.09 - # resource_class: small - # steps: - # - run: - # name: "Noop" - # command: echo Noop - bench-summary: machine: image: ubuntu-2204:2023.07.2 @@ -966,18 +924,19 @@ jobs: name: "Assemble benchmark summary from uploaded logs" command: ./scripts/ci/assemble_e2e_benchmark.sh - # deploy-npm-canary: - # # Deploys next version under 'canary' dist tag. - # machine: - # image: ubuntu-2204:2023.07.2 - # resource_class: medium - # steps: - # - *checkout - # - *setup_env - # # Aztec.js and dependencies - # - run: - # name: "yarn-project" - # command: yarn-project/deploy_npm.sh canary + # Deploy jobs. + deploy-mainnet-fork: + machine: + image: ubuntu-2204:2023.07.2 + resource_class: medium + steps: + - *checkout + - *setup_env + - run: + name: "Deploy mainnet fork" + command: | + should_deploy || exit 0 + deploy_ecr mainnet-fork deploy-npm: machine: @@ -986,32 +945,11 @@ jobs: steps: - *checkout - *setup_env - # Aztec.js and dependencies - run: name: "yarn-project" - command: yarn-project/deploy_npm.sh latest - - # deploy-dockerhub-canary: - # machine: - # image: ubuntu-2204:2023.07.2 - # resource_class: medium - # steps: - # - *checkout - # - *setup_env - # - run: - # name: "Deploy to dockerhub" - # command: yarn-project/deploy_dockerhub.sh canary - - # deploy-ecr: - # machine: - # image: ubuntu-2204:2023.07.2 - # resource_class: medium - # steps: - # - *checkout - # - *setup_env - # - run: - # name: "yarn-project" - # command: yarn-project/deploy_ecr.sh + command: | + should_deploy || exit 0 + yarn-project/deploy_npm.sh latest deploy-dockerhub: machine: @@ -1022,71 +960,17 @@ jobs: - *setup_env - run: name: "Deploy to dockerhub" - command: yarn-project/deploy_dockerhub.sh - - # build-deployment-canary: - # machine: - # image: ubuntu-2204:2023.07.2 - # resource_class: large - # steps: - # - *checkout - # - *setup_env - # - run: - # name: "Build" - # command: build canary true - - # canary-uniswap-test: - # machine: - # image: ubuntu-2204:2023.07.2 - # resource_class: large - # steps: - # - *checkout - # - *setup_env - # - run: - # name: "Test" - # command: run_script canary ./scripts/run_tests ./src/uniswap_trade_on_l1_from_l2.test.ts canary ./scripts/docker-compose.yml - - # canary-browser-test: - # machine: - # image: ubuntu-2204:2023.07.2 - # resource_class: large - # steps: - # - *checkout - # - *setup_env - # - run: - # name: "Test" - # command: run_script canary ./scripts/run_tests ./src/aztec_js_browser.test.ts canary ./scripts/docker-compose-browser.yml - - # canary-cli-test: - # machine: - # image: ubuntu-2204:2023.07.2 - # resource_class: large - # steps: - # - *checkout - # - *setup_env - # - run: - # name: "Test" - # command: run_script canary ./scripts/run_tests ./src/cli.test.ts canary ./scripts/docker-compose.yml + command: | + should_deploy || exit 0 + deploy_dockerhub noir x86_64,arm64 + deploy_dockerhub aztec-sandbox x86_64,arm64 + deploy_dockerhub cli x86_64,arm64 # Repeatable config for defining the workflow below. -tag_regex: &tag_regex /^aztec-packages-v.*/ defaults: &defaults filters: tags: - only: *tag_regex - context: - - build - - slack - post-steps: - - slack/notify: - event: fail - branch_pattern: "master" -deploy_defaults: &deploy_defaults - filters: - tags: - only: *tag_regex - branches: - ignore: /.*/ + only: /^aztec-packages-v.*/ context: - build - slack @@ -1094,20 +978,42 @@ deploy_defaults: &deploy_defaults - slack/notify: event: fail branch_pattern: "master" + +defaults_yarn_project: &defaults_yarn_project + requires: + - yarn-project + <<: *defaults + +defaults_deploy: &defaults_deploy + requires: + - end + <<: *defaults + e2e_test: &e2e_test requires: - e2e-join <<: *defaults + bb_test: &bb_test requires: - barretenberg-x86_64-linux-clang-assert <<: *defaults +# Workflows. workflows: system: when: equal: [system, << pipeline.parameters.workflow >>] jobs: + # Noir + - noir-x86_64: *defaults + - noir-arm64: *defaults + - noir-ecr-manifest: + requires: + - noir-x86_64 + - noir-arm64 + <<: *defaults + # Barretenberg - barretenberg-x86_64-linux-gcc: *defaults - barretenberg-x86_64-linux-clang: *defaults @@ -1122,17 +1028,6 @@ workflows: - barretenberg-stdlib-tests: *bb_test - barretenberg-stdlib-recursion-ultra-tests: *bb_test - barretenberg-join-split-tests: *bb_test - # - barretenberg-benchmark-aggregator: - # requires: - # - barretenberg-tests - # - barretenberg-stdlib-tests - # - barretenberg-stdlib-recursion-ultra-tests - # - barretenberg-join-split-tests - # filters: - # branches: - # only: - # - master - # <<: *defaults - barretenberg-acir-tests-bb: requires: - barretenberg-x86_64-linux-clang-assert @@ -1167,10 +1062,6 @@ workflows: - l1-contracts: *defaults - mainnet-fork: *defaults - - deploy-mainnet-fork: - requires: - - mainnet-fork - <<: *deploy_defaults # Yarn Project - yarn-project-base: @@ -1183,106 +1074,49 @@ workflows: requires: - yarn-project-base <<: *defaults - - yarn-project-formatting: - requires: - - yarn-project - <<: *defaults - - yarn-project-tests: - requires: - - yarn-project - <<: *defaults - - - end-to-end: - requires: - - yarn-project - <<: *defaults - - canary: - requires: - - yarn-project - <<: *defaults - - build-docs: - requires: - - yarn-project - <<: *defaults - - - aztec-sandbox-x86_64: - requires: - - yarn-project - <<: *defaults - - aztec-sandbox-arm64: - requires: - - yarn-project - <<: *defaults - - - cli-x86_64: - requires: - - yarn-project - <<: *defaults - - cli-arm64: - requires: - - yarn-project - <<: *defaults - - - aztec-p2p-bootstrap: - requires: - - yarn-project - <<: *defaults - - - aztec-node: - requires: - - yarn-project - <<: *defaults - - - aztec-faucet: - requires: - - yarn-project - <<: *defaults - - - pxe-x86_64: - requires: - - yarn-project - <<: *defaults - - pxe-arm64: - requires: - - yarn-project - <<: *defaults - + - yarn-project-formatting: *defaults_yarn_project + - yarn-project-tests: *defaults_yarn_project + - end-to-end: *defaults_yarn_project + - build-docs: *defaults_yarn_project + - aztec-sandbox-x86_64: *defaults_yarn_project + - aztec-sandbox-arm64: *defaults_yarn_project + - cli-x86_64: *defaults_yarn_project + - cli-arm64: *defaults_yarn_project + - aztec-faucet: *defaults_yarn_project - ecr-manifest: requires: - aztec-sandbox-x86_64 - aztec-sandbox-arm64 - - pxe-x86_64 - - pxe-arm64 - cli-x86_64 - cli-arm64 <<: *defaults + # Boxes. - boxes-blank-react: requires: - aztec-sandbox-x86_64 <<: *defaults - - boxes-blank: requires: - aztec-sandbox-x86_64 <<: *defaults - - boxes-token: requires: - aztec-sandbox-x86_64 <<: *defaults + # End to end tests. - e2e-join: requires: - end-to-end - ecr-manifest - - canary <<: *defaults - - e2e-2-pxes: *e2e_test - e2e-deploy-contract: *e2e_test - e2e-lending-contract: *e2e_test - e2e-token-contract: *e2e_test + - e2e-blacklist-token-contract: *e2e_test + - e2e-slow-tree: *e2e_test - e2e-sandbox-example: *e2e_test - e2e-block-building: *e2e_test - e2e-nested-contract: *e2e_test @@ -1308,15 +1142,18 @@ workflows: - guides-dapp-testing: *e2e_test - guides-sample-dapp: *e2e_test - guides-up-quick-start: *e2e_test - - bench-publish-rollup: *e2e_test - - bench-process-history: *e2e_test - - e2e-end: + # Everything that must complete before deployment. + - end: requires: + - mainnet-fork + - noir-ecr-manifest - e2e-2-pxes - e2e-deploy-contract - e2e-lending-contract - e2e-token-contract + - e2e-blacklist-token-contract + - e2e-slow-tree - e2e-sandbox-example - e2e-block-building - e2e-nested-contract @@ -1344,64 +1181,16 @@ workflows: - guides-up-quick-start <<: *defaults + # Benchmark jobs. + - bench-publish-rollup: *e2e_test + - bench-process-history: *e2e_test - bench-summary: requires: - - e2e-end - bench-publish-rollup - bench-process-history <<: *defaults - # Deploy under canary tag - # - deploy-npm-canary: - # requires: - # - e2e-end - # <<: *deploy_defaults - # - deploy-dockerhub-canary: - # requires: - # - e2e-end - # <<: *deploy_defaults - # - deploy-ecr: - # requires: - # - e2e-end - # <<: *deploy_defaults - - # - build-deployment-canary: - # requires: - # - deploy-npm-canary - # - deploy-dockerhub-canary - # <<: *deploy_defaults - - # Run canary tests - # - canary-uniswap-test: - # requires: - # - build-deployment-canary - # <<: *deploy_defaults - - # - canary-browser-test: - # requires: - # - build-deployment-canary - # <<: *deploy_defaults - - # - canary-cli-test: - # requires: - # - build-deployment-canary - # <<: *deploy_defaults - - # - canary-end: - # requires: - # - canary-uniswap-test - # - canary-browser-test - # - canary-cli-test - # <<: *deploy_defaults - # Production deployment - - deploy-dockerhub: - requires: - # - canary-end - - e2e-end - <<: *deploy_defaults - - deploy-npm: - requires: - # - canary-end - - e2e-end - <<: *deploy_defaults + - deploy-dockerhub: *defaults_deploy + - deploy-npm: *defaults_deploy + - deploy-mainnet-fork: *defaults_deploy diff --git a/.gitignore b/.gitignore index 46c867ea481..1a18b0f2f7d 100644 --- a/.gitignore +++ b/.gitignore @@ -8,3 +8,6 @@ build/ cmake-build-debug .terraform* .bootstrapped + +# Local Netlify folder +.netlify diff --git a/.release-please-manifest.json b/.release-please-manifest.json index 91819d8f5bf..85dc0dac688 100644 --- a/.release-please-manifest.json +++ b/.release-please-manifest.json @@ -1,5 +1,5 @@ { - ".": "0.14.2", - "barretenberg": "0.14.2", - "barretenberg/ts": "0.14.2" + ".": "0.15.1", + "barretenberg": "0.15.1", + "barretenberg/ts": "0.15.1" } diff --git a/CHANGELOG.md b/CHANGELOG.md index e7d76536ce8..8f1e5eadbeb 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,84 @@ # Changelog +## [0.15.1](https://github.com/AztecProtocol/aztec-packages/compare/aztec-packages-v0.15.0...aztec-packages-v0.15.1) (2023-11-21) + + +### Features + +* **bb:** Add ability to write pk to file or stdout ([#3335](https://github.com/AztecProtocol/aztec-packages/issues/3335)) ([c99862c](https://github.com/AztecProtocol/aztec-packages/commit/c99862c9602d7d37f7fef348e9f014fb137adab1)) +* DataBus PoC (UltraHonk as extension of Ultra) ([#3181](https://github.com/AztecProtocol/aztec-packages/issues/3181)) ([dd9dd84](https://github.com/AztecProtocol/aztec-packages/commit/dd9dd84e9cfc93f8605f28aa25fa36b0004052cb)) +* Deploy docs from CCI w/ netlify-cli ([#3348](https://github.com/AztecProtocol/aztec-packages/issues/3348)) ([624d733](https://github.com/AztecProtocol/aztec-packages/commit/624d7339d6bd9755156487a07c553b00f92c3b4b)) +* Fold batching challenge (alpha) ([#3291](https://github.com/AztecProtocol/aztec-packages/issues/3291)) ([bc99a4f](https://github.com/AztecProtocol/aztec-packages/commit/bc99a4f644824727920b0b4a38ec5ba915d5c0ce)) +* Open transcript polys as univariates in ECCVM ([#3331](https://github.com/AztecProtocol/aztec-packages/issues/3331)) ([436b22e](https://github.com/AztecProtocol/aztec-packages/commit/436b22e35bf8a41f78def237889f2afd2ca79830)) +* Sandbox packages ([#3360](https://github.com/AztecProtocol/aztec-packages/issues/3360)) ([0dc2d58](https://github.com/AztecProtocol/aztec-packages/commit/0dc2d586c60587f62e50bb7af0862d1a3f828688)) +* Slow updates experimentation ([#2732](https://github.com/AztecProtocol/aztec-packages/issues/2732)) ([193e6c8](https://github.com/AztecProtocol/aztec-packages/commit/193e6c8e0afd1646f3b90c30c250fc4c087a4dde)) +* ZM updates for Translator concatenated polys ([#3343](https://github.com/AztecProtocol/aztec-packages/issues/3343)) ([0e425db](https://github.com/AztecProtocol/aztec-packages/commit/0e425dbfc99af9fc2598a957acd8b71f3fd45fe9)) + + +### Bug Fixes + +* Bootstrap bbjs. ([#3337](https://github.com/AztecProtocol/aztec-packages/issues/3337)) ([06aedcb](https://github.com/AztecProtocol/aztec-packages/commit/06aedcbfd601e243d3486763c1306e20c1ae3688)) +* Noir-compiler breadth-first resolver ([#3307](https://github.com/AztecProtocol/aztec-packages/issues/3307)) ([02348cf](https://github.com/AztecProtocol/aztec-packages/commit/02348cf94ff21d585ca43c22be69433af9cd3b98)) +* Update command looks at devDeps ([#3276](https://github.com/AztecProtocol/aztec-packages/issues/3276)) ([54ee38d](https://github.com/AztecProtocol/aztec-packages/commit/54ee38d94f904a94cec948b9db9ca833f097d9c1)), closes [#3275](https://github.com/AztecProtocol/aztec-packages/issues/3275) +* Updating pedersen benchmarks ([#3211](https://github.com/AztecProtocol/aztec-packages/issues/3211)) ([7e89ff3](https://github.com/AztecProtocol/aztec-packages/commit/7e89ff363521dd65e0c9f0c098b3bacea33c2764)) +* Warn on circular imports. ([#3350](https://github.com/AztecProtocol/aztec-packages/issues/3350)) ([5bfbddb](https://github.com/AztecProtocol/aztec-packages/commit/5bfbddb21bc81dd47698f0c8796d0c8dc0a498e0)) + + +### Miscellaneous + +* All hashes in ts ([#3333](https://github.com/AztecProtocol/aztec-packages/issues/3333)) ([6307e12](https://github.com/AztecProtocol/aztec-packages/commit/6307e129770af7791dc5a477859b75ebb112a653)) +* Compute function tree root in ts. ([#3326](https://github.com/AztecProtocol/aztec-packages/issues/3326)) ([48d8c7f](https://github.com/AztecProtocol/aztec-packages/commit/48d8c7fd53c11b2d84c8f8e9e137ce0bb0dc3604)) +* **docs:** Suggest CLI install per project ([#3267](https://github.com/AztecProtocol/aztec-packages/issues/3267)) ([b4c967b](https://github.com/AztecProtocol/aztec-packages/commit/b4c967bcb222e410030fe6066b32aa1802ddb15b)) +* Enforce bracing around blocks. Generally considered easier to read and less error prone. ([#3349](https://github.com/AztecProtocol/aztec-packages/issues/3349)) ([ee11dec](https://github.com/AztecProtocol/aztec-packages/commit/ee11decb8d3ba65d2a74aedf72396b57fccb1db6)) +* Fix circulars in foundation. Also cleanup fields and optimise to be buffer underlying. ([#3351](https://github.com/AztecProtocol/aztec-packages/issues/3351)) ([c4bf8d3](https://github.com/AztecProtocol/aztec-packages/commit/c4bf8d371550e27ee8982ec3ea2a6848bd02a46f)) +* Public kernel tests ([#3325](https://github.com/AztecProtocol/aztec-packages/issues/3325)) ([bace972](https://github.com/AztecProtocol/aztec-packages/commit/bace9722cbb5903ea28ebc8e32ddfa4cf784e62a)) + + +### Documentation + +* Fixed errors in Gas and Fees yellow paper ([#3363](https://github.com/AztecProtocol/aztec-packages/issues/3363)) ([d818206](https://github.com/AztecProtocol/aztec-packages/commit/d818206391801b69fff9ff63d4aeefa1bb3fb72a)) +* Initial network section of yellow paper ([#3341](https://github.com/AztecProtocol/aztec-packages/issues/3341)) ([5a18615](https://github.com/AztecProtocol/aztec-packages/commit/5a18615fe68a25adf33f9d158c03cf9d68fbcfc6)) +* Yellow paper section on Gas and Fees ([#3327](https://github.com/AztecProtocol/aztec-packages/issues/3327)) ([caa7e10](https://github.com/AztecProtocol/aztec-packages/commit/caa7e10565f2b9177085fca5fc3366ccea5f0d33)) + +## [0.15.0](https://github.com/AztecProtocol/aztec-packages/compare/aztec-packages-v0.14.2...aztec-packages-v0.15.0) (2023-11-16) + + +### ⚠ BREAKING CHANGES + +* Replace computing hashes in circuits wasm, with computing them in ts via bb.js pedersen call. ([#3114](https://github.com/AztecProtocol/aztec-packages/issues/3114)) + +### Features + +* **bb:** Add msan preset ([#3284](https://github.com/AztecProtocol/aztec-packages/issues/3284)) ([bcf025c](https://github.com/AztecProtocol/aztec-packages/commit/bcf025ceef07fb2bf5b07f96e7818425ae59e79a)) +* Enable merge and root rollup circuits in noir ([#3248](https://github.com/AztecProtocol/aztec-packages/issues/3248)) ([68555fc](https://github.com/AztecProtocol/aztec-packages/commit/68555fca71746579c7551a78a13b965400d2c865)) +* Protogalaxy combiner quotient ([#3245](https://github.com/AztecProtocol/aztec-packages/issues/3245)) ([db0f3ab](https://github.com/AztecProtocol/aztec-packages/commit/db0f3ab9b3d74e0527116a773bf11d26e6bf7736)) +* Public kernel in noir ([#3186](https://github.com/AztecProtocol/aztec-packages/issues/3186)) ([15a522b](https://github.com/AztecProtocol/aztec-packages/commit/15a522ba731820851f1bf505bc2663314e4efc30)) +* Ultra honk arith from ultra ([#3274](https://github.com/AztecProtocol/aztec-packages/issues/3274)) ([ec2b805](https://github.com/AztecProtocol/aztec-packages/commit/ec2b805e5b35805e2c5e394ae2b6181865e22aa3)) + + +### Bug Fixes + +* Debug build ([#3283](https://github.com/AztecProtocol/aztec-packages/issues/3283)) ([aca2624](https://github.com/AztecProtocol/aztec-packages/commit/aca2624df2d07782f6879d32efc891318b985344)) +* Fix block constraint key divergence bug. ([#3256](https://github.com/AztecProtocol/aztec-packages/issues/3256)) ([1c71a0c](https://github.com/AztecProtocol/aztec-packages/commit/1c71a0cf38cf463efe1964126a6a5741c27bd2eb)) +* Main.md typo ([#3278](https://github.com/AztecProtocol/aztec-packages/issues/3278)) ([cb87c4d](https://github.com/AztecProtocol/aztec-packages/commit/cb87c4df5e37a689e8ea32a138f794bbe099f884)) +* Typo fix roundup ([#3302](https://github.com/AztecProtocol/aztec-packages/issues/3302)) ([9dd778d](https://github.com/AztecProtocol/aztec-packages/commit/9dd778d6856b87107b88e4e8e38d0fc6fc6479fc)) + + +### Miscellaneous + +* **bb:** Remove -Wfatal-errors ([#3318](https://github.com/AztecProtocol/aztec-packages/issues/3318)) ([4229173](https://github.com/AztecProtocol/aztec-packages/commit/4229173e7d794ba7800b34dcc8565d7f3ea5525d)) +* Clarify that barretenberg mirror should not take PRs ([#3303](https://github.com/AztecProtocol/aztec-packages/issues/3303)) ([13f1a1d](https://github.com/AztecProtocol/aztec-packages/commit/13f1a1d4f8cd12ac8f38e2d1a2c6715f2871f4c8)) +* Clean up Plonk widgets ([#3305](https://github.com/AztecProtocol/aztec-packages/issues/3305)) ([4623d91](https://github.com/AztecProtocol/aztec-packages/commit/4623d916d5e8d048cf3c5e06f02d937cf91e6180)) +* **docs:** Aztec.nr logging page ([#3281](https://github.com/AztecProtocol/aztec-packages/issues/3281)) ([11e6ca7](https://github.com/AztecProtocol/aztec-packages/commit/11e6ca732c90dc25eceda00f8ac30620a064ebf6)) +* **docs:** Update netlify.toml and fix build ([#3304](https://github.com/AztecProtocol/aztec-packages/issues/3304)) ([df76636](https://github.com/AztecProtocol/aztec-packages/commit/df76636293091e2761721eff6f2bdf7243b642e1)) +* Explicitly instantiate Goblin translator relations ([#3239](https://github.com/AztecProtocol/aztec-packages/issues/3239)) ([e3b5fb0](https://github.com/AztecProtocol/aztec-packages/commit/e3b5fb0681839bd003804a9e066118dd4693502d)) +* Plain struct flavor entities ([#3277](https://github.com/AztecProtocol/aztec-packages/issues/3277)) ([f109512](https://github.com/AztecProtocol/aztec-packages/commit/f1095124af96d2d69522c8677e5e02cd55063c99)) +* Remove bn254 instantiation of eccvm plus naming changes ([#3330](https://github.com/AztecProtocol/aztec-packages/issues/3330)) ([23d1e2d](https://github.com/AztecProtocol/aztec-packages/commit/23d1e2d307757c42f6a070afcb22f800fae94555)) +* Replace computing hashes in circuits wasm, with computing them in ts via bb.js pedersen call. ([#3114](https://github.com/AztecProtocol/aztec-packages/issues/3114)) ([87eeb71](https://github.com/AztecProtocol/aztec-packages/commit/87eeb715014996ec329de969df85684083b18f83)) +* Revert build-debug folder for debug preset ([#3324](https://github.com/AztecProtocol/aztec-packages/issues/3324)) ([43a2e6b](https://github.com/AztecProtocol/aztec-packages/commit/43a2e6b68853d5c22fac4563949c83baf443827c)) +* Towards plain struct flavor entities ([#3216](https://github.com/AztecProtocol/aztec-packages/issues/3216)) ([3ba89cf](https://github.com/AztecProtocol/aztec-packages/commit/3ba89cf6fe3821b1149f482ee28c5e0716878b15)) +* Typo fixes based on cspell ([#3319](https://github.com/AztecProtocol/aztec-packages/issues/3319)) ([8ae44dd](https://github.com/AztecProtocol/aztec-packages/commit/8ae44dd702987db524ab5e3edd6545881614f56b)) + ## [0.14.2](https://github.com/AztecProtocol/aztec-packages/compare/aztec-packages-v0.14.1...aztec-packages-v0.14.2) (2023-11-07) @@ -601,7 +680,7 @@ * Check that portal address is saved ([#2641](https://github.com/AztecProtocol/aztec-packages/issues/2641)) ([9ebef6e](https://github.com/AztecProtocol/aztec-packages/commit/9ebef6e04d8ddd25649a325f5b3692b42699629e)) * Fixes in deploy scripts ([#2659](https://github.com/AztecProtocol/aztec-packages/issues/2659)) ([f44568b](https://github.com/AztecProtocol/aztec-packages/commit/f44568b8557aac15b4accf901b1ff72efaf2a1da)) * Measure circuit simulation times and input/output sizes ([#2663](https://github.com/AztecProtocol/aztec-packages/issues/2663)) ([027f7ec](https://github.com/AztecProtocol/aztec-packages/commit/027f7ec95f9d761189166936a7c42d08dacf55b7)) -* Remove sandbox base image and force_deploy_build. Generalise in check_rebuild. ([#2645](https://github.com/AztecProtocol/aztec-packages/issues/2645)) ([805fe18](https://github.com/AztecProtocol/aztec-packages/commit/805fe18ec1bd207a713cf3438f6d241bf22317fa)) +* Remove sandbox base image and force_deploy_build. Generalize in check_rebuild. ([#2645](https://github.com/AztecProtocol/aztec-packages/issues/2645)) ([805fe18](https://github.com/AztecProtocol/aztec-packages/commit/805fe18ec1bd207a713cf3438f6d241bf22317fa)) ## [0.8.1](https://github.com/AztecProtocol/aztec-packages/compare/aztec-packages-v0.8.0...aztec-packages-v0.8.1) (2023-10-03) @@ -982,7 +1061,7 @@ * **cli:** Inspect contract command ([#2248](https://github.com/AztecProtocol/aztec-packages/issues/2248)) ([381706e](https://github.com/AztecProtocol/aztec-packages/commit/381706eaaad7054d620855f7b986e2df3cf62a91)), closes [#2180](https://github.com/AztecProtocol/aztec-packages/issues/2180) * Define specific Sandbox version when running docker-compose up ([#2238](https://github.com/AztecProtocol/aztec-packages/issues/2238)) ([71da236](https://github.com/AztecProtocol/aztec-packages/commit/71da2360986e5b57f211ca095b95ade2617f4eb8)) * **docs:** Updated docs explaining Sandbox accounts ([#2235](https://github.com/AztecProtocol/aztec-packages/issues/2235)) ([f560066](https://github.com/AztecProtocol/aztec-packages/commit/f560066394c3fc9725be18f320597794e29dc077)) -* Optimise sandbox startup time by only initialising the BB solver once. ([#2240](https://github.com/AztecProtocol/aztec-packages/issues/2240)) ([e9cac9c](https://github.com/AztecProtocol/aztec-packages/commit/e9cac9ced3604fdef1d6b298091639fc510cb4fb)) +* Optimize sandbox startup time by only initializing the BB solver once. ([#2240](https://github.com/AztecProtocol/aztec-packages/issues/2240)) ([e9cac9c](https://github.com/AztecProtocol/aztec-packages/commit/e9cac9ced3604fdef1d6b298091639fc510cb4fb)) * Remove entrypoint collection ([#2148](https://github.com/AztecProtocol/aztec-packages/issues/2148)) ([e97c94d](https://github.com/AztecProtocol/aztec-packages/commit/e97c94d8bc0659a95f457ba63369fca0dfba47c8)) * Validate nargo version against expected one ([#2254](https://github.com/AztecProtocol/aztec-packages/issues/2254)) ([011c0b7](https://github.com/AztecProtocol/aztec-packages/commit/011c0b7c070f004fcc1c6f9ce8936830c9f496f6)) diff --git a/PROJECT b/PROJECT index bc836bf5f94..5f8dfee5219 100644 --- a/PROJECT +++ b/PROJECT @@ -1 +1 @@ -aztec3-packages \ No newline at end of file +aztec \ No newline at end of file diff --git a/VERSION b/VERSION deleted file mode 100644 index bfec125b8c6..00000000000 --- a/VERSION +++ /dev/null @@ -1 +0,0 @@ -v0.14.2 x-release-please-version diff --git a/barretenberg/.gitrepo b/barretenberg/.gitrepo index cf73fabae20..ce08233762c 100644 --- a/barretenberg/.gitrepo +++ b/barretenberg/.gitrepo @@ -6,7 +6,7 @@ [subrepo] remote = https://github.com/AztecProtocol/barretenberg branch = master - commit = ff31b1d215cc2d2b810321f6db87f5a2aa5887f6 - parent = 87eeb715014996ec329de969df85684083b18f83 + commit = db6c524503467d3cdfcbd846e784a94ae53deabf + parent = d94d88bf626ddbe41dd1b7fe3eb0f11619dde97a method = merge cmdver = 0.4.6 diff --git a/barretenberg/.vscode/settings.json b/barretenberg/.vscode/settings.json index cc597845c66..2016adfd2c9 100644 --- a/barretenberg/.vscode/settings.json +++ b/barretenberg/.vscode/settings.json @@ -115,7 +115,7 @@ }, "typescript.enablePromptUseWorkspaceTsdk": true, "[cpp]": { - // doesn't conflict with barratenberg.code-workspace settings. + // doesn't conflict with barretenberg.code-workspace settings. "editor.defaultFormatter": "llvm-vs-code-extensions.vscode-clangd" }, "[terraform]": { diff --git a/barretenberg/CHANGELOG.md b/barretenberg/CHANGELOG.md index 5892931a3b7..0f5b439ed89 100644 --- a/barretenberg/CHANGELOG.md +++ b/barretenberg/CHANGELOG.md @@ -1,5 +1,60 @@ # Changelog +## [0.15.1](https://github.com/AztecProtocol/aztec-packages/compare/barretenberg-v0.15.0...barretenberg-v0.15.1) (2023-11-21) + + +### Features + +* **bb:** Add ability to write pk to file or stdout ([#3335](https://github.com/AztecProtocol/aztec-packages/issues/3335)) ([c99862c](https://github.com/AztecProtocol/aztec-packages/commit/c99862c9602d7d37f7fef348e9f014fb137adab1)) +* DataBus PoC (UltraHonk as extension of Ultra) ([#3181](https://github.com/AztecProtocol/aztec-packages/issues/3181)) ([dd9dd84](https://github.com/AztecProtocol/aztec-packages/commit/dd9dd84e9cfc93f8605f28aa25fa36b0004052cb)) +* Fold batching challenge (alpha) ([#3291](https://github.com/AztecProtocol/aztec-packages/issues/3291)) ([bc99a4f](https://github.com/AztecProtocol/aztec-packages/commit/bc99a4f644824727920b0b4a38ec5ba915d5c0ce)) +* Open transcript polys as univariates in ECCVM ([#3331](https://github.com/AztecProtocol/aztec-packages/issues/3331)) ([436b22e](https://github.com/AztecProtocol/aztec-packages/commit/436b22e35bf8a41f78def237889f2afd2ca79830)) +* ZM updates for Translator concatenated polys ([#3343](https://github.com/AztecProtocol/aztec-packages/issues/3343)) ([0e425db](https://github.com/AztecProtocol/aztec-packages/commit/0e425dbfc99af9fc2598a957acd8b71f3fd45fe9)) + + +### Bug Fixes + +* Bootstrap bbjs. ([#3337](https://github.com/AztecProtocol/aztec-packages/issues/3337)) ([06aedcb](https://github.com/AztecProtocol/aztec-packages/commit/06aedcbfd601e243d3486763c1306e20c1ae3688)) +* Updating pedersen benchmarks ([#3211](https://github.com/AztecProtocol/aztec-packages/issues/3211)) ([7e89ff3](https://github.com/AztecProtocol/aztec-packages/commit/7e89ff363521dd65e0c9f0c098b3bacea33c2764)) + + +### Miscellaneous + +* All hashes in ts ([#3333](https://github.com/AztecProtocol/aztec-packages/issues/3333)) ([6307e12](https://github.com/AztecProtocol/aztec-packages/commit/6307e129770af7791dc5a477859b75ebb112a653)) + +## [0.15.0](https://github.com/AztecProtocol/aztec-packages/compare/barretenberg-v0.14.2...barretenberg-v0.15.0) (2023-11-16) + + +### ⚠ BREAKING CHANGES + +* Replace computing hashes in circuits wasm, with computing them in ts via bb.js pedersen call. ([#3114](https://github.com/AztecProtocol/aztec-packages/issues/3114)) + +### Features + +* **bb:** Add msan preset ([#3284](https://github.com/AztecProtocol/aztec-packages/issues/3284)) ([bcf025c](https://github.com/AztecProtocol/aztec-packages/commit/bcf025ceef07fb2bf5b07f96e7818425ae59e79a)) +* Protogalaxy combiner quotient ([#3245](https://github.com/AztecProtocol/aztec-packages/issues/3245)) ([db0f3ab](https://github.com/AztecProtocol/aztec-packages/commit/db0f3ab9b3d74e0527116a773bf11d26e6bf7736)) +* Ultra honk arith from ultra ([#3274](https://github.com/AztecProtocol/aztec-packages/issues/3274)) ([ec2b805](https://github.com/AztecProtocol/aztec-packages/commit/ec2b805e5b35805e2c5e394ae2b6181865e22aa3)) + + +### Bug Fixes + +* Debug build ([#3283](https://github.com/AztecProtocol/aztec-packages/issues/3283)) ([aca2624](https://github.com/AztecProtocol/aztec-packages/commit/aca2624df2d07782f6879d32efc891318b985344)) +* Fix block constraint key divergence bug. ([#3256](https://github.com/AztecProtocol/aztec-packages/issues/3256)) ([1c71a0c](https://github.com/AztecProtocol/aztec-packages/commit/1c71a0cf38cf463efe1964126a6a5741c27bd2eb)) + + +### Miscellaneous + +* **bb:** Remove -Wfatal-errors ([#3318](https://github.com/AztecProtocol/aztec-packages/issues/3318)) ([4229173](https://github.com/AztecProtocol/aztec-packages/commit/4229173e7d794ba7800b34dcc8565d7f3ea5525d)) +* Clarify that barretenberg mirror should not take PRs ([#3303](https://github.com/AztecProtocol/aztec-packages/issues/3303)) ([13f1a1d](https://github.com/AztecProtocol/aztec-packages/commit/13f1a1d4f8cd12ac8f38e2d1a2c6715f2871f4c8)) +* Clean up Plonk widgets ([#3305](https://github.com/AztecProtocol/aztec-packages/issues/3305)) ([4623d91](https://github.com/AztecProtocol/aztec-packages/commit/4623d916d5e8d048cf3c5e06f02d937cf91e6180)) +* Explicitly instantiate Goblin translator relations ([#3239](https://github.com/AztecProtocol/aztec-packages/issues/3239)) ([e3b5fb0](https://github.com/AztecProtocol/aztec-packages/commit/e3b5fb0681839bd003804a9e066118dd4693502d)) +* Plain struct flavor entities ([#3277](https://github.com/AztecProtocol/aztec-packages/issues/3277)) ([f109512](https://github.com/AztecProtocol/aztec-packages/commit/f1095124af96d2d69522c8677e5e02cd55063c99)) +* Remove bn254 instantiation of eccvm plus naming changes ([#3330](https://github.com/AztecProtocol/aztec-packages/issues/3330)) ([23d1e2d](https://github.com/AztecProtocol/aztec-packages/commit/23d1e2d307757c42f6a070afcb22f800fae94555)) +* Replace computing hashes in circuits wasm, with computing them in ts via bb.js pedersen call. ([#3114](https://github.com/AztecProtocol/aztec-packages/issues/3114)) ([87eeb71](https://github.com/AztecProtocol/aztec-packages/commit/87eeb715014996ec329de969df85684083b18f83)) +* Revert build-debug folder for debug preset ([#3324](https://github.com/AztecProtocol/aztec-packages/issues/3324)) ([43a2e6b](https://github.com/AztecProtocol/aztec-packages/commit/43a2e6b68853d5c22fac4563949c83baf443827c)) +* Towards plain struct flavor entities ([#3216](https://github.com/AztecProtocol/aztec-packages/issues/3216)) ([3ba89cf](https://github.com/AztecProtocol/aztec-packages/commit/3ba89cf6fe3821b1149f482ee28c5e0716878b15)) +* Typo fixes based on cspell ([#3319](https://github.com/AztecProtocol/aztec-packages/issues/3319)) ([8ae44dd](https://github.com/AztecProtocol/aztec-packages/commit/8ae44dd702987db524ab5e3edd6545881614f56b)) + ## [0.14.2](https://github.com/AztecProtocol/aztec-packages/compare/barretenberg-v0.14.1...barretenberg-v0.14.2) (2023-11-07) diff --git a/barretenberg/README.md b/barretenberg/README.md index c8a3d1fab03..fd24c4fa039 100644 --- a/barretenberg/README.md +++ b/barretenberg/README.md @@ -207,7 +207,7 @@ Fuzzing build turns off building tests and benchmarks, since they are incompatib To turn on address sanitizer add `-DADDRESS_SANITIZER=ON`. Note that address sanitizer can be used to explore crashes. Sometimes you might have to specify the address of llvm-symbolizer. You have to do it with `export ASAN_SYMBOLIZER_PATH=`. -For undefined behaviour sanitizer `-DUNDEFINED_BEHAVIOUR_SANITIZER=ON`. +For undefined behavior sanitizer `-DUNDEFINED_BEHAVIOUR_SANITIZER=ON`. Note that the fuzzer can be orders of magnitude slower with ASan (2-3x slower) or UBSan on, so it is best to run a non-sanitized build first, minimize the testcase and then run it for a bit of time with sanitizers. ### Test coverage build diff --git a/barretenberg/VERSION b/barretenberg/VERSION deleted file mode 100644 index bfec125b8c6..00000000000 --- a/barretenberg/VERSION +++ /dev/null @@ -1 +0,0 @@ -v0.14.2 x-release-please-version diff --git a/barretenberg/acir_tests/flows/all_cmds.sh b/barretenberg/acir_tests/flows/all_cmds.sh index 97f9f8ea4c1..a65159351ed 100755 --- a/barretenberg/acir_tests/flows/all_cmds.sh +++ b/barretenberg/acir_tests/flows/all_cmds.sh @@ -9,6 +9,7 @@ FLAGS="-c $CRS_PATH $VFLAG" $BIN gates $FLAGS $BFLAG > /dev/null $BIN prove -o proof $FLAGS $BFLAG $BIN write_vk -o vk $FLAGS $BFLAG +$BIN write_pk -o pk $FLAGS $BFLAG $BIN verify -k vk -p proof $FLAGS # Check supplemental functions. diff --git a/barretenberg/acir_tests/sol-test/src/index.js b/barretenberg/acir_tests/sol-test/src/index.js index f59ef154505..b63921d2709 100644 --- a/barretenberg/acir_tests/sol-test/src/index.js +++ b/barretenberg/acir_tests/sol-test/src/index.js @@ -59,7 +59,7 @@ var input = { content: base } }, - settings: { // we require the optimiser + settings: { // we require the optimizer optimizer: { enabled: true, runs: 200 diff --git a/barretenberg/bootstrap.sh b/barretenberg/bootstrap.sh index 1f43a5bfcd2..33342fc9f66 100755 --- a/barretenberg/bootstrap.sh +++ b/barretenberg/bootstrap.sh @@ -1,5 +1,8 @@ #!/bin/bash +set -eu + +# Navigate to script folder +cd "$(dirname "$0")" + (cd cpp && ./bootstrap.sh) -cd ts -yarn build -npm link +(cd ts && yarn install --immutable && yarn build && npm link) diff --git a/barretenberg/cpp/CMakeLists.txt b/barretenberg/cpp/CMakeLists.txt index d07d65aebe4..206febdd4a4 100644 --- a/barretenberg/cpp/CMakeLists.txt +++ b/barretenberg/cpp/CMakeLists.txt @@ -6,7 +6,7 @@ cmake_minimum_required(VERSION 3.24) project( Barretenberg DESCRIPTION "BN254 elliptic curve library, and PLONK SNARK prover" - VERSION 0.14.2 # x-release-please-version + VERSION 0.15.1 # x-release-please-version LANGUAGES CXX C ) # Insert version into `bb` config file @@ -95,7 +95,7 @@ elseif(CMAKE_CXX_COMPILER_ID MATCHES "GNU") message(WARNING "GCC <10 is not supported") endif() else() - message(WARNING "Unsuported compiler, use Clang >14 or GCC >10") + message(WARNING "Unsupported compiler, use Clang >14 or GCC >10") endif() if(COVERAGE) diff --git a/barretenberg/cpp/CMakePresets.json b/barretenberg/cpp/CMakePresets.json index c361d25a4e7..b87e10709b6 100644 --- a/barretenberg/cpp/CMakePresets.json +++ b/barretenberg/cpp/CMakePresets.json @@ -46,7 +46,7 @@ "displayName": "Debugging build with Clang-16", "description": "Build with globally installed Clang-16 in debug mode", "inherits": "clang16", - "binaryDir": "build-debug", + "binaryDir": "build", "environment": { "CMAKE_BUILD_TYPE": "Debug", "CFLAGS": "-gdwarf-4", diff --git a/barretenberg/cpp/docs/Fuzzing.md b/barretenberg/cpp/docs/Fuzzing.md index 7dc3a6bde9f..bbe85a62a1f 100644 --- a/barretenberg/cpp/docs/Fuzzing.md +++ b/barretenberg/cpp/docs/Fuzzing.md @@ -17,7 +17,7 @@ Fuzzing build turns off building tests and benchmarks, since they are incompatib To turn on address sanitizer add `-DADDRESS_SANITIZER=ON`. Note that address sanitizer can be used to explore crashes. Sometimes you might have to specify the address of llvm-symbolizer. You have to do it with `export ASAN_SYMBOLIZER_PATH=`. -For undefined behaviour sanitizer `-DUNDEFINED_BEHAVIOUR_SANITIZER=ON`. +For undefined behavior sanitizer `-DUNDEFINED_BEHAVIOUR_SANITIZER=ON`. Note that the fuzzer can be orders of magnitude slower with ASan (2-3x slower) or UBSan on, so it is best to run a non-sanitized build first, minimize the testcase and then run it for a bit of time with sanitizers. Building with clang 13 or later is recommended, since libfuzzer contains and by default utilizes the entropic power schedule, which is considered more efficient diff --git a/barretenberg/cpp/scripts/bb-tests.sh b/barretenberg/cpp/scripts/bb-tests.sh index d0ca404757e..0c16f66e55b 100755 --- a/barretenberg/cpp/scripts/bb-tests.sh +++ b/barretenberg/cpp/scripts/bb-tests.sh @@ -35,6 +35,7 @@ TESTS=( srs_tests sumcheck_tests transcript_tests + translator_vm_tests ultra_honk_tests ) TESTS_STR="${TESTS[@]}" diff --git a/barretenberg/cpp/src/CMakeLists.txt b/barretenberg/cpp/src/CMakeLists.txt index 2637c16bb4a..b74bc2f99b5 100644 --- a/barretenberg/cpp/src/CMakeLists.txt +++ b/barretenberg/cpp/src/CMakeLists.txt @@ -12,7 +12,7 @@ set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib) # Specifying `-Wno-${ERROR_NAME}` will silence the error completely. # To preserve the warning, but prevent them from causing the build to fail, # use the flag `-Wno-error=${ERROR_NAME}` -add_compile_options(-Werror -Wall -Wextra -Wconversion -Wsign-conversion -Wfatal-errors) +add_compile_options(-Werror -Wall -Wextra -Wconversion -Wsign-conversion) if(CMAKE_CXX_COMPILER_ID MATCHES "Clang") add_compile_options(-fcolor-diagnostics -fconstexpr-steps=100000000) @@ -28,7 +28,7 @@ if(CMAKE_CXX_COMPILER_ID MATCHES "GNU") endif() # We enable -O1 level optimsations, even when compiling debug wasm, otherwise we get "local count too large" at runtime. -# We prioritise reducing size of final artifacts in release with -Oz. +# We prioritize reducing size of final artifacts in release with -Oz. if(WASM) set(CMAKE_CXX_FLAGS_DEBUG "-O1 -g") set(CMAKE_C_FLAGS_DEBUG "-O1 -g") @@ -74,6 +74,7 @@ add_subdirectory(barretenberg/srs) add_subdirectory(barretenberg/stdlib) add_subdirectory(barretenberg/sumcheck) add_subdirectory(barretenberg/transcript) +add_subdirectory(barretenberg/translator_vm) add_subdirectory(barretenberg/ultra_honk) add_subdirectory(barretenberg/wasi) @@ -130,8 +131,8 @@ add_library( ) if(WASM) - # With binaryen installed, it seems its wasm backend optimiser gets invoked automatically. - # Due to either a bug in the optimiser, or non-standards compliant c++ in crypto/aes, tests start failing with + # With binaryen installed, it seems its wasm backend optimizer gets invoked automatically. + # Due to either a bug in the optimizer, or non-standards compliant c++ in crypto/aes, tests start failing with # -O3 level optimizations. We force down to -O2 for current workaround. # TODO: Time has passed, check if this is still needed. # UPDATE: Uninstall binaryen and any need downstream. diff --git a/barretenberg/cpp/src/barretenberg/bb/main.cpp b/barretenberg/cpp/src/barretenberg/bb/main.cpp index 11791b2d383..0cc46d9dd24 100644 --- a/barretenberg/cpp/src/barretenberg/bb/main.cpp +++ b/barretenberg/cpp/src/barretenberg/bb/main.cpp @@ -1,5 +1,6 @@ #include "barretenberg/dsl/acir_format/acir_format.hpp" #include "barretenberg/dsl/types.hpp" +#include "barretenberg/plonk/proof_system/proving_key/serialize.hpp" #include "config.hpp" #include "get_bytecode.hpp" #include "get_crs.hpp" @@ -183,7 +184,7 @@ bool verify(const std::string& proof_path, bool recursive, const std::string& vk * @param bytecodePath Path to the file containing the serialized circuit * @param outputPath Path to write the verification key to */ -void writeVk(const std::string& bytecodePath, const std::string& outputPath) +void write_vk(const std::string& bytecodePath, const std::string& outputPath) { auto constraint_system = get_constraint_system(bytecodePath); auto acir_composer = init(constraint_system); @@ -199,6 +200,22 @@ void writeVk(const std::string& bytecodePath, const std::string& outputPath) } } +void write_pk(const std::string& bytecodePath, const std::string& outputPath) +{ + auto constraint_system = get_constraint_system(bytecodePath); + auto acir_composer = init(constraint_system); + auto pk = acir_composer.init_proving_key(constraint_system); + auto serialized_pk = to_buffer(*pk); + + if (outputPath == "-") { + writeRawBytesToStdout(serialized_pk); + vinfo("pk written to stdout"); + } else { + write_file(outputPath, serialized_pk); + vinfo("pk written to: ", outputPath); + } +} + /** * @brief Writes a Solidity verifier contract for an ACIR circuit to a file * @@ -253,7 +270,7 @@ void contract(const std::string& output_path, const std::string& vk_path) * @param vk_path Path to the file containing the serialized verification key * @param output_path Path to write the proof to */ -void proofAsFields(const std::string& proof_path, std::string const& vk_path, const std::string& output_path) +void proof_as_fields(const std::string& proof_path, std::string const& vk_path, const std::string& output_path) { auto acir_composer = init(); auto vk_data = from_buffer(read_file(vk_path)); @@ -282,7 +299,7 @@ void proofAsFields(const std::string& proof_path, std::string const& vk_path, co * @param vk_path Path to the file containing the serialized verification key * @param output_path Path to write the verification key to */ -void vkAsFields(const std::string& vk_path, const std::string& output_path) +void vk_as_fields(const std::string& vk_path, const std::string& output_path) { auto acir_composer = init(); auto vk_data = from_buffer(read_file(vk_path)); @@ -311,7 +328,7 @@ void vkAsFields(const std::string& vk_path, const std::string& output_path) * * @param output_path Path to write the information to */ -void acvmInfo(const std::string& output_path) +void acvm_info(const std::string& output_path) { const char* jsonData = R"({ @@ -335,12 +352,12 @@ void acvmInfo(const std::string& output_path) } } -bool flagPresent(std::vector& args, const std::string& flag) +bool flag_present(std::vector& args, const std::string& flag) { return std::find(args.begin(), args.end(), flag) != args.end(); } -std::string getOption(std::vector& args, const std::string& option, const std::string& defaultValue) +std::string get_option(std::vector& args, const std::string& option, const std::string& defaultValue) { auto itr = std::find(args.begin(), args.end(), option); return (itr != args.end() && std::next(itr) != args.end()) ? *(std::next(itr)) : defaultValue; @@ -350,7 +367,7 @@ int main(int argc, char* argv[]) { try { std::vector args(argv + 1, argv + argc); - verbose = flagPresent(args, "-v") || flagPresent(args, "--verbose"); + verbose = flag_present(args, "-v") || flag_present(args, "--verbose"); if (args.empty()) { std::cerr << "No command provided.\n"; @@ -359,12 +376,13 @@ int main(int argc, char* argv[]) std::string command = args[0]; - std::string bytecode_path = getOption(args, "-b", "./target/acir.gz"); - std::string witness_path = getOption(args, "-w", "./target/witness.gz"); - std::string proof_path = getOption(args, "-p", "./proofs/proof"); - std::string vk_path = getOption(args, "-k", "./target/vk"); - CRS_PATH = getOption(args, "-c", "./crs"); - bool recursive = flagPresent(args, "-r") || flagPresent(args, "--recursive"); + std::string bytecode_path = get_option(args, "-b", "./target/acir.gz"); + std::string witness_path = get_option(args, "-w", "./target/witness.gz"); + std::string proof_path = get_option(args, "-p", "./proofs/proof"); + std::string vk_path = get_option(args, "-k", "./target/vk"); + std::string pk_path = get_option(args, "-r", "./target/pk"); + CRS_PATH = get_option(args, "-c", "./crs"); + bool recursive = flag_present(args, "-r") || flag_present(args, "--recursive"); // Skip CRS initialization for any command which doesn't require the CRS. if (command == "--version") { @@ -372,8 +390,8 @@ int main(int argc, char* argv[]) return 0; } if (command == "info") { - std::string output_path = getOption(args, "-o", "info.json"); - acvmInfo(output_path); + std::string output_path = get_option(args, "-o", "info.json"); + acvm_info(output_path); return 0; } @@ -381,24 +399,27 @@ int main(int argc, char* argv[]) return proveAndVerify(bytecode_path, witness_path, recursive) ? 0 : 1; } if (command == "prove") { - std::string output_path = getOption(args, "-o", "./proofs/proof"); + std::string output_path = get_option(args, "-o", "./proofs/proof"); prove(bytecode_path, witness_path, recursive, output_path); } else if (command == "gates") { gateCount(bytecode_path); } else if (command == "verify") { return verify(proof_path, recursive, vk_path) ? 0 : 1; } else if (command == "contract") { - std::string output_path = getOption(args, "-o", "./target/contract.sol"); + std::string output_path = get_option(args, "-o", "./target/contract.sol"); contract(output_path, vk_path); } else if (command == "write_vk") { - std::string output_path = getOption(args, "-o", "./target/vk"); - writeVk(bytecode_path, output_path); + std::string output_path = get_option(args, "-o", "./target/vk"); + write_vk(bytecode_path, output_path); + } else if (command == "write_pk") { + std::string output_path = get_option(args, "-o", "./target/pk"); + write_pk(bytecode_path, output_path); } else if (command == "proof_as_fields") { - std::string output_path = getOption(args, "-o", proof_path + "_fields.json"); - proofAsFields(proof_path, vk_path, output_path); + std::string output_path = get_option(args, "-o", proof_path + "_fields.json"); + proof_as_fields(proof_path, vk_path, output_path); } else if (command == "vk_as_fields") { - std::string output_path = getOption(args, "-o", vk_path + "_fields.json"); - vkAsFields(vk_path, output_path); + std::string output_path = get_option(args, "-o", vk_path + "_fields.json"); + vk_as_fields(vk_path, output_path); } else { std::cerr << "Unknown command: " << command << "\n"; return 1; diff --git a/barretenberg/cpp/src/barretenberg/benchmark/relations_bench/relations.bench.cpp b/barretenberg/cpp/src/barretenberg/benchmark/relations_bench/relations.bench.cpp index 3300fb4bf8e..d27dc35f9bc 100644 --- a/barretenberg/cpp/src/barretenberg/benchmark/relations_bench/relations.bench.cpp +++ b/barretenberg/cpp/src/barretenberg/benchmark/relations_bench/relations.bench.cpp @@ -1,3 +1,4 @@ +#include "barretenberg/flavor/ecc_vm.hpp" #include "barretenberg/flavor/goblin_translator.hpp" #include "barretenberg/flavor/goblin_ultra.hpp" #include "barretenberg/flavor/ultra.hpp" @@ -7,12 +8,16 @@ namespace { auto& engine = numeric::random::get_debug_engine(); } +using namespace proof_system::honk::sumcheck; + namespace proof_system::benchmark::relations { -using FF = barretenberg::fr; +using Fr = barretenberg::fr; +using Fq = grumpkin::fr; template void execute_relation(::benchmark::State& state) { + using FF = typename Flavor::FF; using AllValues = typename Flavor::AllValues; using SumcheckArrayOfValuesOverSubrelations = typename Relation::SumcheckArrayOfValuesOverSubrelations; @@ -29,82 +34,118 @@ template void execute_relation(::benchmark: } } -void auxiliary_relation(::benchmark::State& state) noexcept +void ultra_auxiliary_relation(::benchmark::State& state) noexcept { - execute_relation>(state); + execute_relation>(state); } -BENCHMARK(auxiliary_relation); +BENCHMARK(ultra_auxiliary_relation); -void elliptic_relation(::benchmark::State& state) noexcept +void ultra_elliptic_relation(::benchmark::State& state) noexcept { - execute_relation>(state); + execute_relation>(state); } -BENCHMARK(elliptic_relation); +BENCHMARK(ultra_elliptic_relation); -void ecc_op_queue_relation(::benchmark::State& state) noexcept +void ultra_ecc_op_queue_relation(::benchmark::State& state) noexcept { - execute_relation>(state); + execute_relation>(state); } -BENCHMARK(ecc_op_queue_relation); +BENCHMARK(ultra_ecc_op_queue_relation); -void gen_perm_sort_relation(::benchmark::State& state) noexcept +void ultra_gen_perm_sort_relation(::benchmark::State& state) noexcept { - execute_relation>(state); + execute_relation>(state); } -BENCHMARK(gen_perm_sort_relation); +BENCHMARK(ultra_gen_perm_sort_relation); -void lookup_relation(::benchmark::State& state) noexcept +void ultralookup_relation(::benchmark::State& state) noexcept { - execute_relation>(state); + execute_relation>(state); } -BENCHMARK(lookup_relation); +BENCHMARK(ultralookup_relation); void ultra_permutation_relation(::benchmark::State& state) noexcept { - execute_relation>(state); + execute_relation>(state); } BENCHMARK(ultra_permutation_relation); void ultra_arithmetic_relation(::benchmark::State& state) noexcept { - execute_relation>(state); + execute_relation>(state); } BENCHMARK(ultra_arithmetic_relation); void translator_decomposition_relation(::benchmark::State& state) noexcept { - execute_relation>(state); + execute_relation>(state); } BENCHMARK(translator_decomposition_relation); void translator_opcode_constraint_relation(::benchmark::State& state) noexcept { - execute_relation>(state); + execute_relation>(state); } BENCHMARK(translator_opcode_constraint_relation); void translator_accumulator_transfer_relation(::benchmark::State& state) noexcept { - execute_relation>(state); + execute_relation>(state); } BENCHMARK(translator_accumulator_transfer_relation); void translator_gen_perm_sort_relation(::benchmark::State& state) noexcept { - execute_relation>(state); + execute_relation>(state); } BENCHMARK(translator_gen_perm_sort_relation); void translator_non_native_field_relation(::benchmark::State& state) noexcept { - execute_relation>(state); + execute_relation>(state); } BENCHMARK(translator_non_native_field_relation); void translator_permutation_relation(::benchmark::State& state) noexcept { - execute_relation>(state); + execute_relation>(state); } BENCHMARK(translator_permutation_relation); +void eccvm_lookup_relation(::benchmark::State& state) noexcept +{ + execute_relation>(state); +} +BENCHMARK(eccvm_lookup_relation); + +void eccvm_msm_relation(::benchmark::State& state) noexcept +{ + execute_relation>(state); +} +BENCHMARK(eccvm_msm_relation); + +void eccvm_point_table_relation(::benchmark::State& state) noexcept +{ + execute_relation>(state); +} +BENCHMARK(eccvm_point_table_relation); + +void eccvm_set_relation(::benchmark::State& state) noexcept +{ + execute_relation>(state); +} +BENCHMARK(eccvm_set_relation); + +void eccvm_transcript_relation(::benchmark::State& state) noexcept +{ + execute_relation>(state); +} +BENCHMARK(eccvm_transcript_relation); + +void eccvm_wnaf_relation(::benchmark::State& state) noexcept +{ + execute_relation>(state); +} +BENCHMARK(eccvm_wnaf_relation); + } // namespace proof_system::benchmark::relations diff --git a/barretenberg/cpp/src/barretenberg/benchmark/widgets_bench/widget.bench.cpp b/barretenberg/cpp/src/barretenberg/benchmark/widgets_bench/widget.bench.cpp index eb2d7ec79da..454625eafd8 100644 --- a/barretenberg/cpp/src/barretenberg/benchmark/widgets_bench/widget.bench.cpp +++ b/barretenberg/cpp/src/barretenberg/benchmark/widgets_bench/widget.bench.cpp @@ -50,10 +50,8 @@ void plookup_auxiliary_kernel(::benchmark::State& state) noexcept for (auto _ : state) { // NOTE: this simply calls the following 3 functions it does NOT try to replicate ProverPlookupAuxiliaryWidget // logic exactly - widget::containers::coefficient_array linear_terms; - FFTKernel::compute_linear_terms(polynomials, challenges, linear_terms, 0); - barretenberg::fr sum_of_linear_terms = FFTKernel::sum_linear_terms(polynomials, challenges, linear_terms, 0); - FFTKernel::compute_non_linear_terms(polynomials, challenges, sum_of_linear_terms, 0); + barretenberg::fr result{ 0 }; + FFTKernel::accumulate_contribution(polynomials, challenges, result, 0); } } BENCHMARK(plookup_auxiliary_kernel); diff --git a/barretenberg/cpp/src/barretenberg/commitment_schemes/shplonk/shplonk.hpp b/barretenberg/cpp/src/barretenberg/commitment_schemes/shplonk/shplonk.hpp index 54fed18e988..06b258644ac 100644 --- a/barretenberg/cpp/src/barretenberg/commitment_schemes/shplonk/shplonk.hpp +++ b/barretenberg/cpp/src/barretenberg/commitment_schemes/shplonk/shplonk.hpp @@ -205,7 +205,7 @@ template class ShplonkVerifier_ { std::vector inverse_vanishing_evals; inverse_vanishing_evals.reserve(num_claims); for (const auto& claim : claims) { - // Note: no need for batch inversion; emulated inverison is cheap. (just show known inverse is valid) + // Note: no need for batch inversion; emulated inversion is cheap. (just show known inverse is valid) inverse_vanishing_evals.emplace_back((z_challenge - claim.opening_pair.challenge).invert()); } diff --git a/barretenberg/cpp/src/barretenberg/commitment_schemes/zeromorph/zeromorph.hpp b/barretenberg/cpp/src/barretenberg/commitment_schemes/zeromorph/zeromorph.hpp index 96c8a980c9b..1cd359ebe69 100644 --- a/barretenberg/cpp/src/barretenberg/commitment_schemes/zeromorph/zeromorph.hpp +++ b/barretenberg/cpp/src/barretenberg/commitment_schemes/zeromorph/zeromorph.hpp @@ -1,6 +1,6 @@ #pragma once +#include "barretenberg/common/zip_view.hpp" #include "barretenberg/polynomials/polynomial.hpp" - namespace proof_system::honk::pcs::zeromorph { /** @@ -188,7 +188,7 @@ template class ZeroMorphProver_ { * * where f_batched = \sum_{i=0}^{m-1}\rho^i*f_i, g_batched = \sum_{i=0}^{l-1}\rho^{m+i}*g_i * - * and concatenation_term = \sum_{i=0}^{concatenation_index}(x^{i * min_N + 1}concatenation_groups_batched_{i}) + * and concatenation_term = \sum_{i=0}^{num_chunks_per_group}(x^{i * min_N + 1}concatenation_groups_batched_{i}) * * @note The concatenation term arises from an implementation detail in the Goblin Translator and is not part of the * conventional ZM protocol @@ -240,7 +240,7 @@ template class ZeroMorphProver_ { } // If necessary, add to Z_x the contribution related to concatenated polynomials: - // \sum_{i=0}^{concatenation_index}(x^{i * min_n + 1}concatenation_groups_batched_{i}). + // \sum_{i=0}^{num_chunks_per_group}(x^{i * min_n + 1}concatenation_groups_batched_{i}). // We are effectively reconstructing concatenated polynomials from their chunks now that we know x // Note: this is an implementation detail related to Goblin Translator and is not part of the standard protocol. if (!concatenation_groups_batched.empty()) { @@ -314,18 +314,20 @@ template class ZeroMorphProver_ { */ static void prove(const auto& f_polynomials, const auto& g_polynomials, - auto& evaluations, + auto&& f_evaluations, + auto&& g_shift_evaluations, auto& multilinear_challenge, auto& commitment_key, - auto& transcript) + auto& transcript, + const std::vector>& concatenated_polynomials = {}, + const std::vector& concatenated_evaluations = {}, + const std::vector>>& concatenation_groups = {}) { // Generate batching challenge \rho and powers 1,...,\rho^{m-1} FF rho = transcript.get_challenge("rho"); - std::vector rhos = powers_of_challenge(rho, evaluations.size()); // Extract multilinear challenge u and claimed multilinear evaluations from Sumcheck output std::span u_challenge = multilinear_challenge; - auto claimed_evaluations = evaluations.pointer_view(); size_t log_N = u_challenge.size(); size_t N = 1 << log_N; @@ -337,24 +339,47 @@ template class ZeroMorphProver_ { // evaluations produced by sumcheck of h_i = g_i_shifted. auto batched_evaluation = FF(0); Polynomial f_batched(N); // batched unshifted polynomials - size_t poly_idx = 0; // TODO(#391) zip - for (auto& f_poly : f_polynomials) { - f_batched.add_scaled(f_poly, rhos[poly_idx]); - batched_evaluation += rhos[poly_idx] * (*claimed_evaluations[poly_idx]); - ++poly_idx; + FF batching_scalar = FF(1); + for (auto [f_poly, f_eval] : zip_view(f_polynomials, f_evaluations)) { + f_batched.add_scaled(f_poly, batching_scalar); + batched_evaluation += batching_scalar * f_eval; + batching_scalar *= rho; } Polynomial g_batched(N); // batched to-be-shifted polynomials - for (auto& g_poly : g_polynomials) { - g_batched.add_scaled(g_poly, rhos[poly_idx]); - batched_evaluation += rhos[poly_idx] * (*claimed_evaluations[poly_idx]); - ++poly_idx; + for (auto [g_poly, g_shift_eval] : zip_view(g_polynomials, g_shift_evaluations)) { + g_batched.add_scaled(g_poly, batching_scalar); + batched_evaluation += batching_scalar * g_shift_eval; + batching_scalar *= rho; }; + size_t num_groups = concatenation_groups.size(); + size_t num_chunks_per_group = concatenation_groups.empty() ? 0 : concatenation_groups[0].size(); + // Concatenated polynomials + // std::vector concatenated_polynomials; + Polynomial concatenated_batched(N); + + // construct concatention_groups_batched + std::vector concatenation_groups_batched; + for (size_t i = 0; i < num_chunks_per_group; ++i) { + concatenation_groups_batched.push_back(Polynomial(N)); + } + // for each group + for (size_t i = 0; i < num_groups; ++i) { + concatenated_batched.add_scaled(concatenated_polynomials[i], batching_scalar); + // for each element in a group + for (size_t j = 0; j < num_chunks_per_group; ++j) { + concatenation_groups_batched[j].add_scaled(concatenation_groups[i][j], batching_scalar); + } + batched_evaluation += batching_scalar * concatenated_evaluations[i]; + batching_scalar *= rho; + } + // Compute the full batched polynomial f = f_batched + g_batched.shifted() = f_batched + h_batched. This is the // polynomial for which we compute the quotients q_k and prove f(u) = v_batched. auto f_polynomial = f_batched; f_polynomial += g_batched.shifted(); + f_polynomial += concatenated_batched; // Compute the multilinear quotients q_k = q_k(X_0, ..., X_{k-1}) auto quotients = compute_multilinear_quotients(f_polynomial, u_challenge); @@ -386,8 +411,13 @@ template class ZeroMorphProver_ { compute_partially_evaluated_degree_check_polynomial(batched_quotient, quotients, y_challenge, x_challenge); // Compute ZeroMorph identity polynomial Z partially evaluated at x - auto Z_x = compute_partially_evaluated_zeromorph_identity_polynomial( - f_batched, g_batched, quotients, batched_evaluation, u_challenge, x_challenge); + auto Z_x = compute_partially_evaluated_zeromorph_identity_polynomial(f_batched, + g_batched, + quotients, + batched_evaluation, + u_challenge, + x_challenge, + concatenation_groups_batched); // Compute batched degree-check and ZM-identity quotient polynomial pi auto pi_polynomial = @@ -468,7 +498,7 @@ template class ZeroMorphVerifier_ { * + concatentation_term * where * - * concatenation_term = \sum{i=0}^{o-1}\sum_{j=0}^{concatenation_index}(rho^{m+l+i} * x^{j * min_N + 1} + * concatenation_term = \sum{i=0}^{o-1}\sum_{j=0}^{num_chunks_per_group}(rho^{m+l+i} * x^{j * min_N + 1} * * concatenation_groups_commitments_{i}_{j}) * * @note The concatenation term arises from an implementation detail in the Goblin Translator and is not part of the @@ -490,7 +520,7 @@ template class ZeroMorphVerifier_ { FF batched_evaluation, FF x_challenge, std::vector u_challenge, - std::vector> concatenation_groups_commitments = {}) + const std::vector>& concatenation_groups_commitments = {}) { size_t log_N = C_q_k.size(); size_t N = 1 << log_N; @@ -600,23 +630,33 @@ template class ZeroMorphVerifier_ { * @param transcript * @return std::array Inputs to the final pairing check */ - static std::array verify(auto& commitments, - auto& claimed_evaluations, - auto& multivariate_challenge, - auto& transcript) + static std::array verify( + auto&& unshifted_commitments, + auto&& to_be_shifted_commitments, + auto&& unshifted_evaluations, + auto&& shifted_evaluations, + auto& multivariate_challenge, + auto& transcript, + const std::vector>& concatenation_group_commitments = {}, + const std::vector& concatenated_evaluations = {}) { size_t log_N = multivariate_challenge.size(); FF rho = transcript.get_challenge("rho"); - // Compute powers of batching challenge rho - std::vector rhos = pcs::zeromorph::powers_of_challenge(rho, claimed_evaluations.size()); - // Construct batched evaluation v = sum_{i=0}^{m-1}\rho^i*f_i(u) + sum_{i=0}^{l-1}\rho^{m+i}*h_i(u) FF batched_evaluation = FF(0); - size_t evaluation_idx = 0; - for (auto& value : claimed_evaluations.get_unshifted_then_shifted()) { - batched_evaluation += value * rhos[evaluation_idx]; - ++evaluation_idx; + FF batching_scalar = FF(1); + for (auto& value : unshifted_evaluations) { + batched_evaluation += value * batching_scalar; + batching_scalar *= rho; + } + for (auto& value : shifted_evaluations) { + batched_evaluation += value * batching_scalar; + batching_scalar *= rho; + } + for (auto& value : concatenated_evaluations) { + batched_evaluation += value * batching_scalar; + batching_scalar *= rho; } // Receive commitments [q_k] @@ -639,13 +679,14 @@ template class ZeroMorphVerifier_ { auto C_zeta_x = compute_C_zeta_x(C_q, C_q_k, y_challenge, x_challenge); // Compute commitment C_{Z_x} - Commitment C_Z_x = compute_C_Z_x(commitments.get_unshifted(), - commitments.get_to_be_shifted(), + Commitment C_Z_x = compute_C_Z_x(unshifted_commitments, + to_be_shifted_commitments, C_q_k, rho, batched_evaluation, x_challenge, - multivariate_challenge); + multivariate_challenge, + concatenation_group_commitments); // Compute commitment C_{\zeta,Z} auto C_zeta_Z = C_zeta_x + C_Z_x * z_challenge; diff --git a/barretenberg/cpp/src/barretenberg/commitment_schemes/zeromorph/zeromorph.test.cpp b/barretenberg/cpp/src/barretenberg/commitment_schemes/zeromorph/zeromorph.test.cpp index 82b6191eca2..30876c73e50 100644 --- a/barretenberg/cpp/src/barretenberg/commitment_schemes/zeromorph/zeromorph.test.cpp +++ b/barretenberg/cpp/src/barretenberg/commitment_schemes/zeromorph/zeromorph.test.cpp @@ -80,135 +80,25 @@ template class ZeroMorphTest : public CommitmentTest { auto prover_transcript = BaseTranscript::prover_init_empty(); // Execute Prover protocol - { - auto rho = prover_transcript.get_challenge("ZM:rho"); - - // Compute batching of f_i and g_i polynomials: sum_{i=0}^{m-1}\rho^i*f_i and - // sum_{i=0}^{l-1}\rho^{m+i}*h_i, and also batched evaluation v = sum_{i=0}^{m-1}\rho^i*v_i + - // sum_{i=0}^{l-1}\rho^{m+i}*w_i. - auto f_batched = Polynomial(N); - auto g_batched = Polynomial(N); - auto v_evaluation = Fr(0); - auto rho_pow = Fr(1); - for (size_t i = 0; i < NUM_UNSHIFTED; ++i) { - f_batched.add_scaled(f_polynomials[i], rho_pow); - v_evaluation += rho_pow * v_evaluations[i]; - rho_pow *= rho; - } - for (size_t i = 0; i < NUM_SHIFTED; ++i) { - g_batched.add_scaled(g_polynomials[i], rho_pow); - v_evaluation += rho_pow * w_evaluations[i]; - rho_pow *= rho; - } - - // The new f is f_batched + g_batched.shifted() = f_batched + h_batched - auto f_polynomial = f_batched; - f_polynomial += g_batched.shifted(); - - // Compute the multilinear quotients q_k = q_k(X_0, ..., X_{k-1}) - auto quotients = ZeroMorphProver::compute_multilinear_quotients(f_polynomial, u_challenge); - - // Compute and send commitments C_{q_k} = [q_k], k = 0,...,d-1 - std::vector q_k_commitments; - q_k_commitments.reserve(log_N); - for (size_t idx = 0; idx < log_N; ++idx) { - q_k_commitments[idx] = this->commit(quotients[idx]); - std::string label = "ZM:C_q_" + std::to_string(idx); - prover_transcript.send_to_verifier(label, q_k_commitments[idx]); - } - - // Get challenge y - auto y_challenge = prover_transcript.get_challenge("ZM:y"); - - // Compute the batched, lifted-degree quotient \hat{q} - auto batched_quotient = ZeroMorphProver::compute_batched_lifted_degree_quotient(quotients, y_challenge, N); - - // Compute and send the commitment C_q = [\hat{q}] - auto q_commitment = this->commit(batched_quotient); - prover_transcript.send_to_verifier("ZM:C_q", q_commitment); - - // Get challenges x and z - auto [x_challenge, z_challenge] = prover_transcript.get_challenges("ZM:x", "ZM:z"); - - // Compute degree check polynomial \zeta partially evaluated at x - auto zeta_x = ZeroMorphProver::compute_partially_evaluated_degree_check_polynomial( - batched_quotient, quotients, y_challenge, x_challenge); - - // Compute ZeroMorph identity polynomial Z partially evaluated at x - auto Z_x = ZeroMorphProver::compute_partially_evaluated_zeromorph_identity_polynomial( - f_batched, g_batched, quotients, v_evaluation, u_challenge, x_challenge); - - // Compute batched degree and ZM-identity quotient polynomial pi - auto pi_polynomial = ZeroMorphProver::compute_batched_evaluation_and_degree_check_quotient( - zeta_x, Z_x, x_challenge, z_challenge); - - // Compute and send proof commitment pi - auto pi_commitment = this->commit(pi_polynomial); - prover_transcript.send_to_verifier("ZM:PI", pi_commitment); - } + ZeroMorphProver::prove(f_polynomials, + g_polynomials, + v_evaluations, + w_evaluations, + u_challenge, + this->commitment_key, + prover_transcript); auto verifier_transcript = BaseTranscript::verifier_init_empty(prover_transcript); // Execute Verifier protocol - { - // Challenge rho - auto rho = verifier_transcript.get_challenge("ZM:rho"); - - // Construct batched evaluation v = sum_{i=0}^{m-1}\rho^i*v_i + sum_{i=0}^{l-1}\rho^{m+i}*w_i - auto v_evaluation = Fr(0); - auto rho_pow = Fr(1); - for (size_t i = 0; i < NUM_UNSHIFTED; ++i) { - v_evaluation += rho_pow * v_evaluations[i]; - rho_pow *= rho; - } - for (size_t i = 0; i < NUM_SHIFTED; ++i) { - v_evaluation += rho_pow * w_evaluations[i]; - rho_pow *= rho; - } - - // Receive commitments [q_k] - std::vector C_q_k; - C_q_k.reserve(log_N); - for (size_t i = 0; i < log_N; ++i) { - C_q_k.emplace_back( - verifier_transcript.template receive_from_prover("ZM:C_q_" + std::to_string(i))); - } - - // Challenge y - auto y_challenge = verifier_transcript.get_challenge("ZM:y"); - - // Receive commitment C_{q} - auto C_q = verifier_transcript.template receive_from_prover("ZM:C_q"); - - // Challenges x, z - auto [x_challenge, z_challenge] = verifier_transcript.get_challenges("ZM:x", "ZM:z"); - - // Compute commitment C_{\zeta_x} - auto C_zeta_x = ZeroMorphVerifier::compute_C_zeta_x(C_q, C_q_k, y_challenge, x_challenge); - - // Compute commitment C_{Z_x} - Commitment C_Z_x = ZeroMorphVerifier::compute_C_Z_x( - f_commitments, g_commitments, C_q_k, rho, v_evaluation, x_challenge, u_challenge); - - // Compute commitment C_{\zeta,Z} - auto C_zeta_Z = C_zeta_x + C_Z_x * z_challenge; + auto pairing_points = ZeroMorphVerifier::verify( + f_commitments, g_commitments, v_evaluations, w_evaluations, u_challenge, verifier_transcript); - // Receive proof commitment \pi - auto C_pi = verifier_transcript.template receive_from_prover("ZM:PI"); + verified = this->vk()->pairing_check(pairing_points[0], pairing_points[1]); - // The prover and verifier manifests should agree - EXPECT_EQ(prover_transcript.get_manifest(), verifier_transcript.get_manifest()); + // The prover and verifier manifests should agree + EXPECT_EQ(prover_transcript.get_manifest(), verifier_transcript.get_manifest()); - // Construct inputs and perform pairing check to verify claimed evaluation - // Note: The pairing check (without the degree check component X^{N_max-N-1}) can be expressed naturally as - // e(C_{\zeta,Z}, [1]_2) = e(pi, [X - x]_2). This can be rearranged (e.g. see the plonk paper) as - // e(C_{\zeta,Z} - x*pi, [1]_2) * e(-pi, [X]_2) = 1, or - // e(P_0, [1]_2) * e(P_1, [X]_2) = 1 - auto P0 = C_zeta_Z + C_pi * x_challenge; - auto P1 = -C_pi; - verified = this->vk()->pairing_check(P0, P1); - // EXPECT_TRUE(verified); - } return verified; } }; @@ -335,159 +225,46 @@ template class ZeroMorphWithConcatenationTest : public CommitmentT // Initialize an empty BaseTranscript auto prover_transcript = BaseTranscript::prover_init_empty(); - // Execute Prover protocol - { - auto rho = prover_transcript.get_challenge("ZM:rho"); - - // Compute batching of f_i and g_i polynomials: sum_{i=0}^{m-1}\rho^i*f_i and - // sum_{i=0}^{l-1}\rho^{m+i}*h_i, and also batched evaluation v = sum_{i=0}^{m-1}\rho^i*v_i + - // sum_{i=0}^{l-1}\rho^{m+i}*w_i. - auto f_batched = Polynomial(N); - auto g_batched = Polynomial(N); - auto concatenated_batched = Polynomial(N); - std::vector concatenation_groups_batched; - auto v_evaluation = Fr(0); - auto rho_pow = Fr(1); - for (size_t i = 0; i < NUM_UNSHIFTED; ++i) { - f_batched.add_scaled(f_polynomials[i], rho_pow); - v_evaluation += rho_pow * v_evaluations[i]; - rho_pow *= rho; - } - for (size_t i = 0; i < NUM_SHIFTED; ++i) { - g_batched.add_scaled(g_polynomials[i], rho_pow); - v_evaluation += rho_pow * w_evaluations[i]; - rho_pow *= rho; - } - for (size_t i = 0; i < concatenation_index; ++i) { - concatenation_groups_batched.push_back(Polynomial(N)); - } - for (size_t i = 0; i < NUM_CONCATENATED; ++i) { - concatenated_batched.add_scaled(concatenated_polynomials[i], rho_pow); - for (size_t j = 0; j < concatenation_index; ++j) { - concatenation_groups_batched[j].add_scaled(concatenation_groups[i][j], rho_pow); - } - v_evaluation += rho_pow * c_evaluations[i]; - rho_pow *= rho; - } + std::vector> concatenated_polynomials_views; + for (auto& poly : concatenated_polynomials) { + concatenated_polynomials_views.emplace_back(poly); + } - // The new f is f_batched + g_batched.shifted() = f_batched + h_batched - auto f_polynomial = f_batched; - f_polynomial += g_batched.shifted(); - f_polynomial += concatenated_batched; - - // Compute the multilinear quotients q_k = q_k(X_0, ..., X_{k-1}) - auto quotients = ZeroMorphProver::compute_multilinear_quotients(f_polynomial, u_challenge); - - // Compute and send commitments C_{q_k} = [q_k], k = 0,...,d-1 - std::vector q_k_commitments; - q_k_commitments.reserve(log_N); - for (size_t idx = 0; idx < log_N; ++idx) { - q_k_commitments[idx] = this->commit(quotients[idx]); - std::string label = "ZM:C_q_" + std::to_string(idx); - prover_transcript.send_to_verifier(label, q_k_commitments[idx]); + std::vector>> concatenation_groups_views(concatenation_groups.size()); + for (auto [group_of_polys, group_of_views] : zip_view(concatenation_groups, concatenation_groups_views)) { + for (auto& poly : group_of_polys) { + group_of_views.emplace_back(poly); } - - // Get challenge y - auto y_challenge = prover_transcript.get_challenge("ZM:y"); - - // Compute the batched, lifted-degree quotient \hat{q} - auto batched_quotient = ZeroMorphProver::compute_batched_lifted_degree_quotient(quotients, y_challenge, N); - - // Compute and send the commitment C_q = [\hat{q}] - auto q_commitment = this->commit(batched_quotient); - prover_transcript.send_to_verifier("ZM:C_q", q_commitment); - - // Get challenges x and z - auto [x_challenge, z_challenge] = prover_transcript.get_challenges("ZM:x", "ZM:z"); - - // Compute degree check polynomial \zeta partially evaluated at x - auto zeta_x = ZeroMorphProver::compute_partially_evaluated_degree_check_polynomial( - batched_quotient, quotients, y_challenge, x_challenge); - - // Compute ZeroMorph identity polynomial Z partially evaluated at x - auto Z_x = ZeroMorphProver::compute_partially_evaluated_zeromorph_identity_polynomial( - f_batched, g_batched, quotients, v_evaluation, u_challenge, x_challenge, concatenation_groups_batched); - - // Compute batched degree and ZM-identity quotient polynomial pi - auto pi_polynomial = ZeroMorphProver::compute_batched_evaluation_and_degree_check_quotient( - zeta_x, Z_x, x_challenge, z_challenge); - - // Compute and send proof commitment pi - auto pi_commitment = this->commit(pi_polynomial); - prover_transcript.send_to_verifier("ZM:PI", pi_commitment); } + // Execute Prover protocol + ZeroMorphProver::prove(f_polynomials, // unshifted + g_polynomials, // to-be-shifted + v_evaluations, // unshifted + w_evaluations, // shifted + u_challenge, + this->commitment_key, + prover_transcript, + concatenated_polynomials_views, + c_evaluations, + concatenation_groups_views); auto verifier_transcript = BaseTranscript::verifier_init_empty(prover_transcript); // Execute Verifier protocol - { - // Challenge rho - auto rho = verifier_transcript.get_challenge("ZM:rho"); - - // Construct batched evaluation v = sum_{i=0}^{m-1}\rho^i*v_i + sum_{i=0}^{l-1}\rho^{m+i}*w_i - auto v_evaluation = Fr(0); - auto rho_pow = Fr(1); - for (size_t i = 0; i < NUM_UNSHIFTED; ++i) { - v_evaluation += rho_pow * v_evaluations[i]; - rho_pow *= rho; - } - for (size_t i = 0; i < NUM_SHIFTED; ++i) { - v_evaluation += rho_pow * w_evaluations[i]; - rho_pow *= rho; - } - for (size_t i = 0; i < NUM_CONCATENATED; ++i) { - v_evaluation += rho_pow * c_evaluations[i]; - rho_pow *= rho; - } - // Receive commitments [q_k] - std::vector C_q_k; - C_q_k.reserve(log_N); - for (size_t i = 0; i < log_N; ++i) { - C_q_k.emplace_back( - verifier_transcript.template receive_from_prover("ZM:C_q_" + std::to_string(i))); - } + auto pairing_points = ZeroMorphVerifier::verify(f_commitments, // unshifted + g_commitments, // to-be-shifted + v_evaluations, // unshifted + w_evaluations, // shifted + u_challenge, + verifier_transcript, + concatenation_groups_commitments, + c_evaluations); + + verified = this->vk()->pairing_check(pairing_points[0], pairing_points[1]); + + // The prover and verifier manifests should agree + EXPECT_EQ(prover_transcript.get_manifest(), verifier_transcript.get_manifest()); - // Challenge y - auto y_challenge = verifier_transcript.get_challenge("ZM:y"); - - // Receive commitment C_{q} - auto C_q = verifier_transcript.template receive_from_prover("ZM:C_q"); - - // Challenges x, z - auto [x_challenge, z_challenge] = verifier_transcript.get_challenges("ZM:x", "ZM:z"); - - // Compute commitment C_{\zeta_x} - auto C_zeta_x = ZeroMorphVerifier::compute_C_zeta_x(C_q, C_q_k, y_challenge, x_challenge); - - // Compute commitment C_{Z_x} - Commitment C_Z_x = ZeroMorphVerifier::compute_C_Z_x(f_commitments, - g_commitments, - C_q_k, - rho, - v_evaluation, - x_challenge, - u_challenge, - concatenation_groups_commitments); - - // Compute commitment C_{\zeta,Z} - auto C_zeta_Z = C_zeta_x + C_Z_x * z_challenge; - - // Receive proof commitment \pi - auto C_pi = verifier_transcript.template receive_from_prover("ZM:PI"); - - // The prover and verifier manifests should agree - EXPECT_EQ(prover_transcript.get_manifest(), verifier_transcript.get_manifest()); - - // Construct inputs and perform pairing check to verify claimed evaluation - // Note: The pairing check (without the degree check component X^{N_max-N-1}) can be expressed naturally as - // e(C_{\zeta,Z}, [1]_2) = e(pi, [X - x]_2). This can be rearranged (e.g. see the plonk paper) as - // e(C_{\zeta,Z} - x*pi, [1]_2) * e(-pi, [X]_2) = 1, or - // e(P_0, [1]_2) * e(P_1, [X]_2) = 1 - auto P0 = C_zeta_Z + C_pi * x_challenge; - auto P1 = -C_pi; - verified = this->vk()->pairing_check(P0, P1); - // EXPECT_TRUE(verified); - } return verified; } }; diff --git a/barretenberg/cpp/src/barretenberg/common/benchmark.hpp b/barretenberg/cpp/src/barretenberg/common/benchmark.hpp index 5d3034bbe6f..98715635c92 100644 --- a/barretenberg/cpp/src/barretenberg/common/benchmark.hpp +++ b/barretenberg/cpp/src/barretenberg/common/benchmark.hpp @@ -8,6 +8,8 @@ #include #include +#pragma GCC diagnostic ignored "-Wunused-result" // GCC13 hits this + namespace { /** * If user provides the env var BENCHMARK_FD write benchmarks to this fd, otherwise default to -1 (disable). diff --git a/barretenberg/cpp/src/barretenberg/common/constexpr_utils.hpp b/barretenberg/cpp/src/barretenberg/common/constexpr_utils.hpp index d25d8a15b88..ed11246196f 100644 --- a/barretenberg/cpp/src/barretenberg/common/constexpr_utils.hpp +++ b/barretenberg/cpp/src/barretenberg/common/constexpr_utils.hpp @@ -85,11 +85,11 @@ template constexpr void constexp * The compiler has no alias `X.template (args)` for `X.template operator()(args)` so we must * write it explicitly here * - * To summarise what the next line tells the compiler... + * To summarize what the next line tells the compiler... * 1. I want to call a member of `f` that expects one or more template parameters * 2. The member of `f` that I want to call is the function operator * 3. The template parameter is `Start` - * 4. The funtion operator itself contains no arguments + * 4. The function operator itself contains no arguments */ f.template operator()(); diff --git a/barretenberg/cpp/src/barretenberg/common/serialize.hpp b/barretenberg/cpp/src/barretenberg/common/serialize.hpp index fb8360ac8b9..7142b99cb32 100644 --- a/barretenberg/cpp/src/barretenberg/common/serialize.hpp +++ b/barretenberg/cpp/src/barretenberg/common/serialize.hpp @@ -181,21 +181,21 @@ inline void write(auto& buf, std::integral auto value) serialize::write(buf, value); } -// Optimised specialisation for reading arrays of bytes from a raw buffer. +// Optimized specialisation for reading arrays of bytes from a raw buffer. template inline void read(uint8_t const*& it, std::array& value) { std::copy(it, it + N, value.data()); it += N; } -// Optimised specialisation for writing arrays of bytes to a raw buffer. +// Optimized specialisation for writing arrays of bytes to a raw buffer. template inline void write(uint8_t*& buf, std::array const& value) { std::copy(value.begin(), value.end(), buf); buf += N; } -// Optimised specialisation for reading vectors of bytes from a raw buffer. +// Optimized specialisation for reading vectors of bytes from a raw buffer. inline void read(uint8_t const*& it, std::vector& value) { uint32_t size = 0; @@ -205,7 +205,7 @@ inline void read(uint8_t const*& it, std::vector& value) it += size; } -// Optimised specialisation for writing vectors of bytes to a raw buffer. +// Optimized specialisation for writing vectors of bytes to a raw buffer. inline void write(uint8_t*& buf, std::vector const& value) { write(buf, static_cast(value.size())); @@ -213,7 +213,7 @@ inline void write(uint8_t*& buf, std::vector const& value) buf += value.size(); } -// Optimised specialisation for reading vectors of bytes from an input stream. +// Optimized specialisation for reading vectors of bytes from an input stream. inline void read(std::istream& is, std::vector& value) { uint32_t size = 0; @@ -222,14 +222,14 @@ inline void read(std::istream& is, std::vector& value) is.read(reinterpret_cast(value.data()), static_cast(size)); } -// Optimised specialisation for writing vectors of bytes to an output stream. +// Optimized specialisation for writing vectors of bytes to an output stream. inline void write(std::ostream& os, std::vector const& value) { write(os, static_cast(value.size())); os.write(reinterpret_cast(value.data()), static_cast(value.size())); } -// Optimised specialisation for writing arrays of bytes to a vector. +// Optimized specialisation for writing arrays of bytes to a vector. template inline void write(std::vector& buf, std::array const& value) { buf.resize(buf.size() + N); @@ -237,7 +237,7 @@ template inline void write(std::vector& buf, std::array inline void write(std::ostream& os, std::array const& value) { os.write(reinterpret_cast(value.data()), value.size()); diff --git a/barretenberg/cpp/src/barretenberg/common/slab_allocator.hpp b/barretenberg/cpp/src/barretenberg/common/slab_allocator.hpp index 050360d4db5..a5d5a691e7d 100644 --- a/barretenberg/cpp/src/barretenberg/common/slab_allocator.hpp +++ b/barretenberg/cpp/src/barretenberg/common/slab_allocator.hpp @@ -13,7 +13,7 @@ namespace barretenberg { /** * Allocates a bunch of memory slabs sized to serve an UltraPLONK proof construction. - * If you want normal memory allocator behaviour, just don't call this init function. + * If you want normal memory allocator behavior, just don't call this init function. * * WARNING: If client code is still holding onto slabs from previous use, when those slabs * are released they'll end up back in the allocator. That's probably not desired as presumably diff --git a/barretenberg/cpp/src/barretenberg/common/zip_view.hpp b/barretenberg/cpp/src/barretenberg/common/zip_view.hpp index a055d82dada..f4683049c35 100644 --- a/barretenberg/cpp/src/barretenberg/common/zip_view.hpp +++ b/barretenberg/cpp/src/barretenberg/common/zip_view.hpp @@ -42,10 +42,10 @@ * iterates untill the first iterator hits end. * extends ownership to the end of the for loop, or untill zip goes out of scope. * - * possibly risky behaviour on clang, gcc for fun(const zip& z) when called as fun(zip(a,b)) + * possibly risky behavior on clang, gcc for fun(const zip& z) when called as fun(zip(a,b)) * * - * Depends on the following behaviour for for loops: + * Depends on the following behavior for for loops: * * // in for(x:zip) * // equiv: diff --git a/barretenberg/cpp/src/barretenberg/crypto/aes128/c_bind.cpp b/barretenberg/cpp/src/barretenberg/crypto/aes128/c_bind.cpp index dee106779f2..1b30cdedbb6 100644 --- a/barretenberg/cpp/src/barretenberg/crypto/aes128/c_bind.cpp +++ b/barretenberg/cpp/src/barretenberg/crypto/aes128/c_bind.cpp @@ -1,18 +1,21 @@ +#include "c_bind.hpp" #include "aes128.hpp" -#include "barretenberg/common/wasm_export.hpp" +#include "barretenberg/common/serialize.hpp" -WASM_EXPORT void aes__encrypt_buffer_cbc(uint8_t* in, uint8_t* iv, const uint8_t* key, const size_t length, uint8_t* r) +WASM_EXPORT void aes_encrypt_buffer_cbc( + uint8_t const* in, uint8_t const* iv, uint8_t const* key, uint32_t const* length, uint8_t** r) { - crypto::aes128::encrypt_buffer_cbc(in, iv, key, length); - for (size_t i = 0; i < length; ++i) { - r[i] = in[i]; - } + auto len = ntohl(*length); + crypto::aes128::encrypt_buffer_cbc((uint8_t*)in, (uint8_t*)iv, key, len); + std::vector result(in, in + len); + *r = to_heap_buffer(result); } -WASM_EXPORT void aes__decrypt_buffer_cbc(uint8_t* in, uint8_t* iv, const uint8_t* key, const size_t length, uint8_t* r) +WASM_EXPORT void aes_decrypt_buffer_cbc( + uint8_t const* in, uint8_t const* iv, uint8_t const* key, uint32_t const* length, uint8_t** r) { - crypto::aes128::decrypt_buffer_cbc(in, iv, key, length); - for (size_t i = 0; i < length; ++i) { - r[i] = in[i]; - } + auto len = ntohl(*length); + crypto::aes128::decrypt_buffer_cbc((uint8_t*)in, (uint8_t*)iv, key, len); + std::vector result(in, in + len); + *r = to_heap_buffer(result); } diff --git a/barretenberg/cpp/src/barretenberg/crypto/aes128/c_bind.hpp b/barretenberg/cpp/src/barretenberg/crypto/aes128/c_bind.hpp new file mode 100644 index 00000000000..e1c6c513a93 --- /dev/null +++ b/barretenberg/cpp/src/barretenberg/crypto/aes128/c_bind.hpp @@ -0,0 +1,11 @@ +#pragma once +#include +#include +#include +#include + +WASM_EXPORT void aes_encrypt_buffer_cbc( + uint8_t const* input, uint8_t const* iv, uint8_t const* key, uint32_t const* length, uint8_t** r); + +WASM_EXPORT void aes_decrypt_buffer_cbc( + uint8_t const* input, uint8_t const* iv, uint8_t const* key, uint32_t const* length, uint8_t** r); diff --git a/barretenberg/cpp/src/barretenberg/crypto/pedersen_hash/c_bind.cpp b/barretenberg/cpp/src/barretenberg/crypto/pedersen_hash/c_bind.cpp index 6f51283ed9b..3f4d41567a8 100644 --- a/barretenberg/cpp/src/barretenberg/crypto/pedersen_hash/c_bind.cpp +++ b/barretenberg/cpp/src/barretenberg/crypto/pedersen_hash/c_bind.cpp @@ -14,4 +14,14 @@ WASM_EXPORT void pedersen_hash(uint8_t const* inputs_buffer, uint32_t const* has auto r = crypto::pedersen_hash::hash(to_hash, ctx); barretenberg::fr::serialize_to_buffer(r, output); } + +WASM_EXPORT void pedersen_hash_buffer(uint8_t const* input_buffer, uint32_t const* hash_index, uint8_t* output) +{ + std::vector to_hash; + read(input_buffer, to_hash); + crypto::GeneratorContext ctx; + ctx.offset = static_cast(ntohl(*hash_index)); + auto r = crypto::pedersen_hash::hash_buffer(to_hash, ctx); + barretenberg::fr::serialize_to_buffer(r, output); +} } \ No newline at end of file diff --git a/barretenberg/cpp/src/barretenberg/crypto/pedersen_hash/c_bind.hpp b/barretenberg/cpp/src/barretenberg/crypto/pedersen_hash/c_bind.hpp index 869418762d2..7369e743c19 100644 --- a/barretenberg/cpp/src/barretenberg/crypto/pedersen_hash/c_bind.hpp +++ b/barretenberg/cpp/src/barretenberg/crypto/pedersen_hash/c_bind.hpp @@ -8,4 +8,6 @@ extern "C" { using namespace barretenberg; WASM_EXPORT void pedersen_hash(fr::vec_in_buf inputs_buffer, uint32_t const* hash_index, fr::out_buf output); + +WASM_EXPORT void pedersen_hash_buffer(uint8_t const* input_buffer, uint32_t const* hash_index, fr::out_buf output); } \ No newline at end of file diff --git a/barretenberg/cpp/src/barretenberg/dsl/acir_format/block_constraint.cpp b/barretenberg/cpp/src/barretenberg/dsl/acir_format/block_constraint.cpp index 49bb94aab50..f655826386b 100644 --- a/barretenberg/cpp/src/barretenberg/dsl/acir_format/block_constraint.cpp +++ b/barretenberg/cpp/src/barretenberg/dsl/acir_format/block_constraint.cpp @@ -34,7 +34,7 @@ void create_block_constraints(Builder& builder, const BlockConstraint constraint ASSERT(op.access_type == 0); field_ct value = poly_to_field_ct(op.value, builder); field_ct index = poly_to_field_ct(op.index, builder); - // For a ROM table, constant read should be optimised out: + // For a ROM table, constant read should be optimized out: // The rom_table won't work with a constant read because the table may not be initialized ASSERT(op.index.q_l != 0); // We create a new witness w to avoid issues with non-valid witness assignements: diff --git a/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/acir_composer.cpp b/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/acir_composer.cpp index 5f7cee439c6..0dc4a117735 100644 --- a/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/acir_composer.cpp +++ b/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/acir_composer.cpp @@ -4,6 +4,7 @@ #include "barretenberg/dsl/acir_format/acir_format.hpp" #include "barretenberg/dsl/acir_format/recursion_constraint.hpp" #include "barretenberg/dsl/types.hpp" +#include "barretenberg/plonk/proof_system/proving_key/proving_key.hpp" #include "barretenberg/plonk/proof_system/proving_key/serialize.hpp" #include "barretenberg/plonk/proof_system/verification_key/sol_gen.hpp" #include "barretenberg/plonk/proof_system/verification_key/verification_key.hpp" @@ -30,12 +31,14 @@ void AcirComposer::create_circuit(acir_format::acir_format& constraint_system) vinfo("gates: ", builder_.get_total_circuit_size()); } -void AcirComposer::init_proving_key(acir_format::acir_format& constraint_system) +std::shared_ptr AcirComposer::init_proving_key( + acir_format::acir_format& constraint_system) { create_circuit(constraint_system); acir_format::Composer composer; vinfo("computing proving key..."); proving_key_ = composer.compute_proving_key(builder_); + return proving_key_; } std::vector AcirComposer::create_proof(acir_format::acir_format& constraint_system, diff --git a/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/acir_composer.hpp b/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/acir_composer.hpp index 32b678268e3..db78f067a22 100644 --- a/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/acir_composer.hpp +++ b/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/acir_composer.hpp @@ -14,7 +14,7 @@ class AcirComposer { void create_circuit(acir_format::acir_format& constraint_system); - void init_proving_key(acir_format::acir_format& constraint_system); + std::shared_ptr init_proving_key(acir_format::acir_format& constraint_system); std::vector create_proof(acir_format::acir_format& constraint_system, acir_format::WitnessVector& witness, diff --git a/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/c_bind.cpp b/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/c_bind.cpp index 0bdfbb519d2..b92213f9724 100644 --- a/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/c_bind.cpp +++ b/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/c_bind.cpp @@ -6,6 +6,7 @@ #include "barretenberg/common/serialize.hpp" #include "barretenberg/common/slab_allocator.hpp" #include "barretenberg/dsl/acir_format/acir_format.hpp" +#include "barretenberg/plonk/proof_system/proving_key/serialize.hpp" #include "barretenberg/plonk/proof_system/verification_key/verification_key.hpp" #include "barretenberg/srs/global_crs.hpp" #include @@ -73,6 +74,15 @@ WASM_EXPORT void acir_get_verification_key(in_ptr acir_composer_ptr, uint8_t** o *out = to_heap_buffer(to_buffer(*vk)); } +WASM_EXPORT void acir_get_proving_key(in_ptr acir_composer_ptr, uint8_t const* acir_vec, uint8_t** out) +{ + auto acir_composer = reinterpret_cast(*acir_composer_ptr); + auto constraint_system = acir_format::circuit_buf_to_acir_format(from_buffer>(acir_vec)); + auto pk = acir_composer->init_proving_key(constraint_system); + // We flatten to a vector first, as that's how we treat it on the calling side. + *out = to_heap_buffer(to_buffer(*pk)); +} + WASM_EXPORT void acir_verify_proof(in_ptr acir_composer_ptr, uint8_t const* proof_buf, bool const* is_recursive, diff --git a/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/c_bind.hpp b/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/c_bind.hpp index e17af7a260d..5ffa298b2fc 100644 --- a/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/c_bind.hpp +++ b/barretenberg/cpp/src/barretenberg/dsl/acir_proofs/c_bind.hpp @@ -38,6 +38,8 @@ WASM_EXPORT void acir_init_verification_key(in_ptr acir_composer_ptr); WASM_EXPORT void acir_get_verification_key(in_ptr acir_composer_ptr, uint8_t** out); +WASM_EXPORT void acir_get_proving_key(in_ptr acir_composer_ptr, uint8_t const* acir_vec, uint8_t** out); + WASM_EXPORT void acir_verify_proof(in_ptr acir_composer_ptr, uint8_t const* proof_buf, bool const* is_recursive, diff --git a/barretenberg/cpp/src/barretenberg/ecc/curves/bn254/fq.test.cpp b/barretenberg/cpp/src/barretenberg/ecc/curves/bn254/fq.test.cpp index 69f2065df0e..779f5c84712 100644 --- a/barretenberg/cpp/src/barretenberg/ecc/curves/bn254/fq.test.cpp +++ b/barretenberg/cpp/src/barretenberg/ecc/curves/bn254/fq.test.cpp @@ -5,7 +5,7 @@ using namespace barretenberg; // Used to ensure variables are evaluated at runtime and not compile time. -// If EXPECT_EQ macro params are evaluated at compile-time, compiler can optimise them away. +// If EXPECT_EQ macro params are evaluated at compile-time, compiler can optimize them away. // This triggers compiler errors due to the gtest suite expecting at least one test statement in a TEST macro void shallow_copy(const fq& in, fq& out) { diff --git a/barretenberg/cpp/src/barretenberg/ecc/pippenger.md b/barretenberg/cpp/src/barretenberg/ecc/pippenger.md index fcf4adf4a27..18f8b85941f 100644 --- a/barretenberg/cpp/src/barretenberg/ecc/pippenger.md +++ b/barretenberg/cpp/src/barretenberg/ecc/pippenger.md @@ -1,4 +1,4 @@ -# Implement cahe-optimised, parallelisable pippenger implementation +# Implement cache-optimized, parallelizable pippenger implementation ## The problem @@ -6,7 +6,7 @@ Pippenger's algorithm for batched scalar multiplications is the fastest known al We currently have pippenger implemented in barretenberg, which can process a scalar multiplication in ~4 micro-seconds (for large batches). -However, the algorithm, as it stands, is not parallelisable, due to the highly non-sequential memory access patterns. +However, the algorithm, as it stands, is not parallelizable, due to the highly non-sequential memory access patterns. ## The algorithm diff --git a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_composer.cpp b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_composer.cpp index 7ed34e4990d..d79ce9f310e 100644 --- a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_composer.cpp +++ b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_composer.cpp @@ -115,6 +115,5 @@ std::shared_ptr ECCVMComposer_::comput return verification_key; } template class ECCVMComposer_; -template class ECCVMComposer_; } // namespace proof_system::honk \ No newline at end of file diff --git a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_composer.hpp b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_composer.hpp index 416e9154f46..a92f06bef5c 100644 --- a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_composer.hpp +++ b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_composer.hpp @@ -32,15 +32,10 @@ template class ECCVMComposer_ { std::vector recursive_proof_public_input_indices; bool contains_recursive_proof = false; bool computed_witness = false; - ECCVMComposer_() - requires(std::same_as) - { - crs_factory_ = barretenberg::srs::get_grumpkin_crs_factory(); - }; ECCVMComposer_() requires(std::same_as) { - crs_factory_ = barretenberg::srs::get_crs_factory(); + crs_factory_ = barretenberg::srs::get_grumpkin_crs_factory(); }; explicit ECCVMComposer_( @@ -75,10 +70,8 @@ template class ECCVMComposer_ { }; }; extern template class ECCVMComposer_; -extern template class ECCVMComposer_; // TODO(#532): this pattern is weird; is this not instantiating the templates? using ECCVMComposer = ECCVMComposer_; -using ECCVMGrumpkinComposer = ECCVMComposer_; } // namespace proof_system::honk diff --git a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_composer.test.cpp b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_composer.test.cpp index e9296485e3d..4ae39747236 100644 --- a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_composer.test.cpp +++ b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_composer.test.cpp @@ -21,7 +21,7 @@ template class ECCVMComposerTests : public ::testing::Test { // TODO(640): The Standard Honk on Grumpkin test suite fails unless the SRS is initialized for every test. void SetUp() override { - if constexpr (std::is_same::value) { + if constexpr (std::is_same::value) { barretenberg::srs::init_grumpkin_crs_factory("../srs_db/grumpkin"); } else { barretenberg::srs::init_crs_factory("../srs_db/ignition"); @@ -29,7 +29,7 @@ template class ECCVMComposerTests : public ::testing::Test { }; }; -using FlavorTypes = ::testing::Types; +using FlavorTypes = ::testing::Types; TYPED_TEST_SUITE(ECCVMComposerTests, FlavorTypes); namespace { @@ -83,6 +83,7 @@ TYPED_TEST(ECCVMComposerTests, BaseCase) auto proof = prover.construct_proof(); auto verifier = composer.create_verifier(circuit_constructor); bool verified = verifier.verify_proof(proof); + ASSERT_TRUE(verified); } diff --git a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_prover.cpp b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_prover.cpp index a5b885f6443..d757c37c5ed 100644 --- a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_prover.cpp +++ b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_prover.cpp @@ -9,14 +9,6 @@ #include "barretenberg/relations/lookup_relation.hpp" #include "barretenberg/relations/permutation_relation.hpp" #include "barretenberg/sumcheck/sumcheck.hpp" -#include -#include -#include -#include -#include -#include -#include -#include namespace proof_system::honk { @@ -43,8 +35,8 @@ ECCVMProver_::ECCVMProver_(std::shared_ptr prover_polynomials.transcript_msm_transition = key->transcript_msm_transition; prover_polynomials.transcript_pc = key->transcript_pc; prover_polynomials.transcript_msm_count = key->transcript_msm_count; - prover_polynomials.transcript_x = key->transcript_x; - prover_polynomials.transcript_y = key->transcript_y; + prover_polynomials.transcript_Px = key->transcript_Px; + prover_polynomials.transcript_Py = key->transcript_Py; prover_polynomials.transcript_z1 = key->transcript_z1; prover_polynomials.transcript_z2 = key->transcript_z2; prover_polynomials.transcript_z1zero = key->transcript_z1zero; @@ -214,8 +206,8 @@ template void ECCVMProver_::execute_relation_check_ using Sumcheck = sumcheck::SumcheckProver; auto sumcheck = Sumcheck(key->circuit_size, transcript); - - sumcheck_output = sumcheck.prove(prover_polynomials, relation_parameters); + auto alpha = transcript.get_challenge("alpha"); + sumcheck_output = sumcheck.prove(prover_polynomials, relation_parameters, alpha); } /** @@ -311,6 +303,64 @@ template void ECCVMProver_::execute_final_pcs_round PCS::compute_opening_proof(commitment_key, shplonk_output.opening_pair, shplonk_output.witness, transcript); } +/** + * @brief Batch open the transcript polynomials as univariates for Translator consistency check + * TODO(#768): Find a better way to do this. See issue for details. + * + * @tparam Flavor + */ +template void ECCVMProver_::execute_transcript_consistency_univariate_opening_round() +{ + // Since IPA cannot currently handle polynomials for which the latter half of the coefficients are 0, we hackily + // batch the constant polynomial 1 in with the 5 transcript polynomials. See issue #768 for more details. + Polynomial hack(key->circuit_size); + for (size_t idx = 0; idx < key->circuit_size; idx++) { + hack[idx] = 1; + } + transcript.send_to_verifier("Translation:hack_commitment", commitment_key->commit(hack)); + + // Get the challenge at which we evaluate the polynomials as univariates + evaluation_challenge_x = transcript.get_challenge("Translation:evaluation_challenge_x"); + + translation_evaluations.op = key->transcript_op.evaluate(evaluation_challenge_x); + translation_evaluations.Px = key->transcript_Px.evaluate(evaluation_challenge_x); + translation_evaluations.Py = key->transcript_Py.evaluate(evaluation_challenge_x); + translation_evaluations.z1 = key->transcript_z1.evaluate(evaluation_challenge_x); + translation_evaluations.z2 = key->transcript_z2.evaluate(evaluation_challenge_x); + + // Add the univariate evaluations to the transcript + transcript.send_to_verifier("Translation:op", translation_evaluations.op); + transcript.send_to_verifier("Translation:Px", translation_evaluations.Px); + transcript.send_to_verifier("Translation:Py", translation_evaluations.Py); + transcript.send_to_verifier("Translation:z1", translation_evaluations.z1); + transcript.send_to_verifier("Translation:z2", translation_evaluations.z2); + transcript.send_to_verifier("Translation:hack_evaluation", hack.evaluate(evaluation_challenge_x)); + + // Get another challenge for batching the univariate claims + FF batching_challenge = transcript.get_challenge("Translation:batching_challenge"); + + // Collect the polynomials and evaluations to be batched + const size_t NUM_UNIVARIATES = 6; // 5 transcript polynomials plus the constant hack poly + std::array univariate_polynomials = { &key->transcript_op, &key->transcript_Px, + &key->transcript_Py, &key->transcript_z1, + &key->transcript_z2, &hack }; + std::array univariate_evaluations; + + // Constuct the batched polynomial and batched evaluation + Polynomial batched_univariate{ key->circuit_size }; + FF batched_evaluation{ 0 }; + auto batching_scalar = FF(1); + for (auto [eval, polynomial] : zip_view(univariate_evaluations, univariate_polynomials)) { + batched_univariate.add_scaled(*polynomial, batching_scalar); + batched_evaluation += eval * batching_scalar; + batching_scalar *= batching_challenge; + } + + // Compute a proof for the batched univariate opening + PCS::compute_opening_proof( + commitment_key, { evaluation_challenge_x, batched_evaluation }, batched_univariate, transcript); +} + template plonk::proof& ECCVMProver_::export_proof() { proof.proof_data = transcript.proof_data; @@ -319,47 +369,31 @@ template plonk::proof& ECCVMProver_::export_proof() template plonk::proof& ECCVMProver_::construct_proof() { - // Add circuit size public input size and public inputs to transcript. execute_preamble_round(); - // Compute first three wire commitments execute_wire_commitments_round(); - // Compute sorted list accumulator and commitment execute_log_derivative_commitments_round(); - // Fiat-Shamir: beta & gamma - // Compute grand product(s) and commitments. execute_grand_product_computation_round(); - // Fiat-Shamir: alpha - // Run sumcheck subprotocol. execute_relation_check_rounds(); - // Fiat-Shamir: rho - // Compute Fold polynomials and their commitments. execute_univariatization_round(); - // Fiat-Shamir: r - // Compute Fold evaluations execute_pcs_evaluation_round(); - // Fiat-Shamir: nu - // Compute Shplonk batched quotient commitment Q execute_shplonk_batched_quotient_round(); - // Fiat-Shamir: z - // Compute partial evaluation Q_z execute_shplonk_partial_evaluation_round(); - // Fiat-Shamir: z - // Compute PCS opening proof (either KZG quotient commitment or IPA opening proof) execute_final_pcs_round(); + execute_transcript_consistency_univariate_opening_round(); + return export_proof(); } template class ECCVMProver_; -template class ECCVMProver_; } // namespace proof_system::honk diff --git a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_prover.hpp b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_prover.hpp index 958c1bad95f..3cd07fb9414 100644 --- a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_prover.hpp +++ b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_prover.hpp @@ -2,6 +2,7 @@ #include "barretenberg/commitment_schemes/gemini/gemini.hpp" #include "barretenberg/commitment_schemes/shplonk/shplonk.hpp" #include "barretenberg/flavor/ecc_vm.hpp" +#include "barretenberg/goblin/translation_evaluations.hpp" #include "barretenberg/plonk/proof_system/types/proof.hpp" #include "barretenberg/relations/relation_parameters.hpp" #include "barretenberg/sumcheck/sumcheck_output.hpp" @@ -22,6 +23,7 @@ template class ECCVMProver_ { using CommitmentLabels = typename Flavor::CommitmentLabels; using Curve = typename Flavor::Curve; using Transcript = typename Flavor::Transcript; + using TranslationEvaluations = barretenberg::TranslationEvaluations; public: explicit ECCVMProver_(std::shared_ptr input_key, std::shared_ptr commitment_key); @@ -36,12 +38,15 @@ template class ECCVMProver_ { void execute_shplonk_batched_quotient_round(); void execute_shplonk_partial_evaluation_round(); void execute_final_pcs_round(); + void execute_transcript_consistency_univariate_opening_round(); plonk::proof& export_proof(); plonk::proof& construct_proof(); Transcript transcript; + TranslationEvaluations translation_evaluations; + std::vector public_inputs; proof_system::RelationParameters relation_parameters; @@ -61,6 +66,8 @@ template class ECCVMProver_ { Polynomial quotient_W; + FF evaluation_challenge_x; + sumcheck::SumcheckOutput sumcheck_output; pcs::gemini::ProverOutput gemini_output; pcs::shplonk::ProverOutput shplonk_output; @@ -74,8 +81,5 @@ template class ECCVMProver_ { }; extern template class ECCVMProver_; -extern template class ECCVMProver_; - -using ECCVMProver = ECCVMProver_; } // namespace proof_system::honk diff --git a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_transcript.test.cpp b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_transcript.test.cpp index 423075428d5..8af4c20cca0 100644 --- a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_transcript.test.cpp +++ b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_transcript.test.cpp @@ -12,7 +12,7 @@ template class ECCVMTranscriptTests : public ::testing::Test { public: void SetUp() override { - if constexpr (std::is_same::value) { + if constexpr (std::is_same::value) { barretenberg::srs::init_grumpkin_crs_factory("../srs_db/grumpkin"); } else { barretenberg::srs::init_crs_factory("../srs_db/ignition"); @@ -54,8 +54,8 @@ template class ECCVMTranscriptTests : public ::testing::Test { manifest_expected.add_entry(round, "TRANSCRIPT_MSM_TRANSITION", size_G); manifest_expected.add_entry(round, "TRANSCRIPT_PC", size_G); manifest_expected.add_entry(round, "TRANSCRIPT_MSM_COUNT", size_G); - manifest_expected.add_entry(round, "TRANSCRIPT_X", size_G); - manifest_expected.add_entry(round, "TRANSCRIPT_Y", size_G); + manifest_expected.add_entry(round, "TRANSCRIPT_PX", size_G); + manifest_expected.add_entry(round, "TRANSCRIPT_PY", size_G); manifest_expected.add_entry(round, "TRANSCRIPT_Z1", size_G); manifest_expected.add_entry(round, "TRANSCRIPT_Z2", size_G); manifest_expected.add_entry(round, "TRANSCRIPT_Z1ZERO", size_G); @@ -126,7 +126,10 @@ template class ECCVMTranscriptTests : public ::testing::Test { round++; manifest_expected.add_entry(round, "LOOKUP_INVERSES", size_G); manifest_expected.add_entry(round, "Z_PERM", size_G); - manifest_expected.add_challenge(round, "Sumcheck:alpha", "Sumcheck:zeta"); + manifest_expected.add_challenge(round, "alpha"); + + round++; + manifest_expected.add_challenge(round, "Sumcheck:zeta"); for (size_t i = 0; i < log_n; ++i) { round++; @@ -158,29 +161,23 @@ template class ECCVMTranscriptTests : public ::testing::Test { manifest_expected.add_entry(round, "Shplonk:Q", size_G); manifest_expected.add_challenge(round, "Shplonk:z"); - // TODO(Mara): Make testing more flavor agnostic so we can test this with all flavors - if constexpr (proof_system::IsGrumpkinFlavor) { - round++; - manifest_expected.add_entry(round, "IPA:poly_degree", size_uint64); - manifest_expected.add_challenge(round, "IPA:generator_challenge"); - - auto log_poly_degree = static_cast(numeric::get_msb(ipa_poly_degree)); - for (size_t i = 0; i < log_poly_degree; ++i) { - round++; - std::string idx = std::to_string(i); - manifest_expected.add_entry(round, "IPA:L_" + idx, size_G); - manifest_expected.add_entry(round, "IPA:R_" + idx, size_G); - std::string label = "IPA:round_challenge_" + idx; - manifest_expected.add_challenge(round, label); - } + round++; + manifest_expected.add_entry(round, "IPA:poly_degree", size_uint64); + manifest_expected.add_challenge(round, "IPA:generator_challenge"); + auto log_poly_degree = static_cast(numeric::get_msb(ipa_poly_degree)); + for (size_t i = 0; i < log_poly_degree; ++i) { round++; - manifest_expected.add_entry(round, "IPA:a_0", size_FF); - } else { - round++; - manifest_expected.add_entry(round, "KZG:W", size_G); + std::string idx = std::to_string(i); + manifest_expected.add_entry(round, "IPA:L_" + idx, size_G); + manifest_expected.add_entry(round, "IPA:R_" + idx, size_G); + std::string label = "IPA:round_challenge_" + idx; + manifest_expected.add_challenge(round, label); } + round++; + manifest_expected.add_entry(round, "IPA:a_0", size_FF); + return manifest_expected; } proof_system::ECCVMCircuitBuilder generate_trace(numeric::random::Engine* engine = nullptr) @@ -221,7 +218,7 @@ template class ECCVMTranscriptTests : public ::testing::Test { numeric::random::Engine& engine = numeric::random::get_debug_engine(); -using FlavorTypes = testing::Types; +using FlavorTypes = testing::Types; TYPED_TEST_SUITE(ECCVMTranscriptTests, FlavorTypes); /** @@ -230,6 +227,8 @@ TYPED_TEST_SUITE(ECCVMTranscriptTests, FlavorTypes); */ TYPED_TEST(ECCVMTranscriptTests, ProverManifestConsistency) { + GTEST_SKIP() << "TODO(https://github.com/AztecProtocol/barretenberg/issues/782): update and reinstate after the " + "protocol is finalized."; using Flavor = TypeParam; // Construct a simple circuit @@ -258,6 +257,9 @@ TYPED_TEST(ECCVMTranscriptTests, ProverManifestConsistency) */ TYPED_TEST(ECCVMTranscriptTests, VerifierManifestConsistency) { + GTEST_SKIP() << "TODO(https://github.com/AztecProtocol/barretenberg/issues/782): update and reinstate after the " + "protocol is finalized."; + using Flavor = TypeParam; // Construct a simple circuit @@ -310,6 +312,9 @@ TYPED_TEST(ECCVMTranscriptTests, ChallengeGenerationTest) TYPED_TEST(ECCVMTranscriptTests, StructureTest) { + GTEST_SKIP() << "TODO(https://github.com/AztecProtocol/barretenberg/issues/782): update and reinstate after the " + "protocol is finalized."; + using Flavor = TypeParam; // Construct a simple circuit @@ -329,7 +334,7 @@ TYPED_TEST(ECCVMTranscriptTests, StructureTest) typename Flavor::Commitment one_group_val = Flavor::Commitment::one(); typename Flavor::FF rand_val = Flavor::FF::random_element(); - prover.transcript.transcript_x_comm = one_group_val * rand_val; // choose random object to modify + prover.transcript.transcript_Px_comm = one_group_val * rand_val; // choose random object to modify EXPECT_TRUE(verifier.verify_proof( prover.export_proof())); // we have not serialized it back to the proof so it should still be fine @@ -337,5 +342,5 @@ TYPED_TEST(ECCVMTranscriptTests, StructureTest) EXPECT_FALSE(verifier.verify_proof(prover.export_proof())); // the proof is now wrong after serializing it prover.transcript.deserialize_full_transcript(); - EXPECT_EQ(static_cast(prover.transcript.transcript_x_comm), one_group_val * rand_val); + EXPECT_EQ(static_cast(prover.transcript.transcript_Px_comm), one_group_val * rand_val); } \ No newline at end of file diff --git a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_verifier.cpp b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_verifier.cpp index 8f1039c6bf6..01aea6e673a 100644 --- a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_verifier.cpp +++ b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_verifier.cpp @@ -45,6 +45,7 @@ template bool ECCVMVerifier_::verify_proof(const plonk using VerifierCommitments = typename Flavor::VerifierCommitments; using CommitmentLabels = typename Flavor::CommitmentLabels; using Transcript = typename Flavor::Transcript; + using OpeningClaim = typename pcs::OpeningClaim; RelationParameters relation_parameters; @@ -59,114 +60,86 @@ template bool ECCVMVerifier_::verify_proof(const plonk return false; } + // Utility for extracting commitments from transcript + const auto receive_commitment = [&](const std::string& label) { + return transcript.template receive_from_prover(label); + }; + // Get commitments to VM wires - commitments.transcript_add = transcript.template receive_from_prover(commitment_labels.transcript_add); - commitments.transcript_mul = transcript.template receive_from_prover(commitment_labels.transcript_mul); - commitments.transcript_eq = transcript.template receive_from_prover(commitment_labels.transcript_eq); - commitments.transcript_collision_check = - transcript.template receive_from_prover(commitment_labels.transcript_collision_check); - commitments.transcript_msm_transition = - transcript.template receive_from_prover(commitment_labels.transcript_msm_transition); - commitments.transcript_pc = transcript.template receive_from_prover(commitment_labels.transcript_pc); - commitments.transcript_msm_count = - transcript.template receive_from_prover(commitment_labels.transcript_msm_count); - commitments.transcript_x = transcript.template receive_from_prover(commitment_labels.transcript_x); - commitments.transcript_y = transcript.template receive_from_prover(commitment_labels.transcript_y); - commitments.transcript_z1 = transcript.template receive_from_prover(commitment_labels.transcript_z1); - commitments.transcript_z2 = transcript.template receive_from_prover(commitment_labels.transcript_z2); - commitments.transcript_z1zero = - transcript.template receive_from_prover(commitment_labels.transcript_z1zero); - commitments.transcript_z2zero = - transcript.template receive_from_prover(commitment_labels.transcript_z2zero); - commitments.transcript_op = transcript.template receive_from_prover(commitment_labels.transcript_op); - commitments.transcript_accumulator_x = - transcript.template receive_from_prover(commitment_labels.transcript_accumulator_x); - commitments.transcript_accumulator_y = - transcript.template receive_from_prover(commitment_labels.transcript_accumulator_y); - commitments.transcript_msm_x = - transcript.template receive_from_prover(commitment_labels.transcript_msm_x); - commitments.transcript_msm_y = - transcript.template receive_from_prover(commitment_labels.transcript_msm_y); - commitments.precompute_pc = transcript.template receive_from_prover(commitment_labels.precompute_pc); - commitments.precompute_point_transition = - transcript.template receive_from_prover(commitment_labels.precompute_point_transition); - commitments.precompute_round = - transcript.template receive_from_prover(commitment_labels.precompute_round); - commitments.precompute_scalar_sum = - transcript.template receive_from_prover(commitment_labels.precompute_scalar_sum); - commitments.precompute_s1hi = - transcript.template receive_from_prover(commitment_labels.precompute_s1hi); - commitments.precompute_s1lo = - transcript.template receive_from_prover(commitment_labels.precompute_s1lo); - commitments.precompute_s2hi = - transcript.template receive_from_prover(commitment_labels.precompute_s2hi); - commitments.precompute_s2lo = - transcript.template receive_from_prover(commitment_labels.precompute_s2lo); - commitments.precompute_s3hi = - transcript.template receive_from_prover(commitment_labels.precompute_s3hi); - commitments.precompute_s3lo = - transcript.template receive_from_prover(commitment_labels.precompute_s3lo); - commitments.precompute_s4hi = - transcript.template receive_from_prover(commitment_labels.precompute_s4hi); - commitments.precompute_s4lo = - transcript.template receive_from_prover(commitment_labels.precompute_s4lo); - commitments.precompute_skew = - transcript.template receive_from_prover(commitment_labels.precompute_skew); - commitments.precompute_dx = transcript.template receive_from_prover(commitment_labels.precompute_dx); - commitments.precompute_dy = transcript.template receive_from_prover(commitment_labels.precompute_dy); - commitments.precompute_tx = transcript.template receive_from_prover(commitment_labels.precompute_tx); - commitments.precompute_ty = transcript.template receive_from_prover(commitment_labels.precompute_ty); - commitments.msm_transition = transcript.template receive_from_prover(commitment_labels.msm_transition); - commitments.msm_add = transcript.template receive_from_prover(commitment_labels.msm_add); - commitments.msm_double = transcript.template receive_from_prover(commitment_labels.msm_double); - commitments.msm_skew = transcript.template receive_from_prover(commitment_labels.msm_skew); - commitments.msm_accumulator_x = - transcript.template receive_from_prover(commitment_labels.msm_accumulator_x); - commitments.msm_accumulator_y = - transcript.template receive_from_prover(commitment_labels.msm_accumulator_y); - commitments.msm_pc = transcript.template receive_from_prover(commitment_labels.msm_pc); - commitments.msm_size_of_msm = - transcript.template receive_from_prover(commitment_labels.msm_size_of_msm); - commitments.msm_count = transcript.template receive_from_prover(commitment_labels.msm_count); - commitments.msm_round = transcript.template receive_from_prover(commitment_labels.msm_round); - commitments.msm_add1 = transcript.template receive_from_prover(commitment_labels.msm_add1); - commitments.msm_add2 = transcript.template receive_from_prover(commitment_labels.msm_add2); - commitments.msm_add3 = transcript.template receive_from_prover(commitment_labels.msm_add3); - commitments.msm_add4 = transcript.template receive_from_prover(commitment_labels.msm_add4); - commitments.msm_x1 = transcript.template receive_from_prover(commitment_labels.msm_x1); - commitments.msm_y1 = transcript.template receive_from_prover(commitment_labels.msm_y1); - commitments.msm_x2 = transcript.template receive_from_prover(commitment_labels.msm_x2); - commitments.msm_y2 = transcript.template receive_from_prover(commitment_labels.msm_y2); - commitments.msm_x3 = transcript.template receive_from_prover(commitment_labels.msm_x3); - commitments.msm_y3 = transcript.template receive_from_prover(commitment_labels.msm_y3); - commitments.msm_x4 = transcript.template receive_from_prover(commitment_labels.msm_x4); - commitments.msm_y4 = transcript.template receive_from_prover(commitment_labels.msm_y4); - commitments.msm_collision_x1 = - transcript.template receive_from_prover(commitment_labels.msm_collision_x1); - commitments.msm_collision_x2 = - transcript.template receive_from_prover(commitment_labels.msm_collision_x2); - commitments.msm_collision_x3 = - transcript.template receive_from_prover(commitment_labels.msm_collision_x3); - commitments.msm_collision_x4 = - transcript.template receive_from_prover(commitment_labels.msm_collision_x4); - commitments.msm_lambda1 = transcript.template receive_from_prover(commitment_labels.msm_lambda1); - commitments.msm_lambda2 = transcript.template receive_from_prover(commitment_labels.msm_lambda2); - commitments.msm_lambda3 = transcript.template receive_from_prover(commitment_labels.msm_lambda3); - commitments.msm_lambda4 = transcript.template receive_from_prover(commitment_labels.msm_lambda4); - commitments.msm_slice1 = transcript.template receive_from_prover(commitment_labels.msm_slice1); - commitments.msm_slice2 = transcript.template receive_from_prover(commitment_labels.msm_slice2); - commitments.msm_slice3 = transcript.template receive_from_prover(commitment_labels.msm_slice3); - commitments.msm_slice4 = transcript.template receive_from_prover(commitment_labels.msm_slice4); - commitments.transcript_accumulator_empty = - transcript.template receive_from_prover(commitment_labels.transcript_accumulator_empty); - commitments.transcript_reset_accumulator = - transcript.template receive_from_prover(commitment_labels.transcript_reset_accumulator); - commitments.precompute_select = - transcript.template receive_from_prover(commitment_labels.precompute_select); - commitments.lookup_read_counts_0 = - transcript.template receive_from_prover(commitment_labels.lookup_read_counts_0); - commitments.lookup_read_counts_1 = - transcript.template receive_from_prover(commitment_labels.lookup_read_counts_1); + commitments.transcript_add = receive_commitment(commitment_labels.transcript_add); + commitments.transcript_mul = receive_commitment(commitment_labels.transcript_mul); + commitments.transcript_eq = receive_commitment(commitment_labels.transcript_eq); + commitments.transcript_collision_check = receive_commitment(commitment_labels.transcript_collision_check); + commitments.transcript_msm_transition = receive_commitment(commitment_labels.transcript_msm_transition); + commitments.transcript_pc = receive_commitment(commitment_labels.transcript_pc); + commitments.transcript_msm_count = receive_commitment(commitment_labels.transcript_msm_count); + commitments.transcript_Px = receive_commitment(commitment_labels.transcript_Px); + commitments.transcript_Py = receive_commitment(commitment_labels.transcript_Py); + commitments.transcript_z1 = receive_commitment(commitment_labels.transcript_z1); + commitments.transcript_z2 = receive_commitment(commitment_labels.transcript_z2); + commitments.transcript_z1zero = receive_commitment(commitment_labels.transcript_z1zero); + commitments.transcript_z2zero = receive_commitment(commitment_labels.transcript_z2zero); + commitments.transcript_op = receive_commitment(commitment_labels.transcript_op); + commitments.transcript_accumulator_x = receive_commitment(commitment_labels.transcript_accumulator_x); + commitments.transcript_accumulator_y = receive_commitment(commitment_labels.transcript_accumulator_y); + commitments.transcript_msm_x = receive_commitment(commitment_labels.transcript_msm_x); + commitments.transcript_msm_y = receive_commitment(commitment_labels.transcript_msm_y); + commitments.precompute_pc = receive_commitment(commitment_labels.precompute_pc); + commitments.precompute_point_transition = receive_commitment(commitment_labels.precompute_point_transition); + commitments.precompute_round = receive_commitment(commitment_labels.precompute_round); + commitments.precompute_scalar_sum = receive_commitment(commitment_labels.precompute_scalar_sum); + commitments.precompute_s1hi = receive_commitment(commitment_labels.precompute_s1hi); + commitments.precompute_s1lo = receive_commitment(commitment_labels.precompute_s1lo); + commitments.precompute_s2hi = receive_commitment(commitment_labels.precompute_s2hi); + commitments.precompute_s2lo = receive_commitment(commitment_labels.precompute_s2lo); + commitments.precompute_s3hi = receive_commitment(commitment_labels.precompute_s3hi); + commitments.precompute_s3lo = receive_commitment(commitment_labels.precompute_s3lo); + commitments.precompute_s4hi = receive_commitment(commitment_labels.precompute_s4hi); + commitments.precompute_s4lo = receive_commitment(commitment_labels.precompute_s4lo); + commitments.precompute_skew = receive_commitment(commitment_labels.precompute_skew); + commitments.precompute_dx = receive_commitment(commitment_labels.precompute_dx); + commitments.precompute_dy = receive_commitment(commitment_labels.precompute_dy); + commitments.precompute_tx = receive_commitment(commitment_labels.precompute_tx); + commitments.precompute_ty = receive_commitment(commitment_labels.precompute_ty); + commitments.msm_transition = receive_commitment(commitment_labels.msm_transition); + commitments.msm_add = receive_commitment(commitment_labels.msm_add); + commitments.msm_double = receive_commitment(commitment_labels.msm_double); + commitments.msm_skew = receive_commitment(commitment_labels.msm_skew); + commitments.msm_accumulator_x = receive_commitment(commitment_labels.msm_accumulator_x); + commitments.msm_accumulator_y = receive_commitment(commitment_labels.msm_accumulator_y); + commitments.msm_pc = receive_commitment(commitment_labels.msm_pc); + commitments.msm_size_of_msm = receive_commitment(commitment_labels.msm_size_of_msm); + commitments.msm_count = receive_commitment(commitment_labels.msm_count); + commitments.msm_round = receive_commitment(commitment_labels.msm_round); + commitments.msm_add1 = receive_commitment(commitment_labels.msm_add1); + commitments.msm_add2 = receive_commitment(commitment_labels.msm_add2); + commitments.msm_add3 = receive_commitment(commitment_labels.msm_add3); + commitments.msm_add4 = receive_commitment(commitment_labels.msm_add4); + commitments.msm_x1 = receive_commitment(commitment_labels.msm_x1); + commitments.msm_y1 = receive_commitment(commitment_labels.msm_y1); + commitments.msm_x2 = receive_commitment(commitment_labels.msm_x2); + commitments.msm_y2 = receive_commitment(commitment_labels.msm_y2); + commitments.msm_x3 = receive_commitment(commitment_labels.msm_x3); + commitments.msm_y3 = receive_commitment(commitment_labels.msm_y3); + commitments.msm_x4 = receive_commitment(commitment_labels.msm_x4); + commitments.msm_y4 = receive_commitment(commitment_labels.msm_y4); + commitments.msm_collision_x1 = receive_commitment(commitment_labels.msm_collision_x1); + commitments.msm_collision_x2 = receive_commitment(commitment_labels.msm_collision_x2); + commitments.msm_collision_x3 = receive_commitment(commitment_labels.msm_collision_x3); + commitments.msm_collision_x4 = receive_commitment(commitment_labels.msm_collision_x4); + commitments.msm_lambda1 = receive_commitment(commitment_labels.msm_lambda1); + commitments.msm_lambda2 = receive_commitment(commitment_labels.msm_lambda2); + commitments.msm_lambda3 = receive_commitment(commitment_labels.msm_lambda3); + commitments.msm_lambda4 = receive_commitment(commitment_labels.msm_lambda4); + commitments.msm_slice1 = receive_commitment(commitment_labels.msm_slice1); + commitments.msm_slice2 = receive_commitment(commitment_labels.msm_slice2); + commitments.msm_slice3 = receive_commitment(commitment_labels.msm_slice3); + commitments.msm_slice4 = receive_commitment(commitment_labels.msm_slice4); + commitments.transcript_accumulator_empty = receive_commitment(commitment_labels.transcript_accumulator_empty); + commitments.transcript_reset_accumulator = receive_commitment(commitment_labels.transcript_reset_accumulator); + commitments.precompute_select = receive_commitment(commitment_labels.precompute_select); + commitments.lookup_read_counts_0 = receive_commitment(commitment_labels.lookup_read_counts_0); + commitments.lookup_read_counts_1 = receive_commitment(commitment_labels.lookup_read_counts_1); // Get challenge for sorted list batching and wire four memory records auto [beta, gamma] = transcript.get_challenges("beta", "gamma"); @@ -180,15 +153,14 @@ template bool ECCVMVerifier_::verify_proof(const plonk relation_parameters.eccvm_set_permutation_delta = relation_parameters.eccvm_set_permutation_delta.invert(); // Get commitment to permutation and lookup grand products - commitments.lookup_inverses = - transcript.template receive_from_prover(commitment_labels.lookup_inverses); - commitments.z_perm = transcript.template receive_from_prover(commitment_labels.z_perm); + commitments.lookup_inverses = receive_commitment(commitment_labels.lookup_inverses); + commitments.z_perm = receive_commitment(commitment_labels.z_perm); // Execute Sumcheck Verifier auto sumcheck = SumcheckVerifier(circuit_size); - + auto alpha = transcript.get_challenge("alpha"); auto [multivariate_challenge, purported_evaluations, sumcheck_verified] = - sumcheck.verify(relation_parameters, transcript); + sumcheck.verify(relation_parameters, alpha, transcript); // If Sumcheck did not verify, return false if (sumcheck_verified.has_value() && !sumcheck_verified.value()) { @@ -255,12 +227,52 @@ template bool ECCVMVerifier_::verify_proof(const plonk auto shplonk_claim = Shplonk::reduce_verification(pcs_verification_key, gemini_claim, transcript); // Verify the Shplonk claim with KZG or IPA - auto verified = PCS::verify(pcs_verification_key, shplonk_claim, transcript); + auto multivariate_opening_verified = PCS::verify(pcs_verification_key, shplonk_claim, transcript); + + // Execute transcript consistency univariate opening round + // TODO(#768): Find a better way to do this. See issue for details. + bool univariate_opening_verified = false; + { + auto hack_commitment = receive_commitment("Translation:hack_commitment"); + + FF evaluation_challenge_x = transcript.get_challenge("Translation:evaluation_challenge_x"); + + // Construct arrays of commitments and evaluations to be batched + const size_t NUM_UNIVARIATES = 6; + std::array transcript_commitments = { + commitments.transcript_op, commitments.transcript_Px, commitments.transcript_Py, + commitments.transcript_z1, commitments.transcript_z2, hack_commitment + }; + std::array transcript_evaluations = { + transcript.template receive_from_prover("Translation:op"), + transcript.template receive_from_prover("Translation:Px"), + transcript.template receive_from_prover("Translation:Py"), + transcript.template receive_from_prover("Translation:z1"), + transcript.template receive_from_prover("Translation:z2"), + transcript.template receive_from_prover("Translation:hack_evaluation") + }; + + FF batching_challenge = transcript.get_challenge("Translation:batching_challenge"); + + // Constuct batched commitment and batched evaluation + auto batched_commitment = transcript_commitments[0]; + auto batched_transcript_eval = transcript_evaluations[0]; + auto batching_scalar = batching_challenge; + for (size_t idx = 1; idx < transcript_commitments.size(); ++idx) { + batched_commitment = batched_commitment + transcript_commitments[idx] * batching_scalar; + batched_transcript_eval += batching_scalar * transcript_evaluations[idx]; + batching_scalar *= batching_challenge; + } + + // Construct and verify batched opening claim + OpeningClaim batched_univariate_claim = { { evaluation_challenge_x, batched_transcript_eval }, + batched_commitment }; + univariate_opening_verified = PCS::verify(pcs_verification_key, batched_univariate_claim, transcript); + } - return sumcheck_verified.value() && verified; + return sumcheck_verified.value() && multivariate_opening_verified && univariate_opening_verified; } template class ECCVMVerifier_; -template class ECCVMVerifier_; } // namespace proof_system::honk diff --git a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_verifier.hpp b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_verifier.hpp index 8145219d024..4e8b32b84da 100644 --- a/barretenberg/cpp/src/barretenberg/eccvm/eccvm_verifier.hpp +++ b/barretenberg/cpp/src/barretenberg/eccvm/eccvm_verifier.hpp @@ -40,9 +40,6 @@ template class ECCVMVerifier_ { }; extern template class ECCVMVerifier_; -extern template class ECCVMVerifier_; - -using ECCVMVerifier = ECCVMVerifier_; -using ECCVMVerifierGrumpkin = ECCVMVerifier_; +using ECCVMVerifierGrumpkin = ECCVMVerifier_; } // namespace proof_system::honk diff --git a/barretenberg/cpp/src/barretenberg/flavor/ecc_vm.hpp b/barretenberg/cpp/src/barretenberg/flavor/ecc_vm.hpp index 0fa3fb0c861..0f1049f109f 100644 --- a/barretenberg/cpp/src/barretenberg/flavor/ecc_vm.hpp +++ b/barretenberg/cpp/src/barretenberg/flavor/ecc_vm.hpp @@ -114,8 +114,8 @@ template class ECCVMBa DataType transcript_msm_transition; // column 4 DataType transcript_pc; // column 5 DataType transcript_msm_count; // column 6 - DataType transcript_x; // column 7 - DataType transcript_y; // column 8 + DataType transcript_Px; // column 7 + DataType transcript_Py; // column 8 DataType transcript_z1; // column 9 DataType transcript_z2; // column 10 DataType transcript_z1zero; // column 11 @@ -192,8 +192,8 @@ template class ECCVMBa &transcript_msm_transition, &transcript_pc, &transcript_msm_count, - &transcript_x, - &transcript_y, + &transcript_Px, + &transcript_Py, &transcript_z1, &transcript_z2, &transcript_z1zero, @@ -271,8 +271,8 @@ template class ECCVMBa transcript_msm_transition, transcript_pc, transcript_msm_count, - transcript_x, - transcript_y, + transcript_Px, + transcript_Py, transcript_z1, transcript_z2, transcript_z1zero, @@ -366,8 +366,8 @@ template class ECCVMBa DataType transcript_msm_transition; // column 7 DataType transcript_pc; // column 8 DataType transcript_msm_count; // column 9 - DataType transcript_x; // column 10 - DataType transcript_y; // column 11 + DataType transcript_Px; // column 10 + DataType transcript_Py; // column 11 DataType transcript_z1; // column 12 DataType transcript_z2; // column 13 DataType transcript_z1zero; // column 14 @@ -484,8 +484,8 @@ template class ECCVMBa &transcript_msm_transition, &transcript_pc, &transcript_msm_count, - &transcript_x, - &transcript_y, + &transcript_Px, + &transcript_Py, &transcript_z1, &transcript_z2, &transcript_z1zero, @@ -589,8 +589,8 @@ template class ECCVMBa transcript_msm_transition, transcript_pc, transcript_msm_count, - transcript_x, - transcript_y, + transcript_Px, + transcript_Py, transcript_z1, transcript_z2, transcript_z1zero, @@ -669,8 +669,8 @@ template class ECCVMBa transcript_eq, transcript_collision_check, transcript_msm_transition, - transcript_x, - transcript_y, + transcript_Px, + transcript_Py, transcript_z1, transcript_z2, transcript_z1zero, @@ -923,8 +923,8 @@ template class ECCVMBa Base::transcript_msm_transition = "TRANSCRIPT_MSM_TRANSITION"; Base::transcript_pc = "TRANSCRIPT_PC"; Base::transcript_msm_count = "TRANSCRIPT_MSM_COUNT"; - Base::transcript_x = "TRANSCRIPT_X"; - Base::transcript_y = "TRANSCRIPT_Y"; + Base::transcript_Px = "TRANSCRIPT_PX"; + Base::transcript_Py = "TRANSCRIPT_PY"; Base::transcript_z1 = "TRANSCRIPT_Z1"; Base::transcript_z2 = "TRANSCRIPT_Z2"; Base::transcript_z1zero = "TRANSCRIPT_Z1ZERO"; @@ -1028,8 +1028,8 @@ template class ECCVMBa Commitment transcript_msm_transition_comm; Commitment transcript_pc_comm; Commitment transcript_msm_count_comm; - Commitment transcript_x_comm; - Commitment transcript_y_comm; + Commitment transcript_Px_comm; + Commitment transcript_Py_comm; Commitment transcript_z1_comm; Commitment transcript_z2_comm; Commitment transcript_z1zero_comm; @@ -1136,9 +1136,9 @@ template class ECCVMBa BaseTranscript::proof_data, num_bytes_read); transcript_msm_count_comm = BaseTranscript::template deserialize_from_buffer( BaseTranscript::proof_data, num_bytes_read); - transcript_x_comm = BaseTranscript::template deserialize_from_buffer( + transcript_Px_comm = BaseTranscript::template deserialize_from_buffer( BaseTranscript::proof_data, num_bytes_read); - transcript_y_comm = BaseTranscript::template deserialize_from_buffer( + transcript_Py_comm = BaseTranscript::template deserialize_from_buffer( BaseTranscript::proof_data, num_bytes_read); transcript_z1_comm = BaseTranscript::template deserialize_from_buffer( BaseTranscript::proof_data, num_bytes_read); @@ -1328,8 +1328,8 @@ template class ECCVMBa BaseTranscript::proof_data); BaseTranscript::template serialize_to_buffer(transcript_pc_comm, BaseTranscript::proof_data); BaseTranscript::template serialize_to_buffer(transcript_msm_count_comm, BaseTranscript::proof_data); - BaseTranscript::template serialize_to_buffer(transcript_x_comm, BaseTranscript::proof_data); - BaseTranscript::template serialize_to_buffer(transcript_y_comm, BaseTranscript::proof_data); + BaseTranscript::template serialize_to_buffer(transcript_Px_comm, BaseTranscript::proof_data); + BaseTranscript::template serialize_to_buffer(transcript_Py_comm, BaseTranscript::proof_data); BaseTranscript::template serialize_to_buffer(transcript_z1_comm, BaseTranscript::proof_data); BaseTranscript::template serialize_to_buffer(transcript_z2_comm, BaseTranscript::proof_data); BaseTranscript::template serialize_to_buffer(transcript_z1zero_comm, BaseTranscript::proof_data); @@ -1433,20 +1433,19 @@ template class ECCVMBa }; }; -class ECCVM : public ECCVMBase> {}; -class ECCVMGrumpkin : public ECCVMBase> {}; +class ECCVM : public ECCVMBase> {}; // NOLINTEND(cppcoreguidelines-avoid-const-or-ref-data-members) } // namespace flavor namespace sumcheck { -extern template class ECCVMTranscriptRelationImpl; -extern template class ECCVMWnafRelationImpl; -extern template class ECCVMPointTableRelationImpl; -extern template class ECCVMMSMRelationImpl; -extern template class ECCVMSetRelationImpl; -extern template class ECCVMLookupRelationImpl; +extern template class ECCVMTranscriptRelationImpl; +extern template class ECCVMWnafRelationImpl; +extern template class ECCVMPointTableRelationImpl; +extern template class ECCVMMSMRelationImpl; +extern template class ECCVMSetRelationImpl; +extern template class ECCVMLookupRelationImpl; DECLARE_SUMCHECK_RELATION_CLASS(ECCVMTranscriptRelationImpl, flavor::ECCVM); DECLARE_SUMCHECK_RELATION_CLASS(ECCVMWnafRelationImpl, flavor::ECCVM); @@ -1455,14 +1454,6 @@ DECLARE_SUMCHECK_RELATION_CLASS(ECCVMMSMRelationImpl, flavor::ECCVM); DECLARE_SUMCHECK_RELATION_CLASS(ECCVMSetRelationImpl, flavor::ECCVM); DECLARE_SUMCHECK_RELATION_CLASS(ECCVMLookupRelationImpl, flavor::ECCVM); -DECLARE_SUMCHECK_RELATION_CLASS(ECCVMTranscriptRelationImpl, flavor::ECCVMGrumpkin); -DECLARE_SUMCHECK_RELATION_CLASS(ECCVMWnafRelationImpl, flavor::ECCVMGrumpkin); -DECLARE_SUMCHECK_RELATION_CLASS(ECCVMPointTableRelationImpl, flavor::ECCVMGrumpkin); -DECLARE_SUMCHECK_RELATION_CLASS(ECCVMMSMRelationImpl, flavor::ECCVMGrumpkin); -DECLARE_SUMCHECK_RELATION_CLASS(ECCVMSetRelationImpl, flavor::ECCVMGrumpkin); -DECLARE_SUMCHECK_RELATION_CLASS(ECCVMLookupRelationImpl, flavor::ECCVMGrumpkin); - DECLARE_SUMCHECK_PERMUTATION_CLASS(ECCVMSetRelationImpl, flavor::ECCVM); -DECLARE_SUMCHECK_PERMUTATION_CLASS(ECCVMSetRelationImpl, flavor::ECCVMGrumpkin); } // namespace sumcheck } // namespace proof_system::honk diff --git a/barretenberg/cpp/src/barretenberg/flavor/flavor.hpp b/barretenberg/cpp/src/barretenberg/flavor/flavor.hpp index 61106f14bb4..4f9f1d515b9 100644 --- a/barretenberg/cpp/src/barretenberg/flavor/flavor.hpp +++ b/barretenberg/cpp/src/barretenberg/flavor/flavor.hpp @@ -243,6 +243,20 @@ template static constexpr size_t compute } } +/** + * @brief Recursive utility function to find the number of subrelations. + * + */ +template static constexpr size_t compute_number_of_subrelations() +{ + if constexpr (Index >= std::tuple_size::value) { + return 0; + } else { + constexpr size_t subrelations_in_relation = + std::tuple_element_t::SUBRELATION_PARTIAL_LENGTHS.size(); + return subrelations_in_relation + compute_number_of_subrelations(); + } +} /** * @brief Recursive utility function to construct a container for the subrelation accumulators of Protogalaxy folding. * @details The size of the outer tuple is equal to the number of relations. Each relation contributes an inner tuple of @@ -301,7 +315,6 @@ template static constexpr auto create_tu namespace proof_system::honk::flavor { class Ultra; class ECCVM; -class ECCVMGrumpkin; class GoblinUltra; template class UltraRecursive_; template class GoblinUltraRecursive_; @@ -343,11 +356,11 @@ concept IsRecursiveFlavor = IsAnyOf, honk::flavor::GoblinUltraRecursive_>; -template concept IsGrumpkinFlavor = IsAnyOf; +template concept IsGrumpkinFlavor = IsAnyOf; template concept UltraFlavor = IsAnyOf; -template concept ECCVMFlavor = IsAnyOf; +template concept ECCVMFlavor = IsAnyOf; // clang-format on } // namespace proof_system diff --git a/barretenberg/cpp/src/barretenberg/flavor/flavor.test.cpp b/barretenberg/cpp/src/barretenberg/flavor/flavor.test.cpp index 92d5c2a980c..8e99d56e397 100644 --- a/barretenberg/cpp/src/barretenberg/flavor/flavor.test.cpp +++ b/barretenberg/cpp/src/barretenberg/flavor/flavor.test.cpp @@ -4,9 +4,6 @@ #include #include -#pragma GCC diagnostic ignored "-Wunused-local-typedefs" -#pragma GCC diagnostic ignored "-Wunused-variable" - namespace proof_system::test_flavor { TEST(Flavor, Getters) { @@ -139,7 +136,6 @@ TEST(Flavor, GetRow) return std::vector({ FF::random_element(), FF::random_element() }); }); Flavor::ProverPolynomials prover_polynomials; - size_t poly_idx = 0; for (auto [poly, entry] : zip_view(prover_polynomials.pointer_view(), data)) { *poly = entry; } diff --git a/barretenberg/cpp/src/barretenberg/flavor/goblin_translator.hpp b/barretenberg/cpp/src/barretenberg/flavor/goblin_translator.hpp index 0bd927302ff..454bbf85b67 100644 --- a/barretenberg/cpp/src/barretenberg/flavor/goblin_translator.hpp +++ b/barretenberg/cpp/src/barretenberg/flavor/goblin_translator.hpp @@ -1,10 +1,7 @@ #pragma once #include "barretenberg/commitment_schemes/commitment_key.hpp" #include "barretenberg/commitment_schemes/kzg/kzg.hpp" -#include "barretenberg/ecc/curves/bn254/bn254.hpp" #include "barretenberg/flavor/flavor.hpp" -#include "barretenberg/polynomials/univariate.hpp" -#include "barretenberg/proof_system/arithmetization/arithmetization.hpp" #include "barretenberg/proof_system/circuit_builder/goblin_translator_circuit_builder.hpp" #include "barretenberg/relations/relation_parameters.hpp" #include "barretenberg/relations/translator_vm/translator_decomposition_relation.hpp" @@ -13,248 +10,16 @@ #include "barretenberg/relations/translator_vm/translator_non_native_field_relation.hpp" #include "barretenberg/relations/translator_vm/translator_permutation_relation.hpp" #include "relation_definitions_fwd.hpp" -#include -#include -#include -#include -#include -#include namespace proof_system::honk::flavor { -template class GoblinTranslator_ { +class GoblinTranslator { public: - /** - * @brief Enum containing IDs of all the polynomials used in Goblin Translator - * - * @details We use the enum for easier updates of structure sizes and for cases where we need to get a particular - * polynomial programmatically - */ - enum ALL_ENTITIES_IDS : size_t { - /*The first 4 wires contain the standard values from the EccOpQueue*/ - OP, - X_LO_Y_HI, - X_HI_Z_1, - Y_LO_Z_2, - /*P.xₗₒ split into 2 NUM_LIMB_BITS bit limbs*/ - P_X_LOW_LIMBS, - /*Low limbs split further into smaller chunks for range constraints*/ - P_X_LOW_LIMBS_RANGE_CONSTRAINT_0, - P_X_LOW_LIMBS_RANGE_CONSTRAINT_1, - P_X_LOW_LIMBS_RANGE_CONSTRAINT_2, - P_X_LOW_LIMBS_RANGE_CONSTRAINT_3, - P_X_LOW_LIMBS_RANGE_CONSTRAINT_4, - P_X_LOW_LIMBS_RANGE_CONSTRAINT_TAIL, - /*P.xₕᵢ split into 2 NUM_LIMB_BITS bit limbs*/ - P_X_HIGH_LIMBS, - /*High limbs split into chunks for range constraints*/ - P_X_HIGH_LIMBS_RANGE_CONSTRAINT_0, - P_X_HIGH_LIMBS_RANGE_CONSTRAINT_1, - P_X_HIGH_LIMBS_RANGE_CONSTRAINT_2, - P_X_HIGH_LIMBS_RANGE_CONSTRAINT_3, - P_X_HIGH_LIMBS_RANGE_CONSTRAINT_4, - P_X_HIGH_LIMBS_RANGE_CONSTRAINT_TAIL, // The tail also contains some leftover values from relation wide limb - // range cosntraints - /*P.yₗₒ split into 2 NUM_LIMB_BITS bit limbs*/ - P_Y_LOW_LIMBS, - /*Low limbs split into chunks for range constraints*/ - P_Y_LOW_LIMBS_RANGE_CONSTRAINT_0, - P_Y_LOW_LIMBS_RANGE_CONSTRAINT_1, - P_Y_LOW_LIMBS_RANGE_CONSTRAINT_2, - P_Y_LOW_LIMBS_RANGE_CONSTRAINT_3, - P_Y_LOW_LIMBS_RANGE_CONSTRAINT_4, - P_Y_LOW_LIMBS_RANGE_CONSTRAINT_TAIL, - /*P.yₕᵢ split into 2 NUM_LIMB_BITS bit limbs*/ - P_Y_HIGH_LIMBS, - /*High limbs split into chunks for range constraints*/ - P_Y_HIGH_LIMBS_RANGE_CONSTRAINT_0, - P_Y_HIGH_LIMBS_RANGE_CONSTRAINT_1, - P_Y_HIGH_LIMBS_RANGE_CONSTRAINT_2, - P_Y_HIGH_LIMBS_RANGE_CONSTRAINT_3, - P_Y_HIGH_LIMBS_RANGE_CONSTRAINT_4, - P_Y_HIGH_LIMBS_RANGE_CONSTRAINT_TAIL, // The tail also contains some leftover values from relation wide limb - // range cosntraints - /*Low limbs of z_1 and z_2*/ - Z_LOW_LIMBS, - /*Range constraints for low limbs of z_1 and z_2*/ - Z_LOW_LIMBS_RANGE_CONSTRAINT_0, - Z_LOW_LIMBS_RANGE_CONSTRAINT_1, - Z_LOW_LIMBS_RANGE_CONSTRAINT_2, - Z_LOW_LIMBS_RANGE_CONSTRAINT_3, - Z_LOW_LIMBS_RANGE_CONSTRAINT_4, - Z_LOW_LIMBS_RANGE_CONSTRAINT_TAIL, - /*High Limbs of z_1 and z_2*/ - Z_HIGH_LIMBS, - /*Range constraints for high limbs of z_1 and z_2*/ - Z_HIGH_LIMBS_RANGE_CONSTRAINT_0, - Z_HIGH_LIMBS_RANGE_CONSTRAINT_1, - Z_HIGH_LIMBS_RANGE_CONSTRAINT_2, - Z_HIGH_LIMBS_RANGE_CONSTRAINT_3, - Z_HIGH_LIMBS_RANGE_CONSTRAINT_4, - Z_HIGH_LIMBS_RANGE_CONSTRAINT_TAIL, - /* Contain NUM_LIMB_BITS-bit limbs of current and previous accumulator (previous at higher indices because of - the nuances of KZG commitment) */ - ACCUMULATORS_BINARY_LIMBS_0, - ACCUMULATORS_BINARY_LIMBS_1, - ACCUMULATORS_BINARY_LIMBS_2, - ACCUMULATORS_BINARY_LIMBS_3, - ACCUMULATOR_LOW_LIMBS_RANGE_CONSTRAINT_0, // Range constraints for the current accumulator limbs (no need to - // redo previous accumulator) - ACCUMULATOR_LOW_LIMBS_RANGE_CONSTRAINT_1, - ACCUMULATOR_LOW_LIMBS_RANGE_CONSTRAINT_2, - ACCUMULATOR_LOW_LIMBS_RANGE_CONSTRAINT_3, - ACCUMULATOR_LOW_LIMBS_RANGE_CONSTRAINT_4, - ACCUMULATOR_LOW_LIMBS_RANGE_CONSTRAINT_TAIL, - ACCUMULATOR_HIGH_LIMBS_RANGE_CONSTRAINT_0, - ACCUMULATOR_HIGH_LIMBS_RANGE_CONSTRAINT_1, - ACCUMULATOR_HIGH_LIMBS_RANGE_CONSTRAINT_2, - ACCUMULATOR_HIGH_LIMBS_RANGE_CONSTRAINT_3, - ACCUMULATOR_HIGH_LIMBS_RANGE_CONSTRAINT_4, - ACCUMULATOR_HIGH_LIMBS_RANGE_CONSTRAINT_TAIL, // The tail also contains some leftover values from relation wide - // limb range constraints - - /* Quotient limbs*/ - QUOTIENT_LOW_BINARY_LIMBS, - QUOTIENT_HIGH_BINARY_LIMBS, - /* Range constraints for quotient */ - QUOTIENT_LOW_LIMBS_RANGE_CONSTRAINT_0, - QUOTIENT_LOW_LIMBS_RANGE_CONSTRAINT_1, - QUOTIENT_LOW_LIMBS_RANGE_CONSTRAINT_2, - QUOTIENT_LOW_LIMBS_RANGE_CONSTRAINT_3, - QUOTIENT_LOW_LIMBS_RANGE_CONSTRAINT_4, - QUOTIENT_LOW_LIMBS_RANGE_CONSTRAINT_TAIL, - QUOTIENT_HIGH_LIMBS_RANGE_CONSTRAINT_0, - QUOTIENT_HIGH_LIMBS_RANGE_CONSTRAINT_1, - QUOTIENT_HIGH_LIMBS_RANGE_CONSTRAINT_2, - QUOTIENT_HIGH_LIMBS_RANGE_CONSTRAINT_3, - QUOTIENT_HIGH_LIMBS_RANGE_CONSTRAINT_4, - QUOTIENT_HIGH_LIMBS_RANGE_CONSTRAINT_TAIL, // The tail also contains some leftover values from relation wide - // limb range constraints - - /* Limbs for checking the correctness of mod 2²⁷² relations*/ - RELATION_WIDE_LIMBS, - RELATION_WIDE_LIMBS_RANGE_CONSTRAINT_0, - RELATION_WIDE_LIMBS_RANGE_CONSTRAINT_1, - RELATION_WIDE_LIMBS_RANGE_CONSTRAINT_2, - RELATION_WIDE_LIMBS_RANGE_CONSTRAINT_3, - /*Concatenations of various range constraint wires*/ - CONCATENATED_RANGE_CONSTRAINTS_0, - CONCATENATED_RANGE_CONSTRAINTS_1, - CONCATENATED_RANGE_CONSTRAINTS_2, - CONCATENATED_RANGE_CONSTRAINTS_3, - /*Values from concatenated range constraints + some additional ones*/ - ORDERED_RANGE_CONSTRAINTS_0, - ORDERED_RANGE_CONSTRAINTS_1, - ORDERED_RANGE_CONSTRAINTS_2, - ORDERED_RANGE_CONSTRAINTS_3, - ORDERED_RANGE_CONSTRAINTS_4, - /*Grand Product Polynomial*/ - Z_PERM, - /*Shifted versions of polynomials*/ - X_LO_Y_HI_SHIFT, - X_HI_Z_1_SHIFT, - Y_LO_Z_2_SHIFT, - P_X_LOW_LIMBS_SHIFT, - P_X_LOW_LIMBS_RANGE_CONSTRAINT_0_SHIFT, - P_X_LOW_LIMBS_RANGE_CONSTRAINT_1_SHIFT, - P_X_LOW_LIMBS_RANGE_CONSTRAINT_2_SHIFT, - P_X_LOW_LIMBS_RANGE_CONSTRAINT_3_SHIFT, - P_X_LOW_LIMBS_RANGE_CONSTRAINT_4_SHIFT, - P_X_LOW_LIMBS_RANGE_CONSTRAINT_TAIL_SHIFT, - P_X_HIGH_LIMBS_SHIFT, - P_X_HIGH_LIMBS_RANGE_CONSTRAINT_0_SHIFT, - P_X_HIGH_LIMBS_RANGE_CONSTRAINT_1_SHIFT, - P_X_HIGH_LIMBS_RANGE_CONSTRAINT_2_SHIFT, - P_X_HIGH_LIMBS_RANGE_CONSTRAINT_3_SHIFT, - P_X_HIGH_LIMBS_RANGE_CONSTRAINT_4_SHIFT, - P_X_HIGH_LIMBS_RANGE_CONSTRAINT_TAIL_SHIFT, - P_Y_LOW_LIMBS_SHIFT, - P_Y_LOW_LIMBS_RANGE_CONSTRAINT_0_SHIFT, - P_Y_LOW_LIMBS_RANGE_CONSTRAINT_1_SHIFT, - P_Y_LOW_LIMBS_RANGE_CONSTRAINT_2_SHIFT, - P_Y_LOW_LIMBS_RANGE_CONSTRAINT_3_SHIFT, - P_Y_LOW_LIMBS_RANGE_CONSTRAINT_4_SHIFT, - P_Y_LOW_LIMBS_RANGE_CONSTRAINT_TAIL_SHIFT, - P_Y_HIGH_LIMBS_SHIFT, - P_Y_HIGH_LIMBS_RANGE_CONSTRAINT_0_SHIFT, - P_Y_HIGH_LIMBS_RANGE_CONSTRAINT_1_SHIFT, - P_Y_HIGH_LIMBS_RANGE_CONSTRAINT_2_SHIFT, - P_Y_HIGH_LIMBS_RANGE_CONSTRAINT_3_SHIFT, - P_Y_HIGH_LIMBS_RANGE_CONSTRAINT_4_SHIFT, - P_Y_HIGH_LIMBS_RANGE_CONSTRAINT_TAIL_SHIFT, - Z_LOW_LIMBS_SHIFT, - Z_LOW_LIMBS_RANGE_CONSTRAINT_0_SHIFT, - Z_LOW_LIMBS_RANGE_CONSTRAINT_1_SHIFT, - Z_LOW_LIMBS_RANGE_CONSTRAINT_2_SHIFT, - Z_LOW_LIMBS_RANGE_CONSTRAINT_3_SHIFT, - Z_LOW_LIMBS_RANGE_CONSTRAINT_4_SHIFT, - Z_LOW_LIMBS_RANGE_CONSTRAINT_TAIL_SHIFT, - Z_HIGH_LIMBS_SHIFT, - Z_HIGH_LIMBS_RANGE_CONSTRAINT_0_SHIFT, - Z_HIGH_LIMBS_RANGE_CONSTRAINT_1_SHIFT, - Z_HIGH_LIMBS_RANGE_CONSTRAINT_2_SHIFT, - Z_HIGH_LIMBS_RANGE_CONSTRAINT_3_SHIFT, - Z_HIGH_LIMBS_RANGE_CONSTRAINT_4_SHIFT, - Z_HIGH_LIMBS_RANGE_CONSTRAINT_TAIL_SHIFT, - ACCUMULATORS_BINARY_LIMBS_0_SHIFT, - ACCUMULATORS_BINARY_LIMBS_1_SHIFT, - ACCUMULATORS_BINARY_LIMBS_2_SHIFT, - ACCUMULATORS_BINARY_LIMBS_3_SHIFT, - ACCUMULATOR_LOW_LIMBS_RANGE_CONSTRAINT_0_SHIFT, - ACCUMULATOR_LOW_LIMBS_RANGE_CONSTRAINT_1_SHIFT, - ACCUMULATOR_LOW_LIMBS_RANGE_CONSTRAINT_2_SHIFT, - ACCUMULATOR_LOW_LIMBS_RANGE_CONSTRAINT_3_SHIFT, - ACCUMULATOR_LOW_LIMBS_RANGE_CONSTRAINT_4_SHIFT, - ACCUMULATOR_LOW_LIMBS_RANGE_CONSTRAINT_TAIL_SHIFT, - ACCUMULATOR_HIGH_LIMBS_RANGE_CONSTRAINT_0_SHIFT, - ACCUMULATOR_HIGH_LIMBS_RANGE_CONSTRAINT_1_SHIFT, - ACCUMULATOR_HIGH_LIMBS_RANGE_CONSTRAINT_2_SHIFT, - ACCUMULATOR_HIGH_LIMBS_RANGE_CONSTRAINT_3_SHIFT, - ACCUMULATOR_HIGH_LIMBS_RANGE_CONSTRAINT_4_SHIFT, - ACCUMULATOR_HIGH_LIMBS_RANGE_CONSTRAINT_TAIL_SHIFT, - QUOTIENT_LOW_BINARY_LIMBS_SHIFT, - QUOTIENT_HIGH_BINARY_LIMBS_SHIFT, - QUOTIENT_LOW_LIMBS_RANGE_CONSTRAINT_0_SHIFT, - QUOTIENT_LOW_LIMBS_RANGE_CONSTRAINT_1_SHIFT, - QUOTIENT_LOW_LIMBS_RANGE_CONSTRAINT_2_SHIFT, - QUOTIENT_LOW_LIMBS_RANGE_CONSTRAINT_3_SHIFT, - QUOTIENT_LOW_LIMBS_RANGE_CONSTRAINT_4_SHIFT, - QUOTIENT_LOW_LIMBS_RANGE_CONSTRAINT_TAIL_SHIFT, - QUOTIENT_HIGH_LIMBS_RANGE_CONSTRAINT_0_SHIFT, - QUOTIENT_HIGH_LIMBS_RANGE_CONSTRAINT_1_SHIFT, - QUOTIENT_HIGH_LIMBS_RANGE_CONSTRAINT_2_SHIFT, - QUOTIENT_HIGH_LIMBS_RANGE_CONSTRAINT_3_SHIFT, - QUOTIENT_HIGH_LIMBS_RANGE_CONSTRAINT_4_SHIFT, - QUOTIENT_HIGH_LIMBS_RANGE_CONSTRAINT_TAIL_SHIFT, - RELATION_WIDE_LIMBS_SHIFT, - RELATION_WIDE_LIMBS_RANGE_CONSTRAINT_0_SHIFT, - RELATION_WIDE_LIMBS_RANGE_CONSTRAINT_1_SHIFT, - RELATION_WIDE_LIMBS_RANGE_CONSTRAINT_2_SHIFT, - RELATION_WIDE_LIMBS_RANGE_CONSTRAINT_3_SHIFT, - ORDERED_RANGE_CONSTRAINTS_0_SHIFT, - ORDERED_RANGE_CONSTRAINTS_1_SHIFT, - ORDERED_RANGE_CONSTRAINTS_2_SHIFT, - ORDERED_RANGE_CONSTRAINTS_3_SHIFT, - ORDERED_RANGE_CONSTRAINTS_4_SHIFT, - - Z_PERM_SHIFT, - /*All precomputed polynomials*/ - LAGRANGE_FIRST, - LAGRANGE_LAST, - LAGRANGE_ODD_IN_MINICIRCUIT, - LAGRANGE_EVEN_IN_MINICIRCUIT, - LAGRANGE_SECOND, - LAGRANGE_SECOND_TO_LAST_IN_MINICIRCUIT, - ORDERED_EXTRA_RANGE_CONSTRAINTS_NUMERATOR, - /*Utility value*/ - TOTAL_COUNT - - }; - + static constexpr size_t mini_circuit_size = 2048; using CircuitBuilder = GoblinTranslatorCircuitBuilder; - using PCS = pcs::kzg::KZG; using Curve = curve::BN254; + using PCS = pcs::kzg::KZG; using GroupElement = Curve::Element; using Commitment = Curve::AffineElement; using CommitmentHandle = Curve::AffineElement; @@ -300,13 +65,12 @@ template class GoblinTranslator_ { // The number of multivariate polynomials on which a sumcheck prover sumcheck operates (including shifts). We often // need containers of this size to hold related data, so we choose a name more agnostic than `NUM_POLYNOMIALS`. // Note: this number does not include the individual sorted list polynomials. - static constexpr size_t NUM_ALL_ENTITIES = ALL_ENTITIES_IDS::TOTAL_COUNT; + static constexpr size_t NUM_ALL_ENTITIES = 184; // The number of polynomials precomputed to describe a circuit and to aid a prover in constructing a satisfying // assignment of witnesses. We again choose a neutral name. - static constexpr size_t NUM_PRECOMPUTED_ENTITIES = ALL_ENTITIES_IDS::TOTAL_COUNT - ALL_ENTITIES_IDS::Z_PERM_SHIFT; + static constexpr size_t NUM_PRECOMPUTED_ENTITIES = 7; // The total number of witness entities not including shifts. - static constexpr size_t NUM_WITNESS_ENTITIES = - ALL_ENTITIES_IDS::TOTAL_COUNT - (ALL_ENTITIES_IDS::Z_PERM_SHIFT - ALL_ENTITIES_IDS::Z_PERM); + static constexpr size_t NUM_WITNESS_ENTITIES = 91; using GrandProductRelations = std::tuple>; // define the tuple of Relations that comprise the Sumcheck relation @@ -328,7 +92,13 @@ template class GoblinTranslator_ { static constexpr size_t NUM_RELATIONS = std::tuple_size_v; // define the containers for storing the contributions from each relation in Sumcheck - using SumcheckTupleOfTuplesOfUnivariates = decltype(create_sumcheck_tuple_of_tuples_of_univariates()); + using SumcheckTupleOfTuplesOfUnivariates = + std::tuple::SumcheckTupleOfUnivariatesOverSubrelations, + typename GoblinTranslatorGenPermSortRelation::SumcheckTupleOfUnivariatesOverSubrelations, + typename GoblinTranslatorOpcodeConstraintRelation::SumcheckTupleOfUnivariatesOverSubrelations, + typename GoblinTranslatorAccumulatorTransferRelation::SumcheckTupleOfUnivariatesOverSubrelations, + typename GoblinTranslatorDecompositionRelation::SumcheckTupleOfUnivariatesOverSubrelations, + typename GoblinTranslatorNonNativeFieldRelation::SumcheckTupleOfUnivariatesOverSubrelations>; using TupleOfArraysOfValues = decltype(create_tuple_of_arrays_of_values()); private: @@ -355,6 +125,7 @@ template class GoblinTranslator_ { &lagrange_second, &lagrange_second_to_last_in_minicircuit, &ordered_extra_range_constraints_numerator); + std::vector get_selectors() { return {}; }; std::vector get_sigma_polynomials() { return {}; }; std::vector get_id_polynomials() { return {}; }; @@ -550,7 +321,7 @@ template class GoblinTranslator_ { &ordered_range_constraints_2, &ordered_range_constraints_3, &ordered_range_constraints_4, - &z_perm, ) + &z_perm) std::vector get_wires() override { @@ -935,7 +706,6 @@ template class GoblinTranslator_ { DataType lagrange_second; // column 181 DataType lagrange_second_to_last_in_minicircuit; // column 182 DataType ordered_extra_range_constraints_numerator; // column 183 - // defines a method pointer_view that returns the following, with const and non-const variants DEFINE_POINTER_VIEW(NUM_ALL_ENTITIES, &op, @@ -1122,7 +892,6 @@ template class GoblinTranslator_ { &lagrange_second, &lagrange_second_to_last_in_minicircuit, &ordered_extra_range_constraints_numerator) - std::vector get_wires() override { @@ -1688,7 +1457,6 @@ template class GoblinTranslator_ { public: using Base = AllEntities; using Base::Base; - AllValues(std::array _data_in) { this->_data = _data_in; } }; /** * @brief A container for the prover polynomials handles; only stores spans. @@ -1728,13 +1496,11 @@ template class GoblinTranslator_ { */ class AllPolynomials : public AllEntities { public: - AllValues get_row(const size_t row_idx) const + [[nodiscard]] AllValues get_row(const size_t row_idx) const { AllValues result; - size_t column_idx = 0; // // TODO(https://github.com/AztecProtocol/barretenberg/issues/391) zip - for (auto& column : this->_data) { - result[column_idx] = column[row_idx]; - column_idx++; + for (auto [result_field, polynomial] : zip_view(result.pointer_view(), this->pointer_view())) { + *result_field = (*polynomial)[row_idx]; } return result; } @@ -1755,8 +1521,8 @@ template class GoblinTranslator_ { PartiallyEvaluatedMultivariates(const size_t circuit_size) { // Storage is only needed after the first partial evaluation, hence polynomials of size (n / 2) - for (auto& poly : this->_data) { - poly = Polynomial(circuit_size / 2); + for (auto* poly : this->pointer_view()) { + *poly = Polynomial(circuit_size / 2); } } }; @@ -1782,7 +1548,6 @@ template class GoblinTranslator_ { public: CommitmentLabels() { - this->op = "OP"; this->x_lo_y_hi = "X_LO_Y_HI"; this->x_hi_z_1 = "X_HI_Z_1"; @@ -1882,10 +1647,9 @@ template class GoblinTranslator_ { class VerifierCommitments : public AllEntities { public: - VerifierCommitments(std::shared_ptr verification_key, + VerifierCommitments([[maybe_unused]] std::shared_ptr verification_key, [[maybe_unused]] const BaseTranscript& transcript) { - static_cast(transcript); this->lagrange_first = verification_key->lagrange_first; this->lagrange_last = verification_key->lagrange_last; this->lagrange_odd_in_minicircuit = verification_key->lagrange_odd_in_minicircuit; @@ -1896,10 +1660,9 @@ template class GoblinTranslator_ { verification_key->ordered_extra_range_constraints_numerator; } }; -}; - -using GoblinTranslator = GoblinTranslator_<2048>; + using Transcript = BaseTranscript; +}; } // namespace proof_system::honk::flavor namespace proof_system { diff --git a/barretenberg/cpp/src/barretenberg/flavor/goblin_ultra.hpp b/barretenberg/cpp/src/barretenberg/flavor/goblin_ultra.hpp index 5b83ffc3154..66127524328 100644 --- a/barretenberg/cpp/src/barretenberg/flavor/goblin_ultra.hpp +++ b/barretenberg/cpp/src/barretenberg/flavor/goblin_ultra.hpp @@ -4,13 +4,16 @@ #include "barretenberg/polynomials/univariate.hpp" #include "barretenberg/proof_system/circuit_builder/goblin_ultra_circuit_builder.hpp" #include "barretenberg/relations/auxiliary_relation.hpp" +#include "barretenberg/relations/databus_lookup_relation.hpp" #include "barretenberg/relations/ecc_op_queue_relation.hpp" #include "barretenberg/relations/elliptic_relation.hpp" #include "barretenberg/relations/gen_perm_sort_relation.hpp" #include "barretenberg/relations/lookup_relation.hpp" #include "barretenberg/relations/permutation_relation.hpp" +#include "barretenberg/relations/relation_parameters.hpp" #include "barretenberg/relations/ultra_arithmetic_relation.hpp" #include "barretenberg/transcript/transcript.hpp" +#include "relation_definitions_fwd.hpp" namespace proof_system::honk::flavor { @@ -32,13 +35,12 @@ class GoblinUltra { // The number of multivariate polynomials on which a sumcheck prover sumcheck operates (including shifts). We often // need containers of this size to hold related data, so we choose a name more agnostic than `NUM_POLYNOMIALS`. // Note: this number does not include the individual sorted list polynomials. - // NUM = 43 (UH) + 4 op wires + 1 op wire "selector" + 3 (calldata + calldata_read_counts + q_busread) - static constexpr size_t NUM_ALL_ENTITIES = 51; + static constexpr size_t NUM_ALL_ENTITIES = 53; // The number of polynomials precomputed to describe a circuit and to aid a prover in constructing a satisfying // assignment of witnesses. We again choose a neutral name. - static constexpr size_t NUM_PRECOMPUTED_ENTITIES = 27; // 25 (UH) + 1 op wire "selector" + q_busread + static constexpr size_t NUM_PRECOMPUTED_ENTITIES = 28; // The total number of witness entities not including shifts. - static constexpr size_t NUM_WITNESS_ENTITIES = 17; // 11 (UH) + 4 op wires + (calldata + calldata_read_counts) + static constexpr size_t NUM_WITNESS_ENTITIES = 18; using GrandProductRelations = std::tuple, proof_system::LookupRelation>; @@ -50,10 +52,14 @@ class GoblinUltra { proof_system::GenPermSortRelation, proof_system::EllipticRelation, proof_system::AuxiliaryRelation, - proof_system::EccOpQueueRelation>; + proof_system::EccOpQueueRelation, + proof_system::DatabusLookupRelation>; + + using LogDerivLookupRelation = proof_system::DatabusLookupRelation; static constexpr size_t MAX_PARTIAL_RELATION_LENGTH = compute_max_partial_relation_length(); static constexpr size_t MAX_TOTAL_RELATION_LENGTH = compute_max_total_relation_length(); + static constexpr size_t NUMBER_OF_SUBRELATIONS = compute_number_of_subrelations(); // BATCHED_RELATION_PARTIAL_LENGTH = algebraic degree of sumcheck relation *after* multiplying by the `pow_zeta` // random polynomial e.g. For \sum(x) [A(x) * B(x) + C(x)] * PowZeta(X), relation length = 2 and random relation @@ -106,6 +112,7 @@ class GoblinUltra { DataType lagrange_first; // column 24 DataType lagrange_last; // column 25 DataType lagrange_ecc_op; // column 26 // indicator poly for ecc op gates + DataType databus_id; // column 27 // id polynomial, i.e. id_i = i DEFINE_POINTER_VIEW(NUM_PRECOMPUTED_ENTITIES, &q_m, @@ -134,7 +141,8 @@ class GoblinUltra { &table_4, &lagrange_first, &lagrange_last, - &lagrange_ecc_op) + &lagrange_ecc_op, + &databus_id) static constexpr CircuitType CIRCUIT_TYPE = CircuitBuilder::CIRCUIT_TYPE; @@ -172,6 +180,7 @@ class GoblinUltra { DataType ecc_op_wire_4; // column 14 DataType calldata; // column 15 DataType calldata_read_counts; // column 16 + DataType lookup_inverses; // column 17 DEFINE_POINTER_VIEW(NUM_WITNESS_ENTITIES, &w_l, @@ -190,7 +199,8 @@ class GoblinUltra { &ecc_op_wire_3, &ecc_op_wire_4, &calldata, - &calldata_read_counts) + &calldata_read_counts, + &lookup_inverses) std::vector get_wires() override { return { w_l, w_r, w_o, w_4 }; }; std::vector get_ecc_op_wires() @@ -240,30 +250,32 @@ class GoblinUltra { DataType lagrange_first; // column 24 DataType lagrange_last; // column 25 DataType lagrange_ecc_op; // column 26 - DataType w_l; // column 27 - DataType w_r; // column 28 - DataType w_o; // column 29 - DataType w_4; // column 30 - DataType sorted_accum; // column 31 - DataType z_perm; // column 32 - DataType z_lookup; // column 33 - DataType ecc_op_wire_1; // column 34 - DataType ecc_op_wire_2; // column 35 - DataType ecc_op_wire_3; // column 36 - DataType ecc_op_wire_4; // column 37 - DataType calldata; // column 38 - DataType calldata_read_counts; // column 39 - DataType table_1_shift; // column 40 - DataType table_2_shift; // column 41 - DataType table_3_shift; // column 42 - DataType table_4_shift; // column 43 - DataType w_l_shift; // column 44 - DataType w_r_shift; // column 45 - DataType w_o_shift; // column 46 - DataType w_4_shift; // column 47 - DataType sorted_accum_shift; // column 48 - DataType z_perm_shift; // column 49 - DataType z_lookup_shift; // column 50 + DataType databus_id; // column 27 + DataType w_l; // column 28 + DataType w_r; // column 29 + DataType w_o; // column 30 + DataType w_4; // column 31 + DataType sorted_accum; // column 32 + DataType z_perm; // column 33 + DataType z_lookup; // column 34 + DataType ecc_op_wire_1; // column 35 + DataType ecc_op_wire_2; // column 36 + DataType ecc_op_wire_3; // column 37 + DataType ecc_op_wire_4; // column 38 + DataType calldata; // column 39 + DataType calldata_read_counts; // column 40 + DataType lookup_inverses; // column 41 + DataType table_1_shift; // column 42 + DataType table_2_shift; // column 43 + DataType table_3_shift; // column 44 + DataType table_4_shift; // column 45 + DataType w_l_shift; // column 46 + DataType w_r_shift; // column 47 + DataType w_o_shift; // column 48 + DataType w_4_shift; // column 49 + DataType sorted_accum_shift; // column 50 + DataType z_perm_shift; // column 51 + DataType z_lookup_shift; // column 52 // defines a method pointer_view that returns the following, with const and non-const variants DEFINE_POINTER_VIEW(NUM_ALL_ENTITIES, @@ -294,6 +306,7 @@ class GoblinUltra { &lagrange_first, &lagrange_last, &lagrange_ecc_op, + &databus_id, &w_l, &w_r, &w_o, @@ -307,6 +320,7 @@ class GoblinUltra { &ecc_op_wire_4, &calldata, &calldata_read_counts, + &lookup_inverses, &table_1_shift, &table_2_shift, &table_3_shift, @@ -354,6 +368,7 @@ class GoblinUltra { lagrange_first, lagrange_last, lagrange_ecc_op, + databus_id, w_l, w_r, w_o, @@ -366,7 +381,8 @@ class GoblinUltra { ecc_op_wire_3, ecc_op_wire_4, calldata, - calldata_read_counts }; + calldata_read_counts, + lookup_inverses }; }; std::vector get_to_be_shifted() override { @@ -422,7 +438,7 @@ class GoblinUltra { PartiallyEvaluatedMultivariates(const size_t circuit_size) { // Storage is only needed after the first partial evaluation, hence polynomials of size (n / 2) - for (auto* poly : pointer_view()) { + for (auto* poly : this->pointer_view()) { *poly = Polynomial(circuit_size / 2); } } @@ -459,7 +475,7 @@ class GoblinUltra { [[nodiscard]] AllValues get_row(size_t row_idx) const { AllValues result; - for (auto [result_field, polynomial] : zip_view(result.pointer_view(), pointer_view())) { + for (auto [result_field, polynomial] : zip_view(result.pointer_view(), this->pointer_view())) { *result_field = (*polynomial)[row_idx]; } return result; @@ -489,6 +505,7 @@ class GoblinUltra { ecc_op_wire_4 = "ECC_OP_WIRE_4"; calldata = "CALLDATA"; calldata_read_counts = "CALLDATA_READ_COUNTS"; + lookup_inverses = "LOOKUP_INVERSES"; // The ones beginning with "__" are only used for debugging q_c = "__Q_C"; @@ -554,6 +571,7 @@ class GoblinUltra { lagrange_first = verification_key->lagrange_first; lagrange_last = verification_key->lagrange_last; lagrange_ecc_op = verification_key->lagrange_ecc_op; + databus_id = verification_key->databus_id; } }; @@ -582,6 +600,7 @@ class GoblinUltra { Commitment ecc_op_wire_4_comm; Commitment calldata_comm; Commitment calldata_read_counts_comm; + Commitment lookup_inverses_comm; Commitment sorted_accum_comm; Commitment w_4_comm; Commitment z_perm_comm; @@ -597,6 +616,7 @@ class GoblinUltra { Transcript(const std::vector& proof) : BaseTranscript(proof) {} + void deserialize_full_transcript() override { // take current proof and put them into the struct @@ -618,6 +638,7 @@ class GoblinUltra { ecc_op_wire_4_comm = deserialize_from_buffer(proof_data, num_bytes_read); calldata_comm = deserialize_from_buffer(proof_data, num_bytes_read); calldata_read_counts_comm = deserialize_from_buffer(proof_data, num_bytes_read); + lookup_inverses_comm = deserialize_from_buffer(proof_data, num_bytes_read); sorted_accum_comm = deserialize_from_buffer(proof_data, num_bytes_read); w_4_comm = deserialize_from_buffer(proof_data, num_bytes_read); z_perm_comm = deserialize_from_buffer(proof_data, num_bytes_read); @@ -656,6 +677,7 @@ class GoblinUltra { serialize_to_buffer(ecc_op_wire_4_comm, proof_data); serialize_to_buffer(calldata_comm, proof_data); serialize_to_buffer(calldata_read_counts_comm, proof_data); + serialize_to_buffer(lookup_inverses_comm, proof_data); serialize_to_buffer(sorted_accum_comm, proof_data); serialize_to_buffer(w_4_comm, proof_data); serialize_to_buffer(z_perm_comm, proof_data); @@ -675,4 +697,4 @@ class GoblinUltra { }; }; -} // namespace proof_system::honk::flavor +} // namespace proof_system::honk::flavor \ No newline at end of file diff --git a/barretenberg/cpp/src/barretenberg/flavor/goblin_ultra_recursive.hpp b/barretenberg/cpp/src/barretenberg/flavor/goblin_ultra_recursive.hpp index e85e9578ce2..f37d26b3adc 100644 --- a/barretenberg/cpp/src/barretenberg/flavor/goblin_ultra_recursive.hpp +++ b/barretenberg/cpp/src/barretenberg/flavor/goblin_ultra_recursive.hpp @@ -61,13 +61,12 @@ template class GoblinUltraRecursive_ { // The number of multivariate polynomials on which a sumcheck prover sumcheck operates (including shifts). We often // need containers of this size to hold related data, so we choose a name more agnostic than `NUM_POLYNOMIALS`. // Note: this number does not include the individual sorted list polynomials. - // NUM = 43 (UH) + 4 op wires + 1 op wire "selector" + 3 (calldata + calldata_read_counts + q_busread) - static constexpr size_t NUM_ALL_ENTITIES = 51; + static constexpr size_t NUM_ALL_ENTITIES = 53; // The number of polynomials precomputed to describe a circuit and to aid a prover in constructing a satisfying // assignment of witnesses. We again choose a neutral name. - static constexpr size_t NUM_PRECOMPUTED_ENTITIES = 27; // 25 (UH) + 1 op wire "selector" + q_busread + static constexpr size_t NUM_PRECOMPUTED_ENTITIES = 28; // The total number of witness entities not including shifts. - static constexpr size_t NUM_WITNESS_ENTITIES = 17; // 11 (UH) + 4 op wires + (calldata + calldata_read_counts) + static constexpr size_t NUM_WITNESS_ENTITIES = 18; // define the tuple of Relations that comprise the Sumcheck relation using Relations = std::tuple, @@ -76,7 +75,8 @@ template class GoblinUltraRecursive_ { proof_system::GenPermSortRelation, proof_system::EllipticRelation, proof_system::AuxiliaryRelation, - proof_system::EccOpQueueRelation>; + proof_system::EccOpQueueRelation, + proof_system::DatabusLookupRelation>; static constexpr size_t MAX_PARTIAL_RELATION_LENGTH = compute_max_partial_relation_length(); @@ -125,6 +125,37 @@ template class GoblinUltraRecursive_ { DataType lagrange_first; // column 24 DataType lagrange_last; // column 25 DataType lagrange_ecc_op; // column 26 // indicator poly for ecc op gates + DataType databus_id; // column 27 // id polynomial, i.e. id_i = i + + DEFINE_POINTER_VIEW(NUM_PRECOMPUTED_ENTITIES, + &q_m, + &q_c, + &q_l, + &q_r, + &q_o, + &q_4, + &q_arith, + &q_sort, + &q_elliptic, + &q_aux, + &q_lookup, + &q_busread, + &sigma_1, + &sigma_2, + &sigma_3, + &sigma_4, + &id_1, + &id_2, + &id_3, + &id_4, + &table_1, + &table_2, + &table_3, + &table_4, + &lagrange_first, + &lagrange_last, + &lagrange_ecc_op, + &databus_id) static constexpr CircuitType CIRCUIT_TYPE = CircuitBuilder::CIRCUIT_TYPE; @@ -162,6 +193,7 @@ template class GoblinUltraRecursive_ { DataType ecc_op_wire_4; // column 14 DataType calldata; // column 15 DataType calldata_read_counts; // column 16 + DataType lookup_inverses; // column 17 DEFINE_POINTER_VIEW(NUM_WITNESS_ENTITIES, &w_l, @@ -180,7 +212,8 @@ template class GoblinUltraRecursive_ { &ecc_op_wire_3, &ecc_op_wire_4, &calldata, - &calldata_read_counts) + &calldata_read_counts, + &lookup_inverses) std::vector get_wires() override { return { w_l, w_r, w_o, w_4 }; }; std::vector get_ecc_op_wires() @@ -230,31 +263,34 @@ template class GoblinUltraRecursive_ { DataType lagrange_first; // column 24 DataType lagrange_last; // column 25 DataType lagrange_ecc_op; // column 26 - DataType w_l; // column 27 - DataType w_r; // column 28 - DataType w_o; // column 29 - DataType w_4; // column 30 - DataType sorted_accum; // column 31 - DataType z_perm; // column 32 - DataType z_lookup; // column 33 - DataType ecc_op_wire_1; // column 34 - DataType ecc_op_wire_2; // column 35 - DataType ecc_op_wire_3; // column 36 - DataType ecc_op_wire_4; // column 37 - DataType calldata; // column 38 - DataType calldata_read_counts; // column 39 - DataType table_1_shift; // column 40 - DataType table_2_shift; // column 41 - DataType table_3_shift; // column 42 - DataType table_4_shift; // column 43 - DataType w_l_shift; // column 44 - DataType w_r_shift; // column 45 - DataType w_o_shift; // column 46 - DataType w_4_shift; // column 47 - DataType sorted_accum_shift; // column 48 - DataType z_perm_shift; // column 49 - DataType z_lookup_shift; // column 50 - + DataType databus_id; // column 27 + DataType w_l; // column 28 + DataType w_r; // column 29 + DataType w_o; // column 30 + DataType w_4; // column 31 + DataType sorted_accum; // column 32 + DataType z_perm; // column 33 + DataType z_lookup; // column 34 + DataType ecc_op_wire_1; // column 35 + DataType ecc_op_wire_2; // column 36 + DataType ecc_op_wire_3; // column 37 + DataType ecc_op_wire_4; // column 38 + DataType calldata; // column 39 + DataType calldata_read_counts; // column 40 + DataType lookup_inverses; // column 41 + DataType table_1_shift; // column 42 + DataType table_2_shift; // column 43 + DataType table_3_shift; // column 44 + DataType table_4_shift; // column 45 + DataType w_l_shift; // column 46 + DataType w_r_shift; // column 47 + DataType w_o_shift; // column 48 + DataType w_4_shift; // column 49 + DataType sorted_accum_shift; // column 50 + DataType z_perm_shift; // column 51 + DataType z_lookup_shift; // column 52 + + // defines a method pointer_view that returns the following, with const and non-const variants DEFINE_POINTER_VIEW(NUM_ALL_ENTITIES, &q_c, &q_l, @@ -283,6 +319,7 @@ template class GoblinUltraRecursive_ { &lagrange_first, &lagrange_last, &lagrange_ecc_op, + &databus_id, &w_l, &w_r, &w_o, @@ -296,6 +333,7 @@ template class GoblinUltraRecursive_ { &ecc_op_wire_4, &calldata, &calldata_read_counts, + &lookup_inverses, &table_1_shift, &table_2_shift, &table_3_shift, @@ -306,7 +344,7 @@ template class GoblinUltraRecursive_ { &w_4_shift, &sorted_accum_shift, &z_perm_shift, - &z_lookup_shift) + &z_lookup_shift); std::vector get_wires() override { return { w_l, w_r, w_o, w_4 }; }; std::vector get_ecc_op_wires() @@ -343,6 +381,7 @@ template class GoblinUltraRecursive_ { lagrange_first, lagrange_last, lagrange_ecc_op, + databus_id, w_l, w_r, w_o, @@ -355,7 +394,8 @@ template class GoblinUltraRecursive_ { ecc_op_wire_3, ecc_op_wire_4, calldata, - calldata_read_counts }; + calldata_read_counts, + lookup_inverses }; }; std::vector get_to_be_shifted() override { @@ -416,6 +456,7 @@ template class GoblinUltraRecursive_ { this->lagrange_first = Commitment::from_witness(builder, native_key->lagrange_first); this->lagrange_last = Commitment::from_witness(builder, native_key->lagrange_last); this->lagrange_ecc_op = Commitment::from_witness(builder, native_key->lagrange_ecc_op); + this->databus_id = Commitment::from_witness(builder, native_key->databus_id); }; }; @@ -453,6 +494,7 @@ template class GoblinUltraRecursive_ { this->ecc_op_wire_4 = "ECC_OP_WIRE_4"; this->calldata = "CALLDATA"; this->calldata_read_counts = "CALLDATA_READ_COUNTS"; + this->lookup_inverses = "LOOKUP_INVERSES"; // The ones beginning with "__" are only used for debugging this->q_c = "__Q_C"; @@ -516,6 +558,7 @@ template class GoblinUltraRecursive_ { this->lagrange_first = verification_key->lagrange_first; this->lagrange_last = verification_key->lagrange_last; this->lagrange_ecc_op = verification_key->lagrange_ecc_op; + this->databus_id = verification_key->databus_id; } }; @@ -539,6 +582,7 @@ template class GoblinUltraRecursive_ { Commitment ecc_op_wire_4_comm; Commitment calldata_comm; Commitment calldata_read_counts_comm; + Commitment lookup_inverses_comm; Commitment sorted_accum_comm; Commitment w_4_comm; Commitment z_perm_comm; @@ -581,6 +625,7 @@ template class GoblinUltraRecursive_ { calldata_comm = deserialize_from_buffer(BaseTranscript::proof_data, num_bytes_read); calldata_read_counts_comm = deserialize_from_buffer(BaseTranscript::proof_data, num_bytes_read); + lookup_inverses_comm = deserialize_from_buffer(BaseTranscript::proof_data, num_bytes_read); sorted_accum_comm = deserialize_from_buffer(BaseTranscript::proof_data, num_bytes_read); w_4_comm = deserialize_from_buffer(BaseTranscript::proof_data, num_bytes_read); z_perm_comm = deserialize_from_buffer(BaseTranscript::proof_data, num_bytes_read); @@ -625,6 +670,7 @@ template class GoblinUltraRecursive_ { serialize_to_buffer(ecc_op_wire_4_comm, BaseTranscript::proof_data); serialize_to_buffer(calldata_comm, BaseTranscript::proof_data); serialize_to_buffer(calldata_read_counts_comm, BaseTranscript::proof_data); + serialize_to_buffer(lookup_inverses_comm, BaseTranscript::proof_data); serialize_to_buffer(sorted_accum_comm, BaseTranscript::proof_data); serialize_to_buffer(w_4_comm, BaseTranscript::proof_data); serialize_to_buffer(z_perm_comm, BaseTranscript::proof_data); diff --git a/barretenberg/cpp/src/barretenberg/flavor/ultra.hpp b/barretenberg/cpp/src/barretenberg/flavor/ultra.hpp index d347c45cc83..da12a3994ef 100644 --- a/barretenberg/cpp/src/barretenberg/flavor/ultra.hpp +++ b/barretenberg/cpp/src/barretenberg/flavor/ultra.hpp @@ -56,6 +56,7 @@ class Ultra { static constexpr size_t MAX_TOTAL_RELATION_LENGTH = compute_max_total_relation_length(); static_assert(MAX_PARTIAL_RELATION_LENGTH == 6); static_assert(MAX_TOTAL_RELATION_LENGTH == 12); + static constexpr size_t NUMBER_OF_SUBRELATIONS = compute_number_of_subrelations(); // BATCHED_RELATION_PARTIAL_LENGTH = algebraic degree of sumcheck relation *after* multiplying by the `pow_zeta` // random polynomial e.g. For \sum(x) [A(x) * B(x) + C(x)] * PowZeta(X), relation length = 2 and random relation @@ -373,7 +374,7 @@ class Ultra { PartiallyEvaluatedMultivariates(const size_t circuit_size) { // Storage is only needed after the first partial evaluation, hence polynomials of size (n / 2) - for (auto* poly : pointer_view()) { + for (auto* poly : this->pointer_view()) { *poly = Polynomial(circuit_size / 2); } } diff --git a/barretenberg/cpp/src/barretenberg/goblin/CMakeLists.txt b/barretenberg/cpp/src/barretenberg/goblin/CMakeLists.txt index f0316f56dcc..248b05c02e6 100644 --- a/barretenberg/cpp/src/barretenberg/goblin/CMakeLists.txt +++ b/barretenberg/cpp/src/barretenberg/goblin/CMakeLists.txt @@ -1 +1 @@ -barretenberg_module(goblin ultra_honk eccvm) \ No newline at end of file +barretenberg_module(goblin ultra_honk eccvm translator_vm) \ No newline at end of file diff --git a/barretenberg/cpp/src/barretenberg/goblin/full_goblin_composer.test.cpp b/barretenberg/cpp/src/barretenberg/goblin/full_goblin_composer.test.cpp index 19f4ceb9ece..e263cb329a4 100644 --- a/barretenberg/cpp/src/barretenberg/goblin/full_goblin_composer.test.cpp +++ b/barretenberg/cpp/src/barretenberg/goblin/full_goblin_composer.test.cpp @@ -1,14 +1,14 @@ -#include -#include -#include - -#include "barretenberg/common/log.hpp" #include "barretenberg/eccvm/eccvm_composer.hpp" +#include "barretenberg/goblin/translation_evaluations.hpp" #include "barretenberg/proof_system/circuit_builder/eccvm/eccvm_circuit_builder.hpp" #include "barretenberg/proof_system/circuit_builder/goblin_ultra_circuit_builder.hpp" #include "barretenberg/proof_system/circuit_builder/ultra_circuit_builder.hpp" +#include "barretenberg/translator_vm/goblin_translator_composer.hpp" #include "barretenberg/ultra_honk/ultra_composer.hpp" -#include "barretenberg/ultra_honk/ultra_prover.hpp" + +#include + +using namespace proof_system::honk; namespace test_full_goblin_composer { @@ -26,22 +26,27 @@ class FullGoblinComposerTests : public ::testing::Test { using Curve = curve::BN254; using FF = Curve::ScalarField; + using Fbase = Curve::BaseField; using Point = Curve::AffineElement; - using CommitmentKey = proof_system::honk::pcs::CommitmentKey; + using CommitmentKey = pcs::CommitmentKey; + using OpQueue = proof_system::ECCOpQueue; using GoblinUltraBuilder = proof_system::GoblinUltraCircuitBuilder; - using GoblinUltraComposer = proof_system::honk::GoblinUltraComposer; - using ECCVMFlavor = proof_system::honk::flavor::ECCVMGrumpkin; + using ECCVMFlavor = flavor::ECCVM; using ECCVMBuilder = proof_system::ECCVMCircuitBuilder; - using ECCVMComposer = proof_system::honk::ECCVMComposer_; - using VMOp = proof_system_eccvm::VMOperation; - static constexpr size_t NUM_OP_QUEUE_COLUMNS = proof_system::honk::flavor::GoblinUltra::NUM_WIRES; + using ECCVMComposer = ECCVMComposer_; + using TranslatorFlavor = flavor::GoblinTranslator; + using TranslatorBuilder = proof_system::GoblinTranslatorCircuitBuilder; + using TranslatorComposer = GoblinTranslatorComposer; + using TranslatorConsistencyData = barretenberg::TranslationEvaluations; + + static constexpr size_t NUM_OP_QUEUE_COLUMNS = flavor::GoblinUltra::NUM_WIRES; /** * @brief Generate a simple test circuit with some ECC op gates and conventional arithmetic gates * * @param builder */ - void generate_test_circuit(auto& builder) + static void generate_test_circuit(GoblinUltraBuilder& builder) { // Add some arbitrary ecc op gates for (size_t i = 0; i < 3; ++i) { @@ -50,7 +55,8 @@ class FullGoblinComposerTests : public ::testing::Test { builder.queue_ecc_add_accum(point); builder.queue_ecc_mul_accum(point, scalar); } - builder.queue_ecc_eq(); + // queues the result of the preceding ECC + builder.queue_ecc_eq(); // should be eq and reset // Add some conventional gates that utilize public inputs for (size_t i = 0; i < 10; ++i) { @@ -75,12 +81,14 @@ class FullGoblinComposerTests : public ::testing::Test { * protocol can proceed nominally. The mock data is valid in the sense that it can be processed by all stages of * Goblin as if it came from a genuine circuit. * + * @todo WOKTODO: this is a zero commitments issue + * * @param op_queue */ static void perform_op_queue_interactions_for_mock_first_circuit( std::shared_ptr& op_queue) { - auto builder = GoblinUltraBuilder(op_queue); + auto builder = GoblinUltraBuilder{ op_queue }; // Add a mul accum op and an equality op auto point = Point::one() * FF::random_element(); @@ -106,7 +114,7 @@ class FullGoblinComposerTests : public ::testing::Test { * @brief Construct and a verify a Honk proof * */ - bool construct_and_verify_honk_proof(auto& composer, auto& builder) + static bool construct_and_verify_honk_proof(GoblinUltraComposer& composer, GoblinUltraBuilder& builder) { auto instance = composer.create_instance(builder); auto prover = composer.create_prover(instance); @@ -121,29 +129,15 @@ class FullGoblinComposerTests : public ::testing::Test { * @brief Construct and verify a Goblin ECC op queue merge proof * */ - bool construct_and_verify_merge_proof(auto& composer, auto& op_queue) + static bool construct_and_verify_merge_proof(GoblinUltraComposer& composer, std::shared_ptr& op_queue) { auto merge_prover = composer.create_merge_prover(op_queue); - auto merge_verifier = composer.create_merge_verifier(10); + auto merge_verifier = composer.create_merge_verifier(/*srs_size=*/10); auto merge_proof = merge_prover.construct_proof(); bool verified = merge_verifier.verify_proof(merge_proof); return verified; } - - /** - * @brief Construct and verify a Goblin ECC op queue merge proof - * - */ - bool construct_and_verify_eccvm_proof(auto& composer, auto& builder) - { - auto prover = composer.create_prover(builder); - auto proof = prover.construct_proof(); - auto verifier = composer.create_verifier(builder); - bool verified = verifier.verify_proof(proof); - - return verified; - } }; /** @@ -163,49 +157,7 @@ TEST_F(FullGoblinComposerTests, SimpleCircuit) // Construct a series of simple Goblin circuits; generate and verify their proofs size_t NUM_CIRCUITS = 3; for (size_t circuit_idx = 0; circuit_idx < NUM_CIRCUITS; ++circuit_idx) { - auto builder = GoblinUltraBuilder(op_queue); - - generate_test_circuit(builder); - - // The same composer is used to manage Honk and Merge prover/verifier - auto composer = GoblinUltraComposer(); - - // Construct and verify Ultra Goblin Honk proof - auto honk_verified = construct_and_verify_honk_proof(composer, builder); - EXPECT_TRUE(honk_verified); - - // Construct and verify op queue merge proof - auto merge_verified = construct_and_verify_merge_proof(composer, op_queue); - EXPECT_TRUE(merge_verified); - } - - // Construct an ECCVM circuit then generate and verify its proof - { - // Instantiate an ECCVM builder with the vm ops stored in the op queue - auto builder = ECCVMBuilder(op_queue->raw_ops); - - // Construct and verify ECCVM proof - auto composer = ECCVMComposer(); - auto eccvm_verified = construct_and_verify_eccvm_proof(composer, builder); - EXPECT_TRUE(eccvm_verified); - } -} - -/** - * @brief Check that ECCVM verification fails if ECC op queue operands are tampered with - * - */ -TEST_F(FullGoblinComposerTests, SimpleCircuitFailureCase) -{ - auto op_queue = std::make_shared(); - - // Add mock data to op queue to simulate interaction with a "first" circuit - perform_op_queue_interactions_for_mock_first_circuit(op_queue); - - // Construct a series of simple Goblin circuits; generate and verify their proofs - size_t NUM_CIRCUITS = 3; - for (size_t circuit_idx = 0; circuit_idx < NUM_CIRCUITS; ++circuit_idx) { - auto builder = GoblinUltraBuilder(op_queue); + auto builder = GoblinUltraBuilder{ op_queue }; generate_test_circuit(builder); @@ -213,27 +165,36 @@ TEST_F(FullGoblinComposerTests, SimpleCircuitFailureCase) auto composer = GoblinUltraComposer(); // Construct and verify Ultra Goblin Honk proof - auto honk_verified = construct_and_verify_honk_proof(composer, builder); + bool honk_verified = construct_and_verify_honk_proof(composer, builder); EXPECT_TRUE(honk_verified); // Construct and verify op queue merge proof - auto merge_verified = construct_and_verify_merge_proof(composer, op_queue); + bool merge_verified = construct_and_verify_merge_proof(composer, op_queue); EXPECT_TRUE(merge_verified); } - // Construct an ECCVM circuit then generate and verify its proof - { - // Instantiate an ECCVM builder with the vm ops stored in the op queue - auto builder = ECCVMBuilder(op_queue->raw_ops); - - // Fiddle with one of the operands to trigger a failure - builder.vm_operations[0].z1 += 1; - - // Construct and verify ECCVM proof - auto composer = ECCVMComposer(); - auto eccvm_verified = construct_and_verify_eccvm_proof(composer, builder); - EXPECT_FALSE(eccvm_verified); - } + // Execute the ECCVM + // TODO(https://github.com/AztecProtocol/barretenberg/issues/785) Properly initialize transcript + auto eccvm_builder = ECCVMBuilder(op_queue); + auto eccvm_composer = ECCVMComposer(); + auto eccvm_prover = eccvm_composer.create_prover(eccvm_builder); + auto eccvm_verifier = eccvm_composer.create_verifier(eccvm_builder); + auto eccvm_proof = eccvm_prover.construct_proof(); + bool eccvm_verified = eccvm_verifier.verify_proof(eccvm_proof); + EXPECT_TRUE(eccvm_verified); + + // Execute the Translator + // TODO(https://github.com/AztecProtocol/barretenberg/issues/786) Properly derive batching_challenge + auto batching_challenge = Fbase::random_element(); + auto evaluation_input = eccvm_prover.evaluation_challenge_x; + auto translator_builder = TranslatorBuilder(batching_challenge, evaluation_input, op_queue); + auto translator_composer = TranslatorComposer(); + auto translator_prover = translator_composer.create_prover(translator_builder); + auto translator_verifier = translator_composer.create_verifier(translator_builder); + auto translator_proof = translator_prover.construct_proof(); + bool accumulator_construction_verified = translator_verifier.verify_proof(translator_proof); + bool translation_verified = translator_verifier.verify_translation(eccvm_prover.translation_evaluations); + EXPECT_TRUE(accumulator_construction_verified && translation_verified); } - +// TODO(https://github.com/AztecProtocol/barretenberg/issues/787) Expand these tests. } // namespace test_full_goblin_composer diff --git a/barretenberg/cpp/src/barretenberg/goblin/translation_evaluations.hpp b/barretenberg/cpp/src/barretenberg/goblin/translation_evaluations.hpp new file mode 100644 index 00000000000..4a0e6d5d835 --- /dev/null +++ b/barretenberg/cpp/src/barretenberg/goblin/translation_evaluations.hpp @@ -0,0 +1,8 @@ +#pragma once +#include "barretenberg/ecc/curves/bn254/fq.hpp" + +namespace barretenberg { +struct TranslationEvaluations { + fq op, Px, Py, z1, z2; +}; +} // namespace barretenberg \ No newline at end of file diff --git a/barretenberg/cpp/src/barretenberg/honk/proof_system/lookup_library.hpp b/barretenberg/cpp/src/barretenberg/honk/proof_system/lookup_library.hpp index b8770040beb..820bc2907a5 100644 --- a/barretenberg/cpp/src/barretenberg/honk/proof_system/lookup_library.hpp +++ b/barretenberg/cpp/src/barretenberg/honk/proof_system/lookup_library.hpp @@ -1,5 +1,4 @@ #pragma once -#include "barretenberg/sumcheck/sumcheck.hpp" #include namespace proof_system::honk::lookup_library { @@ -24,9 +23,7 @@ namespace proof_system::honk::lookup_library { * */ template -void compute_logderivative_inverse(Polynomials& polynomials, - proof_system::RelationParameters& relation_parameters, - const size_t circuit_size) +void compute_logderivative_inverse(Polynomials& polynomials, auto& relation_parameters, const size_t circuit_size) { using FF = typename Flavor::FF; using Accumulator = typename Relation::ValueAccumulator0; @@ -128,6 +125,7 @@ void accumulate_logderivative_lookup_subrelation_contributions(ContainerOverSubr const auto inverse_exists = lookup_relation.template compute_inverse_exists(in); + // Note: the lookup_inverses are computed so that the value is 0 if !inverse_exists std::get<0>(accumulator) += (denominator_accumulator[NUM_TOTAL_TERMS - 1] * lookup_inverses - inverse_exists) * scaling_factor; @@ -150,7 +148,7 @@ void accumulate_logderivative_lookup_subrelation_contributions(ContainerOverSubr // degree of relation = NUM_TOTAL_TERMS + 2 barretenberg::constexpr_for<0, WRITE_TERMS, 1>([&]() { const auto p = lookup_relation.template compute_write_term_predicate(in); - const auto lookup_read_count = View(in.template lookup_read_counts()); + const auto lookup_read_count = lookup_relation.template lookup_read_counts(in); std::get<1>(accumulator) -= p * (denominator_accumulator[i + READ_TERMS] * lookup_read_count); }); } diff --git a/barretenberg/cpp/src/barretenberg/honk/proof_system/permutation_library.hpp b/barretenberg/cpp/src/barretenberg/honk/proof_system/permutation_library.hpp index 0471f818ca6..b4f4573c6f8 100644 --- a/barretenberg/cpp/src/barretenberg/honk/proof_system/permutation_library.hpp +++ b/barretenberg/cpp/src/barretenberg/honk/proof_system/permutation_library.hpp @@ -1,7 +1,6 @@ #pragma once #include "barretenberg/plonk/proof_system/proving_key/proving_key.hpp" #include "barretenberg/polynomials/polynomial.hpp" -#include "barretenberg/sumcheck/sumcheck.hpp" #include namespace proof_system::honk::permutation_library { @@ -401,4 +400,31 @@ template inline void compute_extra_range_constraint_numerator( parallel_for(num_concatenated_wires + 1, fill_with_shift); } +/** + * @brief Compute odd and even largrange polynomials (up to mini_circuit length) and put them in the polynomial cache + * + * @param key Proving key where we will save the polynomials + */ +template inline void compute_lagrange_polynomials_for_goblin_translator(auto proving_key) + +{ + const size_t n = proving_key->circuit_size; + typename Flavor::Polynomial lagrange_polynomial_odd_in_minicircuit(n); + typename Flavor::Polynomial lagrange_polynomial_even_in_minicircut(n); + typename Flavor::Polynomial lagrange_polynomial_second(n); + typename Flavor::Polynomial lagrange_polynomial_second_to_last_in_minicircuit(n); + + for (size_t i = 1; i < Flavor::MINI_CIRCUIT_SIZE - 1; i += 2) { + lagrange_polynomial_odd_in_minicircuit[i] = 1; + lagrange_polynomial_even_in_minicircut[i + 1] = 1; + } + proving_key->lagrange_odd_in_minicircuit = lagrange_polynomial_odd_in_minicircuit; + + proving_key->lagrange_even_in_minicircuit = lagrange_polynomial_even_in_minicircut; + lagrange_polynomial_second[1] = 1; + lagrange_polynomial_second_to_last_in_minicircuit[Flavor::MINI_CIRCUIT_SIZE - 2] = 1; + proving_key->lagrange_second_to_last_in_minicircuit = lagrange_polynomial_second_to_last_in_minicircuit; + proving_key->lagrange_second = lagrange_polynomial_second; +} + } // namespace proof_system::honk::permutation_library \ No newline at end of file diff --git a/barretenberg/cpp/src/barretenberg/join_split_example/proofs/join_split/join_split.test.cpp b/barretenberg/cpp/src/barretenberg/join_split_example/proofs/join_split/join_split.test.cpp index ba978aeaf77..48a169b17bd 100644 --- a/barretenberg/cpp/src/barretenberg/join_split_example/proofs/join_split/join_split.test.cpp +++ b/barretenberg/cpp/src/barretenberg/join_split_example/proofs/join_split/join_split.test.cpp @@ -65,7 +65,7 @@ class join_split_tests : public ::testing::Test { .creator_pubkey = 0, .input_nullifier = fr::random_element() }; - // Initialise value_notes array as default: + // Initialize value_notes array as default: for (auto& value_note : value_notes) { value_note = default_value_note; value_note.input_nullifier = fr::random_element(); // to ensure uniqueness diff --git a/barretenberg/cpp/src/barretenberg/numeric/bitop/count_leading_zeros.hpp b/barretenberg/cpp/src/barretenberg/numeric/bitop/count_leading_zeros.hpp index 72aa82a60a9..c41877610c1 100644 --- a/barretenberg/cpp/src/barretenberg/numeric/bitop/count_leading_zeros.hpp +++ b/barretenberg/cpp/src/barretenberg/numeric/bitop/count_leading_zeros.hpp @@ -8,7 +8,7 @@ namespace numeric { /** * Returns the number of leading 0 bits for a given integer type. * Implemented in terms of intrinsics which will use instructions such as `bsr` or `lzcnt` for best performance. - * Undefined behaviour when input is 0. + * Undefined behavior when input is 0. */ template constexpr inline size_t count_leading_zeros(T const& u); diff --git a/barretenberg/cpp/src/barretenberg/numeric/random/engine.cpp b/barretenberg/cpp/src/barretenberg/numeric/random/engine.cpp index 200e73530be..ff31c6136d8 100644 --- a/barretenberg/cpp/src/barretenberg/numeric/random/engine.cpp +++ b/barretenberg/cpp/src/barretenberg/numeric/random/engine.cpp @@ -114,7 +114,7 @@ class DebugEngine : public Engine { }; /** - * Used by tests to ensure consistent behaviour. + * Used by tests to ensure consistent behavior. */ Engine& get_debug_engine(bool reset) { diff --git a/barretenberg/cpp/src/barretenberg/plonk/composer/ultra_composer.cpp b/barretenberg/cpp/src/barretenberg/plonk/composer/ultra_composer.cpp index 2bd77756555..e34787f26d3 100644 --- a/barretenberg/cpp/src/barretenberg/plonk/composer/ultra_composer.cpp +++ b/barretenberg/cpp/src/barretenberg/plonk/composer/ultra_composer.cpp @@ -120,7 +120,7 @@ void UltraComposer::compute_witness(CircuitBuilder& circuit_constructor) } } - // Initialise the `s_randomness` positions in the s polynomials with 0. + // Initialize the `s_randomness` positions in the s polynomials with 0. // These will be the positions where we will be adding random scalars to add zero knowledge // to plookup (search for `Blinding` in plonk/proof_system/widgets/random_widgets/plookup_widget_impl.hpp // ProverPlookupWidget::compute_sorted_list_polynomial()) @@ -414,7 +414,7 @@ std::shared_ptr UltraComposer::compute_proving_key(CircuitBuilder& } } - // Initialise the last `s_randomness` positions in table polynomials with 0. We don't need to actually randomise + // Initialize the last `s_randomness` positions in table polynomials with 0. We don't need to actually randomize // the table polynomials. for (size_t i = 0; i < s_randomness; ++i) { poly_q_table_column_1[offset] = 0; diff --git a/barretenberg/cpp/src/barretenberg/plonk/proof_system/types/polynomial_manifest.hpp b/barretenberg/cpp/src/barretenberg/plonk/proof_system/types/polynomial_manifest.hpp index d4148be017b..be4a634883f 100644 --- a/barretenberg/cpp/src/barretenberg/plonk/proof_system/types/polynomial_manifest.hpp +++ b/barretenberg/cpp/src/barretenberg/plonk/proof_system/types/polynomial_manifest.hpp @@ -23,7 +23,6 @@ enum PolynomialIndex { Q_FIXED_BASE, Q_RANGE, Q_SORT, - Q_LOGIC, TABLE_1, TABLE_2, TABLE_3, diff --git a/barretenberg/cpp/src/barretenberg/plonk/proof_system/types/program_settings.hpp b/barretenberg/cpp/src/barretenberg/plonk/proof_system/types/program_settings.hpp index 2b5d6b899bf..1b849b49a8d 100644 --- a/barretenberg/cpp/src/barretenberg/plonk/proof_system/types/program_settings.hpp +++ b/barretenberg/cpp/src/barretenberg/plonk/proof_system/types/program_settings.hpp @@ -7,9 +7,7 @@ #include "../widgets/random_widgets/random_widget.hpp" #include "../widgets/transition_widgets/arithmetic_widget.hpp" #include "../widgets/transition_widgets/elliptic_widget.hpp" -#include "../widgets/transition_widgets/fixed_base_widget.hpp" #include "../widgets/transition_widgets/genperm_sort_widget.hpp" -#include "../widgets/transition_widgets/logic_widget.hpp" #include "../widgets/transition_widgets/plookup_arithmetic_widget.hpp" #include "../widgets/transition_widgets/plookup_auxiliary_widget.hpp" #include "./prover_settings.hpp" @@ -64,7 +62,6 @@ class ultra_verifier_settings : public ultra_settings { typedef transcript::StandardTranscript Transcript; typedef VerifierPlookupArithmeticWidget PlookupArithmeticWidget; typedef VerifierGenPermSortWidget GenPermSortWidget; - typedef VerifierLogicWidget LogicWidget; typedef VerifierPermutationWidget PermutationWidget; typedef VerifierPlookupWidget PlookupWidget; typedef VerifierEllipticWidget EllipticWidget; @@ -120,7 +117,6 @@ class ultra_to_standard_verifier_settings : public ultra_verifier_settings { typedef VerifierPlookupArithmeticWidget PlookupArithmeticWidget; typedef VerifierGenPermSortWidget GenPermSortWidget; - typedef VerifierLogicWidget LogicWidget; typedef VerifierPermutationWidget PermutationWidget; typedef VerifierPlookupWidget PlookupWidget; typedef VerifierEllipticWidget EllipticWidget; @@ -136,7 +132,6 @@ class ultra_with_keccak_verifier_settings : public ultra_verifier_settings { typedef VerifierPlookupArithmeticWidget PlookupArithmeticWidget; typedef VerifierGenPermSortWidget GenPermSortWidget; - typedef VerifierLogicWidget LogicWidget; typedef VerifierPermutationWidget PermutationWidget; typedef VerifierPlookupWidget PlookupWidget; typedef VerifierEllipticWidget EllipticWidget; diff --git a/barretenberg/cpp/src/barretenberg/plonk/proof_system/verifier/verifier.cpp b/barretenberg/cpp/src/barretenberg/plonk/proof_system/verifier/verifier.cpp index fe951fbaf13..0e909d8eba4 100644 --- a/barretenberg/cpp/src/barretenberg/plonk/proof_system/verifier/verifier.cpp +++ b/barretenberg/cpp/src/barretenberg/plonk/proof_system/verifier/verifier.cpp @@ -49,7 +49,7 @@ template bool VerifierBase::verify key->program_width = program_settings::program_width; - // Add the proof data to the transcript, according to the manifest. Also initialise the transcript's hash type and + // Add the proof data to the transcript, according to the manifest. Also initialize the transcript's hash type and // challenge bytes. transcript::StandardTranscript transcript = transcript::StandardTranscript( proof.proof_data, manifest, program_settings::hash_type, program_settings::num_challenge_bytes); diff --git a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/random_widgets/permutation_widget_impl.hpp b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/random_widgets/permutation_widget_impl.hpp index 0209756a25f..2b186df01f5 100644 --- a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/random_widgets/permutation_widget_impl.hpp +++ b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/random_widgets/permutation_widget_impl.hpp @@ -346,7 +346,7 @@ barretenberg::fr ProverPermutationWidgetquotient_polynomial_parts[0][key->circuit_size] = 0; key->quotient_polynomial_parts[1][key->circuit_size] = 0; @@ -486,10 +486,10 @@ barretenberg::fr ProverPermutationWidget add linearly independent term (z(X.ω) - 1).(α^3).L{n-1}(X) into the quotient polynomial to check // this diff --git a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/arithmetic_widget.hpp b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/arithmetic_widget.hpp index e21da0141eb..125b5157f2f 100644 --- a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/arithmetic_widget.hpp +++ b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/arithmetic_widget.hpp @@ -28,8 +28,6 @@ template class ArithmeticKe private: // A structure with various challenges, even though only alpha is used here. typedef containers::challenge_array challenge_array; - // Type for the linear terms of the transition - typedef containers::coefficient_array coefficient_array; public: inline static std::set const& get_required_polynomial_ids() @@ -51,10 +49,10 @@ template class ArithmeticKe * @param linear_terms Container for results of computation * @param i Index at which the wire values are sampled. */ - inline static void compute_linear_terms(PolyContainer& polynomials, - const challenge_array&, - coefficient_array& linear_terms, - const size_t i = 0) + inline static void accumulate_contribution(PolyContainer& polynomials, + const challenge_array& challenges, + Field& quotient, + const size_t i = 0) { const Field& w_1 = Getters::template get_value(polynomials, i); @@ -63,34 +61,6 @@ template class ArithmeticKe const Field& w_3 = Getters::template get_value(polynomials, i); - linear_terms[0] = w_1 * w_2; - linear_terms[1] = w_1; - linear_terms[2] = w_2; - linear_terms[3] = w_3; - } - - /** - * @brief Not being used in arithmetic_widget because there are none - * - */ - inline static void compute_non_linear_terms(PolyContainer&, const challenge_array&, Field&, const size_t = 0) {} - - /** - * @brief Scale and sum the linear terms for the final equation. - * - * @details Multiplies the linear terms by selector values and scale the whole sum by alpha before returning - * - * @param polynomials Container with polynomials or their simulation - * @param challenges A structure with various challenges - * @param linear_terms Precomuputed linear terms to be scaled and summed - * @param i The index at which selector/witness values are sampled - * @return Field Scaled sum of values - */ - inline static Field sum_linear_terms(PolyContainer& polynomials, - const challenge_array& challenges, - coefficient_array& linear_terms, - const size_t i = 0) - { const Field& alpha = challenges.alpha_powers[0]; const Field& q_1 = Getters::template get_value(polynomials, i); @@ -103,32 +73,13 @@ template class ArithmeticKe const Field& q_c = Getters::template get_value(polynomials, i); - Field result = linear_terms[0] * q_m; - result += (linear_terms[1] * q_1); - result += (linear_terms[2] * q_2); - result += (linear_terms[3] * q_3); + Field result = (w_1 * w_2) * q_m; + result += w_1 * q_1; + result += w_2 * q_2; + result += w_3 * q_3; result += q_c; result *= alpha; - return result; - } - - /** - * @brief Compute the scaled values of openings - * - * @param linear_terms The original computed linear terms of the product and wires - * @param scalars A map where we put the values - * @param challenges Challenges where we get the alpha - */ - inline static void update_kate_opening_scalars(coefficient_array& linear_terms, - std::map& scalars, - const challenge_array& challenges) - { - const Field& alpha = challenges.alpha_powers[0]; - scalars["Q_M"] += linear_terms[0] * alpha; - scalars["Q_1"] += linear_terms[1] * alpha; - scalars["Q_2"] += linear_terms[2] * alpha; - scalars["Q_3"] += linear_terms[3] * alpha; - scalars["Q_C"] += alpha; + quotient += result; } }; diff --git a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/elliptic_widget.hpp b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/elliptic_widget.hpp index b5d36f1d81c..71eb808eb1c 100644 --- a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/elliptic_widget.hpp +++ b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/elliptic_widget.hpp @@ -72,7 +72,6 @@ template class EllipticKern private: typedef containers::challenge_array challenge_array; - typedef containers::coefficient_array coefficient_array; public: inline static std::set const& get_required_polynomial_ids() @@ -93,10 +92,10 @@ template class EllipticKern * @param linear_terms Output array * @param i Gate index */ - inline static void compute_linear_terms(PolyContainer& polynomials, - const challenge_array& challenges, - coefficient_array& linear_terms, - const size_t i = 0) + inline static void accumulate_contribution(PolyContainer& polynomials, + const challenge_array& challenges, + Field& quotient, + const size_t i = 0) { const Field& x_1 = Getters::template get_value(polynomials, i); @@ -106,6 +105,8 @@ template class EllipticKern const Field& y_2 = Getters::template get_value(polynomials, i); const Field& x_3 = Getters::template get_value(polynomials, i); const Field& y_3 = Getters::template get_value(polynomials, i); + const Field& q_elliptic = + Getters::template get_value(polynomials, i); // sign const Field& q_sign = @@ -139,41 +140,9 @@ template class EllipticKern (q_is_double * (x_identity_double - x_identity_add) + x_identity_add) * challenges.alpha_powers[0]; auto y_identity = (q_is_double * (y_identity_double - y_identity_add) + y_identity_add) * challenges.alpha_powers[1]; - linear_terms[0] = x_identity + y_identity; - } + Field identity = x_identity + y_identity; - /** - * @brief Return the linear term multiplied by elliptic curve addition selector value at gate - * - * @param polynomials Polynomial container or simulator - * @param linear_terms Array of linear terms - * @param i Gate index - * @return Field - */ - inline static Field sum_linear_terms(PolyContainer& polynomials, - const challenge_array&, - coefficient_array& linear_terms, - const size_t i = 0) - { - const Field& q_elliptic = - Getters::template get_value(polynomials, i); - - return linear_terms[0] * q_elliptic; - } - - inline static void compute_non_linear_terms(PolyContainer&, const challenge_array&, Field&, const size_t = 0) {} - - /** - * @brief Update opening scalars with the linear term from elliptic gate - * - * @param linear_terms Contains input scalar - * @param scalars Output map for updates - */ - inline static void update_kate_opening_scalars(coefficient_array& linear_terms, - std::map& scalars, - const challenge_array&) - { - scalars["Q_ELLIPTIC"] += linear_terms[0]; + quotient += identity * q_elliptic; } }; diff --git a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/fixed_base_widget.hpp b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/fixed_base_widget.hpp deleted file mode 100644 index a9071279bfa..00000000000 --- a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/fixed_base_widget.hpp +++ /dev/null @@ -1,295 +0,0 @@ -#pragma once - -#include "./transition_widget.hpp" - -namespace proof_system::plonk { -namespace widget { - -/** - * This gate computes 2-bit NAF elliptic curve addition (aka fixed-based scalar multiplication). - * The theory is explained in detail in [1]. Suppose the (n+1) gates are strutured as following: - * - * +---------+---------+-----------+---------+ - * | w_1 | w_2 | w_3 | w_4 | - * |---------|---------|-----------|---------| - * | x_0 | y_0 | c | a_0 | - * | x_1 | y_1 | x_{α,0} | a_1 | - * | . | . | . | . | - * | . | . | . | . | - * | . | . | . | . | - * | x_i | y_i | x_{α,i-1} | a_i |<- i th gate - * | x_{i+1} | y_{i+1} | x_{α,i} | a_{i+1} | - * | . | . | . | . | - * | . | . | . | . | - * | . | . | . | . | - * | x_n | y_n | x_{α,n-1} | a_n | - * +---------+---------+-----------+---------+ - * - * Here, (x_{i+1}, y_{i+1}) = [(x_i, y_i)] + b_i.[(x_{α,i}, y_{α,i})] for i = {0, 1, ..., n-1}. - * Since the values (a_i) are intermediate sums, the actual quad value b_i is: - * b_i := a_{i+1} - 4 * a_i. - * - * In the implementation below, we call b_i as delta (δ). - * Let P_0 = 4^{n-1}.[g] and P_1 = (1 + 4^{n-1}).[g]. - * We need the following constraints: - * - * - * 0. Quad value is either of {-3, -1, 1, 3}. See page 6 of [1]. - * => (b_i + 3)(b_i + 1)(b_i - 1)(b_i - 3) = 0 - * - * 1. Check if x-coordinate of the point to be added is correct. See page 5 of [1]. - * => q_1 * b_i^2 + q_2 = x_{α,i} - * - * 2. Check if x-coordinate of new point is correct. See page 7 of [1]. - * => (x_{i+1} + x_i + x_{α,i}) * (x_{α,i} - x_i)^2 + - * (2.b_i.y_1) * (q_3.x_{α,i} + q_{ecc,1}) - - * (x_{α,i}^3 + y_i^2 + b_{grumpkin}) = 0 - * - * 3. Check if y-coordinate of new point is correct. See page 7 of [1]. - * => (y_{i+1} + y_i) * (x_{α,i} - x_i) - - * (b_i.(q_3.x_{α,i} + q_{ecc,1}) - y_i) * (x_i - x_{i+1}) = 0 - * - * 4. Initialization: a_0 must be either 1 or (1 + 4^{-n}). See page 7 of [1]. - * => q_c * (1 - a_0).(1 - a_0 - 4^{-n}) = 0 - * - * 5. Initialization: x_0 must be x-coordinate of either P_0 or P_1. - * => q_c * (c.(q_4 - x_0) + q_5.(1 - a_0)) = 0 - * - * 6. Initialization: y_0 must be y-coordinate of either P_0 or P_1. - * => q_c * (c.(q_m - y_0) + q_c.(1 - a_0)) = 0 - * - * - * We combine all of these constraints using powers of the challenge α. Further, the linear and non-linear parts in - * the constraines are computed separately in the functions `compute_linear_terms` and `compute_linear_terms` - * respectively. More details on how selector values for i=0 are specially chosen are explained in [3]. - * - * References: - * [1] The Turbo-PLONK program syntax for specifying SNARK programs, Ariel G and Zac W. - * Link: https://docs.zkproof.org/pages/standards/accepted-workshop3/proposal-turbo_plonk.pdf - * [2] Constant b_{grumpkin} = -17. - * [3] Fixed-base Multiplication gate, Cody G. - * Link: https://hackmd.io/MCmV2bipRYelT1WUNLj02g - * - **/ -template class FixedBaseKernel { - public: - // UltraPlonkComposer only needs 6 independent relations, (α^5 is not added), but we accept the tiny inefficiency - // of computing and storing an extra power of α (we use power 0,1,2,3,4 and 6) to minimize code changes. - static constexpr size_t num_independent_relations = 7; - // We state the challenges required for linear/nonlinear terms computation - static constexpr uint8_t quotient_required_challenges = CHALLENGE_BIT_ALPHA; - // We state the challenges required for updating kate opening scalars - static constexpr uint8_t update_required_challenges = CHALLENGE_BIT_ALPHA; - - private: - typedef containers::challenge_array challenge_array; - typedef containers::coefficient_array coefficient_array; - - public: - inline static std::set const& get_required_polynomial_ids() - { - static const std::set required_polynomial_ids = { - PolynomialIndex::Q_1, PolynomialIndex::Q_2, PolynomialIndex::Q_3, PolynomialIndex::Q_4, - PolynomialIndex::Q_5, PolynomialIndex::Q_M, PolynomialIndex::Q_C, PolynomialIndex::Q_FIXED_BASE, - PolynomialIndex::W_1, PolynomialIndex::W_2, PolynomialIndex::W_3, PolynomialIndex::W_4 - }; - return required_polynomial_ids; - } - - /** - * @brief Quickly checks if the result of all computation will be zero because of the selector or not - * - * @param polynomials Polynomial or simulated container - * @param i Gate index - * @return q_ecc_1[i] != 0 - */ - inline static bool gate_enabled(PolyContainer& polynomials, const size_t i = 0) - { - const Field& q_ecc_1 = - Getters::template get_value(polynomials, i); - return !q_ecc_1.is_zero(); - } - - inline static void compute_linear_terms(PolyContainer& polynomials, - const challenge_array& challenges, - coefficient_array& linear_terms, - const size_t i = 0) - { - - const Field& w_1 = - Getters::template get_value(polynomials, i); - const Field& w_2 = - Getters::template get_value(polynomials, i); - const Field& w_3 = - Getters::template get_value(polynomials, i); - const Field& w_4 = - Getters::template get_value(polynomials, i); - const Field& w_1_omega = - Getters::template get_value(polynomials, i); - const Field& w_3_omega = - Getters::template get_value(polynomials, i); - const Field& w_4_omega = - Getters::template get_value(polynomials, i); - const Field& q_c = - Getters::template get_value(polynomials, i); - const Field& q_fixed_base = - Getters::template get_value(polynomials, i); - - Field delta = w_4_omega - (w_4 + w_4 + w_4 + w_4); - - Field delta_squared = delta.sqr(); - - Field q_1_multiplicand = delta_squared * q_fixed_base * challenges.alpha_powers[1]; - - Field q_2_multiplicand = challenges.alpha_powers[1] * q_fixed_base; - - Field q_3_multiplicand = (w_1_omega - w_1) * delta * w_3_omega * challenges.alpha_powers[3] * q_fixed_base; - Field T1 = delta * w_3_omega * w_2 * challenges.alpha_powers[2]; - q_3_multiplicand = q_3_multiplicand + (T1 + T1) * q_fixed_base; - - Field q_4_multiplicand; - - Field q_5_multiplicand; - - Field q_m_multiplicand = w_3 * q_fixed_base * q_c * challenges.alpha_powers[6]; - - linear_terms[0] = q_m_multiplicand; // α^6 q_fixed_base q_c w_3 - linear_terms[1] = q_1_multiplicand; // α q_fixed_base δ^2 - linear_terms[2] = q_2_multiplicand; // α q_fixed_base - linear_terms[3] = q_3_multiplicand; // α^3 q_fixed_base δ * (w_1,ω - w_1) * w_3,ω - } - - inline static Field sum_linear_terms(PolyContainer& polynomials, - const challenge_array&, - coefficient_array& linear_terms, - const size_t i = 0) - { - const Field& q_1 = - Getters::template get_value(polynomials, i); - const Field& q_2 = - Getters::template get_value(polynomials, i); - const Field& q_3 = - Getters::template get_value(polynomials, i); - const Field& q_4 = - Getters::template get_value(polynomials, i); - const Field& q_m = - Getters::template get_value(polynomials, i); - - Field result = linear_terms[0] * q_m; - result += (linear_terms[1] * q_1); - result += (linear_terms[2] * q_2); - result += (linear_terms[3] * q_3); - return result; - } - - inline static void compute_non_linear_terms(PolyContainer& polynomials, - const challenge_array& challenges, - Field& quotient, - const size_t i = 0) - { - constexpr barretenberg::fr grumpkin_curve_b(-17); - - const Field& w_1 = - Getters::template get_value(polynomials, i); - const Field& w_2 = - Getters::template get_value(polynomials, i); - const Field& w_3 = - Getters::template get_value(polynomials, i); - const Field& w_4 = - Getters::template get_value(polynomials, i); - const Field& w_1_omega = - Getters::template get_value(polynomials, i); - const Field& w_2_omega = - Getters::template get_value(polynomials, i); - const Field& w_3_omega = - Getters::template get_value(polynomials, i); - const Field& w_4_omega = - Getters::template get_value(polynomials, i); - const Field& q_c = - Getters::template get_value(polynomials, i); - const Field& q_fixed_base = - Getters::template get_value(polynomials, i); - - Field delta = w_4_omega - (w_4 + w_4 + w_4 + w_4); - const Field three = Field(3); - Field T1 = (delta + Field(1)); - Field T2 = (delta + three); - Field T3 = (delta - Field(1)); - Field T4 = (delta - three); - - // accumulator_identity = (δ + 3)(δ + 1)(δ - 1)(δ - 3) - Field accumulator_identity = T1 * T2 * T3 * T4 * challenges.alpha_powers[0]; - - // x_alpha_identity = -α w_3,ω - Field x_alpha_identity = -(w_3_omega * challenges.alpha_powers[1]); - - Field T0 = w_1_omega + w_1 + w_3_omega; - T1 = (w_3_omega - w_1).sqr(); - T0 = T0 * T1; - - T1 = w_3_omega.sqr() * w_3_omega; - T2 = w_2.sqr(); - T1 = T1 + T2; - T1 = -(T1 + grumpkin_curve_b); - - T2 = delta * w_2 * q_fixed_base; - T2 = T2 + T2; - - // x_accumulator_identity = α^2 * - // [(w_1,ω + w_1 + w_3,ω) * (w_3,ω - w_1)^2 - (b + w_3,ω^3 + w_2^2) + 2δ * w_2 * q_fixed_base] - Field x_accumulator_identity = (T0 + T1 + T2) * challenges.alpha_powers[2]; - - T0 = (w_2_omega + w_2) * (w_3_omega - w_1); - - T1 = w_1 - w_1_omega; - T2 = w_2 - (q_fixed_base * delta); - T1 = T1 * T2; - - // y_accumulator_identity = α^3 * - // [(w_2,ω + w_2) * (w_3,ω - w_1) + (w_1 - w_1,ω) * (w_2 - q_fixed_base * δ)] - Field y_accumulator_identity = (T0 + T1) * challenges.alpha_powers[3]; - - // accumulator_init_identity = α^4 * (w_4 - 1)(w_4 - 1 - w_3) - T0 = w_4 - Field(1); - T1 = T0 - w_3; - Field accumulator_init_identity = T0 * T1 * challenges.alpha_powers[4]; - - Field x_init_identity; - - // y_init_identity = α^6 * (q_c * (1 - w_4) - w_2 * w_3) - T0 = Field(1) - w_4; - T0 = T0 * q_c; - T1 = w_2 * w_3; - Field y_init_identity = (T0 - T1) * challenges.alpha_powers[6]; - - Field gate_identity = accumulator_init_identity + y_init_identity; - gate_identity = gate_identity * q_c; - gate_identity = - gate_identity + accumulator_identity + x_alpha_identity + x_accumulator_identity + y_accumulator_identity; - gate_identity = gate_identity * q_fixed_base; - - quotient += gate_identity; - } - - inline static void update_kate_opening_scalars(coefficient_array& linear_terms, - std::map& scalars, - const challenge_array&) - { - scalars["Q_M"] += linear_terms[0]; - scalars["Q_1"] += linear_terms[1]; - scalars["Q_2"] += linear_terms[2]; - scalars["Q_3"] += linear_terms[3]; - } -}; - -template -using UltraFixedBaseKernel = FixedBaseKernel; -} // namespace widget - -template -using ProverUltraFixedBaseWidget = widget::TransitionWidget; - -template -using VerifierUltraFixedBaseWidget = - widget::GenericVerifierWidget; -} // namespace proof_system::plonk \ No newline at end of file diff --git a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/genperm_sort_widget.hpp b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/genperm_sort_widget.hpp index ee8afe2dc9c..eda0f942b08 100644 --- a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/genperm_sort_widget.hpp +++ b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/genperm_sort_widget.hpp @@ -15,7 +15,6 @@ template class GenPermSortK private: typedef containers::challenge_array challenge_array; - typedef containers::coefficient_array coefficient_array; public: inline static std::set const& get_required_polynomial_ids() @@ -27,10 +26,10 @@ template class GenPermSortK return required_polynomial_ids; } - inline static void compute_linear_terms(PolyContainer& polynomials, - const challenge_array& challenges, - coefficient_array& linear_terms, - const size_t i = 0) + inline static void accumulate_contribution(PolyContainer& polynomials, + const challenge_array& challenges, + Field& quotient, + const size_t i = 0) { constexpr barretenberg::fr minus_two(-2); constexpr barretenberg::fr minus_three(-3); @@ -47,6 +46,8 @@ template class GenPermSortK Getters::template get_value(polynomials, i); const Field& w_1_omega = Getters::template get_value(polynomials, i); + const Field& q_sort = + Getters::template get_value(polynomials, i); Field alpha_a = alpha_base; Field alpha_b = alpha_a * alpha; @@ -94,27 +95,7 @@ template class GenPermSortK T0 *= alpha_d; range_accumulator += T0; - linear_terms[0] = range_accumulator; - } - - inline static void compute_non_linear_terms(PolyContainer&, const challenge_array&, Field&, const size_t = 0) {} - - inline static Field sum_linear_terms(PolyContainer& polynomials, - const challenge_array&, - coefficient_array& linear_terms, - const size_t i = 0) - { - const Field& q_sort = - Getters::template get_value(polynomials, i); - - return linear_terms[0] * q_sort; - } - - inline static void update_kate_opening_scalars(coefficient_array& linear_terms, - std::map& scalars, - const challenge_array&) - { - scalars["Q_SORT"] += linear_terms[0]; + quotient += range_accumulator * q_sort; } }; diff --git a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/logic_widget.hpp b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/logic_widget.hpp deleted file mode 100644 index 6edc1c1f432..00000000000 --- a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/logic_widget.hpp +++ /dev/null @@ -1,236 +0,0 @@ -#pragma once - -#include "./transition_widget.hpp" - -namespace proof_system::plonk { -namespace widget { - -template class LogicKernel { - public: - static constexpr size_t num_independent_relations = 4; - // We state the challenges required for linear/nonlinear terms computation - static constexpr uint8_t quotient_required_challenges = CHALLENGE_BIT_ALPHA; - // We state the challenges required for updating kate opening scalars - static constexpr uint8_t update_required_challenges = CHALLENGE_BIT_ALPHA; - - private: - typedef containers::challenge_array challenge_array; - typedef containers::coefficient_array coefficient_array; - - public: - inline static std::set const& get_required_polynomial_ids() - { - static const std::set required_polynomial_ids = { - PolynomialIndex::Q_C, PolynomialIndex::Q_LOGIC, PolynomialIndex::W_1, - PolynomialIndex::W_2, PolynomialIndex::W_3, PolynomialIndex::W_4 - }; - return required_polynomial_ids; - } - - /** - * @brief Quickly checks if the result of all computation will be zero because of the selector or not - * - * @param polynomials Polynomial or simulated container - * @param i Gate index - * @return q_logic[i] != 0 - */ - inline static bool gate_enabled(PolyContainer& polynomials, const size_t i = 0) - { - const Field& q_logic = - Getters::template get_value(polynomials, i); - return !q_logic.is_zero(); - } - - inline static void compute_linear_terms(PolyContainer& polynomials, - const challenge_array& challenges, - coefficient_array& linear_terms, - const size_t i = 0) - { - constexpr barretenberg::fr six(6); - constexpr barretenberg::fr eighty_one(81); - constexpr barretenberg::fr eighty_three(83); - - const Field& alpha_base = challenges.alpha_powers[0]; - const Field& alpha = challenges.elements[ChallengeIndex::ALPHA]; - const Field& w_1 = - Getters::template get_value(polynomials, i); - const Field& w_2 = - Getters::template get_value(polynomials, i); - const Field& w_3 = - Getters::template get_value(polynomials, i); - const Field& w_4 = - Getters::template get_value(polynomials, i); - const Field& w_1_omega = - Getters::template get_value(polynomials, i); - const Field& w_2_omega = - Getters::template get_value(polynomials, i); - const Field& w_4_omega = - Getters::template get_value(polynomials, i); - - const Field& q_c = - Getters::template get_value(polynomials, i); - - Field delta_sum; - Field delta_squared_sum; - Field T0; - Field T1; - Field T2; - Field T3; - Field T4; - Field identity; - - T0 = w_1 + w_1; - T0 += T0; - T0 = w_1_omega - T0; - - // T1 = b - T1 = w_2 + w_2; - T1 += T1; - T1 = w_2_omega - T1; - - // delta_sum = a + b - delta_sum = T0 + T1; - - // T2 = a^2, T3 = b^2 - T2 = T0.sqr(); - T3 = T1.sqr(); - - delta_squared_sum = T2 + T3; - - // identity = a^2 + b^2 + 2ab - identity = delta_sum.sqr(); - // identity = 2ab - identity -= delta_squared_sum; - - // identity = 2(ab - w) - T4 = w_3 + w_3; - identity -= T4; - identity *= alpha; - - // T4 = 4w - T4 += T4; - - // T2 = a^2 - a - T2 -= T0; - - // T0 = a^2 - 5a + 6 - T0 += T0; - T0 += T0; - T0 = T2 - T0; - T0 += six; - - // identity = (identity + a(a - 1)(a - 2)(a - 3)) * alpha - T0 *= T2; - identity += T0; - identity *= alpha; - - // T3 = b^2 - b - T3 -= T1; - - // T1 = b^2 - 5b + 6 - T1 += T1; - T1 += T1; - T1 = T3 - T1; - T1 += six; - - // identity = (identity + b(b - 1)(b - 2)(b - 3)) * alpha - T1 *= T3; - identity += T1; - identity *= alpha; - - // T0 = 3(a + b) - T0 = delta_sum + delta_sum; - T0 += delta_sum; - - // T1 = 9(a + b) - T1 = T0 + T0; - T1 += T0; - - // delta_sum = 18(a + b) - delta_sum = T1 + T1; - - // T1 = 81(a + b) - T2 = delta_sum + delta_sum; - T2 += T2; - T1 += T2; - - // delta_squared_sum = 18(a^2 + b^2) - T2 = delta_squared_sum + delta_squared_sum; - T2 += delta_squared_sum; - delta_squared_sum = T2 + T2; - delta_squared_sum += T2; - delta_squared_sum += delta_squared_sum; - - // delta_sum = w(4w - 18(a + b) + 81) - delta_sum = T4 - delta_sum; - delta_sum += eighty_one; - delta_sum *= w_3; - - // T1 = 18(a^2 + b^2) - 81(a + b) + 83 - T1 = delta_squared_sum - T1; - T1 += eighty_three; - - // delta_sum = w ( w ( 4w - 18(a + b) + 81) + 18(a^2 + b^2) - 81(a + b) + 83) - delta_sum += T1; - delta_sum *= w_3; - - // T2 = 3c - T2 = w_4 + w_4; - T2 += T2; - T2 = w_4_omega - T2; - T3 = T2 + T2; - T2 += T3; - - // T3 = 9c - T3 = T2 + T2; - T3 += T2; - - // T3 = q_c * (9c - 3(a + b)) - T3 -= T0; - T3 *= q_c; - - // T2 = 3c + 3(a + b) - 2 * delta_sum - T2 += T0; - delta_sum += delta_sum; - T2 -= delta_sum; - - // T2 = T2 + T3 - T2 += T3; - - // identity = q_logic * alpha_base * (identity + T2) - identity += T2; - identity *= alpha_base; - - linear_terms[0] = identity; - } - - inline static void compute_non_linear_terms(PolyContainer&, const challenge_array&, Field&, const size_t = 0) {} - - inline static Field sum_linear_terms(PolyContainer& polynomials, - const challenge_array&, - coefficient_array& linear_terms, - const size_t i = 0) - { - const Field& q_logic = - Getters::template get_value(polynomials, i); - - return linear_terms[0] * q_logic; - } - - inline static void update_kate_opening_scalars(coefficient_array& linear_terms, - std::map& scalars, - const challenge_array&) - { - scalars["Q_LOGIC"] += linear_terms[0]; - } -}; - -} // namespace widget - -template -using ProverLogicWidget = widget::TransitionWidget; - -template -using VerifierLogicWidget = widget::GenericVerifierWidget; - -} // namespace proof_system::plonk \ No newline at end of file diff --git a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/plookup_arithmetic_widget.hpp b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/plookup_arithmetic_widget.hpp index c22caccd58f..61323ab4619 100644 --- a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/plookup_arithmetic_widget.hpp +++ b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/plookup_arithmetic_widget.hpp @@ -60,8 +60,6 @@ template class PlookupArith private: // A structure with various challenges, even though only alpha is used here. typedef containers::challenge_array challenge_array; - // Type for the linear terms of the transition (not actually used here) - typedef containers::coefficient_array coefficient_array; public: inline static std::set const& get_required_polynomial_ids() @@ -74,15 +72,6 @@ template class PlookupArith return required_polynomial_ids; } - /** - * @brief Stub for computing linear terms. Not used in plookup artihmetic gate - * - */ - inline static void compute_linear_terms(PolyContainer&, - const challenge_array&, - coefficient_array&, - const size_t = 0) - {} /** * @brief Computes the full identity for the arithmetic gate in plookup to be added to the quotient. All the logic * is explained in class description @@ -92,10 +81,10 @@ template class PlookupArith * @param quotient Quotient reference to add the result to * @param i Gate index */ - inline static void compute_non_linear_terms(PolyContainer& polynomials, - const challenge_array& challenges, - Field& quotient, - const size_t i = 0) + inline static void accumulate_contribution(PolyContainer& polynomials, + const challenge_array& challenges, + Field& quotient, + const size_t i = 0) { // For subgroup element i, this term evaluates to W_4(i \omega) * 2 iff Q_ARITH(i \omega) = 2 const Field& q_arith = @@ -169,20 +158,6 @@ template class PlookupArith quotient += identity; } - - inline static Field sum_linear_terms(PolyContainer&, const challenge_array&, coefficient_array&, const size_t = 0) - { - return Field(0); - } - - /** - * @brief Stub for updating opening scalars, since not using linear terms - * - */ - inline static void update_kate_opening_scalars(coefficient_array&, - std::map&, - const challenge_array&) - {} }; } // namespace widget diff --git a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/plookup_auxiliary_widget.hpp b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/plookup_auxiliary_widget.hpp index 093568b58a2..c582d6b1a7d 100644 --- a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/plookup_auxiliary_widget.hpp +++ b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/plookup_auxiliary_widget.hpp @@ -49,7 +49,6 @@ template class PlookupAuxil private: typedef containers::challenge_array challenge_array; - typedef containers::coefficient_array coefficient_array; public: inline static std::set const& get_required_polynomial_ids() @@ -62,16 +61,10 @@ template class PlookupAuxil return required_polynomial_ids; } - inline static void compute_linear_terms(PolyContainer&, - const challenge_array&, - coefficient_array&, - const size_t = 0) - {} - - inline static void compute_non_linear_terms(PolyContainer& polynomials, - const challenge_array& challenges, - Field& quotient, - const size_t i = 0) + inline static void accumulate_contribution(PolyContainer& polynomials, + const challenge_array& challenges, + Field& quotient, + const size_t i = 0) { constexpr barretenberg::fr LIMB_SIZE(uint256_t(1) << 68); constexpr barretenberg::fr SUBLIMB_SHIFT(uint256_t(1) << 14); @@ -326,16 +319,6 @@ template class PlookupAuxil quotient += (auxiliary_identity); } - - inline static Field sum_linear_terms(PolyContainer&, const challenge_array&, coefficient_array&, const size_t = 0) - { - return Field(0); - } - - inline static void update_kate_opening_scalars(coefficient_array&, - std::map&, - const challenge_array&) - {} }; } // namespace widget diff --git a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/transition_widget.hpp b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/transition_widget.hpp index 727d92e796d..9f3cc1cb2b5 100644 --- a/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/transition_widget.hpp +++ b/barretenberg/cpp/src/barretenberg/plonk/proof_system/widgets/transition_widgets/transition_widget.hpp @@ -48,8 +48,6 @@ template struct poly_ptr_map { size_t index_shift; }; -template using coefficient_array = std::array; - } // namespace containers /** @@ -287,7 +285,6 @@ class TransitionWidget : public TransitionWidgetBase { typedef containers::poly_ptr_map poly_ptr_map; typedef containers::poly_array poly_array; typedef containers::challenge_array challenge_array; - typedef containers::coefficient_array coefficient_array; public: typedef getters::EvaluationGetter @@ -330,15 +327,10 @@ class TransitionWidget : public TransitionWidgetBase { FFTGetter::get_challenges(transcript, alpha_base, FFTKernel::quotient_required_challenges); ITERATE_OVER_DOMAIN_START(key->large_domain); - coefficient_array linear_terms; - FFTKernel::compute_linear_terms(polynomials, challenges, linear_terms, i); - Field sum_of_linear_terms = FFTKernel::sum_linear_terms(polynomials, challenges, linear_terms, i); - // populate split quotient components Field& quotient_term = key->quotient_polynomial_parts[i >> key->small_domain.log2_size][i & (key->circuit_size - 1)]; - quotient_term += sum_of_linear_terms; - FFTKernel::compute_non_linear_terms(polynomials, challenges, quotient_term, i); + FFTKernel::accumulate_contribution(polynomials, challenges, quotient_term, i); ITERATE_OVER_DOMAIN_END; return FFTGetter::update_alpha(challenges, FFTKernel::num_independent_relations); @@ -352,7 +344,6 @@ class GenericVerifierWidget { typedef containers::poly_ptr_map poly_ptr_map; typedef containers::poly_array poly_array; typedef containers::challenge_array challenge_array; - typedef containers::coefficient_array coefficient_array; public: typedef getters::EvaluationGetter EvaluationGetter; @@ -368,13 +359,8 @@ class GenericVerifierWidget { challenge_array challenges = EvaluationGetter::get_challenges(transcript, alpha_base, EvaluationKernel::quotient_required_challenges); - // As in the permutation widget, we have vestiges of the linearization trick: the code first computes what - // would be the contribution with linearization, then completes that smaller sum to the full contribution - // without linearization. - coefficient_array linear_terms; - EvaluationKernel::compute_linear_terms(polynomial_evaluations, challenges, linear_terms); - quotient_numerator_eval += EvaluationKernel::sum_linear_terms(polynomial_evaluations, challenges, linear_terms); - EvaluationKernel::compute_non_linear_terms(polynomial_evaluations, challenges, quotient_numerator_eval); + // Accumulate the contribution from the widget into the quotient + EvaluationKernel::accumulate_contribution(polynomial_evaluations, challenges, quotient_numerator_eval); return EvaluationGetter::update_alpha(challenges, num_independent_relations); } diff --git a/barretenberg/cpp/src/barretenberg/polynomials/polynomial.hpp b/barretenberg/cpp/src/barretenberg/polynomials/polynomial.hpp index 63caded798a..caf74ad39dc 100644 --- a/barretenberg/cpp/src/barretenberg/polynomials/polynomial.hpp +++ b/barretenberg/cpp/src/barretenberg/polynomials/polynomial.hpp @@ -105,8 +105,8 @@ template class Polynomial { return coefficients_.get()[i]; }; - Fr evaluate(const Fr& z, const size_t target_size) const; - Fr evaluate(const Fr& z) const; + [[nodiscard]] Fr evaluate(const Fr& z, const size_t target_size) const; + [[nodiscard]] Fr evaluate(const Fr& z) const; Fr compute_barycentric_evaluation(const Fr& z, const EvaluationDomain& domain) requires polynomial_arithmetic::SupportsFFT; diff --git a/barretenberg/cpp/src/barretenberg/polynomials/serialize.hpp b/barretenberg/cpp/src/barretenberg/polynomials/serialize.hpp index b1c6241b573..ac6d400fc58 100644 --- a/barretenberg/cpp/src/barretenberg/polynomials/serialize.hpp +++ b/barretenberg/cpp/src/barretenberg/polynomials/serialize.hpp @@ -3,7 +3,7 @@ namespace barretenberg { -// Highly optimised read / write of polynomials in little endian montgomery form. +// Highly optimized read / write of polynomials in little endian montgomery form. template inline void read(B& buf, polynomial& p) { uint32_t size; diff --git a/barretenberg/cpp/src/barretenberg/polynomials/univariate.hpp b/barretenberg/cpp/src/barretenberg/polynomials/univariate.hpp index b898642be5e..a3770cad6b9 100644 --- a/barretenberg/cpp/src/barretenberg/polynomials/univariate.hpp +++ b/barretenberg/cpp/src/barretenberg/polynomials/univariate.hpp @@ -38,7 +38,8 @@ template class Univariate Univariate(Univariate&& other) noexcept = default; Univariate& operator=(const Univariate& other) = default; Univariate& operator=(Univariate&& other) noexcept = default; - // Construct Univariate from scalar + // Construct constant Univariate from scalar which represents the value that all the points in the domain evaluate + // to explicit Univariate(Fr value) : evaluations{} { @@ -81,6 +82,15 @@ template class Univariate return output; }; + static Univariate zero() + { + auto output = Univariate(); + for (size_t i = 0; i != LENGTH; ++i) { + output.value_at(i) = Fr::zero(); + } + return output; + } + static Univariate random_element() { return get_random(); }; // Operations between Univariate and other Univariate diff --git a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/eccvm/eccvm_circuit_builder.hpp b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/eccvm/eccvm_circuit_builder.hpp index 07175f9df70..6bee55c0d4c 100644 --- a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/eccvm/eccvm_circuit_builder.hpp +++ b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/eccvm/eccvm_circuit_builder.hpp @@ -9,6 +9,7 @@ #include "barretenberg/flavor/ecc_vm.hpp" #include "barretenberg/honk/proof_system/lookup_library.hpp" #include "barretenberg/honk/proof_system/permutation_library.hpp" +#include "barretenberg/proof_system/op_queue/ecc_op_queue.hpp" #include "barretenberg/relations/relation_parameters.hpp" namespace proof_system { @@ -36,19 +37,20 @@ template class ECCVMCircuitBuilder { using MSM = proof_system_eccvm::MSM; using VMOperation = proof_system_eccvm::VMOperation; - std::vector vm_operations; + std::shared_ptr op_queue; using ScalarMul = proof_system_eccvm::ScalarMul; using AllPolynomials = typename Flavor::AllPolynomials; - ECCVMCircuitBuilder() = default; + ECCVMCircuitBuilder() + : op_queue(std::make_shared()){}; - ECCVMCircuitBuilder(std::vector vm_operations) - : vm_operations(vm_operations){}; + ECCVMCircuitBuilder(std::shared_ptr& op_queue) + : op_queue(op_queue){}; [[nodiscard]] uint32_t get_number_of_muls() const { uint32_t num_muls = 0; - for (auto& op : vm_operations) { + for (auto& op : op_queue->raw_ops) { if (op.mul) { if (op.z1 != 0) { num_muls++; @@ -144,7 +146,7 @@ template class ECCVMCircuitBuilder { } }; - for (auto& op : vm_operations) { + for (auto& op : op_queue->raw_ops) { if (op.mul) { process_mul(op.z1, op.base_point); process_mul(op.z2, AffineElement{ op.base_point.x * FF::cube_root_of_unity(), -op.base_point.y }); @@ -177,7 +179,7 @@ template class ECCVMCircuitBuilder { void add_accumulate(const AffineElement& to_add) { - vm_operations.emplace_back(VMOperation{ + op_queue->raw_ops.emplace_back(VMOperation{ .add = true, .mul = false, .eq = false, @@ -197,7 +199,7 @@ template class ECCVMCircuitBuilder { CycleScalar::split_into_endomorphism_scalars(converted, z1, z2); z1 = z1.to_montgomery_form(); z2 = z2.to_montgomery_form(); - vm_operations.emplace_back(VMOperation{ + op_queue->raw_ops.emplace_back(VMOperation{ .add = false, .mul = true, .eq = false, @@ -211,7 +213,7 @@ template class ECCVMCircuitBuilder { void eq_and_reset(const AffineElement& expected) { - vm_operations.emplace_back(VMOperation{ + op_queue->raw_ops.emplace_back(VMOperation{ .add = false, .mul = false, .eq = true, @@ -225,7 +227,7 @@ template class ECCVMCircuitBuilder { void empty_row() { - vm_operations.emplace_back(VMOperation{ + op_queue->raw_ops.emplace_back(VMOperation{ .add = false, .mul = false, .eq = false, @@ -253,8 +255,8 @@ template class ECCVMCircuitBuilder { multiplication? * transcript_pc: point counter for transcript columns * transcript_msm_count: counts number of muls processed in an ongoing multiscalar multiplication - * transcript_x: input transcript point, x-coordinate - * transcript_y: input transcriot point, y-coordinate + * transcript_Px: input transcript point, x-coordinate + * transcript_Py: input transcriot point, y-coordinate * transcript_op: input transcript opcode value * transcript_z1: input transcript scalar multiplier (low component, 128 bits max) * transcript_z2: input transcript scalar multipplier (high component, 128 bits max) @@ -322,7 +324,7 @@ template class ECCVMCircuitBuilder { std::array, 2> point_table_read_counts; const auto transcript_state = - ECCVMTranscriptBuilder::compute_transcript_state(vm_operations, get_number_of_muls()); + ECCVMTranscriptBuilder::compute_transcript_state(op_queue->raw_ops, get_number_of_muls()); const auto precompute_table_state = ECCVMPrecomputedTablesBuilder::compute_precompute_state(flattened_muls); const auto msm_state = @@ -365,8 +367,8 @@ template class ECCVMCircuitBuilder { polys.transcript_msm_transition[i] = transcript_state[i].msm_transition; polys.transcript_pc[i] = transcript_state[i].pc; polys.transcript_msm_count[i] = transcript_state[i].msm_count; - polys.transcript_x[i] = transcript_state[i].base_x; - polys.transcript_y[i] = transcript_state[i].base_y; + polys.transcript_Px[i] = transcript_state[i].base_x; + polys.transcript_Py[i] = transcript_state[i].base_y; polys.transcript_z1[i] = transcript_state[i].z1; polys.transcript_z2[i] = transcript_state[i].z2; polys.transcript_z1zero[i] = transcript_state[i].z1_zero; @@ -568,14 +570,14 @@ template class ECCVMCircuitBuilder { [[nodiscard]] size_t get_num_gates() const { - // TODO(@zac-williamson) once we have a stable base to work off of, optimise this method! + // TODO(@zac-williamson) once we have a stable base to work off of, optimize this method! // (issue #2218) const auto msms = get_msms(); const auto flattened_muls = get_flattened_scalar_muls(msms); std::array, 2> point_table_read_counts; const auto transcript_state = - ECCVMTranscriptBuilder::compute_transcript_state(vm_operations, get_number_of_muls()); + ECCVMTranscriptBuilder::compute_transcript_state(op_queue->raw_ops, get_number_of_muls()); const auto precompute_table_state = ECCVMPrecomputedTablesBuilder::compute_precompute_state(flattened_muls); const auto msm_state = diff --git a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/eccvm/eccvm_circuit_builder.test.cpp b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/eccvm/eccvm_circuit_builder.test.cpp index 9b4c15f385a..ab05d63811c 100644 --- a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/eccvm/eccvm_circuit_builder.test.cpp +++ b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/eccvm/eccvm_circuit_builder.test.cpp @@ -13,7 +13,7 @@ namespace eccvm_circuit_builder_tests { template class ECCVMCircuitBuilderTests : public ::testing::Test {}; -using FlavorTypes = ::testing::Types; +using FlavorTypes = ::testing::Types; TYPED_TEST_SUITE(ECCVMCircuitBuilderTests, FlavorTypes); TYPED_TEST(ECCVMCircuitBuilderTests, BaseCase) diff --git a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_translator_circuit_builder.cpp b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_translator_circuit_builder.cpp index 9384ef1f86d..ee95fd30105 100644 --- a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_translator_circuit_builder.cpp +++ b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_translator_circuit_builder.cpp @@ -599,12 +599,12 @@ GoblinTranslatorCircuitBuilder::AccumulationInput compute_witness_values_for_one batching_challenge_v, evaluation_input_x); } -void GoblinTranslatorCircuitBuilder::feed_ecc_op_queue_into_circuit(ECCOpQueue& ecc_op_queue) +void GoblinTranslatorCircuitBuilder::feed_ecc_op_queue_into_circuit(std::shared_ptr ecc_op_queue) { using Fq = barretenberg::fq; std::vector accumulator_trace; Fq current_accumulator(0); - if (ecc_op_queue.raw_ops.empty()) { + if (ecc_op_queue->raw_ops.empty()) { return; } // Rename for ease of use @@ -613,8 +613,8 @@ void GoblinTranslatorCircuitBuilder::feed_ecc_op_queue_into_circuit(ECCOpQueue& // We need to precompute the accumulators at each step, because in the actual circuit we compute the values starting // from the later indices. We need to know the previous accumulator to create the gate - for (size_t i = 0; i < ecc_op_queue.raw_ops.size(); i++) { - auto& ecc_op = ecc_op_queue.raw_ops[ecc_op_queue.raw_ops.size() - 1 - i]; + for (size_t i = 0; i < ecc_op_queue->raw_ops.size(); i++) { + auto& ecc_op = ecc_op_queue->raw_ops[ecc_op_queue->raw_ops.size() - 1 - i]; current_accumulator *= x; current_accumulator += (Fq(ecc_op.get_opcode_value()) + @@ -625,7 +625,7 @@ void GoblinTranslatorCircuitBuilder::feed_ecc_op_queue_into_circuit(ECCOpQueue& // We don't care about the last value since we'll recompute it during witness generation anyway accumulator_trace.pop_back(); - for (auto& raw_op : ecc_op_queue.raw_ops) { + for (auto& raw_op : ecc_op_queue->raw_ops) { Fq previous_accumulator = 0; // Pop the last value from accumulator trace and use it as previous accumulator if (!accumulator_trace.empty()) { diff --git a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_translator_circuit_builder.hpp b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_translator_circuit_builder.hpp index 2235a934345..92164873e44 100644 --- a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_translator_circuit_builder.hpp +++ b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_translator_circuit_builder.hpp @@ -94,7 +94,6 @@ class GoblinTranslatorCircuitBuilder : public CircuitBuilderBase op_queue) : GoblinTranslatorCircuitBuilder(batching_challenge_v_, evaluation_input_x_) { feed_ecc_op_queue_into_circuit(op_queue); @@ -447,7 +448,7 @@ class GoblinTranslatorCircuitBuilder : public CircuitBuilderBase ecc_op_queue); /** * @brief Check the witness satisifies the circuit diff --git a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_translator_circuit_builder.test.cpp b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_translator_circuit_builder.test.cpp index 6c3b10b33c4..cd019284069 100644 --- a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_translator_circuit_builder.test.cpp +++ b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_translator_circuit_builder.test.cpp @@ -87,9 +87,9 @@ TEST(GoblinTranslatorCircuitBuilder, SeveralOperationCorrectness) auto z = scalar::random_element(); // Add the same operations to the ECC op queue; the native computation is performed under the hood. - ECCOpQueue op_queue; - op_queue.add_accumulate(P1); - op_queue.mul_accumulate(P2, z); + auto op_queue = std::make_shared(); + op_queue->add_accumulate(P1); + op_queue->mul_accumulate(P2, z); Fq op_accumulator = 0; Fq p_x_accumulator = 0; Fq p_y_accumulator = 0; @@ -97,22 +97,22 @@ TEST(GoblinTranslatorCircuitBuilder, SeveralOperationCorrectness) Fq z_2_accumulator = 0; Fq batching_challenge = fq::random_element(); - op_queue.eq(); - op_queue.empty_row(); + op_queue->eq(); + op_queue->empty_row(); // Sample the evaluation input x Fq x = Fq::random_element(); // Get an inverse Fq x_inv = x.invert(); // Compute the batched evaluation of polynomials (multiplying by inverse to go from lower to higher) - for (auto& ecc_op : op_queue.raw_ops) { + for (auto& ecc_op : op_queue->raw_ops) { op_accumulator = op_accumulator * x_inv + ecc_op.get_opcode_value(); p_x_accumulator = p_x_accumulator * x_inv + ecc_op.base_point.x; p_y_accumulator = p_y_accumulator * x_inv + ecc_op.base_point.y; z_1_accumulator = z_1_accumulator * x_inv + ecc_op.z1; z_2_accumulator = z_2_accumulator * x_inv + ecc_op.z2; } - Fq x_pow = x.pow(op_queue.raw_ops.size() - 1); + Fq x_pow = x.pow(op_queue->raw_ops.size() - 1); // Multiply by an appropriate power of x to get rid of the inverses Fq result = ((((z_2_accumulator * batching_challenge + z_1_accumulator) * batching_challenge + p_y_accumulator) * diff --git a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_ultra_circuit_builder.cpp b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_ultra_circuit_builder.cpp index 5611b7ec5a0..610aefccf09 100644 --- a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_ultra_circuit_builder.cpp +++ b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/goblin_ultra_circuit_builder.cpp @@ -22,11 +22,31 @@ template void GoblinUltraCircuitBuilder_::finalize_circuit() // polynomials is zero, which is required for them to be shiftable. template void GoblinUltraCircuitBuilder_::add_gates_to_ensure_all_polys_are_non_zero() { + // Most polynomials are handled via the conventional Ultra method UltraCircuitBuilder_>::add_gates_to_ensure_all_polys_are_non_zero(); - // Additional gate to add a nonzero value to q_busread - this->w_l.emplace_back(this->zero_idx); - this->w_r.emplace_back(this->zero_idx); + // All that remains is to handle databus related polynomials. In what follows we populate the calldata with some + // mock data then constuct a single calldata read gate + + // Populate the calldata with some data + public_calldata.emplace_back(this->add_variable(FF(5))); + public_calldata.emplace_back(this->add_variable(FF(7))); + public_calldata.emplace_back(this->add_variable(FF(9))); + + // Construct read counts with length of calldata + calldata_read_counts.resize(public_calldata.size()); + for (auto& val : calldata_read_counts) { + val = 0; + } + + // Construct gate corresponding to a single calldata read + size_t read_idx = 1; // index into calldata array at which we want to read + this->w_l.emplace_back(public_calldata[read_idx]); // populate with value of calldata at read index + this->w_r.emplace_back(this->add_variable(FF(read_idx))); // populate with read index as witness + calldata_read_counts[read_idx]++; // increment read count at read index + q_busread.emplace_back(1); // read selector on + + // populate all other components with zero this->w_o.emplace_back(this->zero_idx); this->w_4.emplace_back(this->zero_idx); this->q_m.emplace_back(0); @@ -35,18 +55,13 @@ template void GoblinUltraCircuitBuilder_::add_gates_to_ensure_ this->q_3.emplace_back(0); this->q_c.emplace_back(0); this->q_sort.emplace_back(0); - this->q_arith.emplace_back(0); this->q_4.emplace_back(0); this->q_lookup_type.emplace_back(0); this->q_elliptic.emplace_back(0); this->q_aux.emplace_back(0); - q_busread.emplace_back(1); - ++this->num_gates; - // Add some nonzero values to the calldata and corresponding read counts - public_calldata.emplace_back(this->one_idx); - calldata_read_counts.emplace_back(this->one_idx); + ++this->num_gates; } /** diff --git a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/ultra_circuit_builder.cpp b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/ultra_circuit_builder.cpp index da96bd1cf86..e51cacc67d4 100644 --- a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/ultra_circuit_builder.cpp +++ b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/ultra_circuit_builder.cpp @@ -38,15 +38,15 @@ template void UltraCircuitBuilder_:: * circuit size would not be correct (resulting in the code crashing while performing FFT * operations). * - * Therefore, we introduce a boolean flag `circuit_finalised` here. Once we add the rom and range gates, - * our circuit is finalised, and we must not to execute these functions again. + * Therefore, we introduce a boolean flag `circuit_finalized` here. Once we add the rom and range gates, + * our circuit is finalized, and we must not to execute these functions again. */ - if (!circuit_finalised) { + if (!circuit_finalized) { process_non_native_field_multiplications(); process_ROM_arrays(); process_RAM_arrays(); process_range_lists(); - circuit_finalised = true; + circuit_finalized = true; } } @@ -912,7 +912,7 @@ template void UltraCircuitBuilder_:: * data structures: vector of lists, each list contains: * - the range size * - the list of variables in the range - * - a generalised permutation tag + * - a generalized permutation tag * * create range constraint parameters: variable index && range size * @@ -2139,7 +2139,7 @@ void UltraCircuitBuilder_::create_final_sorted_RAM_gate(RamReco } /** - * @brief Create a new updateable memory region + * @brief Create a new updatable memory region * * @details Creates a transcript object, where the inside memory state array is filled with "uninitialized memory" and diff --git a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/ultra_circuit_builder.hpp b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/ultra_circuit_builder.hpp index ae3b25ab436..25c35737a12 100644 --- a/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/ultra_circuit_builder.hpp +++ b/barretenberg/cpp/src/barretenberg/proof_system/circuit_builder/ultra_circuit_builder.hpp @@ -280,7 +280,7 @@ class UltraCircuitBuilder_ : public CircuitBuilderBasememory_read_records; stored_state.memory_write_records = builder->memory_write_records; stored_state.range_lists = builder->range_lists; - stored_state.circuit_finalised = builder->circuit_finalised; + stored_state.circuit_finalized = builder->circuit_finalized; stored_state.num_gates = builder->num_gates; stored_state.cached_partial_non_native_field_multiplications = builder->cached_partial_non_native_field_multiplications; @@ -399,7 +399,7 @@ class UltraCircuitBuilder_ : public CircuitBuilderBasememory_read_records = memory_read_records; builder->memory_write_records = memory_write_records; builder->range_lists = range_lists; - builder->circuit_finalised = circuit_finalised; + builder->circuit_finalized = circuit_finalized; builder->num_gates = num_gates; builder->cached_partial_non_native_field_multiplications = cached_partial_non_native_field_multiplications; builder->w_l.resize(num_gates); @@ -521,7 +521,7 @@ class UltraCircuitBuilder_ : public CircuitBuilderBase cached_partial_non_native_field_multiplications; - bool circuit_finalised = false; + bool circuit_finalized = false; void process_non_native_field_multiplications(); UltraCircuitBuilder_(const size_t size_hint = 0) @@ -614,7 +614,7 @@ class UltraCircuitBuilder_ : public CircuitBuilderBasenum_gates; } size_t count = 0; diff --git a/barretenberg/cpp/src/barretenberg/proof_system/composer/composer_lib.hpp b/barretenberg/cpp/src/barretenberg/proof_system/composer/composer_lib.hpp index 8d3366f2256..93884466ba0 100644 --- a/barretenberg/cpp/src/barretenberg/proof_system/composer/composer_lib.hpp +++ b/barretenberg/cpp/src/barretenberg/proof_system/composer/composer_lib.hpp @@ -7,7 +7,7 @@ namespace proof_system { /** - * @brief Construct selector polynomials from ciruit selector information and put into polynomial cache + * @brief Construct selector polynomials from circuit selector information and put into polynomial cache * * @tparam Flavor * @param circuit_constructor The object holding the circuit diff --git a/barretenberg/cpp/src/barretenberg/protogalaxy/combiner.test.cpp b/barretenberg/cpp/src/barretenberg/protogalaxy/combiner.test.cpp index 162e6cba68b..dce50d0220c 100644 --- a/barretenberg/cpp/src/barretenberg/protogalaxy/combiner.test.cpp +++ b/barretenberg/cpp/src/barretenberg/protogalaxy/combiner.test.cpp @@ -12,6 +12,8 @@ using Polynomial = typename Flavor::Polynomial; using FF = typename Flavor::FF; using RelationParameters = proof_system::RelationParameters; +// TODO(https://github.com/AztecProtocol/barretenberg/issues/780): Improve combiner tests to check more than the +// arithmetic relation so we more than unit test folding relation parameters and alpha as well. TEST(Protogalaxy, CombinerOn2Instances) { constexpr size_t NUM_INSTANCES = 2; @@ -36,7 +38,6 @@ TEST(Protogalaxy, CombinerOn2Instances) std::array, NUM_INSTANCES> storage_arrays; ProtoGalaxyProver prover; std::vector pow_betas = { FF(1), FF(2) }; - auto alpha = FF(0); // focus on the arithmetic relation only for (size_t idx = 0; idx < NUM_INSTANCES; idx++) { auto instance = std::make_shared(); @@ -49,8 +50,9 @@ TEST(Protogalaxy, CombinerOn2Instances) } ProverInstances instances{ instance_data }; + instances.alpha = Univariate(FF(0)); // focus on the arithmetic relation only - auto result = prover.compute_combiner(instances, pow_betas, alpha); + auto result = prover.compute_combiner(instances, pow_betas); auto expected_result = barretenberg::Univariate(std::array{ 87706, 13644570, @@ -71,7 +73,6 @@ TEST(Protogalaxy, CombinerOn2Instances) std::array, NUM_INSTANCES> storage_arrays; ProtoGalaxyProver prover; std::vector pow_betas = { FF(1), FF(2) }; - auto alpha = FF(0); // focus on the arithmetic relation only for (size_t idx = 0; idx < NUM_INSTANCES; idx++) { auto instance = std::make_shared(); @@ -84,6 +85,7 @@ TEST(Protogalaxy, CombinerOn2Instances) } ProverInstances instances{ instance_data }; + instances.alpha = Univariate(FF(0)); // focus on the arithmetic relation only const auto create_add_gate = [](auto& polys, const size_t idx, FF w_l, FF w_r) { polys.w_l[idx] = w_l; @@ -129,7 +131,7 @@ TEST(Protogalaxy, CombinerOn2Instances) relation value: 0 0 0 0 0 0 0 0 0 6 18 36 60 90 */ - auto result = prover.compute_combiner(instances, pow_betas, alpha); + auto result = prover.compute_combiner(instances, pow_betas); auto expected_result = barretenberg::Univariate( std::array{ 0, 0, 12, 36, 72, 120, 180, 252, 336, 432, 540, 660, 792 }); @@ -162,7 +164,6 @@ TEST(Protogalaxy, CombinerOn4Instances) std::vector> instance_data(NUM_INSTANCES); std::array, NUM_INSTANCES> storage_arrays; ProtoGalaxyProver prover; - auto alpha = FF(0); // focus on the arithmetic relation only std::vector pow_betas = { FF(1), FF(2) }; for (size_t idx = 0; idx < NUM_INSTANCES; idx++) { @@ -175,19 +176,20 @@ TEST(Protogalaxy, CombinerOn4Instances) } ProverInstances instances{ instance_data }; + instances.alpha = Univariate(FF(0)); // focus on the arithmetic relation only zero_all_selectors(instances[0]->prover_polynomials); zero_all_selectors(instances[1]->prover_polynomials); zero_all_selectors(instances[2]->prover_polynomials); zero_all_selectors(instances[3]->prover_polynomials); - auto result = prover.compute_combiner(instances, pow_betas, alpha); - std::array zeroes; + auto result = prover.compute_combiner(instances, pow_betas); + std::array zeroes; std::fill(zeroes.begin(), zeroes.end(), 0); - auto expected_result = barretenberg::Univariate(zeroes); + auto expected_result = barretenberg::Univariate(zeroes); EXPECT_EQ(result, expected_result); }; run_test(); }; -} // namespace barretenberg::test_protogalaxy_prover +} // namespace barretenberg::test_protogalaxy_prover \ No newline at end of file diff --git a/barretenberg/cpp/src/barretenberg/protogalaxy/protogalaxy_prover.cpp b/barretenberg/cpp/src/barretenberg/protogalaxy/protogalaxy_prover.cpp index 74b7ae2245b..d52af9f2a84 100644 --- a/barretenberg/cpp/src/barretenberg/protogalaxy/protogalaxy_prover.cpp +++ b/barretenberg/cpp/src/barretenberg/protogalaxy/protogalaxy_prover.cpp @@ -7,7 +7,7 @@ template void ProtoGalaxyProver_::prepa auto idx = 0; for (auto it = instances.begin(); it != instances.end(); it++, idx++) { auto instance = *it; - instance->initialise_prover_polynomials(); + instance->initialize_prover_polynomials(); auto domain_separator = std::to_string(idx); const auto circuit_size = static_cast(instance->proving_key->circuit_size); @@ -27,9 +27,11 @@ template void ProtoGalaxyProver_::prepa domain_separator + "_eta", domain_separator + "_beta", domain_separator + "_gamma"); instance->compute_sorted_accumulator_polynomials(eta); instance->compute_grand_product_polynomials(beta, gamma); + instance->alpha = transcript.get_challenge(domain_separator + "_alpha"); } - fold_parameters(instances); + fold_relation_parameters(instances); + fold_alpha(instances); } // TODO(#https://github.com/AztecProtocol/barretenberg/issues/689): finalise implementation this function @@ -40,13 +42,13 @@ ProverFoldingResult ProtoGalaxyProver_prover_polynomials.get_polynomial_size(); const auto log_instance_size = static_cast(numeric::get_msb(instance_size)); auto deltas = compute_round_challenge_pows(log_instance_size, delta); - auto perturbator = compute_perturbator(accumulator, deltas, alpha); + auto perturbator = compute_perturbator(accumulator, deltas); for (size_t idx = 0; idx <= log_instance_size; idx++) { transcript.send_to_verifier("perturbator_" + std::to_string(idx), perturbator[idx]); } @@ -62,7 +64,7 @@ ProverFoldingResult ProtoGalaxyProver_ ProtoGalaxyProver_ class ProtoGalaxyProver_ { using RowEvaluations = typename Flavor::AllValues; using ProverPolynomials = typename Flavor::ProverPolynomials; using Relations = typename Flavor::Relations; + using AlphaType = typename ProverInstances::AlphaType; using BaseUnivariate = Univariate; - // The length of ExtendedUnivariate is the largest length (==degree + 1) of a univariate polynomial obtained by - // composing a relation with folded instance + challenge data. + // The length of ExtendedUnivariate is the largest length (==max_relation_degree + 1) of a univariate polynomial + // obtained by composing a relation with folded instance + relation parameters . using ExtendedUnivariate = Univariate; + // Represents the total length of the combiner univariate, obtained by combining the already folded relations with + // the folded relation batching challenge. using ExtendedUnivariateWithRandomization = - Univariate; + Univariate; + using ExtendedUnivariates = typename Flavor::template ProverUnivariates; using TupleOfTuplesOfUnivariates = @@ -181,11 +186,10 @@ template class ProtoGalaxyProver_ { * */ static Polynomial compute_perturbator(const std::shared_ptr accumulator, - const std::vector& deltas, - const FF& alpha) + const std::vector& deltas) { - auto full_honk_evaluations = - compute_full_honk_evaluations(accumulator->prover_polynomials, alpha, accumulator->relation_parameters); + auto full_honk_evaluations = compute_full_honk_evaluations( + accumulator->prover_polynomials, accumulator->alpha, accumulator->relation_parameters); const auto betas = accumulator->folding_parameters.gate_separation_challenges; assert(betas.size() == deltas.size()); auto coeffs = construct_perturbator_coefficients(betas, deltas, full_honk_evaluations); @@ -237,8 +241,7 @@ template class ProtoGalaxyProver_ { * */ ExtendedUnivariateWithRandomization compute_combiner(const ProverInstances& instances, - const std::vector& pow_betas_star, - const FF& alpha) + const std::vector& pow_betas_star) { size_t common_circuit_size = instances[0]->prover_polynomials.get_polynomial_size(); @@ -288,24 +291,38 @@ template class ProtoGalaxyProver_ { Utils::add_nested_tuples(univariate_accumulators, accumulators); } // Batch the univariate contributions from each sub-relation to obtain the round univariate - return Utils::template batch_over_relations(univariate_accumulators, - alpha); + return batch_over_relations(univariate_accumulators, instances.alpha); + } + static ExtendedUnivariateWithRandomization batch_over_relations(TupleOfTuplesOfUnivariates univariate_accumulators, + AlphaType alpha) + { + + // First relation does not get multiplied by a batching challenge + auto result = std::get<0>(std::get<0>(univariate_accumulators)) + .template extend_to(); + auto scale_and_sum = [&](auto& element) { + auto extended = element.template extend_to(); + extended *= alpha; + result += extended; + }; + + Utils::template apply_to_tuple_of_tuples<0, 1>(univariate_accumulators, scale_and_sum); + Utils::zero_univariates(univariate_accumulators); + + return result; } /** * @brief Compute the combiner quotient defined as $K$ polynomial in the paper. * - * TODO(https://github.com/AztecProtocol/barretenberg/issues/764): generalise the computation of vanishing + * TODO(https://github.com/AztecProtocol/barretenberg/issues/764): generalize the computation of vanishing * polynomials and Lagrange basis and use batch_invert. * */ - static Univariate - compute_combiner_quotient(FF compressed_perturbator, ExtendedUnivariateWithRandomization combiner) + static Univariate compute_combiner_quotient( + FF compressed_perturbator, ExtendedUnivariateWithRandomization combiner) { - std::array - combiner_quotient_evals = {}; + std::array combiner_quotient_evals = {}; // Compute the combiner quotient polynomial as evaluations on points that are not in the vanishing set. // @@ -318,10 +335,8 @@ template class ProtoGalaxyProver_ { (combiner.value_at(point) - compressed_perturbator * lagrange_0) * vanishing_polynomial.invert(); } - Univariate - combiner_quotient(combiner_quotient_evals); + Univariate combiner_quotient( + combiner_quotient_evals); return combiner_quotient; } @@ -331,7 +346,7 @@ template class ProtoGalaxyProver_ { * univariate (i.e., sum them against an appropriate univariate Lagrange basis) and then extended as needed during * the constuction of the combiner. */ - static void fold_parameters(ProverInstances& instances) + static void fold_relation_parameters(ProverInstances& instances) { // array of parameters to be computed auto& folded_parameters = instances.relation_parameters.to_fold; @@ -347,6 +362,25 @@ template class ProtoGalaxyProver_ { param_idx++; } } + + /** + * @brief Create folded univariate for the relation batching parameter (alpha). + * + */ + // TODO(https://github.com/AztecProtocol/barretenberg/issues/772): At the moment we have a single α per Instance, we + // fold them and then we use the unique folded_α for each folded subrelation that is batched in the combiner. This + // is obviously insecure. We need to generate α_i for each subrelation_i, fold them and then use folded_α_i when + // batching the i-th folded subrelation in the combiner. + static void fold_alpha(ProverInstances& instances) + { + Univariate accumulated_alpha; + size_t instance_idx = 0; + for (auto& instance : instances) { + accumulated_alpha.value_at(instance_idx) = instance->alpha; + instance_idx++; + } + instances.alpha = accumulated_alpha.template extend_to(); + } }; extern template class ProtoGalaxyProver_>; diff --git a/barretenberg/cpp/src/barretenberg/protogalaxy/protogalaxy_verifier.cpp b/barretenberg/cpp/src/barretenberg/protogalaxy/protogalaxy_verifier.cpp index 321c50a1ed6..ed6441dae39 100644 --- a/barretenberg/cpp/src/barretenberg/protogalaxy/protogalaxy_verifier.cpp +++ b/barretenberg/cpp/src/barretenberg/protogalaxy/protogalaxy_verifier.cpp @@ -28,6 +28,7 @@ void ProtoGalaxyVerifier_::prepare_for_folding(std::vector(beta, gamma, inst->instance_size); inst->relation_parameters = RelationParameters{ eta, beta, gamma, public_input_delta, lookup_grand_product_delta }; + inst->alpha = transcript.get_challenge(domain_separator + "_alpha"); } } @@ -38,7 +39,7 @@ VerifierFoldingResult ProtoGalaxyVerifier_< using Flavor = typename VerifierInstances::Flavor; prepare_for_folding(fold_data); - auto [alpha, delta] = transcript.get_challenges("alpha", "delta"); + auto delta = transcript.get_challenge("delta"); auto accumulator = get_accumulator(); auto log_instance_size = static_cast(numeric::get_msb(accumulator->instance_size)); auto deltas = compute_round_challenge_pows(log_instance_size, delta); @@ -51,16 +52,13 @@ VerifierFoldingResult ProtoGalaxyVerifier_< auto perturbator_at_challenge = perturbator.evaluate(perturbator_challenge); // Thed degree of K(X) is dk - k - 1 = k(d - 1) - 1. Hence we need k(d - 1) evaluations to represent it. - std::array - combiner_quotient_evals = {}; - for (size_t idx = 0; idx < (Flavor::BATCHED_RELATION_TOTAL_LENGTH - 2) * (VerifierInstances::NUM - 1); idx++) { + std::array combiner_quotient_evals = {}; + for (size_t idx = 0; idx < VerifierInstances::BATCHED_EXTENDED_LENGTH - VerifierInstances::NUM; idx++) { combiner_quotient_evals[idx] = transcript.template receive_from_prover( "combiner_quotient_" + std::to_string(idx + VerifierInstances::NUM)); } - Univariate - combiner_quotient(combiner_quotient_evals); + Univariate combiner_quotient( + combiner_quotient_evals); auto combiner_challenge = transcript.get_challenge("combiner_quotient_challenge"); auto combiner_quotient_at_challenge = combiner_quotient.evaluate(combiner_challenge); diff --git a/barretenberg/cpp/src/barretenberg/relations/databus_lookup_relation.hpp b/barretenberg/cpp/src/barretenberg/relations/databus_lookup_relation.hpp new file mode 100644 index 00000000000..da659a321e9 --- /dev/null +++ b/barretenberg/cpp/src/barretenberg/relations/databus_lookup_relation.hpp @@ -0,0 +1,178 @@ +#pragma once +#include +#include + +#include "barretenberg/common/constexpr_utils.hpp" +#include "barretenberg/honk/proof_system/lookup_library.hpp" +#include "barretenberg/polynomials/polynomial.hpp" +#include "barretenberg/polynomials/univariate.hpp" +#include "barretenberg/relations/relation_types.hpp" + +namespace proof_system { + +template class DatabusLookupRelationImpl { + public: + using FF = FF_; + static constexpr size_t READ_TERMS = 1; + static constexpr size_t WRITE_TERMS = 1; + // 1 + polynomial degree of this relation + static constexpr size_t LENGTH = READ_TERMS + WRITE_TERMS + 3; + + static constexpr std::array SUBRELATION_PARTIAL_LENGTHS{ + LENGTH, // inverse polynomial correctness subrelation + LENGTH // log-derivative lookup argument subrelation + }; + + // The second subrelation is "linearly dependant" in the sense that it establishes the value of a sum across the + // entire execution trace rather than a per-row identity. + static constexpr std::array SUBRELATION_LINEARLY_INDEPENDENT = { true, false }; + + /** + * @brief Determine whether the inverse I needs to be computed at a given row + * @details The value of the inverse polynomial I(X) only needs to be computed when the databus lookup gate is + * "active". Otherwise it is set to 0. This method allows for determination of when the inverse should be computed. + * + * @tparam AllValues + * @param row + * @return true + * @return false + */ + template static bool lookup_exists_at_row(const AllValues& row) + { + return (row.q_busread == 1 || row.calldata_read_counts > 0); + } + + /** + * @brief Compute the Accumulator whose values indicate whether the inverse is computed or not + * @details This is needed for efficiency since we don't need to compute the inverse unless the log derivative + * lookup relation is active at a given row. + * + */ + template + static Accumulator compute_inverse_exists(const AllEntities& in) + { + using View = typename Accumulator::View; + // TODO(luke): row_has_read should really be a boolean object thats equal to 1 when counts > 0 and 0 otherwise. + // This current structure will lead to failure if call_data_read_counts > 1. + const auto row_has_write = View(in.q_busread); + const auto row_has_read = View(in.calldata_read_counts); + + return row_has_write + row_has_read - (row_has_write * row_has_read); + + return Accumulator(View(in.q_busread) + View(in.calldata_read_counts)); + } + + template + static Accumulator lookup_read_counts(const AllEntities& in) + { + using View = typename Accumulator::View; + + if constexpr (index == 0) { + return Accumulator(View(in.calldata_read_counts)); + } + return Accumulator(1); + } + + /** + * @brief Compute scalar for read term in log derivative lookup argument + * + */ + template + static Accumulator compute_read_term_predicate([[maybe_unused]] const AllEntities& in) + + { + using View = typename Accumulator::View; + + if constexpr (read_index == 0) { + return Accumulator(View(in.q_busread)); + } + return Accumulator(1); + } + + /** + * @brief Compute scalar for write term in log derivative lookup argument + * + */ + template + static Accumulator compute_write_term_predicate(const AllEntities& /*unused*/) + { + return Accumulator(1); + } + + /** + * @brief Compute write term denominator in log derivative lookup argument + * + */ + template + static Accumulator compute_write_term(const AllEntities& in, const Parameters& params) + { + using View = typename Accumulator::View; + using ParameterView = GetParameterView; + + static_assert(write_index < WRITE_TERMS); + + const auto& calldata = View(in.calldata); + const auto& id = View(in.databus_id); + + const auto& gamma = ParameterView(params.gamma); + const auto& beta = ParameterView(params.beta); + + // Construct b_i + idx_i*\beta + \gamma + if constexpr (write_index == 0) { + return calldata + gamma + id * beta; // degree 1 + } + + return Accumulator(1); + } + + /** + * @brief Compute read term denominator in log derivative lookup argument + * + */ + template + static Accumulator compute_read_term(const AllEntities& in, const Parameters& params) + { + using View = typename Accumulator::View; + using ParameterView = GetParameterView; + + static_assert(read_index < READ_TERMS); + + // Bus value stored in w_1, index into bus column stored in w_2 + const auto& w_1 = View(in.w_l); + const auto& w_2 = View(in.w_r); + + const auto& gamma = ParameterView(params.gamma); + const auto& beta = ParameterView(params.beta); + + // Construct value + index*\beta + \gamma + if constexpr (read_index == 0) { + return w_1 + gamma + w_2 * beta; + } + + return Accumulator(1); + } + + /** + * @brief Accumulate the contribution from two surelations for the log derivative databus lookup argument + * @details See lookup_library.hpp for details of the generic log-derivative lookup argument + * + * @param accumulator transformed to `evals + C(in(X)...)*scaling_factor` + * @param in an std::array containing the fully extended Accumulator edges. + * @param params contains beta, gamma, and public_input_delta, .... + * @param scaling_factor optional term to scale the evaluation before adding to evals. + */ + template + static void accumulate(ContainerOverSubrelations& accumulator, + const AllEntities& in, + const Parameters& params, + const FF& scaling_factor) + { + honk::lookup_library::accumulate_logderivative_lookup_subrelation_contributions>( + accumulator, in, params, scaling_factor); + } +}; + +template using DatabusLookupRelation = Relation>; + +} // namespace proof_system diff --git a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_lookup_relation.cpp b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_lookup_relation.cpp index 903da2a7056..1daf3469bc7 100644 --- a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_lookup_relation.cpp +++ b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_lookup_relation.cpp @@ -29,9 +29,7 @@ void ECCVMLookupRelationImpl::accumulate(ContainerOverSubrelations& accumula accumulator, in, params, scaling_factor); } -template class ECCVMLookupRelationImpl; template class ECCVMLookupRelationImpl; DEFINE_SUMCHECK_RELATION_CLASS(ECCVMLookupRelationImpl, flavor::ECCVM); -DEFINE_SUMCHECK_RELATION_CLASS(ECCVMLookupRelationImpl, flavor::ECCVMGrumpkin); } // namespace proof_system::honk::sumcheck diff --git a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_lookup_relation.hpp b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_lookup_relation.hpp index 68f15aa2bd4..35af59f7490 100644 --- a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_lookup_relation.hpp +++ b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_lookup_relation.hpp @@ -40,6 +40,20 @@ template class ECCVMLookupRelationImpl { return row_has_write + row_has_read - (row_has_write * row_has_read); } + template + static Accumulator lookup_read_counts(const AllEntities& in) + { + using View = typename Accumulator::View; + + if constexpr (index == 0) { + return Accumulator(View(in.lookup_read_counts_0)); + } + if constexpr (index == 1) { + return Accumulator(View(in.lookup_read_counts_1)); + } + return Accumulator(1); + } + template static Accumulator compute_read_term_predicate(const AllEntities& in) @@ -137,7 +151,7 @@ template class ECCVMLookupRelationImpl { const auto positive_slice_value = -(precompute_round) + 15; const auto positive_term = precompute_pc + gamma + positive_slice_value * beta + tx * beta_sqr + ty * beta_cube; - // todo optimise this? + // todo optimize this? if constexpr (write_index == 0) { return positive_term; // degree 1 } diff --git a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_msm_relation.cpp b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_msm_relation.cpp index b0efb52113c..f0a3a7ee341 100644 --- a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_msm_relation.cpp +++ b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_msm_relation.cpp @@ -391,8 +391,7 @@ void ECCVMMSMRelationImpl::accumulate(ContainerOverSubrelations& accumulator // perform lookups on (pc / slice_i / x / y) } -template class ECCVMMSMRelationImpl; +template class ECCVMMSMRelationImpl; DEFINE_SUMCHECK_RELATION_CLASS(ECCVMMSMRelationImpl, flavor::ECCVM); -DEFINE_SUMCHECK_RELATION_CLASS(ECCVMMSMRelationImpl, flavor::ECCVMGrumpkin); } // namespace proof_system::honk::sumcheck diff --git a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_point_table_relation.cpp b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_point_table_relation.cpp index f846c265482..a07c46a2b76 100644 --- a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_point_table_relation.cpp +++ b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_point_table_relation.cpp @@ -172,8 +172,7 @@ void ECCVMPointTableRelationImpl::accumulate(ContainerOverSubrelations& accu (-lagrange_first + 1) * (-precompute_point_transition + 1) * y_add_check * scaling_factor; } -template class ECCVMPointTableRelationImpl; +template class ECCVMPointTableRelationImpl; DEFINE_SUMCHECK_RELATION_CLASS(ECCVMPointTableRelationImpl, flavor::ECCVM); -DEFINE_SUMCHECK_RELATION_CLASS(ECCVMPointTableRelationImpl, flavor::ECCVMGrumpkin); } // namespace proof_system::honk::sumcheck diff --git a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_set_relation.cpp b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_set_relation.cpp index f9c9cd96cdd..6e754d3107d 100644 --- a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_set_relation.cpp +++ b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_set_relation.cpp @@ -280,16 +280,16 @@ Accumulator ECCVMSetRelationImpl::compute_permutation_denominator(const AllE } /** - * @brief Second term: tuple of (transcript_pc, transcript_x, transcript_y, z1) OR (transcript_pc, \lambda * - * transcript_x, -transcript_y, z2) for each scalar multiplication in ECCVMTranscriptRelation columns. (the latter + * @brief Second term: tuple of (transcript_pc, transcript_Px, transcript_Py, z1) OR (transcript_pc, \lambda * + * transcript_Px, -transcript_Py, z2) for each scalar multiplication in ECCVMTranscriptRelation columns. (the latter * term uses the curve endomorphism: \lambda = cube root of unity). These values must be equivalent to the second * term values in `compute_permutation_numerator` */ { const auto& transcript_pc = View(in.transcript_pc); - auto transcript_x = View(in.transcript_x); - auto transcript_y = View(in.transcript_y); + auto transcript_Px = View(in.transcript_Px); + auto transcript_Py = View(in.transcript_Py); auto z1 = View(in.transcript_z1); auto z2 = View(in.transcript_z2); auto z1_zero = View(in.transcript_z1zero); @@ -300,9 +300,9 @@ Accumulator ECCVMSetRelationImpl::compute_permutation_denominator(const AllE auto lookup_second = (-z2_zero + 1); FF endomorphism_base_field_shift = FF::cube_root_of_unity(); - auto transcript_input1 = transcript_pc + transcript_x * beta + transcript_y * beta_sqr + z1 * beta_cube; - auto transcript_input2 = (transcript_pc - 1) + transcript_x * endomorphism_base_field_shift * beta - - transcript_y * beta_sqr + z2 * beta_cube; + auto transcript_input1 = transcript_pc + transcript_Px * beta + transcript_Py * beta_sqr + z1 * beta_cube; + auto transcript_input2 = (transcript_pc - 1) + transcript_Px * endomorphism_base_field_shift * beta - + transcript_Py * beta_sqr + z2 * beta_cube; // | q_mul | z2_zero | z1_zero | lookup | // | ----- | ------- | ------- | ---------------------- | @@ -393,10 +393,8 @@ void ECCVMSetRelationImpl::accumulate(ContainerOverSubrelations& accumulator std::get<1>(accumulator) += (lagrange_last * z_perm_shift) * scaling_factor; } -template class ECCVMSetRelationImpl; +template class ECCVMSetRelationImpl; DEFINE_SUMCHECK_RELATION_CLASS(ECCVMSetRelationImpl, flavor::ECCVM); -DEFINE_SUMCHECK_RELATION_CLASS(ECCVMSetRelationImpl, flavor::ECCVMGrumpkin); DEFINE_SUMCHECK_PERMUTATION_CLASS(ECCVMSetRelationImpl, flavor::ECCVM); -DEFINE_SUMCHECK_PERMUTATION_CLASS(ECCVMSetRelationImpl, flavor::ECCVMGrumpkin); } // namespace proof_system::honk::sumcheck diff --git a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_transcript_relation.cpp b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_transcript_relation.cpp index 85b18b65f7f..e944ed3ef2c 100644 --- a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_transcript_relation.cpp +++ b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_transcript_relation.cpp @@ -59,8 +59,8 @@ void ECCVMTranscriptRelationImpl::accumulate(ContainerOverSubrelations& accu auto transcript_accumulator_y = View(in.transcript_accumulator_y); auto transcript_msm_x = View(in.transcript_msm_x); auto transcript_msm_y = View(in.transcript_msm_y); - auto transcript_x = View(in.transcript_x); - auto transcript_y = View(in.transcript_y); + auto transcript_Px = View(in.transcript_Px); + auto transcript_Py = View(in.transcript_Py); auto is_accumulator_empty = View(in.transcript_accumulator_empty); auto lagrange_first = View(in.lagrange_first); auto lagrange_last = View(in.lagrange_last); @@ -169,13 +169,13 @@ void ECCVMTranscriptRelationImpl::accumulate(ContainerOverSubrelations& accu /** * @brief Constrain `add` opcode. * - * add will add the input point in (transcript_x, transcript_y) into the accumulator. + * add will add the input point in (transcript_Px, transcript_Py) into the accumulator. * Correctly handles case where accumulator is point at infinity. * TODO: need to add constraints to rule out point doubling case (x2 != x1) * TODO: need to assert input point is on the curve! */ - x2 = transcript_x; - y2 = transcript_y; + x2 = transcript_Px; + y2 = transcript_Py; auto add_into_accumulator = q_add * (-is_accumulator_empty + 1); tmpx = (x3 + x2 + x1) * (x2 - x1) * (x2 - x1) - (y2 - y1) * (y2 - y1); tmpy = (y3 + y1) * (x2 - x1) - (y2 - y1) * (x1 - x3); @@ -205,12 +205,12 @@ void ECCVMTranscriptRelationImpl::accumulate(ContainerOverSubrelations& accu /** * @brief `eq` opcode. - * If eq = 1, assert transcript_x/y = transcript_accumulator_x/y. + * If eq = 1, assert transcript_Px/y = transcript_accumulator_x/y. * If eq = 1, assert is_accumulator_empty = 0 (input point cannot be point at infinity) */ - std::get<20>(accumulator) += q_eq * (transcript_accumulator_x - transcript_x) * scaling_factor; + std::get<20>(accumulator) += q_eq * (transcript_accumulator_x - transcript_Px) * scaling_factor; std::get<21>(accumulator) += - q_eq * (-is_accumulator_empty + 1) * (transcript_accumulator_y - transcript_y) * scaling_factor; + q_eq * (-is_accumulator_empty + 1) * (transcript_accumulator_y - transcript_Py) * scaling_factor; std::get<22>(accumulator) += q_eq * is_accumulator_empty * scaling_factor; // validate selectors are boolean (put somewhere else? these are low degree) @@ -236,12 +236,12 @@ void ECCVMTranscriptRelationImpl::accumulate(ContainerOverSubrelations& accu /** * @brief On-curve validation checks. - * If q_mul = 1 OR q_add = 1 OR q_eq = 1, require (transcript_x, transcript_y) is valid ecc point + * If q_mul = 1 OR q_add = 1 OR q_eq = 1, require (transcript_Px, transcript_Py) is valid ecc point * q_mul/q_add/q_eq mutually exclusive, can represent as sum of 3 */ const auto validate_on_curve = q_mul; // q_add + q_mul + q_eq; const auto on_curve_check = - transcript_y * transcript_y - transcript_x * transcript_x * transcript_x - get_curve_b(); + transcript_Py * transcript_Py - transcript_Px * transcript_Px * transcript_Px - get_curve_b(); std::get<33>(accumulator) += validate_on_curve * on_curve_check * scaling_factor; /** @@ -251,12 +251,11 @@ void ECCVMTranscriptRelationImpl::accumulate(ContainerOverSubrelations& accu auto x_coordinate_collision_check = add_msm_into_accumulator * ((transcript_msm_x - transcript_accumulator_x) * transcript_collision_check - FF(1)); x_coordinate_collision_check += - add_into_accumulator * ((transcript_x - transcript_accumulator_x) * transcript_collision_check - FF(1)); + add_into_accumulator * ((transcript_Px - transcript_accumulator_x) * transcript_collision_check - FF(1)); std::get<34>(accumulator) += x_coordinate_collision_check * scaling_factor; } -template class ECCVMTranscriptRelationImpl; +template class ECCVMTranscriptRelationImpl; DEFINE_SUMCHECK_RELATION_CLASS(ECCVMTranscriptRelationImpl, flavor::ECCVM); -DEFINE_SUMCHECK_RELATION_CLASS(ECCVMTranscriptRelationImpl, flavor::ECCVMGrumpkin); } // namespace proof_system::honk::sumcheck diff --git a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_wnaf_relation.cpp b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_wnaf_relation.cpp index 56f84de6a87..0f469aedd8e 100644 --- a/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_wnaf_relation.cpp +++ b/barretenberg/cpp/src/barretenberg/relations/ecc_vm/ecc_wnaf_relation.cpp @@ -152,7 +152,7 @@ void ECCVMWnafRelationImpl::accumulate(ContainerOverSubrelations& accumulato * If q_transition = 1, round value at current row = 7 * If q_transition = 1, round value at next row = 0 * Question: is this sufficient? We don't actually range constrain `round` (expensive if we don't need to!). - * Let us analyse... + * Let us analyze... * 1. When `q_transition = 1`, we use a set membership check to map the tuple of (pc, scalar_sum) into a set. * We compare this set with an equivalent set generated from the transcript columns. The sets must match. * 2. Only case where, at row `i`, a Prover can set `round` to value > 7 is if `q_transition = 0` for all j > i. @@ -216,8 +216,7 @@ void ECCVMWnafRelationImpl::accumulate(ContainerOverSubrelations& accumulato // the set equivalence relation } -template class ECCVMWnafRelationImpl; +template class ECCVMWnafRelationImpl; DEFINE_SUMCHECK_RELATION_CLASS(ECCVMWnafRelationImpl, flavor::ECCVM); -DEFINE_SUMCHECK_RELATION_CLASS(ECCVMWnafRelationImpl, flavor::ECCVMGrumpkin); } // namespace proof_system::honk::sumcheck diff --git a/barretenberg/cpp/src/barretenberg/relations/utils.hpp b/barretenberg/cpp/src/barretenberg/relations/utils.hpp index da2b8c111cf..a87f71855f8 100644 --- a/barretenberg/cpp/src/barretenberg/relations/utils.hpp +++ b/barretenberg/cpp/src/barretenberg/relations/utils.hpp @@ -25,7 +25,7 @@ template class RelationUtils { * @param tuple A Tuple of tuples of Univariates * @param operation Operation to apply to Univariates */ - template + template static void apply_to_tuple_of_tuples(auto& tuple, Operation&& operation) { auto& inner_tuple = std::get(tuple); @@ -39,9 +39,9 @@ template class RelationUtils { // Recurse over inner and outer tuples if constexpr (inner_idx + 1 < inner_size) { - apply_to_tuple_of_tuples(tuple, std::forward(operation)); + apply_to_tuple_of_tuples(tuple, std::forward(operation)); } else if constexpr (outer_idx + 1 < outer_size) { - apply_to_tuple_of_tuples(tuple, std::forward(operation)); + apply_to_tuple_of_tuples(tuple, std::forward(operation)); } } @@ -65,7 +65,7 @@ template class RelationUtils { * @param challenge * @param current_scalar power of the challenge */ - static void scale_univariates(auto& tuple, const FF& challenge, FF current_scalar) + static void scale_univariates(auto& tuple, const FF& challenge, FF& current_scalar) { auto scale_by_consecutive_powers_of_challenge = [&](auto& element) { element *= current_scalar; @@ -114,67 +114,6 @@ template class RelationUtils { } } - /** - * @brief Extend Univariates to specified size then sum them - * - * @tparam extended_size Size after extension - * @param tuple A tuple of tuples of Univariates - * @param result A Univariate of length extended_size - * @param pow_univariate Power polynomial univariate. Optional because this concept is only used in sumcheck. When - * using this function in the context of Protogalaxy, the contribution of the pow polynomial has already been added - * to the result as a scalar. - */ - template - static void extend_and_batch_univariates(const TupleOfTuplesOfUnivariates& tuple, - ExtendedUnivariate& result, - const std::optional>& pow_univariate = std::nullopt) - { - ExtendedUnivariate extended_random_polynomial; - // Random poly R(X) = (1-X) + X.zeta_pow - if (pow_univariate.has_value()) { - auto random_polynomial = Univariate({ 1, pow_univariate.value().zeta_pow }); - extended_random_polynomial = random_polynomial.template extend_to(); - } - - auto extend_and_sum = [&](Element& element) { - auto extended = element.template extend_to(); - - using Relation = typename std::tuple_element_t; - const bool is_subrelation_linearly_independent = - proof_system::subrelation_is_linearly_independent(); - // Except from the log derivative subrelation, each other subrelation in part is required to be 0 hence we - // multiply by the power polynomial. As the sumcheck prover is required to send a univariate to the - // verifier, we additionally need a univariate contribution from the pow polynomial. - if (!is_subrelation_linearly_independent || !pow_univariate.has_value()) { - result += extended; - } else { - result += extended * extended_random_polynomial; - } - }; - apply_to_tuple_of_tuples(tuple, extend_and_sum); - } - - /** - * @brief Given a tuple t = (t_0, t_1, ..., t_{NUM_RELATIONS-1}) and a challenge α, - * return t_0 + αt_1 + ... + α^{NUM_RELATIONS-1}t_{NUM_RELATIONS-1}). - */ - template - static ExtendedUnivariate batch_over_relations( - ContainerOverSubrelations& univariate_accumulators, - const FF& challenge, - const std::optional>& pow_univariate = std::nullopt) - { - FF running_challenge = 1; - scale_univariates(univariate_accumulators, challenge, running_challenge); - - auto result = ExtendedUnivariate(0); - extend_and_batch_univariates(univariate_accumulators, result, pow_univariate); - - // Reset all univariate accumulators to 0 before beginning accumulation in the next round - zero_univariates(univariate_accumulators); - return result; - } - /** * @brief Calculate the contribution of each relation to the expected value of the full Honk relation. * diff --git a/barretenberg/cpp/src/barretenberg/smt_verification/README.md b/barretenberg/cpp/src/barretenberg/smt_verification/README.md index 740b01b1479..fdb6d1c2072 100644 --- a/barretenberg/cpp/src/barretenberg/smt_verification/README.md +++ b/barretenberg/cpp/src/barretenberg/smt_verification/README.md @@ -3,7 +3,7 @@ As for now it's required to build cvc5 library manually. + ### Dependencies diff --git a/docs/internal_notes/dev_docs/sandbox/components.md b/docs/internal_notes/dev_docs/sandbox/components.md index 313eff2e1d0..509e851da71 100644 --- a/docs/internal_notes/dev_docs/sandbox/components.md +++ b/docs/internal_notes/dev_docs/sandbox/components.md @@ -239,7 +239,7 @@ Implements: Implementation notes for this milestone: -Closest analogous component in AC is the `WorldStateDb` in bb.js. We can configure the backing store (probably leveldb) to be an in-memory only store. We don't need persistence, we will rebuild the tree at startup. This will ensure we have appropriate sync-from-zero behaviour. +Closest analogous component in AC is the `WorldStateDb` in bb.js. We can configure the backing store (probably leveldb) to be an in-memory only store. We don't need persistence, we will rebuild the tree at startup. This will ensure we have appropriate sync-from-zero behavior. Responsibilities: @@ -279,7 +279,7 @@ Implements: Responsibilities: - Pulls data in from whatever sources are needed, to fully describe a rollup e.g. - - L1 calldata (`provessRollup` and `offchainData` for this milestone.) + - L1 calldata (`provesRollup` and `offchainData` for this milestone.) - ETH blobs (not before they are released). - Other sources that have archived historical ETH blobs (not before they are released). - Combines these sources of data to describe a `Rollup`. diff --git a/docs/sidebars.js b/docs/sidebars.js index 9616058d7d6..5dcccb9c8d1 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -347,10 +347,10 @@ const sidebars = { { label: "Common Patterns", type: "category", - // link: { - // type: "doc", - // id: "dev_docs/contracts/resources/common_patterns/main", - // }, + link: { + type: "doc", + id: "dev_docs/contracts/resources/common_patterns/main", + }, items: [ "dev_docs/contracts/resources/common_patterns/authwit", // "dev_docs/contracts/resources/common_patterns/sending_tokens_to_user", diff --git a/l1-contracts/GUIDE_LINES.md b/l1-contracts/GUIDE_LINES.md index 650c09befbf..e62096e2bab 100644 --- a/l1-contracts/GUIDE_LINES.md +++ b/l1-contracts/GUIDE_LINES.md @@ -39,7 +39,7 @@ The spec should outline the dependencies of the contract (other protocols and to ## 2. Writing a reference implementation A reference implementation is a practical instantiation of the spec that should strive to maximize readability. -It should make it very clear what the purpose is, and how it acheives its goal, meaning that no heavy optimizations should be made. The reference is simply to be used as a good way to understand the code, and to build tests for. To make assessing the coverage of the code easier, ternary operators should be avoid, e.g., instead of `a > b ? a : b` please use explicit `if-else` branching. +It should make it very clear what the purpose is, and how it achieves its goal, meaning that no heavy optimizations should be made. The reference is simply to be used as a good way to understand the code, and to build tests for. To make assessing the coverage of the code easier, ternary operators should be avoid, e.g., instead of `a > b ? a : b` please use explicit `if-else` branching. Having a reference implementation, that can later be optimized, should also make it easier for auditors and external parties to give a first look at the protocol, e.g., if there is a flaw in the business logic, it should be possible to catch at this point. ## 3. Writing tests @@ -80,7 +80,7 @@ Before writing the optimized implementation, the tests should be extended to the - Unchecked math for increment in values where it is known that it will never overflow. ### 7.2 Gas-comparison for reference / optimized -For each of the changes that are made, please add an estimate on the gas savings. If gas is saved and it don't significantly increase the code-size or heavily denegate the readability +For each of the changes that are made, please add an estimate on the gas savings. If gas is saved and it doesn't significantly increase the code-size or heavily degrades the readability ## Reviews There is a couple of different angles when reviewing smart contracts: diff --git a/l1-contracts/README.md b/l1-contracts/README.md index a664f9506e2..cacd6087454 100644 --- a/l1-contracts/README.md +++ b/l1-contracts/README.md @@ -33,7 +33,7 @@ We use `forge fmt` to format. But follow a few general guidelines beyond the sta ## Contracts: -The contracts are in a very early stage, and don't worry about gas costs right now. Instead they prioritise development velocity. +The contracts are in a very early stage, and don't worry about gas costs right now. Instead they prioritize development velocity. ### Decoder diff --git a/l1-contracts/src/core/Rollup.sol b/l1-contracts/src/core/Rollup.sol index ca03d1cf753..341fd34b3da 100644 --- a/l1-contracts/src/core/Rollup.sol +++ b/l1-contracts/src/core/Rollup.sol @@ -28,7 +28,7 @@ contract Rollup is IRollup { bytes32 public rollupStateHash; uint256 public lastBlockTs; - // Tracks the last time time was warped on L2 ("warp" is the testing cheatocde). + // Tracks the last time time was warped on L2 ("warp" is the testing cheatcode). // See https://github.com/AztecProtocol/aztec-packages/issues/1614 uint256 public lastWarpedBlockTs; diff --git a/l1-contracts/src/core/libraries/Decoder.sol b/l1-contracts/src/core/libraries/Decoder.sol index 37aa828a3a9..ccc7d0cf677 100644 --- a/l1-contracts/src/core/libraries/Decoder.sol +++ b/l1-contracts/src/core/libraries/Decoder.sol @@ -99,7 +99,7 @@ library Decoder { // The size of the block header elements uint256 private constant TREES_BLOCK_HEADER_SIZE = 0xd4; - // Where the end of trees metadata begns in the block + // Where the end of trees metadata begins in the block uint256 private constant END_TREES_BLOCK_HEADER_OFFSET = START_TREES_BLOCK_HEADER_OFFSET + TREES_BLOCK_HEADER_SIZE; diff --git a/l1-contracts/test/portals/TokenPortal.t.sol b/l1-contracts/test/portals/TokenPortal.t.sol index aeb110fb830..ae02336b3df 100644 --- a/l1-contracts/test/portals/TokenPortal.t.sol +++ b/l1-contracts/test/portals/TokenPortal.t.sol @@ -2,7 +2,7 @@ pragma solidity >=0.8.18; import "forge-std/Test.sol"; -// Rollup Proccessor +// Rollup Processor import {Rollup} from "../../src/core/Rollup.sol"; import {Inbox} from "../../src/core/messagebridge/Inbox.sol"; import {Registry} from "../../src/core/messagebridge/Registry.sol"; diff --git a/l1-contracts/test/portals/UniswapPortal.sol b/l1-contracts/test/portals/UniswapPortal.sol index 75e32f22c43..8fce7fbed67 100644 --- a/l1-contracts/test/portals/UniswapPortal.sol +++ b/l1-contracts/test/portals/UniswapPortal.sol @@ -50,7 +50,7 @@ contract UniswapPortal { * @param _amountOutMinimum - The minimum amount of output assets to receive from the swap (slippage protection) * @param _aztecRecipient - The aztec address to receive the output assets * @param _secretHashForL1ToL2Message - The hash of the secret consumable message. The hash should be 254 bits (so it can fit in a Field element) - * @param _deadlineForL1ToL2Message - deadline for when the L1 to L2 message (to mint outpiut assets in L2) must be consumed by + * @param _deadlineForL1ToL2Message - deadline for when the L1 to L2 message (to mint output assets in L2) must be consumed by * @param _canceller - The ethereum address that can cancel the deposit * @param _withCaller - When true, using `msg.sender` as the caller, otherwise address(0) * @return The entryKey of the deposit transaction in the Inbox @@ -144,7 +144,7 @@ contract UniswapPortal { * @param _amountOutMinimum - The minimum amount of output assets to receive from the swap (slippage protection) * @param _secretHashForRedeemingMintedNotes - The hash of the secret to redeem minted notes privately on Aztec. The hash should be 254 bits (so it can fit in a Field element) * @param _secretHashForL1ToL2Message - The hash of the secret consumable message. The hash should be 254 bits (so it can fit in a Field element) - * @param _deadlineForL1ToL2Message - deadline for when the L1 to L2 message (to mint outpiut assets in L2) must be consumed by + * @param _deadlineForL1ToL2Message - deadline for when the L1 to L2 message (to mint output assets in L2) must be consumed by * @param _canceller - The ethereum address that can cancel the deposit * @param _withCaller - When true, using `msg.sender` as the caller, otherwise address(0) * @return The entryKey of the deposit transaction in the Inbox diff --git a/l1-contracts/test/portals/UniswapPortal.t.sol b/l1-contracts/test/portals/UniswapPortal.t.sol index 0e769074408..3605e213d03 100644 --- a/l1-contracts/test/portals/UniswapPortal.t.sol +++ b/l1-contracts/test/portals/UniswapPortal.t.sol @@ -2,7 +2,7 @@ pragma solidity >=0.8.18; import "forge-std/Test.sol"; -// Rollup Proccessor +// Rollup Processor import {Rollup} from "../../src/core/Rollup.sol"; import {Inbox} from "../../src/core/messagebridge/Inbox.sol"; import {Registry} from "../../src/core/messagebridge/Registry.sol"; @@ -269,7 +269,7 @@ contract UniswapPortalTest is Test { assertFalse(outbox.contains(swapMsgKey)); } - function testSwapCalledbyAnyoneIfDesignatedCallerNotSet(address _caller) public { + function testSwapCalledByAnyoneIfDesignatedCallerNotSet(address _caller) public { vm.assume(_caller != address(uniswapPortal)); bytes32 daiWithdrawMsgKey = _createDaiWithdrawMessage(address(uniswapPortal), address(uniswapPortal)); diff --git a/noir/.envrc b/noir/.envrc new file mode 100644 index 00000000000..b2f868b1898 --- /dev/null +++ b/noir/.envrc @@ -0,0 +1,20 @@ +# Based on https://github.com/direnv/direnv-vscode/blob/158e8302c2594cc0eaa5f8b4f0cafedd4e1c0315/.envrc + +# You can define your system-specific logic (like Git settings or GH tokens) in .envrc.local +# If that logic is usable by other people and might improve development environment, consider +# contributing it to this file! + +source_env_if_exists .envrc.local + +if [[ -z "${SKIP_NIX:-}" ]] && has nix; then + + if nix flake metadata &>/dev/null && has use_flake; then + # use flakes if possible + use flake + + else + # Otherwise fall back to pure nix + use nix + fi + +fi diff --git a/noir/.eslintrc.js b/noir/.eslintrc.js new file mode 100644 index 00000000000..f47f171561c --- /dev/null +++ b/noir/.eslintrc.js @@ -0,0 +1,19 @@ +module.exports = { + root: true, + parser: '@typescript-eslint/parser', + plugins: ['@typescript-eslint', 'prettier'], + extends: ['eslint:recommended', 'plugin:@typescript-eslint/recommended'], + rules: { + 'comma-spacing': ['error', { before: false, after: true }], + 'no-unused-vars': 'off', + '@typescript-eslint/no-unused-vars': [ + 'warn', // or "error" + { + argsIgnorePattern: '^_', + varsIgnorePattern: '^_', + caughtErrorsIgnorePattern: '^_', + }, + ], + 'prettier/prettier': 'error', + }, +}; diff --git a/noir/.gitattributes b/noir/.gitattributes new file mode 100644 index 00000000000..204cff5c097 --- /dev/null +++ b/noir/.gitattributes @@ -0,0 +1 @@ +*.nr linguist-language=rust diff --git a/noir/.github/Cross.toml b/noir/.github/Cross.toml new file mode 100644 index 00000000000..09e6316a59c --- /dev/null +++ b/noir/.github/Cross.toml @@ -0,0 +1,9 @@ +[build.env] +passthrough = [ + "HOME", + "RUST_BACKTRACE", + "BARRETENBERG_BIN_DIR" +] +volumes = [ + "HOME", +] diff --git a/noir/.github/ISSUE_TEMPLATE/bug_report.yml b/noir/.github/ISSUE_TEMPLATE/bug_report.yml new file mode 100644 index 00000000000..112da342e10 --- /dev/null +++ b/noir/.github/ISSUE_TEMPLATE/bug_report.yml @@ -0,0 +1,99 @@ +name: Bug Report +description: Report an unexpected behavior. +labels: ["bug"] +body: + - type: markdown + attributes: + value: | + # Description + Thanks for taking the time to create the Issue, and welcome to the Noirot family! + - type: textarea + id: aim + attributes: + label: Aim + description: Describe what you tried to achieve. + validations: + required: true + - type: textarea + id: expected + attributes: + label: Expected Behavior + description: Describe what you expected to happen. + validations: + required: true + - type: textarea + id: bug + attributes: + label: Bug + description: Describe the bug. Supply error codes / terminal logs if applicable. + validations: + required: true + - type: textarea + id: reproduction + attributes: + label: To Reproduce + description: Describe the steps to reproduce the behavior. + value: | + 1. + 2. + 3. + 4. + - type: markdown + attributes: + value: | + # Environment + Specify your versions of Noir releases used. + - type: markdown + attributes: + value: | + ## Using Nargo? + - type: dropdown + id: nargo-install + attributes: + label: Installation Method + description: How did you install Nargo? + multiple: false + options: + - Binary + - Compiled from source + - type: input + id: nargo-version + attributes: + label: Nargo Version + description: What is the output of the `nargo --version` command? + placeholder: "nargo 0.6.0 (git version hash: 0181813203a9e3e46c6d8c3169ad5d25971d4282, is dirty: false)" + - type: markdown + attributes: + value: | + ## Using TypeScript? + Please await for our new set of packages. + You can find our target release timeframe on the [Noir Roadmap](https://github.com/orgs/noir-lang/projects/1/views/16). + - type: markdown + attributes: + value: | + # Misc + - type: textarea + id: additional + attributes: + label: Additional Context + description: Supplement further information if applicable. + - type: markdown + attributes: + value: | + # Pull Request + - type: dropdown + id: pr_preference + attributes: + label: Would you like to submit a PR for this Issue? + description: Fellow contributors are happy to provide support where applicable. + options: + - "No" + - "Maybe" + - "Yes" + validations: + required: true + - type: textarea + id: pr_support + attributes: + label: Support Needs + description: Support from other contributors you are looking for to create a PR for this Issue. diff --git a/noir/.github/ISSUE_TEMPLATE/config.yml b/noir/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 00000000000..b5ded344eb9 --- /dev/null +++ b/noir/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,4 @@ +contact_links: + - name: Ideas + url: https://github.com/orgs/noir-lang/discussions/new?category=ideas + about: Share ideas for new features diff --git a/noir/.github/ISSUE_TEMPLATE/idea_action_plan.yml b/noir/.github/ISSUE_TEMPLATE/idea_action_plan.yml new file mode 100644 index 00000000000..02fed1fc48b --- /dev/null +++ b/noir/.github/ISSUE_TEMPLATE/idea_action_plan.yml @@ -0,0 +1,54 @@ +name: Idea Action Plan +description: Outline the scope and steps for implementing an enhancement. Start with "Ideas" instead to request and discuss new features. +labels: ["enhancement"] +body: + - type: markdown + attributes: + value: | + ## Description + Thanks for taking the time to create the Issue, and welcome to the Noirot family! + - type: textarea + id: problem + attributes: + label: Problem + description: Describe what you feel lacking. Supply code / step-by-step examples if applicable. + validations: + required: true + - type: textarea + id: solution + attributes: + label: Happy Case + description: Describe how you think it should work. Supply pseudocode / step-by-step examples if applicable. + validations: + required: true + - type: textarea + id: alternatives + attributes: + label: Alternatives Considered + description: Describe less-happy cases you have considered, if any. + - type: textarea + id: additional + attributes: + label: Additional Context + description: Supplement further information if applicable. + - type: markdown + attributes: + value: | + ## Pull Request + - type: dropdown + id: pr-preference + attributes: + label: Would you like to submit a PR for this Issue? + description: Fellow contributors are happy to provide support where applicable. + multiple: false + options: + - "No" + - "Maybe" + - "Yes" + validations: + required: true + - type: textarea + id: pr-support + attributes: + label: Support Needs + description: Support from other contributors you are looking for to create a PR for this Issue. diff --git a/noir/.github/NIGHTLY_TEST_FAILURE.md b/noir/.github/NIGHTLY_TEST_FAILURE.md new file mode 100644 index 00000000000..05772d82a51 --- /dev/null +++ b/noir/.github/NIGHTLY_TEST_FAILURE.md @@ -0,0 +1,11 @@ +--- +title: "nightly test-integration failed" +assignees: kobyhallx, tomafrench, jonybur +labels: bug +--- + +Something broke our nightly integration test. + +Check the [test]({{env.WORKFLOW_URL}}) workflow for details. + +This issue was raised by the workflow `{{env.WORKFLOW_NAME}}` diff --git a/noir/.github/actions/docs/build-status/action.yml b/noir/.github/actions/docs/build-status/action.yml new file mode 100644 index 00000000000..8206dd64776 --- /dev/null +++ b/noir/.github/actions/docs/build-status/action.yml @@ -0,0 +1,22 @@ +name: 'Get build status' +description: 'Gets the build status of a Netlify site' +inputs: + branch-name: + description: 'Branch name' + required: true + site-id: + description: Netlify site id + required: true +outputs: + deploy_status: + description: "The deploy status" + value: ${{ steps.check_deploy_status.outputs.deploy_status }} +runs: + using: "composite" + steps: + - run: ${{ github.action_path }}/script.sh + shell: bash + id: check_deploy_status + env: + BRANCH_NAME: ${{ inputs.branch-name }} + SITE_ID: ${{ inputs.site-id }} diff --git a/noir/.github/actions/docs/build-status/script.sh b/noir/.github/actions/docs/build-status/script.sh new file mode 100755 index 00000000000..0b282557cf2 --- /dev/null +++ b/noir/.github/actions/docs/build-status/script.sh @@ -0,0 +1,28 @@ +#!/bin/bash + +BRANCH_NAME=$(echo "$BRANCH_NAME" | sed -e "s#refs/[^/]*/##") +DEPLOY_STATUS=$(curl -X GET "https://api.netlify.com/api/v1/sites/$SITE_ID/deploys?branch=$BRANCH_NAME" | jq -r '.[] | select(.created_at != null) | .state' | head -1) + +echo "$SITE_ID" +MAX_RETRIES=10 +COUNT=0 +while [[ "$DEPLOY_STATUS" != "ready" && $COUNT -lt $MAX_RETRIES ]]; do + sleep 20 + DEPLOY_STATUS=$(curl -X GET "https://api.netlify.com/api/v1/sites/$SITE_ID/deploys?branch=$BRANCH_NAME" | jq -r '.[] | select(.created_at != null) | .state' | head -1) + COUNT=$((COUNT+1)) + + echo "Deploy status: $DEPLOY_STATUS" + # If deploy status is ready, set the output and exit successfully + if [[ "$DEPLOY_STATUS" == "ready" ]]; then + echo "deploy_status=success" >> $GITHUB_OUTPUT + exit 0 + elif [[ "$DEPLOY_STATUS" == "error" ]]; then + echo "deploy_status=failure" >> $GITHUB_OUTPUT + exit 1 + fi + + echo "Deploy still running. Retrying..." +done + +echo "deploy_status=failure" >> $GITHUB_OUTPUT +exit 1 diff --git a/noir/.github/actions/install-playwright/action.yml b/noir/.github/actions/install-playwright/action.yml new file mode 100644 index 00000000000..ac412a7dd4a --- /dev/null +++ b/noir/.github/actions/install-playwright/action.yml @@ -0,0 +1,24 @@ +name: Install Playwright +description: Installs Playwright and its dependencies and caches them. + +runs: + using: composite + steps: + - name: Query playwright version + shell: bash + run: echo "PLAYWRIGHT_VERSION=$(yarn workspace @noir-lang/noirc_abi info @web/test-runner-playwright --json | jq .children.Version | tr -d '"')" >> $GITHUB_ENV + + - name: Cache playwright binaries + uses: actions/cache@v3 + id: playwright-cache + with: + path: | + ~/.cache/ms-playwright + key: ${{ runner.os }}-playwright-${{ env.PLAYWRIGHT_VERSION }} + + - name: Install playwright deps + shell: bash + if: steps.playwright-cache.outputs.cache-hit != 'true' + run: | + npx playwright install + npx playwright install-deps diff --git a/noir/.github/actions/nix/action.yml b/noir/.github/actions/nix/action.yml new file mode 100644 index 00000000000..9f008ad0f9d --- /dev/null +++ b/noir/.github/actions/nix/action.yml @@ -0,0 +1,27 @@ +name: Setup Nix +description: Installs and setups Nix components + +inputs: + github-token: + description: 'Github Access Token' + required: true + nix-cache-name: + description: 'Name of the Cachix cache to use' + required: true + cachix-auth-token: + description: 'Cachix Auth Token' + required: true + + +runs: + using: composite + steps: + - uses: cachix/install-nix-action@v22 + with: + nix_path: nixpkgs=channel:nixos-23.05 + github_access_token: ${{ inputs.github-token }} + + - uses: cachix/cachix-action@v12 + with: + name: ${{ inputs.nix-cache-name }} + authToken: ${{ inputs.cachix-auth-token }} diff --git a/noir/.github/actions/setup/action.yml b/noir/.github/actions/setup/action.yml new file mode 100644 index 00000000000..5efe115ddcf --- /dev/null +++ b/noir/.github/actions/setup/action.yml @@ -0,0 +1,16 @@ +name: Install Yarn dependencies +description: Installs the workspace's yarn dependencies and caches them + +runs: + using: composite + steps: + - name: Cache + uses: actions/cache@v3 + id: cache + with: + path: "**/node_modules" + key: yarn-v1-${{ hashFiles('**/yarn.lock') }} + - name: Install + run: yarn --immutable + shell: bash + if: steps.cache.outputs.cache-hit != 'true' diff --git a/noir/.github/pull_request_template.md b/noir/.github/pull_request_template.md new file mode 100644 index 00000000000..e81ede7199d --- /dev/null +++ b/noir/.github/pull_request_template.md @@ -0,0 +1,25 @@ +# Description + +## Problem\* + +Resolves + +## Summary\* + + + +## Additional Context + + + +## Documentation\* + +Check one: +- [ ] No documentation needed. +- [ ] Documentation included in this PR. +- [ ] **[Exceptional Case]** Documentation to be submitted in a separate PR. + +# PR Checklist\* + +- [ ] I have tested the changes locally. +- [ ] I have formatted the changes with [Prettier](https://prettier.io/) and/or `cargo fmt` on default settings. diff --git a/noir/.github/workflows/auto-pr-rebuild-script.yml b/noir/.github/workflows/auto-pr-rebuild-script.yml new file mode 100644 index 00000000000..4937de98e04 --- /dev/null +++ b/noir/.github/workflows/auto-pr-rebuild-script.yml @@ -0,0 +1,130 @@ +name: Rebuild ACIR artifacts + +on: + pull_request: + push: + branches: + - master + +jobs: + check-artifacts-requested: + name: Check if artifacts should be published + runs-on: ubuntu-22.04 + outputs: + publish: ${{ steps.check.outputs.result }} + + steps: + - name: Check if artifacts should be published + id: check + uses: actions/github-script@v6 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + script: | + const { REF_NAME } = process.env; + if (REF_NAME == "master") { + console.log(`publish = true`) + return true; + } + + const labels = context.payload.pull_request.labels.map(label => label.name); + const publish = labels.includes('publish-acir'); + + console.log(`publish = ${publish}`) + return publish; + result-encoding: string + env: + REF_NAME: ${{ github.ref_name }} + + build-nargo: + name: Build nargo binary + if: ${{ needs.check-artifacts-requested.outputs.publish == 'true' }} + runs-on: ubuntu-22.04 + needs: [check-artifacts-requested] + strategy: + matrix: + target: [x86_64-unknown-linux-gnu] + + steps: + - name: Checkout Noir repo + uses: actions/checkout@v4 + + - name: Setup toolchain + uses: dtolnay/rust-toolchain@1.71.1 + + - uses: Swatinem/rust-cache@v2 + with: + key: ${{ matrix.target }} + cache-on-failure: true + save-if: ${{ github.event_name != 'merge_group' }} + + - name: Build Nargo + run: cargo build --package nargo_cli --release + + - name: Package artifacts + run: | + mkdir dist + cp ./target/release/nargo ./dist/nargo + 7z a -ttar -so -an ./dist/* | 7z a -si ./nargo-x86_64-unknown-linux-gnu.tar.gz + + - name: Upload artifact + uses: actions/upload-artifact@v3 + with: + name: nargo + path: ./dist/* + retention-days: 3 + + auto-pr-rebuild-script: + name: Rebuild ACIR artifacts + needs: [build-nargo] + runs-on: ubuntu-latest + + steps: + - name: Check out code + uses: actions/checkout@v4 + + - name: Download nargo binary + uses: actions/download-artifact@v3 + with: + name: nargo + path: ./nargo + + - name: Add Nargo to $PATH + run: | + chmod +x ${{ github.workspace }}/nargo/nargo + echo "${{ github.workspace }}/nargo" >> $GITHUB_PATH + + - name: Set up Git user (Github Action) + run: | + git config --local user.name kevaundray + git config --local user.email kevtheappdev@gmail.com + + - name: Run rebuild script + working-directory: tooling/nargo_cli/tests + run: | + chmod +x ./rebuild.sh + ./rebuild.sh + + - name: Upload ACIR artifacts + uses: actions/upload-artifact@v3 + with: + name: acir-artifacts + path: ./tooling/nargo_cli/tests/acir_artifacts + retention-days: 10 + + - name: Check for changes in acir_artifacts directory + id: check_changes + if: ${{ github.ref_name }} == "master" + run: | + git diff --quiet tooling/nargo_cli/tests/acir_artifacts/ || echo "::set-output name=changes::true" + + - name: Create or Update PR + if: steps.check_changes.outputs.changes == 'true' + uses: peter-evans/create-pull-request@v3 + with: + token: ${{ secrets.NOIR_REPO_TOKEN }} + commit-message: "chore: update acir artifacts" + title: "chore: Update ACIR artifacts" + body: "Automatic PR to update acir artifacts" + add-paths: tooling/nargo_cli/tests/acir_artifacts/*.gz + labels: "auto-pr" + branch: "auto-pr-rebuild-script-branch" diff --git a/noir/.github/workflows/build-aztec-feature-flag.yml b/noir/.github/workflows/build-aztec-feature-flag.yml new file mode 100644 index 00000000000..888a88a7f88 --- /dev/null +++ b/noir/.github/workflows/build-aztec-feature-flag.yml @@ -0,0 +1,45 @@ +name: Build with aztec feature flag + +on: + pull_request: + merge_group: + push: + branches: + - master + +# This will cancel previous runs when a branch or PR is updated +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.ref || github.run_id }} + cancel-in-progress: true + +jobs: + build-aztec-feature-flag: + name: Test on ${{ matrix.os }} + runs-on: ${{ matrix.runner }} + timeout-minutes: 30 + + strategy: + fail-fast: false + matrix: + include: + - os: ubuntu + runner: ubuntu-latest + target: x86_64-unknown-linux-gnu + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup toolchain + uses: dtolnay/rust-toolchain@1.71.1 + with: + targets: ${{ matrix.target }} + + - uses: Swatinem/rust-cache@v2 + with: + key: ${{ matrix.target }} + cache-on-failure: true + save-if: ${{ github.event_name != 'merge_group' }} + + - name: Build with feature flag + run: cargo build --features="noirc_frontend/aztec" diff --git a/noir/.github/workflows/cache-cleanup.yml b/noir/.github/workflows/cache-cleanup.yml new file mode 100644 index 00000000000..cf2b0ec413e --- /dev/null +++ b/noir/.github/workflows/cache-cleanup.yml @@ -0,0 +1,35 @@ +# This workflow cleans up any cache entries associated with a pull request once it has been closed. +# This prevents us from having many refs/pull/PR_NUMBER/merge cache entries which will never be used. +# +# Note that this will affect both PRs being closed with and without being merged. + +name: Cleanup closed PR cache entries + +on: + pull_request: + types: + - closed + +jobs: + cleanup: + runs-on: ubuntu-latest + steps: + - name: Cleanup + run: | + gh extension install actions/gh-actions-cache + + echo "Fetching list of cache key" + cacheKeysForPR=$(gh actions-cache list -R $REPO -B $BRANCH -L 100 | cut -f 1 ) + + ## Setting this to not fail the workflow while deleting cache keys. + set +e + echo "Deleting caches..." + for cacheKey in $cacheKeysForPR + do + gh actions-cache delete $cacheKey -R $REPO -B $BRANCH --confirm + done + echo "Done" + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + REPO: ${{ github.repository }} + BRANCH: refs/pull/${{ github.event.pull_request.number }}/merge diff --git a/noir/.github/workflows/deny.yml b/noir/.github/workflows/deny.yml new file mode 100644 index 00000000000..8ae7d03e076 --- /dev/null +++ b/noir/.github/workflows/deny.yml @@ -0,0 +1,26 @@ +name: deny + +on: + push: + branches: [master] + paths: [Cargo.lock] + pull_request: + branches: [master] + paths: [Cargo.lock] + merge_group: + +env: + RUSTFLAGS: -D warnings + CARGO_TERM_COLOR: always + +concurrency: deny-${{ github.head_ref || github.run_id }} + +jobs: + deny: + name: deny + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: EmbarkStudios/cargo-deny-action@v1 + with: + command: check all \ No newline at end of file diff --git a/noir/.github/workflows/docs-new-version.yml b/noir/.github/workflows/docs-new-version.yml new file mode 100644 index 00000000000..9b109e170bb --- /dev/null +++ b/noir/.github/workflows/docs-new-version.yml @@ -0,0 +1,112 @@ +name: Cut a new version of the docs + +on: + workflow_dispatch: + inputs: + tag: + description: The tag to build Docs for + required: false + +jobs: + publish-docs: + runs-on: ubuntu-latest + if: ${{ inputs.tag != '' }} + permissions: + pull-requests: write + contents: write + steps: + - name: Checkout sources + uses: actions/checkout@v4 + with: + ref: ${{ inputs.tag }} + + - name: Create new branch + run: | + git checkout -b new-docs-version-${{ github.event.inputs.tag }} + + - name: Setup Node.js + uses: actions/setup-node@v2 + with: + node-version: '18' + + - name: Install wasm-bindgen-cli + uses: taiki-e/install-action@v2 + with: + tool: wasm-bindgen-cli@0.2.86 + + - name: Install wasm-opt + run: | + npm i wasm-opt -g + + - name: Install Yarn + run: npm install -g yarn + + - name: Install Yarn dependencies + run: yarn + + - name: Build acvm_js + run: yarn workspace @noir-lang/acvm_js build + + - name: Build noirc_abi + run: yarn workspace @noir-lang/noirc_abi build + + - name: Build noir_js_types + run: yarn workspace @noir-lang/types build + + - name: Build barretenberg wrapper + run: yarn workspace @noir-lang/backend_barretenberg build + + - name: Run noir_js + run: | + yarn workspace @noir-lang/noir_js build + + - name: Build docs + run: + yarn workspace docs build + + - name: Cut a new version + working-directory: ./docs + run: yarn docusaurus docs:version ${{ inputs.tag }} + + - name: Remove pre-releases + id: get_version + run: | + cd docs && yarn setStable + + - name: Commit new documentation version + run: | + git config --local user.name 'signorecello' + git config --local user.email 'github@zepedro.me' + git add . + git commit -m "chore(docs): cut new docs version for tag ${{ github.event.inputs.tag }}" + + - name: Push changes to new branch + run: git push origin new-docs-version-${{ github.event.inputs.tag }} + + - name: Create Pull Request + run: | + gh pr create \ + --title "chore(docs): docs for ${{ github.event.inputs.tag }}" \ + --body "Updates documentation to new version for tag ${{ github.event.inputs.tag }}." \ + --base master \ + --head new-docs-version-${{ github.event.inputs.tag }} \ + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + + - name: Build docs + run: yarn workspace docs build + + - name: Deploy to Netlify + uses: nwtgck/actions-netlify@v2.1 + with: + publish-dir: './docs/build' + production-branch: master + production-deploy: true + github-token: ${{ secrets.GITHUB_TOKEN }} + enable-github-deployment: false + deploy-message: "Deploy from GitHub Actions for tag ${{ inputs.tag }}" + env: + NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }} + NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }} + timeout-minutes: 1 + diff --git a/noir/.github/workflows/docs-pr.yml b/noir/.github/workflows/docs-pr.yml new file mode 100644 index 00000000000..02044c82224 --- /dev/null +++ b/noir/.github/workflows/docs-pr.yml @@ -0,0 +1,113 @@ +name: Deploy preview for PR + +on: + pull_request: + +jobs: + add_label: + runs-on: ubuntu-latest + outputs: + has_label: ${{ steps.check-labels.outputs.result }} + steps: + - name: Check if label is present + id: check-labels + uses: actions/github-script@v3 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + script: | + const labels = context.payload.pull_request.labels.map(label => label.name); + if (labels.includes('documentation')) { + return true; + } + + // Fetch the list of files changed in the PR + const { data: files } = await github.pulls.listFiles({ + owner: context.repo.owner, + repo: context.repo.repo, + pull_number: context.issue.number + }); + + // Check if any file is within the 'docs' folder + const docsChanged = files.some(file => file.filename.startsWith('docs/')); + return docsChanged; + + - name: Add label if not present + if: steps.check-labels.outputs.result == 'true' + uses: actions/github-script@v3 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + script: | + const labels = context.payload.pull_request.labels.map(label => label.name); + if (!labels.includes('documentation')) { + github.issues.addLabels({ + owner: context.repo.owner, + repo: context.repo.repo, + issue_number: context.issue.number, + labels: ['documentation'] + }) + } + + build_and_deploy_preview: + runs-on: ubuntu-latest + permissions: + pull-requests: write + needs: add_label + if: needs.add_label.outputs.has_label == 'true' + steps: + - name: Checkout code + uses: actions/checkout@v2 + + - name: Setup Node.js + uses: actions/setup-node@v2 + with: + node-version: '18' + + - name: Install wasm-bindgen-cli + uses: taiki-e/install-action@v2 + with: + tool: wasm-bindgen-cli@0.2.86 + + - name: Install wasm-opt + run: | + npm i wasm-opt -g + + - name: Install dependencies + run: yarn + + - name: Build acvm_js + run: yarn workspace @noir-lang/acvm_js build + + - name: Build noirc_abi + run: yarn workspace @noir-lang/noirc_abi build + + - name: Build noir_js_types + run: yarn workspace @noir-lang/types build + + - name: Build barretenberg wrapper + run: yarn workspace @noir-lang/backend_barretenberg build + + - name: Run noir_js + run: | + yarn workspace @noir-lang/noir_js build + + - name: Remove pre-releases + working-directory: docs + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: yarn setStable + + - name: Build docs + run: + yarn workspace docs build + + - name: Deploy to Netlify + uses: nwtgck/actions-netlify@v2.1 + with: + publish-dir: './docs/build' + github-token: ${{ secrets.GITHUB_TOKEN }} + enable-github-deployment: false + deploy-message: "Deploy from GitHub Actions for PR ${{ github.event.number }}" + env: + NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }} + NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }} + timeout-minutes: 1 diff --git a/noir/.github/workflows/docs-release.yml b/noir/.github/workflows/docs-release.yml new file mode 100644 index 00000000000..4cd9d9998cb --- /dev/null +++ b/noir/.github/workflows/docs-release.yml @@ -0,0 +1,72 @@ +name: Rebuild docs with the latest release + +on: + release: + types: [released] + workflow_dispatch: + +jobs: + build_and_deploy: + runs-on: ubuntu-latest + permissions: + pull-requests: write + steps: + - name: Checkout code + uses: actions/checkout@v2 + + - name: Setup Node.js + uses: actions/setup-node@v2 + with: + node-version: '18' + + - name: Install wasm-bindgen-cli + uses: taiki-e/install-action@v2 + with: + tool: wasm-bindgen-cli@0.2.86 + + - name: Install wasm-opt + run: | + npm i wasm-opt -g + + - name: Install dependencies + run: yarn + + - name: Build acvm_js + run: yarn workspace @noir-lang/acvm_js build + + - name: Build noirc_abi + run: yarn workspace @noir-lang/noirc_abi build + + - name: Build noir_js_types + run: yarn workspace @noir-lang/types build + + - name: Build barretenberg wrapper + run: yarn workspace @noir-lang/backend_barretenberg build + + - name: Run noir_js + run: | + yarn workspace @noir-lang/noir_js build + + - name: Remove pre-releases + working-directory: docs + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: yarn setStable + + - name: Build docs + run: + yarn workspace docs build + + - name: Deploy to Netlify + uses: nwtgck/actions-netlify@v2.1 + with: + publish-dir: './docs/build' + production-branch: master + production-deploy: true + github-token: ${{ secrets.GITHUB_TOKEN }} + enable-github-deployment: false + deploy-message: "Deploy from GitHub Actions for release" + env: + NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }} + NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }} + timeout-minutes: 1 diff --git a/noir/.github/workflows/formatting.yml b/noir/.github/workflows/formatting.yml new file mode 100644 index 00000000000..97736e2415e --- /dev/null +++ b/noir/.github/workflows/formatting.yml @@ -0,0 +1,65 @@ +name: Formatting + +on: + pull_request: + merge_group: + push: + branches: + - master + +# This will cancel previous runs when a branch or PR is updated +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.ref || github.run_id }} + cancel-in-progress: true + +jobs: + clippy: + name: cargo clippy + runs-on: ${{ matrix.runner }} + timeout-minutes: 30 + env: + RUSTFLAGS: -Dwarnings + + strategy: + fail-fast: false + matrix: + include: + - runner: ubuntu-latest + target: x86_64-unknown-linux-gnu + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup toolchain + uses: dtolnay/rust-toolchain@1.71.1 + with: + targets: ${{ matrix.target }} + components: clippy, rustfmt + + - uses: Swatinem/rust-cache@v2 + with: + key: ${{ matrix.target }} + cache-on-failure: true + save-if: ${{ github.event_name != 'merge_group' }} + + - name: Run `cargo clippy` + run: cargo clippy --workspace --locked --release + + - name: Run `cargo fmt` + run: cargo fmt --all --check + + eslint: + name: eslint + runs-on: ubuntu-latest + timeout-minutes: 30 + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Install Yarn dependencies + uses: ./.github/actions/setup + + - name: Run `yarn lint` + run: yarn lint diff --git a/noir/.github/workflows/gates_report.yml b/noir/.github/workflows/gates_report.yml new file mode 100644 index 00000000000..41a68c65852 --- /dev/null +++ b/noir/.github/workflows/gates_report.yml @@ -0,0 +1,90 @@ +name: Report gates diff + +on: + push: + branches: + - master + pull_request: + +jobs: + build-nargo: + runs-on: ubuntu-latest + strategy: + matrix: + target: [x86_64-unknown-linux-gnu] + + steps: + - name: Checkout Noir repo + uses: actions/checkout@v4 + + - name: Setup toolchain + uses: dtolnay/rust-toolchain@1.71.1 + + - uses: Swatinem/rust-cache@v2 + with: + key: ${{ matrix.target }} + cache-on-failure: true + save-if: ${{ github.event_name != 'merge_group' }} + + - name: Build Nargo + run: cargo build --package nargo_cli --release + + - name: Package artifacts + run: | + mkdir dist + cp ./target/release/nargo ./dist/nargo + 7z a -ttar -so -an ./dist/* | 7z a -si ./nargo-x86_64-unknown-linux-gnu.tar.gz + + - name: Upload artifact + uses: actions/upload-artifact@v3 + with: + name: nargo + path: ./dist/* + retention-days: 3 + + + compare_gas_reports: + needs: [build-nargo] + runs-on: ubuntu-latest + permissions: + pull-requests: write + + steps: + - uses: actions/checkout@v3 + with: + submodules: recursive + + - name: Download nargo binary + uses: actions/download-artifact@v3 + with: + name: nargo + path: ./nargo + + - name: Set nargo on PATH + run: | + nargo_binary="${{ github.workspace }}/nargo/nargo" + chmod +x $nargo_binary + echo "$(dirname $nargo_binary)" >> $GITHUB_PATH + export PATH="$PATH:$(dirname $nargo_binary)" + nargo -V + + - name: Generate gates report + working-directory: ./tooling/nargo_cli/tests + run: | + ./gates_report.sh + mv gates_report.json ../../../gates_report.json + + - name: Compare gates reports + id: gates_diff + uses: TomAFrench/noir-gates-diff@e7cf131b7e7f044c01615f93f0b855f65ddc02d4 + with: + report: gates_report.json + summaryQuantile: 0.9 # only display the 10% most significant circuit size diffs in the summary (defaults to 20%) + + - name: Add gates diff to sticky comment + if: github.event_name == 'pull_request' || github.event_name == 'pull_request_target' + uses: marocchino/sticky-pull-request-comment@v2 + with: + # delete the comment in case changes no longer impact circuit sizes + delete: ${{ !steps.gates_diff.outputs.markdown }} + message: ${{ steps.gates_diff.outputs.markdown }} diff --git a/noir/.github/workflows/lockfile.yml b/noir/.github/workflows/lockfile.yml new file mode 100644 index 00000000000..190e01745af --- /dev/null +++ b/noir/.github/workflows/lockfile.yml @@ -0,0 +1,23 @@ +name: Lockfile check + +on: + pull_request: + +# This will cancel previous runs when a branch or PR is updated +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.ref || github.run_id }} + cancel-in-progress: true + +jobs: + yarn-lock: + runs-on: ubuntu-latest + timeout-minutes: 30 + + steps: + - name: Checkout + uses: actions/checkout@v4 + + # Errors if installation would result in modifications to yarn.lock + - name: Install + run: yarn --immutable + shell: bash diff --git a/noir/.github/workflows/publish-acvm.yml b/noir/.github/workflows/publish-acvm.yml new file mode 100644 index 00000000000..ded669f13d0 --- /dev/null +++ b/noir/.github/workflows/publish-acvm.yml @@ -0,0 +1,70 @@ +name: Publish ACVM crates + +on: + workflow_dispatch: + inputs: + acvm-ref: + description: The acvm reference to checkout + required: true + +jobs: + publish: + name: Publish in order + runs-on: ubuntu-latest + steps: + - name: Checkout sources + uses: actions/checkout@v4 + with: + ref: ${{ inputs.acvm-ref }} + + - name: Setup toolchain + uses: dtolnay/rust-toolchain@1.71.1 + + # These steps are in a specific order so crate dependencies are updated first + - name: Publish acir_field + run: | + cargo publish --package acir_field + env: + CARGO_REGISTRY_TOKEN: ${{ secrets.ACVM_CRATES_IO_TOKEN }} + + - name: Publish brillig + run: | + cargo publish --package brillig + env: + CARGO_REGISTRY_TOKEN: ${{ secrets.ACVM_CRATES_IO_TOKEN }} + + - name: Publish acir + run: | + cargo publish --package acir + env: + CARGO_REGISTRY_TOKEN: ${{ secrets.ACVM_CRATES_IO_TOKEN }} + + - name: Publish acvm_blackbox_solver + run: | + cargo publish --package acvm_blackbox_solver + env: + CARGO_REGISTRY_TOKEN: ${{ secrets.ACVM_CRATES_IO_TOKEN }} + + - name: Publish barretenberg_blackbox_solver + run: | + cargo publish --package barretenberg_blackbox_solver + env: + CARGO_REGISTRY_TOKEN: ${{ secrets.ACVM_CRATES_IO_TOKEN }} + + - name: Publish acvm_stdlib + run: | + cargo publish --package acvm_stdlib + env: + CARGO_REGISTRY_TOKEN: ${{ secrets.ACVM_CRATES_IO_TOKEN }} + + - name: Publish brillig_vm + run: | + cargo publish --package brillig_vm + env: + CARGO_REGISTRY_TOKEN: ${{ secrets.ACVM_CRATES_IO_TOKEN }} + + - name: Publish acvm + run: | + cargo publish --package acvm + env: + CARGO_REGISTRY_TOKEN: ${{ secrets.ACVM_CRATES_IO_TOKEN }} diff --git a/noir/.github/workflows/publish-es-packages.yml b/noir/.github/workflows/publish-es-packages.yml new file mode 100644 index 00000000000..2e88ee2b77f --- /dev/null +++ b/noir/.github/workflows/publish-es-packages.yml @@ -0,0 +1,139 @@ +name: Publish Noir ES Packages + +on: + workflow_dispatch: + inputs: + noir-ref: + description: The noir reference to checkout + required: false + default: 'master' + npm-tag: + description: Repository Tag to publish under + required: false + default: 'nightly' + +run-name: Publish ES Packages from ${{ inputs.noir-ref }} under @${{ inputs.npm-tag }} tag. + +jobs: + build-noir_wasm: + runs-on: ubuntu-latest + steps: + - name: Checkout sources + uses: actions/checkout@v4 + with: + ref: ${{ inputs.noir-ref }} + + - name: Setup Nix + uses: ./.github/actions/nix + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + nix-cache-name: "noir" + cachix-auth-token: ${{ secrets.CACHIXAUTHTOKEN }} + + - name: Enable aztec features + if: ${{ inputs.npm-tag == 'aztec' }} + run: | + echo $'\n'"default = [\"aztec\"]"$'\n' >> compiler/noirc_frontend/Cargo.toml + + - name: Build wasm package + run: | + nix build -L .#noir_wasm + + - uses: actions/upload-artifact@v3 + with: + name: noir_wasm + path: | + result/noir_wasm/nodejs + result/noir_wasm/web + + build-noirc_abi_wasm: + runs-on: ubuntu-latest + steps: + - name: Checkout sources + uses: actions/checkout@v4 + with: + ref: ${{ inputs.noir-ref }} + + - name: Setup Nix + uses: ./.github/actions/nix + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + nix-cache-name: "noir" + cachix-auth-token: ${{ secrets.CACHIXAUTHTOKEN }} + + - name: Build wasm package + run: | + nix build -L .#noirc_abi_wasm + + - uses: actions/upload-artifact@v3 + with: + name: noirc_abi_wasm + path: | + result/noirc_abi_wasm/nodejs + result/noirc_abi_wasm/web + + build-acvm_js: + runs-on: ubuntu-latest + steps: + - name: Checkout sources + uses: actions/checkout@v4 + with: + ref: ${{ inputs.noir-ref }} + + - name: Setup Nix + uses: ./.github/actions/nix + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + nix-cache-name: "noir" + cachix-auth-token: ${{ secrets.CACHIXAUTHTOKEN }} + + - name: Build wasm package + run: | + nix build -L .#acvm_js + + - uses: actions/upload-artifact@v3 + with: + name: acvm_js + path: | + result/acvm_js/nodejs + result/acvm_js/web + + publish-es-packages: + runs-on: ubuntu-latest + needs: [build-acvm_js, build-noirc_abi_wasm, build-noir_wasm] + steps: + + - name: Checkout sources + uses: actions/checkout@v4 + with: + ref: ${{ inputs.noir-ref }} + + - uses: actions/download-artifact@v3 + with: + name: acvm_js + path: acvm-repo/acvm_js + - uses: actions/download-artifact@v3 + with: + name: noir_wasm + path: compiler/wasm + - uses: actions/download-artifact@v3 + with: + name: noirc_abi_wasm + path: tooling/noirc_abi_wasm + + - name: Install Yarn dependencies + run: yarn install + + - name: Build ES Packages + run: yarn build:js:only + + - name: Prepare nightly version + if: ${{ inputs.npm-tag != 'latest' }} + run: | + yarn nightly:version -- .${{ inputs.npm-tag }} + + - name: Authenticate with npm + run: "echo npmAuthToken: ${{ secrets.NPM_TOKEN }} > ~/.yarnrc.yml" + + - name: Publish ES Packages + run: yarn publish:all --access public --tag ${{ inputs.npm-tag }} diff --git a/noir/.github/workflows/publish-nargo.yml b/noir/.github/workflows/publish-nargo.yml new file mode 100644 index 00000000000..59d4fe4cc48 --- /dev/null +++ b/noir/.github/workflows/publish-nargo.yml @@ -0,0 +1,202 @@ +name: Publish Nargo + +on: + workflow_dispatch: + # Allow pushing a manual nightly release + inputs: + tag: + description: The tag to build Nargo from (leave empty to build a nightly release from master) + required: false + features: + description: Extra feature flags to release with + required: false + publish: + description: Whether to publish the build artifacts + type: boolean + default: false + merge_group: + pull_request: + +permissions: + # Necessary to upload new release artifacts + contents: write + +jobs: + build-apple-darwin: + runs-on: macos-latest + env: + CROSS_CONFIG: ${{ github.workspace }}/.github/Cross.toml + NIGHTLY_RELEASE: ${{ inputs.tag == '' }} + strategy: + matrix: + target: [x86_64-apple-darwin, aarch64-apple-darwin] + + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + ref: ${{ inputs.tag || env.GITHUB_REF }} + + - name: Setup for Apple Silicon + if: matrix.target == 'aarch64-apple-darwin' + run: | + sudo xcode-select -s /Applications/Xcode_13.2.1.app/Contents/Developer/ + echo "SDKROOT=$(xcrun -sdk macosx$(sw_vers -productVersion) --show-sdk-path)" >> $GITHUB_ENV + echo "MACOSX_DEPLOYMENT_TARGET=$(xcrun -sdk macosx$(sw_vers -productVersion) --show-sdk-platform-version)" >> $GITHUB_ENV + + - name: Setup toolchain + uses: dtolnay/rust-toolchain@1.71.1 + with: + targets: ${{ matrix.target }} + + - uses: Swatinem/rust-cache@v2 + with: + key: ${{ matrix.target }} + cache-on-failure: true + save-if: ${{ github.event_name != 'merge_group' }} + + - name: Build environment and Compile + run: | + cargo build --package nargo_cli --release --target ${{ matrix.target }} --no-default-features --features "${{ inputs.features }}" + + - name: Package artifacts + run: | + mkdir dist + cp ./target/${{ matrix.target }}/release/nargo ./dist/nargo + 7z a -ttar -so -an ./dist/* | 7z a -si ./nargo-${{ matrix.target }}.tar.gz + + - name: Upload artifact + uses: actions/upload-artifact@v3 + with: + name: nargo-${{ matrix.target }} + path: ./dist/* + retention-days: 3 + + - name: Install Yarn dependencies + if: matrix.target == 'x86_64-apple-darwin' + uses: ./.github/actions/setup + + - name: Test built artifact + if: matrix.target == 'x86_64-apple-darwin' + run: | + cp ./target/${{ matrix.target }}/release/nargo ~/.cargo/bin/ + yarn workspace release-tests test + + - name: Upload binaries to release tag + uses: svenstaro/upload-release-action@v2 + if: ${{ inputs.publish || github.event_name == 'schedule' }} + with: + repo_name: noir-lang/noir + repo_token: ${{ secrets.GITHUB_TOKEN }} + file: ./nargo-${{ matrix.target }}.tar.gz + asset_name: nargo-${{ matrix.target }}.tar.gz + overwrite: true + tag: ${{ inputs.tag || 'nightly' }} # This will fail if `inputs.tag` is not a tag (e.g. testing a branch) + + - name: Get formatted date + id: date + if: ${{ inputs.tag == '' && inputs.publish || github.event_name == 'schedule' }} + run: echo "date=$(date '+%Y-%m-%d')" >> $GITHUB_OUTPUT + + - name: Upload binaries to release with date tag + uses: svenstaro/upload-release-action@v2 + if: ${{ inputs.tag == '' && inputs.publish || github.event_name == 'schedule' }} + with: + repo_name: noir-lang/noir + repo_token: ${{ secrets.GITHUB_TOKEN }} + file: ./nargo-${{ matrix.target }}.tar.gz + asset_name: nargo-${{ matrix.target }}.tar.gz + prerelease: true + make_latest: false + overwrite: true + tag: ${{ format('{0}-{1}', 'nightly', steps.date.outputs.date) }} + + build-linux: + runs-on: ubuntu-22.04 + env: + CROSS_CONFIG: ${{ github.workspace }}/.github/Cross.toml + NIGHTLY_RELEASE: ${{ inputs.tag == '' }} + strategy: + fail-fast: false + matrix: + target: [x86_64-unknown-linux-gnu, x86_64-unknown-linux-musl] + + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + ref: ${{ inputs.tag || env.GITHUB_REF }} + + - name: Setup toolchain + uses: dtolnay/rust-toolchain@1.71.1 + with: + targets: ${{ matrix.target }} + + - uses: Swatinem/rust-cache@v2 + with: + key: ${{ matrix.target }} + cache-on-failure: true + save-if: ${{ github.event_name != 'merge_group' }} + + - name: Install Cross + uses: taiki-e/install-action@v2 + with: + tool: cross@0.2.5 + + - name: Build Nargo + run: cross build --package nargo_cli --release --target=${{ matrix.target }} --no-default-features --features "${{ inputs.features }}" + + - name: Package artifacts + run: | + mkdir dist + cp ./target/${{ matrix.target }}/release/nargo ./dist/nargo + 7z a -ttar -so -an ./dist/* | 7z a -si ./nargo-${{ matrix.target }}.tar.gz + + - name: Upload artifact + uses: actions/upload-artifact@v3 + with: + name: nargo-${{ matrix.target }} + path: ./dist/* + retention-days: 3 + + - name: Install Yarn dependencies + if: startsWith(matrix.target, 'x86_64-unknown-linux') + uses: ./.github/actions/setup + + - name: Test built artifact + if: startsWith(matrix.target, 'x86_64-unknown-linux') + run: | + cp ./target/${{ matrix.target }}/release/nargo ~/.cargo/bin/ + yarn workspace release-tests test + + - name: Upload binaries to release tag + uses: svenstaro/upload-release-action@v2 + if: ${{ inputs.publish }} + with: + repo_name: noir-lang/noir + repo_token: ${{ secrets.GITHUB_TOKEN }} + file: ./nargo-${{ matrix.target }}.tar.gz + asset_name: nargo-${{ matrix.target }}.tar.gz + prerelease: true + overwrite: true + tag: ${{ inputs.tag || 'nightly' }} # This will fail if `inputs.tag` is not a tag (e.g. testing a branch) + + - name: Get formatted date + id: date + if: ${{ env.NIGHTLY_RELEASE && inputs.publish }} + run: echo "date=$(date '+%Y-%m-%d')" >> $GITHUB_OUTPUT + + - name: Upload binaries to release with date tag + uses: svenstaro/upload-release-action@v2 + if: ${{ env.NIGHTLY_RELEASE && inputs.publish }} + with: + repo_name: noir-lang/noir + repo_token: ${{ secrets.GITHUB_TOKEN }} + file: ./nargo-${{ matrix.target }}.tar.gz + asset_name: nargo-${{ matrix.target }}.tar.gz + prerelease: true + make_latest: false + overwrite: true + tag: ${{ format('{0}-{1}', 'nightly', steps.date.outputs.date) }} + + diff --git a/noir/.github/workflows/publish-nightly.yml b/noir/.github/workflows/publish-nightly.yml new file mode 100644 index 00000000000..f5c013883bb --- /dev/null +++ b/noir/.github/workflows/publish-nightly.yml @@ -0,0 +1,26 @@ +name: Publish Nightly release +on: + workflow_dispatch: + schedule: + # Run a nightly release at 2 AM UTC + - cron: "0 2 * * *" + +jobs: + dispatch-publish-es: + runs-on: ubuntu-latest + steps: + - name: Dispatch to publish-nargo + uses: benc-uk/workflow-dispatch@v1 + with: + workflow: publish-nargo.yml + token: ${{ secrets.NOIR_REPO_TOKEN }} + # Omitting a tag results in a nightly + inputs: "{ \"publish\": true }" + + + - name: Dispatch to publish-es-packages + uses: benc-uk/workflow-dispatch@v1 + with: + workflow: publish-es-packages.yml + token: ${{ secrets.NOIR_REPO_TOKEN }} + inputs: "{ \"noir-ref\": \"${{ env.GITHUB_REF }}\", \"npm-tag\": \"nightly\" }" diff --git a/noir/.github/workflows/pull-request-title.yml b/noir/.github/workflows/pull-request-title.yml new file mode 100644 index 00000000000..4b8a626a94e --- /dev/null +++ b/noir/.github/workflows/pull-request-title.yml @@ -0,0 +1,29 @@ +name: Pull Request + +on: + merge_group: + pull_request_target: + types: + - opened + - reopened + - edited + - synchronize + +permissions: + pull-requests: read + +jobs: + conventional-title: + name: Validate PR title is Conventional Commit + runs-on: ubuntu-latest + steps: + - name: Check title + if: github.event_name == 'pull_request_target' + uses: amannn/action-semantic-pull-request@v5 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + types: | + fix + feat + chore diff --git a/noir/.github/workflows/recrawler.yml b/noir/.github/workflows/recrawler.yml new file mode 100644 index 00000000000..ee832e273a1 --- /dev/null +++ b/noir/.github/workflows/recrawler.yml @@ -0,0 +1,22 @@ +name: Algolia Recrawl +on: + push: + branches: [ master ] + workflow_dispatch: + +jobs: + algolia_recrawl: + name: Algolia Recrawl + runs-on: ubuntu-latest + steps: + - name: Algolia crawler creation and crawl + uses: algolia/algoliasearch-crawler-github-actions@v1.1.0 + id: algolia_crawler + with: + crawler-user-id: ${{ secrets.CRAWLER_USER_ID }} + crawler-api-key: ${{ secrets.CRAWLER_API_KEY }} + algolia-app-id: ${{ secrets.ALGOLIA_APP_ID }} + algolia-api-key: ${{ secrets.ALGOLIA_API_KEY }} + site-url: 'https://noir-lang.org/' + crawler-name: noir-lang + override-config: false diff --git a/noir/.github/workflows/release.yml b/noir/.github/workflows/release.yml new file mode 100644 index 00000000000..95da6792f04 --- /dev/null +++ b/noir/.github/workflows/release.yml @@ -0,0 +1,96 @@ +name: Release + +on: + push: + branches: + - master + +jobs: + release-please: + name: Create Release + outputs: + release-pr: ${{ steps.release.outputs.pr }} + tag-name: ${{ steps.release.outputs.tag_name }} + runs-on: ubuntu-latest + steps: + - name: Run release-please + id: release + uses: google-github-actions/release-please-action@v3 + with: + token: ${{ secrets.NOIR_RELEASES_TOKEN }} + command: manifest + + update-lockfile: + name: Update lockfile + needs: [release-please] + if: ${{ needs.release-please.outputs.release-pr }} + runs-on: ubuntu-latest + steps: + - name: Checkout release branch + uses: actions/checkout@v4 + with: + ref: ${{ fromJSON(needs.release-please.outputs.release-pr).headBranchName }} + token: ${{ secrets.NOIR_RELEASES_TOKEN }} + + - name: Setup toolchain + uses: dtolnay/rust-toolchain@1.65.0 + + - name: Update lockfile + run: | + cargo update --workspace + + - name: Configure git + run: | + git config user.name kevaundray + git config user.email kevtheappdev@gmail.com + + - name: Commit updates + run: | + git add Cargo.lock + git commit -m 'chore: Update lockfile' + git push + + build-binaries: + name: Build binaries + needs: [release-please] + if: ${{ needs.release-please.outputs.tag-name }} + runs-on: ubuntu-latest + steps: + - name: Dispatch to publish workflow + uses: benc-uk/workflow-dispatch@v1 + with: + workflow: publish-nargo.yml + repo: noir-lang/noir + ref: master + token: ${{ secrets.GITHUB_TOKEN }} + inputs: '{ "tag": "${{ needs.release-please.outputs.tag-name }}", "publish": true }' + + publish-es-packages: + name: Publish ES packages + needs: [release-please] + if: ${{ needs.release-please.outputs.tag-name }} + runs-on: ubuntu-latest + steps: + - name: Dispatch to publish-es-packages + uses: benc-uk/workflow-dispatch@v1 + with: + workflow: publish-es-packages.yml + ref: master + token: ${{ secrets.NOIR_REPO_TOKEN }} + inputs: '{ "noir-ref": "${{ needs.release-please.outputs.tag-name }}", "npm-tag": "latest" }' + + + publish-docs: + name: Publish docs + needs: [release-please] + if: ${{ needs.release-please.outputs.tag-name }} + runs-on: ubuntu-latest + steps: + - name: Dispatch to publish workflow + uses: benc-uk/workflow-dispatch@v1 + with: + workflow: docs-new-version.yml + repo: noir-lang/noir + ref: master + token: ${{ secrets.GITHUB_TOKEN }} + inputs: '{ "tag": "${{ needs.release-please.outputs.tag-name }}"}' diff --git a/noir/.github/workflows/spellcheck.yml b/noir/.github/workflows/spellcheck.yml new file mode 100644 index 00000000000..89fcdd12d12 --- /dev/null +++ b/noir/.github/workflows/spellcheck.yml @@ -0,0 +1,13 @@ +name: Spellcheck + +on: [pull_request] + +# This will cancel previous runs when a branch or PR is updated +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.ref || github.run_id }} + cancel-in-progress: true + +jobs: + spellcheck: + name: Spellcheck + uses: noir-lang/.github/.github/workflows/spellcheck.yml@main diff --git a/noir/.github/workflows/test-abi_wasm.yml b/noir/.github/workflows/test-abi_wasm.yml new file mode 100644 index 00000000000..52223d874bf --- /dev/null +++ b/noir/.github/workflows/test-abi_wasm.yml @@ -0,0 +1,71 @@ +name: ABI Wasm test + +on: + pull_request: + merge_group: + push: + branches: + - master + +# This will cancel previous runs when a branch or PR is updated +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.ref || github.run_id }} + cancel-in-progress: true + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - name: Checkout sources + uses: actions/checkout@v3 + + - name: Setup Nix + uses: ./.github/actions/nix + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + nix-cache-name: "noir" + cachix-auth-token: ${{ secrets.CACHIXAUTHTOKEN }} + + - name: Build noirc_abi_wasm + run: | + nix build -L .#noirc_abi_wasm + cp -r ./result/noirc_abi_wasm/nodejs ./tooling/noirc_abi_wasm + cp -r ./result/noirc_abi_wasm/web ./tooling/noirc_abi_wasm + + - name: Dereference symlink + run: echo "UPLOAD_PATH=$(readlink -f ./result/noirc_abi_wasm)" >> $GITHUB_ENV + + - name: Upload artifact + uses: actions/upload-artifact@v3 + with: + name: noirc_abi_wasm + path: ${{ env.UPLOAD_PATH }} + retention-days: 10 + + test: + runs-on: ubuntu-latest + needs: [build] + + steps: + - name: Checkout sources + uses: actions/checkout@v3 + + - name: Download wasm package artifact + uses: actions/download-artifact@v3 + with: + name: noirc_abi_wasm + path: ./tooling/noirc_abi_wasm + + - name: Install Yarn dependencies + uses: ./.github/actions/setup + + - name: Run node tests + run: yarn workspace @noir-lang/noirc_abi test + + - name: Install Playwright + uses: ./.github/actions/install-playwright + + - name: Run browser tests + run: yarn workspace @noir-lang/noirc_abi test:browser + diff --git a/noir/.github/workflows/test-acvm-js.yml b/noir/.github/workflows/test-acvm-js.yml new file mode 100644 index 00000000000..14ce5d916c0 --- /dev/null +++ b/noir/.github/workflows/test-acvm-js.yml @@ -0,0 +1,99 @@ +name: Test acvm_js + +on: + pull_request: + merge_group: + push: + branches: + - master + +# This will cancel previous runs when a branch or PR is updated +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.ref || github.run_id }} + cancel-in-progress: true + +jobs: + build-acvm-js-package: + runs-on: ubuntu-latest + steps: + - name: Checkout sources + uses: actions/checkout@v4 + + - name: Setup Nix + uses: ./.github/actions/nix + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + nix-cache-name: "noir" + cachix-auth-token: ${{ secrets.CACHIXAUTHTOKEN }} + + - name: Build acvm-js + run: | + nix build -L .#acvm_js + + - name: Dereference symlink + run: echo "UPLOAD_PATH=$(readlink -f result)" >> $GITHUB_ENV + + - name: Upload artifact + uses: actions/upload-artifact@v3 + with: + name: acvm-js + path: ${{ env.UPLOAD_PATH }} + retention-days: 3 + + test-acvm_js-node: + needs: [build-acvm-js-package] + name: Node.js Tests + runs-on: ubuntu-latest + + steps: + - name: Checkout sources + uses: actions/checkout@v4 + + - name: Download artifact + uses: actions/download-artifact@v3 + with: + name: acvm-js + path: ./result + + - name: Move build artifacts + run: | + mv ./result/acvm_js/nodejs ./acvm-repo/acvm_js/nodejs + mv ./result/acvm_js/web ./acvm-repo/acvm_js/web + + - name: Set up test environment + uses: ./.github/actions/setup + + - name: Run node tests + run: yarn workspace @noir-lang/acvm_js test + + test-acvm_js-browser: + needs: [build-acvm-js-package] + name: Browser Tests + runs-on: ubuntu-latest + + steps: + - name: Checkout sources + uses: actions/checkout@v4 + + - name: Download artifact + uses: actions/download-artifact@v3 + with: + name: acvm-js + path: ./result + + - name: Move build artifacts + run: | + mv ./result/acvm_js/nodejs ./acvm-repo/acvm_js/nodejs + mv ./result/acvm_js/web ./acvm-repo/acvm_js/web + + - name: Set up test environment + uses: ./.github/actions/setup + + - name: Install playwright deps + run: | + npx playwright install + npx playwright install-deps + + - name: Run browser tests + working-directory: ./acvm-repo/acvm_js + run: yarn workspace @noir-lang/acvm_js test:browser diff --git a/noir/.github/workflows/test-integration.yml b/noir/.github/workflows/test-integration.yml new file mode 100644 index 00000000000..ad28d9c8e86 --- /dev/null +++ b/noir/.github/workflows/test-integration.yml @@ -0,0 +1,199 @@ +name: test-integration + +on: + workflow_dispatch: + pull_request: + merge_group: + schedule: + - cron: "0 2 * * *" # Run nightly at 2 AM UTC + +jobs: + build-nargo: + runs-on: ubuntu-latest + strategy: + matrix: + target: [x86_64-unknown-linux-gnu] + + steps: + - name: Checkout Noir repo + uses: actions/checkout@v4 + + - name: Setup toolchain + uses: dtolnay/rust-toolchain@1.71.1 + + - uses: Swatinem/rust-cache@v2 + with: + key: ${{ matrix.target }} + cache-on-failure: true + save-if: ${{ github.event_name != 'merge_group' }} + + - name: Build Nargo + run: cargo build --package nargo_cli --release + + - name: Package artifacts + run: | + mkdir dist + cp ./target/release/nargo ./dist/nargo + 7z a -ttar -so -an ./dist/* | 7z a -si ./nargo-x86_64-unknown-linux-gnu.tar.gz + + - name: Upload artifact + uses: actions/upload-artifact@v3 + with: + name: nargo + path: ./dist/* + retention-days: 3 + + build-acvm-js: + runs-on: ubuntu-latest + steps: + - name: Checkout sources + uses: actions/checkout@v4 + + - name: Setup Nix + uses: ./.github/actions/nix + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + nix-cache-name: "noir" + cachix-auth-token: ${{ secrets.CACHIXAUTHTOKEN }} + + - name: Build acvm-js + run: | + nix build -L .#acvm_js + + - name: Dereference symlink + run: echo "UPLOAD_PATH=$(readlink -f result/acvm_js)" >> $GITHUB_ENV + + - name: Upload artifact + uses: actions/upload-artifact@v3 + with: + name: acvm-js + path: ${{ env.UPLOAD_PATH }} + retention-days: 3 + + build-wasm: + runs-on: ubuntu-latest + + steps: + - name: Checkout sources + uses: actions/checkout@v4 + + - name: Setup Nix + uses: ./.github/actions/nix + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + nix-cache-name: "noir" + cachix-auth-token: ${{ secrets.CACHIXAUTHTOKEN }} + + - name: Build wasm package + run: | + nix build -L .#noir_wasm + + - name: Dereference symlink + run: echo "UPLOAD_PATH=$(readlink -f ./result/noir_wasm)" >> $GITHUB_ENV + + - name: Upload artifact + uses: actions/upload-artifact@v3 + with: + name: noir_wasm + path: ${{ env.UPLOAD_PATH }} + retention-days: 3 + + build-noirc: + runs-on: ubuntu-latest + + steps: + - name: Checkout sources + uses: actions/checkout@v3 + + - name: Setup Nix + uses: ./.github/actions/nix + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + nix-cache-name: "noir" + cachix-auth-token: ${{ secrets.CACHIXAUTHTOKEN }} + + - name: Build noirc_abi_wasm + run: | + nix build -L .#noirc_abi_wasm + cp -r ./result/noirc_abi_wasm/nodejs ./tooling/noirc_abi_wasm + cp -r ./result/noirc_abi_wasm/web ./tooling/noirc_abi_wasm + + - name: Dereference symlink + run: echo "UPLOAD_PATH=$(readlink -f ./result/noirc_abi_wasm)" >> $GITHUB_ENV + + - name: Upload artifact + uses: actions/upload-artifact@v3 + with: + name: noirc_abi_wasm + path: ${{ env.UPLOAD_PATH }} + retention-days: 10 + + test-solidity-verifier: + runs-on: ubuntu-latest + needs: [build-acvm-js, build-wasm, build-nargo, build-noirc] + env: + CACHED_PATH: /tmp/nix-cache + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Download nargo binary + uses: actions/download-artifact@v3 + with: + name: nargo + path: ./nargo + + - name: Download acvm_js package artifact + uses: actions/download-artifact@v3 + with: + name: acvm-js + path: ./acvm-repo/acvm_js + + - name: Download noir_wasm package artifact + uses: actions/download-artifact@v3 + with: + name: noir_wasm + path: ./compiler/wasm + + - name: Download noirc_abi package artifact + uses: actions/download-artifact@v3 + with: + name: noirc_abi_wasm + path: ./tooling/noirc_abi_wasm + + - name: Set nargo on PATH + run: | + nargo_binary="${{ github.workspace }}/nargo/nargo" + chmod +x $nargo_binary + echo "$(dirname $nargo_binary)" >> $GITHUB_PATH + export PATH="$PATH:$(dirname $nargo_binary)" + nargo -V + + - name: Install Yarn dependencies + uses: ./.github/actions/setup + + - name: Install Playwright + uses: ./.github/actions/install-playwright + + - name: Setup `integration-tests` + run: | + yarn workspace @noir-lang/source-resolver build + yarn workspace @noir-lang/types build + yarn workspace @noir-lang/backend_barretenberg build + yarn workspace @noir-lang/noir_js build + + - name: Run `integration-tests` + run: | + yarn test:integration + + - name: Alert on nightly test failure + uses: JasonEtco/create-an-issue@v2 + if: ${{ failure() && github.event_name == 'schedule' }} + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + WORKFLOW_NAME: ${{ github.workflow }} + WORKFLOW_URL: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }} + with: + update_existing: true + filename: .github/NIGHTLY_TEST_FAILURE.md diff --git a/noir/.github/workflows/test-noir-js.yml b/noir/.github/workflows/test-noir-js.yml new file mode 100644 index 00000000000..e74f2ee6015 --- /dev/null +++ b/noir/.github/workflows/test-noir-js.yml @@ -0,0 +1,61 @@ +name: Test Noir Js + +on: + pull_request: + merge_group: + push: + branches: + - master + +jobs: + test-noir-js: + name: Test Noir JS + runs-on: ubuntu-latest + timeout-minutes: 30 + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Install Yarn dependencies + uses: ./.github/actions/setup + + - name: Setup toolchain + uses: dtolnay/rust-toolchain@1.71.1 + with: + targets: wasm32-unknown-unknown + + - uses: Swatinem/rust-cache@v2 + with: + key: wasm32-unknown-unknown-noir-js + cache-on-failure: true + save-if: ${{ github.event_name != 'merge_group' }} + + - name: Install jq + run: sudo apt-get install jq + + - name: Install wasm-bindgen-cli + uses: taiki-e/install-action@v2 + with: + tool: wasm-bindgen-cli@0.2.86 + + - name: Install wasm-opt + run: | + npm i wasm-opt -g + + - name: Build acvm_js + run: yarn workspace @noir-lang/acvm_js build + + - name: Build noirc_abi + run: yarn workspace @noir-lang/noirc_abi build + + - name: Build noir_js_types + run: yarn workspace @noir-lang/types build + + - name: Build barretenberg wrapper + run: yarn workspace @noir-lang/backend_barretenberg build + + - name: Run noir_js tests + run: | + yarn workspace @noir-lang/noir_js build + yarn workspace @noir-lang/noir_js test diff --git a/noir/.github/workflows/test-noir_wasm.yml b/noir/.github/workflows/test-noir_wasm.yml new file mode 100644 index 00000000000..2175e20acd7 --- /dev/null +++ b/noir/.github/workflows/test-noir_wasm.yml @@ -0,0 +1,125 @@ +name: Wasm + +on: + pull_request: + merge_group: + push: + branches: + - master + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.ref || github.run_id }} + cancel-in-progress: true + +jobs: + build-nargo: + runs-on: ubuntu-22.04 + strategy: + matrix: + target: [x86_64-unknown-linux-gnu] + + steps: + - name: Checkout Noir repo + uses: actions/checkout@v4 + + - name: Setup toolchain + uses: dtolnay/rust-toolchain@1.71.1 + + - uses: Swatinem/rust-cache@v2 + with: + key: ${{ matrix.target }} + cache-on-failure: true + save-if: ${{ github.event_name != 'merge_group' }} + + - name: Build Nargo + run: cargo build --package nargo_cli --release + + - name: Package artifacts + run: | + mkdir dist + cp ./target/release/nargo ./dist/nargo + 7z a -ttar -so -an ./dist/* | 7z a -si ./nargo-x86_64-unknown-linux-gnu.tar.gz + + - name: Upload artifact + uses: actions/upload-artifact@v3 + with: + name: nargo + path: ./dist/* + retention-days: 3 + + build-wasm: + runs-on: ubuntu-latest + + steps: + - name: Checkout sources + uses: actions/checkout@v4 + + - name: Setup Nix + uses: ./.github/actions/nix + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + nix-cache-name: "noir" + cachix-auth-token: ${{ secrets.CACHIXAUTHTOKEN }} + + - name: Build wasm package + run: | + nix build -L .#noir_wasm + + - name: Dereference symlink + run: echo "UPLOAD_PATH=$(readlink -f ./result/noir_wasm)" >> $GITHUB_ENV + + - name: Upload artifact + uses: actions/upload-artifact@v3 + with: + name: noir_wasm + path: ${{ env.UPLOAD_PATH }} + retention-days: 3 + + test: + needs: [build-wasm, build-nargo] + name: Test noir_wasm + runs-on: ubuntu-latest + steps: + - name: Checkout noir-lang/noir + uses: actions/checkout@v4 + + - name: Download wasm package artifact + uses: actions/download-artifact@v3 + with: + name: noir_wasm + path: ./compiler/wasm/downloaded + + - name: Download nargo binary + uses: actions/download-artifact@v3 + with: + name: nargo + path: ./nargo + + - name: Compile fixtures with Nargo CLI + working-directory: ./compiler/wasm/fixtures + run: | + nargo_binary=${{ github.workspace }}/nargo/nargo + chmod +x $nargo_binary + for dir in $(ls -d */); do + pushd $dir/noir-script + $nargo_binary compile + popd + done + + - name: Install Yarn dependencies + uses: ./.github/actions/setup + + - name: Install Playwright + uses: ./.github/actions/install-playwright + + - name: Install dependencies + run: | + cp -r ./compiler/wasm/downloaded/nodejs ./compiler/wasm + cp -r ./compiler/wasm/downloaded/web ./compiler/wasm + yarn workspace @noir-lang/source-resolver build + + - name: Run node tests + run: yarn workspace @noir-lang/noir_wasm test:node + + - name: Run browser tests + run: yarn workspace @noir-lang/noir_wasm test:browser diff --git a/noir/.github/workflows/test-rust-workspace.yml b/noir/.github/workflows/test-rust-workspace.yml new file mode 100644 index 00000000000..eccd7585fcf --- /dev/null +++ b/noir/.github/workflows/test-rust-workspace.yml @@ -0,0 +1,45 @@ +name: Test + +on: + pull_request: + merge_group: + push: + branches: + - master + +# This will cancel previous runs when a branch or PR is updated +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.ref || github.run_id }} + cancel-in-progress: true + +jobs: + test: + name: Test on ${{ matrix.os }} + runs-on: ${{ matrix.runner }} + timeout-minutes: 30 + + strategy: + fail-fast: false + matrix: + include: + - os: ubuntu + runner: ubuntu-latest + target: x86_64-unknown-linux-gnu + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup toolchain + uses: dtolnay/rust-toolchain@1.71.1 + with: + targets: ${{ matrix.target }} + + - uses: Swatinem/rust-cache@v2 + with: + key: ${{ matrix.target }} + cache-on-failure: true + save-if: ${{ github.event_name != 'merge_group' }} + + - name: Run tests + run: cargo test --workspace --locked --release diff --git a/noir/.github/workflows/test-source-resolver.yml b/noir/.github/workflows/test-source-resolver.yml new file mode 100644 index 00000000000..5dafe4c5fd9 --- /dev/null +++ b/noir/.github/workflows/test-source-resolver.yml @@ -0,0 +1,26 @@ +name: Test Source Resolver + +on: + push: + paths: + - "compiler/source-resolver/**" + pull_request: + paths: + - "compiler/source-resolver/**" + +jobs: + test: + runs-on: ubuntu-latest + + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Install Yarn dependencies + uses: ./.github/actions/setup + + - name: Build @noir-lang/source-resolver + run: yarn workspace @noir-lang/source-resolver build + + - name: Run tests + run: yarn workspace @noir-lang/source-resolver test diff --git a/noir/.gitignore b/noir/.gitignore new file mode 100644 index 00000000000..169353af2b6 --- /dev/null +++ b/noir/.gitignore @@ -0,0 +1,55 @@ +/target +.DS_Store +examples/**/target/ +examples/9 +node_modules +pkg/ + +# Yarn +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/sdks +!.yarn/versions + +# Source resolver +compiler/source-resolver/node_modules +compiler/source-resolver/lib +compiler/source-resolver/lib-node + +# Noir.js +tooling/noir_js/lib + +# Nix stuff +**/outputs +result +.envrc.local +.direnv/ + +# Nargo output +*.proof +*.acir +*.acir.sha256 +*.tr +*.pk +*.vk +**/Verifier.toml +**/target +!tooling/nargo_cli/tests/acir_artifacts/*/target +!tooling/nargo_cli/tests/acir_artifacts/*/target/witness.gz +!compiler/wasm/noir-script/target + +gates_report.json + +# Github Actions scratch space +# This gives a location to download artifacts into the repository in CI without making git dirty. +libbarretenberg-wasm32 + +# Wasm build atifacts +compiler/wasm/nodejs +compiler/wasm/web +tooling/noirc_abi_wasm/nodejs +tooling/noirc_abi_wasm/web +tooling/noir_js/lib diff --git a/noir/.gitrepo b/noir/.gitrepo new file mode 100644 index 00000000000..5c67b47675c --- /dev/null +++ b/noir/.gitrepo @@ -0,0 +1,12 @@ +; DO NOT EDIT (unless you know what you are doing) +; +; This subdirectory is a git "subrepo", and this file is maintained by the +; git-subrepo command. See https://github.com/ingydotnet/git-subrepo#readme +; +[subrepo] + remote = git@github.com:noir-lang/noir + branch = master + commit = 03ab6a22ea9d2028a303802725c1a66329503379 + parent = 8f1cb832cd0adeff0da69da293bb45a3748583e7 + method = merge + cmdver = 0.4.6 diff --git a/noir/.prettierrc b/noir/.prettierrc new file mode 100644 index 00000000000..ef937f9697a --- /dev/null +++ b/noir/.prettierrc @@ -0,0 +1,6 @@ +{ + "parser": "typescript", + "printWidth": 120, + "singleQuote": true, + "trailingComma": "all" +} \ No newline at end of file diff --git a/noir/.release-please-manifest.json b/noir/.release-please-manifest.json new file mode 100644 index 00000000000..73b991cb8af --- /dev/null +++ b/noir/.release-please-manifest.json @@ -0,0 +1,4 @@ +{ + ".": "0.19.3", + "acvm-repo": "0.34.0" +} \ No newline at end of file diff --git a/noir/.rustfmt.toml b/noir/.rustfmt.toml new file mode 100644 index 00000000000..c13d3e328d4 --- /dev/null +++ b/noir/.rustfmt.toml @@ -0,0 +1,2 @@ +edition = "2018" +use_small_heuristics = "Max" diff --git a/noir/.vscode/extensions.json b/noir/.vscode/extensions.json new file mode 100644 index 00000000000..1fb1def7ce1 --- /dev/null +++ b/noir/.vscode/extensions.json @@ -0,0 +1,16 @@ +{ + // See https://go.microsoft.com/fwlink/?LinkId=827846 to learn about workspace recommendations. + // Extension identifier format: ${publisher}.${name}. Example: vscode.csharp + // List of extensions which should be recommended for users of this workspace. + "recommendations": [ + "mkhl.direnv", + "jnoortheen.nix-ide", + "rust-lang.rust-analyzer", + "redhat.vscode-yaml", + "esbenp.prettier-vscode", + // Spell checking + "streetsidesoftware.code-spell-checker", + ], + // List of extensions recommended by VS Code that should not be recommended for users of this workspace. + "unwantedRecommendations": [] +} diff --git a/noir/.vscode/settings.json b/noir/.vscode/settings.json new file mode 100644 index 00000000000..171d36f4e04 --- /dev/null +++ b/noir/.vscode/settings.json @@ -0,0 +1,22 @@ +{ + "direnv.restart.automatic": true, + "redhat.telemetry.enabled": false, + "yaml.recommendations.show": false, + "nix.serverPath": "nil", + "nix.enableLanguageServer": true, + "nix.serverSettings": { + "nil": { + "formatting": { + "command": [ + "nixpkgs-fmt" + ] + } + } + }, + "yaml.schemas": { + "https://json.schemastore.org/github-workflow.json": 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m,Q,N,U,ce,Se,ht,Bt,qr,hi,as;for(m=b,Q=[],N=He();N!==t;)Q.push(N),N=He();if(Q!==t)if(r.charCodeAt(b)===40?(N=ge,b++):(N=t,I===0&&be(re)),N!==t){for(U=[],ce=He();ce!==t;)U.push(ce),ce=He();if(U!==t)if(ce=Or(),ce!==t){for(Se=[],ht=He();ht!==t;)Se.push(ht),ht=He();if(Se!==t)if(r.charCodeAt(b)===41?(ht=M,b++):(ht=t,I===0&&be(F)),ht!==t){for(Bt=[],qr=He();qr!==t;)Bt.push(qr),qr=He();if(Bt!==t){for(qr=[],hi=Np();hi!==t;)qr.push(hi),hi=Np();if(qr!==t){for(hi=[],as=He();as!==t;)hi.push(as),as=He();hi!==t?(Oe=m,Q=ue(ce,qr),m=Q):(b=m,m=t)}else b=m,m=t}else b=m,m=t}else b=m,m=t;else b=m,m=t}else b=m,m=t;else b=m,m=t}else b=m,m=t;else b=m,m=t;if(m===t){for(m=b,Q=[],N=He();N!==t;)Q.push(N),N=He();if(Q!==t)if(r.charCodeAt(b)===123?(N=pe,b++):(N=t,I===0&&be(ke)),N!==t){for(U=[],ce=He();ce!==t;)U.push(ce),ce=He();if(U!==t)if(ce=Or(),ce!==t){for(Se=[],ht=He();ht!==t;)Se.push(ht),ht=He();if(Se!==t)if(r.charCodeAt(b)===125?(ht=Fe,b++):(ht=t,I===0&&be(Ne)),ht!==t){for(Bt=[],qr=He();qr!==t;)Bt.push(qr),qr=He();if(Bt!==t){for(qr=[],hi=Np();hi!==t;)qr.push(hi),hi=Np();if(qr!==t){for(hi=[],as=He();as!==t;)hi.push(as),as=He();hi!==t?(Oe=m,Q=oe(ce,qr),m=Q):(b=m,m=t)}else b=m,m=t}else b=m,m=t}else b=m,m=t;else b=m,m=t}else b=m,m=t;else b=m,m=t}else b=m,m=t;else b=m,m=t;if(m===t){for(m=b,Q=[],N=He();N!==t;)Q.push(N),N=He();if(Q!==t){for(N=[],U=qE();U!==t;)N.push(U),U=qE();if(N!==t){for(U=[],ce=He();ce!==t;)U.push(ce),ce=He();if(U!==t){if(ce=[],Se=J1(),Se!==t)for(;Se!==t;)ce.push(Se),Se=J1();else ce=t;if(ce!==t){for(Se=[],ht=He();ht!==t;)Se.push(ht),ht=He();Se!==t?(Oe=m,Q=le(N,ce),m=Q):(b=m,m=t)}else b=m,m=t}else b=m,m=t}else b=m,m=t}else b=m,m=t;if(m===t){for(m=b,Q=[],N=He();N!==t;)Q.push(N),N=He();if(Q!==t){if(N=[],U=qE(),U!==t)for(;U!==t;)N.push(U),U=qE();else N=t;if(N!==t){for(U=[],ce=He();ce!==t;)U.push(ce),ce=He();U!==t?(Oe=m,Q=Be(N),m=Q):(b=m,m=t)}else b=m,m=t}else b=m,m=t}}}return m}function q1(){var m,Q,N,U,ce;for(m=b,Q=[],N=He();N!==t;)Q.push(N),N=He();if(Q!==t){if(N=[],U=JE(),U!==t)for(;U!==t;)N.push(U),U=JE();else N=t;if(N!==t){for(U=[],ce=He();ce!==t;)U.push(ce),ce=He();U!==t?(Oe=m,Q=fe(N),m=Q):(b=m,m=t)}else b=m,m=t}else b=m,m=t;return m}function J1(){var m,Q,N;for(m=b,Q=[],N=He();N!==t;)Q.push(N),N=He();if(Q!==t?(N=Np(),N!==t?(Oe=m,Q=ae(N),m=Q):(b=m,m=t)):(b=m,m=t),m===t){for(m=b,Q=[],N=He();N!==t;)Q.push(N),N=He();Q!==t?(N=JE(),N!==t?(Oe=m,Q=ae(N),m=Q):(b=m,m=t)):(b=m,m=t)}return m}function Np(){var m,Q,N,U,ce;for(m=b,Q=[],N=He();N!==t;)Q.push(N),N=He();return Q!==t?(qe.test(r.charAt(b))?(N=r.charAt(b),b++):(N=t,I===0&&be(ne)),N===t&&(N=null),N!==t?(U=mge(),U!==t?(ce=JE(),ce!==t?(Oe=m,Q=Y(N,U,ce),m=Q):(b=m,m=t)):(b=m,m=t)):(b=m,m=t)):(b=m,m=t),m}function mge(){var m;return r.substr(b,2)===he?(m=he,b+=2):(m=t,I===0&&be(ie)),m===t&&(r.substr(b,2)===de?(m=de,b+=2):(m=t,I===0&&be(_e)),m===t&&(r.charCodeAt(b)===62?(m=Pt,b++):(m=t,I===0&&be(It)),m===t&&(r.substr(b,3)===Mr?(m=Mr,b+=3):(m=t,I===0&&be(ii)),m===t&&(r.substr(b,2)===gi?(m=gi,b+=2):(m=t,I===0&&be(hr)),m===t&&(r.charCodeAt(b)===60?(m=fi,b++):(m=t,I===0&&be(ni))))))),m}function JE(){var m,Q,N;for(m=b,Q=[],N=He();N!==t;)Q.push(N),N=He();return Q!==t?(N=W1(),N!==t?(Oe=m,Q=ae(N),m=Q):(b=m,m=t)):(b=m,m=t),m}function W1(){var m,Q,N;if(m=b,Q=[],N=z1(),N!==t)for(;N!==t;)Q.push(N),N=z1();else Q=t;return Q!==t&&(Oe=m,Q=Ks(Q)),m=Q,m}function z1(){var m,Q;return m=b,Q=Ege(),Q!==t&&(Oe=m,Q=pr(Q)),m=Q,m===t&&(m=b,Q=Ige(),Q!==t&&(Oe=m,Q=pr(Q)),m=Q,m===t&&(m=b,Q=yge(),Q!==t&&(Oe=m,Q=pr(Q)),m=Q,m===t&&(m=b,Q=wge(),Q!==t&&(Oe=m,Q=pr(Q)),m=Q))),m}function Ege(){var m,Q,N,U;return m=b,r.substr(b,2)===Ii?(Q=Ii,b+=2):(Q=t,I===0&&be(rs)),Q!==t?(N=Qge(),N!==t?(r.charCodeAt(b)===39?(U=fa,b++):(U=t,I===0&&be(dA)),U!==t?(Oe=m,Q=cg(N),m=Q):(b=m,m=t)):(b=m,m=t)):(b=m,m=t),m}function Ige(){var m,Q,N,U;return m=b,r.charCodeAt(b)===39?(Q=fa,b++):(Q=t,I===0&&be(dA)),Q!==t?(N=Bge(),N!==t?(r.charCodeAt(b)===39?(U=fa,b++):(U=t,I===0&&be(dA)),U!==t?(Oe=m,Q=cg(N),m=Q):(b=m,m=t)):(b=m,m=t)):(b=m,m=t),m}function yge(){var m,Q,N,U;if(m=b,r.substr(b,2)===is?(Q=is,b+=2):(Q=t,I===0&&be(CA)),Q!==t&&(Oe=m,Q=ha()),m=Q,m===t)if(m=b,r.charCodeAt(b)===34?(Q=wp,b++):(Q=t,I===0&&be(mA)),Q!==t){for(N=[],U=V1();U!==t;)N.push(U),U=V1();N!==t?(r.charCodeAt(b)===34?(U=wp,b++):(U=t,I===0&&be(mA)),U!==t?(Oe=m,Q=EA(N),m=Q):(b=m,m=t)):(b=m,m=t)}else b=m,m=t;return m}function wge(){var m,Q,N;if(m=b,Q=[],N=X1(),N!==t)for(;N!==t;)Q.push(N),N=X1();else Q=t;return Q!==t&&(Oe=m,Q=EA(Q)),m=Q,m}function V1(){var m,Q;return m=b,Q=eK(),Q!==t&&(Oe=m,Q=wr(Q)),m=Q,m===t&&(m=b,Q=tK(),Q!==t&&(Oe=m,Q=Tl(Q)),m=Q,m===t&&(m=b,Q=aS(),Q!==t&&(Oe=m,Q=ug(Q)),m=Q,m===t&&(m=b,Q=bge(),Q!==t&&(Oe=m,Q=yo(Q)),m=Q))),m}function X1(){var m,Q;return m=b,Q=eK(),Q!==t&&(Oe=m,Q=gg(Q)),m=Q,m===t&&(m=b,Q=tK(),Q!==t&&(Oe=m,Q=Bp(Q)),m=Q,m===t&&(m=b,Q=aS(),Q!==t&&(Oe=m,Q=bp(Q)),m=Q,m===t&&(m=b,Q=xge(),Q!==t&&(Oe=m,Q=vr(Q)),m=Q,m===t&&(m=b,Q=vge(),Q!==t&&(Oe=m,Q=yo(Q)),m=Q)))),m}function Bge(){var m,Q,N;for(m=b,Q=[],se.test(r.charAt(b))?(N=r.charAt(b),b++):(N=t,I===0&&be(wo));N!==t;)Q.push(N),se.test(r.charAt(b))?(N=r.charAt(b),b++):(N=t,I===0&&be(wo));return Q!==t&&(Oe=m,Q=Fn(Q)),m=Q,m}function bge(){var m,Q,N;if(m=b,Q=[],N=Z1(),N===t&&(fg.test(r.charAt(b))?(N=r.charAt(b),b++):(N=t,I===0&&be(bt))),N!==t)for(;N!==t;)Q.push(N),N=Z1(),N===t&&(fg.test(r.charAt(b))?(N=r.charAt(b),b++):(N=t,I===0&&be(bt)));else Q=t;return Q!==t&&(Oe=m,Q=Fn(Q)),m=Q,m}function Z1(){var m,Q,N;return m=b,r.substr(b,2)===Ll?(Q=Ll,b+=2):(Q=t,I===0&&be(Nn)),Q!==t&&(Oe=m,Q=ns()),m=Q,m===t&&(m=b,r.charCodeAt(b)===92?(Q=ss,b++):(Q=t,I===0&&be(gt)),Q!==t?(Bo.test(r.charAt(b))?(N=r.charAt(b),b++):(N=t,I===0&&be(At)),N!==t?(Oe=m,Q=ln(N),m=Q):(b=m,m=t)):(b=m,m=t)),m}function Qge(){var m,Q,N;for(m=b,Q=[],N=_1(),N===t&&(se.test(r.charAt(b))?(N=r.charAt(b),b++):(N=t,I===0&&be(wo)));N!==t;)Q.push(N),N=_1(),N===t&&(se.test(r.charAt(b))?(N=r.charAt(b),b++):(N=t,I===0&&be(wo)));return Q!==t&&(Oe=m,Q=Fn(Q)),m=Q,m}function _1(){var m,Q,N;return m=b,r.substr(b,2)===S?(Q=S,b+=2):(Q=t,I===0&&be(Lt)),Q!==t&&(Oe=m,Q=hg()),m=Q,m===t&&(m=b,r.substr(b,2)===Ml?(Q=Ml,b+=2):(Q=t,I===0&&be(Qp)),Q!==t&&(Oe=m,Q=Sp()),m=Q,m===t&&(m=b,r.charCodeAt(b)===92?(Q=ss,b++):(Q=t,I===0&&be(gt)),Q!==t?(vp.test(r.charAt(b))?(N=r.charAt(b),b++):(N=t,I===0&&be(xp)),N!==t?(Oe=m,Q=Pp(),m=Q):(b=m,m=t)):(b=m,m=t),m===t&&(m=b,r.substr(b,2)===G?(Q=G,b+=2):(Q=t,I===0&&be(yt)),Q!==t&&(Oe=m,Q=IA()),m=Q,m===t&&(m=b,r.substr(b,2)===zi?(Q=zi,b+=2):(Q=t,I===0&&be(Ol)),Q!==t&&(Oe=m,Q=Xe()),m=Q,m===t&&(m=b,r.substr(b,2)===pa?(Q=pa,b+=2):(Q=t,I===0&&be(pg)),Q!==t&&(Oe=m,Q=ME()),m=Q,m===t&&(m=b,r.substr(b,2)===Dp?(Q=Dp,b+=2):(Q=t,I===0&&be(OE)),Q!==t&&(Oe=m,Q=ar()),m=Q,m===t&&(m=b,r.substr(b,2)===Tn?(Q=Tn,b+=2):(Q=t,I===0&&be(Kl)),Q!==t&&(Oe=m,Q=kp()),m=Q,m===t&&(m=b,r.charCodeAt(b)===92?(Q=ss,b++):(Q=t,I===0&&be(gt)),Q!==t?(Us.test(r.charAt(b))?(N=r.charAt(b),b++):(N=t,I===0&&be(da)),N!==t?(Oe=m,Q=ln(N),m=Q):(b=m,m=t)):(b=m,m=t),m===t&&(m=Sge()))))))))),m}function Sge(){var m,Q,N,U,ce,Se,ht,Bt,qr,hi,as,AS;return m=b,r.charCodeAt(b)===92?(Q=ss,b++):(Q=t,I===0&&be(gt)),Q!==t?(N=nS(),N!==t?(Oe=m,Q=cn(N),m=Q):(b=m,m=t)):(b=m,m=t),m===t&&(m=b,r.substr(b,2)===Le?(Q=Le,b+=2):(Q=t,I===0&&be(dg)),Q!==t?(N=b,U=b,ce=nS(),ce!==t?(Se=On(),Se!==t?(ce=[ce,Se],U=ce):(b=U,U=t)):(b=U,U=t),U===t&&(U=nS()),U!==t?N=r.substring(N,b):N=U,N!==t?(Oe=m,Q=cn(N),m=Q):(b=m,m=t)):(b=m,m=t),m===t&&(m=b,r.substr(b,2)===Ul?(Q=Ul,b+=2):(Q=t,I===0&&be(Hs)),Q!==t?(N=b,U=b,ce=On(),ce!==t?(Se=On(),Se!==t?(ht=On(),ht!==t?(Bt=On(),Bt!==t?(ce=[ce,Se,ht,Bt],U=ce):(b=U,U=t)):(b=U,U=t)):(b=U,U=t)):(b=U,U=t),U!==t?N=r.substring(N,b):N=U,N!==t?(Oe=m,Q=cn(N),m=Q):(b=m,m=t)):(b=m,m=t),m===t&&(m=b,r.substr(b,2)===Hl?(Q=Hl,b+=2):(Q=t,I===0&&be(yA)),Q!==t?(N=b,U=b,ce=On(),ce!==t?(Se=On(),Se!==t?(ht=On(),ht!==t?(Bt=On(),Bt!==t?(qr=On(),qr!==t?(hi=On(),hi!==t?(as=On(),as!==t?(AS=On(),AS!==t?(ce=[ce,Se,ht,Bt,qr,hi,as,AS],U=ce):(b=U,U=t)):(b=U,U=t)):(b=U,U=t)):(b=U,U=t)):(b=U,U=t)):(b=U,U=t)):(b=U,U=t)):(b=U,U=t),U!==t?N=r.substring(N,b):N=U,N!==t?(Oe=m,Q=Cg(N),m=Q):(b=m,m=t)):(b=m,m=t)))),m}function 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p=this.popChar();return{type:"Character",value:n(p)}},r.prototype.classPatternCharacterAtom=function(){switch(this.peekChar()){case` +`:case"\r":case"\u2028":case"\u2029":case"\\":case"]":throw Error("TBD");default:var p=this.popChar();return{type:"Character",value:n(p)}}},r.prototype.characterClass=function(){var p=[],C=!1;for(this.consumeChar("["),this.peekChar(0)==="^"&&(this.consumeChar("^"),C=!0);this.isClassAtom();){var y=this.classAtom(),B=y.type==="Character";if(B&&this.isRangeDash()){this.consumeChar("-");var v=this.classAtom(),D=v.type==="Character";if(D){if(v.value=this.input.length)throw Error("Unexpected end of input");this.idx++},r.prototype.loc=function(p){return{begin:p,end:this.idx}};var e=/[0-9a-fA-F]/,t=/[0-9]/,i=/[1-9]/;function n(p){return p.charCodeAt(0)}function s(p,C){p.length!==void 0?p.forEach(function(y){C.push(y)}):C.push(p)}function o(p,C){if(p[C]===!0)throw"duplicate flag "+C;p[C]=!0}function a(p){if(p===void 0)throw Error("Internal Error - Should never 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ty=w(Xg=>{"use strict";Object.defineProperty(Xg,"__esModule",{value:!0});Xg.clearRegExpParserCache=Xg.getRegExpAst=void 0;var IEe=$I(),ey={},yEe=new IEe.RegExpParser;function wEe(r){var e=r.toString();if(ey.hasOwnProperty(e))return ey[e];var t=yEe.pattern(e);return ey[e]=t,t}Xg.getRegExpAst=wEe;function BEe(){ey={}}Xg.clearRegExpParserCache=BEe});var qY=w(Cn=>{"use strict";var bEe=Cn&&Cn.__extends||function(){var r=function(e,t){return r=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(i,n){i.__proto__=n}||function(i,n){for(var s in n)Object.prototype.hasOwnProperty.call(n,s)&&(i[s]=n[s])},r(e,t)};return function(e,t){if(typeof t!="function"&&t!==null)throw new TypeError("Class extends value "+String(t)+" is not a constructor or null");r(e,t);function i(){this.constructor=e}e.prototype=t===null?Object.create(t):(i.prototype=t.prototype,new 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details.`),(0,gs.PRINT_ERROR)(Cn.failedOptimizationPrefixMsg+` +`+(" Failed parsing: < "+r.toString()+` > +`)+(" Using the regexp-to-ast library version: "+GY.VERSION+` +`)+" Please open an issue at: https://github.com/bd82/regexp-to-ast/issues"+n)}}return[]}Cn.getOptimizedStartCodesIndices=QEe;function iy(r,e,t){switch(r.type){case"Disjunction":for(var i=0;i=xa.minOptimizationVal)for(var f=u.from>=xa.minOptimizationVal?u.from:xa.minOptimizationVal,h=u.to,p=(0,xa.charCodeToOptimizedIndex)(f),C=(0,xa.charCodeToOptimizedIndex)(h),y=p;y<=C;y++)e[y]=y}}});break;case"Group":iy(o.value,e,t);break;default:throw Error("Non Exhaustive Match")}var a=o.quantifier!==void 0&&o.quantifier.atLeast===0;if(o.type==="Group"&&kv(o)===!1||o.type!=="Group"&&a===!1)break}break;default:throw Error("non exhaustive match!")}return(0,gs.values)(e)}Cn.firstCharOptimizedIndices=iy;function ry(r,e,t){var i=(0,xa.charCodeToOptimizedIndex)(r);e[i]=i,t===!0&&SEe(r,e)}function SEe(r,e){var 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https://chevrotain.io/docs/guide/resolving_lexer_errors.html#CUSTOM_OPTIMIZE`),y=!1;return v},[])}),t("ArrayPacking",function(){B=(0,xe.packArray)(B)}),{emptyGroups:p,patternIdxToConfig:C,charCodeToPatternIdxToConfig:B,hasCustom:n,canBeOptimized:y}}Ve.analyzeTokenTypes=kEe;function REe(r,e){var t=[],i=VY(r);t=t.concat(i.errors);var n=XY(i.valid),s=n.valid;return t=t.concat(n.errors),t=t.concat(FEe(s)),t=t.concat(rj(s)),t=t.concat(ij(s,e)),t=t.concat(nj(s)),t}Ve.validatePatterns=REe;function FEe(r){var e=[],t=(0,xe.filter)(r,function(i){return(0,xe.isRegExp)(i[ko])});return e=e.concat(ZY(t)),e=e.concat($Y(t)),e=e.concat(ej(t)),e=e.concat(tj(t)),e=e.concat(_Y(t)),e}function VY(r){var e=(0,xe.filter)(r,function(n){return!(0,xe.has)(n,ko)}),t=(0,xe.map)(e,function(n){return{message:"Token Type: ->"+n.name+"<- missing static 'PATTERN' 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a.visit(o),a.found}catch{return NEe.test(s.source)}}),i=(0,xe.map)(t,function(n){return{message:`Unexpected RegExp Anchor Error: + Token Type: ->`+n.name+`<- static 'PATTERN' cannot contain end of input anchor '$' + See chevrotain.io/docs/guide/resolving_lexer_errors.html#ANCHORS for details.`,type:ir.LexerDefinitionErrorType.EOI_ANCHOR_FOUND,tokenTypes:[n]}});return i}Ve.findEndOfInputAnchor=ZY;function _Y(r){var e=(0,xe.filter)(r,function(i){var n=i[ko];return n.test("")}),t=(0,xe.map)(e,function(i){return{message:"Token Type: ->"+i.name+"<- static 'PATTERN' must not match an empty string",type:ir.LexerDefinitionErrorType.EMPTY_MATCH_PATTERN,tokenTypes:[i]}});return t}Ve.findEmptyMatchRegExps=_Y;var TEe=/[^\\[][\^]|^\^/;function $Y(r){var e=function(n){JY(s,n);function s(){var o=n!==null&&n.apply(this,arguments)||this;return o.found=!1,o}return s.prototype.visitStartAnchor=function(o){this.found=!0},s}(WY.BaseRegExpVisitor),t=(0,xe.filter)(r,function(n){var s=n[ko];try{var 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Mode:"+("<"+s+"> at index: <"+a+`> +`),type:ir.LexerDefinitionErrorType.LEXER_DEFINITION_CANNOT_CONTAIN_UNDEFINED})})}),i}Ve.performRuntimeChecks=OEe;function KEe(r,e,t){var i=[],n=!1,s=(0,xe.compact)((0,xe.flatten)((0,xe.mapValues)(r.modes,function(l){return l}))),o=(0,xe.reject)(s,function(l){return l[ko]===ir.Lexer.NA}),a=Aj(t);return e&&(0,xe.forEach)(o,function(l){var c=oj(l,a);if(c!==!1){var u=aj(l,c),g={message:u,type:c.issue,tokenType:l};i.push(g)}else(0,xe.has)(l,"LINE_BREAKS")?l.LINE_BREAKS===!0&&(n=!0):(0,Zg.canMatchCharCode)(a,l.PATTERN)&&(n=!0)}),e&&!n&&i.push({message:`Warning: No LINE_BREAKS Found. + This Lexer has been defined to track line and column information, + But none of the Token Types can be identified as matching a line terminator. + See https://chevrotain.io/docs/guide/resolving_lexer_errors.html#LINE_BREAKS + for details.`,type:ir.LexerDefinitionErrorType.NO_LINE_BREAKS_FLAGS}),i}Ve.performWarningRuntimeChecks=KEe;function UEe(r){var e={},t=(0,xe.keys)(r);return(0,xe.forEach)(t,function(i){var n=r[i];if((0,xe.isArray)(n))e[i]=[];else throw Error("non exhaustive match")}),e}Ve.cloneEmptyGroups=UEe;function Mv(r){var e=r.PATTERN;if((0,xe.isRegExp)(e))return!1;if((0,xe.isFunction)(e))return!0;if((0,xe.has)(e,"exec"))return!0;if((0,xe.isString)(e))return!1;throw Error("non exhaustive match")}Ve.isCustomPattern=Mv;function sj(r){return(0,xe.isString)(r)&&r.length===1?r.charCodeAt(0):!1}Ve.isShortPattern=sj;Ve.LineTerminatorOptimizedTester={test:function(r){for(var e=r.length,t=this.lastIndex;t Token Type +`)+(" Root cause: "+e.errMsg+`. +`)+" For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#IDENTIFY_TERMINATOR";if(e.issue===ir.LexerDefinitionErrorType.CUSTOM_LINE_BREAK)return`Warning: A Custom Token Pattern should specify the option. +`+(" The problem is in the <"+r.name+`> Token Type +`)+" For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#CUSTOM_LINE_BREAK";throw Error("non exhaustive match")}Ve.buildLineBreakIssueMessage=aj;function Aj(r){var e=(0,xe.map)(r,function(t){return(0,xe.isString)(t)&&t.length>0?t.charCodeAt(0):t});return e}function Fv(r,e,t){r[e]===void 0?r[e]=[t]:r[e].push(t)}Ve.minOptimizationVal=256;var ny=[];function Lv(r){return r255?255+~~(r/255):r}}});var _g=w(Nt=>{"use strict";Object.defineProperty(Nt,"__esModule",{value:!0});Nt.isTokenType=Nt.hasExtendingTokensTypesMapProperty=Nt.hasExtendingTokensTypesProperty=Nt.hasCategoriesProperty=Nt.hasShortKeyProperty=Nt.singleAssignCategoriesToksMap=Nt.assignCategoriesMapProp=Nt.assignCategoriesTokensProp=Nt.assignTokenDefaultProps=Nt.expandCategories=Nt.augmentTokenTypes=Nt.tokenIdxToClass=Nt.tokenShortNameIdx=Nt.tokenStructuredMatcherNoCategories=Nt.tokenStructuredMatcher=void 0;var Zr=Gt();function GEe(r,e){var t=r.tokenTypeIdx;return t===e.tokenTypeIdx?!0:e.isParent===!0&&e.categoryMatchesMap[t]===!0}Nt.tokenStructuredMatcher=GEe;function YEe(r,e){return 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bd={deferDefinitionErrorsHandling:!1,positionTracking:"full",lineTerminatorsPattern:/\n|\r\n?/g,lineTerminatorCharacters:[` +`,"\r"],ensureOptimizations:!1,safeMode:!1,errorMessageProvider:WEe.defaultLexerErrorProvider,traceInitPerf:!1,skipValidations:!1};Object.freeze(bd);var XEe=function(){function r(e,t){var i=this;if(t===void 0&&(t=bd),this.lexerDefinition=e,this.lexerDefinitionErrors=[],this.lexerDefinitionWarning=[],this.patternIdxToConfig={},this.charCodeToPatternIdxToConfig={},this.modes=[],this.emptyGroups={},this.config=void 0,this.trackStartLines=!0,this.trackEndLines=!0,this.hasCustom=!1,this.canModeBeOptimized={},typeof t=="boolean")throw Error(`The second argument to the Lexer constructor is now an ILexerConfig Object. +a boolean 2nd argument is no longer supported`);this.config=(0,nr.merge)(bd,t);var n=this.config.traceInitPerf;n===!0?(this.traceInitMaxIdent=1/0,this.traceInitPerf=!0):typeof 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"+h}),f=`one of these possible Token sequences: +`+g.join(` +`);return o+f+l},buildEarlyExitMessage:function(r){var e=r.expectedIterationPaths,t=r.actual,i=r.customUserDescription,n=r.ruleName,s="Expecting: ",o=(0,eo.first)(t).image,a=` +but found: '`+o+"'";if(i)return s+i+a;var l=(0,eo.map)(e,function(u){return"["+(0,eo.map)(u,function(g){return(0,ef.tokenLabel)(g)}).join(",")+"]"}),c=`expecting at least one iteration which starts with one of these possible Token sequences:: + `+("<"+l.join(" ,")+">");return s+c+a}};Object.freeze(Da.defaultParserErrorProvider);Da.defaultGrammarResolverErrorProvider={buildRuleNotFoundError:function(r,e){var t="Invalid grammar, reference to a rule which is not defined: ->"+e.nonTerminalName+`<- +inside top level rule: ->`+r.name+"<-";return t}};Da.defaultGrammarValidatorErrorProvider={buildDuplicateFoundError:function(r,e){function t(u){return u instanceof Wv.Terminal?u.terminalType.name:u instanceof Wv.NonTerminal?u.nonTerminalName:""}var i=r.name,n=(0,eo.first)(e),s=n.idx,o=(0,Jj.getProductionDslName)(n),a=t(n),l=s>0,c="->"+o+(l?s:"")+"<- "+(a?"with argument: ->"+a+"<-":"")+` + appears more than once (`+e.length+" times) in the top level rule: ->"+i+`<-. + For further details see: https://chevrotain.io/docs/FAQ.html#NUMERICAL_SUFFIXES + `;return c=c.replace(/[ \t]+/g," "),c=c.replace(/\s\s+/g,` +`),c},buildNamespaceConflictError:function(r){var e=`Namespace conflict found in grammar. +`+("The grammar has both a Terminal(Token) and a Non-Terminal(Rule) named: <"+r.name+`>. +`)+`To resolve this make sure each Terminal and Non-Terminal names are unique +This is easy to accomplish by using the convention that Terminal names start with an uppercase letter +and Non-Terminal names start with a lower case letter.`;return e},buildAlternationPrefixAmbiguityError:function(r){var e=(0,eo.map)(r.prefixPath,function(n){return(0,ef.tokenLabel)(n)}).join(", "),t=r.alternation.idx===0?"":r.alternation.idx,i="Ambiguous alternatives: <"+r.ambiguityIndices.join(" ,")+`> due to common lookahead prefix +`+("in inside <"+r.topLevelRule.name+`> Rule, +`)+("<"+e+`> may appears as a prefix path in all these alternatives. +`)+`See: https://chevrotain.io/docs/guide/resolving_grammar_errors.html#COMMON_PREFIX +For Further details.`;return i},buildAlternationAmbiguityError:function(r){var e=(0,eo.map)(r.prefixPath,function(n){return(0,ef.tokenLabel)(n)}).join(", "),t=r.alternation.idx===0?"":r.alternation.idx,i="Ambiguous Alternatives Detected: <"+r.ambiguityIndices.join(" ,")+"> in "+(" inside <"+r.topLevelRule.name+`> Rule, +`)+("<"+e+`> may appears as a prefix path in all these alternatives. +`);return i=i+`See: https://chevrotain.io/docs/guide/resolving_grammar_errors.html#AMBIGUOUS_ALTERNATIVES +For Further details.`,i},buildEmptyRepetitionError:function(r){var e=(0,Jj.getProductionDslName)(r.repetition);r.repetition.idx!==0&&(e+=r.repetition.idx);var t="The repetition <"+e+"> within Rule <"+r.topLevelRule.name+`> can never consume any tokens. +This could lead to an infinite loop.`;return t},buildTokenNameError:function(r){return"deprecated"},buildEmptyAlternationError:function(r){var e="Ambiguous empty alternative: <"+(r.emptyChoiceIdx+1)+">"+(" in inside <"+r.topLevelRule.name+`> Rule. +`)+"Only the last alternative may be an empty alternative.";return e},buildTooManyAlternativesError:function(r){var e=`An Alternation cannot have more than 256 alternatives: +`+(" inside <"+r.topLevelRule.name+`> Rule. + has `+(r.alternation.definition.length+1)+" alternatives.");return e},buildLeftRecursionError:function(r){var e=r.topLevelRule.name,t=IIe.map(r.leftRecursionPath,function(s){return s.name}),i=e+" --> "+t.concat([e]).join(" --> "),n=`Left Recursion found in grammar. +`+("rule: <"+e+`> can be invoked from itself (directly or indirectly) +`)+(`without consuming any Tokens. The grammar path that causes this is: + `+i+` +`)+` To fix this refactor your grammar to remove the left recursion. +see: https://en.wikipedia.org/wiki/LL_parser#Left_Factoring.`;return n},buildInvalidRuleNameError:function(r){return"deprecated"},buildDuplicateRuleNameError:function(r){var e;r.topLevelRule instanceof Wv.Rule?e=r.topLevelRule.name:e=r.topLevelRule;var t="Duplicate definition, rule: ->"+e+"<- is already defined in the grammar: ->"+r.grammarName+"<-";return t}}});var Vj=w(LA=>{"use strict";var yIe=LA&&LA.__extends||function(){var r=function(e,t){return r=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(i,n){i.__proto__=n}||function(i,n){for(var s in n)Object.prototype.hasOwnProperty.call(n,s)&&(i[s]=n[s])},r(e,t)};return function(e,t){if(typeof t!="function"&&t!==null)throw new TypeError("Class extends value "+String(t)+" is not a constructor or null");r(e,t);function i(){this.constructor=e}e.prototype=t===null?Object.create(t):(i.prototype=t.prototype,new 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sye.MismatchedTokenException(p,u,s.LA(0));C.resyncedTokens=(0,hs.dropRight)(l),s.SAVE_ERROR(C)};!c;)if(this.tokenMatcher(g,n)){f();return}else if(i.call(this)){f(),e.apply(this,t);return}else this.tokenMatcher(g,o)?c=!0:(g=this.SKIP_TOKEN(),this.addToResyncTokens(g,l));this.importLexerState(a)},r.prototype.shouldInRepetitionRecoveryBeTried=function(e,t,i){return!(i===!1||e===void 0||t===void 0||this.tokenMatcher(this.LA(1),e)||this.isBackTracking()||this.canPerformInRuleRecovery(e,this.getFollowsForInRuleRecovery(e,t)))},r.prototype.getFollowsForInRuleRecovery=function(e,t){var i=this.getCurrentGrammarPath(e,t),n=this.getNextPossibleTokenTypes(i);return n},r.prototype.tryInRuleRecovery=function(e,t){if(this.canRecoverWithSingleTokenInsertion(e,t)){var i=this.getTokenToInsert(e);return i}if(this.canRecoverWithSingleTokenDeletion(e)){var n=this.SKIP_TOKEN();return this.consumeToken(),n}throw new nx("sad sad panda")},r.prototype.canPerformInRuleRecovery=function(e,t){return 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a=this;this.TRACE_INIT(""+o+(t===0?"":t),function(){var l=(0,ka.buildLookaheadFuncForOptionalProd)(t,e,s||a.maxLookahead,a.dynamicTokensEnabled,n,a.lookAheadBuilderForOptional),c=(0,Ra.getKeyForAutomaticLookahead)(a.fullRuleNameToShort[e.name],i,t);a.setLaFuncCache(c,l)})},r.prototype.lookAheadBuilderForOptional=function(e,t,i){return(0,ka.buildSingleAlternativeLookaheadFunction)(e,t,i)},r.prototype.lookAheadBuilderForAlternatives=function(e,t,i,n){return(0,ka.buildAlternativesLookAheadFunc)(e,t,i,n)},r.prototype.getKeyForAutomaticLookahead=function(e,t){var i=this.getLastExplicitRuleShortName();return(0,Ra.getKeyForAutomaticLookahead)(i,e,t)},r.prototype.getLaFuncFromCache=function(e){},r.prototype.getLaFuncFromMap=function(e){return this.lookAheadFuncsCache.get(e)},r.prototype.getLaFuncFromObj=function(e){return 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this.gastProductionsCache},r.prototype.getSerializedGastProductions=function(){return(0,vye.serializeGrammar)((0,Oq.values)(this.gastProductionsCache))},r}();yy.RecognizerApi=xye});var Yq=w(By=>{"use strict";Object.defineProperty(By,"__esModule",{value:!0});By.RecognizerEngine=void 0;var Pr=Gt(),qn=Cy(),wy=nf(),Uq=kd(),of=Dd(),Hq=jn(),Pye=sx(),Gq=TA(),Td=_g(),Dye=ox(),kye=function(){function r(){}return r.prototype.initRecognizerEngine=function(e,t){if(this.className=(0,Dye.classNameFromInstance)(this),this.shortRuleNameToFull={},this.fullRuleNameToShort={},this.ruleShortNameIdx=256,this.tokenMatcher=Td.tokenStructuredMatcherNoCategories,this.definedRulesNames=[],this.tokensMap={},this.isBackTrackingStack=[],this.RULE_STACK=[],this.RULE_OCCURRENCE_STACK=[],this.gastProductionsCache={},(0,Pr.has)(t,"serializedGrammar"))throw Error(`The Parser's configuration can no longer contain a property. + See: https://chevrotain.io/docs/changes/BREAKING_CHANGES.html#_6-0-0 + For Further details.`);if((0,Pr.isArray)(e)){if((0,Pr.isEmpty)(e))throw Error(`A Token Vocabulary cannot be empty. + Note that the first argument for the parser constructor + is no longer a Token vector (since v4.0).`);if(typeof e[0].startOffset=="number")throw Error(`The Parser constructor no longer accepts a token vector as the first argument. + See: https://chevrotain.io/docs/changes/BREAKING_CHANGES.html#_4-0-0 + For Further details.`)}if((0,Pr.isArray)(e))this.tokensMap=(0,Pr.reduce)(e,function(o,a){return o[a.name]=a,o},{});else if((0,Pr.has)(e,"modes")&&(0,Pr.every)((0,Pr.flatten)((0,Pr.values)(e.modes)),Td.isTokenType)){var i=(0,Pr.flatten)((0,Pr.values)(e.modes)),n=(0,Pr.uniq)(i);this.tokensMap=(0,Pr.reduce)(n,function(o,a){return o[a.name]=a,o},{})}else if((0,Pr.isObject)(e))this.tokensMap=(0,Pr.cloneObj)(e);else throw new Error(" argument must be An Array of Token constructors, A dictionary of Token constructors or an IMultiModeLexerDefinition");this.tokensMap.EOF=Gq.EOF;var s=(0,Pr.every)((0,Pr.values)(e),function(o){return(0,Pr.isEmpty)(o.categoryMatches)});this.tokenMatcher=s?Td.tokenStructuredMatcherNoCategories:Td.tokenStructuredMatcher,(0,Td.augmentTokenTypes)((0,Pr.values)(this.tokensMap))},r.prototype.defineRule=function(e,t,i){if(this.selfAnalysisDone)throw Error("Grammar rule <"+e+`> may not be defined after the 'performSelfAnalysis' method has been called' +Make sure that all grammar rule definitions are done before 'performSelfAnalysis' is called.`);var n=(0,Pr.has)(i,"resyncEnabled")?i.resyncEnabled:Hq.DEFAULT_RULE_CONFIG.resyncEnabled,s=(0,Pr.has)(i,"recoveryValueFunc")?i.recoveryValueFunc:Hq.DEFAULT_RULE_CONFIG.recoveryValueFunc,o=this.ruleShortNameIdx<t},r.prototype.orInternal=function(e,t){var i=this.getKeyForAutomaticLookahead(qn.OR_IDX,t),n=(0,Pr.isArray)(e)?e:e.DEF,s=this.getLaFuncFromCache(i),o=s.call(this,n);if(o!==void 0){var a=n[o];return a.ALT.call(this)}this.raiseNoAltException(t,e.ERR_MSG)},r.prototype.ruleFinallyStateUpdate=function(){if(this.RULE_STACK.pop(),this.RULE_OCCURRENCE_STACK.pop(),this.cstFinallyStateUpdate(),this.RULE_STACK.length===0&&this.isAtEndOfInput()===!1){var e=this.LA(1),t=this.errorMessageProvider.buildNotAllInputParsedMessage({firstRedundant:e,ruleName:this.getCurrRuleFullName()});this.SAVE_ERROR(new wy.NotAllInputParsedException(t,e))}},r.prototype.subruleInternal=function(e,t,i){var n;try{var s=i!==void 0?i.ARGS:void 0;return n=e.call(this,t,s),this.cstPostNonTerminal(n,i!==void 0&&i.LABEL!==void 0?i.LABEL:e.ruleName),n}catch(o){this.subruleInternalError(o,i,e.ruleName)}},r.prototype.subruleInternalError=function(e,t,i){throw(0,wy.isRecognitionException)(e)&&e.partialCstResult!==void 0&&(this.cstPostNonTerminal(e.partialCstResult,t!==void 0&&t.LABEL!==void 0?t.LABEL:i),delete e.partialCstResult),e},r.prototype.consumeInternal=function(e,t,i){var n;try{var s=this.LA(1);this.tokenMatcher(s,e)===!0?(this.consumeToken(),n=s):this.consumeInternalError(e,s,i)}catch(o){n=this.consumeInternalRecovery(e,t,o)}return this.cstPostTerminal(i!==void 0&&i.LABEL!==void 0?i.LABEL:e.name,n),n},r.prototype.consumeInternalError=function(e,t,i){var n,s=this.LA(0);throw i!==void 0&&i.ERR_MSG?n=i.ERR_MSG:n=this.errorMessageProvider.buildMismatchTokenMessage({expected:e,actual:t,previous:s,ruleName:this.getCurrRuleFullName()}),this.SAVE_ERROR(new wy.MismatchedTokenException(n,t,s))},r.prototype.consumeInternalRecovery=function(e,t,i){if(this.recoveryEnabled&&i.name==="MismatchedTokenException"&&!this.isBackTracking()){var n=this.getFollowsForInRuleRecovery(e,t);try{return this.tryInRuleRecovery(e,n)}catch(s){throw s.name===Pye.IN_RULE_RECOVERY_EXCEPTION?i:s}}else throw i},r.prototype.saveRecogState=function(){var e=this.errors,t=(0,Pr.cloneArr)(this.RULE_STACK);return{errors:e,lexerState:this.exportLexerState(),RULE_STACK:t,CST_STACK:this.CST_STACK}},r.prototype.reloadRecogState=function(e){this.errors=e.errors,this.importLexerState(e.lexerState),this.RULE_STACK=e.RULE_STACK},r.prototype.ruleInvocationStateUpdate=function(e,t,i){this.RULE_OCCURRENCE_STACK.push(i),this.RULE_STACK.push(e),this.cstInvocationStateUpdate(t,e)},r.prototype.isBackTracking=function(){return this.isBackTrackingStack.length!==0},r.prototype.getCurrRuleFullName=function(){var e=this.getLastExplicitRuleShortName();return this.shortRuleNameToFull[e]},r.prototype.shortRuleNameToFullName=function(e){return this.shortRuleNameToFull[e]},r.prototype.isAtEndOfInput=function(){return this.tokenMatcher(this.LA(1),Gq.EOF)},r.prototype.reset=function(){this.resetLexerState(),this.isBackTrackingStack=[],this.errors=[],this.RULE_STACK=[],this.CST_STACK=[],this.RULE_OCCURRENCE_STACK=[]},r}();By.RecognizerEngine=kye});var qq=w(by=>{"use strict";Object.defineProperty(by,"__esModule",{value:!0});by.ErrorHandler=void 0;var lx=nf(),cx=Gt(),jq=kd(),Rye=jn(),Fye=function(){function r(){}return r.prototype.initErrorHandler=function(e){this._errors=[],this.errorMessageProvider=(0,cx.has)(e,"errorMessageProvider")?e.errorMessageProvider:Rye.DEFAULT_PARSER_CONFIG.errorMessageProvider},r.prototype.SAVE_ERROR=function(e){if((0,lx.isRecognitionException)(e))return e.context={ruleStack:this.getHumanReadableRuleStack(),ruleOccurrenceStack:(0,cx.cloneArr)(this.RULE_OCCURRENCE_STACK)},this._errors.push(e),e;throw Error("Trying to save an Error which is not a RecognitionException")},Object.defineProperty(r.prototype,"errors",{get:function(){return(0,cx.cloneArr)(this._errors)},set:function(e){this._errors=e},enumerable:!1,configurable:!0}),r.prototype.raiseEarlyExitException=function(e,t,i){for(var n=this.getCurrRuleFullName(),s=this.getGAstProductions()[n],o=(0,jq.getLookaheadPathsForOptionalProd)(e,s,t,this.maxLookahead),a=o[0],l=[],c=1;c<=this.maxLookahead;c++)l.push(this.LA(c));var u=this.errorMessageProvider.buildEarlyExitMessage({expectedIterationPaths:a,actual:l,previous:this.LA(0),customUserDescription:i,ruleName:n});throw this.SAVE_ERROR(new lx.EarlyExitException(u,this.LA(1),this.LA(0)))},r.prototype.raiseNoAltException=function(e,t){for(var i=this.getCurrRuleFullName(),n=this.getGAstProductions()[i],s=(0,jq.getLookaheadPathsForOr)(e,n,this.maxLookahead),o=[],a=1;a<=this.maxLookahead;a++)o.push(this.LA(a));var l=this.LA(0),c=this.errorMessageProvider.buildNoViableAltMessage({expectedPathsPerAlt:s,actual:o,previous:l,customUserDescription:t,ruleName:this.getCurrRuleFullName()});throw this.SAVE_ERROR(new lx.NoViableAltException(c,this.LA(1),l))},r}();by.ErrorHandler=Fye});var zq=w(Qy=>{"use strict";Object.defineProperty(Qy,"__esModule",{value:!0});Qy.ContentAssist=void 0;var Jq=Dd(),Wq=Gt(),Nye=function(){function r(){}return r.prototype.initContentAssist=function(){},r.prototype.computeContentAssist=function(e,t){var i=this.gastProductionsCache[e];if((0,Wq.isUndefined)(i))throw Error("Rule ->"+e+"<- does not exist in this grammar.");return(0,Jq.nextPossibleTokensAfter)([i],t,this.tokenMatcher,this.maxLookahead)},r.prototype.getNextPossibleTokenTypes=function(e){var t=(0,Wq.first)(e.ruleStack),i=this.getGAstProductions(),n=i[t],s=new Jq.NextAfterTokenWalker(n,e).startWalking();return s},r}();Qy.ContentAssist=Nye});var rJ=w(xy=>{"use strict";Object.defineProperty(xy,"__esModule",{value:!0});xy.GastRecorder=void 0;var yn=Gt(),Mo=mn(),Tye=Bd(),_q=_g(),$q=TA(),Lye=jn(),Mye=Cy(),vy={description:"This Object indicates the Parser is during Recording Phase"};Object.freeze(vy);var Vq=!0,Xq=Math.pow(2,Mye.BITS_FOR_OCCURRENCE_IDX)-1,eJ=(0,$q.createToken)({name:"RECORDING_PHASE_TOKEN",pattern:Tye.Lexer.NA});(0,_q.augmentTokenTypes)([eJ]);var tJ=(0,$q.createTokenInstance)(eJ,`This IToken indicates the Parser is in Recording Phase + See: https://chevrotain.io/docs/guide/internals.html#grammar-recording for details`,-1,-1,-1,-1,-1,-1);Object.freeze(tJ);var Oye={name:`This CSTNode indicates the Parser is in Recording Phase + See: https://chevrotain.io/docs/guide/internals.html#grammar-recording for details`,children:{}},Kye=function(){function r(){}return r.prototype.initGastRecorder=function(e){this.recordingProdStack=[],this.RECORDING_PHASE=!1},r.prototype.enableRecording=function(){var e=this;this.RECORDING_PHASE=!0,this.TRACE_INIT("Enable Recording",function(){for(var t=function(n){var s=n>0?n:"";e["CONSUME"+s]=function(o,a){return this.consumeInternalRecord(o,n,a)},e["SUBRULE"+s]=function(o,a){return this.subruleInternalRecord(o,n,a)},e["OPTION"+s]=function(o){return this.optionInternalRecord(o,n)},e["OR"+s]=function(o){return this.orInternalRecord(o,n)},e["MANY"+s]=function(o){this.manyInternalRecord(n,o)},e["MANY_SEP"+s]=function(o){this.manySepFirstInternalRecord(n,o)},e["AT_LEAST_ONE"+s]=function(o){this.atLeastOneInternalRecord(n,o)},e["AT_LEAST_ONE_SEP"+s]=function(o){this.atLeastOneSepFirstInternalRecord(n,o)}},i=0;i<10;i++)t(i);e.consume=function(n,s,o){return this.consumeInternalRecord(s,n,o)},e.subrule=function(n,s,o){return this.subruleInternalRecord(s,n,o)},e.option=function(n,s){return this.optionInternalRecord(s,n)},e.or=function(n,s){return this.orInternalRecord(s,n)},e.many=function(n,s){this.manyInternalRecord(n,s)},e.atLeastOne=function(n,s){this.atLeastOneInternalRecord(n,s)},e.ACTION=e.ACTION_RECORD,e.BACKTRACK=e.BACKTRACK_RECORD,e.LA=e.LA_RECORD})},r.prototype.disableRecording=function(){var e=this;this.RECORDING_PHASE=!1,this.TRACE_INIT("Deleting Recording methods",function(){for(var t=0;t<10;t++){var i=t>0?t:"";delete e["CONSUME"+i],delete e["SUBRULE"+i],delete e["OPTION"+i],delete e["OR"+i],delete e["MANY"+i],delete e["MANY_SEP"+i],delete e["AT_LEAST_ONE"+i],delete e["AT_LEAST_ONE_SEP"+i]}delete e.consume,delete e.subrule,delete e.option,delete e.or,delete e.many,delete e.atLeastOne,delete e.ACTION,delete e.BACKTRACK,delete e.LA})},r.prototype.ACTION_RECORD=function(e){},r.prototype.BACKTRACK_RECORD=function(e,t){return function(){return!0}},r.prototype.LA_RECORD=function(e){return Lye.END_OF_FILE},r.prototype.topLevelRuleRecord=function(e,t){try{var i=new Mo.Rule({definition:[],name:e});return i.name=e,this.recordingProdStack.push(i),t.call(this),this.recordingProdStack.pop(),i}catch(n){if(n.KNOWN_RECORDER_ERROR!==!0)try{n.message=n.message+` + This error was thrown during the "grammar recording phase" For more info see: + https://chevrotain.io/docs/guide/internals.html#grammar-recording`}catch{throw n}throw n}},r.prototype.optionInternalRecord=function(e,t){return Ld.call(this,Mo.Option,e,t)},r.prototype.atLeastOneInternalRecord=function(e,t){Ld.call(this,Mo.RepetitionMandatory,t,e)},r.prototype.atLeastOneSepFirstInternalRecord=function(e,t){Ld.call(this,Mo.RepetitionMandatoryWithSeparator,t,e,Vq)},r.prototype.manyInternalRecord=function(e,t){Ld.call(this,Mo.Repetition,t,e)},r.prototype.manySepFirstInternalRecord=function(e,t){Ld.call(this,Mo.RepetitionWithSeparator,t,e,Vq)},r.prototype.orInternalRecord=function(e,t){return Uye.call(this,e,t)},r.prototype.subruleInternalRecord=function(e,t,i){if(Sy(t),!e||(0,yn.has)(e,"ruleName")===!1){var n=new Error(" argument is invalid"+(" expecting a Parser method reference but got: <"+JSON.stringify(e)+">")+(` + inside top level rule: <`+this.recordingProdStack[0].name+">"));throw n.KNOWN_RECORDER_ERROR=!0,n}var s=(0,yn.peek)(this.recordingProdStack),o=e.ruleName,a=new Mo.NonTerminal({idx:t,nonTerminalName:o,label:i==null?void 0:i.LABEL,referencedRule:void 0});return s.definition.push(a),this.outputCst?Oye:vy},r.prototype.consumeInternalRecord=function(e,t,i){if(Sy(t),!(0,_q.hasShortKeyProperty)(e)){var n=new Error(" argument is invalid"+(" expecting a TokenType reference but got: <"+JSON.stringify(e)+">")+(` + inside top level rule: <`+this.recordingProdStack[0].name+">"));throw n.KNOWN_RECORDER_ERROR=!0,n}var s=(0,yn.peek)(this.recordingProdStack),o=new Mo.Terminal({idx:t,terminalType:e,label:i==null?void 0:i.LABEL});return s.definition.push(o),tJ},r}();xy.GastRecorder=Kye;function Ld(r,e,t,i){i===void 0&&(i=!1),Sy(t);var n=(0,yn.peek)(this.recordingProdStack),s=(0,yn.isFunction)(e)?e:e.DEF,o=new r({definition:[],idx:t});return i&&(o.separator=e.SEP),(0,yn.has)(e,"MAX_LOOKAHEAD")&&(o.maxLookahead=e.MAX_LOOKAHEAD),this.recordingProdStack.push(o),s.call(this),n.definition.push(o),this.recordingProdStack.pop(),vy}function Uye(r,e){var t=this;Sy(e);var i=(0,yn.peek)(this.recordingProdStack),n=(0,yn.isArray)(r)===!1,s=n===!1?r:r.DEF,o=new Mo.Alternation({definition:[],idx:e,ignoreAmbiguities:n&&r.IGNORE_AMBIGUITIES===!0});(0,yn.has)(r,"MAX_LOOKAHEAD")&&(o.maxLookahead=r.MAX_LOOKAHEAD);var a=(0,yn.some)(s,function(l){return(0,yn.isFunction)(l.GATE)});return o.hasPredicates=a,i.definition.push(o),(0,yn.forEach)(s,function(l){var c=new Mo.Alternative({definition:[]});o.definition.push(c),(0,yn.has)(l,"IGNORE_AMBIGUITIES")?c.ignoreAmbiguities=l.IGNORE_AMBIGUITIES:(0,yn.has)(l,"GATE")&&(c.ignoreAmbiguities=!0),t.recordingProdStack.push(c),l.ALT.call(t),t.recordingProdStack.pop()}),vy}function Zq(r){return r===0?"":""+r}function Sy(r){if(r<0||r>Xq){var e=new Error("Invalid DSL Method idx value: <"+r+`> + `+("Idx value must be a none negative value smaller than "+(Xq+1)));throw e.KNOWN_RECORDER_ERROR=!0,e}}});var nJ=w(Py=>{"use strict";Object.defineProperty(Py,"__esModule",{value:!0});Py.PerformanceTracer=void 0;var iJ=Gt(),Hye=jn(),Gye=function(){function r(){}return r.prototype.initPerformanceTracer=function(e){if((0,iJ.has)(e,"traceInitPerf")){var t=e.traceInitPerf,i=typeof t=="number";this.traceInitMaxIdent=i?t:1/0,this.traceInitPerf=i?t>0:t}else this.traceInitMaxIdent=0,this.traceInitPerf=Hye.DEFAULT_PARSER_CONFIG.traceInitPerf;this.traceInitIndent=-1},r.prototype.TRACE_INIT=function(e,t){if(this.traceInitPerf===!0){this.traceInitIndent++;var i=new Array(this.traceInitIndent+1).join(" ");this.traceInitIndent <"+e+">");var n=(0,iJ.timer)(t),s=n.time,o=n.value,a=s>10?console.warn:console.log;return this.traceInitIndent time: "+s+"ms"),this.traceInitIndent--,o}else return t()},r}();Py.PerformanceTracer=Gye});var sJ=w(Dy=>{"use strict";Object.defineProperty(Dy,"__esModule",{value:!0});Dy.applyMixins=void 0;function Yye(r,e){e.forEach(function(t){var i=t.prototype;Object.getOwnPropertyNames(i).forEach(function(n){if(n!=="constructor"){var s=Object.getOwnPropertyDescriptor(i,n);s&&(s.get||s.set)?Object.defineProperty(r.prototype,n,s):r.prototype[n]=t.prototype[n]}})})}Dy.applyMixins=Yye});var jn=w(dr=>{"use strict";var AJ=dr&&dr.__extends||function(){var r=function(e,t){return r=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(i,n){i.__proto__=n}||function(i,n){for(var s in n)Object.prototype.hasOwnProperty.call(n,s)&&(i[s]=n[s])},r(e,t)};return function(e,t){if(typeof t!="function"&&t!==null)throw new TypeError("Class extends value "+String(t)+" is not a constructor or null");r(e,t);function i(){this.constructor=e}e.prototype=t===null?Object.create(t):(i.prototype=t.prototype,new i)}}();Object.defineProperty(dr,"__esModule",{value:!0});dr.EmbeddedActionsParser=dr.CstParser=dr.Parser=dr.EMPTY_ALT=dr.ParserDefinitionErrorType=dr.DEFAULT_RULE_CONFIG=dr.DEFAULT_PARSER_CONFIG=dr.END_OF_FILE=void 0;var en=Gt(),jye=qj(),oJ=TA(),lJ=xd(),aJ=Cq(),qye=sx(),Jye=Qq(),Wye=Tq(),zye=Mq(),Vye=Kq(),Xye=Yq(),Zye=qq(),_ye=zq(),$ye=rJ(),ewe=nJ(),twe=sJ();dr.END_OF_FILE=(0,oJ.createTokenInstance)(oJ.EOF,"",NaN,NaN,NaN,NaN,NaN,NaN);Object.freeze(dr.END_OF_FILE);dr.DEFAULT_PARSER_CONFIG=Object.freeze({recoveryEnabled:!1,maxLookahead:3,dynamicTokensEnabled:!1,outputCst:!0,errorMessageProvider:lJ.defaultParserErrorProvider,nodeLocationTracking:"none",traceInitPerf:!1,skipValidations:!1});dr.DEFAULT_RULE_CONFIG=Object.freeze({recoveryValueFunc:function(){},resyncEnabled:!0});var rwe;(function(r){r[r.INVALID_RULE_NAME=0]="INVALID_RULE_NAME",r[r.DUPLICATE_RULE_NAME=1]="DUPLICATE_RULE_NAME",r[r.INVALID_RULE_OVERRIDE=2]="INVALID_RULE_OVERRIDE",r[r.DUPLICATE_PRODUCTIONS=3]="DUPLICATE_PRODUCTIONS",r[r.UNRESOLVED_SUBRULE_REF=4]="UNRESOLVED_SUBRULE_REF",r[r.LEFT_RECURSION=5]="LEFT_RECURSION",r[r.NONE_LAST_EMPTY_ALT=6]="NONE_LAST_EMPTY_ALT",r[r.AMBIGUOUS_ALTS=7]="AMBIGUOUS_ALTS",r[r.CONFLICT_TOKENS_RULES_NAMESPACE=8]="CONFLICT_TOKENS_RULES_NAMESPACE",r[r.INVALID_TOKEN_NAME=9]="INVALID_TOKEN_NAME",r[r.NO_NON_EMPTY_LOOKAHEAD=10]="NO_NON_EMPTY_LOOKAHEAD",r[r.AMBIGUOUS_PREFIX_ALTS=11]="AMBIGUOUS_PREFIX_ALTS",r[r.TOO_MANY_ALTS=12]="TOO_MANY_ALTS"})(rwe=dr.ParserDefinitionErrorType||(dr.ParserDefinitionErrorType={}));function iwe(r){return r===void 0&&(r=void 0),function(){return r}}dr.EMPTY_ALT=iwe;var ky=function(){function r(e,t){this.definitionErrors=[],this.selfAnalysisDone=!1;var i=this;if(i.initErrorHandler(t),i.initLexerAdapter(),i.initLooksAhead(t),i.initRecognizerEngine(e,t),i.initRecoverable(t),i.initTreeBuilder(t),i.initContentAssist(),i.initGastRecorder(t),i.initPerformanceTracer(t),(0,en.has)(t,"ignoredIssues"))throw new Error(`The IParserConfig property has been deprecated. + Please use the flag on the relevant DSL method instead. + See: https://chevrotain.io/docs/guide/resolving_grammar_errors.html#IGNORING_AMBIGUITIES + For further details.`);this.skipValidations=(0,en.has)(t,"skipValidations")?t.skipValidations:dr.DEFAULT_PARSER_CONFIG.skipValidations}return r.performSelfAnalysis=function(e){throw Error("The **static** `performSelfAnalysis` method has been deprecated. \nUse the **instance** method with the same name instead.")},r.prototype.performSelfAnalysis=function(){var e=this;this.TRACE_INIT("performSelfAnalysis",function(){var t;e.selfAnalysisDone=!0;var i=e.className;e.TRACE_INIT("toFastProps",function(){(0,en.toFastProperties)(e)}),e.TRACE_INIT("Grammar Recording",function(){try{e.enableRecording(),(0,en.forEach)(e.definedRulesNames,function(s){var o=e[s],a=o.originalGrammarAction,l=void 0;e.TRACE_INIT(s+" Rule",function(){l=e.topLevelRuleRecord(s,a)}),e.gastProductionsCache[s]=l})}finally{e.disableRecording()}});var n=[];if(e.TRACE_INIT("Grammar Resolving",function(){n=(0,aJ.resolveGrammar)({rules:(0,en.values)(e.gastProductionsCache)}),e.definitionErrors=e.definitionErrors.concat(n)}),e.TRACE_INIT("Grammar Validations",function(){if((0,en.isEmpty)(n)&&e.skipValidations===!1){var s=(0,aJ.validateGrammar)({rules:(0,en.values)(e.gastProductionsCache),maxLookahead:e.maxLookahead,tokenTypes:(0,en.values)(e.tokensMap),errMsgProvider:lJ.defaultGrammarValidatorErrorProvider,grammarName:i});e.definitionErrors=e.definitionErrors.concat(s)}}),(0,en.isEmpty)(e.definitionErrors)&&(e.recoveryEnabled&&e.TRACE_INIT("computeAllProdsFollows",function(){var s=(0,jye.computeAllProdsFollows)((0,en.values)(e.gastProductionsCache));e.resyncFollows=s}),e.TRACE_INIT("ComputeLookaheadFunctions",function(){e.preComputeLookaheadFunctions((0,en.values)(e.gastProductionsCache))})),!r.DEFER_DEFINITION_ERRORS_HANDLING&&!(0,en.isEmpty)(e.definitionErrors))throw t=(0,en.map)(e.definitionErrors,function(s){return s.message}),new Error(`Parser Definition Errors detected: + `+t.join(` +------------------------------- +`))})},r.DEFER_DEFINITION_ERRORS_HANDLING=!1,r}();dr.Parser=ky;(0,twe.applyMixins)(ky,[qye.Recoverable,Jye.LooksAhead,Wye.TreeBuilder,zye.LexerAdapter,Xye.RecognizerEngine,Vye.RecognizerApi,Zye.ErrorHandler,_ye.ContentAssist,$ye.GastRecorder,ewe.PerformanceTracer]);var nwe=function(r){AJ(e,r);function e(t,i){i===void 0&&(i=dr.DEFAULT_PARSER_CONFIG);var n=this,s=(0,en.cloneObj)(i);return s.outputCst=!0,n=r.call(this,t,s)||this,n}return e}(ky);dr.CstParser=nwe;var swe=function(r){AJ(e,r);function e(t,i){i===void 0&&(i=dr.DEFAULT_PARSER_CONFIG);var n=this,s=(0,en.cloneObj)(i);return s.outputCst=!1,n=r.call(this,t,s)||this,n}return e}(ky);dr.EmbeddedActionsParser=swe});var uJ=w(Ry=>{"use strict";Object.defineProperty(Ry,"__esModule",{value:!0});Ry.createSyntaxDiagramsCode=void 0;var cJ=Dv();function owe(r,e){var t=e===void 0?{}:e,i=t.resourceBase,n=i===void 0?"https://unpkg.com/chevrotain@"+cJ.VERSION+"/diagrams/":i,s=t.css,o=s===void 0?"https://unpkg.com/chevrotain@"+cJ.VERSION+"/diagrams/diagrams.css":s,a=` + + + + + +`,l=` + +`,c=` + +

Very basic Noir app

+
+

Logs

+

Proof

+
+ + +``` + +## Some good old vanilla Javascript + +Create a new file `app.js`, which is where our javascript code will live. Let's start with this code inside: + +```js +document.addEventListener('DOMContentLoaded', async () => { + // here's where the magic happens +}); + +function display(container, msg) { + const c = document.getElementById(container); + const p = document.createElement('p'); + p.textContent = msg; + c.appendChild(p); +} +``` + +We can manipulate our website with this little function, so we can see our website working. + +## Adding Noir + +If you come from the previous page, your folder structure should look like this: + +```tree +├── app.js +├── circuit +│ ├── Nargo.toml +│ ├── src +│ │ └── main.nr +│ └── target +│ └── circuit.json +├── index.html +├── package.json +└── vite.config.js +``` + +You'll see other files and folders showing up (like `package-lock.json`, `yarn.lock`, `node_modules`) but you shouldn't have to care about those. + +## Importing our dependencies + +We're starting with the good stuff now. At the top of the new javascript file, import the packages: + +```ts +import { BarretenbergBackend } from '@noir-lang/backend_barretenberg'; +import { Noir } from '@noir-lang/noir_js'; +``` + +We also need to import the `circuit` JSON file we created. If you have the suggested folder structure, you can add this line: + +```ts +import circuit from './circuit/target/circuit.json'; +``` + +## Write code + +:::note + +We're gonna be adding code inside the `document.addEventListener...etc` block: + +```js +// forget stuff here +document.addEventListener('DOMContentLoaded', async () => { + // here's where the magic happens +}); +// forget stuff here +``` + +::: + +Our dependencies exported two classes: `BarretenbergBackend` and `Noir`. Let's `init` them and add some logs, just to flex: + +```ts +const backend = new BarretenbergBackend(circuit); +const noir = new Noir(circuit, backend); +``` + +## Proving + +Now we're ready to prove stuff! Let's feed some inputs to our circuit and calculate the proof: + +```js +const input = { x: 1, y: 2 }; +display('logs', 'Generating proof... ⌛'); +const proof = await noir.generateFinalProof(input); +display('logs', 'Generating proof... ✅'); +display('results', proof.proof); +``` + +You're probably eager to see stuff happening, so go and run your app now! + +From your terminal, run `npm start` (or `yarn start`). If it doesn't open a browser for you, just visit `localhost:5173`. On a modern laptop, proof will generate in less than 100ms, and you'll see this: + +![Getting Started 0](@site/static/img/noir_getting_started_1.png) + +If you're human, you shouldn't be able to understand anything on the "proof" box. That's OK. We like you, human. + +In any case, this means your proof was generated! But you shouldn't trust me just yet. Add these lines to see it being verified: + +```js +display('logs', 'Verifying proof... ⌛'); +const verification = await noir.verifyFinalProof(proof); +if (verification) display('logs', 'Verifying proof... ✅'); +``` + +By saving, your app will refresh and here's our complete Tiny Noir App! + +You can find the complete app code for this guide [here](https://github.com/noir-lang/tiny-noirjs-app). + +## Further Reading + +You can see how noirjs is used in a full stack Next.js hardhat application in the [noir-starter repo here](https://github.com/noir-lang/noir-starter/tree/main/next-hardhat). The example shows how to calculate a proof in the browser and verify it with a deployed Solidity verifier contract from noirjs. + +You should also check out the more advanced examples in the [noir-examples repo](https://github.com/noir-lang/noir-examples), where you'll find reference usage for some cool apps. diff --git a/noir/docs/docs/noir_js/noir_js.md b/noir/docs/docs/noir_js/noir_js.md new file mode 100644 index 00000000000..f895b22eaf8 --- /dev/null +++ b/noir/docs/docs/noir_js/noir_js.md @@ -0,0 +1,36 @@ +--- +title: NoirJS +description: Interact with Noir in Typescript or Javascript +keywords: [Noir project, javascript, typescript, node.js, browser, react] +--- + +NoirJS is a TypeScript library that make it easy to use Noir on your dapp, webapp, Node.js server, website, etc. + +A typical workflow would be composed of two major elements: + +- NoirJS +- Proving backend of choice's JavaScript package + + + +To install NoirJS, install Node.js if you have not already and run this in your JavaScript project: + +```bash +npm i @noir-lang/noir_js +``` + +## Proving backend + +Since Noir is backend agnostic, you can instantiate NoirJS without any backend (i.e. to execute a function). But for proving, you would have to instantiate NoirJS with any of the supported backends through their own `js` interface. + +### Barretenberg + +Aztec Labs maintains the `barretenberg` proving backend, which you can instantiate and make use of alongside NoirJS. It is also the default proving backend installed and used with Nargo, the Noir CLI tool. + +To install its JavaScript library, run this in your project: + +```bash +npm i @noir-lang/backend_barretenberg +``` + +For more details on how to instantiate and use the libraries, refer to the [Full Noir App Guide](./getting_started/01_tiny_noir_app.md) and [Reference](./reference/noir_js/classes/Noir.md) sections. diff --git a/noir/docs/docs/standard_library/black_box_fns.md b/noir/docs/docs/standard_library/black_box_fns.md new file mode 100644 index 00000000000..1dfabfe8f22 --- /dev/null +++ b/noir/docs/docs/standard_library/black_box_fns.md @@ -0,0 +1,46 @@ +--- +title: Black Box Functions +description: Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. +keywords: [noir, black box functions] +--- + +Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. This makes certain zk-snark unfriendly computations cheaper than if they were implemented in Noir. + +:::warning + +It is likely that not all backends will support a particular black box function. + +::: + +Because it is not guaranteed that all backends will support black box functions, it is possible that certain Noir programs won't compile against a particular backend if they use an unsupported black box function. It is possible to fallback to less efficient implementations written in Noir/ACIR in some cases. + +Black box functions are specified with the `#[foreign(black_box_fn)]` attribute. For example, the SHA256 function in the Noir [source code](https://github.com/noir-lang/noir/blob/v0.5.1/noir_stdlib/src/hash.nr) looks like: + +```rust +#[foreign(sha256)] +fn sha256(_input : [u8; N]) -> [u8; 32] {} +``` + +## Function list + +Here is a list of the current black box functions that are supported by UltraPlonk: + +- AES +- [SHA256](./cryptographic_primitives/hashes#sha256) +- [Schnorr signature verification](./cryptographic_primitives/schnorr) +- [Blake2s](./cryptographic_primitives/hashes#blake2s) +- [Pedersen Hash](./cryptographic_primitives/hashes#pedersen_hash) +- [Pedersen Commitment](./cryptographic_primitives/hashes#pedersen_commitment) +- [HashToField128Security](./cryptographic_primitives/hashes#hash_to_field) +- [ECDSA signature verification](./cryptographic_primitives/ecdsa_sig_verification) +- [Fixed base scalar multiplication](./cryptographic_primitives/scalar) +- [Compute merkle root](./merkle_trees#compute_merkle_root) +- AND +- XOR +- RANGE +- [Keccak256](./cryptographic_primitives/hashes#keccak256) +- [Recursive proof verification](./recursion) + +Most black box functions are included as part of the Noir standard library, however `AND`, `XOR` and `RANGE` are used as part of the Noir language syntax. For instance, using the bitwise operator `&` will invoke the `AND` black box function. To ensure compatibility across backends, the ACVM has fallback implementations of `AND`, `XOR` and `RANGE` defined in its standard library which it can seamlessly fallback to if the backend doesn't support them. + +You can view the black box functions defined in the ACVM code [here](https://github.com/noir-lang/acvm/blob/acir-v0.12.0/acir/src/circuit/black_box_functions.rs). diff --git a/noir/docs/docs/standard_library/cryptographic_primitives.md b/noir/docs/docs/standard_library/cryptographic_primitives.md new file mode 100644 index 00000000000..2df4f929474 --- /dev/null +++ b/noir/docs/docs/standard_library/cryptographic_primitives.md @@ -0,0 +1,14 @@ +--- +title: Cryptographic primitives in Noir +description: + Learn about the cryptographic primitives ready to use for any Noir project +keywords: + [ + cryptographic primitives, + Noir project, + ] +--- + +The Noir team is progressively adding new cryptographic primitives to the standard library. Reach out for news or if you would be interested in adding more of these calculations in Noir. + +Some methods are available thanks to the Aztec backend, not being performed using Noir. When using other backends, these methods may or may not be supplied. diff --git a/noir/docs/docs/standard_library/cryptographic_primitives/00_hashes.mdx b/noir/docs/docs/standard_library/cryptographic_primitives/00_hashes.mdx new file mode 100644 index 00000000000..76745196681 --- /dev/null +++ b/noir/docs/docs/standard_library/cryptographic_primitives/00_hashes.mdx @@ -0,0 +1,167 @@ +--- +title: Hash methods +description: + Learn about the cryptographic primitives ready to use for any Noir project, including sha256, + blake2s, pedersen, mimc_bn254 and mimc +keywords: + [cryptographic primitives, Noir project, sha256, blake2s, pedersen, mimc_bn254, mimc, hash] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## sha256 + +Given an array of bytes, returns the resulting sha256 hash. + +```rust +fn sha256(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::sha256(x); +} +``` + + + +## blake2s + +Given an array of bytes, returns an array with the Blake2 hash + +```rust +fn blake2s(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::blake2s(x); +} +``` + + + +## pedersen_hash + +Given an array of Fields, returns the Pedersen hash. + +```rust +fn pedersen_hash(_input : [Field]) -> Field +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::pedersen_hash(x); +} +``` + + + + + +## pedersen_commitment + +Given an array of Fields, returns the Pedersen commitment. + +```rust +fn pedersen_commitment(_input : [Field]) -> [Field; 2] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let commitment = std::hash::pedersen_commitment(x); +} +``` + + + +## keccak256 + +Given an array of bytes (`u8`), returns the resulting keccak hash as an array of 32 bytes +(`[u8; 32]`). Specify a message_size to hash only the first `message_size` bytes +of the input. + +```rust +fn keccak256(_input : [u8; N], _message_size: u32) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let message_size = 4; + let hash = std::hash::keccak256(x, message_size); +} +``` + + + +## poseidon + +Given an array of Fields, returns a new Field with the Poseidon Hash. Mind that you need to specify +how many inputs are there to your Poseidon function. + +```rust +// example for hash_1, hash_2 accepts an array of length 2, etc +fn hash_1(input: [Field; 1]) -> Field +``` + +example: + +```rust +fn main() +{ + let hash1 = std::hash::poseidon::bn254::hash_2([1, 2]); + assert(hash1 == 0x115cc0f5e7d690413df64c6b9662e9cf2a3617f2743245519e19607a4417189a); +} +``` + +## mimc_bn254 and mimc + +`mimc_bn254` is `mimc`, but with hardcoded parameters for the BN254 curve. You can use it by +providing an array of Fields, and it returns a Field with the hash. You can use the `mimc` method if +you're willing to input your own constants: + +```rust +fn mimc(x: Field, k: Field, constants: [Field; N], exp : Field) -> Field +``` + +otherwise, use the `mimc_bn254` method: + +```rust +fn mimc_bn254(array: [Field; N]) -> Field +``` + +example: + +```rust + +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::mimc::mimc_bn254(x); +} +``` + +## hash_to_field + +```rust +fn hash_to_field(_input : [Field; N]) -> Field {} +``` + +Calculates the `blake2s` hash of the inputs and returns the hash modulo the field modulus to return +a value which can be represented as a `Field`. + + diff --git a/noir/docs/docs/standard_library/cryptographic_primitives/01_scalar.mdx b/noir/docs/docs/standard_library/cryptographic_primitives/01_scalar.mdx new file mode 100644 index 00000000000..c7eed820a80 --- /dev/null +++ b/noir/docs/docs/standard_library/cryptographic_primitives/01_scalar.mdx @@ -0,0 +1,27 @@ +--- +title: Scalar multiplication +description: See how you can perform scalar multiplications over a fixed base in Noir +keywords: [cryptographic primitives, Noir project, scalar multiplication] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## scalar_mul::fixed_base_embedded_curve + +Performs scalar multiplication over the embedded curve whose coordinates are defined by the +configured noir field. For the BN254 scalar field, this is BabyJubJub or Grumpkin. + +```rust +fn fixed_base_embedded_curve(_input : Field) -> [Field; 2] +``` + +example + +```rust +fn main(x : Field) { + let scal = std::scalar_mul::fixed_base_embedded_curve(x); + std::println(scal); +} +``` + + diff --git a/noir/docs/docs/standard_library/cryptographic_primitives/02_schnorr.mdx b/noir/docs/docs/standard_library/cryptographic_primitives/02_schnorr.mdx new file mode 100644 index 00000000000..c184ce28120 --- /dev/null +++ b/noir/docs/docs/standard_library/cryptographic_primitives/02_schnorr.mdx @@ -0,0 +1,37 @@ +--- +title: Schnorr Signatures +description: Learn how you can verify Schnorr signatures using Noir +keywords: [cryptographic primitives, Noir project, schnorr, signatures] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## schnorr::verify_signature + +Verifier for Schnorr signatures over the embedded curve (for BN254 it is Grumpkin). + +```rust +fn verify_signature(_public_key_x: Field, _public_key_y: Field, _signature: [u8; 64], _message: [u8]) -> bool +``` + +where `_signature` can be generated like so using the npm package +[@noir-lang/barretenberg](https://www.npmjs.com/package/@noir-lang/barretenberg) + +```js +const { BarretenbergWasm } = require('@noir-lang/barretenberg/dest/wasm'); +const { Schnorr } = require('@noir-lang/barretenberg/dest/crypto/schnorr'); + +... + +const barretenberg = await BarretenbergWasm.new(); +const schnorr = new Schnorr(barretenberg); +const pubKey = schnorr.computePublicKey(privateKey); +const message = ... +const signature = Array.from( + schnorr.constructSignature(hash, privateKey).toBuffer() +); + +... +``` + + diff --git a/noir/docs/docs/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx b/noir/docs/docs/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx new file mode 100644 index 00000000000..72bce984821 --- /dev/null +++ b/noir/docs/docs/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx @@ -0,0 +1,45 @@ +--- +title: ECDSA Signature Verification +description: Learn about the cryptographic primitives regarding ECDSA over the secp256k1 and secp256r1 curves +keywords: [cryptographic primitives, Noir project, ecdsa, secp256k1, secp256r1, signatures] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +Noir supports ECDSA signatures verification over the secp256k1 and secp256r1 curves. + +## ecdsa_secp256k1::verify_signature + +Verifier for ECDSA Secp256k1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> bool +``` + +example: + +```rust +fn main(hashed_message : [u8;32], pub_key_x : [u8;32], pub_key_y : [u8;32], signature : [u8;64]) { + let valid_signature = std::ecdsa_secp256k1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} +``` + +## ecdsa_secp256r1::verify_signature + +Verifier for ECDSA Secp256r1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> bool +``` + +example: + +```rust +fn main(hashed_message : [u8;32], pub_key_x : [u8;32], pub_key_y : [u8;32], signature : [u8;64]) { + let valid_signature = std::ecdsa_secp256r1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} +``` + + diff --git a/noir/docs/docs/standard_library/cryptographic_primitives/04_ec_primitives.md b/noir/docs/docs/standard_library/cryptographic_primitives/04_ec_primitives.md new file mode 100644 index 00000000000..6e6b19b6861 --- /dev/null +++ b/noir/docs/docs/standard_library/cryptographic_primitives/04_ec_primitives.md @@ -0,0 +1,101 @@ +--- +title: Elliptic Curve Primitives +keywords: [cryptographic primitives, Noir project] +--- + +Data structures and methods on them that allow you to carry out computations involving elliptic +curves over the (mathematical) field corresponding to `Field`. For the field currently at our +disposal, applications would involve a curve embedded in BN254, e.g. the +[Baby Jubjub curve](https://eips.ethereum.org/EIPS/eip-2494). + +## Data structures + +### Elliptic curve configurations + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Curve`), i.e. the specific elliptic +curve you want to use, which would be specified using any one of the methods +`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::new` which take the coefficients in the +defining equation together with a generator point as parameters. You can find more detail in the +comments in +[`noir_stdlib/src/ec.nr`](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr), but +the gist of it is that the elliptic curves of interest are usually expressed in one of the standard +forms implemented here (Twisted Edwards, Montgomery and Short Weierstraß), and in addition to that, +you could choose to use `affine` coordinates (Cartesian coordinates - the usual (x,y) - possibly +together with a point at infinity) or `curvegroup` coordinates (some form of projective coordinates +requiring more coordinates but allowing for more efficient implementations of elliptic curve +operations). Conversions between all of these forms are provided, and under the hood these +conversions are done whenever an operation is more efficient in a different representation (or a +mixed coordinate representation is employed). + +### Points + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Point`), i.e. points lying on the +elliptic curve. For a curve configuration `c` and a point `p`, it may be checked that `p` +does indeed lie on `c` by calling `c.contains(p1)`. + +## Methods + +(given a choice of curve representation, e.g. use `std::ec::tecurve::affine::Curve` and use +`std::ec::tecurve::affine::Point`) + +- The **zero element** is given by `Point::zero()`, and we can verify whether a point `p: Point` is + zero by calling `p.is_zero()`. +- **Equality**: Points `p1: Point` and `p2: Point` may be checked for equality by calling + `p1.eq(p2)`. +- **Addition**: For `c: Curve` and points `p1: Point` and `p2: Point` on the curve, adding these two + points is accomplished by calling `c.add(p1,p2)`. +- **Negation**: For a point `p: Point`, `p.negate()` is its negation. +- **Subtraction**: For `c` and `p1`, `p2` as above, subtracting `p2` from `p1` is accomplished by + calling `c.subtract(p1,p2)`. +- **Scalar multiplication**: For `c` as above, `p: Point` a point on the curve and `n: Field`, + scalar multiplication is given by `c.mul(n,p)`. If instead `n :: [u1; N]`, i.e. `n` is a bit + array, the `bit_mul` method may be used instead: `c.bit_mul(n,p)` +- **Multi-scalar multiplication**: For `c` as above and arrays `n: [Field; N]` and `p: [Point; N]`, + multi-scalar multiplication is given by `c.msm(n,p)`. +- **Coordinate representation conversions**: The `into_group` method converts a point or curve + configuration in the affine representation to one in the CurveGroup representation, and + `into_affine` goes in the other direction. +- **Curve representation conversions**: `tecurve` and `montcurve` curves and points are equivalent + and may be converted between one another by calling `into_montcurve` or `into_tecurve` on their + configurations or points. `swcurve` is more general and a curve c of one of the other two types + may be converted to this representation by calling `c.into_swcurve()`, whereas a point `p` lying + on the curve given by `c` may be mapped to its corresponding `swcurve` point by calling + `c.map_into_swcurve(p)`. +- **Map-to-curve methods**: The Elligator 2 method of mapping a field element `n: Field` into a + `tecurve` or `montcurve` with configuration `c` may be called as `c.elligator2_map(n)`. For all of + the curve configurations, the SWU map-to-curve method may be called as `c.swu_map(z,n)`, where + `z: Field` depends on `Field` and `c` and must be chosen by the user (the conditions it needs to + satisfy are specified in the comments + [here](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr)). + +## Examples + +The +[ec_baby_jubjub test](https://github.com/noir-lang/noir/blob/master/crates/nargo_cli/tests/test_data/ec_baby_jubjub/src/main.nr) +illustrates all of the above primitives on various forms of the Baby Jubjub curve. A couple of more +interesting examples in Noir would be: + +Public-key cryptography: Given an elliptic curve and a 'base point' on it, determine the public key +from the private key. This is a matter of using scalar multiplication. In the case of Baby Jubjub, +for example, this code would do: + +```rust +use dep::std::ec::tecurve::affine::{Curve, Point}; + +fn bjj_pub_key(priv_key: Field) -> Point +{ + + let bjj = Curve::new(168700, 168696, G::new(995203441582195749578291179787384436505546430278305826713579947235728471134,5472060717959818805561601436314318772137091100104008585924551046643952123905)); + + let base_pt = Point::new(5299619240641551281634865583518297030282874472190772894086521144482721001553, 16950150798460657717958625567821834550301663161624707787222815936182638968203); + + bjj.mul(priv_key,base_pt) +} +``` + +This would come in handy in a Merkle proof. + +- EdDSA signature verification: This is a matter of combining these primitives with a suitable hash + function. See + [feat(stdlib): EdDSA sig verification noir#1136](https://github.com/noir-lang/noir/pull/1136) for + the case of Baby Jubjub and the Poseidon hash function. diff --git a/noir/docs/docs/standard_library/cryptographic_primitives/05_eddsa.mdx b/noir/docs/docs/standard_library/cryptographic_primitives/05_eddsa.mdx new file mode 100644 index 00000000000..9a5beb55ee9 --- /dev/null +++ b/noir/docs/docs/standard_library/cryptographic_primitives/05_eddsa.mdx @@ -0,0 +1,17 @@ +--- +title: EdDSA Verification +description: Learn about the cryptographic primitives regarding EdDSA +keywords: [cryptographic primitives, Noir project, eddsa, signatures] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## eddsa::eddsa_poseidon_verify + +Verifier for EdDSA signatures + +```rust +fn eddsa_poseidon_verify(public_key_x : Field, public_key_y : Field, signature_s: Field, signature_r8_x: Field, signature_r8_y: Field, message: Field) -> bool +``` + + diff --git a/noir/docs/docs/standard_library/logging.md b/noir/docs/docs/standard_library/logging.md new file mode 100644 index 00000000000..7e2fd9b9aff --- /dev/null +++ b/noir/docs/docs/standard_library/logging.md @@ -0,0 +1,62 @@ +--- +title: Logging +description: + Learn how to use the println statement for debugging in Noir with this tutorial. Understand the + basics of logging in Noir and how to implement it in your code. +keywords: + [ + noir logging, + println statement, + debugging in noir, + noir std library, + logging tutorial, + basic logging in noir, + noir logging implementation, + noir debugging techniques, + rust, + ] +--- + +The standard library provides a familiar `println` statement you can use. Despite being a limited +implementation of rust's `println!` macro, this construct can be useful for debugging. + +You can print the output of println statements in your Noir code by using the `nargo execute` command or the `--show-output` flag when using `nargo test` (provided there are println statements in your tests). + +It is recommended to use `nargo execute` if you want to debug failing constrains with `println` statements. This is due to every input in a test being a constant rather than a witness, so we issue an error during compilation while we only print during execution (which comes after compilation). `println` will not work for failed constraints caught at compile time. + +The `println` statement is unconstrained, so it works for outputting integers, fields, strings, and even structs or expressions. For example: + +```rust +use dep::std; + +struct Person { + age : Field, + height : Field, +} + +fn main(age : Field, height : Field) { + let person = Person { age : age, height : height }; + std::println(person); + std::println(age + height); + std::println("Hello world!"); +} + +``` + +You can print multiple different types in the same statement and string as well as a new "fmtstr" type. A `fmtstr` can be specified in the same way as a normal string it just should be prepended with an "f" character: + +```rust + let fmt_str = f"i: {i}, j: {j}"; + std::println(fmt_str); + + let s = myStruct { y: x, x: y }; + std::println(s); + + std::println(f"i: {i}, s: {s}"); + + std::println(x); + std::println([x, y]); + + let foo = fooStruct { my_struct: s, foo: 15 }; + std::println(f"s: {s}, foo: {foo}"); +``` diff --git a/noir/docs/docs/standard_library/merkle_trees.md b/noir/docs/docs/standard_library/merkle_trees.md new file mode 100644 index 00000000000..dc383a1426b --- /dev/null +++ b/noir/docs/docs/standard_library/merkle_trees.md @@ -0,0 +1,58 @@ +--- +title: Merkle Trees +description: Learn about Merkle Trees in Noir with this tutorial. Explore the basics of computing a merkle root using a proof, with examples. +keywords: + [ + Merkle trees in Noir, + Noir programming language, + check membership, + computing root from leaf, + Noir Merkle tree implementation, + Merkle tree tutorial, + Merkle tree code examples, + Noir libraries, + pedersen hash., + ] +--- + +## compute_merkle_root + +Returns the root of the tree from the provided leaf and its hash path, using a [Pedersen hash](cryptographic_primitives/00_hashes.mdx#pedersen_hash). + +```rust +fn compute_merkle_root(leaf : Field, index : Field, hash_path: [Field]) -> Field +``` + +example: + +```rust +/** + // these values are for this example only + index = "0" + priv_key = "0x000000000000000000000000000000000000000000000000000000616c696365" + secret = "0x1929ea3ab8d9106a899386883d9428f8256cfedb3c4f6b66bf4aa4d28a79988f" + note_hash_path = [ + "0x1e61bdae0f027b1b2159e1f9d3f8d00fa668a952dddd822fda80dc745d6f65cc", + "0x0e4223f3925f98934393c74975142bd73079ab0621f4ee133cee050a3c194f1a", + "0x2fd7bb412155bf8693a3bd2a3e7581a679c95c68a052f835dddca85fa1569a40" + ] + */ +fn main(index: Field, priv_key: Field, secret: Field, note_hash_path: [Field; 3]) { + + let pubkey = std::scalar_mul::fixed_base_embedded_curve(priv_key); + let pubkey_x = pubkey[0]; + let pubkey_y = pubkey[1]; + let note_commitment = std::hash::pedersen([pubkey_x, pubkey_y, secret]); + + let root = std::merkle::compute_merkle_root(note_commitment[0], index, note_hash_path); + std::println(root); +} +``` + +To check merkle tree membership: + +1. Include a merkle root as a program input. +2. Compute the merkle root of a given leaf, index and hash path. +3. Assert the merkle roots are equal. + +For more info about merkle trees, see the Wikipedia [page](https://en.wikipedia.org/wiki/Merkle_tree). diff --git a/noir/docs/docs/standard_library/options.md b/noir/docs/docs/standard_library/options.md new file mode 100644 index 00000000000..3d3139fb98b --- /dev/null +++ b/noir/docs/docs/standard_library/options.md @@ -0,0 +1,99 @@ +--- +title: Option Type +--- + +The `Option` type is a way to express that a value might be present (`Some(T))` or absent (`None`). It's a safer way to handle potential absence of values, compared to using nulls in many other languages. + +```rust +struct Option { + None, + Some(T), +} +``` + +You can import the Option type into your Noir program like so: + +```rust +use dep::std::option::Option; + +fn main() { + let none = Option::none(); + let some = Option::some(3); +} +``` + +See [this test](https://github.com/noir-lang/noir/blob/5cbfb9c4a06c8865c98ff2b594464b037d821a5c/crates/nargo_cli/tests/test_data/option/src/main.nr) for a more comprehensive set of examples of each of the methods described below. + +## Methods + +### none + +Constructs a none value. + +### some + +Constructs a some wrapper around a given value. + +### is_none + +Returns true if the Option is None. + +### is_some + +Returns true of the Option is Some. + +### unwrap + +Asserts `self.is_some()` and returns the wrapped value. + +### unwrap_unchecked + +Returns the inner value without asserting `self.is_some()`. This method can be useful within an if condition when we already know that `option.is_some()`. If the option is None, there is no guarantee what value will be returned, only that it will be of type T for an `Option`. + +### unwrap_or + +Returns the wrapped value if `self.is_some()`. Otherwise, returns the given default value. + +### unwrap_or_else + +Returns the wrapped value if `self.is_some()`. Otherwise, calls the given function to return a default value. + +### map + +If self is `Some(x)`, this returns `Some(f(x))`. Otherwise, this returns `None`. + +### map_or + +If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns the given default value. + +### map_or_else + +If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns `default()`. + +### and + +Returns None if self is None. Otherwise, this returns `other`. + +### and_then + +If self is None, this returns None. Otherwise, this calls the given function with the Some value contained within self, and returns the result of that call. In some languages this function is called `flat_map` or `bind`. + +### or + +If self is Some, return self. Otherwise, return `other`. + +### or_else + +If self is Some, return self. Otherwise, return `default()`. + +### xor + +If only one of the two Options is Some, return that option. Otherwise, if both options are Some or both are None, None is returned. + +### filter + +Returns `Some(x)` if self is `Some(x)` and `predicate(x)` is true. Otherwise, this returns `None`. + +### flatten + +Flattens an `Option>` into a `Option`. This returns `None` if the outer Option is None. Otherwise, this returns the inner Option. diff --git a/noir/docs/docs/standard_library/recursion.md b/noir/docs/docs/standard_library/recursion.md new file mode 100644 index 00000000000..ff4c63acaa7 --- /dev/null +++ b/noir/docs/docs/standard_library/recursion.md @@ -0,0 +1,96 @@ +--- +title: Recursive Proofs +description: Learn about how to write recursive proofs in Noir. +keywords: [recursion, recursive proofs, verification_key, aggregation object, verify_proof] +--- + +Noir supports recursively verifying proofs, meaning you verify the proof of a Noir program in another Noir program. This enables creating proofs of arbitrary size by doing step-wise verification of smaller components of a large proof. + +The `verify_proof` function takes a verification key, proof and public inputs for a zk program, as well as a key hash and an input aggregation object. The key hash is used to check the validity of the verification key and the input aggregation object is required by some proving systems. The `verify_proof` function returns an output aggregation object that can then be fed into future iterations of the proof verification if required. + +```rust +#[foreign(verify_proof)] +fn verify_proof(_verification_key : [Field], _proof : [Field], _public_input : Field, _key_hash : Field, _input_aggregation_object : [Field]) -> [Field] {} +``` + +:::info + +This is a black box function. Read [this section](./black_box_fns) to learn more about black box functions in Noir. + +::: + +## Aggregation Object + +The purpose of the input aggregation object is a little less clear though (and the output aggregation object that is returned from the `std::verify_proof` method). Recursive zkSNARK schemes do not necessarily "verify a proof" in the sense that you expect a true or false to be spit out by the verifier. Rather an aggregation object is built over the public inputs. In the case of PLONK the recursive aggregation object is two G1 points (expressed as 16 witness values). The final verifier (in our case this is most often the smart contract verifier) has to be aware of this aggregation object to execute a pairing and check the validity of these points (thus completing the recursive verification). + +So for example in this circuit: + +```rust +use dep::std; + +fn main( + verification_key : [Field; 114], + proof : [Field; 94], + public_inputs : [Field; 1], + key_hash : Field, + input_aggregation_object : [Field; 16], + proof_b : [Field; 94], +) -> pub [Field; 16] { + let output_aggregation_object_a = std::verify_proof( + verification_key, + proof, + public_inputs, + key_hash, + input_aggregation_object + ); + + let output_aggregation_object = std::verify_proof( + verification_key, + proof_b, + public_inputs, + key_hash, + output_aggregation_object_a + ); + + let mut output = [0; 16]; + for i in 0..16 { + output[i] = output_aggregation_object[i]; + } + output +} +``` + +In this example we have a circuit, that generates proofs A and B, that is being verified in circuit C. Assuming that the proof being passed in is not already a recursive proof, the `input_aggregation_object` will be all zeros. It will then generate an `output_aggregation_object`. This blob of data then becomes the `input_aggregation_object` of the next recursive aggregation we wish to compute. We can see here as the same public inputs, verification key, and key hash are used that we are verifying two proofs generated from the same circuit in this single circuit. `std::verify_proof` returns a `[Field]` because the size of an aggregation object is proof system dependent--in barretenberg, aggregation objects are two G1 points, while in Halo2, the aggregation object is a list of G1 points that is log the circuit size. So for the final step we convert the slice into an array of size 16 because we are generating proofs using UltraPlonk. + +## Parameters + +### `verification_key` + +The verification key for the zk program that is being verified. + +### `proof` + +The proof for the zk program that is being verified. + +### `public_inputs` + +These represent the public inputs of the proof we are verifying. They should be checked against in the circuit after construction of a new aggregation state. + +### `key_hash` + +A key hash is used to check the validity of the verification key. The circuit implementing this opcode can use this hash to ensure that the key provided to the circuit matches the key produced by the circuit creator. + +### `input_aggregation_object` + +An aggregation object is blob of data that the top-level verifier must run some proof system specific algorithm on to complete verification. The size is proof system specific and will be set by the backend integrating this opcode. The input aggregation object is only not `None` when we are verifying a previous recursive aggregation in the current circuit. If this is the first recursive aggregation there is no input aggregation object. It is left to the backend to determine how to handle when there is no input aggregation object. + +## Return value + +### `output_aggregation_object` + +This is the result of a recursive aggregation and is what will be fed into the next verifier. +The next verifier can either perform a final verification (returning true or false) or perform another recursive aggregation where this output aggregation object will be the input aggregation object of the next recursive aggregation. + +## Example + +You can see an example of how to do recursive proofs in [this example recursion demo repo](https://github.com/noir-lang/noir-examples/tree/master/recursion). diff --git a/noir/docs/docs/standard_library/zeroed.md b/noir/docs/docs/standard_library/zeroed.md new file mode 100644 index 00000000000..97dab02dac2 --- /dev/null +++ b/noir/docs/docs/standard_library/zeroed.md @@ -0,0 +1,25 @@ +--- +title: Zeroed Function +description: + The zeroed function returns a zeroed value of any type. +keywords: + [ + zeroed + ] +--- + +Implements `fn zeroed() -> T` to return a zeroed value of any type. This function is generally unsafe to use as the zeroed bit pattern is not guaranteed to be valid for all types. It can however, be useful in cases when the value is guaranteed not to be used such as in a BoundedVec library implementing a growable vector, up to a certain length, backed by an array. The array can be initialized with zeroed values which are guaranteed to be inaccessible until the vector is pushed to. Similarly, enumerations in noir can be implemented using this method by providing zeroed values for the unused variants. + +You can access the function at `std::unsafe::zeroed`. + +This function currently supports the following types: + +- Field +- Bool +- Uint +- Array +- String +- Tuple +- Function + +Using it on other types could result in unexpected behavior. diff --git a/noir/docs/docusaurus.config.js b/noir/docs/docusaurus.config.js new file mode 100644 index 00000000000..8f62df3d0e9 --- /dev/null +++ b/noir/docs/docusaurus.config.js @@ -0,0 +1,215 @@ +// @ts-check +// Note: type annotations allow type checking and IDEs autocompletion + +const lightCodeTheme = require('prism-react-renderer/themes/github'); +const darkCodeTheme = require('prism-react-renderer/themes/dracula'); + +const math = require('remark-math'); +const katex = require('rehype-katex'); + +/** @type {import('@docusaurus/types').Config} */ +const config = { + title: 'Noir Documentation', + tagline: 'The Universal ZK Circuit Language', + favicon: 'img/favicon.ico', + url: 'https://noir-lang.org', + // Set the // pathname under which your site is served + // For GitHub pages deployment, it is often '//' + baseUrl: '/', + onBrokenLinks: 'throw', + onBrokenMarkdownLinks: 'throw', + + // Even if you don't use internalization, you can use this field to set useful + // metadata like html lang. For example, if your site is Chinese, you may want + // to replace "en" with "zh-Hans". + i18n: { + defaultLocale: 'en', + locales: ['en'], + }, + + presets: [ + [ + '@docusaurus/preset-classic', + { + // gtag: { + // trackingID: 'G-SZQHEQZK3L', + // anonymizeIP: true, + // }, + docs: { + sidebarPath: require.resolve('./sidebars.js'), + routeBasePath: '/', + remarkPlugins: [math], + rehypePlugins: [katex], + versions: { + current: { + label: 'dev', + path: 'dev', + }, + }, + editUrl: ({ versionDocsDirPath, docPath }) => + `https://github.com/noir-lang/noir/edit/master/docs/${versionDocsDirPath}/${docPath}`, + }, + blog: false, + theme: { + customCss: require.resolve('./src/css/custom.css'), + }, + }, + ], + ], + + themeConfig: + /** @type {import('@docusaurus/preset-classic').ThemeConfig} */ + { + // Replace with your project's social card + navbar: { + logo: { + alt: 'Noir Logo', + src: 'img/logo.svg', + srcDark: 'img/logoDark.svg', + href: '/', + }, + items: [ + { + href: 'https://github.com/noir-lang/docs', + label: 'GitHub', + position: 'right', + }, + { + type: 'docsVersionDropdown', + position: 'left', + dropdownActiveClassDisabled: true, + }, + ], + }, + metadata: [ + { + name: 'Noir', + content: 'noir, programming, language, documentation, zk, zero-knowledge, l2, crypto, layer2, ethereum', + }, + ], + footer: { + style: 'dark', + links: [ + { + title: 'Community', + items: [ + { + label: 'Noir Forum', + href: 'https://discourse.aztec.network/c/noir/7', + }, + { + label: 'Twitter', + href: 'https://twitter.com/NoirLang', + }, + { + label: 'Discord', + href: 'https://discord.gg/JtqzkdeQ6G', + }, + ], + }, + { + title: 'Code', + items: [ + { + label: 'Noir GitHub', + href: 'https://github.com/noir-lang', + }, + { + label: 'Docs GitHub', + href: 'https://github.com/noir-lang/docs', + }, + ], + }, + ], + copyright: `Noir will be dual licensed under MIT/Apache (Version 2.0).`, + }, + prism: { + theme: lightCodeTheme, + darkTheme: darkCodeTheme, + additionalLanguages: ['rust', 'powershell', 'solidity', 'toml'], + }, + stylesheets: [ + { + href: 'https://cdn.jsdelivr.net/npm/katex@0.13.24/dist/katex.min.css', + type: 'text/css', + integrity: 'sha384-odtC+0UGzzFL/6PNoE8rX/SPcQDXBJ+uRepguP4QkPCm2LBxH3FA3y+fKSiJ+AmM', + crossorigin: 'anonymous', + }, + ], + algolia: { + // The application ID provided by Algolia + appId: '97APAVUL6H', + + // Public API key: it is safe to commit it + apiKey: 'b9b94d2f1c58f7d509f0bc1f13b381fb', + + indexName: 'noir-lang', + }, + }, + plugins: [ + [ + 'docusaurus-plugin-typedoc', + { + id: 'noir_js', + entryPoints: ['../tooling/noir_js/src/index.ts'], + tsconfig: '../tooling/noir_js/tsconfig.json', + entryPointStrategy: 'resolve', + out: 'docs/noir_js/reference/noir_js', + plugin: ['typedoc-plugin-markdown'], + name: 'Noir JS', + disableSources: true, + excludePrivate: true, + + sidebar: { + filteredIds: ['noir_js/reference/noir_js/index'], + }, + readme: 'none', + hidePageHeader: true, + hideBreadcrumbs: true, + hideInPageTOC: true, + useCodeBlocks: true, + typeDeclarationFormat: 'table', + propertiesFormat: 'table', + parametersFormat: 'table', + enumMembersFormat: 'table', + indexFormat: 'table', + outputFileStrategy: 'members', + memberPageTitle: '{name}', + membersWithOwnFile: ['Interface', 'Class', 'TypeAlias', 'Function'], + }, + ], + [ + 'docusaurus-plugin-typedoc', + { + id: 'noir_js_backend_barretenberg', + entryPoints: ['../tooling/noir_js_backend_barretenberg/src/index.ts'], + tsconfig: '../tooling/noir_js_backend_barretenberg/tsconfig.json', + entryPointStrategy: 'resolve', + out: 'docs/noir_js/reference/backend_barretenberg', + plugin: ['typedoc-plugin-markdown'], + name: 'Backend Barretenberg', + disableSources: true, + excludePrivate: true, + + sidebar: { + filteredIds: ['noir_js/reference/backend_barretenberg/index'], + }, + readme: 'none', + hidePageHeader: true, + hideBreadcrumbs: true, + hideInPageTOC: true, + useCodeBlocks: true, + typeDeclarationFormat: 'table', + propertiesFormat: 'table', + parametersFormat: 'table', + enumMembersFormat: 'table', + indexFormat: 'table', + outputFileStrategy: 'members', + memberPageTitle: '{name}', + membersWithOwnFile: ['Interface', 'Class', 'TypeAlias'], + }, + ], + ], +}; + +module.exports = config; diff --git a/noir/docs/package.json b/noir/docs/package.json new file mode 100644 index 00000000000..09f8d718b56 --- /dev/null +++ b/noir/docs/package.json @@ -0,0 +1,49 @@ +{ + "name": "docs", + "version": "0.0.0", + "private": true, + "scripts": { + "start": "docusaurus start", + "build": "docusaurus build", + "setStable": "node ./scripts/setStable.js" + }, + "dependencies": { + "@docusaurus/core": "^2.4.0", + "@docusaurus/plugin-google-gtag": "^2.4.0", + "@docusaurus/preset-classic": "^2.4.0", + "@easyops-cn/docusaurus-search-local": "^0.35.0", + "@mdx-js/react": "^1.6.22", + "axios": "^1.4.0", + "clsx": "^1.2.1", + "docusaurus-plugin-typedoc": "1.0.0-next.18", + "hast-util-is-element": "^1.1.0", + "prism-react-renderer": "^1.3.5", + "react": "^17.0.2", + "react-dom": "^17.0.2", + "rehype-katex": "^5.0.0", + "remark-math": "^3.0.1", + "typedoc": "^0.25.0", + "typedoc-plugin-frontmatter": "^0.0.2", + "typedoc-plugin-markdown": "4.0.0-next.25", + "typedoc-plugin-merge-modules": "^5.1.0", + "typescript": "^5.2.2" + }, + "devDependencies": { + "@docusaurus/module-type-aliases": "^2.4.0" + }, + "browserslist": { + "production": [ + ">0.5%", + "not dead", + "not op_mini all" + ], + "development": [ + "last 1 chrome version", + "last 1 firefox version", + "last 1 safari version" + ] + }, + "engines": { + "node": ">=16.14" + } +} diff --git a/noir/docs/scripts/setStable.js b/noir/docs/scripts/setStable.js new file mode 100644 index 00000000000..4bbe283f4be --- /dev/null +++ b/noir/docs/scripts/setStable.js @@ -0,0 +1,42 @@ +/* eslint-disable */ +const fs = require('fs'); +const path = require('path'); +const axios = require('axios'); +const { release } = require('os'); + +const IGNORE_VERSIONS = ['0.16.0']; +const NUMBER_OF_VERSIONS_TO_SHOW = 4; + +async function main() { + const versionsFile = path.join(__dirname, '../versions.json'); + + const axiosOpts = { + params: { per_page: 100 }, + }; + + console.log(process.env.GITHUB_TOKEN); + // cool if you have a GITHUB_TOKEN because of rate limiting + // but fine if you don't + if (process.env.GITHUB_TOKEN) axiosOpts.headers = { Authorization: `token ${process.env.GITHUB_TOKEN}` }; + + const { data } = await axios.get('https://api.github.com/repos/noir-lang/noir/releases', axiosOpts); + + const all = data.map((release) => release.tag_name); + console.log('All versions: ', all); + const aztecs = data.filter((release) => release.tag_name.includes('aztec')).map((release) => release.tag_name); + console.log('Removing aztecs: ', aztecs); + const prereleases = data.filter((release) => !release.prerelease).map((release) => release.tag_name); + console.log('Removing prereleases: ', prereleases); + + const stables = data + .filter((release) => !release.prerelease && !release.tag_name.includes('aztec')) + .filter((release) => !IGNORE_VERSIONS.includes(release.tag_name.replace('v', ''))) + .map((release) => release.tag_name) + .slice(0, NUMBER_OF_VERSIONS_TO_SHOW); + + console.log('Stables: ', stables); + + fs.writeFileSync(versionsFile, JSON.stringify(stables, null, 2)); +} + +main(); diff --git a/noir/docs/sidebars.js b/noir/docs/sidebars.js new file mode 100644 index 00000000000..3fd391cf09c --- /dev/null +++ b/noir/docs/sidebars.js @@ -0,0 +1,151 @@ +/** + * Creating a sidebar enables you to: + - create an ordered group of docs + - render a sidebar for each doc of that group + - provide next/previous navigation + + The sidebars can be generated from the filesystem, or explicitly defined here. + + Create as many sidebars as you want. + */ + +// @ts-check + +/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ +const sidebars = { + sidebar: [ + { + type: 'doc', + id: 'index', + label: 'Noir', + }, + { + type: 'category', + label: 'Getting Started', + items: [{ type: 'autogenerated', dirName: 'getting_started' }], + }, + { + type: 'category', + label: 'Examples', + items: [{ type: 'autogenerated', dirName: 'examples' }], + }, + { + type: 'category', + label: 'Nargo', + items: [{ type: 'autogenerated', dirName: 'nargo' }], + }, + { + type: 'category', + label: 'Language Concepts', + items: [ + { + type: 'category', + label: 'Data Types', + link: { + type: 'doc', + id: 'language_concepts/data_types', + }, + items: [ + { + type: 'autogenerated', + dirName: 'language_concepts/data_types', + }, + ], + }, + 'language_concepts/functions', + 'language_concepts/control_flow', + 'language_concepts/ops', + 'language_concepts/assert', + 'language_concepts/unconstrained', + 'language_concepts/generics', + 'language_concepts/mutability', + 'language_concepts/lambdas', + 'language_concepts/comments', + 'language_concepts/distinct', + 'language_concepts/shadowing', + ], + }, + { + type: 'category', + label: 'Noir Standard Library', + items: [ + { + type: 'category', + label: 'Cryptographic Primitives', + link: { + type: 'doc', + id: 'standard_library/cryptographic_primitives', + }, + items: [ + { + type: 'autogenerated', + dirName: 'standard_library/cryptographic_primitives', + }, + ], + }, + 'standard_library/recursion', + 'standard_library/logging', + 'standard_library/merkle_trees', + 'standard_library/zeroed', + 'standard_library/black_box_fns', + 'standard_library/options', + ], + }, + { + type: 'category', + label: 'Modules, Packages and Crates', + items: [{ type: 'autogenerated', dirName: 'modules_packages_crates' }], + }, + { + type: 'category', + label: 'NoirJS', + link: { + type: 'doc', + id: 'noir_js/noir_js', + }, + items: [ + { + type: 'category', + label: 'Guides', + items: [ + { + type: 'autogenerated', + dirName: 'noir_js/getting_started', + }, + ], + }, + { + type: 'category', + label: 'Reference', + items: [ + { + type: 'category', + label: 'Noir JS', + link: { + type: 'doc', + id: 'noir_js/reference/noir_js/index', + }, + items: require('./docs/noir_js/reference/noir_js/typedoc-sidebar.cjs'), + }, + { + type: 'category', + label: 'Backend Barretenberg', + link: { + type: 'doc', + id: 'noir_js/reference/backend_barretenberg/index', + }, + items: require('./docs/noir_js/reference/backend_barretenberg/typedoc-sidebar.cjs'), + }, + ], + }, + ], + }, + { + type: 'doc', + id: 'migration_notes', + label: 'Migration notes', + }, + ], +}; + +module.exports = sidebars; diff --git a/noir/docs/src/components/GithubCode/index.js b/noir/docs/src/components/GithubCode/index.js new file mode 100644 index 00000000000..69ba17ef791 --- /dev/null +++ b/noir/docs/src/components/GithubCode/index.js @@ -0,0 +1,63 @@ +import React, { useEffect, useState } from 'react'; +import axios from 'axios'; +import Highlight, { defaultProps } from "prism-react-renderer"; +import github from 'prism-react-renderer/themes/github'; +//import vsDark from 'prism-react-renderer/themes/vsDark'; + +const GitHubCode = ({ owner, repo, branch = 'master', filePath, language, startLine = 1, endLine = Infinity }) => { + const [code, setCode] = useState(''); + const [response, setResponse] = useState(''); + + useEffect(() => { + const fetchCode = async () => { + const url = `https://api.github.com/repos/${owner}/${repo}/contents/${filePath}?ref=${branch}` + try { + const response = await axios.get(url); + const content = response.data.content; + const decodedContent = atob(content); // Decode Base64 content + + const lines = decodedContent.split('\n'); + const desiredLines = lines.slice(startLine - 1, endLine).join('\n').trimEnd(); + + setResponse(response); + setCode(desiredLines); + } catch (error) { + console.error('Failed to fetch GitHub code:', error); + } + }; + + fetchCode(); + }, [owner, repo, branch, filePath, startLine, endLine]); + + const highlightedCode = ( + + {({ className, style, tokens, getLineProps, getTokenProps }) => ( +
+ 
+                        {tokens.map((line, i) => (
+                            

+                                {/* uncomment for line numbers */}
+                                {/* {i + 1} */}
+                                {line.map((token, key) => (
+                                    
+                                ))}
+                            

+                        ))}
+
+ { + response.data?.html_url ? Link to source code. : '' + } +
+ )} + + ) + + return highlightedCode; +}; + +export default GitHubCode; \ No newline at end of file diff --git a/noir/docs/src/components/HomepageFeatures/index.js b/noir/docs/src/components/HomepageFeatures/index.js new file mode 100644 index 00000000000..78f410ba688 --- /dev/null +++ b/noir/docs/src/components/HomepageFeatures/index.js @@ -0,0 +1,64 @@ +import React from 'react'; +import clsx from 'clsx'; +import styles from './styles.module.css'; + +const FeatureList = [ + { + title: 'Easy to Use', + Svg: require('@site/static/img/undraw_docusaurus_mountain.svg').default, + description: ( + <> + Docusaurus was designed from the ground up to be easily installed and + used to get your website up and running quickly. + + ), + }, + { + title: 'Focus on What Matters', + Svg: require('@site/static/img/undraw_docusaurus_tree.svg').default, + description: ( + <> + Docusaurus lets you focus on your docs, and we'll do the chores. Go + ahead and move your docs into the docs directory. + + ), + }, + { + title: 'Powered by React', + Svg: require('@site/static/img/undraw_docusaurus_react.svg').default, + description: ( + <> + Extend or customize your website layout by reusing React. Docusaurus can + be extended while reusing the same header and footer. + + ), + }, +]; + +function Feature({Svg, title, description}) { + return ( +
+
+ +
+
+

{title}

+

{description}

+
+
+ ); +} + +export default function HomepageFeatures() { + return ( +
+
+
+ {FeatureList.map((props, idx) => ( + + ))} +
+
+
+ ); +} diff --git a/noir/docs/src/components/HomepageFeatures/styles.module.css b/noir/docs/src/components/HomepageFeatures/styles.module.css new file mode 100644 index 00000000000..b248eb2e5de --- /dev/null +++ b/noir/docs/src/components/HomepageFeatures/styles.module.css @@ -0,0 +1,11 @@ +.features { + display: flex; + align-items: center; + padding: 2rem 0; + width: 100%; +} + +.featureSvg { + height: 200px; + width: 200px; +} diff --git a/noir/docs/src/components/Notes/_blackbox.mdx b/noir/docs/src/components/Notes/_blackbox.mdx new file mode 100644 index 00000000000..9fe9b48fbff --- /dev/null +++ b/noir/docs/src/components/Notes/_blackbox.mdx @@ -0,0 +1,5 @@ +:::info + +This is a black box function. Read [this section](../black_box_fns) to learn more about black box functions in Noir. + +::: \ No newline at end of file diff --git a/noir/docs/src/components/Notes/_experimental.mdx b/noir/docs/src/components/Notes/_experimental.mdx new file mode 100644 index 00000000000..da1b0826aa1 --- /dev/null +++ b/noir/docs/src/components/Notes/_experimental.mdx @@ -0,0 +1,6 @@ +:::caution + +This feature is experimental. You should expect it to change in future versions, +cause unexpected behavior, or simply not work at all. + +::: diff --git a/noir/docs/src/css/custom.css b/noir/docs/src/css/custom.css new file mode 100644 index 00000000000..c1faa9d3139 --- /dev/null +++ b/noir/docs/src/css/custom.css @@ -0,0 +1,70 @@ +/** + * Any CSS included here will be global. The classic template + * bundles Infima by default. Infima is a CSS framework designed to + * work well for content-centric websites. + */ + +/* You can override the default Infima variables here. */ +:root { + --ifm-color-primary: #9f3fff; + --ifm-color-primary-dark: #2f1f49; + --ifm-color-primary-darker: #2f1f49; + --ifm-color-primary-darkest: #2f1f49; + --ifm-color-primary-light: #9f3fff; + --ifm-color-primary-lighter: #9f3fff; + --ifm-color-primary-lightest: #9f3fff; + --search-local-highlight-color: #2f1f49; + --ifm-menu-color-background-active: #f6f8fa; + --ifm-code-font-size: 95%; + --ifm-breadcrumb-color-active: white; + --ifm-breadcrumb-item-background-active: #2f1f49; + --ifm-heading-color: #2f1f49; + --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.1); +} + +/* For readability concerns, you should choose a lighter palette in dark mode. */ +[data-theme='dark'] { + --ifm-color-primary: #f5bda9; + --ifm-color-primary-dark: #f5bda9; + --ifm-color-primary-darker: #f5bda9; + --ifm-color-primary-darkest: #f5bda9; + --ifm-color-primary-light: #f5bda9; + --ifm-color-primary-lighter: #f5bda9; + --ifm-color-primary-lightest: #f5bda9; + + --ifm-heading-color: white; + --ifm-menu-color-background-active: #282a36; + --ifm-breadcrumb-color-active: #2f1f49; + --ifm-breadcrumb-item-background-active: #f5bda9; + + --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.3); +} + +html[data-theme='dark'] { + --search-local-highlight-color: #f5bda9; + --search-local-muted-color: white; + --search-local-hit-active-color: #1b1b1d; + --search-local-hit-color: white; +} + +[data-theme='dark'] .footer { + --ifm-footer-background-color: #282a36 + --ifm-footer-color: var(--ifm-footer-link-color); + --ifm-footer-link-color: var(--ifm-color-secondary); + --ifm-footer-title-color: var(--ifm-color-white); +} + +[data-theme='light'] .navbar { + background-color: #f6fbfc; +} + +[data-theme='light'] .footer { + --ifm-footer-background-color: #f6fbfc; + --ifm-footer-color: #2f1f49; + --ifm-footer-link-color: #2f1f49; + --ifm-footer-title-color: #2f1f49; +} + +.katex-html { + display: none; +} diff --git a/docs/docs/dev_docs/contracts/resources/common_patterns/common_patterns.md b/noir/docs/static/.nojekyll similarity index 100% rename from docs/docs/dev_docs/contracts/resources/common_patterns/common_patterns.md rename to noir/docs/static/.nojekyll diff --git a/noir/docs/static/img/codelens_compile_execute.png b/noir/docs/static/img/codelens_compile_execute.png new file mode 100644 index 00000000000..040e3af2704 Binary files /dev/null and b/noir/docs/static/img/codelens_compile_execute.png differ diff --git a/noir/docs/static/img/codelens_run_test.png b/noir/docs/static/img/codelens_run_test.png new file mode 100644 index 00000000000..568869fb839 Binary files /dev/null and b/noir/docs/static/img/codelens_run_test.png differ diff --git a/noir/docs/static/img/codelens_testing_panel.png b/noir/docs/static/img/codelens_testing_panel.png new file mode 100644 index 00000000000..cec1708d405 Binary files /dev/null and b/noir/docs/static/img/codelens_testing_panel.png differ diff --git a/noir/docs/static/img/favicon.ico b/noir/docs/static/img/favicon.ico new file mode 100644 index 00000000000..1c85cef482e Binary files /dev/null and b/noir/docs/static/img/favicon.ico differ diff --git a/noir/docs/static/img/logo.svg b/noir/docs/static/img/logo.svg new file mode 100644 index 00000000000..8095d6a169d --- /dev/null +++ b/noir/docs/static/img/logo.svg @@ -0,0 +1,29 @@ + + + + + + + + + + + + + + diff --git a/noir/docs/static/img/logoDark.svg b/noir/docs/static/img/logoDark.svg new file mode 100644 index 00000000000..dee9f27a6a9 --- /dev/null +++ b/noir/docs/static/img/logoDark.svg @@ -0,0 +1,29 @@ + + + + + + + + + + + + + + diff --git a/noir/docs/static/img/noir_getting_started_1.png b/noir/docs/static/img/noir_getting_started_1.png new file mode 100644 index 00000000000..9de33296e91 Binary files /dev/null and b/noir/docs/static/img/noir_getting_started_1.png differ diff --git a/noir/docs/versioned_docs/version-v0.10.5/examples/merkle-proof.mdx b/noir/docs/versioned_docs/version-v0.10.5/examples/merkle-proof.mdx new file mode 100644 index 00000000000..6430780817c --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/examples/merkle-proof.mdx @@ -0,0 +1,48 @@ +--- +title: Merkle Proof Membership +description: + Learn how to use merkle membership proof in Noir to prove that a given leaf is a member of a + merkle tree with a specified root, at a given index. +keywords: + [merkle proof, merkle membership proof, Noir, rust, hash function, Pedersen, sha256, merkle tree] +--- + +Let's walk through an example of a merkle membership proof in Noir that proves that a given leaf is +in a merkle tree. + +```rust +use dep::std; + +fn main(message : [Field; 62], index : Field, hashpath : [Field; 40], root : Field) { + let leaf = std::hash::hash_to_field(message); + let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); + assert(merkle_root == root); +} + +``` + +The message is hashed using `hash_to_field`. The specific hash function that is being used is chosen +by the backend. The only requirement is that this hash function can heuristically be used as a +random oracle. If only collision resistance is needed, then one can call `std::hash::pedersen` +instead. + +```rust +let leaf = std::hash::hash_to_field(message); +``` + +The leaf is then passed to a compute_merkle_root function with the root, index and hashpath. The returned root can then be asserted to be the same as the provided root. + +```rust +let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); +assert (merkle_root == root); +``` + +> **Note:** It is possible to re-implement the merkle tree implementation without standard library. +> However, for most usecases, it is enough. In general, the standard library will always opt to be +> as conservative as possible, while striking a balance with efficiency. + +An example, the merkle membership proof, only requires a hash function that has collision +resistance, hence a hash function like Pedersen is allowed, which in most cases is more efficient +than the even more conservative sha256. + +[View an example on the starter repo](https://github.com/noir-lang/noir-examples/blob/3ea09545cabfa464124ec2f3ea8e60c608abe6df/stealthdrop/circuits/src/main.nr#L20) diff --git a/noir/docs/versioned_docs/version-v0.10.5/getting_started/00_nargo_installation.md b/noir/docs/versioned_docs/version-v0.10.5/getting_started/00_nargo_installation.md new file mode 100644 index 00000000000..de30869732d --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/getting_started/00_nargo_installation.md @@ -0,0 +1,285 @@ +--- +title: Nargo Installation +description: + nargo is a command line tool for interacting with Noir programs (e.g. compiling, proving, + verifying and more). Learn how to install and use Nargo for your projects with this comprehensive + guide. +keywords: [Nargo, command line tool, Noir programs, installation guide, how to use Nargo] +--- + +`nargo` is a command line tool for interacting with Noir programs (e.g. compiling, proving, +verifying and more). + +Alternatively, the interactions can also be performed in [TypeScript](../typescript). + +### UltraPlonk + +Nargo versions <0.5.0 of `aztec_backend` and `aztec_wasm_backend` are based on the TurboPlonk +version of Aztec Backend, which lacks efficient implementations of useful primitives (e.g. Keccak256 in 18k constraints, ECDSA verification in 36k constraints) that the UltraPlonk version offers. + +## Installation + +There are four approaches for installing Nargo: + +- [Option 1: Noirup](#option-1-noirup) +- [Option 2: Binaries](#option-2-binaries) +- [Option 3: Install via Nix](#option-3-install-via-nix) +- [Option 4: Compile from Source](#option-4-compile-from-source) + +Optionally you can also install [Noir VS Code extension] for syntax highlighting. + +### Option 1: Noirup + +If you're on OSX or Linux, the easiest way to start using Noir and Nargo is via noirup. Just open a +terminal and run: + +```bash +curl -L https://raw.githubusercontent.com/noir-lang/noirup/main/install | bash +``` + +Close the terminal, open another one, and run + +```bash +noirup +``` + +Done, you should have the latest version working. You can check with `nargo --version`. + +You can also install nightlies, specific versions +or branches, check out the [noirup repository](https://github.com/noir-lang/noirup) for more +information. + +#### GitHub Actions + +You can use `noirup` with GitHub Actions for CI/CD and automated testing. It is as simple as +installing `noirup` and running tests in your GitHub Action `yml` file. + +See the +[config file](https://github.com/TomAFrench/noir-hashes/blob/master/.github/workflows/noir.yml) in +this repo containing hash functions in Noir for an example. + +#### Nightly versions + +To install the nightly version of Noir (updated daily) run: + +```bash +noirup -n +``` + +### Option 2: Binaries + +See [GitHub Releases](https://github.com/noir-lang/noir/releases) for the latest and previous +platform specific binaries. + +#### Step 1 + +Paste and run the following in the terminal to extract and install the binary: + +> **macOS / Linux:** If you are prompted with `Permission denied` when running commands, prepend +> `sudo` and re-run it. + +##### macOS (Apple Silicon) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-aarch64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### macOS (Intel) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### Windows (PowerShell) + +Open PowerShell as Administrator and run: + +```powershell +mkdir -f -p "$env:USERPROFILE\.nargo\bin\"; ` +Invoke-RestMethod -Method Get -Uri https://github.com/noir-lang/noir/releases/download/v0.4.1/nargo-x86_64-pc-windows-msvc.zip -Outfile "$env:USERPROFILE\.nargo\bin\nargo-x86_64-pc-windows-msvc.zip"; ` +Expand-Archive -Path "$env:USERPROFILE\.nargo\bin\nargo-x86_64-pc-windows-msvc.zip" -DestinationPath "$env:USERPROFILE\.nargo\bin\"; ` +$Reg = "Registry::HKLM\System\CurrentControlSet\Control\Session Manager\Environment"; ` +$OldPath = (Get-ItemProperty -Path "$Reg" -Name PATH).Path; ` +$NewPath = $OldPath + ’;’ + "$env:USERPROFILE\.nargo\bin\"; ` +Set-ItemProperty -Path "$Reg" -Name PATH –Value "$NewPath"; ` +$env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User") +``` + +##### Linux (Bash) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-unknown-linux-gnu.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -C $HOME/.nargo/bin/ && \ +echo -e '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.bashrc && \ +source ~/.bashrc +``` + +#### Step 2 + +Check if the installation was successful by running `nargo --help`. + +> **macOS:** If you are prompted with an OS alert, right-click and open the _nargo_ executable from +> Finder. Close the new terminal popped up and `nargo` should now be accessible. + +For a successful installation, you should see something similar to the following after running the +command: + +```sh +$ nargo --help + +Noir's package manager + +Usage: nargo + +Commands: + check Checks the constraint system for errors + codegen-verifier Generates a Solidity verifier smart contract for the program + compile Compile the program and its secret execution trace into ACIR format + new Create a new binary project + execute Executes a circuit to calculate its return value + prove Create proof for this program. The proof is returned as a hex encoded string + verify Given a proof and a program, verify whether the proof is valid + test Run the tests for this program + gates Counts the occurrences of different gates in circuit + help Print this message or the help of the given subcommand(s) +``` + +### Option 3: Install via Nix + +Due to the large number of native dependencies, Noir projects can be installed via [Nix](https://nixos.org/). + +#### Installing Nix + +For the best experience, please follow these instructions to setup Nix: + +1. Install Nix following [their guide](https://nixos.org/download.html) for your operating system. +2. Create the file `~/.config/nix/nix.conf` with the contents: + +```ini +experimental-features = nix-command +extra-experimental-features = flakes +``` + +#### Install Nargo into your Nix profile + +1. Use `nix profile` to install Nargo + +```sh +nix profile install github:noir-lang/noir +``` + +### Option 4: Compile from Source + +Due to the large number of native dependencies, Noir projects uses [Nix](https://nixos.org/) and [direnv](https://direnv.net/) to streamline the development experience. + +#### Setting up your environment + +For the best experience, please follow these instructions to setup your environment: + +1. Install Nix following [their guide](https://nixos.org/download.html) for your operating system. +2. Create the file `~/.config/nix/nix.conf` with the contents: + +```ini +experimental-features = nix-command +extra-experimental-features = flakes +``` + +3. Install direnv into your Nix profile by running: + +```sh +nix profile install nixpkgs#direnv +``` + +4. Add direnv to your shell following [their guide](https://direnv.net/docs/hook.html). + 1. For bash or zshell, add `eval "$(direnv hook bash)"` or `eval "$(direnv hook zsh)"` to your ~/.bashrc or ~/.zshrc file, respectively. +5. Restart your shell. + +#### Shell & editor experience + +Now that your environment is set up, you can get to work on the project. + +1. Clone the repository, such as: + +```sh +git clone git@github.com:noir-lang/noir +``` + +> Replacing `noir` with whichever repository you want to work on. + +2. Navigate to the directory: + +```sh +cd noir +``` + +> Replacing `noir` with whichever repository you cloned. + +3. You should see a **direnv error** because projects aren't allowed by default. Make sure you've reviewed and trust our `.envrc` file, then you need to run: + +```sh +direnv allow +``` + +4. Now, wait awhile for all the native dependencies to be built. This will take some time and direnv will warn you that it is taking a long time, but we just need to let it run. + +5. Once you are presented with your prompt again, you can start your editor within the project directory (we recommend [VSCode](https://code.visualstudio.com/)): + +```sh +code . +``` + +6. (Recommended) When launching VSCode for the first time, you should be prompted to install our recommended plugins. We highly recommend installing these for the best development experience. + +#### Building and testing + +Assuming you are using `direnv` to populate your environment, building and testing the project can be done +with the typical `cargo build`, `cargo test`, and `cargo clippy` commands. You'll notice that the `cargo` version matches the version we specify in `flake.nix`, which is 1.66.0 at the time of this writing. + +If you want to build the entire project in an isolated sandbox, you can use Nix commands: + +1. `nix build .` (or `nix build . -L` for verbose output) to build the project in a Nix sandbox. +2. `nix flake check` (or `nix flake check -L` for verbose output) to run clippy and tests in a Nix sandbox. + +#### Without `direnv` + +If you have hesitations with using direnv, you can launch a subshell with `nix develop` and then launch your editor from within the subshell. However, if VSCode was already launched in the project directory, the environment won't be updated. + +Advanced: If you aren't using direnv nor launching your editor within the subshell, you can try to install Barretenberg and other global dependencies the package needs. This is an advanced workflow and likely won't receive support! + +## Uninstalling Nargo + +### Noirup + +If you installed Noir with `noirup`, you can uninstall Noir by removing the files in `~/.nargo`, `~/nargo` and `~/noir_cache`. + +```bash +rm -r ~/.nargo +rm -r ~/nargo +rm -r ~/noir_cache +``` + +### Nix + +If you installed Noir with Nix or from source, you can remove the binary located at `~/.nix-profile/bin/nargo`. + +```bash +rm ~/.nix-profile/bin/nargo +``` + +[git]: https://git-scm.com/book/en/v2/Getting-Started-Installing-Git +[rust]: https://www.rust-lang.org/tools/install +[noir vs code extension]: https://marketplace.visualstudio.com/items?itemName=noir-lang.vscode-noir +[homebrew]: https://brew.sh/ +[cmake]: https://cmake.org/install/ +[llvm]: https://llvm.org/docs/GettingStarted.html +[openmp]: https://openmp.llvm.org/ +[barretenberg]: https://github.com/AztecProtocol/barretenberg diff --git a/noir/docs/versioned_docs/version-v0.10.5/getting_started/01_hello_world.md b/noir/docs/versioned_docs/version-v0.10.5/getting_started/01_hello_world.md new file mode 100644 index 00000000000..8b4416beba1 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/getting_started/01_hello_world.md @@ -0,0 +1,147 @@ +--- +title: Create A Project +description: + Learn how to create and verify your first Noir program using Nargo, a programming language for + zero-knowledge proofs. +keywords: + [ + Nargo, + Noir, + zero-knowledge proofs, + programming language, + create Noir program, + verify Noir program, + step-by-step guide, + ] +--- + +Now that we have installed Nargo, it is time to make our first hello world program! + +## Create a Project Directory + +Noir code can live anywhere on your computer. Let us create a _projects_ folder in the home +directory to house our Noir programs. + +For Linux, macOS, and Windows PowerShell, create the directory and change directory into it by +running: + +```sh +mkdir ~/projects +cd ~/projects +``` + +For Windows CMD, run: + +```sh +> mkdir "%USERPROFILE%\projects" +> cd /d "%USERPROFILE%\projects" +``` + +## Create Our First Nargo Project + +Now that we are in the projects directory, create a new Nargo project by running: + +```sh +nargo new hello_world +``` + +> **Note:** `hello_world` can be any arbitrary project name, we are simply using `hello_world` for +> demonstration. +> +> In production, the common practice is to name the project folder as `circuits` for better +> identifiability when sitting alongside other folders in the codebase (e.g. `contracts`, `scripts`, +> `test`). + +A `hello_world` folder would be created. Similar to Rust, the folder houses _src/main.nr_ and +_Nargo.toml_ that contains the source code and environmental options of your Noir program +respectively. + +### Intro to Noir Syntax + +Let us take a closer look at _main.nr_. The default _main.nr_ generated should look like this: + +```rust +fn main(x : Field, y : pub Field) { + assert(x != y); +} +``` + +The first line of the program specifies the program's inputs: + +```rust +x : Field, y : pub Field +``` + +Program inputs in Noir are private by default (e.g. `x`), but can be labeled public using the +keyword `pub` (e.g. `y`). To learn more about private and public values, check the +[Data Types](../language_concepts/data_types) section. + +The next line of the program specifies its body: + +```rust +assert(x != y); +``` + +The Noir syntax `assert` can be interpreted as something similar to constraints in other zk-contract languages. + +For more Noir syntax, check the [Language Concepts](../language_concepts/comments) chapter. + +## Build In/Output Files + +Change directory into _hello_world_ and build in/output files for your Noir program by running: + +```sh +cd hello_world +nargo check +``` + +Two additional files would be generated in your project directory: + +_Prover.toml_ houses input values, and _Verifier.toml_ houses public values. + +## Prove Our Noir Program + +Now that the project is set up, we can create a proof of correct execution on our Noir program. + +Fill in input values for execution in the _Prover.toml_ file. For example: + +```toml +x = "1" +y = "2" +``` + +Prove the valid execution of your Noir program: + +```sh +nargo prove +``` + +A new folder _proofs_ would then be generated in your project directory, containing the proof file +`.proof`, where the project name is defined in Nargo.toml. + +The _Verifier.toml_ file would also be updated with the public values computed from program +execution (in this case the value of `y`): + +```toml +y = "0x0000000000000000000000000000000000000000000000000000000000000002" +``` + +> **Note:** Values in _Verifier.toml_ are computed as 32-byte hex values. + +## Verify Our Noir Program + +Once a proof is generated, we can verify correct execution of our Noir program by verifying the +proof file. + +Verify your proof by running: + +```sh +nargo verify +``` + +The verification will complete in silence if it is successful. If it fails, it will log the +corresponding error instead. + +Congratulations, you have now created and verified a proof for your very first Noir program! + +In the [next section](breakdown), we will go into more detail on each step performed. diff --git a/noir/docs/versioned_docs/version-v0.10.5/getting_started/02_breakdown.md b/noir/docs/versioned_docs/version-v0.10.5/getting_started/02_breakdown.md new file mode 100644 index 00000000000..3fa3a35766b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/getting_started/02_breakdown.md @@ -0,0 +1,200 @@ +--- +title: Project Breakdown +description: + Learn about the anatomy of a Nargo project, including the purpose of the Prover and Verifier TOML + files, and how to prove and verify your program. +keywords: + [Nargo, Nargo project, Prover.toml, Verifier.toml, proof verification, private asset transfer] +--- + +This section breaks down our hello world program in section _1.2_. We elaborate on the project +structure and what the `prove` and `verify` commands did in the previous section. + +## Anatomy of a Nargo Project + +Upon creating a new project with `nargo new` and building the in/output files with `nargo check` +commands, you would get a minimal Nargo project of the following structure: + + - src + - Prover.toml + - Verifier.toml + - Nargo.toml + +The source directory _src_ holds the source code for your Noir program. By default only a _main.nr_ +file will be generated within it. + +### Prover.toml + +_Prover.toml_ is used for specifying the input values for executing and proving the program. You can specify `toml` files with different names by using the `--prover-name` or `-p` flags, see the [Prover](#provertoml) section below. Optionally you may specify expected output values for prove-time checking as well. + +### Verifier.toml + +_Verifier.toml_ contains public in/output values computed when executing the Noir program. + +### Nargo.toml + +_Nargo.toml_ contains the environmental options of your project. It contains a "package" section and a "dependencies" section. + +Example Nargo.toml: + +```toml +[package] +name = "noirstarter" +type = "bin" +authors = ["Alice"] +compiler_version = "0.9.0" +description = "Getting started with Noir" +entry = "circuit/main.nr" +license = "MIT" + +[dependencies] +ecrecover = {tag = "v0.9.0", git = "https://github.com/colinnielsen/ecrecover-noir.git"} +``` + +Nargo.toml for a [workspace](../modules_packages_crates/workspaces) will look a bit different. For example: + +```toml +[workspace] +members = ["crates/a", "crates/b"] +default-member = "crates/a" +``` + +#### Package section + +The package section requires a number of fields including: + +- `name` (**required**) - the name of the package +- `type` (**required**) - can be "bin", "lib", or "contract" to specify whether its a binary, library or Aztec contract +- `authors` (optional) - authors of the project +- `compiler_version` (optional) - specifies the version of the compiler to use. This is not currently enforced by the compiler, but will be in future versions. +- `description` (optional) +- `entry` (optional) - a relative filepath to use as the entry point into your package (overrides the default of `src/lib.nr` or `src/main.nr`) +- `backend` (optional) +- `license` (optional) + +#### Dependencies section + +This is where you will specify any dependencies for your project. See the [Dependencies page](../modules_packages_crates/dependencies) for more info. + +`./proofs/` and `./contract/` directories will not be immediately visible until you create a proof or +verifier contract respectively. + +### main.nr + +The _main.nr_ file contains a `main` method, this method is the entry point into your Noir program. + +In our sample program, _main.nr_ looks like this: + +```rust +fn main(x : Field, y : Field) { + assert(x != y); +} +``` + +The parameters `x` and `y` can be seen as the API for the program and must be supplied by the +prover. Since neither `x` nor `y` is marked as public, the verifier does not supply any inputs, when +verifying the proof. + +The prover supplies the values for `x` and `y` in the _Prover.toml_ file. + +As for the program body, `assert` ensures the satisfaction of the condition (e.g. `x != y`) is +constrained by the proof of the execution of said program (i.e. if the condition was not met, the +verifier would reject the proof as an invalid proof). + +### Prover.toml + +The _Prover.toml_ file is a file which the prover uses to supply his witness values(both private and +public). + +In our hello world program the _Prover.toml_ file looks like this: + +```toml +x = "1" +y = "2" +``` + +When the command `nargo prove` is executed, two processes happen: + +1. Noir creates a proof that `x` which holds the value of `1` and `y` which holds the value of `2` + is not equal. This not equal constraint is due to the line `assert(x != y)`. + +2. Noir creates and stores the proof of this statement in the _proofs_ directory in a file called your-project.proof. So if your project is named "private_voting" (defined in the project Nargo.toml), the proof will be saved at `./proofs/private_voting.proof`. Opening this file will display the proof in hex format. + +#### Arrays of Structs + +The following code shows how to pass an array of structs to a Noir program to generate a proof. + +```rust +// main.nr +struct Foo { + bar: Field, + baz: Field, +} + +fn main(foos: [Foo; 3]) -> pub Field { + foos[2].bar + foos[2].baz +} +``` + +Prover.toml: + +```toml +[[foos]] # foos[0] +bar = 0 +baz = 0 + +[[foos]] # foos[1] +bar = 0 +baz = 0 + +[[foos]] # foos[2] +bar = 1 +baz = 2 +``` + +#### Custom toml files + +You can specify a `toml` file with a different name to use for proving by using the `--prover-name` or `-p` flags. + +This command looks for proof inputs in the default **Prover.toml** and generates the proof and saves it at `./proofs/.proof`: + +```bash +nargo prove +``` + +This command looks for proof inputs in the custom **OtherProver.toml** and generates proof and saves it at `./proofs/.proof`: + +```bash +nargo prove -p OtherProver +``` + +## Verifying a Proof + +When the command `nargo verify` is executed, two processes happen: + +1. Noir checks in the _proofs_ directory for a proof file with the project name (eg. test_project.proof) + +2. If that file is found, the proof's validity is checked + +> **Note:** The validity of the proof is linked to the current Noir program; if the program is +> changed and the verifier verifies the proof, it will fail because the proof is not valid for the +> _modified_ Noir program. + +In production, the prover and the verifier are usually two separate entities. A prover would +retrieve the necessary inputs, execute the Noir program, generate a proof and pass it to the +verifier. The verifier would then retrieve the public inputs from usually external sources and +verifies the validity of the proof against it. + +Take a private asset transfer as an example: + +A user on browser as the prover would retrieve private inputs (e.g. the user's private key) and +public inputs (e.g. the user's encrypted balance on-chain), compute the transfer, generate a proof +and submit it to the verifier smart contract. + +The verifier contract would then draw the user's encrypted balance directly from the blockchain and +verify the proof submitted against it. If the verification passes, additional functions in the +verifier contract could trigger (e.g. approve the asset transfer). + +Now that you understand the concepts, you'll probably want some editor feedback while you are writing more complex code. + +In the [next section](language_server), we will explain how to utilize the Noir Language Server. diff --git a/noir/docs/versioned_docs/version-v0.10.5/getting_started/03_language_server.md b/noir/docs/versioned_docs/version-v0.10.5/getting_started/03_language_server.md new file mode 100644 index 00000000000..49bd1d24675 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/getting_started/03_language_server.md @@ -0,0 +1,36 @@ +--- +title: Language Server +description: + Learn about the Noir Language Server, how to install the components, and configuration that may be required. +keywords: + [Nargo, Language Server, LSP, VSCode, Visual Studio Code] +--- + +This section helps you install and configure the Noir Language Server. + +The Language Server Protocol (LSP) has two components, the [Server](#language-server) and the [Client](#language-client). Below we describe each in the context of Noir. + +## Language Server + +The Server component is provided by the Nargo command line tool that you installed at the beginning of this guide. +As long as Nargo is installed and you've used it to run other commands in this guide, it should be good to go! + +If you'd like to verify that the `nargo lsp` command is available, you can run `nargo --help` and look for `lsp` in the list of commands. If you see it, you're using a version of Noir with LSP support. + +## Language Client + +The Client component is usually an editor plugin that launches the Server. It communicates LSP messages between the editor and the Server. For example, when you save a file, the Client will alert the Server, so it can try to compile the project and report any errors. + +Currently, Noir provides a Language Client for Visual Studio Code via the [vscode-noir](https://github.com/noir-lang/vscode-noir) extension. You can install it via the [Visual Studio Marketplace](https://marketplace.visualstudio.com/items?itemName=noir-lang.vscode-noir). + +When you language server is running correctly and the VSCode plugin is installed, you should see handy codelens buttons for compilation, execution, and tests: + +![Compile and Execute](@site/static/img/codelens_compile_execute.png) +![Run test](@site/static/img/codelens_run_test.png) + +### Configuration + +* __Noir: Enable LSP__ - If checked, the extension will launch the Language Server via `nargo lsp` and communicate with it. +* __Noir: Nargo Flags__ - Additional flags may be specified if you require them to be added when the extension calls `nargo lsp`. +* __Noir: Nargo Path__ - An absolute path to a Nargo binary with the `lsp` command. This may be useful if Nargo is not within the `PATH` of your editor. +* __Noir > Trace: Server__ - Setting this to `"messages"` or `"verbose"` will log LSP messages between the Client and Server. Useful for debugging. diff --git a/noir/docs/versioned_docs/version-v0.10.5/index.md b/noir/docs/versioned_docs/version-v0.10.5/index.md new file mode 100644 index 00000000000..e56b24bccd8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/index.md @@ -0,0 +1,103 @@ +--- +title: Introducing Noir +description: + Learn about the public alpha release of Noir, a domain specific language heavily influenced by + Rust that compiles to an intermediate language which can be compiled to an arithmetic circuit or a + rank-1 constraint system. +keywords: + [ + Noir, + Domain Specific Language, + Rust, + Intermediate Language, + Arithmetic Circuit, + Rank-1 Constraint System, + Ethereum Developers, + Protocol Developers, + Blockchain Developers, + Proving System, + Smart Contract Language, + ] +slug: / +--- + +This version of the book is being released with the public alpha. There will be a lot of features +that are missing in this version, however the syntax and the feel of the language will mostly be +completed. + +## What is Noir? + +Noir is a Domain Specific Language for SNARK proving systems. It has been designed to use any ACIR compatible proving system. + +It's design choices are influenced heavily by Rust and focuses on a simple, familiar syntax. + +## Who is Noir for? + +Noir can be used for a variety of purposes. + +### Solidity Developers + +Noir currently includes a command to create a Solidity contract which verifies your Noir program. This will +be modularised in the future; however, as of the alpha, you can use the [`nargo codegen-verifier`](./nargo/commands#nargo-codegen-verifier) command to create +a verifier contract. + +### Protocol Developers + +As a protocol developer, you may not want to use the Aztec backend due to it not being a fit for +your stack, or maybe you simply want to use a different proving system. Since Noir does not compile +to a specific proof system, it is possible for protocol developers to replace the PLONK-based +proving system with a different proving system altogether. + +### Blockchain developers + +As a blockchain developer, you will be constrained by parameters set by your blockchain (for example, the +proving system and smart contract language has been pre-defined). In order for you to use Noir in +your blockchain, a proving system backend and a smart contract interface +must be implemented for it. + +## What's new about Noir? + +Noir is simple and flexible in its design, as it does not compile immediately to a fixed +NP-complete language. Instead, Noir compiles to an intermediate language (ACIR), which itself can be compiled +to an arithmetic circuit (if choosing to target Aztec's barretenberg backend) or a rank-1 constraint system (if choosing to target an R1CS backend like Arkwork's Marlin backend, or others). + +This in itself brings up a few challenges within the design process, but allows one to decouple the programming language completely from the backend. This is similar in theory to LLVM. + +## Current Features + +Compiler: + +- Module System +- For expressions +- Arrays +- Bit Operations +- Binary operations (<, <=, >, >=, +, -, \*, /, %) [See documentation for an extensive list] +- Unsigned integers +- If statements +- Structures and Tuples +- Generics + +ACIR Supported OPCODES: + +- Sha256 +- Blake2s +- Schnorr signature verification +- MerkleMembership +- Pedersen +- HashToField + +## Libraries + +Noir does not currently have an official package manager. You can find a list of available Noir libraries in the [awesome-noir repo here](https://github.com/noir-lang/awesome-noir#libraries). + +Some libraries that are available today include: + +- [Standard Library](https://github.com/noir-lang/noir/tree/master/noir_stdlib) - the Noir Standard Library +- [Ethereum Storage Proof Verification](https://github.com/aragonzkresearch/noir-trie-proofs) - a library that contains the primitives necessary for RLP decoding (in the form of look-up table construction) and Ethereum state and storage proof verification (or verification of any trie proof involving 32-byte long keys) +- [BigInt](https://github.com/shuklaayush/noir-bigint) - a library that provides a custom BigUint56 data type, allowing for computations on large unsigned integers +- [ECrecover](https://github.com/colinnielsen/ecrecover-noir/tree/main) - a library to verify an ECDSA signature and return the source Ethereum address +- [Sparse Merkle Tree Verifier](https://github.com/vocdoni/smtverifier-noir/tree/main) - a library for verification of sparse Merkle trees +- [Signed Int](https://github.com/resurgencelabs/signed_int) - a library for accessing a custom Signed Integer data type, allowing access to negative numbers on Noir +- [Fraction](https://github.com/resurgencelabs/fraction) - a library for accessing fractional number data type in Noir, allowing results that aren't whole numbers + +See the section on [dependencies](./modules_packages_crates/dependencies) for more information. diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/01_functions.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/01_functions.md new file mode 100644 index 00000000000..7cb43c4c5f2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/01_functions.md @@ -0,0 +1,99 @@ +--- +title: Functions +description: + Learn how to declare functions and methods in Noir, a programming language with Rust semantics. + This guide covers parameter declaration, return types, call expressions, and more. +keywords: [Noir, Rust, functions, methods, parameter declaration, return types, call expressions] +--- + +Functions in Noir follow the same semantics of Rust, though Noir does not support early returns. + +To declare a function the `fn` keyword is used. + +```rust +fn foo() {} +``` + +All parameters in a function must have a type and all types are known at compile time. The parameter +is pre-pended with a colon and the parameter type. Multiple parameters are separated using a comma. + +```rust +fn foo(x : Field, y : pub Field){} +``` + +The return type of a function can be stated by using the `->` arrow notation. The function below +states that the foo function must return a `Field`. If the function returns no value, then the arrow +is omitted. + +```rust +fn foo(x : Field, y : pub Field) -> Field { + x + y +} +``` + +Note that a `return` keyword is unneeded in this case - the last expression in a function's body is +returned. + +## Call Expressions + +Calling a function in Noir is executed by using the function name and passing in the necessary +arguments. + +Below we show how to call the `foo` function from the `main` function using a call expression: + +```rust +fn main(x : Field, y : Field) { + let z = foo(x); +} + +fn foo(x : Field) -> Field { + x + x +} +``` + +## Methods + +You can define methods in Noir on any struct type in scope. + +```rust +struct MyStruct { + foo: Field, + bar: Field, +} + +impl MyStruct { + fn new(foo: Field) -> MyStruct { + MyStruct { + foo, + bar: 2, + } + } + + fn sum(self) -> Field { + self.foo + self.bar + } +} + +fn main() { + let s = MyStruct::new(40); + assert(s.sum() == 42); +} +``` + +Methods are just syntactic sugar for functions, so if we wanted to we could also call `sum` as +follows: + +```rust +assert(MyStruct::sum(s) == 42); +``` + +## Lambdas + +Lambdas are anonymous functions. They follow the syntax of Rust - `|arg1, arg2, ..., argN| return_expression`. + +```rust +let add_50 = |val| val + 50; +assert(add_50(100) == 150); +``` + +See [Lambdas](./08_lambdas.md) for more details. diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/02_control_flow.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/02_control_flow.md new file mode 100644 index 00000000000..691c514d9a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/02_control_flow.md @@ -0,0 +1,42 @@ +--- +title: Control Flow +description: + Learn how to use loops and if expressions in the Noir programming language. Discover the syntax + and examples for for loops and if-else statements. +keywords: [Noir programming language, loops, for loop, if-else statements, Rust syntax] +--- + +## Loops + +Noir has one kind of loop: the `for` loop. `for` loops allow you to repeat a block of code multiple +times. + +The following block of code between the braces is run 10 times. + +```rust +for i in 0..10 { + // do something +}; +``` + +## If Expressions + +Noir supports `if-else` statements. The syntax is most similar to Rust's where it is not required +for the statement's conditional to be surrounded by parentheses. + +```rust +let a = 0; +let mut x: u32 = 0; + +if a == 0 { + if a != 0 { + x = 6; + } else { + x = 2; + } +} else { + x = 5; + assert(x == 5); +} +assert(x == 2); +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/03_ops.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/03_ops.md new file mode 100644 index 00000000000..da02b126059 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/03_ops.md @@ -0,0 +1,97 @@ +--- +title: Logical Operations +description: + Learn about the supported arithmetic and logical operations in the Noir programming language. + Discover how to perform operations on private input types, integers, and booleans. +keywords: + [ + Noir programming language, + supported operations, + arithmetic operations, + logical operations, + predicate operators, + bitwise operations, + short-circuiting, + backend, + ] +--- + +# Operations + +## Table of Supported Operations + +| Operation | Description | Requirements | +| :-------- | :------------------------------------------------------------: | -------------------------------------: | +| + | Adds two private input types together | Types must be private input | +| - | Subtracts two private input types together | Types must be private input | +| \* | Multiplies two private input types together | Types must be private input | +| / | Divides two private input types together | Types must be private input | +| ^ | XOR two private input types together | Types must be integer | +| & | AND two private input types together | Types must be integer | +| \| | OR two private input types together | Types must be integer | +| << | Left shift an integer by another integer amount | Types must be integer | +| >> | Right shift an integer by another integer amount | Types must be integer | +| ! | Bitwise not of a value | Type must be integer or boolean | +| < | returns a bool if one value is less than the other | Upper bound must have a known bit size | +| <= | returns a bool if one value is less than or equal to the other | Upper bound must have a known bit size | +| > | returns a bool if one value is more than the other | Upper bound must have a known bit size | +| >= | returns a bool if one value is more than or equal to the other | Upper bound must have a known bit size | +| == | returns a bool if one value is equal to the other | Both types must not be constants | +| != | returns a bool if one value is not equal to the other | Both types must not be constants | + +### Predicate Operators + +`<,<=, !=, == , >, >=` are known as predicate/comparison operations because they compare two values. +This differs from the operations such as `+` where the operands are used in _computation_. + +### Bitwise Operations Example + +```rust +fn main(x : Field) { + let y = x as u32; + let z = y & y; +} +``` + +`z` is implicitly constrained to be the result of `y & y`. The `&` operand is used to denote bitwise +`&`. + +> `x & x` would not compile as `x` is a `Field` and not an integer type. + +### Logical Operators + +Noir has no support for the logical operators `||` and `&&`. This is because encoding the +short-circuiting that these operators require can be inefficient for Noir's backend. Instead you can +use the bitwise operators `|` and `&` which operate indentically for booleans, just without the +short-circuiting. + +```rust +let my_val = 5; + +let mut flag = 1; +if (my_val > 6) | (my_val == 0) { + flag = 0; +} +assert(flag == 1); + +if (my_val != 10) & (my_val < 50) { + flag = 0; +} +assert(flag == 0); +``` + +### Shorthand operators + +Noir shorthand operators for most of the above operators, namely `+=, -=, *=, /=, %=, &=, |=, ^=, <<=`, and `>>=`. These allow for more concise syntax. For example: + +```rust +let mut i = 0; +i = i + 1; +``` + +could be written as: + +```rust +let mut i = 0; +i += 1; +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/04_assert.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/04_assert.md new file mode 100644 index 00000000000..a25a946123d --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/04_assert.md @@ -0,0 +1,34 @@ +--- +title: Assert Function +description: + Learn about the assert function in Noir, which can be used to explicitly constrain the predicate or + comparison expression that follows to be true, and what happens if the expression is false at + runtime. +keywords: [Noir programming language, assert statement, predicate expression, comparison expression] +--- + +Noir includes a special `assert` function which will explicitly constrain the predicate/comparison +expression that follows to be true. If this expression is false at runtime, the program will fail to +be proven. + +### Example + +```rust +fn main(x : Field, y : Field) { + assert(x == y); +} +``` + +The above snippet compiles because `==` is a predicate operation. Conversely, the following will not +compile: + +```rust +// INCORRECT + +fn main(x : Field, y : Field) { + assert(x + y); +} +``` + +> The rationale behind this not compiling is due to ambiguity. It is not clear if the above should +> equate to `x + y == 0` or if it should check the truthiness of the result. diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/05_unconstrained.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/05_unconstrained.md new file mode 100644 index 00000000000..6b621eda3eb --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/05_unconstrained.md @@ -0,0 +1,96 @@ +--- +title: Unconstrained Functions +description: "Learn about what unconstrained functions in Noir are, how to use them and when you'd want to." + +keywords: [Noir programming language, unconstrained, open] +--- + + + +Unconstrained functions are functions which do not constrain any of the included computation and allow for non-determinisitic computation. + +## Why? + +Zero-knowledge (ZK) domain-specific languages (DSL) enable developers to generate ZK proofs from their programs by compiling code down to the constraints of an NP complete language (such as R1CS or PLONKish languages). However, the hard bounds of a constraint system can be very limiting to the functionality of a ZK DSL. + +Enabling a circuit language to perform unconstrained execution is a powerful tool. Said another way, unconstrained execution lets developers generate witnesses from code that does not generate any constraints. Being able to execute logic outside of a circuit is critical for both circuit performance and constructing proofs on information that is external to a circuit. + +Fetching information from somewhere external to a circuit can also be used to enable developers to improve circuit efficiency. + +A ZK DSL does not just prove computation, but proves that some computation was handled correctly. Thus, it is necessary that when we switch from performing some operation directly inside of a circuit to inside of an unconstrained environment that the appropriate constraints are still laid down elsewhere in the circuit. + +## Example + +An in depth example might help drive the point home. This example comes from the excellent [post](https://discord.com/channels/1113924620781883405/1124022445054111926/1128747641853972590) by Tom in the Noir Discord. + +Let's look at how we can optimize a function to turn a `u72` into an array of `u8`s. + +```rust +fn main(num: u72) -> pub [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8)) as u72 & 0xff) as u8; + } + + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 91 +Backend circuit size: 3619 +``` + +A lot of the operations in this function are optimized away by the compiler (all the bit-shifts turn into divisions by constants). However we can save a bunch of gates by casting to u8 a bit earlier. This automatically truncates the bit-shifted value to fit in a u8 which allows us to remove the XOR against 0xff. This saves us ~480 gates in total. + +```rust +fn main(num: u72) -> pub [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8)) as u8; + } + + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 75 +Backend circuit size: 3143 +``` + +Those are some nice savings already but we can do better. This code is all constrained so we're proving every step of calculating out using num, but we don't actually care about how we calculate this, just that it's correct. This is where brillig comes in. + +It turns out that truncating a u72 into a u8 is hard to do inside a snark, each time we do as u8 we lay down 4 ACIR opcodes which get converted into multiple gates. It's actually much easier to calculate num from out than the other way around. All we need to do is multiply each element of out by a constant and add them all together, both relatively easy operations inside a snark. + +We can then run u72_to_u8 as unconstrained brillig code in order to calculate out, then use that result in our constrained function and assert that if we were to do the reverse calculation we'd get back num. This looks a little like the below: + +```rust +fn main(num: u72) -> pub [u8; 8] { + let out = u72_to_u8(num); + + let mut reconstructed_num: u72 = 0; + for i in 0..8 { + reconstructed_num += (out[i] as u72 << (56 - (8 * i))); + } + assert(num == reconstructed_num); + out +} + +unconstrained fn u72_to_u8(num: u72) -> [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8))) as u8; + } + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 78 +Backend circuit size: 2902 +``` + +This ends up taking off another ~250 gates from our circuit! We've ended up with more ACIR opcodes than before but they're easier for the backend to prove (resulting in fewer gates). + +Generally we want to use brillig whenever there's something that's easy to verify but hard to compute within the circuit. For example, if you wanted to calculate a square root of a number it'll be a much better idea to calculate this in brillig and then assert that if you square the result you get back your number. diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/06_generics.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/06_generics.md new file mode 100644 index 00000000000..9fb4177c2a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/06_generics.md @@ -0,0 +1,113 @@ +--- +title: Generics +description: Learn how to use Generics in Noir +keywords: [Noir, Rust, generics, functions, structs] +--- + +Generics allow you to use the same functions with multiple different concrete data types. You can +read more about the concept of generics in the Rust documentation +[here](https://doc.rust-lang.org/book/ch10-01-syntax.html). + +Here is a trivial example showing the identity function that supports any type. In Rust, it is +common to refer to the most general type as `T`. We follow the same convention in Noir. + +```rust +fn id(x: T) -> T { + x +} +``` + +## In Structs + +Generics are useful for specifying types in structs. For example, we can specify that a field in a +struct will be of a certain generic type. In this case `value` is of type `T`. + +```rust +struct RepeatedValue { + value: T, + count: Field, +} + +impl RepeatedValue { + fn new(value: T) -> Self { + Self { value, count: 1 } + } + + fn increment(mut repeated: Self) -> Self { + repeated.count += 1; + repeated + } + + fn print(self) { + for _i in 0 .. self.count { + dep::std::println(self.value); + } + } +} + +fn main() { + let mut repeated = RepeatedValue::new("Hello!"); + repeated = repeated.increment(); + repeated.print(); +} +``` + +The `print` function will print `Hello!` an arbitrary number of times, twice in this case. + +If we want to be generic over array lengths (which are type-level integers), we can use numeric +generics. Using these looks just like using regular generics, but these generics can resolve to +integers at compile-time, rather than resolving to types. Here's an example of a struct that is +generic over the size of the array it contains internally: + +```rust +struct BigInt { + limbs: [u32; N], +} + +impl BigInt { + // `N` is in scope of all methods in the impl + fn first(first: BigInt, second: BigInt) -> Self { + assert(first.limbs != second.limbs); + first + + fn second(first: BigInt, second: Self) -> Self { + assert(first.limbs != second.limbs); + second + } +} +``` + +## Calling functions on generic parameters + +Unlike Rust, Noir does not have traits, so how can one translate the equivalent of a trait bound in +Rust into Noir? That is, how can we write a function that is generic over some type `T`, while also +requiring there is a function like `eq: fn(T, T) -> bool` that works on the type? + +The answer is that we can translate this by passing in the function manually. Here's an example of +implementing array equality in Noir: + +```rust +fn array_eq(array1: [T; N], array2: [T; N], elem_eq: fn(T, T) -> bool) -> bool { + if array1.len() != array2.len() { + false + } else { + let mut result = true; + for i in 0 .. array1.len() { + result &= elem_eq(array1[i], array2[i]); + } + result + } +} + +fn main() { + assert(array_eq([1, 2, 3], [1, 2, 3], |a, b| a == b)); + + // We can use array_eq even for arrays of structs, as long as we have + // an equality function for these structs we can pass in + let array = [MyStruct::new(), MyStruct::new()]; + assert(array_eq(array, array, MyStruct::eq)); +} +``` + +You can see an example of generics in the tests +[here](https://github.com/noir-lang/noir/blob/master/tooling/nargo_cli/tests/execution_success/generics/src/main.nr). diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/07_mutability.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/07_mutability.md new file mode 100644 index 00000000000..4641521b1d9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/07_mutability.md @@ -0,0 +1,92 @@ +--- +title: Mutability +description: + Learn about mutable variables, constants, and globals in Noir programming language. Discover how + to declare, modify, and use them in your programs. +keywords: [noir programming language, mutability in noir, mutable variables, constants, globals] +--- + +Variables in noir can be declared mutable via the `mut` keyword. Mutable variables can be reassigned +to via an assignment expression. + +```rust +let x = 2; +x = 3; // error: x must be mutable to be assigned to + +let mut y = 3; +let y = 4; // OK +``` + +The `mut` modifier can also apply to patterns: + +```rust +let (a, mut b) = (1, 2); +a = 11; // error: a must be mutable to be assigned to +b = 12; // OK + +let mut (c, d) = (3, 4); +c = 13; // OK +d = 14; // OK + +// etc. +let MyStruct { x: mut y } = MyStruct { x: a }; +// y is now in scope +``` + +Note that mutability in noir is local and everything is passed by value, so if a called function +mutates its parameters then the parent function will keep the old value of the parameters. + +```rust +fn main() -> Field { + let x = 3; + helper(x); + x // x is still 3 +} + +fn helper(mut x: i32) { + x = 4; +} +``` + +## Comptime Values + +:::warning + +The 'comptime' keyword was removed in version 0.10. The comptime keyword and syntax are currently still kept and parsed for backwards compatibility, but are now deprecated and will issue a warning when used. `comptime` has been removed because it is no longer needed for accessing arrays. + +::: + +## Globals + +Noir also supports global variables. However, they must be known at compile-time. The global type can also be inferred by the compiler entirely. Globals can also be used to specify array +annotations for function parameters and can be imported from submodules. + +```rust +global N: Field = 5; // Same as `global N: Field = 5` + +fn main(x : Field, y : [Field; N]) { + let res = x * N; + + assert(res == y[0]); + + let res2 = x * mysubmodule::N; + assert(res != res2); +} + +mod mysubmodule { + use dep::std; + + global N: Field = 10; + + fn my_helper() -> Field { + let x = N; + x + } +} +``` + +## Why only local mutability? + +Witnesses in a proving system are immutable in nature. Noir aims to _closely_ mirror this setting +without applying additional overhead to the user. Modeling a mutable reference is not as +straightforward as on conventional architectures and would incur some possibly unexpected overhead. diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/08_lambdas.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/08_lambdas.md new file mode 100644 index 00000000000..ae1e6aecab1 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/08_lambdas.md @@ -0,0 +1,80 @@ +--- +title: Lambdas +description: Learn how to use anonymous functions in Noir programming language. +keywords: [Noir programming language, lambda, closure, function, anonymous function] +--- + +## Introduction + +Lambdas are anonymous functions. The syntax is `|arg1, arg2, ..., argN| return_expression`. + +```rust +let add_50 = |val| val + 50; +assert(add_50(100) == 150); +``` + +A block can be used as the body of a lambda, allowing you to declare local variables inside it: + +```rust +let cool = || { + let x = 100; + let y = 100; + x + y +} + +assert(cool() == 200); +``` + +## Closures + +Inside the body of a lambda, you can use variables defined in the enclosing function. Such lambdas are called **closures**. In this example `x` is defined inside `main` and is accessed from within the lambda: + +```rust +fn main() { + let x = 100; + let closure = || x + 150; + assert(closure() == 250); +} +``` + +## Passing closures to higher-order functions + +It may catch you by surprise that the following code fails to compile: + +```rust +fn foo(f: fn () -> Field) -> Field { + f() +} + +fn main() { + let (x, y) = (50, 50); + assert(foo(|| x + y) == 100); // error :( +} +``` + +The reason is that the closure's capture environment affects its type - we have a closure that captures two Fields and `foo` +expects a regular function as an argument - those are incompatible. +:::note + +Variables contained within the `||` are the closure's parameters, and the expression that follows it is the closure's body. The capture environment is comprised of any variables used in the closure's body that are not parameters. + +E.g. in |x| x + y, y would be a captured variable, but x would not be, since it is a parameter of the closure. + +::: +The syntax for the type of a closure is `fn[env](args) -> ret_type`, where `env` is the capture environment of the closure - +in this example that's `(Field, Field)`. + +The best solution in our case is to make `foo` generic over the environment type of its parameter, so that it can be called +with closures with any environment, as well as with regular functions: + +```rust +fn foo(f: fn[Env]() -> Field) -> Field { + f() +} + +fn main() { + let (x, y) = (50, 50); + assert(foo(|| x + y) == 100); // compiles fine + assert(foo(|| 60) == 60); // compiles fine +} +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/09_comments.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/09_comments.md new file mode 100644 index 00000000000..3bb4d2f25a4 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/09_comments.md @@ -0,0 +1,32 @@ +--- +title: Comments +description: + Learn how to write comments in Noir programming language. A comment is a line of code that is + ignored by the compiler, but it can be read by programmers. Single-line and multi-line comments + are supported in Noir. +keywords: [Noir programming language, comments, single-line comments, multi-line comments] +--- + +A comment is a line in your codebase which the compiler ignores, however it can be read by +programmers. + +Here is a single line comment: + +```rust +// This is a comment and is ignored +``` + +`//` is used to tell the compiler to ignore the rest of the line. + +Noir also supports multi-line block comments. Start a block comment with `/*` and end the block with `*/`. + +Noir does not natively support doc comments. You may be able to use [Rust doc comments](https://doc.rust-lang.org/reference/comments.html) in your code to leverage some Rust documentation build tools with Noir code. + +```rust +/* + This is a block comment describing a complex function. +*/ +fn main(x : Field, y : pub Field) { + assert(x != y); +} +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/10_distinct.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/10_distinct.md new file mode 100644 index 00000000000..e7ff7f5017a --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/10_distinct.md @@ -0,0 +1,63 @@ +--- +title: Distinct Witnesses +--- + +The `distinct` keyword prevents repetitions of witness indices in the program's ABI. This ensures +that the witnesses being returned as public inputs are all unique. + +The `distinct` keyword is only used for return values on program entry points (usually the `main()` +function). + +When using `distinct` and `pub` simultaneously, `distinct` comes first. See the example below. + +You can read more about the problem this solves +[here](https://github.com/noir-lang/noir/issues/1183). + +## Example + +Without the `distinct` keyword, the following program + +```rust +fn main(x : pub Field, y : pub Field) -> pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + "return_witnesses": [3, 2, 4, 4] + } +} +``` + +Whereas (with the `distinct` keyword) + +```rust +fn main(x : pub Field, y : pub Field) -> distinct pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + //... + "return_witnesses": [3, 4, 5, 6] + } +} +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/11_shadowing.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/11_shadowing.md new file mode 100644 index 00000000000..efd743e764f --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/11_shadowing.md @@ -0,0 +1,43 @@ +--- +title: Shadowing +--- + +Noir allows for inheriting variables' values and re-declaring them with the same name similar to Rust, known as shadowing. + +For example, the following function is valid in Noir: + +```rust +fn main() { + let x = 5; + + { + let x = x * 2; + assert (x == 10); + } + + assert (x == 5); +} +``` + +In this example, a variable x is first defined with the value 5. + +The local scope that follows shadows the original x, i.e. creates a local mutable x based on the value of the original x. It is given a value of 2 times the original x. + +When we return to the main scope, x once again refers to just the original x, which stays at the value of 5. + +## Temporal mutability + +One way that shadowing is useful, in addition to ergonomics across scopes, is for temporarily mutating variables. + +```rust +fn main() { + let age = 30; + // age = age + 5; // Would error as `age` is immutable by default. + + let mut age = age + 5; // Temporarily mutates `age` with a new value. + + let age = age; // Locks `age`'s mutability again. + + assert (age == 35); +} +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types.md new file mode 100644 index 00000000000..d546cc463a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types.md @@ -0,0 +1,96 @@ +--- +title: Data Types +description: + Get a clear understanding of the two categories of Noir data types - primitive types and compound + types. Learn about their characteristics, differences, and how to use them in your Noir + programming. +keywords: + [ + noir, + data types, + primitive types, + compound types, + private types, + public types, + ] +--- + +Every value in Noir has a type, which determines which operations are valid for it. + +All values in Noir are fundamentally composed of `Field` elements. For a more approachable +developing experience, abstractions are added on top to introduce different data types in Noir. + +Noir has two category of data types: primitive types (e.g. `Field`, integers, `bool`) and compound +types that group primitive types (e.g. arrays, tuples, structs). Each value can either be private or +public. + +## Private & Public Types + +A **private value** is known only to the Prover, while a **public value** is known by both the +Prover and Verifier. Mark values as `private` when the value should only be known to the prover. All +primitive types (including individual fields of compound types) in Noir are private by default, and +can be marked public when certain values are intended to be revealed to the Verifier. + +> **Note:** For public values defined in Noir programs paired with smart contract verifiers, once +> the proofs are verified on-chain the values can be considered known to everyone that has access to +> that blockchain. + +Public data types are treated no differently to private types apart from the fact that their values +will be revealed in proofs generated. Simply changing the value of a public type will not change the +circuit (where the same goes for changing values of private types as well). + +_Private values_ are also referred to as _witnesses_ sometimes. + +> **Note:** The terms private and public when applied to a type (e.g. `pub Field`) have a different +> meaning than when applied to a function (e.g. `pub fn foo() {}`). +> +> The former is a visibility modifier for the Prover to interpret if a value should be made known to +> the Verifier, while the latter is a visibility modifier for the compiler to interpret if a +> function should be made accessible to external Noir programs like in other languages. + +### pub Modifier + +All data types in Noir are private by default. Types are explicitly declared as public using the +`pub` modifier: + +```rust +fn main(x : Field, y : pub Field) -> pub Field { + x + y +} +``` + +In this example, `x` is **private** while `y` and `x + y` (the return value) are **public**. Note +that visibility is handled **per variable**, so it is perfectly valid to have one input that is +private and another that is public. + +> **Note:** Public types can only be declared through parameters on `main`. + +## Type Aliases + +A type alias is a new name for an existing type. Type aliases are declared with the keyword `type`: + +```rust +type Id = u8; + +fn main() { + let id: Id = 1; + let zero: u8 = 0; + assert(zero + 1 == id); +} +``` + +Type aliases can also be used with [generics](./06_generics.md): + +```rust +type Id = Size; + +fn main() { + let id: Id = 1; + let zero: u32 = 0; + assert(zero + 1 == id); +} +``` + +### BigInt + +You can acheive BigInt functionality using the [Noir BigInt](https://github.com/shuklaayush/noir-bigint) library. diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/00_fields.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/00_fields.md new file mode 100644 index 00000000000..658a0441ffb --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/00_fields.md @@ -0,0 +1,165 @@ +--- +title: Fields +description: + Dive deep into the Field data type in Noir. Understand its methods, practical examples, and best practices to effectively use Fields in your Noir programs. +keywords: + [ + noir, + field type, + methods, + examples, + best practices, + ] +--- + +The field type corresponds to the native field type of the proving backend. + +The size of a Noir field depends on the elliptic curve's finite field for the proving backend +adopted. For example, a field would be a 254-bit integer when paired with the default backend that +spans the Grumpkin curve. + +Fields support integer arithmetic and are often used as the default numeric type in Noir: + +```rust +fn main(x : Field, y : Field) { + let z = x + y; +} +``` + +`x`, `y` and `z` are all private fields in this example. Using the `let` keyword we defined a new +private value `z` constrained to be equal to `x + y`. + +If proving efficiency is of priority, fields should be used as a default for solving problems. +Smaller integer types (e.g. `u64`) incur extra range constraints. + +## Methods + +After declaring a Field, you can use these common methods on it: + +### to_le_bits + +Transforms the field into an array of bits, Little Endian. + +```rust +fn to_le_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2; + let bits = field.to_le_bits(32); +} +``` + +### to_be_bits + +Transforms the field into an array of bits, Big Endian. + +```rust +fn to_be_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2; + let bits = field.to_be_bits(32); +} +``` + +### to_le_bytes + +Transforms into an array of bytes, Little Endian + +```rust +fn to_le_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let bytes = field.to_le_bytes(4); +} +``` + +### to_be_bytes + +Transforms into an array of bytes, Big Endian + +```rust +fn to_be_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let bytes = field.to_be_bytes(4); +} +``` + +### to_le_radix + +Decomposes into a vector over the specified base, Little Endian + +```rust +fn to_le_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let radix = field.to_le_radix(256, 4); +} +``` + +### to_be_radix + +Decomposes into a vector over the specified base, Big Endian + +```rust +fn to_be_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let radix = field.to_be_radix(256, 4); +} +``` + +### pow_32 + +Returns the value to the power of the specified exponent + +```rust +fn pow_32(self, exponent: Field) -> Field +``` + +example: + +```rust +fn main() { + let field = 2 + let pow = field.pow_32(4); + assert(pow == 16); +} +``` + +### sgn0 + +Parity of (prime) Field element, i.e. sgn0(x mod p) = 0 if x ∈ {0, ..., p-1} is even, otherwise sgn0(x mod p) = 1. + +```rust +fn sgn0(self) -> u1 +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/01_integers.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/01_integers.md new file mode 100644 index 00000000000..d9c5e20e795 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/01_integers.md @@ -0,0 +1,33 @@ +--- +title: Integers +description: + Explore the Integer data type in Noir. Learn about its methods, see real-world examples, and grasp how to efficiently use Integers in your Noir code. +keywords: + [ + noir, + integer types, + methods, + examples, + arithmetic, + ] +--- + +An integer type is a range constrained field type. The Noir frontend currently supports unsigned, +arbitrary-sized integer types. + +An integer type is specified first with the letter `u`, indicating its unsigned nature, followed by +its length in bits (e.g. `32`). For example, a `u32` variable can store a value in the range of +$\\([0,2^{32}-1]\\)$: + +```rust +fn main(x : Field, y : u32) { + let z = x as u32 + y; +} +``` + +`x`, `y` and `z` are all private values in this example. However, `x` is a field while `y` and `z` +are unsigned 32-bit integers. If `y` or `z` exceeds the range $\\([0,2^{32}-1]\\)$, proofs created +will be rejected by the verifier. + +> **Note:** The default backend supports both even (e.g. `u16`, `u48`) and odd (e.g. `u5`, `u3`) +> sized integer types. diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/02_booleans.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/02_booleans.md new file mode 100644 index 00000000000..885db167d83 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/02_booleans.md @@ -0,0 +1,30 @@ +--- +title: Booleans +description: + Delve into the Boolean data type in Noir. Understand its methods, practical examples, and best practices for using Booleans in your Noir programs. +keywords: + [ + noir, + boolean type, + methods, + examples, + logical operations, + ] +--- + + +The `bool` type in Noir has two possible values: `true` and `false`: + +```rust +fn main() { + let t = true; + let f: bool = false; +} +``` + +> **Note:** When returning a boolean value, it will show up as a value of 1 for `true` and 0 for +> `false` in _Verifier.toml_. + +The boolean type is most commonly used in conditionals like `if` expressions and `assert` +statements. More about conditionals is covered in the [Control Flow](../control_flow) and +[Assert Function](../assert) sections. diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/03_strings.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/03_strings.md new file mode 100644 index 00000000000..ee69853bfba --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/03_strings.md @@ -0,0 +1,43 @@ +--- +title: Strings +description: + Discover the String data type in Noir. Learn about its methods, see real-world examples, and understand how to effectively manipulate and use Strings in Noir. +keywords: + [ + noir, + string type, + methods, + examples, + concatenation, + ] +--- + + +The string type is a fixed length value defined with `str`. + +You can use strings in `assert()` functions or print them with +`std::println()`. See more about [Logging](../../standard_library/logging). + +```rust +use dep::std; + +fn main(message : pub str<11>, hex_as_string : str<4>) { + std::println(message); + assert(message == "hello world"); + assert(hex_as_string == "0x41"); +} +``` + +You can convert a `str` to a byte array by calling `as_bytes()` +or a vector by calling `as_bytes_vec()`. + +```rust +fn main() { + let message = "hello world"; + let message_bytes = message.as_bytes(); + let mut message_vec = message.as_bytes_vec(); + assert(message_bytes.len() == 11); + assert(message_bytes[0] == 104); + assert(message_bytes[0] == message_vec.get(0)); +} +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/04_arrays.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/04_arrays.md new file mode 100644 index 00000000000..f826b39bf39 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/04_arrays.md @@ -0,0 +1,237 @@ +--- +title: Arrays +description: + Dive into the Array data type in Noir. Grasp its methods, practical examples, and best practices for efficiently using Arrays in your Noir code. +keywords: + [ + noir, + array type, + methods, + examples, + indexing, + ] +--- + +An array is one way of grouping together values into one compound type. Array types can be inferred +or explicitly specified via the syntax `[; ]`: + +```rust +fn main(x : Field, y : Field) { + let my_arr = [x, y]; + let your_arr: [Field; 2] = [x, y]; +} +``` + +Here, both `my_arr` and `your_arr` are instantiated as an array containing two `Field` elements. + +Array elements can be accessed using indexing: + +```rust +fn main() { + let a = [1, 2, 3, 4, 5]; + + let first = a[0]; + let second = a[1]; +} +``` + +All elements in an array must be of the same type (i.e. homogeneous). That is, an array cannot group +a `Field` value and a `u8` value together for example. + +You can write mutable arrays, like: + +```rust +fn main() { + let mut arr = [1, 2, 3, 4, 5]; + assert(arr[0] == 1); + + arr[0] = 42; + assert(arr[0] == 42); +} +``` + +You can instantiate a new array of a fixed size with the same value repeated for each element. The following example instantiates an array of length 32 where each element is of type Field and has the value 0. + +```rust +let array: [Field; 32] = [0; 32]; +``` + +Like in Rust, arrays in Noir are a fixed size. However, if you wish to convert an array to a [slice](./slices), you can just call `as_slice` on your array: + +```rust +let array: [Field; 32] = [0; 32]; +let sl = array.as_slice() +``` + +## Types + +You can create arrays of primitive types or structs. There is not yet support for nested arrays +(arrays of arrays) or arrays of structs that contain arrays. + +## Methods + +For convenience, the STD provides some ready-to-use, common methods for arrays: + +### len + +Returns the length of an array + +```rust +fn len(_array: [T; N]) -> comptime Field +``` + +example + +```rust +fn main() { + let array = [42, 42]; + assert(array.len() == 2); +} +``` + +### sort + +Returns a new sorted array. The original array remains untouched. Notice that this function will +only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting +logic it uses internally is optimized specifically for these values. If you need a sort function to +sort any type, you should use the function `sort_via` described below. + +```rust +fn sort(_array: [T; N]) -> [T; N] +``` + +example + +```rust +fn main() { + let arr = [42, 32]; + let sorted = arr.sort(); + assert(sorted == [32, 42]); +} +``` + +### sort_via + +Sorts the array with a custom comparison function + +```rust +fn sort_via(mut a: [T; N], ordering: fn(T, T) -> bool) -> [T; N] +``` + +example + +```rust +fn main() { + let arr = [42, 32] + let sorted_ascending = arr.sort_via(|a, b| a < b); + assert(sorted_ascending == [32, 42]); // verifies + + let sorted_descending = arr.sort_via(|a, b| a > b); + assert(sorted_descending == [32, 42]); // does not verify +} +``` + +### map + +Applies a function to each element of the array, returning a new array containing the mapped elements. + +```rust +fn map(f: fn(T) -> U) -> [U; N] +``` + +example + +```rust +let a = [1, 2, 3]; +let b = a.map(|a| a * 2); // b is now [2, 4, 6] +``` + +### fold + +Applies a function to each element of the array, returning the final accumulated value. The first +parameter is the initial value. + +```rust +fn fold(mut accumulator: U, f: fn(U, T) -> U) -> U +``` + +This is a left fold, so the given function will be applied to the accumulator and first element of +the array, then the second, and so on. For a given call the expected result would be equivalent to: + +```rust +let a1 = [1]; +let a2 = [1, 2]; +let a3 = [1, 2, 3]; + +let f = |a, b| a - b; +a1.fold(10, f) //=> f(10, 1) +a2.fold(10, f) //=> f(f(10, 1), 2) +a3.fold(10, f) //=> f(f(f(10, 1), 2), 3) +``` + +example: + +```rust + +fn main() { + let arr = [2, 2, 2, 2, 2]; + let folded = arr.fold(0, |a, b| a + b); + assert(folded == 10); +} + +``` + +### reduce + +Same as fold, but uses the first element as starting element. + +```rust +fn reduce(f: fn(T, T) -> T) -> T +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 2]; + let reduced = arr.reduce(|a, b| a + b); + assert(reduced == 10); +} +``` + +### all + +Returns true if all the elements satisfy the given predicate + +```rust +fn all(predicate: fn(T) -> bool) -> bool +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 2]; + let all = arr.all(|a| a == 2); + assert(all); +} +``` + +### any + +Returns true if any of the elements satisfy the given predicate + +```rust +fn any(predicate: fn(T) -> bool) -> bool +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 5]; + let any = arr.any(|a| a == 5); + assert(any); +} + +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/05_slices.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/05_slices.md new file mode 100644 index 00000000000..bc7f5c531e7 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/05_slices.md @@ -0,0 +1,157 @@ +--- +title: Slices +description: + Explore the Slice data type in Noir. Understand its methods, see real-world examples, and learn how to effectively use Slices in your Noir programs. +keywords: + [ + noir, + slice type, + methods, + examples, + subarrays, + ] +--- + +:::caution + +This feature is experimental. You should expect it to change in future versions, +cause unexpected behavior, or simply not work at all. + +::: + +A slice is a dynamically-sized view into a sequence of elements. They can be resized at runtime, but because they don't own the data, they cannot be returned from a circuit. You can treat slices as arrays without a constrained size. + +```rust +use dep::std::slice; + +fn main() -> pub Field { + let mut slice: [Field] = [0; 2]; + + let mut new_slice = slice.push_back(6); + new_slice.len() +} +``` + +View the corresponding test file [here]([test-file]. + +[test-file]: https://github.com/noir-lang/noir/blob/f387ec1475129732f72ba294877efdf6857135ac/crates/nargo_cli/tests/test_data_ssa_refactor/slices/src/main.nr + +## Methods + +For convenience, the STD provides some ready-to-use, common methods for slices: + +### push_back + +Pushes a new element to the end of the slice, returning a new slice with a length one greater than the original unmodified slice. + +```rust +fn push_back(_self: [T], _elem: T) -> [T] +``` + +example: + +```rust +fn main() -> pub Field { + let mut slice: [Field] = [0; 2]; + + let mut new_slice = slice.push_back(6); + new_slice.len() +} +``` + +View the corresponding test file [here][test-file]. + +### push_front + +Returns a new array with the specified element inserted at index 0. The existing elements indexes are incremented by 1. + +```rust +fn push_front(_self: Self, _elem: T) -> Self +``` + +Example: + +```rust +let mut new_slice: [Field] = []; +new_slice = new_slice.push_front(20); +assert(new_slice[0] == 20); // returns true +``` + +View the corresponding test file [here][test-file]. + +### pop_front + +Returns a tuple of two items, the first element of the array and the rest of the array. + +```rust +fn pop_front(_self: Self) -> (T, Self) +``` + +Example: + +```rust +let (first_elem, rest_of_slice) = slice.pop_front(); +``` + +View the corresponding test file [here][test-file]. + +### pop_back + +Returns a tuple of two items, the beginning of the array with the last element omitted and the last element. + +```rust +fn pop_back(_self: Self) -> (Self, T) +``` + +Example: + +```rust +let (popped_slice, last_elem) = slice.pop_back(); +``` + +View the corresponding test file [here][test-file]. + +### append + +Loops over a slice and adds it to the end of another. + +```rust +fn append(mut self, other: Self) -> Self +``` + +Example: + +```rust +let append = [1, 2].append([3, 4, 5]); +``` + +### insert + +Inserts an element at a specified index and shifts all following elements by 1. + +```rust +fn insert(_self: Self, _index: Field, _elem: T) -> Self +``` + +Example: + +```rust +new_slice = rest_of_slice.insert(2, 100); +assert(new_slice[2] == 100); +``` + +View the corresponding test file [here][test-file]. + +### remove + +Remove an element at a specified index, shifting all elements after it to the left, returning the altered slice and the removed element. + +```rust +fn remove(_self: Self, _index: Field) -> (Self, T) +``` + +Example: + +```rust +let (remove_slice, removed_elem) = slice.remove(3); +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/06_vectors.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/06_vectors.md new file mode 100644 index 00000000000..c5b74c877f8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/06_vectors.md @@ -0,0 +1,34 @@ +--- +title: Vectors +description: + Delve into the Vector data type in Noir. Learn about its methods, practical examples, and best practices for using Vectors in your Noir code. +keywords: + [ + noir, + vector type, + methods, + examples, + dynamic arrays, + ] +--- + +:::caution + +This feature is experimental. You should expect it to change in future versions, +cause unexpected behavior, or simply not work at all. + +::: + +A vector is a collection type similar to Rust's Vector type. It's convenient way to use slices as mutable arrays. + +Example: + +```rust +use dep::std::collections::vec::Vec; + +let mut vector: Vec = Vec::new(); +for i in 0..5 { + vector.push(i); +} +assert(vector.len() == 5); +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/07_tuples.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/07_tuples.md new file mode 100644 index 00000000000..5f6cab974a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/07_tuples.md @@ -0,0 +1,47 @@ +--- +title: Tuples +description: + Dive into the Tuple data type in Noir. Understand its methods, practical examples, and best practices for efficiently using Tuples in your Noir code. +keywords: + [ + noir, + tuple type, + methods, + examples, + multi-value containers, + ] +--- + +A tuple collects multiple values like an array, but with the added ability to collect values of +different types: + +```rust +fn main() { + let tup: (u8, u64, Field) = (255, 500, 1000); +} +``` + +One way to access tuple elements is via destructuring using pattern matching: + +```rust +fn main() { + let tup = (1, 2); + + let (one, two) = tup; + + let three = one + two; +} +``` + +Another way to access tuple elements is via direct member access, using a period (`.`) followed by +the index of the element we want to access. Index `0` corresponds to the first tuple element, `1` to +the second and so on: + +```rust +fn main() { + let tup = (5, 6, 7, 8); + + let five = tup.0; + let eight = tup.3; +} +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/08_structs.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/08_structs.md new file mode 100644 index 00000000000..85649dfb389 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/08_structs.md @@ -0,0 +1,73 @@ +--- +title: Structs +description: + Explore the Struct data type in Noir. Learn about its methods, see real-world examples, and grasp how to effectively define and use Structs in your Noir programs. +keywords: + [ + noir, + struct type, + methods, + examples, + data structures, + ] +--- + +A struct also allows for grouping multiple values of different types. Unlike tuples, we can also +name each field. + +> **Note:** The usage of _field_ here refers to each element of the struct and is unrelated to the +> field type of Noir. + +Defining a struct requires giving it a name and listing each field within as `: ` pairs: + +```rust +struct Animal { + hands: Field, + legs: Field, + eyes: u8, +} +``` + +An instance of a struct can then be created with actual values in `: ` pairs in any +order. Struct fields are accessible using their given names: + +```rust +fn main() { + let legs = 4; + + let dog = Animal { + eyes: 2, + hands: 0, + legs, + }; + + let zero = dog.hands; +} +``` + +Structs can also be destructured in a pattern, binding each field to a new variable: + +```rust +fn main() { + let Animal { hands, legs: feet, eyes } = get_octopus(); + + let ten = hands + feet + eyes as u8; +} + +fn get_octopus() -> Animal { + let octopus = Animal { + hands: 0, + legs: 8, + eyes: 2, + }; + + octopus +} +``` + +The new variables can be bound with names different from the original struct field names, as +showcased in the `legs --> feet` binding in the example above. + +:::note +You can use Structs as inputs to the `main` function, but you can't output them +::: diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/09_references.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/09_references.md new file mode 100644 index 00000000000..b0c35ce2cb9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/09_references.md @@ -0,0 +1,22 @@ +--- +title: References +--- + +Noir supports first-class references. References are a bit like pointers: they point to a specific address that can be followed to access the data stored at that address. You can use Rust-like syntax to use pointers in Noir: the `&` operator references the variable, the `*` operator dereferences it. + +Example: + +```rust +fn main() { + let mut x = 2; + + // you can reference x as &mut and pass it to multiplyBy2 + multiplyBy2(&mut x); +} + +// you can access &mut here +fn multiplyBy2(x: &mut Field) { + // and dereference it with * + *x = *x * 2; +} +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/10_function_types.md b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/10_function_types.md new file mode 100644 index 00000000000..1ec92efd594 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/language_concepts/data_types/10_function_types.md @@ -0,0 +1,25 @@ +--- +title: Function types +--- + +Noir supports higher-order functions. The syntax for a function type is as follows: + +```rust +fn(arg1_type, arg2_type, ...) -> return_type +``` + +Example: + +```rust +fn assert_returns_100(f: fn() -> Field) { // f takes no args and returns a Field + assert(f() == 100); +} + +fn main() { + assert_returns_100(|| 100); // ok + assert_returns_100(|| 150); // fails +} +``` + +A function type also has an optional capture environment - this is necessary to support closures. +See [Lambdas](../08_lambdas.md) for more details. diff --git a/noir/docs/versioned_docs/version-v0.10.5/modules_packages_crates/crates_and_packages.md b/noir/docs/versioned_docs/version-v0.10.5/modules_packages_crates/crates_and_packages.md new file mode 100644 index 00000000000..fb83a33d94e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/modules_packages_crates/crates_and_packages.md @@ -0,0 +1,42 @@ +--- +title: Crates and Packages +description: Learn how to use Crates and Packages in your Noir project +keywords: [Nargo, dependencies, package management, crates, package] +--- + +## Crates + +A crate is the smallest amount of code that the Noir compiler considers at a time. +Crates can contain modules, and the modules may be defined in other files that get compiled with the crate, as we’ll see in the coming sections. + +### Crate Types + +A Noir crate can come in several forms: binaries, libraries or contracts. + +#### Binaries + +_Binary crates_ are programs which you can compile to an ACIR circuit which you can then create proofs against. Each must have a function called `main` that defines the ACIR circuit which is to be proved. + +#### Libraries + +_Library crates_ don't have a `main` function and they don't compile down to ACIR. Instead they define functionality intended to be shared with multiple projects, and eventually included in a binary crate. + +#### Contracts + +Contract crates are similar to binary crates in that they compile to ACIR which you can create proofs against. They are different in that they do not have a single `main` function, but are a collection of functions to be deployed to the [Aztec network](https://aztec.network). You can learn more about the technical details of Aztec in the [monorepo](https://github.com/AztecProtocol/aztec-packages) or contract [examples](https://github.com/AztecProtocol/aztec-packages/tree/master/yarn-project/noir-contracts/src/contracts). + +### Crate Root + +Every crate has a root, which is the source file that the compiler starts, this is also known as the root module. The Noir compiler does not enforce any conditions on the name of the file which is the crate root, however if you are compiling via Nargo the crate root must be called `lib.nr` or `main.nr` for library or binary crates respectively. + +## Packages + +A Nargo _package_ is a collection of one of more crates that provides a set of functionality. A package must include a Nargo.toml file. + +A package _must_ contain either a library or a binary crate, but not both. + +### Differences from Cargo Packages + +One notable difference between Rust's Cargo and Noir's Nargo is that while Cargo allows a package to contain an unlimited number of binary crates and a single library crate, Nargo currently only allows a package to contain a single crate. + +In future this restriction may be lifted to allow a Nargo package to contain both a binary and library crate or multiple binary crates. diff --git a/noir/docs/versioned_docs/version-v0.10.5/modules_packages_crates/dependencies.md b/noir/docs/versioned_docs/version-v0.10.5/modules_packages_crates/dependencies.md new file mode 100644 index 00000000000..9e6463801b7 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/modules_packages_crates/dependencies.md @@ -0,0 +1,123 @@ +--- +title: Dependencies +description: + Learn how to specify and manage dependencies in Nargo, allowing you to upload packages to GitHub + and use them easily in your project. +keywords: [Nargo, dependencies, GitHub, package management, versioning] +--- + +Nargo allows you to upload packages to GitHub and use them as dependencies. + +## Specifying a dependency + +Specifying a dependency requires a tag to a specific commit and the git url to the url containing +the package. + +Currently, there are no requirements on the tag contents. If requirements are added, it would follow +semver 2.0 guidelines. + +> Note: Without a `tag` , there would be no versioning and dependencies would change each time you +> compile your project. + +For example, to add the [ecrecover-noir library](https://github.com/colinnielsen/ecrecover-noir) to your project, add it to `Nargo.toml`: + +```toml +# Nargo.toml + +[dependencies] +ecrecover = {tag = "v0.8.0", git = "https://github.com/colinnielsen/ecrecover-noir"} +``` + +If the module is in a subdirectory, you can define a subdirectory in your git repository, for example: + +```toml +# Nargo.toml + +[dependencies] +easy_private_token_contract = {tag ="v0.1.0-alpha62", git = "https://github.com/AztecProtocol/aztec-packages", directory = "yarn-project/noir-contracts/src/contracts/easy_private_token_contract"} +``` + +## Specifying a local dependency + +You can also specify dependencies that are local to your machine. + +For example, this file structure has a library and binary crate + +```tree +├── binary_crate +│   ├── Nargo.toml +│   └── src +│   └── main.nr +└── liba + ├── Nargo.toml + └── src + └── lib.nr +``` + +Inside of the binary crate, you can specify: + +```toml +# Nargo.toml + +[dependencies] +libA = { path = "../liba" } +``` + +## Importing dependencies + +You can import a dependency to a Noir file using the following syntax. For example, to import the +ecrecover-noir library and local liba referenced above: + +```rust +use dep::ecrecover; +use dep::libA; +``` + +You can also import only the specific parts of dependency that you want to use, like so: + +```rust +use dep::std::hash::sha256; +use dep::std::scalar_mul::fixed_base; +``` + +Lastly, as demonstrated in the +[elliptic curve example](../standard_library/cryptographic_primitives/ec_primitives#examples), you +can import multiple items in the same line by enclosing them in curly braces: + +```rust +use dep::std::ec::tecurve::affine::{Curve, Point}; +``` + +We don't have a way to consume libraries from inside a [workspace](./workspaces) as external dependencies right now. + +Inside a workspace, these are consumed as `{ path = "../to_lib" }` dependencies in Nargo.toml. + +## Dependencies of Dependencies + +Note that when you import a dependency, you also get access to all of the dependencies of that package. + +For example, the [phy_vector](https://github.com/resurgencelabs/phy_vector) library imports an [fraction](https://github.com/resurgencelabs/fraction) library. If you're importing the phy_vector library, then you can access the functions in fractions library like so: + +```rust +use dep::phy_vector; + +fn main(x : Field, y : pub Field) { + //... + let f = phy_vector::fraction::toFraction(true, 2, 1); + //... +} +``` + +## Available Libraries + +Noir does not currently have an official package manager. You can find a list of available Noir libraries in the [awesome-noir repo here](https://github.com/noir-lang/awesome-noir#libraries). + +Some libraries that are available today include: + +- [Standard Library](https://github.com/noir-lang/noir/tree/master/noir_stdlib) - the Noir Standard Library +- [Ethereum Storage Proof Verification](https://github.com/aragonzkresearch/noir-trie-proofs) - a library that contains the primitives necessary for RLP decoding (in the form of look-up table construction) and Ethereum state and storage proof verification (or verification of any trie proof involving 32-byte long keys) +- [BigInt](https://github.com/shuklaayush/noir-bigint) - a library that provides a custom BigUint56 data type, allowing for computations on large unsigned integers +- [ECrecover](https://github.com/colinnielsen/ecrecover-noir/tree/main) - a library to verify an ECDSA signature and return the source Ethereum address +- [Sparse Merkle Tree Verifier](https://github.com/vocdoni/smtverifier-noir/tree/main) - a library for verification of sparse Merkle trees +- [Signed Int](https://github.com/resurgencelabs/signed_int) - a library for accessing a custom Signed Integer data type, allowing access to negative numbers on Noir +- [Fraction](https://github.com/resurgencelabs/fraction) - a library for accessing fractional number data type in Noir, allowing results that aren't whole numbers diff --git a/noir/docs/versioned_docs/version-v0.10.5/modules_packages_crates/modules.md b/noir/docs/versioned_docs/version-v0.10.5/modules_packages_crates/modules.md new file mode 100644 index 00000000000..147c9b284e8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/modules_packages_crates/modules.md @@ -0,0 +1,104 @@ +--- +title: Modules +description: + Learn how to organize your files using modules in Noir, following the same convention as Rust's + module system. Examples included. +keywords: [Noir, Rust, modules, organizing files, sub-modules] +--- + +Noir's module system follows the same convention as the _newer_ version of Rust's module system. + +## Purpose of Modules + +Modules are used to organise files. Without modules all of your code would need to live in a single +file. In Noir, the compiler does not automatically scan all of your files to detect modules. This +must be done explicitly by the developer. + +## Examples + +### Importing a module in the crate root + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::hello_world(); +} +``` + +Filename : `src/foo.nr` + +```rust +fn from_foo() {} +``` + +In the above snippet, the crate root is the `src/main.nr` file. The compiler sees the module +declaration `mod foo` which prompts it to look for a foo.nr file. + +Visually this module hierarchy looks like the following : + +``` +crate + ├── main + │ + └── foo + └── from_foo + +``` + +### Importing a module throughout the tree + +All modules are accessible from the `crate::` namespace. + +``` +crate + ├── bar + ├── foo + └── main + +``` + +In the above snippet, if `bar` would like to use functions in `foo`, it can do so by `use crate::foo::function_name`. + +### Sub-modules + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::from_foo(); +} +``` + +Filename : `src/foo.nr` + +```rust +mod bar; +fn from_foo() {} +``` + +Filename : `src/foo/bar.nr` + +```rust +fn from_bar() {} +``` + +In the above snippet, we have added an extra module to the module tree; `bar`. `bar` is a submodule +of `foo` hence we declare bar in `foo.nr` with `mod bar`. Since `foo` is not the crate root, the +compiler looks for the file associated with the `bar` module in `src/foo/bar.nr` + +Visually the module hierarchy looks as follows: + +``` +crate + ├── main + │ + └── foo + ├── from_foo + └── bar + └── from_bar +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/modules_packages_crates/workspaces.md b/noir/docs/versioned_docs/version-v0.10.5/modules_packages_crates/workspaces.md new file mode 100644 index 00000000000..eaa09506698 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/modules_packages_crates/workspaces.md @@ -0,0 +1,39 @@ +--- +title: Workspaces +--- + +Workspaces are a feature of nargo that allow you to manage multiple related Noir packages in a single repository. A workspace is essentially a group of related projects that share common build output directories and configurations. + +Each Noir project (with it's own Nargo.toml file) can be thought of as a package. Each package is expected to contain exactly one "named circuit", being the "name" defined in Nargo.toml with the program logic defined in `./src/main.nr`. + +For a project with the following structure: + +```tree +├── crates +│   ├── a +│   │   ├── Nargo.toml +│   │   └── src +│   │   └── main.nr +│   └── b +│   ├── Nargo.toml +│   └── src +│   └── main.nr +├── Nargo.toml +└── Prover.toml +``` + +You can define a workspace in Nargo.toml like so: + +```toml +[workspace] +members = ["crates/a", "crates/b"] +default-member = "crates/a" +``` + +`members` indicates which packages are included in the workspace. As such, all member packages of a workspace will be processed when the `--workspace` flag is used with various commands or if a `default-member` is not specified. + +`default-member` indicates which package various commands process by default. + +Libraries can be defined in a workspace. We just don't have a way to consume libraries from inside a workspace as external dependencies right now. + +Inside a workspace, these are consumed as `{ path = "../to_lib" }` dependencies in Nargo.toml. diff --git a/noir/docs/versioned_docs/version-v0.10.5/nargo/01_commands.md b/noir/docs/versioned_docs/version-v0.10.5/nargo/01_commands.md new file mode 100644 index 00000000000..425a73a34c8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/nargo/01_commands.md @@ -0,0 +1,223 @@ +--- +title: Commands +description: + Noir CLI Commands for Noir Prover and Verifier to create, execute, prove and verify programs, + generate Solidity verifier smart contract and compile into JSON file containing ACIR + representation and ABI of circuit. +keywords: + [ + Nargo, + Noir CLI, + Noir Prover, + Noir Verifier, + generate Solidity verifier, + compile JSON file, + ACIR representation, + ABI of circuit, + TypeScript, + ] +--- + +## General options + +``` +Options: + --show-ssa Emit debug information for the intermediate SSA IR + --deny-warnings Quit execution when warnings are emitted + -h, --help Print help +``` + +## `nargo help [subcommand]` + +Prints the list of available commands or specific information of a subcommand. + +_Arguments_ + +- `` - The subcommand whose help message to display + +## `nargo check` + +Generate the `Prover.toml` and `Verifier.toml` files for specifying prover and verifier in/output +values of the Noir program respectively. + +**Options** + +``` + --package The name of the package to check + --workspace Check all packages in the workspace + --print-acir Display the ACIR for compiled circuit + --deny-warnings Treat all warnings as errors +-h, --help Print help +``` + +## `nargo codegen-verifier` + +Generate a Solidity verifier smart contract for the program. + +**Options** + +``` + --package The name of the package to codegen + --workspace Codegen all packages in the workspace + --print-acir Display the ACIR for compiled circuit + --deny-warnings Treat all warnings as errors +-h, --help Print help +``` + +## `nargo compile` + +Compile the program into a JSON build artifact file containing the ACIR representation and the ABI +of the circuit. This build artifact can then be used to generate and verify proofs. + +You can also use "build" as an alias for compile (e.g. `nargo build`). + +**Options** + +``` + --include-keys Include Proving and Verification keys in the build artifacts + --package The name of the package to compile + --workspace Compile all packages in the workspace + --print-acir Display the ACIR for compiled circuit + --deny-warnings Treat all warnings as errors +-h, --help Print help +``` + +## `nargo new ` + +Creates a new Noir project in a new folder. + +**Arguments** + +``` + The path to save the new project +``` + +**Options** + +``` + --name Name of the package [default: package directory name] + --lib Use a library template + --bin Use a binary template [default] + --contract Use a contract template +-h, --help Print help +``` + +## `nargo init` + +Creates a new Noir project in the current directory. + +**Options** + +``` + --name Name of the package [default: current directory name] + --lib Use a library template + --bin Use a binary template [default] + --contract Use a contract template +-h, --help Print help +``` + +## `nargo execute [WITNESS_NAME]` + +Runs the Noir program and prints its return value. + +**Arguments** + +``` +[WITNESS_NAME] Write the execution witness to named file +``` + +**Options** + +``` +-p, --prover-name The name of the toml file which contains the inputs for the prover [default: Prover] + --package The name of the package to execute + --workspace Execute all packages in the workspace + --print-acir Display the ACIR for compiled circuit + --deny-warnings Treat all warnings as errors +-h, --help Print help +``` + +_Usage_ + +The inputs to the circuit are read from the `Prover.toml` file generated by `nargo check`, which +must be filled in. + +To save the witness to file, run the command with a value for the `WITNESS_NAME` argument. A +`.tr` file will then be saved in the `./target` folder. + +> **Info:** The `.tr` file is the witness file. The witness file can be considered as program inputs +> parsed for your program's ACIR. +> +> This file can be passed along with circuit's ACIR into a TypeScript project for proving and +> verification. See the [TypeScript](../typescript#proving-and-verifying-externally-compiled-files) +> section to learn more. + +## `nargo prove` + +Creates a proof for the program. + +**Options** + +``` +-p, --prover-name The name of the toml file which contains the inputs for the prover [default: Prover] +-v, --verifier-name The name of the toml file which contains the inputs for the verifier [default: Verifier] + --verify Verify proof after proving + --package The name of the package to prove + --workspace Prove all packages in the workspace + --print-acir Display the ACIR for compiled circuit + --deny-warnings Treat all warnings as errors +-h, --help Print help +``` + +## `nargo verify` + +Given a proof and a program, verify whether the proof is valid. + +**Options** + +``` +-v, --verifier-name The name of the toml file which contains the inputs for the verifier [default: Verifier] + --package The name of the package verify + --workspace Verify all packages in the workspace + --print-acir Display the ACIR for compiled circuit + --deny-warnings Treat all warnings as errors +-h, --help Print help +``` + +## `nargo test [TEST_NAME]` + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. To print `println` statements in tests, use the `--show-output` flag. + +Takes an optional `--exact` flag which allows you to select tests based on an exact name. + +See an example on the [testing page](./testing). + +**Options** + +``` + --show-output Display output of `println` statements + --exact Only run tests that match exactly + --package The name of the package to test + --workspace Test all packages in the workspace + --print-acir Display the ACIR for compiled circuit + --deny-warnings Treat all warnings as errors +-h, --help Print help +``` + +## `nargo info` + +Prints a table containing the information of the package. + +Currently the table provide + +1. The number of ACIR opcodes +2. The final number gates in the circuit used by a backend + +If the file contains a contract the table will provide the +above information about each function of the contract. + +## `nargo lsp` + +Start a long-running Language Server process that communicates over stdin/stdout. +Usually this command is not run by a user, but instead will be run by a Language Client, such as [vscode-noir](https://github.com/noir-lang/vscode-noir). diff --git a/noir/docs/versioned_docs/version-v0.10.5/nargo/02_testing.md b/noir/docs/versioned_docs/version-v0.10.5/nargo/02_testing.md new file mode 100644 index 00000000000..106952c44c0 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/nargo/02_testing.md @@ -0,0 +1,42 @@ +--- +title: Testing in Noir +description: Learn how to use Nargo to test your Noir program in a quick and easy way +keywords: [Nargo, testing, Noir, compile, test] +--- + +You can test your Noir programs using Noir circuits. + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. + +For example if you have a program like: + +```rust +fn add(x: u64, y: u64) -> u64 { + x + y +} +#[test] +fn test_add() { + assert(add(2,2) == 4); + assert(add(0,1) == 1); + assert(add(1,0) == 1); +} +``` + +Running `nargo test` will test that the `test_add` function can be executed while satisfying the all +the contraints which allows you to test that add returns the expected values. Test functions can't +have any arguments currently. + +### Test fail + +You can write tests that are expected to fail by using the decorator `#[test(should_fail)]`. For example: + +```rust +fn add(x: u64, y: u64) -> u64 { + x + y +} +#[test(should_fail)] +fn test_add() { + assert(add(2,2) == 5); +} +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/nargo/03_solidity_verifier.md b/noir/docs/versioned_docs/version-v0.10.5/nargo/03_solidity_verifier.md new file mode 100644 index 00000000000..9ac60cb0ba7 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/nargo/03_solidity_verifier.md @@ -0,0 +1,129 @@ +--- +title: Solidity Verifier +description: + Learn how to run the verifier as a smart contract on the blockchain. Compile a Solidity verifier + contract for your Noir program and deploy it on any EVM blockchain acting as a verifier smart + contract. Read more to find out! +keywords: + [ + solidity verifier, + smart contract, + blockchain, + compiler, + plonk_vk.sol, + EVM blockchain, + verifying Noir programs, + proving backend, + Barretenberg, + ] +--- + +For certain applications, it may be desirable to run the verifier as a smart contract instead of on +a local machine. + +Compile a Solidity verifier contract for your Noir program by running: + +```sh +nargo codegen-verifier +``` + +A new `contract` folder would then be generated in your project directory, containing the Solidity +file `plonk_vk.sol`. It can be deployed on any EVM blockchain acting as a verifier smart contract. + +> **Note:** It is possible to compile verifier contracts of Noir programs for other smart contract +> platforms as long as the proving backend supplies an implementation. +> +> Barretenberg, the default proving backend for Nargo, supports compilation of verifier contracts in +> Solidity only for the time being. + +## Verify + +To verify a proof using the Solidity verifier contract, call the `verify` function with the +following signature: + +```solidity +function verify(bytes calldata _proof, bytes32[] calldata _publicInputs) external view returns (bool) +``` + +You can see an example of how the `verify` function is called in the example zk voting application [here](https://github.com/noir-lang/noir-examples/blob/33e598c257e2402ea3a6b68dd4c5ad492bce1b0a/foundry-voting/src/zkVote.sol#L35): + +```solidity +function castVote(bytes calldata proof, uint proposalId, uint vote, bytes32 nullifierHash) public returns (bool) { + // ... + bytes32[] memory publicInputs = new bytes32[](4); + publicInputs[0] = merkleRoot; + publicInputs[1] = bytes32(proposalId); + publicInputs[2] = bytes32(vote); + publicInputs[3] = nullifierHash; + require(verifier.verify(proof, publicInputs), "Invalid proof"); +``` + +### Public Inputs + +:::tip + +A circuit doesn't have the concept of a return value. Return values are just syntactic sugar in +Noir. + +Under the hood, the return value is passed as an input to the circuit and is checked at the end of +the circuit program. + +::: + +The verifier contract uses the output (return) value of a Noir program as a public input. So if you +have the following function + +```rust +fn main( + // Public inputs + pubkey_x: pub Field, + pubkey_y: pub Field, + // Private inputs + priv_key: Field, +) -> pub Field +``` + +then `verify` in `plonk_vk.sol` will expect 3 public inputs. Passing two inputs will result in an +error like `Reason: PUBLIC_INPUT_COUNT_INVALID(3, 2)`. + +In this case the 3 inputs to `verify` would be ordered as `[pubkey_x, pubkey_y, return]`. + +#### Struct inputs + +Consider the following program: + +```rust +struct Type1 { + val1: Field, + val2: Field, +} + +struct Nested { + t1: Type1, + is_true: bool, +} + +fn main(x: pub Field, nested: pub Nested, y: pub Field) { + //... +} +``` + +Structs will be flattened so that the array of inputs is 1-dimensional array. The order of these inputs would be flattened to: `[x, nested.t1.val1, nested.t1.val2, nested.is_true, y]` + +## Noir for EVM chains + +You can currently deploy the Solidity verifier contracts to most EVM compatible chains. EVM chains that have been tested and are known to work include: + +- Optimism +- Arbitrum +- Polygon PoS +- Scroll +- Celo + +Other EVM chains should work, but have not been tested directly by our team. If you test any other chains, please open a PR on this page to update the list. See [this doc](https://github.com/noir-lang/noir-starter/tree/main/with-foundry#testing-on-chain) for more info about testing verifier contracts on different EVM chains. + +### Unsupported chains + +Unfortunately not all "EVM" chains are supported. + +**zkSync** and the **Polygon zkEVM** do _not_ currently support proof verification via Solidity verifier contracts. They are missing the bn256 precompile contract that the verifier contract requires. Once these chains support this precompile, they may work. diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/black_box_fns.md b/noir/docs/versioned_docs/version-v0.10.5/standard_library/black_box_fns.md new file mode 100644 index 00000000000..c758846b688 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/black_box_fns.md @@ -0,0 +1,45 @@ +--- +title: Black Box Functions +description: Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. +keywords: [noir, black box functions] +--- + +Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. This makes certain zk-snark unfriendly computations cheaper than if they were implemented in Noir. + +:::warning + +It is likely that not all backends will support a particular black box function. + +::: + +Because it is not guaranteed that all backends will support black box functions, it is possible that certain Noir programs won't compile against a particular backend if they use an unsupported black box function. It is possible to fallback to less efficient implementations written in Noir/ACIR in some cases. + +Black box functions are specified with the `#[foreign(black_box_fn)]` attribute. For example, the SHA256 function in the Noir [source code](https://github.com/noir-lang/noir/blob/v0.5.1/noir_stdlib/src/hash.nr) looks like: + +```rust +#[foreign(sha256)] +fn sha256(_input : [u8; N]) -> [u8; 32] {} +``` + +## Function list + +Here is a list of the current black box functions that are supported by UltraPlonk: + +- AES +- [SHA256](./cryptographic_primitives/hashes#sha256) +- [Schnorr signature verification](./cryptographic_primitives/schnorr) +- [Blake2s](./cryptographic_primitives/hashes#blake2s) +- [Pedersen](./cryptographic_primitives/hashes#pedersen) +- [HashToField128Security](./cryptographic_primitives/hashes#hash_to_field) +- [ECDSA signature verification](./cryptographic_primitives/ecdsa_sig_verification) +- [Fixed base scalar multiplication](./cryptographic_primitives/scalar) +- [Compute merkle root](./merkle_trees#compute_merkle_root) +- AND +- XOR +- RANGE +- [Keccak256](./cryptographic_primitives/hashes#keccak256) +- [Recursive proof verification](./recursion) + +Most black box functions are included as part of the Noir standard library, however `AND`, `XOR` and `RANGE` are used as part of the Noir language syntax. For instance, using the bitwise operator `&` will invoke the `AND` black box function. To ensure compatibility across backends, the ACVM has fallback implementations of `AND`, `XOR` and `RANGE` defined in its standard library which it can seamlessly fallback to if the backend doesn't support them. + +You can view the black box functions defined in the ACVM code [here](https://github.com/noir-lang/acvm/blob/acir-v0.12.0/acir/src/circuit/black_box_functions.rs). diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives.md b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives.md new file mode 100644 index 00000000000..2df4f929474 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives.md @@ -0,0 +1,14 @@ +--- +title: Cryptographic primitives in Noir +description: + Learn about the cryptographic primitives ready to use for any Noir project +keywords: + [ + cryptographic primitives, + Noir project, + ] +--- + +The Noir team is progressively adding new cryptographic primitives to the standard library. Reach out for news or if you would be interested in adding more of these calculations in Noir. + +Some methods are available thanks to the Aztec backend, not being performed using Noir. When using other backends, these methods may or may not be supplied. diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/00_hashes.mdx b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/00_hashes.mdx new file mode 100644 index 00000000000..1d9b1c7f09c --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/00_hashes.mdx @@ -0,0 +1,146 @@ +--- +title: Hash methods +description: + Learn about the cryptographic primitives ready to use for any Noir project, including sha256, + blake2s, pedersen, mimc_bn254 and mimc +keywords: + [cryptographic primitives, Noir project, sha256, blake2s, pedersen, mimc_bn254, mimc, hash] +--- + +import BlackBoxInfo from './common/_blackbox.mdx'; + +## sha256 + +Given an array of bytes, returns the resulting sha256 hash. + +```rust +fn sha256(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::sha256(x); +} +``` + + + +## blake2s + +Given an array of bytes, returns an array with the Blake2 hash + +```rust +fn blake2s(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::blake2s(x); +} +``` + + + +## pedersen + +Given an array of Fields, returns the Pedersen hash. + +```rust +fn pedersen(_input : [Field]) -> [Field; 2] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::pedersen(x); +} +``` + + + +## keccak256 + +Given an array of bytes (`u8`), returns the resulting keccak hash as an array of 32 bytes +(`[u8; 32]`). Specify a message_size to hash only the first `message_size` bytes +of the input. + +```rust +fn keccak256(_input : [u8; N], _message_size: u32) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let message_size = 4; + let hash = std::hash::keccak256(x, message_size); +} +``` + + + +## poseidon + +Given an array of Fields, returns a new Field with the Poseidon Hash. Mind that you need to specify +how many inputs are there to your Poseidon function. + +```rust +// example for hash_1, hash_2 accepts an array of length 2, etc +fn hash_1(input: [Field; 1]) -> Field +``` + +example: + +```rust +fn main() +{ + let hash1 = std::hash::poseidon::bn254::hash_2([1, 2]); + assert(hash1 == 0x115cc0f5e7d690413df64c6b9662e9cf2a3617f2743245519e19607a4417189a); +} +``` + +## mimc_bn254 and mimc + +`mimc_bn254` is `mimc`, but with hardcoded parameters for the BN254 curve. You can use it by +providing an array of Fields, and it returns a Field with the hash. You can use the `mimc` method if +you're willing to input your own constants: + +```rust +fn mimc(x: Field, k: Field, constants: [Field; N], exp : Field) -> Field +``` + +otherwise, use the `mimc_bn254` method: + +```rust +fn mimc_bn254(array: [Field; N]) -> Field +``` + +example: + +```rust + +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::mimc_bn254(x); +} +``` + +## hash_to_field + +```rust +fn hash_to_field(_input : [Field; N]) -> Field {} +``` + +Calculates the `blake2s` hash of the inputs and returns the hash modulo the field modulus to return +a value which can be represented as a `Field`. + + diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/01_scalar.mdx b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/01_scalar.mdx new file mode 100644 index 00000000000..62265cddb1e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/01_scalar.mdx @@ -0,0 +1,33 @@ +--- +title: Scalar multiplication +description: + See how you can perform scalar multiplications over a fixed base in Noir +keywords: + [ + cryptographic primitives, + Noir project, + scalar multiplication, + ] +--- + +import BlackBoxInfo from './common/\_blackbox.mdx'; + +## scalar_mul::fixed_base + +Performs scalar multiplication over the embedded curve whose coordinates are defined by the +configured noir field. For the BN254 scalar field, this is BabyJubJub or Grumpkin. + +```rust +fn fixed_base(_input : Field) -> [Field; 2] +``` + +example + +```rust +fn main(x : Field) { + let scal = std::scalar_mul::fixed_base(x); + std::println(scal); +} +``` + + diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/02_schnorr.mdx b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/02_schnorr.mdx new file mode 100644 index 00000000000..0e219c0e5ff --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/02_schnorr.mdx @@ -0,0 +1,37 @@ +--- +title: Schnorr Signatures +description: Learn how you can verify Schnorr signatures using Noir +keywords: [cryptographic primitives, Noir project, schnorr, signatures] +--- + +import BlackBoxInfo from './common/_blackbox.mdx'; + +## schnorr::verify_signature + +Verifier for Schnorr signatures over the embedded curve (for BN254 it is Grumpkin). + +```rust +fn verify_signature(_public_key_x: Field, _public_key_y: Field, _signature: [u8; 64], _message: [u8]) -> bool +``` + +where `_signature` can be generated like so using the npm package +[@noir-lang/barretenberg](https://www.npmjs.com/package/@noir-lang/barretenberg) + +```js +const { BarretenbergWasm } = require('@noir-lang/barretenberg/dest/wasm'); +const { Schnorr } = require('@noir-lang/barretenberg/dest/crypto/schnorr'); + +... + +const barretenberg = await BarretenbergWasm.new(); +const schnorr = new Schnorr(barretenberg); +const pubKey = schnorr.computePublicKey(privateKey); +const message = ... +const signature = Array.from( + schnorr.constructSignature(hash, privateKey).toBuffer() +); + +... +``` + + diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx new file mode 100644 index 00000000000..3934a0338d0 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx @@ -0,0 +1,45 @@ +--- +title: ECDSA Signature Verification +description: Learn about the cryptographic primitives regarding ECDSA over the secp256k1 and secp256r1 curves +keywords: [cryptographic primitives, Noir project, ecdsa, secp256k1, secp256r1, signatures] +--- + +import BlackBoxInfo from './common/_blackbox.mdx'; + +Noir supports ECDSA signatures verification over the secp256k1 and secp256r1 curves. + +## ecdsa_secp256k1::verify_signature + +Verifier for ECDSA Secp256k1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> bool +``` + +example: + +```rust +fn main(hashed_message : [u8;32], pub_key_x : [u8;32], pub_key_y : [u8;32], signature : [u8;64]) { + let valid_signature = std::ecdsa_secp256k1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} +``` + +## ecdsa_secp256r1::verify_signature + +Verifier for ECDSA Secp256r1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> bool +``` + +example: + +```rust +fn main(hashed_message : [u8;32], pub_key_x : [u8;32], pub_key_y : [u8;32], signature : [u8;64]) { + let valid_signature = std::ecdsa_secp256r1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} +``` + + diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/04_ec_primitives.md b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/04_ec_primitives.md new file mode 100644 index 00000000000..6e6b19b6861 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/04_ec_primitives.md @@ -0,0 +1,101 @@ +--- +title: Elliptic Curve Primitives +keywords: [cryptographic primitives, Noir project] +--- + +Data structures and methods on them that allow you to carry out computations involving elliptic +curves over the (mathematical) field corresponding to `Field`. For the field currently at our +disposal, applications would involve a curve embedded in BN254, e.g. the +[Baby Jubjub curve](https://eips.ethereum.org/EIPS/eip-2494). + +## Data structures + +### Elliptic curve configurations + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Curve`), i.e. the specific elliptic +curve you want to use, which would be specified using any one of the methods +`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::new` which take the coefficients in the +defining equation together with a generator point as parameters. You can find more detail in the +comments in +[`noir_stdlib/src/ec.nr`](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr), but +the gist of it is that the elliptic curves of interest are usually expressed in one of the standard +forms implemented here (Twisted Edwards, Montgomery and Short Weierstraß), and in addition to that, +you could choose to use `affine` coordinates (Cartesian coordinates - the usual (x,y) - possibly +together with a point at infinity) or `curvegroup` coordinates (some form of projective coordinates +requiring more coordinates but allowing for more efficient implementations of elliptic curve +operations). Conversions between all of these forms are provided, and under the hood these +conversions are done whenever an operation is more efficient in a different representation (or a +mixed coordinate representation is employed). + +### Points + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Point`), i.e. points lying on the +elliptic curve. For a curve configuration `c` and a point `p`, it may be checked that `p` +does indeed lie on `c` by calling `c.contains(p1)`. + +## Methods + +(given a choice of curve representation, e.g. use `std::ec::tecurve::affine::Curve` and use +`std::ec::tecurve::affine::Point`) + +- The **zero element** is given by `Point::zero()`, and we can verify whether a point `p: Point` is + zero by calling `p.is_zero()`. +- **Equality**: Points `p1: Point` and `p2: Point` may be checked for equality by calling + `p1.eq(p2)`. +- **Addition**: For `c: Curve` and points `p1: Point` and `p2: Point` on the curve, adding these two + points is accomplished by calling `c.add(p1,p2)`. +- **Negation**: For a point `p: Point`, `p.negate()` is its negation. +- **Subtraction**: For `c` and `p1`, `p2` as above, subtracting `p2` from `p1` is accomplished by + calling `c.subtract(p1,p2)`. +- **Scalar multiplication**: For `c` as above, `p: Point` a point on the curve and `n: Field`, + scalar multiplication is given by `c.mul(n,p)`. If instead `n :: [u1; N]`, i.e. `n` is a bit + array, the `bit_mul` method may be used instead: `c.bit_mul(n,p)` +- **Multi-scalar multiplication**: For `c` as above and arrays `n: [Field; N]` and `p: [Point; N]`, + multi-scalar multiplication is given by `c.msm(n,p)`. +- **Coordinate representation conversions**: The `into_group` method converts a point or curve + configuration in the affine representation to one in the CurveGroup representation, and + `into_affine` goes in the other direction. +- **Curve representation conversions**: `tecurve` and `montcurve` curves and points are equivalent + and may be converted between one another by calling `into_montcurve` or `into_tecurve` on their + configurations or points. `swcurve` is more general and a curve c of one of the other two types + may be converted to this representation by calling `c.into_swcurve()`, whereas a point `p` lying + on the curve given by `c` may be mapped to its corresponding `swcurve` point by calling + `c.map_into_swcurve(p)`. +- **Map-to-curve methods**: The Elligator 2 method of mapping a field element `n: Field` into a + `tecurve` or `montcurve` with configuration `c` may be called as `c.elligator2_map(n)`. For all of + the curve configurations, the SWU map-to-curve method may be called as `c.swu_map(z,n)`, where + `z: Field` depends on `Field` and `c` and must be chosen by the user (the conditions it needs to + satisfy are specified in the comments + [here](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr)). + +## Examples + +The +[ec_baby_jubjub test](https://github.com/noir-lang/noir/blob/master/crates/nargo_cli/tests/test_data/ec_baby_jubjub/src/main.nr) +illustrates all of the above primitives on various forms of the Baby Jubjub curve. A couple of more +interesting examples in Noir would be: + +Public-key cryptography: Given an elliptic curve and a 'base point' on it, determine the public key +from the private key. This is a matter of using scalar multiplication. In the case of Baby Jubjub, +for example, this code would do: + +```rust +use dep::std::ec::tecurve::affine::{Curve, Point}; + +fn bjj_pub_key(priv_key: Field) -> Point +{ + + let bjj = Curve::new(168700, 168696, G::new(995203441582195749578291179787384436505546430278305826713579947235728471134,5472060717959818805561601436314318772137091100104008585924551046643952123905)); + + let base_pt = Point::new(5299619240641551281634865583518297030282874472190772894086521144482721001553, 16950150798460657717958625567821834550301663161624707787222815936182638968203); + + bjj.mul(priv_key,base_pt) +} +``` + +This would come in handy in a Merkle proof. + +- EdDSA signature verification: This is a matter of combining these primitives with a suitable hash + function. See + [feat(stdlib): EdDSA sig verification noir#1136](https://github.com/noir-lang/noir/pull/1136) for + the case of Baby Jubjub and the Poseidon hash function. diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/05_eddsa.mdx b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/05_eddsa.mdx new file mode 100644 index 00000000000..8f060ed3316 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/05_eddsa.mdx @@ -0,0 +1,17 @@ +--- +title: EdDSA Verification +description: Learn about the cryptographic primitives regarding EdDSA +keywords: [cryptographic primitives, Noir project, eddsa, signatures] +--- + +import BlackBoxInfo from './common/_blackbox.mdx'; + +## eddsa::eddsa_poseidon_verify + +Verifier for EdDSA signatures + +```rust +fn eddsa_poseidon_verify(public_key_x : Field, public_key_y : Field, signature_s: Field, signature_r8_x: Field, signature_r8_y: Field, message: Field) -> bool +``` + + diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/common/_blackbox.mdx b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/common/_blackbox.mdx new file mode 100644 index 00000000000..9fe9b48fbff --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/cryptographic_primitives/common/_blackbox.mdx @@ -0,0 +1,5 @@ +:::info + +This is a black box function. Read [this section](../black_box_fns) to learn more about black box functions in Noir. + +::: \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/logging.md b/noir/docs/versioned_docs/version-v0.10.5/standard_library/logging.md new file mode 100644 index 00000000000..7e2fd9b9aff --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/logging.md @@ -0,0 +1,62 @@ +--- +title: Logging +description: + Learn how to use the println statement for debugging in Noir with this tutorial. Understand the + basics of logging in Noir and how to implement it in your code. +keywords: + [ + noir logging, + println statement, + debugging in noir, + noir std library, + logging tutorial, + basic logging in noir, + noir logging implementation, + noir debugging techniques, + rust, + ] +--- + +The standard library provides a familiar `println` statement you can use. Despite being a limited +implementation of rust's `println!` macro, this construct can be useful for debugging. + +You can print the output of println statements in your Noir code by using the `nargo execute` command or the `--show-output` flag when using `nargo test` (provided there are println statements in your tests). + +It is recommended to use `nargo execute` if you want to debug failing constrains with `println` statements. This is due to every input in a test being a constant rather than a witness, so we issue an error during compilation while we only print during execution (which comes after compilation). `println` will not work for failed constraints caught at compile time. + +The `println` statement is unconstrained, so it works for outputting integers, fields, strings, and even structs or expressions. For example: + +```rust +use dep::std; + +struct Person { + age : Field, + height : Field, +} + +fn main(age : Field, height : Field) { + let person = Person { age : age, height : height }; + std::println(person); + std::println(age + height); + std::println("Hello world!"); +} + +``` + +You can print multiple different types in the same statement and string as well as a new "fmtstr" type. A `fmtstr` can be specified in the same way as a normal string it just should be prepended with an "f" character: + +```rust + let fmt_str = f"i: {i}, j: {j}"; + std::println(fmt_str); + + let s = myStruct { y: x, x: y }; + std::println(s); + + std::println(f"i: {i}, s: {s}"); + + std::println(x); + std::println([x, y]); + + let foo = fooStruct { my_struct: s, foo: 15 }; + std::println(f"s: {s}, foo: {foo}"); +``` diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/merkle_trees.md b/noir/docs/versioned_docs/version-v0.10.5/standard_library/merkle_trees.md new file mode 100644 index 00000000000..57d8c4a9e4f --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/merkle_trees.md @@ -0,0 +1,58 @@ +--- +title: Merkle Trees +description: Learn about Merkle Trees in Noir with this tutorial. Explore the basics of computing a merkle root using a proof, with examples. +keywords: + [ + Merkle trees in Noir, + Noir programming language, + check membership, + computing root from leaf, + Noir Merkle tree implementation, + Merkle tree tutorial, + Merkle tree code examples, + Noir libraries, + pedersen hash., + ] +--- + +## compute_merkle_root + +Returns the root of the tree from the provided leaf and its hash path, using a [Pedersen hash](cryptographic_primitives/00_hashes.mdx#pedersen). + +```rust +fn compute_merkle_root(leaf : Field, index : Field, hash_path: [Field]) -> Field +``` + +example: + +```rust +/** + // these values are for this example only + index = "0" + priv_key = "0x000000000000000000000000000000000000000000000000000000616c696365" + secret = "0x1929ea3ab8d9106a899386883d9428f8256cfedb3c4f6b66bf4aa4d28a79988f" + note_hash_path = [ + "0x1e61bdae0f027b1b2159e1f9d3f8d00fa668a952dddd822fda80dc745d6f65cc", + "0x0e4223f3925f98934393c74975142bd73079ab0621f4ee133cee050a3c194f1a", + "0x2fd7bb412155bf8693a3bd2a3e7581a679c95c68a052f835dddca85fa1569a40" + ] + */ +fn main(index: Field, priv_key: Field, secret: Field, note_hash_path: [Field; 3]) { + + let pubkey = std::scalar_mul::fixed_base(priv_key); + let pubkey_x = pubkey[0]; + let pubkey_y = pubkey[1]; + let note_commitment = std::hash::pedersen([pubkey_x, pubkey_y, secret]); + + let root = std::merkle::compute_merkle_root(note_commitment[0], index, note_hash_path); + std::println(root); +} +``` + +To check merkle tree membership: + +1. Include a merkle root as a program input. +2. Compute the merkle root of a given leaf, index and hash path. +3. Assert the merkle roots are equal. + +For more info about merkle trees, see the Wikipedia [page](https://en.wikipedia.org/wiki/Merkle_tree). diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/options.md b/noir/docs/versioned_docs/version-v0.10.5/standard_library/options.md new file mode 100644 index 00000000000..3d3139fb98b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/options.md @@ -0,0 +1,99 @@ +--- +title: Option Type +--- + +The `Option` type is a way to express that a value might be present (`Some(T))` or absent (`None`). It's a safer way to handle potential absence of values, compared to using nulls in many other languages. + +```rust +struct Option { + None, + Some(T), +} +``` + +You can import the Option type into your Noir program like so: + +```rust +use dep::std::option::Option; + +fn main() { + let none = Option::none(); + let some = Option::some(3); +} +``` + +See [this test](https://github.com/noir-lang/noir/blob/5cbfb9c4a06c8865c98ff2b594464b037d821a5c/crates/nargo_cli/tests/test_data/option/src/main.nr) for a more comprehensive set of examples of each of the methods described below. + +## Methods + +### none + +Constructs a none value. + +### some + +Constructs a some wrapper around a given value. + +### is_none + +Returns true if the Option is None. + +### is_some + +Returns true of the Option is Some. + +### unwrap + +Asserts `self.is_some()` and returns the wrapped value. + +### unwrap_unchecked + +Returns the inner value without asserting `self.is_some()`. This method can be useful within an if condition when we already know that `option.is_some()`. If the option is None, there is no guarantee what value will be returned, only that it will be of type T for an `Option`. + +### unwrap_or + +Returns the wrapped value if `self.is_some()`. Otherwise, returns the given default value. + +### unwrap_or_else + +Returns the wrapped value if `self.is_some()`. Otherwise, calls the given function to return a default value. + +### map + +If self is `Some(x)`, this returns `Some(f(x))`. Otherwise, this returns `None`. + +### map_or + +If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns the given default value. + +### map_or_else + +If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns `default()`. + +### and + +Returns None if self is None. Otherwise, this returns `other`. + +### and_then + +If self is None, this returns None. Otherwise, this calls the given function with the Some value contained within self, and returns the result of that call. In some languages this function is called `flat_map` or `bind`. + +### or + +If self is Some, return self. Otherwise, return `other`. + +### or_else + +If self is Some, return self. Otherwise, return `default()`. + +### xor + +If only one of the two Options is Some, return that option. Otherwise, if both options are Some or both are None, None is returned. + +### filter + +Returns `Some(x)` if self is `Some(x)` and `predicate(x)` is true. Otherwise, this returns `None`. + +### flatten + +Flattens an `Option>` into a `Option`. This returns `None` if the outer Option is None. Otherwise, this returns the inner Option. diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/recursion.md b/noir/docs/versioned_docs/version-v0.10.5/standard_library/recursion.md new file mode 100644 index 00000000000..ff4c63acaa7 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/recursion.md @@ -0,0 +1,96 @@ +--- +title: Recursive Proofs +description: Learn about how to write recursive proofs in Noir. +keywords: [recursion, recursive proofs, verification_key, aggregation object, verify_proof] +--- + +Noir supports recursively verifying proofs, meaning you verify the proof of a Noir program in another Noir program. This enables creating proofs of arbitrary size by doing step-wise verification of smaller components of a large proof. + +The `verify_proof` function takes a verification key, proof and public inputs for a zk program, as well as a key hash and an input aggregation object. The key hash is used to check the validity of the verification key and the input aggregation object is required by some proving systems. The `verify_proof` function returns an output aggregation object that can then be fed into future iterations of the proof verification if required. + +```rust +#[foreign(verify_proof)] +fn verify_proof(_verification_key : [Field], _proof : [Field], _public_input : Field, _key_hash : Field, _input_aggregation_object : [Field]) -> [Field] {} +``` + +:::info + +This is a black box function. Read [this section](./black_box_fns) to learn more about black box functions in Noir. + +::: + +## Aggregation Object + +The purpose of the input aggregation object is a little less clear though (and the output aggregation object that is returned from the `std::verify_proof` method). Recursive zkSNARK schemes do not necessarily "verify a proof" in the sense that you expect a true or false to be spit out by the verifier. Rather an aggregation object is built over the public inputs. In the case of PLONK the recursive aggregation object is two G1 points (expressed as 16 witness values). The final verifier (in our case this is most often the smart contract verifier) has to be aware of this aggregation object to execute a pairing and check the validity of these points (thus completing the recursive verification). + +So for example in this circuit: + +```rust +use dep::std; + +fn main( + verification_key : [Field; 114], + proof : [Field; 94], + public_inputs : [Field; 1], + key_hash : Field, + input_aggregation_object : [Field; 16], + proof_b : [Field; 94], +) -> pub [Field; 16] { + let output_aggregation_object_a = std::verify_proof( + verification_key, + proof, + public_inputs, + key_hash, + input_aggregation_object + ); + + let output_aggregation_object = std::verify_proof( + verification_key, + proof_b, + public_inputs, + key_hash, + output_aggregation_object_a + ); + + let mut output = [0; 16]; + for i in 0..16 { + output[i] = output_aggregation_object[i]; + } + output +} +``` + +In this example we have a circuit, that generates proofs A and B, that is being verified in circuit C. Assuming that the proof being passed in is not already a recursive proof, the `input_aggregation_object` will be all zeros. It will then generate an `output_aggregation_object`. This blob of data then becomes the `input_aggregation_object` of the next recursive aggregation we wish to compute. We can see here as the same public inputs, verification key, and key hash are used that we are verifying two proofs generated from the same circuit in this single circuit. `std::verify_proof` returns a `[Field]` because the size of an aggregation object is proof system dependent--in barretenberg, aggregation objects are two G1 points, while in Halo2, the aggregation object is a list of G1 points that is log the circuit size. So for the final step we convert the slice into an array of size 16 because we are generating proofs using UltraPlonk. + +## Parameters + +### `verification_key` + +The verification key for the zk program that is being verified. + +### `proof` + +The proof for the zk program that is being verified. + +### `public_inputs` + +These represent the public inputs of the proof we are verifying. They should be checked against in the circuit after construction of a new aggregation state. + +### `key_hash` + +A key hash is used to check the validity of the verification key. The circuit implementing this opcode can use this hash to ensure that the key provided to the circuit matches the key produced by the circuit creator. + +### `input_aggregation_object` + +An aggregation object is blob of data that the top-level verifier must run some proof system specific algorithm on to complete verification. The size is proof system specific and will be set by the backend integrating this opcode. The input aggregation object is only not `None` when we are verifying a previous recursive aggregation in the current circuit. If this is the first recursive aggregation there is no input aggregation object. It is left to the backend to determine how to handle when there is no input aggregation object. + +## Return value + +### `output_aggregation_object` + +This is the result of a recursive aggregation and is what will be fed into the next verifier. +The next verifier can either perform a final verification (returning true or false) or perform another recursive aggregation where this output aggregation object will be the input aggregation object of the next recursive aggregation. + +## Example + +You can see an example of how to do recursive proofs in [this example recursion demo repo](https://github.com/noir-lang/noir-examples/tree/master/recursion). diff --git a/noir/docs/versioned_docs/version-v0.10.5/standard_library/zeroed.md b/noir/docs/versioned_docs/version-v0.10.5/standard_library/zeroed.md new file mode 100644 index 00000000000..97dab02dac2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/standard_library/zeroed.md @@ -0,0 +1,25 @@ +--- +title: Zeroed Function +description: + The zeroed function returns a zeroed value of any type. +keywords: + [ + zeroed + ] +--- + +Implements `fn zeroed() -> T` to return a zeroed value of any type. This function is generally unsafe to use as the zeroed bit pattern is not guaranteed to be valid for all types. It can however, be useful in cases when the value is guaranteed not to be used such as in a BoundedVec library implementing a growable vector, up to a certain length, backed by an array. The array can be initialized with zeroed values which are guaranteed to be inaccessible until the vector is pushed to. Similarly, enumerations in noir can be implemented using this method by providing zeroed values for the unused variants. + +You can access the function at `std::unsafe::zeroed`. + +This function currently supports the following types: + +- Field +- Bool +- Uint +- Array +- String +- Tuple +- Function + +Using it on other types could result in unexpected behavior. diff --git a/noir/docs/versioned_docs/version-v0.10.5/typescript.md b/noir/docs/versioned_docs/version-v0.10.5/typescript.md new file mode 100644 index 00000000000..258a2160e92 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.10.5/typescript.md @@ -0,0 +1,237 @@ +--- +title: Working with TypeScript +description: + Learn how to interact with Noir programs using TypeScript. Follow this tutorial to compile your + program, specify inputs, initialize a prover & verifier, and prove and verify your program. +keywords: [TypeScript, Noir, tutorial, compile, inputs, prover, verifier, proof] +--- + +Interactions with Noir programs can also be performed in TypeScript, which can come in handy when +writing tests or when working in TypeScript-based projects like [Hardhat](https://hardhat.org/). + +You can check the complete code for this tutorial here: [browser with next.js](https://github.com/signorecello/noir-min-browser-example) and [node.js](https://github.com/signorecello/noir-min-nodejs-example). If you want just a browser boilerplate to start with, check out the [noir-starter](https://github.com/noir-lang/noir-starter) for an example implementation. + +:::note + +You may find unexpected errors working with some frameworks such as `vite`. This is due to the +nature of `wasm` files and the way Noir uses web workers. As we figure it out, we suggest using +[Create React App](https://create-react-app.dev/), or [Next.js](https://nextjs.org/) for a quick +start. + +::: + +## Setup + +Make sure you are using Noir version >= 0.10.1. + +You can check your current version by running `nargo --version`. + +See the [Installation page](./getting_started/nargo_installation) for more info. + +We're assuming you're using ES6 and ESM for both browser (for example with React), or nodejs. Install [Node.js](https://nodejs.org/en). Init a new project with `npm init` and add `"type": "module"` to your `package.json`, to let `node` know we're using the new ESM sytem: + +```json +{ + "type": "module" + // the rest of your package.json +} +``` + +Install Noir dependencies in your project by running: + +```bash +npm i @aztec/bb.js@0.3.6 https://git@github.com/noir-lang/acvm-simulator-wasm.git#b9d9ca9dfc5140839f23998d9466307215607c42 fflate ethers@5.7.2 +``` + +This will install the `acvm-simulator` that will generate our witness, and the proving backend barretenberg `bb.js`. + +We're also installing `ethers` because we're too lazy to write a function that pads public inputs with 32bytes, and `fflate` to help us decompress our circuit bytecode. + +Since we're with typescript and using `nodejs` types, we also recommend to install the `@types/node` package, otherwise your IDE will scream at you. + +```bash +npm i --save-dev @types/node +``` + +:::note + +While Noir is in rapid development, some packages could interfere with others. For that reason, you +should use these specified versions. Let us know if for some reason you need to use other ones. + +::: + +As for the circuit, run `nargo init` to create a new Noir project. + +We will use a Standard Noir Example and place it in the `src` folder. This program simply multiplies input `x` with input `y` and returns the result `z`. The verifier doesn't know the value of `x`: we're proving that we know it without making it public. + +```rust +// src/main.nr +fn main(x: u32, y: pub u32) -> pub u32 { + let z = x * y; + z +} +``` + +One valid scenario for proving could be `x = 3`, `y = 4` and `return = 12` + +## Compiling + +In order to start proving, we need to compile our circuit into the intermediate representation used by our backend. As of today, you have to do that with `nargo`. Just hop to your circuits folder and run `nargo compile`. + +:::info + +At this time, you need to use a nightly version of nargo. Using [noirup](./getting_started/00_nargo_installation.md#option-1-noirup) you can do this simply by running `noirup -n`. + +::: + +You should have a `json` file in `target/` with your circuit's bytecode. The json file is name based on the project name specified in Nargo.toml, so for a project named "test", it will be at `target/test.json`. You can then import that file normally. + +```ts +import circuit from '../target/test.json' assert { type: 'json' }; +``` + +## Decompressing the circuit + +The compiled circuit comes compressed. We need to decompress it, that's where `fflate` comes in. + +```ts +import { decompressSync } from 'fflate'; + +const acirBuffer = Buffer.from(circuit.bytecode, 'base64'); +const acirBufferUncompressed = decompressSync(acirBuffer); +``` + +From here, it's highly recommended you store `acirBuffer` and `acirBufferUncompressed` close by, as they will be used for witness generation and proving. + +## Initializing ACVM and BB.JS + +:::note + +This step will eventually be abstracted away as Noir tooling matures. For now, you should be fine just literally copy-pasting most of this into your own code. + +::: + +Before proving, `bb.js` needs to be initialized. We need to import some functions and use them + +```ts +import { Crs, newBarretenbergApiAsync, RawBuffer } from '@aztec/bb.js/dest/node/index.js'; + +const api = await newBarretenbergApiAsync(4); + +const [exact, circuitSize, subgroup] = await api.acirGetCircuitSizes(acirBufferUncompressed); +const subgroupSize = Math.pow(2, Math.ceil(Math.log2(circuitSize))); +const crs = await Crs.new(subgroupSize + 1); +await api.commonInitSlabAllocator(subgroupSize); +await api.srsInitSrs(new RawBuffer(crs.getG1Data()), crs.numPoints, new RawBuffer(crs.getG2Data())); + +const acirComposer = await api.acirNewAcirComposer(subgroupSize); +``` + +We should take two very useful objects from here: `api` and `acirComposer`. Make sure to keep these close by! + +:::info + +On the browser, you also need to init the ACVM. You can do that by importing it and calling it like: + +```ts +import initACVM, { executeCircuit, compressWitness } from '@noir-lang/acvm_js'; + +await initACVM(); +// the rest of your code +``` + +::: + +## Generating witnesses + +Witness generation is what allows us to prove with arbitrary inputs (like user inputs on a form, game, etc). In this example, our input is a simple object with our circuit inputs `x`, `y`, and return `z` (fun fact: the return value in Noir is actually a public input!). We're wrapping it in a function, so it can be conveniently called later on. + +```ts +import { ethers } from 'ethers'; // I'm lazy so I'm using ethers to pad my input +import { executeCircuit, compressWitness } from '@noir-lang/acvm_js'; + +async function generateWitness(input: any, acirBuffer: Buffer): Promise { + const initialWitness = new Map(); + initialWitness.set(1, ethers.utils.hexZeroPad(`0x${input.x.toString(16)}`, 32)); + initialWitness.set(2, ethers.utils.hexZeroPad(`0x${input.y.toString(16)}`, 32)); + + const witnessMap = await executeCircuit(acirBuffer, initialWitness, () => { + throw Error('unexpected oracle'); + }); + + const witnessBuff = compressWitness(witnessMap); + return witnessBuff; +} +``` + +## Proving + +Finally, we're ready to prove with our backend. Just like with the witness generation, could be useful to wrap it in its own function: + +```ts +async function generateProof(witness: Uint8Array) { + const proof = await api.acirCreateProof( + acirComposer, + acirBufferUncompressed, + decompressSync(witness), + false, + ); + return proof; +} +``` + +## Verifying + +Our backend should also be ready to verify our proof: + +```ts +async function verifyProof(proof: Uint8Array) { + await api.acirInitProvingKey(acirComposer, acirBufferUncompressed); + const verified = await api.acirVerifyProof(acirComposer, proof, false); + return verified; +} +``` + +## Now for the fun part + +Let's call our functions, and destroy our API! + +```ts +const input = { x: 3, y: 4 }; +const witness = await generateWitness(input, acirBuffer); +console.log('Witness generated!'); +const proof = await generateProof(witness); +console.log('Proof generated!'); +await verifyProof(proof); +console.log('Proof verified!'); +api.destroy(); +``` + +You can use [this](https://gist.github.com/critesjosh/6f3ba19fdc9298b24e90ba4f736247dc) tsconfig.json. You can see the script [here](https://gist.github.com/critesjosh/4aa36e87a0cc3f09feaf1febb4d11348). + +## Verifying with Smart Contract + +Alternatively, a verifier smart contract can be generated and used for verifying Noir proofs in +TypeScript as well. + +This could be useful if the Noir program is designed to be decentrally verified and/or make use of +decentralized states and logics that is handled at the smart contract level. + +This assumes you've already ran `nargo codegen-verifier`, got your smart contract, and deployed it with Hardhat, Foundry, or your tool of choice. You can then verify a Noir proof by simply calling it. + +Currently, `bb.js` appends the public inputs to the proof. However, these inputs need to be fed separately to the verifier contract. A simple solution is to just slice them from the resulting proof, like this: + +```ts +import { ethers } from 'ethers'; // example using ethers v5 +import artifacts from '../artifacts/circuits/contract/plonk_vk.sol/UltraVerifier.json'; // I compiled using Hardhat, so I'm getting my abi from here + +const verifierAddress = '0x123455'; // your verifier address +const provider = new ethers.providers.Web3Provider(window.ethereum); +const signer = this.provider.getSigner(); + +const contract = new ethers.Contract(verifierAddress, artifacts.abi, signer); + +const publicInputs = proof.slice(0, 32); +const slicedProof = proof.slice(32); +await contract.verify(slicedProof, [publicInputs]); +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/examples/merkle-proof.mdx b/noir/docs/versioned_docs/version-v0.17.0/examples/merkle-proof.mdx new file mode 100644 index 00000000000..6430780817c --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/examples/merkle-proof.mdx @@ -0,0 +1,48 @@ +--- +title: Merkle Proof Membership +description: + Learn how to use merkle membership proof in Noir to prove that a given leaf is a member of a + merkle tree with a specified root, at a given index. +keywords: + [merkle proof, merkle membership proof, Noir, rust, hash function, Pedersen, sha256, merkle tree] +--- + +Let's walk through an example of a merkle membership proof in Noir that proves that a given leaf is +in a merkle tree. + +```rust +use dep::std; + +fn main(message : [Field; 62], index : Field, hashpath : [Field; 40], root : Field) { + let leaf = std::hash::hash_to_field(message); + let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); + assert(merkle_root == root); +} + +``` + +The message is hashed using `hash_to_field`. The specific hash function that is being used is chosen +by the backend. The only requirement is that this hash function can heuristically be used as a +random oracle. If only collision resistance is needed, then one can call `std::hash::pedersen` +instead. + +```rust +let leaf = std::hash::hash_to_field(message); +``` + +The leaf is then passed to a compute_merkle_root function with the root, index and hashpath. The returned root can then be asserted to be the same as the provided root. + +```rust +let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); +assert (merkle_root == root); +``` + +> **Note:** It is possible to re-implement the merkle tree implementation without standard library. +> However, for most usecases, it is enough. In general, the standard library will always opt to be +> as conservative as possible, while striking a balance with efficiency. + +An example, the merkle membership proof, only requires a hash function that has collision +resistance, hence a hash function like Pedersen is allowed, which in most cases is more efficient +than the even more conservative sha256. + +[View an example on the starter repo](https://github.com/noir-lang/noir-examples/blob/3ea09545cabfa464124ec2f3ea8e60c608abe6df/stealthdrop/circuits/src/main.nr#L20) diff --git a/noir/docs/versioned_docs/version-v0.17.0/getting_started/00_nargo_installation.md b/noir/docs/versioned_docs/version-v0.17.0/getting_started/00_nargo_installation.md new file mode 100644 index 00000000000..4ff5fc46334 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/getting_started/00_nargo_installation.md @@ -0,0 +1,249 @@ +--- +title: Nargo Installation +description: + nargo is a command line tool for interacting with Noir programs (e.g. compiling, proving, + verifying and more). Learn how to install and use Nargo for your projects with this comprehensive + guide. +keywords: [Nargo, command line tool, Noir programs, installation guide, how to use Nargo] +--- + +`nargo` is a command line tool for interacting with Noir programs (e.g. compiling, proving, +verifying and more). + +Alternatively, the interactions can also be performed in [NoirJS](../noir_js/noir_js.md). + +### UltraPlonk + +Nargo versions <0.5.0 of `aztec_backend` and `aztec_wasm_backend` are based on the TurboPlonk +version of Aztec Backend, which lacks efficient implementations of useful primitives (e.g. Keccak256 in 18k constraints, ECDSA verification in 36k constraints) that the UltraPlonk version offers. + +## Installation + +There are four approaches for installing Nargo: + +- [Option 1: Noirup](#option-1-noirup) +- [Option 2: Binaries](#option-2-binaries) +- [Option 3: Compile from Source](#option-3-compile-from-source) +- [Option 4: WSL for Windows](#option-4-wsl-for-windows) + +Optionally you can also install [Noir VS Code extension] for syntax highlighting. + +### Option 1: Noirup + +If you're on OSX or Linux, the easiest way to start using Noir and Nargo is via noirup. Just open a +terminal and run: + +```bash +curl -L https://raw.githubusercontent.com/noir-lang/noirup/main/install | bash +``` + +Close the terminal, open another one, and run + +```bash +noirup +``` + +Done, you should have the latest version working. You can check with `nargo --version`. + +You can also install nightlies, specific versions +or branches, check out the [noirup repository](https://github.com/noir-lang/noirup) for more +information. + +#### GitHub Actions + +You can use `noirup` with GitHub Actions for CI/CD and automated testing. It is as simple as +installing `noirup` and running tests in your GitHub Action `yml` file. + +See the +[config file](https://github.com/TomAFrench/noir-hashes/blob/master/.github/workflows/noir.yml) in +this repo containing hash functions in Noir for an example. + +#### Nightly versions + +To install the nightly version of Noir (updated daily) run: + +```bash +noirup -n +``` + +### Option 2: Binaries + +See [GitHub Releases](https://github.com/noir-lang/noir/releases) for the latest and previous +platform specific binaries. + +#### Step 1 + +Paste and run the following in the terminal to extract and install the binary: + +> **macOS / Linux:** If you are prompted with `Permission denied` when running commands, prepend +> `sudo` and re-run it. + +##### macOS (Apple Silicon) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-aarch64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### macOS (Intel) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### Linux (Bash) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-unknown-linux-gnu.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -C $HOME/.nargo/bin/ && \ +echo -e '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.bashrc && \ +source ~/.bashrc +``` + +#### Step 2 + +Check if the installation was successful by running `nargo --help`. + +> **macOS:** If you are prompted with an OS alert, right-click and open the _nargo_ executable from +> Finder. Close the new terminal popped up and `nargo` should now be accessible. + +For a successful installation, you should see something similar to the following after running the +command: + +```sh +$ nargo --help + +Noir's package manager + +Usage: nargo + +Commands: + check Checks the constraint system for errors + codegen-verifier Generates a Solidity verifier smart contract for the program + compile Compile the program and its secret execution trace into ACIR format + new Create a new binary project + execute Executes a circuit to calculate its return value + prove Create proof for this program. The proof is returned as a hex encoded string + verify Given a proof and a program, verify whether the proof is valid + test Run the tests for this program + gates Counts the occurrences of different gates in circuit + help Print this message or the help of the given subcommand(s) +``` + +### Option 3: Compile from Source + +Due to the large number of native dependencies, Noir projects uses [Nix](https://nixos.org/) and [direnv](https://direnv.net/) to streamline the development experience. It helps mitigating ssues commonly associated with dependency management, such as conflicts between required package versions for different projects (often referred to as "dependency hell"). + +Combined with direnv, which automatically sets or unsets environment variables based on the directory, it further simplifies the development process by seamlessly integrating with the developer's shell, facilitating an efficient and reliable workflow for managing and deploying Noir projects with multiple dependencies. + +#### Setting up your environment + +For the best experience, please follow these instructions to setup your environment: + +1. Install Nix following [their guide](https://nixos.org/download.html) for your operating system. +2. Create the file `~/.config/nix/nix.conf` with the contents: + +```ini +experimental-features = nix-command +extra-experimental-features = flakes +``` + +3. Install direnv into your Nix profile by running: + +```sh +nix profile install nixpkgs#direnv +``` + +4. Add direnv to your shell following [their guide](https://direnv.net/docs/hook.html). + 1. For bash or zshell, add `eval "$(direnv hook bash)"` or `eval "$(direnv hook zsh)"` to your ~/.bashrc or ~/.zshrc file, respectively. +5. Restart your shell. + +#### Shell & editor experience + +Now that your environment is set up, you can get to work on the project. + +1. Clone the repository, such as: + +```sh +git clone git@github.com:noir-lang/noir +``` + +> Replacing `noir` with whichever repository you want to work on. + +2. Navigate to the directory: + +```sh +cd noir +``` + +> Replacing `noir` with whichever repository you cloned. + +3. You should see a **direnv error** because projects aren't allowed by default. Make sure you've reviewed and trust our `.envrc` file, then you need to run: + +```sh +direnv allow +``` + +4. Now, wait awhile for all the native dependencies to be built. This will take some time and direnv will warn you that it is taking a long time, but we just need to let it run. + +5. Once you are presented with your prompt again, you can start your editor within the project directory (we recommend [VSCode](https://code.visualstudio.com/)): + +```sh +code . +``` + +6. (Recommended) When launching VSCode for the first time, you should be prompted to install our recommended plugins. We highly recommend installing these for the best development experience. + +#### Building and testing + +Assuming you are using `direnv` to populate your environment, building and testing the project can be done +with the typical `cargo build`, `cargo test`, and `cargo clippy` commands. You'll notice that the `cargo` version matches the version we specify in `flake.nix`, which is 1.66.0 at the time of this writing. + +If you want to build the entire project in an isolated sandbox, you can use Nix commands: + +1. `nix build .` (or `nix build . -L` for verbose output) to build the project in a Nix sandbox. +2. `nix flake check` (or `nix flake check -L` for verbose output) to run clippy and tests in a Nix sandbox. + +#### Without `direnv` + +If you have hesitations with using direnv, you can launch a subshell with `nix develop` and then launch your editor from within the subshell. However, if VSCode was already launched in the project directory, the environment won't be updated. + +Advanced: If you aren't using direnv nor launching your editor within the subshell, you can try to install Barretenberg and other global dependencies the package needs. This is an advanced workflow and likely won't receive support! + +### Option 4: WSL (for Windows) + +Windows is not directly supported at this time. To install Noir on a Windows machine, it is recommended to use WSL. + +Step 1: Follow the instructions [here](https://learn.microsoft.com/en-us/windows/wsl/install) to install and run WSL. + +step 2: Follow the [Noirup instructions](#option-1-noirup). + +## Uninstalling Nargo + +### Noirup + +If you installed Noir with `noirup`, you can uninstall Noir by removing the files in `~/.nargo`, `~/nargo` and `~/noir_cache`. + +```bash +rm -r ~/.nargo +rm -r ~/nargo +rm -r ~/noir_cache +``` + +### Nix + +If you installed Noir with Nix or from source, you can remove the binary located at `~/.nix-profile/bin/nargo`. + +```bash +rm ~/.nix-profile/bin/nargo +``` + +[noir vs code extension]: https://marketplace.visualstudio.com/items?itemName=noir-lang.vscode-noir diff --git a/noir/docs/versioned_docs/version-v0.17.0/getting_started/01_hello_world.md b/noir/docs/versioned_docs/version-v0.17.0/getting_started/01_hello_world.md new file mode 100644 index 00000000000..8b4416beba1 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/getting_started/01_hello_world.md @@ -0,0 +1,147 @@ +--- +title: Create A Project +description: + Learn how to create and verify your first Noir program using Nargo, a programming language for + zero-knowledge proofs. +keywords: + [ + Nargo, + Noir, + zero-knowledge proofs, + programming language, + create Noir program, + verify Noir program, + step-by-step guide, + ] +--- + +Now that we have installed Nargo, it is time to make our first hello world program! + +## Create a Project Directory + +Noir code can live anywhere on your computer. Let us create a _projects_ folder in the home +directory to house our Noir programs. + +For Linux, macOS, and Windows PowerShell, create the directory and change directory into it by +running: + +```sh +mkdir ~/projects +cd ~/projects +``` + +For Windows CMD, run: + +```sh +> mkdir "%USERPROFILE%\projects" +> cd /d "%USERPROFILE%\projects" +``` + +## Create Our First Nargo Project + +Now that we are in the projects directory, create a new Nargo project by running: + +```sh +nargo new hello_world +``` + +> **Note:** `hello_world` can be any arbitrary project name, we are simply using `hello_world` for +> demonstration. +> +> In production, the common practice is to name the project folder as `circuits` for better +> identifiability when sitting alongside other folders in the codebase (e.g. `contracts`, `scripts`, +> `test`). + +A `hello_world` folder would be created. Similar to Rust, the folder houses _src/main.nr_ and +_Nargo.toml_ that contains the source code and environmental options of your Noir program +respectively. + +### Intro to Noir Syntax + +Let us take a closer look at _main.nr_. The default _main.nr_ generated should look like this: + +```rust +fn main(x : Field, y : pub Field) { + assert(x != y); +} +``` + +The first line of the program specifies the program's inputs: + +```rust +x : Field, y : pub Field +``` + +Program inputs in Noir are private by default (e.g. `x`), but can be labeled public using the +keyword `pub` (e.g. `y`). To learn more about private and public values, check the +[Data Types](../language_concepts/data_types) section. + +The next line of the program specifies its body: + +```rust +assert(x != y); +``` + +The Noir syntax `assert` can be interpreted as something similar to constraints in other zk-contract languages. + +For more Noir syntax, check the [Language Concepts](../language_concepts/comments) chapter. + +## Build In/Output Files + +Change directory into _hello_world_ and build in/output files for your Noir program by running: + +```sh +cd hello_world +nargo check +``` + +Two additional files would be generated in your project directory: + +_Prover.toml_ houses input values, and _Verifier.toml_ houses public values. + +## Prove Our Noir Program + +Now that the project is set up, we can create a proof of correct execution on our Noir program. + +Fill in input values for execution in the _Prover.toml_ file. For example: + +```toml +x = "1" +y = "2" +``` + +Prove the valid execution of your Noir program: + +```sh +nargo prove +``` + +A new folder _proofs_ would then be generated in your project directory, containing the proof file +`.proof`, where the project name is defined in Nargo.toml. + +The _Verifier.toml_ file would also be updated with the public values computed from program +execution (in this case the value of `y`): + +```toml +y = "0x0000000000000000000000000000000000000000000000000000000000000002" +``` + +> **Note:** Values in _Verifier.toml_ are computed as 32-byte hex values. + +## Verify Our Noir Program + +Once a proof is generated, we can verify correct execution of our Noir program by verifying the +proof file. + +Verify your proof by running: + +```sh +nargo verify +``` + +The verification will complete in silence if it is successful. If it fails, it will log the +corresponding error instead. + +Congratulations, you have now created and verified a proof for your very first Noir program! + +In the [next section](breakdown), we will go into more detail on each step performed. diff --git a/noir/docs/versioned_docs/version-v0.17.0/getting_started/02_breakdown.md b/noir/docs/versioned_docs/version-v0.17.0/getting_started/02_breakdown.md new file mode 100644 index 00000000000..bc0e742fb4e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/getting_started/02_breakdown.md @@ -0,0 +1,198 @@ +--- +title: Project Breakdown +description: + Learn about the anatomy of a Nargo project, including the purpose of the Prover and Verifier TOML + files, and how to prove and verify your program. +keywords: + [Nargo, Nargo project, Prover.toml, Verifier.toml, proof verification, private asset transfer] +--- + +This section breaks down our hello world program in section _1.2_. We elaborate on the project +structure and what the `prove` and `verify` commands did in the previous section. + +## Anatomy of a Nargo Project + +Upon creating a new project with `nargo new` and building the in/output files with `nargo check` +commands, you would get a minimal Nargo project of the following structure: + + - src + - Prover.toml + - Verifier.toml + - Nargo.toml + +The source directory _src_ holds the source code for your Noir program. By default only a _main.nr_ +file will be generated within it. + +### Prover.toml + +_Prover.toml_ is used for specifying the input values for executing and proving the program. You can specify `toml` files with different names by using the `--prover-name` or `-p` flags, see the [Prover](#provertoml) section below. Optionally you may specify expected output values for prove-time checking as well. + +### Verifier.toml + +_Verifier.toml_ contains public in/output values computed when executing the Noir program. + +### Nargo.toml + +_Nargo.toml_ contains the environmental options of your project. It contains a "package" section and a "dependencies" section. + +Example Nargo.toml: + +```toml +[package] +name = "noirstarter" +type = "bin" +authors = ["Alice"] +compiler_version = "0.9.0" +description = "Getting started with Noir" +entry = "circuit/main.nr" +license = "MIT" + +[dependencies] +ecrecover = {tag = "v0.9.0", git = "https://github.com/colinnielsen/ecrecover-noir.git"} +``` + +Nargo.toml for a [workspace](../modules_packages_crates/workspaces) will look a bit different. For example: + +```toml +[workspace] +members = ["crates/a", "crates/b"] +default-member = "crates/a" +``` + +#### Package section + +The package section requires a number of fields including: + +- `name` (**required**) - the name of the package +- `type` (**required**) - can be "bin", "lib", or "contract" to specify whether its a binary, library or Aztec contract +- `authors` (optional) - authors of the project +- `compiler_version` (optional) - specifies the version of the compiler to use. This is not currently enforced by the compiler, but will be in future versions. +- `description` (optional) +- `entry` (optional) - a relative filepath to use as the entry point into your package (overrides the default of `src/lib.nr` or `src/main.nr`) +- `backend` (optional) +- `license` (optional) + +#### Dependencies section + +This is where you will specify any dependencies for your project. See the [Dependencies page](../modules_packages_crates/dependencies) for more info. + +`./proofs/` and `./contract/` directories will not be immediately visible until you create a proof or +verifier contract respectively. + +### main.nr + +The _main.nr_ file contains a `main` method, this method is the entry point into your Noir program. + +In our sample program, _main.nr_ looks like this: + +```rust +fn main(x : Field, y : Field) { + assert(x != y); +} +``` + +The parameters `x` and `y` can be seen as the API for the program and must be supplied by the +prover. Since neither `x` nor `y` is marked as public, the verifier does not supply any inputs, when +verifying the proof. + +The prover supplies the values for `x` and `y` in the _Prover.toml_ file. + +As for the program body, `assert` ensures the satisfaction of the condition (e.g. `x != y`) is +constrained by the proof of the execution of said program (i.e. if the condition was not met, the +verifier would reject the proof as an invalid proof). + +### Prover.toml + +The _Prover.toml_ file is a file which the prover uses to supply his witness values(both private and +public). + +In our hello world program the _Prover.toml_ file looks like this: + +```toml +x = "1" +y = "2" +``` + +When the command `nargo prove` is executed, two processes happen: + +1. Noir creates a proof that `x` which holds the value of `1` and `y` which holds the value of `2` + is not equal. This not equal constraint is due to the line `assert(x != y)`. + +2. Noir creates and stores the proof of this statement in the _proofs_ directory in a file called your-project.proof. So if your project is named "private_voting" (defined in the project Nargo.toml), the proof will be saved at `./proofs/private_voting.proof`. Opening this file will display the proof in hex format. + +#### Arrays of Structs + +The following code shows how to pass an array of structs to a Noir program to generate a proof. + +```rust +// main.nr +struct Foo { + bar: Field, + baz: Field, +} + +fn main(foos: [Foo; 3]) -> pub Field { + foos[2].bar + foos[2].baz +} +``` + +Prover.toml: + +```toml +[[foos]] # foos[0] +bar = 0 +baz = 0 + +[[foos]] # foos[1] +bar = 0 +baz = 0 + +[[foos]] # foos[2] +bar = 1 +baz = 2 +``` + +#### Custom toml files + +You can specify a `toml` file with a different name to use for proving by using the `--prover-name` or `-p` flags. + +This command looks for proof inputs in the default **Prover.toml** and generates the proof and saves it at `./proofs/.proof`: + +```bash +nargo prove +``` + +This command looks for proof inputs in the custom **OtherProver.toml** and generates proof and saves it at `./proofs/.proof`: + +```bash +nargo prove -p OtherProver +``` + +## Verifying a Proof + +When the command `nargo verify` is executed, two processes happen: + +1. Noir checks in the _proofs_ directory for a proof file with the project name (eg. test_project.proof) + +2. If that file is found, the proof's validity is checked + +> **Note:** The validity of the proof is linked to the current Noir program; if the program is +> changed and the verifier verifies the proof, it will fail because the proof is not valid for the +> _modified_ Noir program. + +In production, the prover and the verifier are usually two separate entities. A prover would +retrieve the necessary inputs, execute the Noir program, generate a proof and pass it to the +verifier. The verifier would then retrieve the public inputs from usually external sources and +verifies the validity of the proof against it. + +Take a private asset transfer as an example: + +A user on browser as the prover would retrieve private inputs (e.g. the user's private key) and +public inputs (e.g. the user's encrypted balance on-chain), compute the transfer, generate a proof +and submit it to the verifier smart contract. + +The verifier contract would then draw the user's encrypted balance directly from the blockchain and +verify the proof submitted against it. If the verification passes, additional functions in the +verifier contract could trigger (e.g. approve the asset transfer). + +Now that you understand the concepts, you'll probably want some editor feedback while you are writing more complex code. diff --git a/noir/docs/versioned_docs/version-v0.17.0/index.md b/noir/docs/versioned_docs/version-v0.17.0/index.md new file mode 100644 index 00000000000..9ebe1d54944 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/index.md @@ -0,0 +1,99 @@ +--- +title: Introducing Noir +description: + Learn about the public alpha release of Noir, a domain specific language heavily influenced by + Rust that compiles to an intermediate language which can be compiled to an arithmetic circuit or a + rank-1 constraint system. +keywords: + [ + Noir, + Domain Specific Language, + Rust, + Intermediate Language, + Arithmetic Circuit, + Rank-1 Constraint System, + Ethereum Developers, + Protocol Developers, + Blockchain Developers, + Proving System, + Smart Contract Language, + ] +slug: / +--- + +## What is Noir? + +Noir is a Domain Specific Language for SNARK proving systems. It has been designed to use any ACIR compatible proving system. + +It's design choices are influenced heavily by Rust and focuses on a simple, familiar syntax. + +## Who is Noir for? + +Noir can be used for a variety of purposes. + +### Solidity Developers + +Noir currently includes a command to create a Solidity contract which verifies your Noir program. This will +be modularised in the future; however, as of the alpha, you can use the [`nargo codegen-verifier`](./nargo/commands#nargo-codegen-verifier) command to create +a verifier contract. + +### Protocol Developers + +As a protocol developer, you may not want to use the Aztec backend due to it not being a fit for +your stack, or maybe you simply want to use a different proving system. Since Noir does not compile +to a specific proof system, it is possible for protocol developers to replace the PLONK-based +proving system with a different proving system altogether. + +### Blockchain developers + +As a blockchain developer, you will be constrained by parameters set by your blockchain (for example, the +proving system and smart contract language has been pre-defined). In order for you to use Noir in +your blockchain, a proving system backend and a smart contract interface +must be implemented for it. + +## What's new about Noir? + +Noir is simple and flexible in its design, as it does not compile immediately to a fixed +NP-complete language. Instead, Noir compiles to an intermediate language (ACIR), which itself can be compiled +to an arithmetic circuit (if choosing to target Aztec's barretenberg backend) or a rank-1 constraint system (if choosing to target an R1CS backend like Arkwork's Marlin backend, or others). + +This in itself brings up a few challenges within the design process, but allows one to decouple the programming language completely from the backend. This is similar in theory to LLVM. + +## Current Features + +Compiler: + +- Module System +- For expressions +- Arrays +- Bit Operations +- Binary operations (<, <=, >, >=, +, -, \*, /, %) [See documentation for an extensive list] +- Unsigned integers +- If statements +- Structures and Tuples +- Generics + +ACIR Supported OPCODES: + +- Sha256 +- Blake2s +- Schnorr signature verification +- MerkleMembership +- Pedersen +- HashToField + +## Libraries + +Noir does not currently have an official package manager. You can find a list of available Noir libraries in the [awesome-noir repo here](https://github.com/noir-lang/awesome-noir#libraries). + +Some libraries that are available today include: + +- [Standard Library](https://github.com/noir-lang/noir/tree/master/noir_stdlib) - the Noir Standard Library +- [Ethereum Storage Proof Verification](https://github.com/aragonzkresearch/noir-trie-proofs) - a library that contains the primitives necessary for RLP decoding (in the form of look-up table construction) and Ethereum state and storage proof verification (or verification of any trie proof involving 32-byte long keys) +- [BigInt](https://github.com/shuklaayush/noir-bigint) - a library that provides a custom BigUint56 data type, allowing for computations on large unsigned integers +- [ECrecover](https://github.com/colinnielsen/ecrecover-noir/tree/main) - a library to verify an ECDSA signature and return the source Ethereum address +- [Sparse Merkle Tree Verifier](https://github.com/vocdoni/smtverifier-noir/tree/main) - a library for verification of sparse Merkle trees +- [Signed Int](https://github.com/resurgencelabs/signed_int) - a library for accessing a custom Signed Integer data type, allowing access to negative numbers on Noir +- [Fraction](https://github.com/resurgencelabs/fraction) - a library for accessing fractional number data type in Noir, allowing results that aren't whole numbers + +See the section on [dependencies](./modules_packages_crates/dependencies) for more information. diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/01_functions.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/01_functions.md new file mode 100644 index 00000000000..47cdea0cf04 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/01_functions.md @@ -0,0 +1,225 @@ +--- +title: Functions +description: + Learn how to declare functions and methods in Noir, a programming language with Rust semantics. + This guide covers parameter declaration, return types, call expressions, and more. +keywords: [Noir, Rust, functions, methods, parameter declaration, return types, call expressions] +--- + +Functions in Noir follow the same semantics of Rust, though Noir does not support early returns. + +To declare a function the `fn` keyword is used. + +```rust +fn foo() {} +``` + +By default, functions are visible only within the package they are defined. To make them visible outside of that package (for example, as part of a [library](../modules_packages_crates/crates_and_packages.md#libraries)), you should mark them as `pub`: + +```rust +pub fn foo() {} +``` + +You can also restrict the visibility of the function to only the crate it was defined in, by specifying `pub(crate)`: + +```rust +pub(crate) fn foo() {} //foo can only be called within its crate +``` + +All parameters in a function must have a type and all types are known at compile time. The parameter +is pre-pended with a colon and the parameter type. Multiple parameters are separated using a comma. + +```rust +fn foo(x : Field, y : pub Field){} +``` + +The return type of a function can be stated by using the `->` arrow notation. The function below +states that the foo function must return a `Field`. If the function returns no value, then the arrow +is omitted. + +```rust +fn foo(x : Field, y : pub Field) -> Field { + x + y +} +``` + +Note that a `return` keyword is unneeded in this case - the last expression in a function's body is +returned. + +## Main function + +If you're writing a binary, the `main` function is the starting point of your program. You can pass all types of expressions to it, as long as they have a fixed size at compile time: + +```rust +fn main(x : Field) // this is fine: passing a Field +fn main(x : [Field; 2]) // this is also fine: passing a Field with known size at compile-time +fn main(x : (Field, bool)) // 👌: passing a (Field, bool) tuple means size 2 +fn main(x : str<5>) // this is fine, as long as you pass a string of size 5 + +fn main(x : Vec) // can't compile, has variable size +fn main(x : [Field]) // can't compile, has variable size +fn main(....// i think you got it by now +``` + +Keep in mind [tests](../nargo/02_testing.md) don't differentiate between `main` and any other function. The following snippet passes tests, but won't compile or prove: + +```rust +fn main(x : [Field]) { + assert(x[0] == 1); +} + +#[test] +fn test_one() { + main([1, 2]); +} +``` + +```bash +$ nargo test +[testing] Running 1 test functions +[testing] Testing test_one... ok +[testing] All tests passed + +$ nargo check +The application panicked (crashed). +Message: Cannot have variable sized arrays as a parameter to main +``` + +## Call Expressions + +Calling a function in Noir is executed by using the function name and passing in the necessary +arguments. + +Below we show how to call the `foo` function from the `main` function using a call expression: + +```rust +fn main(x : Field, y : Field) { + let z = foo(x); +} + +fn foo(x : Field) -> Field { + x + x +} +``` + +## Methods + +You can define methods in Noir on any struct type in scope. + +```rust +struct MyStruct { + foo: Field, + bar: Field, +} + +impl MyStruct { + fn new(foo: Field) -> MyStruct { + MyStruct { + foo, + bar: 2, + } + } + + fn sum(self) -> Field { + self.foo + self.bar + } +} + +fn main() { + let s = MyStruct::new(40); + assert(s.sum() == 42); +} +``` + +Methods are just syntactic sugar for functions, so if we wanted to we could also call `sum` as +follows: + +```rust +assert(MyStruct::sum(s) == 42); +``` + +It is also possible to specialize which method is chosen depending on the [generic](./06_generics.md) type that is used. In this example, the `foo` function returns different values depending on its type: + +```rust +struct Foo {} + +impl Foo { + fn foo(self) -> Field { 1 } +} + +impl Foo { + fn foo(self) -> Field { 2 } +} + +fn main() { + let f1: Foo = Foo{}; + let f2: Foo = Foo{}; + assert(f1.foo() + f2.foo() == 3); +} +``` + +Also note that impls with the same method name defined in them cannot overlap. For example, if we already have `foo` defined for `Foo` and `Foo` like we do above, we cannot also define `foo` in an `impl Foo` since it would be ambiguous which version of `foo` to choose. + +```rust +// Including this impl in the same project as the above snippet would +// cause an overlapping impls error +impl Foo { + fn foo(self) -> Field { 3 } +} +``` + +## Lambdas + +Lambdas are anonymous functions. They follow the syntax of Rust - `|arg1, arg2, ..., argN| return_expression`. + +```rust +let add_50 = |val| val + 50; +assert(add_50(100) == 150); +``` + +See [Lambdas](./08_lambdas.md) for more details. + +## Attributes + +Attributes are metadata that can be applied to a function, using the following syntax: `#[attribute(value)]`. + +Supported attributes include: + +- **builtin**: the function is implemented by the compiler, for efficiency purposes. +- **deprecated**: mark the function as _deprecated_. Calling the function will generate a warning: `warning: use of deprecated function` +- **field**: Used to enable conditional compilation of code depending on the field size. See below for more details +- **oracle**: mark the function as _oracle_; meaning it is an external unconstrained function, implemented in noir_js. See [Unconstrained](./05_unconstrained.md) and [NoirJS](../noir_js/noir_js.md) for more details. +- **test**: mark the function as unit tests. See [Tests](../nargo/02_testing.md) for more details + +### Field Attribute + +The field attribute defines which field the function is compatible for. The function is conditionally compiled, under the condition that the field attribute matches the Noir native field. +The field can be defined implicitly, by using the name of the elliptic curve usually associated to it - for instance bn254, bls12_381 - or explicitly by using the field (prime) order, in decimal or hexadecimal form. +As a result, it is possible to define multiple versions of a function with each version specialized for a different field attribute. This can be useful when a function requires different parameters depending on the underlying elliptic curve. + +Example: we define the function `foo()` three times below. Once for the default Noir bn254 curve, once for the field $\mathbb F_{23}$, which will normally never be used by Noir, and once again for the bls12_381 curve. + +```rust +#[field(bn254)] +fn foo() -> u32 { + 1 +} + +#[field(23)] +fn foo() -> u32 { + 2 +} + +// This commented code would not compile as foo would be defined twice because it is the same field as bn254 +// #[field(21888242871839275222246405745257275088548364400416034343698204186575808495617)] +// fn foo() -> u32 { +// 2 +// } + +#[field(bls12_381)] +fn foo() -> u32 { + 3 +} +``` + +If the field name is not known to Noir, it will discard the function. Field names are case insensitive. diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/02_control_flow.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/02_control_flow.md new file mode 100644 index 00000000000..a7f85360197 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/02_control_flow.md @@ -0,0 +1,44 @@ +--- +title: Control Flow +description: + Learn how to use loops and if expressions in the Noir programming language. Discover the syntax + and examples for for loops and if-else statements. +keywords: [Noir programming language, loops, for loop, if-else statements, Rust syntax] +--- + +## Loops + +Noir has one kind of loop: the `for` loop. `for` loops allow you to repeat a block of code multiple +times. + +The following block of code between the braces is run 10 times. + +```rust +for i in 0..10 { + // do something +}; +``` + +The index for loops is of type `u64`. + +## If Expressions + +Noir supports `if-else` statements. The syntax is most similar to Rust's where it is not required +for the statement's conditional to be surrounded by parentheses. + +```rust +let a = 0; +let mut x: u32 = 0; + +if a == 0 { + if a != 0 { + x = 6; + } else { + x = 2; + } +} else { + x = 5; + assert(x == 5); +} +assert(x == 2); +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/03_ops.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/03_ops.md new file mode 100644 index 00000000000..da02b126059 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/03_ops.md @@ -0,0 +1,97 @@ +--- +title: Logical Operations +description: + Learn about the supported arithmetic and logical operations in the Noir programming language. + Discover how to perform operations on private input types, integers, and booleans. +keywords: + [ + Noir programming language, + supported operations, + arithmetic operations, + logical operations, + predicate operators, + bitwise operations, + short-circuiting, + backend, + ] +--- + +# Operations + +## Table of Supported Operations + +| Operation | Description | Requirements | +| :-------- | :------------------------------------------------------------: | -------------------------------------: | +| + | Adds two private input types together | Types must be private input | +| - | Subtracts two private input types together | Types must be private input | +| \* | Multiplies two private input types together | Types must be private input | +| / | Divides two private input types together | Types must be private input | +| ^ | XOR two private input types together | Types must be integer | +| & | AND two private input types together | Types must be integer | +| \| | OR two private input types together | Types must be integer | +| << | Left shift an integer by another integer amount | Types must be integer | +| >> | Right shift an integer by another integer amount | Types must be integer | +| ! | Bitwise not of a value | Type must be integer or boolean | +| < | returns a bool if one value is less than the other | Upper bound must have a known bit size | +| <= | returns a bool if one value is less than or equal to the other | Upper bound must have a known bit size | +| > | returns a bool if one value is more than the other | Upper bound must have a known bit size | +| >= | returns a bool if one value is more than or equal to the other | Upper bound must have a known bit size | +| == | returns a bool if one value is equal to the other | Both types must not be constants | +| != | returns a bool if one value is not equal to the other | Both types must not be constants | + +### Predicate Operators + +`<,<=, !=, == , >, >=` are known as predicate/comparison operations because they compare two values. +This differs from the operations such as `+` where the operands are used in _computation_. + +### Bitwise Operations Example + +```rust +fn main(x : Field) { + let y = x as u32; + let z = y & y; +} +``` + +`z` is implicitly constrained to be the result of `y & y`. The `&` operand is used to denote bitwise +`&`. + +> `x & x` would not compile as `x` is a `Field` and not an integer type. + +### Logical Operators + +Noir has no support for the logical operators `||` and `&&`. This is because encoding the +short-circuiting that these operators require can be inefficient for Noir's backend. Instead you can +use the bitwise operators `|` and `&` which operate indentically for booleans, just without the +short-circuiting. + +```rust +let my_val = 5; + +let mut flag = 1; +if (my_val > 6) | (my_val == 0) { + flag = 0; +} +assert(flag == 1); + +if (my_val != 10) & (my_val < 50) { + flag = 0; +} +assert(flag == 0); +``` + +### Shorthand operators + +Noir shorthand operators for most of the above operators, namely `+=, -=, *=, /=, %=, &=, |=, ^=, <<=`, and `>>=`. These allow for more concise syntax. For example: + +```rust +let mut i = 0; +i = i + 1; +``` + +could be written as: + +```rust +let mut i = 0; +i += 1; +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/04_assert.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/04_assert.md new file mode 100644 index 00000000000..7427ec6cc63 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/04_assert.md @@ -0,0 +1,26 @@ +--- +title: Assert Function +description: + Learn about the assert function in Noir, which can be used to explicitly constrain the predicate or + comparison expression that follows to be true, and what happens if the expression is false at + runtime. +keywords: [Noir programming language, assert statement, predicate expression, comparison expression] +--- + +Noir includes a special `assert` function which will explicitly constrain the predicate/comparison +expression that follows to be true. If this expression is false at runtime, the program will fail to +be proven. Example: + +```rust +fn main(x : Field, y : Field) { + assert(x == y); +} +``` + +You can optionally provide a message to be logged when the assertion fails: + +```rust +assert(x == y, "x and y are not equal"); +``` + +> Assertions only work for predicate operations, such as `==`. If there's any ambiguity on the operation, the program will fail to compile. For example, it is unclear if `assert(x + y)` would check for `x + y == 0` or simply would return `true`. diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/05_unconstrained.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/05_unconstrained.md new file mode 100644 index 00000000000..6b621eda3eb --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/05_unconstrained.md @@ -0,0 +1,96 @@ +--- +title: Unconstrained Functions +description: "Learn about what unconstrained functions in Noir are, how to use them and when you'd want to." + +keywords: [Noir programming language, unconstrained, open] +--- + + + +Unconstrained functions are functions which do not constrain any of the included computation and allow for non-determinisitic computation. + +## Why? + +Zero-knowledge (ZK) domain-specific languages (DSL) enable developers to generate ZK proofs from their programs by compiling code down to the constraints of an NP complete language (such as R1CS or PLONKish languages). However, the hard bounds of a constraint system can be very limiting to the functionality of a ZK DSL. + +Enabling a circuit language to perform unconstrained execution is a powerful tool. Said another way, unconstrained execution lets developers generate witnesses from code that does not generate any constraints. Being able to execute logic outside of a circuit is critical for both circuit performance and constructing proofs on information that is external to a circuit. + +Fetching information from somewhere external to a circuit can also be used to enable developers to improve circuit efficiency. + +A ZK DSL does not just prove computation, but proves that some computation was handled correctly. Thus, it is necessary that when we switch from performing some operation directly inside of a circuit to inside of an unconstrained environment that the appropriate constraints are still laid down elsewhere in the circuit. + +## Example + +An in depth example might help drive the point home. This example comes from the excellent [post](https://discord.com/channels/1113924620781883405/1124022445054111926/1128747641853972590) by Tom in the Noir Discord. + +Let's look at how we can optimize a function to turn a `u72` into an array of `u8`s. + +```rust +fn main(num: u72) -> pub [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8)) as u72 & 0xff) as u8; + } + + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 91 +Backend circuit size: 3619 +``` + +A lot of the operations in this function are optimized away by the compiler (all the bit-shifts turn into divisions by constants). However we can save a bunch of gates by casting to u8 a bit earlier. This automatically truncates the bit-shifted value to fit in a u8 which allows us to remove the XOR against 0xff. This saves us ~480 gates in total. + +```rust +fn main(num: u72) -> pub [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8)) as u8; + } + + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 75 +Backend circuit size: 3143 +``` + +Those are some nice savings already but we can do better. This code is all constrained so we're proving every step of calculating out using num, but we don't actually care about how we calculate this, just that it's correct. This is where brillig comes in. + +It turns out that truncating a u72 into a u8 is hard to do inside a snark, each time we do as u8 we lay down 4 ACIR opcodes which get converted into multiple gates. It's actually much easier to calculate num from out than the other way around. All we need to do is multiply each element of out by a constant and add them all together, both relatively easy operations inside a snark. + +We can then run u72_to_u8 as unconstrained brillig code in order to calculate out, then use that result in our constrained function and assert that if we were to do the reverse calculation we'd get back num. This looks a little like the below: + +```rust +fn main(num: u72) -> pub [u8; 8] { + let out = u72_to_u8(num); + + let mut reconstructed_num: u72 = 0; + for i in 0..8 { + reconstructed_num += (out[i] as u72 << (56 - (8 * i))); + } + assert(num == reconstructed_num); + out +} + +unconstrained fn u72_to_u8(num: u72) -> [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8))) as u8; + } + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 78 +Backend circuit size: 2902 +``` + +This ends up taking off another ~250 gates from our circuit! We've ended up with more ACIR opcodes than before but they're easier for the backend to prove (resulting in fewer gates). + +Generally we want to use brillig whenever there's something that's easy to verify but hard to compute within the circuit. For example, if you wanted to calculate a square root of a number it'll be a much better idea to calculate this in brillig and then assert that if you square the result you get back your number. diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/06_generics.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/06_generics.md new file mode 100644 index 00000000000..9fb4177c2a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/06_generics.md @@ -0,0 +1,113 @@ +--- +title: Generics +description: Learn how to use Generics in Noir +keywords: [Noir, Rust, generics, functions, structs] +--- + +Generics allow you to use the same functions with multiple different concrete data types. You can +read more about the concept of generics in the Rust documentation +[here](https://doc.rust-lang.org/book/ch10-01-syntax.html). + +Here is a trivial example showing the identity function that supports any type. In Rust, it is +common to refer to the most general type as `T`. We follow the same convention in Noir. + +```rust +fn id(x: T) -> T { + x +} +``` + +## In Structs + +Generics are useful for specifying types in structs. For example, we can specify that a field in a +struct will be of a certain generic type. In this case `value` is of type `T`. + +```rust +struct RepeatedValue { + value: T, + count: Field, +} + +impl RepeatedValue { + fn new(value: T) -> Self { + Self { value, count: 1 } + } + + fn increment(mut repeated: Self) -> Self { + repeated.count += 1; + repeated + } + + fn print(self) { + for _i in 0 .. self.count { + dep::std::println(self.value); + } + } +} + +fn main() { + let mut repeated = RepeatedValue::new("Hello!"); + repeated = repeated.increment(); + repeated.print(); +} +``` + +The `print` function will print `Hello!` an arbitrary number of times, twice in this case. + +If we want to be generic over array lengths (which are type-level integers), we can use numeric +generics. Using these looks just like using regular generics, but these generics can resolve to +integers at compile-time, rather than resolving to types. Here's an example of a struct that is +generic over the size of the array it contains internally: + +```rust +struct BigInt { + limbs: [u32; N], +} + +impl BigInt { + // `N` is in scope of all methods in the impl + fn first(first: BigInt, second: BigInt) -> Self { + assert(first.limbs != second.limbs); + first + + fn second(first: BigInt, second: Self) -> Self { + assert(first.limbs != second.limbs); + second + } +} +``` + +## Calling functions on generic parameters + +Unlike Rust, Noir does not have traits, so how can one translate the equivalent of a trait bound in +Rust into Noir? That is, how can we write a function that is generic over some type `T`, while also +requiring there is a function like `eq: fn(T, T) -> bool` that works on the type? + +The answer is that we can translate this by passing in the function manually. Here's an example of +implementing array equality in Noir: + +```rust +fn array_eq(array1: [T; N], array2: [T; N], elem_eq: fn(T, T) -> bool) -> bool { + if array1.len() != array2.len() { + false + } else { + let mut result = true; + for i in 0 .. array1.len() { + result &= elem_eq(array1[i], array2[i]); + } + result + } +} + +fn main() { + assert(array_eq([1, 2, 3], [1, 2, 3], |a, b| a == b)); + + // We can use array_eq even for arrays of structs, as long as we have + // an equality function for these structs we can pass in + let array = [MyStruct::new(), MyStruct::new()]; + assert(array_eq(array, array, MyStruct::eq)); +} +``` + +You can see an example of generics in the tests +[here](https://github.com/noir-lang/noir/blob/master/tooling/nargo_cli/tests/execution_success/generics/src/main.nr). diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/07_mutability.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/07_mutability.md new file mode 100644 index 00000000000..4641521b1d9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/07_mutability.md @@ -0,0 +1,92 @@ +--- +title: Mutability +description: + Learn about mutable variables, constants, and globals in Noir programming language. Discover how + to declare, modify, and use them in your programs. +keywords: [noir programming language, mutability in noir, mutable variables, constants, globals] +--- + +Variables in noir can be declared mutable via the `mut` keyword. Mutable variables can be reassigned +to via an assignment expression. + +```rust +let x = 2; +x = 3; // error: x must be mutable to be assigned to + +let mut y = 3; +let y = 4; // OK +``` + +The `mut` modifier can also apply to patterns: + +```rust +let (a, mut b) = (1, 2); +a = 11; // error: a must be mutable to be assigned to +b = 12; // OK + +let mut (c, d) = (3, 4); +c = 13; // OK +d = 14; // OK + +// etc. +let MyStruct { x: mut y } = MyStruct { x: a }; +// y is now in scope +``` + +Note that mutability in noir is local and everything is passed by value, so if a called function +mutates its parameters then the parent function will keep the old value of the parameters. + +```rust +fn main() -> Field { + let x = 3; + helper(x); + x // x is still 3 +} + +fn helper(mut x: i32) { + x = 4; +} +``` + +## Comptime Values + +:::warning + +The 'comptime' keyword was removed in version 0.10. The comptime keyword and syntax are currently still kept and parsed for backwards compatibility, but are now deprecated and will issue a warning when used. `comptime` has been removed because it is no longer needed for accessing arrays. + +::: + +## Globals + +Noir also supports global variables. However, they must be known at compile-time. The global type can also be inferred by the compiler entirely. Globals can also be used to specify array +annotations for function parameters and can be imported from submodules. + +```rust +global N: Field = 5; // Same as `global N: Field = 5` + +fn main(x : Field, y : [Field; N]) { + let res = x * N; + + assert(res == y[0]); + + let res2 = x * mysubmodule::N; + assert(res != res2); +} + +mod mysubmodule { + use dep::std; + + global N: Field = 10; + + fn my_helper() -> Field { + let x = N; + x + } +} +``` + +## Why only local mutability? + +Witnesses in a proving system are immutable in nature. Noir aims to _closely_ mirror this setting +without applying additional overhead to the user. Modeling a mutable reference is not as +straightforward as on conventional architectures and would incur some possibly unexpected overhead. diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/08_lambdas.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/08_lambdas.md new file mode 100644 index 00000000000..ae1e6aecab1 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/08_lambdas.md @@ -0,0 +1,80 @@ +--- +title: Lambdas +description: Learn how to use anonymous functions in Noir programming language. +keywords: [Noir programming language, lambda, closure, function, anonymous function] +--- + +## Introduction + +Lambdas are anonymous functions. The syntax is `|arg1, arg2, ..., argN| return_expression`. + +```rust +let add_50 = |val| val + 50; +assert(add_50(100) == 150); +``` + +A block can be used as the body of a lambda, allowing you to declare local variables inside it: + +```rust +let cool = || { + let x = 100; + let y = 100; + x + y +} + +assert(cool() == 200); +``` + +## Closures + +Inside the body of a lambda, you can use variables defined in the enclosing function. Such lambdas are called **closures**. In this example `x` is defined inside `main` and is accessed from within the lambda: + +```rust +fn main() { + let x = 100; + let closure = || x + 150; + assert(closure() == 250); +} +``` + +## Passing closures to higher-order functions + +It may catch you by surprise that the following code fails to compile: + +```rust +fn foo(f: fn () -> Field) -> Field { + f() +} + +fn main() { + let (x, y) = (50, 50); + assert(foo(|| x + y) == 100); // error :( +} +``` + +The reason is that the closure's capture environment affects its type - we have a closure that captures two Fields and `foo` +expects a regular function as an argument - those are incompatible. +:::note + +Variables contained within the `||` are the closure's parameters, and the expression that follows it is the closure's body. The capture environment is comprised of any variables used in the closure's body that are not parameters. + +E.g. in |x| x + y, y would be a captured variable, but x would not be, since it is a parameter of the closure. + +::: +The syntax for the type of a closure is `fn[env](args) -> ret_type`, where `env` is the capture environment of the closure - +in this example that's `(Field, Field)`. + +The best solution in our case is to make `foo` generic over the environment type of its parameter, so that it can be called +with closures with any environment, as well as with regular functions: + +```rust +fn foo(f: fn[Env]() -> Field) -> Field { + f() +} + +fn main() { + let (x, y) = (50, 50); + assert(foo(|| x + y) == 100); // compiles fine + assert(foo(|| 60) == 60); // compiles fine +} +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/09_comments.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/09_comments.md new file mode 100644 index 00000000000..3bb4d2f25a4 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/09_comments.md @@ -0,0 +1,32 @@ +--- +title: Comments +description: + Learn how to write comments in Noir programming language. A comment is a line of code that is + ignored by the compiler, but it can be read by programmers. Single-line and multi-line comments + are supported in Noir. +keywords: [Noir programming language, comments, single-line comments, multi-line comments] +--- + +A comment is a line in your codebase which the compiler ignores, however it can be read by +programmers. + +Here is a single line comment: + +```rust +// This is a comment and is ignored +``` + +`//` is used to tell the compiler to ignore the rest of the line. + +Noir also supports multi-line block comments. Start a block comment with `/*` and end the block with `*/`. + +Noir does not natively support doc comments. You may be able to use [Rust doc comments](https://doc.rust-lang.org/reference/comments.html) in your code to leverage some Rust documentation build tools with Noir code. + +```rust +/* + This is a block comment describing a complex function. +*/ +fn main(x : Field, y : pub Field) { + assert(x != y); +} +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/10_distinct.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/10_distinct.md new file mode 100644 index 00000000000..e7ff7f5017a --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/10_distinct.md @@ -0,0 +1,63 @@ +--- +title: Distinct Witnesses +--- + +The `distinct` keyword prevents repetitions of witness indices in the program's ABI. This ensures +that the witnesses being returned as public inputs are all unique. + +The `distinct` keyword is only used for return values on program entry points (usually the `main()` +function). + +When using `distinct` and `pub` simultaneously, `distinct` comes first. See the example below. + +You can read more about the problem this solves +[here](https://github.com/noir-lang/noir/issues/1183). + +## Example + +Without the `distinct` keyword, the following program + +```rust +fn main(x : pub Field, y : pub Field) -> pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + "return_witnesses": [3, 2, 4, 4] + } +} +``` + +Whereas (with the `distinct` keyword) + +```rust +fn main(x : pub Field, y : pub Field) -> distinct pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + //... + "return_witnesses": [3, 4, 5, 6] + } +} +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/11_shadowing.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/11_shadowing.md new file mode 100644 index 00000000000..efd743e764f --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/11_shadowing.md @@ -0,0 +1,43 @@ +--- +title: Shadowing +--- + +Noir allows for inheriting variables' values and re-declaring them with the same name similar to Rust, known as shadowing. + +For example, the following function is valid in Noir: + +```rust +fn main() { + let x = 5; + + { + let x = x * 2; + assert (x == 10); + } + + assert (x == 5); +} +``` + +In this example, a variable x is first defined with the value 5. + +The local scope that follows shadows the original x, i.e. creates a local mutable x based on the value of the original x. It is given a value of 2 times the original x. + +When we return to the main scope, x once again refers to just the original x, which stays at the value of 5. + +## Temporal mutability + +One way that shadowing is useful, in addition to ergonomics across scopes, is for temporarily mutating variables. + +```rust +fn main() { + let age = 30; + // age = age + 5; // Would error as `age` is immutable by default. + + let mut age = age + 5; // Temporarily mutates `age` with a new value. + + let age = age; // Locks `age`'s mutability again. + + assert (age == 35); +} +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types.md new file mode 100644 index 00000000000..d546cc463a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types.md @@ -0,0 +1,96 @@ +--- +title: Data Types +description: + Get a clear understanding of the two categories of Noir data types - primitive types and compound + types. Learn about their characteristics, differences, and how to use them in your Noir + programming. +keywords: + [ + noir, + data types, + primitive types, + compound types, + private types, + public types, + ] +--- + +Every value in Noir has a type, which determines which operations are valid for it. + +All values in Noir are fundamentally composed of `Field` elements. For a more approachable +developing experience, abstractions are added on top to introduce different data types in Noir. + +Noir has two category of data types: primitive types (e.g. `Field`, integers, `bool`) and compound +types that group primitive types (e.g. arrays, tuples, structs). Each value can either be private or +public. + +## Private & Public Types + +A **private value** is known only to the Prover, while a **public value** is known by both the +Prover and Verifier. Mark values as `private` when the value should only be known to the prover. All +primitive types (including individual fields of compound types) in Noir are private by default, and +can be marked public when certain values are intended to be revealed to the Verifier. + +> **Note:** For public values defined in Noir programs paired with smart contract verifiers, once +> the proofs are verified on-chain the values can be considered known to everyone that has access to +> that blockchain. + +Public data types are treated no differently to private types apart from the fact that their values +will be revealed in proofs generated. Simply changing the value of a public type will not change the +circuit (where the same goes for changing values of private types as well). + +_Private values_ are also referred to as _witnesses_ sometimes. + +> **Note:** The terms private and public when applied to a type (e.g. `pub Field`) have a different +> meaning than when applied to a function (e.g. `pub fn foo() {}`). +> +> The former is a visibility modifier for the Prover to interpret if a value should be made known to +> the Verifier, while the latter is a visibility modifier for the compiler to interpret if a +> function should be made accessible to external Noir programs like in other languages. + +### pub Modifier + +All data types in Noir are private by default. Types are explicitly declared as public using the +`pub` modifier: + +```rust +fn main(x : Field, y : pub Field) -> pub Field { + x + y +} +``` + +In this example, `x` is **private** while `y` and `x + y` (the return value) are **public**. Note +that visibility is handled **per variable**, so it is perfectly valid to have one input that is +private and another that is public. + +> **Note:** Public types can only be declared through parameters on `main`. + +## Type Aliases + +A type alias is a new name for an existing type. Type aliases are declared with the keyword `type`: + +```rust +type Id = u8; + +fn main() { + let id: Id = 1; + let zero: u8 = 0; + assert(zero + 1 == id); +} +``` + +Type aliases can also be used with [generics](./06_generics.md): + +```rust +type Id = Size; + +fn main() { + let id: Id = 1; + let zero: u32 = 0; + assert(zero + 1 == id); +} +``` + +### BigInt + +You can acheive BigInt functionality using the [Noir BigInt](https://github.com/shuklaayush/noir-bigint) library. diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/00_fields.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/00_fields.md new file mode 100644 index 00000000000..658a0441ffb --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/00_fields.md @@ -0,0 +1,165 @@ +--- +title: Fields +description: + Dive deep into the Field data type in Noir. Understand its methods, practical examples, and best practices to effectively use Fields in your Noir programs. +keywords: + [ + noir, + field type, + methods, + examples, + best practices, + ] +--- + +The field type corresponds to the native field type of the proving backend. + +The size of a Noir field depends on the elliptic curve's finite field for the proving backend +adopted. For example, a field would be a 254-bit integer when paired with the default backend that +spans the Grumpkin curve. + +Fields support integer arithmetic and are often used as the default numeric type in Noir: + +```rust +fn main(x : Field, y : Field) { + let z = x + y; +} +``` + +`x`, `y` and `z` are all private fields in this example. Using the `let` keyword we defined a new +private value `z` constrained to be equal to `x + y`. + +If proving efficiency is of priority, fields should be used as a default for solving problems. +Smaller integer types (e.g. `u64`) incur extra range constraints. + +## Methods + +After declaring a Field, you can use these common methods on it: + +### to_le_bits + +Transforms the field into an array of bits, Little Endian. + +```rust +fn to_le_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2; + let bits = field.to_le_bits(32); +} +``` + +### to_be_bits + +Transforms the field into an array of bits, Big Endian. + +```rust +fn to_be_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2; + let bits = field.to_be_bits(32); +} +``` + +### to_le_bytes + +Transforms into an array of bytes, Little Endian + +```rust +fn to_le_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let bytes = field.to_le_bytes(4); +} +``` + +### to_be_bytes + +Transforms into an array of bytes, Big Endian + +```rust +fn to_be_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let bytes = field.to_be_bytes(4); +} +``` + +### to_le_radix + +Decomposes into a vector over the specified base, Little Endian + +```rust +fn to_le_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let radix = field.to_le_radix(256, 4); +} +``` + +### to_be_radix + +Decomposes into a vector over the specified base, Big Endian + +```rust +fn to_be_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let radix = field.to_be_radix(256, 4); +} +``` + +### pow_32 + +Returns the value to the power of the specified exponent + +```rust +fn pow_32(self, exponent: Field) -> Field +``` + +example: + +```rust +fn main() { + let field = 2 + let pow = field.pow_32(4); + assert(pow == 16); +} +``` + +### sgn0 + +Parity of (prime) Field element, i.e. sgn0(x mod p) = 0 if x ∈ {0, ..., p-1} is even, otherwise sgn0(x mod p) = 1. + +```rust +fn sgn0(self) -> u1 +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/01_integers.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/01_integers.md new file mode 100644 index 00000000000..d9c5e20e795 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/01_integers.md @@ -0,0 +1,33 @@ +--- +title: Integers +description: + Explore the Integer data type in Noir. Learn about its methods, see real-world examples, and grasp how to efficiently use Integers in your Noir code. +keywords: + [ + noir, + integer types, + methods, + examples, + arithmetic, + ] +--- + +An integer type is a range constrained field type. The Noir frontend currently supports unsigned, +arbitrary-sized integer types. + +An integer type is specified first with the letter `u`, indicating its unsigned nature, followed by +its length in bits (e.g. `32`). For example, a `u32` variable can store a value in the range of +$\\([0,2^{32}-1]\\)$: + +```rust +fn main(x : Field, y : u32) { + let z = x as u32 + y; +} +``` + +`x`, `y` and `z` are all private values in this example. However, `x` is a field while `y` and `z` +are unsigned 32-bit integers. If `y` or `z` exceeds the range $\\([0,2^{32}-1]\\)$, proofs created +will be rejected by the verifier. + +> **Note:** The default backend supports both even (e.g. `u16`, `u48`) and odd (e.g. `u5`, `u3`) +> sized integer types. diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/02_booleans.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/02_booleans.md new file mode 100644 index 00000000000..885db167d83 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/02_booleans.md @@ -0,0 +1,30 @@ +--- +title: Booleans +description: + Delve into the Boolean data type in Noir. Understand its methods, practical examples, and best practices for using Booleans in your Noir programs. +keywords: + [ + noir, + boolean type, + methods, + examples, + logical operations, + ] +--- + + +The `bool` type in Noir has two possible values: `true` and `false`: + +```rust +fn main() { + let t = true; + let f: bool = false; +} +``` + +> **Note:** When returning a boolean value, it will show up as a value of 1 for `true` and 0 for +> `false` in _Verifier.toml_. + +The boolean type is most commonly used in conditionals like `if` expressions and `assert` +statements. More about conditionals is covered in the [Control Flow](../control_flow) and +[Assert Function](../assert) sections. diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/03_strings.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/03_strings.md new file mode 100644 index 00000000000..c42f34ec3ad --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/03_strings.md @@ -0,0 +1,63 @@ +--- +title: Strings +description: + Discover the String data type in Noir. Learn about its methods, see real-world examples, and understand how to effectively manipulate and use Strings in Noir. +keywords: + [ + noir, + string type, + methods, + examples, + concatenation, + ] +--- + + +The string type is a fixed length value defined with `str`. + +You can use strings in `assert()` functions or print them with +`std::println()`. See more about [Logging](../../standard_library/logging). + +```rust +use dep::std; + +fn main(message : pub str<11>, hex_as_string : str<4>) { + std::println(message); + assert(message == "hello world"); + assert(hex_as_string == "0x41"); +} +``` + +You can convert a `str` to a byte array by calling `as_bytes()` +or a vector by calling `as_bytes_vec()`. + +```rust +fn main() { + let message = "hello world"; + let message_bytes = message.as_bytes(); + let mut message_vec = message.as_bytes_vec(); + assert(message_bytes.len() == 11); + assert(message_bytes[0] == 104); + assert(message_bytes[0] == message_vec.get(0)); +} +``` + +## Escape characters + +You can use escape characters for your strings: + +| Escape Sequence | Description | +|-----------------|-----------------| +| `\r` | Carriage Return | +| `\n` | Newline | +| `\t` | Tab | +| `\0` | Null Character | +| `\"` | Double Quote | +| `\\` | Backslash | + +Example: + +```rust +let s = "Hello \"world" // prints "Hello "world" +let s = "hey \tyou"; // prints "hey you" +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/04_arrays.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/04_arrays.md new file mode 100644 index 00000000000..bdbd1798bef --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/04_arrays.md @@ -0,0 +1,244 @@ +--- +title: Arrays +description: + Dive into the Array data type in Noir. Grasp its methods, practical examples, and best practices for efficiently using Arrays in your Noir code. +keywords: + [ + noir, + array type, + methods, + examples, + indexing, + ] +--- + +An array is one way of grouping together values into one compound type. Array types can be inferred +or explicitly specified via the syntax `[; ]`: + +```rust +fn main(x : Field, y : Field) { + let my_arr = [x, y]; + let your_arr: [Field; 2] = [x, y]; +} +``` + +Here, both `my_arr` and `your_arr` are instantiated as an array containing two `Field` elements. + +Array elements can be accessed using indexing: + +```rust +fn main() { + let a = [1, 2, 3, 4, 5]; + + let first = a[0]; + let second = a[1]; +} +``` + +All elements in an array must be of the same type (i.e. homogeneous). That is, an array cannot group +a `Field` value and a `u8` value together for example. + +You can write mutable arrays, like: + +```rust +fn main() { + let mut arr = [1, 2, 3, 4, 5]; + assert(arr[0] == 1); + + arr[0] = 42; + assert(arr[0] == 42); +} +``` + +You can instantiate a new array of a fixed size with the same value repeated for each element. The following example instantiates an array of length 32 where each element is of type Field and has the value 0. + +```rust +let array: [Field; 32] = [0; 32]; +``` + +Like in Rust, arrays in Noir are a fixed size. However, if you wish to convert an array to a [slice](./slices), you can just call `as_slice` on your array: + +```rust +let array: [Field; 32] = [0; 32]; +let sl = array.as_slice() +``` + +You can define multidimensional arrays: + +```rust +let array : [[Field; 2]; 2]; +let element = array[0][0]; +``` + +## Types + +You can create arrays of primitive types or structs. There is not yet support for nested arrays +(arrays of arrays) or arrays of structs that contain arrays. + +## Methods + +For convenience, the STD provides some ready-to-use, common methods for arrays: + +### len + +Returns the length of an array + +```rust +fn len(_array: [T; N]) -> comptime Field +``` + +example + +```rust +fn main() { + let array = [42, 42]; + assert(array.len() == 2); +} +``` + +### sort + +Returns a new sorted array. The original array remains untouched. Notice that this function will +only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting +logic it uses internally is optimized specifically for these values. If you need a sort function to +sort any type, you should use the function `sort_via` described below. + +```rust +fn sort(_array: [T; N]) -> [T; N] +``` + +example + +```rust +fn main() { + let arr = [42, 32]; + let sorted = arr.sort(); + assert(sorted == [32, 42]); +} +``` + +### sort_via + +Sorts the array with a custom comparison function + +```rust +fn sort_via(mut a: [T; N], ordering: fn(T, T) -> bool) -> [T; N] +``` + +example + +```rust +fn main() { + let arr = [42, 32] + let sorted_ascending = arr.sort_via(|a, b| a < b); + assert(sorted_ascending == [32, 42]); // verifies + + let sorted_descending = arr.sort_via(|a, b| a > b); + assert(sorted_descending == [32, 42]); // does not verify +} +``` + +### map + +Applies a function to each element of the array, returning a new array containing the mapped elements. + +```rust +fn map(f: fn(T) -> U) -> [U; N] +``` + +example + +```rust +let a = [1, 2, 3]; +let b = a.map(|a| a * 2); // b is now [2, 4, 6] +``` + +### fold + +Applies a function to each element of the array, returning the final accumulated value. The first +parameter is the initial value. + +```rust +fn fold(mut accumulator: U, f: fn(U, T) -> U) -> U +``` + +This is a left fold, so the given function will be applied to the accumulator and first element of +the array, then the second, and so on. For a given call the expected result would be equivalent to: + +```rust +let a1 = [1]; +let a2 = [1, 2]; +let a3 = [1, 2, 3]; + +let f = |a, b| a - b; +a1.fold(10, f) //=> f(10, 1) +a2.fold(10, f) //=> f(f(10, 1), 2) +a3.fold(10, f) //=> f(f(f(10, 1), 2), 3) +``` + +example: + +```rust + +fn main() { + let arr = [2, 2, 2, 2, 2]; + let folded = arr.fold(0, |a, b| a + b); + assert(folded == 10); +} + +``` + +### reduce + +Same as fold, but uses the first element as starting element. + +```rust +fn reduce(f: fn(T, T) -> T) -> T +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 2]; + let reduced = arr.reduce(|a, b| a + b); + assert(reduced == 10); +} +``` + +### all + +Returns true if all the elements satisfy the given predicate + +```rust +fn all(predicate: fn(T) -> bool) -> bool +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 2]; + let all = arr.all(|a| a == 2); + assert(all); +} +``` + +### any + +Returns true if any of the elements satisfy the given predicate + +```rust +fn any(predicate: fn(T) -> bool) -> bool +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 5]; + let any = arr.any(|a| a == 5); + assert(any); +} + +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/05_slices.mdx b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/05_slices.mdx new file mode 100644 index 00000000000..f45f56daee1 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/05_slices.mdx @@ -0,0 +1,146 @@ +--- +title: Slices +description: Explore the Slice data type in Noir. Understand its methods, see real-world examples, and learn how to effectively use Slices in your Noir programs. +keywords: [noir, slice type, methods, examples, subarrays] +--- + +import Experimental from '@site/src/components/Notes/_experimental.mdx'; + + + +A slice is a dynamically-sized view into a sequence of elements. They can be resized at runtime, but because they don't own the data, they cannot be returned from a circuit. You can treat slices as arrays without a constrained size. + +```rust +use dep::std::slice; + +fn main() -> pub Field { + let mut slice: [Field] = [0; 2]; + + let mut new_slice = slice.push_back(6); + new_slice.len() +} +``` + +View the corresponding test file [here]([test-file]. + +[test-file]: https://github.com/noir-lang/noir/blob/f387ec1475129732f72ba294877efdf6857135ac/crates/nargo_cli/tests/test_data_ssa_refactor/slices/src/main.nr + +## Methods + +For convenience, the STD provides some ready-to-use, common methods for slices: + +### push_back + +Pushes a new element to the end of the slice, returning a new slice with a length one greater than the original unmodified slice. + +```rust +fn push_back(_self: [T], _elem: T) -> [T] +``` + +example: + +```rust +fn main() -> pub Field { + let mut slice: [Field] = [0; 2]; + + let mut new_slice = slice.push_back(6); + new_slice.len() +} +``` + +View the corresponding test file [here][test-file]. + +### push_front + +Returns a new array with the specified element inserted at index 0. The existing elements indexes are incremented by 1. + +```rust +fn push_front(_self: Self, _elem: T) -> Self +``` + +Example: + +```rust +let mut new_slice: [Field] = []; +new_slice = new_slice.push_front(20); +assert(new_slice[0] == 20); // returns true +``` + +View the corresponding test file [here][test-file]. + +### pop_front + +Returns a tuple of two items, the first element of the array and the rest of the array. + +```rust +fn pop_front(_self: Self) -> (T, Self) +``` + +Example: + +```rust +let (first_elem, rest_of_slice) = slice.pop_front(); +``` + +View the corresponding test file [here][test-file]. + +### pop_back + +Returns a tuple of two items, the beginning of the array with the last element omitted and the last element. + +```rust +fn pop_back(_self: Self) -> (Self, T) +``` + +Example: + +```rust +let (popped_slice, last_elem) = slice.pop_back(); +``` + +View the corresponding test file [here][test-file]. + +### append + +Loops over a slice and adds it to the end of another. + +```rust +fn append(mut self, other: Self) -> Self +``` + +Example: + +```rust +let append = [1, 2].append([3, 4, 5]); +``` + +### insert + +Inserts an element at a specified index and shifts all following elements by 1. + +```rust +fn insert(_self: Self, _index: Field, _elem: T) -> Self +``` + +Example: + +```rust +new_slice = rest_of_slice.insert(2, 100); +assert(new_slice[2] == 100); +``` + +View the corresponding test file [here][test-file]. + +### remove + +Remove an element at a specified index, shifting all elements after it to the left, returning the altered slice and the removed element. + +```rust +fn remove(_self: Self, _index: Field) -> (Self, T) +``` + +Example: + +```rust +let (remove_slice, removed_elem) = slice.remove(3); +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/06_vectors.mdx b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/06_vectors.mdx new file mode 100644 index 00000000000..4617e90d038 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/06_vectors.mdx @@ -0,0 +1,172 @@ +--- +title: Vectors +description: Delve into the Vector data type in Noir. Learn about its methods, practical examples, and best practices for using Vectors in your Noir code. +keywords: [noir, vector type, methods, examples, dynamic arrays] +--- + +import Experimental from '@site/src/components/Notes/_experimental.mdx'; + + + +A vector is a collection type similar to Rust's Vector type. It's convenient way to use slices as mutable arrays. + +Example: + +```rust +use dep::std::collections::vec::Vec; + +let mut vector: Vec = Vec::new(); +for i in 0..5 { + vector.push(i); +} +assert(vector.len() == 5); +``` + +## Methods + +### new + +Creates a new, empty vector. + +```rust +pub fn new() -> Self { + Self { slice: [] } +} +``` + +Example: + +```rust +let empty_vector: Vec = Vec::new(); +assert(empty_vector.len() == 0); +``` + +### from_slice + +Creates a vector containing each element from a given slice. Mutations to the resulting vector will not affect the original slice. + +```rust +pub fn from_slice(slice: [T]) -> Self { + Self { slice } +} +``` + +Example: + +```rust +let arr: [Field] = [1, 2, 3]; +let vector_from_slice = Vec::from_slice(arr); +assert(vector_from_slice.len() == 3); +``` + +### get + +Retrieves an element from the vector at a given index. Panics if the index points beyond the vector's end. + +```rust +pub fn get(self, index: Field) -> T { + self.slice[index] +} +``` + +Example: + +```rust +let vector: Vec = Vec::from_slice([10, 20, 30]); +assert(vector.get(1) == 20); +``` + +### push + +Adds a new element to the vector's end, returning a new vector with a length one greater than the original unmodified vector. + +```rust +pub fn push(&mut self, elem: T) { + self.slice = self.slice.push_back(elem); +} +``` + +Example: + +```rust +let mut vector: Vec = Vec::new(); +vector.push(10); +assert(vector.len() == 1); +``` + +### pop + +Removes an element from the vector's end, returning a new vector with a length one less than the original vector, along with the removed element. Panics if the vector's length is zero. + +```rust +pub fn pop(&mut self) -> T { + let (popped_slice, last_elem) = self.slice.pop_back(); + self.slice = popped_slice; + last_elem +} +``` + +Example: + +```rust +let mut vector = Vec::from_slice([10, 20]); +let popped_elem = vector.pop(); +assert(popped_elem == 20); +assert(vector.len() == 1); +``` + +### insert + +Inserts an element at a specified index, shifting subsequent elements to the right. + +```rust +pub fn insert(&mut self, index: Field, elem: T) { + self.slice = self.slice.insert(index, elem); +} +``` + +Example: + +```rust +let mut vector = Vec::from_slice([10, 30]); +vector.insert(1, 20); +assert(vector.get(1) == 20); +``` + +### remove + +Removes an element at a specified index, shifting subsequent elements to the left, and returns the removed element. + +```rust +pub fn remove(&mut self, index: Field) -> T { + let (new_slice, elem) = self.slice.remove(index); + self.slice = new_slice; + elem +} +``` + +Example: + +```rust +let mut vector = Vec::from_slice([10, 20, 30]); +let removed_elem = vector.remove(1); +assert(removed_elem == 20); +assert(vector.len() == 2); +``` + +### len + +Returns the number of elements in the vector. + +```rust +pub fn len(self) -> Field { + self.slice.len() +} +``` + +Example: + +```rust +let empty_vector: Vec = Vec::new(); +assert(empty_vector.len() == 0); +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/07_tuples.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/07_tuples.md new file mode 100644 index 00000000000..5f6cab974a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/07_tuples.md @@ -0,0 +1,47 @@ +--- +title: Tuples +description: + Dive into the Tuple data type in Noir. Understand its methods, practical examples, and best practices for efficiently using Tuples in your Noir code. +keywords: + [ + noir, + tuple type, + methods, + examples, + multi-value containers, + ] +--- + +A tuple collects multiple values like an array, but with the added ability to collect values of +different types: + +```rust +fn main() { + let tup: (u8, u64, Field) = (255, 500, 1000); +} +``` + +One way to access tuple elements is via destructuring using pattern matching: + +```rust +fn main() { + let tup = (1, 2); + + let (one, two) = tup; + + let three = one + two; +} +``` + +Another way to access tuple elements is via direct member access, using a period (`.`) followed by +the index of the element we want to access. Index `0` corresponds to the first tuple element, `1` to +the second and so on: + +```rust +fn main() { + let tup = (5, 6, 7, 8); + + let five = tup.0; + let eight = tup.3; +} +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/08_structs.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/08_structs.md new file mode 100644 index 00000000000..85649dfb389 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/08_structs.md @@ -0,0 +1,73 @@ +--- +title: Structs +description: + Explore the Struct data type in Noir. Learn about its methods, see real-world examples, and grasp how to effectively define and use Structs in your Noir programs. +keywords: + [ + noir, + struct type, + methods, + examples, + data structures, + ] +--- + +A struct also allows for grouping multiple values of different types. Unlike tuples, we can also +name each field. + +> **Note:** The usage of _field_ here refers to each element of the struct and is unrelated to the +> field type of Noir. + +Defining a struct requires giving it a name and listing each field within as `: ` pairs: + +```rust +struct Animal { + hands: Field, + legs: Field, + eyes: u8, +} +``` + +An instance of a struct can then be created with actual values in `: ` pairs in any +order. Struct fields are accessible using their given names: + +```rust +fn main() { + let legs = 4; + + let dog = Animal { + eyes: 2, + hands: 0, + legs, + }; + + let zero = dog.hands; +} +``` + +Structs can also be destructured in a pattern, binding each field to a new variable: + +```rust +fn main() { + let Animal { hands, legs: feet, eyes } = get_octopus(); + + let ten = hands + feet + eyes as u8; +} + +fn get_octopus() -> Animal { + let octopus = Animal { + hands: 0, + legs: 8, + eyes: 2, + }; + + octopus +} +``` + +The new variables can be bound with names different from the original struct field names, as +showcased in the `legs --> feet` binding in the example above. + +:::note +You can use Structs as inputs to the `main` function, but you can't output them +::: diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/09_references.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/09_references.md new file mode 100644 index 00000000000..b0c35ce2cb9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/09_references.md @@ -0,0 +1,22 @@ +--- +title: References +--- + +Noir supports first-class references. References are a bit like pointers: they point to a specific address that can be followed to access the data stored at that address. You can use Rust-like syntax to use pointers in Noir: the `&` operator references the variable, the `*` operator dereferences it. + +Example: + +```rust +fn main() { + let mut x = 2; + + // you can reference x as &mut and pass it to multiplyBy2 + multiplyBy2(&mut x); +} + +// you can access &mut here +fn multiplyBy2(x: &mut Field) { + // and dereference it with * + *x = *x * 2; +} +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/10_function_types.md b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/10_function_types.md new file mode 100644 index 00000000000..1ec92efd594 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/language_concepts/data_types/10_function_types.md @@ -0,0 +1,25 @@ +--- +title: Function types +--- + +Noir supports higher-order functions. The syntax for a function type is as follows: + +```rust +fn(arg1_type, arg2_type, ...) -> return_type +``` + +Example: + +```rust +fn assert_returns_100(f: fn() -> Field) { // f takes no args and returns a Field + assert(f() == 100); +} + +fn main() { + assert_returns_100(|| 100); // ok + assert_returns_100(|| 150); // fails +} +``` + +A function type also has an optional capture environment - this is necessary to support closures. +See [Lambdas](../08_lambdas.md) for more details. diff --git a/noir/docs/versioned_docs/version-v0.17.0/migration_notes.md b/noir/docs/versioned_docs/version-v0.17.0/migration_notes.md new file mode 100644 index 00000000000..48a8abcf22e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/migration_notes.md @@ -0,0 +1,83 @@ +--- +title: Migration notes +description: Read about migration notes from previous versions, which could solve problems while updating +keywords: [Noir, notes, migration, updating, upgrading] +--- + +Noir is in full-speed development. Things break fast, wild, and often. This page attempts to leave some notes on errors you might encounter when upgrading and how to resolve them until proper patches are built. + +## ≥0.14 + +The index of the [for loops](./language_concepts/02_control_flow.md#loops) is now of type `u64` instead of `Field`. An example refactor would be: + +```rust +for i in 0..10 { + let i = i as Field; +} +``` + +## ≥v0.11.0 and Nargo backend + +From this version onwards, Nargo starts managing backends through the `nargo backend` command. Upgrading to the versions per usual steps might lead to: + +### `backend encountered an error` + +This is likely due to the existing locally installed version of proving backend (e.g. barretenberg) is incompatible with the version of Nargo in use. + +To fix the issue: + +1. Uninstall the existing backend + +```bash +nargo backend uninstall acvm-backend-barretenberg +``` + +You may replace _acvm-backend-barretenberg_ with the name of your backend listed in `nargo backend ls` or in ~/.nargo/backends. + +2. Reinstall a compatible version of the proving backend. + +If you are using the default barretenberg backend, simply run: + +``` +nargo prove +``` + +with you Noir program. + +This will trigger the download and installation of the latest version of barretenberg compatible with your Nargo in use. + +### `backend encountered an error: illegal instruction` + +On certain Intel-based systems, an `illegal instruction` error may arise due to incompatibility of barretenberg with certain CPU instructions. + +To fix the issue: + +1. Uninstall the existing backend + +```bash +nargo backend uninstall acvm-backend-barretenberg +``` + +You may replace _acvm-backend-barretenberg_ with the name of your backend listed in `nargo backend ls` or in ~/.nargo/backends. + +2. Reinstall a compatible version of the proving backend. + +If you are using the default barretenberg backend, simply run: + +``` +nargo backend install acvm-backend-barretenberg https://github.com/noir-lang/barretenberg-js-binary/raw/master/run-bb.tar.gz +``` + +This downloads and installs a specific bb.js based version of barretenberg binary from GitHub. + +The gzipped filed is running this bash script: , where we need to gzip it as the Nargo currently expect the backend to be zipped up. + +Then run: + +``` +DESIRED_BINARY_VERSION=0.8.1 nargo info +``` + +This overrides the bb native binary with a bb.js node application instead, which should be compatible with most if not all hardware. This does come with the drawback of being generally slower than native binary. + +0.8.1 indicates bb.js version 0.8.1, so if you change that it will update to a different version or the default version in the script if none was supplied. diff --git a/noir/docs/versioned_docs/version-v0.17.0/modules_packages_crates/crates_and_packages.md b/noir/docs/versioned_docs/version-v0.17.0/modules_packages_crates/crates_and_packages.md new file mode 100644 index 00000000000..fb83a33d94e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/modules_packages_crates/crates_and_packages.md @@ -0,0 +1,42 @@ +--- +title: Crates and Packages +description: Learn how to use Crates and Packages in your Noir project +keywords: [Nargo, dependencies, package management, crates, package] +--- + +## Crates + +A crate is the smallest amount of code that the Noir compiler considers at a time. +Crates can contain modules, and the modules may be defined in other files that get compiled with the crate, as we’ll see in the coming sections. + +### Crate Types + +A Noir crate can come in several forms: binaries, libraries or contracts. + +#### Binaries + +_Binary crates_ are programs which you can compile to an ACIR circuit which you can then create proofs against. Each must have a function called `main` that defines the ACIR circuit which is to be proved. + +#### Libraries + +_Library crates_ don't have a `main` function and they don't compile down to ACIR. Instead they define functionality intended to be shared with multiple projects, and eventually included in a binary crate. + +#### Contracts + +Contract crates are similar to binary crates in that they compile to ACIR which you can create proofs against. They are different in that they do not have a single `main` function, but are a collection of functions to be deployed to the [Aztec network](https://aztec.network). You can learn more about the technical details of Aztec in the [monorepo](https://github.com/AztecProtocol/aztec-packages) or contract [examples](https://github.com/AztecProtocol/aztec-packages/tree/master/yarn-project/noir-contracts/src/contracts). + +### Crate Root + +Every crate has a root, which is the source file that the compiler starts, this is also known as the root module. The Noir compiler does not enforce any conditions on the name of the file which is the crate root, however if you are compiling via Nargo the crate root must be called `lib.nr` or `main.nr` for library or binary crates respectively. + +## Packages + +A Nargo _package_ is a collection of one of more crates that provides a set of functionality. A package must include a Nargo.toml file. + +A package _must_ contain either a library or a binary crate, but not both. + +### Differences from Cargo Packages + +One notable difference between Rust's Cargo and Noir's Nargo is that while Cargo allows a package to contain an unlimited number of binary crates and a single library crate, Nargo currently only allows a package to contain a single crate. + +In future this restriction may be lifted to allow a Nargo package to contain both a binary and library crate or multiple binary crates. diff --git a/noir/docs/versioned_docs/version-v0.17.0/modules_packages_crates/dependencies.md b/noir/docs/versioned_docs/version-v0.17.0/modules_packages_crates/dependencies.md new file mode 100644 index 00000000000..75f95aaa305 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/modules_packages_crates/dependencies.md @@ -0,0 +1,123 @@ +--- +title: Dependencies +description: + Learn how to specify and manage dependencies in Nargo, allowing you to upload packages to GitHub + and use them easily in your project. +keywords: [Nargo, dependencies, GitHub, package management, versioning] +--- + +Nargo allows you to upload packages to GitHub and use them as dependencies. + +## Specifying a dependency + +Specifying a dependency requires a tag to a specific commit and the git url to the url containing +the package. + +Currently, there are no requirements on the tag contents. If requirements are added, it would follow +semver 2.0 guidelines. + +> Note: Without a `tag` , there would be no versioning and dependencies would change each time you +> compile your project. + +For example, to add the [ecrecover-noir library](https://github.com/colinnielsen/ecrecover-noir) to your project, add it to `Nargo.toml`: + +```toml +# Nargo.toml + +[dependencies] +ecrecover = {tag = "v0.8.0", git = "https://github.com/colinnielsen/ecrecover-noir"} +``` + +If the module is in a subdirectory, you can define a subdirectory in your git repository, for example: + +```toml +# Nargo.toml + +[dependencies] +easy_private_token_contract = {tag ="v0.1.0-alpha62", git = "https://github.com/AztecProtocol/aztec-packages", directory = "yarn-project/noir-contracts/src/contracts/easy_private_token_contract"} +``` + +## Specifying a local dependency + +You can also specify dependencies that are local to your machine. + +For example, this file structure has a library and binary crate + +```tree +├── binary_crate +│   ├── Nargo.toml +│   └── src +│   └── main.nr +└── liba + ├── Nargo.toml + └── src + └── lib.nr +``` + +Inside of the binary crate, you can specify: + +```toml +# Nargo.toml + +[dependencies] +libA = { path = "../liba" } +``` + +## Importing dependencies + +You can import a dependency to a Noir file using the following syntax. For example, to import the +ecrecover-noir library and local liba referenced above: + +```rust +use dep::ecrecover; +use dep::libA; +``` + +You can also import only the specific parts of dependency that you want to use, like so: + +```rust +use dep::std::hash::sha256; +use dep::std::scalar_mul::fixed_base_embedded_curve; +``` + +Lastly, as demonstrated in the +[elliptic curve example](../standard_library/cryptographic_primitives/ec_primitives#examples), you +can import multiple items in the same line by enclosing them in curly braces: + +```rust +use dep::std::ec::tecurve::affine::{Curve, Point}; +``` + +We don't have a way to consume libraries from inside a [workspace](./workspaces) as external dependencies right now. + +Inside a workspace, these are consumed as `{ path = "../to_lib" }` dependencies in Nargo.toml. + +## Dependencies of Dependencies + +Note that when you import a dependency, you also get access to all of the dependencies of that package. + +For example, the [phy_vector](https://github.com/resurgencelabs/phy_vector) library imports an [fraction](https://github.com/resurgencelabs/fraction) library. If you're importing the phy_vector library, then you can access the functions in fractions library like so: + +```rust +use dep::phy_vector; + +fn main(x : Field, y : pub Field) { + //... + let f = phy_vector::fraction::toFraction(true, 2, 1); + //... +} +``` + +## Available Libraries + +Noir does not currently have an official package manager. You can find a list of available Noir libraries in the [awesome-noir repo here](https://github.com/noir-lang/awesome-noir#libraries). + +Some libraries that are available today include: + +- [Standard Library](https://github.com/noir-lang/noir/tree/master/noir_stdlib) - the Noir Standard Library +- [Ethereum Storage Proof Verification](https://github.com/aragonzkresearch/noir-trie-proofs) - a library that contains the primitives necessary for RLP decoding (in the form of look-up table construction) and Ethereum state and storage proof verification (or verification of any trie proof involving 32-byte long keys) +- [BigInt](https://github.com/shuklaayush/noir-bigint) - a library that provides a custom BigUint56 data type, allowing for computations on large unsigned integers +- [ECrecover](https://github.com/colinnielsen/ecrecover-noir/tree/main) - a library to verify an ECDSA signature and return the source Ethereum address +- [Sparse Merkle Tree Verifier](https://github.com/vocdoni/smtverifier-noir/tree/main) - a library for verification of sparse Merkle trees +- [Signed Int](https://github.com/resurgencelabs/signed_int) - a library for accessing a custom Signed Integer data type, allowing access to negative numbers on Noir +- [Fraction](https://github.com/resurgencelabs/fraction) - a library for accessing fractional number data type in Noir, allowing results that aren't whole numbers diff --git a/noir/docs/versioned_docs/version-v0.17.0/modules_packages_crates/modules.md b/noir/docs/versioned_docs/version-v0.17.0/modules_packages_crates/modules.md new file mode 100644 index 00000000000..147c9b284e8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/modules_packages_crates/modules.md @@ -0,0 +1,104 @@ +--- +title: Modules +description: + Learn how to organize your files using modules in Noir, following the same convention as Rust's + module system. Examples included. +keywords: [Noir, Rust, modules, organizing files, sub-modules] +--- + +Noir's module system follows the same convention as the _newer_ version of Rust's module system. + +## Purpose of Modules + +Modules are used to organise files. Without modules all of your code would need to live in a single +file. In Noir, the compiler does not automatically scan all of your files to detect modules. This +must be done explicitly by the developer. + +## Examples + +### Importing a module in the crate root + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::hello_world(); +} +``` + +Filename : `src/foo.nr` + +```rust +fn from_foo() {} +``` + +In the above snippet, the crate root is the `src/main.nr` file. The compiler sees the module +declaration `mod foo` which prompts it to look for a foo.nr file. + +Visually this module hierarchy looks like the following : + +``` +crate + ├── main + │ + └── foo + └── from_foo + +``` + +### Importing a module throughout the tree + +All modules are accessible from the `crate::` namespace. + +``` +crate + ├── bar + ├── foo + └── main + +``` + +In the above snippet, if `bar` would like to use functions in `foo`, it can do so by `use crate::foo::function_name`. + +### Sub-modules + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::from_foo(); +} +``` + +Filename : `src/foo.nr` + +```rust +mod bar; +fn from_foo() {} +``` + +Filename : `src/foo/bar.nr` + +```rust +fn from_bar() {} +``` + +In the above snippet, we have added an extra module to the module tree; `bar`. `bar` is a submodule +of `foo` hence we declare bar in `foo.nr` with `mod bar`. Since `foo` is not the crate root, the +compiler looks for the file associated with the `bar` module in `src/foo/bar.nr` + +Visually the module hierarchy looks as follows: + +``` +crate + ├── main + │ + └── foo + ├── from_foo + └── bar + └── from_bar +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/modules_packages_crates/workspaces.md b/noir/docs/versioned_docs/version-v0.17.0/modules_packages_crates/workspaces.md new file mode 100644 index 00000000000..d9ac92667c9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/modules_packages_crates/workspaces.md @@ -0,0 +1,39 @@ +--- +title: Workspaces +--- + +Workspaces are a feature of nargo that allow you to manage multiple related Noir packages in a single repository. A workspace is essentially a group of related projects that share common build output directories and configurations. + +Each Noir project (with it's own Nargo.toml file) can be thought of as a package. Each package is expected to contain exactly one "named circuit", being the "name" defined in Nargo.toml with the program logic defined in `./src/main.nr`. + +For a project with the following structure: + +```tree +├── crates +│   ├── a +│   │   ├── Nargo.toml +│   │   └── src +│   │   └── main.nr +│   └── b +│   ├── Nargo.toml +│   └── src +│   └── main.nr +├── Nargo.toml +└── Prover.toml +``` + +You can define a workspace in Nargo.toml like so: + +```toml +[workspace] +members = ["crates/a", "crates/b"] +default-member = "crates/a" +``` + +`members` indicates which packages are included in the workspace. As such, all member packages of a workspace will be processed when the `--workspace` flag is used with various commands or if a `default-member` is not specified. + +`default-member` indicates which package various commands process by default. + +Libraries can be defined in a workspace. Inside a workspace, these are consumed as `{ path = "../to_lib" }` dependencies in Nargo.toml. + +Inside a workspace, these are consumed as `{ path = "../to_lib" }` dependencies in Nargo.toml. diff --git a/noir/docs/versioned_docs/version-v0.17.0/nargo/01_commands.md b/noir/docs/versioned_docs/version-v0.17.0/nargo/01_commands.md new file mode 100644 index 00000000000..65e2bdb44e3 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/nargo/01_commands.md @@ -0,0 +1,250 @@ +--- +title: Commands +description: + Noir CLI Commands for Noir Prover and Verifier to create, execute, prove and verify programs, + generate Solidity verifier smart contract and compile into JSON file containing ACIR + representation and ABI of circuit. +keywords: + [ + Nargo, + Noir CLI, + Noir Prover, + Noir Verifier, + generate Solidity verifier, + compile JSON file, + ACIR representation, + ABI of circuit, + TypeScript, + ] +--- + +## General options + +| Option | Description | +| -------------------- | -------------------------------------------------- | +| `--show-ssa` | Emit debug information for the intermediate SSA IR | +| `--deny-warnings` | Quit execution when warnings are emitted | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo help [subcommand]` + +Prints the list of available commands or specific information of a subcommand. + +_Arguments_ + +| Argument | Description | +| -------------- | -------------------------------------------- | +| `` | The subcommand whose help message to display | + +## `nargo backend` + +Installs and selects custom backends used to generate and verify proofs. + +### Commands + +| Command | Description | +| ----------- | --------------------------------------------------------- | +| `current` | Prints the name of the currently active backend | +| `ls` | Prints the list of currently installed backends | +| `use` | Select the backend to use | +| `install` | Install a new backend from a URL | +| `uninstall` | Uninstalls a backend | +| `help` | Print this message or the help of the given subcommand(s) | + +### Options + +| Option | Description | +| ------------ | ----------- | +| `-h, --help` | Print help | + +## `nargo check` + +Generate the `Prover.toml` and `Verifier.toml` files for specifying prover and verifier in/output +values of the Noir program respectively. + +### Options + +| Option | Description | +| --------------------- | ------------------------------------- | +| `--package ` | The name of the package to check | +| `--workspace` | Check all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +### `nargo codegen-verifier` + +Generate a Solidity verifier smart contract for the program. + +### Options + +| Option | Description | +| --------------------- | ------------------------------------- | +| `--package ` | The name of the package to codegen | +| `--workspace` | Codegen all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo compile` + +Compile the program into a JSON build artifact file containing the ACIR representation and the ABI +of the circuit. This build artifact can then be used to generate and verify proofs. + +You can also use "build" as an alias for compile (e.g. `nargo build`). + +### Options + +| Option | Description | +| --------------------- | ------------------------------------------------------------ | +| `--include-keys` | Include Proving and Verification keys in the build artifacts | +| `--package ` | The name of the package to compile | +| `--workspace` | Compile all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo new ` + +Creates a new Noir project in a new folder. + +**Arguments** + +| Argument | Description | +| -------- | -------------------------------- | +| `` | The path to save the new project | + +### Options + +| Option | Description | +| --------------- | ----------------------------------------------------- | +| `--name ` | Name of the package [default: package directory name] | +| `--lib` | Use a library template | +| `--bin` | Use a binary template [default] | +| `--contract` | Use a contract template | +| `-h, --help` | Print help | + +## `nargo init` + +Creates a new Noir project in the current directory. + +### Options + +| Option | Description | +| --------------- | ----------------------------------------------------- | +| `--name ` | Name of the package [default: current directory name] | +| `--lib` | Use a library template | +| `--bin` | Use a binary template [default] | +| `--contract` | Use a contract template | +| `-h, --help` | Print help | + +## `nargo execute [WITNESS_NAME]` + +Runs the Noir program and prints its return value. + +**Arguments** + +| Argument | Description | +| ---------------- | ----------------------------------------- | +| `[WITNESS_NAME]` | Write the execution witness to named file | + +### Options + +| Option | Description | +| --------------------------------- | ------------------------------------------------------------------------------------ | +| `-p, --prover-name ` | The name of the toml file which contains the inputs for the prover [default: Prover] | +| `--package ` | The name of the package to execute | +| `--workspace` | Execute all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +_Usage_ + +The inputs to the circuit are read from the `Prover.toml` file generated by `nargo check`, which +must be filled in. + +To save the witness to file, run the command with a value for the `WITNESS_NAME` argument. A +`.tr` file will then be saved in the `./target` folder. + +## `nargo prove` + +Creates a proof for the program. + +### Options + +| Option | Description | +| ------------------------------------- | ---------------------------------------------------------------------------------------- | +| `-p, --prover-name ` | The name of the toml file which contains the inputs for the prover [default: Prover] | +| `-v, --verifier-name ` | The name of the toml file which contains the inputs for the verifier [default: Verifier] | +| `--verify` | Verify proof after proving | +| `--package ` | The name of the package to prove | +| `--workspace` | Prove all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo verify` + +Given a proof and a program, verify whether the proof is valid. + +### Options + +| Option | Description | +| ------------------------------------- | ---------------------------------------------------------------------------------------- | +| `-v, --verifier-name ` | The name of the toml file which contains the inputs for the verifier [default: Verifier] | +| `--package ` | The name of the package to verify | +| `--workspace` | Verify all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo test [TEST_NAME]` + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. To print `println` statements in tests, use the `--show-output` flag. + +Takes an optional `--exact` flag which allows you to select tests based on an exact name. + +See an example on the [testing page](./testing). + +### Options + +| Option | Description | +| --------------------- | -------------------------------------- | +| `--show-output` | Display output of `println` statements | +| `--exact` | Only run tests that match exactly | +| `--package ` | The name of the package to test | +| `--workspace` | Test all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo info` + +Prints a table containing the information of the package. + +Currently the table provide + +1. The number of ACIR opcodes +2. The final number gates in the circuit used by a backend + +If the file contains a contract the table will provide the +above information about each function of the contract. + +## `nargo lsp` + +Start a long-running Language Server process that communicates over stdin/stdout. +Usually this command is not run by a user, but instead will be run by a Language Client, such as [vscode-noir](https://github.com/noir-lang/vscode-noir). + +## `nargo fmt` + +Automatically formats your Noir source code based on the default formatting settings. diff --git a/noir/docs/versioned_docs/version-v0.17.0/nargo/02_testing.md b/noir/docs/versioned_docs/version-v0.17.0/nargo/02_testing.md new file mode 100644 index 00000000000..da767274efd --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/nargo/02_testing.md @@ -0,0 +1,61 @@ +--- +title: Testing in Noir +description: Learn how to use Nargo to test your Noir program in a quick and easy way +keywords: [Nargo, testing, Noir, compile, test] +--- + +You can test your Noir programs using Noir circuits. + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. + +For example if you have a program like: + +```rust +fn add(x: u64, y: u64) -> u64 { + x + y +} +#[test] +fn test_add() { + assert(add(2,2) == 4); + assert(add(0,1) == 1); + assert(add(1,0) == 1); +} +``` + +Running `nargo test` will test that the `test_add` function can be executed while satisfying the all +the contraints which allows you to test that add returns the expected values. Test functions can't +have any arguments currently. + +### Test fail + +You can write tests that are expected to fail by using the decorator `#[test(should_fail)]`. For example: + +```rust +fn add(x: u64, y: u64) -> u64 { + x + y +} +#[test(should_fail)] +fn test_add() { + assert(add(2,2) == 5); +} +``` + +You can be more specific and make it fail with a specific reason by using `should_fail_with = "`: + +```rust +fn main(african_swallow_avg_speed : Field) { + assert(african_swallow_avg_speed == 65, "What is the airspeed velocity of an unladen swallow"); +} + +#[test] +fn test_king_arthur() { + main(65); +} + +#[test(should_fail_with = "What is the airspeed velocity of an unladen swallow")] +fn test_bridgekeeper() { + main(32); +} + +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/nargo/03_solidity_verifier.md b/noir/docs/versioned_docs/version-v0.17.0/nargo/03_solidity_verifier.md new file mode 100644 index 00000000000..9ac60cb0ba7 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/nargo/03_solidity_verifier.md @@ -0,0 +1,129 @@ +--- +title: Solidity Verifier +description: + Learn how to run the verifier as a smart contract on the blockchain. Compile a Solidity verifier + contract for your Noir program and deploy it on any EVM blockchain acting as a verifier smart + contract. Read more to find out! +keywords: + [ + solidity verifier, + smart contract, + blockchain, + compiler, + plonk_vk.sol, + EVM blockchain, + verifying Noir programs, + proving backend, + Barretenberg, + ] +--- + +For certain applications, it may be desirable to run the verifier as a smart contract instead of on +a local machine. + +Compile a Solidity verifier contract for your Noir program by running: + +```sh +nargo codegen-verifier +``` + +A new `contract` folder would then be generated in your project directory, containing the Solidity +file `plonk_vk.sol`. It can be deployed on any EVM blockchain acting as a verifier smart contract. + +> **Note:** It is possible to compile verifier contracts of Noir programs for other smart contract +> platforms as long as the proving backend supplies an implementation. +> +> Barretenberg, the default proving backend for Nargo, supports compilation of verifier contracts in +> Solidity only for the time being. + +## Verify + +To verify a proof using the Solidity verifier contract, call the `verify` function with the +following signature: + +```solidity +function verify(bytes calldata _proof, bytes32[] calldata _publicInputs) external view returns (bool) +``` + +You can see an example of how the `verify` function is called in the example zk voting application [here](https://github.com/noir-lang/noir-examples/blob/33e598c257e2402ea3a6b68dd4c5ad492bce1b0a/foundry-voting/src/zkVote.sol#L35): + +```solidity +function castVote(bytes calldata proof, uint proposalId, uint vote, bytes32 nullifierHash) public returns (bool) { + // ... + bytes32[] memory publicInputs = new bytes32[](4); + publicInputs[0] = merkleRoot; + publicInputs[1] = bytes32(proposalId); + publicInputs[2] = bytes32(vote); + publicInputs[3] = nullifierHash; + require(verifier.verify(proof, publicInputs), "Invalid proof"); +``` + +### Public Inputs + +:::tip + +A circuit doesn't have the concept of a return value. Return values are just syntactic sugar in +Noir. + +Under the hood, the return value is passed as an input to the circuit and is checked at the end of +the circuit program. + +::: + +The verifier contract uses the output (return) value of a Noir program as a public input. So if you +have the following function + +```rust +fn main( + // Public inputs + pubkey_x: pub Field, + pubkey_y: pub Field, + // Private inputs + priv_key: Field, +) -> pub Field +``` + +then `verify` in `plonk_vk.sol` will expect 3 public inputs. Passing two inputs will result in an +error like `Reason: PUBLIC_INPUT_COUNT_INVALID(3, 2)`. + +In this case the 3 inputs to `verify` would be ordered as `[pubkey_x, pubkey_y, return]`. + +#### Struct inputs + +Consider the following program: + +```rust +struct Type1 { + val1: Field, + val2: Field, +} + +struct Nested { + t1: Type1, + is_true: bool, +} + +fn main(x: pub Field, nested: pub Nested, y: pub Field) { + //... +} +``` + +Structs will be flattened so that the array of inputs is 1-dimensional array. The order of these inputs would be flattened to: `[x, nested.t1.val1, nested.t1.val2, nested.is_true, y]` + +## Noir for EVM chains + +You can currently deploy the Solidity verifier contracts to most EVM compatible chains. EVM chains that have been tested and are known to work include: + +- Optimism +- Arbitrum +- Polygon PoS +- Scroll +- Celo + +Other EVM chains should work, but have not been tested directly by our team. If you test any other chains, please open a PR on this page to update the list. See [this doc](https://github.com/noir-lang/noir-starter/tree/main/with-foundry#testing-on-chain) for more info about testing verifier contracts on different EVM chains. + +### Unsupported chains + +Unfortunately not all "EVM" chains are supported. + +**zkSync** and the **Polygon zkEVM** do _not_ currently support proof verification via Solidity verifier contracts. They are missing the bn256 precompile contract that the verifier contract requires. Once these chains support this precompile, they may work. diff --git a/noir/docs/versioned_docs/version-v0.17.0/nargo/04_language_server.md b/noir/docs/versioned_docs/version-v0.17.0/nargo/04_language_server.md new file mode 100644 index 00000000000..48c01465f6e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/nargo/04_language_server.md @@ -0,0 +1,42 @@ +--- +title: Language Server +description: Learn about the Noir Language Server, how to install the components, and configuration that may be required. +keywords: [Nargo, Language Server, LSP, VSCode, Visual Studio Code] +--- + +This section helps you install and configure the Noir Language Server. + +The Language Server Protocol (LSP) has two components, the [Server](#language-server) and the [Client](#language-client). Below we describe each in the context of Noir. + +## Language Server + +The Server component is provided by the Nargo command line tool that you installed at the beginning of this guide. +As long as Nargo is installed and you've used it to run other commands in this guide, it should be good to go! + +If you'd like to verify that the `nargo lsp` command is available, you can run `nargo --help` and look for `lsp` in the list of commands. If you see it, you're using a version of Noir with LSP support. + +## Language Client + +The Client component is usually an editor plugin that launches the Server. It communicates LSP messages between the editor and the Server. For example, when you save a file, the Client will alert the Server, so it can try to compile the project and report any errors. + +Currently, Noir provides a Language Client for Visual Studio Code via the [vscode-noir](https://github.com/noir-lang/vscode-noir) extension. You can install it via the [Visual Studio Marketplace](https://marketplace.visualstudio.com/items?itemName=noir-lang.vscode-noir). + +> **Note:** Noir's Language Server Protocol support currently assumes users' VSCode workspace root to be the same as users' Noir project root (i.e. where Nargo.toml lies). +> +> If LSP features seem to be missing / malfunctioning, make sure you are opening your Noir project directly (instead of as a sub-folder) in your VSCode instance. + +When your language server is running correctly and the VSCode plugin is installed, you should see handy codelens buttons for compilation, measuring circuit size, execution, and tests: + +![Compile and Execute](@site/static/img/codelens_compile_execute.png) +![Run test](@site/static/img/codelens_run_test.png) + +You should also see your tests in the `testing` panel: + +![Testing panel](@site/static/img/codelens_testing_panel.png) + +### Configuration + +- **Noir: Enable LSP** - If checked, the extension will launch the Language Server via `nargo lsp` and communicate with it. +- **Noir: Nargo Flags** - Additional flags may be specified if you require them to be added when the extension calls `nargo lsp`. +- **Noir: Nargo Path** - An absolute path to a Nargo binary with the `lsp` command. This may be useful if Nargo is not within the `PATH` of your editor. +- **Noir > Trace: Server** - Setting this to `"messages"` or `"verbose"` will log LSP messages between the Client and Server. Useful for debugging. diff --git a/noir/docs/versioned_docs/version-v0.17.0/noir_js/getting_started/01_tiny_noir_app.md b/noir/docs/versioned_docs/version-v0.17.0/noir_js/getting_started/01_tiny_noir_app.md new file mode 100644 index 00000000000..6955f7a1e64 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/noir_js/getting_started/01_tiny_noir_app.md @@ -0,0 +1,256 @@ +--- +title: End-to-end +description: Learn how to setup a new app that uses Noir to generate and verify zero-knowledge SNARK proofs in a typescript or javascript environment +keywords: [how to, guide, javascript, typescript, noir, barretenberg, zero-knowledge, proofs] +--- + +NoirJS works both on the browser and on the server, and works for both ESM and CJS module systems. In this page, we will learn how can we write a simple test and a simple web app to verify the standard Noir example. + +## Before we start + +:::note + +Feel free to use whatever versions, just keep in mind that Nargo and the NoirJS packages are meant to be in sync. For example, Nargo 0.18.x matches `noir_js@0.18.x`, etc. + +In this guide, we will be pinned to 0.17.0. + +::: + +Make sure you have Node installed on your machine by opening a terminal and executing `node --version`. If you don't see a version, you should install [node](https://github.com/nvm-sh/nvm). You can also use `yarn` if you prefer that package manager over npm (which comes with node). + +First of all, follow the the [Nargo guide](../../getting_started/00_nargo_installation.md) to install nargo version 0.17.0 and create a new project with `nargo new circuit`. Once there, `cd` into the `circuit` folder. You should then be able to compile your circuit into `json` format and see it inside the `target` folder: + +```bash +nargo compile +``` + +Your folder structure should look like: + +```tree +. +└── circuit + ├── Nargo.toml + ├── src + │ └── main.nr + └── target + └── circuit.json +``` + +## Starting a new project + +Go back to the previous folder and start a new project by running run `npm init`. You can configure your project or just leave the defaults, and see a `package.json` appear in your root folder. + +## Installing dependencies + +We'll need two `npm` packages. These packages will provide us the methods we need to run and verify proofs: + +```bash +npm i @noir-lang/backend_barretenberg@^0.17.0 @noir-lang/noir_js@^0.17.0 +``` + +To serve our page, we can use a build tool such as `vite`. Because we're gonna use some `wasm` files, we need to install a plugin as well. Run: + +```bash +npm i --save-dev vite rollup-plugin-copy +``` + +Since we're on the dependency world, we may as well define a nice starting script. Vite makes it easy. Just open `package.json`, find the block "scripts" and add this just below the line with `"test" : "echo......."`: + +```json + "start": "vite --open" +``` + +If you want do build a static website, you can also add some build and preview scripts: + +```json + "build": "vite build", + "preview": "vite preview" +``` + +## Vite plugins + +Vite is great, but support from `wasm` doesn't work out-of-the-box. We're gonna write a quick plugin and use another one. Just copy and paste this into a file named `vite.config.js`. You don't need to understand it, just trust me bro. + +```js +import { defineConfig } from 'vite'; +import copy from 'rollup-plugin-copy'; +import fs from 'fs'; +import path from 'path'; + +const wasmContentTypePlugin = { + name: 'wasm-content-type-plugin', + configureServer(server) { + server.middlewares.use(async (req, res, next) => { + if (req.url.endsWith('.wasm')) { + res.setHeader('Content-Type', 'application/wasm'); + const newPath = req.url.replace('deps', 'dist'); + const targetPath = path.join(__dirname, newPath); + const wasmContent = fs.readFileSync(targetPath); + return res.end(wasmContent); + } + next(); + }); + }, +}; + +export default defineConfig(({ command }) => { + if (command === 'serve') { + return { + plugins: [ + copy({ + targets: [{ src: 'node_modules/**/*.wasm', dest: 'node_modules/.vite/dist' }], + copySync: true, + hook: 'buildStart', + }), + command === 'serve' ? wasmContentTypePlugin : [], + ], + }; + } + + return {}; +}); +``` + +## HTML + +Here's the simplest HTML with some terrible UI. Create a file called `index.html` and paste this: + +```html + + + + + + +

Very basic Noir app

+
+

Logs

+

Proof

+
+ + +``` + +## Some good old vanilla Javascript + +Create a new file `app.js`, which is where our javascript code will live. Let's start with this code inside: + +```js +document.addEventListener('DOMContentLoaded', async () => { + // here's where the magic happens +}); + +function display(container, msg) { + const c = document.getElementById(container); + const p = document.createElement('p'); + p.textContent = msg; + c.appendChild(p); +} +``` + +We can manipulate our website with this little function, so we can see our website working. + +## Adding Noir + +If you come from the previous page, your folder structure should look like this: + +```tree +├── app.js +├── circuit +│ ├── Nargo.toml +│ ├── src +│ │ └── main.nr +│ └── target +│ └── circuit.json +├── index.html +├── package.json +└── vite.config.js +``` + +You'll see other files and folders showing up (like `package-lock.json`, `yarn.lock`, `node_modules`) but you shouldn't have to care about those. + +## Importing our dependencies + +We're starting with the good stuff now. At the top of the new javascript file, import the packages: + +```ts +import { BarretenbergBackend } from '@noir-lang/backend_barretenberg'; +import { Noir } from '@noir-lang/noir_js'; +``` + +We also need to import the `circuit` JSON file we created. If you have the suggested folder structure, you can add this line: + +```ts +import circuit from './circuit/target/circuit.json'; +``` + +## Write code + +:::note + +We're gonna be adding code inside the `document.addEventListener...etc` block: + +```js +// forget stuff here +document.addEventListener('DOMContentLoaded', async () => { + // here's where the magic happens +}); +// forget stuff here +``` + +::: + +Our dependencies exported two classes: `BarretenbergBackend` and `Noir`. Let's `init` them and add some logs, just to flex: + +```ts +const backend = new BarretenbergBackend(circuit); +const noir = new Noir(circuit, backend); +``` + +## Proving + +Now we're ready to prove stuff! Let's feed some inputs to our circuit and calculate the proof: + +```js +const input = { x: 1, y: 2 }; +display('logs', 'Generating proof... ⌛'); +const proof = await noir.generateFinalProof(input); +display('logs', 'Generating proof... ✅'); +display('results', proof.proof); +``` + +You're probably eager to see stuff happening, so go and run your app now! + +From your terminal, run `npm start` (or `yarn start`). If it doesn't open a browser for you, just visit `localhost:5173`. On a modern laptop, proof will generate in less than 100ms, and you'll see this: + +![Getting Started 0](@site/static/img/noir_getting_started_1.png) + +If you're human, you shouldn't be able to understand anything on the "proof" box. That's OK. We like you, human. + +In any case, this means your proof was generated! But you shouldn't trust me just yet. Add these lines to see it being verified: + +```js +display('logs', 'Verifying proof... ⌛'); +const verification = await noir.verifyFinalProof(proof); +if (verification) display('logs', 'Verifying proof... ✅'); +``` + +By saving, your app will refresh and here's our complete Tiny Noir App! + +## Further Reading + +You can see how noirjs is used in a full stack Next.js hardhat application in the [noir-starter repo here](https://github.com/noir-lang/noir-starter/tree/main/next-hardhat). The example shows how to calculate a proof in the browser and verify it with a deployed Solidity verifier contract from noirjs. + +You should also check out the more advanced examples in the [noir-examples repo](https://github.com/noir-lang/noir-examples), where you'll find reference usage for some cool apps. diff --git a/noir/docs/versioned_docs/version-v0.17.0/noir_js/noir_js.md b/noir/docs/versioned_docs/version-v0.17.0/noir_js/noir_js.md new file mode 100644 index 00000000000..23ea550e156 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/noir_js/noir_js.md @@ -0,0 +1,36 @@ +--- +title: NoirJS +description: Interact with Noir in Typescript or Javascript +keywords: [Noir project, javascript, typescript, node.js, browser, react] +--- + +NoirJS is a TypeScript library that make it easy to use Noir on your dapp, webapp, Node.js server, website, etc. + +A typical workflow would be composed of two major elements: + +- NoirJS +- Proving backend of choice's JavaScript package + + + +To install NoirJS, install Node.js if you have not already and run this in your JavaScript project: + +```bash +npm i @noir-lang/noir_js +``` + +## Proving backend + +Since Noir is backend agnostic, you can instantiate NoirJS without any backend (i.e. to execute a function). But for proving, you would have to instantiate NoirJS with any of the supported backends through their own `js` interface. + +### Barretenberg + +Aztec Labs maintains the `barretenberg` proving backend, which you can instantiate and make use of alongside NoirJS. It is also the default proving backend installed and used with Nargo, the Noir CLI tool. + +To install its JavaScript library, run this in your project: + +```bash +npm i @noir-lang/backend_barretenberg +``` + +For more details on how to instantiate and use the libraries, refer to the [Full Noir App Guide](./getting_started/01_tiny_noir_app.md) and [Reference](./reference/01_noirjs.md) sections. diff --git a/noir/docs/versioned_docs/version-v0.17.0/noir_js/reference/01_noirjs.md b/noir/docs/versioned_docs/version-v0.17.0/noir_js/reference/01_noirjs.md new file mode 100644 index 00000000000..d9e5a0c6115 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/noir_js/reference/01_noirjs.md @@ -0,0 +1,143 @@ +--- +title: Noir +description: Reference to noir_js library and the Noir class +keywords: [Noir project, javascript, typescript, node.js, browser, react, class, reference] +--- + +## Table of Contents + +- [constructor](#constructor) +- [init](#init) +- [generateFinalProof](#generatefinalproof) +- [verifyFinalProof](#verifyfinalproof) + +## `constructor` + +The `constructor` is a method used to create and initialize objects created within the `Noir` class. In the `Noir` class constructor, you need to pass two parameters: `circuit` and `backend`. + +### Syntax + +```js +constructor(circuit, backend); +``` + +### Parameters + +| Parameter | Type | Description | +| --------- | ------ | ------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `circuit` | Object | A circuit represented in a `json` format, containing the ABI and bytecode. Typically obtained by running [`nargo compile`](../../nargo/01_commands.md) | +| `backend` | Object | A backend instance, before initialization. | + +### Usage + +```js +const noir = new Noir(circuit, backend); +``` + +## `init` + +This async method should be called after class instantiation. It will run processes on the ACVM, instantiate your backend, etc. + +### Syntax + +```js +async init() +``` + +### Parameters + +This method takes no parameters + +### Usage + +```js +await noirInstance.init(); +``` + +## `execute` + +This async method allows to execute a circuit to get its witness and return value. [`generateFinalProof`](#generatefinalproof) calls it for you, but you can call it directly (i.e. to feed directly to a backend, or to get the return value). + +### Syntax + +```js +async execute(inputs) +``` + +### Parameters + +| Parameter | Type | Description | +| --------- | ------ | ------------------------------------------------ | +| `inputs` | Object | An object containing the inputs to your circuit. | + +### Returns + +| Return value | Type | Description | +| ------------ | --------------------- | --------------------------------------------------- | +| `witness` | Promise | The witness | +| `returnValue` | Promise | The return value | + +### Usage + +```js +const { witness, returnValue } = await noir.execute(inputs) +``` + +## `generateFinalProof` + +This async method generates a witness and a proof given an object as input. + +### Syntax + +```js +async generateFinalproof(input) +``` + +### Parameters + +| Parameter | Type | Description | +| --------- | ------ | ------------------------------------------------ | +| `input` | Object | An object containing the inputs to your circuit. | + +### Returns + +| Return value | Type | Description | +| ------------ | --------------------- | --------------------------------------------------- | +| `proof` | Promise | An array with the byte representation of the proof. | + +### Usage + +```js +// consider the Standard Noir Example given with nargo init +const input = { x: 1, y: 2 }; +noirInstance.generateProof(input); +``` + +## `verifyFinalProof` + +This async method instantiates the verification key and verifies your proof. + +### Syntax + +```js +async verifyFinalProof(proof) +``` + +### Parameters + +| Parameter | Type | Description | +| --------- | ---------- | --------------------------------------------------------------------------------------------- | +| `proof` | Uint8Array | The Uint8Array representation of your proof, usually obtained by calling `generateFinalProof` | + +### Returns + +| Return value | Type | Description | +| ------------ | ------------------ | -------------------------------------------- | +| `verified` | Promise | A boolean for whether the proof was verified | + +### Usage + +```js +const proof = noirInstance.generateProof(input); +noirInstance.verifyFinalProof(proof); +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/noir_js/reference/02_bb_backend.md b/noir/docs/versioned_docs/version-v0.17.0/noir_js/reference/02_bb_backend.md new file mode 100644 index 00000000000..21c2ff32b57 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/noir_js/reference/02_bb_backend.md @@ -0,0 +1,272 @@ +--- +title: BarretenbergBackend +description: Reference documentation for the barretenberg_backend library and the BarretenbergBackend class +keywords: + [ + BarretenbergBackend, + Barretenberg, + javascript, + typescript, + node.js, + browser, + class, + reference, + noir_js, + ] +--- + +## Table of Contents + +- [constructor](#constructor) +- [generateFinalProof](#generatefinalproof) +- [generateIntermediateProof](#generateintermediateproof) +- [generateProof](#generateproof) +- [generateIntermediateProofArtifacts](#generateintermediateproofartifacts) +- [verifyFinalProof](#verifyfinalproof) +- [verifyIntermediateProof](#verifyintermediateproof) +- [verifyProof](#verifyproof) +- [destroy](#destroy) + +## `constructor` + +The `constructor` is a method used to create and initialize objects created within the `BarretenbergBackend` class. In this class, you should pass at least one argument for the `circuit`. + +### Syntax + +```js +constructor(acirCircuit, (numberOfThreads = 1)); +``` + +### Parameters + +| Parameter | Type | Description | +| ----------------- | ----------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `acirCircuit` | Object | A circuit represented in a `json` format, containing the ABI and bytecode Tipically obtained by running [`nargo compile`](../../nargo/01_commands.md). This is the same circuit expected to be passed to [the Noir class](01_noirjs.md) | +| `numberOfThreads` | Number (optional) | The number of threads to be used by the backend. Defaults to 1. | + +### Usage + +```js +const backend = new BarretenbergBackend(acirCircuit); +``` + +## `generateFinalProof` + +An async wrapper around the [generateProof](#generateproof) method that passes a `false` flag. Usually called by the Noir class. + +### Syntax + +```js +async generateFinalProof(decompressedWitness) +``` + +### Parameters + +| Parameter | Type | Description | +| --------------------- | ------ | -------------------------------------------------------- | +| `decompressedWitness` | Object | The decompressed witness for generating the final proof. | + +### Returns + +| Return value | Type | Description | +| ------------ | -------------------- | --------------------------------------------------------- | +| `proof` | Promise | An array with the byte representation of the final proof. | + +### Usage + +```js +const finalProof = await backend.generateFinalProof(decompressedWitness); +``` + +## `generateIntermediateProof` + +An async wrapper around the [generateProof](#generateproof) method that passes a `true` flag. It's not currently being used by the Noir class, but developers can call this method directly to use Noir's recursive features. + +### Syntax + +```js +async generateIntermediateProof(witness) +``` + +### Parameters + +| Parameter | Type | Description | +| --------- | ------ | -------------------------------------------------- | +| `witness` | Object | The witness for generating the intermediate proof. | + +### Returns + +| Return value | Type | Description | +| ------------ | -------------------- | --------------------------------------------------------------- | +| `proof` | Promise | An array with the byte representation of the intermediate proof | + +### Usage + +```js +const intermediateProof = await backend.generateIntermediateProof(witness); +``` + +## `generateProof` + +This async method generates a proof. Takes the witness generated by ACVM, and a boolean that evaluates to `true` when the proof _is_ meant to be verified in another circuit. Not currently used by the Noir class. + +### Syntax + +```js +async generateProof(decompressedWitness, makeEasyToVerifyInCircuit) +``` + +### Parameters + +| Parameter | Type | Description | +| --------------------------- | ------- | ---------------------------------------------------------------------------------------------- | +| `decompressedWitness` | Object | The decompressed witness for generating the proof. | +| `makeEasyToVerifyInCircuit` | Boolean | A flag indicating whether to generate proof components for easy verification within a circuit. | + +### Returns + +| Return value | Type | Description | +| ------------ | -------------------- | -------------------------------------------------- | +| `proof` | Promise | An array with the byte representation of the proof | + +### Usage + +```js +const proof = await backend.generateProof(decompressedWitness, makeEasyToVerifyInCircuit); +``` + +## `generateIntermediateProofArtifacts` + +This async method returns the artifacts needed to verify the intermediate proof in another circuit. It's not currently being used by the Noir class, but developers can call this method directly to use Noir's recursive features. + +### Syntax + +```js +async generateIntermediateProofArtifacts(proof, numOfPublicInputs = 0) +``` + +### Parameters + +| Parameter | Type | Description | +| ------------------- | ----------------- | ---------------------------------------------------------------- | +| `proof` | Object | The proof object. | +| `numOfPublicInputs` | Number (optional) | The number of public inputs in the inner proof, defaulting to 0. | + +### Returns + +| Return value | Type | Description | +| --------------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `proofAsFields` | string[] | An array of strings with the hexadecimal representation of the [Fields](../../language_concepts/data_types/00_fields.md) that make up a proof | +| `vkAsFields` | string[] | An array of strings with the hexadecimal representation of the [Fields](../../language_concepts/data_types/00_fields.md) that make up the verification key | +| `vkHash` | string | A pedersen hash of the verification key | + +### Usage + +```js +const artifacts = await backend.generateIntermediateProofArtifacts(proof, numOfPublicInputs); +``` + +## `verifyFinalProof` + +An async wrapper around [verifyProof](#verifyproof) that sets the `false` flag. Usually called by the Noir class. + +### Syntax + +```js +async verifyFinalProof(proof) +``` + +### Parameters + +| Parameter | Type | Description | +| --------- | ------ | --------------------------- | +| `proof` | Object | The proof object to verify. | + +### Returns + +| Return value | Type | Description | +| ------------ | ------------------ | -------------------------------------------- | +| `verified` | Promise | A boolean for whether the proof was verified | + +### Usage + +```js +const isValidFinal = await backend.verifyFinalProof(proof); +``` + +## `verifyIntermediateProof` + +An async wrapper around [verifyProof](#verifyproof) that sets the `true` flag. It's not currently being used by the Noir class, but developers can call this method directly to use Noir's recursive features. + +### Syntax + +```js +async verifyIntermediateProof(proof) +``` + +### Parameters + +| Parameter | Type | Description | +| --------- | ------ | ---------------------------------------- | +| `proof` | Object | The intermediate proof object to verify. | + +### Returns + +| Return value | Type | Description | +| ------------ | ------------------ | -------------------------------------------- | +| `verified` | Promise | A boolean for whether the proof was verified | + +### Usage + +```js +const isValidIntermediate = await backend.verifyIntermediateProof(proof); +``` + +## `verifyProof` + +This async method verifies a proof. Takes the proof, and a boolean that evaluates to `true` when the proof is intermediate. + +### Syntax + +```js +async verifyProof(proof, makeEasyToVerifyInCircuit) +``` + +### Parameters + +| Parameter | Type | Description | +| --------------------------- | ------- | ------------------------------------------------------------ | +| `proof` | Object | The proof object to verify | +| `makeEasyToVerifyInCircuit` | Boolean | A flag indicating whether the proof is intermediate or final | + +### Returns + +| Parameter | Type | Description | +| ---------- | ------------------ | -------------------------------------------- | +| `verified` | Promise\ | A boolean for whether the proof was verified | + +### Usage + +```js +const isValid = await backend.verifyProof(proof, makeEasyToVerifyInCircuit); +``` + +## `destroy` + +This method destroys the resources allocated in the [instantiate](#instantiate) method. Noir doesn't currently call this method, but it's highly recommended that developers do so in order to save resources. + +### Syntax + +```js +async destroy() +``` + +### Parameters + +This method takes no parameters. + +### Usage + +```js +await backend.destroy(); +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/standard_library/black_box_fns.md b/noir/docs/versioned_docs/version-v0.17.0/standard_library/black_box_fns.md new file mode 100644 index 00000000000..c758846b688 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/standard_library/black_box_fns.md @@ -0,0 +1,45 @@ +--- +title: Black Box Functions +description: Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. +keywords: [noir, black box functions] +--- + +Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. This makes certain zk-snark unfriendly computations cheaper than if they were implemented in Noir. + +:::warning + +It is likely that not all backends will support a particular black box function. + +::: + +Because it is not guaranteed that all backends will support black box functions, it is possible that certain Noir programs won't compile against a particular backend if they use an unsupported black box function. It is possible to fallback to less efficient implementations written in Noir/ACIR in some cases. + +Black box functions are specified with the `#[foreign(black_box_fn)]` attribute. For example, the SHA256 function in the Noir [source code](https://github.com/noir-lang/noir/blob/v0.5.1/noir_stdlib/src/hash.nr) looks like: + +```rust +#[foreign(sha256)] +fn sha256(_input : [u8; N]) -> [u8; 32] {} +``` + +## Function list + +Here is a list of the current black box functions that are supported by UltraPlonk: + +- AES +- [SHA256](./cryptographic_primitives/hashes#sha256) +- [Schnorr signature verification](./cryptographic_primitives/schnorr) +- [Blake2s](./cryptographic_primitives/hashes#blake2s) +- [Pedersen](./cryptographic_primitives/hashes#pedersen) +- [HashToField128Security](./cryptographic_primitives/hashes#hash_to_field) +- [ECDSA signature verification](./cryptographic_primitives/ecdsa_sig_verification) +- [Fixed base scalar multiplication](./cryptographic_primitives/scalar) +- [Compute merkle root](./merkle_trees#compute_merkle_root) +- AND +- XOR +- RANGE +- [Keccak256](./cryptographic_primitives/hashes#keccak256) +- [Recursive proof verification](./recursion) + +Most black box functions are included as part of the Noir standard library, however `AND`, `XOR` and `RANGE` are used as part of the Noir language syntax. For instance, using the bitwise operator `&` will invoke the `AND` black box function. To ensure compatibility across backends, the ACVM has fallback implementations of `AND`, `XOR` and `RANGE` defined in its standard library which it can seamlessly fallback to if the backend doesn't support them. + +You can view the black box functions defined in the ACVM code [here](https://github.com/noir-lang/acvm/blob/acir-v0.12.0/acir/src/circuit/black_box_functions.rs). diff --git a/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives.md b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives.md new file mode 100644 index 00000000000..2df4f929474 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives.md @@ -0,0 +1,14 @@ +--- +title: Cryptographic primitives in Noir +description: + Learn about the cryptographic primitives ready to use for any Noir project +keywords: + [ + cryptographic primitives, + Noir project, + ] +--- + +The Noir team is progressively adding new cryptographic primitives to the standard library. Reach out for news or if you would be interested in adding more of these calculations in Noir. + +Some methods are available thanks to the Aztec backend, not being performed using Noir. When using other backends, these methods may or may not be supplied. diff --git a/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/00_hashes.mdx b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/00_hashes.mdx new file mode 100644 index 00000000000..2cc3cd81e4e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/00_hashes.mdx @@ -0,0 +1,146 @@ +--- +title: Hash methods +description: + Learn about the cryptographic primitives ready to use for any Noir project, including sha256, + blake2s, pedersen, mimc_bn254 and mimc +keywords: + [cryptographic primitives, Noir project, sha256, blake2s, pedersen, mimc_bn254, mimc, hash] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## sha256 + +Given an array of bytes, returns the resulting sha256 hash. + +```rust +fn sha256(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::sha256(x); +} +``` + + + +## blake2s + +Given an array of bytes, returns an array with the Blake2 hash + +```rust +fn blake2s(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::blake2s(x); +} +``` + + + +## pedersen + +Given an array of Fields, returns the Pedersen hash. + +```rust +fn pedersen(_input : [Field]) -> [Field; 2] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::pedersen(x); +} +``` + + + +## keccak256 + +Given an array of bytes (`u8`), returns the resulting keccak hash as an array of 32 bytes +(`[u8; 32]`). Specify a message_size to hash only the first `message_size` bytes +of the input. + +```rust +fn keccak256(_input : [u8; N], _message_size: u32) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let message_size = 4; + let hash = std::hash::keccak256(x, message_size); +} +``` + + + +## poseidon + +Given an array of Fields, returns a new Field with the Poseidon Hash. Mind that you need to specify +how many inputs are there to your Poseidon function. + +```rust +// example for hash_1, hash_2 accepts an array of length 2, etc +fn hash_1(input: [Field; 1]) -> Field +``` + +example: + +```rust +fn main() +{ + let hash1 = std::hash::poseidon::bn254::hash_2([1, 2]); + assert(hash1 == 0x115cc0f5e7d690413df64c6b9662e9cf2a3617f2743245519e19607a4417189a); +} +``` + +## mimc_bn254 and mimc + +`mimc_bn254` is `mimc`, but with hardcoded parameters for the BN254 curve. You can use it by +providing an array of Fields, and it returns a Field with the hash. You can use the `mimc` method if +you're willing to input your own constants: + +```rust +fn mimc(x: Field, k: Field, constants: [Field; N], exp : Field) -> Field +``` + +otherwise, use the `mimc_bn254` method: + +```rust +fn mimc_bn254(array: [Field; N]) -> Field +``` + +example: + +```rust + +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::mimc_bn254(x); +} +``` + +## hash_to_field + +```rust +fn hash_to_field(_input : [Field; N]) -> Field {} +``` + +Calculates the `blake2s` hash of the inputs and returns the hash modulo the field modulus to return +a value which can be represented as a `Field`. + + diff --git a/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/01_scalar.mdx b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/01_scalar.mdx new file mode 100644 index 00000000000..c7eed820a80 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/01_scalar.mdx @@ -0,0 +1,27 @@ +--- +title: Scalar multiplication +description: See how you can perform scalar multiplications over a fixed base in Noir +keywords: [cryptographic primitives, Noir project, scalar multiplication] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## scalar_mul::fixed_base_embedded_curve + +Performs scalar multiplication over the embedded curve whose coordinates are defined by the +configured noir field. For the BN254 scalar field, this is BabyJubJub or Grumpkin. + +```rust +fn fixed_base_embedded_curve(_input : Field) -> [Field; 2] +``` + +example + +```rust +fn main(x : Field) { + let scal = std::scalar_mul::fixed_base_embedded_curve(x); + std::println(scal); +} +``` + + diff --git a/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/02_schnorr.mdx b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/02_schnorr.mdx new file mode 100644 index 00000000000..c184ce28120 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/02_schnorr.mdx @@ -0,0 +1,37 @@ +--- +title: Schnorr Signatures +description: Learn how you can verify Schnorr signatures using Noir +keywords: [cryptographic primitives, Noir project, schnorr, signatures] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## schnorr::verify_signature + +Verifier for Schnorr signatures over the embedded curve (for BN254 it is Grumpkin). + +```rust +fn verify_signature(_public_key_x: Field, _public_key_y: Field, _signature: [u8; 64], _message: [u8]) -> bool +``` + +where `_signature` can be generated like so using the npm package +[@noir-lang/barretenberg](https://www.npmjs.com/package/@noir-lang/barretenberg) + +```js +const { BarretenbergWasm } = require('@noir-lang/barretenberg/dest/wasm'); +const { Schnorr } = require('@noir-lang/barretenberg/dest/crypto/schnorr'); + +... + +const barretenberg = await BarretenbergWasm.new(); +const schnorr = new Schnorr(barretenberg); +const pubKey = schnorr.computePublicKey(privateKey); +const message = ... +const signature = Array.from( + schnorr.constructSignature(hash, privateKey).toBuffer() +); + +... +``` + + diff --git a/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx new file mode 100644 index 00000000000..72bce984821 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx @@ -0,0 +1,45 @@ +--- +title: ECDSA Signature Verification +description: Learn about the cryptographic primitives regarding ECDSA over the secp256k1 and secp256r1 curves +keywords: [cryptographic primitives, Noir project, ecdsa, secp256k1, secp256r1, signatures] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +Noir supports ECDSA signatures verification over the secp256k1 and secp256r1 curves. + +## ecdsa_secp256k1::verify_signature + +Verifier for ECDSA Secp256k1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> bool +``` + +example: + +```rust +fn main(hashed_message : [u8;32], pub_key_x : [u8;32], pub_key_y : [u8;32], signature : [u8;64]) { + let valid_signature = std::ecdsa_secp256k1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} +``` + +## ecdsa_secp256r1::verify_signature + +Verifier for ECDSA Secp256r1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> bool +``` + +example: + +```rust +fn main(hashed_message : [u8;32], pub_key_x : [u8;32], pub_key_y : [u8;32], signature : [u8;64]) { + let valid_signature = std::ecdsa_secp256r1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} +``` + + diff --git a/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/04_ec_primitives.md b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/04_ec_primitives.md new file mode 100644 index 00000000000..6e6b19b6861 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/04_ec_primitives.md @@ -0,0 +1,101 @@ +--- +title: Elliptic Curve Primitives +keywords: [cryptographic primitives, Noir project] +--- + +Data structures and methods on them that allow you to carry out computations involving elliptic +curves over the (mathematical) field corresponding to `Field`. For the field currently at our +disposal, applications would involve a curve embedded in BN254, e.g. the +[Baby Jubjub curve](https://eips.ethereum.org/EIPS/eip-2494). + +## Data structures + +### Elliptic curve configurations + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Curve`), i.e. the specific elliptic +curve you want to use, which would be specified using any one of the methods +`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::new` which take the coefficients in the +defining equation together with a generator point as parameters. You can find more detail in the +comments in +[`noir_stdlib/src/ec.nr`](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr), but +the gist of it is that the elliptic curves of interest are usually expressed in one of the standard +forms implemented here (Twisted Edwards, Montgomery and Short Weierstraß), and in addition to that, +you could choose to use `affine` coordinates (Cartesian coordinates - the usual (x,y) - possibly +together with a point at infinity) or `curvegroup` coordinates (some form of projective coordinates +requiring more coordinates but allowing for more efficient implementations of elliptic curve +operations). Conversions between all of these forms are provided, and under the hood these +conversions are done whenever an operation is more efficient in a different representation (or a +mixed coordinate representation is employed). + +### Points + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Point`), i.e. points lying on the +elliptic curve. For a curve configuration `c` and a point `p`, it may be checked that `p` +does indeed lie on `c` by calling `c.contains(p1)`. + +## Methods + +(given a choice of curve representation, e.g. use `std::ec::tecurve::affine::Curve` and use +`std::ec::tecurve::affine::Point`) + +- The **zero element** is given by `Point::zero()`, and we can verify whether a point `p: Point` is + zero by calling `p.is_zero()`. +- **Equality**: Points `p1: Point` and `p2: Point` may be checked for equality by calling + `p1.eq(p2)`. +- **Addition**: For `c: Curve` and points `p1: Point` and `p2: Point` on the curve, adding these two + points is accomplished by calling `c.add(p1,p2)`. +- **Negation**: For a point `p: Point`, `p.negate()` is its negation. +- **Subtraction**: For `c` and `p1`, `p2` as above, subtracting `p2` from `p1` is accomplished by + calling `c.subtract(p1,p2)`. +- **Scalar multiplication**: For `c` as above, `p: Point` a point on the curve and `n: Field`, + scalar multiplication is given by `c.mul(n,p)`. If instead `n :: [u1; N]`, i.e. `n` is a bit + array, the `bit_mul` method may be used instead: `c.bit_mul(n,p)` +- **Multi-scalar multiplication**: For `c` as above and arrays `n: [Field; N]` and `p: [Point; N]`, + multi-scalar multiplication is given by `c.msm(n,p)`. +- **Coordinate representation conversions**: The `into_group` method converts a point or curve + configuration in the affine representation to one in the CurveGroup representation, and + `into_affine` goes in the other direction. +- **Curve representation conversions**: `tecurve` and `montcurve` curves and points are equivalent + and may be converted between one another by calling `into_montcurve` or `into_tecurve` on their + configurations or points. `swcurve` is more general and a curve c of one of the other two types + may be converted to this representation by calling `c.into_swcurve()`, whereas a point `p` lying + on the curve given by `c` may be mapped to its corresponding `swcurve` point by calling + `c.map_into_swcurve(p)`. +- **Map-to-curve methods**: The Elligator 2 method of mapping a field element `n: Field` into a + `tecurve` or `montcurve` with configuration `c` may be called as `c.elligator2_map(n)`. For all of + the curve configurations, the SWU map-to-curve method may be called as `c.swu_map(z,n)`, where + `z: Field` depends on `Field` and `c` and must be chosen by the user (the conditions it needs to + satisfy are specified in the comments + [here](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr)). + +## Examples + +The +[ec_baby_jubjub test](https://github.com/noir-lang/noir/blob/master/crates/nargo_cli/tests/test_data/ec_baby_jubjub/src/main.nr) +illustrates all of the above primitives on various forms of the Baby Jubjub curve. A couple of more +interesting examples in Noir would be: + +Public-key cryptography: Given an elliptic curve and a 'base point' on it, determine the public key +from the private key. This is a matter of using scalar multiplication. In the case of Baby Jubjub, +for example, this code would do: + +```rust +use dep::std::ec::tecurve::affine::{Curve, Point}; + +fn bjj_pub_key(priv_key: Field) -> Point +{ + + let bjj = Curve::new(168700, 168696, G::new(995203441582195749578291179787384436505546430278305826713579947235728471134,5472060717959818805561601436314318772137091100104008585924551046643952123905)); + + let base_pt = Point::new(5299619240641551281634865583518297030282874472190772894086521144482721001553, 16950150798460657717958625567821834550301663161624707787222815936182638968203); + + bjj.mul(priv_key,base_pt) +} +``` + +This would come in handy in a Merkle proof. + +- EdDSA signature verification: This is a matter of combining these primitives with a suitable hash + function. See + [feat(stdlib): EdDSA sig verification noir#1136](https://github.com/noir-lang/noir/pull/1136) for + the case of Baby Jubjub and the Poseidon hash function. diff --git a/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/05_eddsa.mdx b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/05_eddsa.mdx new file mode 100644 index 00000000000..9a5beb55ee9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/standard_library/cryptographic_primitives/05_eddsa.mdx @@ -0,0 +1,17 @@ +--- +title: EdDSA Verification +description: Learn about the cryptographic primitives regarding EdDSA +keywords: [cryptographic primitives, Noir project, eddsa, signatures] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## eddsa::eddsa_poseidon_verify + +Verifier for EdDSA signatures + +```rust +fn eddsa_poseidon_verify(public_key_x : Field, public_key_y : Field, signature_s: Field, signature_r8_x: Field, signature_r8_y: Field, message: Field) -> bool +``` + + diff --git a/noir/docs/versioned_docs/version-v0.17.0/standard_library/logging.md b/noir/docs/versioned_docs/version-v0.17.0/standard_library/logging.md new file mode 100644 index 00000000000..7e2fd9b9aff --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/standard_library/logging.md @@ -0,0 +1,62 @@ +--- +title: Logging +description: + Learn how to use the println statement for debugging in Noir with this tutorial. Understand the + basics of logging in Noir and how to implement it in your code. +keywords: + [ + noir logging, + println statement, + debugging in noir, + noir std library, + logging tutorial, + basic logging in noir, + noir logging implementation, + noir debugging techniques, + rust, + ] +--- + +The standard library provides a familiar `println` statement you can use. Despite being a limited +implementation of rust's `println!` macro, this construct can be useful for debugging. + +You can print the output of println statements in your Noir code by using the `nargo execute` command or the `--show-output` flag when using `nargo test` (provided there are println statements in your tests). + +It is recommended to use `nargo execute` if you want to debug failing constrains with `println` statements. This is due to every input in a test being a constant rather than a witness, so we issue an error during compilation while we only print during execution (which comes after compilation). `println` will not work for failed constraints caught at compile time. + +The `println` statement is unconstrained, so it works for outputting integers, fields, strings, and even structs or expressions. For example: + +```rust +use dep::std; + +struct Person { + age : Field, + height : Field, +} + +fn main(age : Field, height : Field) { + let person = Person { age : age, height : height }; + std::println(person); + std::println(age + height); + std::println("Hello world!"); +} + +``` + +You can print multiple different types in the same statement and string as well as a new "fmtstr" type. A `fmtstr` can be specified in the same way as a normal string it just should be prepended with an "f" character: + +```rust + let fmt_str = f"i: {i}, j: {j}"; + std::println(fmt_str); + + let s = myStruct { y: x, x: y }; + std::println(s); + + std::println(f"i: {i}, s: {s}"); + + std::println(x); + std::println([x, y]); + + let foo = fooStruct { my_struct: s, foo: 15 }; + std::println(f"s: {s}, foo: {foo}"); +``` diff --git a/noir/docs/versioned_docs/version-v0.17.0/standard_library/merkle_trees.md b/noir/docs/versioned_docs/version-v0.17.0/standard_library/merkle_trees.md new file mode 100644 index 00000000000..9761105f4f2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/standard_library/merkle_trees.md @@ -0,0 +1,58 @@ +--- +title: Merkle Trees +description: Learn about Merkle Trees in Noir with this tutorial. Explore the basics of computing a merkle root using a proof, with examples. +keywords: + [ + Merkle trees in Noir, + Noir programming language, + check membership, + computing root from leaf, + Noir Merkle tree implementation, + Merkle tree tutorial, + Merkle tree code examples, + Noir libraries, + pedersen hash., + ] +--- + +## compute_merkle_root + +Returns the root of the tree from the provided leaf and its hash path, using a [Pedersen hash](cryptographic_primitives/00_hashes.mdx#pedersen). + +```rust +fn compute_merkle_root(leaf : Field, index : Field, hash_path: [Field]) -> Field +``` + +example: + +```rust +/** + // these values are for this example only + index = "0" + priv_key = "0x000000000000000000000000000000000000000000000000000000616c696365" + secret = "0x1929ea3ab8d9106a899386883d9428f8256cfedb3c4f6b66bf4aa4d28a79988f" + note_hash_path = [ + "0x1e61bdae0f027b1b2159e1f9d3f8d00fa668a952dddd822fda80dc745d6f65cc", + "0x0e4223f3925f98934393c74975142bd73079ab0621f4ee133cee050a3c194f1a", + "0x2fd7bb412155bf8693a3bd2a3e7581a679c95c68a052f835dddca85fa1569a40" + ] + */ +fn main(index: Field, priv_key: Field, secret: Field, note_hash_path: [Field; 3]) { + + let pubkey = std::scalar_mul::fixed_base_embedded_curve(priv_key); + let pubkey_x = pubkey[0]; + let pubkey_y = pubkey[1]; + let note_commitment = std::hash::pedersen([pubkey_x, pubkey_y, secret]); + + let root = std::merkle::compute_merkle_root(note_commitment[0], index, note_hash_path); + std::println(root); +} +``` + +To check merkle tree membership: + +1. Include a merkle root as a program input. +2. Compute the merkle root of a given leaf, index and hash path. +3. Assert the merkle roots are equal. + +For more info about merkle trees, see the Wikipedia [page](https://en.wikipedia.org/wiki/Merkle_tree). diff --git a/noir/docs/versioned_docs/version-v0.17.0/standard_library/options.md b/noir/docs/versioned_docs/version-v0.17.0/standard_library/options.md new file mode 100644 index 00000000000..3d3139fb98b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/standard_library/options.md @@ -0,0 +1,99 @@ +--- +title: Option Type +--- + +The `Option` type is a way to express that a value might be present (`Some(T))` or absent (`None`). It's a safer way to handle potential absence of values, compared to using nulls in many other languages. + +```rust +struct Option { + None, + Some(T), +} +``` + +You can import the Option type into your Noir program like so: + +```rust +use dep::std::option::Option; + +fn main() { + let none = Option::none(); + let some = Option::some(3); +} +``` + +See [this test](https://github.com/noir-lang/noir/blob/5cbfb9c4a06c8865c98ff2b594464b037d821a5c/crates/nargo_cli/tests/test_data/option/src/main.nr) for a more comprehensive set of examples of each of the methods described below. + +## Methods + +### none + +Constructs a none value. + +### some + +Constructs a some wrapper around a given value. + +### is_none + +Returns true if the Option is None. + +### is_some + +Returns true of the Option is Some. + +### unwrap + +Asserts `self.is_some()` and returns the wrapped value. + +### unwrap_unchecked + +Returns the inner value without asserting `self.is_some()`. This method can be useful within an if condition when we already know that `option.is_some()`. If the option is None, there is no guarantee what value will be returned, only that it will be of type T for an `Option`. + +### unwrap_or + +Returns the wrapped value if `self.is_some()`. Otherwise, returns the given default value. + +### unwrap_or_else + +Returns the wrapped value if `self.is_some()`. Otherwise, calls the given function to return a default value. + +### map + +If self is `Some(x)`, this returns `Some(f(x))`. Otherwise, this returns `None`. + +### map_or + +If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns the given default value. + +### map_or_else + +If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns `default()`. + +### and + +Returns None if self is None. Otherwise, this returns `other`. + +### and_then + +If self is None, this returns None. Otherwise, this calls the given function with the Some value contained within self, and returns the result of that call. In some languages this function is called `flat_map` or `bind`. + +### or + +If self is Some, return self. Otherwise, return `other`. + +### or_else + +If self is Some, return self. Otherwise, return `default()`. + +### xor + +If only one of the two Options is Some, return that option. Otherwise, if both options are Some or both are None, None is returned. + +### filter + +Returns `Some(x)` if self is `Some(x)` and `predicate(x)` is true. Otherwise, this returns `None`. + +### flatten + +Flattens an `Option>` into a `Option`. This returns `None` if the outer Option is None. Otherwise, this returns the inner Option. diff --git a/noir/docs/versioned_docs/version-v0.17.0/standard_library/recursion.md b/noir/docs/versioned_docs/version-v0.17.0/standard_library/recursion.md new file mode 100644 index 00000000000..ff4c63acaa7 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/standard_library/recursion.md @@ -0,0 +1,96 @@ +--- +title: Recursive Proofs +description: Learn about how to write recursive proofs in Noir. +keywords: [recursion, recursive proofs, verification_key, aggregation object, verify_proof] +--- + +Noir supports recursively verifying proofs, meaning you verify the proof of a Noir program in another Noir program. This enables creating proofs of arbitrary size by doing step-wise verification of smaller components of a large proof. + +The `verify_proof` function takes a verification key, proof and public inputs for a zk program, as well as a key hash and an input aggregation object. The key hash is used to check the validity of the verification key and the input aggregation object is required by some proving systems. The `verify_proof` function returns an output aggregation object that can then be fed into future iterations of the proof verification if required. + +```rust +#[foreign(verify_proof)] +fn verify_proof(_verification_key : [Field], _proof : [Field], _public_input : Field, _key_hash : Field, _input_aggregation_object : [Field]) -> [Field] {} +``` + +:::info + +This is a black box function. Read [this section](./black_box_fns) to learn more about black box functions in Noir. + +::: + +## Aggregation Object + +The purpose of the input aggregation object is a little less clear though (and the output aggregation object that is returned from the `std::verify_proof` method). Recursive zkSNARK schemes do not necessarily "verify a proof" in the sense that you expect a true or false to be spit out by the verifier. Rather an aggregation object is built over the public inputs. In the case of PLONK the recursive aggregation object is two G1 points (expressed as 16 witness values). The final verifier (in our case this is most often the smart contract verifier) has to be aware of this aggregation object to execute a pairing and check the validity of these points (thus completing the recursive verification). + +So for example in this circuit: + +```rust +use dep::std; + +fn main( + verification_key : [Field; 114], + proof : [Field; 94], + public_inputs : [Field; 1], + key_hash : Field, + input_aggregation_object : [Field; 16], + proof_b : [Field; 94], +) -> pub [Field; 16] { + let output_aggregation_object_a = std::verify_proof( + verification_key, + proof, + public_inputs, + key_hash, + input_aggregation_object + ); + + let output_aggregation_object = std::verify_proof( + verification_key, + proof_b, + public_inputs, + key_hash, + output_aggregation_object_a + ); + + let mut output = [0; 16]; + for i in 0..16 { + output[i] = output_aggregation_object[i]; + } + output +} +``` + +In this example we have a circuit, that generates proofs A and B, that is being verified in circuit C. Assuming that the proof being passed in is not already a recursive proof, the `input_aggregation_object` will be all zeros. It will then generate an `output_aggregation_object`. This blob of data then becomes the `input_aggregation_object` of the next recursive aggregation we wish to compute. We can see here as the same public inputs, verification key, and key hash are used that we are verifying two proofs generated from the same circuit in this single circuit. `std::verify_proof` returns a `[Field]` because the size of an aggregation object is proof system dependent--in barretenberg, aggregation objects are two G1 points, while in Halo2, the aggregation object is a list of G1 points that is log the circuit size. So for the final step we convert the slice into an array of size 16 because we are generating proofs using UltraPlonk. + +## Parameters + +### `verification_key` + +The verification key for the zk program that is being verified. + +### `proof` + +The proof for the zk program that is being verified. + +### `public_inputs` + +These represent the public inputs of the proof we are verifying. They should be checked against in the circuit after construction of a new aggregation state. + +### `key_hash` + +A key hash is used to check the validity of the verification key. The circuit implementing this opcode can use this hash to ensure that the key provided to the circuit matches the key produced by the circuit creator. + +### `input_aggregation_object` + +An aggregation object is blob of data that the top-level verifier must run some proof system specific algorithm on to complete verification. The size is proof system specific and will be set by the backend integrating this opcode. The input aggregation object is only not `None` when we are verifying a previous recursive aggregation in the current circuit. If this is the first recursive aggregation there is no input aggregation object. It is left to the backend to determine how to handle when there is no input aggregation object. + +## Return value + +### `output_aggregation_object` + +This is the result of a recursive aggregation and is what will be fed into the next verifier. +The next verifier can either perform a final verification (returning true or false) or perform another recursive aggregation where this output aggregation object will be the input aggregation object of the next recursive aggregation. + +## Example + +You can see an example of how to do recursive proofs in [this example recursion demo repo](https://github.com/noir-lang/noir-examples/tree/master/recursion). diff --git a/noir/docs/versioned_docs/version-v0.17.0/standard_library/zeroed.md b/noir/docs/versioned_docs/version-v0.17.0/standard_library/zeroed.md new file mode 100644 index 00000000000..97dab02dac2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.17.0/standard_library/zeroed.md @@ -0,0 +1,25 @@ +--- +title: Zeroed Function +description: + The zeroed function returns a zeroed value of any type. +keywords: + [ + zeroed + ] +--- + +Implements `fn zeroed() -> T` to return a zeroed value of any type. This function is generally unsafe to use as the zeroed bit pattern is not guaranteed to be valid for all types. It can however, be useful in cases when the value is guaranteed not to be used such as in a BoundedVec library implementing a growable vector, up to a certain length, backed by an array. The array can be initialized with zeroed values which are guaranteed to be inaccessible until the vector is pushed to. Similarly, enumerations in noir can be implemented using this method by providing zeroed values for the unused variants. + +You can access the function at `std::unsafe::zeroed`. + +This function currently supports the following types: + +- Field +- Bool +- Uint +- Array +- String +- Tuple +- Function + +Using it on other types could result in unexpected behavior. diff --git a/noir/docs/versioned_docs/version-v0.19.1/examples/merkle-proof.mdx b/noir/docs/versioned_docs/version-v0.19.1/examples/merkle-proof.mdx new file mode 100644 index 00000000000..832fb4bb55e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/examples/merkle-proof.mdx @@ -0,0 +1,48 @@ +--- +title: Merkle Proof Membership +description: + Learn how to use merkle membership proof in Noir to prove that a given leaf is a member of a + merkle tree with a specified root, at a given index. +keywords: + [merkle proof, merkle membership proof, Noir, rust, hash function, Pedersen, sha256, merkle tree] +--- + +Let's walk through an example of a merkle membership proof in Noir that proves that a given leaf is +in a merkle tree. + +```rust +use dep::std; + +fn main(message : [Field; 62], index : Field, hashpath : [Field; 40], root : Field) { + let leaf = std::hash::hash_to_field(message); + let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); + assert(merkle_root == root); +} + +``` + +The message is hashed using `hash_to_field`. The specific hash function that is being used is chosen +by the backend. The only requirement is that this hash function can heuristically be used as a +random oracle. If only collision resistance is needed, then one can call `std::hash::pedersen_hash` +instead. + +```rust +let leaf = std::hash::hash_to_field(message); +``` + +The leaf is then passed to a compute_merkle_root function with the root, index and hashpath. The returned root can then be asserted to be the same as the provided root. + +```rust +let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); +assert (merkle_root == root); +``` + +> **Note:** It is possible to re-implement the merkle tree implementation without standard library. +> However, for most usecases, it is enough. In general, the standard library will always opt to be +> as conservative as possible, while striking a balance with efficiency. + +An example, the merkle membership proof, only requires a hash function that has collision +resistance, hence a hash function like Pedersen is allowed, which in most cases is more efficient +than the even more conservative sha256. + +[View an example on the starter repo](https://github.com/noir-lang/noir-examples/blob/3ea09545cabfa464124ec2f3ea8e60c608abe6df/stealthdrop/circuits/src/main.nr#L20) diff --git a/noir/docs/versioned_docs/version-v0.19.1/getting_started/00_nargo_installation.md b/noir/docs/versioned_docs/version-v0.19.1/getting_started/00_nargo_installation.md new file mode 100644 index 00000000000..725c5f4d373 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/getting_started/00_nargo_installation.md @@ -0,0 +1,249 @@ +--- +title: Nargo Installation +description: + nargo is a command line tool for interacting with Noir programs (e.g. compiling, proving, + verifying and more). Learn how to install and use Nargo for your projects with this comprehensive + guide. +keywords: [Nargo, command line tool, Noir programs, installation guide, how to use Nargo] +--- + +`nargo` is a command line tool for interacting with Noir programs (e.g. compiling, proving, +verifying and more). + +Alternatively, the interactions can also be performed in [NoirJS](../noir_js/noir_js.md). + +### UltraPlonk + +Nargo versions <0.5.0 of `aztec_backend` and `aztec_wasm_backend` are based on the TurboPlonk +version of Aztec Backend, which lacks efficient implementations of useful primitives (e.g. Keccak256 in 18k constraints, ECDSA verification in 36k constraints) that the UltraPlonk version offers. + +## Installation + +There are four approaches for installing Nargo: + +- [Option 1: Noirup](#option-1-noirup) +- [Option 2: Binaries](#option-2-binaries) +- [Option 3: Compile from Source](#option-3-compile-from-source) +- [Option 4: WSL for Windows](#option-4-wsl-for-windows) + +Optionally you can also install [Noir VS Code extension] for syntax highlighting. + +### Option 1: Noirup + +If you're on OSX or Linux, the easiest way to start using Noir and Nargo is via noirup. Just open a +terminal and run: + +```bash +curl -L https://raw.githubusercontent.com/noir-lang/noirup/main/install | bash +``` + +Close the terminal, open another one, and run + +```bash +noirup +``` + +Done, you should have the latest version working. You can check with `nargo --version`. + +You can also install nightlies, specific versions +or branches, check out the [noirup repository](https://github.com/noir-lang/noirup) for more +information. + +#### GitHub Actions + +You can use `noirup` with GitHub Actions for CI/CD and automated testing. It is as simple as +installing `noirup` and running tests in your GitHub Action `yml` file. + +See the +[config file](https://github.com/TomAFrench/noir-hashes/blob/master/.github/workflows/noir.yml) in +this repo containing hash functions in Noir for an example. + +#### Nightly versions + +To install the nightly version of Noir (updated daily) run: + +```bash +noirup -n +``` + +### Option 2: Binaries + +See [GitHub Releases](https://github.com/noir-lang/noir/releases) for the latest and previous +platform specific binaries. + +#### Step 1 + +Paste and run the following in the terminal to extract and install the binary: + +> **macOS / Linux:** If you are prompted with `Permission denied` when running commands, prepend +> `sudo` and re-run it. + +##### macOS (Apple Silicon) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-aarch64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### macOS (Intel) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### Linux (Bash) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-unknown-linux-gnu.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -C $HOME/.nargo/bin/ && \ +echo -e '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.bashrc && \ +source ~/.bashrc +``` + +#### Step 2 + +Check if the installation was successful by running `nargo --help`. + +> **macOS:** If you are prompted with an OS alert, right-click and open the _nargo_ executable from +> Finder. Close the new terminal popped up and `nargo` should now be accessible. + +For a successful installation, you should see something similar to the following after running the +command: + +```sh +$ nargo --help + +Noir's package manager + +Usage: nargo + +Commands: + check Checks the constraint system for errors + codegen-verifier Generates a Solidity verifier smart contract for the program + compile Compile the program and its secret execution trace into ACIR format + new Create a new binary project + execute Executes a circuit to calculate its return value + prove Create proof for this program. The proof is returned as a hex encoded string + verify Given a proof and a program, verify whether the proof is valid + test Run the tests for this program + gates Counts the occurrences of different gates in circuit + help Print this message or the help of the given subcommand(s) +``` + +### Option 3: Compile from Source + +Due to the large number of native dependencies, Noir projects uses [Nix](https://nixos.org/) and [direnv](https://direnv.net/) to streamline the development experience. It helps mitigating ssues commonly associated with dependency management, such as conflicts between required package versions for different projects (often referred to as "dependency hell"). + +Combined with direnv, which automatically sets or unsets environment variables based on the directory, it further simplifies the development process by seamlessly integrating with the developer's shell, facilitating an efficient and reliable workflow for managing and deploying Noir projects with multiple dependencies. + +#### Setting up your environment + +For the best experience, please follow these instructions to setup your environment: + +1. Install Nix following [their guide](https://nixos.org/download.html) for your operating system. +2. Create the file `~/.config/nix/nix.conf` with the contents: + +```ini +experimental-features = nix-command +extra-experimental-features = flakes +``` + +3. Install direnv into your Nix profile by running: + +```sh +nix profile install nixpkgs#direnv +``` + +4. Add direnv to your shell following [their guide](https://direnv.net/docs/hook.html). + 1. For bash or zshell, add `eval "$(direnv hook bash)"` or `eval "$(direnv hook zsh)"` to your ~/.bashrc or ~/.zshrc file, respectively. +5. Restart your shell. + +#### Shell & editor experience + +Now that your environment is set up, you can get to work on the project. + +1. Clone the repository, such as: + +```sh +git clone git@github.com:noir-lang/noir +``` + +> Replacing `noir` with whichever repository you want to work on. + +2. Navigate to the directory: + +```sh +cd noir +``` + +> Replacing `noir` with whichever repository you cloned. + +3. You should see a **direnv error** because projects aren't allowed by default. Make sure you've reviewed and trust our `.envrc` file, then you need to run: + +```sh +direnv allow +``` + +4. Now, wait awhile for all the native dependencies to be built. This will take some time and direnv will warn you that it is taking a long time, but we just need to let it run. + +5. Once you are presented with your prompt again, you can start your editor within the project directory (we recommend [VSCode](https://code.visualstudio.com/)): + +```sh +code . +``` + +6. (Recommended) When launching VSCode for the first time, you should be prompted to install our recommended plugins. We highly recommend installing these for the best development experience. + +#### Building and testing + +Assuming you are using `direnv` to populate your environment, building and testing the project can be done +with the typical `cargo build`, `cargo test`, and `cargo clippy` commands. You'll notice that the `cargo` version matches the version we specify in `rust-toolchain.toml`, which is 1.71.1 at the time of this writing. + +If you want to build the entire project in an isolated sandbox, you can use Nix commands: + +1. `nix build .` (or `nix build . -L` for verbose output) to build the project in a Nix sandbox. +2. `nix flake check` (or `nix flake check -L` for verbose output) to run clippy and tests in a Nix sandbox. + +#### Without `direnv` + +If you have hesitations with using direnv, you can launch a subshell with `nix develop` and then launch your editor from within the subshell. However, if VSCode was already launched in the project directory, the environment won't be updated. + +Advanced: If you aren't using direnv nor launching your editor within the subshell, you can try to install Barretenberg and other global dependencies the package needs. This is an advanced workflow and likely won't receive support! + +### Option 4: WSL (for Windows) + +The default backend for Noir (Barretenberg) doesn't provide Windows binaries at this time. For that reason, Noir cannot be installed nativerly. However, it is available by using Windows Subsystem for Linux (WSL). + +Step 1: Follow the instructions [here](https://learn.microsoft.com/en-us/windows/wsl/install) to install and run WSL. + +step 2: Follow the [Noirup instructions](#option-1-noirup). + +## Uninstalling Nargo + +### Noirup + +If you installed Noir with `noirup`, you can uninstall Noir by removing the files in `~/.nargo`, `~/nargo` and `~/noir_cache`. + +```bash +rm -r ~/.nargo +rm -r ~/nargo +rm -r ~/noir_cache +``` + +### Nix + +If you installed Noir with Nix or from source, you can remove the binary located at `~/.nix-profile/bin/nargo`. + +```bash +rm ~/.nix-profile/bin/nargo +``` + +[noir vs code extension]: https://marketplace.visualstudio.com/items?itemName=noir-lang.vscode-noir diff --git a/noir/docs/versioned_docs/version-v0.19.1/getting_started/01_hello_world.md b/noir/docs/versioned_docs/version-v0.19.1/getting_started/01_hello_world.md new file mode 100644 index 00000000000..8b4416beba1 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/getting_started/01_hello_world.md @@ -0,0 +1,147 @@ +--- +title: Create A Project +description: + Learn how to create and verify your first Noir program using Nargo, a programming language for + zero-knowledge proofs. +keywords: + [ + Nargo, + Noir, + zero-knowledge proofs, + programming language, + create Noir program, + verify Noir program, + step-by-step guide, + ] +--- + +Now that we have installed Nargo, it is time to make our first hello world program! + +## Create a Project Directory + +Noir code can live anywhere on your computer. Let us create a _projects_ folder in the home +directory to house our Noir programs. + +For Linux, macOS, and Windows PowerShell, create the directory and change directory into it by +running: + +```sh +mkdir ~/projects +cd ~/projects +``` + +For Windows CMD, run: + +```sh +> mkdir "%USERPROFILE%\projects" +> cd /d "%USERPROFILE%\projects" +``` + +## Create Our First Nargo Project + +Now that we are in the projects directory, create a new Nargo project by running: + +```sh +nargo new hello_world +``` + +> **Note:** `hello_world` can be any arbitrary project name, we are simply using `hello_world` for +> demonstration. +> +> In production, the common practice is to name the project folder as `circuits` for better +> identifiability when sitting alongside other folders in the codebase (e.g. `contracts`, `scripts`, +> `test`). + +A `hello_world` folder would be created. Similar to Rust, the folder houses _src/main.nr_ and +_Nargo.toml_ that contains the source code and environmental options of your Noir program +respectively. + +### Intro to Noir Syntax + +Let us take a closer look at _main.nr_. The default _main.nr_ generated should look like this: + +```rust +fn main(x : Field, y : pub Field) { + assert(x != y); +} +``` + +The first line of the program specifies the program's inputs: + +```rust +x : Field, y : pub Field +``` + +Program inputs in Noir are private by default (e.g. `x`), but can be labeled public using the +keyword `pub` (e.g. `y`). To learn more about private and public values, check the +[Data Types](../language_concepts/data_types) section. + +The next line of the program specifies its body: + +```rust +assert(x != y); +``` + +The Noir syntax `assert` can be interpreted as something similar to constraints in other zk-contract languages. + +For more Noir syntax, check the [Language Concepts](../language_concepts/comments) chapter. + +## Build In/Output Files + +Change directory into _hello_world_ and build in/output files for your Noir program by running: + +```sh +cd hello_world +nargo check +``` + +Two additional files would be generated in your project directory: + +_Prover.toml_ houses input values, and _Verifier.toml_ houses public values. + +## Prove Our Noir Program + +Now that the project is set up, we can create a proof of correct execution on our Noir program. + +Fill in input values for execution in the _Prover.toml_ file. For example: + +```toml +x = "1" +y = "2" +``` + +Prove the valid execution of your Noir program: + +```sh +nargo prove +``` + +A new folder _proofs_ would then be generated in your project directory, containing the proof file +`.proof`, where the project name is defined in Nargo.toml. + +The _Verifier.toml_ file would also be updated with the public values computed from program +execution (in this case the value of `y`): + +```toml +y = "0x0000000000000000000000000000000000000000000000000000000000000002" +``` + +> **Note:** Values in _Verifier.toml_ are computed as 32-byte hex values. + +## Verify Our Noir Program + +Once a proof is generated, we can verify correct execution of our Noir program by verifying the +proof file. + +Verify your proof by running: + +```sh +nargo verify +``` + +The verification will complete in silence if it is successful. If it fails, it will log the +corresponding error instead. + +Congratulations, you have now created and verified a proof for your very first Noir program! + +In the [next section](breakdown), we will go into more detail on each step performed. diff --git a/noir/docs/versioned_docs/version-v0.19.1/getting_started/02_breakdown.md b/noir/docs/versioned_docs/version-v0.19.1/getting_started/02_breakdown.md new file mode 100644 index 00000000000..9a17f5d6360 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/getting_started/02_breakdown.md @@ -0,0 +1,198 @@ +--- +title: Project Breakdown +description: + Learn about the anatomy of a Nargo project, including the purpose of the Prover and Verifier TOML + files, and how to prove and verify your program. +keywords: + [Nargo, Nargo project, Prover.toml, Verifier.toml, proof verification, private asset transfer] +--- + +This section breaks down our hello world program in section _1.2_. We elaborate on the project +structure and what the `prove` and `verify` commands did in the previous section. + +## Anatomy of a Nargo Project + +Upon creating a new project with `nargo new` and building the in/output files with `nargo check` +commands, you would get a minimal Nargo project of the following structure: + + - src + - Prover.toml + - Verifier.toml + - Nargo.toml + +The source directory _src_ holds the source code for your Noir program. By default only a _main.nr_ +file will be generated within it. + +### Prover.toml + +_Prover.toml_ is used for specifying the input values for executing and proving the program. You can specify `toml` files with different names by using the `--prover-name` or `-p` flags, see the [Prover](#provertoml) section below. Optionally you may specify expected output values for prove-time checking as well. + +### Verifier.toml + +_Verifier.toml_ contains public in/output values computed when executing the Noir program. + +### Nargo.toml + +_Nargo.toml_ contains the environmental options of your project. It contains a "package" section and a "dependencies" section. + +Example Nargo.toml: + +```toml +[package] +name = "noirstarter" +type = "bin" +authors = ["Alice"] +compiler_version = "0.9.0" +description = "Getting started with Noir" +entry = "circuit/main.nr" +license = "MIT" + +[dependencies] +ecrecover = {tag = "v0.9.0", git = "https://github.com/colinnielsen/ecrecover-noir.git"} +``` + +Nargo.toml for a [workspace](../modules_packages_crates/workspaces) will look a bit different. For example: + +```toml +[workspace] +members = ["crates/a", "crates/b"] +default-member = "crates/a" +``` + +#### Package section + +The package section requires a number of fields including: + +- `name` (**required**) - the name of the package +- `type` (**required**) - can be "bin", "lib", or "contract" to specify whether its a binary, library or Aztec contract +- `authors` (optional) - authors of the project +- `compiler_version` - specifies the version of the compiler to use. This is enforced by the compiler and follow's [Rust's versioning](https://doc.rust-lang.org/cargo/reference/manifest.html#the-version-field), so a `compiler_version = 0.18.0` will enforce Nargo version 0.18.0, `compiler_version = ^0.18.0` will enforce anything above 0.18.0 but below 0.19.0, etc. For more information, see how [Rust handles these operators](https://docs.rs/semver/latest/semver/enum.Op.html) +- `description` (optional) +- `entry` (optional) - a relative filepath to use as the entry point into your package (overrides the default of `src/lib.nr` or `src/main.nr`) +- `backend` (optional) +- `license` (optional) + +#### Dependencies section + +This is where you will specify any dependencies for your project. See the [Dependencies page](../modules_packages_crates/dependencies) for more info. + +`./proofs/` and `./contract/` directories will not be immediately visible until you create a proof or +verifier contract respectively. + +### main.nr + +The _main.nr_ file contains a `main` method, this method is the entry point into your Noir program. + +In our sample program, _main.nr_ looks like this: + +```rust +fn main(x : Field, y : Field) { + assert(x != y); +} +``` + +The parameters `x` and `y` can be seen as the API for the program and must be supplied by the +prover. Since neither `x` nor `y` is marked as public, the verifier does not supply any inputs, when +verifying the proof. + +The prover supplies the values for `x` and `y` in the _Prover.toml_ file. + +As for the program body, `assert` ensures the satisfaction of the condition (e.g. `x != y`) is +constrained by the proof of the execution of said program (i.e. if the condition was not met, the +verifier would reject the proof as an invalid proof). + +### Prover.toml + +The _Prover.toml_ file is a file which the prover uses to supply his witness values(both private and +public). + +In our hello world program the _Prover.toml_ file looks like this: + +```toml +x = "1" +y = "2" +``` + +When the command `nargo prove` is executed, two processes happen: + +1. Noir creates a proof that `x` which holds the value of `1` and `y` which holds the value of `2` + is not equal. This not equal constraint is due to the line `assert(x != y)`. + +2. Noir creates and stores the proof of this statement in the _proofs_ directory in a file called your-project.proof. So if your project is named "private_voting" (defined in the project Nargo.toml), the proof will be saved at `./proofs/private_voting.proof`. Opening this file will display the proof in hex format. + +#### Arrays of Structs + +The following code shows how to pass an array of structs to a Noir program to generate a proof. + +```rust +// main.nr +struct Foo { + bar: Field, + baz: Field, +} + +fn main(foos: [Foo; 3]) -> pub Field { + foos[2].bar + foos[2].baz +} +``` + +Prover.toml: + +```toml +[[foos]] # foos[0] +bar = 0 +baz = 0 + +[[foos]] # foos[1] +bar = 0 +baz = 0 + +[[foos]] # foos[2] +bar = 1 +baz = 2 +``` + +#### Custom toml files + +You can specify a `toml` file with a different name to use for proving by using the `--prover-name` or `-p` flags. + +This command looks for proof inputs in the default **Prover.toml** and generates the proof and saves it at `./proofs/.proof`: + +```bash +nargo prove +``` + +This command looks for proof inputs in the custom **OtherProver.toml** and generates proof and saves it at `./proofs/.proof`: + +```bash +nargo prove -p OtherProver +``` + +## Verifying a Proof + +When the command `nargo verify` is executed, two processes happen: + +1. Noir checks in the _proofs_ directory for a proof file with the project name (eg. test_project.proof) + +2. If that file is found, the proof's validity is checked + +> **Note:** The validity of the proof is linked to the current Noir program; if the program is +> changed and the verifier verifies the proof, it will fail because the proof is not valid for the +> _modified_ Noir program. + +In production, the prover and the verifier are usually two separate entities. A prover would +retrieve the necessary inputs, execute the Noir program, generate a proof and pass it to the +verifier. The verifier would then retrieve the public inputs from usually external sources and +verifies the validity of the proof against it. + +Take a private asset transfer as an example: + +A user on browser as the prover would retrieve private inputs (e.g. the user's private key) and +public inputs (e.g. the user's encrypted balance on-chain), compute the transfer, generate a proof +and submit it to the verifier smart contract. + +The verifier contract would then draw the user's encrypted balance directly from the blockchain and +verify the proof submitted against it. If the verification passes, additional functions in the +verifier contract could trigger (e.g. approve the asset transfer). + +Now that you understand the concepts, you'll probably want some editor feedback while you are writing more complex code. diff --git a/noir/docs/versioned_docs/version-v0.19.1/index.md b/noir/docs/versioned_docs/version-v0.19.1/index.md new file mode 100644 index 00000000000..75e1abf2932 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/index.md @@ -0,0 +1,100 @@ +--- +title: Introducing Noir +description: + Learn about the public alpha release of Noir, a domain specific language heavily influenced by + Rust that compiles to an intermediate language which can be compiled to an arithmetic circuit or a + rank-1 constraint system. +keywords: + [ + Noir, + Domain Specific Language, + Rust, + Intermediate Language, + Arithmetic Circuit, + Rank-1 Constraint System, + Ethereum Developers, + Protocol Developers, + Blockchain Developers, + Proving System, + Smart Contract Language, + ] +slug: / +--- + +## What is Noir? + +Noir is a Domain Specific Language for SNARK proving systems. It has been designed to use any ACIR compatible proving system. + +It's design choices are influenced heavily by Rust and focuses on a simple, familiar syntax. + +## Who is Noir for? + +Noir can be used for a variety of purposes. + +### Solidity Developers + +Noir currently includes a command to create a Solidity contract which verifies your Noir program. This will +be modularised in the future; however, as of the alpha, you can use the [`nargo codegen-verifier`](./nargo/commands#nargo-codegen-verifier) command to create +a verifier contract. + +### Protocol Developers + +As a protocol developer, you may not want to use the Aztec backend due to it not being a fit for +your stack, or maybe you simply want to use a different proving system. Since Noir does not compile +to a specific proof system, it is possible for protocol developers to replace the PLONK-based +proving system with a different proving system altogether. + +### Blockchain developers + +As a blockchain developer, you will be constrained by parameters set by your blockchain (for example, the +proving system and smart contract language has been pre-defined). In order for you to use Noir in +your blockchain, a proving system backend and a smart contract interface +must be implemented for it. + +## What's new about Noir? + +Noir is simple and flexible in its design, as it does not compile immediately to a fixed +NP-complete language. Instead, Noir compiles to an intermediate language (ACIR), which itself can be compiled +to an arithmetic circuit (if choosing to target Aztec's barretenberg backend) or a rank-1 constraint system (if choosing to target an R1CS backend like Arkwork's Marlin backend, or others). + +This in itself brings up a few challenges within the design process, but allows one to decouple the programming language completely from the backend. This is similar in theory to LLVM. + +## Current Features + +Compiler: + +- Module System +- For expressions +- Arrays +- Bit Operations +- Binary operations (<, <=, >, >=, +, -, \*, /, %) [See documentation for an extensive list] +- Unsigned integers +- If statements +- Structures and Tuples +- Generics + +ACIR Supported OPCODES: + +- Sha256 +- Blake2s +- Schnorr signature verification +- MerkleMembership +- Pedersen Commitment +- Pedersen Hash +- HashToField + +## Libraries + +Noir does not currently have an official package manager. You can find a list of available Noir libraries in the [awesome-noir repo here](https://github.com/noir-lang/awesome-noir#libraries). + +Some libraries that are available today include: + +- [Standard Library](https://github.com/noir-lang/noir/tree/master/noir_stdlib) - the Noir Standard Library +- [Ethereum Storage Proof Verification](https://github.com/aragonzkresearch/noir-trie-proofs) - a library that contains the primitives necessary for RLP decoding (in the form of look-up table construction) and Ethereum state and storage proof verification (or verification of any trie proof involving 32-byte long keys) +- [BigInt](https://github.com/shuklaayush/noir-bigint) - a library that provides a custom BigUint56 data type, allowing for computations on large unsigned integers +- [ECrecover](https://github.com/colinnielsen/ecrecover-noir/tree/main) - a library to verify an ECDSA signature and return the source Ethereum address +- [Sparse Merkle Tree Verifier](https://github.com/vocdoni/smtverifier-noir/tree/main) - a library for verification of sparse Merkle trees +- [Signed Int](https://github.com/resurgencelabs/signed_int) - a library for accessing a custom Signed Integer data type, allowing access to negative numbers on Noir +- [Fraction](https://github.com/resurgencelabs/fraction) - a library for accessing fractional number data type in Noir, allowing results that aren't whole numbers + +See the section on [dependencies](./modules_packages_crates/dependencies) for more information. diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/01_functions.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/01_functions.md new file mode 100644 index 00000000000..47cdea0cf04 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/01_functions.md @@ -0,0 +1,225 @@ +--- +title: Functions +description: + Learn how to declare functions and methods in Noir, a programming language with Rust semantics. + This guide covers parameter declaration, return types, call expressions, and more. +keywords: [Noir, Rust, functions, methods, parameter declaration, return types, call expressions] +--- + +Functions in Noir follow the same semantics of Rust, though Noir does not support early returns. + +To declare a function the `fn` keyword is used. + +```rust +fn foo() {} +``` + +By default, functions are visible only within the package they are defined. To make them visible outside of that package (for example, as part of a [library](../modules_packages_crates/crates_and_packages.md#libraries)), you should mark them as `pub`: + +```rust +pub fn foo() {} +``` + +You can also restrict the visibility of the function to only the crate it was defined in, by specifying `pub(crate)`: + +```rust +pub(crate) fn foo() {} //foo can only be called within its crate +``` + +All parameters in a function must have a type and all types are known at compile time. The parameter +is pre-pended with a colon and the parameter type. Multiple parameters are separated using a comma. + +```rust +fn foo(x : Field, y : pub Field){} +``` + +The return type of a function can be stated by using the `->` arrow notation. The function below +states that the foo function must return a `Field`. If the function returns no value, then the arrow +is omitted. + +```rust +fn foo(x : Field, y : pub Field) -> Field { + x + y +} +``` + +Note that a `return` keyword is unneeded in this case - the last expression in a function's body is +returned. + +## Main function + +If you're writing a binary, the `main` function is the starting point of your program. You can pass all types of expressions to it, as long as they have a fixed size at compile time: + +```rust +fn main(x : Field) // this is fine: passing a Field +fn main(x : [Field; 2]) // this is also fine: passing a Field with known size at compile-time +fn main(x : (Field, bool)) // 👌: passing a (Field, bool) tuple means size 2 +fn main(x : str<5>) // this is fine, as long as you pass a string of size 5 + +fn main(x : Vec) // can't compile, has variable size +fn main(x : [Field]) // can't compile, has variable size +fn main(....// i think you got it by now +``` + +Keep in mind [tests](../nargo/02_testing.md) don't differentiate between `main` and any other function. The following snippet passes tests, but won't compile or prove: + +```rust +fn main(x : [Field]) { + assert(x[0] == 1); +} + +#[test] +fn test_one() { + main([1, 2]); +} +``` + +```bash +$ nargo test +[testing] Running 1 test functions +[testing] Testing test_one... ok +[testing] All tests passed + +$ nargo check +The application panicked (crashed). +Message: Cannot have variable sized arrays as a parameter to main +``` + +## Call Expressions + +Calling a function in Noir is executed by using the function name and passing in the necessary +arguments. + +Below we show how to call the `foo` function from the `main` function using a call expression: + +```rust +fn main(x : Field, y : Field) { + let z = foo(x); +} + +fn foo(x : Field) -> Field { + x + x +} +``` + +## Methods + +You can define methods in Noir on any struct type in scope. + +```rust +struct MyStruct { + foo: Field, + bar: Field, +} + +impl MyStruct { + fn new(foo: Field) -> MyStruct { + MyStruct { + foo, + bar: 2, + } + } + + fn sum(self) -> Field { + self.foo + self.bar + } +} + +fn main() { + let s = MyStruct::new(40); + assert(s.sum() == 42); +} +``` + +Methods are just syntactic sugar for functions, so if we wanted to we could also call `sum` as +follows: + +```rust +assert(MyStruct::sum(s) == 42); +``` + +It is also possible to specialize which method is chosen depending on the [generic](./06_generics.md) type that is used. In this example, the `foo` function returns different values depending on its type: + +```rust +struct Foo {} + +impl Foo { + fn foo(self) -> Field { 1 } +} + +impl Foo { + fn foo(self) -> Field { 2 } +} + +fn main() { + let f1: Foo = Foo{}; + let f2: Foo = Foo{}; + assert(f1.foo() + f2.foo() == 3); +} +``` + +Also note that impls with the same method name defined in them cannot overlap. For example, if we already have `foo` defined for `Foo` and `Foo` like we do above, we cannot also define `foo` in an `impl Foo` since it would be ambiguous which version of `foo` to choose. + +```rust +// Including this impl in the same project as the above snippet would +// cause an overlapping impls error +impl Foo { + fn foo(self) -> Field { 3 } +} +``` + +## Lambdas + +Lambdas are anonymous functions. They follow the syntax of Rust - `|arg1, arg2, ..., argN| return_expression`. + +```rust +let add_50 = |val| val + 50; +assert(add_50(100) == 150); +``` + +See [Lambdas](./08_lambdas.md) for more details. + +## Attributes + +Attributes are metadata that can be applied to a function, using the following syntax: `#[attribute(value)]`. + +Supported attributes include: + +- **builtin**: the function is implemented by the compiler, for efficiency purposes. +- **deprecated**: mark the function as _deprecated_. Calling the function will generate a warning: `warning: use of deprecated function` +- **field**: Used to enable conditional compilation of code depending on the field size. See below for more details +- **oracle**: mark the function as _oracle_; meaning it is an external unconstrained function, implemented in noir_js. See [Unconstrained](./05_unconstrained.md) and [NoirJS](../noir_js/noir_js.md) for more details. +- **test**: mark the function as unit tests. See [Tests](../nargo/02_testing.md) for more details + +### Field Attribute + +The field attribute defines which field the function is compatible for. The function is conditionally compiled, under the condition that the field attribute matches the Noir native field. +The field can be defined implicitly, by using the name of the elliptic curve usually associated to it - for instance bn254, bls12_381 - or explicitly by using the field (prime) order, in decimal or hexadecimal form. +As a result, it is possible to define multiple versions of a function with each version specialized for a different field attribute. This can be useful when a function requires different parameters depending on the underlying elliptic curve. + +Example: we define the function `foo()` three times below. Once for the default Noir bn254 curve, once for the field $\mathbb F_{23}$, which will normally never be used by Noir, and once again for the bls12_381 curve. + +```rust +#[field(bn254)] +fn foo() -> u32 { + 1 +} + +#[field(23)] +fn foo() -> u32 { + 2 +} + +// This commented code would not compile as foo would be defined twice because it is the same field as bn254 +// #[field(21888242871839275222246405745257275088548364400416034343698204186575808495617)] +// fn foo() -> u32 { +// 2 +// } + +#[field(bls12_381)] +fn foo() -> u32 { + 3 +} +``` + +If the field name is not known to Noir, it will discard the function. Field names are case insensitive. diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/02_control_flow.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/02_control_flow.md new file mode 100644 index 00000000000..a7f85360197 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/02_control_flow.md @@ -0,0 +1,44 @@ +--- +title: Control Flow +description: + Learn how to use loops and if expressions in the Noir programming language. Discover the syntax + and examples for for loops and if-else statements. +keywords: [Noir programming language, loops, for loop, if-else statements, Rust syntax] +--- + +## Loops + +Noir has one kind of loop: the `for` loop. `for` loops allow you to repeat a block of code multiple +times. + +The following block of code between the braces is run 10 times. + +```rust +for i in 0..10 { + // do something +}; +``` + +The index for loops is of type `u64`. + +## If Expressions + +Noir supports `if-else` statements. The syntax is most similar to Rust's where it is not required +for the statement's conditional to be surrounded by parentheses. + +```rust +let a = 0; +let mut x: u32 = 0; + +if a == 0 { + if a != 0 { + x = 6; + } else { + x = 2; + } +} else { + x = 5; + assert(x == 5); +} +assert(x == 2); +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/03_ops.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/03_ops.md new file mode 100644 index 00000000000..da02b126059 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/03_ops.md @@ -0,0 +1,97 @@ +--- +title: Logical Operations +description: + Learn about the supported arithmetic and logical operations in the Noir programming language. + Discover how to perform operations on private input types, integers, and booleans. +keywords: + [ + Noir programming language, + supported operations, + arithmetic operations, + logical operations, + predicate operators, + bitwise operations, + short-circuiting, + backend, + ] +--- + +# Operations + +## Table of Supported Operations + +| Operation | Description | Requirements | +| :-------- | :------------------------------------------------------------: | -------------------------------------: | +| + | Adds two private input types together | Types must be private input | +| - | Subtracts two private input types together | Types must be private input | +| \* | Multiplies two private input types together | Types must be private input | +| / | Divides two private input types together | Types must be private input | +| ^ | XOR two private input types together | Types must be integer | +| & | AND two private input types together | Types must be integer | +| \| | OR two private input types together | Types must be integer | +| << | Left shift an integer by another integer amount | Types must be integer | +| >> | Right shift an integer by another integer amount | Types must be integer | +| ! | Bitwise not of a value | Type must be integer or boolean | +| < | returns a bool if one value is less than the other | Upper bound must have a known bit size | +| <= | returns a bool if one value is less than or equal to the other | Upper bound must have a known bit size | +| > | returns a bool if one value is more than the other | Upper bound must have a known bit size | +| >= | returns a bool if one value is more than or equal to the other | Upper bound must have a known bit size | +| == | returns a bool if one value is equal to the other | Both types must not be constants | +| != | returns a bool if one value is not equal to the other | Both types must not be constants | + +### Predicate Operators + +`<,<=, !=, == , >, >=` are known as predicate/comparison operations because they compare two values. +This differs from the operations such as `+` where the operands are used in _computation_. + +### Bitwise Operations Example + +```rust +fn main(x : Field) { + let y = x as u32; + let z = y & y; +} +``` + +`z` is implicitly constrained to be the result of `y & y`. The `&` operand is used to denote bitwise +`&`. + +> `x & x` would not compile as `x` is a `Field` and not an integer type. + +### Logical Operators + +Noir has no support for the logical operators `||` and `&&`. This is because encoding the +short-circuiting that these operators require can be inefficient for Noir's backend. Instead you can +use the bitwise operators `|` and `&` which operate indentically for booleans, just without the +short-circuiting. + +```rust +let my_val = 5; + +let mut flag = 1; +if (my_val > 6) | (my_val == 0) { + flag = 0; +} +assert(flag == 1); + +if (my_val != 10) & (my_val < 50) { + flag = 0; +} +assert(flag == 0); +``` + +### Shorthand operators + +Noir shorthand operators for most of the above operators, namely `+=, -=, *=, /=, %=, &=, |=, ^=, <<=`, and `>>=`. These allow for more concise syntax. For example: + +```rust +let mut i = 0; +i = i + 1; +``` + +could be written as: + +```rust +let mut i = 0; +i += 1; +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/04_assert.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/04_assert.md new file mode 100644 index 00000000000..7427ec6cc63 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/04_assert.md @@ -0,0 +1,26 @@ +--- +title: Assert Function +description: + Learn about the assert function in Noir, which can be used to explicitly constrain the predicate or + comparison expression that follows to be true, and what happens if the expression is false at + runtime. +keywords: [Noir programming language, assert statement, predicate expression, comparison expression] +--- + +Noir includes a special `assert` function which will explicitly constrain the predicate/comparison +expression that follows to be true. If this expression is false at runtime, the program will fail to +be proven. Example: + +```rust +fn main(x : Field, y : Field) { + assert(x == y); +} +``` + +You can optionally provide a message to be logged when the assertion fails: + +```rust +assert(x == y, "x and y are not equal"); +``` + +> Assertions only work for predicate operations, such as `==`. If there's any ambiguity on the operation, the program will fail to compile. For example, it is unclear if `assert(x + y)` would check for `x + y == 0` or simply would return `true`. diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/05_unconstrained.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/05_unconstrained.md new file mode 100644 index 00000000000..6b621eda3eb --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/05_unconstrained.md @@ -0,0 +1,96 @@ +--- +title: Unconstrained Functions +description: "Learn about what unconstrained functions in Noir are, how to use them and when you'd want to." + +keywords: [Noir programming language, unconstrained, open] +--- + + + +Unconstrained functions are functions which do not constrain any of the included computation and allow for non-determinisitic computation. + +## Why? + +Zero-knowledge (ZK) domain-specific languages (DSL) enable developers to generate ZK proofs from their programs by compiling code down to the constraints of an NP complete language (such as R1CS or PLONKish languages). However, the hard bounds of a constraint system can be very limiting to the functionality of a ZK DSL. + +Enabling a circuit language to perform unconstrained execution is a powerful tool. Said another way, unconstrained execution lets developers generate witnesses from code that does not generate any constraints. Being able to execute logic outside of a circuit is critical for both circuit performance and constructing proofs on information that is external to a circuit. + +Fetching information from somewhere external to a circuit can also be used to enable developers to improve circuit efficiency. + +A ZK DSL does not just prove computation, but proves that some computation was handled correctly. Thus, it is necessary that when we switch from performing some operation directly inside of a circuit to inside of an unconstrained environment that the appropriate constraints are still laid down elsewhere in the circuit. + +## Example + +An in depth example might help drive the point home. This example comes from the excellent [post](https://discord.com/channels/1113924620781883405/1124022445054111926/1128747641853972590) by Tom in the Noir Discord. + +Let's look at how we can optimize a function to turn a `u72` into an array of `u8`s. + +```rust +fn main(num: u72) -> pub [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8)) as u72 & 0xff) as u8; + } + + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 91 +Backend circuit size: 3619 +``` + +A lot of the operations in this function are optimized away by the compiler (all the bit-shifts turn into divisions by constants). However we can save a bunch of gates by casting to u8 a bit earlier. This automatically truncates the bit-shifted value to fit in a u8 which allows us to remove the XOR against 0xff. This saves us ~480 gates in total. + +```rust +fn main(num: u72) -> pub [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8)) as u8; + } + + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 75 +Backend circuit size: 3143 +``` + +Those are some nice savings already but we can do better. This code is all constrained so we're proving every step of calculating out using num, but we don't actually care about how we calculate this, just that it's correct. This is where brillig comes in. + +It turns out that truncating a u72 into a u8 is hard to do inside a snark, each time we do as u8 we lay down 4 ACIR opcodes which get converted into multiple gates. It's actually much easier to calculate num from out than the other way around. All we need to do is multiply each element of out by a constant and add them all together, both relatively easy operations inside a snark. + +We can then run u72_to_u8 as unconstrained brillig code in order to calculate out, then use that result in our constrained function and assert that if we were to do the reverse calculation we'd get back num. This looks a little like the below: + +```rust +fn main(num: u72) -> pub [u8; 8] { + let out = u72_to_u8(num); + + let mut reconstructed_num: u72 = 0; + for i in 0..8 { + reconstructed_num += (out[i] as u72 << (56 - (8 * i))); + } + assert(num == reconstructed_num); + out +} + +unconstrained fn u72_to_u8(num: u72) -> [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8))) as u8; + } + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 78 +Backend circuit size: 2902 +``` + +This ends up taking off another ~250 gates from our circuit! We've ended up with more ACIR opcodes than before but they're easier for the backend to prove (resulting in fewer gates). + +Generally we want to use brillig whenever there's something that's easy to verify but hard to compute within the circuit. For example, if you wanted to calculate a square root of a number it'll be a much better idea to calculate this in brillig and then assert that if you square the result you get back your number. diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/06_generics.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/06_generics.md new file mode 100644 index 00000000000..9fb4177c2a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/06_generics.md @@ -0,0 +1,113 @@ +--- +title: Generics +description: Learn how to use Generics in Noir +keywords: [Noir, Rust, generics, functions, structs] +--- + +Generics allow you to use the same functions with multiple different concrete data types. You can +read more about the concept of generics in the Rust documentation +[here](https://doc.rust-lang.org/book/ch10-01-syntax.html). + +Here is a trivial example showing the identity function that supports any type. In Rust, it is +common to refer to the most general type as `T`. We follow the same convention in Noir. + +```rust +fn id(x: T) -> T { + x +} +``` + +## In Structs + +Generics are useful for specifying types in structs. For example, we can specify that a field in a +struct will be of a certain generic type. In this case `value` is of type `T`. + +```rust +struct RepeatedValue { + value: T, + count: Field, +} + +impl RepeatedValue { + fn new(value: T) -> Self { + Self { value, count: 1 } + } + + fn increment(mut repeated: Self) -> Self { + repeated.count += 1; + repeated + } + + fn print(self) { + for _i in 0 .. self.count { + dep::std::println(self.value); + } + } +} + +fn main() { + let mut repeated = RepeatedValue::new("Hello!"); + repeated = repeated.increment(); + repeated.print(); +} +``` + +The `print` function will print `Hello!` an arbitrary number of times, twice in this case. + +If we want to be generic over array lengths (which are type-level integers), we can use numeric +generics. Using these looks just like using regular generics, but these generics can resolve to +integers at compile-time, rather than resolving to types. Here's an example of a struct that is +generic over the size of the array it contains internally: + +```rust +struct BigInt { + limbs: [u32; N], +} + +impl BigInt { + // `N` is in scope of all methods in the impl + fn first(first: BigInt, second: BigInt) -> Self { + assert(first.limbs != second.limbs); + first + + fn second(first: BigInt, second: Self) -> Self { + assert(first.limbs != second.limbs); + second + } +} +``` + +## Calling functions on generic parameters + +Unlike Rust, Noir does not have traits, so how can one translate the equivalent of a trait bound in +Rust into Noir? That is, how can we write a function that is generic over some type `T`, while also +requiring there is a function like `eq: fn(T, T) -> bool` that works on the type? + +The answer is that we can translate this by passing in the function manually. Here's an example of +implementing array equality in Noir: + +```rust +fn array_eq(array1: [T; N], array2: [T; N], elem_eq: fn(T, T) -> bool) -> bool { + if array1.len() != array2.len() { + false + } else { + let mut result = true; + for i in 0 .. array1.len() { + result &= elem_eq(array1[i], array2[i]); + } + result + } +} + +fn main() { + assert(array_eq([1, 2, 3], [1, 2, 3], |a, b| a == b)); + + // We can use array_eq even for arrays of structs, as long as we have + // an equality function for these structs we can pass in + let array = [MyStruct::new(), MyStruct::new()]; + assert(array_eq(array, array, MyStruct::eq)); +} +``` + +You can see an example of generics in the tests +[here](https://github.com/noir-lang/noir/blob/master/tooling/nargo_cli/tests/execution_success/generics/src/main.nr). diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/07_mutability.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/07_mutability.md new file mode 100644 index 00000000000..4641521b1d9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/07_mutability.md @@ -0,0 +1,92 @@ +--- +title: Mutability +description: + Learn about mutable variables, constants, and globals in Noir programming language. Discover how + to declare, modify, and use them in your programs. +keywords: [noir programming language, mutability in noir, mutable variables, constants, globals] +--- + +Variables in noir can be declared mutable via the `mut` keyword. Mutable variables can be reassigned +to via an assignment expression. + +```rust +let x = 2; +x = 3; // error: x must be mutable to be assigned to + +let mut y = 3; +let y = 4; // OK +``` + +The `mut` modifier can also apply to patterns: + +```rust +let (a, mut b) = (1, 2); +a = 11; // error: a must be mutable to be assigned to +b = 12; // OK + +let mut (c, d) = (3, 4); +c = 13; // OK +d = 14; // OK + +// etc. +let MyStruct { x: mut y } = MyStruct { x: a }; +// y is now in scope +``` + +Note that mutability in noir is local and everything is passed by value, so if a called function +mutates its parameters then the parent function will keep the old value of the parameters. + +```rust +fn main() -> Field { + let x = 3; + helper(x); + x // x is still 3 +} + +fn helper(mut x: i32) { + x = 4; +} +``` + +## Comptime Values + +:::warning + +The 'comptime' keyword was removed in version 0.10. The comptime keyword and syntax are currently still kept and parsed for backwards compatibility, but are now deprecated and will issue a warning when used. `comptime` has been removed because it is no longer needed for accessing arrays. + +::: + +## Globals + +Noir also supports global variables. However, they must be known at compile-time. The global type can also be inferred by the compiler entirely. Globals can also be used to specify array +annotations for function parameters and can be imported from submodules. + +```rust +global N: Field = 5; // Same as `global N: Field = 5` + +fn main(x : Field, y : [Field; N]) { + let res = x * N; + + assert(res == y[0]); + + let res2 = x * mysubmodule::N; + assert(res != res2); +} + +mod mysubmodule { + use dep::std; + + global N: Field = 10; + + fn my_helper() -> Field { + let x = N; + x + } +} +``` + +## Why only local mutability? + +Witnesses in a proving system are immutable in nature. Noir aims to _closely_ mirror this setting +without applying additional overhead to the user. Modeling a mutable reference is not as +straightforward as on conventional architectures and would incur some possibly unexpected overhead. diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/08_lambdas.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/08_lambdas.md new file mode 100644 index 00000000000..ae1e6aecab1 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/08_lambdas.md @@ -0,0 +1,80 @@ +--- +title: Lambdas +description: Learn how to use anonymous functions in Noir programming language. +keywords: [Noir programming language, lambda, closure, function, anonymous function] +--- + +## Introduction + +Lambdas are anonymous functions. The syntax is `|arg1, arg2, ..., argN| return_expression`. + +```rust +let add_50 = |val| val + 50; +assert(add_50(100) == 150); +``` + +A block can be used as the body of a lambda, allowing you to declare local variables inside it: + +```rust +let cool = || { + let x = 100; + let y = 100; + x + y +} + +assert(cool() == 200); +``` + +## Closures + +Inside the body of a lambda, you can use variables defined in the enclosing function. Such lambdas are called **closures**. In this example `x` is defined inside `main` and is accessed from within the lambda: + +```rust +fn main() { + let x = 100; + let closure = || x + 150; + assert(closure() == 250); +} +``` + +## Passing closures to higher-order functions + +It may catch you by surprise that the following code fails to compile: + +```rust +fn foo(f: fn () -> Field) -> Field { + f() +} + +fn main() { + let (x, y) = (50, 50); + assert(foo(|| x + y) == 100); // error :( +} +``` + +The reason is that the closure's capture environment affects its type - we have a closure that captures two Fields and `foo` +expects a regular function as an argument - those are incompatible. +:::note + +Variables contained within the `||` are the closure's parameters, and the expression that follows it is the closure's body. The capture environment is comprised of any variables used in the closure's body that are not parameters. + +E.g. in |x| x + y, y would be a captured variable, but x would not be, since it is a parameter of the closure. + +::: +The syntax for the type of a closure is `fn[env](args) -> ret_type`, where `env` is the capture environment of the closure - +in this example that's `(Field, Field)`. + +The best solution in our case is to make `foo` generic over the environment type of its parameter, so that it can be called +with closures with any environment, as well as with regular functions: + +```rust +fn foo(f: fn[Env]() -> Field) -> Field { + f() +} + +fn main() { + let (x, y) = (50, 50); + assert(foo(|| x + y) == 100); // compiles fine + assert(foo(|| 60) == 60); // compiles fine +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/09_comments.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/09_comments.md new file mode 100644 index 00000000000..3bb4d2f25a4 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/09_comments.md @@ -0,0 +1,32 @@ +--- +title: Comments +description: + Learn how to write comments in Noir programming language. A comment is a line of code that is + ignored by the compiler, but it can be read by programmers. Single-line and multi-line comments + are supported in Noir. +keywords: [Noir programming language, comments, single-line comments, multi-line comments] +--- + +A comment is a line in your codebase which the compiler ignores, however it can be read by +programmers. + +Here is a single line comment: + +```rust +// This is a comment and is ignored +``` + +`//` is used to tell the compiler to ignore the rest of the line. + +Noir also supports multi-line block comments. Start a block comment with `/*` and end the block with `*/`. + +Noir does not natively support doc comments. You may be able to use [Rust doc comments](https://doc.rust-lang.org/reference/comments.html) in your code to leverage some Rust documentation build tools with Noir code. + +```rust +/* + This is a block comment describing a complex function. +*/ +fn main(x : Field, y : pub Field) { + assert(x != y); +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/10_distinct.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/10_distinct.md new file mode 100644 index 00000000000..e7ff7f5017a --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/10_distinct.md @@ -0,0 +1,63 @@ +--- +title: Distinct Witnesses +--- + +The `distinct` keyword prevents repetitions of witness indices in the program's ABI. This ensures +that the witnesses being returned as public inputs are all unique. + +The `distinct` keyword is only used for return values on program entry points (usually the `main()` +function). + +When using `distinct` and `pub` simultaneously, `distinct` comes first. See the example below. + +You can read more about the problem this solves +[here](https://github.com/noir-lang/noir/issues/1183). + +## Example + +Without the `distinct` keyword, the following program + +```rust +fn main(x : pub Field, y : pub Field) -> pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + "return_witnesses": [3, 2, 4, 4] + } +} +``` + +Whereas (with the `distinct` keyword) + +```rust +fn main(x : pub Field, y : pub Field) -> distinct pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + //... + "return_witnesses": [3, 4, 5, 6] + } +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/11_shadowing.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/11_shadowing.md new file mode 100644 index 00000000000..efd743e764f --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/11_shadowing.md @@ -0,0 +1,43 @@ +--- +title: Shadowing +--- + +Noir allows for inheriting variables' values and re-declaring them with the same name similar to Rust, known as shadowing. + +For example, the following function is valid in Noir: + +```rust +fn main() { + let x = 5; + + { + let x = x * 2; + assert (x == 10); + } + + assert (x == 5); +} +``` + +In this example, a variable x is first defined with the value 5. + +The local scope that follows shadows the original x, i.e. creates a local mutable x based on the value of the original x. It is given a value of 2 times the original x. + +When we return to the main scope, x once again refers to just the original x, which stays at the value of 5. + +## Temporal mutability + +One way that shadowing is useful, in addition to ergonomics across scopes, is for temporarily mutating variables. + +```rust +fn main() { + let age = 30; + // age = age + 5; // Would error as `age` is immutable by default. + + let mut age = age + 5; // Temporarily mutates `age` with a new value. + + let age = age; // Locks `age`'s mutability again. + + assert (age == 35); +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types.md new file mode 100644 index 00000000000..d546cc463a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types.md @@ -0,0 +1,96 @@ +--- +title: Data Types +description: + Get a clear understanding of the two categories of Noir data types - primitive types and compound + types. Learn about their characteristics, differences, and how to use them in your Noir + programming. +keywords: + [ + noir, + data types, + primitive types, + compound types, + private types, + public types, + ] +--- + +Every value in Noir has a type, which determines which operations are valid for it. + +All values in Noir are fundamentally composed of `Field` elements. For a more approachable +developing experience, abstractions are added on top to introduce different data types in Noir. + +Noir has two category of data types: primitive types (e.g. `Field`, integers, `bool`) and compound +types that group primitive types (e.g. arrays, tuples, structs). Each value can either be private or +public. + +## Private & Public Types + +A **private value** is known only to the Prover, while a **public value** is known by both the +Prover and Verifier. Mark values as `private` when the value should only be known to the prover. All +primitive types (including individual fields of compound types) in Noir are private by default, and +can be marked public when certain values are intended to be revealed to the Verifier. + +> **Note:** For public values defined in Noir programs paired with smart contract verifiers, once +> the proofs are verified on-chain the values can be considered known to everyone that has access to +> that blockchain. + +Public data types are treated no differently to private types apart from the fact that their values +will be revealed in proofs generated. Simply changing the value of a public type will not change the +circuit (where the same goes for changing values of private types as well). + +_Private values_ are also referred to as _witnesses_ sometimes. + +> **Note:** The terms private and public when applied to a type (e.g. `pub Field`) have a different +> meaning than when applied to a function (e.g. `pub fn foo() {}`). +> +> The former is a visibility modifier for the Prover to interpret if a value should be made known to +> the Verifier, while the latter is a visibility modifier for the compiler to interpret if a +> function should be made accessible to external Noir programs like in other languages. + +### pub Modifier + +All data types in Noir are private by default. Types are explicitly declared as public using the +`pub` modifier: + +```rust +fn main(x : Field, y : pub Field) -> pub Field { + x + y +} +``` + +In this example, `x` is **private** while `y` and `x + y` (the return value) are **public**. Note +that visibility is handled **per variable**, so it is perfectly valid to have one input that is +private and another that is public. + +> **Note:** Public types can only be declared through parameters on `main`. + +## Type Aliases + +A type alias is a new name for an existing type. Type aliases are declared with the keyword `type`: + +```rust +type Id = u8; + +fn main() { + let id: Id = 1; + let zero: u8 = 0; + assert(zero + 1 == id); +} +``` + +Type aliases can also be used with [generics](./06_generics.md): + +```rust +type Id = Size; + +fn main() { + let id: Id = 1; + let zero: u32 = 0; + assert(zero + 1 == id); +} +``` + +### BigInt + +You can acheive BigInt functionality using the [Noir BigInt](https://github.com/shuklaayush/noir-bigint) library. diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/00_fields.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/00_fields.md new file mode 100644 index 00000000000..658a0441ffb --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/00_fields.md @@ -0,0 +1,165 @@ +--- +title: Fields +description: + Dive deep into the Field data type in Noir. Understand its methods, practical examples, and best practices to effectively use Fields in your Noir programs. +keywords: + [ + noir, + field type, + methods, + examples, + best practices, + ] +--- + +The field type corresponds to the native field type of the proving backend. + +The size of a Noir field depends on the elliptic curve's finite field for the proving backend +adopted. For example, a field would be a 254-bit integer when paired with the default backend that +spans the Grumpkin curve. + +Fields support integer arithmetic and are often used as the default numeric type in Noir: + +```rust +fn main(x : Field, y : Field) { + let z = x + y; +} +``` + +`x`, `y` and `z` are all private fields in this example. Using the `let` keyword we defined a new +private value `z` constrained to be equal to `x + y`. + +If proving efficiency is of priority, fields should be used as a default for solving problems. +Smaller integer types (e.g. `u64`) incur extra range constraints. + +## Methods + +After declaring a Field, you can use these common methods on it: + +### to_le_bits + +Transforms the field into an array of bits, Little Endian. + +```rust +fn to_le_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2; + let bits = field.to_le_bits(32); +} +``` + +### to_be_bits + +Transforms the field into an array of bits, Big Endian. + +```rust +fn to_be_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2; + let bits = field.to_be_bits(32); +} +``` + +### to_le_bytes + +Transforms into an array of bytes, Little Endian + +```rust +fn to_le_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let bytes = field.to_le_bytes(4); +} +``` + +### to_be_bytes + +Transforms into an array of bytes, Big Endian + +```rust +fn to_be_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let bytes = field.to_be_bytes(4); +} +``` + +### to_le_radix + +Decomposes into a vector over the specified base, Little Endian + +```rust +fn to_le_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let radix = field.to_le_radix(256, 4); +} +``` + +### to_be_radix + +Decomposes into a vector over the specified base, Big Endian + +```rust +fn to_be_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let radix = field.to_be_radix(256, 4); +} +``` + +### pow_32 + +Returns the value to the power of the specified exponent + +```rust +fn pow_32(self, exponent: Field) -> Field +``` + +example: + +```rust +fn main() { + let field = 2 + let pow = field.pow_32(4); + assert(pow == 16); +} +``` + +### sgn0 + +Parity of (prime) Field element, i.e. sgn0(x mod p) = 0 if x ∈ {0, ..., p-1} is even, otherwise sgn0(x mod p) = 1. + +```rust +fn sgn0(self) -> u1 +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/01_integers.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/01_integers.md new file mode 100644 index 00000000000..b1e7ad11bfd --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/01_integers.md @@ -0,0 +1,112 @@ +--- +title: Integers +description: Explore the Integer data type in Noir. Learn about its methods, see real-world examples, and grasp how to efficiently use Integers in your Noir code. +keywords: [noir, integer types, methods, examples, arithmetic] +--- + +An integer type is a range constrained field type. The Noir frontend supports arbitrarily-sized, both unsigned and signed integer types. + +:::info + +When an integer is defined in Noir without a specific type, it will default to `Field`. + +The one exception is for loop indices which default to `u64` since comparisons on `Field`s are not possible. + +::: + +## Unsigned Integers + +An unsigned integer type is specified first with the letter `u` (indicating its unsigned nature) followed by its bit size (e.g. `8`): + +```rust +fn main() { + let x: u8 = 1; + let y: u8 = 1; + let z = x + y; + assert (z == 2); +} +``` + +The bit size determines the maximum value the integer type can store. For example, a `u8` variable can store a value in the range of 0 to 255 (i.e. $\\2^{8}-1\\$). + +## Signed Integers + +A signed integer type is specified first with the letter `i` (which stands for integer) followed by its bit size (e.g. `8`): + +```rust +fn main() { + let x: i8 = -1; + let y: i8 = -1; + let z = x + y; + assert (z == -2); +} +``` + +The bit size determines the maximum and minimum range of value the integer type can store. For example, an `i8` variable can store a value in the range of -128 to 127 (i.e. $\\-2^{7}\\$ to $\\2^{7}-1\\$). + +:::tip + +If you are using the default proving backend with Noir, both even (e.g. _u2_, _i2_) and odd (e.g. _u3_, _i3_) arbitrarily-sized integer types up to 127 bits (i.e. _u127_ and _i127_) are supported. + +::: + +## Overflows + +Computations that exceed the type boundaries will result in overflow errors. This happens with both signed and unsigned integers. For example, attempting to prove: + +```rust +fn main(x: u8, y: u8) { + let z = x + y; +} +``` + +With: + +```toml +x = "255" +y = "1" +``` + +Would result in: + +``` +$ nargo prove +error: Assertion failed: 'attempt to add with overflow' +┌─ ~/src/main.nr:9:13 +│ +│ let z = x + y; +│ ----- +│ += Call stack: + ... +``` + +A similar error would happen with signed integers: + +```rust +fn main() { + let x: i8 = -118; + let y: i8 = -11; + let z = x + y; +} +``` + +### Wrapping methods + +Although integer overflow is expected to error, some use-cases rely on wrapping. For these use-cases, the standard library provides `wrapping` variants of certain common operations: + +```rust +fn wrapping_add(x: T, y: T) -> T; +fn wrapping_sub(x: T, y: T) -> T; +fn wrapping_mul(x: T, y: T) -> T; +``` + +Example of how it is used: + +```rust +use dep::std; + +fn main(x: u8, y: u8) -> pub u8 { + std::wrapping_add(x + y) +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/02_booleans.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/02_booleans.md new file mode 100644 index 00000000000..885db167d83 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/02_booleans.md @@ -0,0 +1,30 @@ +--- +title: Booleans +description: + Delve into the Boolean data type in Noir. Understand its methods, practical examples, and best practices for using Booleans in your Noir programs. +keywords: + [ + noir, + boolean type, + methods, + examples, + logical operations, + ] +--- + + +The `bool` type in Noir has two possible values: `true` and `false`: + +```rust +fn main() { + let t = true; + let f: bool = false; +} +``` + +> **Note:** When returning a boolean value, it will show up as a value of 1 for `true` and 0 for +> `false` in _Verifier.toml_. + +The boolean type is most commonly used in conditionals like `if` expressions and `assert` +statements. More about conditionals is covered in the [Control Flow](../control_flow) and +[Assert Function](../assert) sections. diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/03_strings.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/03_strings.md new file mode 100644 index 00000000000..c42f34ec3ad --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/03_strings.md @@ -0,0 +1,63 @@ +--- +title: Strings +description: + Discover the String data type in Noir. Learn about its methods, see real-world examples, and understand how to effectively manipulate and use Strings in Noir. +keywords: + [ + noir, + string type, + methods, + examples, + concatenation, + ] +--- + + +The string type is a fixed length value defined with `str`. + +You can use strings in `assert()` functions or print them with +`std::println()`. See more about [Logging](../../standard_library/logging). + +```rust +use dep::std; + +fn main(message : pub str<11>, hex_as_string : str<4>) { + std::println(message); + assert(message == "hello world"); + assert(hex_as_string == "0x41"); +} +``` + +You can convert a `str` to a byte array by calling `as_bytes()` +or a vector by calling `as_bytes_vec()`. + +```rust +fn main() { + let message = "hello world"; + let message_bytes = message.as_bytes(); + let mut message_vec = message.as_bytes_vec(); + assert(message_bytes.len() == 11); + assert(message_bytes[0] == 104); + assert(message_bytes[0] == message_vec.get(0)); +} +``` + +## Escape characters + +You can use escape characters for your strings: + +| Escape Sequence | Description | +|-----------------|-----------------| +| `\r` | Carriage Return | +| `\n` | Newline | +| `\t` | Tab | +| `\0` | Null Character | +| `\"` | Double Quote | +| `\\` | Backslash | + +Example: + +```rust +let s = "Hello \"world" // prints "Hello "world" +let s = "hey \tyou"; // prints "hey you" +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/04_arrays.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/04_arrays.md new file mode 100644 index 00000000000..bdbd1798bef --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/04_arrays.md @@ -0,0 +1,244 @@ +--- +title: Arrays +description: + Dive into the Array data type in Noir. Grasp its methods, practical examples, and best practices for efficiently using Arrays in your Noir code. +keywords: + [ + noir, + array type, + methods, + examples, + indexing, + ] +--- + +An array is one way of grouping together values into one compound type. Array types can be inferred +or explicitly specified via the syntax `[; ]`: + +```rust +fn main(x : Field, y : Field) { + let my_arr = [x, y]; + let your_arr: [Field; 2] = [x, y]; +} +``` + +Here, both `my_arr` and `your_arr` are instantiated as an array containing two `Field` elements. + +Array elements can be accessed using indexing: + +```rust +fn main() { + let a = [1, 2, 3, 4, 5]; + + let first = a[0]; + let second = a[1]; +} +``` + +All elements in an array must be of the same type (i.e. homogeneous). That is, an array cannot group +a `Field` value and a `u8` value together for example. + +You can write mutable arrays, like: + +```rust +fn main() { + let mut arr = [1, 2, 3, 4, 5]; + assert(arr[0] == 1); + + arr[0] = 42; + assert(arr[0] == 42); +} +``` + +You can instantiate a new array of a fixed size with the same value repeated for each element. The following example instantiates an array of length 32 where each element is of type Field and has the value 0. + +```rust +let array: [Field; 32] = [0; 32]; +``` + +Like in Rust, arrays in Noir are a fixed size. However, if you wish to convert an array to a [slice](./slices), you can just call `as_slice` on your array: + +```rust +let array: [Field; 32] = [0; 32]; +let sl = array.as_slice() +``` + +You can define multidimensional arrays: + +```rust +let array : [[Field; 2]; 2]; +let element = array[0][0]; +``` + +## Types + +You can create arrays of primitive types or structs. There is not yet support for nested arrays +(arrays of arrays) or arrays of structs that contain arrays. + +## Methods + +For convenience, the STD provides some ready-to-use, common methods for arrays: + +### len + +Returns the length of an array + +```rust +fn len(_array: [T; N]) -> comptime Field +``` + +example + +```rust +fn main() { + let array = [42, 42]; + assert(array.len() == 2); +} +``` + +### sort + +Returns a new sorted array. The original array remains untouched. Notice that this function will +only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting +logic it uses internally is optimized specifically for these values. If you need a sort function to +sort any type, you should use the function `sort_via` described below. + +```rust +fn sort(_array: [T; N]) -> [T; N] +``` + +example + +```rust +fn main() { + let arr = [42, 32]; + let sorted = arr.sort(); + assert(sorted == [32, 42]); +} +``` + +### sort_via + +Sorts the array with a custom comparison function + +```rust +fn sort_via(mut a: [T; N], ordering: fn(T, T) -> bool) -> [T; N] +``` + +example + +```rust +fn main() { + let arr = [42, 32] + let sorted_ascending = arr.sort_via(|a, b| a < b); + assert(sorted_ascending == [32, 42]); // verifies + + let sorted_descending = arr.sort_via(|a, b| a > b); + assert(sorted_descending == [32, 42]); // does not verify +} +``` + +### map + +Applies a function to each element of the array, returning a new array containing the mapped elements. + +```rust +fn map(f: fn(T) -> U) -> [U; N] +``` + +example + +```rust +let a = [1, 2, 3]; +let b = a.map(|a| a * 2); // b is now [2, 4, 6] +``` + +### fold + +Applies a function to each element of the array, returning the final accumulated value. The first +parameter is the initial value. + +```rust +fn fold(mut accumulator: U, f: fn(U, T) -> U) -> U +``` + +This is a left fold, so the given function will be applied to the accumulator and first element of +the array, then the second, and so on. For a given call the expected result would be equivalent to: + +```rust +let a1 = [1]; +let a2 = [1, 2]; +let a3 = [1, 2, 3]; + +let f = |a, b| a - b; +a1.fold(10, f) //=> f(10, 1) +a2.fold(10, f) //=> f(f(10, 1), 2) +a3.fold(10, f) //=> f(f(f(10, 1), 2), 3) +``` + +example: + +```rust + +fn main() { + let arr = [2, 2, 2, 2, 2]; + let folded = arr.fold(0, |a, b| a + b); + assert(folded == 10); +} + +``` + +### reduce + +Same as fold, but uses the first element as starting element. + +```rust +fn reduce(f: fn(T, T) -> T) -> T +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 2]; + let reduced = arr.reduce(|a, b| a + b); + assert(reduced == 10); +} +``` + +### all + +Returns true if all the elements satisfy the given predicate + +```rust +fn all(predicate: fn(T) -> bool) -> bool +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 2]; + let all = arr.all(|a| a == 2); + assert(all); +} +``` + +### any + +Returns true if any of the elements satisfy the given predicate + +```rust +fn any(predicate: fn(T) -> bool) -> bool +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 5]; + let any = arr.any(|a| a == 5); + assert(any); +} + +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/05_slices.mdx b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/05_slices.mdx new file mode 100644 index 00000000000..1be0ec4a137 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/05_slices.mdx @@ -0,0 +1,146 @@ +--- +title: Slices +description: Explore the Slice data type in Noir. Understand its methods, see real-world examples, and learn how to effectively use Slices in your Noir programs. +keywords: [noir, slice type, methods, examples, subarrays] +--- + +import Experimental from '@site/src/components/Notes/_experimental.mdx'; + + + +A slice is a dynamically-sized view into a sequence of elements. They can be resized at runtime, but because they don't own the data, they cannot be returned from a circuit. You can treat slices as arrays without a constrained size. + +```rust +use dep::std::slice; + +fn main() -> pub Field { + let mut slice: [Field] = [0; 2]; + + let mut new_slice = slice.push_back(6); + new_slice.len() +} +``` + +View the corresponding test file [here][test-file]. + +[test-file]: https://github.com/noir-lang/noir/blob/f387ec1475129732f72ba294877efdf6857135ac/crates/nargo_cli/tests/test_data_ssa_refactor/slices/src/main.nr + +## Methods + +For convenience, the STD provides some ready-to-use, common methods for slices: + +### push_back + +Pushes a new element to the end of the slice, returning a new slice with a length one greater than the original unmodified slice. + +```rust +fn push_back(_self: [T], _elem: T) -> [T] +``` + +example: + +```rust +fn main() -> pub Field { + let mut slice: [Field] = [0; 2]; + + let mut new_slice = slice.push_back(6); + new_slice.len() +} +``` + +View the corresponding test file [here][test-file]. + +### push_front + +Returns a new array with the specified element inserted at index 0. The existing elements indexes are incremented by 1. + +```rust +fn push_front(_self: Self, _elem: T) -> Self +``` + +Example: + +```rust +let mut new_slice: [Field] = []; +new_slice = new_slice.push_front(20); +assert(new_slice[0] == 20); // returns true +``` + +View the corresponding test file [here][test-file]. + +### pop_front + +Returns a tuple of two items, the first element of the array and the rest of the array. + +```rust +fn pop_front(_self: Self) -> (T, Self) +``` + +Example: + +```rust +let (first_elem, rest_of_slice) = slice.pop_front(); +``` + +View the corresponding test file [here][test-file]. + +### pop_back + +Returns a tuple of two items, the beginning of the array with the last element omitted and the last element. + +```rust +fn pop_back(_self: Self) -> (Self, T) +``` + +Example: + +```rust +let (popped_slice, last_elem) = slice.pop_back(); +``` + +View the corresponding test file [here][test-file]. + +### append + +Loops over a slice and adds it to the end of another. + +```rust +fn append(mut self, other: Self) -> Self +``` + +Example: + +```rust +let append = [1, 2].append([3, 4, 5]); +``` + +### insert + +Inserts an element at a specified index and shifts all following elements by 1. + +```rust +fn insert(_self: Self, _index: Field, _elem: T) -> Self +``` + +Example: + +```rust +new_slice = rest_of_slice.insert(2, 100); +assert(new_slice[2] == 100); +``` + +View the corresponding test file [here][test-file]. + +### remove + +Remove an element at a specified index, shifting all elements after it to the left, returning the altered slice and the removed element. + +```rust +fn remove(_self: Self, _index: Field) -> (Self, T) +``` + +Example: + +```rust +let (remove_slice, removed_elem) = slice.remove(3); +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/06_vectors.mdx b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/06_vectors.mdx new file mode 100644 index 00000000000..4617e90d038 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/06_vectors.mdx @@ -0,0 +1,172 @@ +--- +title: Vectors +description: Delve into the Vector data type in Noir. Learn about its methods, practical examples, and best practices for using Vectors in your Noir code. +keywords: [noir, vector type, methods, examples, dynamic arrays] +--- + +import Experimental from '@site/src/components/Notes/_experimental.mdx'; + + + +A vector is a collection type similar to Rust's Vector type. It's convenient way to use slices as mutable arrays. + +Example: + +```rust +use dep::std::collections::vec::Vec; + +let mut vector: Vec = Vec::new(); +for i in 0..5 { + vector.push(i); +} +assert(vector.len() == 5); +``` + +## Methods + +### new + +Creates a new, empty vector. + +```rust +pub fn new() -> Self { + Self { slice: [] } +} +``` + +Example: + +```rust +let empty_vector: Vec = Vec::new(); +assert(empty_vector.len() == 0); +``` + +### from_slice + +Creates a vector containing each element from a given slice. Mutations to the resulting vector will not affect the original slice. + +```rust +pub fn from_slice(slice: [T]) -> Self { + Self { slice } +} +``` + +Example: + +```rust +let arr: [Field] = [1, 2, 3]; +let vector_from_slice = Vec::from_slice(arr); +assert(vector_from_slice.len() == 3); +``` + +### get + +Retrieves an element from the vector at a given index. Panics if the index points beyond the vector's end. + +```rust +pub fn get(self, index: Field) -> T { + self.slice[index] +} +``` + +Example: + +```rust +let vector: Vec = Vec::from_slice([10, 20, 30]); +assert(vector.get(1) == 20); +``` + +### push + +Adds a new element to the vector's end, returning a new vector with a length one greater than the original unmodified vector. + +```rust +pub fn push(&mut self, elem: T) { + self.slice = self.slice.push_back(elem); +} +``` + +Example: + +```rust +let mut vector: Vec = Vec::new(); +vector.push(10); +assert(vector.len() == 1); +``` + +### pop + +Removes an element from the vector's end, returning a new vector with a length one less than the original vector, along with the removed element. Panics if the vector's length is zero. + +```rust +pub fn pop(&mut self) -> T { + let (popped_slice, last_elem) = self.slice.pop_back(); + self.slice = popped_slice; + last_elem +} +``` + +Example: + +```rust +let mut vector = Vec::from_slice([10, 20]); +let popped_elem = vector.pop(); +assert(popped_elem == 20); +assert(vector.len() == 1); +``` + +### insert + +Inserts an element at a specified index, shifting subsequent elements to the right. + +```rust +pub fn insert(&mut self, index: Field, elem: T) { + self.slice = self.slice.insert(index, elem); +} +``` + +Example: + +```rust +let mut vector = Vec::from_slice([10, 30]); +vector.insert(1, 20); +assert(vector.get(1) == 20); +``` + +### remove + +Removes an element at a specified index, shifting subsequent elements to the left, and returns the removed element. + +```rust +pub fn remove(&mut self, index: Field) -> T { + let (new_slice, elem) = self.slice.remove(index); + self.slice = new_slice; + elem +} +``` + +Example: + +```rust +let mut vector = Vec::from_slice([10, 20, 30]); +let removed_elem = vector.remove(1); +assert(removed_elem == 20); +assert(vector.len() == 2); +``` + +### len + +Returns the number of elements in the vector. + +```rust +pub fn len(self) -> Field { + self.slice.len() +} +``` + +Example: + +```rust +let empty_vector: Vec = Vec::new(); +assert(empty_vector.len() == 0); +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/07_tuples.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/07_tuples.md new file mode 100644 index 00000000000..5f6cab974a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/07_tuples.md @@ -0,0 +1,47 @@ +--- +title: Tuples +description: + Dive into the Tuple data type in Noir. Understand its methods, practical examples, and best practices for efficiently using Tuples in your Noir code. +keywords: + [ + noir, + tuple type, + methods, + examples, + multi-value containers, + ] +--- + +A tuple collects multiple values like an array, but with the added ability to collect values of +different types: + +```rust +fn main() { + let tup: (u8, u64, Field) = (255, 500, 1000); +} +``` + +One way to access tuple elements is via destructuring using pattern matching: + +```rust +fn main() { + let tup = (1, 2); + + let (one, two) = tup; + + let three = one + two; +} +``` + +Another way to access tuple elements is via direct member access, using a period (`.`) followed by +the index of the element we want to access. Index `0` corresponds to the first tuple element, `1` to +the second and so on: + +```rust +fn main() { + let tup = (5, 6, 7, 8); + + let five = tup.0; + let eight = tup.3; +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/08_structs.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/08_structs.md new file mode 100644 index 00000000000..35421734639 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/08_structs.md @@ -0,0 +1,69 @@ +--- +title: Structs +description: + Explore the Struct data type in Noir. Learn about its methods, see real-world examples, and grasp how to effectively define and use Structs in your Noir programs. +keywords: + [ + noir, + struct type, + methods, + examples, + data structures, + ] +--- + +A struct also allows for grouping multiple values of different types. Unlike tuples, we can also +name each field. + +> **Note:** The usage of _field_ here refers to each element of the struct and is unrelated to the +> field type of Noir. + +Defining a struct requires giving it a name and listing each field within as `: ` pairs: + +```rust +struct Animal { + hands: Field, + legs: Field, + eyes: u8, +} +``` + +An instance of a struct can then be created with actual values in `: ` pairs in any +order. Struct fields are accessible using their given names: + +```rust +fn main() { + let legs = 4; + + let dog = Animal { + eyes: 2, + hands: 0, + legs, + }; + + let zero = dog.hands; +} +``` + +Structs can also be destructured in a pattern, binding each field to a new variable: + +```rust +fn main() { + let Animal { hands, legs: feet, eyes } = get_octopus(); + + let ten = hands + feet + eyes as u8; +} + +fn get_octopus() -> Animal { + let octopus = Animal { + hands: 0, + legs: 8, + eyes: 2, + }; + + octopus +} +``` + +The new variables can be bound with names different from the original struct field names, as +showcased in the `legs --> feet` binding in the example above. diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/09_references.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/09_references.md new file mode 100644 index 00000000000..b0c35ce2cb9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/09_references.md @@ -0,0 +1,22 @@ +--- +title: References +--- + +Noir supports first-class references. References are a bit like pointers: they point to a specific address that can be followed to access the data stored at that address. You can use Rust-like syntax to use pointers in Noir: the `&` operator references the variable, the `*` operator dereferences it. + +Example: + +```rust +fn main() { + let mut x = 2; + + // you can reference x as &mut and pass it to multiplyBy2 + multiplyBy2(&mut x); +} + +// you can access &mut here +fn multiplyBy2(x: &mut Field) { + // and dereference it with * + *x = *x * 2; +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/10_function_types.md b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/10_function_types.md new file mode 100644 index 00000000000..1ec92efd594 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/language_concepts/data_types/10_function_types.md @@ -0,0 +1,25 @@ +--- +title: Function types +--- + +Noir supports higher-order functions. The syntax for a function type is as follows: + +```rust +fn(arg1_type, arg2_type, ...) -> return_type +``` + +Example: + +```rust +fn assert_returns_100(f: fn() -> Field) { // f takes no args and returns a Field + assert(f() == 100); +} + +fn main() { + assert_returns_100(|| 100); // ok + assert_returns_100(|| 150); // fails +} +``` + +A function type also has an optional capture environment - this is necessary to support closures. +See [Lambdas](../08_lambdas.md) for more details. diff --git a/noir/docs/versioned_docs/version-v0.19.1/migration_notes.md b/noir/docs/versioned_docs/version-v0.19.1/migration_notes.md new file mode 100644 index 00000000000..e87eb1feaba --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/migration_notes.md @@ -0,0 +1,91 @@ +--- +title: Migration notes +description: Read about migration notes from previous versions, which could solve problems while updating +keywords: [Noir, notes, migration, updating, upgrading] +--- + +Noir is in full-speed development. Things break fast, wild, and often. This page attempts to leave some notes on errors you might encounter when upgrading and how to resolve them until proper patches are built. + +## ≥0.19 + +### Enforcing `compiler_version` + +From this version on, the compiler will check for the `compiler_version` field in `Nargo.toml`, and will error if it doesn't match the current Nargo version in use. + +To update, please make sure this field in `Nargo.toml` matches the output of `nargo --version`. + +## ≥0.14 + +The index of the [for loops](./language_concepts/02_control_flow.md#loops) is now of type `u64` instead of `Field`. An example refactor would be: + +```rust +for i in 0..10 { + let i = i as Field; +} +``` + +## ≥v0.11.0 and Nargo backend + +From this version onwards, Nargo starts managing backends through the `nargo backend` command. Upgrading to the versions per usual steps might lead to: + +### `backend encountered an error` + +This is likely due to the existing locally installed version of proving backend (e.g. barretenberg) is incompatible with the version of Nargo in use. + +To fix the issue: + +1. Uninstall the existing backend + +```bash +nargo backend uninstall acvm-backend-barretenberg +``` + +You may replace _acvm-backend-barretenberg_ with the name of your backend listed in `nargo backend ls` or in ~/.nargo/backends. + +2. Reinstall a compatible version of the proving backend. + +If you are using the default barretenberg backend, simply run: + +``` +nargo prove +``` + +with you Noir program. + +This will trigger the download and installation of the latest version of barretenberg compatible with your Nargo in use. + +### `backend encountered an error: illegal instruction` + +On certain Intel-based systems, an `illegal instruction` error may arise due to incompatibility of barretenberg with certain CPU instructions. + +To fix the issue: + +1. Uninstall the existing backend + +```bash +nargo backend uninstall acvm-backend-barretenberg +``` + +You may replace _acvm-backend-barretenberg_ with the name of your backend listed in `nargo backend ls` or in ~/.nargo/backends. + +2. Reinstall a compatible version of the proving backend. + +If you are using the default barretenberg backend, simply run: + +``` +nargo backend install acvm-backend-barretenberg https://github.com/noir-lang/barretenberg-js-binary/raw/master/run-bb.tar.gz +``` + +This downloads and installs a specific bb.js based version of barretenberg binary from GitHub. + +The gzipped filed is running this bash script: , where we need to gzip it as the Nargo currently expect the backend to be zipped up. + +Then run: + +``` +DESIRED_BINARY_VERSION=0.8.1 nargo info +``` + +This overrides the bb native binary with a bb.js node application instead, which should be compatible with most if not all hardware. This does come with the drawback of being generally slower than native binary. + +0.8.1 indicates bb.js version 0.8.1, so if you change that it will update to a different version or the default version in the script if none was supplied. diff --git a/noir/docs/versioned_docs/version-v0.19.1/modules_packages_crates/crates_and_packages.md b/noir/docs/versioned_docs/version-v0.19.1/modules_packages_crates/crates_and_packages.md new file mode 100644 index 00000000000..fb83a33d94e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/modules_packages_crates/crates_and_packages.md @@ -0,0 +1,42 @@ +--- +title: Crates and Packages +description: Learn how to use Crates and Packages in your Noir project +keywords: [Nargo, dependencies, package management, crates, package] +--- + +## Crates + +A crate is the smallest amount of code that the Noir compiler considers at a time. +Crates can contain modules, and the modules may be defined in other files that get compiled with the crate, as we’ll see in the coming sections. + +### Crate Types + +A Noir crate can come in several forms: binaries, libraries or contracts. + +#### Binaries + +_Binary crates_ are programs which you can compile to an ACIR circuit which you can then create proofs against. Each must have a function called `main` that defines the ACIR circuit which is to be proved. + +#### Libraries + +_Library crates_ don't have a `main` function and they don't compile down to ACIR. Instead they define functionality intended to be shared with multiple projects, and eventually included in a binary crate. + +#### Contracts + +Contract crates are similar to binary crates in that they compile to ACIR which you can create proofs against. They are different in that they do not have a single `main` function, but are a collection of functions to be deployed to the [Aztec network](https://aztec.network). You can learn more about the technical details of Aztec in the [monorepo](https://github.com/AztecProtocol/aztec-packages) or contract [examples](https://github.com/AztecProtocol/aztec-packages/tree/master/yarn-project/noir-contracts/src/contracts). + +### Crate Root + +Every crate has a root, which is the source file that the compiler starts, this is also known as the root module. The Noir compiler does not enforce any conditions on the name of the file which is the crate root, however if you are compiling via Nargo the crate root must be called `lib.nr` or `main.nr` for library or binary crates respectively. + +## Packages + +A Nargo _package_ is a collection of one of more crates that provides a set of functionality. A package must include a Nargo.toml file. + +A package _must_ contain either a library or a binary crate, but not both. + +### Differences from Cargo Packages + +One notable difference between Rust's Cargo and Noir's Nargo is that while Cargo allows a package to contain an unlimited number of binary crates and a single library crate, Nargo currently only allows a package to contain a single crate. + +In future this restriction may be lifted to allow a Nargo package to contain both a binary and library crate or multiple binary crates. diff --git a/noir/docs/versioned_docs/version-v0.19.1/modules_packages_crates/dependencies.md b/noir/docs/versioned_docs/version-v0.19.1/modules_packages_crates/dependencies.md new file mode 100644 index 00000000000..75f95aaa305 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/modules_packages_crates/dependencies.md @@ -0,0 +1,123 @@ +--- +title: Dependencies +description: + Learn how to specify and manage dependencies in Nargo, allowing you to upload packages to GitHub + and use them easily in your project. +keywords: [Nargo, dependencies, GitHub, package management, versioning] +--- + +Nargo allows you to upload packages to GitHub and use them as dependencies. + +## Specifying a dependency + +Specifying a dependency requires a tag to a specific commit and the git url to the url containing +the package. + +Currently, there are no requirements on the tag contents. If requirements are added, it would follow +semver 2.0 guidelines. + +> Note: Without a `tag` , there would be no versioning and dependencies would change each time you +> compile your project. + +For example, to add the [ecrecover-noir library](https://github.com/colinnielsen/ecrecover-noir) to your project, add it to `Nargo.toml`: + +```toml +# Nargo.toml + +[dependencies] +ecrecover = {tag = "v0.8.0", git = "https://github.com/colinnielsen/ecrecover-noir"} +``` + +If the module is in a subdirectory, you can define a subdirectory in your git repository, for example: + +```toml +# Nargo.toml + +[dependencies] +easy_private_token_contract = {tag ="v0.1.0-alpha62", git = "https://github.com/AztecProtocol/aztec-packages", directory = "yarn-project/noir-contracts/src/contracts/easy_private_token_contract"} +``` + +## Specifying a local dependency + +You can also specify dependencies that are local to your machine. + +For example, this file structure has a library and binary crate + +```tree +├── binary_crate +│   ├── Nargo.toml +│   └── src +│   └── main.nr +└── liba + ├── Nargo.toml + └── src + └── lib.nr +``` + +Inside of the binary crate, you can specify: + +```toml +# Nargo.toml + +[dependencies] +libA = { path = "../liba" } +``` + +## Importing dependencies + +You can import a dependency to a Noir file using the following syntax. For example, to import the +ecrecover-noir library and local liba referenced above: + +```rust +use dep::ecrecover; +use dep::libA; +``` + +You can also import only the specific parts of dependency that you want to use, like so: + +```rust +use dep::std::hash::sha256; +use dep::std::scalar_mul::fixed_base_embedded_curve; +``` + +Lastly, as demonstrated in the +[elliptic curve example](../standard_library/cryptographic_primitives/ec_primitives#examples), you +can import multiple items in the same line by enclosing them in curly braces: + +```rust +use dep::std::ec::tecurve::affine::{Curve, Point}; +``` + +We don't have a way to consume libraries from inside a [workspace](./workspaces) as external dependencies right now. + +Inside a workspace, these are consumed as `{ path = "../to_lib" }` dependencies in Nargo.toml. + +## Dependencies of Dependencies + +Note that when you import a dependency, you also get access to all of the dependencies of that package. + +For example, the [phy_vector](https://github.com/resurgencelabs/phy_vector) library imports an [fraction](https://github.com/resurgencelabs/fraction) library. If you're importing the phy_vector library, then you can access the functions in fractions library like so: + +```rust +use dep::phy_vector; + +fn main(x : Field, y : pub Field) { + //... + let f = phy_vector::fraction::toFraction(true, 2, 1); + //... +} +``` + +## Available Libraries + +Noir does not currently have an official package manager. You can find a list of available Noir libraries in the [awesome-noir repo here](https://github.com/noir-lang/awesome-noir#libraries). + +Some libraries that are available today include: + +- [Standard Library](https://github.com/noir-lang/noir/tree/master/noir_stdlib) - the Noir Standard Library +- [Ethereum Storage Proof Verification](https://github.com/aragonzkresearch/noir-trie-proofs) - a library that contains the primitives necessary for RLP decoding (in the form of look-up table construction) and Ethereum state and storage proof verification (or verification of any trie proof involving 32-byte long keys) +- [BigInt](https://github.com/shuklaayush/noir-bigint) - a library that provides a custom BigUint56 data type, allowing for computations on large unsigned integers +- [ECrecover](https://github.com/colinnielsen/ecrecover-noir/tree/main) - a library to verify an ECDSA signature and return the source Ethereum address +- [Sparse Merkle Tree Verifier](https://github.com/vocdoni/smtverifier-noir/tree/main) - a library for verification of sparse Merkle trees +- [Signed Int](https://github.com/resurgencelabs/signed_int) - a library for accessing a custom Signed Integer data type, allowing access to negative numbers on Noir +- [Fraction](https://github.com/resurgencelabs/fraction) - a library for accessing fractional number data type in Noir, allowing results that aren't whole numbers diff --git a/noir/docs/versioned_docs/version-v0.19.1/modules_packages_crates/modules.md b/noir/docs/versioned_docs/version-v0.19.1/modules_packages_crates/modules.md new file mode 100644 index 00000000000..147c9b284e8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/modules_packages_crates/modules.md @@ -0,0 +1,104 @@ +--- +title: Modules +description: + Learn how to organize your files using modules in Noir, following the same convention as Rust's + module system. Examples included. +keywords: [Noir, Rust, modules, organizing files, sub-modules] +--- + +Noir's module system follows the same convention as the _newer_ version of Rust's module system. + +## Purpose of Modules + +Modules are used to organise files. Without modules all of your code would need to live in a single +file. In Noir, the compiler does not automatically scan all of your files to detect modules. This +must be done explicitly by the developer. + +## Examples + +### Importing a module in the crate root + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::hello_world(); +} +``` + +Filename : `src/foo.nr` + +```rust +fn from_foo() {} +``` + +In the above snippet, the crate root is the `src/main.nr` file. The compiler sees the module +declaration `mod foo` which prompts it to look for a foo.nr file. + +Visually this module hierarchy looks like the following : + +``` +crate + ├── main + │ + └── foo + └── from_foo + +``` + +### Importing a module throughout the tree + +All modules are accessible from the `crate::` namespace. + +``` +crate + ├── bar + ├── foo + └── main + +``` + +In the above snippet, if `bar` would like to use functions in `foo`, it can do so by `use crate::foo::function_name`. + +### Sub-modules + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::from_foo(); +} +``` + +Filename : `src/foo.nr` + +```rust +mod bar; +fn from_foo() {} +``` + +Filename : `src/foo/bar.nr` + +```rust +fn from_bar() {} +``` + +In the above snippet, we have added an extra module to the module tree; `bar`. `bar` is a submodule +of `foo` hence we declare bar in `foo.nr` with `mod bar`. Since `foo` is not the crate root, the +compiler looks for the file associated with the `bar` module in `src/foo/bar.nr` + +Visually the module hierarchy looks as follows: + +``` +crate + ├── main + │ + └── foo + ├── from_foo + └── bar + └── from_bar +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/modules_packages_crates/workspaces.md b/noir/docs/versioned_docs/version-v0.19.1/modules_packages_crates/workspaces.md new file mode 100644 index 00000000000..d9ac92667c9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/modules_packages_crates/workspaces.md @@ -0,0 +1,39 @@ +--- +title: Workspaces +--- + +Workspaces are a feature of nargo that allow you to manage multiple related Noir packages in a single repository. A workspace is essentially a group of related projects that share common build output directories and configurations. + +Each Noir project (with it's own Nargo.toml file) can be thought of as a package. Each package is expected to contain exactly one "named circuit", being the "name" defined in Nargo.toml with the program logic defined in `./src/main.nr`. + +For a project with the following structure: + +```tree +├── crates +│   ├── a +│   │   ├── Nargo.toml +│   │   └── src +│   │   └── main.nr +│   └── b +│   ├── Nargo.toml +│   └── src +│   └── main.nr +├── Nargo.toml +└── Prover.toml +``` + +You can define a workspace in Nargo.toml like so: + +```toml +[workspace] +members = ["crates/a", "crates/b"] +default-member = "crates/a" +``` + +`members` indicates which packages are included in the workspace. As such, all member packages of a workspace will be processed when the `--workspace` flag is used with various commands or if a `default-member` is not specified. + +`default-member` indicates which package various commands process by default. + +Libraries can be defined in a workspace. Inside a workspace, these are consumed as `{ path = "../to_lib" }` dependencies in Nargo.toml. + +Inside a workspace, these are consumed as `{ path = "../to_lib" }` dependencies in Nargo.toml. diff --git a/noir/docs/versioned_docs/version-v0.19.1/nargo/01_commands.md b/noir/docs/versioned_docs/version-v0.19.1/nargo/01_commands.md new file mode 100644 index 00000000000..65e2bdb44e3 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/nargo/01_commands.md @@ -0,0 +1,250 @@ +--- +title: Commands +description: + Noir CLI Commands for Noir Prover and Verifier to create, execute, prove and verify programs, + generate Solidity verifier smart contract and compile into JSON file containing ACIR + representation and ABI of circuit. +keywords: + [ + Nargo, + Noir CLI, + Noir Prover, + Noir Verifier, + generate Solidity verifier, + compile JSON file, + ACIR representation, + ABI of circuit, + TypeScript, + ] +--- + +## General options + +| Option | Description | +| -------------------- | -------------------------------------------------- | +| `--show-ssa` | Emit debug information for the intermediate SSA IR | +| `--deny-warnings` | Quit execution when warnings are emitted | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo help [subcommand]` + +Prints the list of available commands or specific information of a subcommand. + +_Arguments_ + +| Argument | Description | +| -------------- | -------------------------------------------- | +| `` | The subcommand whose help message to display | + +## `nargo backend` + +Installs and selects custom backends used to generate and verify proofs. + +### Commands + +| Command | Description | +| ----------- | --------------------------------------------------------- | +| `current` | Prints the name of the currently active backend | +| `ls` | Prints the list of currently installed backends | +| `use` | Select the backend to use | +| `install` | Install a new backend from a URL | +| `uninstall` | Uninstalls a backend | +| `help` | Print this message or the help of the given subcommand(s) | + +### Options + +| Option | Description | +| ------------ | ----------- | +| `-h, --help` | Print help | + +## `nargo check` + +Generate the `Prover.toml` and `Verifier.toml` files for specifying prover and verifier in/output +values of the Noir program respectively. + +### Options + +| Option | Description | +| --------------------- | ------------------------------------- | +| `--package ` | The name of the package to check | +| `--workspace` | Check all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +### `nargo codegen-verifier` + +Generate a Solidity verifier smart contract for the program. + +### Options + +| Option | Description | +| --------------------- | ------------------------------------- | +| `--package ` | The name of the package to codegen | +| `--workspace` | Codegen all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo compile` + +Compile the program into a JSON build artifact file containing the ACIR representation and the ABI +of the circuit. This build artifact can then be used to generate and verify proofs. + +You can also use "build" as an alias for compile (e.g. `nargo build`). + +### Options + +| Option | Description | +| --------------------- | ------------------------------------------------------------ | +| `--include-keys` | Include Proving and Verification keys in the build artifacts | +| `--package ` | The name of the package to compile | +| `--workspace` | Compile all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo new ` + +Creates a new Noir project in a new folder. + +**Arguments** + +| Argument | Description | +| -------- | -------------------------------- | +| `` | The path to save the new project | + +### Options + +| Option | Description | +| --------------- | ----------------------------------------------------- | +| `--name ` | Name of the package [default: package directory name] | +| `--lib` | Use a library template | +| `--bin` | Use a binary template [default] | +| `--contract` | Use a contract template | +| `-h, --help` | Print help | + +## `nargo init` + +Creates a new Noir project in the current directory. + +### Options + +| Option | Description | +| --------------- | ----------------------------------------------------- | +| `--name ` | Name of the package [default: current directory name] | +| `--lib` | Use a library template | +| `--bin` | Use a binary template [default] | +| `--contract` | Use a contract template | +| `-h, --help` | Print help | + +## `nargo execute [WITNESS_NAME]` + +Runs the Noir program and prints its return value. + +**Arguments** + +| Argument | Description | +| ---------------- | ----------------------------------------- | +| `[WITNESS_NAME]` | Write the execution witness to named file | + +### Options + +| Option | Description | +| --------------------------------- | ------------------------------------------------------------------------------------ | +| `-p, --prover-name ` | The name of the toml file which contains the inputs for the prover [default: Prover] | +| `--package ` | The name of the package to execute | +| `--workspace` | Execute all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +_Usage_ + +The inputs to the circuit are read from the `Prover.toml` file generated by `nargo check`, which +must be filled in. + +To save the witness to file, run the command with a value for the `WITNESS_NAME` argument. A +`.tr` file will then be saved in the `./target` folder. + +## `nargo prove` + +Creates a proof for the program. + +### Options + +| Option | Description | +| ------------------------------------- | ---------------------------------------------------------------------------------------- | +| `-p, --prover-name ` | The name of the toml file which contains the inputs for the prover [default: Prover] | +| `-v, --verifier-name ` | The name of the toml file which contains the inputs for the verifier [default: Verifier] | +| `--verify` | Verify proof after proving | +| `--package ` | The name of the package to prove | +| `--workspace` | Prove all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo verify` + +Given a proof and a program, verify whether the proof is valid. + +### Options + +| Option | Description | +| ------------------------------------- | ---------------------------------------------------------------------------------------- | +| `-v, --verifier-name ` | The name of the toml file which contains the inputs for the verifier [default: Verifier] | +| `--package ` | The name of the package to verify | +| `--workspace` | Verify all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo test [TEST_NAME]` + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. To print `println` statements in tests, use the `--show-output` flag. + +Takes an optional `--exact` flag which allows you to select tests based on an exact name. + +See an example on the [testing page](./testing). + +### Options + +| Option | Description | +| --------------------- | -------------------------------------- | +| `--show-output` | Display output of `println` statements | +| `--exact` | Only run tests that match exactly | +| `--package ` | The name of the package to test | +| `--workspace` | Test all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo info` + +Prints a table containing the information of the package. + +Currently the table provide + +1. The number of ACIR opcodes +2. The final number gates in the circuit used by a backend + +If the file contains a contract the table will provide the +above information about each function of the contract. + +## `nargo lsp` + +Start a long-running Language Server process that communicates over stdin/stdout. +Usually this command is not run by a user, but instead will be run by a Language Client, such as [vscode-noir](https://github.com/noir-lang/vscode-noir). + +## `nargo fmt` + +Automatically formats your Noir source code based on the default formatting settings. diff --git a/noir/docs/versioned_docs/version-v0.19.1/nargo/02_testing.md b/noir/docs/versioned_docs/version-v0.19.1/nargo/02_testing.md new file mode 100644 index 00000000000..da767274efd --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/nargo/02_testing.md @@ -0,0 +1,61 @@ +--- +title: Testing in Noir +description: Learn how to use Nargo to test your Noir program in a quick and easy way +keywords: [Nargo, testing, Noir, compile, test] +--- + +You can test your Noir programs using Noir circuits. + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. + +For example if you have a program like: + +```rust +fn add(x: u64, y: u64) -> u64 { + x + y +} +#[test] +fn test_add() { + assert(add(2,2) == 4); + assert(add(0,1) == 1); + assert(add(1,0) == 1); +} +``` + +Running `nargo test` will test that the `test_add` function can be executed while satisfying the all +the contraints which allows you to test that add returns the expected values. Test functions can't +have any arguments currently. + +### Test fail + +You can write tests that are expected to fail by using the decorator `#[test(should_fail)]`. For example: + +```rust +fn add(x: u64, y: u64) -> u64 { + x + y +} +#[test(should_fail)] +fn test_add() { + assert(add(2,2) == 5); +} +``` + +You can be more specific and make it fail with a specific reason by using `should_fail_with = "`: + +```rust +fn main(african_swallow_avg_speed : Field) { + assert(african_swallow_avg_speed == 65, "What is the airspeed velocity of an unladen swallow"); +} + +#[test] +fn test_king_arthur() { + main(65); +} + +#[test(should_fail_with = "What is the airspeed velocity of an unladen swallow")] +fn test_bridgekeeper() { + main(32); +} + +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/nargo/03_solidity_verifier.md b/noir/docs/versioned_docs/version-v0.19.1/nargo/03_solidity_verifier.md new file mode 100644 index 00000000000..9ac60cb0ba7 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/nargo/03_solidity_verifier.md @@ -0,0 +1,129 @@ +--- +title: Solidity Verifier +description: + Learn how to run the verifier as a smart contract on the blockchain. Compile a Solidity verifier + contract for your Noir program and deploy it on any EVM blockchain acting as a verifier smart + contract. Read more to find out! +keywords: + [ + solidity verifier, + smart contract, + blockchain, + compiler, + plonk_vk.sol, + EVM blockchain, + verifying Noir programs, + proving backend, + Barretenberg, + ] +--- + +For certain applications, it may be desirable to run the verifier as a smart contract instead of on +a local machine. + +Compile a Solidity verifier contract for your Noir program by running: + +```sh +nargo codegen-verifier +``` + +A new `contract` folder would then be generated in your project directory, containing the Solidity +file `plonk_vk.sol`. It can be deployed on any EVM blockchain acting as a verifier smart contract. + +> **Note:** It is possible to compile verifier contracts of Noir programs for other smart contract +> platforms as long as the proving backend supplies an implementation. +> +> Barretenberg, the default proving backend for Nargo, supports compilation of verifier contracts in +> Solidity only for the time being. + +## Verify + +To verify a proof using the Solidity verifier contract, call the `verify` function with the +following signature: + +```solidity +function verify(bytes calldata _proof, bytes32[] calldata _publicInputs) external view returns (bool) +``` + +You can see an example of how the `verify` function is called in the example zk voting application [here](https://github.com/noir-lang/noir-examples/blob/33e598c257e2402ea3a6b68dd4c5ad492bce1b0a/foundry-voting/src/zkVote.sol#L35): + +```solidity +function castVote(bytes calldata proof, uint proposalId, uint vote, bytes32 nullifierHash) public returns (bool) { + // ... + bytes32[] memory publicInputs = new bytes32[](4); + publicInputs[0] = merkleRoot; + publicInputs[1] = bytes32(proposalId); + publicInputs[2] = bytes32(vote); + publicInputs[3] = nullifierHash; + require(verifier.verify(proof, publicInputs), "Invalid proof"); +``` + +### Public Inputs + +:::tip + +A circuit doesn't have the concept of a return value. Return values are just syntactic sugar in +Noir. + +Under the hood, the return value is passed as an input to the circuit and is checked at the end of +the circuit program. + +::: + +The verifier contract uses the output (return) value of a Noir program as a public input. So if you +have the following function + +```rust +fn main( + // Public inputs + pubkey_x: pub Field, + pubkey_y: pub Field, + // Private inputs + priv_key: Field, +) -> pub Field +``` + +then `verify` in `plonk_vk.sol` will expect 3 public inputs. Passing two inputs will result in an +error like `Reason: PUBLIC_INPUT_COUNT_INVALID(3, 2)`. + +In this case the 3 inputs to `verify` would be ordered as `[pubkey_x, pubkey_y, return]`. + +#### Struct inputs + +Consider the following program: + +```rust +struct Type1 { + val1: Field, + val2: Field, +} + +struct Nested { + t1: Type1, + is_true: bool, +} + +fn main(x: pub Field, nested: pub Nested, y: pub Field) { + //... +} +``` + +Structs will be flattened so that the array of inputs is 1-dimensional array. The order of these inputs would be flattened to: `[x, nested.t1.val1, nested.t1.val2, nested.is_true, y]` + +## Noir for EVM chains + +You can currently deploy the Solidity verifier contracts to most EVM compatible chains. EVM chains that have been tested and are known to work include: + +- Optimism +- Arbitrum +- Polygon PoS +- Scroll +- Celo + +Other EVM chains should work, but have not been tested directly by our team. If you test any other chains, please open a PR on this page to update the list. See [this doc](https://github.com/noir-lang/noir-starter/tree/main/with-foundry#testing-on-chain) for more info about testing verifier contracts on different EVM chains. + +### Unsupported chains + +Unfortunately not all "EVM" chains are supported. + +**zkSync** and the **Polygon zkEVM** do _not_ currently support proof verification via Solidity verifier contracts. They are missing the bn256 precompile contract that the verifier contract requires. Once these chains support this precompile, they may work. diff --git a/noir/docs/versioned_docs/version-v0.19.1/nargo/04_language_server.md b/noir/docs/versioned_docs/version-v0.19.1/nargo/04_language_server.md new file mode 100644 index 00000000000..48c01465f6e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/nargo/04_language_server.md @@ -0,0 +1,42 @@ +--- +title: Language Server +description: Learn about the Noir Language Server, how to install the components, and configuration that may be required. +keywords: [Nargo, Language Server, LSP, VSCode, Visual Studio Code] +--- + +This section helps you install and configure the Noir Language Server. + +The Language Server Protocol (LSP) has two components, the [Server](#language-server) and the [Client](#language-client). Below we describe each in the context of Noir. + +## Language Server + +The Server component is provided by the Nargo command line tool that you installed at the beginning of this guide. +As long as Nargo is installed and you've used it to run other commands in this guide, it should be good to go! + +If you'd like to verify that the `nargo lsp` command is available, you can run `nargo --help` and look for `lsp` in the list of commands. If you see it, you're using a version of Noir with LSP support. + +## Language Client + +The Client component is usually an editor plugin that launches the Server. It communicates LSP messages between the editor and the Server. For example, when you save a file, the Client will alert the Server, so it can try to compile the project and report any errors. + +Currently, Noir provides a Language Client for Visual Studio Code via the [vscode-noir](https://github.com/noir-lang/vscode-noir) extension. You can install it via the [Visual Studio Marketplace](https://marketplace.visualstudio.com/items?itemName=noir-lang.vscode-noir). + +> **Note:** Noir's Language Server Protocol support currently assumes users' VSCode workspace root to be the same as users' Noir project root (i.e. where Nargo.toml lies). +> +> If LSP features seem to be missing / malfunctioning, make sure you are opening your Noir project directly (instead of as a sub-folder) in your VSCode instance. + +When your language server is running correctly and the VSCode plugin is installed, you should see handy codelens buttons for compilation, measuring circuit size, execution, and tests: + +![Compile and Execute](@site/static/img/codelens_compile_execute.png) +![Run test](@site/static/img/codelens_run_test.png) + +You should also see your tests in the `testing` panel: + +![Testing panel](@site/static/img/codelens_testing_panel.png) + +### Configuration + +- **Noir: Enable LSP** - If checked, the extension will launch the Language Server via `nargo lsp` and communicate with it. +- **Noir: Nargo Flags** - Additional flags may be specified if you require them to be added when the extension calls `nargo lsp`. +- **Noir: Nargo Path** - An absolute path to a Nargo binary with the `lsp` command. This may be useful if Nargo is not within the `PATH` of your editor. +- **Noir > Trace: Server** - Setting this to `"messages"` or `"verbose"` will log LSP messages between the Client and Server. Useful for debugging. diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/getting_started/01_tiny_noir_app.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/getting_started/01_tiny_noir_app.md new file mode 100644 index 00000000000..c51ed61de52 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/getting_started/01_tiny_noir_app.md @@ -0,0 +1,260 @@ +--- +title: End-to-end +description: Learn how to setup a new app that uses Noir to generate and verify zero-knowledge SNARK proofs in a typescript or javascript environment +keywords: [how to, guide, javascript, typescript, noir, barretenberg, zero-knowledge, proofs] +--- + +NoirJS works both on the browser and on the server, and works for both ESM and CJS module systems. In this page, we will learn how can we write a simple test and a simple web app to verify the standard Noir example. + +You can find the complete app code for this guide [here](https://github.com/noir-lang/tiny-noirjs-app). + +## Before we start + +:::note + +Feel free to use whatever versions, just keep in mind that Nargo and the NoirJS packages are meant to be in sync. For example, Nargo 0.18.x matches `noir_js@0.18.x`, etc. + +In this guide, we will be pinned to 0.17.0. + +::: + +Make sure you have Node installed on your machine by opening a terminal and executing `node --version`. If you don't see a version, you should install [node](https://github.com/nvm-sh/nvm). You can also use `yarn` if you prefer that package manager over npm (which comes with node). + +First of all, follow the the [Nargo guide](../../getting_started/00_nargo_installation.md) to install nargo version 0.17.0 and create a new project with `nargo new circuit`. Once there, `cd` into the `circuit` folder. You should then be able to compile your circuit into `json` format and see it inside the `target` folder: + +```bash +nargo compile +``` + +Your folder structure should look like: + +```tree +. +└── circuit + ├── Nargo.toml + ├── src + │ └── main.nr + └── target + └── circuit.json +``` + +## Starting a new project + +Go back to the previous folder and start a new project by running run `npm init`. You can configure your project or just leave the defaults, and see a `package.json` appear in your root folder. + +## Installing dependencies + +We'll need two `npm` packages. These packages will provide us the methods we need to run and verify proofs: + +```bash +npm i @noir-lang/backend_barretenberg@^0.17.0 @noir-lang/noir_js@^0.17.0 +``` + +To serve our page, we can use a build tool such as `vite`. Because we're gonna use some `wasm` files, we need to install a plugin as well. Run: + +```bash +npm i --save-dev vite rollup-plugin-copy +``` + +Since we're on the dependency world, we may as well define a nice starting script. Vite makes it easy. Just open `package.json`, find the block "scripts" and add this just below the line with `"test" : "echo......."`: + +```json + "start": "vite --open" +``` + +If you want do build a static website, you can also add some build and preview scripts: + +```json + "build": "vite build", + "preview": "vite preview" +``` + +## Vite plugins + +Vite is great, but support from `wasm` doesn't work out-of-the-box. We're gonna write a quick plugin and use another one. Just copy and paste this into a file named `vite.config.js`. You don't need to understand it, just trust me bro. + +```js +import { defineConfig } from 'vite'; +import copy from 'rollup-plugin-copy'; +import fs from 'fs'; +import path from 'path'; + +const wasmContentTypePlugin = { + name: 'wasm-content-type-plugin', + configureServer(server) { + server.middlewares.use(async (req, res, next) => { + if (req.url.endsWith('.wasm')) { + res.setHeader('Content-Type', 'application/wasm'); + const newPath = req.url.replace('deps', 'dist'); + const targetPath = path.join(__dirname, newPath); + const wasmContent = fs.readFileSync(targetPath); + return res.end(wasmContent); + } + next(); + }); + }, +}; + +export default defineConfig(({ command }) => { + if (command === 'serve') { + return { + plugins: [ + copy({ + targets: [{ src: 'node_modules/**/*.wasm', dest: 'node_modules/.vite/dist' }], + copySync: true, + hook: 'buildStart', + }), + command === 'serve' ? wasmContentTypePlugin : [], + ], + }; + } + + return {}; +}); +``` + +## HTML + +Here's the simplest HTML with some terrible UI. Create a file called `index.html` and paste this: + +```html + + + + + + +

Very basic Noir app

+
+

Logs

+

Proof

+
+ + +``` + +## Some good old vanilla Javascript + +Create a new file `app.js`, which is where our javascript code will live. Let's start with this code inside: + +```js +document.addEventListener('DOMContentLoaded', async () => { + // here's where the magic happens +}); + +function display(container, msg) { + const c = document.getElementById(container); + const p = document.createElement('p'); + p.textContent = msg; + c.appendChild(p); +} +``` + +We can manipulate our website with this little function, so we can see our website working. + +## Adding Noir + +If you come from the previous page, your folder structure should look like this: + +```tree +├── app.js +├── circuit +│ ├── Nargo.toml +│ ├── src +│ │ └── main.nr +│ └── target +│ └── circuit.json +├── index.html +├── package.json +└── vite.config.js +``` + +You'll see other files and folders showing up (like `package-lock.json`, `yarn.lock`, `node_modules`) but you shouldn't have to care about those. + +## Importing our dependencies + +We're starting with the good stuff now. At the top of the new javascript file, import the packages: + +```ts +import { BarretenbergBackend } from '@noir-lang/backend_barretenberg'; +import { Noir } from '@noir-lang/noir_js'; +``` + +We also need to import the `circuit` JSON file we created. If you have the suggested folder structure, you can add this line: + +```ts +import circuit from './circuit/target/circuit.json'; +``` + +## Write code + +:::note + +We're gonna be adding code inside the `document.addEventListener...etc` block: + +```js +// forget stuff here +document.addEventListener('DOMContentLoaded', async () => { + // here's where the magic happens +}); +// forget stuff here +``` + +::: + +Our dependencies exported two classes: `BarretenbergBackend` and `Noir`. Let's `init` them and add some logs, just to flex: + +```ts +const backend = new BarretenbergBackend(circuit); +const noir = new Noir(circuit, backend); +``` + +## Proving + +Now we're ready to prove stuff! Let's feed some inputs to our circuit and calculate the proof: + +```js +const input = { x: 1, y: 2 }; +display('logs', 'Generating proof... ⌛'); +const proof = await noir.generateFinalProof(input); +display('logs', 'Generating proof... ✅'); +display('results', proof.proof); +``` + +You're probably eager to see stuff happening, so go and run your app now! + +From your terminal, run `npm start` (or `yarn start`). If it doesn't open a browser for you, just visit `localhost:5173`. On a modern laptop, proof will generate in less than 100ms, and you'll see this: + +![Getting Started 0](@site/static/img/noir_getting_started_1.png) + +If you're human, you shouldn't be able to understand anything on the "proof" box. That's OK. We like you, human. + +In any case, this means your proof was generated! But you shouldn't trust me just yet. Add these lines to see it being verified: + +```js +display('logs', 'Verifying proof... ⌛'); +const verification = await noir.verifyFinalProof(proof); +if (verification) display('logs', 'Verifying proof... ✅'); +``` + +By saving, your app will refresh and here's our complete Tiny Noir App! + +You can find the complete app code for this guide [here](https://github.com/noir-lang/tiny-noirjs-app). + +## Further Reading + +You can see how noirjs is used in a full stack Next.js hardhat application in the [noir-starter repo here](https://github.com/noir-lang/noir-starter/tree/main/next-hardhat). The example shows how to calculate a proof in the browser and verify it with a deployed Solidity verifier contract from noirjs. + +You should also check out the more advanced examples in the [noir-examples repo](https://github.com/noir-lang/noir-examples), where you'll find reference usage for some cool apps. diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/noir_js.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/noir_js.md new file mode 100644 index 00000000000..f895b22eaf8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/noir_js.md @@ -0,0 +1,36 @@ +--- +title: NoirJS +description: Interact with Noir in Typescript or Javascript +keywords: [Noir project, javascript, typescript, node.js, browser, react] +--- + +NoirJS is a TypeScript library that make it easy to use Noir on your dapp, webapp, Node.js server, website, etc. + +A typical workflow would be composed of two major elements: + +- NoirJS +- Proving backend of choice's JavaScript package + + + +To install NoirJS, install Node.js if you have not already and run this in your JavaScript project: + +```bash +npm i @noir-lang/noir_js +``` + +## Proving backend + +Since Noir is backend agnostic, you can instantiate NoirJS without any backend (i.e. to execute a function). But for proving, you would have to instantiate NoirJS with any of the supported backends through their own `js` interface. + +### Barretenberg + +Aztec Labs maintains the `barretenberg` proving backend, which you can instantiate and make use of alongside NoirJS. It is also the default proving backend installed and used with Nargo, the Noir CLI tool. + +To install its JavaScript library, run this in your project: + +```bash +npm i @noir-lang/backend_barretenberg +``` + +For more details on how to instantiate and use the libraries, refer to the [Full Noir App Guide](./getting_started/01_tiny_noir_app.md) and [Reference](./reference/noir_js/classes/Noir.md) sections. diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/.nojekyll b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/.nojekyll new file mode 100644 index 00000000000..e2ac6616add --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/.nojekyll @@ -0,0 +1 @@ +TypeDoc added this file to prevent GitHub Pages from using Jekyll. You can turn off this behavior by setting the `githubPages` option to false. \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/classes/BarretenbergBackend.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/classes/BarretenbergBackend.md new file mode 100644 index 00000000000..5cbe9421b92 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/classes/BarretenbergBackend.md @@ -0,0 +1,185 @@ +# BarretenbergBackend + +## Implements + +- [`Backend`](../interfaces/Backend.md) + +## Constructors + +### new BarretenbergBackend(acirCircuit, options) + +```ts +new BarretenbergBackend(acirCircuit, options): BarretenbergBackend +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `acirCircuit` | [`CompiledCircuit`](../type-aliases/CompiledCircuit.md) | +| `options` | [`BackendOptions`](../type-aliases/BackendOptions.md) | + +#### Returns + +[`BarretenbergBackend`](BarretenbergBackend.md) + +## Methods + +### destroy() + +```ts +destroy(): Promise +``` + +#### Returns + +`Promise`\<`void`\> + +#### Implementation of + +[`Backend`](../interfaces/Backend.md).[`destroy`](../interfaces/Backend.md#destroy) + +#### Description + +Destroys the backend + +*** + +### generateFinalProof() + +```ts +generateFinalProof(decompressedWitness): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `decompressedWitness` | `Uint8Array` | + +#### Returns + +`Promise`\<[`ProofData`](../type-aliases/ProofData.md)\> + +#### Implementation of + +[`Backend`](../interfaces/Backend.md).[`generateFinalProof`](../interfaces/Backend.md#generatefinalproof) + +#### Description + +Generates a final proof (not meant to be verified in another circuit) + +*** + +### generateIntermediateProof() + +```ts +generateIntermediateProof(witness): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `witness` | `Uint8Array` | + +#### Returns + +`Promise`\<[`ProofData`](../type-aliases/ProofData.md)\> + +#### Implementation of + +[`Backend`](../interfaces/Backend.md).[`generateIntermediateProof`](../interfaces/Backend.md#generateintermediateproof) + +#### Example + +```typescript +const intermediateProof = await backend.generateIntermediateProof(witness); +``` + +*** + +### generateIntermediateProofArtifacts() + +```ts +generateIntermediateProofArtifacts(proofData, numOfPublicInputs): Promise +``` + +#### Parameters + +| Parameter | Type | Default value | +| :------ | :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | `undefined` | +| `numOfPublicInputs` | `number` | `0` | + +#### Returns + +`Promise`\<`object`\> + +#### Implementation of + +[`Backend`](../interfaces/Backend.md).[`generateIntermediateProofArtifacts`](../interfaces/Backend.md#generateintermediateproofartifacts) + +#### Example + +```typescript +const artifacts = await backend.generateIntermediateProofArtifacts(proof, numOfPublicInputs); +``` + +*** + +### verifyFinalProof() + +```ts +verifyFinalProof(proofData): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | + +#### Returns + +`Promise`\<`boolean`\> + +#### Implementation of + +[`Backend`](../interfaces/Backend.md).[`verifyFinalProof`](../interfaces/Backend.md#verifyfinalproof) + +#### Description + +Verifies a final proof + +*** + +### verifyIntermediateProof() + +```ts +verifyIntermediateProof(proofData): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | + +#### Returns + +`Promise`\<`boolean`\> + +#### Implementation of + +[`Backend`](../interfaces/Backend.md).[`verifyIntermediateProof`](../interfaces/Backend.md#verifyintermediateproof) + +#### Example + +```typescript +const isValidIntermediate = await backend.verifyIntermediateProof(proof); +``` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/index.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/index.md new file mode 100644 index 00000000000..3680ba3ca77 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/index.md @@ -0,0 +1,27 @@ +# Backend Barretenberg + +## Exports + +### Classes + +| Class | Description | +| :------ | :------ | +| [BarretenbergBackend](classes/BarretenbergBackend.md) | - | + +### Interfaces + +| Interface | Description | +| :------ | :------ | +| [Backend](interfaces/Backend.md) | - | + +### Type Aliases + +| Type alias | Description | +| :------ | :------ | +| [BackendOptions](type-aliases/BackendOptions.md) | - | +| [CompiledCircuit](type-aliases/CompiledCircuit.md) | - | +| [ProofData](type-aliases/ProofData.md) | - | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/interfaces/Backend.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/interfaces/Backend.md new file mode 100644 index 00000000000..3eb9645c8d2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/interfaces/Backend.md @@ -0,0 +1,132 @@ +# Backend + +## Methods + +### destroy() + +```ts +destroy(): Promise +``` + +#### Returns + +`Promise`\<`void`\> + +#### Description + +Destroys the backend + +*** + +### generateFinalProof() + +```ts +generateFinalProof(decompressedWitness): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `decompressedWitness` | `Uint8Array` | + +#### Returns + +`Promise`\<[`ProofData`](../type-aliases/ProofData.md)\> + +#### Description + +Generates a final proof (not meant to be verified in another circuit) + +*** + +### generateIntermediateProof() + +```ts +generateIntermediateProof(decompressedWitness): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `decompressedWitness` | `Uint8Array` | + +#### Returns + +`Promise`\<[`ProofData`](../type-aliases/ProofData.md)\> + +#### Description + +Generates an intermediate proof (meant to be verified in another circuit) + +*** + +### generateIntermediateProofArtifacts() + +```ts +generateIntermediateProofArtifacts(proofData, numOfPublicInputs): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | +| `numOfPublicInputs` | `number` | + +#### Returns + +`Promise`\<`object`\> + +#### Description + +Retrieves the artifacts from a proof in the Field format + +*** + +### verifyFinalProof() + +```ts +verifyFinalProof(proofData): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | + +#### Returns + +`Promise`\<`boolean`\> + +#### Description + +Verifies a final proof + +*** + +### verifyIntermediateProof() + +```ts +verifyIntermediateProof(proofData): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | + +#### Returns + +`Promise`\<`boolean`\> + +#### Description + +Verifies an intermediate proof + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/type-aliases/BackendOptions.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/type-aliases/BackendOptions.md new file mode 100644 index 00000000000..266ade75d17 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/type-aliases/BackendOptions.md @@ -0,0 +1,19 @@ +# BackendOptions + +```ts +type BackendOptions: object; +``` + +## Description + +An options object, currently only used to specify the number of threads to use. + +## Type declaration + +| Member | Type | Description | +| :------ | :------ | :------ | +| `threads` | `number` | **Description**

Number of threads | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/type-aliases/CompiledCircuit.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/type-aliases/CompiledCircuit.md new file mode 100644 index 00000000000..34e0dd04205 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/type-aliases/CompiledCircuit.md @@ -0,0 +1,20 @@ +# CompiledCircuit + +```ts +type CompiledCircuit: object; +``` + +## Description + +The representation of a compiled circuit + +## Type declaration + +| Member | Type | Description | +| :------ | :------ | :------ | +| `abi` | `Abi` | **Description**

ABI representation of the circuit | +| `bytecode` | `string` | **Description**

The bytecode of the circuit | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/type-aliases/ProofData.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/type-aliases/ProofData.md new file mode 100644 index 00000000000..4aeff73d3e4 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/type-aliases/ProofData.md @@ -0,0 +1,20 @@ +# ProofData + +```ts +type ProofData: object; +``` + +## Description + +The representation of a proof + +## Type declaration + +| Member | Type | Description | +| :------ | :------ | :------ | +| `proof` | `Uint8Array` | **Description**

An byte array representing the proof | +| `publicInputs` | `Uint8Array`[] | **Description**

Public inputs of a proof | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/typedoc-sidebar.cjs b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/typedoc-sidebar.cjs new file mode 100644 index 00000000000..04e662c845f --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/backend_barretenberg/typedoc-sidebar.cjs @@ -0,0 +1,4 @@ +// @ts-check +/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ +const typedocSidebar = { items: [{"type":"category","label":"Classes","items":[{"type":"doc","id":"noir_js/reference/backend_barretenberg/classes/BarretenbergBackend","label":"BarretenbergBackend"}]},{"type":"category","label":"Interfaces","items":[{"type":"doc","id":"noir_js/reference/backend_barretenberg/interfaces/Backend","label":"Backend"}]},{"type":"category","label":"Type Aliases","items":[{"type":"doc","id":"noir_js/reference/backend_barretenberg/type-aliases/BackendOptions","label":"BackendOptions"},{"type":"doc","id":"noir_js/reference/backend_barretenberg/type-aliases/CompiledCircuit","label":"CompiledCircuit"},{"type":"doc","id":"noir_js/reference/backend_barretenberg/type-aliases/ProofData","label":"ProofData"}]}]}; +module.exports = typedocSidebar.items; \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/.nojekyll b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/.nojekyll new file mode 100644 index 00000000000..e2ac6616add --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/.nojekyll @@ -0,0 +1 @@ +TypeDoc added this file to prevent GitHub Pages from using Jekyll. You can turn off this behavior by setting the `githubPages` option to false. \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/classes/Noir.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/classes/Noir.md new file mode 100644 index 00000000000..a8a0bb451c1 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/classes/Noir.md @@ -0,0 +1,131 @@ +# Noir + +## Constructors + +### new Noir(circuit, backend) + +```ts +new Noir(circuit, backend?): Noir +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `circuit` | [`CompiledCircuit`](../type-aliases/CompiledCircuit.md) | +| `backend`? | `Backend` | + +#### Returns + +[`Noir`](Noir.md) + +## Methods + +### destroy() + +```ts +destroy(): Promise +``` + +#### Returns + +`Promise`\<`void`\> + +#### Description + +Destroys the underlying backend instance. + +#### Example + +```typescript +await noir.destroy(); +``` + +*** + +### execute() + +```ts +execute(inputs, foreignCallHandler?): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `inputs` | `InputMap` | +| `foreignCallHandler`? | [`ForeignCallHandler`](../type-aliases/ForeignCallHandler.md) | + +#### Returns + +`Promise`\<`object`\> + +#### Description + +Allows to execute a circuit to get its witness and return value. + +#### Example + +```typescript +async execute(inputs) +``` + +*** + +### generateFinalProof() + +```ts +generateFinalProof(inputs): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `inputs` | `InputMap` | + +#### Returns + +`Promise`\<[`ProofData`](../type-aliases/ProofData.md)\> + +#### Description + +Generates a witness and a proof given an object as input. + +#### Example + +```typescript +async generateFinalproof(input) +``` + +*** + +### verifyFinalProof() + +```ts +verifyFinalProof(proofData): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | + +#### Returns + +`Promise`\<`boolean`\> + +#### Description + +Instantiates the verification key and verifies a proof. + +#### Example + +```typescript +async verifyFinalProof(proof) +``` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/and.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/and.md new file mode 100644 index 00000000000..c783283e396 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/and.md @@ -0,0 +1,22 @@ +# and() + +```ts +and(lhs, rhs): string +``` + +Performs a bitwise AND operation between `lhs` and `rhs` + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `lhs` | `string` | | +| `rhs` | `string` | | + +## Returns + +`string` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/blake2s256.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/blake2s256.md new file mode 100644 index 00000000000..7882d0da8d5 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/blake2s256.md @@ -0,0 +1,21 @@ +# blake2s256() + +```ts +blake2s256(inputs): Uint8Array +``` + +Calculates the Blake2s256 hash of the input bytes + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `inputs` | `Uint8Array` | | + +## Returns + +`Uint8Array` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/ecdsa_secp256k1_verify.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/ecdsa_secp256k1_verify.md new file mode 100644 index 00000000000..0ba5783f0d5 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/ecdsa_secp256k1_verify.md @@ -0,0 +1,29 @@ +# ecdsa\_secp256k1\_verify() + +```ts +ecdsa_secp256k1_verify( + hashed_msg, + public_key_x_bytes, + public_key_y_bytes, + signature): boolean +``` + +Calculates the Blake2s256 hash of the input bytes and represents these as a single field element. +Verifies a ECDSA signature over the secp256k1 curve. + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `hashed_msg` | `Uint8Array` | | +| `public_key_x_bytes` | `Uint8Array` | | +| `public_key_y_bytes` | `Uint8Array` | | +| `signature` | `Uint8Array` | | + +## Returns + +`boolean` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/ecdsa_secp256r1_verify.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/ecdsa_secp256r1_verify.md new file mode 100644 index 00000000000..0b20ff68957 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/ecdsa_secp256r1_verify.md @@ -0,0 +1,28 @@ +# ecdsa\_secp256r1\_verify() + +```ts +ecdsa_secp256r1_verify( + hashed_msg, + public_key_x_bytes, + public_key_y_bytes, + signature): boolean +``` + +Verifies a ECDSA signature over the secp256r1 curve. + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `hashed_msg` | `Uint8Array` | | +| `public_key_x_bytes` | `Uint8Array` | | +| `public_key_y_bytes` | `Uint8Array` | | +| `signature` | `Uint8Array` | | + +## Returns + +`boolean` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/keccak256.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/keccak256.md new file mode 100644 index 00000000000..d10f155ce86 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/keccak256.md @@ -0,0 +1,21 @@ +# keccak256() + +```ts +keccak256(inputs): Uint8Array +``` + +Calculates the Keccak256 hash of the input bytes + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `inputs` | `Uint8Array` | | + +## Returns + +`Uint8Array` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/sha256.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/sha256.md new file mode 100644 index 00000000000..6ba4ecac022 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/sha256.md @@ -0,0 +1,21 @@ +# sha256() + +```ts +sha256(inputs): Uint8Array +``` + +Calculates the SHA256 hash of the input bytes + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `inputs` | `Uint8Array` | | + +## Returns + +`Uint8Array` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/xor.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/xor.md new file mode 100644 index 00000000000..8d762b895d3 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/functions/xor.md @@ -0,0 +1,22 @@ +# xor() + +```ts +xor(lhs, rhs): string +``` + +Performs a bitwise XOR operation between `lhs` and `rhs` + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `lhs` | `string` | | +| `rhs` | `string` | | + +## Returns + +`string` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/index.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/index.md new file mode 100644 index 00000000000..58902c17b99 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/index.md @@ -0,0 +1,36 @@ +# Noir JS + +## Exports + +### Classes + +| Class | Description | +| :------ | :------ | +| [Noir](classes/Noir.md) | - | + +### Type Aliases + +| Type alias | Description | +| :------ | :------ | +| [CompiledCircuit](type-aliases/CompiledCircuit.md) | - | +| [ForeignCallHandler](type-aliases/ForeignCallHandler.md) | A callback which performs an foreign call and returns the response. | +| [ForeignCallInput](type-aliases/ForeignCallInput.md) | - | +| [ForeignCallOutput](type-aliases/ForeignCallOutput.md) | - | +| [ProofData](type-aliases/ProofData.md) | - | +| [WitnessMap](type-aliases/WitnessMap.md) | - | + +### Functions + +| Function | Description | +| :------ | :------ | +| [and](functions/and.md) | Performs a bitwise AND operation between `lhs` and `rhs` | +| [blake2s256](functions/blake2s256.md) | Calculates the Blake2s256 hash of the input bytes | +| [ecdsa\_secp256k1\_verify](functions/ecdsa_secp256k1_verify.md) | Calculates the Blake2s256 hash of the input bytes and represents these as a single field element. | +| [ecdsa\_secp256r1\_verify](functions/ecdsa_secp256r1_verify.md) | Verifies a ECDSA signature over the secp256r1 curve. | +| [keccak256](functions/keccak256.md) | Calculates the Keccak256 hash of the input bytes | +| [sha256](functions/sha256.md) | Calculates the SHA256 hash of the input bytes | +| [xor](functions/xor.md) | Performs a bitwise XOR operation between `lhs` and `rhs` | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/CompiledCircuit.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/CompiledCircuit.md new file mode 100644 index 00000000000..34e0dd04205 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/CompiledCircuit.md @@ -0,0 +1,20 @@ +# CompiledCircuit + +```ts +type CompiledCircuit: object; +``` + +## Description + +The representation of a compiled circuit + +## Type declaration + +| Member | Type | Description | +| :------ | :------ | :------ | +| `abi` | `Abi` | **Description**

ABI representation of the circuit | +| `bytecode` | `string` | **Description**

The bytecode of the circuit | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/ForeignCallHandler.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/ForeignCallHandler.md new file mode 100644 index 00000000000..812b8b16481 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/ForeignCallHandler.md @@ -0,0 +1,24 @@ +# ForeignCallHandler + +```ts +type ForeignCallHandler: (name, inputs) => Promise; +``` + +A callback which performs an foreign call and returns the response. + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `name` | `string` | The identifier for the type of foreign call being performed. | +| `inputs` | [`ForeignCallInput`](ForeignCallInput.md)[] | An array of hex encoded inputs to the foreign call. | + +## Returns + +`Promise`\<[`ForeignCallOutput`](ForeignCallOutput.md)[]\> + +outputs - An array of hex encoded outputs containing the results of the foreign call. + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/ForeignCallInput.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/ForeignCallInput.md new file mode 100644 index 00000000000..dd95809186a --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/ForeignCallInput.md @@ -0,0 +1,9 @@ +# ForeignCallInput + +```ts +type ForeignCallInput: string[]; +``` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/ForeignCallOutput.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/ForeignCallOutput.md new file mode 100644 index 00000000000..b71fb78a946 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/ForeignCallOutput.md @@ -0,0 +1,9 @@ +# ForeignCallOutput + +```ts +type ForeignCallOutput: string | string[]; +``` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/ProofData.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/ProofData.md new file mode 100644 index 00000000000..4aeff73d3e4 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/ProofData.md @@ -0,0 +1,20 @@ +# ProofData + +```ts +type ProofData: object; +``` + +## Description + +The representation of a proof + +## Type declaration + +| Member | Type | Description | +| :------ | :------ | :------ | +| `proof` | `Uint8Array` | **Description**

An byte array representing the proof | +| `publicInputs` | `Uint8Array`[] | **Description**

Public inputs of a proof | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/WitnessMap.md b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/WitnessMap.md new file mode 100644 index 00000000000..258c46f9d0c --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/type-aliases/WitnessMap.md @@ -0,0 +1,9 @@ +# WitnessMap + +```ts +type WitnessMap: Map; +``` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/typedoc-sidebar.cjs b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/typedoc-sidebar.cjs new file mode 100644 index 00000000000..c18318850d0 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/noir_js/reference/noir_js/typedoc-sidebar.cjs @@ -0,0 +1,4 @@ +// @ts-check +/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ +const typedocSidebar = { items: [{"type":"category","label":"Classes","items":[{"type":"doc","id":"noir_js/reference/noir_js/classes/Noir","label":"Noir"}]},{"type":"category","label":"Type Aliases","items":[{"type":"doc","id":"noir_js/reference/noir_js/type-aliases/CompiledCircuit","label":"CompiledCircuit"},{"type":"doc","id":"noir_js/reference/noir_js/type-aliases/ForeignCallHandler","label":"ForeignCallHandler"},{"type":"doc","id":"noir_js/reference/noir_js/type-aliases/ForeignCallInput","label":"ForeignCallInput"},{"type":"doc","id":"noir_js/reference/noir_js/type-aliases/ForeignCallOutput","label":"ForeignCallOutput"},{"type":"doc","id":"noir_js/reference/noir_js/type-aliases/ProofData","label":"ProofData"},{"type":"doc","id":"noir_js/reference/noir_js/type-aliases/WitnessMap","label":"WitnessMap"}]},{"type":"category","label":"Functions","items":[{"type":"doc","id":"noir_js/reference/noir_js/functions/and","label":"and"},{"type":"doc","id":"noir_js/reference/noir_js/functions/blake2s256","label":"blake2s256"},{"type":"doc","id":"noir_js/reference/noir_js/functions/ecdsa_secp256k1_verify","label":"ecdsa_secp256k1_verify"},{"type":"doc","id":"noir_js/reference/noir_js/functions/ecdsa_secp256r1_verify","label":"ecdsa_secp256r1_verify"},{"type":"doc","id":"noir_js/reference/noir_js/functions/keccak256","label":"keccak256"},{"type":"doc","id":"noir_js/reference/noir_js/functions/sha256","label":"sha256"},{"type":"doc","id":"noir_js/reference/noir_js/functions/xor","label":"xor"}]}]}; +module.exports = typedocSidebar.items; \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.19.1/standard_library/black_box_fns.md b/noir/docs/versioned_docs/version-v0.19.1/standard_library/black_box_fns.md new file mode 100644 index 00000000000..1dfabfe8f22 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/standard_library/black_box_fns.md @@ -0,0 +1,46 @@ +--- +title: Black Box Functions +description: Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. +keywords: [noir, black box functions] +--- + +Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. This makes certain zk-snark unfriendly computations cheaper than if they were implemented in Noir. + +:::warning + +It is likely that not all backends will support a particular black box function. + +::: + +Because it is not guaranteed that all backends will support black box functions, it is possible that certain Noir programs won't compile against a particular backend if they use an unsupported black box function. It is possible to fallback to less efficient implementations written in Noir/ACIR in some cases. + +Black box functions are specified with the `#[foreign(black_box_fn)]` attribute. For example, the SHA256 function in the Noir [source code](https://github.com/noir-lang/noir/blob/v0.5.1/noir_stdlib/src/hash.nr) looks like: + +```rust +#[foreign(sha256)] +fn sha256(_input : [u8; N]) -> [u8; 32] {} +``` + +## Function list + +Here is a list of the current black box functions that are supported by UltraPlonk: + +- AES +- [SHA256](./cryptographic_primitives/hashes#sha256) +- [Schnorr signature verification](./cryptographic_primitives/schnorr) +- [Blake2s](./cryptographic_primitives/hashes#blake2s) +- [Pedersen Hash](./cryptographic_primitives/hashes#pedersen_hash) +- [Pedersen Commitment](./cryptographic_primitives/hashes#pedersen_commitment) +- [HashToField128Security](./cryptographic_primitives/hashes#hash_to_field) +- [ECDSA signature verification](./cryptographic_primitives/ecdsa_sig_verification) +- [Fixed base scalar multiplication](./cryptographic_primitives/scalar) +- [Compute merkle root](./merkle_trees#compute_merkle_root) +- AND +- XOR +- RANGE +- [Keccak256](./cryptographic_primitives/hashes#keccak256) +- [Recursive proof verification](./recursion) + +Most black box functions are included as part of the Noir standard library, however `AND`, `XOR` and `RANGE` are used as part of the Noir language syntax. For instance, using the bitwise operator `&` will invoke the `AND` black box function. To ensure compatibility across backends, the ACVM has fallback implementations of `AND`, `XOR` and `RANGE` defined in its standard library which it can seamlessly fallback to if the backend doesn't support them. + +You can view the black box functions defined in the ACVM code [here](https://github.com/noir-lang/acvm/blob/acir-v0.12.0/acir/src/circuit/black_box_functions.rs). diff --git a/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives.md b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives.md new file mode 100644 index 00000000000..2df4f929474 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives.md @@ -0,0 +1,14 @@ +--- +title: Cryptographic primitives in Noir +description: + Learn about the cryptographic primitives ready to use for any Noir project +keywords: + [ + cryptographic primitives, + Noir project, + ] +--- + +The Noir team is progressively adding new cryptographic primitives to the standard library. Reach out for news or if you would be interested in adding more of these calculations in Noir. + +Some methods are available thanks to the Aztec backend, not being performed using Noir. When using other backends, these methods may or may not be supplied. diff --git a/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/00_hashes.mdx b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/00_hashes.mdx new file mode 100644 index 00000000000..76745196681 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/00_hashes.mdx @@ -0,0 +1,167 @@ +--- +title: Hash methods +description: + Learn about the cryptographic primitives ready to use for any Noir project, including sha256, + blake2s, pedersen, mimc_bn254 and mimc +keywords: + [cryptographic primitives, Noir project, sha256, blake2s, pedersen, mimc_bn254, mimc, hash] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## sha256 + +Given an array of bytes, returns the resulting sha256 hash. + +```rust +fn sha256(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::sha256(x); +} +``` + + + +## blake2s + +Given an array of bytes, returns an array with the Blake2 hash + +```rust +fn blake2s(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::blake2s(x); +} +``` + + + +## pedersen_hash + +Given an array of Fields, returns the Pedersen hash. + +```rust +fn pedersen_hash(_input : [Field]) -> Field +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::pedersen_hash(x); +} +``` + + + + + +## pedersen_commitment + +Given an array of Fields, returns the Pedersen commitment. + +```rust +fn pedersen_commitment(_input : [Field]) -> [Field; 2] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let commitment = std::hash::pedersen_commitment(x); +} +``` + + + +## keccak256 + +Given an array of bytes (`u8`), returns the resulting keccak hash as an array of 32 bytes +(`[u8; 32]`). Specify a message_size to hash only the first `message_size` bytes +of the input. + +```rust +fn keccak256(_input : [u8; N], _message_size: u32) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let message_size = 4; + let hash = std::hash::keccak256(x, message_size); +} +``` + + + +## poseidon + +Given an array of Fields, returns a new Field with the Poseidon Hash. Mind that you need to specify +how many inputs are there to your Poseidon function. + +```rust +// example for hash_1, hash_2 accepts an array of length 2, etc +fn hash_1(input: [Field; 1]) -> Field +``` + +example: + +```rust +fn main() +{ + let hash1 = std::hash::poseidon::bn254::hash_2([1, 2]); + assert(hash1 == 0x115cc0f5e7d690413df64c6b9662e9cf2a3617f2743245519e19607a4417189a); +} +``` + +## mimc_bn254 and mimc + +`mimc_bn254` is `mimc`, but with hardcoded parameters for the BN254 curve. You can use it by +providing an array of Fields, and it returns a Field with the hash. You can use the `mimc` method if +you're willing to input your own constants: + +```rust +fn mimc(x: Field, k: Field, constants: [Field; N], exp : Field) -> Field +``` + +otherwise, use the `mimc_bn254` method: + +```rust +fn mimc_bn254(array: [Field; N]) -> Field +``` + +example: + +```rust + +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::mimc::mimc_bn254(x); +} +``` + +## hash_to_field + +```rust +fn hash_to_field(_input : [Field; N]) -> Field {} +``` + +Calculates the `blake2s` hash of the inputs and returns the hash modulo the field modulus to return +a value which can be represented as a `Field`. + + diff --git a/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/01_scalar.mdx b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/01_scalar.mdx new file mode 100644 index 00000000000..c7eed820a80 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/01_scalar.mdx @@ -0,0 +1,27 @@ +--- +title: Scalar multiplication +description: See how you can perform scalar multiplications over a fixed base in Noir +keywords: [cryptographic primitives, Noir project, scalar multiplication] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## scalar_mul::fixed_base_embedded_curve + +Performs scalar multiplication over the embedded curve whose coordinates are defined by the +configured noir field. For the BN254 scalar field, this is BabyJubJub or Grumpkin. + +```rust +fn fixed_base_embedded_curve(_input : Field) -> [Field; 2] +``` + +example + +```rust +fn main(x : Field) { + let scal = std::scalar_mul::fixed_base_embedded_curve(x); + std::println(scal); +} +``` + + diff --git a/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/02_schnorr.mdx b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/02_schnorr.mdx new file mode 100644 index 00000000000..c184ce28120 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/02_schnorr.mdx @@ -0,0 +1,37 @@ +--- +title: Schnorr Signatures +description: Learn how you can verify Schnorr signatures using Noir +keywords: [cryptographic primitives, Noir project, schnorr, signatures] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## schnorr::verify_signature + +Verifier for Schnorr signatures over the embedded curve (for BN254 it is Grumpkin). + +```rust +fn verify_signature(_public_key_x: Field, _public_key_y: Field, _signature: [u8; 64], _message: [u8]) -> bool +``` + +where `_signature` can be generated like so using the npm package +[@noir-lang/barretenberg](https://www.npmjs.com/package/@noir-lang/barretenberg) + +```js +const { BarretenbergWasm } = require('@noir-lang/barretenberg/dest/wasm'); +const { Schnorr } = require('@noir-lang/barretenberg/dest/crypto/schnorr'); + +... + +const barretenberg = await BarretenbergWasm.new(); +const schnorr = new Schnorr(barretenberg); +const pubKey = schnorr.computePublicKey(privateKey); +const message = ... +const signature = Array.from( + schnorr.constructSignature(hash, privateKey).toBuffer() +); + +... +``` + + diff --git a/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx new file mode 100644 index 00000000000..72bce984821 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx @@ -0,0 +1,45 @@ +--- +title: ECDSA Signature Verification +description: Learn about the cryptographic primitives regarding ECDSA over the secp256k1 and secp256r1 curves +keywords: [cryptographic primitives, Noir project, ecdsa, secp256k1, secp256r1, signatures] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +Noir supports ECDSA signatures verification over the secp256k1 and secp256r1 curves. + +## ecdsa_secp256k1::verify_signature + +Verifier for ECDSA Secp256k1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> bool +``` + +example: + +```rust +fn main(hashed_message : [u8;32], pub_key_x : [u8;32], pub_key_y : [u8;32], signature : [u8;64]) { + let valid_signature = std::ecdsa_secp256k1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} +``` + +## ecdsa_secp256r1::verify_signature + +Verifier for ECDSA Secp256r1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> bool +``` + +example: + +```rust +fn main(hashed_message : [u8;32], pub_key_x : [u8;32], pub_key_y : [u8;32], signature : [u8;64]) { + let valid_signature = std::ecdsa_secp256r1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} +``` + + diff --git a/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/04_ec_primitives.md b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/04_ec_primitives.md new file mode 100644 index 00000000000..6e6b19b6861 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/04_ec_primitives.md @@ -0,0 +1,101 @@ +--- +title: Elliptic Curve Primitives +keywords: [cryptographic primitives, Noir project] +--- + +Data structures and methods on them that allow you to carry out computations involving elliptic +curves over the (mathematical) field corresponding to `Field`. For the field currently at our +disposal, applications would involve a curve embedded in BN254, e.g. the +[Baby Jubjub curve](https://eips.ethereum.org/EIPS/eip-2494). + +## Data structures + +### Elliptic curve configurations + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Curve`), i.e. the specific elliptic +curve you want to use, which would be specified using any one of the methods +`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::new` which take the coefficients in the +defining equation together with a generator point as parameters. You can find more detail in the +comments in +[`noir_stdlib/src/ec.nr`](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr), but +the gist of it is that the elliptic curves of interest are usually expressed in one of the standard +forms implemented here (Twisted Edwards, Montgomery and Short Weierstraß), and in addition to that, +you could choose to use `affine` coordinates (Cartesian coordinates - the usual (x,y) - possibly +together with a point at infinity) or `curvegroup` coordinates (some form of projective coordinates +requiring more coordinates but allowing for more efficient implementations of elliptic curve +operations). Conversions between all of these forms are provided, and under the hood these +conversions are done whenever an operation is more efficient in a different representation (or a +mixed coordinate representation is employed). + +### Points + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Point`), i.e. points lying on the +elliptic curve. For a curve configuration `c` and a point `p`, it may be checked that `p` +does indeed lie on `c` by calling `c.contains(p1)`. + +## Methods + +(given a choice of curve representation, e.g. use `std::ec::tecurve::affine::Curve` and use +`std::ec::tecurve::affine::Point`) + +- The **zero element** is given by `Point::zero()`, and we can verify whether a point `p: Point` is + zero by calling `p.is_zero()`. +- **Equality**: Points `p1: Point` and `p2: Point` may be checked for equality by calling + `p1.eq(p2)`. +- **Addition**: For `c: Curve` and points `p1: Point` and `p2: Point` on the curve, adding these two + points is accomplished by calling `c.add(p1,p2)`. +- **Negation**: For a point `p: Point`, `p.negate()` is its negation. +- **Subtraction**: For `c` and `p1`, `p2` as above, subtracting `p2` from `p1` is accomplished by + calling `c.subtract(p1,p2)`. +- **Scalar multiplication**: For `c` as above, `p: Point` a point on the curve and `n: Field`, + scalar multiplication is given by `c.mul(n,p)`. If instead `n :: [u1; N]`, i.e. `n` is a bit + array, the `bit_mul` method may be used instead: `c.bit_mul(n,p)` +- **Multi-scalar multiplication**: For `c` as above and arrays `n: [Field; N]` and `p: [Point; N]`, + multi-scalar multiplication is given by `c.msm(n,p)`. +- **Coordinate representation conversions**: The `into_group` method converts a point or curve + configuration in the affine representation to one in the CurveGroup representation, and + `into_affine` goes in the other direction. +- **Curve representation conversions**: `tecurve` and `montcurve` curves and points are equivalent + and may be converted between one another by calling `into_montcurve` or `into_tecurve` on their + configurations or points. `swcurve` is more general and a curve c of one of the other two types + may be converted to this representation by calling `c.into_swcurve()`, whereas a point `p` lying + on the curve given by `c` may be mapped to its corresponding `swcurve` point by calling + `c.map_into_swcurve(p)`. +- **Map-to-curve methods**: The Elligator 2 method of mapping a field element `n: Field` into a + `tecurve` or `montcurve` with configuration `c` may be called as `c.elligator2_map(n)`. For all of + the curve configurations, the SWU map-to-curve method may be called as `c.swu_map(z,n)`, where + `z: Field` depends on `Field` and `c` and must be chosen by the user (the conditions it needs to + satisfy are specified in the comments + [here](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr)). + +## Examples + +The +[ec_baby_jubjub test](https://github.com/noir-lang/noir/blob/master/crates/nargo_cli/tests/test_data/ec_baby_jubjub/src/main.nr) +illustrates all of the above primitives on various forms of the Baby Jubjub curve. A couple of more +interesting examples in Noir would be: + +Public-key cryptography: Given an elliptic curve and a 'base point' on it, determine the public key +from the private key. This is a matter of using scalar multiplication. In the case of Baby Jubjub, +for example, this code would do: + +```rust +use dep::std::ec::tecurve::affine::{Curve, Point}; + +fn bjj_pub_key(priv_key: Field) -> Point +{ + + let bjj = Curve::new(168700, 168696, G::new(995203441582195749578291179787384436505546430278305826713579947235728471134,5472060717959818805561601436314318772137091100104008585924551046643952123905)); + + let base_pt = Point::new(5299619240641551281634865583518297030282874472190772894086521144482721001553, 16950150798460657717958625567821834550301663161624707787222815936182638968203); + + bjj.mul(priv_key,base_pt) +} +``` + +This would come in handy in a Merkle proof. + +- EdDSA signature verification: This is a matter of combining these primitives with a suitable hash + function. See + [feat(stdlib): EdDSA sig verification noir#1136](https://github.com/noir-lang/noir/pull/1136) for + the case of Baby Jubjub and the Poseidon hash function. diff --git a/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/05_eddsa.mdx b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/05_eddsa.mdx new file mode 100644 index 00000000000..9a5beb55ee9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/standard_library/cryptographic_primitives/05_eddsa.mdx @@ -0,0 +1,17 @@ +--- +title: EdDSA Verification +description: Learn about the cryptographic primitives regarding EdDSA +keywords: [cryptographic primitives, Noir project, eddsa, signatures] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## eddsa::eddsa_poseidon_verify + +Verifier for EdDSA signatures + +```rust +fn eddsa_poseidon_verify(public_key_x : Field, public_key_y : Field, signature_s: Field, signature_r8_x: Field, signature_r8_y: Field, message: Field) -> bool +``` + + diff --git a/noir/docs/versioned_docs/version-v0.19.1/standard_library/logging.md b/noir/docs/versioned_docs/version-v0.19.1/standard_library/logging.md new file mode 100644 index 00000000000..7e2fd9b9aff --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/standard_library/logging.md @@ -0,0 +1,62 @@ +--- +title: Logging +description: + Learn how to use the println statement for debugging in Noir with this tutorial. Understand the + basics of logging in Noir and how to implement it in your code. +keywords: + [ + noir logging, + println statement, + debugging in noir, + noir std library, + logging tutorial, + basic logging in noir, + noir logging implementation, + noir debugging techniques, + rust, + ] +--- + +The standard library provides a familiar `println` statement you can use. Despite being a limited +implementation of rust's `println!` macro, this construct can be useful for debugging. + +You can print the output of println statements in your Noir code by using the `nargo execute` command or the `--show-output` flag when using `nargo test` (provided there are println statements in your tests). + +It is recommended to use `nargo execute` if you want to debug failing constrains with `println` statements. This is due to every input in a test being a constant rather than a witness, so we issue an error during compilation while we only print during execution (which comes after compilation). `println` will not work for failed constraints caught at compile time. + +The `println` statement is unconstrained, so it works for outputting integers, fields, strings, and even structs or expressions. For example: + +```rust +use dep::std; + +struct Person { + age : Field, + height : Field, +} + +fn main(age : Field, height : Field) { + let person = Person { age : age, height : height }; + std::println(person); + std::println(age + height); + std::println("Hello world!"); +} + +``` + +You can print multiple different types in the same statement and string as well as a new "fmtstr" type. A `fmtstr` can be specified in the same way as a normal string it just should be prepended with an "f" character: + +```rust + let fmt_str = f"i: {i}, j: {j}"; + std::println(fmt_str); + + let s = myStruct { y: x, x: y }; + std::println(s); + + std::println(f"i: {i}, s: {s}"); + + std::println(x); + std::println([x, y]); + + let foo = fooStruct { my_struct: s, foo: 15 }; + std::println(f"s: {s}, foo: {foo}"); +``` diff --git a/noir/docs/versioned_docs/version-v0.19.1/standard_library/merkle_trees.md b/noir/docs/versioned_docs/version-v0.19.1/standard_library/merkle_trees.md new file mode 100644 index 00000000000..dc383a1426b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/standard_library/merkle_trees.md @@ -0,0 +1,58 @@ +--- +title: Merkle Trees +description: Learn about Merkle Trees in Noir with this tutorial. Explore the basics of computing a merkle root using a proof, with examples. +keywords: + [ + Merkle trees in Noir, + Noir programming language, + check membership, + computing root from leaf, + Noir Merkle tree implementation, + Merkle tree tutorial, + Merkle tree code examples, + Noir libraries, + pedersen hash., + ] +--- + +## compute_merkle_root + +Returns the root of the tree from the provided leaf and its hash path, using a [Pedersen hash](cryptographic_primitives/00_hashes.mdx#pedersen_hash). + +```rust +fn compute_merkle_root(leaf : Field, index : Field, hash_path: [Field]) -> Field +``` + +example: + +```rust +/** + // these values are for this example only + index = "0" + priv_key = "0x000000000000000000000000000000000000000000000000000000616c696365" + secret = "0x1929ea3ab8d9106a899386883d9428f8256cfedb3c4f6b66bf4aa4d28a79988f" + note_hash_path = [ + "0x1e61bdae0f027b1b2159e1f9d3f8d00fa668a952dddd822fda80dc745d6f65cc", + "0x0e4223f3925f98934393c74975142bd73079ab0621f4ee133cee050a3c194f1a", + "0x2fd7bb412155bf8693a3bd2a3e7581a679c95c68a052f835dddca85fa1569a40" + ] + */ +fn main(index: Field, priv_key: Field, secret: Field, note_hash_path: [Field; 3]) { + + let pubkey = std::scalar_mul::fixed_base_embedded_curve(priv_key); + let pubkey_x = pubkey[0]; + let pubkey_y = pubkey[1]; + let note_commitment = std::hash::pedersen([pubkey_x, pubkey_y, secret]); + + let root = std::merkle::compute_merkle_root(note_commitment[0], index, note_hash_path); + std::println(root); +} +``` + +To check merkle tree membership: + +1. Include a merkle root as a program input. +2. Compute the merkle root of a given leaf, index and hash path. +3. Assert the merkle roots are equal. + +For more info about merkle trees, see the Wikipedia [page](https://en.wikipedia.org/wiki/Merkle_tree). diff --git a/noir/docs/versioned_docs/version-v0.19.1/standard_library/options.md b/noir/docs/versioned_docs/version-v0.19.1/standard_library/options.md new file mode 100644 index 00000000000..3d3139fb98b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/standard_library/options.md @@ -0,0 +1,99 @@ +--- +title: Option Type +--- + +The `Option` type is a way to express that a value might be present (`Some(T))` or absent (`None`). It's a safer way to handle potential absence of values, compared to using nulls in many other languages. + +```rust +struct Option { + None, + Some(T), +} +``` + +You can import the Option type into your Noir program like so: + +```rust +use dep::std::option::Option; + +fn main() { + let none = Option::none(); + let some = Option::some(3); +} +``` + +See [this test](https://github.com/noir-lang/noir/blob/5cbfb9c4a06c8865c98ff2b594464b037d821a5c/crates/nargo_cli/tests/test_data/option/src/main.nr) for a more comprehensive set of examples of each of the methods described below. + +## Methods + +### none + +Constructs a none value. + +### some + +Constructs a some wrapper around a given value. + +### is_none + +Returns true if the Option is None. + +### is_some + +Returns true of the Option is Some. + +### unwrap + +Asserts `self.is_some()` and returns the wrapped value. + +### unwrap_unchecked + +Returns the inner value without asserting `self.is_some()`. This method can be useful within an if condition when we already know that `option.is_some()`. If the option is None, there is no guarantee what value will be returned, only that it will be of type T for an `Option`. + +### unwrap_or + +Returns the wrapped value if `self.is_some()`. Otherwise, returns the given default value. + +### unwrap_or_else + +Returns the wrapped value if `self.is_some()`. Otherwise, calls the given function to return a default value. + +### map + +If self is `Some(x)`, this returns `Some(f(x))`. Otherwise, this returns `None`. + +### map_or + +If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns the given default value. + +### map_or_else + +If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns `default()`. + +### and + +Returns None if self is None. Otherwise, this returns `other`. + +### and_then + +If self is None, this returns None. Otherwise, this calls the given function with the Some value contained within self, and returns the result of that call. In some languages this function is called `flat_map` or `bind`. + +### or + +If self is Some, return self. Otherwise, return `other`. + +### or_else + +If self is Some, return self. Otherwise, return `default()`. + +### xor + +If only one of the two Options is Some, return that option. Otherwise, if both options are Some or both are None, None is returned. + +### filter + +Returns `Some(x)` if self is `Some(x)` and `predicate(x)` is true. Otherwise, this returns `None`. + +### flatten + +Flattens an `Option>` into a `Option`. This returns `None` if the outer Option is None. Otherwise, this returns the inner Option. diff --git a/noir/docs/versioned_docs/version-v0.19.1/standard_library/recursion.md b/noir/docs/versioned_docs/version-v0.19.1/standard_library/recursion.md new file mode 100644 index 00000000000..ff4c63acaa7 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/standard_library/recursion.md @@ -0,0 +1,96 @@ +--- +title: Recursive Proofs +description: Learn about how to write recursive proofs in Noir. +keywords: [recursion, recursive proofs, verification_key, aggregation object, verify_proof] +--- + +Noir supports recursively verifying proofs, meaning you verify the proof of a Noir program in another Noir program. This enables creating proofs of arbitrary size by doing step-wise verification of smaller components of a large proof. + +The `verify_proof` function takes a verification key, proof and public inputs for a zk program, as well as a key hash and an input aggregation object. The key hash is used to check the validity of the verification key and the input aggregation object is required by some proving systems. The `verify_proof` function returns an output aggregation object that can then be fed into future iterations of the proof verification if required. + +```rust +#[foreign(verify_proof)] +fn verify_proof(_verification_key : [Field], _proof : [Field], _public_input : Field, _key_hash : Field, _input_aggregation_object : [Field]) -> [Field] {} +``` + +:::info + +This is a black box function. Read [this section](./black_box_fns) to learn more about black box functions in Noir. + +::: + +## Aggregation Object + +The purpose of the input aggregation object is a little less clear though (and the output aggregation object that is returned from the `std::verify_proof` method). Recursive zkSNARK schemes do not necessarily "verify a proof" in the sense that you expect a true or false to be spit out by the verifier. Rather an aggregation object is built over the public inputs. In the case of PLONK the recursive aggregation object is two G1 points (expressed as 16 witness values). The final verifier (in our case this is most often the smart contract verifier) has to be aware of this aggregation object to execute a pairing and check the validity of these points (thus completing the recursive verification). + +So for example in this circuit: + +```rust +use dep::std; + +fn main( + verification_key : [Field; 114], + proof : [Field; 94], + public_inputs : [Field; 1], + key_hash : Field, + input_aggregation_object : [Field; 16], + proof_b : [Field; 94], +) -> pub [Field; 16] { + let output_aggregation_object_a = std::verify_proof( + verification_key, + proof, + public_inputs, + key_hash, + input_aggregation_object + ); + + let output_aggregation_object = std::verify_proof( + verification_key, + proof_b, + public_inputs, + key_hash, + output_aggregation_object_a + ); + + let mut output = [0; 16]; + for i in 0..16 { + output[i] = output_aggregation_object[i]; + } + output +} +``` + +In this example we have a circuit, that generates proofs A and B, that is being verified in circuit C. Assuming that the proof being passed in is not already a recursive proof, the `input_aggregation_object` will be all zeros. It will then generate an `output_aggregation_object`. This blob of data then becomes the `input_aggregation_object` of the next recursive aggregation we wish to compute. We can see here as the same public inputs, verification key, and key hash are used that we are verifying two proofs generated from the same circuit in this single circuit. `std::verify_proof` returns a `[Field]` because the size of an aggregation object is proof system dependent--in barretenberg, aggregation objects are two G1 points, while in Halo2, the aggregation object is a list of G1 points that is log the circuit size. So for the final step we convert the slice into an array of size 16 because we are generating proofs using UltraPlonk. + +## Parameters + +### `verification_key` + +The verification key for the zk program that is being verified. + +### `proof` + +The proof for the zk program that is being verified. + +### `public_inputs` + +These represent the public inputs of the proof we are verifying. They should be checked against in the circuit after construction of a new aggregation state. + +### `key_hash` + +A key hash is used to check the validity of the verification key. The circuit implementing this opcode can use this hash to ensure that the key provided to the circuit matches the key produced by the circuit creator. + +### `input_aggregation_object` + +An aggregation object is blob of data that the top-level verifier must run some proof system specific algorithm on to complete verification. The size is proof system specific and will be set by the backend integrating this opcode. The input aggregation object is only not `None` when we are verifying a previous recursive aggregation in the current circuit. If this is the first recursive aggregation there is no input aggregation object. It is left to the backend to determine how to handle when there is no input aggregation object. + +## Return value + +### `output_aggregation_object` + +This is the result of a recursive aggregation and is what will be fed into the next verifier. +The next verifier can either perform a final verification (returning true or false) or perform another recursive aggregation where this output aggregation object will be the input aggregation object of the next recursive aggregation. + +## Example + +You can see an example of how to do recursive proofs in [this example recursion demo repo](https://github.com/noir-lang/noir-examples/tree/master/recursion). diff --git a/noir/docs/versioned_docs/version-v0.19.1/standard_library/zeroed.md b/noir/docs/versioned_docs/version-v0.19.1/standard_library/zeroed.md new file mode 100644 index 00000000000..97dab02dac2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.1/standard_library/zeroed.md @@ -0,0 +1,25 @@ +--- +title: Zeroed Function +description: + The zeroed function returns a zeroed value of any type. +keywords: + [ + zeroed + ] +--- + +Implements `fn zeroed() -> T` to return a zeroed value of any type. This function is generally unsafe to use as the zeroed bit pattern is not guaranteed to be valid for all types. It can however, be useful in cases when the value is guaranteed not to be used such as in a BoundedVec library implementing a growable vector, up to a certain length, backed by an array. The array can be initialized with zeroed values which are guaranteed to be inaccessible until the vector is pushed to. Similarly, enumerations in noir can be implemented using this method by providing zeroed values for the unused variants. + +You can access the function at `std::unsafe::zeroed`. + +This function currently supports the following types: + +- Field +- Bool +- Uint +- Array +- String +- Tuple +- Function + +Using it on other types could result in unexpected behavior. diff --git a/noir/docs/versioned_docs/version-v0.19.2/examples/merkle-proof.mdx b/noir/docs/versioned_docs/version-v0.19.2/examples/merkle-proof.mdx new file mode 100644 index 00000000000..832fb4bb55e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/examples/merkle-proof.mdx @@ -0,0 +1,48 @@ +--- +title: Merkle Proof Membership +description: + Learn how to use merkle membership proof in Noir to prove that a given leaf is a member of a + merkle tree with a specified root, at a given index. +keywords: + [merkle proof, merkle membership proof, Noir, rust, hash function, Pedersen, sha256, merkle tree] +--- + +Let's walk through an example of a merkle membership proof in Noir that proves that a given leaf is +in a merkle tree. + +```rust +use dep::std; + +fn main(message : [Field; 62], index : Field, hashpath : [Field; 40], root : Field) { + let leaf = std::hash::hash_to_field(message); + let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); + assert(merkle_root == root); +} + +``` + +The message is hashed using `hash_to_field`. The specific hash function that is being used is chosen +by the backend. The only requirement is that this hash function can heuristically be used as a +random oracle. If only collision resistance is needed, then one can call `std::hash::pedersen_hash` +instead. + +```rust +let leaf = std::hash::hash_to_field(message); +``` + +The leaf is then passed to a compute_merkle_root function with the root, index and hashpath. The returned root can then be asserted to be the same as the provided root. + +```rust +let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); +assert (merkle_root == root); +``` + +> **Note:** It is possible to re-implement the merkle tree implementation without standard library. +> However, for most usecases, it is enough. In general, the standard library will always opt to be +> as conservative as possible, while striking a balance with efficiency. + +An example, the merkle membership proof, only requires a hash function that has collision +resistance, hence a hash function like Pedersen is allowed, which in most cases is more efficient +than the even more conservative sha256. + +[View an example on the starter repo](https://github.com/noir-lang/noir-examples/blob/3ea09545cabfa464124ec2f3ea8e60c608abe6df/stealthdrop/circuits/src/main.nr#L20) diff --git a/noir/docs/versioned_docs/version-v0.19.2/getting_started/00_nargo_installation.md b/noir/docs/versioned_docs/version-v0.19.2/getting_started/00_nargo_installation.md new file mode 100644 index 00000000000..725c5f4d373 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/getting_started/00_nargo_installation.md @@ -0,0 +1,249 @@ +--- +title: Nargo Installation +description: + nargo is a command line tool for interacting with Noir programs (e.g. compiling, proving, + verifying and more). Learn how to install and use Nargo for your projects with this comprehensive + guide. +keywords: [Nargo, command line tool, Noir programs, installation guide, how to use Nargo] +--- + +`nargo` is a command line tool for interacting with Noir programs (e.g. compiling, proving, +verifying and more). + +Alternatively, the interactions can also be performed in [NoirJS](../noir_js/noir_js.md). + +### UltraPlonk + +Nargo versions <0.5.0 of `aztec_backend` and `aztec_wasm_backend` are based on the TurboPlonk +version of Aztec Backend, which lacks efficient implementations of useful primitives (e.g. Keccak256 in 18k constraints, ECDSA verification in 36k constraints) that the UltraPlonk version offers. + +## Installation + +There are four approaches for installing Nargo: + +- [Option 1: Noirup](#option-1-noirup) +- [Option 2: Binaries](#option-2-binaries) +- [Option 3: Compile from Source](#option-3-compile-from-source) +- [Option 4: WSL for Windows](#option-4-wsl-for-windows) + +Optionally you can also install [Noir VS Code extension] for syntax highlighting. + +### Option 1: Noirup + +If you're on OSX or Linux, the easiest way to start using Noir and Nargo is via noirup. Just open a +terminal and run: + +```bash +curl -L https://raw.githubusercontent.com/noir-lang/noirup/main/install | bash +``` + +Close the terminal, open another one, and run + +```bash +noirup +``` + +Done, you should have the latest version working. You can check with `nargo --version`. + +You can also install nightlies, specific versions +or branches, check out the [noirup repository](https://github.com/noir-lang/noirup) for more +information. + +#### GitHub Actions + +You can use `noirup` with GitHub Actions for CI/CD and automated testing. It is as simple as +installing `noirup` and running tests in your GitHub Action `yml` file. + +See the +[config file](https://github.com/TomAFrench/noir-hashes/blob/master/.github/workflows/noir.yml) in +this repo containing hash functions in Noir for an example. + +#### Nightly versions + +To install the nightly version of Noir (updated daily) run: + +```bash +noirup -n +``` + +### Option 2: Binaries + +See [GitHub Releases](https://github.com/noir-lang/noir/releases) for the latest and previous +platform specific binaries. + +#### Step 1 + +Paste and run the following in the terminal to extract and install the binary: + +> **macOS / Linux:** If you are prompted with `Permission denied` when running commands, prepend +> `sudo` and re-run it. + +##### macOS (Apple Silicon) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-aarch64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### macOS (Intel) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### Linux (Bash) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-unknown-linux-gnu.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -C $HOME/.nargo/bin/ && \ +echo -e '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.bashrc && \ +source ~/.bashrc +``` + +#### Step 2 + +Check if the installation was successful by running `nargo --help`. + +> **macOS:** If you are prompted with an OS alert, right-click and open the _nargo_ executable from +> Finder. Close the new terminal popped up and `nargo` should now be accessible. + +For a successful installation, you should see something similar to the following after running the +command: + +```sh +$ nargo --help + +Noir's package manager + +Usage: nargo + +Commands: + check Checks the constraint system for errors + codegen-verifier Generates a Solidity verifier smart contract for the program + compile Compile the program and its secret execution trace into ACIR format + new Create a new binary project + execute Executes a circuit to calculate its return value + prove Create proof for this program. The proof is returned as a hex encoded string + verify Given a proof and a program, verify whether the proof is valid + test Run the tests for this program + gates Counts the occurrences of different gates in circuit + help Print this message or the help of the given subcommand(s) +``` + +### Option 3: Compile from Source + +Due to the large number of native dependencies, Noir projects uses [Nix](https://nixos.org/) and [direnv](https://direnv.net/) to streamline the development experience. It helps mitigating ssues commonly associated with dependency management, such as conflicts between required package versions for different projects (often referred to as "dependency hell"). + +Combined with direnv, which automatically sets or unsets environment variables based on the directory, it further simplifies the development process by seamlessly integrating with the developer's shell, facilitating an efficient and reliable workflow for managing and deploying Noir projects with multiple dependencies. + +#### Setting up your environment + +For the best experience, please follow these instructions to setup your environment: + +1. Install Nix following [their guide](https://nixos.org/download.html) for your operating system. +2. Create the file `~/.config/nix/nix.conf` with the contents: + +```ini +experimental-features = nix-command +extra-experimental-features = flakes +``` + +3. Install direnv into your Nix profile by running: + +```sh +nix profile install nixpkgs#direnv +``` + +4. Add direnv to your shell following [their guide](https://direnv.net/docs/hook.html). + 1. For bash or zshell, add `eval "$(direnv hook bash)"` or `eval "$(direnv hook zsh)"` to your ~/.bashrc or ~/.zshrc file, respectively. +5. Restart your shell. + +#### Shell & editor experience + +Now that your environment is set up, you can get to work on the project. + +1. Clone the repository, such as: + +```sh +git clone git@github.com:noir-lang/noir +``` + +> Replacing `noir` with whichever repository you want to work on. + +2. Navigate to the directory: + +```sh +cd noir +``` + +> Replacing `noir` with whichever repository you cloned. + +3. You should see a **direnv error** because projects aren't allowed by default. Make sure you've reviewed and trust our `.envrc` file, then you need to run: + +```sh +direnv allow +``` + +4. Now, wait awhile for all the native dependencies to be built. This will take some time and direnv will warn you that it is taking a long time, but we just need to let it run. + +5. Once you are presented with your prompt again, you can start your editor within the project directory (we recommend [VSCode](https://code.visualstudio.com/)): + +```sh +code . +``` + +6. (Recommended) When launching VSCode for the first time, you should be prompted to install our recommended plugins. We highly recommend installing these for the best development experience. + +#### Building and testing + +Assuming you are using `direnv` to populate your environment, building and testing the project can be done +with the typical `cargo build`, `cargo test`, and `cargo clippy` commands. You'll notice that the `cargo` version matches the version we specify in `rust-toolchain.toml`, which is 1.71.1 at the time of this writing. + +If you want to build the entire project in an isolated sandbox, you can use Nix commands: + +1. `nix build .` (or `nix build . -L` for verbose output) to build the project in a Nix sandbox. +2. `nix flake check` (or `nix flake check -L` for verbose output) to run clippy and tests in a Nix sandbox. + +#### Without `direnv` + +If you have hesitations with using direnv, you can launch a subshell with `nix develop` and then launch your editor from within the subshell. However, if VSCode was already launched in the project directory, the environment won't be updated. + +Advanced: If you aren't using direnv nor launching your editor within the subshell, you can try to install Barretenberg and other global dependencies the package needs. This is an advanced workflow and likely won't receive support! + +### Option 4: WSL (for Windows) + +The default backend for Noir (Barretenberg) doesn't provide Windows binaries at this time. For that reason, Noir cannot be installed nativerly. However, it is available by using Windows Subsystem for Linux (WSL). + +Step 1: Follow the instructions [here](https://learn.microsoft.com/en-us/windows/wsl/install) to install and run WSL. + +step 2: Follow the [Noirup instructions](#option-1-noirup). + +## Uninstalling Nargo + +### Noirup + +If you installed Noir with `noirup`, you can uninstall Noir by removing the files in `~/.nargo`, `~/nargo` and `~/noir_cache`. + +```bash +rm -r ~/.nargo +rm -r ~/nargo +rm -r ~/noir_cache +``` + +### Nix + +If you installed Noir with Nix or from source, you can remove the binary located at `~/.nix-profile/bin/nargo`. + +```bash +rm ~/.nix-profile/bin/nargo +``` + +[noir vs code extension]: https://marketplace.visualstudio.com/items?itemName=noir-lang.vscode-noir diff --git a/noir/docs/versioned_docs/version-v0.19.2/getting_started/01_hello_world.md b/noir/docs/versioned_docs/version-v0.19.2/getting_started/01_hello_world.md new file mode 100644 index 00000000000..8b4416beba1 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/getting_started/01_hello_world.md @@ -0,0 +1,147 @@ +--- +title: Create A Project +description: + Learn how to create and verify your first Noir program using Nargo, a programming language for + zero-knowledge proofs. +keywords: + [ + Nargo, + Noir, + zero-knowledge proofs, + programming language, + create Noir program, + verify Noir program, + step-by-step guide, + ] +--- + +Now that we have installed Nargo, it is time to make our first hello world program! + +## Create a Project Directory + +Noir code can live anywhere on your computer. Let us create a _projects_ folder in the home +directory to house our Noir programs. + +For Linux, macOS, and Windows PowerShell, create the directory and change directory into it by +running: + +```sh +mkdir ~/projects +cd ~/projects +``` + +For Windows CMD, run: + +```sh +> mkdir "%USERPROFILE%\projects" +> cd /d "%USERPROFILE%\projects" +``` + +## Create Our First Nargo Project + +Now that we are in the projects directory, create a new Nargo project by running: + +```sh +nargo new hello_world +``` + +> **Note:** `hello_world` can be any arbitrary project name, we are simply using `hello_world` for +> demonstration. +> +> In production, the common practice is to name the project folder as `circuits` for better +> identifiability when sitting alongside other folders in the codebase (e.g. `contracts`, `scripts`, +> `test`). + +A `hello_world` folder would be created. Similar to Rust, the folder houses _src/main.nr_ and +_Nargo.toml_ that contains the source code and environmental options of your Noir program +respectively. + +### Intro to Noir Syntax + +Let us take a closer look at _main.nr_. The default _main.nr_ generated should look like this: + +```rust +fn main(x : Field, y : pub Field) { + assert(x != y); +} +``` + +The first line of the program specifies the program's inputs: + +```rust +x : Field, y : pub Field +``` + +Program inputs in Noir are private by default (e.g. `x`), but can be labeled public using the +keyword `pub` (e.g. `y`). To learn more about private and public values, check the +[Data Types](../language_concepts/data_types) section. + +The next line of the program specifies its body: + +```rust +assert(x != y); +``` + +The Noir syntax `assert` can be interpreted as something similar to constraints in other zk-contract languages. + +For more Noir syntax, check the [Language Concepts](../language_concepts/comments) chapter. + +## Build In/Output Files + +Change directory into _hello_world_ and build in/output files for your Noir program by running: + +```sh +cd hello_world +nargo check +``` + +Two additional files would be generated in your project directory: + +_Prover.toml_ houses input values, and _Verifier.toml_ houses public values. + +## Prove Our Noir Program + +Now that the project is set up, we can create a proof of correct execution on our Noir program. + +Fill in input values for execution in the _Prover.toml_ file. For example: + +```toml +x = "1" +y = "2" +``` + +Prove the valid execution of your Noir program: + +```sh +nargo prove +``` + +A new folder _proofs_ would then be generated in your project directory, containing the proof file +`.proof`, where the project name is defined in Nargo.toml. + +The _Verifier.toml_ file would also be updated with the public values computed from program +execution (in this case the value of `y`): + +```toml +y = "0x0000000000000000000000000000000000000000000000000000000000000002" +``` + +> **Note:** Values in _Verifier.toml_ are computed as 32-byte hex values. + +## Verify Our Noir Program + +Once a proof is generated, we can verify correct execution of our Noir program by verifying the +proof file. + +Verify your proof by running: + +```sh +nargo verify +``` + +The verification will complete in silence if it is successful. If it fails, it will log the +corresponding error instead. + +Congratulations, you have now created and verified a proof for your very first Noir program! + +In the [next section](breakdown), we will go into more detail on each step performed. diff --git a/noir/docs/versioned_docs/version-v0.19.2/getting_started/02_breakdown.md b/noir/docs/versioned_docs/version-v0.19.2/getting_started/02_breakdown.md new file mode 100644 index 00000000000..9a17f5d6360 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/getting_started/02_breakdown.md @@ -0,0 +1,198 @@ +--- +title: Project Breakdown +description: + Learn about the anatomy of a Nargo project, including the purpose of the Prover and Verifier TOML + files, and how to prove and verify your program. +keywords: + [Nargo, Nargo project, Prover.toml, Verifier.toml, proof verification, private asset transfer] +--- + +This section breaks down our hello world program in section _1.2_. We elaborate on the project +structure and what the `prove` and `verify` commands did in the previous section. + +## Anatomy of a Nargo Project + +Upon creating a new project with `nargo new` and building the in/output files with `nargo check` +commands, you would get a minimal Nargo project of the following structure: + + - src + - Prover.toml + - Verifier.toml + - Nargo.toml + +The source directory _src_ holds the source code for your Noir program. By default only a _main.nr_ +file will be generated within it. + +### Prover.toml + +_Prover.toml_ is used for specifying the input values for executing and proving the program. You can specify `toml` files with different names by using the `--prover-name` or `-p` flags, see the [Prover](#provertoml) section below. Optionally you may specify expected output values for prove-time checking as well. + +### Verifier.toml + +_Verifier.toml_ contains public in/output values computed when executing the Noir program. + +### Nargo.toml + +_Nargo.toml_ contains the environmental options of your project. It contains a "package" section and a "dependencies" section. + +Example Nargo.toml: + +```toml +[package] +name = "noirstarter" +type = "bin" +authors = ["Alice"] +compiler_version = "0.9.0" +description = "Getting started with Noir" +entry = "circuit/main.nr" +license = "MIT" + +[dependencies] +ecrecover = {tag = "v0.9.0", git = "https://github.com/colinnielsen/ecrecover-noir.git"} +``` + +Nargo.toml for a [workspace](../modules_packages_crates/workspaces) will look a bit different. For example: + +```toml +[workspace] +members = ["crates/a", "crates/b"] +default-member = "crates/a" +``` + +#### Package section + +The package section requires a number of fields including: + +- `name` (**required**) - the name of the package +- `type` (**required**) - can be "bin", "lib", or "contract" to specify whether its a binary, library or Aztec contract +- `authors` (optional) - authors of the project +- `compiler_version` - specifies the version of the compiler to use. This is enforced by the compiler and follow's [Rust's versioning](https://doc.rust-lang.org/cargo/reference/manifest.html#the-version-field), so a `compiler_version = 0.18.0` will enforce Nargo version 0.18.0, `compiler_version = ^0.18.0` will enforce anything above 0.18.0 but below 0.19.0, etc. For more information, see how [Rust handles these operators](https://docs.rs/semver/latest/semver/enum.Op.html) +- `description` (optional) +- `entry` (optional) - a relative filepath to use as the entry point into your package (overrides the default of `src/lib.nr` or `src/main.nr`) +- `backend` (optional) +- `license` (optional) + +#### Dependencies section + +This is where you will specify any dependencies for your project. See the [Dependencies page](../modules_packages_crates/dependencies) for more info. + +`./proofs/` and `./contract/` directories will not be immediately visible until you create a proof or +verifier contract respectively. + +### main.nr + +The _main.nr_ file contains a `main` method, this method is the entry point into your Noir program. + +In our sample program, _main.nr_ looks like this: + +```rust +fn main(x : Field, y : Field) { + assert(x != y); +} +``` + +The parameters `x` and `y` can be seen as the API for the program and must be supplied by the +prover. Since neither `x` nor `y` is marked as public, the verifier does not supply any inputs, when +verifying the proof. + +The prover supplies the values for `x` and `y` in the _Prover.toml_ file. + +As for the program body, `assert` ensures the satisfaction of the condition (e.g. `x != y`) is +constrained by the proof of the execution of said program (i.e. if the condition was not met, the +verifier would reject the proof as an invalid proof). + +### Prover.toml + +The _Prover.toml_ file is a file which the prover uses to supply his witness values(both private and +public). + +In our hello world program the _Prover.toml_ file looks like this: + +```toml +x = "1" +y = "2" +``` + +When the command `nargo prove` is executed, two processes happen: + +1. Noir creates a proof that `x` which holds the value of `1` and `y` which holds the value of `2` + is not equal. This not equal constraint is due to the line `assert(x != y)`. + +2. Noir creates and stores the proof of this statement in the _proofs_ directory in a file called your-project.proof. So if your project is named "private_voting" (defined in the project Nargo.toml), the proof will be saved at `./proofs/private_voting.proof`. Opening this file will display the proof in hex format. + +#### Arrays of Structs + +The following code shows how to pass an array of structs to a Noir program to generate a proof. + +```rust +// main.nr +struct Foo { + bar: Field, + baz: Field, +} + +fn main(foos: [Foo; 3]) -> pub Field { + foos[2].bar + foos[2].baz +} +``` + +Prover.toml: + +```toml +[[foos]] # foos[0] +bar = 0 +baz = 0 + +[[foos]] # foos[1] +bar = 0 +baz = 0 + +[[foos]] # foos[2] +bar = 1 +baz = 2 +``` + +#### Custom toml files + +You can specify a `toml` file with a different name to use for proving by using the `--prover-name` or `-p` flags. + +This command looks for proof inputs in the default **Prover.toml** and generates the proof and saves it at `./proofs/.proof`: + +```bash +nargo prove +``` + +This command looks for proof inputs in the custom **OtherProver.toml** and generates proof and saves it at `./proofs/.proof`: + +```bash +nargo prove -p OtherProver +``` + +## Verifying a Proof + +When the command `nargo verify` is executed, two processes happen: + +1. Noir checks in the _proofs_ directory for a proof file with the project name (eg. test_project.proof) + +2. If that file is found, the proof's validity is checked + +> **Note:** The validity of the proof is linked to the current Noir program; if the program is +> changed and the verifier verifies the proof, it will fail because the proof is not valid for the +> _modified_ Noir program. + +In production, the prover and the verifier are usually two separate entities. A prover would +retrieve the necessary inputs, execute the Noir program, generate a proof and pass it to the +verifier. The verifier would then retrieve the public inputs from usually external sources and +verifies the validity of the proof against it. + +Take a private asset transfer as an example: + +A user on browser as the prover would retrieve private inputs (e.g. the user's private key) and +public inputs (e.g. the user's encrypted balance on-chain), compute the transfer, generate a proof +and submit it to the verifier smart contract. + +The verifier contract would then draw the user's encrypted balance directly from the blockchain and +verify the proof submitted against it. If the verification passes, additional functions in the +verifier contract could trigger (e.g. approve the asset transfer). + +Now that you understand the concepts, you'll probably want some editor feedback while you are writing more complex code. diff --git a/noir/docs/versioned_docs/version-v0.19.2/index.md b/noir/docs/versioned_docs/version-v0.19.2/index.md new file mode 100644 index 00000000000..75e1abf2932 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/index.md @@ -0,0 +1,100 @@ +--- +title: Introducing Noir +description: + Learn about the public alpha release of Noir, a domain specific language heavily influenced by + Rust that compiles to an intermediate language which can be compiled to an arithmetic circuit or a + rank-1 constraint system. +keywords: + [ + Noir, + Domain Specific Language, + Rust, + Intermediate Language, + Arithmetic Circuit, + Rank-1 Constraint System, + Ethereum Developers, + Protocol Developers, + Blockchain Developers, + Proving System, + Smart Contract Language, + ] +slug: / +--- + +## What is Noir? + +Noir is a Domain Specific Language for SNARK proving systems. It has been designed to use any ACIR compatible proving system. + +It's design choices are influenced heavily by Rust and focuses on a simple, familiar syntax. + +## Who is Noir for? + +Noir can be used for a variety of purposes. + +### Solidity Developers + +Noir currently includes a command to create a Solidity contract which verifies your Noir program. This will +be modularised in the future; however, as of the alpha, you can use the [`nargo codegen-verifier`](./nargo/commands#nargo-codegen-verifier) command to create +a verifier contract. + +### Protocol Developers + +As a protocol developer, you may not want to use the Aztec backend due to it not being a fit for +your stack, or maybe you simply want to use a different proving system. Since Noir does not compile +to a specific proof system, it is possible for protocol developers to replace the PLONK-based +proving system with a different proving system altogether. + +### Blockchain developers + +As a blockchain developer, you will be constrained by parameters set by your blockchain (for example, the +proving system and smart contract language has been pre-defined). In order for you to use Noir in +your blockchain, a proving system backend and a smart contract interface +must be implemented for it. + +## What's new about Noir? + +Noir is simple and flexible in its design, as it does not compile immediately to a fixed +NP-complete language. Instead, Noir compiles to an intermediate language (ACIR), which itself can be compiled +to an arithmetic circuit (if choosing to target Aztec's barretenberg backend) or a rank-1 constraint system (if choosing to target an R1CS backend like Arkwork's Marlin backend, or others). + +This in itself brings up a few challenges within the design process, but allows one to decouple the programming language completely from the backend. This is similar in theory to LLVM. + +## Current Features + +Compiler: + +- Module System +- For expressions +- Arrays +- Bit Operations +- Binary operations (<, <=, >, >=, +, -, \*, /, %) [See documentation for an extensive list] +- Unsigned integers +- If statements +- Structures and Tuples +- Generics + +ACIR Supported OPCODES: + +- Sha256 +- Blake2s +- Schnorr signature verification +- MerkleMembership +- Pedersen Commitment +- Pedersen Hash +- HashToField + +## Libraries + +Noir does not currently have an official package manager. You can find a list of available Noir libraries in the [awesome-noir repo here](https://github.com/noir-lang/awesome-noir#libraries). + +Some libraries that are available today include: + +- [Standard Library](https://github.com/noir-lang/noir/tree/master/noir_stdlib) - the Noir Standard Library +- [Ethereum Storage Proof Verification](https://github.com/aragonzkresearch/noir-trie-proofs) - a library that contains the primitives necessary for RLP decoding (in the form of look-up table construction) and Ethereum state and storage proof verification (or verification of any trie proof involving 32-byte long keys) +- [BigInt](https://github.com/shuklaayush/noir-bigint) - a library that provides a custom BigUint56 data type, allowing for computations on large unsigned integers +- [ECrecover](https://github.com/colinnielsen/ecrecover-noir/tree/main) - a library to verify an ECDSA signature and return the source Ethereum address +- [Sparse Merkle Tree Verifier](https://github.com/vocdoni/smtverifier-noir/tree/main) - a library for verification of sparse Merkle trees +- [Signed Int](https://github.com/resurgencelabs/signed_int) - a library for accessing a custom Signed Integer data type, allowing access to negative numbers on Noir +- [Fraction](https://github.com/resurgencelabs/fraction) - a library for accessing fractional number data type in Noir, allowing results that aren't whole numbers + +See the section on [dependencies](./modules_packages_crates/dependencies) for more information. diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/01_functions.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/01_functions.md new file mode 100644 index 00000000000..47cdea0cf04 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/01_functions.md @@ -0,0 +1,225 @@ +--- +title: Functions +description: + Learn how to declare functions and methods in Noir, a programming language with Rust semantics. + This guide covers parameter declaration, return types, call expressions, and more. +keywords: [Noir, Rust, functions, methods, parameter declaration, return types, call expressions] +--- + +Functions in Noir follow the same semantics of Rust, though Noir does not support early returns. + +To declare a function the `fn` keyword is used. + +```rust +fn foo() {} +``` + +By default, functions are visible only within the package they are defined. To make them visible outside of that package (for example, as part of a [library](../modules_packages_crates/crates_and_packages.md#libraries)), you should mark them as `pub`: + +```rust +pub fn foo() {} +``` + +You can also restrict the visibility of the function to only the crate it was defined in, by specifying `pub(crate)`: + +```rust +pub(crate) fn foo() {} //foo can only be called within its crate +``` + +All parameters in a function must have a type and all types are known at compile time. The parameter +is pre-pended with a colon and the parameter type. Multiple parameters are separated using a comma. + +```rust +fn foo(x : Field, y : pub Field){} +``` + +The return type of a function can be stated by using the `->` arrow notation. The function below +states that the foo function must return a `Field`. If the function returns no value, then the arrow +is omitted. + +```rust +fn foo(x : Field, y : pub Field) -> Field { + x + y +} +``` + +Note that a `return` keyword is unneeded in this case - the last expression in a function's body is +returned. + +## Main function + +If you're writing a binary, the `main` function is the starting point of your program. You can pass all types of expressions to it, as long as they have a fixed size at compile time: + +```rust +fn main(x : Field) // this is fine: passing a Field +fn main(x : [Field; 2]) // this is also fine: passing a Field with known size at compile-time +fn main(x : (Field, bool)) // 👌: passing a (Field, bool) tuple means size 2 +fn main(x : str<5>) // this is fine, as long as you pass a string of size 5 + +fn main(x : Vec) // can't compile, has variable size +fn main(x : [Field]) // can't compile, has variable size +fn main(....// i think you got it by now +``` + +Keep in mind [tests](../nargo/02_testing.md) don't differentiate between `main` and any other function. The following snippet passes tests, but won't compile or prove: + +```rust +fn main(x : [Field]) { + assert(x[0] == 1); +} + +#[test] +fn test_one() { + main([1, 2]); +} +``` + +```bash +$ nargo test +[testing] Running 1 test functions +[testing] Testing test_one... ok +[testing] All tests passed + +$ nargo check +The application panicked (crashed). +Message: Cannot have variable sized arrays as a parameter to main +``` + +## Call Expressions + +Calling a function in Noir is executed by using the function name and passing in the necessary +arguments. + +Below we show how to call the `foo` function from the `main` function using a call expression: + +```rust +fn main(x : Field, y : Field) { + let z = foo(x); +} + +fn foo(x : Field) -> Field { + x + x +} +``` + +## Methods + +You can define methods in Noir on any struct type in scope. + +```rust +struct MyStruct { + foo: Field, + bar: Field, +} + +impl MyStruct { + fn new(foo: Field) -> MyStruct { + MyStruct { + foo, + bar: 2, + } + } + + fn sum(self) -> Field { + self.foo + self.bar + } +} + +fn main() { + let s = MyStruct::new(40); + assert(s.sum() == 42); +} +``` + +Methods are just syntactic sugar for functions, so if we wanted to we could also call `sum` as +follows: + +```rust +assert(MyStruct::sum(s) == 42); +``` + +It is also possible to specialize which method is chosen depending on the [generic](./06_generics.md) type that is used. In this example, the `foo` function returns different values depending on its type: + +```rust +struct Foo {} + +impl Foo { + fn foo(self) -> Field { 1 } +} + +impl Foo { + fn foo(self) -> Field { 2 } +} + +fn main() { + let f1: Foo = Foo{}; + let f2: Foo = Foo{}; + assert(f1.foo() + f2.foo() == 3); +} +``` + +Also note that impls with the same method name defined in them cannot overlap. For example, if we already have `foo` defined for `Foo` and `Foo` like we do above, we cannot also define `foo` in an `impl Foo` since it would be ambiguous which version of `foo` to choose. + +```rust +// Including this impl in the same project as the above snippet would +// cause an overlapping impls error +impl Foo { + fn foo(self) -> Field { 3 } +} +``` + +## Lambdas + +Lambdas are anonymous functions. They follow the syntax of Rust - `|arg1, arg2, ..., argN| return_expression`. + +```rust +let add_50 = |val| val + 50; +assert(add_50(100) == 150); +``` + +See [Lambdas](./08_lambdas.md) for more details. + +## Attributes + +Attributes are metadata that can be applied to a function, using the following syntax: `#[attribute(value)]`. + +Supported attributes include: + +- **builtin**: the function is implemented by the compiler, for efficiency purposes. +- **deprecated**: mark the function as _deprecated_. Calling the function will generate a warning: `warning: use of deprecated function` +- **field**: Used to enable conditional compilation of code depending on the field size. See below for more details +- **oracle**: mark the function as _oracle_; meaning it is an external unconstrained function, implemented in noir_js. See [Unconstrained](./05_unconstrained.md) and [NoirJS](../noir_js/noir_js.md) for more details. +- **test**: mark the function as unit tests. See [Tests](../nargo/02_testing.md) for more details + +### Field Attribute + +The field attribute defines which field the function is compatible for. The function is conditionally compiled, under the condition that the field attribute matches the Noir native field. +The field can be defined implicitly, by using the name of the elliptic curve usually associated to it - for instance bn254, bls12_381 - or explicitly by using the field (prime) order, in decimal or hexadecimal form. +As a result, it is possible to define multiple versions of a function with each version specialized for a different field attribute. This can be useful when a function requires different parameters depending on the underlying elliptic curve. + +Example: we define the function `foo()` three times below. Once for the default Noir bn254 curve, once for the field $\mathbb F_{23}$, which will normally never be used by Noir, and once again for the bls12_381 curve. + +```rust +#[field(bn254)] +fn foo() -> u32 { + 1 +} + +#[field(23)] +fn foo() -> u32 { + 2 +} + +// This commented code would not compile as foo would be defined twice because it is the same field as bn254 +// #[field(21888242871839275222246405745257275088548364400416034343698204186575808495617)] +// fn foo() -> u32 { +// 2 +// } + +#[field(bls12_381)] +fn foo() -> u32 { + 3 +} +``` + +If the field name is not known to Noir, it will discard the function. Field names are case insensitive. diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/02_control_flow.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/02_control_flow.md new file mode 100644 index 00000000000..a7f85360197 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/02_control_flow.md @@ -0,0 +1,44 @@ +--- +title: Control Flow +description: + Learn how to use loops and if expressions in the Noir programming language. Discover the syntax + and examples for for loops and if-else statements. +keywords: [Noir programming language, loops, for loop, if-else statements, Rust syntax] +--- + +## Loops + +Noir has one kind of loop: the `for` loop. `for` loops allow you to repeat a block of code multiple +times. + +The following block of code between the braces is run 10 times. + +```rust +for i in 0..10 { + // do something +}; +``` + +The index for loops is of type `u64`. + +## If Expressions + +Noir supports `if-else` statements. The syntax is most similar to Rust's where it is not required +for the statement's conditional to be surrounded by parentheses. + +```rust +let a = 0; +let mut x: u32 = 0; + +if a == 0 { + if a != 0 { + x = 6; + } else { + x = 2; + } +} else { + x = 5; + assert(x == 5); +} +assert(x == 2); +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/03_ops.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/03_ops.md new file mode 100644 index 00000000000..da02b126059 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/03_ops.md @@ -0,0 +1,97 @@ +--- +title: Logical Operations +description: + Learn about the supported arithmetic and logical operations in the Noir programming language. + Discover how to perform operations on private input types, integers, and booleans. +keywords: + [ + Noir programming language, + supported operations, + arithmetic operations, + logical operations, + predicate operators, + bitwise operations, + short-circuiting, + backend, + ] +--- + +# Operations + +## Table of Supported Operations + +| Operation | Description | Requirements | +| :-------- | :------------------------------------------------------------: | -------------------------------------: | +| + | Adds two private input types together | Types must be private input | +| - | Subtracts two private input types together | Types must be private input | +| \* | Multiplies two private input types together | Types must be private input | +| / | Divides two private input types together | Types must be private input | +| ^ | XOR two private input types together | Types must be integer | +| & | AND two private input types together | Types must be integer | +| \| | OR two private input types together | Types must be integer | +| << | Left shift an integer by another integer amount | Types must be integer | +| >> | Right shift an integer by another integer amount | Types must be integer | +| ! | Bitwise not of a value | Type must be integer or boolean | +| < | returns a bool if one value is less than the other | Upper bound must have a known bit size | +| <= | returns a bool if one value is less than or equal to the other | Upper bound must have a known bit size | +| > | returns a bool if one value is more than the other | Upper bound must have a known bit size | +| >= | returns a bool if one value is more than or equal to the other | Upper bound must have a known bit size | +| == | returns a bool if one value is equal to the other | Both types must not be constants | +| != | returns a bool if one value is not equal to the other | Both types must not be constants | + +### Predicate Operators + +`<,<=, !=, == , >, >=` are known as predicate/comparison operations because they compare two values. +This differs from the operations such as `+` where the operands are used in _computation_. + +### Bitwise Operations Example + +```rust +fn main(x : Field) { + let y = x as u32; + let z = y & y; +} +``` + +`z` is implicitly constrained to be the result of `y & y`. The `&` operand is used to denote bitwise +`&`. + +> `x & x` would not compile as `x` is a `Field` and not an integer type. + +### Logical Operators + +Noir has no support for the logical operators `||` and `&&`. This is because encoding the +short-circuiting that these operators require can be inefficient for Noir's backend. Instead you can +use the bitwise operators `|` and `&` which operate indentically for booleans, just without the +short-circuiting. + +```rust +let my_val = 5; + +let mut flag = 1; +if (my_val > 6) | (my_val == 0) { + flag = 0; +} +assert(flag == 1); + +if (my_val != 10) & (my_val < 50) { + flag = 0; +} +assert(flag == 0); +``` + +### Shorthand operators + +Noir shorthand operators for most of the above operators, namely `+=, -=, *=, /=, %=, &=, |=, ^=, <<=`, and `>>=`. These allow for more concise syntax. For example: + +```rust +let mut i = 0; +i = i + 1; +``` + +could be written as: + +```rust +let mut i = 0; +i += 1; +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/04_assert.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/04_assert.md new file mode 100644 index 00000000000..7427ec6cc63 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/04_assert.md @@ -0,0 +1,26 @@ +--- +title: Assert Function +description: + Learn about the assert function in Noir, which can be used to explicitly constrain the predicate or + comparison expression that follows to be true, and what happens if the expression is false at + runtime. +keywords: [Noir programming language, assert statement, predicate expression, comparison expression] +--- + +Noir includes a special `assert` function which will explicitly constrain the predicate/comparison +expression that follows to be true. If this expression is false at runtime, the program will fail to +be proven. Example: + +```rust +fn main(x : Field, y : Field) { + assert(x == y); +} +``` + +You can optionally provide a message to be logged when the assertion fails: + +```rust +assert(x == y, "x and y are not equal"); +``` + +> Assertions only work for predicate operations, such as `==`. If there's any ambiguity on the operation, the program will fail to compile. For example, it is unclear if `assert(x + y)` would check for `x + y == 0` or simply would return `true`. diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/05_unconstrained.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/05_unconstrained.md new file mode 100644 index 00000000000..6b621eda3eb --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/05_unconstrained.md @@ -0,0 +1,96 @@ +--- +title: Unconstrained Functions +description: "Learn about what unconstrained functions in Noir are, how to use them and when you'd want to." + +keywords: [Noir programming language, unconstrained, open] +--- + + + +Unconstrained functions are functions which do not constrain any of the included computation and allow for non-determinisitic computation. + +## Why? + +Zero-knowledge (ZK) domain-specific languages (DSL) enable developers to generate ZK proofs from their programs by compiling code down to the constraints of an NP complete language (such as R1CS or PLONKish languages). However, the hard bounds of a constraint system can be very limiting to the functionality of a ZK DSL. + +Enabling a circuit language to perform unconstrained execution is a powerful tool. Said another way, unconstrained execution lets developers generate witnesses from code that does not generate any constraints. Being able to execute logic outside of a circuit is critical for both circuit performance and constructing proofs on information that is external to a circuit. + +Fetching information from somewhere external to a circuit can also be used to enable developers to improve circuit efficiency. + +A ZK DSL does not just prove computation, but proves that some computation was handled correctly. Thus, it is necessary that when we switch from performing some operation directly inside of a circuit to inside of an unconstrained environment that the appropriate constraints are still laid down elsewhere in the circuit. + +## Example + +An in depth example might help drive the point home. This example comes from the excellent [post](https://discord.com/channels/1113924620781883405/1124022445054111926/1128747641853972590) by Tom in the Noir Discord. + +Let's look at how we can optimize a function to turn a `u72` into an array of `u8`s. + +```rust +fn main(num: u72) -> pub [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8)) as u72 & 0xff) as u8; + } + + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 91 +Backend circuit size: 3619 +``` + +A lot of the operations in this function are optimized away by the compiler (all the bit-shifts turn into divisions by constants). However we can save a bunch of gates by casting to u8 a bit earlier. This automatically truncates the bit-shifted value to fit in a u8 which allows us to remove the XOR against 0xff. This saves us ~480 gates in total. + +```rust +fn main(num: u72) -> pub [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8)) as u8; + } + + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 75 +Backend circuit size: 3143 +``` + +Those are some nice savings already but we can do better. This code is all constrained so we're proving every step of calculating out using num, but we don't actually care about how we calculate this, just that it's correct. This is where brillig comes in. + +It turns out that truncating a u72 into a u8 is hard to do inside a snark, each time we do as u8 we lay down 4 ACIR opcodes which get converted into multiple gates. It's actually much easier to calculate num from out than the other way around. All we need to do is multiply each element of out by a constant and add them all together, both relatively easy operations inside a snark. + +We can then run u72_to_u8 as unconstrained brillig code in order to calculate out, then use that result in our constrained function and assert that if we were to do the reverse calculation we'd get back num. This looks a little like the below: + +```rust +fn main(num: u72) -> pub [u8; 8] { + let out = u72_to_u8(num); + + let mut reconstructed_num: u72 = 0; + for i in 0..8 { + reconstructed_num += (out[i] as u72 << (56 - (8 * i))); + } + assert(num == reconstructed_num); + out +} + +unconstrained fn u72_to_u8(num: u72) -> [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8))) as u8; + } + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 78 +Backend circuit size: 2902 +``` + +This ends up taking off another ~250 gates from our circuit! We've ended up with more ACIR opcodes than before but they're easier for the backend to prove (resulting in fewer gates). + +Generally we want to use brillig whenever there's something that's easy to verify but hard to compute within the circuit. For example, if you wanted to calculate a square root of a number it'll be a much better idea to calculate this in brillig and then assert that if you square the result you get back your number. diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/06_generics.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/06_generics.md new file mode 100644 index 00000000000..9fb4177c2a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/06_generics.md @@ -0,0 +1,113 @@ +--- +title: Generics +description: Learn how to use Generics in Noir +keywords: [Noir, Rust, generics, functions, structs] +--- + +Generics allow you to use the same functions with multiple different concrete data types. You can +read more about the concept of generics in the Rust documentation +[here](https://doc.rust-lang.org/book/ch10-01-syntax.html). + +Here is a trivial example showing the identity function that supports any type. In Rust, it is +common to refer to the most general type as `T`. We follow the same convention in Noir. + +```rust +fn id(x: T) -> T { + x +} +``` + +## In Structs + +Generics are useful for specifying types in structs. For example, we can specify that a field in a +struct will be of a certain generic type. In this case `value` is of type `T`. + +```rust +struct RepeatedValue { + value: T, + count: Field, +} + +impl RepeatedValue { + fn new(value: T) -> Self { + Self { value, count: 1 } + } + + fn increment(mut repeated: Self) -> Self { + repeated.count += 1; + repeated + } + + fn print(self) { + for _i in 0 .. self.count { + dep::std::println(self.value); + } + } +} + +fn main() { + let mut repeated = RepeatedValue::new("Hello!"); + repeated = repeated.increment(); + repeated.print(); +} +``` + +The `print` function will print `Hello!` an arbitrary number of times, twice in this case. + +If we want to be generic over array lengths (which are type-level integers), we can use numeric +generics. Using these looks just like using regular generics, but these generics can resolve to +integers at compile-time, rather than resolving to types. Here's an example of a struct that is +generic over the size of the array it contains internally: + +```rust +struct BigInt { + limbs: [u32; N], +} + +impl BigInt { + // `N` is in scope of all methods in the impl + fn first(first: BigInt, second: BigInt) -> Self { + assert(first.limbs != second.limbs); + first + + fn second(first: BigInt, second: Self) -> Self { + assert(first.limbs != second.limbs); + second + } +} +``` + +## Calling functions on generic parameters + +Unlike Rust, Noir does not have traits, so how can one translate the equivalent of a trait bound in +Rust into Noir? That is, how can we write a function that is generic over some type `T`, while also +requiring there is a function like `eq: fn(T, T) -> bool` that works on the type? + +The answer is that we can translate this by passing in the function manually. Here's an example of +implementing array equality in Noir: + +```rust +fn array_eq(array1: [T; N], array2: [T; N], elem_eq: fn(T, T) -> bool) -> bool { + if array1.len() != array2.len() { + false + } else { + let mut result = true; + for i in 0 .. array1.len() { + result &= elem_eq(array1[i], array2[i]); + } + result + } +} + +fn main() { + assert(array_eq([1, 2, 3], [1, 2, 3], |a, b| a == b)); + + // We can use array_eq even for arrays of structs, as long as we have + // an equality function for these structs we can pass in + let array = [MyStruct::new(), MyStruct::new()]; + assert(array_eq(array, array, MyStruct::eq)); +} +``` + +You can see an example of generics in the tests +[here](https://github.com/noir-lang/noir/blob/master/tooling/nargo_cli/tests/execution_success/generics/src/main.nr). diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/07_mutability.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/07_mutability.md new file mode 100644 index 00000000000..4641521b1d9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/07_mutability.md @@ -0,0 +1,92 @@ +--- +title: Mutability +description: + Learn about mutable variables, constants, and globals in Noir programming language. Discover how + to declare, modify, and use them in your programs. +keywords: [noir programming language, mutability in noir, mutable variables, constants, globals] +--- + +Variables in noir can be declared mutable via the `mut` keyword. Mutable variables can be reassigned +to via an assignment expression. + +```rust +let x = 2; +x = 3; // error: x must be mutable to be assigned to + +let mut y = 3; +let y = 4; // OK +``` + +The `mut` modifier can also apply to patterns: + +```rust +let (a, mut b) = (1, 2); +a = 11; // error: a must be mutable to be assigned to +b = 12; // OK + +let mut (c, d) = (3, 4); +c = 13; // OK +d = 14; // OK + +// etc. +let MyStruct { x: mut y } = MyStruct { x: a }; +// y is now in scope +``` + +Note that mutability in noir is local and everything is passed by value, so if a called function +mutates its parameters then the parent function will keep the old value of the parameters. + +```rust +fn main() -> Field { + let x = 3; + helper(x); + x // x is still 3 +} + +fn helper(mut x: i32) { + x = 4; +} +``` + +## Comptime Values + +:::warning + +The 'comptime' keyword was removed in version 0.10. The comptime keyword and syntax are currently still kept and parsed for backwards compatibility, but are now deprecated and will issue a warning when used. `comptime` has been removed because it is no longer needed for accessing arrays. + +::: + +## Globals + +Noir also supports global variables. However, they must be known at compile-time. The global type can also be inferred by the compiler entirely. Globals can also be used to specify array +annotations for function parameters and can be imported from submodules. + +```rust +global N: Field = 5; // Same as `global N: Field = 5` + +fn main(x : Field, y : [Field; N]) { + let res = x * N; + + assert(res == y[0]); + + let res2 = x * mysubmodule::N; + assert(res != res2); +} + +mod mysubmodule { + use dep::std; + + global N: Field = 10; + + fn my_helper() -> Field { + let x = N; + x + } +} +``` + +## Why only local mutability? + +Witnesses in a proving system are immutable in nature. Noir aims to _closely_ mirror this setting +without applying additional overhead to the user. Modeling a mutable reference is not as +straightforward as on conventional architectures and would incur some possibly unexpected overhead. diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/08_lambdas.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/08_lambdas.md new file mode 100644 index 00000000000..ae1e6aecab1 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/08_lambdas.md @@ -0,0 +1,80 @@ +--- +title: Lambdas +description: Learn how to use anonymous functions in Noir programming language. +keywords: [Noir programming language, lambda, closure, function, anonymous function] +--- + +## Introduction + +Lambdas are anonymous functions. The syntax is `|arg1, arg2, ..., argN| return_expression`. + +```rust +let add_50 = |val| val + 50; +assert(add_50(100) == 150); +``` + +A block can be used as the body of a lambda, allowing you to declare local variables inside it: + +```rust +let cool = || { + let x = 100; + let y = 100; + x + y +} + +assert(cool() == 200); +``` + +## Closures + +Inside the body of a lambda, you can use variables defined in the enclosing function. Such lambdas are called **closures**. In this example `x` is defined inside `main` and is accessed from within the lambda: + +```rust +fn main() { + let x = 100; + let closure = || x + 150; + assert(closure() == 250); +} +``` + +## Passing closures to higher-order functions + +It may catch you by surprise that the following code fails to compile: + +```rust +fn foo(f: fn () -> Field) -> Field { + f() +} + +fn main() { + let (x, y) = (50, 50); + assert(foo(|| x + y) == 100); // error :( +} +``` + +The reason is that the closure's capture environment affects its type - we have a closure that captures two Fields and `foo` +expects a regular function as an argument - those are incompatible. +:::note + +Variables contained within the `||` are the closure's parameters, and the expression that follows it is the closure's body. The capture environment is comprised of any variables used in the closure's body that are not parameters. + +E.g. in |x| x + y, y would be a captured variable, but x would not be, since it is a parameter of the closure. + +::: +The syntax for the type of a closure is `fn[env](args) -> ret_type`, where `env` is the capture environment of the closure - +in this example that's `(Field, Field)`. + +The best solution in our case is to make `foo` generic over the environment type of its parameter, so that it can be called +with closures with any environment, as well as with regular functions: + +```rust +fn foo(f: fn[Env]() -> Field) -> Field { + f() +} + +fn main() { + let (x, y) = (50, 50); + assert(foo(|| x + y) == 100); // compiles fine + assert(foo(|| 60) == 60); // compiles fine +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/09_comments.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/09_comments.md new file mode 100644 index 00000000000..3bb4d2f25a4 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/09_comments.md @@ -0,0 +1,32 @@ +--- +title: Comments +description: + Learn how to write comments in Noir programming language. A comment is a line of code that is + ignored by the compiler, but it can be read by programmers. Single-line and multi-line comments + are supported in Noir. +keywords: [Noir programming language, comments, single-line comments, multi-line comments] +--- + +A comment is a line in your codebase which the compiler ignores, however it can be read by +programmers. + +Here is a single line comment: + +```rust +// This is a comment and is ignored +``` + +`//` is used to tell the compiler to ignore the rest of the line. + +Noir also supports multi-line block comments. Start a block comment with `/*` and end the block with `*/`. + +Noir does not natively support doc comments. You may be able to use [Rust doc comments](https://doc.rust-lang.org/reference/comments.html) in your code to leverage some Rust documentation build tools with Noir code. + +```rust +/* + This is a block comment describing a complex function. +*/ +fn main(x : Field, y : pub Field) { + assert(x != y); +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/10_distinct.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/10_distinct.md new file mode 100644 index 00000000000..e7ff7f5017a --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/10_distinct.md @@ -0,0 +1,63 @@ +--- +title: Distinct Witnesses +--- + +The `distinct` keyword prevents repetitions of witness indices in the program's ABI. This ensures +that the witnesses being returned as public inputs are all unique. + +The `distinct` keyword is only used for return values on program entry points (usually the `main()` +function). + +When using `distinct` and `pub` simultaneously, `distinct` comes first. See the example below. + +You can read more about the problem this solves +[here](https://github.com/noir-lang/noir/issues/1183). + +## Example + +Without the `distinct` keyword, the following program + +```rust +fn main(x : pub Field, y : pub Field) -> pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + "return_witnesses": [3, 2, 4, 4] + } +} +``` + +Whereas (with the `distinct` keyword) + +```rust +fn main(x : pub Field, y : pub Field) -> distinct pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + //... + "return_witnesses": [3, 4, 5, 6] + } +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/11_shadowing.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/11_shadowing.md new file mode 100644 index 00000000000..efd743e764f --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/11_shadowing.md @@ -0,0 +1,43 @@ +--- +title: Shadowing +--- + +Noir allows for inheriting variables' values and re-declaring them with the same name similar to Rust, known as shadowing. + +For example, the following function is valid in Noir: + +```rust +fn main() { + let x = 5; + + { + let x = x * 2; + assert (x == 10); + } + + assert (x == 5); +} +``` + +In this example, a variable x is first defined with the value 5. + +The local scope that follows shadows the original x, i.e. creates a local mutable x based on the value of the original x. It is given a value of 2 times the original x. + +When we return to the main scope, x once again refers to just the original x, which stays at the value of 5. + +## Temporal mutability + +One way that shadowing is useful, in addition to ergonomics across scopes, is for temporarily mutating variables. + +```rust +fn main() { + let age = 30; + // age = age + 5; // Would error as `age` is immutable by default. + + let mut age = age + 5; // Temporarily mutates `age` with a new value. + + let age = age; // Locks `age`'s mutability again. + + assert (age == 35); +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types.md new file mode 100644 index 00000000000..d546cc463a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types.md @@ -0,0 +1,96 @@ +--- +title: Data Types +description: + Get a clear understanding of the two categories of Noir data types - primitive types and compound + types. Learn about their characteristics, differences, and how to use them in your Noir + programming. +keywords: + [ + noir, + data types, + primitive types, + compound types, + private types, + public types, + ] +--- + +Every value in Noir has a type, which determines which operations are valid for it. + +All values in Noir are fundamentally composed of `Field` elements. For a more approachable +developing experience, abstractions are added on top to introduce different data types in Noir. + +Noir has two category of data types: primitive types (e.g. `Field`, integers, `bool`) and compound +types that group primitive types (e.g. arrays, tuples, structs). Each value can either be private or +public. + +## Private & Public Types + +A **private value** is known only to the Prover, while a **public value** is known by both the +Prover and Verifier. Mark values as `private` when the value should only be known to the prover. All +primitive types (including individual fields of compound types) in Noir are private by default, and +can be marked public when certain values are intended to be revealed to the Verifier. + +> **Note:** For public values defined in Noir programs paired with smart contract verifiers, once +> the proofs are verified on-chain the values can be considered known to everyone that has access to +> that blockchain. + +Public data types are treated no differently to private types apart from the fact that their values +will be revealed in proofs generated. Simply changing the value of a public type will not change the +circuit (where the same goes for changing values of private types as well). + +_Private values_ are also referred to as _witnesses_ sometimes. + +> **Note:** The terms private and public when applied to a type (e.g. `pub Field`) have a different +> meaning than when applied to a function (e.g. `pub fn foo() {}`). +> +> The former is a visibility modifier for the Prover to interpret if a value should be made known to +> the Verifier, while the latter is a visibility modifier for the compiler to interpret if a +> function should be made accessible to external Noir programs like in other languages. + +### pub Modifier + +All data types in Noir are private by default. Types are explicitly declared as public using the +`pub` modifier: + +```rust +fn main(x : Field, y : pub Field) -> pub Field { + x + y +} +``` + +In this example, `x` is **private** while `y` and `x + y` (the return value) are **public**. Note +that visibility is handled **per variable**, so it is perfectly valid to have one input that is +private and another that is public. + +> **Note:** Public types can only be declared through parameters on `main`. + +## Type Aliases + +A type alias is a new name for an existing type. Type aliases are declared with the keyword `type`: + +```rust +type Id = u8; + +fn main() { + let id: Id = 1; + let zero: u8 = 0; + assert(zero + 1 == id); +} +``` + +Type aliases can also be used with [generics](./06_generics.md): + +```rust +type Id = Size; + +fn main() { + let id: Id = 1; + let zero: u32 = 0; + assert(zero + 1 == id); +} +``` + +### BigInt + +You can acheive BigInt functionality using the [Noir BigInt](https://github.com/shuklaayush/noir-bigint) library. diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/00_fields.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/00_fields.md new file mode 100644 index 00000000000..658a0441ffb --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/00_fields.md @@ -0,0 +1,165 @@ +--- +title: Fields +description: + Dive deep into the Field data type in Noir. Understand its methods, practical examples, and best practices to effectively use Fields in your Noir programs. +keywords: + [ + noir, + field type, + methods, + examples, + best practices, + ] +--- + +The field type corresponds to the native field type of the proving backend. + +The size of a Noir field depends on the elliptic curve's finite field for the proving backend +adopted. For example, a field would be a 254-bit integer when paired with the default backend that +spans the Grumpkin curve. + +Fields support integer arithmetic and are often used as the default numeric type in Noir: + +```rust +fn main(x : Field, y : Field) { + let z = x + y; +} +``` + +`x`, `y` and `z` are all private fields in this example. Using the `let` keyword we defined a new +private value `z` constrained to be equal to `x + y`. + +If proving efficiency is of priority, fields should be used as a default for solving problems. +Smaller integer types (e.g. `u64`) incur extra range constraints. + +## Methods + +After declaring a Field, you can use these common methods on it: + +### to_le_bits + +Transforms the field into an array of bits, Little Endian. + +```rust +fn to_le_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2; + let bits = field.to_le_bits(32); +} +``` + +### to_be_bits + +Transforms the field into an array of bits, Big Endian. + +```rust +fn to_be_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2; + let bits = field.to_be_bits(32); +} +``` + +### to_le_bytes + +Transforms into an array of bytes, Little Endian + +```rust +fn to_le_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let bytes = field.to_le_bytes(4); +} +``` + +### to_be_bytes + +Transforms into an array of bytes, Big Endian + +```rust +fn to_be_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let bytes = field.to_be_bytes(4); +} +``` + +### to_le_radix + +Decomposes into a vector over the specified base, Little Endian + +```rust +fn to_le_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let radix = field.to_le_radix(256, 4); +} +``` + +### to_be_radix + +Decomposes into a vector over the specified base, Big Endian + +```rust +fn to_be_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2; + let radix = field.to_be_radix(256, 4); +} +``` + +### pow_32 + +Returns the value to the power of the specified exponent + +```rust +fn pow_32(self, exponent: Field) -> Field +``` + +example: + +```rust +fn main() { + let field = 2 + let pow = field.pow_32(4); + assert(pow == 16); +} +``` + +### sgn0 + +Parity of (prime) Field element, i.e. sgn0(x mod p) = 0 if x ∈ {0, ..., p-1} is even, otherwise sgn0(x mod p) = 1. + +```rust +fn sgn0(self) -> u1 +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/01_integers.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/01_integers.md new file mode 100644 index 00000000000..b1e7ad11bfd --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/01_integers.md @@ -0,0 +1,112 @@ +--- +title: Integers +description: Explore the Integer data type in Noir. Learn about its methods, see real-world examples, and grasp how to efficiently use Integers in your Noir code. +keywords: [noir, integer types, methods, examples, arithmetic] +--- + +An integer type is a range constrained field type. The Noir frontend supports arbitrarily-sized, both unsigned and signed integer types. + +:::info + +When an integer is defined in Noir without a specific type, it will default to `Field`. + +The one exception is for loop indices which default to `u64` since comparisons on `Field`s are not possible. + +::: + +## Unsigned Integers + +An unsigned integer type is specified first with the letter `u` (indicating its unsigned nature) followed by its bit size (e.g. `8`): + +```rust +fn main() { + let x: u8 = 1; + let y: u8 = 1; + let z = x + y; + assert (z == 2); +} +``` + +The bit size determines the maximum value the integer type can store. For example, a `u8` variable can store a value in the range of 0 to 255 (i.e. $\\2^{8}-1\\$). + +## Signed Integers + +A signed integer type is specified first with the letter `i` (which stands for integer) followed by its bit size (e.g. `8`): + +```rust +fn main() { + let x: i8 = -1; + let y: i8 = -1; + let z = x + y; + assert (z == -2); +} +``` + +The bit size determines the maximum and minimum range of value the integer type can store. For example, an `i8` variable can store a value in the range of -128 to 127 (i.e. $\\-2^{7}\\$ to $\\2^{7}-1\\$). + +:::tip + +If you are using the default proving backend with Noir, both even (e.g. _u2_, _i2_) and odd (e.g. _u3_, _i3_) arbitrarily-sized integer types up to 127 bits (i.e. _u127_ and _i127_) are supported. + +::: + +## Overflows + +Computations that exceed the type boundaries will result in overflow errors. This happens with both signed and unsigned integers. For example, attempting to prove: + +```rust +fn main(x: u8, y: u8) { + let z = x + y; +} +``` + +With: + +```toml +x = "255" +y = "1" +``` + +Would result in: + +``` +$ nargo prove +error: Assertion failed: 'attempt to add with overflow' +┌─ ~/src/main.nr:9:13 +│ +│ let z = x + y; +│ ----- +│ += Call stack: + ... +``` + +A similar error would happen with signed integers: + +```rust +fn main() { + let x: i8 = -118; + let y: i8 = -11; + let z = x + y; +} +``` + +### Wrapping methods + +Although integer overflow is expected to error, some use-cases rely on wrapping. For these use-cases, the standard library provides `wrapping` variants of certain common operations: + +```rust +fn wrapping_add(x: T, y: T) -> T; +fn wrapping_sub(x: T, y: T) -> T; +fn wrapping_mul(x: T, y: T) -> T; +``` + +Example of how it is used: + +```rust +use dep::std; + +fn main(x: u8, y: u8) -> pub u8 { + std::wrapping_add(x + y) +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/02_booleans.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/02_booleans.md new file mode 100644 index 00000000000..885db167d83 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/02_booleans.md @@ -0,0 +1,30 @@ +--- +title: Booleans +description: + Delve into the Boolean data type in Noir. Understand its methods, practical examples, and best practices for using Booleans in your Noir programs. +keywords: + [ + noir, + boolean type, + methods, + examples, + logical operations, + ] +--- + + +The `bool` type in Noir has two possible values: `true` and `false`: + +```rust +fn main() { + let t = true; + let f: bool = false; +} +``` + +> **Note:** When returning a boolean value, it will show up as a value of 1 for `true` and 0 for +> `false` in _Verifier.toml_. + +The boolean type is most commonly used in conditionals like `if` expressions and `assert` +statements. More about conditionals is covered in the [Control Flow](../control_flow) and +[Assert Function](../assert) sections. diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/03_strings.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/03_strings.md new file mode 100644 index 00000000000..c42f34ec3ad --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/03_strings.md @@ -0,0 +1,63 @@ +--- +title: Strings +description: + Discover the String data type in Noir. Learn about its methods, see real-world examples, and understand how to effectively manipulate and use Strings in Noir. +keywords: + [ + noir, + string type, + methods, + examples, + concatenation, + ] +--- + + +The string type is a fixed length value defined with `str`. + +You can use strings in `assert()` functions or print them with +`std::println()`. See more about [Logging](../../standard_library/logging). + +```rust +use dep::std; + +fn main(message : pub str<11>, hex_as_string : str<4>) { + std::println(message); + assert(message == "hello world"); + assert(hex_as_string == "0x41"); +} +``` + +You can convert a `str` to a byte array by calling `as_bytes()` +or a vector by calling `as_bytes_vec()`. + +```rust +fn main() { + let message = "hello world"; + let message_bytes = message.as_bytes(); + let mut message_vec = message.as_bytes_vec(); + assert(message_bytes.len() == 11); + assert(message_bytes[0] == 104); + assert(message_bytes[0] == message_vec.get(0)); +} +``` + +## Escape characters + +You can use escape characters for your strings: + +| Escape Sequence | Description | +|-----------------|-----------------| +| `\r` | Carriage Return | +| `\n` | Newline | +| `\t` | Tab | +| `\0` | Null Character | +| `\"` | Double Quote | +| `\\` | Backslash | + +Example: + +```rust +let s = "Hello \"world" // prints "Hello "world" +let s = "hey \tyou"; // prints "hey you" +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/04_arrays.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/04_arrays.md new file mode 100644 index 00000000000..bdbd1798bef --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/04_arrays.md @@ -0,0 +1,244 @@ +--- +title: Arrays +description: + Dive into the Array data type in Noir. Grasp its methods, practical examples, and best practices for efficiently using Arrays in your Noir code. +keywords: + [ + noir, + array type, + methods, + examples, + indexing, + ] +--- + +An array is one way of grouping together values into one compound type. Array types can be inferred +or explicitly specified via the syntax `[; ]`: + +```rust +fn main(x : Field, y : Field) { + let my_arr = [x, y]; + let your_arr: [Field; 2] = [x, y]; +} +``` + +Here, both `my_arr` and `your_arr` are instantiated as an array containing two `Field` elements. + +Array elements can be accessed using indexing: + +```rust +fn main() { + let a = [1, 2, 3, 4, 5]; + + let first = a[0]; + let second = a[1]; +} +``` + +All elements in an array must be of the same type (i.e. homogeneous). That is, an array cannot group +a `Field` value and a `u8` value together for example. + +You can write mutable arrays, like: + +```rust +fn main() { + let mut arr = [1, 2, 3, 4, 5]; + assert(arr[0] == 1); + + arr[0] = 42; + assert(arr[0] == 42); +} +``` + +You can instantiate a new array of a fixed size with the same value repeated for each element. The following example instantiates an array of length 32 where each element is of type Field and has the value 0. + +```rust +let array: [Field; 32] = [0; 32]; +``` + +Like in Rust, arrays in Noir are a fixed size. However, if you wish to convert an array to a [slice](./slices), you can just call `as_slice` on your array: + +```rust +let array: [Field; 32] = [0; 32]; +let sl = array.as_slice() +``` + +You can define multidimensional arrays: + +```rust +let array : [[Field; 2]; 2]; +let element = array[0][0]; +``` + +## Types + +You can create arrays of primitive types or structs. There is not yet support for nested arrays +(arrays of arrays) or arrays of structs that contain arrays. + +## Methods + +For convenience, the STD provides some ready-to-use, common methods for arrays: + +### len + +Returns the length of an array + +```rust +fn len(_array: [T; N]) -> comptime Field +``` + +example + +```rust +fn main() { + let array = [42, 42]; + assert(array.len() == 2); +} +``` + +### sort + +Returns a new sorted array. The original array remains untouched. Notice that this function will +only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting +logic it uses internally is optimized specifically for these values. If you need a sort function to +sort any type, you should use the function `sort_via` described below. + +```rust +fn sort(_array: [T; N]) -> [T; N] +``` + +example + +```rust +fn main() { + let arr = [42, 32]; + let sorted = arr.sort(); + assert(sorted == [32, 42]); +} +``` + +### sort_via + +Sorts the array with a custom comparison function + +```rust +fn sort_via(mut a: [T; N], ordering: fn(T, T) -> bool) -> [T; N] +``` + +example + +```rust +fn main() { + let arr = [42, 32] + let sorted_ascending = arr.sort_via(|a, b| a < b); + assert(sorted_ascending == [32, 42]); // verifies + + let sorted_descending = arr.sort_via(|a, b| a > b); + assert(sorted_descending == [32, 42]); // does not verify +} +``` + +### map + +Applies a function to each element of the array, returning a new array containing the mapped elements. + +```rust +fn map(f: fn(T) -> U) -> [U; N] +``` + +example + +```rust +let a = [1, 2, 3]; +let b = a.map(|a| a * 2); // b is now [2, 4, 6] +``` + +### fold + +Applies a function to each element of the array, returning the final accumulated value. The first +parameter is the initial value. + +```rust +fn fold(mut accumulator: U, f: fn(U, T) -> U) -> U +``` + +This is a left fold, so the given function will be applied to the accumulator and first element of +the array, then the second, and so on. For a given call the expected result would be equivalent to: + +```rust +let a1 = [1]; +let a2 = [1, 2]; +let a3 = [1, 2, 3]; + +let f = |a, b| a - b; +a1.fold(10, f) //=> f(10, 1) +a2.fold(10, f) //=> f(f(10, 1), 2) +a3.fold(10, f) //=> f(f(f(10, 1), 2), 3) +``` + +example: + +```rust + +fn main() { + let arr = [2, 2, 2, 2, 2]; + let folded = arr.fold(0, |a, b| a + b); + assert(folded == 10); +} + +``` + +### reduce + +Same as fold, but uses the first element as starting element. + +```rust +fn reduce(f: fn(T, T) -> T) -> T +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 2]; + let reduced = arr.reduce(|a, b| a + b); + assert(reduced == 10); +} +``` + +### all + +Returns true if all the elements satisfy the given predicate + +```rust +fn all(predicate: fn(T) -> bool) -> bool +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 2]; + let all = arr.all(|a| a == 2); + assert(all); +} +``` + +### any + +Returns true if any of the elements satisfy the given predicate + +```rust +fn any(predicate: fn(T) -> bool) -> bool +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 5]; + let any = arr.any(|a| a == 5); + assert(any); +} + +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/05_slices.mdx b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/05_slices.mdx new file mode 100644 index 00000000000..1be0ec4a137 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/05_slices.mdx @@ -0,0 +1,146 @@ +--- +title: Slices +description: Explore the Slice data type in Noir. Understand its methods, see real-world examples, and learn how to effectively use Slices in your Noir programs. +keywords: [noir, slice type, methods, examples, subarrays] +--- + +import Experimental from '@site/src/components/Notes/_experimental.mdx'; + + + +A slice is a dynamically-sized view into a sequence of elements. They can be resized at runtime, but because they don't own the data, they cannot be returned from a circuit. You can treat slices as arrays without a constrained size. + +```rust +use dep::std::slice; + +fn main() -> pub Field { + let mut slice: [Field] = [0; 2]; + + let mut new_slice = slice.push_back(6); + new_slice.len() +} +``` + +View the corresponding test file [here][test-file]. + +[test-file]: https://github.com/noir-lang/noir/blob/f387ec1475129732f72ba294877efdf6857135ac/crates/nargo_cli/tests/test_data_ssa_refactor/slices/src/main.nr + +## Methods + +For convenience, the STD provides some ready-to-use, common methods for slices: + +### push_back + +Pushes a new element to the end of the slice, returning a new slice with a length one greater than the original unmodified slice. + +```rust +fn push_back(_self: [T], _elem: T) -> [T] +``` + +example: + +```rust +fn main() -> pub Field { + let mut slice: [Field] = [0; 2]; + + let mut new_slice = slice.push_back(6); + new_slice.len() +} +``` + +View the corresponding test file [here][test-file]. + +### push_front + +Returns a new array with the specified element inserted at index 0. The existing elements indexes are incremented by 1. + +```rust +fn push_front(_self: Self, _elem: T) -> Self +``` + +Example: + +```rust +let mut new_slice: [Field] = []; +new_slice = new_slice.push_front(20); +assert(new_slice[0] == 20); // returns true +``` + +View the corresponding test file [here][test-file]. + +### pop_front + +Returns a tuple of two items, the first element of the array and the rest of the array. + +```rust +fn pop_front(_self: Self) -> (T, Self) +``` + +Example: + +```rust +let (first_elem, rest_of_slice) = slice.pop_front(); +``` + +View the corresponding test file [here][test-file]. + +### pop_back + +Returns a tuple of two items, the beginning of the array with the last element omitted and the last element. + +```rust +fn pop_back(_self: Self) -> (Self, T) +``` + +Example: + +```rust +let (popped_slice, last_elem) = slice.pop_back(); +``` + +View the corresponding test file [here][test-file]. + +### append + +Loops over a slice and adds it to the end of another. + +```rust +fn append(mut self, other: Self) -> Self +``` + +Example: + +```rust +let append = [1, 2].append([3, 4, 5]); +``` + +### insert + +Inserts an element at a specified index and shifts all following elements by 1. + +```rust +fn insert(_self: Self, _index: Field, _elem: T) -> Self +``` + +Example: + +```rust +new_slice = rest_of_slice.insert(2, 100); +assert(new_slice[2] == 100); +``` + +View the corresponding test file [here][test-file]. + +### remove + +Remove an element at a specified index, shifting all elements after it to the left, returning the altered slice and the removed element. + +```rust +fn remove(_self: Self, _index: Field) -> (Self, T) +``` + +Example: + +```rust +let (remove_slice, removed_elem) = slice.remove(3); +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/06_vectors.mdx b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/06_vectors.mdx new file mode 100644 index 00000000000..4617e90d038 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/06_vectors.mdx @@ -0,0 +1,172 @@ +--- +title: Vectors +description: Delve into the Vector data type in Noir. Learn about its methods, practical examples, and best practices for using Vectors in your Noir code. +keywords: [noir, vector type, methods, examples, dynamic arrays] +--- + +import Experimental from '@site/src/components/Notes/_experimental.mdx'; + + + +A vector is a collection type similar to Rust's Vector type. It's convenient way to use slices as mutable arrays. + +Example: + +```rust +use dep::std::collections::vec::Vec; + +let mut vector: Vec = Vec::new(); +for i in 0..5 { + vector.push(i); +} +assert(vector.len() == 5); +``` + +## Methods + +### new + +Creates a new, empty vector. + +```rust +pub fn new() -> Self { + Self { slice: [] } +} +``` + +Example: + +```rust +let empty_vector: Vec = Vec::new(); +assert(empty_vector.len() == 0); +``` + +### from_slice + +Creates a vector containing each element from a given slice. Mutations to the resulting vector will not affect the original slice. + +```rust +pub fn from_slice(slice: [T]) -> Self { + Self { slice } +} +``` + +Example: + +```rust +let arr: [Field] = [1, 2, 3]; +let vector_from_slice = Vec::from_slice(arr); +assert(vector_from_slice.len() == 3); +``` + +### get + +Retrieves an element from the vector at a given index. Panics if the index points beyond the vector's end. + +```rust +pub fn get(self, index: Field) -> T { + self.slice[index] +} +``` + +Example: + +```rust +let vector: Vec = Vec::from_slice([10, 20, 30]); +assert(vector.get(1) == 20); +``` + +### push + +Adds a new element to the vector's end, returning a new vector with a length one greater than the original unmodified vector. + +```rust +pub fn push(&mut self, elem: T) { + self.slice = self.slice.push_back(elem); +} +``` + +Example: + +```rust +let mut vector: Vec = Vec::new(); +vector.push(10); +assert(vector.len() == 1); +``` + +### pop + +Removes an element from the vector's end, returning a new vector with a length one less than the original vector, along with the removed element. Panics if the vector's length is zero. + +```rust +pub fn pop(&mut self) -> T { + let (popped_slice, last_elem) = self.slice.pop_back(); + self.slice = popped_slice; + last_elem +} +``` + +Example: + +```rust +let mut vector = Vec::from_slice([10, 20]); +let popped_elem = vector.pop(); +assert(popped_elem == 20); +assert(vector.len() == 1); +``` + +### insert + +Inserts an element at a specified index, shifting subsequent elements to the right. + +```rust +pub fn insert(&mut self, index: Field, elem: T) { + self.slice = self.slice.insert(index, elem); +} +``` + +Example: + +```rust +let mut vector = Vec::from_slice([10, 30]); +vector.insert(1, 20); +assert(vector.get(1) == 20); +``` + +### remove + +Removes an element at a specified index, shifting subsequent elements to the left, and returns the removed element. + +```rust +pub fn remove(&mut self, index: Field) -> T { + let (new_slice, elem) = self.slice.remove(index); + self.slice = new_slice; + elem +} +``` + +Example: + +```rust +let mut vector = Vec::from_slice([10, 20, 30]); +let removed_elem = vector.remove(1); +assert(removed_elem == 20); +assert(vector.len() == 2); +``` + +### len + +Returns the number of elements in the vector. + +```rust +pub fn len(self) -> Field { + self.slice.len() +} +``` + +Example: + +```rust +let empty_vector: Vec = Vec::new(); +assert(empty_vector.len() == 0); +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/07_tuples.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/07_tuples.md new file mode 100644 index 00000000000..5f6cab974a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/07_tuples.md @@ -0,0 +1,47 @@ +--- +title: Tuples +description: + Dive into the Tuple data type in Noir. Understand its methods, practical examples, and best practices for efficiently using Tuples in your Noir code. +keywords: + [ + noir, + tuple type, + methods, + examples, + multi-value containers, + ] +--- + +A tuple collects multiple values like an array, but with the added ability to collect values of +different types: + +```rust +fn main() { + let tup: (u8, u64, Field) = (255, 500, 1000); +} +``` + +One way to access tuple elements is via destructuring using pattern matching: + +```rust +fn main() { + let tup = (1, 2); + + let (one, two) = tup; + + let three = one + two; +} +``` + +Another way to access tuple elements is via direct member access, using a period (`.`) followed by +the index of the element we want to access. Index `0` corresponds to the first tuple element, `1` to +the second and so on: + +```rust +fn main() { + let tup = (5, 6, 7, 8); + + let five = tup.0; + let eight = tup.3; +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/08_structs.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/08_structs.md new file mode 100644 index 00000000000..9255455bdcb --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/08_structs.md @@ -0,0 +1,69 @@ +--- +title: Structs +description: + Explore the Struct data type in Noir. Learn about its methods, see real-world examples, and grasp how to effectively define and use Structs in your Noir programs. +keywords: + [ + noir, + struct type, + methods, + examples, + data structures, + ] +--- + +A struct also allows for grouping multiple values of different types. Unlike tuples, we can also +name each field. + +> **Note:** The usage of _field_ here refers to each element of the struct and is unrelated to the +> field type of Noir. + +Defining a struct requires giving it a name and listing each field within as `: ` pairs: + +```rust +struct Animal { + hands: Field, + legs: Field, + eyes: u8, +} +``` + +An instance of a struct can then be created with actual values in `: ` pairs in any +order. Struct fields are accessible using their given names: + +```rust +fn main() { + let legs = 4; + + let dog = Animal { + eyes: 2, + hands: 0, + legs, + }; + + let zero = dog.hands; +} +``` + +Structs can also be destructured in a pattern, binding each field to a new variable: + +```rust +fn main() { + let Animal { hands, legs: feet, eyes } = get_octopus(); + + let ten = hands + feet + eyes as u8; +} + +fn get_octopus() -> Animal { + let octopus = Animal { + hands: 0, + legs: 8, + eyes: 2, + }; + + octopus +} +``` + +The new variables can be bound with names different from the original struct field names, as +showcased in the `legs --> feet` binding in the example above. \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/09_references.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/09_references.md new file mode 100644 index 00000000000..b0c35ce2cb9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/09_references.md @@ -0,0 +1,22 @@ +--- +title: References +--- + +Noir supports first-class references. References are a bit like pointers: they point to a specific address that can be followed to access the data stored at that address. You can use Rust-like syntax to use pointers in Noir: the `&` operator references the variable, the `*` operator dereferences it. + +Example: + +```rust +fn main() { + let mut x = 2; + + // you can reference x as &mut and pass it to multiplyBy2 + multiplyBy2(&mut x); +} + +// you can access &mut here +fn multiplyBy2(x: &mut Field) { + // and dereference it with * + *x = *x * 2; +} +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/10_function_types.md b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/10_function_types.md new file mode 100644 index 00000000000..1ec92efd594 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/language_concepts/data_types/10_function_types.md @@ -0,0 +1,25 @@ +--- +title: Function types +--- + +Noir supports higher-order functions. The syntax for a function type is as follows: + +```rust +fn(arg1_type, arg2_type, ...) -> return_type +``` + +Example: + +```rust +fn assert_returns_100(f: fn() -> Field) { // f takes no args and returns a Field + assert(f() == 100); +} + +fn main() { + assert_returns_100(|| 100); // ok + assert_returns_100(|| 150); // fails +} +``` + +A function type also has an optional capture environment - this is necessary to support closures. +See [Lambdas](../08_lambdas.md) for more details. diff --git a/noir/docs/versioned_docs/version-v0.19.2/migration_notes.md b/noir/docs/versioned_docs/version-v0.19.2/migration_notes.md new file mode 100644 index 00000000000..e87eb1feaba --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/migration_notes.md @@ -0,0 +1,91 @@ +--- +title: Migration notes +description: Read about migration notes from previous versions, which could solve problems while updating +keywords: [Noir, notes, migration, updating, upgrading] +--- + +Noir is in full-speed development. Things break fast, wild, and often. This page attempts to leave some notes on errors you might encounter when upgrading and how to resolve them until proper patches are built. + +## ≥0.19 + +### Enforcing `compiler_version` + +From this version on, the compiler will check for the `compiler_version` field in `Nargo.toml`, and will error if it doesn't match the current Nargo version in use. + +To update, please make sure this field in `Nargo.toml` matches the output of `nargo --version`. + +## ≥0.14 + +The index of the [for loops](./language_concepts/02_control_flow.md#loops) is now of type `u64` instead of `Field`. An example refactor would be: + +```rust +for i in 0..10 { + let i = i as Field; +} +``` + +## ≥v0.11.0 and Nargo backend + +From this version onwards, Nargo starts managing backends through the `nargo backend` command. Upgrading to the versions per usual steps might lead to: + +### `backend encountered an error` + +This is likely due to the existing locally installed version of proving backend (e.g. barretenberg) is incompatible with the version of Nargo in use. + +To fix the issue: + +1. Uninstall the existing backend + +```bash +nargo backend uninstall acvm-backend-barretenberg +``` + +You may replace _acvm-backend-barretenberg_ with the name of your backend listed in `nargo backend ls` or in ~/.nargo/backends. + +2. Reinstall a compatible version of the proving backend. + +If you are using the default barretenberg backend, simply run: + +``` +nargo prove +``` + +with you Noir program. + +This will trigger the download and installation of the latest version of barretenberg compatible with your Nargo in use. + +### `backend encountered an error: illegal instruction` + +On certain Intel-based systems, an `illegal instruction` error may arise due to incompatibility of barretenberg with certain CPU instructions. + +To fix the issue: + +1. Uninstall the existing backend + +```bash +nargo backend uninstall acvm-backend-barretenberg +``` + +You may replace _acvm-backend-barretenberg_ with the name of your backend listed in `nargo backend ls` or in ~/.nargo/backends. + +2. Reinstall a compatible version of the proving backend. + +If you are using the default barretenberg backend, simply run: + +``` +nargo backend install acvm-backend-barretenberg https://github.com/noir-lang/barretenberg-js-binary/raw/master/run-bb.tar.gz +``` + +This downloads and installs a specific bb.js based version of barretenberg binary from GitHub. + +The gzipped filed is running this bash script: , where we need to gzip it as the Nargo currently expect the backend to be zipped up. + +Then run: + +``` +DESIRED_BINARY_VERSION=0.8.1 nargo info +``` + +This overrides the bb native binary with a bb.js node application instead, which should be compatible with most if not all hardware. This does come with the drawback of being generally slower than native binary. + +0.8.1 indicates bb.js version 0.8.1, so if you change that it will update to a different version or the default version in the script if none was supplied. diff --git a/noir/docs/versioned_docs/version-v0.19.2/modules_packages_crates/crates_and_packages.md b/noir/docs/versioned_docs/version-v0.19.2/modules_packages_crates/crates_and_packages.md new file mode 100644 index 00000000000..fb83a33d94e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/modules_packages_crates/crates_and_packages.md @@ -0,0 +1,42 @@ +--- +title: Crates and Packages +description: Learn how to use Crates and Packages in your Noir project +keywords: [Nargo, dependencies, package management, crates, package] +--- + +## Crates + +A crate is the smallest amount of code that the Noir compiler considers at a time. +Crates can contain modules, and the modules may be defined in other files that get compiled with the crate, as we’ll see in the coming sections. + +### Crate Types + +A Noir crate can come in several forms: binaries, libraries or contracts. + +#### Binaries + +_Binary crates_ are programs which you can compile to an ACIR circuit which you can then create proofs against. Each must have a function called `main` that defines the ACIR circuit which is to be proved. + +#### Libraries + +_Library crates_ don't have a `main` function and they don't compile down to ACIR. Instead they define functionality intended to be shared with multiple projects, and eventually included in a binary crate. + +#### Contracts + +Contract crates are similar to binary crates in that they compile to ACIR which you can create proofs against. They are different in that they do not have a single `main` function, but are a collection of functions to be deployed to the [Aztec network](https://aztec.network). You can learn more about the technical details of Aztec in the [monorepo](https://github.com/AztecProtocol/aztec-packages) or contract [examples](https://github.com/AztecProtocol/aztec-packages/tree/master/yarn-project/noir-contracts/src/contracts). + +### Crate Root + +Every crate has a root, which is the source file that the compiler starts, this is also known as the root module. The Noir compiler does not enforce any conditions on the name of the file which is the crate root, however if you are compiling via Nargo the crate root must be called `lib.nr` or `main.nr` for library or binary crates respectively. + +## Packages + +A Nargo _package_ is a collection of one of more crates that provides a set of functionality. A package must include a Nargo.toml file. + +A package _must_ contain either a library or a binary crate, but not both. + +### Differences from Cargo Packages + +One notable difference between Rust's Cargo and Noir's Nargo is that while Cargo allows a package to contain an unlimited number of binary crates and a single library crate, Nargo currently only allows a package to contain a single crate. + +In future this restriction may be lifted to allow a Nargo package to contain both a binary and library crate or multiple binary crates. diff --git a/noir/docs/versioned_docs/version-v0.19.2/modules_packages_crates/dependencies.md b/noir/docs/versioned_docs/version-v0.19.2/modules_packages_crates/dependencies.md new file mode 100644 index 00000000000..75f95aaa305 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/modules_packages_crates/dependencies.md @@ -0,0 +1,123 @@ +--- +title: Dependencies +description: + Learn how to specify and manage dependencies in Nargo, allowing you to upload packages to GitHub + and use them easily in your project. +keywords: [Nargo, dependencies, GitHub, package management, versioning] +--- + +Nargo allows you to upload packages to GitHub and use them as dependencies. + +## Specifying a dependency + +Specifying a dependency requires a tag to a specific commit and the git url to the url containing +the package. + +Currently, there are no requirements on the tag contents. If requirements are added, it would follow +semver 2.0 guidelines. + +> Note: Without a `tag` , there would be no versioning and dependencies would change each time you +> compile your project. + +For example, to add the [ecrecover-noir library](https://github.com/colinnielsen/ecrecover-noir) to your project, add it to `Nargo.toml`: + +```toml +# Nargo.toml + +[dependencies] +ecrecover = {tag = "v0.8.0", git = "https://github.com/colinnielsen/ecrecover-noir"} +``` + +If the module is in a subdirectory, you can define a subdirectory in your git repository, for example: + +```toml +# Nargo.toml + +[dependencies] +easy_private_token_contract = {tag ="v0.1.0-alpha62", git = "https://github.com/AztecProtocol/aztec-packages", directory = "yarn-project/noir-contracts/src/contracts/easy_private_token_contract"} +``` + +## Specifying a local dependency + +You can also specify dependencies that are local to your machine. + +For example, this file structure has a library and binary crate + +```tree +├── binary_crate +│   ├── Nargo.toml +│   └── src +│   └── main.nr +└── liba + ├── Nargo.toml + └── src + └── lib.nr +``` + +Inside of the binary crate, you can specify: + +```toml +# Nargo.toml + +[dependencies] +libA = { path = "../liba" } +``` + +## Importing dependencies + +You can import a dependency to a Noir file using the following syntax. For example, to import the +ecrecover-noir library and local liba referenced above: + +```rust +use dep::ecrecover; +use dep::libA; +``` + +You can also import only the specific parts of dependency that you want to use, like so: + +```rust +use dep::std::hash::sha256; +use dep::std::scalar_mul::fixed_base_embedded_curve; +``` + +Lastly, as demonstrated in the +[elliptic curve example](../standard_library/cryptographic_primitives/ec_primitives#examples), you +can import multiple items in the same line by enclosing them in curly braces: + +```rust +use dep::std::ec::tecurve::affine::{Curve, Point}; +``` + +We don't have a way to consume libraries from inside a [workspace](./workspaces) as external dependencies right now. + +Inside a workspace, these are consumed as `{ path = "../to_lib" }` dependencies in Nargo.toml. + +## Dependencies of Dependencies + +Note that when you import a dependency, you also get access to all of the dependencies of that package. + +For example, the [phy_vector](https://github.com/resurgencelabs/phy_vector) library imports an [fraction](https://github.com/resurgencelabs/fraction) library. If you're importing the phy_vector library, then you can access the functions in fractions library like so: + +```rust +use dep::phy_vector; + +fn main(x : Field, y : pub Field) { + //... + let f = phy_vector::fraction::toFraction(true, 2, 1); + //... +} +``` + +## Available Libraries + +Noir does not currently have an official package manager. You can find a list of available Noir libraries in the [awesome-noir repo here](https://github.com/noir-lang/awesome-noir#libraries). + +Some libraries that are available today include: + +- [Standard Library](https://github.com/noir-lang/noir/tree/master/noir_stdlib) - the Noir Standard Library +- [Ethereum Storage Proof Verification](https://github.com/aragonzkresearch/noir-trie-proofs) - a library that contains the primitives necessary for RLP decoding (in the form of look-up table construction) and Ethereum state and storage proof verification (or verification of any trie proof involving 32-byte long keys) +- [BigInt](https://github.com/shuklaayush/noir-bigint) - a library that provides a custom BigUint56 data type, allowing for computations on large unsigned integers +- [ECrecover](https://github.com/colinnielsen/ecrecover-noir/tree/main) - a library to verify an ECDSA signature and return the source Ethereum address +- [Sparse Merkle Tree Verifier](https://github.com/vocdoni/smtverifier-noir/tree/main) - a library for verification of sparse Merkle trees +- [Signed Int](https://github.com/resurgencelabs/signed_int) - a library for accessing a custom Signed Integer data type, allowing access to negative numbers on Noir +- [Fraction](https://github.com/resurgencelabs/fraction) - a library for accessing fractional number data type in Noir, allowing results that aren't whole numbers diff --git a/noir/docs/versioned_docs/version-v0.19.2/modules_packages_crates/modules.md b/noir/docs/versioned_docs/version-v0.19.2/modules_packages_crates/modules.md new file mode 100644 index 00000000000..147c9b284e8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/modules_packages_crates/modules.md @@ -0,0 +1,104 @@ +--- +title: Modules +description: + Learn how to organize your files using modules in Noir, following the same convention as Rust's + module system. Examples included. +keywords: [Noir, Rust, modules, organizing files, sub-modules] +--- + +Noir's module system follows the same convention as the _newer_ version of Rust's module system. + +## Purpose of Modules + +Modules are used to organise files. Without modules all of your code would need to live in a single +file. In Noir, the compiler does not automatically scan all of your files to detect modules. This +must be done explicitly by the developer. + +## Examples + +### Importing a module in the crate root + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::hello_world(); +} +``` + +Filename : `src/foo.nr` + +```rust +fn from_foo() {} +``` + +In the above snippet, the crate root is the `src/main.nr` file. The compiler sees the module +declaration `mod foo` which prompts it to look for a foo.nr file. + +Visually this module hierarchy looks like the following : + +``` +crate + ├── main + │ + └── foo + └── from_foo + +``` + +### Importing a module throughout the tree + +All modules are accessible from the `crate::` namespace. + +``` +crate + ├── bar + ├── foo + └── main + +``` + +In the above snippet, if `bar` would like to use functions in `foo`, it can do so by `use crate::foo::function_name`. + +### Sub-modules + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::from_foo(); +} +``` + +Filename : `src/foo.nr` + +```rust +mod bar; +fn from_foo() {} +``` + +Filename : `src/foo/bar.nr` + +```rust +fn from_bar() {} +``` + +In the above snippet, we have added an extra module to the module tree; `bar`. `bar` is a submodule +of `foo` hence we declare bar in `foo.nr` with `mod bar`. Since `foo` is not the crate root, the +compiler looks for the file associated with the `bar` module in `src/foo/bar.nr` + +Visually the module hierarchy looks as follows: + +``` +crate + ├── main + │ + └── foo + ├── from_foo + └── bar + └── from_bar +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/modules_packages_crates/workspaces.md b/noir/docs/versioned_docs/version-v0.19.2/modules_packages_crates/workspaces.md new file mode 100644 index 00000000000..d9ac92667c9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/modules_packages_crates/workspaces.md @@ -0,0 +1,39 @@ +--- +title: Workspaces +--- + +Workspaces are a feature of nargo that allow you to manage multiple related Noir packages in a single repository. A workspace is essentially a group of related projects that share common build output directories and configurations. + +Each Noir project (with it's own Nargo.toml file) can be thought of as a package. Each package is expected to contain exactly one "named circuit", being the "name" defined in Nargo.toml with the program logic defined in `./src/main.nr`. + +For a project with the following structure: + +```tree +├── crates +│   ├── a +│   │   ├── Nargo.toml +│   │   └── src +│   │   └── main.nr +│   └── b +│   ├── Nargo.toml +│   └── src +│   └── main.nr +├── Nargo.toml +└── Prover.toml +``` + +You can define a workspace in Nargo.toml like so: + +```toml +[workspace] +members = ["crates/a", "crates/b"] +default-member = "crates/a" +``` + +`members` indicates which packages are included in the workspace. As such, all member packages of a workspace will be processed when the `--workspace` flag is used with various commands or if a `default-member` is not specified. + +`default-member` indicates which package various commands process by default. + +Libraries can be defined in a workspace. Inside a workspace, these are consumed as `{ path = "../to_lib" }` dependencies in Nargo.toml. + +Inside a workspace, these are consumed as `{ path = "../to_lib" }` dependencies in Nargo.toml. diff --git a/noir/docs/versioned_docs/version-v0.19.2/nargo/01_commands.md b/noir/docs/versioned_docs/version-v0.19.2/nargo/01_commands.md new file mode 100644 index 00000000000..65e2bdb44e3 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/nargo/01_commands.md @@ -0,0 +1,250 @@ +--- +title: Commands +description: + Noir CLI Commands for Noir Prover and Verifier to create, execute, prove and verify programs, + generate Solidity verifier smart contract and compile into JSON file containing ACIR + representation and ABI of circuit. +keywords: + [ + Nargo, + Noir CLI, + Noir Prover, + Noir Verifier, + generate Solidity verifier, + compile JSON file, + ACIR representation, + ABI of circuit, + TypeScript, + ] +--- + +## General options + +| Option | Description | +| -------------------- | -------------------------------------------------- | +| `--show-ssa` | Emit debug information for the intermediate SSA IR | +| `--deny-warnings` | Quit execution when warnings are emitted | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo help [subcommand]` + +Prints the list of available commands or specific information of a subcommand. + +_Arguments_ + +| Argument | Description | +| -------------- | -------------------------------------------- | +| `` | The subcommand whose help message to display | + +## `nargo backend` + +Installs and selects custom backends used to generate and verify proofs. + +### Commands + +| Command | Description | +| ----------- | --------------------------------------------------------- | +| `current` | Prints the name of the currently active backend | +| `ls` | Prints the list of currently installed backends | +| `use` | Select the backend to use | +| `install` | Install a new backend from a URL | +| `uninstall` | Uninstalls a backend | +| `help` | Print this message or the help of the given subcommand(s) | + +### Options + +| Option | Description | +| ------------ | ----------- | +| `-h, --help` | Print help | + +## `nargo check` + +Generate the `Prover.toml` and `Verifier.toml` files for specifying prover and verifier in/output +values of the Noir program respectively. + +### Options + +| Option | Description | +| --------------------- | ------------------------------------- | +| `--package ` | The name of the package to check | +| `--workspace` | Check all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +### `nargo codegen-verifier` + +Generate a Solidity verifier smart contract for the program. + +### Options + +| Option | Description | +| --------------------- | ------------------------------------- | +| `--package ` | The name of the package to codegen | +| `--workspace` | Codegen all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo compile` + +Compile the program into a JSON build artifact file containing the ACIR representation and the ABI +of the circuit. This build artifact can then be used to generate and verify proofs. + +You can also use "build" as an alias for compile (e.g. `nargo build`). + +### Options + +| Option | Description | +| --------------------- | ------------------------------------------------------------ | +| `--include-keys` | Include Proving and Verification keys in the build artifacts | +| `--package ` | The name of the package to compile | +| `--workspace` | Compile all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo new ` + +Creates a new Noir project in a new folder. + +**Arguments** + +| Argument | Description | +| -------- | -------------------------------- | +| `` | The path to save the new project | + +### Options + +| Option | Description | +| --------------- | ----------------------------------------------------- | +| `--name ` | Name of the package [default: package directory name] | +| `--lib` | Use a library template | +| `--bin` | Use a binary template [default] | +| `--contract` | Use a contract template | +| `-h, --help` | Print help | + +## `nargo init` + +Creates a new Noir project in the current directory. + +### Options + +| Option | Description | +| --------------- | ----------------------------------------------------- | +| `--name ` | Name of the package [default: current directory name] | +| `--lib` | Use a library template | +| `--bin` | Use a binary template [default] | +| `--contract` | Use a contract template | +| `-h, --help` | Print help | + +## `nargo execute [WITNESS_NAME]` + +Runs the Noir program and prints its return value. + +**Arguments** + +| Argument | Description | +| ---------------- | ----------------------------------------- | +| `[WITNESS_NAME]` | Write the execution witness to named file | + +### Options + +| Option | Description | +| --------------------------------- | ------------------------------------------------------------------------------------ | +| `-p, --prover-name ` | The name of the toml file which contains the inputs for the prover [default: Prover] | +| `--package ` | The name of the package to execute | +| `--workspace` | Execute all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +_Usage_ + +The inputs to the circuit are read from the `Prover.toml` file generated by `nargo check`, which +must be filled in. + +To save the witness to file, run the command with a value for the `WITNESS_NAME` argument. A +`.tr` file will then be saved in the `./target` folder. + +## `nargo prove` + +Creates a proof for the program. + +### Options + +| Option | Description | +| ------------------------------------- | ---------------------------------------------------------------------------------------- | +| `-p, --prover-name ` | The name of the toml file which contains the inputs for the prover [default: Prover] | +| `-v, --verifier-name ` | The name of the toml file which contains the inputs for the verifier [default: Verifier] | +| `--verify` | Verify proof after proving | +| `--package ` | The name of the package to prove | +| `--workspace` | Prove all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo verify` + +Given a proof and a program, verify whether the proof is valid. + +### Options + +| Option | Description | +| ------------------------------------- | ---------------------------------------------------------------------------------------- | +| `-v, --verifier-name ` | The name of the toml file which contains the inputs for the verifier [default: Verifier] | +| `--package ` | The name of the package to verify | +| `--workspace` | Verify all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo test [TEST_NAME]` + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. To print `println` statements in tests, use the `--show-output` flag. + +Takes an optional `--exact` flag which allows you to select tests based on an exact name. + +See an example on the [testing page](./testing). + +### Options + +| Option | Description | +| --------------------- | -------------------------------------- | +| `--show-output` | Display output of `println` statements | +| `--exact` | Only run tests that match exactly | +| `--package ` | The name of the package to test | +| `--workspace` | Test all packages in the workspace | +| `--print-acir` | Display the ACIR for compiled circuit | +| `--deny-warnings` | Treat all warnings as errors | +| `--silence-warnings` | Suppress warnings | +| `-h, --help` | Print help | + +## `nargo info` + +Prints a table containing the information of the package. + +Currently the table provide + +1. The number of ACIR opcodes +2. The final number gates in the circuit used by a backend + +If the file contains a contract the table will provide the +above information about each function of the contract. + +## `nargo lsp` + +Start a long-running Language Server process that communicates over stdin/stdout. +Usually this command is not run by a user, but instead will be run by a Language Client, such as [vscode-noir](https://github.com/noir-lang/vscode-noir). + +## `nargo fmt` + +Automatically formats your Noir source code based on the default formatting settings. diff --git a/noir/docs/versioned_docs/version-v0.19.2/nargo/02_testing.md b/noir/docs/versioned_docs/version-v0.19.2/nargo/02_testing.md new file mode 100644 index 00000000000..da767274efd --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/nargo/02_testing.md @@ -0,0 +1,61 @@ +--- +title: Testing in Noir +description: Learn how to use Nargo to test your Noir program in a quick and easy way +keywords: [Nargo, testing, Noir, compile, test] +--- + +You can test your Noir programs using Noir circuits. + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. + +For example if you have a program like: + +```rust +fn add(x: u64, y: u64) -> u64 { + x + y +} +#[test] +fn test_add() { + assert(add(2,2) == 4); + assert(add(0,1) == 1); + assert(add(1,0) == 1); +} +``` + +Running `nargo test` will test that the `test_add` function can be executed while satisfying the all +the contraints which allows you to test that add returns the expected values. Test functions can't +have any arguments currently. + +### Test fail + +You can write tests that are expected to fail by using the decorator `#[test(should_fail)]`. For example: + +```rust +fn add(x: u64, y: u64) -> u64 { + x + y +} +#[test(should_fail)] +fn test_add() { + assert(add(2,2) == 5); +} +``` + +You can be more specific and make it fail with a specific reason by using `should_fail_with = "`: + +```rust +fn main(african_swallow_avg_speed : Field) { + assert(african_swallow_avg_speed == 65, "What is the airspeed velocity of an unladen swallow"); +} + +#[test] +fn test_king_arthur() { + main(65); +} + +#[test(should_fail_with = "What is the airspeed velocity of an unladen swallow")] +fn test_bridgekeeper() { + main(32); +} + +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/nargo/03_solidity_verifier.md b/noir/docs/versioned_docs/version-v0.19.2/nargo/03_solidity_verifier.md new file mode 100644 index 00000000000..9ac60cb0ba7 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/nargo/03_solidity_verifier.md @@ -0,0 +1,129 @@ +--- +title: Solidity Verifier +description: + Learn how to run the verifier as a smart contract on the blockchain. Compile a Solidity verifier + contract for your Noir program and deploy it on any EVM blockchain acting as a verifier smart + contract. Read more to find out! +keywords: + [ + solidity verifier, + smart contract, + blockchain, + compiler, + plonk_vk.sol, + EVM blockchain, + verifying Noir programs, + proving backend, + Barretenberg, + ] +--- + +For certain applications, it may be desirable to run the verifier as a smart contract instead of on +a local machine. + +Compile a Solidity verifier contract for your Noir program by running: + +```sh +nargo codegen-verifier +``` + +A new `contract` folder would then be generated in your project directory, containing the Solidity +file `plonk_vk.sol`. It can be deployed on any EVM blockchain acting as a verifier smart contract. + +> **Note:** It is possible to compile verifier contracts of Noir programs for other smart contract +> platforms as long as the proving backend supplies an implementation. +> +> Barretenberg, the default proving backend for Nargo, supports compilation of verifier contracts in +> Solidity only for the time being. + +## Verify + +To verify a proof using the Solidity verifier contract, call the `verify` function with the +following signature: + +```solidity +function verify(bytes calldata _proof, bytes32[] calldata _publicInputs) external view returns (bool) +``` + +You can see an example of how the `verify` function is called in the example zk voting application [here](https://github.com/noir-lang/noir-examples/blob/33e598c257e2402ea3a6b68dd4c5ad492bce1b0a/foundry-voting/src/zkVote.sol#L35): + +```solidity +function castVote(bytes calldata proof, uint proposalId, uint vote, bytes32 nullifierHash) public returns (bool) { + // ... + bytes32[] memory publicInputs = new bytes32[](4); + publicInputs[0] = merkleRoot; + publicInputs[1] = bytes32(proposalId); + publicInputs[2] = bytes32(vote); + publicInputs[3] = nullifierHash; + require(verifier.verify(proof, publicInputs), "Invalid proof"); +``` + +### Public Inputs + +:::tip + +A circuit doesn't have the concept of a return value. Return values are just syntactic sugar in +Noir. + +Under the hood, the return value is passed as an input to the circuit and is checked at the end of +the circuit program. + +::: + +The verifier contract uses the output (return) value of a Noir program as a public input. So if you +have the following function + +```rust +fn main( + // Public inputs + pubkey_x: pub Field, + pubkey_y: pub Field, + // Private inputs + priv_key: Field, +) -> pub Field +``` + +then `verify` in `plonk_vk.sol` will expect 3 public inputs. Passing two inputs will result in an +error like `Reason: PUBLIC_INPUT_COUNT_INVALID(3, 2)`. + +In this case the 3 inputs to `verify` would be ordered as `[pubkey_x, pubkey_y, return]`. + +#### Struct inputs + +Consider the following program: + +```rust +struct Type1 { + val1: Field, + val2: Field, +} + +struct Nested { + t1: Type1, + is_true: bool, +} + +fn main(x: pub Field, nested: pub Nested, y: pub Field) { + //... +} +``` + +Structs will be flattened so that the array of inputs is 1-dimensional array. The order of these inputs would be flattened to: `[x, nested.t1.val1, nested.t1.val2, nested.is_true, y]` + +## Noir for EVM chains + +You can currently deploy the Solidity verifier contracts to most EVM compatible chains. EVM chains that have been tested and are known to work include: + +- Optimism +- Arbitrum +- Polygon PoS +- Scroll +- Celo + +Other EVM chains should work, but have not been tested directly by our team. If you test any other chains, please open a PR on this page to update the list. See [this doc](https://github.com/noir-lang/noir-starter/tree/main/with-foundry#testing-on-chain) for more info about testing verifier contracts on different EVM chains. + +### Unsupported chains + +Unfortunately not all "EVM" chains are supported. + +**zkSync** and the **Polygon zkEVM** do _not_ currently support proof verification via Solidity verifier contracts. They are missing the bn256 precompile contract that the verifier contract requires. Once these chains support this precompile, they may work. diff --git a/noir/docs/versioned_docs/version-v0.19.2/nargo/04_language_server.md b/noir/docs/versioned_docs/version-v0.19.2/nargo/04_language_server.md new file mode 100644 index 00000000000..48c01465f6e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/nargo/04_language_server.md @@ -0,0 +1,42 @@ +--- +title: Language Server +description: Learn about the Noir Language Server, how to install the components, and configuration that may be required. +keywords: [Nargo, Language Server, LSP, VSCode, Visual Studio Code] +--- + +This section helps you install and configure the Noir Language Server. + +The Language Server Protocol (LSP) has two components, the [Server](#language-server) and the [Client](#language-client). Below we describe each in the context of Noir. + +## Language Server + +The Server component is provided by the Nargo command line tool that you installed at the beginning of this guide. +As long as Nargo is installed and you've used it to run other commands in this guide, it should be good to go! + +If you'd like to verify that the `nargo lsp` command is available, you can run `nargo --help` and look for `lsp` in the list of commands. If you see it, you're using a version of Noir with LSP support. + +## Language Client + +The Client component is usually an editor plugin that launches the Server. It communicates LSP messages between the editor and the Server. For example, when you save a file, the Client will alert the Server, so it can try to compile the project and report any errors. + +Currently, Noir provides a Language Client for Visual Studio Code via the [vscode-noir](https://github.com/noir-lang/vscode-noir) extension. You can install it via the [Visual Studio Marketplace](https://marketplace.visualstudio.com/items?itemName=noir-lang.vscode-noir). + +> **Note:** Noir's Language Server Protocol support currently assumes users' VSCode workspace root to be the same as users' Noir project root (i.e. where Nargo.toml lies). +> +> If LSP features seem to be missing / malfunctioning, make sure you are opening your Noir project directly (instead of as a sub-folder) in your VSCode instance. + +When your language server is running correctly and the VSCode plugin is installed, you should see handy codelens buttons for compilation, measuring circuit size, execution, and tests: + +![Compile and Execute](@site/static/img/codelens_compile_execute.png) +![Run test](@site/static/img/codelens_run_test.png) + +You should also see your tests in the `testing` panel: + +![Testing panel](@site/static/img/codelens_testing_panel.png) + +### Configuration + +- **Noir: Enable LSP** - If checked, the extension will launch the Language Server via `nargo lsp` and communicate with it. +- **Noir: Nargo Flags** - Additional flags may be specified if you require them to be added when the extension calls `nargo lsp`. +- **Noir: Nargo Path** - An absolute path to a Nargo binary with the `lsp` command. This may be useful if Nargo is not within the `PATH` of your editor. +- **Noir > Trace: Server** - Setting this to `"messages"` or `"verbose"` will log LSP messages between the Client and Server. Useful for debugging. diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/getting_started/01_tiny_noir_app.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/getting_started/01_tiny_noir_app.md new file mode 100644 index 00000000000..c51ed61de52 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/getting_started/01_tiny_noir_app.md @@ -0,0 +1,260 @@ +--- +title: End-to-end +description: Learn how to setup a new app that uses Noir to generate and verify zero-knowledge SNARK proofs in a typescript or javascript environment +keywords: [how to, guide, javascript, typescript, noir, barretenberg, zero-knowledge, proofs] +--- + +NoirJS works both on the browser and on the server, and works for both ESM and CJS module systems. In this page, we will learn how can we write a simple test and a simple web app to verify the standard Noir example. + +You can find the complete app code for this guide [here](https://github.com/noir-lang/tiny-noirjs-app). + +## Before we start + +:::note + +Feel free to use whatever versions, just keep in mind that Nargo and the NoirJS packages are meant to be in sync. For example, Nargo 0.18.x matches `noir_js@0.18.x`, etc. + +In this guide, we will be pinned to 0.17.0. + +::: + +Make sure you have Node installed on your machine by opening a terminal and executing `node --version`. If you don't see a version, you should install [node](https://github.com/nvm-sh/nvm). You can also use `yarn` if you prefer that package manager over npm (which comes with node). + +First of all, follow the the [Nargo guide](../../getting_started/00_nargo_installation.md) to install nargo version 0.17.0 and create a new project with `nargo new circuit`. Once there, `cd` into the `circuit` folder. You should then be able to compile your circuit into `json` format and see it inside the `target` folder: + +```bash +nargo compile +``` + +Your folder structure should look like: + +```tree +. +└── circuit + ├── Nargo.toml + ├── src + │ └── main.nr + └── target + └── circuit.json +``` + +## Starting a new project + +Go back to the previous folder and start a new project by running run `npm init`. You can configure your project or just leave the defaults, and see a `package.json` appear in your root folder. + +## Installing dependencies + +We'll need two `npm` packages. These packages will provide us the methods we need to run and verify proofs: + +```bash +npm i @noir-lang/backend_barretenberg@^0.17.0 @noir-lang/noir_js@^0.17.0 +``` + +To serve our page, we can use a build tool such as `vite`. Because we're gonna use some `wasm` files, we need to install a plugin as well. Run: + +```bash +npm i --save-dev vite rollup-plugin-copy +``` + +Since we're on the dependency world, we may as well define a nice starting script. Vite makes it easy. Just open `package.json`, find the block "scripts" and add this just below the line with `"test" : "echo......."`: + +```json + "start": "vite --open" +``` + +If you want do build a static website, you can also add some build and preview scripts: + +```json + "build": "vite build", + "preview": "vite preview" +``` + +## Vite plugins + +Vite is great, but support from `wasm` doesn't work out-of-the-box. We're gonna write a quick plugin and use another one. Just copy and paste this into a file named `vite.config.js`. You don't need to understand it, just trust me bro. + +```js +import { defineConfig } from 'vite'; +import copy from 'rollup-plugin-copy'; +import fs from 'fs'; +import path from 'path'; + +const wasmContentTypePlugin = { + name: 'wasm-content-type-plugin', + configureServer(server) { + server.middlewares.use(async (req, res, next) => { + if (req.url.endsWith('.wasm')) { + res.setHeader('Content-Type', 'application/wasm'); + const newPath = req.url.replace('deps', 'dist'); + const targetPath = path.join(__dirname, newPath); + const wasmContent = fs.readFileSync(targetPath); + return res.end(wasmContent); + } + next(); + }); + }, +}; + +export default defineConfig(({ command }) => { + if (command === 'serve') { + return { + plugins: [ + copy({ + targets: [{ src: 'node_modules/**/*.wasm', dest: 'node_modules/.vite/dist' }], + copySync: true, + hook: 'buildStart', + }), + command === 'serve' ? wasmContentTypePlugin : [], + ], + }; + } + + return {}; +}); +``` + +## HTML + +Here's the simplest HTML with some terrible UI. Create a file called `index.html` and paste this: + +```html + + + + + + +

Very basic Noir app

+
+

Logs

+

Proof

+
+ + +``` + +## Some good old vanilla Javascript + +Create a new file `app.js`, which is where our javascript code will live. Let's start with this code inside: + +```js +document.addEventListener('DOMContentLoaded', async () => { + // here's where the magic happens +}); + +function display(container, msg) { + const c = document.getElementById(container); + const p = document.createElement('p'); + p.textContent = msg; + c.appendChild(p); +} +``` + +We can manipulate our website with this little function, so we can see our website working. + +## Adding Noir + +If you come from the previous page, your folder structure should look like this: + +```tree +├── app.js +├── circuit +│ ├── Nargo.toml +│ ├── src +│ │ └── main.nr +│ └── target +│ └── circuit.json +├── index.html +├── package.json +└── vite.config.js +``` + +You'll see other files and folders showing up (like `package-lock.json`, `yarn.lock`, `node_modules`) but you shouldn't have to care about those. + +## Importing our dependencies + +We're starting with the good stuff now. At the top of the new javascript file, import the packages: + +```ts +import { BarretenbergBackend } from '@noir-lang/backend_barretenberg'; +import { Noir } from '@noir-lang/noir_js'; +``` + +We also need to import the `circuit` JSON file we created. If you have the suggested folder structure, you can add this line: + +```ts +import circuit from './circuit/target/circuit.json'; +``` + +## Write code + +:::note + +We're gonna be adding code inside the `document.addEventListener...etc` block: + +```js +// forget stuff here +document.addEventListener('DOMContentLoaded', async () => { + // here's where the magic happens +}); +// forget stuff here +``` + +::: + +Our dependencies exported two classes: `BarretenbergBackend` and `Noir`. Let's `init` them and add some logs, just to flex: + +```ts +const backend = new BarretenbergBackend(circuit); +const noir = new Noir(circuit, backend); +``` + +## Proving + +Now we're ready to prove stuff! Let's feed some inputs to our circuit and calculate the proof: + +```js +const input = { x: 1, y: 2 }; +display('logs', 'Generating proof... ⌛'); +const proof = await noir.generateFinalProof(input); +display('logs', 'Generating proof... ✅'); +display('results', proof.proof); +``` + +You're probably eager to see stuff happening, so go and run your app now! + +From your terminal, run `npm start` (or `yarn start`). If it doesn't open a browser for you, just visit `localhost:5173`. On a modern laptop, proof will generate in less than 100ms, and you'll see this: + +![Getting Started 0](@site/static/img/noir_getting_started_1.png) + +If you're human, you shouldn't be able to understand anything on the "proof" box. That's OK. We like you, human. + +In any case, this means your proof was generated! But you shouldn't trust me just yet. Add these lines to see it being verified: + +```js +display('logs', 'Verifying proof... ⌛'); +const verification = await noir.verifyFinalProof(proof); +if (verification) display('logs', 'Verifying proof... ✅'); +``` + +By saving, your app will refresh and here's our complete Tiny Noir App! + +You can find the complete app code for this guide [here](https://github.com/noir-lang/tiny-noirjs-app). + +## Further Reading + +You can see how noirjs is used in a full stack Next.js hardhat application in the [noir-starter repo here](https://github.com/noir-lang/noir-starter/tree/main/next-hardhat). The example shows how to calculate a proof in the browser and verify it with a deployed Solidity verifier contract from noirjs. + +You should also check out the more advanced examples in the [noir-examples repo](https://github.com/noir-lang/noir-examples), where you'll find reference usage for some cool apps. diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/noir_js.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/noir_js.md new file mode 100644 index 00000000000..f895b22eaf8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/noir_js.md @@ -0,0 +1,36 @@ +--- +title: NoirJS +description: Interact with Noir in Typescript or Javascript +keywords: [Noir project, javascript, typescript, node.js, browser, react] +--- + +NoirJS is a TypeScript library that make it easy to use Noir on your dapp, webapp, Node.js server, website, etc. + +A typical workflow would be composed of two major elements: + +- NoirJS +- Proving backend of choice's JavaScript package + + + +To install NoirJS, install Node.js if you have not already and run this in your JavaScript project: + +```bash +npm i @noir-lang/noir_js +``` + +## Proving backend + +Since Noir is backend agnostic, you can instantiate NoirJS without any backend (i.e. to execute a function). But for proving, you would have to instantiate NoirJS with any of the supported backends through their own `js` interface. + +### Barretenberg + +Aztec Labs maintains the `barretenberg` proving backend, which you can instantiate and make use of alongside NoirJS. It is also the default proving backend installed and used with Nargo, the Noir CLI tool. + +To install its JavaScript library, run this in your project: + +```bash +npm i @noir-lang/backend_barretenberg +``` + +For more details on how to instantiate and use the libraries, refer to the [Full Noir App Guide](./getting_started/01_tiny_noir_app.md) and [Reference](./reference/noir_js/classes/Noir.md) sections. diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/.nojekyll b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/.nojekyll new file mode 100644 index 00000000000..e2ac6616add --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/.nojekyll @@ -0,0 +1 @@ +TypeDoc added this file to prevent GitHub Pages from using Jekyll. You can turn off this behavior by setting the `githubPages` option to false. \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/classes/BarretenbergBackend.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/classes/BarretenbergBackend.md new file mode 100644 index 00000000000..5cbe9421b92 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/classes/BarretenbergBackend.md @@ -0,0 +1,185 @@ +# BarretenbergBackend + +## Implements + +- [`Backend`](../interfaces/Backend.md) + +## Constructors + +### new BarretenbergBackend(acirCircuit, options) + +```ts +new BarretenbergBackend(acirCircuit, options): BarretenbergBackend +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `acirCircuit` | [`CompiledCircuit`](../type-aliases/CompiledCircuit.md) | +| `options` | [`BackendOptions`](../type-aliases/BackendOptions.md) | + +#### Returns + +[`BarretenbergBackend`](BarretenbergBackend.md) + +## Methods + +### destroy() + +```ts +destroy(): Promise +``` + +#### Returns + +`Promise`\<`void`\> + +#### Implementation of + +[`Backend`](../interfaces/Backend.md).[`destroy`](../interfaces/Backend.md#destroy) + +#### Description + +Destroys the backend + +*** + +### generateFinalProof() + +```ts +generateFinalProof(decompressedWitness): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `decompressedWitness` | `Uint8Array` | + +#### Returns + +`Promise`\<[`ProofData`](../type-aliases/ProofData.md)\> + +#### Implementation of + +[`Backend`](../interfaces/Backend.md).[`generateFinalProof`](../interfaces/Backend.md#generatefinalproof) + +#### Description + +Generates a final proof (not meant to be verified in another circuit) + +*** + +### generateIntermediateProof() + +```ts +generateIntermediateProof(witness): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `witness` | `Uint8Array` | + +#### Returns + +`Promise`\<[`ProofData`](../type-aliases/ProofData.md)\> + +#### Implementation of + +[`Backend`](../interfaces/Backend.md).[`generateIntermediateProof`](../interfaces/Backend.md#generateintermediateproof) + +#### Example + +```typescript +const intermediateProof = await backend.generateIntermediateProof(witness); +``` + +*** + +### generateIntermediateProofArtifacts() + +```ts +generateIntermediateProofArtifacts(proofData, numOfPublicInputs): Promise +``` + +#### Parameters + +| Parameter | Type | Default value | +| :------ | :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | `undefined` | +| `numOfPublicInputs` | `number` | `0` | + +#### Returns + +`Promise`\<`object`\> + +#### Implementation of + +[`Backend`](../interfaces/Backend.md).[`generateIntermediateProofArtifacts`](../interfaces/Backend.md#generateintermediateproofartifacts) + +#### Example + +```typescript +const artifacts = await backend.generateIntermediateProofArtifacts(proof, numOfPublicInputs); +``` + +*** + +### verifyFinalProof() + +```ts +verifyFinalProof(proofData): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | + +#### Returns + +`Promise`\<`boolean`\> + +#### Implementation of + +[`Backend`](../interfaces/Backend.md).[`verifyFinalProof`](../interfaces/Backend.md#verifyfinalproof) + +#### Description + +Verifies a final proof + +*** + +### verifyIntermediateProof() + +```ts +verifyIntermediateProof(proofData): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | + +#### Returns + +`Promise`\<`boolean`\> + +#### Implementation of + +[`Backend`](../interfaces/Backend.md).[`verifyIntermediateProof`](../interfaces/Backend.md#verifyintermediateproof) + +#### Example + +```typescript +const isValidIntermediate = await backend.verifyIntermediateProof(proof); +``` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/index.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/index.md new file mode 100644 index 00000000000..3680ba3ca77 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/index.md @@ -0,0 +1,27 @@ +# Backend Barretenberg + +## Exports + +### Classes + +| Class | Description | +| :------ | :------ | +| [BarretenbergBackend](classes/BarretenbergBackend.md) | - | + +### Interfaces + +| Interface | Description | +| :------ | :------ | +| [Backend](interfaces/Backend.md) | - | + +### Type Aliases + +| Type alias | Description | +| :------ | :------ | +| [BackendOptions](type-aliases/BackendOptions.md) | - | +| [CompiledCircuit](type-aliases/CompiledCircuit.md) | - | +| [ProofData](type-aliases/ProofData.md) | - | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/interfaces/Backend.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/interfaces/Backend.md new file mode 100644 index 00000000000..3eb9645c8d2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/interfaces/Backend.md @@ -0,0 +1,132 @@ +# Backend + +## Methods + +### destroy() + +```ts +destroy(): Promise +``` + +#### Returns + +`Promise`\<`void`\> + +#### Description + +Destroys the backend + +*** + +### generateFinalProof() + +```ts +generateFinalProof(decompressedWitness): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `decompressedWitness` | `Uint8Array` | + +#### Returns + +`Promise`\<[`ProofData`](../type-aliases/ProofData.md)\> + +#### Description + +Generates a final proof (not meant to be verified in another circuit) + +*** + +### generateIntermediateProof() + +```ts +generateIntermediateProof(decompressedWitness): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `decompressedWitness` | `Uint8Array` | + +#### Returns + +`Promise`\<[`ProofData`](../type-aliases/ProofData.md)\> + +#### Description + +Generates an intermediate proof (meant to be verified in another circuit) + +*** + +### generateIntermediateProofArtifacts() + +```ts +generateIntermediateProofArtifacts(proofData, numOfPublicInputs): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | +| `numOfPublicInputs` | `number` | + +#### Returns + +`Promise`\<`object`\> + +#### Description + +Retrieves the artifacts from a proof in the Field format + +*** + +### verifyFinalProof() + +```ts +verifyFinalProof(proofData): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | + +#### Returns + +`Promise`\<`boolean`\> + +#### Description + +Verifies a final proof + +*** + +### verifyIntermediateProof() + +```ts +verifyIntermediateProof(proofData): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | + +#### Returns + +`Promise`\<`boolean`\> + +#### Description + +Verifies an intermediate proof + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/type-aliases/BackendOptions.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/type-aliases/BackendOptions.md new file mode 100644 index 00000000000..266ade75d17 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/type-aliases/BackendOptions.md @@ -0,0 +1,19 @@ +# BackendOptions + +```ts +type BackendOptions: object; +``` + +## Description + +An options object, currently only used to specify the number of threads to use. + +## Type declaration + +| Member | Type | Description | +| :------ | :------ | :------ | +| `threads` | `number` | **Description**

Number of threads | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/type-aliases/CompiledCircuit.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/type-aliases/CompiledCircuit.md new file mode 100644 index 00000000000..34e0dd04205 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/type-aliases/CompiledCircuit.md @@ -0,0 +1,20 @@ +# CompiledCircuit + +```ts +type CompiledCircuit: object; +``` + +## Description + +The representation of a compiled circuit + +## Type declaration + +| Member | Type | Description | +| :------ | :------ | :------ | +| `abi` | `Abi` | **Description**

ABI representation of the circuit | +| `bytecode` | `string` | **Description**

The bytecode of the circuit | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/type-aliases/ProofData.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/type-aliases/ProofData.md new file mode 100644 index 00000000000..4aeff73d3e4 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/type-aliases/ProofData.md @@ -0,0 +1,20 @@ +# ProofData + +```ts +type ProofData: object; +``` + +## Description + +The representation of a proof + +## Type declaration + +| Member | Type | Description | +| :------ | :------ | :------ | +| `proof` | `Uint8Array` | **Description**

An byte array representing the proof | +| `publicInputs` | `Uint8Array`[] | **Description**

Public inputs of a proof | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/typedoc-sidebar.cjs b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/typedoc-sidebar.cjs new file mode 100644 index 00000000000..04e662c845f --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/backend_barretenberg/typedoc-sidebar.cjs @@ -0,0 +1,4 @@ +// @ts-check +/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ +const typedocSidebar = { items: [{"type":"category","label":"Classes","items":[{"type":"doc","id":"noir_js/reference/backend_barretenberg/classes/BarretenbergBackend","label":"BarretenbergBackend"}]},{"type":"category","label":"Interfaces","items":[{"type":"doc","id":"noir_js/reference/backend_barretenberg/interfaces/Backend","label":"Backend"}]},{"type":"category","label":"Type Aliases","items":[{"type":"doc","id":"noir_js/reference/backend_barretenberg/type-aliases/BackendOptions","label":"BackendOptions"},{"type":"doc","id":"noir_js/reference/backend_barretenberg/type-aliases/CompiledCircuit","label":"CompiledCircuit"},{"type":"doc","id":"noir_js/reference/backend_barretenberg/type-aliases/ProofData","label":"ProofData"}]}]}; +module.exports = typedocSidebar.items; \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/.nojekyll b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/.nojekyll new file mode 100644 index 00000000000..e2ac6616add --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/.nojekyll @@ -0,0 +1 @@ +TypeDoc added this file to prevent GitHub Pages from using Jekyll. You can turn off this behavior by setting the `githubPages` option to false. \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/classes/Noir.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/classes/Noir.md new file mode 100644 index 00000000000..a8a0bb451c1 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/classes/Noir.md @@ -0,0 +1,131 @@ +# Noir + +## Constructors + +### new Noir(circuit, backend) + +```ts +new Noir(circuit, backend?): Noir +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `circuit` | [`CompiledCircuit`](../type-aliases/CompiledCircuit.md) | +| `backend`? | `Backend` | + +#### Returns + +[`Noir`](Noir.md) + +## Methods + +### destroy() + +```ts +destroy(): Promise +``` + +#### Returns + +`Promise`\<`void`\> + +#### Description + +Destroys the underlying backend instance. + +#### Example + +```typescript +await noir.destroy(); +``` + +*** + +### execute() + +```ts +execute(inputs, foreignCallHandler?): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `inputs` | `InputMap` | +| `foreignCallHandler`? | [`ForeignCallHandler`](../type-aliases/ForeignCallHandler.md) | + +#### Returns + +`Promise`\<`object`\> + +#### Description + +Allows to execute a circuit to get its witness and return value. + +#### Example + +```typescript +async execute(inputs) +``` + +*** + +### generateFinalProof() + +```ts +generateFinalProof(inputs): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `inputs` | `InputMap` | + +#### Returns + +`Promise`\<[`ProofData`](../type-aliases/ProofData.md)\> + +#### Description + +Generates a witness and a proof given an object as input. + +#### Example + +```typescript +async generateFinalproof(input) +``` + +*** + +### verifyFinalProof() + +```ts +verifyFinalProof(proofData): Promise +``` + +#### Parameters + +| Parameter | Type | +| :------ | :------ | +| `proofData` | [`ProofData`](../type-aliases/ProofData.md) | + +#### Returns + +`Promise`\<`boolean`\> + +#### Description + +Instantiates the verification key and verifies a proof. + +#### Example + +```typescript +async verifyFinalProof(proof) +``` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/and.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/and.md new file mode 100644 index 00000000000..c783283e396 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/and.md @@ -0,0 +1,22 @@ +# and() + +```ts +and(lhs, rhs): string +``` + +Performs a bitwise AND operation between `lhs` and `rhs` + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `lhs` | `string` | | +| `rhs` | `string` | | + +## Returns + +`string` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/blake2s256.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/blake2s256.md new file mode 100644 index 00000000000..7882d0da8d5 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/blake2s256.md @@ -0,0 +1,21 @@ +# blake2s256() + +```ts +blake2s256(inputs): Uint8Array +``` + +Calculates the Blake2s256 hash of the input bytes + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `inputs` | `Uint8Array` | | + +## Returns + +`Uint8Array` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/ecdsa_secp256k1_verify.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/ecdsa_secp256k1_verify.md new file mode 100644 index 00000000000..0ba5783f0d5 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/ecdsa_secp256k1_verify.md @@ -0,0 +1,29 @@ +# ecdsa\_secp256k1\_verify() + +```ts +ecdsa_secp256k1_verify( + hashed_msg, + public_key_x_bytes, + public_key_y_bytes, + signature): boolean +``` + +Calculates the Blake2s256 hash of the input bytes and represents these as a single field element. +Verifies a ECDSA signature over the secp256k1 curve. + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `hashed_msg` | `Uint8Array` | | +| `public_key_x_bytes` | `Uint8Array` | | +| `public_key_y_bytes` | `Uint8Array` | | +| `signature` | `Uint8Array` | | + +## Returns + +`boolean` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/ecdsa_secp256r1_verify.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/ecdsa_secp256r1_verify.md new file mode 100644 index 00000000000..0b20ff68957 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/ecdsa_secp256r1_verify.md @@ -0,0 +1,28 @@ +# ecdsa\_secp256r1\_verify() + +```ts +ecdsa_secp256r1_verify( + hashed_msg, + public_key_x_bytes, + public_key_y_bytes, + signature): boolean +``` + +Verifies a ECDSA signature over the secp256r1 curve. + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `hashed_msg` | `Uint8Array` | | +| `public_key_x_bytes` | `Uint8Array` | | +| `public_key_y_bytes` | `Uint8Array` | | +| `signature` | `Uint8Array` | | + +## Returns + +`boolean` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/keccak256.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/keccak256.md new file mode 100644 index 00000000000..d10f155ce86 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/keccak256.md @@ -0,0 +1,21 @@ +# keccak256() + +```ts +keccak256(inputs): Uint8Array +``` + +Calculates the Keccak256 hash of the input bytes + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `inputs` | `Uint8Array` | | + +## Returns + +`Uint8Array` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/sha256.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/sha256.md new file mode 100644 index 00000000000..6ba4ecac022 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/sha256.md @@ -0,0 +1,21 @@ +# sha256() + +```ts +sha256(inputs): Uint8Array +``` + +Calculates the SHA256 hash of the input bytes + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `inputs` | `Uint8Array` | | + +## Returns + +`Uint8Array` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/xor.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/xor.md new file mode 100644 index 00000000000..8d762b895d3 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/functions/xor.md @@ -0,0 +1,22 @@ +# xor() + +```ts +xor(lhs, rhs): string +``` + +Performs a bitwise XOR operation between `lhs` and `rhs` + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `lhs` | `string` | | +| `rhs` | `string` | | + +## Returns + +`string` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/index.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/index.md new file mode 100644 index 00000000000..58902c17b99 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/index.md @@ -0,0 +1,36 @@ +# Noir JS + +## Exports + +### Classes + +| Class | Description | +| :------ | :------ | +| [Noir](classes/Noir.md) | - | + +### Type Aliases + +| Type alias | Description | +| :------ | :------ | +| [CompiledCircuit](type-aliases/CompiledCircuit.md) | - | +| [ForeignCallHandler](type-aliases/ForeignCallHandler.md) | A callback which performs an foreign call and returns the response. | +| [ForeignCallInput](type-aliases/ForeignCallInput.md) | - | +| [ForeignCallOutput](type-aliases/ForeignCallOutput.md) | - | +| [ProofData](type-aliases/ProofData.md) | - | +| [WitnessMap](type-aliases/WitnessMap.md) | - | + +### Functions + +| Function | Description | +| :------ | :------ | +| [and](functions/and.md) | Performs a bitwise AND operation between `lhs` and `rhs` | +| [blake2s256](functions/blake2s256.md) | Calculates the Blake2s256 hash of the input bytes | +| [ecdsa\_secp256k1\_verify](functions/ecdsa_secp256k1_verify.md) | Calculates the Blake2s256 hash of the input bytes and represents these as a single field element. | +| [ecdsa\_secp256r1\_verify](functions/ecdsa_secp256r1_verify.md) | Verifies a ECDSA signature over the secp256r1 curve. | +| [keccak256](functions/keccak256.md) | Calculates the Keccak256 hash of the input bytes | +| [sha256](functions/sha256.md) | Calculates the SHA256 hash of the input bytes | +| [xor](functions/xor.md) | Performs a bitwise XOR operation between `lhs` and `rhs` | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/CompiledCircuit.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/CompiledCircuit.md new file mode 100644 index 00000000000..34e0dd04205 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/CompiledCircuit.md @@ -0,0 +1,20 @@ +# CompiledCircuit + +```ts +type CompiledCircuit: object; +``` + +## Description + +The representation of a compiled circuit + +## Type declaration + +| Member | Type | Description | +| :------ | :------ | :------ | +| `abi` | `Abi` | **Description**

ABI representation of the circuit | +| `bytecode` | `string` | **Description**

The bytecode of the circuit | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/ForeignCallHandler.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/ForeignCallHandler.md new file mode 100644 index 00000000000..812b8b16481 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/ForeignCallHandler.md @@ -0,0 +1,24 @@ +# ForeignCallHandler + +```ts +type ForeignCallHandler: (name, inputs) => Promise; +``` + +A callback which performs an foreign call and returns the response. + +## Parameters + +| Parameter | Type | Description | +| :------ | :------ | :------ | +| `name` | `string` | The identifier for the type of foreign call being performed. | +| `inputs` | [`ForeignCallInput`](ForeignCallInput.md)[] | An array of hex encoded inputs to the foreign call. | + +## Returns + +`Promise`\<[`ForeignCallOutput`](ForeignCallOutput.md)[]\> + +outputs - An array of hex encoded outputs containing the results of the foreign call. + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/ForeignCallInput.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/ForeignCallInput.md new file mode 100644 index 00000000000..dd95809186a --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/ForeignCallInput.md @@ -0,0 +1,9 @@ +# ForeignCallInput + +```ts +type ForeignCallInput: string[]; +``` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/ForeignCallOutput.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/ForeignCallOutput.md new file mode 100644 index 00000000000..b71fb78a946 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/ForeignCallOutput.md @@ -0,0 +1,9 @@ +# ForeignCallOutput + +```ts +type ForeignCallOutput: string | string[]; +``` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/ProofData.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/ProofData.md new file mode 100644 index 00000000000..4aeff73d3e4 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/ProofData.md @@ -0,0 +1,20 @@ +# ProofData + +```ts +type ProofData: object; +``` + +## Description + +The representation of a proof + +## Type declaration + +| Member | Type | Description | +| :------ | :------ | :------ | +| `proof` | `Uint8Array` | **Description**

An byte array representing the proof | +| `publicInputs` | `Uint8Array`[] | **Description**

Public inputs of a proof | + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/WitnessMap.md b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/WitnessMap.md new file mode 100644 index 00000000000..258c46f9d0c --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/type-aliases/WitnessMap.md @@ -0,0 +1,9 @@ +# WitnessMap + +```ts +type WitnessMap: Map; +``` + +*** + +Generated using [typedoc-plugin-markdown](https://www.npmjs.com/package/typedoc-plugin-markdown) and [TypeDoc](https://typedoc.org/) diff --git a/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/typedoc-sidebar.cjs b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/typedoc-sidebar.cjs new file mode 100644 index 00000000000..c18318850d0 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/noir_js/reference/noir_js/typedoc-sidebar.cjs @@ -0,0 +1,4 @@ +// @ts-check +/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ +const typedocSidebar = { items: [{"type":"category","label":"Classes","items":[{"type":"doc","id":"noir_js/reference/noir_js/classes/Noir","label":"Noir"}]},{"type":"category","label":"Type Aliases","items":[{"type":"doc","id":"noir_js/reference/noir_js/type-aliases/CompiledCircuit","label":"CompiledCircuit"},{"type":"doc","id":"noir_js/reference/noir_js/type-aliases/ForeignCallHandler","label":"ForeignCallHandler"},{"type":"doc","id":"noir_js/reference/noir_js/type-aliases/ForeignCallInput","label":"ForeignCallInput"},{"type":"doc","id":"noir_js/reference/noir_js/type-aliases/ForeignCallOutput","label":"ForeignCallOutput"},{"type":"doc","id":"noir_js/reference/noir_js/type-aliases/ProofData","label":"ProofData"},{"type":"doc","id":"noir_js/reference/noir_js/type-aliases/WitnessMap","label":"WitnessMap"}]},{"type":"category","label":"Functions","items":[{"type":"doc","id":"noir_js/reference/noir_js/functions/and","label":"and"},{"type":"doc","id":"noir_js/reference/noir_js/functions/blake2s256","label":"blake2s256"},{"type":"doc","id":"noir_js/reference/noir_js/functions/ecdsa_secp256k1_verify","label":"ecdsa_secp256k1_verify"},{"type":"doc","id":"noir_js/reference/noir_js/functions/ecdsa_secp256r1_verify","label":"ecdsa_secp256r1_verify"},{"type":"doc","id":"noir_js/reference/noir_js/functions/keccak256","label":"keccak256"},{"type":"doc","id":"noir_js/reference/noir_js/functions/sha256","label":"sha256"},{"type":"doc","id":"noir_js/reference/noir_js/functions/xor","label":"xor"}]}]}; +module.exports = typedocSidebar.items; \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.19.2/standard_library/black_box_fns.md b/noir/docs/versioned_docs/version-v0.19.2/standard_library/black_box_fns.md new file mode 100644 index 00000000000..1dfabfe8f22 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/standard_library/black_box_fns.md @@ -0,0 +1,46 @@ +--- +title: Black Box Functions +description: Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. +keywords: [noir, black box functions] +--- + +Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. This makes certain zk-snark unfriendly computations cheaper than if they were implemented in Noir. + +:::warning + +It is likely that not all backends will support a particular black box function. + +::: + +Because it is not guaranteed that all backends will support black box functions, it is possible that certain Noir programs won't compile against a particular backend if they use an unsupported black box function. It is possible to fallback to less efficient implementations written in Noir/ACIR in some cases. + +Black box functions are specified with the `#[foreign(black_box_fn)]` attribute. For example, the SHA256 function in the Noir [source code](https://github.com/noir-lang/noir/blob/v0.5.1/noir_stdlib/src/hash.nr) looks like: + +```rust +#[foreign(sha256)] +fn sha256(_input : [u8; N]) -> [u8; 32] {} +``` + +## Function list + +Here is a list of the current black box functions that are supported by UltraPlonk: + +- AES +- [SHA256](./cryptographic_primitives/hashes#sha256) +- [Schnorr signature verification](./cryptographic_primitives/schnorr) +- [Blake2s](./cryptographic_primitives/hashes#blake2s) +- [Pedersen Hash](./cryptographic_primitives/hashes#pedersen_hash) +- [Pedersen Commitment](./cryptographic_primitives/hashes#pedersen_commitment) +- [HashToField128Security](./cryptographic_primitives/hashes#hash_to_field) +- [ECDSA signature verification](./cryptographic_primitives/ecdsa_sig_verification) +- [Fixed base scalar multiplication](./cryptographic_primitives/scalar) +- [Compute merkle root](./merkle_trees#compute_merkle_root) +- AND +- XOR +- RANGE +- [Keccak256](./cryptographic_primitives/hashes#keccak256) +- [Recursive proof verification](./recursion) + +Most black box functions are included as part of the Noir standard library, however `AND`, `XOR` and `RANGE` are used as part of the Noir language syntax. For instance, using the bitwise operator `&` will invoke the `AND` black box function. To ensure compatibility across backends, the ACVM has fallback implementations of `AND`, `XOR` and `RANGE` defined in its standard library which it can seamlessly fallback to if the backend doesn't support them. + +You can view the black box functions defined in the ACVM code [here](https://github.com/noir-lang/acvm/blob/acir-v0.12.0/acir/src/circuit/black_box_functions.rs). diff --git a/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives.md b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives.md new file mode 100644 index 00000000000..2df4f929474 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives.md @@ -0,0 +1,14 @@ +--- +title: Cryptographic primitives in Noir +description: + Learn about the cryptographic primitives ready to use for any Noir project +keywords: + [ + cryptographic primitives, + Noir project, + ] +--- + +The Noir team is progressively adding new cryptographic primitives to the standard library. Reach out for news or if you would be interested in adding more of these calculations in Noir. + +Some methods are available thanks to the Aztec backend, not being performed using Noir. When using other backends, these methods may or may not be supplied. diff --git a/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/00_hashes.mdx b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/00_hashes.mdx new file mode 100644 index 00000000000..76745196681 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/00_hashes.mdx @@ -0,0 +1,167 @@ +--- +title: Hash methods +description: + Learn about the cryptographic primitives ready to use for any Noir project, including sha256, + blake2s, pedersen, mimc_bn254 and mimc +keywords: + [cryptographic primitives, Noir project, sha256, blake2s, pedersen, mimc_bn254, mimc, hash] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## sha256 + +Given an array of bytes, returns the resulting sha256 hash. + +```rust +fn sha256(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::sha256(x); +} +``` + + + +## blake2s + +Given an array of bytes, returns an array with the Blake2 hash + +```rust +fn blake2s(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::blake2s(x); +} +``` + + + +## pedersen_hash + +Given an array of Fields, returns the Pedersen hash. + +```rust +fn pedersen_hash(_input : [Field]) -> Field +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::pedersen_hash(x); +} +``` + + + + + +## pedersen_commitment + +Given an array of Fields, returns the Pedersen commitment. + +```rust +fn pedersen_commitment(_input : [Field]) -> [Field; 2] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let commitment = std::hash::pedersen_commitment(x); +} +``` + + + +## keccak256 + +Given an array of bytes (`u8`), returns the resulting keccak hash as an array of 32 bytes +(`[u8; 32]`). Specify a message_size to hash only the first `message_size` bytes +of the input. + +```rust +fn keccak256(_input : [u8; N], _message_size: u32) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let message_size = 4; + let hash = std::hash::keccak256(x, message_size); +} +``` + + + +## poseidon + +Given an array of Fields, returns a new Field with the Poseidon Hash. Mind that you need to specify +how many inputs are there to your Poseidon function. + +```rust +// example for hash_1, hash_2 accepts an array of length 2, etc +fn hash_1(input: [Field; 1]) -> Field +``` + +example: + +```rust +fn main() +{ + let hash1 = std::hash::poseidon::bn254::hash_2([1, 2]); + assert(hash1 == 0x115cc0f5e7d690413df64c6b9662e9cf2a3617f2743245519e19607a4417189a); +} +``` + +## mimc_bn254 and mimc + +`mimc_bn254` is `mimc`, but with hardcoded parameters for the BN254 curve. You can use it by +providing an array of Fields, and it returns a Field with the hash. You can use the `mimc` method if +you're willing to input your own constants: + +```rust +fn mimc(x: Field, k: Field, constants: [Field; N], exp : Field) -> Field +``` + +otherwise, use the `mimc_bn254` method: + +```rust +fn mimc_bn254(array: [Field; N]) -> Field +``` + +example: + +```rust + +fn main() { + let x = [163, 117, 178, 149]; // some random bytes + let hash = std::hash::mimc::mimc_bn254(x); +} +``` + +## hash_to_field + +```rust +fn hash_to_field(_input : [Field; N]) -> Field {} +``` + +Calculates the `blake2s` hash of the inputs and returns the hash modulo the field modulus to return +a value which can be represented as a `Field`. + + diff --git a/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/01_scalar.mdx b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/01_scalar.mdx new file mode 100644 index 00000000000..c7eed820a80 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/01_scalar.mdx @@ -0,0 +1,27 @@ +--- +title: Scalar multiplication +description: See how you can perform scalar multiplications over a fixed base in Noir +keywords: [cryptographic primitives, Noir project, scalar multiplication] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## scalar_mul::fixed_base_embedded_curve + +Performs scalar multiplication over the embedded curve whose coordinates are defined by the +configured noir field. For the BN254 scalar field, this is BabyJubJub or Grumpkin. + +```rust +fn fixed_base_embedded_curve(_input : Field) -> [Field; 2] +``` + +example + +```rust +fn main(x : Field) { + let scal = std::scalar_mul::fixed_base_embedded_curve(x); + std::println(scal); +} +``` + + diff --git a/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/02_schnorr.mdx b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/02_schnorr.mdx new file mode 100644 index 00000000000..c184ce28120 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/02_schnorr.mdx @@ -0,0 +1,37 @@ +--- +title: Schnorr Signatures +description: Learn how you can verify Schnorr signatures using Noir +keywords: [cryptographic primitives, Noir project, schnorr, signatures] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## schnorr::verify_signature + +Verifier for Schnorr signatures over the embedded curve (for BN254 it is Grumpkin). + +```rust +fn verify_signature(_public_key_x: Field, _public_key_y: Field, _signature: [u8; 64], _message: [u8]) -> bool +``` + +where `_signature` can be generated like so using the npm package +[@noir-lang/barretenberg](https://www.npmjs.com/package/@noir-lang/barretenberg) + +```js +const { BarretenbergWasm } = require('@noir-lang/barretenberg/dest/wasm'); +const { Schnorr } = require('@noir-lang/barretenberg/dest/crypto/schnorr'); + +... + +const barretenberg = await BarretenbergWasm.new(); +const schnorr = new Schnorr(barretenberg); +const pubKey = schnorr.computePublicKey(privateKey); +const message = ... +const signature = Array.from( + schnorr.constructSignature(hash, privateKey).toBuffer() +); + +... +``` + + diff --git a/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx new file mode 100644 index 00000000000..72bce984821 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx @@ -0,0 +1,45 @@ +--- +title: ECDSA Signature Verification +description: Learn about the cryptographic primitives regarding ECDSA over the secp256k1 and secp256r1 curves +keywords: [cryptographic primitives, Noir project, ecdsa, secp256k1, secp256r1, signatures] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +Noir supports ECDSA signatures verification over the secp256k1 and secp256r1 curves. + +## ecdsa_secp256k1::verify_signature + +Verifier for ECDSA Secp256k1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> bool +``` + +example: + +```rust +fn main(hashed_message : [u8;32], pub_key_x : [u8;32], pub_key_y : [u8;32], signature : [u8;64]) { + let valid_signature = std::ecdsa_secp256k1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} +``` + +## ecdsa_secp256r1::verify_signature + +Verifier for ECDSA Secp256r1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> bool +``` + +example: + +```rust +fn main(hashed_message : [u8;32], pub_key_x : [u8;32], pub_key_y : [u8;32], signature : [u8;64]) { + let valid_signature = std::ecdsa_secp256r1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} +``` + + diff --git a/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/04_ec_primitives.md b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/04_ec_primitives.md new file mode 100644 index 00000000000..6e6b19b6861 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/04_ec_primitives.md @@ -0,0 +1,101 @@ +--- +title: Elliptic Curve Primitives +keywords: [cryptographic primitives, Noir project] +--- + +Data structures and methods on them that allow you to carry out computations involving elliptic +curves over the (mathematical) field corresponding to `Field`. For the field currently at our +disposal, applications would involve a curve embedded in BN254, e.g. the +[Baby Jubjub curve](https://eips.ethereum.org/EIPS/eip-2494). + +## Data structures + +### Elliptic curve configurations + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Curve`), i.e. the specific elliptic +curve you want to use, which would be specified using any one of the methods +`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::new` which take the coefficients in the +defining equation together with a generator point as parameters. You can find more detail in the +comments in +[`noir_stdlib/src/ec.nr`](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr), but +the gist of it is that the elliptic curves of interest are usually expressed in one of the standard +forms implemented here (Twisted Edwards, Montgomery and Short Weierstraß), and in addition to that, +you could choose to use `affine` coordinates (Cartesian coordinates - the usual (x,y) - possibly +together with a point at infinity) or `curvegroup` coordinates (some form of projective coordinates +requiring more coordinates but allowing for more efficient implementations of elliptic curve +operations). Conversions between all of these forms are provided, and under the hood these +conversions are done whenever an operation is more efficient in a different representation (or a +mixed coordinate representation is employed). + +### Points + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Point`), i.e. points lying on the +elliptic curve. For a curve configuration `c` and a point `p`, it may be checked that `p` +does indeed lie on `c` by calling `c.contains(p1)`. + +## Methods + +(given a choice of curve representation, e.g. use `std::ec::tecurve::affine::Curve` and use +`std::ec::tecurve::affine::Point`) + +- The **zero element** is given by `Point::zero()`, and we can verify whether a point `p: Point` is + zero by calling `p.is_zero()`. +- **Equality**: Points `p1: Point` and `p2: Point` may be checked for equality by calling + `p1.eq(p2)`. +- **Addition**: For `c: Curve` and points `p1: Point` and `p2: Point` on the curve, adding these two + points is accomplished by calling `c.add(p1,p2)`. +- **Negation**: For a point `p: Point`, `p.negate()` is its negation. +- **Subtraction**: For `c` and `p1`, `p2` as above, subtracting `p2` from `p1` is accomplished by + calling `c.subtract(p1,p2)`. +- **Scalar multiplication**: For `c` as above, `p: Point` a point on the curve and `n: Field`, + scalar multiplication is given by `c.mul(n,p)`. If instead `n :: [u1; N]`, i.e. `n` is a bit + array, the `bit_mul` method may be used instead: `c.bit_mul(n,p)` +- **Multi-scalar multiplication**: For `c` as above and arrays `n: [Field; N]` and `p: [Point; N]`, + multi-scalar multiplication is given by `c.msm(n,p)`. +- **Coordinate representation conversions**: The `into_group` method converts a point or curve + configuration in the affine representation to one in the CurveGroup representation, and + `into_affine` goes in the other direction. +- **Curve representation conversions**: `tecurve` and `montcurve` curves and points are equivalent + and may be converted between one another by calling `into_montcurve` or `into_tecurve` on their + configurations or points. `swcurve` is more general and a curve c of one of the other two types + may be converted to this representation by calling `c.into_swcurve()`, whereas a point `p` lying + on the curve given by `c` may be mapped to its corresponding `swcurve` point by calling + `c.map_into_swcurve(p)`. +- **Map-to-curve methods**: The Elligator 2 method of mapping a field element `n: Field` into a + `tecurve` or `montcurve` with configuration `c` may be called as `c.elligator2_map(n)`. For all of + the curve configurations, the SWU map-to-curve method may be called as `c.swu_map(z,n)`, where + `z: Field` depends on `Field` and `c` and must be chosen by the user (the conditions it needs to + satisfy are specified in the comments + [here](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr)). + +## Examples + +The +[ec_baby_jubjub test](https://github.com/noir-lang/noir/blob/master/crates/nargo_cli/tests/test_data/ec_baby_jubjub/src/main.nr) +illustrates all of the above primitives on various forms of the Baby Jubjub curve. A couple of more +interesting examples in Noir would be: + +Public-key cryptography: Given an elliptic curve and a 'base point' on it, determine the public key +from the private key. This is a matter of using scalar multiplication. In the case of Baby Jubjub, +for example, this code would do: + +```rust +use dep::std::ec::tecurve::affine::{Curve, Point}; + +fn bjj_pub_key(priv_key: Field) -> Point +{ + + let bjj = Curve::new(168700, 168696, G::new(995203441582195749578291179787384436505546430278305826713579947235728471134,5472060717959818805561601436314318772137091100104008585924551046643952123905)); + + let base_pt = Point::new(5299619240641551281634865583518297030282874472190772894086521144482721001553, 16950150798460657717958625567821834550301663161624707787222815936182638968203); + + bjj.mul(priv_key,base_pt) +} +``` + +This would come in handy in a Merkle proof. + +- EdDSA signature verification: This is a matter of combining these primitives with a suitable hash + function. See + [feat(stdlib): EdDSA sig verification noir#1136](https://github.com/noir-lang/noir/pull/1136) for + the case of Baby Jubjub and the Poseidon hash function. diff --git a/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/05_eddsa.mdx b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/05_eddsa.mdx new file mode 100644 index 00000000000..9a5beb55ee9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/standard_library/cryptographic_primitives/05_eddsa.mdx @@ -0,0 +1,17 @@ +--- +title: EdDSA Verification +description: Learn about the cryptographic primitives regarding EdDSA +keywords: [cryptographic primitives, Noir project, eddsa, signatures] +--- + +import BlackBoxInfo from '@site/src/components/Notes/_blackbox.mdx'; + +## eddsa::eddsa_poseidon_verify + +Verifier for EdDSA signatures + +```rust +fn eddsa_poseidon_verify(public_key_x : Field, public_key_y : Field, signature_s: Field, signature_r8_x: Field, signature_r8_y: Field, message: Field) -> bool +``` + + diff --git a/noir/docs/versioned_docs/version-v0.19.2/standard_library/logging.md b/noir/docs/versioned_docs/version-v0.19.2/standard_library/logging.md new file mode 100644 index 00000000000..7e2fd9b9aff --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/standard_library/logging.md @@ -0,0 +1,62 @@ +--- +title: Logging +description: + Learn how to use the println statement for debugging in Noir with this tutorial. Understand the + basics of logging in Noir and how to implement it in your code. +keywords: + [ + noir logging, + println statement, + debugging in noir, + noir std library, + logging tutorial, + basic logging in noir, + noir logging implementation, + noir debugging techniques, + rust, + ] +--- + +The standard library provides a familiar `println` statement you can use. Despite being a limited +implementation of rust's `println!` macro, this construct can be useful for debugging. + +You can print the output of println statements in your Noir code by using the `nargo execute` command or the `--show-output` flag when using `nargo test` (provided there are println statements in your tests). + +It is recommended to use `nargo execute` if you want to debug failing constrains with `println` statements. This is due to every input in a test being a constant rather than a witness, so we issue an error during compilation while we only print during execution (which comes after compilation). `println` will not work for failed constraints caught at compile time. + +The `println` statement is unconstrained, so it works for outputting integers, fields, strings, and even structs or expressions. For example: + +```rust +use dep::std; + +struct Person { + age : Field, + height : Field, +} + +fn main(age : Field, height : Field) { + let person = Person { age : age, height : height }; + std::println(person); + std::println(age + height); + std::println("Hello world!"); +} + +``` + +You can print multiple different types in the same statement and string as well as a new "fmtstr" type. A `fmtstr` can be specified in the same way as a normal string it just should be prepended with an "f" character: + +```rust + let fmt_str = f"i: {i}, j: {j}"; + std::println(fmt_str); + + let s = myStruct { y: x, x: y }; + std::println(s); + + std::println(f"i: {i}, s: {s}"); + + std::println(x); + std::println([x, y]); + + let foo = fooStruct { my_struct: s, foo: 15 }; + std::println(f"s: {s}, foo: {foo}"); +``` diff --git a/noir/docs/versioned_docs/version-v0.19.2/standard_library/merkle_trees.md b/noir/docs/versioned_docs/version-v0.19.2/standard_library/merkle_trees.md new file mode 100644 index 00000000000..dc383a1426b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/standard_library/merkle_trees.md @@ -0,0 +1,58 @@ +--- +title: Merkle Trees +description: Learn about Merkle Trees in Noir with this tutorial. Explore the basics of computing a merkle root using a proof, with examples. +keywords: + [ + Merkle trees in Noir, + Noir programming language, + check membership, + computing root from leaf, + Noir Merkle tree implementation, + Merkle tree tutorial, + Merkle tree code examples, + Noir libraries, + pedersen hash., + ] +--- + +## compute_merkle_root + +Returns the root of the tree from the provided leaf and its hash path, using a [Pedersen hash](cryptographic_primitives/00_hashes.mdx#pedersen_hash). + +```rust +fn compute_merkle_root(leaf : Field, index : Field, hash_path: [Field]) -> Field +``` + +example: + +```rust +/** + // these values are for this example only + index = "0" + priv_key = "0x000000000000000000000000000000000000000000000000000000616c696365" + secret = "0x1929ea3ab8d9106a899386883d9428f8256cfedb3c4f6b66bf4aa4d28a79988f" + note_hash_path = [ + "0x1e61bdae0f027b1b2159e1f9d3f8d00fa668a952dddd822fda80dc745d6f65cc", + "0x0e4223f3925f98934393c74975142bd73079ab0621f4ee133cee050a3c194f1a", + "0x2fd7bb412155bf8693a3bd2a3e7581a679c95c68a052f835dddca85fa1569a40" + ] + */ +fn main(index: Field, priv_key: Field, secret: Field, note_hash_path: [Field; 3]) { + + let pubkey = std::scalar_mul::fixed_base_embedded_curve(priv_key); + let pubkey_x = pubkey[0]; + let pubkey_y = pubkey[1]; + let note_commitment = std::hash::pedersen([pubkey_x, pubkey_y, secret]); + + let root = std::merkle::compute_merkle_root(note_commitment[0], index, note_hash_path); + std::println(root); +} +``` + +To check merkle tree membership: + +1. Include a merkle root as a program input. +2. Compute the merkle root of a given leaf, index and hash path. +3. Assert the merkle roots are equal. + +For more info about merkle trees, see the Wikipedia [page](https://en.wikipedia.org/wiki/Merkle_tree). diff --git a/noir/docs/versioned_docs/version-v0.19.2/standard_library/options.md b/noir/docs/versioned_docs/version-v0.19.2/standard_library/options.md new file mode 100644 index 00000000000..3d3139fb98b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/standard_library/options.md @@ -0,0 +1,99 @@ +--- +title: Option Type +--- + +The `Option` type is a way to express that a value might be present (`Some(T))` or absent (`None`). It's a safer way to handle potential absence of values, compared to using nulls in many other languages. + +```rust +struct Option { + None, + Some(T), +} +``` + +You can import the Option type into your Noir program like so: + +```rust +use dep::std::option::Option; + +fn main() { + let none = Option::none(); + let some = Option::some(3); +} +``` + +See [this test](https://github.com/noir-lang/noir/blob/5cbfb9c4a06c8865c98ff2b594464b037d821a5c/crates/nargo_cli/tests/test_data/option/src/main.nr) for a more comprehensive set of examples of each of the methods described below. + +## Methods + +### none + +Constructs a none value. + +### some + +Constructs a some wrapper around a given value. + +### is_none + +Returns true if the Option is None. + +### is_some + +Returns true of the Option is Some. + +### unwrap + +Asserts `self.is_some()` and returns the wrapped value. + +### unwrap_unchecked + +Returns the inner value without asserting `self.is_some()`. This method can be useful within an if condition when we already know that `option.is_some()`. If the option is None, there is no guarantee what value will be returned, only that it will be of type T for an `Option`. + +### unwrap_or + +Returns the wrapped value if `self.is_some()`. Otherwise, returns the given default value. + +### unwrap_or_else + +Returns the wrapped value if `self.is_some()`. Otherwise, calls the given function to return a default value. + +### map + +If self is `Some(x)`, this returns `Some(f(x))`. Otherwise, this returns `None`. + +### map_or + +If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns the given default value. + +### map_or_else + +If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns `default()`. + +### and + +Returns None if self is None. Otherwise, this returns `other`. + +### and_then + +If self is None, this returns None. Otherwise, this calls the given function with the Some value contained within self, and returns the result of that call. In some languages this function is called `flat_map` or `bind`. + +### or + +If self is Some, return self. Otherwise, return `other`. + +### or_else + +If self is Some, return self. Otherwise, return `default()`. + +### xor + +If only one of the two Options is Some, return that option. Otherwise, if both options are Some or both are None, None is returned. + +### filter + +Returns `Some(x)` if self is `Some(x)` and `predicate(x)` is true. Otherwise, this returns `None`. + +### flatten + +Flattens an `Option>` into a `Option`. This returns `None` if the outer Option is None. Otherwise, this returns the inner Option. diff --git a/noir/docs/versioned_docs/version-v0.19.2/standard_library/recursion.md b/noir/docs/versioned_docs/version-v0.19.2/standard_library/recursion.md new file mode 100644 index 00000000000..ff4c63acaa7 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/standard_library/recursion.md @@ -0,0 +1,96 @@ +--- +title: Recursive Proofs +description: Learn about how to write recursive proofs in Noir. +keywords: [recursion, recursive proofs, verification_key, aggregation object, verify_proof] +--- + +Noir supports recursively verifying proofs, meaning you verify the proof of a Noir program in another Noir program. This enables creating proofs of arbitrary size by doing step-wise verification of smaller components of a large proof. + +The `verify_proof` function takes a verification key, proof and public inputs for a zk program, as well as a key hash and an input aggregation object. The key hash is used to check the validity of the verification key and the input aggregation object is required by some proving systems. The `verify_proof` function returns an output aggregation object that can then be fed into future iterations of the proof verification if required. + +```rust +#[foreign(verify_proof)] +fn verify_proof(_verification_key : [Field], _proof : [Field], _public_input : Field, _key_hash : Field, _input_aggregation_object : [Field]) -> [Field] {} +``` + +:::info + +This is a black box function. Read [this section](./black_box_fns) to learn more about black box functions in Noir. + +::: + +## Aggregation Object + +The purpose of the input aggregation object is a little less clear though (and the output aggregation object that is returned from the `std::verify_proof` method). Recursive zkSNARK schemes do not necessarily "verify a proof" in the sense that you expect a true or false to be spit out by the verifier. Rather an aggregation object is built over the public inputs. In the case of PLONK the recursive aggregation object is two G1 points (expressed as 16 witness values). The final verifier (in our case this is most often the smart contract verifier) has to be aware of this aggregation object to execute a pairing and check the validity of these points (thus completing the recursive verification). + +So for example in this circuit: + +```rust +use dep::std; + +fn main( + verification_key : [Field; 114], + proof : [Field; 94], + public_inputs : [Field; 1], + key_hash : Field, + input_aggregation_object : [Field; 16], + proof_b : [Field; 94], +) -> pub [Field; 16] { + let output_aggregation_object_a = std::verify_proof( + verification_key, + proof, + public_inputs, + key_hash, + input_aggregation_object + ); + + let output_aggregation_object = std::verify_proof( + verification_key, + proof_b, + public_inputs, + key_hash, + output_aggregation_object_a + ); + + let mut output = [0; 16]; + for i in 0..16 { + output[i] = output_aggregation_object[i]; + } + output +} +``` + +In this example we have a circuit, that generates proofs A and B, that is being verified in circuit C. Assuming that the proof being passed in is not already a recursive proof, the `input_aggregation_object` will be all zeros. It will then generate an `output_aggregation_object`. This blob of data then becomes the `input_aggregation_object` of the next recursive aggregation we wish to compute. We can see here as the same public inputs, verification key, and key hash are used that we are verifying two proofs generated from the same circuit in this single circuit. `std::verify_proof` returns a `[Field]` because the size of an aggregation object is proof system dependent--in barretenberg, aggregation objects are two G1 points, while in Halo2, the aggregation object is a list of G1 points that is log the circuit size. So for the final step we convert the slice into an array of size 16 because we are generating proofs using UltraPlonk. + +## Parameters + +### `verification_key` + +The verification key for the zk program that is being verified. + +### `proof` + +The proof for the zk program that is being verified. + +### `public_inputs` + +These represent the public inputs of the proof we are verifying. They should be checked against in the circuit after construction of a new aggregation state. + +### `key_hash` + +A key hash is used to check the validity of the verification key. The circuit implementing this opcode can use this hash to ensure that the key provided to the circuit matches the key produced by the circuit creator. + +### `input_aggregation_object` + +An aggregation object is blob of data that the top-level verifier must run some proof system specific algorithm on to complete verification. The size is proof system specific and will be set by the backend integrating this opcode. The input aggregation object is only not `None` when we are verifying a previous recursive aggregation in the current circuit. If this is the first recursive aggregation there is no input aggregation object. It is left to the backend to determine how to handle when there is no input aggregation object. + +## Return value + +### `output_aggregation_object` + +This is the result of a recursive aggregation and is what will be fed into the next verifier. +The next verifier can either perform a final verification (returning true or false) or perform another recursive aggregation where this output aggregation object will be the input aggregation object of the next recursive aggregation. + +## Example + +You can see an example of how to do recursive proofs in [this example recursion demo repo](https://github.com/noir-lang/noir-examples/tree/master/recursion). diff --git a/noir/docs/versioned_docs/version-v0.19.2/standard_library/zeroed.md b/noir/docs/versioned_docs/version-v0.19.2/standard_library/zeroed.md new file mode 100644 index 00000000000..97dab02dac2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.19.2/standard_library/zeroed.md @@ -0,0 +1,25 @@ +--- +title: Zeroed Function +description: + The zeroed function returns a zeroed value of any type. +keywords: + [ + zeroed + ] +--- + +Implements `fn zeroed() -> T` to return a zeroed value of any type. This function is generally unsafe to use as the zeroed bit pattern is not guaranteed to be valid for all types. It can however, be useful in cases when the value is guaranteed not to be used such as in a BoundedVec library implementing a growable vector, up to a certain length, backed by an array. The array can be initialized with zeroed values which are guaranteed to be inaccessible until the vector is pushed to. Similarly, enumerations in noir can be implemented using this method by providing zeroed values for the unused variants. + +You can access the function at `std::unsafe::zeroed`. + +This function currently supports the following types: + +- Field +- Bool +- Uint +- Array +- String +- Tuple +- Function + +Using it on other types could result in unexpected behavior. diff --git a/noir/docs/versioned_docs/version-v0.6.0/examples/merkle-proof.md b/noir/docs/versioned_docs/version-v0.6.0/examples/merkle-proof.md new file mode 100644 index 00000000000..4696b4a1426 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/examples/merkle-proof.md @@ -0,0 +1,48 @@ +--- +title: Merkle Proof Membership +description: + Learn how to use merkle membership proof in Noir to prove that a given leaf is a member of a + merkle tree with a specified root, at a given index. +keywords: + [merkle proof, merkle membership proof, Noir, rust, hash function, Pedersen, sha256, merkle tree] +--- + +Let's walk through an example of a merkle membership proof in Noir that proves that a given leaf is +in a merkle tree. + +```rust +use dep::std; + +fn main(message : [Field; 62], index : Field, hashpath : [Field; 40], root : Field) { + let leaf = std::hash::hash_to_field(message); + let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); + assert(merkle_root == root); +} + +``` + +The message is hashed using `hash_to_field`. The specific hash function that is being used is chosen +by the backend. The only requirement is that this hash function can heuristically be used as a +random oracle. If only collision resistance is needed, then one can call `std::hash::pedersen` +instead. + +```rust +let leaf = std::hash::hash_to_field(message); +``` + +The leaf is then passed to a compute_merkle_root function with the root, index and hashpath. The returned root can then be asserted to be the same as the provided root. + +```rust +let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); +assert (merkle_root == root); +``` + +> **Note:** It is possible to re-implement the merkle tree implementation without standard library. +> However, for most usecases, it is enough. In general, the standard library will always opt to be +> as conservative as possible, while striking a balance with efficiency. + +An example, the merkle membership proof, only requires a hash function that has collision +resistance, hence a hash function like Pedersen is allowed, which in most cases is more efficient +than the even more conservative sha256. + +[view an example on the starter repo](https://github.com/noir-lang/noir-examples/blob/3ea09545cabfa464124ec2f3ea8e60c608abe6df/stealthdrop/circuits/src/main.nr#L20) diff --git a/noir/docs/versioned_docs/version-v0.6.0/getting_started/00_nargo_installation.md b/noir/docs/versioned_docs/version-v0.6.0/getting_started/00_nargo_installation.md new file mode 100644 index 00000000000..432884b709e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/getting_started/00_nargo_installation.md @@ -0,0 +1,283 @@ +--- +title: Nargo Installation +description: + nargo is a command line tool for interacting with Noir programs (e.g. compiling, proving, + verifying and more). Learn how to install and use Nargo for your projects with this comprehensive + guide. +keywords: [Nargo, command line tool, Noir programs, installation guide, how to use Nargo] +--- + +`nargo` is a command line tool for interacting with Noir programs (e.g. compiling, proving, +verifying and more). + +Alternatively, the interactions can also be performed in [TypeScript](../typescript). + +### UltraPlonk + +Nargo versions <0.5.0 of `aztec_backend` and `aztec_wasm_backend` are based on the TurboPlonk +version of Aztec Backend, which lacks efficient implementations of useful primitives (e.g. Keccak256 in 18k constraints, ECDSA verification in 36k constraints) that the UltraPlonk version offers. + +## Installation + +There are four approaches for installing Nargo: + +- [Option 1: Noirup](#option-1-noirup) +- [Option 2: Binaries](#option-2-binaries) +- [Option 3: Install via Nix](#option-3-install-via-nix) +- [Option 4: Compile from Source](#option-4-compile-from-source) + +Optionally you can also install [Noir VS Code extension] for syntax highlighting. + +### Option 1: Noirup + +If you're on OSX or Linux, the easiest way to start using Noir and Nargo is via noirup. Just open a +terminal and run: + +```bash +curl -L https://raw.githubusercontent.com/noir-lang/noirup/main/install | bash +``` + +Close the terminal, open another one, and run + +```bash +noirup -v 0.6.0 +``` + +Done, you should have the latest version working. You can check with `nargo --version`. + +You can also install nightlies, specific versions +or branches, check out the [noirup repository](https://github.com/noir-lang/noirup) for more +information. + +#### GitHub Actions + +You can use `noirup` with GitHub Actions for CI/CD and automated testing. It is as simple as +installing `noirup` and running tests in your GitHub Action `yml` file. + +See the +[config file](https://github.com/TomAFrench/noir-hashes/blob/master/.github/workflows/noir.yml) in +this repo containing hash functions in Noir for an example. + +#### Nightly versions + +To install the nightly version of Noir (updated daily) run: + +```bash +noirup -n +``` + +### Option 2: Binaries + +See [GitHub Releases](https://github.com/noir-lang/noir/releases) for the latest and previous +platform specific binaries. + +#### Step 1 + +Paste and run the following in the terminal to extract and install the binary: + +> **macOS / Linux:** If you are prompted with `Permission denied` when running commands, prepend +> `sudo` and re-run it. + +##### macOS (Apple Silicon) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-aarch64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### macOS (Intel) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### Windows (PowerShell) + +Open PowerShell as Administrator and run: + +```powershell +mkdir -f -p "$env:USERPROFILE\.nargo\bin\"; ` +Invoke-RestMethod -Method Get -Uri https://github.com/noir-lang/noir/releases/download/v0.4.1/nargo-x86_64-pc-windows-msvc.zip -Outfile "$env:USERPROFILE\.nargo\bin\nargo-x86_64-pc-windows-msvc.zip"; ` +Expand-Archive -Path "$env:USERPROFILE\.nargo\bin\nargo-x86_64-pc-windows-msvc.zip" -DestinationPath "$env:USERPROFILE\.nargo\bin\"; ` +$Reg = "Registry::HKLM\System\CurrentControlSet\Control\Session Manager\Environment"; ` +$OldPath = (Get-ItemProperty -Path "$Reg" -Name PATH).Path; ` +$NewPath = $OldPath + ’;’ + "$env:USERPROFILE\.nargo\bin\"; ` +Set-ItemProperty -Path "$Reg" -Name PATH –Value "$NewPath"; ` +$env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User") +``` + +##### Linux (Bash) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-unknown-linux-gnu.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -C $HOME/.nargo/bin/ && \ +echo -e '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.bashrc && \ +source ~/.bashrc +``` + +#### Step 2 + +Check if the installation was successful by running `nargo --help`. + +> **macOS:** If you are prompted with an OS alert, right-click and open the _nargo_ executable from +> Finder. Close the new terminal popped up and `nargo` should now be accessible. + +For a successful installation, you should see something similar to the following after running the +command: + +```sh +$ nargo --help + +Noir's package manager + +Usage: nargo + +Commands: + check Checks the constraint system for errors + codegen-verifier Generates a Solidity verifier smart contract for the program + compile Compile the program and its secret execution trace into ACIR format + new Create a new binary project + execute Executes a circuit to calculate its return value + prove Create proof for this program. The proof is returned as a hex encoded string + verify Given a proof and a program, verify whether the proof is valid + test Run the tests for this program + gates Counts the occurrences of different gates in circuit + help Print this message or the help of the given subcommand(s) +``` + +### Option 3: Install via Nix + +Due to the large number of native dependencies, Noir projects can be installed via [Nix](https://nixos.org/). + +#### Installing Nix + +For the best experience, please follow these instructions to setup Nix: + +1. Install Nix following [their guide](https://nixos.org/download.html) for your operating system. +2. Create the file `~/.config/nix/nix.conf` with the contents: + +```ini +experimental-features = nix-command +extra-experimental-features = flakes +``` + +#### Install Nargo into your Nix profile + +1. Use `nix profile` to install Nargo + +```sh +nix profile install github:noir-lang/noir +``` + +### Option 4: Compile from Source + +Due to the large number of native dependencies, Noir projects uses [Nix](https://nixos.org/) and [direnv](https://direnv.net/) to streamline the development experience. + +#### Setting up your environment + +For the best experience, please follow these instructions to setup your environment: + +1. Install Nix following [their guide](https://nixos.org/download.html) for your operating system. +2. Create the file `~/.config/nix/nix.conf` with the contents: + +```ini +experimental-features = nix-command +extra-experimental-features = flakes +``` + +3. Install direnv into your Nix profile by running: + +```sh +nix profile install nixpkgs#direnv +``` + +4. Add direnv to your shell following [their guide](https://direnv.net/docs/hook.html). +5. Restart your shell. + +#### Shell & editor experience + +Now that your environment is set up, you can get to work on the project. + +1. Clone the repository, such as: + +```sh +git clone git@github.com:noir-lang/noir +``` +> Replacing `noir` with whichever repository you want to work on. + +2. Navigate to the directory: + +```sh +cd noir +``` +> Replacing `noir` with whichever repository you cloned. + +3. You should see a __direnv error__ because projects aren't allowed by default. Make sure you've reviewed and trust our `.envrc` file, then you need to run: + +```sh +direnv allow +``` + +4. Now, wait awhile for all the native dependencies to be built. This will take some time and direnv will warn you that it is taking a long time, but we just need to let it run. + +5. Once you are presented with your prompt again, you can start your editor within the project directory (we recommend [VSCode](https://code.visualstudio.com/)): + +```sh +code . +``` + +6. (Recommended) When launching VSCode for the first time, you should be prompted to install our recommended plugins. We highly recommend installing these for the best development experience. + +#### Building and testing + +Assuming you are using `direnv` to populate your environment, building and testing the project can be done +with the typical `cargo build`, `cargo test`, and `cargo clippy` commands. You'll notice that the `cargo` version matches the version we specify in `flake.nix`, which is 1.66.0 at the time of this writing. + +If you want to build the entire project in an isolated sandbox, you can use Nix commands: + +1. `nix build .` (or `nix build . -L` for verbose output) to build the project in a Nix sandbox. +2. `nix flake check` (or `nix flake check -L` for verbose output) to run clippy and tests in a Nix sandbox. + +#### Without `direnv` + +If you have hesitations with using direnv, you can launch a subshell with `nix develop` and then launch your editor from within the subshell. However, if VSCode was already launched in the project directory, the environment won't be updated. + +Advanced: If you aren't using direnv nor launching your editor within the subshell, you can try to install Barretenberg and other global dependencies the package needs. This is an advanced workflow and likely won't receive support! + +## Uninstalling Nargo + +### Noirup + +If you installed Noir with `noirup`, you can uninstall Noir by removing the files in `~/.nargo`, `~/nargo` and `~/noir_cache`. + +```bash +rm -r ~/.nargo +rm -r ~/nargo +rm -r ~/noir_cache +``` + +### Nix + +If you installed Noir with Nix or from source, you can remove the binary located at `~/.nix-profile/bin/nargo`. + +```bash +rm ~/.nix-profile/bin/nargo +``` + +[git]: https://git-scm.com/book/en/v2/Getting-Started-Installing-Git +[rust]: https://www.rust-lang.org/tools/install +[noir vs code extension]: + https://marketplace.visualstudio.com/items?itemName=noir-lang.noir-programming-language-syntax-highlighter +[homebrew]: https://brew.sh/ +[cmake]: https://cmake.org/install/ +[llvm]: https://llvm.org/docs/GettingStarted.html +[openmp]: https://openmp.llvm.org/ +[barretenberg]: https://github.com/AztecProtocol/barretenberg diff --git a/noir/docs/versioned_docs/version-v0.6.0/getting_started/01_hello_world.md b/noir/docs/versioned_docs/version-v0.6.0/getting_started/01_hello_world.md new file mode 100644 index 00000000000..0f21ad45569 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/getting_started/01_hello_world.md @@ -0,0 +1,147 @@ +--- +title: Create A Project +description: + Learn how to create and verify your first Noir program using Nargo, a programming language for + zero-knowledge proofs. +keywords: + [ + Nargo, + Noir, + zero-knowledge proofs, + programming language, + create Noir program, + verify Noir program, + step-by-step guide, + ] +--- + +Now that we have installed Nargo, it is time to make our first hello world program! + +## Create a Project Directory + +Noir code can live anywhere on your computer. Let us create a _projects_ folder in the home +directory to house our Noir programs. + +For Linux, macOS, and Windows PowerShell, create the directory and change directory into it by +running: + +```sh +mkdir ~/projects +cd ~/projects +``` + +For Windows CMD, run: + +```sh +> mkdir "%USERPROFILE%\projects" +> cd /d "%USERPROFILE%\projects" +``` + +## Create Our First Nargo Project + +Now that we are in the projects directory, create a new Nargo project by running: + +```sh +nargo new hello_world +``` + +> **Note:** `hello_world` can be any arbitrary project name, we are simply using `hello_world` for +> demonstration. +> +> In production, the common practice is to name the project folder as `circuits` for better +> identifiability when sitting alongside other folders in the codebase (e.g. `contracts`, `scripts`, +> `test`). + +A `hello_world` folder would be created. Similar to Rust, the folder houses _src/main.nr_ and +_Nargo.toml_ that contains the source code and environmental options of your Noir program +respectively. + +### Intro to Noir Syntax + +Let us take a closer look at _main.nr_. The default _main.nr_ generated should look like this: + +```rust +fn main(x : Field, y : pub Field) { + assert(x != y); +} +``` + +The first line of the program specifies the program's inputs: + +```rust +x : Field, y : pub Field +``` + +Program inputs in Noir are private by default (e.g. `x`), but can be labeled public using the +keyword `pub` (e.g. `y`). To learn more about private and public values, check the +[Data Types](../language_concepts/data_types) section. + +The next line of the program specifies its body: + +```rust +assert(x != y); +``` + +The Noir syntax `assert` can be interpreted as something similar to constraints in other zk-contract languages. + +For more Noir syntax, check the [Language Concepts](../language_concepts/comments) chapter. + +## Build In/Output Files + +Change directory into _hello_world_ and build in/output files for your Noir program by running: + +```sh +cd hello_world +nargo check +``` + +Two additional files would be generated in your project directory: + +_Prover.toml_ houses input values, and _Verifier.toml_ houses public values. + +## Prove Our Noir Program + +Now that the project is set up, we can create a proof of correct execution on our Noir program. + +Fill in input values for execution in the _Prover.toml_ file. For example: + +```toml +x = "1" +y = "2" +``` + +Prove the valid execution of your Noir program with your preferred proof name, for example `p`: + +```sh +nargo prove p +``` + +A new folder _proofs_ would then be generated in your project directory, containing the proof file +`p.proof`. + +The _Verifier.toml_ file would also be updated with the public values computed from program +execution (in this case the value of `y`): + +```toml +y = "0x0000000000000000000000000000000000000000000000000000000000000002" +``` + +> **Note:** Values in _Verifier.toml_ are computed as 32-byte hex values. + +## Verify Our Noir Program + +Once a proof is generated, we can verify correct execution of our Noir program by verifying the +proof file. + +Verify your proof of name `p` by running: + +```sh +nargo verify p +``` + +The verification will complete in silence if it is successful. If it fails, it will log the +corresponding error instead. + +Congratulations, you have now created and verified a proof for your very first Noir program! + +In the [next section](breakdown), we will go into more detail on each step performed. diff --git a/noir/docs/versioned_docs/version-v0.6.0/getting_started/02_breakdown.md b/noir/docs/versioned_docs/version-v0.6.0/getting_started/02_breakdown.md new file mode 100644 index 00000000000..5f4f00a3f36 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/getting_started/02_breakdown.md @@ -0,0 +1,103 @@ +--- +title: Project Breakdown +description: + Learn about the anatomy of a Nargo project, including the purpose of the Prover and Verifier TOML + files, and how to prove and verify your program. +keywords: + [Nargo, Nargo project, Prover.toml, Verifier.toml, proof verification, private asset transfer] +--- + +This section breaks down our hello world program in section _1.2_. We elaborate on the project +structure and what the `prove` and `verify` commands did in the previous section. + +## Anatomy of a Nargo Project + +Upon creating a new project with `nargo new` and building the in/output files with `nargo check` +commands, you would get a minimal Nargo project of the following structure: + + - src + - Prover.toml + - Verifier.toml + - Nargo.toml + +The source directory _src_ holds the source code for your Noir program. By default only a _main.nr_ +file will be generated within it. + +_Prover.toml_ is used for specifying the input values for executing and proving the program. +Optionally you may specify expected output values for prove-time checking as well. + +_Verifier.toml_ contains public in/output values computed when executing the Noir program. + +_Nargo.toml_ contains the environmental options of your project. + +_proofs_ and _contract_ directories will not be immediately visible until you create a proof or +verifier contract respectively. + +### main.nr + +The _main.nr_ file contains a `main` method, this method is the entry point into your Noir program. + +In our sample program, _main.nr_ looks like this: + +```rust +fn main(x : Field, y : Field) { + constrain x != y; +} +``` + +The parameters `x` and `y` can be seen as the API for the program and must be supplied by the +prover. Since neither `x` nor `y` is marked as public, the verifier does not supply any inputs, when +verifying the proof. + +The prover supplies the values for `x` and `y` in the _Prover.toml_ file. + +As for the program body, `constrain` ensures the satisfaction of the condition (e.g. `x != y`) is +constrained by the proof of the execution of said program (i.e. if the condition was not met, the +verifier would reject the proof as an invalid proof). + +### Prover.toml + +The _Prover.toml_ file is a file which the prover uses to supply his witness values(both private and +public). + +In our hello world program the _Prover.toml_ file looks like this: + +```toml +x = "1" +y = "2" +``` + +When the command `nargo prove my_proof` is executed, two processes happen: + +1. Noir creates a proof that `x` which holds the value of `1` and `y` which holds the value of `2` + is not equal. This not equal constraint is due to the line `constrain x != y`. + +2. Noir creates and stores the proof of this statement in the _proofs_ directory and names the proof + file _my_proof_. Opening this file will display the proof in hex format. + +## Verifying a Proof + +When the command `nargo verify my_proof` is executed, two processes happen: + +1. Noir checks in the _proofs_ directory for a file called _my_proof_ + +2. If that file is found, the proof's validity is checked + +> **Note:** The validity of the proof is linked to the current Noir program; if the program is +> changed and the verifier verifies the proof, it will fail because the proof is not valid for the +> _modified_ Noir program. + +In production, the prover and the verifier are usually two separate entities. A prover would +retrieve the necessary inputs, execute the Noir program, generate a proof and pass it to the +verifier. The verifier would then retrieve the public inputs from usually external sources and +verifies the validity of the proof against it. + +Take a private asset transfer as an example: + +A user on browser as the prover would retrieve private inputs (e.g. the user's private key) and +public inputs (e.g. the user's encrypted balance on-chain), compute the transfer, generate a proof +and submit it to the verifier smart contract. + +The verifier contract would then draw the user's encrypted balance directly from the blockchain and +verify the proof submitted against it. If the verification passes, additional functions in the +verifier contract could trigger (e.g. approve the asset transfer). diff --git a/noir/docs/versioned_docs/version-v0.6.0/index.md b/noir/docs/versioned_docs/version-v0.6.0/index.md new file mode 100644 index 00000000000..f4706182ffa --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/index.md @@ -0,0 +1,65 @@ +--- +title: Introducing Noir +description: + Learn about the public alpha release of Noir, a domain specific language heavily influenced by + Rust that compiles to an intermediate language which can be compiled to an arithmetic circuit or a + rank-1 constraint system. +keywords: + [ + Noir, + Domain Specific Language, + Rust, + Intermediate Language, + Arithmetic Circuit, + Rank-1 Constraint System, + Ethereum Developers, + Protocol Developers, + Blockchain Developers, + Proving System, + Smart Contract Language, + ] +slug: / +--- + +This version of the book is being released with the public alpha. There will be a lot of features +that are missing in this version, however the syntax and the feel of the language will mostly be +completed. + +## What is Noir? + +Noir is a domain specific language for creating and verifying proofs. It's design choices are +influenced heavily by Rust. + +## What's new about Noir? + +Noir is simple and flexible in its design, as it does not compile immediately to a fixed +NP-complete language. Instead, Noir compiles to an intermediate language (ACIR), which itself can be compiled +to an arithmetic circuit (if choosing to target Aztec's barretenberg backend) or a rank-1 constraint system (if choosing to target an R1CS backend like Arkwork's Marlin backend, or others). + +This in itself brings up a few challenges +within the design process, but allows one to decouple the programming language completely from the +backend. This is similar in theory to LLVM. + +## Who is Noir for? + +Noir can be used for a variety of purposes. + +### Ethereum Developers + +Noir currently includes a command to publish a contract which verifies your Noir program. This will +be modularised in the future; however, as of the alpha, you can use the `contract` command to create +a verifier contract. + +### Protocol Developers + +As a protocol developer, you may not want to use the Aztec backend due to it not being a fit for +your stack, or maybe you simply want to use a different proving system. Since Noir does not compile +to a specific proof system, it is possible for protocol developers to replace the PLONK-based +proving system with a different proving system altogether. + +### Blockchain developers + +As a blockchain developer, you will be constrained by parameters set by your blockchain (for example, the +proving system and smart contract language has been pre-defined). In order for you to use Noir in +your blockchain, a proving system backend and a smart contract interface +must be implemented for it. diff --git a/noir/docs/versioned_docs/version-v0.6.0/language_concepts/00_data_types.md b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/00_data_types.md new file mode 100644 index 00000000000..3a711fc8922 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/00_data_types.md @@ -0,0 +1,301 @@ +--- +title: Data Types +description: + Get a clear understanding of the two categories of Noir data types - primitive types and compound + types. Learn about their characteristics, differences, and how to use them in your Noir + programming. +keywords: + [ + noir, + data types, + primitive types, + compound types, + private types, + public types, + field type, + integer types, + boolean type, + array type, + tuple type, + struct type, + ] +--- + +Every value in Noir has a type, which determines which operations are valid for it. + +All values in Noir are fundamentally composed of `Field` elements. For a more approachable +developing experience, abstractions are added on top to introduce different data types in Noir. + +Noir has two category of data types: primitive types (e.g. `Field`, integers, `bool`) and compound +types that group primitive types (e.g. arrays, tuples, structs). Each value can either be private or +public. + +## Private & Public Types + +A **private value** is known only to the Prover, while a **public value** is known by both the +Prover and Verifier. Mark values as `private` when the value should only be known to the prover. All +primitive types (including individual fields of compound types) in Noir are private by default, and +can be marked public when certain values are intended to be revealed to the Verifier. + +> **Note:** For public values defined in Noir programs paired with smart contract verifiers, once +> the proofs are verified on-chain the values can be considered known to everyone that has access to +> that blockchain. + +Public data types are treated no differently to private types apart from the fact that their values +will be revealed in proofs generated. Simply changing the value of a public type will not change the +circuit (where the same goes for changing values of private types as well). + +_Private values_ are also referred to as _witnesses_ sometimes. + +> **Note:** The terms private and public when applied to a type (e.g. `pub Field`) have a different +> meaning than when applied to a function (e.g. `pub fn foo() {}`). +> +> The former is a visibility modifier for the Prover to interpret if a value should be made known to +> the Verifier, while the latter is a visibility modifier for the compiler to interpret if a +> function should be made accessible to external Noir programs like in other languages. + +### pub Modifier + +All data types in Noir are private by default. Types are explicitly declared as public using the +`pub` modifier: + +```rust +fn main(x : Field, y : pub Field) -> pub Field { + x + y +} +``` + +In this example, `x` is **private** while `y` and `x + y` (the return value) are **public**. Note +that visibility is handled **per variable**, so it is perfectly valid to have one input that is +private and another that is public. + +> **Note:** Public types can only be declared through parameters on `main`. + +## Primitive Types + +A primitive type represents a single value. They can be private or public. + +### Fields + +The field type corresponds to the native field type of the proving backend. + +The size of a Noir field depends on the elliptic curve's finite field for the proving backend +adopted. For example, a field would be a 254-bit integer when paired with the default backend that +spans the Grumpkin curve. + +Fields support integer arithmetic and are often used as the default numeric type in Noir: + +```rust +fn main(x : Field, y : Field) { + let z = x + y; +} +``` + +`x`, `y` and `z` are all private fields in this example. Using the `let` keyword we defined a new +private value `z` constrained to be equal to `x + y`. + +If proving efficiency is of priority, fields should be used as a default for solving problems. +Smaller integer types (e.g. `u64`) incur extra range constraints. + +### Integers + +An integer type is a range constrained field type. The Noir frontend currently supports unsigned, +arbitrary-sized integer types. + +An integer type is specified first with the letter `u`, indicating its unsigned nature, followed by +its length in bits (e.g. `32`). For example, a `u32` variable can store a value in the range of +$\\([0,2^{32}-1]\\)$: + +```rust +fn main(x : Field, y : u32) { + let z = x as u32 + y; +} +``` + +`x`, `y` and `z` are all private values in this example. However, `x` is a field while `y` and `z` +are unsigned 32-bit integers. If `y` or `z` exceeds the range $\\([0,2^{32}-1]\\)$, proofs created +will be rejected by the verifier. + +> **Note:** The default backend supports both even (e.g. `u16`, `u48`) and odd (e.g. `u5`, `u3`) +> sized integer types. + +### Booleans + +The `bool` type in Noir has two possible values: `true` and `false`: + +```rust +fn main() { + let t = true; + let f: bool = false; +} +``` + +> **Note:** When returning a boolean value, it will show up as a value of 1 for `true` and 0 for +> `false` in _Verifier.toml_. + +The boolean type is most commonly used in conditionals like `if` expressions and `constrain` +statements. More about conditionals is covered in the [Control Flow](./control_flow) and +[Constrain Statement](./constrain) sections. + +### Strings + +The string type is a fixed length value defined with `str`. + +You can use strings in `constrain` statements, `assert()` functions or print them with +`std::println()`. + +```rust +fn main(message : pub str<11>, hex_as_string : str<4>) { + std::println(message); + assert(message == "hello world"); + assert(hex_as_string == "0x41"); +} +``` + +## Compound Types + +A compound type groups together multiple values into one type. Elements within a compound type can +be private or public. + +### Arrays + +An array is one way of grouping together values into one compound type. Array types can be inferred +or explicitly specified via the syntax `[; ]`: + +```rust +fn main(x : Field, y : Field) { + let my_arr = [x, y]; + let your_arr: [Field; 2] = [x, y]; +} +``` + +Here, both `my_arr` and `your_arr` are instantiated as an array containing two `Field` elements. + +Array elements can be accessed using indexing: + +```rust +fn main() { + let a = [1, 2, 3, 4, 5]; + + let first = a[0]; + let second = a[1]; +} +``` + +All elements in an array must be of the same type (i.e. homogeneous). That is, an array cannot group +a `Field` value and a `u8` value together for example. + +You can write mutable arrays, like: + +```rust +fn main() { + let mut arr = [1, 2, 3, 4, 5]; + assert(arr[0] == 1); + + arr[0] = 42; + assert(arr[0] == 42); +} +``` + +#### Types + +You can create arrays of primitive types or structs. There is not yet support for nested arrays +(arrays of arrays) or arrays of structs that contain arrays. + +### Tuples + +A tuple collects multiple values like an array, but with the added ability to collect values of +different types: + +```rust +fn main() { + let tup: (u8, u64, Field) = (255, 500, 1000); +} +``` + +One way to access tuple elements is via destructuring using pattern matching: + +```rust +fn main() { + let tup = (1, 2); + + let (one, two) = tup; + + let three = one + two; +} +``` + +Another way to access tuple elements is via direct member access, using a period (`.`) followed by +the index of the element we want to access. Index `0` corresponds to the first tuple element, `1` to +the second and so on: + +```rust +fn main() { + let tup = (5, 6, 7, 8); + + let five = tup.0; + let eight = tup.3; +} +``` + +### Structs + +A struct also allows for grouping multiple values of different types. Unlike tuples, we can also +name each field. + +> **Note:** The usage of _field_ here refers to each element of the struct and is unrelated to the +> field type of Noir. + +Defining a struct requires giving it a name and listing each field within as `: ` pairs: + +```rust +struct Animal { + hands: Field, + legs: Field, + eyes: u8, +} +``` + +An instance of a struct can then be created with actual values in `: ` pairs in any +order. Struct fields are accessible using their given names: + +```rust +fn main() { + let legs = 4; + + let dog = Animal { + eyes: 2, + hands: 0, + legs, + }; + + let zero = dog.hands; +} +``` + +Structs can also be destructured in a pattern, binding each field to a new variable: + +```rust +fn main() { + let Animal { hands, legs: feet, eyes } = get_octopus(); + + let ten = hands + feet + eyes as u8; +} + +fn get_octopus() -> Animal { + let octopus = Animal { + hands: 0, + legs: 8, + eyes: 2, + }; + + octopus +} +``` + +The new variables can be bound with names different from the original struct field names, as +showcased in the `legs --> feet` binding in the example above. + +:::note +You can use Structs as inputs to the `main` function, but you can't output them +::: diff --git a/noir/docs/versioned_docs/version-v0.6.0/language_concepts/01_functions.md b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/01_functions.md new file mode 100644 index 00000000000..c4bc0545a1c --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/01_functions.md @@ -0,0 +1,88 @@ +--- +title: Functions +description: + Learn how to declare functions and methods in Noir, a programming language with Rust semantics. + This guide covers parameter declaration, return types, call expressions, and more. +keywords: [Noir, Rust, functions, methods, parameter declaration, return types, call expressions] +--- + +Functions in Noir follow the same semantics of Rust, though Noir does not support early returns. + +To declare a function the `fn` keyword is used. + +```rust +fn foo() {} +``` + +All parameters in a function must have a type and all types are known at compile time. The parameter +is pre-pended with a colon and the parameter type. Multiple parameters are separated using a comma. + +```rust +fn foo(x : Field, y : pub Field){} +``` + +The return type of a function can be stated by using the `->` arrow notation. The function below +states that the foo function must return a `Field`. If the function returns no value, then the arrow +is omitted. + +```rust +fn foo(x : Field, y : pub Field) -> Field { + x + y +} +``` + +Note that a `return` keyword is unneeded in this case - the last expression in a function's body is +returned. + +## Call Expressions + +Calling a function in Noir is executed by using the function name and passing in the necessary +arguments. + +Below we show how to call the `foo` function from the `main` function using a call expression: + +```rust +fn main(x : Field, y : Field) { + let z = foo(x); +} + +fn foo(x : Field) -> Field { + x + x +} +``` + +## Methods + +You can define methods in Noir on any struct type in scope. + +```rust +struct MyStruct { + foo: Field, + bar: Field, +} + +impl MyStruct { + fn new(foo: Field) -> MyStruct { + MyStruct { + foo, + bar: 2, + } + } + + fn sum(self) -> Field { + self.foo + self.bar + } +} + +fn main() { + let s = MyStruct::new(40); + constrain s.sum() == 42; +} +``` + +Methods are just syntactic sugar for functions, so if we wanted to we could also call `sum` as +follows: + +```rust +constrain MyStruct::sum(s) == 42 +``` diff --git a/noir/docs/versioned_docs/version-v0.6.0/language_concepts/02_control_flow.md b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/02_control_flow.md new file mode 100644 index 00000000000..29108dd2634 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/02_control_flow.md @@ -0,0 +1,42 @@ +--- +title: Control Flow +description: + Learn how to use loops and if expressions in the Noir programming language. Discover the syntax + and examples for for loops and if-else statements. +keywords: [Noir programming language, loops, for loop, if-else statements, Rust syntax] +--- + +## Loops + +Noir has one kind of loop: the `for` loop. `for` loops allow you to repeat a block of code multiple +times. + +The following block of code between the braces is run 10 times. + +```rust +for i in 0..10 { + // do something +}; +``` + +## If Expressions + +Noir supports `if-else` statements. The syntax is most similar to Rust's where it is not required +for the statement's conditonal to be surrounded by parentheses. + +```rust +let a = 0; +let mut x: u32 = 0; + +if a == 0 { + if a != 0 { + x = 6; + } else { + x = 2; + } +} else { + x = 5; + constrain x == 5; +} +constrain x == 2; +``` diff --git a/noir/docs/versioned_docs/version-v0.6.0/language_concepts/03_ops.md b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/03_ops.md new file mode 100644 index 00000000000..d08df2094a5 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/03_ops.md @@ -0,0 +1,96 @@ +--- +title: Logical Operations +description: + Learn about the supported arithmetic and logical operations in the Noir programming language. + Discover how to perform operations on private input types, integers, and booleans. +keywords: + [ + Noir programming language, + supported operations, + arithmetic operations, + logical operations, + predicate operators, + bitwise operations, + short-circuiting, + backend, + ] +--- + +# Operations + +## Table of Supported Operations + +| Operation | Description | Requirements | +| :-------- | :------------------------------------------------------------: | -------------------------------------: | +| + | Adds two private input types together | Types must be private input | +| - | Subtracts two private input types together | Types must be private input | +| \* | Multiplies two private input types together | Types must be private input | +| / | Divides two private input types together | Types must be private input | +| ^ | XOR two private input types together | Types must be integer | +| & | AND two private input types together | Types must be integer | +| << | Left shift an integer by another integer amount | Types must be integer | +| >> | Right shift an integer by another integer amount | Types must be integer | +| ! | Bitwise not of a value | Type must be integer or boolean | +| < | returns a bool if one value is less than the other | Upper bound must have a known bit size | +| <= | returns a bool if one value is less than or equal to the other | Upper bound must have a known bit size | +| > | returns a bool if one value is more than the other | Upper bound must have a known bit size | +| >= | returns a bool if one value is more than or equal to the other | Upper bound must have a known bit size | +| == | returns a bool if one value is equal to the other | Both types must not be constants | +| != | returns a bool if one value is not equal to the other | Both types must not be constants | + +### Predicate Operators + +`<,<=, !=, == , >, >=` are known as predicate/comparison operations because they compare two values. +This differs from the operations such as `+` where the operands are used in _computation_. + +### Bitwise Operations Example + +```rust +fn main(x : Field) { + let y = x as u32; + let z = y & y; +} +``` + +`z` is implicitly constrained to be the result of `y & y`. The `&` operand is used to denote bitwise +`&`. + +> `x & x` would not compile as `x` is a `Field` and not an integer type. + +### Logical Operators + +Noir has no support for the logical operators `||` and `&&`. This is because encoding the +short-circuiting that these operators require can be inefficient for Noir's backend. Instead you can +use the bitwise operators `|` and `&` which operate indentically for booleans, just without the +short-circuiting. + +```rust +let my_val = 5; + +let mut flag = 1; +if (my_val > 6) | (my_val == 0) { + flag = 0; +} +constrain flag == 1; + +if (my_val != 10) & (my_val < 50) { + flag = 0; +} +constrain flag == 0; +``` + +### Shorthand operators + +Noir shorthand operators for most of the above operators, namely `+=, -=, *=, /=, %=, &=, |=, ^=, <<=`, and `>>=`. These allow for more concise syntax. For example: + +```rust +let mut i = 0; +i = i + 1; +``` + +could be written as: + +```rust +let mut i = 0; +i += 1; +``` diff --git a/noir/docs/versioned_docs/version-v0.6.0/language_concepts/04_assert.md b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/04_assert.md new file mode 100644 index 00000000000..a25a946123d --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/04_assert.md @@ -0,0 +1,34 @@ +--- +title: Assert Function +description: + Learn about the assert function in Noir, which can be used to explicitly constrain the predicate or + comparison expression that follows to be true, and what happens if the expression is false at + runtime. +keywords: [Noir programming language, assert statement, predicate expression, comparison expression] +--- + +Noir includes a special `assert` function which will explicitly constrain the predicate/comparison +expression that follows to be true. If this expression is false at runtime, the program will fail to +be proven. + +### Example + +```rust +fn main(x : Field, y : Field) { + assert(x == y); +} +``` + +The above snippet compiles because `==` is a predicate operation. Conversely, the following will not +compile: + +```rust +// INCORRECT + +fn main(x : Field, y : Field) { + assert(x + y); +} +``` + +> The rationale behind this not compiling is due to ambiguity. It is not clear if the above should +> equate to `x + y == 0` or if it should check the truthiness of the result. diff --git a/noir/docs/versioned_docs/version-v0.6.0/language_concepts/05_constrain.md b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/05_constrain.md new file mode 100644 index 00000000000..9ba1e260e8c --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/05_constrain.md @@ -0,0 +1,40 @@ +--- +title: Constrain Statements +description: + Learn about the constrain keyword in Noir, which can be used to explicitly constrain the predicate + or comparison expression that follows to be true, and what happens if the expression is false at + runtime. +keywords: + [Noir programming language, constrain statement, predicate expression, comparison expression] +--- + +:::danger + +In versions >=0.5.0 use the [`assert`](./04_assert.md) syntax. The `constrain` statement will be +maintained for some time for backwards compatibility but will be deprecated in the future. + +::: + +Noir includes a special keyword `constrain` which will explicitly constrain the predicate/comparison +expression that follows to be true. If this expression is false at runtime, the program will fail to +be proven. + +### Constrain statement example + +```rust +fn main(x : Field, y : Field) { + constrain x == y; +} +``` + +The above snippet compiles because `==` is a predicate operation. Conversely, the following will not +compile: + +```rust +fn main(x : Field, y : Field) { + constrain x + y; +} +``` + +> The rationale behind this not compiling is due to ambiguity. It is not clear if the above should +> equate to `x + y == 0` or if it should check the truthiness of the result. diff --git a/noir/docs/versioned_docs/version-v0.6.0/language_concepts/06_generics.md b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/06_generics.md new file mode 100644 index 00000000000..4d6c01fd797 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/06_generics.md @@ -0,0 +1,116 @@ +--- +title: Generics +description: + Learn how to use Generics in Noir +keywords: [Noir, Rust, generics, functions, structs] +--- + +# Generics + +Generics allow you to use the same functions with multiple different concrete data types. You can +read more about the concept of generics in the Rust documentation +[here](https://doc.rust-lang.org/book/ch10-01-syntax.html). + +Here is a trivial example showing the identity function that supports any type. In Rust, it is +common to refer to the most general type as `T`. We follow the same convention in Noir. + +```rust +fn id(x: T) -> T { + x +} +``` + +## In Structs + +Generics are useful for specifying types in structs. For example, we can specify that a field in a +struct will be of a certain generic type. In this case `value` is of type `T`. + +```rust +struct RepeatedValue { + value: T, + count: comptime Field, +} + +impl RepeatedValue { + fn new(value: T) -> Self { + Self { value, count: 1 } + } + + fn increment(mut repeated: Self) -> Self { + repeated.count += 1; + repeated + } + + fn print(self) { + for _i in 0 .. self.count { + dep::std::println(self.value); + } + } +} + +fn main() { + let mut repeated = RepeatedValue::new("Hello!"); + repeated = repeated.increment(); + repeated.print(); +} +``` + +The `print` function will print `Hello!` an arbitrary number of times, twice in this case. + +If we want to be generic over array lengths (which are type-level integers), we can use numeric +generics. Using these looks just like using regular generics, but these generics can resolve to +integers at compile-time, rather than resolving to types. Here's an example of a struct that is +generic over the size of the array it contains internally: + +```rust +struct BigInt { + limbs: [u32; N], +} + +impl BigInt { + // `N` is in scope of all methods in the impl + fn first(first: BigInt, second: BigInt) -> Self { + constrain first.limbs != second.limbs; + first + + fn second(first: BigInt, second: Self) -> Self { + constrain first.limbs != second.limbs; + second + } +} +``` + +## Calling functions on generic parameters + +Unlike Rust, Noir does not have traits, so how can one translate the equivalent of a trait bound in +Rust into Noir? That is, how can we write a function that is generic over some type `T`, while also +requiring there is a function like `eq: fn(T, T) -> bool` that works on the type? + +The answer is that we can translate this by passing in the function manually. Here's an example of +implementing array equality in Noir: + +```rust +fn array_eq(array1: [T; N], array2: [T; N], elem_eq: fn(T, T) -> bool) -> bool { + if array1.len() != array2.len() { + false + } else { + let mut result = true; + for i in 0 .. array1.len() { + result &= elem_eq(array1[i], array2[i]); + } + result + } +} + +fn main() { + constrain array_eq([1, 2, 3], [1, 2, 3], |a, b| a == b); + + // We can use array_eq even for arrays of structs, as long as we have + // an equality function for these structs we can pass in + let array = [MyStruct::new(), MyStruct::new()]; + constrain array_eq(array, array, MyStruct::eq); +} +``` + +You can see an example of generics in the tests +[here](https://github.com/noir-lang/noir/blob/master/tooling/nargo_cli/tests/execution_success/generics/src/main.nr). diff --git a/noir/docs/versioned_docs/version-v0.6.0/language_concepts/07_mutability.md b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/07_mutability.md new file mode 100644 index 00000000000..c8ccb4f8b9f --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/07_mutability.md @@ -0,0 +1,118 @@ +--- +title: Mutability +description: + Learn about mutable variables, constants, and globals in Noir programming language. Discover how + to declare, modify, and use them in your programs. +keywords: [noir programming language, mutability in noir, mutable variables, constants, globals] +--- + +# Mutability + +Variables in noir can be declared mutable via the `mut` keyword. Mutable variables can be reassigned +to via an assignment expression. + +```rust +let x = 2; +x = 3; // error: x must be mutable to be assigned to + +let mut y = 3; +let y = 4; // OK +``` + +The `mut` modifier can also apply to patterns: + +```rust +let (a, mut b) = (1, 2); +a = 11; // error: a must be mutable to be assigned to +b = 12; // OK + +let mut (c, d) = (3, 4); +c = 13; // OK +d = 14; // OK + +// etc. +let MyStruct { x: mut y } = MyStruct { x: a } +// y is now in scope +``` + +Note that mutability in noir is local and everything is passed by value, so if a called function +mutates its parameters then the parent function will keep the old value of the parameters. + +```rust +fn main() -> Field { + let x = 3; + helper(x); + x // x is still 3 +} + +fn helper(mut x: i32) { + x = 4; +} +``` + +## Comptime values + +Comptime value are values that are known at compile-time. This is different to a witness +which changes per proof. If a comptime value that is being used in your program is changed, then your +circuit will also change. + +Below we show how to declare a comptime value: + +```rust +fn main() { + let a: comptime Field = 5; + + // `comptime Field` can also be inferred: + let a = 5; +} +``` + +Note that variables declared as mutable may not be comptime: + +```rust +fn main() { + // error: Cannot mark a comptime type as mutable + let mut a: comptime Field = 5; + + // a inferred as a private Field here + let mut a = 5; +} +``` + +## Globals + +Noir also supports global variables. However, they must be compile-time variables. If `comptime` is +not explicitly written in the type annotation the compiler will implicitly specify the declaration +as compile-time. They can then be used like any other compile-time variable inside functions. The +global type can also be inferred by the compiler entirely. Globals can also be used to specify array +annotations for function parameters and can be imported from submodules. + +```rust +global N: Field = 5; // Same as `global N: comptime Field = 5` + +fn main(x : Field, y : [Field; N]) { + let res = x * N; + + constrain res == y[0]; + + let res2 = x * mysubmodule::N; + constrain res != res2; +} + +mod mysubmodule { + use dep::std; + + global N: Field = 10; + + fn my_helper() -> comptime Field { + let x = N; + x + } +} +``` + +## Why only local mutability? + +Witnesses in a proving system are immutable in nature. Noir aims to _closely_ mirror this setting +without applying additional overhead to the user. Modeling a mutable reference is not as +straightforward as on conventional architectures and would incur some possibly unexpected overhead. diff --git a/noir/docs/versioned_docs/version-v0.6.0/language_concepts/08_comments.md b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/08_comments.md new file mode 100644 index 00000000000..5b1d9fa38f2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/08_comments.md @@ -0,0 +1,27 @@ +--- +title: Comments +description: + Learn how to write comments in Noir programming language. A comment is a line of code that is + ignored by the compiler, but it can be read by programmers. Single-line and multi-line comments + are supported in Noir. +keywords: [Noir programming language, comments, single-line comments, multi-line comments] +--- + +A comment is a line in your codebase which the compiler ignores, however it can be read by +programmers. + +Here is a single line comment: + +```rust +// This is a comment and is ignored +``` + +`//` is used to tell the compiler to ignore the rest of the line. + +Noir doesn't have multi-line comments, but you can emulate them via using `//` on each line + +```rust +// This is a multi line +// comment, that is ignored by +// the compiler +``` diff --git a/noir/docs/versioned_docs/version-v0.6.0/language_concepts/09_distinct.md b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/09_distinct.md new file mode 100644 index 00000000000..03759d4bb4a --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/language_concepts/09_distinct.md @@ -0,0 +1,63 @@ +--- +title: Distinct Witnesses +--- + +The `distinct` keyword prevents repetitions of witness indices in the program's ABI. This ensures +that the witnesses being returned as public inputs are all unique. + +The `distinct` keyword is only used for return values on program entry points (usually the `main()` +function). + +When using `disctinct` and `pub` simultaneously, `distinct` comes first. See the example below. + +You can read more about the problem this solves +[here](https://github.com/noir-lang/noir/issues/1183). + +## Example + +Without the `distinct` keyword, the following program + +```rust +fn main(x : pub Field, y : pub Field) -> pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + "return_witnesses": [3, 2, 4, 4] + } +} +``` + +Whereas (with the `distinct` keyword) + +```rust +fn main(x : pub Field, y : pub Field) -> distinct pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + //... + "return_witnesses": [3, 4, 5, 6] + } +} +``` diff --git a/noir/docs/versioned_docs/version-v0.6.0/modules_packages_crates/crates_and_packages.md b/noir/docs/versioned_docs/version-v0.6.0/modules_packages_crates/crates_and_packages.md new file mode 100644 index 00000000000..34f28a71148 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/modules_packages_crates/crates_and_packages.md @@ -0,0 +1,35 @@ +--- +title: Crates and Packages +description: + Learn how to use Crates and Packages in your Noir project +keywords: [Nargo, dependencies, package management, crates, package] +--- + +## Crates + +A crate is the smallest amount of code that the Noir compiler considers at a time. +Crates can contain modules, and the modules may be defined in other files that get compiled with the crate, as we’ll see in the coming sections. + +### Crate Types + +A Noir crate can come in one of two forms: a binary crate or a library crate. + +_Binary crates_ are programs which you can compile to an ACIR circuit which you can then create proofs against. Each must have a function called `main` that defines the ACIR circuit which is to be proved. + +_Library crates_ don't have a `main` function and they don't compile down to ACIR. Instead they define functionality intended to be shared with multiple projects, and eventually included in a binary crate. + +### Crate Root + +Every crate has a root, which is the source file that the compiler starts, this is also known as the root module. The Noir compiler does not enforce any conditions on the name of the file which is the crate root, however if you are compiling via Nargo the crate root must be called `lib.nr` or `main.nr` for library or binary crates respectively. + +## Packages + +A Nargo _package_ is a collection of one of more crates that provides a set of functionality. A package must include a Nargo.toml file. + +A package _must_ contain either a library or a binary crate, but not both. + +### Differences from Cargo Packages + +One notable difference between Rust's Cargo and Noir's Nargo is that while Cargo allows a package to contain an unlimited number of binary crates and a single library crate, Nargo currently only allows a package to contain a single crate. + +In future this restriction may be lifted to allow a Nargo package to contain both a binary and library crate or multiple binary crates. diff --git a/noir/docs/versioned_docs/version-v0.6.0/modules_packages_crates/dependencies.md b/noir/docs/versioned_docs/version-v0.6.0/modules_packages_crates/dependencies.md new file mode 100644 index 00000000000..f3b40eb849b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/modules_packages_crates/dependencies.md @@ -0,0 +1,85 @@ +--- +title: Managing Dependencies +description: + Learn how to specify and manage dependencies in Nargo, allowing you to upload packages to GitHub + and use them easily in your project. +keywords: [Nargo, dependencies, GitHub, package management, versioning] +--- + +Nargo allows you to upload packages to GitHub and use them as dependencies. + +## Specifying a dependency + +Specifying a dependency requires a tag to a specific commit and the git url to the url containing +the package. + +Currently, there are no requirements on the tag contents. If requirements are added, it would follow +semver 2.0 guidelines. + +> Note: Without a `tag` , there would be no versioning and dependencies would change each time you +> compile your project. + +For example, to add the [ecrecover-noir library](https://github.com/colinnielsen/ecrecover-noir) to your project, add it to `Nargo.toml`: + +```toml +# Nargo.toml + +[dependencies] +ecrecover = {tag = "v0.2.0", git = "https://github.com/colinnielsen/ecrecover-noir"} +``` + +## Specifying a local dependency + +You can also specify dependencies that are local to your machine. + +For example, this file structure has a library and binary crate + +``` +├── binary_crate +│   ├── Nargo.toml +│   └── src +│   └── main.nr +└── liba + ├── Nargo.toml + └── src + └── lib.nr +``` + +Inside of the binary crate, you can specify: + +```toml +# Nargo.toml + +[dependencies] +libA = { path = "../liba" } +``` + +## Importing dependencies + +You can import a dependency to a Noir file using the following syntax. For example, to import the +ecrecover-noir library and local liba referenced above: + +```rust +use dep::ecrecover; +use dep::libA; +``` + +You can also import only the specific parts of dependency that you want to use. For example, +demonstrated in the +[elliptic curve example](../standard_library/cryptographic_primitives/ec_primitives#examples), you +can import just the `Point` and the `Curve` that you want to use. + +```rust +use dep::std::ec::tecurve::affine::Curve; +use dep::std::ec::tecurve::affine::Point; +``` + +## Available Libraries + +Noir does not currently have an official package manager. You can find a list of available Noir libraries in the [awesome-noir repo here](https://github.com/noir-lang/awesome-noir#libraries). + +Some libraries that are available today include: + +- BigInt +- "`ecrecover`" +- sparse merkle tree verifier diff --git a/noir/docs/versioned_docs/version-v0.6.0/modules_packages_crates/modules.md b/noir/docs/versioned_docs/version-v0.6.0/modules_packages_crates/modules.md new file mode 100644 index 00000000000..e429b336511 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/modules_packages_crates/modules.md @@ -0,0 +1,104 @@ +--- +title: Understanding Modules +description: + Learn how to organize your files using modules in Noir, following the same convention as Rust's + module system. Examples included. +keywords: [Noir, Rust, modules, organizing files, sub-modules] +--- + +# Modules + +Noir's module system follows the same convention as the _newer_ version of Rust's module system. + +## Purpose of Modules + +Modules are used to organise files. Without modules all of your code would need to live in a single +file. In Noir, the compiler does not automatically scan all of your files to detect modules. This +must be done explicitly by the developer. + +## Examples + +### Importing a module in the crate root + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::hello_world(); +} +``` + +Filename : `src/foo.nr` + +```rust +fn from_foo() {} +``` + +In the above snippet, the crate root is the `src/main.nr` file. The compiler sees the module +declaration `mod foo` which prompts it to look for a foo.nr file. + +Visually this module hierarchy looks like the following : + +``` +crate + ├── main + │ + └── foo + └── from_foo + +``` + +### Importing a module throughout the tree +All modules are accessible from the ``crate::`` namespace. + +``` +crate + ├── bar + ├── foo + └── main + +``` +In the above snippet, if ``bar`` would like to use functions in ``foo``, it can do so by ``use crate::foo::function_name``. + +### Sub-modules + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::from_foo(); +} +``` + +Filename : `src/foo.nr` + +```rust +mod bar; +fn from_foo() {} +``` + +Filename : `src/foo/bar.nr` + +```rust +fn from_bar() {} +``` + +In the above snippet, we have added an extra module to the module tree; `bar`. `bar` is a submodule +of `foo` hence we declare bar in `foo.nr` with `mod bar`. Since `foo` is not the crate root, the +compiler looks for the file associated with the `bar` module in `src/foo/bar.nr` + +Visually the module hierarchy looks as follows: + +``` +crate + ├── main + │ + └── foo + ├── from_foo + └── bar + └── from_bar +``` diff --git a/noir/docs/versioned_docs/version-v0.6.0/nargo/01_commands.md b/noir/docs/versioned_docs/version-v0.6.0/nargo/01_commands.md new file mode 100644 index 00000000000..f9d9ddb77ff --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/nargo/01_commands.md @@ -0,0 +1,130 @@ +--- +title: Commands +description: + Noir CLI Commands for Noir Prover and Verifier to create, execute, prove and verify programs, + generate Solidity verifier smart contract and compile into JSON file containing ACIR + representation and ABI of circuit. +keywords: + [ + Nargo, + Noir CLI, + Noir Prover, + Noir Verifier, + generate Solidity verifier, + compile JSON file, + ACIR representation, + ABI of circuit, + TypeScript, + ] +--- + +## General options + +``` +Options: + -s, --show-ssa Emit debug information for the intermediate SSA IR + -d, --deny-warnings Quit execution when warnings are emitted + --show-output Display output of `println` statements during tests + -h, --help Print help +``` + +## `nargo help [subcommand]` + +Prints the list of available commands or specific information of a subcommand. + +_Arguments_ + +- `` - The subcommand whose help message to display + +## `nargo check` + +Generate the `Prover.toml` and `Verifier.toml` files for specifying prover and verifier in/output +values of the Noir program respectively. + +## `nargo codegen-verifier` + +Generate a Solidity verifier smart contract for the program. + +## `nargo compile ` + +Compile the program into a JSON build artifact file containing the ACIR representation and the ABI +of the circuit. This build artifact can then be used to generate and verify proofs. + +_Arguments_ + +- `` - The name of the circuit file + +_Options_ + +- `-c, --contracts` - Compile each contract function used within the program + +## `nargo new [path]` + +Creates a new Noir project. + +_Arguments_ + +- `` - Name of the package +- `[path]` - The path to save the new project + +## `nargo execute [witness_name]` + +Runs the Noir program and prints its return value. + +_Arguments_ + +- `[witness_name]` - The name of the witness + +_Usage_ + +The inputs to the circuit are read from the `Prover.toml` file generated by `nargo check`, which +must be filled in. + +To save the witness to file, run the command with a value for the `witness-name` argument. A +`.tr` file will then be saved in the `build` folder. + +> **Info:** The `.tr` file is the witness file. The witness file can be considered as program inputs +> parsed for your program's ACIR. +> +> This file can be passed along with circuit's ACIR into a TypeScript project for proving and +> verification. See the [TypeScript](../typescript#proving-and-verifying-externally-compiled-files) +> section to learn more. + +## `nargo prove ` + +Creates a proof for the program. + +_Arguments_ + +- `` - The name of the proof + +_Options_ + +- `-v, --verify` - Verify proof after proving + +## `nargo verify ` + +Given a proof and a program, verify whether the proof is valid. + +_Arguments_ + +- `` - The proof to verify + +## `nargo test ` + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. + +See an example on the [testing page](./testing). + +_Arguments_ + +- `` - a pattern to indicate to only run tests with names containing the pattern + +## `nargo gates` + +Counts the occurrences of different gates in circuit + +## `nargo print-acir` + +Print a compiled circuit to stdout such that the ACIR can be inspected. diff --git a/noir/docs/versioned_docs/version-v0.6.0/nargo/02_testing.md b/noir/docs/versioned_docs/version-v0.6.0/nargo/02_testing.md new file mode 100644 index 00000000000..73f91c72bfd --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/nargo/02_testing.md @@ -0,0 +1,32 @@ +--- +title: Testing in Noir +description: Learn how to use Nargo to test your Noir program in a quick and easy way +keywords: [Nargo, testing, Noir, compile, test] +--- + +You can test your Noir programs using Noir circuits. + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. + +For example if you have a program like: + +```rust +fn add(x: u64, y: u64) -> u64 { + x + y +} +#[test] +fn test_add() { + constrain add(2,2) == 4; + constrain add(0,1) == 1; + constrain add(1,0) == 1; +} +``` + +Running `nargo test` will test that the `test_add` function can be executed while satisfying the all +the contraints which allows you to test that add returns the expected values. Test functions can't +have any arguments currently. + +This is much faster compared to testing in Typescript but the only downside is that you can't +explicitly test that a certain set of inputs are invalid. i.e. you can't say that you want +add(2^64-1, 2^64-1) to fail. diff --git a/noir/docs/versioned_docs/version-v0.6.0/nargo/03_solidity_verifier.md b/noir/docs/versioned_docs/version-v0.6.0/nargo/03_solidity_verifier.md new file mode 100644 index 00000000000..69a5607f1b2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/nargo/03_solidity_verifier.md @@ -0,0 +1,116 @@ +--- +title: Solidity Verifier +description: + Learn how to run the verifier as a smart contract on the blockchain. Compile a Solidity verifier + contract for your Noir program and deploy it on any EVM blockchain acting as a verifier smart + contract. Read more to find out! +keywords: + [ + solidity verifier, + smart contract, + blockchain, + compiler, + plonk_vk.sol, + EVM blockchain, + verifying Noir programs, + proving backend, + Barretenberg, + ] +--- + +For certain applications, it may be desirable to run the verifier as a smart contract instead of on +a local machine. + +Compile a Solidity verifier contract for your Noir program by running: + +```sh +nargo codegen-verifier +``` + +A new `contract` folder would then be generated in your project directory, containing the Solidity +file `plonk_vk.sol`. It can be deployed on any EVM blockchain acting as a verifier smart contract. + +> **Note:** It is possible to compile verifier contracts of Noir programs for other smart contract +> platforms as long as the proving backend supplies an implementation. +> +> Barretenberg, the default proving backend for Nargo, supports compilation of verifier contracts in +> Solidity only for the time being. + +## Verify + +To verify a proof using the Solidity verifier contract, call the `verify` function with the +following signature: + +```solidity +function verify(bytes calldata _proof, bytes32[] calldata _publicInputs) external view returns (bool) +``` + +### Public Inputs + +:::tip + +A circuit doesn't have the concept of a return value. Return values are just syntactic sugar in +Noir. + +Under the hood, the return value is passed as an input to the circuit and is checked at the end of +the circuit program. + +::: + +The verifier contract uses the output (return) value of a Noir program as a public input. So if you +have the following function + +```rust +fn main( + // Public inputs + pubkey_x: pub Field, + pubkey_y: pub Field, + // Private inputs + priv_key: Field, +) -> pub Field +``` + +then `verify` in `plonk_vk.sol` will expect 3 public inputs. Passing two inputs will result in an +error like `Reason: PUBLIC_INPUT_COUNT_INVALID(3, 2)`. + +In this case the 3 inputs to `verify` would be ordered as `[pubkey_x, pubkey_y, return]`. + +#### Struct inputs + +Consider the following program: + +```rust +struct Type1 { + val1: Field, + val2: Field, +} + +struct Nested { + t1: Type1, + is_true: bool, +} + +fn main(x: pub Field, nested: pub Nested, y: pub Field) { + //... +} +``` + +Structs will be flattened so that the array of inputs is 1-dimensional array. The order of these inputs would be flattened to: `[x, nested.t1.val1, nested.t1.val2, nested.is_true, y]` + +## Noir for EVM chains + +You can currently deploy the Solidity verifier contracts to most EVM compatible chains. EVM chains that have been tested and are known to work include: + +- Optimism +- Arbitrum +- Polygon PoS +- Scroll +- Celo + +Other EVM chains should work, but have not been tested directly by our team. If you test any other chains, please open a PR on this page to update the list. See [this doc](https://github.com/noir-lang/noir-starter/tree/main/with-foundry#testing-on-chain) for more info about testing verifier contracts on different EVM chains. + +### Unsupported chains + +Unfortunately not all "EVM" chains are supported. + +**zkSync** and the **Polygon zkEVM** do *not* currently support proof verification via Solidity verifier contracts. They are missing the bn256 precompile contract that the verifier contract requires. Once these chains support this precompile, they may work. diff --git a/noir/docs/versioned_docs/version-v0.6.0/standard_library/array_methods.md b/noir/docs/versioned_docs/version-v0.6.0/standard_library/array_methods.md new file mode 100644 index 00000000000..701590ccf54 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/standard_library/array_methods.md @@ -0,0 +1,180 @@ +--- +title: Array Methods +description: + Learn about the commonly used methods available for arrays in Noir, including len, sort, fold, + reduce, all, and any. +keywords: [rust, array, methods, len, sort, fold, reduce, all, any] +--- + +# Array + +For convenience, the STD provides some ready-to-use, common methods for arrays[^migrationnote]: + +## len + +Returns the length of an array + +```rust +fn len(_array: [T; N]) -> comptime Field +``` + +example + +```rust +fn main() { + let array = [42, 42]; + constrain array.len() == 2; +} +``` + +## sort + +Returns a new sorted array. The original array remains untouched. Notice that this function will +only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting +logic it uses internally is optimized specifically for these values. If you need a sort function to +sort any type, you should use the function `sort_via` described below. + +```rust +fn sort(_array: [T; N]) -> [T; N] +``` + +example + +```rust +fn main() { + let arr = [42, 32] + let sorted = arr.sort(); + constrain sorted == [32, 42]; +} +``` + +## sort_via + +Sorts the array with a custom comparison function + +```rust +fn sort_via(mut a: [T; N], ordering: fn(T, T) -> bool) -> [T; N] +``` + +example + +```rust +fn main() { + let arr = [42, 32] + let sorted_ascending = arr.sort_via(|a, b| a < b); + constrain sorted_ascending == [32, 42]; // verifies + + let sorted_descending = arr.sort_via(|a, b| a > b); + constrain sorted_descending == [32, 42]; // does not verify +} +``` + +## map + +Applies a function to each element of the array, returning a new array containing the mapped elements. + +```rust +fn map(f: fn(T) -> U) -> [U; N] +``` + +example + +```rust +let a = [1, 2, 3]; +let b = a.map(|a| a * 2) // b is now [2, 4, 6] +``` + +## fold + +Applies a function to each element of the array, returning the final accumulated value. The first +parameter is the initial value. + +```rust +fn fold(mut accumulator: U, f: fn(U, T) -> U) -> U +``` + +This is a left fold, so the given function will be applied to the accumulator and first element of +the array, then the second, and so on. For a given call the expected result would be equivalent to: + +```rust +let a1 = [1]; +let a2 = [1, 2]; +let a3 = [1, 2, 3]; + +let f = |a, b| a - b; +a1.fold(10, f) //=> f(10, 1) +a2.fold(10, f) //=> f(f(10, 1), 2) +a3.fold(10, f) //=> f(f(f(10, 1), 2), 3) +``` + +example: + +```rust + +fn main() { + let arr = [2,2,2,2,2] + let folded = arr.fold(0, |a, b| a + b); + constrain folded == 10; +} + +``` + +## reduce + +Same as fold, but uses the first element as starting element. + +```rust +fn reduce(f: fn(T, T) -> T) -> T +``` + +example: + +```rust +fn main() { + let arr = [2,2,2,2,2] + let reduced = arr.reduce(|a, b| a + b); + constrain reduced == 10; +} +``` + +## all + +Returns true if all the elements satisfy the given predicate + +```rust +fn all(predicate: fn(T) -> bool) -> bool +``` + +example: + +```rust +fn main() { + let arr = [2,2,2,2,2] + let all = arr.all(|a| a == 2); + constrain all; +} +``` + +## any + +Returns true if any of the elements satisfy the given predicate + +```rust +fn any(predicate: fn(T) -> bool) -> bool +``` + +example: + +```rust +fn main() { + let arr = [2,2,2,2,5] + let any = arr.any(|a| a == 5); + constrain any; +} + +``` + +[^migrationnote]: + Migration Note: These methods were previously free functions, called via `std::array::len()`. + For the sake of ease of use and readability, these functions are now methods and the old syntax + for them is now deprecated. diff --git a/noir/docs/versioned_docs/version-v0.6.0/standard_library/black_box_fns.md b/noir/docs/versioned_docs/version-v0.6.0/standard_library/black_box_fns.md new file mode 100644 index 00000000000..3063e71c147 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/standard_library/black_box_fns.md @@ -0,0 +1,44 @@ +--- +title: Black Box Functions +description: Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. +keywords: [noir, black box functions] +--- + +Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. This makes certain zk-snark unfriendly computations cheaper than if they were implemented in Noir. + +:::warning + +It is likely that not all backends will support a particular black box function. + +::: + +Because it is not guaranteed that all backends will support black box functions, it is possible that certain Noir programs won't compile against a particular backend if they use an unsupported black box function. It is possible to fallback to less efficient implementations written in Noir/ACIR in some cases. + +Black box functions are specified with the `#[foreign(black_box_fn)]` attribute. For example, the SHA256 function in the Noir [source code](https://github.com/noir-lang/noir/blob/v0.5.1/noir_stdlib/src/hash.nr) looks like: + +```rust +#[foreign(sha256)] +fn sha256(_input : [u8; N]) -> [u8; 32] {} +``` + +## Function list + +Here is a list of the current black box functions that are supported by UltraPlonk: + +- AES +- [SHA256](./cryptographic_primitives/hashes#sha256) +- [Schnorr signature verification](./cryptographic_primitives/schnorr) +- [Blake2s](./cryptographic_primitives/hashes#blake2s) +- [Pedersen](./cryptographic_primitives/hashes#pedersen) +- [HashToField128Security](./cryptographic_primitives/hashes#hash_to_field) +- [ECDSA signature verification](./cryptographic_primitives/ecdsa_secp256k1) +- [Fixed base scalar multiplication](./cryptographic_primitives/scalar) +- [Compute merkle root](./merkle_trees#compute_merkle_root) +- AND +- XOR +- RANGE +- [Keccak256](./cryptographic_primitives/hashes#keccak256) + +Most black box functions are included as part of the Noir standard library, however `AND`, `XOR` and `RANGE` are used as part of the Noir language syntax. For instance, using the bitwise operator `&` will invoke the `AND` black box function. To ensure compatibility across backends, the ACVM has fallback implementations of `AND`, `XOR` and `RANGE` defined in its standard library which it can seamlessly fallback to if the backend doesn't support them. + +You can view the black box functions defined in the ACVM code [here](https://github.com/noir-lang/acvm/blob/acir-v0.12.0/acir/src/circuit/black_box_functions.rs). diff --git a/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives.md b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives.md new file mode 100644 index 00000000000..2df4f929474 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives.md @@ -0,0 +1,14 @@ +--- +title: Cryptographic primitives in Noir +description: + Learn about the cryptographic primitives ready to use for any Noir project +keywords: + [ + cryptographic primitives, + Noir project, + ] +--- + +The Noir team is progressively adding new cryptographic primitives to the standard library. Reach out for news or if you would be interested in adding more of these calculations in Noir. + +Some methods are available thanks to the Aztec backend, not being performed using Noir. When using other backends, these methods may or may not be supplied. diff --git a/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/00_hashes.mdx b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/00_hashes.mdx new file mode 100644 index 00000000000..c373f10ca6a --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/00_hashes.mdx @@ -0,0 +1,149 @@ +--- +title: Hash methods +description: + Learn about the cryptographic primitives ready to use for any Noir project, including sha256, blake2s, pedersen, mimc_bn254 and mimc +keywords: + [ + cryptographic primitives, + Noir project, + sha256, + blake2s, + pedersen, + mimc_bn254, + mimc, + hash + ] +--- + +import BlackBoxInfo from './common/\_blackbox.mdx'; + +## sha256 + +Given an array of bytes, returns the resulting sha256 hash. + +```rust +fn sha256(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let hash = std::hash::sha256(x); +} +``` + + + +## blake2s + +Given an array of bytes, returns an array with the Blake2 hash + +```rust +fn blake2s(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let hash = std::hash::blake2s(x); +} +``` + + + +## pedersen + +Given an array of Fields, returns the Pedersen hash. + +```rust +fn pedersen(_input : [Field]) -> [Field; 2] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let hash = std::hash::pedersen(x); +} +``` + + + +## keccak256 + +Given an array of bytes (`u8`), returns the resulting keccak hash as an array of 32 bytes (`[u8; 32]`). + +```rust +fn keccak256(_input : [u8; N]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let hash = std::hash::keccak256(x); +} +``` + + + +## poseidon + +Given an array of Fields, returns a new Field with the Poseidon Hash. Mind that you need to specify how many inputs are there to your Poseidon function. + +```rust +// example for hash_1, hash_2 accepts an array of length 2, etc +fn hash_1(input: [Field; 1]) -> Field +``` + +example: + +```rust +fn main() +{ + let hash1 = std::hash::poseidon::bn254::hash_2([1, 2]); + constrain hash1 == 0x115cc0f5e7d690413df64c6b9662e9cf2a3617f2743245519e19607a4417189a; +} +``` + +## mimc_bn254 and mimc + +`mimc_bn254` is `mimc`, but with hardcoded parameters for the BN254 curve. You can use it by +providing an array of Fields, and it returns a Field with the hash. You can use the `mimc` method if +you're willing to input your own constants: + +```rust +fn mimc(x: Field, k: Field, constants: [Field; N], exp : Field) -> Field +``` + +otherwise, use the `mimc_bn254` method: + +```rust +fn mimc_bn254(array: [Field; N]) -> Field +``` + +example: + +```rust + +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let hash = std::hash::mimc_bn254(x); +} +``` + +## hash_to_field + +```rust +fn hash_to_field(_input : [Field; N]) -> Field {} +``` + +Calculates the `blake2s` hash of the inputs and returns the hash modulo the field modulus to return a value which can be represented as a `Field`. + + diff --git a/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/01_scalar.mdx b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/01_scalar.mdx new file mode 100644 index 00000000000..62265cddb1e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/01_scalar.mdx @@ -0,0 +1,33 @@ +--- +title: Scalar multiplication +description: + See how you can perform scalar multiplications over a fixed base in Noir +keywords: + [ + cryptographic primitives, + Noir project, + scalar multiplication, + ] +--- + +import BlackBoxInfo from './common/\_blackbox.mdx'; + +## scalar_mul::fixed_base + +Performs scalar multiplication over the embedded curve whose coordinates are defined by the +configured noir field. For the BN254 scalar field, this is BabyJubJub or Grumpkin. + +```rust +fn fixed_base(_input : Field) -> [Field; 2] +``` + +example + +```rust +fn main(x : Field) { + let scal = std::scalar_mul::fixed_base(x); + std::println(scal); +} +``` + + diff --git a/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/02_schnorr.mdx b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/02_schnorr.mdx new file mode 100644 index 00000000000..c2c6f3ae19a --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/02_schnorr.mdx @@ -0,0 +1,37 @@ +--- +title: Schnorr Signatures +description: Learn how you can verify Schnorr signatures using Noir +keywords: [cryptographic primitives, Noir project, schnorr, signatures] +--- + +import BlackBoxInfo from './common/\_blackbox.mdx'; + +## schnorr::verify_signature + +Verifier for Schnorr signatures over the embedded curve (for BN254 it is Grumpkin). + +```rust +fn verify_signature(_public_key_x: Field, _public_key_y: Field, _signature: [u8; 64], _message: [u8]) -> Field +``` + +where `_signature` can be generated like so using the npm package +[@noir-lang/barretenberg](https://www.npmjs.com/package/@noir-lang/barretenberg) + +```js +const { BarretenbergWasm } = require('@noir-lang/barretenberg/dest/wasm'); +const { Schnorr } = require('@noir-lang/barretenberg/dest/crypto/schnorr'); + +... + +const barretenberg = await BarretenbergWasm.new(); +const schnorr = new Schnorr(barretenberg); +const pubKey = schnorr.computePublicKey(privateKey); +const message = ... +const signature = Array.from( + schnorr.constructSignature(hash, privateKey).toBuffer() +); + +... +``` + + diff --git a/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/03_ecdsa_secp256k1.mdx b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/03_ecdsa_secp256k1.mdx new file mode 100644 index 00000000000..88892712fb6 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/03_ecdsa_secp256k1.mdx @@ -0,0 +1,25 @@ +--- +title: ECDSA Verification +description: + Learn about the cryptographic primitives regarding ECDSA over the secp256k1 curve +keywords: + [ + cryptographic primitives, + Noir project, + ecdsa, + secp256k1, + signatures, + ] +--- + +import BlackBoxInfo from './common/\_blackbox.mdx'; + +## ecdsa_secp256k1::verify_signature + +Verifier for ECDSA Secp256k1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> Field +``` + + diff --git a/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/04_ec_primitives.md b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/04_ec_primitives.md new file mode 100644 index 00000000000..2d4ed0f8fae --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/04_ec_primitives.md @@ -0,0 +1,102 @@ +--- +title: Elliptic Curve Primitives +keywords: [cryptographic primitives, Noir project] +--- + +Data structures and methods on them that allow you to carry out computations involving elliptic +curves over the (mathematical) field corresponding to `Field`. For the field currently at our +disposal, applications would involve a curve embedded in BN254, e.g. the +[Baby Jubjub curve](https://eips.ethereum.org/EIPS/eip-2494). + +## Data structures + +### Elliptic curve configurations + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Curve`), i.e. the specific elliptic +curve you want to use, which would be specified using any one of the methods +`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::new` which take the coefficients in the +defining equation together with a generator point as parameters. You can find more detail in the +comments in +[`noir_stdlib/src/ec.nr`](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr), but +the gist of it is that the elliptic curves of interest are usually expressed in one of the standard +forms implemented here (Twisted Edwards, Montgomery and Short Weierstraß), and in addition to that, +you could choose to use `affine` coordinates (Cartesian coordinates - the usual (x,y) - possibly +together with a point at infinity) or `curvegroup` coordinates (some form of projective coordinates +requiring more coordinates but allowing for more efficient implementations of elliptic curve +operations). Conversions between all of these forms are provided, and under the hood these +conversions are done whenever an operation is more efficient in a different representation (or a +mixed coordinate representation is employed). + +### Points + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Point`), i.e. points lying on the +elliptic curve. For a curve configuration `c` and a point `p`, it may be checked checked that `p` +does indeed lie on `c` by calling `c.contains(p1)`. + +## Methods + +(given a choice of curve representation, e.g. use `std::ec::tecurve::affine::Curve` and use +`std::ec::tecurve::affine::Point`) + +- The **zero element** is given by `Point::zero()`, and we can verify whether a point `p: Point` is + zero by calling `p.is_zero()`. +- **Equality**: Points `p1: Point` and `p2: Point` may be checked for equality by calling + `p1.eq(p2)`. +- **Addition**: For `c: Curve` and points `p1: Point` and `p2: Point` on the curve, adding these two + points is accomplished by calling `c.add(p1,p2)`. +- **Negation**: For a point `p: Point`, `p.negate()` is its negation. +- **Subtraction**: For `c` and `p1`, `p2` as above, subtracting `p2` from `p1` is accomplished by + calling `c.subtract(p1,p2)`. +- **Scalar multiplication**: For `c` as above, `p: Point` a point on the curve and `n: Field`, + scalar multiplication is given by `c.mul(n,p)`. If instead `n :: [u1; N]`, i.e. `n` is a bit + array, the `bit_mul` method may be used instead: `c.bit_mul(n,p)` +- **Multi-scalar multiplication**: For `c` as above and arrays `n: [Field; N]` and `p: [Point; N]`, + multi-scalar multiplication is given by `c.msm(n,p)`. +- **Coordinate representation conversions**: The `into_group` method converts a point or curve + configuration in the affine representation to one in the CurveGroup representation, and + `into_affine` goes in the other direction. +- **Curve representation conversions**: `tecurve` and `montcurve` curves and points are equivalent + and may be converted between one another by calling `into_montcurve` or `into_tecurve` on their + configurations or points. `swcurve` is more general and a curve c of one of the other two types + may be converted to this representation by calling `c.into_swcurve()`, whereas a point `p` lying + on the curve given by `c` may be mapped to its corresponding `swcurve` point by calling + `c.map_into_swcurve(p)`. +- **Map-to-curve methods**: The Elligator 2 method of mapping a field element `n: Field` into a + `tecurve` or `montcurve` with configuration `c` may be called as `c.elligator2_map(n)`. For all of + the curve configurations, the SWU map-to-curve method may be called as `c.swu_map(z,n)`, where + `z: Field` depends on `Field` and `c` and must be chosen by the user (the conditions it needs to + satisfy are specified in the comments + [here](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr)). + +## Examples + +The +[ec_baby_jubjub test](https://github.com/noir-lang/noir/blob/master/crates/nargo_cli/tests/test_data/ec_baby_jubjub/src/main.nr) +illustrates all of the above primitives on various forms of the Baby Jubjub curve. A couple of more +interesting examples in Noir would be: + +Public-key cryptography: Given an elliptic curve and a 'base point' on it, determine the public key +from the private key. This is a matter of using scalar multiplication. In the case of Baby Jubjub, +for example, this code would do: + +```rust +use dep::std::ec::tecurve::affine::Curve; +use dep::std::ec::tecurve::affine::Point; + +fn bjj_pub_key(priv_key: Field) -> Point +{ + + let bjj = Curve::new(168700, 168696, G::new(995203441582195749578291179787384436505546430278305826713579947235728471134,5472060717959818805561601436314318772137091100104008585924551046643952123905)); + + let base_pt = Point::new(5299619240641551281634865583518297030282874472190772894086521144482721001553, 16950150798460657717958625567821834550301663161624707787222815936182638968203); + + bjj.mul(priv_key,base_pt) +} +``` + +This would come in handy in a Merkle proof. + +- EdDSA signature verification: This is a matter of combining these primitives with a suitable hash + function. See + [feat(stdlib): EdDSA sig verification noir#1136](https://github.com/noir-lang/noir/pull/1136) for + the case of Baby Jubjub and the Poseidon hash function. diff --git a/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/common/_blackbox.mdx b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/common/_blackbox.mdx new file mode 100644 index 00000000000..9fe9b48fbff --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/standard_library/cryptographic_primitives/common/_blackbox.mdx @@ -0,0 +1,5 @@ +:::info + +This is a black box function. Read [this section](../black_box_fns) to learn more about black box functions in Noir. + +::: \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.6.0/standard_library/field_methods.md b/noir/docs/versioned_docs/version-v0.6.0/standard_library/field_methods.md new file mode 100644 index 00000000000..7cea9846102 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/standard_library/field_methods.md @@ -0,0 +1,149 @@ +--- +title: Field Methods +description: + Learn about common methods on Noir Field, including to_le_bits, to_le_bytes, to_le_radix, + to_be_radix, pow_32, etc, and see code examples. +keywords: + [ + Noir Field, + to_le_bits, + to_le_bytes, + to_le_radix, + to_be_radix, + pow_32, + Little Endian, + Big Endian, + Vector, + Exponent, + ] +--- + +After declaring a Field, you can use these common methods on it: + +## to_le_bits + +Transforms the field into an array of bits, Little Endian. + +```rust +fn to_le_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2 + let bits = field.to_le_bits(32); +} +``` + +## to_be_bits + +Transforms the field into an array of bits, Big Endian. + +```rust +fn to_be_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2 + let bits = field.to_be_bits(32); +} +``` + +## to_le_bytes + +Transforms into an array of bytes, Little Endian + +```rust +fn to_le_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2 + let bytes = field.to_le_bytes(4); +} +``` + +## to_be_bytes + +Transforms into an array of bytes, Big Endian + +```rust +fn to_be_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2 + let bytes = field.to_be_bytes(4); +} +``` + +## to_le_radix + +Decomposes into a vector over the specified base, Little Endian + +```rust +fn to_le_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2 + let radix = field.to_le_radix(256, 4); +} +``` + +## to_be_radix + +Decomposes into a vector over the specified base, Big Endian + +```rust +fn to_be_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2 + let radix = field.to_be_radix(256, 4); +} +``` + +## pow_32 + +Returns the value to the power of the specified exponent + +```rust +fn pow_32(self, exponent: Field) -> Field +``` + +example: + +```rust +fn main() { + let field = 2 + let pow = field.pow_32(4); + constrain pow == 16; +} +``` + +## sgn0 + +Parity of (prime) Field element, i.e. sgn0(x mod p) = 0 if x ∈ {0, ..., p-1} is even, otherwise sgn0(x mod p) = 1. + +```rust +fn sgn0(self) -> u1 +``` diff --git a/noir/docs/versioned_docs/version-v0.6.0/standard_library/logging.md b/noir/docs/versioned_docs/version-v0.6.0/standard_library/logging.md new file mode 100644 index 00000000000..649d35a3f0b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/standard_library/logging.md @@ -0,0 +1,55 @@ +--- +title: Logging +description: + Learn how to use the println statement for debugging in Noir with this tutorial. Understand the + basics of logging in Noir and how to implement it in your code. +keywords: + [ + noir logging, + println statement, + debugging in noir, + noir std library, + logging tutorial, + basic logging in noir, + noir logging implementation, + noir debugging techniques, + rust, + ] +--- + +# Logging + +The standard library provides a familiar `println` statement you can use. Despite being a limited +implementation of rust's `println!` macro, this construct can be useful for debugging. + +The `println` statement only works for fields, integers and arrays (including strings). + +```rust +use dep::std; + +fn main(string: pub str<5>) { + let x = 5; + std::println(x) +} + +``` + +To view the output of the `println` statement you need to set the `--show-output` flag. + +``` +$ nargo prove --help +Create proof for this program. The proof is returned as a hex encoded string + +Usage: nargo prove [OPTIONS] [PROOF_NAME] [CIRCUIT_NAME] + +Arguments: + [PROOF_NAME] The name of the proof + [CIRCUIT_NAME] The name of the circuit build files (ACIR, proving and verification keys) + +Options: + -v, --verify Verify proof after proving + -s, --show-ssa Emit debug information for the intermediate SSA IR + -d, --deny-warnings Quit execution when warnings are emitted + --show-output Display output of `println` statements during tests + -h, --help Print help +``` diff --git a/noir/docs/versioned_docs/version-v0.6.0/standard_library/merkle_trees.md b/noir/docs/versioned_docs/version-v0.6.0/standard_library/merkle_trees.md new file mode 100644 index 00000000000..fc8909a4795 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/standard_library/merkle_trees.md @@ -0,0 +1,59 @@ +--- +title: Merkle Trees +description: + Learn about Merkle Trees in Noir with this tutorial. Explore the basics of computing a merkle root using a proof, with examples. +keywords: + [ + Merkle trees in Noir, + Noir programming language, + check membership, + computing root from leaf, + Noir Merkle tree implementation, + Merkle tree tutorial, + Merkle tree code examples, + Noir libraries, + pedersen hash., + ] +--- + +## compute_merkle_root + +Returns the root of the tree from the provided leaf and its hash path, using a [Pedersen hash](cryptographic_primitives/00_hashes.mdx#pedersen). + +```rust +fn compute_merkle_root(leaf : Field, index : Field, hash_path: [Field]) -> Field +``` + +example: + +```rust +/** + // these values are for this example only + index = "0" + priv_key = "0x000000000000000000000000000000000000000000000000000000616c696365" + secret = "0x1929ea3ab8d9106a899386883d9428f8256cfedb3c4f6b66bf4aa4d28a79988f" + note_hash_path = [ + "0x1e61bdae0f027b1b2159e1f9d3f8d00fa668a952dddd822fda80dc745d6f65cc", + "0x0e4223f3925f98934393c74975142bd73079ab0621f4ee133cee050a3c194f1a", + "0x2fd7bb412155bf8693a3bd2a3e7581a679c95c68a052f835dddca85fa1569a40" + ] + */ +fn main(index: Field, priv_key: Field, secret: Field, note_hash_path: [Field; 3]) { + + let pubkey = std::scalar_mul::fixed_base(priv_key); + let pubkey_x = pubkey[0]; + let pubkey_y = pubkey[1]; + let note_commitment = std::hash::pedersen([pubkey_x, pubkey_y, secret]); + + let root = std::merkle::compute_merkle_root(note_commitment[0], index, note_hash_path); + std::println(root); +} +``` + +To check merkle tree membership: + +1. Include a merkle root as a program input. +2. Compute the merkle root of a given leaf, index and hash path. +3. Assert the merkle roots are equal. + +For more info about merkle trees, see the Wikipedia [page](https://en.wikipedia.org/wiki/Merkle_tree). \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.6.0/standard_library/zeroed.md b/noir/docs/versioned_docs/version-v0.6.0/standard_library/zeroed.md new file mode 100644 index 00000000000..97dab02dac2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/standard_library/zeroed.md @@ -0,0 +1,25 @@ +--- +title: Zeroed Function +description: + The zeroed function returns a zeroed value of any type. +keywords: + [ + zeroed + ] +--- + +Implements `fn zeroed() -> T` to return a zeroed value of any type. This function is generally unsafe to use as the zeroed bit pattern is not guaranteed to be valid for all types. It can however, be useful in cases when the value is guaranteed not to be used such as in a BoundedVec library implementing a growable vector, up to a certain length, backed by an array. The array can be initialized with zeroed values which are guaranteed to be inaccessible until the vector is pushed to. Similarly, enumerations in noir can be implemented using this method by providing zeroed values for the unused variants. + +You can access the function at `std::unsafe::zeroed`. + +This function currently supports the following types: + +- Field +- Bool +- Uint +- Array +- String +- Tuple +- Function + +Using it on other types could result in unexpected behavior. diff --git a/noir/docs/versioned_docs/version-v0.6.0/typescript.md b/noir/docs/versioned_docs/version-v0.6.0/typescript.md new file mode 100644 index 00000000000..fae002dfd28 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.6.0/typescript.md @@ -0,0 +1,262 @@ +--- +title: Working with TypeScript +description: + Learn how to interact with Noir programs using TypeScript. Follow this tutorial to compile your + program, specify inputs, initialize a prover & verifier, and prove and verify your program. +keywords: [TypeScript, Noir, tutorial, compile, inputs, prover, verifier, proof] +--- + +Interactions with Noir programs can also be performed in TypeScript, which can come in handy when +writing tests or when working in TypeScript-based projects like [Hardhat](https://hardhat.org/). + +This guide is based on the [noir-starter](https://github.com/signorecello/noir-starter) example. +Please refer to it for an example implementation. + +:::note + +You may find unexpected errors working with some frameworks such as `vite`. This is due to the +nature of `wasm` files and the way Noir uses web workers. As we figure it out, we suggest using +[Create React App](https://create-react-app.dev/), or [Next.js](https://nextjs.org/) for a quick +start. + +::: + +## Setup + +We're assuming you're using ES6 for both browser (for example with React), or nodejs. + +Install [Yarn](https://yarnpkg.com/) or [Node.js](https://nodejs.org/en). Init a new project with +`npm init`. Install Noir dependencies in your project by running: + +```bash +npm i @noir-lang/noir_wasm@0.3.2-fa0e9cff github:noir-lang/barretenberg#39a1547875f941ef6640217a42d8f34972425c97 @noir-lang/aztec_backend@0.1.0-0c3b2f2 +``` + +:::note + +While Noir is in rapid development, some packages could interfere with others. For that reason, you +should use these specified versions. Let us know if for some reason you need to use other ones. + +::: + +As for the circuit, we will use the _Standard Noir Example_ and place it in the `src` folder. Feel +free to use any other, as long as you refactor the below examples accordingly. + +This standard example is a program that multiplies input `x` with input `y` and returns the result: + +```rust +// src/main.nr +fn main(x: u32, y: pub u32) -> pub u32 { + let z = x * y; + z +} +``` + +One valid scenario for proving could be `x = 3`, `y = 4` and `return = 12` + +## Imports + +We need some imports, for both the `noir_wasm` library (which will compile the circuit into `wasm` +executables) and `aztec_backend` which is the actual proving backend we will be using. + +We also need to tell the compiler where to find the `.nr` files, so we need to import +`initialiseResolver`. + +```ts +import initNoirWasm, { acir_read_bytes, compile } from '@noir-lang/noir_wasm'; +import initialiseAztecBackend from '@noir-lang/aztec_backend'; +import { initialiseResolver } from '@noir-lang/noir-source-resolver'; +``` + +## Compiling + +We'll go over the code line-by-line later: + +```ts +export const compileCircuit = async () => { + await initNoirWasm(); + + return await fetch(new URL('../src/main.nr', import.meta.url)) + .then(r => r.text()) + .then(code => { + initialiseResolver((id: any) => { + return code; + }); + }) + .then(() => { + try { + const compiled_noir = compile({}); + return compiled_noir; + } catch (e) { + console.log('Error while compiling:', e); + } + }); +}; +``` + +1. First we're calling `initNoirWasm`. This is required on the browser only. +2. We then pass an URL that points to our `main.nr` file, and call `.then` on it so we can get the + actual text of the source code +3. We call `initialiseResolver` returning the source code +4. Finally, we can call the `compile` function + +This function should return us the compiled circuit. + +:::note + +You can use as many files as you need, +[importing them as you would do with Nargo](./modules_packages_crates/dependencies), and you don't +need to set them up in the `src` folder. Just mind the following particularities about +`initialiseResolver`: + +1. The `compile` function expects a `main.nr` file as an entry point. If you need another one, just + pass it as a `entry_point` parameter to `compile`. Check the + [noir starter](https://github.com/signorecello/noir-starter) for an example on multiple files and + a non-default entry point. +2. `initialiseResolver` needs to be synchronous +3. Different frameworks use different ways of fetching files. It's beyond the scope of this guide to + explain why and how, but for reference, + [noir starter](https://github.com/signorecello/noir-starter) uses both Next.js and node.js for + testing. + +Quick tip: an easy way to deal with `initialiseResolver` is just to prepare a +`{fileName: "literally_the_code"}` object beforehand + +::: + +## ACIR + +Noir compiles to two properties: + +1. The ACIR, which is the intermediate language used by backends such as Barretenberg +2. The ABI, which tells you which inputs are to be read + +Let's write a little function that gets us both, initializes the backend, and returns the ACIR as +bytes: + +```ts +export const getAcir = async () => { + const { circuit, abi } = await compileCircuit(); + await initialiseAztecBackend(); + + let acir_bytes = new Uint8Array(Buffer.from(circuit, 'hex')); + return acir_read_bytes(acir_bytes); +}; +``` + +Calling `getAcir()` now should return us the ACIR of the circuit, ready to be used in proofs. + +## Initializing Prover & Verifier + +Prior to proving and verifying, the prover and verifier have to first be initialized by calling +`barretenberg`'s `setup_generic_prover_and_verifier` with your Noir program's ACIR: + +```ts +let [prover, verifier] = await setup_generic_prover_and_verifier(acir); +``` + +This is probably a good time to store this prover and verifier into your state like React Context, +Redux, or others. + +## Proving + +The Noir program can then be executed and proved by calling `barretenberg`'s `create_proof` +function: + +```ts +const proof = await create_proof(prover, acir, abi); +``` + +On the browser, this proof can fail as it requires heavy loads to be run on worker threads. Here's a +quick example of a worker: + +```ts +// worker.ts +onmessage = async event => { + try { + await initializeAztecBackend(); + const { acir, input } = event.data; + const [prover, verifier] = await setup_generic_prover_and_verifier(acir); + const proof = await create_proof(prover, acir, input); + postMessage(proof); + } catch (er) { + postMessage(er); + } finally { + close(); + } +}; +``` + +Which would be called like this, for example: + +```ts +// index.ts +const worker = new Worker(new URL('./worker.ts', import.meta.url)); +worker.onmessage = e => { + if (e.data instanceof Error) { + // oh no! + } else { + // yey! + } +}; +worker.postMessage({ acir, input: { x: 3, y: 4 } }); +``` + +## Verifying + +The `proof` obtained can be verified by calling `barretenberg`'s `verify_proof` function: + +```ts +// 1_mul.ts +const verified = await verify_proof(verifier, proof); +``` + +The function should return `true` if the entire process is working as intended, which can be +asserted if you are writing a test script: + +```ts +expect(verified).eq(true); +``` + +## Verifying with Smart Contract + +Alternatively, a verifier smart contract can be generated and used for verifying Noir proofs in +TypeScript as well. + +This could be useful if the Noir program is designed to be decentrally verified and/or make use of +decentralized states and logics that is handled at the smart contract level. + +To generate the verifier smart contract using typescript: + +```ts +// generator.ts +import { writeFileSync } from 'fs'; + +const sc = verifier.SmartContract(); +syncWriteFile('../contracts/plonk_vk.sol', sc); + +function syncWriteFile(filename: string, data: any) { + writeFileSync(join(__dirname, filename), data, { + flag: 'w', + }); +} +``` + +You can then verify a Noir proof using the verifier contract, for example using Hardhat: + +```ts +// verifier.ts +import { ethers } from 'hardhat'; +import { Contract, ContractFactory, utils } from 'ethers'; + +let Verifier: ContractFactory; +let verifierContract: Contract; + +before(async () => { + Verifier = await ethers.getContractFactory('TurboVerifier'); + verifierContract = await Verifier.deploy(); +}); + +// Verify proof +const sc_verified = await verifierContract.verify(proof); +``` diff --git a/noir/docs/versioned_docs/version-v0.7.1/examples/merkle-proof.md b/noir/docs/versioned_docs/version-v0.7.1/examples/merkle-proof.md new file mode 100644 index 00000000000..4696b4a1426 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/examples/merkle-proof.md @@ -0,0 +1,48 @@ +--- +title: Merkle Proof Membership +description: + Learn how to use merkle membership proof in Noir to prove that a given leaf is a member of a + merkle tree with a specified root, at a given index. +keywords: + [merkle proof, merkle membership proof, Noir, rust, hash function, Pedersen, sha256, merkle tree] +--- + +Let's walk through an example of a merkle membership proof in Noir that proves that a given leaf is +in a merkle tree. + +```rust +use dep::std; + +fn main(message : [Field; 62], index : Field, hashpath : [Field; 40], root : Field) { + let leaf = std::hash::hash_to_field(message); + let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); + assert(merkle_root == root); +} + +``` + +The message is hashed using `hash_to_field`. The specific hash function that is being used is chosen +by the backend. The only requirement is that this hash function can heuristically be used as a +random oracle. If only collision resistance is needed, then one can call `std::hash::pedersen` +instead. + +```rust +let leaf = std::hash::hash_to_field(message); +``` + +The leaf is then passed to a compute_merkle_root function with the root, index and hashpath. The returned root can then be asserted to be the same as the provided root. + +```rust +let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); +assert (merkle_root == root); +``` + +> **Note:** It is possible to re-implement the merkle tree implementation without standard library. +> However, for most usecases, it is enough. In general, the standard library will always opt to be +> as conservative as possible, while striking a balance with efficiency. + +An example, the merkle membership proof, only requires a hash function that has collision +resistance, hence a hash function like Pedersen is allowed, which in most cases is more efficient +than the even more conservative sha256. + +[view an example on the starter repo](https://github.com/noir-lang/noir-examples/blob/3ea09545cabfa464124ec2f3ea8e60c608abe6df/stealthdrop/circuits/src/main.nr#L20) diff --git a/noir/docs/versioned_docs/version-v0.7.1/getting_started/00_nargo_installation.md b/noir/docs/versioned_docs/version-v0.7.1/getting_started/00_nargo_installation.md new file mode 100644 index 00000000000..fb86a966e75 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/getting_started/00_nargo_installation.md @@ -0,0 +1,284 @@ +--- +title: Nargo Installation +description: + nargo is a command line tool for interacting with Noir programs (e.g. compiling, proving, + verifying and more). Learn how to install and use Nargo for your projects with this comprehensive + guide. +keywords: [Nargo, command line tool, Noir programs, installation guide, how to use Nargo] +--- + +`nargo` is a command line tool for interacting with Noir programs (e.g. compiling, proving, +verifying and more). + +Alternatively, the interactions can also be performed in [TypeScript](../typescript). + +### UltraPlonk + +Nargo versions <0.5.0 of `aztec_backend` and `aztec_wasm_backend` are based on the TurboPlonk +version of Aztec Backend, which lacks efficient implementations of useful primitives (e.g. Keccak256 in 18k constraints, ECDSA verification in 36k constraints) that the UltraPlonk version offers. + +## Installation + +There are four approaches for installing Nargo: + +- [Option 1: Noirup](#option-1-noirup) +- [Option 2: Binaries](#option-2-binaries) +- [Option 3: Install via Nix](#option-3-install-via-nix) +- [Option 4: Compile from Source](#option-4-compile-from-source) + +Optionally you can also install [Noir VS Code extension] for syntax highlighting. + +### Option 1: Noirup + +If you're on OSX or Linux, the easiest way to start using Noir and Nargo is via noirup. Just open a +terminal and run: + +```bash +curl -L https://raw.githubusercontent.com/noir-lang/noirup/main/install | bash +``` + +Close the terminal, open another one, and run + +```bash +noirup +``` + +Done, you should have the latest version working. You can check with `nargo --version`. + +You can also install nightlies, specific versions +or branches, check out the [noirup repository](https://github.com/noir-lang/noirup) for more +information. + +#### GitHub Actions + +You can use `noirup` with GitHub Actions for CI/CD and automated testing. It is as simple as +installing `noirup` and running tests in your GitHub Action `yml` file. + +See the +[config file](https://github.com/TomAFrench/noir-hashes/blob/master/.github/workflows/noir.yml) in +this repo containing hash functions in Noir for an example. + +#### Nightly versions + +To install the nightly version of Noir (updated daily) run: + +```bash +noirup -n +``` + +### Option 2: Binaries + +See [GitHub Releases](https://github.com/noir-lang/noir/releases) for the latest and previous +platform specific binaries. + +#### Step 1 + +Paste and run the following in the terminal to extract and install the binary: + +> **macOS / Linux:** If you are prompted with `Permission denied` when running commands, prepend +> `sudo` and re-run it. + +##### macOS (Apple Silicon) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-aarch64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### macOS (Intel) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### Windows (PowerShell) + +Open PowerShell as Administrator and run: + +```powershell +mkdir -f -p "$env:USERPROFILE\.nargo\bin\"; ` +Invoke-RestMethod -Method Get -Uri https://github.com/noir-lang/noir/releases/download/v0.4.1/nargo-x86_64-pc-windows-msvc.zip -Outfile "$env:USERPROFILE\.nargo\bin\nargo-x86_64-pc-windows-msvc.zip"; ` +Expand-Archive -Path "$env:USERPROFILE\.nargo\bin\nargo-x86_64-pc-windows-msvc.zip" -DestinationPath "$env:USERPROFILE\.nargo\bin\"; ` +$Reg = "Registry::HKLM\System\CurrentControlSet\Control\Session Manager\Environment"; ` +$OldPath = (Get-ItemProperty -Path "$Reg" -Name PATH).Path; ` +$NewPath = $OldPath + ’;’ + "$env:USERPROFILE\.nargo\bin\"; ` +Set-ItemProperty -Path "$Reg" -Name PATH –Value "$NewPath"; ` +$env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User") +``` + +##### Linux (Bash) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-unknown-linux-gnu.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -C $HOME/.nargo/bin/ && \ +echo -e '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.bashrc && \ +source ~/.bashrc +``` + +#### Step 2 + +Check if the installation was successful by running `nargo --help`. + +> **macOS:** If you are prompted with an OS alert, right-click and open the _nargo_ executable from +> Finder. Close the new terminal popped up and `nargo` should now be accessible. + +For a successful installation, you should see something similar to the following after running the +command: + +```sh +$ nargo --help + +Noir's package manager + +Usage: nargo + +Commands: + check Checks the constraint system for errors + codegen-verifier Generates a Solidity verifier smart contract for the program + compile Compile the program and its secret execution trace into ACIR format + new Create a new binary project + execute Executes a circuit to calculate its return value + prove Create proof for this program. The proof is returned as a hex encoded string + verify Given a proof and a program, verify whether the proof is valid + test Run the tests for this program + gates Counts the occurrences of different gates in circuit + help Print this message or the help of the given subcommand(s) +``` + +### Option 3: Install via Nix + +Due to the large number of native dependencies, Noir projects can be installed via [Nix](https://nixos.org/). + +#### Installing Nix + +For the best experience, please follow these instructions to setup Nix: + +1. Install Nix following [their guide](https://nixos.org/download.html) for your operating system. +2. Create the file `~/.config/nix/nix.conf` with the contents: + +```ini +experimental-features = nix-command +extra-experimental-features = flakes +``` + +#### Install Nargo into your Nix profile + +1. Use `nix profile` to install Nargo + +```sh +nix profile install github:noir-lang/noir +``` + +### Option 4: Compile from Source + +Due to the large number of native dependencies, Noir projects uses [Nix](https://nixos.org/) and [direnv](https://direnv.net/) to streamline the development experience. + +#### Setting up your environment + +For the best experience, please follow these instructions to setup your environment: + +1. Install Nix following [their guide](https://nixos.org/download.html) for your operating system. +2. Create the file `~/.config/nix/nix.conf` with the contents: + +```ini +experimental-features = nix-command +extra-experimental-features = flakes +``` + +3. Install direnv into your Nix profile by running: + +```sh +nix profile install nixpkgs#direnv +``` + +4. Add direnv to your shell following [their guide](https://direnv.net/docs/hook.html). +5. Restart your shell. + +#### Shell & editor experience + +Now that your environment is set up, you can get to work on the project. + +1. Clone the repository, such as: + +```sh +git clone git@github.com:noir-lang/noir +``` + +> Replacing `noir` with whichever repository you want to work on. + +2. Navigate to the directory: + +```sh +cd noir +``` + +> Replacing `noir` with whichever repository you cloned. + +3. You should see a **direnv error** because projects aren't allowed by default. Make sure you've reviewed and trust our `.envrc` file, then you need to run: + +```sh +direnv allow +``` + +4. Now, wait awhile for all the native dependencies to be built. This will take some time and direnv will warn you that it is taking a long time, but we just need to let it run. + +5. Once you are presented with your prompt again, you can start your editor within the project directory (we recommend [VSCode](https://code.visualstudio.com/)): + +```sh +code . +``` + +6. (Recommended) When launching VSCode for the first time, you should be prompted to install our recommended plugins. We highly recommend installing these for the best development experience. + +#### Building and testing + +Assuming you are using `direnv` to populate your environment, building and testing the project can be done +with the typical `cargo build`, `cargo test`, and `cargo clippy` commands. You'll notice that the `cargo` version matches the version we specify in `flake.nix`, which is 1.66.0 at the time of this writing. + +If you want to build the entire project in an isolated sandbox, you can use Nix commands: + +1. `nix build .` (or `nix build . -L` for verbose output) to build the project in a Nix sandbox. +2. `nix flake check` (or `nix flake check -L` for verbose output) to run clippy and tests in a Nix sandbox. + +#### Without `direnv` + +If you have hesitations with using direnv, you can launch a subshell with `nix develop` and then launch your editor from within the subshell. However, if VSCode was already launched in the project directory, the environment won't be updated. + +Advanced: If you aren't using direnv nor launching your editor within the subshell, you can try to install Barretenberg and other global dependencies the package needs. This is an advanced workflow and likely won't receive support! + +## Uninstalling Nargo + +### Noirup + +If you installed Noir with `noirup`, you can uninstall Noir by removing the files in `~/.nargo`, `~/nargo` and `~/noir_cache`. + +```bash +rm -r ~/.nargo +rm -r ~/nargo +rm -r ~/noir_cache +``` + +### Nix + +If you installed Noir with Nix or from source, you can remove the binary located at `~/.nix-profile/bin/nargo`. + +```bash +rm ~/.nix-profile/bin/nargo +``` + +[git]: https://git-scm.com/book/en/v2/Getting-Started-Installing-Git +[rust]: https://www.rust-lang.org/tools/install +[noir vs code extension]: https://marketplace.visualstudio.com/items?itemName=noir-lang.vscode-noir +[homebrew]: https://brew.sh/ +[cmake]: https://cmake.org/install/ +[llvm]: https://llvm.org/docs/GettingStarted.html +[openmp]: https://openmp.llvm.org/ +[barretenberg]: https://github.com/AztecProtocol/barretenberg diff --git a/noir/docs/versioned_docs/version-v0.7.1/getting_started/01_hello_world.md b/noir/docs/versioned_docs/version-v0.7.1/getting_started/01_hello_world.md new file mode 100644 index 00000000000..0f21ad45569 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/getting_started/01_hello_world.md @@ -0,0 +1,147 @@ +--- +title: Create A Project +description: + Learn how to create and verify your first Noir program using Nargo, a programming language for + zero-knowledge proofs. +keywords: + [ + Nargo, + Noir, + zero-knowledge proofs, + programming language, + create Noir program, + verify Noir program, + step-by-step guide, + ] +--- + +Now that we have installed Nargo, it is time to make our first hello world program! + +## Create a Project Directory + +Noir code can live anywhere on your computer. Let us create a _projects_ folder in the home +directory to house our Noir programs. + +For Linux, macOS, and Windows PowerShell, create the directory and change directory into it by +running: + +```sh +mkdir ~/projects +cd ~/projects +``` + +For Windows CMD, run: + +```sh +> mkdir "%USERPROFILE%\projects" +> cd /d "%USERPROFILE%\projects" +``` + +## Create Our First Nargo Project + +Now that we are in the projects directory, create a new Nargo project by running: + +```sh +nargo new hello_world +``` + +> **Note:** `hello_world` can be any arbitrary project name, we are simply using `hello_world` for +> demonstration. +> +> In production, the common practice is to name the project folder as `circuits` for better +> identifiability when sitting alongside other folders in the codebase (e.g. `contracts`, `scripts`, +> `test`). + +A `hello_world` folder would be created. Similar to Rust, the folder houses _src/main.nr_ and +_Nargo.toml_ that contains the source code and environmental options of your Noir program +respectively. + +### Intro to Noir Syntax + +Let us take a closer look at _main.nr_. The default _main.nr_ generated should look like this: + +```rust +fn main(x : Field, y : pub Field) { + assert(x != y); +} +``` + +The first line of the program specifies the program's inputs: + +```rust +x : Field, y : pub Field +``` + +Program inputs in Noir are private by default (e.g. `x`), but can be labeled public using the +keyword `pub` (e.g. `y`). To learn more about private and public values, check the +[Data Types](../language_concepts/data_types) section. + +The next line of the program specifies its body: + +```rust +assert(x != y); +``` + +The Noir syntax `assert` can be interpreted as something similar to constraints in other zk-contract languages. + +For more Noir syntax, check the [Language Concepts](../language_concepts/comments) chapter. + +## Build In/Output Files + +Change directory into _hello_world_ and build in/output files for your Noir program by running: + +```sh +cd hello_world +nargo check +``` + +Two additional files would be generated in your project directory: + +_Prover.toml_ houses input values, and _Verifier.toml_ houses public values. + +## Prove Our Noir Program + +Now that the project is set up, we can create a proof of correct execution on our Noir program. + +Fill in input values for execution in the _Prover.toml_ file. For example: + +```toml +x = "1" +y = "2" +``` + +Prove the valid execution of your Noir program with your preferred proof name, for example `p`: + +```sh +nargo prove p +``` + +A new folder _proofs_ would then be generated in your project directory, containing the proof file +`p.proof`. + +The _Verifier.toml_ file would also be updated with the public values computed from program +execution (in this case the value of `y`): + +```toml +y = "0x0000000000000000000000000000000000000000000000000000000000000002" +``` + +> **Note:** Values in _Verifier.toml_ are computed as 32-byte hex values. + +## Verify Our Noir Program + +Once a proof is generated, we can verify correct execution of our Noir program by verifying the +proof file. + +Verify your proof of name `p` by running: + +```sh +nargo verify p +``` + +The verification will complete in silence if it is successful. If it fails, it will log the +corresponding error instead. + +Congratulations, you have now created and verified a proof for your very first Noir program! + +In the [next section](breakdown), we will go into more detail on each step performed. diff --git a/noir/docs/versioned_docs/version-v0.7.1/getting_started/02_breakdown.md b/noir/docs/versioned_docs/version-v0.7.1/getting_started/02_breakdown.md new file mode 100644 index 00000000000..64d04c4e062 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/getting_started/02_breakdown.md @@ -0,0 +1,122 @@ +--- +title: Project Breakdown +description: + Learn about the anatomy of a Nargo project, including the purpose of the Prover and Verifier TOML + files, and how to prove and verify your program. +keywords: + [Nargo, Nargo project, Prover.toml, Verifier.toml, proof verification, private asset transfer] +--- + +This section breaks down our hello world program in section _1.2_. We elaborate on the project +structure and what the `prove` and `verify` commands did in the previous section. + +## Anatomy of a Nargo Project + +Upon creating a new project with `nargo new` and building the in/output files with `nargo check` +commands, you would get a minimal Nargo project of the following structure: + + - src + - Prover.toml + - Verifier.toml + - Nargo.toml + +The source directory _src_ holds the source code for your Noir program. By default only a _main.nr_ +file will be generated within it. + +_Prover.toml_ is used for specifying the input values for executing and proving the program. You can specify `toml` files with different names by using the `--prover-name` or `-p` flags, see the [Prover](#provertoml) section below. Optionally you may specify expected output values for prove-time checking as well. + +_Verifier.toml_ contains public in/output values computed when executing the Noir program. + +_Nargo.toml_ contains the environmental options of your project. + +_proofs_ and _contract_ directories will not be immediately visible until you create a proof or +verifier contract respectively. + +### main.nr + +The _main.nr_ file contains a `main` method, this method is the entry point into your Noir program. + +In our sample program, _main.nr_ looks like this: + +```rust +fn main(x : Field, y : Field) { + assert(x != y); +} +``` + +The parameters `x` and `y` can be seen as the API for the program and must be supplied by the +prover. Since neither `x` nor `y` is marked as public, the verifier does not supply any inputs, when +verifying the proof. + +The prover supplies the values for `x` and `y` in the _Prover.toml_ file. + +As for the program body, `assert` ensures the satisfaction of the condition (e.g. `x != y`) is +constrained by the proof of the execution of said program (i.e. if the condition was not met, the +verifier would reject the proof as an invalid proof). + +### Prover.toml + +The _Prover.toml_ file is a file which the prover uses to supply his witness values(both private and +public). + +In our hello world program the _Prover.toml_ file looks like this: + +```toml +x = "1" +y = "2" +``` + +When the command `nargo prove my_proof` is executed, two processes happen: + +1. Noir creates a proof that `x` which holds the value of `1` and `y` which holds the value of `2` + is not equal. This not equal constraint is due to the line `assert(x != y)`. + +2. Noir creates and stores the proof of this statement in the _proofs_ directory and names the proof + file _my_proof_. Opening this file will display the proof in hex format. + +#### Custom toml files + +You can specify a `toml` file with a different name to use for proving by using the `--prover-name` or `-p` flags. + +This command looks for proof inputs in the default **Prover.toml** and generates proof `p`: + +```bash +nargo prove p +``` + +This command looks for proof inputs in the custom **OtherProver.toml** and generates proof `pp`: + +```bash +nargo prove -p OtherProver pp +``` + +## Verifying a Proof + +When the command `nargo verify my_proof` is executed, two processes happen: + +1. Noir checks in the _proofs_ directory for a file called _my_proof_ + +2. If that file is found, the proof's validity is checked + +> **Note:** The validity of the proof is linked to the current Noir program; if the program is +> changed and the verifier verifies the proof, it will fail because the proof is not valid for the +> _modified_ Noir program. + +In production, the prover and the verifier are usually two separate entities. A prover would +retrieve the necessary inputs, execute the Noir program, generate a proof and pass it to the +verifier. The verifier would then retrieve the public inputs from usually external sources and +verifies the validity of the proof against it. + +Take a private asset transfer as an example: + +A user on browser as the prover would retrieve private inputs (e.g. the user's private key) and +public inputs (e.g. the user's encrypted balance on-chain), compute the transfer, generate a proof +and submit it to the verifier smart contract. + +The verifier contract would then draw the user's encrypted balance directly from the blockchain and +verify the proof submitted against it. If the verification passes, additional functions in the +verifier contract could trigger (e.g. approve the asset transfer). + +Now that you understand the concepts, you'll probably want some editor feedback while you are writing more complex code. + +In the [next section](language_server), we will explain how to utilize the Noir Language Server. diff --git a/noir/docs/versioned_docs/version-v0.7.1/getting_started/03_language_server.md b/noir/docs/versioned_docs/version-v0.7.1/getting_started/03_language_server.md new file mode 100644 index 00000000000..e6f5dfc2faa --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/getting_started/03_language_server.md @@ -0,0 +1,31 @@ +--- +title: Language Server +description: + Learn about the Noir Language Server, how to install the components, and configuration that may be required. +keywords: + [Nargo, Language Server, LSP, VSCode, Visual Studio Code] +--- + +This section helps you install and configure the Noir Language Server. + +The Language Server Protocol (LSP) has two components, the [Server](#language-server) and the [Client](#language-client). Below we describe each in the context of Noir. + +## Language Server + +The Server component is provided by the Nargo command line tool that you installed at the beginning of this guide. +As long as Nargo is installed and you've used it to run other commands in this guide, it should be good to go! + +If you'd like to verify that the `nargo lsp` command is available, you can run `nargo --help` and look for `lsp` in the list of commands. If you see it, you're using a version of Noir with LSP support. + +## Language Client + +The Client component is usually an editor plugin that launches the Server. It communicates LSP messages between the editor and the Server. For example, when you save a file, the Client will alert the Server, so it can try to compile the project and report any errors. + +Currently, Noir provides a Language Client for Visual Studio Code via the [vscode-noir](https://github.com/noir-lang/vscode-noir) extension. You can install it via the [Visual Studio Marketplace](https://marketplace.visualstudio.com/items?itemName=noir-lang.vscode-noir). + +### Configuration + +* __Noir: Enable LSP__ - If checked, the extension will launch the Language Server via `nargo lsp` and communicate with it. +* __Noir: Nargo Flags__ - Additional flags may be specified if you require them to be added when the extension calls `nargo lsp`. +* __Noir: Nargo Path__ - An absolute path to a Nargo binary with the `lsp` command. This may be useful if Nargo is not within the `PATH` of your editor. +* __Noir > Trace: Server__ - Setting this to `"messages"` or `"verbose"` will log LSP messages between the Client and Server. Useful for debugging. diff --git a/noir/docs/versioned_docs/version-v0.7.1/index.md b/noir/docs/versioned_docs/version-v0.7.1/index.md new file mode 100644 index 00000000000..e56b24bccd8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/index.md @@ -0,0 +1,103 @@ +--- +title: Introducing Noir +description: + Learn about the public alpha release of Noir, a domain specific language heavily influenced by + Rust that compiles to an intermediate language which can be compiled to an arithmetic circuit or a + rank-1 constraint system. +keywords: + [ + Noir, + Domain Specific Language, + Rust, + Intermediate Language, + Arithmetic Circuit, + Rank-1 Constraint System, + Ethereum Developers, + Protocol Developers, + Blockchain Developers, + Proving System, + Smart Contract Language, + ] +slug: / +--- + +This version of the book is being released with the public alpha. There will be a lot of features +that are missing in this version, however the syntax and the feel of the language will mostly be +completed. + +## What is Noir? + +Noir is a Domain Specific Language for SNARK proving systems. It has been designed to use any ACIR compatible proving system. + +It's design choices are influenced heavily by Rust and focuses on a simple, familiar syntax. + +## Who is Noir for? + +Noir can be used for a variety of purposes. + +### Solidity Developers + +Noir currently includes a command to create a Solidity contract which verifies your Noir program. This will +be modularised in the future; however, as of the alpha, you can use the [`nargo codegen-verifier`](./nargo/commands#nargo-codegen-verifier) command to create +a verifier contract. + +### Protocol Developers + +As a protocol developer, you may not want to use the Aztec backend due to it not being a fit for +your stack, or maybe you simply want to use a different proving system. Since Noir does not compile +to a specific proof system, it is possible for protocol developers to replace the PLONK-based +proving system with a different proving system altogether. + +### Blockchain developers + +As a blockchain developer, you will be constrained by parameters set by your blockchain (for example, the +proving system and smart contract language has been pre-defined). In order for you to use Noir in +your blockchain, a proving system backend and a smart contract interface +must be implemented for it. + +## What's new about Noir? + +Noir is simple and flexible in its design, as it does not compile immediately to a fixed +NP-complete language. Instead, Noir compiles to an intermediate language (ACIR), which itself can be compiled +to an arithmetic circuit (if choosing to target Aztec's barretenberg backend) or a rank-1 constraint system (if choosing to target an R1CS backend like Arkwork's Marlin backend, or others). + +This in itself brings up a few challenges within the design process, but allows one to decouple the programming language completely from the backend. This is similar in theory to LLVM. + +## Current Features + +Compiler: + +- Module System +- For expressions +- Arrays +- Bit Operations +- Binary operations (<, <=, >, >=, +, -, \*, /, %) [See documentation for an extensive list] +- Unsigned integers +- If statements +- Structures and Tuples +- Generics + +ACIR Supported OPCODES: + +- Sha256 +- Blake2s +- Schnorr signature verification +- MerkleMembership +- Pedersen +- HashToField + +## Libraries + +Noir does not currently have an official package manager. You can find a list of available Noir libraries in the [awesome-noir repo here](https://github.com/noir-lang/awesome-noir#libraries). + +Some libraries that are available today include: + +- [Standard Library](https://github.com/noir-lang/noir/tree/master/noir_stdlib) - the Noir Standard Library +- [Ethereum Storage Proof Verification](https://github.com/aragonzkresearch/noir-trie-proofs) - a library that contains the primitives necessary for RLP decoding (in the form of look-up table construction) and Ethereum state and storage proof verification (or verification of any trie proof involving 32-byte long keys) +- [BigInt](https://github.com/shuklaayush/noir-bigint) - a library that provides a custom BigUint56 data type, allowing for computations on large unsigned integers +- [ECrecover](https://github.com/colinnielsen/ecrecover-noir/tree/main) - a library to verify an ECDSA signature and return the source Ethereum address +- [Sparse Merkle Tree Verifier](https://github.com/vocdoni/smtverifier-noir/tree/main) - a library for verification of sparse Merkle trees +- [Signed Int](https://github.com/resurgencelabs/signed_int) - a library for accessing a custom Signed Integer data type, allowing access to negative numbers on Noir +- [Fraction](https://github.com/resurgencelabs/fraction) - a library for accessing fractional number data type in Noir, allowing results that aren't whole numbers + +See the section on [dependencies](./modules_packages_crates/dependencies) for more information. diff --git a/noir/docs/versioned_docs/version-v0.7.1/language_concepts/00_data_types.md b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/00_data_types.md new file mode 100644 index 00000000000..6ac494ad404 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/00_data_types.md @@ -0,0 +1,305 @@ +--- +title: Data Types +description: + Get a clear understanding of the two categories of Noir data types - primitive types and compound + types. Learn about their characteristics, differences, and how to use them in your Noir + programming. +keywords: + [ + noir, + data types, + primitive types, + compound types, + private types, + public types, + field type, + integer types, + boolean type, + array type, + tuple type, + struct type, + ] +--- + +Every value in Noir has a type, which determines which operations are valid for it. + +All values in Noir are fundamentally composed of `Field` elements. For a more approachable +developing experience, abstractions are added on top to introduce different data types in Noir. + +Noir has two category of data types: primitive types (e.g. `Field`, integers, `bool`) and compound +types that group primitive types (e.g. arrays, tuples, structs). Each value can either be private or +public. + +## Private & Public Types + +A **private value** is known only to the Prover, while a **public value** is known by both the +Prover and Verifier. Mark values as `private` when the value should only be known to the prover. All +primitive types (including individual fields of compound types) in Noir are private by default, and +can be marked public when certain values are intended to be revealed to the Verifier. + +> **Note:** For public values defined in Noir programs paired with smart contract verifiers, once +> the proofs are verified on-chain the values can be considered known to everyone that has access to +> that blockchain. + +Public data types are treated no differently to private types apart from the fact that their values +will be revealed in proofs generated. Simply changing the value of a public type will not change the +circuit (where the same goes for changing values of private types as well). + +_Private values_ are also referred to as _witnesses_ sometimes. + +> **Note:** The terms private and public when applied to a type (e.g. `pub Field`) have a different +> meaning than when applied to a function (e.g. `pub fn foo() {}`). +> +> The former is a visibility modifier for the Prover to interpret if a value should be made known to +> the Verifier, while the latter is a visibility modifier for the compiler to interpret if a +> function should be made accessible to external Noir programs like in other languages. + +### pub Modifier + +All data types in Noir are private by default. Types are explicitly declared as public using the +`pub` modifier: + +```rust +fn main(x : Field, y : pub Field) -> pub Field { + x + y +} +``` + +In this example, `x` is **private** while `y` and `x + y` (the return value) are **public**. Note +that visibility is handled **per variable**, so it is perfectly valid to have one input that is +private and another that is public. + +> **Note:** Public types can only be declared through parameters on `main`. + +## Primitive Types + +A primitive type represents a single value. They can be private or public. + +### Fields + +The field type corresponds to the native field type of the proving backend. + +The size of a Noir field depends on the elliptic curve's finite field for the proving backend +adopted. For example, a field would be a 254-bit integer when paired with the default backend that +spans the Grumpkin curve. + +Fields support integer arithmetic and are often used as the default numeric type in Noir: + +```rust +fn main(x : Field, y : Field) { + let z = x + y; +} +``` + +`x`, `y` and `z` are all private fields in this example. Using the `let` keyword we defined a new +private value `z` constrained to be equal to `x + y`. + +If proving efficiency is of priority, fields should be used as a default for solving problems. +Smaller integer types (e.g. `u64`) incur extra range constraints. + +### Integers + +An integer type is a range constrained field type. The Noir frontend currently supports unsigned, +arbitrary-sized integer types. + +An integer type is specified first with the letter `u`, indicating its unsigned nature, followed by +its length in bits (e.g. `32`). For example, a `u32` variable can store a value in the range of +$\\([0,2^{32}-1]\\)$: + +```rust +fn main(x : Field, y : u32) { + let z = x as u32 + y; +} +``` + +`x`, `y` and `z` are all private values in this example. However, `x` is a field while `y` and `z` +are unsigned 32-bit integers. If `y` or `z` exceeds the range $\\([0,2^{32}-1]\\)$, proofs created +will be rejected by the verifier. + +> **Note:** The default backend supports both even (e.g. `u16`, `u48`) and odd (e.g. `u5`, `u3`) +> sized integer types. + +### Booleans + +The `bool` type in Noir has two possible values: `true` and `false`: + +```rust +fn main() { + let t = true; + let f: bool = false; +} +``` + +> **Note:** When returning a boolean value, it will show up as a value of 1 for `true` and 0 for +> `false` in _Verifier.toml_. + +The boolean type is most commonly used in conditionals like `if` expressions and `assert` +statements. More about conditionals is covered in the [Control Flow](./control_flow) and +[Assert Function](./assert) sections. + +### Strings + +The string type is a fixed length value defined with `str`. + +You can use strings in `assert()` functions or print them with +`std::println()`. + +```rust +fn main(message : pub str<11>, hex_as_string : str<4>) { + std::println(message); + assert(message == "hello world"); + assert(hex_as_string == "0x41"); +} +``` + +## Compound Types + +A compound type groups together multiple values into one type. Elements within a compound type can +be private or public. + +### Arrays + +An array is one way of grouping together values into one compound type. Array types can be inferred +or explicitly specified via the syntax `[; ]`: + +```rust +fn main(x : Field, y : Field) { + let my_arr = [x, y]; + let your_arr: [Field; 2] = [x, y]; +} +``` + +Here, both `my_arr` and `your_arr` are instantiated as an array containing two `Field` elements. + +Array elements can be accessed using indexing: + +```rust +fn main() { + let a = [1, 2, 3, 4, 5]; + + let first = a[0]; + let second = a[1]; +} +``` + +All elements in an array must be of the same type (i.e. homogeneous). That is, an array cannot group +a `Field` value and a `u8` value together for example. + +You can write mutable arrays, like: + +```rust +fn main() { + let mut arr = [1, 2, 3, 4, 5]; + assert(arr[0] == 1); + + arr[0] = 42; + assert(arr[0] == 42); +} +``` + +#### Types + +You can create arrays of primitive types or structs. There is not yet support for nested arrays +(arrays of arrays) or arrays of structs that contain arrays. + +### Tuples + +A tuple collects multiple values like an array, but with the added ability to collect values of +different types: + +```rust +fn main() { + let tup: (u8, u64, Field) = (255, 500, 1000); +} +``` + +One way to access tuple elements is via destructuring using pattern matching: + +```rust +fn main() { + let tup = (1, 2); + + let (one, two) = tup; + + let three = one + two; +} +``` + +Another way to access tuple elements is via direct member access, using a period (`.`) followed by +the index of the element we want to access. Index `0` corresponds to the first tuple element, `1` to +the second and so on: + +```rust +fn main() { + let tup = (5, 6, 7, 8); + + let five = tup.0; + let eight = tup.3; +} +``` + +### Structs + +A struct also allows for grouping multiple values of different types. Unlike tuples, we can also +name each field. + +> **Note:** The usage of _field_ here refers to each element of the struct and is unrelated to the +> field type of Noir. + +Defining a struct requires giving it a name and listing each field within as `: ` pairs: + +```rust +struct Animal { + hands: Field, + legs: Field, + eyes: u8, +} +``` + +An instance of a struct can then be created with actual values in `: ` pairs in any +order. Struct fields are accessible using their given names: + +```rust +fn main() { + let legs = 4; + + let dog = Animal { + eyes: 2, + hands: 0, + legs, + }; + + let zero = dog.hands; +} +``` + +Structs can also be destructured in a pattern, binding each field to a new variable: + +```rust +fn main() { + let Animal { hands, legs: feet, eyes } = get_octopus(); + + let ten = hands + feet + eyes as u8; +} + +fn get_octopus() -> Animal { + let octopus = Animal { + hands: 0, + legs: 8, + eyes: 2, + }; + + octopus +} +``` + +The new variables can be bound with names different from the original struct field names, as +showcased in the `legs --> feet` binding in the example above. + +:::note +You can use Structs as inputs to the `main` function, but you can't output them +::: + +### BigInt + +You can acheive BigInt functionality using the [Noir BigInt](https://github.com/shuklaayush/noir-bigint) library. diff --git a/noir/docs/versioned_docs/version-v0.7.1/language_concepts/01_functions.md b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/01_functions.md new file mode 100644 index 00000000000..54c618599d2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/01_functions.md @@ -0,0 +1,88 @@ +--- +title: Functions +description: + Learn how to declare functions and methods in Noir, a programming language with Rust semantics. + This guide covers parameter declaration, return types, call expressions, and more. +keywords: [Noir, Rust, functions, methods, parameter declaration, return types, call expressions] +--- + +Functions in Noir follow the same semantics of Rust, though Noir does not support early returns. + +To declare a function the `fn` keyword is used. + +```rust +fn foo() {} +``` + +All parameters in a function must have a type and all types are known at compile time. The parameter +is pre-pended with a colon and the parameter type. Multiple parameters are separated using a comma. + +```rust +fn foo(x : Field, y : pub Field){} +``` + +The return type of a function can be stated by using the `->` arrow notation. The function below +states that the foo function must return a `Field`. If the function returns no value, then the arrow +is omitted. + +```rust +fn foo(x : Field, y : pub Field) -> Field { + x + y +} +``` + +Note that a `return` keyword is unneeded in this case - the last expression in a function's body is +returned. + +## Call Expressions + +Calling a function in Noir is executed by using the function name and passing in the necessary +arguments. + +Below we show how to call the `foo` function from the `main` function using a call expression: + +```rust +fn main(x : Field, y : Field) { + let z = foo(x); +} + +fn foo(x : Field) -> Field { + x + x +} +``` + +## Methods + +You can define methods in Noir on any struct type in scope. + +```rust +struct MyStruct { + foo: Field, + bar: Field, +} + +impl MyStruct { + fn new(foo: Field) -> MyStruct { + MyStruct { + foo, + bar: 2, + } + } + + fn sum(self) -> Field { + self.foo + self.bar + } +} + +fn main() { + let s = MyStruct::new(40); + assert(s.sum() == 42); +} +``` + +Methods are just syntactic sugar for functions, so if we wanted to we could also call `sum` as +follows: + +```rust +assert(MyStruct::sum(s) == 42); +``` diff --git a/noir/docs/versioned_docs/version-v0.7.1/language_concepts/02_control_flow.md b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/02_control_flow.md new file mode 100644 index 00000000000..691c514d9a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/02_control_flow.md @@ -0,0 +1,42 @@ +--- +title: Control Flow +description: + Learn how to use loops and if expressions in the Noir programming language. Discover the syntax + and examples for for loops and if-else statements. +keywords: [Noir programming language, loops, for loop, if-else statements, Rust syntax] +--- + +## Loops + +Noir has one kind of loop: the `for` loop. `for` loops allow you to repeat a block of code multiple +times. + +The following block of code between the braces is run 10 times. + +```rust +for i in 0..10 { + // do something +}; +``` + +## If Expressions + +Noir supports `if-else` statements. The syntax is most similar to Rust's where it is not required +for the statement's conditional to be surrounded by parentheses. + +```rust +let a = 0; +let mut x: u32 = 0; + +if a == 0 { + if a != 0 { + x = 6; + } else { + x = 2; + } +} else { + x = 5; + assert(x == 5); +} +assert(x == 2); +``` diff --git a/noir/docs/versioned_docs/version-v0.7.1/language_concepts/03_ops.md b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/03_ops.md new file mode 100644 index 00000000000..da02b126059 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/03_ops.md @@ -0,0 +1,97 @@ +--- +title: Logical Operations +description: + Learn about the supported arithmetic and logical operations in the Noir programming language. + Discover how to perform operations on private input types, integers, and booleans. +keywords: + [ + Noir programming language, + supported operations, + arithmetic operations, + logical operations, + predicate operators, + bitwise operations, + short-circuiting, + backend, + ] +--- + +# Operations + +## Table of Supported Operations + +| Operation | Description | Requirements | +| :-------- | :------------------------------------------------------------: | -------------------------------------: | +| + | Adds two private input types together | Types must be private input | +| - | Subtracts two private input types together | Types must be private input | +| \* | Multiplies two private input types together | Types must be private input | +| / | Divides two private input types together | Types must be private input | +| ^ | XOR two private input types together | Types must be integer | +| & | AND two private input types together | Types must be integer | +| \| | OR two private input types together | Types must be integer | +| << | Left shift an integer by another integer amount | Types must be integer | +| >> | Right shift an integer by another integer amount | Types must be integer | +| ! | Bitwise not of a value | Type must be integer or boolean | +| < | returns a bool if one value is less than the other | Upper bound must have a known bit size | +| <= | returns a bool if one value is less than or equal to the other | Upper bound must have a known bit size | +| > | returns a bool if one value is more than the other | Upper bound must have a known bit size | +| >= | returns a bool if one value is more than or equal to the other | Upper bound must have a known bit size | +| == | returns a bool if one value is equal to the other | Both types must not be constants | +| != | returns a bool if one value is not equal to the other | Both types must not be constants | + +### Predicate Operators + +`<,<=, !=, == , >, >=` are known as predicate/comparison operations because they compare two values. +This differs from the operations such as `+` where the operands are used in _computation_. + +### Bitwise Operations Example + +```rust +fn main(x : Field) { + let y = x as u32; + let z = y & y; +} +``` + +`z` is implicitly constrained to be the result of `y & y`. The `&` operand is used to denote bitwise +`&`. + +> `x & x` would not compile as `x` is a `Field` and not an integer type. + +### Logical Operators + +Noir has no support for the logical operators `||` and `&&`. This is because encoding the +short-circuiting that these operators require can be inefficient for Noir's backend. Instead you can +use the bitwise operators `|` and `&` which operate indentically for booleans, just without the +short-circuiting. + +```rust +let my_val = 5; + +let mut flag = 1; +if (my_val > 6) | (my_val == 0) { + flag = 0; +} +assert(flag == 1); + +if (my_val != 10) & (my_val < 50) { + flag = 0; +} +assert(flag == 0); +``` + +### Shorthand operators + +Noir shorthand operators for most of the above operators, namely `+=, -=, *=, /=, %=, &=, |=, ^=, <<=`, and `>>=`. These allow for more concise syntax. For example: + +```rust +let mut i = 0; +i = i + 1; +``` + +could be written as: + +```rust +let mut i = 0; +i += 1; +``` diff --git a/noir/docs/versioned_docs/version-v0.7.1/language_concepts/04_assert.md b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/04_assert.md new file mode 100644 index 00000000000..a25a946123d --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/04_assert.md @@ -0,0 +1,34 @@ +--- +title: Assert Function +description: + Learn about the assert function in Noir, which can be used to explicitly constrain the predicate or + comparison expression that follows to be true, and what happens if the expression is false at + runtime. +keywords: [Noir programming language, assert statement, predicate expression, comparison expression] +--- + +Noir includes a special `assert` function which will explicitly constrain the predicate/comparison +expression that follows to be true. If this expression is false at runtime, the program will fail to +be proven. + +### Example + +```rust +fn main(x : Field, y : Field) { + assert(x == y); +} +``` + +The above snippet compiles because `==` is a predicate operation. Conversely, the following will not +compile: + +```rust +// INCORRECT + +fn main(x : Field, y : Field) { + assert(x + y); +} +``` + +> The rationale behind this not compiling is due to ambiguity. It is not clear if the above should +> equate to `x + y == 0` or if it should check the truthiness of the result. diff --git a/noir/docs/versioned_docs/version-v0.7.1/language_concepts/06_generics.md b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/06_generics.md new file mode 100644 index 00000000000..66f2e85e16b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/06_generics.md @@ -0,0 +1,116 @@ +--- +title: Generics +description: + Learn how to use Generics in Noir +keywords: [Noir, Rust, generics, functions, structs] +--- + +# Generics + +Generics allow you to use the same functions with multiple different concrete data types. You can +read more about the concept of generics in the Rust documentation +[here](https://doc.rust-lang.org/book/ch10-01-syntax.html). + +Here is a trivial example showing the identity function that supports any type. In Rust, it is +common to refer to the most general type as `T`. We follow the same convention in Noir. + +```rust +fn id(x: T) -> T { + x +} +``` + +## In Structs + +Generics are useful for specifying types in structs. For example, we can specify that a field in a +struct will be of a certain generic type. In this case `value` is of type `T`. + +```rust +struct RepeatedValue { + value: T, + count: comptime Field, +} + +impl RepeatedValue { + fn new(value: T) -> Self { + Self { value, count: 1 } + } + + fn increment(mut repeated: Self) -> Self { + repeated.count += 1; + repeated + } + + fn print(self) { + for _i in 0 .. self.count { + dep::std::println(self.value); + } + } +} + +fn main() { + let mut repeated = RepeatedValue::new("Hello!"); + repeated = repeated.increment(); + repeated.print(); +} +``` + +The `print` function will print `Hello!` an arbitrary number of times, twice in this case. + +If we want to be generic over array lengths (which are type-level integers), we can use numeric +generics. Using these looks just like using regular generics, but these generics can resolve to +integers at compile-time, rather than resolving to types. Here's an example of a struct that is +generic over the size of the array it contains internally: + +```rust +struct BigInt { + limbs: [u32; N], +} + +impl BigInt { + // `N` is in scope of all methods in the impl + fn first(first: BigInt, second: BigInt) -> Self { + assert(first.limbs != second.limbs); + first + + fn second(first: BigInt, second: Self) -> Self { + assert(first.limbs != second.limbs); + second + } +} +``` + +## Calling functions on generic parameters + +Unlike Rust, Noir does not have traits, so how can one translate the equivalent of a trait bound in +Rust into Noir? That is, how can we write a function that is generic over some type `T`, while also +requiring there is a function like `eq: fn(T, T) -> bool` that works on the type? + +The answer is that we can translate this by passing in the function manually. Here's an example of +implementing array equality in Noir: + +```rust +fn array_eq(array1: [T; N], array2: [T; N], elem_eq: fn(T, T) -> bool) -> bool { + if array1.len() != array2.len() { + false + } else { + let mut result = true; + for i in 0 .. array1.len() { + result &= elem_eq(array1[i], array2[i]); + } + result + } +} + +fn main() { + assert(array_eq([1, 2, 3], [1, 2, 3], |a, b| a == b)); + + // We can use array_eq even for arrays of structs, as long as we have + // an equality function for these structs we can pass in + let array = [MyStruct::new(), MyStruct::new()]; + assert(array_eq(array, array, MyStruct::eq)); +} +``` + +You can see an example of generics in the tests +[here](https://github.com/noir-lang/noir/blob/master/tooling/nargo_cli/tests/execution_success/generics/src/main.nr). diff --git a/noir/docs/versioned_docs/version-v0.7.1/language_concepts/07_mutability.md b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/07_mutability.md new file mode 100644 index 00000000000..5631a322659 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/07_mutability.md @@ -0,0 +1,118 @@ +--- +title: Mutability +description: + Learn about mutable variables, constants, and globals in Noir programming language. Discover how + to declare, modify, and use them in your programs. +keywords: [noir programming language, mutability in noir, mutable variables, constants, globals] +--- + +Variables in Noir can be declared mutable via the `mut` keyword. Mutable variables can be reassigned +to via an assignment expression. + +```rust +let x = 2; +x = 3; // error: x must be mutable to be assigned to + +let mut y = 3; +let y = 4; // OK +``` + +The `mut` modifier can also apply to patterns: + +```rust +let (a, mut b) = (1, 2); +a = 11; // error: a must be mutable to be assigned to +b = 12; // OK + +let mut (c, d) = (3, 4); +c = 13; // OK +d = 14; // OK + +// etc. +let MyStruct { x: mut y } = MyStruct { x: a } +// y is now in scope +``` + +Note that mutability in noir is local and everything is passed by value, so if a called function +mutates its parameters then the parent function will keep the old value of the parameters. + +```rust +fn main() -> Field { + let x = 3; + helper(x); + x // x is still 3 +} + +fn helper(mut x: i32) { + x = 4; +} +``` + +## Comptime Values + +Comptime values are values that are known at compile-time. This is different to a witness +which changes per proof. If a comptime value that is being used in your program is changed, then your +circuit will also change. + +Comptime is slightly different from Rust's `const`. Namely, it is a bit more flexible in that normal functions can accept comptime parameters. For example, this is used to verify an array index is known at compile-time. Note that the "known at compile-time" here means "known after function inlining is performed while optimizing the program" and not "known during type-checking." + +Below we show how to declare a comptime value: + +```rust +fn main() { + let a: comptime Field = 5; + + // `comptime Field` can also be inferred: + let a = 5; +} +``` + +Note that variables declared as mutable may not be comptime: + +```rust +fn main() { + // error: Cannot mark a comptime type as mutable + let mut a: comptime Field = 5; + + // a inferred as a private Field here + let mut a = 5; +} +``` + +## Globals + +Noir also supports global variables. However, they must be compile-time variables. If `comptime` is +not explicitly written in the type annotation the compiler will implicitly specify the declaration +as compile-time. They can then be used like any other compile-time variable inside functions. The +global type can also be inferred by the compiler entirely. Globals can also be used to specify array +annotations for function parameters and can be imported from submodules. + +```rust +global N: Field = 5; // Same as `global N: comptime Field = 5` + +fn main(x : Field, y : [Field; N]) { + let res = x * N; + + assert(res == y[0]); + + let res2 = x * mysubmodule::N; + assert(res != res2); +} + +mod mysubmodule { + use dep::std; + + global N: Field = 10; + + fn my_helper() -> comptime Field { + let x = N; + x + } +} +``` + +## Why only local mutability? + +Witnesses in a proving system are immutable in nature. Noir aims to _closely_ mirror this setting +without applying additional overhead to the user. Modeling a mutable reference is not as +straightforward as on conventional architectures and would incur some possibly unexpected overhead. diff --git a/noir/docs/versioned_docs/version-v0.7.1/language_concepts/08_comments.md b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/08_comments.md new file mode 100644 index 00000000000..5b1d9fa38f2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/08_comments.md @@ -0,0 +1,27 @@ +--- +title: Comments +description: + Learn how to write comments in Noir programming language. A comment is a line of code that is + ignored by the compiler, but it can be read by programmers. Single-line and multi-line comments + are supported in Noir. +keywords: [Noir programming language, comments, single-line comments, multi-line comments] +--- + +A comment is a line in your codebase which the compiler ignores, however it can be read by +programmers. + +Here is a single line comment: + +```rust +// This is a comment and is ignored +``` + +`//` is used to tell the compiler to ignore the rest of the line. + +Noir doesn't have multi-line comments, but you can emulate them via using `//` on each line + +```rust +// This is a multi line +// comment, that is ignored by +// the compiler +``` diff --git a/noir/docs/versioned_docs/version-v0.7.1/language_concepts/09_distinct.md b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/09_distinct.md new file mode 100644 index 00000000000..03759d4bb4a --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/language_concepts/09_distinct.md @@ -0,0 +1,63 @@ +--- +title: Distinct Witnesses +--- + +The `distinct` keyword prevents repetitions of witness indices in the program's ABI. This ensures +that the witnesses being returned as public inputs are all unique. + +The `distinct` keyword is only used for return values on program entry points (usually the `main()` +function). + +When using `disctinct` and `pub` simultaneously, `distinct` comes first. See the example below. + +You can read more about the problem this solves +[here](https://github.com/noir-lang/noir/issues/1183). + +## Example + +Without the `distinct` keyword, the following program + +```rust +fn main(x : pub Field, y : pub Field) -> pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + "return_witnesses": [3, 2, 4, 4] + } +} +``` + +Whereas (with the `distinct` keyword) + +```rust +fn main(x : pub Field, y : pub Field) -> distinct pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + //... + "return_witnesses": [3, 4, 5, 6] + } +} +``` diff --git a/noir/docs/versioned_docs/version-v0.7.1/modules_packages_crates/crates_and_packages.md b/noir/docs/versioned_docs/version-v0.7.1/modules_packages_crates/crates_and_packages.md new file mode 100644 index 00000000000..34f28a71148 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/modules_packages_crates/crates_and_packages.md @@ -0,0 +1,35 @@ +--- +title: Crates and Packages +description: + Learn how to use Crates and Packages in your Noir project +keywords: [Nargo, dependencies, package management, crates, package] +--- + +## Crates + +A crate is the smallest amount of code that the Noir compiler considers at a time. +Crates can contain modules, and the modules may be defined in other files that get compiled with the crate, as we’ll see in the coming sections. + +### Crate Types + +A Noir crate can come in one of two forms: a binary crate or a library crate. + +_Binary crates_ are programs which you can compile to an ACIR circuit which you can then create proofs against. Each must have a function called `main` that defines the ACIR circuit which is to be proved. + +_Library crates_ don't have a `main` function and they don't compile down to ACIR. Instead they define functionality intended to be shared with multiple projects, and eventually included in a binary crate. + +### Crate Root + +Every crate has a root, which is the source file that the compiler starts, this is also known as the root module. The Noir compiler does not enforce any conditions on the name of the file which is the crate root, however if you are compiling via Nargo the crate root must be called `lib.nr` or `main.nr` for library or binary crates respectively. + +## Packages + +A Nargo _package_ is a collection of one of more crates that provides a set of functionality. A package must include a Nargo.toml file. + +A package _must_ contain either a library or a binary crate, but not both. + +### Differences from Cargo Packages + +One notable difference between Rust's Cargo and Noir's Nargo is that while Cargo allows a package to contain an unlimited number of binary crates and a single library crate, Nargo currently only allows a package to contain a single crate. + +In future this restriction may be lifted to allow a Nargo package to contain both a binary and library crate or multiple binary crates. diff --git a/noir/docs/versioned_docs/version-v0.7.1/modules_packages_crates/dependencies.md b/noir/docs/versioned_docs/version-v0.7.1/modules_packages_crates/dependencies.md new file mode 100644 index 00000000000..2807ad52046 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/modules_packages_crates/dependencies.md @@ -0,0 +1,110 @@ +--- +title: Managing Dependencies +description: + Learn how to specify and manage dependencies in Nargo, allowing you to upload packages to GitHub + and use them easily in your project. +keywords: [Nargo, dependencies, GitHub, package management, versioning] +--- + +Nargo allows you to upload packages to GitHub and use them as dependencies. + +## Specifying a dependency + +Specifying a dependency requires a tag to a specific commit and the git url to the url containing +the package. + +Currently, there are no requirements on the tag contents. If requirements are added, it would follow +semver 2.0 guidelines. + +> Note: Without a `tag` , there would be no versioning and dependencies would change each time you +> compile your project. + +For example, to add the [ecrecover-noir library](https://github.com/colinnielsen/ecrecover-noir) to your project, add it to `Nargo.toml`: + +```toml +# Nargo.toml + +[dependencies] +ecrecover = {tag = "v0.8.0", git = "https://github.com/colinnielsen/ecrecover-noir"} +``` + +## Specifying a local dependency + +You can also specify dependencies that are local to your machine. + +For example, this file structure has a library and binary crate + +``` +├── binary_crate +│   ├── Nargo.toml +│   └── src +│   └── main.nr +└── liba + ├── Nargo.toml + └── src + └── lib.nr +``` + +Inside of the binary crate, you can specify: + +```toml +# Nargo.toml + +[dependencies] +libA = { path = "../liba" } +``` + +## Importing dependencies + +You can import a dependency to a Noir file using the following syntax. For example, to import the +ecrecover-noir library and local liba referenced above: + +```rust +use dep::ecrecover; +use dep::libA; +``` + +You can also import only the specific parts of dependency that you want to use, like so: + +```rust +use dep::std::hash::sha256; +use dep::std::scalar_mul::fixed_base; +``` + +Lastly, as demonstrated in the +[elliptic curve example](../standard_library/cryptographic_primitives/ec_primitives#examples), you +can import multiple items in the same line by enclosing them in curly braces: + +```rust +use dep::std::ec::tecurve::affine::{Curve, Point}; +``` + +## Dependencies of Dependencies + +Note that when you import a dependency, you also get access to all of the dependencies of that package. + +For example, the [phy_vector](https://github.com/resurgencelabs/phy_vector) library imports an [fraction](https://github.com/resurgencelabs/fraction) library. If you're importing the phy_vector library, then you can access the functions in fractions library like so: + +```rust +use dep::phy_vector; + +fn main(x : Field, y : pub Field) { + //... + let f = phy_vector::fraction::toFraction(true, 2, 1); + //... +} +``` + +## Available Libraries + +Noir does not currently have an official package manager. You can find a list of available Noir libraries in the [awesome-noir repo here](https://github.com/noir-lang/awesome-noir#libraries). + +Some libraries that are available today include: + +- [Standard Library](https://github.com/noir-lang/noir/tree/master/noir_stdlib) - the Noir Standard Library +- [Ethereum Storage Proof Verification](https://github.com/aragonzkresearch/noir-trie-proofs) - a library that contains the primitives necessary for RLP decoding (in the form of look-up table construction) and Ethereum state and storage proof verification (or verification of any trie proof involving 32-byte long keys) +- [BigInt](https://github.com/shuklaayush/noir-bigint) - a library that provides a custom BigUint56 data type, allowing for computations on large unsigned integers +- [ECrecover](https://github.com/colinnielsen/ecrecover-noir/tree/main) - a library to verify an ECDSA signature and return the source Ethereum address +- [Sparse Merkle Tree Verifier](https://github.com/vocdoni/smtverifier-noir/tree/main) - a library for verification of sparse Merkle trees +- [Signed Int](https://github.com/resurgencelabs/signed_int) - a library for accessing a custom Signed Integer data type, allowing access to negative numbers on Noir +- [Fraction](https://github.com/resurgencelabs/fraction) - a library for accessing fractional number data type in Noir, allowing results that aren't whole numbers diff --git a/noir/docs/versioned_docs/version-v0.7.1/modules_packages_crates/modules.md b/noir/docs/versioned_docs/version-v0.7.1/modules_packages_crates/modules.md new file mode 100644 index 00000000000..e429b336511 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/modules_packages_crates/modules.md @@ -0,0 +1,104 @@ +--- +title: Understanding Modules +description: + Learn how to organize your files using modules in Noir, following the same convention as Rust's + module system. Examples included. +keywords: [Noir, Rust, modules, organizing files, sub-modules] +--- + +# Modules + +Noir's module system follows the same convention as the _newer_ version of Rust's module system. + +## Purpose of Modules + +Modules are used to organise files. Without modules all of your code would need to live in a single +file. In Noir, the compiler does not automatically scan all of your files to detect modules. This +must be done explicitly by the developer. + +## Examples + +### Importing a module in the crate root + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::hello_world(); +} +``` + +Filename : `src/foo.nr` + +```rust +fn from_foo() {} +``` + +In the above snippet, the crate root is the `src/main.nr` file. The compiler sees the module +declaration `mod foo` which prompts it to look for a foo.nr file. + +Visually this module hierarchy looks like the following : + +``` +crate + ├── main + │ + └── foo + └── from_foo + +``` + +### Importing a module throughout the tree +All modules are accessible from the ``crate::`` namespace. + +``` +crate + ├── bar + ├── foo + └── main + +``` +In the above snippet, if ``bar`` would like to use functions in ``foo``, it can do so by ``use crate::foo::function_name``. + +### Sub-modules + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::from_foo(); +} +``` + +Filename : `src/foo.nr` + +```rust +mod bar; +fn from_foo() {} +``` + +Filename : `src/foo/bar.nr` + +```rust +fn from_bar() {} +``` + +In the above snippet, we have added an extra module to the module tree; `bar`. `bar` is a submodule +of `foo` hence we declare bar in `foo.nr` with `mod bar`. Since `foo` is not the crate root, the +compiler looks for the file associated with the `bar` module in `src/foo/bar.nr` + +Visually the module hierarchy looks as follows: + +``` +crate + ├── main + │ + └── foo + ├── from_foo + └── bar + └── from_bar +``` diff --git a/noir/docs/versioned_docs/version-v0.7.1/nargo/01_commands.md b/noir/docs/versioned_docs/version-v0.7.1/nargo/01_commands.md new file mode 100644 index 00000000000..8efa286b5f0 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/nargo/01_commands.md @@ -0,0 +1,132 @@ +--- +title: Commands +description: + Noir CLI Commands for Noir Prover and Verifier to create, execute, prove and verify programs, + generate Solidity verifier smart contract and compile into JSON file containing ACIR + representation and ABI of circuit. +keywords: + [ + Nargo, + Noir CLI, + Noir Prover, + Noir Verifier, + generate Solidity verifier, + compile JSON file, + ACIR representation, + ABI of circuit, + TypeScript, + ] +--- + +## General options + +``` +Options: + -s, --show-ssa Emit debug information for the intermediate SSA IR + -d, --deny-warnings Quit execution when warnings are emitted + --show-output Display output of `println` statements during tests + -h, --help Print help +``` + +## `nargo help [subcommand]` + +Prints the list of available commands or specific information of a subcommand. + +_Arguments_ + +- `` - The subcommand whose help message to display + +## `nargo check` + +Generate the `Prover.toml` and `Verifier.toml` files for specifying prover and verifier in/output +values of the Noir program respectively. + +## `nargo codegen-verifier` + +Generate a Solidity verifier smart contract for the program. + +## `nargo compile ` + +Compile the program into a JSON build artifact file containing the ACIR representation and the ABI +of the circuit. This build artifact can then be used to generate and verify proofs. + +_Arguments_ + +- `` - The name of the circuit file + +_Options_ + +- `-c, --contracts` - Compile each contract function used within the program +- `--print-acir` - Displays the ACIR for the compiled circuit + +## `nargo new [path]` + +Creates a new Noir project. + +_Arguments_ + +- `` - Name of the package +- `[path]` - The path to save the new project + +## `nargo execute [witness_name]` + +Runs the Noir program and prints its return value. + +_Arguments_ + +- `[witness_name]` - The name of the witness + +_Usage_ + +The inputs to the circuit are read from the `Prover.toml` file generated by `nargo check`, which +must be filled in. + +To save the witness to file, run the command with a value for the `witness-name` argument. A +`.tr` file will then be saved in the `build` folder. + +> **Info:** The `.tr` file is the witness file. The witness file can be considered as program inputs +> parsed for your program's ACIR. +> +> This file can be passed along with circuit's ACIR into a TypeScript project for proving and +> verification. See the [TypeScript](../typescript#proving-and-verifying-externally-compiled-files) +> section to learn more. + +## `nargo prove ` + +Creates a proof for the program. + +_Arguments_ + +- `` - The name of the proof + +_Options_ + +- `-v, --verify` - Verify proof after proving + +## `nargo verify ` + +Given a proof and a program, verify whether the proof is valid. + +_Arguments_ + +- `` - The proof to verify + +## `nargo test ` + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. + +See an example on the [testing page](./testing). + +_Arguments_ + +- `` - a pattern to indicate to only run tests with names containing the pattern + +## `nargo gates` + +Counts the occurrences of different gates in circuit + +## `nargo lsp` + +Start a long-running Language Server process that communicates over stdin/stdout. +Usually this command is not run by a user, but instead will be run by a Language Client, such as [vscode-noir](https://github.com/noir-lang/vscode-noir). diff --git a/noir/docs/versioned_docs/version-v0.7.1/nargo/02_testing.md b/noir/docs/versioned_docs/version-v0.7.1/nargo/02_testing.md new file mode 100644 index 00000000000..ba0bebd658b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/nargo/02_testing.md @@ -0,0 +1,32 @@ +--- +title: Testing in Noir +description: Learn how to use Nargo to test your Noir program in a quick and easy way +keywords: [Nargo, testing, Noir, compile, test] +--- + +You can test your Noir programs using Noir circuits. + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. + +For example if you have a program like: + +```rust +fn add(x: u64, y: u64) -> u64 { + x + y +} +#[test] +fn test_add() { + assert(add(2,2) == 4); + assert(add(0,1) == 1); + assert(add(1,0) == 1); +} +``` + +Running `nargo test` will test that the `test_add` function can be executed while satisfying the all +the contraints which allows you to test that add returns the expected values. Test functions can't +have any arguments currently. + +This is much faster compared to testing in Typescript but the only downside is that you can't +explicitly test that a certain set of inputs are invalid. i.e. you can't say that you want +add(2^64-1, 2^64-1) to fail. diff --git a/noir/docs/versioned_docs/version-v0.7.1/nargo/03_solidity_verifier.md b/noir/docs/versioned_docs/version-v0.7.1/nargo/03_solidity_verifier.md new file mode 100644 index 00000000000..9ac60cb0ba7 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/nargo/03_solidity_verifier.md @@ -0,0 +1,129 @@ +--- +title: Solidity Verifier +description: + Learn how to run the verifier as a smart contract on the blockchain. Compile a Solidity verifier + contract for your Noir program and deploy it on any EVM blockchain acting as a verifier smart + contract. Read more to find out! +keywords: + [ + solidity verifier, + smart contract, + blockchain, + compiler, + plonk_vk.sol, + EVM blockchain, + verifying Noir programs, + proving backend, + Barretenberg, + ] +--- + +For certain applications, it may be desirable to run the verifier as a smart contract instead of on +a local machine. + +Compile a Solidity verifier contract for your Noir program by running: + +```sh +nargo codegen-verifier +``` + +A new `contract` folder would then be generated in your project directory, containing the Solidity +file `plonk_vk.sol`. It can be deployed on any EVM blockchain acting as a verifier smart contract. + +> **Note:** It is possible to compile verifier contracts of Noir programs for other smart contract +> platforms as long as the proving backend supplies an implementation. +> +> Barretenberg, the default proving backend for Nargo, supports compilation of verifier contracts in +> Solidity only for the time being. + +## Verify + +To verify a proof using the Solidity verifier contract, call the `verify` function with the +following signature: + +```solidity +function verify(bytes calldata _proof, bytes32[] calldata _publicInputs) external view returns (bool) +``` + +You can see an example of how the `verify` function is called in the example zk voting application [here](https://github.com/noir-lang/noir-examples/blob/33e598c257e2402ea3a6b68dd4c5ad492bce1b0a/foundry-voting/src/zkVote.sol#L35): + +```solidity +function castVote(bytes calldata proof, uint proposalId, uint vote, bytes32 nullifierHash) public returns (bool) { + // ... + bytes32[] memory publicInputs = new bytes32[](4); + publicInputs[0] = merkleRoot; + publicInputs[1] = bytes32(proposalId); + publicInputs[2] = bytes32(vote); + publicInputs[3] = nullifierHash; + require(verifier.verify(proof, publicInputs), "Invalid proof"); +``` + +### Public Inputs + +:::tip + +A circuit doesn't have the concept of a return value. Return values are just syntactic sugar in +Noir. + +Under the hood, the return value is passed as an input to the circuit and is checked at the end of +the circuit program. + +::: + +The verifier contract uses the output (return) value of a Noir program as a public input. So if you +have the following function + +```rust +fn main( + // Public inputs + pubkey_x: pub Field, + pubkey_y: pub Field, + // Private inputs + priv_key: Field, +) -> pub Field +``` + +then `verify` in `plonk_vk.sol` will expect 3 public inputs. Passing two inputs will result in an +error like `Reason: PUBLIC_INPUT_COUNT_INVALID(3, 2)`. + +In this case the 3 inputs to `verify` would be ordered as `[pubkey_x, pubkey_y, return]`. + +#### Struct inputs + +Consider the following program: + +```rust +struct Type1 { + val1: Field, + val2: Field, +} + +struct Nested { + t1: Type1, + is_true: bool, +} + +fn main(x: pub Field, nested: pub Nested, y: pub Field) { + //... +} +``` + +Structs will be flattened so that the array of inputs is 1-dimensional array. The order of these inputs would be flattened to: `[x, nested.t1.val1, nested.t1.val2, nested.is_true, y]` + +## Noir for EVM chains + +You can currently deploy the Solidity verifier contracts to most EVM compatible chains. EVM chains that have been tested and are known to work include: + +- Optimism +- Arbitrum +- Polygon PoS +- Scroll +- Celo + +Other EVM chains should work, but have not been tested directly by our team. If you test any other chains, please open a PR on this page to update the list. See [this doc](https://github.com/noir-lang/noir-starter/tree/main/with-foundry#testing-on-chain) for more info about testing verifier contracts on different EVM chains. + +### Unsupported chains + +Unfortunately not all "EVM" chains are supported. + +**zkSync** and the **Polygon zkEVM** do _not_ currently support proof verification via Solidity verifier contracts. They are missing the bn256 precompile contract that the verifier contract requires. Once these chains support this precompile, they may work. diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/array_methods.md b/noir/docs/versioned_docs/version-v0.7.1/standard_library/array_methods.md new file mode 100644 index 00000000000..8d8a9531243 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/array_methods.md @@ -0,0 +1,180 @@ +--- +title: Array Methods +description: + Learn about the commonly used methods available for arrays in Noir, including len, sort, fold, + reduce, all, and any. +keywords: [rust, array, methods, len, sort, fold, reduce, all, any] +--- + +# Array + +For convenience, the STD provides some ready-to-use, common methods for arrays[^migrationnote]: + +## len + +Returns the length of an array + +```rust +fn len(_array: [T; N]) -> comptime Field +``` + +example + +```rust +fn main() { + let array = [42, 42]; + assert(array.len() == 2); +} +``` + +## sort + +Returns a new sorted array. The original array remains untouched. Notice that this function will +only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting +logic it uses internally is optimized specifically for these values. If you need a sort function to +sort any type, you should use the function `sort_via` described below. + +```rust +fn sort(_array: [T; N]) -> [T; N] +``` + +example + +```rust +fn main() { + let arr = [42, 32]; + let sorted = arr.sort(); + assert(sorted == [32, 42]); +} +``` + +## sort_via + +Sorts the array with a custom comparison function + +```rust +fn sort_via(mut a: [T; N], ordering: fn(T, T) -> bool) -> [T; N] +``` + +example + +```rust +fn main() { + let arr = [42, 32] + let sorted_ascending = arr.sort_via(|a, b| a < b); + assert(sorted_ascending == [32, 42]); // verifies + + let sorted_descending = arr.sort_via(|a, b| a > b); + assert(sorted_descending == [32, 42]); // does not verify +} +``` + +## map + +Applies a function to each element of the array, returning a new array containing the mapped elements. + +```rust +fn map(f: fn(T) -> U) -> [U; N] +``` + +example + +```rust +let a = [1, 2, 3]; +let b = a.map(|a| a * 2); // b is now [2, 4, 6] +``` + +## fold + +Applies a function to each element of the array, returning the final accumulated value. The first +parameter is the initial value. + +```rust +fn fold(mut accumulator: U, f: fn(U, T) -> U) -> U +``` + +This is a left fold, so the given function will be applied to the accumulator and first element of +the array, then the second, and so on. For a given call the expected result would be equivalent to: + +```rust +let a1 = [1]; +let a2 = [1, 2]; +let a3 = [1, 2, 3]; + +let f = |a, b| a - b; +a1.fold(10, f) //=> f(10, 1) +a2.fold(10, f) //=> f(f(10, 1), 2) +a3.fold(10, f) //=> f(f(f(10, 1), 2), 3) +``` + +example: + +```rust + +fn main() { + let arr = [2, 2, 2, 2, 2]; + let folded = arr.fold(0, |a, b| a + b); + assert(folded == 10); +} + +``` + +## reduce + +Same as fold, but uses the first element as starting element. + +```rust +fn reduce(f: fn(T, T) -> T) -> T +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 2]; + let reduced = arr.reduce(|a, b| a + b); + assert(reduced == 10); +} +``` + +## all + +Returns true if all the elements satisfy the given predicate + +```rust +fn all(predicate: fn(T) -> bool) -> bool +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 2]; + let all = arr.all(|a| a == 2); + assert(all); +} +``` + +## any + +Returns true if any of the elements satisfy the given predicate + +```rust +fn any(predicate: fn(T) -> bool) -> bool +``` + +example: + +```rust +fn main() { + let arr = [2, 2, 2, 2, 5]; + let any = arr.any(|a| a == 5); + assert(any); +} + +``` + +[^migrationnote]: + Migration Note: These methods were previously free functions, called via `std::array::len()`. + For the sake of ease of use and readability, these functions are now methods and the old syntax + for them is now deprecated. diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/black_box_fns.md b/noir/docs/versioned_docs/version-v0.7.1/standard_library/black_box_fns.md new file mode 100644 index 00000000000..3063e71c147 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/black_box_fns.md @@ -0,0 +1,44 @@ +--- +title: Black Box Functions +description: Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. +keywords: [noir, black box functions] +--- + +Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. This makes certain zk-snark unfriendly computations cheaper than if they were implemented in Noir. + +:::warning + +It is likely that not all backends will support a particular black box function. + +::: + +Because it is not guaranteed that all backends will support black box functions, it is possible that certain Noir programs won't compile against a particular backend if they use an unsupported black box function. It is possible to fallback to less efficient implementations written in Noir/ACIR in some cases. + +Black box functions are specified with the `#[foreign(black_box_fn)]` attribute. For example, the SHA256 function in the Noir [source code](https://github.com/noir-lang/noir/blob/v0.5.1/noir_stdlib/src/hash.nr) looks like: + +```rust +#[foreign(sha256)] +fn sha256(_input : [u8; N]) -> [u8; 32] {} +``` + +## Function list + +Here is a list of the current black box functions that are supported by UltraPlonk: + +- AES +- [SHA256](./cryptographic_primitives/hashes#sha256) +- [Schnorr signature verification](./cryptographic_primitives/schnorr) +- [Blake2s](./cryptographic_primitives/hashes#blake2s) +- [Pedersen](./cryptographic_primitives/hashes#pedersen) +- [HashToField128Security](./cryptographic_primitives/hashes#hash_to_field) +- [ECDSA signature verification](./cryptographic_primitives/ecdsa_secp256k1) +- [Fixed base scalar multiplication](./cryptographic_primitives/scalar) +- [Compute merkle root](./merkle_trees#compute_merkle_root) +- AND +- XOR +- RANGE +- [Keccak256](./cryptographic_primitives/hashes#keccak256) + +Most black box functions are included as part of the Noir standard library, however `AND`, `XOR` and `RANGE` are used as part of the Noir language syntax. For instance, using the bitwise operator `&` will invoke the `AND` black box function. To ensure compatibility across backends, the ACVM has fallback implementations of `AND`, `XOR` and `RANGE` defined in its standard library which it can seamlessly fallback to if the backend doesn't support them. + +You can view the black box functions defined in the ACVM code [here](https://github.com/noir-lang/acvm/blob/acir-v0.12.0/acir/src/circuit/black_box_functions.rs). diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives.md b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives.md new file mode 100644 index 00000000000..2df4f929474 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives.md @@ -0,0 +1,14 @@ +--- +title: Cryptographic primitives in Noir +description: + Learn about the cryptographic primitives ready to use for any Noir project +keywords: + [ + cryptographic primitives, + Noir project, + ] +--- + +The Noir team is progressively adding new cryptographic primitives to the standard library. Reach out for news or if you would be interested in adding more of these calculations in Noir. + +Some methods are available thanks to the Aztec backend, not being performed using Noir. When using other backends, these methods may or may not be supplied. diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/00_hashes.mdx b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/00_hashes.mdx new file mode 100644 index 00000000000..31a84cdb753 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/00_hashes.mdx @@ -0,0 +1,146 @@ +--- +title: Hash methods +description: + Learn about the cryptographic primitives ready to use for any Noir project, including sha256, + blake2s, pedersen, mimc_bn254 and mimc +keywords: + [cryptographic primitives, Noir project, sha256, blake2s, pedersen, mimc_bn254, mimc, hash] +--- + +import BlackBoxInfo from './common/_blackbox.mdx'; + +## sha256 + +Given an array of bytes, returns the resulting sha256 hash. + +```rust +fn sha256(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let hash = std::hash::sha256(x); +} +``` + + + +## blake2s + +Given an array of bytes, returns an array with the Blake2 hash + +```rust +fn blake2s(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let hash = std::hash::blake2s(x); +} +``` + + + +## pedersen + +Given an array of Fields, returns the Pedersen hash. + +```rust +fn pedersen(_input : [Field]) -> [Field; 2] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let hash = std::hash::pedersen(x); +} +``` + + + +## keccak256 + +Given an array of bytes (`u8`), returns the resulting keccak hash as an array of 32 bytes +(`[u8; 32]`). Specify a message_size to hash only the first `message_size` bytes +of the input. + +```rust +fn keccak256(_input : [u8; N], _message_size: u32) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let message_size = 4; + let hash = std::hash::keccak256(x, message_size); +} +``` + + + +## poseidon + +Given an array of Fields, returns a new Field with the Poseidon Hash. Mind that you need to specify +how many inputs are there to your Poseidon function. + +```rust +// example for hash_1, hash_2 accepts an array of length 2, etc +fn hash_1(input: [Field; 1]) -> Field +``` + +example: + +```rust +fn main() +{ + let hash1 = std::hash::poseidon::bn254::hash_2([1, 2]); + assert(hash1 == 0x115cc0f5e7d690413df64c6b9662e9cf2a3617f2743245519e19607a4417189a); +} +``` + +## mimc_bn254 and mimc + +`mimc_bn254` is `mimc`, but with hardcoded parameters for the BN254 curve. You can use it by +providing an array of Fields, and it returns a Field with the hash. You can use the `mimc` method if +you're willing to input your own constants: + +```rust +fn mimc(x: Field, k: Field, constants: [Field; N], exp : Field) -> Field +``` + +otherwise, use the `mimc_bn254` method: + +```rust +fn mimc_bn254(array: [Field; N]) -> Field +``` + +example: + +```rust + +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let hash = std::hash::mimc_bn254(x); +} +``` + +## hash_to_field + +```rust +fn hash_to_field(_input : [Field; N]) -> Field {} +``` + +Calculates the `blake2s` hash of the inputs and returns the hash modulo the field modulus to return +a value which can be represented as a `Field`. + + diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/01_scalar.mdx b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/01_scalar.mdx new file mode 100644 index 00000000000..62265cddb1e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/01_scalar.mdx @@ -0,0 +1,33 @@ +--- +title: Scalar multiplication +description: + See how you can perform scalar multiplications over a fixed base in Noir +keywords: + [ + cryptographic primitives, + Noir project, + scalar multiplication, + ] +--- + +import BlackBoxInfo from './common/\_blackbox.mdx'; + +## scalar_mul::fixed_base + +Performs scalar multiplication over the embedded curve whose coordinates are defined by the +configured noir field. For the BN254 scalar field, this is BabyJubJub or Grumpkin. + +```rust +fn fixed_base(_input : Field) -> [Field; 2] +``` + +example + +```rust +fn main(x : Field) { + let scal = std::scalar_mul::fixed_base(x); + std::println(scal); +} +``` + + diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/02_schnorr.mdx b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/02_schnorr.mdx new file mode 100644 index 00000000000..0e219c0e5ff --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/02_schnorr.mdx @@ -0,0 +1,37 @@ +--- +title: Schnorr Signatures +description: Learn how you can verify Schnorr signatures using Noir +keywords: [cryptographic primitives, Noir project, schnorr, signatures] +--- + +import BlackBoxInfo from './common/_blackbox.mdx'; + +## schnorr::verify_signature + +Verifier for Schnorr signatures over the embedded curve (for BN254 it is Grumpkin). + +```rust +fn verify_signature(_public_key_x: Field, _public_key_y: Field, _signature: [u8; 64], _message: [u8]) -> bool +``` + +where `_signature` can be generated like so using the npm package +[@noir-lang/barretenberg](https://www.npmjs.com/package/@noir-lang/barretenberg) + +```js +const { BarretenbergWasm } = require('@noir-lang/barretenberg/dest/wasm'); +const { Schnorr } = require('@noir-lang/barretenberg/dest/crypto/schnorr'); + +... + +const barretenberg = await BarretenbergWasm.new(); +const schnorr = new Schnorr(barretenberg); +const pubKey = schnorr.computePublicKey(privateKey); +const message = ... +const signature = Array.from( + schnorr.constructSignature(hash, privateKey).toBuffer() +); + +... +``` + + diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/03_ecdsa_secp256k1.mdx b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/03_ecdsa_secp256k1.mdx new file mode 100644 index 00000000000..0b800fdbc5f --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/03_ecdsa_secp256k1.mdx @@ -0,0 +1,17 @@ +--- +title: ECDSA Verification +description: Learn about the cryptographic primitives regarding ECDSA over the secp256k1 curve +keywords: [cryptographic primitives, Noir project, ecdsa, secp256k1, signatures] +--- + +import BlackBoxInfo from './common/_blackbox.mdx'; + +## ecdsa_secp256k1::verify_signature + +Verifier for ECDSA Secp256k1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> bool +``` + + diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/04_ec_primitives.md b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/04_ec_primitives.md new file mode 100644 index 00000000000..26fb4c09e88 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/04_ec_primitives.md @@ -0,0 +1,101 @@ +--- +title: Elliptic Curve Primitives +keywords: [cryptographic primitives, Noir project] +--- + +Data structures and methods on them that allow you to carry out computations involving elliptic +curves over the (mathematical) field corresponding to `Field`. For the field currently at our +disposal, applications would involve a curve embedded in BN254, e.g. the +[Baby Jubjub curve](https://eips.ethereum.org/EIPS/eip-2494). + +## Data structures + +### Elliptic curve configurations + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Curve`), i.e. the specific elliptic +curve you want to use, which would be specified using any one of the methods +`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::new` which take the coefficients in the +defining equation together with a generator point as parameters. You can find more detail in the +comments in +[`noir_stdlib/src/ec.nr`](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr), but +the gist of it is that the elliptic curves of interest are usually expressed in one of the standard +forms implemented here (Twisted Edwards, Montgomery and Short Weierstraß), and in addition to that, +you could choose to use `affine` coordinates (Cartesian coordinates - the usual (x,y) - possibly +together with a point at infinity) or `curvegroup` coordinates (some form of projective coordinates +requiring more coordinates but allowing for more efficient implementations of elliptic curve +operations). Conversions between all of these forms are provided, and under the hood these +conversions are done whenever an operation is more efficient in a different representation (or a +mixed coordinate representation is employed). + +### Points + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Point`), i.e. points lying on the +elliptic curve. For a curve configuration `c` and a point `p`, it may be checked checked that `p` +does indeed lie on `c` by calling `c.contains(p1)`. + +## Methods + +(given a choice of curve representation, e.g. use `std::ec::tecurve::affine::Curve` and use +`std::ec::tecurve::affine::Point`) + +- The **zero element** is given by `Point::zero()`, and we can verify whether a point `p: Point` is + zero by calling `p.is_zero()`. +- **Equality**: Points `p1: Point` and `p2: Point` may be checked for equality by calling + `p1.eq(p2)`. +- **Addition**: For `c: Curve` and points `p1: Point` and `p2: Point` on the curve, adding these two + points is accomplished by calling `c.add(p1,p2)`. +- **Negation**: For a point `p: Point`, `p.negate()` is its negation. +- **Subtraction**: For `c` and `p1`, `p2` as above, subtracting `p2` from `p1` is accomplished by + calling `c.subtract(p1,p2)`. +- **Scalar multiplication**: For `c` as above, `p: Point` a point on the curve and `n: Field`, + scalar multiplication is given by `c.mul(n,p)`. If instead `n :: [u1; N]`, i.e. `n` is a bit + array, the `bit_mul` method may be used instead: `c.bit_mul(n,p)` +- **Multi-scalar multiplication**: For `c` as above and arrays `n: [Field; N]` and `p: [Point; N]`, + multi-scalar multiplication is given by `c.msm(n,p)`. +- **Coordinate representation conversions**: The `into_group` method converts a point or curve + configuration in the affine representation to one in the CurveGroup representation, and + `into_affine` goes in the other direction. +- **Curve representation conversions**: `tecurve` and `montcurve` curves and points are equivalent + and may be converted between one another by calling `into_montcurve` or `into_tecurve` on their + configurations or points. `swcurve` is more general and a curve c of one of the other two types + may be converted to this representation by calling `c.into_swcurve()`, whereas a point `p` lying + on the curve given by `c` may be mapped to its corresponding `swcurve` point by calling + `c.map_into_swcurve(p)`. +- **Map-to-curve methods**: The Elligator 2 method of mapping a field element `n: Field` into a + `tecurve` or `montcurve` with configuration `c` may be called as `c.elligator2_map(n)`. For all of + the curve configurations, the SWU map-to-curve method may be called as `c.swu_map(z,n)`, where + `z: Field` depends on `Field` and `c` and must be chosen by the user (the conditions it needs to + satisfy are specified in the comments + [here](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr)). + +## Examples + +The +[ec_baby_jubjub test](https://github.com/noir-lang/noir/blob/master/crates/nargo_cli/tests/test_data/ec_baby_jubjub/src/main.nr) +illustrates all of the above primitives on various forms of the Baby Jubjub curve. A couple of more +interesting examples in Noir would be: + +Public-key cryptography: Given an elliptic curve and a 'base point' on it, determine the public key +from the private key. This is a matter of using scalar multiplication. In the case of Baby Jubjub, +for example, this code would do: + +```rust +use dep::std::ec::tecurve::affine::{Curve, Point}; + +fn bjj_pub_key(priv_key: Field) -> Point +{ + + let bjj = Curve::new(168700, 168696, G::new(995203441582195749578291179787384436505546430278305826713579947235728471134,5472060717959818805561601436314318772137091100104008585924551046643952123905)); + + let base_pt = Point::new(5299619240641551281634865583518297030282874472190772894086521144482721001553, 16950150798460657717958625567821834550301663161624707787222815936182638968203); + + bjj.mul(priv_key,base_pt) +} +``` + +This would come in handy in a Merkle proof. + +- EdDSA signature verification: This is a matter of combining these primitives with a suitable hash + function. See + [feat(stdlib): EdDSA sig verification noir#1136](https://github.com/noir-lang/noir/pull/1136) for + the case of Baby Jubjub and the Poseidon hash function. diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/05_eddsa.mdx b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/05_eddsa.mdx new file mode 100644 index 00000000000..8f060ed3316 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/05_eddsa.mdx @@ -0,0 +1,17 @@ +--- +title: EdDSA Verification +description: Learn about the cryptographic primitives regarding EdDSA +keywords: [cryptographic primitives, Noir project, eddsa, signatures] +--- + +import BlackBoxInfo from './common/_blackbox.mdx'; + +## eddsa::eddsa_poseidon_verify + +Verifier for EdDSA signatures + +```rust +fn eddsa_poseidon_verify(public_key_x : Field, public_key_y : Field, signature_s: Field, signature_r8_x: Field, signature_r8_y: Field, message: Field) -> bool +``` + + diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/common/_blackbox.mdx b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/common/_blackbox.mdx new file mode 100644 index 00000000000..9fe9b48fbff --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/cryptographic_primitives/common/_blackbox.mdx @@ -0,0 +1,5 @@ +:::info + +This is a black box function. Read [this section](../black_box_fns) to learn more about black box functions in Noir. + +::: \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/field_methods.md b/noir/docs/versioned_docs/version-v0.7.1/standard_library/field_methods.md new file mode 100644 index 00000000000..4d1cdc953e9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/field_methods.md @@ -0,0 +1,149 @@ +--- +title: Field Methods +description: + Learn about common methods on Noir Field, including to_le_bits, to_le_bytes, to_le_radix, + to_be_radix, pow_32, etc, and see code examples. +keywords: + [ + Noir Field, + to_le_bits, + to_le_bytes, + to_le_radix, + to_be_radix, + pow_32, + Little Endian, + Big Endian, + Vector, + Exponent, + ] +--- + +After declaring a Field, you can use these common methods on it: + +## to_le_bits + +Transforms the field into an array of bits, Little Endian. + +```rust +fn to_le_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2 + let bits = field.to_le_bits(32); +} +``` + +## to_be_bits + +Transforms the field into an array of bits, Big Endian. + +```rust +fn to_be_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2 + let bits = field.to_be_bits(32); +} +``` + +## to_le_bytes + +Transforms into an array of bytes, Little Endian + +```rust +fn to_le_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2 + let bytes = field.to_le_bytes(4); +} +``` + +## to_be_bytes + +Transforms into an array of bytes, Big Endian + +```rust +fn to_be_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2 + let bytes = field.to_be_bytes(4); +} +``` + +## to_le_radix + +Decomposes into a vector over the specified base, Little Endian + +```rust +fn to_le_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2 + let radix = field.to_le_radix(256, 4); +} +``` + +## to_be_radix + +Decomposes into a vector over the specified base, Big Endian + +```rust +fn to_be_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2 + let radix = field.to_be_radix(256, 4); +} +``` + +## pow_32 + +Returns the value to the power of the specified exponent + +```rust +fn pow_32(self, exponent: Field) -> Field +``` + +example: + +```rust +fn main() { + let field = 2 + let pow = field.pow_32(4); + assert(pow == 16); +} +``` + +## sgn0 + +Parity of (prime) Field element, i.e. sgn0(x mod p) = 0 if x ∈ {0, ..., p-1} is even, otherwise sgn0(x mod p) = 1. + +```rust +fn sgn0(self) -> u1 +``` diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/logging.md b/noir/docs/versioned_docs/version-v0.7.1/standard_library/logging.md new file mode 100644 index 00000000000..649d35a3f0b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/logging.md @@ -0,0 +1,55 @@ +--- +title: Logging +description: + Learn how to use the println statement for debugging in Noir with this tutorial. Understand the + basics of logging in Noir and how to implement it in your code. +keywords: + [ + noir logging, + println statement, + debugging in noir, + noir std library, + logging tutorial, + basic logging in noir, + noir logging implementation, + noir debugging techniques, + rust, + ] +--- + +# Logging + +The standard library provides a familiar `println` statement you can use. Despite being a limited +implementation of rust's `println!` macro, this construct can be useful for debugging. + +The `println` statement only works for fields, integers and arrays (including strings). + +```rust +use dep::std; + +fn main(string: pub str<5>) { + let x = 5; + std::println(x) +} + +``` + +To view the output of the `println` statement you need to set the `--show-output` flag. + +``` +$ nargo prove --help +Create proof for this program. The proof is returned as a hex encoded string + +Usage: nargo prove [OPTIONS] [PROOF_NAME] [CIRCUIT_NAME] + +Arguments: + [PROOF_NAME] The name of the proof + [CIRCUIT_NAME] The name of the circuit build files (ACIR, proving and verification keys) + +Options: + -v, --verify Verify proof after proving + -s, --show-ssa Emit debug information for the intermediate SSA IR + -d, --deny-warnings Quit execution when warnings are emitted + --show-output Display output of `println` statements during tests + -h, --help Print help +``` diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/merkle_trees.md b/noir/docs/versioned_docs/version-v0.7.1/standard_library/merkle_trees.md new file mode 100644 index 00000000000..fc8909a4795 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/merkle_trees.md @@ -0,0 +1,59 @@ +--- +title: Merkle Trees +description: + Learn about Merkle Trees in Noir with this tutorial. Explore the basics of computing a merkle root using a proof, with examples. +keywords: + [ + Merkle trees in Noir, + Noir programming language, + check membership, + computing root from leaf, + Noir Merkle tree implementation, + Merkle tree tutorial, + Merkle tree code examples, + Noir libraries, + pedersen hash., + ] +--- + +## compute_merkle_root + +Returns the root of the tree from the provided leaf and its hash path, using a [Pedersen hash](cryptographic_primitives/00_hashes.mdx#pedersen). + +```rust +fn compute_merkle_root(leaf : Field, index : Field, hash_path: [Field]) -> Field +``` + +example: + +```rust +/** + // these values are for this example only + index = "0" + priv_key = "0x000000000000000000000000000000000000000000000000000000616c696365" + secret = "0x1929ea3ab8d9106a899386883d9428f8256cfedb3c4f6b66bf4aa4d28a79988f" + note_hash_path = [ + "0x1e61bdae0f027b1b2159e1f9d3f8d00fa668a952dddd822fda80dc745d6f65cc", + "0x0e4223f3925f98934393c74975142bd73079ab0621f4ee133cee050a3c194f1a", + "0x2fd7bb412155bf8693a3bd2a3e7581a679c95c68a052f835dddca85fa1569a40" + ] + */ +fn main(index: Field, priv_key: Field, secret: Field, note_hash_path: [Field; 3]) { + + let pubkey = std::scalar_mul::fixed_base(priv_key); + let pubkey_x = pubkey[0]; + let pubkey_y = pubkey[1]; + let note_commitment = std::hash::pedersen([pubkey_x, pubkey_y, secret]); + + let root = std::merkle::compute_merkle_root(note_commitment[0], index, note_hash_path); + std::println(root); +} +``` + +To check merkle tree membership: + +1. Include a merkle root as a program input. +2. Compute the merkle root of a given leaf, index and hash path. +3. Assert the merkle roots are equal. + +For more info about merkle trees, see the Wikipedia [page](https://en.wikipedia.org/wiki/Merkle_tree). \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.7.1/standard_library/zeroed.md b/noir/docs/versioned_docs/version-v0.7.1/standard_library/zeroed.md new file mode 100644 index 00000000000..97dab02dac2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/standard_library/zeroed.md @@ -0,0 +1,25 @@ +--- +title: Zeroed Function +description: + The zeroed function returns a zeroed value of any type. +keywords: + [ + zeroed + ] +--- + +Implements `fn zeroed() -> T` to return a zeroed value of any type. This function is generally unsafe to use as the zeroed bit pattern is not guaranteed to be valid for all types. It can however, be useful in cases when the value is guaranteed not to be used such as in a BoundedVec library implementing a growable vector, up to a certain length, backed by an array. The array can be initialized with zeroed values which are guaranteed to be inaccessible until the vector is pushed to. Similarly, enumerations in noir can be implemented using this method by providing zeroed values for the unused variants. + +You can access the function at `std::unsafe::zeroed`. + +This function currently supports the following types: + +- Field +- Bool +- Uint +- Array +- String +- Tuple +- Function + +Using it on other types could result in unexpected behavior. diff --git a/noir/docs/versioned_docs/version-v0.7.1/typescript.md b/noir/docs/versioned_docs/version-v0.7.1/typescript.md new file mode 100644 index 00000000000..fae002dfd28 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.7.1/typescript.md @@ -0,0 +1,262 @@ +--- +title: Working with TypeScript +description: + Learn how to interact with Noir programs using TypeScript. Follow this tutorial to compile your + program, specify inputs, initialize a prover & verifier, and prove and verify your program. +keywords: [TypeScript, Noir, tutorial, compile, inputs, prover, verifier, proof] +--- + +Interactions with Noir programs can also be performed in TypeScript, which can come in handy when +writing tests or when working in TypeScript-based projects like [Hardhat](https://hardhat.org/). + +This guide is based on the [noir-starter](https://github.com/signorecello/noir-starter) example. +Please refer to it for an example implementation. + +:::note + +You may find unexpected errors working with some frameworks such as `vite`. This is due to the +nature of `wasm` files and the way Noir uses web workers. As we figure it out, we suggest using +[Create React App](https://create-react-app.dev/), or [Next.js](https://nextjs.org/) for a quick +start. + +::: + +## Setup + +We're assuming you're using ES6 for both browser (for example with React), or nodejs. + +Install [Yarn](https://yarnpkg.com/) or [Node.js](https://nodejs.org/en). Init a new project with +`npm init`. Install Noir dependencies in your project by running: + +```bash +npm i @noir-lang/noir_wasm@0.3.2-fa0e9cff github:noir-lang/barretenberg#39a1547875f941ef6640217a42d8f34972425c97 @noir-lang/aztec_backend@0.1.0-0c3b2f2 +``` + +:::note + +While Noir is in rapid development, some packages could interfere with others. For that reason, you +should use these specified versions. Let us know if for some reason you need to use other ones. + +::: + +As for the circuit, we will use the _Standard Noir Example_ and place it in the `src` folder. Feel +free to use any other, as long as you refactor the below examples accordingly. + +This standard example is a program that multiplies input `x` with input `y` and returns the result: + +```rust +// src/main.nr +fn main(x: u32, y: pub u32) -> pub u32 { + let z = x * y; + z +} +``` + +One valid scenario for proving could be `x = 3`, `y = 4` and `return = 12` + +## Imports + +We need some imports, for both the `noir_wasm` library (which will compile the circuit into `wasm` +executables) and `aztec_backend` which is the actual proving backend we will be using. + +We also need to tell the compiler where to find the `.nr` files, so we need to import +`initialiseResolver`. + +```ts +import initNoirWasm, { acir_read_bytes, compile } from '@noir-lang/noir_wasm'; +import initialiseAztecBackend from '@noir-lang/aztec_backend'; +import { initialiseResolver } from '@noir-lang/noir-source-resolver'; +``` + +## Compiling + +We'll go over the code line-by-line later: + +```ts +export const compileCircuit = async () => { + await initNoirWasm(); + + return await fetch(new URL('../src/main.nr', import.meta.url)) + .then(r => r.text()) + .then(code => { + initialiseResolver((id: any) => { + return code; + }); + }) + .then(() => { + try { + const compiled_noir = compile({}); + return compiled_noir; + } catch (e) { + console.log('Error while compiling:', e); + } + }); +}; +``` + +1. First we're calling `initNoirWasm`. This is required on the browser only. +2. We then pass an URL that points to our `main.nr` file, and call `.then` on it so we can get the + actual text of the source code +3. We call `initialiseResolver` returning the source code +4. Finally, we can call the `compile` function + +This function should return us the compiled circuit. + +:::note + +You can use as many files as you need, +[importing them as you would do with Nargo](./modules_packages_crates/dependencies), and you don't +need to set them up in the `src` folder. Just mind the following particularities about +`initialiseResolver`: + +1. The `compile` function expects a `main.nr` file as an entry point. If you need another one, just + pass it as a `entry_point` parameter to `compile`. Check the + [noir starter](https://github.com/signorecello/noir-starter) for an example on multiple files and + a non-default entry point. +2. `initialiseResolver` needs to be synchronous +3. Different frameworks use different ways of fetching files. It's beyond the scope of this guide to + explain why and how, but for reference, + [noir starter](https://github.com/signorecello/noir-starter) uses both Next.js and node.js for + testing. + +Quick tip: an easy way to deal with `initialiseResolver` is just to prepare a +`{fileName: "literally_the_code"}` object beforehand + +::: + +## ACIR + +Noir compiles to two properties: + +1. The ACIR, which is the intermediate language used by backends such as Barretenberg +2. The ABI, which tells you which inputs are to be read + +Let's write a little function that gets us both, initializes the backend, and returns the ACIR as +bytes: + +```ts +export const getAcir = async () => { + const { circuit, abi } = await compileCircuit(); + await initialiseAztecBackend(); + + let acir_bytes = new Uint8Array(Buffer.from(circuit, 'hex')); + return acir_read_bytes(acir_bytes); +}; +``` + +Calling `getAcir()` now should return us the ACIR of the circuit, ready to be used in proofs. + +## Initializing Prover & Verifier + +Prior to proving and verifying, the prover and verifier have to first be initialized by calling +`barretenberg`'s `setup_generic_prover_and_verifier` with your Noir program's ACIR: + +```ts +let [prover, verifier] = await setup_generic_prover_and_verifier(acir); +``` + +This is probably a good time to store this prover and verifier into your state like React Context, +Redux, or others. + +## Proving + +The Noir program can then be executed and proved by calling `barretenberg`'s `create_proof` +function: + +```ts +const proof = await create_proof(prover, acir, abi); +``` + +On the browser, this proof can fail as it requires heavy loads to be run on worker threads. Here's a +quick example of a worker: + +```ts +// worker.ts +onmessage = async event => { + try { + await initializeAztecBackend(); + const { acir, input } = event.data; + const [prover, verifier] = await setup_generic_prover_and_verifier(acir); + const proof = await create_proof(prover, acir, input); + postMessage(proof); + } catch (er) { + postMessage(er); + } finally { + close(); + } +}; +``` + +Which would be called like this, for example: + +```ts +// index.ts +const worker = new Worker(new URL('./worker.ts', import.meta.url)); +worker.onmessage = e => { + if (e.data instanceof Error) { + // oh no! + } else { + // yey! + } +}; +worker.postMessage({ acir, input: { x: 3, y: 4 } }); +``` + +## Verifying + +The `proof` obtained can be verified by calling `barretenberg`'s `verify_proof` function: + +```ts +// 1_mul.ts +const verified = await verify_proof(verifier, proof); +``` + +The function should return `true` if the entire process is working as intended, which can be +asserted if you are writing a test script: + +```ts +expect(verified).eq(true); +``` + +## Verifying with Smart Contract + +Alternatively, a verifier smart contract can be generated and used for verifying Noir proofs in +TypeScript as well. + +This could be useful if the Noir program is designed to be decentrally verified and/or make use of +decentralized states and logics that is handled at the smart contract level. + +To generate the verifier smart contract using typescript: + +```ts +// generator.ts +import { writeFileSync } from 'fs'; + +const sc = verifier.SmartContract(); +syncWriteFile('../contracts/plonk_vk.sol', sc); + +function syncWriteFile(filename: string, data: any) { + writeFileSync(join(__dirname, filename), data, { + flag: 'w', + }); +} +``` + +You can then verify a Noir proof using the verifier contract, for example using Hardhat: + +```ts +// verifier.ts +import { ethers } from 'hardhat'; +import { Contract, ContractFactory, utils } from 'ethers'; + +let Verifier: ContractFactory; +let verifierContract: Contract; + +before(async () => { + Verifier = await ethers.getContractFactory('TurboVerifier'); + verifierContract = await Verifier.deploy(); +}); + +// Verify proof +const sc_verified = await verifierContract.verify(proof); +``` diff --git a/noir/docs/versioned_docs/version-v0.9.0/examples/merkle-proof.mdx b/noir/docs/versioned_docs/version-v0.9.0/examples/merkle-proof.mdx new file mode 100644 index 00000000000..6430780817c --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/examples/merkle-proof.mdx @@ -0,0 +1,48 @@ +--- +title: Merkle Proof Membership +description: + Learn how to use merkle membership proof in Noir to prove that a given leaf is a member of a + merkle tree with a specified root, at a given index. +keywords: + [merkle proof, merkle membership proof, Noir, rust, hash function, Pedersen, sha256, merkle tree] +--- + +Let's walk through an example of a merkle membership proof in Noir that proves that a given leaf is +in a merkle tree. + +```rust +use dep::std; + +fn main(message : [Field; 62], index : Field, hashpath : [Field; 40], root : Field) { + let leaf = std::hash::hash_to_field(message); + let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); + assert(merkle_root == root); +} + +``` + +The message is hashed using `hash_to_field`. The specific hash function that is being used is chosen +by the backend. The only requirement is that this hash function can heuristically be used as a +random oracle. If only collision resistance is needed, then one can call `std::hash::pedersen` +instead. + +```rust +let leaf = std::hash::hash_to_field(message); +``` + +The leaf is then passed to a compute_merkle_root function with the root, index and hashpath. The returned root can then be asserted to be the same as the provided root. + +```rust +let merkle_root = std::merkle::compute_merkle_root(leaf, index, hashpath); +assert (merkle_root == root); +``` + +> **Note:** It is possible to re-implement the merkle tree implementation without standard library. +> However, for most usecases, it is enough. In general, the standard library will always opt to be +> as conservative as possible, while striking a balance with efficiency. + +An example, the merkle membership proof, only requires a hash function that has collision +resistance, hence a hash function like Pedersen is allowed, which in most cases is more efficient +than the even more conservative sha256. + +[View an example on the starter repo](https://github.com/noir-lang/noir-examples/blob/3ea09545cabfa464124ec2f3ea8e60c608abe6df/stealthdrop/circuits/src/main.nr#L20) diff --git a/noir/docs/versioned_docs/version-v0.9.0/getting_started/00_nargo_installation.md b/noir/docs/versioned_docs/version-v0.9.0/getting_started/00_nargo_installation.md new file mode 100644 index 00000000000..de30869732d --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/getting_started/00_nargo_installation.md @@ -0,0 +1,285 @@ +--- +title: Nargo Installation +description: + nargo is a command line tool for interacting with Noir programs (e.g. compiling, proving, + verifying and more). Learn how to install and use Nargo for your projects with this comprehensive + guide. +keywords: [Nargo, command line tool, Noir programs, installation guide, how to use Nargo] +--- + +`nargo` is a command line tool for interacting with Noir programs (e.g. compiling, proving, +verifying and more). + +Alternatively, the interactions can also be performed in [TypeScript](../typescript). + +### UltraPlonk + +Nargo versions <0.5.0 of `aztec_backend` and `aztec_wasm_backend` are based on the TurboPlonk +version of Aztec Backend, which lacks efficient implementations of useful primitives (e.g. Keccak256 in 18k constraints, ECDSA verification in 36k constraints) that the UltraPlonk version offers. + +## Installation + +There are four approaches for installing Nargo: + +- [Option 1: Noirup](#option-1-noirup) +- [Option 2: Binaries](#option-2-binaries) +- [Option 3: Install via Nix](#option-3-install-via-nix) +- [Option 4: Compile from Source](#option-4-compile-from-source) + +Optionally you can also install [Noir VS Code extension] for syntax highlighting. + +### Option 1: Noirup + +If you're on OSX or Linux, the easiest way to start using Noir and Nargo is via noirup. Just open a +terminal and run: + +```bash +curl -L https://raw.githubusercontent.com/noir-lang/noirup/main/install | bash +``` + +Close the terminal, open another one, and run + +```bash +noirup +``` + +Done, you should have the latest version working. You can check with `nargo --version`. + +You can also install nightlies, specific versions +or branches, check out the [noirup repository](https://github.com/noir-lang/noirup) for more +information. + +#### GitHub Actions + +You can use `noirup` with GitHub Actions for CI/CD and automated testing. It is as simple as +installing `noirup` and running tests in your GitHub Action `yml` file. + +See the +[config file](https://github.com/TomAFrench/noir-hashes/blob/master/.github/workflows/noir.yml) in +this repo containing hash functions in Noir for an example. + +#### Nightly versions + +To install the nightly version of Noir (updated daily) run: + +```bash +noirup -n +``` + +### Option 2: Binaries + +See [GitHub Releases](https://github.com/noir-lang/noir/releases) for the latest and previous +platform specific binaries. + +#### Step 1 + +Paste and run the following in the terminal to extract and install the binary: + +> **macOS / Linux:** If you are prompted with `Permission denied` when running commands, prepend +> `sudo` and re-run it. + +##### macOS (Apple Silicon) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-aarch64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-aarch64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### macOS (Intel) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-apple-darwin.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-apple-darwin.tar.gz -C $HOME/.nargo/bin/ && \ +echo '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.zshrc && \ +source ~/.zshrc +``` + +##### Windows (PowerShell) + +Open PowerShell as Administrator and run: + +```powershell +mkdir -f -p "$env:USERPROFILE\.nargo\bin\"; ` +Invoke-RestMethod -Method Get -Uri https://github.com/noir-lang/noir/releases/download/v0.4.1/nargo-x86_64-pc-windows-msvc.zip -Outfile "$env:USERPROFILE\.nargo\bin\nargo-x86_64-pc-windows-msvc.zip"; ` +Expand-Archive -Path "$env:USERPROFILE\.nargo\bin\nargo-x86_64-pc-windows-msvc.zip" -DestinationPath "$env:USERPROFILE\.nargo\bin\"; ` +$Reg = "Registry::HKLM\System\CurrentControlSet\Control\Session Manager\Environment"; ` +$OldPath = (Get-ItemProperty -Path "$Reg" -Name PATH).Path; ` +$NewPath = $OldPath + ’;’ + "$env:USERPROFILE\.nargo\bin\"; ` +Set-ItemProperty -Path "$Reg" -Name PATH –Value "$NewPath"; ` +$env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User") +``` + +##### Linux (Bash) + +```bash +mkdir -p $HOME/.nargo/bin && \ +curl -o $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -L https://github.com/noir-lang/noir/releases/download/v0.6.0/nargo-x86_64-unknown-linux-gnu.tar.gz && \ +tar -xvf $HOME/.nargo/bin/nargo-x86_64-unknown-linux-gnu.tar.gz -C $HOME/.nargo/bin/ && \ +echo -e '\nexport PATH=$PATH:$HOME/.nargo/bin' >> ~/.bashrc && \ +source ~/.bashrc +``` + +#### Step 2 + +Check if the installation was successful by running `nargo --help`. + +> **macOS:** If you are prompted with an OS alert, right-click and open the _nargo_ executable from +> Finder. Close the new terminal popped up and `nargo` should now be accessible. + +For a successful installation, you should see something similar to the following after running the +command: + +```sh +$ nargo --help + +Noir's package manager + +Usage: nargo + +Commands: + check Checks the constraint system for errors + codegen-verifier Generates a Solidity verifier smart contract for the program + compile Compile the program and its secret execution trace into ACIR format + new Create a new binary project + execute Executes a circuit to calculate its return value + prove Create proof for this program. The proof is returned as a hex encoded string + verify Given a proof and a program, verify whether the proof is valid + test Run the tests for this program + gates Counts the occurrences of different gates in circuit + help Print this message or the help of the given subcommand(s) +``` + +### Option 3: Install via Nix + +Due to the large number of native dependencies, Noir projects can be installed via [Nix](https://nixos.org/). + +#### Installing Nix + +For the best experience, please follow these instructions to setup Nix: + +1. Install Nix following [their guide](https://nixos.org/download.html) for your operating system. +2. Create the file `~/.config/nix/nix.conf` with the contents: + +```ini +experimental-features = nix-command +extra-experimental-features = flakes +``` + +#### Install Nargo into your Nix profile + +1. Use `nix profile` to install Nargo + +```sh +nix profile install github:noir-lang/noir +``` + +### Option 4: Compile from Source + +Due to the large number of native dependencies, Noir projects uses [Nix](https://nixos.org/) and [direnv](https://direnv.net/) to streamline the development experience. + +#### Setting up your environment + +For the best experience, please follow these instructions to setup your environment: + +1. Install Nix following [their guide](https://nixos.org/download.html) for your operating system. +2. Create the file `~/.config/nix/nix.conf` with the contents: + +```ini +experimental-features = nix-command +extra-experimental-features = flakes +``` + +3. Install direnv into your Nix profile by running: + +```sh +nix profile install nixpkgs#direnv +``` + +4. Add direnv to your shell following [their guide](https://direnv.net/docs/hook.html). + 1. For bash or zshell, add `eval "$(direnv hook bash)"` or `eval "$(direnv hook zsh)"` to your ~/.bashrc or ~/.zshrc file, respectively. +5. Restart your shell. + +#### Shell & editor experience + +Now that your environment is set up, you can get to work on the project. + +1. Clone the repository, such as: + +```sh +git clone git@github.com:noir-lang/noir +``` + +> Replacing `noir` with whichever repository you want to work on. + +2. Navigate to the directory: + +```sh +cd noir +``` + +> Replacing `noir` with whichever repository you cloned. + +3. You should see a **direnv error** because projects aren't allowed by default. Make sure you've reviewed and trust our `.envrc` file, then you need to run: + +```sh +direnv allow +``` + +4. Now, wait awhile for all the native dependencies to be built. This will take some time and direnv will warn you that it is taking a long time, but we just need to let it run. + +5. Once you are presented with your prompt again, you can start your editor within the project directory (we recommend [VSCode](https://code.visualstudio.com/)): + +```sh +code . +``` + +6. (Recommended) When launching VSCode for the first time, you should be prompted to install our recommended plugins. We highly recommend installing these for the best development experience. + +#### Building and testing + +Assuming you are using `direnv` to populate your environment, building and testing the project can be done +with the typical `cargo build`, `cargo test`, and `cargo clippy` commands. You'll notice that the `cargo` version matches the version we specify in `flake.nix`, which is 1.66.0 at the time of this writing. + +If you want to build the entire project in an isolated sandbox, you can use Nix commands: + +1. `nix build .` (or `nix build . -L` for verbose output) to build the project in a Nix sandbox. +2. `nix flake check` (or `nix flake check -L` for verbose output) to run clippy and tests in a Nix sandbox. + +#### Without `direnv` + +If you have hesitations with using direnv, you can launch a subshell with `nix develop` and then launch your editor from within the subshell. However, if VSCode was already launched in the project directory, the environment won't be updated. + +Advanced: If you aren't using direnv nor launching your editor within the subshell, you can try to install Barretenberg and other global dependencies the package needs. This is an advanced workflow and likely won't receive support! + +## Uninstalling Nargo + +### Noirup + +If you installed Noir with `noirup`, you can uninstall Noir by removing the files in `~/.nargo`, `~/nargo` and `~/noir_cache`. + +```bash +rm -r ~/.nargo +rm -r ~/nargo +rm -r ~/noir_cache +``` + +### Nix + +If you installed Noir with Nix or from source, you can remove the binary located at `~/.nix-profile/bin/nargo`. + +```bash +rm ~/.nix-profile/bin/nargo +``` + +[git]: https://git-scm.com/book/en/v2/Getting-Started-Installing-Git +[rust]: https://www.rust-lang.org/tools/install +[noir vs code extension]: https://marketplace.visualstudio.com/items?itemName=noir-lang.vscode-noir +[homebrew]: https://brew.sh/ +[cmake]: https://cmake.org/install/ +[llvm]: https://llvm.org/docs/GettingStarted.html +[openmp]: https://openmp.llvm.org/ +[barretenberg]: https://github.com/AztecProtocol/barretenberg diff --git a/noir/docs/versioned_docs/version-v0.9.0/getting_started/01_hello_world.md b/noir/docs/versioned_docs/version-v0.9.0/getting_started/01_hello_world.md new file mode 100644 index 00000000000..0f21ad45569 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/getting_started/01_hello_world.md @@ -0,0 +1,147 @@ +--- +title: Create A Project +description: + Learn how to create and verify your first Noir program using Nargo, a programming language for + zero-knowledge proofs. +keywords: + [ + Nargo, + Noir, + zero-knowledge proofs, + programming language, + create Noir program, + verify Noir program, + step-by-step guide, + ] +--- + +Now that we have installed Nargo, it is time to make our first hello world program! + +## Create a Project Directory + +Noir code can live anywhere on your computer. Let us create a _projects_ folder in the home +directory to house our Noir programs. + +For Linux, macOS, and Windows PowerShell, create the directory and change directory into it by +running: + +```sh +mkdir ~/projects +cd ~/projects +``` + +For Windows CMD, run: + +```sh +> mkdir "%USERPROFILE%\projects" +> cd /d "%USERPROFILE%\projects" +``` + +## Create Our First Nargo Project + +Now that we are in the projects directory, create a new Nargo project by running: + +```sh +nargo new hello_world +``` + +> **Note:** `hello_world` can be any arbitrary project name, we are simply using `hello_world` for +> demonstration. +> +> In production, the common practice is to name the project folder as `circuits` for better +> identifiability when sitting alongside other folders in the codebase (e.g. `contracts`, `scripts`, +> `test`). + +A `hello_world` folder would be created. Similar to Rust, the folder houses _src/main.nr_ and +_Nargo.toml_ that contains the source code and environmental options of your Noir program +respectively. + +### Intro to Noir Syntax + +Let us take a closer look at _main.nr_. The default _main.nr_ generated should look like this: + +```rust +fn main(x : Field, y : pub Field) { + assert(x != y); +} +``` + +The first line of the program specifies the program's inputs: + +```rust +x : Field, y : pub Field +``` + +Program inputs in Noir are private by default (e.g. `x`), but can be labeled public using the +keyword `pub` (e.g. `y`). To learn more about private and public values, check the +[Data Types](../language_concepts/data_types) section. + +The next line of the program specifies its body: + +```rust +assert(x != y); +``` + +The Noir syntax `assert` can be interpreted as something similar to constraints in other zk-contract languages. + +For more Noir syntax, check the [Language Concepts](../language_concepts/comments) chapter. + +## Build In/Output Files + +Change directory into _hello_world_ and build in/output files for your Noir program by running: + +```sh +cd hello_world +nargo check +``` + +Two additional files would be generated in your project directory: + +_Prover.toml_ houses input values, and _Verifier.toml_ houses public values. + +## Prove Our Noir Program + +Now that the project is set up, we can create a proof of correct execution on our Noir program. + +Fill in input values for execution in the _Prover.toml_ file. For example: + +```toml +x = "1" +y = "2" +``` + +Prove the valid execution of your Noir program with your preferred proof name, for example `p`: + +```sh +nargo prove p +``` + +A new folder _proofs_ would then be generated in your project directory, containing the proof file +`p.proof`. + +The _Verifier.toml_ file would also be updated with the public values computed from program +execution (in this case the value of `y`): + +```toml +y = "0x0000000000000000000000000000000000000000000000000000000000000002" +``` + +> **Note:** Values in _Verifier.toml_ are computed as 32-byte hex values. + +## Verify Our Noir Program + +Once a proof is generated, we can verify correct execution of our Noir program by verifying the +proof file. + +Verify your proof of name `p` by running: + +```sh +nargo verify p +``` + +The verification will complete in silence if it is successful. If it fails, it will log the +corresponding error instead. + +Congratulations, you have now created and verified a proof for your very first Noir program! + +In the [next section](breakdown), we will go into more detail on each step performed. diff --git a/noir/docs/versioned_docs/version-v0.9.0/getting_started/02_breakdown.md b/noir/docs/versioned_docs/version-v0.9.0/getting_started/02_breakdown.md new file mode 100644 index 00000000000..7c320cef9c5 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/getting_started/02_breakdown.md @@ -0,0 +1,154 @@ +--- +title: Project Breakdown +description: + Learn about the anatomy of a Nargo project, including the purpose of the Prover and Verifier TOML + files, and how to prove and verify your program. +keywords: + [Nargo, Nargo project, Prover.toml, Verifier.toml, proof verification, private asset transfer] +--- + +This section breaks down our hello world program in section _1.2_. We elaborate on the project +structure and what the `prove` and `verify` commands did in the previous section. + +## Anatomy of a Nargo Project + +Upon creating a new project with `nargo new` and building the in/output files with `nargo check` +commands, you would get a minimal Nargo project of the following structure: + + - src + - Prover.toml + - Verifier.toml + - Nargo.toml + +The source directory _src_ holds the source code for your Noir program. By default only a _main.nr_ +file will be generated within it. + +_Prover.toml_ is used for specifying the input values for executing and proving the program. You can specify `toml` files with different names by using the `--prover-name` or `-p` flags, see the [Prover](#provertoml) section below. Optionally you may specify expected output values for prove-time checking as well. + +_Verifier.toml_ contains public in/output values computed when executing the Noir program. + +_Nargo.toml_ contains the environmental options of your project. + +_proofs_ and _contract_ directories will not be immediately visible until you create a proof or +verifier contract respectively. + +### main.nr + +The _main.nr_ file contains a `main` method, this method is the entry point into your Noir program. + +In our sample program, _main.nr_ looks like this: + +```rust +fn main(x : Field, y : Field) { + assert(x != y); +} +``` + +The parameters `x` and `y` can be seen as the API for the program and must be supplied by the +prover. Since neither `x` nor `y` is marked as public, the verifier does not supply any inputs, when +verifying the proof. + +The prover supplies the values for `x` and `y` in the _Prover.toml_ file. + +As for the program body, `assert` ensures the satisfaction of the condition (e.g. `x != y`) is +constrained by the proof of the execution of said program (i.e. if the condition was not met, the +verifier would reject the proof as an invalid proof). + +### Prover.toml + +The _Prover.toml_ file is a file which the prover uses to supply his witness values(both private and +public). + +In our hello world program the _Prover.toml_ file looks like this: + +```toml +x = "1" +y = "2" +``` + +When the command `nargo prove my_proof` is executed, two processes happen: + +1. Noir creates a proof that `x` which holds the value of `1` and `y` which holds the value of `2` + is not equal. This not equal constraint is due to the line `assert(x != y)`. + +2. Noir creates and stores the proof of this statement in the _proofs_ directory and names the proof + file _my_proof_. Opening this file will display the proof in hex format. + +#### Arrays of Structs + +The following code shows how to pass an array of structs to a Noir program to generate a proof. + +```rust +// main.nr +struct Foo { + bar: Field, + baz: Field, +} + +fn main(foos: [Foo; 3]) -> pub Field { + foos[2].bar + foos[2].baz +} +``` + +Prover.toml: + +```toml +[[foos]] # foos[0] +bar = 0 +baz = 0 + +[[foos]] # foos[1] +bar = 0 +baz = 0 + +[[foos]] # foos[2] +bar = 1 +baz = 2 +``` + +#### Custom toml files + +You can specify a `toml` file with a different name to use for proving by using the `--prover-name` or `-p` flags. + +This command looks for proof inputs in the default **Prover.toml** and generates proof `p`: + +```bash +nargo prove p +``` + +This command looks for proof inputs in the custom **OtherProver.toml** and generates proof `pp`: + +```bash +nargo prove -p OtherProver pp +``` + +## Verifying a Proof + +When the command `nargo verify my_proof` is executed, two processes happen: + +1. Noir checks in the _proofs_ directory for a file called _my_proof_ + +2. If that file is found, the proof's validity is checked + +> **Note:** The validity of the proof is linked to the current Noir program; if the program is +> changed and the verifier verifies the proof, it will fail because the proof is not valid for the +> _modified_ Noir program. + +In production, the prover and the verifier are usually two separate entities. A prover would +retrieve the necessary inputs, execute the Noir program, generate a proof and pass it to the +verifier. The verifier would then retrieve the public inputs from usually external sources and +verifies the validity of the proof against it. + +Take a private asset transfer as an example: + +A user on browser as the prover would retrieve private inputs (e.g. the user's private key) and +public inputs (e.g. the user's encrypted balance on-chain), compute the transfer, generate a proof +and submit it to the verifier smart contract. + +The verifier contract would then draw the user's encrypted balance directly from the blockchain and +verify the proof submitted against it. If the verification passes, additional functions in the +verifier contract could trigger (e.g. approve the asset transfer). + +Now that you understand the concepts, you'll probably want some editor feedback while you are writing more complex code. + +In the [next section](language_server), we will explain how to utilize the Noir Language Server. diff --git a/noir/docs/versioned_docs/version-v0.9.0/getting_started/03_language_server.md b/noir/docs/versioned_docs/version-v0.9.0/getting_started/03_language_server.md new file mode 100644 index 00000000000..e6f5dfc2faa --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/getting_started/03_language_server.md @@ -0,0 +1,31 @@ +--- +title: Language Server +description: + Learn about the Noir Language Server, how to install the components, and configuration that may be required. +keywords: + [Nargo, Language Server, LSP, VSCode, Visual Studio Code] +--- + +This section helps you install and configure the Noir Language Server. + +The Language Server Protocol (LSP) has two components, the [Server](#language-server) and the [Client](#language-client). Below we describe each in the context of Noir. + +## Language Server + +The Server component is provided by the Nargo command line tool that you installed at the beginning of this guide. +As long as Nargo is installed and you've used it to run other commands in this guide, it should be good to go! + +If you'd like to verify that the `nargo lsp` command is available, you can run `nargo --help` and look for `lsp` in the list of commands. If you see it, you're using a version of Noir with LSP support. + +## Language Client + +The Client component is usually an editor plugin that launches the Server. It communicates LSP messages between the editor and the Server. For example, when you save a file, the Client will alert the Server, so it can try to compile the project and report any errors. + +Currently, Noir provides a Language Client for Visual Studio Code via the [vscode-noir](https://github.com/noir-lang/vscode-noir) extension. You can install it via the [Visual Studio Marketplace](https://marketplace.visualstudio.com/items?itemName=noir-lang.vscode-noir). + +### Configuration + +* __Noir: Enable LSP__ - If checked, the extension will launch the Language Server via `nargo lsp` and communicate with it. +* __Noir: Nargo Flags__ - Additional flags may be specified if you require them to be added when the extension calls `nargo lsp`. +* __Noir: Nargo Path__ - An absolute path to a Nargo binary with the `lsp` command. This may be useful if Nargo is not within the `PATH` of your editor. +* __Noir > Trace: Server__ - Setting this to `"messages"` or `"verbose"` will log LSP messages between the Client and Server. Useful for debugging. diff --git a/noir/docs/versioned_docs/version-v0.9.0/index.md b/noir/docs/versioned_docs/version-v0.9.0/index.md new file mode 100644 index 00000000000..e56b24bccd8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/index.md @@ -0,0 +1,103 @@ +--- +title: Introducing Noir +description: + Learn about the public alpha release of Noir, a domain specific language heavily influenced by + Rust that compiles to an intermediate language which can be compiled to an arithmetic circuit or a + rank-1 constraint system. +keywords: + [ + Noir, + Domain Specific Language, + Rust, + Intermediate Language, + Arithmetic Circuit, + Rank-1 Constraint System, + Ethereum Developers, + Protocol Developers, + Blockchain Developers, + Proving System, + Smart Contract Language, + ] +slug: / +--- + +This version of the book is being released with the public alpha. There will be a lot of features +that are missing in this version, however the syntax and the feel of the language will mostly be +completed. + +## What is Noir? + +Noir is a Domain Specific Language for SNARK proving systems. It has been designed to use any ACIR compatible proving system. + +It's design choices are influenced heavily by Rust and focuses on a simple, familiar syntax. + +## Who is Noir for? + +Noir can be used for a variety of purposes. + +### Solidity Developers + +Noir currently includes a command to create a Solidity contract which verifies your Noir program. This will +be modularised in the future; however, as of the alpha, you can use the [`nargo codegen-verifier`](./nargo/commands#nargo-codegen-verifier) command to create +a verifier contract. + +### Protocol Developers + +As a protocol developer, you may not want to use the Aztec backend due to it not being a fit for +your stack, or maybe you simply want to use a different proving system. Since Noir does not compile +to a specific proof system, it is possible for protocol developers to replace the PLONK-based +proving system with a different proving system altogether. + +### Blockchain developers + +As a blockchain developer, you will be constrained by parameters set by your blockchain (for example, the +proving system and smart contract language has been pre-defined). In order for you to use Noir in +your blockchain, a proving system backend and a smart contract interface +must be implemented for it. + +## What's new about Noir? + +Noir is simple and flexible in its design, as it does not compile immediately to a fixed +NP-complete language. Instead, Noir compiles to an intermediate language (ACIR), which itself can be compiled +to an arithmetic circuit (if choosing to target Aztec's barretenberg backend) or a rank-1 constraint system (if choosing to target an R1CS backend like Arkwork's Marlin backend, or others). + +This in itself brings up a few challenges within the design process, but allows one to decouple the programming language completely from the backend. This is similar in theory to LLVM. + +## Current Features + +Compiler: + +- Module System +- For expressions +- Arrays +- Bit Operations +- Binary operations (<, <=, >, >=, +, -, \*, /, %) [See documentation for an extensive list] +- Unsigned integers +- If statements +- Structures and Tuples +- Generics + +ACIR Supported OPCODES: + +- Sha256 +- Blake2s +- Schnorr signature verification +- MerkleMembership +- Pedersen +- HashToField + +## Libraries + +Noir does not currently have an official package manager. You can find a list of available Noir libraries in the [awesome-noir repo here](https://github.com/noir-lang/awesome-noir#libraries). + +Some libraries that are available today include: + +- [Standard Library](https://github.com/noir-lang/noir/tree/master/noir_stdlib) - the Noir Standard Library +- [Ethereum Storage Proof Verification](https://github.com/aragonzkresearch/noir-trie-proofs) - a library that contains the primitives necessary for RLP decoding (in the form of look-up table construction) and Ethereum state and storage proof verification (or verification of any trie proof involving 32-byte long keys) +- [BigInt](https://github.com/shuklaayush/noir-bigint) - a library that provides a custom BigUint56 data type, allowing for computations on large unsigned integers +- [ECrecover](https://github.com/colinnielsen/ecrecover-noir/tree/main) - a library to verify an ECDSA signature and return the source Ethereum address +- [Sparse Merkle Tree Verifier](https://github.com/vocdoni/smtverifier-noir/tree/main) - a library for verification of sparse Merkle trees +- [Signed Int](https://github.com/resurgencelabs/signed_int) - a library for accessing a custom Signed Integer data type, allowing access to negative numbers on Noir +- [Fraction](https://github.com/resurgencelabs/fraction) - a library for accessing fractional number data type in Noir, allowing results that aren't whole numbers + +See the section on [dependencies](./modules_packages_crates/dependencies) for more information. diff --git a/noir/docs/versioned_docs/version-v0.9.0/language_concepts/00_data_types.md b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/00_data_types.md new file mode 100644 index 00000000000..abbadca86be --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/00_data_types.md @@ -0,0 +1,405 @@ +--- +title: Data Types +description: + Get a clear understanding of the two categories of Noir data types - primitive types and compound + types. Learn about their characteristics, differences, and how to use them in your Noir + programming. +keywords: + [ + noir, + data types, + primitive types, + compound types, + private types, + public types, + field type, + integer types, + boolean type, + array type, + tuple type, + struct type, + ] +--- + +Every value in Noir has a type, which determines which operations are valid for it. + +All values in Noir are fundamentally composed of `Field` elements. For a more approachable +developing experience, abstractions are added on top to introduce different data types in Noir. + +Noir has two category of data types: primitive types (e.g. `Field`, integers, `bool`) and compound +types that group primitive types (e.g. arrays, tuples, structs). Each value can either be private or +public. + +## Private & Public Types + +A **private value** is known only to the Prover, while a **public value** is known by both the +Prover and Verifier. Mark values as `private` when the value should only be known to the prover. All +primitive types (including individual fields of compound types) in Noir are private by default, and +can be marked public when certain values are intended to be revealed to the Verifier. + +> **Note:** For public values defined in Noir programs paired with smart contract verifiers, once +> the proofs are verified on-chain the values can be considered known to everyone that has access to +> that blockchain. + +Public data types are treated no differently to private types apart from the fact that their values +will be revealed in proofs generated. Simply changing the value of a public type will not change the +circuit (where the same goes for changing values of private types as well). + +_Private values_ are also referred to as _witnesses_ sometimes. + +> **Note:** The terms private and public when applied to a type (e.g. `pub Field`) have a different +> meaning than when applied to a function (e.g. `pub fn foo() {}`). +> +> The former is a visibility modifier for the Prover to interpret if a value should be made known to +> the Verifier, while the latter is a visibility modifier for the compiler to interpret if a +> function should be made accessible to external Noir programs like in other languages. + +### pub Modifier + +All data types in Noir are private by default. Types are explicitly declared as public using the +`pub` modifier: + +```rust +fn main(x : Field, y : pub Field) -> pub Field { + x + y +} +``` + +In this example, `x` is **private** while `y` and `x + y` (the return value) are **public**. Note +that visibility is handled **per variable**, so it is perfectly valid to have one input that is +private and another that is public. + +> **Note:** Public types can only be declared through parameters on `main`. + +## Primitive Types + +A primitive type represents a single value. They can be private or public. + +### Fields + +The field type corresponds to the native field type of the proving backend. + +The size of a Noir field depends on the elliptic curve's finite field for the proving backend +adopted. For example, a field would be a 254-bit integer when paired with the default backend that +spans the Grumpkin curve. + +Fields support integer arithmetic and are often used as the default numeric type in Noir: + +```rust +fn main(x : Field, y : Field) { + let z = x + y; +} +``` + +`x`, `y` and `z` are all private fields in this example. Using the `let` keyword we defined a new +private value `z` constrained to be equal to `x + y`. + +If proving efficiency is of priority, fields should be used as a default for solving problems. +Smaller integer types (e.g. `u64`) incur extra range constraints. + +### Integers + +An integer type is a range constrained field type. The Noir frontend currently supports unsigned, +arbitrary-sized integer types. + +An integer type is specified first with the letter `u`, indicating its unsigned nature, followed by +its length in bits (e.g. `32`). For example, a `u32` variable can store a value in the range of +$\\([0,2^{32}-1]\\)$: + +```rust +fn main(x : Field, y : u32) { + let z = x as u32 + y; +} +``` + +`x`, `y` and `z` are all private values in this example. However, `x` is a field while `y` and `z` +are unsigned 32-bit integers. If `y` or `z` exceeds the range $\\([0,2^{32}-1]\\)$, proofs created +will be rejected by the verifier. + +> **Note:** The default backend supports both even (e.g. `u16`, `u48`) and odd (e.g. `u5`, `u3`) +> sized integer types. + +### Booleans + +The `bool` type in Noir has two possible values: `true` and `false`: + +```rust +fn main() { + let t = true; + let f: bool = false; +} +``` + +> **Note:** When returning a boolean value, it will show up as a value of 1 for `true` and 0 for +> `false` in _Verifier.toml_. + +The boolean type is most commonly used in conditionals like `if` expressions and `assert` +statements. More about conditionals is covered in the [Control Flow](./control_flow) and +[Assert Function](./assert) sections. + +### Strings + +The string type is a fixed length value defined with `str`. + +You can use strings in `assert()` functions or print them with +`std::println()`. + +```rust +fn main(message : pub str<11>, hex_as_string : str<4>) { + std::println(message); + assert(message == "hello world"); + assert(hex_as_string == "0x41"); +} +``` + +## Compound Types + +A compound type groups together multiple values into one type. Elements within a compound type can +be private or public. + +### Arrays + +An array is one way of grouping together values into one compound type. Array types can be inferred +or explicitly specified via the syntax `[; ]`: + +```rust +fn main(x : Field, y : Field) { + let my_arr = [x, y]; + let your_arr: [Field; 2] = [x, y]; +} +``` + +Here, both `my_arr` and `your_arr` are instantiated as an array containing two `Field` elements. + +Array elements can be accessed using indexing: + +```rust +fn main() { + let a = [1, 2, 3, 4, 5]; + + let first = a[0]; + let second = a[1]; +} +``` + +All elements in an array must be of the same type (i.e. homogeneous). That is, an array cannot group +a `Field` value and a `u8` value together for example. + +You can write mutable arrays, like: + +```rust +fn main() { + let mut arr = [1, 2, 3, 4, 5]; + assert(arr[0] == 1); + + arr[0] = 42; + assert(arr[0] == 42); +} +``` + +You can instantiate a new array of a fixed size with the same value repeated for each element. The following example instantiates an array of length 32 where each element is of type Field and has the value 0. + +```rust +let array: [Field; 32] = [0; 32]; +``` + +#### Types + +You can create arrays of primitive types or structs. There is not yet support for nested arrays +(arrays of arrays) or arrays of structs that contain arrays. + +### Slices + +:::caution + +This feature is experimental. You should expect it to change in future versions, +cause unexpected behavior, or simply not work at all. + +::: + +A slice is a dynamically-sized view into a sequence of elements. They can be resized at runtime, but because they don't own the data, they cannot be returned from a circuit. You can treat slices as arrays without a constrained size. + +Slices are part of the [noir standard library](../standard_library/slice_methods) so you need to import the respective module in order to work with it. For example: + +```rust +use dep::std::slice; + +fn main() -> pub Field { + let mut slice: [Field] = [0; 2]; + + let mut new_slice = slice.push_back(6); + new_slice.len() +} +``` + +### Vectors + +:::caution + +This feature is experimental. You should expect it to change in future versions, +cause unexpected behavior, or simply not work at all. + +::: + +A vector is a collection type similar to Rust's Vector type. It's convenient way to use slices as mutable arrays. + +Example: + +```rust +use std::collections::vec::Vec; + +let mut vector: Vec = Vec::new(); +for i in 0..5 { + vector.push(i); +} +assert(vector.len() == 5); +``` + +### Tuples + +A tuple collects multiple values like an array, but with the added ability to collect values of +different types: + +```rust +fn main() { + let tup: (u8, u64, Field) = (255, 500, 1000); +} +``` + +One way to access tuple elements is via destructuring using pattern matching: + +```rust +fn main() { + let tup = (1, 2); + + let (one, two) = tup; + + let three = one + two; +} +``` + +Another way to access tuple elements is via direct member access, using a period (`.`) followed by +the index of the element we want to access. Index `0` corresponds to the first tuple element, `1` to +the second and so on: + +```rust +fn main() { + let tup = (5, 6, 7, 8); + + let five = tup.0; + let eight = tup.3; +} +``` + +### Structs + +A struct also allows for grouping multiple values of different types. Unlike tuples, we can also +name each field. + +> **Note:** The usage of _field_ here refers to each element of the struct and is unrelated to the +> field type of Noir. + +Defining a struct requires giving it a name and listing each field within as `: ` pairs: + +```rust +struct Animal { + hands: Field, + legs: Field, + eyes: u8, +} +``` + +An instance of a struct can then be created with actual values in `: ` pairs in any +order. Struct fields are accessible using their given names: + +```rust +fn main() { + let legs = 4; + + let dog = Animal { + eyes: 2, + hands: 0, + legs, + }; + + let zero = dog.hands; +} +``` + +Structs can also be destructured in a pattern, binding each field to a new variable: + +```rust +fn main() { + let Animal { hands, legs: feet, eyes } = get_octopus(); + + let ten = hands + feet + eyes as u8; +} + +fn get_octopus() -> Animal { + let octopus = Animal { + hands: 0, + legs: 8, + eyes: 2, + }; + + octopus +} +``` + +The new variables can be bound with names different from the original struct field names, as +showcased in the `legs --> feet` binding in the example above. + +:::note +You can use Structs as inputs to the `main` function, but you can't output them +::: + +### Type Aliases + +A type alias is a new name for an existing type. Type aliases are declared with the keyword `type`: + +```rust +type Id = u8; + +fn main() { + let id: Id = 1; + let zero: u8 = 0; + assert(zero + 1 == id); +} +``` + +Type aliases can also be used with [generics](./06_generics.md): + +```rust +type Id = Size; + +fn main() { + let id: Id = 1; + let zero: u32 = 0; + assert(zero + 1 == id); +} +``` + +### BigInt + +You can acheive BigInt functionality using the [Noir BigInt](https://github.com/shuklaayush/noir-bigint) library. + +## References + +Noir supports first-class references. References are a bit like pointers: they point to a specific address that can be followed to access the data stored at that address. You can use Rust-like syntax to use pointers in Noir: the `&` operator references the variable, the `*` operator dereferences it. + +Example: + +```rust +fn main() { + let mut x = 2; + + // you can reference x as &mut and pass it to multiplyBy2 + multiplyBy2(&mut x); +} + +// you can access &mut here +fn multiplyBy2(x: &mut Field) { + // and dereference it with * + *x = *x * 2; +} +``` diff --git a/noir/docs/versioned_docs/version-v0.9.0/language_concepts/01_functions.md b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/01_functions.md new file mode 100644 index 00000000000..54c618599d2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/01_functions.md @@ -0,0 +1,88 @@ +--- +title: Functions +description: + Learn how to declare functions and methods in Noir, a programming language with Rust semantics. + This guide covers parameter declaration, return types, call expressions, and more. +keywords: [Noir, Rust, functions, methods, parameter declaration, return types, call expressions] +--- + +Functions in Noir follow the same semantics of Rust, though Noir does not support early returns. + +To declare a function the `fn` keyword is used. + +```rust +fn foo() {} +``` + +All parameters in a function must have a type and all types are known at compile time. The parameter +is pre-pended with a colon and the parameter type. Multiple parameters are separated using a comma. + +```rust +fn foo(x : Field, y : pub Field){} +``` + +The return type of a function can be stated by using the `->` arrow notation. The function below +states that the foo function must return a `Field`. If the function returns no value, then the arrow +is omitted. + +```rust +fn foo(x : Field, y : pub Field) -> Field { + x + y +} +``` + +Note that a `return` keyword is unneeded in this case - the last expression in a function's body is +returned. + +## Call Expressions + +Calling a function in Noir is executed by using the function name and passing in the necessary +arguments. + +Below we show how to call the `foo` function from the `main` function using a call expression: + +```rust +fn main(x : Field, y : Field) { + let z = foo(x); +} + +fn foo(x : Field) -> Field { + x + x +} +``` + +## Methods + +You can define methods in Noir on any struct type in scope. + +```rust +struct MyStruct { + foo: Field, + bar: Field, +} + +impl MyStruct { + fn new(foo: Field) -> MyStruct { + MyStruct { + foo, + bar: 2, + } + } + + fn sum(self) -> Field { + self.foo + self.bar + } +} + +fn main() { + let s = MyStruct::new(40); + assert(s.sum() == 42); +} +``` + +Methods are just syntactic sugar for functions, so if we wanted to we could also call `sum` as +follows: + +```rust +assert(MyStruct::sum(s) == 42); +``` diff --git a/noir/docs/versioned_docs/version-v0.9.0/language_concepts/02_control_flow.md b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/02_control_flow.md new file mode 100644 index 00000000000..691c514d9a8 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/02_control_flow.md @@ -0,0 +1,42 @@ +--- +title: Control Flow +description: + Learn how to use loops and if expressions in the Noir programming language. Discover the syntax + and examples for for loops and if-else statements. +keywords: [Noir programming language, loops, for loop, if-else statements, Rust syntax] +--- + +## Loops + +Noir has one kind of loop: the `for` loop. `for` loops allow you to repeat a block of code multiple +times. + +The following block of code between the braces is run 10 times. + +```rust +for i in 0..10 { + // do something +}; +``` + +## If Expressions + +Noir supports `if-else` statements. The syntax is most similar to Rust's where it is not required +for the statement's conditional to be surrounded by parentheses. + +```rust +let a = 0; +let mut x: u32 = 0; + +if a == 0 { + if a != 0 { + x = 6; + } else { + x = 2; + } +} else { + x = 5; + assert(x == 5); +} +assert(x == 2); +``` diff --git a/noir/docs/versioned_docs/version-v0.9.0/language_concepts/03_ops.md b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/03_ops.md new file mode 100644 index 00000000000..da02b126059 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/03_ops.md @@ -0,0 +1,97 @@ +--- +title: Logical Operations +description: + Learn about the supported arithmetic and logical operations in the Noir programming language. + Discover how to perform operations on private input types, integers, and booleans. +keywords: + [ + Noir programming language, + supported operations, + arithmetic operations, + logical operations, + predicate operators, + bitwise operations, + short-circuiting, + backend, + ] +--- + +# Operations + +## Table of Supported Operations + +| Operation | Description | Requirements | +| :-------- | :------------------------------------------------------------: | -------------------------------------: | +| + | Adds two private input types together | Types must be private input | +| - | Subtracts two private input types together | Types must be private input | +| \* | Multiplies two private input types together | Types must be private input | +| / | Divides two private input types together | Types must be private input | +| ^ | XOR two private input types together | Types must be integer | +| & | AND two private input types together | Types must be integer | +| \| | OR two private input types together | Types must be integer | +| << | Left shift an integer by another integer amount | Types must be integer | +| >> | Right shift an integer by another integer amount | Types must be integer | +| ! | Bitwise not of a value | Type must be integer or boolean | +| < | returns a bool if one value is less than the other | Upper bound must have a known bit size | +| <= | returns a bool if one value is less than or equal to the other | Upper bound must have a known bit size | +| > | returns a bool if one value is more than the other | Upper bound must have a known bit size | +| >= | returns a bool if one value is more than or equal to the other | Upper bound must have a known bit size | +| == | returns a bool if one value is equal to the other | Both types must not be constants | +| != | returns a bool if one value is not equal to the other | Both types must not be constants | + +### Predicate Operators + +`<,<=, !=, == , >, >=` are known as predicate/comparison operations because they compare two values. +This differs from the operations such as `+` where the operands are used in _computation_. + +### Bitwise Operations Example + +```rust +fn main(x : Field) { + let y = x as u32; + let z = y & y; +} +``` + +`z` is implicitly constrained to be the result of `y & y`. The `&` operand is used to denote bitwise +`&`. + +> `x & x` would not compile as `x` is a `Field` and not an integer type. + +### Logical Operators + +Noir has no support for the logical operators `||` and `&&`. This is because encoding the +short-circuiting that these operators require can be inefficient for Noir's backend. Instead you can +use the bitwise operators `|` and `&` which operate indentically for booleans, just without the +short-circuiting. + +```rust +let my_val = 5; + +let mut flag = 1; +if (my_val > 6) | (my_val == 0) { + flag = 0; +} +assert(flag == 1); + +if (my_val != 10) & (my_val < 50) { + flag = 0; +} +assert(flag == 0); +``` + +### Shorthand operators + +Noir shorthand operators for most of the above operators, namely `+=, -=, *=, /=, %=, &=, |=, ^=, <<=`, and `>>=`. These allow for more concise syntax. For example: + +```rust +let mut i = 0; +i = i + 1; +``` + +could be written as: + +```rust +let mut i = 0; +i += 1; +``` diff --git a/noir/docs/versioned_docs/version-v0.9.0/language_concepts/04_assert.md b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/04_assert.md new file mode 100644 index 00000000000..a25a946123d --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/04_assert.md @@ -0,0 +1,34 @@ +--- +title: Assert Function +description: + Learn about the assert function in Noir, which can be used to explicitly constrain the predicate or + comparison expression that follows to be true, and what happens if the expression is false at + runtime. +keywords: [Noir programming language, assert statement, predicate expression, comparison expression] +--- + +Noir includes a special `assert` function which will explicitly constrain the predicate/comparison +expression that follows to be true. If this expression is false at runtime, the program will fail to +be proven. + +### Example + +```rust +fn main(x : Field, y : Field) { + assert(x == y); +} +``` + +The above snippet compiles because `==` is a predicate operation. Conversely, the following will not +compile: + +```rust +// INCORRECT + +fn main(x : Field, y : Field) { + assert(x + y); +} +``` + +> The rationale behind this not compiling is due to ambiguity. It is not clear if the above should +> equate to `x + y == 0` or if it should check the truthiness of the result. diff --git a/noir/docs/versioned_docs/version-v0.9.0/language_concepts/05_unconstrained.md b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/05_unconstrained.md new file mode 100644 index 00000000000..6b621eda3eb --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/05_unconstrained.md @@ -0,0 +1,96 @@ +--- +title: Unconstrained Functions +description: "Learn about what unconstrained functions in Noir are, how to use them and when you'd want to." + +keywords: [Noir programming language, unconstrained, open] +--- + + + +Unconstrained functions are functions which do not constrain any of the included computation and allow for non-determinisitic computation. + +## Why? + +Zero-knowledge (ZK) domain-specific languages (DSL) enable developers to generate ZK proofs from their programs by compiling code down to the constraints of an NP complete language (such as R1CS or PLONKish languages). However, the hard bounds of a constraint system can be very limiting to the functionality of a ZK DSL. + +Enabling a circuit language to perform unconstrained execution is a powerful tool. Said another way, unconstrained execution lets developers generate witnesses from code that does not generate any constraints. Being able to execute logic outside of a circuit is critical for both circuit performance and constructing proofs on information that is external to a circuit. + +Fetching information from somewhere external to a circuit can also be used to enable developers to improve circuit efficiency. + +A ZK DSL does not just prove computation, but proves that some computation was handled correctly. Thus, it is necessary that when we switch from performing some operation directly inside of a circuit to inside of an unconstrained environment that the appropriate constraints are still laid down elsewhere in the circuit. + +## Example + +An in depth example might help drive the point home. This example comes from the excellent [post](https://discord.com/channels/1113924620781883405/1124022445054111926/1128747641853972590) by Tom in the Noir Discord. + +Let's look at how we can optimize a function to turn a `u72` into an array of `u8`s. + +```rust +fn main(num: u72) -> pub [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8)) as u72 & 0xff) as u8; + } + + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 91 +Backend circuit size: 3619 +``` + +A lot of the operations in this function are optimized away by the compiler (all the bit-shifts turn into divisions by constants). However we can save a bunch of gates by casting to u8 a bit earlier. This automatically truncates the bit-shifted value to fit in a u8 which allows us to remove the XOR against 0xff. This saves us ~480 gates in total. + +```rust +fn main(num: u72) -> pub [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8)) as u8; + } + + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 75 +Backend circuit size: 3143 +``` + +Those are some nice savings already but we can do better. This code is all constrained so we're proving every step of calculating out using num, but we don't actually care about how we calculate this, just that it's correct. This is where brillig comes in. + +It turns out that truncating a u72 into a u8 is hard to do inside a snark, each time we do as u8 we lay down 4 ACIR opcodes which get converted into multiple gates. It's actually much easier to calculate num from out than the other way around. All we need to do is multiply each element of out by a constant and add them all together, both relatively easy operations inside a snark. + +We can then run u72_to_u8 as unconstrained brillig code in order to calculate out, then use that result in our constrained function and assert that if we were to do the reverse calculation we'd get back num. This looks a little like the below: + +```rust +fn main(num: u72) -> pub [u8; 8] { + let out = u72_to_u8(num); + + let mut reconstructed_num: u72 = 0; + for i in 0..8 { + reconstructed_num += (out[i] as u72 << (56 - (8 * i))); + } + assert(num == reconstructed_num); + out +} + +unconstrained fn u72_to_u8(num: u72) -> [u8; 8] { + let mut out: [u8; 8] = [0; 8]; + for i in 0..8 { + out[i] = (num >> (56 - (i * 8))) as u8; + } + out +} +``` + +``` +Total ACIR opcodes generated for language PLONKCSat { width: 3 }: 78 +Backend circuit size: 2902 +``` + +This ends up taking off another ~250 gates from our circuit! We've ended up with more ACIR opcodes than before but they're easier for the backend to prove (resulting in fewer gates). + +Generally we want to use brillig whenever there's something that's easy to verify but hard to compute within the circuit. For example, if you wanted to calculate a square root of a number it'll be a much better idea to calculate this in brillig and then assert that if you square the result you get back your number. diff --git a/noir/docs/versioned_docs/version-v0.9.0/language_concepts/06_generics.md b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/06_generics.md new file mode 100644 index 00000000000..66f2e85e16b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/06_generics.md @@ -0,0 +1,116 @@ +--- +title: Generics +description: + Learn how to use Generics in Noir +keywords: [Noir, Rust, generics, functions, structs] +--- + +# Generics + +Generics allow you to use the same functions with multiple different concrete data types. You can +read more about the concept of generics in the Rust documentation +[here](https://doc.rust-lang.org/book/ch10-01-syntax.html). + +Here is a trivial example showing the identity function that supports any type. In Rust, it is +common to refer to the most general type as `T`. We follow the same convention in Noir. + +```rust +fn id(x: T) -> T { + x +} +``` + +## In Structs + +Generics are useful for specifying types in structs. For example, we can specify that a field in a +struct will be of a certain generic type. In this case `value` is of type `T`. + +```rust +struct RepeatedValue { + value: T, + count: comptime Field, +} + +impl RepeatedValue { + fn new(value: T) -> Self { + Self { value, count: 1 } + } + + fn increment(mut repeated: Self) -> Self { + repeated.count += 1; + repeated + } + + fn print(self) { + for _i in 0 .. self.count { + dep::std::println(self.value); + } + } +} + +fn main() { + let mut repeated = RepeatedValue::new("Hello!"); + repeated = repeated.increment(); + repeated.print(); +} +``` + +The `print` function will print `Hello!` an arbitrary number of times, twice in this case. + +If we want to be generic over array lengths (which are type-level integers), we can use numeric +generics. Using these looks just like using regular generics, but these generics can resolve to +integers at compile-time, rather than resolving to types. Here's an example of a struct that is +generic over the size of the array it contains internally: + +```rust +struct BigInt { + limbs: [u32; N], +} + +impl BigInt { + // `N` is in scope of all methods in the impl + fn first(first: BigInt, second: BigInt) -> Self { + assert(first.limbs != second.limbs); + first + + fn second(first: BigInt, second: Self) -> Self { + assert(first.limbs != second.limbs); + second + } +} +``` + +## Calling functions on generic parameters + +Unlike Rust, Noir does not have traits, so how can one translate the equivalent of a trait bound in +Rust into Noir? That is, how can we write a function that is generic over some type `T`, while also +requiring there is a function like `eq: fn(T, T) -> bool` that works on the type? + +The answer is that we can translate this by passing in the function manually. Here's an example of +implementing array equality in Noir: + +```rust +fn array_eq(array1: [T; N], array2: [T; N], elem_eq: fn(T, T) -> bool) -> bool { + if array1.len() != array2.len() { + false + } else { + let mut result = true; + for i in 0 .. array1.len() { + result &= elem_eq(array1[i], array2[i]); + } + result + } +} + +fn main() { + assert(array_eq([1, 2, 3], [1, 2, 3], |a, b| a == b)); + + // We can use array_eq even for arrays of structs, as long as we have + // an equality function for these structs we can pass in + let array = [MyStruct::new(), MyStruct::new()]; + assert(array_eq(array, array, MyStruct::eq)); +} +``` + +You can see an example of generics in the tests +[here](https://github.com/noir-lang/noir/blob/master/tooling/nargo_cli/tests/execution_success/generics/src/main.nr). diff --git a/noir/docs/versioned_docs/version-v0.9.0/language_concepts/07_mutability.md b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/07_mutability.md new file mode 100644 index 00000000000..69798c7a276 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/07_mutability.md @@ -0,0 +1,120 @@ +--- +title: Mutability +description: + Learn about mutable variables, constants, and globals in Noir programming language. Discover how + to declare, modify, and use them in your programs. +keywords: [noir programming language, mutability in noir, mutable variables, constants, globals] +--- + +Variables in noir can be declared mutable via the `mut` keyword. Mutable variables can be reassigned +to via an assignment expression. + +```rust +let x = 2; +x = 3; // error: x must be mutable to be assigned to + +let mut y = 3; +let y = 4; // OK +``` + +The `mut` modifier can also apply to patterns: + +```rust +let (a, mut b) = (1, 2); +a = 11; // error: a must be mutable to be assigned to +b = 12; // OK + +let mut (c, d) = (3, 4); +c = 13; // OK +d = 14; // OK + +// etc. +let MyStruct { x: mut y } = MyStruct { x: a } +// y is now in scope +``` + +Note that mutability in noir is local and everything is passed by value, so if a called function +mutates its parameters then the parent function will keep the old value of the parameters. + +```rust +fn main() -> Field { + let x = 3; + helper(x); + x // x is still 3 +} + +fn helper(mut x: i32) { + x = 4; +} +``` + +## Comptime Values + +Comptime values are values that are known at compile-time. This is different to a witness +which changes per proof. If a comptime value that is being used in your program is changed, then your +circuit will also change. + +Comptime is slightly different from Rust's `const`. Namely, it is a bit more flexible in that normal functions can accept comptime parameters. For example, this is used to verify an array index is known at compile-time. Note that the "known at compile-time" here means "known after function inlining is performed while optimizing the program" and not "known during type-checking." + +Below we show how to declare a comptime value: + +```rust +fn main() { + let a: comptime Field = 5; + + // `comptime Field` can also be inferred: + let a = 5; +} +``` + +Comptime variables can be mutuable, but must be known at compile time: + +```rust +fn main(runtime_var: Field) -> pub Field { + let known_at_compile_time: comptime Field = 1; + + // The next line will cause an error + let bad_var: comptime Field = runtime_var; + +} +``` + +As `runtime_var` is a argument to the circuit it cannot be known at compile time and so assigning it to a comptime variable should fail. A circuit's arguments is the only way in which non-comptime variables can enter the circuit (excluding [brillig](./unconstrained) foreign calls). + +## Globals + +Noir also supports global variables. However, they must be compile-time variables. If `comptime` is +not explicitly written in the type annotation the compiler will implicitly specify the declaration +as compile-time. They can then be used like any other compile-time variable inside functions. The +global type can also be inferred by the compiler entirely. Globals can also be used to specify array +annotations for function parameters and can be imported from submodules. + +```rust +global N: Field = 5; // Same as `global N: comptime Field = 5` + +fn main(x : Field, y : [Field; N]) { + let res = x * N; + + assert(res == y[0]); + + let res2 = x * mysubmodule::N; + assert(res != res2); +} + +mod mysubmodule { + use dep::std; + + global N: Field = 10; + + fn my_helper() -> comptime Field { + let x = N; + x + } +} +``` + +## Why only local mutability? + +Witnesses in a proving system are immutable in nature. Noir aims to _closely_ mirror this setting +without applying additional overhead to the user. Modeling a mutable reference is not as +straightforward as on conventional architectures and would incur some possibly unexpected overhead. diff --git a/noir/docs/versioned_docs/version-v0.9.0/language_concepts/08_comments.md b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/08_comments.md new file mode 100644 index 00000000000..3bb4d2f25a4 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/08_comments.md @@ -0,0 +1,32 @@ +--- +title: Comments +description: + Learn how to write comments in Noir programming language. A comment is a line of code that is + ignored by the compiler, but it can be read by programmers. Single-line and multi-line comments + are supported in Noir. +keywords: [Noir programming language, comments, single-line comments, multi-line comments] +--- + +A comment is a line in your codebase which the compiler ignores, however it can be read by +programmers. + +Here is a single line comment: + +```rust +// This is a comment and is ignored +``` + +`//` is used to tell the compiler to ignore the rest of the line. + +Noir also supports multi-line block comments. Start a block comment with `/*` and end the block with `*/`. + +Noir does not natively support doc comments. You may be able to use [Rust doc comments](https://doc.rust-lang.org/reference/comments.html) in your code to leverage some Rust documentation build tools with Noir code. + +```rust +/* + This is a block comment describing a complex function. +*/ +fn main(x : Field, y : pub Field) { + assert(x != y); +} +``` diff --git a/noir/docs/versioned_docs/version-v0.9.0/language_concepts/09_distinct.md b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/09_distinct.md new file mode 100644 index 00000000000..e7ff7f5017a --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/09_distinct.md @@ -0,0 +1,63 @@ +--- +title: Distinct Witnesses +--- + +The `distinct` keyword prevents repetitions of witness indices in the program's ABI. This ensures +that the witnesses being returned as public inputs are all unique. + +The `distinct` keyword is only used for return values on program entry points (usually the `main()` +function). + +When using `distinct` and `pub` simultaneously, `distinct` comes first. See the example below. + +You can read more about the problem this solves +[here](https://github.com/noir-lang/noir/issues/1183). + +## Example + +Without the `distinct` keyword, the following program + +```rust +fn main(x : pub Field, y : pub Field) -> pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + "return_witnesses": [3, 2, 4, 4] + } +} +``` + +Whereas (with the `distinct` keyword) + +```rust +fn main(x : pub Field, y : pub Field) -> distinct pub [Field; 4] { + let a = 1; + let b = 1; + [x + 1, y, a, b] +} +``` + +compiles to + +```json +{ + //... + "abi": { + //... + "param_witnesses": { "x": [1], "y": [2] }, + //... + "return_witnesses": [3, 4, 5, 6] + } +} +``` diff --git a/noir/docs/versioned_docs/version-v0.9.0/language_concepts/10_shadowing.md b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/10_shadowing.md new file mode 100644 index 00000000000..efd743e764f --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/language_concepts/10_shadowing.md @@ -0,0 +1,43 @@ +--- +title: Shadowing +--- + +Noir allows for inheriting variables' values and re-declaring them with the same name similar to Rust, known as shadowing. + +For example, the following function is valid in Noir: + +```rust +fn main() { + let x = 5; + + { + let x = x * 2; + assert (x == 10); + } + + assert (x == 5); +} +``` + +In this example, a variable x is first defined with the value 5. + +The local scope that follows shadows the original x, i.e. creates a local mutable x based on the value of the original x. It is given a value of 2 times the original x. + +When we return to the main scope, x once again refers to just the original x, which stays at the value of 5. + +## Temporal mutability + +One way that shadowing is useful, in addition to ergonomics across scopes, is for temporarily mutating variables. + +```rust +fn main() { + let age = 30; + // age = age + 5; // Would error as `age` is immutable by default. + + let mut age = age + 5; // Temporarily mutates `age` with a new value. + + let age = age; // Locks `age`'s mutability again. + + assert (age == 35); +} +``` diff --git a/noir/docs/versioned_docs/version-v0.9.0/modules_packages_crates/crates_and_packages.md b/noir/docs/versioned_docs/version-v0.9.0/modules_packages_crates/crates_and_packages.md new file mode 100644 index 00000000000..34f28a71148 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/modules_packages_crates/crates_and_packages.md @@ -0,0 +1,35 @@ +--- +title: Crates and Packages +description: + Learn how to use Crates and Packages in your Noir project +keywords: [Nargo, dependencies, package management, crates, package] +--- + +## Crates + +A crate is the smallest amount of code that the Noir compiler considers at a time. +Crates can contain modules, and the modules may be defined in other files that get compiled with the crate, as we’ll see in the coming sections. + +### Crate Types + +A Noir crate can come in one of two forms: a binary crate or a library crate. + +_Binary crates_ are programs which you can compile to an ACIR circuit which you can then create proofs against. Each must have a function called `main` that defines the ACIR circuit which is to be proved. + +_Library crates_ don't have a `main` function and they don't compile down to ACIR. Instead they define functionality intended to be shared with multiple projects, and eventually included in a binary crate. + +### Crate Root + +Every crate has a root, which is the source file that the compiler starts, this is also known as the root module. The Noir compiler does not enforce any conditions on the name of the file which is the crate root, however if you are compiling via Nargo the crate root must be called `lib.nr` or `main.nr` for library or binary crates respectively. + +## Packages + +A Nargo _package_ is a collection of one of more crates that provides a set of functionality. A package must include a Nargo.toml file. + +A package _must_ contain either a library or a binary crate, but not both. + +### Differences from Cargo Packages + +One notable difference between Rust's Cargo and Noir's Nargo is that while Cargo allows a package to contain an unlimited number of binary crates and a single library crate, Nargo currently only allows a package to contain a single crate. + +In future this restriction may be lifted to allow a Nargo package to contain both a binary and library crate or multiple binary crates. diff --git a/noir/docs/versioned_docs/version-v0.9.0/modules_packages_crates/dependencies.md b/noir/docs/versioned_docs/version-v0.9.0/modules_packages_crates/dependencies.md new file mode 100644 index 00000000000..2807ad52046 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/modules_packages_crates/dependencies.md @@ -0,0 +1,110 @@ +--- +title: Managing Dependencies +description: + Learn how to specify and manage dependencies in Nargo, allowing you to upload packages to GitHub + and use them easily in your project. +keywords: [Nargo, dependencies, GitHub, package management, versioning] +--- + +Nargo allows you to upload packages to GitHub and use them as dependencies. + +## Specifying a dependency + +Specifying a dependency requires a tag to a specific commit and the git url to the url containing +the package. + +Currently, there are no requirements on the tag contents. If requirements are added, it would follow +semver 2.0 guidelines. + +> Note: Without a `tag` , there would be no versioning and dependencies would change each time you +> compile your project. + +For example, to add the [ecrecover-noir library](https://github.com/colinnielsen/ecrecover-noir) to your project, add it to `Nargo.toml`: + +```toml +# Nargo.toml + +[dependencies] +ecrecover = {tag = "v0.8.0", git = "https://github.com/colinnielsen/ecrecover-noir"} +``` + +## Specifying a local dependency + +You can also specify dependencies that are local to your machine. + +For example, this file structure has a library and binary crate + +``` +├── binary_crate +│   ├── Nargo.toml +│   └── src +│   └── main.nr +└── liba + ├── Nargo.toml + └── src + └── lib.nr +``` + +Inside of the binary crate, you can specify: + +```toml +# Nargo.toml + +[dependencies] +libA = { path = "../liba" } +``` + +## Importing dependencies + +You can import a dependency to a Noir file using the following syntax. For example, to import the +ecrecover-noir library and local liba referenced above: + +```rust +use dep::ecrecover; +use dep::libA; +``` + +You can also import only the specific parts of dependency that you want to use, like so: + +```rust +use dep::std::hash::sha256; +use dep::std::scalar_mul::fixed_base; +``` + +Lastly, as demonstrated in the +[elliptic curve example](../standard_library/cryptographic_primitives/ec_primitives#examples), you +can import multiple items in the same line by enclosing them in curly braces: + +```rust +use dep::std::ec::tecurve::affine::{Curve, Point}; +``` + +## Dependencies of Dependencies + +Note that when you import a dependency, you also get access to all of the dependencies of that package. + +For example, the [phy_vector](https://github.com/resurgencelabs/phy_vector) library imports an [fraction](https://github.com/resurgencelabs/fraction) library. If you're importing the phy_vector library, then you can access the functions in fractions library like so: + +```rust +use dep::phy_vector; + +fn main(x : Field, y : pub Field) { + //... + let f = phy_vector::fraction::toFraction(true, 2, 1); + //... +} +``` + +## Available Libraries + +Noir does not currently have an official package manager. You can find a list of available Noir libraries in the [awesome-noir repo here](https://github.com/noir-lang/awesome-noir#libraries). + +Some libraries that are available today include: + +- [Standard Library](https://github.com/noir-lang/noir/tree/master/noir_stdlib) - the Noir Standard Library +- [Ethereum Storage Proof Verification](https://github.com/aragonzkresearch/noir-trie-proofs) - a library that contains the primitives necessary for RLP decoding (in the form of look-up table construction) and Ethereum state and storage proof verification (or verification of any trie proof involving 32-byte long keys) +- [BigInt](https://github.com/shuklaayush/noir-bigint) - a library that provides a custom BigUint56 data type, allowing for computations on large unsigned integers +- [ECrecover](https://github.com/colinnielsen/ecrecover-noir/tree/main) - a library to verify an ECDSA signature and return the source Ethereum address +- [Sparse Merkle Tree Verifier](https://github.com/vocdoni/smtverifier-noir/tree/main) - a library for verification of sparse Merkle trees +- [Signed Int](https://github.com/resurgencelabs/signed_int) - a library for accessing a custom Signed Integer data type, allowing access to negative numbers on Noir +- [Fraction](https://github.com/resurgencelabs/fraction) - a library for accessing fractional number data type in Noir, allowing results that aren't whole numbers diff --git a/noir/docs/versioned_docs/version-v0.9.0/modules_packages_crates/modules.md b/noir/docs/versioned_docs/version-v0.9.0/modules_packages_crates/modules.md new file mode 100644 index 00000000000..e429b336511 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/modules_packages_crates/modules.md @@ -0,0 +1,104 @@ +--- +title: Understanding Modules +description: + Learn how to organize your files using modules in Noir, following the same convention as Rust's + module system. Examples included. +keywords: [Noir, Rust, modules, organizing files, sub-modules] +--- + +# Modules + +Noir's module system follows the same convention as the _newer_ version of Rust's module system. + +## Purpose of Modules + +Modules are used to organise files. Without modules all of your code would need to live in a single +file. In Noir, the compiler does not automatically scan all of your files to detect modules. This +must be done explicitly by the developer. + +## Examples + +### Importing a module in the crate root + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::hello_world(); +} +``` + +Filename : `src/foo.nr` + +```rust +fn from_foo() {} +``` + +In the above snippet, the crate root is the `src/main.nr` file. The compiler sees the module +declaration `mod foo` which prompts it to look for a foo.nr file. + +Visually this module hierarchy looks like the following : + +``` +crate + ├── main + │ + └── foo + └── from_foo + +``` + +### Importing a module throughout the tree +All modules are accessible from the ``crate::`` namespace. + +``` +crate + ├── bar + ├── foo + └── main + +``` +In the above snippet, if ``bar`` would like to use functions in ``foo``, it can do so by ``use crate::foo::function_name``. + +### Sub-modules + +Filename : `src/main.nr` + +```rust +mod foo; + +fn main() { + foo::from_foo(); +} +``` + +Filename : `src/foo.nr` + +```rust +mod bar; +fn from_foo() {} +``` + +Filename : `src/foo/bar.nr` + +```rust +fn from_bar() {} +``` + +In the above snippet, we have added an extra module to the module tree; `bar`. `bar` is a submodule +of `foo` hence we declare bar in `foo.nr` with `mod bar`. Since `foo` is not the crate root, the +compiler looks for the file associated with the `bar` module in `src/foo/bar.nr` + +Visually the module hierarchy looks as follows: + +``` +crate + ├── main + │ + └── foo + ├── from_foo + └── bar + └── from_bar +``` diff --git a/noir/docs/versioned_docs/version-v0.9.0/nargo/01_commands.md b/noir/docs/versioned_docs/version-v0.9.0/nargo/01_commands.md new file mode 100644 index 00000000000..d550e137258 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/nargo/01_commands.md @@ -0,0 +1,139 @@ +--- +title: Commands +description: + Noir CLI Commands for Noir Prover and Verifier to create, execute, prove and verify programs, + generate Solidity verifier smart contract and compile into JSON file containing ACIR + representation and ABI of circuit. +keywords: + [ + Nargo, + Noir CLI, + Noir Prover, + Noir Verifier, + generate Solidity verifier, + compile JSON file, + ACIR representation, + ABI of circuit, + TypeScript, + ] +--- + +## General options + +``` +Options: + -s, --show-ssa Emit debug information for the intermediate SSA IR + -d, --deny-warnings Quit execution when warnings are emitted + -h, --help Print help +``` + +## `nargo help [subcommand]` + +Prints the list of available commands or specific information of a subcommand. + +_Arguments_ + +- `` - The subcommand whose help message to display + +## `nargo check` + +Generate the `Prover.toml` and `Verifier.toml` files for specifying prover and verifier in/output +values of the Noir program respectively. + +## `nargo codegen-verifier` + +Generate a Solidity verifier smart contract for the program. + +## `nargo compile ` + +Compile the program into a JSON build artifact file containing the ACIR representation and the ABI +of the circuit. This build artifact can then be used to generate and verify proofs. + +You can also use "build" as an alias for compile. + +For example, `nargo build `. + +_Arguments_ + +- `` - The name of the circuit file + +_Options_ + +- `-c, --contracts` - Compile each contract function used within the program +- `--print-acir` - Displays the ACIR for the compiled circuit + +## `nargo new [path]` + +Creates a new Noir project in a new folder called `` - Name of the package +- `[path]` - The path to save the new project + +## `nargo init` + +Creates a new Noir project in the current directory. + +## `nargo execute [witness_name]` + +Runs the Noir program and prints its return value. + +_Arguments_ + +- `[witness_name]` - The name of the witness + +_Usage_ + +The inputs to the circuit are read from the `Prover.toml` file generated by `nargo check`, which +must be filled in. + +To save the witness to file, run the command with a value for the `witness-name` argument. A +`.tr` file will then be saved in the `build` folder. + +> **Info:** The `.tr` file is the witness file. The witness file can be considered as program inputs +> parsed for your program's ACIR. +> +> This file can be passed along with circuit's ACIR into a TypeScript project for proving and +> verification. See the [TypeScript](../typescript#proving-and-verifying-externally-compiled-files) +> section to learn more. + +## `nargo prove ` + +Creates a proof for the program. + +_Arguments_ + +- `` - The name of the proof + +_Options_ + +- `-v, --verify` - Verify proof after proving + +## `nargo verify ` + +Given a proof and a program, verify whether the proof is valid. + +_Arguments_ + +- `` - The proof to verify + +## `nargo test ` + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. + +See an example on the [testing page](./testing). + +_Arguments_ + +- `` - a pattern to indicate to only run tests with names containing the pattern + +## `nargo gates` + +Counts the occurrences of different gates in circuit + +## `nargo lsp` + +Start a long-running Language Server process that communicates over stdin/stdout. +Usually this command is not run by a user, but instead will be run by a Language Client, such as [vscode-noir](https://github.com/noir-lang/vscode-noir). diff --git a/noir/docs/versioned_docs/version-v0.9.0/nargo/02_testing.md b/noir/docs/versioned_docs/version-v0.9.0/nargo/02_testing.md new file mode 100644 index 00000000000..ba0bebd658b --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/nargo/02_testing.md @@ -0,0 +1,32 @@ +--- +title: Testing in Noir +description: Learn how to use Nargo to test your Noir program in a quick and easy way +keywords: [Nargo, testing, Noir, compile, test] +--- + +You can test your Noir programs using Noir circuits. + +Nargo will automatically compile and run any functions which have the decorator `#[test]` on them if +you run `nargo test`. + +For example if you have a program like: + +```rust +fn add(x: u64, y: u64) -> u64 { + x + y +} +#[test] +fn test_add() { + assert(add(2,2) == 4); + assert(add(0,1) == 1); + assert(add(1,0) == 1); +} +``` + +Running `nargo test` will test that the `test_add` function can be executed while satisfying the all +the contraints which allows you to test that add returns the expected values. Test functions can't +have any arguments currently. + +This is much faster compared to testing in Typescript but the only downside is that you can't +explicitly test that a certain set of inputs are invalid. i.e. you can't say that you want +add(2^64-1, 2^64-1) to fail. diff --git a/noir/docs/versioned_docs/version-v0.9.0/nargo/03_solidity_verifier.md b/noir/docs/versioned_docs/version-v0.9.0/nargo/03_solidity_verifier.md new file mode 100644 index 00000000000..9ac60cb0ba7 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/nargo/03_solidity_verifier.md @@ -0,0 +1,129 @@ +--- +title: Solidity Verifier +description: + Learn how to run the verifier as a smart contract on the blockchain. Compile a Solidity verifier + contract for your Noir program and deploy it on any EVM blockchain acting as a verifier smart + contract. Read more to find out! +keywords: + [ + solidity verifier, + smart contract, + blockchain, + compiler, + plonk_vk.sol, + EVM blockchain, + verifying Noir programs, + proving backend, + Barretenberg, + ] +--- + +For certain applications, it may be desirable to run the verifier as a smart contract instead of on +a local machine. + +Compile a Solidity verifier contract for your Noir program by running: + +```sh +nargo codegen-verifier +``` + +A new `contract` folder would then be generated in your project directory, containing the Solidity +file `plonk_vk.sol`. It can be deployed on any EVM blockchain acting as a verifier smart contract. + +> **Note:** It is possible to compile verifier contracts of Noir programs for other smart contract +> platforms as long as the proving backend supplies an implementation. +> +> Barretenberg, the default proving backend for Nargo, supports compilation of verifier contracts in +> Solidity only for the time being. + +## Verify + +To verify a proof using the Solidity verifier contract, call the `verify` function with the +following signature: + +```solidity +function verify(bytes calldata _proof, bytes32[] calldata _publicInputs) external view returns (bool) +``` + +You can see an example of how the `verify` function is called in the example zk voting application [here](https://github.com/noir-lang/noir-examples/blob/33e598c257e2402ea3a6b68dd4c5ad492bce1b0a/foundry-voting/src/zkVote.sol#L35): + +```solidity +function castVote(bytes calldata proof, uint proposalId, uint vote, bytes32 nullifierHash) public returns (bool) { + // ... + bytes32[] memory publicInputs = new bytes32[](4); + publicInputs[0] = merkleRoot; + publicInputs[1] = bytes32(proposalId); + publicInputs[2] = bytes32(vote); + publicInputs[3] = nullifierHash; + require(verifier.verify(proof, publicInputs), "Invalid proof"); +``` + +### Public Inputs + +:::tip + +A circuit doesn't have the concept of a return value. Return values are just syntactic sugar in +Noir. + +Under the hood, the return value is passed as an input to the circuit and is checked at the end of +the circuit program. + +::: + +The verifier contract uses the output (return) value of a Noir program as a public input. So if you +have the following function + +```rust +fn main( + // Public inputs + pubkey_x: pub Field, + pubkey_y: pub Field, + // Private inputs + priv_key: Field, +) -> pub Field +``` + +then `verify` in `plonk_vk.sol` will expect 3 public inputs. Passing two inputs will result in an +error like `Reason: PUBLIC_INPUT_COUNT_INVALID(3, 2)`. + +In this case the 3 inputs to `verify` would be ordered as `[pubkey_x, pubkey_y, return]`. + +#### Struct inputs + +Consider the following program: + +```rust +struct Type1 { + val1: Field, + val2: Field, +} + +struct Nested { + t1: Type1, + is_true: bool, +} + +fn main(x: pub Field, nested: pub Nested, y: pub Field) { + //... +} +``` + +Structs will be flattened so that the array of inputs is 1-dimensional array. The order of these inputs would be flattened to: `[x, nested.t1.val1, nested.t1.val2, nested.is_true, y]` + +## Noir for EVM chains + +You can currently deploy the Solidity verifier contracts to most EVM compatible chains. EVM chains that have been tested and are known to work include: + +- Optimism +- Arbitrum +- Polygon PoS +- Scroll +- Celo + +Other EVM chains should work, but have not been tested directly by our team. If you test any other chains, please open a PR on this page to update the list. See [this doc](https://github.com/noir-lang/noir-starter/tree/main/with-foundry#testing-on-chain) for more info about testing verifier contracts on different EVM chains. + +### Unsupported chains + +Unfortunately not all "EVM" chains are supported. + +**zkSync** and the **Polygon zkEVM** do _not_ currently support proof verification via Solidity verifier contracts. They are missing the bn256 precompile contract that the verifier contract requires. Once these chains support this precompile, they may work. diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/black_box_fns.md b/noir/docs/versioned_docs/version-v0.9.0/standard_library/black_box_fns.md new file mode 100644 index 00000000000..c758846b688 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/black_box_fns.md @@ -0,0 +1,45 @@ +--- +title: Black Box Functions +description: Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. +keywords: [noir, black box functions] +--- + +Black box functions are functions in Noir that rely on backends implementing support for specialized constraints. This makes certain zk-snark unfriendly computations cheaper than if they were implemented in Noir. + +:::warning + +It is likely that not all backends will support a particular black box function. + +::: + +Because it is not guaranteed that all backends will support black box functions, it is possible that certain Noir programs won't compile against a particular backend if they use an unsupported black box function. It is possible to fallback to less efficient implementations written in Noir/ACIR in some cases. + +Black box functions are specified with the `#[foreign(black_box_fn)]` attribute. For example, the SHA256 function in the Noir [source code](https://github.com/noir-lang/noir/blob/v0.5.1/noir_stdlib/src/hash.nr) looks like: + +```rust +#[foreign(sha256)] +fn sha256(_input : [u8; N]) -> [u8; 32] {} +``` + +## Function list + +Here is a list of the current black box functions that are supported by UltraPlonk: + +- AES +- [SHA256](./cryptographic_primitives/hashes#sha256) +- [Schnorr signature verification](./cryptographic_primitives/schnorr) +- [Blake2s](./cryptographic_primitives/hashes#blake2s) +- [Pedersen](./cryptographic_primitives/hashes#pedersen) +- [HashToField128Security](./cryptographic_primitives/hashes#hash_to_field) +- [ECDSA signature verification](./cryptographic_primitives/ecdsa_sig_verification) +- [Fixed base scalar multiplication](./cryptographic_primitives/scalar) +- [Compute merkle root](./merkle_trees#compute_merkle_root) +- AND +- XOR +- RANGE +- [Keccak256](./cryptographic_primitives/hashes#keccak256) +- [Recursive proof verification](./recursion) + +Most black box functions are included as part of the Noir standard library, however `AND`, `XOR` and `RANGE` are used as part of the Noir language syntax. For instance, using the bitwise operator `&` will invoke the `AND` black box function. To ensure compatibility across backends, the ACVM has fallback implementations of `AND`, `XOR` and `RANGE` defined in its standard library which it can seamlessly fallback to if the backend doesn't support them. + +You can view the black box functions defined in the ACVM code [here](https://github.com/noir-lang/acvm/blob/acir-v0.12.0/acir/src/circuit/black_box_functions.rs). diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives.md b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives.md new file mode 100644 index 00000000000..2df4f929474 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives.md @@ -0,0 +1,14 @@ +--- +title: Cryptographic primitives in Noir +description: + Learn about the cryptographic primitives ready to use for any Noir project +keywords: + [ + cryptographic primitives, + Noir project, + ] +--- + +The Noir team is progressively adding new cryptographic primitives to the standard library. Reach out for news or if you would be interested in adding more of these calculations in Noir. + +Some methods are available thanks to the Aztec backend, not being performed using Noir. When using other backends, these methods may or may not be supplied. diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/00_hashes.mdx b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/00_hashes.mdx new file mode 100644 index 00000000000..31a84cdb753 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/00_hashes.mdx @@ -0,0 +1,146 @@ +--- +title: Hash methods +description: + Learn about the cryptographic primitives ready to use for any Noir project, including sha256, + blake2s, pedersen, mimc_bn254 and mimc +keywords: + [cryptographic primitives, Noir project, sha256, blake2s, pedersen, mimc_bn254, mimc, hash] +--- + +import BlackBoxInfo from './common/_blackbox.mdx'; + +## sha256 + +Given an array of bytes, returns the resulting sha256 hash. + +```rust +fn sha256(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let hash = std::hash::sha256(x); +} +``` + + + +## blake2s + +Given an array of bytes, returns an array with the Blake2 hash + +```rust +fn blake2s(_input : [u8]) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let hash = std::hash::blake2s(x); +} +``` + + + +## pedersen + +Given an array of Fields, returns the Pedersen hash. + +```rust +fn pedersen(_input : [Field]) -> [Field; 2] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let hash = std::hash::pedersen(x); +} +``` + + + +## keccak256 + +Given an array of bytes (`u8`), returns the resulting keccak hash as an array of 32 bytes +(`[u8; 32]`). Specify a message_size to hash only the first `message_size` bytes +of the input. + +```rust +fn keccak256(_input : [u8; N], _message_size: u32) -> [u8; 32] +``` + +example: + +```rust +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let message_size = 4; + let hash = std::hash::keccak256(x, message_size); +} +``` + + + +## poseidon + +Given an array of Fields, returns a new Field with the Poseidon Hash. Mind that you need to specify +how many inputs are there to your Poseidon function. + +```rust +// example for hash_1, hash_2 accepts an array of length 2, etc +fn hash_1(input: [Field; 1]) -> Field +``` + +example: + +```rust +fn main() +{ + let hash1 = std::hash::poseidon::bn254::hash_2([1, 2]); + assert(hash1 == 0x115cc0f5e7d690413df64c6b9662e9cf2a3617f2743245519e19607a4417189a); +} +``` + +## mimc_bn254 and mimc + +`mimc_bn254` is `mimc`, but with hardcoded parameters for the BN254 curve. You can use it by +providing an array of Fields, and it returns a Field with the hash. You can use the `mimc` method if +you're willing to input your own constants: + +```rust +fn mimc(x: Field, k: Field, constants: [Field; N], exp : Field) -> Field +``` + +otherwise, use the `mimc_bn254` method: + +```rust +fn mimc_bn254(array: [Field; N]) -> Field +``` + +example: + +```rust + +fn main() { + let x = [163, 117, 178, 149] // some random bytes + let hash = std::hash::mimc_bn254(x); +} +``` + +## hash_to_field + +```rust +fn hash_to_field(_input : [Field; N]) -> Field {} +``` + +Calculates the `blake2s` hash of the inputs and returns the hash modulo the field modulus to return +a value which can be represented as a `Field`. + + diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/01_scalar.mdx b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/01_scalar.mdx new file mode 100644 index 00000000000..62265cddb1e --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/01_scalar.mdx @@ -0,0 +1,33 @@ +--- +title: Scalar multiplication +description: + See how you can perform scalar multiplications over a fixed base in Noir +keywords: + [ + cryptographic primitives, + Noir project, + scalar multiplication, + ] +--- + +import BlackBoxInfo from './common/\_blackbox.mdx'; + +## scalar_mul::fixed_base + +Performs scalar multiplication over the embedded curve whose coordinates are defined by the +configured noir field. For the BN254 scalar field, this is BabyJubJub or Grumpkin. + +```rust +fn fixed_base(_input : Field) -> [Field; 2] +``` + +example + +```rust +fn main(x : Field) { + let scal = std::scalar_mul::fixed_base(x); + std::println(scal); +} +``` + + diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/02_schnorr.mdx b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/02_schnorr.mdx new file mode 100644 index 00000000000..0e219c0e5ff --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/02_schnorr.mdx @@ -0,0 +1,37 @@ +--- +title: Schnorr Signatures +description: Learn how you can verify Schnorr signatures using Noir +keywords: [cryptographic primitives, Noir project, schnorr, signatures] +--- + +import BlackBoxInfo from './common/_blackbox.mdx'; + +## schnorr::verify_signature + +Verifier for Schnorr signatures over the embedded curve (for BN254 it is Grumpkin). + +```rust +fn verify_signature(_public_key_x: Field, _public_key_y: Field, _signature: [u8; 64], _message: [u8]) -> bool +``` + +where `_signature` can be generated like so using the npm package +[@noir-lang/barretenberg](https://www.npmjs.com/package/@noir-lang/barretenberg) + +```js +const { BarretenbergWasm } = require('@noir-lang/barretenberg/dest/wasm'); +const { Schnorr } = require('@noir-lang/barretenberg/dest/crypto/schnorr'); + +... + +const barretenberg = await BarretenbergWasm.new(); +const schnorr = new Schnorr(barretenberg); +const pubKey = schnorr.computePublicKey(privateKey); +const message = ... +const signature = Array.from( + schnorr.constructSignature(hash, privateKey).toBuffer() +); + +... +``` + + diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx new file mode 100644 index 00000000000..3934a0338d0 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/03_ecdsa_sig_verification.mdx @@ -0,0 +1,45 @@ +--- +title: ECDSA Signature Verification +description: Learn about the cryptographic primitives regarding ECDSA over the secp256k1 and secp256r1 curves +keywords: [cryptographic primitives, Noir project, ecdsa, secp256k1, secp256r1, signatures] +--- + +import BlackBoxInfo from './common/_blackbox.mdx'; + +Noir supports ECDSA signatures verification over the secp256k1 and secp256r1 curves. + +## ecdsa_secp256k1::verify_signature + +Verifier for ECDSA Secp256k1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> bool +``` + +example: + +```rust +fn main(hashed_message : [u8;32], pub_key_x : [u8;32], pub_key_y : [u8;32], signature : [u8;64]) { + let valid_signature = std::ecdsa_secp256k1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} +``` + +## ecdsa_secp256r1::verify_signature + +Verifier for ECDSA Secp256r1 signatures + +```rust +fn verify_signature(_public_key_x : [u8; 32], _public_key_y : [u8; 32], _signature: [u8; 64], _message: [u8]) -> bool +``` + +example: + +```rust +fn main(hashed_message : [u8;32], pub_key_x : [u8;32], pub_key_y : [u8;32], signature : [u8;64]) { + let valid_signature = std::ecdsa_secp256r1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} +``` + + diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/04_ec_primitives.md b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/04_ec_primitives.md new file mode 100644 index 00000000000..6e6b19b6861 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/04_ec_primitives.md @@ -0,0 +1,101 @@ +--- +title: Elliptic Curve Primitives +keywords: [cryptographic primitives, Noir project] +--- + +Data structures and methods on them that allow you to carry out computations involving elliptic +curves over the (mathematical) field corresponding to `Field`. For the field currently at our +disposal, applications would involve a curve embedded in BN254, e.g. the +[Baby Jubjub curve](https://eips.ethereum.org/EIPS/eip-2494). + +## Data structures + +### Elliptic curve configurations + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Curve`), i.e. the specific elliptic +curve you want to use, which would be specified using any one of the methods +`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::new` which take the coefficients in the +defining equation together with a generator point as parameters. You can find more detail in the +comments in +[`noir_stdlib/src/ec.nr`](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr), but +the gist of it is that the elliptic curves of interest are usually expressed in one of the standard +forms implemented here (Twisted Edwards, Montgomery and Short Weierstraß), and in addition to that, +you could choose to use `affine` coordinates (Cartesian coordinates - the usual (x,y) - possibly +together with a point at infinity) or `curvegroup` coordinates (some form of projective coordinates +requiring more coordinates but allowing for more efficient implementations of elliptic curve +operations). Conversions between all of these forms are provided, and under the hood these +conversions are done whenever an operation is more efficient in a different representation (or a +mixed coordinate representation is employed). + +### Points + +(`std::ec::{tecurve,montcurve,swcurve}::{affine,curvegroup}::Point`), i.e. points lying on the +elliptic curve. For a curve configuration `c` and a point `p`, it may be checked that `p` +does indeed lie on `c` by calling `c.contains(p1)`. + +## Methods + +(given a choice of curve representation, e.g. use `std::ec::tecurve::affine::Curve` and use +`std::ec::tecurve::affine::Point`) + +- The **zero element** is given by `Point::zero()`, and we can verify whether a point `p: Point` is + zero by calling `p.is_zero()`. +- **Equality**: Points `p1: Point` and `p2: Point` may be checked for equality by calling + `p1.eq(p2)`. +- **Addition**: For `c: Curve` and points `p1: Point` and `p2: Point` on the curve, adding these two + points is accomplished by calling `c.add(p1,p2)`. +- **Negation**: For a point `p: Point`, `p.negate()` is its negation. +- **Subtraction**: For `c` and `p1`, `p2` as above, subtracting `p2` from `p1` is accomplished by + calling `c.subtract(p1,p2)`. +- **Scalar multiplication**: For `c` as above, `p: Point` a point on the curve and `n: Field`, + scalar multiplication is given by `c.mul(n,p)`. If instead `n :: [u1; N]`, i.e. `n` is a bit + array, the `bit_mul` method may be used instead: `c.bit_mul(n,p)` +- **Multi-scalar multiplication**: For `c` as above and arrays `n: [Field; N]` and `p: [Point; N]`, + multi-scalar multiplication is given by `c.msm(n,p)`. +- **Coordinate representation conversions**: The `into_group` method converts a point or curve + configuration in the affine representation to one in the CurveGroup representation, and + `into_affine` goes in the other direction. +- **Curve representation conversions**: `tecurve` and `montcurve` curves and points are equivalent + and may be converted between one another by calling `into_montcurve` or `into_tecurve` on their + configurations or points. `swcurve` is more general and a curve c of one of the other two types + may be converted to this representation by calling `c.into_swcurve()`, whereas a point `p` lying + on the curve given by `c` may be mapped to its corresponding `swcurve` point by calling + `c.map_into_swcurve(p)`. +- **Map-to-curve methods**: The Elligator 2 method of mapping a field element `n: Field` into a + `tecurve` or `montcurve` with configuration `c` may be called as `c.elligator2_map(n)`. For all of + the curve configurations, the SWU map-to-curve method may be called as `c.swu_map(z,n)`, where + `z: Field` depends on `Field` and `c` and must be chosen by the user (the conditions it needs to + satisfy are specified in the comments + [here](https://github.com/noir-lang/noir/blob/master/noir_stdlib/src/ec.nr)). + +## Examples + +The +[ec_baby_jubjub test](https://github.com/noir-lang/noir/blob/master/crates/nargo_cli/tests/test_data/ec_baby_jubjub/src/main.nr) +illustrates all of the above primitives on various forms of the Baby Jubjub curve. A couple of more +interesting examples in Noir would be: + +Public-key cryptography: Given an elliptic curve and a 'base point' on it, determine the public key +from the private key. This is a matter of using scalar multiplication. In the case of Baby Jubjub, +for example, this code would do: + +```rust +use dep::std::ec::tecurve::affine::{Curve, Point}; + +fn bjj_pub_key(priv_key: Field) -> Point +{ + + let bjj = Curve::new(168700, 168696, G::new(995203441582195749578291179787384436505546430278305826713579947235728471134,5472060717959818805561601436314318772137091100104008585924551046643952123905)); + + let base_pt = Point::new(5299619240641551281634865583518297030282874472190772894086521144482721001553, 16950150798460657717958625567821834550301663161624707787222815936182638968203); + + bjj.mul(priv_key,base_pt) +} +``` + +This would come in handy in a Merkle proof. + +- EdDSA signature verification: This is a matter of combining these primitives with a suitable hash + function. See + [feat(stdlib): EdDSA sig verification noir#1136](https://github.com/noir-lang/noir/pull/1136) for + the case of Baby Jubjub and the Poseidon hash function. diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/05_eddsa.mdx b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/05_eddsa.mdx new file mode 100644 index 00000000000..8f060ed3316 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/05_eddsa.mdx @@ -0,0 +1,17 @@ +--- +title: EdDSA Verification +description: Learn about the cryptographic primitives regarding EdDSA +keywords: [cryptographic primitives, Noir project, eddsa, signatures] +--- + +import BlackBoxInfo from './common/_blackbox.mdx'; + +## eddsa::eddsa_poseidon_verify + +Verifier for EdDSA signatures + +```rust +fn eddsa_poseidon_verify(public_key_x : Field, public_key_y : Field, signature_s: Field, signature_r8_x: Field, signature_r8_y: Field, message: Field) -> bool +``` + + diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/common/_blackbox.mdx b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/common/_blackbox.mdx new file mode 100644 index 00000000000..9fe9b48fbff --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/cryptographic_primitives/common/_blackbox.mdx @@ -0,0 +1,5 @@ +:::info + +This is a black box function. Read [this section](../black_box_fns) to learn more about black box functions in Noir. + +::: \ No newline at end of file diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/field_methods.md b/noir/docs/versioned_docs/version-v0.9.0/standard_library/field_methods.md new file mode 100644 index 00000000000..4d1cdc953e9 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/field_methods.md @@ -0,0 +1,149 @@ +--- +title: Field Methods +description: + Learn about common methods on Noir Field, including to_le_bits, to_le_bytes, to_le_radix, + to_be_radix, pow_32, etc, and see code examples. +keywords: + [ + Noir Field, + to_le_bits, + to_le_bytes, + to_le_radix, + to_be_radix, + pow_32, + Little Endian, + Big Endian, + Vector, + Exponent, + ] +--- + +After declaring a Field, you can use these common methods on it: + +## to_le_bits + +Transforms the field into an array of bits, Little Endian. + +```rust +fn to_le_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2 + let bits = field.to_le_bits(32); +} +``` + +## to_be_bits + +Transforms the field into an array of bits, Big Endian. + +```rust +fn to_be_bits(_x : Field, _bit_size: u32) -> [u1; N] +``` + +example: + +```rust +fn main() { + let field = 2 + let bits = field.to_be_bits(32); +} +``` + +## to_le_bytes + +Transforms into an array of bytes, Little Endian + +```rust +fn to_le_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2 + let bytes = field.to_le_bytes(4); +} +``` + +## to_be_bytes + +Transforms into an array of bytes, Big Endian + +```rust +fn to_be_bytes(_x : Field, byte_size: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2 + let bytes = field.to_be_bytes(4); +} +``` + +## to_le_radix + +Decomposes into a vector over the specified base, Little Endian + +```rust +fn to_le_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2 + let radix = field.to_le_radix(256, 4); +} +``` + +## to_be_radix + +Decomposes into a vector over the specified base, Big Endian + +```rust +fn to_be_radix(_x : Field, _radix: u32, _result_len: u32) -> [u8] +``` + +example: + +```rust +fn main() { + let field = 2 + let radix = field.to_be_radix(256, 4); +} +``` + +## pow_32 + +Returns the value to the power of the specified exponent + +```rust +fn pow_32(self, exponent: Field) -> Field +``` + +example: + +```rust +fn main() { + let field = 2 + let pow = field.pow_32(4); + assert(pow == 16); +} +``` + +## sgn0 + +Parity of (prime) Field element, i.e. sgn0(x mod p) = 0 if x ∈ {0, ..., p-1} is even, otherwise sgn0(x mod p) = 1. + +```rust +fn sgn0(self) -> u1 +``` diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/logging.md b/noir/docs/versioned_docs/version-v0.9.0/standard_library/logging.md new file mode 100644 index 00000000000..42a84be1992 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/logging.md @@ -0,0 +1,42 @@ +--- +title: Logging +description: + Learn how to use the println statement for debugging in Noir with this tutorial. Understand the + basics of logging in Noir and how to implement it in your code. +keywords: + [ + noir logging, + println statement, + debugging in noir, + noir std library, + logging tutorial, + basic logging in noir, + noir logging implementation, + noir debugging techniques, + rust, + ] +--- + +# Logging + +The standard library provides a familiar `println` statement you can use. Despite being a limited +implementation of rust's `println!` macro, this construct can be useful for debugging. + +The `println` statement is unconstrained, so it works for outputting integers, fields, strings, and even structs or expressions. For example: + +```rust +use dep::std; + +struct Person { + age : Field, + height : Field, +} + +fn main(age : Field, height : Field) { + let person = Person { age : age, height : height }; + std::println(person); + std::println(age + height); + std::println("Hello world!"); +} + +``` diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/merkle_trees.md b/noir/docs/versioned_docs/version-v0.9.0/standard_library/merkle_trees.md new file mode 100644 index 00000000000..57d8c4a9e4f --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/merkle_trees.md @@ -0,0 +1,58 @@ +--- +title: Merkle Trees +description: Learn about Merkle Trees in Noir with this tutorial. Explore the basics of computing a merkle root using a proof, with examples. +keywords: + [ + Merkle trees in Noir, + Noir programming language, + check membership, + computing root from leaf, + Noir Merkle tree implementation, + Merkle tree tutorial, + Merkle tree code examples, + Noir libraries, + pedersen hash., + ] +--- + +## compute_merkle_root + +Returns the root of the tree from the provided leaf and its hash path, using a [Pedersen hash](cryptographic_primitives/00_hashes.mdx#pedersen). + +```rust +fn compute_merkle_root(leaf : Field, index : Field, hash_path: [Field]) -> Field +``` + +example: + +```rust +/** + // these values are for this example only + index = "0" + priv_key = "0x000000000000000000000000000000000000000000000000000000616c696365" + secret = "0x1929ea3ab8d9106a899386883d9428f8256cfedb3c4f6b66bf4aa4d28a79988f" + note_hash_path = [ + "0x1e61bdae0f027b1b2159e1f9d3f8d00fa668a952dddd822fda80dc745d6f65cc", + "0x0e4223f3925f98934393c74975142bd73079ab0621f4ee133cee050a3c194f1a", + "0x2fd7bb412155bf8693a3bd2a3e7581a679c95c68a052f835dddca85fa1569a40" + ] + */ +fn main(index: Field, priv_key: Field, secret: Field, note_hash_path: [Field; 3]) { + + let pubkey = std::scalar_mul::fixed_base(priv_key); + let pubkey_x = pubkey[0]; + let pubkey_y = pubkey[1]; + let note_commitment = std::hash::pedersen([pubkey_x, pubkey_y, secret]); + + let root = std::merkle::compute_merkle_root(note_commitment[0], index, note_hash_path); + std::println(root); +} +``` + +To check merkle tree membership: + +1. Include a merkle root as a program input. +2. Compute the merkle root of a given leaf, index and hash path. +3. Assert the merkle roots are equal. + +For more info about merkle trees, see the Wikipedia [page](https://en.wikipedia.org/wiki/Merkle_tree). diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/recursion.md b/noir/docs/versioned_docs/version-v0.9.0/standard_library/recursion.md new file mode 100644 index 00000000000..4705ae6c575 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/recursion.md @@ -0,0 +1,96 @@ +--- +title: Recursive Proofs +description: Learn about how to write recursive proofs in Noir. +keywords: [recursion, recursive proofs, verification_key, aggregation object, verify_proof] +--- + +Noir supports recursively verifying proofs, meaning you verify the proof of a Noir program in another Noir program. This enables creating proofs of arbitrary size by doing step-wise verification of smaller components of a large proof. + +The `verify_proof` function takes a verification key, proof and public inputs for a zk program, as well as a key hash and an input aggregation object. The key hash is used to check the validity of the verification key and the input aggregation object is required by some proving systems. The `verify_proof` function returns an output aggregation object that can then be fed into future iterations of the proof verification if required. + +```rust +#[foreign(verify_proof)] +fn verify_proof(_verification_key : [Field], _proof : [Field], _public_input : Field, _key_hash : Field, _input_aggregation_object : [Field]) -> [Field] {} +``` + +:::info + +This is a black box function. Read [this section](./black_box_fns) to learn more about black box functions in Noir. + +::: + +## Aggregation Object + +The purpose of the input aggregation object is a little less clear though (and the output aggregation object that is returned from the `std::verify_proof` method). Recursive zkSNARK schemes do not necessarily "verify a proof" in the sense that you expect a true or false to be spit out by the verifier. Rather an aggregation object is built over the public inputs. In the case of PLONK the recursive aggregation object is two G1 points (expressed as 16 witness values). The final verifier (in our case this is most often the smart contract verifier) has to be aware of this aggregation object to execute a pairing and check the validity of these points (thus completing the recursive verification). + +So for example in this circuit: + +```rust +use dep::std; + +fn main( + verification_key : [Field; 114], + proof : [Field; 94], + public_inputs : [Field; 1], + key_hash : Field, + input_aggregation_object : [Field; 16], + proof_b : [Field; 94], +) -> pub [Field; 16] { + let output_aggregation_object_a = std::verify_proof( + verification_key, + proof, + public_inputs, + key_hash, + input_aggregation_object + ); + + let output_aggregation_object = std::verify_proof( + verification_key, + proof_b, + public_inputs, + key_hash, + output_aggregation_object_a + ); + + let mut output = [0; 16]; + for i in 0..16 { + output[i] = output_aggregation_object[i]; + } + output +} +``` + +In this example we have a circuit, that generates proofs A and B, that is being verified in circuit C. Assuming that the proof being passed in is not already a recursive proof, the `input_aggregation_object` will be all zeros. It will then generate an `output_aggregation_object`. This blob of data then becomes the `input_aggregation_object` of the next recursive aggregation we wish to compute. We can see here as the same public inputs, verification key, and key hash are used that we are verifying two proofs generated from the same circuit in this single circuit. `std::verify_proof` returns a `[Field]` because the size of an aggregation object is proof system dependent--in barretenberg, aggregation objects are two G1 points, while in Halo2, the aggregation object is a list of G1 points that is log the circuit size. So for the final step we convert the slice into an array of size 16 because we are generating proofs using UltraPlonk. + +## Parameters + +### `verification_key` + +The verification key for the zk program that is being verified. + +### `proof` + +The proof for the zk program that is being verified. + +### `public_inputs` + +These represent the public inputs of the proof we are verifying. They should be checked against in the circuit after construction of a new aggregation state. + +### `key_hash` + +A key hash is used to check the validity of the verification key. The circuit implementing this opcode can use this hash to ensure that the key provided to the circuit matches the key produced by the circuit creator. + +### `input_aggregation_object` + +An aggregation object is blob of data that the top-level verifier must run some proof system specific algorithm on to complete verification. The size is proof system specific and will be set by the backend integrating this opcode. The input aggregation object is only not `None` when we are verifying a previous recursive aggregation in the current circuit. If this is the first recursive aggregation there is no input aggregation object. It is left to the backend to determine how to handle when there is no input aggregation object. + +## Return value + +### `output_aggregation_object` + +This is the result of a recursive aggregation and is what will be fed into the next verifier. +The next verifier can either perform a final verification (returning true or false) or perform another recursive aggregation where this output aggregation object will be the input aggregation object of the next recursive aggregation. + +## Example + +You can see an example of how to do recursive proofs in [this example recursion demo repo](https://github.com/Savio-Sou/recursion-demo/tree/main). diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/slice_methods.md b/noir/docs/versioned_docs/version-v0.9.0/standard_library/slice_methods.md new file mode 100644 index 00000000000..8b93d8ea427 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/slice_methods.md @@ -0,0 +1,279 @@ +--- +title: Slice Methods +description: + Learn about the commonly used methods available for slices in Noir, including push_back, len, srt, map, fold, reduce, all, and any. +keywords: [rust, slice, methods, push_back, len, sort, fold, reduce, all, any] +--- + +For convenience, the STD provides some ready-to-use, common methods for slices: + +## push_back + +Pushes a new element to the end of the slice, returning a new slice with a length one greater than the original unmodified slice. + +```rust +fn push_back(_self: [T], _elem: T) -> [T] +``` + +example: + +```rust +fn main() -> pub Field { + let mut slice: [Field] = [0; 2]; + + let mut new_slice = slice.push_back(6); + new_slice.len() +} +``` + +View the corresponding test file [here][test-file]. + +## push_front + +Returns a new array with the specified element inserted at index 0. The existing elements indexes are incremented by 1. + +```rust +fn push_front(_self: Self, _elem: T) -> Self +``` + +Example: + +```rust +let mut new_slice: [Field] = []; +new_slice = new_slice.push_front(20); +assert(new_slice[0] == 20); // returns true +``` + +View the corresponding test file [here][test-file]. + +## pop_front + +Returns a tuple of two items, the first element of the array and the rest of the array. + +```rust +fn pop_front(_self: Self) -> (T, Self) +``` + +Example: + +```rust +let (first_elem, rest_of_slice) = slice.pop_front(); +``` + +View the corresponding test file [here][test-file]. + +## pop_back + +Returns a tuple of two items, the beginning of the array with the last element omitted and the last element. + +```rust +fn pop_back(_self: Self) -> (Self, T) +``` + +Example: + +```rust +let (popped_slice, last_elem) = slice.pop_back(); +``` + +View the corresponding test file [here][test-file]. + +## insert + +Inserts an element at a specified index and shifts all following elements by 1. + +```rust +fn insert(_self: Self, _index: Field, _elem: T) -> Self +``` + +Example: + +```rust + new_slice = rest_of_slice.insert(2, 100); +assert(new_slice[2] == 100); +``` + +View the corresponding test file [here][test-file]. + +## remove + +Remove an element at a specified index, shifting all elements after it to the left, returning the altered slice and the removed element. + +```rust +fn remove(_self: Self, _index: Field) -> (Self, T) +``` + +Example: + +```rust +let (remove_slice, removed_elem) = slice.remove(3); +``` + +View the corresponding test file [here]([test-file]. + +## len + +Returns the length of a slice + +```rust +fn len(_slice: [T]) -> comptime Field +``` + +Example: + +```rust +fn main() { + let slic = [42, 42] + assert(slic.len() == 2); +} +``` + +## sort + +Returns a new sorted slice. The original slice remains untouched. Notice that this function will +only work for slices of fields or integers, not for any arbitrary type. This is because the sorting +logic the function uses internally is optimized specifically for these values. If you need a sort function to +sort any type, you should use the function `sort_via` described below. + +```rust +fn sort(_slice: [T]) -> [T] +``` + +Example: + +```rust +fn main() { + let slic = [42, 32] + let sorted = slic.sort(); + assert(sorted == [32, 42]); +} +``` + +## sort_via + +Sorts the slice with a custom comparison function + +```rust +fn sort_via(mut a: [T], ordering: fn(T, T) -> bool) -> [T] +``` + +Example: + +```rust +fn main() { + let slic = [42, 32] + let sorted_ascending = slic.sort_via(|a, b| a < b); + assert(sorted_ascending == [32, 42]); // verifies + + let sorted_descending = slic.sort_via(|a, b| a > b); + assert(sorted_descending == [32, 42]); // does not verify +} +``` + +## map + +Applies a function to each element of the slice, returning a new slice containing the mapped elements. + +```rust +fn map(f: fn(T) -> U) -> [U] +``` + +Example: + +```rust +let a = [1, 2, 3]; +let b = a.map(|a| a * 2) // b is now [2, 4, 6] +``` + +## fold + +Applies a function to each element of the slice, returning the final accumulated value. The first +parameter is the initial value. + +```rust +fn fold(mut accumulator: U, f: fn(U, T) -> U) -> U +``` + +This is a left fold, so the given function will be applied to the accumulator and first element of +the slice, then the second, and so on. For a given call the expected result would be equivalent to: + +```rust +let a1 = [1]; +let a2 = [1, 2]; +let a3 = [1, 2, 3]; + +let f = |a, b| a - b; +a1.fold(10, f) //=> f(10, 1) +a2.fold(10, f) //=> f(f(10, 1), 2) +a3.fold(10, f) //=> f(f(f(10, 1), 2), 3) +``` + +Example: + +```rust + +fn main() { + let slic = [2,2,2,2,2] + let folded = slic.fold(0, |a, b| a + b); + assert(folded == 10); +} + +``` + +## reduce + +Same as fold, but uses the first element as starting element. + +```rust +fn reduce(f: fn(T, T) -> T) -> T +``` + +Example: + +```rust +fn main() { + let slic = [2,2,2,2,2] + let reduced = slic.reduce(|a, b| a + b); + assert(reduced == 10); +} +``` + +## all + +Returns true if all the elements satisfy the given predicate + +```rust +fn all(predicate: fn(T) -> bool) -> bool +``` + +Example: + +```rust +fn main() { + let slic = [2,2,2,2,2] + let all = slic.all(|a| a == 2); + assert(all); +} +``` + +## any + +Returns true if any of the elements satisfy the given predicate + +```rust +fn any(predicate: fn(T) -> bool) -> bool +``` + +Example: + +```rust +fn main() { + let slic = [2,2,2,2,5] + let any = slic.any(|a| a == 5); + assert(any); +} + +``` + +[test-file]: https://github.com/noir-lang/noir/blob/f387ec1475129732f72ba294877efdf6857135ac/crates/nargo_cli/tests/test_data_ssa_refactor/slices/src/main.nr + diff --git a/noir/docs/versioned_docs/version-v0.9.0/standard_library/zeroed.md b/noir/docs/versioned_docs/version-v0.9.0/standard_library/zeroed.md new file mode 100644 index 00000000000..97dab02dac2 --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/standard_library/zeroed.md @@ -0,0 +1,25 @@ +--- +title: Zeroed Function +description: + The zeroed function returns a zeroed value of any type. +keywords: + [ + zeroed + ] +--- + +Implements `fn zeroed() -> T` to return a zeroed value of any type. This function is generally unsafe to use as the zeroed bit pattern is not guaranteed to be valid for all types. It can however, be useful in cases when the value is guaranteed not to be used such as in a BoundedVec library implementing a growable vector, up to a certain length, backed by an array. The array can be initialized with zeroed values which are guaranteed to be inaccessible until the vector is pushed to. Similarly, enumerations in noir can be implemented using this method by providing zeroed values for the unused variants. + +You can access the function at `std::unsafe::zeroed`. + +This function currently supports the following types: + +- Field +- Bool +- Uint +- Array +- String +- Tuple +- Function + +Using it on other types could result in unexpected behavior. diff --git a/noir/docs/versioned_docs/version-v0.9.0/typescript.md b/noir/docs/versioned_docs/version-v0.9.0/typescript.md new file mode 100644 index 00000000000..8608783784c --- /dev/null +++ b/noir/docs/versioned_docs/version-v0.9.0/typescript.md @@ -0,0 +1,243 @@ +--- +title: Working with TypeScript +description: + Learn how to interact with Noir programs using TypeScript. Follow this tutorial to compile your + program, specify inputs, initialize a prover & verifier, and prove and verify your program. +keywords: [TypeScript, Noir, tutorial, compile, inputs, prover, verifier, proof] +--- + +Interactions with Noir programs can also be performed in TypeScript, which can come in handy when +writing tests or when working in TypeScript-based projects like [Hardhat](https://hardhat.org/). + +You can check the complete code for this tutorial here: [browser with next.js](https://github.com/signorecello/noir-min-browser-example) and [node.js](https://github.com/signorecello/noir-min-nodejs-example). If you want just a browser boilerplate to start with, check out the [noir-starter](https://github.com/noir-lang/noir-starter) for an example implementation. + +:::note + +You may find unexpected errors working with some frameworks such as `vite`. This is due to the +nature of `wasm` files and the way Noir uses web workers. As we figure it out, we suggest using +[Create React App](https://create-react-app.dev/), or [Next.js](https://nextjs.org/) for a quick +start. + +::: + +## Setup + +Make sure you are using Noir version >= 0.10.1. + +You can check your current version by running `nargo --version`. + +You can install version 0.10.1 with noirup with + +```bash +noirup -v 0.10.1 +``` + +See the [Installation page](./getting_started/nargo_installation) for more info. + +We're assuming you're using ES6 and ESM for both browser (for example with React), or nodejs. Install [Node.js](https://nodejs.org/en). Init a new project with `npm init` and add `"type": "module"` to your `package.json`, to let `node` know we're using the new ESM sytem: + +```json +{ + "type": "module" + // the rest of your package.json +} +``` + +Install Noir dependencies in your project by running: + +```bash +npm i @aztec/bb.js@0.3.6 https://git@github.com/noir-lang/acvm-simulator-wasm.git#b9d9ca9dfc5140839f23998d9466307215607c42 fflate ethers@5.7.2 +``` + +This will install the `acvm-simulator` that will generate our witness, and the proving backend barretenberg `bb.js`. + +We're also installing `ethers` because we're too lazy to write a function that pads public inputs with 32bytes, and `fflate` to help us decompress our circuit bytecode. + +Since we're with typescript and using `nodejs` types, we also recommend to install the `@types/node` package, otherwise your IDE will scream at you. + +```bash +npm i --save-dev @types/node +``` + +:::note + +While Noir is in rapid development, some packages could interfere with others. For that reason, you +should use these specified versions. Let us know if for some reason you need to use other ones. + +::: + +As for the circuit, run `nargo init` to create a new Noir project. + +We will use a Standard Noir Example and place it in the `src` folder. This program simply multiplies input `x` with input `y` and returns the result `z`. The verifier doesn't know the value of `x`: we're proving that we know it without making it public. + +```rust +// src/main.nr +fn main(x: u32, y: pub u32) -> pub u32 { + let z = x * y; + z +} +``` + +One valid scenario for proving could be `x = 3`, `y = 4` and `return = 12` + +## Compiling + +In order to start proving, we need to compile our circuit into the intermediate representation used by our backend. As of today, you have to do that with `nargo`. Just hop to your circuits folder and run `nargo compile`. + +:::info + +At this time, you need to use a nightly version of nargo. Using [noirup](./getting_started/00_nargo_installation.md#option-1-noirup) you can do this simply by running `noirup -n`. + +::: + +You should have a `json` file in `target/` with your circuit's bytecode. The json file is name based on the project name specified in Nargo.toml, so for a project named "test", it will be at `target/test.json`. You can then import that file normally. + +```ts +import circuit from '../target/test.json' assert { type: 'json' }; +``` + +## Decompressing the circuit + +The compiled circuit comes compressed. We need to decompress it, that's where `fflate` comes in. + +```ts +import { decompressSync } from 'fflate'; + +const acirBuffer = Buffer.from(circuit.bytecode, 'base64'); +const acirBufferUncompressed = decompressSync(acirBuffer); +``` + +From here, it's highly recommended you store `acirBuffer` and `acirBufferUncompressed` close by, as they will be used for witness generation and proving. + +## Initializing ACVM and BB.JS + +:::note + +This step will eventually be abstracted away as Noir tooling matures. For now, you should be fine just literally copy-pasting most of this into your own code. + +::: + +Before proving, `bb.js` needs to be initialized. We need to import some functions and use them + +```ts +import { Crs, newBarretenbergApiAsync, RawBuffer } from '@aztec/bb.js/dest/node/index.js'; + +const api = await newBarretenbergApiAsync(4); + +const [exact, circuitSize, subgroup] = await api.acirGetCircuitSizes(acirBufferUncompressed); +const subgroupSize = Math.pow(2, Math.ceil(Math.log2(circuitSize))); +const crs = await Crs.new(subgroupSize + 1); +await api.commonInitSlabAllocator(subgroupSize); +await api.srsInitSrs(new RawBuffer(crs.getG1Data()), crs.numPoints, new RawBuffer(crs.getG2Data())); + +const acirComposer = await api.acirNewAcirComposer(subgroupSize); +``` + +We should take two very useful objects from here: `api` and `acirComposer`. Make sure to keep these close by! + +:::info + +On the browser, you also need to init the ACVM. You can do that by importing it and calling it like: + +```ts +import initACVM, { executeCircuit, compressWitness } from '@noir-lang/acvm_js'; + +await initACVM(); +// the rest of your code +``` + +::: + +## Generating witnesses + +Witness generation is what allows us to prove with arbitrary inputs (like user inputs on a form, game, etc). In this example, our input is a simple object with our circuit inputs `x`, `y`, and return `z` (fun fact: the return value in Noir is actually a public input!). We're wrapping it in a function, so it can be conveniently called later on. + +```ts +import { ethers } from 'ethers'; // I'm lazy so I'm using ethers to pad my input +import { executeCircuit, compressWitness } from '@noir-lang/acvm_js'; + +async function generateWitness(input: any, acirBuffer: Buffer): Promise { + const initialWitness = new Map(); + initialWitness.set(1, ethers.utils.hexZeroPad(`0x${input.x.toString(16)}`, 32)); + initialWitness.set(2, ethers.utils.hexZeroPad(`0x${input.y.toString(16)}`, 32)); + + const witnessMap = await executeCircuit(acirBuffer, initialWitness, () => { + throw Error('unexpected oracle'); + }); + + const witnessBuff = compressWitness(witnessMap); + return witnessBuff; +} +``` + +## Proving + +Finally, we're ready to prove with our backend. Just like with the witness generation, could be useful to wrap it in its own function: + +```ts +async function generateProof(witness: Uint8Array) { + const proof = await api.acirCreateProof( + acirComposer, + acirBufferUncompressed, + decompressSync(witness), + false, + ); + return proof; +} +``` + +## Verifying + +Our backend should also be ready to verify our proof: + +```ts +async function verifyProof(proof: Uint8Array) { + await api.acirInitProvingKey(acirComposer, acirBufferUncompressed); + const verified = await api.acirVerifyProof(acirComposer, proof, false); + return verified; +} +``` + +## Now for the fun part + +Let's call our functions, and destroy our API! + +```ts +const input = { x: 3, y: 4 }; +const witness = await generateWitness(input, acirBuffer); +console.log('Witness generated!'); +const proof = await generateProof(witness); +console.log('Proof generated!'); +await verifyProof(proof); +console.log('Proof verified!'); +api.destroy(); +``` + +You can use [this](https://gist.github.com/critesjosh/6f3ba19fdc9298b24e90ba4f736247dc) tsconfig.json. You can see the script [here](https://gist.github.com/critesjosh/4aa36e87a0cc3f09feaf1febb4d11348). + +## Verifying with Smart Contract + +Alternatively, a verifier smart contract can be generated and used for verifying Noir proofs in +TypeScript as well. + +This could be useful if the Noir program is designed to be decentrally verified and/or make use of +decentralized states and logics that is handled at the smart contract level. + +This assumes you've already ran `nargo codegen-verifier`, got your smart contract, and deployed it with Hardhat, Foundry, or your tool of choice. You can then verify a Noir proof by simply calling it. + +Currently, `bb.js` appends the public inputs to the proof. However, these inputs need to be fed separately to the verifier contract. A simple solution is to just slice them from the resulting proof, like this: + +```ts +import { ethers } from 'ethers'; // example using ethers v5 +import artifacts from '../artifacts/circuits/contract/plonk_vk.sol/UltraVerifier.json'; // I compiled using Hardhat, so I'm getting my abi from here + +const verifierAddress = '0x123455'; // your verifier address +const provider = new ethers.providers.Web3Provider(window.ethereum); +const signer = this.provider.getSigner(); + +const contract = new ethers.Contract(verifierAddress, artifacts.abi, signer); + +const publicInputs = proof.slice(0, 32); +const slicedProof = proof.slice(32); +await contract.verify(slicedProof, [publicInputs]); +``` diff --git a/noir/docs/versioned_sidebars/version-v0.10.5-sidebars.json b/noir/docs/versioned_sidebars/version-v0.10.5-sidebars.json new file mode 100644 index 00000000000..a51ebf87643 --- /dev/null +++ b/noir/docs/versioned_sidebars/version-v0.10.5-sidebars.json @@ -0,0 +1,111 @@ +{ + "sidebar": [ + { + 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b/noir/docs/versioned_sidebars/version-v0.6.0-sidebars.json @@ -0,0 +1,90 @@ +{ + "sidebar": [ + { + "type": "doc", + "id": "index", + "label": "Noir" + }, + { + "type": "category", + "label": "Getting Started", + "items": [ + { + "type": "autogenerated", + "dirName": "getting_started" + } + ] + }, + { + "type": "category", + "label": "Examples", + "items": [ + { + "type": "autogenerated", + "dirName": "examples" + } + ] + }, + { + "type": "category", + "label": "Nargo", + "items": [ + { + "type": "autogenerated", + "dirName": "nargo" + } + ] + }, + { + "type": "category", + "label": "Language Concepts", + "items": [ + { + "type": "autogenerated", + "dirName": "language_concepts" + } + ] + }, + { + "type": "category", + "label": "Noir Standard Library", + "items": [ + { + "type": "category", + "label": "Cryptographic Primitives", + "link": { + "type": "doc", + "id": "standard_library/cryptographic_primitives" + }, + "items": [ + { + "type": "autogenerated", + "dirName": "standard_library/cryptographic_primitives" + } + ] + }, + "standard_library/array_methods", + "standard_library/field_methods", + "standard_library/logging", + "standard_library/merkle_trees", + "standard_library/zeroed", + "standard_library/black_box_fns" + ] + }, + { + "type": "category", + "label": "Modules, Packages and Crates", + "items": [ + { + "type": "autogenerated", + "dirName": "modules_packages_crates" + } + ] + }, + { + "type": "doc", + "id": "typescript", + "label": "Working with Typescript" + } + ] +} diff --git a/noir/docs/versioned_sidebars/version-v0.7.1-sidebars.json b/noir/docs/versioned_sidebars/version-v0.7.1-sidebars.json new file mode 100644 index 00000000000..7323ae1c504 --- /dev/null +++ b/noir/docs/versioned_sidebars/version-v0.7.1-sidebars.json @@ -0,0 +1,90 @@ +{ + "sidebar": [ + { + "type": "doc", + "id": "index", + "label": "Noir" + }, + { + "type": "category", + "label": "Getting Started", + "items": [ + { + "type": "autogenerated", + "dirName": "getting_started" + } + ] + }, + { + "type": "category", + "label": "Examples", + "items": [ + { + "type": "autogenerated", + "dirName": "examples" + } + ] + }, + { + "type": "category", + "label": "Nargo", + "items": [ + { + "type": "autogenerated", + "dirName": "nargo" + } + ] + }, + { + "type": "category", + "label": "Language Concepts", + "items": [ + { + "type": "autogenerated", + "dirName": "language_concepts" + } + ] + }, + { + "type": "category", + "label": "Noir Standard Library", + "items": [ + { + "type": "category", + "label": "Cryptographic Primitives", + "link": { + "type": "doc", + "id": "standard_library/cryptographic_primitives" + }, + "items": [ + { + "type": "autogenerated", + "dirName": "standard_library/cryptographic_primitives" + } + ] + }, + "standard_library/array_methods", + "standard_library/field_methods", + "standard_library/logging", + "standard_library/merkle_trees", + "standard_library/zeroed", + "standard_library/black_box_fns" + ] + }, + { + "type": "category", + "label": "Modules, Packages and Crates", + "items": [ + { + "type": "autogenerated", + "dirName": "modules_packages_crates" + } + ] + }, + { + "type": "doc", + "id": "typescript", + "label": "Working with Typescript" + } + ] +} diff --git a/noir/docs/versioned_sidebars/version-v0.9.0-sidebars.json b/noir/docs/versioned_sidebars/version-v0.9.0-sidebars.json new file mode 100644 index 00000000000..190363917e0 --- /dev/null +++ b/noir/docs/versioned_sidebars/version-v0.9.0-sidebars.json @@ -0,0 +1,91 @@ +{ + "sidebar": [ + { + "type": "doc", + "id": "index", + "label": "Noir" + }, + { + "type": "category", + "label": "Getting Started", + "items": [ + { + "type": "autogenerated", + "dirName": "getting_started" + } + ] + }, + { + "type": "category", + "label": "Examples", + "items": [ + { + "type": "autogenerated", + "dirName": "examples" + } + ] + }, + { + "type": "category", + "label": "Nargo", + "items": [ + { + "type": "autogenerated", + "dirName": "nargo" + } + ] + }, + { + "type": "category", + "label": "Language Concepts", + "items": [ + { + "type": "autogenerated", + "dirName": "language_concepts" + } + ] + }, + { + "type": "category", + "label": "Noir Standard Library", + "items": [ + { + "type": "category", + "label": "Cryptographic Primitives", + "link": { + "type": "doc", + "id": "standard_library/cryptographic_primitives" + }, + "items": [ + { + "type": "autogenerated", + "dirName": "standard_library/cryptographic_primitives" + } + ] + }, + "standard_library/slice_methods", + "standard_library/field_methods", + "standard_library/recursion", + "standard_library/logging", + "standard_library/merkle_trees", + "standard_library/zeroed", + "standard_library/black_box_fns" + ] + }, + { + "type": "category", + "label": "Modules, Packages and Crates", + "items": [ + { + "type": "autogenerated", + "dirName": "modules_packages_crates" + } + ] + }, + { + "type": "doc", + "id": "typescript", + "label": "Working with Typescript" + } + ] +} diff --git a/noir/docs/versions.json b/noir/docs/versions.json new file mode 100644 index 00000000000..ebd565cb0ae --- /dev/null +++ b/noir/docs/versions.json @@ -0,0 +1,5 @@ +[ + "v0.19.2", + "v0.17.0", + "v0.10.5" +] diff --git a/noir/flake.lock b/noir/flake.lock new file mode 100644 index 00000000000..5a9f9470a1f --- /dev/null +++ b/noir/flake.lock @@ -0,0 +1,170 @@ +{ + "nodes": { + "crane": { + "inputs": { + "flake-compat": [ + "flake-compat" + ], + "flake-utils": [ + "flake-utils" + ], + "nixpkgs": [ + "nixpkgs" + ], + "rust-overlay": "rust-overlay" + }, + "locked": { + "lastModified": 1681177078, + "narHash": "sha256-ZNIjBDou2GOabcpctiQykEQVkI8BDwk7TyvlWlI4myE=", + "owner": "ipetkov", + "repo": "crane", + "rev": "0c9f468ff00576577d83f5019a66c557ede5acf6", + "type": "github" + }, + "original": { + "owner": "ipetkov", + "repo": "crane", + "type": "github" + } + }, + "fenix": { + "inputs": { + "nixpkgs": [ + "nixpkgs" + ], + "rust-analyzer-src": "rust-analyzer-src" + }, + "locked": { + 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"rust-overlay": { + "inputs": { + "flake-utils": [ + "crane", + "flake-utils" + ], + "nixpkgs": [ + "crane", + "nixpkgs" + ] + }, + "locked": { + "lastModified": 1694484610, + "narHash": "sha256-aeSDkp7fkAqtVjW3QUn7vq7BKNlFul/BiGgdv7rK+mA=", + "owner": "oxalica", + "repo": "rust-overlay", + "rev": "c5b977a7e6a295697fa1f9c42174fd6313b38df4", + "type": "github" + }, + "original": { + "owner": "oxalica", + "repo": "rust-overlay", + "type": "github" + } + }, + "systems": { + "locked": { + "lastModified": 1681028828, + "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=", + "owner": "nix-systems", + "repo": "default", + "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e", + "type": "github" + }, + "original": { + "owner": "nix-systems", + "repo": "default", + "type": "github" + } + } + }, + "root": "root", + "version": 7 +} diff --git a/noir/flake.nix b/noir/flake.nix new file mode 100644 index 00000000000..f9c458dc6ea --- /dev/null +++ b/noir/flake.nix @@ -0,0 +1,285 @@ +{ + description = "Build the Noir programming language"; + + # All of these inputs (a.k.a. dependencies) need to align with inputs we + # use so they use the `inputs.*.follows` syntax to reference our inputs + inputs = { + nixpkgs = { + url = "github:NixOS/nixpkgs/nixos-23.05"; + }; + + flake-utils = { + url = "github:numtide/flake-utils"; + }; + + flake-compat = { + url = "github:edolstra/flake-compat"; + flake = false; + }; + + fenix = { + url = "github:nix-community/fenix"; + inputs = { + nixpkgs.follows = "nixpkgs"; + }; + }; + + crane = { + url = "github:ipetkov/crane"; + inputs = { + nixpkgs.follows = "nixpkgs"; + flake-utils.follows = "flake-utils"; + flake-compat.follows = "flake-compat"; + }; + }; + }; + + outputs = + { self, nixpkgs, crane, flake-utils, fenix, ... }: + flake-utils.lib.eachDefaultSystem (system: + let + pkgs = import nixpkgs { + inherit system; + }; + + rustToolchain = fenix.packages.${system}.fromToolchainFile { + file = ./rust-toolchain.toml; + sha256 = "sha256-R0F0Risbr74xg9mEYydyebx/z0Wu6HI0/KWwrV30vZo="; + }; + + craneLib = (crane.mkLib pkgs).overrideToolchain rustToolchain; + + # The `self.rev` property is only available when the working tree is not dirty + GIT_COMMIT = if (self ? rev) then self.rev else "unknown"; + GIT_DIRTY = if (self ? rev) then "false" else "true"; + + extraBuildInputs = [ ] ++ pkgs.lib.optionals pkgs.stdenv.isDarwin [ + # Need libiconv and apple Security on Darwin. See https://github.com/ipetkov/crane/issues/156 + pkgs.libiconv + pkgs.darwin.apple_sdk.frameworks.Security + ]; + + environment = { + # We enable backtraces on any failure for help with debugging + RUST_BACKTRACE = "1"; + + # We download the Wasm version of `acvm_backend` in the barretenberg releases for the ACVM `blackbox_solver` + BARRETENBERG_ARCHIVE = pkgs.fetchurl { + url = "https://github.com/AztecProtocol/barretenberg/releases/download/barretenberg-v0.4.5/acvm_backend.wasm.tar.gz"; + sha256 = "sha256-xONt5pTKWf/YbVnX/NXl/VNBbtKd+CP7CLkB1jf0RHw="; + }; + }; + + # Configuration shared between builds + config = { + # x-release-please-start-version + version = "0.19.3"; + # x-release-please-end + + src = pkgs.lib.cleanSourceWith { + src = craneLib.path ./.; + # Custom filter with various file extensions that we rely upon to build packages + # Currently: `.nr`, `.sol`, `.sh`, `.json`, `.md` and `.wasm` + filter = path: type: + (builtins.match ".*\.(nr|sol|sh|json|md|wasm)$" path != null) || (craneLib.filterCargoSources path type); + }; + + # TODO(#1198): It'd be nice to include these flags when running `cargo clippy` in a devShell. + cargoClippyExtraArgs = "--all-targets -- -D warnings"; + + # TODO(#1198): It'd be nice to include this flag when running `cargo test` in a devShell. + cargoTestExtraArgs = "--workspace"; + }; + + # Combine the environment and other configuration needed for Crane to build our Rust packages + nativeConfig = environment // config // { + nativeBuildInputs = [ ]; + + buildInputs = [ ] ++ extraBuildInputs; + }; + + # Combine the environmnet and other configuration needed for Crane to build our Wasm packages + wasmConfig = environment // config // { + CARGO_TARGET_DIR = "./target"; + + nativeBuildInputs = with pkgs; [ + which + git + jq + rustToolchain + wasm-bindgen-cli + binaryen + ]; + + buildInputs = [ ] ++ extraBuildInputs; + }; + + # Build *just* the cargo dependencies, so we can reuse all of that work between runs + native-cargo-artifacts = craneLib.buildDepsOnly (nativeConfig // { + pname = "nargo"; + }); + noir-wasm-cargo-artifacts = craneLib.buildDepsOnly (wasmConfig // { + pname = "noir_wasm"; + }); + noirc-abi-wasm-cargo-artifacts = craneLib.buildDepsOnly (wasmConfig // { + pname = "noirc_abi_wasm"; + }); + acvm-js-cargo-artifacts = craneLib.buildDepsOnly (wasmConfig // { + pname = "acvm_js"; + }); + + nargo = craneLib.buildPackage (nativeConfig // { + pname = "nargo"; + + inherit GIT_COMMIT GIT_DIRTY; + + cargoArtifacts = native-cargo-artifacts; + + # We don't want to run tests because they don't work in the Nix sandbox + doCheck = false; + }); + + noir_wasm = craneLib.buildPackage (wasmConfig // { + pname = "noir_wasm"; + + inherit GIT_COMMIT GIT_DIRTY; + + cargoArtifacts = noir-wasm-cargo-artifacts; + + buildPhaseCargoCommand = '' + bash compiler/wasm/buildPhaseCargoCommand.sh release + ''; + + installPhase = '' + bash compiler/wasm/installPhase.sh + ''; + + # We don't want to run tests because they don't work in the Nix sandbox + doCheck = false; + }); + + noirc_abi_wasm = craneLib.buildPackage (wasmConfig // rec { + pname = "noirc_abi_wasm"; + + inherit GIT_COMMIT GIT_DIRTY; + + cargoArtifacts = noirc-abi-wasm-cargo-artifacts; + + cargoExtraArgs = "--package ${pname} --target wasm32-unknown-unknown"; + + buildPhaseCargoCommand = '' + bash tooling/noirc_abi_wasm/buildPhaseCargoCommand.sh release + ''; + + installPhase = '' + bash tooling/noirc_abi_wasm/installPhase.sh + ''; + + # We don't want to run tests because they don't work in the Nix sandbox + doCheck = false; + }); + + acvm_js = craneLib.buildPackage (wasmConfig // rec { + pname = "acvm_js"; + + inherit GIT_COMMIT GIT_DIRTY; + + cargoArtifacts = acvm-js-cargo-artifacts; + + cargoExtraArgs = "--package ${pname} --target wasm32-unknown-unknown"; + + buildPhaseCargoCommand = '' + bash acvm-repo/acvm_js/buildPhaseCargoCommand.sh release + ''; + + installPhase = '' + bash acvm-repo/acvm_js/installPhase.sh + ''; + + # We don't want to run tests because they don't work in the Nix sandbox + doCheck = false; + }); + + wasm-bindgen-cli = pkgs.callPackage ./wasm-bindgen-cli.nix { + rustPlatform = pkgs.makeRustPlatform { + rustc = rustToolchain; + cargo = rustToolchain; + }; + }; + in + { + # We use `checks` to run `cargo clippy` and `cargo fmt` since we disable checks in the primary derivations + checks = { + cargo-clippy = craneLib.cargoClippy (nativeConfig // { + pname = "noir"; + + inherit GIT_COMMIT GIT_DIRTY; + + cargoArtifacts = native-cargo-artifacts; + }); + + cargo-fmt = craneLib.cargoFmt (nativeConfig // { + pname = "noir"; + + inherit GIT_COMMIT GIT_DIRTY; + + cargoArtifacts = native-cargo-artifacts; + }); + }; + + packages = { + default = nargo; + + # Nix flakes cannot build more than one derivation in one command (see https://github.com/NixOS/nix/issues/5591) + # so we use `symlinkJoin` to build everything as the "all" package. + all = pkgs.symlinkJoin { name = "all"; paths = [ nargo noir_wasm noirc_abi_wasm acvm_js ]; }; + all_wasm = pkgs.symlinkJoin { name = "all_wasm"; paths = [ noir_wasm noirc_abi_wasm acvm_js ]; }; + + # We also export individual packages to enable `nix build .#nargo -L`, etc. + inherit nargo; + inherit noir_wasm; + inherit noirc_abi_wasm; + inherit acvm_js; + + # We expose the `*-cargo-artifacts` derivations so we can cache our cargo dependencies in CI + inherit native-cargo-artifacts; + inherit noir-wasm-cargo-artifacts; + inherit noirc-abi-wasm-cargo-artifacts; + inherit acvm-js-cargo-artifacts; + }; + + # Setup the environment to match the environment settings, the inputs from our checks derivations, + # and extra tooling via `nativeBuildInputs` + devShells.default = pkgs.mkShell (environment // { + inputsFrom = [ + nargo + noir_wasm + noirc_abi_wasm + acvm_js + ]; + + # Additional tools that weren't included as `nativeBuildInputs` of any of the derivations in `inputsFrom` + nativeBuildInputs = with pkgs; [ + # Rust toolchain + rustToolchain + # Other tools + starship + yarn + nodejs-18_x + # Used by the `bb` binary + curl + gzip + # This ensures the right lldb is in the environment for running rust-lldb + llvmPackages.lldb + # Nix tools + nil + nixpkgs-fmt + ]; + + shellHook = '' + eval "$(starship init bash)" + ''; + }); + }); +} + diff --git a/noir/noir_stdlib/Nargo.toml b/noir/noir_stdlib/Nargo.toml new file mode 100644 index 00000000000..d2f8ebcb636 --- /dev/null +++ b/noir/noir_stdlib/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "std" +type = "lib" +authors = [""] +[dependencies] diff --git a/noir/noir_stdlib/src/array.nr b/noir/noir_stdlib/src/array.nr new file mode 100644 index 00000000000..bcdf56dd7aa --- /dev/null +++ b/noir/noir_stdlib/src/array.nr @@ -0,0 +1,85 @@ + +// TODO: Once we fully move to the new SSA pass this module can be removed and replaced +// by the methods in the `slice` module +impl [T; N] { + #[builtin(array_len)] + pub fn len(_self: Self) -> Field {} + + #[builtin(arraysort)] + pub fn sort(_self: Self) -> Self {} + + // Sort with a custom sorting function. + pub fn sort_via(mut a: Self, ordering: fn[Env](T, T) -> bool) -> Self { + for i in 1 .. a.len() { + for j in 0..i { + if ordering(a[i], a[j]) { + let old_a_j = a[j]; + a[j] = a[i]; + a[i] = old_a_j; + } + } + } + a + } + + // Converts an array into a slice. + pub fn as_slice(self) -> [T] { + let mut slice = []; + for elem in self { + slice = slice.push_back(elem); + } + slice + } + + // Apply a function to each element of an array, returning a new array + // containing the mapped elements. + pub fn map(self, f: fn[Env](T) -> U) -> [U; N] { + let first_elem = f(self[0]); + let mut ret = [first_elem; N]; + + for i in 1 .. self.len() { + ret[i] = f(self[i]); + } + + ret + } + + // Apply a function to each element of the array and an accumulator value, + // returning the final accumulated value. This function is also sometimes + // called `foldl`, `fold_left`, `reduce`, or `inject`. + pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U { + for elem in self { + accumulator = f(accumulator, elem); + } + accumulator + } + + // Apply a function to each element of the array and an accumulator value, + // returning the final accumulated value. Unlike fold, reduce uses the first + // element of the given array as its starting accumulator value. + pub fn reduce(self, f: fn[Env](T, T) -> T) -> T { + let mut accumulator = self[0]; + for i in 1 .. self.len() { + accumulator = f(accumulator, self[i]); + } + accumulator + } + + // Returns true if all elements in the array satisfy the predicate + pub fn all(self, predicate: fn[Env](T) -> bool) -> bool { + let mut ret = true; + for elem in self { + ret &= predicate(elem); + } + ret + } + + // Returns true if any element in the array satisfies the predicate + pub fn any(self, predicate: fn[Env](T) -> bool) -> bool { + let mut ret = false; + for elem in self { + ret |= predicate(elem); + } + ret + } +} diff --git a/noir/noir_stdlib/src/collections.nr b/noir/noir_stdlib/src/collections.nr new file mode 100644 index 00000000000..e06c662e658 --- /dev/null +++ b/noir/noir_stdlib/src/collections.nr @@ -0,0 +1 @@ +mod vec; diff --git a/noir/noir_stdlib/src/collections/vec.nr b/noir/noir_stdlib/src/collections/vec.nr new file mode 100644 index 00000000000..43d68e1d1e7 --- /dev/null +++ b/noir/noir_stdlib/src/collections/vec.nr @@ -0,0 +1,59 @@ +struct Vec { + slice: [T] +} +// A mutable vector type implemented as a wrapper around immutable slices. +// A separate type is technically not needed but helps differentiate which operations are mutable. +impl Vec { + pub fn new() -> Self { + Self { slice: [] } + } + + // Create a Vec containing each element from the given slice. + // Mutations to the resulting Vec will not affect the original slice. + pub fn from_slice(slice: [T]) -> Self { + Self { slice } + } + + /// Get an element from the vector at the given index. + /// Panics if the given index + /// points beyond the end of the vector. + pub fn get(self, index: Field) -> T { + self.slice[index] + } + + /// Push a new element to the end of the vector, returning a + /// new vector with a length one greater than the + /// original unmodified vector. + pub fn push(&mut self, elem: T) { + self.slice = self.slice.push_back(elem); + } + + /// Pop an element from the end of the given vector, returning + /// a new vector with a length of one less than the given vector, + /// as well as the popped element. + /// Panics if the given vector's length is zero. + pub fn pop(&mut self) -> T { + let (popped_slice, last_elem) = self.slice.pop_back(); + self.slice = popped_slice; + last_elem + } + + /// Insert an element at a specified index, shifting all elements + /// after it to the right + pub fn insert(&mut self, index: Field, elem: T) { + self.slice = self.slice.insert(index, elem); + } + + /// Remove an element at a specified index, shifting all elements + /// after it to the left, returning the removed element + pub fn remove(&mut self, index: Field) -> T { + let (new_slice, elem) = self.slice.remove(index); + self.slice = new_slice; + elem + } + + /// Returns the number of elements in the vector + pub fn len(self) -> Field { + self.slice.len() + } +} diff --git a/noir/noir_stdlib/src/compat.nr b/noir/noir_stdlib/src/compat.nr new file mode 100644 index 00000000000..5d80c422c33 --- /dev/null +++ b/noir/noir_stdlib/src/compat.nr @@ -0,0 +1,4 @@ +pub fn is_bn254() -> bool { + // bn254 truncates its curve order to 0 + 21888242871839275222246405745257275088548364400416034343698204186575808495617 == 0 +} diff --git a/noir/noir_stdlib/src/ec.nr b/noir/noir_stdlib/src/ec.nr new file mode 100644 index 00000000000..86fb201408f --- /dev/null +++ b/noir/noir_stdlib/src/ec.nr @@ -0,0 +1,198 @@ +// Elliptic curve implementation +// Overview +// ======== +// The following three elliptic curve representations are admissible: +mod tecurve; // Twisted Edwards curves +mod swcurve; // Elliptic curves in Short Weierstraß form +mod montcurve; // Montgomery curves +mod consts; // Commonly used curve presets +// +// Note that Twisted Edwards and Montgomery curves are (birationally) equivalent, so that +// they may be freely converted between one another, whereas Short Weierstraß curves are +// more general. Diagramatically: +// +// tecurve == montcurve ⊂ swcurve +// +// Each module is further divided into two submodules, 'affine' and 'curvegroup', depending +// on the preferred coordinate representation. Affine coordinates are none other than the usual +// two-dimensional Cartesian coordinates used in the definitions of these curves, whereas +// 'CurveGroup' coordinates (terminology borrowed from Arkworks, whose conventions we try +// to follow) are special coordinate systems with respect to which the group operations may be +// implemented more efficiently, usually by means of an appropriate choice of projective coordinates. +// +// In each of these submodules, there is a Point struct and a Curve struct, the former +// representing a point in the coordinate system and the latter a curve configuration. +// +// Points +// ====== +// Points may be instantiated using the associated function `new`, which takes coordinates +// as its arguments. For instance, +// +// `let p = swcurve::Point::new(1,1);` +// +// The additive identity may be constructed by a call to the associated function `zero` of no +// arguments: +// +// `let zero = swcurve::Point::zero();` +// +// Points may be tested for equality by calling the method `eq`: +// +// `let pred = p.eq(zero);` +// +// There is also the method `is_zero` to explicitly check whether a point is the additive identity: +// +// `constrain pred == p.is_zero();` +// +// Points may be negated by calling the `negate` method and converted to CurveGroup (or affine) +// coordinates by calling the `into_group` (resp. `into_affine`) method on them. Finally, +// Points may be freely mapped between their respective Twisted Edwards and Montgomery +// representations by calling the `into_montcurve` or `into_tecurve` methods. For mappings +// between Twisted Edwards/Montgomery curves and Short Weierstraß curves, see the Curve section +// below, as the underlying mappings are those of curves rather than ambient spaces. +// As a rule, Points in affine (or CurveGroup) coordinates are mapped to Points in affine +// (resp. CurveGroup) coordinates. +// +// Curves +// ====== +// A curve configuration (Curve) is completely determined by the Field coefficients of its defining +// equation (a and b in the case of swcurve, a and d in the case of tecurve, and j and k in +// the case of montcurve) together with a generator (`gen`) in the corresponding coordinate system. +// For example, the Baby Jubjub curve configuration as defined in ERC-2494 may be instantiated as a Twisted +// Edwards curve in affine coordinates as follows: +// +// `let bjj_affine = tecurve::Curve::new(168700, 168696, tecurve::Point::new(995203441582195749578291179787384436505546430278305826713579947235728471134,5472060717959818805561601436314318772137091100104008585924551046643952123905));` +// +// The `contains` method may be used to check whether a Point lies on a given curve: +// +// `constrain bjj_affine.contains(tecurve::Point::zero());` +// +// The elliptic curve group's addition operation is exposed as the `add` method, e.g. +// +// `let p = bjj_affine.add(bjj_affine.gen, bjj_affine.gen);` +// +// subtraction as the `subtract` method, e.g. +// +// `constrain tecurve::Point::zero().eq(bjj_affine.subtract(bjj_affine.gen, bjj_affine.gen));` +// +// scalar multiplication as the `mul` method, where the scalar is assumed to be a Field* element, e.g. +// +// `constrain tecurve::Point::zero().eq(bjj_affine.mul(2, tecurve::Point::zero());` +// +// There is a scalar multiplication method (`bit_mul`) provided where the scalar input is expected to be +// an array of bits (little-endian convention), as well as a multi-scalar multiplication method** (`msm`) +// which takes an array of Field elements and an array of elliptic curve points as arguments, both assumed +// to be of the same length. +// +// Curve configurations may be converted between different coordinate representations by calling the `into_group` +// and `into_affine` methods on them, e.g. +// +// `let bjj_curvegroup = bjj_affine.into_group();` +// +// Curve configurations may also be converted between different curve representations by calling the `into_swcurve`, +// `into_montcurve` and `into_tecurve` methods subject to the relation between the curve representations mentioned +// above. Note that it is possible to map Points from a Twisted Edwards/Montgomery curve to the corresponding +// Short Weierstraß representation and back, and the methods to do so are exposed as `map_into_swcurve` and +// `map_from_swcurve`, which each take one argument, the point to be mapped. +// +// Curve maps +// ========== +// There are a few different ways of mapping Field elements to elliptic curves. Here we provide the simplified +// Shallue-van de Woestijne-Ulas and Elligator 2 methods, the former being applicable to all curve types +// provided above subject to the constraint that the coefficients of the corresponding Short Weierstraß curve satisfies +// a*b != 0 and the latter being applicable to Montgomery and Twisted Edwards curves subject to the constraint that +// the coefficients of the corresponding Montgomery curve satisfy j*k != 0 and (j^2 - 4)/k^2 is non-square. +// +// The simplified Shallue-van de Woestijne-Ulas method is exposed as the method `swu_map` on the Curve configuration and +// depends on two parameters, a Field element z != -1 for which g(x) - z is irreducible over Field and g(b/(z*a)) is +// square, where g(x) = x^3 + a*x + b is the right-hand side of the defining equation of the corresponding Short +// Weierstraß curve, and a Field element u to be mapped onto the curve. For example, in the case of bjj_affine above, +// it may be determined using the scripts provided at that z = 5. +// +// The Elligator 2 method is exposed as the method `elligator2_map` on the Curve configurations of Montgomery and +// Twisted Edwards curves. Like the simplified SWU method above, it depends on a certain non-square element of Field, +// but this element need not satisfy any further conditions, so it is included as the (Field-dependent) constant +//`ZETA` below. Thus, the `elligator2_map` method depends only on one parameter, the Field element to be mapped onto +// the curve. +// +// For details on all of the above in the context of hashing to elliptic curves, see . +// +// +// *TODO: Replace Field with Bigint. +// **TODO: Support arrays of structs to make this work. +// Field-dependent constant ZETA = a non-square element of Field +// Required for Elligator 2 map +// TODO: Replace with built-in constant. +global ZETA = 5; +// Field-dependent constants for Tonelli-Shanks algorithm (see sqrt function below) +// TODO: Possibly make this built-in. +global C1 = 28; +global C3 = 40770029410420498293352137776570907027550720424234931066070132305055; +global C5 = 19103219067921713944291392827692070036145651957329286315305642004821462161904; +// Higher-order version of scalar multiplication +// TODO: Make this work so that the submodules' bit_mul may be defined in terms of it. +//fn bit_mul(add: fn(T,T) -> T, e: T, bits: [u1; N], p: T) -> T { +// let mut out = e; +// let n = bits.len(); +// +// for i in 0..n { +// out = add( +// add(out, out), +// if(bits[n - i - 1] == 0) {e} else {p}); +// } +// +// out +//} +// TODO: Make this built-in. +pub fn safe_inverse(x: Field) -> Field { + if x == 0 { 0 } else { 1 / x } +} +// Boolean indicating whether Field element is a square, i.e. whether there exists a y in Field s.t. x = y*y. +pub fn is_square(x: Field) -> bool { + let v = pow(x, 0 - 1 / 2); + + v * (v - 1) == 0 +} +// Power function of two Field arguments of arbitrary size. +// Adapted from std::field::pow_32. +pub fn pow(x: Field, y: Field) -> Field { + // As in tests with minor modifications + let N_BITS = crate::field::modulus_num_bits(); + + let mut r = 1 as Field; + let b = y.to_le_bits(N_BITS as u32); + + for i in 0..N_BITS { + r *= r; + r *= (b[N_BITS - 1 - i] as Field)*x + (1-b[N_BITS - 1 - i] as Field); + } + + r +} +// Tonelli-Shanks algorithm for computing the square root of a Field element. +// Requires C1 = max{c: 2^c divides (p-1)}, where p is the order of Field +// as well as C3 = (C2 - 1)/2, where C2 = (p-1)/(2^c1), +// and C5 = ZETA^C2, where ZETA is a non-square element of Field. +// These are pre-computed above as globals. +pub fn sqrt(x: Field) -> Field { + let mut z = pow(x, C3); + let mut t = z * z * x; + z *= x; + let mut b = t; + let mut c = C5; + + for i in 0..(C1 - 1) { + for _j in 1..(C1 - i - 1) { + b *= b; + } + + z *= if b == 1 { 1 } else { c }; + + c *= c; + + t *= if b == 1 { 1 } else { c }; + + b = t; + } + + z +} diff --git a/noir/noir_stdlib/src/ec/consts.nr b/noir/noir_stdlib/src/ec/consts.nr new file mode 100644 index 00000000000..f4d67e7a92c --- /dev/null +++ b/noir/noir_stdlib/src/ec/consts.nr @@ -0,0 +1 @@ +mod te; diff --git a/noir/noir_stdlib/src/ec/consts/te.nr b/noir/noir_stdlib/src/ec/consts/te.nr new file mode 100644 index 00000000000..469493a35ee --- /dev/null +++ b/noir/noir_stdlib/src/ec/consts/te.nr @@ -0,0 +1,26 @@ +use crate::compat; +use crate::ec::tecurve::affine::Point as TEPoint; +use crate::ec::tecurve::affine::Curve as TECurve; + +struct BabyJubjub { + curve: TECurve, + base8: TEPoint, + suborder: Field, +} + +#[field(bn254)] +pub fn baby_jubjub() -> BabyJubjub { + BabyJubjub { + // Baby Jubjub (ERC-2494) parameters in affine representation + curve: TECurve::new(168700, + 168696, + // G + TEPoint::new(995203441582195749578291179787384436505546430278305826713579947235728471134, + 5472060717959818805561601436314318772137091100104008585924551046643952123905)), + // [8]G precalculated + base8: TEPoint::new(5299619240641551281634865583518297030282874472190772894086521144482721001553, + 16950150798460657717958625567821834550301663161624707787222815936182638968203), + // The size of the group formed from multiplying the base field by 8. + suborder: 2736030358979909402780800718157159386076813972158567259200215660948447373041 + } +} diff --git a/noir/noir_stdlib/src/ec/montcurve.nr b/noir/noir_stdlib/src/ec/montcurve.nr new file mode 100644 index 00000000000..82d22837b46 --- /dev/null +++ b/noir/noir_stdlib/src/ec/montcurve.nr @@ -0,0 +1,377 @@ +mod affine { + // Affine representation of Montgomery curves + // Points are represented by two-dimensional Cartesian coordinates. + // All group operations are induced by those of the corresponding Twisted Edwards curve. + // See e.g. for details on the correspondences. + use crate::ec::montcurve::curvegroup; + use crate::ec::swcurve::affine::Curve as SWCurve; + use crate::ec::swcurve::affine::Point as SWPoint; + use crate::ec::tecurve::affine::Curve as TECurve; + use crate::ec::tecurve::affine::Point as TEPoint; + use crate::ec::is_square; + use crate::ec::safe_inverse; + use crate::ec::sqrt; + use crate::ec::ZETA; + // Curve specification + struct Curve { // Montgomery Curve configuration (ky^2 = x^3 + j*x^2 + x) + j: Field, + k: Field, + // Generator as point in Cartesian coordinates + gen: Point + } + // Point in Cartesian coordinates + struct Point { + x: Field, + y: Field, + infty: bool // Indicator for point at infinity + } + + impl Point { + // Point constructor + pub fn new(x: Field, y: Field) -> Self { + Self {x, y, infty: false} + } + + // Check for equality + fn eq(self, p: Self) -> bool { + (self.infty & p.infty) | (!self.infty & !p.infty & (self.x == p.x) & (self.y == p.y)) + } + + // Check if zero + pub fn is_zero(self) -> bool { + self.infty + } + + // Conversion to CurveGroup coordinates + fn into_group(self) -> curvegroup::Point { + if self.is_zero() { + curvegroup::Point::zero() + } else { + let (x,y) = (self.x, self.y); + curvegroup::Point::new(x,y,1) + } + } + + // Additive identity + pub fn zero() -> Self { + Self {x: 0, y: 0, infty: true} + } + + // Negation + fn negate(self) -> Self { + let Self {x, y, infty} = self; + + Self {x, y: 0-y, infty} + } + + // Map into equivalent Twisted Edwards curve + fn into_tecurve(self) -> TEPoint { + let Self {x, y, infty} = self; + + if infty | (y*(x+1) == 0) { + TEPoint::zero() + } else { + TEPoint::new(x/y, (x-1)/(x+1)) + } + } + } + + impl Curve { + // Curve constructor + pub fn new(j: Field, k: Field, gen: Point) -> Self { + // Check curve coefficients + assert(k != 0); + assert(j*j != 4); + + let curve = Self {j, k, gen}; + + // gen should be on the curve + assert(curve.contains(curve.gen)); + + curve + } + + // Conversion to CurveGroup coordinates + fn into_group(self) -> curvegroup::Curve { + curvegroup::Curve::new(self.j, self.k, self.gen.into_group()) + } + + // Membership check + pub fn contains(self, p: Point) -> bool { + let Self {j, k, gen: _gen} = self; + let Point {x, y, infty: infty} = p; + + infty | (k*y*y == x*(x*x + j*x + 1)) + } + + // Point addition + pub fn add(self, p1: Point, p2: Point) -> Point { + self.into_tecurve().add(p1.into_tecurve(), p2.into_tecurve()).into_montcurve() + } + + // Scalar multiplication with scalar represented by a bit array (little-endian convention). + // If k is the natural number represented by `bits`, then this computes p + ... + p k times. + fn bit_mul(self, bits: [u1; N], p: Point) -> Point { + self.into_tecurve().bit_mul(bits, p.into_tecurve()).into_montcurve() + } + + // Scalar multiplication (p + ... + p n times) + fn mul(self, n: Field, p: Point) -> Point { + self.into_tecurve().mul(n, p.into_tecurve()).into_montcurve() + } + + // Multi-scalar multiplication (n[0]*p[0] + ... + n[N]*p[N], where * denotes scalar multiplication) + fn msm(self, n: [Field; N], p: [Point; N]) -> Point { + let mut out = Point::zero(); + + for i in 0..N { + out = self.add(out, self.mul(n[i], p[i])); + } + + out + } + + // Point subtraction + fn subtract(self, p1: Point, p2: Point) -> Point { + self.add(p1, p2.negate()) + } + + // Conversion to equivalent Twisted Edwards curve + fn into_tecurve(self) -> TECurve { + let Self {j, k, gen} = self; + TECurve::new((j+2)/k, (j-2)/k, gen.into_tecurve()) + } + + // Conversion to equivalent Short Weierstraß curve + pub fn into_swcurve(self) -> SWCurve { + let j = self.j; + let k = self.k; + let a0 = (3-j*j)/(3*k*k); + let b0 = (2*j*j*j - 9*j)/(27*k*k*k); + + SWCurve::new(a0, b0, self.map_into_swcurve(self.gen)) + } + + // Point mapping into equivalent Short Weierstraß curve + pub fn map_into_swcurve(self, p: Point) -> SWPoint { + if p.is_zero() { + SWPoint::zero() + } else { + SWPoint::new((3*p.x + self.j)/(3*self.k), + p.y/self.k) + } + } + + // Point mapping from equivalent Short Weierstraß curve + fn map_from_swcurve(self, p: SWPoint) -> Point { + let SWPoint {x, y, infty} = p; + let j = self.j; + let k = self.k; + + Point {x: (3*k*x - j)/3, y: y*k, infty} + } + + // Elligator 2 map-to-curve method; see . + fn elligator2_map(self, u: Field) -> Point { + let j = self.j; + let k = self.k; + let z = ZETA; // Non-square Field element required for map + + // Check whether curve is admissible + assert(j != 0); + let l = (j*j - 4)/(k*k); + assert(l != 0); + assert(is_square(l) == false); + + let x1 = safe_inverse(1+z*u*u)*(0 - (j/k)); + + let gx1 = x1*x1*x1 + (j/k)*x1*x1 + x1/(k*k); + let x2 = 0 - x1 - (j/k); + let gx2 = x2*x2*x2 + (j/k)*x2*x2 + x2/(k*k); + + let x = if is_square(gx1) { x1 } else { x2 }; + + let y = if is_square(gx1) { + let y0 = sqrt(gx1); + if y0.sgn0() == 1 { y0 } else { 0 - y0 } + } else { + let y0 = sqrt(gx2); + if y0.sgn0() == 0 { y0 } else { 0 - y0 } + }; + + Point::new(x*k, y*k) + + } + + // SWU map-to-curve method (via rational map) + fn swu_map(self, z: Field, u: Field) -> Point { + self.map_from_swcurve(self.into_swcurve().swu_map(z,u)) + } + } +} +mod curvegroup { + // Affine representation of Montgomery curves + // Points are represented by three-dimensional projective (homogeneous) coordinates. + // All group operations are induced by those of the corresponding Twisted Edwards curve. + // See e.g. for details on the correspondences. + use crate::ec::montcurve::affine; + use crate::ec::swcurve::curvegroup::Curve as SWCurve; + use crate::ec::swcurve::curvegroup::Point as SWPoint; + use crate::ec::tecurve::curvegroup::Curve as TECurve; + use crate::ec::tecurve::curvegroup::Point as TEPoint; + + struct Curve { // Montgomery Curve configuration (ky^2 z = x*(x^2 + j*x*z + z*z)) + j: Field, + k: Field, + // Generator as point in projective coordinates + gen: Point + } + // Point in projective coordinates + struct Point { + x: Field, + y: Field, + z: Field + } + + impl Point { + // Point constructor + pub fn new(x: Field, y: Field, z: Field) -> Self { + Self {x, y, z} + } + + // Check for equality + fn eq(self, p: Self) -> bool { + (self.z == p.z) | (((self.x * self.z) == (p.x * p.z)) & ((self.y * self.z) == (p.y * p.z))) + } + + // Check if zero + pub fn is_zero(self) -> bool { + self.z == 0 + } + + // Conversion to affine coordinates + fn into_affine(self) -> affine::Point { + if self.is_zero() { + affine::Point::zero() + } else { + let (x,y,z) = (self.x, self.y, self.z); + affine::Point::new(x/z, y/z) + } + } + + // Additive identity + pub fn zero() -> Self { + Self {x: 0, y: 1,z: 0} + } + + // Negation + fn negate(self) -> Self { + let Self {x, y, z} = self; + + Point::new(x, 0-y, z) + } + + // Map into equivalent Twisted Edwards curve + fn into_tecurve(self) -> TEPoint { + self.into_affine().into_tecurve().into_group() + } + } + + impl Curve { + // Curve constructor + pub fn new(j: Field, k: Field, gen: Point) -> Self { + // Check curve coefficients + assert(k != 0); + assert(j*j != 4); + + let curve = Self {j, k, gen}; + + // gen should be on the curve + assert(curve.contains(curve.gen)); + + curve + } + + // Conversion to affine coordinates + fn into_affine(self) -> affine::Curve { + affine::Curve::new(self.j, self.k, self.gen.into_affine()) + } + + // Membership check + pub fn contains(self, p: Point) -> bool { + let Self {j, k, gen: _gen} = self; + let Point {x, y, z} = p; + + k*y*y*z == x*(x*x + j*x*z + z*z) + } + + // Point addition + pub fn add(self, p1: Point, p2: Point) -> Point { + self.into_affine().add(p1.into_affine(), p2.into_affine()).into_group() + } + + // Scalar multiplication with scalar represented by a bit array (little-endian convention). + // If k is the natural number represented by `bits`, then this computes p + ... + p k times. + fn bit_mul(self, bits: [u1; N], p: Point) -> Point { + self.into_tecurve().bit_mul(bits, p.into_tecurve()).into_montcurve() + } + + // Scalar multiplication (p + ... + p n times) + pub fn mul(self, n: Field, p: Point) -> Point { + self.into_tecurve().mul(n, p.into_tecurve()).into_montcurve() + } + + // Multi-scalar multiplication (n[0]*p[0] + ... + n[N]*p[N], where * denotes scalar multiplication) + fn msm(self, n: [Field; N], p: [Point; N]) -> Point { + let mut out = Point::zero(); + + for i in 0..N { + out = self.add(out, self.mul(n[i], p[i])); + } + + out + } + + // Point subtraction + pub fn subtract(self, p1: Point, p2: Point) -> Point { + self.add(p1, p2.negate()) + } + + // Conversion to equivalent Twisted Edwards curve + fn into_tecurve(self) -> TECurve { + let Self {j, k, gen} = self; + TECurve::new((j+2)/k, (j-2)/k, gen.into_tecurve()) + } + + // Conversion to equivalent Short Weierstraß curve + fn into_swcurve(self) -> SWCurve { + let j = self.j; + let k = self.k; + let a0 = (3-j*j)/(3*k*k); + let b0 = (2*j*j*j - 9*j)/(27*k*k*k); + + SWCurve::new(a0, b0, + self.map_into_swcurve(self.gen)) + } + + // Point mapping into equivalent Short Weierstraß curve + pub fn map_into_swcurve(self, p: Point) -> SWPoint { + self.into_affine().map_into_swcurve(p.into_affine()).into_group() + } + + // Point mapping from equivalent Short Weierstraß curve + fn map_from_swcurve(self, p: SWPoint) -> Point { + self.into_affine().map_from_swcurve(p.into_affine()).into_group() + } + + // Elligator 2 map-to-curve method + fn elligator2_map(self, u: Field) -> Point { + self.into_affine().elligator2_map(u).into_group() + } + + // SWU map-to-curve method (via rational map) + fn swu_map(self, z: Field, u: Field) -> Point { + self.into_affine().swu_map(z,u).into_group() + } + } +} diff --git a/noir/noir_stdlib/src/ec/swcurve.nr b/noir/noir_stdlib/src/ec/swcurve.nr new file mode 100644 index 00000000000..e9b6f661843 --- /dev/null +++ b/noir/noir_stdlib/src/ec/swcurve.nr @@ -0,0 +1,375 @@ +mod affine { + // Affine representation of Short Weierstraß curves + // Points are represented by two-dimensional Cartesian coordinates. + // Group operations are implemented in terms of those in CurveGroup (in this case, extended Twisted Edwards) coordinates + // for reasons of efficiency, cf. . + use crate::ec::swcurve::curvegroup; + use crate::ec::safe_inverse; + use crate::ec::is_square; + use crate::ec::sqrt; + // Curve specification + struct Curve { // Short Weierstraß curve + // Coefficients in defining equation y^2 = x^3 + ax + b + a: Field, + b: Field, + // Generator as point in Cartesian coordinates + gen: Point + } + // Point in Cartesian coordinates + struct Point { + x: Field, + y: Field, + infty: bool // Indicator for point at infinity + } + + impl Point { + // Point constructor + pub fn new(x: Field, y: Field) -> Self { + Self {x, y, infty: false} + } + + // Check for equality + fn eq(self, p: Point) -> bool { + let Self {x: x1, y: y1, infty: inf1} = self; + let Self {x: x2, y: y2, infty: inf2} = p; + + (inf1 & inf2) + | (!inf1 & !inf2 & (x1 == x2) & (y1 == y2)) + } + + // Check if zero + pub fn is_zero(self) -> bool { + self.eq(Point::zero()) + } + + // Conversion to CurveGroup coordinates + fn into_group(self) -> curvegroup::Point { + let Self {x, y, infty} = self; + + if infty { + curvegroup::Point::zero() + } else { + curvegroup::Point::new(x, y, 1) + } + } + + // Additive identity + pub fn zero() -> Self { + Self {x: 0, y: 0, infty: true} + } + + // Negation + fn negate(self) -> Self { + let Self {x, y, infty} = self; + Self {x, y: 0-y, infty} + } + } + + impl Curve { + // Curve constructor + pub fn new(a: Field, b: Field, gen: Point) -> Curve { + // Check curve coefficients + assert(4*a*a*a + 27*b*b != 0); + + let curve = Curve { a, b, gen }; + + // gen should be on the curve + assert(curve.contains(curve.gen)); + + curve + } + + // Conversion to CurveGroup coordinates + fn into_group(self) -> curvegroup::Curve { + let Curve{a, b, gen} = self; + + curvegroup::Curve {a, b, gen: gen.into_group()} + } + + // Membership check + pub fn contains(self, p: Point) -> bool { + let Point {x, y, infty} = p; + infty | (y*y == x*x*x + self.a*x + self.b) + } + + // Point addition, implemented in terms of mixed addition for reasons of efficiency + pub fn add(self, p1: Point, p2: Point) -> Point { + self.mixed_add(p1, p2.into_group()).into_affine() + } + + // Mixed point addition, i.e. first argument in affine, second in CurveGroup coordinates. + fn mixed_add(self, p1: Point, p2: curvegroup::Point) -> curvegroup::Point { + if p1.is_zero() { + p2 + } else if p2.is_zero() { + p1.into_group() + } else { + let Point {x: x1, y: y1, infty: _inf} = p1; + let curvegroup::Point {x: x2, y: y2, z: z2} = p2; + let you1 = x1*z2*z2; + let you2 = x2; + let s1 = y1*z2*z2*z2; + let s2 = y2; + + if you1 == you2 { + if s1 != s2 { + curvegroup::Point::zero() + } else { + self.into_group().double(p2) + } + } else + { + let h = you2 - you1; + let r = s2 - s1; + let x3 = r*r - h*h*h - 2*you1*h*h; + let y3 = r*(you1*h*h - x3) - s1*h*h*h; + let z3 = h*z2; + + curvegroup::Point::new(x3,y3,z3) + } + } + } + + // Scalar multiplication with scalar represented by a bit array (little-endian convention). + // If k is the natural number represented by `bits`, then this computes p + ... + p k times. + fn bit_mul(self, bits: [u1; N], p: Point) -> Point { + self.into_group().bit_mul(bits, p.into_group()).into_affine() + } + + // Scalar multiplication (p + ... + p n times) + pub fn mul(self, n: Field, p: Point) -> Point { + self.into_group().mul(n, p.into_group()).into_affine() + } + + // Multi-scalar multiplication (n[0]*p[0] + ... + n[N]*p[N], where * denotes scalar multiplication) + pub fn msm(self, n: [Field; N], p: [Point; N]) -> Point { + let mut out = Point::zero(); + + for i in 0..N { + out = self.add(out, self.mul(n[i], p[i])); + } + + out + } + + // Point subtraction + pub fn subtract(self, p1: Point, p2: Point) -> Point { + self.add(p1, p2.negate()) + } + + // Simplified Shallue-van de Woestijne-Ulas map-to-curve method; see . + // First determine non-square z != -1 in Field s.t. g(x) - z irreducible over Field and g(b/(z*a)) is square, + // where g(x) = x^3 + a*x + b. swu_map(c,z,.) then maps a Field element to a point on curve c. + fn swu_map(self, z: Field, u: Field) -> Point { + // Check whether curve is admissible + assert(self.a*self.b != 0); + + let Curve {a, b, gen: _gen} = self; + + let tv1 = safe_inverse(z*z*u*u*u*u + u*u*z); + let x1 = if tv1 == 0 {b/(z*a)} else {(0-b/a)*(1 + tv1)}; + let gx1 = x1*x1*x1 + a*x1 + b; + let x2 = z*u*u*x1; + let gx2 = x2*x2*x2 + a*x2 + b; + let (x,y) = if is_square(gx1) {(x1, sqrt(gx1))} else {(x2, sqrt(gx2))}; + Point::new(x, if u.sgn0() != y.sgn0() {0-y} else {y}) + } + } +} + +mod curvegroup { + // CurveGroup representation of Weierstraß curves + // Points are represented by three-dimensional Jacobian coordinates. + // See for details. + use crate::ec::swcurve::affine; + // Curve specification + struct Curve { // Short Weierstraß curve + // Coefficients in defining equation y^2 = x^3 + axz^4 + bz^6 + a: Field, + b: Field, + // Generator as point in Cartesian coordinates + gen: Point + } + // Point in three-dimensional Jacobian coordinates + struct Point { + x: Field, + y: Field, + z: Field // z = 0 corresponds to point at infinity. + } + + impl Point { + // Point constructor + pub fn new(x: Field, y: Field, z: Field) -> Self { + Self {x, y, z} + } + + // Check for equality + fn eq(self, p: Point) -> bool { + let Self {x: x1, y: y1, z: z1} = self; + let Self {x: x2, y: y2, z: z2} = p; + + ((z1 == 0) & (z2 == 0)) | ((z1 != 0) & (z2 != 0) & (x1*z2*z2 == x2*z1*z1) & (y1*z2*z2*z2 == y2*z1*z1*z1)) + } + + // Check if zero + pub fn is_zero(self) -> bool { + self.eq(Point::zero()) + } + + // Conversion to affine coordinates + pub fn into_affine(self) -> affine::Point { + let Self {x, y, z} = self; + + if z == 0 { + affine::Point::zero() + } else { + affine::Point::new(x/(z*z), y/(z*z*z)) + } + } + + // Additive identity + pub fn zero() -> Self { + Self {x: 0, y: 0, z: 0} + } + + + // Negation + fn negate(self) -> Self { + let Self {x, y, z} = self; + Self {x, y: 0-y, z} + } + } + + impl Curve { + // Curve constructor + pub fn new(a: Field, b: Field, gen: Point) -> Curve { + // Check curve coefficients + assert(4*a*a*a + 27*b*b != 0); + + let curve = Curve { a, b, gen }; + + // gen should be on the curve + assert(curve.contains(curve.gen)); + + curve + } + + // Conversion to affine coordinates + pub fn into_affine(self) -> affine::Curve { + let Curve{a, b, gen} = self; + + affine::Curve {a, b, gen: gen.into_affine()} + } + + // Membership check + pub fn contains(self, p: Point) -> bool { + let Point {x, y, z} = p; + if z == 0 { + true + } else { + y*y == x*x*x + self.a*x*z*z*z*z + self.b*z*z*z*z*z*z + } + } + + // Addition + pub fn add(self, p1: Point, p2: Point) -> Point { + + if p1.is_zero() { + p2 + } else if p2.is_zero() { + p1 + } else { + let Point {x: x1, y: y1, z: z1} = p1; + let Point {x: x2, y: y2, z: z2} = p2; + let you1 = x1*z2*z2; + let you2 = x2*z1*z1; + let s1 = y1*z2*z2*z2; + let s2 = y2*z1*z1*z1; + + if you1 == you2 { + if s1 != s2 { + Point::zero() + } else { + self.double(p1) + } + } else { + let h = you2 - you1; + let r = s2 - s1; + let x3 = r*r - h*h*h - 2*you1*h*h; + let y3 = r*(you1*h*h - x3) - s1*h*h*h; + let z3 = h*z1*z2; + + Point::new(x3,y3,z3) + } + } + } + + // Point doubling + pub fn double(self, p: Point) -> Point { + let Point {x, y, z} = p; + + if p.is_zero() { + p + } else if y == 0 { + Point::zero() + } else { + let s = 4*x*y*y; + let m = 3*x*x + self.a*z*z*z*z; + let x0 = m*m - 2*s; + let y0 = m*(s-x0) - 8*y*y*y*y; + let z0 = 2*y*z; + + Point::new(x0,y0,z0) + } + } + + // Scalar multiplication with scalar represented by a bit array (little-endian convention). + // If k is the natural number represented by `bits`, then this computes p + ... + p k times. + fn bit_mul(self, bits: [u1; N], p: Point) -> Point { + let mut out = Point::zero(); + + for i in 0..N { + out = self.add( + self.add(out, out), + if(bits[N - i - 1] == 0) {Point::zero()} else {p}); + } + + out + } + + // Scalar multiplication (p + ... + p n times) + pub fn mul(self, n: Field, p: Point) -> Point { + let N_BITS = crate::field::modulus_num_bits(); + + // TODO: temporary workaround until issue 1354 is solved + let mut n_as_bits: [u1; 254] = [0; 254]; + let tmp = n.to_le_bits(N_BITS as u32); + for i in 0..254 { + n_as_bits[i] = tmp[i]; + } + + self.bit_mul(n_as_bits, p) + } + + // Multi-scalar multiplication (n[0]*p[0] + ... + n[N]*p[N], where * denotes scalar multiplication) + fn msm(self, n: [Field; N], p: [Point; N]) -> Point { + let mut out = Point::zero(); + + for i in 0..N { + out = self.add(out, self.mul(n[i], p[i])); + } + + out + } + + // Point subtraction + pub fn subtract(self, p1: Point, p2: Point) -> Point { + self.add(p1, p2.negate()) + } + + // Simplified SWU map-to-curve method + fn swu_map(self, z: Field, u: Field) -> Point { + self.into_affine().swu_map(z,u).into_group() + } + } +} diff --git a/noir/noir_stdlib/src/ec/tecurve.nr b/noir/noir_stdlib/src/ec/tecurve.nr new file mode 100644 index 00000000000..849b45ff012 --- /dev/null +++ b/noir/noir_stdlib/src/ec/tecurve.nr @@ -0,0 +1,407 @@ +mod affine { + // Affine coordinate representation of Twisted Edwards curves + // Points are represented by two-dimensional Cartesian coordinates. + // Group operations are implemented in terms of those in CurveGroup (in this case, extended Twisted Edwards) coordinates + // for reasons of efficiency. + // See for details. + use crate::ec::tecurve::curvegroup; + use crate::ec::montcurve::affine::Curve as MCurve; + use crate::ec::montcurve::affine::Point as MPoint; + use crate::ec::swcurve::affine::Curve as SWCurve; + use crate::ec::swcurve::affine::Point as SWPoint; + // Curve specification + struct Curve { // Twisted Edwards curve + // Coefficients in defining equation ax^2 + y^2 = 1 + dx^2y^2 + a: Field, + d: Field, + // Generator as point in Cartesian coordinates + gen: Point + } + // Point in Cartesian coordinates + struct Point { + x: Field, + y: Field + } + + impl Point { + // Point constructor + pub fn new(x: Field, y: Field) -> Self { + Self { x, y } + } + + // Check for equality + fn eq(self, p: Point) -> bool { + let Self {x: x1, y: y1} = self; + let Self {x: x2, y: y2} = p; + + (x1 == x2) & (y1 == y2) + } + + // Check if zero + pub fn is_zero(self) -> bool { + self.eq(Point::zero()) + } + + // Conversion to CurveGroup coordinates + fn into_group(self) -> curvegroup::Point { + let Self {x, y} = self; + + curvegroup::Point::new(x, y, x*y, 1) + } + + // Additive identity + pub fn zero() -> Self { + Point::new(0,1) + } + + // Negation + fn negate(self) -> Self { + let Self {x, y} = self; + Point::new(0-x, y) + } + + // Map into prime-order subgroup of equivalent Montgomery curve + fn into_montcurve(self) -> MPoint { + if self.is_zero() { + MPoint::zero() + } else { + let Self {x, y} = self; + let x0 = (1+y)/(1-y); + let y0 = (1+y)/(x*(1-y)); + + MPoint::new(x0,y0) + } + } + } + + impl Curve { + // Curve constructor + pub fn new(a: Field, d: Field, gen: Point) -> Curve { + // Check curve coefficients + assert(a*d*(a-d) != 0); + + let curve = Curve {a, d, gen}; + + // gen should be on the curve + assert(curve.contains(curve.gen)); + + curve + } + + // Conversion to CurveGroup coordinates + fn into_group(self) -> curvegroup::Curve { + let Curve{a, d, gen} = self; + + curvegroup::Curve {a, d, gen: gen.into_group()} + } + + // Membership check + pub fn contains(self, p: Point) -> bool { + let Point {x, y} = p; + self.a*x*x + y*y == 1 + self.d*x*x*y*y + } + + // Point addition, implemented in terms of mixed addition for reasons of efficiency + pub fn add(self, p1: Point, p2: Point) -> Point { + self.mixed_add(p1, p2.into_group()).into_affine() + } + + // Mixed point addition, i.e. first argument in affine, second in CurveGroup coordinates. + fn mixed_add(self, p1: Point, p2: curvegroup::Point) -> curvegroup::Point { + let Point{x: x1, y: y1} = p1; + let curvegroup::Point{x: x2, y: y2, t: t2, z: z2} = p2; + + let a = x1*x2; + let b = y1*y2; + let c = self.d*x1*y1*t2; + let e = (x1 + y1)*(x2 + y2) - a - b; + let f = z2 - c; + let g = z2 + c; + let h = b - self.a*a; + + let x = e*f; + let y = g*h; + let t = e*h; + let z = f*g; + + curvegroup::Point::new(x,y,t,z) + } + + // Scalar multiplication with scalar represented by a bit array (little-endian convention). + // If k is the natural number represented by `bits`, then this computes p + ... + p k times. + fn bit_mul(self, bits: [u1; N], p: Point) -> Point { + self.into_group().bit_mul(bits, p.into_group()).into_affine() + } + + // Scalar multiplication (p + ... + p n times) + fn mul(self, n: Field, p: Point) -> Point { + self.into_group().mul(n, p.into_group()).into_affine() + } + + // Multi-scalar multiplication (n[0]*p[0] + ... + n[N]*p[N], where * denotes scalar multiplication) + fn msm(self, n: [Field; N], p: [Point; N]) -> Point { + let mut out = Point::zero(); + + for i in 0..N { + out = self.add(out, self.mul(n[i], p[i])); + } + + out + } + + // Point subtraction + fn subtract(self, p1: Point, p2: Point) -> Point { + self.add(p1, p2.negate()) + } + + // Conversion to equivalent Montgomery curve + pub fn into_montcurve(self) -> MCurve { + let j = 2*(self.a + self.d)/(self.a - self.d); + let k = 4/(self.a - self.d); + let gen_montcurve = self.gen.into_montcurve(); + + MCurve::new(j, k, gen_montcurve) + } + + // Conversion to equivalent Short Weierstraß curve + pub fn into_swcurve(self) -> SWCurve { + self.into_montcurve().into_swcurve() + } + + // Point mapping into equivalent Short Weierstraß curve + pub fn map_into_swcurve(self, p: Point) -> SWPoint { + self.into_montcurve().map_into_swcurve(p.into_montcurve()) + } + + // Point mapping from equivalent Short Weierstraß curve + fn map_from_swcurve(self, p: SWPoint) -> Point { + self.into_montcurve().map_from_swcurve(p).into_tecurve() + } + + // Elligator 2 map-to-curve method (via rational map) + fn elligator2_map(self, u: Field) -> Point { + self.into_montcurve().elligator2_map(u).into_tecurve() + } + + // Simplified SWU map-to-curve method (via rational map) + fn swu_map(self, z: Field, u: Field) -> Point { + self.into_montcurve().swu_map(z,u).into_tecurve() + } + } +} +mod curvegroup { + // CurveGroup coordinate representation of Twisted Edwards curves + // Points are represented by four-dimensional projective coordinates, viz. extended Twisted Edwards coordinates. + // See §3 of for details. + use crate::ec::tecurve::affine; + use crate::ec::montcurve::curvegroup::Curve as MCurve; + use crate::ec::montcurve::curvegroup::Point as MPoint; + use crate::ec::swcurve::curvegroup::Curve as SWCurve; + use crate::ec::swcurve::curvegroup::Point as SWPoint; + // Curve specification + struct Curve { // Twisted Edwards curve + // Coefficients in defining equation a(x^2 + y^2)z^2 = z^4 + dx^2y^2 + a: Field, + d: Field, + // Generator as point in projective coordinates + gen: Point + } + // Point in extended twisted Edwards coordinates + struct Point { + x: Field, + y: Field, + t: Field, + z: Field + } + + impl Point { + // Point constructor + pub fn new(x: Field, y: Field, t: Field, z: Field) -> Self { + Self {x, y, t, z} + } + + // Check for equality + fn eq(self, p: Point) -> bool { + let Self {x: x1, y: y1, t: _t1, z: z1} = self; + let Self {x: x2, y: y2, t: _t2, z:z2} = p; + + (x1*z2 == x2*z1) & (y1*z2 == y2*z1) + } + + // Check if zero + pub fn is_zero(self) -> bool { + let Self {x, y, t, z} = self; + (x == 0) & (y == z) & (y != 0) & (t == 0) + } + + // Conversion to affine coordinates + pub fn into_affine(self) -> affine::Point { + let Self {x, y, t: _t, z} = self; + + affine::Point::new(x/z, y/z) + } + + // Additive identity + pub fn zero() -> Self { + Point::new(0,1,0,1) + } + + // Negation + fn negate(self) -> Self { + let Self {x, y, t, z} = self; + + Point::new(0-x, y, 0-t, z) + } + + // Map into prime-order subgroup of equivalent Montgomery curve + fn into_montcurve(self) -> MPoint { + self.into_affine().into_montcurve().into_group() + } + } + + impl Curve { + // Curve constructor + pub fn new(a: Field, d: Field, gen: Point) -> Curve { + // Check curve coefficients + assert(a*d*(a-d) != 0); + + let curve = Curve { a, d, gen }; + + // gen should be on the curve + assert(curve.contains(curve.gen)); + + curve + } + + // Conversion to affine coordinates + pub fn into_affine(self) -> affine::Curve { + let Curve{a, d, gen} = self; + + affine::Curve {a, d, gen: gen.into_affine()} + } + + // Membership check + pub fn contains(self, p: Point) -> bool { + let Point {x, y, t, z} = p; + + (z != 0) & (z*t == x*y) & (z*z*(self.a*x*x + y*y) == z*z*z*z + self.d*x*x*y*y) + } + + // Point addition + pub fn add(self, p1: Point, p2: Point) -> Point { + let Point{x: x1, y: y1, t: t1, z: z1} = p1; + let Point{x: x2, y: y2, t: t2, z: z2} = p2; + + let a = x1*x2; + let b = y1*y2; + let c = self.d*t1*t2; + let d = z1*z2; + let e = (x1 + y1)*(x2 + y2) - a - b; + let f = d - c; + let g = d + c; + let h = b - self.a*a; + + let x = e*f; + let y = g*h; + let t = e*h; + let z = f*g; + + Point::new(x,y,t,z) + } + + // Point doubling, cf. §3.3 + pub fn double(self, p: Point) -> Point { + let Point{x, y, t: _t, z} = p; + + let a = x*x; + let b = y*y; + let c = 2*z*z; + let d = self.a*a; + let e = (x + y)*(x + y) - a - b; + let g = d + b; + let f = g - c; + let h = d - b; + + let x0 = e*f; + let y0 = g*h; + let t0 = e*h; + let z0 = f*g; + + Point::new(x0, y0, t0, z0) + } + + // Scalar multiplication with scalar represented by a bit array (little-endian convention). + // If k is the natural number represented by `bits`, then this computes p + ... + p k times. + fn bit_mul(self, bits: [u1; N], p: Point) -> Point { + let mut out = Point::zero(); + + for i in 0..N { + out = self.add( + self.add(out, out), + if(bits[N - i - 1] == 0) {Point::zero()} else {p}); + } + + out + } + + // Scalar multiplication (p + ... + p n times) + pub fn mul(self, n: Field, p: Point) -> Point { + let N_BITS = crate::field::modulus_num_bits(); + + // TODO: temporary workaround until issue 1354 is solved + let mut n_as_bits: [u1; 254] = [0; 254]; + let tmp = n.to_le_bits(N_BITS as u32); + for i in 0..254 { + n_as_bits[i] = tmp[i]; + } + + self.bit_mul(n_as_bits, p) + } + + // Multi-scalar multiplication (n[0]*p[0] + ... + n[N]*p[N], where * denotes scalar multiplication) + fn msm(self, n: [Field; N], p: [Point; N]) -> Point { + let mut out = Point::zero(); + + for i in 0..N { + out = self.add(out, self.mul(n[i], p[i])); + } + + out + } + + // Point subtraction + fn subtract(self, p1: Point, p2: Point) -> Point { + self.add(p1, p2.negate()) + } + + // Conversion to equivalent Montgomery curve + fn into_montcurve(self) -> MCurve { + self.into_affine().into_montcurve().into_group() + } + + // Conversion to equivalent Short Weierstraß curve + fn into_swcurve(self) -> SWCurve { + self.into_montcurve().into_swcurve() + } + + // Point mapping into equivalent short Weierstraß curve + pub fn map_into_swcurve(self, p: Point) -> SWPoint { + self.into_montcurve().map_into_swcurve(p.into_montcurve()) + } + + // Point mapping from equivalent short Weierstraß curve + fn map_from_swcurve(self, p: SWPoint) -> Point { + self.into_montcurve().map_from_swcurve(p).into_tecurve() + } + + // Elligator 2 map-to-curve method (via rational maps) + fn elligator2_map(self, u: Field) -> Point { + self.into_montcurve().elligator2_map(u).into_tecurve() + } + + // Simplified SWU map-to-curve method (via rational map) + fn swu_map(self, z: Field, u: Field) -> Point { + self.into_montcurve().swu_map(z,u).into_tecurve() + } + } +} diff --git a/noir/noir_stdlib/src/ecdsa_secp256k1.nr b/noir/noir_stdlib/src/ecdsa_secp256k1.nr new file mode 100644 index 00000000000..61687f557fe --- /dev/null +++ b/noir/noir_stdlib/src/ecdsa_secp256k1.nr @@ -0,0 +1,2 @@ +#[foreign(ecdsa_secp256k1)] +pub fn verify_signature(_public_key_x: [u8; 32], _public_key_y: [u8; 32], _signature: [u8; 64], _message_hash: [u8; N]) -> bool {} diff --git a/noir/noir_stdlib/src/ecdsa_secp256r1.nr b/noir/noir_stdlib/src/ecdsa_secp256r1.nr new file mode 100644 index 00000000000..7af37407edd --- /dev/null +++ b/noir/noir_stdlib/src/ecdsa_secp256r1.nr @@ -0,0 +1,2 @@ +#[foreign(ecdsa_secp256r1)] +pub fn verify_signature(_public_key_x: [u8; 32], _public_key_y: [u8; 32], _signature: [u8; 64], _message_hash: [u8; N]) -> bool {} diff --git a/noir/noir_stdlib/src/eddsa.nr b/noir/noir_stdlib/src/eddsa.nr new file mode 100644 index 00000000000..39051e23233 --- /dev/null +++ b/noir/noir_stdlib/src/eddsa.nr @@ -0,0 +1,58 @@ +use crate::hash::poseidon; +use crate::ec::consts::te::baby_jubjub; +use crate::ec::tecurve::affine::Point as TEPoint; +// Returns true if x is less than y +fn lt_bytes32(x: Field, y: Field) -> bool { + let x_bytes = x.to_le_bytes(32); + let y_bytes = y.to_le_bytes(32); + let mut x_is_lt = false; + let mut done = false; + for i in 0..32 { + if (!done) { + let x_byte = x_bytes[31 - i] as u8; + let y_byte = y_bytes[31 - i] as u8; + let bytes_match = x_byte == y_byte; + if !bytes_match { + x_is_lt = x_byte < y_byte; + done = true; + } + } + } + x_is_lt +} +// Returns true if signature is valid +pub fn eddsa_poseidon_verify( + pub_key_x: Field, + pub_key_y: Field, + signature_s: Field, + signature_r8_x: Field, + signature_r8_y: Field, + message: Field +) -> bool { + // Verifies by testing: + // S * B8 = R8 + H(R8, A, m) * A8 + let bjj = baby_jubjub(); + + let pub_key = TEPoint::new(pub_key_x, pub_key_y); + assert(bjj.curve.contains(pub_key)); + + let signature_r8 = TEPoint::new(signature_r8_x, signature_r8_y); + assert(bjj.curve.contains(signature_r8)); + // Ensure S < Subgroup Order + assert(lt_bytes32(signature_s, bjj.suborder)); + // Calculate the h = H(R, A, msg) + let hash: Field = poseidon::bn254::hash_5([signature_r8_x, signature_r8_y, pub_key_x, pub_key_y, message]); + // Calculate second part of the right side: right2 = h*8*A + // Multiply by 8 by doubling 3 times. This also ensures that the result is in the subgroup. + let pub_key_mul_2 = bjj.curve.add(pub_key, pub_key); + let pub_key_mul_4 = bjj.curve.add(pub_key_mul_2, pub_key_mul_2); + let pub_key_mul_8 = bjj.curve.add(pub_key_mul_4, pub_key_mul_4); + // We check that A8 is not zero. + assert(!pub_key_mul_8.is_zero()); + // Compute the right side: R8 + h * A8 + let right = bjj.curve.add(signature_r8, bjj.curve.mul(hash, pub_key_mul_8)); + // Calculate left side of equation left = S * B8 + let left = bjj.curve.mul(signature_s, bjj.base8); + + left.eq(right) +} diff --git a/noir/noir_stdlib/src/field.nr b/noir/noir_stdlib/src/field.nr new file mode 100644 index 00000000000..b4cb9b64e3c --- /dev/null +++ b/noir/noir_stdlib/src/field.nr @@ -0,0 +1,98 @@ +impl Field { + pub fn to_le_bits(self: Self, bit_size: u32) -> [u1] { + crate::assert_constant(bit_size); + self.__to_le_bits(bit_size) + } + + pub fn to_be_bits(self: Self, bit_size: u32) -> [u1] { + crate::assert_constant(bit_size); + self.__to_be_bits(bit_size) + } + + #[builtin(to_le_bits)] + fn __to_le_bits(_self: Self, _bit_size: u32) -> [u1] {} + + #[builtin(to_be_bits)] + fn __to_be_bits(_self: Self, _bit_size: u32) -> [u1] {} + + pub fn to_le_bytes(self: Self, byte_size: u32) -> [u8] { + self.to_le_radix(256, byte_size) + } + + pub fn to_be_bytes(self: Self, byte_size: u32) -> [u8] { + self.to_be_radix(256, byte_size) + } + + + pub fn to_le_radix(self: Self, radix: u32, result_len: u32) -> [u8] { + crate::assert_constant(radix); + crate::assert_constant(result_len); + self.__to_le_radix(radix, result_len) + } + + pub fn to_be_radix(self: Self, radix: u32, result_len: u32) -> [u8] { + crate::assert_constant(radix); + crate::assert_constant(result_len); + self.__to_be_radix(radix, result_len) + } + + + + // decompose `_self` into a `_result_len` vector over the `_radix` basis + // `_radix` must be less than 256 + #[builtin(to_le_radix)] + fn __to_le_radix(_self: Self, _radix: u32, _result_len: u32) -> [u8] {} + + #[builtin(to_be_radix)] + fn __to_be_radix(_self: Self, _radix: u32, _result_len: u32) -> [u8] {} + + + // Returns self to the power of the given exponent value. + // Caution: we assume the exponent fits into 32 bits + // using a bigger bit size impacts negatively the performance and should be done only if the exponent does not fit in 32 bits + pub fn pow_32(self, exponent: Field) -> Field { + let mut r: Field = 1; + let b = exponent.to_le_bits(32); + + for i in 1..33 { + r *= r; + r = (b[32-i] as Field) * (r * self) + (1 - b[32-i] as Field) * r; + } + r + } + + // Parity of (prime) Field element, i.e. sgn0(x mod p) = 0 if x ∈ {0, ..., p-1} is even, otherwise sgn0(x mod p) = 1. + pub fn sgn0(self) -> u1 { + self as u1 + } +} + +#[builtin(modulus_num_bits)] +pub fn modulus_num_bits() -> Field {} + +#[builtin(modulus_be_bits)] +pub fn modulus_be_bits() -> [u1] {} + +#[builtin(modulus_le_bits)] +pub fn modulus_le_bits() -> [u1] {} + +#[builtin(modulus_be_bytes)] +pub fn modulus_be_bytes() -> [u8] {} + +#[builtin(modulus_le_bytes)] +pub fn modulus_le_bytes() -> [u8] {} +// Convert a 32 byte array to a field element +pub fn bytes32_to_field(bytes32: [u8; 32]) -> Field { + // Convert it to a field element + let mut v = 1; + let mut high = 0 as Field; + let mut low = 0 as Field; + + for i in 0..16 { + high = high + (bytes32[15 - i] as Field) * v; + low = low + (bytes32[16 + 15 - i] as Field) * v; + v = v * 256; + } + // Abuse that a % p + b % p = (a + b) % p and that low < p + low + high * v +} diff --git a/noir/noir_stdlib/src/grumpkin_scalar.nr b/noir/noir_stdlib/src/grumpkin_scalar.nr new file mode 100644 index 00000000000..d05158488f4 --- /dev/null +++ b/noir/noir_stdlib/src/grumpkin_scalar.nr @@ -0,0 +1,21 @@ +struct GrumpkinScalar { + low: Field, + high: Field, +} + +impl GrumpkinScalar { + pub fn new(low: Field, high: Field) -> Self { + // TODO: check that the low and high value fit within the grumpkin modulus + GrumpkinScalar { low, high } + } +} + +global GRUMPKIN_SCALAR_SERIALIZED_LEN: Field = 2; + +pub fn deserialize_grumpkin_scalar(fields: [Field; GRUMPKIN_SCALAR_SERIALIZED_LEN]) -> GrumpkinScalar { + GrumpkinScalar { low: fields[0], high: fields[1] } +} + +pub fn serialize_grumpkin_scalar(scalar: GrumpkinScalar) -> [Field; GRUMPKIN_SCALAR_SERIALIZED_LEN] { + [scalar.low, scalar.high] +} diff --git a/noir/noir_stdlib/src/grumpkin_scalar_mul.nr b/noir/noir_stdlib/src/grumpkin_scalar_mul.nr new file mode 100644 index 00000000000..06d30d62332 --- /dev/null +++ b/noir/noir_stdlib/src/grumpkin_scalar_mul.nr @@ -0,0 +1,7 @@ +use crate::grumpkin_scalar::GrumpkinScalar; +use crate::scalar_mul::fixed_base_embedded_curve; + +pub fn grumpkin_fixed_base(scalar: GrumpkinScalar) -> [Field; 2] { + // TODO: this should use both the low and high limbs to do the scalar multiplication + fixed_base_embedded_curve(scalar.low, scalar.high) +} diff --git a/noir/noir_stdlib/src/hash.nr b/noir/noir_stdlib/src/hash.nr new file mode 100644 index 00000000000..157d6518367 --- /dev/null +++ b/noir/noir_stdlib/src/hash.nr @@ -0,0 +1,50 @@ +mod poseidon; +mod mimc; + +#[foreign(sha256)] +pub fn sha256(_input: [u8; N]) -> [u8; 32] {} + +#[foreign(blake2s)] +pub fn blake2s(_input: [u8; N]) -> [u8; 32] {} + +struct PedersenPoint { + x : Field, + y : Field, +} + +pub fn pedersen_commitment(input: [Field; N]) -> PedersenPoint { + pedersen_commitment_with_separator(input, 0) +} + +#[foreign(pedersen)] +pub fn __pedersen_commitment_with_separator(_input: [Field; N], _separator: u32) -> [Field; 2] {} + +pub fn pedersen_commitment_with_separator(input: [Field; N], separator: u32) -> PedersenPoint { + let values = __pedersen_commitment_with_separator(input, separator); + PedersenPoint { x: values[0], y: values[1] } +} + +pub fn pedersen_hash(input: [Field; N]) -> Field { + pedersen_hash_with_separator(input, 0) +} + +#[foreign(pedersen_hash)] +pub fn pedersen_hash_with_separator(_input: [Field; N], _separator: u32) -> Field {} + +pub fn hash_to_field(_input: [Field; N]) -> Field { + let mut inputs_as_bytes = []; + + for i in 0..N { + let input_bytes = _input[i].to_le_bytes(32); + for i in 0..32 { + inputs_as_bytes = inputs_as_bytes.push_back(input_bytes[i]); + } + } + + let hashed_input = blake2s(inputs_as_bytes); + crate::field::bytes32_to_field(hashed_input) +} + +#[foreign(keccak256)] +pub fn keccak256(_input: [u8; N], _message_size: u32) -> [u8; 32] {} + diff --git a/noir/noir_stdlib/src/hash/mimc.nr b/noir/noir_stdlib/src/hash/mimc.nr new file mode 100644 index 00000000000..10c0a48917c --- /dev/null +++ b/noir/noir_stdlib/src/hash/mimc.nr @@ -0,0 +1,125 @@ +// mimc-p/p implementation +// constants are (publicly generated) random numbers, for instance using keccak as a ROM. +// You must use constants generated for the native field +// Rounds number should be ~ log(p)/log(exp) +// For 254 bit primes, exponent 7 and 91 rounds seems to be recommended +fn mimc(x: Field, k: Field, constants: [Field; N], exp: Field) -> Field { + //round 0 + let mut t = x + k; + let mut h = t.pow_32(exp); + //next rounds + for i in 1..constants.len() { + t = h + k + constants[i]; + h = t.pow_32(exp); + } + h + k +} + +global MIMC_BN254_ROUNDS = 91; +//mimc implementation with hardcoded parameters for BN254 curve. +#[field(bn254)] +pub fn mimc_bn254(array: [Field; N]) -> Field { + //mimc parameters + let exponent = 7; + //generated from seed "mimc" using keccak256 + let constants: [Field; MIMC_BN254_ROUNDS] = [ + 0, + 20888961410941983456478427210666206549300505294776164667214940546594746570981, + 15265126113435022738560151911929040668591755459209400716467504685752745317193, + 8334177627492981984476504167502758309043212251641796197711684499645635709656, + 1374324219480165500871639364801692115397519265181803854177629327624133579404, + 11442588683664344394633565859260176446561886575962616332903193988751292992472, + 2558901189096558760448896669327086721003508630712968559048179091037845349145, + 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in array { + let h = mimc(elem, r, constants, exponent); + r = r + elem + h; + } + r +} diff --git a/noir/noir_stdlib/src/hash/poseidon.nr b/noir/noir_stdlib/src/hash/poseidon.nr new file mode 100644 index 00000000000..b3cb32cd357 --- /dev/null +++ b/noir/noir_stdlib/src/hash/poseidon.nr @@ -0,0 +1,95 @@ +mod bn254; // Instantiations of Poseidon for prime field of the same order as BN254 +use crate::field::modulus_num_bits; + +struct PoseidonConfig { + t: Field, // Width, i.e. state size + rf: u8, // Number of full rounds; should be even + rp: u8, // Number of partial rounds + alpha: Field, // S-box power; depends on the underlying field + ark: [Field; M], // Additive round keys + mds: [Field; N] // MDS Matrix in row-major order +} + +pub fn config(t: Field, rf: u8, rp: u8, alpha: Field, ark: [Field; M], mds: [Field; N]) -> PoseidonConfig { + // Input checks + let mul = crate::wrapping_mul(t as u8, (rf + rp)); + assert(mul == ark.len() as u8); + assert(t * t == mds.len()); + assert(alpha != 0); + + PoseidonConfig { t, rf, rp, alpha, ark, mds } +} +// General Poseidon permutation on elements of type Field +fn permute(pos_conf: PoseidonConfig, mut state: [Field; O]) -> [Field; O] { + let PoseidonConfig {t, rf, rp, alpha, ark, mds} = pos_conf; + + assert(t == state.len()); + + let mut count = 0; + // for r in 0..rf + rp + for r in 0..(ark.len() / state.len()) { + for i in 0..state.len() { + state[i] = state[i] + ark[count + i]; + } // Shift by round constants + state[0] = state[0].pow_32(alpha); + // Check whether we are in a full round + if (r as u8 < rf / 2) | (r as u8 >= rf / 2 + rp) { + for i in 1..state.len() { + state[i] = state[i].pow_32(alpha); + } + } + + state = apply_matrix(mds, state); // Apply MDS matrix + count = count + t; + } + + state +} +// Absorption. Fully absorbs input message. +fn absorb( + pos_conf: PoseidonConfig, + mut state: [Field; O], // Initial state; usually [0; O] + rate: Field, // Rate + capacity: Field, // Capacity; usually 1 + msg: [Field; P] +) -> [Field; O] { + assert(pos_conf.t == rate + capacity); + + let mut i = 0; + + for k in 0..msg.len() { + // Add current block to state + state[capacity + i] += msg[k]; + i = i+1; + // Enough to absorb + if i == rate { + state = permute(pos_conf, state); + i = 0; + } + } + // If we have one more block to permute + if i != 0 { + state = permute(pos_conf, state); + } + + state +} +// Check security of sponge instantiation +fn check_security(rate: Field, width: Field, security: Field) -> bool { + let n = modulus_num_bits(); + + ((n - 1) * (width - rate) / 2) as u8 > security as u8 +} +// A*x where A is an n x n matrix in row-major order and x an n-vector +fn apply_matrix(a: [Field; M], x: [Field; N]) -> [Field; N] { + let mut y = x; + + for i in 0..x.len() { + y[i] = 0; + for j in 0..x.len() { + y[i] = y[i] + a[x.len()*i + j]* x[j]; + } + } + + y +} diff --git a/noir/noir_stdlib/src/hash/poseidon/bn254.nr b/noir/noir_stdlib/src/hash/poseidon/bn254.nr new file mode 100644 index 00000000000..0db6d9546dc --- /dev/null +++ b/noir/noir_stdlib/src/hash/poseidon/bn254.nr @@ -0,0 +1,236 @@ +// Instantiations of Poseidon constants, permutations and sponge for prime field of the same order as BN254 +mod perm; +mod consts; + +use crate::hash::poseidon::PoseidonConfig; +use crate::hash::poseidon::apply_matrix; +// Optimised permutation for this particular field; uses hardcoded rf and rp values, +// which should agree with those in pos_conf. +#[field(bn254)] +pub fn permute(pos_conf: PoseidonConfig, mut state: [Field; O]) -> [Field; O] { + let PoseidonConfig {t, rf: config_rf, rp: config_rp, alpha, ark, mds} = pos_conf; + let rf = 8; + let rp = [56, 57, 56, 60, 60, 63, 64, 63, 60, 66, 60, 65, 70, 60, 64, 68][state.len() - 2]; + + assert(t == state.len()); + assert(rf == config_rf as Field); + assert(rp == config_rp as Field); + + let mut count = 0; + // First half of full rounds + for _r in 0..rf / 2 { + for i in 0..state.len() { + state[i] = state[i] + ark[count + i]; + } // Shift by round constants + for i in 0..state.len() { + state[i] = state[i].pow_32(alpha); + } + + state = apply_matrix(mds, state); // Apply MDS matrix + count = count + t; + } + // Partial rounds + for _r in 0..rp { + for i in 0..state.len() { + state[i] = state[i] + ark[count + i]; + } // Shift by round constants + state[0] = state[0].pow_32(alpha); + + state = apply_matrix(mds, state); // Apply MDS matrix + count = count + t; + } + // Second half of full rounds + for _r in 0..rf / 2 { + for i in 0..state.len() { + state[i] = state[i] + ark[count + i]; + } // Shift by round constants + for i in 0..state.len() { + state[i] = state[i].pow_32(alpha); + } + + state = apply_matrix(mds, state); // Apply MDS matrix + count = count + t; + } + + state +} +// Corresponding absorption. +#[field(bn254)] +fn absorb( + pos_conf: PoseidonConfig, + mut state: [Field; O], // Initial state; usually [0; O] + rate: Field, // Rate + capacity: Field, // Capacity; usually 1 + msg: [Field; P] // Arbitrary length message +) -> [Field; O] { + assert(pos_conf.t == rate + capacity); + + let mut i = 0; + + for k in 0..msg.len() { + // Add current block to state + state[capacity + i] += msg[k]; + i = i+1; + // Enough to absorb + if i == rate { + state = permute(pos_conf, state); + i = 0; + } + } + // If we have one more block to permute + if i != 0 { + state = permute(pos_conf, state); + } + + state +} +// Variable-length Poseidon-128 sponge as suggested in second bullet point of §3 of https://eprint.iacr.org/2019/458.pdf +#[field(bn254)] +pub fn sponge(msg: [Field; N]) -> Field { + absorb(consts::x5_5_config(), [0; 5], 4, 1, msg)[1] +} +// Various instances of the Poseidon hash function +// Consistent with Circom's implementation +pub fn hash_1(input: [Field; 1]) -> Field { + let mut state = [0; 2]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_2(state)[0] +} + +pub fn hash_2(input: [Field; 2]) -> Field { + let mut state = [0; 3]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_3(state)[0] +} + +pub fn hash_3(input: [Field; 3]) -> Field { + let mut state = [0; 4]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_4(state)[0] +} + +pub fn hash_4(input: [Field; 4]) -> Field { + let mut state = [0; 5]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_5(state)[0] +} + +pub fn hash_5(input: [Field; 5]) -> Field { + let mut state = [0; 6]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_6(state)[0] +} + +pub fn hash_6(input: [Field; 6]) -> Field { + let mut state = [0; 7]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_7(state)[0] +} + +pub fn hash_7(input: [Field; 7]) -> Field { + let mut state = [0; 8]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_8(state)[0] +} + +pub fn hash_8(input: [Field; 8]) -> Field { + let mut state = [0; 9]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_9(state)[0] +} + +pub fn hash_9(input: [Field; 9]) -> Field { + let mut state = [0; 10]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_10(state)[0] +} + +pub fn hash_10(input: [Field; 10]) -> Field { + let mut state = [0; 11]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_11(state)[0] +} + +pub fn hash_11(input: [Field; 11]) -> Field { + let mut state = [0; 12]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_12(state)[0] +} + +pub fn hash_12(input: [Field; 12]) -> Field { + let mut state = [0; 13]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_13(state)[0] +} + +pub fn hash_13(input: [Field; 13]) -> Field { + let mut state = [0; 14]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_14(state)[0] +} + +pub fn hash_14(input: [Field; 14]) -> Field { + let mut state = [0; 15]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_15(state)[0] +} + +pub fn hash_15(input: [Field; 15]) -> Field { + let mut state = [0; 16]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_16(state)[0] +} + +pub fn hash_16(input: [Field; 16]) -> Field { + let mut state = [0; 17]; + for i in 0..input.len() { + state[i+1] = input[i]; + } + + perm::x5_17(state)[0] +} diff --git a/noir/noir_stdlib/src/hash/poseidon/bn254/consts.nr b/noir/noir_stdlib/src/hash/poseidon/bn254/consts.nr new file mode 100644 index 00000000000..62b5f4b5212 --- /dev/null +++ b/noir/noir_stdlib/src/hash/poseidon/bn254/consts.nr @@ -0,0 +1,192 @@ +// Constants for various Poseidon instances in the case of the prime field of the same order as BN254. +// Consistent with https://github.com/iden3/circomlib/blob/master/circuits/poseidon.circom and https://github.com/iden3/circomlib/blob/master/circuits/poseidon_constants.circom +use crate::hash::poseidon::PoseidonConfig; +use crate::hash::poseidon::config; +// Number of full rounds +global rf = 8; +// Number of partial rounds +fn rp() -> [u8; 16] { + [56, 57, 56, 60, 60, 63, 64, 63, 60, 66, 60, 65, 70, 60, 64, 68] +} +// S-box power +fn alpha() -> Field { + 5 +} +// Poseidon configurations for states of size 2 to 17. +// noir-fmt:ignore +pub fn x5_2_config() -> PoseidonConfig<128, 4> { + config( + 2, + 8, + 56, + alpha(), + 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+ [2910766817845651019878574839501801340070030115151021261302834310722729507541,19727366863391167538122140361473584127147630672623100827934084310230022599144,5776684794125549462448597414050232243778680302179439492664047328281728356345,8348174920934122550483593999453880006756108121341067172388445916328941978568] + ) + +} +// noir-fmt:ignore +pub fn x5_3_config() -> PoseidonConfig<195, 9> { + config( + 3, + 8, + 57, + 5, + 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333941013553002921430306,13409242754315411433193860530743374419854094495153957441316635981078068351329], + [7511745149465107256748700652201246547602992235352608707588321460060273774987,10370080108974718697676803824769673834027675643658433702224577712625900127200,19705173408229649878903981084052839426532978878058043055305024233888854471533,18732019378264290557468133440468564866454307626475683536618613112504878618481,20870176810702568768751421378473869562658540583882454726129544628203806653987,7266061498423634438633389053804536045105766754026813321943009179476902321146,9131299761947733513298312097611845208338517739621853568979632113419485819303,10595341252162738537912664445405114076324478519622938027420701542910180337937,11597556804922396090267472882856054602429588299176362916247939723151043581408]) + +} +// noir-fmt:ignore +pub fn x5_4_config() -> PoseidonConfig<256, 16> { + config( + 4, + 8, + 56, + alpha(), + 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+ [16023668707004248971294664614290028914393192768609916554276071736843535714477,17849615858846139011678879517964683507928512741474025695659909954675835121177,1013663139540921998616312712475594638459213772728467613870351821911056489570,13211800058103802189838759488224684841774731021206389709687693993627918500545,19204974983793400699898444372535256207646557857575315905278218870961389967884,3722304780857845144568029505892077496425786544014166938942516810831732569870,11920634922168932145084219049241528148129057802067880076377897257847125830511,6085682566123812000257211683010755099394491689511511633947011263229442977967,14672613178263529785795301930884172260797190868602674472542654261498546023746,20850178060552184587113773087797340350525370429749200838012809627359404457643,7082289538076771741936674361200789891432311337766695368327626572220036527624,1787876543469562003404632310460227730887431311758627706450615128255538398187,21407770160218607278833379114951608489910182969042472165261557405353704846967,16058955581309173858487265533260133430557379878452348481750737813742488209262,593311177550138061601452020934455734040559402531605836278498327468203888086,341662423637860635938968460722645910313598807845686354625820505885069260074] + ) +} +// noir-fmt:ignore +pub fn x5_5_config() -> PoseidonConfig<340, 25> { + config( + 5, + 8, + 60, + alpha(), + 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+ 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+ ) +} +// noir-fmt:ignore +pub fn x5_6_config() -> PoseidonConfig<408, 36> { + config( + 6, + 8, + 60, + alpha(), + 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+ ) +} +// noir-fmt:ignore +pub fn x5_7_config() -> PoseidonConfig<497, 49> { + config( + 7, + 8, + 63, + alpha(), + 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+ ) +} +// noir-fmt:ignore +pub fn x5_8_config() -> PoseidonConfig<576, 64> { + config( + 8, + 8, + 64, + alpha(), + 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+ ) +} +// noir-fmt:ignore +pub fn x5_9_config() -> PoseidonConfig<639, 81> { + config( + 9, + 8, + 63, + alpha(), + 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+ ) +} +// noir-fmt:ignore +pub fn x5_10_config() -> PoseidonConfig<680, 100> { + config( + 10, + 8, + 60, + alpha(), + 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+ ) +} +// noir-fmt:ignore +pub fn x5_11_config() -> PoseidonConfig<814, 121> { + config( + 11, + 8, + 66, + alpha(), + 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+ ) +} +// noir-fmt:ignore +pub fn x5_12_config() -> PoseidonConfig<816, 144> { + config( + 12, + 8, + 60, + alpha(), + 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+ ) +} +// noir-fmt:ignore +pub fn x5_13_config() -> PoseidonConfig<949, 169> { + config( + 13, + 8, + 65, + alpha(), + [8798508051216852101945770298446195263426534955832706109793971883081428107277,18986509933027080134736570007084643366993581436980149915398754460582000677690,12888632176077676513526604615863975427335143923557716212154194444095746278696,15218363940991075171925464923999816479227567707705088260324253815128900522726,5123856245344699442960347063644568841985144075867098178729206123344695866632,937497109650566529304426914695525196119182564119552807475810542139513078711,824338985948318627675352935105995425917124279032809525354560876904860400681,13942203133811236390194976449250445722833065335401645546668049734024419803027,21193497547768556525501812324063541319364865851059424372272833186043647143451,8250588221665279725951753271334754148372733354841465281007604695074534688729,19810263445128123914159130235504848033923366950322026683325764129239721093372,14320576994914109414332501339118128158228930675887122896979269439627163060693,18889264041663920880871940494605753485014632588652805059026615325416030193719,10698608730721598729809918763148392473410663742558667693804099019738511427166,5471531298285601691838160103499608876767872950664004448938449413930813358696,11457601248269622750970644898105156673192281665198367584690238105651871243849,16992951642443963917535033612483493956841253647152989134657936764458589935104,10681874111209657199770620844475073359855652721773538207614365548304774934508,21875342192304323235337862160226992419479798345905340775869689347883470787957,18783598503862573040343207522848147282351537545213737188311758281500392127423,1221270316640374234025430293653174054626512367600614548099335037611485538579,3803127350594514565431185919859613836470412459545124433755604579955078714169,4692704173297526575662787527414813729052368393667564364621548376076502682045,14877344233698668087821753402566363996983618742746012792982725192235615274582,1054560579819817802724755404438987869385528807680314510667741074193700268397,8052216208184446695555642950254618937388238814298100142455645263386380520432,21528486310986712871728288619461268100617012373791428855787397091733571140849,9607239927604256097021258716815750648977424271874860033430637795556811442682,2196312812082021938532541315765264710074289763312450521728804241715253452502,6605540160838083938636248853297421723407665107595785929587574408377531474397,6525948153406525772960169117380395975817023461160134354401655738067058124631,1003556227519998429556476568497227440137917634405973503866888411893650518666,13677584968475474622689223355399150668790874783709262452797580070344541448836,4272686670447940608576804106249107479440735969672981187306042096242258850880,7723948150633857131095670967217469617626942590283045283879017721699420758313,9563761958679910727099148022742805150896356950128100201760652748527446140573,2850052690445041465388681891454371728435601558220304706587137488583335087257,8526775100524058290507772716277462650712450825299014127801740914862834880076,3867865860920444582361195032750323847757212456942796852256870663055781206979,9531131437985240947539412881613144640386246585015365913532093261866651903759,7876176264763636659141490167245054739857019306622806711043600468363729864093,19195073274099628437757763039411307254419435216466391625117296911023053159082,17201596318516267959242478674525331020962456681235714852429128354797936423512,16429987001981270427843548313178399452962937303668954688438246688799503164984,2808058398896238113910841463946229560728543614115874664163904810633100174930,14219106904819518684382581834179422938681605045169559176622588393807041033595,10530037691301443797595179975755234137332257307745168342967468344289118325757,1780683171090951191330938815635375996828930714110381590102230999415067289139,20631152509228411005601100836698224543066495368069041690921190101674287477942,808499207021654020396580110067573161997188131641925849629551231827774305643,16396331554632517617174441423319546296247483740125085437207362257874071846324,2856734177282633594896606301173852733142847026457749184584827572766646232552,10322006004350928347565262203595059371944943723449117022023179276501731876345,2683049508597401695548071522846046195699870016352617057472448981851954812059,16247545910658018105089170383534659574306111291405960552997108542031333797235,18622054704352443244328095600847746501599243235049513145116519278941866455272,8702183185743408572844482710294629749653378107594727106707156268893318308842,14401120539669001367195063486710670290450638416936122540739824928865783084126,1008932491940705147356558882115440120351684984366727805937095853000783157110,3335415470820584035672268458205991064638866247863327497697585974180788121327,675348271572506248948949205284720732296364868854507020989147277054109697307,11425495584918585085965114263893597096866227224211705734884180990929134229634,3139501819004172358132939651256683854267531704642601593201346365873114559764,5783084359807455756426365693604321435940832494660634548089377362001475784943,1209509486454700107070921930482941186558988074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+ ) +} +// noir-fmt:ignore +pub fn x5_14_config() -> PoseidonConfig<1092, 196> { + config( + 14, + 8, + 70, + alpha(), + [21845584790817078371458083471368949437776490472877850604640045078191512294989,19653529167194186573342031346879012675435131167180408423801998487681049609228,2161640783454164110262374377277313793192503897274785966059544028153063342839,3054385704838408049711788708109646820127990212588286684954516786776077717445,16635301713639076283966918721405743045341739008997361549904617279107633739991,16225725689449395070421553385264743934675104858051374243666267106604469686859,13459300344588917210133884568970491767131781128923804903594902942358152425193,18076808066595657160589765822638228194586496301618971676220813553508533309824,15694780501519741286439086116053845024521602672078716577385749258673935540935,16872540725652861460604107748085417998883467929955001131816309200004825208678,18265717256656085201140362928095712147159090935193139243454994794524622317396,19947609676398163035598483882491861002323351300468831661031717096486749401185,7065348755377637300800426777670517297442798705186719592553630280930183590981,5932961695686545421777788171001317503890223201383043783424823453778021729657,2576876576710667081195577161905871971928185892200731156106299121736713352612,12555802030075275558510564984903031995058560722557285963258561359487710166944,6675427211912119210966904017515062933064977336124153512189140864737663166184,12222599141329943348111473622063042423140762381594560767061296463113725630598,10362975561623981137760844569177506781739504467939943515494131756563631401384,12107531632347826132438108083855485290921771214734424618157420540758495466114,3236413362865617508850894632024597913345632839215458268170009274560894755059,13269640842689962370480760671727240164372687082286848502921603339956981268889,8634391395282326489812410144806386699066840792624899435283359752045476380023,14222253145252965302168372301361324351891663598874510374220732501857922860417,8638897321585802770244834010345336579513350450350318876059595307827518698264,3865974821285567306249300995778666703191077355251395536574998983854299535502,7110975263857442529682465175399830853738223819847928136560110618272170902664,20747849667914457283564964533975989346418433637631755372433299771504842519646,8525337218586373689263383636693181730826089466845186214607593322372086164902,15284698092379128147447314807096025894174546974949762301780223877793581148749,7697267363768037767923915437621109821518467158489394234232803683407232938777,18552464418403713636184903318257896949762751716606049583572368219578636617127,8197992130246671682417102475937137101180310399693221498504232752936385287840,6267215711691005252092821645736755217101102985763928035625443919819600833817,17433952956528062441211440388577119958754746388457390005635829394680376947181,4412646415700345908595327060988016573431087490581498127967913815901104779675,15833191543444816612091389727640172975373200401937212078659643034429446372420,2496738022051529758808536908421531340554055955151436808600608028881270179985,18865821309368781007158439319264225124435320863391412688953283839208730853556,17356853444859379852104523062654303386446700475095103588258687890090805160214,13809314487709569674040696128771324530575306163394456992422515709961741509179,16441057642509466688507318755613056920894191930223829210993196564779122458374,13126203992288988176378067141418363395032818736472538500506274256104409609416,13787967697762688988712839398243982920760030565931489906532854488857802399817,19656786571615920765259702874045834039087950126970888730192726308970074475638,9496155930249199891836650339371004045437843955429376625371983436016490755144,6133650980214690439646563353939486183333374330790371938934927663849630407389,9878750392407472855744714788091164958447986838940220983459727015586370325700,17172636845539329981401043696407061375416986816086343858560846660524543673690,18266068074968659293646594793202575012585251834459743250538629833034512472364,88170009072279647351671114053985696295758726185186407584170034409102883821,7161623553009701438320582200845261728475099875569530553253147102879319777702,19053465421328592764143131116469165876378776722378567877265112443326967288533,15787821054928042708896412640384007326676625326396861263135967977164294783653,9615173323505112477217305395762776033870692647990635021315350642536385774258,10748585844981412663149047472971001536312431184916168605042111548221680655573,17193496554920138070905215774356694517155445370515924892481894322048517125340,20981027561373479728773182418686452188471940655764396684028110979140369006827,21262972686125563821794309741130556914165641373895499794098515843451753652777,17107658081493676403630365275304097003600155220274082934263833389505162130032,12511894931761241561787052333055670047472612011302194769826693935710455626001,17806912269440585736864672280987816575618315620498245554921738316327046304192,19545292022072695979212345737390709562681030627834519269858193519314537426533,7543800958185166098093959246645025070235714806654195385000743554473922936618,462547508253414923216140573122460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24819089112009057667,8823950326593148986044014943552213420762296615584865468926316064433145020153,3784973153813546220636380916429273484146041804997715013288159720673291711004] + ) +} +// noir-fmt:ignore +pub fn x5_15_config() -> PoseidonConfig<1020, 225> { + config( + 15, + 8, + 60, + alpha(), + 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+ ) +} +// noir-fmt:ignore +pub fn x5_16_config() -> PoseidonConfig<1152, 256> { + config( + 16, + 8, + 64, + alpha(), + 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+ ) +} +// noir-fmt:ignore +pub fn x5_17_config() -> PoseidonConfig<1292, 289> { + config( + 17, + 8, + 68, + alpha(), + 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+ ) +} diff --git a/noir/noir_stdlib/src/hash/poseidon/bn254/perm.nr b/noir/noir_stdlib/src/hash/poseidon/bn254/perm.nr new file mode 100644 index 00000000000..890f54fdb3f --- /dev/null +++ b/noir/noir_stdlib/src/hash/poseidon/bn254/perm.nr @@ -0,0 +1,148 @@ +// Instantiations of Poseidon permutation for the prime field of the same order as BN254 +use crate::hash::poseidon::bn254::consts; +use crate::hash::poseidon::bn254::permute; +use crate::hash::poseidon::PoseidonConfig; + +#[field(bn254)] +pub fn x5_2(mut state: [Field; 2]) -> [Field; 2] { + state = permute( + consts::x5_2_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_3(mut state: [Field; 3]) -> [Field; 3] { + state = permute( + consts::x5_3_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_4(mut state: [Field; 4]) -> [Field; 4] { + state = permute( + consts::x5_4_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_5(mut state: [Field; 5]) -> [Field; 5] { + state = permute( + consts::x5_5_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_6(mut state: [Field; 6]) -> [Field; 6] { + state = permute( + consts::x5_6_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_7(mut state: [Field; 7]) -> [Field; 7] { + state = permute( + consts::x5_7_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_8(mut state: [Field; 8]) -> [Field; 8] { + state = permute( + consts::x5_8_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_9(mut state: [Field; 9]) -> [Field; 9] { + state = permute( + consts::x5_9_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_10(mut state: [Field; 10]) -> [Field; 10] { + state = permute( + consts::x5_10_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_11(mut state: [Field; 11]) -> [Field; 11] { + state = permute( + consts::x5_11_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_12(mut state: [Field; 12]) -> [Field; 12] { + state = permute( + consts::x5_12_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_13(mut state: [Field; 13]) -> [Field; 13] { + state = permute( + consts::x5_13_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_14(mut state: [Field; 14]) -> [Field; 14] { + state = permute( + consts::x5_14_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_15(mut state: [Field; 15]) -> [Field; 15] { + state = permute( + consts::x5_15_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_16(mut state: [Field; 16]) -> [Field; 16] { + state = permute( + consts::x5_16_config(), + state); + + state +} + +#[field(bn254)] +pub fn x5_17(mut state: [Field; 17]) -> [Field; 17] { + state = permute( + consts::x5_17_config(), + state); + + state +} diff --git a/noir/noir_stdlib/src/lib.nr b/noir/noir_stdlib/src/lib.nr new file mode 100644 index 00000000000..8d878eecbb3 --- /dev/null +++ b/noir/noir_stdlib/src/lib.nr @@ -0,0 +1,63 @@ +mod hash; +mod array; +mod slice; +mod merkle; +mod schnorr; +mod ecdsa_secp256k1; +mod ecdsa_secp256r1; +mod eddsa; +mod grumpkin_scalar; +mod grumpkin_scalar_mul; +mod scalar_mul; +mod sha256; +mod sha512; +mod field; +mod ec; +mod unsafe; +mod collections; +mod compat; +mod option; +mod string; +mod test; +// Oracle calls are required to be wrapped in an unconstrained function +// Thus, the only argument to the `println` oracle is expected to always be an ident +#[oracle(println)] +unconstrained fn println_oracle(_input: T) {} + +unconstrained pub fn println(input: T) { + println_oracle(input); +} + +#[foreign(recursive_aggregation)] +pub fn verify_proof( + _verification_key: [Field], + _proof: [Field], + _public_inputs: [Field], + _key_hash: Field, + _input_aggregation_object: [Field; N] +) -> [Field; N] {} +// Asserts that the given value is known at compile-time. +// Useful for debugging for-loop bounds. +#[builtin(assert_constant)] +pub fn assert_constant(_x: T) {} +// from_field and as_field are private since they are not valid for every type. +// `as` should be the default for users to cast between primitive types, and in the future +// traits can be used to work with generic types. +#[builtin(from_field)] +fn from_field(_x: Field) -> T {} + +#[builtin(as_field)] +fn as_field(_x: T) -> Field {} + +pub fn wrapping_add(x: T, y: T) -> T { + crate::from_field(crate::as_field(x) + crate::as_field(y)) +} + +pub fn wrapping_sub(x: T, y: T) -> T { + //340282366920938463463374607431768211456 is 2^128, it is used to avoid underflow + crate::from_field(crate::as_field(x) + 340282366920938463463374607431768211456 - crate::as_field(y)) +} + +pub fn wrapping_mul(x: T, y: T) -> T { + crate::from_field(crate::as_field(x) * crate::as_field(y)) +} diff --git a/noir/noir_stdlib/src/merkle.nr b/noir/noir_stdlib/src/merkle.nr new file mode 100644 index 00000000000..9b15fe7313d --- /dev/null +++ b/noir/noir_stdlib/src/merkle.nr @@ -0,0 +1,19 @@ +// Regular merkle tree means a append-only merkle tree (Explain why this is the only way to have privacy and alternatives if you don't want it) +// Currently we assume that it is a binary tree, so depth k implies a width of 2^k +// XXX: In the future we can add an arity parameter +// Returns the merkle root of the tree from the provided leaf, its hashpath, using a pedersen hash function. +pub fn compute_merkle_root(leaf: Field, index: Field, hash_path: [Field; N]) -> Field { + let n = hash_path.len(); + let index_bits = index.to_le_bits(n as u32); + let mut current = leaf; + for i in 0..n { + let path_bit = index_bits[i] as bool; + let (hash_left, hash_right) = if path_bit { + (hash_path[i], current) + } else { + (current, hash_path[i]) + }; + current = crate::hash::pedersen_hash([hash_left, hash_right]); + } + current +} diff --git a/noir/noir_stdlib/src/option.nr b/noir/noir_stdlib/src/option.nr new file mode 100644 index 00000000000..137d57f33db --- /dev/null +++ b/noir/noir_stdlib/src/option.nr @@ -0,0 +1,164 @@ +struct Option { + _is_some: bool, + _value: T, +} + +impl Option { + /// Constructs a None value + pub fn none() -> Self { + Self { _is_some: false, _value: crate::unsafe::zeroed() } + } + + /// Constructs a Some wrapper around the given value + pub fn some(_value: T) -> Self { + Self { _is_some: true, _value } + } + + /// True if this Option is None + pub fn is_none(self) -> bool { + !self._is_some + } + + /// True if this Option is Some + pub fn is_some(self) -> bool { + self._is_some + } + + /// Asserts `self.is_some()` and returns the wrapped value. + pub fn unwrap(self) -> T { + assert(self._is_some); + self._value + } + + /// Returns the inner value without asserting `self.is_some()` + /// Note that if `self` is `None`, there is no guarantee what value will be returned, + /// only that it will be of type `T`. + pub fn unwrap_unchecked(self) -> T { + self._value + } + + /// Returns the wrapped value if `self.is_some()`. Otherwise, returns the given default value. + pub fn unwrap_or(self, default: T) -> T { + if self._is_some { + self._value + } else { + default + } + } + + /// Returns the wrapped value if `self.is_some()`. Otherwise, calls the given function to return + /// a default value. + pub fn unwrap_or_else(self, default: fn[Env]() -> T) -> T { + if self._is_some { + self._value + } else { + default() + } + } + + /// If self is `Some(x)`, this returns `Some(f(x))`. Otherwise, this returns `None`. + pub fn map(self, f: fn[Env](T) -> U) -> Option { + if self._is_some { + Option::some(f(self._value)) + } else { + Option::none() + } + } + + /// If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns the given default value. + pub fn map_or(self, default: U, f: fn[Env](T) -> U) -> U { + if self._is_some { + f(self._value) + } else { + default + } + } + + /// If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns `default()`. + pub fn map_or_else(self, default: fn[Env1]() -> U, f: fn[Env2](T) -> U) -> U { + if self._is_some { + f(self._value) + } else { + default() + } + } + + /// Returns None if self is None. Otherwise, this returns `other`. + pub fn and(self, other: Self) -> Self { + if self.is_none() { + Option::none() + } else { + other + } + } + + /// If self is None, this returns None. Otherwise, this calls the given function + /// with the Some value contained within self, and returns the result of that call. + /// + /// In some languages this function is called `flat_map` or `bind`. + pub fn and_then(self, f: fn[Env](T) -> Option) -> Option { + if self._is_some { + f(self._value) + } else { + Option::none() + } + } + + /// If self is Some, return self. Otherwise, return `other`. + pub fn or(self, other: Self) -> Self { + if self._is_some { + self + } else { + other + } + } + + /// If self is Some, return self. Otherwise, return `default()`. + pub fn or_else(self, default: fn[Env]() -> Self) -> Self { + if self._is_some { + self + } else { + default() + } + } + + // If only one of the two Options is Some, return that option. + // Otherwise, if both options are Some or both are None, None is returned. + pub fn xor(self, other: Self) -> Self { + if self._is_some { + if other._is_some { + Option::none() + } else { + self + } + } else if other._is_some { + other + } else { + Option::none() + } + } + + /// Returns `Some(x)` if self is `Some(x)` and `predicate(x)` is true. + /// Otherwise, this returns `None` + pub fn filter(self, predicate: fn[Env](T) -> bool) -> Self { + if self._is_some { + if predicate(self._value) { + self + } else { + Option::none() + } + } else { + Option::none() + } + } + + /// Flattens an Option> into a Option. + /// This returns None if the outer Option is None. Otherwise, this returns the inner Option. + pub fn flatten(option: Option>) -> Option { + if option._is_some { + option._value + } else { + Option::none() + } + } +} diff --git a/noir/noir_stdlib/src/scalar_mul.nr b/noir/noir_stdlib/src/scalar_mul.nr new file mode 100644 index 00000000000..37cd935cdb9 --- /dev/null +++ b/noir/noir_stdlib/src/scalar_mul.nr @@ -0,0 +1,8 @@ +// Computes a fixed base scalar multiplication over the embedded curve. +// For bn254, We have Grumpkin and Baby JubJub. +// For bls12-381, we have JubJub and Bandersnatch. +// +// The embedded curve being used is decided by the +// underlying proof system. +#[foreign(fixed_base_scalar_mul)] +pub fn fixed_base_embedded_curve(_low: Field, _high: Field) -> [Field; 2] {} diff --git a/noir/noir_stdlib/src/schnorr.nr b/noir/noir_stdlib/src/schnorr.nr new file mode 100644 index 00000000000..c78eae37243 --- /dev/null +++ b/noir/noir_stdlib/src/schnorr.nr @@ -0,0 +1,2 @@ +#[foreign(schnorr_verify)] +pub fn verify_signature(_public_key_x: Field, _public_key_y: Field, _signature: [u8; 64], _message: [u8; N]) -> bool {} diff --git a/noir/noir_stdlib/src/sha256.nr b/noir/noir_stdlib/src/sha256.nr new file mode 100644 index 00000000000..8bb4a19f717 --- /dev/null +++ b/noir/noir_stdlib/src/sha256.nr @@ -0,0 +1,172 @@ +// Implementation of SHA-256 mapping a byte array of variable length to +// 32 bytes. +// Internal functions act on 32-bit unsigned integers for simplicity. +// Auxiliary mappings; names as in FIPS PUB 180-4 +fn rotr32(a: u32, b: u32) -> u32 // 32-bit right rotation +{ + // None of the bits overlap between `(a >> b)` and `(a << (32 - b))` + // Addition is then equivalent to OR, with fewer constraints. + (a >> b) + (a << (32 - b)) +} + +fn ch(x: u32, y: u32, z: u32) -> u32 { + (x & y) ^ ((!x) & z) +} + +fn maj(x: u32, y: u32, z: u32) -> u32 { + (x & y) ^ (x & z) ^ (y & z) +} + +fn bigma0(x: u32) -> u32 { + rotr32(x, 2) ^ rotr32(x, 13) ^ rotr32(x, 22) +} + +fn bigma1(x: u32) -> u32 { + rotr32(x, 6) ^ rotr32(x, 11) ^ rotr32(x, 25) +} + +fn sigma0(x: u32) -> u32 { + rotr32(x, 7) ^ rotr32(x, 18) ^ (x >> 3) +} + +fn sigma1(x: u32) -> u32 { + rotr32(x, 17) ^ rotr32(x, 19) ^ (x >> 10) +} + +fn sha_w(msg: [u32; 16]) -> [u32; 64] // Expanded message blocks +{ + let mut w: [u32;64] = [0; 64]; + + for j in 0..16 { + w[j] = msg[j]; + } + + for j in 16..64 { + w[j] = crate::wrapping_add( + crate::wrapping_add(sigma1(w[j-2]), w[j-7]), + crate::wrapping_add(sigma0(w[j-15]), w[j-16]), + ); + } + + w +} +// SHA-256 compression function +fn sha_c(msg: [u32; 16], hash: [u32; 8]) -> [u32; 8] { + let K: [u32; 64] = [ + 1116352408, 1899447441, 3049323471, 3921009573, 961987163, 1508970993, 2453635748, + 2870763221, 3624381080, 310598401, 607225278, 1426881987, 1925078388, 2162078206, + 2614888103, 3248222580, 3835390401, 4022224774, 264347078, 604807628, 770255983, 1249150122, + 1555081692, 1996064986, 2554220882, 2821834349, 2952996808, 3210313671, 3336571891, + 3584528711, 113926993, 338241895, 666307205, 773529912, 1294757372, 1396182291, 1695183700, + 1986661051, 2177026350, 2456956037, 2730485921, 2820302411, 3259730800, 3345764771, + 3516065817, 3600352804, 4094571909, 275423344, 430227734, 506948616, 659060556, 883997877, + 958139571, 1322822218, 1537002063, 1747873779, 1955562222, 2024104815, 2227730452, + 2361852424, 2428436474, 2756734187, 3204031479, 3329325298 + ]; // first 32 bits of fractional parts of cube roots of first 64 primes + let mut out_h: [u32; 8] = hash; + let w = sha_w(msg); + for j in 0..64 { + let t1 = crate::wrapping_add(crate::wrapping_add(crate::wrapping_add(out_h[7], bigma1(out_h[4])), + ch(out_h[4], out_h[5], out_h[6])), + crate::wrapping_add(K[j], w[j])); + let t2 = crate::wrapping_add(bigma0(out_h[0]), maj(out_h[0], out_h[1], out_h[2])); + out_h[7] = out_h[6]; + out_h[6] = out_h[5]; + out_h[5] = out_h[4]; + out_h[4] = crate::wrapping_add(out_h[3], t1); + out_h[3] = out_h[2]; + out_h[2] = out_h[1]; + out_h[1] = out_h[0]; + out_h[0] = crate::wrapping_add(t1, t2); + } + + out_h +} +// Convert 64-byte array to array of 16 u32s +fn msg_u8_to_u32(msg: [u8; 64]) -> [u32; 16] { + let mut msg32: [u32; 16] = [0; 16]; + + for i in 0..16 { + for j in 0..4 { + msg32[15 - i] = (msg32[15 - i] << 8) + msg[64 - 4*(i + 1) + j] as u32; + } + } + + msg32 +} +// SHA-256 hash function +pub fn digest(msg: [u8; N]) -> [u8; 32] { + let mut msg_block: [u8; 64] = [0; 64]; + let mut h: [u32; 8] = [1779033703, 3144134277, 1013904242, 2773480762, 1359893119, 2600822924, 528734635, 1541459225]; // Intermediate hash, starting with the canonical initial value + let mut c: [u32; 8] = [0; 8]; // Compression of current message block as sequence of u32 + let mut out_h: [u8; 32] = [0; 32]; // Digest as sequence of bytes + let mut i: u64 = 0; // Message byte pointer + for k in 0..msg.len() { + // Populate msg_block + msg_block[i as Field] = msg[k]; + i = i + 1; + if i == 64 { + // Enough to hash block + c = sha_c(msg_u8_to_u32(msg_block), h); + for j in 0..8 { + h[j] = crate::wrapping_add(c[j], h[j]); + } + + i = 0; + } + } + // Pad the rest such that we have a [u32; 2] block at the end representing the length + // of the message, and a block of 1 0 ... 0 following the message (i.e. [1 << 7, 0, ..., 0]). + msg_block[i as Field] = 1 << 7; + i = i + 1; + // If i >= 57, there aren't enough bits in the current message block to accomplish this, so + // the 1 and 0s fill up the current block, which we then compress accordingly. + if i >= 57 { + // Not enough bits (64) to store length. Fill up with zeros. + if i < 64 { + for _i in 57..64 { + if i <= 63 { + msg_block[i as Field] = 0; + i += 1; + } + } + } + c = h; + c = sha_c(msg_u8_to_u32(msg_block), c); + for j in 0..8 { + h[j] = crate::wrapping_add(h[j], c[j]); + } + + i = 0; + } + + for _i in 0..64 { + // In any case, fill blocks up with zeros until the last 64 (i.e. until i = 56). + if i < 56 { + msg_block[i as Field] = 0; + i = i + 1; + } else if i < 64 { + let mut len = 8 * msg.len() as u64; + for j in 0..8 { + msg_block[63 - j] = len as u8; + len >>= 8; + } + i += 8; + } + } + // Hash final padded block + c = h; + c = sha_c(msg_u8_to_u32(msg_block), c); + for j in 0..8 { + h[j] = crate::wrapping_add(h[j], c[j]); + } + // Return final hash as byte array + for j in 0..8 { + for k in 0..4 { + out_h[31 - 4*j - k] = h[7 - j] as u8; + h[7-j] >>= 8; + } + } + + out_h +} diff --git a/noir/noir_stdlib/src/sha512.nr b/noir/noir_stdlib/src/sha512.nr new file mode 100644 index 00000000000..155ba593bba --- /dev/null +++ b/noir/noir_stdlib/src/sha512.nr @@ -0,0 +1,161 @@ +// Implementation of SHA-512 mapping a byte array of variable length to +// 64 bytes. +// Internal functions act on 64-bit unsigned integers for simplicity. +// Auxiliary mappings; names as in FIPS PUB 180-4 +fn rotr64(a: u64, b: u64) -> u64 // 64-bit right rotation +{ + // None of the bits overlap between `(a >> b)` and `(a << (64 - b))` + // Addition is then equivalent to OR, with fewer constraints. + (a >> b) + (a << (64 - b)) +} + +fn sha_ch(x: u64, y: u64, z: u64) -> u64 { + (x & y) ^ (!x & z) +} + +fn sha_maj(x: u64, y: u64, z: u64) -> u64 { + (x & y) ^ (x & z) ^ (y & z) +} + +fn sha_bigma0(x: u64) -> u64 { + rotr64(x, 28) ^ rotr64(x, 34) ^ rotr64(x, 39) +} + +fn sha_bigma1(x: u64) -> u64 { + rotr64(x, 14) ^ rotr64(x, 18) ^ rotr64(x, 41) +} + +fn sha_sigma0(x: u64) -> u64 { + rotr64(x, 1) ^ rotr64(x, 8) ^ (x >> 7) +} + +fn sha_sigma1(x: u64) -> u64 { + rotr64(x, 19) ^ rotr64(x, 61) ^ (x >> 6) +} + +fn sha_w(msg: [u64; 16]) -> [u64; 80] // Expanded message blocks +{ + let mut w: [u64;80] = [0; 80]; + + for j in 0..16 { + w[j] = msg[j]; + } + + for j in 16..80 { + w[j] = crate::wrapping_add( + crate::wrapping_add(sha_sigma1(w[j-2]), w[j-7]), + crate::wrapping_add(sha_sigma0(w[j-15]), w[j-16]), + ); + } + w +} +// SHA-512 compression function +fn sha_c(msg: [u64; 16], hash: [u64; 8]) -> [u64; 8] { + // noir-fmt:ignore + let K: [u64; 80] = [4794697086780616226, 8158064640168781261, 13096744586834688815, 16840607885511220156, 4131703408338449720, 6480981068601479193, 10538285296894168987, 12329834152419229976, 15566598209576043074, 1334009975649890238, 2608012711638119052, 6128411473006802146, 8268148722764581231, 9286055187155687089, 11230858885718282805, 13951009754708518548, 16472876342353939154, 17275323862435702243, 1135362057144423861, 2597628984639134821, 3308224258029322869, 5365058923640841347, 6679025012923562964, 8573033837759648693, 10970295158949994411, 12119686244451234320, 12683024718118986047, 13788192230050041572, 14330467153632333762, 15395433587784984357, 489312712824947311, 1452737877330783856, 2861767655752347644, 3322285676063803686, 5560940570517711597, 5996557281743188959, 7280758554555802590, 8532644243296465576, 9350256976987008742, 10552545826968843579, 11727347734174303076, 12113106623233404929, 14000437183269869457, 14369950271660146224, 15101387698204529176, 15463397548674623760, 17586052441742319658, 1182934255886127544, 1847814050463011016, 2177327727835720531, 2830643537854262169, 3796741975233480872, 4115178125766777443, 5681478168544905931, 6601373596472566643, 7507060721942968483, 8399075790359081724, 8693463985226723168, 9568029438360202098, 10144078919501101548, 10430055236837252648, 11840083180663258601, 13761210420658862357, 14299343276471374635, 14566680578165727644, 15097957966210449927, 16922976911328602910, 17689382322260857208, 500013540394364858, 748580250866718886, 1242879168328830382, 1977374033974150939, 2944078676154940804, 3659926193048069267, 4368137639120453308, 4836135668995329356, 5532061633213252278, 6448918945643986474, 6902733635092675308, 7801388544844847127]; // first 64 bits of fractional parts of cube roots of first 80 primes + let mut out_h: [u64; 8] = hash; + let w = sha_w(msg); + for j in 0..80 { + let out1 = crate::wrapping_add(out_h[7], sha_bigma1(out_h[4])); + let out2 = crate::wrapping_add(out1, sha_ch(out_h[4], out_h[5], out_h[6])); + let t1 = crate::wrapping_add(crate::wrapping_add(out2, K[j]), w[j]); + let t2 = crate::wrapping_add(sha_bigma0(out_h[0]), sha_maj(out_h[0], out_h[1], out_h[2])); + out_h[7] = out_h[6]; + out_h[6] = out_h[5]; + out_h[5] = out_h[4]; + out_h[4] = crate::wrapping_add(out_h[3] , t1); + out_h[3] = out_h[2]; + out_h[2] = out_h[1]; + out_h[1] = out_h[0]; + out_h[0] = crate::wrapping_add(t1, t2); + } + + out_h +} +// Convert 128-byte array to array of 16 u64s +fn msg_u8_to_u64(msg: [u8; 128]) -> [u64; 16] { + let mut msg64: [u64; 16] = [0; 16]; + + for i in 0..16 { + for j in 0..8 { + msg64[15 - i] = (msg64[15 - i] << 8) + msg[128 - 8*(i + 1) + j] as u64; + } + } + + msg64 +} +// SHA-512 hash function +pub fn digest(msg: [u8; N]) -> [u8; 64] { + let mut msg_block: [u8; 128] = [0; 128]; + // noir-fmt:ignore + let mut h: [u64; 8] = [7640891576956012808, 13503953896175478587, 4354685564936845355, 11912009170470909681, 5840696475078001361, 11170449401992604703, 2270897969802886507, 6620516959819538809]; // Intermediate hash, starting with the canonical initial value + let mut c: [u64; 8] = [0; 8]; // Compression of current message block as sequence of u64 + let mut out_h: [u8; 64] = [0; 64]; // Digest as sequence of bytes + let mut i: u64 = 0; // Message byte pointer + for k in 0..msg.len() { + // Populate msg_block + msg_block[i as Field] = msg[k]; + i = i + 1; + if i == 128 { + // Enough to hash block + c = sha_c(msg_u8_to_u64(msg_block), h); + for j in 0..8 { + h[j] = crate::wrapping_add(h[j], c[j]); + } + + i = 0; + } + } + // Pad the rest such that we have a [u64; 2] block at the end representing the length + // of the message, and a block of 1 0 ... 0 following the message (i.e. [1 << 7, 0, ..., 0]). + msg_block[i as Field] = 1 << 7; + i += 1; + // If i >= 113, there aren't enough bits in the current message block to accomplish this, so + // the 1 and 0s fill up the current block, which we then compress accordingly. + if i >= 113 { + // Not enough bits (128) to store length. Fill up with zeros. + if i < 128 { + for _i in 113..128 { + if i <= 127 { + msg_block[i as Field] = 0; + i += 1; + } + } + } + c = sha_c(msg_u8_to_u64(msg_block), h); + for j in 0..8 { + h[j] = crate::wrapping_add(h[j], c[j]); + } + + i = 0; + } + + for _i in 0..128 { + // In any case, fill blocks up with zeros until the last 128 (i.e. until i = 112). + if i < 112 { + msg_block[i as Field] = 0; + i += 1; + } else if i < 128 { + let mut len = 8 * msg.len() as u64; // u128 unsupported + for j in 0..16 { + msg_block[127 - j] = len as u8; + len >>= 8; + } + i += 16; // Done. + } + } + // Hash final padded block + c = sha_c(msg_u8_to_u64(msg_block), h); + for j in 0..8 { + h[j] = crate::wrapping_add(h[j], c[j]); + } + // Return final hash as byte array + for j in 0..8 { + for k in 0..8 { + out_h[63 - 8*j - k] = h[7 - j] as u8; + h[7-j] >>= 8; + } + } + + out_h +} diff --git a/noir/noir_stdlib/src/slice.nr b/noir/noir_stdlib/src/slice.nr new file mode 100644 index 00000000000..ca06e2aae44 --- /dev/null +++ b/noir/noir_stdlib/src/slice.nr @@ -0,0 +1,55 @@ +impl [T] { + /// Push a new element to the end of the slice, returning a + /// new slice with a length one greater than the + /// original unmodified slice. + #[builtin(slice_push_back)] + pub fn push_back(_self: Self, _elem: T) -> Self { } + + /// Push a new element to the front of the slice, returning a + /// new slice with a length one greater than the + /// original unmodified slice. + #[builtin(slice_push_front)] + pub fn push_front(_self: Self, _elem: T) -> Self { } + + /// Remove the last element of the slice, returning the + /// popped slice and the element in a tuple + #[builtin(slice_pop_back)] + pub fn pop_back(_self: Self) -> (Self, T) { } + + /// Remove the first element of the slice, returning the + /// element and the popped slice in a tuple + #[builtin(slice_pop_front)] + pub fn pop_front(_self: Self) -> (T, Self) { } + + pub fn insert(self, _index: Field, _elem: T) -> Self { + // TODO(#2462): Slice insert with a dynamic index + crate::assert_constant(_index); + self.__slice_insert(_index, _elem) + } + + /// Insert an element at a specified index, shifting all elements + /// after it to the right + #[builtin(slice_insert)] + fn __slice_insert(_self: Self, _index: Field, _elem: T) -> Self { } + + pub fn remove(self, _index: Field) -> (Self, T) { + // TODO(#2462): Slice remove with a dynamic index + crate::assert_constant(_index); + self.__slice_remove(_index) + } + + /// Remove an element at a specified index, shifting all elements + /// after it to the left, returning the altered slice and + /// the removed element + #[builtin(slice_remove)] + fn __slice_remove(_self: Self, _index: Field) -> (Self, T) { } + + // Append each element of the `other` slice to the end of `self`. + // This returns a new slice and leaves both input slices unchanged. + pub fn append(mut self, other: Self) -> Self { + for elem in other { + self = self.push_back(elem); + } + self + } +} diff --git a/noir/noir_stdlib/src/string.nr b/noir/noir_stdlib/src/string.nr new file mode 100644 index 00000000000..e402abf9ab6 --- /dev/null +++ b/noir/noir_stdlib/src/string.nr @@ -0,0 +1,11 @@ +use crate::collections::vec::Vec; +impl str { + /// Converts the given string into a byte array + #[builtin(str_as_bytes)] + pub fn as_bytes(_self: Self) -> [u8; N] { } + + /// return a byte vector of the str content + pub fn as_bytes_vec(self: Self) -> Vec { + Vec::from_slice(self.as_bytes().as_slice()) + } +} diff --git a/noir/noir_stdlib/src/test.nr b/noir/noir_stdlib/src/test.nr new file mode 100644 index 00000000000..47b31f4acea --- /dev/null +++ b/noir/noir_stdlib/src/test.nr @@ -0,0 +1,45 @@ +#[oracle(create_mock)] +unconstrained fn create_mock_oracle(_name: str) -> Field {} + +#[oracle(set_mock_params)] +unconstrained fn set_mock_params_oracle

(_id: Field, _params: P) {} + +#[oracle(set_mock_returns)] +unconstrained fn set_mock_returns_oracle(_id: Field, _returns: R) {} + +#[oracle(set_mock_times)] +unconstrained fn set_mock_times_oracle(_id: Field, _times: u64) {} + +#[oracle(clear_mock)] +unconstrained fn clear_mock_oracle(_id: Field) {} + +struct OracleMock { + id: Field, +} + +impl OracleMock { + unconstrained pub fn mock(name: str) -> Self { + Self { + id: create_mock_oracle(name), + } + } + + unconstrained pub fn with_params

(self, params: P) -> Self { + set_mock_params_oracle(self.id, params); + self + } + + unconstrained pub fn returns(self, returns: R) -> Self { + set_mock_returns_oracle(self.id, returns); + self + } + + unconstrained pub fn times(self, times: u64) -> Self { + set_mock_times_oracle(self.id, times); + self + } + + unconstrained pub fn clear(self) { + clear_mock_oracle(self.id); + } +} diff --git a/noir/noir_stdlib/src/unsafe.nr b/noir/noir_stdlib/src/unsafe.nr new file mode 100644 index 00000000000..542bd31fa84 --- /dev/null +++ b/noir/noir_stdlib/src/unsafe.nr @@ -0,0 +1,5 @@ +/// For any type, return an instance of that type by initializing +/// all of its fields to 0. This is considered to be unsafe since there +/// is no guarantee that all zeroes is a valid bit pattern for every type. +#[builtin(zeroed)] +pub fn zeroed() -> T {} diff --git a/noir/package.json b/noir/package.json new file mode 100644 index 00000000000..0e86b100b7c --- /dev/null +++ b/noir/package.json @@ -0,0 +1,50 @@ +{ + "name": "@noir-lang/root", + "private": true, + "workspaces": [ + "compiler/wasm", + "compiler/source-resolver", + "compiler/integration-tests", + "tooling/noir_js_types", + "tooling/noirc_abi_wasm", + "tooling/noir_js", + "tooling/noir_codegen", + "tooling/noir_js_backend_barretenberg", + "acvm-repo/acvm_js", + "release-tests", + "docs" + ], + "scripts": { + "build": "yarn workspaces foreach --parallel --topological-dev --verbose run build", + "test": "yarn workspaces foreach run test", + "test:integration": "yarn workspace integration-tests test", + "clean:workspaces": "yarn workspaces foreach --exclude @noir-lang/root run clean", + "clean:root": "rm -rf ./result ./target", + "clean": "yarn clean:workspaces && yarn clean:root", + "lint": "yarn workspaces foreach --verbose run lint", + "install:acvm_js": "yarn workspace @noir-lang/acvm_js run install:from:nix", + "install:noir_wasm": "yarn workspace @noir-lang/noir_wasm run install:from:nix", + "install:noirc_abi_wasm": "yarn workspace @noir-lang/noirc_abi run install:from:nix", + "install:from:nix": "yarn install:acvm_js && yarn install:noir_wasm && yarn install:noirc_abi_wasm", + "build:types": "yarn workspace @noir-lang/types run build", + "build:source-resolver": "yarn workspace @noir-lang/source-resolver run build", + "build:backend_barretenberg": "yarn workspace @noir-lang/backend_barretenberg run build", + "build:noir_js": "yarn workspace @noir-lang/noir_js run build", + "build:js:only": "yarn build:types && yarn build:source-resolver && yarn build:backend_barretenberg && yarn build:noir_js", + "prepare:publish": "yarn clean && yarn install:from:nix && yarn build:js:only", + "nightly:version": "yarn workspaces foreach run nightly:version", + "publish:all": "yarn install && yarn workspaces foreach run publish" + }, + "devDependencies": { + "@typescript-eslint/eslint-plugin": "^6.7.3", + "@typescript-eslint/parser": "^6.7.3", + "chai": "^4.3.7", + "eslint": "^8.50.0", + "eslint-plugin-prettier": "^5.0.0", + "mocha": "^10.2.0", + "prettier": "3.0.3", + "ts-node": "^10.9.1", + "typescript": "^5.0.4" + }, + "packageManager": "yarn@3.6.4" +} diff --git a/noir/release-please-config.json b/noir/release-please-config.json new file mode 100644 index 00000000000..afc0bfd420d --- /dev/null +++ b/noir/release-please-config.json @@ -0,0 +1,81 @@ +{ + "release-type": "simple", + "bump-minor-pre-major": true, + "bump-patch-for-minor-pre-major": true, + "prerelease": true, + "pull-request-title-pattern": "chore: Release Noir(${version})", + "group-pull-request-title-pattern": "chore: Release Noir(${version})", + "packages": { + ".": { + "release-type": "simple", + "component": "noir", + "package-name": "noir", + "include-component-in-tag": false, + "extra-files": [ + "Cargo.toml", + "flake.nix", + { + "type": "json", + "path": "compiler/source-resolver/package.json", + "jsonpath": "$.version" + }, + { + "type": "json", + "path": "compiler/wasm/package.json", + "jsonpath": "$.version" + }, + { + "type": "json", + "path": "tooling/noir_codegen/package.json", + "jsonpath": "$.version" + }, + { + "type": "json", + "path": "tooling/noir_js/package.json", + "jsonpath": "$.version" + }, + { + "type": "json", + "path": "tooling/noir_js_backend_barretenberg/package.json", + "jsonpath": "$.version" + }, + { + "type": "json", + "path": "tooling/noir_js_types/package.json", + "jsonpath": "$.version" + }, + { + "type": "json", + "path": "tooling/noirc_abi_wasm/package.json", + "jsonpath": "$.version" + } + ] + }, + "acvm-repo": { + "release-type": "simple", + "package-name": "acvm", + "component": "acvm", + "include-component-in-tag": false, + "extra-files": [ + "acir/Cargo.toml", + "acir_field/Cargo.toml", + "acvm/Cargo.toml", + "acvm_js/Cargo.toml", + "barretenberg_blackbox_solver/Cargo.toml", + "blackbox_solver/Cargo.toml", + "brillig/Cargo.toml", + "brillig_vm/Cargo.toml", + "stdlib/Cargo.toml", + "flake.nix", + { + "type": "json", + "path": "acvm_js/package.json", + "jsonpath": "$.version" + } + ] + } + }, + "plugins": [ + "sentence-case" + ] +} diff --git a/noir/release-tests/package.json b/noir/release-tests/package.json new file mode 100644 index 00000000000..3b73ad18574 --- /dev/null +++ b/noir/release-tests/package.json @@ -0,0 +1,14 @@ +{ + "name": "release-tests", + "main": "index.js", + "license": "(MIT OR Apache-2.0)", + "private": true, + "type": "module", + "scripts": { + "test": "uvu test" + }, + "devDependencies": { + "uvu": "0.5.6", + "zx": "7.1.1" + } +} diff --git a/noir/release-tests/test/6_array.test.js b/noir/release-tests/test/6_array.test.js new file mode 100644 index 00000000000..530b7f85bf4 --- /dev/null +++ b/noir/release-tests/test/6_array.test.js @@ -0,0 +1,49 @@ +import { suite } from "uvu"; +import { cd } from "zx"; +import { NARGO_BIN } from "./utils/nargo.js"; +import "./utils/zx.js"; + +const test = suite("nargo"); + +// Helps detect unresolved ProcessPromise. +let promiseResolved = false; +process.on("exit", () => { + if (!promiseResolved) { + console.error("Error: ProcessPromise never resolved."); + process.exitCode = 1; + } +}); + +test("promise resolved", async () => { + await $`echo PromiseHelper`; + promiseResolved = true; +}); + +test("nargo builds ../tooling/nargo_cli/tests/execution_success/6_array sucessfully", async () => { + await within(async () => { + cd("../tooling/nargo_cli/tests/execution_success/6_array"); + const command = `${NARGO_BIN} check`; + + await $`${command}`.nothrow(); + }); +}); + +test("nargo creates proof ../tooling/nargo_cli/tests/execution_success/6_array sucessfully", async () => { + await within(async () => { + cd("../tooling/nargo_cli/tests/execution_success/6_array"); + const command = `${NARGO_BIN} prove 6_array`; + + await $`${command}`.nothrow(); + }); +}); + +test("nargo verifies proof ../tooling/nargo_cli/tests/execution_success/6_array sucessfully", async () => { + await within(async () => { + cd("../tooling/nargo_cli/tests/execution_success/6_array"); + const command = `${NARGO_BIN} verify 6_array`; + + await $`${command}`.nothrow(); + }); +}); + +test.run(); diff --git a/noir/release-tests/test/utils/nargo.js b/noir/release-tests/test/utils/nargo.js new file mode 100644 index 00000000000..537cdfc8be5 --- /dev/null +++ b/noir/release-tests/test/utils/nargo.js @@ -0,0 +1,3 @@ +import { default as path } from "node:path"; + +export const NARGO_BIN = process.env.NARGO_BIN ? path.resolve(process.env.NARGO_BIN) : "nargo"; diff --git a/noir/release-tests/test/utils/zx.js b/noir/release-tests/test/utils/zx.js new file mode 100644 index 00000000000..a8ab500aec0 --- /dev/null +++ b/noir/release-tests/test/utils/zx.js @@ -0,0 +1,11 @@ +import "zx/globals"; + +// We perform any common setup for zx here to avoid repetition across test files. + +if (process.platform == "win32") { + $.shell = "powershell"; +} + +$.quote = (arg) => arg; + +$.verbose = true; diff --git a/noir/release-tests/test/version.test.js b/noir/release-tests/test/version.test.js new file mode 100644 index 00000000000..7a70639d83e --- /dev/null +++ b/noir/release-tests/test/version.test.js @@ -0,0 +1,35 @@ +import { suite } from "uvu"; +import * as assert from "uvu/assert"; +import { NARGO_BIN } from "./utils/nargo.js"; +import "./utils/zx.js"; + +const test = suite("nargo"); + +// Helps detect unresolved ProcessPromise. +let promiseResolved = false; +process.on("exit", () => { + if (!promiseResolved) { + console.error("Error: ProcessPromise never resolved."); + process.exitCode = 1; + } +}); + +test("promise resolved", async () => { + await $`echo PromiseHelper`; + promiseResolved = true; +}); + +test("prints version", async () => { + const processOutput = (await $`${NARGO_BIN} --version`).toString(); + + // Regex to match the "nargo version" part of the output + assert.match(processOutput, /nargo version = \d{1,2}\.\d{1,2}\.\d{1,2}/); +}); + + +test("reports a clean commit", async () => { + const processOutput = (await $`${NARGO_BIN} --version`).toString(); + assert.not.match(processOutput, /is dirty: true/) +}); + +test.run(); diff --git a/noir/rust-toolchain.toml b/noir/rust-toolchain.toml new file mode 100644 index 00000000000..b6f7edc4bde --- /dev/null +++ b/noir/rust-toolchain.toml @@ -0,0 +1,5 @@ +[toolchain] +channel = "1.71.1" +components = [ "rust-src" ] +targets = [ "wasm32-unknown-unknown", "wasm32-wasi", "aarch64-apple-darwin" ] +profile = "default" diff --git a/noir/shell.nix b/noir/shell.nix new file mode 100644 index 00000000000..b72d4a4697b --- /dev/null +++ b/noir/shell.nix @@ -0,0 +1,13 @@ +let + lock = builtins.fromJSON (builtins.readFile ./flake.lock); + flakeCompatRev = lock.nodes.flake-compat.locked.rev; + flakeCompatHash = lock.nodes.flake-compat.locked.narHash; + flakeCompat = fetchTarball { + url = "https://github.com/edolstra/flake-compat/archive/${flakeCompatRev}.tar.gz"; + sha256 = flakeCompatHash; + }; + compat = import flakeCompat { + src = ./.; + }; +in +compat.shellNix diff --git a/noir/tooling/backend_interface/CHANGELOG.md b/noir/tooling/backend_interface/CHANGELOG.md new file mode 100644 index 00000000000..4387d8ccb5f --- /dev/null +++ b/noir/tooling/backend_interface/CHANGELOG.md @@ -0,0 +1,233 @@ +# Changelog + +## [0.11.0](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.10.1...v0.11.0) (2023-08-18) + + +### ⚠ BREAKING CHANGES + +* Update `acvm` to 0.22.0 ([#240](https://github.com/noir-lang/acvm-backend-barretenberg/issues/240)) + +### Features + +* Update `acvm` to 0.22.0 ([#240](https://github.com/noir-lang/acvm-backend-barretenberg/issues/240)) ([d8342fd](https://github.com/noir-lang/acvm-backend-barretenberg/commit/d8342fd6da605ac3bbd889edf89cd122bc4689ce)) + +## [0.10.1](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.10.0...v0.10.1) (2023-08-18) + + +### Features + +* Migrate to `wasmer` 3.3.0 ([#236](https://github.com/noir-lang/acvm-backend-barretenberg/issues/236)) ([e115e38](https://github.com/noir-lang/acvm-backend-barretenberg/commit/e115e38856887c6b1eeead3534534ac7e6327ea9)) + +## [0.10.0](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.9.1...v0.10.0) (2023-07-26) + + +### ⚠ BREAKING CHANGES + +* Migrate to ACVM 0.21.0 ([#234](https://github.com/noir-lang/acvm-backend-barretenberg/issues/234)) + +### Features + +* Migrate to ACVM 0.21.0 ([#234](https://github.com/noir-lang/acvm-backend-barretenberg/issues/234)) ([15c8676](https://github.com/noir-lang/acvm-backend-barretenberg/commit/15c86768685d2946a767c350f6ef5972c86677eb)) + +## [0.9.1](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.9.0...v0.9.1) (2023-07-21) + + +### Features + +* add support for atomic memory opcodes ([#232](https://github.com/noir-lang/acvm-backend-barretenberg/issues/232)) ([a7aa6e9](https://github.com/noir-lang/acvm-backend-barretenberg/commit/a7aa6e9505bb402c1b3db0a990845ed26928e7aa)) + +## [0.9.0](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.8.0...v0.9.0) (2023-07-17) + + +### ⚠ BREAKING CHANGES + +* update to ACVM 0.19.0 ([#230](https://github.com/noir-lang/acvm-backend-barretenberg/issues/230)) + +### Miscellaneous Chores + +* update to ACVM 0.19.0 ([#230](https://github.com/noir-lang/acvm-backend-barretenberg/issues/230)) ([3f1d967](https://github.com/noir-lang/acvm-backend-barretenberg/commit/3f1d9674b904acb02c2a3e52481be8a6104c3a9d)) + +## [0.8.0](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.7.0...v0.8.0) (2023-07-12) + + +### ⚠ BREAKING CHANGES + +* Update to acvm 0.18.1 ([#228](https://github.com/noir-lang/acvm-backend-barretenberg/issues/228)) + +### Features + +* Update to acvm 0.18.1 ([#228](https://github.com/noir-lang/acvm-backend-barretenberg/issues/228)) ([397098b](https://github.com/noir-lang/acvm-backend-barretenberg/commit/397098b239efbe16785b1c9af108ca9fc4e24497)) + +## [0.7.0](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.6.1...v0.7.0) (2023-07-08) + + +### ⚠ BREAKING CHANGES + +* **bberg:** add secp256r1 builtin to barretenberg ([#223](https://github.com/noir-lang/acvm-backend-barretenberg/issues/223)) + +### Features + +* **bberg:** add secp256r1 builtin to barretenberg ([#223](https://github.com/noir-lang/acvm-backend-barretenberg/issues/223)) ([ceb4770](https://github.com/noir-lang/acvm-backend-barretenberg/commit/ceb47705a492fcdcea1f3c098aaab42ea8edbf2e)) + +## [0.6.1](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.6.0...v0.6.1) (2023-07-06) + + +### Features + +* switch RecursiveAggregation support to true ([#225](https://github.com/noir-lang/acvm-backend-barretenberg/issues/225)) ([e9462ae](https://github.com/noir-lang/acvm-backend-barretenberg/commit/e9462ae015ec0dfb0a23ccbb89562071f87940f5)) + +## [0.6.0](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.5.1...v0.6.0) (2023-07-06) + + +### ⚠ BREAKING CHANGES + +* Update to ACVM 0.16.0 ([#221](https://github.com/noir-lang/acvm-backend-barretenberg/issues/221)) + +### Features + +* Update to ACVM 0.16.0 ([#221](https://github.com/noir-lang/acvm-backend-barretenberg/issues/221)) ([062d5ed](https://github.com/noir-lang/acvm-backend-barretenberg/commit/062d5ed9b476fab8ac8d3ca13371699fb2aac332)) + +## [0.5.1](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.5.0...v0.5.1) (2023-06-20) + + +### Bug Fixes + +* Remove wasm32 target ([#219](https://github.com/noir-lang/acvm-backend-barretenberg/issues/219)) ([e4cbb6d](https://github.com/noir-lang/acvm-backend-barretenberg/commit/e4cbb6d476e8746de33c38506e2fcb970f1c866a)) + +## [0.5.0](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.4.0...v0.5.0) (2023-06-15) + + +### ⚠ BREAKING CHANGES + +* Update to target ACVM 0.15.0 ([#217](https://github.com/noir-lang/acvm-backend-barretenberg/issues/217)) + +### Features + +* Update to target ACVM 0.15.0 ([#217](https://github.com/noir-lang/acvm-backend-barretenberg/issues/217)) ([9331898](https://github.com/noir-lang/acvm-backend-barretenberg/commit/9331898f161321c8b6a82d5ea850f197952b2ed2)) + +## [0.4.0](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.3.0...v0.4.0) (2023-06-07) + + +### ⚠ BREAKING CHANGES + +* Recursion ([#207](https://github.com/noir-lang/acvm-backend-barretenberg/issues/207)) + +### Features + +* Recursion ([#207](https://github.com/noir-lang/acvm-backend-barretenberg/issues/207)) ([6fc479b](https://github.com/noir-lang/acvm-backend-barretenberg/commit/6fc479b9ae99d59bbfeb1b895d63cdbea469dcaa)) + +## [0.3.0](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.2.0...v0.3.0) (2023-06-01) + + +### ⚠ BREAKING CHANGES + +* Update to ACVM 0.13.0 ([#205](https://github.com/noir-lang/acvm-backend-barretenberg/issues/205)) +* added keccakvar constraints ([#213](https://github.com/noir-lang/acvm-backend-barretenberg/issues/213)) +* update pedersen hashes for new implementation ([#212](https://github.com/noir-lang/acvm-backend-barretenberg/issues/212)) + +### Features + +* added keccakvar constraints ([91ea65f](https://github.com/noir-lang/acvm-backend-barretenberg/commit/91ea65f6af7039095c7a3af7bc1e4ce302a68a8d)) +* added keccakvar constraints ([#213](https://github.com/noir-lang/acvm-backend-barretenberg/issues/213)) ([91ea65f](https://github.com/noir-lang/acvm-backend-barretenberg/commit/91ea65f6af7039095c7a3af7bc1e4ce302a68a8d)) +* Update to ACVM 0.13.0 ([#205](https://github.com/noir-lang/acvm-backend-barretenberg/issues/205)) ([298446e](https://github.com/noir-lang/acvm-backend-barretenberg/commit/298446ef8b69f528b6e2fd2abb2298d7b0a8118e)) + + +### Bug Fixes + +* Add or cleanup implementations for JS target ([#199](https://github.com/noir-lang/acvm-backend-barretenberg/issues/199)) ([f6134b7](https://github.com/noir-lang/acvm-backend-barretenberg/commit/f6134b7b502cb74882300b0046ab91ab000daf3c)) +* update pedersen hashes for new impl ([9a233ce](https://github.com/noir-lang/acvm-backend-barretenberg/commit/9a233ce8db9984b29b9cce0603f758d5281c89c9)) +* update pedersen hashes for new implementation ([#212](https://github.com/noir-lang/acvm-backend-barretenberg/issues/212)) ([9a233ce](https://github.com/noir-lang/acvm-backend-barretenberg/commit/9a233ce8db9984b29b9cce0603f758d5281c89c9)) + +## [0.2.0](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.1.2...v0.2.0) (2023-05-22) + + +### ⚠ BREAKING CHANGES + +* Update to acvm 0.12.0 ([#165](https://github.com/noir-lang/acvm-backend-barretenberg/issues/165)) +* Add serialization logic for RAM and ROM opcodes ([#153](https://github.com/noir-lang/acvm-backend-barretenberg/issues/153)) + +### Features + +* Add serde to `ConstraintSystem` types ([#196](https://github.com/noir-lang/acvm-backend-barretenberg/issues/196)) ([4c04a79](https://github.com/noir-lang/acvm-backend-barretenberg/commit/4c04a79e6d2b0115f3b4526c60f9f7dae8b464ae)) +* Add serialization logic for RAM and ROM opcodes ([#153](https://github.com/noir-lang/acvm-backend-barretenberg/issues/153)) ([3d3847d](https://github.com/noir-lang/acvm-backend-barretenberg/commit/3d3847de70e74a8f65c64e165ad15ae3d31f5350)) +* Update to acvm 0.12.0 ([#165](https://github.com/noir-lang/acvm-backend-barretenberg/issues/165)) ([d613c79](https://github.com/noir-lang/acvm-backend-barretenberg/commit/d613c79584a599f4adbd11d2ce3b61403c185b73)) + +## [0.1.2](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.1.1...v0.1.2) (2023-05-11) + + +### Bug Fixes + +* Remove star dependencies to allow publishing ([#182](https://github.com/noir-lang/acvm-backend-barretenberg/issues/182)) ([1727a79](https://github.com/noir-lang/acvm-backend-barretenberg/commit/1727a79ce7e66d95528f70c445cb4ec1b1ece636)) + +## [0.1.1](https://github.com/noir-lang/acvm-backend-barretenberg/compare/v0.1.0...v0.1.1) (2023-05-11) + + +### Bug Fixes + +* Add description so crate can be published ([#180](https://github.com/noir-lang/acvm-backend-barretenberg/issues/180)) ([caabf94](https://github.com/noir-lang/acvm-backend-barretenberg/commit/caabf9434031c6023a5e3a436c87fba0a1072539)) + +## 0.1.0 (2023-05-10) + + +### ⚠ BREAKING CHANGES + +* Update to ACVM v0.11.0 ([#151](https://github.com/noir-lang/acvm-backend-barretenberg/issues/151)) +* Add Keccak constraints ([#150](https://github.com/noir-lang/acvm-backend-barretenberg/issues/150)) +* migrate to ACVM 0.10.3 ([#148](https://github.com/noir-lang/acvm-backend-barretenberg/issues/148)) +* remove all crates other than `acvm-backend-barretenberg` and remove workspace ([#147](https://github.com/noir-lang/acvm-backend-barretenberg/issues/147)) +* merge `barretenberg_static_lib` and `barretenberg_wasm` ([#117](https://github.com/noir-lang/acvm-backend-barretenberg/issues/117)) +* remove dead blake2 code ([#137](https://github.com/noir-lang/acvm-backend-barretenberg/issues/137)) +* Implement pseudo-builder pattern for ConstraintSystem & hide struct fields ([#120](https://github.com/noir-lang/acvm-backend-barretenberg/issues/120)) +* return boolean rather than `FieldElement` from `verify_signature` ([#123](https://github.com/noir-lang/acvm-backend-barretenberg/issues/123)) +* avoid exposing internals of Assignments type ([#119](https://github.com/noir-lang/acvm-backend-barretenberg/issues/119)) +* update to acvm 0.9.0 ([#106](https://github.com/noir-lang/acvm-backend-barretenberg/issues/106)) +* Depend upon upstream barretenberg & switch to UltraPlonk ([#84](https://github.com/noir-lang/acvm-backend-barretenberg/issues/84)) +* update to ACVM 0.7.0 ([#90](https://github.com/noir-lang/acvm-backend-barretenberg/issues/90)) +* Remove create_proof and verify functions ([#82](https://github.com/noir-lang/acvm-backend-barretenberg/issues/82)) +* update to acvm v0.5.0 ([#60](https://github.com/noir-lang/acvm-backend-barretenberg/issues/60)) + +### Features + +* **acvm_interop:** Updates to reflect new acvm methods using pk/vk ([#50](https://github.com/noir-lang/acvm-backend-barretenberg/issues/50)) ([cff757d](https://github.com/noir-lang/acvm-backend-barretenberg/commit/cff757dca7971161e4bd25e7a744d910c37c22be)) +* Add Keccak constraints ([#150](https://github.com/noir-lang/acvm-backend-barretenberg/issues/150)) ([ce2b9ed](https://github.com/noir-lang/acvm-backend-barretenberg/commit/ce2b9ed456bd8d2ad8357c15736d62c2a5812add)) +* allow overriding transcript location with BARRETENBERG_TRANSCRIPT env var ([#86](https://github.com/noir-lang/acvm-backend-barretenberg/issues/86)) ([af92b99](https://github.com/noir-lang/acvm-backend-barretenberg/commit/af92b99c7b5f37e9659931af378a851b3658a80b)) +* **ci:** add concurrency group for rust workflow ([#63](https://github.com/noir-lang/acvm-backend-barretenberg/issues/63)) ([5c936bc](https://github.com/noir-lang/acvm-backend-barretenberg/commit/5c936bc63cc3adcf9d43c9c4ce69053566089ad9)) +* Depend upon upstream barretenberg & switch to UltraPlonk ([#84](https://github.com/noir-lang/acvm-backend-barretenberg/issues/84)) ([8437bf7](https://github.com/noir-lang/acvm-backend-barretenberg/commit/8437bf7e08acadf43b55b307545336596a9fe766)) +* Implement pseudo-builder pattern for ConstraintSystem & hide struct fields ([#120](https://github.com/noir-lang/acvm-backend-barretenberg/issues/120)) ([8ed67d6](https://github.com/noir-lang/acvm-backend-barretenberg/commit/8ed67d68c71d655e1a6a5c38fa9ea1c3566f771d)) +* Leverage rustls when using downloader crate ([#46](https://github.com/noir-lang/acvm-backend-barretenberg/issues/46)) ([9de36b6](https://github.com/noir-lang/acvm-backend-barretenberg/commit/9de36b642d125d1fb4facd1bf60db67946be70ae)) +* merge `barretenberg_static_lib` and `barretenberg_wasm` ([#117](https://github.com/noir-lang/acvm-backend-barretenberg/issues/117)) ([ba1d0d6](https://github.com/noir-lang/acvm-backend-barretenberg/commit/ba1d0d61b94de91b15044d97608907c21bfb5299)) +* migrate to ACVM 0.10.3 ([#148](https://github.com/noir-lang/acvm-backend-barretenberg/issues/148)) ([c9fb9e8](https://github.com/noir-lang/acvm-backend-barretenberg/commit/c9fb9e806f1400a2ff7594a0669bec56025220bb)) +* remove all crates other than `acvm-backend-barretenberg` and remove workspace ([#147](https://github.com/noir-lang/acvm-backend-barretenberg/issues/147)) ([8fe7111](https://github.com/noir-lang/acvm-backend-barretenberg/commit/8fe7111ebdcb043764a83436744662e8c3ca5abc)) +* remove dead blake2 code ([#137](https://github.com/noir-lang/acvm-backend-barretenberg/issues/137)) ([14d8a5b](https://github.com/noir-lang/acvm-backend-barretenberg/commit/14d8a5b893eb1cb91d5bde908643b487b41809d6)) +* replace `downloader` dependency with `reqwest` ([#114](https://github.com/noir-lang/acvm-backend-barretenberg/issues/114)) ([dd62231](https://github.com/noir-lang/acvm-backend-barretenberg/commit/dd62231b8bfcee32e1029d31a07895b16159339c)) +* return boolean from `verify_signature` ([e560602](https://github.com/noir-lang/acvm-backend-barretenberg/commit/e560602ebbd547386ca4cab35735ffa92e98ac4b)) +* return boolean rather than `FieldElement` from `check_membership` ([#124](https://github.com/noir-lang/acvm-backend-barretenberg/issues/124)) ([a0a338e](https://github.com/noir-lang/acvm-backend-barretenberg/commit/a0a338e2295635a07f6b9e497c029160a5f323bc)) +* return boolean rather than `FieldElement` from `verify_signature` ([#123](https://github.com/noir-lang/acvm-backend-barretenberg/issues/123)) ([e560602](https://github.com/noir-lang/acvm-backend-barretenberg/commit/e560602ebbd547386ca4cab35735ffa92e98ac4b)) +* store transcript in `.nargo/backends` directory ([#91](https://github.com/noir-lang/acvm-backend-barretenberg/issues/91)) ([c6b5023](https://github.com/noir-lang/acvm-backend-barretenberg/commit/c6b50231da065e7550bfe8bddf8e46f4cd8002d7)) +* update `aztec_backend_wasm` to use new serialization ([#94](https://github.com/noir-lang/acvm-backend-barretenberg/issues/94)) ([28014d8](https://github.com/noir-lang/acvm-backend-barretenberg/commit/28014d803d052a7f459e03dbd7b5b9210449b1d0)) +* update to acvm 0.9.0 ([#106](https://github.com/noir-lang/acvm-backend-barretenberg/issues/106)) ([ff350fb](https://github.com/noir-lang/acvm-backend-barretenberg/commit/ff350fb111043964b8a14fc0df62508c87506423)) +* Update to ACVM v0.11.0 ([#151](https://github.com/noir-lang/acvm-backend-barretenberg/issues/151)) ([9202415](https://github.com/noir-lang/acvm-backend-barretenberg/commit/92024155532e15f25acb2f3ed8d5ca78da0fddd9)) +* update to acvm v0.5.0 ([#60](https://github.com/noir-lang/acvm-backend-barretenberg/issues/60)) ([74b4d8d](https://github.com/noir-lang/acvm-backend-barretenberg/commit/74b4d8d8b118e4477880c04149e5e9d93d388384)) + + +### Bug Fixes + +* Avoid exposing internals of Assignments type ([614c81b](https://github.com/noir-lang/acvm-backend-barretenberg/commit/614c81b0ea5e110bbf5a61a526bb0173f4fe377a)) +* avoid exposing internals of Assignments type ([#119](https://github.com/noir-lang/acvm-backend-barretenberg/issues/119)) ([614c81b](https://github.com/noir-lang/acvm-backend-barretenberg/commit/614c81b0ea5e110bbf5a61a526bb0173f4fe377a)) +* fix serialisation of arithmetic expressions ([#145](https://github.com/noir-lang/acvm-backend-barretenberg/issues/145)) ([7f42535](https://github.com/noir-lang/acvm-backend-barretenberg/commit/7f4253570257d9dedcfa8c8fb96b9d097ef06419)) +* Implement random_get for wasm backend ([#102](https://github.com/noir-lang/acvm-backend-barretenberg/issues/102)) ([9c0f06e](https://github.com/noir-lang/acvm-backend-barretenberg/commit/9c0f06ef56f23e2b5794e810f433e36ff2c5d6b5)) +* rename gates to opcodes ([#59](https://github.com/noir-lang/acvm-backend-barretenberg/issues/59)) ([6e05307](https://github.com/noir-lang/acvm-backend-barretenberg/commit/6e053072d8b9c5d93c296f10782251ccb597f902)) +* reorganize and ensure contracts can be compiled in Remix ([#112](https://github.com/noir-lang/acvm-backend-barretenberg/issues/112)) ([7ec5693](https://github.com/noir-lang/acvm-backend-barretenberg/commit/7ec5693f194a79c379ae2952bc17a31ee63a42b9)) +* replace `serialize_circuit` function with `from<&Circuit>` ([#118](https://github.com/noir-lang/acvm-backend-barretenberg/issues/118)) ([94f83a7](https://github.com/noir-lang/acvm-backend-barretenberg/commit/94f83a78e32d91dfb7ae9824923695d9b4c425b0)) +* Replace serialize_circuit function with `from<&Circuit>` ([94f83a7](https://github.com/noir-lang/acvm-backend-barretenberg/commit/94f83a78e32d91dfb7ae9824923695d9b4c425b0)) +* Update bb-sys to resolve bugs in some environments ([#129](https://github.com/noir-lang/acvm-backend-barretenberg/issues/129)) ([e3d4504](https://github.com/noir-lang/acvm-backend-barretenberg/commit/e3d4504f15e1295e637c4da80b1d08c87c267c45)) +* Update dependency containing pk write fix for large general circuits ([#78](https://github.com/noir-lang/acvm-backend-barretenberg/issues/78)) ([2cb523d](https://github.com/noir-lang/acvm-backend-barretenberg/commit/2cb523d2ab95249157b22e198d9dcd6841c3eed8)) +* Update to bb-sys 0.1.1 and update bb in lockfile ([00bb157](https://github.com/noir-lang/acvm-backend-barretenberg/commit/00bb15779dfb64539eeb3f3bb4c4deeba106f2fe)) +* update to bb-sys 0.1.1 and update bb in lockfile ([#111](https://github.com/noir-lang/acvm-backend-barretenberg/issues/111)) ([00bb157](https://github.com/noir-lang/acvm-backend-barretenberg/commit/00bb15779dfb64539eeb3f3bb4c4deeba106f2fe)) +* use `Barretenberg.call` to query circuit size from wasm ([#121](https://github.com/noir-lang/acvm-backend-barretenberg/issues/121)) ([a775af1](https://github.com/noir-lang/acvm-backend-barretenberg/commit/a775af14137cc7bc2e9d8a063fa718a5a9abe6cb)) + + +### Miscellaneous Chores + +* Remove create_proof and verify functions ([#82](https://github.com/noir-lang/acvm-backend-barretenberg/issues/82)) ([ad0c422](https://github.com/noir-lang/acvm-backend-barretenberg/commit/ad0c4228488457bd155ff381186ecf583f18bfac)) +* update to ACVM 0.7.0 ([#90](https://github.com/noir-lang/acvm-backend-barretenberg/issues/90)) ([6c03687](https://github.com/noir-lang/acvm-backend-barretenberg/commit/6c036870a6a8e26612ab8b4f90a162f7540b42e2)) diff --git a/noir/tooling/backend_interface/Cargo.toml b/noir/tooling/backend_interface/Cargo.toml new file mode 100644 index 00000000000..14b1609dd4a --- /dev/null +++ b/noir/tooling/backend_interface/Cargo.toml @@ -0,0 +1,35 @@ +[package] +name = "backend-interface" +description = "The definition of the backend CLI interface which Nargo uses for proving/verifying ACIR circuits." +version = "0.11.0" +authors.workspace = true +edition.workspace = true +rust-version.workspace = true +license.workspace = true + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + +[dependencies] +acvm.workspace = true +dirs.workspace = true +thiserror.workspace = true +serde.workspace = true +serde_json.workspace = true +bb_abstraction_leaks.workspace = true + +tempfile = "3.6.0" + +## bb binary downloading +tar = "~0.4.15" +flate2 = "~1.0.1" +reqwest = { version = "0.11.20", default-features = false, features = [ + "rustls-tls", + "blocking", +] } + +[dev-dependencies] +test-binary = "3.0.1" + +[build-dependencies] +build-target = "0.4.0" +const_format.workspace = true diff --git a/noir/tooling/backend_interface/src/cli/contract.rs b/noir/tooling/backend_interface/src/cli/contract.rs new file mode 100644 index 00000000000..e83fc1909b6 --- /dev/null +++ b/noir/tooling/backend_interface/src/cli/contract.rs @@ -0,0 +1,71 @@ +use std::path::{Path, PathBuf}; + +use crate::BackendError; + +use super::string_from_stderr; + +/// VerifyCommand will call the barretenberg binary +/// to return a solidity library with the verification key +/// that can be used to verify proofs on-chain. +/// +/// This does not return a Solidity file that is able +/// to verify a proof. See acvm_interop/contract.sol for the +/// remaining logic that is missing. +pub(crate) struct ContractCommand { + pub(crate) crs_path: PathBuf, + pub(crate) vk_path: PathBuf, +} + +impl ContractCommand { + pub(crate) fn run(self, binary_path: &Path) -> Result { + let mut command = std::process::Command::new(binary_path); + + command + .arg("contract") + .arg("-c") + .arg(self.crs_path) + .arg("-k") + .arg(self.vk_path) + .arg("-o") + .arg("-"); + + let output = command.output()?; + + if output.status.success() { + String::from_utf8(output.stdout) + .map_err(|error| BackendError::InvalidUTF8Vector(error.into_bytes())) + } else { + Err(BackendError::CommandFailed(string_from_stderr(&output.stderr))) + } + } +} + +#[test] +fn contract_command() -> Result<(), BackendError> { + use tempfile::tempdir; + + let backend = crate::get_mock_backend()?; + + let temp_directory = tempdir().expect("could not create a temporary directory"); + let temp_directory_path = temp_directory.path(); + let bytecode_path = temp_directory_path.join("acir.gz"); + let vk_path = temp_directory_path.join("vk"); + + let crs_path = backend.backend_directory(); + + std::fs::File::create(&bytecode_path).expect("file should be created"); + + let write_vk_command = super::WriteVkCommand { + bytecode_path, + vk_path_output: vk_path.clone(), + crs_path: crs_path.clone(), + }; + write_vk_command.run(backend.binary_path())?; + + let contract_command = ContractCommand { vk_path, crs_path }; + contract_command.run(backend.binary_path())?; + + drop(temp_directory); + + Ok(()) +} diff --git a/noir/tooling/backend_interface/src/cli/gates.rs b/noir/tooling/backend_interface/src/cli/gates.rs new file mode 100644 index 00000000000..aca05f0232a --- /dev/null +++ b/noir/tooling/backend_interface/src/cli/gates.rs @@ -0,0 +1,64 @@ +use std::path::{Path, PathBuf}; + +use crate::BackendError; + +use super::string_from_stderr; + +/// GatesCommand will call the barretenberg binary +/// to return the number of gates needed to create a proof +/// for the given bytecode. +pub(crate) struct GatesCommand { + pub(crate) crs_path: PathBuf, + pub(crate) bytecode_path: PathBuf, +} + +impl GatesCommand { + pub(crate) fn run(self, binary_path: &Path) -> Result { + let output = std::process::Command::new(binary_path) + .arg("gates") + .arg("-c") + .arg(self.crs_path) + .arg("-b") + .arg(self.bytecode_path) + .output()?; + + if !output.status.success() { + return Err(BackendError::CommandFailed(string_from_stderr(&output.stderr))); + } + // Note: barretenberg includes the newline, so that subsequent prints to stdout + // are not on the same line as the gates output. + + const EXPECTED_BYTES: usize = 8; + let gates_bytes: [u8; EXPECTED_BYTES] = + output.stdout.as_slice().try_into().map_err(|_| { + BackendError::UnexpectedNumberOfBytes(EXPECTED_BYTES, output.stdout.clone()) + })?; + + // Convert bytes to u64 in little-endian format + let value = u64::from_le_bytes(gates_bytes); + + Ok(value as u32) + } +} + +#[test] +fn gate_command() -> Result<(), BackendError> { + use tempfile::tempdir; + + let backend = crate::get_mock_backend()?; + + let temp_directory = tempdir().expect("could not create a temporary directory"); + let temp_directory_path = temp_directory.path(); + let bytecode_path = temp_directory_path.join("acir.gz"); + let crs_path = backend.backend_directory(); + + std::fs::File::create(&bytecode_path).expect("file should be created"); + + let gate_command = GatesCommand { crs_path, bytecode_path }; + + let output = gate_command.run(backend.binary_path())?; + // Mock backend always returns zero gates. + assert_eq!(output, 0); + + Ok(()) +} diff --git a/noir/tooling/backend_interface/src/cli/info.rs b/noir/tooling/backend_interface/src/cli/info.rs new file mode 100644 index 00000000000..d3fd89bd2bc --- /dev/null +++ b/noir/tooling/backend_interface/src/cli/info.rs @@ -0,0 +1,81 @@ +use acvm::Language; +use serde::Deserialize; +use std::collections::HashSet; +use std::path::{Path, PathBuf}; + +use crate::{BackendError, BackendOpcodeSupport}; + +use super::string_from_stderr; + +pub(crate) struct InfoCommand { + pub(crate) crs_path: PathBuf, +} + +#[derive(Deserialize)] +struct InfoResponse { + language: LanguageResponse, + opcodes_supported: Vec, + black_box_functions_supported: Vec, +} + +#[derive(Deserialize)] +struct LanguageResponse { + name: String, + width: Option, +} + +impl BackendOpcodeSupport { + fn new(info: InfoResponse) -> Self { + let opcodes: HashSet = info.opcodes_supported.into_iter().collect(); + let black_box_functions: HashSet = + info.black_box_functions_supported.into_iter().collect(); + Self { opcodes, black_box_functions } + } +} + +impl InfoCommand { + pub(crate) fn run( + self, + binary_path: &Path, + ) -> Result<(Language, BackendOpcodeSupport), BackendError> { + let mut command = std::process::Command::new(binary_path); + + command.arg("info").arg("-c").arg(self.crs_path).arg("-o").arg("-"); + + let output = command.output()?; + + if !output.status.success() { + return Err(BackendError::CommandFailed(string_from_stderr(&output.stderr))); + } + + let backend_info: InfoResponse = + serde_json::from_slice(&output.stdout).expect("Backend should return valid json"); + let language: Language = match backend_info.language.name.as_str() { + "PLONK-CSAT" => { + let width = backend_info.language.width.unwrap(); + Language::PLONKCSat { width } + } + "R1CS" => Language::R1CS, + _ => panic!("Unknown langauge"), + }; + + Ok((language, BackendOpcodeSupport::new(backend_info))) + } +} + +#[test] +fn info_command() -> Result<(), BackendError> { + use acvm::acir::circuit::opcodes::Opcode; + + use acvm::acir::native_types::Expression; + + let backend = crate::get_mock_backend()?; + let crs_path = backend.backend_directory(); + + let (language, opcode_support) = InfoCommand { crs_path }.run(backend.binary_path())?; + + assert!(matches!(language, Language::PLONKCSat { width: 3 })); + assert!(opcode_support.is_opcode_supported(&Opcode::Arithmetic(Expression::default()))); + + Ok(()) +} diff --git a/noir/tooling/backend_interface/src/cli/mod.rs b/noir/tooling/backend_interface/src/cli/mod.rs new file mode 100644 index 00000000000..3ea65f28103 --- /dev/null +++ b/noir/tooling/backend_interface/src/cli/mod.rs @@ -0,0 +1,41 @@ +// Reference: https://github.com/AztecProtocol/aztec-packages/blob/master/circuits/cpp/barretenberg/cpp/src/barretenberg/bb/main.cpp + +mod contract; +mod gates; +mod info; +mod proof_as_fields; +mod prove; +mod verify; +mod version; +mod vk_as_fields; +mod write_vk; + +pub(crate) use contract::ContractCommand; +pub(crate) use gates::GatesCommand; +pub(crate) use info::InfoCommand; +pub(crate) use proof_as_fields::ProofAsFieldsCommand; +pub(crate) use prove::ProveCommand; +pub(crate) use verify::VerifyCommand; +pub(crate) use version::VersionCommand; +pub(crate) use vk_as_fields::VkAsFieldsCommand; +pub(crate) use write_vk::WriteVkCommand; + +#[test] +fn no_command_provided_works() -> Result<(), crate::BackendError> { + // This is a simple test to check that the binaries work + + let backend = crate::get_mock_backend()?; + + let output = std::process::Command::new(backend.binary_path()).output()?; + + let stderr = string_from_stderr(&output.stderr); + // Assert help message is printed due to no command being provided. + assert!(stderr.contains("Usage: mock_backend ")); + + Ok(()) +} + +// Converts a stderr byte array to a string (including invalid characters) +fn string_from_stderr(stderr: &[u8]) -> String { + String::from_utf8_lossy(stderr).to_string() +} diff --git a/noir/tooling/backend_interface/src/cli/proof_as_fields.rs b/noir/tooling/backend_interface/src/cli/proof_as_fields.rs new file mode 100644 index 00000000000..7eb1c1ef35c --- /dev/null +++ b/noir/tooling/backend_interface/src/cli/proof_as_fields.rs @@ -0,0 +1,38 @@ +use std::path::{Path, PathBuf}; + +use acvm::FieldElement; + +use crate::BackendError; + +use super::string_from_stderr; + +/// `ProofAsFieldsCommand` will call the barretenberg binary +/// to split a proof into a representation as [`FieldElement`]s. +pub(crate) struct ProofAsFieldsCommand { + pub(crate) proof_path: PathBuf, + pub(crate) vk_path: PathBuf, +} + +impl ProofAsFieldsCommand { + pub(crate) fn run(self, binary_path: &Path) -> Result, BackendError> { + let mut command = std::process::Command::new(binary_path); + + command + .arg("proof_as_fields") + .arg("-p") + .arg(self.proof_path) + .arg("-k") + .arg(self.vk_path) + .arg("-o") + .arg("-"); + + let output = command.output()?; + if output.status.success() { + let string_output = String::from_utf8(output.stdout).unwrap(); + serde_json::from_str(&string_output) + .map_err(|err| BackendError::CommandFailed(err.to_string())) + } else { + Err(BackendError::CommandFailed(string_from_stderr(&output.stderr))) + } + } +} diff --git a/noir/tooling/backend_interface/src/cli/prove.rs b/noir/tooling/backend_interface/src/cli/prove.rs new file mode 100644 index 00000000000..c12e516db57 --- /dev/null +++ b/noir/tooling/backend_interface/src/cli/prove.rs @@ -0,0 +1,71 @@ +use std::path::{Path, PathBuf}; + +use crate::BackendError; + +use super::string_from_stderr; + +/// ProveCommand will call the barretenberg binary +/// to create a proof, given the witness and the bytecode. +/// +/// Note:Internally barretenberg will create and discard the +/// proving key, so this is not returned. +/// +/// The proof will be written to the specified output file. +pub(crate) struct ProveCommand { + pub(crate) crs_path: PathBuf, + pub(crate) is_recursive: bool, + pub(crate) bytecode_path: PathBuf, + pub(crate) witness_path: PathBuf, +} + +impl ProveCommand { + pub(crate) fn run(self, binary_path: &Path) -> Result, BackendError> { + let mut command = std::process::Command::new(binary_path); + + command + .arg("prove") + .arg("-c") + .arg(self.crs_path) + .arg("-b") + .arg(self.bytecode_path) + .arg("-w") + .arg(self.witness_path) + .arg("-o") + .arg("-"); + + if self.is_recursive { + command.arg("-r"); + } + + let output = command.output()?; + if output.status.success() { + Ok(output.stdout) + } else { + Err(BackendError::CommandFailed(string_from_stderr(&output.stderr))) + } + } +} + +#[test] +fn prove_command() -> Result<(), BackendError> { + use tempfile::tempdir; + + let backend = crate::get_mock_backend()?; + + let temp_directory = tempdir().expect("could not create a temporary directory"); + let temp_directory_path = temp_directory.path(); + let bytecode_path = temp_directory_path.join("acir.gz"); + let witness_path = temp_directory_path.join("witness.tr"); + + std::fs::File::create(&bytecode_path).expect("file should be created"); + std::fs::File::create(&witness_path).expect("file should be created"); + + let crs_path = backend.backend_directory(); + let prove_command = ProveCommand { crs_path, bytecode_path, witness_path, is_recursive: false }; + + let proof = prove_command.run(backend.binary_path())?; + assert_eq!(proof, "proof".as_bytes()); + drop(temp_directory); + + Ok(()) +} diff --git a/noir/tooling/backend_interface/src/cli/verify.rs b/noir/tooling/backend_interface/src/cli/verify.rs new file mode 100644 index 00000000000..a31f476d84c --- /dev/null +++ b/noir/tooling/backend_interface/src/cli/verify.rs @@ -0,0 +1,85 @@ +use std::path::{Path, PathBuf}; + +use crate::BackendError; + +/// VerifyCommand will call the barretenberg binary +/// to verify a proof +pub(crate) struct VerifyCommand { + pub(crate) crs_path: PathBuf, + pub(crate) is_recursive: bool, + pub(crate) proof_path: PathBuf, + pub(crate) vk_path: PathBuf, +} + +impl VerifyCommand { + pub(crate) fn run(self, binary_path: &Path) -> Result { + let mut command = std::process::Command::new(binary_path); + + command + .arg("verify") + .arg("-c") + .arg(self.crs_path) + .arg("-p") + .arg(self.proof_path) + .arg("-k") + .arg(self.vk_path); + + if self.is_recursive { + command.arg("-r"); + } + + let output = command.output()?; + + // We currently do not distinguish between an invalid proof and an error inside the backend. + Ok(output.status.success()) + } +} + +#[test] +fn verify_command() -> Result<(), BackendError> { + use tempfile::tempdir; + + use super::{ProveCommand, WriteVkCommand}; + use crate::proof_system::write_to_file; + + let backend = crate::get_mock_backend()?; + + let temp_directory = tempdir().expect("could not create a temporary directory"); + let temp_directory_path = temp_directory.path(); + let bytecode_path = temp_directory_path.join("acir.gz"); + let witness_path = temp_directory_path.join("witness.tr"); + let proof_path = temp_directory_path.join("1_mul.proof"); + let vk_path_output = temp_directory_path.join("vk"); + + let crs_path = backend.backend_directory(); + + std::fs::File::create(&bytecode_path).expect("file should be created"); + std::fs::File::create(&witness_path).expect("file should be created"); + + let write_vk_command = WriteVkCommand { + bytecode_path: bytecode_path.clone(), + crs_path: crs_path.clone(), + vk_path_output: vk_path_output.clone(), + }; + + write_vk_command.run(backend.binary_path())?; + + let prove_command = ProveCommand { + crs_path: crs_path.clone(), + is_recursive: false, + bytecode_path, + witness_path, + }; + let proof = prove_command.run(backend.binary_path())?; + + write_to_file(&proof, &proof_path); + + let verify_command = + VerifyCommand { crs_path, is_recursive: false, proof_path, vk_path: vk_path_output }; + + let verified = verify_command.run(backend.binary_path())?; + assert!(verified); + + drop(temp_directory); + Ok(()) +} diff --git a/noir/tooling/backend_interface/src/cli/version.rs b/noir/tooling/backend_interface/src/cli/version.rs new file mode 100644 index 00000000000..83ab72a870e --- /dev/null +++ b/noir/tooling/backend_interface/src/cli/version.rs @@ -0,0 +1,29 @@ +use std::path::Path; + +use crate::BackendError; + +use super::string_from_stderr; + +/// VersionCommand will call the backend binary +/// to query installed version. +pub(crate) struct VersionCommand; + +impl VersionCommand { + pub(crate) fn run(self, binary_path: &Path) -> Result { + let mut command = std::process::Command::new(binary_path); + + command.arg("--version"); + + let output = command.output()?; + if output.status.success() { + match String::from_utf8(output.stdout) { + Ok(result) => Ok(result), + Err(_) => Err(BackendError::CommandFailed( + "Unexpected output from --version check.".to_owned(), + )), + } + } else { + Err(BackendError::CommandFailed(string_from_stderr(&output.stderr))) + } + } +} diff --git a/noir/tooling/backend_interface/src/cli/vk_as_fields.rs b/noir/tooling/backend_interface/src/cli/vk_as_fields.rs new file mode 100644 index 00000000000..1b0212241c4 --- /dev/null +++ b/noir/tooling/backend_interface/src/cli/vk_as_fields.rs @@ -0,0 +1,39 @@ +use std::path::{Path, PathBuf}; + +use acvm::FieldElement; + +use crate::BackendError; + +use super::string_from_stderr; + +/// VkAsFieldsCommand will call the barretenberg binary +/// to split a verification key into a representation as [`FieldElement`]s. +/// +/// The hash of the verification key will also be returned. +pub(crate) struct VkAsFieldsCommand { + pub(crate) vk_path: PathBuf, +} + +impl VkAsFieldsCommand { + pub(crate) fn run( + self, + binary_path: &Path, + ) -> Result<(FieldElement, Vec), BackendError> { + let mut command = std::process::Command::new(binary_path); + + command.arg("vk_as_fields").arg("-k").arg(self.vk_path).arg("-o").arg("-"); + + let output = command.output()?; + if output.status.success() { + let string_output = String::from_utf8(output.stdout).unwrap(); + let mut fields: Vec = serde_json::from_str(&string_output) + .map_err(|err| BackendError::CommandFailed(err.to_string()))?; + + // The first element of this vector is the hash of the verification key, we want to split that off. + let hash = fields.remove(0); + Ok((hash, fields)) + } else { + Err(BackendError::CommandFailed(string_from_stderr(&output.stderr))) + } + } +} diff --git a/noir/tooling/backend_interface/src/cli/write_vk.rs b/noir/tooling/backend_interface/src/cli/write_vk.rs new file mode 100644 index 00000000000..8d4aa9cc7e3 --- /dev/null +++ b/noir/tooling/backend_interface/src/cli/write_vk.rs @@ -0,0 +1,57 @@ +use std::path::{Path, PathBuf}; + +use super::string_from_stderr; +use crate::BackendError; + +/// WriteCommand will call the barretenberg binary +/// to write a verification key to a file +pub(crate) struct WriteVkCommand { + pub(crate) crs_path: PathBuf, + pub(crate) bytecode_path: PathBuf, + pub(crate) vk_path_output: PathBuf, +} + +impl WriteVkCommand { + pub(crate) fn run(self, binary_path: &Path) -> Result<(), BackendError> { + let mut command = std::process::Command::new(binary_path); + + command + .arg("write_vk") + .arg("-c") + .arg(self.crs_path) + .arg("-b") + .arg(self.bytecode_path) + .arg("-o") + .arg(self.vk_path_output); + + let output = command.output()?; + if output.status.success() { + Ok(()) + } else { + Err(BackendError::CommandFailed(string_from_stderr(&output.stderr))) + } + } +} + +#[test] +fn write_vk_command() -> Result<(), BackendError> { + use tempfile::tempdir; + + let backend = crate::get_mock_backend()?; + + let temp_directory = tempdir().expect("could not create a temporary directory"); + let temp_directory_path = temp_directory.path(); + let bytecode_path = temp_directory_path.join("acir.gz"); + let vk_path_output = temp_directory.path().join("vk"); + + let crs_path = backend.backend_directory(); + + std::fs::File::create(&bytecode_path).expect("file should be created"); + + let write_vk_command = WriteVkCommand { bytecode_path, crs_path, vk_path_output }; + + write_vk_command.run(backend.binary_path())?; + drop(temp_directory); + + Ok(()) +} diff --git a/noir/tooling/backend_interface/src/download.rs b/noir/tooling/backend_interface/src/download.rs new file mode 100644 index 00000000000..27aab7ef351 --- /dev/null +++ b/noir/tooling/backend_interface/src/download.rs @@ -0,0 +1,54 @@ +use std::{ + io::{Cursor, ErrorKind}, + path::Path, +}; + +/// Downloads a zipped archive and unpacks the backend binary to `destination_path`. +/// +/// # Backend Requirements +/// +/// In order for a backend to be compatible with this function: +/// - `backend_url` must serve a gzipped tarball. +/// - The tarball must only contain the backend's binary. +/// - The binary file must be located at the archive root. +pub fn download_backend(backend_url: &str, destination_path: &Path) -> std::io::Result<()> { + use flate2::read::GzDecoder; + use tar::Archive; + use tempfile::tempdir; + + // Download sources + let compressed_file: Cursor> = download_binary_from_url(backend_url) + .map_err(|_| std::io::Error::from(ErrorKind::Other))?; + + // Unpack the tarball + let gz_decoder = GzDecoder::new(compressed_file); + let mut archive = Archive::new(gz_decoder); + + let temp_directory = tempdir()?; + archive.unpack(&temp_directory)?; + + // Assume that the archive contains a single file which is the backend binary. + let mut archive_files = std::fs::read_dir(&temp_directory)?; + let temp_binary_path = archive_files.next().unwrap()?.path(); + + // Create directory to place binary in. + std::fs::create_dir_all(destination_path.parent().unwrap())?; + + // Rename the binary to the desired name + std::fs::copy(temp_binary_path, destination_path)?; + + drop(temp_directory); + + Ok(()) +} + +/// Try to download the specified URL into a buffer which is returned. +fn download_binary_from_url(url: &str) -> Result>, reqwest::Error> { + let response = reqwest::blocking::get(url)?; + + let bytes = response.bytes()?; + + // TODO: Check SHA of downloaded binary + + Ok(Cursor::new(bytes.to_vec())) +} diff --git a/noir/tooling/backend_interface/src/lib.rs b/noir/tooling/backend_interface/src/lib.rs new file mode 100644 index 00000000000..36ebe5ebb91 --- /dev/null +++ b/noir/tooling/backend_interface/src/lib.rs @@ -0,0 +1,198 @@ +#![warn(unused_crate_dependencies, unused_extern_crates)] +#![warn(unreachable_pub)] + +use std::{collections::HashSet, path::PathBuf}; + +mod cli; +mod download; +mod proof_system; +mod smart_contract; + +use acvm::acir::circuit::Opcode; +use bb_abstraction_leaks::ACVM_BACKEND_BARRETENBERG; +use bb_abstraction_leaks::BB_VERSION; +use cli::VersionCommand; +pub use download::download_backend; + +const BACKENDS_DIR: &str = ".nargo/backends"; + +pub fn backends_directory() -> PathBuf { + let home_directory = dirs::home_dir().unwrap(); + home_directory.join(BACKENDS_DIR) +} + +#[cfg(test)] +test_binary::build_test_binary_once!(mock_backend, "test-binaries"); + +#[cfg(test)] +fn get_mock_backend() -> Result { + std::env::set_var("NARGO_BACKEND_PATH", path_to_mock_backend()); + + let mock_backend = Backend::new("mock_backend".to_string()); + mock_backend.assert_binary_exists()?; + + Ok(mock_backend) +} + +#[derive(Debug, thiserror::Error)] +pub enum BackendError { + #[error(transparent)] + IoError(#[from] std::io::Error), + + #[error("Backend binary does not exist")] + MissingBinary, + + #[error("The backend responded with a malformed UTF8 byte vector: {0:?}")] + InvalidUTF8Vector(Vec), + + #[error( + "The backend responded with a unexpected number of bytes. Expected: {0} but got {} ({1:?})", .1.len() + )] + UnexpectedNumberOfBytes(usize, Vec), + + #[error("The backend encountered an error: {0:?}")] + CommandFailed(String), +} + +#[derive(Debug)] +pub struct Backend { + name: String, + binary_path: PathBuf, +} + +impl Backend { + pub fn new(name: String) -> Backend { + let binary_path = if let Some(binary_path) = std::env::var_os("NARGO_BACKEND_PATH") { + PathBuf::from(binary_path) + } else { + const BINARY_NAME: &str = "backend_binary"; + + backends_directory().join(&name).join(BINARY_NAME) + }; + Backend { name, binary_path } + } + + pub fn name(&self) -> &str { + &self.name + } + + fn binary_path(&self) -> &PathBuf { + &self.binary_path + } + + fn assert_binary_exists(&self) -> Result<&PathBuf, BackendError> { + let binary_path = self.binary_path(); + if binary_path.is_file() { + Ok(binary_path) + } else { + if self.name == ACVM_BACKEND_BARRETENBERG { + // If we're trying to use barretenberg, automatically go and install it. + let bb_url = std::env::var("BB_BINARY_URL") + .unwrap_or_else(|_| bb_abstraction_leaks::BB_DOWNLOAD_URL.to_owned()); + download_backend(&bb_url, binary_path)?; + return Ok(binary_path); + } + Err(BackendError::MissingBinary) + } + } + + fn backend_directory(&self) -> PathBuf { + self.binary_path() + .parent() + .expect("backend binary should have a parent directory") + .to_path_buf() + } + + fn crs_directory(&self) -> PathBuf { + self.backend_directory().join("crs") + } + + fn assert_correct_version(&self) -> Result<&PathBuf, BackendError> { + let binary_path = self.binary_path(); + if binary_path.to_string_lossy().contains(ACVM_BACKEND_BARRETENBERG) { + match VersionCommand.run(binary_path) { + // If version matches then do nothing. + Ok(version_string) if version_string == BB_VERSION => (), + + // If version doesn't match then download the correct version. + Ok(version_string) => { + println!("`{ACVM_BACKEND_BARRETENBERG}` version `{version_string}` is different from expected `{BB_VERSION}`. Downloading expected version..."); + let bb_url = std::env::var("BB_BINARY_URL") + .unwrap_or_else(|_| bb_abstraction_leaks::BB_DOWNLOAD_URL.to_owned()); + download_backend(&bb_url, binary_path)?; + } + + // If `bb` fails to report its version, then attempt to fix it by re-downloading the binary. + Err(_) => { + println!("Could not determine version of `{ACVM_BACKEND_BARRETENBERG}`. Downloading expected version..."); + let bb_url = std::env::var("BB_BINARY_URL") + .unwrap_or_else(|_| bb_abstraction_leaks::BB_DOWNLOAD_URL.to_owned()); + download_backend(&bb_url, binary_path)?; + } + } + } + Ok(binary_path) + } +} + +pub struct BackendOpcodeSupport { + opcodes: HashSet, + black_box_functions: HashSet, +} + +impl BackendOpcodeSupport { + pub fn is_opcode_supported(&self, opcode: &Opcode) -> bool { + match opcode { + Opcode::Arithmetic(_) => self.opcodes.contains("arithmetic"), + Opcode::Directive(_) => self.opcodes.contains("directive"), + Opcode::Brillig(_) => self.opcodes.contains("brillig"), + Opcode::MemoryInit { .. } => self.opcodes.contains("memory_init"), + Opcode::MemoryOp { .. } => self.opcodes.contains("memory_op"), + Opcode::BlackBoxFuncCall(func) => { + self.black_box_functions.contains(func.get_black_box_func().name()) + } + } + } + + pub fn all() -> BackendOpcodeSupport { + BackendOpcodeSupport { + opcodes: HashSet::from([ + "arithmetic".to_string(), + "directive".to_string(), + "brillig".to_string(), + "memory_init".to_string(), + "memory_op".to_string(), + ]), + black_box_functions: HashSet::from([ + "sha256".to_string(), + "schnorr_verify".to_string(), + "blake2s".to_string(), + "pedersen".to_string(), + "pedersen_hash".to_string(), + "hash_to_field_128_security".to_string(), + "ecdsa_secp256k1".to_string(), + "fixed_base_scalar_mul".to_string(), + "and".to_string(), + "xor".to_string(), + "range".to_string(), + "keccak256".to_string(), + "recursive_aggregation".to_string(), + "ecdsa_secp256r1".to_string(), + ]), + } + } +} + +#[cfg(test)] +mod backend { + use crate::{Backend, BackendError}; + + #[test] + fn raises_error_on_missing_binary() { + let bad_backend = Backend::new("i_dont_exist".to_string()); + + let binary_path = bad_backend.assert_binary_exists(); + + assert!(matches!(binary_path, Err(BackendError::MissingBinary))); + } +} diff --git a/noir/tooling/backend_interface/src/proof_system.rs b/noir/tooling/backend_interface/src/proof_system.rs new file mode 100644 index 00000000000..dcf1dad56b0 --- /dev/null +++ b/noir/tooling/backend_interface/src/proof_system.rs @@ -0,0 +1,188 @@ +use std::fs::File; +use std::io::Write; +use std::path::Path; + +use acvm::acir::{circuit::Circuit, native_types::WitnessMap}; +use acvm::FieldElement; +use acvm::Language; +use tempfile::tempdir; + +use crate::cli::{ + GatesCommand, InfoCommand, ProofAsFieldsCommand, ProveCommand, VerifyCommand, + VkAsFieldsCommand, WriteVkCommand, +}; +use crate::{Backend, BackendError, BackendOpcodeSupport}; + +impl Backend { + pub fn get_exact_circuit_size(&self, circuit: &Circuit) -> Result { + let binary_path = self.assert_binary_exists()?; + self.assert_correct_version()?; + + let temp_directory = tempdir().expect("could not create a temporary directory"); + let temp_directory = temp_directory.path().to_path_buf(); + + // Create a temporary file for the circuit + let circuit_path = temp_directory.join("circuit").with_extension("bytecode"); + let serialized_circuit = Circuit::serialize_circuit(circuit); + write_to_file(&serialized_circuit, &circuit_path); + + GatesCommand { crs_path: self.crs_directory(), bytecode_path: circuit_path } + .run(binary_path) + } + + pub fn get_backend_info(&self) -> Result<(Language, BackendOpcodeSupport), BackendError> { + let binary_path = self.assert_binary_exists()?; + self.assert_correct_version()?; + InfoCommand { crs_path: self.crs_directory() }.run(binary_path) + } + + /// If we cannot get a valid backend, returns the default backend which supports all the opcodes + /// and uses Plonk with width 3 + /// The function also prints a message saying we could not find a backend + pub fn get_backend_info_or_default(&self) -> (Language, BackendOpcodeSupport) { + if let Ok(backend_info) = self.get_backend_info() { + (backend_info.0, backend_info.1) + } else { + println!("No valid backend found, defaulting to Plonk with width 3 and all opcodes supported"); + (Language::PLONKCSat { width: 3 }, BackendOpcodeSupport::all()) + } + } + + pub fn prove( + &self, + circuit: &Circuit, + witness_values: WitnessMap, + is_recursive: bool, + ) -> Result, BackendError> { + let binary_path = self.assert_binary_exists()?; + self.assert_correct_version()?; + + let temp_directory = tempdir().expect("could not create a temporary directory"); + let temp_directory = temp_directory.path().to_path_buf(); + + // Create a temporary file for the witness + let serialized_witnesses: Vec = + witness_values.try_into().expect("could not serialize witness map"); + let witness_path = temp_directory.join("witness").with_extension("tr"); + write_to_file(&serialized_witnesses, &witness_path); + + // Create a temporary file for the circuit + // + let bytecode_path = temp_directory.join("circuit").with_extension("bytecode"); + let serialized_circuit = Circuit::serialize_circuit(circuit); + write_to_file(&serialized_circuit, &bytecode_path); + + // Create proof and store it in the specified path + let proof_with_public_inputs = ProveCommand { + crs_path: self.crs_directory(), + is_recursive, + bytecode_path, + witness_path, + } + .run(binary_path)?; + + let proof = bb_abstraction_leaks::remove_public_inputs( + circuit.public_inputs().0.len(), + &proof_with_public_inputs, + ); + Ok(proof) + } + + pub fn verify( + &self, + proof: &[u8], + public_inputs: WitnessMap, + circuit: &Circuit, + is_recursive: bool, + ) -> Result { + let binary_path = self.assert_binary_exists()?; + self.assert_correct_version()?; + + let temp_directory = tempdir().expect("could not create a temporary directory"); + let temp_directory = temp_directory.path().to_path_buf(); + + // Create a temporary file for the proof + let proof_with_public_inputs = + bb_abstraction_leaks::prepend_public_inputs(proof.to_vec(), public_inputs); + let proof_path = temp_directory.join("proof").with_extension("proof"); + write_to_file(&proof_with_public_inputs, &proof_path); + + // Create a temporary file for the circuit + let bytecode_path = temp_directory.join("circuit").with_extension("bytecode"); + let serialized_circuit = Circuit::serialize_circuit(circuit); + write_to_file(&serialized_circuit, &bytecode_path); + + // Create the verification key and write it to the specified path + let vk_path = temp_directory.join("vk"); + + WriteVkCommand { + crs_path: self.crs_directory(), + bytecode_path, + vk_path_output: vk_path.clone(), + } + .run(binary_path)?; + + // Verify the proof + VerifyCommand { crs_path: self.crs_directory(), is_recursive, proof_path, vk_path } + .run(binary_path) + } + + pub fn get_intermediate_proof_artifacts( + &self, + circuit: &Circuit, + proof: &[u8], + public_inputs: WitnessMap, + ) -> Result<(Vec, FieldElement, Vec), BackendError> { + let binary_path = self.assert_binary_exists()?; + self.assert_correct_version()?; + + let temp_directory = tempdir().expect("could not create a temporary directory"); + let temp_directory = temp_directory.path().to_path_buf(); + + // Create a temporary file for the circuit + // + let bytecode_path = temp_directory.join("circuit").with_extension("bytecode"); + let serialized_circuit = Circuit::serialize_circuit(circuit); + write_to_file(&serialized_circuit, &bytecode_path); + + // Create the verification key and write it to the specified path + let vk_path = temp_directory.join("vk"); + + WriteVkCommand { + crs_path: self.crs_directory(), + bytecode_path, + vk_path_output: vk_path.clone(), + } + .run(binary_path)?; + + // Create a temporary file for the proof + + let proof_with_public_inputs = + bb_abstraction_leaks::prepend_public_inputs(proof.to_vec(), public_inputs); + let proof_path = temp_directory.join("proof").with_extension("proof"); + write_to_file(&proof_with_public_inputs, &proof_path); + + // Now ready to generate intermediate artifacts. + + let proof_as_fields = + ProofAsFieldsCommand { proof_path, vk_path: vk_path.clone() }.run(binary_path)?; + + let (vk_hash, vk_as_fields) = VkAsFieldsCommand { vk_path }.run(binary_path)?; + + Ok((proof_as_fields, vk_hash, vk_as_fields)) + } +} + +pub(super) fn write_to_file(bytes: &[u8], path: &Path) -> String { + let display = path.display(); + + let mut file = match File::create(path) { + Err(why) => panic!("couldn't create {display}: {why}"), + Ok(file) => file, + }; + + match file.write_all(bytes) { + Err(why) => panic!("couldn't write to {display}: {why}"), + Ok(_) => display.to_string(), + } +} diff --git a/noir/tooling/backend_interface/src/smart_contract.rs b/noir/tooling/backend_interface/src/smart_contract.rs new file mode 100644 index 00000000000..5dac57c4072 --- /dev/null +++ b/noir/tooling/backend_interface/src/smart_contract.rs @@ -0,0 +1,67 @@ +use super::proof_system::write_to_file; +use crate::{ + cli::{ContractCommand, WriteVkCommand}, + Backend, BackendError, +}; +use acvm::acir::circuit::Circuit; +use tempfile::tempdir; + +impl Backend { + pub fn eth_contract(&self, circuit: &Circuit) -> Result { + let binary_path = self.assert_binary_exists()?; + self.assert_correct_version()?; + + let temp_directory = tempdir().expect("could not create a temporary directory"); + let temp_directory_path = temp_directory.path().to_path_buf(); + + // Create a temporary file for the circuit + let bytecode_path = temp_directory_path.join("circuit").with_extension("bytecode"); + let serialized_circuit = Circuit::serialize_circuit(circuit); + write_to_file(&serialized_circuit, &bytecode_path); + + // Create the verification key and write it to the specified path + let vk_path = temp_directory_path.join("vk"); + + WriteVkCommand { + crs_path: self.crs_directory(), + bytecode_path, + vk_path_output: vk_path.clone(), + } + .run(binary_path)?; + + ContractCommand { crs_path: self.crs_directory(), vk_path }.run(binary_path) + } +} + +#[cfg(test)] +mod tests { + use std::collections::BTreeSet; + + use acvm::acir::{ + circuit::{Circuit, Opcode, PublicInputs}, + native_types::{Expression, Witness}, + }; + + use crate::{get_mock_backend, BackendError}; + + #[test] + fn test_smart_contract() -> Result<(), BackendError> { + let expression = &(Witness(1) + Witness(2)) - &Expression::from(Witness(3)); + let constraint = Opcode::Arithmetic(expression); + + let circuit = Circuit { + current_witness_index: 4, + opcodes: vec![constraint], + private_parameters: BTreeSet::from([Witness(1), Witness(2)]), + public_parameters: PublicInputs::default(), + return_values: PublicInputs::default(), + assert_messages: Default::default(), + }; + + let contract = get_mock_backend()?.eth_contract(&circuit)?; + + assert!(contract.contains("contract VerifierContract")); + + Ok(()) + } +} diff --git a/noir/tooling/backend_interface/test-binaries/mock_backend/Cargo.lock b/noir/tooling/backend_interface/test-binaries/mock_backend/Cargo.lock new file mode 100644 index 00000000000..c43d1b84915 --- /dev/null +++ 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b/noir/tooling/backend_interface/test-binaries/mock_backend/Cargo.toml new file mode 100644 index 00000000000..f527b03a7b9 --- /dev/null +++ b/noir/tooling/backend_interface/test-binaries/mock_backend/Cargo.toml @@ -0,0 +1,11 @@ +[workspace] + +[package] +name = "mock_backend" +version = "0.1.0" +edition = "2021" + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + +[dependencies] +clap = { version = "4.3.19", features = ["derive"] } diff --git a/noir/tooling/backend_interface/test-binaries/mock_backend/src/contract_cmd.rs b/noir/tooling/backend_interface/test-binaries/mock_backend/src/contract_cmd.rs new file mode 100644 index 00000000000..7ee41121d61 --- /dev/null +++ b/noir/tooling/backend_interface/test-binaries/mock_backend/src/contract_cmd.rs @@ -0,0 +1,21 @@ +use clap::Args; +use std::io::Write; +use std::path::PathBuf; + +#[derive(Debug, Clone, Args)] +pub(crate) struct ContractCommand { + #[clap(short = 'c')] + pub(crate) crs_path: Option, + + #[clap(short = 'k')] + pub(crate) vk_path: PathBuf, + + #[clap(short = 'o')] + pub(crate) contract_path: PathBuf, +} + +pub(crate) fn run(args: ContractCommand) { + assert!(args.vk_path.is_file(), "Could not find vk file at provided path"); + + std::io::stdout().write_all(b"contract VerifierContract {}").unwrap(); +} diff --git a/noir/tooling/backend_interface/test-binaries/mock_backend/src/gates_cmd.rs b/noir/tooling/backend_interface/test-binaries/mock_backend/src/gates_cmd.rs new file mode 100644 index 00000000000..3cc397d3292 --- /dev/null +++ b/noir/tooling/backend_interface/test-binaries/mock_backend/src/gates_cmd.rs @@ -0,0 +1,18 @@ +use clap::Args; +use std::io::Write; +use std::path::PathBuf; + +#[derive(Debug, Clone, Args)] +pub(crate) struct GatesCommand { + #[clap(short = 'c')] + pub(crate) crs_path: Option, + + #[clap(short = 'b')] + pub(crate) bytecode_path: PathBuf, +} + +pub(crate) fn run(args: GatesCommand) { + assert!(args.bytecode_path.is_file(), "Could not find bytecode file at provided path"); + + std::io::stdout().write_all(&0u64.to_le_bytes()).unwrap(); +} diff --git a/noir/tooling/backend_interface/test-binaries/mock_backend/src/info_cmd.rs b/noir/tooling/backend_interface/test-binaries/mock_backend/src/info_cmd.rs new file mode 100644 index 00000000000..e9a7842ba24 --- /dev/null +++ b/noir/tooling/backend_interface/test-binaries/mock_backend/src/info_cmd.rs @@ -0,0 +1,40 @@ +use clap::Args; +use std::io::Write; +use std::path::PathBuf; + +const INFO_RESPONSE: &str = r#"{ + "language": { + "name": "PLONK-CSAT", + "width": 3 + }, + "opcodes_supported": ["arithmetic", "directive", "brillig", "memory_init", "memory_op"], + "black_box_functions_supported": [ + "and", + "xor", + "range", + "sha256", + "blake2s", + "keccak256", + "schnorr_verify", + "pedersen", + "pedersen_hash", + "hash_to_field_128_security", + "ecdsa_secp256k1", + "ecdsa_secp256r1", + "fixed_base_scalar_mul", + "recursive_aggregation" + ] +}"#; + +#[derive(Debug, Clone, Args)] +pub(crate) struct InfoCommand { + #[clap(short = 'c')] + pub(crate) crs_path: Option, + + #[clap(short = 'o')] + pub(crate) info_path: Option, +} + +pub(crate) fn run(_args: InfoCommand) { + std::io::stdout().write_all(INFO_RESPONSE.as_bytes()).unwrap(); +} diff --git a/noir/tooling/backend_interface/test-binaries/mock_backend/src/main.rs b/noir/tooling/backend_interface/test-binaries/mock_backend/src/main.rs new file mode 100644 index 00000000000..ef8819af94b --- /dev/null +++ b/noir/tooling/backend_interface/test-binaries/mock_backend/src/main.rs @@ -0,0 +1,44 @@ +#![forbid(unsafe_code)] +#![warn(unreachable_pub)] +#![warn(clippy::semicolon_if_nothing_returned)] +#![cfg_attr(not(test), warn(unused_crate_dependencies, unused_extern_crates))] + +use clap::{Parser, Subcommand}; + +mod contract_cmd; +mod gates_cmd; +mod info_cmd; +mod prove_cmd; +mod verify_cmd; +mod write_vk_cmd; + +#[derive(Parser, Debug)] +#[command(name = "mock_backend")] +struct BackendCli { + #[command(subcommand)] + command: BackendCommand, +} + +#[derive(Subcommand, Clone, Debug)] +enum BackendCommand { + Info(info_cmd::InfoCommand), + Contract(contract_cmd::ContractCommand), + Gates(gates_cmd::GatesCommand), + Prove(prove_cmd::ProveCommand), + Verify(verify_cmd::VerifyCommand), + #[command(name = "write_vk")] + WriteVk(write_vk_cmd::WriteVkCommand), +} + +fn main() { + let BackendCli { command } = BackendCli::parse(); + + match command { + BackendCommand::Info(args) => info_cmd::run(args), + BackendCommand::Contract(args) => contract_cmd::run(args), + BackendCommand::Gates(args) => gates_cmd::run(args), + BackendCommand::Prove(args) => prove_cmd::run(args), + BackendCommand::Verify(args) => verify_cmd::run(args), + BackendCommand::WriteVk(args) => write_vk_cmd::run(args), + }; +} diff --git a/noir/tooling/backend_interface/test-binaries/mock_backend/src/prove_cmd.rs b/noir/tooling/backend_interface/test-binaries/mock_backend/src/prove_cmd.rs new file mode 100644 index 00000000000..3967778d4e8 --- /dev/null +++ b/noir/tooling/backend_interface/test-binaries/mock_backend/src/prove_cmd.rs @@ -0,0 +1,25 @@ +use clap::Args; +use std::io::Write; +use std::path::PathBuf; + +#[derive(Debug, Clone, Args)] +pub(crate) struct ProveCommand { + #[clap(short = 'c')] + pub(crate) crs_path: Option, + + #[clap(short = 'b')] + pub(crate) bytecode_path: PathBuf, + + #[clap(short = 'w')] + pub(crate) witness_path: PathBuf, + + #[clap(short = 'o')] + pub(crate) proof_path: PathBuf, +} + +pub(crate) fn run(args: ProveCommand) { + assert!(args.bytecode_path.is_file(), "Could not find bytecode file at provided path"); + assert!(args.witness_path.is_file(), "Could not find witness file at provided path"); + + std::io::stdout().write_all(b"proof").unwrap(); +} diff --git a/noir/tooling/backend_interface/test-binaries/mock_backend/src/verify_cmd.rs b/noir/tooling/backend_interface/test-binaries/mock_backend/src/verify_cmd.rs new file mode 100644 index 00000000000..1a715eea880 --- /dev/null +++ b/noir/tooling/backend_interface/test-binaries/mock_backend/src/verify_cmd.rs @@ -0,0 +1,24 @@ +use clap::Args; +use std::path::PathBuf; + +#[derive(Debug, Clone, Args)] +pub(crate) struct VerifyCommand { + #[clap(short = 'c')] + pub(crate) crs_path: Option, + + #[clap(short = 'p')] + pub(crate) proof_path: PathBuf, + + #[clap(short = 'k')] + pub(crate) vk_path: PathBuf, + + #[clap(short = 'r')] + pub(crate) is_recursive: bool, +} + +pub(crate) fn run(args: VerifyCommand) { + assert!(args.vk_path.is_file(), "Could not find verification key file at provided path"); + assert!(args.proof_path.is_file(), "Could not find proof file at provided path"); + + std::fs::write(args.proof_path, "proof").unwrap(); +} diff --git a/noir/tooling/backend_interface/test-binaries/mock_backend/src/write_vk_cmd.rs b/noir/tooling/backend_interface/test-binaries/mock_backend/src/write_vk_cmd.rs new file mode 100644 index 00000000000..fcee224e85b --- /dev/null +++ b/noir/tooling/backend_interface/test-binaries/mock_backend/src/write_vk_cmd.rs @@ -0,0 +1,20 @@ +use clap::Args; +use std::path::PathBuf; + +#[derive(Debug, Clone, Args)] +pub(crate) struct WriteVkCommand { + #[clap(short = 'c')] + pub(crate) crs_path: Option, + + #[clap(short = 'b')] + pub(crate) bytecode_path: PathBuf, + + #[clap(short = 'o')] + pub(crate) vk_path: PathBuf, +} + +pub(crate) fn run(args: WriteVkCommand) { + assert!(args.bytecode_path.is_file(), "Could not find bytecode file at provided path"); + + std::fs::write(args.vk_path, "vk").unwrap(); +} diff --git a/noir/tooling/bb_abstraction_leaks/Cargo.toml b/noir/tooling/bb_abstraction_leaks/Cargo.toml new file mode 100644 index 00000000000..972c78831a7 --- /dev/null +++ b/noir/tooling/bb_abstraction_leaks/Cargo.toml @@ -0,0 +1,17 @@ +[package] +name = "bb_abstraction_leaks" +description = "A crate which encapsulates knowledge about Barretenberg which is currently leaking into Nargo" +version = "0.11.0" +authors.workspace = true +edition.workspace = true +rust-version.workspace = true +license.workspace = true + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + +[dependencies] +acvm.workspace = true + +[build-dependencies] +build-target = "0.4.0" +const_format.workspace = true diff --git a/noir/tooling/bb_abstraction_leaks/build.rs b/noir/tooling/bb_abstraction_leaks/build.rs new file mode 100644 index 00000000000..9c5e51a8115 --- /dev/null +++ b/noir/tooling/bb_abstraction_leaks/build.rs @@ -0,0 +1,58 @@ +use build_target::{Arch, Os}; +use const_format::formatcp; + +// Useful for printing debugging messages during the build +// macro_rules! p { +// ($($tokens: tt)*) => { +// println!("cargo:warning={}", format!($($tokens)*)) +// } +// } + +const USERNAME: &str = "AztecProtocol"; +const REPO: &str = "aztec-packages"; +const VERSION: &str = "0.15.1"; +const TAG: &str = formatcp!("aztec-packages-v{}", VERSION); + +const API_URL: &str = + formatcp!("https://github.com/{}/{}/releases/download/{}", USERNAME, REPO, TAG); + +fn main() -> Result<(), String> { + // We need to inject which OS we're building for so that we can download the correct barretenberg binary. + let os = match build_target::target_os().unwrap() { + os @ (Os::Linux | Os::MacOs) => os, + Os::Windows => todo!("Windows is not currently supported"), + os_name => panic!("Unsupported OS {os_name}"), + }; + + let arch = match build_target::target_arch().unwrap() { + arch @ (Arch::X86_64 | Arch::AARCH64) => arch, + arch_name => panic!("Unsupported Architecture {arch_name}"), + }; + + // Arm builds of linux are not supported + // We do not panic because we allow users to run nargo without a backend. + if let (Os::Linux, Arch::AARCH64) = (&os, &arch) { + println!( + "cargo:warning=ARM64 builds of linux are not supported for the barretenberg binary" + ); + }; + + println!("cargo:rustc-env=BB_BINARY_URL={}", get_bb_download_url(arch, os)); + println!("cargo:rustc-env=BB_VERSION={}", VERSION); + + Ok(()) +} + +fn get_bb_download_url(target_arch: Arch, target_os: Os) -> String { + let archive_name = match target_os { + Os::Linux => "barretenberg-x86_64-linux-gnu.tar.gz", + Os::MacOs => match target_arch { + Arch::AARCH64 => "barretenberg-aarch64-apple-darwin.tar.gz", + Arch::X86_64 => "barretenberg-x86_64-apple-darwin.tar.gz", + arch => panic!("unsupported arch {arch}"), + }, + os => panic!("Unsupported OS {os}"), + }; + + format!("{API_URL}/{archive_name}") +} diff --git a/noir/tooling/bb_abstraction_leaks/src/contract.sol b/noir/tooling/bb_abstraction_leaks/src/contract.sol new file mode 100644 index 00000000000..814c81d235e --- /dev/null +++ b/noir/tooling/bb_abstraction_leaks/src/contract.sol @@ -0,0 +1,2575 @@ +/** + * @title Ultra Plonk proof verification contract + * @dev Top level Plonk proof verification contract, which allows Plonk proof to be verified + */ +abstract contract BaseUltraVerifier { + // VERIFICATION KEY MEMORY LOCATIONS + uint256 internal constant N_LOC = 0x380; + uint256 internal constant NUM_INPUTS_LOC = 0x3a0; + uint256 internal constant OMEGA_LOC = 0x3c0; + uint256 internal constant DOMAIN_INVERSE_LOC = 0x3e0; + uint256 internal constant Q1_X_LOC = 0x400; + uint256 internal constant Q1_Y_LOC = 0x420; + uint256 internal constant Q2_X_LOC = 0x440; + uint256 internal constant Q2_Y_LOC = 0x460; + uint256 internal constant Q3_X_LOC = 0x480; + uint256 internal constant Q3_Y_LOC = 0x4a0; + uint256 internal constant Q4_X_LOC = 0x4c0; + uint256 internal constant Q4_Y_LOC = 0x4e0; + uint256 internal constant QM_X_LOC = 0x500; + uint256 internal constant QM_Y_LOC = 0x520; + uint256 internal constant QC_X_LOC = 0x540; + uint256 internal constant QC_Y_LOC = 0x560; + uint256 internal constant QARITH_X_LOC = 0x580; + uint256 internal constant QARITH_Y_LOC = 0x5a0; + uint256 internal constant QSORT_X_LOC = 0x5c0; + uint256 internal constant QSORT_Y_LOC = 0x5e0; + uint256 internal constant QELLIPTIC_X_LOC = 0x600; + uint256 internal constant QELLIPTIC_Y_LOC = 0x620; + uint256 internal constant QAUX_X_LOC = 0x640; + uint256 internal constant QAUX_Y_LOC = 0x660; + uint256 internal constant SIGMA1_X_LOC = 0x680; + uint256 internal constant SIGMA1_Y_LOC = 0x6a0; + uint256 internal constant SIGMA2_X_LOC = 0x6c0; + uint256 internal constant SIGMA2_Y_LOC = 0x6e0; + uint256 internal constant SIGMA3_X_LOC = 0x700; + uint256 internal constant SIGMA3_Y_LOC = 0x720; + uint256 internal constant SIGMA4_X_LOC = 0x740; + uint256 internal constant SIGMA4_Y_LOC = 0x760; + uint256 internal constant TABLE1_X_LOC = 0x780; + uint256 internal constant TABLE1_Y_LOC = 0x7a0; + uint256 internal constant TABLE2_X_LOC = 0x7c0; + uint256 internal constant TABLE2_Y_LOC = 0x7e0; + uint256 internal constant TABLE3_X_LOC = 0x800; + uint256 internal constant TABLE3_Y_LOC = 0x820; + uint256 internal constant TABLE4_X_LOC = 0x840; + uint256 internal constant TABLE4_Y_LOC = 0x860; + uint256 internal constant TABLE_TYPE_X_LOC = 0x880; + uint256 internal constant TABLE_TYPE_Y_LOC = 0x8a0; + uint256 internal constant ID1_X_LOC = 0x8c0; + uint256 internal constant ID1_Y_LOC = 0x8e0; + uint256 internal constant ID2_X_LOC = 0x900; + uint256 internal constant ID2_Y_LOC = 0x920; + uint256 internal constant ID3_X_LOC = 0x940; + uint256 internal constant ID3_Y_LOC = 0x960; + uint256 internal constant ID4_X_LOC = 0x980; + uint256 internal constant ID4_Y_LOC = 0x9a0; + uint256 internal constant CONTAINS_RECURSIVE_PROOF_LOC = 0x9c0; + uint256 internal constant RECURSIVE_PROOF_PUBLIC_INPUT_INDICES_LOC = 0x9e0; + uint256 internal constant G2X_X0_LOC = 0xa00; + uint256 internal constant G2X_X1_LOC = 0xa20; + uint256 internal constant G2X_Y0_LOC = 0xa40; + uint256 internal constant G2X_Y1_LOC = 0xa60; + + // ### PROOF DATA MEMORY LOCATIONS + uint256 internal constant W1_X_LOC = 0x1200; + uint256 internal constant W1_Y_LOC = 0x1220; + uint256 internal constant W2_X_LOC = 0x1240; + uint256 internal constant W2_Y_LOC = 0x1260; + uint256 internal constant W3_X_LOC = 0x1280; + uint256 internal constant W3_Y_LOC = 0x12a0; + uint256 internal constant W4_X_LOC = 0x12c0; + uint256 internal constant W4_Y_LOC = 0x12e0; + uint256 internal constant S_X_LOC = 0x1300; + uint256 internal constant S_Y_LOC = 0x1320; + uint256 internal constant Z_X_LOC = 0x1340; + uint256 internal constant Z_Y_LOC = 0x1360; + uint256 internal constant Z_LOOKUP_X_LOC = 0x1380; + uint256 internal constant Z_LOOKUP_Y_LOC = 0x13a0; + uint256 internal constant T1_X_LOC = 0x13c0; + uint256 internal constant T1_Y_LOC = 0x13e0; + uint256 internal constant T2_X_LOC = 0x1400; + uint256 internal constant T2_Y_LOC = 0x1420; + uint256 internal constant T3_X_LOC = 0x1440; + uint256 internal constant T3_Y_LOC = 0x1460; + uint256 internal constant T4_X_LOC = 0x1480; + uint256 internal constant T4_Y_LOC = 0x14a0; + + uint256 internal constant W1_EVAL_LOC = 0x1600; + uint256 internal constant W2_EVAL_LOC = 0x1620; + uint256 internal constant W3_EVAL_LOC = 0x1640; + uint256 internal constant W4_EVAL_LOC = 0x1660; + uint256 internal constant S_EVAL_LOC = 0x1680; + uint256 internal constant Z_EVAL_LOC = 0x16a0; + uint256 internal constant Z_LOOKUP_EVAL_LOC = 0x16c0; + uint256 internal constant Q1_EVAL_LOC = 0x16e0; + uint256 internal constant Q2_EVAL_LOC = 0x1700; + uint256 internal constant Q3_EVAL_LOC = 0x1720; + uint256 internal constant Q4_EVAL_LOC = 0x1740; + uint256 internal constant QM_EVAL_LOC = 0x1760; + uint256 internal constant QC_EVAL_LOC = 0x1780; + uint256 internal constant QARITH_EVAL_LOC = 0x17a0; + uint256 internal constant QSORT_EVAL_LOC = 0x17c0; + uint256 internal constant QELLIPTIC_EVAL_LOC = 0x17e0; + uint256 internal constant QAUX_EVAL_LOC = 0x1800; + uint256 internal constant TABLE1_EVAL_LOC = 0x1840; + uint256 internal constant TABLE2_EVAL_LOC = 0x1860; + uint256 internal constant TABLE3_EVAL_LOC = 0x1880; + uint256 internal constant TABLE4_EVAL_LOC = 0x18a0; + uint256 internal constant TABLE_TYPE_EVAL_LOC = 0x18c0; + uint256 internal constant ID1_EVAL_LOC = 0x18e0; + uint256 internal constant ID2_EVAL_LOC = 0x1900; + uint256 internal constant ID3_EVAL_LOC = 0x1920; + uint256 internal constant ID4_EVAL_LOC = 0x1940; + uint256 internal constant SIGMA1_EVAL_LOC = 0x1960; + uint256 internal constant SIGMA2_EVAL_LOC = 0x1980; + uint256 internal constant SIGMA3_EVAL_LOC = 0x19a0; + uint256 internal constant SIGMA4_EVAL_LOC = 0x19c0; + uint256 internal constant W1_OMEGA_EVAL_LOC = 0x19e0; + uint256 internal constant W2_OMEGA_EVAL_LOC = 0x2000; + uint256 internal constant W3_OMEGA_EVAL_LOC = 0x2020; + uint256 internal constant W4_OMEGA_EVAL_LOC = 0x2040; + uint256 internal constant S_OMEGA_EVAL_LOC = 0x2060; + uint256 internal constant Z_OMEGA_EVAL_LOC = 0x2080; + uint256 internal constant Z_LOOKUP_OMEGA_EVAL_LOC = 0x20a0; + uint256 internal constant TABLE1_OMEGA_EVAL_LOC = 0x20c0; + uint256 internal constant TABLE2_OMEGA_EVAL_LOC = 0x20e0; + uint256 internal constant TABLE3_OMEGA_EVAL_LOC = 0x2100; + uint256 internal constant TABLE4_OMEGA_EVAL_LOC = 0x2120; + + uint256 internal constant PI_Z_X_LOC = 0x2300; + uint256 internal constant PI_Z_Y_LOC = 0x2320; + uint256 internal constant PI_Z_OMEGA_X_LOC = 0x2340; + uint256 internal constant PI_Z_OMEGA_Y_LOC = 0x2360; + + // Used for elliptic widget. These are alias names for wire + shifted wire evaluations + uint256 internal constant X1_EVAL_LOC = W2_EVAL_LOC; + uint256 internal constant X2_EVAL_LOC = W1_OMEGA_EVAL_LOC; + uint256 internal constant X3_EVAL_LOC = W2_OMEGA_EVAL_LOC; + uint256 internal constant Y1_EVAL_LOC = W3_EVAL_LOC; + uint256 internal constant Y2_EVAL_LOC = W4_OMEGA_EVAL_LOC; + uint256 internal constant Y3_EVAL_LOC = W3_OMEGA_EVAL_LOC; + uint256 internal constant QBETA_LOC = Q3_EVAL_LOC; + uint256 internal constant QBETA_SQR_LOC = Q4_EVAL_LOC; + uint256 internal constant QSIGN_LOC = Q1_EVAL_LOC; + + // ### CHALLENGES MEMORY OFFSETS + + uint256 internal constant C_BETA_LOC = 0x2600; + uint256 internal constant C_GAMMA_LOC = 0x2620; + uint256 internal constant C_ALPHA_LOC = 0x2640; + uint256 internal constant C_ETA_LOC = 0x2660; + uint256 internal constant C_ETA_SQR_LOC = 0x2680; + uint256 internal constant C_ETA_CUBE_LOC = 0x26a0; + + uint256 internal constant C_ZETA_LOC = 0x26c0; + uint256 internal constant C_CURRENT_LOC = 0x26e0; + uint256 internal constant C_V0_LOC = 0x2700; + uint256 internal constant C_V1_LOC = 0x2720; + uint256 internal constant C_V2_LOC = 0x2740; + uint256 internal constant C_V3_LOC = 0x2760; + uint256 internal constant C_V4_LOC = 0x2780; + uint256 internal constant C_V5_LOC = 0x27a0; + uint256 internal constant C_V6_LOC = 0x27c0; + uint256 internal constant C_V7_LOC = 0x27e0; + uint256 internal constant C_V8_LOC = 0x2800; + uint256 internal constant C_V9_LOC = 0x2820; + uint256 internal constant C_V10_LOC = 0x2840; + uint256 internal constant C_V11_LOC = 0x2860; + uint256 internal constant C_V12_LOC = 0x2880; + uint256 internal constant C_V13_LOC = 0x28a0; + uint256 internal constant C_V14_LOC = 0x28c0; + uint256 internal constant C_V15_LOC = 0x28e0; + uint256 internal constant C_V16_LOC = 0x2900; + uint256 internal constant C_V17_LOC = 0x2920; + uint256 internal constant C_V18_LOC = 0x2940; + uint256 internal constant C_V19_LOC = 0x2960; + uint256 internal constant C_V20_LOC = 0x2980; + uint256 internal constant C_V21_LOC = 0x29a0; + uint256 internal constant C_V22_LOC = 0x29c0; + uint256 internal constant C_V23_LOC = 0x29e0; + uint256 internal constant C_V24_LOC = 0x2a00; + uint256 internal constant C_V25_LOC = 0x2a20; + uint256 internal constant C_V26_LOC = 0x2a40; + uint256 internal constant C_V27_LOC = 0x2a60; + uint256 internal constant C_V28_LOC = 0x2a80; + uint256 internal constant C_V29_LOC = 0x2aa0; + uint256 internal constant C_V30_LOC = 0x2ac0; + + uint256 internal constant C_U_LOC = 0x2b00; + + // ### LOCAL VARIABLES MEMORY OFFSETS + uint256 internal constant DELTA_NUMERATOR_LOC = 0x3000; + uint256 internal constant DELTA_DENOMINATOR_LOC = 0x3020; + uint256 internal constant ZETA_POW_N_LOC = 0x3040; + uint256 internal constant PUBLIC_INPUT_DELTA_LOC = 0x3060; + uint256 internal constant ZERO_POLY_LOC = 0x3080; + uint256 internal constant L_START_LOC = 0x30a0; + uint256 internal constant L_END_LOC = 0x30c0; + uint256 internal constant R_ZERO_EVAL_LOC = 0x30e0; + + uint256 internal constant PLOOKUP_DELTA_NUMERATOR_LOC = 0x3100; + uint256 internal constant PLOOKUP_DELTA_DENOMINATOR_LOC = 0x3120; + uint256 internal constant PLOOKUP_DELTA_LOC = 0x3140; + + uint256 internal constant ACCUMULATOR_X_LOC = 0x3160; + uint256 internal constant ACCUMULATOR_Y_LOC = 0x3180; + uint256 internal constant ACCUMULATOR2_X_LOC = 0x31a0; + uint256 internal constant ACCUMULATOR2_Y_LOC = 0x31c0; + uint256 internal constant PAIRING_LHS_X_LOC = 0x31e0; + uint256 internal constant PAIRING_LHS_Y_LOC = 0x3200; + uint256 internal constant PAIRING_RHS_X_LOC = 0x3220; + uint256 internal constant PAIRING_RHS_Y_LOC = 0x3240; + + // ### SUCCESS FLAG MEMORY LOCATIONS + uint256 internal constant GRAND_PRODUCT_SUCCESS_FLAG = 0x3300; + uint256 internal constant ARITHMETIC_TERM_SUCCESS_FLAG = 0x3020; + uint256 internal constant BATCH_OPENING_SUCCESS_FLAG = 0x3340; + uint256 internal constant OPENING_COMMITMENT_SUCCESS_FLAG = 0x3360; + uint256 internal constant PAIRING_PREAMBLE_SUCCESS_FLAG = 0x3380; + uint256 internal constant PAIRING_SUCCESS_FLAG = 0x33a0; + uint256 internal constant RESULT_FLAG = 0x33c0; + + // misc stuff + uint256 internal constant OMEGA_INVERSE_LOC = 0x3400; + uint256 internal constant C_ALPHA_SQR_LOC = 0x3420; + uint256 internal constant C_ALPHA_CUBE_LOC = 0x3440; + uint256 internal constant C_ALPHA_QUAD_LOC = 0x3460; + uint256 internal constant C_ALPHA_BASE_LOC = 0x3480; + + // ### RECURSION VARIABLE MEMORY LOCATIONS + uint256 internal constant RECURSIVE_P1_X_LOC = 0x3500; + uint256 internal constant RECURSIVE_P1_Y_LOC = 0x3520; + uint256 internal constant RECURSIVE_P2_X_LOC = 0x3540; + uint256 internal constant RECURSIVE_P2_Y_LOC = 0x3560; + + uint256 internal constant PUBLIC_INPUTS_HASH_LOCATION = 0x3580; + + // sub-identity storage + uint256 internal constant PERMUTATION_IDENTITY = 0x3600; + uint256 internal constant PLOOKUP_IDENTITY = 0x3620; + uint256 internal constant ARITHMETIC_IDENTITY = 0x3640; + uint256 internal constant SORT_IDENTITY = 0x3660; + uint256 internal constant ELLIPTIC_IDENTITY = 0x3680; + uint256 internal constant AUX_IDENTITY = 0x36a0; + uint256 internal constant AUX_NON_NATIVE_FIELD_EVALUATION = 0x36c0; + uint256 internal constant AUX_LIMB_ACCUMULATOR_EVALUATION = 0x36e0; + uint256 internal constant AUX_RAM_CONSISTENCY_EVALUATION = 0x3700; + uint256 internal constant AUX_ROM_CONSISTENCY_EVALUATION = 0x3720; + uint256 internal constant AUX_MEMORY_EVALUATION = 0x3740; + + uint256 internal constant QUOTIENT_EVAL_LOC = 0x3760; + uint256 internal constant ZERO_POLY_INVERSE_LOC = 0x3780; + + // when hashing public inputs we use memory at NU_CHALLENGE_INPUT_LOC_A, as the hash input size is unknown at compile time + uint256 internal constant NU_CHALLENGE_INPUT_LOC_A = 0x37a0; + uint256 internal constant NU_CHALLENGE_INPUT_LOC_B = 0x37c0; + uint256 internal constant NU_CHALLENGE_INPUT_LOC_C = 0x37e0; + + bytes4 internal constant PUBLIC_INPUT_INVALID_BN128_G1_POINT_SELECTOR = 0xeba9f4a6; + bytes4 internal constant PUBLIC_INPUT_GE_P_SELECTOR = 0x374a972f; + bytes4 internal constant MOD_EXP_FAILURE_SELECTOR = 0xf894a7bc; + bytes4 internal constant EC_SCALAR_MUL_FAILURE_SELECTOR = 0xf755f369; + bytes4 internal constant PROOF_FAILURE_SELECTOR = 0x0711fcec; + + uint256 internal constant ETA_INPUT_LENGTH = 0xc0; // W1, W2, W3 = 6 * 0x20 bytes + + // We need to hash 41 field elements when generating the NU challenge + // w1, w2, w3, w4, s, z, z_lookup, q1, q2, q3, q4, qm, qc, qarith (14) + // qsort, qelliptic, qaux, sigma1, sigma2, sigma, sigma4, (7) + // table1, table2, table3, table4, tabletype, id1, id2, id3, id4, (9) + // w1_omega, w2_omega, w3_omega, w4_omega, s_omega, z_omega, z_lookup_omega, (7) + // table1_omega, table2_omega, table3_omega, table4_omega (4) + uint256 internal constant NU_INPUT_LENGTH = 0x520; // 0x520 = 41 * 0x20 + + // There are ELEVEN G1 group elements added into the transcript in the `beta` round, that we need to skip over + // W1, W2, W3, W4, S, Z, Z_LOOKUP, T1, T2, T3, T4 + uint256 internal constant NU_CALLDATA_SKIP_LENGTH = 0x2c0; // 11 * 0x40 = 0x2c0 + + uint256 internal constant NEGATIVE_INVERSE_OF_2_MODULO_P = + 0x183227397098d014dc2822db40c0ac2e9419f4243cdcb848a1f0fac9f8000000; + uint256 internal constant LIMB_SIZE = 0x100000000000000000; // 2<<68 + uint256 internal constant SUBLIMB_SHIFT = 0x4000; // 2<<14 + + // y^2 = x^3 + ax + b + // for Grumpkin, a = 0 and b = -17. We use b in a custom gate relation that evaluates elliptic curve arithmetic + uint256 internal constant GRUMPKIN_CURVE_B_PARAMETER_NEGATED = 17; + error PUBLIC_INPUT_COUNT_INVALID(uint256 expected, uint256 actual); + error PUBLIC_INPUT_INVALID_BN128_G1_POINT(); + error PUBLIC_INPUT_GE_P(); + error MOD_EXP_FAILURE(); + error EC_SCALAR_MUL_FAILURE(); + error PROOF_FAILURE(); + + function getVerificationKeyHash() public pure virtual returns (bytes32); + + function loadVerificationKey(uint256 _vk, uint256 _omegaInverseLoc) internal pure virtual; + + /** + * @notice Verify a Ultra Plonk proof + * @param _proof - The serialized proof + * @param _publicInputs - An array of the public inputs + * @return True if proof is valid, reverts otherwise + */ + function verify(bytes calldata _proof, bytes32[] calldata _publicInputs) external view returns (bool) { + loadVerificationKey(N_LOC, OMEGA_INVERSE_LOC); + + uint256 requiredPublicInputCount; + assembly { + requiredPublicInputCount := mload(NUM_INPUTS_LOC) + } + if (requiredPublicInputCount != _publicInputs.length) { + revert PUBLIC_INPUT_COUNT_INVALID(requiredPublicInputCount, _publicInputs.length); + } + + assembly { + let q := 21888242871839275222246405745257275088696311157297823662689037894645226208583 // EC group order + let p := 21888242871839275222246405745257275088548364400416034343698204186575808495617 // Prime field order + + /** + * LOAD PROOF FROM CALLDATA + */ + { + let data_ptr := add(calldataload(0x04), 0x24) + + mstore(W1_Y_LOC, mod(calldataload(data_ptr), q)) + mstore(W1_X_LOC, mod(calldataload(add(data_ptr, 0x20)), q)) + + mstore(W2_Y_LOC, mod(calldataload(add(data_ptr, 0x40)), q)) + mstore(W2_X_LOC, mod(calldataload(add(data_ptr, 0x60)), q)) + + mstore(W3_Y_LOC, mod(calldataload(add(data_ptr, 0x80)), q)) + mstore(W3_X_LOC, mod(calldataload(add(data_ptr, 0xa0)), q)) + + mstore(W4_Y_LOC, mod(calldataload(add(data_ptr, 0xc0)), q)) + mstore(W4_X_LOC, mod(calldataload(add(data_ptr, 0xe0)), q)) + + mstore(S_Y_LOC, mod(calldataload(add(data_ptr, 0x100)), q)) + mstore(S_X_LOC, mod(calldataload(add(data_ptr, 0x120)), q)) + mstore(Z_Y_LOC, mod(calldataload(add(data_ptr, 0x140)), q)) + mstore(Z_X_LOC, mod(calldataload(add(data_ptr, 0x160)), q)) + mstore(Z_LOOKUP_Y_LOC, mod(calldataload(add(data_ptr, 0x180)), q)) + mstore(Z_LOOKUP_X_LOC, mod(calldataload(add(data_ptr, 0x1a0)), q)) + mstore(T1_Y_LOC, mod(calldataload(add(data_ptr, 0x1c0)), q)) + mstore(T1_X_LOC, mod(calldataload(add(data_ptr, 0x1e0)), q)) + + mstore(T2_Y_LOC, mod(calldataload(add(data_ptr, 0x200)), q)) + mstore(T2_X_LOC, mod(calldataload(add(data_ptr, 0x220)), q)) + + mstore(T3_Y_LOC, mod(calldataload(add(data_ptr, 0x240)), q)) + mstore(T3_X_LOC, mod(calldataload(add(data_ptr, 0x260)), q)) + + mstore(T4_Y_LOC, mod(calldataload(add(data_ptr, 0x280)), q)) + mstore(T4_X_LOC, mod(calldataload(add(data_ptr, 0x2a0)), q)) + + mstore(W1_EVAL_LOC, mod(calldataload(add(data_ptr, 0x2c0)), p)) + mstore(W2_EVAL_LOC, mod(calldataload(add(data_ptr, 0x2e0)), p)) + mstore(W3_EVAL_LOC, mod(calldataload(add(data_ptr, 0x300)), p)) + mstore(W4_EVAL_LOC, mod(calldataload(add(data_ptr, 0x320)), p)) + mstore(S_EVAL_LOC, mod(calldataload(add(data_ptr, 0x340)), p)) + mstore(Z_EVAL_LOC, mod(calldataload(add(data_ptr, 0x360)), p)) + mstore(Z_LOOKUP_EVAL_LOC, mod(calldataload(add(data_ptr, 0x380)), p)) + mstore(Q1_EVAL_LOC, mod(calldataload(add(data_ptr, 0x3a0)), p)) + mstore(Q2_EVAL_LOC, mod(calldataload(add(data_ptr, 0x3c0)), p)) + mstore(Q3_EVAL_LOC, mod(calldataload(add(data_ptr, 0x3e0)), p)) + mstore(Q4_EVAL_LOC, mod(calldataload(add(data_ptr, 0x400)), p)) + mstore(QM_EVAL_LOC, mod(calldataload(add(data_ptr, 0x420)), p)) + mstore(QC_EVAL_LOC, mod(calldataload(add(data_ptr, 0x440)), p)) + mstore(QARITH_EVAL_LOC, mod(calldataload(add(data_ptr, 0x460)), p)) + mstore(QSORT_EVAL_LOC, mod(calldataload(add(data_ptr, 0x480)), p)) + mstore(QELLIPTIC_EVAL_LOC, mod(calldataload(add(data_ptr, 0x4a0)), p)) + mstore(QAUX_EVAL_LOC, mod(calldataload(add(data_ptr, 0x4c0)), p)) + + mstore(SIGMA1_EVAL_LOC, mod(calldataload(add(data_ptr, 0x4e0)), p)) + mstore(SIGMA2_EVAL_LOC, mod(calldataload(add(data_ptr, 0x500)), p)) + + mstore(SIGMA3_EVAL_LOC, mod(calldataload(add(data_ptr, 0x520)), p)) + mstore(SIGMA4_EVAL_LOC, mod(calldataload(add(data_ptr, 0x540)), p)) + + mstore(TABLE1_EVAL_LOC, mod(calldataload(add(data_ptr, 0x560)), p)) + mstore(TABLE2_EVAL_LOC, mod(calldataload(add(data_ptr, 0x580)), p)) + mstore(TABLE3_EVAL_LOC, mod(calldataload(add(data_ptr, 0x5a0)), p)) + mstore(TABLE4_EVAL_LOC, mod(calldataload(add(data_ptr, 0x5c0)), p)) + mstore(TABLE_TYPE_EVAL_LOC, mod(calldataload(add(data_ptr, 0x5e0)), p)) + + mstore(ID1_EVAL_LOC, mod(calldataload(add(data_ptr, 0x600)), p)) + mstore(ID2_EVAL_LOC, mod(calldataload(add(data_ptr, 0x620)), p)) + mstore(ID3_EVAL_LOC, mod(calldataload(add(data_ptr, 0x640)), p)) + mstore(ID4_EVAL_LOC, mod(calldataload(add(data_ptr, 0x660)), p)) + + mstore(W1_OMEGA_EVAL_LOC, mod(calldataload(add(data_ptr, 0x680)), p)) + mstore(W2_OMEGA_EVAL_LOC, mod(calldataload(add(data_ptr, 0x6a0)), p)) + mstore(W3_OMEGA_EVAL_LOC, mod(calldataload(add(data_ptr, 0x6c0)), p)) + mstore(W4_OMEGA_EVAL_LOC, mod(calldataload(add(data_ptr, 0x6e0)), p)) + mstore(S_OMEGA_EVAL_LOC, mod(calldataload(add(data_ptr, 0x700)), p)) + + mstore(Z_OMEGA_EVAL_LOC, mod(calldataload(add(data_ptr, 0x720)), p)) + + mstore(Z_LOOKUP_OMEGA_EVAL_LOC, mod(calldataload(add(data_ptr, 0x740)), p)) + mstore(TABLE1_OMEGA_EVAL_LOC, mod(calldataload(add(data_ptr, 0x760)), p)) + mstore(TABLE2_OMEGA_EVAL_LOC, mod(calldataload(add(data_ptr, 0x780)), p)) + mstore(TABLE3_OMEGA_EVAL_LOC, mod(calldataload(add(data_ptr, 0x7a0)), p)) + mstore(TABLE4_OMEGA_EVAL_LOC, mod(calldataload(add(data_ptr, 0x7c0)), p)) + + mstore(PI_Z_Y_LOC, mod(calldataload(add(data_ptr, 0x7e0)), q)) + mstore(PI_Z_X_LOC, mod(calldataload(add(data_ptr, 0x800)), q)) + + mstore(PI_Z_OMEGA_Y_LOC, mod(calldataload(add(data_ptr, 0x820)), q)) + mstore(PI_Z_OMEGA_X_LOC, mod(calldataload(add(data_ptr, 0x840)), q)) + } + + /** + * LOAD RECURSIVE PROOF INTO MEMORY + */ + { + if mload(CONTAINS_RECURSIVE_PROOF_LOC) { + let public_inputs_ptr := add(calldataload(0x24), 0x24) + let index_counter := add(shl(5, mload(RECURSIVE_PROOF_PUBLIC_INPUT_INDICES_LOC)), public_inputs_ptr) + + let x0 := calldataload(index_counter) + x0 := add(x0, shl(68, calldataload(add(index_counter, 0x20)))) + x0 := add(x0, shl(136, calldataload(add(index_counter, 0x40)))) + x0 := add(x0, shl(204, calldataload(add(index_counter, 0x60)))) + let y0 := calldataload(add(index_counter, 0x80)) + y0 := add(y0, shl(68, calldataload(add(index_counter, 0xa0)))) + y0 := add(y0, shl(136, calldataload(add(index_counter, 0xc0)))) + y0 := add(y0, shl(204, calldataload(add(index_counter, 0xe0)))) + let x1 := calldataload(add(index_counter, 0x100)) + x1 := add(x1, shl(68, calldataload(add(index_counter, 0x120)))) + x1 := add(x1, shl(136, calldataload(add(index_counter, 0x140)))) + x1 := add(x1, shl(204, calldataload(add(index_counter, 0x160)))) + let y1 := calldataload(add(index_counter, 0x180)) + y1 := add(y1, shl(68, calldataload(add(index_counter, 0x1a0)))) + y1 := add(y1, shl(136, calldataload(add(index_counter, 0x1c0)))) + y1 := add(y1, shl(204, calldataload(add(index_counter, 0x1e0)))) + mstore(RECURSIVE_P1_X_LOC, x0) + mstore(RECURSIVE_P1_Y_LOC, y0) + mstore(RECURSIVE_P2_X_LOC, x1) + mstore(RECURSIVE_P2_Y_LOC, y1) + + // validate these are valid bn128 G1 points + if iszero(and(and(lt(x0, q), lt(x1, q)), and(lt(y0, q), lt(y1, q)))) { + mstore(0x00, PUBLIC_INPUT_INVALID_BN128_G1_POINT_SELECTOR) + revert(0x00, 0x04) + } + } + } + + { + /** + * Generate initial challenge + */ + mstore(0x00, shl(224, mload(N_LOC))) + mstore(0x04, shl(224, mload(NUM_INPUTS_LOC))) + let challenge := keccak256(0x00, 0x08) + + /** + * Generate eta challenge + */ + mstore(PUBLIC_INPUTS_HASH_LOCATION, challenge) + // The public input location is stored at 0x24, we then add 0x24 to skip selector and the length of public inputs + let public_inputs_start := add(calldataload(0x24), 0x24) + // copy the public inputs over + let public_input_size := mul(mload(NUM_INPUTS_LOC), 0x20) + calldatacopy(add(PUBLIC_INPUTS_HASH_LOCATION, 0x20), public_inputs_start, public_input_size) + + // copy W1, W2, W3 into challenge. Each point is 0x40 bytes, so load 0xc0 = 3 * 0x40 bytes (ETA input length) + let w_start := add(calldataload(0x04), 0x24) + calldatacopy(add(add(PUBLIC_INPUTS_HASH_LOCATION, 0x20), public_input_size), w_start, ETA_INPUT_LENGTH) + + // Challenge is the old challenge + public inputs + W1, W2, W3 (0x20 + public_input_size + 0xc0) + let challenge_bytes_size := add(0x20, add(public_input_size, ETA_INPUT_LENGTH)) + + challenge := keccak256(PUBLIC_INPUTS_HASH_LOCATION, challenge_bytes_size) + { + let eta := mod(challenge, p) + mstore(C_ETA_LOC, eta) + mstore(C_ETA_SQR_LOC, mulmod(eta, eta, p)) + mstore(C_ETA_CUBE_LOC, mulmod(mload(C_ETA_SQR_LOC), eta, p)) + } + + /** + * Generate beta challenge + */ + mstore(0x00, challenge) + mstore(0x20, mload(W4_Y_LOC)) + mstore(0x40, mload(W4_X_LOC)) + mstore(0x60, mload(S_Y_LOC)) + mstore(0x80, mload(S_X_LOC)) + challenge := keccak256(0x00, 0xa0) + mstore(C_BETA_LOC, mod(challenge, p)) + + /** + * Generate gamma challenge + */ + mstore(0x00, challenge) + mstore8(0x20, 0x01) + challenge := keccak256(0x00, 0x21) + mstore(C_GAMMA_LOC, mod(challenge, p)) + + /** + * Generate alpha challenge + */ + mstore(0x00, challenge) + mstore(0x20, mload(Z_Y_LOC)) + mstore(0x40, mload(Z_X_LOC)) + mstore(0x60, mload(Z_LOOKUP_Y_LOC)) + mstore(0x80, mload(Z_LOOKUP_X_LOC)) + challenge := keccak256(0x00, 0xa0) + mstore(C_ALPHA_LOC, mod(challenge, p)) + + /** + * Compute and store some powers of alpha for future computations + */ + let alpha := mload(C_ALPHA_LOC) + mstore(C_ALPHA_SQR_LOC, mulmod(alpha, alpha, p)) + mstore(C_ALPHA_CUBE_LOC, mulmod(mload(C_ALPHA_SQR_LOC), alpha, p)) + mstore(C_ALPHA_QUAD_LOC, mulmod(mload(C_ALPHA_CUBE_LOC), alpha, p)) + mstore(C_ALPHA_BASE_LOC, alpha) + + /** + * Generate zeta challenge + */ + mstore(0x00, challenge) + mstore(0x20, mload(T1_Y_LOC)) + mstore(0x40, mload(T1_X_LOC)) + mstore(0x60, mload(T2_Y_LOC)) + mstore(0x80, mload(T2_X_LOC)) + mstore(0xa0, mload(T3_Y_LOC)) + mstore(0xc0, mload(T3_X_LOC)) + mstore(0xe0, mload(T4_Y_LOC)) + mstore(0x100, mload(T4_X_LOC)) + + challenge := keccak256(0x00, 0x120) + + mstore(C_ZETA_LOC, mod(challenge, p)) + mstore(C_CURRENT_LOC, challenge) + } + + /** + * EVALUATE FIELD OPERATIONS + */ + + /** + * COMPUTE PUBLIC INPUT DELTA + * ΔPI = ∏ᵢ∈ℓ(wᵢ + β σ(i) + γ) / ∏ᵢ∈ℓ(wᵢ + β σ'(i) + γ) + */ + { + let beta := mload(C_BETA_LOC) // β + let gamma := mload(C_GAMMA_LOC) // γ + let work_root := mload(OMEGA_LOC) // ω + let numerator_value := 1 + let denominator_value := 1 + + let p_clone := p // move p to the front of the stack + let valid_inputs := true + + // Load the starting point of the public inputs (jump over the selector and the length of public inputs [0x24]) + let public_inputs_ptr := add(calldataload(0x24), 0x24) + + // endpoint_ptr = public_inputs_ptr + num_inputs * 0x20. // every public input is 0x20 bytes + let endpoint_ptr := add(public_inputs_ptr, mul(mload(NUM_INPUTS_LOC), 0x20)) + + // root_1 = β * 0x05 + let root_1 := mulmod(beta, 0x05, p_clone) // k1.β + // root_2 = β * 0x0c + let root_2 := mulmod(beta, 0x0c, p_clone) + // @note 0x05 + 0x07 == 0x0c == external coset generator + + for {} lt(public_inputs_ptr, endpoint_ptr) { public_inputs_ptr := add(public_inputs_ptr, 0x20) } { + /** + * input = public_input[i] + * valid_inputs &= input < p + * temp = input + gamma + * numerator_value *= (β.σ(i) + wᵢ + γ) // σ(i) = 0x05.ωⁱ + * denominator_value *= (β.σ'(i) + wᵢ + γ) // σ'(i) = 0x0c.ωⁱ + * root_1 *= ω + * root_2 *= ω + */ + + let input := calldataload(public_inputs_ptr) + valid_inputs := and(valid_inputs, lt(input, p_clone)) + let temp := addmod(input, gamma, p_clone) + + numerator_value := mulmod(numerator_value, add(root_1, temp), p_clone) + denominator_value := mulmod(denominator_value, add(root_2, temp), p_clone) + + root_1 := mulmod(root_1, work_root, p_clone) + root_2 := mulmod(root_2, work_root, p_clone) + } + + // Revert if not all public inputs are field elements (i.e. < p) + if iszero(valid_inputs) { + mstore(0x00, PUBLIC_INPUT_GE_P_SELECTOR) + revert(0x00, 0x04) + } + + mstore(DELTA_NUMERATOR_LOC, numerator_value) + mstore(DELTA_DENOMINATOR_LOC, denominator_value) + } + + /** + * Compute Plookup delta factor [γ(1 + β)]^{n-k} + * k = num roots cut out of Z_H = 4 + */ + { + let delta_base := mulmod(mload(C_GAMMA_LOC), addmod(mload(C_BETA_LOC), 1, p), p) + let delta_numerator := delta_base + { + let exponent := mload(N_LOC) + let count := 1 + for {} lt(count, exponent) { count := add(count, count) } { + delta_numerator := mulmod(delta_numerator, delta_numerator, p) + } + } + mstore(PLOOKUP_DELTA_NUMERATOR_LOC, delta_numerator) + + let delta_denominator := mulmod(delta_base, delta_base, p) + delta_denominator := mulmod(delta_denominator, delta_denominator, p) + mstore(PLOOKUP_DELTA_DENOMINATOR_LOC, delta_denominator) + } + /** + * Compute lagrange poly and vanishing poly fractions + */ + { + /** + * vanishing_numerator = zeta + * ZETA_POW_N = zeta^n + * vanishing_numerator -= 1 + * accumulating_root = omega_inverse + * work_root = p - accumulating_root + * domain_inverse = domain_inverse + * vanishing_denominator = zeta + work_root + * work_root *= accumulating_root + * vanishing_denominator *= (zeta + work_root) + * work_root *= accumulating_root + * vanishing_denominator *= (zeta + work_root) + * vanishing_denominator *= (zeta + (zeta + accumulating_root)) + * work_root = omega + * lagrange_numerator = vanishing_numerator * domain_inverse + * l_start_denominator = zeta - 1 + * accumulating_root = work_root^2 + * l_end_denominator = accumulating_root^2 * work_root * zeta - 1 + * Note: l_end_denominator term contains a term \omega^5 to cut out 5 roots of unity from vanishing poly + */ + + let zeta := mload(C_ZETA_LOC) + + // compute zeta^n, where n is a power of 2 + let vanishing_numerator := zeta + { + // pow_small + let exponent := mload(N_LOC) + let count := 1 + for {} lt(count, exponent) { count := add(count, count) } { + vanishing_numerator := mulmod(vanishing_numerator, vanishing_numerator, p) + } + } + mstore(ZETA_POW_N_LOC, vanishing_numerator) + vanishing_numerator := addmod(vanishing_numerator, sub(p, 1), p) + + let accumulating_root := mload(OMEGA_INVERSE_LOC) + let work_root := sub(p, accumulating_root) + let domain_inverse := mload(DOMAIN_INVERSE_LOC) + + let vanishing_denominator := addmod(zeta, work_root, p) + work_root := mulmod(work_root, accumulating_root, p) + vanishing_denominator := mulmod(vanishing_denominator, addmod(zeta, work_root, p), p) + work_root := mulmod(work_root, accumulating_root, p) + vanishing_denominator := mulmod(vanishing_denominator, addmod(zeta, work_root, p), p) + vanishing_denominator := + mulmod(vanishing_denominator, addmod(zeta, mulmod(work_root, accumulating_root, p), p), p) + + work_root := mload(OMEGA_LOC) + + let lagrange_numerator := mulmod(vanishing_numerator, domain_inverse, p) + let l_start_denominator := addmod(zeta, sub(p, 1), p) + + accumulating_root := mulmod(work_root, work_root, p) + + let l_end_denominator := + addmod( + mulmod(mulmod(mulmod(accumulating_root, accumulating_root, p), work_root, p), zeta, p), sub(p, 1), p + ) + + /** + * Compute inversions using Montgomery's batch inversion trick + */ + let accumulator := mload(DELTA_DENOMINATOR_LOC) + let t0 := accumulator + accumulator := mulmod(accumulator, vanishing_denominator, p) + let t1 := accumulator + accumulator := mulmod(accumulator, vanishing_numerator, p) + let t2 := accumulator + accumulator := mulmod(accumulator, l_start_denominator, p) + let t3 := accumulator + accumulator := mulmod(accumulator, mload(PLOOKUP_DELTA_DENOMINATOR_LOC), p) + let t4 := accumulator + { + mstore(0, 0x20) + mstore(0x20, 0x20) + mstore(0x40, 0x20) + mstore(0x60, mulmod(accumulator, l_end_denominator, p)) + mstore(0x80, sub(p, 2)) + mstore(0xa0, p) + if iszero(staticcall(gas(), 0x05, 0x00, 0xc0, 0x00, 0x20)) { + mstore(0x0, MOD_EXP_FAILURE_SELECTOR) + revert(0x00, 0x04) + } + accumulator := mload(0x00) + } + + t4 := mulmod(accumulator, t4, p) + accumulator := mulmod(accumulator, l_end_denominator, p) + + t3 := mulmod(accumulator, t3, p) + accumulator := mulmod(accumulator, mload(PLOOKUP_DELTA_DENOMINATOR_LOC), p) + + t2 := mulmod(accumulator, t2, p) + accumulator := mulmod(accumulator, l_start_denominator, p) + + t1 := mulmod(accumulator, t1, p) + accumulator := mulmod(accumulator, vanishing_numerator, p) + + t0 := mulmod(accumulator, t0, p) + accumulator := mulmod(accumulator, vanishing_denominator, p) + + accumulator := mulmod(mulmod(accumulator, accumulator, p), mload(DELTA_DENOMINATOR_LOC), p) + + mstore(PUBLIC_INPUT_DELTA_LOC, mulmod(mload(DELTA_NUMERATOR_LOC), accumulator, p)) + mstore(ZERO_POLY_LOC, mulmod(vanishing_numerator, t0, p)) + mstore(ZERO_POLY_INVERSE_LOC, mulmod(vanishing_denominator, t1, p)) + mstore(L_START_LOC, mulmod(lagrange_numerator, t2, p)) + mstore(PLOOKUP_DELTA_LOC, mulmod(mload(PLOOKUP_DELTA_NUMERATOR_LOC), t3, p)) + mstore(L_END_LOC, mulmod(lagrange_numerator, t4, p)) + } + + /** + * UltraPlonk Widget Ordering: + * + * 1. Permutation widget + * 2. Plookup widget + * 3. Arithmetic widget + * 4. Fixed base widget (?) + * 5. GenPermSort widget + * 6. Elliptic widget + * 7. Auxiliary widget + */ + + /** + * COMPUTE PERMUTATION WIDGET EVALUATION + */ + { + let alpha := mload(C_ALPHA_LOC) + let beta := mload(C_BETA_LOC) + let gamma := mload(C_GAMMA_LOC) + + /** + * t1 = (W1 + gamma + beta * ID1) * (W2 + gamma + beta * ID2) + * t2 = (W3 + gamma + beta * ID3) * (W4 + gamma + beta * ID4) + * result = alpha_base * z_eval * t1 * t2 + * t1 = (W1 + gamma + beta * sigma_1_eval) * (W2 + gamma + beta * sigma_2_eval) + * t2 = (W2 + gamma + beta * sigma_3_eval) * (W3 + gamma + beta * sigma_4_eval) + * result -= (alpha_base * z_omega_eval * t1 * t2) + */ + let t1 := + mulmod( + add(add(mload(W1_EVAL_LOC), gamma), mulmod(beta, mload(ID1_EVAL_LOC), p)), + add(add(mload(W2_EVAL_LOC), gamma), mulmod(beta, mload(ID2_EVAL_LOC), p)), + p + ) + let t2 := + mulmod( + add(add(mload(W3_EVAL_LOC), gamma), mulmod(beta, mload(ID3_EVAL_LOC), p)), + add(add(mload(W4_EVAL_LOC), gamma), mulmod(beta, mload(ID4_EVAL_LOC), p)), + p + ) + let result := mulmod(mload(C_ALPHA_BASE_LOC), mulmod(mload(Z_EVAL_LOC), mulmod(t1, t2, p), p), p) + t1 := + mulmod( + add(add(mload(W1_EVAL_LOC), gamma), mulmod(beta, mload(SIGMA1_EVAL_LOC), p)), + add(add(mload(W2_EVAL_LOC), gamma), mulmod(beta, mload(SIGMA2_EVAL_LOC), p)), + p + ) + t2 := + mulmod( + add(add(mload(W3_EVAL_LOC), gamma), mulmod(beta, mload(SIGMA3_EVAL_LOC), p)), + add(add(mload(W4_EVAL_LOC), gamma), mulmod(beta, mload(SIGMA4_EVAL_LOC), p)), + p + ) + result := + addmod( + result, + sub(p, mulmod(mload(C_ALPHA_BASE_LOC), mulmod(mload(Z_OMEGA_EVAL_LOC), mulmod(t1, t2, p), p), p)), + p + ) + + /** + * alpha_base *= alpha + * result += alpha_base . (L_{n-k}(ʓ) . (z(ʓ.ω) - ∆_{PI})) + * alpha_base *= alpha + * result += alpha_base . (L_1(ʓ)(Z(ʓ) - 1)) + * alpha_Base *= alpha + */ + mstore(C_ALPHA_BASE_LOC, mulmod(mload(C_ALPHA_BASE_LOC), mload(C_ALPHA_LOC), p)) + result := + addmod( + result, + mulmod( + mload(C_ALPHA_BASE_LOC), + mulmod( + mload(L_END_LOC), + addmod(mload(Z_OMEGA_EVAL_LOC), sub(p, mload(PUBLIC_INPUT_DELTA_LOC)), p), + p + ), + p + ), + p + ) + mstore(C_ALPHA_BASE_LOC, mulmod(mload(C_ALPHA_BASE_LOC), mload(C_ALPHA_LOC), p)) + mstore( + PERMUTATION_IDENTITY, + addmod( + result, + mulmod( + mload(C_ALPHA_BASE_LOC), + mulmod(mload(L_START_LOC), addmod(mload(Z_EVAL_LOC), sub(p, 1), p), p), + p + ), + p + ) + ) + mstore(C_ALPHA_BASE_LOC, mulmod(mload(C_ALPHA_BASE_LOC), mload(C_ALPHA_LOC), p)) + } + + /** + * COMPUTE PLOOKUP WIDGET EVALUATION + */ + { + /** + * Goal: f = (w1(z) + q2.w1(zω)) + η(w2(z) + qm.w2(zω)) + η²(w3(z) + qc.w_3(zω)) + q3(z).η³ + * f = η.q3(z) + * f += (w3(z) + qc.w_3(zω)) + * f *= η + * f += (w2(z) + qm.w2(zω)) + * f *= η + * f += (w1(z) + q2.w1(zω)) + */ + let f := mulmod(mload(C_ETA_LOC), mload(Q3_EVAL_LOC), p) + f := + addmod(f, addmod(mload(W3_EVAL_LOC), mulmod(mload(QC_EVAL_LOC), mload(W3_OMEGA_EVAL_LOC), p), p), p) + f := mulmod(f, mload(C_ETA_LOC), p) + f := + addmod(f, addmod(mload(W2_EVAL_LOC), mulmod(mload(QM_EVAL_LOC), mload(W2_OMEGA_EVAL_LOC), p), p), p) + f := mulmod(f, mload(C_ETA_LOC), p) + f := + addmod(f, addmod(mload(W1_EVAL_LOC), mulmod(mload(Q2_EVAL_LOC), mload(W1_OMEGA_EVAL_LOC), p), p), p) + + // t(z) = table4(z).η³ + table3(z).η² + table2(z).η + table1(z) + let t := + addmod( + addmod( + addmod( + mulmod(mload(TABLE4_EVAL_LOC), mload(C_ETA_CUBE_LOC), p), + mulmod(mload(TABLE3_EVAL_LOC), mload(C_ETA_SQR_LOC), p), + p + ), + mulmod(mload(TABLE2_EVAL_LOC), mload(C_ETA_LOC), p), + p + ), + mload(TABLE1_EVAL_LOC), + p + ) + + // t(zw) = table4(zw).η³ + table3(zw).η² + table2(zw).η + table1(zw) + let t_omega := + addmod( + addmod( + addmod( + mulmod(mload(TABLE4_OMEGA_EVAL_LOC), mload(C_ETA_CUBE_LOC), p), + mulmod(mload(TABLE3_OMEGA_EVAL_LOC), mload(C_ETA_SQR_LOC), p), + p + ), + mulmod(mload(TABLE2_OMEGA_EVAL_LOC), mload(C_ETA_LOC), p), + p + ), + mload(TABLE1_OMEGA_EVAL_LOC), + p + ) + + /** + * Goal: numerator = (TABLE_TYPE_EVAL * f(z) + γ) * (t(z) + βt(zω) + γ(β + 1)) * (β + 1) + * gamma_beta_constant = γ(β + 1) + * numerator = f * TABLE_TYPE_EVAL + gamma + * temp0 = t(z) + t(zω) * β + gamma_beta_constant + * numerator *= temp0 + * numerator *= (β + 1) + * temp0 = alpha * l_1 + * numerator += temp0 + * numerator *= z_lookup(z) + * numerator -= temp0 + */ + let gamma_beta_constant := mulmod(mload(C_GAMMA_LOC), addmod(mload(C_BETA_LOC), 1, p), p) + let numerator := addmod(mulmod(f, mload(TABLE_TYPE_EVAL_LOC), p), mload(C_GAMMA_LOC), p) + let temp0 := addmod(addmod(t, mulmod(t_omega, mload(C_BETA_LOC), p), p), gamma_beta_constant, p) + numerator := mulmod(numerator, temp0, p) + numerator := mulmod(numerator, addmod(mload(C_BETA_LOC), 1, p), p) + temp0 := mulmod(mload(C_ALPHA_LOC), mload(L_START_LOC), p) + numerator := addmod(numerator, temp0, p) + numerator := mulmod(numerator, mload(Z_LOOKUP_EVAL_LOC), p) + numerator := addmod(numerator, sub(p, temp0), p) + + /** + * Goal: denominator = z_lookup(zω)*[s(z) + βs(zω) + γ(1 + β)] - [z_lookup(zω) - [γ(1 + β)]^{n-k}]*α²L_end(z) + * note: delta_factor = [γ(1 + β)]^{n-k} + * denominator = s(z) + βs(zω) + γ(β + 1) + * temp1 = α²L_end(z) + * denominator -= temp1 + * denominator *= z_lookup(zω) + * denominator += temp1 * delta_factor + * PLOOKUP_IDENTITY = (numerator - denominator).alpha_base + * alpha_base *= alpha^3 + */ + let denominator := + addmod( + addmod(mload(S_EVAL_LOC), mulmod(mload(S_OMEGA_EVAL_LOC), mload(C_BETA_LOC), p), p), + gamma_beta_constant, + p + ) + let temp1 := mulmod(mload(C_ALPHA_SQR_LOC), mload(L_END_LOC), p) + denominator := addmod(denominator, sub(p, temp1), p) + denominator := mulmod(denominator, mload(Z_LOOKUP_OMEGA_EVAL_LOC), p) + denominator := addmod(denominator, mulmod(temp1, mload(PLOOKUP_DELTA_LOC), p), p) + + mstore(PLOOKUP_IDENTITY, mulmod(addmod(numerator, sub(p, denominator), p), mload(C_ALPHA_BASE_LOC), p)) + + // update alpha + mstore(C_ALPHA_BASE_LOC, mulmod(mload(C_ALPHA_BASE_LOC), mload(C_ALPHA_CUBE_LOC), p)) + } + + /** + * COMPUTE ARITHMETIC WIDGET EVALUATION + */ + { + /** + * The basic arithmetic gate identity in standard plonk is as follows. + * (w_1 . w_2 . q_m) + (w_1 . q_1) + (w_2 . q_2) + (w_3 . q_3) + (w_4 . q_4) + q_c = 0 + * However, for Ultraplonk, we extend this to support "passing" wires between rows (shown without alpha scaling below): + * q_arith * ( ( (-1/2) * (q_arith - 3) * q_m * w_1 * w_2 + q_1 * w_1 + q_2 * w_2 + q_3 * w_3 + q_4 * w_4 + q_c ) + + * (q_arith - 1)*( α * (q_arith - 2) * (w_1 + w_4 - w_1_omega + q_m) + w_4_omega) ) = 0 + * + * This formula results in several cases depending on q_arith: + * 1. q_arith == 0: Arithmetic gate is completely disabled + * + * 2. q_arith == 1: Everything in the minigate on the right is disabled. The equation is just a standard plonk equation + * with extra wires: q_m * w_1 * w_2 + q_1 * w_1 + q_2 * w_2 + q_3 * w_3 + q_4 * w_4 + q_c = 0 + * + * 3. q_arith == 2: The (w_1 + w_4 - ...) term is disabled. THe equation is: + * (1/2) * q_m * w_1 * w_2 + q_1 * w_1 + q_2 * w_2 + q_3 * w_3 + q_4 * w_4 + q_c + w_4_omega = 0 + * It allows defining w_4 at next index (w_4_omega) in terms of current wire values + * + * 4. q_arith == 3: The product of w_1 and w_2 is disabled, but a mini addition gate is enabled. α allows us to split + * the equation into two: + * + * q_1 * w_1 + q_2 * w_2 + q_3 * w_3 + q_4 * w_4 + q_c + 2 * w_4_omega = 0 + * and + * w_1 + w_4 - w_1_omega + q_m = 0 (we are reusing q_m here) + * + * 5. q_arith > 3: The product of w_1 and w_2 is scaled by (q_arith - 3), while the w_4_omega term is scaled by (q_arith - 1). + * The equation can be split into two: + * + * (q_arith - 3)* q_m * w_1 * w_ 2 + q_1 * w_1 + q_2 * w_2 + q_3 * w_3 + q_4 * w_4 + q_c + (q_arith - 1) * w_4_omega = 0 + * and + * w_1 + w_4 - w_1_omega + q_m = 0 + * + * The problem that q_m is used both in both equations can be dealt with by appropriately changing selector values at + * the next gate. Then we can treat (q_arith - 1) as a simulated q_6 selector and scale q_m to handle (q_arith - 3) at + * product. + */ + + let w1q1 := mulmod(mload(W1_EVAL_LOC), mload(Q1_EVAL_LOC), p) + let w2q2 := mulmod(mload(W2_EVAL_LOC), mload(Q2_EVAL_LOC), p) + let w3q3 := mulmod(mload(W3_EVAL_LOC), mload(Q3_EVAL_LOC), p) + let w4q3 := mulmod(mload(W4_EVAL_LOC), mload(Q4_EVAL_LOC), p) + + // @todo - Add a explicit test that hits QARITH == 3 + // w1w2qm := (w_1 . w_2 . q_m . (QARITH_EVAL_LOC - 3)) / 2 + let w1w2qm := + mulmod( + mulmod( + mulmod(mulmod(mload(W1_EVAL_LOC), mload(W2_EVAL_LOC), p), mload(QM_EVAL_LOC), p), + addmod(mload(QARITH_EVAL_LOC), sub(p, 3), p), + p + ), + NEGATIVE_INVERSE_OF_2_MODULO_P, + p + ) + + // (w_1 . w_2 . q_m . (q_arith - 3)) / -2) + (w_1 . q_1) + (w_2 . q_2) + (w_3 . q_3) + (w_4 . q_4) + q_c + let identity := + addmod( + mload(QC_EVAL_LOC), addmod(w4q3, addmod(w3q3, addmod(w2q2, addmod(w1q1, w1w2qm, p), p), p), p), p + ) + + // if q_arith == 3 we evaluate an additional mini addition gate (on top of the regular one), where: + // w_1 + w_4 - w_1_omega + q_m = 0 + // we use this gate to save an addition gate when adding or subtracting non-native field elements + // α * (q_arith - 2) * (w_1 + w_4 - w_1_omega + q_m) + let extra_small_addition_gate_identity := + mulmod( + mload(C_ALPHA_LOC), + mulmod( + addmod(mload(QARITH_EVAL_LOC), sub(p, 2), p), + addmod( + mload(QM_EVAL_LOC), + addmod( + sub(p, mload(W1_OMEGA_EVAL_LOC)), addmod(mload(W1_EVAL_LOC), mload(W4_EVAL_LOC), p), p + ), + p + ), + p + ), + p + ) + + // if q_arith == 2 OR q_arith == 3 we add the 4th wire of the NEXT gate into the arithmetic identity + // N.B. if q_arith > 2, this wire value will be scaled by (q_arith - 1) relative to the other gate wires! + // alpha_base * q_arith * (identity + (q_arith - 1) * (w_4_omega + extra_small_addition_gate_identity)) + mstore( + ARITHMETIC_IDENTITY, + mulmod( + mload(C_ALPHA_BASE_LOC), + mulmod( + mload(QARITH_EVAL_LOC), + addmod( + identity, + mulmod( + addmod(mload(QARITH_EVAL_LOC), sub(p, 1), p), + addmod(mload(W4_OMEGA_EVAL_LOC), extra_small_addition_gate_identity, p), + p + ), + p + ), + p + ), + p + ) + ) + + // update alpha + mstore(C_ALPHA_BASE_LOC, mulmod(mload(C_ALPHA_BASE_LOC), mload(C_ALPHA_SQR_LOC), p)) + } + + /** + * COMPUTE GENPERMSORT WIDGET EVALUATION + */ + { + /** + * D1 = (w2 - w1) + * D2 = (w3 - w2) + * D3 = (w4 - w3) + * D4 = (w1_omega - w4) + * + * α_a = alpha_base + * α_b = alpha_base * α + * α_c = alpha_base * α^2 + * α_d = alpha_base * α^3 + * + * range_accumulator = ( + * D1(D1 - 1)(D1 - 2)(D1 - 3).α_a + + * D2(D2 - 1)(D2 - 2)(D2 - 3).α_b + + * D3(D3 - 1)(D3 - 2)(D3 - 3).α_c + + * D4(D4 - 1)(D4 - 2)(D4 - 3).α_d + + * ) . q_sort + */ + let minus_two := sub(p, 2) + let minus_three := sub(p, 3) + let d1 := addmod(mload(W2_EVAL_LOC), sub(p, mload(W1_EVAL_LOC)), p) + let d2 := addmod(mload(W3_EVAL_LOC), sub(p, mload(W2_EVAL_LOC)), p) + let d3 := addmod(mload(W4_EVAL_LOC), sub(p, mload(W3_EVAL_LOC)), p) + let d4 := addmod(mload(W1_OMEGA_EVAL_LOC), sub(p, mload(W4_EVAL_LOC)), p) + + let range_accumulator := + mulmod( + mulmod( + mulmod(addmod(mulmod(d1, d1, p), sub(p, d1), p), addmod(d1, minus_two, p), p), + addmod(d1, minus_three, p), + p + ), + mload(C_ALPHA_BASE_LOC), + p + ) + range_accumulator := + addmod( + range_accumulator, + mulmod( + mulmod( + mulmod(addmod(mulmod(d2, d2, p), sub(p, d2), p), addmod(d2, minus_two, p), p), + addmod(d2, minus_three, p), + p + ), + mulmod(mload(C_ALPHA_BASE_LOC), mload(C_ALPHA_LOC), p), + p + ), + p + ) + range_accumulator := + addmod( + range_accumulator, + mulmod( + mulmod( + mulmod(addmod(mulmod(d3, d3, p), sub(p, d3), p), addmod(d3, minus_two, p), p), + addmod(d3, minus_three, p), + p + ), + mulmod(mload(C_ALPHA_BASE_LOC), mload(C_ALPHA_SQR_LOC), p), + p + ), + p + ) + range_accumulator := + addmod( + range_accumulator, + mulmod( + mulmod( + mulmod(addmod(mulmod(d4, d4, p), sub(p, d4), p), addmod(d4, minus_two, p), p), + addmod(d4, minus_three, p), + p + ), + mulmod(mload(C_ALPHA_BASE_LOC), mload(C_ALPHA_CUBE_LOC), p), + p + ), + p + ) + range_accumulator := mulmod(range_accumulator, mload(QSORT_EVAL_LOC), p) + + mstore(SORT_IDENTITY, range_accumulator) + + // update alpha + mstore(C_ALPHA_BASE_LOC, mulmod(mload(C_ALPHA_BASE_LOC), mload(C_ALPHA_QUAD_LOC), p)) + } + + /** + * COMPUTE ELLIPTIC WIDGET EVALUATION + */ + { + /** + * endo_term = (-x_2) * x_1 * (x_3 * 2 + x_1) * q_beta + * endo_sqr_term = x_2^2 + * endo_sqr_term *= (x_3 - x_1) + * endo_sqr_term *= q_beta^2 + * leftovers = x_2^2 + * leftovers *= x_2 + * leftovers += x_1^2 * (x_3 + x_1) @follow-up Invalid comment in BB widget + * leftovers -= (y_2^2 + y_1^2) + * sign_term = y_2 * y_1 + * sign_term += sign_term + * sign_term *= q_sign + */ + // q_elliptic * (x3 + x2 + x1)(x2 - x1)(x2 - x1) - y2^2 - y1^2 + 2(y2y1)*q_sign = 0 + let x_diff := addmod(mload(X2_EVAL_LOC), sub(p, mload(X1_EVAL_LOC)), p) + let y2_sqr := mulmod(mload(Y2_EVAL_LOC), mload(Y2_EVAL_LOC), p) + let y1_sqr := mulmod(mload(Y1_EVAL_LOC), mload(Y1_EVAL_LOC), p) + let y1y2 := mulmod(mulmod(mload(Y1_EVAL_LOC), mload(Y2_EVAL_LOC), p), mload(QSIGN_LOC), p) + + let x_add_identity := + addmod( + mulmod( + addmod(mload(X3_EVAL_LOC), addmod(mload(X2_EVAL_LOC), mload(X1_EVAL_LOC), p), p), + mulmod(x_diff, x_diff, p), + p + ), + addmod( + sub( + p, + addmod(y2_sqr, y1_sqr, p) + ), + addmod(y1y2, y1y2, p), + p + ), + p + ) + x_add_identity := + mulmod( + mulmod( + x_add_identity, + addmod( + 1, + sub(p, mload(QM_EVAL_LOC)), + p + ), + p + ), + mload(C_ALPHA_BASE_LOC), + p + ) + + // q_elliptic * (x3 + x2 + x1)(x2 - x1)(x2 - x1) - y2^2 - y1^2 + 2(y2y1)*q_sign = 0 + let y1_plus_y3 := addmod( + mload(Y1_EVAL_LOC), + mload(Y3_EVAL_LOC), + p + ) + let y_diff := addmod(mulmod(mload(Y2_EVAL_LOC), mload(QSIGN_LOC), p), sub(p, mload(Y1_EVAL_LOC)), p) + let y_add_identity := + addmod( + mulmod(y1_plus_y3, x_diff, p), + mulmod(addmod(mload(X3_EVAL_LOC), sub(p, mload(X1_EVAL_LOC)), p), y_diff, p), + p + ) + y_add_identity := + mulmod( + mulmod(y_add_identity, addmod(1, sub(p, mload(QM_EVAL_LOC)), p), p), + mulmod(mload(C_ALPHA_BASE_LOC), mload(C_ALPHA_LOC), p), + p + ) + + // ELLIPTIC_IDENTITY = (x_identity + y_identity) * Q_ELLIPTIC_EVAL + mstore( + ELLIPTIC_IDENTITY, mulmod(addmod(x_add_identity, y_add_identity, p), mload(QELLIPTIC_EVAL_LOC), p) + ) + } + { + /** + * x_pow_4 = (y_1_sqr - curve_b) * x_1; + * y_1_sqr_mul_4 = y_1_sqr + y_1_sqr; + * y_1_sqr_mul_4 += y_1_sqr_mul_4; + * x_1_pow_4_mul_9 = x_pow_4; + * x_1_pow_4_mul_9 += x_1_pow_4_mul_9; + * x_1_pow_4_mul_9 += x_1_pow_4_mul_9; + * x_1_pow_4_mul_9 += x_1_pow_4_mul_9; + * x_1_pow_4_mul_9 += x_pow_4; + * x_1_sqr_mul_3 = x_1_sqr + x_1_sqr + x_1_sqr; + * x_double_identity = (x_3 + x_1 + x_1) * y_1_sqr_mul_4 - x_1_pow_4_mul_9; + * y_double_identity = x_1_sqr_mul_3 * (x_1 - x_3) - (y_1 + y_1) * (y_1 + y_3); + */ + // (x3 + x1 + x1) (4y1*y1) - 9 * x1 * x1 * x1 * x1 = 0 + let x1_sqr := mulmod(mload(X1_EVAL_LOC), mload(X1_EVAL_LOC), p) + let y1_sqr := mulmod(mload(Y1_EVAL_LOC), mload(Y1_EVAL_LOC), p) + let x_pow_4 := mulmod(addmod(y1_sqr, GRUMPKIN_CURVE_B_PARAMETER_NEGATED, p), mload(X1_EVAL_LOC), p) + let y1_sqr_mul_4 := mulmod(y1_sqr, 4, p) + let x1_pow_4_mul_9 := mulmod(x_pow_4, 9, p) + let x1_sqr_mul_3 := mulmod(x1_sqr, 3, p) + let x_double_identity := + addmod( + mulmod( + addmod(mload(X3_EVAL_LOC), addmod(mload(X1_EVAL_LOC), mload(X1_EVAL_LOC), p), p), + y1_sqr_mul_4, + p + ), + sub(p, x1_pow_4_mul_9), + p + ) + // (y1 + y1) (2y1) - (3 * x1 * x1)(x1 - x3) = 0 + let y_double_identity := + addmod( + mulmod(x1_sqr_mul_3, addmod(mload(X1_EVAL_LOC), sub(p, mload(X3_EVAL_LOC)), p), p), + sub( + p, + mulmod( + addmod(mload(Y1_EVAL_LOC), mload(Y1_EVAL_LOC), p), + addmod(mload(Y1_EVAL_LOC), mload(Y3_EVAL_LOC), p), + p + ) + ), + p + ) + x_double_identity := mulmod(x_double_identity, mload(C_ALPHA_BASE_LOC), p) + y_double_identity := + mulmod(y_double_identity, mulmod(mload(C_ALPHA_BASE_LOC), mload(C_ALPHA_LOC), p), p) + x_double_identity := mulmod(x_double_identity, mload(QM_EVAL_LOC), p) + y_double_identity := mulmod(y_double_identity, mload(QM_EVAL_LOC), p) + // ELLIPTIC_IDENTITY += (x_double_identity + y_double_identity) * Q_DOUBLE_EVAL + mstore( + ELLIPTIC_IDENTITY, + addmod( + mload(ELLIPTIC_IDENTITY), + mulmod(addmod(x_double_identity, y_double_identity, p), mload(QELLIPTIC_EVAL_LOC), p), + p + ) + ) + + // update alpha + mstore(C_ALPHA_BASE_LOC, mulmod(mload(C_ALPHA_BASE_LOC), mload(C_ALPHA_QUAD_LOC), p)) + } + + /** + * COMPUTE AUXILIARY WIDGET EVALUATION + */ + { + { + /** + * Non native field arithmetic gate 2 + * _ _ + * / _ _ _ 14 \ + * q_2 . q_4 | (w_1 . w_2) + (w_1 . w_2) + (w_1 . w_4 + w_2 . w_3 - w_3) . 2 - w_3 - w_4 | + * \_ _/ + * + * limb_subproduct = w_1 . w_2_omega + w_1_omega . w_2 + * non_native_field_gate_2 = w_1 * w_4 + w_4 * w_3 - w_3_omega + * non_native_field_gate_2 = non_native_field_gate_2 * limb_size + * non_native_field_gate_2 -= w_4_omega + * non_native_field_gate_2 += limb_subproduct + * non_native_field_gate_2 *= q_4 + * limb_subproduct *= limb_size + * limb_subproduct += w_1_omega * w_2_omega + * non_native_field_gate_1 = (limb_subproduct + w_3 + w_4) * q_3 + * non_native_field_gate_3 = (limb_subproduct + w_4 - (w_3_omega + w_4_omega)) * q_m + * non_native_field_identity = (non_native_field_gate_1 + non_native_field_gate_2 + non_native_field_gate_3) * q_2 + */ + + let limb_subproduct := + addmod( + mulmod(mload(W1_EVAL_LOC), mload(W2_OMEGA_EVAL_LOC), p), + mulmod(mload(W1_OMEGA_EVAL_LOC), mload(W2_EVAL_LOC), p), + p + ) + + let non_native_field_gate_2 := + addmod( + addmod( + mulmod(mload(W1_EVAL_LOC), mload(W4_EVAL_LOC), p), + mulmod(mload(W2_EVAL_LOC), mload(W3_EVAL_LOC), p), + p + ), + sub(p, mload(W3_OMEGA_EVAL_LOC)), + p + ) + non_native_field_gate_2 := mulmod(non_native_field_gate_2, LIMB_SIZE, p) + non_native_field_gate_2 := addmod(non_native_field_gate_2, sub(p, mload(W4_OMEGA_EVAL_LOC)), p) + non_native_field_gate_2 := addmod(non_native_field_gate_2, limb_subproduct, p) + non_native_field_gate_2 := mulmod(non_native_field_gate_2, mload(Q4_EVAL_LOC), p) + limb_subproduct := mulmod(limb_subproduct, LIMB_SIZE, p) + limb_subproduct := + addmod(limb_subproduct, mulmod(mload(W1_OMEGA_EVAL_LOC), mload(W2_OMEGA_EVAL_LOC), p), p) + let non_native_field_gate_1 := + mulmod( + addmod(limb_subproduct, sub(p, addmod(mload(W3_EVAL_LOC), mload(W4_EVAL_LOC), p)), p), + mload(Q3_EVAL_LOC), + p + ) + let non_native_field_gate_3 := + mulmod( + addmod( + addmod(limb_subproduct, mload(W4_EVAL_LOC), p), + sub(p, addmod(mload(W3_OMEGA_EVAL_LOC), mload(W4_OMEGA_EVAL_LOC), p)), + p + ), + mload(QM_EVAL_LOC), + p + ) + let non_native_field_identity := + mulmod( + addmod(addmod(non_native_field_gate_1, non_native_field_gate_2, p), non_native_field_gate_3, p), + mload(Q2_EVAL_LOC), + p + ) + + mstore(AUX_NON_NATIVE_FIELD_EVALUATION, non_native_field_identity) + } + + { + /** + * limb_accumulator_1 = w_2_omega; + * limb_accumulator_1 *= SUBLIMB_SHIFT; + * limb_accumulator_1 += w_1_omega; + * limb_accumulator_1 *= SUBLIMB_SHIFT; + * limb_accumulator_1 += w_3; + * limb_accumulator_1 *= SUBLIMB_SHIFT; + * limb_accumulator_1 += w_2; + * limb_accumulator_1 *= SUBLIMB_SHIFT; + * limb_accumulator_1 += w_1; + * limb_accumulator_1 -= w_4; + * limb_accumulator_1 *= q_4; + */ + let limb_accumulator_1 := mulmod(mload(W2_OMEGA_EVAL_LOC), SUBLIMB_SHIFT, p) + limb_accumulator_1 := addmod(limb_accumulator_1, mload(W1_OMEGA_EVAL_LOC), p) + limb_accumulator_1 := mulmod(limb_accumulator_1, SUBLIMB_SHIFT, p) + limb_accumulator_1 := addmod(limb_accumulator_1, mload(W3_EVAL_LOC), p) + limb_accumulator_1 := mulmod(limb_accumulator_1, SUBLIMB_SHIFT, p) + limb_accumulator_1 := addmod(limb_accumulator_1, mload(W2_EVAL_LOC), p) + limb_accumulator_1 := mulmod(limb_accumulator_1, SUBLIMB_SHIFT, p) + limb_accumulator_1 := addmod(limb_accumulator_1, mload(W1_EVAL_LOC), p) + limb_accumulator_1 := addmod(limb_accumulator_1, sub(p, mload(W4_EVAL_LOC)), p) + limb_accumulator_1 := mulmod(limb_accumulator_1, mload(Q4_EVAL_LOC), p) + + /** + * limb_accumulator_2 = w_3_omega; + * limb_accumulator_2 *= SUBLIMB_SHIFT; + * limb_accumulator_2 += w_2_omega; + * limb_accumulator_2 *= SUBLIMB_SHIFT; + * limb_accumulator_2 += w_1_omega; + * limb_accumulator_2 *= SUBLIMB_SHIFT; + * limb_accumulator_2 += w_4; + * limb_accumulator_2 *= SUBLIMB_SHIFT; + * limb_accumulator_2 += w_3; + * limb_accumulator_2 -= w_4_omega; + * limb_accumulator_2 *= q_m; + */ + let limb_accumulator_2 := mulmod(mload(W3_OMEGA_EVAL_LOC), SUBLIMB_SHIFT, p) + limb_accumulator_2 := addmod(limb_accumulator_2, mload(W2_OMEGA_EVAL_LOC), p) + limb_accumulator_2 := mulmod(limb_accumulator_2, SUBLIMB_SHIFT, p) + limb_accumulator_2 := addmod(limb_accumulator_2, mload(W1_OMEGA_EVAL_LOC), p) + limb_accumulator_2 := mulmod(limb_accumulator_2, SUBLIMB_SHIFT, p) + limb_accumulator_2 := addmod(limb_accumulator_2, mload(W4_EVAL_LOC), p) + limb_accumulator_2 := mulmod(limb_accumulator_2, SUBLIMB_SHIFT, p) + limb_accumulator_2 := addmod(limb_accumulator_2, mload(W3_EVAL_LOC), p) + limb_accumulator_2 := addmod(limb_accumulator_2, sub(p, mload(W4_OMEGA_EVAL_LOC)), p) + limb_accumulator_2 := mulmod(limb_accumulator_2, mload(QM_EVAL_LOC), p) + + mstore( + AUX_LIMB_ACCUMULATOR_EVALUATION, + mulmod(addmod(limb_accumulator_1, limb_accumulator_2, p), mload(Q3_EVAL_LOC), p) + ) + } + + { + /** + * memory_record_check = w_3; + * memory_record_check *= eta; + * memory_record_check += w_2; + * memory_record_check *= eta; + * memory_record_check += w_1; + * memory_record_check *= eta; + * memory_record_check += q_c; + * + * partial_record_check = memory_record_check; + * + * memory_record_check -= w_4; + */ + + let memory_record_check := mulmod(mload(W3_EVAL_LOC), mload(C_ETA_LOC), p) + memory_record_check := addmod(memory_record_check, mload(W2_EVAL_LOC), p) + memory_record_check := mulmod(memory_record_check, mload(C_ETA_LOC), p) + memory_record_check := addmod(memory_record_check, mload(W1_EVAL_LOC), p) + memory_record_check := mulmod(memory_record_check, mload(C_ETA_LOC), p) + memory_record_check := addmod(memory_record_check, mload(QC_EVAL_LOC), p) + + let partial_record_check := memory_record_check + memory_record_check := addmod(memory_record_check, sub(p, mload(W4_EVAL_LOC)), p) + + mstore(AUX_MEMORY_EVALUATION, memory_record_check) + + // index_delta = w_1_omega - w_1 + let index_delta := addmod(mload(W1_OMEGA_EVAL_LOC), sub(p, mload(W1_EVAL_LOC)), p) + // record_delta = w_4_omega - w_4 + let record_delta := addmod(mload(W4_OMEGA_EVAL_LOC), sub(p, mload(W4_EVAL_LOC)), p) + // index_is_monotonically_increasing = index_delta * (index_delta - 1) + let index_is_monotonically_increasing := mulmod(index_delta, addmod(index_delta, sub(p, 1), p), p) + + // adjacent_values_match_if_adjacent_indices_match = record_delta * (1 - index_delta) + let adjacent_values_match_if_adjacent_indices_match := + mulmod(record_delta, addmod(1, sub(p, index_delta), p), p) + + // AUX_ROM_CONSISTENCY_EVALUATION = ((adjacent_values_match_if_adjacent_indices_match * alpha) + index_is_monotonically_increasing) * alpha + partial_record_check + mstore( + AUX_ROM_CONSISTENCY_EVALUATION, + addmod( + mulmod( + addmod( + mulmod(adjacent_values_match_if_adjacent_indices_match, mload(C_ALPHA_LOC), p), + index_is_monotonically_increasing, + p + ), + mload(C_ALPHA_LOC), + p + ), + memory_record_check, + p + ) + ) + + { + /** + * next_gate_access_type = w_3_omega; + * next_gate_access_type *= eta; + * next_gate_access_type += w_2_omega; + * next_gate_access_type *= eta; + * next_gate_access_type += w_1_omega; + * next_gate_access_type *= eta; + * next_gate_access_type = w_4_omega - next_gate_access_type; + */ + let next_gate_access_type := mulmod(mload(W3_OMEGA_EVAL_LOC), mload(C_ETA_LOC), p) + next_gate_access_type := addmod(next_gate_access_type, mload(W2_OMEGA_EVAL_LOC), p) + next_gate_access_type := mulmod(next_gate_access_type, mload(C_ETA_LOC), p) + next_gate_access_type := addmod(next_gate_access_type, mload(W1_OMEGA_EVAL_LOC), p) + next_gate_access_type := mulmod(next_gate_access_type, mload(C_ETA_LOC), p) + next_gate_access_type := addmod(mload(W4_OMEGA_EVAL_LOC), sub(p, next_gate_access_type), p) + + // value_delta = w_3_omega - w_3 + let value_delta := addmod(mload(W3_OMEGA_EVAL_LOC), sub(p, mload(W3_EVAL_LOC)), p) + // adjacent_values_match_if_adjacent_indices_match_and_next_access_is_a_read_operation = (1 - index_delta) * value_delta * (1 - next_gate_access_type); + + let adjacent_values_match_if_adjacent_indices_match_and_next_access_is_a_read_operation := + mulmod( + addmod(1, sub(p, index_delta), p), + mulmod(value_delta, addmod(1, sub(p, next_gate_access_type), p), p), + p + ) + + // AUX_RAM_CONSISTENCY_EVALUATION + + /** + * access_type = w_4 - partial_record_check + * access_check = access_type^2 - access_type + * next_gate_access_type_is_boolean = next_gate_access_type^2 - next_gate_access_type + * RAM_consistency_check_identity = adjacent_values_match_if_adjacent_indices_match_and_next_access_is_a_read_operation; + * RAM_consistency_check_identity *= alpha; + * RAM_consistency_check_identity += index_is_monotonically_increasing; + * RAM_consistency_check_identity *= alpha; + * RAM_consistency_check_identity += next_gate_access_type_is_boolean; + * RAM_consistency_check_identity *= alpha; + * RAM_consistency_check_identity += access_check; + */ + + let access_type := addmod(mload(W4_EVAL_LOC), sub(p, partial_record_check), p) + let access_check := mulmod(access_type, addmod(access_type, sub(p, 1), p), p) + let next_gate_access_type_is_boolean := + mulmod(next_gate_access_type, addmod(next_gate_access_type, sub(p, 1), p), p) + let RAM_cci := + mulmod( + adjacent_values_match_if_adjacent_indices_match_and_next_access_is_a_read_operation, + mload(C_ALPHA_LOC), + p + ) + RAM_cci := addmod(RAM_cci, index_is_monotonically_increasing, p) + RAM_cci := mulmod(RAM_cci, mload(C_ALPHA_LOC), p) + RAM_cci := addmod(RAM_cci, next_gate_access_type_is_boolean, p) + RAM_cci := mulmod(RAM_cci, mload(C_ALPHA_LOC), p) + RAM_cci := addmod(RAM_cci, access_check, p) + + mstore(AUX_RAM_CONSISTENCY_EVALUATION, RAM_cci) + } + + { + // timestamp_delta = w_2_omega - w_2 + let timestamp_delta := addmod(mload(W2_OMEGA_EVAL_LOC), sub(p, mload(W2_EVAL_LOC)), p) + + // RAM_timestamp_check_identity = (1 - index_delta) * timestamp_delta - w_3 + let RAM_timestamp_check_identity := + addmod( + mulmod(timestamp_delta, addmod(1, sub(p, index_delta), p), p), sub(p, mload(W3_EVAL_LOC)), p + ) + + /** + * memory_identity = ROM_consistency_check_identity * q_2; + * memory_identity += RAM_timestamp_check_identity * q_4; + * memory_identity += memory_record_check * q_m; + * memory_identity *= q_1; + * memory_identity += (RAM_consistency_check_identity * q_arith); + * + * auxiliary_identity = memory_identity + non_native_field_identity + limb_accumulator_identity; + * auxiliary_identity *= q_aux; + * auxiliary_identity *= alpha_base; + */ + let memory_identity := mulmod(mload(AUX_ROM_CONSISTENCY_EVALUATION), mload(Q2_EVAL_LOC), p) + memory_identity := + addmod(memory_identity, mulmod(RAM_timestamp_check_identity, mload(Q4_EVAL_LOC), p), p) + memory_identity := + addmod(memory_identity, mulmod(mload(AUX_MEMORY_EVALUATION), mload(QM_EVAL_LOC), p), p) + memory_identity := mulmod(memory_identity, mload(Q1_EVAL_LOC), p) + memory_identity := + addmod( + memory_identity, mulmod(mload(AUX_RAM_CONSISTENCY_EVALUATION), mload(QARITH_EVAL_LOC), p), p + ) + + let auxiliary_identity := addmod(memory_identity, mload(AUX_NON_NATIVE_FIELD_EVALUATION), p) + auxiliary_identity := addmod(auxiliary_identity, mload(AUX_LIMB_ACCUMULATOR_EVALUATION), p) + auxiliary_identity := mulmod(auxiliary_identity, mload(QAUX_EVAL_LOC), p) + auxiliary_identity := mulmod(auxiliary_identity, mload(C_ALPHA_BASE_LOC), p) + + mstore(AUX_IDENTITY, auxiliary_identity) + + // update alpha + mstore(C_ALPHA_BASE_LOC, mulmod(mload(C_ALPHA_BASE_LOC), mload(C_ALPHA_CUBE_LOC), p)) + } + } + } + + { + /** + * quotient = ARITHMETIC_IDENTITY + * quotient += PERMUTATION_IDENTITY + * quotient += PLOOKUP_IDENTITY + * quotient += SORT_IDENTITY + * quotient += ELLIPTIC_IDENTITY + * quotient += AUX_IDENTITY + * quotient *= ZERO_POLY_INVERSE + */ + mstore( + QUOTIENT_EVAL_LOC, + mulmod( + addmod( + addmod( + addmod( + addmod( + addmod(mload(PERMUTATION_IDENTITY), mload(PLOOKUP_IDENTITY), p), + mload(ARITHMETIC_IDENTITY), + p + ), + mload(SORT_IDENTITY), + p + ), + mload(ELLIPTIC_IDENTITY), + p + ), + mload(AUX_IDENTITY), + p + ), + mload(ZERO_POLY_INVERSE_LOC), + p + ) + ) + } + + /** + * GENERATE NU AND SEPARATOR CHALLENGES + */ + { + let current_challenge := mload(C_CURRENT_LOC) + // get a calldata pointer that points to the start of the data we want to copy + let calldata_ptr := add(calldataload(0x04), 0x24) + + calldata_ptr := add(calldata_ptr, NU_CALLDATA_SKIP_LENGTH) + + mstore(NU_CHALLENGE_INPUT_LOC_A, current_challenge) + mstore(NU_CHALLENGE_INPUT_LOC_B, mload(QUOTIENT_EVAL_LOC)) + calldatacopy(NU_CHALLENGE_INPUT_LOC_C, calldata_ptr, NU_INPUT_LENGTH) + + // hash length = (0x20 + num field elements), we include the previous challenge in the hash + let challenge := keccak256(NU_CHALLENGE_INPUT_LOC_A, add(NU_INPUT_LENGTH, 0x40)) + + mstore(C_V0_LOC, mod(challenge, p)) + // We need THIRTY-ONE independent nu challenges! + mstore(0x00, challenge) + mstore8(0x20, 0x01) + mstore(C_V1_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x02) + mstore(C_V2_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x03) + mstore(C_V3_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x04) + mstore(C_V4_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x05) + mstore(C_V5_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x06) + mstore(C_V6_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x07) + mstore(C_V7_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x08) + mstore(C_V8_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x09) + mstore(C_V9_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x0a) + mstore(C_V10_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x0b) + mstore(C_V11_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x0c) + mstore(C_V12_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x0d) + mstore(C_V13_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x0e) + mstore(C_V14_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x0f) + mstore(C_V15_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x10) + mstore(C_V16_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x11) + mstore(C_V17_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x12) + mstore(C_V18_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x13) + mstore(C_V19_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x14) + mstore(C_V20_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x15) + mstore(C_V21_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x16) + mstore(C_V22_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x17) + mstore(C_V23_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x18) + mstore(C_V24_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x19) + mstore(C_V25_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x1a) + mstore(C_V26_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x1b) + mstore(C_V27_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x1c) + mstore(C_V28_LOC, mod(keccak256(0x00, 0x21), p)) + mstore8(0x20, 0x1d) + mstore(C_V29_LOC, mod(keccak256(0x00, 0x21), p)) + + // @follow-up - Why are both v29 and v30 using appending 0x1d to the prior challenge and hashing, should it not change? + mstore8(0x20, 0x1d) + challenge := keccak256(0x00, 0x21) + mstore(C_V30_LOC, mod(challenge, p)) + + // separator + mstore(0x00, challenge) + mstore(0x20, mload(PI_Z_Y_LOC)) + mstore(0x40, mload(PI_Z_X_LOC)) + mstore(0x60, mload(PI_Z_OMEGA_Y_LOC)) + mstore(0x80, mload(PI_Z_OMEGA_X_LOC)) + + mstore(C_U_LOC, mod(keccak256(0x00, 0xa0), p)) + } + + let success := 0 + // VALIDATE T1 + { + let x := mload(T1_X_LOC) + let y := mload(T1_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q)) + mstore(ACCUMULATOR_X_LOC, x) + mstore(add(ACCUMULATOR_X_LOC, 0x20), y) + } + // VALIDATE T2 + { + let x := mload(T2_X_LOC) // 0x1400 + let y := mload(T2_Y_LOC) // 0x1420 + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(ZETA_POW_N_LOC)) + // accumulator_2 = [T2].zeta^n + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = [T1] + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE T3 + { + let x := mload(T3_X_LOC) + let y := mload(T3_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(mload(ZETA_POW_N_LOC), mload(ZETA_POW_N_LOC), p)) + // accumulator_2 = [T3].zeta^{2n} + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE T4 + { + let x := mload(T4_X_LOC) + let y := mload(T4_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(mulmod(mload(ZETA_POW_N_LOC), mload(ZETA_POW_N_LOC), p), mload(ZETA_POW_N_LOC), p)) + // accumulator_2 = [T4].zeta^{3n} + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE W1 + { + let x := mload(W1_X_LOC) + let y := mload(W1_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(addmod(mload(C_U_LOC), 0x1, p), mload(C_V0_LOC), p)) + // accumulator_2 = v0.(u + 1).[W1] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE W2 + { + let x := mload(W2_X_LOC) + let y := mload(W2_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(addmod(mload(C_U_LOC), 0x1, p), mload(C_V1_LOC), p)) + // accumulator_2 = v1.(u + 1).[W2] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE W3 + { + let x := mload(W3_X_LOC) + let y := mload(W3_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(addmod(mload(C_U_LOC), 0x1, p), mload(C_V2_LOC), p)) + // accumulator_2 = v2.(u + 1).[W3] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE W4 + { + let x := mload(W4_X_LOC) + let y := mload(W4_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(addmod(mload(C_U_LOC), 0x1, p), mload(C_V3_LOC), p)) + // accumulator_2 = v3.(u + 1).[W4] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE S + { + let x := mload(S_X_LOC) + let y := mload(S_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(addmod(mload(C_U_LOC), 0x1, p), mload(C_V4_LOC), p)) + // accumulator_2 = v4.(u + 1).[S] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE Z + { + let x := mload(Z_X_LOC) + let y := mload(Z_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(addmod(mload(C_U_LOC), 0x1, p), mload(C_V5_LOC), p)) + // accumulator_2 = v5.(u + 1).[Z] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE Z_LOOKUP + { + let x := mload(Z_LOOKUP_X_LOC) + let y := mload(Z_LOOKUP_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(addmod(mload(C_U_LOC), 0x1, p), mload(C_V6_LOC), p)) + // accumulator_2 = v6.(u + 1).[Z_LOOKUP] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE Q1 + { + let x := mload(Q1_X_LOC) + let y := mload(Q1_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V7_LOC)) + // accumulator_2 = v7.[Q1] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE Q2 + { + let x := mload(Q2_X_LOC) + let y := mload(Q2_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V8_LOC)) + // accumulator_2 = v8.[Q2] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE Q3 + { + let x := mload(Q3_X_LOC) + let y := mload(Q3_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V9_LOC)) + // accumulator_2 = v9.[Q3] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE Q4 + { + let x := mload(Q4_X_LOC) + let y := mload(Q4_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V10_LOC)) + // accumulator_2 = v10.[Q4] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE QM + { + let x := mload(QM_X_LOC) + let y := mload(QM_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V11_LOC)) + // accumulator_2 = v11.[Q;] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE QC + { + let x := mload(QC_X_LOC) + let y := mload(QC_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V12_LOC)) + // accumulator_2 = v12.[QC] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE QARITH + { + let x := mload(QARITH_X_LOC) + let y := mload(QARITH_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V13_LOC)) + // accumulator_2 = v13.[QARITH] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE QSORT + { + let x := mload(QSORT_X_LOC) + let y := mload(QSORT_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V14_LOC)) + // accumulator_2 = v14.[QSORT] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE QELLIPTIC + { + let x := mload(QELLIPTIC_X_LOC) + let y := mload(QELLIPTIC_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V15_LOC)) + // accumulator_2 = v15.[QELLIPTIC] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE QAUX + { + let x := mload(QAUX_X_LOC) + let y := mload(QAUX_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V16_LOC)) + // accumulator_2 = v15.[Q_AUX] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE SIGMA1 + { + let x := mload(SIGMA1_X_LOC) + let y := mload(SIGMA1_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V17_LOC)) + // accumulator_2 = v17.[sigma1] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE SIGMA2 + { + let x := mload(SIGMA2_X_LOC) + let y := mload(SIGMA2_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V18_LOC)) + // accumulator_2 = v18.[sigma2] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE SIGMA3 + { + let x := mload(SIGMA3_X_LOC) + let y := mload(SIGMA3_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V19_LOC)) + // accumulator_2 = v19.[sigma3] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE SIGMA4 + { + let x := mload(SIGMA4_X_LOC) + let y := mload(SIGMA4_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V20_LOC)) + // accumulator_2 = v20.[sigma4] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE TABLE1 + { + let x := mload(TABLE1_X_LOC) + let y := mload(TABLE1_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(addmod(mload(C_U_LOC), 0x1, p), mload(C_V21_LOC), p)) + // accumulator_2 = u.[table1] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE TABLE2 + { + let x := mload(TABLE2_X_LOC) + let y := mload(TABLE2_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(addmod(mload(C_U_LOC), 0x1, p), mload(C_V22_LOC), p)) + // accumulator_2 = u.[table2] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE TABLE3 + { + let x := mload(TABLE3_X_LOC) + let y := mload(TABLE3_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(addmod(mload(C_U_LOC), 0x1, p), mload(C_V23_LOC), p)) + // accumulator_2 = u.[table3] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE TABLE4 + { + let x := mload(TABLE4_X_LOC) + let y := mload(TABLE4_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(addmod(mload(C_U_LOC), 0x1, p), mload(C_V24_LOC), p)) + // accumulator_2 = u.[table4] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE TABLE_TYPE + { + let x := mload(TABLE_TYPE_X_LOC) + let y := mload(TABLE_TYPE_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V25_LOC)) + // accumulator_2 = v25.[TableType] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE ID1 + { + let x := mload(ID1_X_LOC) + let y := mload(ID1_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V26_LOC)) + // accumulator_2 = v26.[ID1] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE ID2 + { + let x := mload(ID2_X_LOC) + let y := mload(ID2_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V27_LOC)) + // accumulator_2 = v27.[ID2] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE ID3 + { + let x := mload(ID3_X_LOC) + let y := mload(ID3_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V28_LOC)) + // accumulator_2 = v28.[ID3] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE ID4 + { + let x := mload(ID4_X_LOC) + let y := mload(ID4_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mload(C_V29_LOC)) + // accumulator_2 = v29.[ID4] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + /** + * COMPUTE BATCH EVALUATION SCALAR MULTIPLIER + */ + { + /** + * batch_evaluation = v0 * (w_1_omega * u + w_1_eval) + * batch_evaluation += v1 * (w_2_omega * u + w_2_eval) + * batch_evaluation += v2 * (w_3_omega * u + w_3_eval) + * batch_evaluation += v3 * (w_4_omega * u + w_4_eval) + * batch_evaluation += v4 * (s_omega_eval * u + s_eval) + * batch_evaluation += v5 * (z_omega_eval * u + z_eval) + * batch_evaluation += v6 * (z_lookup_omega_eval * u + z_lookup_eval) + */ + let batch_evaluation := + mulmod( + mload(C_V0_LOC), + addmod(mulmod(mload(W1_OMEGA_EVAL_LOC), mload(C_U_LOC), p), mload(W1_EVAL_LOC), p), + p + ) + batch_evaluation := + addmod( + batch_evaluation, + mulmod( + mload(C_V1_LOC), + addmod(mulmod(mload(W2_OMEGA_EVAL_LOC), mload(C_U_LOC), p), mload(W2_EVAL_LOC), p), + p + ), + p + ) + batch_evaluation := + addmod( + batch_evaluation, + mulmod( + mload(C_V2_LOC), + addmod(mulmod(mload(W3_OMEGA_EVAL_LOC), mload(C_U_LOC), p), mload(W3_EVAL_LOC), p), + p + ), + p + ) + batch_evaluation := + addmod( + batch_evaluation, + mulmod( + mload(C_V3_LOC), + addmod(mulmod(mload(W4_OMEGA_EVAL_LOC), mload(C_U_LOC), p), mload(W4_EVAL_LOC), p), + p + ), + p + ) + batch_evaluation := + addmod( + batch_evaluation, + mulmod( + mload(C_V4_LOC), + addmod(mulmod(mload(S_OMEGA_EVAL_LOC), mload(C_U_LOC), p), mload(S_EVAL_LOC), p), + p + ), + p + ) + batch_evaluation := + addmod( + batch_evaluation, + mulmod( + mload(C_V5_LOC), + addmod(mulmod(mload(Z_OMEGA_EVAL_LOC), mload(C_U_LOC), p), mload(Z_EVAL_LOC), p), + p + ), + p + ) + batch_evaluation := + addmod( + batch_evaluation, + mulmod( + mload(C_V6_LOC), + addmod(mulmod(mload(Z_LOOKUP_OMEGA_EVAL_LOC), mload(C_U_LOC), p), mload(Z_LOOKUP_EVAL_LOC), p), + p + ), + p + ) + + /** + * batch_evaluation += v7 * Q1_EVAL + * batch_evaluation += v8 * Q2_EVAL + * batch_evaluation += v9 * Q3_EVAL + * batch_evaluation += v10 * Q4_EVAL + * batch_evaluation += v11 * QM_EVAL + * batch_evaluation += v12 * QC_EVAL + * batch_evaluation += v13 * QARITH_EVAL + * batch_evaluation += v14 * QSORT_EVAL_LOC + * batch_evaluation += v15 * QELLIPTIC_EVAL_LOC + * batch_evaluation += v16 * QAUX_EVAL_LOC + * batch_evaluation += v17 * SIGMA1_EVAL_LOC + * batch_evaluation += v18 * SIGMA2_EVAL_LOC + * batch_evaluation += v19 * SIGMA3_EVAL_LOC + * batch_evaluation += v20 * SIGMA4_EVAL_LOC + */ + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V7_LOC), mload(Q1_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V8_LOC), mload(Q2_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V9_LOC), mload(Q3_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V10_LOC), mload(Q4_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V11_LOC), mload(QM_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V12_LOC), mload(QC_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V13_LOC), mload(QARITH_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V14_LOC), mload(QSORT_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V15_LOC), mload(QELLIPTIC_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V16_LOC), mload(QAUX_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V17_LOC), mload(SIGMA1_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V18_LOC), mload(SIGMA2_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V19_LOC), mload(SIGMA3_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V20_LOC), mload(SIGMA4_EVAL_LOC), p), p) + + /** + * batch_evaluation += v21 * (table1(zw) * u + table1(z)) + * batch_evaluation += v22 * (table2(zw) * u + table2(z)) + * batch_evaluation += v23 * (table3(zw) * u + table3(z)) + * batch_evaluation += v24 * (table4(zw) * u + table4(z)) + * batch_evaluation += v25 * table_type_eval + * batch_evaluation += v26 * id1_eval + * batch_evaluation += v27 * id2_eval + * batch_evaluation += v28 * id3_eval + * batch_evaluation += v29 * id4_eval + * batch_evaluation += quotient_eval + */ + batch_evaluation := + addmod( + batch_evaluation, + mulmod( + mload(C_V21_LOC), + addmod(mulmod(mload(TABLE1_OMEGA_EVAL_LOC), mload(C_U_LOC), p), mload(TABLE1_EVAL_LOC), p), + p + ), + p + ) + batch_evaluation := + addmod( + batch_evaluation, + mulmod( + mload(C_V22_LOC), + addmod(mulmod(mload(TABLE2_OMEGA_EVAL_LOC), mload(C_U_LOC), p), mload(TABLE2_EVAL_LOC), p), + p + ), + p + ) + batch_evaluation := + addmod( + batch_evaluation, + mulmod( + mload(C_V23_LOC), + addmod(mulmod(mload(TABLE3_OMEGA_EVAL_LOC), mload(C_U_LOC), p), mload(TABLE3_EVAL_LOC), p), + p + ), + p + ) + batch_evaluation := + addmod( + batch_evaluation, + mulmod( + mload(C_V24_LOC), + addmod(mulmod(mload(TABLE4_OMEGA_EVAL_LOC), mload(C_U_LOC), p), mload(TABLE4_EVAL_LOC), p), + p + ), + p + ) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V25_LOC), mload(TABLE_TYPE_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V26_LOC), mload(ID1_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V27_LOC), mload(ID2_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V28_LOC), mload(ID3_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mulmod(mload(C_V29_LOC), mload(ID4_EVAL_LOC), p), p) + batch_evaluation := addmod(batch_evaluation, mload(QUOTIENT_EVAL_LOC), p) + + mstore(0x00, 0x01) // [1].x + mstore(0x20, 0x02) // [1].y + mstore(0x40, sub(p, batch_evaluation)) + // accumulator_2 = -[1].(batch_evaluation) + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + mstore(OPENING_COMMITMENT_SUCCESS_FLAG, success) + } + + /** + * PERFORM PAIRING PREAMBLE + */ + { + let u := mload(C_U_LOC) + let zeta := mload(C_ZETA_LOC) + // VALIDATE PI_Z + { + let x := mload(PI_Z_X_LOC) + let y := mload(PI_Z_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q)) + mstore(0x00, x) + mstore(0x20, y) + } + // compute zeta.[PI_Z] and add into accumulator + mstore(0x40, zeta) + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // accumulator = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, ACCUMULATOR_X_LOC, 0x40)) + + // VALIDATE PI_Z_OMEGA + { + let x := mload(PI_Z_OMEGA_X_LOC) + let y := mload(PI_Z_OMEGA_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + mstore(0x40, mulmod(mulmod(u, zeta, p), mload(OMEGA_LOC), p)) + // accumulator_2 = u.zeta.omega.[PI_Z_OMEGA] + success := and(success, staticcall(gas(), 7, 0x00, 0x60, ACCUMULATOR2_X_LOC, 0x40)) + // PAIRING_RHS = accumulator + accumulator_2 + success := and(success, staticcall(gas(), 6, ACCUMULATOR_X_LOC, 0x80, PAIRING_RHS_X_LOC, 0x40)) + + mstore(0x00, mload(PI_Z_X_LOC)) + mstore(0x20, mload(PI_Z_Y_LOC)) + mstore(0x40, mload(PI_Z_OMEGA_X_LOC)) + mstore(0x60, mload(PI_Z_OMEGA_Y_LOC)) + mstore(0x80, u) + success := and(success, staticcall(gas(), 7, 0x40, 0x60, 0x40, 0x40)) + // PAIRING_LHS = [PI_Z] + [PI_Z_OMEGA] * u + success := and(success, staticcall(gas(), 6, 0x00, 0x80, PAIRING_LHS_X_LOC, 0x40)) + // negate lhs y-coordinate + mstore(PAIRING_LHS_Y_LOC, sub(q, mload(PAIRING_LHS_Y_LOC))) + + if mload(CONTAINS_RECURSIVE_PROOF_LOC) { + // VALIDATE RECURSIVE P1 + { + let x := mload(RECURSIVE_P1_X_LOC) + let y := mload(RECURSIVE_P1_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + + // compute u.u.[recursive_p1] and write into 0x60 + mstore(0x40, mulmod(u, u, p)) + success := and(success, staticcall(gas(), 7, 0x00, 0x60, 0x60, 0x40)) + // VALIDATE RECURSIVE P2 + { + let x := mload(RECURSIVE_P2_X_LOC) + let y := mload(RECURSIVE_P2_Y_LOC) + let xx := mulmod(x, x, q) + // validate on curve + success := and(success, eq(mulmod(y, y, q), addmod(mulmod(x, xx, q), 3, q))) + mstore(0x00, x) + mstore(0x20, y) + } + // compute u.u.[recursive_p2] and write into 0x00 + // 0x40 still contains u*u + success := and(success, staticcall(gas(), 7, 0x00, 0x60, 0x00, 0x40)) + + // compute u.u.[recursiveP1] + rhs and write into rhs + mstore(0xa0, mload(PAIRING_RHS_X_LOC)) + mstore(0xc0, mload(PAIRING_RHS_Y_LOC)) + success := and(success, staticcall(gas(), 6, 0x60, 0x80, PAIRING_RHS_X_LOC, 0x40)) + + // compute u.u.[recursiveP2] + lhs and write into lhs + mstore(0x40, mload(PAIRING_LHS_X_LOC)) + mstore(0x60, mload(PAIRING_LHS_Y_LOC)) + success := and(success, staticcall(gas(), 6, 0x00, 0x80, PAIRING_LHS_X_LOC, 0x40)) + } + + if iszero(success) { + mstore(0x0, EC_SCALAR_MUL_FAILURE_SELECTOR) + revert(0x00, 0x04) + } + mstore(PAIRING_PREAMBLE_SUCCESS_FLAG, success) + } + + /** + * PERFORM PAIRING + */ + { + // rhs paired with [1]_2 + // lhs paired with [x]_2 + + mstore(0x00, mload(PAIRING_RHS_X_LOC)) + mstore(0x20, mload(PAIRING_RHS_Y_LOC)) + mstore(0x40, 0x198e9393920d483a7260bfb731fb5d25f1aa493335a9e71297e485b7aef312c2) // this is [1]_2 + mstore(0x60, 0x1800deef121f1e76426a00665e5c4479674322d4f75edadd46debd5cd992f6ed) + mstore(0x80, 0x090689d0585ff075ec9e99ad690c3395bc4b313370b38ef355acdadcd122975b) + mstore(0xa0, 0x12c85ea5db8c6deb4aab71808dcb408fe3d1e7690c43d37b4ce6cc0166fa7daa) + + mstore(0xc0, mload(PAIRING_LHS_X_LOC)) + mstore(0xe0, mload(PAIRING_LHS_Y_LOC)) + mstore(0x100, mload(G2X_X0_LOC)) + mstore(0x120, mload(G2X_X1_LOC)) + mstore(0x140, mload(G2X_Y0_LOC)) + mstore(0x160, mload(G2X_Y1_LOC)) + + success := staticcall(gas(), 8, 0x00, 0x180, 0x00, 0x20) + mstore(PAIRING_SUCCESS_FLAG, success) + mstore(RESULT_FLAG, mload(0x00)) + } + if iszero( + and( + and(and(mload(PAIRING_SUCCESS_FLAG), mload(RESULT_FLAG)), mload(PAIRING_PREAMBLE_SUCCESS_FLAG)), + mload(OPENING_COMMITMENT_SUCCESS_FLAG) + ) + ) { + mstore(0x0, PROOF_FAILURE_SELECTOR) + revert(0x00, 0x04) + } + { + mstore(0x00, 0x01) + return(0x00, 0x20) // Proof succeeded! + } + } + } +} + +contract UltraVerifier is BaseUltraVerifier { + function getVerificationKeyHash() public pure override(BaseUltraVerifier) returns (bytes32) { + return UltraVerificationKey.verificationKeyHash(); + } + + function loadVerificationKey(uint256 vk, uint256 _omegaInverseLoc) internal pure virtual override(BaseUltraVerifier) { + UltraVerificationKey.loadVerificationKey(vk, _omegaInverseLoc); + } +} diff --git a/noir/tooling/bb_abstraction_leaks/src/lib.rs b/noir/tooling/bb_abstraction_leaks/src/lib.rs new file mode 100644 index 00000000000..fec53809ad4 --- /dev/null +++ b/noir/tooling/bb_abstraction_leaks/src/lib.rs @@ -0,0 +1,33 @@ +#![warn(unused_crate_dependencies, unused_extern_crates)] +#![warn(unreachable_pub)] + +use acvm::{acir::native_types::WitnessMap, FieldElement}; + +pub const ACVM_BACKEND_BARRETENBERG: &str = "acvm-backend-barretenberg"; +pub const BB_DOWNLOAD_URL: &str = env!("BB_BINARY_URL"); +pub const BB_VERSION: &str = env!("BB_VERSION"); + +/// Embed the Solidity verifier file +const ULTRA_VERIFIER_CONTRACT: &str = include_str!("contract.sol"); + +pub fn complete_barretenberg_verifier_contract(contract: String) -> String { + format!("{contract}{ULTRA_VERIFIER_CONTRACT}") +} + +/// Removes the public inputs which are prepended to a proof by Barretenberg. +pub fn remove_public_inputs(num_pub_inputs: usize, proof: &[u8]) -> Vec { + // Barretenberg prepends the public inputs onto the proof so we need to remove + // the first `num_pub_inputs` field elements. + let num_bytes_to_remove = num_pub_inputs * (FieldElement::max_num_bytes() as usize); + proof[num_bytes_to_remove..].to_vec() +} + +/// Prepends a set of public inputs to a proof. +pub fn prepend_public_inputs(proof: Vec, public_inputs: WitnessMap) -> Vec { + // We omit any unassigned witnesses. + // Witness values should be ordered by their index but we skip over any indices without an assignment. + let public_inputs_bytes = + public_inputs.into_iter().flat_map(|(_, assignment)| assignment.to_be_bytes()); + + public_inputs_bytes.chain(proof).collect() +} diff --git a/noir/tooling/debugger/Cargo.toml b/noir/tooling/debugger/Cargo.toml new file mode 100644 index 00000000000..53c71754da4 --- /dev/null +++ b/noir/tooling/debugger/Cargo.toml @@ -0,0 +1,19 @@ +[package] +name = "noir_debugger" +description = "Debugger for Noir" +version.workspace = true +authors.workspace = true +edition.workspace = true +license.workspace = true + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + +[dependencies] +acvm.workspace = true +nargo.workspace = true +noirc_printable_type.workspace = true +noirc_errors.workspace = true +thiserror.workspace = true +codespan-reporting.workspace = true +easy-repl = "0.2.1" +owo-colors = "3" diff --git a/noir/tooling/debugger/src/context.rs b/noir/tooling/debugger/src/context.rs new file mode 100644 index 00000000000..40507aaed3a --- /dev/null +++ b/noir/tooling/debugger/src/context.rs @@ -0,0 +1,525 @@ +use acvm::acir::circuit::{Circuit, Opcode, OpcodeLocation}; +use acvm::acir::native_types::{Witness, WitnessMap}; +use acvm::brillig_vm::{brillig::Value, Registers}; +use acvm::pwg::{ + ACVMStatus, BrilligSolver, BrilligSolverStatus, ForeignCallWaitInfo, StepResult, ACVM, +}; +use acvm::{BlackBoxFunctionSolver, FieldElement}; + +use nargo::artifacts::debug::DebugArtifact; +use nargo::errors::{ExecutionError, Location}; +use nargo::ops::{DefaultForeignCallExecutor, ForeignCallExecutor}; +use nargo::NargoError; + +use std::collections::{hash_set::Iter, HashSet}; + +#[derive(Debug)] +pub(super) enum DebugCommandResult { + Done, + Ok, + BreakpointReached(OpcodeLocation), + Error(NargoError), +} + +pub(super) struct DebugContext<'a, B: BlackBoxFunctionSolver> { + acvm: ACVM<'a, B>, + brillig_solver: Option>, + foreign_call_executor: DefaultForeignCallExecutor, + debug_artifact: &'a DebugArtifact, + breakpoints: HashSet, +} + +impl<'a, B: BlackBoxFunctionSolver> DebugContext<'a, B> { + pub(super) fn new( + blackbox_solver: &'a B, + circuit: &'a Circuit, + debug_artifact: &'a DebugArtifact, + initial_witness: WitnessMap, + ) -> Self { + Self { + acvm: ACVM::new(blackbox_solver, &circuit.opcodes, initial_witness), + brillig_solver: None, + foreign_call_executor: DefaultForeignCallExecutor::new(true), + debug_artifact, + breakpoints: HashSet::new(), + } + } + + pub(super) fn get_opcodes(&self) -> &[Opcode] { + self.acvm.opcodes() + } + + pub(super) fn get_witness_map(&self) -> &WitnessMap { + self.acvm.witness_map() + } + + pub(super) fn overwrite_witness( + &mut self, + witness: Witness, + value: FieldElement, + ) -> Option { + self.acvm.overwrite_witness(witness, value) + } + + pub(super) fn get_current_opcode_location(&self) -> Option { + let ip = self.acvm.instruction_pointer(); + if ip >= self.get_opcodes().len() { + None + } else if let Some(ref solver) = self.brillig_solver { + Some(OpcodeLocation::Brillig { + acir_index: ip, + brillig_index: solver.program_counter(), + }) + } else { + Some(OpcodeLocation::Acir(ip)) + } + } + + /// Returns the callstack in source code locations for the currently + /// executing opcode. This can be `None` if the execution finished (and + /// `get_current_opcode_location()` returns `None`) or if the opcode is not + /// mapped to a specific source location in the debug artifact (which can + /// happen for certain opcodes inserted synthetically by the compiler) + pub(super) fn get_current_source_location(&self) -> Option> { + self.get_current_opcode_location() + .as_ref() + .and_then(|location| self.debug_artifact.debug_symbols[0].opcode_location(location)) + } + + fn step_brillig_opcode(&mut self) -> DebugCommandResult { + let Some(mut solver) = self.brillig_solver.take() else { + unreachable!("Missing Brillig solver"); + }; + match solver.step() { + Ok(BrilligSolverStatus::InProgress) => { + self.brillig_solver = Some(solver); + if self.breakpoint_reached() { + DebugCommandResult::BreakpointReached( + self.get_current_opcode_location() + .expect("Breakpoint reached but we have no location"), + ) + } else { + DebugCommandResult::Ok + } + } + Ok(BrilligSolverStatus::Finished) => { + let status = self.acvm.finish_brillig_with_solver(solver); + self.handle_acvm_status(status) + } + Ok(BrilligSolverStatus::ForeignCallWait(foreign_call)) => { + self.brillig_solver = Some(solver); + self.handle_foreign_call(foreign_call) + } + Err(err) => DebugCommandResult::Error(NargoError::ExecutionError( + ExecutionError::SolvingError(err), + )), + } + } + + fn handle_foreign_call(&mut self, foreign_call: ForeignCallWaitInfo) -> DebugCommandResult { + let foreign_call_result = self.foreign_call_executor.execute(&foreign_call); + match foreign_call_result { + Ok(foreign_call_result) => { + if let Some(mut solver) = self.brillig_solver.take() { + solver.resolve_pending_foreign_call(foreign_call_result); + self.brillig_solver = Some(solver); + } else { + self.acvm.resolve_pending_foreign_call(foreign_call_result); + } + // TODO: should we retry executing the opcode somehow in this case? + DebugCommandResult::Ok + } + Err(error) => DebugCommandResult::Error(error.into()), + } + } + + fn handle_acvm_status(&mut self, status: ACVMStatus) -> DebugCommandResult { + if let ACVMStatus::RequiresForeignCall(foreign_call) = status { + return self.handle_foreign_call(foreign_call); + } + + match status { + ACVMStatus::Solved => DebugCommandResult::Done, + ACVMStatus::InProgress => { + if self.breakpoint_reached() { + DebugCommandResult::BreakpointReached( + self.get_current_opcode_location() + .expect("Breakpoint reached but we have no location"), + ) + } else { + DebugCommandResult::Ok + } + } + ACVMStatus::Failure(error) => DebugCommandResult::Error(NargoError::ExecutionError( + ExecutionError::SolvingError(error), + )), + ACVMStatus::RequiresForeignCall(_) => { + unreachable!("Unexpected pending foreign call resolution"); + } + } + } + + pub(super) fn step_into_opcode(&mut self) -> DebugCommandResult { + if self.brillig_solver.is_some() { + return self.step_brillig_opcode(); + } + + match self.acvm.step_into_brillig_opcode() { + StepResult::IntoBrillig(solver) => { + self.brillig_solver = Some(solver); + self.step_brillig_opcode() + } + StepResult::Status(status) => self.handle_acvm_status(status), + } + } + + fn currently_executing_brillig(&self) -> bool { + if self.brillig_solver.is_some() { + return true; + } + + match self.get_current_opcode_location() { + Some(OpcodeLocation::Brillig { .. }) => true, + Some(OpcodeLocation::Acir(acir_index)) => { + matches!(self.get_opcodes()[acir_index], Opcode::Brillig(_)) + } + _ => false, + } + } + + fn get_current_acir_index(&self) -> Option { + self.get_current_opcode_location().map(|opcode_location| match opcode_location { + OpcodeLocation::Acir(acir_index) => acir_index, + OpcodeLocation::Brillig { acir_index, .. } => acir_index, + }) + } + + fn step_out_of_brillig_opcode(&mut self) -> DebugCommandResult { + let Some(start_acir_index) = self.get_current_acir_index() else { + return DebugCommandResult::Done; + }; + loop { + let result = self.step_into_opcode(); + if !matches!(result, DebugCommandResult::Ok) { + return result; + } + let new_acir_index = self.get_current_acir_index().unwrap(); + if new_acir_index != start_acir_index { + return DebugCommandResult::Ok; + } + } + } + + pub(super) fn step_acir_opcode(&mut self) -> DebugCommandResult { + if self.currently_executing_brillig() { + self.step_out_of_brillig_opcode() + } else { + let status = self.acvm.solve_opcode(); + self.handle_acvm_status(status) + } + } + + pub(super) fn next(&mut self) -> DebugCommandResult { + let start_location = self.get_current_source_location(); + loop { + let result = self.step_into_opcode(); + if !matches!(result, DebugCommandResult::Ok) { + return result; + } + let new_location = self.get_current_source_location(); + if new_location.is_some() && new_location != start_location { + return DebugCommandResult::Ok; + } + } + } + + pub(super) fn cont(&mut self) -> DebugCommandResult { + loop { + let result = self.step_into_opcode(); + if !matches!(result, DebugCommandResult::Ok) { + return result; + } + } + } + + pub(super) fn is_executing_brillig(&self) -> bool { + let opcodes = self.get_opcodes(); + let acir_index = self.acvm.instruction_pointer(); + acir_index < opcodes.len() && matches!(opcodes[acir_index], Opcode::Brillig(..)) + } + + pub(super) fn get_brillig_registers(&self) -> Option<&Registers> { + self.brillig_solver.as_ref().map(|solver| solver.get_registers()) + } + + pub(super) fn set_brillig_register(&mut self, register_index: usize, value: FieldElement) { + if let Some(solver) = self.brillig_solver.as_mut() { + solver.set_register(register_index, value.into()); + } + } + + pub(super) fn get_brillig_memory(&self) -> Option<&[Value]> { + self.brillig_solver.as_ref().map(|solver| solver.get_memory()) + } + + pub(super) fn write_brillig_memory(&mut self, ptr: usize, value: FieldElement) { + if let Some(solver) = self.brillig_solver.as_mut() { + solver.write_memory_at(ptr, value.into()); + } + } + + fn breakpoint_reached(&self) -> bool { + if let Some(location) = self.get_current_opcode_location() { + self.breakpoints.contains(&location) + } else { + false + } + } + + pub(super) fn is_valid_opcode_location(&self, location: &OpcodeLocation) -> bool { + let opcodes = self.get_opcodes(); + match *location { + OpcodeLocation::Acir(acir_index) => acir_index < opcodes.len(), + OpcodeLocation::Brillig { acir_index, brillig_index } => { + acir_index < opcodes.len() + && matches!(opcodes[acir_index], Opcode::Brillig(..)) + && { + if let Opcode::Brillig(ref brillig) = opcodes[acir_index] { + brillig_index < brillig.bytecode.len() + } else { + false + } + } + } + } + } + + pub(super) fn is_breakpoint_set(&self, location: &OpcodeLocation) -> bool { + self.breakpoints.contains(location) + } + + pub(super) fn add_breakpoint(&mut self, location: OpcodeLocation) -> bool { + self.breakpoints.insert(location) + } + + pub(super) fn delete_breakpoint(&mut self, location: &OpcodeLocation) -> bool { + self.breakpoints.remove(location) + } + + pub(super) fn iterate_breakpoints(&self) -> Iter<'_, OpcodeLocation> { + self.breakpoints.iter() + } + + pub(super) fn is_solved(&self) -> bool { + matches!(self.acvm.get_status(), ACVMStatus::Solved) + } + + pub fn finalize(self) -> WitnessMap { + self.acvm.finalize() + } +} + +#[cfg(test)] +struct StubbedSolver; + +#[cfg(test)] +impl BlackBoxFunctionSolver for StubbedSolver { + fn schnorr_verify( + &self, + _public_key_x: &FieldElement, + _public_key_y: &FieldElement, + _signature: &[u8], + _message: &[u8], + ) -> Result { + unimplemented!(); + } + + fn pedersen_commitment( + &self, + _inputs: &[FieldElement], + _domain_separator: u32, + ) -> Result<(FieldElement, FieldElement), acvm::BlackBoxResolutionError> { + unimplemented!(); + } + + fn pedersen_hash( + &self, + _inputs: &[FieldElement], + _domain_separator: u32, + ) -> Result { + unimplemented!(); + } + + fn fixed_base_scalar_mul( + &self, + _low: &FieldElement, + _high: &FieldElement, + ) -> Result<(FieldElement, FieldElement), acvm::BlackBoxResolutionError> { + unimplemented!(); + } +} + +#[cfg(test)] +#[test] +fn test_resolve_foreign_calls_stepping_into_brillig() { + use std::collections::BTreeMap; + + use acvm::acir::{ + brillig::{Opcode as BrilligOpcode, RegisterIndex, RegisterOrMemory}, + circuit::brillig::{Brillig, BrilligInputs}, + native_types::Expression, + }; + + let fe_0 = FieldElement::zero(); + let fe_1 = FieldElement::one(); + let w_x = Witness(1); + + let blackbox_solver = &StubbedSolver; + + let brillig_opcodes = Brillig { + inputs: vec![BrilligInputs::Single(Expression { + linear_combinations: vec![(fe_1, w_x)], + ..Expression::default() + })], + outputs: vec![], + bytecode: vec![ + BrilligOpcode::Const { destination: RegisterIndex::from(1), value: Value::from(fe_0) }, + BrilligOpcode::ForeignCall { + function: "clear_mock".into(), + destinations: vec![], + inputs: vec![RegisterOrMemory::RegisterIndex(RegisterIndex::from(0))], + }, + BrilligOpcode::Stop, + ], + predicate: None, + }; + let opcodes = vec![Opcode::Brillig(brillig_opcodes)]; + let current_witness_index = 2; + let circuit = &Circuit { current_witness_index, opcodes, ..Circuit::default() }; + + let debug_symbols = vec![]; + let file_map = BTreeMap::new(); + let warnings = vec![]; + let debug_artifact = &DebugArtifact { debug_symbols, file_map, warnings }; + + let initial_witness = BTreeMap::from([(Witness(1), fe_1)]).into(); + + let mut context = DebugContext::new(blackbox_solver, circuit, debug_artifact, initial_witness); + + assert_eq!(context.get_current_opcode_location(), Some(OpcodeLocation::Acir(0))); + + // execute the first Brillig opcode (const) + let result = context.step_into_opcode(); + assert!(matches!(result, DebugCommandResult::Ok)); + assert_eq!( + context.get_current_opcode_location(), + Some(OpcodeLocation::Brillig { acir_index: 0, brillig_index: 1 }) + ); + + // try to execute the second Brillig opcode (and resolve the foreign call) + let result = context.step_into_opcode(); + assert!(matches!(result, DebugCommandResult::Ok)); + assert_eq!( + context.get_current_opcode_location(), + Some(OpcodeLocation::Brillig { acir_index: 0, brillig_index: 1 }) + ); + + // retry the second Brillig opcode (foreign call should be finished) + let result = context.step_into_opcode(); + assert!(matches!(result, DebugCommandResult::Ok)); + assert_eq!( + context.get_current_opcode_location(), + Some(OpcodeLocation::Brillig { acir_index: 0, brillig_index: 2 }) + ); + + // last Brillig opcode + let result = context.step_into_opcode(); + assert!(matches!(result, DebugCommandResult::Done)); + assert_eq!(context.get_current_opcode_location(), None); +} + +#[cfg(test)] +#[test] +fn test_break_brillig_block_while_stepping_acir_opcodes() { + use std::collections::BTreeMap; + + use acvm::acir::{ + brillig::{Opcode as BrilligOpcode, RegisterIndex}, + circuit::brillig::{Brillig, BrilligInputs, BrilligOutputs}, + native_types::Expression, + }; + use acvm::brillig_vm::brillig::BinaryFieldOp; + + let fe_0 = FieldElement::zero(); + let fe_1 = FieldElement::one(); + let w_x = Witness(1); + let w_y = Witness(2); + let w_z = Witness(3); + + let blackbox_solver = &StubbedSolver; + + // This Brillig block is equivalent to: z = x + y + let brillig_opcodes = Brillig { + inputs: vec![ + BrilligInputs::Single(Expression { + linear_combinations: vec![(fe_1, w_x)], + ..Expression::default() + }), + BrilligInputs::Single(Expression { + linear_combinations: vec![(fe_1, w_y)], + ..Expression::default() + }), + ], + outputs: vec![BrilligOutputs::Simple(w_z)], + bytecode: vec![ + BrilligOpcode::BinaryFieldOp { + destination: RegisterIndex::from(0), + op: BinaryFieldOp::Add, + lhs: RegisterIndex::from(0), + rhs: RegisterIndex::from(1), + }, + BrilligOpcode::Stop, + ], + predicate: None, + }; + let opcodes = vec![ + // z = x + y + Opcode::Brillig(brillig_opcodes), + // x + y - z = 0 + Opcode::Arithmetic(Expression { + mul_terms: vec![], + linear_combinations: vec![(fe_1, w_x), (fe_1, w_y), (-fe_1, w_z)], + q_c: fe_0, + }), + ]; + let current_witness_index = 3; + let circuit = &Circuit { current_witness_index, opcodes, ..Circuit::default() }; + + let debug_symbols = vec![]; + let file_map = BTreeMap::new(); + let warnings = vec![]; + let debug_artifact = &DebugArtifact { debug_symbols, file_map, warnings }; + + let initial_witness = BTreeMap::from([(Witness(1), fe_1), (Witness(2), fe_1)]).into(); + + let mut context = DebugContext::new(blackbox_solver, circuit, debug_artifact, initial_witness); + + // set breakpoint + let breakpoint_location = OpcodeLocation::Brillig { acir_index: 0, brillig_index: 1 }; + assert!(context.add_breakpoint(breakpoint_location.clone())); + + // execute the first ACIR opcode (Brillig block) -> should reach the breakpoint instead + let result = context.step_acir_opcode(); + assert!(matches!(result, DebugCommandResult::BreakpointReached(_))); + assert_eq!(context.get_current_opcode_location(), Some(breakpoint_location)); + + // continue execution to the next ACIR opcode + let result = context.step_acir_opcode(); + assert!(matches!(result, DebugCommandResult::Ok)); + assert_eq!(context.get_current_opcode_location(), Some(OpcodeLocation::Acir(1))); + + // last ACIR opcode + let result = context.step_acir_opcode(); + assert!(matches!(result, DebugCommandResult::Done)); + assert_eq!(context.get_current_opcode_location(), None); +} diff --git a/noir/tooling/debugger/src/lib.rs b/noir/tooling/debugger/src/lib.rs new file mode 100644 index 00000000000..7c6a9e9f618 --- /dev/null +++ b/noir/tooling/debugger/src/lib.rs @@ -0,0 +1,18 @@ +mod context; +mod repl; + +use acvm::BlackBoxFunctionSolver; +use acvm::{acir::circuit::Circuit, acir::native_types::WitnessMap}; + +use nargo::artifacts::debug::DebugArtifact; + +use nargo::NargoError; + +pub fn debug_circuit( + blackbox_solver: &B, + circuit: &Circuit, + debug_artifact: DebugArtifact, + initial_witness: WitnessMap, +) -> Result, NargoError> { + repl::run(blackbox_solver, circuit, &debug_artifact, initial_witness) +} diff --git a/noir/tooling/debugger/src/repl.rs b/noir/tooling/debugger/src/repl.rs new file mode 100644 index 00000000000..23abe83c38d --- /dev/null +++ b/noir/tooling/debugger/src/repl.rs @@ -0,0 +1,568 @@ +use crate::context::{DebugCommandResult, DebugContext}; + +use acvm::acir::circuit::{Circuit, Opcode, OpcodeLocation}; +use acvm::acir::native_types::{Witness, WitnessMap}; +use acvm::{BlackBoxFunctionSolver, FieldElement}; + +use nargo::artifacts::debug::DebugArtifact; +use nargo::NargoError; + +use easy_repl::{command, CommandStatus, Repl}; +use std::cell::RefCell; + +use codespan_reporting::files::Files; +use noirc_errors::Location; + +use owo_colors::OwoColorize; + +use std::ops::Range; + +pub struct ReplDebugger<'a, B: BlackBoxFunctionSolver> { + context: DebugContext<'a, B>, + blackbox_solver: &'a B, + circuit: &'a Circuit, + debug_artifact: &'a DebugArtifact, + initial_witness: WitnessMap, + last_result: DebugCommandResult, +} + +impl<'a, B: BlackBoxFunctionSolver> ReplDebugger<'a, B> { + pub fn new( + blackbox_solver: &'a B, + circuit: &'a Circuit, + debug_artifact: &'a DebugArtifact, + initial_witness: WitnessMap, + ) -> Self { + let context = + DebugContext::new(blackbox_solver, circuit, debug_artifact, initial_witness.clone()); + Self { + context, + blackbox_solver, + circuit, + debug_artifact, + initial_witness, + last_result: DebugCommandResult::Ok, + } + } + + pub fn show_current_vm_status(&self) { + let location = self.context.get_current_opcode_location(); + let opcodes = self.context.get_opcodes(); + + match location { + None => println!("Finished execution"), + Some(location) => { + match location { + OpcodeLocation::Acir(ip) => { + println!("At opcode {}: {}", ip, opcodes[ip]) + } + OpcodeLocation::Brillig { acir_index, brillig_index } => { + let Opcode::Brillig(ref brillig) = opcodes[acir_index] else { + unreachable!("Brillig location does not contain a Brillig block"); + }; + println!( + "At opcode {}.{}: {:?}", + acir_index, brillig_index, brillig.bytecode[brillig_index] + ); + } + } + self.show_source_code_location(&location); + } + } + } + + fn print_location_path(&self, loc: Location) { + let line_number = self.debug_artifact.location_line_number(loc).unwrap(); + let column_number = self.debug_artifact.location_column_number(loc).unwrap(); + + println!( + "At {}:{line_number}:{column_number}", + self.debug_artifact.name(loc.file).unwrap() + ); + } + + fn show_source_code_location(&self, location: &OpcodeLocation) { + let locations = self.debug_artifact.debug_symbols[0].opcode_location(location); + let Some(locations) = locations else { return }; + for loc in locations { + self.print_location_path(loc); + + let loc_line_index = self.debug_artifact.location_line_index(loc).unwrap(); + + // How many lines before or after the location's line we + // print + let context_lines = 5; + + let first_line_to_print = + if loc_line_index < context_lines { 0 } else { loc_line_index - context_lines }; + + let last_line_index = self.debug_artifact.last_line_index(loc).unwrap(); + let last_line_to_print = std::cmp::min(loc_line_index + context_lines, last_line_index); + + let source = self.debug_artifact.location_source_code(loc).unwrap(); + for (current_line_index, line) in source.lines().enumerate() { + let current_line_number = current_line_index + 1; + + if current_line_index < first_line_to_print { + // Ignore lines before range starts + continue; + } else if current_line_index == first_line_to_print && current_line_index > 0 { + // Denote that there's more lines before but we're not showing them + print_line_of_ellipsis(current_line_index); + } + + if current_line_index > last_line_to_print { + // Denote that there's more lines after but we're not showing them, + // and stop printing + print_line_of_ellipsis(current_line_number); + break; + } + + if current_line_index == loc_line_index { + // Highlight current location + let Range { start: loc_start, end: loc_end } = + self.debug_artifact.location_in_line(loc).unwrap(); + println!( + "{:>3} {:2} {}{}{}", + current_line_number, + "->", + &line[0..loc_start].to_string().dimmed(), + &line[loc_start..loc_end], + &line[loc_end..].to_string().dimmed() + ); + } else { + print_dimmed_line(current_line_number, line); + } + } + } + } + + fn display_opcodes(&self) { + let opcodes = self.context.get_opcodes(); + let current_opcode_location = self.context.get_current_opcode_location(); + let current_acir_index = match current_opcode_location { + Some(OpcodeLocation::Acir(ip)) => Some(ip), + Some(OpcodeLocation::Brillig { acir_index, .. }) => Some(acir_index), + None => None, + }; + let current_brillig_index = match current_opcode_location { + Some(OpcodeLocation::Brillig { brillig_index, .. }) => brillig_index, + _ => 0, + }; + let outer_marker = |acir_index| { + if current_acir_index == Some(acir_index) { + "->" + } else if self.context.is_breakpoint_set(&OpcodeLocation::Acir(acir_index)) { + " *" + } else { + "" + } + }; + let brillig_marker = |acir_index, brillig_index| { + if current_acir_index == Some(acir_index) && brillig_index == current_brillig_index { + "->" + } else if self + .context + .is_breakpoint_set(&OpcodeLocation::Brillig { acir_index, brillig_index }) + { + " *" + } else { + "" + } + }; + for (acir_index, opcode) in opcodes.iter().enumerate() { + let marker = outer_marker(acir_index); + if let Opcode::Brillig(brillig) = opcode { + println!("{:>3} {:2} BRILLIG inputs={:?}", acir_index, marker, brillig.inputs); + println!(" | outputs={:?}", brillig.outputs); + for (brillig_index, brillig_opcode) in brillig.bytecode.iter().enumerate() { + println!( + "{:>3}.{:<2} |{:2} {:?}", + acir_index, + brillig_index, + brillig_marker(acir_index, brillig_index), + brillig_opcode + ); + } + } else { + println!("{:>3} {:2} {:?}", acir_index, marker, opcode); + } + } + } + + fn add_breakpoint_at(&mut self, location: OpcodeLocation) { + if !self.context.is_valid_opcode_location(&location) { + println!("Invalid opcode location {location}"); + } else if self.context.add_breakpoint(location) { + println!("Added breakpoint at opcode {location}"); + } else { + println!("Breakpoint at opcode {location} already set"); + } + } + + fn delete_breakpoint_at(&mut self, location: OpcodeLocation) { + if self.context.delete_breakpoint(&location) { + println!("Breakpoint at opcode {location} deleted"); + } else { + println!("Breakpoint at opcode {location} not set"); + } + } + + fn validate_in_progress(&self) -> bool { + match self.last_result { + DebugCommandResult::Ok | DebugCommandResult::BreakpointReached(..) => true, + DebugCommandResult::Done => { + println!("Execution finished"); + false + } + DebugCommandResult::Error(ref error) => { + println!("ERROR: {}", error); + self.show_current_vm_status(); + false + } + } + } + + fn handle_debug_command_result(&mut self, result: DebugCommandResult) { + match &result { + DebugCommandResult::BreakpointReached(location) => { + println!("Stopped at breakpoint in opcode {}", location); + } + DebugCommandResult::Error(error) => { + println!("ERROR: {}", error); + } + _ => (), + } + self.last_result = result; + self.show_current_vm_status(); + } + + fn step_acir_opcode(&mut self) { + if self.validate_in_progress() { + let result = self.context.step_acir_opcode(); + self.handle_debug_command_result(result); + } + } + + fn step_into_opcode(&mut self) { + if self.validate_in_progress() { + let result = self.context.step_into_opcode(); + self.handle_debug_command_result(result); + } + } + + fn next(&mut self) { + if self.validate_in_progress() { + let result = self.context.next(); + self.handle_debug_command_result(result); + } + } + + fn cont(&mut self) { + if self.validate_in_progress() { + println!("(Continuing execution...)"); + let result = self.context.cont(); + self.handle_debug_command_result(result); + } + } + + fn restart_session(&mut self) { + let breakpoints: Vec = + self.context.iterate_breakpoints().copied().collect(); + self.context = DebugContext::new( + self.blackbox_solver, + self.circuit, + self.debug_artifact, + self.initial_witness.clone(), + ); + for opcode_location in breakpoints { + self.context.add_breakpoint(opcode_location); + } + self.last_result = DebugCommandResult::Ok; + println!("Restarted debugging session."); + self.show_current_vm_status(); + } + + pub fn show_witness_map(&self) { + let witness_map = self.context.get_witness_map(); + // NOTE: we need to clone() here to get the iterator + for (witness, value) in witness_map.clone().into_iter() { + println!("_{} = {value}", witness.witness_index()); + } + } + + pub fn show_witness(&self, index: u32) { + if let Some(value) = self.context.get_witness_map().get_index(index) { + println!("_{} = {value}", index); + } + } + + pub fn update_witness(&mut self, index: u32, value: String) { + let Some(field_value) = FieldElement::try_from_str(&value) else { + println!("Invalid witness value: {value}"); + return; + }; + + let witness = Witness::from(index); + _ = self.context.overwrite_witness(witness, field_value); + println!("_{} = {value}", index); + } + + pub fn show_brillig_registers(&self) { + if !self.context.is_executing_brillig() { + println!("Not executing a Brillig block"); + return; + } + + let Some(registers) = self.context.get_brillig_registers() else { + // this can happen when just entering the Brillig block since ACVM + // would have not initialized the Brillig VM yet; in fact, the + // Brillig code may be skipped altogether + println!("Brillig VM registers not available"); + return; + }; + + for (index, value) in registers.inner.iter().enumerate() { + println!("{index} = {}", value.to_field()); + } + } + + pub fn set_brillig_register(&mut self, index: usize, value: String) { + let Some(field_value) = FieldElement::try_from_str(&value) else { + println!("Invalid value: {value}"); + return; + }; + if !self.context.is_executing_brillig() { + println!("Not executing a Brillig block"); + return; + } + self.context.set_brillig_register(index, field_value); + } + + pub fn show_brillig_memory(&self) { + if !self.context.is_executing_brillig() { + println!("Not executing a Brillig block"); + return; + } + + let Some(memory) = self.context.get_brillig_memory() else { + // this can happen when just entering the Brillig block since ACVM + // would have not initialized the Brillig VM yet; in fact, the + // Brillig code may be skipped altogether + println!("Brillig VM memory not available"); + return; + }; + + for (index, value) in memory.iter().enumerate() { + println!("{index} = {}", value.to_field()); + } + } + + pub fn write_brillig_memory(&mut self, index: usize, value: String) { + let Some(field_value) = FieldElement::try_from_str(&value) else { + println!("Invalid value: {value}"); + return; + }; + if !self.context.is_executing_brillig() { + println!("Not executing a Brillig block"); + return; + } + self.context.write_brillig_memory(index, field_value); + } + + fn is_solved(&self) -> bool { + self.context.is_solved() + } + + fn finalize(self) -> WitnessMap { + self.context.finalize() + } +} + +fn print_line_of_ellipsis(line_number: usize) { + println!("{}", format!("{:>3} {}", line_number, "...").dimmed()); +} + +fn print_dimmed_line(line_number: usize, line: &str) { + println!("{}", format!("{:>3} {:2} {}", line_number, "", line).dimmed()); +} + +pub fn run( + blackbox_solver: &B, + circuit: &Circuit, + debug_artifact: &DebugArtifact, + initial_witness: WitnessMap, +) -> Result, NargoError> { + let context = + RefCell::new(ReplDebugger::new(blackbox_solver, circuit, debug_artifact, initial_witness)); + let ref_context = &context; + + ref_context.borrow().show_current_vm_status(); + + let mut repl = Repl::builder() + .add( + "step", + command! { + "step to the next ACIR opcode", + () => || { + ref_context.borrow_mut().step_acir_opcode(); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "into", + command! { + "step into to the next opcode", + () => || { + ref_context.borrow_mut().step_into_opcode(); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "next", + command! { + "step until a new source location is reached", + () => || { + ref_context.borrow_mut().next(); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "continue", + command! { + "continue execution until the end of the program", + () => || { + ref_context.borrow_mut().cont(); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "restart", + command! { + "restart the debugging session", + () => || { + ref_context.borrow_mut().restart_session(); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "opcodes", + command! { + "display ACIR opcodes", + () => || { + ref_context.borrow().display_opcodes(); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "break", + command! { + "add a breakpoint at an opcode location", + (LOCATION:OpcodeLocation) => |location| { + ref_context.borrow_mut().add_breakpoint_at(location); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "delete", + command! { + "delete breakpoint at an opcode location", + (LOCATION:OpcodeLocation) => |location| { + ref_context.borrow_mut().delete_breakpoint_at(location); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "witness", + command! { + "show witness map", + () => || { + ref_context.borrow().show_witness_map(); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "witness", + command! { + "display a single witness from the witness map", + (index: u32) => |index| { + ref_context.borrow().show_witness(index); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "witness", + command! { + "update a witness with the given value", + (index: u32, value: String) => |index, value| { + ref_context.borrow_mut().update_witness(index, value); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "registers", + command! { + "show Brillig registers (valid when executing a Brillig block)", + () => || { + ref_context.borrow().show_brillig_registers(); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "regset", + command! { + "update a Brillig register with the given value", + (index: usize, value: String) => |index, value| { + ref_context.borrow_mut().set_brillig_register(index, value); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "memory", + command! { + "show Brillig memory (valid when executing a Brillig block)", + () => || { + ref_context.borrow().show_brillig_memory(); + Ok(CommandStatus::Done) + } + }, + ) + .add( + "memset", + command! { + "update a Brillig memory cell with the given value", + (index: usize, value: String) => |index, value| { + ref_context.borrow_mut().write_brillig_memory(index, value); + Ok(CommandStatus::Done) + } + }, + ) + .build() + .expect("Failed to initialize debugger repl"); + + repl.run().expect("Debugger error"); + + // REPL execution has finished. + // Drop it so that we can move fields out from `context` again. + drop(repl); + + if context.borrow().is_solved() { + let solved_witness = context.into_inner().finalize(); + Ok(Some(solved_witness)) + } else { + Ok(None) + } +} diff --git a/noir/tooling/lsp/Cargo.toml b/noir/tooling/lsp/Cargo.toml new file mode 100644 index 00000000000..67778c744db --- /dev/null +++ b/noir/tooling/lsp/Cargo.toml @@ -0,0 +1,34 @@ +[package] +name = "noir_lsp" +description = "Language server for Noir" +version.workspace = true +authors.workspace = true +edition.workspace = true +license.workspace = true + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + +[dependencies] +acvm.workspace = true +codespan-lsp.workspace = true +codespan-reporting.workspace = true +lsp-types.workspace = true +nargo.workspace = true +nargo_fmt.workspace = true +nargo_toml.workspace = true +noirc_driver.workspace = true +noirc_errors.workspace = true +noirc_frontend.workspace = true +serde.workspace = true +serde_json.workspace = true +tower.workspace = true +cfg-if.workspace = true +async-lsp = { workspace = true, features = ["omni-trait"] } +serde_with = "3.2.0" +fm.workspace = true + +[target.'cfg(all(target_arch = "wasm32", not(target_os = "wasi")))'.dependencies] +wasm-bindgen.workspace = true + +[dev-dependencies] +tokio = { version = "1.0", features = ["macros", "rt"] } diff --git a/noir/tooling/lsp/src/lib.rs b/noir/tooling/lsp/src/lib.rs new file mode 100644 index 00000000000..d8a992155dd --- /dev/null +++ b/noir/tooling/lsp/src/lib.rs @@ -0,0 +1,203 @@ +#![forbid(unsafe_code)] +#![warn(unreachable_pub)] +#![warn(clippy::semicolon_if_nothing_returned)] +#![cfg_attr(not(test), warn(unused_crate_dependencies, unused_extern_crates))] + +use std::{ + collections::HashMap, + future::Future, + ops::{self, ControlFlow}, + path::{Path, PathBuf}, + pin::Pin, + task::{self, Poll}, +}; + +use acvm::BlackBoxFunctionSolver; +use async_lsp::{ + router::Router, AnyEvent, AnyNotification, AnyRequest, ClientSocket, Error, LspService, + ResponseError, +}; +use codespan_reporting::files; +use noirc_frontend::{ + graph::{CrateId, CrateName}, + hir::{Context, FunctionNameMatch}, +}; +use notifications::{ + on_did_change_configuration, on_did_change_text_document, on_did_close_text_document, + on_did_open_text_document, on_did_save_text_document, on_exit, on_initialized, +}; +use requests::{ + on_code_lens_request, on_formatting, on_initialize, on_profile_run_request, on_shutdown, + on_test_run_request, on_tests_request, +}; +use serde_json::Value as JsonValue; +use tower::Service; + +mod notifications; +mod requests; +mod solver; +mod types; + +use solver::WrapperSolver; +use types::{notification, request, NargoTest, NargoTestId, Position, Range, Url}; + +// State for the LSP gets implemented on this struct and is internal to the implementation +pub struct LspState { + root_path: Option, + client: ClientSocket, + solver: WrapperSolver, + input_files: HashMap, +} + +impl LspState { + fn new(client: &ClientSocket, solver: impl BlackBoxFunctionSolver + 'static) -> Self { + Self { + client: client.clone(), + root_path: None, + solver: WrapperSolver(Box::new(solver)), + input_files: HashMap::new(), + } + } +} + +pub struct NargoLspService { + router: Router, +} + +impl NargoLspService { + pub fn new(client: &ClientSocket, solver: impl BlackBoxFunctionSolver + 'static) -> Self { + let state = LspState::new(client, solver); + let mut router = Router::new(state); + router + .request::(on_initialize) + .request::(on_formatting) + .request::(on_shutdown) + .request::(on_code_lens_request) + .request::(on_tests_request) + .request::(on_test_run_request) + .request::(on_profile_run_request) + .notification::(on_initialized) + .notification::(on_did_change_configuration) + .notification::(on_did_open_text_document) + .notification::(on_did_change_text_document) + .notification::(on_did_close_text_document) + .notification::(on_did_save_text_document) + .notification::(on_exit); + Self { router } + } +} + +// This trait implemented as a passthrough to the router, which makes +// our `NargoLspService` a normal Service as far as Tower is concerned. +impl Service for NargoLspService { + type Response = JsonValue; + type Error = ResponseError; + type Future = Pin> + Send>>; + + fn poll_ready(&mut self, cx: &mut task::Context<'_>) -> Poll> { + self.router.poll_ready(cx) + } + + fn call(&mut self, req: AnyRequest) -> Self::Future { + self.router.call(req) + } +} + +// This trait implemented as a passthrough to the router, which makes +// our `NargoLspService` able to accept the `async-lsp` middleware. +impl LspService for NargoLspService { + fn notify(&mut self, notification: AnyNotification) -> ControlFlow> { + self.router.notify(notification) + } + + fn emit(&mut self, event: AnyEvent) -> ControlFlow> { + self.router.emit(event) + } +} + +fn get_package_tests_in_crate( + context: &Context, + crate_id: &CrateId, + crate_name: &CrateName, +) -> Option> { + let fm = &context.file_manager; + let files = fm.as_file_map(); + let tests = + context.get_all_test_functions_in_crate_matching(crate_id, FunctionNameMatch::Anything); + + let package_tests: Vec<_> = tests + .into_iter() + .map(|(func_name, test_function)| { + let location = context.function_meta(&test_function.get_id()).name.location; + let file_id = location.file; + + let range = + byte_span_to_range(files, file_id, location.span.into()).unwrap_or_default(); + let file_uri = Url::from_file_path(fm.path(file_id)) + .expect("Expected a valid file path that can be converted into a URI"); + + NargoTest { + id: NargoTestId::new(crate_name.clone(), func_name.clone()), + label: func_name, + uri: file_uri, + range, + } + }) + .collect(); + + if package_tests.is_empty() { + None + } else { + Some(package_tests) + } +} + +fn byte_span_to_range<'a, F: files::Files<'a> + ?Sized>( + files: &'a F, + file_id: F::FileId, + span: ops::Range, +) -> Option { + if let Ok(codespan_range) = codespan_lsp::byte_span_to_range(files, file_id, span) { + // We have to manually construct a Range because the codespan_lsp restricts lsp-types to the wrong version range + // TODO: codespan is unmaintained and we should probably subsume it. Ref https://github.com/brendanzab/codespan/issues/345 + let range = Range { + start: Position { + line: codespan_range.start.line, + character: codespan_range.start.character, + }, + end: Position { + line: codespan_range.end.line, + character: codespan_range.end.character, + }, + }; + Some(range) + } else { + None + } +} + +cfg_if::cfg_if! { + if #[cfg(all(target_arch = "wasm32", not(target_os = "wasi")))] { + use wasm_bindgen::{prelude::*, JsValue}; + + #[wasm_bindgen(module = "@noir-lang/source-resolver")] + extern "C" { + + #[wasm_bindgen(catch)] + fn read_file(path: &str) -> Result; + + } + + fn get_non_stdlib_asset(path_to_file: &Path) -> std::io::Result { + let path_str = path_to_file.to_str().unwrap(); + match read_file(path_str) { + Ok(buffer) => Ok(buffer), + Err(_) => Err(Error::new(ErrorKind::Other, "could not read file using wasm")), + } + } + } else { + fn get_non_stdlib_asset(path_to_file: &Path) -> std::io::Result { + std::fs::read_to_string(path_to_file) + } + } +} diff --git a/noir/tooling/lsp/src/notifications/mod.rs b/noir/tooling/lsp/src/notifications/mod.rs new file mode 100644 index 00000000000..f6484f49d48 --- /dev/null +++ b/noir/tooling/lsp/src/notifications/mod.rs @@ -0,0 +1,181 @@ +use std::ops::ControlFlow; + +use async_lsp::{ErrorCode, LanguageClient, ResponseError}; +use nargo::prepare_package; +use nargo_toml::{find_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_driver::{check_crate, NOIR_ARTIFACT_VERSION_STRING}; +use noirc_errors::{DiagnosticKind, FileDiagnostic}; + +use crate::types::{ + notification, Diagnostic, DiagnosticSeverity, DidChangeConfigurationParams, + DidChangeTextDocumentParams, DidCloseTextDocumentParams, DidOpenTextDocumentParams, + DidSaveTextDocumentParams, InitializedParams, LogMessageParams, MessageType, NargoPackageTests, + PublishDiagnosticsParams, +}; + +use crate::{byte_span_to_range, get_non_stdlib_asset, get_package_tests_in_crate, LspState}; + +pub(super) fn on_initialized( + _state: &mut LspState, + _params: InitializedParams, +) -> ControlFlow> { + ControlFlow::Continue(()) +} + +pub(super) fn on_did_change_configuration( + _state: &mut LspState, + _params: DidChangeConfigurationParams, +) -> ControlFlow> { + ControlFlow::Continue(()) +} + +pub(super) fn on_did_open_text_document( + state: &mut LspState, + params: DidOpenTextDocumentParams, +) -> ControlFlow> { + state.input_files.insert(params.text_document.uri.to_string(), params.text_document.text); + ControlFlow::Continue(()) +} + +pub(super) fn on_did_change_text_document( + state: &mut LspState, + params: DidChangeTextDocumentParams, +) -> ControlFlow> { + let text = params.content_changes.into_iter().next().unwrap().text; + state.input_files.insert(params.text_document.uri.to_string(), text); + ControlFlow::Continue(()) +} + +pub(super) fn on_did_close_text_document( + state: &mut LspState, + params: DidCloseTextDocumentParams, +) -> ControlFlow> { + state.input_files.remove(¶ms.text_document.uri.to_string()); + ControlFlow::Continue(()) +} + +pub(super) fn on_did_save_text_document( + state: &mut LspState, + params: DidSaveTextDocumentParams, +) -> ControlFlow> { + let file_path = match params.text_document.uri.to_file_path() { + Ok(file_path) => file_path, + Err(()) => { + return ControlFlow::Break(Err(ResponseError::new( + ErrorCode::REQUEST_FAILED, + "URI is not a valid file path", + ) + .into())) + } + }; + + let root_path = match &state.root_path { + Some(root) => root, + None => { + return ControlFlow::Break(Err(ResponseError::new( + ErrorCode::REQUEST_FAILED, + "Could not find project root", + ) + .into())); + } + }; + + let toml_path = match find_package_manifest(root_path, &file_path) { + Ok(toml_path) => toml_path, + Err(err) => { + // If we cannot find a manifest, we log a warning but return no diagnostics + // We can reconsider this when we can build a file without the need for a Nargo.toml file to resolve deps + let _ = state.client.log_message(LogMessageParams { + typ: MessageType::WARNING, + message: format!("{err}"), + }); + return ControlFlow::Continue(()); + } + }; + let workspace = match resolve_workspace_from_toml( + &toml_path, + PackageSelection::All, + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + ) { + Ok(workspace) => workspace, + Err(err) => { + // If we found a manifest, but the workspace is invalid, we raise an error about it + return ControlFlow::Break(Err(ResponseError::new( + ErrorCode::REQUEST_FAILED, + format!("{err}"), + ) + .into())); + } + }; + + let diagnostics: Vec<_> = workspace + .into_iter() + .flat_map(|package| -> Vec { + let (mut context, crate_id) = prepare_package(package, Box::new(get_non_stdlib_asset)); + + let file_diagnostics = match check_crate(&mut context, crate_id, false) { + Ok(((), warnings)) => warnings, + Err(errors_and_warnings) => errors_and_warnings, + }; + + // We don't add test headings for a package if it contains no `#[test]` functions + if let Some(tests) = get_package_tests_in_crate(&context, &crate_id, &package.name) { + let _ = state.client.notify::(NargoPackageTests { + package: package.name.to_string(), + tests, + }); + } + + let fm = &context.file_manager; + let files = fm.as_file_map(); + + file_diagnostics + .into_iter() + .filter_map(|FileDiagnostic { file_id, diagnostic, call_stack: _ }| { + // Ignore diagnostics for any file that wasn't the file we saved + // TODO: In the future, we could create "related" diagnostics for these files + if fm.path(file_id) != file_path { + return None; + } + + // TODO: Should this be the first item in secondaries? Should we bail when we find a range? + let range = diagnostic + .secondaries + .into_iter() + .filter_map(|sec| byte_span_to_range(files, file_id, sec.span.into())) + .last() + .unwrap_or_default(); + + let severity = match diagnostic.kind { + DiagnosticKind::Error => DiagnosticSeverity::ERROR, + DiagnosticKind::Warning => DiagnosticSeverity::WARNING, + }; + Some(Diagnostic { + range, + severity: Some(severity), + message: diagnostic.message, + ..Default::default() + }) + }) + .collect() + }) + .collect(); + + // We need to refresh lenses when we compile since that's the only time they can be accurately reflected + std::mem::drop(state.client.code_lens_refresh(())); + + let _ = state.client.publish_diagnostics(PublishDiagnosticsParams { + uri: params.text_document.uri, + version: None, + diagnostics, + }); + + ControlFlow::Continue(()) +} + +pub(super) fn on_exit( + _state: &mut LspState, + _params: (), +) -> ControlFlow> { + ControlFlow::Continue(()) +} diff --git a/noir/tooling/lsp/src/requests/code_lens_request.rs b/noir/tooling/lsp/src/requests/code_lens_request.rs new file mode 100644 index 00000000000..602ed268981 --- /dev/null +++ b/noir/tooling/lsp/src/requests/code_lens_request.rs @@ -0,0 +1,235 @@ +use std::future::{self, Future}; + +use async_lsp::{ErrorCode, LanguageClient, ResponseError}; + +use nargo::{package::Package, prepare_package, workspace::Workspace}; +use nargo_toml::{find_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_driver::{check_crate, NOIR_ARTIFACT_VERSION_STRING}; +use noirc_frontend::hir::FunctionNameMatch; + +use crate::{ + byte_span_to_range, get_non_stdlib_asset, + types::{CodeLens, CodeLensParams, CodeLensResult, Command, LogMessageParams, MessageType}, + LspState, +}; + +const ARROW: &str = "▶\u{fe0e}"; +const TEST_COMMAND: &str = "nargo.test"; +const TEST_CODELENS_TITLE: &str = "Run Test"; +const COMPILE_COMMAND: &str = "nargo.compile"; +const COMPILE_CODELENS_TITLE: &str = "Compile"; +const INFO_COMMAND: &str = "nargo.info"; +const INFO_CODELENS_TITLE: &str = "Info"; +const EXECUTE_COMMAND: &str = "nargo.execute"; +const EXECUTE_CODELENS_TITLE: &str = "Execute"; + +const PROFILE_COMMAND: &str = "nargo.profile"; +const PROFILE_CODELENS_TITLE: &str = "Profile"; + +fn with_arrow(title: &str) -> String { + format!("{ARROW} {title}") +} + +fn package_selection_args(workspace: &Workspace, package: &Package) -> Vec { + vec![ + "--program-dir".into(), + workspace.root_dir.display().to_string().into(), + "--package".into(), + package.name.to_string().into(), + ] +} + +pub(crate) fn on_code_lens_request( + state: &mut LspState, + params: CodeLensParams, +) -> impl Future> { + future::ready(on_code_lens_request_inner(state, params)) +} + +fn on_code_lens_request_inner( + state: &mut LspState, + params: CodeLensParams, +) -> Result { + let file_path = params.text_document.uri.to_file_path().map_err(|_| { + ResponseError::new(ErrorCode::REQUEST_FAILED, "URI is not a valid file path") + })?; + + let root_path = state.root_path.as_deref().ok_or_else(|| { + ResponseError::new(ErrorCode::REQUEST_FAILED, "Could not find project root") + })?; + + let toml_path = match find_package_manifest(root_path, &file_path) { + Ok(toml_path) => toml_path, + Err(err) => { + // If we cannot find a manifest, we log a warning but return no code lenses + // We can reconsider this when we can build a file without the need for a Nargo.toml file to resolve deps + let _ = state.client.log_message(LogMessageParams { + typ: MessageType::WARNING, + message: err.to_string(), + }); + return Ok(None); + } + }; + let workspace = resolve_workspace_from_toml( + &toml_path, + PackageSelection::All, + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + ) + .map_err(|err| { + // If we found a manifest, but the workspace is invalid, we raise an error about it + ResponseError::new(ErrorCode::REQUEST_FAILED, err) + })?; + + let mut lenses: Vec = vec![]; + + for package in &workspace { + let (mut context, crate_id) = prepare_package(package, Box::new(get_non_stdlib_asset)); + // We ignore the warnings and errors produced by compilation for producing code lenses + // because we can still get the test functions even if compilation fails + let _ = check_crate(&mut context, crate_id, false); + + let fm = &context.file_manager; + let files = fm.as_file_map(); + let tests = context + .get_all_test_functions_in_crate_matching(&crate_id, FunctionNameMatch::Anything); + + for (func_name, test_function) in tests { + let location = context.function_meta(&test_function.get_id()).name.location; + let file_id = location.file; + + // Ignore diagnostics for any file that wasn't the file we saved + // TODO: In the future, we could create "related" diagnostics for these files + if fm.path(file_id) != file_path { + continue; + } + + let range = + byte_span_to_range(files, file_id, location.span.into()).unwrap_or_default(); + + let test_command = Command { + title: with_arrow(TEST_CODELENS_TITLE), + command: TEST_COMMAND.into(), + arguments: Some( + [ + package_selection_args(&workspace, package), + vec!["--exact".into(), func_name.into()], + ] + .concat(), + ), + }; + + let test_lens = CodeLens { range, command: Some(test_command), data: None }; + + lenses.push(test_lens); + } + + if package.is_binary() { + if let Some(main_func_id) = context.get_main_function(&crate_id) { + let location = context.function_meta(&main_func_id).name.location; + let file_id = location.file; + + // Ignore diagnostics for any file that wasn't the file we saved + // TODO: In the future, we could create "related" diagnostics for these files + if fm.path(file_id) != file_path { + continue; + } + + let range = + byte_span_to_range(files, file_id, location.span.into()).unwrap_or_default(); + + let compile_command = Command { + title: with_arrow(COMPILE_CODELENS_TITLE), + command: COMPILE_COMMAND.into(), + arguments: Some(package_selection_args(&workspace, package)), + }; + + let compile_lens = CodeLens { range, command: Some(compile_command), data: None }; + + lenses.push(compile_lens); + + let info_command = Command { + title: INFO_CODELENS_TITLE.to_string(), + command: INFO_COMMAND.into(), + arguments: Some(package_selection_args(&workspace, package)), + }; + + let info_lens = CodeLens { range, command: Some(info_command), data: None }; + + lenses.push(info_lens); + + let execute_command = Command { + title: EXECUTE_CODELENS_TITLE.to_string(), + command: EXECUTE_COMMAND.into(), + arguments: Some(package_selection_args(&workspace, package)), + }; + + let execute_lens = CodeLens { range, command: Some(execute_command), data: None }; + + lenses.push(execute_lens); + + let profile_command = Command { + title: PROFILE_CODELENS_TITLE.to_string(), + command: PROFILE_COMMAND.into(), + arguments: Some(package_selection_args(&workspace, package)), + }; + + let profile_lens = CodeLens { range, command: Some(profile_command), data: None }; + + lenses.push(profile_lens); + } + } + + if package.is_contract() { + // Currently not looking to deduplicate this since we don't have a clear decision on if the Contract stuff is staying + for contract in context.get_all_contracts(&crate_id) { + let location = contract.location; + let file_id = location.file; + + // Ignore diagnostics for any file that wasn't the file we saved + // TODO: In the future, we could create "related" diagnostics for these files + if fm.path(file_id) != file_path { + continue; + } + + let range = + byte_span_to_range(files, file_id, location.span.into()).unwrap_or_default(); + + let compile_command = Command { + title: with_arrow(COMPILE_CODELENS_TITLE), + command: COMPILE_COMMAND.into(), + arguments: Some(package_selection_args(&workspace, package)), + }; + + let compile_lens = CodeLens { range, command: Some(compile_command), data: None }; + + lenses.push(compile_lens); + + let info_command = Command { + title: INFO_CODELENS_TITLE.to_string(), + command: INFO_COMMAND.into(), + arguments: Some(package_selection_args(&workspace, package)), + }; + + let info_lens = CodeLens { range, command: Some(info_command), data: None }; + + lenses.push(info_lens); + + let profile_command = Command { + title: PROFILE_CODELENS_TITLE.to_string(), + command: PROFILE_COMMAND.into(), + arguments: Some(package_selection_args(&workspace, package)), + }; + + let profile_lens = CodeLens { range, command: Some(profile_command), data: None }; + + lenses.push(profile_lens); + } + } + } + + if lenses.is_empty() { + Ok(None) + } else { + Ok(Some(lenses)) + } +} diff --git a/noir/tooling/lsp/src/requests/mod.rs b/noir/tooling/lsp/src/requests/mod.rs new file mode 100644 index 00000000000..a319f2593a4 --- /dev/null +++ b/noir/tooling/lsp/src/requests/mod.rs @@ -0,0 +1,139 @@ +use std::future::Future; + +use crate::types::{CodeLensOptions, InitializeParams}; +use async_lsp::ResponseError; +use lsp_types::{Position, TextDocumentSyncCapability, TextDocumentSyncKind}; +use nargo_fmt::Config; + +use crate::{ + types::{InitializeResult, NargoCapability, NargoTestsOptions, ServerCapabilities}, + LspState, +}; + +// Handlers +// The handlers for `request` are not `async` because it compiles down to lifetimes that can't be added to +// the router. To return a future that fits the trait, it is easiest wrap your implementations in an `async {}` +// block but you can also use `std::future::ready`. +// +// Additionally, the handlers for `notification` aren't async at all. +// +// They are not attached to the `NargoLspService` struct so they can be unit tested with only `LspState` +// and params passed in. + +mod code_lens_request; +mod profile_run; +mod test_run; +mod tests; + +pub(crate) use { + code_lens_request::on_code_lens_request, profile_run::on_profile_run_request, + test_run::on_test_run_request, tests::on_tests_request, +}; + +pub(crate) fn on_initialize( + state: &mut LspState, + params: InitializeParams, +) -> impl Future> { + state.root_path = params.root_uri.and_then(|root_uri| root_uri.to_file_path().ok()); + + async { + let text_document_sync = TextDocumentSyncCapability::Kind(TextDocumentSyncKind::FULL); + + let code_lens = CodeLensOptions { resolve_provider: Some(false) }; + + let nargo = NargoCapability { + tests: Some(NargoTestsOptions { + fetch: Some(true), + run: Some(true), + update: Some(true), + }), + }; + + Ok(InitializeResult { + capabilities: ServerCapabilities { + text_document_sync: Some(text_document_sync), + code_lens_provider: Some(code_lens), + document_formatting_provider: true, + nargo: Some(nargo), + }, + server_info: None, + }) + } +} + +pub(crate) fn on_formatting( + state: &mut LspState, + params: lsp_types::DocumentFormattingParams, +) -> impl Future>, ResponseError>> { + std::future::ready(on_formatting_inner(state, params)) +} + +fn on_formatting_inner( + state: &LspState, + params: lsp_types::DocumentFormattingParams, +) -> Result>, ResponseError> { + let path = params.text_document.uri.to_string(); + + if let Some(source) = state.input_files.get(&path) { + let (module, errors) = noirc_frontend::parse_program(source); + if !errors.is_empty() { + return Ok(None); + } + + let new_text = nargo_fmt::format(source, module, &Config::default()); + + let start_position = Position { line: 0, character: 0 }; + let end_position = Position { + line: source.lines().count() as u32, + character: source.chars().count() as u32, + }; + + Ok(Some(vec![lsp_types::TextEdit { + range: lsp_types::Range::new(start_position, end_position), + new_text, + }])) + } else { + Ok(None) + } +} + +pub(crate) fn on_shutdown( + _state: &mut LspState, + _params: (), +) -> impl Future> { + async { Ok(()) } +} + +#[cfg(test)] +mod initialization { + use async_lsp::ClientSocket; + use lsp_types::{ + CodeLensOptions, InitializeParams, TextDocumentSyncCapability, TextDocumentSyncKind, + }; + use tokio::test; + + use crate::{ + requests::on_initialize, solver::MockBackend, types::ServerCapabilities, LspState, + }; + + #[test] + async fn test_on_initialize() { + let client = ClientSocket::new_closed(); + let solver = MockBackend; + let mut state = LspState::new(&client, solver); + let params = InitializeParams::default(); + let response = on_initialize(&mut state, params).await.unwrap(); + assert!(matches!( + response.capabilities, + ServerCapabilities { + text_document_sync: Some(TextDocumentSyncCapability::Kind( + TextDocumentSyncKind::FULL + )), + code_lens_provider: Some(CodeLensOptions { resolve_provider: Some(false) }), + document_formatting_provider: true, + .. + } + )); + assert!(response.server_info.is_none()); + } +} diff --git a/noir/tooling/lsp/src/requests/profile_run.rs b/noir/tooling/lsp/src/requests/profile_run.rs new file mode 100644 index 00000000000..84888d30ba5 --- /dev/null +++ b/noir/tooling/lsp/src/requests/profile_run.rs @@ -0,0 +1,102 @@ +use std::{ + collections::{BTreeMap, HashMap}, + future::{self, Future}, +}; + +use acvm::{acir::circuit::Opcode, Language}; +use async_lsp::{ErrorCode, ResponseError}; +use nargo::artifacts::debug::DebugArtifact; +use nargo_toml::{find_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_driver::{CompileOptions, DebugFile, NOIR_ARTIFACT_VERSION_STRING}; +use noirc_errors::{debug_info::OpCodesCount, Location}; + +use crate::{ + types::{NargoProfileRunParams, NargoProfileRunResult}, + LspState, +}; +use fm::FileId; + +pub(crate) fn on_profile_run_request( + state: &mut LspState, + params: NargoProfileRunParams, +) -> impl Future> { + future::ready(on_profile_run_request_inner(state, params)) +} + +fn on_profile_run_request_inner( + state: &LspState, + params: NargoProfileRunParams, +) -> Result { + let root_path = state.root_path.as_deref().ok_or_else(|| { + ResponseError::new(ErrorCode::REQUEST_FAILED, "Could not find project root") + })?; + + let toml_path = find_package_manifest(root_path, root_path).map_err(|err| { + // If we cannot find a manifest, we can't run the test + ResponseError::new(ErrorCode::REQUEST_FAILED, err) + })?; + + let crate_name = params.package; + + let workspace = resolve_workspace_from_toml( + &toml_path, + PackageSelection::DefaultOrAll, + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + ) + .map_err(|err| { + // If we found a manifest, but the workspace is invalid, we raise an error about it + ResponseError::new(ErrorCode::REQUEST_FAILED, err) + })?; + + // Since we filtered on crate name, this should be the only item in the iterator + match workspace.into_iter().next() { + Some(_package) => { + let (binary_packages, contract_packages): (Vec<_>, Vec<_>) = workspace + .into_iter() + .filter(|package| !package.is_library()) + .cloned() + .partition(|package| package.is_binary()); + + // # TODO(#3504): Consider how to incorporate Backend relevant information in wider context. + let is_opcode_supported = |_opcode: &Opcode| true; + let np_language = Language::PLONKCSat { width: 3 }; + + let (compiled_programs, compiled_contracts) = nargo::ops::compile_workspace( + &workspace, + &binary_packages, + &contract_packages, + np_language, + is_opcode_supported, + &CompileOptions::default(), + ) + .map_err(|err| ResponseError::new(ErrorCode::REQUEST_FAILED, err))?; + + let mut opcodes_counts: HashMap = HashMap::new(); + let mut file_map: BTreeMap = BTreeMap::new(); + for compiled_program in &compiled_programs { + let span_opcodes = compiled_program.debug.count_span_opcodes(); + let debug_artifact: DebugArtifact = compiled_program.clone().into(); + opcodes_counts.extend(span_opcodes); + file_map.extend(debug_artifact.file_map); + } + + for compiled_contract in &compiled_contracts { + let functions = &compiled_contract.functions; + let debug_artifact: DebugArtifact = compiled_contract.clone().into(); + file_map.extend(debug_artifact.file_map); + for contract_function in functions { + let span_opcodes = contract_function.debug.count_span_opcodes(); + opcodes_counts.extend(span_opcodes); + } + } + + let result = NargoProfileRunResult { file_map, opcodes_counts }; + + Ok(result) + } + None => Err(ResponseError::new( + ErrorCode::REQUEST_FAILED, + format!("Could not locate package named: {crate_name}"), + )), + } +} diff --git a/noir/tooling/lsp/src/requests/test_run.rs b/noir/tooling/lsp/src/requests/test_run.rs new file mode 100644 index 00000000000..962fe99a49b --- /dev/null +++ b/noir/tooling/lsp/src/requests/test_run.rs @@ -0,0 +1,102 @@ +use std::future::{self, Future}; + +use async_lsp::{ErrorCode, ResponseError}; +use nargo::{ + ops::{run_test, TestStatus}, + prepare_package, +}; +use nargo_toml::{find_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_driver::{check_crate, CompileOptions, NOIR_ARTIFACT_VERSION_STRING}; +use noirc_frontend::hir::FunctionNameMatch; + +use crate::{ + get_non_stdlib_asset, + types::{NargoTestRunParams, NargoTestRunResult}, + LspState, +}; + +pub(crate) fn on_test_run_request( + state: &mut LspState, + params: NargoTestRunParams, +) -> impl Future> { + future::ready(on_test_run_request_inner(state, params)) +} + +fn on_test_run_request_inner( + state: &LspState, + params: NargoTestRunParams, +) -> Result { + let root_path = state.root_path.as_deref().ok_or_else(|| { + ResponseError::new(ErrorCode::REQUEST_FAILED, "Could not find project root") + })?; + + let toml_path = find_package_manifest(root_path, root_path).map_err(|err| { + // If we cannot find a manifest, we can't run the test + ResponseError::new(ErrorCode::REQUEST_FAILED, err) + })?; + + let crate_name = params.id.crate_name(); + let function_name = params.id.function_name(); + + let workspace = resolve_workspace_from_toml( + &toml_path, + PackageSelection::Selected(crate_name.clone()), + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + ) + .map_err(|err| { + // If we found a manifest, but the workspace is invalid, we raise an error about it + ResponseError::new(ErrorCode::REQUEST_FAILED, err) + })?; + + // Since we filtered on crate name, this should be the only item in the iterator + match workspace.into_iter().next() { + Some(package) => { + let (mut context, crate_id) = prepare_package(package, Box::new(get_non_stdlib_asset)); + if check_crate(&mut context, crate_id, false).is_err() { + let result = NargoTestRunResult { + id: params.id.clone(), + result: "error".to_string(), + message: Some("The project failed to compile".into()), + }; + return Ok(result); + }; + + let test_functions = context.get_all_test_functions_in_crate_matching( + &crate_id, + FunctionNameMatch::Exact(function_name), + ); + + let (_, test_function) = test_functions.into_iter().next().ok_or_else(|| { + ResponseError::new( + ErrorCode::REQUEST_FAILED, + format!("Could not locate test named: {function_name} in {crate_name}"), + ) + })?; + + let test_result = + run_test(&state.solver, &context, test_function, false, &CompileOptions::default()); + let result = match test_result { + TestStatus::Pass => NargoTestRunResult { + id: params.id.clone(), + result: "pass".to_string(), + message: None, + }, + TestStatus::Fail { message, .. } => NargoTestRunResult { + id: params.id.clone(), + result: "fail".to_string(), + message: Some(message), + }, + TestStatus::CompileError(diag) => NargoTestRunResult { + id: params.id.clone(), + result: "error".to_string(), + message: Some(diag.diagnostic.message), + }, + }; + Ok(result) + } + None => Err(ResponseError::new( + ErrorCode::REQUEST_FAILED, + format!("Could not locate package named: {crate_name}"), + )), + } +} diff --git a/noir/tooling/lsp/src/requests/tests.rs b/noir/tooling/lsp/src/requests/tests.rs new file mode 100644 index 00000000000..6b94b921a06 --- /dev/null +++ b/noir/tooling/lsp/src/requests/tests.rs @@ -0,0 +1,72 @@ +use std::future::{self, Future}; + +use async_lsp::{ErrorCode, LanguageClient, ResponseError}; +use lsp_types::{LogMessageParams, MessageType}; +use nargo::prepare_package; +use nargo_toml::{find_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_driver::{check_crate, NOIR_ARTIFACT_VERSION_STRING}; + +use crate::{ + get_non_stdlib_asset, get_package_tests_in_crate, + types::{NargoPackageTests, NargoTestsParams, NargoTestsResult}, + LspState, +}; + +pub(crate) fn on_tests_request( + state: &mut LspState, + params: NargoTestsParams, +) -> impl Future> { + future::ready(on_tests_request_inner(state, params)) +} + +fn on_tests_request_inner( + state: &mut LspState, + _params: NargoTestsParams, +) -> Result { + let root_path = state.root_path.as_deref().ok_or_else(|| { + ResponseError::new(ErrorCode::REQUEST_FAILED, "Could not find project root") + })?; + + let toml_path = match find_package_manifest(root_path, root_path) { + Ok(toml_path) => toml_path, + Err(err) => { + // If we cannot find a manifest, we log a warning but return no code lenses + // We can reconsider this when we can build a file without the need for a Nargo.toml file to resolve deps + let _ = state.client.log_message(LogMessageParams { + typ: MessageType::WARNING, + message: err.to_string(), + }); + return Ok(None); + } + }; + + let workspace = resolve_workspace_from_toml( + &toml_path, + PackageSelection::All, + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + ) + .map_err(|err| { + // If we found a manifest, but the workspace is invalid, we raise an error about it + ResponseError::new(ErrorCode::REQUEST_FAILED, err) + })?; + + let package_tests: Vec<_> = workspace + .into_iter() + .filter_map(|package| { + let (mut context, crate_id) = prepare_package(package, Box::new(get_non_stdlib_asset)); + // We ignore the warnings and errors produced by compilation for producing tests + // because we can still get the test functions even if compilation fails + let _ = check_crate(&mut context, crate_id, false); + + // We don't add test headings for a package if it contains no `#[test]` functions + get_package_tests_in_crate(&context, &crate_id, &package.name) + .map(|tests| NargoPackageTests { package: package.name.to_string(), tests }) + }) + .collect(); + + if package_tests.is_empty() { + Ok(None) + } else { + Ok(Some(package_tests)) + } +} diff --git a/noir/tooling/lsp/src/solver.rs b/noir/tooling/lsp/src/solver.rs new file mode 100644 index 00000000000..090f71d63b4 --- /dev/null +++ b/noir/tooling/lsp/src/solver.rs @@ -0,0 +1,84 @@ +use acvm::BlackBoxFunctionSolver; + +// This is a struct that wraps a dynamically dispatched `BlackBoxFunctionSolver` +// where we proxy the unimplemented stuff to the wrapped backend, but it +// allows us to avoid changing function signatures to include the `Box` +pub(super) struct WrapperSolver(pub(super) Box); + +impl BlackBoxFunctionSolver for WrapperSolver { + fn schnorr_verify( + &self, + public_key_x: &acvm::FieldElement, + public_key_y: &acvm::FieldElement, + signature: &[u8], + message: &[u8], + ) -> Result { + self.0.schnorr_verify(public_key_x, public_key_y, signature, message) + } + + fn pedersen_commitment( + &self, + inputs: &[acvm::FieldElement], + domain_separator: u32, + ) -> Result<(acvm::FieldElement, acvm::FieldElement), acvm::BlackBoxResolutionError> { + self.0.pedersen_commitment(inputs, domain_separator) + } + + fn fixed_base_scalar_mul( + &self, + low: &acvm::FieldElement, + high: &acvm::FieldElement, + ) -> Result<(acvm::FieldElement, acvm::FieldElement), acvm::BlackBoxResolutionError> { + self.0.fixed_base_scalar_mul(low, high) + } + + fn pedersen_hash( + &self, + inputs: &[acvm::FieldElement], + domain_separator: u32, + ) -> Result { + self.0.pedersen_hash(inputs, domain_separator) + } +} + +// We also have a mocked implementation of the `BlackBoxFunctionSolver` trait for use in tests + +#[cfg(test)] +pub(crate) struct MockBackend; + +#[cfg(test)] +impl BlackBoxFunctionSolver for MockBackend { + fn schnorr_verify( + &self, + _public_key_x: &acvm::FieldElement, + _public_key_y: &acvm::FieldElement, + _signature: &[u8], + _message: &[u8], + ) -> Result { + unimplemented!() + } + + fn pedersen_commitment( + &self, + _inputs: &[acvm::FieldElement], + _domain_separator: u32, + ) -> Result<(acvm::FieldElement, acvm::FieldElement), acvm::BlackBoxResolutionError> { + unimplemented!() + } + + fn fixed_base_scalar_mul( + &self, + _low: &acvm::FieldElement, + _high: &acvm::FieldElement, + ) -> Result<(acvm::FieldElement, acvm::FieldElement), acvm::BlackBoxResolutionError> { + unimplemented!() + } + + fn pedersen_hash( + &self, + _inputs: &[acvm::FieldElement], + _domain_separator: u32, + ) -> Result { + unimplemented!() + } +} diff --git a/noir/tooling/lsp/src/types.rs b/noir/tooling/lsp/src/types.rs new file mode 100644 index 00000000000..ba964cba0c1 --- /dev/null +++ b/noir/tooling/lsp/src/types.rs @@ -0,0 +1,217 @@ +use fm::FileId; +use noirc_driver::DebugFile; +use noirc_errors::{debug_info::OpCodesCount, Location}; +use noirc_frontend::graph::CrateName; +use serde::{Deserialize, Serialize}; +use serde_with::serde_as; +use std::collections::{BTreeMap, HashMap}; + +// Re-providing lsp_types that we don't need to override +pub(crate) use lsp_types::{ + CodeLens, CodeLensOptions, CodeLensParams, Command, Diagnostic, DiagnosticSeverity, + DidChangeConfigurationParams, DidChangeTextDocumentParams, DidCloseTextDocumentParams, + DidOpenTextDocumentParams, DidSaveTextDocumentParams, InitializeParams, InitializedParams, + LogMessageParams, MessageType, Position, PublishDiagnosticsParams, Range, ServerInfo, + TextDocumentSyncCapability, Url, +}; + +pub(crate) mod request { + use lsp_types::{request::Request, InitializeParams}; + + use super::{ + InitializeResult, NargoProfileRunParams, NargoProfileRunResult, NargoTestRunParams, + NargoTestRunResult, NargoTestsParams, NargoTestsResult, + }; + + // Re-providing lsp_types that we don't need to override + pub(crate) use lsp_types::request::{CodeLensRequest as CodeLens, Formatting, Shutdown}; + + #[derive(Debug)] + pub(crate) struct Initialize; + impl Request for Initialize { + type Params = InitializeParams; + type Result = InitializeResult; + const METHOD: &'static str = "initialize"; + } + + #[derive(Debug)] + pub(crate) struct NargoTestRun; + impl Request for NargoTestRun { + type Params = NargoTestRunParams; + type Result = NargoTestRunResult; + const METHOD: &'static str = "nargo/tests/run"; + } + + #[derive(Debug)] + pub(crate) struct NargoTests; + impl Request for NargoTests { + type Params = NargoTestsParams; + type Result = NargoTestsResult; + const METHOD: &'static str = "nargo/tests"; + } + + #[derive(Debug)] + pub(crate) struct NargoProfileRun; + impl Request for NargoProfileRun { + type Params = NargoProfileRunParams; + type Result = NargoProfileRunResult; + const METHOD: &'static str = "nargo/profile/run"; + } +} + +pub(crate) mod notification { + use lsp_types::notification::Notification; + + use super::NargoPackageTests; + + // Re-providing lsp_types that we don't need to override + pub(crate) use lsp_types::notification::{ + DidChangeConfiguration, DidChangeTextDocument, DidCloseTextDocument, DidOpenTextDocument, + DidSaveTextDocument, Exit, Initialized, + }; + + pub(crate) struct NargoUpdateTests; + impl Notification for NargoUpdateTests { + type Params = NargoPackageTests; + const METHOD: &'static str = "nargo/tests/update"; + } +} + +#[derive(Debug, Eq, PartialEq, Clone, Default, Deserialize, Serialize)] +#[serde(rename_all = "camelCase")] +pub(crate) struct NargoTestsOptions { + /// Tests can be requested from the server. + #[serde(skip_serializing_if = "Option::is_none")] + pub(crate) fetch: Option, + + /// Tests runs can be requested from the server. + #[serde(skip_serializing_if = "Option::is_none")] + pub(crate) run: Option, + + /// The server will send notifications to update tests. + #[serde(skip_serializing_if = "Option::is_none")] + pub(crate) update: Option, +} + +#[derive(Debug, Eq, PartialEq, Clone, Default, Deserialize, Serialize)] +#[serde(rename_all = "camelCase")] +pub(crate) struct NargoCapability { + /// The server will provide various features related to testing within Nargo. + #[serde(skip_serializing_if = "Option::is_none")] + pub(crate) tests: Option, +} + +#[derive(Debug, PartialEq, Clone, Default, Deserialize, Serialize)] +#[serde(rename_all = "camelCase")] +pub(crate) struct ServerCapabilities { + /// Defines how text documents are synced. + #[serde(skip_serializing_if = "Option::is_none")] + pub(crate) text_document_sync: Option, + + /// The server provides code lens. + #[serde(skip_serializing_if = "Option::is_none")] + pub(crate) code_lens_provider: Option, + + /// The server provides document formatting. + pub(crate) document_formatting_provider: bool, + + /// The server handles and provides custom nargo messages. + #[serde(skip_serializing_if = "Option::is_none")] + pub(crate) nargo: Option, +} + +#[derive(Debug, PartialEq, Clone, Default, Deserialize, Serialize)] +#[serde(rename_all = "camelCase")] +pub(crate) struct InitializeResult { + /// The capabilities the language server provides. + pub(crate) capabilities: ServerCapabilities, + + /// Information about the server. + #[serde(skip_serializing_if = "Option::is_none")] + pub(crate) server_info: Option, +} + +#[derive(Debug, Clone, Serialize, Deserialize)] +#[serde(try_from = "String", into = "String")] +pub(crate) struct NargoTestId { + package: CrateName, + fully_qualified_path: String, +} + +impl TryFrom for NargoTestId { + type Error = String; + + fn try_from(value: String) -> Result { + if let Some((crate_name, function_name)) = value.split_once('/') { + let crate_name = crate_name.parse()?; + Ok(Self { package: crate_name, fully_qualified_path: function_name.to_string() }) + } else { + Err("NargoTestId should be serialized as package_name/fully_qualified_path".to_string()) + } + } +} + +impl From for String { + fn from(value: NargoTestId) -> Self { + format!("{}/{}", value.package, value.fully_qualified_path) + } +} + +impl NargoTestId { + pub(crate) fn new(crate_name: CrateName, function_name: String) -> Self { + Self { package: crate_name, fully_qualified_path: function_name } + } + + pub(crate) fn crate_name(&self) -> &CrateName { + &self.package + } + + pub(crate) fn function_name(&self) -> &String { + &self.fully_qualified_path + } +} + +#[derive(Debug, Serialize, Deserialize)] +pub(crate) struct NargoTest { + pub(crate) id: NargoTestId, + /// Fully-qualified path to the test within the crate + pub(crate) label: String, + pub(crate) range: Range, + pub(crate) uri: Url, +} + +#[derive(Debug, Serialize, Deserialize)] +pub(crate) struct NargoPackageTests { + pub(crate) package: String, + pub(crate) tests: Vec, +} + +#[derive(Debug, Serialize, Deserialize)] +pub(crate) struct NargoTestsParams {} + +pub(crate) type NargoTestsResult = Option>; + +#[derive(Debug, Serialize, Deserialize)] +pub(crate) struct NargoTestRunParams { + pub(crate) id: NargoTestId, +} + +#[derive(Debug, Serialize, Deserialize)] +pub(crate) struct NargoTestRunResult { + pub(crate) id: NargoTestId, + pub(crate) result: String, + pub(crate) message: Option, +} +#[derive(Debug, Serialize, Deserialize)] +pub(crate) struct NargoProfileRunParams { + pub(crate) package: CrateName, +} +#[serde_as] +#[derive(Debug, Serialize, Deserialize)] +pub(crate) struct NargoProfileRunResult { + pub(crate) file_map: BTreeMap, + #[serde_as(as = "Vec<(_, _)>")] + pub(crate) opcodes_counts: HashMap, +} + +pub(crate) type CodeLensResult = Option>; diff --git a/noir/tooling/nargo/Cargo.toml b/noir/tooling/nargo/Cargo.toml new file mode 100644 index 00000000000..f8269459968 --- /dev/null +++ b/noir/tooling/nargo/Cargo.toml @@ -0,0 +1,27 @@ +[package] +name = "nargo" +description = "Noir's package manager" +version.workspace = true +authors.workspace = true +edition.workspace = true +license.workspace = true + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + +[build-dependencies] +rustc_version = "0.4.0" + +[dependencies] +acvm.workspace = true +fm.workspace = true +noirc_abi.workspace = true +noirc_driver.workspace = true +noirc_errors.workspace = true +noirc_evaluator.workspace = true +noirc_frontend.workspace = true +noirc_printable_type.workspace = true +iter-extended.workspace = true +serde.workspace = true +thiserror.workspace = true +codespan-reporting.workspace = true +rayon = "1.8.0" \ No newline at end of file diff --git a/noir/tooling/nargo/build.rs b/noir/tooling/nargo/build.rs new file mode 100644 index 00000000000..4fa7f58892a --- /dev/null +++ b/noir/tooling/nargo/build.rs @@ -0,0 +1,12 @@ +use rustc_version::{version, Version}; + +fn check_rustc_version() { + assert!( + version().unwrap() >= Version::parse("1.71.1").unwrap(), + "The minimal supported rustc version is 1.71.1." + ); +} + +fn main() { + check_rustc_version(); +} diff --git a/noir/tooling/nargo/src/artifacts/contract.rs b/noir/tooling/nargo/src/artifacts/contract.rs new file mode 100644 index 00000000000..f9e8d45b02e --- /dev/null +++ b/noir/tooling/nargo/src/artifacts/contract.rs @@ -0,0 +1,44 @@ +use acvm::acir::circuit::Circuit; +use noirc_abi::{Abi, ContractEvent}; +use noirc_driver::ContractFunctionType; +use serde::{Deserialize, Serialize}; + +/// `PreprocessedContract` represents a Noir contract which has been preprocessed by a particular backend proving system. +/// +/// This differs from a generic Noir contract artifact in that: +/// - The ACIR bytecode has had an optimization pass applied to tailor it for the backend. +/// - Proving and verification keys have been pregenerated based on this ACIR. +#[derive(Serialize, Deserialize)] +pub struct PreprocessedContract { + /// Version of noir used to compile this contract + pub noir_version: String, + /// The name of the contract. + pub name: String, + /// The identifier of the proving backend which this contract has been compiled for. + pub backend: String, + /// Each of the contract's functions are compiled into a separate program stored in this `Vec`. + pub functions: Vec, + /// All the events defined inside the contract scope. + pub events: Vec, +} + +/// Each function in the contract will be compiled as a separate noir program. +/// +/// A contract function unlike a regular Noir program however can have additional properties. +/// One of these being a function type. +#[derive(Debug, Serialize, Deserialize)] +pub struct PreprocessedContractFunction { + pub name: String, + + pub function_type: ContractFunctionType, + + pub is_internal: bool, + + pub abi: Abi, + + #[serde( + serialize_with = "Circuit::serialize_circuit_base64", + deserialize_with = "Circuit::deserialize_circuit_base64" + )] + pub bytecode: Circuit, +} diff --git a/noir/tooling/nargo/src/artifacts/debug.rs b/noir/tooling/nargo/src/artifacts/debug.rs new file mode 100644 index 00000000000..40acc7db8f8 --- /dev/null +++ b/noir/tooling/nargo/src/artifacts/debug.rs @@ -0,0 +1,151 @@ +use codespan_reporting::files::{Error, Files, SimpleFile}; +use noirc_driver::{CompiledContract, CompiledProgram, DebugFile}; +use noirc_errors::{debug_info::DebugInfo, Location}; +use noirc_evaluator::errors::SsaReport; +use serde::{Deserialize, Serialize}; +use std::{ + collections::{BTreeMap, BTreeSet}, + ops::Range, +}; + +use fm::{FileId, FileManager, PathString}; + +/// A Debug Artifact stores, for a given program, the debug info for every function +/// along with a map of file Id to the source code so locations in debug info can be mapped to source code they point to. +#[derive(Debug, Serialize, Deserialize)] +pub struct DebugArtifact { + pub debug_symbols: Vec, + pub file_map: BTreeMap, + pub warnings: Vec, +} + +impl DebugArtifact { + pub fn new(debug_symbols: Vec, file_manager: &FileManager) -> Self { + let mut file_map = BTreeMap::new(); + + let files_with_debug_symbols: BTreeSet = debug_symbols + .iter() + .flat_map(|function_symbols| { + function_symbols + .locations + .values() + .flat_map(|call_stack| call_stack.iter().map(|location| location.file)) + }) + .collect(); + + for file_id in files_with_debug_symbols { + let file_source = file_manager.fetch_file(file_id).source(); + + file_map.insert( + file_id, + DebugFile { + source: file_source.to_string(), + path: file_manager.path(file_id).to_path_buf(), + }, + ); + } + + Self { debug_symbols, file_map, warnings: Vec::new() } + } + + /// Given a location, returns its file's source code + pub fn location_source_code(&self, location: Location) -> Result<&str, Error> { + self.source(location.file) + } + + /// Given a location, returns the index of the line it starts at + pub fn location_line_index(&self, location: Location) -> Result { + let location_start = location.span.start() as usize; + self.line_index(location.file, location_start) + } + + /// Given a location, returns the line number it starts at + pub fn location_line_number(&self, location: Location) -> Result { + let location_start = location.span.start() as usize; + let line_index = self.line_index(location.file, location_start)?; + self.line_number(location.file, line_index) + } + + /// Given a location, returns the column number it starts at + pub fn location_column_number(&self, location: Location) -> Result { + let location_start = location.span.start() as usize; + let line_index = self.line_index(location.file, location_start)?; + self.column_number(location.file, line_index, location_start) + } + + /// Given a location, returns a Span relative to its line's + /// position in the file. This is useful when processing a file's + /// contents on a per-line-basis. + pub fn location_in_line(&self, location: Location) -> Result, Error> { + let location_start = location.span.start() as usize; + let location_end = location.span.end() as usize; + let line_index = self.line_index(location.file, location_start)?; + let line_span = self.line_range(location.file, line_index)?; + + let start_in_line = location_start - line_span.start; + let end_in_line = location_end - line_span.start; + + Ok(Range { start: start_in_line, end: end_in_line }) + } + + /// Given a location, returns the last line index + /// of its file + pub fn last_line_index(&self, location: Location) -> Result { + let source = self.source(location.file)?; + self.line_index(location.file, source.len()) + } +} + +impl From for DebugArtifact { + fn from(compiled_program: CompiledProgram) -> Self { + DebugArtifact { + debug_symbols: vec![compiled_program.debug], + file_map: compiled_program.file_map, + warnings: compiled_program.warnings, + } + } +} + +impl From<&CompiledContract> for DebugArtifact { + fn from(compiled_artifact: &CompiledContract) -> Self { + let all_functions_debug: Vec = compiled_artifact + .functions + .iter() + .map(|contract_function| contract_function.debug.clone()) + .collect(); + + DebugArtifact { + debug_symbols: all_functions_debug, + file_map: compiled_artifact.file_map.clone(), + warnings: compiled_artifact.warnings.clone(), + } + } +} + +impl<'a> Files<'a> for DebugArtifact { + type FileId = FileId; + type Name = PathString; + type Source = &'a str; + + fn name(&self, file_id: Self::FileId) -> Result { + self.file_map.get(&file_id).ok_or(Error::FileMissing).map(|file| file.path.clone().into()) + } + + fn source(&'a self, file_id: Self::FileId) -> Result { + self.file_map.get(&file_id).ok_or(Error::FileMissing).map(|file| file.source.as_ref()) + } + + fn line_index(&self, file_id: Self::FileId, byte_index: usize) -> Result { + self.file_map.get(&file_id).ok_or(Error::FileMissing).and_then(|file| { + SimpleFile::new(PathString::from(file.path.clone()), file.source.clone()) + .line_index((), byte_index) + }) + } + + fn line_range(&self, file_id: Self::FileId, line_index: usize) -> Result, Error> { + self.file_map.get(&file_id).ok_or(Error::FileMissing).and_then(|file| { + SimpleFile::new(PathString::from(file.path.clone()), file.source.clone()) + .line_range((), line_index) + }) + } +} diff --git a/noir/tooling/nargo/src/artifacts/mod.rs b/noir/tooling/nargo/src/artifacts/mod.rs new file mode 100644 index 00000000000..180a900fd81 --- /dev/null +++ b/noir/tooling/nargo/src/artifacts/mod.rs @@ -0,0 +1,8 @@ +//! This module defines the structure of Nargo's different compilation artifacts. +//! +//! These artifacts are intended to remain independent of any applications being built on top of Noir. +//! Should any projects require/desire a different artifact format, it's expected that they will write a transformer +//! to generate them using these artifacts as a starting point. +pub mod contract; +pub mod debug; +pub mod program; diff --git a/noir/tooling/nargo/src/artifacts/program.rs b/noir/tooling/nargo/src/artifacts/program.rs new file mode 100644 index 00000000000..890b6c55f7d --- /dev/null +++ b/noir/tooling/nargo/src/artifacts/program.rs @@ -0,0 +1,28 @@ +use acvm::acir::circuit::Circuit; +use noirc_abi::Abi; +use serde::{Deserialize, Serialize}; + +/// `PreprocessedProgram` represents a Noir program which has been preprocessed by a particular backend proving system. +/// +/// This differs from a generic Noir program artifact in that: +/// - The ACIR bytecode has had an optimization pass applied to tailor it for the backend. +/// - Proving and verification keys have been pregenerated based on this ACIR. +#[derive(Serialize, Deserialize, Debug)] +pub struct PreprocessedProgram { + pub noir_version: String, + + /// Hash of the [`Program`][noirc_frontend::monomorphization::ast::Program] from which this [`PreprocessedProgram`] + /// was compiled. + /// + /// Used to short-circuit compilation in the case of the source code not changing since the last compilation. + pub hash: u64, + + pub backend: String, + pub abi: Abi, + + #[serde( + serialize_with = "Circuit::serialize_circuit_base64", + deserialize_with = "Circuit::deserialize_circuit_base64" + )] + pub bytecode: Circuit, +} diff --git a/noir/tooling/nargo/src/constants.rs b/noir/tooling/nargo/src/constants.rs new file mode 100644 index 00000000000..5e448277694 --- /dev/null +++ b/noir/tooling/nargo/src/constants.rs @@ -0,0 +1,23 @@ +// Directories +/// The directory for the `nargo contract` command output +pub const CONTRACT_DIR: &str = "contract"; +/// The directory to store serialized circuit proofs. +pub const PROOFS_DIR: &str = "proofs"; +/// The directory to store Noir source files +pub const SRC_DIR: &str = "src"; +/// The directory to store circuits' serialized ACIR representations. +pub const TARGET_DIR: &str = "target"; + +// Files +/// The file from which Nargo pulls prover inputs +pub const PROVER_INPUT_FILE: &str = "Prover"; +/// The file from which Nargo pulls verifier inputs +pub const VERIFIER_INPUT_FILE: &str = "Verifier"; +/// The package definition file for a Noir project. +pub const PKG_FILE: &str = "Nargo.toml"; + +// Extensions +/// The extension for files containing circuit proofs. +pub const PROOF_EXT: &str = "proof"; +/// The extension for files containing proof witnesses. +pub const WITNESS_EXT: &str = "tr"; diff --git a/noir/tooling/nargo/src/errors.rs b/noir/tooling/nargo/src/errors.rs new file mode 100644 index 00000000000..bca8ca24767 --- /dev/null +++ b/noir/tooling/nargo/src/errors.rs @@ -0,0 +1,165 @@ +use acvm::{ + acir::circuit::OpcodeLocation, + pwg::{ErrorLocation, OpcodeResolutionError}, +}; +use noirc_errors::{ + debug_info::DebugInfo, reporter::ReportedErrors, CustomDiagnostic, FileDiagnostic, +}; + +pub use noirc_errors::Location; + +use noirc_frontend::graph::CrateName; +use noirc_printable_type::ForeignCallError; +use thiserror::Error; + +/// Errors covering situations where a package cannot be compiled. +#[derive(Debug, Error)] +pub enum CompileError { + #[error("Package `{0}` has type `lib` but only `bin` types can be compiled")] + LibraryCrate(CrateName), + + #[error("Package `{0}` is expected to have a `main` function but it does not")] + MissingMainFunction(CrateName), + + /// Errors encountered while compiling the Noir program. + /// These errors are already written to stderr. + #[error("Aborting due to {} previous error{}", .0.error_count, if .0.error_count == 1 { "" } else { "s" })] + ReportedErrors(ReportedErrors), +} +impl From for CompileError { + fn from(errors: ReportedErrors) -> Self { + Self::ReportedErrors(errors) + } +} + +#[derive(Debug, Error)] +pub enum NargoError { + /// Error while compiling Noir into ACIR. + #[error("Failed to compile circuit")] + CompilationError, + + /// ACIR circuit execution error + #[error(transparent)] + ExecutionError(#[from] ExecutionError), + + /// Oracle handling error + #[error(transparent)] + ForeignCallError(#[from] ForeignCallError), +} + +impl From for NargoError { + fn from(_: acvm::compiler::CompileError) -> Self { + NargoError::CompilationError + } +} + +impl NargoError { + /// Extracts the user defined failure message from the ExecutionError + /// If one exists. + /// + /// We want to extract the user defined error so that we can compare it + /// in tests to expected failure messages + pub fn user_defined_failure_message(&self) -> Option<&str> { + let execution_error = match self { + NargoError::ExecutionError(error) => error, + _ => return None, + }; + + match execution_error { + ExecutionError::AssertionFailed(message, _) => Some(message), + ExecutionError::SolvingError(error) => match error { + OpcodeResolutionError::IndexOutOfBounds { .. } + | OpcodeResolutionError::UnsupportedBlackBoxFunc(_) + | OpcodeResolutionError::OpcodeNotSolvable(_) + | OpcodeResolutionError::UnsatisfiedConstrain { .. } => None, + OpcodeResolutionError::BrilligFunctionFailed { message, .. } => Some(message), + OpcodeResolutionError::BlackBoxFunctionFailed(_, reason) => Some(reason), + }, + } + } +} + +#[derive(Debug, Error)] +pub enum ExecutionError { + #[error("Failed assertion: '{}'", .0)] + AssertionFailed(String, Vec), + + #[error(transparent)] + SolvingError(#[from] OpcodeResolutionError), +} + +/// Extracts the opcode locations from a nargo error. +fn extract_locations_from_error( + error: &ExecutionError, + debug: &DebugInfo, +) -> Option> { + let mut opcode_locations = match error { + ExecutionError::SolvingError(OpcodeResolutionError::BrilligFunctionFailed { + call_stack, + .. + }) + | ExecutionError::AssertionFailed(_, call_stack) => Some(call_stack.clone()), + ExecutionError::SolvingError(OpcodeResolutionError::IndexOutOfBounds { + opcode_location: error_location, + .. + }) + | ExecutionError::SolvingError(OpcodeResolutionError::UnsatisfiedConstrain { + opcode_location: error_location, + }) => match error_location { + ErrorLocation::Unresolved => { + unreachable!("Cannot resolve index for unsatisfied constraint") + } + ErrorLocation::Resolved(opcode_location) => Some(vec![*opcode_location]), + }, + _ => None, + }?; + + if let Some(OpcodeLocation::Brillig { acir_index, .. }) = opcode_locations.first() { + opcode_locations.insert(0, OpcodeLocation::Acir(*acir_index)); + } + + Some( + opcode_locations + .iter() + .flat_map(|opcode_location| debug.opcode_location(opcode_location).unwrap_or_default()) + .collect(), + ) +} + +/// Tries to generate a runtime diagnostic from a nargo error. It will successfully do so if it's a runtime error with a call stack. +pub fn try_to_diagnose_runtime_error( + nargo_err: &NargoError, + debug: &DebugInfo, +) -> Option { + let execution_error = match nargo_err { + NargoError::ExecutionError(execution_error) => execution_error, + _ => return None, + }; + + let source_locations = extract_locations_from_error(execution_error, debug)?; + + // The location of the error itself will be the location at the top + // of the call stack (the last item in the Vec). + let location = source_locations.last()?; + + let message = match nargo_err { + NargoError::ExecutionError(ExecutionError::AssertionFailed(message, _)) => { + format!("Assertion failed: '{message}'") + } + NargoError::ExecutionError(ExecutionError::SolvingError( + OpcodeResolutionError::IndexOutOfBounds { index, array_size, .. }, + )) => { + format!("Index out of bounds, array has size {array_size:?}, but index was {index:?}") + } + NargoError::ExecutionError(ExecutionError::SolvingError( + OpcodeResolutionError::UnsatisfiedConstrain { .. }, + )) => "Failed constraint".into(), + _ => nargo_err.to_string(), + }; + + Some( + CustomDiagnostic::simple_error(message, String::new(), location.span) + .in_file(location.file) + .with_call_stack(source_locations), + ) +} diff --git a/noir/tooling/nargo/src/lib.rs b/noir/tooling/nargo/src/lib.rs new file mode 100644 index 00000000000..ef014fb436b --- /dev/null +++ b/noir/tooling/nargo/src/lib.rs @@ -0,0 +1,56 @@ +#![forbid(unsafe_code)] +#![warn(unused_crate_dependencies, unused_extern_crates)] +#![warn(unreachable_pub)] +#![warn(clippy::semicolon_if_nothing_returned)] + +//! Nargo is the package manager for Noir +//! This name was used because it sounds like `cargo` and +//! Noir Package Manager abbreviated is npm, which is already taken. + +pub mod artifacts; +pub mod constants; +pub mod errors; +pub mod ops; +pub mod package; +pub mod workspace; + +use std::collections::BTreeMap; + +use fm::{FileManager, FileReader}; +use noirc_driver::{add_dep, prepare_crate, prepare_dependency}; +use noirc_frontend::{ + graph::{CrateGraph, CrateId, CrateName}, + hir::Context, +}; +use package::{Dependency, Package}; + +pub use self::errors::NargoError; + +pub fn prepare_dependencies( + context: &mut Context, + parent_crate: CrateId, + dependencies: &BTreeMap, +) { + for (dep_name, dep) in dependencies.iter() { + match dep { + Dependency::Remote { package } | Dependency::Local { package } => { + let crate_id = prepare_dependency(context, &package.entry_path); + add_dep(context, parent_crate, crate_id, dep_name.clone()); + prepare_dependencies(context, crate_id, &package.dependencies); + } + } + } +} + +pub fn prepare_package(package: &Package, file_reader: Box) -> (Context, CrateId) { + // TODO: FileManager continues to leak into various crates + let fm = FileManager::new(&package.root_dir, file_reader); + let graph = CrateGraph::default(); + let mut context = Context::new(fm, graph); + + let crate_id = prepare_crate(&mut context, &package.entry_path); + + prepare_dependencies(&mut context, crate_id, &package.dependencies); + + (context, crate_id) +} diff --git a/noir/tooling/nargo/src/ops/compile.rs b/noir/tooling/nargo/src/ops/compile.rs new file mode 100644 index 00000000000..d4164eaa865 --- /dev/null +++ b/noir/tooling/nargo/src/ops/compile.rs @@ -0,0 +1,154 @@ +use acvm::{acir::circuit::Opcode, Language}; +use fm::FileManager; +use noirc_driver::{CompilationResult, CompileOptions, CompiledContract, CompiledProgram}; + +use crate::errors::CompileError; +use crate::prepare_package; +use crate::{package::Package, workspace::Workspace}; + +use rayon::prelude::*; + +/// Compiles workspace. +/// +/// # Errors +/// +/// This function will return an error if there are any compilations errors reported. +pub fn compile_workspace( + workspace: &Workspace, + binary_packages: &[Package], + contract_packages: &[Package], + np_language: Language, + is_opcode_supported: impl Fn(&Opcode) -> bool + std::marker::Sync, + compile_options: &CompileOptions, +) -> Result<(Vec, Vec), CompileError> { + // Compile all of the packages in parallel. + let program_results: Vec<(FileManager, CompilationResult)> = binary_packages + .par_iter() + .map(|package| { + compile_program(workspace, package, compile_options, np_language, &is_opcode_supported) + }) + .collect(); + let contract_results: Vec<(FileManager, CompilationResult)> = + contract_packages + .par_iter() + .map(|package| { + compile_contract(package, compile_options, np_language, &is_opcode_supported) + }) + .collect(); + + // Report any warnings/errors which were encountered during compilation. + let compiled_programs: Vec = program_results + .into_iter() + .map(|(file_manager, compilation_result)| { + report_errors( + compilation_result, + &file_manager, + compile_options.deny_warnings, + compile_options.silence_warnings, + ) + }) + .collect::>()?; + let compiled_contracts: Vec = contract_results + .into_iter() + .map(|(file_manager, compilation_result)| { + report_errors( + compilation_result, + &file_manager, + compile_options.deny_warnings, + compile_options.silence_warnings, + ) + }) + .collect::>()?; + + Ok((compiled_programs, compiled_contracts)) +} + +pub fn compile_program( + workspace: &Workspace, + package: &Package, + compile_options: &CompileOptions, + np_language: Language, + is_opcode_supported: &impl Fn(&Opcode) -> bool, +) -> (FileManager, CompilationResult) { + let (mut context, crate_id) = + prepare_package(package, Box::new(|path| std::fs::read_to_string(path))); + + let program_artifact_path = workspace.package_build_path(package); + let mut debug_artifact_path = program_artifact_path.clone(); + debug_artifact_path.set_file_name(format!("debug_{}.json", package.name)); + + let (program, warnings) = + match noirc_driver::compile_main(&mut context, crate_id, compile_options, None, true) { + Ok(program_and_warnings) => program_and_warnings, + Err(errors) => { + return (context.file_manager, Err(errors)); + } + }; + + // TODO: we say that pedersen hashing is supported by all backends for now + let is_opcode_supported_pedersen_hash = |opcode: &Opcode| -> bool { + if let Opcode::BlackBoxFuncCall( + acvm::acir::circuit::opcodes::BlackBoxFuncCall::PedersenHash { .. }, + ) = opcode + { + true + } else { + is_opcode_supported(opcode) + } + }; + + // Apply backend specific optimizations. + let optimized_program = + crate::ops::optimize_program(program, np_language, &is_opcode_supported_pedersen_hash) + .expect("Backend does not support an opcode that is in the IR"); + + (context.file_manager, Ok((optimized_program, warnings))) +} + +fn compile_contract( + package: &Package, + compile_options: &CompileOptions, + np_language: Language, + is_opcode_supported: &impl Fn(&Opcode) -> bool, +) -> (FileManager, CompilationResult) { + let (mut context, crate_id) = + prepare_package(package, Box::new(|path| std::fs::read_to_string(path))); + let (contract, warnings) = + match noirc_driver::compile_contract(&mut context, crate_id, compile_options) { + Ok(contracts_and_warnings) => contracts_and_warnings, + Err(errors) => { + return (context.file_manager, Err(errors)); + } + }; + + let optimized_contract = + crate::ops::optimize_contract(contract, np_language, &is_opcode_supported) + .expect("Backend does not support an opcode that is in the IR"); + + (context.file_manager, Ok((optimized_contract, warnings))) +} + +pub(crate) fn report_errors( + result: CompilationResult, + file_manager: &FileManager, + deny_warnings: bool, + silence_warnings: bool, +) -> Result { + let (t, warnings) = result.map_err(|errors| { + noirc_errors::reporter::report_all( + file_manager.as_file_map(), + &errors, + deny_warnings, + silence_warnings, + ) + })?; + + noirc_errors::reporter::report_all( + file_manager.as_file_map(), + &warnings, + deny_warnings, + silence_warnings, + ); + + Ok(t) +} diff --git a/noir/tooling/nargo/src/ops/execute.rs b/noir/tooling/nargo/src/ops/execute.rs new file mode 100644 index 00000000000..d7cb44188c4 --- /dev/null +++ b/noir/tooling/nargo/src/ops/execute.rs @@ -0,0 +1,59 @@ +use acvm::pwg::{ACVMStatus, ErrorLocation, OpcodeResolutionError, ACVM}; +use acvm::BlackBoxFunctionSolver; +use acvm::{acir::circuit::Circuit, acir::native_types::WitnessMap}; + +use crate::errors::ExecutionError; +use crate::NargoError; + +use super::foreign_calls::ForeignCallExecutor; + +pub fn execute_circuit( + circuit: &Circuit, + initial_witness: WitnessMap, + blackbox_solver: &B, + foreign_call_executor: &mut F, +) -> Result { + let mut acvm = ACVM::new(blackbox_solver, &circuit.opcodes, initial_witness); + + loop { + let solver_status = acvm.solve(); + + match solver_status { + ACVMStatus::Solved => break, + ACVMStatus::InProgress => { + unreachable!("Execution should not stop while in `InProgress` state.") + } + ACVMStatus::Failure(error) => { + let call_stack = match &error { + OpcodeResolutionError::UnsatisfiedConstrain { + opcode_location: ErrorLocation::Resolved(opcode_location), + } => Some(vec![*opcode_location]), + OpcodeResolutionError::BrilligFunctionFailed { call_stack, .. } => { + Some(call_stack.clone()) + } + _ => None, + }; + + return Err(NargoError::ExecutionError(match call_stack { + Some(call_stack) => { + if let Some(assert_message) = circuit.get_assert_message( + *call_stack.last().expect("Call stacks should not be empty"), + ) { + ExecutionError::AssertionFailed(assert_message.to_owned(), call_stack) + } else { + ExecutionError::SolvingError(error) + } + } + None => ExecutionError::SolvingError(error), + })); + } + ACVMStatus::RequiresForeignCall(foreign_call) => { + let foreign_call_result = foreign_call_executor.execute(&foreign_call)?; + acvm.resolve_pending_foreign_call(foreign_call_result); + } + } + } + + let solved_witness = acvm.finalize(); + Ok(solved_witness) +} diff --git a/noir/tooling/nargo/src/ops/foreign_calls.rs b/noir/tooling/nargo/src/ops/foreign_calls.rs new file mode 100644 index 00000000000..6cc78febab3 --- /dev/null +++ b/noir/tooling/nargo/src/ops/foreign_calls.rs @@ -0,0 +1,242 @@ +use acvm::{ + acir::brillig::{ForeignCallParam, ForeignCallResult, Value}, + pwg::ForeignCallWaitInfo, +}; +use iter_extended::vecmap; +use noirc_printable_type::{decode_string_value, ForeignCallError, PrintableValueDisplay}; + +pub trait ForeignCallExecutor { + fn execute( + &mut self, + foreign_call: &ForeignCallWaitInfo, + ) -> Result; +} + +/// This enumeration represents the Brillig foreign calls that are natively supported by nargo. +/// After resolution of a foreign call, nargo will restart execution of the ACVM +pub(crate) enum ForeignCall { + Println, + Sequence, + ReverseSequence, + CreateMock, + SetMockParams, + SetMockReturns, + SetMockTimes, + ClearMock, +} + +impl std::fmt::Display for ForeignCall { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + write!(f, "{}", self.name()) + } +} + +impl ForeignCall { + pub(crate) fn name(&self) -> &'static str { + match self { + ForeignCall::Println => "println", + ForeignCall::Sequence => "get_number_sequence", + ForeignCall::ReverseSequence => "get_reverse_number_sequence", + ForeignCall::CreateMock => "create_mock", + ForeignCall::SetMockParams => "set_mock_params", + ForeignCall::SetMockReturns => "set_mock_returns", + ForeignCall::SetMockTimes => "set_mock_times", + ForeignCall::ClearMock => "clear_mock", + } + } + + pub(crate) fn lookup(op_name: &str) -> Option { + match op_name { + "println" => Some(ForeignCall::Println), + "get_number_sequence" => Some(ForeignCall::Sequence), + "get_reverse_number_sequence" => Some(ForeignCall::ReverseSequence), + "create_mock" => Some(ForeignCall::CreateMock), + "set_mock_params" => Some(ForeignCall::SetMockParams), + "set_mock_returns" => Some(ForeignCall::SetMockReturns), + "set_mock_times" => Some(ForeignCall::SetMockTimes), + "clear_mock" => Some(ForeignCall::ClearMock), + _ => None, + } + } +} + +/// This struct represents an oracle mock. It can be used for testing programs that use oracles. +#[derive(Debug, PartialEq, Eq, Clone)] +struct MockedCall { + /// The id of the mock, used to update or remove it + id: usize, + /// The oracle it's mocking + name: String, + /// Optionally match the parameters + params: Option>, + /// The result to return when this mock is called + result: ForeignCallResult, + /// How many times should this mock be called before it is removed + times_left: Option, +} + +impl MockedCall { + fn new(id: usize, name: String) -> Self { + Self { + id, + name, + params: None, + result: ForeignCallResult { values: vec![] }, + times_left: None, + } + } +} + +impl MockedCall { + fn matches(&self, name: &str, params: &Vec) -> bool { + self.name == name && (self.params.is_none() || self.params.as_ref() == Some(params)) + } +} + +#[derive(Debug, Default)] +pub struct DefaultForeignCallExecutor { + /// Mocks have unique ids used to identify them in Noir, allowing to update or remove them. + last_mock_id: usize, + /// The registered mocks + mocked_responses: Vec, + /// Whether to print [`ForeignCall::Println`] output. + show_output: bool, +} + +impl DefaultForeignCallExecutor { + pub fn new(show_output: bool) -> Self { + DefaultForeignCallExecutor { show_output, ..DefaultForeignCallExecutor::default() } + } +} + +impl DefaultForeignCallExecutor { + fn extract_mock_id( + foreign_call_inputs: &[ForeignCallParam], + ) -> Result<(usize, &[ForeignCallParam]), ForeignCallError> { + let (id, params) = + foreign_call_inputs.split_first().ok_or(ForeignCallError::MissingForeignCallInputs)?; + Ok((id.unwrap_value().to_usize(), params)) + } + + fn find_mock_by_id(&mut self, id: usize) -> Option<&mut MockedCall> { + self.mocked_responses.iter_mut().find(|response| response.id == id) + } + + fn parse_string(param: &ForeignCallParam) -> String { + let fields: Vec<_> = param.values().into_iter().map(|value| value.to_field()).collect(); + decode_string_value(&fields) + } + + fn execute_println(foreign_call_inputs: &[ForeignCallParam]) -> Result<(), ForeignCallError> { + let display_values: PrintableValueDisplay = foreign_call_inputs.try_into()?; + println!("{display_values}"); + Ok(()) + } +} + +impl ForeignCallExecutor for DefaultForeignCallExecutor { + fn execute( + &mut self, + foreign_call: &ForeignCallWaitInfo, + ) -> Result { + let foreign_call_name = foreign_call.function.as_str(); + match ForeignCall::lookup(foreign_call_name) { + Some(ForeignCall::Println) => { + if self.show_output { + Self::execute_println(&foreign_call.inputs)?; + } + Ok(ForeignCallResult { values: vec![] }) + } + Some(ForeignCall::Sequence) => { + let sequence_length: u128 = + foreign_call.inputs[0].unwrap_value().to_field().to_u128(); + let sequence = vecmap(0..sequence_length, Value::from); + + Ok(ForeignCallResult { + values: vec![ + ForeignCallParam::Single(sequence_length.into()), + ForeignCallParam::Array(sequence), + ], + }) + } + Some(ForeignCall::ReverseSequence) => { + let sequence_length: u128 = + foreign_call.inputs[0].unwrap_value().to_field().to_u128(); + let sequence = vecmap((0..sequence_length).rev(), Value::from); + + Ok(ForeignCallResult { + values: vec![ + ForeignCallParam::Single(sequence_length.into()), + ForeignCallParam::Array(sequence), + ], + }) + } + Some(ForeignCall::CreateMock) => { + let mock_oracle_name = Self::parse_string(&foreign_call.inputs[0]); + assert!(ForeignCall::lookup(&mock_oracle_name).is_none()); + let id = self.last_mock_id; + self.mocked_responses.push(MockedCall::new(id, mock_oracle_name)); + self.last_mock_id += 1; + + Ok(ForeignCallResult { values: vec![Value::from(id).into()] }) + } + Some(ForeignCall::SetMockParams) => { + let (id, params) = Self::extract_mock_id(&foreign_call.inputs)?; + self.find_mock_by_id(id) + .unwrap_or_else(|| panic!("Unknown mock id {}", id)) + .params = Some(params.to_vec()); + + Ok(ForeignCallResult { values: vec![] }) + } + Some(ForeignCall::SetMockReturns) => { + let (id, params) = Self::extract_mock_id(&foreign_call.inputs)?; + self.find_mock_by_id(id) + .unwrap_or_else(|| panic!("Unknown mock id {}", id)) + .result = ForeignCallResult { values: params.to_vec() }; + + Ok(ForeignCallResult { values: vec![] }) + } + Some(ForeignCall::SetMockTimes) => { + let (id, params) = Self::extract_mock_id(&foreign_call.inputs)?; + let times = params[0] + .unwrap_value() + .to_field() + .try_to_u64() + .expect("Invalid bit size of times"); + + self.find_mock_by_id(id) + .unwrap_or_else(|| panic!("Unknown mock id {}", id)) + .times_left = Some(times); + + Ok(ForeignCallResult { values: vec![] }) + } + Some(ForeignCall::ClearMock) => { + let (id, _) = Self::extract_mock_id(&foreign_call.inputs)?; + self.mocked_responses.retain(|response| response.id != id); + Ok(ForeignCallResult { values: vec![] }) + } + None => { + let response_position = self + .mocked_responses + .iter() + .position(|response| response.matches(foreign_call_name, &foreign_call.inputs)) + .unwrap_or_else(|| panic!("Unknown foreign call {}", foreign_call_name)); + + let mock = self + .mocked_responses + .get_mut(response_position) + .expect("Invalid position of mocked response"); + let result = mock.result.values.clone(); + + if let Some(times_left) = &mut mock.times_left { + *times_left -= 1; + if *times_left == 0 { + self.mocked_responses.remove(response_position); + } + } + + Ok(ForeignCallResult { values: result }) + } + } + } +} diff --git a/noir/tooling/nargo/src/ops/mod.rs b/noir/tooling/nargo/src/ops/mod.rs new file mode 100644 index 00000000000..34487ed9770 --- /dev/null +++ b/noir/tooling/nargo/src/ops/mod.rs @@ -0,0 +1,11 @@ +pub use self::compile::{compile_program, compile_workspace}; +pub use self::execute::execute_circuit; +pub use self::foreign_calls::{DefaultForeignCallExecutor, ForeignCallExecutor}; +pub use self::optimize::{optimize_contract, optimize_program}; +pub use self::test::{run_test, TestStatus}; + +mod compile; +mod execute; +mod foreign_calls; +mod optimize; +mod test; diff --git a/noir/tooling/nargo/src/ops/optimize.rs b/noir/tooling/nargo/src/ops/optimize.rs new file mode 100644 index 00000000000..54e2432aa40 --- /dev/null +++ b/noir/tooling/nargo/src/ops/optimize.rs @@ -0,0 +1,34 @@ +use acvm::{acir::circuit::Opcode, Language}; +use iter_extended::try_vecmap; +use noirc_driver::{CompiledContract, CompiledProgram}; + +use crate::NargoError; + +pub fn optimize_program( + mut program: CompiledProgram, + np_language: Language, + is_opcode_supported: &impl Fn(&Opcode) -> bool, +) -> Result { + let (optimized_circuit, location_map) = + acvm::compiler::compile(program.circuit, np_language, is_opcode_supported)?; + + program.circuit = optimized_circuit; + program.debug.update_acir(location_map); + Ok(program) +} + +pub fn optimize_contract( + contract: CompiledContract, + np_language: Language, + is_opcode_supported: &impl Fn(&Opcode) -> bool, +) -> Result { + let functions = try_vecmap(contract.functions, |mut func| { + let (optimized_bytecode, location_map) = + acvm::compiler::compile(func.bytecode, np_language, is_opcode_supported)?; + func.bytecode = optimized_bytecode; + func.debug.update_acir(location_map); + Ok::<_, NargoError>(func) + })?; + + Ok(CompiledContract { functions, ..contract }) +} diff --git a/noir/tooling/nargo/src/ops/test.rs b/noir/tooling/nargo/src/ops/test.rs new file mode 100644 index 00000000000..5bfdd6d15d0 --- /dev/null +++ b/noir/tooling/nargo/src/ops/test.rs @@ -0,0 +1,136 @@ +use acvm::{acir::native_types::WitnessMap, BlackBoxFunctionSolver}; +use noirc_driver::{compile_no_check, CompileOptions}; +use noirc_errors::{debug_info::DebugInfo, FileDiagnostic}; +use noirc_evaluator::errors::RuntimeError; +use noirc_frontend::hir::{def_map::TestFunction, Context}; + +use crate::{errors::try_to_diagnose_runtime_error, NargoError}; + +use super::{execute_circuit, DefaultForeignCallExecutor}; + +pub enum TestStatus { + Pass, + Fail { message: String, error_diagnostic: Option }, + CompileError(FileDiagnostic), +} + +pub fn run_test( + blackbox_solver: &B, + context: &Context, + test_function: TestFunction, + show_output: bool, + config: &CompileOptions, +) -> TestStatus { + let program = compile_no_check(context, config, test_function.get_id(), None, false); + match program { + Ok(program) => { + // Run the backend to ensure the PWG evaluates functions like std::hash::pedersen, + // otherwise constraints involving these expressions will not error. + let circuit_execution = execute_circuit( + &program.circuit, + WitnessMap::new(), + blackbox_solver, + &mut DefaultForeignCallExecutor::new(show_output), + ); + test_status_program_compile_pass(test_function, program.debug, circuit_execution) + } + Err(err) => test_status_program_compile_fail(err, test_function), + } +} + +/// Test function failed to compile +/// +/// Note: This could be because the compiler was able to deduce +/// that a constraint was never satisfiable. +/// An example of this is the program `assert(false)` +/// In that case, we check if the test function should fail, and if so, we return `TestStatus::Pass`. +fn test_status_program_compile_fail(err: RuntimeError, test_function: TestFunction) -> TestStatus { + // The test has failed compilation, but it should never fail. Report error. + if !test_function.should_fail() { + return TestStatus::CompileError(err.into()); + } + + // The test has failed compilation, extract the assertion message if present and check if it's expected. + let assert_message = if let RuntimeError::FailedConstraint { assert_message, .. } = &err { + assert_message.clone() + } else { + None + }; + + check_expected_failure_message(test_function, assert_message, Some(err.into())) +} + +/// The test function compiled successfully. +/// +/// We now check whether execution passed/failed and whether it should have +/// passed/failed to determine the test status. +fn test_status_program_compile_pass( + test_function: TestFunction, + debug: DebugInfo, + circuit_execution: Result, +) -> TestStatus { + let circuit_execution_err = match circuit_execution { + // Circuit execution was successful; ie no errors or unsatisfied constraints + // were encountered. + Ok(_) => { + if test_function.should_fail() { + return TestStatus::Fail { + message: "error: Test passed when it should have failed".to_string(), + error_diagnostic: None, + }; + } + return TestStatus::Pass; + } + Err(err) => err, + }; + + // If we reach here, then the circuit execution failed. + // + // Check if the function should have passed + let diagnostic = try_to_diagnose_runtime_error(&circuit_execution_err, &debug); + let test_should_have_passed = !test_function.should_fail(); + if test_should_have_passed { + return TestStatus::Fail { + message: circuit_execution_err.to_string(), + error_diagnostic: diagnostic, + }; + } + + check_expected_failure_message( + test_function, + circuit_execution_err.user_defined_failure_message().map(|s| s.to_string()), + diagnostic, + ) +} + +fn check_expected_failure_message( + test_function: TestFunction, + failed_assertion: Option, + error_diagnostic: Option, +) -> TestStatus { + // Extract the expected failure message, if there was one + // + // #[test(should_fail)] will not produce any message + // #[test(should_fail_with = "reason")] will produce a message + // + let expected_failure_message = match test_function.failure_reason() { + Some(reason) => reason, + None => return TestStatus::Pass, + }; + + let expected_failure_message_matches = + matches!(&failed_assertion, Some(message) if message == expected_failure_message); + if expected_failure_message_matches { + return TestStatus::Pass; + } + + // The expected failure message does not match the actual failure message + TestStatus::Fail { + message: format!( + "\nerror: Test failed with the wrong message. \nExpected: {} \nGot: {}", + test_function.failure_reason().unwrap_or_default(), + failed_assertion.unwrap_or_default().trim_matches('\'') + ), + error_diagnostic, + } +} diff --git a/noir/tooling/nargo/src/package.rs b/noir/tooling/nargo/src/package.rs new file mode 100644 index 00000000000..94c7c5b9c98 --- /dev/null +++ b/noir/tooling/nargo/src/package.rs @@ -0,0 +1,78 @@ +use std::{collections::BTreeMap, fmt::Display, path::PathBuf}; + +use noirc_frontend::graph::CrateName; + +use crate::constants::{PROVER_INPUT_FILE, VERIFIER_INPUT_FILE}; + +#[derive(Debug, Copy, Clone, PartialEq, Eq)] +pub enum PackageType { + Library, + Binary, + Contract, +} + +impl Display for PackageType { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + match self { + Self::Library => write!(f, "lib"), + Self::Binary => write!(f, "bin"), + Self::Contract => write!(f, "contract"), + } + } +} + +#[derive(Clone)] +pub enum Dependency { + Local { package: Package }, + Remote { package: Package }, +} + +impl Dependency { + pub fn is_binary(&self) -> bool { + match self { + Self::Local { package } | Self::Remote { package } => package.is_binary(), + } + } + + pub fn package_name(&self) -> &CrateName { + match self { + Self::Local { package } | Self::Remote { package } => &package.name, + } + } +} + +#[derive(Clone)] +pub struct Package { + // A semver string which specifies the compiler version required to compile this package + pub compiler_required_version: Option, + pub root_dir: PathBuf, + pub package_type: PackageType, + pub entry_path: PathBuf, + pub name: CrateName, + pub dependencies: BTreeMap, +} + +impl Package { + pub fn prover_input_path(&self) -> PathBuf { + // TODO: This should be configurable, such as if we are looking for .json or .toml or custom paths + // For now it is hard-coded to be toml. + self.root_dir.join(format!("{PROVER_INPUT_FILE}.toml")) + } + pub fn verifier_input_path(&self) -> PathBuf { + // TODO: This should be configurable, such as if we are looking for .json or .toml or custom paths + // For now it is hard-coded to be toml. + self.root_dir.join(format!("{VERIFIER_INPUT_FILE}.toml")) + } + + pub fn is_binary(&self) -> bool { + self.package_type == PackageType::Binary + } + + pub fn is_contract(&self) -> bool { + self.package_type == PackageType::Contract + } + + pub fn is_library(&self) -> bool { + self.package_type == PackageType::Library + } +} diff --git a/noir/tooling/nargo/src/workspace.rs b/noir/tooling/nargo/src/workspace.rs new file mode 100644 index 00000000000..65f9ab7e0d9 --- /dev/null +++ b/noir/tooling/nargo/src/workspace.rs @@ -0,0 +1,78 @@ +// We will say that a cargo unit must contain either a binary or a library +// Then we use workspace to allow more than one. In the future, do not allow there to be +// both a binary and a library. +// - library will be default + +use std::{ + iter::{once, Once}, + path::PathBuf, + slice, +}; + +use crate::{ + constants::{CONTRACT_DIR, PROOFS_DIR, TARGET_DIR}, + package::Package, +}; + +#[derive(Clone)] +pub struct Workspace { + pub root_dir: PathBuf, + pub members: Vec, + // If `Some()`, the `selected_package_index` is used to select the only `Package` when iterating a Workspace + pub selected_package_index: Option, +} + +impl Workspace { + pub fn package_build_path(&self, package: &Package) -> PathBuf { + let name: String = package.name.clone().into(); + self.target_directory_path().join(name).with_extension("json") + } + + pub fn contracts_directory_path(&self, package: &Package) -> PathBuf { + let name: String = package.name.clone().into(); + self.root_dir.join(CONTRACT_DIR).join(name) + } + + pub fn proofs_directory_path(&self) -> PathBuf { + self.root_dir.join(PROOFS_DIR) + } + + pub fn target_directory_path(&self) -> PathBuf { + self.root_dir.join(TARGET_DIR) + } +} + +pub enum IntoIter<'a, T> { + Only(Once<&'a T>), + All(slice::Iter<'a, T>), +} + +impl<'a> IntoIterator for &'a Workspace { + type Item = &'a Package; + type IntoIter = IntoIter<'a, Package>; + + fn into_iter(self) -> Self::IntoIter { + if let Some(index) = self.selected_package_index { + // Precondition: The selected_package_index was verified to be in-bounds before constructing workspace + let member = self + .members + .get(index) + .expect("Workspace constructed with invalid selected_package_index"); + + IntoIter::Only(once(member)) + } else { + IntoIter::All(self.members.iter()) + } + } +} + +impl<'a> Iterator for IntoIter<'a, Package> { + type Item = &'a Package; + + fn next(&mut self) -> Option { + match self { + Self::Only(iter) => iter.next(), + Self::All(iter) => iter.next(), + } + } +} diff --git a/noir/tooling/nargo_cli/Cargo.toml b/noir/tooling/nargo_cli/Cargo.toml new file mode 100644 index 00000000000..1a08514dc5f --- /dev/null +++ b/noir/tooling/nargo_cli/Cargo.toml @@ -0,0 +1,80 @@ +[package] +name = "nargo_cli" +description = "Noir's package manager" +version.workspace = true +authors.workspace = true +edition.workspace = true +license.workspace = true + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + +# Rename binary from `nargo_cli` to `nargo` +[[bin]] +name = "nargo" +path = "src/main.rs" + +[build-dependencies] +rustc_version = "0.4.0" +build-data.workspace = true +toml.workspace = true + +[dependencies] +clap.workspace = true +fm.workspace = true +iter-extended.workspace = true +nargo.workspace = true +nargo_fmt.workspace = true +nargo_toml.workspace = true +noir_lsp.workspace = true +noir_debugger.workspace = true +noirc_driver.workspace = true +noirc_frontend.workspace = true +noirc_abi.workspace = true +noirc_errors.workspace = true +acvm.workspace = true +barretenberg_blackbox_solver.workspace = true +toml.workspace = true +serde.workspace = true +serde_json.workspace = true +prettytable-rs = "0.10" +rayon = "1.8.0" +thiserror.workspace = true +tower.workspace = true +async-lsp = { workspace = true, features = ["client-monitor", "stdio", "tracing", "tokio"] } +const_format.workspace = true +hex.workspace = true +termcolor = "1.1.2" +color-eyre = "0.6.2" +tokio = { version = "1.0", features = ["io-std"] } + +# Backends +backend-interface = { path = "../backend_interface" } +bb_abstraction_leaks.workspace = true + +[target.'cfg(not(unix))'.dependencies] +tokio-util = { version = "0.7.8", features = ["compat"] } + +[dev-dependencies] +tempfile = "3.6.0" +dirs.workspace = true +assert_cmd = "2.0.8" +assert_fs = "1.0.10" +predicates = "2.1.5" +fm.workspace = true +criterion = "0.5.0" +paste = "1.0.14" +pprof = { version = "0.12", features = [ + "flamegraph", + "frame-pointer", + "criterion", +] } +iai = "0.1.1" +test-binary = "3.0.1" + +[[bench]] +name = "criterion" +harness = false + +[[bench]] +name = "iai" +harness = false diff --git a/noir/tooling/nargo_cli/benches/criterion.rs b/noir/tooling/nargo_cli/benches/criterion.rs new file mode 100644 index 00000000000..a7b094fd7aa --- /dev/null +++ b/noir/tooling/nargo_cli/benches/criterion.rs @@ -0,0 +1,33 @@ +//! Select representative tests to bench with criterion +use assert_cmd::prelude::{CommandCargoExt, OutputAssertExt}; +use criterion::{criterion_group, criterion_main, Criterion}; +use paste::paste; +use pprof::criterion::{Output, PProfProfiler}; +use std::process::Command; +include!("./utils.rs"); + +macro_rules! criterion_command { + ($command_name:tt, $command_string:expr) => { + paste! { + fn [](c: &mut Criterion) { + let test_program_dirs = get_selected_tests(); + for test_program_dir in test_program_dirs { + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.arg("--program-dir").arg(&test_program_dir); + cmd.arg($command_string); + + c.bench_function(&format!("{}_{}", test_program_dir.file_name().unwrap().to_str().unwrap(), $command_string), |b| { + b.iter(|| cmd.assert()) + }); + } + } + } + }; +} +criterion_command!(execution, "execute"); +criterion_group! { + name = benches; + config = Criterion::default().sample_size(20).with_profiler(PProfProfiler::new(100, Output::Flamegraph(None))); + targets = criterion_selected_tests_execution +} +criterion_main!(benches); diff --git a/noir/tooling/nargo_cli/benches/iai.rs b/noir/tooling/nargo_cli/benches/iai.rs new file mode 100644 index 00000000000..bcd60111ccf --- /dev/null +++ b/noir/tooling/nargo_cli/benches/iai.rs @@ -0,0 +1,25 @@ +//! Select representative tests to bench with iai +use assert_cmd::prelude::{CommandCargoExt, OutputAssertExt}; +use iai::black_box; +use paste::paste; +use std::process::Command; +include!("./utils.rs"); + +macro_rules! iai_command { + ($command_name:tt, $command_string:expr) => { + paste! { + fn []() { + let test_program_dirs = get_selected_tests(); + for test_program_dir in test_program_dirs { + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.arg("--program-dir").arg(&test_program_dir); + cmd.arg($command_string); + + black_box(cmd.assert()); + } + } + } + }; +} +iai_command!(execution, "execute"); +iai::main!(iai_selected_tests_execution); diff --git a/noir/tooling/nargo_cli/benches/utils.rs b/noir/tooling/nargo_cli/benches/utils.rs new file mode 100644 index 00000000000..52a6b718c44 --- /dev/null +++ b/noir/tooling/nargo_cli/benches/utils.rs @@ -0,0 +1,14 @@ +use std::path::PathBuf; + +#[allow(unused)] +fn get_selected_tests() -> Vec { + let manifest_dir = match std::env::var("CARGO_MANIFEST_DIR") { + Ok(dir) => PathBuf::from(dir), + Err(_) => std::env::current_dir().unwrap().join("crates").join("nargo_cli"), + }; + let test_dir = manifest_dir.join("tests").join("execution_success"); + + let selected_tests = + vec!["8_integration", "sha256_blocks", "struct", "eddsa", "regression", "regression_2099"]; + selected_tests.into_iter().map(|t| test_dir.join(t)).collect() +} diff --git a/noir/tooling/nargo_cli/build.rs b/noir/tooling/nargo_cli/build.rs new file mode 100644 index 00000000000..0d1acca6ee9 --- /dev/null +++ b/noir/tooling/nargo_cli/build.rs @@ -0,0 +1,289 @@ +use rustc_version::{version, Version}; +use std::fs::File; +use std::io::Write; +use std::path::{Path, PathBuf}; +use std::{env, fs}; + +fn check_rustc_version() { + assert!( + version().unwrap() >= Version::parse("1.71.1").unwrap(), + "The minimal supported rustc version is 1.71.1." + ); +} + +const GIT_COMMIT: &&str = &"GIT_COMMIT"; + +fn main() { + // Rebuild if the tests have changed + println!("cargo:rerun-if-changed=tests"); + + check_rustc_version(); + + // Only use build_data if the environment variable isn't set + // The environment variable is always set when working via Nix + if std::env::var(GIT_COMMIT).is_err() { + build_data::set_GIT_COMMIT(); + build_data::set_GIT_DIRTY(); + build_data::no_debug_rebuilds(); + } + + let out_dir = env::var("OUT_DIR").unwrap(); + let destination = Path::new(&out_dir).join("execute.rs"); + let mut test_file = File::create(destination).unwrap(); + + // Try to find the directory that Cargo sets when it is running; otherwise fallback to assuming the CWD + // is the root of the repository and append the crate path + let manifest_dir = match std::env::var("CARGO_MANIFEST_DIR") { + Ok(dir) => PathBuf::from(dir), + Err(_) => std::env::current_dir().unwrap().join("crates").join("nargo_cli"), + }; + let test_dir = manifest_dir.join("tests"); + + generate_execution_success_tests(&mut test_file, &test_dir); + generate_noir_test_success_tests(&mut test_file, &test_dir); + generate_noir_test_failure_tests(&mut test_file, &test_dir); + generate_compile_success_empty_tests(&mut test_file, &test_dir); + generate_compile_success_contract_tests(&mut test_file, &test_dir); + generate_compile_failure_tests(&mut test_file, &test_dir); +} + +fn generate_execution_success_tests(test_file: &mut File, test_data_dir: &Path) { + let test_sub_dir = "execution_success"; + let test_data_dir = test_data_dir.join(test_sub_dir); + + let test_case_dirs = + fs::read_dir(test_data_dir).unwrap().flatten().filter(|c| c.path().is_dir()); + + for test_dir in test_case_dirs { + let test_name = + test_dir.file_name().into_string().expect("Directory can't be converted to string"); + if test_name.contains('-') { + panic!( + "Invalid test directory: {test_name}. Cannot include `-`, please convert to `_`" + ); + }; + let test_dir = &test_dir.path(); + + write!( + test_file, + r#" +#[test] +fn execution_success_{test_name}() {{ + let test_program_dir = PathBuf::from("{test_dir}"); + + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.env("NARGO_BACKEND_PATH", path_to_mock_backend()); + cmd.arg("--program-dir").arg(test_program_dir); + cmd.arg("execute"); + + cmd.assert().success(); +}} + "#, + test_dir = test_dir.display(), + ) + .expect("Could not write templated test file."); + } +} + +fn generate_noir_test_success_tests(test_file: &mut File, test_data_dir: &Path) { + let test_sub_dir = "noir_test_success"; + let test_data_dir = test_data_dir.join(test_sub_dir); + + let test_case_dirs = + fs::read_dir(test_data_dir).unwrap().flatten().filter(|c| c.path().is_dir()); + + for test_dir in test_case_dirs { + let test_name = + test_dir.file_name().into_string().expect("Directory can't be converted to string"); + if test_name.contains('-') { + panic!( + "Invalid test directory: {test_name}. Cannot include `-`, please convert to `_`" + ); + }; + let test_dir = &test_dir.path(); + + write!( + test_file, + r#" +#[test] +fn noir_test_success_{test_name}() {{ + let test_program_dir = PathBuf::from("{test_dir}"); + + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.env("NARGO_BACKEND_PATH", path_to_mock_backend()); + cmd.arg("--program-dir").arg(test_program_dir); + cmd.arg("test"); + + cmd.assert().success(); +}} + "#, + test_dir = test_dir.display(), + ) + .expect("Could not write templated test file."); + } +} + +fn generate_noir_test_failure_tests(test_file: &mut File, test_data_dir: &Path) { + let test_sub_dir = "noir_test_failure"; + let test_data_dir = test_data_dir.join(test_sub_dir); + + let test_case_dirs = + fs::read_dir(test_data_dir).unwrap().flatten().filter(|c| c.path().is_dir()); + + for test_dir in test_case_dirs { + let test_name = + test_dir.file_name().into_string().expect("Directory can't be converted to string"); + if test_name.contains('-') { + panic!( + "Invalid test directory: {test_name}. Cannot include `-`, please convert to `_`" + ); + }; + let test_dir = &test_dir.path(); + + write!( + test_file, + r#" +#[test] +fn noir_test_failure_{test_name}() {{ + let test_program_dir = PathBuf::from("{test_dir}"); + + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.env("NARGO_BACKEND_PATH", path_to_mock_backend()); + cmd.arg("--program-dir").arg(test_program_dir); + cmd.arg("test"); + + cmd.assert().failure(); +}} + "#, + test_dir = test_dir.display(), + ) + .expect("Could not write templated test file."); + } +} + +fn generate_compile_success_empty_tests(test_file: &mut File, test_data_dir: &Path) { + let test_sub_dir = "compile_success_empty"; + let test_data_dir = test_data_dir.join(test_sub_dir); + + let test_case_dirs = + fs::read_dir(test_data_dir).unwrap().flatten().filter(|c| c.path().is_dir()); + + for test_dir in test_case_dirs { + let test_name = + test_dir.file_name().into_string().expect("Directory can't be converted to string"); + if test_name.contains('-') { + panic!( + "Invalid test directory: {test_name}. Cannot include `-`, please convert to `_`" + ); + }; + let test_dir = &test_dir.path(); + + write!( + test_file, + r#" +#[test] +fn compile_success_empty_{test_name}() {{ + + // We use a mocked backend for this test as we do not rely on the returned circuit size + // but we must call a backend as part of querying the number of opcodes in the circuit. + + let test_program_dir = PathBuf::from("{test_dir}"); + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.env("NARGO_BACKEND_PATH", path_to_mock_backend()); + cmd.arg("--program-dir").arg(test_program_dir); + cmd.arg("info"); + cmd.arg("--json"); + + let output = cmd.output().expect("Failed to execute command"); + + if !output.status.success() {{ + panic!("`nargo info` failed with: {{}}", String::from_utf8(output.stderr).unwrap()); + }} + + // `compile_success_empty` tests should be able to compile down to an empty circuit. + let json: serde_json::Value = serde_json::from_slice(&output.stdout).expect("JSON was not well-formatted"); + let num_opcodes = &json["programs"][0]["acir_opcodes"]; + assert_eq!(num_opcodes.as_u64().unwrap(), 0); +}} + "#, + test_dir = test_dir.display(), + ) + .expect("Could not write templated test file."); + } +} + +fn generate_compile_success_contract_tests(test_file: &mut File, test_data_dir: &Path) { + let test_sub_dir = "compile_success_contract"; + let test_data_dir = test_data_dir.join(test_sub_dir); + + let test_case_dirs = + fs::read_dir(test_data_dir).unwrap().flatten().filter(|c| c.path().is_dir()); + + for test_dir in test_case_dirs { + let test_name = + test_dir.file_name().into_string().expect("Directory can't be converted to string"); + if test_name.contains('-') { + panic!( + "Invalid test directory: {test_name}. Cannot include `-`, please convert to `_`" + ); + }; + let test_dir = &test_dir.path(); + + write!( + test_file, + r#" +#[test] +fn compile_success_contract_{test_name}() {{ + let test_program_dir = PathBuf::from("{test_dir}"); + + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.env("NARGO_BACKEND_PATH", path_to_mock_backend()); + cmd.arg("--program-dir").arg(test_program_dir); + cmd.arg("compile"); + + cmd.assert().success(); +}} + "#, + test_dir = test_dir.display(), + ) + .expect("Could not write templated test file."); + } +} + +fn generate_compile_failure_tests(test_file: &mut File, test_data_dir: &Path) { + let test_sub_dir = "compile_failure"; + let test_data_dir = test_data_dir.join(test_sub_dir); + + let test_case_dirs = + fs::read_dir(test_data_dir).unwrap().flatten().filter(|c| c.path().is_dir()); + + for test_dir in test_case_dirs { + let test_name = + test_dir.file_name().into_string().expect("Directory can't be converted to string"); + if test_name.contains('-') { + panic!( + "Invalid test directory: {test_name}. Cannot include `-`, please convert to `_`" + ); + }; + let test_dir = &test_dir.path(); + + write!( + test_file, + r#" +#[test] +fn compile_failure_{test_name}() {{ + let test_program_dir = PathBuf::from("{test_dir}"); + + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.env("NARGO_BACKEND_PATH", path_to_mock_backend()); + cmd.arg("--program-dir").arg(test_program_dir); + cmd.arg("execute"); + + cmd.assert().failure().stderr(predicate::str::contains("The application panicked (crashed).").not()); +}} + "#, + test_dir = test_dir.display(), + ) + .expect("Could not write templated test file."); + } +} diff --git a/noir/tooling/nargo_cli/src/backends.rs b/noir/tooling/nargo_cli/src/backends.rs new file mode 100644 index 00000000000..8b1da2cd118 --- /dev/null +++ b/noir/tooling/nargo_cli/src/backends.rs @@ -0,0 +1,39 @@ +use std::path::PathBuf; + +use backend_interface::backends_directory; +pub(crate) use backend_interface::Backend; + +fn active_backend_file_path() -> PathBuf { + backends_directory().join(".selected_backend") +} + +pub(crate) const ACVM_BACKEND_BARRETENBERG: &str = "acvm-backend-barretenberg"; + +pub(crate) fn clear_active_backend() { + let active_backend_file = active_backend_file_path(); + if active_backend_file.is_file() { + std::fs::remove_file(active_backend_file_path()) + .expect("should delete active backend file"); + } +} + +pub(crate) fn set_active_backend(backend_name: &str) { + let active_backend_file = active_backend_file_path(); + let backends_directory = + active_backend_file.parent().expect("active backend file should have parent"); + + std::fs::create_dir_all(backends_directory).expect("Could not create backends directory"); + std::fs::write(active_backend_file, backend_name.as_bytes()) + .expect("Could not write to active backend file"); +} + +pub(crate) fn get_active_backend() -> String { + let active_backend_file = active_backend_file_path(); + + if !active_backend_file.is_file() { + set_active_backend(ACVM_BACKEND_BARRETENBERG); + return ACVM_BACKEND_BARRETENBERG.to_string(); + } + + std::fs::read_to_string(active_backend_file).expect("Could not read active backend file") +} diff --git a/noir/tooling/nargo_cli/src/cli/backend_cmd/current_cmd.rs b/noir/tooling/nargo_cli/src/cli/backend_cmd/current_cmd.rs new file mode 100644 index 00000000000..5aba00764d3 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/backend_cmd/current_cmd.rs @@ -0,0 +1,13 @@ +use clap::Args; + +use crate::{backends::get_active_backend, errors::CliError}; + +/// Prints the name of the currently active backend +#[derive(Debug, Clone, Args)] +pub(crate) struct CurrentCommand; + +pub(crate) fn run(_args: CurrentCommand) -> Result<(), CliError> { + println!("{}", get_active_backend()); + + Ok(()) +} diff --git a/noir/tooling/nargo_cli/src/cli/backend_cmd/install_cmd.rs b/noir/tooling/nargo_cli/src/cli/backend_cmd/install_cmd.rs new file mode 100644 index 00000000000..974db9ff7f5 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/backend_cmd/install_cmd.rs @@ -0,0 +1,30 @@ +use clap::Args; + +use backend_interface::{backends_directory, download_backend}; + +use crate::errors::{BackendError, CliError}; + +use super::ls_cmd::get_available_backends; + +/// Install a new backend from a URL. +#[derive(Debug, Clone, Args)] +pub(crate) struct InstallCommand { + /// The name of the backend to install. + backend: String, + + /// The URL from which to download the backend. + url: String, +} + +pub(crate) fn run(args: InstallCommand) -> Result<(), CliError> { + let installed_backends = get_available_backends(); + + if installed_backends.contains(&args.backend) { + return Err(BackendError::AlreadyInstalled(args.backend).into()); + } + + download_backend(&args.url, &backends_directory().join(args.backend).join("backend_binary")) + .map_err(BackendError::from)?; + + Ok(()) +} diff --git a/noir/tooling/nargo_cli/src/cli/backend_cmd/ls_cmd.rs b/noir/tooling/nargo_cli/src/cli/backend_cmd/ls_cmd.rs new file mode 100644 index 00000000000..da37b104d65 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/backend_cmd/ls_cmd.rs @@ -0,0 +1,34 @@ +use backend_interface::backends_directory; +use clap::Args; + +use crate::errors::CliError; + +/// Prints the list of currently installed backends +#[derive(Debug, Clone, Args)] +pub(crate) struct LsCommand; + +pub(crate) fn run(_args: LsCommand) -> Result<(), CliError> { + for backend in get_available_backends() { + println!("{backend}"); + } + + Ok(()) +} + +pub(super) fn get_available_backends() -> Vec { + let backend_directory_contents = std::fs::read_dir(backends_directory()) + .expect("Could not read backends directory contents"); + + // TODO: Highlight the currently active backend. + backend_directory_contents + .into_iter() + .filter_map(|entry| { + let path = entry.ok()?.path(); + if path.is_dir() { + path.file_name().map(|name| name.to_string_lossy().to_string()) + } else { + None + } + }) + .collect() +} diff --git a/noir/tooling/nargo_cli/src/cli/backend_cmd/mod.rs b/noir/tooling/nargo_cli/src/cli/backend_cmd/mod.rs new file mode 100644 index 00000000000..985dbbdb934 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/backend_cmd/mod.rs @@ -0,0 +1,41 @@ +use clap::{Args, Subcommand}; + +use crate::errors::CliError; + +mod current_cmd; +mod install_cmd; +mod ls_cmd; +mod uninstall_cmd; +mod use_cmd; + +#[non_exhaustive] +#[derive(Args, Clone, Debug)] +/// Install and select custom backends used to generate and verify proofs. +pub(crate) struct BackendCommand { + #[command(subcommand)] + command: BackendCommands, +} + +#[non_exhaustive] +#[derive(Subcommand, Clone, Debug)] +pub(crate) enum BackendCommands { + Current(current_cmd::CurrentCommand), + Ls(ls_cmd::LsCommand), + Use(use_cmd::UseCommand), + Install(install_cmd::InstallCommand), + Uninstall(uninstall_cmd::UninstallCommand), +} + +pub(crate) fn run(cmd: BackendCommand) -> Result<(), CliError> { + let BackendCommand { command } = cmd; + + match command { + BackendCommands::Current(args) => current_cmd::run(args), + BackendCommands::Ls(args) => ls_cmd::run(args), + BackendCommands::Use(args) => use_cmd::run(args), + BackendCommands::Install(args) => install_cmd::run(args), + BackendCommands::Uninstall(args) => uninstall_cmd::run(args), + }?; + + Ok(()) +} diff --git a/noir/tooling/nargo_cli/src/cli/backend_cmd/uninstall_cmd.rs b/noir/tooling/nargo_cli/src/cli/backend_cmd/uninstall_cmd.rs new file mode 100644 index 00000000000..7497f1bc2f6 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/backend_cmd/uninstall_cmd.rs @@ -0,0 +1,59 @@ +use clap::Args; + +use backend_interface::backends_directory; + +use crate::{ + backends::{ + clear_active_backend, get_active_backend, set_active_backend, ACVM_BACKEND_BARRETENBERG, + }, + errors::{BackendError, CliError}, +}; + +use super::ls_cmd::get_available_backends; + +/// Uninstalls a backend +#[derive(Debug, Clone, Args)] +pub(crate) struct UninstallCommand { + /// The name of the backend to uninstall. + backend: String, +} + +pub(crate) fn run(args: UninstallCommand) -> Result<(), CliError> { + let installed_backends = get_available_backends(); + + if !installed_backends.contains(&args.backend) { + return Err(BackendError::UnknownBackend(args.backend).into()); + } + + let active_backend = get_active_backend(); + + // Handle the case where we're uninstalling the currently active backend. + if active_backend == args.backend { + let barretenberg_is_installed = + installed_backends.iter().any(|backend_name| backend_name == ACVM_BACKEND_BARRETENBERG); + + let new_active_backend = + if args.backend != ACVM_BACKEND_BARRETENBERG && barretenberg_is_installed { + // Prefer switching to barretenberg if possible. + Some(ACVM_BACKEND_BARRETENBERG) + } else { + // Otherwise pick the first backend which isn't being uninstalled. + installed_backends + .iter() + .find(|&backend_name| backend_name != &args.backend) + .map(|name| name.as_str()) + }; + + if let Some(backend) = new_active_backend { + set_active_backend(backend); + } else { + // We've deleted the last backend. Clear the active backend file to be recreated once we install a new one. + clear_active_backend(); + } + } + + std::fs::remove_dir_all(backends_directory().join(args.backend)) + .expect("backend directory should be deleted"); + + Ok(()) +} diff --git a/noir/tooling/nargo_cli/src/cli/backend_cmd/use_cmd.rs b/noir/tooling/nargo_cli/src/cli/backend_cmd/use_cmd.rs new file mode 100644 index 00000000000..66a129c2148 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/backend_cmd/use_cmd.rs @@ -0,0 +1,26 @@ +use clap::Args; + +use crate::{ + backends::set_active_backend, + errors::{BackendError, CliError}, +}; + +use super::ls_cmd::get_available_backends; + +/// Select the backend to use +#[derive(Debug, Clone, Args)] +pub(crate) struct UseCommand { + backend: String, +} + +pub(crate) fn run(args: UseCommand) -> Result<(), CliError> { + let backends = get_available_backends(); + + if !backends.contains(&args.backend) { + return Err(BackendError::UnknownBackend(args.backend).into()); + } + + set_active_backend(&args.backend); + + Ok(()) +} diff --git a/noir/tooling/nargo_cli/src/cli/check_cmd.rs b/noir/tooling/nargo_cli/src/cli/check_cmd.rs new file mode 100644 index 00000000000..57b36b8932b --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/check_cmd.rs @@ -0,0 +1,194 @@ +use crate::backends::Backend; +use crate::errors::CliError; + +use clap::Args; +use iter_extended::btree_map; +use nargo::{errors::CompileError, package::Package, prepare_package}; +use nargo_toml::{get_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_abi::{AbiParameter, AbiType, MAIN_RETURN_NAME}; +use noirc_driver::{ + check_crate, compute_function_abi, CompileOptions, NOIR_ARTIFACT_VERSION_STRING, +}; +use noirc_frontend::{ + graph::{CrateId, CrateName}, + hir::Context, +}; + +use super::fs::write_to_file; +use super::NargoConfig; + +/// Checks the constraint system for errors +#[derive(Debug, Clone, Args)] +pub(crate) struct CheckCommand { + /// The name of the package to check + #[clap(long, conflicts_with = "workspace")] + package: Option, + + /// Check all packages in the workspace + #[clap(long, conflicts_with = "package")] + workspace: bool, + + #[clap(flatten)] + compile_options: CompileOptions, +} + +pub(crate) fn run( + _backend: &Backend, + args: CheckCommand, + config: NargoConfig, +) -> Result<(), CliError> { + let toml_path = get_package_manifest(&config.program_dir)?; + let default_selection = + if args.workspace { PackageSelection::All } else { PackageSelection::DefaultOrAll }; + let selection = args.package.map_or(default_selection, PackageSelection::Selected); + let workspace = resolve_workspace_from_toml( + &toml_path, + selection, + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + )?; + + for package in &workspace { + check_package(package, &args.compile_options)?; + println!("[{}] Constraint system successfully built!", package.name); + } + Ok(()) +} + +fn check_package(package: &Package, compile_options: &CompileOptions) -> Result<(), CompileError> { + let (mut context, crate_id) = + prepare_package(package, Box::new(|path| std::fs::read_to_string(path))); + check_crate_and_report_errors( + &mut context, + crate_id, + compile_options.deny_warnings, + compile_options.silence_warnings, + )?; + + if package.is_library() || package.is_contract() { + // Libraries do not have ABIs while contracts have many, so we cannot generate a `Prover.toml` file. + Ok(()) + } else { + // XXX: We can have a --overwrite flag to determine if you want to overwrite the Prover/Verifier.toml files + if let Some((parameters, return_type)) = compute_function_abi(&context, &crate_id) { + let path_to_prover_input = package.prover_input_path(); + let path_to_verifier_input = package.verifier_input_path(); + + // If they are not available, then create them and populate them based on the ABI + if !path_to_prover_input.exists() { + let prover_toml = create_input_toml_template(parameters.clone(), None); + write_to_file(prover_toml.as_bytes(), &path_to_prover_input); + } + if !path_to_verifier_input.exists() { + let public_inputs = + parameters.into_iter().filter(|param| param.is_public()).collect(); + + let verifier_toml = create_input_toml_template(public_inputs, return_type); + write_to_file(verifier_toml.as_bytes(), &path_to_verifier_input); + } + + Ok(()) + } else { + Err(CompileError::MissingMainFunction(package.name.clone())) + } + } +} + +/// Generates the contents of a toml file with fields for each of the passed parameters. +fn create_input_toml_template( + parameters: Vec, + return_type: Option, +) -> String { + /// Returns a default placeholder `toml::Value` for `typ` which + /// complies with the structure of the specified `AbiType`. + fn default_value(typ: AbiType) -> toml::Value { + match typ { + AbiType::Array { length, typ } => { + let default_value_vec = std::iter::repeat(default_value(*typ)) + .take(length.try_into().unwrap()) + .collect(); + toml::Value::Array(default_value_vec) + } + AbiType::Struct { fields, .. } => { + let default_value_map = toml::map::Map::from_iter( + fields.into_iter().map(|(name, typ)| (name, default_value(typ))), + ); + toml::Value::Table(default_value_map) + } + _ => toml::Value::String("".to_owned()), + } + } + + let mut map = + btree_map(parameters, |AbiParameter { name, typ, .. }| (name, default_value(typ))); + + if let Some(typ) = return_type { + map.insert(MAIN_RETURN_NAME.to_owned(), default_value(typ)); + } + + toml::to_string(&map).unwrap() +} + +#[cfg(test)] +mod tests { + use noirc_abi::{AbiParameter, AbiType, AbiVisibility, Sign}; + + use super::create_input_toml_template; + + #[test] + fn valid_toml_template() { + let typed_param = |name: &str, typ: AbiType| AbiParameter { + name: name.to_string(), + typ, + visibility: AbiVisibility::Public, + }; + let parameters = vec![ + typed_param("a", AbiType::Field), + typed_param("b", AbiType::Integer { sign: Sign::Unsigned, width: 32 }), + typed_param("c", AbiType::Array { length: 2, typ: Box::new(AbiType::Field) }), + typed_param( + "d", + AbiType::Struct { + path: String::from("MyStruct"), + fields: vec![ + (String::from("d1"), AbiType::Field), + ( + String::from("d2"), + AbiType::Array { length: 3, typ: Box::new(AbiType::Field) }, + ), + ], + }, + ), + typed_param("e", AbiType::Boolean), + ]; + + let toml_str = create_input_toml_template(parameters, None); + + let expected_toml_str = r#"a = "" +b = "" +c = ["", ""] +e = "" + +[d] +d1 = "" +d2 = ["", "", ""] +"#; + assert_eq!(toml_str, expected_toml_str); + } +} + +/// Run the lexing, parsing, name resolution, and type checking passes and report any warnings +/// and errors found. +pub(crate) fn check_crate_and_report_errors( + context: &mut Context, + crate_id: CrateId, + deny_warnings: bool, + silence_warnings: bool, +) -> Result<(), CompileError> { + let result = check_crate(context, crate_id, deny_warnings); + super::compile_cmd::report_errors( + result, + &context.file_manager, + deny_warnings, + silence_warnings, + ) +} diff --git a/noir/tooling/nargo_cli/src/cli/codegen_verifier_cmd.rs b/noir/tooling/nargo_cli/src/cli/codegen_verifier_cmd.rs new file mode 100644 index 00000000000..02c83adb59a --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/codegen_verifier_cmd.rs @@ -0,0 +1,90 @@ +use super::NargoConfig; +use super::{ + compile_cmd::compile_bin_package, + fs::{create_named_dir, write_to_file}, +}; +use crate::backends::Backend; +use crate::errors::CliError; + +use acvm::Language; +use backend_interface::BackendOpcodeSupport; +use bb_abstraction_leaks::ACVM_BACKEND_BARRETENBERG; +use clap::Args; +use nargo::package::Package; +use nargo::workspace::Workspace; +use nargo_toml::{get_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_driver::{CompileOptions, NOIR_ARTIFACT_VERSION_STRING}; +use noirc_frontend::graph::CrateName; + +/// Generates a Solidity verifier smart contract for the program +#[derive(Debug, Clone, Args)] +pub(crate) struct CodegenVerifierCommand { + /// The name of the package to codegen + #[clap(long, conflicts_with = "workspace")] + package: Option, + + /// Codegen all packages in the workspace + #[clap(long, conflicts_with = "package")] + workspace: bool, + + #[clap(flatten)] + compile_options: CompileOptions, +} + +pub(crate) fn run( + backend: &Backend, + args: CodegenVerifierCommand, + config: NargoConfig, +) -> Result<(), CliError> { + let toml_path = get_package_manifest(&config.program_dir)?; + let default_selection = + if args.workspace { PackageSelection::All } else { PackageSelection::DefaultOrAll }; + let selection = args.package.map_or(default_selection, PackageSelection::Selected); + let workspace = resolve_workspace_from_toml( + &toml_path, + selection, + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + )?; + + let (np_language, opcode_support) = backend.get_backend_info()?; + for package in &workspace { + let smart_contract_string = smart_contract_for_package( + &workspace, + backend, + package, + &args.compile_options, + np_language, + &opcode_support, + )?; + + let contract_dir = workspace.contracts_directory_path(package); + create_named_dir(&contract_dir, "contract"); + let contract_path = contract_dir.join("plonk_vk").with_extension("sol"); + + let path = write_to_file(smart_contract_string.as_bytes(), &contract_path); + println!("[{}] Contract successfully created and located at {path}", package.name); + } + + Ok(()) +} + +fn smart_contract_for_package( + workspace: &Workspace, + backend: &Backend, + package: &Package, + compile_options: &CompileOptions, + np_language: Language, + opcode_support: &BackendOpcodeSupport, +) -> Result { + let program = + compile_bin_package(workspace, package, compile_options, np_language, opcode_support)?; + + let mut smart_contract_string = backend.eth_contract(&program.circuit)?; + + if backend.name() == ACVM_BACKEND_BARRETENBERG { + smart_contract_string = + bb_abstraction_leaks::complete_barretenberg_verifier_contract(smart_contract_string); + } + + Ok(smart_contract_string) +} diff --git a/noir/tooling/nargo_cli/src/cli/compile_cmd.rs b/noir/tooling/nargo_cli/src/cli/compile_cmd.rs new file mode 100644 index 00000000000..69533292bbd --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/compile_cmd.rs @@ -0,0 +1,347 @@ +use std::path::Path; + +use acvm::acir::circuit::opcodes::BlackBoxFuncCall; +use acvm::acir::circuit::Opcode; +use acvm::Language; +use backend_interface::BackendOpcodeSupport; +use fm::FileManager; +use iter_extended::vecmap; +use nargo::artifacts::contract::PreprocessedContract; +use nargo::artifacts::contract::PreprocessedContractFunction; +use nargo::artifacts::debug::DebugArtifact; +use nargo::artifacts::program::PreprocessedProgram; +use nargo::errors::CompileError; +use nargo::package::Package; +use nargo::prepare_package; +use nargo::workspace::Workspace; +use nargo_toml::{get_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_driver::NOIR_ARTIFACT_VERSION_STRING; +use noirc_driver::{CompilationResult, CompileOptions, CompiledContract, CompiledProgram}; +use noirc_frontend::graph::CrateName; + +use clap::Args; + +use crate::backends::Backend; +use crate::errors::CliError; + +use super::fs::program::{ + read_debug_artifact_from_file, read_program_from_file, save_contract_to_file, + save_debug_artifact_to_file, save_program_to_file, +}; +use super::NargoConfig; +use rayon::prelude::*; + +// TODO(#1388): pull this from backend. +const BACKEND_IDENTIFIER: &str = "acvm-backend-barretenberg"; + +/// Compile the program and its secret execution trace into ACIR format +#[derive(Debug, Clone, Args)] +pub(crate) struct CompileCommand { + /// Include Proving and Verification keys in the build artifacts. + #[arg(long)] + include_keys: bool, + + /// The name of the package to compile + #[clap(long, conflicts_with = "workspace")] + package: Option, + + /// Compile all packages in the workspace + #[clap(long, conflicts_with = "package")] + workspace: bool, + + #[clap(flatten)] + compile_options: CompileOptions, +} + +pub(crate) fn run( + backend: &Backend, + args: CompileCommand, + config: NargoConfig, +) -> Result<(), CliError> { + let toml_path = get_package_manifest(&config.program_dir)?; + let default_selection = + if args.workspace { PackageSelection::All } else { PackageSelection::DefaultOrAll }; + let selection = args.package.map_or(default_selection, PackageSelection::Selected); + + let workspace = resolve_workspace_from_toml( + &toml_path, + selection, + Some(NOIR_ARTIFACT_VERSION_STRING.to_owned()), + )?; + let circuit_dir = workspace.target_directory_path(); + + let (binary_packages, contract_packages): (Vec<_>, Vec<_>) = workspace + .into_iter() + .filter(|package| !package.is_library()) + .cloned() + .partition(|package| package.is_binary()); + + let (np_language, opcode_support) = backend.get_backend_info_or_default(); + let (_, compiled_contracts) = compile_workspace( + &workspace, + &binary_packages, + &contract_packages, + np_language, + &opcode_support, + &args.compile_options, + )?; + + // Save build artifacts to disk. + for (package, contract) in contract_packages.into_iter().zip(compiled_contracts) { + save_contract(contract, &package, &circuit_dir); + } + + Ok(()) +} + +pub(super) fn compile_workspace( + workspace: &Workspace, + binary_packages: &[Package], + contract_packages: &[Package], + np_language: Language, + opcode_support: &BackendOpcodeSupport, + compile_options: &CompileOptions, +) -> Result<(Vec, Vec), CliError> { + // Compile all of the packages in parallel. + let program_results: Vec<(FileManager, CompilationResult)> = binary_packages + .par_iter() + .map(|package| { + let is_opcode_supported = |opcode: &_| opcode_support.is_opcode_supported(opcode); + compile_program(workspace, package, compile_options, np_language, &is_opcode_supported) + }) + .collect(); + let contract_results: Vec<(FileManager, CompilationResult)> = + contract_packages + .par_iter() + .map(|package| { + let is_opcode_supported = |opcode: &_| opcode_support.is_opcode_supported(opcode); + compile_contract(package, compile_options, np_language, &is_opcode_supported) + }) + .collect(); + + // Report any warnings/errors which were encountered during compilation. + let compiled_programs: Vec = program_results + .into_iter() + .map(|(file_manager, compilation_result)| { + report_errors( + compilation_result, + &file_manager, + compile_options.deny_warnings, + compile_options.silence_warnings, + ) + }) + .collect::>()?; + let compiled_contracts: Vec = contract_results + .into_iter() + .map(|(file_manager, compilation_result)| { + report_errors( + compilation_result, + &file_manager, + compile_options.deny_warnings, + compile_options.silence_warnings, + ) + }) + .collect::>()?; + + Ok((compiled_programs, compiled_contracts)) +} + +pub(crate) fn compile_bin_package( + workspace: &Workspace, + package: &Package, + compile_options: &CompileOptions, + np_language: Language, + opcode_support: &BackendOpcodeSupport, +) -> Result { + if package.is_library() { + return Err(CompileError::LibraryCrate(package.name.clone()).into()); + } + + let (file_manager, compilation_result) = + compile_program(workspace, package, compile_options, np_language, &|opcode| { + opcode_support.is_opcode_supported(opcode) + }); + + let program = report_errors( + compilation_result, + &file_manager, + compile_options.deny_warnings, + compile_options.silence_warnings, + )?; + + Ok(program) +} + +fn compile_program( + workspace: &Workspace, + package: &Package, + compile_options: &CompileOptions, + np_language: Language, + is_opcode_supported: &impl Fn(&Opcode) -> bool, +) -> (FileManager, CompilationResult) { + let (mut context, crate_id) = + prepare_package(package, Box::new(|path| std::fs::read_to_string(path))); + + let program_artifact_path = workspace.package_build_path(package); + let mut debug_artifact_path = program_artifact_path.clone(); + debug_artifact_path.set_file_name(format!("debug_{}.json", package.name)); + let cached_program = if let (Ok(preprocessed_program), Ok(mut debug_artifact)) = ( + read_program_from_file(program_artifact_path), + read_debug_artifact_from_file(debug_artifact_path), + ) { + Some(CompiledProgram { + hash: preprocessed_program.hash, + circuit: preprocessed_program.bytecode, + abi: preprocessed_program.abi, + noir_version: preprocessed_program.noir_version, + debug: debug_artifact.debug_symbols.remove(0), + file_map: debug_artifact.file_map, + warnings: debug_artifact.warnings, + }) + } else { + None + }; + + let force_recompile = + cached_program.as_ref().map_or(false, |p| p.noir_version != NOIR_ARTIFACT_VERSION_STRING); + let (program, warnings) = match noirc_driver::compile_main( + &mut context, + crate_id, + compile_options, + cached_program, + force_recompile, + ) { + Ok(program_and_warnings) => program_and_warnings, + Err(errors) => { + return (context.file_manager, Err(errors)); + } + }; + + // TODO: we say that pedersen hashing is supported by all backends for now + let is_opcode_supported_pedersen_hash = |opcode: &Opcode| -> bool { + if let Opcode::BlackBoxFuncCall(BlackBoxFuncCall::PedersenHash { .. }) = opcode { + true + } else { + is_opcode_supported(opcode) + } + }; + + // Apply backend specific optimizations. + let optimized_program = + nargo::ops::optimize_program(program, np_language, &is_opcode_supported_pedersen_hash) + .expect("Backend does not support an opcode that is in the IR"); + + save_program(optimized_program.clone(), package, &workspace.target_directory_path()); + + (context.file_manager, Ok((optimized_program, warnings))) +} + +fn compile_contract( + package: &Package, + compile_options: &CompileOptions, + np_language: Language, + is_opcode_supported: &impl Fn(&Opcode) -> bool, +) -> (FileManager, CompilationResult) { + let (mut context, crate_id) = + prepare_package(package, Box::new(|path| std::fs::read_to_string(path))); + let (contract, warnings) = + match noirc_driver::compile_contract(&mut context, crate_id, compile_options) { + Ok(contracts_and_warnings) => contracts_and_warnings, + Err(errors) => { + return (context.file_manager, Err(errors)); + } + }; + + let optimized_contract = + nargo::ops::optimize_contract(contract, np_language, &is_opcode_supported) + .expect("Backend does not support an opcode that is in the IR"); + + (context.file_manager, Ok((optimized_contract, warnings))) +} + +fn save_program(program: CompiledProgram, package: &Package, circuit_dir: &Path) { + let preprocessed_program = PreprocessedProgram { + hash: program.hash, + backend: String::from(BACKEND_IDENTIFIER), + abi: program.abi, + noir_version: program.noir_version, + bytecode: program.circuit, + }; + + save_program_to_file(&preprocessed_program, &package.name, circuit_dir); + + let debug_artifact = DebugArtifact { + debug_symbols: vec![program.debug], + file_map: program.file_map, + warnings: program.warnings, + }; + let circuit_name: String = (&package.name).into(); + save_debug_artifact_to_file(&debug_artifact, &circuit_name, circuit_dir); +} + +fn save_contract(contract: CompiledContract, package: &Package, circuit_dir: &Path) { + // TODO(#1389): I wonder if it is incorrect for nargo-core to know anything about contracts. + // As can be seen here, It seems like a leaky abstraction where ContractFunctions (essentially CompiledPrograms) + // are compiled via nargo-core and then the PreprocessedContract is constructed here. + // This is due to EACH function needing it's own CRS, PKey, and VKey from the backend. + let debug_artifact = DebugArtifact { + debug_symbols: contract.functions.iter().map(|function| function.debug.clone()).collect(), + file_map: contract.file_map, + warnings: contract.warnings, + }; + + let preprocessed_functions = vecmap(contract.functions, |func| PreprocessedContractFunction { + name: func.name, + function_type: func.function_type, + is_internal: func.is_internal, + abi: func.abi, + bytecode: func.bytecode, + }); + + let preprocessed_contract = PreprocessedContract { + noir_version: contract.noir_version, + name: contract.name, + backend: String::from(BACKEND_IDENTIFIER), + functions: preprocessed_functions, + events: contract.events, + }; + + save_contract_to_file( + &preprocessed_contract, + &format!("{}-{}", package.name, preprocessed_contract.name), + circuit_dir, + ); + + save_debug_artifact_to_file( + &debug_artifact, + &format!("{}-{}", package.name, preprocessed_contract.name), + circuit_dir, + ); +} + +/// Helper function for reporting any errors in a `CompilationResult` +/// structure that is commonly used as a return result in this file. +pub(crate) fn report_errors( + result: CompilationResult, + file_manager: &FileManager, + deny_warnings: bool, + silence_warnings: bool, +) -> Result { + let (t, warnings) = result.map_err(|errors| { + noirc_errors::reporter::report_all( + file_manager.as_file_map(), + &errors, + deny_warnings, + silence_warnings, + ) + })?; + + noirc_errors::reporter::report_all( + file_manager.as_file_map(), + &warnings, + deny_warnings, + silence_warnings, + ); + + Ok(t) +} diff --git a/noir/tooling/nargo_cli/src/cli/debug_cmd.rs b/noir/tooling/nargo_cli/src/cli/debug_cmd.rs new file mode 100644 index 00000000000..0e7579b0721 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/debug_cmd.rs @@ -0,0 +1,132 @@ +use acvm::acir::native_types::WitnessMap; +use clap::Args; + +use nargo::artifacts::debug::DebugArtifact; +use nargo::constants::PROVER_INPUT_FILE; +use nargo::package::Package; +use nargo_toml::{get_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_abi::input_parser::{Format, InputValue}; +use noirc_abi::InputMap; +use noirc_driver::{CompileOptions, CompiledProgram, NOIR_ARTIFACT_VERSION_STRING}; +use noirc_frontend::graph::CrateName; + +use super::compile_cmd::compile_bin_package; +use super::fs::{inputs::read_inputs_from_file, witness::save_witness_to_dir}; +use super::NargoConfig; +use crate::backends::Backend; +use crate::errors::CliError; + +/// Executes a circuit in debug mode +#[derive(Debug, Clone, Args)] +pub(crate) struct DebugCommand { + /// Write the execution witness to named file + witness_name: Option, + + /// The name of the toml file which contains the inputs for the prover + #[clap(long, short, default_value = PROVER_INPUT_FILE)] + prover_name: String, + + /// The name of the package to execute + #[clap(long)] + package: Option, + + #[clap(flatten)] + compile_options: CompileOptions, +} + +pub(crate) fn run( + backend: &Backend, + args: DebugCommand, + config: NargoConfig, +) -> Result<(), CliError> { + let toml_path = get_package_manifest(&config.program_dir)?; + let selection = args.package.map_or(PackageSelection::DefaultOrAll, PackageSelection::Selected); + let workspace = resolve_workspace_from_toml( + &toml_path, + selection, + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + )?; + let target_dir = &workspace.target_directory_path(); + let (np_language, opcode_support) = backend.get_backend_info()?; + + let Some(package) = workspace.into_iter().find(|p| p.is_binary()) else { + println!( + "No matching binary packages found in workspace. Only binary packages can be debugged." + ); + return Ok(()); + }; + + let compiled_program = compile_bin_package( + &workspace, + package, + &args.compile_options, + np_language, + &opcode_support, + )?; + + println!("[{}] Starting debugger", package.name); + let (return_value, solved_witness) = + debug_program_and_decode(compiled_program, package, &args.prover_name)?; + + if let Some(solved_witness) = solved_witness { + println!("[{}] Circuit witness successfully solved", package.name); + + if let Some(return_value) = return_value { + println!("[{}] Circuit output: {return_value:?}", package.name); + } + + if let Some(witness_name) = &args.witness_name { + let witness_path = save_witness_to_dir(solved_witness, witness_name, target_dir)?; + + println!("[{}] Witness saved to {}", package.name, witness_path.display()); + } + } else { + println!("Debugger execution halted."); + } + + Ok(()) +} + +fn debug_program_and_decode( + program: CompiledProgram, + package: &Package, + prover_name: &str, +) -> Result<(Option, Option), CliError> { + // Parse the initial witness values from Prover.toml + let (inputs_map, _) = + read_inputs_from_file(&package.root_dir, prover_name, Format::Toml, &program.abi)?; + let solved_witness = debug_program(&program, &inputs_map)?; + let public_abi = program.abi.public_abi(); + + match solved_witness { + Some(witness) => { + let (_, return_value) = public_abi.decode(&witness)?; + Ok((return_value, Some(witness))) + } + None => Ok((None, None)), + } +} + +pub(crate) fn debug_program( + compiled_program: &CompiledProgram, + inputs_map: &InputMap, +) -> Result, CliError> { + #[allow(deprecated)] + let blackbox_solver = barretenberg_blackbox_solver::BarretenbergSolver::new(); + + let initial_witness = compiled_program.abi.encode(inputs_map, None)?; + + let debug_artifact = DebugArtifact { + debug_symbols: vec![compiled_program.debug.clone()], + file_map: compiled_program.file_map.clone(), + warnings: compiled_program.warnings.clone(), + }; + + noir_debugger::debug_circuit( + &blackbox_solver, + &compiled_program.circuit, + debug_artifact, + initial_witness, + ) + .map_err(CliError::from) +} diff --git a/noir/tooling/nargo_cli/src/cli/execute_cmd.rs b/noir/tooling/nargo_cli/src/cli/execute_cmd.rs new file mode 100644 index 00000000000..2f69b4c7df7 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/execute_cmd.rs @@ -0,0 +1,131 @@ +use acvm::acir::native_types::WitnessMap; +use clap::Args; + +use nargo::artifacts::debug::DebugArtifact; +use nargo::constants::PROVER_INPUT_FILE; +use nargo::errors::try_to_diagnose_runtime_error; +use nargo::ops::DefaultForeignCallExecutor; +use nargo::package::Package; +use nargo_toml::{get_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_abi::input_parser::{Format, InputValue}; +use noirc_abi::InputMap; +use noirc_driver::{CompileOptions, CompiledProgram, NOIR_ARTIFACT_VERSION_STRING}; +use noirc_frontend::graph::CrateName; + +use super::compile_cmd::compile_bin_package; +use super::fs::{inputs::read_inputs_from_file, witness::save_witness_to_dir}; +use super::NargoConfig; +use crate::backends::Backend; +use crate::errors::CliError; + +/// Executes a circuit to calculate its return value +#[derive(Debug, Clone, Args)] +pub(crate) struct ExecuteCommand { + /// Write the execution witness to named file + witness_name: Option, + + /// The name of the toml file which contains the inputs for the prover + #[clap(long, short, default_value = PROVER_INPUT_FILE)] + prover_name: String, + + /// The name of the package to execute + #[clap(long, conflicts_with = "workspace")] + package: Option, + + /// Execute all packages in the workspace + #[clap(long, conflicts_with = "package")] + workspace: bool, + + #[clap(flatten)] + compile_options: CompileOptions, +} + +pub(crate) fn run( + backend: &Backend, + args: ExecuteCommand, + config: NargoConfig, +) -> Result<(), CliError> { + let toml_path = get_package_manifest(&config.program_dir)?; + let default_selection = + if args.workspace { PackageSelection::All } else { PackageSelection::DefaultOrAll }; + let selection = args.package.map_or(default_selection, PackageSelection::Selected); + let workspace = resolve_workspace_from_toml( + &toml_path, + selection, + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + )?; + let target_dir = &workspace.target_directory_path(); + + let (np_language, opcode_support) = backend.get_backend_info_or_default(); + for package in &workspace { + let compiled_program = compile_bin_package( + &workspace, + package, + &args.compile_options, + np_language, + &opcode_support, + )?; + + let (return_value, solved_witness) = + execute_program_and_decode(compiled_program, package, &args.prover_name)?; + + println!("[{}] Circuit witness successfully solved", package.name); + if let Some(return_value) = return_value { + println!("[{}] Circuit output: {return_value:?}", package.name); + } + if let Some(witness_name) = &args.witness_name { + let witness_path = save_witness_to_dir(solved_witness, witness_name, target_dir)?; + + println!("[{}] Witness saved to {}", package.name, witness_path.display()); + } + } + Ok(()) +} + +fn execute_program_and_decode( + program: CompiledProgram, + package: &Package, + prover_name: &str, +) -> Result<(Option, WitnessMap), CliError> { + // Parse the initial witness values from Prover.toml + let (inputs_map, _) = + read_inputs_from_file(&package.root_dir, prover_name, Format::Toml, &program.abi)?; + let solved_witness = execute_program(&program, &inputs_map)?; + let public_abi = program.abi.public_abi(); + let (_, return_value) = public_abi.decode(&solved_witness)?; + + Ok((return_value, solved_witness)) +} + +pub(crate) fn execute_program( + compiled_program: &CompiledProgram, + inputs_map: &InputMap, +) -> Result { + #[allow(deprecated)] + let blackbox_solver = barretenberg_blackbox_solver::BarretenbergSolver::new(); + + let initial_witness = compiled_program.abi.encode(inputs_map, None)?; + + let solved_witness_err = nargo::ops::execute_circuit( + &compiled_program.circuit, + initial_witness, + &blackbox_solver, + &mut DefaultForeignCallExecutor::new(true), + ); + match solved_witness_err { + Ok(solved_witness) => Ok(solved_witness), + Err(err) => { + let debug_artifact = DebugArtifact { + debug_symbols: vec![compiled_program.debug.clone()], + file_map: compiled_program.file_map.clone(), + warnings: compiled_program.warnings.clone(), + }; + + if let Some(diagnostic) = try_to_diagnose_runtime_error(&err, &compiled_program.debug) { + diagnostic.report(&debug_artifact, false); + } + + Err(crate::errors::CliError::NargoError(err)) + } + } +} diff --git a/noir/tooling/nargo_cli/src/cli/fmt_cmd.rs b/noir/tooling/nargo_cli/src/cli/fmt_cmd.rs new file mode 100644 index 00000000000..638eaa64b3e --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/fmt_cmd.rs @@ -0,0 +1,85 @@ +use std::{fs::DirEntry, path::Path}; + +use clap::Args; +use fm::FileManager; +use nargo_toml::{get_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_driver::NOIR_ARTIFACT_VERSION_STRING; +use noirc_errors::CustomDiagnostic; +use noirc_frontend::{hir::def_map::parse_file, parser::ParserError}; + +use crate::errors::CliError; + +use super::NargoConfig; + +/// Format the Noir files in a workspace +#[derive(Debug, Clone, Args)] +pub(crate) struct FormatCommand {} + +pub(crate) fn run(_args: FormatCommand, config: NargoConfig) -> Result<(), CliError> { + let toml_path = get_package_manifest(&config.program_dir)?; + let workspace = resolve_workspace_from_toml( + &toml_path, + PackageSelection::All, + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + )?; + + let config = nargo_fmt::Config::read(&config.program_dir) + .map_err(|err| CliError::Generic(err.to_string()))?; + + for package in &workspace { + let mut file_manager = + FileManager::new(&package.root_dir, Box::new(|path| std::fs::read_to_string(path))); + + visit_noir_files(&package.root_dir.join("src"), &mut |entry| { + let file_id = file_manager.add_file(&entry.path()).expect("file exists"); + let (parsed_module, errors) = parse_file(&file_manager, file_id); + + let is_all_warnings = errors.iter().all(ParserError::is_warning); + if !is_all_warnings { + let errors = errors + .into_iter() + .map(|error| { + let error: CustomDiagnostic = error.into(); + error.in_file(file_id) + }) + .collect(); + + let _ = super::compile_cmd::report_errors::<()>( + Err(errors), + &file_manager, + false, + false, + ); + return Ok(()); + } + + let source = nargo_fmt::format( + file_manager.fetch_file(file_id).source(), + parsed_module, + &config, + ); + + std::fs::write(entry.path(), source) + }) + .map_err(|error| CliError::Generic(error.to_string()))?; + } + Ok(()) +} + +fn visit_noir_files( + dir: &Path, + cb: &mut dyn FnMut(&DirEntry) -> std::io::Result<()>, +) -> std::io::Result<()> { + if dir.is_dir() { + for entry in std::fs::read_dir(dir)? { + let entry = entry?; + let path = entry.path(); + if path.is_dir() { + visit_noir_files(&path, cb)?; + } else if entry.path().extension().map_or(false, |extension| extension == "nr") { + cb(&entry)?; + } + } + } + Ok(()) +} diff --git a/noir/tooling/nargo_cli/src/cli/fs/inputs.rs b/noir/tooling/nargo_cli/src/cli/fs/inputs.rs new file mode 100644 index 00000000000..f3f0baf10f4 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/fs/inputs.rs @@ -0,0 +1,130 @@ +use noirc_abi::{ + input_parser::{Format, InputValue}, + Abi, InputMap, MAIN_RETURN_NAME, +}; +use std::{collections::BTreeMap, path::Path}; + +use crate::errors::FilesystemError; + +use super::write_to_file; + +/// Returns the circuit's parameters and its return value, if one exists. +/// # Examples +/// +/// ```ignore +/// let (input_map, return_value): (InputMap, Option) = +/// read_inputs_from_file(path, "Verifier", Format::Toml, &abi)?; +/// ``` +pub(crate) fn read_inputs_from_file>( + path: P, + file_name: &str, + format: Format, + abi: &Abi, +) -> Result<(InputMap, Option), FilesystemError> { + if abi.is_empty() { + return Ok((BTreeMap::new(), None)); + } + + let file_path = path.as_ref().join(file_name).with_extension(format.ext()); + if !file_path.exists() { + return Err(FilesystemError::MissingTomlFile(file_name.to_owned(), file_path)); + } + + let input_string = std::fs::read_to_string(file_path).unwrap(); + let mut input_map = format.parse(&input_string, abi)?; + let return_value = input_map.remove(MAIN_RETURN_NAME); + + Ok((input_map, return_value)) +} + +pub(crate) fn write_inputs_to_file>( + input_map: &InputMap, + return_value: &Option, + abi: &Abi, + path: P, + file_name: &str, + format: Format, +) -> Result<(), FilesystemError> { + let file_path = path.as_ref().join(file_name).with_extension(format.ext()); + + // We must insert the return value into the `InputMap` in order for it to be written to file. + let serialized_output = match return_value { + // Parameters and return values are kept separate except for when they're being written to file. + // As a result, we don't want to modify the original map and must clone it before insertion. + Some(return_value) => { + let mut input_map = input_map.clone(); + input_map.insert(MAIN_RETURN_NAME.to_owned(), return_value.clone()); + format.serialize(&input_map, abi)? + } + // If no return value exists, then we can serialize the original map directly. + None => format.serialize(input_map, abi)?, + }; + + write_to_file(serialized_output.as_bytes(), &file_path); + + Ok(()) +} + +#[cfg(test)] +mod tests { + use std::{collections::BTreeMap, vec}; + + use acvm::FieldElement; + use nargo::constants::VERIFIER_INPUT_FILE; + use noirc_abi::{ + input_parser::{Format, InputValue}, + Abi, AbiParameter, AbiType, AbiVisibility, + }; + use tempfile::TempDir; + + use super::{read_inputs_from_file, write_inputs_to_file}; + + #[test] + fn write_and_read_recovers_inputs_and_return_value() { + let input_dir = TempDir::new().unwrap().into_path(); + + // We purposefully test a simple ABI here as we're focussing on `fs`. + // Tests for serializing complex types should exist in `noirc_abi`. + let abi = Abi { + parameters: vec![ + AbiParameter { + name: "foo".into(), + typ: AbiType::Field, + visibility: AbiVisibility::Public, + }, + AbiParameter { + name: "bar".into(), + typ: AbiType::String { length: 11 }, + visibility: AbiVisibility::Private, + }, + ], + return_type: Some(AbiType::Field), + + // Input serialization is only dependent on types, not position in witness map. + // Neither of these should be relevant so we leave them empty. + param_witnesses: BTreeMap::new(), + return_witnesses: Vec::new(), + }; + let input_map = BTreeMap::from([ + ("foo".to_owned(), InputValue::Field(42u128.into())), + ("bar".to_owned(), InputValue::String("hello world".to_owned())), + ]); + let return_value = Some(InputValue::Field(FieldElement::zero())); + + write_inputs_to_file( + &input_map, + &return_value, + &abi, + &input_dir, + VERIFIER_INPUT_FILE, + Format::Toml, + ) + .unwrap(); + + let (loaded_inputs, loaded_return_value) = + read_inputs_from_file(input_dir, VERIFIER_INPUT_FILE, Format::Toml, &abi).unwrap(); + + assert_eq!(loaded_inputs, input_map); + assert_eq!(loaded_return_value, return_value); + } +} diff --git a/noir/tooling/nargo_cli/src/cli/fs/mod.rs b/noir/tooling/nargo_cli/src/cli/fs/mod.rs new file mode 100644 index 00000000000..4ebce3b3325 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/fs/mod.rs @@ -0,0 +1,42 @@ +use std::{ + fs::File, + io::Write, + path::{Path, PathBuf}, +}; + +use crate::errors::FilesystemError; + +pub(super) mod inputs; +pub(super) mod program; +pub(super) mod proof; +pub(super) mod witness; + +pub(super) fn create_named_dir(named_dir: &Path, name: &str) -> PathBuf { + std::fs::create_dir_all(named_dir) + .unwrap_or_else(|_| panic!("could not create the `{name}` directory")); + + PathBuf::from(named_dir) +} + +pub(super) fn write_to_file(bytes: &[u8], path: &Path) -> String { + let display = path.display(); + + let mut file = match File::create(path) { + Err(why) => panic!("couldn't create {display}: {why}"), + Ok(file) => file, + }; + + match file.write_all(bytes) { + Err(why) => panic!("couldn't write to {display}: {why}"), + Ok(_) => display.to_string(), + } +} + +pub(super) fn load_hex_data>(path: P) -> Result, FilesystemError> { + let hex_data: Vec<_> = std::fs::read(&path) + .map_err(|_| FilesystemError::PathNotValid(path.as_ref().to_path_buf()))?; + + let raw_bytes = hex::decode(hex_data).map_err(FilesystemError::HexArtifactNotValid)?; + + Ok(raw_bytes) +} diff --git a/noir/tooling/nargo_cli/src/cli/fs/program.rs b/noir/tooling/nargo_cli/src/cli/fs/program.rs new file mode 100644 index 00000000000..e82f2d55264 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/fs/program.rs @@ -0,0 +1,73 @@ +use std::path::{Path, PathBuf}; + +use nargo::artifacts::{ + contract::PreprocessedContract, debug::DebugArtifact, program::PreprocessedProgram, +}; +use noirc_frontend::graph::CrateName; + +use crate::errors::FilesystemError; + +use super::{create_named_dir, write_to_file}; + +pub(crate) fn save_program_to_file>( + compiled_program: &PreprocessedProgram, + crate_name: &CrateName, + circuit_dir: P, +) -> PathBuf { + let circuit_name: String = crate_name.into(); + save_build_artifact_to_file(compiled_program, &circuit_name, circuit_dir) +} + +pub(crate) fn save_contract_to_file>( + compiled_contract: &PreprocessedContract, + circuit_name: &str, + circuit_dir: P, +) -> PathBuf { + save_build_artifact_to_file(compiled_contract, circuit_name, circuit_dir) +} + +pub(crate) fn save_debug_artifact_to_file>( + debug_artifact: &DebugArtifact, + circuit_name: &str, + circuit_dir: P, +) -> PathBuf { + let artifact_name = format!("debug_{circuit_name}"); + save_build_artifact_to_file(debug_artifact, &artifact_name, circuit_dir) +} + +fn save_build_artifact_to_file, T: ?Sized + serde::Serialize>( + build_artifact: &T, + artifact_name: &str, + circuit_dir: P, +) -> PathBuf { + create_named_dir(circuit_dir.as_ref(), "target"); + let circuit_path = circuit_dir.as_ref().join(artifact_name).with_extension("json"); + + write_to_file(&serde_json::to_vec(build_artifact).unwrap(), &circuit_path); + + circuit_path +} + +pub(crate) fn read_program_from_file>( + circuit_path: P, +) -> Result { + let file_path = circuit_path.as_ref().with_extension("json"); + + let input_string = + std::fs::read(&file_path).map_err(|_| FilesystemError::PathNotValid(file_path))?; + let program = serde_json::from_slice(&input_string) + .map_err(|err| FilesystemError::ProgramSerializationError(err.to_string()))?; + + Ok(program) +} + +pub(crate) fn read_debug_artifact_from_file>( + debug_artifact_path: P, +) -> Result { + let input_string = std::fs::read(&debug_artifact_path) + .map_err(|_| FilesystemError::PathNotValid(debug_artifact_path.as_ref().into()))?; + let program = serde_json::from_slice(&input_string) + .map_err(|err| FilesystemError::ProgramSerializationError(err.to_string()))?; + + Ok(program) +} diff --git a/noir/tooling/nargo_cli/src/cli/fs/proof.rs b/noir/tooling/nargo_cli/src/cli/fs/proof.rs new file mode 100644 index 00000000000..d2b3050708b --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/fs/proof.rs @@ -0,0 +1,20 @@ +use std::path::{Path, PathBuf}; + +use nargo::constants::PROOF_EXT; + +use crate::errors::FilesystemError; + +use super::{create_named_dir, write_to_file}; + +pub(crate) fn save_proof_to_dir>( + proof: &[u8], + proof_name: &str, + proof_dir: P, +) -> Result { + create_named_dir(proof_dir.as_ref(), "proof"); + let proof_path = proof_dir.as_ref().join(proof_name).with_extension(PROOF_EXT); + + write_to_file(hex::encode(proof).as_bytes(), &proof_path); + + Ok(proof_path) +} diff --git a/noir/tooling/nargo_cli/src/cli/fs/witness.rs b/noir/tooling/nargo_cli/src/cli/fs/witness.rs new file mode 100644 index 00000000000..1a2cf88f4a1 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/fs/witness.rs @@ -0,0 +1,22 @@ +use std::path::{Path, PathBuf}; + +use acvm::acir::native_types::WitnessMap; +use nargo::constants::WITNESS_EXT; + +use super::{create_named_dir, write_to_file}; +use crate::errors::FilesystemError; + +pub(crate) fn save_witness_to_dir>( + witnesses: WitnessMap, + witness_name: &str, + witness_dir: P, +) -> Result { + create_named_dir(witness_dir.as_ref(), "witness"); + let witness_path = witness_dir.as_ref().join(witness_name).with_extension(WITNESS_EXT); + + let buf: Vec = witnesses.try_into()?; + + write_to_file(buf.as_slice(), &witness_path); + + Ok(witness_path) +} diff --git a/noir/tooling/nargo_cli/src/cli/info_cmd.rs b/noir/tooling/nargo_cli/src/cli/info_cmd.rs new file mode 100644 index 00000000000..b0f771bfc1c --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/info_cmd.rs @@ -0,0 +1,283 @@ +use std::collections::HashMap; + +use acvm::Language; +use backend_interface::BackendError; +use clap::Args; +use iter_extended::vecmap; +use nargo::{artifacts::debug::DebugArtifact, package::Package}; +use nargo_toml::{get_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_driver::{ + CompileOptions, CompiledContract, CompiledProgram, NOIR_ARTIFACT_VERSION_STRING, +}; +use noirc_errors::{debug_info::OpCodesCount, Location}; +use noirc_frontend::graph::CrateName; +use prettytable::{row, table, Row}; +use rayon::prelude::*; +use serde::Serialize; + +use crate::backends::Backend; +use crate::errors::CliError; + +use super::{compile_cmd::compile_workspace, NargoConfig}; + +/// Provides detailed information on a circuit +/// +/// Current information provided: +/// 1. The number of ACIR opcodes +/// 2. Counts the final number gates in the circuit used by a backend +#[derive(Debug, Clone, Args)] +pub(crate) struct InfoCommand { + /// The name of the package to detail + #[clap(long, conflicts_with = "workspace")] + package: Option, + + /// Detail all packages in the workspace + #[clap(long, conflicts_with = "package")] + workspace: bool, + + /// Output a JSON formatted report. Changes to this format are not currently considered breaking. + #[clap(long, hide = true)] + json: bool, + + #[clap(long, hide = true)] + profile_info: bool, + + #[clap(flatten)] + compile_options: CompileOptions, +} + +pub(crate) fn run( + backend: &Backend, + args: InfoCommand, + config: NargoConfig, +) -> Result<(), CliError> { + let toml_path = get_package_manifest(&config.program_dir)?; + let default_selection = + if args.workspace { PackageSelection::All } else { PackageSelection::DefaultOrAll }; + let selection = args.package.map_or(default_selection, PackageSelection::Selected); + let workspace = resolve_workspace_from_toml( + &toml_path, + selection, + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + )?; + + let (binary_packages, contract_packages): (Vec<_>, Vec<_>) = workspace + .into_iter() + .filter(|package| !package.is_library()) + .cloned() + .partition(|package| package.is_binary()); + + let (np_language, opcode_support) = backend.get_backend_info_or_default(); + let (compiled_programs, compiled_contracts) = compile_workspace( + &workspace, + &binary_packages, + &contract_packages, + np_language, + &opcode_support, + &args.compile_options, + )?; + + if args.profile_info { + for compiled_program in &compiled_programs { + let span_opcodes = compiled_program.debug.count_span_opcodes(); + let debug_artifact: DebugArtifact = compiled_program.clone().into(); + print_span_opcodes(span_opcodes, &debug_artifact); + } + + for compiled_contract in &compiled_contracts { + let debug_artifact: DebugArtifact = compiled_contract.clone().into(); + let functions = &compiled_contract.functions; + for contract_function in functions { + let span_opcodes = contract_function.debug.count_span_opcodes(); + print_span_opcodes(span_opcodes, &debug_artifact); + } + } + } + + let program_info = binary_packages + .into_par_iter() + .zip(compiled_programs) + .map(|(package, program)| { + count_opcodes_and_gates_in_program(backend, program, &package, np_language) + }) + .collect::>()?; + + let contract_info = compiled_contracts + .into_par_iter() + .map(|contract| count_opcodes_and_gates_in_contract(backend, contract, np_language)) + .collect::>()?; + + let info_report = InfoReport { programs: program_info, contracts: contract_info }; + + if args.json { + // Expose machine-readable JSON data. + println!("{}", serde_json::to_string(&info_report).unwrap()); + } else { + // Otherwise print human-readable table. + if !info_report.programs.is_empty() { + let mut program_table = table!([Fm->"Package", Fm->"Language", Fm->"ACIR Opcodes", Fm->"Backend Circuit Size"]); + + for program in info_report.programs { + program_table.add_row(program.into()); + } + program_table.printstd(); + } + if !info_report.contracts.is_empty() { + let mut contract_table = table!([ + Fm->"Contract", + Fm->"Function", + Fm->"Language", + Fm->"ACIR Opcodes", + Fm->"Backend Circuit Size" + ]); + for contract_info in info_report.contracts { + let contract_rows: Vec = contract_info.into(); + for row in contract_rows { + contract_table.add_row(row); + } + } + + contract_table.printstd(); + } + } + + Ok(()) +} + +/// Provides profiling information on +/// +/// Number of OpCodes in relation to Noir source file +/// and line number information +fn print_span_opcodes( + span_opcodes_map: HashMap, + debug_artifact: &DebugArtifact, +) { + let mut pairs: Vec<(&Location, &OpCodesCount)> = span_opcodes_map.iter().collect(); + + pairs.sort_by(|a, b| { + a.1.acir_size.cmp(&b.1.acir_size).then_with(|| a.1.brillig_size.cmp(&b.1.brillig_size)) + }); + + for (location, opcodes_count) in pairs { + let debug_file = debug_artifact.file_map.get(&location.file).unwrap(); + + let start_byte = byte_index(&debug_file.source, location.span.start() + 1); + let end_byte = byte_index(&debug_file.source, location.span.end() + 1); + let range = start_byte..end_byte; + let span_content = &debug_file.source[range]; + let line = debug_artifact.location_line_index(*location).unwrap() + 1; + println!( + "Ln. {}: {} (ACIR:{}, Brillig:{} opcode|s) in file: {}", + line, + span_content, + opcodes_count.acir_size, + opcodes_count.brillig_size, + debug_file.path.to_str().unwrap() + ); + } +} +fn byte_index(string: &str, index: u32) -> usize { + let mut byte_index = 0; + let mut char_index = 0; + + #[allow(clippy::explicit_counter_loop)] + for (byte_offset, _) in string.char_indices() { + if char_index == index { + return byte_index; + } + + byte_index = byte_offset; + char_index += 1; + } + + byte_index +} + +#[derive(Debug, Default, Serialize)] +struct InfoReport { + programs: Vec, + contracts: Vec, +} + +#[derive(Debug, Serialize)] +struct ProgramInfo { + name: String, + #[serde(skip)] + language: Language, + acir_opcodes: usize, + circuit_size: u32, +} + +impl From for Row { + fn from(program_info: ProgramInfo) -> Self { + row![ + Fm->format!("{}", program_info.name), + format!("{:?}", program_info.language), + Fc->format!("{}", program_info.acir_opcodes), + Fc->format!("{}", program_info.circuit_size), + ] + } +} + +#[derive(Debug, Serialize)] +struct ContractInfo { + name: String, + #[serde(skip)] + language: Language, + functions: Vec, +} + +#[derive(Debug, Serialize)] +struct FunctionInfo { + name: String, + acir_opcodes: usize, + circuit_size: u32, +} + +impl From for Vec { + fn from(contract_info: ContractInfo) -> Self { + vecmap(contract_info.functions, |function| { + row![ + Fm->format!("{}", contract_info.name), + Fc->format!("{}", function.name), + format!("{:?}", contract_info.language), + Fc->format!("{}", function.acir_opcodes), + Fc->format!("{}", function.circuit_size), + ] + }) + } +} + +fn count_opcodes_and_gates_in_program( + backend: &Backend, + compiled_program: CompiledProgram, + package: &Package, + language: Language, +) -> Result { + Ok(ProgramInfo { + name: package.name.to_string(), + language, + acir_opcodes: compiled_program.circuit.opcodes.len(), + circuit_size: backend.get_exact_circuit_size(&compiled_program.circuit)?, + }) +} + +fn count_opcodes_and_gates_in_contract( + backend: &Backend, + contract: CompiledContract, + language: Language, +) -> Result { + let functions = contract + .functions + .into_par_iter() + .map(|function| -> Result<_, BackendError> { + Ok(FunctionInfo { + name: function.name, + acir_opcodes: function.bytecode.opcodes.len(), + circuit_size: backend.get_exact_circuit_size(&function.bytecode)?, + }) + }) + .collect::>()?; + + Ok(ContractInfo { name: contract.name, language, functions }) +} diff --git a/noir/tooling/nargo_cli/src/cli/init_cmd.rs b/noir/tooling/nargo_cli/src/cli/init_cmd.rs new file mode 100644 index 00000000000..e53c2e4cdc9 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/init_cmd.rs @@ -0,0 +1,91 @@ +use crate::backends::Backend; +use crate::errors::CliError; + +use super::fs::{create_named_dir, write_to_file}; +use super::NargoConfig; +use clap::Args; +use nargo::constants::{PKG_FILE, SRC_DIR}; +use nargo::package::PackageType; +use noirc_driver::NOIRC_VERSION; +use noirc_frontend::graph::CrateName; +use std::path::PathBuf; + +/// Create a Noir project in the current directory. +#[derive(Debug, Clone, Args)] +pub(crate) struct InitCommand { + /// Name of the package [default: current directory name] + #[clap(long)] + name: Option, + + /// Use a library template + #[arg(long, conflicts_with = "bin", conflicts_with = "contract")] + pub(crate) lib: bool, + + /// Use a binary template [default] + #[arg(long, conflicts_with = "lib", conflicts_with = "contract")] + pub(crate) bin: bool, + + /// Use a contract template + #[arg(long, conflicts_with = "lib", conflicts_with = "bin")] + pub(crate) contract: bool, +} + +const BIN_EXAMPLE: &str = include_str!("./noir_template_files/binary.nr"); +const CONTRACT_EXAMPLE: &str = include_str!("./noir_template_files/contract.nr"); +const LIB_EXAMPLE: &str = include_str!("./noir_template_files/library.nr"); + +pub(crate) fn run( + // Backend is currently unused, but we might want to use it to inform the "new" template in the future + _backend: &Backend, + args: InitCommand, + config: NargoConfig, +) -> Result<(), CliError> { + let package_name = match args.name { + Some(name) => name, + None => { + let name = config.program_dir.file_name().unwrap().to_str().unwrap(); + name.parse().map_err(|_| CliError::InvalidPackageName(name.into()))? + } + }; + + let package_type = if args.lib { + PackageType::Library + } else if args.contract { + PackageType::Contract + } else { + PackageType::Binary + }; + initialize_project(config.program_dir, package_name, package_type); + Ok(()) +} + +/// Initializes a new Noir project in `package_dir`. +pub(crate) fn initialize_project( + package_dir: PathBuf, + package_name: CrateName, + package_type: PackageType, +) { + let src_dir = package_dir.join(SRC_DIR); + create_named_dir(&src_dir, "src"); + + let toml_contents = format!( + r#"[package] +name = "{package_name}" +type = "{package_type}" +authors = [""] +compiler_version = ">={NOIRC_VERSION}" + +[dependencies]"# + ); + + write_to_file(toml_contents.as_bytes(), &package_dir.join(PKG_FILE)); + // This uses the `match` syntax instead of `if` so we get a compile error when we add new package types (which likely need new template files) + match package_type { + PackageType::Binary => write_to_file(BIN_EXAMPLE.as_bytes(), &src_dir.join("main.nr")), + PackageType::Contract => { + write_to_file(CONTRACT_EXAMPLE.as_bytes(), &src_dir.join("main.nr")) + } + PackageType::Library => write_to_file(LIB_EXAMPLE.as_bytes(), &src_dir.join("lib.nr")), + }; + println!("Project successfully created! It is located at {}", package_dir.display()); +} diff --git a/noir/tooling/nargo_cli/src/cli/lsp_cmd.rs b/noir/tooling/nargo_cli/src/cli/lsp_cmd.rs new file mode 100644 index 00000000000..a41bb877991 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/lsp_cmd.rs @@ -0,0 +1,61 @@ +use async_lsp::{ + client_monitor::ClientProcessMonitorLayer, concurrency::ConcurrencyLayer, + panic::CatchUnwindLayer, server::LifecycleLayer, tracing::TracingLayer, +}; +use clap::Args; +use noir_lsp::NargoLspService; +use tower::ServiceBuilder; + +use super::NargoConfig; +use crate::backends::Backend; +use crate::errors::CliError; + +/// Starts the Noir LSP server +/// +/// Starts an LSP server which allows IDEs such as VS Code to display diagnostics in Noir source. +/// +/// VS Code Noir Language Support: https://marketplace.visualstudio.com/items?itemName=noir-lang.vscode-noir +#[derive(Debug, Clone, Args)] +pub(crate) struct LspCommand; + +pub(crate) fn run( + // Backend is currently unused, but we might want to use it to inform the lsp in the future + _backend: &Backend, + _args: LspCommand, + _config: NargoConfig, +) -> Result<(), CliError> { + use tokio::runtime::Builder; + + let runtime = Builder::new_current_thread().enable_all().build().unwrap(); + + runtime.block_on(async { + let (server, _) = async_lsp::MainLoop::new_server(|client| { + #[allow(deprecated)] + let blackbox_solver = barretenberg_blackbox_solver::BarretenbergSolver::new(); + let router = NargoLspService::new(&client, blackbox_solver); + + ServiceBuilder::new() + .layer(TracingLayer::default()) + .layer(LifecycleLayer::default()) + .layer(CatchUnwindLayer::default()) + .layer(ConcurrencyLayer::default()) + .layer(ClientProcessMonitorLayer::new(client)) + .service(router) + }); + + // Prefer truly asynchronous piped stdin/stdout without blocking tasks. + #[cfg(unix)] + let (stdin, stdout) = ( + async_lsp::stdio::PipeStdin::lock_tokio().unwrap(), + async_lsp::stdio::PipeStdout::lock_tokio().unwrap(), + ); + // Fallback to spawn blocking read/write otherwise. + #[cfg(not(unix))] + let (stdin, stdout) = ( + tokio_util::compat::TokioAsyncReadCompatExt::compat(tokio::io::stdin()), + tokio_util::compat::TokioAsyncWriteCompatExt::compat_write(tokio::io::stdout()), + ); + + server.run_buffered(stdin, stdout).await.map_err(CliError::LspError) + }) +} diff --git a/noir/tooling/nargo_cli/src/cli/mod.rs b/noir/tooling/nargo_cli/src/cli/mod.rs new file mode 100644 index 00000000000..88c6b57a98c --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/mod.rs @@ -0,0 +1,119 @@ +use clap::{Args, Parser, Subcommand}; +use const_format::formatcp; +use nargo_toml::find_package_root; +use noirc_driver::NOIR_ARTIFACT_VERSION_STRING; +use std::path::PathBuf; + +use color_eyre::eyre; + +use crate::backends::get_active_backend; + +mod fs; + +mod backend_cmd; +mod check_cmd; +mod codegen_verifier_cmd; +mod compile_cmd; +mod debug_cmd; +mod execute_cmd; +mod fmt_cmd; +mod info_cmd; +mod init_cmd; +mod lsp_cmd; +mod new_cmd; +mod prove_cmd; +mod test_cmd; +mod verify_cmd; + +const GIT_HASH: &str = env!("GIT_COMMIT"); +const IS_DIRTY: &str = env!("GIT_DIRTY"); +const NARGO_VERSION: &str = env!("CARGO_PKG_VERSION"); + +static VERSION_STRING: &str = formatcp!( + "version = {}\nnoirc version = {}\n(git version hash: {}, is dirty: {})", + NARGO_VERSION, + NOIR_ARTIFACT_VERSION_STRING, + GIT_HASH, + IS_DIRTY +); + +#[derive(Parser, Debug)] +#[command(name="nargo", author, version=VERSION_STRING, about, long_about = None)] +struct NargoCli { + #[command(subcommand)] + command: NargoCommand, + + #[clap(flatten)] + config: NargoConfig, +} + +#[non_exhaustive] +#[derive(Args, Clone, Debug)] +pub(crate) struct NargoConfig { + // REMINDER: Also change this flag in the LSP test lens if renamed + #[arg(long, hide = true, global = true, default_value = "./")] + program_dir: PathBuf, +} + +#[non_exhaustive] +#[derive(Subcommand, Clone, Debug)] +enum NargoCommand { + Backend(backend_cmd::BackendCommand), + Check(check_cmd::CheckCommand), + Fmt(fmt_cmd::FormatCommand), + CodegenVerifier(codegen_verifier_cmd::CodegenVerifierCommand), + #[command(alias = "build")] + Compile(compile_cmd::CompileCommand), + New(new_cmd::NewCommand), + Init(init_cmd::InitCommand), + Execute(execute_cmd::ExecuteCommand), + #[command(hide = true)] // Hidden while the feature is being built out + Debug(debug_cmd::DebugCommand), + Prove(prove_cmd::ProveCommand), + Verify(verify_cmd::VerifyCommand), + Test(test_cmd::TestCommand), + Info(info_cmd::InfoCommand), + Lsp(lsp_cmd::LspCommand), +} + +pub(crate) fn start_cli() -> eyre::Result<()> { + let NargoCli { command, mut config } = NargoCli::parse(); + + // If the provided `program_dir` is relative, make it absolute by joining it to the current directory. + if !config.program_dir.is_absolute() { + config.program_dir = std::env::current_dir().unwrap().join(config.program_dir); + } + + // Search through parent directories to find package root if necessary. + if !matches!( + command, + NargoCommand::New(_) + | NargoCommand::Init(_) + | NargoCommand::Lsp(_) + | NargoCommand::Backend(_) + ) { + config.program_dir = find_package_root(&config.program_dir)?; + } + + let active_backend = get_active_backend(); + let backend = crate::backends::Backend::new(active_backend); + + match command { + NargoCommand::New(args) => new_cmd::run(&backend, args, config), + NargoCommand::Init(args) => init_cmd::run(&backend, args, config), + NargoCommand::Check(args) => check_cmd::run(&backend, args, config), + NargoCommand::Compile(args) => compile_cmd::run(&backend, args, config), + NargoCommand::Debug(args) => debug_cmd::run(&backend, args, config), + NargoCommand::Execute(args) => execute_cmd::run(&backend, args, config), + NargoCommand::Prove(args) => prove_cmd::run(&backend, args, config), + NargoCommand::Verify(args) => verify_cmd::run(&backend, args, config), + NargoCommand::Test(args) => test_cmd::run(&backend, args, config), + NargoCommand::Info(args) => info_cmd::run(&backend, args, config), + NargoCommand::CodegenVerifier(args) => codegen_verifier_cmd::run(&backend, args, config), + NargoCommand::Backend(args) => backend_cmd::run(args), + NargoCommand::Lsp(args) => lsp_cmd::run(&backend, args, config), + NargoCommand::Fmt(args) => fmt_cmd::run(args, config), + }?; + + Ok(()) +} diff --git a/noir/tooling/nargo_cli/src/cli/new_cmd.rs b/noir/tooling/nargo_cli/src/cli/new_cmd.rs new file mode 100644 index 00000000000..b4c823d0c1e --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/new_cmd.rs @@ -0,0 +1,61 @@ +use crate::backends::Backend; +use crate::errors::CliError; + +use super::{init_cmd::initialize_project, NargoConfig}; +use clap::Args; +use nargo::package::PackageType; +use noirc_frontend::graph::CrateName; +use std::path::PathBuf; + +/// Create a Noir project in a new directory. +#[derive(Debug, Clone, Args)] +pub(crate) struct NewCommand { + /// The path to save the new project + path: PathBuf, + + /// Name of the package [default: package directory name] + #[clap(long)] + name: Option, + + /// Use a library template + #[arg(long, conflicts_with = "bin", conflicts_with = "contract")] + pub(crate) lib: bool, + + /// Use a binary template [default] + #[arg(long, conflicts_with = "lib", conflicts_with = "contract")] + pub(crate) bin: bool, + + /// Use a contract template + #[arg(long, conflicts_with = "lib", conflicts_with = "bin")] + pub(crate) contract: bool, +} + +pub(crate) fn run( + // Backend is currently unused, but we might want to use it to inform the "new" template in the future + _backend: &Backend, + args: NewCommand, + config: NargoConfig, +) -> Result<(), CliError> { + let package_dir = config.program_dir.join(&args.path); + + if package_dir.exists() { + return Err(CliError::DestinationAlreadyExists(package_dir)); + } + + let package_name = match args.name { + Some(name) => name, + None => { + let name = args.path.file_name().unwrap().to_str().unwrap(); + name.parse().map_err(|_| CliError::InvalidPackageName(name.into()))? + } + }; + let package_type = if args.lib { + PackageType::Library + } else if args.contract { + PackageType::Contract + } else { + PackageType::Binary + }; + initialize_project(package_dir, package_name, package_type); + Ok(()) +} diff --git a/noir/tooling/nargo_cli/src/cli/noir_template_files/binary.nr b/noir/tooling/nargo_cli/src/cli/noir_template_files/binary.nr new file mode 100644 index 00000000000..3c30bf08424 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/noir_template_files/binary.nr @@ -0,0 +1,11 @@ +fn main(x: Field, y: pub Field) { + assert(x != y); +} + +#[test] +fn test_main() { + main(1, 2); + + // Uncomment to make test fail + // main(1, 1); +} diff --git a/noir/tooling/nargo_cli/src/cli/noir_template_files/contract.nr b/noir/tooling/nargo_cli/src/cli/noir_template_files/contract.nr new file mode 100644 index 00000000000..e126726393d --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/noir_template_files/contract.nr @@ -0,0 +1,5 @@ +contract Main { + internal fn double(x: Field) -> pub Field { x * 2 } + fn triple(x: Field) -> pub Field { x * 3 } + fn quadruple(x: Field) -> pub Field { double(double(x)) } +} diff --git a/noir/tooling/nargo_cli/src/cli/noir_template_files/library.nr b/noir/tooling/nargo_cli/src/cli/noir_template_files/library.nr new file mode 100644 index 00000000000..b874c958a9b --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/noir_template_files/library.nr @@ -0,0 +1,11 @@ +fn not_equal(x: Field, y: Field) -> bool { + x != y +} + +#[test] +fn test_not_equal() { + assert(not_equal(1, 2)); + + // Uncomment to make test fail + // assert(not_equal(1, 1)); +} diff --git a/noir/tooling/nargo_cli/src/cli/prove_cmd.rs b/noir/tooling/nargo_cli/src/cli/prove_cmd.rs new file mode 100644 index 00000000000..54b148ec3a2 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/prove_cmd.rs @@ -0,0 +1,127 @@ +use clap::Args; +use nargo::constants::{PROVER_INPUT_FILE, VERIFIER_INPUT_FILE}; +use nargo::package::Package; +use nargo::workspace::Workspace; +use nargo_toml::{get_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_abi::input_parser::Format; +use noirc_driver::{CompileOptions, CompiledProgram, NOIR_ARTIFACT_VERSION_STRING}; +use noirc_frontend::graph::CrateName; + +use super::compile_cmd::compile_bin_package; +use super::fs::{ + inputs::{read_inputs_from_file, write_inputs_to_file}, + proof::save_proof_to_dir, +}; +use super::NargoConfig; +use crate::{backends::Backend, cli::execute_cmd::execute_program, errors::CliError}; + +/// Create proof for this program. The proof is returned as a hex encoded string. +#[derive(Debug, Clone, Args)] +pub(crate) struct ProveCommand { + /// The name of the toml file which contains the inputs for the prover + #[clap(long, short, default_value = PROVER_INPUT_FILE)] + prover_name: String, + + /// The name of the toml file which contains the inputs for the verifier + #[clap(long, short, default_value = VERIFIER_INPUT_FILE)] + verifier_name: String, + + /// Verify proof after proving + #[arg(long)] + verify: bool, + + /// The name of the package to prove + #[clap(long, conflicts_with = "workspace")] + package: Option, + + /// Prove all packages in the workspace + #[clap(long, conflicts_with = "package")] + workspace: bool, + + #[clap(flatten)] + compile_options: CompileOptions, +} + +pub(crate) fn run( + backend: &Backend, + args: ProveCommand, + config: NargoConfig, +) -> Result<(), CliError> { + let toml_path = get_package_manifest(&config.program_dir)?; + let default_selection = + if args.workspace { PackageSelection::All } else { PackageSelection::DefaultOrAll }; + let selection = args.package.map_or(default_selection, PackageSelection::Selected); + let workspace = resolve_workspace_from_toml( + &toml_path, + selection, + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + )?; + + let (np_language, opcode_support) = backend.get_backend_info()?; + for package in &workspace { + let program = compile_bin_package( + &workspace, + package, + &args.compile_options, + np_language, + &opcode_support, + )?; + + prove_package( + backend, + &workspace, + package, + program, + &args.prover_name, + &args.verifier_name, + args.verify, + )?; + } + + Ok(()) +} + +pub(crate) fn prove_package( + backend: &Backend, + workspace: &Workspace, + package: &Package, + compiled_program: CompiledProgram, + prover_name: &str, + verifier_name: &str, + check_proof: bool, +) -> Result<(), CliError> { + // Parse the initial witness values from Prover.toml + let (inputs_map, _) = + read_inputs_from_file(&package.root_dir, prover_name, Format::Toml, &compiled_program.abi)?; + + let solved_witness = execute_program(&compiled_program, &inputs_map)?; + + // Write public inputs into Verifier.toml + let public_abi = compiled_program.abi.public_abi(); + let (public_inputs, return_value) = public_abi.decode(&solved_witness)?; + + write_inputs_to_file( + &public_inputs, + &return_value, + &public_abi, + &package.root_dir, + verifier_name, + Format::Toml, + )?; + + let proof = backend.prove(&compiled_program.circuit, solved_witness, false)?; + + if check_proof { + let public_inputs = public_abi.encode(&public_inputs, return_value)?; + let valid_proof = + backend.verify(&proof, public_inputs, &compiled_program.circuit, false)?; + + if !valid_proof { + return Err(CliError::InvalidProof("".into())); + } + } + + save_proof_to_dir(&proof, &String::from(&package.name), workspace.proofs_directory_path())?; + + Ok(()) +} diff --git a/noir/tooling/nargo_cli/src/cli/test_cmd.rs b/noir/tooling/nargo_cli/src/cli/test_cmd.rs new file mode 100644 index 00000000000..e117d8555a5 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/test_cmd.rs @@ -0,0 +1,160 @@ +use std::io::Write; + +use acvm::BlackBoxFunctionSolver; +use clap::Args; +use nargo::{ + ops::{run_test, TestStatus}, + package::Package, + prepare_package, +}; +use nargo_toml::{get_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_driver::{CompileOptions, NOIR_ARTIFACT_VERSION_STRING}; +use noirc_frontend::{graph::CrateName, hir::FunctionNameMatch}; +use termcolor::{Color, ColorChoice, ColorSpec, StandardStream, WriteColor}; + +use crate::{backends::Backend, cli::check_cmd::check_crate_and_report_errors, errors::CliError}; + +use super::NargoConfig; + +/// Run the tests for this program +#[derive(Debug, Clone, Args)] +pub(crate) struct TestCommand { + /// If given, only tests with names containing this string will be run + test_name: Option, + + /// Display output of `println` statements + #[arg(long)] + show_output: bool, + + /// Only run tests that match exactly + #[clap(long)] + exact: bool, + + /// The name of the package to test + #[clap(long, conflicts_with = "workspace")] + package: Option, + + /// Test all packages in the workspace + #[clap(long, conflicts_with = "package")] + workspace: bool, + + #[clap(flatten)] + compile_options: CompileOptions, +} + +pub(crate) fn run( + _backend: &Backend, + args: TestCommand, + config: NargoConfig, +) -> Result<(), CliError> { + let toml_path = get_package_manifest(&config.program_dir)?; + let default_selection = + if args.workspace { PackageSelection::All } else { PackageSelection::DefaultOrAll }; + let selection = args.package.map_or(default_selection, PackageSelection::Selected); + let workspace = resolve_workspace_from_toml( + &toml_path, + selection, + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + )?; + + let pattern = match &args.test_name { + Some(name) => { + if args.exact { + FunctionNameMatch::Exact(name) + } else { + FunctionNameMatch::Contains(name) + } + } + None => FunctionNameMatch::Anything, + }; + + #[allow(deprecated)] + let blackbox_solver = barretenberg_blackbox_solver::BarretenbergSolver::new(); + for package in &workspace { + // By unwrapping here with `?`, we stop the test runner upon a package failing + // TODO: We should run the whole suite even if there are failures in a package + run_tests(&blackbox_solver, package, pattern, args.show_output, &args.compile_options)?; + } + + Ok(()) +} + +fn run_tests( + blackbox_solver: &S, + package: &Package, + test_name: FunctionNameMatch, + show_output: bool, + compile_options: &CompileOptions, +) -> Result<(), CliError> { + let (mut context, crate_id) = + prepare_package(package, Box::new(|path| std::fs::read_to_string(path))); + check_crate_and_report_errors( + &mut context, + crate_id, + compile_options.deny_warnings, + compile_options.silence_warnings, + )?; + + let test_functions = context.get_all_test_functions_in_crate_matching(&crate_id, test_name); + + println!("[{}] Running {} test functions", package.name, test_functions.len()); + let mut failing = 0; + + let writer = StandardStream::stderr(ColorChoice::Always); + let mut writer = writer.lock(); + + for (test_name, test_function) in test_functions { + write!(writer, "[{}] Testing {test_name}... ", package.name) + .expect("Failed to write to stdout"); + writer.flush().expect("Failed to flush writer"); + + match run_test(blackbox_solver, &context, test_function, show_output, compile_options) { + TestStatus::Pass { .. } => { + writer + .set_color(ColorSpec::new().set_fg(Some(Color::Green))) + .expect("Failed to set color"); + writeln!(writer, "ok").expect("Failed to write to stdout"); + } + TestStatus::Fail { message, error_diagnostic } => { + let writer = StandardStream::stderr(ColorChoice::Always); + let mut writer = writer.lock(); + writer + .set_color(ColorSpec::new().set_fg(Some(Color::Red))) + .expect("Failed to set color"); + writeln!(writer, "{message}").expect("Failed to write to stdout"); + writer.reset().expect("Failed to reset writer"); + if let Some(diag) = error_diagnostic { + noirc_errors::reporter::report_all( + context.file_manager.as_file_map(), + &[diag], + compile_options.deny_warnings, + compile_options.silence_warnings, + ); + } + failing += 1; + } + TestStatus::CompileError(err) => { + noirc_errors::reporter::report_all( + context.file_manager.as_file_map(), + &[err], + compile_options.deny_warnings, + compile_options.silence_warnings, + ); + failing += 1; + } + } + writer.reset().expect("Failed to reset writer"); + } + + if failing == 0 { + write!(writer, "[{}] ", package.name).expect("Failed to write to stdout"); + writer.set_color(ColorSpec::new().set_fg(Some(Color::Green))).expect("Failed to set color"); + writeln!(writer, "All tests passed").expect("Failed to write to stdout"); + } else { + let plural = if failing == 1 { "" } else { "s" }; + return Err(CliError::Generic(format!("[{}] {failing} test{plural} failed", package.name))); + } + + writer.reset().expect("Failed to reset writer"); + Ok(()) +} diff --git a/noir/tooling/nargo_cli/src/cli/verify_cmd.rs b/noir/tooling/nargo_cli/src/cli/verify_cmd.rs new file mode 100644 index 00000000000..2f8a6efbba4 --- /dev/null +++ b/noir/tooling/nargo_cli/src/cli/verify_cmd.rs @@ -0,0 +1,93 @@ +use super::NargoConfig; +use super::{ + compile_cmd::compile_bin_package, + fs::{inputs::read_inputs_from_file, load_hex_data}, +}; +use crate::{backends::Backend, errors::CliError}; + +use clap::Args; +use nargo::constants::{PROOF_EXT, VERIFIER_INPUT_FILE}; +use nargo::package::Package; +use nargo::workspace::Workspace; +use nargo_toml::{get_package_manifest, resolve_workspace_from_toml, PackageSelection}; +use noirc_abi::input_parser::Format; +use noirc_driver::{CompileOptions, CompiledProgram, NOIR_ARTIFACT_VERSION_STRING}; +use noirc_frontend::graph::CrateName; + +/// Given a proof and a program, verify whether the proof is valid +#[derive(Debug, Clone, Args)] +pub(crate) struct VerifyCommand { + /// The name of the toml file which contains the inputs for the verifier + #[clap(long, short, default_value = VERIFIER_INPUT_FILE)] + verifier_name: String, + + /// The name of the package verify + #[clap(long, conflicts_with = "workspace")] + package: Option, + + /// Verify all packages in the workspace + #[clap(long, conflicts_with = "package")] + workspace: bool, + + #[clap(flatten)] + compile_options: CompileOptions, +} + +pub(crate) fn run( + backend: &Backend, + args: VerifyCommand, + config: NargoConfig, +) -> Result<(), CliError> { + let toml_path = get_package_manifest(&config.program_dir)?; + let default_selection = + if args.workspace { PackageSelection::All } else { PackageSelection::DefaultOrAll }; + let selection = args.package.map_or(default_selection, PackageSelection::Selected); + let workspace = resolve_workspace_from_toml( + &toml_path, + selection, + Some(NOIR_ARTIFACT_VERSION_STRING.to_string()), + )?; + + let (np_language, opcode_support) = backend.get_backend_info()?; + for package in &workspace { + let program = compile_bin_package( + &workspace, + package, + &args.compile_options, + np_language, + &opcode_support, + )?; + + verify_package(backend, &workspace, package, program, &args.verifier_name)?; + } + + Ok(()) +} + +fn verify_package( + backend: &Backend, + workspace: &Workspace, + package: &Package, + compiled_program: CompiledProgram, + verifier_name: &str, +) -> Result<(), CliError> { + // Load public inputs (if any) from `verifier_name`. + let public_abi = compiled_program.abi.public_abi(); + let (public_inputs_map, return_value) = + read_inputs_from_file(&package.root_dir, verifier_name, Format::Toml, &public_abi)?; + + let public_inputs = public_abi.encode(&public_inputs_map, return_value)?; + + let proof_path = + workspace.proofs_directory_path().join(package.name.to_string()).with_extension(PROOF_EXT); + + let proof = load_hex_data(&proof_path)?; + + let valid_proof = backend.verify(&proof, public_inputs, &compiled_program.circuit, false)?; + + if valid_proof { + Ok(()) + } else { + Err(CliError::InvalidProof(proof_path)) + } +} diff --git a/noir/tooling/nargo_cli/src/errors.rs b/noir/tooling/nargo_cli/src/errors.rs new file mode 100644 index 00000000000..92da74c71d4 --- /dev/null +++ b/noir/tooling/nargo_cli/src/errors.rs @@ -0,0 +1,87 @@ +use acvm::acir::native_types::WitnessMapError; +use hex::FromHexError; +use nargo::{errors::CompileError, NargoError}; +use nargo_toml::ManifestError; +use noirc_abi::errors::{AbiError, InputParserError}; +use std::path::PathBuf; +use thiserror::Error; + +#[derive(Debug, Error)] +pub(crate) enum FilesystemError { + #[error("Error: {} is not a valid path\nRun either `nargo compile` to generate missing build artifacts or `nargo prove` to construct a proof", .0.display())] + PathNotValid(PathBuf), + #[error("Error: could not parse hex build artifact (proof, proving and/or verification keys, ACIR checksum) ({0})")] + HexArtifactNotValid(FromHexError), + #[error( + " Error: cannot find {0}.toml file.\n Expected location: {1:?} \n Please generate this file at the expected location." + )] + MissingTomlFile(String, PathBuf), + + /// Input parsing error + #[error(transparent)] + InputParserError(#[from] InputParserError), + + /// WitnessMap serialization error + #[error(transparent)] + WitnessMapSerialization(#[from] WitnessMapError), + + #[error("Error: could not deserialize build program: {0}")] + ProgramSerializationError(String), +} + +#[derive(Debug, Error)] +pub(crate) enum CliError { + #[error("{0}")] + Generic(String), + #[error("Error: destination {} already exists", .0.display())] + DestinationAlreadyExists(PathBuf), + + #[error("Failed to verify proof {}", .0.display())] + InvalidProof(PathBuf), + + #[error("Invalid package name {0}. Did you mean to use `--name`?")] + InvalidPackageName(String), + + /// ABI encoding/decoding error + #[error(transparent)] + AbiError(#[from] AbiError), + + /// Filesystem errors + #[error(transparent)] + FilesystemError(#[from] FilesystemError), + + #[error(transparent)] + LspError(#[from] async_lsp::Error), + + /// Error from Nargo + #[error(transparent)] + NargoError(#[from] NargoError), + + /// Error from Manifest + #[error(transparent)] + ManifestError(#[from] ManifestError), + + /// Error from the compilation pipeline + #[error(transparent)] + CompileError(#[from] CompileError), + + /// Error related to backend selection/installation. + #[error(transparent)] + BackendError(#[from] BackendError), + + /// Error related to communication with backend. + #[error(transparent)] + BackendCommunicationError(#[from] backend_interface::BackendError), +} + +#[derive(Debug, thiserror::Error)] +pub(crate) enum BackendError { + #[error("No backend is installed with the name {0}")] + UnknownBackend(String), + + #[error("The backend {0} is already installed")] + AlreadyInstalled(String), + + #[error("Backend installation failed: {0}")] + InstallationError(#[from] std::io::Error), +} diff --git a/noir/tooling/nargo_cli/src/main.rs b/noir/tooling/nargo_cli/src/main.rs new file mode 100644 index 00000000000..92bd7b94988 --- /dev/null +++ b/noir/tooling/nargo_cli/src/main.rs @@ -0,0 +1,28 @@ +#![forbid(unsafe_code)] +#![warn(unreachable_pub)] +#![warn(clippy::semicolon_if_nothing_returned)] +#![cfg_attr(not(test), warn(unused_crate_dependencies, unused_extern_crates))] + +//! Nargo is the package manager for Noir +//! This name was used because it sounds like `cargo` and +//! Noir Package Manager abbreviated is npm, which is already taken. + +mod backends; +mod cli; +mod errors; + +use color_eyre::config::HookBuilder; + +const PANIC_MESSAGE: &str = "This is a bug. We may have already fixed this in newer versions of Nargo so try searching for similar issues at https://github.com/noir-lang/noir/issues/.\nIf there isn't an open issue for this bug, consider opening one at https://github.com/noir-lang/noir/issues/new?labels=bug&template=bug_report.yml"; + +fn main() { + // Register a panic hook to display more readable panic messages to end-users + let (panic_hook, _) = + HookBuilder::default().display_env_section(false).panic_section(PANIC_MESSAGE).into_hooks(); + panic_hook.install(); + + if let Err(report) = cli::start_cli() { + eprintln!("{report}"); + std::process::exit(1); + } +} diff --git a/noir/tooling/nargo_cli/tests/README.md b/noir/tooling/nargo_cli/tests/README.md new file mode 100644 index 00000000000..71e609ebf24 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/README.md @@ -0,0 +1,43 @@ +# Integration test directory structure + +Integration tests for the Noir compiler are broken down into the following directories: + +- `compile_failure`: programs which are not valid or unsatisfiable Noir code and so the compiler should reject. +- `compile_success_empty`: programs which are valid satisfiable Noir code but have no opcodes. +- `compile_success_contract`: contracts which are valid Noir code. +- `execution_success`: programs which are valid Noir satisfiable code and have opcodes. + +The current testing flow can be thought of as shown: +```mermaid +flowchart TD + + subgraph compile_failure + A1[Attempt to compile] --> A2[Assert compilation fails] + end + + subgraph compile_success_empty + B1[Attempt to compile] --> B2[Assert compilation succeeds] + B2 --> B3[Assert empty circuit] + end + + subgraph execution_success + C1[Attempt to compile] --> C2[Assert compilation succeeds] + C2 --> C3[Write circuit to file] + C3 --> C4[Assert execution succeeds] + C4 --> C5[Write witness to file] + + C6[Publish witness + circuit as artifact] + C3 --> C6 + C5 --> C6 + end + + subgraph compile_success_contract + D1[Attempt to compile] --> D2[Assert compilation succeeds] + end +``` + +## `execution_success` vs `compile_success_empty` + +Note that `execution_success` and `compile_success_empty` are distinct as `compile_success_empty` is expected to compile down to an empty circuit. This may not be possible for some argument-less circuits in the situation where instructions have side-effects or certain compiler optimizations are missing, but once moved to `compile_success_empty` a program compiling down to a non-empty circuit is a compiler regression. + + diff --git a/noir/tooling/nargo_cli/tests/acir_artifacts/1327_concrete_in_generic/target/acir.gz b/noir/tooling/nargo_cli/tests/acir_artifacts/1327_concrete_in_generic/target/acir.gz new file mode 100644 index 00000000000..cc92863a4a8 Binary files /dev/null and b/noir/tooling/nargo_cli/tests/acir_artifacts/1327_concrete_in_generic/target/acir.gz differ diff --git a/noir/tooling/nargo_cli/tests/acir_artifacts/1327_concrete_in_generic/target/witness.gz b/noir/tooling/nargo_cli/tests/acir_artifacts/1327_concrete_in_generic/target/witness.gz new file mode 100644 index 00000000000..454a9b75e04 Binary files /dev/null and b/noir/tooling/nargo_cli/tests/acir_artifacts/1327_concrete_in_generic/target/witness.gz differ 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a/noir/tooling/nargo_cli/tests/acir_artifacts/xor/target/acir.gz b/noir/tooling/nargo_cli/tests/acir_artifacts/xor/target/acir.gz new file mode 100644 index 00000000000..eda28c748c5 Binary files /dev/null and b/noir/tooling/nargo_cli/tests/acir_artifacts/xor/target/acir.gz differ diff --git a/noir/tooling/nargo_cli/tests/acir_artifacts/xor/target/witness.gz b/noir/tooling/nargo_cli/tests/acir_artifacts/xor/target/witness.gz new file mode 100644 index 00000000000..444c8e01cd1 Binary files /dev/null and b/noir/tooling/nargo_cli/tests/acir_artifacts/xor/target/witness.gz differ diff --git a/noir/tooling/nargo_cli/tests/codegen-verifier.rs b/noir/tooling/nargo_cli/tests/codegen-verifier.rs new file mode 100644 index 00000000000..f991f72b108 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/codegen-verifier.rs @@ -0,0 +1,37 @@ +//! This integration test aims to check that the `nargo codegen-verifier` will successfully create a +//! file containing a verifier for a simple program. + +use assert_cmd::prelude::*; +use predicates::prelude::*; +use std::process::Command; + +use assert_fs::prelude::{PathAssert, PathChild}; + +#[test] +fn simple_verifier_codegen() { + let test_dir = assert_fs::TempDir::new().unwrap(); + std::env::set_current_dir(&test_dir).unwrap(); + + // Create trivial program + let project_name = "hello_world"; + let project_dir = test_dir.child(project_name); + + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.arg("new").arg(project_name); + cmd.assert().success(); + + std::env::set_current_dir(&project_dir).unwrap(); + + // Run `nargo codegen-verifier` + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.arg("codegen-verifier"); + cmd.assert() + .success() + .stdout(predicate::str::contains("Contract successfully created and located at")); + + project_dir + .child("contract") + .child("hello_world") + .child("plonk_vk.sol") + .assert(predicate::path::is_file()); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/assert_constant_fail/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/assert_constant_fail/Nargo.toml new file mode 100644 index 00000000000..2eaf67246e1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/assert_constant_fail/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "assert_constant_fail" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/assert_constant_fail/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/assert_constant_fail/src/main.nr new file mode 100644 index 00000000000..cf682607083 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/assert_constant_fail/src/main.nr @@ -0,0 +1,10 @@ +use dep::std::assert_constant; + +fn main(x: Field) { + foo(5, x); +} + +fn foo(constant: Field, non_constant: Field) { + assert_constant(constant); + assert_constant(non_constant); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/assert_eq_struct/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/assert_eq_struct/Nargo.toml new file mode 100644 index 00000000000..9fcdcc10bb7 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/assert_eq_struct/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "assert_eq_struct" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/assert_eq_struct/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/assert_eq_struct/src/main.nr new file mode 100644 index 00000000000..54b937e5f1d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/assert_eq_struct/src/main.nr @@ -0,0 +1,5 @@ +struct myStruct {} +// `assert_eq` should not allow asserting equality between types for which `==` is not defined. +fn main(x: myStruct, y: pub myStruct) { + assert_eq(x, y); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/brillig_assert_fail/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/brillig_assert_fail/Nargo.toml new file mode 100644 index 00000000000..7ce776e5ce6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/brillig_assert_fail/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_assert_fail" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/brillig_assert_fail/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/brillig_assert_fail/Prover.toml new file mode 100644 index 00000000000..11497a473bc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/brillig_assert_fail/Prover.toml @@ -0,0 +1 @@ +x = "0" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/brillig_assert_fail/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/brillig_assert_fail/src/main.nr new file mode 100644 index 00000000000..da9d4ec1ac8 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/brillig_assert_fail/src/main.nr @@ -0,0 +1,11 @@ +// Tests a very simple program. +// +// The features being tested is using assert on brillig +fn main(x: Field) { + assert(1 == conditional(x as bool)); +} + +unconstrained fn conditional(x: bool) -> Field { + assert(x); + 1 +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/constrain_typo/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/constrain_typo/Nargo.toml new file mode 100644 index 00000000000..191121e59b7 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/constrain_typo/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "constrain_typo" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/constrain_typo/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/constrain_typo/src/main.nr new file mode 100644 index 00000000000..ee99663da46 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/constrain_typo/src/main.nr @@ -0,0 +1,7 @@ + +// This should not compile as the keyword +// is `constrain` and not `constrai` + +fn main(x : Field, y : Field) { + constrai x != y; +} \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/custom_entry_not_found/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/custom_entry_not_found/Nargo.toml new file mode 100644 index 00000000000..a32696d3370 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/custom_entry_not_found/Nargo.toml @@ -0,0 +1,8 @@ +[package] +name = "custom_entry" +type = "bin" +# Testing that this file is missing and doesn't fallback to default `main.nr` file +entry = "src/foobarbaz.nr" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/custom_entry_not_found/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/custom_entry_not_found/Prover.toml new file mode 100644 index 00000000000..4dd6b405159 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/custom_entry_not_found/Prover.toml @@ -0,0 +1 @@ +x = "1" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/custom_entry_not_found/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/custom_entry_not_found/src/main.nr new file mode 100644 index 00000000000..00e94414c0b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/custom_entry_not_found/src/main.nr @@ -0,0 +1,3 @@ +fn main(x: Field) { + assert(x == 1); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dep_impl_primitive/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/dep_impl_primitive/Nargo.toml new file mode 100644 index 00000000000..afe5a5134ca --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dep_impl_primitive/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "dep_impl_primitive" +type = "bin" +authors = [""] +[dependencies] +bad_impl = { path = "../../test_libraries/bad_impl" } diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dep_impl_primitive/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/dep_impl_primitive/Prover.toml new file mode 100644 index 00000000000..7d4290a117a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dep_impl_primitive/Prover.toml @@ -0,0 +1 @@ +x = 1 diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dep_impl_primitive/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/dep_impl_primitive/src/main.nr new file mode 100644 index 00000000000..e61ae82b62c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dep_impl_primitive/src/main.nr @@ -0,0 +1,5 @@ +use dep::bad_impl; + +fn main(x: Field) { + x.something(); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/depend_on_bin/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/depend_on_bin/Nargo.toml new file mode 100644 index 00000000000..8334a38eb4b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/depend_on_bin/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "depend_on_bin" +type = "bin" +authors = [""] +[dependencies] +bin_dep = { path = "../../test_libraries/bin_dep" } diff --git a/noir/tooling/nargo_cli/tests/compile_failure/depend_on_bin/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/depend_on_bin/Prover.toml new file mode 100644 index 00000000000..7d4290a117a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/depend_on_bin/Prover.toml @@ -0,0 +1 @@ +x = 1 diff --git a/noir/tooling/nargo_cli/tests/compile_failure/depend_on_bin/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/depend_on_bin/src/main.nr new file mode 100644 index 00000000000..4e03e8eb41e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/depend_on_bin/src/main.nr @@ -0,0 +1,5 @@ +use dep::bin_dep; + +fn main(x : Field) { + assert(x == 1); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_constants/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_constants/Nargo.toml new file mode 100644 index 00000000000..aad13c7d97f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_constants/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "divide_by_zero" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/docs/docs/dev_docs/contracts/resources/common_patterns/interacting_with_l1.md b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_constants/Prover.toml similarity index 100% rename from docs/docs/dev_docs/contracts/resources/common_patterns/interacting_with_l1.md rename to noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_constants/Prover.toml diff --git a/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_constants/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_constants/src/main.nr new file mode 100644 index 00000000000..58adc5444b1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_constants/src/main.nr @@ -0,0 +1,6 @@ +use dep::std; + +fn main() { + let a: Field = 3 / 0; + std::println(a); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_modulo/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_modulo/Nargo.toml new file mode 100644 index 00000000000..22ff18075cd --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_modulo/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "div_by_zero_modulo" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/docs/docs/dev_docs/contracts/resources/common_patterns/sending_tokens_to_contract.md b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_modulo/Prover.toml similarity index 100% rename from docs/docs/dev_docs/contracts/resources/common_patterns/sending_tokens_to_contract.md rename to noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_modulo/Prover.toml diff --git a/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_modulo/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_modulo/src/main.nr new file mode 100644 index 00000000000..d938ab7fca8 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_modulo/src/main.nr @@ -0,0 +1,7 @@ +fn main() { + let a: u32 = 6; + let b = 3; + let c = 0; + let res = (a * b) % c; + assert(res != 5); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_numerator_witness/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_numerator_witness/Nargo.toml new file mode 100644 index 00000000000..30a7eb62645 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_numerator_witness/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "div_by_zero_numerator_witness" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_numerator_witness/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_numerator_witness/Prover.toml new file mode 100644 index 00000000000..07890234a19 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_numerator_witness/Prover.toml @@ -0,0 +1 @@ +x = "3" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_numerator_witness/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_numerator_witness/src/main.nr new file mode 100644 index 00000000000..f51b26d5ba1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_numerator_witness/src/main.nr @@ -0,0 +1,6 @@ +use dep::std; + +fn main(x: Field) { + let a: Field = x / 0; + std::println(a); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_witness/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_witness/Nargo.toml new file mode 100644 index 00000000000..e4968f6182d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_witness/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "div_by_zero_witness" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_witness/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_witness/Prover.toml new file mode 100644 index 00000000000..a1f166bf325 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_witness/Prover.toml @@ -0,0 +1,2 @@ +x = "3" +y = "0" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_witness/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_witness/src/main.nr new file mode 100644 index 00000000000..a814f88f320 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/div_by_zero_witness/src/main.nr @@ -0,0 +1,6 @@ +use dep::std; +// It is expected that `y` must be equal to 0. +fn main(x: Field, y: pub Field) { + let a: Field = x / y; + std::println(a); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/Nargo.toml new file mode 100644 index 00000000000..18352cb34b5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "dup_trait_implementation_4" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/docs/docs/dev_docs/contracts/resources/common_patterns/sending_tokens_to_user.md b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/Prover.toml similarity index 100% rename from docs/docs/dev_docs/contracts/resources/common_patterns/sending_tokens_to_user.md rename to noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/Prover.toml diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/src/main.nr new file mode 100644 index 00000000000..e03fe8b3eeb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/src/main.nr @@ -0,0 +1,5 @@ +mod module1; +mod module2; +mod module3; + +fn main() {} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/src/module1.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/src/module1.nr new file mode 100644 index 00000000000..4d41ff2909a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/src/module1.nr @@ -0,0 +1,2 @@ +trait MyTrait { +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/src/module2.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/src/module2.nr new file mode 100644 index 00000000000..3cadb6d78cb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/src/module2.nr @@ -0,0 +1,2 @@ +struct MyStruct { +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/src/module3.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/src/module3.nr new file mode 100644 index 00000000000..17874893190 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_4/src/module3.nr @@ -0,0 +1,6 @@ +use crate::module1::MyTrait; +use crate::module2::MyStruct; +// those are not the same 'Path', but they refer to the same trait & impl +// so a Duplicate error should be thrown +impl MyTrait for MyStruct {} +impl crate::module1::MyTrait for crate::module2::MyStruct { } diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/Nargo.toml new file mode 100644 index 00000000000..40b7c3d0269 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "dup_trait_implementation_5" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/main.nr new file mode 100644 index 00000000000..b5719009898 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/main.nr @@ -0,0 +1,6 @@ +mod module1; +mod module2; +mod module3; +mod module4; + +fn main() {} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/module1.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/module1.nr new file mode 100644 index 00000000000..4d41ff2909a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/module1.nr @@ -0,0 +1,2 @@ +trait MyTrait { +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/module2.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/module2.nr new file mode 100644 index 00000000000..3cadb6d78cb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/module2.nr @@ -0,0 +1,2 @@ +struct MyStruct { +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/module3.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/module3.nr new file mode 100644 index 00000000000..ac886d441cd --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/module3.nr @@ -0,0 +1,4 @@ +use crate::module1::MyTrait; +use crate::module2::MyStruct; + +impl MyTrait for MyStruct {} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/module4.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/module4.nr new file mode 100644 index 00000000000..dfa660f80b1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_implementation_5/src/module4.nr @@ -0,0 +1,3 @@ +// another module in the crate implements the same trait + struct +// a Duplicate error should be thrown +impl crate::module1::MyTrait for crate::module2::MyStruct { } diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_1/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_1/Nargo.toml new file mode 100644 index 00000000000..078a336c282 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_1/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "dup_trait_items_1" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_1/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_1/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_1/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_1/src/main.nr new file mode 100644 index 00000000000..863139333f7 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_1/src/main.nr @@ -0,0 +1,6 @@ +trait MyTrait { + fn SomeFunc(); + fn SomeFunc(); +} + +fn main() {} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_2/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_2/Nargo.toml new file mode 100644 index 00000000000..41e1a3b3fd9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_2/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "dup_trait_items_2" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_2/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_2/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_2/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_2/src/main.nr new file mode 100644 index 00000000000..cdcac745208 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_2/src/main.nr @@ -0,0 +1,6 @@ +trait MyTrait { + let SomeConst: u32; + let SomeConst: Field; +} + +fn main() {} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_3/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_3/Nargo.toml new file mode 100644 index 00000000000..1388f3c83ee --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_3/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "dup_trait_items_3" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_3/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_3/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_3/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_3/src/main.nr new file mode 100644 index 00000000000..f6ed35468b9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_3/src/main.nr @@ -0,0 +1,6 @@ +trait MyTrait { + type SomeType; + type SomeType; +} + +fn main() {} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_4/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_4/Nargo.toml new file mode 100644 index 00000000000..43e8096548b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_4/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "dup_trait_items_4" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_4/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_4/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_4/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_4/src/main.nr new file mode 100644 index 00000000000..fc0a7011a5d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_4/src/main.nr @@ -0,0 +1,6 @@ +trait MyTrait { + let MyItem: u32; + fn MyItem(); +} + +fn main() {} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_5/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_5/Nargo.toml new file mode 100644 index 00000000000..0af8ef88cea --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_5/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "dup_trait_items_5" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_5/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_5/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_5/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_5/src/main.nr new file mode 100644 index 00000000000..da42d27ac36 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_5/src/main.nr @@ -0,0 +1,6 @@ +trait MyTrait { + fn MyItem(); + let MyItem: u32; +} + +fn main() {} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_6/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_6/Nargo.toml new file mode 100644 index 00000000000..dd5f13438ec --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_6/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "dup_trait_items_6" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_6/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_6/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_6/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_6/src/main.nr new file mode 100644 index 00000000000..a1a731d943b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dup_trait_items_6/src/main.nr @@ -0,0 +1,15 @@ +trait MyTrait { + fn SomeFunc() { }; + fn SomeFunc() { }; +} + +struct MyStruct { +} + +impl MyTrait for MyStruct { + fn SomeFunc() { + } +} + +fn main() { +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/duplicate_declaration/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/duplicate_declaration/Nargo.toml new file mode 100644 index 00000000000..a4c82c70642 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/duplicate_declaration/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "duplicate_declaration" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/duplicate_declaration/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/duplicate_declaration/src/main.nr new file mode 100644 index 00000000000..e4433ef4078 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/duplicate_declaration/src/main.nr @@ -0,0 +1,8 @@ +// Duplicate functions should not compile +fn hello(x: Field) -> Field { + x +} + +fn hello(x: Field) -> Field { + x +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dyn_index_fail_nested_array/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/dyn_index_fail_nested_array/Nargo.toml new file mode 100644 index 00000000000..7f1c2c097a4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dyn_index_fail_nested_array/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "dyn_index_fail_nested_array" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dyn_index_fail_nested_array/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/dyn_index_fail_nested_array/Prover.toml new file mode 100644 index 00000000000..00ffa6e4620 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dyn_index_fail_nested_array/Prover.toml @@ -0,0 +1,13 @@ +y = "2" + +[[x]] +a = "1" +b = "2" + +[[x]] +a = "3" +b = "4" + +[[x]] +a = "5" +b = "6" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dyn_index_fail_nested_array/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/dyn_index_fail_nested_array/src/main.nr new file mode 100644 index 00000000000..954d2e77c6e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dyn_index_fail_nested_array/src/main.nr @@ -0,0 +1,8 @@ +struct Foo { + a: Field, + b: Field, +} + +fn main(mut x: [Foo; 3], y: pub Field) { + assert(x[y + 2].a == 5); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dynamic_index_failure/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/dynamic_index_failure/Nargo.toml new file mode 100644 index 00000000000..2c44a5356c3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dynamic_index_failure/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "dynamic_index_failure" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dynamic_index_failure/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/dynamic_index_failure/Prover.toml new file mode 100644 index 00000000000..caf3448c56f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dynamic_index_failure/Prover.toml @@ -0,0 +1,2 @@ +x = [104, 101, 108, 108, 111] +z = "4" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/dynamic_index_failure/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/dynamic_index_failure/src/main.nr new file mode 100644 index 00000000000..0af5f90eea6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/dynamic_index_failure/src/main.nr @@ -0,0 +1,21 @@ +fn main(mut x: [u32; 5], z: Field) { + let idx = z + 10; + + x[z] = 4; + // Dynamic index is greater than length of the array + assert(x[idx] != 0); + // TODO(#2133): Provide more accurate call stacks for arrays merged in if statements + // if z != 20 { + // x[0] = x[4]; + // } else { + // // TODO: Dynamic predicate still gives index out of bounds error + // if idx as u32 < 3 { + // x[idx] = 10; + // } + // x[idx] = 10; + // for i in 0..5 { + // x[idx] = x[i]; + // } + // } + // assert(x[idx] != 0); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/field_modulo/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/field_modulo/Nargo.toml new file mode 100644 index 00000000000..94934f13e56 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/field_modulo/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "field_modulo" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/field_modulo/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/field_modulo/src/main.nr new file mode 100644 index 00000000000..a482b68806b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/field_modulo/src/main.nr @@ -0,0 +1,3 @@ +fn main(x: Field) -> pub Field { + x % 2 +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/integer_literal_overflow/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/integer_literal_overflow/Nargo.toml new file mode 100644 index 00000000000..ed607023878 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/integer_literal_overflow/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "integer_literal_overflow" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/integer_literal_overflow/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/integer_literal_overflow/src/main.nr new file mode 100644 index 00000000000..d89505c0085 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/integer_literal_overflow/src/main.nr @@ -0,0 +1,5 @@ +fn main() { + foo(1234) +} + +fn foo(_x: u4) {} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/invalid_dependency_name/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/invalid_dependency_name/Nargo.toml new file mode 100644 index 00000000000..99d184acdcf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/invalid_dependency_name/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "invalid_dependency_name" +type = "bin" +authors = [""] +[dependencies] +bad_name = { path = "../../test_libraries/bad_name" } diff --git a/noir/tooling/nargo_cli/tests/compile_failure/invalid_dependency_name/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/invalid_dependency_name/src/main.nr new file mode 100644 index 00000000000..faf1ba0045a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/invalid_dependency_name/src/main.nr @@ -0,0 +1 @@ +fn main(x: Field) { } diff --git a/noir/tooling/nargo_cli/tests/compile_failure/multiple_contracts/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/multiple_contracts/Nargo.toml new file mode 100644 index 00000000000..d6e4e632f95 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/multiple_contracts/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "multiple_contracts" +type = "contract" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/multiple_contracts/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/multiple_contracts/src/main.nr new file mode 100644 index 00000000000..a6c49d75378 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/multiple_contracts/src/main.nr @@ -0,0 +1,3 @@ +contract Foo {} + +contract Bar {} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/multiple_primary_attributes_fail/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/multiple_primary_attributes_fail/Nargo.toml new file mode 100644 index 00000000000..9aa843d16dc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/multiple_primary_attributes_fail/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "multiple_primary_attributes_fail" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/multiple_primary_attributes_fail/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/multiple_primary_attributes_fail/src/main.nr new file mode 100644 index 00000000000..c8d8b0a1969 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/multiple_primary_attributes_fail/src/main.nr @@ -0,0 +1,6 @@ + +#[oracle(oracleName)] +#[builtin(builtinName)] +fn main(x: Field) -> pub Field { + x + 1 +} \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/mutability_regression_2911/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/mutability_regression_2911/Nargo.toml new file mode 100644 index 00000000000..05db8252f3b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/mutability_regression_2911/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "mutability_regression_2911" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/mutability_regression_2911/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/mutability_regression_2911/src/main.nr new file mode 100644 index 00000000000..a0d53706f97 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/mutability_regression_2911/src/main.nr @@ -0,0 +1,5 @@ +// Expect 'Variable must be mutable to be assigned to' error +fn main() { + let slice : &mut [Field] = &mut []; + slice = &mut (*slice).push_back(1); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/no_impl_from_function/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/no_impl_from_function/Nargo.toml new file mode 100644 index 00000000000..0d243d0029d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/no_impl_from_function/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "no_impl_from_function" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/no_impl_from_function/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/no_impl_from_function/src/main.nr new file mode 100644 index 00000000000..b0c485c2bf5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/no_impl_from_function/src/main.nr @@ -0,0 +1,26 @@ +fn main() { + let array: [Field; 3] = [1, 2, 3]; + assert(foo(array)); + + // Ensure this still works if we have to infer the type of the integer literals + let array = [1, 2, 3]; + assert(foo(array)); +} + +fn foo(x: T) -> bool where T: Eq { + x.eq(x) +} + +trait Eq { + fn eq(self, other: Self) -> bool; +} + +impl Eq for [T; N] where T: Eq { + fn eq(self, other: Self) -> bool { + let mut ret = true; + for i in 0 .. self.len() { + ret &= self[i].eq(other[i]); + } + ret + } +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/no_nested_impl/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/no_nested_impl/Nargo.toml new file mode 100644 index 00000000000..5179c0f6a5c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/no_nested_impl/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "no_nested_impl" +type = "bin" +authors = [""] +compiler_version = ">=0.18.0" + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/no_nested_impl/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/no_nested_impl/src/main.nr new file mode 100644 index 00000000000..916567a7c04 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/no_nested_impl/src/main.nr @@ -0,0 +1,21 @@ +fn main() { + let a: [[[[Field; 2]; 2]; 2]; 2] = [[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]]; + assert(a.eq(a)); +} + +trait Eq { + fn eq(self, other: Self) -> bool; +} + +impl Eq for [T; 2] where T: Eq { + fn eq(self, other: Self) -> bool { + self[0].eq(other[0]) + & self[0].eq(other[0]) + } +} +// Impl for u32 but not Field +impl Eq for u32 { + fn eq(self, other: Self) -> bool { + self == other + } +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/Nargo.toml new file mode 100644 index 00000000000..89f30bb1ba5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "orphaned_trait_impl" +type = "bin" +authors = [""] +[dependencies] +crate1 = { path = "crate1" } +crate2 = { path = "crate2" } diff --git a/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/Prover.toml new file mode 100644 index 00000000000..2c1854573a4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/Prover.toml @@ -0,0 +1,2 @@ +x = 1 +y = 2 diff --git a/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/crate1/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/crate1/Nargo.toml new file mode 100644 index 00000000000..de1eddf2cf0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/crate1/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "crate1" +type = "lib" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/crate1/src/lib.nr b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/crate1/src/lib.nr new file mode 100644 index 00000000000..4d41ff2909a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/crate1/src/lib.nr @@ -0,0 +1,2 @@ +trait MyTrait { +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/crate2/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/crate2/Nargo.toml new file mode 100644 index 00000000000..c86c5edf51a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/crate2/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "crate2" +type = "lib" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/crate2/src/lib.nr b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/crate2/src/lib.nr new file mode 100644 index 00000000000..3cadb6d78cb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/crate2/src/lib.nr @@ -0,0 +1,2 @@ +struct MyStruct { +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/src/main.nr new file mode 100644 index 00000000000..dfd88d8f074 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/orphaned_trait_impl/src/main.nr @@ -0,0 +1,6 @@ +impl dep::crate1::MyTrait for dep::crate2::MyStruct { +} + +fn main(x: Field, y: pub Field) { + assert(x != y); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/overflowing_assignment/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/overflowing_assignment/Nargo.toml new file mode 100644 index 00000000000..612e3e7aaf6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/overflowing_assignment/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "overflowing_assignment" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/overflowing_assignment/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/overflowing_assignment/src/main.nr new file mode 100644 index 00000000000..6b529103ca3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/overflowing_assignment/src/main.nr @@ -0,0 +1,5 @@ +fn main() { + let x:u8 = -1; + let y:u8 = 300; + assert(x != y); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/overlapping_generic_impls/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/overlapping_generic_impls/Nargo.toml new file mode 100644 index 00000000000..a279bfb053a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/overlapping_generic_impls/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "overlapping_generic_impls" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/overlapping_generic_impls/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/overlapping_generic_impls/src/main.nr new file mode 100644 index 00000000000..95e4d53bf93 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/overlapping_generic_impls/src/main.nr @@ -0,0 +1,6 @@ +trait Trait { fn t(self); } + +impl Trait for T { fn t(self){} } +impl Trait for u32 { fn t(self){} } + +fn main() {} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/package_name_empty/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/package_name_empty/Nargo.toml new file mode 100644 index 00000000000..88382674780 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/package_name_empty/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/package_name_empty/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/package_name_empty/src/main.nr new file mode 100644 index 00000000000..faf1ba0045a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/package_name_empty/src/main.nr @@ -0,0 +1 @@ +fn main(x: Field) { } diff --git a/noir/tooling/nargo_cli/tests/compile_failure/package_name_hyphen/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/package_name_hyphen/Nargo.toml new file mode 100644 index 00000000000..4882b83b8bf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/package_name_hyphen/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "hyphenated-name" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/package_name_hyphen/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/package_name_hyphen/src/main.nr new file mode 100644 index 00000000000..faf1ba0045a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/package_name_hyphen/src/main.nr @@ -0,0 +1 @@ +fn main(x: Field) { } diff --git a/noir/tooling/nargo_cli/tests/compile_failure/primary_attribute_struct/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/primary_attribute_struct/Nargo.toml new file mode 100644 index 00000000000..7c5e7f70774 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/primary_attribute_struct/Nargo.toml @@ -0,0 +1,7 @@ + +[package] +name = "primary_attribute_struct" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/primary_attribute_struct/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/primary_attribute_struct/src/main.nr new file mode 100644 index 00000000000..8922ef60091 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/primary_attribute_struct/src/main.nr @@ -0,0 +1,8 @@ +// An primary attribute should not be able to be added to a struct defintion +#[oracle(some_oracle)] +struct SomeStruct{ + x: Field, + y: Field +} + +fn main() {} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/radix_non_constant_length/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/radix_non_constant_length/Nargo.toml new file mode 100644 index 00000000000..349698cc32d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/radix_non_constant_length/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "radix_non_constant_length" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/radix_non_constant_length/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/radix_non_constant_length/Prover.toml new file mode 100644 index 00000000000..f28f2f8cc48 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/radix_non_constant_length/Prover.toml @@ -0,0 +1,2 @@ +x = "5" +y = "10" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/radix_non_constant_length/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/radix_non_constant_length/src/main.nr new file mode 100644 index 00000000000..c6dd68d925c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/radix_non_constant_length/src/main.nr @@ -0,0 +1,4 @@ +fn main(x: Field, y: pub u32) { + let bytes = x.to_be_bytes(y); + assert(bytes[0] == 0); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/slice_access_failure/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/slice_access_failure/Nargo.toml new file mode 100644 index 00000000000..fc159b7efc1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/slice_access_failure/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "slice_access_failure" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/slice_access_failure/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/slice_access_failure/Prover.toml new file mode 100644 index 00000000000..f28f2f8cc48 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/slice_access_failure/Prover.toml @@ -0,0 +1,2 @@ +x = "5" +y = "10" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/slice_access_failure/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/slice_access_failure/src/main.nr new file mode 100644 index 00000000000..6e8b5c7d841 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/slice_access_failure/src/main.nr @@ -0,0 +1,13 @@ +fn main(x: Field, y: pub Field) { + let mut slice = [0; 2]; + if x == y { + slice = slice.push_back(y); + slice = slice.push_back(x); + } else { + slice = slice.push_back(x); + } + // This constraint should fail as the slice length is 3 and the index is 3 + // The right hand side AND case ensures that the circuit inputs have not changed + // and we always hit the else case in the if statement above. + assert((slice[3] == 0) & (slice[2] != y)); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/slice_insert_failure/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/slice_insert_failure/Nargo.toml new file mode 100644 index 00000000000..5134032264c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/slice_insert_failure/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "slice_insert_failure" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/slice_insert_failure/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/slice_insert_failure/Prover.toml new file mode 100644 index 00000000000..f28f2f8cc48 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/slice_insert_failure/Prover.toml @@ -0,0 +1,2 @@ +x = "5" +y = "10" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/slice_insert_failure/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/slice_insert_failure/src/main.nr new file mode 100644 index 00000000000..38892f01e12 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/slice_insert_failure/src/main.nr @@ -0,0 +1,11 @@ +fn main(x: Field, y: pub Field) { + let mut slice = [0; 2]; + if x == y { + slice = slice.push_back(y); + slice = slice.push_back(x); + } else { + slice = slice.push_back(x); + } + + slice = slice.insert(10, 100); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/slice_remove_failure/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/slice_remove_failure/Nargo.toml new file mode 100644 index 00000000000..1e13dfe6483 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/slice_remove_failure/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "slice_remove_failure" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/slice_remove_failure/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/slice_remove_failure/Prover.toml new file mode 100644 index 00000000000..f28f2f8cc48 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/slice_remove_failure/Prover.toml @@ -0,0 +1,2 @@ +x = "5" +y = "10" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/slice_remove_failure/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/slice_remove_failure/src/main.nr new file mode 100644 index 00000000000..f9faa25384b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/slice_remove_failure/src/main.nr @@ -0,0 +1,11 @@ +fn main(x: Field, y: pub Field) { + let mut slice = [0; 2]; + if x == y { + slice = slice.push_back(y); + slice = slice.push_back(x); + } else { + slice = slice.push_back(x); + } + + let (removed_slice, removed_elem) = slice.remove(10); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/trait_incorrect_generic_count/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/trait_incorrect_generic_count/Nargo.toml new file mode 100644 index 00000000000..379e964d70a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/trait_incorrect_generic_count/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "trait_incorrect_generic_count" +type = "bin" +authors = [""] +compiler_version = ">=0.18.0" + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_failure/trait_incorrect_generic_count/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/trait_incorrect_generic_count/src/main.nr new file mode 100644 index 00000000000..d65d55894ea --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/trait_incorrect_generic_count/src/main.nr @@ -0,0 +1,12 @@ +fn main() { + let x: u32 = 0; + x.trait_fn(); +} + +trait Trait { + fn trait_fn(x: T) -> T {} +} + +impl Trait for u32 { + fn trait_fn(x: A) -> A { x } +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/Nargo.toml new file mode 100644 index 00000000000..36db098686f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/Nargo.toml @@ -0,0 +1,2 @@ +[workspace] +members = ["crates/a", "crates/b"] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/a/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/a/Nargo.toml new file mode 100644 index 00000000000..8d0e1aca4a9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/a/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "a" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/a/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/a/Prover.toml new file mode 100644 index 00000000000..b76c88bf536 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/a/Prover.toml @@ -0,0 +1,3 @@ +# Deliberately setting these to fail to prove this is being executed +x = "1" +y = "2" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/a/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/a/src/main.nr new file mode 100644 index 00000000000..cf72627da2e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/a/src/main.nr @@ -0,0 +1,3 @@ +fn main(x: Field, y: pub Field) { + assert(x == y); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/b/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/b/Nargo.toml new file mode 100644 index 00000000000..f316511340f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/b/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "b" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/b/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/b/Prover.toml new file mode 100644 index 00000000000..a0397e89477 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/b/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "0" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/b/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/b/src/main.nr new file mode 100644 index 00000000000..4e1fd3c9035 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/workspace_fail/crates/b/src/main.nr @@ -0,0 +1,3 @@ +fn main(x: Field, y: pub Field) { + assert(x != y); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/Nargo.toml new file mode 100644 index 00000000000..36db098686f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/Nargo.toml @@ -0,0 +1,2 @@ +[workspace] +members = ["crates/a", "crates/b"] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/a/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/a/Prover.toml new file mode 100644 index 00000000000..465ef562de4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/a/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "1" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/a/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/a/src/main.nr new file mode 100644 index 00000000000..550e5034a7b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/a/src/main.nr @@ -0,0 +1,3 @@ +fn main(x : Field, y : pub Field) { + assert(x == y); +} diff --git a/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/b/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/b/Nargo.toml new file mode 100644 index 00000000000..f316511340f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/b/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "b" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/b/Prover.toml b/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/b/Prover.toml new file mode 100644 index 00000000000..a0397e89477 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/b/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "0" diff --git a/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/b/src/main.nr b/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/b/src/main.nr new file mode 100644 index 00000000000..6e170de75fc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_failure/workspace_missing_toml/crates/b/src/main.nr @@ -0,0 +1,3 @@ +fn main(x : Field, y : pub Field) { + assert(x != y); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_contract/contract_with_impl/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_contract/contract_with_impl/Nargo.toml new file mode 100644 index 00000000000..ed5e9ec0910 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_contract/contract_with_impl/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "simple_contract" +type = "contract" +authors = [""] + +[dependencies] + diff --git a/noir/tooling/nargo_cli/tests/compile_success_contract/contract_with_impl/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_contract/contract_with_impl/src/main.nr new file mode 100644 index 00000000000..ddcb5d54d78 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_contract/contract_with_impl/src/main.nr @@ -0,0 +1,7 @@ +contract Foo { + struct T { x: [Field] } + + impl T { + fn t(self){} + } +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_contract/non_entry_point_method/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_contract/non_entry_point_method/Nargo.toml new file mode 100644 index 00000000000..fb1969acc43 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_contract/non_entry_point_method/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "no_entry_points" +type = "contract" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_contract/non_entry_point_method/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_contract/non_entry_point_method/src/main.nr new file mode 100644 index 00000000000..b768653262a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_contract/non_entry_point_method/src/main.nr @@ -0,0 +1,6 @@ +contract Foo { + struct PlaceholderStruct{x : u32 } + + #[contract_library_method] + fn has_mut(_context: &mut PlaceholderStruct) {} +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_contract/simple_contract/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_contract/simple_contract/Nargo.toml new file mode 100644 index 00000000000..ed5e9ec0910 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_contract/simple_contract/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "simple_contract" +type = "contract" +authors = [""] + +[dependencies] + diff --git a/noir/tooling/nargo_cli/tests/compile_success_contract/simple_contract/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_contract/simple_contract/src/main.nr new file mode 100644 index 00000000000..88edd4ac2c3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_contract/simple_contract/src/main.nr @@ -0,0 +1,14 @@ +contract Foo { + fn double(x: Field) -> pub Field { + x * 2 + } + fn triple(x: Field) -> pub Field { + x * 3 + } + internal fn quadruple(x: Field) -> pub Field { + x * 4 + } + open internal fn skibbidy(x: Field) -> pub Field { + x * 5 + } +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/attributes_multiple/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/attributes_multiple/Nargo.toml new file mode 100644 index 00000000000..78b0e5b7b18 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/attributes_multiple/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "attributes_multiple" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/attributes_multiple/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/attributes_multiple/src/main.nr new file mode 100644 index 00000000000..581fb989b4c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/attributes_multiple/src/main.nr @@ -0,0 +1,7 @@ +fn main() { + another_func() +} + +#[aztec(private)] +#[internal] +fn another_func() {} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/attributes_struct/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/attributes_struct/Nargo.toml new file mode 100644 index 00000000000..e05ccb8d925 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/attributes_struct/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "attributes_struct" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/attributes_struct/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/attributes_struct/src/main.nr new file mode 100644 index 00000000000..0bad42aee57 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/attributes_struct/src/main.nr @@ -0,0 +1,8 @@ +#[some_attribute] +#[another_attribute] +struct SomeStruct { + a: Field, + b: Field +} + +fn main() {} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/auto_deref/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/auto_deref/Nargo.toml new file mode 100644 index 00000000000..ed2a51c0fb0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/auto_deref/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "auto_deref" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/auto_deref/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/auto_deref/src/main.nr new file mode 100644 index 00000000000..d69aff7e0d4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/auto_deref/src/main.nr @@ -0,0 +1,7 @@ +fn main() { + let a = &mut &mut &mut [1, 2, 3]; + assert(a[0] == 1); + + a[0] = 4; + assert(a[0] == 4); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_cast/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_cast/Nargo.toml new file mode 100644 index 00000000000..cb5a7f3f2bd --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_cast/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_cast" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_cast/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_cast/src/main.nr new file mode 100644 index 00000000000..3ba29b52982 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_cast/src/main.nr @@ -0,0 +1,49 @@ +// Tests a very simple Brillig function. +// +// The features being tested are cast operations on brillig +fn main() { + bool_casts(); + field_casts(); + uint_casts(); + int_casts(); + mixed_casts(); +} + +unconstrained fn bool_casts() { + assert(false == 0 as bool); + assert(true == 1 as bool); + assert(true == 3 as bool); +} + +unconstrained fn field_casts() { + assert(5 as u8 as Field == 5); + assert(16 as u4 as Field == 0); +} + +unconstrained fn uint_casts() { + let x: u32 = 100; + assert(x as u2 == 0); + assert(x as u4 == 4); + assert(x as u6 == 36); + assert(x as u8 == 100); + assert(x as u64 == 100); + assert(x as u126 == 100); +} + +unconstrained fn int_casts() { + let x: i32 = 100; + assert(x as i2 == 0); + assert(x as i4 == 4); + assert(x as i6 == -28 as i6); + assert(x as i8 == 100); + assert(x as i8 == 100); + assert(x as i8 == 100); +} + +unconstrained fn mixed_casts() { + assert(100 as u32 as i32 as u32 == 100); + assert(13 as u4 as i2 as u32 == 1); + assert(15 as u4 as i2 as u32 == 3); + assert(1 as u8 as bool == true); + assert(true as i8 == 1); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_field_binary_operations/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_field_binary_operations/Nargo.toml new file mode 100644 index 00000000000..fb17e881f4c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_field_binary_operations/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_field_binary_operations" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_field_binary_operations/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_field_binary_operations/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_field_binary_operations/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_field_binary_operations/src/main.nr new file mode 100644 index 00000000000..54f06858846 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_field_binary_operations/src/main.nr @@ -0,0 +1,25 @@ +// Tests arithmetic operations on fields +fn main() { + let x = 4; + let y = 2; + assert((x + y) == add(x, y)); + assert((x - y) == sub(x, y)); + assert((x * y) == mul(x, y)); + assert((x / y) == div(x, y)); +} + +unconstrained fn add(x: Field, y: Field) -> Field { + x + y +} + +unconstrained fn sub(x: Field, y: Field) -> Field { + x - y +} + +unconstrained fn mul(x: Field, y: Field) -> Field { + x * y +} + +unconstrained fn div(x: Field, y: Field) -> Field { + x / y +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_integer_binary_operations/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_integer_binary_operations/Nargo.toml new file mode 100644 index 00000000000..cd550ce0252 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_integer_binary_operations/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_integer_binary_operations" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_integer_binary_operations/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_integer_binary_operations/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_integer_binary_operations/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_integer_binary_operations/src/main.nr new file mode 100644 index 00000000000..a873bcd5dbd --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_integer_binary_operations/src/main.nr @@ -0,0 +1,77 @@ +// Tests arithmetic operations on integers +fn main() { + let x: u32 = 6; + let y: u32 = 2; + + assert((x + y) == add(x, y)); + + assert((x - y) == sub(x, y)); + + assert((x * y) == mul(x, y)); + + assert((x / y) == div(x, y)); + // TODO SSA => ACIR has some issues with i32 ops + assert(check_signed_div(6, 2, 3)); + + assert(eq(1, 2) == false); + assert(eq(1, 1)); + + assert(lt(x, y) == false); + assert(lt(y, x)); + + assert((x & y) == and(x, y)); + assert((x | y) == or(x, y)); + // TODO SSA => ACIR has some issues with xor ops + assert(check_xor(x, y, 4)); + assert((x >> y) == shr(x, y)); + assert((x << y) == shl(x, y)); +} + +unconstrained fn add(x: u32, y: u32) -> u32 { + x + y +} + +unconstrained fn sub(x: u32, y: u32) -> u32 { + x - y +} + +unconstrained fn mul(x: u32, y: u32) -> u32 { + x * y +} + +unconstrained fn div(x: u32, y: u32) -> u32 { + x / y +} + +unconstrained fn check_signed_div(x: i32, y: i32, result: i32) -> bool { + (x / y) == result +} + +unconstrained fn eq(x: u32, y: u32) -> bool { + x == y +} + +unconstrained fn lt(x: u32, y: u32) -> bool { + x < y +} + +unconstrained fn and(x: u32, y: u32) -> u32 { + x & y +} + +unconstrained fn or(x: u32, y: u32) -> u32 { + x | y +} + +unconstrained fn check_xor(x: u32, y: u32, result: u32) -> bool { + (x ^ y) == result +} + +unconstrained fn shr(x: u32, y: u32) -> u32 { + x >> y +} + +unconstrained fn shl(x: u32, y: u32) -> u32 { + x << y +} + diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_modulo/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_modulo/Nargo.toml new file mode 100644 index 00000000000..7bac4a06607 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_modulo/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_modulo" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_modulo/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_modulo/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_modulo/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_modulo/src/main.nr new file mode 100644 index 00000000000..ed0353b101a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_modulo/src/main.nr @@ -0,0 +1,27 @@ +// Tests a very simple program. +// +// The features being tested is modulo operations on brillig +fn main() { + assert(modulo(47, 3) == 2); + assert(modulo(2, 3) == 2); + assert(signed_modulo(5, 3) == 2); + assert(signed_modulo(2, 3) == 2); + + let minus_two: i4 = -2; // 14 + let minus_three: i4 = -3; // 13 + let minus_five: i4 = -5; // 11 + // (5 / -3) * -3 + 2 = -1 * -3 + 2 = 3 + 2 = 5 + assert(signed_modulo(5, minus_three) == 2); + // (-5 / 3) * 3 - 2 = -1 * 3 - 2 = -3 - 2 = -5 + assert(signed_modulo(minus_five, 3) == minus_two); + // (-5 / -3) * -3 - 2 = 1 * -3 - 2 = -3 - 2 = -5 + assert(signed_modulo(minus_five, minus_three) == minus_two); +} + +unconstrained fn modulo(x: u32, y: u32) -> u32 { + x % y +} + +unconstrained fn signed_modulo(x: i4, y: i4) -> i4 { + x % y +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_to_bits/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_to_bits/Nargo.toml new file mode 100644 index 00000000000..a18b769550d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_to_bits/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "brillig_to_bits" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_to_bits/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_to_bits/src/main.nr new file mode 100644 index 00000000000..7ff3d2467b5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/brillig_to_bits/src/main.nr @@ -0,0 +1,23 @@ +use dep::std; + +unconstrained fn main() { + let field = 1000; + let be_bits = field.to_be_bits(16); + let le_bits = field.to_le_bits(16); + + for i in 0..16 { + let x = be_bits[i]; + let y = le_bits[15 - i]; + assert(x == y); + } + + let x = 3; + let be_bits_x = x.to_be_bits(4); + let le_bits_x = x.to_le_bits(4); + + for i in 0..4 { + let be_bit = be_bits_x[i]; + let le_bit = le_bits_x[3 - i]; + assert(be_bit == le_bit); + } +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/closure_explicit_types/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/closure_explicit_types/Nargo.toml new file mode 100644 index 00000000000..86ddff27910 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/closure_explicit_types/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "closure_explicit_types" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/closure_explicit_types/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/closure_explicit_types/src/main.nr new file mode 100644 index 00000000000..eec2b90b5b2 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/closure_explicit_types/src/main.nr @@ -0,0 +1,71 @@ +fn ret_normal_lambda1() -> fn() -> Field { + || 10 +} +// return lamda that captures a thing +fn ret_closure1() -> fn[(Field,)]() -> Field { + let x = 20; + || x + 10 +} +// return lamda that captures two things +fn ret_closure2() -> fn[(Field,Field)]() -> Field { + let x = 20; + let y = 10; + || x + y + 10 +} +// return lamda that captures two things with different types +fn ret_closure3() -> fn[(u32,u64)]() -> u64 { + let x: u32 = 20; + let y: u64 = 10; + || x as u64 + y + 10 +} +// accepts closure that has 1 thing in its env, calls it and returns the result +fn accepts_closure1(f: fn[(Field,)]() -> Field) -> Field { + f() +} +// accepts closure that has 1 thing in its env and returns it +fn accepts_closure2(f: fn[(Field,)]() -> Field) -> fn[(Field,)]() -> Field { + f +} +// accepts closure with different types in the capture group +fn accepts_closure3(f: fn[(u32, u64)]() -> u64) -> u64 { + f() +} +// generic over closure environments +fn add_results(f1: fn[Env1]() -> Field, f2: fn[Env2]() -> Field) -> Field { + f1() + f2() +} +// a *really* generic function +fn map(arr: [T; N], f: fn[Env](T) -> U) -> [U; N] { + let first_elem = f(arr[0]); + let mut ret = [first_elem; N]; + + for i in 1..N { + ret[i] = f(arr[i]); + } + + ret +} + +fn main() { + assert(ret_normal_lambda1()() == 10); + assert(ret_closure1()() == 30); + assert(ret_closure2()() == 40); + assert(ret_closure3()() == 40); + + let x = 50; + assert(accepts_closure1(|| x) == 50); + assert(accepts_closure2(|| x + 10)() == 60); + + let y: u32 = 30; + let z: u64 = 40; + assert(accepts_closure3(|| y as u64 + z) == 70); + + let w = 50; + assert(add_results(|| 100, || x) == 150); + assert(add_results(|| x + 100, || w + x) == 250); + + let arr = [1, 2, 3, 4]; + + assert(map(arr, |n| n + 1) == [2, 3, 4, 5]); + assert(map(arr, |n| n + x) == [51, 52, 53, 54]); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_recursion_regression/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_recursion_regression/Nargo.toml new file mode 100644 index 00000000000..74710a6ac2a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_recursion_regression/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "comptime_recursion_regression" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_recursion_regression/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_recursion_regression/Prover.toml new file mode 100644 index 00000000000..745ce7c2361 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_recursion_regression/Prover.toml @@ -0,0 +1,2 @@ +x = 5 +y = 6 diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_recursion_regression/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_recursion_regression/src/main.nr new file mode 100644 index 00000000000..0461fd9c4cb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_recursion_regression/src/main.nr @@ -0,0 +1,4 @@ +fn main(x: Field, y: Field) { + let flag = (x == 1) | (y == 2); + assert(flag | false == flag); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_sort/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_sort/Nargo.toml new file mode 100644 index 00000000000..7d215a22496 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_sort/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "comptime_sort" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_sort/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_sort/src/main.nr new file mode 100644 index 00000000000..a24a6ebaba6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/comptime_sort/src/main.nr @@ -0,0 +1,7 @@ +fn main() { + let unsorted: [u8; 3] = [3, 1, 2]; + let sorted = unsorted.sort(); + assert(sorted[0] == 1); + assert(sorted[1] == 2); + assert(sorted[2] == 3); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_547/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_547/Nargo.toml new file mode 100644 index 00000000000..1a62c5af20c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_547/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "conditional_regression_547" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_547/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_547/Prover.toml new file mode 100644 index 00000000000..3d2b4b14efe --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_547/Prover.toml @@ -0,0 +1 @@ +x = 1 \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_547/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_547/src/main.nr new file mode 100644 index 00000000000..e47d23516a5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_547/src/main.nr @@ -0,0 +1,16 @@ +fn main(x: Field) -> pub Field { + // Regression test for issue #547 + // Warning: it must be kept at the start of main + let arr: [u8; 2] = [1, 2]; + if arr[0] != arr[1] { + for i in 0..1 { + assert(i != 2); + } + } + // Regression for predicate simplification + x + safe_inverse(0) +} + +fn safe_inverse(n: Field) -> Field { + if n == 0 { 0 } else { 1 / n } +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_579/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_579/Nargo.toml new file mode 100644 index 00000000000..b440cb26e46 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_579/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "conditional_regression_579" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_579/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_579/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_579/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_579/src/main.nr new file mode 100644 index 00000000000..a479a7a6fbf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_579/src/main.nr @@ -0,0 +1,28 @@ +fn main() { + //Regression for Issue #579 + let result1_true = test(true); + assert(result1_true.array_param[0] == 1); + let result1_false = test(false); + assert(result1_false.array_param[0] == 0); +} + +struct MyStruct579 { + array_param: [u32; 2] +} + +impl MyStruct579 { + fn new(array_param: [u32; 2]) -> MyStruct579 { + MyStruct579 { + array_param: array_param + } + } +} + +fn test(flag: bool) -> MyStruct579 { + let mut my_struct = MyStruct579::new([0; 2]); + + if flag == true { + my_struct= MyStruct579::new([1; 2]); + } + my_struct +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_to_bits/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_to_bits/Nargo.toml new file mode 100644 index 00000000000..f2b13c4d547 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_to_bits/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "conditional_regression_to_bits" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_to_bits/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_to_bits/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_to_bits/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_to_bits/src/main.nr new file mode 100644 index 00000000000..5446cfbb1e4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/conditional_regression_to_bits/src/main.nr @@ -0,0 +1,28 @@ +use dep::std; + +fn main() { + //Regression for to_le_bits() constant evaluation + // binary array representation of u8 1 + let arr: [u8; 2] = [1, 2]; + let as_bits_hardcode_1 = [1, 0]; + let mut c1 = 0; + for i in 0..2 { + let mut as_bits = (arr[i] as Field).to_le_bits(2); + c1 = c1 + as_bits[0] as Field; + + if i == 0 { + assert(arr[i] == 1); // 1 + for k in 0..2 { + assert(as_bits_hardcode_1[k] == as_bits[k]); + } + } + if i == 1 { + assert(arr[i] == 2); //2 + for k in 0..2 { + assert(as_bits_hardcode_1[k] != as_bits[k]); + } + } + } + assert(c1 == 1); +} + diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/ec_baby_jubjub/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/ec_baby_jubjub/Nargo.toml new file mode 100644 index 00000000000..fdb0df17112 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/ec_baby_jubjub/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "ec_baby_jubjub" +description = "Baby Jubjub sanity checks" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/ec_baby_jubjub/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/ec_baby_jubjub/src/main.nr new file mode 100644 index 00000000000..e7f32999454 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/ec_baby_jubjub/src/main.nr @@ -0,0 +1,174 @@ +// Tests may be checked against https://github.com/cfrg/draft-irtf-cfrg-hash-to-curve/tree/main/poc +use dep::std::ec::tecurve::affine::Curve as AffineCurve; +use dep::std::ec::tecurve::affine::Point as Gaffine; +use dep::std::ec::tecurve::curvegroup::Curve; +use dep::std::ec::tecurve::curvegroup::Point as G; + +use dep::std::ec::swcurve::affine::Point as SWGaffine; +use dep::std::ec::swcurve::curvegroup::Point as SWG; + +use dep::std::ec::montcurve::affine::Point as MGaffine; +use dep::std::ec::montcurve::curvegroup::Point as MG; + +fn main() { + // This test only makes sense if Field is the right prime field. + if 21888242871839275222246405745257275088548364400416034343698204186575808495617 == 0 { + // Define Baby Jubjub (ERC-2494) parameters in affine representation + let bjj_affine = AffineCurve::new(168700, + 168696, + Gaffine::new(995203441582195749578291179787384436505546430278305826713579947235728471134, + 5472060717959818805561601436314318772137091100104008585924551046643952123905)); + // Test addition + let p1_affine = Gaffine::new(17777552123799933955779906779655732241715742912184938656739573121738514868268, + 2626589144620713026669568689430873010625803728049924121243784502389097019475); + let p2_affine = Gaffine::new(16540640123574156134436876038791482806971768689494387082833631921987005038935, + 20819045374670962167435360035096875258406992893633759881276124905556507972311); + + let p3_affine = bjj_affine.add(p1_affine, p2_affine); + assert(p3_affine.eq(Gaffine::new(7916061937171219682591368294088513039687205273691143098332585753343424131937, + 14035240266687799601661095864649209771790948434046947201833777492504781204499))); + // Test scalar multiplication + let p4_affine = bjj_affine.mul(2, p1_affine); + assert(p4_affine.eq(Gaffine::new(6890855772600357754907169075114257697580319025794532037257385534741338397365, + 4338620300185947561074059802482547481416142213883829469920100239455078257889))); + assert(p4_affine.eq(bjj_affine.bit_mul([0, 1], p1_affine))); + // Test subtraction + let p5_affine = bjj_affine.subtract(p3_affine, p3_affine); + assert(p5_affine.eq(Gaffine::zero())); + // Check that these points are on the curve + assert(bjj_affine.contains(bjj_affine.gen) + & bjj_affine.contains(p1_affine) + & bjj_affine.contains(p2_affine) + & bjj_affine.contains(p3_affine) + & bjj_affine.contains(p4_affine) + & bjj_affine.contains(p5_affine)); + // Test CurveGroup equivalents + let bjj = bjj_affine.into_group(); // Baby Jubjub + let p1 = p1_affine.into_group(); + let p2 = p2_affine.into_group(); + let p3 = p3_affine.into_group(); + let p4 = p4_affine.into_group(); + let p5 = p5_affine.into_group(); + // Test addition + assert(p3.eq(bjj.add(p1, p2))); + // Test scalar multiplication + assert(p4.eq(bjj.mul(2, p1))); + assert(p4.eq(bjj.bit_mul([0, 1], p1))); + // Test subtraction + assert(G::zero().eq(bjj.subtract(p3, p3))); + assert(p5.eq(G::zero())); + // Check that these points are on the curve + assert(bjj.contains(bjj.gen) + & bjj.contains(p1) + & bjj.contains(p2) + & bjj.contains(p3) + & bjj.contains(p4) + & bjj.contains(p5)); + // Test SWCurve equivalents of the above + // First the affine representation + let bjj_swcurve_affine = bjj_affine.into_swcurve(); + + let p1_swcurve_affine = bjj_affine.map_into_swcurve(p1_affine); + let p2_swcurve_affine = bjj_affine.map_into_swcurve(p2_affine); + let p3_swcurve_affine = bjj_affine.map_into_swcurve(p3_affine); + let p4_swcurve_affine = bjj_affine.map_into_swcurve(p4_affine); + let p5_swcurve_affine = bjj_affine.map_into_swcurve(p5_affine); + // Addition + assert(p3_swcurve_affine.eq(bjj_swcurve_affine.add(p1_swcurve_affine, p2_swcurve_affine))); + // Doubling + assert(p4_swcurve_affine.eq(bjj_swcurve_affine.mul(2, p1_swcurve_affine))); + assert(p4_swcurve_affine.eq(bjj_swcurve_affine.bit_mul([0, 1], p1_swcurve_affine))); + // Subtraction + assert(SWGaffine::zero().eq(bjj_swcurve_affine.subtract(p3_swcurve_affine, p3_swcurve_affine))); + assert(p5_swcurve_affine.eq(SWGaffine::zero())); + // Check that these points are on the curve + assert(bjj_swcurve_affine.contains(bjj_swcurve_affine.gen) + & bjj_swcurve_affine.contains(p1_swcurve_affine) + & bjj_swcurve_affine.contains(p2_swcurve_affine) + & bjj_swcurve_affine.contains(p3_swcurve_affine) + & bjj_swcurve_affine.contains(p4_swcurve_affine) + & bjj_swcurve_affine.contains(p5_swcurve_affine)); + // Then the CurveGroup representation + let bjj_swcurve = bjj.into_swcurve(); + + let p1_swcurve = bjj.map_into_swcurve(p1); + let p2_swcurve = bjj.map_into_swcurve(p2); + let p3_swcurve = bjj.map_into_swcurve(p3); + let p4_swcurve = bjj.map_into_swcurve(p4); + let p5_swcurve = bjj.map_into_swcurve(p5); + // Addition + assert(p3_swcurve.eq(bjj_swcurve.add(p1_swcurve, p2_swcurve))); + // Doubling + assert(p4_swcurve.eq(bjj_swcurve.mul(2, p1_swcurve))); + assert(p4_swcurve.eq(bjj_swcurve.bit_mul([0, 1], p1_swcurve))); + // Subtraction + assert(SWG::zero().eq(bjj_swcurve.subtract(p3_swcurve, p3_swcurve))); + assert(p5_swcurve.eq(SWG::zero())); + // Check that these points are on the curve + assert(bjj_swcurve.contains(bjj_swcurve.gen) + & bjj_swcurve.contains(p1_swcurve) + & bjj_swcurve.contains(p2_swcurve) + & bjj_swcurve.contains(p3_swcurve) + & bjj_swcurve.contains(p4_swcurve) + & bjj_swcurve.contains(p5_swcurve)); + // Test MontCurve conversions + // First the affine representation + let bjj_montcurve_affine = bjj_affine.into_montcurve(); + + let p1_montcurve_affine = p1_affine.into_montcurve(); + let p2_montcurve_affine = p2_affine.into_montcurve(); + let p3_montcurve_affine = p3_affine.into_montcurve(); + let p4_montcurve_affine = p4_affine.into_montcurve(); + let p5_montcurve_affine = p5_affine.into_montcurve(); + // Addition + assert(p3_montcurve_affine.eq(bjj_montcurve_affine.add(p1_montcurve_affine, p2_montcurve_affine))); + // Doubling + assert(p4_montcurve_affine.eq(bjj_montcurve_affine.mul(2, p1_montcurve_affine))); + assert(p4_montcurve_affine.eq(bjj_montcurve_affine.bit_mul([0, 1], p1_montcurve_affine))); + // Subtraction + assert(MGaffine::zero().eq(bjj_montcurve_affine.subtract(p3_montcurve_affine, p3_montcurve_affine))); + assert(p5_montcurve_affine.eq(MGaffine::zero())); + // Check that these points are on the curve + assert(bjj_montcurve_affine.contains(bjj_montcurve_affine.gen) + & bjj_montcurve_affine.contains(p1_montcurve_affine) + & bjj_montcurve_affine.contains(p2_montcurve_affine) + & bjj_montcurve_affine.contains(p3_montcurve_affine) + & bjj_montcurve_affine.contains(p4_montcurve_affine) + & bjj_montcurve_affine.contains(p5_montcurve_affine)); + // Then the CurveGroup representation + let bjj_montcurve = bjj.into_montcurve(); + + let p1_montcurve = p1_montcurve_affine.into_group(); + let p2_montcurve = p2_montcurve_affine.into_group(); + let p3_montcurve = p3_montcurve_affine.into_group(); + let p4_montcurve = p4_montcurve_affine.into_group(); + let p5_montcurve = p5_montcurve_affine.into_group(); + // Addition + assert(p3_montcurve.eq(bjj_montcurve.add(p1_montcurve, p2_montcurve))); + // Doubling + assert(p4_montcurve.eq(bjj_montcurve.mul(2, p1_montcurve))); + assert(p4_montcurve.eq(bjj_montcurve.bit_mul([0, 1], p1_montcurve))); + // Subtraction + assert(MG::zero().eq(bjj_montcurve.subtract(p3_montcurve, p3_montcurve))); + assert(p5_montcurve.eq(MG::zero())); + // Check that these points are on the curve + assert(bjj_montcurve.contains(bjj_montcurve.gen) + & bjj_montcurve.contains(p1_montcurve) + & bjj_montcurve.contains(p2_montcurve) + & bjj_montcurve.contains(p3_montcurve) + & bjj_montcurve.contains(p4_montcurve) + & bjj_montcurve.contains(p5_montcurve)); + // Elligator 2 map-to-curve + let ell2_pt_map = bjj_affine.elligator2_map(27); + + assert(ell2_pt_map.eq(MGaffine::new(7972459279704486422145701269802978968072470631857513331988813812334797879121, + 8142420778878030219043334189293412482212146646099536952861607542822144507872).into_tecurve())); + // SWU map-to-curve + let swu_pt_map = bjj_affine.swu_map(5, 27); + + assert(swu_pt_map.eq(bjj_affine.map_from_swcurve( + SWGaffine::new(2162719247815120009132293839392097468339661471129795280520343931405114293888, + 5341392251743377373758788728206293080122949448990104760111875914082289313973) + ))); + } +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/generators/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/generators/Nargo.toml new file mode 100644 index 00000000000..58d8a993e16 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/generators/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "generators" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/generators/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/generators/src/main.nr new file mode 100644 index 00000000000..20bdedee50f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/generators/src/main.nr @@ -0,0 +1,56 @@ +// TODO? +// the syntax for these return types is very difficult to get right :/ +// for arguments this can be handled with a generic Env (or with Fn traits when we add them) +// but for return types neither fo these will help, you need to type out the exact type +fn make_counter() -> fn[(&mut Field,)]() -> Field { + let mut x = &mut 0; + + || { + *x = *x + 1; + *x + } +} + +fn fibonacci_generator() -> fn[(&mut Field, &mut Field)]() -> Field { + let mut x = &mut 1; + let mut y = &mut 2; + + || { + let old_x = *x; + let old_y = *y; + + *y = *x + *y; + *x = old_y; + + old_x + } +} +// we'll be able to un-hardcode the array length if we have the ::<> syntax proposed in https://github.com/noir-lang/noir/issues/2458 +fn get_some(generator: fn[Env]() -> Field) -> [Field; 5] { + [0, 0, 0, 0, 0].map(|_| generator()) +} + +fn test_fib() { + let fib = fibonacci_generator(); + + assert(fib() == 1); + assert(fib() == 2); + assert(fib() == 3); + assert(fib() == 5); + + assert(get_some(fib) == [8, 13, 21, 34, 55]); +} + +fn test_counter() { + let counter = make_counter(); + assert(counter() == 1); + assert(counter() == 2); + assert(counter() == 3); + + assert(get_some(counter) == [4, 5, 6, 7, 8]); +} + +fn main() { + test_fib(); + test_counter(); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/higher_order_fn_selector/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/higher_order_fn_selector/Nargo.toml new file mode 100644 index 00000000000..968fc5300e6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/higher_order_fn_selector/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "higher_order_fn_selector" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/higher_order_fn_selector/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/higher_order_fn_selector/src/main.nr new file mode 100644 index 00000000000..99093c581b5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/higher_order_fn_selector/src/main.nr @@ -0,0 +1,30 @@ +fn g(x: &mut Field) -> () { + *x *= 2; +} + +fn h(x: &mut Field) -> () { + *x *= 3; +} + +fn selector(flag: &mut bool) -> fn(&mut Field) -> () { + let my_func = if *flag { g } else { h }; + // Flip the flag for the next function call + *flag = !(*flag); + my_func +} + +fn main() { + let mut flag: bool = true; + + let mut x: Field = 100; + let returned_func = selector(&mut flag); + returned_func(&mut x); + + assert(x == 200); + + let mut y: Field = 100; + let returned_func2 = selector(&mut flag); + returned_func2(&mut y); + + assert(y == 300); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/impl_with_where_clause/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/impl_with_where_clause/Nargo.toml new file mode 100644 index 00000000000..ef9bdce2640 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/impl_with_where_clause/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "impl_with_where_clause" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/impl_with_where_clause/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/impl_with_where_clause/src/main.nr new file mode 100644 index 00000000000..de3078be8ba --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/impl_with_where_clause/src/main.nr @@ -0,0 +1,27 @@ +fn main() { + let array: [Field; 3] = [1, 2, 3]; + assert(array.eq(array)); + // Ensure this still works if we have to infer the type of the integer literals + let array = [1, 2, 3]; + assert(array.eq(array)); +} + +trait Eq { + fn eq(self, other: Self) -> bool; +} + +impl Eq for [T; 3] where T: Eq { + fn eq(self, other: Self) -> bool { + let mut ret = true; + for i in 0 .. self.len() { + ret &= self[i].eq(other[i]); + } + ret + } +} + +impl Eq for Field { + fn eq(self, other: Field) -> bool { + self == other + } +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/inner_outer_cl/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/inner_outer_cl/Nargo.toml new file mode 100644 index 00000000000..073456fa7f6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/inner_outer_cl/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "inner_outer_cl" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/inner_outer_cl/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/inner_outer_cl/src/main.nr new file mode 100644 index 00000000000..ce847b56b93 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/inner_outer_cl/src/main.nr @@ -0,0 +1,12 @@ +fn main() { + let z1 = 0; + let z2 = 1; + let cl_outer = |x| { + let cl_inner = |y| { + x + y + z2 + }; + cl_inner(1) + z1 + }; + let result = cl_outer(1); + assert(result == 3); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/instruction_deduplication/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/instruction_deduplication/Nargo.toml new file mode 100644 index 00000000000..4786a87ff65 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/instruction_deduplication/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "instruction_deduplication" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/instruction_deduplication/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/instruction_deduplication/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/instruction_deduplication/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/instruction_deduplication/src/main.nr new file mode 100644 index 00000000000..43c0a382185 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/instruction_deduplication/src/main.nr @@ -0,0 +1,5 @@ +fn main(x: Field) { + // This is a regression test for #2450. + // The compiler should recognize that the `(x as u32)` instructions are duplicates and so have the same output. + assert(x as u32 == x as u32); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/intrinsic_die/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/intrinsic_die/Nargo.toml new file mode 100644 index 00000000000..539c536b944 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/intrinsic_die/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "intrinsic_die" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/intrinsic_die/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/intrinsic_die/src/main.nr new file mode 100644 index 00000000000..88f7a3634c1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/intrinsic_die/src/main.nr @@ -0,0 +1,8 @@ +use dep::std; +// This test checks that we perform dead-instruction-elimination on intrinsic functions. +fn main(x: Field) { + let bytes = x.to_be_bytes(32); + + let hash = std::hash::pedersen_commitment([x]); + let _p1 = std::scalar_mul::fixed_base_embedded_curve(x, 0); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/let_stmt/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/let_stmt/Nargo.toml new file mode 100644 index 00000000000..def6e33b39a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/let_stmt/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "let_stmt" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/let_stmt/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/let_stmt/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/let_stmt/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/let_stmt/src/main.nr new file mode 100644 index 00000000000..6c63ecd6e12 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/let_stmt/src/main.nr @@ -0,0 +1,11 @@ +struct Foo { + a: u64, +} + +fn main() { + let _ = 42; + let _ = 42; + + let Foo { a: _ } = Foo { a: 42 }; + let _regression_2786 = [Foo { a: 1 }; 8]; +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/main_return/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/main_return/Nargo.toml new file mode 100644 index 00000000000..793ae612730 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/main_return/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "main_return" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/main_return/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/main_return/Prover.toml new file mode 100644 index 00000000000..63e9878811a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/main_return/Prover.toml @@ -0,0 +1 @@ +x = "8" diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/main_return/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/main_return/src/main.nr new file mode 100644 index 00000000000..06347eb0919 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/main_return/src/main.nr @@ -0,0 +1,3 @@ +fn main(x: pub Field) -> pub Field { + x +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/numeric_generics/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/numeric_generics/Nargo.toml new file mode 100644 index 00000000000..835c945c3b8 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/numeric_generics/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "numeric_generics" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/numeric_generics/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/numeric_generics/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/numeric_generics/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/numeric_generics/src/main.nr new file mode 100644 index 00000000000..1e03a382fed --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/numeric_generics/src/main.nr @@ -0,0 +1,39 @@ +fn main() { + let a = id([1, 2]); + let b = id([1, 2, 3]); + + let itWorks1 = MyStruct { data: a }; + assert(itWorks1.data[1] == 2); + let itWorks2 = MyStruct { data: b }; + assert(itWorks2.data[1] == 2); + + let c = [1, 2]; + let itAlsoWorks = MyStruct { data: c }; + assert(itAlsoWorks.data[1] == 2); + + assert(foo(itWorks2).data[0] == itWorks2.data[0] + 1); +} + +fn id(x: [Field; I]) -> [Field; I] { + x +} + +struct MyStruct { + data: [Field; S], +} + +impl MyStruct { + fn insert(mut self: Self, index: Field, elem: Field) -> Self { + // Regression test for numeric generics on impls + assert(index as u64 < S as u64); + + self.data[index] = elem; + self + } +} + +fn foo(mut s: MyStruct<2+1>) -> MyStruct<10/2-2> { + s.data[0] = s.data[0] + 1; + s +} + diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/option/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/option/Nargo.toml new file mode 100644 index 00000000000..d9bec3c3096 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/option/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "option" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/option/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/option/src/main.nr new file mode 100644 index 00000000000..1f879bd375f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/option/src/main.nr @@ -0,0 +1,63 @@ +use dep::std::option::Option; + +fn main() { + let ten = 10; // giving this a name, to ensure that the Option functions work with closures + let none = Option::none(); + let some = Option::some(3); + + assert(none.is_none()); + assert(some.is_some()); + + assert(some.unwrap() == 3); + + assert(none.unwrap_or(2) == 2); + assert(some.unwrap_or(2) == 3); + + assert(none.unwrap_or_else(|| 5) == 5); + assert(some.unwrap_or_else(|| 5) == 3); + assert(none.unwrap_or_else(|| ten + 5) == 15); + assert(some.unwrap_or_else(|| ten + 5) == 3); + + assert(none.map(|x| x * 2).is_none()); + assert(some.map(|x| x * 2).unwrap() == 6); + assert(some.map(|x| x * ten).unwrap() == 30); + + assert(none.map_or(0, |x| x * 2) == 0); + assert(some.map_or(0, |x| x * 2) == 6); + assert(none.map_or(0, |x| x * ten) == 0); + assert(some.map_or(0, |x| x * ten) == 30); + + assert(none.map_or_else(|| 0, |x| x * 2) == 0); + assert(some.map_or_else(|| 0, |x| x * 2) == 6); + assert(none.map_or_else(|| 0, |x| x * ten) == 0); + assert(some.map_or_else(|| ten, |x| x * 2) == 6); + + assert(none.and(none).is_none()); + assert(none.and(some).is_none()); + assert(some.and(none).is_none()); + assert(some.and(some).is_some()); + + let add1_u64 = |value: Field| Option::some(value as u64 + 1); + + assert(none.and_then(|_value| Option::none()).is_none()); + assert(none.and_then(add1_u64).is_none()); + assert(some.and_then(|_value| Option::none()).is_none()); + assert(some.and_then(add1_u64).unwrap() == 4); + assert(some.and_then(|x| Option::some(x + ten)).unwrap() == 13); + + assert(none.or(none).is_none()); + assert(none.or(some).is_some()); + assert(some.or(none).is_some()); + assert(some.or(some).is_some()); + + assert(none.or_else(|| Option::none()).is_none()); + assert(none.or_else(|| Option::some(5)).is_some()); + assert(some.or_else(|| Option::none()).is_some()); + assert(some.or_else(|| Option::some(5)).is_some()); + assert(some.or_else(|| Option::some(ten)).is_some()); + + assert(none.xor(none).is_none()); + assert(none.xor(some).is_some()); + assert(some.xor(none).is_some()); + assert(some.xor(some).is_none()); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/references_aliasing/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/references_aliasing/Nargo.toml new file mode 100644 index 00000000000..0310e55ed7e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/references_aliasing/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "references_aliasing" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/references_aliasing/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/references_aliasing/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/references_aliasing/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/references_aliasing/src/main.nr new file mode 100644 index 00000000000..0d96bc2a734 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/references_aliasing/src/main.nr @@ -0,0 +1,28 @@ +fn main() { + let mut x = 100; + let mut xref = &mut x; + increment(xref); + assert(*xref == 101); + + regression_2445(); +} + +fn increment(mut r: &mut Field) { + *r = *r + 1; +} +// If aliasing within arrays and constant folding within the mem2reg pass aren't +// handled, we'll fail to optimize out all the references in this function. +fn regression_2445() { + let mut var = 0; + let ref = &mut &mut var; + + let mut array = [ref, ref]; + + **array[0] = 1; + **array[1] = 2; + + assert(var == 2); + assert(**ref == 2); + assert(**array[0] == 2); + assert(**array[1] == 2); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/regression_2099/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/regression_2099/Nargo.toml new file mode 100644 index 00000000000..6b9f9a24038 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/regression_2099/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "regression_2099" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/regression_2099/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/regression_2099/src/main.nr new file mode 100644 index 00000000000..185a6426cb3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/regression_2099/src/main.nr @@ -0,0 +1,34 @@ +use dep::std::ec::tecurve::affine::Curve as AffineCurve; +use dep::std::ec::tecurve::affine::Point as Gaffine; +use dep::std::ec::tecurve::curvegroup::Curve; +use dep::std::ec::tecurve::curvegroup::Point as G; + +use dep::std::ec::swcurve::affine::Point as SWGaffine; +use dep::std::ec::swcurve::curvegroup::Point as SWG; + +use dep::std::ec::montcurve::affine::Point as MGaffine; +use dep::std::ec::montcurve::curvegroup::Point as MG; + +fn main() { + // Define Baby Jubjub (ERC-2494) parameters in affine representation + let bjj_affine = AffineCurve::new(168700, + 168696, + Gaffine::new(995203441582195749578291179787384436505546430278305826713579947235728471134, + 5472060717959818805561601436314318772137091100104008585924551046643952123905)); + // Test addition + let p1_affine = Gaffine::new(17777552123799933955779906779655732241715742912184938656739573121738514868268, + 2626589144620713026669568689430873010625803728049924121243784502389097019475); + let p2_affine = Gaffine::new(16540640123574156134436876038791482806971768689494387082833631921987005038935, + 20819045374670962167435360035096875258406992893633759881276124905556507972311); + let _p3_affine = bjj_affine.add(p1_affine, p2_affine); + // Test SWCurve equivalents of the above + // First the affine representation + let bjj_swcurve_affine = bjj_affine.into_swcurve(); + + let p1_swcurve_affine = bjj_affine.map_into_swcurve(p1_affine); + let p2_swcurve_affine = bjj_affine.map_into_swcurve(p2_affine); + + let _p3_swcurve_affine_from_add = bjj_swcurve_affine.add(p1_swcurve_affine, p2_swcurve_affine); + // Check that these points are on the curve + assert(bjj_swcurve_affine.contains(p1_swcurve_affine)); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/ret_fn_ret_cl/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/ret_fn_ret_cl/Nargo.toml new file mode 100644 index 00000000000..4f778574417 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/ret_fn_ret_cl/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "ret_fn_ret_cl" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/ret_fn_ret_cl/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/ret_fn_ret_cl/Prover.toml new file mode 100644 index 00000000000..3a627b9188b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/ret_fn_ret_cl/Prover.toml @@ -0,0 +1 @@ +x = "10" diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/ret_fn_ret_cl/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/ret_fn_ret_cl/src/main.nr new file mode 100644 index 00000000000..89083b076b6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/ret_fn_ret_cl/src/main.nr @@ -0,0 +1,33 @@ +fn f(x: Field) -> Field { + x + 1 +} + +fn ret_fn() -> fn(Field) -> Field { + f +} +// TODO: in the advanced implicitly generic function with closures branch +// which would support higher-order functions in a better way +// support returning closures: +// +// fn ret_closure() -> fn(Field) -> Field { +// let y = 1; +// let inner_closure = |z| -> Field{ +// z + y +// }; +// inner_closure +// } +fn ret_lambda() -> fn(Field) -> Field { + let cl = |z: Field| -> Field { + z + 1 + }; + cl +} + +fn main(x: Field) { + let result_fn = ret_fn(); + assert(result_fn(x) == x + 1); + // let result_closure = ret_closure(); + // assert(result_closure(x) == x + 1); + let result_lambda = ret_lambda(); + assert(result_lambda(x) == x + 1); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/simple_array_param/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_array_param/Nargo.toml new file mode 100644 index 00000000000..4f1811cc669 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_array_param/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "simple_array_param" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/simple_array_param/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_array_param/Prover.toml new file mode 100644 index 00000000000..66f0b9ccc1c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_array_param/Prover.toml @@ -0,0 +1 @@ +xs = [0, 1] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/simple_array_param/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_array_param/src/main.nr new file mode 100644 index 00000000000..6dd7c34dab5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_array_param/src/main.nr @@ -0,0 +1,6 @@ +// This program tests: +// - the allocation of virtual arrays for array params to main +// - load instructions for such arrays +fn main(xs: [Field; 2]) -> pub Field { + xs[1] +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/simple_program_no_body/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_program_no_body/Nargo.toml new file mode 100644 index 00000000000..13178336eea --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_program_no_body/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "simple_program_no_body" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/simple_program_no_body/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_program_no_body/Prover.toml new file mode 100644 index 00000000000..c2b2ccfd9f1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_program_no_body/Prover.toml @@ -0,0 +1,2 @@ +_x = "3" +_y = "4" diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/simple_program_no_body/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_program_no_body/src/main.nr new file mode 100644 index 00000000000..21719018f3f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_program_no_body/src/main.nr @@ -0,0 +1,9 @@ +// Tests a very simple program. +// +// The features being tested are: +// - Abi generation of private and public +// main parameters. +// +// This program will never fail since there are +// no assertions being applied. +fn main(_x: Field, _y: pub Field) {} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/simple_range/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_range/Nargo.toml new file mode 100644 index 00000000000..1db9099be7e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_range/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "simple_range" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/simple_range/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_range/Prover.toml new file mode 100644 index 00000000000..07890234a19 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_range/Prover.toml @@ -0,0 +1 @@ +x = "3" diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/simple_range/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_range/src/main.nr new file mode 100644 index 00000000000..3f595cfd817 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/simple_range/src/main.nr @@ -0,0 +1,6 @@ +// Tests a very simple program. +// +// The features being tested is casting to an integer +fn main(x: Field) { + let _z = x as u32; +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/specialization/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/specialization/Nargo.toml new file mode 100644 index 00000000000..aaccd85a444 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/specialization/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "specialization" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/specialization/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/specialization/src/main.nr new file mode 100644 index 00000000000..9cd32e0f1eb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/specialization/src/main.nr @@ -0,0 +1,15 @@ +struct Foo {} + +impl Foo { + fn foo(_self: Self) -> Field { 1 } +} + +impl Foo { + fn foo(_self: Self) -> Field { 2 } +} + +fn main() { + let f1: Foo = Foo {}; + let f2: Foo = Foo {}; + assert(f1.foo() + f2.foo() == 3); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/str_as_bytes/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/str_as_bytes/Nargo.toml new file mode 100644 index 00000000000..6b9bbd9b65f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/str_as_bytes/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "str_as_bytes" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/str_as_bytes/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/str_as_bytes/src/main.nr new file mode 100644 index 00000000000..6fdd926ce7f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/str_as_bytes/src/main.nr @@ -0,0 +1,18 @@ +use dep::std; +fn main() { + let a = "hello"; + let b = a.as_bytes(); + assert(b[0] == 104); + assert(b[1] == 101); + assert(b[2] == 108); + assert(b[3] == 108); + assert(b[4] == 111); + assert(b.len() == 5); + let mut c = a.as_bytes_vec(); + assert(c.get(0) == 104); + assert(c.get(1) == 101); + assert(c.get(2) == 108); + assert(c.get(3) == 108); + assert(c.get(4) == 111); + assert(c.len() == 5); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/to_bits/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/to_bits/Nargo.toml new file mode 100644 index 00000000000..ef47f3b4ba1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/to_bits/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "to_bits" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/to_bits/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/to_bits/src/main.nr new file mode 100644 index 00000000000..84ace83903a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/to_bits/src/main.nr @@ -0,0 +1,21 @@ +fn main() { + let field = 1000; + let be_bits = field.to_be_bits(16); + let le_bits = field.to_le_bits(16); + + for i in 0..16 { + let x = be_bits[i]; + let y = le_bits[15 - i]; + assert(x == y); + } + + let x = 3; + let be_bits_x = x.to_be_bits(4); + let le_bits_x = x.to_le_bits(4); + + for i in 0..4 { + let be_bit = be_bits_x[i]; + let le_bit = le_bits_x[3 - i]; + assert(be_bit == le_bit); + } +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_allowed_item_name_matches/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_allowed_item_name_matches/Nargo.toml new file mode 100644 index 00000000000..bd31596297a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_allowed_item_name_matches/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "trait_allowed_item_name_matches" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_allowed_item_name_matches/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_allowed_item_name_matches/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_allowed_item_name_matches/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_allowed_item_name_matches/src/main.nr new file mode 100644 index 00000000000..44cad58c2a6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_allowed_item_name_matches/src/main.nr @@ -0,0 +1,25 @@ +trait Trait1 { + // types and consts with the same name are allowed + type Tralala; + let Tralala: u32; +} + +trait Trait2 { + // consts and types with the same name are allowed + let Tralala: u32; + type Tralala; +} + +trait Trait3 { + // types and functions with the same name are allowed + type Tralala; + fn Tralala(); +} + +trait Trait4 { + // functions and types with the same name are allowed + fn Tralala(); + type Tralala; +} + +fn main() {} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_associated_member_names_clashes/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_associated_member_names_clashes/Nargo.toml new file mode 100644 index 00000000000..79d78c1aa51 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_associated_member_names_clashes/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "trait_associated_member_names_clashes" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_associated_member_names_clashes/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_associated_member_names_clashes/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_associated_member_names_clashes/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_associated_member_names_clashes/src/main.nr new file mode 100644 index 00000000000..412a75010f6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_associated_member_names_clashes/src/main.nr @@ -0,0 +1,36 @@ +trait Trait1 { + fn tralala() -> Field; +} + +trait Trait2 { + fn tralala() -> Field; +} + +struct Struct1 { +} + +impl Struct1 { + fn tralala() -> Field { + 123456 + } +} + +impl Trait1 for Struct1 { + fn tralala() -> Field { + 111111 + } +} + +impl Trait2 for Struct1 { + fn tralala() -> Field { + 222222 + } +} + +fn main() { + // the struct impl takes priority over trait methods + assert(Struct1::tralala() == 123456); + // TODO: uncomment these, once we support the :: syntax + //assert(::tralala() == 111111); + //assert(::tralala() == 222222); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_default_implementation/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_default_implementation/Nargo.toml new file mode 100644 index 00000000000..938e224ec02 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_default_implementation/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "trait_default_implementation" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_default_implementation/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_default_implementation/Prover.toml new file mode 100644 index 00000000000..71805e71e8e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_default_implementation/Prover.toml @@ -0,0 +1,2 @@ +x = "5" +y = "1" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_default_implementation/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_default_implementation/src/main.nr new file mode 100644 index 00000000000..e1f29ce3f48 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_default_implementation/src/main.nr @@ -0,0 +1,26 @@ +use dep::std; + +trait Default { + fn default(x: Field, y: Field) -> Self; + + fn method2(x: Field) -> Field { + x + } + +} + +struct Foo { + bar: Field, + array: [Field; 2], +} + +impl Default for Foo { + fn default(x: Field,y: Field) -> Self { + Self { bar: x, array: [x,y] } + } +} + +fn main(x: Field) { + let first = Foo::method2(x); + assert(first == x); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_function_calls/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_function_calls/Nargo.toml new file mode 100644 index 00000000000..1bb7522d6e1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_function_calls/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "trait_function_calls" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_function_calls/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_function_calls/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_function_calls/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_function_calls/src/main.nr new file mode 100644 index 00000000000..39d28a5a3b3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_function_calls/src/main.nr @@ -0,0 +1,615 @@ +// a more thorough test for trait function/method calls from within trait function/method calls +// This tests all combinations of caller+callee pairs. For each of these, the following combination of properties are tried: +// *) method (has self parameter) vs function (no self parameter) +// *) default vs overriden vs overriden (no default) +// test order is: +// 1) trait method -> trait method +// 1a) trait default method -> trait default method +// 1b) trait default method -> trait overriden method +// 1c) trait default method -> trait overriden (no default) method +// 1d) trait overriden method -> trait default method +// 1e) trait overriden method -> trait overriden method +// 1f) trait overriden method -> trait overriden (no default) method +// 1g) trait overriden (no default) method -> trait default method +// 1h) trait overriden (no default) method -> trait overriden method +// 1i) trait overriden (no default) method -> trait overriden (no default) method +// 2) trait method -> trait function +// 2a) - subcases are the same as the above +// ... +// 2i) +// 3{a..i}) trait function -> trait method +// 4{a..i}) trait function -> trait function +// 1) trait method -> trait method +// 1a) trait default method -> trait default method +trait Trait1a { + fn trait_method1(self) -> Field { + self.trait_method2() * 7892 - self.vl + } + fn trait_method2(self) -> Field { + 43278 + } +} +struct Struct1a { vl: Field } +impl Trait1a for Struct1a { } +// 1b) trait default method -> trait overriden method +trait Trait1b { + fn trait_method1(self) -> Field { + self.trait_method2() * 2832 - self.vl + } + fn trait_method2(self) -> Field { + 9323 + } +} +struct Struct1b { vl: Field } +impl Trait1b for Struct1b { + fn trait_method2(self) -> Field { + 2394 + } +} +// 1c) trait default method -> trait overriden (no default) method +trait Trait1c { + fn trait_method1(self) -> Field { + self.trait_method2() * 7635 - self.vl + } + fn trait_method2(self) -> Field; +} +struct Struct1c { vl: Field } +impl Trait1c for Struct1c { + fn trait_method2(self) -> Field { + 5485 + } +} +// 1d) trait overriden method -> trait default method +trait Trait1d { + fn trait_method1(self) -> Field { + self.trait_method2() * 2825 - self.vl + } + fn trait_method2(self) -> Field { + 29341 + } +} +struct Struct1d { vl: Field } +impl Trait1d for Struct1d { + fn trait_method1(self) -> Field { + self.trait_method2() * 9342 - self.vl + } +} +// 1e) trait overriden method -> trait overriden method +trait Trait1e { + fn trait_method1(self) -> Field { + self.trait_method2() * 85465 - self.vl + } + fn trait_method2(self) -> Field { + 2381 + } +} +struct Struct1e { vl: Field } +impl Trait1e for Struct1e { + fn trait_method1(self) -> Field { + self.trait_method2() * 47324 - self.vl + } + fn trait_method2(self) -> Field { + 58945 + } +} +// 1f) trait overriden method -> trait overriden (no default) method +trait Trait1f { + fn trait_method1(self) -> Field { + self.trait_method2() * 43257 - self.vl + } + fn trait_method2(self) -> Field; +} +struct Struct1f { vl: Field } +impl Trait1f for Struct1f { + fn trait_method1(self) -> Field { + self.trait_method2() * 34875 - self.vl + } + fn trait_method2(self) -> Field { + 5748 + } +} +// 1g) trait overriden (no default) method -> trait default method +trait Trait1g { + fn trait_method1(self) -> Field; + fn trait_method2(self) -> Field { + 37845 + } +} +struct Struct1g { vl: Field } +impl Trait1g for Struct1g { + fn trait_method1(self) -> Field { + self.trait_method2() * 7854 - self.vl + } +} +// 1h) trait overriden (no default) method -> trait overriden method +trait Trait1h { + fn trait_method1(self) -> Field; + fn trait_method2(self) -> Field { + 7823 + } +} +struct Struct1h { vl: Field } +impl Trait1h for Struct1h { + fn trait_method1(self) -> Field { + self.trait_method2() * 3482 - self.vl + } + fn trait_method2(self) -> Field { + 8542 + } +} +// 1i) trait overriden (no default) method -> trait overriden (no default) method +trait Trait1i { + fn trait_method1(self) -> Field; + fn trait_method2(self) -> Field; +} +struct Struct1i { vl: Field } +impl Trait1i for Struct1i { + fn trait_method1(self) -> Field { + self.trait_method2() * 23478 - self.vl + } + fn trait_method2(self) -> Field { + 98543 + } +} +// 2) trait method -> trait function +// 2a) trait default method -> trait default function +trait Trait2a { + fn trait_method1(self) -> Field { + Self::trait_function2() * 2385 - self.vl + } + fn trait_function2() -> Field { + 7843 + } +} +struct Struct2a { vl: Field } +impl Trait2a for Struct2a { } +// 2b) trait default method -> trait overriden function +trait Trait2b { + fn trait_method1(self) -> Field { + Self::trait_function2() * 6583 - self.vl + } + fn trait_function2() -> Field { + 3752 + } +} +struct Struct2b { vl: Field } +impl Trait2b for Struct2b { + fn trait_function2() -> Field { + 8477 + } +} +// 2c) trait default method -> trait overriden (no default) function +trait Trait2c { + fn trait_method1(self) -> Field { + Self::trait_function2() * 2831 - self.vl + } + fn trait_function2() -> Field; +} +struct Struct2c { vl: Field } +impl Trait2c for Struct2c { + fn trait_function2() -> Field { + 8342 + } +} +// 2d) trait overriden method -> trait default function +trait Trait2d { + fn trait_method1(self) -> Field { + Self::trait_function2() * 924 - self.vl + } + fn trait_function2() -> Field { + 384 + } +} +struct Struct2d { vl: Field } +impl Trait2d for Struct2d { + fn trait_method1(self) -> Field { + Self::trait_function2() * 3984 - self.vl + } +} +// 2e) trait overriden method -> trait overriden function +trait Trait2e { + fn trait_method1(self) -> Field { + Self::trait_function2() * 3642 - self.vl + } + fn trait_function2() -> Field { + 97342 + } +} +struct Struct2e { vl: Field } +impl Trait2e for Struct2e { + fn trait_method1(self) -> Field { + Self::trait_function2() * 7363 - self.vl + } + fn trait_function2() -> Field { + 39400 + } +} +// 2f) trait overriden method -> trait overriden (no default) function +trait Trait2f { + fn trait_method1(self) -> Field { + Self::trait_function2() * 2783 - self.vl + } + fn trait_function2() -> Field; +} +struct Struct2f { vl: Field } +impl Trait2f for Struct2f { + fn trait_method1(self) -> Field { + Self::trait_function2() * 6362 - self.vl + } + fn trait_function2() -> Field { + 72311 + } +} +// 2g) trait overriden (no default) method -> trait default function +trait Trait2g { + fn trait_method1(self) -> Field; + fn trait_function2() -> Field { + 19273 + } +} +struct Struct2g { vl: Field } +impl Trait2g for Struct2g { + fn trait_method1(self) -> Field { + Self::trait_function2() * 9123 - self.vl + } +} +// 2h) trait overriden (no default) method -> trait overriden function +trait Trait2h { + fn trait_method1(self) -> Field; + fn trait_function2() -> Field { + 1281 + } +} +struct Struct2h { vl: Field } +impl Trait2h for Struct2h { + fn trait_method1(self) -> Field { + Self::trait_function2() * 4833 - self.vl + } + fn trait_function2() -> Field { + 5335 + } +} +// 2i) trait overriden (no default) method -> trait overriden (no default) function +trait Trait2i { + fn trait_method1(self) -> Field; + fn trait_function2() -> Field; +} +struct Struct2i { vl: Field } +impl Trait2i for Struct2i { + fn trait_method1(self) -> Field { + Self::trait_function2() * 2291 - self.vl + } + fn trait_function2() -> Field { + 3322 + } +} +// 3 trait function -> trait method +// 3a) trait default function -> trait default method +trait Trait3a { + fn trait_function1(a: Field, b: Self) -> Field { + b.trait_method2() * 8344 - b.vl + a + } + fn trait_method2(self) -> Field { + 19212 + } +} +struct Struct3a { vl: Field } +impl Trait3a for Struct3a { } +// 3b) trait default function -> trait overriden method +trait Trait3b { + fn trait_function1(a: Field, b: Self) -> Field { + b.trait_method2() * 9233 - b.vl + a + } + fn trait_method2(self) -> Field { + 9111 + } +} +struct Struct3b { vl: Field } +impl Trait3b for Struct3b { + fn trait_method2(self) -> Field { + 2392 + } +} +// 3c) trait default function -> trait overriden (no default) method +trait Trait3c { + fn trait_function1(a: Field, b: Self) -> Field { + b.trait_method2() * 2822 - b.vl + a + } + fn trait_method2(self) -> Field; +} +struct Struct3c { vl: Field } +impl Trait3c for Struct3c { + fn trait_method2(self) -> Field { + 7743 + } +} +// 3d) trait overriden function -> trait default method +trait Trait3d { + fn trait_function1(a: Field, b: Self) -> Field { + b.trait_method2() * 291 - b.vl + a + } + fn trait_method2(self) -> Field { + 3328 + } +} +struct Struct3d { vl: Field } +impl Trait3d for Struct3d { + fn trait_function1(a: Field, b: Self) -> Field { + b.trait_method2() * 4933 - b.vl + a + } +} +// 3e) trait overriden function -> trait overriden method +trait Trait3e { + fn trait_function1(a: Field, b: Self) -> Field { + b.trait_method2() * 71231 - b.vl + a + } + fn trait_method2(self) -> Field { + 373 + } +} +struct Struct3e { vl: Field } +impl Trait3e for Struct3e { + fn trait_function1(a: Field, b: Self) -> Field { + b.trait_method2() * 81232 - b.vl + a + } + fn trait_method2(self) -> Field { + 80002 + } +} +// 3f) trait overriden function -> trait overriden (no default) method +trait Trait3f { + fn trait_function1(a: Field, b: Self) -> Field { + b.trait_method2() * 28223 - b.vl + a + } + fn trait_method2(self) -> Field; +} +struct Struct3f { vl: Field } +impl Trait3f for Struct3f { + fn trait_function1(a: Field, b: Self) -> Field { + b.trait_method2() * 29223 - b.vl + a + } + fn trait_method2(self) -> Field { + 63532 + } +} +// 3g) trait overriden (no default) function -> trait default method +trait Trait3g { + fn trait_function1(a: Field, b: Self) -> Field; + fn trait_method2(self) -> Field { + 8887 + } +} +struct Struct3g { vl: Field } +impl Trait3g for Struct3g { + fn trait_function1(a: Field, b: Self) -> Field { + b.trait_method2() * 31337 - b.vl + a + } +} +// 3h) trait overriden (no default) function -> trait overriden method +trait Trait3h { + fn trait_function1(a: Field, b: Self) -> Field; + fn trait_method2(self) -> Field { + 293 + } +} +struct Struct3h { vl: Field } +impl Trait3h for Struct3h { + fn trait_function1(a: Field, b: Self) -> Field { + b.trait_method2() * 74747 - b.vl + a + } + fn trait_method2(self) -> Field { + 6283 + } +} +// 3i) trait overriden (no default) function -> trait overriden (no default) method +trait Trait3i { + fn trait_function1(a: Field, b: Self) -> Field; + fn trait_method2(self) -> Field; +} +struct Struct3i { vl: Field } +impl Trait3i for Struct3i { + fn trait_function1(a: Field, b: Self) -> Field { + b.trait_method2() * 1237 - b.vl + a + } + fn trait_method2(self) -> Field { + 84352 + } +} +// 4) trait function -> trait function +// 4a) trait default function -> trait default function +trait Trait4a { + fn trait_function1() -> Field { + Self::trait_function2() * 3842 + } + fn trait_function2() -> Field { + 2932 + } +} +struct Struct4a { vl: Field } +impl Trait4a for Struct4a { } +// 4b) trait default function -> trait overriden function +trait Trait4b { + fn trait_function1() -> Field { + Self::trait_function2() * 3842 + } + fn trait_function2() -> Field { + 2932 + } +} +struct Struct4b { vl: Field } +impl Trait4b for Struct4b { + fn trait_function2() -> Field { + 9353 + } +} +// 4c) trait default function -> trait overriden (no default) function +trait Trait4c { + fn trait_function1() -> Field { + Self::trait_function2() * 7832 + } + fn trait_function2() -> Field; +} +struct Struct4c { vl: Field } +impl Trait4c for Struct4c { + fn trait_function2() -> Field { + 2928 + } +} +// 4d) trait overriden function -> trait default function +trait Trait4d { + fn trait_function1() -> Field { + Self::trait_function2() * 2283 + } + fn trait_function2() -> Field { + 9332 + } +} +struct Struct4d { vl: Field } +impl Trait4d for Struct4d { + fn trait_function1() -> Field { + Self::trait_function2() * 8374 + } +} +// 4e) trait overriden function -> trait overriden function +trait Trait4e { + fn trait_function1() -> Field { + Self::trait_function2() * 94329 + } + fn trait_function2() -> Field { + 28328 + } +} +struct Struct4e { vl: Field } +impl Trait4e for Struct4e { + fn trait_function1() -> Field { + Self::trait_function2() * 12323 + } + fn trait_function2() -> Field { + 38434 + } +} +// 4f) trait overriden function -> trait overriden (no default) function +trait Trait4f { + fn trait_function1() -> Field { + Self::trait_function2() * 23723 + } + fn trait_function2() -> Field; +} +struct Struct4f { vl: Field } +impl Trait4f for Struct4f { + fn trait_function1() -> Field { + Self::trait_function2() * 21392 + } + fn trait_function2() -> Field { + 4394 + } +} +// 4g) trait overriden (no default) function -> trait default function +trait Trait4g { + fn trait_function1() -> Field; + fn trait_function2() -> Field { + 2932 + } +} +struct Struct4g { vl: Field } +impl Trait4g for Struct4g { + fn trait_function1() -> Field { + Self::trait_function2() * 3345 + } +} +// 4h) trait overriden (no default) function -> trait overriden function +trait Trait4h { + fn trait_function1() -> Field; + fn trait_function2() -> Field { + 5756 + } +} +struct Struct4h { vl: Field } +impl Trait4h for Struct4h { + fn trait_function1() -> Field { + Self::trait_function2() * 6478 + } + fn trait_function2() -> Field { + 5435 + } +} +// 4i) trait overriden (no default) function -> trait overriden (no default) function +trait Trait4i { + fn trait_function1() -> Field; + fn trait_function2() -> Field; +} +struct Struct4i { vl: Field } +impl Trait4i for Struct4i { + fn trait_function1() -> Field { + Self::trait_function2() * 8239 + } + fn trait_function2() -> Field { + 2032 + } +} + +fn main() { + let t1a = Struct1a { vl: 1234 }; + assert(t1a.trait_method1() == 341548742); + let t1b = Struct1b { vl: 4444 }; + assert(t1b.trait_method1() == 6775364); + let t1c = Struct1c { vl: 3946 }; + assert(t1c.trait_method1() == 41874029); + let t1d = Struct1d { vl: 9234 }; + assert(t1d.trait_method1() == 274094388); + let t1e = Struct1e { vl: 5438 }; + assert(t1e.trait_method1() == 2789507742); + let t1f = Struct1f { vl: 6237 }; + assert(t1f.trait_method1() == 200455263); + let t1g = Struct1g { vl: 43587 }; + assert(t1g.trait_method1() == 297191043); + let t1h = Struct1h { vl: 3984 }; + assert(t1h.trait_method1() == 29739260); + let t1i = Struct1i { vl: 9234 }; + assert(t1i.trait_method1() == 2313583320); + let t2a = Struct2a { vl: 4362 }; + assert(t2a.trait_method1() == 18701193); + let t2b = Struct2b { vl: 8347 }; + assert(t2b.trait_method1() == 55795744); + let t2c = Struct2c { vl: 1923 }; + assert(t2c.trait_method1() == 23614279); + let t2d = Struct2d { vl: 92384 }; + assert(t2d.trait_method1() == 1437472); + let t2e = Struct2e { vl: 83943 }; + assert(t2e.trait_method1() == 290018257); + let t2f = Struct2f { vl: 8237 }; + assert(t2f.trait_method1() == 460034345); + let t2g = Struct2g { vl: 1232 }; + assert(t2g.trait_method1() == 175826347); + let t2h = Struct2h { vl: 7222 }; + assert(t2h.trait_method1() == 25776833); + let t2i = Struct2i { vl: 1821 }; + assert(t2i.trait_method1() == 7608881); + let t3a = Struct3a { vl: 93248 }; + assert(Struct3a::trait_function1(5, t3a) == 160211685); + let t3b = Struct3b { vl: 76763 }; + assert(Struct3b::trait_function1(62, t3b) == 22008635); + let t3c = Struct3c { vl: 3833 }; + assert(Struct3c::trait_function1(25, t3c) == 21846938); + let t3d = Struct3d { vl: 5645 }; + assert(Struct3d::trait_function1(73, t3d) == 16411452); + let t3e = Struct3e { vl: 22912 }; + assert(Struct3e::trait_function1(92, t3e) == 6498699644); + let t3f = Struct3f { vl: 3256 }; + assert(Struct3f::trait_function1(77, t3f) == 1856592457); + let t3g = Struct3g { vl: 22832 }; + assert(Struct3g::trait_function1(23, t3g) == 278469110); + let t3h = Struct3h { vl: 4933 }; + assert(Struct3h::trait_function1(17, t3h) == 469630485); + let t3i = Struct3i { vl: 39432 }; + assert(Struct3i::trait_function1(54, t3i) == 104304046); + assert(Struct4a::trait_function1() == 11264744); + assert(Struct4b::trait_function1() == 35934226); + assert(Struct4c::trait_function1() == 22932096); + assert(Struct4d::trait_function1() == 78146168); + assert(Struct4e::trait_function1() == 473622182); + assert(Struct4f::trait_function1() == 93996448); + assert(Struct4g::trait_function1() == 9807540); + assert(Struct4h::trait_function1() == 35207930); + assert(Struct4i::trait_function1() == 16741648); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_generics/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_generics/Nargo.toml new file mode 100644 index 00000000000..c1b5d0aaa6c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_generics/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "trait_generics" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_generics/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_generics/src/main.nr new file mode 100644 index 00000000000..bb6d6e74726 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_generics/src/main.nr @@ -0,0 +1,59 @@ +struct Empty {} + +trait Foo { + fn foo(self) -> u32; +} + +impl Foo for Empty { + fn foo(_self: Self) -> u32 { 32 } +} + +impl Foo for Empty { + fn foo(_self: Self) -> u32 { 64 } +} + +fn main() { + let x: Empty = Empty {}; + let y: Empty = Empty {}; + let z = Empty {}; + + assert(x.foo() == 32); + assert(y.foo() == 64); + // Types matching multiple impls will currently choose + // the first matching one instead of erroring + assert(z.foo() == 32); + + call_impl_with_generic_struct(); + call_impl_with_generic_function(); +} +// Ensure we can call a generic impl +fn call_impl_with_generic_struct() { + let x: u8 = 7; + let y: i8 = 8; + let s2_u8 = S2 { x }; + let s2_i8 = S2 { x: y }; + assert(s2_u8.t2().x == 7); + assert(s2_i8.t2().x == 8); +} + +trait T2 { + fn t2(self) -> Self; +} + +struct S2 { x: T } + +impl T2 for S2 { + fn t2(self) -> Self { self } +} + +fn call_impl_with_generic_function() { + assert(3.t3(7) == 7); +} + +trait T3 { + fn t3(self, x: T) -> T; +} + +impl T3 for u32 { + fn t3(self, y: U) -> U { y } +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/Nargo.toml new file mode 100644 index 00000000000..efa176099cf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "trait_multi_module_test" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/main.nr new file mode 100644 index 00000000000..77c30ce602b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/main.nr @@ -0,0 +1,8 @@ +mod module1; +mod module2; +mod module3; +mod module4; +mod module5; +mod module6; + +fn main() {} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module1.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module1.nr new file mode 100644 index 00000000000..4d41ff2909a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module1.nr @@ -0,0 +1,2 @@ +trait MyTrait { +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module2.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module2.nr new file mode 100644 index 00000000000..3cadb6d78cb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module2.nr @@ -0,0 +1,2 @@ +struct MyStruct { +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module3.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module3.nr new file mode 100644 index 00000000000..2485a2ba7a1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module3.nr @@ -0,0 +1,4 @@ +use crate::module1::MyTrait; +use crate::module2::MyStruct; +// ensure we can implement traits that are imported with the `use` syntax +impl MyTrait for MyStruct {} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module4.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module4.nr new file mode 100644 index 00000000000..f9458e83c4a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module4.nr @@ -0,0 +1,2 @@ +trait MyTrait4 { +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module5.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module5.nr new file mode 100644 index 00000000000..cd9b7f0bf39 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module5.nr @@ -0,0 +1,2 @@ +struct MyStruct5 { +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module6.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module6.nr new file mode 100644 index 00000000000..35f5ce3a183 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_multi_module_test/src/module6.nr @@ -0,0 +1,2 @@ +// ensure we can implement traits using the Path syntax +impl crate::module4::MyTrait4 for crate::module5::MyStruct5 { } diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_override_implementation/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_override_implementation/Nargo.toml new file mode 100644 index 00000000000..1ccf15adc14 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_override_implementation/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "trait_override_implementation" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_override_implementation/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_override_implementation/Prover.toml new file mode 100644 index 00000000000..71805e71e8e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_override_implementation/Prover.toml @@ -0,0 +1,2 @@ +x = "5" +y = "1" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_override_implementation/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_override_implementation/src/main.nr new file mode 100644 index 00000000000..a385efc63fd --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_override_implementation/src/main.nr @@ -0,0 +1,71 @@ +use dep::std; + +trait Default { + fn default(x: Field, y: Field) -> Self; + + fn method2(x: Field) -> Field { + x + } +} + +struct Foo { + bar: Field, + array: [Field; 2], +} + +impl Default for Foo { + fn default(x: Field,y: Field) -> Self { + Self { bar: x, array: [x,y] } + } + + fn method2(x: Field) -> Field { + x * 3 + } +} + +trait F { + fn f1(self) -> Field; + fn f2(self) -> Field { 2 } + fn f3(self) -> Field { 3 } + fn f4(self) -> Field { 4 } + fn f5(self) -> Field { 5 } +} + +struct Bar {} + +impl F for Bar { + fn f5(self) -> Field { 50 } + fn f1(self) -> Field { 10 } + fn f3(self) -> Field { 30 } +} +// Impls on mutable references are temporarily disabled +// impl F for &mut Bar { +// fn f1(self) -> Field { 101 } +// fn f5(self) -> Field { 505 } +// } +fn main(x: Field) { + let first = Foo::method2(x); + assert(first == 3 * x); + + let bar = Bar {}; + + assert(bar.f1() == 10, "1"); + assert(bar.f2() == 2, "2"); + assert(bar.f3() == 30, "3"); + assert(bar.f4() == 4, "4"); + assert(bar.f5() == 50, "5"); + + let mut bar_mut = Bar {}; + // Impls on mutable references are temporarily disabled + // assert_eq((&mut bar_mut).f1(), 101); + // assert((&mut bar_mut).f2() == 2, "7"); + // assert((&mut bar_mut).f3() == 3, "8"); + // assert((&mut bar_mut).f4() == 4, "9"); + // assert((&mut bar_mut).f5() == 505, "10"); + assert(bar_mut.f1() == 10, "10"); + assert(bar_mut.f2() == 2, "12"); + assert(bar_mut.f3() == 30, "13"); + assert(bar_mut.f4() == 4, "14"); + assert(bar_mut.f5() == 50, "15"); +} + diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_static_methods/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_static_methods/Nargo.toml new file mode 100644 index 00000000000..71c541ccd4f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_static_methods/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "trait_self" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_static_methods/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_static_methods/src/main.nr new file mode 100644 index 00000000000..0150da68315 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_static_methods/src/main.nr @@ -0,0 +1,41 @@ +trait ATrait { + fn asd() -> Self; + + fn static_method() -> Field { + Self::static_method_2() + } + + fn static_method_2() -> Field { + 100 + } +} + +struct Foo { + x: Field +} +impl ATrait for Foo { + fn asd() -> Self { + // This should pass as Self should be bound to Foo while typechecking this + Foo{x: 100} + } +} + +struct Bar { + x: Field +} +impl ATrait for Bar { + // The trait method is declared as returning `Self` + // but explicitly specifying the type in the impl should work + fn asd() -> Bar { + Bar{x: 100} + } + + fn static_method_2() -> Field { + 200 + } +} + +fn main() { + assert(Foo::static_method() == 100); + assert(Bar::static_method() == 200); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_where_clause/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_where_clause/Nargo.toml new file mode 100644 index 00000000000..1bbde8bfd79 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_where_clause/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "trait_where_clause" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_where_clause/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_where_clause/src/main.nr new file mode 100644 index 00000000000..5fd7e78abf4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_where_clause/src/main.nr @@ -0,0 +1,56 @@ +// TODO(#2568): Currently we only support trait constraints in a few cases. +// There's a bunch of other places where they can pop up: +// - trait methods (trait Foo where T: ... { ) +// - structs (struct Foo where T: ...) +// import the traits from another module to ensure the where clauses are ok with that +mod the_trait; +use crate::the_trait::Asd; +use crate::the_trait::StaticTrait; + +struct Add10 { x: Field, } +struct Add20 { x: Field, } +struct Add30 { x: Field, } +struct AddXY { x: Field, y: Field, } + +impl Asd for Add10 { fn asd(self) -> Field { self.x + 10 } } +impl Asd for Add20 { fn asd(self) -> Field { self.x + 20 } } +impl Asd for Add30 { fn asd(self) -> Field { self.x + 30 } } + +impl Asd for AddXY { + fn asd(self) -> Field { + self.x + self.y + } +} + +struct Static100 {} +impl StaticTrait for Static100 { + // use default implementation for static_function, which returns 100 +} + +struct Static200 {} +impl StaticTrait for Static200 { + fn static_function(slf: Self) -> Field { 200 } +} + +fn assert_asd_eq_100(t: T) where T: crate::the_trait::Asd { + assert(t.asd() == 100); +} + +fn add_one_to_static_function(t: T) -> Field where T: StaticTrait { + T::static_function(t) + 1 +} + +fn main() { + let x = Add10 { x: 90 }; + let z = Add20 { x: 80 }; + let a = Add30 { x: 70 }; + let xy = AddXY { x: 30, y: 70 }; + + assert_asd_eq_100(x); + assert_asd_eq_100(z); + assert_asd_eq_100(a); + assert_asd_eq_100(xy); + + assert(add_one_to_static_function(Static100 {}) == 101); + assert(add_one_to_static_function(Static200 {}) == 201); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/trait_where_clause/src/the_trait.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_where_clause/src/the_trait.nr new file mode 100644 index 00000000000..c5cac4a1186 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/trait_where_clause/src/the_trait.nr @@ -0,0 +1,9 @@ +trait Asd { + fn asd(self) -> Field; +} + +trait StaticTrait { + fn static_function(slf: Self) -> Field { + 100 + } +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/traits/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/traits/Nargo.toml new file mode 100644 index 00000000000..a33e8681807 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/traits/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "traits" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/traits/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/traits/Prover.toml new file mode 100644 index 00000000000..71805e71e8e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/traits/Prover.toml @@ -0,0 +1,2 @@ +x = "5" +y = "1" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/traits/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/traits/src/main.nr new file mode 100644 index 00000000000..784ff01a883 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/traits/src/main.nr @@ -0,0 +1,21 @@ +use dep::std; + +trait Default { + fn default(x: Field, y: Field) -> Self; +} + +struct Foo { + bar: Field, + array: [Field; 2], +} + +impl Default for Foo { + fn default(x: Field,y: Field) -> Self { + Self { bar: x, array: [x,y] } + } +} + +fn main(x: Field, y: Field) { + let first = Foo::default(x, y); + assert(first.bar == x); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/unary_operators/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/unary_operators/Nargo.toml new file mode 100644 index 00000000000..8ab6f708b8a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/unary_operators/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "unary_operators" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/unary_operators/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/unary_operators/src/main.nr new file mode 100644 index 00000000000..ef622fd3eb9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/unary_operators/src/main.nr @@ -0,0 +1,7 @@ +fn main() { + let x = -1; + assert(x == 1 - 2); + + let y: i32 = -1; + assert(x == 1 - 2); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/unconstrained_empty/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/unconstrained_empty/Nargo.toml new file mode 100644 index 00000000000..48d0f5938d8 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/unconstrained_empty/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "unconstrained_empty" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/unconstrained_empty/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/unconstrained_empty/src/main.nr new file mode 100644 index 00000000000..5e5fb297236 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/unconstrained_empty/src/main.nr @@ -0,0 +1 @@ +unconstrained fn main() {} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/unit/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/unit/Nargo.toml new file mode 100644 index 00000000000..7a15bd803c0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/unit/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "unit" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/unit/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/unit/src/main.nr new file mode 100644 index 00000000000..603829ec6db --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/unit/src/main.nr @@ -0,0 +1,18 @@ +fn main() { + let _a = (); + let _b: () = _a; + let _c: () = (); + let _d = f1(); + let _e: () = f2(); + let _f: () = f3(); + let _g = f4(); +} + +fn f1() {} +fn f2() { + () +} +fn f3() -> () {} +fn f4() -> () { + () +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/unused_variables/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/unused_variables/Nargo.toml new file mode 100644 index 00000000000..1447f07d81d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/unused_variables/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "unused_variables" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/unused_variables/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/unused_variables/src/main.nr new file mode 100644 index 00000000000..f82cace0509 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/unused_variables/src/main.nr @@ -0,0 +1 @@ +fn main(x: Field, y: pub Field) {} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/vectors/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/vectors/Nargo.toml new file mode 100644 index 00000000000..e4c3889c685 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/vectors/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "vectors" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/vectors/Prover.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/vectors/Prover.toml new file mode 100644 index 00000000000..f28f2f8cc48 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/vectors/Prover.toml @@ -0,0 +1,2 @@ +x = "5" +y = "10" diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/vectors/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/vectors/src/main.nr new file mode 100644 index 00000000000..28187a4f619 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/vectors/src/main.nr @@ -0,0 +1,32 @@ +use dep::std::collections::vec::Vec; + +fn main(x: Field, y: pub Field) { + let mut vector = Vec::new(); + + assert(vector.len() == 0); + for i in 0..5 { + vector.push(i); + } + assert(vector.len() == 5); + for i in 0..5 { + assert(i == vector.get(i)); + } + + let last_elem = vector.pop(); + assert(last_elem == 4); + assert(vector.len() == 4); + + vector.insert(2, 100); + assert(vector.get(2) == 100); + assert(vector.get(4) == 3); + assert(vector.len() == 5); + + let removed_elem = vector.remove(3); + assert(removed_elem == 2); + assert(vector.get(3) == 3); + assert(vector.len() == 4); + + let mut inputs_vector = Vec::from_slice([x, y]); + assert(inputs_vector.get(0) == x); + assert(inputs_vector.get(1) == y); +} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/Nargo.toml new file mode 100644 index 00000000000..ea2ffc0d2eb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/Nargo.toml @@ -0,0 +1,7 @@ +[workspace] +members = [ + "library", + "library2", + "binary" +] +default-member = "binary" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/binary/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/binary/Nargo.toml new file mode 100644 index 00000000000..90d8321589b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/binary/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "binary" +type = "bin" +authors = [""] +[dependencies] +library = { path = "../library" } \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/binary/src/main.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/binary/src/main.nr new file mode 100644 index 00000000000..ab0ae9a48b8 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/binary/src/main.nr @@ -0,0 +1,2 @@ +use dep::library::ReExportMeFromAnotherLib; +fn main(_x: ReExportMeFromAnotherLib) {} diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/library/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/library/Nargo.toml new file mode 100644 index 00000000000..88831bada4e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/library/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "library" +type = "lib" +authors = [""] + +[dependencies] +library2 = { path = "../library2"} \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/library/src/lib.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/library/src/lib.nr new file mode 100644 index 00000000000..8e84662ed03 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/library/src/lib.nr @@ -0,0 +1,2 @@ +// Re-export +use dep::library2::ReExportMeFromAnotherLib; diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/library2/Nargo.toml b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/library2/Nargo.toml new file mode 100644 index 00000000000..f2c20c0bf4a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/library2/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "library" +type = "lib" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/library2/src/lib.nr b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/library2/src/lib.nr new file mode 100644 index 00000000000..7e5a29a1424 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/compile_success_empty/workspace_reexport_bug/library2/src/lib.nr @@ -0,0 +1,5 @@ +// When we re-export this type from another library and then use it in +// main, we get a panic +struct ReExportMeFromAnotherLib { + x : Field, +} diff --git a/noir/tooling/nargo_cli/tests/execute.rs b/noir/tooling/nargo_cli/tests/execute.rs new file mode 100644 index 00000000000..e2bef43b571 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execute.rs @@ -0,0 +1,19 @@ +#[allow(unused_imports)] +#[cfg(test)] +mod tests { + // Some of these imports are consumed by the injected tests + use assert_cmd::prelude::*; + use predicates::prelude::*; + + use std::collections::BTreeMap; + use std::fs; + use std::path::PathBuf; + use std::process::Command; + + use super::*; + + test_binary::build_test_binary_once!(mock_backend, "../backend_interface/test-binaries"); + + // include tests generated by `build.rs` + include!(concat!(env!("OUT_DIR"), "/execute.rs")); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/1327_concrete_in_generic/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/1327_concrete_in_generic/Nargo.toml new file mode 100644 index 00000000000..3adf969538c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/1327_concrete_in_generic/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "1327_concrete_in_generic" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/1327_concrete_in_generic/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/1327_concrete_in_generic/Prover.toml new file mode 100644 index 00000000000..e5fc42da053 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/1327_concrete_in_generic/Prover.toml @@ -0,0 +1 @@ +input = 1 \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/1327_concrete_in_generic/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/1327_concrete_in_generic/src/main.nr new file mode 100644 index 00000000000..e1d601b13c9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/1327_concrete_in_generic/src/main.nr @@ -0,0 +1,64 @@ +// --- +fn new_concrete_c_over_d() -> C { + let d_method_interface = get_d_method_interface(); + C::new(d_method_interface) +} +// --- +// Map +struct B { + new_concrete_t_c_constructor: fn()->T_C, + } + +impl B { + fn new(new_concrete_t_c_constructor: fn () -> T_C) -> B { + B { new_concrete_t_c_constructor } + } + + fn get_t_c(self) -> T_C { + let new_concrete_t_c_constructor = self.new_concrete_t_c_constructor; + new_concrete_t_c_constructor() + } + } +// --- +// Set +struct C { + t_d_interface: MethodInterface, + } + +impl C { + fn new (t_d_interface: MethodInterface) -> Self { + C { t_d_interface } + } + + fn call_method_of_t_d(self, t_d: T_D) -> Field { + let some_method_on_t_d = self.t_d_interface.some_method_on_t_d; + some_method_on_t_d(t_d) + } + } +// --- +struct MethodInterface { + some_method_on_t_d: fn(T_D)->Field, + } +// --- +// Note +struct D { + d: Field, + } + +fn d_method(input: D) -> Field { + input.d * input.d +} + +fn get_d_method_interface() -> MethodInterface { + MethodInterface { some_method_on_t_d: d_method } +} +// --- +fn main(input: Field) -> pub Field { + let b: B> = B::new(new_concrete_c_over_d); + let c: C = b.get_t_c(); // Singleton + let d: D = D { d: input }; // Note + let output = c.call_method_of_t_d(d); + + output +} +// --- diff --git a/noir/tooling/nargo_cli/tests/execution_success/1_mul/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/1_mul/Nargo.toml new file mode 100644 index 00000000000..a0fd8d98027 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/1_mul/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "1_mul" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/1_mul/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/1_mul/Prover.toml new file mode 100644 index 00000000000..9bff601c75a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/1_mul/Prover.toml @@ -0,0 +1,3 @@ +x = "3" +y = "4" +z = "429981696" diff --git a/noir/tooling/nargo_cli/tests/execution_success/1_mul/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/1_mul/src/main.nr new file mode 100644 index 00000000000..8f4032dbd75 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/1_mul/src/main.nr @@ -0,0 +1,9 @@ +// Test unsafe integer multiplication with overflow: 12^8 = 429 981 696 +// The circuit should handle properly the growth of the bit size +fn main(mut x: u32, y: u32, z: u32) { + x *= y; + x *= x; //144 + x *= x; //20736 + x *= x; //429 981 696 + assert(x == z); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/2_div/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/2_div/Nargo.toml new file mode 100644 index 00000000000..f689c97f760 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/2_div/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "2_div" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/2_div/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/2_div/Prover.toml new file mode 100644 index 00000000000..ee6f0ef229a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/2_div/Prover.toml @@ -0,0 +1,3 @@ +x = "7" +y = "3" +z = "2" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/2_div/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/2_div/src/main.nr new file mode 100644 index 00000000000..e16cc5be229 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/2_div/src/main.nr @@ -0,0 +1,7 @@ +// Testing integer division: 7/3 = 2 +fn main(mut x: u32, y: u32, z: u32) { + let a = x % y; + assert(x / y == z); + assert(a == x - z * y); + assert((50 as u64) % (9 as u64) == 5); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/3_add/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/3_add/Nargo.toml new file mode 100644 index 00000000000..0bd2caef024 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/3_add/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "3_add" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/3_add/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/3_add/Prover.toml new file mode 100644 index 00000000000..5d777c023db --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/3_add/Prover.toml @@ -0,0 +1,3 @@ +x = "3" +y = "4" +z = "7" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/3_add/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/3_add/src/main.nr new file mode 100644 index 00000000000..480348dc1cf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/3_add/src/main.nr @@ -0,0 +1,8 @@ +// Test integer addition: 3 + 4 = 7 +fn main(mut x: u32, y: u32, z: u32) { + x += y; + assert(x == z); + + x *= 8; + assert(x > 9); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/4_sub/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/4_sub/Nargo.toml new file mode 100644 index 00000000000..b282bfe6287 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/4_sub/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "4_sub" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/4_sub/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/4_sub/Prover.toml new file mode 100644 index 00000000000..1240475dee3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/4_sub/Prover.toml @@ -0,0 +1,3 @@ +x = "12" +y = "2418266113" +z = "1876701195" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/4_sub/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/4_sub/src/main.nr new file mode 100644 index 00000000000..6aef8e7b208 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/4_sub/src/main.nr @@ -0,0 +1,6 @@ +use dep::std; +// Test unsafe integer subtraction with underflow: 12 - 2418266113 = 1876701195 modulo 2^32 +fn main(mut x: u32, y: u32, z: u32) { + x = std::wrapping_sub(x,y); + assert(x == z); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/5_over/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/5_over/Nargo.toml new file mode 100644 index 00000000000..dd9a21b44bf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/5_over/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "5_over" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/5_over/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/5_over/Prover.toml new file mode 100644 index 00000000000..9a1986329ca --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/5_over/Prover.toml @@ -0,0 +1,2 @@ +x = "43046721" +y = "3793632897" diff --git a/noir/tooling/nargo_cli/tests/execution_success/5_over/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/5_over/src/main.nr new file mode 100644 index 00000000000..f24ff06cb2a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/5_over/src/main.nr @@ -0,0 +1,10 @@ +use dep::std; +// Test unsafe integer arithmetic +// Test odd bits integer +fn main(mut x: u32, y: u32) { + x = std::wrapping_mul(x,x); + assert(y == x); + + let c: u3 = 2; + assert(c > x as u3); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/6/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/6/Nargo.toml new file mode 100644 index 00000000000..9774a5d8c47 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/6/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "6" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/6/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/6/Prover.toml new file mode 100644 index 00000000000..1c52aef063c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/6/Prover.toml @@ -0,0 +1,39 @@ + +# hello as bytes +# used : https://emn178.github.io/online-tools/sha256.html +x = [104, 101, 108, 108, 111] + +result = [ + 0x2c, + 0xf2, + 0x4d, + 0xba, + 0x5f, + 0xb0, + 0xa3, + 0x0e, + 0x26, + 0xe8, + 0x3b, + 0x2a, + 0xc5, + 0xb9, + 0xe2, + 0x9e, + 0x1b, + 0x16, + 0x1e, + 0x5c, + 0x1f, + 0xa7, + 0x42, + 0x5e, + 0x73, + 0x04, + 0x33, + 0x62, + 0x93, + 0x8b, + 0x98, + 0x24, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/6/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/6/src/main.nr new file mode 100644 index 00000000000..5ecb809e68b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/6/src/main.nr @@ -0,0 +1,19 @@ +// Sha256 circuit where the input is 5 bytes +// not five field elements since sha256 operates over +// bytes. +// +// If you do not cast, it will take all the bytes from the field element! +// Mimc input is an array of field elements +// The function is called mimc_bn254 to emphasize its parameters are chosen for bn254 curve, it should be used only with a proving system using the same curve (e.g Plonk from Aztec) +use dep::std; + +fn main(x: [u8; 5], result: pub [u8; 32]) { + let mut digest = std::hash::sha256(x); + digest[0] = 5 as u8; + digest = std::hash::sha256(x); + assert(digest == result); + + let y = [12, 45, 78, 41]; + let h = std::hash::mimc::mimc_bn254(y); + assert(h == 18226366069841799622585958305961373004333097209608110160936134895615261821931); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/6_array/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/6_array/Nargo.toml new file mode 100644 index 00000000000..c75df3e468e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/6_array/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "6_array" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/6_array/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/6_array/Prover.toml new file mode 100644 index 00000000000..2e2ed310ba1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/6_array/Prover.toml @@ -0,0 +1,8 @@ +x = [104, 101, 108, 108, 111] +y = [10, 81, 18, 48, 0] +z = "59" +t = "10" + +#7128 +#15309 +#16349 \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/6_array/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/6_array/src/main.nr new file mode 100644 index 00000000000..6aa05f58b71 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/6_array/src/main.nr @@ -0,0 +1,53 @@ +use dep::std; +//Basic tests for arrays +fn main(x: [u32; 5], y: [u32; 5], mut z: u32, t: u32) { + let mut c = 2301; + z = y[4]; + //Test 1: + for i in 0..5 { + c = z*z*y[i]; + z -= c; + } + assert(z == 0); //y[4]=0, so c and z are always 0 + //Test 2: + c = 2301 as u32; + for i in 0..5 { + c = t+2 as u32; + c = std::wrapping_mul(std::wrapping_mul(z,z),x[i]); + z =std::wrapping_add(z, std::wrapping_sub(x[i]*y[i] , c)); + } + assert(z == 3814912846); + //Test 3: + c = 2300001 as u32; + z = y[4]; + for i in 0..5 { + z = z + x[i]*y[i]; + for _i in 0..3 { + c = std::wrapping_sub(i as u32,2 as u32); + z = std::wrapping_mul(z,c); + } + } + assert(z == 41472); + //Test 4: + z = y[4]; + for i in 0..3 { + z += x[i] * y[i]; + for j in 0..2 { + z += x[i+j] - y[i+j]; + } + } + assert(z == 11539); + //Test 5: + let cc = if z < 1 { x } else { y }; + assert(cc[0] == y[0]); + // Test 6: for-each loops + for y_elem in y { + for x_elem in x { + assert(x_elem != y_elem); + } + } + // Test 7: Arrays of tuples/structs + let mut tuple_array = [(1, 2), (3, 4), (5, 6)]; + tuple_array[1] = (7, 8); + assert(tuple_array[1].1 == 8); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/7/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/7/Nargo.toml new file mode 100644 index 00000000000..f69f89468d7 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/7/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "7" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/7/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/7/Prover.toml new file mode 100644 index 00000000000..bc3784726d2 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/7/Prover.toml @@ -0,0 +1,38 @@ + +# hello as bytes +# https://toolkitbay.com/tkb/tool/BLAKE2s_256 +x = [104, 101, 108, 108, 111] +result = [ + 0x19, + 0x21, + 0x3b, + 0xac, + 0xc5, + 0x8d, + 0xee, + 0x6d, + 0xbd, + 0xe3, + 0xce, + 0xb9, + 0xa4, + 0x7c, + 0xbb, + 0x33, + 0x0b, + 0x3d, + 0x86, + 0xf8, + 0xcc, + 0xa8, + 0x99, + 0x7e, + 0xb0, + 0x0b, + 0xe4, + 0x56, + 0xf1, + 0x40, + 0xca, + 0x25, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/7/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/7/src/main.nr new file mode 100644 index 00000000000..a6bba978644 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/7/src/main.nr @@ -0,0 +1,10 @@ +// This is the same as Blake2s example. +// +// Pre-alpha dependencies must now be prefixed with the word "dep". +// The line below indicates that we would like to pull in the standard library dependency. +use dep::std; + +fn main(x: [u8; 5], result: [u8; 32]) { + let digest = std::hash::blake2s(x); + assert(digest == result); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/7_function/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/7_function/Nargo.toml new file mode 100644 index 00000000000..cec14ff7d6a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/7_function/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "7_function" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/7_function/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/7_function/Prover.toml new file mode 100644 index 00000000000..9140e7f7530 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/7_function/Prover.toml @@ -0,0 +1,6 @@ +x = "59" +y = "5" +a = "1" + +arr1=[3320379920, 1938147428, 1942509796, 1795943184, 24853, 0, 0, 0, 0] +arr2=[2912727897, 3590519536, 1687587470, 3896107618, 1092831095, 0, 0, 0, 0] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/7_function/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/7_function/src/main.nr new file mode 100644 index 00000000000..95568dd4ccd --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/7_function/src/main.nr @@ -0,0 +1,139 @@ +//Tests for function calling +fn f1(mut x: Field) -> Field { + x = x + 1; + x = f2(x); + x +} + +fn f2(mut x: Field) -> Field { + x += 2; + x +} +// Simple example +fn test0(mut a: Field) { + a = f2(a); + assert(a == 3); +} +// Nested call +fn test1(mut a: Field) { + a = f1(a); + assert(a == 4); +} + +fn test2(z: Field, t: u32) { + let a = z + t as Field; + assert(a == 64); + let e = pow(z, t as Field); + assert(e == 714924299); +} + +fn pow(base: Field, exponent: Field) -> Field { + let mut r = 1 as Field; + let b = exponent.to_le_bits(32 as u32); + for i in 1..33 { + r = r*r; + r = (b[32-i] as Field) * (r * base) + (1 - b[32-i] as Field) * r; + } + r +} + +fn test3(x: [u8; 3]) -> [u8; 3] { + let mut buffer = [0 as u8; 3]; + for i in 0..3 { + buffer[i] = x[i]; + } + assert(buffer == x); + buffer +} + +fn test_multiple(x: u32, y: u32) -> (u32, u32) { + (y, x) +} + +fn test_multiple2() -> my_struct { + my_struct { a: 5 as u32, b: 7 as u32 } +} + +fn test_multiple3(x: u32, y: u32) { + assert(x == y); +} + +struct my_struct { + a: u32, + b: u32, +} + +struct my2 { + aa: my_struct, + bb: my_struct, +} + +fn test_multiple4(s: my_struct) { + assert(s.a == s.b + 2); +} + +fn test_multiple5(a: (u32, u32)) { + assert(a.0 == a.1 + 2); +} + +fn test_multiple6(a: my2, b: my_struct, c: (my2, my_struct)) { + test_multiple4(a.aa); + test_multiple5((b.a, b.b)); + assert(c.0.aa.a == c.1.a); +} + +fn foo(a: [Field; N]) -> [Field; N] { + a +} + +fn bar() -> [Field; 1] { + foo([0]) +} + +fn main(x: u32, y: u32, a: Field, arr1: [u32; 9], arr2: [u32; 9]) { + let mut ss: my_struct = my_struct { b: x, a: x + 2 }; + test_multiple4(ss); + test_multiple5((ss.a, ss.b)); + let my = my2 { aa: ss, bb: ss }; + ss.a = 61; + test_multiple6(my, ss, (my, ss)); + + let my_block = { + let mut ab = f2(a); + ab = ab + a; + (x, ab) + }; + assert(my_block.1 == 4); + + test0(a); + test1(a); + test2(x as Field, y); + assert(bar()[0] == 0); + + let mut b = [0 as u8, 5 as u8, 2 as u8]; + let c = test3(b); + assert(b == c); + b[0] = 1 as u8; + let cc = test3(b); + assert(c != cc); + let e = test_multiple(x, y); + assert(e.1 == e.0 + 54 as u32); + let d = test_multiple2(); + assert(d.b == d.a + 2 as u32); + test_multiple3(y, y); + //Regression test for issue #628: + let result = first(arr_to_field(arr1), arr_to_field(arr2)); + assert(result[0] == arr1[0] as Field); +} +// Issue #628 +fn arr_to_field(arr: [u32; 9]) -> [Field; 9] { + let mut as_field: [Field; 9] = [0 as Field; 9]; + for i in 0..9 { + as_field[i] = arr[i] as Field; + } + as_field +} + +fn first(a: [Field; 9], _b: [Field; 9]) -> [Field; 9] { + a +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/arithmetic_binary_operations/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/arithmetic_binary_operations/Nargo.toml new file mode 100644 index 00000000000..c9160aa2592 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/arithmetic_binary_operations/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "arithmetic_binary_operations" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/arithmetic_binary_operations/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/arithmetic_binary_operations/Prover.toml new file mode 100644 index 00000000000..63382a9f640 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/arithmetic_binary_operations/Prover.toml @@ -0,0 +1,3 @@ +x = "3" +y = "4" +z = "5" diff --git a/noir/tooling/nargo_cli/tests/execution_success/arithmetic_binary_operations/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/arithmetic_binary_operations/src/main.nr new file mode 100644 index 00000000000..69554f413a4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/arithmetic_binary_operations/src/main.nr @@ -0,0 +1,15 @@ +// Tests a very simple program. +// +// The features being tested are: +// Binary addition, multiplication, division, constant modulo +// x = 3, y = 4, z = 5 +fn main(x: Field, y: Field, z: Field) -> pub Field { + //cast + assert(y as u1 == 0); + + let a = x + x; // 3 + 3 = 6 + let b = a - y; // 6 - 4 = 2 + let c = b * z; // 2 * 5 = 10 + let d = c / a; // 10 / 6 (This uses field inversion, so we test it by multiplying by `a`) + d * a +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_dynamic/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/array_dynamic/Nargo.toml new file mode 100644 index 00000000000..25684b00377 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_dynamic/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "array_dynamic" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_dynamic/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/array_dynamic/Prover.toml new file mode 100644 index 00000000000..750b3129ec9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_dynamic/Prover.toml @@ -0,0 +1,9 @@ +x = [104, 101, 108, 108, 111] +z = "59" +t = "10" +index = [0,1,2,3,4] +index2 = [0,1,2,3,4] +offset = 1 +sublen = 2 + + diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_dynamic/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/array_dynamic/src/main.nr new file mode 100644 index 00000000000..9e21334e510 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_dynamic/src/main.nr @@ -0,0 +1,30 @@ +fn main(x: [u32; 5], mut z: u32, t: u32, index: [Field;5], index2: [Field;5], offset: Field, sublen: Field) { + let idx = (z - 5 * t - 5) as Field; + //dynamic array test + dyn_array(x, idx, idx - 3); + //regression for issue 1283 + let mut s = 0; + let x3 = [246, 159, 32, 176, 8]; + for i in 0..5 { + s += x3[index[i]]; + } + assert(s != 0); + + if 3 < (sublen as u32) { + assert(index[offset + 3] == index2[3]); + } +} + +fn dyn_array(mut x: [u32; 5], y: Field, z: Field) { + assert(x[y] == 111); + assert(x[z] == 101); + x[z] = 0; + assert(x[y] == 111); + assert(x[1] == 0); + if y as u32 < 10 { + x[y] = x[y] - 2; + } else { + x[y] = 0; + } + assert(x[4] == 109); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_eq/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/array_eq/Nargo.toml new file mode 100644 index 00000000000..1020d4905b1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_eq/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "array_eq" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_eq/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/array_eq/Prover.toml new file mode 100644 index 00000000000..ecfed7de213 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_eq/Prover.toml @@ -0,0 +1,2 @@ +a = [77,75,108,209,54,16,50,202,155,210,174,185,217,0,170,77,69,217,234,216,10,201,66,51,116,196,81,167,37,77,7,102] +b = [77,75,108,209,54,16,50,202,155,210,174,185,217,0,170,77,69,217,234,216,10,201,66,51,116,196,81,167,37,77,7,102] diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_eq/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/array_eq/src/main.nr new file mode 100644 index 00000000000..5bbd595898c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_eq/src/main.nr @@ -0,0 +1,4 @@ +// Simple example of checking where two arrays are equal +fn main(a: [Field; 32], b: [Field; 32]) { + assert(a == b); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_len/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/array_len/Nargo.toml new file mode 100644 index 00000000000..24ac6ff7f13 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_len/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "array_len" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_len/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/array_len/Prover.toml new file mode 100644 index 00000000000..a5ffe607b73 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_len/Prover.toml @@ -0,0 +1,3 @@ +len3 = [1, 2, 3] +len4 = [1, 2, 3, 4] +x = 123 diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_len/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/array_len/src/main.nr new file mode 100644 index 00000000000..b60762f4636 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_len/src/main.nr @@ -0,0 +1,24 @@ +fn len_plus_1(array: [T; N]) -> Field { + array.len() + 1 +} + +fn add_lens(a: [T; N], b: [Field; M]) -> Field { + a.len() + b.len() +} + +fn nested_call(b: [Field; N]) -> Field { + len_plus_1(b) +} + +fn main(x: Field, len3: [u8; 3], len4: [Field; 4]) { + assert(len_plus_1(len3) == 4); + assert(len_plus_1(len4) == 5); + assert(add_lens(len3, len4) == 7); + assert(nested_call(len4) == 5); + // std::array::len returns a compile-time known value + assert(len4[len3.len()] == 4); + // Regression for #1023, ensure .len still works after calling to_le_bytes on a witness. + // This was needed because normally .len is evaluated before acir-gen where to_le_bytes + // on a witness is only evaluated during/after acir-gen. + assert(x.to_le_bytes(8).len() != 0); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_neq/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/array_neq/Nargo.toml new file mode 100644 index 00000000000..2e4bbdf97ee --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_neq/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "array_neq" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_neq/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/array_neq/Prover.toml new file mode 100644 index 00000000000..3aad77f6d4d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_neq/Prover.toml @@ -0,0 +1,2 @@ +a = [77,75,108,209,54,16,50,202,155,210,174,185,217,0,170,77,69,217,234,216,10,201,66,51,116,196,81,167,37,77,7,102] +b = [44,75,108,209,54,16,50,202,155,210,174,185,217,0,170,77,69,217,234,216,10,201,66,51,116,196,81,167,37,77,7,102] diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_neq/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/array_neq/src/main.nr new file mode 100644 index 00000000000..a3e51dc5066 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_neq/src/main.nr @@ -0,0 +1,4 @@ +// Simple example of checking where two arrays are different +fn main(a: [Field; 32], b: [Field; 32]) { + assert(a != b); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_sort/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/array_sort/Nargo.toml new file mode 100644 index 00000000000..28e9c65a0dc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_sort/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "array_sort" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_sort/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/array_sort/Prover.toml new file mode 100644 index 00000000000..e0d79da4da6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_sort/Prover.toml @@ -0,0 +1 @@ +xs = [2, 1, 3] diff --git a/noir/tooling/nargo_cli/tests/execution_success/array_sort/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/array_sort/src/main.nr new file mode 100644 index 00000000000..4f40a2cee84 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/array_sort/src/main.nr @@ -0,0 +1,6 @@ +fn main(xs: [u8; 3]) { + let sorted = xs.sort(); + assert(sorted[0] == 1); + assert(sorted[1] == 2); + assert(sorted[2] == 3); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/assert/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/assert/Nargo.toml new file mode 100644 index 00000000000..7780d53459e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/assert/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "assert" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/assert/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/assert/Prover.toml new file mode 100644 index 00000000000..4dd6b405159 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/assert/Prover.toml @@ -0,0 +1 @@ +x = "1" diff --git a/noir/tooling/nargo_cli/tests/execution_success/assert/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/assert/src/main.nr new file mode 100644 index 00000000000..00e94414c0b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/assert/src/main.nr @@ -0,0 +1,3 @@ +fn main(x: Field) { + assert(x == 1); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/assert_statement/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/assert_statement/Nargo.toml new file mode 100644 index 00000000000..c6a1daaed19 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/assert_statement/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "assert_statement" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/assert_statement/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/assert_statement/Prover.toml new file mode 100644 index 00000000000..5d1dc99124f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/assert_statement/Prover.toml @@ -0,0 +1,2 @@ +x = "3" +y = "3" diff --git a/noir/tooling/nargo_cli/tests/execution_success/assert_statement/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/assert_statement/src/main.nr new file mode 100644 index 00000000000..2646a0b85c2 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/assert_statement/src/main.nr @@ -0,0 +1,7 @@ +// Tests a very simple program. +// +// The features being tested is assertion +fn main(x: Field, y: pub Field) { + assert(x == y, "x and y are not equal"); + assert_eq(x, y, "x and y are not equal"); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/assign_ex/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/assign_ex/Nargo.toml new file mode 100644 index 00000000000..4ef7b3aa5a6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/assign_ex/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "assign_ex" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/assign_ex/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/assign_ex/Prover.toml new file mode 100644 index 00000000000..8c12ebba6cf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/assign_ex/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "2" diff --git a/noir/tooling/nargo_cli/tests/execution_success/assign_ex/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/assign_ex/src/main.nr new file mode 100644 index 00000000000..b5cfc162cc4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/assign_ex/src/main.nr @@ -0,0 +1,14 @@ +fn main(x: Field, y: Field) { + let mut z = x + y; + assert(z == 3); + z = x * y; + assert(z == 2); + + regression_3057(); +} +// Ensure parsing parenthesized lvalues works +fn regression_3057() { + let mut array = [[0, 1], [2, 3]]; + (array[0])[1] = 2; + assert(array[0][1] == 2); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/bit_and/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/bit_and/Nargo.toml new file mode 100644 index 00000000000..e9a28577079 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bit_and/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "bit_and" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/bit_and/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/bit_and/Prover.toml new file mode 100644 index 00000000000..40ce2b0bc27 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bit_and/Prover.toml @@ -0,0 +1,2 @@ +x = "0x00" +y = "0x10" diff --git a/noir/tooling/nargo_cli/tests/execution_success/bit_and/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/bit_and/src/main.nr new file mode 100644 index 00000000000..0bc1d9a49bd --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bit_and/src/main.nr @@ -0,0 +1,16 @@ +// You can only do bit operations with integers. +// (Kobi/Daira/Circom/#37) https://github.com/iden3/circom/issues/37 +fn main(x: Field, y: Field) { + let x_as_u8 = x as u8; + let y_as_u8 = y as u8; + + assert((x_as_u8 & y_as_u8) == x_as_u8); + //bitwise and with 1 bit: + let flag = (x == 0) & (y == 16); + assert(flag); + //bitwise and with odd bits: + let x_as_u11 = x as u11; + let y_as_u11 = y as u11; + assert((x_as_u11 & y_as_u11) == x_as_u11); +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_comptime/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_comptime/Nargo.toml new file mode 100644 index 00000000000..81272be4e98 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_comptime/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "bit_shifts_comptime" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_comptime/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_comptime/Prover.toml new file mode 100644 index 00000000000..cfd62c406cb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_comptime/Prover.toml @@ -0,0 +1 @@ +x = 64 diff --git a/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_comptime/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_comptime/src/main.nr new file mode 100644 index 00000000000..a03376d062a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_comptime/src/main.nr @@ -0,0 +1,24 @@ +fn main(x: u64) { + let two: u64 = 2; + let three: u64 = 3; + // shifts on constant values + assert(two << 2 == 8); + assert((two << 3) / 8 == two); + assert((three >> 1) == 1); + // shifts on runtime values + assert(x << 1 == 128); + assert(x >> 2 == 16); + + regression_2250(); + + //regression for 3481 + assert(x << 63 == 0); +} + +fn regression_2250() { + let a: u1 = 1 >> 1; + assert(a == 0); + + let b: u32 = 1 >> 32; + assert(b == 0); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_runtime/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_runtime/Nargo.toml new file mode 100644 index 00000000000..f60c7dbe658 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_runtime/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "bit_shifts_runtime" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_runtime/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_runtime/Prover.toml new file mode 100644 index 00000000000..98d8630792e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_runtime/Prover.toml @@ -0,0 +1,2 @@ +x = 64 +y = 1 \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_runtime/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_runtime/src/main.nr new file mode 100644 index 00000000000..a2c873a7e7f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bit_shifts_runtime/src/main.nr @@ -0,0 +1,8 @@ +fn main(x: u64, y: u64) { + // runtime shifts on compile-time known values + assert(64 << y == 128); + assert(64 >> y == 32); + // runtime shifts on runtime values + assert(x << y == 128); + assert(x >> y == 32); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/bool_not/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/bool_not/Nargo.toml new file mode 100644 index 00000000000..fe42e1efa11 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bool_not/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "bool_not" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/bool_not/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/bool_not/Prover.toml new file mode 100644 index 00000000000..4dd6b405159 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bool_not/Prover.toml @@ -0,0 +1 @@ +x = "1" diff --git a/noir/tooling/nargo_cli/tests/execution_success/bool_not/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/bool_not/src/main.nr new file mode 100644 index 00000000000..935d8cc074d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bool_not/src/main.nr @@ -0,0 +1,4 @@ +fn main(x: u1) { + assert(!x == 0); +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/bool_or/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/bool_or/Nargo.toml new file mode 100644 index 00000000000..e139a7a2947 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bool_or/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "bool_or" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/bool_or/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/bool_or/Prover.toml new file mode 100644 index 00000000000..a0397e89477 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bool_or/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "0" diff --git a/noir/tooling/nargo_cli/tests/execution_success/bool_or/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/bool_or/src/main.nr new file mode 100644 index 00000000000..6cb959e61e6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/bool_or/src/main.nr @@ -0,0 +1,6 @@ +fn main(x: u1, y: u1) { + assert(x | y == 1); + + assert(x | y | x == 1); +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_acir_as_brillig/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_acir_as_brillig/Nargo.toml new file mode 100644 index 00000000000..aef4fd56d4f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_acir_as_brillig/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_acir_as_brillig" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_acir_as_brillig/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_acir_as_brillig/Prover.toml new file mode 100644 index 00000000000..11497a473bc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_acir_as_brillig/Prover.toml @@ -0,0 +1 @@ +x = "0" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_acir_as_brillig/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_acir_as_brillig/src/main.nr new file mode 100644 index 00000000000..5bd6ce0adb2 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_acir_as_brillig/src/main.nr @@ -0,0 +1,42 @@ +fn main(x: u32) { + assert(entry_point(x) == 2); + swap_entry_point(x, x + 1); + assert(deep_entry_point(x) == 4); +} + +fn inner(x: u32) -> u32 { + x + 1 +} + +unconstrained fn entry_point(x: u32) -> u32 { + inner(x + 1) +} + +fn swap(x: u32, y: u32) -> (u32, u32) { + (y, x) +} + +unconstrained fn swap_entry_point(x: u32, y: u32) { + let swapped = swap(x, y); + assert(swapped.0 == y); + assert(swapped.1 == x); + let swapped_twice = swap(swapped.0, swapped.1); + assert(swapped_twice.0 == x); + assert(swapped_twice.1 == y); +} + +fn level_3(x: u32) -> u32 { + x + 1 +} + +fn level_2(x: u32) -> u32 { + level_3(x + 1) +} + +fn level_1(x: u32) -> u32 { + level_2(x + 1) +} + +unconstrained fn deep_entry_point(x: u32) -> u32 { + level_1(x + 1) +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_arrays/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_arrays/Nargo.toml new file mode 100644 index 00000000000..a4433e380a9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_arrays/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_arrays" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_arrays/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_arrays/Prover.toml new file mode 100644 index 00000000000..6371ea2b28b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_arrays/Prover.toml @@ -0,0 +1 @@ +x = ["1", "2", "3"] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_arrays/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_arrays/src/main.nr new file mode 100644 index 00000000000..e535b6001a4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_arrays/src/main.nr @@ -0,0 +1,28 @@ +// Tests a very simple program. +// +// The features being tested are array reads and writes +fn main(x: [Field; 3]) { + read_array(x); + read_write_array(x); +} + +unconstrained fn read_array(x: [Field; 3]) { + assert(x[0] == 1); + let y = [1, 5, 27]; + + assert(y[x[0]] == 5); +} + +unconstrained fn read_write_array(x: [Field; 3]) { + let mut y = x; + + y[0] = 5; + + assert(y[0] == 5); + assert(y[1] == 2); + assert(y[2] == 3); + + assert(x[0] == 1); + assert(x[1] == 2); + assert(x[2] == 3); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_assert/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_assert/Nargo.toml new file mode 100644 index 00000000000..b7d9231ab75 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_assert/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_assert" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_assert/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_assert/Prover.toml new file mode 100644 index 00000000000..4dd6b405159 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_assert/Prover.toml @@ -0,0 +1 @@ +x = "1" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_assert/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_assert/src/main.nr new file mode 100644 index 00000000000..91e4cebd9d3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_assert/src/main.nr @@ -0,0 +1,12 @@ +// Tests a very simple program. +// +// The features being tested is using assert on brillig +fn main(x: Field) { + assert(1 == conditional(x as bool)); +} + +unconstrained fn conditional(x: bool) -> Field { + assert(x, "x is false"); + assert_eq(x, true, "x is false"); + 1 +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_blake2s/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_blake2s/Nargo.toml new file mode 100644 index 00000000000..a497b6f48cc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_blake2s/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_blake2s" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_blake2s/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_blake2s/Prover.toml new file mode 100644 index 00000000000..8f95d95505f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_blake2s/Prover.toml @@ -0,0 +1,35 @@ +x = [104, 101, 108, 108, 111] +result = [ + 0x19, + 0x21, + 0x3b, + 0xac, + 0xc5, + 0x8d, + 0xee, + 0x6d, + 0xbd, + 0xe3, + 0xce, + 0xb9, + 0xa4, + 0x7c, + 0xbb, + 0x33, + 0x0b, + 0x3d, + 0x86, + 0xf8, + 0xcc, + 0xa8, + 0x99, + 0x7e, + 0xb0, + 0x0b, + 0xe4, + 0x56, + 0xf1, + 0x40, + 0xca, + 0x25, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_blake2s/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_blake2s/src/main.nr new file mode 100644 index 00000000000..5bd52666ae9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_blake2s/src/main.nr @@ -0,0 +1,11 @@ +use dep::std; +// Tests a very simple program. +// +// The features being tested is blake2s in brillig +fn main(x: [u8; 5], result: [u8; 32]) { + assert(blake2s(x) == result); +} + +unconstrained fn blake2s(x: [u8; 5]) -> [u8; 32] { + std::hash::blake2s(x) +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_calls/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls/Nargo.toml new file mode 100644 index 00000000000..7cc53a33a8a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_calls" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_calls/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls/Prover.toml new file mode 100644 index 00000000000..11497a473bc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls/Prover.toml @@ -0,0 +1 @@ +x = "0" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_calls/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls/src/main.nr new file mode 100644 index 00000000000..5c39713f5bb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls/src/main.nr @@ -0,0 +1,58 @@ +// Tests a very simple program. +// +// The features being tested is brillig calls +fn main(x: u32) { + assert(entry_point(x) == 2); + swap_entry_point(x, x + 1); + assert(deep_entry_point(x) == 4); + multiple_values_entry_point(x); +} + +unconstrained fn returns_multiple_values(x: u32) -> (u32, u32, u32, u32) { + (x + 1, x + 2, x + 3, x + 4) +} + +unconstrained fn multiple_values_entry_point(x: u32) { + let (a, b, c, d) = returns_multiple_values(x); + assert(a == x + 1); + assert(b == x + 2); + assert(c == x + 3); + assert(d == x + 4); +} + +unconstrained fn inner(x: u32) -> u32 { + x + 1 +} + +unconstrained fn entry_point(x: u32) -> u32 { + inner(x + 1) +} + +unconstrained fn swap(x: u32, y: u32) -> (u32, u32) { + (y, x) +} + +unconstrained fn swap_entry_point(x: u32, y: u32) { + let swapped = swap(x, y); + assert(swapped.0 == y); + assert(swapped.1 == x); + let swapped_twice = swap(swapped.0, swapped.1); + assert(swapped_twice.0 == x); + assert(swapped_twice.1 == y); +} + +unconstrained fn level_3(x: u32) -> u32 { + x + 1 +} + +unconstrained fn level_2(x: u32) -> u32 { + level_3(x + 1) +} + +unconstrained fn level_1(x: u32) -> u32 { + level_2(x + 1) +} + +unconstrained fn deep_entry_point(x: u32) -> u32 { + level_1(x + 1) +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_array/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_array/Nargo.toml new file mode 100644 index 00000000000..e360f2d6338 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_array/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_calls_array" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_array/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_array/Prover.toml new file mode 100644 index 00000000000..99580ca45bc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_array/Prover.toml @@ -0,0 +1 @@ +x = ["1","2","3"] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_array/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_array/src/main.nr new file mode 100644 index 00000000000..1b1d89f6366 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_array/src/main.nr @@ -0,0 +1,33 @@ +// Tests a very simple program. +// +// The features being tested is brillig calls passing arrays around +fn main(x: [u32; 3]) { + assert(entry_point(x) == 9); + another_entry_point(x); +} + +unconstrained fn inner(x: [u32; 3]) -> [u32; 3] { + [x[0] + 1, x[1] + 1, x[2] + 1] +} + +unconstrained fn entry_point(x: [u32; 3]) -> u32 { + let y = inner(x); + y[0] + y[1] + y[2] +} + +unconstrained fn nested_fn_that_allocates(value: u32) -> u32 { + let x = [value, value, value]; + let y = inner(x); + y[0] + y[1] + y[2] +} + +unconstrained fn another_entry_point(x: [u32; 3]) { + assert(x[0] == 1); + assert(x[1] == 2); + assert(x[2] == 3); + assert(nested_fn_that_allocates(1) == 6); + // x should be unchanged + assert(x[0] == 1); + assert(x[1] == 2); + assert(x[2] == 3); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_conditionals/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_conditionals/Nargo.toml new file mode 100644 index 00000000000..1553cd525ff --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_conditionals/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_calls_conditionals" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_conditionals/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_conditionals/Prover.toml new file mode 100644 index 00000000000..99580ca45bc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_conditionals/Prover.toml @@ -0,0 +1 @@ +x = ["1","2","3"] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_conditionals/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_conditionals/src/main.nr new file mode 100644 index 00000000000..0a1718d0171 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_calls_conditionals/src/main.nr @@ -0,0 +1,36 @@ +// Tests a very simple program. +// +// The features being tested is brillig calls with conditionals +fn main(x: [u32; 3]) { + assert(entry_point(x[0]) == 7); + assert(entry_point(x[1]) == 8); + assert(entry_point(x[2]) == 9); + assert(entry_point(42) == 0); +} + +unconstrained fn inner_1() -> u32 { + 7 +} + +unconstrained fn inner_2() -> u32 { + 8 +} + +unconstrained fn inner_3() -> u32 { + 9 +} + +unconstrained fn entry_point(x: u32) -> u32 { + let mut result: u32 = 0; + + if x == 1 { + result = inner_1(); + } else if x == 2 { + result = inner_2(); + } else if x == 3 { + result = inner_3(); + } + + result +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_conditional/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_conditional/Nargo.toml new file mode 100644 index 00000000000..6a8be620fee --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_conditional/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_conditional" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_conditional/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_conditional/Prover.toml new file mode 100644 index 00000000000..4dd6b405159 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_conditional/Prover.toml @@ -0,0 +1 @@ +x = "1" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_conditional/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_conditional/src/main.nr new file mode 100644 index 00000000000..a59336a877b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_conditional/src/main.nr @@ -0,0 +1,10 @@ +// Tests a very simple program. +// +// The features being tested is basic conditonal on brillig +fn main(x: Field) { + assert(4 == conditional(x == 1)); +} + +unconstrained fn conditional(x: bool) -> Field { + if x { 4 } else { 5 } +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_ecdsa/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_ecdsa/Nargo.toml new file mode 100644 index 00000000000..972dd9ce93b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_ecdsa/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_ecdsa" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_ecdsa/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_ecdsa/Prover.toml new file mode 100644 index 00000000000..e78fc19cb71 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_ecdsa/Prover.toml @@ -0,0 +1,169 @@ + +hashed_message = [ + 0x3a, + 0x73, + 0xf4, + 0x12, + 0x3a, + 0x5c, + 0xd2, + 0x12, + 0x1f, + 0x21, + 0xcd, + 0x7e, + 0x8d, + 0x35, + 0x88, + 0x35, + 0x47, + 0x69, + 0x49, + 0xd0, + 0x35, + 0xd9, + 0xc2, + 0xda, + 0x68, + 0x06, + 0xb4, + 0x63, + 0x3a, + 0xc8, + 0xc1, + 0xe2, +] +pub_key_x = [ + 0xa0, + 0x43, + 0x4d, + 0x9e, + 0x47, + 0xf3, + 0xc8, + 0x62, + 0x35, + 0x47, + 0x7c, + 0x7b, + 0x1a, + 0xe6, + 0xae, + 0x5d, + 0x34, + 0x42, + 0xd4, + 0x9b, + 0x19, + 0x43, + 0xc2, + 0xb7, + 0x52, + 0xa6, + 0x8e, + 0x2a, + 0x47, + 0xe2, + 0x47, + 0xc7, +] +pub_key_y = [ + 0x89, + 0x3a, + 0xba, + 0x42, + 0x54, + 0x19, + 0xbc, + 0x27, + 0xa3, + 0xb6, + 0xc7, + 0xe6, + 0x93, + 0xa2, + 0x4c, + 0x69, + 0x6f, + 0x79, + 0x4c, + 0x2e, + 0xd8, + 0x77, + 0xa1, + 0x59, + 0x3c, + 0xbe, + 0xe5, + 0x3b, + 0x03, + 0x73, + 0x68, + 0xd7, +] +signature = [ + 0xe5, + 0x08, + 0x1c, + 0x80, + 0xab, + 0x42, + 0x7d, + 0xc3, + 0x70, + 0x34, + 0x6f, + 0x4a, + 0x0e, + 0x31, + 0xaa, + 0x2b, + 0xad, + 0x8d, + 0x97, + 0x98, + 0xc3, + 0x80, + 0x61, + 0xdb, + 0x9a, + 0xe5, + 0x5a, + 0x4e, + 0x8d, + 0xf4, + 0x54, + 0xfd, + 0x28, + 0x11, + 0x98, + 0x94, + 0x34, + 0x4e, + 0x71, + 0xb7, + 0x87, + 0x70, + 0xcc, + 0x93, + 0x1d, + 0x61, + 0xf4, + 0x80, + 0xec, + 0xbb, + 0x0b, + 0x89, + 0xd6, + 0xeb, + 0x69, + 0x69, + 0x01, + 0x61, + 0xe4, + 0x9a, + 0x71, + 0x5f, + 0xcd, + 0x55, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_ecdsa/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_ecdsa/src/main.nr new file mode 100644 index 00000000000..23f017aa336 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_ecdsa/src/main.nr @@ -0,0 +1,11 @@ +use dep::std; +// Tests a very simple program. +// +// The features being tested is ecdsa in brillig +fn main(hashed_message: [u8;32], pub_key_x: [u8;32], pub_key_y: [u8;32], signature: [u8;64]) { + assert(ecdsa(hashed_message, pub_key_x, pub_key_y, signature)); +} + +unconstrained fn ecdsa(hashed_message: [u8;32], pub_key_x: [u8;32], pub_key_y: [u8;32], signature: [u8;64]) -> bool { + std::ecdsa_secp256k1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message) +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_fns_as_values/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_fns_as_values/Nargo.toml new file mode 100644 index 00000000000..decd9a9412a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_fns_as_values/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_fns_as_values" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_fns_as_values/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_fns_as_values/Prover.toml new file mode 100644 index 00000000000..11497a473bc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_fns_as_values/Prover.toml @@ -0,0 +1 @@ +x = "0" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_fns_as_values/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_fns_as_values/src/main.nr new file mode 100644 index 00000000000..2f5d14583d5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_fns_as_values/src/main.nr @@ -0,0 +1,36 @@ +use dep::std; + +struct MyStruct { + operation: fn (u32) -> u32, +} + +fn main(x: u32) { + assert(wrapper(increment, x) == x + 1); + assert(wrapper(increment_acir, x) == x + 1); + assert(wrapper(decrement, x) == std::wrapping_sub(x, 1)); + assert(wrapper_with_struct(MyStruct { operation: increment }, x) == x + 1); + assert(wrapper_with_struct(MyStruct { operation: decrement }, x) == std::wrapping_sub(x, 1)); + // https://github.com/noir-lang/noir/issues/1975 + assert(increment(x) == x + 1); +} + +unconstrained fn wrapper(func: fn (u32) -> u32, param: u32) -> u32 { + func(param) +} + +unconstrained fn increment(x: u32) -> u32 { + x + 1 +} + +unconstrained fn decrement(x: u32) -> u32 { + x - 1 +} + +unconstrained fn wrapper_with_struct(my_struct: MyStruct, param: u32) -> u32 { + let func = my_struct.operation; + func(param) +} + +fn increment_acir(x: u32) -> u32 { + x + 1 +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_hash_to_field/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_hash_to_field/Nargo.toml new file mode 100644 index 00000000000..7cfcc745f0d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_hash_to_field/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_hash_to_field" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_hash_to_field/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_hash_to_field/Prover.toml new file mode 100644 index 00000000000..ecdcfd1fb00 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_hash_to_field/Prover.toml @@ -0,0 +1 @@ +input = "27" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_hash_to_field/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_hash_to_field/src/main.nr new file mode 100644 index 00000000000..4b4177a521e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_hash_to_field/src/main.nr @@ -0,0 +1,11 @@ +use dep::std; +// Tests a very simple program. +// +// The features being tested is hash_to_field in brillig +fn main(input: Field) -> pub Field { + hash_to_field(input) +} + +unconstrained fn hash_to_field(input: Field) -> Field { + std::hash::hash_to_field([input]) +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_identity_function/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_identity_function/Nargo.toml new file mode 100644 index 00000000000..8196884fe2b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_identity_function/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_identity_function" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_identity_function/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_identity_function/Prover.toml new file mode 100644 index 00000000000..55cccb955a9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_identity_function/Prover.toml @@ -0,0 +1,2 @@ +x = "3" + diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_identity_function/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_identity_function/src/main.nr new file mode 100644 index 00000000000..f41188b1f0d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_identity_function/src/main.nr @@ -0,0 +1,32 @@ +struct myStruct { + foo: Field, + foo_arr: [Field; 2], +} +// Tests a very simple program. +// +// The features being tested is the identity function in Brillig +fn main(x: Field) { + assert(x == identity(x)); + // TODO: add support for array comparison + let arr = identity_array([x, x]); + assert(x == arr[0]); + assert(x == arr[1]); + + let s = myStruct { foo: x, foo_arr: [x, x] }; + let identity_struct = identity_struct(s); + assert(x == identity_struct.foo); + assert(x == identity_struct.foo_arr[0]); + assert(x == identity_struct.foo_arr[1]); +} + +unconstrained fn identity(x: Field) -> Field { + x +} + +unconstrained fn identity_array(arr: [Field; 2]) -> [Field; 2] { + arr +} + +unconstrained fn identity_struct(s: myStruct) -> myStruct { + s +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_keccak/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_keccak/Nargo.toml new file mode 100644 index 00000000000..8cacf2186b8 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_keccak/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_keccak" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_keccak/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_keccak/Prover.toml new file mode 100644 index 00000000000..d65c4011d3f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_keccak/Prover.toml @@ -0,0 +1,35 @@ +x = 0xbd +result = [ + 0x5a, + 0x50, + 0x2f, + 0x9f, + 0xca, + 0x46, + 0x7b, + 0x26, + 0x6d, + 0x5b, + 0x78, + 0x33, + 0x65, + 0x19, + 0x37, + 0xe8, + 0x05, + 0x27, + 0x0c, + 0xa3, + 0xf3, + 0xaf, + 0x1c, + 0x0d, + 0xd2, + 0x46, + 0x2d, + 0xca, + 0x4b, + 0x3b, + 0x1a, + 0xbf, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_keccak/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_keccak/src/main.nr new file mode 100644 index 00000000000..1e9b65a6eb4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_keccak/src/main.nr @@ -0,0 +1,25 @@ +use dep::std; +// Tests a very simple program. +// +// The features being tested is keccak256 in brillig +fn main(x: Field, result: [u8; 32]) { + // We use the `as` keyword here to denote the fact that we want to take just the first byte from the x Field + // The padding is taken care of by the program + let digest = keccak256([x as u8], 1); + assert(digest == result); + //#1399: variable meesage size + let message_size = 4; + let hash_a = keccak256([1, 2, 3, 4], message_size); + let hash_b = keccak256([1, 2, 3, 4, 0, 0, 0, 0], message_size); + + assert(hash_a == hash_b); + + let message_size_big = 8; + let hash_c = keccak256([1, 2, 3, 4, 0, 0, 0, 0], message_size_big); + + assert(hash_a != hash_c); +} + +unconstrained fn keccak256(data: [u8; N], msg_len: u32) -> [u8; 32] { + std::hash::keccak256(data, msg_len) +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_loop/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_loop/Nargo.toml new file mode 100644 index 00000000000..1212397c4db --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_loop/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_loop" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_loop/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_loop/Prover.toml new file mode 100644 index 00000000000..22cd5b7c12f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_loop/Prover.toml @@ -0,0 +1 @@ +sum = "6" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_loop/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_loop/src/main.nr new file mode 100644 index 00000000000..05d35469342 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_loop/src/main.nr @@ -0,0 +1,23 @@ +// Tests a very simple program. +// +// The features being tested is basic looping on brillig +fn main(sum: u32) { + assert(loop(4) == sum); + assert(plain_loop() == sum); +} + +unconstrained fn loop(x: u32) -> u32 { + let mut sum = 0; + for i in 0..x { + sum = sum + i; + } + sum +} + +unconstrained fn plain_loop() -> u32 { + let mut sum = 0; + for i in 0..4 { + sum = sum + i; + } + sum +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_arrays/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_arrays/Nargo.toml new file mode 100644 index 00000000000..c36c8a0a2bd --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_arrays/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "brillig_nested_arrays" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_arrays/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_arrays/Prover.toml new file mode 100644 index 00000000000..26fdbc19975 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_arrays/Prover.toml @@ -0,0 +1,2 @@ +x = "0" +y = "1" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_arrays/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_arrays/src/main.nr new file mode 100644 index 00000000000..d0a60ac0a58 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_arrays/src/main.nr @@ -0,0 +1,30 @@ +struct Header { + params: [Field; 3], +} + +struct MyNote { + plain: Field, + array: [Field; 2], + header: Header, +} + +unconstrained fn access_nested(notes: [MyNote; 2], x: Field, y: Field) -> Field { + notes[x].array[y] + notes[y].array[x] + notes[x].plain + notes[y].header.params[x] +} + +unconstrained fn create_inside_brillig(x: Field, y: Field) { + let header = Header { params: [1, 2, 3] }; + let note0 = MyNote { array: [1, 2], plain: 3, header }; + let note1 = MyNote { array: [4, 5], plain: 6, header }; + assert(access_nested([note0, note1], x, y) == (2 + 4 + 3 + 1)); +} + +fn main(x: Field, y: Field) { + let header = Header { params: [1, 2, 3] }; + let note0 = MyNote { array: [1, 2], plain: 3, header }; + let note1 = MyNote { array: [4, 5], plain: 6, header }; + + create_inside_brillig(x, y); + assert(access_nested([note0, note1], x, y) == (2 + 4 + 3 + 1)); +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_slices/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_slices/Nargo.toml new file mode 100644 index 00000000000..d84338cdbb4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_slices/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "brillig_nested_slices" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_slices/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_slices/Prover.toml new file mode 100644 index 00000000000..c52564de922 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_slices/Prover.toml @@ -0,0 +1,2 @@ +a = "5" +b = "10" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_slices/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_slices/src/main.nr new file mode 100644 index 00000000000..3d8a6748ccf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_nested_slices/src/main.nr @@ -0,0 +1,72 @@ +use dep::std::slice; +// Tests nested slice passing to/from functions +unconstrained fn push_back_to_slice(slice: [T], item: T) -> [T] { + slice.push_back(item) +} + +struct NestedSliceStruct { + id: Field, + arr: [Field] +} + +unconstrained fn create_foo(id: Field, value: Field) -> NestedSliceStruct { + let mut arr = [id]; + arr = arr.push_back(value); + NestedSliceStruct { id, arr } +} + +unconstrained fn main(a: Field, b: Field) { + let mut slice = [create_foo(a, b), create_foo(b, a)]; + assert(slice.len() == 2); + + assert(slice[0].id == a); + assert(slice[0].arr[0] == a); + assert(slice[1].id == b); + assert(slice[1].arr[1] == a); + + slice = push_back_to_slice(slice, create_foo(0, 42)); + assert(slice.len() == 3); + + assert(slice[0].id == a); + assert(slice[0].arr[0] == a); + assert(slice[1].id == b); + assert(slice[1].arr[1] == a); + + assert(slice[2].id == 0); + assert(slice[2].arr[0] == 0); + assert(slice[2].arr[1] == 42); + + slice = slice.push_front(create_foo(1, 43)); + slice = slice.push_back(create_foo(2, 44)); + + assert(slice.len() == 5); + + let pop_front_result = slice.pop_front(); + slice = pop_front_result.1; + assert(pop_front_result.0.id == 1); + + let pop_back_result = slice.pop_back(); + slice = pop_back_result.0; + assert(pop_back_result.1.id == 2); + + assert(slice.len() == 3); + + let mut remove_result = slice.remove(0); + slice = remove_result.0; + let mut removed_item = remove_result.1; + assert(removed_item.arr[0] == a); + + remove_result = slice.remove(1); + slice = remove_result.0; + removed_item = remove_result.1; + assert(removed_item.arr[0] == 0); + + let last_item = slice[0]; + + assert(last_item.id == b); + slice = slice.insert(1, removed_item); + + assert(slice.len() == 2); + assert(slice[0].id == b); + assert(slice[1].id == 0); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_not/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_not/Nargo.toml new file mode 100644 index 00000000000..ef882ea8da0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_not/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_not" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_not/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_not/Prover.toml new file mode 100644 index 00000000000..a0397e89477 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_not/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "0" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_not/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_not/src/main.nr new file mode 100644 index 00000000000..d34b3edb4b6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_not/src/main.nr @@ -0,0 +1,11 @@ +// Tests a very simple Brillig function. +// +// The features being tested is not instruction on brillig +fn main(x: Field, y: Field) { + assert(false == not_operator(x as bool)); + assert(true == not_operator(y as bool)); +} + +unconstrained fn not_operator(x: bool) -> bool { + !x +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_oracle/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_oracle/Nargo.toml new file mode 100644 index 00000000000..13bd913cef7 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_oracle/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_oracle" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_oracle/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_oracle/Prover.toml new file mode 100644 index 00000000000..2b26a4ce471 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_oracle/Prover.toml @@ -0,0 +1,2 @@ +x = "10" + diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_oracle/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_oracle/src/main.nr new file mode 100644 index 00000000000..86cf6ff1498 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_oracle/src/main.nr @@ -0,0 +1,26 @@ +use dep::std::slice; +// Tests oracle usage in brillig/unconstrained functions +fn main(x: Field) { + get_number_sequence_wrapper(20); +} +// TODO(#1911): This function does not need to be an oracle but acts +// as a useful test while we finalize code generation for slices in Brillig +#[oracle(get_number_sequence)] +unconstrained fn get_number_sequence(_size: Field) -> [Field] {} +// TODO(#1911) +#[oracle(get_reverse_number_sequence)] +unconstrained fn get_reverse_number_sequence(_size: Field) -> [Field] {} + +unconstrained fn get_number_sequence_wrapper(size: Field) { + let slice = get_number_sequence(size); + for i in 0..19 as u32 { + assert(slice[i] == i as Field); + } + + let reversed_slice = get_reverse_number_sequence(size); + // Regression test that we have not overwritten memory + for i in 0..19 as u32 { + assert(slice[i] == reversed_slice[19 - i]); + } +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_pedersen/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_pedersen/Nargo.toml new file mode 100644 index 00000000000..6327caa0a09 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_pedersen/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_pedersen" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_pedersen/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_pedersen/Prover.toml new file mode 100644 index 00000000000..db1ebdf6c51 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_pedersen/Prover.toml @@ -0,0 +1,7 @@ +x = "0" +y = "1" +salt = "42" + +out_x = "0x054aa86a73cb8a34525e5bbed6e43ba1198e860f5f3950268f71df4591bde402" +out_y = "0x209dcfbf2cfb57f9f6046f44d71ac6faf87254afc7407c04eb621a6287cac126" +out_hash = "0x0d98561fb02ca04d00801dfdc118b2a24cea0351963587712a28d368041370e1" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_pedersen/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_pedersen/src/main.nr new file mode 100644 index 00000000000..2379818c454 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_pedersen/src/main.nr @@ -0,0 +1,22 @@ +use dep::std; + +unconstrained fn main(x: Field, y: Field, salt: Field, out_x: Field, out_y: Field, out_hash: Field) { + let res = std::hash::pedersen_commitment_with_separator([x, y], 0); + assert(res.x == out_x); + assert(res.y == out_y); + + let res_hash = std::hash::pedersen_hash_with_separator([x, y], 0); + assert_eq(res_hash, out_hash); + + assert(res_hash != res.x); + + let raw_data = [x, y]; + let mut state = 0; + for i in 0..(2 as u32) { + state = state * 8 + raw_data[i]; + } + state += salt; + let hash = std::hash::pedersen_commitment_with_separator([state], 0); + assert(std::hash::pedersen_commitment_with_separator([43], 0).x == hash.x); +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_recursion/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_recursion/Nargo.toml new file mode 100644 index 00000000000..087eeefd5ac --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_recursion/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_recursion" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_recursion/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_recursion/Prover.toml new file mode 100644 index 00000000000..3a627b9188b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_recursion/Prover.toml @@ -0,0 +1 @@ +x = "10" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_recursion/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_recursion/src/main.nr new file mode 100644 index 00000000000..a87ef28bc56 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_recursion/src/main.nr @@ -0,0 +1,14 @@ +// Tests a very simple program. +// +// The feature being tested is brillig recursion +fn main(x: u32) { + assert(fibonacci(x) == 55); +} + +unconstrained fn fibonacci(x: u32) -> u32 { + if x <= 1 { + x + } else { + fibonacci(x - 1) + fibonacci(x - 2) + } +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_references/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_references/Nargo.toml new file mode 100644 index 00000000000..0f64b862ba0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_references/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_references" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_references/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_references/Prover.toml new file mode 100644 index 00000000000..151faa5a9b1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_references/Prover.toml @@ -0,0 +1 @@ +x = "2" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_references/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_references/src/main.nr new file mode 100644 index 00000000000..e1f906beb0a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_references/src/main.nr @@ -0,0 +1,49 @@ +unconstrained fn main(mut x: Field) { + add1(&mut x); + assert(x == 3); + // https://github.com/noir-lang/noir/issues/1899 + // let mut s = S { y: x }; + // s.add2(); + // assert(s.y == 5); + // Test that normal mutable variables are still copied + let mut a = 0; + mutate_copy(a); + assert(a == 0); + // Test something 3 allocations deep + let mut nested_allocations = Nested { y: &mut &mut 0 }; + add1(*nested_allocations.y); + assert(**nested_allocations.y == 1); + // Test nested struct allocations with a mutable reference to an array. + let mut c = C { foo: 0, bar: &mut C2 { array: &mut [1, 2] } }; + *c.bar.array = [3, 4]; + let arr: [Field; 2] = *c.bar.array; + assert(arr[0] == 3); + assert(arr[1] == 4); +} + +unconstrained fn add1(x: &mut Field) { + *x += 1; +} + +struct S { y: Field } + +struct Nested { y: &mut &mut Field } + +struct C { + foo: Field, + bar: &mut C2, +} + +struct C2 { + array: &mut [Field; 2] +} + +impl S { + unconstrained fn add2(&mut self) { + self.y += 2; + } +} + +unconstrained fn mutate_copy(mut a: Field) { + a = 7; +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_scalar_mul/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_scalar_mul/Nargo.toml new file mode 100644 index 00000000000..eefd041b899 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_scalar_mul/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_scalar_mul" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_scalar_mul/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_scalar_mul/Prover.toml new file mode 100644 index 00000000000..69b91cb5f31 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_scalar_mul/Prover.toml @@ -0,0 +1,7 @@ +a = "1" +a_pub_x = "0x0000000000000000000000000000000000000000000000000000000000000001" +a_pub_y = "0x0000000000000002cf135e7506a45d632d270d45f1181294833fc48d823f272c" + +b = "2" +b_pub_x = "0x06ce1b0827aafa85ddeb49cdaa36306d19a74caa311e13d46d8bc688cdbffffe" +b_pub_y = "0x1c122f81a3a14964909ede0ba2a6855fc93faf6fa1a788bf467be7e7a43f80ac" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_scalar_mul/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_scalar_mul/src/main.nr new file mode 100644 index 00000000000..30c51ce0f50 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_scalar_mul/src/main.nr @@ -0,0 +1,16 @@ +use dep::std; + +unconstrained fn main(a: Field, a_pub_x: pub Field, a_pub_y: pub Field, b: Field, b_pub_x: pub Field, b_pub_y: pub Field) { + let mut priv_key = a; + let mut pub_x: Field = a_pub_x; + let mut pub_y: Field = a_pub_y; + if a != 1 { + // Change `a` in Prover.toml to test input `b` + priv_key = b; + pub_x = b_pub_x; + pub_y = b_pub_y; + } + let res = std::scalar_mul::fixed_base_embedded_curve(priv_key, 0); + assert(res[0] == pub_x); + assert(res[1] == pub_y); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_schnorr/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_schnorr/Nargo.toml new file mode 100644 index 00000000000..1b598abbf74 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_schnorr/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_schnorr" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_schnorr/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_schnorr/Prover.toml new file mode 100644 index 00000000000..2faf2018e07 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_schnorr/Prover.toml @@ -0,0 +1,70 @@ +message = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] +message_field = "0x010203040506070809" +pub_key_x = "0x04b260954662e97f00cab9adb773a259097f7a274b83b113532bce27fa3fb96a" +pub_key_y = "0x2fd51571db6c08666b0edfbfbc57d432068bccd0110a39b166ab243da0037197" +signature = [ + 1, + 13, + 119, + 112, + 212, + 39, + 233, + 41, + 84, + 235, + 255, + 93, + 245, + 172, + 186, + 83, + 157, + 253, + 76, + 77, + 33, + 128, + 178, + 15, + 214, + 67, + 105, + 107, + 177, + 234, + 77, + 48, + 27, + 237, + 155, + 84, + 39, + 84, + 247, + 27, + 22, + 8, + 176, + 230, + 24, + 115, + 145, + 220, + 254, + 122, + 135, + 179, + 171, + 4, + 214, + 202, + 64, + 199, + 19, + 84, + 239, + 138, + 124, + 12, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_schnorr/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_schnorr/src/main.nr new file mode 100644 index 00000000000..107b311f6ab --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_schnorr/src/main.nr @@ -0,0 +1,19 @@ +use dep::std; +// Note: If main has any unsized types, then the verifier will never be able +// to figure out the circuit instance +unconstrained fn main(message: [u8; 10], message_field: Field, pub_key_x: Field, pub_key_y: Field, signature: [u8; 64]) { + // Regression for issue #2421 + // We want to make sure that we can accurately verify a signature whose message is a slice vs. an array + let message_field_bytes = message_field.to_be_bytes(10); + for i in 0..10 { + assert(message[i] == message_field_bytes[i]); + } + // Is there ever a situation where someone would want + // to ensure that a signature was invalid? + // Check that passing a slice as the message is valid + let valid_signature = std::schnorr::verify_signature(pub_key_x, pub_key_y, signature, message_field_bytes); + assert(valid_signature); + // Check that passing an array as the message is valid + let valid_signature = std::schnorr::verify_signature(pub_key_x, pub_key_y, signature, message); + assert(valid_signature); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_sha256/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_sha256/Nargo.toml new file mode 100644 index 00000000000..7140fa0fd0b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_sha256/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_sha256" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_sha256/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_sha256/Prover.toml new file mode 100644 index 00000000000..374ae90ad78 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_sha256/Prover.toml @@ -0,0 +1,35 @@ +x = 0xbd +result = [ + 0x68, + 0x32, + 0x57, + 0x20, + 0xaa, + 0xbd, + 0x7c, + 0x82, + 0xf3, + 0x0f, + 0x55, + 0x4b, + 0x31, + 0x3d, + 0x05, + 0x70, + 0xc9, + 0x5a, + 0xcc, + 0xbb, + 0x7d, + 0xc4, + 0xb5, + 0xaa, + 0xe1, + 0x12, + 0x04, + 0xc0, + 0x8f, + 0xfe, + 0x73, + 0x2b, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_sha256/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_sha256/src/main.nr new file mode 100644 index 00000000000..e76109df9c3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_sha256/src/main.nr @@ -0,0 +1,14 @@ +use dep::std; +// Tests a very simple program. +// +// The features being tested is sha256 in brillig +fn main(x: Field, result: [u8; 32]) { + assert(result == sha256(x)); +} + +unconstrained fn sha256(x: Field) -> [u8; 32] { + // We use the `as` keyword here to denote the fact that we want to take just the first byte from the x Field + // The padding is taken care of by the program + std::hash::sha256([x as u8]) +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_slices/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_slices/Nargo.toml new file mode 100644 index 00000000000..5f6caad088a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_slices/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "brillig_slices" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_slices/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_slices/Prover.toml new file mode 100644 index 00000000000..f28f2f8cc48 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_slices/Prover.toml @@ -0,0 +1,2 @@ +x = "5" +y = "10" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_slices/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_slices/src/main.nr new file mode 100644 index 00000000000..48bc8a76bb8 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_slices/src/main.nr @@ -0,0 +1,140 @@ +use dep::std::slice; +unconstrained fn main(x: Field, y: Field) { + let mut slice: [Field] = [y, x]; + assert(slice.len() == 2); + + slice = slice.push_back(7); + assert(slice.len() == 3); + assert(slice[0] == y); + assert(slice[1] == x); + assert(slice[2] == 7); + // Array set on slice target + slice[0] = x; + slice[1] = y; + slice[2] = 1; + + assert(slice[0] == x); + assert(slice[1] == y); + assert(slice[2] == 1); + + slice = push_front_to_slice(slice, 2); + assert(slice.len() == 4); + assert(slice[0] == 2); + assert(slice[1] == x); + assert(slice[2] == y); + assert(slice[3] == 1); + + let (item, popped_front_slice) = slice.pop_front(); + slice = popped_front_slice; + assert(item == 2); + + assert(slice.len() == 3); + assert(slice[0] == x); + assert(slice[1] == y); + assert(slice[2] == 1); + + let (popped_back_slice, another_item) = slice.pop_back(); + slice = popped_back_slice; + assert(another_item == 1); + + assert(slice.len() == 2); + assert(slice[0] == x); + assert(slice[1] == y); + + slice = slice.insert(1, 2); + assert(slice.len() == 3); + assert(slice[0] == x); + assert(slice[1] == 2); + assert(slice[2] == y); + + let (removed_slice, should_be_2) = slice.remove(1); + slice = removed_slice; + assert(should_be_2 == 2); + + assert(slice.len() == 2); + assert(slice[0] == x); + assert(slice[1] == y); + + let (slice_with_only_x, should_be_y) = slice.remove(1); + slice = slice_with_only_x; + assert(should_be_y == y); + + assert(slice.len() == 1); + assert(slice[0] == x); + + let (empty_slice, should_be_x) = slice.remove(0); + assert(should_be_x == x); + assert(empty_slice.len() == 0); + + regression_merge_slices(x, y); +} +// Tests slice passing to/from functions +unconstrained fn push_front_to_slice(slice: [T], item: T) -> [T] { + slice.push_front(item) +} +// The parameters to this function must come from witness values (inputs to main) +unconstrained fn regression_merge_slices(x: Field, y: Field) { + merge_slices_if(x, y); + merge_slices_else(x); +} + +unconstrained fn merge_slices_if(x: Field, y: Field) { + let slice = merge_slices_return(x, y); + assert(slice[2] == 10); + assert(slice.len() == 3); + + let slice = merge_slices_mutate(x, y); + assert(slice[3] == 5); + assert(slice.len() == 4); + + let slice = merge_slices_mutate_in_loop(x, y); + assert(slice[6] == 4); + assert(slice.len() == 7); +} + +unconstrained fn merge_slices_else(x: Field) { + let slice = merge_slices_return(x, 5); + assert(slice[0] == 0); + assert(slice[1] == 0); + assert(slice.len() == 2); + + let slice = merge_slices_mutate(x, 5); + assert(slice[2] == 5); + assert(slice.len() == 3); + + let slice = merge_slices_mutate_in_loop(x, 5); + assert(slice[2] == 5); + assert(slice.len() == 3); +} +// Test returning a merged slice without a mutation +unconstrained fn merge_slices_return(x: Field, y: Field) -> [Field] { + let slice = [0; 2]; + if x != y { + if x != 20 { slice.push_back(y) } else { slice } + } else { + slice + } +} +// Test mutating a slice inside of an if statement +unconstrained fn merge_slices_mutate(x: Field, y: Field) -> [Field] { + let mut slice = [0; 2]; + if x != y { + slice = slice.push_back(y); + slice = slice.push_back(x); + } else { + slice = slice.push_back(x); + } + slice +} +// Test mutating a slice inside of a loop in an if statement +unconstrained fn merge_slices_mutate_in_loop(x: Field, y: Field) -> [Field] { + let mut slice = [0; 2]; + if x != y { + for i in 0..5 { + slice = slice.push_back(i); + } + } else { + slice = slice.push_back(x); + } + slice +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_to_be_bytes/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_be_bytes/Nargo.toml new file mode 100644 index 00000000000..df6c818c90f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_be_bytes/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_to_be_bytes" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_to_be_bytes/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_be_bytes/Prover.toml new file mode 100644 index 00000000000..07fe857ac7c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_be_bytes/Prover.toml @@ -0,0 +1 @@ +x = "2040124" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_to_be_bytes/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_be_bytes/src/main.nr new file mode 100644 index 00000000000..9d78411f060 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_be_bytes/src/main.nr @@ -0,0 +1,12 @@ +unconstrained fn main(x: Field) -> pub [u8; 31] { + // The result of this byte array will be big-endian + let byte_array = x.to_be_bytes(31); + let mut bytes = [0; 31]; + for i in 0..31 { + bytes[i] = byte_array[i]; + } + assert(bytes[30] == 60); + assert(bytes[29] == 33); + assert(bytes[28] == 31); + bytes +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_to_bytes_integration/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_bytes_integration/Nargo.toml new file mode 100644 index 00000000000..991f3d1e46c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_bytes_integration/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_to_bytes_integration" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_to_bytes_integration/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_bytes_integration/Prover.toml new file mode 100644 index 00000000000..23f7acea449 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_bytes_integration/Prover.toml @@ -0,0 +1,2 @@ +x = "2040124" +_y = "0x2000000000000000000000000000000000000000000000000000000000000000" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_to_bytes_integration/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_bytes_integration/src/main.nr new file mode 100644 index 00000000000..e8e5b9db9ca --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_bytes_integration/src/main.nr @@ -0,0 +1,27 @@ +use dep::std; + +unconstrained fn main(x: Field, _y: Field) { + // The result of this byte array will be big-endian + let y: Field = 2040124; + let be_byte_array = y.to_be_bytes(31); + // The result of this byte array will be little-endian + let le_byte_array = x.to_le_bytes(31); + + assert(le_byte_array[0] == 60); + assert(le_byte_array[0] == be_byte_array[30]); + assert(le_byte_array[1] == be_byte_array[29]); + assert(le_byte_array[2] == be_byte_array[28]); + + let z = 0 - 1; + let p_bytes = std::field::modulus_le_bytes(); + let z_bytes = z.to_le_bytes(32); + assert(p_bytes[10] == z_bytes[10]); + assert(p_bytes[0] == z_bytes[0] as u8 + 1 as u8); + + let p_bits = std::field::modulus_le_bits(); + let z_bits = z.to_le_bits(std::field::modulus_num_bits() as u32); + assert(z_bits[0] == 0); + assert(p_bits[100] == z_bits[100]); + + _y.to_le_bits(std::field::modulus_num_bits() as u32); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_to_le_bytes/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_le_bytes/Nargo.toml new file mode 100644 index 00000000000..c2ce8ad01b5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_le_bytes/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_to_le_bytes" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_to_le_bytes/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_le_bytes/Prover.toml new file mode 100644 index 00000000000..07fe857ac7c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_le_bytes/Prover.toml @@ -0,0 +1 @@ +x = "2040124" diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_to_le_bytes/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_le_bytes/src/main.nr new file mode 100644 index 00000000000..77d292cf01b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_to_le_bytes/src/main.nr @@ -0,0 +1,10 @@ +unconstrained fn main(x: Field) -> pub [u8; 31] { + // The result of this byte array will be little-endian + let byte_array = x.to_le_bytes(31); + assert(byte_array.len() == 31); + let mut bytes = [0; 31]; + for i in 0..31 { + bytes[i] = byte_array[i]; + } + bytes +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_top_level/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_top_level/Nargo.toml new file mode 100644 index 00000000000..f74a2a82964 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_top_level/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_top_level" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_top_level/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_top_level/Prover.toml new file mode 100644 index 00000000000..a0150a0e562 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_top_level/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +array = ["4", "5", "6"] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_top_level/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_top_level/src/main.nr new file mode 100644 index 00000000000..6dfd98b2c3e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_top_level/src/main.nr @@ -0,0 +1,6 @@ +// Tests a very simple program. +// +// The feature being tested is brillig as the entry point. +unconstrained fn main(array: [Field; 3], x: pub Field) -> pub [Field; 2] { + [array[x], array[x + 1]] +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_unitialised_arrays/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_unitialised_arrays/Nargo.toml new file mode 100644 index 00000000000..f23ecc787d0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_unitialised_arrays/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "brillig_unitialised_arrays" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_unitialised_arrays/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/brillig_unitialised_arrays/Prover.toml new file mode 100644 index 00000000000..b6626a67e19 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_unitialised_arrays/Prover.toml @@ -0,0 +1,2 @@ +x = 1 +y = 0 diff --git a/noir/tooling/nargo_cli/tests/execution_success/brillig_unitialised_arrays/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/brillig_unitialised_arrays/src/main.nr new file mode 100644 index 00000000000..5ec657b0d35 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/brillig_unitialised_arrays/src/main.nr @@ -0,0 +1,12 @@ +fn main(x: Field, y: Field) -> pub Field { + let notes = create_notes(x, y); + sum_x(notes, x, y) +} + +fn sum_x(notes: [Field; 2], x: Field, y: Field) -> Field { + notes[x] + notes[y] +} + +unconstrained fn create_notes(x: Field, y: Field) -> [Field; 2] { + [x, y] +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/cast_bool/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/cast_bool/Nargo.toml new file mode 100644 index 00000000000..6571019494e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/cast_bool/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "cast_bool" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/cast_bool/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/cast_bool/Prover.toml new file mode 100644 index 00000000000..f489cbac003 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/cast_bool/Prover.toml @@ -0,0 +1,2 @@ +x = "10" +y = "10" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/cast_bool/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/cast_bool/src/main.nr new file mode 100644 index 00000000000..422d3b98f83 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/cast_bool/src/main.nr @@ -0,0 +1,6 @@ +fn main(x: Field, y: Field) { + let z = x == y; + let t = z as u8; + assert(t == 1); +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/closures_mut_ref/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/closures_mut_ref/Nargo.toml new file mode 100644 index 00000000000..01936c7be65 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/closures_mut_ref/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "closures_mut_ref" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/closures_mut_ref/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/closures_mut_ref/Prover.toml new file mode 100644 index 00000000000..11497a473bc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/closures_mut_ref/Prover.toml @@ -0,0 +1 @@ +x = "0" diff --git a/noir/tooling/nargo_cli/tests/execution_success/closures_mut_ref/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/closures_mut_ref/src/main.nr new file mode 100644 index 00000000000..5a743d1b633 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/closures_mut_ref/src/main.nr @@ -0,0 +1,30 @@ +fn main(mut x: Field) { + let one = 1; + let add1 = |z| { + *z = *z + one; + }; + + let two = 2; + let add2 = |z| { + *z = *z + two; + }; + + add1(&mut x); + assert(x == 1); + + add2(&mut x); + assert(x == 3); + + issue_2120(); +} +// https://github.com/noir-lang/noir/issues/2120 +fn issue_2120() { + let x1 = &mut 42; + let set_x1 = |y| { *x1 = y; }; + + assert(*x1 == 42); + set_x1(44); + assert(*x1 == 44); + set_x1(*x1); + assert(*x1 == 44); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_1/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/conditional_1/Nargo.toml new file mode 100644 index 00000000000..28ca01332df --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_1/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "conditional_1" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_1/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/conditional_1/Prover.toml new file mode 100644 index 00000000000..baad8be126a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_1/Prover.toml @@ -0,0 +1,38 @@ +c=[2, 4, 3, 0, ] +a=0 +x = [104, 101, 108, 108, 111] + +result = [ + 0x2c, + 0xf2, + 0x4d, + 0xba, + 0x5f, + 0xb0, + 0xa3, + 0x0e, + 0x26, + 0xe8, + 0x3b, + 0x2a, + 0xc5, + 0xb9, + 0xe2, + 0x9e, + 0x1b, + 0x16, + 0x1e, + 0x5c, + 0x1f, + 0xa7, + 0x42, + 0x5e, + 0x73, + 0x04, + 0x33, + 0x62, + 0x93, + 0x8b, + 0x98, + 0x24, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_1/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/conditional_1/src/main.nr new file mode 100644 index 00000000000..5064c82bce9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_1/src/main.nr @@ -0,0 +1,93 @@ +use dep::std; + +fn sort(mut a: [u32; 4]) -> [u32; 4] { + for i in 1..4 { + for j in 0..i { + if a[i] < a[j] { + let c = a[j]; + a[j] = a[i]; + a[i] = c; + } + } + } + a +} + +fn must_be_zero(x: u8) { + assert(x == 0); +} + +fn main(a: u32, mut c: [u32; 4], x: [u8; 5], result: pub [u8; 32]) { + //Test case for short-circuit + let mut data = [0 as u32; 32]; + let mut ba = a; + for i in 0..32 { + let i_u32 = i as u32; + if i_u32 == a { + for j in 0..4 { + data[i + j] = c[4 - 1 - j]; + for k in 0..4 { + ba = ba +data[k]; + } + if ba == 4864 { + c[3]=ba; + } + } + } + } + assert(data[31] == 0); + assert(ba != 13); + //Test case for conditional with arrays from function parameters + let b = sort([1, 2, 3, 4]); + assert(b[0] == 1); + + if a == 0 { + must_be_zero(0); + c[0] = 3; + } else { + must_be_zero(1); + c[0] = 1; + c[1] = c[2] / a + 11 % a; + let f1 = a as Field; + assert(10 / f1 != 0); + } + assert(c[0] == 3); + + let mut y = 0; + if a == 0 { + let digest = std::hash::sha256(x); + y = digest[0]; + } else { + y = 5; + } + assert(y == result[0]); + c = sort(c); + assert(c[0] == 0); + //test 1 + let mut x: u32 = 0; + if a == 0 { + c[0] = 12; + if a != 0 { + x = 6; + } else { + x = 2; + assert(x == 2); + } + } else { + x = 5; + assert(x == 5); + } + if c[0] == 0 { + x = 3; + } + assert(x == 2); + //test2: loops + let mut x: u32 = 0; + x = a - a; + for i in 0..4 { + if c[i] == 0 { + x = i as u32 +2; + } + } + assert(x == 0); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_2/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/conditional_2/Nargo.toml new file mode 100644 index 00000000000..153c5d38b99 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_2/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "conditional_2" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_2/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/conditional_2/Prover.toml new file mode 100644 index 00000000000..73fa4a5e31a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_2/Prover.toml @@ -0,0 +1,2 @@ +c=[2, 4, 3, 0, ] +a=0 diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_2/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/conditional_2/src/main.nr new file mode 100644 index 00000000000..5b3f64f6be5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_2/src/main.nr @@ -0,0 +1,51 @@ +use dep::std; + +fn must_be_zero(x: u8) { + assert(x == 0); +} + +fn test3(x: u8) { + if x == 0 { + must_be_zero(x); + } +} + +fn test4() -> [u32; 4] { + let b: [u32; 4] = [1, 2, 3, 4]; + b +} + +fn main(a: u32, mut c: [u32; 4]) { + test3(1); + + if a == 0 { + c = test4(); + } else { + assert(c[1] != 2); + } + if false { + c[1] = 5; + } + assert(c[1] == 2); + + test5(4); + // Test case for function synchronisation + let mut c_sync = 0; + if a == 42 { + c_sync = foo2(); + } else { + c_sync = foo2() + foo2(); + } + assert(c_sync == 6); +} + +fn test5(a: u32) { + if a > 1 { + let q = a / 2; + assert(q == 2); + } +} + +fn foo2() -> Field { + 3 +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_421/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_421/Nargo.toml new file mode 100644 index 00000000000..987b51ae126 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_421/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "conditional_regression_421" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_421/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_421/Prover.toml new file mode 100644 index 00000000000..73fa4a5e31a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_421/Prover.toml @@ -0,0 +1,2 @@ +c=[2, 4, 3, 0, ] +a=0 diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_421/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_421/src/main.nr new file mode 100644 index 00000000000..5789afb76db --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_421/src/main.nr @@ -0,0 +1,10 @@ +fn main(a: u32, mut c: [u32; 4]) { + //Issue reported in #421 + if a == c[0] { + assert(c[0] == 0); + } else if a == c[1] { + assert(c[1] == 0); + } else if a == c[2] { + assert(c[2] == 0); + } +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_661/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_661/Nargo.toml new file mode 100644 index 00000000000..ed11341c5b0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_661/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "conditional_regression_661" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_661/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_661/Prover.toml new file mode 100644 index 00000000000..baad8be126a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_661/Prover.toml @@ -0,0 +1,38 @@ +c=[2, 4, 3, 0, ] +a=0 +x = [104, 101, 108, 108, 111] + +result = [ + 0x2c, + 0xf2, + 0x4d, + 0xba, + 0x5f, + 0xb0, + 0xa3, + 0x0e, + 0x26, + 0xe8, + 0x3b, + 0x2a, + 0xc5, + 0xb9, + 0xe2, + 0x9e, + 0x1b, + 0x16, + 0x1e, + 0x5c, + 0x1f, + 0xa7, + 0x42, + 0x5e, + 0x73, + 0x04, + 0x33, + 0x62, + 0x93, + 0x8b, + 0x98, + 0x24, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_661/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_661/src/main.nr new file mode 100644 index 00000000000..03102eb775e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_661/src/main.nr @@ -0,0 +1,28 @@ +fn main(a: u32, mut c: [u32; 4]) { + // Regression for issue #661: + let mut c_661: [u32; 1] = [0]; + if a > 5 { + c_661 = issue_661_foo(issue_661_bar(c), a); + } else { + c_661 = issue_661_foo(issue_661_bar(c), a + 2); + } + assert(c_661[0] < 20000); +} + +fn test5(a: u32) { + if a > 1 { + let q = a / 2; + assert(q == 2); + } +} + +fn issue_661_foo(array: [u32;4], b: u32) -> [u32;1] { + [array[0] + b] +} + +fn issue_661_bar(a: [u32;4]) -> [u32;4] { + let mut b: [u32; 4] = [0; 4]; + b[0]=a[0]+1; + b +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_short_circuit/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_short_circuit/Nargo.toml new file mode 100644 index 00000000000..92f5a869bc4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_short_circuit/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "conditional_regression_short_circuit" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_short_circuit/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_short_circuit/Prover.toml new file mode 100644 index 00000000000..baad8be126a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_short_circuit/Prover.toml @@ -0,0 +1,38 @@ +c=[2, 4, 3, 0, ] +a=0 +x = [104, 101, 108, 108, 111] + +result = [ + 0x2c, + 0xf2, + 0x4d, + 0xba, + 0x5f, + 0xb0, + 0xa3, + 0x0e, + 0x26, + 0xe8, + 0x3b, + 0x2a, + 0xc5, + 0xb9, + 0xe2, + 0x9e, + 0x1b, + 0x16, + 0x1e, + 0x5c, + 0x1f, + 0xa7, + 0x42, + 0x5e, + 0x73, + 0x04, + 0x33, + 0x62, + 0x93, + 0x8b, + 0x98, + 0x24, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_short_circuit/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_short_circuit/src/main.nr new file mode 100644 index 00000000000..d260fa49dc3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_short_circuit/src/main.nr @@ -0,0 +1,38 @@ +use dep::std; + +fn main(a: u32, mut c: [u32; 4], x: [u8; 5], result: pub [u8; 32]) { + //regression for short-circuit2 + if 35 == a { + assert(false); + } + bar(a as Field); + + if a == 3 { + c = test4(); + } + assert(c[1] != 2); + call_intrinsic(x, result); +} + +fn foo() { + let mut x = 1; + x /= 0; +} + +fn bar(x: Field) { + if x == 15 { + foo(); + } +} + +fn call_intrinsic(x: [u8; 5], result: [u8; 32]) { + let mut digest = std::hash::sha256(x); + digest[0] = 5 as u8; + digest = std::hash::sha256(x); + assert(digest == result); +} + +fn test4() -> [u32; 4] { + let b: [u32; 4] = [1, 2, 3, 4]; + b +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_underflow/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_underflow/Nargo.toml new file mode 100644 index 00000000000..75c4fb43b2f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_underflow/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "conditional_regression_underflow" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_underflow/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_underflow/Prover.toml new file mode 100644 index 00000000000..ca65e8b9428 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_underflow/Prover.toml @@ -0,0 +1 @@ +x = "4" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_underflow/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_underflow/src/main.nr new file mode 100644 index 00000000000..be70b67be5e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/conditional_regression_underflow/src/main.nr @@ -0,0 +1,15 @@ +// Regression test for https://github.com/noir-lang/noir/issues/3493 +fn main(x: u4) { + if x == 10 { + x + 15; + } + if x == 9 { + x << 3; + } + if x == 8 { + x * 3; + } + if x == 7 { + x - 8; + } +} \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/custom_entry/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/custom_entry/Nargo.toml new file mode 100644 index 00000000000..35cf2e080c1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/custom_entry/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "custom_entry" +type = "bin" +entry = "src/foobarbaz.nr" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/custom_entry/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/custom_entry/Prover.toml new file mode 100644 index 00000000000..4dd6b405159 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/custom_entry/Prover.toml @@ -0,0 +1 @@ +x = "1" diff --git a/noir/tooling/nargo_cli/tests/execution_success/custom_entry/src/foobarbaz.nr b/noir/tooling/nargo_cli/tests/execution_success/custom_entry/src/foobarbaz.nr new file mode 100644 index 00000000000..00e94414c0b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/custom_entry/src/foobarbaz.nr @@ -0,0 +1,3 @@ +fn main(x: Field) { + assert(x == 1); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/debug_logs/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/debug_logs/Nargo.toml new file mode 100644 index 00000000000..9550d50cf15 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/debug_logs/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "debug_logs" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/debug_logs/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/debug_logs/Prover.toml new file mode 100644 index 00000000000..f28f2f8cc48 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/debug_logs/Prover.toml @@ -0,0 +1,2 @@ +x = "5" +y = "10" diff --git a/noir/tooling/nargo_cli/tests/execution_success/debug_logs/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/debug_logs/src/main.nr new file mode 100644 index 00000000000..6accdf725d9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/debug_logs/src/main.nr @@ -0,0 +1,81 @@ +use dep::std; + +fn main(x: Field, y: pub Field) { + let string = "i: {i}, j: {j}"; + std::println(string); + // A `fmtstr` lets you easily perform string interpolation. + let fmt_str: fmtstr<14, (Field, Field)> = f"i: {x}, j: {y}"; + let fmt_str = string_identity(fmt_str); + std::println(fmt_str); + + let fmt_str_no_type = f"i: {x}, j: {y}"; + std::println(fmt_str_no_type); + + let fmt_str_generic = string_with_generics(fmt_str_no_type); + std::println(fmt_str_generic); + + let s = myStruct { y: x, x: y }; + std::println(s); + + std::println(f"randomstring{x}{x}"); + + let fmt_str = string_with_partial_generics(f"i: {x}, s: {s}"); + std::println(fmt_str); + + std::println(x); + std::println([x, y]); + + let foo = fooStruct { my_struct: s, foo: 15 }; + std::println(f"s: {s}, foo: {foo}"); + + std::println(f"x: 0, y: 1"); + + let s_2 = myStruct { x: 20, y: 30 }; + std::println(f"s1: {s}, s2: {s_2}"); + + let bar = fooStruct { my_struct: s_2, foo: 20 }; + std::println(f"foo1: {foo}, foo2: {bar}"); + + let struct_string = if x != 5 { f"{foo}" } else { f"{bar}" }; + std::println(struct_string); + + regression_2906(); +} + +fn string_identity(string: fmtstr<14, (Field, Field)>) -> fmtstr<14, (Field, Field)> { + string +} + +fn string_with_generics(string: fmtstr) -> fmtstr { + string +} + +fn string_with_partial_generics(string: fmtstr) -> fmtstr { + string +} + +struct myStruct { + y: Field, + x: Field, +} + +struct fooStruct { + my_struct: myStruct, + foo: Field, +} + +fn regression_2906() { + let array_two_vals = [1, 2]; + dep::std::println(f"array_two_vals: {array_two_vals}"); + + let label_two_vals = "12"; + dep::std::println(f"label_two_vals: {label_two_vals}"); + + let array_five_vals = [1, 2, 3, 4, 5]; + dep::std::println(f"array_five_vals: {array_five_vals}"); + + let label_five_vals = "12345"; + dep::std::println(f"label_five_vals: {label_five_vals}"); + + dep::std::println(f"array_five_vals: {array_five_vals}, label_five_vals: {label_five_vals}"); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/diamond_deps_0/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/diamond_deps_0/Nargo.toml new file mode 100644 index 00000000000..8b75d71ae12 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/diamond_deps_0/Nargo.toml @@ -0,0 +1,8 @@ +[package] +name = "diamond_deps_0" +type = "bin" +authors = [""] + +[dependencies] +dep1 = { path = "../../test_libraries/diamond_deps_1" } +dep2 = { path = "../../test_libraries/diamond_deps_2" } diff --git a/noir/tooling/nargo_cli/tests/execution_success/diamond_deps_0/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/diamond_deps_0/Prover.toml new file mode 100644 index 00000000000..a713241e7dd --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/diamond_deps_0/Prover.toml @@ -0,0 +1,3 @@ +x = 1 +y = 1 +return = 5 \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/diamond_deps_0/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/diamond_deps_0/src/main.nr new file mode 100644 index 00000000000..ca95c6e0aa8 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/diamond_deps_0/src/main.nr @@ -0,0 +1,7 @@ +use dep::dep1::call_dep1_then_dep2; +use dep::dep2::call_dep2; +use dep::dep2::RESOLVE_THIS; + +fn main(x: Field, y: pub Field) -> pub Field { + call_dep1_then_dep2(x, y) + call_dep2(x, y) + RESOLVE_THIS +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/distinct_keyword/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/distinct_keyword/Nargo.toml new file mode 100644 index 00000000000..3f1b1386ba7 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/distinct_keyword/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "distinct_keyword" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/distinct_keyword/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/distinct_keyword/Prover.toml new file mode 100644 index 00000000000..07890234a19 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/distinct_keyword/Prover.toml @@ -0,0 +1 @@ +x = "3" diff --git a/noir/tooling/nargo_cli/tests/execution_success/distinct_keyword/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/distinct_keyword/src/main.nr new file mode 100644 index 00000000000..0e55a011a48 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/distinct_keyword/src/main.nr @@ -0,0 +1,4 @@ +// Example that uses the distinct keyword +fn main(x: pub Field) -> distinct pub [Field;2] { + [x + 1, x] +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/double_verify_proof/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/double_verify_proof/Nargo.toml new file mode 100644 index 00000000000..a4edd2e4288 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/double_verify_proof/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "double_verify_proof" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/double_verify_proof/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/double_verify_proof/Prover.toml new file mode 100644 index 00000000000..3e6d996d0e1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/double_verify_proof/Prover.toml @@ -0,0 +1,12 @@ +input_aggregation_object = ["0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"] +# key_hash = "0x17a5d2b205c1bf45b015ba33bc2f0beb7fbb36682f31f953b8d4d093c8644be5" +# proof = 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\ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/double_verify_proof/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/double_verify_proof/src/main.nr new file mode 100644 index 00000000000..ecc00bbd560 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/double_verify_proof/src/main.nr @@ -0,0 +1,28 @@ +use dep::std; + +fn main( + verification_key: [Field; 114], + proof: [Field; 94], + public_inputs: [Field; 1], + key_hash: Field, + input_aggregation_object: [Field; 16], + proof_b: [Field; 94] +) -> pub [Field; 16] { + let output_aggregation_object_a = std::verify_proof(verification_key.as_slice(), + proof.as_slice(), + public_inputs.as_slice(), + key_hash, + input_aggregation_object); + + let output_aggregation_object = std::verify_proof(verification_key.as_slice(), + proof_b.as_slice(), + public_inputs.as_slice(), + key_hash, + output_aggregation_object_a); + + let mut output = [0; 16]; + for i in 0..16 { + output[i] = output_aggregation_object[i]; + } + output +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256k1/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256k1/Nargo.toml new file mode 100644 index 00000000000..58fdf96f0e3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256k1/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "ecdsa_secp256k1" +description = "ECDSA secp256k1 verification" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256k1/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256k1/Prover.toml new file mode 100644 index 00000000000..412c7b36e4c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256k1/Prover.toml @@ -0,0 +1,209 @@ + +hashed_message = [ + 0x3a, + 0x73, + 0xf4, + 0x12, + 0x3a, + 0x5c, + 0xd2, + 0x12, + 0x1f, + 0x21, + 0xcd, + 0x7e, + 0x8d, + 0x35, + 0x88, + 0x35, + 0x47, + 0x69, + 0x49, + 0xd0, + 0x35, + 0xd9, + 0xc2, + 0xda, + 0x68, + 0x06, + 0xb4, + 0x63, + 0x3a, + 0xc8, + 0xc1, + 0xe2, +] +message = [ + 0x49, + 0x6e, + 0x73, + 0x74, + 0x72, + 0x75, + 0x63, + 0x74, + 0x69, + 0x6f, + 0x6e, + 0x73, + 0x20, + 0x75, + 0x6e, + 0x63, + 0x6c, + 0x65, + 0x61, + 0x72, + 0x2c, + 0x20, + 0x61, + 0x73, + 0x6b, + 0x20, + 0x61, + 0x67, + 0x61, + 0x69, + 0x6e, + 0x20, + 0x6c, + 0x61, + 0x74, + 0x65, + 0x72, + 0x2e, +] +pub_key_x = [ + 0xa0, + 0x43, + 0x4d, + 0x9e, + 0x47, + 0xf3, + 0xc8, + 0x62, + 0x35, + 0x47, + 0x7c, + 0x7b, + 0x1a, + 0xe6, + 0xae, + 0x5d, + 0x34, + 0x42, + 0xd4, + 0x9b, + 0x19, + 0x43, + 0xc2, + 0xb7, + 0x52, + 0xa6, + 0x8e, + 0x2a, + 0x47, + 0xe2, + 0x47, + 0xc7, +] +pub_key_y = [ + 0x89, + 0x3a, + 0xba, + 0x42, + 0x54, + 0x19, + 0xbc, + 0x27, + 0xa3, + 0xb6, + 0xc7, + 0xe6, + 0x93, + 0xa2, + 0x4c, + 0x69, + 0x6f, + 0x79, + 0x4c, + 0x2e, + 0xd8, + 0x77, + 0xa1, + 0x59, + 0x3c, + 0xbe, + 0xe5, + 0x3b, + 0x03, + 0x73, + 0x68, + 0xd7, +] +signature = [ + 0xe5, + 0x08, + 0x1c, + 0x80, + 0xab, + 0x42, + 0x7d, + 0xc3, + 0x70, + 0x34, + 0x6f, + 0x4a, + 0x0e, + 0x31, + 0xaa, + 0x2b, + 0xad, + 0x8d, + 0x97, + 0x98, + 0xc3, + 0x80, + 0x61, + 0xdb, + 0x9a, + 0xe5, + 0x5a, + 0x4e, + 0x8d, + 0xf4, + 0x54, + 0xfd, + 0x28, + 0x11, + 0x98, + 0x94, + 0x34, + 0x4e, + 0x71, + 0xb7, + 0x87, + 0x70, + 0xcc, + 0x93, + 0x1d, + 0x61, + 0xf4, + 0x80, + 0xec, + 0xbb, + 0x0b, + 0x89, + 0xd6, + 0xeb, + 0x69, + 0x69, + 0x01, + 0x61, + 0xe4, + 0x9a, + 0x71, + 0x5f, + 0xcd, + 0x55, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256k1/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256k1/src/main.nr new file mode 100644 index 00000000000..552fcea9af7 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256k1/src/main.nr @@ -0,0 +1,10 @@ +use dep::std; + +fn main(message: [u8;38], hashed_message: [u8;32], pub_key_x: [u8;32], pub_key_y: [u8;32], signature: [u8;64]) { + // Hash the message, since secp256k1 expects a hashed_message + let expected = std::hash::sha256(message); + assert(hashed_message == expected); + + let valid_signature = std::ecdsa_secp256k1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256r1/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256r1/Nargo.toml new file mode 100644 index 00000000000..87e8d529566 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256r1/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "ecdsa_secp256r1" +description = "ECDSA secp256r1 verification" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256r1/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256r1/Prover.toml new file mode 100644 index 00000000000..a45f799877b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256r1/Prover.toml @@ -0,0 +1,20 @@ +hashed_message = [ + 84, 112, 91, 163, 186, 175, 219, 223, 186, 140, 95, 154, 112, 247, 168, 155, 238, 152, + 217, 6, 181, 62, 49, 7, 77, 167, 186, 236, 220, 13, 169, 173, +] +pub_key_x = [ + 85, 15, 71, 16, 3, 243, 223, 151, 195, 223, 80, 106, 199, 151, 246, 114, 31, 177, 161, + 251, 123, 143, 111, 131, 210, 36, 73, 138, 101, 200, 142, 36, +] +pub_key_y = [ + 19, 96, 147, 215, 1, 46, 80, 154, 115, 113, 92, 189, 11, 0, 163, 204, 15, 244, 181, + 192, 27, 63, 250, 25, 106, 177, 251, 50, 112, 54, 184, 230, +] +signature = [ + 44, 112, 168, 208, 132, 182, 43, 252, 92, 224, 54, 65, 202, 249, 247, 42, + 212, 218, 140, 129, 191, 230, 236, 148, 135, 187, 94, 27, 239, 98, 161, 50, + 24, 173, 158, 226, 158, 175, 53, 31, 220, 80, 241, 82, 12, 66, 94, 155, + 144, 138, 7, 39, 139, 67, 176, 236, 123, 135, 39, 120, 193, 78, 7, 132 +] + + diff --git a/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256r1/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256r1/src/main.nr new file mode 100644 index 00000000000..d23573d13a6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/ecdsa_secp256r1/src/main.nr @@ -0,0 +1,6 @@ +use dep::std; + +fn main(hashed_message: [u8;32], pub_key_x: [u8;32], pub_key_y: [u8;32], signature: [u8;64]) { + let valid_signature = std::ecdsa_secp256r1::verify_signature(pub_key_x, pub_key_y, signature, hashed_message); + assert(valid_signature); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/eddsa/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/eddsa/Nargo.toml new file mode 100644 index 00000000000..0f545c2febc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/eddsa/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "eddsa" +description = "Eddsa verification" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/eddsa/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/eddsa/Prover.toml new file mode 100644 index 00000000000..53555202ca6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/eddsa/Prover.toml @@ -0,0 +1,3 @@ +_priv_key_a = 123 +_priv_key_b = 456 +msg = 789 diff --git a/noir/tooling/nargo_cli/tests/execution_success/eddsa/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/eddsa/src/main.nr new file mode 100644 index 00000000000..12e8ea92785 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/eddsa/src/main.nr @@ -0,0 +1,46 @@ +use dep::std::compat; +use dep::std::ec::consts::te::baby_jubjub; +use dep::std::hash; +use dep::std::eddsa::eddsa_poseidon_verify; +fn main(msg: pub Field, _priv_key_a: Field, _priv_key_b: Field) { + // Skip this test for non-bn254 backends + if compat::is_bn254() { + let bjj = baby_jubjub(); + + let pub_key_a = bjj.curve.mul(_priv_key_a, bjj.curve.gen); + // let pub_key_b = bjj.curve.mul(_priv_key_b, bjj.curve.gen); + // Manually computed as fields can't use modulo. Importantantly the commitment is within + // the subgroup order. Note that choice of hash is flexible for this step. + // let r_a = hash::pedersen_commitment([_priv_key_a, msg])[0] % bjj.suborder; // modulus computed manually + let r_a = 1414770703199880747815475415092878800081323795074043628810774576767372531818; + // let r_b = hash::pedersen_commitment([_priv_key_b, msg])[0] % bjj.suborder; // modulus computed manually + let r_b = 571799555715456644614141527517766533395606396271089506978608487688924659618; + + let r8_a = bjj.curve.mul(r_a, bjj.base8); + let r8_b = bjj.curve.mul(r_b, bjj.base8); + // let h_a: [Field; 6] = hash::poseidon::bn254::hash_5([ + // r8_a.x, + // r8_a.y, + // pub_key_a.x, + // pub_key_a.y, + // msg, + // ]); + // let h_b: [Field; 6] = hash::poseidon::bn254::hash_5([ + // r8_b.x, + // r8_b.y, + // pub_key_b.x, + // pub_key_b.y, + // msg, + // ]); + // let s_a = (r_a + _priv_key_a * h_a) % bjj.suborder; // modulus computed manually + let s_a = 30333430637424319196043722294837632681219980330991241982145549329256671548; + // let s_b = (r_b + _priv_key_b * h_b) % bjj.suborder; // modulus computed manually + let s_b = 1646085314320208098241070054368798527940102577261034947654839408482102287019; + // User A verifies their signature over the message + assert(eddsa_poseidon_verify(pub_key_a.x, pub_key_a.y, s_a, r8_a.x, r8_a.y, msg)); + // User B's signature over the message can't be used with user A's pub key + assert(!eddsa_poseidon_verify(pub_key_a.x, pub_key_a.y, s_b, r8_b.x, r8_b.y, msg)); + // User A's signature over the message can't be used with another message + assert(!eddsa_poseidon_verify(pub_key_a.x, pub_key_a.y, s_a, r8_a.x, r8_a.y, msg + 1)); + } +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/field_attribute/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/field_attribute/Nargo.toml new file mode 100644 index 00000000000..37487d0043c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/field_attribute/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "field_attribute" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/field_attribute/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/field_attribute/Prover.toml new file mode 100644 index 00000000000..07890234a19 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/field_attribute/Prover.toml @@ -0,0 +1 @@ +x = "3" diff --git a/noir/tooling/nargo_cli/tests/execution_success/field_attribute/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/field_attribute/src/main.nr new file mode 100644 index 00000000000..dda83db40de --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/field_attribute/src/main.nr @@ -0,0 +1,19 @@ +// Test integer addition: 3 + 4 = 7 +fn main(mut x: u32) { + assert(x > foo()); +} + +#[field(bn254)] +fn foo() -> u32 { + 1 +} + +#[field(23)] +fn foo() -> u32 { + 2 +} + +#[field(bls12_381)] +fn foo() -> u32 { + 3 +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/generics/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/generics/Nargo.toml new file mode 100644 index 00000000000..19c8ff28ec7 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/generics/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "generics" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/generics/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/generics/Prover.toml new file mode 100644 index 00000000000..85f1e9f96f2 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/generics/Prover.toml @@ -0,0 +1,2 @@ +x = "2" +y = "2" diff --git a/noir/tooling/nargo_cli/tests/execution_success/generics/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/generics/src/main.nr new file mode 100644 index 00000000000..3edce1ed8e7 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/generics/src/main.nr @@ -0,0 +1,67 @@ +struct Bar { + one: Field, + two: Field, + other: T, +} + +fn foo(bar: Bar) { + assert(bar.one == bar.two); +} + +struct BigInt { + limbs: [u32; N], +} + +impl BigInt { + // `N` is in scope of all methods in the impl + fn first(first: BigInt, second: BigInt) -> Self { + assert(first.limbs != second.limbs); + first + } + + fn second(first: BigInt, second: Self) -> Self { + assert(first.limbs != second.limbs); + second + } +} + +impl Bar { + fn get_other(self) -> Field { + self.other + } +} + +fn main(x: Field, y: Field) { + let bar1: Bar = Bar { one: x, two: y, other: 0 }; + let bar2 = Bar { one: x, two: y, other: [0] }; + + foo(bar1); + foo(bar2); + // Test generic impls + let int1 = BigInt { limbs: [1] }; + let int2 = BigInt { limbs: [2] }; + let BigInt { limbs } = int1.second(int2).first(int1); + assert(limbs == int2.limbs); + // Test impl exclusively for Bar + assert(bar1.get_other() == bar1.other); + // Expected type error + // assert(bar2.get_other() == bar2.other); + let one = x; + let two = y; + let nested_generics: Bar> = Bar { one, two, other: Bar { one, two, other: 0 } }; + assert(nested_generics.other.other == bar1.get_other()); + + let _ = regression_2055([1, 2, 3]); +} + +fn regression_2055(bytes: [u8; LEN]) -> Field { + let mut f = 0; + let mut b = 1; + let mut len = LEN - 1; // FAILS + for i in 0..LEN { + let j = len - i; + f += (bytes[j] as Field) * b; + b *= 256; + } + f +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/global_consts/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/global_consts/Nargo.toml new file mode 100644 index 00000000000..3b4d6be2353 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/global_consts/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "global_consts" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/global_consts/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/global_consts/Prover.toml new file mode 100644 index 00000000000..66f7feb1dda --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/global_consts/Prover.toml @@ -0,0 +1,4 @@ +a = [77,75,108,209,54,16,50,202,155,210,174,185,217,0,170,77,69,217,234,216,10,201,66,51,116,196,81,167,37,77,7,102] +b = [44,75,108,209,54,16,50,202,155,210,174,185,217,0,170,77,69,217,234,216,10,201,66,51,116,196,81,167,37,77,7,102] +c = [3, 3, 3] +d = [5, 5, 5, 5, 5] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/global_consts/src/baz.nr b/noir/tooling/nargo_cli/tests/execution_success/global_consts/src/baz.nr new file mode 100644 index 00000000000..4271de81118 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/global_consts/src/baz.nr @@ -0,0 +1,5 @@ +pub fn from_baz(x: [Field; crate::foo::MAGIC_NUMBER]) { + for i in 0..crate::foo::MAGIC_NUMBER { + assert(x[i] == crate::foo::MAGIC_NUMBER); + } +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/global_consts/src/foo.nr b/noir/tooling/nargo_cli/tests/execution_success/global_consts/src/foo.nr new file mode 100644 index 00000000000..7b0ae75b74b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/global_consts/src/foo.nr @@ -0,0 +1,11 @@ +mod bar; + +global N: Field = 5; +global MAGIC_NUMBER: Field = 3; +global TYPE_INFERRED = 42; + +pub fn from_foo(x: [Field; bar::N]) { + for i in 0..bar::N { + assert(x[i] == bar::N); + } +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/global_consts/src/foo/bar.nr b/noir/tooling/nargo_cli/tests/execution_success/global_consts/src/foo/bar.nr new file mode 100644 index 00000000000..b8d0b85b0f3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/global_consts/src/foo/bar.nr @@ -0,0 +1,5 @@ +global N: Field = 5; + +pub fn from_bar(x: Field) -> Field { + x * N +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/global_consts/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/global_consts/src/main.nr new file mode 100644 index 00000000000..a749ec77da6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/global_consts/src/main.nr @@ -0,0 +1,96 @@ +mod foo; +mod baz; + +global M: Field = 32; +global L: Field = 10; // Unused globals currently allowed +global N: Field = 5; +global T_LEN = 2; // Type inference is allowed on globals +//global N: Field = 5; // Uncomment to see duplicate globals error +struct Dummy { + x: [Field; N], + y: [Field; foo::MAGIC_NUMBER] +} + +struct Test { + v: Field, +} +global VALS: [Test; 1] = [Test { v: 100 }]; +global NESTED = [VALS, VALS]; + +fn main( + a: [Field; M + N - N], + b: [Field; 30 + N / 2], + c: pub [Field; foo::MAGIC_NUMBER], + d: [Field; foo::bar::N] +) { + let test_struct = Dummy { x: d, y: c }; + + for i in 0..foo::MAGIC_NUMBER { + assert(c[i] == foo::MAGIC_NUMBER); + assert(test_struct.y[i] == foo::MAGIC_NUMBER); + assert(test_struct.y[i] != NESTED[1][0].v); + } + + assert(N != M); + + let expected: u32 = 42; + assert(foo::TYPE_INFERRED == expected); + + let mut y = 5; + let mut x = M; + for i in 0..N * N { + let M: Field = 10; + x = M; + + y = i; + } + assert(y == 24); + assert(x == 10); + + let q = multiplyByM(3); + assert(q == 96); + + arrays_neq(a, b); + + let t: [Field; T_LEN] = [N, M]; + assert(t[1] == 32); + + assert(15 == mysubmodule::my_helper()); + + let add_submodules_N = mysubmodule::N + foo::bar::N; + assert(15 == add_submodules_N); + let add_from_bar_N = mysubmodule::N + foo::bar::from_bar(1); + assert(15 == add_from_bar_N); + // Example showing an array filled with (mysubmodule::N + 2) 0's + let sugared = [0; mysubmodule::N + 2]; + assert(sugared[mysubmodule::N + 1] == 0); + + let arr: [Field; mysubmodule::N] = [N; 10]; + assert((arr[0] == 5) & (arr[9] == 5)); + + foo::from_foo(d); + baz::from_baz(c); +} + +fn multiplyByM(x: Field) -> Field { + x * M +} + +fn arrays_neq(a: [Field; M], b: [Field; M]) { + assert(a != b); +} + +mod mysubmodule { + global N: Field = 10; + global L: Field = 50; + + fn my_bool_or(x: u1, y: u1) { + assert(x | y == 1); + } + + pub fn my_helper() -> Field { + let N: Field = 15; // Like in Rust, local variables override globals + let x = N; + x + } +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/hash_to_field/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/hash_to_field/Nargo.toml new file mode 100644 index 00000000000..a00a7bdc050 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/hash_to_field/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "hash_to_field" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/hash_to_field/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/hash_to_field/Prover.toml new file mode 100644 index 00000000000..f6597d3f78a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/hash_to_field/Prover.toml @@ -0,0 +1 @@ +input = "1" diff --git a/noir/tooling/nargo_cli/tests/execution_success/hash_to_field/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/hash_to_field/src/main.nr new file mode 100644 index 00000000000..5af1c5af55e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/hash_to_field/src/main.nr @@ -0,0 +1,5 @@ +use dep::std; + +fn main(input: Field) -> pub Field { + std::hash::hash_to_field([input]) +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/higher_order_functions/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/higher_order_functions/Nargo.toml new file mode 100644 index 00000000000..8f8a79e7e83 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/higher_order_functions/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "higher_order_functions" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/higher_order_functions/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/higher_order_functions/Prover.toml new file mode 100644 index 00000000000..b373bb827c4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/higher_order_functions/Prover.toml @@ -0,0 +1 @@ +w = 1 \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/higher_order_functions/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/higher_order_functions/src/main.nr new file mode 100644 index 00000000000..479cdbbe7bf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/higher_order_functions/src/main.nr @@ -0,0 +1,97 @@ +fn main(w: Field) -> pub Field { + let f = if 3 * 7 > 200 as u32 { foo } else { bar }; + assert(f()[1] == 2); + // Lambdas: + assert(twice(|x| x * 2, 5) == 20); + assert((|x, y| x + y + 1)(2, 3) == 6); + // nested lambdas + assert((|a, b| { + a + (|c| c + 2)(b) + })(0, 1) + == 3); + // Closures: + let a = 42; + let g = || a; + assert(g() == 42); + // When you copy mutable variables, + // the capture of the copies shouldn't change: + let mut x = 2; + x = x + 1; + let z = x; + // Add extra mutations to ensure we can mutate x without the + // captured z changing. + x = x + 1; + assert((|y| y + z)(1) == 4); + // When you capture mutable variables, + // again, the captured variable doesn't change: + let closure_capturing_mutable = (|y| y + x); + assert(closure_capturing_mutable(1) == 5); + x += 1; + assert(closure_capturing_mutable(1) == 5); + + regression_2154(); + + let ret = twice(add1, 3); + + test_array_functions(); + w + ret +} +/// Test the array functions in std::array +fn test_array_functions() { + let two = 2; // giving this a name, to ensure that the Option functions work with closures + let myarray: [i32; 3] = [1, 2, 3]; + assert(myarray.any(|n| n > 2)); + assert(myarray.any(|n| n > two)); + + let evens: [i32; 3] = myarray.map(|n| n * two); // [2, 4, 6] + assert(evens.all(|n| n > 1)); + assert(evens.all(|n| n >= two)); + + assert(evens.fold(0, |a, b| a + b) == 12); + assert(evens.fold(0, |a, b| a + b + two) == 18); + assert(evens.reduce(|a, b| a + b) == 12); + assert(evens.reduce(|a, b| a + b + two) == 16); + // TODO: is this a sort_via issue with the new backend, + // or something more general? + // + // currently it fails only with `--experimental-ssa` with + // "not yet implemented: Cast into signed" + // but it worked with the original ssa backend + // (before dropping it) + // + // opened #2121 for it + // https://github.com/noir-lang/noir/issues/2121 + // let descending = myarray.sort_via(|a, b| a > b); + // assert(descending == [3, 2, 1]); + assert(evens.map(|n| n / 2) == myarray); + assert(evens.map(|n| n / two) == myarray); +} + +fn foo() -> [u32; 2] { + [1, 3] +} + +fn bar() -> [u32; 2] { + [3, 2] +} + +fn add1(x: Field) -> Field { + x + 1 +} + +fn twice(f: fn(Field) -> Field, x: Field) -> Field { + f(f(x)) +} +// Fixing an ICE, where rewriting the closures +// during monomorphization didn't correspond +// to an internal `if` type +// found by @jfecher: +// https://github.com/noir-lang/noir/pull/1959#issuecomment-1658992989 +// issue https://github.com/noir-lang/noir/issues/2154 +fn regression_2154() { + let x: u32 = 32; + + let closure_if_else = if x > 2 { || x } else { || x + 2342 }; + + assert(closure_if_else() == 32); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/if_else_chain/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/if_else_chain/Nargo.toml new file mode 100644 index 00000000000..2f0dc93d120 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/if_else_chain/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "if_else_chain" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/if_else_chain/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/if_else_chain/Prover.toml new file mode 100644 index 00000000000..84aeb36ac21 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/if_else_chain/Prover.toml @@ -0,0 +1,2 @@ +a=0 +c=[2, 4, 3, 0, ] diff --git a/noir/tooling/nargo_cli/tests/execution_success/if_else_chain/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/if_else_chain/src/main.nr new file mode 100644 index 00000000000..2705d5b3111 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/if_else_chain/src/main.nr @@ -0,0 +1,15 @@ +fn main(a: u32, mut c: [u32; 4]) { + if a == c[0] { + assert(c[0] == 0); + } else if a == c[1] { + assert(c[1] == 0); + } else if a == c[2] { + assert(c[2] == 0); + } else if a == c[3] { + // expect to match this case + assert(c[3] == 0); + } else { + assert(c[0] == 10); + } +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/import/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/import/Nargo.toml new file mode 100644 index 00000000000..c92328f106c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/import/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "import" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/import/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/import/Prover.toml new file mode 100644 index 00000000000..8c12ebba6cf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/import/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "2" diff --git a/noir/tooling/nargo_cli/tests/execution_success/import/src/import.nr b/noir/tooling/nargo_cli/tests/execution_success/import/src/import.nr new file mode 100644 index 00000000000..ef3f0d94c28 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/import/src/import.nr @@ -0,0 +1,3 @@ +pub fn hello(x: Field) -> Field { + x +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/import/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/import/src/main.nr new file mode 100644 index 00000000000..7dcc16fed16 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/import/src/main.nr @@ -0,0 +1,9 @@ +mod import; +use crate::import::hello; + +fn main(x: Field, y: Field) { + let _k = dep::std::hash::pedersen_commitment([x]); + let _l = hello(x); + + assert(x != import::hello(y)); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/integer_array_indexing/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/integer_array_indexing/Nargo.toml new file mode 100644 index 00000000000..6e639b64f58 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/integer_array_indexing/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "integer_array_indexing" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/integer_array_indexing/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/integer_array_indexing/Prover.toml new file mode 100644 index 00000000000..1496028f60a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/integer_array_indexing/Prover.toml @@ -0,0 +1,2 @@ +arr = [1, 2, 3] +x = 2 diff --git a/noir/tooling/nargo_cli/tests/execution_success/integer_array_indexing/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/integer_array_indexing/src/main.nr new file mode 100644 index 00000000000..1698c68161b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/integer_array_indexing/src/main.nr @@ -0,0 +1,10 @@ +global ARRAY_LEN: u32 = 3; + +fn main(arr: [Field; ARRAY_LEN], x: u32) -> pub Field { + let mut value = arr[ARRAY_LEN - 1]; + + value += arr[0 as u32]; + value += arr[1 as Field]; + + value + x as Field +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/keccak256/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/keccak256/Nargo.toml new file mode 100644 index 00000000000..7e48c3b342c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/keccak256/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "keccak256" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/keccak256/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/keccak256/Prover.toml new file mode 100644 index 00000000000..d65c4011d3f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/keccak256/Prover.toml @@ -0,0 +1,35 @@ +x = 0xbd +result = [ + 0x5a, + 0x50, + 0x2f, + 0x9f, + 0xca, + 0x46, + 0x7b, + 0x26, + 0x6d, + 0x5b, + 0x78, + 0x33, + 0x65, + 0x19, + 0x37, + 0xe8, + 0x05, + 0x27, + 0x0c, + 0xa3, + 0xf3, + 0xaf, + 0x1c, + 0x0d, + 0xd2, + 0x46, + 0x2d, + 0xca, + 0x4b, + 0x3b, + 0x1a, + 0xbf, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/keccak256/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/keccak256/src/main.nr new file mode 100644 index 00000000000..ff2167694d6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/keccak256/src/main.nr @@ -0,0 +1,21 @@ +// Keccak256 example +// +use dep::std; + +fn main(x: Field, result: [u8; 32]) { + // We use the `as` keyword here to denote the fact that we want to take just the first byte from the x Field + // The padding is taken care of by the program + let digest = std::hash::keccak256([x as u8], 1); + assert(digest == result); + //#1399: variable meesage size + let message_size = 4; + let hash_a = std::hash::keccak256([1, 2, 3, 4], message_size); + let hash_b = std::hash::keccak256([1, 2, 3, 4, 0, 0, 0, 0], message_size); + + assert(hash_a == hash_b); + + let message_size_big = 8; + let hash_c = std::hash::keccak256([1, 2, 3, 4, 0, 0, 0, 0], message_size_big); + + assert(hash_a != hash_c); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/main_bool_arg/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/main_bool_arg/Nargo.toml new file mode 100644 index 00000000000..c1cedb6c657 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/main_bool_arg/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "main_bool_arg" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/main_bool_arg/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/main_bool_arg/Prover.toml new file mode 100644 index 00000000000..f932e0b4817 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/main_bool_arg/Prover.toml @@ -0,0 +1,2 @@ +x = true +y = [true, false] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/main_bool_arg/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/main_bool_arg/src/main.nr new file mode 100644 index 00000000000..111a23ec0c2 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/main_bool_arg/src/main.nr @@ -0,0 +1,8 @@ +fn main(x: bool, y: [bool;2]) { + if x { + assert(1 != 2); + } + + assert(x); + assert(y[0] != y[1]); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/merkle_insert/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/merkle_insert/Nargo.toml new file mode 100644 index 00000000000..f09458411d8 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/merkle_insert/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "merkle_insert" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/merkle_insert/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/merkle_insert/Prover.toml new file mode 100644 index 00000000000..0656908c063 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/merkle_insert/Prover.toml @@ -0,0 +1,11 @@ +old_root = "0x2131c74af6250d1246797588ff075b672e3d2e6805f9c6b313ade8f6b9e12802" +old_leaf = "0x2df8b940e5890e4e1377e05373fae69a1d754f6935e6a780b666947431f2cdcd" +old_hash_path = [ + "0x03542cb720369f19a74fd05b4edfbedb27a78514ad3283f1b3270a1656cced8e", + "0x1278272f6e617666b18c52349c4bbe94249a8ab59add2ee0a142168a92b7ffc2", + "0x2547cfd7699ad1f331fb77b30a3445c6043a4c62b5b1215356b2924607bf937b", +] +new_root = "0x25e2a8ee5b85e5b546ae27832b9b53f5fae5b371e3e7f8cddda839f41669fc68" +leaf = "0x23fe6c8f2426b793f0f156f57efbecbea52e951af761634a85e80cc1a9fb5003" +index = "0" +mimc_input = [12, 45, 78, 41] diff --git a/noir/tooling/nargo_cli/tests/execution_success/merkle_insert/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/merkle_insert/src/main.nr new file mode 100644 index 00000000000..ac9a7b34ea3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/merkle_insert/src/main.nr @@ -0,0 +1,22 @@ +use dep::std; +use dep::std::hash::mimc; + +fn main( + old_root: Field, + old_leaf: Field, + old_hash_path: [Field; 3], + new_root: pub Field, + leaf: Field, + index: Field, + mimc_input: [Field; 4] +) { + assert(old_root == std::merkle::compute_merkle_root(old_leaf, index, old_hash_path)); + + let calculated_root = std::merkle::compute_merkle_root(leaf, index, old_hash_path); + assert(new_root == calculated_root); + + let h = mimc::mimc_bn254(mimc_input); + // Regression test for PR #891 + std::println(h); + assert(h == 18226366069841799622585958305961373004333097209608110160936134895615261821931); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/mock_oracle/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/mock_oracle/Nargo.toml new file mode 100644 index 00000000000..b2916487e8c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/mock_oracle/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "mock_oracle" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/mock_oracle/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/mock_oracle/Prover.toml new file mode 100644 index 00000000000..2b26a4ce471 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/mock_oracle/Prover.toml @@ -0,0 +1,2 @@ +x = "10" + diff --git a/noir/tooling/nargo_cli/tests/execution_success/mock_oracle/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/mock_oracle/src/main.nr new file mode 100644 index 00000000000..90fca7993cc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/mock_oracle/src/main.nr @@ -0,0 +1,27 @@ +use dep::std::test::OracleMock; + +struct Point { + x: Field, + y: Field, +} + +#[oracle(foo)] +unconstrained fn foo_oracle(_point: Point, _array: [Field; 4]) -> Field {} + +unconstrained fn main() { + let array = [1, 2, 3, 4]; + let another_array = [4, 3, 2, 1]; + let point = Point { x: 14, y: 27 }; + + OracleMock::mock("foo").returns(42).times(1); + let mock = OracleMock::mock("foo").returns(0); + assert_eq(42, foo_oracle(point, array)); + assert_eq(0, foo_oracle(point, array)); + mock.clear(); + + OracleMock::mock("foo").with_params((point, array)).returns(10); + OracleMock::mock("foo").with_params((point, another_array)).returns(20); + assert_eq(10, foo_oracle(point, array)); + assert_eq(20, foo_oracle(point, another_array)); +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/modules/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/modules/Nargo.toml new file mode 100644 index 00000000000..ab1d6e962d7 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/modules/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "modules" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/modules/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/modules/Prover.toml new file mode 100644 index 00000000000..c0a0cdfbeb0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/modules/Prover.toml @@ -0,0 +1,2 @@ +x = "2" +y = "13" diff --git a/noir/tooling/nargo_cli/tests/execution_success/modules/src/foo.nr b/noir/tooling/nargo_cli/tests/execution_success/modules/src/foo.nr new file mode 100644 index 00000000000..ef3f0d94c28 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/modules/src/foo.nr @@ -0,0 +1,3 @@ +pub fn hello(x: Field) -> Field { + x +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/modules/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/modules/src/main.nr new file mode 100644 index 00000000000..167f7e671a0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/modules/src/main.nr @@ -0,0 +1,14 @@ +mod foo; +// This is a comment. +// +// `main` is the entry point to a binary +// +// You can have a `Binary` or a `Library` +// Release : 0.2 +// +// To run a proof on the command line, type `cargo run prove {proof_name}` +// +// To verify that proof, type `cargo run verify {proof_name}` +fn main(x: Field, y: pub Field) { + assert(x != foo::hello(y)); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/modules_more/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/modules_more/Nargo.toml new file mode 100644 index 00000000000..bfcfe08fa79 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/modules_more/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "modules_more" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/modules_more/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/modules_more/Prover.toml new file mode 100644 index 00000000000..39a4ddb9d15 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/modules_more/Prover.toml @@ -0,0 +1,4 @@ + + x = "5" + y = "15" + \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/modules_more/src/foo.nr b/noir/tooling/nargo_cli/tests/execution_success/modules_more/src/foo.nr new file mode 100644 index 00000000000..fa531a1a2f0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/modules_more/src/foo.nr @@ -0,0 +1,5 @@ +mod bar; + +fn hello(x: Field) -> Field { + x +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/modules_more/src/foo/bar.nr b/noir/tooling/nargo_cli/tests/execution_success/modules_more/src/foo/bar.nr new file mode 100644 index 00000000000..1665f720be6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/modules_more/src/foo/bar.nr @@ -0,0 +1,3 @@ +pub fn from_bar(x: Field) -> Field { + x +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/modules_more/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/modules_more/src/main.nr new file mode 100644 index 00000000000..93b76d62845 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/modules_more/src/main.nr @@ -0,0 +1,5 @@ +mod foo; +// An example of the module system +fn main(x: Field, y: Field) { + assert(x != foo::bar::from_bar(y)); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/modulus/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/modulus/Nargo.toml new file mode 100644 index 00000000000..256eeef058f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/modulus/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "modulus" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/modulus/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/modulus/Prover.toml new file mode 100644 index 00000000000..d435609bb1a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/modulus/Prover.toml @@ -0,0 +1,290 @@ +bn254_modulus_be_bytes = [ + 48, + 100, + 78, + 114, + 225, + 49, + 160, + 41, + 184, + 80, + 69, + 182, + 129, + 129, + 88, + 93, + 40, + 51, + 232, + 72, + 121, + 185, + 112, + 145, + 67, + 225, + 245, + 147, + 240, + 0, + 0, + 1, +] +bn254_modulus_be_bits = [ + 1, + 1, + 0, + 0, + 0, + 0, + 0, + 1, + 1, + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + 0, + 0, + 1, + 0, + 1, + 1, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 1, + 1, + 0, + 0, + 0, + 1, + 1, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 1, + 0, + 0, + 1, + 1, + 0, + 1, + 1, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 1, + 1, + 0, + 1, + 1, + 0, + 1, + 1, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 1, + 0, + 1, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 1, + 1, + 1, + 0, + 1, + 0, + 0, + 1, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + 1, + 1, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 0, + 1, + 1, + 1, + 0, + 0, + 1, + 0, + 1, + 1, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 1, + 1, + 1, + 1, + 0, + 0, + 0, + 0, + 1, + 1, + 1, + 1, + 1, + 0, + 1, + 0, + 1, + 1, + 0, + 0, + 1, + 0, + 0, + 1, + 1, + 1, + 1, + 1, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/modulus/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/modulus/src/main.nr new file mode 100644 index 00000000000..35f63fdc8c5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/modulus/src/main.nr @@ -0,0 +1,25 @@ +use dep::std; + +fn main(bn254_modulus_be_bytes: [u8; 32], bn254_modulus_be_bits: [u1; 254]) { + let modulus_size = std::field::modulus_num_bits(); + // NOTE: The constraints used in this circuit will only work when testing nargo with the plonk bn254 backend + assert(modulus_size == 254); + + let modulus_be_byte_array = std::field::modulus_be_bytes(); + for i in 0..32 { + assert(modulus_be_byte_array[i] == bn254_modulus_be_bytes[i]); + } + let modulus_le_byte_array = std::field::modulus_le_bytes(); + for i in 0..32 { + assert(modulus_le_byte_array[i] == bn254_modulus_be_bytes[31 - i]); + } + + let modulus_be_bits = std::field::modulus_be_bits(); + for i in 0..254 { + assert(modulus_be_bits[i] == bn254_modulus_be_bits[i]); + } + let modulus_le_bits = std::field::modulus_le_bits(); + for i in 0..254 { + assert(modulus_le_bits[i] == bn254_modulus_be_bits[253 - i]); + } +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/nested_array_dynamic/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/nested_array_dynamic/Nargo.toml new file mode 100644 index 00000000000..72642bebc15 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/nested_array_dynamic/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "nested_array_dynamic" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/nested_array_dynamic/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/nested_array_dynamic/Prover.toml new file mode 100644 index 00000000000..6c7e77b581d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/nested_array_dynamic/Prover.toml @@ -0,0 +1,29 @@ +y = "3" + +[[x]] +a = "1" +b = ["2", "3", "20"] + +[x.bar] +inner = ["100", "101", "102"] + +[[x]] +a = "4" # idx = 3, flattened start idx = 7 +b = ["5", "6", "21"] # idx = 4, flattened start idx = 8 + +[x.bar] +inner = ["103", "104", "105"] # idx = 5, flattened start idx = 11 + +[[x]] +a = "7" +b = ["8", "9", "22"] + +[x.bar] +inner = ["106", "107", "108"] + +[[x]] +a = "10" # idx = 9, flattened start idx = 21 +b = ["11", "12", "23"] # idx = 10, flattened start idx = 22 + +[x.bar] +inner = ["109", "110", "111"] # idx = 11, flattened start idx = 25 diff --git a/noir/tooling/nargo_cli/tests/execution_success/nested_array_dynamic/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/nested_array_dynamic/src/main.nr new file mode 100644 index 00000000000..2c53822d6b9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/nested_array_dynamic/src/main.nr @@ -0,0 +1,76 @@ +struct Bar { + inner: [Field; 3], +} + +struct Foo { + a: Field, + b: [Field; 3], + bar: Bar, +} + +struct FooParent { + array: [Field; 3], + foos: [Foo; 4], +} + +fn main(mut x: [Foo; 4], y: pub Field) { + assert(x[y - 3].a == 1); + assert(x[y - 3].b == [2, 3, 20]); + assert(x[y - 2].a == 4); + assert(x[y - 2].b == [5, 6, 21]); + assert(x[y - 1].a == 7); + assert(x[y - 1].b == [8, 9, 22]); + assert(x[y].a == 10); + assert(x[y].b == [11, 12, 23]); + assert(x[y].bar.inner == [109, 110, 111]); + // Check dynamic array set + if y != 2 { + x[y].a = 50; + } else { + x[y].a = 100; + } + assert(x[3].a == 50); + + if y == 2 { + x[y - 1].b = [50, 51, 52]; + } else { + x[y - 1].b = [100, 101, 102]; + } + assert(x[2].b == [100, 101, 102]); + + assert(x[y - 3].bar.inner == [100, 101, 102]); + assert(x[y - 2].bar.inner == [103, 104, 105]); + assert(x[y - 1].bar.inner == [106, 107, 108]); + assert(x[y].bar.inner == [109, 110, 111]); + + let foo_parent_one = FooParent { array: [0, 1, 2], foos: x }; + let foo_parent_two = FooParent { array: [3, 4, 5], foos: x }; + let mut foo_parents = [foo_parent_one, foo_parent_two]; + + assert(foo_parents[y - 3].foos[y - 3].b == [2, 3, 20]); + assert(foo_parents[y - 3].foos[y - 2].b == [5, 6, 21]); + assert(foo_parents[y - 3].foos[y - 1].b == [100, 101, 102]); + assert(foo_parents[y - 3].foos[y].b == [11, 12, 23]); + + assert(foo_parents[y - 3].foos[y].a == 50); + + assert(foo_parents[1].foos[1].b == [5, 6, 21]); + if y == 2 { + foo_parents[y - 2].foos[y - 2].b = [10, 9, 8]; + } else { + foo_parents[y - 2].foos[y - 2].b = [20, 19, 18]; + } + assert(foo_parents[1].foos[1].b == [20, 19, 18]); + + assert(foo_parents[1].foos[1].b[2] == 18); + if y == 3 { + foo_parents[y - 2].foos[y - 2].b[y - 1] = 5000; + } else { + foo_parents[y - 2].foos[y - 2].b[y - 1] = 1000; + } + assert(foo_parents[1].foos[1].b[2] == 5000); + // Set a dynamic array value + foo_parents[y - 2].foos[y - 3].b = foo_parents[y - 2].foos[y - 2].b; + assert(foo_parents[1].foos[0].b == [20, 19, 5000]); +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/nested_arrays_from_brillig/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/nested_arrays_from_brillig/Nargo.toml new file mode 100644 index 00000000000..02d2e6e6a4e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/nested_arrays_from_brillig/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "nested_arrays_from_brillig" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/nested_arrays_from_brillig/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/nested_arrays_from_brillig/Prover.toml new file mode 100644 index 00000000000..1a59cd124a7 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/nested_arrays_from_brillig/Prover.toml @@ -0,0 +1 @@ +values = [1,2,3,4,5,6] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/nested_arrays_from_brillig/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/nested_arrays_from_brillig/src/main.nr new file mode 100644 index 00000000000..1bcbd7d5421 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/nested_arrays_from_brillig/src/main.nr @@ -0,0 +1,26 @@ +struct Header { + params: [Field; 3], +} + +struct MyNote { + plain: Field, + array: [Field; 2], + header: Header, +} + +fn access_nested(notes: [MyNote; 2]) -> Field { + notes[0].array[1] + notes[1].array[0] + notes[0].plain + notes[1].header.params[0] +} + +unconstrained fn create_inside_brillig(values: [Field; 6]) -> [MyNote; 2] { + let header = Header { params: [values[0], values[1], values[2]] }; + let note0 = MyNote { array: [values[0], values[1]], plain: values[2], header }; + let note1 = MyNote { array: [values[3], values[4]], plain: values[5], header }; + [note0, note1] +} + +fn main(values: [Field; 6]) { + let notes = create_inside_brillig(values); + assert(access_nested(notes) == (2 + 4 + 3 + 1)); +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/nested_slice_dynamic/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/nested_slice_dynamic/Nargo.toml new file mode 100644 index 00000000000..c8925ed97b4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/nested_slice_dynamic/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "nested_slice_dynamic" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/nested_slice_dynamic/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/nested_slice_dynamic/Prover.toml new file mode 100644 index 00000000000..7127baac5bf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/nested_slice_dynamic/Prover.toml @@ -0,0 +1 @@ +y = "3" diff --git a/noir/tooling/nargo_cli/tests/execution_success/nested_slice_dynamic/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/nested_slice_dynamic/src/main.nr new file mode 100644 index 00000000000..a3007d5d0dc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/nested_slice_dynamic/src/main.nr @@ -0,0 +1,49 @@ +struct Bar { + inner: [Field; 3], +} + +struct Foo { + a: Field, + b: [Field; 3], + bar: Bar, +} + +fn main(y: Field) { + let foo_one = Foo { a: 1, b: [2, 3, 20], bar: Bar { inner: [100, 101, 102] } }; + let foo_two = Foo { a: 4, b: [5, 6, 21], bar: Bar { inner: [103, 104, 105] } }; + let foo_three = Foo { a: 7, b: [8, 9, 22], bar: Bar { inner: [106, 107, 108] } }; + let foo_four = Foo { a: 10, b: [11, 12, 23], bar: Bar { inner: [109, 110, 111] } }; + let mut x = [foo_one]; + x = x.push_back(foo_two); + x = x.push_back(foo_three); + x = x.push_back(foo_four); + + assert(x[y - 3].a == 1); + assert(x[y - 3].b == [2, 3, 20]); + assert(x[y - 2].a == 4); + assert(x[y - 2].b == [5, 6, 21]); + assert(x[y - 1].a == 7); + assert(x[y - 1].b == [8, 9, 22]); + assert(x[y].a == 10); + assert(x[y].b == [11, 12, 23]); + assert(x[y].bar.inner == [109, 110, 111]); + + if y != 2 { + x[y - 2].a = 50; + } else { + x[y - 2].a = 100; + } + assert(x[y - 2].a == 50); + + if y == 2 { + x[y - 1].b = [50, 51, 52]; + } else { + x[y - 1].b = [100, 101, 102]; + } + assert(x[2].b == [100, 101, 102]); + + assert(x[y - 3].bar.inner == [100, 101, 102]); + assert(x[y - 2].bar.inner == [103, 104, 105]); + assert(x[y - 1].bar.inner == [106, 107, 108]); + assert(x[y].bar.inner == [109, 110, 111]); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/pedersen_check/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/pedersen_check/Nargo.toml new file mode 100644 index 00000000000..65fd3b5cf59 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/pedersen_check/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "pedersen_check" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/pedersen_check/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/pedersen_check/Prover.toml new file mode 100644 index 00000000000..db1ebdf6c51 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/pedersen_check/Prover.toml @@ -0,0 +1,7 @@ +x = "0" +y = "1" +salt = "42" + +out_x = "0x054aa86a73cb8a34525e5bbed6e43ba1198e860f5f3950268f71df4591bde402" +out_y = "0x209dcfbf2cfb57f9f6046f44d71ac6faf87254afc7407c04eb621a6287cac126" +out_hash = "0x0d98561fb02ca04d00801dfdc118b2a24cea0351963587712a28d368041370e1" diff --git a/noir/tooling/nargo_cli/tests/execution_success/pedersen_check/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/pedersen_check/src/main.nr new file mode 100644 index 00000000000..90ef218249b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/pedersen_check/src/main.nr @@ -0,0 +1,22 @@ +use dep::std; + +fn main(x: Field, y: Field, salt: Field, out_x: Field, out_y: Field, out_hash: Field) { + let res = std::hash::pedersen_commitment([x, y]); + assert(res.x == out_x); + assert(res.y == out_y); + + let res_hash = std::hash::pedersen_hash_with_separator([x, y], 0); + assert_eq(res_hash, out_hash); + + assert(res_hash != res.x); + + let raw_data = [x, y]; + let mut state = 0; + for i in 0..2 { + state = state * 8 + raw_data[i]; + } + state += salt; + let hash = std::hash::pedersen_commitment([state]); + assert(std::hash::pedersen_commitment([43]).x == hash.x); +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/poseidon_bn254_hash/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/poseidon_bn254_hash/Nargo.toml new file mode 100644 index 00000000000..a8e2b3d5a2c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/poseidon_bn254_hash/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "poseidon_bn254_hash" +description = "Poseidon 254-bit permutation test on 3 elements with alpha = 5" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/poseidon_bn254_hash/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/poseidon_bn254_hash/Prover.toml new file mode 100644 index 00000000000..8eecf9a3db2 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/poseidon_bn254_hash/Prover.toml @@ -0,0 +1,4 @@ +x1 = [1,2] +y1 = "0x115cc0f5e7d690413df64c6b9662e9cf2a3617f2743245519e19607a4417189a" +x2 = [1,2,3,4] +y2 = "0x299c867db6c1fdd79dcefa40e4510b9837e60ebb1ce0663dbaa525df65250465" diff --git a/noir/tooling/nargo_cli/tests/execution_success/poseidon_bn254_hash/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/poseidon_bn254_hash/src/main.nr new file mode 100644 index 00000000000..3d30ebad279 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/poseidon_bn254_hash/src/main.nr @@ -0,0 +1,9 @@ +use dep::std::hash::poseidon; + +fn main(x1: [Field; 2], y1: pub Field, x2: [Field; 4], y2: pub Field) { + let hash1 = poseidon::bn254::hash_2(x1); + assert(hash1 == y1); + + let hash2 = poseidon::bn254::hash_4(x2); + assert(hash2 == y2); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/poseidonsponge_x5_254/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/poseidonsponge_x5_254/Nargo.toml new file mode 100644 index 00000000000..de4b1a44668 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/poseidonsponge_x5_254/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "poseidonsponge_x5_254" +description = "Variable-length Poseidon-128 sponge test on 7 elements with alpha = 5" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/poseidonsponge_x5_254/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/poseidonsponge_x5_254/Prover.toml new file mode 100644 index 00000000000..f8a6be57b24 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/poseidonsponge_x5_254/Prover.toml @@ -0,0 +1 @@ +x = [1,2,3,4,5,6,7] diff --git a/noir/tooling/nargo_cli/tests/execution_success/poseidonsponge_x5_254/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/poseidonsponge_x5_254/src/main.nr new file mode 100644 index 00000000000..910a17c8c89 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/poseidonsponge_x5_254/src/main.nr @@ -0,0 +1,8 @@ +use dep::std::hash::poseidon; + +fn main(x: [Field; 7]) { + // Test optimized sponge + let result = poseidon::bn254::sponge(x); + + assert(result == 0x080ae1669d62f0197190573d4a325bfb8d8fc201ce3127cbac0c47a7ac81ac48); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/pred_eq/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/pred_eq/Nargo.toml new file mode 100644 index 00000000000..b5b4a9fdf61 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/pred_eq/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "pred_eq" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/pred_eq/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/pred_eq/Prover.toml new file mode 100644 index 00000000000..465ef562de4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/pred_eq/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "1" diff --git a/noir/tooling/nargo_cli/tests/execution_success/pred_eq/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/pred_eq/src/main.nr new file mode 100644 index 00000000000..d1e79a3e408 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/pred_eq/src/main.nr @@ -0,0 +1,4 @@ +fn main(x: Field, y: Field) { + let p = x == y; + assert(p == true); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/references/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/references/Nargo.toml new file mode 100644 index 00000000000..c4be4d4218d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/references/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "references" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/references/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/references/Prover.toml new file mode 100644 index 00000000000..151faa5a9b1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/references/Prover.toml @@ -0,0 +1 @@ +x = "2" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/references/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/references/src/main.nr new file mode 100644 index 00000000000..1a9be5f82b9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/references/src/main.nr @@ -0,0 +1,226 @@ +fn main(mut x: Field) { + add1(&mut x); + assert(x == 3); + + let mut s = S { y: x }; + s.add2(); + assert(s.y == 5); + // Regression for #1946: Method resolution error when calling &mut methods with a variable of type &mut T + let s_ref = &mut s; + s_ref.add2(); + assert(s.y == 7); + // Test that normal mutable variables are still copied + let mut a = 0; + mutate_copy(a); + assert(a == 0); + // Test something 3 allocations deep + let mut nested_allocations = Nested { y: &mut &mut 0 }; + add1(*nested_allocations.y); + assert(**nested_allocations.y == 1); + // Test nested struct allocations with a mutable reference to an array. + let mut c = C { foo: 0, bar: &mut C2 { array: &mut [1, 2] } }; + *c.bar.array = [3, 4]; + assert(*c.bar.array == [3, 4]); + + regression_1887(); + regression_2054(); + regression_2030(); + regression_2255(); + + assert(x == 3); + regression_2218_if_inner_if(x, 10); + regression_2218_if_inner_else(20, x); + regression_2218_else(x, 3); + regression_2218_loop(x, 10); + + regression_2560(s_ref); +} + +fn add1(x: &mut Field) { + *x += 1; +} + +struct S { y: Field } + +struct Nested { y: &mut &mut Field } + +struct C { + foo: Field, + bar: &mut C2, +} + +struct C2 { + array: &mut [Field; 2] +} + +impl S { + fn add2(&mut self) { + self.y += 2; + } + + fn get_y(self) -> Field { + self.y + } +} + +fn mutate_copy(mut a: Field) { + a = 7; +} +// Previously the `foo.bar` in `foo.bar.mutate()` would insert an automatic dereference +// of `foo` which caused the method to wrongly be mutating a copy of bar rather than the original. +fn regression_1887() { + let foo = &mut Foo { bar: Bar { x: 0 } }; + foo.bar.mutate(); + assert(foo.bar.x == 32); +} + +struct Foo { bar: Bar } +struct Bar { x: Field } + +impl Bar { + fn mutate(&mut self) { + self.x = 32; + } +} +// Ensure that mutating a variable does not also mutate its copy +fn regression_2054() { + let mut x = 2; + let z = x; + + x += 1; + assert(z == 2); +} +// The compiler was still trying to convert an LValue from an array of structs to struct of arrays indexing, +// even though this conversion was mostly removed elsewhere. +fn regression_2030() { + let ref = &mut 0; + let mut array = [ref, ref]; + let _ = *array[0]; + *array[0] = 1; +} +// The `mut x: &mut ...` caught a bug handling lvalues where a double-dereference would occur internally +// in one step rather than being tracked by two separate steps. This lead to assigning the 1 value to the +// incorrect outer `mut` reference rather than the correct `&mut` reference. +fn regression_2255() { + let x = &mut 0; + regression_2255_helper(x); + assert(*x == 1); +} + +fn regression_2255_helper(mut x: &mut Field) { + *x = 1; +} + +fn regression_2218(x: Field, y: Field) -> Field { + let q = &mut &mut 0; + let q1 = *q; + let q2 = *q; + + if x != y { + *q1 = 1; + // Make sure that we correct load reference aliases through multiple blocks + if x != 20 { + *q1 = 10; + *q2 = 2; // now we'd expect q1 == q2 == 2 + assert(*q1 == 2); + } else { + *q2 = 15; + assert(*q1 == 15); + } + } else { + *q2 = 20; + assert(*q1 == 20); + } + // Have to assign value to return it + let value = *q1; + value +} + +fn regression_2218_if_inner_if(x: Field, y: Field) { + let value = regression_2218(x, y); + assert(value == 2); +} + +fn regression_2218_if_inner_else(x: Field, y: Field) { + let value = regression_2218(x, y); + assert(value == 15); +} + +fn regression_2218_else(x: Field, y: Field) { + let value = regression_2218(x, y); + assert(value == 20); +} + +fn regression_2218_loop(x: Field, y: Field) { + let q = &mut &mut 0; + let q1 = *q; + let q2 = *q; + + for _ in 0..1 { + if x != y { + *q1 = 10; + *q2 = 2; // now we'd expect q1 == q2 == 2 + assert(*q1 == 2); + } else { + *q2 = 20; + assert(*q1 == 20); + } + } + assert(*q1 == 2); + + for _ in 0..1 { + for _ in 0..5 { + if x != y { + *q1 = 1; + // Make sure that we correct load reference aliases through multiple blocks + if x != 20 { + *q1 = 10; + *q2 = 2; // now we'd expect q1 == q2 == 2 + assert(*q1 == 2); + } + } else { + *q2 = 20; + assert(*q1 == 20); + } + } + if x != y { + *q1 = 1; + for _ in 0..5 { + // Make sure that we correct load reference aliases through multiple blocks + if x != 20 { + *q1 = 10; + *q2 = 2; // now we'd expect q1 == q2 == 2 + assert(*q1 == 2); + } + } + } else { + *q2 = 20; + assert(*q1 == 20); + } + } + assert(*q1 == 2); + + if x != y { + for _ in 0..5 { + if x != y { + *q1 = 1; + // Make sure that we correct load reference aliases through multiple blocks + if x != 20 { + *q1 = 10; + *q2 = 2; // now we'd expect q1 == q2 == 2 + assert(*q1 == 2); + } + } + } + } else { + *q2 = 20; + assert(*q1 == 20); + } + assert(*q1 == 2); +} +// This is more a feature test than a proper regression. +// Before, we never automatically dereferenced objects in method calls to their value types. +// Now, we insert as many `*` as necessary to get to `S`. +fn regression_2560(s_ref: &mut S) { + assert(s_ref.get_y() == 7); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/regression/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/regression/Nargo.toml new file mode 100644 index 00000000000..0f6961c41d3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/regression/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "regression" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/regression/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/regression/Prover.toml new file mode 100644 index 00000000000..2875190982f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/regression/Prover.toml @@ -0,0 +1,2 @@ +x = [0x3f, 0x1c, 0xb8, 0x99, 0xab] +z = 3 diff --git a/noir/tooling/nargo_cli/tests/execution_success/regression/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/regression/src/main.nr new file mode 100644 index 00000000000..ed3dbf23a24 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/regression/src/main.nr @@ -0,0 +1,107 @@ +global NIBBLE_LENGTH: Field = 16; + +fn compact_decode(input: [u8; N], length: Field) -> ([u4; NIBBLE_LENGTH], Field) { + assert(2 * input.len() as u64 <= NIBBLE_LENGTH as u64); + assert(length as u64 <= input.len() as u64); + + let mut nibble = [0 as u4; NIBBLE_LENGTH]; + + let first_nibble = (input[0] >> 4) as u4; + let parity = first_nibble as u1; + + if parity == 1 { + nibble[0] = (input[0] & 0x0f) as u4; + for i in 1..input.len() { + if i as u64 < length as u64 { + let x = input[i]; + nibble[2*i - 1] = (x >> 4) as u4; + nibble[2*i] = (x & 0x0f) as u4; + } + } + } else { + for i in 0..2 { + if (i as u64) < length as u64 - 1 { + let x = input[i + 1]; + nibble[2*i] = (x >> 4) as u4; + nibble[2*i + 1] = (x & 0x0f) as u4; + } + } + } + + let out = (nibble, 2 * length + (parity as Field) - 2); + + out +} + +fn enc(value: [u8; N], value_length: Field) -> ([u8; 32], Field) { + assert(value.len() as u8 >= value_length as u8); + let mut out_value = [0; 32]; + if value_length == 0 { + let out = (out_value, value_length); + out + } else if value_length as u8 < 31 { + out_value[0] = 0x80 + value_length as u8; + + for i in 1..value.len() { + out_value[i] = value[i-1]; + } + + let out = (out_value, value_length + 1); + + out + } else { + let out = (out_value, 32); + out + } +} + +fn bitshift_literal_0() -> u64 { + let mut bits: u64 = 0; + bits |= 1 << 0; + + bits +} +fn bitshift_literal_4() -> u64 { + let mut bits: u64 = 0; + bits |= 1 << 4; + + bits +} +fn bitshift_variable(idx: u64) -> u64 { + let mut bits: u64 = 0; + bits |= 1 << idx; + + bits +} + +fn main(x: [u8; 5], z: Field) { + //Issue 1144 + let (nib, len) = compact_decode(x, z); + assert(len == 5); + assert([nib[0], nib[1], nib[2], nib[3], nib[4]] == [15, 1, 12, 11, 8]); + // Issue 1169 + let val1 = [ + 0xb8, 0x8f, 0x61, 0xe6, 0xfb, 0xda, 0x83, 0xfb, 0xff, 0xfa, 0xbe, 0x36, 0x41, 0x12, 0x13, + 0x74, 0x80, 0x39, 0x80, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00 + ]; + let val1_length = 20; + + let enc_val1 = enc(val1, val1_length); + + assert(enc_val1.0 == [ + 0x94, 0xb8, 0x8f, 0x61, 0xe6, 0xfb, 0xda, 0x83, 0xfb, 0xff, 0xfa, 0xbe, 0x36, 0x41, + 0x12, 0x13, 0x74, 0x80, 0x39, 0x80, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00 + ]); + assert(enc_val1.1 == 21); + // Issue 2399 + let result_0 = bitshift_literal_0(); + assert(result_0 == 1); + let result_4 = bitshift_literal_4(); + assert(result_4 == 16); + let result_0 = bitshift_variable(0); + assert(result_0 == 1); + let result_4 = bitshift_variable(4); + assert(result_4 == 16); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/regression_2854/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/regression_2854/Nargo.toml new file mode 100644 index 00000000000..fb2b3c42fdd --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/regression_2854/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "regression_2854" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/regression_2854/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/regression_2854/Prover.toml new file mode 100644 index 00000000000..07890234a19 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/regression_2854/Prover.toml @@ -0,0 +1 @@ +x = "3" diff --git a/noir/tooling/nargo_cli/tests/execution_success/regression_2854/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/regression_2854/src/main.nr new file mode 100644 index 00000000000..eccff8225b6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/regression_2854/src/main.nr @@ -0,0 +1,3 @@ +fn main(x: Field) -> pub i127 { + x as i127 +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/regression_mem_op_predicate/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/regression_mem_op_predicate/Nargo.toml new file mode 100644 index 00000000000..93a0ba6f25a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/regression_mem_op_predicate/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "regression_mem_op_predicate" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/regression_mem_op_predicate/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/regression_mem_op_predicate/Prover.toml new file mode 100644 index 00000000000..3621f24082c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/regression_mem_op_predicate/Prover.toml @@ -0,0 +1,2 @@ +x = [4,3,1,5,111] +idx = 12 \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/regression_mem_op_predicate/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/regression_mem_op_predicate/src/main.nr new file mode 100644 index 00000000000..4b5ca67f6de --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/regression_mem_op_predicate/src/main.nr @@ -0,0 +1,8 @@ +fn main(mut x: [u32; 5], idx: Field) { + // We should not hit out of bounds here as we have a predicate + // that should not be hit + if idx as u32 < 3 { + x[idx] = 10; + } + assert(x[4] == 111); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/regression_method_cannot_be_found/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/regression_method_cannot_be_found/Nargo.toml new file mode 100644 index 00000000000..aed6aa06714 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/regression_method_cannot_be_found/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "regression_method_cannot_be_found" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/regression_method_cannot_be_found/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/regression_method_cannot_be_found/Prover.toml new file mode 100644 index 00000000000..8b137891791 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/regression_method_cannot_be_found/Prover.toml @@ -0,0 +1 @@ + diff --git a/noir/tooling/nargo_cli/tests/execution_success/regression_method_cannot_be_found/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/regression_method_cannot_be_found/src/main.nr new file mode 100644 index 00000000000..1a6931a6870 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/regression_method_cannot_be_found/src/main.nr @@ -0,0 +1,23 @@ +use dep::std; +struct Item { + id: Field, +} + +impl Item { + fn log(self) { + let id = self.id; + std::println(id); + } +} + +fn create(something: V) -> V { + something +} + +fn main() { + let a = Item { id: 1 }; + let b = create(a); + let _id = b.id; + // Regression for: cannot find this method + b.log(); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/scalar_mul/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/scalar_mul/Nargo.toml new file mode 100644 index 00000000000..926114ec374 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/scalar_mul/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "scalar_mul" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/scalar_mul/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/scalar_mul/Prover.toml new file mode 100644 index 00000000000..69b91cb5f31 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/scalar_mul/Prover.toml @@ -0,0 +1,7 @@ +a = "1" +a_pub_x = "0x0000000000000000000000000000000000000000000000000000000000000001" +a_pub_y = "0x0000000000000002cf135e7506a45d632d270d45f1181294833fc48d823f272c" + +b = "2" +b_pub_x = "0x06ce1b0827aafa85ddeb49cdaa36306d19a74caa311e13d46d8bc688cdbffffe" +b_pub_y = "0x1c122f81a3a14964909ede0ba2a6855fc93faf6fa1a788bf467be7e7a43f80ac" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/scalar_mul/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/scalar_mul/src/main.nr new file mode 100644 index 00000000000..4f0a83ea9e5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/scalar_mul/src/main.nr @@ -0,0 +1,16 @@ +use dep::std; + +fn main(a: Field, a_pub_x: pub Field, a_pub_y: pub Field, b: Field, b_pub_x: pub Field, b_pub_y: pub Field) { + let mut priv_key = a; + let mut pub_x: Field = a_pub_x; + let mut pub_y: Field = a_pub_y; + if a != 1 { + // Change `a` in Prover.toml to test input `b` + priv_key = b; + pub_x = b_pub_x; + pub_y = b_pub_y; + } + let res = std::scalar_mul::fixed_base_embedded_curve(priv_key, 0); + assert(res[0] == pub_x); + assert(res[1] == pub_y); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/schnorr/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/schnorr/Nargo.toml new file mode 100644 index 00000000000..aa24a2f3caf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/schnorr/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "schnorr" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/schnorr/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/schnorr/Prover.toml new file mode 100644 index 00000000000..2faf2018e07 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/schnorr/Prover.toml @@ -0,0 +1,70 @@ +message = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] +message_field = "0x010203040506070809" +pub_key_x = "0x04b260954662e97f00cab9adb773a259097f7a274b83b113532bce27fa3fb96a" +pub_key_y = "0x2fd51571db6c08666b0edfbfbc57d432068bccd0110a39b166ab243da0037197" +signature = [ + 1, + 13, + 119, + 112, + 212, + 39, + 233, + 41, + 84, + 235, + 255, + 93, + 245, + 172, + 186, + 83, + 157, + 253, + 76, + 77, + 33, + 128, + 178, + 15, + 214, + 67, + 105, + 107, + 177, + 234, + 77, + 48, + 27, + 237, + 155, + 84, + 39, + 84, + 247, + 27, + 22, + 8, + 176, + 230, + 24, + 115, + 145, + 220, + 254, + 122, + 135, + 179, + 171, + 4, + 214, + 202, + 64, + 199, + 19, + 84, + 239, + 138, + 124, + 12, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/schnorr/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/schnorr/src/main.nr new file mode 100644 index 00000000000..d5eb92fd5f8 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/schnorr/src/main.nr @@ -0,0 +1,19 @@ +use dep::std; +// Note: If main has any unsized types, then the verifier will never be able +// to figure out the circuit instance +fn main(message: [u8; 10], message_field: Field, pub_key_x: Field, pub_key_y: Field, signature: [u8; 64]) { + // Regression for issue #2421 + // We want to make sure that we can accurately verify a signature whose message is a slice vs. an array + let message_field_bytes = message_field.to_be_bytes(10); + for i in 0..10 { + assert(message[i] == message_field_bytes[i]); + } + // Is there ever a situation where someone would want + // to ensure that a signature was invalid? + // Check that passing a slice as the message is valid + let valid_signature = std::schnorr::verify_signature(pub_key_x, pub_key_y, signature, message_field_bytes); + assert(valid_signature); + // Check that passing an array as the message is valid + let valid_signature = std::schnorr::verify_signature(pub_key_x, pub_key_y, signature, message); + assert(valid_signature); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/sha256/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/sha256/Nargo.toml new file mode 100644 index 00000000000..255d2156ef6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/sha256/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "sha256" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/sha256/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/sha256/Prover.toml new file mode 100644 index 00000000000..c4df1b749bb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/sha256/Prover.toml @@ -0,0 +1,36 @@ + +x = 0xbd +result = [ + 0x68, + 0x32, + 0x57, + 0x20, + 0xaa, + 0xbd, + 0x7c, + 0x82, + 0xf3, + 0x0f, + 0x55, + 0x4b, + 0x31, + 0x3d, + 0x05, + 0x70, + 0xc9, + 0x5a, + 0xcc, + 0xbb, + 0x7d, + 0xc4, + 0xb5, + 0xaa, + 0xe1, + 0x12, + 0x04, + 0xc0, + 0x8f, + 0xfe, + 0x73, + 0x2b, +] diff --git a/noir/tooling/nargo_cli/tests/execution_success/sha256/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/sha256/src/main.nr new file mode 100644 index 00000000000..fd5340e2384 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/sha256/src/main.nr @@ -0,0 +1,19 @@ +// Sha256 example +// +// Calls Sha256 from the standard library. +// +// The Compiler sees this special function and creates an ACIR gate +// +// The ACIR SHA256 gate is passed to PLONK who should +// know how to create the necessary constraints. +// +// Not yet here: For R1CS, it is more about manipulating arithmetic gates to get performance +// This can be done in ACIR! +use dep::std; + +fn main(x: Field, result: [u8; 32]) { + // We use the `as` keyword here to denote the fact that we want to take just the first byte from the x Field + // The padding is taken care of by the program + let digest = std::hash::sha256([x as u8]); + assert(digest == result); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/sha2_byte/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/sha2_byte/Nargo.toml new file mode 100644 index 00000000000..efd691fce58 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/sha2_byte/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "sha2_byte" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/sha2_byte/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/sha2_byte/Prover.toml new file mode 100644 index 00000000000..2f82f14a58d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/sha2_byte/Prover.toml @@ -0,0 +1,5 @@ +# Values obtainable from https://emn178.github.io/online-tools/sha256.html and https://emn178.github.io/online-tools/sha512.html +x = 0xbd +result256 = [0x68, 0x32, 0x57, 0x20, 0xaa, 0xbd, 0x7c, 0x82, 0xf3, 0x0f, 0x55, 0x4b, 0x31, 0x3d, 0x05, 0x70, 0xc9, 0x5a, 0xcc, 0xbb, 0x7d, 0xc4, 0xb5, 0xaa, 0xe1, 0x12, 0x04, 0xc0, 0x8f, 0xfe, 0x73, 0x2b] +result512 = [0x29, 0x6e, 0x22, 0x67, 0xd7, 0x4c, 0x27, 0x8d, 0xaa, 0xaa, 0x94, 0x0d, 0x17, 0xb0, 0xcf, 0xb7, 0x4a, 0x50, 0x83, 0xf8, 0xe0, 0x69, 0x72, 0x6d, 0x8c, 0x84, 0x1c, 0xbe, 0x59, 0x6e, 0x04, 0x31, 0xcb, 0x77, 0x41, 0xa5, 0xb5, 0x0f, 0x71, 0x66, 0x6c, 0xfd, 0x54, 0xba, 0xcb, 0x7b, 0x00, 0xae, 0xa8, 0x91, 0x49, 0x9c, 0xf4, 0xef, 0x6a, 0x03, 0xc8, 0xa8, 0x3f, 0xe3, 0x7c, 0x3f, 0x7b, 0xaf] + diff --git a/noir/tooling/nargo_cli/tests/execution_success/sha2_byte/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/sha2_byte/src/main.nr new file mode 100644 index 00000000000..fa8ddfbdf69 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/sha2_byte/src/main.nr @@ -0,0 +1,10 @@ +// Test Noir implementations of SHA256 and SHA512 on a one-byte message. +use dep::std; + +fn main(x: Field, result256: [u8; 32], result512: [u8; 64]) { + let digest256 = std::sha256::digest([x as u8]); + assert(digest256 == result256); + + let digest512 = std::sha512::digest([x as u8]); + assert(digest512 == result512); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/signed_arithmetic/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/signed_arithmetic/Nargo.toml new file mode 100644 index 00000000000..47676a806e8 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/signed_arithmetic/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "signed_arithmetic" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/signed_arithmetic/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/signed_arithmetic/Prover.toml new file mode 100644 index 00000000000..e0e584b7380 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/signed_arithmetic/Prover.toml @@ -0,0 +1,3 @@ +x = "5" +y = "8" +z = "-15" diff --git a/noir/tooling/nargo_cli/tests/execution_success/signed_arithmetic/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/signed_arithmetic/src/main.nr new file mode 100644 index 00000000000..7a997d177ba --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/signed_arithmetic/src/main.nr @@ -0,0 +1,32 @@ +fn main(mut x: i32, mut y: i32, z: i32) { + let mut s1: i8 = 5; + let mut s2: i8 = 8; + assert(s1 + s2 == 13); + assert(x + y == 13); + + s2= -8; + y = -y; + assert(s1 + s2 == -3); + assert(x + y == -3); + + s1 = -15; + assert(s1 - s2 == -7); + assert(z - y == -7); + + s1 = -5; + s2 = 8; + x = -x; + y = -y; + assert(s1 - s2 == -13); + assert(x - y == -13); + + s2 = -8; + y = -y; + assert(s1 * s2 == 40); + assert(x * y == 40); + + s1 = 1; + s2 = -8; + assert(s1 * s2 == -8); + assert(x / x * y == -8); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/signed_division/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/signed_division/Nargo.toml new file mode 100644 index 00000000000..0d631a00b05 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/signed_division/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "signed_division" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/signed_division/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/signed_division/Prover.toml new file mode 100644 index 00000000000..ee6f0ef229a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/signed_division/Prover.toml @@ -0,0 +1,3 @@ +x = "7" +y = "3" +z = "2" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/signed_division/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/signed_division/src/main.nr new file mode 100644 index 00000000000..6289a2f9ed9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/signed_division/src/main.nr @@ -0,0 +1,21 @@ +use dep::std; +// Testing signed integer division: +// 7/3 = 2 +// -7/3 = -2 +// -7/-3 = 2 +// 7/-3 = -2 +fn main(mut x: i32, mut y: i32, mut z: i32) { + // 7/3 = 2 + assert(x / y == z); + // -7/3 = -2 + let minus_x = std::wrapping_sub(0, x); + let minus_z = std::wrapping_sub(0, z); + let minus_y = std::wrapping_sub(0, y); + assert(x + minus_x == 0); + assert(z + minus_z == 0); + assert(minus_x / y == minus_z); + // -7/-3 = 2 + assert(minus_x / minus_y == z); + // 7/-3 = -2 + assert(x / minus_y == minus_z); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_2d_array/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_2d_array/Nargo.toml new file mode 100644 index 00000000000..b6d22d82631 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_2d_array/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "simple_2d_array" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_2d_array/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_2d_array/Prover.toml new file mode 100644 index 00000000000..a4616f907bb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_2d_array/Prover.toml @@ -0,0 +1,3 @@ +x = "1" +y = "2" +array_input = [[1, 2], [3, 3]] diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_2d_array/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/simple_2d_array/src/main.nr new file mode 100644 index 00000000000..2ecdd4bc15f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_2d_array/src/main.nr @@ -0,0 +1,8 @@ +// Test accessing a multidimensional array +fn main(x: Field, y: Field, array_input: [[Field; 2]; 2]) { + assert(array_input[0][0] == x); + assert(array_input[0][1] == y); + + let arr: [[Field; 2]; 1] = [[3, 3]]; + assert_eq(arr[0], array_input[1]); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_add_and_ret_arr/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_add_and_ret_arr/Nargo.toml new file mode 100644 index 00000000000..6c5b0e4ca9a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_add_and_ret_arr/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "simple_add_and_ret_arr" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_add_and_ret_arr/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_add_and_ret_arr/Prover.toml new file mode 100644 index 00000000000..3d2b4b14efe --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_add_and_ret_arr/Prover.toml @@ -0,0 +1 @@ +x = 1 \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_add_and_ret_arr/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/simple_add_and_ret_arr/src/main.nr new file mode 100644 index 00000000000..016c4fedf40 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_add_and_ret_arr/src/main.nr @@ -0,0 +1,8 @@ +// A simple program to test that SSA array values elements +// aren't disconnected from their instruction results, and +// that dead instruction elemination looks inside of arrays +// when deciding whether of not an instruction should be +// retained. +fn main(x: Field) -> pub [Field; 1] { + [x + 1] +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_bitwise/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_bitwise/Nargo.toml new file mode 100644 index 00000000000..be425f22e5c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_bitwise/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "simple_bitwise" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_bitwise/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_bitwise/Prover.toml new file mode 100644 index 00000000000..aa3715f9b3d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_bitwise/Prover.toml @@ -0,0 +1,4 @@ +a = 1 +b = 0 +c = "10" +d = "11" diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_bitwise/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/simple_bitwise/src/main.nr new file mode 100644 index 00000000000..9ab738ae04d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_bitwise/src/main.nr @@ -0,0 +1,9 @@ +fn main(a: bool, b: bool, c: u8, d: u8) -> pub u8 { + let i = a & b; + let j = a ^ b; + let k = a | b; + let x = c & d; + let y = c ^ d; + let z = c | d; + (i as u8) + (j as u8) + (k as u8) + x + y + z +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_comparison/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_comparison/Nargo.toml new file mode 100644 index 00000000000..06685628444 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_comparison/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "simple_comparison" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_comparison/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_comparison/Prover.toml new file mode 100644 index 00000000000..ed94d313315 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_comparison/Prover.toml @@ -0,0 +1,2 @@ +x = "3" +y = "4" diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_comparison/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/simple_comparison/src/main.nr new file mode 100644 index 00000000000..05800440459 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_comparison/src/main.nr @@ -0,0 +1,6 @@ +// Tests a very simple program. +// +// The features being tested is comparison +fn main(x: Field, y: Field) { + assert(x as u32 < y as u32); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_mut/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_mut/Nargo.toml new file mode 100644 index 00000000000..c00e60bdbc0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_mut/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "simple_mut" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_mut/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_mut/Prover.toml new file mode 100644 index 00000000000..7d4290a117a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_mut/Prover.toml @@ -0,0 +1 @@ +x = 1 diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_mut/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/simple_mut/src/main.nr new file mode 100644 index 00000000000..d0715dbafe0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_mut/src/main.nr @@ -0,0 +1,6 @@ +// A simple program to test mutable variables +fn main(x: Field) -> pub Field { + let mut y = 2; + y += x; + y +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_not/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_not/Nargo.toml new file mode 100644 index 00000000000..52c86b36efb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_not/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "simple_not" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_not/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_not/Prover.toml new file mode 100644 index 00000000000..b3accc9dd32 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_not/Prover.toml @@ -0,0 +1 @@ +x = false \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_not/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/simple_not/src/main.nr new file mode 100644 index 00000000000..ba172625fe4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_not/src/main.nr @@ -0,0 +1,4 @@ +// A simple program for testing the NOT op +fn main(x: bool) -> pub bool { + !x +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_print/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_print/Nargo.toml new file mode 100644 index 00000000000..cd8422ac482 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_print/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "simple_print" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_print/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_print/Prover.toml new file mode 100644 index 00000000000..2c1854573a4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_print/Prover.toml @@ -0,0 +1,2 @@ +x = 1 +y = 2 diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_print/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/simple_print/src/main.nr new file mode 100644 index 00000000000..6038b995af0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_print/src/main.nr @@ -0,0 +1,8 @@ +// Simple program for testing the logging +// of single witnesses and witness arrays. +use dep::std; + +fn main(x: Field, y: pub Field) { + std::println(x); + std::println([x, y]); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_program_addition/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_program_addition/Nargo.toml new file mode 100644 index 00000000000..28967a28a55 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_program_addition/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "simple_program_addition" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_program_addition/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_program_addition/Prover.toml new file mode 100644 index 00000000000..07890234a19 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_program_addition/Prover.toml @@ -0,0 +1 @@ +x = "3" diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_program_addition/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/simple_program_addition/src/main.nr new file mode 100644 index 00000000000..0390d79e83b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_program_addition/src/main.nr @@ -0,0 +1,5 @@ +// The feature being tested is handling of +// a binary operation. +fn main(x: Field) -> pub Field { + x + 1 +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_radix/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_radix/Nargo.toml new file mode 100644 index 00000000000..12cbc8da1d3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_radix/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "simple_radix" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_radix/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_radix/Prover.toml new file mode 100644 index 00000000000..1ddfb7dc8e4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_radix/Prover.toml @@ -0,0 +1 @@ +x = 2 \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_radix/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/simple_radix/src/main.nr new file mode 100644 index 00000000000..4a335e1bade --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_radix/src/main.nr @@ -0,0 +1,7 @@ +// Simple program to test to_radix +fn main(x: Field) { + let bits = x.to_le_bits(3); + assert(bits[0] == 0); + assert(bits[1] == 1); + assert(bits[2] == 0); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_shield/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_shield/Nargo.toml new file mode 100644 index 00000000000..7e9579b0af4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_shield/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "simple_shield" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_shield/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_shield/Prover.toml new file mode 100644 index 00000000000..7878e4b8281 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_shield/Prover.toml @@ -0,0 +1,11 @@ +# Random test key +priv_key = "0x000000000000000000000000000000000000000000000000000000616c696365" +note_root = "0x02278fc806225cd696bd502049f7bb98624536de56d09343f8a26ac1876b23a0" +index = "0" +note_hash_path = [ + "0x1cdcf02431ba623767fe389337d011df1048dcc24b98ed81cec97627bab454a0", + "0x0b5e9666e7323ce925c28201a97ddf4144ac9d148448ed6f49f9008719c1b85b", + "0x22ec636f8ad30ef78c42b7fe2be4a4cacf5a445cfb5948224539f59a11d70775", +] +to_pubkey_x = "0x0000000000000000000000000000000000000000000000000000000000000001" +to_pubkey_y = "0x0000000000000002cf135e7506a45d632d270d45f1181294833fc48d823f272c" diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_shield/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/simple_shield/src/main.nr new file mode 100644 index 00000000000..c46d3b4594c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_shield/src/main.nr @@ -0,0 +1,29 @@ +use dep::std; + +fn main( + // Public key of note + // all notes have the same denomination + priv_key: Field, + // Merkle membership proof + note_root: pub Field, + index: Field, + note_hash_path: [Field; 3], + // Receiver public key + to_pubkey_x: Field, + to_pubkey_y: Field +) -> pub [Field; 2] { + // Compute public key from private key to show ownership + let pubkey = std::scalar_mul::fixed_base_embedded_curve(priv_key, 0); + let pubkey_x = pubkey[0]; + let pubkey_y = pubkey[1]; + // Compute input note commitment + let note_commitment = std::hash::pedersen_commitment([pubkey_x, pubkey_y]); + // Compute input note nullifier + let nullifier = std::hash::pedersen_commitment([note_commitment.x, index, priv_key]); + // Compute output note nullifier + let receiver_note_commitment = std::hash::pedersen_commitment([to_pubkey_x, to_pubkey_y]); + // Check that the input note nullifier is in the root + assert(note_root == std::merkle::compute_merkle_root(note_commitment.x, index, note_hash_path)); + + [nullifier.x, receiver_note_commitment.x] +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_shift_left_right/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_shift_left_right/Nargo.toml new file mode 100644 index 00000000000..2e75d5feada --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_shift_left_right/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "simple_shift_left_right" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_shift_left_right/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/simple_shift_left_right/Prover.toml new file mode 100644 index 00000000000..07890234a19 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_shift_left_right/Prover.toml @@ -0,0 +1 @@ +x = "3" diff --git a/noir/tooling/nargo_cli/tests/execution_success/simple_shift_left_right/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/simple_shift_left_right/src/main.nr new file mode 100644 index 00000000000..40698af6ce7 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/simple_shift_left_right/src/main.nr @@ -0,0 +1,8 @@ +// Tests a very simple program. +// +// The features being tested are left and right shifts. +fn main(x: u32) { + let z = x >> 4; + let t = x << 4; + assert(z == t >> 8); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/slice_dynamic_index/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/slice_dynamic_index/Nargo.toml new file mode 100644 index 00000000000..a02faeae27a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/slice_dynamic_index/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "slice_dynamic_index" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/slice_dynamic_index/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/slice_dynamic_index/Prover.toml new file mode 100644 index 00000000000..0e5dfd5638d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/slice_dynamic_index/Prover.toml @@ -0,0 +1 @@ +x = "5" diff --git a/noir/tooling/nargo_cli/tests/execution_success/slice_dynamic_index/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/slice_dynamic_index/src/main.nr new file mode 100644 index 00000000000..2e5c0122dfb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/slice_dynamic_index/src/main.nr @@ -0,0 +1,308 @@ +fn main(x: Field) { + // The parameters to this function must come directly from witness values (inputs to main). + regression_dynamic_slice_index(x - 1, x - 4); +} + +fn regression_dynamic_slice_index(x: Field, y: Field) { + let mut slice = []; + for i in 0..5 { + slice = slice.push_back(i); + } + assert(slice.len() == 5); + + dynamic_slice_index_set_if(slice, x, y); + dynamic_slice_index_set_else(slice, x, y); + dynamic_slice_index_set_nested_if_else_else(slice, x, y); + dynamic_slice_index_set_nested_if_else_if(slice, x, y + 1); + dynamic_slice_index_if(slice, x); + dynamic_array_index_if([0, 1, 2, 3, 4], x); + dynamic_slice_index_else(slice, x); + + dynamic_slice_merge_if(slice, x); + dynamic_slice_merge_else(slice, x); + dynamic_slice_merge_two_ifs(slice, x); + dynamic_slice_merge_mutate_between_ifs(slice, x, y); + dynamic_slice_merge_push_then_pop(slice, x, y); +} + +fn dynamic_slice_index_set_if(mut slice: [Field], x: Field, y: Field) { + assert(slice[x] == 4); + assert(slice[y] == 1); + slice[y] = 0; + assert(slice[x] == 4); + assert(slice[1] == 0); + if x as u32 < 10 { + assert(slice[x] == 4); + slice[x] = slice[x] - 2; + slice[x - 1] = slice[x]; + } else { + slice[x] = 0; + } + assert(slice[3] == 2); + assert(slice[4] == 2); +} + +fn dynamic_slice_index_set_else(mut slice: [Field], x: Field, y: Field) { + assert(slice[x] == 4); + assert(slice[y] == 1); + slice[y] = 0; + assert(slice[x] == 4); + assert(slice[1] == 0); + if x as u32 > 10 { + assert(slice[x] == 4); + slice[x] = slice[x] - 2; + slice[x - 1] = slice[x]; + } else { + slice[x] = 0; + } + assert(slice[4] == 0); +} +// This tests the case of missing a store instruction in the else branch +// of merging slices +fn dynamic_slice_index_if(mut slice: [Field], x: Field) { + if x as u32 < 10 { + assert(slice[x] == 4); + slice[x] = slice[x] - 2; + } else { + assert(slice[x] == 0); + } + assert(slice[4] == 2); +} + +fn dynamic_array_index_if(mut array: [Field; 5], x: Field) { + if x as u32 < 10 { + assert(array[x] == 4); + array[x] = array[x] - 2; + } else { + assert(array[x] == 0); + } + assert(array[4] == 2); +} +// This tests the case of missing a store instruction in the then branch +// of merging slices +fn dynamic_slice_index_else(mut slice: [Field], x: Field) { + if x as u32 > 10 { + assert(slice[x] == 0); + } else { + assert(slice[x] == 4); + slice[x] = slice[x] - 2; + } + assert(slice[4] == 2); +} + +fn dynamic_slice_merge_if(mut slice: [Field], x: Field) { + if x as u32 < 10 { + assert(slice[x] == 4); + slice[x] = slice[x] - 2; + + slice = slice.push_back(10); + // Having an array set here checks whether we appropriately + // handle a slice length that is not yet resolving to a constant + // during flattening + slice[x] = 10; + assert(slice[slice.len() - 1] == 10); + assert(slice.len() == 6); + + slice[x] = 20; + slice[x] = slice[x] + 10; + + slice = slice.push_front(11); + assert(slice[0] == 11); + assert(slice.len() == 7); + assert(slice[5] == 30); + + slice = slice.push_front(12); + assert(slice[0] == 12); + assert(slice.len() == 8); + assert(slice[6] == 30); + + let (popped_slice, last_elem) = slice.pop_back(); + assert(last_elem == 10); + assert(popped_slice.len() == 7); + + let (first_elem, rest_of_slice) = popped_slice.pop_front(); + assert(first_elem == 12); + assert(rest_of_slice.len() == 6); + // TODO(#2462): SliceInsert and SliceRemove with a dynamic index are not yet implemented in ACIR gen + slice = rest_of_slice.insert(2, 20); + assert(slice[2] == 20); + assert(slice[6] == 30); + assert(slice.len() == 7); + // TODO(#2462): SliceInsert and SliceRemove with a dynamic index are not yet implemented in ACIR gen + let (removed_slice, removed_elem) = slice.remove(3); + // The deconstructed tuple assigns to the slice but is not seen outside of the if statement + // without a direct assignment + slice = removed_slice; + + assert(removed_elem == 1); + assert(slice.len() == 6); + } else { + assert(slice[x] == 0); + slice = slice.push_back(20); + } + + assert(slice.len() == 6); + assert(slice[slice.len() - 1] == 30); +} + +fn dynamic_slice_merge_else(mut slice: [Field], x: Field) { + if x as u32 > 10 { + assert(slice[x] == 0); + slice[x] = 2; + } else { + assert(slice[x] == 4); + slice[x] = slice[x] - 2; + slice = slice.push_back(10); + } + assert(slice.len() == 6); + assert(slice[slice.len() - 1] == 10); + + slice = slice.push_back(20); + assert(slice.len() == 7); + assert(slice[slice.len() - 1] == 20); +} + +fn dynamic_slice_index_set_nested_if_else_else(mut slice: [Field], x: Field, y: Field) { + assert(slice[x] == 4); + assert(slice[y] == 1); + slice[y] = 0; + assert(slice[x] == 4); + assert(slice[1] == 0); + if x as u32 < 10 { + slice[x] = slice[x] - 2; + if y != 1 { + slice[x] = slice[x] + 20; + } else { + if x == 5 { + // We should not hit this case + assert(slice[x] == 22); + } else { + slice[x] = 10; + slice = slice.push_back(15); + assert(slice.len() == 6); + } + assert(slice[4] == 10); + } + } else { + slice[x] = 0; + } + assert(slice[4] == 10); + assert(slice.len() == 6); + assert(slice[slice.len() - 1] == 15); + + slice = slice.push_back(20); + assert(slice.len() == 7); + assert(slice[slice.len() - 1] == 20); +} + +fn dynamic_slice_index_set_nested_if_else_if(mut slice: [Field], x: Field, y: Field) { + assert(slice[x] == 4); + assert(slice[y] == 2); + slice[y] = 0; + assert(slice[x] == 4); + assert(slice[2] == 0); + if x as u32 < 10 { + slice[x] = slice[x] - 2; + // TODO: this panics as we have a load for the slice in flattening + if y == 1 { + slice[x] = slice[x] + 20; + } else { + if x == 4 { + slice[x] = 5; + } + assert(slice[4] == 5); + } + } else { + slice[x] = 0; + } + assert(slice[4] == 5); +} + +fn dynamic_slice_merge_two_ifs(mut slice: [Field], x: Field) { + if x as u32 > 10 { + assert(slice[x] == 0); + slice[x] = 2; + } else { + assert(slice[x] == 4); + slice[x] = slice[x] - 2; + slice = slice.push_back(10); + } + + assert(slice.len() == 6); + assert(slice[slice.len() - 1] == 10); + + if x == 20 { + slice = slice.push_back(20); + } + + slice = slice.push_back(15); + + assert(slice.len() == 7); + assert(slice[slice.len() - 1] == 15); + + slice = slice.push_back(20); + assert(slice.len() == 8); + assert(slice[slice.len() - 1] == 20); +} + +fn dynamic_slice_merge_mutate_between_ifs(mut slice: [Field], x: Field, y: Field) { + if x != y { + slice[x] = 50; + slice = slice.push_back(y); + slice = slice.push_back(x); + } else { + slice[x] = slice[x] - 2; + slice = slice.push_back(x); + } + + slice = slice.push_back(30); + assert(slice.len() == 8); + + if x == 20 { + slice = slice.push_back(20); + } + + slice = slice.push_back(15); + + if x != 20 { + slice = slice.push_back(50); + } + + slice = slice.push_back(60); + assert(slice.len() == 11); + assert(slice[x] == 50); + assert(slice[slice.len() - 4] == 30); + assert(slice[slice.len() - 3] == 15); + assert(slice[slice.len() - 2] == 50); + assert(slice[slice.len() - 1] == 60); +} + +fn dynamic_slice_merge_push_then_pop(mut slice: [Field], x: Field, y: Field) { + if x != y { + slice[x] = 5; + slice = slice.push_back(y); + slice = slice.push_back(x); + assert(slice.len() == 7); + + let (popped_slice, elem) = slice.pop_back(); + assert(slice.len() == 7); + assert(elem == x); + slice = popped_slice; + } else { + slice = slice.push_back(x); + } + + slice = slice.push_back(30); + assert(slice.len() == 7); + + if x == 20 { + slice = slice.push_back(20); + } + + let (slice, elem) = slice.pop_back(); + assert(elem == 30); + + let (_, elem) = slice.pop_back(); + assert(elem == y); +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/slice_struct_field/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/slice_struct_field/Nargo.toml new file mode 100644 index 00000000000..9530ebf9271 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/slice_struct_field/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "slice_struct_field" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/slice_struct_field/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/slice_struct_field/Prover.toml new file mode 100644 index 00000000000..7127baac5bf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/slice_struct_field/Prover.toml @@ -0,0 +1 @@ +y = "3" diff --git a/noir/tooling/nargo_cli/tests/execution_success/slice_struct_field/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/slice_struct_field/src/main.nr new file mode 100644 index 00000000000..c00fdf85180 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/slice_struct_field/src/main.nr @@ -0,0 +1,177 @@ +struct FooParent { + parent_arr: [Field; 3], + foos: [Foo], +} + +struct Bar { + inner: [Field; 3], +} + +struct Foo { + a: Field, + b: [Field], + bar: Bar, +} + +fn main(y: pub Field) { + let mut b_one = [2, 3, 20]; + b_one = b_one.push_back(20); + let foo_one = Foo { a: 1, b: b_one, bar: Bar { inner: [100, 101, 102] } }; + + let mut b_two = [5, 6, 21]; + b_two = b_two.push_back(21); + let foo_two = Foo { a: 4, b: b_two, bar: Bar { inner: [103, 104, 105] } }; + + let foo_three = Foo { a: 7, b: [8, 9, 22], bar: Bar { inner: [106, 107, 108] } }; + let foo_four = Foo { a: 10, b: [11, 12, 23], bar: Bar { inner: [109, 110, 111] } }; + + let mut x = [foo_one, foo_two]; + x = x.push_back(foo_three); + x = x.push_back(foo_four); + + assert(x[y - 3].a == 1); + let struct_slice = x[y - 3].b; + for i in 0..4 { + assert(struct_slice[i] == b_one[i]); + } + + assert(x[y - 2].a == 4); + let struct_slice = x[y - 2].b; + for i in 0..4 { + assert(struct_slice[i] == b_two[i]); + } + + assert(x[y - 1].a == 7); + let struct_slice = x[y - 1].b; + assert(struct_slice[0] == 8); + assert(struct_slice[1] == 9); + assert(struct_slice[2] == 22); + + assert(x[y].a == 10); + let struct_slice = x[y].b; + assert(struct_slice[0] == 11); + assert(struct_slice[1] == 12); + assert(struct_slice[2] == 23); + assert(x[y].bar.inner == [109, 110, 111]); + + assert(x[y - 3].bar.inner == [100, 101, 102]); + assert(x[y - 2].bar.inner == [103, 104, 105]); + assert(x[y - 1].bar.inner == [106, 107, 108]); + assert(x[y].bar.inner == [109, 110, 111]); + // Check that switching the lhs and rhs is still valid + assert([109, 110, 111] == x[y].bar.inner); + // TODO: Enable merging nested slices + // if y != 2 { + // x[y].a = 50; + // } else { + // x[y].a = 100; + // } + // assert(x[3].a == 50); + // if y == 2 { + // x[y - 1].b = [50, 51, 52]; + // } else { + // x[y - 1].b = [100, 101, 102]; + // } + // assert(x[2].b[0] == 100); + // assert(x[2].b[1] == 101); + // assert(x[2].b[2] == 102); + let q = x.push_back(foo_four); + let foo_parent_one = FooParent { parent_arr: [0, 1, 2], foos: x }; + let foo_parent_two = FooParent { parent_arr: [3, 4, 5], foos: q }; + let mut foo_parents = [foo_parent_one]; + foo_parents = foo_parents.push_back(foo_parent_two); + // TODO: make a separate test for compile time + // foo_parents[1].foos.push_back(foo_four); + // TODO: Merging nested slices is broken + // if y == 3 { + // foo_parents[y - 2].foos[y - 1].b[y - 1] = 5000; + // } else { + // foo_parents[y - 2].foos[y - 1].b[y - 1] = 1000; + // } + assert(foo_parents[y - 2].foos[y - 2].b[y - 1] == 21); + foo_parents[y - 2].foos[y - 2].b[y - 1] = 5000; + assert(foo_parents[y - 2].foos[y - 2].b[y - 1] == 5000); + + let b_array = foo_parents[y - 2].foos[y - 3].b; + assert(foo_parents[y - 2].foos[y - 3].a == 1); + assert(b_array[0] == 2); + assert(b_array[1] == 3); + assert(b_array[2] == 20); + assert(b_array[3] == 20); + + let b_array = foo_parents[y - 2].foos[y - 2].b; + assert(foo_parents[y - 2].foos[y - 2].a == 4); + assert(b_array[0] == 5); + assert(b_array[1] == 6); + assert(b_array[2] == 5000); + assert(b_array[3] == 21); + + assert(foo_parents[y - 2].foos[y - 1].a == 7); + foo_parents[y - 2].foos[y - 1].a = 50; + + let b_array = foo_parents[y - 2].foos[y - 1].b; + assert(b_array[2] == 22); + assert(b_array.len() == 3); + // Test setting a nested array with non-dynamic + let x = [5, 6, 5000, 21, 100, 101].as_slice(); + foo_parents[y - 2].foos[y - 1].b = x; + + assert(foo_parents[y - 2].foos[y - 1].b.len() == 6); + assert(foo_parents[y - 2].foos[y - 1].b[4] == 100); + assert(foo_parents[y - 2].foos[y - 1].b[5] == 101); + + test_basic_intrinsics_nested_slices(foo_parents, y); + // TODO(#3364): still have to enable slice intrinsics on dynamic nested slices + // assert(foo_parents[y - 2].foos.len() == 5); + // foo_parents[y - 2].foos = foo_parents[y - 2].foos.push_back(foo_four); + // assert(foo_parents[y - 2].foos.len() == 6); + let b_array = foo_parents[y - 2].foos[y - 1].b; + assert(b_array[0] == 5); + assert(b_array[1] == 6); + assert(b_array[2] == 5000); + assert(b_array[3] == 21); + + let b_array = foo_parents[y - 2].foos[y].b; + assert(foo_parents[y - 2].foos[y].a == 10); + assert(b_array[0] == 11); + assert(b_array[1] == 12); + assert(b_array[2] == 23); + + assert(foo_parents[y - 2].foos[y - 3].bar.inner == [100, 101, 102]); + assert(foo_parents[y - 2].foos[y - 2].bar.inner == [103, 104, 105]); + assert(foo_parents[y - 2].foos[y - 1].bar.inner == [106, 107, 108]); + assert(foo_parents[y - 2].foos[y].bar.inner == [109, 110, 111]); +} + +fn test_basic_intrinsics_nested_slices(mut foo_parents: [FooParent], y: Field) { + foo_parents[y - 2].foos[y - 1].b = foo_parents[y - 2].foos[y - 1].b.push_back(500); + assert(foo_parents[y - 2].foos[y - 1].b.len() == 7); + assert(foo_parents[y - 2].foos[y - 1].b[6] == 500); + + let (popped_slice, last_elem) = foo_parents[y - 2].foos[y - 1].b.pop_back(); + foo_parents[y - 2].foos[y - 1].b = popped_slice; + assert(foo_parents[y - 2].foos[y - 1].b.len() == 6); + assert(last_elem == 500); + + foo_parents[y - 2].foos[y - 1].b = foo_parents[y - 2].foos[y - 1].b.push_front(11); + assert(foo_parents[y - 2].foos[y - 1].b.len() == 7); + assert(foo_parents[y - 2].foos[y - 1].b[0] == 11); + + let (first_elem, rest_of_slice) = foo_parents[y - 2].foos[y - 1].b.pop_front(); + foo_parents[y - 2].foos[y - 1].b = rest_of_slice; + assert(foo_parents[y - 2].foos[y - 1].b.len() == 6); + assert(first_elem == 11); + + foo_parents[y - 2].foos[y - 1].b = foo_parents[y - 2].foos[y - 1].b.insert(2, 20); + assert(foo_parents[y - 2].foos[y - 1].b.len() == 7); + assert(foo_parents[y - 2].foos[y - 1].b[y - 1] == 20); + assert(foo_parents[y - 2].foos[y - 1].b[y] == 5000); + assert(foo_parents[y - 2].foos[y - 1].b[6] == 101); + + let (rest_of_slice, removed_elem) = foo_parents[y - 2].foos[y - 1].b.remove(3); + foo_parents[y - 2].foos[y - 1].b = rest_of_slice; + assert(removed_elem == 5000); + assert(foo_parents[y - 2].foos[y - 1].b.len() == 6); + assert(foo_parents[y - 2].foos[y - 1].b[2] == 20); + assert(foo_parents[y - 2].foos[y - 1].b[3] == 21); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/slices/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/slices/Nargo.toml new file mode 100644 index 00000000000..3eec413bc07 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/slices/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "slices" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/slices/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/slices/Prover.toml new file mode 100644 index 00000000000..f28f2f8cc48 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/slices/Prover.toml @@ -0,0 +1,2 @@ +x = "5" +y = "10" diff --git a/noir/tooling/nargo_cli/tests/execution_success/slices/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/slices/src/main.nr new file mode 100644 index 00000000000..c377d2e5b2f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/slices/src/main.nr @@ -0,0 +1,299 @@ +use dep::std::slice; +use dep::std; + +fn main(x: Field, y: pub Field) { + let mut slice = [0; 2]; + assert(slice[0] == 0); + assert(slice[0] != 1); + slice[0] = x; + assert(slice[0] == x); + + let slice_plus_10 = slice.push_back(y); + assert(slice_plus_10[2] == 10); + assert(slice_plus_10[2] != 8); + assert(slice_plus_10.len() == 3); + + let mut new_slice = []; + for i in 0..5 { + new_slice = new_slice.push_back(i); + } + assert(new_slice.len() == 5); + + new_slice = new_slice.push_front(20); + assert(new_slice[0] == 20); + assert(new_slice.len() == 6); + + let (popped_slice, last_elem) = new_slice.pop_back(); + assert(last_elem == 4); + assert(popped_slice.len() == 5); + + let (first_elem, rest_of_slice) = popped_slice.pop_front(); + assert(first_elem == 20); + assert(rest_of_slice.len() == 4); + + new_slice = rest_of_slice.insert(2, 100); + assert(new_slice[2] == 100); + assert(new_slice[4] == 3); + assert(new_slice.len() == 5); + + let (remove_slice, removed_elem) = new_slice.remove(3); + assert(removed_elem == 2); + assert(remove_slice[3] == 3); + assert(remove_slice.len() == 4); + + let append = [1, 2].append([3, 4, 5]); + assert(append.len() == 5); + assert(append[0] == 1); + assert(append[4] == 5); + + regression_2083(); + // The parameters to this function must come from witness values (inputs to main) + regression_merge_slices(x, y); + regression_2370(); +} +// Ensure that slices of struct/tuple values work. +fn regression_2083() { + let y = [(1, 2)]; + let y = y.push_back((3, 4)); // [(1, 2), (3, 4)] + let y = y.push_back((5, 6)); // [(1, 2), (3, 4), (5, 6)] + assert(y[2].1 == 6); + + let y = y.push_front((10, 11)); // [(10, 11), (1, 2), (3, 4), (5, 6)] + let y = y.push_front((12, 13)); // [(12, 13), (10, 11), (1, 2), (3, 4), (5, 6)] + assert(y[1].0 == 10); + + let y = y.insert(1, (55, 56)); // [(12, 13), (55, 56), (10, 11), (1, 2), (3, 4), (5, 6)] + assert(y[0].1 == 13); + assert(y[1].1 == 56); + assert(y[2].0 == 10); + + let (y, x) = y.remove(2); // [(12, 13), (55, 56), (1, 2), (3, 4), (5, 6)] + assert(y[2].0 == 1); + assert(x.0 == 10); + assert(x.1 == 11); + + let (x, y) = y.pop_front(); // [(55, 56), (1, 2), (3, 4), (5, 6)] + assert(y[0].0 == 55); + assert(x.0 == 12); + assert(x.1 == 13); + + let (y, x) = y.pop_back(); // [(55, 56), (1, 2), (3, 4)] + assert(y.len() == 3); + assert(x.0 == 5); + assert(x.1 == 6); +} +// The parameters to this function must come from witness values (inputs to main) +fn regression_merge_slices(x: Field, y: Field) { + merge_slices_if(x, y); + merge_slices_else(x); +} + +fn merge_slices_if(x: Field, y: Field) { + let slice = merge_slices_return(x, y); + assert(slice.len() == 3); + assert(slice[2] == 10); + + let slice = merge_slices_mutate(x, y); + assert(slice.len() == 4); + assert(slice[3] == 5); + + let slice = merge_slices_mutate_in_loop(x, y); + assert(slice.len() == 7); + assert(slice[6] == 4); + + let slice = merge_slices_mutate_two_ifs(x, y); + assert(slice.len() == 6); + assert(slice[3] == 5); + assert(slice[4] == 15); + assert(slice[5] == 30); + + let slice = merge_slices_mutate_between_ifs(x, y); + assert(slice.len() == 8); + assert(slice[3] == 5); + assert(slice[4] == 30); + assert(slice[5] == 15); + assert(slice[6] == 50); + assert(slice[7] == 60); + + merge_slices_push_then_pop(x, y); + + let slice = merge_slices_push_then_insert(x, y); + assert(slice.len() == 7); + assert(slice[1] == 50); + assert(slice[2] == 0); + assert(slice[5] == 30); + assert(slice[6] == 100); + + let slice = merge_slices_remove_between_ifs(x, y); + assert(slice.len() == 5); +} + +fn merge_slices_else(x: Field) { + let slice = merge_slices_return(x, 5); + assert(slice[0] == 0); + assert(slice[1] == 0); + assert(slice.len() == 2); + + let slice = merge_slices_mutate(x, 5); + assert(slice[2] == 5); + assert(slice.len() == 3); + + let slice = merge_slices_mutate_in_loop(x, 5); + assert(slice[2] == 5); + assert(slice.len() == 3); +} +// Test returning a merged slice without a mutation +fn merge_slices_return(x: Field, y: Field) -> [Field] { + let slice = [0; 2]; + if x != y { + if x != 20 { slice.push_back(y) } else { slice } + } else { + slice + } +} +// Test mutating a slice inside of an if statement +fn merge_slices_mutate(x: Field, y: Field) -> [Field] { + let mut slice = [0; 2]; + if x != y { + slice = slice.push_back(y); + slice = slice.push_back(x); + } else { + slice = slice.push_back(x); + } + slice +} +// Test mutating a slice inside of a loop in an if statement +fn merge_slices_mutate_in_loop(x: Field, y: Field) -> [Field] { + let mut slice = [0; 2]; + if x != y { + for i in 0..5 { + slice = slice.push_back(i); + } + } else { + slice = slice.push_back(x); + } + slice +} + +fn merge_slices_mutate_two_ifs(x: Field, y: Field) -> [Field] { + let mut slice = [0; 2]; + if x != y { + slice = slice.push_back(y); + slice = slice.push_back(x); + } else { + slice = slice.push_back(x); + } + + if x == 20 { + slice = slice.push_back(20); + } + + slice = slice.push_back(15); + slice = slice.push_back(30); + + slice +} + +fn merge_slices_mutate_between_ifs(x: Field, y: Field) -> [Field] { + let mut slice = [0; 2]; + if x != y { + slice = slice.push_back(y); + slice = slice.push_back(x); + } else { + slice = slice.push_back(x); + } + + slice = slice.push_back(30); + + if x == 20 { + slice = slice.push_back(20); + } + + slice = slice.push_back(15); + + if x != 20 { + slice = slice.push_back(50); + } + + slice = slice.push_back(60); + + slice +} + +fn merge_slices_push_then_pop(x: Field, y: Field) { + let mut slice = [0; 2]; + if x != y { + slice = slice.push_back(y); + slice = slice.push_back(x); + } else { + slice = slice.push_back(x); + } + + slice = slice.push_back(30); + + if x == 20 { + slice = slice.push_back(20); + } + + let (slice, elem) = slice.pop_back(); + assert(slice.len() == 4); + assert(elem == 30); + + let (slice, elem) = slice.pop_back(); + assert(slice.len() == 3); + assert(elem == x); +} + +fn merge_slices_push_then_insert(x: Field, y: Field) -> [Field] { + let mut slice = [0; 2]; + if x != y { + slice = slice.push_back(y); + slice = slice.push_back(x); + } else { + slice = slice.push_back(x); + } + + slice = slice.push_back(30); + + if x == 20 { + slice = slice.push_back(20); + slice = slice.push_back(15); + } + + slice = slice.insert(1, 50); + // Test that we can use slice insert the same as slice push back + slice = slice.insert(6, 100); + + slice +} + +fn merge_slices_remove_between_ifs(x: Field, y: Field) -> [Field] { + let mut slice = [0; 2]; + if x != y { + slice = slice.push_back(y); + slice = slice.push_back(x); + } else { + slice = slice.push_back(x); + } + + let (mut slice, elem) = slice.remove(2); + assert(elem == y); + + if x == 20 { + slice = slice.push_back(20); + } + + slice = slice.push_back(15); + + if x != 20 { + slice = slice.push_back(50); + } + + slice +} +// Previously, we'd get a type error when trying to assign an array of a different size to +// an existing array variable. Now, we infer the variable must be a slice. +fn regression_2370() { + let mut slice = []; + slice = [1, 2, 3]; +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/strings/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/strings/Nargo.toml new file mode 100644 index 00000000000..76a9beb6909 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/strings/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "strings" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/strings/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/strings/Prover.toml new file mode 100644 index 00000000000..0d1bd92b5de --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/strings/Prover.toml @@ -0,0 +1,4 @@ +message = "hello world" +y = 5 +hex_as_string = "0x41" +hex_as_field = "0x41" diff --git a/noir/tooling/nargo_cli/tests/execution_success/strings/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/strings/src/main.nr new file mode 100644 index 00000000000..cb2218837f3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/strings/src/main.nr @@ -0,0 +1,73 @@ +use dep::std; +// Test global string literals +global HELLO_WORLD = "hello world"; + +fn main(message: pub str<11>, y: Field, hex_as_string: str<4>, hex_as_field: Field) { + let mut bad_message = "hello world"; + + assert(message == "hello world"); + assert(message == HELLO_WORLD); + let x = 10; + let z = x * 5; + std::println(10); + + std::println(z); // x * 5 in println not yet supported + std::println(x); + + let array = [1, 2, 3, 5, 8]; + assert(y == 5); // Change to y != 5 to see how the later print statements are not called + std::println(array); + + bad_message = "hell\0\"world"; + std::println(bad_message); + assert(message != bad_message); + + let hash = std::hash::pedersen_commitment([x]); + std::println(hash); + + assert(hex_as_string == "0x41"); + // assert(hex_as_string != 0x41); This will fail with a type mismatch between str[4] and Field + assert(hex_as_field == 0x41); +} + +#[test] +fn test_prints_strings() { + let message = "hello world!"; + + std::println(message); + std::println("goodbye world"); +} + +#[test] +fn test_prints_array() { + let array = [1, 2, 3, 5, 8]; + + let s = Test { a: 1, b: 2, c: [3, 4] }; + std::println(s); + + std::println(array); + + let hash = std::hash::pedersen_commitment(array); + std::println(hash); +} + +fn failed_constraint(hex_as_field: Field) { + // TODO(#2116): Note that `println` will not work if a failed constraint can be + // evaluated at compile time. + // When this method is called from a test method or with constant values + // a `Failed constraint` compile error will be caught before this `println` + // is executed as the input will be a constant. + std::println(hex_as_field); + assert(hex_as_field != 0x41); +} + +#[test] +fn test_failed_constraint() { + failed_constraint(0x41); +} + +struct Test { + a: Field, + b: Field, + c: [Field; 2], +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/struct/Nargo.toml new file mode 100644 index 00000000000..6622edd0653 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "struct" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/struct/Prover.toml new file mode 100644 index 00000000000..7d59cc81807 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct/Prover.toml @@ -0,0 +1,2 @@ +x = "0" +y = "1" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/struct/src/main.nr new file mode 100644 index 00000000000..45c5e347e5a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct/src/main.nr @@ -0,0 +1,75 @@ +struct Foo { + bar: Field, + array: [Field; 2], +} + +struct Pair { + first: Foo, + second: Field, +} + +impl Foo { + fn default(x: Field,y: Field) -> Self { + Self { bar: 0, array: [x,y] } + } +} + +impl Pair { + fn foo(p: Self) -> Foo { + p.first + } + + fn bar(self) -> Field { + self.foo().bar + } +} + +struct Nested { + a: Field, + b: Field +} +struct MyStruct { + my_bool: bool, + my_int: u32, + my_nest: Nested, +} +fn test_struct_in_tuple(a_bool: bool, x: Field, y: Field) -> (MyStruct, bool) { + let my_struct = MyStruct { my_bool: a_bool, my_int: 5, my_nest: Nested { a: x, b: y } }; + (my_struct, a_bool) +} + +struct Animal { + legs: Field, + eyes: u8, +} + +fn get_dog() -> Animal { + let dog = Animal { legs: 4, eyes: 2 }; + dog +} + +struct Unit; + +fn main(x: Field, y: Field) { + let unit = Unit {}; + + let first = Foo::default(x, y); + let p = Pair { first, second: 1 }; + + assert(p.bar() == x); + assert(p.second == y); + assert(p.first.array[0] != p.first.array[1]); + // Nested structs + let (struct_from_tuple, a_bool) = test_struct_in_tuple(true, x, y); + assert(struct_from_tuple.my_bool == true); + assert(a_bool == true); + assert(struct_from_tuple.my_int == 5); + assert(struct_from_tuple.my_nest.a == 0); + // Regression test for issue #670 + let Animal { legs, eyes } = get_dog(); + let six = legs + eyes as Field; + + assert(six == 6); + + let Animal { legs: _, eyes: _ } = get_dog(); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct_array_inputs/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/struct_array_inputs/Nargo.toml new file mode 100644 index 00000000000..40160fb397f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct_array_inputs/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "struct_array_inputs" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct_array_inputs/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/struct_array_inputs/Prover.toml new file mode 100644 index 00000000000..c2212c1a9c9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct_array_inputs/Prover.toml @@ -0,0 +1,11 @@ +[[foos]] +bar = 0 +baz = 0 + +[[foos]] +bar = 0 +baz = 0 + +[[foos]] +bar = 1 +baz = 2 diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct_array_inputs/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/struct_array_inputs/src/main.nr new file mode 100644 index 00000000000..4a367558671 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct_array_inputs/src/main.nr @@ -0,0 +1,8 @@ +struct Foo { + bar: Field, + baz: Field, +} + +fn main(foos: [Foo; 3]) -> pub Field { + foos[2].bar + foos[2].baz +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct_fields_ordering/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/struct_fields_ordering/Nargo.toml new file mode 100644 index 00000000000..767f51acda3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct_fields_ordering/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "struct_fields_ordering" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct_fields_ordering/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/struct_fields_ordering/Prover.toml new file mode 100644 index 00000000000..70640bba4cc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct_fields_ordering/Prover.toml @@ -0,0 +1,3 @@ +[y] +foo = "5" +bar = "7" diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct_fields_ordering/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/struct_fields_ordering/src/main.nr new file mode 100644 index 00000000000..1a2e2d462e2 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct_fields_ordering/src/main.nr @@ -0,0 +1,12 @@ +// Note that fields are not in alphabetical order. +// We want to check that this ordering is maintained +struct myStruct { + foo: u32, + bar: Field, +} + +fn main(y: pub myStruct) { + assert(y.foo == 5); + assert(y.bar == 7); +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/Nargo.toml new file mode 100644 index 00000000000..7dd6f78b750 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "struct_inputs" +type = "bin" +authors = [""] + +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/Prover.toml new file mode 100644 index 00000000000..339da5b1a00 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/Prover.toml @@ -0,0 +1,19 @@ +x = "5" + +[y] +foo = "5" +bar = "10" +message = "hello" + +[z] +val = "1" +array = [0, 1] +message = "helld" + +[a] +baz = 0 + +[a.bar_struct] +val = "1" +array = [0, 1] +message = "hello" diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/src/foo.nr b/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/src/foo.nr new file mode 100644 index 00000000000..ea3a6bbe25f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/src/foo.nr @@ -0,0 +1,6 @@ +mod bar; + +struct fooStruct { + bar_struct: bar::barStruct, + baz: Field, +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/src/foo/bar.nr b/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/src/foo/bar.nr new file mode 100644 index 00000000000..6d879326677 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/src/foo/bar.nr @@ -0,0 +1,7 @@ +global N = 2; + +struct barStruct { + val: Field, + array: [Field; 2], + message: str<5>, +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/src/main.nr new file mode 100644 index 00000000000..5b03483cbaf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/struct_inputs/src/main.nr @@ -0,0 +1,34 @@ +mod foo; + +struct myStruct { + foo: u32, + bar: Field, + message: str<5>, +} + +fn main(x: Field, y: pub myStruct, z: pub foo::bar::barStruct, a: pub foo::fooStruct) -> pub Field { + let struct_from_bar = foo::bar::barStruct { val: 1, array: [0, 1], message: "hello" }; + + check_inner_struct(a, z); + + for i in 0..struct_from_bar.array.len() { + assert(struct_from_bar.array[i] == z.array[i]); + } + assert(z.val == struct_from_bar.val); + + assert((struct_from_bar.val * x) == x); + + assert(x != y.bar); + + assert(y.message == "hello"); + assert(a.bar_struct.message == struct_from_bar.message); + + a.bar_struct.array[1] +} + +fn check_inner_struct(a: foo::fooStruct, z: foo::bar::barStruct) { + assert(a.bar_struct.val == z.val); + for i in 0..a.bar_struct.array.len() { + assert(a.bar_struct.array[i] == z.array[i]); + } +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/submodules/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/submodules/Nargo.toml new file mode 100644 index 00000000000..082afdf3f10 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/submodules/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "submodules" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/submodules/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/submodules/Prover.toml new file mode 100644 index 00000000000..b6626a67e19 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/submodules/Prover.toml @@ -0,0 +1,2 @@ +x = 1 +y = 0 diff --git a/noir/tooling/nargo_cli/tests/execution_success/submodules/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/submodules/src/main.nr new file mode 100644 index 00000000000..813f3a26a20 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/submodules/src/main.nr @@ -0,0 +1,15 @@ +use mysubmodule::my_helper; + +fn main(x: u1, y: u1) { + my_helper(); + mysubmodule::my_bool_or(x, y); +} + +mod mysubmodule { + pub fn my_bool_or(x: u1, y: u1) { + assert(x | y == 1); + } + + pub fn my_helper() {} +} + diff --git a/noir/tooling/nargo_cli/tests/execution_success/to_be_bytes/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/to_be_bytes/Nargo.toml new file mode 100644 index 00000000000..e9d2e30b4c4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/to_be_bytes/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "to_be_bytes" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/to_be_bytes/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/to_be_bytes/Prover.toml new file mode 100644 index 00000000000..07fe857ac7c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/to_be_bytes/Prover.toml @@ -0,0 +1 @@ +x = "2040124" diff --git a/noir/tooling/nargo_cli/tests/execution_success/to_be_bytes/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/to_be_bytes/src/main.nr new file mode 100644 index 00000000000..64d54ddff66 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/to_be_bytes/src/main.nr @@ -0,0 +1,12 @@ +fn main(x: Field) -> pub [u8; 31] { + // The result of this byte array will be big-endian + let byte_array = x.to_be_bytes(31); + let mut bytes = [0; 31]; + for i in 0..31 { + bytes[i] = byte_array[i]; + } + if (bytes[30] != 60) | (bytes[29] != 33) | (bytes[28] != 31) { + assert(false); + } + bytes +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/to_bytes_consistent/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/to_bytes_consistent/Nargo.toml new file mode 100644 index 00000000000..1e117b83883 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/to_bytes_consistent/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "to_bytes_consistent" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/to_bytes_consistent/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/to_bytes_consistent/Prover.toml new file mode 100644 index 00000000000..07fe857ac7c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/to_bytes_consistent/Prover.toml @@ -0,0 +1 @@ +x = "2040124" diff --git a/noir/tooling/nargo_cli/tests/execution_success/to_bytes_consistent/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/to_bytes_consistent/src/main.nr new file mode 100644 index 00000000000..638b34c9bab --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/to_bytes_consistent/src/main.nr @@ -0,0 +1,13 @@ +// This test aims to check that we have consistent behavior +// between a `to_be_bytes` call (which is radix decomposition under the hood) +// with constant inputs or with witness inputs. +// x = 2040124 +fn main(x: Field) { + let byte_array = x.to_be_bytes(31); + let x_as_constant = 2040124; + let constant_byte_array = x_as_constant.to_be_bytes(31); + assert(constant_byte_array.len() == byte_array.len()); + for i in 0..constant_byte_array.len() { + assert(constant_byte_array[i] == byte_array[i]); + } +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/to_bytes_integration/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/to_bytes_integration/Nargo.toml new file mode 100644 index 00000000000..c89c32eb06c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/to_bytes_integration/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "to_bytes_integration" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/to_bytes_integration/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/to_bytes_integration/Prover.toml new file mode 100644 index 00000000000..bc4693e434a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/to_bytes_integration/Prover.toml @@ -0,0 +1,2 @@ +x = "2040124" +a = "0x2000000000000000000000000000000000000000000000000000000000000000" diff --git a/noir/tooling/nargo_cli/tests/execution_success/to_bytes_integration/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/to_bytes_integration/src/main.nr new file mode 100644 index 00000000000..3c43caf1806 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/to_bytes_integration/src/main.nr @@ -0,0 +1,25 @@ +use dep::std; + +fn main(x: Field, a: Field) { + let y: Field = 2040124; + let be_byte_array = y.to_be_bytes(31); + let le_byte_array = x.to_le_bytes(31); + + assert(le_byte_array[0] == 60); + assert(le_byte_array[0] == be_byte_array[30]); + assert(le_byte_array[1] == be_byte_array[29]); + assert(le_byte_array[2] == be_byte_array[28]); + + let z = 0 - 1; + let p_bytes = std::field::modulus_le_bytes(); + let z_bytes = z.to_le_bytes(32); + assert(p_bytes[10] == z_bytes[10]); + assert(p_bytes[0] == z_bytes[0] as u8 + 1 as u8); + + let p_bits = std::field::modulus_le_bits(); + let z_bits = z.to_le_bits(std::field::modulus_num_bits() as u32); + assert(z_bits[0] == 0); + assert(p_bits[100] == z_bits[100]); + + a.to_le_bits(std::field::modulus_num_bits() as u32); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/to_le_bytes/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/to_le_bytes/Nargo.toml new file mode 100644 index 00000000000..3d02c3510fd --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/to_le_bytes/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "to_le_bytes" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/to_le_bytes/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/to_le_bytes/Prover.toml new file mode 100644 index 00000000000..07fe857ac7c --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/to_le_bytes/Prover.toml @@ -0,0 +1 @@ +x = "2040124" diff --git a/noir/tooling/nargo_cli/tests/execution_success/to_le_bytes/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/to_le_bytes/src/main.nr new file mode 100644 index 00000000000..05eefc0f143 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/to_le_bytes/src/main.nr @@ -0,0 +1,11 @@ +fn main(x: Field) -> pub [u8; 31] { + // The result of this byte array will be little-endian + let byte_array = x.to_le_bytes(31); + assert(byte_array.len() == 31); + + let mut bytes = [0; 31]; + for i in 0..31 { + bytes[i] = byte_array[i]; + } + bytes +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/trait_as_return_type/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/trait_as_return_type/Nargo.toml new file mode 100644 index 00000000000..afd89f0c3a1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/trait_as_return_type/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "trait_as_return_type" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/trait_as_return_type/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/trait_as_return_type/Prover.toml new file mode 100644 index 00000000000..a0cd58138b6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/trait_as_return_type/Prover.toml @@ -0,0 +1 @@ +x = "5" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/trait_as_return_type/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/trait_as_return_type/src/main.nr new file mode 100644 index 00000000000..f6828a356c1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/trait_as_return_type/src/main.nr @@ -0,0 +1,51 @@ +trait SomeTrait { + fn magic_number(self) -> Field; +} + +struct A {} +struct B {} +struct C { + x: Field +} + +impl SomeTrait for A { + fn magic_number(self) -> Field { + 2 + } +} + +impl SomeTrait for B { + fn magic_number(self) -> Field { + 4 + } +} + +impl SomeTrait for C { + fn magic_number(self) -> Field { + self.x + } +} + +fn factory_a() -> impl SomeTrait { + A {} +} + +fn factory_b() -> impl SomeTrait { + B {} +} + +fn factory_c(x: Field) -> impl SomeTrait { + C { x } +} +// x = 15 +fn main(x: u32) { + let a = factory_a(); + let b = B {}; + let b2 = factory_b(); + assert(a.magic_number() == 2); + assert(b.magic_number() == 4); + assert(b2.magic_number() == 4); + let c = factory_c(10); + assert(c.magic_number() == 10); + assert(factory_c(13).magic_number() == 13); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/trait_impl_base_type/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/trait_impl_base_type/Nargo.toml new file mode 100644 index 00000000000..845498737c1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/trait_impl_base_type/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "trait_impl_base_type" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/trait_impl_base_type/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/trait_impl_base_type/Prover.toml new file mode 100644 index 00000000000..a0cd58138b6 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/trait_impl_base_type/Prover.toml @@ -0,0 +1 @@ +x = "5" \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/trait_impl_base_type/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/trait_impl_base_type/src/main.nr new file mode 100644 index 00000000000..bb11dcab73a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/trait_impl_base_type/src/main.nr @@ -0,0 +1,115 @@ +trait Fieldable { + fn to_field(self) -> Field; +} + +impl Fieldable for u32 { + fn to_field(self) -> Field { + let res = self as Field; + res * 3 + } +} + +impl Fieldable for [u32; 3] { + fn to_field(self) -> Field { + let res = self[0] + self[1] + self[2]; + res as Field + } +} + +impl Fieldable for bool { + fn to_field(self) -> Field { + if self { + 14 + } else { + 3 + } + } +} + +impl Fieldable for (u32, bool) { + fn to_field(self) -> Field { + if self.1 { + self.0 as Field + } else { + 32 + } + } +} + +impl Fieldable for Field { + fn to_field(self) -> Field { + self + } +} + +impl Fieldable for str<6> { + fn to_field(self) -> Field { + 6 + } +} + +impl Fieldable for () { + fn to_field(self) -> Field { + 0 + } +} + +type Point2D = [Field; 2]; +type Point2DAlias = Point2D; + +impl Fieldable for Point2DAlias { + fn to_field(self) -> Field { + self[0] + self[1] + } +} + +impl Fieldable for fmtstr<14, (Field, Field)> { + fn to_field(self) -> Field { + 52 + } +} + +impl Fieldable for fn(u32) -> u32 { + fn to_field(self) -> Field { + self(10) as Field + } +} + +fn some_func(x: u32) -> u32 { + x * 2 - 3 +} + +impl Fieldable for u64 { + fn to_field(self) -> Field { + 66 as Field + } +} +// x = 15 +fn main(x: u32) { + assert(x.to_field() == 15); + let arr: [u32; 3] = [3, 5, 8]; + assert(arr.to_field() == 16); + let b_true = 2 == 2; + assert(b_true.to_field() == 14); + let b_false = 2 == 3; + assert(b_false.to_field() == 3); + let f = 13 as Field; + assert(f.to_field() == 13); + let k_true = (12 as u32, true); + assert(k_true.to_field() == 12); + let k_false = (11 as u32, false); + assert(k_false.to_field() == 32); + let m = "String"; + assert(m.to_field() == 6); + let unit = (); + assert(unit.to_field() == 0); + let point: Point2DAlias = [2, 3]; + assert(point.to_field() == 5); + let i: Field = 2; + let j: Field = 6; + assert(f"i: {i}, j: {j}".to_field() == 52); + assert(some_func.to_field() == 17); + + let mut y = 0 as u64; + assert(y.to_field() == 66); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/Nargo.toml new file mode 100644 index 00000000000..555c3b38b98 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "traits_in_crates_1" +type = "bin" +authors = [""] +[dependencies] +crate1 = { path = "crate1" } +crate2 = { path = "crate2" } diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/Prover.toml new file mode 100644 index 00000000000..c2005d59807 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/Prover.toml @@ -0,0 +1,2 @@ +x = 1 +y = 11 diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/crate1/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/crate1/Nargo.toml new file mode 100644 index 00000000000..f49603ba203 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/crate1/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "crate1" +type = "lib" +authors = [""] +[dependencies] +crate2 = { path = "../crate2" } diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/crate1/src/lib.nr b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/crate1/src/lib.nr new file mode 100644 index 00000000000..62dd5a2c111 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/crate1/src/lib.nr @@ -0,0 +1,9 @@ +trait MyTrait { + fn Add10(&mut self); +} + +impl MyTrait for dep::crate2::MyStruct { + fn Add10(&mut self) { + self.Q += 10; + } +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/crate2/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/crate2/Nargo.toml new file mode 100644 index 00000000000..c86c5edf51a --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/crate2/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "crate2" +type = "lib" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/crate2/src/lib.nr b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/crate2/src/lib.nr new file mode 100644 index 00000000000..c59bf0387c1 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/crate2/src/lib.nr @@ -0,0 +1,3 @@ +struct MyStruct { + Q: Field, +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/src/main.nr new file mode 100644 index 00000000000..7ba2f63c5c0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_1/src/main.nr @@ -0,0 +1,5 @@ +fn main(x: Field, y: pub Field) { + let mut V = dep::crate2::MyStruct { Q: x }; + V.Add10(); + assert(V.Q == y); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/Nargo.toml new file mode 100644 index 00000000000..11a86a08402 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/Nargo.toml @@ -0,0 +1,7 @@ +[package] +name = "traits_in_crates_2" +type = "bin" +authors = [""] +[dependencies] +crate1 = { path = "crate1" } +crate2 = { path = "crate2" } diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/Prover.toml new file mode 100644 index 00000000000..c2005d59807 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/Prover.toml @@ -0,0 +1,2 @@ +x = 1 +y = 11 diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/crate1/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/crate1/Nargo.toml new file mode 100644 index 00000000000..de1eddf2cf0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/crate1/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "crate1" +type = "lib" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/crate1/src/lib.nr b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/crate1/src/lib.nr new file mode 100644 index 00000000000..59a28a50c79 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/crate1/src/lib.nr @@ -0,0 +1,3 @@ +trait MyTrait { + fn Add10(&mut self); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/crate2/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/crate2/Nargo.toml new file mode 100644 index 00000000000..885d41f0a27 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/crate2/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "crate2" +type = "lib" +authors = [""] +[dependencies] +crate1 = { path = "../crate1" } diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/crate2/src/lib.nr b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/crate2/src/lib.nr new file mode 100644 index 00000000000..38870489131 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/crate2/src/lib.nr @@ -0,0 +1,9 @@ +struct MyStruct { + Q: Field, +} + +impl dep::crate1::MyTrait for MyStruct { + fn Add10(&mut self) { + self.Q += 10; + } +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/src/main.nr new file mode 100644 index 00000000000..7ba2f63c5c0 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/traits_in_crates_2/src/main.nr @@ -0,0 +1,5 @@ +fn main(x: Field, y: pub Field) { + let mut V = dep::crate2::MyStruct { Q: x }; + V.Add10(); + assert(V.Q == y); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/tuple_inputs/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/tuple_inputs/Nargo.toml new file mode 100644 index 00000000000..f4af6cfe6e3 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/tuple_inputs/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "tuple_inputs" +type = "bin" +authors = [""] +[dependencies] \ No newline at end of file diff --git a/noir/tooling/nargo_cli/tests/execution_success/tuple_inputs/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/tuple_inputs/Prover.toml new file mode 100644 index 00000000000..43d62a7a15b --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/tuple_inputs/Prover.toml @@ -0,0 +1,12 @@ +pair = [1, 0] +x = [[0, 1, 2], [3, 4, 5]] + +[[struct_pair]] +a = "1" +b = ["2", "3", "20"] + +[struct_pair.bar] +inner = ["100", "101", "102"] + +[[struct_pair]] +inner = ["103", "104", "105"] diff --git a/noir/tooling/nargo_cli/tests/execution_success/tuple_inputs/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/tuple_inputs/src/main.nr new file mode 100644 index 00000000000..38fec58f14f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/tuple_inputs/src/main.nr @@ -0,0 +1,35 @@ +struct Bar { + inner: [Field; 3], +} + +struct Foo { + a: Field, + b: [Field; 3], + bar: Bar, +} + +fn main(pair: (Field, Field), x: [(u8, u8, u8); 2], struct_pair: (Foo, Bar)) -> pub (Field, u8) { + let mut start_val = 0; + for i in 0..2 { + assert(x[i].0 == start_val); + assert(x[i].1 == start_val + 1); + assert(x[i].2 == start_val + 2); + start_val += 3; + } + + assert(struct_pair.0.a == 1); + assert(struct_pair.0.b == [2, 3, 20]); + assert(struct_pair.0.bar.inner == [100, 101, 102]); + assert(struct_pair.1.inner == [103, 104, 105]); + + let (u, v) = if pair.0 as u32 < 1 { + (pair.0, pair.0 + 1) + } else { + (pair.0 + 1, pair.0) + }; + + assert(u == pair.0 + 1); + assert(v == pair.0); + + (u, v as u8) +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/tuples/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/tuples/Nargo.toml new file mode 100644 index 00000000000..f6ed9f1e649 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/tuples/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "tuples" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/tuples/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/tuples/Prover.toml new file mode 100644 index 00000000000..a0397e89477 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/tuples/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "0" diff --git a/noir/tooling/nargo_cli/tests/execution_success/tuples/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/tuples/src/main.nr new file mode 100644 index 00000000000..5526fcad422 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/tuples/src/main.nr @@ -0,0 +1,22 @@ +fn main(x: Field, y: Field) { + let pair = (x, y); + assert(pair.0 == 1); + assert(pair.1 == 0); + + let (a, b) = if true { (0, 1) } else { (2, 3) }; + assert(a == 0); + assert(b == 1); + + let (u, v) = if x as u32 < 1 { (x, x + 1) } else { (x + 1, x) }; + assert(u == x + 1); + assert(v == x); + // Test mutating tuples + let mut mutable = ((0, 0), 1, 2, 3); + mutable.0 = (x, y); + mutable.2 = 7; + assert(mutable.0.0 == 1); + assert(mutable.0.1 == 0); + assert(mutable.1 == 1); + assert(mutable.2 == 7); + assert(mutable.3 == 3); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/type_aliases/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/type_aliases/Nargo.toml new file mode 100644 index 00000000000..a12df601a91 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/type_aliases/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "type_aliases" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/type_aliases/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/type_aliases/Prover.toml new file mode 100644 index 00000000000..771df41899d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/type_aliases/Prover.toml @@ -0,0 +1 @@ +x = [2, 3] diff --git a/noir/tooling/nargo_cli/tests/execution_success/type_aliases/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/type_aliases/src/main.nr new file mode 100644 index 00000000000..ee62b0b7260 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/type_aliases/src/main.nr @@ -0,0 +1,33 @@ +type Foo = [T; 2]; + +type Bar = Field; + +type One = (A, B); +type Two = One; +type Three = Two; + +struct MyStruct { + foo: Bar, +} + +fn main(x: [Field; 2]) { + let a: Foo = [1, 2]; + assert(a[0] != x[0]); + + let b: Bar = 2; + assert(x[0] == b); + + let c: u8 = 1; + let d: u32 = 2; + let e: Three = (c, d); + assert(e.0 == 1); + + let s = MyStruct { foo: 10 }; + assert(s.foo == 10); + + let _regression2502: Regression2502Alias = Regression2502 {}; +} +// An ICE was occurring if a type alias referred to a struct before it was initialized +// during name resolution. The fix was to initialize structs during def collection instead. +type Regression2502Alias = Regression2502; +struct Regression2502 {} diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/workspace/Nargo.toml new file mode 100644 index 00000000000..36db098686f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace/Nargo.toml @@ -0,0 +1,2 @@ +[workspace] +members = ["crates/a", "crates/b"] diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/workspace/Prover.toml new file mode 100644 index 00000000000..a0397e89477 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "0" diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/a/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/a/Nargo.toml new file mode 100644 index 00000000000..8d0e1aca4a9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/a/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "a" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/a/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/a/Prover.toml new file mode 100644 index 00000000000..465ef562de4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/a/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "1" diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/a/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/a/src/main.nr new file mode 100644 index 00000000000..cf72627da2e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/a/src/main.nr @@ -0,0 +1,3 @@ +fn main(x: Field, y: pub Field) { + assert(x == y); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/b/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/b/Nargo.toml new file mode 100644 index 00000000000..f316511340f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/b/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "b" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/b/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/b/Prover.toml new file mode 100644 index 00000000000..a0397e89477 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/b/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "0" diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/b/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/b/src/main.nr new file mode 100644 index 00000000000..4e1fd3c9035 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace/crates/b/src/main.nr @@ -0,0 +1,3 @@ +fn main(x: Field, y: pub Field) { + assert(x != y); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/Nargo.toml new file mode 100644 index 00000000000..3828c5d0677 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/Nargo.toml @@ -0,0 +1,3 @@ +[workspace] +members = ["a"] +default-member = "a" diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/Prover.toml new file mode 100644 index 00000000000..8c12ebba6cf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "2" diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/a/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/a/Nargo.toml new file mode 100644 index 00000000000..8d0e1aca4a9 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/a/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "a" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/a/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/a/Prover.toml new file mode 100644 index 00000000000..465ef562de4 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/a/Prover.toml @@ -0,0 +1,2 @@ +x = "1" +y = "1" diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/a/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/a/src/main.nr new file mode 100644 index 00000000000..cf72627da2e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/a/src/main.nr @@ -0,0 +1,3 @@ +fn main(x: Field, y: pub Field) { + assert(x == y); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/b/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/b/Nargo.toml new file mode 100644 index 00000000000..f316511340f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/b/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "b" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/b/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/b/Prover.toml new file mode 100644 index 00000000000..83fcd8678e7 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/b/Prover.toml @@ -0,0 +1,3 @@ +# Deliberately setting these to fail to prove this is NOT executed since a default is specified +x = "1" +y = "1" diff --git a/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/b/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/b/src/main.nr new file mode 100644 index 00000000000..6e170de75fc --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/workspace_default_member/b/src/main.nr @@ -0,0 +1,3 @@ +fn main(x : Field, y : pub Field) { + assert(x != y); +} diff --git a/noir/tooling/nargo_cli/tests/execution_success/xor/Nargo.toml b/noir/tooling/nargo_cli/tests/execution_success/xor/Nargo.toml new file mode 100644 index 00000000000..ddf3b6d86c5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/xor/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "xor" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/execution_success/xor/Prover.toml b/noir/tooling/nargo_cli/tests/execution_success/xor/Prover.toml new file mode 100644 index 00000000000..f28f2f8cc48 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/xor/Prover.toml @@ -0,0 +1,2 @@ +x = "5" +y = "10" diff --git a/noir/tooling/nargo_cli/tests/execution_success/xor/src/main.nr b/noir/tooling/nargo_cli/tests/execution_success/xor/src/main.nr new file mode 100644 index 00000000000..4ceef1b518f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/execution_success/xor/src/main.nr @@ -0,0 +1,5 @@ +fn main(x: u32, y: pub u32) { + let m = x ^ y; + + assert(m != 10); +} diff --git a/noir/tooling/nargo_cli/tests/gates_report.sh b/noir/tooling/nargo_cli/tests/gates_report.sh new file mode 100755 index 00000000000..e06e6812e9d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/gates_report.sh @@ -0,0 +1,36 @@ +#!/bin/bash +set -e + +# These tests are incompatible with gas reporting +excluded_dirs=("workspace" "workspace_default_member") + +# These tests cause failures in CI with a stack overflow for some reason. +ci_excluded_dirs=("eddsa") + +current_dir=$(pwd) +base_path="$current_dir/execution_success" +test_dirs=$(ls $base_path) + +# We generate a Noir workspace which contains all of the test cases +# This allows us to generate a gates report using `nargo info` for all of them at once. + +echo "[workspace]" > Nargo.toml +echo "members = [" >> Nargo.toml + +for dir in $test_dirs; do + if [[ " ${excluded_dirs[@]} " =~ " ${dir} " ]]; then + continue + fi + + if [[ ${CI-false} = "true" ]] && [[ " ${ci_excluded_dirs[@]} " =~ " ${dir} " ]]; then + continue + fi + + echo " \"execution_success/$dir\"," >> Nargo.toml +done + +echo "]" >> Nargo.toml + +nargo info --json > gates_report.json + +rm Nargo.toml diff --git a/noir/tooling/nargo_cli/tests/hello_world.rs b/noir/tooling/nargo_cli/tests/hello_world.rs new file mode 100644 index 00000000000..bc7022d1567 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/hello_world.rs @@ -0,0 +1,55 @@ +//! This integration test aims to mirror the steps taken by a new user using Nargo for the first time. +//! It then follows the steps published at https://noir-lang.org/getting_started/hello_world.html +//! Any modifications to the commands run here MUST be documented in the noir-lang book. + +use assert_cmd::prelude::*; +use predicates::prelude::*; +use std::process::Command; + +use assert_fs::prelude::{FileWriteStr, PathAssert, PathChild}; + +#[test] +fn hello_world_example() { + let test_dir = assert_fs::TempDir::new().unwrap(); + std::env::set_current_dir(&test_dir).unwrap(); + + let project_name = "hello_world"; + let project_dir = test_dir.child(project_name); + + // `nargo new hello_world` + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.arg("new").arg(project_name); + cmd.assert().success().stdout(predicate::str::contains("Project successfully created!")); + + project_dir.child("src").assert(predicate::path::is_dir()); + project_dir.child("Nargo.toml").assert(predicate::path::is_file()); + + std::env::set_current_dir(&project_dir).unwrap(); + + // `nargo check` + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.arg("check"); + cmd.assert() + .success() + .stdout(predicate::str::contains("Constraint system successfully built!")); + + project_dir.child("Prover.toml").assert(predicate::path::is_file()); + project_dir.child("Verifier.toml").assert(predicate::path::is_file()); + + // `nargo prove` + project_dir.child("Prover.toml").write_str("x = 1\ny = 2").unwrap(); + + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.arg("prove"); + cmd.assert().success(); + + project_dir + .child("proofs") + .child(format!("{project_name}.proof")) + .assert(predicate::path::is_file()); + + // `nargo verify p` + let mut cmd = Command::cargo_bin("nargo").unwrap(); + cmd.arg("verify"); + cmd.assert().success(); +} diff --git a/noir/tooling/nargo_cli/tests/noir_test_failure/should_fail_mismatch/Nargo.toml b/noir/tooling/nargo_cli/tests/noir_test_failure/should_fail_mismatch/Nargo.toml new file mode 100644 index 00000000000..3d2cf2c6096 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/noir_test_failure/should_fail_mismatch/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "should_fail_with_mismatch" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/noir_test_failure/should_fail_mismatch/Prover.toml b/noir/tooling/nargo_cli/tests/noir_test_failure/should_fail_mismatch/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/noir_test_failure/should_fail_mismatch/src/main.nr b/noir/tooling/nargo_cli/tests/noir_test_failure/should_fail_mismatch/src/main.nr new file mode 100644 index 00000000000..a677b10b0cd --- /dev/null +++ b/noir/tooling/nargo_cli/tests/noir_test_failure/should_fail_mismatch/src/main.nr @@ -0,0 +1,14 @@ +#[test(should_fail_with = "Not equal")] +fn test_different_string() { + assert_eq(0, 1, "Different string"); +} +// The assert message has a space +#[test(should_fail_with = "Not equal")] +fn test_with_extra_space() { + assert_eq(0, 1, "Not equal "); +} +// The assert message has a space +#[test(should_fail_with = "Not equal")] +fn test_runtime_mismatch() { + assert_eq(dep::std::hash::pedersen_commitment([27])[0], 0, "Not equal "); +} diff --git a/noir/tooling/nargo_cli/tests/noir_test_success/should_fail_with_matches/Nargo.toml b/noir/tooling/nargo_cli/tests/noir_test_success/should_fail_with_matches/Nargo.toml new file mode 100644 index 00000000000..21bce3d2b2d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/noir_test_success/should_fail_with_matches/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "should_fail_with_match" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/noir_test_success/should_fail_with_matches/Prover.toml b/noir/tooling/nargo_cli/tests/noir_test_success/should_fail_with_matches/Prover.toml new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/noir_test_success/should_fail_with_matches/src/main.nr b/noir/tooling/nargo_cli/tests/noir_test_success/should_fail_with_matches/src/main.nr new file mode 100644 index 00000000000..d2b7d155a32 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/noir_test_success/should_fail_with_matches/src/main.nr @@ -0,0 +1,19 @@ +#[test(should_fail_with = "Not equal")] +fn test_should_fail_with_match() { + assert_eq(0, 1, "Not equal"); +} + +#[test(should_fail)] +fn test_should_fail_without_match() { + assert_eq(0, 1); +} + +#[test(should_fail_with = "Not equal")] +fn test_should_fail_with_runtime_match() { + assert_eq(dep::std::hash::pedersen_commitment([27]).x, 0, "Not equal"); +} + +#[test(should_fail)] +fn test_should_fail_without_runtime_match() { + assert_eq(dep::std::hash::pedersen_commitment([27]).x, 0); +} diff --git a/noir/tooling/nargo_cli/tests/rebuild.sh b/noir/tooling/nargo_cli/tests/rebuild.sh new file mode 100755 index 00000000000..d89402fb1cf --- /dev/null +++ b/noir/tooling/nargo_cli/tests/rebuild.sh @@ -0,0 +1,56 @@ +#!/bin/bash +set -e + +excluded_dirs=("workspace" "workspace_default_member") + +current_dir=$(pwd) +base_path="$current_dir/execution_success" + +# Clear the acir_artifacts directory of any existing artifacts +rm -rf $current_dir/acir_artifacts +mkdir -p $current_dir/acir_artifacts + +# Loop over every directory +for dir in $base_path/*; do + if [[ ! -d $dir ]]; then + continue + fi + + dir_name=$(basename "$dir") + + if [[ ! " ${excluded_dirs[@]} " =~ " ${dir_name} " ]]; then + if [[ ! -d "$current_dir/acir_artifacts/$dir_name" ]]; then + mkdir -p $current_dir/acir_artifacts/$dir_name + fi + + cd $dir + if [ -d ./target/ ]; then + rm -r ./target/ + fi + nargo compile && nargo execute witness + + # Rename witness.tr to witness.gz + if [ -f ./target/witness.tr ]; then + mv ./target/witness.tr ./target/witness.gz + fi + + # Extract bytecode field from JSON, base64 decode it, and save it to the target directory + if [ -f ./target/${dir_name}.json ]; then + jq -r '.bytecode' ./target/${dir_name}.json | base64 -d > ./target/acir.gz + fi + + # Delete the JSON file after extracting bytecode field + rm ./target/${dir_name}.json + + # Clear the target directory in acir_artifacts + if [ -d "$current_dir/acir_artifacts/$dir_name/target" ]; then + rm -r "$current_dir/acir_artifacts/$dir_name/target" + fi + mkdir $current_dir/acir_artifacts/$dir_name/target + + # Move the artifacts from the target directory to the corresponding directory in acir_artifacts + mv ./target/*.gz $current_dir/acir_artifacts/$dir_name/target/ + + cd $base_path + fi +done diff --git a/noir/tooling/nargo_cli/tests/test_libraries/bad_impl/Nargo.toml b/noir/tooling/nargo_cli/tests/test_libraries/bad_impl/Nargo.toml new file mode 100644 index 00000000000..62ffe95922d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/test_libraries/bad_impl/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "bad_impl" +type = "lib" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/test_libraries/bad_impl/src/lib.nr b/noir/tooling/nargo_cli/tests/test_libraries/bad_impl/src/lib.nr new file mode 100644 index 00000000000..a96a6cbf91f --- /dev/null +++ b/noir/tooling/nargo_cli/tests/test_libraries/bad_impl/src/lib.nr @@ -0,0 +1,5 @@ +impl Field { + fn something(self) -> Field { + self + } +} diff --git a/noir/tooling/nargo_cli/tests/test_libraries/bad_name/Nargo.toml b/noir/tooling/nargo_cli/tests/test_libraries/bad_name/Nargo.toml new file mode 100644 index 00000000000..74f6f66524d --- /dev/null +++ b/noir/tooling/nargo_cli/tests/test_libraries/bad_name/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "bad-name" +type = "lib" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/test_libraries/bad_name/src/lib.nr b/noir/tooling/nargo_cli/tests/test_libraries/bad_name/src/lib.nr new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_cli/tests/test_libraries/bin_dep/Nargo.toml b/noir/tooling/nargo_cli/tests/test_libraries/bin_dep/Nargo.toml new file mode 100644 index 00000000000..7cc99c1c5c5 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/test_libraries/bin_dep/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "bin_dep" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/test_libraries/bin_dep/src/main.nr b/noir/tooling/nargo_cli/tests/test_libraries/bin_dep/src/main.nr new file mode 100644 index 00000000000..042c85a8afb --- /dev/null +++ b/noir/tooling/nargo_cli/tests/test_libraries/bin_dep/src/main.nr @@ -0,0 +1,3 @@ +fn call_dep1_then_dep2(x: Field, y: Field) { + assert(x == y); +} diff --git a/noir/tooling/nargo_cli/tests/test_libraries/diamond_deps_1/Nargo.toml b/noir/tooling/nargo_cli/tests/test_libraries/diamond_deps_1/Nargo.toml new file mode 100644 index 00000000000..f7770e07469 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/test_libraries/diamond_deps_1/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "diamond_deps_1" +type = "lib" +authors = [""] +[dependencies] +dep2 = { path = "../diamond_deps_2" } diff --git a/noir/tooling/nargo_cli/tests/test_libraries/diamond_deps_1/src/lib.nr b/noir/tooling/nargo_cli/tests/test_libraries/diamond_deps_1/src/lib.nr new file mode 100644 index 00000000000..60c001ec64e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/test_libraries/diamond_deps_1/src/lib.nr @@ -0,0 +1,5 @@ +use dep::dep2::call_dep2; + +pub fn call_dep1_then_dep2(x: Field, y: Field) -> Field { + call_dep2(x, y) +} diff --git a/noir/tooling/nargo_cli/tests/test_libraries/diamond_deps_2/Nargo.toml b/noir/tooling/nargo_cli/tests/test_libraries/diamond_deps_2/Nargo.toml new file mode 100644 index 00000000000..2dbba1b7a9e --- /dev/null +++ b/noir/tooling/nargo_cli/tests/test_libraries/diamond_deps_2/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "diamond_deps_2" +type = "lib" +authors = [""] +[dependencies] diff --git a/noir/tooling/nargo_cli/tests/test_libraries/diamond_deps_2/src/lib.nr b/noir/tooling/nargo_cli/tests/test_libraries/diamond_deps_2/src/lib.nr new file mode 100644 index 00000000000..46dce3d5600 --- /dev/null +++ b/noir/tooling/nargo_cli/tests/test_libraries/diamond_deps_2/src/lib.nr @@ -0,0 +1,5 @@ +global RESOLVE_THIS = 3; + +pub fn call_dep2(x: Field, y: Field) -> Field { + x + y +} diff --git a/noir/tooling/nargo_fmt/Cargo.toml b/noir/tooling/nargo_fmt/Cargo.toml new file mode 100644 index 00000000000..921c9893ab5 --- /dev/null +++ b/noir/tooling/nargo_fmt/Cargo.toml @@ -0,0 +1,16 @@ +[package] +name = "nargo_fmt" +version.workspace = true +authors.workspace = true +edition.workspace = true +license.workspace = true + +[dependencies] +bytecount = "0.6.3" +noirc_frontend.workspace = true +serde.workspace = true +toml.workspace = true +thiserror.workspace = true + +[dev-dependencies] +similar-asserts = "1.5.0" diff --git a/noir/tooling/nargo_fmt/build.rs b/noir/tooling/nargo_fmt/build.rs new file mode 100644 index 00000000000..cd93866ece9 --- /dev/null +++ b/noir/tooling/nargo_fmt/build.rs @@ -0,0 +1,73 @@ +use std::fs::File; +use std::io::Write; +use std::path::{Path, PathBuf}; +use std::{env, fs}; + +fn main() { + let out_dir = env::var("OUT_DIR").unwrap(); + let destination = Path::new(&out_dir).join("execute.rs"); + let mut test_file = File::create(destination).unwrap(); + + // Try to find the directory that Cargo sets when it is running; otherwise fallback to assuming the CWD + // is the root of the repository and append the crate path + let manifest_dir = match std::env::var("CARGO_MANIFEST_DIR") { + Ok(dir) => PathBuf::from(dir), + Err(_) => std::env::current_dir().unwrap().join("crates").join("nargo_cli"), + }; + let test_dir = manifest_dir.join("tests"); + + generate_formatter_tests(&mut test_file, &test_dir); +} + +fn generate_formatter_tests(test_file: &mut File, test_data_dir: &Path) { + let inputs_dir = test_data_dir.join("input"); + let outputs_dir = test_data_dir.join("expected"); + + let test_case_files = + fs::read_dir(inputs_dir).unwrap().flatten().filter(|c| c.path().is_file()); + + for file in test_case_files { + let file_path = file.path(); + let file_name = file_path.file_name().unwrap(); + let test_name = file_path.file_stem().unwrap().to_str().unwrap(); + + if test_name.contains('-') { + panic!( + "Invalid test directory: {test_name}. Cannot include `-`, please convert to `_`" + ); + }; + + let input_source_path = file.path(); + let input_source = std::fs::read_to_string(input_source_path).unwrap(); + + let config = input_source + .lines() + .flat_map(|line| line.strip_prefix("//@")) + .collect::>() + .join("\n"); + + let output_source_path = outputs_dir.join(file_name); + let output_source = std::fs::read_to_string(output_source_path).unwrap(); + + write!( + test_file, + r##" +#[test] +fn format_{test_name}() {{ + let input = r#"{input_source}"#; + let expected_output = r#"{output_source}"#; + + + let (parsed_module, errors) = noirc_frontend::parse_program(&input); + + let config = nargo_fmt::Config::of("{config}").unwrap(); + let fmt_text = nargo_fmt::format(&input, parsed_module, &config); + + + similar_asserts::assert_eq!(fmt_text, expected_output); +}} + "## + ) + .expect("Could not write templated test file."); + } +} diff --git a/noir/tooling/nargo_fmt/src/config.rs b/noir/tooling/nargo_fmt/src/config.rs new file mode 100644 index 00000000000..2bb5d97c0af --- /dev/null +++ b/noir/tooling/nargo_fmt/src/config.rs @@ -0,0 +1,74 @@ +use std::path::Path; + +use crate::errors::ConfigError; + +macro_rules! config { + ($($field_name:ident: $field_ty:ty, $default_value:expr, $description:expr );+ $(;)*) => ( + pub struct Config { + $( + #[doc = $description] + pub $field_name: $field_ty + ),+ + } + + impl Config { + pub fn fill_from_toml(&mut self, toml: TomlConfig) { + $( + if let Some(value) = toml.$field_name { + self.$field_name = value; + } + )+ + } + } + + impl Default for Config { + fn default() -> Self { + Self { + $( + $field_name: $default_value, + )+ + } + } + } + + #[derive(serde::Deserialize, serde::Serialize, Clone)] + pub struct TomlConfig { + $( + #[doc = $description] + pub $field_name: Option<$field_ty> + ),+ + } + ) +} + +config! { + max_width: usize, 100, "Maximum width of each line"; + tab_spaces: usize, 4, "Number of spaces per tab"; + remove_nested_parens: bool, true, "Remove nested parens"; + error_on_lost_comment: bool, true, "Error if unable to get comments"; + short_array_element_width_threshold: usize, 10, "Width threshold for an array element to be considered short"; + array_width: usize, 100, "Maximum width of an array literal before falling back to vertical formatting"; + fn_call_width: usize, 60, "Maximum width of the args of a function call before falling back to vertical formatting"; + single_line_if_else_max_width: usize, 50, "Maximum line length for single line if-else expressions"; +} + +impl Config { + pub fn read(path: &Path) -> Result { + let config_path = path.join("noirfmt.toml"); + + let input = match std::fs::read_to_string(&config_path) { + Ok(input) => input, + Err(cause) if cause.kind() == std::io::ErrorKind::NotFound => String::new(), + Err(cause) => return Err(ConfigError::ReadFailed(config_path, cause)), + }; + + Self::of(&input) + } + + pub fn of(s: &str) -> Result { + let mut config = Self::default(); + let toml = toml::from_str(s).map_err(ConfigError::MalformedFile)?; + config.fill_from_toml(toml); + Ok(config) + } +} diff --git a/noir/tooling/nargo_fmt/src/errors.rs b/noir/tooling/nargo_fmt/src/errors.rs new file mode 100644 index 00000000000..e0a1758ae0f --- /dev/null +++ b/noir/tooling/nargo_fmt/src/errors.rs @@ -0,0 +1,12 @@ +use std::path::PathBuf; + +use thiserror::Error; + +#[derive(Debug, Error)] +pub enum ConfigError { + #[error("Cannot read file {0} - {1}")] + ReadFailed(PathBuf, std::io::Error), + + #[error("noirfmt.toml is badly formed, could not parse.\n\n {0}")] + MalformedFile(#[from] toml::de::Error), +} diff --git a/noir/tooling/nargo_fmt/src/lib.rs b/noir/tooling/nargo_fmt/src/lib.rs new file mode 100644 index 00000000000..d731934c3c3 --- /dev/null +++ b/noir/tooling/nargo_fmt/src/lib.rs @@ -0,0 +1,36 @@ +#![forbid(unsafe_code)] +#![warn(unreachable_pub)] +#![warn(clippy::semicolon_if_nothing_returned)] +#![cfg_attr(not(test), warn(unused_crate_dependencies, unused_extern_crates))] + +/// A Rust code formatting utility designed to manage and format untouched fragments of source code, +/// including comments, whitespace, and other characters. While the module doesn't directly address comments, +/// it treats them as unchanged fragments, ensuring their original placement and content remain preserved. +/// +/// Key methods include: +/// - `format_missing`: Addresses characters between the last processed position and a given end position, +/// capturing comments and other untouched sequences. +/// - `format_missing_indent`: Functions similarly to `format_missing`, but introduces added indentation. +/// - `format_missing_inner`: The core method for handling missing fragments, appending them to the output buffer. +/// Pure whitespace fragments might be replaced or adjusted based on context. +/// - `push_vertical_spaces`: Standardizes vertical spacing, eliminating potential excessive empty lines +/// or ensuring adequate vertical separation. +/// +/// By recognizing and properly handling these untouched fragments, the utility ensures comments remain intact +/// in both placement and content during the formatting process. +mod config; +pub mod errors; +mod rewrite; +mod utils; +mod visitor; + +use noirc_frontend::ParsedModule; +use visitor::FmtVisitor; + +pub use config::Config; + +pub fn format(source: &str, parsed_module: ParsedModule, config: &Config) -> String { + let mut fmt = FmtVisitor::new(source, config); + fmt.visit_file(parsed_module); + fmt.finish() +} diff --git a/noir/tooling/nargo_fmt/src/rewrite.rs b/noir/tooling/nargo_fmt/src/rewrite.rs new file mode 100644 index 00000000000..c1ac585985e --- /dev/null +++ b/noir/tooling/nargo_fmt/src/rewrite.rs @@ -0,0 +1,5 @@ +mod array; +mod infix; + +pub(crate) use array::rewrite as array; +pub(crate) use infix::rewrite as infix; diff --git a/noir/tooling/nargo_fmt/src/rewrite/array.rs b/noir/tooling/nargo_fmt/src/rewrite/array.rs new file mode 100644 index 00000000000..9c49d827528 --- /dev/null +++ b/noir/tooling/nargo_fmt/src/rewrite/array.rs @@ -0,0 +1,85 @@ +use noirc_frontend::{hir::resolution::errors::Span, token::Token, Expression}; + +use crate::{ + utils::{Expr, FindToken}, + visitor::FmtVisitor, +}; + +pub(crate) fn rewrite(mut visitor: FmtVisitor, array: Vec, array_span: Span) -> String { + let pattern: &[_] = &[' ', '\t']; + + visitor.indent.block_indent(visitor.config); + let nested_indent = visitor.shape(); + + let indent_str = nested_indent.indent.to_string(); + + let mut last_position = array_span.start() + 1; + let end_position = array_span.end() - 1; + + let mut items = array.into_iter().peekable(); + + let mut result = Vec::new(); + while let Some(item) = items.next() { + let item_span = item.span; + + let start: u32 = last_position; + let end = item_span.start(); + + let leading = visitor.slice(start..end).trim_matches(pattern); + let item = visitor.format_sub_expr(item); + let next_start = items.peek().map_or(end_position, |expr| expr.span.start()); + let trailing = visitor.slice(item_span.end()..next_start); + let offset = trailing + .find_token(Token::Comma) + .map(|span| span.end() as usize) + .unwrap_or(trailing.len()); + let trailing = trailing[..offset].trim_end_matches(',').trim_matches(pattern); + last_position = item_span.end() + offset as u32; + + let (leading, _) = visitor.format_comment_in_block(leading); + let (trailing, _) = visitor.format_comment_in_block(trailing); + + result.push(Expr { leading, value: item, trailing, different_line: false }); + } + + let slice = visitor.slice(last_position..end_position); + let (comment, _) = visitor.format_comment_in_block(slice); + result.push(Expr { + leading: "".into(), + value: "".into(), + trailing: comment, + different_line: false, + }); + + visitor.indent.block_unindent(visitor.config); + + let mut items_str = String::new(); + let mut items = result.into_iter().peekable(); + while let Some(next) = items.next() { + items_str.push_str(&next.leading); + if next.leading.contains('\n') && !next.value.is_empty() { + items_str.push_str(&indent_str); + } + items_str.push_str(&next.value); + items_str.push_str(&next.trailing); + + if let Some(item) = items.peek() { + if !item.value.is_empty() { + items_str.push(','); + } + + if !item.leading.contains('\n') && !next.value.is_empty() { + items_str.push(' '); + } + } + } + + crate::visitor::expr::wrap_exprs( + "[", + "]", + items_str.trim().into(), + nested_indent, + visitor.shape(), + true, + ) +} diff --git a/noir/tooling/nargo_fmt/src/rewrite/infix.rs b/noir/tooling/nargo_fmt/src/rewrite/infix.rs new file mode 100644 index 00000000000..0fbfa07a841 --- /dev/null +++ b/noir/tooling/nargo_fmt/src/rewrite/infix.rs @@ -0,0 +1,112 @@ +use std::iter::zip; + +use noirc_frontend::{Expression, ExpressionKind}; + +use crate::{ + utils::{first_line_width, is_single_line}, + visitor::{ExpressionType, FmtVisitor, Shape}, +}; + +pub(crate) fn rewrite(visitor: FmtVisitor, expr: Expression, shape: Shape) -> String { + match flatten(visitor.fork(), &expr) { + Some((exprs, separators)) => rewrite_single_line(shape, &exprs, &separators) + .unwrap_or_else(|| rewrite_multiline(visitor, &exprs, &separators)), + None => { + let ExpressionKind::Infix(infix) = expr.kind else { unreachable!() }; + + format!( + "{} {} {}", + visitor.format_sub_expr(infix.lhs), + infix.operator.contents.as_string(), + visitor.format_sub_expr(infix.rhs) + ) + } + } +} + +fn rewrite_single_line(shape: Shape, exprs: &[String], separators: &[String]) -> Option { + let mut result = String::new(); + + for (rewrite, separator) in zip(exprs, separators) { + if !is_single_line(rewrite) || result.len() > shape.width { + return None; + } + + result.push_str(rewrite); + result.push(' '); + result.push_str(separator); + result.push(' '); + } + + let last = exprs.last().unwrap(); + result.push_str(last); + + if first_line_width(&result) > shape.width { + return None; + } + + result.into() +} + +fn rewrite_multiline(visitor: FmtVisitor, exprs: &[String], separators: &[String]) -> String { + let mut visitor = visitor.fork(); + visitor.indent.block_indent(visitor.config); + let indent_str = visitor.indent.to_string_with_newline(); + + let mut result = exprs[0].clone(); + + for (rewrite, separator) in exprs[1..].iter().zip(separators.iter()) { + result.push_str(&indent_str); + result.push_str(separator); + result.push(' '); + result.push_str(rewrite); + } + + result +} + +pub(crate) fn flatten( + mut visitor: FmtVisitor, + mut node: &Expression, +) -> Option<(Vec, Vec)> { + let top_operator = match node.kind { + ExpressionKind::Infix(ref infix) => infix.operator.contents, + _ => return None, + }; + + let mut result = Vec::new(); + + let mut stack: Vec<&Expression> = Vec::new(); + let mut separators = Vec::new(); + + loop { + match &node.kind { + ExpressionKind::Infix(infix) if top_operator == infix.operator.contents => { + stack.push(node); + node = &infix.lhs; + } + _ => { + let rewrite = if result.is_empty() { + visitor.format_expr(node.clone(), ExpressionType::SubExpression) + } else { + visitor.indent.block_indent(visitor.config); + visitor.format_expr(node.clone(), ExpressionType::SubExpression) + }; + + result.push(rewrite); + + let Some(pop) = stack.pop() else { break; }; + + match &pop.kind { + ExpressionKind::Infix(infix) => { + separators.push(infix.operator.contents.to_string()); + node = &infix.rhs; + } + _ => unreachable!(), + } + } + } + } + + (result, separators).into() +} diff --git a/noir/tooling/nargo_fmt/src/utils.rs b/noir/tooling/nargo_fmt/src/utils.rs new file mode 100644 index 00000000000..13938079e68 --- /dev/null +++ b/noir/tooling/nargo_fmt/src/utils.rs @@ -0,0 +1,277 @@ +use crate::visitor::FmtVisitor; +use noirc_frontend::hir::resolution::errors::Span; +use noirc_frontend::lexer::Lexer; +use noirc_frontend::token::Token; +use noirc_frontend::{Expression, Ident, Param, Visibility}; + +pub(crate) fn changed_comment_content(original: &str, new: &str) -> bool { + comments(original).ne(comments(new)) +} + +pub(crate) fn comments(source: &str) -> impl Iterator + '_ { + Lexer::new(source).skip_comments(false).flatten().filter_map(|spanned| { + if let Token::LineComment(content, _) | Token::BlockComment(content, _) = + spanned.into_token() + { + Some(content) + } else { + None + } + }) +} + +#[derive(Debug)] +pub(crate) struct Expr { + pub(crate) leading: String, + pub(crate) value: String, + pub(crate) trailing: String, + pub(crate) different_line: bool, +} + +impl Expr { + pub(crate) fn total_width(&self) -> usize { + comment_len(&self.leading) + self.value.chars().count() + comment_len(&self.trailing) + } + + pub(crate) fn is_multiline(&self) -> bool { + self.leading.contains('\n') || self.trailing.contains('\n') + } +} + +pub(crate) struct Exprs<'me, T> { + pub(crate) visitor: &'me FmtVisitor<'me>, + pub(crate) elements: std::iter::Peekable>, + pub(crate) last_position: u32, + pub(crate) end_position: u32, +} + +impl<'me, T: Item> Exprs<'me, T> { + pub(crate) fn new(visitor: &'me FmtVisitor<'me>, span: Span, elements: Vec) -> Self { + Self { + visitor, + last_position: span.start() + 1, /*(*/ + end_position: span.end() - 1, /*)*/ + elements: elements.into_iter().peekable(), + } + } +} + +impl Iterator for Exprs<'_, T> { + type Item = Expr; + + fn next(&mut self) -> Option { + let element = self.elements.next()?; + let element_span = element.span(); + + let start = self.last_position; + let end = element_span.start(); + + let is_last = self.elements.peek().is_none(); + let next_start = self.elements.peek().map_or(self.end_position, |expr| expr.start()); + + let (leading, different_line) = self.leading(start, end); + let expr = element.format(self.visitor); + let trailing = self.trailing(element_span.end(), next_start, is_last); + + Expr { leading, value: expr, trailing, different_line }.into() + } +} + +impl<'me, T> Exprs<'me, T> { + pub(crate) fn leading(&mut self, start: u32, end: u32) -> (String, bool) { + let mut different_line = false; + + let leading = self.visitor.slice(start..end); + let leading_trimmed = leading.trim(); + + let starts_with_block_comment = leading_trimmed.starts_with("/*"); + let ends_with_block_comment = leading_trimmed.ends_with("*/"); + let starts_with_single_line_comment = leading_trimmed.starts_with("//"); + + if ends_with_block_comment { + let comment_end = leading_trimmed.rfind(|c| c == '/').unwrap(); + + if leading[comment_end..].contains('\n') { + different_line = true; + } + } else if starts_with_single_line_comment || starts_with_block_comment { + different_line = true; + }; + + (leading_trimmed.to_string(), different_line) + } + + pub(crate) fn trailing(&mut self, start: u32, end: u32, is_last: bool) -> String { + let slice = self.visitor.slice(start..end); + let comment_end = find_comment_end(slice, is_last); + let trailing = slice[..comment_end].trim_matches(',').trim(); + self.last_position = start + (comment_end as u32); + trailing.to_string() + } +} + +pub(crate) trait FindToken { + fn find_token(&self, token: Token) -> Option; + fn find_token_with(&self, f: impl Fn(&Token) -> bool) -> Option; +} + +impl FindToken for str { + fn find_token(&self, token: Token) -> Option { + Lexer::new(self).flatten().find_map(|it| (it.token() == &token).then(|| it.to_span())) + } + + fn find_token_with(&self, f: impl Fn(&Token) -> bool) -> Option { + Lexer::new(self) + .skip_comments(false) + .flatten() + .find_map(|spanned| f(spanned.token()).then(|| spanned.to_span().end())) + } +} + +pub(crate) fn find_comment_end(slice: &str, is_last: bool) -> usize { + fn find_comment_end(slice: &str) -> usize { + slice + .find_token_with(|token| { + matches!(token, Token::LineComment(_, _) | Token::BlockComment(_, _)) + }) + .map(|index| index as usize) + .unwrap_or(slice.len()) + } + + if is_last { + return slice.len(); + } + + let mut block_open_index = slice.find("/*"); + if let Some(index) = block_open_index { + match slice.find('/') { + Some(slash) if slash < index => block_open_index = None, + _ if slice[..index].ends_with('/') => block_open_index = None, + _ => (), + } + } + + let newline_index = slice.find('\n'); + if let Some(separator_index) = + slice.find_token(Token::Comma).map(|index| index.start() as usize) + { + match (block_open_index, newline_index) { + (Some(block), None) if block > separator_index => separator_index + 1, + (Some(block), None) => { + let slice = &slice[block..]; + std::cmp::max(find_comment_end(slice) + block, separator_index + 1) + } + (Some(block), Some(newline)) if block < newline => { + let slice = &slice[block..]; + std::cmp::max(find_comment_end(slice) + block, separator_index + 1) + } + (_, Some(newline)) if newline > separator_index => newline + 1, + _ => slice.len(), + } + } else if let Some(newline_index) = newline_index { + newline_index + 1 + } else { + 0 + } +} + +fn comment_len(comment: &str) -> usize { + match comment { + "" => 0, + _ => { + let len = comment.trim().len(); + if len > 0 { + len + 6 + } else { + len + } + } + } +} + +pub(crate) fn count_newlines(slice: &str) -> usize { + bytecount::count(slice.as_bytes(), b'\n') +} + +pub(crate) trait Item { + fn span(&self) -> Span; + + fn format(self, visitor: &FmtVisitor) -> String; + + fn start(&self) -> u32 { + self.span().start() + } + + fn end(&self) -> u32 { + self.span().end() + } +} + +impl Item for Expression { + fn span(&self) -> Span { + self.span + } + + fn format(self, visitor: &FmtVisitor) -> String { + visitor.format_sub_expr(self) + } +} + +impl Item for (Ident, Expression) { + fn span(&self) -> Span { + let (name, value) = self; + (name.span().start()..value.span.end()).into() + } + + fn format(self, visitor: &FmtVisitor) -> String { + let (name, expr) = self; + + let name = name.0.contents; + let expr = visitor.format_sub_expr(expr); + + if name == expr { + name + } else { + format!("{name}: {expr}") + } + } +} + +impl Item for Param { + fn span(&self) -> Span { + self.span + } + + fn format(self, visitor: &FmtVisitor) -> String { + let visibility = match self.visibility { + Visibility::Public => "pub ", + Visibility::Private => "", + }; + let pattern = visitor.slice(self.pattern.span()); + let ty = visitor.slice(self.typ.span.unwrap()); + + format!("{pattern}: {visibility}{ty}") + } +} + +impl Item for Ident { + fn span(&self) -> Span { + self.span() + } + + fn format(self, visitor: &FmtVisitor) -> String { + visitor.slice(self.span()).into() + } +} + +pub(crate) fn first_line_width(exprs: &str) -> usize { + exprs.lines().next().map_or(0, |line: &str| line.chars().count()) +} + +pub(crate) fn is_single_line(s: &str) -> bool { + !s.chars().any(|c| c == '\n') +} + +pub(crate) fn last_line_contains_single_line_comment(s: &str) -> bool { + s.lines().last().map_or(false, |line| line.contains("//")) +} diff --git a/noir/tooling/nargo_fmt/src/visitor.rs b/noir/tooling/nargo_fmt/src/visitor.rs new file mode 100644 index 00000000000..cf3b3a41e8a --- /dev/null +++ b/noir/tooling/nargo_fmt/src/visitor.rs @@ -0,0 +1,284 @@ +pub(crate) mod expr; +mod item; +mod stmt; + +use noirc_frontend::{hir::resolution::errors::Span, lexer::Lexer, token::Token}; + +use crate::{ + config::Config, + utils::{self, FindToken}, +}; + +pub(crate) struct FmtVisitor<'me> { + ignore_next_node: bool, + pub(crate) config: &'me Config, + buffer: String, + pub(crate) source: &'me str, + pub(crate) indent: Indent, + last_position: u32, +} + +impl<'me> FmtVisitor<'me> { + pub(crate) fn new(source: &'me str, config: &'me Config) -> Self { + Self { + ignore_next_node: false, + buffer: String::new(), + config, + source, + last_position: 0, + indent: Indent { block_indent: 0 }, + } + } + + pub(crate) fn slice(&self, span: impl Into) -> &'me str { + let span = span.into(); + &self.source[span.start() as usize..span.end() as usize] + } + + fn span_after(&self, span: impl Into, token: Token) -> Span { + let span = span.into(); + + let slice = self.slice(span); + let offset = slice.find_token(token).unwrap().end(); + + (span.start() + offset..span.end()).into() + } + + fn span_before(&self, span: impl Into, token: Token) -> Span { + let span = span.into(); + + let slice = self.slice(span); + let offset = slice.find_token(token).unwrap().start(); + + (span.start() + offset..span.end()).into() + } + + pub(crate) fn shape(&self) -> Shape { + Shape { + width: self.config.max_width.saturating_sub(self.indent.width()), + indent: self.indent, + } + } + + pub(crate) fn fork(&self) -> Self { + Self { + buffer: String::new(), + ignore_next_node: self.ignore_next_node, + config: self.config, + source: self.source, + last_position: self.last_position, + indent: self.indent, + } + } + + pub(crate) fn finish(self) -> String { + self.buffer + } + + fn at_start(&self) -> bool { + self.buffer.is_empty() + } + + fn push_str(&mut self, s: &str) { + let comments = Lexer::new(s).skip_comments(false).flatten().flat_map(|token| { + if let Token::LineComment(content, _) | Token::BlockComment(content, _) = + token.into_token() + { + let content = content.trim(); + content.strip_prefix("noir-fmt:").map(ToOwned::to_owned) + } else { + None + } + }); + + for comment in comments { + match comment.as_str() { + "ignore" => self.ignore_next_node = true, + this => unreachable!("unknown settings {this}"), + } + } + + self.buffer.push_str(s); + } + + #[track_caller] + fn push_rewrite(&mut self, rewrite: String, span: Span) { + let original = self.slice(span); + let changed_comment_content = utils::changed_comment_content(original, &rewrite); + + if changed_comment_content && self.config.error_on_lost_comment { + panic!("not formatted because a comment would be lost: {rewrite:?}"); + } + + self.format_missing_indent(span.start(), true); + + let rewrite = if changed_comment_content || std::mem::take(&mut self.ignore_next_node) { + original.to_string() + } else { + rewrite + }; + + self.push_str(&rewrite); + + if rewrite.starts_with('{') && rewrite.ends_with('}') { + self.ignore_next_node = false; + } + } + + #[track_caller] + fn format_missing_indent(&mut self, end: u32, should_indent: bool) { + self.format_missing_inner(end, |this, last_slice, slice| { + this.push_str(last_slice.trim_end()); + + if (last_slice == slice && !this.at_start()) || this.buffer.ends_with("*/") { + this.push_str("\n"); + } + + if should_indent { + let indent = this.indent.to_string(); + this.push_str(&indent); + } + }); + } + + #[track_caller] + fn format_missing_inner( + &mut self, + end: u32, + process_last_slice: impl Fn(&mut Self, &str, &str), + ) { + let start = self.last_position; + + if start == end { + if !self.at_start() { + process_last_slice(self, "", ""); + } + return; + } + + let slice = self.slice(start..end); + self.last_position = end; + + if slice.trim().is_empty() && !self.at_start() { + self.push_vertical_spaces(slice); + process_last_slice(self, "", slice); + } else { + let (result, last_end) = self.format_comment_in_block(slice); + if result.trim().is_empty() { + process_last_slice(self, slice, slice); + } else { + let last_snippet = &slice[last_end as usize..]; + self.push_str(&result); + process_last_slice(self, last_snippet, &result); + } + } + } + + pub(crate) fn format_comment_in_block(&mut self, slice: &str) -> (String, u32) { + let mut result = String::new(); + let comments = Lexer::new(slice).skip_comments(false).skip_whitespaces(false).flatten(); + + let indent = self.indent.to_string(); + for comment in comments { + let span = comment.to_span(); + + match comment.token() { + Token::LineComment(_, _) | Token::BlockComment(_, _) => { + let comment = &slice[span.start() as usize..span.end() as usize]; + if result.ends_with('\n') { + result.push_str(&indent); + } else if !self.at_start() { + result.push(' '); + } + result.push_str(comment); + } + Token::Whitespace(whitespaces) => { + let mut visitor = self.fork(); + if whitespaces.contains('\n') { + visitor.push_vertical_spaces(whitespaces.trim_matches(' ')); + result.push_str(&visitor.finish()); + } + } + _ => {} + } + } + + (result, slice.len() as u32) + } + + fn push_vertical_spaces(&mut self, slice: &str) { + let newline_upper_bound = 2; + let newline_lower_bound = 1; + + let mut newline_count = utils::count_newlines(slice); + let offset = self.buffer.chars().rev().take_while(|c| *c == '\n').count(); + + if newline_count + offset > newline_upper_bound { + if offset >= newline_upper_bound { + newline_count = 0; + } else { + newline_count = newline_upper_bound - offset; + } + } else if newline_count + offset < newline_lower_bound { + if offset >= newline_lower_bound { + newline_count = 0; + } else { + newline_count = newline_lower_bound - offset; + } + } + + let blank_lines = "\n".repeat(newline_count); + self.push_str(&blank_lines); + } + + pub(crate) fn format_comment(&self, span: Span) -> String { + let slice = self.slice(span).trim(); + let pos = slice.find('/'); + + if !slice.is_empty() && pos.is_some() { + slice.to_string() + } else { + String::new() + } + } +} + +#[derive(Clone, Copy, Debug, Default)] +pub(crate) struct Indent { + block_indent: usize, +} + +impl Indent { + pub(crate) fn width(&self) -> usize { + self.block_indent + } + + pub(crate) fn block_indent(&mut self, config: &Config) { + self.block_indent += config.tab_spaces; + } + + pub(crate) fn block_unindent(&mut self, config: &Config) { + self.block_indent -= config.tab_spaces; + } + + pub(crate) fn to_string_with_newline(self) -> String { + "\n".to_string() + &self.to_string() + } + + #[allow(clippy::inherent_to_string)] + pub(crate) fn to_string(self) -> String { + " ".repeat(self.block_indent) + } +} + +#[derive(Clone, Copy, Debug)] +pub(crate) struct Shape { + pub(crate) width: usize, + pub(crate) indent: Indent, +} + +#[derive(PartialEq, Eq, Debug)] +pub(crate) enum ExpressionType { + Statement, + SubExpression, +} diff --git a/noir/tooling/nargo_fmt/src/visitor/expr.rs b/noir/tooling/nargo_fmt/src/visitor/expr.rs new file mode 100644 index 00000000000..0eb192569c3 --- /dev/null +++ b/noir/tooling/nargo_fmt/src/visitor/expr.rs @@ -0,0 +1,632 @@ +use noirc_frontend::{ + hir::resolution::errors::Span, lexer::Lexer, token::Token, ArrayLiteral, BlockExpression, + ConstructorExpression, Expression, ExpressionKind, IfExpression, Literal, Statement, + StatementKind, UnaryOp, +}; + +use super::{ExpressionType, FmtVisitor, Indent, Shape}; +use crate::{ + rewrite, + utils::{self, first_line_width, Expr, FindToken, Item}, + Config, +}; + +impl FmtVisitor<'_> { + pub(crate) fn visit_expr(&mut self, expr: Expression, expr_type: ExpressionType) { + let span = expr.span; + let rewrite = self.format_expr(expr, expr_type); + self.push_rewrite(rewrite, span); + self.last_position = span.end(); + } + + pub(crate) fn format_sub_expr(&self, expression: Expression) -> String { + self.format_expr(expression, ExpressionType::SubExpression) + } + + pub(crate) fn format_expr( + &self, + Expression { kind, mut span }: Expression, + expr_type: ExpressionType, + ) -> String { + match kind { + ExpressionKind::Block(block) => { + let mut visitor = self.fork(); + visitor.visit_block(block, span); + visitor.buffer + } + ExpressionKind::Prefix(prefix) => { + let op = match prefix.operator { + UnaryOp::Minus => "-", + UnaryOp::Not => "!", + UnaryOp::MutableReference => "&mut ", + UnaryOp::Dereference { implicitly_added } => { + if implicitly_added { + "" + } else { + "*" + } + } + }; + + format!("{op}{}", self.format_sub_expr(prefix.rhs)) + } + ExpressionKind::Cast(cast) => { + format!("{} as {}", self.format_sub_expr(cast.lhs), cast.r#type) + } + kind @ ExpressionKind::Infix(_) => { + let shape = self.shape(); + rewrite::infix(self.fork(), Expression { kind, span }, shape) + } + ExpressionKind::Call(call_expr) => { + let args_span = + self.span_before(call_expr.func.span.end()..span.end(), Token::LeftParen); + + let callee = self.format_sub_expr(*call_expr.func); + let args = format_parens( + self.config.fn_call_width.into(), + self.fork(), + false, + call_expr.arguments, + args_span, + true, + ); + + format!("{callee}{args}") + } + ExpressionKind::MethodCall(method_call_expr) => { + let args_span = self.span_before( + method_call_expr.method_name.span().end()..span.end(), + Token::LeftParen, + ); + + let object = self.format_sub_expr(method_call_expr.object); + let method = method_call_expr.method_name.to_string(); + let args = format_parens( + self.config.fn_call_width.into(), + self.fork(), + false, + method_call_expr.arguments, + args_span, + true, + ); + + format!("{object}.{method}{args}") + } + ExpressionKind::MemberAccess(member_access_expr) => { + let lhs_str = self.format_sub_expr(member_access_expr.lhs); + format!("{}.{}", lhs_str, member_access_expr.rhs) + } + ExpressionKind::Index(index_expr) => { + let index_span = self + .span_before(index_expr.collection.span.end()..span.end(), Token::LeftBracket); + + let collection = self.format_sub_expr(index_expr.collection); + let index = format_brackets(self.fork(), false, vec![index_expr.index], index_span); + + format!("{collection}{index}") + } + ExpressionKind::Tuple(exprs) => { + format_parens(None, self.fork(), exprs.len() == 1, exprs, span, false) + } + ExpressionKind::Literal(literal) => match literal { + Literal::Integer(_) | Literal::Bool(_) | Literal::Str(_) | Literal::FmtStr(_) => { + self.slice(span).to_string() + } + Literal::Array(ArrayLiteral::Repeated { repeated_element, length }) => { + let repeated = self.format_sub_expr(*repeated_element); + let length = self.format_sub_expr(*length); + + format!("[{repeated}; {length}]") + } + Literal::Array(ArrayLiteral::Standard(exprs)) => { + rewrite::array(self.fork(), exprs, span) + } + Literal::Unit => "()".to_string(), + }, + ExpressionKind::Parenthesized(mut sub_expr) => { + let remove_nested_parens = self.config.remove_nested_parens; + + let mut leading; + let mut trailing; + + loop { + let leading_span = span.start() + 1..sub_expr.span.start(); + let trailing_span = sub_expr.span.end()..span.end() - 1; + + leading = self.format_comment(leading_span.into()); + trailing = self.format_comment(trailing_span.into()); + + if let ExpressionKind::Parenthesized(ref sub_sub_expr) = sub_expr.kind { + if remove_nested_parens && leading.is_empty() && trailing.is_empty() { + span = sub_expr.span; + sub_expr = sub_sub_expr.clone(); + continue; + } + } + + break; + } + + if !leading.contains("//") && !trailing.contains("//") { + let sub_expr = self.format_sub_expr(*sub_expr); + format!("({leading}{sub_expr}{trailing})") + } else { + let mut visitor = self.fork(); + + let indent = visitor.indent.to_string_with_newline(); + visitor.indent.block_indent(self.config); + let nested_indent = visitor.indent.to_string_with_newline(); + + let sub_expr = visitor.format_sub_expr(*sub_expr); + + let mut result = String::new(); + result.push('('); + + if !leading.is_empty() { + result.push_str(&nested_indent); + result.push_str(&leading); + } + + result.push_str(&nested_indent); + result.push_str(&sub_expr); + + if !trailing.is_empty() { + result.push_str(&nested_indent); + result.push_str(&trailing); + } + + result.push_str(&indent); + result.push(')'); + + result + } + } + ExpressionKind::Constructor(constructor) => { + let type_name = self.slice(span.start()..constructor.type_name.span().end()); + let fields_span = self + .span_before(constructor.type_name.span().end()..span.end(), Token::LeftBrace); + + self.format_struct_lit(type_name, fields_span, *constructor) + } + ExpressionKind::If(if_expr) => { + let allow_single_line = expr_type == ExpressionType::SubExpression; + + if allow_single_line { + let mut visitor = self.fork(); + visitor.indent = Indent::default(); + if let Some(line) = visitor.format_if_single_line(*if_expr.clone()) { + return line; + } + } + + self.format_if(*if_expr) + } + ExpressionKind::Lambda(_) | ExpressionKind::Variable(_) => self.slice(span).to_string(), + ExpressionKind::Error => unreachable!(), + } + } + + fn format_if(&self, if_expr: IfExpression) -> String { + let condition_str = self.format_sub_expr(if_expr.condition); + let consequence_str = self.format_sub_expr(if_expr.consequence); + + let mut result = format!("if {condition_str} {consequence_str}"); + + if let Some(alternative) = if_expr.alternative { + let alternative = if let Some(ExpressionKind::If(if_expr)) = + extract_simple_expr(alternative.clone()).map(|expr| expr.kind) + { + self.format_if(*if_expr) + } else { + self.format_expr(alternative, ExpressionType::Statement) + }; + + result.push_str(" else "); + result.push_str(&alternative); + }; + + result + } + + fn format_if_single_line(&self, if_expr: IfExpression) -> Option { + let condition_str = self.format_sub_expr(if_expr.condition); + let consequence_str = self.format_sub_expr(extract_simple_expr(if_expr.consequence)?); + + let if_str = if let Some(alternative) = if_expr.alternative { + let alternative_str = if let Some(ExpressionKind::If(_)) = + extract_simple_expr(alternative.clone()).map(|expr| expr.kind) + { + return None; + } else { + self.format_expr(extract_simple_expr(alternative)?, ExpressionType::Statement) + }; + + format!("if {} {{ {} }} else {{ {} }}", condition_str, consequence_str, alternative_str) + } else { + format!("if {{{}}} {{{}}}", condition_str, consequence_str) + }; + + (if_str.len() <= self.config.single_line_if_else_max_width).then_some(if_str) + } + + fn format_struct_lit( + &self, + type_name: &str, + fields_span: Span, + constructor: ConstructorExpression, + ) -> String { + let fields = { + let mut visitor = self.fork(); + let is_unit_struct = constructor.fields.is_empty(); + + visitor.indent.block_indent(visitor.config); + + let nested_indent = visitor.shape(); + let exprs: Vec<_> = + utils::Exprs::new(&visitor, fields_span, constructor.fields).collect(); + let exprs = format_exprs( + visitor.config, + Tactic::HorizontalVertical, + false, + exprs, + nested_indent, + ); + + visitor.indent.block_unindent(visitor.config); + + if exprs.contains('\n') { + format!( + "{}{exprs}{}", + nested_indent.indent.to_string_with_newline(), + visitor.shape().indent.to_string_with_newline() + ) + } else if is_unit_struct { + exprs + } else { + format!(" {exprs} ") + } + }; + + format!("{type_name} {{{fields}}}") + } + + pub(crate) fn visit_block(&mut self, block: BlockExpression, block_span: Span) { + if block.is_empty() { + self.visit_empty_block(block_span); + return; + } + + self.last_position = block_span.start() + 1; // `{` + self.push_str("{"); + + self.trim_spaces_after_opening_brace(&block.0); + + self.indent.block_indent(self.config); + + self.visit_stmts(block.0); + + let span = (self.last_position..block_span.end() - 1).into(); + self.close_block(span); + self.last_position = block_span.end(); + } + + fn trim_spaces_after_opening_brace(&mut self, block: &[Statement]) { + if let Some(first_stmt) = block.first() { + let slice = self.slice(self.last_position..first_stmt.span.start()); + let len = + slice.chars().take_while(|ch| ch.is_whitespace()).collect::().rfind('\n'); + self.last_position += len.unwrap_or(0) as u32; + } + } + + pub(crate) fn visit_empty_block(&mut self, block_span: Span) { + let slice = self.slice(block_span); + let comment_str = slice[1..slice.len() - 1].trim(); + + if comment_str.is_empty() { + self.push_str("{}"); + } else { + self.push_str("{"); + self.indent.block_indent(self.config); + self.close_block(block_span); + }; + + self.last_position = block_span.end(); + } + + pub(crate) fn close_block(&mut self, span: Span) { + let slice = self.slice(span); + + for spanned in Lexer::new(slice).skip_comments(false).flatten() { + match spanned.token() { + Token::LineComment(_, _) | Token::BlockComment(_, _) => { + let token_span = spanned.to_span(); + + let offset = token_span.start(); + let sub_slice = &slice[token_span.start() as usize..token_span.end() as usize]; + + let span_in_between = span.start()..span.start() + offset; + let slice_in_between = self.slice(span_in_between); + let comment_on_same_line = !slice_in_between.contains('\n'); + + if comment_on_same_line { + self.push_str(" "); + self.push_str(sub_slice); + } else { + self.push_str(&self.indent.to_string_with_newline()); + self.push_str(sub_slice); + } + } + _ => {} + } + } + + self.indent.block_unindent(self.config); + self.push_str(&self.indent.to_string_with_newline()); + self.push_str("}"); + } +} + +#[allow(clippy::too_many_arguments)] +pub(crate) fn format_seq( + prefix: &str, + suffix: &str, + mut visitor: FmtVisitor, + trailing_comma: bool, + exprs: Vec, + span: Span, + tactic: Tactic, + soft_newline: bool, +) -> String { + visitor.indent.block_indent(visitor.config); + + let nested_indent = visitor.shape(); + let exprs: Vec<_> = utils::Exprs::new(&visitor, span, exprs).collect(); + let exprs = format_exprs(visitor.config, tactic, trailing_comma, exprs, nested_indent); + + visitor.indent.block_unindent(visitor.config); + + wrap_exprs(prefix, suffix, exprs, nested_indent, visitor.shape(), soft_newline) +} + +fn format_brackets( + visitor: FmtVisitor, + trailing_comma: bool, + exprs: Vec, + span: Span, +) -> String { + let array_width = visitor.config.array_width; + format_seq( + "[", + "]", + visitor, + trailing_comma, + exprs, + span, + Tactic::LimitedHorizontalVertical(array_width), + false, + ) +} + +fn format_parens( + max_width: Option, + visitor: FmtVisitor, + trailing_comma: bool, + exprs: Vec, + span: Span, + soft_newline: bool, +) -> String { + let tactic = max_width.map(Tactic::LimitedHorizontalVertical).unwrap_or(Tactic::Horizontal); + format_seq("(", ")", visitor, trailing_comma, exprs, span, tactic, soft_newline) +} + +fn format_exprs( + config: &Config, + tactic: Tactic, + trailing_comma: bool, + exprs: Vec, + shape: Shape, +) -> String { + let mut result = String::new(); + let indent_str = shape.indent.to_string(); + + let tactic = tactic.definitive(&exprs, config.short_array_element_width_threshold); + let mut exprs = exprs.into_iter().enumerate().peekable(); + let mut line_len = 0; + let mut prev_expr_trailing_comment = false; + + while let Some((index, expr)) = exprs.next() { + let is_first = index == 0; + let separate = exprs.peek().is_some() || trailing_comma; + let separate_len = usize::from(separate); + + match tactic { + DefinitiveTactic::Vertical + if !is_first && !expr.value.is_empty() && !result.is_empty() => + { + result.push('\n'); + result.push_str(&indent_str); + } + DefinitiveTactic::Horizontal if !is_first => { + result.push(' '); + } + DefinitiveTactic::Mixed => { + let total_width = expr.total_width() + separate_len; + + if line_len > 0 && line_len + 1 + total_width > shape.width + || prev_expr_trailing_comment + { + result.push('\n'); + result.push_str(&indent_str); + line_len = 0; + } else if line_len > 0 { + result.push(' '); + line_len += 1; + } + + line_len += total_width; + } + _ => {} + } + + result.push_str(&expr.leading); + + if expr.different_line { + result.push('\n'); + result.push_str(&indent_str); + line_len = expr.value.chars().count(); + } else if !expr.leading.is_empty() { + result.push(' '); + } + + result.push_str(&expr.value); + + if tactic == DefinitiveTactic::Horizontal { + result.push_str(&expr.trailing); + } + + if separate && expr.trailing.find_token(Token::Comma).is_none() { + result.push(','); + } + + if tactic != DefinitiveTactic::Horizontal { + prev_expr_trailing_comment = !expr.trailing.is_empty(); + + if !expr.different_line && !expr.trailing.is_empty() { + result.push(' '); + } + + result.push_str(&expr.trailing); + } + } + + result +} + +pub(crate) fn wrap_exprs( + prefix: &str, + suffix: &str, + exprs: String, + nested_shape: Shape, + shape: Shape, + soft_newline: bool, +) -> String { + let first_line_width = first_line_width(&exprs); + + let force_one_line = + if soft_newline { !exprs.contains('\n') } else { first_line_width <= shape.width }; + + if force_one_line { + let allow_trailing_newline = exprs + .lines() + .last() + .unwrap_or_default() + .find_token_with(|token| matches!(token, Token::LineComment(_, _))) + .is_some(); + + let trailing_newline = if allow_trailing_newline { + shape.indent.to_string_with_newline() + } else { + String::new() + }; + + format!("{prefix}{exprs}{trailing_newline}{suffix}") + } else { + let nested_indent_str = nested_shape.indent.to_string_with_newline(); + let indent_str = shape.indent.to_string_with_newline(); + + format!("{prefix}{nested_indent_str}{exprs}{indent_str}{suffix}") + } +} + +#[derive(PartialEq, Eq, Clone, Copy)] +pub(crate) enum Tactic { + Horizontal, + HorizontalVertical, + LimitedHorizontalVertical(usize), + Mixed, +} + +impl Tactic { + fn definitive( + self, + exprs: &[Expr], + short_array_element_width_threshold: usize, + ) -> DefinitiveTactic { + let tactic = || { + let has_single_line_comment = exprs.iter().any(|item| { + has_single_line_comment(&item.leading) || has_single_line_comment(&item.trailing) + }); + + let limit = match self { + _ if has_single_line_comment => return DefinitiveTactic::Vertical, + + Tactic::Horizontal => return DefinitiveTactic::Horizontal, + Tactic::LimitedHorizontalVertical(limit) => limit, + Tactic::HorizontalVertical | Tactic::Mixed => 100, + }; + + let (sep_count, total_width): (usize, usize) = exprs + .iter() + .map(|expr| expr.total_width()) + .fold((0, 0), |(sep_count, total_width), width| { + (sep_count + 1, total_width + width) + }); + + let total_sep_len = sep_count.saturating_sub(1); + let real_total = total_width + total_sep_len; + + if real_total <= limit && !exprs.iter().any(|expr| expr.is_multiline()) { + DefinitiveTactic::Horizontal + } else if self == Tactic::Mixed { + DefinitiveTactic::Mixed + } else { + DefinitiveTactic::Vertical + } + }; + + tactic().reduce(exprs, short_array_element_width_threshold) + } +} + +#[derive(Debug, PartialEq, Eq, Clone, Copy)] +enum DefinitiveTactic { + Vertical, + Horizontal, + Mixed, +} + +impl DefinitiveTactic { + fn reduce(self, exprs: &[Expr], short_array_element_width_threshold: usize) -> Self { + match self { + DefinitiveTactic::Vertical + if no_long_exprs(exprs, short_array_element_width_threshold) => + { + DefinitiveTactic::Mixed + } + DefinitiveTactic::Vertical | DefinitiveTactic::Horizontal | DefinitiveTactic::Mixed => { + self + } + } + } +} + +fn has_single_line_comment(slice: &str) -> bool { + slice.trim_start().starts_with("//") +} + +fn no_long_exprs(exprs: &[Expr], max_width: usize) -> bool { + exprs.iter().all(|expr| expr.value.len() <= max_width) +} + +fn extract_simple_expr(expr: Expression) -> Option { + if let ExpressionKind::Block(mut block) = expr.kind { + if block.len() == 1 { + if let StatementKind::Expression(expr) = block.pop().unwrap() { + return expr.into(); + } + } + } + + None +} diff --git a/noir/tooling/nargo_fmt/src/visitor/item.rs b/noir/tooling/nargo_fmt/src/visitor/item.rs new file mode 100644 index 00000000000..7774618afea --- /dev/null +++ b/noir/tooling/nargo_fmt/src/visitor/item.rs @@ -0,0 +1,149 @@ +use noirc_frontend::{ + parser::{Item, ItemKind}, + token::Token, + Distinctness, NoirFunction, ParsedModule, Visibility, +}; + +use crate::{utils::last_line_contains_single_line_comment, visitor::expr::format_seq}; + +use super::expr::Tactic::LimitedHorizontalVertical; + +impl super::FmtVisitor<'_> { + fn format_fn_before_block(&self, func: NoirFunction, start: u32) -> (String, bool) { + let tactic = LimitedHorizontalVertical(self.config.max_width); + + let name_span = func.name_ident().span(); + let func_span = func.span(); + + let mut result = self.slice(start..name_span.end()).to_owned(); + + let params_open = + self.span_before(name_span.end()..func_span.start(), Token::LeftParen).start(); + + let last_span = if func.parameters().is_empty() { + params_open..func_span.start() + } else { + func.parameters().last().unwrap().span.end()..func_span.start() + }; + + let params_end = self.span_after(last_span, Token::RightParen).start(); + + let params_span = params_open..params_end; + let return_type_span = func.return_type().span; + let parameters = func.def.parameters; + + if !func.def.generics.is_empty() { + let full_span = name_span.end()..params_open; + let start = name_span.end(); + let end = self.span_after(full_span, Token::Greater).start(); + + let generics = func.def.generics; + let span = (start..end).into(); + let generics = format_seq("<", ">", self.fork(), false, generics, span, tactic, false); + + result.push_str(&generics); + } + + let parameters = if parameters.is_empty() { + self.slice(params_span).into() + } else { + format_seq("(", ")", self.fork(), false, parameters, params_span.into(), tactic, true) + }; + + result.push_str(¶meters); + + if let Some(span) = return_type_span { + result.push_str(" -> "); + + if let Distinctness::Distinct = func.def.return_distinctness { + result.push_str("distinct "); + } + + if let Visibility::Public = func.def.return_visibility { + result.push_str("pub "); + } + + result.push_str(self.slice(span)); + + let slice = self.slice(span.end()..func_span.start()); + if !slice.trim().is_empty() { + result.push_str(slice); + } + } else { + result.push_str(self.slice(params_end..func_span.start())); + } + + let maybe_comment = self.slice(params_end..func_span.start()); + + (result.trim_end().to_string(), last_line_contains_single_line_comment(maybe_comment)) + } + + pub(crate) fn visit_file(&mut self, module: ParsedModule) { + self.visit_module(module); + self.format_missing_indent(self.source.len() as u32, false); + } + + fn visit_module(&mut self, module: ParsedModule) { + for Item { kind, span } in module.items { + match kind { + ItemKind::Function(func) => { + self.format_missing_indent(span.start(), true); + + if std::mem::take(&mut self.ignore_next_node) { + self.push_str(self.slice(span)); + self.last_position = span.end(); + continue; + } + + let (fn_before_block, force_brace_newline) = + self.format_fn_before_block(func.clone(), span.start()); + + self.push_str(&fn_before_block); + self.push_str(if force_brace_newline { "\n" } else { " " }); + + self.visit_block(func.def.body, func.def.span); + } + ItemKind::Submodules(module) => { + self.format_missing_indent(span.start(), true); + + if std::mem::take(&mut self.ignore_next_node) { + self.push_str(self.slice(span)); + self.last_position = span.end(); + continue; + } + + let name = module.name; + let after_brace = self.span_after(span, Token::LeftBrace).start(); + self.last_position = after_brace; + + let keyword = if module.is_contract { "contract" } else { "mod" }; + + self.push_str(&format!("{keyword} {name} ")); + + if module.contents.items.is_empty() { + self.visit_empty_block((after_brace - 1..span.end()).into()); + continue; + } else { + self.push_str("{"); + self.indent.block_indent(self.config); + self.visit_module(module.contents); + } + + self.close_block((self.last_position..span.end() - 1).into()); + self.last_position = span.end(); + } + ItemKind::Import(_) + | ItemKind::Struct(_) + | ItemKind::Trait(_) + | ItemKind::TraitImpl(_) + | ItemKind::Impl(_) + | ItemKind::TypeAlias(_) + | ItemKind::Global(_) + | ItemKind::ModuleDecl(_) => { + self.push_rewrite(self.slice(span).to_string(), span); + self.last_position = span.end(); + } + } + } + } +} diff --git a/noir/tooling/nargo_fmt/src/visitor/stmt.rs b/noir/tooling/nargo_fmt/src/visitor/stmt.rs new file mode 100644 index 00000000000..b6dd67323fa --- /dev/null +++ b/noir/tooling/nargo_fmt/src/visitor/stmt.rs @@ -0,0 +1,84 @@ +use std::iter::zip; + +use noirc_frontend::{ + ConstrainKind, ConstrainStatement, ExpressionKind, ForRange, Statement, StatementKind, +}; + +use super::ExpressionType; + +impl super::FmtVisitor<'_> { + pub(crate) fn visit_stmts(&mut self, stmts: Vec) { + let len = stmts.len(); + + for (Statement { kind, span }, index) in zip(stmts, 1..) { + let is_last = index == len; + + match kind { + StatementKind::Expression(expr) => self.visit_expr( + expr, + if is_last { ExpressionType::SubExpression } else { ExpressionType::Statement }, + ), + StatementKind::Semi(expr) => { + self.visit_expr(expr, ExpressionType::Statement); + self.push_str(";"); + } + StatementKind::Let(let_stmt) => { + let let_str = + self.slice(span.start()..let_stmt.expression.span.start()).trim_end(); + let expr_str = + self.format_expr(let_stmt.expression, ExpressionType::SubExpression); + + self.push_rewrite(format!("{let_str} {expr_str};"), span); + } + StatementKind::Constrain(ConstrainStatement(expr, message, kind)) => { + let message = + message.map_or(String::new(), |message| format!(", \"{message}\"")); + let constrain = match kind { + ConstrainKind::Assert => { + let assertion = self.format_sub_expr(expr); + + format!("assert({assertion}{message});") + } + ConstrainKind::AssertEq => { + if let ExpressionKind::Infix(infix) = expr.kind { + let lhs = self.format_sub_expr(infix.lhs); + let rhs = self.format_sub_expr(infix.rhs); + + format!("assert_eq({lhs}, {rhs}{message});") + } else { + unreachable!() + } + } + ConstrainKind::Constrain => { + let expr = self.format_sub_expr(expr); + format!("constrain {expr};") + } + }; + + self.push_rewrite(constrain, span); + } + StatementKind::For(for_stmt) => { + let identifier = self.slice(for_stmt.identifier.span()); + let range = match for_stmt.range { + ForRange::Range(start, end) => format!( + "{}..{}", + self.format_sub_expr(start), + self.format_sub_expr(end) + ), + ForRange::Array(array) => self.format_sub_expr(array), + }; + let block = self.format_sub_expr(for_stmt.block); + + let result = format!("for {identifier} in {range} {block}"); + self.push_rewrite(result, span); + } + StatementKind::Assign(_) => { + self.push_rewrite(self.slice(span).to_string(), span); + } + StatementKind::Error => unreachable!(), + } + + self.last_position = span.end(); + } + } +} diff --git a/noir/tooling/nargo_fmt/tests/execute.rs b/noir/tooling/nargo_fmt/tests/execute.rs new file mode 100644 index 00000000000..d8802fd926b --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/execute.rs @@ -0,0 +1,5 @@ +#[cfg(test)] +mod tests { + // include tests generated by `build.rs` + include!(concat!(env!("OUT_DIR"), "/execute.rs")); +} diff --git a/noir/tooling/nargo_fmt/tests/expected/add.nr b/noir/tooling/nargo_fmt/tests/expected/add.nr new file mode 100644 index 00000000000..341ed06f3e6 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/add.nr @@ -0,0 +1,7 @@ +fn main(mut x: u32, y: u32, z: u32) { + x += y; + assert(x == z); + + x *= 8; + assert(x > 9); +} diff --git a/noir/tooling/nargo_fmt/tests/expected/array.nr b/noir/tooling/nargo_fmt/tests/expected/array.nr new file mode 100644 index 00000000000..3341afb31a5 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/array.nr @@ -0,0 +1,39 @@ +fn big_array() { + [ + 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000, 100000000, 1000000000, 10000000000, 100000000000, 1000000000000, 10000000000000, 100000000000000, 1000000000000000, 10000000000000000, 100000000000000000, 1000000000000000000, + 10000000000000000000, + 100000000000000000000, + 1000000000000000000000, + 10000000000000000000000, + 100000000000000000000000, + 1000000000000000000000000 + ]; + + [ + 1, + 10 + ]; + + [ + // hello! + 1, + 10 + ]; + + [ + // hello! + 1, + // asd + 10 + ]; + + [ + // hello! + 1, + // asd + 10 + // asdasd + ]; + + [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]], [[13, 14, 15], [16, 17, 18]]]; +} diff --git a/noir/tooling/nargo_fmt/tests/expected/call.nr b/noir/tooling/nargo_fmt/tests/expected/call.nr new file mode 100644 index 00000000000..7824ba37089 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/call.nr @@ -0,0 +1,48 @@ +fn foo() { + my_function(10, some_value, another_func(20, 30)); + + outer_function( + some_function(), // Original inner function call + another_function() // Original inner function call + ); + + outer_function( + some_function(), // Original inner function call + another_function() // Original inner function call + ); + + my_function( + // Comment + some_value, + /* Multiline + Comment */ + another_func(20, 30) + ); + + my_function( + some_function(10, "arg1", another_function()), + another_func(20, some_function(), 30) + ); + + outer_function( + some_function(), + another_function(some_function(), some_value) + ); + + assert_eq(x, y); + + assert_eq(x, y, "message"); + + assert(x); + + assert(x, "message"); + + assert(x == y); + + assert(p4_affine.eq( + Gaffine::new( + 6890855772600357754907169075114257697580319025794532037257385534741338397365, + 4338620300185947561074059802482547481416142213883829469920100239455078257889 + ) + )); +} diff --git a/noir/tooling/nargo_fmt/tests/expected/cast.nr b/noir/tooling/nargo_fmt/tests/expected/cast.nr new file mode 100644 index 00000000000..63008168f84 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/cast.nr @@ -0,0 +1,3 @@ +fn main() { + x as u8 +} diff --git a/noir/tooling/nargo_fmt/tests/expected/comment.nr b/noir/tooling/nargo_fmt/tests/expected/comment.nr new file mode 100644 index 00000000000..fae425acfd0 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/comment.nr @@ -0,0 +1,27 @@ +fn comment1() { + // +} + +// random comment + +fn comment2() { // Test +} + +fn comment3() // some comment +{} + +fn comment4() +// some comment +{} + +fn comment5() // some comment +{} + +fn comment6() // some comment some comment some comment some comment some comment some comment so +{} + +fn comment7() +// some comment some comment some comment some comment some comment some comment some comment +{} + +fn comment8(/*test*/) {} diff --git a/noir/tooling/nargo_fmt/tests/expected/contract.nr b/noir/tooling/nargo_fmt/tests/expected/contract.nr new file mode 100644 index 00000000000..8a77d97bbba --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/contract.nr @@ -0,0 +1,80 @@ +// Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. +// Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. +// Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. +// Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. +contract Benchmarking { + use dep::value_note::{ + utils::{increment, decrement}, + value_note::{VALUE_NOTE_LEN, ValueNote, ValueNoteMethods}, + }; + + use dep::aztec::{ + context::{Context}, + note::{utils as note_utils, note_getter_options::NoteGetterOptions, note_header::NoteHeader}, + selector::compute_selector, + log::emit_unencrypted_log, + state_vars::{map::Map, public_state::PublicState, set::Set}, + types::type_serialization::field_serialization::{FieldSerializationMethods, FIELD_SERIALIZED_LEN}, + types::address::{AztecAddress}, + }; + + struct Storage { + notes: Map>, + balances: Map>, + } + + impl Storage { + fn init(context: Context) -> pub Self { + Storage { + notes: Map::new(context, 1, |context, slot| { Set::new(context, slot, ValueNoteMethods) }), + balances: Map::new(context, 2, |context, slot| { PublicState::new(context, slot, FieldSerializationMethods) }), + } + } + } + + #[aztec(private)] + fn constructor() {} + + // Nec tincidunt praesent semper feugiat nibh sed pulvinar. Nibh nisl condimentum id venenatis a. + #[aztec(private)] + fn create_note(owner: Field, value: Field) { + increment(storage.notes.at(owner), value, owner); + } + + // Diam quam nulla porttitor massa id. Elit ullamcorper dignissim cras tincidunt lobortis feugiat. + #[aztec(private)] + fn recreate_note(owner: Field, index: u32) { + let owner_notes = storage.notes.at(owner); + let getter_options = NoteGetterOptions::new().set_limit(1).set_offset(index); + let notes = owner_notes.get_notes(getter_options); + let note = notes[0].unwrap_unchecked(); + owner_notes.remove(note); + increment(owner_notes, note.value, owner); + } + + // Ultrices in iaculis nunc sed augue lacus. + #[aztec(public)] + fn increment_balance(owner: Field, value: Field) { + let current = storage.balances.at(owner).read(); + storage.balances.at(owner).write(current + value); + let _callStackItem1 = context.call_public_function( + context.this_address(), + compute_selector("broadcast(Field)"), + [owner] + ); + } + + // Est ultricies integer quis auctor elit sed. In nibh mauris cursus mattis molestie a iaculis. + #[aztec(public)] + fn broadcast(owner: Field) { + emit_unencrypted_log(&mut context, storage.balances.at(owner).read()); + } + + unconstrained fn compute_note_hash_and_nullifier(contract_address: Field, nonce: Field, storage_slot: Field, preimage: [Field; VALUE_NOTE_LEN]) -> [Field; 4] { + let note_header = NoteHeader::new(contract_address, nonce, storage_slot); + note_utils::compute_note_hash_and_nullifier(ValueNoteMethods, note_header, preimage) + } +} + +// Uses the token bridge contract, which tells which input token we need to talk to and handles the exit funds to L1 +contract Uniswap {} diff --git a/noir/tooling/nargo_fmt/tests/expected/empty.nr b/noir/tooling/nargo_fmt/tests/expected/empty.nr new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_fmt/tests/expected/expr.nr b/noir/tooling/nargo_fmt/tests/expected/expr.nr new file mode 100644 index 00000000000..03a26835ee3 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/expr.nr @@ -0,0 +1,137 @@ +// Test some empty blocks. +fn qux() { + {} + + { /* a block with a comment */ + } + {} + { + // A block with a comment. + } + + { + { + { + // A block with a comment. + } + } + } +} + +fn foo_return() { + "yay" +} + +fn fooblock() { + { + "inner-block" + } +} + +fn fooblock() { + { + { + { + "inner-block" + } + } + } +} + +fn comment() { + // this is a test comment + 1 +} + +fn only_comment() { + // Keep this here +} + +fn only_comments() { + // Keep this here + // Keep this here +} + +fn only_comments() { + // Keep this here + // Keep this here +} + +fn commnet() { + 1 + // +} + +fn test() { + 34 +} + +fn test() { + // 324 + 34 +} + +fn line() { + 42; // 42 +} + +fn line() { + 42; // 42 + 42; + // hello +} + +fn line() { + 42; // 42 + // 42 + // hello +} + +fn parenthesized() { + value + (x as Field) +} + +fn parenthesized() { + (i as u8) + (j as u8) + (k as u8) + x + y + z +} + +fn parenthesized() { + value + (/*test*/x as Field/*test*/) +} + +fn parenthesized() { + value + ( + // line + x as Field + ) +} + +fn constructor() { + Point { x: 5, y: 10 }; +} + +fn if_expr() { + if true { + println("Hello :D"); + } +} + +fn return_if_expr() { + if true { 42 } else { 40 + 2 } +} + +fn return_if_expr() { + if true { + 42 + }; + + if true { 42 } else { 40 + 2 } +} + +fn if_if() { + if cond { + some(); + } else { + none(); + }.bar().baz(); +} diff --git a/noir/tooling/nargo_fmt/tests/expected/fn.nr b/noir/tooling/nargo_fmt/tests/expected/fn.nr new file mode 100644 index 00000000000..5bca2dd8bb1 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/fn.nr @@ -0,0 +1,33 @@ +fn main(x: pub u8, y: u8) {} + +fn main(x: pub u8, y: u8) -> pub Field {} + +fn main(x: A, y: B) -> pub Field where A: Eq, B: Eq {} + +fn main() +// hello +{} + +fn main( + // hello +) {} + +fn main( + // hello + unit: () +) {} + +fn main() where T: Eq {} + +fn main( + tape: [Field; TAPE_LEN], + initial_registers: [Field; REGISTER_COUNT], + initial_memory: [Field; MEM_COUNT], + initial_program_counter: Field, + initial_call_stack: [Field; MAX_CALL_STACK], + initial_call_stack_pointer: u64 +) -> pub ExecutionResult {} + +fn apply_binary_field_op(lhs: RegisterIndex, rhs: RegisterIndex, result: RegisterIndex, op: u8, registers: &mut Registers) -> bool {} + +fn main() -> distinct pub [Field;2] {} diff --git a/noir/tooling/nargo_fmt/tests/expected/for.nr b/noir/tooling/nargo_fmt/tests/expected/for.nr new file mode 100644 index 00000000000..98dff672bef --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/for.nr @@ -0,0 +1,21 @@ +fn for_stmt() { + for elem in self { + ret &= predicate(elem); + } +} + +fn for_stmt() { + for i in 0..(C1 - 1) { + for _j in 1..(C1 - i - 1) { + b *= b; + } + + z *= if b == 1 { 1 } else { c }; + + c *= c; + + t *= if b == 1 { 1 } else { c }; + + b = t; + } +} diff --git a/noir/tooling/nargo_fmt/tests/expected/global.nr b/noir/tooling/nargo_fmt/tests/expected/global.nr new file mode 100644 index 00000000000..e73cf96ccbe --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/global.nr @@ -0,0 +1,9 @@ +//! Super module :] + +// super global variable +global answer = 42; + +// Super module :] + +// super global variable +global answer = 42; diff --git a/noir/tooling/nargo_fmt/tests/expected/if.nr b/noir/tooling/nargo_fmt/tests/expected/if.nr new file mode 100644 index 00000000000..39ad7d18cdd --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/if.nr @@ -0,0 +1,53 @@ +//@error_on_lost_comment=false +fn main() { + let (x,y) = if is_square(gx1) { + (x1, sqrt(gx1)) + } else { + (x2, sqrt(gx2)) + }; + + let n = if x != y { + if x != 20 { slice.push_back(y) } else { slice } + } else { + slice + }; + + if false { + (); + (); + } + + if false // lone if comment + { + (); + (); + } + + let a = if 0 > 1 { 0 } else { 0 }; + + if true { + (); + } else if false { + (); + (); + } else { + (); + (); + (); + } + + if true // else-if-chain if comment + { + (); + } + else if false // else-if-chain else-if comment + { + (); + (); + } else // else-if-chain else comment + { + (); + (); + (); + } +} diff --git a/noir/tooling/nargo_fmt/tests/expected/ignore.nr b/noir/tooling/nargo_fmt/tests/expected/ignore.nr new file mode 100644 index 00000000000..1c84e178f34 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/ignore.nr @@ -0,0 +1,26 @@ +fn main() { + // noir-fmt:ignore + assert( x != y ); + assert(x != y); + { + // noir-fmt:ignore + }; + assert(x != y); +} +// noir-fmt:ignore +fn main() { +1; +2; +3; +} +// noir-fmt:ignore +mod items { +fn hello() {} +} + +fn mk_array() { + // noir-fmt:ignore + let array = [1, + ]; + let array = [1]; +} diff --git a/noir/tooling/nargo_fmt/tests/expected/index.nr b/noir/tooling/nargo_fmt/tests/expected/index.nr new file mode 100644 index 00000000000..54f2ed2cf39 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/index.nr @@ -0,0 +1,9 @@ +fn foo() { + let arr = [10, 20, 30, 40]; + arr[2]; + arr[2]; + arr[/*test*/ 2]; + arr[2/*test*/]; + arr[// test + 2]; +} diff --git a/noir/tooling/nargo_fmt/tests/expected/infix.nr b/noir/tooling/nargo_fmt/tests/expected/infix.nr new file mode 100644 index 00000000000..cbc73045fe3 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/infix.nr @@ -0,0 +1,18 @@ +//@error_on_lost_comment=false +fn foo() { + 40 + 2; + !40 + 2; + 40 + 2 == 42; + + 40/*test*/ + 2 == 42; + 40 + 2/*test*/ == 42; +} + +fn big() { + assert(bjj_affine.contains(bjj_affine.gen) + & bjj_affine.contains(p1_affine) + & bjj_affine.contains(p2_affine) + & bjj_affine.contains(p3_affine) + & bjj_affine.contains(p4_affine) + & bjj_affine.contains(p5_affine)); +} diff --git a/noir/tooling/nargo_fmt/tests/expected/let.nr b/noir/tooling/nargo_fmt/tests/expected/let.nr new file mode 100644 index 00000000000..017a724b099 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/let.nr @@ -0,0 +1,56 @@ +//@error_on_lost_comment=false +fn let_() { + let fn_call = my_function( + some_function(10, "arg1", another_function()), + another_func(20, some_function(), 30) + ); + let array = [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]], [[13, 14, 15], [16, 17, 18]]]; + + let padded_sha256_hash: [u8; 259] = [ + // Padded hash + 209, 50, 135, 178, 4, 155, 190, 229, 228, 111, 61, 174, 8, 49, 48, 116, 90, 226, 77, 7, 111, 27, 19, 113, 154, 48, 138, 136, 138, 15, 230, 132, 32, 4, 0, 5, 1, 2, 4, 3, 101, 1, 72, 134, 96, 9, 6, 13, 48, 49, + 48, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 1, 0, + // Rest is padded with 0s until max bytes + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + ]; + + let a = BigUint56 { limbs: [1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] }; + + let person = Person { + first_name: "John", + last_name: "Doe", + home_address: Address { street: "123 Main St", city: "Exampleville", zip_code: "12345" } + }; + + let person = Person { + first_name: "John", + last_name: "Doe", + home_address: Address { + street: "123 Main St", + city: "Exampleville", + zip_code: "12345", + master: Person { + first_name: "John", + last_name: "Doe", + home_address: Address { street: "123 Main St", city: "Exampleville", zip_code: "12345" } + } + } + }; + + let expr = Expr { + // A boolean literal (true, false). + kind: ExprKind::Bool(true) + }; + + let expr = Expr { /*A boolean literal (true, false).*/ kind: ExprKind::Bool(true) }; + + let mut V = dep::crate2::MyStruct { Q: x }; + let mut V = dep::crate2::MyStruct {}; + let mut V = dep::crate2::MyStruct {/*test*/}; + let mut V = dep::crate2::MyStruct { + // sad + }; +} diff --git a/noir/tooling/nargo_fmt/tests/expected/literals.nr b/noir/tooling/nargo_fmt/tests/expected/literals.nr new file mode 100644 index 00000000000..5a9a735337f --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/literals.nr @@ -0,0 +1,24 @@ +//@error_on_lost_comment=false +fn main() { + [1, 2, 3, 4, 5]; + + [1; 5]; + + [0xff; 5]; + + [0 as u8; MAX_BYTES]; + + true; + + "hello world"; + + "hell\0\"world"; + + f"i: {i}, j: {j}"; + + (); + + (/*test*/); + + () +} diff --git a/noir/tooling/nargo_fmt/tests/expected/member_access.nr b/noir/tooling/nargo_fmt/tests/expected/member_access.nr new file mode 100644 index 00000000000..2330ec826c7 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/member_access.nr @@ -0,0 +1,15 @@ +struct Address { + city: String, + country: String, +} + +struct Person { + name: String, + age: u8, + address: Address, +} + +fn foo(p: Person) { + p.name; + p.address.country; +} diff --git a/noir/tooling/nargo_fmt/tests/expected/method_call.nr b/noir/tooling/nargo_fmt/tests/expected/method_call.nr new file mode 100644 index 00000000000..c2ee71237f8 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/method_call.nr @@ -0,0 +1,3 @@ +fn foo() { + my_object.some_method(10, var_value, inner_method(20, 30)); +} diff --git a/noir/tooling/nargo_fmt/tests/expected/module.nr b/noir/tooling/nargo_fmt/tests/expected/module.nr new file mode 100644 index 00000000000..e419543dbc4 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/module.nr @@ -0,0 +1,23 @@ +mod a { + mod b { + struct Data { + a: Field + } + } + + fn data(a: Field) -> Data { + Data { a } + } + + fn data2(a: Field) -> Data2 { + Data2 { a } + } + + mod tests { + #[test] + fn test() { + data(1); + data2(1); + } + } +} diff --git a/noir/tooling/nargo_fmt/tests/expected/nested_if_else.nr b/noir/tooling/nargo_fmt/tests/expected/nested_if_else.nr new file mode 100644 index 00000000000..dfd203189e8 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/nested_if_else.nr @@ -0,0 +1,9 @@ +fn nested_if_else() { + if false { + 1 + } else if false { + 2 + } else { + 3 + } +} diff --git a/noir/tooling/nargo_fmt/tests/expected/nested_parens.nr b/noir/tooling/nargo_fmt/tests/expected/nested_parens.nr new file mode 100644 index 00000000000..53eaa63c279 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/nested_parens.nr @@ -0,0 +1,5 @@ +//@remove_nested_parens=false +fn main() { + ((())); + ((((((((())))))))); +} diff --git a/noir/tooling/nargo_fmt/tests/expected/parens.nr b/noir/tooling/nargo_fmt/tests/expected/parens.nr new file mode 100644 index 00000000000..e6c4f91879c --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/parens.nr @@ -0,0 +1,46 @@ +fn main(x: u64, y: pub u64) { + ( + // + 1 + ); + + ( + // a + 1 + // b + ); + + ( + // test + 1 + ); + + ( + // 1 + ( + // 2 + 2 + ) + ); + + (/*a*/( + // test + 1 + )/*b*/); + + ( + // test + 1 + ); + + (/*a*/1); + + (1/*b*/); + + (/*c*/1/*d*/); + + (/*test*/1/*test 2*/); + + (()); + (()); +} diff --git a/noir/tooling/nargo_fmt/tests/expected/print.nr b/noir/tooling/nargo_fmt/tests/expected/print.nr new file mode 100644 index 00000000000..e169f565455 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/print.nr @@ -0,0 +1,5 @@ +use dep::std; + +fn main() { + std::println("Hello world"); +} diff --git a/noir/tooling/nargo_fmt/tests/expected/print2.nr b/noir/tooling/nargo_fmt/tests/expected/print2.nr new file mode 100644 index 00000000000..80284444af8 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/print2.nr @@ -0,0 +1,5 @@ +use dep::std; + +fn main( ) { + std::println("Hello world"); +} diff --git a/noir/tooling/nargo_fmt/tests/expected/read_array.nr b/noir/tooling/nargo_fmt/tests/expected/read_array.nr new file mode 100644 index 00000000000..d2619884b5d --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/read_array.nr @@ -0,0 +1,6 @@ +fn read_array(x: [Field; 3]) { + assert(x[0] == 1); + let y = [1, 5, 27]; + + assert(y[x[0]] == 5); +} diff --git a/noir/tooling/nargo_fmt/tests/expected/single_fn.nr b/noir/tooling/nargo_fmt/tests/expected/single_fn.nr new file mode 100644 index 00000000000..b02291fc50d --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/single_fn.nr @@ -0,0 +1,3 @@ +fn main() { + // hello +} diff --git a/noir/tooling/nargo_fmt/tests/expected/single_mod.nr b/noir/tooling/nargo_fmt/tests/expected/single_mod.nr new file mode 100644 index 00000000000..3c04a12d34b --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/single_mod.nr @@ -0,0 +1 @@ +mod hello {} diff --git a/noir/tooling/nargo_fmt/tests/expected/struct.nr b/noir/tooling/nargo_fmt/tests/expected/struct.nr new file mode 100644 index 00000000000..cf1795892d2 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/struct.nr @@ -0,0 +1,73 @@ +struct Foo { + bar: Field, + array: [Field; 2], +} + +struct Pair { + first: Foo, + second: Field, +} + +impl Foo { + fn default(x: Field,y: Field) -> Self { + Self { bar: 0, array: [x,y] } + } +} + +impl Pair { + fn foo(p: Self) -> Foo { + p.first + } + + fn bar(self) -> Field { + self.foo().bar + } +} + +struct Nested { + a: Field, + b: Field +} +struct MyStruct { + my_bool: bool, + my_int: u32, + my_nest: Nested, +} +fn test_struct_in_tuple(a_bool: bool, x: Field, y: Field) -> (MyStruct, bool) { + let my_struct = MyStruct { my_bool: a_bool, my_int: 5, my_nest: Nested { a: x, b: y } }; + (my_struct, a_bool) +} + +struct Animal { + legs: Field, + eyes: u8, +} + +fn get_dog() -> Animal { + let dog = Animal { legs: 4, eyes: 2 }; + dog +} + +fn main(x: Field, y: Field) { + let first = Foo::default(x, y); + let p = Pair { first, second: 1 }; + + assert(p.bar() == x); + assert(p.second == y); + assert(p.first.array[0] != p.first.array[1]); + + // Nested structs + let (struct_from_tuple, a_bool) = test_struct_in_tuple(true, x, y); + assert(struct_from_tuple.my_bool == true); + assert(a_bool == true); + assert(struct_from_tuple.my_int == 5); + assert(struct_from_tuple.my_nest.a == 0); + + // Regression test for issue #670 + let Animal { legs, eyes } = get_dog(); + let six = legs + eyes as Field; + + assert(six == 6); + + let Animal { legs: _, eyes: _ } = get_dog(); +} diff --git a/noir/tooling/nargo_fmt/tests/expected/submodule.nr b/noir/tooling/nargo_fmt/tests/expected/submodule.nr new file mode 100644 index 00000000000..88e005b7ac2 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/submodule.nr @@ -0,0 +1,30 @@ +mod a { + // hello +} + +mod a { + // hello + mod b { + // hello + } +} + +mod a { + mod b { + mod c {} + } +} + +mod a { + // 1 + // 2 + // 3 + /*test*/ +} + +mod a { + /**/ + mod b { + mod c {} + } +} diff --git a/noir/tooling/nargo_fmt/tests/expected/tuple.nr b/noir/tooling/nargo_fmt/tests/expected/tuple.nr new file mode 100644 index 00000000000..c3b32904f15 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/tuple.nr @@ -0,0 +1,32 @@ +fn main() { + (1,); + (// hello + 1,); + (/*hello*/ 1,); + (1/*hello*/,); + (1,); + (/*test*/ 1,); + (/*a*/ 1/*b*/,); + (/*a*/ 1/*b*/, /*c*/ 2/*d*/, /*c*/ 2/*d*/); + (/*a*/ 1/*b*/, /*c*/ 2/*d*/, /*c*/ 2/*d*/, /*e*/ 3/*f*/); + + (1/*1*/, 2/* 2*/); + + (1/*test*/,); + + (// + 1,); + + (// 1 + 1, // 2, + 2); + + (/*1*/ 1, /*2*/ 2); + + // FIXME: + (((//2 + 1,),),); + (/*a*/ + 1/*b*/, +/*c*/ 2/*d*/, /*c*/ 2/*d*/, /*e*/ 3/*f*/); +} diff --git a/noir/tooling/nargo_fmt/tests/expected/unary_operators.nr b/noir/tooling/nargo_fmt/tests/expected/unary_operators.nr new file mode 100644 index 00000000000..ffea1713c06 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/unary_operators.nr @@ -0,0 +1,10 @@ +//@error_on_lost_comment=false +fn main() { + -1; + -/*test*/1; + -/*test*/1; + + &mut 1; + *v; + ***v; +} diff --git a/noir/tooling/nargo_fmt/tests/expected/vec.nr b/noir/tooling/nargo_fmt/tests/expected/vec.nr new file mode 100644 index 00000000000..1c9a791961e --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/expected/vec.nr @@ -0,0 +1,60 @@ +struct Vec { + slice: [T] +} + +// A mutable vector type implemented as a wrapper around immutable slices. +// A separate type is technically not needed but helps differentiate which operations are mutable. +impl Vec { + pub fn new() -> Self { + Self { slice: [] } + } + + // Create a Vec containing each element from the given slice. + // Mutations to the resulting Vec will not affect the original slice. + pub fn from_slice(slice: [T]) -> Self { + Self { slice } + } + + /// Get an element from the vector at the given index. + /// Panics if the given index + /// points beyond the end of the vector. + pub fn get(self, index: Field) -> T { + self.slice[index] + } + + /// Push a new element to the end of the vector, returning a + /// new vector with a length one greater than the + /// original unmodified vector. + pub fn push(&mut self, elem: T) { + self.slice = self.slice.push_back(elem); + } + + /// Pop an element from the end of the given vector, returning + /// a new vector with a length of one less than the given vector, + /// as well as the popped element. + /// Panics if the given vector's length is zero. + pub fn pop(&mut self) -> T { + let (popped_slice, last_elem) = self.slice.pop_back(); + self.slice = popped_slice; + last_elem + } + + /// Insert an element at a specified index, shifting all elements + /// after it to the right + pub fn insert(&mut self, index: Field, elem: T) { + self.slice = self.slice.insert(index, elem); + } + + /// Remove an element at a specified index, shifting all elements + /// after it to the left, returning the removed element + pub fn remove(&mut self, index: Field) -> T { + let (new_slice, elem) = self.slice.remove(index); + self.slice = new_slice; + elem + } + + /// Returns the number of elements in the vector + pub fn len(self) -> Field { + self.slice.len() + } +} diff --git a/noir/tooling/nargo_fmt/tests/input/add.nr b/noir/tooling/nargo_fmt/tests/input/add.nr new file mode 100644 index 00000000000..6f2892942c1 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/add.nr @@ -0,0 +1,7 @@ +fn main(mut x: u32, y: u32, z: u32) { + x += y; + assert(x == z); + + x *= 8; + assert(x>9); +} diff --git a/noir/tooling/nargo_fmt/tests/input/array.nr b/noir/tooling/nargo_fmt/tests/input/array.nr new file mode 100644 index 00000000000..73651ef76bd --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/array.nr @@ -0,0 +1,39 @@ +fn big_array() { + [ + 1,10,100, 1000, 10000, 100000, 1000000, 10000000, 100000000, 1000000000, 10000000000, 100000000000, 1000000000000, 10000000000000, 100000000000000, 1000000000000000, 10000000000000000, 100000000000000000, 1000000000000000000, + 10000000000000000000, + 100000000000000000000, + 1000000000000000000000, + 10000000000000000000000, + 100000000000000000000000, + 1000000000000000000000000, + ]; + + [ + 1, + 10, + ]; + + [ +// hello! +1, +10, + ]; + + [ +// hello! +1, +// asd +10, + ]; + + [ +// hello! +1, +// asd +10, +// asdasd + ]; + + [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]], [[13, 14, 15], [16, 17, 18]]]; +} diff --git a/noir/tooling/nargo_fmt/tests/input/call.nr b/noir/tooling/nargo_fmt/tests/input/call.nr new file mode 100644 index 00000000000..2e9a612e6d8 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/call.nr @@ -0,0 +1,47 @@ +fn foo() { + my_function( 10,some_value,another_func( 20 , 30) ); + + outer_function(some_function(), // Original inner function call + another_function(), // Original inner function call + ); + + outer_function( + + + some_function(), // Original inner function call + another_function(), // Original inner function call + ); + + my_function( + // Comment + some_value, + /* Multiline + Comment */ + another_func( 20, 30 ) + ); + + my_function( + some_function( 10, "arg1", another_function() ), + another_func (20, some_function() , 30 ) + ); + + outer_function( + some_function(), + + + another_function( + some_function(), some_value) + ); + + assert_eq( x, y ); + + assert_eq( x, y, "message" ); + + assert( x ); + + assert( x, "message" ); + + assert( x == y ); + + assert(p4_affine.eq(Gaffine::new(6890855772600357754907169075114257697580319025794532037257385534741338397365, 4338620300185947561074059802482547481416142213883829469920100239455078257889))); +} diff --git a/noir/tooling/nargo_fmt/tests/input/cast.nr b/noir/tooling/nargo_fmt/tests/input/cast.nr new file mode 100644 index 00000000000..dfbb370e2bb --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/cast.nr @@ -0,0 +1,4 @@ +fn main() { + +x as u8 +} diff --git a/noir/tooling/nargo_fmt/tests/input/comment.nr b/noir/tooling/nargo_fmt/tests/input/comment.nr new file mode 100644 index 00000000000..0e203a82d66 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/comment.nr @@ -0,0 +1,32 @@ +fn comment1() { + // +} + +// random comment + +fn comment2() { // Test +} + +fn comment3() // some comment +{ +} + +fn comment4() +// some comment +{ +} + +fn comment5() // some comment +{ +} + +fn comment6() // some comment some comment some comment some comment some comment some comment so +{ +} + +fn comment7() +// some comment some comment some comment some comment some comment some comment some comment +{ +} + +fn comment8(/*test*/) {} diff --git a/noir/tooling/nargo_fmt/tests/input/contract.nr b/noir/tooling/nargo_fmt/tests/input/contract.nr new file mode 100644 index 00000000000..6bc5c552110 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/contract.nr @@ -0,0 +1,76 @@ +// Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. +// Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. +// Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. +// Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. +contract Benchmarking { + use dep::value_note::{ + utils::{increment, decrement}, + value_note::{VALUE_NOTE_LEN, ValueNote, ValueNoteMethods}, + }; + + use dep::aztec::{ + context::{Context}, + note::{utils as note_utils, note_getter_options::NoteGetterOptions, note_header::NoteHeader}, + selector::compute_selector, + log::emit_unencrypted_log, + state_vars::{map::Map, public_state::PublicState, set::Set}, + types::type_serialization::field_serialization::{FieldSerializationMethods, FIELD_SERIALIZED_LEN}, + types::address::{AztecAddress}, + }; + + struct Storage { + notes: Map>, + balances: Map>, + } + + impl Storage { + fn init(context: Context) -> pub Self { + Storage { + notes: Map::new(context, 1, |context, slot| { Set::new(context, slot, ValueNoteMethods) }), + balances: Map::new(context, 2, |context, slot| { PublicState::new(context, slot, FieldSerializationMethods) }), + } + } + } + + #[aztec(private)] + fn constructor() {} + + // Nec tincidunt praesent semper feugiat nibh sed pulvinar. Nibh nisl condimentum id venenatis a. + #[aztec(private)] + fn create_note(owner: Field, value: Field) { + increment(storage.notes.at(owner), value, owner); + } + + // Diam quam nulla porttitor massa id. Elit ullamcorper dignissim cras tincidunt lobortis feugiat. + #[aztec(private)] + fn recreate_note(owner: Field, index: u32) { + let owner_notes = storage.notes.at(owner); + let getter_options = NoteGetterOptions::new().set_limit(1).set_offset(index); + let notes = owner_notes.get_notes(getter_options); + let note = notes[0].unwrap_unchecked(); + owner_notes.remove(note); + increment(owner_notes, note.value, owner); + } + + // Ultrices in iaculis nunc sed augue lacus. + #[aztec(public)] + fn increment_balance(owner: Field, value: Field) { + let current = storage.balances.at(owner).read(); + storage.balances.at(owner).write(current + value); + let _callStackItem1 = context.call_public_function(context.this_address(), compute_selector("broadcast(Field)"), [owner]); + } + + // Est ultricies integer quis auctor elit sed. In nibh mauris cursus mattis molestie a iaculis. + #[aztec(public)] + fn broadcast(owner: Field) { + emit_unencrypted_log(&mut context, storage.balances.at(owner).read()); + } + + unconstrained fn compute_note_hash_and_nullifier(contract_address: Field, nonce: Field, storage_slot: Field, preimage: [Field; VALUE_NOTE_LEN]) -> [Field; 4] { + let note_header = NoteHeader::new(contract_address, nonce, storage_slot); + note_utils::compute_note_hash_and_nullifier(ValueNoteMethods, note_header, preimage) + } +} + +// Uses the token bridge contract, which tells which input token we need to talk to and handles the exit funds to L1 +contract Uniswap {} diff --git a/noir/tooling/nargo_fmt/tests/input/empty.nr b/noir/tooling/nargo_fmt/tests/input/empty.nr new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/nargo_fmt/tests/input/expr.nr b/noir/tooling/nargo_fmt/tests/input/expr.nr new file mode 100644 index 00000000000..b4edcbbed5f --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/expr.nr @@ -0,0 +1,153 @@ +// Test some empty blocks. +fn qux() { + {} + + { /* a block with a comment */ } + { + + } + { + // A block with a comment. + } + + { + { + { + // A block with a comment. + } + } + } +} + +fn foo_return() { + "yay" +} + +fn fooblock() { + { + "inner-block" + } +} + +fn fooblock() { + { + { + { + "inner-block" + } + } + } +} + +fn comment() { + // this is a test comment + 1 +} + +fn only_comment() { + // Keep this here +} + +fn only_comments() { +// Keep this here +// Keep this here +} + +fn only_comments() { + // Keep this here + // Keep this here +} + +fn commnet() { + 1 + // +} + +fn test() { + + + + + + 34 +} + +fn test() { + + + + + // 324 + 34 +} + +fn line() { + 42; // 42 +} + + +fn line() { + 42;// 42 + 42; +// hello +} + + +fn line() { + 42;// 42 + // 42 +// hello +} + +fn parenthesized() { + value + ( x as Field ) +} + +fn parenthesized() { + ( i as u8 ) + ( j as u8 ) + ( k as u8 ) + x + y + z +} + +fn parenthesized() { + value + ( /*test*/x as Field /*test*/ ) +} + +fn parenthesized() { + value + ( +// line + x as Field + ) +} + +fn constructor() { + Point{x :5, + y: 10 }; +} + +fn if_expr() { + if true { println("Hello :D"); } +} + +fn return_if_expr() { + if true { +42 +} +else +{ 40 + 2 } +} + +fn return_if_expr() { + if true {42}; + + if true { + 42 + } + else { + 40 + + 2 } +} + +fn if_if() { +if cond { some(); } else { none(); } + .bar() + .baz(); +} \ No newline at end of file diff --git a/noir/tooling/nargo_fmt/tests/input/fn.nr b/noir/tooling/nargo_fmt/tests/input/fn.nr new file mode 100644 index 00000000000..f503db99853 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/fn.nr @@ -0,0 +1,26 @@ +fn main(x: pub u8, y: u8) {} + +fn main(x: pub u8, y: u8) -> pub Field {} + +fn main(x: A, y: B) -> pub Field where A: Eq, B: Eq {} + +fn main() +// hello +{} + +fn main( + // hello +) {} + +fn main( + // hello + unit: () +) {} + +fn main() where T: Eq {} + +fn main(tape: [Field; TAPE_LEN], initial_registers: [Field; REGISTER_COUNT], initial_memory: [Field; MEM_COUNT], initial_program_counter: Field, initial_call_stack: [Field; MAX_CALL_STACK], initial_call_stack_pointer: u64) -> pub ExecutionResult {} + +fn apply_binary_field_op(lhs: RegisterIndex, rhs: RegisterIndex, result: RegisterIndex, op: u8, registers: &mut Registers) -> bool {} + +fn main() -> distinct pub [Field;2] {} diff --git a/noir/tooling/nargo_fmt/tests/input/for.nr b/noir/tooling/nargo_fmt/tests/input/for.nr new file mode 100644 index 00000000000..99b796df820 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/for.nr @@ -0,0 +1,24 @@ +fn for_stmt() { + for elem in self { + ret &= predicate(elem); + } +} + +fn for_stmt() { + for i in 0..(C1-1) { + + for _j in 1..(C1-i-1) { + + b *= b; + + } + + z *= if b == 1 { 1 } else { c }; + + c *= c; + + t *= if b == 1 { 1 } else { c }; + + b = t; + } +} diff --git a/noir/tooling/nargo_fmt/tests/input/global.nr b/noir/tooling/nargo_fmt/tests/input/global.nr new file mode 100644 index 00000000000..bf023c61805 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/global.nr @@ -0,0 +1,17 @@ +//! Super module :] + +// super global variable +global answer = 42; + +// Super module :] + + + + + + + + + +// super global variable +global answer = 42; diff --git a/noir/tooling/nargo_fmt/tests/input/if.nr b/noir/tooling/nargo_fmt/tests/input/if.nr new file mode 100644 index 00000000000..be72eb79a18 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/if.nr @@ -0,0 +1,57 @@ +//@error_on_lost_comment=false +fn main() { + let (x,y) = if is_square(gx1) {(x1, sqrt(gx1))} else {(x2, sqrt(gx2))}; + + let n = if x != y { if x != 20 { slice.push_back(y) } else { slice } } else { slice }; + + if false + { + (); + (); + } + + if false // lone if comment + { + (); + (); + } + + + let a = + if 0 > 1 { + 0 + } + else + { + 0 + }; + + + if true + { + (); + } else if false { + (); + (); + } + else { + (); + (); + (); + } + + if true // else-if-chain if comment + { + (); + } + else if false // else-if-chain else-if comment + { + (); + (); + } else // else-if-chain else comment + { + (); + (); + (); + } +} diff --git a/noir/tooling/nargo_fmt/tests/input/ignore.nr b/noir/tooling/nargo_fmt/tests/input/ignore.nr new file mode 100644 index 00000000000..e095d2e9f1f --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/ignore.nr @@ -0,0 +1,27 @@ +fn main() { + // noir-fmt:ignore + assert( x != y ); + assert( x != y ); + { + // noir-fmt:ignore + }; + assert( x != y ); +} +// noir-fmt:ignore +fn main() { +1; +2; +3; +} +// noir-fmt:ignore +mod items { +fn hello() {} +} + +fn mk_array() { + // noir-fmt:ignore + let array = [1, + ]; + let array = [1, + ]; +} diff --git a/noir/tooling/nargo_fmt/tests/input/index.nr b/noir/tooling/nargo_fmt/tests/input/index.nr new file mode 100644 index 00000000000..7d10e897b8d --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/index.nr @@ -0,0 +1,10 @@ +fn foo() { + let arr = [10, 20, 30, 40]; + arr [2]; + arr [2]; + arr [/*test*/2]; + arr [2/*test*/]; + arr [ + // test + 2]; +} \ No newline at end of file diff --git a/noir/tooling/nargo_fmt/tests/input/infix.nr b/noir/tooling/nargo_fmt/tests/input/infix.nr new file mode 100644 index 00000000000..059e58c6b64 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/infix.nr @@ -0,0 +1,13 @@ +//@error_on_lost_comment=false +fn foo() { + 40 + 2; + !40+2; + 40 + 2 == 42; + +40/*test*/ + 2 == 42; + 40 + 2/*test*/ == 42; +} + +fn big() { + assert(bjj_affine.contains(bjj_affine.gen) & bjj_affine.contains(p1_affine) & bjj_affine.contains(p2_affine) & bjj_affine.contains(p3_affine) & bjj_affine.contains(p4_affine) & bjj_affine.contains(p5_affine)); +} diff --git a/noir/tooling/nargo_fmt/tests/input/let.nr b/noir/tooling/nargo_fmt/tests/input/let.nr new file mode 100644 index 00000000000..67c4ab8bd52 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/let.nr @@ -0,0 +1,35 @@ +//@error_on_lost_comment=false +fn let_() { + let fn_call = my_function(some_function( 10, "arg1", another_function() ),another_func (20, some_function() , 30 )); + let array = [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]], [[13, 14, 15], [16, 17, 18]]]; + + let padded_sha256_hash: [u8; 259] = [ + // Padded hash + 209, 50, 135, 178, 4, 155, 190, 229, 228, 111, 61, 174, 8, 49, 48, 116, 90, 226, 77, 7, 111, 27, 19, 113, 154, 48, 138, 136, 138, 15, 230, 132, 32, 4, 0, 5, 1, 2, 4, 3, 101, 1, 72, 134, 96, 9, 6, 13, 48, 49, + 48, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 1, 0, + // Rest is padded with 0s until max bytes + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + ]; + + let a = BigUint56 {limbs:[1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}; + + let person = Person { first_name: "John", last_name: "Doe", home_address: Address { street: "123 Main St", city: "Exampleville", zip_code: "12345" } }; + + let person = Person { first_name: "John", last_name: "Doe", home_address: Address { street: "123 Main St", city: "Exampleville", zip_code: "12345", master: Person { first_name: "John", last_name: "Doe", home_address: Address { street: "123 Main St", city: "Exampleville", zip_code: "12345" } } } }; + + let expr = Expr {// A boolean literal (true, false). +kind: ExprKind::Bool(true), + }; + + let expr = Expr {/*A boolean literal (true, false).*/kind: ExprKind::Bool(true),}; + + let mut V = dep::crate2::MyStruct { Q: x }; + let mut V = dep::crate2::MyStruct {}; + let mut V = dep::crate2::MyStruct {/*test*/}; + let mut V = dep::crate2::MyStruct { + // sad + }; +} diff --git a/noir/tooling/nargo_fmt/tests/input/literals.nr b/noir/tooling/nargo_fmt/tests/input/literals.nr new file mode 100644 index 00000000000..fbdc7676845 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/literals.nr @@ -0,0 +1,25 @@ +//@error_on_lost_comment=false +fn main() { + [1,2,3,4,5]; + + + [1;5]; + + [0xff;5]; + + [0 as u8; MAX_BYTES]; + + true; + + "hello world"; + + "hell\0\"world"; + + f"i: {i}, j: {j}"; + + ( ); + + (/*test*/); + + () +} diff --git a/noir/tooling/nargo_fmt/tests/input/member_access.nr b/noir/tooling/nargo_fmt/tests/input/member_access.nr new file mode 100644 index 00000000000..abcab671836 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/member_access.nr @@ -0,0 +1,15 @@ +struct Address { + city: String, + country: String, +} + +struct Person { + name: String, + age: u8, + address: Address, +} + +fn foo(p: Person) { + p . name; + p.address . country; +} diff --git a/noir/tooling/nargo_fmt/tests/input/method_call.nr b/noir/tooling/nargo_fmt/tests/input/method_call.nr new file mode 100644 index 00000000000..175647b4650 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/method_call.nr @@ -0,0 +1,3 @@ +fn foo() { + my_object . some_method( 10,var_value,inner_method( 20 , 30) ); +} diff --git a/noir/tooling/nargo_fmt/tests/input/module.nr b/noir/tooling/nargo_fmt/tests/input/module.nr new file mode 100644 index 00000000000..e419543dbc4 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/module.nr @@ -0,0 +1,23 @@ +mod a { + mod b { + struct Data { + a: Field + } + } + + fn data(a: Field) -> Data { + Data { a } + } + + fn data2(a: Field) -> Data2 { + Data2 { a } + } + + mod tests { + #[test] + fn test() { + data(1); + data2(1); + } + } +} diff --git a/noir/tooling/nargo_fmt/tests/input/nested_if_else.nr b/noir/tooling/nargo_fmt/tests/input/nested_if_else.nr new file mode 100644 index 00000000000..8aa120e3b18 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/nested_if_else.nr @@ -0,0 +1,3 @@ +fn nested_if_else() { + if false { 1 } else if false { 2 } else { 3 } +} diff --git a/noir/tooling/nargo_fmt/tests/input/nested_parens.nr b/noir/tooling/nargo_fmt/tests/input/nested_parens.nr new file mode 100644 index 00000000000..53eaa63c279 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/nested_parens.nr @@ -0,0 +1,5 @@ +//@remove_nested_parens=false +fn main() { + ((())); + ((((((((())))))))); +} diff --git a/noir/tooling/nargo_fmt/tests/input/parens.nr b/noir/tooling/nargo_fmt/tests/input/parens.nr new file mode 100644 index 00000000000..769b477a34d --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/parens.nr @@ -0,0 +1,53 @@ +fn main(x : u64, y : pub u64) { + ( +// +1 + ); + +( +// a +1 +// b +); + +( +// test + 1 + ); + + ( +// 1 +( +// 2 + 2 +) +); + + ( + + /*a*/ + + ( + + // test + 1 + ) + /*b*/ +); + + ( + // test + 1 + ); + + ( /*a*/ 1 ); + + ( 1 /*b*/ ); + + ( /*c*/ 1 /*d*/ ); + + ( /*test*/ 1 /*test 2*/ ); + + ((())); + (((((((((((((((()))))))))))))))); +} diff --git a/noir/tooling/nargo_fmt/tests/input/print.nr b/noir/tooling/nargo_fmt/tests/input/print.nr new file mode 100644 index 00000000000..8afa562dada --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/print.nr @@ -0,0 +1,3 @@ +use dep::std; + +fn main() { std::println("Hello world"); } diff --git a/noir/tooling/nargo_fmt/tests/input/print2.nr b/noir/tooling/nargo_fmt/tests/input/print2.nr new file mode 100644 index 00000000000..07ef9dd0386 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/print2.nr @@ -0,0 +1,5 @@ +use dep::std; + +fn main( ) { +std::println("Hello world"); +} diff --git a/noir/tooling/nargo_fmt/tests/input/read_array.nr b/noir/tooling/nargo_fmt/tests/input/read_array.nr new file mode 100644 index 00000000000..d2619884b5d --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/read_array.nr @@ -0,0 +1,6 @@ +fn read_array(x: [Field; 3]) { + assert(x[0] == 1); + let y = [1, 5, 27]; + + assert(y[x[0]] == 5); +} diff --git a/noir/tooling/nargo_fmt/tests/input/single_fn.nr b/noir/tooling/nargo_fmt/tests/input/single_fn.nr new file mode 100644 index 00000000000..c7ee3bbd3aa --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/single_fn.nr @@ -0,0 +1,3 @@ +fn main() { + // hello +} \ No newline at end of file diff --git a/noir/tooling/nargo_fmt/tests/input/single_mod.nr b/noir/tooling/nargo_fmt/tests/input/single_mod.nr new file mode 100644 index 00000000000..f02dbfb5464 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/single_mod.nr @@ -0,0 +1 @@ +mod hello {} \ No newline at end of file diff --git a/noir/tooling/nargo_fmt/tests/input/struct.nr b/noir/tooling/nargo_fmt/tests/input/struct.nr new file mode 100644 index 00000000000..5e3530e8364 --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/struct.nr @@ -0,0 +1,77 @@ +struct Foo { + bar: Field, + array: [Field; 2], +} + +struct Pair { + first: Foo, + second: Field, +} + +impl Foo { + fn default(x: Field,y: Field) -> Self { + Self { bar: 0, array: [x,y] } + } +} + +impl Pair { + fn foo(p: Self) -> Foo { + p.first + } + + fn bar(self) -> Field { + self.foo().bar + } +} + +struct Nested { + a: Field, + b: Field +} +struct MyStruct { + my_bool: bool, + my_int: u32, + my_nest: Nested, +} +fn test_struct_in_tuple(a_bool : bool,x:Field, y:Field) -> (MyStruct, bool) { + let my_struct = MyStruct { + my_bool: a_bool, + my_int: 5, + my_nest: Nested{a:x,b:y}, + }; + (my_struct, a_bool) +} + +struct Animal { + legs: Field, + eyes: u8, +} + +fn get_dog() -> Animal { + let dog = Animal { legs: 4, eyes: 2 }; + dog +} + +fn main(x: Field, y: Field) { + let first = Foo::default(x,y); + let p = Pair { first, second: 1 }; + + assert(p.bar() == x); + assert(p.second == y); + assert(p.first.array[0] != p.first.array[1]); + + // Nested structs + let (struct_from_tuple, a_bool) = test_struct_in_tuple(true,x,y); + assert(struct_from_tuple.my_bool == true); + assert(a_bool == true); + assert(struct_from_tuple.my_int == 5); + assert(struct_from_tuple.my_nest.a == 0); + + // Regression test for issue #670 + let Animal { legs, eyes } = get_dog(); + let six = legs + eyes as Field; + + assert(six == 6); + + let Animal { legs: _, eyes: _ } = get_dog(); +} diff --git a/noir/tooling/nargo_fmt/tests/input/submodule.nr b/noir/tooling/nargo_fmt/tests/input/submodule.nr new file mode 100644 index 00000000000..d5b162fd28d --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/submodule.nr @@ -0,0 +1,25 @@ +mod a { +// hello +} + +mod a { +// hello + mod b { +// hello + } +} + +mod a {mod b {mod c {}}} + +mod a { +// 1 +// 2 +// 3 +/*test*/ +} + +mod a { + /**/ mod b { + mod c {} + } +} diff --git a/noir/tooling/nargo_fmt/tests/input/tuple.nr b/noir/tooling/nargo_fmt/tests/input/tuple.nr new file mode 100644 index 00000000000..da3b6ed597b --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/tuple.nr @@ -0,0 +1,48 @@ +fn main() { +(1,); +( +// hello +1,); +(/*hello*/1,); +(1,/*hello*/); + ( 1, ); +( /*test*/1, ); +( /*a*/1/*b*/, ); +( /*a*/1/*b*/, /*c*/2/*d*/, /*c*/2/*d*/ ); +( /*a*/1/*b*/, /*c*/2/*d*/, /*c*/2/*d*/, /*e*/3/*f*/ ); + +( 1 /*1*/ , 2 /* 2*/ ); + + + + +( 1, /*test*/ ); + + ( +// +1, +); + +( +// 1 +1, +// 2, +2, +); + +(/*1*/1, /*2*/2); + +// FIXME: +((( +//2 +1,),),); +( + /*a*/ + 1 + /*b*/, +/*c*/ +2/*d*/, +/*c*/2/*d*/, +/*e*/3/*f*/ +); +} diff --git a/noir/tooling/nargo_fmt/tests/input/unary_operators.nr b/noir/tooling/nargo_fmt/tests/input/unary_operators.nr new file mode 100644 index 00000000000..4324b8045cc --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/unary_operators.nr @@ -0,0 +1,10 @@ +//@error_on_lost_comment=false +fn main() { + -1; +-/*test*/1; + -/*test*/1; + + & mut 1; + * v; + * * * v; +} \ No newline at end of file diff --git a/noir/tooling/nargo_fmt/tests/input/vec.nr b/noir/tooling/nargo_fmt/tests/input/vec.nr new file mode 100644 index 00000000000..1c9a791961e --- /dev/null +++ b/noir/tooling/nargo_fmt/tests/input/vec.nr @@ -0,0 +1,60 @@ +struct Vec { + slice: [T] +} + +// A mutable vector type implemented as a wrapper around immutable slices. +// A separate type is technically not needed but helps differentiate which operations are mutable. +impl Vec { + pub fn new() -> Self { + Self { slice: [] } + } + + // Create a Vec containing each element from the given slice. + // Mutations to the resulting Vec will not affect the original slice. + pub fn from_slice(slice: [T]) -> Self { + Self { slice } + } + + /// Get an element from the vector at the given index. + /// Panics if the given index + /// points beyond the end of the vector. + pub fn get(self, index: Field) -> T { + self.slice[index] + } + + /// Push a new element to the end of the vector, returning a + /// new vector with a length one greater than the + /// original unmodified vector. + pub fn push(&mut self, elem: T) { + self.slice = self.slice.push_back(elem); + } + + /// Pop an element from the end of the given vector, returning + /// a new vector with a length of one less than the given vector, + /// as well as the popped element. + /// Panics if the given vector's length is zero. + pub fn pop(&mut self) -> T { + let (popped_slice, last_elem) = self.slice.pop_back(); + self.slice = popped_slice; + last_elem + } + + /// Insert an element at a specified index, shifting all elements + /// after it to the right + pub fn insert(&mut self, index: Field, elem: T) { + self.slice = self.slice.insert(index, elem); + } + + /// Remove an element at a specified index, shifting all elements + /// after it to the left, returning the removed element + pub fn remove(&mut self, index: Field) -> T { + let (new_slice, elem) = self.slice.remove(index); + self.slice = new_slice; + elem + } + + /// Returns the number of elements in the vector + pub fn len(self) -> Field { + self.slice.len() + } +} diff --git a/noir/tooling/nargo_toml/Cargo.toml b/noir/tooling/nargo_toml/Cargo.toml new file mode 100644 index 00000000000..c835ddd936c --- /dev/null +++ b/noir/tooling/nargo_toml/Cargo.toml @@ -0,0 +1,22 @@ +[package] +name = "nargo_toml" +description = "Utilities for working with Nargo.toml files" +version.workspace = true +authors.workspace = true +edition.workspace = true +license.workspace = true + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + +[dependencies] +dirs.workspace = true +fm.workspace = true +nargo.workspace = true +noirc_frontend.workspace = true +serde.workspace = true +thiserror.workspace = true +toml.workspace = true +url.workspace = true +semver = "1.0.20" + +[dev-dependencies] diff --git a/noir/tooling/nargo_toml/src/errors.rs b/noir/tooling/nargo_toml/src/errors.rs new file mode 100644 index 00000000000..fdbdc317bf9 --- /dev/null +++ b/noir/tooling/nargo_toml/src/errors.rs @@ -0,0 +1,84 @@ +use std::path::PathBuf; + +use nargo::package::PackageType; +use noirc_frontend::graph::CrateName; +use thiserror::Error; + +/// Errors covering situations where a package is either missing, malformed or does not pass semver +/// validation checks. +#[derive(Debug, Error)] +pub enum ManifestError { + /// Package doesn't have a manifest file + #[error("cannot find a Nargo.toml for {0}")] + MissingFile(PathBuf), + + #[error("Cannot read file {0} - does it exist?")] + ReadFailed(PathBuf), + + #[error("Nargo.toml is missing a parent directory")] + MissingParent, + + #[error("Missing `type` field in {0}")] + MissingPackageType(PathBuf), + + #[error("Cannot use `{1}` for `type` field in {0}")] + InvalidPackageType(PathBuf, String), + + /// Package manifest is unreadable. + #[error("Nargo.toml is badly formed, could not parse.\n\n {0}")] + MalformedFile(#[from] toml::de::Error), + + #[error("Unexpected workspace definition found in {0}")] + UnexpectedWorkspace(PathBuf), + + #[error("Cannot find file {entry} which was specified as the `entry` field in {toml}")] + MissingEntryFile { toml: PathBuf, entry: PathBuf }, + + #[error( + r#"Cannot find file {entry} which is defaulted due to specifying `type = "{package_type}"` in {toml}"# + )] + MissingDefaultEntryFile { toml: PathBuf, entry: PathBuf, package_type: PackageType }, + + #[error("{} found in {toml}", if name.is_empty() { "Empty package name".into() } else { format!("Invalid package name `{name}`") })] + InvalidPackageName { toml: PathBuf, name: String }, + + #[error("{} found in {toml}", if name.is_empty() { "Empty dependency name".into() } else { format!("Invalid dependency name `{name}`") })] + InvalidDependencyName { toml: PathBuf, name: String }, + + #[error("Invalid directory path {directory} in {toml}: It must point to a subdirectory")] + InvalidDirectory { toml: PathBuf, directory: PathBuf }, + + /// Encountered error while downloading git repository. + #[error("{0}")] + GitError(String), + + #[error("Selected package `{0}` was not found")] + MissingSelectedPackage(CrateName), + + #[error("Default package was not found. Does {0} exist in your workspace?")] + MissingDefaultPackage(PathBuf), + + #[error("Package `{0}` has type `bin` but you cannot depend on binary packages")] + BinaryDependency(CrateName), + + #[error("Missing `name` field in {toml}")] + MissingNameField { toml: PathBuf }, + + #[error("No common ancestor between {root} and {current}")] + NoCommonAncestor { root: PathBuf, current: PathBuf }, + + #[error(transparent)] + SemverError(SemverError), +} + +#[derive(Error, Debug, PartialEq, Eq, Clone)] +pub enum SemverError { + #[error("Incompatible compiler version in package {package_name}. Required compiler version is {required_compiler_version} but the compiler version is {compiler_version_found}.\n Update the compiler_version field in Nargo.toml to >={compiler_version_found} or compile this project with version {compiler_version_found}")] + IncompatibleVersion { + package_name: CrateName, + required_compiler_version: String, + compiler_version_found: String, + }, + #[error("Could not parse the required compiler version for package {package_name} in Nargo.toml. Error: {error}")] + CouldNotParseRequiredVersion { package_name: String, error: String }, +} diff --git a/noir/tooling/nargo_toml/src/git.rs b/noir/tooling/nargo_toml/src/git.rs new file mode 100644 index 00000000000..80e57247ae6 --- /dev/null +++ b/noir/tooling/nargo_toml/src/git.rs @@ -0,0 +1,55 @@ +use std::path::PathBuf; + +/// Creates a unique folder name for a GitHub repo +/// by using its URL and tag +fn resolve_folder_name(base: &url::Url, tag: &str) -> String { + let mut folder_name = base.domain().unwrap().to_owned(); + folder_name.push_str(base.path()); + folder_name.push_str(tag); + folder_name +} + +fn nargo_crates() -> PathBuf { + dirs::home_dir().unwrap().join("nargo") +} + +fn git_dep_location(base: &url::Url, tag: &str) -> PathBuf { + let folder_name = resolve_folder_name(base, tag); + + nargo_crates().join(folder_name) +} + +/// XXX: I'd prefer to use a GitHub library however, there +/// does not seem to be an easy way to download a repo at a specific +/// tag +/// github-rs looks promising, however it seems to require an API token +/// +/// One advantage of using "git clone" is that there is effectively no rate limit +pub(crate) fn clone_git_repo(url: &str, tag: &str) -> Result { + use std::process::Command; + + let base = match url::Url::parse(url) { + Ok(base) => base, + Err(err) => return Err(err.to_string()), + }; + + let loc = git_dep_location(&base, tag); + if loc.exists() { + return Ok(loc); + } + + Command::new("git") + .arg("-c") + .arg("advice.detachedHead=false") + .arg("clone") + .arg("--depth") + .arg("1") + .arg("--branch") + .arg(tag) + .arg(base.as_str()) + .arg(&loc) + .status() + .expect("git clone command failed to start"); + + Ok(loc) +} diff --git a/noir/tooling/nargo_toml/src/lib.rs b/noir/tooling/nargo_toml/src/lib.rs new file mode 100644 index 00000000000..223ed2da081 --- /dev/null +++ b/noir/tooling/nargo_toml/src/lib.rs @@ -0,0 +1,459 @@ +#![forbid(unsafe_code)] +#![warn(unreachable_pub)] +#![warn(clippy::semicolon_if_nothing_returned)] +#![cfg_attr(not(test), warn(unused_crate_dependencies, unused_extern_crates))] + +use std::{ + collections::BTreeMap, + path::{Component, Path, PathBuf}, +}; + +use fm::{NormalizePath, FILE_EXTENSION}; +use nargo::{ + package::{Dependency, Package, PackageType}, + workspace::Workspace, +}; +use noirc_frontend::graph::CrateName; +use serde::Deserialize; + +mod errors; +mod git; +mod semver; + +pub use errors::ManifestError; +use git::clone_git_repo; + +/// Returns the [PathBuf] of the directory containing the `Nargo.toml` by searching from `current_path` to the root of its [Path]. +/// +/// Returns a [ManifestError] if no parent directories of `current_path` contain a manifest file. +pub fn find_package_root(current_path: &Path) -> Result { + let root = path_root(current_path); + let manifest_path = find_package_manifest(&root, current_path)?; + + let package_root = + manifest_path.parent().expect("infallible: manifest file path can't be root directory"); + + Ok(package_root.to_path_buf()) +} + +// TODO(#2323): We are probably going to need a "filepath utils" crate soon +fn path_root(path: &Path) -> PathBuf { + let mut components = path.components(); + + match (components.next(), components.next()) { + // Preserve prefix if one exists + (Some(prefix @ Component::Prefix(_)), Some(root @ Component::RootDir)) => { + PathBuf::from(prefix.as_os_str()).join(root.as_os_str()) + } + (Some(root @ Component::RootDir), _) => PathBuf::from(root.as_os_str()), + _ => PathBuf::new(), + } +} + +/// Returns the [PathBuf] of the `Nargo.toml` file by searching from `current_path` and stopping at `root_path`. +/// +/// Returns a [ManifestError] if no parent directories of `current_path` contain a manifest file. +pub fn find_package_manifest( + root_path: &Path, + current_path: &Path, +) -> Result { + if current_path.starts_with(root_path) { + let mut found_toml_paths = Vec::new(); + for path in current_path.ancestors() { + if let Ok(toml_path) = get_package_manifest(path) { + found_toml_paths.push(toml_path); + } + // While traversing, break once we process the root specified + if path == root_path { + break; + } + } + + // Return the shallowest Nargo.toml, which will be the last in the list + found_toml_paths.pop().ok_or_else(|| ManifestError::MissingFile(current_path.to_path_buf())) + } else { + Err(ManifestError::NoCommonAncestor { + root: root_path.to_path_buf(), + current: current_path.to_path_buf(), + }) + } +} +/// Returns the [PathBuf] of the `Nargo.toml` file in the `current_path` directory. +/// +/// Returns a [ManifestError] if `current_path` does not contain a manifest file. +pub fn get_package_manifest(current_path: &Path) -> Result { + let toml_path = current_path.join("Nargo.toml"); + if toml_path.exists() { + Ok(toml_path) + } else { + Err(ManifestError::MissingFile(current_path.to_path_buf())) + } +} + +#[derive(Debug, Deserialize, Clone)] +struct PackageConfig { + package: PackageMetadata, + #[serde(default)] + dependencies: BTreeMap, +} + +impl PackageConfig { + fn resolve_to_package(&self, root_dir: &Path) -> Result { + let name = if let Some(name) = &self.package.name { + name.parse().map_err(|_| ManifestError::InvalidPackageName { + toml: root_dir.join("Nargo.toml"), + name: name.into(), + })? + } else { + return Err(ManifestError::MissingNameField { toml: root_dir.join("Nargo.toml") }); + }; + + let mut dependencies: BTreeMap = BTreeMap::new(); + for (name, dep_config) in self.dependencies.iter() { + let name = name.parse().map_err(|_| ManifestError::InvalidDependencyName { + toml: root_dir.join("Nargo.toml"), + name: name.into(), + })?; + let resolved_dep = dep_config.resolve_to_dependency(root_dir)?; + + dependencies.insert(name, resolved_dep); + } + + let package_type = match self.package.package_type.as_deref() { + Some("lib") => PackageType::Library, + Some("bin") => PackageType::Binary, + Some("contract") => PackageType::Contract, + Some(invalid) => { + return Err(ManifestError::InvalidPackageType( + root_dir.join("Nargo.toml"), + invalid.to_string(), + )) + } + None => return Err(ManifestError::MissingPackageType(root_dir.join("Nargo.toml"))), + }; + + let entry_path = if let Some(entry_path) = &self.package.entry { + let custom_entry_path = root_dir.join(entry_path); + if custom_entry_path.exists() { + custom_entry_path + } else { + return Err(ManifestError::MissingEntryFile { + toml: root_dir.join("Nargo.toml"), + entry: custom_entry_path, + }); + } + } else { + let default_entry_path = match package_type { + PackageType::Library => { + root_dir.join("src").join("lib").with_extension(FILE_EXTENSION) + } + PackageType::Binary | PackageType::Contract => { + root_dir.join("src").join("main").with_extension(FILE_EXTENSION) + } + }; + + if default_entry_path.exists() { + default_entry_path + } else { + return Err(ManifestError::MissingDefaultEntryFile { + toml: root_dir.join("Nargo.toml"), + entry: default_entry_path, + package_type, + }); + } + }; + + Ok(Package { + compiler_required_version: self.package.compiler_version.clone(), + root_dir: root_dir.to_path_buf(), + entry_path, + package_type, + name, + dependencies, + }) + } +} + +/// Contains all the information about a package, as loaded from a `Nargo.toml`. +#[derive(Debug, Deserialize, Clone)] +#[serde(untagged)] +enum Config { + /// Represents a `Nargo.toml` with package fields. + Package { + #[serde(flatten)] + package_config: PackageConfig, + }, + /// Represents a `Nargo.toml` with workspace fields. + Workspace { + #[serde(alias = "workspace")] + workspace_config: WorkspaceConfig, + }, +} + +impl TryFrom for Config { + type Error = toml::de::Error; + + fn try_from(toml: String) -> Result { + toml::from_str(&toml) + } +} + +impl TryFrom<&str> for Config { + type Error = toml::de::Error; + + fn try_from(toml: &str) -> Result { + toml::from_str(toml) + } +} + +/// Tracks the root_dir of a `Nargo.toml` and the contents inside the file. +struct NargoToml { + root_dir: PathBuf, + config: Config, +} + +#[derive(Default, Debug, Deserialize, Clone)] +#[serde(rename_all = "kebab-case")] +struct WorkspaceConfig { + /// List of members in this workspace. + members: Vec, + /// Specifies the default crate to interact with in the context (similarly to how we have nargo as the default crate in this repository). + default_member: Option, +} + +#[allow(dead_code)] +#[derive(Default, Debug, Deserialize, Clone)] +struct PackageMetadata { + name: Option, + #[serde(alias = "type")] + package_type: Option, + entry: Option, + description: Option, + authors: Option>, + // If no compiler version is supplied, the latest is used + // For now, we state that all packages must be compiled under the same + // compiler version. + // We also state that ACIR and the compiler will upgrade in lockstep. + // so you will not need to supply an ACIR and compiler version + compiler_version: Option, + backend: Option, + license: Option, +} + +#[derive(Debug, Deserialize, Clone)] +#[serde(untagged)] +/// Enum representing the different types of ways to +/// supply a source for the dependency +enum DependencyConfig { + Github { git: String, tag: String, directory: Option }, + Path { path: String }, +} + +impl DependencyConfig { + fn resolve_to_dependency(&self, pkg_root: &Path) -> Result { + let dep = match self { + Self::Github { git, tag, directory } => { + let dir_path = clone_git_repo(git, tag).map_err(ManifestError::GitError)?; + let project_path = if let Some(directory) = directory { + let internal_path = dir_path.join(directory).normalize(); + if !internal_path.starts_with(&dir_path) { + return Err(ManifestError::InvalidDirectory { + toml: pkg_root.join("Nargo.toml"), + directory: directory.into(), + }); + } + internal_path + } else { + dir_path + }; + let toml_path = project_path.join("Nargo.toml"); + let package = resolve_package_from_toml(&toml_path)?; + Dependency::Remote { package } + } + Self::Path { path } => { + let dir_path = pkg_root.join(path); + let toml_path = dir_path.join("Nargo.toml"); + let package = resolve_package_from_toml(&toml_path)?; + Dependency::Local { package } + } + }; + + // Cannot depend on a binary + // TODO: Can we depend upon contracts? + if dep.is_binary() { + Err(ManifestError::BinaryDependency(dep.package_name().clone())) + } else { + Ok(dep) + } + } +} + +fn toml_to_workspace( + nargo_toml: NargoToml, + package_selection: PackageSelection, +) -> Result { + let workspace = match nargo_toml.config { + Config::Package { package_config } => { + let member = package_config.resolve_to_package(&nargo_toml.root_dir)?; + match &package_selection { + PackageSelection::Selected(selected_name) if selected_name != &member.name => { + return Err(ManifestError::MissingSelectedPackage(member.name)) + } + _ => Workspace { + root_dir: nargo_toml.root_dir, + selected_package_index: Some(0), + members: vec![member], + }, + } + } + Config::Workspace { workspace_config } => { + let mut members = Vec::new(); + let mut selected_package_index = None; + for (index, member_path) in workspace_config.members.into_iter().enumerate() { + let package_root_dir = nargo_toml.root_dir.join(&member_path); + let package_toml_path = package_root_dir.join("Nargo.toml"); + let member = resolve_package_from_toml(&package_toml_path)?; + + match &package_selection { + PackageSelection::Selected(selected_name) => { + if &member.name == selected_name { + selected_package_index = Some(index); + } + } + PackageSelection::DefaultOrAll => { + if Some(&member_path) == workspace_config.default_member.as_ref() { + selected_package_index = Some(index); + } + } + PackageSelection::All => selected_package_index = None, + } + + members.push(member); + } + + // If the selected_package_index is still `None` but we have see a default_member or selected package, + // we want to present an error to users + match package_selection { + PackageSelection::Selected(selected_name) => { + if selected_package_index.is_none() { + return Err(ManifestError::MissingSelectedPackage(selected_name)); + } + } + PackageSelection::DefaultOrAll => match workspace_config.default_member { + // If `default-member` is specified but we don't have a selected_package_index, we need to fail + Some(default_path) if selected_package_index.is_none() => { + return Err(ManifestError::MissingDefaultPackage(default_path)); + } + // However, if there wasn't a `default-member`, we select All, so no error is needed + _ => (), + }, + PackageSelection::All => (), + } + + Workspace { root_dir: nargo_toml.root_dir, members, selected_package_index } + } + }; + + Ok(workspace) +} + +fn read_toml(toml_path: &Path) -> Result { + let toml_path = toml_path.normalize(); + let toml_as_string = std::fs::read_to_string(&toml_path) + .map_err(|_| ManifestError::ReadFailed(toml_path.to_path_buf()))?; + let root_dir = toml_path.parent().ok_or(ManifestError::MissingParent)?; + let nargo_toml = + NargoToml { root_dir: root_dir.to_path_buf(), config: toml_as_string.try_into()? }; + + Ok(nargo_toml) +} + +/// Resolves a Nargo.toml file into a `Package` struct as defined by our `nargo` core. +fn resolve_package_from_toml(toml_path: &Path) -> Result { + let nargo_toml = read_toml(toml_path)?; + + match nargo_toml.config { + Config::Package { package_config } => { + package_config.resolve_to_package(&nargo_toml.root_dir) + } + Config::Workspace { .. } => { + Err(ManifestError::UnexpectedWorkspace(toml_path.to_path_buf())) + } + } +} + +#[derive(Debug, PartialEq, Eq)] +pub enum PackageSelection { + Selected(CrateName), + DefaultOrAll, + All, +} + +/// Resolves a Nargo.toml file into a `Workspace` struct as defined by our `nargo` core. +pub fn resolve_workspace_from_toml( + toml_path: &Path, + package_selection: PackageSelection, + current_compiler_version: Option, +) -> Result { + let nargo_toml = read_toml(toml_path)?; + let workspace = toml_to_workspace(nargo_toml, package_selection)?; + if let Some(current_compiler_version) = current_compiler_version { + semver::semver_check_workspace(workspace.clone(), current_compiler_version)?; + } + Ok(workspace) +} + +#[test] +fn parse_standard_toml() { + let src = r#" + + [package] + name = "test" + authors = ["kev", "foo"] + compiler_version = "*" + + [dependencies] + rand = { tag = "next", git = "https://github.com/rust-lang-nursery/rand"} + cool = { tag = "next", git = "https://github.com/rust-lang-nursery/rand"} + hello = {path = "./noir_driver"} + "#; + + assert!(Config::try_from(String::from(src)).is_ok()); + assert!(Config::try_from(src).is_ok()); +} + +#[test] +fn parse_package_toml_no_deps() { + let src = r#" + [package] + name = "test" + authors = ["kev", "foo"] + compiler_version = "*" + "#; + + assert!(Config::try_from(String::from(src)).is_ok()); + assert!(Config::try_from(src).is_ok()); +} + +#[test] +fn parse_workspace_toml() { + let src = r#" + [workspace] + members = ["a", "b"] + "#; + + assert!(Config::try_from(String::from(src)).is_ok()); + assert!(Config::try_from(src).is_ok()); +} + +#[test] +fn parse_workspace_default_member_toml() { + let src = r#" + [workspace] + members = ["a", "b"] + default-member = "a" + "#; + + assert!(Config::try_from(String::from(src)).is_ok()); + assert!(Config::try_from(src).is_ok()); +} diff --git a/noir/tooling/nargo_toml/src/semver.rs b/noir/tooling/nargo_toml/src/semver.rs new file mode 100644 index 00000000000..de722f06bd8 --- /dev/null +++ b/noir/tooling/nargo_toml/src/semver.rs @@ -0,0 +1,196 @@ +use crate::{errors::SemverError, ManifestError}; +use nargo::{ + package::{Dependency, Package}, + workspace::Workspace, +}; +use semver::{Version, VersionReq}; + +// Check that all of the packages in the workspace are compatible with the current compiler version +pub(crate) fn semver_check_workspace( + workspace: Workspace, + current_compiler_version: String, +) -> Result<(), ManifestError> { + let version = Version::parse(¤t_compiler_version) + .expect("The compiler version is not a valid semver version"); + for package in &workspace.members { + semver_check_package(package, &version).map_err(ManifestError::SemverError)?; + } + + Ok(()) +} + +// Check that a package and all of its dependencies are compatible with the current compiler version +pub(crate) fn semver_check_package( + package: &Package, + compiler_version: &Version, +) -> Result<(), SemverError> { + // Check that this package's compiler version requirements are satisfied + if let Some(version) = &package.compiler_required_version { + let version_req = match VersionReq::parse(version) { + Ok(version_req) => version_req, + Err(err) => { + return Err(SemverError::CouldNotParseRequiredVersion { + package_name: package.name.clone().into(), + error: err.to_string(), + }) + } + }; + if !version_req.matches(compiler_version) { + return Err(SemverError::IncompatibleVersion { + package_name: package.name.clone(), + required_compiler_version: version.clone(), + compiler_version_found: strip_build_meta_data(compiler_version), + }); + }; + } + + // Check that all of this package's dependencies' compiler version requirements are satisfied + for dep in package.dependencies.values() { + match dep { + Dependency::Local { package } | Dependency::Remote { package } => { + semver_check_package(package, compiler_version)?; + } + } + } + + Ok(()) +} + +// Strip the build meta data from the version string since it is ignored by semver. +fn strip_build_meta_data(version: &Version) -> String { + let version_string = version.to_string(); + let mut split = version_string.split('+'); + split.next().expect("split was called on an empty string").to_string() +} + +#[cfg(test)] +mod tests { + use std::{collections::BTreeMap, path::PathBuf, str::FromStr}; + + use nargo::package::PackageType; + use noirc_frontend::graph::CrateName; + + use super::*; + + #[test] + fn test_semver_check_smoke() { + let compiler_version = Version::parse("0.1.0").unwrap(); + + let mut package = Package { + compiler_required_version: Some("0.1.0".to_string()), + root_dir: PathBuf::new(), + package_type: PackageType::Library, + entry_path: PathBuf::new(), + name: CrateName::from_str("test").unwrap(), + dependencies: BTreeMap::new(), + }; + if let Err(err) = semver_check_package(&package, &compiler_version) { + panic!("semver check should have passed. compiler version is 0.1.0 and required version from the package is 0.1.0\n error: {err:?}") + }; + + package.compiler_required_version = Some("0.2.0".to_string()); + let got_err = match semver_check_package(&package, &compiler_version) { + Ok(_) => panic!("semver check should have failed. compiler version is 0.1.0 and required version from the package is 0.2.0"), + Err(err) => err, + }; + + let expected_version_error = SemverError::IncompatibleVersion { + package_name: CrateName::from_str("test").unwrap(), + required_compiler_version: "0.2.0".to_string(), + compiler_version_found: "0.1.0".to_string(), + }; + assert_eq!(got_err, expected_version_error); + } + + #[test] + fn test_semver_dependency_check_smoke() { + let compiler_version = Version::parse("0.1.0").unwrap(); + + let mut package = Package { + compiler_required_version: Some("0.1.0".to_string()), + root_dir: PathBuf::new(), + package_type: PackageType::Library, + entry_path: PathBuf::new(), + name: CrateName::from_str("test").unwrap(), + dependencies: BTreeMap::new(), + }; + + let valid_dependency = Package { + compiler_required_version: Some("0.1.0".to_string()), + root_dir: PathBuf::new(), + package_type: PackageType::Library, + entry_path: PathBuf::new(), + name: CrateName::from_str("good_dependency").unwrap(), + dependencies: BTreeMap::new(), + }; + let invalid_dependency = Package { + compiler_required_version: Some("0.2.0".to_string()), + root_dir: PathBuf::new(), + package_type: PackageType::Library, + entry_path: PathBuf::new(), + name: CrateName::from_str("bad_dependency").unwrap(), + dependencies: BTreeMap::new(), + }; + + package.dependencies.insert( + CrateName::from_str("test_dep_valid").unwrap(), + Dependency::Local { package: valid_dependency.clone() }, + ); + + if let Err(err) = semver_check_package(&package, &compiler_version) { + panic!("semver check should have passed. compiler version is 0.1.0 and required version from the package is 0.1.0\n error: {err:?}") + }; + + package.dependencies.insert( + CrateName::from_str("test_dep_invalid").unwrap(), + Dependency::Local { package: invalid_dependency.clone() }, + ); + let got_err = match semver_check_package(&package,&compiler_version) { + Ok(_) => panic!("semver check should have failed. compiler version is 0.1.0 and required version from the package is 0.2.0"), + Err(err) => err, + }; + + let expected_version_error = SemverError::IncompatibleVersion { + package_name: CrateName::from_str("bad_dependency").unwrap(), + required_compiler_version: "0.2.0".to_string(), + compiler_version_found: "0.1.0".to_string(), + }; + assert_eq!(got_err, expected_version_error); + } + + #[test] + fn test_semver_carrot() { + let compiler_version = Version::parse("0.2.0").unwrap(); + + let package = Package { + compiler_required_version: Some(">=0.1.0".to_string()), + root_dir: PathBuf::new(), + package_type: PackageType::Library, + entry_path: PathBuf::new(), + name: CrateName::from_str("test").unwrap(), + dependencies: BTreeMap::new(), + }; + + if let Err(err) = semver_check_package(&package, &compiler_version) { + panic!("semver check should have passed. compiler version is 0.2.0 and required version from the package is >=0.1.0\n error: {err:?}") + }; + } + + #[test] + fn test_semver_build_data() { + let compiler_version = Version::parse("0.1.0+this-is-ignored-by-semver").unwrap(); + + let package = Package { + compiler_required_version: Some("0.1.0".to_string()), + root_dir: PathBuf::new(), + package_type: PackageType::Library, + entry_path: PathBuf::new(), + name: CrateName::from_str("test").unwrap(), + dependencies: BTreeMap::new(), + }; + + if let Err(err) = semver_check_package(&package, &compiler_version) { + panic!("semver check should have passed. compiler version is 0.1.0+build_data and required version from the package is 0.1.0\n The build data should be ignored\n error: {err:?}") + }; + } +} diff --git a/noir/tooling/noir_codegen/.eslintignore b/noir/tooling/noir_codegen/.eslintignore new file mode 100644 index 00000000000..491fc35975b --- /dev/null +++ b/noir/tooling/noir_codegen/.eslintignore @@ -0,0 +1,2 @@ +node_modules +lib diff --git a/noir/tooling/noir_codegen/.eslintrc.cjs b/noir/tooling/noir_codegen/.eslintrc.cjs new file mode 100644 index 00000000000..33335c2a877 --- /dev/null +++ b/noir/tooling/noir_codegen/.eslintrc.cjs @@ -0,0 +1,3 @@ +module.exports = { + extends: ["../../.eslintrc.js"], +}; diff --git a/noir/tooling/noir_codegen/.gitignore b/noir/tooling/noir_codegen/.gitignore new file mode 100644 index 00000000000..721d05448d6 --- /dev/null +++ b/noir/tooling/noir_codegen/.gitignore @@ -0,0 +1,4 @@ +crs +lib + +!test/*/target diff --git a/noir/tooling/noir_codegen/.mocharc.json b/noir/tooling/noir_codegen/.mocharc.json new file mode 100644 index 00000000000..82855d2ddf4 --- /dev/null +++ b/noir/tooling/noir_codegen/.mocharc.json @@ -0,0 +1,11 @@ +{ + "require": "ts-node/register", + "loader": "ts-node/esm", + "extensions": [ + "ts", + "cjs" + ], + "spec": [ + "test/**/*.test.ts*" + ] +} \ No newline at end of file diff --git a/noir/tooling/noir_codegen/README.md b/noir/tooling/noir_codegen/README.md new file mode 100644 index 00000000000..c24c12ff987 --- /dev/null +++ b/noir/tooling/noir_codegen/README.md @@ -0,0 +1,4 @@ + +## Acknowledgements + +`noir-codegen` repurposes the CLI code from https://github.com/dethcrypto/TypeChain, used under the MIT license. \ No newline at end of file diff --git a/noir/tooling/noir_codegen/package.json b/noir/tooling/noir_codegen/package.json new file mode 100644 index 00000000000..14f9bad6df6 --- /dev/null +++ b/noir/tooling/noir_codegen/package.json @@ -0,0 +1,53 @@ +{ + "name": "@noir-lang/noir_codegen", + "collaborators": [ + "The Noir Team " + ], + "version": "0.19.3", + "packageManager": "yarn@3.5.1", + "license": "(MIT OR Apache-2.0)", + "type": "module", + "dependencies": { + "@noir-lang/types": "workspace:*", + "glob": "^10.3.10", + "lodash": "^4.17.21", + "ts-command-line-args": "^2.5.1" + }, + "files": [ + "lib", + "package.json" + ], + "source": "src/index.ts", + "main": "lib/index.js", + "types": "lib/index.d.ts", + "bin": { + "noir-codegen": "lib/main.js" + }, + "scripts": { + "dev": "tsc-multi --watch", + "build": "tsc", + "test": "ts-node --esm src/main.ts ./test/assert_lt/target/** --out-dir ./test/codegen && yarn test:node && rm -rf ./test/codegen", + "test:node": "mocha --timeout 25000 --exit --config ./.mocharc.json", + "prettier": "prettier 'src/**/*.ts'", + "prettier:fix": "prettier --write 'src/**/*.ts' 'test/**/*.ts'", + "lint": "NODE_NO_WARNINGS=1 eslint . --ext .ts --ignore-path ./.eslintignore --max-warnings 0", + "nightly:version": "jq --arg new_version \"-$(git rev-parse --short HEAD)$1\" '.version = .version + $new_version' package.json > package-tmp.json && mv package-tmp.json package.json", + "publish": "echo 📡 publishing `$npm_package_name` && yarn npm publish", + "clean": "rm -rf ./lib" + }, + "devDependencies": { + "@noir-lang/noir_js": "workspace:*", + "@types/chai": "^4", + "@types/lodash": "^4", + "@types/mocha": "^10.0.1", + "@types/node": "^20.6.2", + "@types/prettier": "^3", + "chai": "^4.3.8", + "eslint": "^8.50.0", + "eslint-plugin-prettier": "^5.0.0", + "mocha": "^10.2.0", + "prettier": "3.0.3", + "ts-node": "^10.9.1", + "typescript": "^5.2.2" + } +} diff --git a/noir/tooling/noir_codegen/src/index.ts b/noir/tooling/noir_codegen/src/index.ts new file mode 100644 index 00000000000..1f8d2d183d4 --- /dev/null +++ b/noir/tooling/noir_codegen/src/index.ts @@ -0,0 +1,28 @@ +import { CompiledCircuit } from '@noir-lang/types'; + +const codegenImports = `import { InputMap, InputValue } from "@noir-lang/noirc_abi" +import { Noir } from "@noir-lang/noir_js"`; + +const codegenFunction = ( + name: string, + compiled_program: CompiledCircuit, +) => `export async function ${name}(args: InputMap): Promise { + const program = new Noir(${JSON.stringify(compiled_program)}); + const { returnValue } = await program.execute(args); + return returnValue; +}`; + +export const codegen = (programs: [string, CompiledCircuit][]): string => { + const results = [codegenImports]; + for (const [name, program] of programs) { + results.push(codegenFunction(name, stripUnwantedFields(program))); + } + + return results.join('\n\n'); +}; + +// eslint-disable-next-line @typescript-eslint/no-explicit-any +function stripUnwantedFields(value: any): CompiledCircuit { + const { abi, bytecode } = value; + return { abi, bytecode }; +} diff --git a/noir/tooling/noir_codegen/src/main.ts b/noir/tooling/noir_codegen/src/main.ts new file mode 100644 index 00000000000..591e7420dba --- /dev/null +++ b/noir/tooling/noir_codegen/src/main.ts @@ -0,0 +1,47 @@ +#! /usr/bin/env node + +import { CompiledCircuit } from '@noir-lang/types'; +import fs from 'fs'; +import path from 'path'; +import { parseArgs } from './parseArgs.js'; +import { glob } from './utils/glob.js'; +import { codegen } from './index.js'; + +function main() { + const cliConfig = parseArgs(); + const cwd = process.cwd(); + + const files = getFilesToProcess(cwd, cliConfig.files); + if (files.length === 0) { + throw new Error('No files passed.' + '\n' + `\`${cliConfig.files}\` didn't match any input files in ${cwd}`); + } + + const programs = files.map((file_path): [string, CompiledCircuit] => { + const program_name = path.parse(file_path).name; + const file_contents = fs.readFileSync(file_path).toString(); + const { abi, bytecode } = JSON.parse(file_contents); + + return [program_name, { abi, bytecode }]; + }); + + const result = codegen(programs); + + const outputDir = path.resolve(cliConfig.outDir ?? './codegen'); + const outputFile = path.join(outputDir, 'index.ts'); + if (!fs.existsSync(outputDir)) fs.mkdirSync(outputDir); + fs.writeFileSync(outputFile, result); +} + +function getFilesToProcess(cwd: string, filesOrPattern: string[]) { + let res = glob(cwd, filesOrPattern); + + if (res.length === 0) { + // If there are no files found, but first parameter is surrounded with single quotes, we try again without quotes + const match = filesOrPattern[0].match(/'([\s\S]*)'/)?.[1]; + if (match) res = glob(cwd, [match]); + } + + return res; +} + +main(); diff --git a/noir/tooling/noir_codegen/src/parseArgs.ts b/noir/tooling/noir_codegen/src/parseArgs.ts new file mode 100644 index 00000000000..58468c1b8f8 --- /dev/null +++ b/noir/tooling/noir_codegen/src/parseArgs.ts @@ -0,0 +1,64 @@ +import { parse as commandLineArgs } from 'ts-command-line-args'; + +const DEFAULT_GLOB_PATTERN = './target/**/*.json'; + +export interface ParsedArgs { + files: string[]; + outDir?: string | undefined; + inputDir?: string | undefined; +} + +export function parseArgs(): ParsedArgs { + const rawOptions = commandLineArgs( + { + glob: { + type: String, + defaultOption: true, + multiple: true, + defaultValue: [DEFAULT_GLOB_PATTERN], + description: + 'Pattern that will be used to find program artifacts. Remember about adding quotes: noir-codegen "**/*.json".', + }, + 'out-dir': { type: String, optional: true, description: 'Output directory for generated files.' }, + 'input-dir': { + type: String, + optional: true, + description: + 'Directory containing program artifact files. Inferred as lowest common path of all files if not specified.', + }, + help: { type: Boolean, defaultValue: false, alias: 'h', description: 'Prints this message.' }, + }, + { + helpArg: 'help', + headerContentSections: [ + { + content: `\ + noir-codegen generates TypeScript wrappers for Noir programs to simplify replicating your Noir logic in JS.`, + }, + ], + footerContentSections: [ + { + header: 'Example Usage', + content: `\ + noir-codegen --out-dir app/noir_programs './target/*.json' + + + You can read more about noir-codegen at {underline https://github.com/noir-lang/noir}.`, + }, + ], + }, + ); + + return { + files: rawOptions.glob, + outDir: rawOptions['out-dir'], + inputDir: rawOptions['input-dir'], + }; +} + +interface CommandLineArgs { + glob: string[]; + 'out-dir'?: string; + 'input-dir'?: string; + help: boolean; +} diff --git a/noir/tooling/noir_codegen/src/utils/glob.ts b/noir/tooling/noir_codegen/src/utils/glob.ts new file mode 100644 index 00000000000..15deaf72e44 --- /dev/null +++ b/noir/tooling/noir_codegen/src/utils/glob.ts @@ -0,0 +1,9 @@ +import { sync as globSync } from 'glob'; +import _ from 'lodash'; +const { flatten, uniq } = _; + +export function glob(cwd: string, patternsOrFiles: string[]): string[] { + const matches = patternsOrFiles.map((p) => globSync(p, { ignore: 'node_modules/**', absolute: true, cwd })); + + return uniq(flatten(matches)); +} diff --git a/noir/tooling/noir_codegen/test/assert_lt/Nargo.toml b/noir/tooling/noir_codegen/test/assert_lt/Nargo.toml new file mode 100644 index 00000000000..f32ec18cae7 --- /dev/null +++ b/noir/tooling/noir_codegen/test/assert_lt/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "assert_lt" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/noir_codegen/test/assert_lt/src/main.nr b/noir/tooling/noir_codegen/test/assert_lt/src/main.nr new file mode 100644 index 00000000000..0698cbce4a8 --- /dev/null +++ b/noir/tooling/noir_codegen/test/assert_lt/src/main.nr @@ -0,0 +1,4 @@ +fn main(x: u64, y: pub u64) -> pub u64 { + assert(x < y); + x + y +} diff --git a/noir/tooling/noir_codegen/test/assert_lt/target/assert_lt.json b/noir/tooling/noir_codegen/test/assert_lt/target/assert_lt.json new file mode 100644 index 00000000000..3b2b1b2c5a1 --- /dev/null +++ b/noir/tooling/noir_codegen/test/assert_lt/target/assert_lt.json @@ -0,0 +1 @@ +{"hash":13834844072603749544,"backend":"acvm-backend-barretenberg","abi":{"parameters":[{"name":"x","type":{"kind":"integer","sign":"unsigned","width":64},"visibility":"private"},{"name":"y","type":{"kind":"integer","sign":"unsigned","width":64},"visibility":"public"}],"param_witnesses":{"x":[1],"y":[2]},"return_type":{"kind":"integer","sign":"unsigned","width":64},"return_witnesses":[12]},"bytecode":"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"} \ No newline at end of file diff --git a/noir/tooling/noir_codegen/test/index.test.ts b/noir/tooling/noir_codegen/test/index.test.ts new file mode 100644 index 00000000000..702ba1f9cbb --- /dev/null +++ b/noir/tooling/noir_codegen/test/index.test.ts @@ -0,0 +1,11 @@ +import { expect } from 'chai'; +import { assert_lt } from './codegen/index.js'; + +it('codegens a callable function', async () => { + const result = await assert_lt({ + x: '2', + y: '3', + }); + + expect(result).to.be.eq('0x05'); +}); diff --git a/noir/tooling/noir_codegen/tsconfig.json b/noir/tooling/noir_codegen/tsconfig.json new file mode 100644 index 00000000000..30dd2a7ee5f --- /dev/null +++ b/noir/tooling/noir_codegen/tsconfig.json @@ -0,0 +1,20 @@ +{ + "compilerOptions": { + "target": "esnext", + "declaration": true, + "emitDeclarationOnly": false, + "module": "NodeNext", + "moduleResolution": "NodeNext", + "outDir": "./lib", + "esModuleInterop": true, + "resolveJsonModule": true, + "strict": true, + "noImplicitAny": false, + }, + "include": [ + "src/**/*.ts" + ], + "exclude": [ + "node_modules" + ] +} diff --git a/noir/tooling/noir_js/.eslintignore b/noir/tooling/noir_js/.eslintignore new file mode 100644 index 00000000000..e34f38f3450 --- /dev/null +++ b/noir/tooling/noir_js/.eslintignore @@ -0,0 +1,3 @@ +node_modules +test/backend/barretenberg.ts +lib diff --git a/noir/tooling/noir_js/.eslintrc.cjs b/noir/tooling/noir_js/.eslintrc.cjs new file mode 100644 index 00000000000..33335c2a877 --- /dev/null +++ b/noir/tooling/noir_js/.eslintrc.cjs @@ -0,0 +1,3 @@ +module.exports = { + extends: ["../../.eslintrc.js"], +}; diff --git a/noir/tooling/noir_js/.gitignore b/noir/tooling/noir_js/.gitignore new file mode 100644 index 00000000000..5b57ba1708d --- /dev/null +++ b/noir/tooling/noir_js/.gitignore @@ -0,0 +1,3 @@ +crs + +!test/noir_compiled_examples/*/target diff --git a/noir/tooling/noir_js/.mocharc.cjs.json b/noir/tooling/noir_js/.mocharc.cjs.json new file mode 100644 index 00000000000..c1c37373f18 --- /dev/null +++ b/noir/tooling/noir_js/.mocharc.cjs.json @@ -0,0 +1,6 @@ +{ + "extensions": ["cjs"], + "spec": [ + "test/node/**/*.test.cjs" + ] +} \ No newline at end of file diff --git a/noir/tooling/noir_js/.mocharc.json b/noir/tooling/noir_js/.mocharc.json new file mode 100644 index 00000000000..c2e70b73d0f --- /dev/null +++ b/noir/tooling/noir_js/.mocharc.json @@ -0,0 +1,8 @@ +{ + "require": "ts-node/register", + "loader": "ts-node/esm", + "extensions": ["ts", "cjs"], + "spec": [ + "test/node/**/*.test.ts*" + ] +} \ No newline at end of file diff --git a/noir/tooling/noir_js/package.json b/noir/tooling/noir_js/package.json new file mode 100644 index 00000000000..440bd8dec63 --- /dev/null +++ b/noir/tooling/noir_js/package.json @@ -0,0 +1,55 @@ +{ + "name": "@noir-lang/noir_js", + "collaborators": [ + "The Noir Team " + ], + "version": "0.19.3", + "packageManager": "yarn@3.5.1", + "license": "(MIT OR Apache-2.0)", + "type": "module", + "dependencies": { + "@noir-lang/acvm_js": "workspace:*", + "@noir-lang/noirc_abi": "workspace:*", + "@noir-lang/types": "workspace:*" + }, + "files": [ + "lib", + "package.json" + ], + "source": "src/index.ts", + "main": "lib/index.cjs", + "module": "lib/index.mjs", + "exports": { + "require": "./lib/index.cjs", + "types": "./lib/index.d.ts", + "default": "./lib/index.mjs" + }, + "types": "lib/index.d.ts", + "scripts": { + "dev": "tsc-multi --watch", + "build": "tsc-multi", + "test": "yarn test:node:esm && yarn test:node:cjs", + "test:node:esm": "mocha --timeout 25000 --exit --config ./.mocharc.json", + "test:node:cjs": "mocha --timeout 25000 --exit --config ./.mocharc.cjs.json", + "prettier": "prettier 'src/**/*.ts'", + "prettier:fix": "prettier --write 'src/**/*.ts' 'test/**/*.ts'", + "lint": "NODE_NO_WARNINGS=1 eslint . --ext .ts --ignore-path ./.eslintignore --max-warnings 0", + "nightly:version": "jq --arg new_version \"-$(git rev-parse --short HEAD)$1\" '.version = .version + $new_version' package.json > package-tmp.json && mv package-tmp.json package.json", + "publish": "echo 📡 publishing `$npm_package_name` && yarn npm publish", + "clean": "rm -rf ./lib" + }, + "devDependencies": { + "@types/chai": "^4", + "@types/mocha": "^10.0.1", + "@types/node": "^20.6.2", + "@types/prettier": "^3", + "chai": "^4.3.8", + "eslint": "^8.50.0", + "eslint-plugin-prettier": "^5.0.0", + "mocha": "^10.2.0", + "prettier": "3.0.3", + "ts-node": "^10.9.1", + "tsc-multi": "^1.1.0", + "typescript": "^5.2.2" + } +} diff --git a/noir/tooling/noir_js/src/base64_decode.ts b/noir/tooling/noir_js/src/base64_decode.ts new file mode 100644 index 00000000000..d53aed187c7 --- /dev/null +++ b/noir/tooling/noir_js/src/base64_decode.ts @@ -0,0 +1,13 @@ +// Since this is a simple function, we can use feature detection to +// see if we are in the nodeJs environment or the browser environment. +export function base64Decode(input: string): Uint8Array { + if (typeof Buffer !== 'undefined') { + // Node.js environment + return Buffer.from(input, 'base64'); + } else if (typeof atob === 'function') { + // Browser environment + return Uint8Array.from(atob(input), (c) => c.charCodeAt(0)); + } else { + throw new Error('No implementation found for base64 decoding.'); + } +} diff --git a/noir/tooling/noir_js/src/index.ts b/noir/tooling/noir_js/src/index.ts new file mode 100644 index 00000000000..5e700a5b583 --- /dev/null +++ b/noir/tooling/noir_js/src/index.ts @@ -0,0 +1,23 @@ +import * as acvm from '@noir-lang/acvm_js'; +import * as abi from '@noir-lang/noirc_abi'; +import { CompiledCircuit, ProofData } from '@noir-lang/types'; + +export { + ecdsa_secp256r1_verify, + ecdsa_secp256k1_verify, + keccak256, + blake2s256, + sha256, + xor, + and, +} from '@noir-lang/acvm_js'; + +export { WitnessMap, ForeignCallHandler, ForeignCallInput, ForeignCallOutput } from '@noir-lang/acvm_js'; + +export { Noir } from './program.js'; + +/** @ignore */ +export { acvm, abi }; + +// type exports for typedoc +export { CompiledCircuit, ProofData }; diff --git a/noir/tooling/noir_js/src/program.ts b/noir/tooling/noir_js/src/program.ts new file mode 100644 index 00000000000..711413bbc84 --- /dev/null +++ b/noir/tooling/noir_js/src/program.ts @@ -0,0 +1,94 @@ +/* eslint-disable @typescript-eslint/no-explicit-any */ +import { Backend, CompiledCircuit, ProofData } from '@noir-lang/types'; +import { generateWitness } from './witness_generation.js'; +import initAbi, { abiDecode, InputMap, InputValue } from '@noir-lang/noirc_abi'; +import initACVM, { compressWitness, ForeignCallHandler } from '@noir-lang/acvm_js'; + +export class Noir { + constructor( + private circuit: CompiledCircuit, + private backend?: Backend, + ) {} + + /** @ignore */ + async init(): Promise { + // If these are available, then we are in the + // web environment. For the node environment, this + // is a no-op. + if (typeof initAbi === 'function') { + await Promise.all([initAbi(), initACVM()]); + } + } + + /** + * + * @description + * Destroys the underlying backend instance. + * + * @example + * ```typescript + * await noir.destroy(); + * ``` + * + */ + async destroy(): Promise { + await this.backend?.destroy(); + } + + private getBackend(): Backend { + if (this.backend === undefined) throw new Error('Operation requires a backend but none was provided'); + return this.backend; + } + + // Initial inputs to your program + /** + * @description + * Allows to execute a circuit to get its witness and return value. + * + * @example + * ```typescript + * async execute(inputs) + * ``` + */ + async execute( + inputs: InputMap, + foreignCallHandler?: ForeignCallHandler, + ): Promise<{ witness: Uint8Array; returnValue: InputValue }> { + await this.init(); + const witness = await generateWitness(this.circuit, inputs, foreignCallHandler); + const { return_value: returnValue } = abiDecode(this.circuit.abi, witness); + return { witness: compressWitness(witness), returnValue }; + } + + /** + * + * @description + * Generates a witness and a proof given an object as input. + * + * @example + * ```typescript + * async generateFinalproof(input) + * ``` + * + */ + async generateFinalProof(inputs: InputMap): Promise { + const { witness } = await this.execute(inputs); + return this.getBackend().generateFinalProof(witness); + } + + /** + * + * @description + * Instantiates the verification key and verifies a proof. + * + * + * @example + * ```typescript + * async verifyFinalProof(proof) + * ``` + * + */ + async verifyFinalProof(proofData: ProofData): Promise { + return this.getBackend().verifyFinalProof(proofData); + } +} diff --git a/noir/tooling/noir_js/src/witness_generation.ts b/noir/tooling/noir_js/src/witness_generation.ts new file mode 100644 index 00000000000..e3ddb1a2a21 --- /dev/null +++ b/noir/tooling/noir_js/src/witness_generation.ts @@ -0,0 +1,27 @@ +import { abiEncode, InputMap } from '@noir-lang/noirc_abi'; +import { base64Decode } from './base64_decode.js'; +import { executeCircuit, WitnessMap, ForeignCallHandler, ForeignCallInput } from '@noir-lang/acvm_js'; +import { CompiledCircuit } from '@noir-lang/types'; + +const defaultForeignCallHandler: ForeignCallHandler = (name: string, args: ForeignCallInput[]) => { + throw Error(`Unexpected oracle during execution: ${name}(${args.join(', ')})`); +}; + +// Generates the witnesses needed to feed into the chosen proving system +export async function generateWitness( + compiledProgram: CompiledCircuit, + inputs: InputMap, + foreignCallHandler: ForeignCallHandler = defaultForeignCallHandler, +): Promise { + // Throws on ABI encoding error + const witnessMap = abiEncode(compiledProgram.abi, inputs); + + // Execute the circuit to generate the rest of the witnesses and serialize + // them into a Uint8Array. + try { + const solvedWitness = await executeCircuit(base64Decode(compiledProgram.bytecode), witnessMap, foreignCallHandler); + return solvedWitness; + } catch (err) { + throw new Error(`Circuit execution failed: ${err}`); + } +} diff --git a/noir/tooling/noir_js/test/node/cjs.test.cjs b/noir/tooling/noir_js/test/node/cjs.test.cjs new file mode 100644 index 00000000000..8698f9dfe2b --- /dev/null +++ b/noir/tooling/noir_js/test/node/cjs.test.cjs @@ -0,0 +1,80 @@ +/* eslint-disable no-undef */ +/* eslint-disable @typescript-eslint/no-var-requires */ +const chai = require('chai'); +const assert_lt_json = require('../noir_compiled_examples/assert_lt/target/assert_lt.json'); +const { Noir } = require('@noir-lang/noir_js'); + +it('generates witnesses successfully', async () => { + const inputs = { + x: '2', + y: '3', + }; + const _solvedWitness = await new Noir(assert_lt_json).execute(inputs); +}); + +it('string input and number input are the same', async () => { + const inputsString = { + x: '2', + y: '3', + }; + const inputsNumber = { + x: 2, + y: 3, + }; + const solvedWitnessString = await new Noir(assert_lt_json).execute(inputsString); + const solvedWitnessNumber = await new Noir(assert_lt_json).execute(inputsNumber); + chai.expect(solvedWitnessString).to.deep.equal(solvedWitnessNumber); +}); + +it('string input and number input are the same', async () => { + const inputsString = { + x: '2', + y: '3', + }; + const inputsNumber = { + x: 2, + y: 3, + }; + + const solvedWitnessString = await new Noir(assert_lt_json).execute(inputsString); + const solvedWitnessNumber = await new Noir(assert_lt_json).execute(inputsNumber); + chai.expect(solvedWitnessString).to.deep.equal(solvedWitnessNumber); +}); + +it('0x prefixed string input for inputs will throw', async () => { + const inputsHexPrefix = { + x: '0x2', + y: '0x3', + }; + + try { + await new Noir(assert_lt_json).execute(inputsHexPrefix); + chai.expect.fail( + 'Expected generatedWitness to throw, due to inputs being prefixed with 0x. Currently not supported', + ); + } catch (error) { + // Successfully errored due to 0x not being supported. Update this test once/if we choose + // to support 0x prefixed inputs. + } +}); + +describe('input validation', () => { + it('x should be a uint64 not a string', async () => { + const inputs = { + x: 'foo', + y: '3', + }; + + try { + await new Noir(assert_lt_json).execute(inputs); + chai.expect.fail('Expected generatedWitness to throw, due to x not being convertible to a uint64'); + } catch (error) { + const knownError = error; + chai + .expect(knownError.message) + .to.equal( + 'Expected witness values to be integers, provided value causes `invalid digit found in string` error', + ); + } + }); +}); diff --git a/noir/tooling/noir_js/test/node/e2e.test.ts b/noir/tooling/noir_js/test/node/e2e.test.ts new file mode 100644 index 00000000000..33d64377b06 --- /dev/null +++ b/noir/tooling/noir_js/test/node/e2e.test.ts @@ -0,0 +1,128 @@ +import { expect } from 'chai'; +import assert_lt_json from '../noir_compiled_examples/assert_lt/target/assert_lt.json' assert { type: 'json' }; +import { Noir } from '@noir-lang/noir_js'; +import { BarretenbergBackend as Backend } from '@noir-lang/backend_barretenberg'; +import { CompiledCircuit } from '@noir-lang/types'; + +const assert_lt_program = assert_lt_json as CompiledCircuit; + +it('end-to-end proof creation and verification (outer)', async () => { + // Noir.Js part + const inputs = { + x: '2', + y: '3', + }; + + const program = new Noir(assert_lt_program); + + const { witness } = await program.execute(inputs); + + // bb.js part + // + // Proof creation + const prover = new Backend(assert_lt_program); + const proof = await prover.generateFinalProof(witness); + + // Proof verification + const isValid = await prover.verifyFinalProof(proof); + expect(isValid).to.be.true; +}); + +it('end-to-end proof creation and verification (outer) -- Program API', async () => { + // Noir.Js part + const inputs = { + x: '2', + y: '3', + }; + + // Initialize backend + const backend = new Backend(assert_lt_program); + // Initialize program + const program = new Noir(assert_lt_program, backend); + // Generate proof + const proof = await program.generateFinalProof(inputs); + + // Proof verification + const isValid = await program.verifyFinalProof(proof); + expect(isValid).to.be.true; +}); + +it('end-to-end proof creation and verification (inner)', async () => { + // Noir.Js part + const inputs = { + x: '2', + y: '3', + }; + + const program = new Noir(assert_lt_program); + + const { witness } = await program.execute(inputs); + + // bb.js part + // + // Proof creation + const prover = new Backend(assert_lt_program); + const proof = await prover.generateIntermediateProof(witness); + + // Proof verification + const isValid = await prover.verifyIntermediateProof(proof); + expect(isValid).to.be.true; +}); + +it('end-to-end proving and verification with different instances', async () => { + // Noir.Js part + const inputs = { + x: '2', + y: '3', + }; + + const program = new Noir(assert_lt_program); + + const { witness } = await program.execute(inputs); + + // bb.js part + const prover = new Backend(assert_lt_program); + + const proof = await prover.generateFinalProof(witness); + + const verifier = new Backend(assert_lt_program); + const proof_is_valid = await verifier.verifyFinalProof(proof); + expect(proof_is_valid).to.be.true; +}); + +// This bug occurs when we use the same backend to create an inner proof and then an outer proof +// and then try to verify either one of them. +// +// The panic occurs when we try to verify the outer/inner proof that was created. +// If we only create one type of proof, then this works as expected. +// +// If we do not create an inner proof, then this will work as expected. +it('[BUG] -- bb.js null function or function signature mismatch (outer-inner) ', async () => { + // Noir.Js part + const inputs = { + x: '2', + y: '3', + }; + + const program = new Noir(assert_lt_program); + + const { witness } = await program.execute(inputs); + + // bb.js part + // + // Proof creation + // + const prover = new Backend(assert_lt_program); + // Create a proof using both proving systems, the majority of the time + // one would only use outer proofs. + const proofOuter = await prover.generateFinalProof(witness); + const _proofInner = await prover.generateIntermediateProof(witness); + + // Proof verification + // + const isValidOuter = await prover.verifyFinalProof(proofOuter); + expect(isValidOuter).to.be.true; + // We can also try verifying an inner proof and it will fail. + const isValidInner = await prover.verifyIntermediateProof(_proofInner); + expect(isValidInner).to.be.true; +}); diff --git a/noir/tooling/noir_js/test/node/execute.test.ts b/noir/tooling/noir_js/test/node/execute.test.ts new file mode 100644 index 00000000000..bfaf80882ab --- /dev/null +++ b/noir/tooling/noir_js/test/node/execute.test.ts @@ -0,0 +1,16 @@ +import assert_lt_json from '../noir_compiled_examples/assert_lt/target/assert_lt.json' assert { type: 'json' }; +import { Noir } from '@noir-lang/noir_js'; +import { CompiledCircuit } from '@noir-lang/types'; +import { expect } from 'chai'; + +const assert_lt_program = assert_lt_json as CompiledCircuit; + +it('returns the return value of the circuit', async () => { + const inputs = { + x: '2', + y: '3', + }; + const { returnValue } = await new Noir(assert_lt_program).execute(inputs); + + expect(returnValue).to.be.eq('0x05'); +}); diff --git a/noir/tooling/noir_js/test/node/smoke.test.ts b/noir/tooling/noir_js/test/node/smoke.test.ts new file mode 100644 index 00000000000..6993a44f66e --- /dev/null +++ b/noir/tooling/noir_js/test/node/smoke.test.ts @@ -0,0 +1,77 @@ +import { expect } from 'chai'; +import assert_lt_json from '../noir_compiled_examples/assert_lt/target/assert_lt.json' assert { type: 'json' }; +import { CompiledCircuit } from '@noir-lang/types'; +import { Noir } from '@noir-lang/noir_js'; + +const assert_lt_program = assert_lt_json as CompiledCircuit; + +it('generates witnesses successfully', async () => { + const inputs = { + x: '2', + y: '3', + }; + expect(() => new Noir(assert_lt_program).execute(inputs)).to.not.throw; +}); + +it('string input and number input are the same', async () => { + const inputsString = { + x: '2', + y: '3', + }; + const inputsNumber = { + x: 2, + y: 3, + }; + const solvedWitnessString = await new Noir(assert_lt_program).execute(inputsString); + const solvedWitnessNumber = await new Noir(assert_lt_program).execute(inputsNumber); + expect(solvedWitnessString).to.deep.equal(solvedWitnessNumber); +}); + +it('string input and number input are the same', async () => { + const inputsString = { + x: '2', + y: '3', + }; + const inputsNumber = { + x: 2, + y: 3, + }; + + const solvedWitnessString = await new Noir(assert_lt_program).execute(inputsString); + const solvedWitnessNumber = await new Noir(assert_lt_program).execute(inputsNumber); + expect(solvedWitnessString).to.deep.equal(solvedWitnessNumber); +}); + +it('0x prefixed string input for inputs will throw', async () => { + const inputsHexPrefix = { + x: '0x2', + y: '0x3', + }; + + try { + await new Noir(assert_lt_program).execute(inputsHexPrefix); + expect.fail('Expected generatedWitness to throw, due to inputs being prefixed with 0x. Currently not supported'); + } catch (error) { + // Successfully errored due to 0x not being supported. Update this test once/if we choose + // to support 0x prefixed inputs. + } +}); + +describe('input validation', () => { + it('x should be a uint64 not a string', async () => { + const inputs = { + x: 'foo', + y: '3', + }; + + try { + await new Noir(assert_lt_program).execute(inputs); + expect.fail('Expected generatedWitness to throw, due to x not being convertible to a uint64'); + } catch (error) { + const knownError = error as Error; + expect(knownError.message).to.equal( + 'Expected witness values to be integers, provided value causes `invalid digit found in string` error', + ); + } + }); +}); diff --git a/noir/tooling/noir_js/test/noir_compiled_examples/assert_lt/Nargo.toml b/noir/tooling/noir_js/test/noir_compiled_examples/assert_lt/Nargo.toml new file mode 100644 index 00000000000..f32ec18cae7 --- /dev/null +++ b/noir/tooling/noir_js/test/noir_compiled_examples/assert_lt/Nargo.toml @@ -0,0 +1,5 @@ +[package] +name = "assert_lt" +type = "bin" +authors = [""] +[dependencies] diff --git a/noir/tooling/noir_js/test/noir_compiled_examples/assert_lt/src/main.nr b/noir/tooling/noir_js/test/noir_compiled_examples/assert_lt/src/main.nr new file mode 100644 index 00000000000..0698cbce4a8 --- /dev/null +++ b/noir/tooling/noir_js/test/noir_compiled_examples/assert_lt/src/main.nr @@ -0,0 +1,4 @@ +fn main(x: u64, y: pub u64) -> pub u64 { + assert(x < y); + x + y +} diff --git a/noir/tooling/noir_js/test/noir_compiled_examples/assert_lt/target/assert_lt.json b/noir/tooling/noir_js/test/noir_compiled_examples/assert_lt/target/assert_lt.json new file mode 100644 index 00000000000..c01b2c5d3f5 --- /dev/null +++ b/noir/tooling/noir_js/test/noir_compiled_examples/assert_lt/target/assert_lt.json @@ -0,0 +1 @@ +{"hash":13834844072603749544,"backend":"acvm-backend-barretenberg","abi":{"parameters":[{"name":"x","type":{"kind":"integer","sign":"unsigned","width":64},"visibility":"private"},{"name":"y","type":{"kind":"integer","sign":"unsigned","width":64},"visibility":"public"}],"param_witnesses":{"x":[{ "start": 1, "end": 2 }],"y":[{ "start": 2, "end": 3 }]},"return_type":{"kind":"integer","sign":"unsigned","width":64},"return_witnesses":[12]},"bytecode":"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"} \ No newline at end of file diff --git a/noir/tooling/noir_js/test/noir_compiled_examples/readme.md b/noir/tooling/noir_js/test/noir_compiled_examples/readme.md new file mode 100644 index 00000000000..26c571850ae --- /dev/null +++ b/noir/tooling/noir_js/test/noir_compiled_examples/readme.md @@ -0,0 +1,3 @@ +## Why + +While we do not automatically pull the .json files from .artifacts, we have these hard-coded versions of examples. diff --git a/noir/tooling/noir_js/tsc-multi.json b/noir/tooling/noir_js/tsc-multi.json new file mode 100644 index 00000000000..af26d1e4aa6 --- /dev/null +++ b/noir/tooling/noir_js/tsc-multi.json @@ -0,0 +1,7 @@ +{ + "targets": [ + { "extname": ".cjs", "module": "commonjs" }, + { "extname": ".mjs", "module": "esnext" } + ], + "projects": ["packages/*/tsconfig.json"] +} \ No newline at end of file diff --git a/noir/tooling/noir_js/tsconfig.json b/noir/tooling/noir_js/tsconfig.json new file mode 100644 index 00000000000..0dbc5204556 --- /dev/null +++ b/noir/tooling/noir_js/tsconfig.json @@ -0,0 +1,20 @@ +{ + "compilerOptions": { + "target": "esnext", + "declaration": true, + "module": "ESNext", + "moduleResolution": "node", + "outDir": "./lib", + "esModuleInterop": true, + "resolveJsonModule": true, + "strict": true, + "noImplicitAny": false, + }, + "include": ["src/**/*.ts"], + "exclude": ["node_modules"], + "references": [ + { + "path": "../noir_js_types" + }, + ] +} diff --git a/noir/tooling/noir_js_backend_barretenberg/.eslintignore b/noir/tooling/noir_js_backend_barretenberg/.eslintignore new file mode 100644 index 00000000000..b512c09d476 --- /dev/null +++ b/noir/tooling/noir_js_backend_barretenberg/.eslintignore @@ -0,0 +1 @@ +node_modules \ No newline at end of file diff --git a/noir/tooling/noir_js_backend_barretenberg/.eslintrc.cjs b/noir/tooling/noir_js_backend_barretenberg/.eslintrc.cjs new file mode 100644 index 00000000000..33335c2a877 --- /dev/null +++ b/noir/tooling/noir_js_backend_barretenberg/.eslintrc.cjs @@ -0,0 +1,3 @@ +module.exports = { + extends: ["../../.eslintrc.js"], +}; diff --git a/noir/tooling/noir_js_backend_barretenberg/.gitignore b/noir/tooling/noir_js_backend_barretenberg/.gitignore new file mode 100644 index 00000000000..689b3ca0701 --- /dev/null +++ b/noir/tooling/noir_js_backend_barretenberg/.gitignore @@ -0,0 +1,2 @@ +crs +lib diff --git a/noir/tooling/noir_js_backend_barretenberg/fixup.sh b/noir/tooling/noir_js_backend_barretenberg/fixup.sh new file mode 100755 index 00000000000..80bd7ec71a5 --- /dev/null +++ b/noir/tooling/noir_js_backend_barretenberg/fixup.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash + +# Put these package.json files in the cjs and +# mjs directory respectively, so that +# tools can recognise that the .js files are either +# commonjs or ESM files. +self_path=$(dirname "$(readlink -f "$0")") + +cjs_package='{ + "type": "commonjs" +}' + +esm_package='{ + "type": "module" +}' + +echo "$cjs_package" > $self_path/lib/cjs/package.json +echo "$esm_package" > $self_path/lib/esm/package.json \ No newline at end of file diff --git a/noir/tooling/noir_js_backend_barretenberg/package.json b/noir/tooling/noir_js_backend_barretenberg/package.json new file mode 100644 index 00000000000..360b3e70ec0 --- /dev/null +++ b/noir/tooling/noir_js_backend_barretenberg/package.json @@ -0,0 +1,47 @@ +{ + "name": "@noir-lang/backend_barretenberg", + "collaborators": [ + "The Noir Team " + ], + "version": "0.19.3", + "packageManager": "yarn@3.5.1", + "license": "(MIT OR Apache-2.0)", + "type": "module", + "source": "src/index.ts", + "main": "lib/cjs/index.js", + "module": "lib/esm/index.js", + "exports": { + "require": "./lib/cjs/index.js", + "types": "./lib/esm/index.d.ts", + "default": "./lib/esm/index.js" + }, + "types": "lib/esm/index.d.ts", + "files": [ + "lib", + "package.json" + ], + "scripts": { + "dev": "tsc --watch", + "generate:package": "bash ./fixup.sh", + "build": "yarn clean && tsc && tsc -p ./tsconfig.cjs.json && yarn generate:package", + "clean": "rm -rf ./lib", + "prettier": "prettier 'src/**/*.ts'", + "prettier:fix": "prettier --write 'src/**/*.ts' 'test/**/*.ts'", + "nightly:version": "jq --arg new_version \"-$(git rev-parse --short HEAD)$1\" '.version = .version + $new_version' package.json > package-tmp.json && mv package-tmp.json package.json", + "publish": "echo 📡 publishing `$npm_package_name` && yarn npm publish", + "lint": "NODE_NO_WARNINGS=1 eslint . --ext .ts --ignore-path ./.eslintignore --max-warnings 0" + }, + "dependencies": { + "@aztec/bb.js": "0.15.1", + "@noir-lang/types": "workspace:*", + "fflate": "^0.8.0" + }, + "devDependencies": { + "@types/node": "^20.6.2", + "@types/prettier": "^3", + "eslint": "^8.50.0", + "eslint-plugin-prettier": "^5.0.0", + "prettier": "3.0.3", + "typescript": "5.1.5" + } +} diff --git a/noir/tooling/noir_js_backend_barretenberg/src/base64_decode.ts b/noir/tooling/noir_js_backend_barretenberg/src/base64_decode.ts new file mode 100644 index 00000000000..d53aed187c7 --- /dev/null +++ b/noir/tooling/noir_js_backend_barretenberg/src/base64_decode.ts @@ -0,0 +1,13 @@ +// Since this is a simple function, we can use feature detection to +// see if we are in the nodeJs environment or the browser environment. +export function base64Decode(input: string): Uint8Array { + if (typeof Buffer !== 'undefined') { + // Node.js environment + return Buffer.from(input, 'base64'); + } else if (typeof atob === 'function') { + // Browser environment + return Uint8Array.from(atob(input), (c) => c.charCodeAt(0)); + } else { + throw new Error('No implementation found for base64 decoding.'); + } +} diff --git a/noir/tooling/noir_js_backend_barretenberg/src/index.ts b/noir/tooling/noir_js_backend_barretenberg/src/index.ts new file mode 100644 index 00000000000..820cda93c83 --- /dev/null +++ b/noir/tooling/noir_js_backend_barretenberg/src/index.ts @@ -0,0 +1,210 @@ +/* eslint-disable @typescript-eslint/no-explicit-any */ +import { decompressSync as gunzip } from 'fflate'; +import { acirToUint8Array } from './serialize.js'; +import { Backend, CompiledCircuit, ProofData } from '@noir-lang/types'; +import { BackendOptions } from './types.js'; + +// This is the number of bytes in a UltraPlonk proof +// minus the public inputs. +const numBytesInProofWithoutPublicInputs: number = 2144; + +export class BarretenbergBackend implements Backend { + // These type assertions are used so that we don't + // have to initialize `api` and `acirComposer` in the constructor. + // These are initialized asynchronously in the `init` function, + // constructors cannot be asynchronous which is why we do this. + private api: any; + private acirComposer: any; + private acirUncompressedBytecode: Uint8Array; + + constructor( + acirCircuit: CompiledCircuit, + private options: BackendOptions = { threads: 1 }, + ) { + const acirBytecodeBase64 = acirCircuit.bytecode; + this.acirUncompressedBytecode = acirToUint8Array(acirBytecodeBase64); + } + + /** @ignore */ + async instantiate(): Promise { + if (!this.api) { + // eslint-disable-next-line @typescript-eslint/ban-ts-comment + //@ts-ignore + const { Barretenberg, RawBuffer, Crs } = await import('@aztec/bb.js'); + const api = await Barretenberg.new(this.options.threads); + + const [_exact, _total, subgroupSize] = await api.acirGetCircuitSizes(this.acirUncompressedBytecode); + const crs = await Crs.new(subgroupSize + 1); + await api.commonInitSlabAllocator(subgroupSize); + await api.srsInitSrs(new RawBuffer(crs.getG1Data()), crs.numPoints, new RawBuffer(crs.getG2Data())); + + this.acirComposer = await api.acirNewAcirComposer(subgroupSize); + await api.acirInitProvingKey(this.acirComposer, this.acirUncompressedBytecode); + this.api = api; + } + } + + // Generate an outer proof. This is the proof for the circuit which will verify + // inner proofs and or can be seen as the proof created for regular circuits. + // + // The settings for this proof are the same as the settings for a "normal" proof + // ie one that is not in the recursive setting. + async generateFinalProof(decompressedWitness: Uint8Array): Promise { + const makeEasyToVerifyInCircuit = false; + return this.generateProof(decompressedWitness, makeEasyToVerifyInCircuit); + } + + // Generates an inner proof. This is the proof that will be verified + // in another circuit. + // + // This is sometimes referred to as a recursive proof. + // We avoid this terminology as the only property of this proof + // that matters, is the fact that it is easy to verify in another + // circuit. We _could_ choose to verify this proof in the CLI. + // + // We set `makeEasyToVerifyInCircuit` to true, which will tell the backend to + // generate the proof using components that will make the proof + // easier to verify in a circuit. + + /** + * + * @example + * ```typescript + * const intermediateProof = await backend.generateIntermediateProof(witness); + * ``` + */ + async generateIntermediateProof(witness: Uint8Array): Promise { + const makeEasyToVerifyInCircuit = true; + return this.generateProof(witness, makeEasyToVerifyInCircuit); + } + + /** @ignore */ + async generateProof(compressedWitness: Uint8Array, makeEasyToVerifyInCircuit: boolean): Promise { + await this.instantiate(); + const proofWithPublicInputs = await this.api.acirCreateProof( + this.acirComposer, + this.acirUncompressedBytecode, + gunzip(compressedWitness), + makeEasyToVerifyInCircuit, + ); + + const splitIndex = proofWithPublicInputs.length - numBytesInProofWithoutPublicInputs; + + const publicInputsConcatenated = proofWithPublicInputs.slice(0, splitIndex); + + const publicInputSize = 32; + const publicInputs: Uint8Array[] = []; + + for (let i = 0; i < publicInputsConcatenated.length; i += publicInputSize) { + const publicInput = publicInputsConcatenated.slice(i, i + publicInputSize); + publicInputs.push(publicInput); + } + + const proof = proofWithPublicInputs.slice(splitIndex); + + return { proof, publicInputs }; + } + + // Generates artifacts that will be passed to a circuit that will verify this proof. + // + // Instead of passing the proof and verification key as a byte array, we pass them + // as fields which makes it cheaper to verify in a circuit. + // + // The proof that is passed here will have been created using the `generateInnerProof` + // method. + // + // The number of public inputs denotes how many public inputs are in the inner proof. + + /** + * + * @example + * ```typescript + * const artifacts = await backend.generateIntermediateProofArtifacts(proof, numOfPublicInputs); + * ``` + */ + async generateIntermediateProofArtifacts( + proofData: ProofData, + numOfPublicInputs = 0, + ): Promise<{ + proofAsFields: string[]; + vkAsFields: string[]; + vkHash: string; + }> { + await this.instantiate(); + const proof = reconstructProofWithPublicInputs(proofData); + const proofAsFields = await this.api.acirSerializeProofIntoFields(this.acirComposer, proof, numOfPublicInputs); + + // TODO: perhaps we should put this in the init function. Need to benchmark + // TODO how long it takes. + await this.api.acirInitVerificationKey(this.acirComposer); + + // Note: If you don't init verification key, `acirSerializeVerificationKeyIntoFields`` will just hang on serialization + const vk = await this.api.acirSerializeVerificationKeyIntoFields(this.acirComposer); + + return { + proofAsFields: proofAsFields.map((p) => p.toString()), + vkAsFields: vk[0].map((vk) => vk.toString()), + vkHash: vk[1].toString(), + }; + } + + async verifyFinalProof(proofData: ProofData): Promise { + const proof = reconstructProofWithPublicInputs(proofData); + const makeEasyToVerifyInCircuit = false; + const verified = await this.verifyProof(proof, makeEasyToVerifyInCircuit); + return verified; + } + + /** + * + * @example + * ```typescript + * const isValidIntermediate = await backend.verifyIntermediateProof(proof); + * ``` + */ + async verifyIntermediateProof(proofData: ProofData): Promise { + const proof = reconstructProofWithPublicInputs(proofData); + const makeEasyToVerifyInCircuit = true; + return this.verifyProof(proof, makeEasyToVerifyInCircuit); + } + + /** @ignore */ + async verifyProof(proof: Uint8Array, makeEasyToVerifyInCircuit: boolean): Promise { + await this.instantiate(); + await this.api.acirInitVerificationKey(this.acirComposer); + return await this.api.acirVerifyProof(this.acirComposer, proof, makeEasyToVerifyInCircuit); + } + + async destroy(): Promise { + if (!this.api) { + return; + } + await this.api.destroy(); + } +} + +function reconstructProofWithPublicInputs(proofData: ProofData): Uint8Array { + // Flatten publicInputs + const publicInputsConcatenated = flattenUint8Arrays(proofData.publicInputs); + + // Concatenate publicInputs and proof + const proofWithPublicInputs = Uint8Array.from([...publicInputsConcatenated, ...proofData.proof]); + + return proofWithPublicInputs; +} + +function flattenUint8Arrays(arrays: Uint8Array[]): Uint8Array { + const totalLength = arrays.reduce((acc, val) => acc + val.length, 0); + const result = new Uint8Array(totalLength); + + let offset = 0; + for (const arr of arrays) { + result.set(arr, offset); + offset += arr.length; + } + + return result; +} + +// typedoc exports +export { Backend, BackendOptions, CompiledCircuit, ProofData }; diff --git a/noir/tooling/noir_js_backend_barretenberg/src/serialize.ts b/noir/tooling/noir_js_backend_barretenberg/src/serialize.ts new file mode 100644 index 00000000000..b15931848a0 --- /dev/null +++ b/noir/tooling/noir_js_backend_barretenberg/src/serialize.ts @@ -0,0 +1,8 @@ +import { decompressSync as gunzip } from 'fflate'; +import { base64Decode } from './base64_decode.js'; + +// Converts bytecode from a base64 string to a Uint8Array +export function acirToUint8Array(base64EncodedBytecode: string): Uint8Array { + const compressedByteCode = base64Decode(base64EncodedBytecode); + return gunzip(compressedByteCode); +} diff --git a/noir/tooling/noir_js_backend_barretenberg/src/types.ts b/noir/tooling/noir_js_backend_barretenberg/src/types.ts new file mode 100644 index 00000000000..041e36fdf91 --- /dev/null +++ b/noir/tooling/noir_js_backend_barretenberg/src/types.ts @@ -0,0 +1,8 @@ +/** + * @description + * An options object, currently only used to specify the number of threads to use. + */ +export type BackendOptions = { + /** @description Number of threads */ + threads: number; +}; diff --git a/noir/tooling/noir_js_backend_barretenberg/tsconfig.cjs.json b/noir/tooling/noir_js_backend_barretenberg/tsconfig.cjs.json new file mode 100644 index 00000000000..15d273af62e --- /dev/null +++ b/noir/tooling/noir_js_backend_barretenberg/tsconfig.cjs.json @@ -0,0 +1,7 @@ +{ + "extends": "./tsconfig.json", + "compilerOptions": { + "module": "CommonJS", + "outDir": "./lib/cjs" + }, +} diff --git a/noir/tooling/noir_js_backend_barretenberg/tsconfig.json b/noir/tooling/noir_js_backend_barretenberg/tsconfig.json new file mode 100644 index 00000000000..1e28c044bba --- /dev/null +++ b/noir/tooling/noir_js_backend_barretenberg/tsconfig.json @@ -0,0 +1,21 @@ +{ + "compilerOptions": { + "target": "esnext", + "declaration": true, + "emitDeclarationOnly": false, + "module": "NodeNext", + "moduleResolution": "NodeNext", + "outDir": "./lib/esm", + "esModuleInterop": true, + "resolveJsonModule": true, + "strict": true, + "noImplicitAny": false, + }, + "include": ["src/**/*.ts"], + "exclude": ["node_modules"], + "references": [ + { + "path": "../noir_js_types" + } + ] +} diff --git a/noir/tooling/noir_js_types/.eslintignore b/noir/tooling/noir_js_types/.eslintignore new file mode 100644 index 00000000000..3c3629e647f --- /dev/null +++ b/noir/tooling/noir_js_types/.eslintignore @@ -0,0 +1 @@ +node_modules diff --git a/noir/tooling/noir_js_types/.eslintrc.cjs b/noir/tooling/noir_js_types/.eslintrc.cjs new file mode 100644 index 00000000000..5a2cc7f1ec0 --- /dev/null +++ b/noir/tooling/noir_js_types/.eslintrc.cjs @@ -0,0 +1,3 @@ +module.exports = { + extends: ['../../.eslintrc.js'], +}; diff --git a/noir/tooling/noir_js_types/.gitignore b/noir/tooling/noir_js_types/.gitignore new file mode 100644 index 00000000000..e92523b6247 --- /dev/null +++ b/noir/tooling/noir_js_types/.gitignore @@ -0,0 +1,3 @@ +lib + +*.tsbuildinfo diff --git a/noir/tooling/noir_js_types/.prettierrc b/noir/tooling/noir_js_types/.prettierrc new file mode 100644 index 00000000000..ef937f9697a --- /dev/null +++ b/noir/tooling/noir_js_types/.prettierrc @@ -0,0 +1,6 @@ +{ + "parser": "typescript", + "printWidth": 120, + "singleQuote": true, + "trailingComma": "all" +} \ No newline at end of file diff --git a/noir/tooling/noir_js_types/package.json b/noir/tooling/noir_js_types/package.json new file mode 100644 index 00000000000..57bb2f050b7 --- /dev/null +++ b/noir/tooling/noir_js_types/package.json @@ -0,0 +1,41 @@ +{ + "name": "@noir-lang/types", + "collaborators": [ + "The Noir Team " + ], + "packageManager": "yarn@3.5.1", + "version": "0.19.3", + "license": "(MIT OR Apache-2.0)", + "files": [ + "lib", + "package.json" + ], + "main": "lib/cjs/types.js", + "module": "lib/esm/types.js", + "types": "lib/esm/types.d.ts", + "scripts": { + "build:esm": "tsc", + "build:cjs": "tsc --module CommonJS --outDir lib/cjs", + "build": "yarn run build:cjs && yarn run build:esm", + "nightly:version": "jq --arg new_version \"-$(git rev-parse --short HEAD)$1\" '.version = .version + $new_version' package.json > package-tmp.json && mv package-tmp.json package.json", + "publish": "echo 📡 publishing `$npm_package_name` && yarn npm publish", + "lint": "NODE_NO_WARNINGS=1 eslint . --ext .ts --ignore-path ./.eslintignore --max-warnings 0" + }, + "exports": { + ".": { + "import": "./lib/esm/types.js", + "require": "./lib/cjs/types.js", + "types": "./lib/esm/types.d.ts" + } + }, + "dependencies": { + "@noir-lang/noirc_abi": "workspace:*" + }, + "devDependencies": { + "@types/prettier": "^3", + "eslint": "^8.50.0", + "eslint-plugin-prettier": "^5.0.0", + "prettier": "3.0.3", + "typescript": "^5.2.2" + } +} diff --git a/noir/tooling/noir_js_types/src/types.ts b/noir/tooling/noir_js_types/src/types.ts new file mode 100644 index 00000000000..5ed6b1721e9 --- /dev/null +++ b/noir/tooling/noir_js_types/src/types.ts @@ -0,0 +1,60 @@ +import { Abi } from '@noir-lang/noirc_abi'; + +export interface Backend { + /** + * @description Generates a final proof (not meant to be verified in another circuit) */ + generateFinalProof(decompressedWitness: Uint8Array): Promise; + + /** + * @description Generates an intermediate proof (meant to be verified in another circuit) */ + generateIntermediateProof(decompressedWitness: Uint8Array): Promise; + + /** + * + * @description Retrieves the artifacts from a proof in the Field format + */ + generateIntermediateProofArtifacts( + proofData: ProofData, + numOfPublicInputs: number, + ): Promise<{ + /** @description An array of Fields containing the proof */ + proofAsFields: string[]; + /** @description An array of Fields containing the verification key */ + vkAsFields: string[]; + /** @description A Field containing the verification key hash */ + vkHash: string; + }>; + + /** + * @description Verifies a final proof */ + verifyFinalProof(proofData: ProofData): Promise; + + /** @description Verifies an intermediate proof */ + verifyIntermediateProof(proofData: ProofData): Promise; + + /** + * @description Destroys the backend */ + destroy(): Promise; +} + +/** + * @description + * The representation of a proof + * */ +export type ProofData = { + /** @description Public inputs of a proof */ + publicInputs: Uint8Array[]; + /** @description An byte array representing the proof */ + proof: Uint8Array; +}; + +/** + * @description + * The representation of a compiled circuit + * */ +export type CompiledCircuit = { + /** @description The bytecode of the circuit */ + bytecode: string; + /** @description ABI representation of the circuit */ + abi: Abi; +}; diff --git a/noir/tooling/noir_js_types/tsconfig.json b/noir/tooling/noir_js_types/tsconfig.json new file mode 100644 index 00000000000..0d5441cc5a2 --- /dev/null +++ b/noir/tooling/noir_js_types/tsconfig.json @@ -0,0 +1,13 @@ +{ + "compilerOptions": { + "composite": true, + "declaration": true, + "module": "ESNext", + "moduleResolution": "node", + "outDir": "lib/esm", + "target": "ES2020", + "rootDir": "./src", + }, + "include": ["src/**/*.ts"], + "exclude": ["node_modules"] +} diff --git a/noir/tooling/noirc_abi/Cargo.toml b/noir/tooling/noirc_abi/Cargo.toml new file mode 100644 index 00000000000..b7fe1ef8084 --- /dev/null +++ b/noir/tooling/noirc_abi/Cargo.toml @@ -0,0 +1,23 @@ +[package] +name = "noirc_abi" +version.workspace = true +authors.workspace = true +edition.workspace = true +license.workspace = true + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + +[dependencies] +acvm.workspace = true +iter-extended.workspace = true +noirc_frontend.workspace = true +toml.workspace = true +serde_json = "1.0" +serde.workspace = true +thiserror.workspace = true +num-bigint = "0.4" +num-traits = "0.2" + +[dev-dependencies] +strum = "0.24" +strum_macros = "0.24" diff --git a/noir/tooling/noirc_abi/src/errors.rs b/noir/tooling/noirc_abi/src/errors.rs new file mode 100644 index 00000000000..687fecfcc1d --- /dev/null +++ b/noir/tooling/noirc_abi/src/errors.rs @@ -0,0 +1,55 @@ +use crate::{input_parser::InputValue, AbiParameter, AbiType}; +use acvm::acir::native_types::Witness; +use thiserror::Error; + +#[derive(Debug, Error)] +pub enum InputParserError { + #[error("input file is badly formed, could not parse, {0}")] + ParseInputMap(String), + #[error("Expected witness values to be integers, provided value causes `{0}` error")] + ParseStr(String), + #[error("Could not parse hex value {0}")] + ParseHexStr(String), + #[error("cannot parse value into {0:?}")] + AbiTypeMismatch(AbiType), + #[error("Expected argument `{0}`, but none was found")] + MissingArgument(String), +} + +impl From for InputParserError { + fn from(err: toml::ser::Error) -> Self { + Self::ParseInputMap(err.to_string()) + } +} + +impl From for InputParserError { + fn from(err: toml::de::Error) -> Self { + Self::ParseInputMap(err.to_string()) + } +} + +impl From for InputParserError { + fn from(err: serde_json::Error) -> Self { + Self::ParseInputMap(err.to_string()) + } +} + +#[derive(Debug, Error)] +pub enum AbiError { + #[error("Received parameters not expected by ABI: {0:?}")] + UnexpectedParams(Vec), + #[error("The parameter {} is expected to be a {:?} but found incompatible value {value:?}", .param.name, .param.typ)] + TypeMismatch { param: AbiParameter, value: InputValue }, + #[error("ABI expects the parameter `{0}`, but this was not found")] + MissingParam(String), + #[error( + "Could not read witness value at index {witness_index:?} (required for parameter \"{name}\")" + )] + MissingParamWitnessValue { name: String, witness_index: Witness }, + #[error("Attempted to write to witness index {0:?} but it is already initialized to a different value")] + InconsistentWitnessAssignment(Witness), + #[error("The return value is expected to be a {return_type:?} but found incompatible value {value:?}")] + ReturnTypeMismatch { return_type: AbiType, value: InputValue }, + #[error("No return value is expected but received {0:?}")] + UnexpectedReturnValue(InputValue), +} diff --git a/noir/tooling/noirc_abi/src/input_parser/json.rs b/noir/tooling/noirc_abi/src/input_parser/json.rs new file mode 100644 index 00000000000..e2b1a83ee6b --- /dev/null +++ b/noir/tooling/noirc_abi/src/input_parser/json.rs @@ -0,0 +1,191 @@ +use super::{parse_str_to_field, InputValue}; +use crate::{errors::InputParserError, Abi, AbiType, MAIN_RETURN_NAME}; +use acvm::FieldElement; +use iter_extended::{try_btree_map, try_vecmap}; +use serde::{Deserialize, Serialize}; +use std::collections::BTreeMap; + +pub(crate) fn parse_json( + input_string: &str, + abi: &Abi, +) -> Result, InputParserError> { + // Parse input.json into a BTreeMap. + let data: BTreeMap = serde_json::from_str(input_string)?; + + // Convert arguments to field elements. + let mut parsed_inputs = try_btree_map(abi.to_btree_map(), |(arg_name, abi_type)| { + // Check that json contains a value for each argument in the ABI. + let value = data + .get(&arg_name) + .ok_or_else(|| InputParserError::MissingArgument(arg_name.clone()))?; + + InputValue::try_from_json(value.clone(), &abi_type, &arg_name) + .map(|input_value| (arg_name, input_value)) + })?; + + // If the json file also includes a return value then we parse it as well. + // This isn't required as the prover calculates the return value itself. + if let (Some(return_type), Some(json_return_value)) = + (&abi.return_type, data.get(MAIN_RETURN_NAME)) + { + let return_value = + InputValue::try_from_json(json_return_value.clone(), return_type, MAIN_RETURN_NAME)?; + parsed_inputs.insert(MAIN_RETURN_NAME.to_owned(), return_value); + } + + Ok(parsed_inputs) +} + +pub(crate) fn serialize_to_json( + input_map: &BTreeMap, + abi: &Abi, +) -> Result { + let mut json_map = try_btree_map(abi.to_btree_map(), |(key, param_type)| { + JsonTypes::try_from_input_value(&input_map[&key], ¶m_type) + .map(|value| (key.to_owned(), value)) + })?; + + if let (Some(return_type), Some(return_value)) = + (&abi.return_type, input_map.get(MAIN_RETURN_NAME)) + { + let return_value = JsonTypes::try_from_input_value(return_value, return_type)?; + json_map.insert(MAIN_RETURN_NAME.to_owned(), return_value); + } + + let json_string = serde_json::to_string(&json_map)?; + + Ok(json_string) +} + +#[derive(Debug, Deserialize, Serialize, Clone)] +#[serde(untagged)] +pub enum JsonTypes { + // This is most likely going to be a hex string + // But it is possible to support UTF-8 + String(String), + // Just a regular integer, that can fit in 64 bits. + // + // The JSON spec does not specify any limit on the size of integer number types, + // however we restrict the allowable size. Values which do not fit in a u64 should be passed + // as a string. + Integer(u64), + // Simple boolean flag + Bool(bool), + // Array of JsonTypes + Array(Vec), + // Struct of JsonTypes + Table(BTreeMap), +} + +impl JsonTypes { + pub fn try_from_input_value( + value: &InputValue, + abi_type: &AbiType, + ) -> Result { + let json_value = match (value, abi_type) { + (InputValue::Field(f), AbiType::Field | AbiType::Integer { .. }) => { + JsonTypes::String(Self::format_field_string(*f)) + } + (InputValue::Field(f), AbiType::Boolean) => JsonTypes::Bool(f.is_one()), + + (InputValue::Vec(vector), AbiType::Array { typ, .. }) => { + let array = + try_vecmap(vector, |value| JsonTypes::try_from_input_value(value, typ))?; + JsonTypes::Array(array) + } + + (InputValue::String(s), AbiType::String { .. }) => JsonTypes::String(s.to_string()), + + (InputValue::Struct(map), AbiType::Struct { fields, .. }) => { + let map_with_json_types = try_btree_map(fields, |(key, field_type)| { + JsonTypes::try_from_input_value(&map[key], field_type) + .map(|json_value| (key.to_owned(), json_value)) + })?; + JsonTypes::Table(map_with_json_types) + } + + (InputValue::Vec(vector), AbiType::Tuple { fields }) => { + let fields = try_vecmap(vector.iter().zip(fields), |(value, typ)| { + JsonTypes::try_from_input_value(value, typ) + })?; + JsonTypes::Array(fields) + } + + _ => return Err(InputParserError::AbiTypeMismatch(abi_type.clone())), + }; + Ok(json_value) + } + + /// This trims any leading zeroes. + /// A singular '0' will be prepended as well if the trimmed string has an odd length. + /// A hex string's length needs to be even to decode into bytes, as two digits correspond to + /// one byte. + fn format_field_string(field: FieldElement) -> String { + if field.is_zero() { + return "0x00".to_owned(); + } + let mut trimmed_field = field.to_hex().trim_start_matches('0').to_owned(); + if trimmed_field.len() % 2 != 0 { + trimmed_field = "0".to_owned() + &trimmed_field; + } + "0x".to_owned() + &trimmed_field + } +} + +impl InputValue { + pub fn try_from_json( + value: JsonTypes, + param_type: &AbiType, + arg_name: &str, + ) -> Result { + let input_value = match (value, param_type) { + (JsonTypes::String(string), AbiType::String { .. }) => InputValue::String(string), + ( + JsonTypes::String(string), + AbiType::Field | AbiType::Integer { .. } | AbiType::Boolean, + ) => InputValue::Field(parse_str_to_field(&string)?), + + ( + JsonTypes::Integer(integer), + AbiType::Field | AbiType::Integer { .. } | AbiType::Boolean, + ) => { + let new_value = FieldElement::from(i128::from(integer)); + + InputValue::Field(new_value) + } + + (JsonTypes::Bool(boolean), AbiType::Boolean) => InputValue::Field(boolean.into()), + + (JsonTypes::Array(array), AbiType::Array { typ, .. }) => { + let array_elements = + try_vecmap(array, |value| InputValue::try_from_json(value, typ, arg_name))?; + InputValue::Vec(array_elements) + } + + (JsonTypes::Table(table), AbiType::Struct { fields, .. }) => { + let native_table = try_btree_map(fields, |(field_name, abi_type)| { + // Check that json contains a value for each field of the struct. + let field_id = format!("{arg_name}.{field_name}"); + let value = table + .get(field_name) + .ok_or_else(|| InputParserError::MissingArgument(field_id.clone()))?; + InputValue::try_from_json(value.clone(), abi_type, &field_id) + .map(|input_value| (field_name.to_string(), input_value)) + })?; + + InputValue::Struct(native_table) + } + + (JsonTypes::Array(array), AbiType::Tuple { fields }) => { + let tuple_fields = try_vecmap(array.into_iter().zip(fields), |(value, typ)| { + InputValue::try_from_json(value, typ, arg_name) + })?; + InputValue::Vec(tuple_fields) + } + + (_, _) => return Err(InputParserError::AbiTypeMismatch(param_type.clone())), + }; + + Ok(input_value) + } +} diff --git a/noir/tooling/noirc_abi/src/input_parser/mod.rs b/noir/tooling/noirc_abi/src/input_parser/mod.rs new file mode 100644 index 00000000000..7bbcb42296c --- /dev/null +++ b/noir/tooling/noirc_abi/src/input_parser/mod.rs @@ -0,0 +1,299 @@ +use num_bigint::{BigInt, BigUint}; +use num_traits::{Num, Zero}; +use std::collections::BTreeMap; + +use acvm::FieldElement; +use serde::Serialize; + +use crate::errors::InputParserError; +use crate::{Abi, AbiType}; + +pub mod json; +mod toml; + +/// This is what all formats eventually transform into +/// For example, a toml file will parse into TomlTypes +/// and those TomlTypes will be mapped to Value +#[derive(Debug, Clone, Serialize, PartialEq)] +pub enum InputValue { + Field(FieldElement), + String(String), + Vec(Vec), + Struct(BTreeMap), +} + +impl InputValue { + /// Checks whether the ABI type matches the InputValue type + /// and also their arity + pub fn matches_abi(&self, abi_param: &AbiType) -> bool { + match (self, abi_param) { + (InputValue::Field(_), AbiType::Field) => true, + (InputValue::Field(field_element), AbiType::Integer { width, .. }) => { + field_element.num_bits() <= *width + } + (InputValue::Field(field_element), AbiType::Boolean) => { + field_element.is_one() || field_element.is_zero() + } + + (InputValue::Vec(array_elements), AbiType::Array { length, typ, .. }) => { + if array_elements.len() != *length as usize { + return false; + } + // Check that all of the array's elements' values match the ABI as well. + array_elements.iter().all(|input_value| input_value.matches_abi(typ)) + } + + (InputValue::String(string), AbiType::String { length }) => { + string.len() == *length as usize + } + + (InputValue::Struct(map), AbiType::Struct { fields, .. }) => { + if map.len() != fields.len() { + return false; + } + + let field_types = BTreeMap::from_iter(fields.iter().cloned()); + + // Check that all of the struct's fields' values match the ABI as well. + map.iter().all(|(field_name, field_value)| { + if let Some(field_type) = field_types.get(field_name) { + field_value.matches_abi(field_type) + } else { + false + } + }) + } + + (InputValue::Vec(vec_elements), AbiType::Tuple { fields }) => { + if vec_elements.len() != fields.len() { + return false; + } + + vec_elements + .iter() + .zip(fields) + .all(|(input_value, abi_param)| input_value.matches_abi(abi_param)) + } + + // All other InputValue-AbiType combinations are fundamentally incompatible. + _ => false, + } + } +} + +/// The different formats that are supported when parsing +/// the initial witness values +#[cfg_attr(test, derive(strum_macros::EnumIter))] +pub enum Format { + Json, + Toml, +} + +impl Format { + pub fn ext(&self) -> &'static str { + match self { + Format::Json => "json", + Format::Toml => "toml", + } + } +} + +impl Format { + pub fn parse( + &self, + input_string: &str, + abi: &Abi, + ) -> Result, InputParserError> { + match self { + Format::Json => json::parse_json(input_string, abi), + Format::Toml => toml::parse_toml(input_string, abi), + } + } + + pub fn serialize( + &self, + input_map: &BTreeMap, + abi: &Abi, + ) -> Result { + match self { + Format::Json => json::serialize_to_json(input_map, abi), + Format::Toml => toml::serialize_to_toml(input_map, abi), + } + } +} + +#[cfg(test)] +mod serialization_tests { + use std::collections::BTreeMap; + + use acvm::FieldElement; + use strum::IntoEnumIterator; + + use crate::{ + input_parser::InputValue, Abi, AbiParameter, AbiType, AbiVisibility, Sign, MAIN_RETURN_NAME, + }; + + use super::Format; + + #[test] + fn serialization_round_trip() { + let abi = Abi { + parameters: vec![ + AbiParameter { + name: "foo".into(), + typ: AbiType::Field, + visibility: AbiVisibility::Private, + }, + AbiParameter { + name: "bar".into(), + typ: AbiType::Struct { + path: "MyStruct".into(), + fields: vec![ + ("field1".into(), AbiType::Integer { sign: Sign::Unsigned, width: 8 }), + ( + "field2".into(), + AbiType::Array { length: 2, typ: Box::new(AbiType::Boolean) }, + ), + ], + }, + visibility: AbiVisibility::Private, + }, + ], + return_type: Some(AbiType::String { length: 5 }), + // These two fields are unused when serializing/deserializing to file. + param_witnesses: BTreeMap::new(), + return_witnesses: Vec::new(), + }; + + let input_map: BTreeMap = BTreeMap::from([ + ("foo".into(), InputValue::Field(FieldElement::one())), + ( + "bar".into(), + InputValue::Struct(BTreeMap::from([ + ("field1".into(), InputValue::Field(255u128.into())), + ( + "field2".into(), + InputValue::Vec(vec![ + InputValue::Field(true.into()), + InputValue::Field(false.into()), + ]), + ), + ])), + ), + (MAIN_RETURN_NAME.into(), InputValue::String("hello".to_owned())), + ]); + + for format in Format::iter() { + let serialized_inputs = format.serialize(&input_map, &abi).unwrap(); + + let reconstructed_input_map = format.parse(&serialized_inputs, &abi).unwrap(); + + assert_eq!(input_map, reconstructed_input_map); + } + } +} + +fn parse_str_to_field(value: &str) -> Result { + let big_num = if let Some(hex) = value.strip_prefix("0x") { + BigUint::from_str_radix(hex, 16) + } else { + BigUint::from_str_radix(value, 10) + }; + big_num.map_err(|err_msg| InputParserError::ParseStr(err_msg.to_string())).and_then(|bigint| { + if bigint < FieldElement::modulus() { + Ok(field_from_big_uint(bigint)) + } else { + Err(InputParserError::ParseStr(format!( + "Input exceeds field modulus. Values must fall within [0, {})", + FieldElement::modulus(), + ))) + } + }) +} + +fn parse_str_to_signed(value: &str, witdh: u32) -> Result { + let big_num = if let Some(hex) = value.strip_prefix("0x") { + BigInt::from_str_radix(hex, 16) + } else { + BigInt::from_str_radix(value, 10) + }; + + big_num.map_err(|err_msg| InputParserError::ParseStr(err_msg.to_string())).and_then(|bigint| { + let modulus: BigInt = FieldElement::modulus().into(); + let bigint = if bigint.sign() == num_bigint::Sign::Minus { + BigInt::from(2).pow(witdh) + bigint + } else { + bigint + }; + if bigint.is_zero() || (bigint.sign() == num_bigint::Sign::Plus && bigint < modulus) { + Ok(field_from_big_int(bigint)) + } else { + Err(InputParserError::ParseStr(format!( + "Input exceeds field modulus. Values must fall within [0, {})", + FieldElement::modulus(), + ))) + } + }) +} + +fn field_from_big_uint(bigint: BigUint) -> FieldElement { + FieldElement::from_be_bytes_reduce(&bigint.to_bytes_be()) +} + +fn field_from_big_int(bigint: BigInt) -> FieldElement { + match bigint.sign() { + num_bigint::Sign::Minus => { + unreachable!( + "Unsupported negative value; it should only be called with a positive value" + ) + } + num_bigint::Sign::NoSign => FieldElement::zero(), + num_bigint::Sign::Plus => FieldElement::from_be_bytes_reduce(&bigint.to_bytes_be().1), + } +} + +#[cfg(test)] +mod test { + use acvm::FieldElement; + use num_bigint::BigUint; + + use super::parse_str_to_field; + + fn big_uint_from_field(field: FieldElement) -> BigUint { + BigUint::from_bytes_be(&field.to_be_bytes()) + } + + #[test] + fn parse_empty_str_fails() { + // Check that this fails appropriately rather than being treated as 0, etc. + assert!(parse_str_to_field("").is_err()); + } + + #[test] + fn parse_fields_from_strings() { + let fields = vec![ + FieldElement::zero(), + FieldElement::one(), + FieldElement::from(u128::MAX) + FieldElement::one(), + // Equivalent to `FieldElement::modulus() - 1` + -FieldElement::one(), + ]; + + for field in fields { + let hex_field = format!("0x{}", field.to_hex()); + let field_from_hex = parse_str_to_field(&hex_field).unwrap(); + assert_eq!(field_from_hex, field); + + let dec_field = big_uint_from_field(field).to_string(); + let field_from_dec = parse_str_to_field(&dec_field).unwrap(); + assert_eq!(field_from_dec, field); + } + } + + #[test] + fn rejects_noncanonical_fields() { + let noncanonical_field = FieldElement::modulus().to_string(); + let parsed_field = parse_str_to_field(&noncanonical_field); + println!("{parsed_field:?}"); + } +} diff --git a/noir/tooling/noirc_abi/src/input_parser/toml.rs b/noir/tooling/noirc_abi/src/input_parser/toml.rs new file mode 100644 index 00000000000..645b59b00cd --- /dev/null +++ b/noir/tooling/noirc_abi/src/input_parser/toml.rs @@ -0,0 +1,178 @@ +use super::{parse_str_to_field, parse_str_to_signed, InputValue}; +use crate::{errors::InputParserError, Abi, AbiType, MAIN_RETURN_NAME}; +use acvm::FieldElement; +use iter_extended::{try_btree_map, try_vecmap}; +use serde::{Deserialize, Serialize}; +use std::collections::BTreeMap; + +pub(crate) fn parse_toml( + input_string: &str, + abi: &Abi, +) -> Result, InputParserError> { + // Parse input.toml into a BTreeMap. + let data: BTreeMap = toml::from_str(input_string)?; + + // Convert arguments to field elements. + let mut parsed_inputs = try_btree_map(abi.to_btree_map(), |(arg_name, abi_type)| { + // Check that toml contains a value for each argument in the ABI. + let value = data + .get(&arg_name) + .ok_or_else(|| InputParserError::MissingArgument(arg_name.clone()))?; + + InputValue::try_from_toml(value.clone(), &abi_type, &arg_name) + .map(|input_value| (arg_name, input_value)) + })?; + + // If the toml file also includes a return value then we parse it as well. + // This isn't required as the prover calculates the return value itself. + if let (Some(return_type), Some(toml_return_value)) = + (&abi.return_type, data.get(MAIN_RETURN_NAME)) + { + let return_value = + InputValue::try_from_toml(toml_return_value.clone(), return_type, MAIN_RETURN_NAME)?; + parsed_inputs.insert(MAIN_RETURN_NAME.to_owned(), return_value); + } + + Ok(parsed_inputs) +} + +pub(crate) fn serialize_to_toml( + input_map: &BTreeMap, + abi: &Abi, +) -> Result { + let mut toml_map = try_btree_map(abi.to_btree_map(), |(key, param_type)| { + TomlTypes::try_from_input_value(&input_map[&key], ¶m_type) + .map(|toml_value| (key.clone(), toml_value)) + })?; + + if let (Some(return_type), Some(return_value)) = + (&abi.return_type, input_map.get(MAIN_RETURN_NAME)) + { + let return_value = TomlTypes::try_from_input_value(return_value, return_type)?; + toml_map.insert(MAIN_RETURN_NAME.to_owned(), return_value); + } + + let toml_string = toml::to_string(&toml_map)?; + + Ok(toml_string) +} + +#[derive(Debug, Deserialize, Serialize, Clone)] +#[serde(untagged)] +enum TomlTypes { + // This is most likely going to be a hex string + // But it is possible to support UTF-8 + String(String), + // Just a regular integer, that can fit in 64 bits + // Note that the toml spec specifies that all numbers are represented as `i64`s. + Integer(u64), + // Simple boolean flag + Bool(bool), + // Array of TomlTypes + Array(Vec), + // Struct of TomlTypes + Table(BTreeMap), +} + +impl TomlTypes { + fn try_from_input_value( + value: &InputValue, + abi_type: &AbiType, + ) -> Result { + let toml_value = match (value, abi_type) { + (InputValue::Field(f), AbiType::Field | AbiType::Integer { .. }) => { + let f_str = format!("0x{}", f.to_hex()); + TomlTypes::String(f_str) + } + (InputValue::Field(f), AbiType::Boolean) => TomlTypes::Bool(f.is_one()), + + (InputValue::Vec(vector), AbiType::Array { typ, .. }) => { + let array = + try_vecmap(vector, |value| TomlTypes::try_from_input_value(value, typ))?; + TomlTypes::Array(array) + } + + (InputValue::String(s), AbiType::String { .. }) => TomlTypes::String(s.to_string()), + + (InputValue::Struct(map), AbiType::Struct { fields, .. }) => { + let map_with_toml_types = try_btree_map(fields, |(key, field_type)| { + TomlTypes::try_from_input_value(&map[key], field_type) + .map(|toml_value| (key.to_owned(), toml_value)) + })?; + TomlTypes::Table(map_with_toml_types) + } + + (InputValue::Vec(vector), AbiType::Tuple { fields }) => { + let fields = try_vecmap(vector.iter().zip(fields), |(value, typ)| { + TomlTypes::try_from_input_value(value, typ) + })?; + TomlTypes::Array(fields) + } + + _ => return Err(InputParserError::AbiTypeMismatch(abi_type.clone())), + }; + Ok(toml_value) + } +} + +impl InputValue { + fn try_from_toml( + value: TomlTypes, + param_type: &AbiType, + arg_name: &str, + ) -> Result { + let input_value = match (value, param_type) { + (TomlTypes::String(string), AbiType::String { .. }) => InputValue::String(string), + ( + TomlTypes::String(string), + AbiType::Field + | AbiType::Integer { sign: crate::Sign::Unsigned, .. } + | AbiType::Boolean, + ) => InputValue::Field(parse_str_to_field(&string)?), + (TomlTypes::String(string), AbiType::Integer { sign: crate::Sign::Signed, width }) => { + InputValue::Field(parse_str_to_signed(&string, *width)?) + } + ( + TomlTypes::Integer(integer), + AbiType::Field | AbiType::Integer { .. } | AbiType::Boolean, + ) => { + let new_value = FieldElement::from(i128::from(integer)); + + InputValue::Field(new_value) + } + + (TomlTypes::Bool(boolean), AbiType::Boolean) => InputValue::Field(boolean.into()), + + (TomlTypes::Array(array), AbiType::Array { typ, .. }) => { + let array_elements = + try_vecmap(array, |value| InputValue::try_from_toml(value, typ, arg_name))?; + InputValue::Vec(array_elements) + } + + (TomlTypes::Table(table), AbiType::Struct { fields, .. }) => { + let native_table = try_btree_map(fields, |(field_name, abi_type)| { + // Check that json contains a value for each field of the struct. + let field_id = format!("{arg_name}.{field_name}"); + let value = table + .get(field_name) + .ok_or_else(|| InputParserError::MissingArgument(field_id.clone()))?; + InputValue::try_from_toml(value.clone(), abi_type, &field_id) + .map(|input_value| (field_name.to_string(), input_value)) + })?; + + InputValue::Struct(native_table) + } + + (TomlTypes::Array(array), AbiType::Tuple { fields }) => { + let tuple_fields = try_vecmap(array.into_iter().zip(fields), |(value, typ)| { + InputValue::try_from_toml(value, typ, arg_name) + })?; + InputValue::Vec(tuple_fields) + } + + (_, _) => return Err(InputParserError::AbiTypeMismatch(param_type.clone())), + }; + + Ok(input_value) + } +} diff --git a/noir/tooling/noirc_abi/src/lib.rs b/noir/tooling/noirc_abi/src/lib.rs new file mode 100644 index 00000000000..7092f05c26e --- /dev/null +++ b/noir/tooling/noirc_abi/src/lib.rs @@ -0,0 +1,597 @@ +#![forbid(unsafe_code)] +#![warn(unused_crate_dependencies, unused_extern_crates)] +#![warn(unreachable_pub)] +#![warn(clippy::semicolon_if_nothing_returned)] + +use acvm::{ + acir::native_types::{Witness, WitnessMap}, + FieldElement, +}; +use errors::AbiError; +use input_parser::InputValue; +use iter_extended::{try_btree_map, try_vecmap, vecmap}; +use noirc_frontend::{ + hir::Context, Signedness, StructType, Type, TypeBinding, TypeVariableKind, Visibility, +}; +use serde::{Deserialize, Serialize}; +use std::ops::Range; +use std::{collections::BTreeMap, str}; +// This is the ABI used to bridge the different TOML formats for the initial +// witness, the partial witness generator and the interpreter. +// +// This ABI has nothing to do with ACVM or ACIR. Although they implicitly have a relationship + +pub mod errors; +pub mod input_parser; +mod serialization; + +/// A map from the fields in an TOML/JSON file which correspond to some ABI to their values +pub type InputMap = BTreeMap; + +pub const MAIN_RETURN_NAME: &str = "return"; + +#[derive(Clone, Debug, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "kind", rename_all = "lowercase")] +/// Types that are allowed in the (main function in binary) +/// +/// we use this separation so that we can have types like Strings +/// without needing to introduce this in the Noir types +/// +/// NOTE: If Strings are introduced as a native type, the translation will +/// be straightforward. Whether exotic types like String will be natively supported +/// depends on the types of programs that users want to do. I don't envision string manipulation +/// in programs, however it is possible to support, with many complications like encoding character set +/// support. +pub enum AbiType { + Field, + Array { + length: u64, + #[serde(rename = "type")] + typ: Box, + }, + Integer { + sign: Sign, + width: u32, + }, + Boolean, + Struct { + path: String, + #[serde( + serialize_with = "serialization::serialize_struct_fields", + deserialize_with = "serialization::deserialize_struct_fields" + )] + fields: Vec<(String, AbiType)>, + }, + Tuple { + fields: Vec, + }, + String { + length: u64, + }, +} + +#[derive(Clone, Copy, Debug, PartialEq, Eq, Serialize, Deserialize)] +#[serde(rename_all = "lowercase")] +/// Represents whether the parameter is public or known only to the prover. +pub enum AbiVisibility { + Public, + // Constants are not allowed in the ABI for main at the moment. + // Constant, + Private, +} + +impl From for AbiVisibility { + fn from(value: Visibility) -> Self { + match value { + Visibility::Public => AbiVisibility::Public, + Visibility::Private => AbiVisibility::Private, + } + } +} + +impl From<&Visibility> for AbiVisibility { + fn from(value: &Visibility) -> Self { + match value { + Visibility::Public => AbiVisibility::Public, + Visibility::Private => AbiVisibility::Private, + } + } +} + +#[derive(Clone, Copy, Debug, PartialEq, Eq, Serialize, Deserialize)] +#[serde(rename_all = "lowercase")] +/// Represents whether the return value should compromise of unique witness indices such that no +/// index occurs within the program's abi more than once. +/// +/// This is useful for application stacks that require an uniform abi across across multiple +/// circuits. When index duplication is allowed, the compiler may identify that a public input +/// reaches the output unaltered and is thus referenced directly, causing the input and output +/// witness indices to overlap. Similarly, repetitions of copied values in the output may be +/// optimized away. +pub enum AbiDistinctness { + Distinct, + DuplicationAllowed, +} + +#[derive(Clone, Copy, Debug, PartialEq, Eq, Serialize, Deserialize)] +#[serde(rename_all = "lowercase")] +pub enum Sign { + Unsigned, + Signed, +} + +impl AbiType { + pub fn from_type(context: &Context, typ: &Type) -> Self { + // Note; use strict_eq instead of partial_eq when comparing field types + // in this method, you most likely want to distinguish between public and private + match typ { + Type::FieldElement => Self::Field, + Type::Array(size, typ) => { + let length = size + .evaluate_to_u64() + .expect("Cannot have variable sized arrays as a parameter to main"); + let typ = typ.as_ref(); + Self::Array { length, typ: Box::new(Self::from_type(context, typ)) } + } + Type::Integer(sign, bit_width) => { + let sign = match sign { + Signedness::Unsigned => Sign::Unsigned, + Signedness::Signed => Sign::Signed, + }; + + Self::Integer { sign, width: *bit_width } + } + Type::TypeVariable(binding, TypeVariableKind::IntegerOrField) => { + match &*binding.borrow() { + TypeBinding::Bound(typ) => Self::from_type(context, typ), + TypeBinding::Unbound(_) => Self::from_type(context, &Type::default_int_type()), + } + } + Type::Bool => Self::Boolean, + Type::String(size) => { + let size = size + .evaluate_to_u64() + .expect("Cannot have variable sized strings as a parameter to main"); + Self::String { length: size } + } + Type::FmtString(_, _) => unreachable!("format strings cannot be used in the abi"), + Type::Error => unreachable!(), + Type::Unit => unreachable!(), + Type::Constant(_) => unreachable!(), + Type::TraitAsType(_) => unreachable!(), + Type::Struct(def, ref args) => { + let struct_type = def.borrow(); + let fields = struct_type.get_fields(args); + let fields = vecmap(fields, |(name, typ)| (name, Self::from_type(context, &typ))); + // For the ABI, we always want to resolve the struct paths from the root crate + let path = + context.fully_qualified_struct_path(context.root_crate_id(), struct_type.id); + Self::Struct { fields, path } + } + Type::Tuple(fields) => { + let fields = vecmap(fields, |typ| Self::from_type(context, typ)); + Self::Tuple { fields } + } + Type::TypeVariable(_, _) => unreachable!(), + Type::NamedGeneric(..) => unreachable!(), + Type::Forall(..) => unreachable!(), + Type::Function(_, _, _) => unreachable!(), + Type::MutableReference(_) => unreachable!("&mut cannot be used in the abi"), + Type::NotConstant => unreachable!(), + } + } + + /// Returns the number of field elements required to represent the type once encoded. + pub fn field_count(&self) -> u32 { + match self { + AbiType::Field | AbiType::Integer { .. } | AbiType::Boolean => 1, + AbiType::Array { length, typ } => typ.field_count() * (*length as u32), + AbiType::Struct { fields, .. } => { + fields.iter().fold(0, |acc, (_, field_type)| acc + field_type.field_count()) + } + AbiType::Tuple { fields } => { + fields.iter().fold(0, |acc, field_typ| acc + field_typ.field_count()) + } + AbiType::String { length } => *length as u32, + } + } +} + +#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)] +/// An argument or return value of the circuit's `main` function. +pub struct AbiParameter { + pub name: String, + #[serde(rename = "type")] + pub typ: AbiType, + pub visibility: AbiVisibility, +} + +impl AbiParameter { + pub fn is_public(&self) -> bool { + self.visibility == AbiVisibility::Public + } +} + +#[derive(Clone, Debug, Serialize, Deserialize)] +pub struct Abi { + /// An ordered list of the arguments to the program's `main` function, specifying their types and visibility. + pub parameters: Vec, + /// A map from the ABI's parameters to the indices they are written to in the [`WitnessMap`]. + /// This defines how to convert between the [`InputMap`] and [`WitnessMap`]. + pub param_witnesses: BTreeMap>>, + pub return_type: Option, + pub return_witnesses: Vec, +} + +impl Abi { + pub fn parameter_names(&self) -> Vec<&String> { + self.parameters.iter().map(|x| &x.name).collect() + } + + pub fn num_parameters(&self) -> usize { + self.parameters.len() + } + + /// Returns the number of field elements required to represent the ABI's input once encoded. + pub fn field_count(&self) -> u32 { + self.parameters.iter().map(|param| param.typ.field_count()).sum() + } + + /// Returns whether any values are needed to be made public for verification. + pub fn has_public_inputs(&self) -> bool { + self.return_type.is_some() || self.parameters.iter().any(|param| param.is_public()) + } + + /// Returns `true` if the ABI contains no parameters or return value. + pub fn is_empty(&self) -> bool { + self.return_type.is_none() && self.parameters.is_empty() + } + + pub fn to_btree_map(&self) -> BTreeMap { + let mut map = BTreeMap::new(); + for param in self.parameters.iter() { + map.insert(param.name.clone(), param.typ.clone()); + } + map + } + + /// ABI with only the public parameters + #[must_use] + pub fn public_abi(self) -> Abi { + let parameters: Vec<_> = + self.parameters.into_iter().filter(|param| param.is_public()).collect(); + let param_witnesses = self + .param_witnesses + .into_iter() + .filter(|(param_name, _)| parameters.iter().any(|param| ¶m.name == param_name)) + .collect(); + Abi { + parameters, + param_witnesses, + return_type: self.return_type, + return_witnesses: self.return_witnesses, + } + } + + /// Encode a set of inputs as described in the ABI into a `WitnessMap`. + pub fn encode( + &self, + input_map: &InputMap, + return_value: Option, + ) -> Result { + // Check that no extra witness values have been provided. + let param_names = self.parameter_names(); + if param_names.len() < input_map.len() { + let unexpected_params: Vec = + input_map.keys().filter(|param| !param_names.contains(param)).cloned().collect(); + return Err(AbiError::UnexpectedParams(unexpected_params)); + } + + // First encode each input separately, performing any input validation. + let encoded_input_map: BTreeMap> = self + .to_btree_map() + .into_iter() + .map(|(param_name, expected_type)| { + let value = input_map + .get(¶m_name) + .ok_or_else(|| AbiError::MissingParam(param_name.clone()))? + .clone(); + + if !value.matches_abi(&expected_type) { + let param = self + .parameters + .iter() + .find(|param| param.name == param_name) + .unwrap() + .clone(); + return Err(AbiError::TypeMismatch { param, value }); + } + + Self::encode_value(value, &expected_type).map(|v| (param_name, v)) + }) + .collect::>()?; + + // Write input field elements into witness indices specified in `self.param_witnesses`. + let mut witness_map: BTreeMap = encoded_input_map + .iter() + .flat_map(|(param_name, encoded_param_fields)| { + let param_witness_indices = range_to_vec(&self.param_witnesses[param_name]); + param_witness_indices + .iter() + .zip(encoded_param_fields.iter()) + .map(|(&witness, &field_element)| (witness, field_element)) + .collect::>() + }) + .collect::>(); + + // When encoding public inputs to be passed to the verifier, the user can must provide a return value + // to be inserted into the witness map. This is not needed when generating a witness when proving the circuit. + match (&self.return_type, return_value) { + (Some(return_type), Some(return_value)) => { + if !return_value.matches_abi(return_type) { + return Err(AbiError::ReturnTypeMismatch { + return_type: return_type.clone(), + value: return_value, + }); + } + let encoded_return_fields = Self::encode_value(return_value, return_type)?; + + // We need to be more careful when writing the return value's witness values. + // This is as it may share witness indices with other public inputs so we must check that when + // this occurs the witness values are consistent with each other. + self.return_witnesses.iter().zip(encoded_return_fields.iter()).try_for_each( + |(&witness, &field_element)| match witness_map.insert(witness, field_element) { + Some(existing_value) if existing_value != field_element => { + Err(AbiError::InconsistentWitnessAssignment(witness)) + } + _ => Ok(()), + }, + )?; + } + (None, Some(return_value)) => { + return Err(AbiError::UnexpectedReturnValue(return_value)) + } + // We allow not passing a return value despite the circuit defining one + // in order to generate the initial partial witness. + (_, None) => {} + } + + Ok(witness_map.into()) + } + + fn encode_value(value: InputValue, abi_type: &AbiType) -> Result, AbiError> { + let mut encoded_value = Vec::new(); + match (value, abi_type) { + (InputValue::Field(elem), _) => encoded_value.push(elem), + + (InputValue::Vec(vec_elements), AbiType::Array { typ, .. }) => { + for elem in vec_elements { + encoded_value.extend(Self::encode_value(elem, typ)?); + } + } + + (InputValue::String(string), _) => { + let str_as_fields = + string.bytes().map(|byte| FieldElement::from_be_bytes_reduce(&[byte])); + encoded_value.extend(str_as_fields); + } + + (InputValue::Struct(object), AbiType::Struct { fields, .. }) => { + for (field, typ) in fields { + encoded_value.extend(Self::encode_value(object[field].clone(), typ)?); + } + } + (InputValue::Vec(vec_elements), AbiType::Tuple { fields }) => { + for (value, typ) in vec_elements.into_iter().zip(fields) { + encoded_value.extend(Self::encode_value(value, typ)?); + } + } + _ => unreachable!("value should have already been checked to match abi type"), + } + Ok(encoded_value) + } + + /// Decode a `WitnessMap` into the types specified in the ABI. + pub fn decode( + &self, + witness_map: &WitnessMap, + ) -> Result<(InputMap, Option), AbiError> { + let public_inputs_map = + try_btree_map(self.parameters.clone(), |AbiParameter { name, typ, .. }| { + let param_witness_values = + try_vecmap(range_to_vec(&self.param_witnesses[&name]), |witness_index| { + witness_map + .get(&witness_index) + .ok_or_else(|| AbiError::MissingParamWitnessValue { + name: name.clone(), + witness_index, + }) + .copied() + })?; + + decode_value(&mut param_witness_values.into_iter(), &typ) + .map(|input_value| (name.clone(), input_value)) + })?; + + // We also attempt to decode the circuit's return value from `witness_map`. + let return_value = if let Some(return_type) = &self.return_type { + if let Ok(return_witness_values) = + try_vecmap(self.return_witnesses.clone(), |witness_index| { + witness_map + .get(&witness_index) + .ok_or_else(|| AbiError::MissingParamWitnessValue { + name: MAIN_RETURN_NAME.to_string(), + witness_index, + }) + .copied() + }) + { + Some(decode_value(&mut return_witness_values.into_iter(), return_type)?) + } else { + // Unlike for the circuit inputs, we tolerate not being able to find the witness values for the return value. + // This is because the user may be decoding a partial witness map for which is hasn't been calculated yet. + // If a return value is expected, this should be checked for by the user. + None + } + } else { + None + }; + + Ok((public_inputs_map, return_value)) + } +} + +fn decode_value( + field_iterator: &mut impl Iterator, + value_type: &AbiType, +) -> Result { + // This function assumes that `field_iterator` contains enough `FieldElement`s in order to decode a `value_type` + // `Abi.decode` enforces that the encoded inputs matches the expected length defined by the ABI so this is safe. + let value = match value_type { + AbiType::Field | AbiType::Integer { .. } | AbiType::Boolean => { + let field_element = field_iterator.next().unwrap(); + + InputValue::Field(field_element) + } + AbiType::Array { length, typ } => { + let length = *length as usize; + let mut array_elements = Vec::with_capacity(length); + for _ in 0..length { + array_elements.push(decode_value(field_iterator, typ)?); + } + + InputValue::Vec(array_elements) + } + AbiType::String { length } => { + let field_elements: Vec = field_iterator.take(*length as usize).collect(); + + InputValue::String(decode_string_value(&field_elements)) + } + AbiType::Struct { fields, .. } => { + let mut struct_map = BTreeMap::new(); + + for (field_key, param_type) in fields { + let field_value = decode_value(field_iterator, param_type)?; + + struct_map.insert(field_key.to_owned(), field_value); + } + + InputValue::Struct(struct_map) + } + AbiType::Tuple { fields } => { + let mut tuple_elements = Vec::with_capacity(fields.len()); + for field_typ in fields { + tuple_elements.push(decode_value(field_iterator, field_typ)?); + } + + InputValue::Vec(tuple_elements) + } + }; + + Ok(value) +} + +fn decode_string_value(field_elements: &[FieldElement]) -> String { + let string_as_slice = vecmap(field_elements, |e| { + let mut field_as_bytes = e.to_be_bytes(); + let char_byte = field_as_bytes.pop().unwrap(); // A character in a string is represented by a u8, thus we just want the last byte of the element + assert!(field_as_bytes.into_iter().all(|b| b == 0)); // Assert that the rest of the field element's bytes are empty + char_byte + }); + + let final_string = str::from_utf8(&string_as_slice).unwrap(); + final_string.to_owned() +} + +#[derive(Debug, Serialize, Deserialize)] +pub struct ContractEvent { + /// Event name + name: String, + /// The fully qualified path to the event definition + path: String, + + /// Fields of the event + #[serde( + serialize_with = "serialization::serialize_struct_fields", + deserialize_with = "serialization::deserialize_struct_fields" + )] + fields: Vec<(String, AbiType)>, +} + +impl ContractEvent { + pub fn from_struct_type(context: &Context, struct_type: &StructType) -> Self { + let fields = vecmap(struct_type.get_fields(&[]), |(name, typ)| { + (name, AbiType::from_type(context, &typ)) + }); + // For the ABI, we always want to resolve the struct paths from the root crate + let path = context.fully_qualified_struct_path(context.root_crate_id(), struct_type.id); + + Self { name: struct_type.name.0.contents.clone(), path, fields } + } +} + +fn range_to_vec(ranges: &[Range]) -> Vec { + let mut result = Vec::new(); + for range in ranges { + for witness in range.start.witness_index()..range.end.witness_index() { + result.push(witness.into()); + } + } + result +} + +#[cfg(test)] +mod test { + use std::collections::BTreeMap; + + use acvm::{acir::native_types::Witness, FieldElement}; + + use crate::{input_parser::InputValue, Abi, AbiParameter, AbiType, AbiVisibility, InputMap}; + + #[test] + fn witness_encoding_roundtrip() { + let abi = Abi { + parameters: vec![ + AbiParameter { + name: "thing1".to_string(), + typ: AbiType::Array { length: 2, typ: Box::new(AbiType::Field) }, + visibility: AbiVisibility::Public, + }, + AbiParameter { + name: "thing2".to_string(), + typ: AbiType::Field, + visibility: AbiVisibility::Public, + }, + ], + // Note that the return value shares a witness with `thing2` + param_witnesses: BTreeMap::from([ + ("thing1".to_string(), vec![(Witness(1)..Witness(3))]), + ("thing2".to_string(), vec![(Witness(3)..Witness(4))]), + ]), + return_type: Some(AbiType::Field), + return_witnesses: vec![Witness(3)], + }; + + // Note we omit return value from inputs + let inputs: InputMap = BTreeMap::from([ + ( + "thing1".to_string(), + InputValue::Vec(vec![ + InputValue::Field(FieldElement::one()), + InputValue::Field(FieldElement::one()), + ]), + ), + ("thing2".to_string(), InputValue::Field(FieldElement::zero())), + ]); + + let witness_map = abi.encode(&inputs, None).unwrap(); + let (reconstructed_inputs, return_value) = abi.decode(&witness_map).unwrap(); + + for (key, expected_value) in inputs { + assert_eq!(reconstructed_inputs[&key], expected_value); + } + + // We also decode the return value (we can do this immediately as we know it shares a witness with an input). + assert_eq!(return_value.unwrap(), reconstructed_inputs["thing2"]); + } +} diff --git a/noir/tooling/noirc_abi/src/serialization.rs b/noir/tooling/noirc_abi/src/serialization.rs new file mode 100644 index 00000000000..ed838803fab --- /dev/null +++ b/noir/tooling/noirc_abi/src/serialization.rs @@ -0,0 +1,137 @@ +use iter_extended::vecmap; +use serde::{Deserialize, Deserializer, Serialize, Serializer}; + +use crate::AbiType; + +// This module exposes a custom serializer and deserializer for `BTreeMap` +// (representing the fields of a struct) to serialize it as a `Vec`. +// +// This is required as the struct is flattened into an array of field elements so the ordering of the struct's fields +// must be maintained. However, several serialization formats (notably JSON) do not provide strong guarantees about +// the ordering of elements in a map, this creates potential for improper ABI encoding of structs if the fields are +// deserialized into a different order. To prevent this, we store the fields in an array to create an unambiguous ordering. + +#[derive(Serialize, Deserialize)] +struct StructField { + name: String, + #[serde(rename = "type")] + typ: AbiType, +} + +pub(crate) fn serialize_struct_fields( + fields: &[(String, AbiType)], + s: S, +) -> Result +where + S: Serializer, +{ + let fields_vector = + vecmap(fields, |(name, typ)| StructField { name: name.to_owned(), typ: typ.to_owned() }); + + fields_vector.serialize(s) +} + +pub(crate) fn deserialize_struct_fields<'de, D>( + deserializer: D, +) -> Result, D::Error> +where + D: Deserializer<'de>, +{ + let fields_vector = Vec::::deserialize(deserializer)?; + Ok(vecmap(fields_vector, |StructField { name, typ }| (name, typ))) +} + +#[cfg(test)] +mod tests { + use crate::{AbiParameter, AbiType, AbiVisibility, Sign}; + + #[test] + fn abi_parameter_serialization() { + let serialized_field = "{ + \"name\": \"thing1\", + \"visibility\": \"public\", + \"type\": { + \"kind\": \"field\" + } + }"; + + let expected_field = AbiParameter { + name: "thing1".to_string(), + typ: AbiType::Field, + visibility: AbiVisibility::Public, + }; + let deserialized_field: AbiParameter = serde_json::from_str(serialized_field).unwrap(); + assert_eq!(deserialized_field, expected_field); + + let serialized_array = "{ + \"name\": \"thing2\", + \"visibility\": \"private\", + \"type\": { + \"kind\": \"array\", + \"length\": 2, + \"type\": { + \"kind\": \"integer\", + \"width\": 3, + \"sign\": \"unsigned\" + } + } + }"; + + let expected_array = AbiParameter { + name: "thing2".to_string(), + typ: AbiType::Array { + length: 2, + typ: Box::new(AbiType::Integer { sign: Sign::Unsigned, width: 3 }), + }, + visibility: AbiVisibility::Private, + }; + let deserialized_array: AbiParameter = serde_json::from_str(serialized_array).unwrap(); + assert_eq!(deserialized_array, expected_array); + + let serialized_struct = "{ + \"name\":\"thing3\", + \"type\": { + \"kind\":\"struct\", + \"path\": \"MyStruct\", + \"fields\": [ + { + \"name\": \"field1\", + \"type\": { + \"kind\": \"integer\", + \"sign\": \"unsigned\", + \"width\": 3 + } + }, + { + \"name\":\"field2\", + \"type\": { + \"kind\":\"array\", + \"length\": 2, + \"type\": { + \"kind\":\"field\" + } + } + } + ] + }, + \"visibility\":\"private\" + }"; + + let expected_struct = AbiParameter { + name: "thing3".to_string(), + typ: AbiType::Struct { + path: "MyStruct".to_string(), + fields: vec![ + ("field1".to_string(), AbiType::Integer { sign: Sign::Unsigned, width: 3 }), + ( + "field2".to_string(), + AbiType::Array { length: 2, typ: Box::new(AbiType::Field) }, + ), + ], + }, + visibility: AbiVisibility::Private, + }; + let deserialized_struct: AbiParameter = serde_json::from_str(serialized_struct).unwrap(); + assert_eq!(deserialized_struct, expected_struct); + } +} diff --git a/noir/tooling/noirc_abi_wasm/.eslintignore b/noir/tooling/noirc_abi_wasm/.eslintignore new file mode 100644 index 00000000000..200ae222150 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/.eslintignore @@ -0,0 +1,2 @@ +node_modules +pkg \ No newline at end of file diff --git a/noir/tooling/noirc_abi_wasm/.eslintrc.js b/noir/tooling/noirc_abi_wasm/.eslintrc.js new file mode 100644 index 00000000000..33335c2a877 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/.eslintrc.js @@ -0,0 +1,3 @@ +module.exports = { + extends: ["../../.eslintrc.js"], +}; diff --git a/noir/tooling/noirc_abi_wasm/.mocharc.json b/noir/tooling/noirc_abi_wasm/.mocharc.json new file mode 100644 index 00000000000..27273835070 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/.mocharc.json @@ -0,0 +1,5 @@ +{ + "extension": ["ts"], + "spec": "test/node/**/*.test.ts", + "require": "ts-node/register" +} \ No newline at end of file diff --git a/noir/tooling/noirc_abi_wasm/CHANGELOG.md b/noir/tooling/noirc_abi_wasm/CHANGELOG.md new file mode 100644 index 00000000000..e69de29bb2d diff --git a/noir/tooling/noirc_abi_wasm/Cargo.toml b/noir/tooling/noirc_abi_wasm/Cargo.toml new file mode 100644 index 00000000000..c78c3ead0c3 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/Cargo.toml @@ -0,0 +1,33 @@ +[package] +name = "noirc_abi_wasm" +version.workspace = true +authors.workspace = true +edition.workspace = true +license.workspace = true + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + + +[lib] +crate-type = ["cdylib"] + +[dependencies] +acvm.workspace = true +noirc_abi.workspace = true +iter-extended.workspace = true +wasm-bindgen.workspace = true +serde.workspace = true +js-sys.workspace = true +console_error_panic_hook.workspace = true +gloo-utils.workspace = true + + +# This is an unused dependency, we are adding it +# so that we can enable the js feature in getrandom. +getrandom = { workspace = true, features = ["js"] } + +[build-dependencies] +build-data.workspace = true + +[dev-dependencies] +wasm-bindgen-test.workspace = true diff --git a/noir/tooling/noirc_abi_wasm/README.md b/noir/tooling/noirc_abi_wasm/README.md new file mode 100644 index 00000000000..77bc1f5fae2 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/README.md @@ -0,0 +1,17 @@ +# Noir Lang ABI JavaScript Package + +This JavaScript package enables users to ABI encode inputs to a Noir program, i.e. generating an initial witness. + +## Building from source + +Outside of the [noir repo](https://github.com/noir-lang/noir), this package can be built using the command below: + +```bash +nix build -L github:noir-lang/noir/master#abi_wasm +``` + +If you are within the noir repo and would like to build local changes, you can use: + +```bash +nix build -L #abi_wasm +``` diff --git a/noir/tooling/noirc_abi_wasm/build.rs b/noir/tooling/noirc_abi_wasm/build.rs new file mode 100644 index 00000000000..3b96be74ef3 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/build.rs @@ -0,0 +1,14 @@ +const GIT_COMMIT: &&str = &"GIT_COMMIT"; + +fn main() { + // Only use build_data if the environment variable isn't set + // The environment variable is always set when working via Nix + if std::env::var(GIT_COMMIT).is_err() { + build_data::set_GIT_COMMIT(); + build_data::set_GIT_DIRTY(); + build_data::no_debug_rebuilds(); + } + + build_data::set_SOURCE_TIMESTAMP(); + build_data::no_debug_rebuilds(); +} diff --git a/noir/tooling/noirc_abi_wasm/build.sh b/noir/tooling/noirc_abi_wasm/build.sh new file mode 100755 index 00000000000..37f2fd0a5a9 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/build.sh @@ -0,0 +1,47 @@ +#!/usr/bin/env bash + +function require_command { + if ! command -v "$1" >/dev/null 2>&1; then + echo "Error: $1 is required but not installed." >&2 + exit 1 + fi +} +function check_installed { + if ! command -v "$1" >/dev/null 2>&1; then + echo "$1 is not installed. Please install it." >&2 + return 1 + fi + return 0 +} +function run_or_fail { + "$@" + local status=$? + if [ $status -ne 0 ]; then + echo "Command '$*' failed with exit code $status" >&2 + exit $status + fi +} + +require_command jq +require_command cargo +require_command wasm-bindgen +check_installed wasm-opt + +self_path=$(dirname "$(readlink -f "$0")") +export pname=$(cargo read-manifest | jq -r '.name') +export CARGO_TARGET_DIR=$self_path/target + +rm -rf $self_path/outputs >/dev/null 2>&1 +rm -rf $self_path/result >/dev/null 2>&1 + +if [ -v out ]; then + echo "Will install package to $out (defined outside installPhase.sh script)" +else + export out="$self_path/outputs/out" + echo "Will install package to $out" +fi + +run_or_fail $self_path/buildPhaseCargoCommand.sh +run_or_fail $self_path/installPhase.sh + +ln -s $out $self_path/result diff --git a/noir/tooling/noirc_abi_wasm/buildPhaseCargoCommand.sh b/noir/tooling/noirc_abi_wasm/buildPhaseCargoCommand.sh new file mode 100755 index 00000000000..1188d00953e --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/buildPhaseCargoCommand.sh @@ -0,0 +1,39 @@ +#!/usr/bin/env bash + +function run_or_fail { + "$@" + local status=$? + if [ $status -ne 0 ]; then + echo "Command '$*' failed with exit code $status" >&2 + exit $status + fi +} +function run_if_available { + if command -v "$1" >/dev/null 2>&1; then + "$@" + else + echo "$1 is not installed. Please install it to use this feature." >&2 + fi +} + +export self_path=$(dirname "$(readlink -f "$0")") + +# Clear out the existing build artifacts as these aren't automatically removed by wasm-pack. +if [ -d ./pkg/ ]; then + rm -rf $self_path/pkg/ +fi + +TARGET=wasm32-unknown-unknown +WASM_BINARY=$CARGO_TARGET_DIR/$TARGET/release/${pname}.wasm + +NODE_DIR=$self_path/nodejs/ +BROWSER_DIR=$self_path/web/ +NODE_WASM=${NODE_DIR}/${pname}_bg.wasm +BROWSER_WASM=${BROWSER_DIR}/${pname}_bg.wasm + +# Build the new wasm package +run_or_fail cargo build --lib --release --target $TARGET --package ${pname} +run_or_fail wasm-bindgen $WASM_BINARY --out-dir $NODE_DIR --typescript --target nodejs +run_or_fail wasm-bindgen $WASM_BINARY --out-dir $BROWSER_DIR --typescript --target web +run_if_available wasm-opt $NODE_WASM -o $NODE_WASM -O +run_if_available wasm-opt $BROWSER_WASM -o $BROWSER_WASM -O \ No newline at end of file diff --git a/noir/tooling/noirc_abi_wasm/installPhase.sh b/noir/tooling/noirc_abi_wasm/installPhase.sh new file mode 100755 index 00000000000..d9b94f2d171 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/installPhase.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash +export self_path=$(dirname "$(readlink -f "$0")") + +export out_path=$out/noirc_abi_wasm + +mkdir -p $out_path +cp $self_path/README.md $out_path/ +cp $self_path/package.json $out_path/ +cp -r $self_path/nodejs $out_path/ +cp -r $self_path/web $out_path/ diff --git a/noir/tooling/noirc_abi_wasm/package.json b/noir/tooling/noirc_abi_wasm/package.json new file mode 100644 index 00000000000..d679ec37194 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/package.json @@ -0,0 +1,43 @@ +{ + "name": "@noir-lang/noirc_abi", + "collaborators": [ + "The Noir Team " + ], + "version": "0.19.3", + "license": "(MIT OR Apache-2.0)", + "files": [ + "nodejs", + "web", + "package.json" + ], + "publishConfig": { + "access": "public" + }, + "main": "./nodejs/noirc_abi_wasm.js", + "types": "./web/noirc_abi_wasm.d.ts", + "module": "./web/noirc_abi_wasm.js", + "sideEffects": false, + "repository": { + "type": "git", + "url": "https://github.com/noir-lang/noir.git" + }, + "scripts": { + "build": "bash ./build.sh", + "test": "env TS_NODE_COMPILER_OPTIONS='{\"module\": \"commonjs\" }' mocha", + "test:browser": "web-test-runner", + "clean": "chmod u+w web nodejs || true && rm -rf ./nodejs ./web ./target ./result", + "nightly:version": "jq --arg new_version \"-$(git rev-parse --short HEAD)$1\" '.version = .version + $new_version' package.json > package-tmp.json && mv package-tmp.json package.json", + "publish": "echo 📡 publishing `$npm_package_name` && yarn npm publish", + "lint": "NODE_NO_WARNINGS=1 eslint . --ext .ts --ignore-path ./.eslintignore --max-warnings 0", + "build:nix": "nix build -L .#noirc_abi_wasm", + "install:from:nix": "yarn clean && yarn build:nix && cp -rL ./result/noirc_abi_wasm/nodejs ./ && cp -rL ./result/noirc_abi_wasm/web ./" + }, + "devDependencies": { + "@esm-bundle/chai": "^4.3.4-fix.0", + "@web/dev-server-esbuild": "^0.3.6", + "@web/test-runner": "^0.15.3", + "@web/test-runner-playwright": "^0.10.0", + "eslint": "^8.50.0", + "mocha": "^10.2.0" + } +} diff --git a/noir/tooling/noirc_abi_wasm/src/errors.rs b/noir/tooling/noirc_abi_wasm/src/errors.rs new file mode 100644 index 00000000000..14ee2d5fd8e --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/src/errors.rs @@ -0,0 +1,47 @@ +use js_sys::{Error, JsString}; +use noirc_abi::errors::{AbiError, InputParserError}; +use wasm_bindgen::prelude::wasm_bindgen; + +#[wasm_bindgen(typescript_custom_section)] +const ABI_ERROR: &'static str = r#" +export type ABIError = Error; +"#; + +/// JsAbiError is a raw js error. +/// It'd be ideal that ABI error was a subclass of Error, but for that we'd need to use JS snippets or a js module. +/// Currently JS snippets don't work with a nodejs target. And a module would be too much for just a custom error type. +#[wasm_bindgen] +extern "C" { + #[wasm_bindgen(extends = Error, js_name = "AbiError", typescript_type = "AbiError")] + #[derive(Clone, Debug, PartialEq, Eq)] + pub type JsAbiError; + + #[wasm_bindgen(constructor, js_class = "Error")] + fn constructor(message: JsString) -> JsAbiError; +} + +impl JsAbiError { + /// Creates a new execution error with the given call stack. + /// Call stacks won't be optional in the future, after removing ErrorLocation in ACVM. + pub fn new(message: String) -> Self { + JsAbiError::constructor(JsString::from(message)) + } +} + +impl From for JsAbiError { + fn from(value: String) -> Self { + JsAbiError::new(value) + } +} + +impl From for JsAbiError { + fn from(value: AbiError) -> Self { + JsAbiError::new(value.to_string()) + } +} + +impl From for JsAbiError { + fn from(value: InputParserError) -> Self { + JsAbiError::new(value.to_string()) + } +} diff --git a/noir/tooling/noirc_abi_wasm/src/js_witness_map.rs b/noir/tooling/noirc_abi_wasm/src/js_witness_map.rs new file mode 100644 index 00000000000..fcc6e75f18c --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/src/js_witness_map.rs @@ -0,0 +1,114 @@ +//! This can most likely be imported from acvm_js to avoid redefining it here. + +use acvm::{ + acir::native_types::{Witness, WitnessMap}, + FieldElement, +}; +use js_sys::{JsString, Map}; +use wasm_bindgen::prelude::{wasm_bindgen, JsValue}; + +#[wasm_bindgen(typescript_custom_section)] +const WITNESS_MAP: &'static str = r#" +// Map from witness index to hex string value of witness. +export type WitnessMap = Map; +"#; + +// WitnessMap +#[wasm_bindgen] +extern "C" { + #[wasm_bindgen(extends = Map, js_name = "WitnessMap", typescript_type = "WitnessMap")] + #[derive(Clone, Debug, PartialEq, Eq)] + pub type JsWitnessMap; + + #[wasm_bindgen(constructor, js_class = "Map")] + pub fn new() -> JsWitnessMap; + +} + +impl Default for JsWitnessMap { + fn default() -> Self { + Self::new() + } +} + +impl From for JsWitnessMap { + fn from(witness_map: WitnessMap) -> Self { + let js_map = JsWitnessMap::new(); + for (key, value) in witness_map { + js_map.set( + &js_sys::Number::from(key.witness_index()), + &field_element_to_js_string(&value), + ); + } + js_map + } +} + +impl From for WitnessMap { + fn from(js_map: JsWitnessMap) -> Self { + let mut witness_map = WitnessMap::new(); + js_map.for_each(&mut |value, key| { + let witness_index = Witness(key.as_f64().unwrap() as u32); + let witness_value = js_value_to_field_element(value).unwrap(); + witness_map.insert(witness_index, witness_value); + }); + witness_map + } +} + +pub(crate) fn js_value_to_field_element(js_value: JsValue) -> Result { + let hex_str = js_value.as_string().ok_or("failed to parse field element from non-string")?; + + FieldElement::from_hex(&hex_str) + .ok_or_else(|| format!("Invalid hex string: '{}'", hex_str).into()) +} + +pub(crate) fn field_element_to_js_string(field_element: &FieldElement) -> JsString { + // This currently maps `0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000000` + // to the bigint `-1n`. This fails when converting back to a `FieldElement`. + // js_sys::BigInt::from_str(&value.to_hex()).unwrap() + + format!("0x{}", field_element.to_hex()).into() +} + +#[cfg(test)] +mod test { + use wasm_bindgen_test::wasm_bindgen_test as test; + + use std::collections::BTreeMap; + + use acvm::{ + acir::native_types::{Witness, WitnessMap}, + FieldElement, + }; + use wasm_bindgen::JsValue; + + use crate::JsWitnessMap; + + #[test] + fn test_witness_map_to_js() { + let witness_map = BTreeMap::from([ + (Witness(1), FieldElement::one()), + (Witness(2), FieldElement::zero()), + (Witness(3), -FieldElement::one()), + ]); + let witness_map = WitnessMap::from(witness_map); + + let js_map = JsWitnessMap::from(witness_map); + + assert_eq!( + js_map.get(&JsValue::from(1)), + JsValue::from_str("0x0000000000000000000000000000000000000000000000000000000000000001") + ); + assert_eq!( + js_map.get(&JsValue::from(2)), + JsValue::from_str("0x0000000000000000000000000000000000000000000000000000000000000000") + ); + assert_eq!( + js_map.get(&JsValue::from(3)), + // Equal to 21888242871839275222246405745257275088548364400416034343698204186575808495616, + // which is field modulus - 1: https://docs.rs/ark-bn254/latest/ark_bn254/ + JsValue::from_str("0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000000") + ); + } +} diff --git a/noir/tooling/noirc_abi_wasm/src/lib.rs b/noir/tooling/noirc_abi_wasm/src/lib.rs new file mode 100644 index 00000000000..734ca1ff40e --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/src/lib.rs @@ -0,0 +1,149 @@ +#![warn(unused_crate_dependencies, unused_extern_crates)] +#![warn(unreachable_pub)] +#![warn(clippy::semicolon_if_nothing_returned)] + +// See Cargo.toml for explanation. +use getrandom as _; + +use acvm::acir::native_types::WitnessMap; +use iter_extended::try_btree_map; +use noirc_abi::{ + errors::InputParserError, + input_parser::{json::JsonTypes, InputValue}, + Abi, MAIN_RETURN_NAME, +}; +use serde::Serialize; +use std::collections::BTreeMap; + +use gloo_utils::format::JsValueSerdeExt; +use wasm_bindgen::{prelude::wasm_bindgen, JsValue}; + +mod errors; +mod js_witness_map; + +use errors::JsAbiError; +use js_witness_map::JsWitnessMap; + +#[wasm_bindgen(typescript_custom_section)] +const INPUT_MAP: &'static str = r#" +export type Field = string | number | boolean; +export type InputValue = Field | Field[] | InputMap; +export type InputMap = { [key: string]: InputValue }; +"#; + +#[wasm_bindgen] +extern "C" { + #[wasm_bindgen(extends = js_sys::Object, js_name = "InputMap", typescript_type = "InputMap")] + #[derive(Clone, Debug, PartialEq, Eq)] + pub type JsInputMap; +} + +#[wasm_bindgen] +extern "C" { + #[wasm_bindgen(extends = js_sys::Object, js_name = "InputValue", typescript_type = "InputValue")] + #[derive(Clone, Debug, PartialEq, Eq)] + pub type JsInputValue; +} + +#[wasm_bindgen(typescript_custom_section)] +const ABI: &'static str = r#" +export type Visibility = "public" | "private"; +export type Sign = "unsigned" | "signed"; +export type AbiType = + { kind: "field" } | + { kind: "boolean" } | + { kind: "string", length: number } | + { kind: "integer", sign: Sign, width: number } | + { kind: "array", length: number, type: AbiType } | + { kind: "tuple", fields: AbiType[] } | + { kind: "struct", path: string, fields: [string, AbiType][] }; + +export type AbiParameter = { + name: string, + type: AbiType, + visibility: Visibility, +}; + +export type Abi = { + parameters: AbiParameter[], + param_witnesses: Record, + return_type: AbiType | null, + return_witnesses: number[], +} +"#; + +#[wasm_bindgen] +extern "C" { + #[wasm_bindgen(extends = js_sys::Object, js_name = "Abi", typescript_type = "Abi")] + #[derive(Clone, Debug, PartialEq, Eq)] + pub type JsAbi; +} + +#[wasm_bindgen(js_name = abiEncode)] +pub fn abi_encode( + abi: JsAbi, + inputs: JsInputMap, + return_value: Option, +) -> Result { + console_error_panic_hook::set_once(); + let abi: Abi = + JsValueSerdeExt::into_serde(&JsValue::from(abi)).map_err(|err| err.to_string())?; + let inputs: BTreeMap = + JsValueSerdeExt::into_serde(&JsValue::from(inputs)).map_err(|err| err.to_string())?; + let return_value: Option = return_value + .map(|return_value| { + let toml_return_value = JsValueSerdeExt::into_serde(&JsValue::from(return_value)) + .expect("could not decode return value"); + InputValue::try_from_json( + toml_return_value, + abi.return_type.as_ref().unwrap(), + MAIN_RETURN_NAME, + ) + }) + .transpose()?; + + let abi_map = abi.to_btree_map(); + let parsed_inputs: BTreeMap = + try_btree_map(abi_map, |(arg_name, abi_type)| { + // Check that toml contains a value for each argument in the ABI. + let value = inputs + .get(&arg_name) + .ok_or_else(|| InputParserError::MissingArgument(arg_name.clone()))?; + InputValue::try_from_json(value.clone(), &abi_type, &arg_name) + .map(|input_value| (arg_name, input_value)) + })?; + + let witness_map = abi.encode(&parsed_inputs, return_value)?; + + Ok(witness_map.into()) +} + +#[wasm_bindgen(js_name = abiDecode)] +pub fn abi_decode(abi: JsAbi, witness_map: JsWitnessMap) -> Result { + console_error_panic_hook::set_once(); + let abi: Abi = + JsValueSerdeExt::into_serde(&JsValue::from(abi)).map_err(|err| err.to_string())?; + + let witness_map = WitnessMap::from(witness_map); + + let (inputs, return_value) = abi.decode(&witness_map)?; + + let abi_types = abi.to_btree_map(); + let inputs_map: BTreeMap = try_btree_map(inputs, |(key, value)| { + JsonTypes::try_from_input_value(&value, &abi_types[&key]).map(|value| (key, value)) + })?; + + let return_value = return_value + .map(|value| JsonTypes::try_from_input_value(&value, &abi.return_type.unwrap())) + .transpose()?; + + #[derive(Serialize)] + struct InputsAndReturn { + inputs: BTreeMap, + return_value: Option, + } + + let return_struct = InputsAndReturn { inputs: inputs_map, return_value }; + ::from_serde(&return_struct) + .map_err(|err| err.to_string().into()) +} diff --git a/noir/tooling/noirc_abi_wasm/test/browser/abi_encode.test.ts b/noir/tooling/noirc_abi_wasm/test/browser/abi_encode.test.ts new file mode 100644 index 00000000000..e1aaf0dc2c0 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/test/browser/abi_encode.test.ts @@ -0,0 +1,21 @@ +import { expect } from '@esm-bundle/chai'; +import initNoirAbi, { abiEncode, abiDecode, WitnessMap, Field } from '@noir-lang/noirc_abi'; +import { DecodedInputs } from '../types'; + +beforeEach(async () => { + await initNoirAbi(); +}); + +it('recovers original inputs when abi encoding and decoding', async () => { + const { abi, inputs } = await import('../shared/abi_encode'); + + const initial_witness: WitnessMap = abiEncode(abi, inputs); + const decoded_inputs: DecodedInputs = abiDecode(abi, initial_witness); + + const foo: Field = inputs.foo as Field; + const bar: Field[] = inputs.bar as Field[]; + expect(BigInt(decoded_inputs.inputs.foo)).to.be.equal(BigInt(foo)); + expect(BigInt(decoded_inputs.inputs.bar[0])).to.be.equal(BigInt(bar[0])); + expect(BigInt(decoded_inputs.inputs.bar[1])).to.be.equal(BigInt(bar[1])); + expect(decoded_inputs.return_value).to.be.null; +}); diff --git a/noir/tooling/noirc_abi_wasm/test/browser/errors.test.ts b/noir/tooling/noirc_abi_wasm/test/browser/errors.test.ts new file mode 100644 index 00000000000..429a2d446a3 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/test/browser/errors.test.ts @@ -0,0 +1,26 @@ +import { expect } from '@esm-bundle/chai'; +import initNoirAbi, { abiEncode } from '@noir-lang/noirc_abi'; + +beforeEach(async () => { + await initNoirAbi(); +}); + +it('errors when an integer input overflows', async () => { + const { abi, inputs } = await import('../shared/uint_overflow'); + + expect(() => abiEncode(abi, inputs)).to.throw( + 'The parameter foo is expected to be a Integer { sign: Unsigned, width: 32 } but found incompatible value Field(2³⁸)', + ); +}); + +it('errors when passing a field in place of an array', async () => { + const { abi, inputs } = await import('../shared/field_as_array'); + + expect(() => abiEncode(abi, inputs)).to.throw('cannot parse value into Array { length: 2, typ: Field }'); +}); + +it('errors when passing an array in place of a field', async () => { + const { abi, inputs } = await import('../shared/array_as_field'); + + expect(() => abiEncode(abi, inputs)).to.throw('cannot parse value into Field'); +}); diff --git a/noir/tooling/noirc_abi_wasm/test/node/abi_encode.test.ts b/noir/tooling/noirc_abi_wasm/test/node/abi_encode.test.ts new file mode 100644 index 00000000000..a49c10b6ea6 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/test/node/abi_encode.test.ts @@ -0,0 +1,17 @@ +import { expect } from 'chai'; +import { abiEncode, abiDecode, WitnessMap, Field } from '@noir-lang/noirc_abi'; +import { DecodedInputs } from '../types'; + +it('recovers original inputs when abi encoding and decoding', async () => { + const { abi, inputs } = await import('../shared/abi_encode'); + + const initial_witness: WitnessMap = abiEncode(abi, inputs); + const decoded_inputs: DecodedInputs = abiDecode(abi, initial_witness); + + const foo: Field = inputs.foo as Field; + const bar: Field[] = inputs.bar as Field[]; + expect(BigInt(decoded_inputs.inputs.foo)).to.be.equal(BigInt(foo)); + expect(BigInt(decoded_inputs.inputs.bar[0])).to.be.equal(BigInt(bar[0])); + expect(BigInt(decoded_inputs.inputs.bar[1])).to.be.equal(BigInt(bar[1])); + expect(decoded_inputs.return_value).to.be.null; +}); diff --git a/noir/tooling/noirc_abi_wasm/test/node/errors.test.ts b/noir/tooling/noirc_abi_wasm/test/node/errors.test.ts new file mode 100644 index 00000000000..0d007e64803 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/test/node/errors.test.ts @@ -0,0 +1,22 @@ +import { expect } from 'chai'; +import { abiEncode } from '@noir-lang/noirc_abi'; + +it('errors when an integer input overflows', async () => { + const { abi, inputs } = await import('../shared/uint_overflow'); + + expect(() => abiEncode(abi, inputs)).to.throw( + 'The parameter foo is expected to be a Integer { sign: Unsigned, width: 32 } but found incompatible value Field(2³⁸)', + ); +}); + +it('errors when passing a field in place of an array', async () => { + const { abi, inputs } = await import('../shared/field_as_array'); + + expect(() => abiEncode(abi, inputs)).to.throw('cannot parse value into Array { length: 2, typ: Field }'); +}); + +it('errors when passing an array in place of a field', async () => { + const { abi, inputs } = await import('../shared/array_as_field'); + + expect(() => abiEncode(abi, inputs)).to.throw('cannot parse value into Field'); +}); diff --git a/noir/tooling/noirc_abi_wasm/test/shared/abi_encode.ts b/noir/tooling/noirc_abi_wasm/test/shared/abi_encode.ts new file mode 100644 index 00000000000..cb80c6710ba --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/test/shared/abi_encode.ts @@ -0,0 +1,20 @@ +import { Abi, InputMap } from '@noir-lang/noirc_abi'; + +export const abi: Abi = { + parameters: [ + { name: 'foo', type: { kind: 'field' }, visibility: 'private' }, + { + name: 'bar', + type: { kind: 'array', length: 2, type: { kind: 'field' } }, + visibility: 'private', + }, + ], + param_witnesses: { foo: [{ start: 1, end: 2 }], bar: [{ start: 2, end: 4 }] }, + return_type: null, + return_witnesses: [], +}; + +export const inputs: InputMap = { + foo: '1', + bar: ['1', '2'], +}; diff --git a/noir/tooling/noirc_abi_wasm/test/shared/array_as_field.ts b/noir/tooling/noirc_abi_wasm/test/shared/array_as_field.ts new file mode 100644 index 00000000000..0cc0035fa68 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/test/shared/array_as_field.ts @@ -0,0 +1,18 @@ +import { Abi, InputMap } from '@noir-lang/noirc_abi'; + +export const abi: Abi = { + parameters: [ + { + name: 'foo', + type: { kind: 'field' }, + visibility: 'private', + }, + ], + param_witnesses: { foo: [{ start: 1, end: 3 }] }, + return_type: null, + return_witnesses: [], +}; + +export const inputs: InputMap = { + foo: ['1', '2'], +}; diff --git a/noir/tooling/noirc_abi_wasm/test/shared/field_as_array.ts b/noir/tooling/noirc_abi_wasm/test/shared/field_as_array.ts new file mode 100644 index 00000000000..6ae709459de --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/test/shared/field_as_array.ts @@ -0,0 +1,18 @@ +import { Abi, InputMap } from '@noir-lang/noirc_abi'; + +export const abi: Abi = { + parameters: [ + { + name: 'foo', + type: { kind: 'array', length: 2, type: { kind: 'field' } }, + visibility: 'private', + }, + ], + param_witnesses: { foo: [{ start: 1, end: 3 }] }, + return_type: null, + return_witnesses: [], +}; + +export const inputs: InputMap = { + foo: '1', +}; diff --git a/noir/tooling/noirc_abi_wasm/test/shared/uint_overflow.ts b/noir/tooling/noirc_abi_wasm/test/shared/uint_overflow.ts new file mode 100644 index 00000000000..c6e066e2bcd --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/test/shared/uint_overflow.ts @@ -0,0 +1,18 @@ +import { Abi, InputMap } from '@noir-lang/noirc_abi'; + +export const abi: Abi = { + parameters: [ + { + name: 'foo', + type: { kind: 'integer', sign: 'unsigned', width: 32 }, + visibility: 'private', + }, + ], + param_witnesses: { foo: [{ start: 1, end: 2 }] }, + return_type: null, + return_witnesses: [], +}; + +export const inputs: InputMap = { + foo: `0x${(1n << 38n).toString(16)}`, +}; diff --git a/noir/tooling/noirc_abi_wasm/test/types.ts b/noir/tooling/noirc_abi_wasm/test/types.ts new file mode 100644 index 00000000000..3a72544b8d0 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/test/types.ts @@ -0,0 +1,2 @@ +// eslint-disable-next-line @typescript-eslint/no-explicit-any +export type DecodedInputs = { inputs: Record; return_value: any }; diff --git a/noir/tooling/noirc_abi_wasm/tsconfig.json b/noir/tooling/noirc_abi_wasm/tsconfig.json new file mode 100644 index 00000000000..eef2ad84833 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/tsconfig.json @@ -0,0 +1,17 @@ +{ + "compilerOptions": { + "moduleResolution": "node", + "outDir": "lib", + "target": "ESNext", + "module": "ESNext", + "strict": true, + "experimentalDecorators": true, + "esModuleInterop": true, + "noImplicitAny": true, + "removeComments": false, + "preserveConstEnums": true, + "sourceMap": true, + "resolveJsonModule": true, + "importHelpers": true + } +} \ No newline at end of file diff --git a/noir/tooling/noirc_abi_wasm/web-test-runner.config.mjs b/noir/tooling/noirc_abi_wasm/web-test-runner.config.mjs new file mode 100644 index 00000000000..282c19c1cd2 --- /dev/null +++ b/noir/tooling/noirc_abi_wasm/web-test-runner.config.mjs @@ -0,0 +1,32 @@ +import { defaultReporter } from "@web/test-runner"; +import { summaryReporter } from "@web/test-runner"; +import { fileURLToPath } from "url"; +import { esbuildPlugin } from "@web/dev-server-esbuild"; +import { playwrightLauncher } from "@web/test-runner-playwright"; + +// eslint-disable-next-line no-undef +const reporter = process.env.CI ? summaryReporter() : defaultReporter(); + +export default { + browsers: [ + playwrightLauncher({ product: "chromium" }), + // playwrightLauncher({ product: "webkit" }), + // playwrightLauncher({ product: "firefox" }), + ], + plugins: [ + esbuildPlugin({ + ts: true, + }), + ], + files: ["test/browser/**/*.test.ts"], + nodeResolve: true, + testFramework: { + config: { + ui: "bdd", + timeout: 40000, + }, + }, + // eslint-disable-next-line no-undef + rootDir: fileURLToPath(new URL("./../../", import.meta.url)), + reporters: [reporter], +}; diff --git a/noir/tooling/readme.md b/noir/tooling/readme.md new file mode 100644 index 00000000000..20d1b560b5b --- /dev/null +++ b/noir/tooling/readme.md @@ -0,0 +1,15 @@ +# Structure + +Below we briefly describe the purpose of each tool-related crate in this repository. + +## nargo + +This is the default package manager used by Noir. One may draw similarities to Rusts' Cargo. + +## nargo_fmt + +This is the default formatter used by Noir, analogous to Rust's rustfmt. + +## lsp + +This is the platform agnostic implementation of Noir's Language Server. It implements the various features supported, but doesn't bind to any particular transport. Binding to a transport must be done when consuming the crate. diff --git a/noir/wasm-bindgen-cli.nix b/noir/wasm-bindgen-cli.nix new file mode 100644 index 00000000000..7c3910f032e --- /dev/null +++ b/noir/wasm-bindgen-cli.nix @@ -0,0 +1,43 @@ +{ lib +, rustPlatform +, fetchCrate +, nodejs +, pkg-config +, openssl +, stdenv +, curl +, darwin +, libiconv +, runCommand +}: + +rustPlatform.buildRustPackage rec { + pname = "wasm-bindgen-cli"; + version = "0.2.86"; + + src = fetchCrate { + inherit pname version; + sha256 = "sha256-56EOiLbdgAcoTrkyvB3t9TjtLaRvGxFUXx4haLwE2QY="; + }; + + cargoSha256 = "sha256-4CPBmz92PuPN6KeGDTdYPAf5+vTFk9EN5Cmx4QJy6yI="; + + nativeBuildInputs = [ pkg-config ]; + + buildInputs = [ openssl ] ++ lib.optionals stdenv.isDarwin [ + curl + # Need libiconv and apple Security on Darwin. See https://github.com/ipetkov/crane/issues/156 + libiconv + darwin.apple_sdk.frameworks.Security + ]; + + doCheck = false; + + meta = with lib; { + homepage = "https://rustwasm.github.io/docs/wasm-bindgen/"; + license = with licenses; [ asl20 /* or */ mit ]; + description = "Facilitating high-level interactions between wasm modules and JavaScript"; + maintainers = with maintainers; [ nitsky rizary ]; + mainProgram = "wasm-bindgen"; + }; +} diff --git a/noir/yarn.lock b/noir/yarn.lock new file mode 100644 index 00000000000..9c6a447d718 --- /dev/null +++ b/noir/yarn.lock @@ -0,0 +1,17858 @@ +# This file is generated by running "yarn install" inside your project. +# Manual changes might be lost - proceed with caution! + +__metadata: + version: 6 + cacheKey: 8 + +"@75lb/deep-merge@npm:^1.1.1": + version: 1.1.1 + resolution: "@75lb/deep-merge@npm:1.1.1" + dependencies: + lodash.assignwith: ^4.2.0 + typical: ^7.1.1 + checksum: 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ps-tree: ^1.2.0 + which: ^2.0.2 + yaml: ^2.1.1 + bin: + zx: build/cli.js + checksum: 510c56366a62220108247624a83fc088bfc8f393e3f20168d69a67e3c7aff1a4b790c118ee70e58171dcbc91b53ab079bba3074e1c40829f6179a7f48a38be78 + languageName: node + linkType: hard diff --git a/release-please-config.json b/release-please-config.json index fa44df2381a..09f9e248f7e 100644 --- a/release-please-config.json +++ b/release-please-config.json @@ -19,8 +19,7 @@ ".": { "release-type": "simple", "component": "aztec-packages", - "package-name": "aztec-packages", - "extra-files": ["VERSION"] + "package-name": "aztec-packages" }, "barretenberg/ts": { "release-type": "node", @@ -31,12 +30,7 @@ "release-type": "simple", "component": "barretenberg", "package-name": "barretenberg", - "extra-files": [ - "cpp/CMakeLists.txt", - "VERSION", - "barretenberg.nix", - "barretenberg-wasm.nix" - ] + "extra-files": ["cpp/CMakeLists.txt"] } }, "plugins": [ diff --git a/yarn-project/acir-simulator/README.md b/yarn-project/acir-simulator/README.md index 0885fabd147..bc61142d327 100644 --- a/yarn-project/acir-simulator/README.md +++ b/yarn-project/acir-simulator/README.md @@ -10,7 +10,7 @@ It's able to simulate three different types of functions: ### Private Functions -Private functions are simulated and proved clientside, and verified clientside in the private kernel circuit. +Private functions are simulated and proved client-side, and verified client-side in the private kernel circuit. The public inputs of private functions is defined [here](../circuits.js/src/structs/private_circuit_public_inputs.ts). @@ -30,7 +30,7 @@ Public functions can call other public function, but no private functions. ### Unconstrained (view) Functions -Unconstrained functions are used to extract useful data for users, such as the user balance. They are not proved, and are simulated clientside. +Unconstrained functions are used to extract useful data for users, such as the user balance. They are not proved, and are simulated client-side. They are run with the assistance of a DB oracle that provides any private data requested by the function. diff --git a/yarn-project/acir-simulator/package.json b/yarn-project/acir-simulator/package.json index c1a7dda3a84..f5c2a79bfbc 100644 --- a/yarn-project/acir-simulator/package.json +++ b/yarn-project/acir-simulator/package.json @@ -15,7 +15,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" }, "inherits": [ @@ -42,7 +42,6 @@ "@aztec/merkle-tree": "workspace:^", "@aztec/noir-contracts": "workspace:^", "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/levelup": "^5.1.3", "@types/memdown": "^3.0.2", diff --git a/yarn-project/acir-simulator/src/acvm/acvm.ts b/yarn-project/acir-simulator/src/acvm/acvm.ts index 68a91c61fac..396dd8d5e07 100644 --- a/yarn-project/acir-simulator/src/acvm/acvm.ts +++ b/yarn-project/acir-simulator/src/acvm/acvm.ts @@ -7,23 +7,13 @@ import { ForeignCallInput, ForeignCallOutput, WasmBlackBoxFunctionSolver, - WitnessMap, executeCircuitWithBlackBoxSolver, } from '@noir-lang/acvm_js'; import { traverseCauseChain } from '../common/errors.js'; +import { ACVMWitness } from './acvm_types.js'; import { ORACLE_NAMES } from './oracle/index.js'; -/** - * The format for fields on the ACVM. - */ -export type ACVMField = string; - -/** - * The format for witnesses of the ACVM. - */ -export type ACVMWitness = WitnessMap; - /** * The callback interface for the ACIR. */ diff --git a/yarn-project/acir-simulator/src/acvm/acvm_types.ts b/yarn-project/acir-simulator/src/acvm/acvm_types.ts new file mode 100644 index 00000000000..d155d668048 --- /dev/null +++ b/yarn-project/acir-simulator/src/acvm/acvm_types.ts @@ -0,0 +1,11 @@ +import { WitnessMap } from '@noir-lang/acvm_js'; + +/** + * ACVMField + */ +export type ACVMField = string; + +/** + * The format for witnesses of the ACVM. + */ +export type ACVMWitness = WitnessMap; diff --git a/yarn-project/acir-simulator/src/acvm/deserialize.ts b/yarn-project/acir-simulator/src/acvm/deserialize.ts index 811be00abbe..d82640a42e9 100644 --- a/yarn-project/acir-simulator/src/acvm/deserialize.ts +++ b/yarn-project/acir-simulator/src/acvm/deserialize.ts @@ -26,7 +26,7 @@ import { Tuple } from '@aztec/foundation/serialize'; import { getReturnWitness } from '@noir-lang/acvm_js'; -import { ACVMField, ACVMWitness } from './acvm.js'; +import { ACVMField, ACVMWitness } from './acvm_types.js'; /** * Converts an ACVM field to a Buffer. @@ -105,7 +105,9 @@ export class PublicInputsReader { */ public readField(): Fr { const acvmField = this.publicInputs.shift(); - if (!acvmField) throw new Error('Not enough public inputs'); + if (!acvmField) { + throw new Error('Not enough public inputs'); + } return fromACVMField(acvmField); } diff --git a/yarn-project/acir-simulator/src/acvm/index.ts b/yarn-project/acir-simulator/src/acvm/index.ts index 8845721ded2..23067f6094b 100644 --- a/yarn-project/acir-simulator/src/acvm/index.ts +++ b/yarn-project/acir-simulator/src/acvm/index.ts @@ -1,4 +1,5 @@ export * from './acvm.js'; +export * from './acvm_types.js'; export * from './deserialize.js'; export * from './oracle/index.js'; export * from './serialize.js'; diff --git a/yarn-project/acir-simulator/src/acvm/oracle/debug.ts b/yarn-project/acir-simulator/src/acvm/oracle/debug.ts index abe82e084ef..e3080d16ac1 100644 --- a/yarn-project/acir-simulator/src/acvm/oracle/debug.ts +++ b/yarn-project/acir-simulator/src/acvm/oracle/debug.ts @@ -1,6 +1,6 @@ import { ForeignCallInput } from '@noir-lang/acvm_js'; -import { ACVMField } from '../acvm.js'; +import { ACVMField } from '../acvm_types.js'; /** * Convert an array of ACVMFields to a string. diff --git a/yarn-project/acir-simulator/src/acvm/oracle/oracle.ts b/yarn-project/acir-simulator/src/acvm/oracle/oracle.ts index 08944796c0c..443b1cda7bb 100644 --- a/yarn-project/acir-simulator/src/acvm/oracle/oracle.ts +++ b/yarn-project/acir-simulator/src/acvm/oracle/oracle.ts @@ -6,8 +6,8 @@ import { Fr, Point } from '@aztec/foundation/fields'; import { createDebugLogger } from '@aztec/foundation/log'; import { UnencryptedL2Log } from '@aztec/types'; -import { ACVMField } from '../acvm.js'; -import { convertACVMFieldToBuffer, fromACVMField } from '../deserialize.js'; +import { ACVMField } from '../acvm_types.js'; +import { fromACVMField } from '../deserialize.js'; import { toACVMField, toAcvmCallPrivateStackItem, @@ -49,10 +49,20 @@ export class Oracle { async getAuthWitness([messageHash]: ACVMField[]): Promise { const messageHashField = fromACVMField(messageHash); const witness = await this.typedOracle.getAuthWitness(messageHashField); - if (!witness) throw new Error(`Authorization not found for message hash ${messageHashField}`); + if (!witness) { + throw new Error(`Authorization not found for message hash ${messageHashField}`); + } return witness.map(toACVMField); } + async popCapsule(): Promise { + const capsule = await this.typedOracle.popCapsule(); + if (!capsule) { + throw new Error(`No capsules available`); + } + return capsule.map(toACVMField); + } + async getNotes( [storageSlot]: ACVMField[], [numSelects]: ACVMField[], @@ -161,7 +171,7 @@ export class Oracle { } emitUnencryptedLog([contractAddress]: ACVMField[], [eventSelector]: ACVMField[], message: ACVMField[]): ACVMField { - const logPayload = Buffer.concat(message.map(charBuffer => convertACVMFieldToBuffer(charBuffer).subarray(-1))); + const logPayload = Buffer.concat(message.map(charBuffer => Fr.fromString(charBuffer).toBuffer().subarray(-1))); const log = new UnencryptedL2Log( AztecAddress.fromString(contractAddress), FunctionSelector.fromField(fromACVMField(eventSelector)), // TODO https://github.com/AztecProtocol/aztec-packages/issues/2632 diff --git a/yarn-project/acir-simulator/src/acvm/oracle/typed_oracle.ts b/yarn-project/acir-simulator/src/acvm/oracle/typed_oracle.ts index a864471a722..0dfbd9affcd 100644 --- a/yarn-project/acir-simulator/src/acvm/oracle/typed_oracle.ts +++ b/yarn-project/acir-simulator/src/acvm/oracle/typed_oracle.ts @@ -80,6 +80,10 @@ export abstract class TypedOracle { throw new Error('Not available.'); } + popCapsule(): Promise { + throw new Error('Not available.'); + } + getNotes( _storageSlot: Fr, _numSelects: number, diff --git a/yarn-project/acir-simulator/src/acvm/serialize.ts b/yarn-project/acir-simulator/src/acvm/serialize.ts index bfb63d3ffbd..dff75ab6cb9 100644 --- a/yarn-project/acir-simulator/src/acvm/serialize.ts +++ b/yarn-project/acir-simulator/src/acvm/serialize.ts @@ -12,8 +12,8 @@ import { AztecAddress } from '@aztec/foundation/aztec-address'; import { EthAddress } from '@aztec/foundation/eth-address'; import { Fr } from '@aztec/foundation/fields'; -import { ACVMField } from './acvm.js'; -import { MessageLoadOracleInputs } from './oracle/index.js'; +import { ACVMField } from './acvm_types.js'; +import { MessageLoadOracleInputs } from './oracle/typed_oracle.js'; /** * Adapts the buffer to the field size. @@ -147,8 +147,8 @@ export function toACVMPublicInputs(publicInputs: PrivateCircuitPublicInputs): AC ...publicInputs.newCommitments.map(toACVMField), ...publicInputs.newNullifiers.map(toACVMField), ...publicInputs.nullifiedCommitments.map(toACVMField), - ...publicInputs.privateCallStack.map(toACVMField), - ...publicInputs.publicCallStack.map(toACVMField), + ...publicInputs.privateCallStackHashes.map(toACVMField), + ...publicInputs.publicCallStackHashes.map(toACVMField), ...publicInputs.newL2ToL1Msgs.map(toACVMField), ...publicInputs.encryptedLogsHash.map(toACVMField), ...publicInputs.unencryptedLogsHash.map(toACVMField), diff --git a/yarn-project/acir-simulator/src/client/client_execution_context.ts b/yarn-project/acir-simulator/src/client/client_execution_context.ts index 579771f109d..5ccbbcc2b99 100644 --- a/yarn-project/acir-simulator/src/client/client_execution_context.ts +++ b/yarn-project/acir-simulator/src/client/client_execution_context.ts @@ -38,7 +38,7 @@ import { ViewDataOracle } from './view_data_oracle.js'; export class ClientExecutionContext extends ViewDataOracle { /** * New notes created during this execution. - * It's possible that a note in this list has been nullified (in the same or other executions) and doen't exist in the ExecutionNoteCache and the final proof data. + * It's possible that a note in this list has been nullified (in the same or other executions) and doesn't exist in the ExecutionNoteCache and the final proof data. * But we still include those notes in the execution result because their commitments are still in the public inputs of this execution. * This information is only for references (currently used for tests), and is not used for any sort of constrains. * Users can also use this to get a clearer idea of what's happened during a simulation. diff --git a/yarn-project/acir-simulator/src/client/db_oracle.ts b/yarn-project/acir-simulator/src/client/db_oracle.ts index 6dd86e95d45..7b61a60e6bd 100644 --- a/yarn-project/acir-simulator/src/client/db_oracle.ts +++ b/yarn-project/acir-simulator/src/client/db_oracle.ts @@ -35,6 +35,13 @@ export interface DBOracle extends CommitmentsDB { */ getAuthWitness(messageHash: Fr): Promise; + /** + * Retrieve a capsule from the capsule dispenser. + * @returns A promise that resolves to an array of field elements representing the capsule. + * @remarks A capsule is a "blob" of data that is passed to the contract through an oracle. + */ + popCapsule(): Promise; + /** * Retrieve the secret key associated with a specific public key. * The function only allows access to the secret keys of the transaction creator, diff --git a/yarn-project/acir-simulator/src/client/pick_notes.ts b/yarn-project/acir-simulator/src/client/pick_notes.ts index ca4bbaf1576..e36e193b2ae 100644 --- a/yarn-project/acir-simulator/src/client/pick_notes.ts +++ b/yarn-project/acir-simulator/src/client/pick_notes.ts @@ -78,10 +78,14 @@ const selectNotes = (noteDatas: T[], selects: Select[]): noteDatas.filter(noteData => selects.every(({ index, value }) => noteData.note.items[index]?.equals(value))); const sortNotes = (a: Fr[], b: Fr[], sorts: Sort[], level = 0): number => { - if (sorts[level] === undefined) return 0; + if (sorts[level] === undefined) { + return 0; + } const { index, order } = sorts[level]; - if (order === 0) return 0; + if (order === 0) { + return 0; + } const dir = order === 1 ? [-1, 1] : [1, -1]; return a[index].value === b[index].value diff --git a/yarn-project/acir-simulator/src/client/private_execution.test.ts b/yarn-project/acir-simulator/src/client/private_execution.test.ts index 61ac920141e..fd0a52fac57 100644 --- a/yarn-project/acir-simulator/src/client/private_execution.test.ts +++ b/yarn-project/acir-simulator/src/client/private_execution.test.ts @@ -1,6 +1,5 @@ import { CallContext, - CircuitsWasm, CompleteAddress, ContractDeploymentData, EMPTY_NULLIFIED_COMMITMENT, @@ -15,7 +14,6 @@ import { TxContext, } from '@aztec/circuits.js'; import { - computeCallStackItemHash, computeCommitmentNonce, computeSecretMessageHash, computeVarArgsHash, @@ -144,11 +142,11 @@ describe('Private Execution test suite', () => { const hashFields = (data: Fr[]) => Fr.fromBuffer(pedersenHash(data.map(f => f.toBuffer()))); - beforeAll(async () => { + beforeAll(() => { logger = createDebugLogger('aztec:test:private_execution'); - ownerCompleteAddress = await CompleteAddress.fromPrivateKeyAndPartialAddress(ownerPk, Fr.random()); - recipientCompleteAddress = await CompleteAddress.fromPrivateKeyAndPartialAddress(recipientPk, Fr.random()); + ownerCompleteAddress = CompleteAddress.fromPrivateKeyAndPartialAddress(ownerPk, Fr.random()); + recipientCompleteAddress = CompleteAddress.fromPrivateKeyAndPartialAddress(recipientPk, Fr.random()); owner = ownerCompleteAddress.address; recipient = recipientCompleteAddress.address; @@ -157,8 +155,12 @@ describe('Private Execution test suite', () => { beforeEach(() => { oracle = mock(); oracle.getSecretKey.mockImplementation((contractAddress: AztecAddress, pubKey: PublicKey) => { - if (pubKey.equals(ownerCompleteAddress.publicKey)) return Promise.resolve(ownerPk); - if (pubKey.equals(recipientCompleteAddress.publicKey)) return Promise.resolve(recipientPk); + if (pubKey.equals(ownerCompleteAddress.publicKey)) { + return Promise.resolve(ownerPk); + } + if (pubKey.equals(recipientCompleteAddress.publicKey)) { + return Promise.resolve(recipientPk); + } throw new Error(`Unknown address ${pubKey}`); }); oracle.getHistoricBlockData.mockResolvedValue(blockData); @@ -210,8 +212,12 @@ describe('Private Execution test suite', () => { beforeEach(() => { oracle.getCompleteAddress.mockImplementation((address: AztecAddress) => { - if (address.equals(owner)) return Promise.resolve(ownerCompleteAddress); - if (address.equals(recipient)) return Promise.resolve(recipientCompleteAddress); + if (address.equals(owner)) { + return Promise.resolve(ownerCompleteAddress); + } + if (address.equals(recipient)) { + return Promise.resolve(recipientCompleteAddress); + } throw new Error(`Unknown address ${address}`); }); @@ -364,9 +370,8 @@ describe('Private Execution test suite', () => { expect(result.nestedExecutions[0].callStackItem.publicInputs.returnValues[0]).toEqual(new Fr(privateIncrement)); // check that Aztec.nr calculated the call stack item hash like cpp does - const wasm = await CircuitsWasm.get(); - const expectedCallStackItemHash = computeCallStackItemHash(wasm, result.nestedExecutions[0].callStackItem); - expect(result.callStackItem.publicInputs.privateCallStack[0]).toEqual(expectedCallStackItemHash); + const expectedCallStackItemHash = result.nestedExecutions[0].callStackItem.hash(); + expect(result.callStackItem.publicInputs.privateCallStackHashes[0]).toEqual(expectedCallStackItemHash); }); }); @@ -422,7 +427,9 @@ describe('Private Execution test suite', () => { beforeEach(() => { oracle.getCompleteAddress.mockImplementation((address: AztecAddress) => { - if (address.equals(recipient)) return Promise.resolve(recipientCompleteAddress); + if (address.equals(recipient)) { + return Promise.resolve(recipientCompleteAddress); + } throw new Error(`Unknown address ${address}`); }); }); @@ -549,21 +556,20 @@ describe('Private Execution test suite', () => { sideEffectCounter: 0, }); - const publicCallRequestHash = computeCallStackItemHash( - await CircuitsWasm.get(), - publicCallRequest.toPublicCallStackItem(), - ); + const publicCallRequestHash = publicCallRequest.toPublicCallStackItem().hash(); expect(result.enqueuedPublicFunctionCalls).toHaveLength(1); expect(result.enqueuedPublicFunctionCalls[0]).toEqual(publicCallRequest); - expect(result.callStackItem.publicInputs.publicCallStack[0]).toEqual(publicCallRequestHash); + expect(result.callStackItem.publicInputs.publicCallStackHashes[0]).toEqual(publicCallRequestHash); }); }); describe('pending commitments contract', () => { beforeEach(() => { oracle.getCompleteAddress.mockImplementation((address: AztecAddress) => { - if (address.equals(owner)) return Promise.resolve(ownerCompleteAddress); + if (address.equals(owner)) { + return Promise.resolve(ownerCompleteAddress); + } throw new Error(`Unknown address ${address}`); }); }); diff --git a/yarn-project/acir-simulator/src/client/simulator.test.ts b/yarn-project/acir-simulator/src/client/simulator.test.ts index e24d2de37a2..ea4878704e2 100644 --- a/yarn-project/acir-simulator/src/client/simulator.test.ts +++ b/yarn-project/acir-simulator/src/client/simulator.test.ts @@ -22,8 +22,8 @@ describe('Simulator', () => { const hashFields = (data: Fr[]) => Fr.fromBuffer(pedersenHash(data.map(f => f.toBuffer()))); - beforeAll(async () => { - ownerCompleteAddress = await CompleteAddress.fromPrivateKeyAndPartialAddress(ownerPk, Fr.random()); + beforeAll(() => { + ownerCompleteAddress = CompleteAddress.fromPrivateKeyAndPartialAddress(ownerPk, Fr.random()); owner = ownerCompleteAddress.address; }); diff --git a/yarn-project/acir-simulator/src/client/simulator.ts b/yarn-project/acir-simulator/src/client/simulator.ts index 092fa49b735..cf7222cbe20 100644 --- a/yarn-project/acir-simulator/src/client/simulator.ts +++ b/yarn-project/acir-simulator/src/client/simulator.ts @@ -45,7 +45,9 @@ export class AcirSimulator { * @returns ACVM WasmBlackBoxFunctionSolver */ public static getSolver(): Promise { - if (!this.solver) this.solver = createBlackBoxSolver(); + if (!this.solver) { + this.solver = createBlackBoxSolver(); + } return this.solver; } @@ -75,7 +77,7 @@ export class AcirSimulator { ); } - const curve = await Grumpkin.new(); + const curve = new Grumpkin(); const historicBlockData = await this.db.getHistoricBlockData(); const callContext = new CallContext( @@ -94,7 +96,7 @@ export class AcirSimulator { callContext, historicBlockData, request.authWitnesses, - await PackedArgsCache.create(request.packedArguments), + PackedArgsCache.create(request.packedArguments), new ExecutionNoteCache(), new SideEffectCounter(), this.db, diff --git a/yarn-project/acir-simulator/src/client/unconstrained_execution.test.ts b/yarn-project/acir-simulator/src/client/unconstrained_execution.test.ts index 8cfdf278a2b..7e7eb3716d4 100644 --- a/yarn-project/acir-simulator/src/client/unconstrained_execution.test.ts +++ b/yarn-project/acir-simulator/src/client/unconstrained_execution.test.ts @@ -28,12 +28,14 @@ describe('Unconstrained Execution test suite', () => { return new Note([new Fr(amount), owner.toField(), Fr.random()]); }; - beforeEach(async () => { - const ownerCompleteAddress = await CompleteAddress.fromPrivateKeyAndPartialAddress(ownerPk, Fr.random()); + beforeEach(() => { + const ownerCompleteAddress = CompleteAddress.fromPrivateKeyAndPartialAddress(ownerPk, Fr.random()); owner = ownerCompleteAddress.address; oracle.getCompleteAddress.mockImplementation((address: AztecAddress) => { - if (address.equals(owner)) return Promise.resolve(ownerCompleteAddress); + if (address.equals(owner)) { + return Promise.resolve(ownerCompleteAddress); + } throw new Error(`Unknown address ${address}`); }); }); diff --git a/yarn-project/acir-simulator/src/client/view_data_oracle.ts b/yarn-project/acir-simulator/src/client/view_data_oracle.ts index 97825133574..cc168317756 100644 --- a/yarn-project/acir-simulator/src/client/view_data_oracle.ts +++ b/yarn-project/acir-simulator/src/client/view_data_oracle.ts @@ -57,7 +57,17 @@ export class ViewDataOracle extends TypedOracle { } /** - * Gets some notes for a storage slot. + * Pops a capsule from the capsule dispenser + * @returns The capsule values + * @remarks A capsule is a "blob" of data that is passed to the contract through an oracle. + */ + public popCapsule(): Promise { + return this.db.popCapsule(); + } + + /** + * Gets some notes for a contract address and storage slot. + * Returns a flattened array containing filtered notes. * * @remarks * Check for pending notes with matching slot. diff --git a/yarn-project/acir-simulator/src/common/packed_args_cache.ts b/yarn-project/acir-simulator/src/common/packed_args_cache.ts index fc120f7daf7..30c8263efbd 100644 --- a/yarn-project/acir-simulator/src/common/packed_args_cache.ts +++ b/yarn-project/acir-simulator/src/common/packed_args_cache.ts @@ -1,4 +1,4 @@ -import { CircuitsWasm, Fr } from '@aztec/circuits.js'; +import { Fr } from '@aztec/circuits.js'; import { PackedArguments } from '@aztec/types'; /** @@ -7,10 +7,10 @@ import { PackedArguments } from '@aztec/types'; export class PackedArgsCache { private cache: Map; - constructor(initialArguments: PackedArguments[] = [], private wasm: CircuitsWasm) { + constructor(initialArguments: PackedArguments[] = []) { this.cache = new Map(); for (const initialArg of initialArguments) { - this.cache.set(initialArg.hash.value, initialArg.args); + this.cache.set(initialArg.hash.toBigInt(), initialArg.args); } } @@ -19,9 +19,8 @@ export class PackedArgsCache { * @param initialArguments - The initial arguments to add to the cache. * @returns The new packed arguments cache. */ - public static async create(initialArguments: PackedArguments[] = []): Promise { - const wasm = await CircuitsWasm.get(); - return new PackedArgsCache(initialArguments, wasm); + public static create(initialArguments: PackedArguments[] = []) { + return new PackedArgsCache(initialArguments); } /** @@ -30,7 +29,7 @@ export class PackedArgsCache { * @returns The unpacked arguments. */ public unpack(hash: Fr): Fr[] { - if (hash.equals(Fr.zero())) { + if (hash.equals(Fr.ZERO)) { return []; } const packedArgs = this.cache.get(hash.value); @@ -47,7 +46,7 @@ export class PackedArgsCache { */ public pack(args: Fr[]) { if (args.length === 0) { - return Fr.zero(); + return Fr.ZERO; } const packedArguments = PackedArguments.fromArgs(args); this.cache.set(packedArguments.hash.value, packedArguments.args); diff --git a/yarn-project/acir-simulator/src/public/db.ts b/yarn-project/acir-simulator/src/public/db.ts index fecfe87fc1e..0f7823dcb12 100644 --- a/yarn-project/acir-simulator/src/public/db.ts +++ b/yarn-project/acir-simulator/src/public/db.ts @@ -24,6 +24,18 @@ export interface PublicStateDB { * @returns Nothing. */ storageWrite(contract: AztecAddress, slot: Fr, newValue: Fr): Promise; + + /** + * Commit the pending changes to the DB. + * @returns Nothing. + */ + commit(): Promise; + + /** + * Rollback the pending changes. + * @returns Nothing. + */ + rollback(): Promise; } /** diff --git a/yarn-project/acir-simulator/src/public/executor.ts b/yarn-project/acir-simulator/src/public/executor.ts index ca626a1812b..fdcb691898d 100644 --- a/yarn-project/acir-simulator/src/public/executor.ts +++ b/yarn-project/acir-simulator/src/public/executor.ts @@ -93,11 +93,13 @@ export class PublicExecutor { public async simulate(execution: PublicExecution, globalVariables: GlobalVariables): Promise { const selector = execution.functionData.selector; const acir = await this.contractsDb.getBytecode(execution.contractAddress, selector); - if (!acir) throw new Error(`Bytecode not found for ${execution.contractAddress}:${selector}`); + if (!acir) { + throw new Error(`Bytecode not found for ${execution.contractAddress}:${selector}`); + } // Functions can request to pack arguments before calling other functions. // We use this cache to hold the packed arguments. - const packedArgs = await PackedArgsCache.create([]); + const packedArgs = PackedArgsCache.create([]); const sideEffectCounter = new SideEffectCounter(); diff --git a/yarn-project/acir-simulator/src/public/index.test.ts b/yarn-project/acir-simulator/src/public/index.test.ts index 38ef76b8190..74abeab2e0b 100644 --- a/yarn-project/acir-simulator/src/public/index.test.ts +++ b/yarn-project/acir-simulator/src/public/index.test.ts @@ -86,8 +86,6 @@ describe('ACIR public execution simulator', () => { const execution: PublicExecution = { contractAddress, functionData, args, callContext }; const result = await executor.simulate(execution, GlobalVariables.empty()); - expect(result.returnValues[0]).toEqual(new Fr(1n)); - const recipientBalanceStorageSlot = computeSlotForMapping(new Fr(6n), recipient.toField()); const totalSupplyStorageSlot = new Fr(4n); @@ -182,8 +180,6 @@ describe('ACIR public execution simulator', () => { const expectedRecipientBalance = new Fr(160n); const expectedSenderBalance = new Fr(60n); - expect(result.returnValues[0]).toEqual(new Fr(1n)); - expect(result.contractStorageUpdateRequests).toEqual([ { storageSlot: senderStorageSlot, diff --git a/yarn-project/acir-simulator/src/public/public_execution_context.ts b/yarn-project/acir-simulator/src/public/public_execution_context.ts index 12393742c35..6db7acfd3fd 100644 --- a/yarn-project/acir-simulator/src/public/public_execution_context.ts +++ b/yarn-project/acir-simulator/src/public/public_execution_context.ts @@ -185,7 +185,9 @@ export class PublicExecutionContext extends TypedOracle { } const acir = await this.contractsDb.getBytecode(targetContractAddress, functionSelector); - if (!acir) throw new Error(`Bytecode not found for ${targetContractAddress}:${functionSelector}`); + if (!acir) { + throw new Error(`Bytecode not found for ${targetContractAddress}:${functionSelector}`); + } const functionData = new FunctionData(functionSelector, isInternal, false, false); diff --git a/yarn-project/acir-simulator/src/public/state_actions.ts b/yarn-project/acir-simulator/src/public/state_actions.ts index b9297d2793c..81b6406914c 100644 --- a/yarn-project/acir-simulator/src/public/state_actions.ts +++ b/yarn-project/acir-simulator/src/public/state_actions.ts @@ -39,9 +39,13 @@ export class ContractStorageActionsCollector { public async read(storageSlot: Fr, sideEffectCounter: number): Promise { const slot = storageSlot.value; const updateRequest = this.contractStorageUpdateRequests.get(slot); - if (updateRequest) return updateRequest.newValue; + if (updateRequest) { + return updateRequest.newValue; + } const read = this.contractStorageReads.get(slot); - if (read) return read.currentValue; + if (read) { + return read.currentValue; + } const value = await this.db.storageRead(this.address, storageSlot); this.contractStorageReads.set(slot, { currentValue: value, sideEffectCounter }); return value; diff --git a/yarn-project/acir-simulator/src/test/utils.ts b/yarn-project/acir-simulator/src/test/utils.ts index 8d076822c50..f879dff69d9 100644 --- a/yarn-project/acir-simulator/src/test/utils.ts +++ b/yarn-project/acir-simulator/src/test/utils.ts @@ -1,7 +1,7 @@ import { AztecAddress, EthAddress, Fr } from '@aztec/circuits.js'; import { computeSecretMessageHash } from '@aztec/circuits.js/abis'; import { ContractArtifact, FunctionSelector, getFunctionDebugMetadata } from '@aztec/foundation/abi'; -import { sha256ToField } from '@aztec/foundation/crypto'; +import { sha256 } from '@aztec/foundation/crypto'; import { L1Actor, L1ToL2Message, L2Actor } from '@aztec/types'; import { FunctionArtifactWithDebugMetadata } from '../index.js'; @@ -24,7 +24,7 @@ export const buildL1ToL2Message = ( const selectorBuf = Buffer.from(selector, 'hex'); const contentBuf = Buffer.concat([selectorBuf, ...contentPreimage.map(field => field.toBuffer())]); - const content = sha256ToField(contentBuf); + const content = Fr.fromBufferReduce(sha256(contentBuf)); const secretHash = computeSecretMessageHash(secret); diff --git a/yarn-project/archiver/package.json b/yarn-project/archiver/package.json index 368309c1bb7..86b50d71c57 100644 --- a/yarn-project/archiver/package.json +++ b/yarn-project/archiver/package.json @@ -15,7 +15,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests", "start": "node ./dest", "start:dev": "tsc-watch -p tsconfig.json --onSuccess 'yarn start'", @@ -49,7 +49,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.2.0", "@types/debug": "^4.1.7", "@types/jest": "^29.5.0", "@types/node": "^18.15.11", diff --git a/yarn-project/archiver/src/archiver/archiver.test.ts b/yarn-project/archiver/src/archiver/archiver.test.ts index f05129af94f..c9d8790f2ea 100644 --- a/yarn-project/archiver/src/archiver/archiver.test.ts +++ b/yarn-project/archiver/src/archiver/archiver.test.ts @@ -18,7 +18,7 @@ describe('Archiver', () => { const inboxAddress = EthAddress.ZERO.toString(); const registryAddress = EthAddress.ZERO.toString(); const contractDeploymentEmitterAddress = '0x0000000000000000000000000000000000000001'; - const blockNums = [1, 2, 3]; + const blockNumbers = [1, 2, 3]; let publicClient: MockProxy>; let archiverStore: ArchiverDataStore; @@ -42,31 +42,31 @@ describe('Archiver', () => { let latestBlockNum = await archiver.getBlockNumber(); expect(latestBlockNum).toEqual(0); - const blocks = blockNums.map(x => L2Block.random(x, 4, x, x + 1, x * 2, x * 3)); + const blocks = blockNumbers.map(x => L2Block.random(x, 4, x, x + 1, x * 2, x * 3)); const rollupTxs = blocks.map(makeRollupTx); // `L2Block.random(x)` creates some l1 to l2 messages. We add those, // since it is expected by the test that these would be consumed. // Archiver removes such messages from pending store. // Also create some more messages to cancel and some that will stay pending. - const messageToCancel1 = Fr.random().toString(true); - const messageToCancel2 = Fr.random().toString(true); + const messageToCancel1 = Fr.random().toString(); + const messageToCancel2 = Fr.random().toString(); const l1ToL2MessagesToCancel = [messageToCancel1, messageToCancel2]; - const messageToStayPending1 = Fr.random().toString(true); - const messageToStayPending2 = Fr.random().toString(true); + const messageToStayPending1 = Fr.random().toString(); + const messageToStayPending2 = Fr.random().toString(); const l1ToL2MessageAddedEvents = [ makeL1ToL2MessageAddedEvents( 100n, - blocks[0].newL1ToL2Messages.map(key => key.toString(true)), + blocks[0].newL1ToL2Messages.map(key => key.toString()), ), makeL1ToL2MessageAddedEvents( 100n, - blocks[1].newL1ToL2Messages.map(key => key.toString(true)), + blocks[1].newL1ToL2Messages.map(key => key.toString()), ), makeL1ToL2MessageAddedEvents( 2501n, - blocks[2].newL1ToL2Messages.map(key => key.toString(true)), + blocks[2].newL1ToL2Messages.map(key => key.toString()), ), makeL1ToL2MessageAddedEvents(2502n, [ messageToCancel1, @@ -105,23 +105,23 @@ describe('Archiver', () => { l1ToL2MessageAddedEvents[3][2].args.entryKey, l1ToL2MessageAddedEvents[3][3].args.entryKey, ]; - const actualPendingMessageKeys = (await archiver.getPendingL1ToL2Messages(10)).map(key => key.toString(true)); + const actualPendingMessageKeys = (await archiver.getPendingL1ToL2Messages(10)).map(key => key.toString()); expect(expectedPendingMessageKeys).toEqual(actualPendingMessageKeys); // Expect logs to correspond to what is set by L2Block.random(...) const encryptedLogs = await archiver.getLogs(1, 100, LogType.ENCRYPTED); - expect(encryptedLogs.length).toEqual(blockNums.length); + expect(encryptedLogs.length).toEqual(blockNumbers.length); - for (const [index, x] of blockNums.entries()) { + for (const [index, x] of blockNumbers.entries()) { const expectedTotalNumEncryptedLogs = 4 * x * (x * 2); const totalNumEncryptedLogs = L2BlockL2Logs.unrollLogs([encryptedLogs[index]]).length; expect(totalNumEncryptedLogs).toEqual(expectedTotalNumEncryptedLogs); } const unencryptedLogs = await archiver.getLogs(1, 100, LogType.UNENCRYPTED); - expect(unencryptedLogs.length).toEqual(blockNums.length); + expect(unencryptedLogs.length).toEqual(blockNumbers.length); - blockNums.forEach((x, index) => { + blockNumbers.forEach((x, index) => { const expectedTotalNumUnencryptedLogs = 4 * (x + 1) * (x * 3); const totalNumUnencryptedLogs = L2BlockL2Logs.unrollLogs([unencryptedLogs[index]]).length; expect(totalNumUnencryptedLogs).toEqual(expectedTotalNumUnencryptedLogs); @@ -147,10 +147,10 @@ describe('Archiver', () => { expect(latestBlockNum).toEqual(0); const createL1ToL2Messages = () => { - return [Fr.random().toString(true), Fr.random().toString(true)]; + return [Fr.random().toString(), Fr.random().toString()]; }; - const blocks = blockNums.map(x => L2Block.random(x, 4, x, x + 1, x * 2, x * 3)); + const blocks = blockNumbers.map(x => L2Block.random(x, 4, x, x + 1, x * 2, x * 3)); const rollupTxs = blocks.map(makeRollupTx); // `L2Block.random(x)` creates some l1 to l2 messages. We add those, // since it is expected by the test that these would be consumed. @@ -163,11 +163,11 @@ describe('Archiver', () => { const l1ToL2MessageAddedEvents = [ makeL1ToL2MessageAddedEvents( 100n, - blocks[0].newL1ToL2Messages.map(key => key.toString(true)), + blocks[0].newL1ToL2Messages.map(key => key.toString()), ), makeL1ToL2MessageAddedEvents( 101n, - blocks[1].newL1ToL2Messages.map(key => key.toString(true)), + blocks[1].newL1ToL2Messages.map(key => key.toString()), ), makeL1ToL2MessageAddedEvents(102n, additionalL1ToL2MessagesBlock102), makeL1ToL2MessageAddedEvents(103n, additionalL1ToL2MessagesBlock103), @@ -201,7 +201,7 @@ describe('Archiver', () => { // Check that the only pending L1 to L2 messages are those from eth bock 102 const expectedPendingMessageKeys = additionalL1ToL2MessagesBlock102; - const actualPendingMessageKeys = (await archiver.getPendingL1ToL2Messages(100)).map(key => key.toString(true)); + const actualPendingMessageKeys = (await archiver.getPendingL1ToL2Messages(100)).map(key => key.toString()); expect(actualPendingMessageKeys).toEqual(expectedPendingMessageKeys); await archiver.stop(); @@ -234,7 +234,7 @@ describe('Archiver', () => { .mockResolvedValueOnce( makeL1ToL2MessageAddedEvents( 100n, - block.newL1ToL2Messages.map(x => x.toString(true)), + block.newL1ToL2Messages.map(x => x.toString()), ), ) .mockResolvedValueOnce([]) @@ -292,9 +292,9 @@ function makeContractDeploymentEvent(l1BlockNum: bigint, l2Block: L2Block) { aztecAddress: extendedContractData.contractData.contractAddress.toString(), portalAddress: extendedContractData.contractData.portalContractAddress.toString(), l2BlockHash: `0x${l2Block.getCalldataHash().toString('hex')}`, - partialAddress: extendedContractData.partialAddress.toString(true), - pubKeyX: extendedContractData.publicKey.x.toString(true), - pubKeyY: extendedContractData.publicKey.y.toString(true), + partialAddress: extendedContractData.partialAddress.toString(), + pubKeyX: extendedContractData.publicKey.x.toString(), + pubKeyY: extendedContractData.publicKey.y.toString(), acir: '0x' + acir, }, transactionHash: `0x${l2Block.number}`, @@ -316,8 +316,8 @@ function makeL1ToL2MessageAddedEvents(l1BlockNum: bigint, entryKeys: string[]) { senderChainId: 1n, recipient: AztecAddress.random().toString(), recipientVersion: 1n, - content: Fr.random().toString(true), - secretHash: Fr.random().toString(true), + content: Fr.random().toString(), + secretHash: Fr.random().toString(), deadline: 100, fee: 1n, entryKey: entryKey, diff --git a/yarn-project/archiver/src/archiver/archiver.ts b/yarn-project/archiver/src/archiver/archiver.ts index 1c4b8be8816..845b2b0a5c8 100644 --- a/yarn-project/archiver/src/archiver/archiver.ts +++ b/yarn-project/archiver/src/archiver/archiver.ts @@ -206,7 +206,7 @@ export class Archiver implements L2BlockSource, L2LogsSource, ContractDataSource currentL1BlockNumber, ); - // TODO (#717): optimise this - there could be messages in confirmed that are also in pending. + // TODO (#717): optimize this - there could be messages in confirmed that are also in pending. // Or messages in pending that are also cancelled in the same block. No need to modify storage for them. // Store l1 to l2 messages this.log('Adding pending l1 to l2 messages to store'); diff --git a/yarn-project/archiver/src/archiver/archiver_store.ts b/yarn-project/archiver/src/archiver/archiver_store.ts index 21f1c655ce9..266c42aa131 100644 --- a/yarn-project/archiver/src/archiver/archiver_store.ts +++ b/yarn-project/archiver/src/archiver/archiver_store.ts @@ -507,7 +507,9 @@ export class MemoryArchiverStore implements ArchiverDataStore { * @returns The number of the latest L2 block processed. */ public getBlockNumber(): Promise { - if (this.l2BlockContexts.length === 0) return Promise.resolve(INITIAL_L2_BLOCK_NUM - 1); + if (this.l2BlockContexts.length === 0) { + return Promise.resolve(INITIAL_L2_BLOCK_NUM - 1); + } return Promise.resolve(this.l2BlockContexts[this.l2BlockContexts.length - 1].block.number); } } diff --git a/yarn-project/archiver/src/archiver/eth_log_handlers.ts b/yarn-project/archiver/src/archiver/eth_log_handlers.ts index ebbbd926748..9a8e82ee228 100644 --- a/yarn-project/archiver/src/archiver/eth_log_handlers.ts +++ b/yarn-project/archiver/src/archiver/eth_log_handlers.ts @@ -102,7 +102,9 @@ async function getBlockFromCallData( abi: RollupAbi.filter(item => item.type.toString() !== 'constructor'), data, }); - if (functionName !== 'process') throw new Error(`Unexpected method called ${functionName}`); + if (functionName !== 'process') { + throw new Error(`Unexpected method called ${functionName}`); + } const [, l2BlockHex] = args! as [Hex, Hex]; const block = L2Block.fromBufferWithLogs(Buffer.from(hexToBytes(l2BlockHex))); if (BigInt(block.number) !== l2BlockNum) { diff --git a/yarn-project/archiver/src/archiver/l1_to_l2_message_store.test.ts b/yarn-project/archiver/src/archiver/l1_to_l2_message_store.test.ts index 095bc0eb639..0901d7a479f 100644 --- a/yarn-project/archiver/src/archiver/l1_to_l2_message_store.test.ts +++ b/yarn-project/archiver/src/archiver/l1_to_l2_message_store.test.ts @@ -91,8 +91,8 @@ describe('pending_l1_to_l2_message_store', () => { ); store.addMessage(entryKey, msg); }); - const expectedMessgeFees = [4n, 3n, 3n, 3n]; // the top 4. + const expectedMessageFees = [4n, 3n, 3n, 3n]; // the top 4. const receivedMessageFees = store.getMessageKeys(4).map(key => key.value); - expect(receivedMessageFees).toEqual(expectedMessgeFees); + expect(receivedMessageFees).toEqual(expectedMessageFees); }); }); diff --git a/yarn-project/archiver/src/archiver/l1_to_l2_message_store.ts b/yarn-project/archiver/src/archiver/l1_to_l2_message_store.ts index 28fab82865a..441a0a81783 100644 --- a/yarn-project/archiver/src/archiver/l1_to_l2_message_store.ts +++ b/yarn-project/archiver/src/archiver/l1_to_l2_message_store.ts @@ -59,7 +59,9 @@ export class PendingL1ToL2MessageStore extends L1ToL2MessageStore { removeMessage(messageKey: Fr) { // ignore 0 - messageKey is a hash, so a 0 can probabilistically never occur. It is best to skip it. - if (messageKey.equals(Fr.ZERO)) return; + if (messageKey.equals(Fr.ZERO)) { + return; + } const messageKeyBigInt = messageKey.value; const msgAndCount = this.store.get(messageKeyBigInt); if (!msgAndCount) { diff --git a/yarn-project/aztec-faucet/package.json b/yarn-project/aztec-faucet/package.json index fcf3526dbd5..52bc9ec488a 100644 --- a/yarn-project/aztec-faucet/package.json +++ b/yarn-project/aztec-faucet/package.json @@ -17,7 +17,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" }, "inherits": [ @@ -41,7 +41,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/node": "^18.7.23", "jest": "^29.5.0", diff --git a/yarn-project/aztec-node/.dockerignore b/yarn-project/aztec-node/.dockerignore deleted file mode 100644 index 2b30eaf4896..00000000000 --- a/yarn-project/aztec-node/.dockerignore +++ /dev/null @@ -1,4 +0,0 @@ -data -dest -node_modules -Dockerfile \ No newline at end of file diff --git a/yarn-project/aztec-node/Dockerfile b/yarn-project/aztec-node/Dockerfile deleted file mode 100644 index d01aa4ede31..00000000000 --- a/yarn-project/aztec-node/Dockerfile +++ /dev/null @@ -1,14 +0,0 @@ -FROM 278380418400.dkr.ecr.eu-west-2.amazonaws.com/yarn-project AS builder - -WORKDIR /usr/src/yarn-project/aztec-node - -# Productionify. See comment in yarn-project-base/Dockerfile. -RUN yarn cache clean && yarn workspaces focus --production - -# Create final, minimal size image. -FROM node:18-alpine -COPY --from=builder /usr/src/ /usr/src/ -WORKDIR /usr/src/yarn-project/aztec-node -ENTRYPOINT ["yarn"] -CMD [ "start" ] -EXPOSE 8080 diff --git a/yarn-project/aztec-node/package.json b/yarn-project/aztec-node/package.json index 7e0fed0de53..a4532c23e6f 100644 --- a/yarn-project/aztec-node/package.json +++ b/yarn-project/aztec-node/package.json @@ -18,7 +18,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" }, "inherits": [ @@ -51,7 +51,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/leveldown": "^4.0.4", "@types/levelup": "^5.1.2", diff --git a/yarn-project/aztec-node/src/aztec-node/server.ts b/yarn-project/aztec-node/src/aztec-node/server.ts index bbf42324341..b528a699343 100644 --- a/yarn-project/aztec-node/src/aztec-node/server.ts +++ b/yarn-project/aztec-node/src/aztec-node/server.ts @@ -34,6 +34,7 @@ import { LogFilter, LogType, MerkleTreeId, + SequencerConfig, SiblingPath, Tx, TxHash, @@ -104,7 +105,7 @@ export class AztecNodeService implements AztecNode { // create the tx pool and the p2p client, which will need the l2 block source const p2pClient = await createP2PClient(config, new InMemoryTxPool(), archiver); - // now create the merkle trees and the world state syncher + // now create the merkle trees and the world state synchronizer const db = await openDb(config); const merkleTrees = await MerkleTrees.new(db); const worldStateConfig: WorldStateConfig = getWorldStateConfig(); @@ -434,6 +435,11 @@ export class AztecNodeService implements AztecNode { this.log.info(`Simulated tx ${await tx.getTxHash()} succeeds`); } + public setConfig(config: Partial): Promise { + this.sequencer?.updateSequencerConfig(config); + return Promise.resolve(); + } + /** * Returns an instance of MerkleTreeOperations having first ensured the world state is fully synched * @returns An instance of a committed MerkleTreeOperations diff --git a/yarn-project/aztec-nr/.gitrepo b/yarn-project/aztec-nr/.gitrepo index e8454da92ff..89ff6ba5475 100644 --- a/yarn-project/aztec-nr/.gitrepo +++ b/yarn-project/aztec-nr/.gitrepo @@ -6,7 +6,7 @@ [subrepo] remote = https://github.com/AztecProtocol/aztec-nr branch = master - commit = 87bdae5f0392ab74e3ccc727e35477a2a2fddf94 + commit = c29afb2043035f51a0d379124715256f3502b9cb method = merge cmdver = 0.4.6 - parent = 87eeb715014996ec329de969df85684083b18f83 + parent = d94d88bf626ddbe41dd1b7fe3eb0f11619dde97a diff --git a/yarn-project/aztec-nr/address-note/src/address_note.nr b/yarn-project/aztec-nr/address-note/src/address_note.nr index 6259cd92f74..1e8f037ac20 100644 --- a/yarn-project/aztec-nr/address-note/src/address_note.nr +++ b/yarn-project/aztec-nr/address-note/src/address_note.nr @@ -18,6 +18,7 @@ use dep::aztec::{ global ADDRESS_NOTE_LEN: Field = 3; +// docs:start:address_note_def // Stores an address struct AddressNote { address: Field, @@ -36,6 +37,8 @@ impl AddressNote { header: NoteHeader::empty(), } } +// docs:end:address_note_def + pub fn serialize(self) -> [Field; ADDRESS_NOTE_LEN]{ [self.address, self.owner, self.randomness] @@ -89,7 +92,7 @@ fn deserialize(serialized_note: [Field; ADDRESS_NOTE_LEN]) -> AddressNote { AddressNote::deserialize(serialized_note) } -fn serialize(note: AddressNote) -> [Field; ADDRESS_NOTE_LEN]{ +fn serialize(note: AddressNote) -> [Field; ADDRESS_NOTE_LEN] { note.serialize() } diff --git a/yarn-project/aztec-nr/address-note/src/lib.nr b/yarn-project/aztec-nr/address-note/src/lib.nr index f016584e48c..5a96025fa25 100644 --- a/yarn-project/aztec-nr/address-note/src/lib.nr +++ b/yarn-project/aztec-nr/address-note/src/lib.nr @@ -1 +1 @@ -mod address_note; \ No newline at end of file +mod address_note; diff --git a/yarn-project/aztec-nr/authwit/src/account.nr b/yarn-project/aztec-nr/authwit/src/account.nr index d2f541c5dff..7a79d089f68 100644 --- a/yarn-project/aztec-nr/authwit/src/account.nr +++ b/yarn-project/aztec-nr/authwit/src/account.nr @@ -66,4 +66,4 @@ impl AccountActions { pub fn internal_set_is_valid_storage(self, message_hash: Field, value: bool) { self.approved_action.at(message_hash).write(value); } -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/authwit/src/auth.nr b/yarn-project/aztec-nr/authwit/src/auth.nr index 7094fae9c53..61952330832 100644 --- a/yarn-project/aztec-nr/authwit/src/auth.nr +++ b/yarn-project/aztec-nr/authwit/src/auth.nr @@ -14,54 +14,46 @@ global IS_VALID_PUBLIC_SELECTOR = 0xf3661153; // docs:start:assert_valid_authwit // Assert that `on_behalf_of` have authorized `message_hash` with a valid authentication witness pub fn assert_valid_authwit(context: &mut PrivateContext, on_behalf_of: AztecAddress, message_hash: Field) { - let result = context.call_private_function(on_behalf_of.address, IS_VALID_SELECTOR, [message_hash])[0]; - context.push_new_nullifier(message_hash, EMPTY_NULLIFIED_COMMITMENT); - assert(result == IS_VALID_SELECTOR, "Message not authorized by account"); + let result = context.call_private_function(on_behalf_of.address, IS_VALID_SELECTOR, [message_hash])[0]; + context.push_new_nullifier(message_hash, EMPTY_NULLIFIED_COMMITMENT); + assert(result == IS_VALID_SELECTOR, "Message not authorized by account"); } // docs:end:assert_valid_authwit // docs:start:assert_current_call_valid_authwit // Assert that `on_behalf_of` have authorized the current call with a valid authentication witness pub fn assert_current_call_valid_authwit(context: &mut PrivateContext, on_behalf_of: AztecAddress) { - // message_hash = H(caller, contract_this, selector, args_hash) - let message_hash = pedersen_hash( - [context.msg_sender(), context.this_address(), context.selector(), context.args_hash], - GENERATOR_INDEX__SIGNATURE_PAYLOAD - ); - assert_valid_authwit(context, on_behalf_of, message_hash); + // message_hash = H(caller, contract_this, selector, args_hash) + let message_hash = pedersen_hash([context.msg_sender(), context.this_address(), context.selector(), context.args_hash], + GENERATOR_INDEX__SIGNATURE_PAYLOAD); + assert_valid_authwit(context, on_behalf_of, message_hash); } // docs:end:assert_current_call_valid_authwit // docs:start:assert_valid_authwit_public // Assert that `on_behalf_of` have authorized `message_hash` in a public context pub fn assert_valid_authwit_public(context: &mut PublicContext, on_behalf_of: AztecAddress, message_hash: Field) { - let result = context.call_public_function(on_behalf_of.address, IS_VALID_PUBLIC_SELECTOR, [message_hash])[0]; - context.push_new_nullifier(message_hash, EMPTY_NULLIFIED_COMMITMENT); - assert(result == IS_VALID_SELECTOR, "Message not authorized by account"); + let result = context.call_public_function(on_behalf_of.address, IS_VALID_PUBLIC_SELECTOR, [message_hash])[0]; + context.push_new_nullifier(message_hash, EMPTY_NULLIFIED_COMMITMENT); + assert(result == IS_VALID_SELECTOR, "Message not authorized by account"); } // docs:end:assert_valid_authwit_public // docs:start:assert_current_call_valid_authwit_public // Assert that `on_behalf_of` have authorized the current call in a public context pub fn assert_current_call_valid_authwit_public(context: &mut PublicContext, on_behalf_of: AztecAddress) { - // message_hash = H(caller, contract_this, selector, args_hash) - let message_hash = pedersen_hash( - [context.msg_sender(), context.this_address(), context.selector(), context.args_hash], - GENERATOR_INDEX__SIGNATURE_PAYLOAD - ); - assert_valid_authwit_public(context, on_behalf_of, message_hash); + // message_hash = H(caller, contract_this, selector, args_hash) + let message_hash = pedersen_hash([context.msg_sender(), context.this_address(), context.selector(), context.args_hash], + GENERATOR_INDEX__SIGNATURE_PAYLOAD); + assert_valid_authwit_public(context, on_behalf_of, message_hash); } // docs:end:assert_current_call_valid_authwit_public // docs:start:compute_authwit_message_hash // Compute the message hash to be used by an authentication witness -pub fn compute_authwit_message_hash( - caller: AztecAddress, - target: AztecAddress, - selector: Field, - args: [Field; N] -) -> Field { - let args_hash = hash_args(args); - pedersen_hash([caller.address, target.address, selector, args_hash], GENERATOR_INDEX__SIGNATURE_PAYLOAD) +pub fn compute_authwit_message_hash(caller: AztecAddress, target: AztecAddress, selector: Field, args: [Field; N]) -> Field { + let args_hash = hash_args(args); + pedersen_hash([caller.address, target.address, selector, args_hash], + GENERATOR_INDEX__SIGNATURE_PAYLOAD) } // docs:end:compute_authwit_message_hash \ No newline at end of file diff --git a/yarn-project/aztec-nr/authwit/src/auth_witness.nr b/yarn-project/aztec-nr/authwit/src/auth_witness.nr index 7ef0577f28e..e51567a618d 100644 --- a/yarn-project/aztec-nr/authwit/src/auth_witness.nr +++ b/yarn-project/aztec-nr/authwit/src/auth_witness.nr @@ -3,4 +3,4 @@ fn get_auth_witness_oracle(_message_hash: Field) -> [Field; N] {} unconstrained pub fn get_auth_witness(message_hash: Field) -> [Field; N] { get_auth_witness_oracle(message_hash) -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/authwit/src/entrypoint.nr b/yarn-project/aztec-nr/authwit/src/entrypoint.nr index 4d97421f2b8..227be7d17fc 100644 --- a/yarn-project/aztec-nr/authwit/src/entrypoint.nr +++ b/yarn-project/aztec-nr/authwit/src/entrypoint.nr @@ -3,7 +3,7 @@ use dep::aztec::types::vec::BoundedVec; use dep::aztec::hash::pedersen_hash; use dep::aztec::context::PrivateContext; use dep::aztec::private_call_stack_item::PrivateCallStackItem; -use dep::aztec::public_call_stack_item::PublicCallStackItem; +use dep::aztec::public_call_stack_item::PublicCallStackItem; use dep::aztec::constants_gen::GENERATOR_INDEX__SIGNATURE_PAYLOAD; global ACCOUNT_MAX_CALLS: Field = 4; @@ -105,4 +105,4 @@ impl EntrypointPayload { } } // docs:end:entrypoint-execute-calls -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/authwit/src/lib.nr b/yarn-project/aztec-nr/authwit/src/lib.nr index ec0b9239404..e56460fd701 100644 --- a/yarn-project/aztec-nr/authwit/src/lib.nr +++ b/yarn-project/aztec-nr/authwit/src/lib.nr @@ -1,4 +1,4 @@ mod account; mod auth_witness; mod auth; -mod entrypoint; \ No newline at end of file +mod entrypoint; diff --git a/yarn-project/aztec-nr/aztec/src/abi.nr b/yarn-project/aztec-nr/aztec/src/abi.nr index 314e5b3fe7e..541dc73cf3a 100644 --- a/yarn-project/aztec-nr/aztec/src/abi.nr +++ b/yarn-project/aztec-nr/aztec/src/abi.nr @@ -302,7 +302,6 @@ impl ContractStorageUpdateRequest { } } - struct PublicCircuitPublicInputs { call_context: CallContext, args_hash: Field, diff --git a/yarn-project/aztec-nr/aztec/src/address.nr b/yarn-project/aztec-nr/aztec/src/address.nr index 162949df483..f1f4224513c 100644 --- a/yarn-project/aztec-nr/aztec/src/address.nr +++ b/yarn-project/aztec-nr/aztec/src/address.nr @@ -2,5 +2,6 @@ use crate::constants_gen::GENERATOR_INDEX__CONTRACT_ADDRESS; use crate::hash::pedersen_hash; pub fn compute_address(pub_key_x: Field, pub_key_y: Field, partial_address: Field) -> Field { - pedersen_hash([pub_key_x, pub_key_y, partial_address], GENERATOR_INDEX__CONTRACT_ADDRESS) -} \ No newline at end of file + pedersen_hash([pub_key_x, pub_key_y, partial_address], + GENERATOR_INDEX__CONTRACT_ADDRESS) +} diff --git a/yarn-project/aztec-nr/aztec/src/context.nr b/yarn-project/aztec-nr/aztec/src/context.nr index 7926e159a3d..a2f34867c6c 100644 --- a/yarn-project/aztec-nr/aztec/src/context.nr +++ b/yarn-project/aztec-nr/aztec/src/context.nr @@ -616,4 +616,4 @@ impl Context { private: Option::none() } } -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/hash.nr b/yarn-project/aztec-nr/aztec/src/hash.nr index 7e3d0071605..f8d3abb51aa 100644 --- a/yarn-project/aztec-nr/aztec/src/hash.nr +++ b/yarn-project/aztec-nr/aztec/src/hash.nr @@ -5,30 +5,30 @@ use crate::constants_gen::{ }; pub fn sha256_to_field(bytes_to_hash: [u8; N]) -> Field { - let sha256_hashed = sha256(bytes_to_hash); + let sha256_hashed = sha256(bytes_to_hash); - // Convert it to a field element - let mut v = 1; - let mut high = 0 as Field; - let mut low = 0 as Field; + // Convert it to a field element + let mut v = 1; + let mut high = 0 as Field; + let mut low = 0 as Field; - for i in 0..16 { - high = high + (sha256_hashed[15 - i] as Field) * v; - low = low + (sha256_hashed[16 + 15 - i] as Field) * v; - v = v * 256; - } + for i in 0..16 { + high = high + (sha256_hashed[15 - i] as Field) * v; + low = low + (sha256_hashed[16 + 15 - i] as Field) * v; + v = v * 256; + } - // Abuse that a % p + b % p = (a + b) % p and that low < p - let hash_in_a_field = low + high * v; + // Abuse that a % p + b % p = (a + b) % p and that low < p + let hash_in_a_field = low + high * v; - hash_in_a_field + hash_in_a_field } pub fn compute_secret_hash(secret: Field) -> Field { - // TODO(#1205) This is probably not the right index to use - pedersen_hash([secret], GENERATOR_INDEX__L1_TO_L2_MESSAGE_SECRET) + // TODO(#1205) This is probably not the right index to use + pedersen_hash([secret], GENERATOR_INDEX__L1_TO_L2_MESSAGE_SECRET) } pub fn pedersen_hash(inputs: [Field; N], hash_index: u32) -> Field { - pedersen_hash_with_separator(inputs, hash_index) -} \ No newline at end of file + pedersen_hash_with_separator(inputs, hash_index) +} diff --git a/yarn-project/aztec-nr/aztec/src/lib.nr b/yarn-project/aztec-nr/aztec/src/lib.nr index 10330e9c1d0..d1080f9056f 100644 --- a/yarn-project/aztec-nr/aztec/src/lib.nr +++ b/yarn-project/aztec-nr/aztec/src/lib.nr @@ -12,4 +12,4 @@ mod public_call_stack_item; mod selector; mod state_vars; mod types; -mod utils; \ No newline at end of file +mod utils; diff --git a/yarn-project/aztec-nr/aztec/src/log.nr b/yarn-project/aztec-nr/aztec/src/log.nr index d38775dcb72..35bc5ee690c 100644 --- a/yarn-project/aztec-nr/aztec/src/log.nr +++ b/yarn-project/aztec-nr/aztec/src/log.nr @@ -7,16 +7,13 @@ pub fn emit_encrypted_log( contract_address: Field, storage_slot: Field, encryption_pub_key: Point, - log: [Field; N], + log: [Field; N] ) { let _ = oracle::logs::emit_encrypted_log(contract_address, storage_slot, encryption_pub_key, log); context.accumulate_encrypted_logs(log); } -pub fn emit_unencrypted_log( - context: &mut PublicContext, - log: T, -) { +pub fn emit_unencrypted_log(context: &mut PublicContext, log: T) { let contract_address = context.this_address(); let event_selector = 5; // TODO: compute actual event selector. let _ = oracle::logs::emit_unencrypted_log(contract_address, event_selector, log); @@ -27,10 +24,7 @@ pub fn emit_unencrypted_log( // --> might be a better approach to force devs to make a public function call that emits the log if needed then // it would be less easy to accidentally leak information. // If we decide to keep this function around would make sense to wait for traits and then merge it with emit_unencrypted_log. -pub fn emit_unencrypted_log_from_private( - context: &mut PrivateContext, - log: T, -) { +pub fn emit_unencrypted_log_from_private(context: &mut PrivateContext, log: T) { let contract_address = context.this_address(); let event_selector = 5; // TODO: compute actual event selector. let _ = oracle::logs::emit_unencrypted_log(contract_address, event_selector, log); diff --git a/yarn-project/aztec-nr/aztec/src/messaging.nr b/yarn-project/aztec-nr/aztec/src/messaging.nr index 7e7aeae4500..185733de9a8 100644 --- a/yarn-project/aztec-nr/aztec/src/messaging.nr +++ b/yarn-project/aztec-nr/aztec/src/messaging.nr @@ -6,10 +6,8 @@ use l1_to_l2_message_getter_data::make_l1_to_l2_message_getter_data; use crate::abi::PublicContextInputs; use crate::oracle::get_l1_to_l2_message::get_l1_to_l2_message_call; - // Returns the nullifier for the message -pub fn process_l1_to_l2_message(l1_to_l2_root: Field, storage_contract_address: Field, msg_key: Field, content: Field, secret: Field) -> Field{ - +pub fn process_l1_to_l2_message(l1_to_l2_root: Field, storage_contract_address: Field, msg_key: Field, content: Field, secret: Field) -> Field { let returned_message = get_l1_to_l2_message_call(msg_key); let l1_to_l2_message_data = make_l1_to_l2_message_getter_data(returned_message, 0, secret); @@ -27,4 +25,4 @@ pub fn process_l1_to_l2_message(l1_to_l2_root: Field, storage_contract_address: // Compute Nullifier l1_to_l2_message_data.message.compute_nullifier() -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/messaging/l1_to_l2_message_getter_data.nr b/yarn-project/aztec-nr/aztec/src/messaging/l1_to_l2_message_getter_data.nr index c8bd586e053..396d758006a 100644 --- a/yarn-project/aztec-nr/aztec/src/messaging/l1_to_l2_message_getter_data.nr +++ b/yarn-project/aztec-nr/aztec/src/messaging/l1_to_l2_message_getter_data.nr @@ -18,9 +18,13 @@ pub fn l1_to_l2_message_getter_len() -> Field { pub fn make_l1_to_l2_message_getter_data(fields: [Field; N], start: Field, secret: Field) -> L1ToL2MessageGetterData { L1ToL2MessageGetterData { - message: L1ToL2Message::deserialize(arr_copy_slice(fields, [0; L1_TO_L2_MESSAGE_LENGTH], start), secret, fields[start + L1_TO_L2_MESSAGE_LENGTH]), + message: L1ToL2Message::deserialize(arr_copy_slice(fields, [0; L1_TO_L2_MESSAGE_LENGTH], start), + secret, + fields[start + L1_TO_L2_MESSAGE_LENGTH]), leaf_index: fields[start + L1_TO_L2_MESSAGE_LENGTH], - sibling_path: arr_copy_slice(fields, [0; L1_TO_L2_MSG_TREE_HEIGHT], L1_TO_L2_MESSAGE_LENGTH + 1), - root: fields[start + L1_TO_L2_MESSAGE_LENGTH + L1_TO_L2_MSG_TREE_HEIGHT + 1], + sibling_path: arr_copy_slice(fields, + [0; L1_TO_L2_MSG_TREE_HEIGHT], + L1_TO_L2_MESSAGE_LENGTH + 1), + root: fields[start + L1_TO_L2_MESSAGE_LENGTH + L1_TO_L2_MSG_TREE_HEIGHT + 1] } -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/note.nr b/yarn-project/aztec-nr/aztec/src/note.nr index d76f7090689..5df51e71dd4 100644 --- a/yarn-project/aztec-nr/aztec/src/note.nr +++ b/yarn-project/aztec-nr/aztec/src/note.nr @@ -5,4 +5,4 @@ mod note_hash; mod note_header; mod note_interface; mod note_viewer_options; -mod utils; \ No newline at end of file +mod utils; diff --git a/yarn-project/aztec-nr/aztec/src/note/lifecycle.nr b/yarn-project/aztec-nr/aztec/src/note/lifecycle.nr index 2930c4430f7..f44d8ab31ec 100644 --- a/yarn-project/aztec-nr/aztec/src/note/lifecycle.nr +++ b/yarn-project/aztec-nr/aztec/src/note/lifecycle.nr @@ -16,7 +16,7 @@ pub fn create_note( storage_slot: Field, note: &mut Note, note_interface: NoteInterface, - broadcast: bool, + broadcast: bool ) { let contract_address = (*context).this_address(); @@ -41,7 +41,7 @@ pub fn create_note_hash_from_public( context: &mut PublicContext, storage_slot: Field, note: &mut Note, - note_interface: NoteInterface, + note_interface: NoteInterface ) { let contract_address = (*context).this_address(); @@ -53,11 +53,7 @@ pub fn create_note_hash_from_public( context.push_new_note_hash(inner_note_hash); } -pub fn destroy_note( - context: &mut PrivateContext, - note: Note, - note_interface: NoteInterface, -) { +pub fn destroy_note(context: &mut PrivateContext, note: Note, note_interface: NoteInterface) { let mut nullifier = 0; let mut nullified_commitment: Field = EMPTY_NULLIFIED_COMMITMENT; let compute_nullifier = note_interface.compute_nullifier; @@ -78,4 +74,4 @@ pub fn destroy_note( assert(notify_nullified_note(nullifier, nullified_commitment) == 0); context.push_new_nullifier(nullifier, nullified_commitment) -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/note/note_getter.nr b/yarn-project/aztec-nr/aztec/src/note/note_getter.nr index 1ed8b14830d..1e5131a22ae 100644 --- a/yarn-project/aztec-nr/aztec/src/note/note_getter.nr +++ b/yarn-project/aztec-nr/aztec/src/note/note_getter.nr @@ -16,12 +16,7 @@ use crate::note::{ use crate::oracle; use crate::types::vec::BoundedVec; -fn check_note_header( - context: PrivateContext, - storage_slot: Field, - note_interface: NoteInterface, - note: Note, -) { +fn check_note_header(context: PrivateContext, storage_slot: Field, note_interface: NoteInterface, note: Note) { let get_header = note_interface.get_header; let header = get_header(note); let contract_address = context.this_address(); @@ -29,10 +24,7 @@ fn check_note_header( assert(header.storage_slot == storage_slot); } -fn check_note_fields( - fields: [Field; N], - selects: BoundedVec, N>, -) { +fn check_note_fields(fields: [Field; N], selects: BoundedVec, N>) { for i in 0..selects.len { let select = selects.get_unchecked(i).unwrap_unchecked(); assert(fields[select.field_index] == select.value, "Mismatch return note field."); @@ -52,11 +44,7 @@ fn check_notes_order(fields_0: [Field; N], fields_1: [Field; N], sorts: Bound } } -pub fn get_note( - context: &mut PrivateContext, - storage_slot: Field, - note_interface: NoteInterface, -) -> Note { +pub fn get_note(context: &mut PrivateContext, storage_slot: Field, note_interface: NoteInterface) -> Note { let note = get_note_internal(storage_slot, note_interface); check_note_header(*context, storage_slot, note_interface, note); @@ -71,7 +59,7 @@ pub fn get_notes( context: &mut PrivateContext, storage_slot: Field, note_interface: NoteInterface, - options: NoteGetterOptions, + options: NoteGetterOptions ) -> [Option; MAX_READ_REQUESTS_PER_CALL] { let opt_notes = get_notes_internal(storage_slot, note_interface, options); let mut num_notes = 0; @@ -96,21 +84,17 @@ pub fn get_notes( num_notes += 1; }; - }; + } if options.limit != 0 { assert(num_notes <= options.limit, "Invalid number of return notes."); } opt_notes } -unconstrained fn get_note_internal( - storage_slot: Field, - note_interface: NoteInterface, -) -> Note { +unconstrained fn get_note_internal(storage_slot: Field, note_interface: NoteInterface) -> Note { let placeholder_note = [Option::none()]; let placeholder_fields = [0; GET_NOTE_ORACLE_RETURN_LENGTH]; - oracle::notes::get_notes( - storage_slot, + oracle::notes::get_notes(storage_slot, note_interface, 0, [], @@ -120,20 +104,18 @@ unconstrained fn get_note_internal( 1, // limit 0, // offset placeholder_note, - placeholder_fields, - )[0].unwrap() // Notice: we don't allow dummies to be returned from get_note (singular). + placeholder_fields)[0].unwrap() // Notice: we don't allow dummies to be returned from get_note (singular). } unconstrained fn get_notes_internal( storage_slot: Field, note_interface: NoteInterface, - options: NoteGetterOptions, + options: NoteGetterOptions ) -> [Option; MAX_READ_REQUESTS_PER_CALL] { let (num_selects, select_by, select_values, sort_by, sort_order) = flatten_options(options.selects, options.sorts); let placeholder_opt_notes = [Option::none(); MAX_READ_REQUESTS_PER_CALL]; let placeholder_fields = [0; GET_NOTES_ORACLE_RETURN_LENGTH]; - let opt_notes = oracle::notes::get_notes( - storage_slot, + let opt_notes = oracle::notes::get_notes(storage_slot, note_interface, num_selects, select_by, @@ -143,24 +125,18 @@ unconstrained fn get_notes_internal( options.limit, options.offset, placeholder_opt_notes, - placeholder_fields, - ); + placeholder_fields); let filter = options.filter; let filter_args = options.filter_args; filter(opt_notes, filter_args) } -unconstrained pub fn view_notes( - storage_slot: Field, - note_interface: NoteInterface, - options: NoteViewerOptions, -) -> [Option; MAX_NOTES_PER_PAGE] { +unconstrained pub fn view_notes(storage_slot: Field, note_interface: NoteInterface, options: NoteViewerOptions) -> [Option; MAX_NOTES_PER_PAGE] { let (num_selects, select_by, select_values, sort_by, sort_order) = flatten_options(options.selects, options.sorts); let placeholder_opt_notes = [Option::none(); MAX_NOTES_PER_PAGE]; let placeholder_fields = [0; VIEW_NOTE_ORACLE_RETURN_LENGTH]; - oracle::notes::get_notes( - storage_slot, + oracle::notes::get_notes(storage_slot, note_interface, num_selects, select_by, @@ -170,14 +146,10 @@ unconstrained pub fn view_notes( options.limit, options.offset, placeholder_opt_notes, - placeholder_fields, - ) + placeholder_fields) } -unconstrained fn flatten_options( - selects: BoundedVec, N>, - sorts: BoundedVec, N>, -) -> (u8, [u8; N], [Field; N], [u8; N], [u2; N]) { +unconstrained fn flatten_options(selects: BoundedVec, N>, sorts: BoundedVec, N>) -> (u8, [u8; N], [Field; N], [u8; N], [u2; N]) { let mut num_selects = 0; let mut select_by = [0; N]; let mut select_values = [0; N]; @@ -188,7 +160,7 @@ unconstrained fn flatten_options( select_values[num_selects] = select.unwrap_unchecked().value; num_selects += 1; }; - }; + } let mut sort_by = [0; N]; let mut sort_order = [0; N]; @@ -198,7 +170,7 @@ unconstrained fn flatten_options( sort_by[i] = sort.unwrap_unchecked().field_index; sort_order[i] = sort.unwrap_unchecked().order; }; - }; + } (num_selects, select_by, select_values, sort_by, sort_order) -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/note/note_header.nr b/yarn-project/aztec-nr/aztec/src/note/note_header.nr index 6f82cebf149..45450638401 100644 --- a/yarn-project/aztec-nr/aztec/src/note/note_header.nr +++ b/yarn-project/aztec-nr/aztec/src/note/note_header.nr @@ -15,4 +15,4 @@ impl NoteHeader { pub fn empty() -> Self { NoteHeader { contract_address: 0, nonce: 0, storage_slot: 0, is_transient: false } } -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/note/utils.nr b/yarn-project/aztec-nr/aztec/src/note/utils.nr index 74d006c0a18..bce81ff225e 100644 --- a/yarn-project/aztec-nr/aztec/src/note/utils.nr +++ b/yarn-project/aztec-nr/aztec/src/note/utils.nr @@ -5,10 +5,7 @@ use crate::note::{ }; use crate::utils::arr_copy_slice; -pub fn compute_inner_note_hash( - note_interface: NoteInterface, - note: Note, -) -> Field { +pub fn compute_inner_note_hash(note_interface: NoteInterface, note: Note) -> Field { let get_header = note_interface.get_header; let header = get_header(note); @@ -18,10 +15,7 @@ pub fn compute_inner_note_hash( compute_inner_hash(header.storage_slot, note_hash) } -pub fn compute_siloed_note_hash( - note_interface: NoteInterface, - note_with_header: Note, -) -> Field { +pub fn compute_siloed_note_hash(note_interface: NoteInterface, note_with_header: Note) -> Field { let get_header = note_interface.get_header; let header = get_header(note_with_header); @@ -30,10 +24,7 @@ pub fn compute_siloed_note_hash( compute_siloed_hash(header.contract_address, inner_note_hash) } -pub fn compute_unique_siloed_note_hash( - note_interface: NoteInterface, - note_with_header: Note, -) -> Field { +pub fn compute_unique_siloed_note_hash(note_interface: NoteInterface, note_with_header: Note) -> Field { let get_header = note_interface.get_header; let header = get_header(note_with_header); @@ -42,10 +33,7 @@ pub fn compute_unique_siloed_note_hash( compute_unique_hash(header.nonce, siloed_note_hash) } -pub fn compute_note_hash_for_read_or_nullify( - note_interface: NoteInterface, - note_with_header: Note, -) -> Field { +pub fn compute_note_hash_for_read_or_nullify(note_interface: NoteInterface, note_with_header: Note) -> Field { let get_header = note_interface.get_header; let header = get_header(note_with_header); @@ -63,14 +51,9 @@ pub fn compute_note_hash_for_read_or_nullify( // data tree, so the kernel can just perform a membership check directly on this hash/leaf. compute_unique_siloed_note_hash(note_interface, note_with_header) } - } -pub fn compute_note_hash_and_nullifier( - note_interface: NoteInterface, - note_header: NoteHeader, - serialized_note: [Field; S], -) -> [Field; 4] { +pub fn compute_note_hash_and_nullifier(note_interface: NoteInterface, note_header: NoteHeader, serialized_note: [Field; S]) -> [Field; 4] { let deserialize = note_interface.deserialize; let set_header = note_interface.set_header; let mut note = deserialize(arr_copy_slice(serialized_note, [0; N], 0)); diff --git a/yarn-project/aztec-nr/aztec/src/oracle/call_private_function.nr b/yarn-project/aztec-nr/aztec/src/oracle/call_private_function.nr index 13a9e5f24c5..a5ab1eb95f3 100644 --- a/yarn-project/aztec-nr/aztec/src/oracle/call_private_function.nr +++ b/yarn-project/aztec-nr/aztec/src/oracle/call_private_function.nr @@ -1,20 +1,8 @@ use crate::constants_gen::CALL_PRIVATE_FUNCTION_RETURN_SIZE; #[oracle(callPrivateFunction)] -fn call_private_function_oracle( - _contract_address: Field, - _function_selector: Field, - _args_hash: Field -) -> [Field; CALL_PRIVATE_FUNCTION_RETURN_SIZE] {} +fn call_private_function_oracle(_contract_address: Field, _function_selector: Field, _args_hash: Field) -> [Field; CALL_PRIVATE_FUNCTION_RETURN_SIZE] {} -unconstrained pub fn call_private_function_internal( - contract_address: Field, - function_selector: Field, - args_hash: Field -) -> [Field; CALL_PRIVATE_FUNCTION_RETURN_SIZE] { - call_private_function_oracle( - contract_address, - function_selector, - args_hash, - ) -} \ No newline at end of file +unconstrained pub fn call_private_function_internal(contract_address: Field, function_selector: Field, args_hash: Field) -> [Field; CALL_PRIVATE_FUNCTION_RETURN_SIZE] { + call_private_function_oracle(contract_address, function_selector, args_hash) +} diff --git a/yarn-project/aztec-nr/aztec/src/oracle/context.nr b/yarn-project/aztec-nr/aztec/src/oracle/context.nr index 8200f9fe18a..5895ec26187 100644 --- a/yarn-project/aztec-nr/aztec/src/oracle/context.nr +++ b/yarn-project/aztec-nr/aztec/src/oracle/context.nr @@ -4,4 +4,4 @@ fn _get_portal_address(_contract_address: Field) -> Field {} unconstrained pub fn get_portal_address(contract_address: Field) -> Field { let portal_address = _get_portal_address(contract_address); portal_address -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/oracle/create_commitment.nr b/yarn-project/aztec-nr/aztec/src/oracle/create_commitment.nr index 4f9b99cf716..62c848c1d71 100644 --- a/yarn-project/aztec-nr/aztec/src/oracle/create_commitment.nr +++ b/yarn-project/aztec-nr/aztec/src/oracle/create_commitment.nr @@ -1,4 +1,3 @@ - #[oracle(createCommitment)] fn create_commitment_oracle(_commitment: Field) -> Field {} diff --git a/yarn-project/aztec-nr/aztec/src/oracle/enqueue_public_function_call.nr b/yarn-project/aztec-nr/aztec/src/oracle/enqueue_public_function_call.nr index 17ea7f2f67b..9d6e1cf80e7 100644 --- a/yarn-project/aztec-nr/aztec/src/oracle/enqueue_public_function_call.nr +++ b/yarn-project/aztec-nr/aztec/src/oracle/enqueue_public_function_call.nr @@ -7,20 +7,8 @@ global ENQUEUE_PUBLIC_FUNCTION_CALL_RETURN_SIZE: Field = 13; #[oracle(enqueuePublicFunctionCall)] -fn enqueue_public_function_call_oracle( - _contract_address: Field, - _function_selector: Field, - _args_hash: Field, -) -> [Field; ENQUEUE_PUBLIC_FUNCTION_CALL_RETURN_SIZE] {} +fn enqueue_public_function_call_oracle(_contract_address: Field, _function_selector: Field, _args_hash: Field) -> [Field; ENQUEUE_PUBLIC_FUNCTION_CALL_RETURN_SIZE] {} -unconstrained pub fn enqueue_public_function_call_internal( - contract_address: Field, - function_selector: Field, - args_hash: Field -) -> [Field; ENQUEUE_PUBLIC_FUNCTION_CALL_RETURN_SIZE] { - enqueue_public_function_call_oracle( - contract_address, - function_selector, - args_hash, - ) -} \ No newline at end of file +unconstrained pub fn enqueue_public_function_call_internal(contract_address: Field, function_selector: Field, args_hash: Field) -> [Field; ENQUEUE_PUBLIC_FUNCTION_CALL_RETURN_SIZE] { + enqueue_public_function_call_oracle(contract_address, function_selector, args_hash) +} diff --git a/yarn-project/aztec-nr/aztec/src/oracle/get_l1_to_l2_message.nr b/yarn-project/aztec-nr/aztec/src/oracle/get_l1_to_l2_message.nr index 32a1f0e719d..358511db402 100644 --- a/yarn-project/aztec-nr/aztec/src/oracle/get_l1_to_l2_message.nr +++ b/yarn-project/aztec-nr/aztec/src/oracle/get_l1_to_l2_message.nr @@ -1,4 +1,3 @@ - use crate::constants_gen::L1_TO_L2_MESSAGE_ORACLE_CALL_LENGTH; // Checks if a msg is within the l1ToL2Msg tree @@ -7,4 +6,4 @@ fn get_l1_to_l2_msg_oracle(_msg_key: Field) -> [Field; L1_TO_L2_MESSAGE_ORACLE_C unconstrained pub fn get_l1_to_l2_message_call(msg_key: Field) -> [Field; L1_TO_L2_MESSAGE_ORACLE_CALL_LENGTH] { get_l1_to_l2_msg_oracle(msg_key) -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/oracle/get_public_key.nr b/yarn-project/aztec-nr/aztec/src/oracle/get_public_key.nr index 5afaa258a21..e2e1684e36f 100644 --- a/yarn-project/aztec-nr/aztec/src/oracle/get_public_key.nr +++ b/yarn-project/aztec-nr/aztec/src/oracle/get_public_key.nr @@ -5,17 +5,17 @@ use crate::address::compute_address; fn get_public_key_and_partial_address_oracle(_address: Field) -> [Field; 3] {} unconstrained fn get_public_key_and_partial_address_internal(address: Field) -> [Field; 3] { - get_public_key_and_partial_address_oracle(address) + get_public_key_and_partial_address_oracle(address) } pub fn get_public_key(address: Field) -> Point { - let result = get_public_key_and_partial_address_internal(address); - let pub_key_x = result[0]; - let pub_key_y = result[1]; - let partial_address = result[2]; - - let calculated_address = compute_address(pub_key_x, pub_key_y, partial_address); - assert(calculated_address == address); - - Point::new(pub_key_x, pub_key_y) + let result = get_public_key_and_partial_address_internal(address); + let pub_key_x = result[0]; + let pub_key_y = result[1]; + let partial_address = result[2]; + + let calculated_address = compute_address(pub_key_x, pub_key_y, partial_address); + assert(calculated_address == address); + + Point::new(pub_key_x, pub_key_y) } diff --git a/yarn-project/aztec-nr/aztec/src/oracle/get_secret_key.nr b/yarn-project/aztec-nr/aztec/src/oracle/get_secret_key.nr index 4ec21bcf5e8..fb7de3e56ed 100644 --- a/yarn-project/aztec-nr/aztec/src/oracle/get_secret_key.nr +++ b/yarn-project/aztec-nr/aztec/src/oracle/get_secret_key.nr @@ -2,10 +2,7 @@ use crate::oracle::get_public_key::get_public_key; use crate::types::point::Point; #[oracle(getSecretKey)] -fn get_secret_key_oracle( - _owner: Point, -) -> [Field; dep::std::grumpkin_scalar::GRUMPKIN_SCALAR_SERIALIZED_LEN] { -} +fn get_secret_key_oracle(_owner: Point) -> [Field; dep::std::grumpkin_scalar::GRUMPKIN_SCALAR_SERIALIZED_LEN] {} unconstrained fn get_secret_key_internal(owner_public_key: Point) -> dep::std::grumpkin_scalar::GrumpkinScalar { dep::std::grumpkin_scalar::deserialize_grumpkin_scalar(get_secret_key_oracle(owner_public_key)) diff --git a/yarn-project/aztec-nr/aztec/src/oracle/logs.nr b/yarn-project/aztec-nr/aztec/src/oracle/logs.nr index bc658e67bdd..caab7b2a13c 100644 --- a/yarn-project/aztec-nr/aztec/src/oracle/logs.nr +++ b/yarn-project/aztec-nr/aztec/src/oracle/logs.nr @@ -3,31 +3,16 @@ use crate::constants_gen::NUM_FIELDS_PER_SHA256; // TODO: Should take encrypted data. #[oracle(emitEncryptedLog)] -fn emit_encrypted_log_oracle( - _contract_address: Field, - _storage_slot: Field, - _encryption_pub_key: Point, - _preimage: [Field; N], -) -> Field {} +fn emit_encrypted_log_oracle(_contract_address: Field, _storage_slot: Field, _encryption_pub_key: Point, _preimage: [Field; N]) -> Field {} -unconstrained pub fn emit_encrypted_log( - contract_address: Field, - storage_slot: Field, - encryption_pub_key: Point, - preimage: [Field; N], -) -> [Field; NUM_FIELDS_PER_SHA256] { - [emit_encrypted_log_oracle( - contract_address, - storage_slot, - encryption_pub_key, - preimage, - ), 0] +unconstrained pub fn emit_encrypted_log(contract_address: Field, storage_slot: Field, encryption_pub_key: Point, preimage: [Field; N]) -> [Field; NUM_FIELDS_PER_SHA256] { + [emit_encrypted_log_oracle(contract_address, storage_slot, encryption_pub_key, preimage), 0] } #[oracle(emitUnencryptedLog)] -fn emit_unencrypted_log_oracle(_contract_address: Field, _event_selector: Field, _message: T) -> Field {} +fn emit_unencrypted_log_oracle(_contract_address: Field, _event_selector: Field, _message: T) -> Field {} unconstrained pub fn emit_unencrypted_log(contract_address: Field, event_selector: Field, message: T) -> [Field; NUM_FIELDS_PER_SHA256] { // https://github.com/AztecProtocol/aztec-packages/issues/885 [emit_unencrypted_log_oracle(contract_address, event_selector, message), 0] -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/oracle/notes.nr b/yarn-project/aztec-nr/aztec/src/oracle/notes.nr index c9e61ee28a2..e3790168cd9 100644 --- a/yarn-project/aztec-nr/aztec/src/oracle/notes.nr +++ b/yarn-project/aztec-nr/aztec/src/oracle/notes.nr @@ -6,30 +6,16 @@ use crate::note::{ use crate::utils::arr_copy_slice; #[oracle(notifyCreatedNote)] -fn notify_created_note_oracle( - _storage_slot: Field, - _serialized_note: [Field; N], - _inner_note_hash: Field, -) -> Field {} +fn notify_created_note_oracle(_storage_slot: Field, _serialized_note: [Field; N], _inner_note_hash: Field) -> Field {} -unconstrained pub fn notify_created_note( - storage_slot: Field, - serialized_note: [Field; N], - inner_note_hash: Field, -) -> Field { +unconstrained pub fn notify_created_note(storage_slot: Field, serialized_note: [Field; N], inner_note_hash: Field) -> Field { notify_created_note_oracle(storage_slot, serialized_note, inner_note_hash) } #[oracle(notifyNullifiedNote)] -fn notify_nullified_note_oracle( - _nullifier: Field, - _inner_note_hash: Field, -) -> Field {} +fn notify_nullified_note_oracle(_nullifier: Field, _inner_note_hash: Field) -> Field {} -unconstrained pub fn notify_nullified_note( - nullifier: Field, - inner_note_hash: Field, -) -> Field { +unconstrained pub fn notify_nullified_note(nullifier: Field, inner_note_hash: Field) -> Field { notify_nullified_note_oracle(nullifier, inner_note_hash) } @@ -44,7 +30,7 @@ fn get_notes_oracle( _limit: u32, _offset: u32, _return_size: u32, - _placeholder_fields: [Field; S], + _placeholder_fields: [Field; S] ) -> [Field; S] {} unconstrained fn get_notes_oracle_wrapper( @@ -56,10 +42,19 @@ unconstrained fn get_notes_oracle_wrapper( sort_order: [u2; N], limit: u32, offset: u32, - mut placeholder_fields: [Field; S], -)-> [Field; S] { + mut placeholder_fields: [Field; S] +) -> [Field; S] { let return_size = placeholder_fields.len() as u32; - get_notes_oracle(storage_slot, num_selects, select_by, select_values, sort_by, sort_order, limit, offset, return_size, placeholder_fields) + get_notes_oracle(storage_slot, + num_selects, + select_by, + select_values, + sort_by, + sort_order, + limit, + offset, + return_size, + placeholder_fields) } unconstrained pub fn get_notes( @@ -73,9 +68,17 @@ unconstrained pub fn get_notes( limit: u32, offset: u32, mut placeholder_opt_notes: [Option; S], // TODO: Remove it and use `limit` to initialize the note array. - placeholder_fields: [Field; NS], // TODO: Remove it and use `limit` to initialize the note array. + placeholder_fields: [Field; NS] // TODO: Remove it and use `limit` to initialize the note array. ) -> [Option; S] { - let fields = get_notes_oracle_wrapper(storage_slot, num_selects, select_by, select_values, sort_by, sort_order, limit, offset, placeholder_fields); + let fields = get_notes_oracle_wrapper(storage_slot, + num_selects, + select_by, + select_values, + sort_by, + sort_order, + limit, + offset, + placeholder_fields); let num_notes = fields[0] as u32; let contract_address = fields[1]; let deserialize = note_interface.deserialize; @@ -94,15 +97,13 @@ unconstrained pub fn get_notes( set_header(&mut note, header); placeholder_opt_notes[i] = Option::some(note); }; - }; + } placeholder_opt_notes } #[oracle(checkNullifierExists)] -fn check_nullifier_exists_oracle( - _inner_nullifier: Field, -) -> Field {} +fn check_nullifier_exists_oracle(_inner_nullifier: Field) -> Field {} unconstrained pub fn check_nullifier_exists(inner_nullifier: Field) -> bool { check_nullifier_exists_oracle(inner_nullifier) == 1 -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/oracle/public_call.nr b/yarn-project/aztec-nr/aztec/src/oracle/public_call.nr index a2caf7ef23f..cda65354018 100644 --- a/yarn-project/aztec-nr/aztec/src/oracle/public_call.nr +++ b/yarn-project/aztec-nr/aztec/src/oracle/public_call.nr @@ -1,20 +1,8 @@ use crate::constants_gen::RETURN_VALUES_LENGTH; #[oracle(callPublicFunction)] -fn call_public_function_oracle( - _contract_address: Field, - _function_selector: Field, - _args_hash: Field -) -> [Field; RETURN_VALUES_LENGTH] {} +fn call_public_function_oracle(_contract_address: Field, _function_selector: Field, _args_hash: Field) -> [Field; RETURN_VALUES_LENGTH] {} -unconstrained pub fn call_public_function_internal( - contract_address: Field, - function_selector: Field, - args_hash: Field -) -> [Field; RETURN_VALUES_LENGTH] { - call_public_function_oracle( - contract_address, - function_selector, - args_hash, - ) +unconstrained pub fn call_public_function_internal(contract_address: Field, function_selector: Field, args_hash: Field) -> [Field; RETURN_VALUES_LENGTH] { + call_public_function_oracle(contract_address, function_selector, args_hash) } diff --git a/yarn-project/aztec-nr/aztec/src/oracle/rand.nr b/yarn-project/aztec-nr/aztec/src/oracle/rand.nr index b21f6ad93af..0d417e69422 100644 --- a/yarn-project/aztec-nr/aztec/src/oracle/rand.nr +++ b/yarn-project/aztec-nr/aztec/src/oracle/rand.nr @@ -1,5 +1,3 @@ - - #[oracle(getRandomField)] fn rand_oracle() -> Field {} diff --git a/yarn-project/aztec-nr/aztec/src/oracle/storage.nr b/yarn-project/aztec-nr/aztec/src/oracle/storage.nr index 0b5d96997d2..b2c2c434c1f 100644 --- a/yarn-project/aztec-nr/aztec/src/oracle/storage.nr +++ b/yarn-project/aztec-nr/aztec/src/oracle/storage.nr @@ -1,32 +1,19 @@ - #[oracle(storageRead)] -fn storage_read_oracle( - _storage_slot: Field, - _number_of_elements: Field, -) -> [Field; N] {} +fn storage_read_oracle(_storage_slot: Field, _number_of_elements: Field) -> [Field; N] {} -unconstrained fn storage_read_oracle_wrapper(_storage_slot: Field)-> [Field; N] { +unconstrained fn storage_read_oracle_wrapper(_storage_slot: Field) -> [Field; N] { storage_read_oracle(_storage_slot, N) } -pub fn storage_read( - storage_slot: Field, - deserialize: fn ([Field; N]) -> T, -) -> T { +pub fn storage_read(storage_slot: Field, deserialize: fn ([Field; N]) -> T) -> T { let fields = storage_read_oracle_wrapper(storage_slot); deserialize(fields) } #[oracle(storageWrite)] -fn storage_write_oracle( - _storage_slot: Field, - _values: [Field; N], -) -> [Field; N] {} +fn storage_write_oracle(_storage_slot: Field, _values: [Field; N]) -> [Field; N] {} // TODO: Remove return value. -unconstrained pub fn storage_write( - storage_slot: Field, - fields: [Field; N] -) { +unconstrained pub fn storage_write(storage_slot: Field, fields: [Field; N]) { let _hash = storage_write_oracle(storage_slot, fields); -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/private_call_stack_item.nr b/yarn-project/aztec-nr/aztec/src/private_call_stack_item.nr index 0900e601d27..9742106c152 100644 --- a/yarn-project/aztec-nr/aztec/src/private_call_stack_item.nr +++ b/yarn-project/aztec-nr/aztec/src/private_call_stack_item.nr @@ -18,4 +18,4 @@ impl PrivateCallStackItem { self.public_inputs.hash(), ], GENERATOR_INDEX__CALL_STACK_ITEM) } -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/selector.nr b/yarn-project/aztec-nr/aztec/src/selector.nr index 9f5d368e5d7..6b81188571d 100644 --- a/yarn-project/aztec-nr/aztec/src/selector.nr +++ b/yarn-project/aztec-nr/aztec/src/selector.nr @@ -5,11 +5,11 @@ global SELECTOR_SIZE = 4; pub fn compute_selector(signature: str) -> Field { let bytes = signature.as_bytes(); let hash = dep::std::hash::keccak256(bytes, bytes.len() as u32); - + let mut selector_be_bytes = [0; SELECTOR_SIZE]; for i in 0..SELECTOR_SIZE { selector_be_bytes[i] = hash[i]; } field_from_bytes(selector_be_bytes, true) -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/state_vars.nr b/yarn-project/aztec-nr/aztec/src/state_vars.nr index b35df0399d1..e1e813891ff 100644 --- a/yarn-project/aztec-nr/aztec/src/state_vars.nr +++ b/yarn-project/aztec-nr/aztec/src/state_vars.nr @@ -2,4 +2,4 @@ mod immutable_singleton; mod map; mod public_state; mod set; -mod singleton; \ No newline at end of file +mod singleton; diff --git a/yarn-project/aztec-nr/aztec/src/state_vars/singleton.nr b/yarn-project/aztec-nr/aztec/src/state_vars/singleton.nr index 2ec2979bc59..9f64faf3c82 100644 --- a/yarn-project/aztec-nr/aztec/src/state_vars/singleton.nr +++ b/yarn-project/aztec-nr/aztec/src/state_vars/singleton.nr @@ -16,15 +16,10 @@ use crate::hash::pedersen_hash; pub fn compute_singleton_initialization_nullifier(storage_slot: Field, owner: Option) -> Field { if owner.is_some() { let secret = get_secret_key(owner.unwrap_unchecked()); - pedersen_hash( - [storage_slot, secret.low, secret.high], - GENERATOR_INDEX__INITIALIZATION_NULLIFIER, - ) + pedersen_hash([storage_slot, secret.low, secret.high], + GENERATOR_INDEX__INITIALIZATION_NULLIFIER) } else { - pedersen_hash( - [storage_slot], - GENERATOR_INDEX__INITIALIZATION_NULLIFIER, - ) + pedersen_hash([storage_slot], GENERATOR_INDEX__INITIALIZATION_NULLIFIER) } } diff --git a/yarn-project/aztec-nr/aztec/src/types.nr b/yarn-project/aztec-nr/aztec/src/types.nr index a93267973be..07b38d9b988 100644 --- a/yarn-project/aztec-nr/aztec/src/types.nr +++ b/yarn-project/aztec-nr/aztec/src/types.nr @@ -1,4 +1,4 @@ mod address; mod point; mod vec; // This can/should be moved out into an official noir library -mod type_serialization; \ No newline at end of file +mod type_serialization; diff --git a/yarn-project/aztec-nr/aztec/src/types/address.nr b/yarn-project/aztec-nr/aztec/src/types/address.nr index c238b491950..c0654e55de0 100644 --- a/yarn-project/aztec-nr/aztec/src/types/address.nr +++ b/yarn-project/aztec-nr/aztec/src/types/address.nr @@ -50,4 +50,4 @@ impl EthereumAddress { address: fields[0] } } -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/types/point.nr b/yarn-project/aztec-nr/aztec/src/types/point.nr index 5a51780a81b..f50be1fe2bb 100644 --- a/yarn-project/aztec-nr/aztec/src/types/point.nr +++ b/yarn-project/aztec-nr/aztec/src/types/point.nr @@ -14,10 +14,7 @@ impl Point { global POINT_SERIALIZED_LEN: Field = 2; fn deserializePoint(fields: [Field; POINT_SERIALIZED_LEN]) -> Point { - Point { - x: fields[0], - y: fields[1], - } + Point { x: fields[0], y: fields[1] } } fn serializePoint(point: Point) -> [Field; POINT_SERIALIZED_LEN] { @@ -27,4 +24,4 @@ fn serializePoint(point: Point) -> [Field; POINT_SERIALIZED_LEN] { global PointSerializationMethods = TypeSerializationInterface { deserialize: deserializePoint, serialize: serializePoint, -}; \ No newline at end of file +}; diff --git a/yarn-project/aztec-nr/aztec/src/types/type_serialization/aztec_address_serialization.nr b/yarn-project/aztec-nr/aztec/src/types/type_serialization/aztec_address_serialization.nr index 8ce68ec513f..2e53171542d 100644 --- a/yarn-project/aztec-nr/aztec/src/types/type_serialization/aztec_address_serialization.nr +++ b/yarn-project/aztec-nr/aztec/src/types/type_serialization/aztec_address_serialization.nr @@ -14,4 +14,4 @@ fn serialize(value: AztecAddress) -> [Field; AZTEC_ADDRESS_SERIALIZED_LEN] { global AztecAddressSerializationMethods = TypeSerializationInterface { deserialize, serialize, -}; \ No newline at end of file +}; diff --git a/yarn-project/aztec-nr/aztec/src/types/type_serialization/bool_serialization.nr b/yarn-project/aztec-nr/aztec/src/types/type_serialization/bool_serialization.nr index ac9829ecfb0..255f519234c 100644 --- a/yarn-project/aztec-nr/aztec/src/types/type_serialization/bool_serialization.nr +++ b/yarn-project/aztec-nr/aztec/src/types/type_serialization/bool_serialization.nr @@ -13,4 +13,4 @@ fn serializeBool(value: bool) -> [Field; BOOL_SERIALIZED_LEN] { global BoolSerializationMethods = TypeSerializationInterface { deserialize: deserializeBool, serialize: serializeBool, -}; \ No newline at end of file +}; diff --git a/yarn-project/aztec-nr/aztec/src/types/type_serialization/u32_serialization.nr b/yarn-project/aztec-nr/aztec/src/types/type_serialization/u32_serialization.nr index e04cf948a53..2517532b420 100644 --- a/yarn-project/aztec-nr/aztec/src/types/type_serialization/u32_serialization.nr +++ b/yarn-project/aztec-nr/aztec/src/types/type_serialization/u32_serialization.nr @@ -13,4 +13,4 @@ fn serializeU32(value: u32) -> [Field; U32_SERIALIZED_LEN] { global U32SerializationMethods = TypeSerializationInterface { deserialize: deserializeU32, serialize: serializeU32, -}; \ No newline at end of file +}; diff --git a/yarn-project/aztec-nr/aztec/src/types/vec.nr b/yarn-project/aztec-nr/aztec/src/types/vec.nr index 4851a1200c3..135bbbd53c3 100644 --- a/yarn-project/aztec-nr/aztec/src/types/vec.nr +++ b/yarn-project/aztec-nr/aztec/src/types/vec.nr @@ -1,4 +1,3 @@ - struct BoundedVec { storage: [T; MaxLen], len: Field, @@ -124,4 +123,4 @@ fn test_vec_any_not_default() { let mut vec: BoundedVec = BoundedVec::new(default_value); vec.push_array([2, 4]); assert(vec.any(|v| v == default_value) == false); -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/aztec/src/utils.nr b/yarn-project/aztec-nr/aztec/src/utils.nr index c80e360de56..205b19fd49a 100644 --- a/yarn-project/aztec-nr/aztec/src/utils.nr +++ b/yarn-project/aztec-nr/aztec/src/utils.nr @@ -1,8 +1,4 @@ -pub fn arr_copy_slice( - src: [T; N], - mut dst: [T; M], - offset: Field, -) -> [T; M] { +pub fn arr_copy_slice(src: [T; N], mut dst: [T; M], offset: Field) -> [T; M] { for i in 0..dst.len() { dst[i] = src[i + offset]; } @@ -23,4 +19,4 @@ pub fn field_from_bytes(bytes: [u8; N], big_endian: bool) -> Field { } as_field -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/easy-private-state/src/lib.nr b/yarn-project/aztec-nr/easy-private-state/src/lib.nr index 5a483393d84..2a29ab85139 100644 --- a/yarn-project/aztec-nr/easy-private-state/src/lib.nr +++ b/yarn-project/aztec-nr/easy-private-state/src/lib.nr @@ -1 +1 @@ -mod easy_private_state; \ No newline at end of file +mod easy_private_state; diff --git a/yarn-project/aztec-nr/field-note/src/field_note.nr b/yarn-project/aztec-nr/field-note/src/field_note.nr index da8f7d286eb..6e0e133476c 100644 --- a/yarn-project/aztec-nr/field-note/src/field_note.nr +++ b/yarn-project/aztec-nr/field-note/src/field_note.nr @@ -55,7 +55,7 @@ fn deserialize(serialized_note: [Field; FIELD_NOTE_LEN]) -> FieldNote { FieldNote::deserialize(serialized_note) } -fn serialize(note: FieldNote) -> [Field; FIELD_NOTE_LEN]{ +fn serialize(note: FieldNote) -> [Field; FIELD_NOTE_LEN] { note.serialize() } diff --git a/yarn-project/aztec-nr/field-note/src/lib.nr b/yarn-project/aztec-nr/field-note/src/lib.nr index 2625d3a2277..71523fc462b 100644 --- a/yarn-project/aztec-nr/field-note/src/lib.nr +++ b/yarn-project/aztec-nr/field-note/src/lib.nr @@ -1 +1 @@ -mod field_note; \ No newline at end of file +mod field_note; diff --git a/yarn-project/aztec-nr/safe-math/src/lib.nr b/yarn-project/aztec-nr/safe-math/src/lib.nr index f8f829b67e3..7e29a39a85e 100644 --- a/yarn-project/aztec-nr/safe-math/src/lib.nr +++ b/yarn-project/aztec-nr/safe-math/src/lib.nr @@ -1,3 +1,3 @@ mod safe_u120; -use crate::safe_u120::SafeU120; \ No newline at end of file +use crate::safe_u120::SafeU120; diff --git a/yarn-project/aztec-nr/safe-math/src/safe_u120.nr b/yarn-project/aztec-nr/safe-math/src/safe_u120.nr index b7ebfa2de82..8412f45a43a 100644 --- a/yarn-project/aztec-nr/safe-math/src/safe_u120.nr +++ b/yarn-project/aztec-nr/safe-math/src/safe_u120.nr @@ -163,7 +163,6 @@ fn test_lt() { assert(b.lt(a) == false); } - #[test] fn test_le() { let a = SafeU120::new(2); @@ -182,7 +181,6 @@ fn test_gt() { assert(a.gt(b) == false); } - #[test] fn test_ge() { let a = SafeU120::new(2); @@ -302,7 +300,6 @@ fn test_mul_div_up_non_rounding() { assert(d.value == 9); } - #[test(should_fail)] fn test_mul_div_up_ghost_overflow() { let a = SafeU120::max(); diff --git a/yarn-project/aztec-nr/slow-updates-tree/Nargo.toml b/yarn-project/aztec-nr/slow-updates-tree/Nargo.toml new file mode 100644 index 00000000000..9460f7c31cf --- /dev/null +++ b/yarn-project/aztec-nr/slow-updates-tree/Nargo.toml @@ -0,0 +1,8 @@ +[package] +name = "slow_updates_tree" +authors = ["aztec-labs"] +compiler_version = ">=0.18.0" +type = "lib" + +[dependencies] +aztec = { path = "../aztec" } \ No newline at end of file diff --git a/yarn-project/aztec-nr/slow-updates-tree/src/lib.nr b/yarn-project/aztec-nr/slow-updates-tree/src/lib.nr new file mode 100644 index 00000000000..72a8c1ad19f --- /dev/null +++ b/yarn-project/aztec-nr/slow-updates-tree/src/lib.nr @@ -0,0 +1 @@ +mod slow_map; diff --git a/yarn-project/aztec-nr/slow-updates-tree/src/slow_map.nr b/yarn-project/aztec-nr/slow-updates-tree/src/slow_map.nr new file mode 100644 index 00000000000..f36d9f79593 --- /dev/null +++ b/yarn-project/aztec-nr/slow-updates-tree/src/slow_map.nr @@ -0,0 +1,272 @@ +use dep::aztec::context::{PrivateContext, PublicContext, Context}; +use dep::aztec::types::type_serialization::TypeSerializationInterface; +use dep::aztec::oracle::storage::{storage_read, storage_write}; + +use dep::std::hash::pedersen_hash; +use dep::std::merkle::compute_merkle_root; +use dep::std::option::Option; + +// The epoch length is just a random number for now. +global EPOCH_LENGTH: u120 = 100; + +fn compute_next_change(time: Field) -> Field { + ((time as u120 / EPOCH_LENGTH + 1) * EPOCH_LENGTH) as Field +} + +// A leaf in the tree. +struct Leaf { + next_change: Field, + before: Field, + after: Field, +} + +fn serialize_leaf(leaf: Leaf) -> [Field; 3] { + [leaf.next_change, leaf.before, leaf.after] +} + +fn deserialize_leaf(serialized: [Field; 3]) -> Leaf { + Leaf { next_change: serialized[0], before: serialized[1], after: serialized[2] } +} + +impl Leaf { + fn serialize(self: Self) -> [Field; 3] { + serialize_leaf(self) + } + fn deserialize(serialized: [Field; 3]) -> Self { + deserialize_leaf(serialized) + } +} + +// Subset of the MembershipProof that is needed for the slow update. +struct SlowUpdateInner { + value: Field, // Value only really used for the private flow though :thinking: + sibling_path: [Field; N], +} + +// The slow update proof. Containing two merkle paths +// One for the before and one for the after trees. +// M = 2 * N + 4 +struct SlowUpdateProof { + index: Field, + new_value: Field, + before: SlowUpdateInner, + after: SlowUpdateInner, +} + +pub fn deserialize_slow_update_proof(serialized: [Field; M]) -> SlowUpdateProof { + SlowUpdateProof::deserialize(serialized) +} + +impl SlowUpdateProof { + pub fn serialize(self: Self) -> [Field; M] { + let mut serialized = [0; M]; + serialized[0] = self.index; + serialized[1] = self.new_value; + serialized[2] = self.before.value; + serialized[3 + N] = self.after.value; + + for i in 0..N { + serialized[3 + i] = self.before.sibling_path[i]; + serialized[4 + N + i] = self.after.sibling_path[i]; + } + serialized + } + + pub fn deserialize(serialized: [Field; M]) -> Self { + let mut before_sibling_path = [0; N]; + let mut after_sibling_path = [0; N]; + + for i in 0..N { + before_sibling_path[i] = serialized[3 + i]; + after_sibling_path[i] = serialized[4 + N + i]; + } + + Self { + index: serialized[0], + new_value: serialized[1], + before: SlowUpdateInner { + value: serialized[2], + sibling_path: before_sibling_path, + }, + after: SlowUpdateInner { + value: serialized[3 + N], + sibling_path: after_sibling_path, + }, + } + } +} + +// The simple slow map which stores a sparse tree +struct SlowMap { + context: Context, + storage_slot: Field +} + +impl SlowMap { + pub fn new( + context: Context, + storage_slot: Field + ) -> Self { + assert(storage_slot != 0, "Storage slot 0 not allowed. Storage slots must start from 1."); + Self { + context, + storage_slot, + } + } + + pub fn read_root(self: Self) -> Leaf { + storage_read(self.storage_slot, deserialize_leaf) + } + + // Beware that the initial root could include much state that is not shown by the public storage! + pub fn initialize(self: Self, initial_root: Field) { + let mut root_object = self.read_root(); + assert(root_object.next_change == 0, "cannot initialize twice"); + root_object = Leaf { + next_change: 0xffffffffffffffffffffffffffffff, + before: initial_root, + after: initial_root, + }; + let fields = root_object.serialize(); + storage_write(self.storage_slot, fields); + } + + // Reads the "CURRENT" value of the root + pub fn current_root(self: Self) -> Field { + let time = self.context.public.unwrap().timestamp() as u120; + let root_object = self.read_root(); + if time <= root_object.next_change as u120 { + root_object.before + } else { + root_object.after + } + } + + pub fn read_leaf_at(self: Self, key: Field) -> Leaf { + let derived_storage_slot = pedersen_hash([self.storage_slot, key]); + storage_read(derived_storage_slot, deserialize_leaf) + } + + // Reads the "CURRENT" value of the leaf + pub fn read_at(self: Self, key: Field) -> Field { + let time = self.context.public.unwrap().timestamp() as u120; + let leaf = self.read_leaf_at(key); + if time <= leaf.next_change as u120 { + leaf.before + } else { + leaf.after + } + } + + // Will update values in the "AFTER" tree + // - updates the leaf and root to follow current values, moving from after to before if + // needed. + // - checks that the provided merkle paths match state values + // - update the leaf and compute the net root + // Should only be used when updates from public are desired, since the hashing will be + // costly since done by sequencer. + pub fn update_at(self: Self, p: SlowUpdateProof) { + // The calling function should ensure that the index is within the tree. + // This must be done separately to ensure we are not constraining too tight here. + + let time = self.context.public.unwrap().timestamp() as u120; + let next_change = compute_next_change(time as Field); + + let mut root = self.read_root(); + let mut leaf = self.read_leaf_at(p.index); + + // Move leaf if needed + if time > leaf.next_change as u120 { + leaf.before = leaf.after; + } + + // Move root if needed + if time > root.next_change as u120 { + root.before = root.after; + } + + // Ensures that when before is active, it is not altered by this update + assert( + root.before == compute_merkle_root(leaf.before, p.index, p.before.sibling_path), + "Before root don't match" + ); + + // Ensures that the provided sibling path is valid for the CURRENT "after" tree. + // Without this check, someone could provide a sibling path for a different tree + // and update the entire "after" tree at once, causing it to be out of sync with leaf storage. + assert( + root.after == compute_merkle_root(leaf.after, p.index, p.after.sibling_path), + "After root don't match" + ); + + // Update the leaf + leaf.after = p.new_value; + leaf.next_change = next_change; + + // Update the after root + root.after = compute_merkle_root(leaf.after, p.index, p.after.sibling_path); + root.next_change = next_change; + + self.update_unsafe(p.index, leaf, root); + } + + // A variation of `update_at` that skips the merkle-membership checks. + // To be used by a contract which has already checked the merkle-membership. + // This allows us to check the merkle-memberships in private and then update + // in public, limiting the cost of the update. + pub fn update_unsafe_at(self: Self, index: Field, leaf_value: Field, new_root: Field) { + // User must ensure that the checks from update_at is performed for safety + let time = self.context.public.unwrap().timestamp() as u120; + let next_change = compute_next_change(time as Field); + + let mut root = self.read_root(); + let mut leaf = self.read_leaf_at(index); + + // Move leaf if needed + if time > leaf.next_change as u120 { + leaf.before = leaf.after; + } + + // Move root if needed + if time > root.next_change as u120 { + root.before = root.after; + } + + // Update the leaf + leaf.after = leaf_value; + leaf.next_change = next_change; + + // Update the root + root.after = new_root; + root.next_change = next_change; + + self.update_unsafe(index, leaf, root); + } + + // Updates the value in the in storage with no checks. + fn update_unsafe(self: Self, index: Field, leaf: Leaf, root: Leaf) { + let derived_storage_slot = pedersen_hash([self.storage_slot, index]); + let fields = leaf.serialize(); + storage_write(derived_storage_slot, fields); + + let fields = root.serialize(); + storage_write(self.storage_slot, fields); + } +} + +/*pub fn compute_merkle_root(leaf: Field, index: Field, hash_path: [Field; N]) -> Field { + let n = hash_path.len(); + let index_bits = index.to_le_bits(n as u32); + let mut current = leaf; + for i in 0..n { + let path_bit = index_bits[i] as bool; + let (hash_left, hash_right) = if path_bit { + (hash_path[i], current) + } else { + (current, hash_path[i]) + }; + current = pedersen_hash([hash_left, hash_right]); + }; + current +} +*/ diff --git a/yarn-project/aztec-nr/value-note/src/balance_utils.nr b/yarn-project/aztec-nr/value-note/src/balance_utils.nr index eedefb3c27e..cd577b97af7 100644 --- a/yarn-project/aztec-nr/value-note/src/balance_utils.nr +++ b/yarn-project/aztec-nr/value-note/src/balance_utils.nr @@ -26,4 +26,4 @@ unconstrained pub fn get_balance_with_offset(set: Set } balance -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/value-note/src/filter.nr b/yarn-project/aztec-nr/value-note/src/filter.nr index 05424756218..8657358cf10 100644 --- a/yarn-project/aztec-nr/value-note/src/filter.nr +++ b/yarn-project/aztec-nr/value-note/src/filter.nr @@ -13,4 +13,4 @@ pub fn filter_notes_min_sum(notes: [Option; MAX_READ_REQUESTS_PER_CAL } } selected -} \ No newline at end of file +} diff --git a/yarn-project/aztec-nr/value-note/src/lib.nr b/yarn-project/aztec-nr/value-note/src/lib.nr index 83b1454e9b7..c48740b98d2 100644 --- a/yarn-project/aztec-nr/value-note/src/lib.nr +++ b/yarn-project/aztec-nr/value-note/src/lib.nr @@ -1,4 +1,4 @@ mod balance_utils; mod filter; mod utils; -mod value_note; \ No newline at end of file +mod value_note; diff --git a/yarn-project/aztec-nr/value-note/src/utils.nr b/yarn-project/aztec-nr/value-note/src/utils.nr index f5abca9ffcc..5751bba16f1 100644 --- a/yarn-project/aztec-nr/value-note/src/utils.nr +++ b/yarn-project/aztec-nr/value-note/src/utils.nr @@ -16,11 +16,7 @@ pub fn create_note_getter_options_for_decreasing_balance(amount: Field) -> NoteG // Creates a new note for the recipient. // Inserts it to the recipient's set of notes. -pub fn increment( - balance: Set, - amount: Field, - recipient: Field, -) { +pub fn increment(balance: Set, amount: Field, recipient: Field) { let mut note = ValueNote::new(amount, recipient); // Insert the new note to the owner's set of notes and emit the log if value is non-zero. balance.insert(&mut note, amount != 0); @@ -30,11 +26,7 @@ pub fn increment( // Remove those notes. // If the value of the removed notes exceeds the requested `amount`, create a new note containing the excess value, so that exactly `amount` is removed. // Fail if the sum of the selected notes is less than the amount. -pub fn decrement( - balance: Set, - amount: Field, - owner: Field, -) { +pub fn decrement(balance: Set, amount: Field, owner: Field) { let sum = decrement_by_at_most(balance, amount, owner); assert(sum == amount, "Balance too low"); } @@ -47,11 +39,7 @@ pub fn decrement( // equal `amount`. // // It returns the decremented amount, which should be less than or equal to max_amount. -pub fn decrement_by_at_most( - balance: Set, - max_amount: Field, - owner: Field, -) -> Field { +pub fn decrement_by_at_most(balance: Set, max_amount: Field, owner: Field) -> Field { let options = create_note_getter_options_for_decreasing_balance(max_amount); let opt_notes = balance.get_notes(options); @@ -75,16 +63,12 @@ pub fn decrement_by_at_most( // Removes the note from the owner's set of notes. // Returns the value of the destroyed note. -pub fn destroy_note( - balance: Set, - owner: Field, - note: ValueNote, -) -> Field { +pub fn destroy_note(balance: Set, owner: Field, note: ValueNote) -> Field { // Ensure the note is actually owned by the owner (to prevent user from generating a valid proof while // spending someone else's notes). assert(note.owner == owner); balance.remove(note); - + note.value } diff --git a/yarn-project/aztec-sandbox/package.json b/yarn-project/aztec-sandbox/package.json index 8e11e03d62a..0739a8d9cc4 100644 --- a/yarn-project/aztec-sandbox/package.json +++ b/yarn-project/aztec-sandbox/package.json @@ -18,7 +18,7 @@ "start": "node --no-warnings ./dest/bin", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "build:dev": "tsc -b --watch", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests", "run:example:token": "DEBUG='aztec:*' node ./dest/examples/token.js" @@ -35,6 +35,7 @@ "@aztec/l1-artifacts": "workspace:^", "@aztec/noir-compiler": "workspace:^", "@aztec/noir-contracts": "workspace:^", + "@aztec/p2p": "workspace:^", "@aztec/pxe": "workspace:^", "@aztec/types": "workspace:^", "abitype": "^0.8.11", diff --git a/yarn-project/aztec-sandbox/src/bin/index.ts b/yarn-project/aztec-sandbox/src/bin/index.ts index 340a1286cfe..31dde1ce825 100644 --- a/yarn-project/aztec-sandbox/src/bin/index.ts +++ b/yarn-project/aztec-sandbox/src/bin/index.ts @@ -1,27 +1,47 @@ #!/usr/bin/env -S node --no-warnings -import { createAztecNodeRpcServer } from '@aztec/aztec-node'; -import { deployInitialSandboxAccounts } from '@aztec/aztec.js'; +import { createAztecNodeRpcServer, getConfigEnvVars as getNodeConfigEnvVars } from '@aztec/aztec-node'; +import { AccountManager, createAztecNodeClient, deployInitialSandboxAccounts } from '@aztec/aztec.js'; +import { NULL_KEY } from '@aztec/ethereum'; import { createDebugLogger } from '@aztec/foundation/log'; import { fileURLToPath } from '@aztec/foundation/url'; import { NoirWasmVersion } from '@aztec/noir-compiler/versions'; -import { createPXERpcServer } from '@aztec/pxe'; +import { BootstrapNode, getP2PConfigEnvVars } from '@aztec/p2p'; +import { GrumpkinScalar, PXEService, createPXERpcServer } from '@aztec/pxe'; import { readFileSync } from 'fs'; import { dirname, resolve } from 'path'; +import { mnemonicToAccount } from 'viem/accounts'; import { setupFileDebugLog } from '../logging.js'; -import { createSandbox } from '../sandbox.js'; +import { MNEMONIC, createAztecNode, createAztecPXE, createSandbox, deployContractsToL1 } from '../sandbox.js'; import { startHttpRpcServer } from '../server.js'; import { github, splash } from '../splash.js'; -const { AZTEC_NODE_PORT = 8079, PXE_PORT = 8080 } = process.env; +/** + * The mode in which the sandbox should be run. + */ +enum SandboxMode { + Sandbox = 'sandbox', + Node = 'node', + PXE = 'pxe', + P2PBootstrap = 'p2p-bootstrap', +} -const logger = createDebugLogger('aztec:sandbox'); +const { + AZTEC_NODE_URL = 'http://localhost:8079', + AZTEC_NODE_PORT = 8079, + PXE_PORT = 8080, + MODE = 'sandbox', + TEST_ACCOUNTS = 'true', + DEPLOY_AZTEC_CONTRACTS = 'true', +} = process.env; + +const logger = createDebugLogger(`aztec:${MODE}`); /** * Creates the sandbox from provided config and deploys any initial L1 and L2 contracts */ -async function createAndInitialiseSandbox() { +async function createAndInitialiseSandbox(deployTestAccounts: boolean) { const { aztecNodeConfig, node, pxe, stop } = await createSandbox(); if (aztecNodeConfig.p2pEnabled) { logger.info(`Not setting up test accounts as we are connecting to a network`); @@ -33,8 +53,11 @@ async function createAndInitialiseSandbox() { accounts: [], }; } - logger.info('Setting up test accounts...'); - const accounts = await deployInitialSandboxAccounts(pxe); + let accounts; + if (deployTestAccounts) { + logger.info('Setting up test accounts...'); + accounts = await deployInitialSandboxAccounts(pxe); + } return { aztecNodeConfig, pxe, @@ -48,45 +71,143 @@ async function createAndInitialiseSandbox() { * Create and start a new Aztec RPC HTTP Server */ async function main() { + const deployTestAccounts = TEST_ACCOUNTS === 'true'; + const deployAztecContracts = DEPLOY_AZTEC_CONTRACTS === 'true'; + + const mode = MODE as SandboxMode; + + const createShutdown = (cb?: () => Promise) => async () => { + logger.info('Shutting down...'); + if (cb) { + await cb(); + } + process.exit(0); + }; + + let shutdown: () => Promise; + + const logStrings = []; + const logPath = setupFileDebugLog(); + logger.info(`Debug logs will be written to ${logPath}`); + + // Get Sandbox version const packageJsonPath = resolve(dirname(fileURLToPath(import.meta.url)), '../../package.json'); const version = JSON.parse(readFileSync(packageJsonPath).toString()).version; - logger.info(`Setting up Aztec Sandbox v${version} (noir v${NoirWasmVersion}), please stand by...`); + // Code path for starting Sandbox + if (mode === SandboxMode.Sandbox) { + logger.info(`Setting up Aztec Sandbox v${version} (noir v${NoirWasmVersion}), please stand by...`); - const { pxe, node, stop, accounts } = await createAndInitialiseSandbox(); + const { pxe, node, stop, accounts } = await createAndInitialiseSandbox(deployTestAccounts); - const shutdown = async () => { - logger.info('Shutting down...'); - await stop(); - process.exit(0); - }; + // Create shutdown cleanup function + shutdown = createShutdown(stop); + // Start Node and PXE JSON-RPC servers + startHttpRpcServer(node, createAztecNodeRpcServer, AZTEC_NODE_PORT); + logger.info(`Aztec Node JSON-RPC Server listening on port ${AZTEC_NODE_PORT}`); + startHttpRpcServer(pxe, createPXERpcServer, PXE_PORT); + logger.info(`PXE JSON-RPC Server listening on port ${PXE_PORT}`); + + // Log initial accounts details + if (accounts?.length) { + const accountLogStrings = await createAccountLogs(accounts, pxe); + logStrings.push(...accountLogStrings); + } + logStrings.push(`Aztec Sandbox v${version} (noir v${NoirWasmVersion}) is now ready for use!`); + } else if (mode === SandboxMode.Node) { + // Code path for starting Node only + const nodeConfig = getNodeConfigEnvVars(); + const hdAccount = mnemonicToAccount(MNEMONIC); + + // Deploy L1 Aztec Contracts if needed + if (deployAztecContracts) { + await deployContractsToL1(nodeConfig, hdAccount); + if (nodeConfig.publisherPrivateKey === NULL_KEY) { + const privKey = hdAccount.getHdKey().privateKey; + nodeConfig.publisherPrivateKey = `0x${Buffer.from(privKey!).toString('hex')}`; + } + } + + const node = await createAztecNode(nodeConfig); + shutdown = createShutdown(node.stop); + + // Start Node JSON-RPC server + startHttpRpcServer(node, createAztecNodeRpcServer, 8080); // Use standard 8080 when no PXE is running + logStrings.push( + `Aztec Node v${version} (noir v${NoirWasmVersion}) is now ready for use in port ${AZTEC_NODE_PORT}!`, + ); + } else if (mode === SandboxMode.PXE) { + // Code path for starting PXE only + + // Create a Node client to connect to the PXE + const node = createAztecNodeClient(AZTEC_NODE_URL); + + const pxe = await createAztecPXE(node); + shutdown = createShutdown(pxe.stop); + + // Start PXE JSON-RPC server + startHttpRpcServer(pxe, createPXERpcServer, PXE_PORT); + + if (deployTestAccounts) { + logger.info('Setting up test accounts...'); + const accounts = await deployInitialSandboxAccounts(pxe); + const accountLogStrings = await createAccountLogs(accounts, pxe); + logStrings.push(...accountLogStrings); + } + + logStrings.push(`PXE v${version} (noir v${NoirWasmVersion}) is now ready for use in port ${PXE_PORT}!`); + } else if (mode === SandboxMode.P2PBootstrap) { + // Code path for starting a P2P bootstrap node + const config = getP2PConfigEnvVars(); + const bootstrapNode = new BootstrapNode(logger); + await bootstrapNode.start(config); + shutdown = createShutdown(bootstrapNode.stop); + logStrings.push( + `Bootstrap P2P node is now ready for use. Listening on: ${config.tcpListenIp}:${config.tcpListenPort}.`, + ); + } else { + shutdown = createShutdown(); + } + + // Log startup details + logger.info(`${splash}\n${github}\n\n`.concat(...logStrings)); process.once('SIGINT', shutdown); process.once('SIGTERM', shutdown); +} - startHttpRpcServer(node, createAztecNodeRpcServer, AZTEC_NODE_PORT); - logger.info(`Aztec Node JSON-RPC Server listening on port ${AZTEC_NODE_PORT}`); - startHttpRpcServer(pxe, createPXERpcServer, PXE_PORT); - logger.info(`PXE JSON-RPC Server listening on port ${PXE_PORT}`); - logger.info(`Debug logs will be written to ${logPath}`); - const accountStrings = accounts.length ? [`Initial Accounts:\n\n`] : []; - +/** + * Creates logs for the initial accounts + * @param accounts - The initial accounts + * @param pxe - A PXE instance to get the registered accounts + * @returns A string array containing the initial accounts details + */ +async function createAccountLogs( + accounts: { + /** + * The account object + */ + account: AccountManager; + /** + * The private key of the account + */ + privateKey: GrumpkinScalar; + }[], + pxe: PXEService, +) { const registeredAccounts = await pxe.getRegisteredAccounts(); + const accountLogStrings = [`Initial Accounts:\n\n`]; for (const account of accounts) { - const completeAddress = await account.account.getCompleteAddress(); + const completeAddress = account.account.getCompleteAddress(); if (registeredAccounts.find(a => a.equals(completeAddress))) { - accountStrings.push(` Address: ${completeAddress.address.toString()}\n`); - accountStrings.push(` Partial Address: ${completeAddress.partialAddress.toString()}\n`); - accountStrings.push(` Private Key: ${account.privateKey.toString()}\n`); - accountStrings.push(` Public Key: ${completeAddress.publicKey.toString()}\n\n`); + accountLogStrings.push(` Address: ${completeAddress.address.toString()}\n`); + accountLogStrings.push(` Partial Address: ${completeAddress.partialAddress.toString()}\n`); + accountLogStrings.push(` Private Key: ${account.privateKey.toString()}\n`); + accountLogStrings.push(` Public Key: ${completeAddress.publicKey.toString()}\n\n`); } } - logger.info( - `${splash}\n${github}\n\n` - .concat(...accountStrings) - .concat(`Aztec Sandbox v${version} (noir v${NoirWasmVersion}) is now ready for use!`), - ); + return accountLogStrings; } main().catch(err => { diff --git a/yarn-project/aztec-sandbox/src/logging.ts b/yarn-project/aztec-sandbox/src/logging.ts index 5a592496a49..a9de4c40415 100644 --- a/yarn-project/aztec-sandbox/src/logging.ts +++ b/yarn-project/aztec-sandbox/src/logging.ts @@ -9,7 +9,7 @@ const { format } = winston; const CURRENT_LOG_FILE_NAME = 'aztec-sandbox.debug.log'; const LOG_DIR = 'log'; -/** Creates a winston logger that logs everyting to a local rotating file */ +/** Creates a winston logger that logs everything to a local rotating file */ function createWinstonLogger() { // See https://www.npmjs.com/package/winston-daily-rotate-file#user-content-options const transport: DailyRotateFile = new DailyRotateFile({ diff --git a/yarn-project/aztec-sandbox/src/sandbox.ts b/yarn-project/aztec-sandbox/src/sandbox.ts index 7a16d5a19fd..d10248a6947 100644 --- a/yarn-project/aztec-sandbox/src/sandbox.ts +++ b/yarn-project/aztec-sandbox/src/sandbox.ts @@ -21,13 +21,14 @@ import { RollupAbi, RollupBytecode, } from '@aztec/l1-artifacts'; -import { createPXEService, getPXEServiceConfig } from '@aztec/pxe'; +import { PXEServiceConfig, createPXEService, getPXEServiceConfig } from '@aztec/pxe'; +import { AztecNode } from '@aztec/types'; import { HDAccount, createPublicClient, http as httpViemTransport } from 'viem'; import { mnemonicToAccount } from 'viem/accounts'; import { foundry } from 'viem/chains'; -const { MNEMONIC = 'test test test test test test test test test test test junk' } = process.env; +export const { MNEMONIC = 'test test test test test test test test test test test junk' } = process.env; const logger = createDebugLogger('aztec:sandbox'); @@ -71,7 +72,7 @@ async function waitThenDeploy(config: AztecNodeConfig, deployFunction: () => Pro * @param aztecNodeConfig - The Aztec Node Config * @param hdAccount - Account for publishing L1 contracts */ -async function deployContractsToL1(aztecNodeConfig: AztecNodeConfig, hdAccount: HDAccount) { +export async function deployContractsToL1(aztecNodeConfig: AztecNodeConfig, hdAccount: HDAccount) { const l1Artifacts: L1ContractArtifactsForDeployment = { contractDeploymentEmitter: { contractAbi: ContractDeploymentEmitterAbi, @@ -117,7 +118,6 @@ export type SandboxConfig = AztecNodeConfig & { */ export async function createSandbox(config: Partial = {}) { const aztecNodeConfig: AztecNodeConfig = { ...getConfigEnvVars(), ...config }; - const pxeServiceConfig = getPXEServiceConfig(); const hdAccount = mnemonicToAccount(config.l1Mnemonic ?? MNEMONIC); if (aztecNodeConfig.publisherPrivateKey === NULL_KEY) { const privKey = hdAccount.getHdKey().privateKey; @@ -128,8 +128,8 @@ export async function createSandbox(config: Partial = {}) { await deployContractsToL1(aztecNodeConfig, hdAccount); } - const node = await AztecNodeService.createAndSync(aztecNodeConfig); - const pxe = await createPXEService(node, pxeServiceConfig); + const node = await createAztecNode(aztecNodeConfig); + const pxe = await createAztecPXE(node); const stop = async () => { await pxe.stop(); @@ -138,3 +138,23 @@ export async function createSandbox(config: Partial = {}) { return { node, pxe, aztecNodeConfig, stop }; } + +/** + * Create and start a new Aztec RPC HTTP Server + * @param config - Optional Aztec node settings. + */ +export async function createAztecNode(config: Partial = {}) { + const aztecNodeConfig: AztecNodeConfig = { ...getConfigEnvVars(), ...config }; + const node = await AztecNodeService.createAndSync(aztecNodeConfig); + return node; +} + +/** + * Create and start a new Aztec PXE HTTP Server + * @param config - Optional PXE settings. + */ +export async function createAztecPXE(node: AztecNode, config: Partial = {}) { + const pxeServiceConfig: PXEServiceConfig = { ...getPXEServiceConfig(), ...config }; + const pxe = await createPXEService(node, pxeServiceConfig); + return pxe; +} diff --git a/yarn-project/aztec-sandbox/tsconfig.json b/yarn-project/aztec-sandbox/tsconfig.json index b2d07377c20..89a05426e77 100644 --- a/yarn-project/aztec-sandbox/tsconfig.json +++ b/yarn-project/aztec-sandbox/tsconfig.json @@ -30,6 +30,9 @@ { "path": "../noir-contracts" }, + { + "path": "../p2p" + }, { "path": "../pxe" }, diff --git a/yarn-project/aztec.js/package.json b/yarn-project/aztec.js/package.json index c0008cd00c4..637c4dfed82 100644 --- a/yarn-project/aztec.js/package.json +++ b/yarn-project/aztec.js/package.json @@ -22,7 +22,7 @@ "build:ts": "tsc -b", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" }, "inherits": [ @@ -49,7 +49,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/lodash.every": "^4.6.7", "@types/lodash.partition": "^4.6.0", diff --git a/yarn-project/aztec.js/src/account/contract/base_account_contract.ts b/yarn-project/aztec.js/src/account/contract/base_account_contract.ts index d749a767fe8..c0123c8ad14 100644 --- a/yarn-project/aztec.js/src/account/contract/base_account_contract.ts +++ b/yarn-project/aztec.js/src/account/contract/base_account_contract.ts @@ -11,7 +11,7 @@ import { AccountContract } from './index.js'; */ export abstract class BaseAccountContract implements AccountContract { abstract getAuthWitnessProvider(address: CompleteAddress): AuthWitnessProvider; - abstract getDeploymentArgs(): Promise; + abstract getDeploymentArgs(): any[]; constructor(private artifact: ContractArtifact) {} @@ -19,7 +19,7 @@ export abstract class BaseAccountContract implements AccountContract { return this.artifact; } - getInterface(address: CompleteAddress, nodeInfo: NodeInfo): Promise { - return Promise.resolve(new DefaultAccountInterface(this.getAuthWitnessProvider(address), address, nodeInfo)); + getInterface(address: CompleteAddress, nodeInfo: NodeInfo): AccountInterface { + return new DefaultAccountInterface(this.getAuthWitnessProvider(address), address, nodeInfo); } } diff --git a/yarn-project/aztec.js/src/account/contract/ecdsa_account_contract.ts b/yarn-project/aztec.js/src/account/contract/ecdsa_account_contract.ts index d238b8c417f..8820fbced7f 100644 --- a/yarn-project/aztec.js/src/account/contract/ecdsa_account_contract.ts +++ b/yarn-project/aztec.js/src/account/contract/ecdsa_account_contract.ts @@ -16,8 +16,8 @@ export class EcdsaAccountContract extends BaseAccountContract { super(EcdsaAccountContractArtifact as ContractArtifact); } - async getDeploymentArgs() { - const signingPublicKey = await Ecdsa.new().then(e => e.computePublicKey(this.signingPrivateKey)); + getDeploymentArgs() { + const signingPublicKey = new Ecdsa().computePublicKey(this.signingPrivateKey); return [signingPublicKey.subarray(0, 32), signingPublicKey.subarray(32, 64)]; } @@ -30,9 +30,9 @@ export class EcdsaAccountContract extends BaseAccountContract { class EcdsaAuthWitnessProvider implements AuthWitnessProvider { constructor(private signingPrivateKey: Buffer) {} - async createAuthWitness(message: Fr): Promise { - const ecdsa = await Ecdsa.new(); + createAuthWitness(message: Fr): Promise { + const ecdsa = new Ecdsa(); const signature = ecdsa.constructSignature(message.toBuffer(), this.signingPrivateKey); - return new AuthWitness(message, [...signature.r, ...signature.s]); + return Promise.resolve(new AuthWitness(message, [...signature.r, ...signature.s])); } } diff --git a/yarn-project/aztec.js/src/account/contract/index.ts b/yarn-project/aztec.js/src/account/contract/index.ts index 611721b64c8..f0ac2fcd717 100644 --- a/yarn-project/aztec.js/src/account/contract/index.ts +++ b/yarn-project/aztec.js/src/account/contract/index.ts @@ -22,7 +22,7 @@ export interface AccountContract { /** * Returns the deployment arguments for this instance. */ - getDeploymentArgs(): Promise; + getDeploymentArgs(): any[]; /** * Returns the account interface for this account contract given a deployment at the provided address. @@ -32,6 +32,6 @@ export interface AccountContract { * @param nodeInfo - Info on the chain where it is deployed. * @returns An account interface instance for creating tx requests and authorizing actions. */ - getInterface(address: CompleteAddress, nodeInfo: NodeInfo): Promise; + getInterface(address: CompleteAddress, nodeInfo: NodeInfo): AccountInterface; } // docs:end:account-contract-interface diff --git a/yarn-project/aztec.js/src/account/contract/schnorr_account_contract.ts b/yarn-project/aztec.js/src/account/contract/schnorr_account_contract.ts index 2f963a468de..cd6cec3791b 100644 --- a/yarn-project/aztec.js/src/account/contract/schnorr_account_contract.ts +++ b/yarn-project/aztec.js/src/account/contract/schnorr_account_contract.ts @@ -16,8 +16,8 @@ export class SchnorrAccountContract extends BaseAccountContract { super(SchnorrAccountContractArtifact as ContractArtifact); } - async getDeploymentArgs() { - const signingPublicKey = await Schnorr.new().then(e => e.computePublicKey(this.signingPrivateKey)); + getDeploymentArgs() { + const signingPublicKey = new Schnorr().computePublicKey(this.signingPrivateKey); return [signingPublicKey.x, signingPublicKey.y]; } @@ -30,9 +30,9 @@ export class SchnorrAccountContract extends BaseAccountContract { class SchnorrAuthWitnessProvider implements AuthWitnessProvider { constructor(private signingPrivateKey: GrumpkinPrivateKey) {} - async createAuthWitness(message: Fr): Promise { - const schnorr = await Schnorr.new(); + createAuthWitness(message: Fr): Promise { + const schnorr = new Schnorr(); const signature = schnorr.constructSignature(message.toBuffer(), this.signingPrivateKey).toBuffer(); - return new AuthWitness(message, [...signature]); + return Promise.resolve(new AuthWitness(message, [...signature])); } } diff --git a/yarn-project/aztec.js/src/account/contract/single_key_account_contract.ts b/yarn-project/aztec.js/src/account/contract/single_key_account_contract.ts index 63cfc44524e..c2b1bffa2fb 100644 --- a/yarn-project/aztec.js/src/account/contract/single_key_account_contract.ts +++ b/yarn-project/aztec.js/src/account/contract/single_key_account_contract.ts @@ -18,8 +18,8 @@ export class SingleKeyAccountContract extends BaseAccountContract { super(SchnorrSingleKeyAccountContractArtifact as ContractArtifact); } - getDeploymentArgs(): Promise { - return Promise.resolve([]); + getDeploymentArgs(): any[] { + return []; } getAuthWitnessProvider({ partialAddress }: CompleteAddress): AuthWitnessProvider { @@ -35,11 +35,11 @@ export class SingleKeyAccountContract extends BaseAccountContract { class SingleKeyAuthWitnessProvider implements AuthWitnessProvider { constructor(private privateKey: GrumpkinPrivateKey, private partialAddress: PartialAddress) {} - async createAuthWitness(message: Fr): Promise { - const schnorr = await Schnorr.new(); + createAuthWitness(message: Fr): Promise { + const schnorr = new Schnorr(); const signature = schnorr.constructSignature(message.toBuffer(), this.privateKey); - const publicKey = await generatePublicKey(this.privateKey); + const publicKey = generatePublicKey(this.privateKey); const witness = [...publicKey.toFields(), ...signature.toBuffer(), this.partialAddress]; - return new AuthWitness(message, witness); + return Promise.resolve(new AuthWitness(message, witness)); } } diff --git a/yarn-project/aztec.js/src/account/defaults/default_interface.ts b/yarn-project/aztec.js/src/account/defaults/default_interface.ts index f59bbdf7dae..ff981933292 100644 --- a/yarn-project/aztec.js/src/account/defaults/default_interface.ts +++ b/yarn-project/aztec.js/src/account/defaults/default_interface.ts @@ -27,9 +27,11 @@ export class DefaultAccountInterface implements AccountInterface { createTxExecutionRequest(executions: FunctionCall[]): Promise { return this.entrypoint.createTxExecutionRequest(executions); } + createAuthWitness(message: Fr): Promise { return this.authWitnessProvider.createAuthWitness(message); } + getCompleteAddress(): CompleteAddress { return this.address; } diff --git a/yarn-project/aztec.js/src/account/index.ts b/yarn-project/aztec.js/src/account/index.ts index 9c240bba333..eab0d934492 100644 --- a/yarn-project/aztec.js/src/account/index.ts +++ b/yarn-project/aztec.js/src/account/index.ts @@ -119,6 +119,6 @@ export async function getWallet( throw new Error(`Account ${address} not found`); } const nodeInfo = await pxe.getNodeInfo(); - const entrypoint = await accountContract.getInterface(completeAddress, nodeInfo); + const entrypoint = accountContract.getInterface(completeAddress, nodeInfo); return new AccountWallet(pxe, entrypoint); } diff --git a/yarn-project/aztec.js/src/account/manager/index.ts b/yarn-project/aztec.js/src/account/manager/index.ts index ace65bf7999..98a36321c7f 100644 --- a/yarn-project/aztec.js/src/account/manager/index.ts +++ b/yarn-project/aztec.js/src/account/manager/index.ts @@ -40,9 +40,9 @@ export class AccountManager { } } - protected async getEncryptionPublicKey() { + protected getEncryptionPublicKey() { if (!this.encryptionPublicKey) { - this.encryptionPublicKey = await generatePublicKey(this.encryptionPrivateKey); + this.encryptionPublicKey = generatePublicKey(this.encryptionPrivateKey); } return this.encryptionPublicKey; } @@ -53,7 +53,7 @@ export class AccountManager { */ public async getAccount(): Promise { const nodeInfo = await this.pxe.getNodeInfo(); - const completeAddress = await this.getCompleteAddress(); + const completeAddress = this.getCompleteAddress(); return this.accountContract.getInterface(completeAddress, nodeInfo); } @@ -62,12 +62,12 @@ export class AccountManager { * Does not require the account to be deployed or registered. * @returns The address, partial address, and encryption public key. */ - public async getCompleteAddress(): Promise { + public getCompleteAddress(): CompleteAddress { if (!this.completeAddress) { - const encryptionPublicKey = await generatePublicKey(this.encryptionPrivateKey); - const contractDeploymentInfo = await getContractDeploymentInfo( + const encryptionPublicKey = generatePublicKey(this.encryptionPrivateKey); + const contractDeploymentInfo = getContractDeploymentInfo( this.accountContract.getContractArtifact(), - await this.accountContract.getDeploymentArgs(), + this.accountContract.getDeploymentArgs(), this.salt!, encryptionPublicKey, ); @@ -107,11 +107,13 @@ export class AccountManager { */ public async getDeployMethod() { if (!this.deployMethod) { - if (!this.salt) throw new Error(`Cannot deploy account contract without known salt.`); + if (!this.salt) { + throw new Error(`Cannot deploy account contract without known salt.`); + } await this.#register(); - const encryptionPublicKey = await this.getEncryptionPublicKey(); + const encryptionPublicKey = this.getEncryptionPublicKey(); const deployer = new ContractDeployer(this.accountContract.getContractArtifact(), this.pxe, encryptionPublicKey); - const args = await this.accountContract.getDeploymentArgs(); + const args = this.accountContract.getDeploymentArgs(); this.deployMethod = deployer.deploy(...args); } return this.deployMethod; @@ -147,7 +149,7 @@ export class AccountManager { } async #register(): Promise { - const completeAddress = await this.getCompleteAddress(); + const completeAddress = this.getCompleteAddress(); await this.pxe.registerAccount(this.encryptionPrivateKey, completeAddress.partialAddress); return completeAddress; } diff --git a/yarn-project/aztec.js/src/contract/checker.test.ts b/yarn-project/aztec.js/src/contract/checker.test.ts index 7214f924f86..9b4183cbba6 100644 --- a/yarn-project/aztec.js/src/contract/checker.test.ts +++ b/yarn-project/aztec.js/src/contract/checker.test.ts @@ -23,7 +23,7 @@ describe('abiChecker', () => { expect(() => abiChecker(abi)).toThrowError('ABI function has no name'); }); - it('should error if ABI function has unrecognised type', () => { + it('should error if ABI function has unrecognized type', () => { abi = { name: 'TEST_ABI', functions: [ @@ -34,7 +34,7 @@ describe('abiChecker', () => { }, ], }; - expect(() => abiChecker(abi)).toThrowError('ABI function parameter has an unrecognised type'); + expect(() => abiChecker(abi)).toThrowError('ABI function parameter has an unrecognized type'); }); it('should error if integer is incorrectly formed', () => { @@ -48,7 +48,7 @@ describe('abiChecker', () => { }, ], }; - expect(() => abiChecker(abi)).toThrowError('Unrecognised attribute on type integer'); + expect(() => abiChecker(abi)).toThrowError('Unrecognized attribute on type integer'); }); it('should error if string is incorrectly formed', () => { @@ -62,7 +62,7 @@ describe('abiChecker', () => { }, ], }; - expect(() => abiChecker(abi)).toThrowError('Unrecognised attribute on type string'); + expect(() => abiChecker(abi)).toThrowError('Unrecognized attribute on type string'); }); it('should error if struct is incorrectly formed', () => { @@ -82,7 +82,7 @@ describe('abiChecker', () => { }, ], }; - expect(() => abiChecker(abi)).toThrowError('Unrecognised attribute on type struct'); + expect(() => abiChecker(abi)).toThrowError('Unrecognized attribute on type struct'); }); it('should error if array is incorrectly formed', () => { diff --git a/yarn-project/aztec.js/src/contract/checker.ts b/yarn-project/aztec.js/src/contract/checker.ts index 616b4840ae7..77bab7b39aa 100644 --- a/yarn-project/aztec.js/src/contract/checker.ts +++ b/yarn-project/aztec.js/src/contract/checker.ts @@ -68,7 +68,7 @@ function abiParameterTypeChecker(type: ABIType): boolean { case 'struct': return checkAttributes(type, { fields: 'object', path: 'string' }) && checkStruct(type); default: - throw new Error('ABI function parameter has an unrecognised type'); + throw new Error('ABI function parameter has an unrecognized type'); } } @@ -93,7 +93,7 @@ function checkStruct(type: StructType) { * Check if a provided ABI type has the correct attributes and their associated types. * This function compares the given 'type' object's keys with the expected attribute types * specified in 'incompleteAttributes', as well as the required 'kind' property. - * Throws an error if there are any unrecognised attributes or incorrect attribute types. + * Throws an error if there are any unrecognized attributes or incorrect attribute types. * * @param type - The ABI type object to be checked for correct attributes. * @param incompleteAttributes - An object representing the expected attribute types without the 'kind' property. @@ -104,7 +104,7 @@ function checkAttributes>(type: T, incompleteAttribu const attributes = { ...incompleteAttributes, kind: 'string' }; if (typeKeys.length !== Object.keys(attributes).length) { - throw new Error(`Unrecognised attribute on type ${type.kind}`); + throw new Error(`Unrecognized attribute on type ${type.kind}`); } typeKeys.forEach(element => { diff --git a/yarn-project/aztec.js/src/contract/sent_tx.ts b/yarn-project/aztec.js/src/contract/sent_tx.ts index 92f96a96967..33af8912162 100644 --- a/yarn-project/aztec.js/src/contract/sent_tx.ts +++ b/yarn-project/aztec.js/src/contract/sent_tx.ts @@ -1,4 +1,4 @@ -import { FieldsOf } from '@aztec/circuits.js'; +import { FieldsOf, MAX_NEW_NULLIFIERS_PER_TX } from '@aztec/circuits.js'; import { retryUntil } from '@aztec/foundation/retry'; import { ExtendedNote, GetUnencryptedLogsResponse, PXE, TxHash, TxReceipt, TxStatus } from '@aztec/types'; @@ -65,8 +65,36 @@ export class SentTx { throw new Error('Cannot set getNotes to true if waitForNotesSync is false'); } const receipt = await this.waitForReceipt(opts); - if (receipt.status !== TxStatus.MINED) - throw new Error(`Transaction ${await this.getTxHash()} was ${receipt.status}`); + if (receipt.status !== TxStatus.MINED) { + // ############## Temporary code start ############## + // A few logs placed here temporarily to help me (@benesjan) debug an issue with intermittent failure of + // 2 pixies test. + // https://github.com/AztecProtocol/aztec-packages/issues/3357 + // For whatever reason (according to logs) the tx which appears here as dropped seems to be correctly included + // in a block and the block built and submitted on-chain. I will try to fetch the latest block here and see + // which txs it contains to check if the block source used here has the relevant block. If it doesn't then + // the test is probably misconfigured and an incorrect block source is used (pxe pointing to a different aztec + // node or smt like that) or there is some bigger issue with block sync somewhere. + const blockNum = await this.pxe.getBlockNumber(); + const block = await this.pxe.getBlock(blockNum); + if (block) { + const txHashes = []; + for (let txIndex = 0; txIndex < block!.numberOfTxs; txIndex++) { + const txHash = block!.newNullifiers[MAX_NEW_NULLIFIERS_PER_TX * txIndex]; + txHashes.push(txHash.toString()); + } + throw new Error( + `Transaction ${await this.getTxHash()} was ${ + receipt.status + }.\nTx hashes of txs included in the last synced block ${block?.number} are: ${txHashes}`, + ); + } else { + throw new Error(`Transaction ${await this.getTxHash()} was ${receipt.status}. Block ${blockNum} not found.`); + } + // ############## Temporary code end ############## + + // throw new Error(`Transaction ${await this.getTxHash()} was ${receipt.status}`); + } if (opts?.debug) { const txHash = await this.getTxHash(); const tx = (await this.pxe.getTx(txHash))!; @@ -110,12 +138,18 @@ export class SentTx { async () => { const txReceipt = await this.pxe.getTxReceipt(txHash); // If receipt is not yet available, try again - if (txReceipt.status === TxStatus.PENDING) return undefined; + if (txReceipt.status === TxStatus.PENDING) { + return undefined; + } // If the tx was dropped, return it - if (txReceipt.status === TxStatus.DROPPED) return txReceipt; + if (txReceipt.status === TxStatus.DROPPED) { + return txReceipt; + } // If we don't care about waiting for notes to be synced, return the receipt const waitForNotesSync = opts?.waitForNotesSync ?? DefaultWaitOpts.waitForNotesSync; - if (!waitForNotesSync) return txReceipt; + if (!waitForNotesSync) { + return txReceipt; + } // Check if all sync blocks on the PXE Service are greater or equal than the block in which the tx was mined const { blocks, notes } = await this.pxe.getSyncStatus(); const targetBlock = txReceipt.blockNumber!; diff --git a/yarn-project/aztec.js/src/contract_deployer/deploy_method.ts b/yarn-project/aztec.js/src/contract_deployer/deploy_method.ts index d2ad1a95416..e8a9ad18f28 100644 --- a/yarn-project/aztec.js/src/contract_deployer/deploy_method.ts +++ b/yarn-project/aztec.js/src/contract_deployer/deploy_method.ts @@ -48,7 +48,9 @@ export class DeployMethod extends Bas ) { super(pxe); const constructorArtifact = artifact.functions.find(f => f.name === 'constructor'); - if (!constructorArtifact) throw new Error('Cannot find constructor in the artifact.'); + if (!constructorArtifact) { + throw new Error('Cannot find constructor in the artifact.'); + } this.constructorArtifact = constructorArtifact; } @@ -67,7 +69,7 @@ export class DeployMethod extends Bas const { chainId, protocolVersion } = await this.pxe.getNodeInfo(); - const { completeAddress, constructorHash, functionTreeRoot } = await getContractDeploymentInfo( + const { completeAddress, constructorHash, functionTreeRoot } = getContractDeploymentInfo( this.artifact, this.args, contractAddressSalt, diff --git a/yarn-project/aztec.js/src/contract_deployer/deploy_sent_tx.ts b/yarn-project/aztec.js/src/contract_deployer/deploy_sent_tx.ts index 23a2e0bfb06..7dad9ff2dde 100644 --- a/yarn-project/aztec.js/src/contract_deployer/deploy_sent_tx.ts +++ b/yarn-project/aztec.js/src/contract_deployer/deploy_sent_tx.ts @@ -60,8 +60,12 @@ export class DeploySentTx extends SentTx protected getContractInstance(wallet?: Wallet, address?: AztecAddress): Promise { const isWallet = (pxe: PXE | Wallet): pxe is Wallet => !!(pxe as Wallet).createTxExecutionRequest; const contractWallet = wallet ?? (isWallet(this.pxe) && this.pxe); - if (!contractWallet) throw new Error(`A wallet is required for creating a contract instance`); - if (!address) throw new Error(`Contract address is missing from transaction receipt`); + if (!contractWallet) { + throw new Error(`A wallet is required for creating a contract instance`); + } + if (!address) { + throw new Error(`Contract address is missing from transaction receipt`); + } return Contract.at(address, this.artifact, contractWallet) as Promise; } } diff --git a/yarn-project/aztec.js/src/utils/cheat_codes.ts b/yarn-project/aztec.js/src/utils/cheat_codes.ts index 9833ef02398..4b378c3cd9f 100644 --- a/yarn-project/aztec.js/src/utils/cheat_codes.ts +++ b/yarn-project/aztec.js/src/utils/cheat_codes.ts @@ -1,4 +1,4 @@ -import { AztecAddress, CircuitsWasm, EthAddress, Fr } from '@aztec/circuits.js'; +import { AztecAddress, EthAddress, Fr } from '@aztec/circuits.js'; import { toBigIntBE, toHex } from '@aztec/foundation/bigint-buffer'; import { keccak, pedersenHash } from '@aztec/foundation/crypto'; import { createDebugLogger } from '@aztec/foundation/log'; @@ -21,9 +21,9 @@ export class CheatCodes { public aztec: AztecCheatCodes, ) {} - static async create(rpcUrl: string, pxe: PXE): Promise { + static create(rpcUrl: string, pxe: PXE): CheatCodes { const ethCheatCodes = new EthCheatCodes(rpcUrl); - const aztecCheatCodes = new AztecCheatCodes(pxe, await CircuitsWasm.get(), ethCheatCodes); + const aztecCheatCodes = new AztecCheatCodes(pxe, ethCheatCodes); return new CheatCodes(ethCheatCodes, aztecCheatCodes); } } @@ -87,7 +87,9 @@ export class EthCheatCodes { */ public async mine(numberOfBlocks = 1): Promise { const res = await this.rpcCall('hardhat_mine', [numberOfBlocks]); - if (res.error) throw new Error(`Error mining: ${res.error.message}`); + if (res.error) { + throw new Error(`Error mining: ${res.error.message}`); + } this.logger(`Mined ${numberOfBlocks} blocks`); } @@ -97,7 +99,9 @@ export class EthCheatCodes { */ public async setNextBlockTimestamp(timestamp: number): Promise { const res = await this.rpcCall('evm_setNextBlockTimestamp', [timestamp]); - if (res.error) throw new Error(`Error setting next block timestamp: ${res.error.message}`); + if (res.error) { + throw new Error(`Error setting next block timestamp: ${res.error.message}`); + } this.logger(`Set next block timestamp to ${timestamp}`); } @@ -107,7 +111,9 @@ export class EthCheatCodes { */ public async dumpChainState(fileName: string): Promise { const res = await this.rpcCall('hardhat_dumpState', []); - if (res.error) throw new Error(`Error dumping state: ${res.error.message}`); + if (res.error) { + throw new Error(`Error dumping state: ${res.error.message}`); + } const jsonContent = JSON.stringify(res.result); fs.writeFileSync(`${fileName}.json`, jsonContent, 'utf8'); this.logger(`Dumped state to ${fileName}`); @@ -120,7 +126,9 @@ export class EthCheatCodes { public async loadChainState(fileName: string): Promise { const data = JSON.parse(fs.readFileSync(`${fileName}.json`, 'utf8')); const res = await this.rpcCall('hardhat_loadState', [data]); - if (res.error) throw new Error(`Error loading state: ${res.error.message}`); + if (res.error) { + throw new Error(`Error loading state: ${res.error.message}`); + } this.logger(`Loaded state from ${fileName}`); } @@ -144,7 +152,9 @@ export class EthCheatCodes { public async store(contract: EthAddress, slot: bigint, value: bigint): Promise { // for the rpc call, we need to change value to be a 32 byte hex string. const res = await this.rpcCall('hardhat_setStorageAt', [contract.toString(), toHex(slot), toHex(value, true)]); - if (res.error) throw new Error(`Error setting storage for contract ${contract} at ${slot}: ${res.error.message}`); + if (res.error) { + throw new Error(`Error setting storage for contract ${contract} at ${slot}: ${res.error.message}`); + } this.logger(`Set storage for contract ${contract} at ${slot} to ${value}`); } @@ -166,7 +176,9 @@ export class EthCheatCodes { */ public async startImpersonating(who: EthAddress): Promise { const res = await this.rpcCall('hardhat_impersonateAccount', [who.toString()]); - if (res.error) throw new Error(`Error impersonating ${who}: ${res.error.message}`); + if (res.error) { + throw new Error(`Error impersonating ${who}: ${res.error.message}`); + } this.logger(`Impersonating ${who}`); } @@ -176,7 +188,9 @@ export class EthCheatCodes { */ public async stopImpersonating(who: EthAddress): Promise { const res = await this.rpcCall('hardhat_stopImpersonatingAccount', [who.toString()]); - if (res.error) throw new Error(`Error when stopping the impersonation of ${who}: ${res.error.message}`); + if (res.error) { + throw new Error(`Error when stopping the impersonation of ${who}: ${res.error.message}`); + } this.logger(`Stopped impersonating ${who}`); } @@ -187,7 +201,9 @@ export class EthCheatCodes { */ public async etch(contract: EthAddress, bytecode: `0x${string}`): Promise { const res = await this.rpcCall('hardhat_setCode', [contract.toString(), bytecode]); - if (res.error) throw new Error(`Error setting bytecode for ${contract}: ${res.error.message}`); + if (res.error) { + throw new Error(`Error setting bytecode for ${contract}: ${res.error.message}`); + } this.logger(`Set bytecode for ${contract} to ${bytecode}`); } @@ -211,10 +227,6 @@ export class AztecCheatCodes { * The PXE Service to use for interacting with the chain */ public pxe: PXE, - /** - * The circuits wasm module used for pedersen hashing - */ - public wasm: CircuitsWasm, /** * The eth cheat codes. */ diff --git a/yarn-project/aztec.js/src/utils/pub_key.ts b/yarn-project/aztec.js/src/utils/pub_key.ts index 14b317569de..5e04f00a1e2 100644 --- a/yarn-project/aztec.js/src/utils/pub_key.ts +++ b/yarn-project/aztec.js/src/utils/pub_key.ts @@ -6,7 +6,7 @@ import { Grumpkin } from '@aztec/circuits.js/barretenberg'; * @param privateKey - The private key. * @returns The generated public key. */ -export async function generatePublicKey(privateKey: GrumpkinPrivateKey): Promise { - const grumpkin = await Grumpkin.new(); +export function generatePublicKey(privateKey: GrumpkinPrivateKey): PublicKey { + const grumpkin = new Grumpkin(); return grumpkin.mul(grumpkin.generator(), privateKey); } diff --git a/yarn-project/aztec.js/src/wallet/base_wallet.ts b/yarn-project/aztec.js/src/wallet/base_wallet.ts index 48e259aeca1..17b6ea22e03 100644 --- a/yarn-project/aztec.js/src/wallet/base_wallet.ts +++ b/yarn-project/aztec.js/src/wallet/base_wallet.ts @@ -35,6 +35,10 @@ export abstract class BaseWallet implements Wallet { abstract createAuthWitness(message: Fr): Promise; + addCapsule(capsule: Fr[]): Promise { + return this.pxe.addCapsule(capsule); + } + registerAccount(privKey: GrumpkinPrivateKey, partialAddress: PartialAddress): Promise { return this.pxe.registerAccount(privKey, partialAddress); } diff --git a/yarn-project/bootstrap.sh b/yarn-project/bootstrap.sh index 56dfd3fe574..e35876c5498 100755 --- a/yarn-project/bootstrap.sh +++ b/yarn-project/bootstrap.sh @@ -22,12 +22,10 @@ for package in "@aztec/foundation" "@aztec/noir-compiler"; do yarn workspace $package build done -# Run remake bindings before building Aztec.nr contracts or l1 contracts as they depend on files created by it. -yarn workspace @aztec/circuits.js remake-bindings +# Run remake constants before building Aztec.nr contracts or l1 contracts as they depend on files created by it. yarn workspace @aztec/circuits.js remake-constants (cd noir-contracts && ./bootstrap.sh) -(cd boxes && ./bootstrap.sh) (cd .. && l1-contracts/bootstrap.sh) # We do not need to build individual packages, yarn build will build the root tsconfig.json diff --git a/yarn-project/boxes/blank-react/src/app/components/copy.tsx b/yarn-project/boxes/blank-react/src/app/components/copy.tsx index 6caa3312159..04c9aa0ecd6 100644 --- a/yarn-project/boxes/blank-react/src/app/components/copy.tsx +++ b/yarn-project/boxes/blank-react/src/app/components/copy.tsx @@ -17,7 +17,7 @@ export function Copy({ value }: { value: string }) { }) .catch(() => { // eslint-disable-next-line no-console - console.error('Couldnt copy address'); + console.error('Could not copy address'); }); }} src={copied ? 'check.svg' : 'copy.svg'} diff --git a/yarn-project/boxes/run_tests b/yarn-project/boxes/run_tests index 80ee1ed87e6..24a089b203c 100755 --- a/yarn-project/boxes/run_tests +++ b/yarn-project/boxes/run_tests @@ -23,41 +23,39 @@ YARN_PROJECT=yarn-project SANDBOX_IMAGE_URI=$(calculate_image_uri $SANDBOX) YP_IMAGE_URI=$(calculate_image_uri $YARN_PROJECT) -################### CHECK REBUILD ################### +################### CHECK REBUILD ################### # Before doing any work we check if we need to rebuild # Each box is suffixed with the box name, such that rebuilding of each box is independent, if one fails we don't rebuild the others ensure_repo $SANDBOX $ECR_REGION refresh_lifecycle -CONTENT_HASH=$(calculate_content_hash $SANDBOX) -BASE_TAG=cache-$CONTENT_HASH-$BOX_NAME # Append the box name to seperate the tag +BASE_TAG=$(calculate_image_tag $SANDBOX) SUCCESS_TAG=$BASE_TAG -echo "Content hash: $CONTENT_HASH" +echo "Success tag: $SUCCESS_TAG" if check_rebuild $SUCCESS_TAG $SANDBOX; then echo "No rebuild required." retry tag_remote_image $SANDBOX $BASE_TAG $SUCCESS_TAG exit 0 fi -################### PREAMBLE ################### +################### PREAMBLE ################### -# Pull images from ecr and retag for the docker compsoe -SANDBOX_IMAGE=$SANDBOX_IMAGE_URI-x86_64 -echo "pulling docker image for $SANDBOX $SANDBOX_IMAGE" -retry docker pull $SANDBOX_IMAGE -retry docker tag $SANDBOX_IMAGE aztecprotocol/$SANDBOX:latest +# Pull images from ecr and retag for the docker compose +echo "pulling docker image for $SANDBOX $SANDBOX_IMAGE_URI" +retry docker pull $SANDBOX_IMAGE_URI +retry docker tag $SANDBOX_IMAGE_URI aztecprotocol/$SANDBOX:latest echo "pulling docker image for $YARN_PROJECT $YP_IMAGE_URI" retry docker pull $YP_IMAGE_URI retry docker tag $YP_IMAGE_URI aztecprotocol/$YARN_PROJECT:latest -################### RUN TESTS ################### +################### RUN TESTS ################### docker-compose rm -f docker-compose -f $COMPOSE_FILE up --exit-code-from boxes-$BOX_NAME -################### POST TEST ################### +################### POST TEST ################### # Success - push a new tag for the commit hash with the box name appended IMAGE_COMMIT_URI=$SANDBOX_IMAGE_URI-$BOX_NAME -retry docker tag $SANDBOX_IMAGE $IMAGE_COMMIT_URI +retry docker tag $SANDBOX_IMAGE_URI $IMAGE_COMMIT_URI retry docker push $IMAGE_COMMIT_URI > /dev/null 2>&1 \ No newline at end of file diff --git a/yarn-project/boxes/token/README.md b/yarn-project/boxes/token/README.md index 99b8eb3fe68..b4b38457432 100644 --- a/yarn-project/boxes/token/README.md +++ b/yarn-project/boxes/token/README.md @@ -49,11 +49,11 @@ This folder should have the following directory structure: |— private_token_contract.json |— private_token.ts |— tests - | A simple end2end test deploying and testing the PrivateToken on a local sandbox + | A simple end2end test deploying and testing the Token on a local sandbox | using the front end helper methods in app/scripts/ | The test requires the sandbox and anvil to be running (yarn start:sandbox). | You can run it via `yarn test:integration`. - |- privatetoken.test.ts + |- token.test.ts ``` Most relevant to you is likely `src/contracts/main.nr` (and the build config `src/contracts/Nargo.toml`). This contains the example PrivateToken logic that the frontend interacts with and is a good place to start writing Noir. diff --git a/yarn-project/boxes/token/src/app/components/copy.tsx b/yarn-project/boxes/token/src/app/components/copy.tsx index 6caa3312159..04c9aa0ecd6 100644 --- a/yarn-project/boxes/token/src/app/components/copy.tsx +++ b/yarn-project/boxes/token/src/app/components/copy.tsx @@ -17,7 +17,7 @@ export function Copy({ value }: { value: string }) { }) .catch(() => { // eslint-disable-next-line no-console - console.error('Couldnt copy address'); + console.error('Could not copy address'); }); }} src={copied ? 'check.svg' : 'copy.svg'} diff --git a/yarn-project/canary/package.json b/yarn-project/canary/package.json index 8952f796622..dc95e326550 100644 --- a/yarn-project/canary/package.json +++ b/yarn-project/canary/package.json @@ -41,9 +41,6 @@ "typescript": "^5.0.4", "viem": "^1.2.5" }, - "devDependencies": { - "@rushstack/eslint-patch": "^1.1.4" - }, "files": [ "dest", "src", diff --git a/yarn-project/circuits.js/package.json b/yarn-project/circuits.js/package.json index 120d66964e5..5b49a3393d5 100644 --- a/yarn-project/circuits.js/package.json +++ b/yarn-project/circuits.js/package.json @@ -22,8 +22,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", - "remake-bindings": "DEBUG=wasm ts-node-esm src/cbind/circuits.in.ts && prettier -w src/cbind/circuits.gen.ts", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "remake-constants": "ts-node-esm src/cbind/constants.in.ts && prettier -w src/cbind/constants.gen.ts", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" }, @@ -39,6 +38,7 @@ "rootDir": "./src" }, "dependencies": { + "@aztec/bb.js": "portal:../../barretenberg/ts", "@aztec/foundation": "workspace:^", "@msgpack/msgpack": "^3.0.0-beta2", "@noble/curves": "^1.0.0", diff --git a/yarn-project/circuits.js/src/abis/__snapshots__/abis.test.ts.snap b/yarn-project/circuits.js/src/abis/__snapshots__/abis.test.ts.snap index 52da5e51621..61ad9e089cd 100644 --- a/yarn-project/circuits.js/src/abis/__snapshots__/abis.test.ts.snap +++ b/yarn-project/circuits.js/src/abis/__snapshots__/abis.test.ts.snap @@ -2,38 +2,224 @@ exports[`abis wasm bindings compute globals hash 1`] = ` Fr { - "value": 19996198784166720428914107076917074510032365849254400404611644441094528984289n, + "asBigInt": 19996198784166720428914107076917074510032365849254400404611644441094528984289n, + "asBuffer": { + "data": [ + 44, + 53, + 114, + 139, + 52, + 188, + 28, + 197, + 40, + 157, + 117, + 130, + 246, + 163, + 68, + 239, + 11, + 227, + 228, + 184, + 250, + 189, + 231, + 11, + 41, + 32, + 78, + 168, + 174, + 60, + 100, + 225, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings compute private call stack item hash 1`] = ` Fr { - "value": 3230879723118793997125108252427471059552102155544558929105689542823769599571n, + "asBigInt": 3230879723118793997125108252427471059552102155544558929105689542823769599571n, + "asBuffer": { + "data": [ + 7, + 36, + 156, + 245, + 105, + 70, + 179, + 10, + 128, + 189, + 85, + 143, + 184, + 14, + 255, + 238, + 59, + 2, + 250, + 190, + 165, + 228, + 189, + 154, + 182, + 111, + 18, + 122, + 180, + 216, + 238, + 83, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings compute public call stack item hash 1`] = ` Fr { - "value": 1369626479296628599511967895314683808250163038382688147156251959177959447826n, + "asBigInt": 1369626479296628599511967895314683808250163038382688147156251959177959447826n, + "asBuffer": { + "data": [ + 3, + 7, + 46, + 93, + 156, + 140, + 48, + 165, + 187, + 71, + 235, + 199, + 129, + 159, + 8, + 25, + 33, + 227, + 241, + 253, + 4, + 14, + 185, + 7, + 30, + 112, + 136, + 232, + 202, + 252, + 29, + 18, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings compute secret message hash 1`] = ` Fr { - "value": 6220068662483113241527007349428551778669520744131373768997518428761948042030n, + "asBigInt": 6220068662483113241527007349428551778669520744131373768997518428761948042030n, + "asBuffer": { + "data": [ + 13, + 192, + 111, + 33, + 103, + 226, + 205, + 25, + 173, + 247, + 56, + 209, + 243, + 132, + 105, + 215, + 248, + 191, + 241, + 226, + 107, + 2, + 152, + 22, + 232, + 35, + 11, + 205, + 106, + 182, + 51, + 46, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings compute tx hash 1`] = ` Fr { - "value": 11028384086470399424960241201065432836500251313078792361310973684322431200173n, + "asBigInt": 11028384086470399424960241201065432836500251313078792361310973684322431200173n, + "asBuffer": { + "data": [ + 24, + 97, + 216, + 28, + 219, + 86, + 198, + 156, + 231, + 143, + 46, + 78, + 221, + 7, + 18, + 240, + 152, + 126, + 198, + 240, + 27, + 12, + 209, + 163, + 188, + 112, + 63, + 126, + 44, + 152, + 235, + 173, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings computes a complete address 1`] = ` CompleteAddress { "address": AztecAddress { - "buffer": { + "asBigInt": 11147987456032716744065611270804907827731877483785437429582589518717493164615n, + "asBuffer": { "data": [ 24, 165, @@ -72,15 +258,126 @@ CompleteAddress { }, }, "partialAddress": Fr { - "value": 12921923968526873580423865450965452589013784321633824918807523389752605479568n, + "asBigInt": 12921923968526873580423865450965452589013784321633824918807523389752605479568n, + "asBuffer": { + "data": [ + 28, + 145, + 140, + 190, + 160, + 180, + 72, + 234, + 8, + 38, + 34, + 189, + 150, + 49, + 183, + 78, + 127, + 175, + 204, + 179, + 163, + 133, + 166, + 183, + 238, + 243, + 37, + 56, + 25, + 29, + 18, + 144, + ], + "type": "Buffer", + }, }, "publicKey": Point { "kind": "point", "x": Fr { - "value": 1n, + "asBigInt": 1n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + ], + "type": "Buffer", + }, }, "y": Fr { - "value": 2n, + "asBigInt": 2n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 2, + ], + "type": "Buffer", + }, }, }, } @@ -88,7 +385,8 @@ CompleteAddress { exports[`abis wasm bindings computes a contract address from partial 1`] = ` AztecAddress { - "buffer": { + "asBigInt": 15451914702384811781262267264665450579243110288870049455159710794697789874663n, + "asBuffer": { "data": [ 34, 41, @@ -130,7 +428,44 @@ AztecAddress { exports[`abis wasm bindings computes a function leaf 1`] = ` Fr { - "value": 8957681167943973616438847631514238173699549883609341685749385526208761312950n, + "asBigInt": 8957681167943973616438847631514238173699549883609341685749385526208761312950n, + "asBuffer": { + "data": [ + 19, + 205, + 222, + 44, + 215, + 32, + 246, + 85, + 182, + 194, + 113, + 211, + 193, + 23, + 61, + 121, + 173, + 10, + 133, + 48, + 24, + 129, + 231, + 172, + 246, + 68, + 18, + 40, + 227, + 134, + 54, + 182, + ], + "type": "Buffer", + }, } `; @@ -148,61 +483,431 @@ exports[`abis wasm bindings computes a function selector 1`] = ` exports[`abis wasm bindings computes block hash with globals 1`] = ` Fr { - "value": 7177915431153102916601456081755280584460785548870192083974540199919775120827n, + "asBigInt": 7177915431153102916601456081755280584460785548870192083974540199919775120827n, + "asBuffer": { + "data": [ + 15, + 222, + 142, + 96, + 169, + 217, + 84, + 90, + 55, + 110, + 52, + 172, + 167, + 236, + 97, + 56, + 185, + 98, + 133, + 50, + 113, + 88, + 184, + 54, + 59, + 75, + 252, + 181, + 66, + 59, + 253, + 187, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings computes commitment nonce 1`] = ` Fr { - "value": 7653394882992289714855533169019502055399179742531912347686813547951736946253n, + "asBigInt": 7653394882992289714855533169019502055399179742531912347686813547951736946253n, + "asBuffer": { + "data": [ + 16, + 235, + 171, + 1, + 188, + 129, + 50, + 99, + 239, + 146, + 237, + 113, + 185, + 199, + 129, + 173, + 62, + 245, + 128, + 25, + 182, + 106, + 143, + 113, + 48, + 77, + 47, + 114, + 215, + 222, + 254, + 77, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings computes contract leaf 1`] = ` Fr { - "value": 16936439505140127403707082829495438425474294111732402569080458144683824428771n, + "asBigInt": 16936439505140127403707082829495438425474294111732402569080458144683824428771n, + "asBuffer": { + "data": [ + 37, + 113, + 175, + 95, + 213, + 5, + 72, + 186, + 142, + 170, + 107, + 231, + 135, + 49, + 45, + 131, + 55, + 162, + 222, + 230, + 65, + 158, + 24, + 95, + 238, + 19, + 196, + 149, + 177, + 246, + 182, + 227, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings computes function tree root 1`] = ` Fr { - "value": 21090938391652199999535994101952511109725456907725564751008805452452294036010n, + "asBigInt": 21090938391652199999535994101952511109725456907725564751008805452452294036010n, + "asBuffer": { + "data": [ + 46, + 161, + 12, + 80, + 15, + 148, + 101, + 167, + 237, + 48, + 42, + 247, + 155, + 146, + 117, + 106, + 78, + 36, + 159, + 144, + 106, + 160, + 190, + 170, + 211, + 213, + 47, + 102, + 180, + 244, + 10, + 42, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings computes public data tree index 1`] = ` Fr { - "value": 9076808949980998475110411569159266589807853958487763065147292518713994520820n, + "asBigInt": 9076808949980998475110411569159266589807853958487763065147292518713994520820n, + "asBuffer": { + "data": [ + 20, + 17, + 74, + 179, + 219, + 221, + 10, + 28, + 204, + 92, + 79, + 230, + 141, + 213, + 118, + 243, + 198, + 205, + 121, + 112, + 135, + 112, + 224, + 106, + 180, + 8, + 99, + 152, + 205, + 216, + 40, + 244, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings computes public data tree value 1`] = ` Fr { - "value": 3n, + "asBigInt": 3n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 3, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings computes siloed commitment 1`] = ` Fr { - "value": 7262347077404413274044670947879391583109741657896097604752287127491776887739n, + "asBigInt": 7262347077404413274044670947879391583109741657896097604752287127491776887739n, + "asBuffer": { + "data": [ + 16, + 14, + 87, + 192, + 122, + 182, + 219, + 134, + 244, + 174, + 67, + 245, + 167, + 212, + 53, + 92, + 87, + 197, + 161, + 226, + 82, + 55, + 70, + 224, + 251, + 22, + 172, + 41, + 240, + 220, + 59, + 187, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings computes siloed nullifier 1`] = ` Fr { - "value": 10521714890263159342521589827909786073566333899903358017581007142888032736115n, + "asBigInt": 10521714890263159342521589827909786073566333899903358017581007142888032736115n, + "asBuffer": { + "data": [ + 23, + 67, + 20, + 95, + 222, + 16, + 62, + 170, + 136, + 175, + 87, + 110, + 5, + 98, + 230, + 29, + 133, + 235, + 165, + 144, + 253, + 223, + 1, + 209, + 149, + 80, + 228, + 240, + 36, + 112, + 147, + 115, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings computes unique commitment 1`] = ` Fr { - "value": 13000121189816960667906065043973697710794195579570622293955220571859166513128n, + "asBigInt": 13000121189816960667906065043973697710794195579570622293955220571859166513128n, + "asBuffer": { + "data": [ + 28, + 189, + 206, + 206, + 196, + 254, + 146, + 246, + 99, + 142, + 182, + 168, + 218, + 222, + 150, + 202, + 53, + 142, + 203, + 164, + 149, + 76, + 245, + 151, + 195, + 99, + 25, + 159, + 174, + 61, + 71, + 232, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings computes zero contract leaf 1`] = ` Fr { - "value": 0n, + "asBigInt": 0n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ], + "type": "Buffer", + }, } `; @@ -288,24 +993,172 @@ exports[`abis wasm bindings hashes a tx request 1`] = ` exports[`abis wasm bindings hashes constructor info 1`] = ` Fr { - "value": 8554563246648466207128694080427818093826588702256491720155365584899927973422n, + "asBigInt": 8554563246648466207128694080427818093826588702256491720155365584899927973422n, + "asBuffer": { + "data": [ + 18, + 233, + 182, + 18, + 27, + 239, + 249, + 139, + 157, + 45, + 92, + 189, + 121, + 152, + 157, + 73, + 211, + 211, + 253, + 135, + 52, + 199, + 134, + 233, + 242, + 74, + 6, + 239, + 86, + 0, + 30, + 46, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings hashes empty function args 1`] = ` Fr { - "value": 0n, + "asBigInt": 0n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings hashes function args 1`] = ` Fr { - "value": 11839099223661714814196842233383119055519657007373713796026764119292399532830n, + "asBigInt": 11839099223661714814196842233383119055519657007373713796026764119292399532830n, + "asBuffer": { + "data": [ + 26, + 44, + 177, + 84, + 151, + 13, + 83, + 84, + 26, + 98, + 206, + 96, + 113, + 195, + 152, + 109, + 8, + 146, + 63, + 234, + 71, + 75, + 232, + 160, + 170, + 26, + 191, + 135, + 24, + 39, + 59, + 30, + ], + "type": "Buffer", + }, } `; exports[`abis wasm bindings hashes many function args 1`] = ` Fr { - "value": 9368119665570837995905174888524883816390941475336228173888734493993721486827n, + "asBigInt": 9368119665570837995905174888524883816390941475336228173888734493993721486827n, + "asBuffer": { + "data": [ + 20, + 182, + 42, + 246, + 214, + 208, + 1, + 110, + 254, + 196, + 157, + 194, + 3, + 246, + 106, + 69, + 102, + 180, + 241, + 249, + 168, + 116, + 85, + 53, + 209, + 138, + 127, + 164, + 10, + 109, + 93, + 235, + ], + "type": "Buffer", + }, } `; diff --git a/yarn-project/circuits.js/src/abis/abis.test.ts b/yarn-project/circuits.js/src/abis/abis.test.ts index 79413a36c10..9550c12e96c 100644 --- a/yarn-project/circuits.js/src/abis/abis.test.ts +++ b/yarn-project/circuits.js/src/abis/abis.test.ts @@ -18,10 +18,8 @@ import { makeTxRequest, makeVerificationKey, } from '../tests/factories.js'; -import { CircuitsWasm } from '../wasm/circuits_wasm.js'; import { computeBlockHashWithGlobals, - computeCallStackItemHash, computeCommitmentNonce, computeCompleteAddress, computeContractAddressFromPartial, @@ -30,6 +28,8 @@ import { computeFunctionSelector, computeFunctionTreeRoot, computeGlobalsHash, + computePrivateCallStackItemHash, + computePublicCallStackItemHash, computePublicDataTreeIndex, computePublicDataTreeValue, computeSecretMessageHash, @@ -44,11 +44,6 @@ import { } from './abis.js'; describe('abis wasm bindings', () => { - let wasm: CircuitsWasm; - beforeAll(async () => { - wasm = await CircuitsWasm.get(); - }); - it('hashes a tx request', () => { const txRequest = makeTxRequest(); const hash = hashTxRequest(txRequest); @@ -63,7 +58,7 @@ describe('abis wasm bindings', () => { it('hashes VK', () => { const vk = makeVerificationKey(); - const res = hashVK(wasm, vk.toBuffer()); + const res = hashVK(vk.toBuffer()); expect(res).toMatchSnapshot(); }); @@ -74,7 +69,7 @@ describe('abis wasm bindings', () => { }); it('computes function tree root', () => { - const res = computeFunctionTreeRoot(wasm, [new Fr(0n), new Fr(0n), new Fr(0n), new Fr(0n)]); + const res = computeFunctionTreeRoot([new Fr(0n), new Fr(0n), new Fr(0n), new Fr(0n)]); expect(res).toMatchSnapshot(); }); @@ -215,13 +210,13 @@ describe('abis wasm bindings', () => { it('compute private call stack item hash', () => { const item = makePrivateCallStackItem(); - const hash = computeCallStackItemHash(wasm, item); + const hash = computePrivateCallStackItemHash(item); expect(hash).toMatchSnapshot(); }); it('compute public call stack item hash', () => { const item = makePublicCallStackItem(); - const hash = computeCallStackItemHash(wasm, item); + const hash = computePublicCallStackItemHash(item); expect(hash).toMatchSnapshot(); }); diff --git a/yarn-project/circuits.js/src/abis/abis.ts b/yarn-project/circuits.js/src/abis/abis.ts index 88911dd1c67..909d33aa635 100644 --- a/yarn-project/circuits.js/src/abis/abis.ts +++ b/yarn-project/circuits.js/src/abis/abis.ts @@ -1,65 +1,41 @@ import { padArrayEnd } from '@aztec/foundation/collection'; -import { keccak, pedersenHash } from '@aztec/foundation/crypto'; -import { numToUInt32BE } from '@aztec/foundation/serialize'; -import { IWasmModule } from '@aztec/foundation/wasm'; +import { keccak, pedersenHash, pedersenHashBuffer } from '@aztec/foundation/crypto'; +import { numToUInt8, numToUInt16BE, numToUInt32BE } from '@aztec/foundation/serialize'; import { Buffer } from 'buffer'; import chunk from 'lodash.chunk'; import { AztecAddress, + CallContext, CompleteAddress, ContractDeploymentData, + ContractStorageRead, + ContractStorageUpdateRequest, FUNCTION_SELECTOR_NUM_BYTES, + FUNCTION_TREE_HEIGHT, Fr, FunctionData, FunctionLeafPreimage, GeneratorIndex, GlobalVariables, NewContractData, + PRIVATE_CIRCUIT_PUBLIC_INPUTS_HASH_INPUT_LENGTH, + PUBLIC_CIRCUIT_PUBLIC_INPUTS_HASH_INPUT_LENGTH, PrivateCallStackItem, + PrivateCircuitPublicInputs, PublicCallStackItem, + PublicCircuitPublicInputs, PublicKey, TxContext, TxRequest, + VerificationKey, } from '../index.js'; -import { boolToBuffer, serializeBufferArrayToVector } from '../utils/serialize.js'; - -/** - * Synchronously calls a wasm function. - * @param wasm - The wasm wrapper. - * @param fnName - The name of the function to call. - * @param input - The input buffer or object serializable to a buffer. - * @param expectedOutputLength - The expected length of the output buffer. - * @returns The output buffer. - */ -function wasmSyncCall( - wasm: IWasmModule, - fnName: string, - input: - | Buffer - | { - /** - * Signature of the target serialization function. - */ - toBuffer: () => Buffer; - }, - expectedOutputLength: number, -): Buffer { - const inputData: Buffer = input instanceof Buffer ? input : input.toBuffer(); - const outputBuf = wasm.call('bbmalloc', expectedOutputLength); - const inputBuf = wasm.call('bbmalloc', inputData.length); - wasm.writeMemory(inputBuf, inputData); - wasm.call(fnName, inputBuf, outputBuf); - const buf = Buffer.from(wasm.getMemorySlice(outputBuf, outputBuf + expectedOutputLength)); - wasm.call('bbfree', outputBuf); - wasm.call('bbfree', inputBuf); - return buf; -} +import { boolToBuffer } from '../utils/serialize.js'; +import { MerkleTreeCalculator } from './merkle_tree_calculator.js'; /** * Computes a hash of a transaction request. - * @param wasm - A module providing low-level wasm access. * @param txRequest - The transaction request. * @returns The hash of the transaction request. */ @@ -69,7 +45,6 @@ export function hashTxRequest(txRequest: TxRequest): Buffer { /** * Computes a function selector from a given function signature. - * @param wasm - A module providing low-level wasm access. * @param funcSig - The function signature. * @returns The function selector. */ @@ -79,22 +54,52 @@ export function computeFunctionSelector(funcSig: string): Buffer { /** * Computes a hash of a given verification key. - * @param wasm - A module providing low-level wasm access. * @param vkBuf - The verification key. * @returns The hash of the verification key. */ -export function hashVK(wasm: IWasmModule, vkBuf: Buffer) { - return wasmSyncCall(wasm, 'abis__hash_vk', vkBuf, 32); +export function hashVK(vkBuf: Buffer) { + const vk = VerificationKey.fromBuffer(vkBuf); + const toHash = Buffer.concat([ + numToUInt8(vk.circuitType), + numToUInt16BE(5), // fr::coset_generator(0)? + numToUInt32BE(vk.circuitSize), + numToUInt32BE(vk.numPublicInputs), + ...Object.values(vk.commitments) + .map(e => [e.y.toBuffer(), e.x.toBuffer()]) + .flat(), + // Montgomery form of fr::one()? Not sure. But if so, why? + Buffer.from('1418144d5b080fcac24cdb7649bdadf246a6cb2426e324bedb94fb05118f023a', 'hex'), + ]); + return pedersenHashBuffer(toHash); + // barretenberg::evaluation_domain eval_domain = barretenberg::evaluation_domain(circuit_size); + + // std::vector preimage_data; + + // preimage_data.push_back(static_cast(proof_system::CircuitType(circuit_type))); + + // const uint256_t domain = eval_domain.domain; // montgomery form of circuit_size + // const uint256_t generator = eval_domain.generator; //coset_generator(0) + // const uint256_t public_inputs = num_public_inputs; + + // write(preimage_data, static_cast(uint256_t(generator))); // maybe 1? + // write(preimage_data, static_cast(uint256_t(domain))); // try circuit_size + // write(preimage_data, static_cast(public_inputs)); + // for (const auto& [tag, selector] : commitments) { + // write(preimage_data, selector.y); + // write(preimage_data, selector.x); + // } + + // write(preimage_data, eval_domain.root); // fr::one() + + // return crypto::pedersen_hash::hash_buffer(preimage_data, hash_index); } /** * Computes a function leaf from a given preimage. - * @param wasm - A module providing low-level wasm access. * @param fnLeaf - The function leaf preimage. * @returns The function leaf. */ export function computeFunctionLeaf(fnLeaf: FunctionLeafPreimage): Fr { - // return Fr.fromBuffer(wasmSyncCall(wasm, 'abis__compute_function_leaf', fnLeaf, 32)); return Fr.fromBuffer( pedersenHash( [ @@ -109,21 +114,33 @@ export function computeFunctionLeaf(fnLeaf: FunctionLeafPreimage): Fr { ); } +// The "zero leaf" of the function tree is the hash of 5 zero fields. +// TODO: Why can we not just use a zero field as the zero leaf? Complicates things perhaps unnecessarily? +const functionTreeZeroLeaf = pedersenHash(new Array(5).fill(Buffer.alloc(32))); +const functionTreeRootCalculator = new MerkleTreeCalculator(FUNCTION_TREE_HEIGHT, functionTreeZeroLeaf); + +/** + * Computes a function tree from function leaves. + * @param fnLeaves - The function leaves to be included in the contract function tree. + * @returns All nodes of the tree. + */ +export function computeFunctionTree(fnLeaves: Fr[]) { + const leaves = fnLeaves.map(fr => fr.toBuffer()); + return functionTreeRootCalculator.computeTree(leaves).map(b => Fr.fromBuffer(b)); +} + /** * Computes a function tree root from function leaves. - * @param wasm - A module providing low-level wasm access. * @param fnLeaves - The function leaves to be included in the contract function tree. * @returns The function tree root. */ -export function computeFunctionTreeRoot(wasm: IWasmModule, fnLeaves: Fr[]) { - const inputVector = serializeBufferArrayToVector(fnLeaves.map(fr => fr.toBuffer())); - const result = wasmSyncCall(wasm, 'abis__compute_function_tree_root', inputVector, 32); - return Fr.fromBuffer(result); +export function computeFunctionTreeRoot(fnLeaves: Fr[]) { + const leaves = fnLeaves.map(fr => fr.toBuffer()); + return Fr.fromBuffer(functionTreeRootCalculator.computeTreeRoot(leaves)); } /** * Computes a constructor hash. - * @param wasm - A module providing low-level wasm access. * @param functionData - Constructor's function data. * @param argsHash - Constructor's arguments hashed. * @param constructorVKHash - Hash of the constructor's verification key. @@ -140,7 +157,6 @@ export function hashConstructor(functionData: FunctionData, argsHash: Fr, constr /** * Computes a complete address. - * @param wasm - A module providing low-level wasm access. * @param deployerPubKey - The pubkey of the contract deployer. * @param contractAddrSalt - The salt used as one of the inputs of the contract address computation. * @param fnTreeRoot - The function tree root of the contract being deployed. @@ -181,7 +197,6 @@ function computePartialAddress(contractAddrSalt: Fr, fnTreeRoot: Fr, constructor /** * Computes a contract address from its partial address and the pubkey. - * @param wasm - A module providing low-level wasm access. * @param partial - The salt used as one of the inputs of the contract address computation. * @param fnTreeRoot - The function tree root of the contract being deployed. * @param constructorHash - The hash of the constructor. @@ -197,7 +212,6 @@ export function computeContractAddressFromPartial(pubKey: PublicKey, partialAddr /** * Computes a commitment nonce, which will be used to create a unique commitment. - * @param wasm - A module providing low-level wasm access. * @param nullifierZero - The first nullifier in the tx. * @param commitmentIndex - The index of the commitment. * @returns A commitment nonce. @@ -211,7 +225,6 @@ export function computeCommitmentNonce(nullifierZero: Fr, commitmentIndex: numbe /** * Computes a siloed commitment, given the contract address and the commitment itself. * A siloed commitment effectively namespaces a commitment to a specific contract. - * @param wasm - A module providing low-level wasm access. * @param contract - The contract address * @param innerCommitment - The commitment to silo. * @returns A siloed commitment. @@ -224,7 +237,6 @@ export function siloCommitment(contract: AztecAddress, innerCommitment: Fr): Fr /** * Computes a unique commitment. It includes a nonce which contains data that guarantees the commitment will be unique. - * @param wasm - A module providing low-level wasm access. * @param nonce - The contract address. * @param siloedCommitment - An siloed commitment. * @returns A unique commitment. @@ -236,7 +248,6 @@ export function computeUniqueCommitment(nonce: Fr, siloedCommitment: Fr): Fr { /** * Computes a siloed nullifier, given the contract address and the inner nullifier. * A siloed nullifier effectively namespaces a nullifier to a specific contract. - * @param wasm - A module providing low-level wasm access. * @param contract - The contract address. * @param innerNullifier - The nullifier to silo. * @returns A siloed nullifier. @@ -247,7 +258,6 @@ export function siloNullifier(contract: AztecAddress, innerNullifier: Fr): Fr { /** * Computes the block hash given the blocks globals and roots. - * @param wasm - A module providing low-level wasm access. * @param globals - The global variables to put into the block hash. * @param noteHashTree - The root of the note hash tree. * @param nullifierTreeRoot - The root of the nullifier tree. @@ -276,7 +286,6 @@ export function computeBlockHashWithGlobals( /** * Computes the block hash given the blocks globals and roots. - * @param wasm - A module providing low-level wasm access. * @param globalsHash - The global variables hash to put into the block hash. * @param noteHashTree - The root of the note hash tree. * @param nullifierTreeRoot - The root of the nullifier tree. @@ -310,7 +319,6 @@ export function computeBlockHash( /** * Computes the globals hash given the globals. - * @param wasm - A module providing low-level wasm access. * @param globals - The global variables to put into the block hash. * @returns The globals hash. */ @@ -330,7 +338,6 @@ export function computeGlobalsHash(globals: GlobalVariables): Fr { /** * Computes a public data tree value ready for insertion. - * @param wasm - A module providing low-level wasm access. * @param value - Raw public data tree value to hash into a tree-insertion-ready value. * @returns Value hash into a tree-insertion-ready value. @@ -341,7 +348,6 @@ export function computePublicDataTreeValue(value: Fr): Fr { /** * Computes a public data tree index from contract address and storage slot. - * @param wasm - A module providing low-level wasm access. * @param contractAddress - Contract where insertion is occurring. * @param storageSlot - Storage slot where insertion is occurring. * @returns Public data tree index computed from contract address and storage slot. @@ -358,40 +364,43 @@ const ARGS_HASH_CHUNK_COUNT = 16; /** * Computes the hash of a list of arguments. - * @param wasm - A module providing low-level wasm access. * @param args - Arguments to hash. * @returns Pedersen hash of the arguments. */ export function computeVarArgsHash(args: Fr[]) { - if (args.length === 0) return Fr.ZERO; - if (args.length > ARGS_HASH_CHUNK_SIZE * ARGS_HASH_CHUNK_COUNT) + if (args.length === 0) { + return Fr.ZERO; + } + if (args.length > ARGS_HASH_CHUNK_SIZE * ARGS_HASH_CHUNK_COUNT) { throw new Error(`Cannot hash more than ${ARGS_HASH_CHUNK_SIZE * ARGS_HASH_CHUNK_COUNT} arguments`); - - const wasmComputeVarArgs = (args: Fr[]) => - Fr.fromBuffer( - pedersenHash( - args.map(a => a.toBuffer()), - GeneratorIndex.FUNCTION_ARGS, - ), - ); + } let chunksHashes = chunk(args, ARGS_HASH_CHUNK_SIZE).map(c => { if (c.length < ARGS_HASH_CHUNK_SIZE) { c = padArrayEnd(c, Fr.ZERO, ARGS_HASH_CHUNK_SIZE); } - return wasmComputeVarArgs(c); + return Fr.fromBuffer( + pedersenHash( + c.map(a => a.toBuffer()), + GeneratorIndex.FUNCTION_ARGS, + ), + ); }); if (chunksHashes.length < ARGS_HASH_CHUNK_COUNT) { chunksHashes = padArrayEnd(chunksHashes, Fr.ZERO, ARGS_HASH_CHUNK_COUNT); } - return wasmComputeVarArgs(chunksHashes); + return Fr.fromBuffer( + pedersenHash( + chunksHashes.map(a => a.toBuffer()), + GeneratorIndex.FUNCTION_ARGS, + ), + ); } /** * Computes a contract leaf of the given contract. - * @param wasm - Relevant WASM wrapper. * @param cd - The contract data of the deployed contract. * @returns The contract leaf. */ @@ -409,7 +418,6 @@ export function computeContractLeaf(cd: NewContractData): Fr { /** * Computes tx hash of a given transaction request. - * @param wasm - Relevant WASM wrapper. * @param txRequest - The signed transaction request. * @returns The transaction hash. */ @@ -483,64 +491,154 @@ function computeContractDeploymentDataHash(data: ContractDeploymentData): Fr { } /** - * Computes a call stack item hash. - * @param wasm - Relevant WASM wrapper. - * @param callStackItem - The call stack item. - * @returns The call stack item hash. + * */ -export function computeCallStackItemHash( - wasm: IWasmModule, - callStackItem: PrivateCallStackItem | PublicCallStackItem, -): Fr { - if (callStackItem instanceof PrivateCallStackItem) { - return computePrivateCallStackItemHash(wasm, callStackItem); - } else if (callStackItem instanceof PublicCallStackItem) { - return computePublicCallStackItemHash(wasm, callStackItem); - } else { - throw new Error(`Unexpected call stack item type`); +function computeCallContextHash(input: CallContext) { + return pedersenHash( + [ + input.msgSender.toBuffer(), + input.storageContractAddress.toBuffer(), + input.portalContractAddress.toBuffer(), + input.functionSelector.toBuffer(), + boolToBuffer(input.isDelegateCall, 32), + boolToBuffer(input.isStaticCall, 32), + boolToBuffer(input.isContractDeployment, 32), + ], + GeneratorIndex.CALL_CONTEXT, + ); +} + +/** + * + */ +function computePrivateInputsHash(input: PrivateCircuitPublicInputs) { + const toHash = [ + computeCallContextHash(input.callContext), + input.argsHash.toBuffer(), + ...input.returnValues.map(fr => fr.toBuffer()), + ...input.readRequests.map(fr => fr.toBuffer()), + ...input.pendingReadRequests.map(fr => fr.toBuffer()), + ...input.newCommitments.map(fr => fr.toBuffer()), + ...input.newNullifiers.map(fr => fr.toBuffer()), + ...input.nullifiedCommitments.map(fr => fr.toBuffer()), + ...input.privateCallStackHashes.map(fr => fr.toBuffer()), + ...input.publicCallStackHashes.map(fr => fr.toBuffer()), + ...input.newL2ToL1Msgs.map(fr => fr.toBuffer()), + ...input.encryptedLogsHash.map(fr => fr.toBuffer()), + ...input.unencryptedLogsHash.map(fr => fr.toBuffer()), + input.encryptedLogPreimagesLength.toBuffer(), + input.unencryptedLogPreimagesLength.toBuffer(), + input.historicBlockData.noteHashTreeRoot.toBuffer(), + input.historicBlockData.nullifierTreeRoot.toBuffer(), + input.historicBlockData.contractTreeRoot.toBuffer(), + input.historicBlockData.l1ToL2MessagesTreeRoot.toBuffer(), + input.historicBlockData.blocksTreeRoot.toBuffer(), + input.historicBlockData.publicDataTreeRoot.toBuffer(), + input.historicBlockData.globalVariablesHash.toBuffer(), + computeContractDeploymentDataHash(input.contractDeploymentData).toBuffer(), + input.chainId.toBuffer(), + input.version.toBuffer(), + ]; + if (toHash.length != PRIVATE_CIRCUIT_PUBLIC_INPUTS_HASH_INPUT_LENGTH) { + throw new Error('Incorrect number of input fields when hashing PrivateCircuitPublicInputs'); } + return pedersenHash(toHash, GeneratorIndex.PRIVATE_CIRCUIT_PUBLIC_INPUTS); } /** * Computes a call stack item hash. - * @param wasm - Relevant WASM wrapper. * @param callStackItem - The call stack item. * @returns The call stack item hash. */ -export function computePrivateCallStackItemHash(wasm: IWasmModule, callStackItem: PrivateCallStackItem): Fr { - const value = wasmSyncCall(wasm, 'abis__compute_private_call_stack_item_hash', callStackItem, 32); - return Fr.fromBuffer(value); - // return Fr.fromBuffer( - // pedersenHashWithHashIndex( - // [ - // callStackItem.contractAddress.toBuffer(), - // computeFunctionDataHash(callStackItem.functionData).toBuffer(), - // computePublicInputsHash(callStackItem.publicInputs).toBuffer(), - // ], - // GeneratorIndex.CALL_STACK_ITEM, - // ), - // ); +export function computePrivateCallStackItemHash(callStackItem: PrivateCallStackItem): Fr { + return Fr.fromBuffer( + pedersenHash( + [ + callStackItem.contractAddress.toBuffer(), + computeFunctionDataHash(callStackItem.functionData).toBuffer(), + computePrivateInputsHash(callStackItem.publicInputs), + ], + GeneratorIndex.CALL_STACK_ITEM, + ), + ); +} + +/** + * + */ +function computeContractStorageUpdateRequestHash(input: ContractStorageUpdateRequest) { + return pedersenHash( + [input.storageSlot.toBuffer(), input.oldValue.toBuffer(), input.newValue.toBuffer()], + GeneratorIndex.PUBLIC_DATA_UPDATE_REQUEST, + ); +} + +/** + * + */ +function computeContractStorageReadsHash(input: ContractStorageRead) { + return pedersenHash([input.storageSlot.toBuffer(), input.currentValue.toBuffer()], GeneratorIndex.PUBLIC_DATA_READ); +} + +/** + * + */ +function computePublicInputsHash(input: PublicCircuitPublicInputs) { + const toHash = [ + computeCallContextHash(input.callContext), + input.argsHash.toBuffer(), + ...input.returnValues.map(fr => fr.toBuffer()), + ...input.contractStorageUpdateRequests.map(computeContractStorageUpdateRequestHash), + ...input.contractStorageReads.map(computeContractStorageReadsHash), + ...input.publicCallStackHashes.map(fr => fr.toBuffer()), + ...input.newCommitments.map(fr => fr.toBuffer()), + ...input.newNullifiers.map(fr => fr.toBuffer()), + ...input.newL2ToL1Msgs.map(fr => fr.toBuffer()), + ...input.unencryptedLogsHash.map(fr => fr.toBuffer()), + input.unencryptedLogPreimagesLength.toBuffer(), + input.historicBlockData.noteHashTreeRoot.toBuffer(), + input.historicBlockData.nullifierTreeRoot.toBuffer(), + input.historicBlockData.contractTreeRoot.toBuffer(), + input.historicBlockData.l1ToL2MessagesTreeRoot.toBuffer(), + input.historicBlockData.blocksTreeRoot.toBuffer(), + input.historicBlockData.publicDataTreeRoot.toBuffer(), + input.historicBlockData.globalVariablesHash.toBuffer(), + input.proverAddress.toBuffer(), + ]; + if (toHash.length != PUBLIC_CIRCUIT_PUBLIC_INPUTS_HASH_INPUT_LENGTH) { + throw new Error('Incorrect number of input fields when hashing PublicCircuitPublicInputs'); + } + return pedersenHash(toHash, GeneratorIndex.PUBLIC_CIRCUIT_PUBLIC_INPUTS); } /** * Computes a call stack item hash. - * @param wasm - Relevant WASM wrapper. * @param callStackItem - The call stack item. * @returns The call stack item hash. */ -export function computePublicCallStackItemHash(wasm: IWasmModule, callStackItem: PublicCallStackItem): Fr { - const value = wasmSyncCall(wasm, 'abis__compute_public_call_stack_item_hash', callStackItem, 32); - return Fr.fromBuffer(value); - // return Fr.fromBuffer( - // pedersenHashWithHashIndex( - // [ - // callStackItem.contractAddress.toBuffer(), - // callStackItem.functionData.toBuffer(), - // callStackItem.publicInputs.toBuffer(), - // ], - // GeneratorIndex.CALL_STACK_ITEM, - // ), - // ); +export function computePublicCallStackItemHash({ + contractAddress, + functionData, + publicInputs, + isExecutionRequest, +}: PublicCallStackItem): Fr { + if (isExecutionRequest) { + const { callContext, argsHash } = publicInputs; + publicInputs = PublicCircuitPublicInputs.empty(); + publicInputs.callContext = callContext; + publicInputs.argsHash = argsHash; + } + + return Fr.fromBuffer( + pedersenHash( + [ + contractAddress.toBuffer(), + computeFunctionDataHash(functionData).toBuffer(), + computePublicInputsHash(publicInputs), + ], + GeneratorIndex.CALL_STACK_ITEM, + ), + ); } /** diff --git a/yarn-project/circuits.js/src/abis/merkle_tree_calculator.test.ts b/yarn-project/circuits.js/src/abis/merkle_tree_calculator.test.ts new file mode 100644 index 00000000000..1be2f815b50 --- /dev/null +++ b/yarn-project/circuits.js/src/abis/merkle_tree_calculator.test.ts @@ -0,0 +1,40 @@ +import { Fr } from '@aztec/foundation/fields'; + +import { MerkleTreeCalculator } from './merkle_tree_calculator.js'; + +describe('merkle tree root calculator', () => { + it('should correctly handle no leaves', () => { + // Height of 3 is 8 leaves. + const calculator = new MerkleTreeCalculator(4); + const expected = calculator.computeTreeRoot(new Array(8).fill(new Fr(0)).map(fr => fr.toBuffer())); + expect(calculator.computeTreeRoot()).toEqual(expected); + }); + + it('should correctly leverage zero hashes', () => { + const calculator = new MerkleTreeCalculator(4); + const leaves = Array.from({ length: 5 }).map((_, i) => new Fr(i).toBuffer()); + const padded = [...leaves, ...new Array(3).fill(Buffer.alloc(32))]; + const expected = calculator.computeTreeRoot(padded); + const result = calculator.computeTreeRoot(leaves); + expect(result).not.toBeUndefined(); + expect(result).toEqual(expected); + }); + + it('should correctly handle non default zero leaf', () => { + const zeroLeaf = new Fr(666).toBuffer(); + const calculator = new MerkleTreeCalculator(4, zeroLeaf); + const leaves = Array.from({ length: 5 }).map((_, i) => new Fr(i).toBuffer()); + const padded = [...leaves, ...new Array(3).fill(zeroLeaf)]; + const expected = calculator.computeTreeRoot(padded); + expect(calculator.computeTreeRoot(leaves)).toEqual(expected); + }); + + it('should compute entire tree', () => { + const calculator = new MerkleTreeCalculator(4); + const leaves = Array.from({ length: 5 }).map((_, i) => new Fr(i).toBuffer()); + const expectedRoot = calculator.computeTreeRoot(leaves); + const result = calculator.computeTree(leaves); + expect(result.length).toEqual(31); + expect(result[result.length - 1]).toEqual(expectedRoot); + }); +}); diff --git a/yarn-project/circuits.js/src/abis/merkle_tree_calculator.ts b/yarn-project/circuits.js/src/abis/merkle_tree_calculator.ts new file mode 100644 index 00000000000..a4de23b3911 --- /dev/null +++ b/yarn-project/circuits.js/src/abis/merkle_tree_calculator.ts @@ -0,0 +1,57 @@ +import { pedersenHash } from '@aztec/foundation/crypto'; + +/** + * Merkle tree calculator. + */ +export class MerkleTreeCalculator { + private zeroHashes: Buffer[]; + + constructor(private height: number, zeroLeaf = Buffer.alloc(32)) { + this.zeroHashes = Array.from({ length: height }).reduce( + (acc: Buffer[], _, i) => [...acc, pedersenHash([acc[i], acc[i]])], + [zeroLeaf], + ); + } + + computeTree(leaves: Buffer[] = []) { + if (leaves.length === 0) { + return [this.zeroHashes[this.zeroHashes.length - 1]]; + } + + let result = leaves.slice(); + + for (let i = 0; i < this.height; ++i) { + const numLeaves = 2 ** (this.height - i); + const newLeaves: Buffer[] = []; + for (let j = 0; j < leaves.length / 2; ++j) { + const l = leaves[j * 2]; + const r = leaves[j * 2 + 1] || this.zeroHashes[i]; + newLeaves[j] = pedersenHash([l, r]); + } + result = result.concat(new Array(numLeaves - leaves.length).fill(this.zeroHashes[i]), newLeaves); + leaves = newLeaves; + } + + return result; + } + + computeTreeRoot(leaves: Buffer[] = []) { + if (leaves.length === 0) { + return this.zeroHashes[this.zeroHashes.length - 1]; + } + + leaves = leaves.slice(); + + for (let i = 0; i < this.height; ++i) { + let j = 0; + for (; j < leaves.length / 2; ++j) { + const l = leaves[j * 2]; + const r = leaves[j * 2 + 1] || this.zeroHashes[i]; + leaves[j] = pedersenHash([l, r]); + } + leaves = leaves.slice(0, j); + } + + return leaves[0]; + } +} diff --git a/yarn-project/circuits.js/src/abis/merkle_tree_root_calculator.test.ts b/yarn-project/circuits.js/src/abis/merkle_tree_root_calculator.test.ts new file mode 100644 index 00000000000..58966728949 --- /dev/null +++ b/yarn-project/circuits.js/src/abis/merkle_tree_root_calculator.test.ts @@ -0,0 +1,31 @@ +import { Fr } from '@aztec/foundation/fields'; + +import { MerkleTreeRootCalculator } from './merkle_tree_root_calculator.js'; + +describe('merkle tree root calculator', () => { + it('should correctly handle no leaves', () => { + // Height of 3 is 8 leaves. + const calculator = new MerkleTreeRootCalculator(4); + const expected = calculator.computeTreeRoot(new Array(8).fill(new Fr(0)).map(fr => fr.toBuffer())); + expect(calculator.computeTreeRoot()).toEqual(expected); + }); + + it('should correctly leverage zero hashes', () => { + const calculator = new MerkleTreeRootCalculator(4); + const leaves = Array.from({ length: 5 }).map((_, i) => new Fr(i).toBuffer()); + const padded = [...leaves, ...new Array(3).fill(Buffer.alloc(32))]; + const expected = calculator.computeTreeRoot(padded); + const result = calculator.computeTreeRoot(leaves); + expect(result).not.toBeUndefined(); + expect(result).toEqual(expected); + }); + + it('should correctly handle non default zero leaf', () => { + const zeroLeaf = new Fr(666).toBuffer(); + const calculator = new MerkleTreeRootCalculator(4, zeroLeaf); + const leaves = Array.from({ length: 5 }).map((_, i) => new Fr(i).toBuffer()); + const padded = [...leaves, ...new Array(3).fill(zeroLeaf)]; + const expected = calculator.computeTreeRoot(padded); + expect(calculator.computeTreeRoot(leaves)).toEqual(expected); + }); +}); diff --git a/yarn-project/circuits.js/src/abis/merkle_tree_root_calculator.ts b/yarn-project/circuits.js/src/abis/merkle_tree_root_calculator.ts new file mode 100644 index 00000000000..904eec35776 --- /dev/null +++ b/yarn-project/circuits.js/src/abis/merkle_tree_root_calculator.ts @@ -0,0 +1,33 @@ +import { pedersenHash } from '@aztec/foundation/crypto'; + +/** + * Calculates the root of a merkle tree. + */ +export class MerkleTreeRootCalculator { + private zeroHashes: Buffer[]; + + constructor(private height: number, zeroLeaf = Buffer.alloc(32)) { + this.zeroHashes = Array.from({ length: height }).reduce( + (acc: Buffer[], _, i) => [...acc, pedersenHash([acc[i], acc[i]])], + [zeroLeaf], + ); + } + + computeTreeRoot(leaves: Buffer[] = []) { + if (leaves.length === 0) { + return this.zeroHashes[this.zeroHashes.length - 1]; + } + + for (let i = 0; i < this.height; ++i) { + let j = 0; + for (; j < leaves.length / 2; ++j) { + const l = leaves[j * 2]; + const r = leaves[j * 2 + 1] || this.zeroHashes[i]; + leaves[j] = pedersenHash([l, r]); + } + leaves = leaves.slice(0, j); + } + + return leaves[0]; + } +} diff --git a/yarn-project/circuits.js/src/barretenberg/crypto/aes128/index.test.ts b/yarn-project/circuits.js/src/barretenberg/crypto/aes128/index.test.ts index e8a1f91b533..9b8afc328e2 100644 --- a/yarn-project/circuits.js/src/barretenberg/crypto/aes128/index.test.ts +++ b/yarn-project/circuits.js/src/barretenberg/crypto/aes128/index.test.ts @@ -1,16 +1,12 @@ -import { CircuitsWasm } from '@aztec/circuits.js'; - import { createCipheriv, createDecipheriv, randomBytes } from 'crypto'; import { Aes128 } from './index.js'; describe('aes128', () => { - let barretenberg!: CircuitsWasm; let aes128!: Aes128; - beforeAll(async () => { - barretenberg = await CircuitsWasm.get(); - aes128 = new Aes128(barretenberg); + beforeAll(() => { + aes128 = new Aes128(); }); it('should correctly encrypt input', () => { diff --git a/yarn-project/circuits.js/src/barretenberg/crypto/aes128/index.ts b/yarn-project/circuits.js/src/barretenberg/crypto/aes128/index.ts index 8d92a2779ab..20e0e133b9c 100644 --- a/yarn-project/circuits.js/src/barretenberg/crypto/aes128/index.ts +++ b/yarn-project/circuits.js/src/barretenberg/crypto/aes128/index.ts @@ -1,13 +1,15 @@ -import { IWasmModule } from '@aztec/foundation/wasm'; +import { BarretenbergSync, RawBuffer } from '@aztec/bb.js'; import { Buffer } from 'buffer'; +// Get the singleton. This constructs (if not already) the barretenberg sync api within bb.js itself. +// This can be called from multiple other modules as needed, and it ensures it's only constructed once. +const api = await BarretenbergSync.getSingleton(); + /** * AES-128-CBC encryption/decryption. */ export class Aes128 { - constructor(private wasm: IWasmModule) {} - /** * Encrypt a buffer using AES-128-CBC. * @param data - Data to encrypt. @@ -25,26 +27,10 @@ export class Aes128 { paddingBuffer.fill(numPaddingBytes); } const input = Buffer.concat([data, paddingBuffer]); - const mem = this.wasm.call('bbmalloc', input.length + key.length + iv.length + input.length); - this.wasm.writeMemory(mem, input); - this.wasm.writeMemory(mem + input.length, iv); - this.wasm.writeMemory(mem + input.length + iv.length, key); - this.wasm.call( - 'aes__encrypt_buffer_cbc', - mem, - mem + input.length, - mem + input.length + iv.length, - input.length, - mem + input.length + iv.length + key.length, - ); - const result: Buffer = Buffer.from( - this.wasm.getMemorySlice( - mem + input.length + key.length + iv.length, - mem + input.length + key.length + iv.length + input.length, - ), + + return Buffer.from( + api.aesEncryptBufferCbc(new RawBuffer(input), new RawBuffer(iv), new RawBuffer(key), input.length), ); - this.wasm.call('bbfree', mem); - return result; } /** @@ -55,25 +41,8 @@ export class Aes128 { * @returns Decrypted data. */ public decryptBufferCBC(data: Uint8Array, iv: Uint8Array, key: Uint8Array) { - const mem = this.wasm.call('bbmalloc', data.length + key.length + iv.length + data.length); - this.wasm.writeMemory(mem, data); - this.wasm.writeMemory(mem + data.length, iv); - this.wasm.writeMemory(mem + data.length + iv.length, key); - this.wasm.call( - 'aes__decrypt_buffer_cbc', - mem, - mem + data.length, - mem + data.length + iv.length, - data.length, - mem + data.length + iv.length + key.length, - ); - const result: Buffer = Buffer.from( - this.wasm.getMemorySlice( - mem + data.length + key.length + iv.length, - mem + data.length + key.length + iv.length + data.length, - ), + return Buffer.from( + api.aesDecryptBufferCbc(new RawBuffer(data), new RawBuffer(iv), new RawBuffer(key), data.length), ); - this.wasm.call('bbfree', mem); - return result; } } diff --git a/yarn-project/circuits.js/src/barretenberg/crypto/ecdsa/index.test.ts b/yarn-project/circuits.js/src/barretenberg/crypto/ecdsa/index.test.ts index a05f16f9b8c..57a61da3124 100644 --- a/yarn-project/circuits.js/src/barretenberg/crypto/ecdsa/index.test.ts +++ b/yarn-project/circuits.js/src/barretenberg/crypto/ecdsa/index.test.ts @@ -1,20 +1,18 @@ import { TextEncoder } from 'util'; -import { CircuitsWasm } from '../../../index.js'; import { Ecdsa } from './index.js'; describe('ecdsa', () => { let ecdsa!: Ecdsa; - beforeAll(async () => { - const wasm = await CircuitsWasm.get(); - ecdsa = new Ecdsa(wasm); + beforeAll(() => { + ecdsa = new Ecdsa(); }); it('should verify signature', () => { // prettier-ignore const privateKey = Buffer.from([ - 0x0b, 0x9b, 0x3a, 0xde, 0xe6, 0xb3, 0xd8, 0x1b, 0x28, 0xa0, 0x88, 0x6b, 0x2a, 0x84, 0x15, 0xc7, + 0x0b, 0x9b, 0x3a, 0xde, 0xe6, 0xb3, 0xd8, 0x1b, 0x28, 0xa0, 0x88, 0x6b, 0x2a, 0x84, 0x15, 0xc7, 0xda, 0x31, 0x29, 0x1a, 0x5e, 0x96, 0xbb, 0x7a, 0x56, 0x63, 0x9e, 0x17, 0x7d, 0x30, 0x1b, 0xeb, ]); const pubKey = ecdsa.computePublicKey(privateKey); @@ -28,7 +26,7 @@ describe('ecdsa', () => { it('should recover public key from signature', () => { // prettier-ignore const privateKey = Buffer.from([ - 0x0b, 0x9b, 0x3a, 0xde, 0xe6, 0xb3, 0xd8, 0x1b, 0x28, 0xa0, 0x88, 0x6b, 0x2a, 0x84, 0x15, 0xc7, + 0x0b, 0x9b, 0x3a, 0xde, 0xe6, 0xb3, 0xd8, 0x1b, 0x28, 0xa0, 0x88, 0x6b, 0x2a, 0x84, 0x15, 0xc7, 0xda, 0x31, 0x29, 0x1a, 0x5e, 0x96, 0xbb, 0x7a, 0x56, 0x63, 0x9e, 0x17, 0x7d, 0x30, 0x1b, 0xeb, ]); const pubKey = ecdsa.computePublicKey(privateKey); diff --git a/yarn-project/circuits.js/src/barretenberg/crypto/ecdsa/index.ts b/yarn-project/circuits.js/src/barretenberg/crypto/ecdsa/index.ts index 6efa8cc5568..e52933eccf4 100644 --- a/yarn-project/circuits.js/src/barretenberg/crypto/ecdsa/index.ts +++ b/yarn-project/circuits.js/src/barretenberg/crypto/ecdsa/index.ts @@ -1,33 +1,25 @@ -import { IWasmModule } from '@aztec/foundation/wasm'; +import { BarretenbergSync } from '@aztec/bb.js'; -import { CircuitsWasm } from '../../../index.js'; import { EcdsaSignature } from './signature.js'; export * from './signature.js'; +const api = await BarretenbergSync.getSingleton(); +const wasm = api.getWasm(); + /** * ECDSA signature construction and helper operations. */ export class Ecdsa { - /** - * Creates a new Ecdsa instance. - * @returns New Ecdsa instance. - */ - public static async new() { - return new this(await CircuitsWasm.get()); - } - - constructor(private wasm: IWasmModule) {} - /** * Computes a secp256k1 public key from a private key. * @param privateKey - Secp256k1 private key. * @returns A secp256k1 public key. */ public computePublicKey(privateKey: Buffer): Buffer { - this.wasm.writeMemory(0, privateKey); - this.wasm.call('ecdsa__compute_public_key', 0, 32); - return Buffer.from(this.wasm.getMemorySlice(32, 96)); + wasm.writeMemory(0, privateKey); + wasm.call('ecdsa__compute_public_key', 0, 32); + return Buffer.from(wasm.getMemorySlice(32, 96)); } /** @@ -37,15 +29,15 @@ export class Ecdsa { * @returns An ECDSA signature of the form (r, s, v). */ public constructSignature(msg: Uint8Array, privateKey: Buffer) { - const mem = this.wasm.call('bbmalloc', msg.length); - this.wasm.writeMemory(0, privateKey); - this.wasm.writeMemory(mem, msg); - this.wasm.call('ecdsa__construct_signature', mem, msg.length, 0, 32, 64, 96); + const mem = wasm.call('bbmalloc', msg.length); + wasm.writeMemory(0, privateKey); + wasm.writeMemory(mem, msg); + wasm.call('ecdsa__construct_signature', mem, msg.length, 0, 32, 64, 96); return new EcdsaSignature( - Buffer.from(this.wasm.getMemorySlice(32, 64)), - Buffer.from(this.wasm.getMemorySlice(64, 96)), - Buffer.from(this.wasm.getMemorySlice(96, 97)), + Buffer.from(wasm.getMemorySlice(32, 64)), + Buffer.from(wasm.getMemorySlice(64, 96)), + Buffer.from(wasm.getMemorySlice(96, 97)), ); } @@ -56,14 +48,14 @@ export class Ecdsa { * @returns The secp256k1 public key of the signer. */ public recoverPublicKey(msg: Uint8Array, sig: EcdsaSignature): Buffer { - const mem = this.wasm.call('bbmalloc', msg.length); - this.wasm.writeMemory(0, sig.r); - this.wasm.writeMemory(32, sig.s); - this.wasm.writeMemory(64, sig.v); - this.wasm.writeMemory(mem, msg); - this.wasm.call('ecdsa__recover_public_key_from_signature', mem, msg.length, 0, 32, 64, 65); + const mem = wasm.call('bbmalloc', msg.length); + wasm.writeMemory(0, sig.r); + wasm.writeMemory(32, sig.s); + wasm.writeMemory(64, sig.v); + wasm.writeMemory(mem, msg); + wasm.call('ecdsa__recover_public_key_from_signature', mem, msg.length, 0, 32, 64, 65); - return Buffer.from(this.wasm.getMemorySlice(65, 129)); + return Buffer.from(wasm.getMemorySlice(65, 129)); } /** @@ -74,12 +66,12 @@ export class Ecdsa { * @returns True or false. */ public verifySignature(msg: Uint8Array, pubKey: Buffer, sig: EcdsaSignature) { - const mem = this.wasm.call('bbmalloc', msg.length); - this.wasm.writeMemory(0, pubKey); - this.wasm.writeMemory(64, sig.r); - this.wasm.writeMemory(96, sig.s); - this.wasm.writeMemory(128, sig.v); - this.wasm.writeMemory(mem, msg); - return this.wasm.call('ecdsa__verify_signature', mem, msg.length, 0, 64, 96, 128) ? true : false; + const mem = wasm.call('bbmalloc', msg.length); + wasm.writeMemory(0, pubKey); + wasm.writeMemory(64, sig.r); + wasm.writeMemory(96, sig.s); + wasm.writeMemory(128, sig.v); + wasm.writeMemory(mem, msg); + return wasm.call('ecdsa__verify_signature', mem, msg.length, 0, 64, 96, 128) ? true : false; } } diff --git a/yarn-project/circuits.js/src/barretenberg/crypto/grumpkin/index.test.ts b/yarn-project/circuits.js/src/barretenberg/crypto/grumpkin/index.test.ts index 2a79d619b98..154ab39075f 100644 --- a/yarn-project/circuits.js/src/barretenberg/crypto/grumpkin/index.test.ts +++ b/yarn-project/circuits.js/src/barretenberg/crypto/grumpkin/index.test.ts @@ -1,17 +1,15 @@ import { createDebugLogger } from '@aztec/foundation/log'; -import { CircuitsWasm, GrumpkinScalar, Point } from '../../../index.js'; +import { GrumpkinScalar, Point } from '../../../index.js'; import { Grumpkin } from './index.js'; const debug = createDebugLogger('bb:grumpkin_test'); describe('grumpkin', () => { - let barretenberg!: CircuitsWasm; let grumpkin!: Grumpkin; - beforeAll(async () => { - barretenberg = await CircuitsWasm.get(); - grumpkin = new Grumpkin(barretenberg); + beforeAll(() => { + grumpkin = new Grumpkin(); }); it('should correctly perform scalar muls', () => { diff --git a/yarn-project/circuits.js/src/barretenberg/crypto/grumpkin/index.ts b/yarn-project/circuits.js/src/barretenberg/crypto/grumpkin/index.ts index 35a0cb068e5..a41c0af1fa0 100644 --- a/yarn-project/circuits.js/src/barretenberg/crypto/grumpkin/index.ts +++ b/yarn-project/circuits.js/src/barretenberg/crypto/grumpkin/index.ts @@ -1,24 +1,15 @@ +import { BarretenbergSync } from '@aztec/bb.js'; import { Fr, Point } from '@aztec/foundation/fields'; -import { IWasmModule } from '@aztec/foundation/wasm'; -import { CircuitsWasm, GrumpkinScalar } from '../../../index.js'; +import { GrumpkinScalar } from '../../../index.js'; -// TODO: Establish if these needs high performance and consider refactoring and using the grumpkin curve in pedersen ts. +const api = await BarretenbergSync.getSingleton(); +const wasm = api.getWasm(); /** * Grumpkin elliptic curve operations. */ export class Grumpkin { - /** - * Creates a new Grumpkin instance. - * @returns New Grumpkin instance. - */ - public static async new() { - return new this(await CircuitsWasm.get()); - } - - constructor(private wasm: IWasmModule) {} - // prettier-ignore static generator = Point.fromBuffer(Buffer.from([ 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, @@ -42,10 +33,10 @@ export class Grumpkin { * @returns Result of the multiplication. */ public mul(point: Point, scalar: GrumpkinScalar): Point { - this.wasm.writeMemory(0, point.toBuffer()); - this.wasm.writeMemory(64, scalar.toBuffer()); - this.wasm.call('ecc_grumpkin__mul', 0, 64, 96); - return Point.fromBuffer(Buffer.from(this.wasm.getMemorySlice(96, 160))); + wasm.writeMemory(0, point.toBuffer()); + wasm.writeMemory(64, scalar.toBuffer()); + wasm.call('ecc_grumpkin__mul', 0, 64, 96); + return Point.fromBuffer(Buffer.from(wasm.getMemorySlice(96, 160))); } /** @@ -58,16 +49,16 @@ export class Grumpkin { const concatenatedPoints: Buffer = Buffer.concat(points.map(point => point.toBuffer())); const pointsByteLength = points.length * Point.SIZE_IN_BYTES; - const mem = this.wasm.call('bbmalloc', pointsByteLength * 2); + const mem = wasm.call('bbmalloc', pointsByteLength * 2); - this.wasm.writeMemory(mem, concatenatedPoints); - this.wasm.writeMemory(0, scalar.toBuffer()); - this.wasm.call('ecc_grumpkin__batch_mul', mem, 0, points.length, mem + pointsByteLength); + wasm.writeMemory(mem, concatenatedPoints); + wasm.writeMemory(0, scalar.toBuffer()); + wasm.call('ecc_grumpkin__batch_mul', mem, 0, points.length, mem + pointsByteLength); const result: Buffer = Buffer.from( - this.wasm.getMemorySlice(mem + pointsByteLength, mem + pointsByteLength + pointsByteLength), + wasm.getMemorySlice(mem + pointsByteLength, mem + pointsByteLength + pointsByteLength), ); - this.wasm.call('bbfree', mem); + wasm.call('bbfree', mem); const parsedResult: Point[] = []; for (let i = 0; i < pointsByteLength; i += 64) { @@ -81,8 +72,8 @@ export class Grumpkin { * @returns Random field element. */ public getRandomFr(): Fr { - this.wasm.call('ecc_grumpkin__get_random_scalar_mod_circuit_modulus', 0); - return Fr.fromBuffer(Buffer.from(this.wasm.getMemorySlice(0, 32))); + wasm.call('ecc_grumpkin__get_random_scalar_mod_circuit_modulus', 0); + return Fr.fromBuffer(Buffer.from(wasm.getMemorySlice(0, 32))); } /** @@ -91,8 +82,8 @@ export class Grumpkin { * @returns Buffer representation of the field element. */ public reduce512BufferToFr(uint512Buf: Buffer): Fr { - this.wasm.writeMemory(0, uint512Buf); - this.wasm.call('ecc_grumpkin__reduce512_buffer_mod_circuit_modulus', 0, 64); - return Fr.fromBuffer(Buffer.from(this.wasm.getMemorySlice(64, 96))); + wasm.writeMemory(0, uint512Buf); + wasm.call('ecc_grumpkin__reduce512_buffer_mod_circuit_modulus', 0, 64); + return Fr.fromBuffer(Buffer.from(wasm.getMemorySlice(64, 96))); } } diff --git a/yarn-project/circuits.js/src/barretenberg/crypto/schnorr/index.test.ts b/yarn-project/circuits.js/src/barretenberg/crypto/schnorr/index.test.ts index c5a2b7b8687..db2e2101e6e 100644 --- a/yarn-project/circuits.js/src/barretenberg/crypto/schnorr/index.test.ts +++ b/yarn-project/circuits.js/src/barretenberg/crypto/schnorr/index.test.ts @@ -1,19 +1,19 @@ import { TextEncoder } from 'util'; -import { CircuitsWasm, GrumpkinScalar } from '../../../index.js'; +import { GrumpkinScalar } from '../../../index.js'; import { Schnorr } from './index.js'; describe('schnorr', () => { let schnorr!: Schnorr; - beforeAll(async () => { - schnorr = new Schnorr(await CircuitsWasm.get()); + beforeAll(() => { + schnorr = new Schnorr(); }); it('should verify signature', () => { // prettier-ignore const privateKey = GrumpkinScalar.fromBuffer(Buffer.from([ - 0x0b, 0x9b, 0x3a, 0xde, 0xe6, 0xb3, 0xd8, 0x1b, 0x28, 0xa0, 0x88, 0x6b, 0x2a, 0x84, 0x15, 0xc7, + 0x0b, 0x9b, 0x3a, 0xde, 0xe6, 0xb3, 0xd8, 0x1b, 0x28, 0xa0, 0x88, 0x6b, 0x2a, 0x84, 0x15, 0xc7, 0xda, 0x31, 0x29, 0x1a, 0x5e, 0x96, 0xbb, 0x7a, 0x56, 0x63, 0x9e, 0x17, 0x7d, 0x30, 0x1b, 0xeb, ])); const pubKey = schnorr.computePublicKey(privateKey); @@ -27,7 +27,7 @@ describe('schnorr', () => { it('should fail invalid signature', () => { // prettier-ignore const privateKey = GrumpkinScalar.fromBuffer(Buffer.from([ - 0x0b, 0x9b, 0x3a, 0xde, 0xe6, 0xb3, 0xd8, 0x1b, 0x28, 0xa0, 0x88, 0x6b, 0x2a, 0x84, 0x15, 0xc7, + 0x0b, 0x9b, 0x3a, 0xde, 0xe6, 0xb3, 0xd8, 0x1b, 0x28, 0xa0, 0x88, 0x6b, 0x2a, 0x84, 0x15, 0xc7, 0xda, 0x31, 0x29, 0x1a, 0x5e, 0x96, 0xbb, 0x7a, 0x56, 0x63, 0x9e, 0x17, 0x7d, 0x30, 0x1b, 0xeb, ])); const pubKey = schnorr.computePublicKey(privateKey); diff --git a/yarn-project/circuits.js/src/barretenberg/crypto/schnorr/index.ts b/yarn-project/circuits.js/src/barretenberg/crypto/schnorr/index.ts index e850d63e1d3..4ab41700e7d 100644 --- a/yarn-project/circuits.js/src/barretenberg/crypto/schnorr/index.ts +++ b/yarn-project/circuits.js/src/barretenberg/crypto/schnorr/index.ts @@ -1,34 +1,27 @@ +import { BarretenbergSync } from '@aztec/bb.js'; import { numToUInt32BE } from '@aztec/foundation/serialize'; -import { IWasmModule } from '@aztec/foundation/wasm'; -import { CircuitsWasm, GrumpkinPrivateKey, Point, PublicKey } from '../../../index.js'; +import { GrumpkinPrivateKey, Point, PublicKey } from '../../../index.js'; import { SchnorrSignature } from './signature.js'; export * from './signature.js'; +const api = await BarretenbergSync.getSingleton(); +const wasm = api.getWasm(); + /** * Schnorr signature construction and helper operations. */ export class Schnorr { - /** - * Creates a new Schnorr instance. - * @returns New Schnorr instance. - */ - public static async new() { - return new this(await CircuitsWasm.get()); - } - - constructor(private wasm: IWasmModule) {} - /** * Computes a grumpkin public key from a private key. * @param privateKey - The private key. * @returns A grumpkin public key. */ public computePublicKey(privateKey: GrumpkinPrivateKey): PublicKey { - this.wasm.writeMemory(0, privateKey.toBuffer()); - this.wasm.call('schnorr_compute_public_key', 0, 32); - return Point.fromBuffer(Buffer.from(this.wasm.getMemorySlice(32, 96))); + wasm.writeMemory(0, privateKey.toBuffer()); + wasm.call('schnorr_compute_public_key', 0, 32); + return Point.fromBuffer(Buffer.from(wasm.getMemorySlice(32, 96))); } /** @@ -38,12 +31,12 @@ export class Schnorr { * @returns A Schnorr signature of the form (s, e). */ public constructSignature(msg: Uint8Array, privateKey: GrumpkinPrivateKey) { - const mem = this.wasm.call('bbmalloc', msg.length + 4); - this.wasm.writeMemory(0, privateKey.toBuffer()); - this.wasm.writeMemory(mem, Buffer.concat([numToUInt32BE(msg.length), msg])); - this.wasm.call('schnorr_construct_signature', mem, 0, 32, 64); + const mem = wasm.call('bbmalloc', msg.length + 4); + wasm.writeMemory(0, privateKey.toBuffer()); + wasm.writeMemory(mem, Buffer.concat([numToUInt32BE(msg.length), msg])); + wasm.call('schnorr_construct_signature', mem, 0, 32, 64); - return new SchnorrSignature(Buffer.from(this.wasm.getMemorySlice(32, 96))); + return new SchnorrSignature(Buffer.from(wasm.getMemorySlice(32, 96))); } /** @@ -54,13 +47,13 @@ export class Schnorr { * @returns True or false. */ public verifySignature(msg: Uint8Array, pubKey: PublicKey, sig: SchnorrSignature) { - const mem = this.wasm.call('bbmalloc', msg.length + 4); - this.wasm.writeMemory(0, pubKey.toBuffer()); - this.wasm.writeMemory(64, sig.s); - this.wasm.writeMemory(96, sig.e); - this.wasm.writeMemory(mem, Buffer.concat([numToUInt32BE(msg.length), msg])); - this.wasm.call('schnorr_verify_signature', mem, 0, 64, 96, 128); - const result = this.wasm.getMemorySlice(128, 129); + const mem = wasm.call('bbmalloc', msg.length + 4); + wasm.writeMemory(0, pubKey.toBuffer()); + wasm.writeMemory(64, sig.s); + wasm.writeMemory(96, sig.e); + wasm.writeMemory(mem, Buffer.concat([numToUInt32BE(msg.length), msg])); + wasm.call('schnorr_verify_signature', mem, 0, 64, 96, 128); + const result = wasm.getMemorySlice(128, 129); return !Buffer.alloc(1, 0).equals(result); } } diff --git a/yarn-project/circuits.js/src/barretenberg/crypto/secp256k1/index.test.ts b/yarn-project/circuits.js/src/barretenberg/crypto/secp256k1/index.test.ts index 801b45d796e..9da5a362323 100644 --- a/yarn-project/circuits.js/src/barretenberg/crypto/secp256k1/index.test.ts +++ b/yarn-project/circuits.js/src/barretenberg/crypto/secp256k1/index.test.ts @@ -1,6 +1,5 @@ import { randomBytes } from '@aztec/foundation/crypto'; -import { CircuitsWasm } from '../../../index.js'; import { Ecdsa } from '../ecdsa/index.js'; import { Secp256k1 } from './index.js'; @@ -8,10 +7,9 @@ describe('secp256k1', () => { let secp256k1!: Secp256k1; let ecdsa!: Ecdsa; - beforeAll(async () => { - const wasm = await CircuitsWasm.get(); - secp256k1 = new Secp256k1(wasm); - ecdsa = new Ecdsa(wasm); + beforeAll(() => { + secp256k1 = new Secp256k1(); + ecdsa = new Ecdsa(); }); it('should correctly compute public key', () => { diff --git a/yarn-project/circuits.js/src/barretenberg/crypto/secp256k1/index.ts b/yarn-project/circuits.js/src/barretenberg/crypto/secp256k1/index.ts index 28c3554e26e..a388ce602fb 100644 --- a/yarn-project/circuits.js/src/barretenberg/crypto/secp256k1/index.ts +++ b/yarn-project/circuits.js/src/barretenberg/crypto/secp256k1/index.ts @@ -1,21 +1,12 @@ -import { IWasmModule } from '@aztec/foundation/wasm'; +import { BarretenbergSync } from '@aztec/bb.js'; -import { CircuitsWasm } from '../../../index.js'; +const api = await BarretenbergSync.getSingleton(); +const wasm = api.getWasm(); /** * Secp256k1 elliptic curve operations. */ export class Secp256k1 { - /** - * Creates a new Secp256k1 instance. - * @returns New Secp256k1 instance. - */ - public static async new() { - return new this(await CircuitsWasm.get()); - } - - constructor(private wasm: IWasmModule) {} - // prettier-ignore static generator = Buffer.from([ 0x79, 0xbe, 0x66, 0x7e, 0xf9, 0xdc, 0xbb, 0xac, 0x55, 0xa0, 0x62, 0x95, 0xce, 0x87, 0x0b, 0x07, @@ -39,10 +30,10 @@ export class Secp256k1 { * @returns Result of the multiplication. */ public mul(point: Uint8Array, scalar: Uint8Array) { - this.wasm.writeMemory(0, point); - this.wasm.writeMemory(64, scalar); - this.wasm.call('ecc_secp256k1__mul', 0, 64, 96); - return Buffer.from(this.wasm.getMemorySlice(96, 160)); + wasm.writeMemory(0, point); + wasm.writeMemory(64, scalar); + wasm.call('ecc_secp256k1__mul', 0, 64, 96); + return Buffer.from(wasm.getMemorySlice(96, 160)); } /** @@ -50,8 +41,8 @@ export class Secp256k1 { * @returns Random field element. */ public getRandomFr() { - this.wasm.call('ecc_secp256k1__get_random_scalar_mod_circuit_modulus', 0); - return Buffer.from(this.wasm.getMemorySlice(0, 32)); + wasm.call('ecc_secp256k1__get_random_scalar_mod_circuit_modulus', 0); + return Buffer.from(wasm.getMemorySlice(0, 32)); } /** @@ -60,8 +51,8 @@ export class Secp256k1 { * @returns Buffer representation of the field element. */ public reduce512BufferToFr(uint512Buf: Buffer) { - this.wasm.writeMemory(0, uint512Buf); - this.wasm.call('ecc_secp256k1__reduce512_buffer_mod_circuit_modulus', 0, 64); - return Buffer.from(this.wasm.getMemorySlice(64, 96)); + wasm.writeMemory(0, uint512Buf); + wasm.call('ecc_secp256k1__reduce512_buffer_mod_circuit_modulus', 0, 64); + return Buffer.from(wasm.getMemorySlice(64, 96)); } } diff --git a/yarn-project/circuits.js/src/cbind/circuits.gen.ts b/yarn-project/circuits.js/src/cbind/circuits.gen.ts index b7479945630..9886c0ed884 100644 --- a/yarn-project/circuits.js/src/cbind/circuits.gen.ts +++ b/yarn-project/circuits.js/src/cbind/circuits.gen.ts @@ -1,27 +1,22 @@ -/* eslint-disable */ -// GENERATED FILE DO NOT EDIT, RUN yarn remake-bindings +/* eslint-disable camelcase,jsdoc/require-jsdoc */ +// TODO: Remove this file as we no longer generate types from cpp. import { Tuple, mapTuple } from '@aztec/foundation/serialize'; import { IWasmModule } from '@aztec/foundation/wasm'; import { Buffer } from 'buffer'; import mapValues from 'lodash.mapvalues'; +import { CallRequest } from '../structs/call_request.js'; import { callCbind } from './cbind.js'; import { - Address, AppendOnlyTreeSnapshot, BaseOrMergeRollupPublicInputs, BaseRollupInputs, - CallContext, CircuitError, CombinedAccumulatedData, CombinedConstantData, - CompleteAddress, ConstantRollupData, ContractDeploymentData, - ContractStorageRead, - ContractStorageUpdateRequest, - FinalAccumulatedData, Fq, Fr, FunctionData, @@ -30,37 +25,17 @@ import { GlobalVariables, HistoricBlockData, KernelCircuitPublicInputs, - KernelCircuitPublicInputsFinal, - MembershipWitness4, - MembershipWitness8, MembershipWitness16, MembershipWitness20, - MergeRollupInputs, NativeAggregationState, NewContractData, NullifierLeafPreimage, OptionallyRevealedData, Point, PreviousKernelData, - PreviousRollupData, - PrivateCallData, - PrivateCallStackItem, - PrivateCircuitPublicInputs, - PrivateKernelInputsInit, - PrivateKernelInputsInner, - PrivateKernelInputsOrdering, - Proof, - PublicCallData, - PublicCallStackItem, - PublicCircuitPublicInputs, PublicDataRead, PublicDataUpdateRequest, - PublicKernelInputs, - ReadRequestMembershipWitness, - RootRollupInputs, - RootRollupPublicInputs, TxContext, - TxRequest, VerificationKeyData, toBuffer, } from './types.js'; @@ -70,16 +45,6 @@ interface MsgpackPoint { y: Buffer; } -export function toPoint(o: MsgpackPoint): Point { - if (o.x === undefined) { - throw new Error('Expected x in Point deserialization'); - } - if (o.y === undefined) { - throw new Error('Expected y in Point deserialization'); - } - return new Point(Fr.fromBuffer(o.x), Fr.fromBuffer(o.y)); -} - export function fromPoint(o: Point): MsgpackPoint { if (o.x === undefined) { throw new Error('Expected x in Point serialization'); @@ -93,42 +58,6 @@ export function fromPoint(o: Point): MsgpackPoint { }; } -interface MsgpackCompleteAddress { - address: Buffer; - public_key: MsgpackPoint; - partial_address: Buffer; -} - -export function toCompleteAddress(o: MsgpackCompleteAddress): CompleteAddress { - if (o.address === undefined) { - throw new Error('Expected address in CompleteAddress deserialization'); - } - if (o.public_key === undefined) { - throw new Error('Expected public_key in CompleteAddress deserialization'); - } - if (o.partial_address === undefined) { - throw new Error('Expected partial_address in CompleteAddress deserialization'); - } - return new CompleteAddress(Address.fromBuffer(o.address), toPoint(o.public_key), Fr.fromBuffer(o.partial_address)); -} - -export function fromCompleteAddress(o: CompleteAddress): MsgpackCompleteAddress { - if (o.address === undefined) { - throw new Error('Expected address in CompleteAddress serialization'); - } - if (o.publicKey === undefined) { - throw new Error('Expected publicKey in CompleteAddress serialization'); - } - if (o.partialAddress === undefined) { - throw new Error('Expected partialAddress in CompleteAddress serialization'); - } - return { - address: toBuffer(o.address), - public_key: fromPoint(o.publicKey), - partial_address: toBuffer(o.partialAddress), - }; -} - interface MsgpackGlobalVariables { chain_id: Buffer; version: Buffer; @@ -270,23 +199,6 @@ interface MsgpackNewContractData { function_tree_root: Buffer; } -export function toNewContractData(o: MsgpackNewContractData): NewContractData { - if (o.contract_address === undefined) { - throw new Error('Expected contract_address in NewContractData deserialization'); - } - if (o.portal_contract_address === undefined) { - throw new Error('Expected portal_contract_address in NewContractData deserialization'); - } - if (o.function_tree_root === undefined) { - throw new Error('Expected function_tree_root in NewContractData deserialization'); - } - return new NewContractData( - Address.fromBuffer(o.contract_address), - Address.fromBuffer(o.portal_contract_address), - Fr.fromBuffer(o.function_tree_root), - ); -} - export function fromNewContractData(o: NewContractData): MsgpackNewContractData { if (o.contractAddress === undefined) { throw new Error('Expected contractAddress in NewContractData serialization'); @@ -308,13 +220,6 @@ interface MsgpackFunctionSelector { value: number; } -export function toFunctionSelector(o: MsgpackFunctionSelector): FunctionSelector { - if (o.value === undefined) { - throw new Error('Expected value in FunctionSelector deserialization'); - } - return new FunctionSelector(o.value); -} - export function fromFunctionSelector(o: FunctionSelector): MsgpackFunctionSelector { if (o.value === undefined) { throw new Error('Expected value in FunctionSelector serialization'); @@ -331,22 +236,6 @@ interface MsgpackFunctionData { is_constructor: boolean; } -export function toFunctionData(o: MsgpackFunctionData): FunctionData { - if (o.selector === undefined) { - throw new Error('Expected selector in FunctionData deserialization'); - } - if (o.is_internal === undefined) { - throw new Error('Expected is_internal in FunctionData deserialization'); - } - if (o.is_private === undefined) { - throw new Error('Expected is_private in FunctionData deserialization'); - } - if (o.is_constructor === undefined) { - throw new Error('Expected is_constructor in FunctionData deserialization'); - } - return new FunctionData(toFunctionSelector(o.selector), o.is_internal, o.is_private, o.is_constructor); -} - export function fromFunctionData(o: FunctionData): MsgpackFunctionData { if (o.selector === undefined) { throw new Error('Expected selector in FunctionData serialization'); @@ -379,43 +268,6 @@ interface MsgpackOptionallyRevealedData { called_from_public_l2: boolean; } -export function toOptionallyRevealedData(o: MsgpackOptionallyRevealedData): OptionallyRevealedData { - if (o.call_stack_item_hash === undefined) { - throw new Error('Expected call_stack_item_hash in OptionallyRevealedData deserialization'); - } - if (o.function_data === undefined) { - throw new Error('Expected function_data in OptionallyRevealedData deserialization'); - } - if (o.vk_hash === undefined) { - throw new Error('Expected vk_hash in OptionallyRevealedData deserialization'); - } - if (o.portal_contract_address === undefined) { - throw new Error('Expected portal_contract_address in OptionallyRevealedData deserialization'); - } - if (o.pay_fee_from_l1 === undefined) { - throw new Error('Expected pay_fee_from_l1 in OptionallyRevealedData deserialization'); - } - if (o.pay_fee_from_public_l2 === undefined) { - throw new Error('Expected pay_fee_from_public_l2 in OptionallyRevealedData deserialization'); - } - if (o.called_from_l1 === undefined) { - throw new Error('Expected called_from_l1 in OptionallyRevealedData deserialization'); - } - if (o.called_from_public_l2 === undefined) { - throw new Error('Expected called_from_public_l2 in OptionallyRevealedData deserialization'); - } - return new OptionallyRevealedData( - Fr.fromBuffer(o.call_stack_item_hash), - toFunctionData(o.function_data), - Fr.fromBuffer(o.vk_hash), - Address.fromBuffer(o.portal_contract_address), - o.pay_fee_from_l1, - o.pay_fee_from_public_l2, - o.called_from_l1, - o.called_from_public_l2, - ); -} - export function fromOptionallyRevealedData(o: OptionallyRevealedData): MsgpackOptionallyRevealedData { if (o.callStackItemHash === undefined) { throw new Error('Expected callStackItemHash in OptionallyRevealedData serialization'); @@ -459,23 +311,6 @@ interface MsgpackPublicDataUpdateRequest { new_value: Buffer; } -export function toPublicDataUpdateRequest(o: MsgpackPublicDataUpdateRequest): PublicDataUpdateRequest { - if (o.leaf_index === undefined) { - throw new Error('Expected leaf_index in PublicDataUpdateRequest deserialization'); - } - if (o.old_value === undefined) { - throw new Error('Expected old_value in PublicDataUpdateRequest deserialization'); - } - if (o.new_value === undefined) { - throw new Error('Expected new_value in PublicDataUpdateRequest deserialization'); - } - return new PublicDataUpdateRequest( - Fr.fromBuffer(o.leaf_index), - Fr.fromBuffer(o.old_value), - Fr.fromBuffer(o.new_value), - ); -} - export function fromPublicDataUpdateRequest(o: PublicDataUpdateRequest): MsgpackPublicDataUpdateRequest { if (o.leafIndex === undefined) { throw new Error('Expected leafIndex in PublicDataUpdateRequest serialization'); @@ -498,16 +333,6 @@ interface MsgpackPublicDataRead { value: Buffer; } -export function toPublicDataRead(o: MsgpackPublicDataRead): PublicDataRead { - if (o.leaf_index === undefined) { - throw new Error('Expected leaf_index in PublicDataRead deserialization'); - } - if (o.value === undefined) { - throw new Error('Expected value in PublicDataRead deserialization'); - } - return new PublicDataRead(Fr.fromBuffer(o.leaf_index), Fr.fromBuffer(o.value)); -} - export function fromPublicDataRead(o: PublicDataRead): MsgpackPublicDataRead { if (o.leafIndex === undefined) { throw new Error('Expected leafIndex in PublicDataRead serialization'); @@ -541,79 +366,6 @@ interface MsgpackCombinedAccumulatedData { public_data_reads: Tuple; } -export function toCombinedAccumulatedData(o: MsgpackCombinedAccumulatedData): CombinedAccumulatedData { - if (o.aggregation_object === undefined) { - throw new Error('Expected aggregation_object in CombinedAccumulatedData deserialization'); - } - if (o.read_requests === undefined) { - throw new Error('Expected read_requests in CombinedAccumulatedData deserialization'); - } - if (o.pending_read_requests === undefined) { - throw new Error('Expected pending_read_requests in CombinedAccumulatedData deserialization'); - } - if (o.new_commitments === undefined) { - throw new Error('Expected new_commitments in CombinedAccumulatedData deserialization'); - } - if (o.new_nullifiers === undefined) { - throw new Error('Expected new_nullifiers in CombinedAccumulatedData deserialization'); - } - if (o.nullified_commitments === undefined) { - throw new Error('Expected nullified_commitments in CombinedAccumulatedData deserialization'); - } - if (o.private_call_stack === undefined) { - throw new Error('Expected private_call_stack in CombinedAccumulatedData deserialization'); - } - if (o.public_call_stack === undefined) { - throw new Error('Expected public_call_stack in CombinedAccumulatedData deserialization'); - } - if (o.new_l2_to_l1_msgs === undefined) { - throw new Error('Expected new_l2_to_l1_msgs in CombinedAccumulatedData deserialization'); - } - if (o.encrypted_logs_hash === undefined) { - throw new Error('Expected encrypted_logs_hash in CombinedAccumulatedData deserialization'); - } - if (o.unencrypted_logs_hash === undefined) { - throw new Error('Expected unencrypted_logs_hash in CombinedAccumulatedData deserialization'); - } - if (o.encrypted_log_preimages_length === undefined) { - throw new Error('Expected encrypted_log_preimages_length in CombinedAccumulatedData deserialization'); - } - if (o.unencrypted_log_preimages_length === undefined) { - throw new Error('Expected unencrypted_log_preimages_length in CombinedAccumulatedData deserialization'); - } - if (o.new_contracts === undefined) { - throw new Error('Expected new_contracts in CombinedAccumulatedData deserialization'); - } - if (o.optionally_revealed_data === undefined) { - throw new Error('Expected optionally_revealed_data in CombinedAccumulatedData deserialization'); - } - if (o.public_data_update_requests === undefined) { - throw new Error('Expected public_data_update_requests in CombinedAccumulatedData deserialization'); - } - if (o.public_data_reads === undefined) { - throw new Error('Expected public_data_reads in CombinedAccumulatedData deserialization'); - } - return new CombinedAccumulatedData( - toNativeAggregationState(o.aggregation_object), - mapTuple(o.read_requests, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.pending_read_requests, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_commitments, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_nullifiers, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.nullified_commitments, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.private_call_stack, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.public_call_stack, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_l2_to_l1_msgs, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.encrypted_logs_hash, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.unencrypted_logs_hash, (v: Buffer) => Fr.fromBuffer(v)), - Fr.fromBuffer(o.encrypted_log_preimages_length), - Fr.fromBuffer(o.unencrypted_log_preimages_length), - mapTuple(o.new_contracts, (v: MsgpackNewContractData) => toNewContractData(v)), - mapTuple(o.optionally_revealed_data, (v: MsgpackOptionallyRevealedData) => toOptionallyRevealedData(v)), - mapTuple(o.public_data_update_requests, (v: MsgpackPublicDataUpdateRequest) => toPublicDataUpdateRequest(v)), - mapTuple(o.public_data_reads, (v: MsgpackPublicDataRead) => toPublicDataRead(v)), - ); -} - export function fromCombinedAccumulatedData(o: CombinedAccumulatedData): MsgpackCombinedAccumulatedData { if (o.aggregationObject === undefined) { throw new Error('Expected aggregationObject in CombinedAccumulatedData serialization'); @@ -673,8 +425,8 @@ export function fromCombinedAccumulatedData(o: CombinedAccumulatedData): Msgpack new_commitments: mapTuple(o.newCommitments, (v: Fr) => toBuffer(v)), new_nullifiers: mapTuple(o.newNullifiers, (v: Fr) => toBuffer(v)), nullified_commitments: mapTuple(o.nullifiedCommitments, (v: Fr) => toBuffer(v)), - private_call_stack: mapTuple(o.privateCallStack, (v: Fr) => toBuffer(v)), - public_call_stack: mapTuple(o.publicCallStack, (v: Fr) => toBuffer(v)), + private_call_stack: mapTuple(o.privateCallStack, (v: CallRequest) => toBuffer(v.hash)), + public_call_stack: mapTuple(o.publicCallStack, (v: CallRequest) => toBuffer(v.hash)), new_l2_to_l1_msgs: mapTuple(o.newL2ToL1Msgs, (v: Fr) => toBuffer(v)), encrypted_logs_hash: mapTuple(o.encryptedLogsHash, (v: Fr) => toBuffer(v)), unencrypted_logs_hash: mapTuple(o.unencryptedLogsHash, (v: Fr) => toBuffer(v)), @@ -702,43 +454,6 @@ interface MsgpackHistoricBlockData { global_variables_hash: Buffer; } -export function toHistoricBlockData(o: MsgpackHistoricBlockData): HistoricBlockData { - if (o.note_hash_tree_root === undefined) { - throw new Error('Expected note_hash_tree_root in HistoricBlockData deserialization'); - } - if (o.nullifier_tree_root === undefined) { - throw new Error('Expected nullifier_tree_root in HistoricBlockData deserialization'); - } - if (o.contract_tree_root === undefined) { - throw new Error('Expected contract_tree_root in HistoricBlockData deserialization'); - } - if (o.l1_to_l2_messages_tree_root === undefined) { - throw new Error('Expected l1_to_l2_messages_tree_root in HistoricBlockData deserialization'); - } - if (o.blocks_tree_root === undefined) { - throw new Error('Expected blocks_tree_root in HistoricBlockData deserialization'); - } - if (o.private_kernel_vk_tree_root === undefined) { - throw new Error('Expected private_kernel_vk_tree_root in HistoricBlockData deserialization'); - } - if (o.public_data_tree_root === undefined) { - throw new Error('Expected public_data_tree_root in HistoricBlockData deserialization'); - } - if (o.global_variables_hash === undefined) { - throw new Error('Expected global_variables_hash in HistoricBlockData deserialization'); - } - return new HistoricBlockData( - Fr.fromBuffer(o.note_hash_tree_root), - Fr.fromBuffer(o.nullifier_tree_root), - Fr.fromBuffer(o.contract_tree_root), - Fr.fromBuffer(o.l1_to_l2_messages_tree_root), - Fr.fromBuffer(o.blocks_tree_root), - Fr.fromBuffer(o.private_kernel_vk_tree_root), - Fr.fromBuffer(o.public_data_tree_root), - Fr.fromBuffer(o.global_variables_hash), - ); -} - export function fromHistoricBlockData(o: HistoricBlockData): MsgpackHistoricBlockData { if (o.noteHashTreeRoot === undefined) { throw new Error('Expected noteHashTreeRoot in HistoricBlockData serialization'); @@ -784,31 +499,6 @@ interface MsgpackContractDeploymentData { portal_contract_address: Buffer; } -export function toContractDeploymentData(o: MsgpackContractDeploymentData): ContractDeploymentData { - if (o.deployer_public_key === undefined) { - throw new Error('Expected deployer_public_key in ContractDeploymentData deserialization'); - } - if (o.constructor_vk_hash === undefined) { - throw new Error('Expected constructor_vk_hash in ContractDeploymentData deserialization'); - } - if (o.function_tree_root === undefined) { - throw new Error('Expected function_tree_root in ContractDeploymentData deserialization'); - } - if (o.contract_address_salt === undefined) { - throw new Error('Expected contract_address_salt in ContractDeploymentData deserialization'); - } - if (o.portal_contract_address === undefined) { - throw new Error('Expected portal_contract_address in ContractDeploymentData deserialization'); - } - return new ContractDeploymentData( - toPoint(o.deployer_public_key), - Fr.fromBuffer(o.constructor_vk_hash), - Fr.fromBuffer(o.function_tree_root), - Fr.fromBuffer(o.contract_address_salt), - Address.fromBuffer(o.portal_contract_address), - ); -} - export function fromContractDeploymentData(o: ContractDeploymentData): MsgpackContractDeploymentData { if (o.deployerPublicKey === undefined) { throw new Error('Expected deployerPublicKey in ContractDeploymentData serialization'); @@ -843,35 +533,6 @@ interface MsgpackTxContext { version: Buffer; } -export function toTxContext(o: MsgpackTxContext): TxContext { - if (o.is_fee_payment_tx === undefined) { - throw new Error('Expected is_fee_payment_tx in TxContext deserialization'); - } - if (o.is_rebate_payment_tx === undefined) { - throw new Error('Expected is_rebate_payment_tx in TxContext deserialization'); - } - if (o.is_contract_deployment_tx === undefined) { - throw new Error('Expected is_contract_deployment_tx in TxContext deserialization'); - } - if (o.contract_deployment_data === undefined) { - throw new Error('Expected contract_deployment_data in TxContext deserialization'); - } - if (o.chain_id === undefined) { - throw new Error('Expected chain_id in TxContext deserialization'); - } - if (o.version === undefined) { - throw new Error('Expected version in TxContext deserialization'); - } - return new TxContext( - o.is_fee_payment_tx, - o.is_rebate_payment_tx, - o.is_contract_deployment_tx, - toContractDeploymentData(o.contract_deployment_data), - Fr.fromBuffer(o.chain_id), - Fr.fromBuffer(o.version), - ); -} - export function fromTxContext(o: TxContext): MsgpackTxContext { if (o.isFeePaymentTx === undefined) { throw new Error('Expected isFeePaymentTx in TxContext serialization'); @@ -906,16 +567,6 @@ interface MsgpackCombinedConstantData { tx_context: MsgpackTxContext; } -export function toCombinedConstantData(o: MsgpackCombinedConstantData): CombinedConstantData { - if (o.block_data === undefined) { - throw new Error('Expected block_data in CombinedConstantData deserialization'); - } - if (o.tx_context === undefined) { - throw new Error('Expected tx_context in CombinedConstantData deserialization'); - } - return new CombinedConstantData(toHistoricBlockData(o.block_data), toTxContext(o.tx_context)); -} - export function fromCombinedConstantData(o: CombinedConstantData): MsgpackCombinedConstantData { if (o.blockData === undefined) { throw new Error('Expected blockData in CombinedConstantData serialization'); @@ -935,23 +586,6 @@ interface MsgpackKernelCircuitPublicInputs { is_private: boolean; } -export function toKernelCircuitPublicInputs(o: MsgpackKernelCircuitPublicInputs): KernelCircuitPublicInputs { - if (o.end === undefined) { - throw new Error('Expected end in KernelCircuitPublicInputs deserialization'); - } - if (o.constants === undefined) { - throw new Error('Expected constants in KernelCircuitPublicInputs deserialization'); - } - if (o.is_private === undefined) { - throw new Error('Expected is_private in KernelCircuitPublicInputs deserialization'); - } - return new KernelCircuitPublicInputs( - toCombinedAccumulatedData(o.end), - toCombinedConstantData(o.constants), - o.is_private, - ); -} - export function fromKernelCircuitPublicInputs(o: KernelCircuitPublicInputs): MsgpackKernelCircuitPublicInputs { if (o.end === undefined) { throw new Error('Expected end in KernelCircuitPublicInputs serialization'); @@ -978,35 +612,6 @@ interface MsgpackVerificationKeyData { recursive_proof_public_input_indices: number[]; } -export function toVerificationKeyData(o: MsgpackVerificationKeyData): VerificationKeyData { - if (o.circuit_type === undefined) { - throw new Error('Expected circuit_type in VerificationKeyData deserialization'); - } - if (o.circuit_size === undefined) { - throw new Error('Expected circuit_size in VerificationKeyData deserialization'); - } - if (o.num_public_inputs === undefined) { - throw new Error('Expected num_public_inputs in VerificationKeyData deserialization'); - } - if (o.commitments === undefined) { - throw new Error('Expected commitments in VerificationKeyData deserialization'); - } - if (o.contains_recursive_proof === undefined) { - throw new Error('Expected contains_recursive_proof in VerificationKeyData deserialization'); - } - if (o.recursive_proof_public_input_indices === undefined) { - throw new Error('Expected recursive_proof_public_input_indices in VerificationKeyData deserialization'); - } - return new VerificationKeyData( - o.circuit_type, - o.circuit_size, - o.num_public_inputs, - mapValues(o.commitments, (v: MsgpackG1AffineElement) => toG1AffineElement(v)), - o.contains_recursive_proof, - o.recursive_proof_public_input_indices.map((v: number) => v), - ); -} - export function fromVerificationKeyData(o: VerificationKeyData): MsgpackVerificationKeyData { if (o.circuitType === undefined) { throw new Error('Expected circuitType in VerificationKeyData serialization'); @@ -1044,31 +649,6 @@ interface MsgpackPreviousKernelData { vk_path: Tuple; } -export function toPreviousKernelData(o: MsgpackPreviousKernelData): PreviousKernelData { - if (o.public_inputs === undefined) { - throw new Error('Expected public_inputs in PreviousKernelData deserialization'); - } - if (o.proof === undefined) { - throw new Error('Expected proof in PreviousKernelData deserialization'); - } - if (o.vk === undefined) { - throw new Error('Expected vk in PreviousKernelData deserialization'); - } - if (o.vk_index === undefined) { - throw new Error('Expected vk_index in PreviousKernelData deserialization'); - } - if (o.vk_path === undefined) { - throw new Error('Expected vk_path in PreviousKernelData deserialization'); - } - return new PreviousKernelData( - toKernelCircuitPublicInputs(o.public_inputs), - Proof.fromMsgpackBuffer(o.proof), - toVerificationKeyData(o.vk), - o.vk_index, - mapTuple(o.vk_path, (v: Buffer) => Fr.fromBuffer(v)), - ); -} - export function fromPreviousKernelData(o: PreviousKernelData): MsgpackPreviousKernelData { if (o.publicInputs === undefined) { throw new Error('Expected publicInputs in PreviousKernelData serialization'); @@ -1094,1316 +674,115 @@ export function fromPreviousKernelData(o: PreviousKernelData): MsgpackPreviousKe }; } -interface MsgpackTxRequest { - origin: Buffer; - function_data: MsgpackFunctionData; - args_hash: Buffer; - tx_context: MsgpackTxContext; +interface MsgpackMembershipWitness16 { + leaf_index: Buffer; + sibling_path: Tuple; } -export function toTxRequest(o: MsgpackTxRequest): TxRequest { - if (o.origin === undefined) { - throw new Error('Expected origin in TxRequest deserialization'); +export function fromMembershipWitness16(o: MembershipWitness16): MsgpackMembershipWitness16 { + if (o.leafIndex === undefined) { + throw new Error('Expected leafIndex in MembershipWitness16 serialization'); } - if (o.function_data === undefined) { - throw new Error('Expected function_data in TxRequest deserialization'); + if (o.siblingPath === undefined) { + throw new Error('Expected siblingPath in MembershipWitness16 serialization'); } - if (o.args_hash === undefined) { - throw new Error('Expected args_hash in TxRequest deserialization'); + return { + leaf_index: toBuffer(o.leafIndex), + sibling_path: mapTuple(o.siblingPath, (v: Fr) => toBuffer(v)), + }; +} + +interface MsgpackCircuitError { + code: number; + message: string; +} + +export function toCircuitError(o: MsgpackCircuitError): CircuitError { + if (o.code === undefined) { + throw new Error('Expected code in CircuitError deserialization'); } - if (o.tx_context === undefined) { - throw new Error('Expected tx_context in TxRequest deserialization'); + if (o.message === undefined) { + throw new Error('Expected message in CircuitError deserialization'); } - return new TxRequest( - Address.fromBuffer(o.origin), - toFunctionData(o.function_data), - Fr.fromBuffer(o.args_hash), - toTxContext(o.tx_context), - ); + return new CircuitError(o.code, o.message); +} + +interface MsgpackAppendOnlyTreeSnapshot { + root: Buffer; + next_available_leaf_index: number; } -export function fromTxRequest(o: TxRequest): MsgpackTxRequest { - if (o.origin === undefined) { - throw new Error('Expected origin in TxRequest serialization'); +export function toAppendOnlyTreeSnapshot(o: MsgpackAppendOnlyTreeSnapshot): AppendOnlyTreeSnapshot { + if (o.root === undefined) { + throw new Error('Expected root in AppendOnlyTreeSnapshot deserialization'); } - if (o.functionData === undefined) { - throw new Error('Expected functionData in TxRequest serialization'); + if (o.next_available_leaf_index === undefined) { + throw new Error('Expected next_available_leaf_index in AppendOnlyTreeSnapshot deserialization'); } - if (o.argsHash === undefined) { - throw new Error('Expected argsHash in TxRequest serialization'); + return new AppendOnlyTreeSnapshot(Fr.fromBuffer(o.root), o.next_available_leaf_index); +} + +export function fromAppendOnlyTreeSnapshot(o: AppendOnlyTreeSnapshot): MsgpackAppendOnlyTreeSnapshot { + if (o.root === undefined) { + throw new Error('Expected root in AppendOnlyTreeSnapshot serialization'); } - if (o.txContext === undefined) { - throw new Error('Expected txContext in TxRequest serialization'); + if (o.nextAvailableLeafIndex === undefined) { + throw new Error('Expected nextAvailableLeafIndex in AppendOnlyTreeSnapshot serialization'); } return { - origin: toBuffer(o.origin), - function_data: fromFunctionData(o.functionData), - args_hash: toBuffer(o.argsHash), - tx_context: fromTxContext(o.txContext), + root: toBuffer(o.root), + next_available_leaf_index: o.nextAvailableLeafIndex, }; } -interface MsgpackCallContext { - msg_sender: Buffer; - storage_contract_address: Buffer; - portal_contract_address: Buffer; - function_selector: MsgpackFunctionSelector; - is_delegate_call: boolean; - is_static_call: boolean; - is_contract_deployment: boolean; +interface MsgpackNullifierLeafPreimage { + leaf_value: Buffer; + next_value: Buffer; + next_index: number; } -export function toCallContext(o: MsgpackCallContext): CallContext { - if (o.msg_sender === undefined) { - throw new Error('Expected msg_sender in CallContext deserialization'); - } - if (o.storage_contract_address === undefined) { - throw new Error('Expected storage_contract_address in CallContext deserialization'); - } - if (o.portal_contract_address === undefined) { - throw new Error('Expected portal_contract_address in CallContext deserialization'); - } - if (o.function_selector === undefined) { - throw new Error('Expected function_selector in CallContext deserialization'); - } - if (o.is_delegate_call === undefined) { - throw new Error('Expected is_delegate_call in CallContext deserialization'); +export function fromNullifierLeafPreimage(o: NullifierLeafPreimage): MsgpackNullifierLeafPreimage { + if (o.leafValue === undefined) { + throw new Error('Expected leafValue in NullifierLeafPreimage serialization'); } - if (o.is_static_call === undefined) { - throw new Error('Expected is_static_call in CallContext deserialization'); + if (o.nextValue === undefined) { + throw new Error('Expected nextValue in NullifierLeafPreimage serialization'); } - if (o.is_contract_deployment === undefined) { - throw new Error('Expected is_contract_deployment in CallContext deserialization'); + if (o.nextIndex === undefined) { + throw new Error('Expected nextIndex in NullifierLeafPreimage serialization'); } - return new CallContext( - Address.fromBuffer(o.msg_sender), - Address.fromBuffer(o.storage_contract_address), - Fr.fromBuffer(o.portal_contract_address), - toFunctionSelector(o.function_selector), - o.is_delegate_call, - o.is_static_call, - o.is_contract_deployment, - ); + return { + leaf_value: toBuffer(o.leafValue), + next_value: toBuffer(o.nextValue), + next_index: o.nextIndex, + }; } -export function fromCallContext(o: CallContext): MsgpackCallContext { - if (o.msgSender === undefined) { - throw new Error('Expected msgSender in CallContext serialization'); - } - if (o.storageContractAddress === undefined) { - throw new Error('Expected storageContractAddress in CallContext serialization'); - } - if (o.portalContractAddress === undefined) { - throw new Error('Expected portalContractAddress in CallContext serialization'); - } - if (o.functionSelector === undefined) { - throw new Error('Expected functionSelector in CallContext serialization'); - } - if (o.isDelegateCall === undefined) { - throw new Error('Expected isDelegateCall in CallContext serialization'); - } - if (o.isStaticCall === undefined) { - throw new Error('Expected isStaticCall in CallContext serialization'); +interface MsgpackMembershipWitness20 { + leaf_index: Buffer; + sibling_path: Tuple; +} + +export function fromMembershipWitness20(o: MembershipWitness20): MsgpackMembershipWitness20 { + if (o.leafIndex === undefined) { + throw new Error('Expected leafIndex in MembershipWitness20 serialization'); } - if (o.isContractDeployment === undefined) { - throw new Error('Expected isContractDeployment in CallContext serialization'); + if (o.siblingPath === undefined) { + throw new Error('Expected siblingPath in MembershipWitness20 serialization'); } return { - msg_sender: toBuffer(o.msgSender), - storage_contract_address: toBuffer(o.storageContractAddress), - portal_contract_address: toBuffer(o.portalContractAddress), - function_selector: fromFunctionSelector(o.functionSelector), - is_delegate_call: o.isDelegateCall, - is_static_call: o.isStaticCall, - is_contract_deployment: o.isContractDeployment, + leaf_index: toBuffer(o.leafIndex), + sibling_path: mapTuple(o.siblingPath, (v: Fr) => toBuffer(v)), }; } -interface MsgpackPrivateCircuitPublicInputs { - call_context: MsgpackCallContext; - args_hash: Buffer; - return_values: Tuple; - read_requests: Tuple; - pending_read_requests: Tuple; - new_commitments: Tuple; - new_nullifiers: Tuple; - nullified_commitments: Tuple; - private_call_stack: Tuple; - public_call_stack: Tuple; - new_l2_to_l1_msgs: Tuple; - encrypted_logs_hash: Tuple; - unencrypted_logs_hash: Tuple; - encrypted_log_preimages_length: Buffer; - unencrypted_log_preimages_length: Buffer; - historic_block_data: MsgpackHistoricBlockData; - contract_deployment_data: MsgpackContractDeploymentData; - chain_id: Buffer; - version: Buffer; -} - -export function toPrivateCircuitPublicInputs(o: MsgpackPrivateCircuitPublicInputs): PrivateCircuitPublicInputs { - if (o.call_context === undefined) { - throw new Error('Expected call_context in PrivateCircuitPublicInputs deserialization'); - } - if (o.args_hash === undefined) { - throw new Error('Expected args_hash in PrivateCircuitPublicInputs deserialization'); - } - if (o.return_values === undefined) { - throw new Error('Expected return_values in PrivateCircuitPublicInputs deserialization'); - } - if (o.read_requests === undefined) { - throw new Error('Expected read_requests in PrivateCircuitPublicInputs deserialization'); - } - if (o.pending_read_requests === undefined) { - throw new Error('Expected pending_read_requests in PrivateCircuitPublicInputs deserialization'); - } - if (o.new_commitments === undefined) { - throw new Error('Expected new_commitments in PrivateCircuitPublicInputs deserialization'); - } - if (o.new_nullifiers === undefined) { - throw new Error('Expected new_nullifiers in PrivateCircuitPublicInputs deserialization'); - } - if (o.nullified_commitments === undefined) { - throw new Error('Expected nullified_commitments in PrivateCircuitPublicInputs deserialization'); - } - if (o.private_call_stack === undefined) { - throw new Error('Expected private_call_stack in PrivateCircuitPublicInputs deserialization'); - } - if (o.public_call_stack === undefined) { - throw new Error('Expected public_call_stack in PrivateCircuitPublicInputs deserialization'); - } - if (o.new_l2_to_l1_msgs === undefined) { - throw new Error('Expected new_l2_to_l1_msgs in PrivateCircuitPublicInputs deserialization'); - } - if (o.encrypted_logs_hash === undefined) { - throw new Error('Expected encrypted_logs_hash in PrivateCircuitPublicInputs deserialization'); - } - if (o.unencrypted_logs_hash === undefined) { - throw new Error('Expected unencrypted_logs_hash in PrivateCircuitPublicInputs deserialization'); - } - if (o.encrypted_log_preimages_length === undefined) { - throw new Error('Expected encrypted_log_preimages_length in PrivateCircuitPublicInputs deserialization'); - } - if (o.unencrypted_log_preimages_length === undefined) { - throw new Error('Expected unencrypted_log_preimages_length in PrivateCircuitPublicInputs deserialization'); - } - if (o.historic_block_data === undefined) { - throw new Error('Expected historic_block_data in PrivateCircuitPublicInputs deserialization'); - } - if (o.contract_deployment_data === undefined) { - throw new Error('Expected contract_deployment_data in PrivateCircuitPublicInputs deserialization'); - } - if (o.chain_id === undefined) { - throw new Error('Expected chain_id in PrivateCircuitPublicInputs deserialization'); - } - if (o.version === undefined) { - throw new Error('Expected version in PrivateCircuitPublicInputs deserialization'); - } - return new PrivateCircuitPublicInputs( - toCallContext(o.call_context), - Fr.fromBuffer(o.args_hash), - mapTuple(o.return_values, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.read_requests, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.pending_read_requests, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_commitments, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_nullifiers, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.nullified_commitments, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.private_call_stack, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.public_call_stack, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_l2_to_l1_msgs, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.encrypted_logs_hash, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.unencrypted_logs_hash, (v: Buffer) => Fr.fromBuffer(v)), - Fr.fromBuffer(o.encrypted_log_preimages_length), - Fr.fromBuffer(o.unencrypted_log_preimages_length), - toHistoricBlockData(o.historic_block_data), - toContractDeploymentData(o.contract_deployment_data), - Fr.fromBuffer(o.chain_id), - Fr.fromBuffer(o.version), - ); -} - -export function fromPrivateCircuitPublicInputs(o: PrivateCircuitPublicInputs): MsgpackPrivateCircuitPublicInputs { - if (o.callContext === undefined) { - throw new Error('Expected callContext in PrivateCircuitPublicInputs serialization'); - } - if (o.argsHash === undefined) { - throw new Error('Expected argsHash in PrivateCircuitPublicInputs serialization'); - } - if (o.returnValues === undefined) { - throw new Error('Expected returnValues in PrivateCircuitPublicInputs serialization'); - } - if (o.readRequests === undefined) { - throw new Error('Expected readRequests in PrivateCircuitPublicInputs serialization'); - } - if (o.pendingReadRequests === undefined) { - throw new Error('Expected pendingReadRequests in PrivateCircuitPublicInputs serialization'); - } - if (o.newCommitments === undefined) { - throw new Error('Expected newCommitments in PrivateCircuitPublicInputs serialization'); - } - if (o.newNullifiers === undefined) { - throw new Error('Expected newNullifiers in PrivateCircuitPublicInputs serialization'); - } - if (o.nullifiedCommitments === undefined) { - throw new Error('Expected nullifiedCommitments in PrivateCircuitPublicInputs serialization'); - } - if (o.privateCallStack === undefined) { - throw new Error('Expected privateCallStack in PrivateCircuitPublicInputs serialization'); - } - if (o.publicCallStack === undefined) { - throw new Error('Expected publicCallStack in PrivateCircuitPublicInputs serialization'); - } - if (o.newL2ToL1Msgs === undefined) { - throw new Error('Expected newL2ToL1Msgs in PrivateCircuitPublicInputs serialization'); - } - if (o.encryptedLogsHash === undefined) { - throw new Error('Expected encryptedLogsHash in PrivateCircuitPublicInputs serialization'); - } - if (o.unencryptedLogsHash === undefined) { - throw new Error('Expected unencryptedLogsHash in PrivateCircuitPublicInputs serialization'); - } - if (o.encryptedLogPreimagesLength === undefined) { - throw new Error('Expected encryptedLogPreimagesLength in PrivateCircuitPublicInputs serialization'); - } - if (o.unencryptedLogPreimagesLength === undefined) { - throw new Error('Expected unencryptedLogPreimagesLength in PrivateCircuitPublicInputs serialization'); - } - if (o.historicBlockData === undefined) { - throw new Error('Expected historicBlockData in PrivateCircuitPublicInputs serialization'); - } - if (o.contractDeploymentData === undefined) { - throw new Error('Expected contractDeploymentData in PrivateCircuitPublicInputs serialization'); - } - if (o.chainId === undefined) { - throw new Error('Expected chainId in PrivateCircuitPublicInputs serialization'); - } - if (o.version === undefined) { - throw new Error('Expected version in PrivateCircuitPublicInputs serialization'); - } - return { - call_context: fromCallContext(o.callContext), - args_hash: toBuffer(o.argsHash), - return_values: mapTuple(o.returnValues, (v: Fr) => toBuffer(v)), - read_requests: mapTuple(o.readRequests, (v: Fr) => toBuffer(v)), - pending_read_requests: mapTuple(o.pendingReadRequests, (v: Fr) => toBuffer(v)), - new_commitments: mapTuple(o.newCommitments, (v: Fr) => toBuffer(v)), - new_nullifiers: mapTuple(o.newNullifiers, (v: Fr) => toBuffer(v)), - nullified_commitments: mapTuple(o.nullifiedCommitments, (v: Fr) => toBuffer(v)), - private_call_stack: mapTuple(o.privateCallStack, (v: Fr) => toBuffer(v)), - public_call_stack: mapTuple(o.publicCallStack, (v: Fr) => toBuffer(v)), - new_l2_to_l1_msgs: mapTuple(o.newL2ToL1Msgs, (v: Fr) => toBuffer(v)), - encrypted_logs_hash: mapTuple(o.encryptedLogsHash, (v: Fr) => toBuffer(v)), - unencrypted_logs_hash: mapTuple(o.unencryptedLogsHash, (v: Fr) => toBuffer(v)), - encrypted_log_preimages_length: toBuffer(o.encryptedLogPreimagesLength), - unencrypted_log_preimages_length: toBuffer(o.unencryptedLogPreimagesLength), - historic_block_data: fromHistoricBlockData(o.historicBlockData), - contract_deployment_data: fromContractDeploymentData(o.contractDeploymentData), - chain_id: toBuffer(o.chainId), - version: toBuffer(o.version), - }; -} - -interface MsgpackPrivateCallStackItem { - contract_address: Buffer; - function_data: MsgpackFunctionData; - public_inputs: MsgpackPrivateCircuitPublicInputs; - is_execution_request: boolean; -} - -export function toPrivateCallStackItem(o: MsgpackPrivateCallStackItem): PrivateCallStackItem { - if (o.contract_address === undefined) { - throw new Error('Expected contract_address in PrivateCallStackItem deserialization'); - } - if (o.function_data === undefined) { - throw new Error('Expected function_data in PrivateCallStackItem deserialization'); - } - if (o.public_inputs === undefined) { - throw new Error('Expected public_inputs in PrivateCallStackItem deserialization'); - } - if (o.is_execution_request === undefined) { - throw new Error('Expected is_execution_request in PrivateCallStackItem deserialization'); - } - return new PrivateCallStackItem( - Address.fromBuffer(o.contract_address), - toFunctionData(o.function_data), - toPrivateCircuitPublicInputs(o.public_inputs), - o.is_execution_request, - ); -} - -export function fromPrivateCallStackItem(o: PrivateCallStackItem): MsgpackPrivateCallStackItem { - if (o.contractAddress === undefined) { - throw new Error('Expected contractAddress in PrivateCallStackItem serialization'); - } - if (o.functionData === undefined) { - throw new Error('Expected functionData in PrivateCallStackItem serialization'); - } - if (o.publicInputs === undefined) { - throw new Error('Expected publicInputs in PrivateCallStackItem serialization'); - } - if (o.isExecutionRequest === undefined) { - throw new Error('Expected isExecutionRequest in PrivateCallStackItem serialization'); - } - return { - contract_address: toBuffer(o.contractAddress), - function_data: fromFunctionData(o.functionData), - public_inputs: fromPrivateCircuitPublicInputs(o.publicInputs), - is_execution_request: o.isExecutionRequest, - }; -} - -interface MsgpackMembershipWitness4 { - leaf_index: Buffer; - sibling_path: Tuple; -} - -export function toMembershipWitness4(o: MsgpackMembershipWitness4): MembershipWitness4 { - if (o.leaf_index === undefined) { - throw new Error('Expected leaf_index in MembershipWitness4 deserialization'); - } - if (o.sibling_path === undefined) { - throw new Error('Expected sibling_path in MembershipWitness4 deserialization'); - } - return new MembershipWitness4( - Fr.fromBuffer(o.leaf_index), - mapTuple(o.sibling_path, (v: Buffer) => Fr.fromBuffer(v)), - ); -} - -export function fromMembershipWitness4(o: MembershipWitness4): MsgpackMembershipWitness4 { - if (o.leafIndex === undefined) { - throw new Error('Expected leafIndex in MembershipWitness4 serialization'); - } - if (o.siblingPath === undefined) { - throw new Error('Expected siblingPath in MembershipWitness4 serialization'); - } - return { - leaf_index: toBuffer(o.leafIndex), - sibling_path: mapTuple(o.siblingPath, (v: Fr) => toBuffer(v)), - }; -} - -interface MsgpackMembershipWitness16 { - leaf_index: Buffer; - sibling_path: Tuple; -} - -export function toMembershipWitness16(o: MsgpackMembershipWitness16): MembershipWitness16 { - if (o.leaf_index === undefined) { - throw new Error('Expected leaf_index in MembershipWitness16 deserialization'); - } - if (o.sibling_path === undefined) { - throw new Error('Expected sibling_path in MembershipWitness16 deserialization'); - } - return new MembershipWitness16( - Fr.fromBuffer(o.leaf_index), - mapTuple(o.sibling_path, (v: Buffer) => Fr.fromBuffer(v)), - ); -} - -export function fromMembershipWitness16(o: MembershipWitness16): MsgpackMembershipWitness16 { - if (o.leafIndex === undefined) { - throw new Error('Expected leafIndex in MembershipWitness16 serialization'); - } - if (o.siblingPath === undefined) { - throw new Error('Expected siblingPath in MembershipWitness16 serialization'); - } - return { - leaf_index: toBuffer(o.leafIndex), - sibling_path: mapTuple(o.siblingPath, (v: Fr) => toBuffer(v)), - }; -} - -interface MsgpackReadRequestMembershipWitness { - leaf_index: Buffer; - sibling_path: Tuple; - is_transient: boolean; - hint_to_commitment: Buffer; -} - -export function toReadRequestMembershipWitness(o: MsgpackReadRequestMembershipWitness): ReadRequestMembershipWitness { - if (o.leaf_index === undefined) { - throw new Error('Expected leaf_index in ReadRequestMembershipWitness deserialization'); - } - if (o.sibling_path === undefined) { - throw new Error('Expected sibling_path in ReadRequestMembershipWitness deserialization'); - } - if (o.is_transient === undefined) { - throw new Error('Expected is_transient in ReadRequestMembershipWitness deserialization'); - } - if (o.hint_to_commitment === undefined) { - throw new Error('Expected hint_to_commitment in ReadRequestMembershipWitness deserialization'); - } - return new ReadRequestMembershipWitness( - Fr.fromBuffer(o.leaf_index), - mapTuple(o.sibling_path, (v: Buffer) => Fr.fromBuffer(v)), - o.is_transient, - Fr.fromBuffer(o.hint_to_commitment), - ); -} - -export function fromReadRequestMembershipWitness(o: ReadRequestMembershipWitness): MsgpackReadRequestMembershipWitness { - if (o.leafIndex === undefined) { - throw new Error('Expected leafIndex in ReadRequestMembershipWitness serialization'); - } - if (o.siblingPath === undefined) { - throw new Error('Expected siblingPath in ReadRequestMembershipWitness serialization'); - } - if (o.isTransient === undefined) { - throw new Error('Expected isTransient in ReadRequestMembershipWitness serialization'); - } - if (o.hintToCommitment === undefined) { - throw new Error('Expected hintToCommitment in ReadRequestMembershipWitness serialization'); - } - return { - leaf_index: toBuffer(o.leafIndex), - sibling_path: mapTuple(o.siblingPath, (v: Fr) => toBuffer(v)), - is_transient: o.isTransient, - hint_to_commitment: toBuffer(o.hintToCommitment), - }; -} - -interface MsgpackPrivateCallData { - call_stack_item: MsgpackPrivateCallStackItem; - private_call_stack_preimages: Tuple; - proof: Buffer; - vk: MsgpackVerificationKeyData; - function_leaf_membership_witness: MsgpackMembershipWitness4; - contract_leaf_membership_witness: MsgpackMembershipWitness16; - read_request_membership_witnesses: Tuple; - portal_contract_address: Buffer; - acir_hash: Buffer; -} - -export function toPrivateCallData(o: MsgpackPrivateCallData): PrivateCallData { - if (o.call_stack_item === undefined) { - throw new Error('Expected call_stack_item in PrivateCallData deserialization'); - } - if (o.private_call_stack_preimages === undefined) { - throw new Error('Expected private_call_stack_preimages in PrivateCallData deserialization'); - } - if (o.proof === undefined) { - throw new Error('Expected proof in PrivateCallData deserialization'); - } - if (o.vk === undefined) { - throw new Error('Expected vk in PrivateCallData deserialization'); - } - if (o.function_leaf_membership_witness === undefined) { - throw new Error('Expected function_leaf_membership_witness in PrivateCallData deserialization'); - } - if (o.contract_leaf_membership_witness === undefined) { - throw new Error('Expected contract_leaf_membership_witness in PrivateCallData deserialization'); - } - if (o.read_request_membership_witnesses === undefined) { - throw new Error('Expected read_request_membership_witnesses in PrivateCallData deserialization'); - } - if (o.portal_contract_address === undefined) { - throw new Error('Expected portal_contract_address in PrivateCallData deserialization'); - } - if (o.acir_hash === undefined) { - throw new Error('Expected acir_hash in PrivateCallData deserialization'); - } - return new PrivateCallData( - toPrivateCallStackItem(o.call_stack_item), - mapTuple(o.private_call_stack_preimages, (v: MsgpackPrivateCallStackItem) => toPrivateCallStackItem(v)), - Proof.fromMsgpackBuffer(o.proof), - toVerificationKeyData(o.vk), - toMembershipWitness4(o.function_leaf_membership_witness), - toMembershipWitness16(o.contract_leaf_membership_witness), - mapTuple(o.read_request_membership_witnesses, (v: MsgpackReadRequestMembershipWitness) => - toReadRequestMembershipWitness(v), - ), - Fr.fromBuffer(o.portal_contract_address), - Fr.fromBuffer(o.acir_hash), - ); -} - -export function fromPrivateCallData(o: PrivateCallData): MsgpackPrivateCallData { - if (o.callStackItem === undefined) { - throw new Error('Expected callStackItem in PrivateCallData serialization'); - } - if (o.privateCallStackPreimages === undefined) { - throw new Error('Expected privateCallStackPreimages in PrivateCallData serialization'); - } - if (o.proof === undefined) { - throw new Error('Expected proof in PrivateCallData serialization'); - } - if (o.vk === undefined) { - throw new Error('Expected vk in PrivateCallData serialization'); - } - if (o.functionLeafMembershipWitness === undefined) { - throw new Error('Expected functionLeafMembershipWitness in PrivateCallData serialization'); - } - if (o.contractLeafMembershipWitness === undefined) { - throw new Error('Expected contractLeafMembershipWitness in PrivateCallData serialization'); - } - if (o.readRequestMembershipWitnesses === undefined) { - throw new Error('Expected readRequestMembershipWitnesses in PrivateCallData serialization'); - } - if (o.portalContractAddress === undefined) { - throw new Error('Expected portalContractAddress in PrivateCallData serialization'); - } - if (o.acirHash === undefined) { - throw new Error('Expected acirHash in PrivateCallData serialization'); - } - return { - call_stack_item: fromPrivateCallStackItem(o.callStackItem), - private_call_stack_preimages: mapTuple(o.privateCallStackPreimages, (v: PrivateCallStackItem) => - fromPrivateCallStackItem(v), - ), - proof: o.proof.toMsgpackBuffer(), - vk: fromVerificationKeyData(o.vk), - function_leaf_membership_witness: fromMembershipWitness4(o.functionLeafMembershipWitness), - contract_leaf_membership_witness: fromMembershipWitness16(o.contractLeafMembershipWitness), - read_request_membership_witnesses: mapTuple(o.readRequestMembershipWitnesses, (v: ReadRequestMembershipWitness) => - fromReadRequestMembershipWitness(v), - ), - portal_contract_address: toBuffer(o.portalContractAddress), - acir_hash: toBuffer(o.acirHash), - }; -} - -interface MsgpackPrivateKernelInputsInit { - tx_request: MsgpackTxRequest; - private_call: MsgpackPrivateCallData; -} - -export function toPrivateKernelInputsInit(o: MsgpackPrivateKernelInputsInit): PrivateKernelInputsInit { - if (o.tx_request === undefined) { - throw new Error('Expected tx_request in PrivateKernelInputsInit deserialization'); - } - if (o.private_call === undefined) { - throw new Error('Expected private_call in PrivateKernelInputsInit deserialization'); - } - return new PrivateKernelInputsInit(toTxRequest(o.tx_request), toPrivateCallData(o.private_call)); -} - -export function fromPrivateKernelInputsInit(o: PrivateKernelInputsInit): MsgpackPrivateKernelInputsInit { - if (o.txRequest === undefined) { - throw new Error('Expected txRequest in PrivateKernelInputsInit serialization'); - } - if (o.privateCall === undefined) { - throw new Error('Expected privateCall in PrivateKernelInputsInit serialization'); - } - return { - tx_request: fromTxRequest(o.txRequest), - private_call: fromPrivateCallData(o.privateCall), - }; -} - -interface MsgpackCircuitError { - code: number; - message: string; -} - -export function toCircuitError(o: MsgpackCircuitError): CircuitError { - if (o.code === undefined) { - throw new Error('Expected code in CircuitError deserialization'); - } - if (o.message === undefined) { - throw new Error('Expected message in CircuitError deserialization'); - } - return new CircuitError(o.code, o.message); -} - -export function fromCircuitError(o: CircuitError): MsgpackCircuitError { - if (o.code === undefined) { - throw new Error('Expected code in CircuitError serialization'); - } - if (o.message === undefined) { - throw new Error('Expected message in CircuitError serialization'); - } - return { - code: o.code, - message: o.message, - }; -} - -interface MsgpackPrivateKernelInputsInner { - previous_kernel: MsgpackPreviousKernelData; - private_call: MsgpackPrivateCallData; -} - -export function toPrivateKernelInputsInner(o: MsgpackPrivateKernelInputsInner): PrivateKernelInputsInner { - if (o.previous_kernel === undefined) { - throw new Error('Expected previous_kernel in PrivateKernelInputsInner deserialization'); - } - if (o.private_call === undefined) { - throw new Error('Expected private_call in PrivateKernelInputsInner deserialization'); - } - return new PrivateKernelInputsInner(toPreviousKernelData(o.previous_kernel), toPrivateCallData(o.private_call)); -} - -export function fromPrivateKernelInputsInner(o: PrivateKernelInputsInner): MsgpackPrivateKernelInputsInner { - if (o.previousKernel === undefined) { - throw new Error('Expected previousKernel in PrivateKernelInputsInner serialization'); - } - if (o.privateCall === undefined) { - throw new Error('Expected privateCall in PrivateKernelInputsInner serialization'); - } - return { - previous_kernel: fromPreviousKernelData(o.previousKernel), - private_call: fromPrivateCallData(o.privateCall), - }; -} - -interface MsgpackPrivateKernelInputsOrdering { - previous_kernel: MsgpackPreviousKernelData; - read_commitment_hints: Tuple; - nullifier_commitment_hints: Tuple; -} - -export function toPrivateKernelInputsOrdering(o: MsgpackPrivateKernelInputsOrdering): PrivateKernelInputsOrdering { - if (o.previous_kernel === undefined) { - throw new Error('Expected previous_kernel in PrivateKernelInputsOrdering deserialization'); - } - if (o.read_commitment_hints === undefined) { - throw new Error('Expected read_commitment_hints in PrivateKernelInputsOrdering deserialization'); - } - if (o.nullifier_commitment_hints === undefined) { - throw new Error('Expected nullifier_commitment_hints in PrivateKernelInputsOrdering deserialization'); - } - return new PrivateKernelInputsOrdering( - toPreviousKernelData(o.previous_kernel), - mapTuple(o.read_commitment_hints, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.nullifier_commitment_hints, (v: Buffer) => Fr.fromBuffer(v)), - ); -} - -export function fromPrivateKernelInputsOrdering(o: PrivateKernelInputsOrdering): MsgpackPrivateKernelInputsOrdering { - if (o.previousKernel === undefined) { - throw new Error('Expected previousKernel in PrivateKernelInputsOrdering serialization'); - } - if (o.readCommitmentHints === undefined) { - throw new Error('Expected readCommitmentHints in PrivateKernelInputsOrdering serialization'); - } - if (o.nullifierCommitmentHints === undefined) { - throw new Error('Expected nullifierCommitmentHints in PrivateKernelInputsOrdering serialization'); - } - return { - previous_kernel: fromPreviousKernelData(o.previousKernel), - read_commitment_hints: mapTuple(o.readCommitmentHints, (v: Fr) => toBuffer(v)), - nullifier_commitment_hints: mapTuple(o.nullifierCommitmentHints, (v: Fr) => toBuffer(v)), - }; -} - -interface MsgpackFinalAccumulatedData { - aggregation_object: MsgpackNativeAggregationState; - new_commitments: Tuple; - new_nullifiers: Tuple; - nullified_commitments: Tuple; - private_call_stack: Tuple; - public_call_stack: Tuple; - new_l2_to_l1_msgs: Tuple; - encrypted_logs_hash: Tuple; - unencrypted_logs_hash: Tuple; - encrypted_log_preimages_length: Buffer; - unencrypted_log_preimages_length: Buffer; - new_contracts: Tuple; - optionally_revealed_data: Tuple; -} - -export function toFinalAccumulatedData(o: MsgpackFinalAccumulatedData): FinalAccumulatedData { - if (o.aggregation_object === undefined) { - throw new Error('Expected aggregation_object in FinalAccumulatedData deserialization'); - } - if (o.new_commitments === undefined) { - throw new Error('Expected new_commitments in FinalAccumulatedData deserialization'); - } - if (o.new_nullifiers === undefined) { - throw new Error('Expected new_nullifiers in FinalAccumulatedData deserialization'); - } - if (o.nullified_commitments === undefined) { - throw new Error('Expected nullified_commitments in FinalAccumulatedData deserialization'); - } - if (o.private_call_stack === undefined) { - throw new Error('Expected private_call_stack in FinalAccumulatedData deserialization'); - } - if (o.public_call_stack === undefined) { - throw new Error('Expected public_call_stack in FinalAccumulatedData deserialization'); - } - if (o.new_l2_to_l1_msgs === undefined) { - throw new Error('Expected new_l2_to_l1_msgs in FinalAccumulatedData deserialization'); - } - if (o.encrypted_logs_hash === undefined) { - throw new Error('Expected encrypted_logs_hash in FinalAccumulatedData deserialization'); - } - if (o.unencrypted_logs_hash === undefined) { - throw new Error('Expected unencrypted_logs_hash in FinalAccumulatedData deserialization'); - } - if (o.encrypted_log_preimages_length === undefined) { - throw new Error('Expected encrypted_log_preimages_length in FinalAccumulatedData deserialization'); - } - if (o.unencrypted_log_preimages_length === undefined) { - throw new Error('Expected unencrypted_log_preimages_length in FinalAccumulatedData deserialization'); - } - if (o.new_contracts === undefined) { - throw new Error('Expected new_contracts in FinalAccumulatedData deserialization'); - } - if (o.optionally_revealed_data === undefined) { - throw new Error('Expected optionally_revealed_data in FinalAccumulatedData deserialization'); - } - return new FinalAccumulatedData( - toNativeAggregationState(o.aggregation_object), - mapTuple(o.new_commitments, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_nullifiers, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.nullified_commitments, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.private_call_stack, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.public_call_stack, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_l2_to_l1_msgs, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.encrypted_logs_hash, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.unencrypted_logs_hash, (v: Buffer) => Fr.fromBuffer(v)), - Fr.fromBuffer(o.encrypted_log_preimages_length), - Fr.fromBuffer(o.unencrypted_log_preimages_length), - mapTuple(o.new_contracts, (v: MsgpackNewContractData) => toNewContractData(v)), - mapTuple(o.optionally_revealed_data, (v: MsgpackOptionallyRevealedData) => toOptionallyRevealedData(v)), - ); -} - -export function fromFinalAccumulatedData(o: FinalAccumulatedData): MsgpackFinalAccumulatedData { - if (o.aggregationObject === undefined) { - throw new Error('Expected aggregationObject in FinalAccumulatedData serialization'); - } - if (o.newCommitments === undefined) { - throw new Error('Expected newCommitments in FinalAccumulatedData serialization'); - } - if (o.newNullifiers === undefined) { - throw new Error('Expected newNullifiers in FinalAccumulatedData serialization'); - } - if (o.nullifiedCommitments === undefined) { - throw new Error('Expected nullifiedCommitments in FinalAccumulatedData serialization'); - } - if (o.privateCallStack === undefined) { - throw new Error('Expected privateCallStack in FinalAccumulatedData serialization'); - } - if (o.publicCallStack === undefined) { - throw new Error('Expected publicCallStack in FinalAccumulatedData serialization'); - } - if (o.newL2ToL1Msgs === undefined) { - throw new Error('Expected newL2ToL1Msgs in FinalAccumulatedData serialization'); - } - if (o.encryptedLogsHash === undefined) { - throw new Error('Expected encryptedLogsHash in FinalAccumulatedData serialization'); - } - if (o.unencryptedLogsHash === undefined) { - throw new Error('Expected unencryptedLogsHash in FinalAccumulatedData serialization'); - } - if (o.encryptedLogPreimagesLength === undefined) { - throw new Error('Expected encryptedLogPreimagesLength in FinalAccumulatedData serialization'); - } - if (o.unencryptedLogPreimagesLength === undefined) { - throw new Error('Expected unencryptedLogPreimagesLength in FinalAccumulatedData serialization'); - } - if (o.newContracts === undefined) { - throw new Error('Expected newContracts in FinalAccumulatedData serialization'); - } - if (o.optionallyRevealedData === undefined) { - throw new Error('Expected optionallyRevealedData in FinalAccumulatedData serialization'); - } - return { - aggregation_object: fromNativeAggregationState(o.aggregationObject), - new_commitments: mapTuple(o.newCommitments, (v: Fr) => toBuffer(v)), - new_nullifiers: mapTuple(o.newNullifiers, (v: Fr) => toBuffer(v)), - nullified_commitments: mapTuple(o.nullifiedCommitments, (v: Fr) => toBuffer(v)), - private_call_stack: mapTuple(o.privateCallStack, (v: Fr) => toBuffer(v)), - public_call_stack: mapTuple(o.publicCallStack, (v: Fr) => toBuffer(v)), - new_l2_to_l1_msgs: mapTuple(o.newL2ToL1Msgs, (v: Fr) => toBuffer(v)), - encrypted_logs_hash: mapTuple(o.encryptedLogsHash, (v: Fr) => toBuffer(v)), - unencrypted_logs_hash: mapTuple(o.unencryptedLogsHash, (v: Fr) => toBuffer(v)), - encrypted_log_preimages_length: toBuffer(o.encryptedLogPreimagesLength), - unencrypted_log_preimages_length: toBuffer(o.unencryptedLogPreimagesLength), - new_contracts: mapTuple(o.newContracts, (v: NewContractData) => fromNewContractData(v)), - optionally_revealed_data: mapTuple(o.optionallyRevealedData, (v: OptionallyRevealedData) => - fromOptionallyRevealedData(v), - ), - }; -} - -interface MsgpackKernelCircuitPublicInputsFinal { - end: MsgpackFinalAccumulatedData; - constants: MsgpackCombinedConstantData; - is_private: boolean; -} - -export function toKernelCircuitPublicInputsFinal( - o: MsgpackKernelCircuitPublicInputsFinal, -): KernelCircuitPublicInputsFinal { - if (o.end === undefined) { - throw new Error('Expected end in KernelCircuitPublicInputsFinal deserialization'); - } - if (o.constants === undefined) { - throw new Error('Expected constants in KernelCircuitPublicInputsFinal deserialization'); - } - if (o.is_private === undefined) { - throw new Error('Expected is_private in KernelCircuitPublicInputsFinal deserialization'); - } - return new KernelCircuitPublicInputsFinal( - toFinalAccumulatedData(o.end), - toCombinedConstantData(o.constants), - o.is_private, - ); -} - -export function fromKernelCircuitPublicInputsFinal( - o: KernelCircuitPublicInputsFinal, -): MsgpackKernelCircuitPublicInputsFinal { - if (o.end === undefined) { - throw new Error('Expected end in KernelCircuitPublicInputsFinal serialization'); - } - if (o.constants === undefined) { - throw new Error('Expected constants in KernelCircuitPublicInputsFinal serialization'); - } - if (o.isPrivate === undefined) { - throw new Error('Expected isPrivate in KernelCircuitPublicInputsFinal serialization'); - } - return { - end: fromFinalAccumulatedData(o.end), - constants: fromCombinedConstantData(o.constants), - is_private: o.isPrivate, - }; -} - -interface MsgpackContractStorageUpdateRequest { - storage_slot: Buffer; - old_value: Buffer; - new_value: Buffer; -} - -export function toContractStorageUpdateRequest(o: MsgpackContractStorageUpdateRequest): ContractStorageUpdateRequest { - if (o.storage_slot === undefined) { - throw new Error('Expected storage_slot in ContractStorageUpdateRequest deserialization'); - } - if (o.old_value === undefined) { - throw new Error('Expected old_value in ContractStorageUpdateRequest deserialization'); - } - if (o.new_value === undefined) { - throw new Error('Expected new_value in ContractStorageUpdateRequest deserialization'); - } - return new ContractStorageUpdateRequest( - Fr.fromBuffer(o.storage_slot), - Fr.fromBuffer(o.old_value), - Fr.fromBuffer(o.new_value), - ); -} - -export function fromContractStorageUpdateRequest(o: ContractStorageUpdateRequest): MsgpackContractStorageUpdateRequest { - if (o.storageSlot === undefined) { - throw new Error('Expected storageSlot in ContractStorageUpdateRequest serialization'); - } - if (o.oldValue === undefined) { - throw new Error('Expected oldValue in ContractStorageUpdateRequest serialization'); - } - if (o.newValue === undefined) { - throw new Error('Expected newValue in ContractStorageUpdateRequest serialization'); - } - return { - storage_slot: toBuffer(o.storageSlot), - old_value: toBuffer(o.oldValue), - new_value: toBuffer(o.newValue), - }; -} - -interface MsgpackContractStorageRead { - storage_slot: Buffer; - current_value: Buffer; -} - -export function toContractStorageRead(o: MsgpackContractStorageRead): ContractStorageRead { - if (o.storage_slot === undefined) { - throw new Error('Expected storage_slot in ContractStorageRead deserialization'); - } - if (o.current_value === undefined) { - throw new Error('Expected current_value in ContractStorageRead deserialization'); - } - return new ContractStorageRead(Fr.fromBuffer(o.storage_slot), Fr.fromBuffer(o.current_value)); -} - -export function fromContractStorageRead(o: ContractStorageRead): MsgpackContractStorageRead { - if (o.storageSlot === undefined) { - throw new Error('Expected storageSlot in ContractStorageRead serialization'); - } - if (o.currentValue === undefined) { - throw new Error('Expected currentValue in ContractStorageRead serialization'); - } - return { - storage_slot: toBuffer(o.storageSlot), - current_value: toBuffer(o.currentValue), - }; -} - -interface MsgpackPublicCircuitPublicInputs { - call_context: MsgpackCallContext; - args_hash: Buffer; - return_values: Tuple; - contract_storage_update_requests: Tuple; - contract_storage_reads: Tuple; - public_call_stack: Tuple; - new_commitments: Tuple; - new_nullifiers: Tuple; - new_l2_to_l1_msgs: Tuple; - unencrypted_logs_hash: Tuple; - unencrypted_log_preimages_length: Buffer; - historic_block_data: MsgpackHistoricBlockData; - prover_address: Buffer; -} - -export function toPublicCircuitPublicInputs(o: MsgpackPublicCircuitPublicInputs): PublicCircuitPublicInputs { - if (o.call_context === undefined) { - throw new Error('Expected call_context in PublicCircuitPublicInputs deserialization'); - } - if (o.args_hash === undefined) { - throw new Error('Expected args_hash in PublicCircuitPublicInputs deserialization'); - } - if (o.return_values === undefined) { - throw new Error('Expected return_values in PublicCircuitPublicInputs deserialization'); - } - if (o.contract_storage_update_requests === undefined) { - throw new Error('Expected contract_storage_update_requests in PublicCircuitPublicInputs deserialization'); - } - if (o.contract_storage_reads === undefined) { - throw new Error('Expected contract_storage_reads in PublicCircuitPublicInputs deserialization'); - } - if (o.public_call_stack === undefined) { - throw new Error('Expected public_call_stack in PublicCircuitPublicInputs deserialization'); - } - if (o.new_commitments === undefined) { - throw new Error('Expected new_commitments in PublicCircuitPublicInputs deserialization'); - } - if (o.new_nullifiers === undefined) { - throw new Error('Expected new_nullifiers in PublicCircuitPublicInputs deserialization'); - } - if (o.new_l2_to_l1_msgs === undefined) { - throw new Error('Expected new_l2_to_l1_msgs in PublicCircuitPublicInputs deserialization'); - } - if (o.unencrypted_logs_hash === undefined) { - throw new Error('Expected unencrypted_logs_hash in PublicCircuitPublicInputs deserialization'); - } - if (o.unencrypted_log_preimages_length === undefined) { - throw new Error('Expected unencrypted_log_preimages_length in PublicCircuitPublicInputs deserialization'); - } - if (o.historic_block_data === undefined) { - throw new Error('Expected historic_block_data in PublicCircuitPublicInputs deserialization'); - } - if (o.prover_address === undefined) { - throw new Error('Expected prover_address in PublicCircuitPublicInputs deserialization'); - } - return new PublicCircuitPublicInputs( - toCallContext(o.call_context), - Fr.fromBuffer(o.args_hash), - mapTuple(o.return_values, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.contract_storage_update_requests, (v: MsgpackContractStorageUpdateRequest) => - toContractStorageUpdateRequest(v), - ), - mapTuple(o.contract_storage_reads, (v: MsgpackContractStorageRead) => toContractStorageRead(v)), - mapTuple(o.public_call_stack, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_commitments, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_nullifiers, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_l2_to_l1_msgs, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.unencrypted_logs_hash, (v: Buffer) => Fr.fromBuffer(v)), - Fr.fromBuffer(o.unencrypted_log_preimages_length), - toHistoricBlockData(o.historic_block_data), - Address.fromBuffer(o.prover_address), - ); -} - -export function fromPublicCircuitPublicInputs(o: PublicCircuitPublicInputs): MsgpackPublicCircuitPublicInputs { - if (o.callContext === undefined) { - throw new Error('Expected callContext in PublicCircuitPublicInputs serialization'); - } - if (o.argsHash === undefined) { - throw new Error('Expected argsHash in PublicCircuitPublicInputs serialization'); - } - if (o.returnValues === undefined) { - throw new Error('Expected returnValues in PublicCircuitPublicInputs serialization'); - } - if (o.contractStorageUpdateRequests === undefined) { - throw new Error('Expected contractStorageUpdateRequests in PublicCircuitPublicInputs serialization'); - } - if (o.contractStorageReads === undefined) { - throw new Error('Expected contractStorageReads in PublicCircuitPublicInputs serialization'); - } - if (o.publicCallStack === undefined) { - throw new Error('Expected publicCallStack in PublicCircuitPublicInputs serialization'); - } - if (o.newCommitments === undefined) { - throw new Error('Expected newCommitments in PublicCircuitPublicInputs serialization'); - } - if (o.newNullifiers === undefined) { - throw new Error('Expected newNullifiers in PublicCircuitPublicInputs serialization'); - } - if (o.newL2ToL1Msgs === undefined) { - throw new Error('Expected newL2ToL1Msgs in PublicCircuitPublicInputs serialization'); - } - if (o.unencryptedLogsHash === undefined) { - throw new Error('Expected unencryptedLogsHash in PublicCircuitPublicInputs serialization'); - } - if (o.unencryptedLogPreimagesLength === undefined) { - throw new Error('Expected unencryptedLogPreimagesLength in PublicCircuitPublicInputs serialization'); - } - if (o.historicBlockData === undefined) { - throw new Error('Expected historicBlockData in PublicCircuitPublicInputs serialization'); - } - if (o.proverAddress === undefined) { - throw new Error('Expected proverAddress in PublicCircuitPublicInputs serialization'); - } - return { - call_context: fromCallContext(o.callContext), - args_hash: toBuffer(o.argsHash), - return_values: mapTuple(o.returnValues, (v: Fr) => toBuffer(v)), - contract_storage_update_requests: mapTuple(o.contractStorageUpdateRequests, (v: ContractStorageUpdateRequest) => - fromContractStorageUpdateRequest(v), - ), - contract_storage_reads: mapTuple(o.contractStorageReads, (v: ContractStorageRead) => fromContractStorageRead(v)), - public_call_stack: mapTuple(o.publicCallStack, (v: Fr) => toBuffer(v)), - new_commitments: mapTuple(o.newCommitments, (v: Fr) => toBuffer(v)), - new_nullifiers: mapTuple(o.newNullifiers, (v: Fr) => toBuffer(v)), - new_l2_to_l1_msgs: mapTuple(o.newL2ToL1Msgs, (v: Fr) => toBuffer(v)), - unencrypted_logs_hash: mapTuple(o.unencryptedLogsHash, (v: Fr) => toBuffer(v)), - unencrypted_log_preimages_length: toBuffer(o.unencryptedLogPreimagesLength), - historic_block_data: fromHistoricBlockData(o.historicBlockData), - prover_address: toBuffer(o.proverAddress), - }; -} - -interface MsgpackPublicCallStackItem { - contract_address: Buffer; - function_data: MsgpackFunctionData; - public_inputs: MsgpackPublicCircuitPublicInputs; - is_execution_request: boolean; -} - -export function toPublicCallStackItem(o: MsgpackPublicCallStackItem): PublicCallStackItem { - if (o.contract_address === undefined) { - throw new Error('Expected contract_address in PublicCallStackItem deserialization'); - } - if (o.function_data === undefined) { - throw new Error('Expected function_data in PublicCallStackItem deserialization'); - } - if (o.public_inputs === undefined) { - throw new Error('Expected public_inputs in PublicCallStackItem deserialization'); - } - if (o.is_execution_request === undefined) { - throw new Error('Expected is_execution_request in PublicCallStackItem deserialization'); - } - return new PublicCallStackItem( - Address.fromBuffer(o.contract_address), - toFunctionData(o.function_data), - toPublicCircuitPublicInputs(o.public_inputs), - o.is_execution_request, - ); -} - -export function fromPublicCallStackItem(o: PublicCallStackItem): MsgpackPublicCallStackItem { - if (o.contractAddress === undefined) { - throw new Error('Expected contractAddress in PublicCallStackItem serialization'); - } - if (o.functionData === undefined) { - throw new Error('Expected functionData in PublicCallStackItem serialization'); - } - if (o.publicInputs === undefined) { - throw new Error('Expected publicInputs in PublicCallStackItem serialization'); - } - if (o.isExecutionRequest === undefined) { - throw new Error('Expected isExecutionRequest in PublicCallStackItem serialization'); - } - return { - contract_address: toBuffer(o.contractAddress), - function_data: fromFunctionData(o.functionData), - public_inputs: fromPublicCircuitPublicInputs(o.publicInputs), - is_execution_request: o.isExecutionRequest, - }; -} - -interface MsgpackPublicCallData { - call_stack_item: MsgpackPublicCallStackItem; - public_call_stack_preimages: Tuple; - proof: Buffer; - portal_contract_address: Buffer; - bytecode_hash: Buffer; -} - -export function toPublicCallData(o: MsgpackPublicCallData): PublicCallData { - if (o.call_stack_item === undefined) { - throw new Error('Expected call_stack_item in PublicCallData deserialization'); - } - if (o.public_call_stack_preimages === undefined) { - throw new Error('Expected public_call_stack_preimages in PublicCallData deserialization'); - } - if (o.proof === undefined) { - throw new Error('Expected proof in PublicCallData deserialization'); - } - if (o.portal_contract_address === undefined) { - throw new Error('Expected portal_contract_address in PublicCallData deserialization'); - } - if (o.bytecode_hash === undefined) { - throw new Error('Expected bytecode_hash in PublicCallData deserialization'); - } - return new PublicCallData( - toPublicCallStackItem(o.call_stack_item), - mapTuple(o.public_call_stack_preimages, (v: MsgpackPublicCallStackItem) => toPublicCallStackItem(v)), - Proof.fromMsgpackBuffer(o.proof), - Fr.fromBuffer(o.portal_contract_address), - Fr.fromBuffer(o.bytecode_hash), - ); -} - -export function fromPublicCallData(o: PublicCallData): MsgpackPublicCallData { - if (o.callStackItem === undefined) { - throw new Error('Expected callStackItem in PublicCallData serialization'); - } - if (o.publicCallStackPreimages === undefined) { - throw new Error('Expected publicCallStackPreimages in PublicCallData serialization'); - } - if (o.proof === undefined) { - throw new Error('Expected proof in PublicCallData serialization'); - } - if (o.portalContractAddress === undefined) { - throw new Error('Expected portalContractAddress in PublicCallData serialization'); - } - if (o.bytecodeHash === undefined) { - throw new Error('Expected bytecodeHash in PublicCallData serialization'); - } - return { - call_stack_item: fromPublicCallStackItem(o.callStackItem), - public_call_stack_preimages: mapTuple(o.publicCallStackPreimages, (v: PublicCallStackItem) => - fromPublicCallStackItem(v), - ), - proof: o.proof.toMsgpackBuffer(), - portal_contract_address: toBuffer(o.portalContractAddress), - bytecode_hash: toBuffer(o.bytecodeHash), - }; -} - -interface MsgpackPublicKernelInputs { - previous_kernel: MsgpackPreviousKernelData; - public_call: MsgpackPublicCallData; -} - -export function toPublicKernelInputs(o: MsgpackPublicKernelInputs): PublicKernelInputs { - if (o.previous_kernel === undefined) { - throw new Error('Expected previous_kernel in PublicKernelInputs deserialization'); - } - if (o.public_call === undefined) { - throw new Error('Expected public_call in PublicKernelInputs deserialization'); - } - return new PublicKernelInputs(toPreviousKernelData(o.previous_kernel), toPublicCallData(o.public_call)); -} - -export function fromPublicKernelInputs(o: PublicKernelInputs): MsgpackPublicKernelInputs { - if (o.previousKernel === undefined) { - throw new Error('Expected previousKernel in PublicKernelInputs serialization'); - } - if (o.publicCall === undefined) { - throw new Error('Expected publicCall in PublicKernelInputs serialization'); - } - return { - previous_kernel: fromPreviousKernelData(o.previousKernel), - public_call: fromPublicCallData(o.publicCall), - }; -} - -interface MsgpackAppendOnlyTreeSnapshot { - root: Buffer; - next_available_leaf_index: number; -} - -export function toAppendOnlyTreeSnapshot(o: MsgpackAppendOnlyTreeSnapshot): AppendOnlyTreeSnapshot { - if (o.root === undefined) { - throw new Error('Expected root in AppendOnlyTreeSnapshot deserialization'); - } - if (o.next_available_leaf_index === undefined) { - throw new Error('Expected next_available_leaf_index in AppendOnlyTreeSnapshot deserialization'); - } - return new AppendOnlyTreeSnapshot(Fr.fromBuffer(o.root), o.next_available_leaf_index); -} - -export function fromAppendOnlyTreeSnapshot(o: AppendOnlyTreeSnapshot): MsgpackAppendOnlyTreeSnapshot { - if (o.root === undefined) { - throw new Error('Expected root in AppendOnlyTreeSnapshot serialization'); - } - if (o.nextAvailableLeafIndex === undefined) { - throw new Error('Expected nextAvailableLeafIndex in AppendOnlyTreeSnapshot serialization'); - } - return { - root: toBuffer(o.root), - next_available_leaf_index: o.nextAvailableLeafIndex, - }; -} - -interface MsgpackNullifierLeafPreimage { - leaf_value: Buffer; - next_value: Buffer; - next_index: number; -} - -export function toNullifierLeafPreimage(o: MsgpackNullifierLeafPreimage): NullifierLeafPreimage { - if (o.leaf_value === undefined) { - throw new Error('Expected leaf_value in NullifierLeafPreimage deserialization'); - } - if (o.next_value === undefined) { - throw new Error('Expected next_value in NullifierLeafPreimage deserialization'); - } - if (o.next_index === undefined) { - throw new Error('Expected next_index in NullifierLeafPreimage deserialization'); - } - return new NullifierLeafPreimage(Fr.fromBuffer(o.leaf_value), Fr.fromBuffer(o.next_value), o.next_index); -} - -export function fromNullifierLeafPreimage(o: NullifierLeafPreimage): MsgpackNullifierLeafPreimage { - if (o.leafValue === undefined) { - throw new Error('Expected leafValue in NullifierLeafPreimage serialization'); - } - if (o.nextValue === undefined) { - throw new Error('Expected nextValue in NullifierLeafPreimage serialization'); - } - if (o.nextIndex === undefined) { - throw new Error('Expected nextIndex in NullifierLeafPreimage serialization'); - } - return { - leaf_value: toBuffer(o.leafValue), - next_value: toBuffer(o.nextValue), - next_index: o.nextIndex, - }; -} - -interface MsgpackMembershipWitness20 { - leaf_index: Buffer; - sibling_path: Tuple; -} - -export function toMembershipWitness20(o: MsgpackMembershipWitness20): MembershipWitness20 { - if (o.leaf_index === undefined) { - throw new Error('Expected leaf_index in MembershipWitness20 deserialization'); - } - if (o.sibling_path === undefined) { - throw new Error('Expected sibling_path in MembershipWitness20 deserialization'); - } - return new MembershipWitness20( - Fr.fromBuffer(o.leaf_index), - mapTuple(o.sibling_path, (v: Buffer) => Fr.fromBuffer(v)), - ); -} - -export function fromMembershipWitness20(o: MembershipWitness20): MsgpackMembershipWitness20 { - if (o.leafIndex === undefined) { - throw new Error('Expected leafIndex in MembershipWitness20 serialization'); - } - if (o.siblingPath === undefined) { - throw new Error('Expected siblingPath in MembershipWitness20 serialization'); - } - return { - leaf_index: toBuffer(o.leafIndex), - sibling_path: mapTuple(o.siblingPath, (v: Fr) => toBuffer(v)), - }; -} - -interface MsgpackConstantRollupData { - start_historic_blocks_tree_roots_snapshot: MsgpackAppendOnlyTreeSnapshot; - private_kernel_vk_tree_root: Buffer; - public_kernel_vk_tree_root: Buffer; - base_rollup_vk_hash: Buffer; - merge_rollup_vk_hash: Buffer; - global_variables: MsgpackGlobalVariables; +interface MsgpackConstantRollupData { + start_historic_blocks_tree_roots_snapshot: MsgpackAppendOnlyTreeSnapshot; + private_kernel_vk_tree_root: Buffer; + public_kernel_vk_tree_root: Buffer; + base_rollup_vk_hash: Buffer; + merge_rollup_vk_hash: Buffer; + global_variables: MsgpackGlobalVariables; } export function toConstantRollupData(o: MsgpackConstantRollupData): ConstantRollupData { @@ -2482,77 +861,6 @@ interface MsgpackBaseRollupInputs { constants: MsgpackConstantRollupData; } -export function toBaseRollupInputs(o: MsgpackBaseRollupInputs): BaseRollupInputs { - if (o.kernel_data === undefined) { - throw new Error('Expected kernel_data in BaseRollupInputs deserialization'); - } - if (o.start_note_hash_tree_snapshot === undefined) { - throw new Error('Expected start_note_hash_tree_snapshot in BaseRollupInputs deserialization'); - } - if (o.start_nullifier_tree_snapshot === undefined) { - throw new Error('Expected start_nullifier_tree_snapshot in BaseRollupInputs deserialization'); - } - if (o.start_contract_tree_snapshot === undefined) { - throw new Error('Expected start_contract_tree_snapshot in BaseRollupInputs deserialization'); - } - if (o.start_public_data_tree_root === undefined) { - throw new Error('Expected start_public_data_tree_root in BaseRollupInputs deserialization'); - } - if (o.start_historic_blocks_tree_snapshot === undefined) { - throw new Error('Expected start_historic_blocks_tree_snapshot in BaseRollupInputs deserialization'); - } - if (o.low_nullifier_leaf_preimages === undefined) { - throw new Error('Expected low_nullifier_leaf_preimages in BaseRollupInputs deserialization'); - } - if (o.low_nullifier_membership_witness === undefined) { - throw new Error('Expected low_nullifier_membership_witness in BaseRollupInputs deserialization'); - } - if (o.new_commitments_subtree_sibling_path === undefined) { - throw new Error('Expected new_commitments_subtree_sibling_path in BaseRollupInputs deserialization'); - } - if (o.new_nullifiers_subtree_sibling_path === undefined) { - throw new Error('Expected new_nullifiers_subtree_sibling_path in BaseRollupInputs deserialization'); - } - if (o.new_contracts_subtree_sibling_path === undefined) { - throw new Error('Expected new_contracts_subtree_sibling_path in BaseRollupInputs deserialization'); - } - if (o.new_public_data_update_requests_sibling_paths === undefined) { - throw new Error('Expected new_public_data_update_requests_sibling_paths in BaseRollupInputs deserialization'); - } - if (o.new_public_data_reads_sibling_paths === undefined) { - throw new Error('Expected new_public_data_reads_sibling_paths in BaseRollupInputs deserialization'); - } - if (o.historic_blocks_tree_root_membership_witnesses === undefined) { - throw new Error('Expected historic_blocks_tree_root_membership_witnesses in BaseRollupInputs deserialization'); - } - if (o.constants === undefined) { - throw new Error('Expected constants in BaseRollupInputs deserialization'); - } - return new BaseRollupInputs( - mapTuple(o.kernel_data, (v: MsgpackPreviousKernelData) => toPreviousKernelData(v)), - toAppendOnlyTreeSnapshot(o.start_note_hash_tree_snapshot), - toAppendOnlyTreeSnapshot(o.start_nullifier_tree_snapshot), - toAppendOnlyTreeSnapshot(o.start_contract_tree_snapshot), - Fr.fromBuffer(o.start_public_data_tree_root), - toAppendOnlyTreeSnapshot(o.start_historic_blocks_tree_snapshot), - mapTuple(o.low_nullifier_leaf_preimages, (v: MsgpackNullifierLeafPreimage) => toNullifierLeafPreimage(v)), - mapTuple(o.low_nullifier_membership_witness, (v: MsgpackMembershipWitness20) => toMembershipWitness20(v)), - mapTuple(o.new_commitments_subtree_sibling_path, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_nullifiers_subtree_sibling_path, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_contracts_subtree_sibling_path, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_public_data_update_requests_sibling_paths, (v: Tuple) => - mapTuple(v, (v: Buffer) => Fr.fromBuffer(v)), - ), - mapTuple(o.new_public_data_reads_sibling_paths, (v: Tuple) => - mapTuple(v, (v: Buffer) => Fr.fromBuffer(v)), - ), - mapTuple(o.historic_blocks_tree_root_membership_witnesses, (v: MsgpackMembershipWitness16) => - toMembershipWitness16(v), - ), - toConstantRollupData(o.constants), - ); -} - export function fromBaseRollupInputs(o: BaseRollupInputs): MsgpackBaseRollupInputs { if (o.kernelData === undefined) { throw new Error('Expected kernelData in BaseRollupInputs serialization'); @@ -2705,605 +1013,6 @@ export function toBaseOrMergeRollupPublicInputs( ); } -export function fromBaseOrMergeRollupPublicInputs( - o: BaseOrMergeRollupPublicInputs, -): MsgpackBaseOrMergeRollupPublicInputs { - if (o.rollupType === undefined) { - throw new Error('Expected rollupType in BaseOrMergeRollupPublicInputs serialization'); - } - if (o.rollupSubtreeHeight === undefined) { - throw new Error('Expected rollupSubtreeHeight in BaseOrMergeRollupPublicInputs serialization'); - } - if (o.endAggregationObject === undefined) { - throw new Error('Expected endAggregationObject in BaseOrMergeRollupPublicInputs serialization'); - } - if (o.constants === undefined) { - throw new Error('Expected constants in BaseOrMergeRollupPublicInputs serialization'); - } - if (o.startNoteHashTreeSnapshot === undefined) { - throw new Error('Expected startNoteHashTreeSnapshot in BaseOrMergeRollupPublicInputs serialization'); - } - if (o.endNoteHashTreeSnapshot === undefined) { - throw new Error('Expected endNoteHashTreeSnapshot in BaseOrMergeRollupPublicInputs serialization'); - } - if (o.startNullifierTreeSnapshot === undefined) { - throw new Error('Expected startNullifierTreeSnapshot in BaseOrMergeRollupPublicInputs serialization'); - } - if (o.endNullifierTreeSnapshot === undefined) { - throw new Error('Expected endNullifierTreeSnapshot in BaseOrMergeRollupPublicInputs serialization'); - } - if (o.startContractTreeSnapshot === undefined) { - throw new Error('Expected startContractTreeSnapshot in BaseOrMergeRollupPublicInputs serialization'); - } - if (o.endContractTreeSnapshot === undefined) { - throw new Error('Expected endContractTreeSnapshot in BaseOrMergeRollupPublicInputs serialization'); - } - if (o.startPublicDataTreeRoot === undefined) { - throw new Error('Expected startPublicDataTreeRoot in BaseOrMergeRollupPublicInputs serialization'); - } - if (o.endPublicDataTreeRoot === undefined) { - throw new Error('Expected endPublicDataTreeRoot in BaseOrMergeRollupPublicInputs serialization'); - } - if (o.calldataHash === undefined) { - throw new Error('Expected calldataHash in BaseOrMergeRollupPublicInputs serialization'); - } - return { - rollup_type: o.rollupType, - rollup_subtree_height: toBuffer(o.rollupSubtreeHeight), - end_aggregation_object: fromNativeAggregationState(o.endAggregationObject), - constants: fromConstantRollupData(o.constants), - start_note_hash_tree_snapshot: fromAppendOnlyTreeSnapshot(o.startNoteHashTreeSnapshot), - end_note_hash_tree_snapshot: fromAppendOnlyTreeSnapshot(o.endNoteHashTreeSnapshot), - start_nullifier_tree_snapshot: fromAppendOnlyTreeSnapshot(o.startNullifierTreeSnapshot), - end_nullifier_tree_snapshot: fromAppendOnlyTreeSnapshot(o.endNullifierTreeSnapshot), - start_contract_tree_snapshot: fromAppendOnlyTreeSnapshot(o.startContractTreeSnapshot), - end_contract_tree_snapshot: fromAppendOnlyTreeSnapshot(o.endContractTreeSnapshot), - start_public_data_tree_root: toBuffer(o.startPublicDataTreeRoot), - end_public_data_tree_root: toBuffer(o.endPublicDataTreeRoot), - calldata_hash: mapTuple(o.calldataHash, (v: Fr) => toBuffer(v)), - }; -} - -interface MsgpackMembershipWitness8 { - leaf_index: Buffer; - sibling_path: Tuple; -} - -export function toMembershipWitness8(o: MsgpackMembershipWitness8): MembershipWitness8 { - if (o.leaf_index === undefined) { - throw new Error('Expected leaf_index in MembershipWitness8 deserialization'); - } - if (o.sibling_path === undefined) { - throw new Error('Expected sibling_path in MembershipWitness8 deserialization'); - } - return new MembershipWitness8( - Fr.fromBuffer(o.leaf_index), - mapTuple(o.sibling_path, (v: Buffer) => Fr.fromBuffer(v)), - ); -} - -export function fromMembershipWitness8(o: MembershipWitness8): MsgpackMembershipWitness8 { - if (o.leafIndex === undefined) { - throw new Error('Expected leafIndex in MembershipWitness8 serialization'); - } - if (o.siblingPath === undefined) { - throw new Error('Expected siblingPath in MembershipWitness8 serialization'); - } - return { - leaf_index: toBuffer(o.leafIndex), - sibling_path: mapTuple(o.siblingPath, (v: Fr) => toBuffer(v)), - }; -} - -interface MsgpackPreviousRollupData { - base_or_merge_rollup_public_inputs: MsgpackBaseOrMergeRollupPublicInputs; - proof: Buffer; - vk: MsgpackVerificationKeyData; - vk_index: number; - vk_sibling_path: MsgpackMembershipWitness8; -} - -export function toPreviousRollupData(o: MsgpackPreviousRollupData): PreviousRollupData { - if (o.base_or_merge_rollup_public_inputs === undefined) { - throw new Error('Expected base_or_merge_rollup_public_inputs in PreviousRollupData deserialization'); - } - if (o.proof === undefined) { - throw new Error('Expected proof in PreviousRollupData deserialization'); - } - if (o.vk === undefined) { - throw new Error('Expected vk in PreviousRollupData deserialization'); - } - if (o.vk_index === undefined) { - throw new Error('Expected vk_index in PreviousRollupData deserialization'); - } - if (o.vk_sibling_path === undefined) { - throw new Error('Expected vk_sibling_path in PreviousRollupData deserialization'); - } - return new PreviousRollupData( - toBaseOrMergeRollupPublicInputs(o.base_or_merge_rollup_public_inputs), - Proof.fromMsgpackBuffer(o.proof), - toVerificationKeyData(o.vk), - o.vk_index, - toMembershipWitness8(o.vk_sibling_path), - ); -} - -export function fromPreviousRollupData(o: PreviousRollupData): MsgpackPreviousRollupData { - if (o.baseOrMergeRollupPublicInputs === undefined) { - throw new Error('Expected baseOrMergeRollupPublicInputs in PreviousRollupData serialization'); - } - if (o.proof === undefined) { - throw new Error('Expected proof in PreviousRollupData serialization'); - } - if (o.vk === undefined) { - throw new Error('Expected vk in PreviousRollupData serialization'); - } - if (o.vkIndex === undefined) { - throw new Error('Expected vkIndex in PreviousRollupData serialization'); - } - if (o.vkSiblingPath === undefined) { - throw new Error('Expected vkSiblingPath in PreviousRollupData serialization'); - } - return { - base_or_merge_rollup_public_inputs: fromBaseOrMergeRollupPublicInputs(o.baseOrMergeRollupPublicInputs), - proof: o.proof.toMsgpackBuffer(), - vk: fromVerificationKeyData(o.vk), - vk_index: o.vkIndex, - vk_sibling_path: fromMembershipWitness8(o.vkSiblingPath), - }; -} - -interface MsgpackMergeRollupInputs { - previous_rollup_data: Tuple; -} - -export function toMergeRollupInputs(o: MsgpackMergeRollupInputs): MergeRollupInputs { - if (o.previous_rollup_data === undefined) { - throw new Error('Expected previous_rollup_data in MergeRollupInputs deserialization'); - } - return new MergeRollupInputs( - mapTuple(o.previous_rollup_data, (v: MsgpackPreviousRollupData) => toPreviousRollupData(v)), - ); -} - -export function fromMergeRollupInputs(o: MergeRollupInputs): MsgpackMergeRollupInputs { - if (o.previousRollupData === undefined) { - throw new Error('Expected previousRollupData in MergeRollupInputs serialization'); - } - return { - previous_rollup_data: mapTuple(o.previousRollupData, (v: PreviousRollupData) => fromPreviousRollupData(v)), - }; -} - -interface MsgpackRootRollupInputs { - previous_rollup_data: Tuple; - new_l1_to_l2_messages: Tuple; - new_l1_to_l2_messages_tree_root_sibling_path: Tuple; - start_l1_to_l2_messages_tree_snapshot: MsgpackAppendOnlyTreeSnapshot; - start_historic_blocks_tree_snapshot: MsgpackAppendOnlyTreeSnapshot; - new_historic_blocks_tree_sibling_path: Tuple; -} - -export function toRootRollupInputs(o: MsgpackRootRollupInputs): RootRollupInputs { - if (o.previous_rollup_data === undefined) { - throw new Error('Expected previous_rollup_data in RootRollupInputs deserialization'); - } - if (o.new_l1_to_l2_messages === undefined) { - throw new Error('Expected new_l1_to_l2_messages in RootRollupInputs deserialization'); - } - if (o.new_l1_to_l2_messages_tree_root_sibling_path === undefined) { - throw new Error('Expected new_l1_to_l2_messages_tree_root_sibling_path in RootRollupInputs deserialization'); - } - if (o.start_l1_to_l2_messages_tree_snapshot === undefined) { - throw new Error('Expected start_l1_to_l2_messages_tree_snapshot in RootRollupInputs deserialization'); - } - if (o.start_historic_blocks_tree_snapshot === undefined) { - throw new Error('Expected start_historic_blocks_tree_snapshot in RootRollupInputs deserialization'); - } - if (o.new_historic_blocks_tree_sibling_path === undefined) { - throw new Error('Expected new_historic_blocks_tree_sibling_path in RootRollupInputs deserialization'); - } - return new RootRollupInputs( - mapTuple(o.previous_rollup_data, (v: MsgpackPreviousRollupData) => toPreviousRollupData(v)), - mapTuple(o.new_l1_to_l2_messages, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.new_l1_to_l2_messages_tree_root_sibling_path, (v: Buffer) => Fr.fromBuffer(v)), - toAppendOnlyTreeSnapshot(o.start_l1_to_l2_messages_tree_snapshot), - toAppendOnlyTreeSnapshot(o.start_historic_blocks_tree_snapshot), - mapTuple(o.new_historic_blocks_tree_sibling_path, (v: Buffer) => Fr.fromBuffer(v)), - ); -} - -export function fromRootRollupInputs(o: RootRollupInputs): MsgpackRootRollupInputs { - if (o.previousRollupData === undefined) { - throw new Error('Expected previousRollupData in RootRollupInputs serialization'); - } - if (o.newL1ToL2Messages === undefined) { - throw new Error('Expected newL1ToL2Messages in RootRollupInputs serialization'); - } - if (o.newL1ToL2MessagesTreeRootSiblingPath === undefined) { - throw new Error('Expected newL1ToL2MessagesTreeRootSiblingPath in RootRollupInputs serialization'); - } - if (o.startL1ToL2MessagesTreeSnapshot === undefined) { - throw new Error('Expected startL1ToL2MessagesTreeSnapshot in RootRollupInputs serialization'); - } - if (o.startHistoricBlocksTreeSnapshot === undefined) { - throw new Error('Expected startHistoricBlocksTreeSnapshot in RootRollupInputs serialization'); - } - if (o.newHistoricBlocksTreeSiblingPath === undefined) { - throw new Error('Expected newHistoricBlocksTreeSiblingPath in RootRollupInputs serialization'); - } - return { - previous_rollup_data: mapTuple(o.previousRollupData, (v: PreviousRollupData) => fromPreviousRollupData(v)), - new_l1_to_l2_messages: mapTuple(o.newL1ToL2Messages, (v: Fr) => toBuffer(v)), - new_l1_to_l2_messages_tree_root_sibling_path: mapTuple(o.newL1ToL2MessagesTreeRootSiblingPath, (v: Fr) => - toBuffer(v), - ), - start_l1_to_l2_messages_tree_snapshot: fromAppendOnlyTreeSnapshot(o.startL1ToL2MessagesTreeSnapshot), - start_historic_blocks_tree_snapshot: fromAppendOnlyTreeSnapshot(o.startHistoricBlocksTreeSnapshot), - new_historic_blocks_tree_sibling_path: mapTuple(o.newHistoricBlocksTreeSiblingPath, (v: Fr) => toBuffer(v)), - }; -} - -interface MsgpackRootRollupPublicInputs { - end_aggregation_object: MsgpackNativeAggregationState; - global_variables: MsgpackGlobalVariables; - start_note_hash_tree_snapshot: MsgpackAppendOnlyTreeSnapshot; - end_note_hash_tree_snapshot: MsgpackAppendOnlyTreeSnapshot; - start_nullifier_tree_snapshot: MsgpackAppendOnlyTreeSnapshot; - end_nullifier_tree_snapshot: MsgpackAppendOnlyTreeSnapshot; - start_contract_tree_snapshot: MsgpackAppendOnlyTreeSnapshot; - end_contract_tree_snapshot: MsgpackAppendOnlyTreeSnapshot; - start_public_data_tree_root: Buffer; - end_public_data_tree_root: Buffer; - start_tree_of_historic_note_hash_tree_roots_snapshot: MsgpackAppendOnlyTreeSnapshot; - end_tree_of_historic_note_hash_tree_roots_snapshot: MsgpackAppendOnlyTreeSnapshot; - start_tree_of_historic_contract_tree_roots_snapshot: MsgpackAppendOnlyTreeSnapshot; - end_tree_of_historic_contract_tree_roots_snapshot: MsgpackAppendOnlyTreeSnapshot; - start_l1_to_l2_messages_tree_snapshot: MsgpackAppendOnlyTreeSnapshot; - end_l1_to_l2_messages_tree_snapshot: MsgpackAppendOnlyTreeSnapshot; - start_tree_of_historic_l1_to_l2_messages_tree_roots_snapshot: MsgpackAppendOnlyTreeSnapshot; - end_tree_of_historic_l1_to_l2_messages_tree_roots_snapshot: MsgpackAppendOnlyTreeSnapshot; - start_historic_blocks_tree_snapshot: MsgpackAppendOnlyTreeSnapshot; - end_historic_blocks_tree_snapshot: MsgpackAppendOnlyTreeSnapshot; - calldata_hash: Tuple; - l1_to_l2_messages_hash: Tuple; -} - -export function toRootRollupPublicInputs(o: MsgpackRootRollupPublicInputs): RootRollupPublicInputs { - if (o.end_aggregation_object === undefined) { - throw new Error('Expected end_aggregation_object in RootRollupPublicInputs deserialization'); - } - if (o.global_variables === undefined) { - throw new Error('Expected global_variables in RootRollupPublicInputs deserialization'); - } - if (o.start_note_hash_tree_snapshot === undefined) { - throw new Error('Expected start_note_hash_tree_snapshot in RootRollupPublicInputs deserialization'); - } - if (o.end_note_hash_tree_snapshot === undefined) { - throw new Error('Expected end_note_hash_tree_snapshot in RootRollupPublicInputs deserialization'); - } - if (o.start_nullifier_tree_snapshot === undefined) { - throw new Error('Expected start_nullifier_tree_snapshot in RootRollupPublicInputs deserialization'); - } - if (o.end_nullifier_tree_snapshot === undefined) { - throw new Error('Expected end_nullifier_tree_snapshot in RootRollupPublicInputs deserialization'); - } - if (o.start_contract_tree_snapshot === undefined) { - throw new Error('Expected start_contract_tree_snapshot in RootRollupPublicInputs deserialization'); - } - if (o.end_contract_tree_snapshot === undefined) { - throw new Error('Expected end_contract_tree_snapshot in RootRollupPublicInputs deserialization'); - } - if (o.start_public_data_tree_root === undefined) { - throw new Error('Expected start_public_data_tree_root in RootRollupPublicInputs deserialization'); - } - if (o.end_public_data_tree_root === undefined) { - throw new Error('Expected end_public_data_tree_root in RootRollupPublicInputs deserialization'); - } - if (o.start_tree_of_historic_note_hash_tree_roots_snapshot === undefined) { - throw new Error( - 'Expected start_tree_of_historic_note_hash_tree_roots_snapshot in RootRollupPublicInputs deserialization', - ); - } - if (o.end_tree_of_historic_note_hash_tree_roots_snapshot === undefined) { - throw new Error( - 'Expected end_tree_of_historic_note_hash_tree_roots_snapshot in RootRollupPublicInputs deserialization', - ); - } - if (o.start_tree_of_historic_contract_tree_roots_snapshot === undefined) { - throw new Error( - 'Expected start_tree_of_historic_contract_tree_roots_snapshot in RootRollupPublicInputs deserialization', - ); - } - if (o.end_tree_of_historic_contract_tree_roots_snapshot === undefined) { - throw new Error( - 'Expected end_tree_of_historic_contract_tree_roots_snapshot in RootRollupPublicInputs deserialization', - ); - } - if (o.start_l1_to_l2_messages_tree_snapshot === undefined) { - throw new Error('Expected start_l1_to_l2_messages_tree_snapshot in RootRollupPublicInputs deserialization'); - } - if (o.end_l1_to_l2_messages_tree_snapshot === undefined) { - throw new Error('Expected end_l1_to_l2_messages_tree_snapshot in RootRollupPublicInputs deserialization'); - } - if (o.start_tree_of_historic_l1_to_l2_messages_tree_roots_snapshot === undefined) { - throw new Error( - 'Expected start_tree_of_historic_l1_to_l2_messages_tree_roots_snapshot in RootRollupPublicInputs deserialization', - ); - } - if (o.end_tree_of_historic_l1_to_l2_messages_tree_roots_snapshot === undefined) { - throw new Error( - 'Expected end_tree_of_historic_l1_to_l2_messages_tree_roots_snapshot in RootRollupPublicInputs deserialization', - ); - } - if (o.start_historic_blocks_tree_snapshot === undefined) { - throw new Error('Expected start_historic_blocks_tree_snapshot in RootRollupPublicInputs deserialization'); - } - if (o.end_historic_blocks_tree_snapshot === undefined) { - throw new Error('Expected end_historic_blocks_tree_snapshot in RootRollupPublicInputs deserialization'); - } - if (o.calldata_hash === undefined) { - throw new Error('Expected calldata_hash in RootRollupPublicInputs deserialization'); - } - if (o.l1_to_l2_messages_hash === undefined) { - throw new Error('Expected l1_to_l2_messages_hash in RootRollupPublicInputs deserialization'); - } - return new RootRollupPublicInputs( - toNativeAggregationState(o.end_aggregation_object), - toGlobalVariables(o.global_variables), - toAppendOnlyTreeSnapshot(o.start_note_hash_tree_snapshot), - toAppendOnlyTreeSnapshot(o.end_note_hash_tree_snapshot), - toAppendOnlyTreeSnapshot(o.start_nullifier_tree_snapshot), - toAppendOnlyTreeSnapshot(o.end_nullifier_tree_snapshot), - toAppendOnlyTreeSnapshot(o.start_contract_tree_snapshot), - toAppendOnlyTreeSnapshot(o.end_contract_tree_snapshot), - Fr.fromBuffer(o.start_public_data_tree_root), - Fr.fromBuffer(o.end_public_data_tree_root), - toAppendOnlyTreeSnapshot(o.start_tree_of_historic_note_hash_tree_roots_snapshot), - toAppendOnlyTreeSnapshot(o.end_tree_of_historic_note_hash_tree_roots_snapshot), - toAppendOnlyTreeSnapshot(o.start_tree_of_historic_contract_tree_roots_snapshot), - toAppendOnlyTreeSnapshot(o.end_tree_of_historic_contract_tree_roots_snapshot), - toAppendOnlyTreeSnapshot(o.start_l1_to_l2_messages_tree_snapshot), - toAppendOnlyTreeSnapshot(o.end_l1_to_l2_messages_tree_snapshot), - toAppendOnlyTreeSnapshot(o.start_tree_of_historic_l1_to_l2_messages_tree_roots_snapshot), - toAppendOnlyTreeSnapshot(o.end_tree_of_historic_l1_to_l2_messages_tree_roots_snapshot), - toAppendOnlyTreeSnapshot(o.start_historic_blocks_tree_snapshot), - toAppendOnlyTreeSnapshot(o.end_historic_blocks_tree_snapshot), - mapTuple(o.calldata_hash, (v: Buffer) => Fr.fromBuffer(v)), - mapTuple(o.l1_to_l2_messages_hash, (v: Buffer) => Fr.fromBuffer(v)), - ); -} - -export function fromRootRollupPublicInputs(o: RootRollupPublicInputs): MsgpackRootRollupPublicInputs { - if (o.endAggregationObject === undefined) { - throw new Error('Expected endAggregationObject in RootRollupPublicInputs serialization'); - } - if (o.globalVariables === undefined) { - throw new Error('Expected globalVariables in RootRollupPublicInputs serialization'); - } - if (o.startNoteHashTreeSnapshot === undefined) { - throw new Error('Expected startNoteHashTreeSnapshot in RootRollupPublicInputs serialization'); - } - if (o.endNoteHashTreeSnapshot === undefined) { - throw new Error('Expected endNoteHashTreeSnapshot in RootRollupPublicInputs serialization'); - } - if (o.startNullifierTreeSnapshot === undefined) { - throw new Error('Expected startNullifierTreeSnapshot in RootRollupPublicInputs serialization'); - } - if (o.endNullifierTreeSnapshot === undefined) { - throw new Error('Expected endNullifierTreeSnapshot in RootRollupPublicInputs serialization'); - } - if (o.startContractTreeSnapshot === undefined) { - throw new Error('Expected startContractTreeSnapshot in RootRollupPublicInputs serialization'); - } - if (o.endContractTreeSnapshot === undefined) { - throw new Error('Expected endContractTreeSnapshot in RootRollupPublicInputs serialization'); - } - if (o.startPublicDataTreeRoot === undefined) { - throw new Error('Expected startPublicDataTreeRoot in RootRollupPublicInputs serialization'); - } - if (o.endPublicDataTreeRoot === undefined) { - throw new Error('Expected endPublicDataTreeRoot in RootRollupPublicInputs serialization'); - } - if (o.startTreeOfHistoricNoteHashTreeRootsSnapshot === undefined) { - throw new Error('Expected startTreeOfHistoricNoteHashTreeRootsSnapshot in RootRollupPublicInputs serialization'); - } - if (o.endTreeOfHistoricNoteHashTreeRootsSnapshot === undefined) { - throw new Error('Expected endTreeOfHistoricNoteHashTreeRootsSnapshot in RootRollupPublicInputs serialization'); - } - if (o.startTreeOfHistoricContractTreeRootsSnapshot === undefined) { - throw new Error('Expected startTreeOfHistoricContractTreeRootsSnapshot in RootRollupPublicInputs serialization'); - } - if (o.endTreeOfHistoricContractTreeRootsSnapshot === undefined) { - throw new Error('Expected endTreeOfHistoricContractTreeRootsSnapshot in RootRollupPublicInputs serialization'); - } - if (o.startL1ToL2MessagesTreeSnapshot === undefined) { - throw new Error('Expected startL1ToL2MessagesTreeSnapshot in RootRollupPublicInputs serialization'); - } - if (o.endL1ToL2MessagesTreeSnapshot === undefined) { - throw new Error('Expected endL1ToL2MessagesTreeSnapshot in RootRollupPublicInputs serialization'); - } - if (o.startTreeOfHistoricL1ToL2MessagesTreeRootsSnapshot === undefined) { - throw new Error( - 'Expected startTreeOfHistoricL1ToL2MessagesTreeRootsSnapshot in RootRollupPublicInputs serialization', - ); - } - if (o.endTreeOfHistoricL1ToL2MessagesTreeRootsSnapshot === undefined) { - throw new Error( - 'Expected endTreeOfHistoricL1ToL2MessagesTreeRootsSnapshot in RootRollupPublicInputs serialization', - ); - } - if (o.startHistoricBlocksTreeSnapshot === undefined) { - throw new Error('Expected startHistoricBlocksTreeSnapshot in RootRollupPublicInputs serialization'); - } - if (o.endHistoricBlocksTreeSnapshot === undefined) { - throw new Error('Expected endHistoricBlocksTreeSnapshot in RootRollupPublicInputs serialization'); - } - if (o.calldataHash === undefined) { - throw new Error('Expected calldataHash in RootRollupPublicInputs serialization'); - } - if (o.l1ToL2MessagesHash === undefined) { - throw new Error('Expected l1ToL2MessagesHash in RootRollupPublicInputs serialization'); - } - return { - end_aggregation_object: fromNativeAggregationState(o.endAggregationObject), - global_variables: fromGlobalVariables(o.globalVariables), - start_note_hash_tree_snapshot: fromAppendOnlyTreeSnapshot(o.startNoteHashTreeSnapshot), - end_note_hash_tree_snapshot: fromAppendOnlyTreeSnapshot(o.endNoteHashTreeSnapshot), - start_nullifier_tree_snapshot: fromAppendOnlyTreeSnapshot(o.startNullifierTreeSnapshot), - end_nullifier_tree_snapshot: fromAppendOnlyTreeSnapshot(o.endNullifierTreeSnapshot), - start_contract_tree_snapshot: fromAppendOnlyTreeSnapshot(o.startContractTreeSnapshot), - end_contract_tree_snapshot: fromAppendOnlyTreeSnapshot(o.endContractTreeSnapshot), - start_public_data_tree_root: toBuffer(o.startPublicDataTreeRoot), - end_public_data_tree_root: toBuffer(o.endPublicDataTreeRoot), - start_tree_of_historic_note_hash_tree_roots_snapshot: fromAppendOnlyTreeSnapshot( - o.startTreeOfHistoricNoteHashTreeRootsSnapshot, - ), - end_tree_of_historic_note_hash_tree_roots_snapshot: fromAppendOnlyTreeSnapshot( - o.endTreeOfHistoricNoteHashTreeRootsSnapshot, - ), - start_tree_of_historic_contract_tree_roots_snapshot: fromAppendOnlyTreeSnapshot( - o.startTreeOfHistoricContractTreeRootsSnapshot, - ), - end_tree_of_historic_contract_tree_roots_snapshot: fromAppendOnlyTreeSnapshot( - o.endTreeOfHistoricContractTreeRootsSnapshot, - ), - start_l1_to_l2_messages_tree_snapshot: fromAppendOnlyTreeSnapshot(o.startL1ToL2MessagesTreeSnapshot), - end_l1_to_l2_messages_tree_snapshot: fromAppendOnlyTreeSnapshot(o.endL1ToL2MessagesTreeSnapshot), - start_tree_of_historic_l1_to_l2_messages_tree_roots_snapshot: fromAppendOnlyTreeSnapshot( - o.startTreeOfHistoricL1ToL2MessagesTreeRootsSnapshot, - ), - end_tree_of_historic_l1_to_l2_messages_tree_roots_snapshot: fromAppendOnlyTreeSnapshot( - o.endTreeOfHistoricL1ToL2MessagesTreeRootsSnapshot, - ), - start_historic_blocks_tree_snapshot: fromAppendOnlyTreeSnapshot(o.startHistoricBlocksTreeSnapshot), - end_historic_blocks_tree_snapshot: fromAppendOnlyTreeSnapshot(o.endHistoricBlocksTreeSnapshot), - calldata_hash: mapTuple(o.calldataHash, (v: Fr) => toBuffer(v)), - l1_to_l2_messages_hash: mapTuple(o.l1ToL2MessagesHash, (v: Fr) => toBuffer(v)), - }; -} - -export function abisComputeCompleteAddress( - wasm: IWasmModule, - arg0: Point, - arg1: Fr, - arg2: Fr, - arg3: Fr, -): CompleteAddress { - return toCompleteAddress( - callCbind(wasm, 'abis__compute_complete_address', [ - fromPoint(arg0), - toBuffer(arg1), - toBuffer(arg2), - toBuffer(arg3), - ]), - ); -} -export function abisComputeCommitmentNonce(wasm: IWasmModule, arg0: Fr, arg1: Fr): Fr { - return Fr.fromBuffer(callCbind(wasm, 'abis__compute_commitment_nonce', [toBuffer(arg0), toBuffer(arg1)])); -} -export function abisComputeUniqueCommitment(wasm: IWasmModule, arg0: Fr, arg1: Fr): Fr { - return Fr.fromBuffer(callCbind(wasm, 'abis__compute_unique_commitment', [toBuffer(arg0), toBuffer(arg1)])); -} -export function abisSiloCommitment(wasm: IWasmModule, arg0: Address, arg1: Fr): Fr { - return Fr.fromBuffer(callCbind(wasm, 'abis__silo_commitment', [toBuffer(arg0), toBuffer(arg1)])); -} -export function abisSiloNullifier(wasm: IWasmModule, arg0: Address, arg1: Fr): Fr { - return Fr.fromBuffer(callCbind(wasm, 'abis__silo_nullifier', [toBuffer(arg0), toBuffer(arg1)])); -} -export function abisComputeBlockHash( - wasm: IWasmModule, - arg0: Fr, - arg1: Fr, - arg2: Fr, - arg3: Fr, - arg4: Fr, - arg5: Fr, -): Fr { - return Fr.fromBuffer( - callCbind(wasm, 'abis__compute_block_hash', [ - toBuffer(arg0), - toBuffer(arg1), - toBuffer(arg2), - toBuffer(arg3), - toBuffer(arg4), - toBuffer(arg5), - ]), - ); -} -export function abisComputeBlockHashWithGlobals( - wasm: IWasmModule, - arg0: GlobalVariables, - arg1: Fr, - arg2: Fr, - arg3: Fr, - arg4: Fr, - arg5: Fr, -): Fr { - return Fr.fromBuffer( - callCbind(wasm, 'abis__compute_block_hash_with_globals', [ - fromGlobalVariables(arg0), - toBuffer(arg1), - toBuffer(arg2), - toBuffer(arg3), - toBuffer(arg4), - toBuffer(arg5), - ]), - ); -} -export function abisComputeGlobalsHash(wasm: IWasmModule, arg0: GlobalVariables): Fr { - return Fr.fromBuffer(callCbind(wasm, 'abis__compute_globals_hash', [fromGlobalVariables(arg0)])); -} -export function abisComputePublicDataTreeValue(wasm: IWasmModule, arg0: Fr): Fr { - return Fr.fromBuffer(callCbind(wasm, 'abis__compute_public_data_tree_value', [toBuffer(arg0)])); -} -export function abisComputePublicDataTreeIndex(wasm: IWasmModule, arg0: Address, arg1: Fr): Fr { - return Fr.fromBuffer(callCbind(wasm, 'abis__compute_public_data_tree_index', [toBuffer(arg0), toBuffer(arg1)])); -} -export function privateKernelDummyPreviousKernel(wasm: IWasmModule): PreviousKernelData { - return toPreviousKernelData(callCbind(wasm, 'private_kernel__dummy_previous_kernel', [])); -} -export function privateKernelSimInit( - wasm: IWasmModule, - arg0: PrivateKernelInputsInit, -): CircuitError | KernelCircuitPublicInputs { - return ((v: [number, MsgpackCircuitError | MsgpackKernelCircuitPublicInputs]) => - v[0] == 0 - ? toCircuitError(v[1] as MsgpackCircuitError) - : toKernelCircuitPublicInputs(v[1] as MsgpackKernelCircuitPublicInputs))( - callCbind(wasm, 'private_kernel__sim_init', [fromPrivateKernelInputsInit(arg0)]), - ); -} -export function privateKernelSimInner( - wasm: IWasmModule, - arg0: PrivateKernelInputsInner, -): CircuitError | KernelCircuitPublicInputs { - return ((v: [number, MsgpackCircuitError | MsgpackKernelCircuitPublicInputs]) => - v[0] == 0 - ? toCircuitError(v[1] as MsgpackCircuitError) - : toKernelCircuitPublicInputs(v[1] as MsgpackKernelCircuitPublicInputs))( - callCbind(wasm, 'private_kernel__sim_inner', [fromPrivateKernelInputsInner(arg0)]), - ); -} -export function privateKernelSimOrdering( - wasm: IWasmModule, - arg0: PrivateKernelInputsOrdering, -): CircuitError | KernelCircuitPublicInputsFinal { - return ((v: [number, MsgpackCircuitError | MsgpackKernelCircuitPublicInputsFinal]) => - v[0] == 0 - ? toCircuitError(v[1] as MsgpackCircuitError) - : toKernelCircuitPublicInputsFinal(v[1] as MsgpackKernelCircuitPublicInputsFinal))( - callCbind(wasm, 'private_kernel__sim_ordering', [fromPrivateKernelInputsOrdering(arg0)]), - ); -} -export function publicKernelSim(wasm: IWasmModule, arg0: PublicKernelInputs): CircuitError | KernelCircuitPublicInputs { - return ((v: [number, MsgpackCircuitError | MsgpackKernelCircuitPublicInputs]) => - v[0] == 0 - ? toCircuitError(v[1] as MsgpackCircuitError) - : toKernelCircuitPublicInputs(v[1] as MsgpackKernelCircuitPublicInputs))( - callCbind(wasm, 'public_kernel__sim', [fromPublicKernelInputs(arg0)]), - ); -} export function baseRollupSim(wasm: IWasmModule, arg0: BaseRollupInputs): CircuitError | BaseOrMergeRollupPublicInputs { return ((v: [number, MsgpackCircuitError | MsgpackBaseOrMergeRollupPublicInputs]) => v[0] == 0 @@ -3312,22 +1021,3 @@ export function baseRollupSim(wasm: IWasmModule, arg0: BaseRollupInputs): Circui callCbind(wasm, 'base_rollup__sim', [fromBaseRollupInputs(arg0)]), ); } -export function mergeRollupSim( - wasm: IWasmModule, - arg0: MergeRollupInputs, -): CircuitError | BaseOrMergeRollupPublicInputs { - return ((v: [number, MsgpackCircuitError | MsgpackBaseOrMergeRollupPublicInputs]) => - v[0] == 0 - ? toCircuitError(v[1] as MsgpackCircuitError) - : toBaseOrMergeRollupPublicInputs(v[1] as MsgpackBaseOrMergeRollupPublicInputs))( - callCbind(wasm, 'merge_rollup__sim', [fromMergeRollupInputs(arg0)]), - ); -} -export function rootRollupSim(wasm: IWasmModule, arg0: RootRollupInputs): CircuitError | RootRollupPublicInputs { - return ((v: [number, MsgpackCircuitError | MsgpackRootRollupPublicInputs]) => - v[0] == 0 - ? toCircuitError(v[1] as MsgpackCircuitError) - : toRootRollupPublicInputs(v[1] as MsgpackRootRollupPublicInputs))( - callCbind(wasm, 'root_rollup__sim', [fromRootRollupInputs(arg0)]), - ); -} diff --git a/yarn-project/circuits.js/src/cbind/compiler.ts b/yarn-project/circuits.js/src/cbind/compiler.ts index 46dff5bec47..5b9262f7abb 100644 --- a/yarn-project/circuits.js/src/cbind/compiler.ts +++ b/yarn-project/circuits.js/src/cbind/compiler.ts @@ -69,12 +69,12 @@ export interface TypeInfo { */ isTuple?: boolean; /** - * Indicates if the schama represents an array. + * Indicates if the schema represents an array. * If so, stores the array's subtype elements. */ arraySubtype?: TypeInfo; /** - * Indicates if the schama represents a variant. + * Indicates if the schema represents a variant. * If so, stores the variant's subtype elements. */ variantSubtypes?: TypeInfo[]; @@ -351,7 +351,9 @@ export class CbindCompiler { // Raw object, used as return value of fromType() generated functions. let result = `interface Msgpack${name} {\n`; for (const [key, value] of Object.entries(type)) { - if (key === '__typename') continue; + if (key === '__typename') { + continue; + } result += ` ${key}: ${this.getMsgpackTypename(value)};\n`; } result += '}'; @@ -370,7 +372,9 @@ export class CbindCompiler { const checkerSyntax = () => { const statements: string[] = []; for (const [key] of Object.entries(type)) { - if (key === '__typename') continue; + if (key === '__typename') { + continue; + } statements.push( ` if (o.${key} === undefined) { throw new Error("Expected ${key} in ${typename} deserialization"); }`, ); @@ -382,7 +386,9 @@ export class CbindCompiler { const constructorBodySyntax = () => { const statements: string[] = []; for (const [key, value] of Object.entries(type)) { - if (key === '__typename') continue; + if (key === '__typename') { + continue; + } statements.push(` ${msgpackConverterExpr(this.getTypeInfo(value), `o.${key}`)},`); } return statements.join('\n'); @@ -411,7 +417,9 @@ return ${callSyntax.call(this)}; const checkerSyntax = () => { const statements: string[] = []; for (const [key] of Object.entries(type)) { - if (key === '__typename') continue; + if (key === '__typename') { + continue; + } statements.push( ` if (o.${camelCase(key)} === undefined) { throw new Error("Expected ${camelCase( key, @@ -423,7 +431,9 @@ return ${callSyntax.call(this)}; const bodySyntax = () => { const statements: string[] = []; for (const [key, value] of Object.entries(type)) { - if (key === '__typename') continue; + if (key === '__typename') { + continue; + } statements.push(` ${key}: ${classConverterExpr(this.getTypeInfo(value), `o.${camelCase(key)}`)},`); } return statements.join('\n'); diff --git a/yarn-project/circuits.js/src/contract/contract_deployment_info.ts b/yarn-project/circuits.js/src/contract/contract_deployment_info.ts index 5914d6c840b..38a15d1cfcc 100644 --- a/yarn-project/circuits.js/src/contract/contract_deployment_info.ts +++ b/yarn-project/circuits.js/src/contract/contract_deployment_info.ts @@ -6,7 +6,7 @@ import { } from '@aztec/circuits.js/abis'; import { ContractArtifact, FunctionSelector, encodeArguments } from '@aztec/foundation/abi'; -import { CircuitsWasm, DeploymentInfo, Fr, FunctionData, PublicKey } from '../index.js'; +import { DeploymentInfo, Fr, FunctionData, PublicKey } from '../index.js'; import { generateFunctionLeaves, hashVKStr, isConstructor } from './contract_tree/contract_tree.js'; /** @@ -17,12 +17,12 @@ import { generateFunctionLeaves, hashVKStr, isConstructor } from './contract_tre * @param publicKey - The account public key * @returns - The contract deployment info */ -export async function getContractDeploymentInfo( +export function getContractDeploymentInfo( artifact: ContractArtifact, args: any[], contractAddressSalt: Fr, publicKey: PublicKey, -): Promise { +): DeploymentInfo { const constructorArtifact = artifact.functions.find(isConstructor); if (!constructorArtifact) { throw new Error('Cannot find constructor in the artifact.'); @@ -31,15 +31,14 @@ export async function getContractDeploymentInfo( throw new Error('Missing verification key for the constructor.'); } - const wasm = await CircuitsWasm.get(); - const vkHash = hashVKStr(constructorArtifact.verificationKey, wasm); + const vkHash = hashVKStr(constructorArtifact.verificationKey); const constructorVkHash = Fr.fromBuffer(vkHash); const functions = artifact.functions.map(f => ({ ...f, selector: FunctionSelector.fromNameAndParameters(f.name, f.parameters), })); const leaves = generateFunctionLeaves(functions); - const functionTreeRoot = computeFunctionTreeRoot(wasm, leaves); + const functionTreeRoot = computeFunctionTreeRoot(leaves); const functionData = FunctionData.fromAbi(constructorArtifact); const flatArgs = encodeArguments(constructorArtifact, args); const argsHash = computeVarArgsHash(flatArgs); diff --git a/yarn-project/circuits.js/src/contract/contract_tree/contract_tree.ts b/yarn-project/circuits.js/src/contract/contract_tree/contract_tree.ts index b74a372849b..f8da811530e 100644 --- a/yarn-project/circuits.js/src/contract/contract_tree/contract_tree.ts +++ b/yarn-project/circuits.js/src/contract/contract_tree/contract_tree.ts @@ -1,4 +1,4 @@ -import { CircuitsWasm, ContractFunctionDao, Fr, FunctionData, FunctionLeafPreimage } from '@aztec/circuits.js'; +import { ContractFunctionDao, Fr, FunctionData, FunctionLeafPreimage } from '@aztec/circuits.js'; import { computeFunctionLeaf, hashVK } from '@aztec/circuits.js/abis'; import { FunctionSelector, FunctionType } from '@aztec/foundation/abi'; @@ -11,9 +11,9 @@ import { FunctionSelector, FunctionType } from '@aztec/foundation/abi'; * @param wasm - An instance of CircuitsWasm class used for hashing. * @returns A Promise resolving to a Buffer containing the hash of the verification key. */ -export function hashVKStr(vk: string, wasm: CircuitsWasm) { +export function hashVKStr(vk: string) { // TODO - check consistent encoding - return hashVK(wasm, Buffer.from(vk, 'hex')); + return hashVK(Buffer.from(vk, 'hex')); } /** diff --git a/yarn-project/circuits.js/src/index.ts b/yarn-project/circuits.js/src/index.ts index 14006be9dca..1753b07f299 100644 --- a/yarn-project/circuits.js/src/index.ts +++ b/yarn-project/circuits.js/src/index.ts @@ -1,6 +1,5 @@ export * from './structs/index.js'; export * from './wasm/index.js'; -export * from './kernel/index.js'; export * from './rollup/index.js'; export * from './utils/jsUtils.js'; export * from './contract/index.js'; diff --git a/yarn-project/circuits.js/src/kernel/index.ts b/yarn-project/circuits.js/src/kernel/index.ts deleted file mode 100644 index 9200e9aeab1..00000000000 --- a/yarn-project/circuits.js/src/kernel/index.ts +++ /dev/null @@ -1,2 +0,0 @@ -export * from './private_kernel.js'; -export * from './public_kernel.js'; diff --git a/yarn-project/circuits.js/src/kernel/private_kernel.test.ts b/yarn-project/circuits.js/src/kernel/private_kernel.test.ts deleted file mode 100644 index 3ac47d213ae..00000000000 --- a/yarn-project/circuits.js/src/kernel/private_kernel.test.ts +++ /dev/null @@ -1,32 +0,0 @@ -import times from 'lodash.times'; - -import { computeFunctionTreeRoot } from '../abis/abis.js'; -import { privateKernelDummyPreviousKernel } from '../cbind/circuits.gen.js'; -import { FUNCTION_TREE_HEIGHT } from '../cbind/constants.gen.js'; -import { fr } from '../tests/factories.js'; -import { CircuitsWasm } from '../wasm/circuits_wasm.js'; -import { computeFunctionTree } from './private_kernel.js'; - -describe('kernel/private_kernel', () => { - let wasm: CircuitsWasm; - - beforeAll(async () => { - wasm = await CircuitsWasm.get(); - }); - - it('gets dummy kernel data', () => { - expect(privateKernelDummyPreviousKernel(wasm)).toBeDefined(); - }); - - it('computes function tree', () => { - const numLeaves = 4; - const leaves = times(numLeaves, i => fr(i)); - const tree = computeFunctionTree(wasm, leaves); - - expect(tree).toHaveLength(2 ** (FUNCTION_TREE_HEIGHT + 1) - 1); - expect(tree.slice(0, numLeaves)).toEqual(leaves); - - const root = tree[tree.length - 1]; - expect(root).toEqual(computeFunctionTreeRoot(wasm, leaves)); - }); -}); diff --git a/yarn-project/circuits.js/src/kernel/private_kernel.ts b/yarn-project/circuits.js/src/kernel/private_kernel.ts deleted file mode 100644 index a1f0e254fde..00000000000 --- a/yarn-project/circuits.js/src/kernel/private_kernel.ts +++ /dev/null @@ -1,39 +0,0 @@ -import { BufferReader } from '@aztec/foundation/serialize'; - -import { Buffer } from 'buffer'; - -import { FUNCTION_TREE_HEIGHT, Fr } from '../index.js'; -import { serializeBufferArrayToVector } from '../utils/serialize.js'; -import { CircuitsWasm } from '../wasm/index.js'; - -export { privateKernelSimOrdering, privateKernelSimInit, privateKernelSimInner } from '../cbind/circuits.gen.js'; - -/** - * Computes contract's function tree from the given leaves. - * @param wasm - The circuits wasm instance. - * @param leaves - The leaves of the function tree. - * @returns All of a function tree's nodes. - */ -export function computeFunctionTree(wasm: CircuitsWasm, leaves: Fr[]): Fr[] { - // Size of the tree is 2^height times size of each element, - // plus 4 for the size used in the std::vector serialization - const outputBufSize = 2 ** (FUNCTION_TREE_HEIGHT + 1) * Fr.SIZE_IN_BYTES + 4; - - // Allocate memory for the input and output buffers, and populate input buffer - const inputVector = serializeBufferArrayToVector(leaves.map(fr => fr.toBuffer())); - const inputBufPtr = wasm.call('bbmalloc', inputVector.length); - const outputBufPtr = wasm.call('bbmalloc', outputBufSize * 100); - wasm.writeMemory(inputBufPtr, inputVector); - - // Run and read outputs - wasm.call('abis__compute_function_tree', inputBufPtr, outputBufPtr); - const outputBuf = Buffer.from(wasm.getMemorySlice(outputBufPtr, outputBufPtr + outputBufSize)); - const reader = new BufferReader(outputBuf); - const output = reader.readVector(Fr); - - // Free memory - wasm.call('bbfree', outputBufPtr); - wasm.call('bbfree', inputBufPtr); - - return output; -} diff --git a/yarn-project/circuits.js/src/kernel/public_kernel.test.ts b/yarn-project/circuits.js/src/kernel/public_kernel.test.ts deleted file mode 100644 index c8b28c2daa7..00000000000 --- a/yarn-project/circuits.js/src/kernel/public_kernel.test.ts +++ /dev/null @@ -1,51 +0,0 @@ -import { - CircuitError, - Fr, - MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, - MAX_PUBLIC_DATA_READS_PER_CALL, - MAX_PUBLIC_DATA_READS_PER_TX, - makeTuple, - simulatePublicKernelCircuit, -} from '../index.js'; -import { makePublicDataRead, makePublicKernelInputsWithTweak } from '../tests/factories.js'; - -describe('kernel/public_kernel', () => { - it('simulates public kernel circuit with previous public kernel', async function () { - const input = await makePublicKernelInputsWithTweak(1, input => { - input.publicCall.callStackItem.functionData.isConstructor = false; - input.publicCall.callStackItem.functionData.isPrivate = false; - input.previousKernel.publicInputs.isPrivate = false; - }); - const result = await simulatePublicKernelCircuit(input); - expect(result).toBeDefined(); - }); - - it('simulates public kernel circuit with previous private kernel', async function () { - const input = await makePublicKernelInputsWithTweak(1, input => { - input.previousKernel.publicInputs.isPrivate = true; - input.previousKernel.publicInputs.end.privateCallStack = makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, Fr.zero); - }); - const result = await simulatePublicKernelCircuit(input); - expect(result).toBeDefined(); - }); - - it('simulating public kernel circuit fails when aggregating proofs will overflow', async function () { - const input = await makePublicKernelInputsWithTweak(1, input => { - input.publicCall.callStackItem.functionData.isConstructor = false; - input.publicCall.callStackItem.functionData.isPrivate = false; - input.previousKernel.publicInputs.isPrivate = false; - - // Cause array overflow - const fullStateReadsObject = makeTuple(MAX_PUBLIC_DATA_READS_PER_TX, makePublicDataRead, 0x01); - input.previousKernel.publicInputs.end.publicDataReads = fullStateReadsObject; - }); - - await expect(simulatePublicKernelCircuit(input)).rejects.toThrow( - new CircuitError( - 7009, - `public_kernel_circuit: too many public data reads in one tx - array_push: capacity exceeded. Limit: ${MAX_PUBLIC_DATA_READS_PER_CALL} -Refer to https://docs.aztec.network/aztec/protocol/errors for more information.`, - ), - ); - }); -}); diff --git a/yarn-project/circuits.js/src/kernel/public_kernel.ts b/yarn-project/circuits.js/src/kernel/public_kernel.ts deleted file mode 100644 index eb1e933bd97..00000000000 --- a/yarn-project/circuits.js/src/kernel/public_kernel.ts +++ /dev/null @@ -1,16 +0,0 @@ -import { publicKernelSim } from '../cbind/circuits.gen.js'; -import { CircuitError, CircuitsWasm, KernelCircuitPublicInputs, PublicKernelInputs } from '../index.js'; - -/** - * Computes the public inputs of the kernel circuit. - * @param input - The kernel circuit inputs. - * @returns The public inputs. - */ -export async function simulatePublicKernelCircuit(input: PublicKernelInputs): Promise { - const result = publicKernelSim(await CircuitsWasm.get(), input); - if (result instanceof CircuitError) { - result.message += '\nRefer to https://docs.aztec.network/aztec/protocol/errors for more information.'; - throw new CircuitError(result.code, result.message); - } - return result; -} diff --git a/yarn-project/circuits.js/src/rollup/index.ts b/yarn-project/circuits.js/src/rollup/index.ts index acb407b5c0f..9c505b9e337 100644 --- a/yarn-project/circuits.js/src/rollup/index.ts +++ b/yarn-project/circuits.js/src/rollup/index.ts @@ -1 +1 @@ -export { baseRollupSim, mergeRollupSim, rootRollupSim } from '../cbind/circuits.gen.js'; +export { baseRollupSim } from '../cbind/circuits.gen.js'; diff --git a/yarn-project/circuits.js/src/rollup/rollup_wasm_wrapper.test.ts b/yarn-project/circuits.js/src/rollup/rollup_wasm_wrapper.test.ts index fa380f412ba..1ca3cbc8dd2 100644 --- a/yarn-project/circuits.js/src/rollup/rollup_wasm_wrapper.test.ts +++ b/yarn-project/circuits.js/src/rollup/rollup_wasm_wrapper.test.ts @@ -1,16 +1,5 @@ -import { - AggregationObject, - BaseOrMergeRollupPublicInputs, - CircuitError, - MergeRollupInputs, - RootRollupInputs, - RootRollupPublicInputs, - VerificationKey, - baseRollupSim, - mergeRollupSim, - rootRollupSim, -} from '../index.js'; -import { makeBaseRollupInputs, makeMergeRollupInputs, makeRootRollupInputs } from '../tests/factories.js'; +import { AggregationObject, BaseOrMergeRollupPublicInputs, VerificationKey, baseRollupSim } from '../index.js'; +import { makeBaseRollupInputs } from '../tests/factories.js'; import { CircuitsWasm } from '../wasm/circuits_wasm.js'; describe('rollup/rollup_wasm_wrapper', () => { @@ -29,29 +18,6 @@ describe('rollup/rollup_wasm_wrapper', () => { return input; }; - const fixPreviousRollupInputs = (input: MergeRollupInputs | RootRollupInputs) => { - input.previousRollupData[1].baseOrMergeRollupPublicInputs.constants = - input.previousRollupData[0].baseOrMergeRollupPublicInputs.constants; - input.previousRollupData[1].baseOrMergeRollupPublicInputs.startNoteHashTreeSnapshot = - input.previousRollupData[0].baseOrMergeRollupPublicInputs.endNoteHashTreeSnapshot; - input.previousRollupData[1].baseOrMergeRollupPublicInputs.startNullifierTreeSnapshot = - input.previousRollupData[0].baseOrMergeRollupPublicInputs.endNullifierTreeSnapshot; - input.previousRollupData[1].baseOrMergeRollupPublicInputs.startContractTreeSnapshot = - input.previousRollupData[0].baseOrMergeRollupPublicInputs.endContractTreeSnapshot; - input.previousRollupData[1].baseOrMergeRollupPublicInputs.startPublicDataTreeRoot = - input.previousRollupData[0].baseOrMergeRollupPublicInputs.endPublicDataTreeRoot; - }; - - const makeMergeRollupInputsForCircuit = () => { - const input = makeMergeRollupInputs(); - for (const previousData of input.previousRollupData) { - previousData.vk = VerificationKey.makeFake(); - previousData.baseOrMergeRollupPublicInputs.endAggregationObject = AggregationObject.makeFake(); - } - fixPreviousRollupInputs(input); - return input; - }; - // Task to repair this test: https://github.com/AztecProtocol/aztec-packages/issues/1586 it.skip('calls base_rollup__sim', () => { const input = makeBaseRollupInputsForCircuit(); @@ -63,62 +29,4 @@ describe('rollup/rollup_wasm_wrapper', () => { expect(publicInputs.startNullifierTreeSnapshot).toEqual(input.startNullifierTreeSnapshot); expect(publicInputs.startNoteHashTreeSnapshot).toEqual(input.startNoteHashTreeSnapshot); }); - - it('calls merge_rollup__sim', () => { - const input = makeMergeRollupInputsForCircuit(); - - const output = mergeRollupSim(wasm, input); - if (output instanceof CircuitError) { - throw new CircuitError(output.code, output.message); - } - - expect(output.rollupType).toEqual(1); - expect(output.startContractTreeSnapshot).toEqual( - input.previousRollupData[0].baseOrMergeRollupPublicInputs.startContractTreeSnapshot, - ); - expect(output.startNullifierTreeSnapshot).toEqual( - input.previousRollupData[0].baseOrMergeRollupPublicInputs.startNullifierTreeSnapshot, - ); - expect(output.startNoteHashTreeSnapshot).toEqual( - input.previousRollupData[0].baseOrMergeRollupPublicInputs.startNoteHashTreeSnapshot, - ); - expect(output.endNoteHashTreeSnapshot).toEqual( - input.previousRollupData[1].baseOrMergeRollupPublicInputs.endNoteHashTreeSnapshot, - ); - }); - - it('calling merge_rollup__sim with different constants should fail', () => { - const input = makeMergeRollupInputs(); - - const output = mergeRollupSim(wasm, input); - expect(output instanceof CircuitError).toBeTruthy(); - - const err = output as CircuitError; - expect(err.message).toEqual( - `input proofs have different constants`, - // Refer to https://docs.aztec.network/aztec/protocol/errors for more information.`, - ); - expect(err.code).toEqual(7003); - }); - - // Task to repair this test: https://github.com/AztecProtocol/aztec-packages/issues/1586 - it.skip('calls root_rollup__sim', () => { - const input = makeRootRollupInputs(); - for (const rd of input.previousRollupData) { - rd.vk = VerificationKey.makeFake(); - rd.baseOrMergeRollupPublicInputs.endAggregationObject = AggregationObject.makeFake(); - const output = baseRollupSim(wasm, makeBaseRollupInputsForCircuit()); - expect(output instanceof BaseOrMergeRollupPublicInputs).toBeTruthy(); - rd.baseOrMergeRollupPublicInputs = output as BaseOrMergeRollupPublicInputs; - } - fixPreviousRollupInputs(input); - - const output = rootRollupSim(wasm, input); - expect(output instanceof RootRollupPublicInputs).toBeTruthy(); - - const publicInputs = output as RootRollupPublicInputs; - expect(publicInputs.startNullifierTreeSnapshot).toEqual( - input.previousRollupData[0].baseOrMergeRollupPublicInputs.startNullifierTreeSnapshot, - ); - }, 15_000); }); diff --git a/yarn-project/circuits.js/src/structs/call_context.ts b/yarn-project/circuits.js/src/structs/call_context.ts index ad45890458f..860b2fc05ea 100644 --- a/yarn-project/circuits.js/src/structs/call_context.ts +++ b/yarn-project/circuits.js/src/structs/call_context.ts @@ -118,4 +118,16 @@ export class CallContext { reader.readBoolean(), ); } + + equals(callContext: CallContext) { + return ( + callContext.msgSender.equals(this.msgSender) && + callContext.storageContractAddress.equals(this.storageContractAddress) && + callContext.portalContractAddress.equals(this.portalContractAddress) && + callContext.functionSelector.equals(this.functionSelector) && + callContext.isDelegateCall === this.isDelegateCall && + callContext.isStaticCall === this.isStaticCall && + callContext.isContractDeployment === this.isContractDeployment + ); + } } diff --git a/yarn-project/circuits.js/src/structs/call_request.ts b/yarn-project/circuits.js/src/structs/call_request.ts new file mode 100644 index 00000000000..da7e3304206 --- /dev/null +++ b/yarn-project/circuits.js/src/structs/call_request.ts @@ -0,0 +1,121 @@ +import { AztecAddress } from '@aztec/foundation/aztec-address'; +import { Fr } from '@aztec/foundation/fields'; +import { BufferReader } from '@aztec/foundation/serialize'; +import { FieldsOf } from '@aztec/foundation/types'; + +import { serializeToBuffer } from '../utils/serialize.js'; + +/** + * Caller context. + */ +export class CallerContext { + constructor( + /** + * Address of the caller contract. + */ + public msgSender: AztecAddress, + /** + * Storage contract address of the caller contract. + */ + public storageContractAddress: AztecAddress, + ) {} + + /** + * Returns a new instance of CallerContext with zero values. + * @returns A new instance of CallerContext with zero values. + */ + public static empty(): CallerContext { + return new CallerContext(AztecAddress.ZERO, AztecAddress.ZERO); + } + + isEmpty() { + return this.msgSender.isZero() && this.storageContractAddress.isZero(); + } + + static from(fields: FieldsOf): CallerContext { + return new CallerContext(...CallerContext.getFields(fields)); + } + + static getFields(fields: FieldsOf) { + return [fields.msgSender, fields.storageContractAddress] as const; + } + + /** + * Serialize this as a buffer. + * @returns The buffer. + */ + toBuffer() { + return serializeToBuffer(...CallerContext.getFields(this)); + } + + /** + * Deserialize this from a buffer. + * @param buffer - The bufferable type from which to deserialize. + * @returns The deserialized instance of PublicCallRequest. + */ + static fromBuffer(buffer: Buffer | BufferReader) { + const reader = BufferReader.asReader(buffer); + return new CallerContext(new AztecAddress(reader.readBytes(32)), new AztecAddress(reader.readBytes(32))); + } + + equals(callerContext: CallerContext) { + return ( + callerContext.msgSender.equals(this.msgSender) && + callerContext.storageContractAddress.equals(this.storageContractAddress) + ); + } +} + +/** + * Call request. + */ +export class CallRequest { + constructor( + /** + * The hash of the call stack item. + */ + public hash: Fr, + /** + * The address of the contract calling the function. + */ + public callerContractAddress: AztecAddress, + /** + * The call context of the contract calling the function. + */ + public callerContext: CallerContext, + ) {} + + toBuffer() { + return serializeToBuffer(this.hash, this.callerContractAddress, this.callerContext); + } + + /** + * Deserializes from a buffer or reader. + * @param buffer - Buffer or reader to read from. + * @returns The deserialized instance of CallRequest. + */ + public static fromBuffer(buffer: Buffer | BufferReader) { + const reader = BufferReader.asReader(buffer); + return new CallRequest(Fr.fromBuffer(reader), reader.readObject(AztecAddress), reader.readObject(CallerContext)); + } + + isEmpty() { + return this.hash.isZero() && this.callerContractAddress.isZero() && this.callerContext.isEmpty(); + } + + /** + * Returns a new instance of CallRequest with zero hash, caller contract address and caller context. + * @returns A new instance of CallRequest with zero hash, caller contract address and caller context. + */ + public static empty() { + return new CallRequest(Fr.ZERO, AztecAddress.ZERO, CallerContext.empty()); + } + + equals(callRequest: CallRequest) { + return ( + callRequest.hash.equals(this.hash) && + callRequest.callerContractAddress.equals(this.callerContractAddress) && + callRequest.callerContext.equals(this.callerContext) + ); + } +} diff --git a/yarn-project/circuits.js/src/structs/call_stack_item.ts b/yarn-project/circuits.js/src/structs/call_stack_item.ts index 581fc315f41..9e1a67857a2 100644 --- a/yarn-project/circuits.js/src/structs/call_stack_item.ts +++ b/yarn-project/circuits.js/src/structs/call_stack_item.ts @@ -1,8 +1,8 @@ import { AztecAddress } from '@aztec/foundation/aztec-address'; -import { computePublicCallStackItemHash } from '../abis/abis.js'; -import { CircuitsWasm } from '../index.js'; +import { computePrivateCallStackItemHash, computePublicCallStackItemHash } from '../abis/abis.js'; import { serializeToBuffer } from '../utils/serialize.js'; +import { CallRequest, CallerContext } from './call_request.js'; import { FunctionData } from './function_data.js'; import { PrivateCircuitPublicInputs } from './private_circuit_public_inputs.js'; import { PublicCircuitPublicInputs } from './public_circuit_public_inputs.js'; @@ -51,6 +51,34 @@ export class PrivateCallStackItem { false, ); } + + isEmpty() { + return this.contractAddress.isZero() && this.functionData.isEmpty() && this.publicInputs.isEmpty(); + } + + /** + * Computes this call stack item hash. + * @returns Hash. + */ + public hash() { + return computePrivateCallStackItemHash(this); + } + + /** + * Creates a new CallRequest with values of the calling contract. + * @returns A CallRequest instance with the contract address, caller context, and the hash of the call stack item. + */ + public toCallRequest() { + if (this.isEmpty()) { + return CallRequest.empty(); + } + + const callContext = this.publicInputs.callContext; + const callerContext = callContext.isDelegateCall + ? new CallerContext(callContext.msgSender, callContext.storageContractAddress) + : CallerContext.empty(); + return new CallRequest(this.hash(), callContext.msgSender, callerContext); + } } /** @@ -102,7 +130,23 @@ export class PublicCallStackItem { * Computes this call stack item hash. * @returns Hash. */ - public async hash() { - return computePublicCallStackItemHash(await CircuitsWasm.get(), this); + public hash() { + return computePublicCallStackItemHash(this); + } + + /** + * Creates a new CallRequest with values of the calling contract. + * @returns A CallRequest instance with the contract address, caller context, and the hash of the call stack item. + */ + public toCallRequest() { + if (this.isEmpty()) { + return CallRequest.empty(); + } + + const callContext = this.publicInputs.callContext; + const callerContext = callContext.isDelegateCall + ? new CallerContext(callContext.msgSender, callContext.storageContractAddress) + : CallerContext.empty(); + return new CallRequest(this.hash(), callContext.msgSender, callerContext); } } diff --git a/yarn-project/circuits.js/src/structs/complete_address.ts b/yarn-project/circuits.js/src/structs/complete_address.ts index e0f86e1419d..b2fd575e4d5 100644 --- a/yarn-project/circuits.js/src/structs/complete_address.ts +++ b/yarn-project/circuits.js/src/structs/complete_address.ts @@ -4,7 +4,7 @@ import { BufferReader } from '@aztec/foundation/serialize'; import { computeContractAddressFromPartial } from '../abis/abis.js'; import { Grumpkin } from '../barretenberg/index.js'; -import { CircuitsWasm, GrumpkinPrivateKey, PartialAddress, PublicKey } from '../index.js'; +import { GrumpkinPrivateKey, PartialAddress, PublicKey } from '../index.js'; /** * A complete address is a combination of an Aztec address, a public key and a partial address. @@ -47,12 +47,8 @@ export class CompleteAddress { return new CompleteAddress(address, pubKey, partialAddress); } - static async fromPrivateKeyAndPartialAddress( - privateKey: GrumpkinPrivateKey, - partialAddress: Fr, - ): Promise { - const wasm = await CircuitsWasm.get(); - const grumpkin = new Grumpkin(wasm); + static fromPrivateKeyAndPartialAddress(privateKey: GrumpkinPrivateKey, partialAddress: Fr): CompleteAddress { + const grumpkin = new Grumpkin(); const pubKey = grumpkin.mul(Grumpkin.generator, privateKey); const address = computeContractAddressFromPartial(pubKey, partialAddress); return new CompleteAddress(address, pubKey, partialAddress); diff --git a/yarn-project/circuits.js/src/structs/function_leaf_preimage.ts b/yarn-project/circuits.js/src/structs/function_leaf_preimage.ts index b9f781202f6..0aed310a4e0 100644 --- a/yarn-project/circuits.js/src/structs/function_leaf_preimage.ts +++ b/yarn-project/circuits.js/src/structs/function_leaf_preimage.ts @@ -51,8 +51,8 @@ export class FunctionLeafPreimage { reader.readObject(FunctionSelector), reader.readBoolean(), reader.readBoolean(), - reader.readFr(), - reader.readFr(), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), ); } } diff --git a/yarn-project/circuits.js/src/structs/global_variables.ts b/yarn-project/circuits.js/src/structs/global_variables.ts index c9bd2cb7e1e..f93af7b67ea 100644 --- a/yarn-project/circuits.js/src/structs/global_variables.ts +++ b/yarn-project/circuits.js/src/structs/global_variables.ts @@ -32,12 +32,17 @@ export class GlobalVariables { } static empty(): GlobalVariables { - return new GlobalVariables(Fr.zero(), Fr.zero(), Fr.zero(), Fr.zero()); + return new GlobalVariables(Fr.ZERO, Fr.ZERO, Fr.ZERO, Fr.ZERO); } static fromBuffer(buffer: Buffer | BufferReader): GlobalVariables { const reader = BufferReader.asReader(buffer); - return new GlobalVariables(reader.readFr(), reader.readFr(), reader.readFr(), reader.readFr()); + return new GlobalVariables( + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), + ); } static fromJSON(obj: any): GlobalVariables { diff --git a/yarn-project/circuits.js/src/structs/index.ts b/yarn-project/circuits.js/src/structs/index.ts index da643b14915..1b23387e994 100644 --- a/yarn-project/circuits.js/src/structs/index.ts +++ b/yarn-project/circuits.js/src/structs/index.ts @@ -18,6 +18,7 @@ export * from './kernel/public_inputs_final.js'; export * from './private_circuit_public_inputs.js'; export * from './public_circuit_public_inputs.js'; export * from './circuit_error.js'; +export * from './call_request.js'; export * from './call_stack_item.js'; export * from './shared.js'; export * from './proof.js'; diff --git a/yarn-project/circuits.js/src/structs/kernel/combined_accumulated_data.ts b/yarn-project/circuits.js/src/structs/kernel/combined_accumulated_data.ts index d58b46e7e91..75d7cccb52c 100644 --- a/yarn-project/circuits.js/src/structs/kernel/combined_accumulated_data.ts +++ b/yarn-project/circuits.js/src/structs/kernel/combined_accumulated_data.ts @@ -17,6 +17,7 @@ import { } from '../../cbind/constants.gen.js'; import { makeTuple } from '../../index.js'; import { serializeToBuffer } from '../../utils/serialize.js'; +import { CallRequest } from '../call_request.js'; import { AggregationObject, AztecAddress, EthAddress, Fr, FunctionData } from '../index.js'; /** @@ -60,7 +61,11 @@ export class NewContractData { */ static fromBuffer(buffer: Buffer | BufferReader): NewContractData { const reader = BufferReader.asReader(buffer); - return new NewContractData(reader.readObject(AztecAddress), new EthAddress(reader.readBytes(32)), reader.readFr()); + return new NewContractData( + reader.readObject(AztecAddress), + new EthAddress(reader.readBytes(32)), + Fr.fromBuffer(reader), + ); } static empty() { @@ -142,9 +147,9 @@ export class OptionallyRevealedData { static fromBuffer(buffer: Buffer | BufferReader): OptionallyRevealedData { const reader = BufferReader.asReader(buffer); return new OptionallyRevealedData( - reader.readFr(), + Fr.fromBuffer(reader), reader.readObject(FunctionData), - reader.readFr(), + Fr.fromBuffer(reader), new EthAddress(reader.readBytes(32)), reader.readBoolean(), reader.readBoolean(), @@ -205,7 +210,7 @@ export class PublicDataRead { static fromBuffer(buffer: Buffer | BufferReader) { const reader = BufferReader.asReader(buffer); - return new PublicDataRead(reader.readFr(), reader.readFr()); + return new PublicDataRead(Fr.fromBuffer(reader), Fr.fromBuffer(reader)); } static empty() { @@ -263,7 +268,7 @@ export class PublicDataUpdateRequest { static fromBuffer(buffer: Buffer | BufferReader) { const reader = BufferReader.asReader(buffer); - return new PublicDataUpdateRequest(reader.readFr(), reader.readFr(), reader.readFr()); + return new PublicDataUpdateRequest(Fr.fromBuffer(reader), Fr.fromBuffer(reader), Fr.fromBuffer(reader)); } static empty() { @@ -308,11 +313,11 @@ export class CombinedAccumulatedData { /** * Current private call stack. */ - public privateCallStack: Tuple, + public privateCallStack: Tuple, /** * Current public call stack. */ - public publicCallStack: Tuple, + public publicCallStack: Tuple, /** * All the new L2 to L1 messages created in this transaction. */ @@ -393,13 +398,13 @@ export class CombinedAccumulatedData { reader.readArray(MAX_NEW_COMMITMENTS_PER_TX, Fr), reader.readArray(MAX_NEW_NULLIFIERS_PER_TX, Fr), reader.readArray(MAX_NEW_NULLIFIERS_PER_TX, Fr), - reader.readArray(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, Fr), - reader.readArray(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, Fr), + reader.readArray(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, CallRequest), + reader.readArray(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, CallRequest), reader.readArray(MAX_NEW_L2_TO_L1_MSGS_PER_TX, Fr), reader.readArray(2, Fr), reader.readArray(2, Fr), - reader.readFr(), - reader.readFr(), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), reader.readArray(MAX_NEW_CONTRACTS_PER_TX, NewContractData), reader.readArray(MAX_OPTIONALLY_REVEALED_DATA_LENGTH_PER_TX, OptionallyRevealedData), reader.readArray(MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX, PublicDataUpdateRequest), @@ -446,8 +451,8 @@ export class CombinedAccumulatedData { makeTuple(MAX_NEW_COMMITMENTS_PER_TX, Fr.zero), makeTuple(MAX_NEW_NULLIFIERS_PER_TX, Fr.zero), makeTuple(MAX_NEW_NULLIFIERS_PER_TX, Fr.zero), - makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, Fr.zero), - makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, Fr.zero), + makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, CallRequest.empty), + makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, CallRequest.empty), makeTuple(MAX_NEW_L2_TO_L1_MSGS_PER_TX, Fr.zero), makeTuple(2, Fr.zero), makeTuple(2, Fr.zero), @@ -487,11 +492,11 @@ export class FinalAccumulatedData { /** * Current private call stack. */ - public privateCallStack: Tuple, + public privateCallStack: Tuple, /** * Current public call stack. */ - public publicCallStack: Tuple, + public publicCallStack: Tuple, /** * All the new L2 to L1 messages created in this transaction. */ @@ -558,13 +563,13 @@ export class FinalAccumulatedData { reader.readArray(MAX_NEW_COMMITMENTS_PER_TX, Fr), reader.readArray(MAX_NEW_NULLIFIERS_PER_TX, Fr), reader.readArray(MAX_NEW_NULLIFIERS_PER_TX, Fr), - reader.readArray(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, Fr), - reader.readArray(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, Fr), + reader.readArray(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, CallRequest), + reader.readArray(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, CallRequest), reader.readArray(MAX_NEW_L2_TO_L1_MSGS_PER_TX, Fr), reader.readArray(2, Fr), reader.readArray(2, Fr), - reader.readFr(), - reader.readFr(), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), reader.readArray(MAX_NEW_CONTRACTS_PER_TX, NewContractData), reader.readArray(MAX_OPTIONALLY_REVEALED_DATA_LENGTH_PER_TX, OptionallyRevealedData), ); @@ -585,8 +590,8 @@ export class FinalAccumulatedData { makeTuple(MAX_NEW_COMMITMENTS_PER_TX, Fr.zero), makeTuple(MAX_NEW_NULLIFIERS_PER_TX, Fr.zero), makeTuple(MAX_NEW_NULLIFIERS_PER_TX, Fr.zero), - makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, Fr.zero), - makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, Fr.zero), + makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, CallRequest.empty), + makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, CallRequest.empty), makeTuple(MAX_NEW_L2_TO_L1_MSGS_PER_TX, Fr.zero), makeTuple(2, Fr.zero), makeTuple(2, Fr.zero), diff --git a/yarn-project/circuits.js/src/structs/kernel/historic_block_data.ts b/yarn-project/circuits.js/src/structs/kernel/historic_block_data.ts index b8a2040cdfe..f3d09abc864 100644 --- a/yarn-project/circuits.js/src/structs/kernel/historic_block_data.ts +++ b/yarn-project/circuits.js/src/structs/kernel/historic_block_data.ts @@ -107,14 +107,14 @@ export class HistoricBlockData { static fromBuffer(buffer: Buffer | BufferReader) { const reader = BufferReader.asReader(buffer); return new HistoricBlockData( - reader.readFr(), - reader.readFr(), - reader.readFr(), - reader.readFr(), - reader.readFr(), - reader.readFr(), - reader.readFr(), - reader.readFr(), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), ); } diff --git a/yarn-project/circuits.js/src/structs/kernel/index.test.ts b/yarn-project/circuits.js/src/structs/kernel/index.test.ts index efc97550c87..f81fa01c4b4 100644 --- a/yarn-project/circuits.js/src/structs/kernel/index.test.ts +++ b/yarn-project/circuits.js/src/structs/kernel/index.test.ts @@ -11,7 +11,8 @@ import { makeSchnorrSignature, } from '../../tests/factories.js'; -describe('structs/kernel', () => { +// TODO: Remove `expectSerialize` and `expectMsgpack` and just test .toBuffer() and .fromBuffer() of each struct. +describe.skip('structs/kernel', () => { it(`serializes and prints previous_kernel_data`, async () => { const previousKernelData = makePreviousKernelData(); await expectSerializeToMatchSnapshot( @@ -71,7 +72,7 @@ describe('structs/kernel', () => { }); it(`serializes and prints public_kernel_inputs`, async () => { - const kernelInputs = await makePublicKernelInputs(); + const kernelInputs = makePublicKernelInputs(); await expectSerializeToMatchSnapshot( kernelInputs.toBuffer(), 'abis__test_roundtrip_serialize_public_kernel_inputs', diff --git a/yarn-project/circuits.js/src/structs/kernel/previous_kernel_data.ts b/yarn-project/circuits.js/src/structs/kernel/previous_kernel_data.ts index 8db9eb5be31..f03ad99fde8 100644 --- a/yarn-project/circuits.js/src/structs/kernel/previous_kernel_data.ts +++ b/yarn-project/circuits.js/src/structs/kernel/previous_kernel_data.ts @@ -1,8 +1,7 @@ import { Fr } from '@aztec/foundation/fields'; import { BufferReader, Tuple } from '@aztec/foundation/serialize'; -import { privateKernelDummyPreviousKernel } from '../../cbind/circuits.gen.js'; -import { CircuitsWasm, VK_TREE_HEIGHT, makeTuple } from '../../index.js'; +import { VK_TREE_HEIGHT, makeTuple } from '../../index.js'; import { serializeToBuffer } from '../../utils/serialize.js'; import { Proof, makeEmptyProof } from '../proof.js'; import { UInt32 } from '../shared.js'; @@ -74,34 +73,3 @@ export class PreviousKernelData { ); } } - -/** - * Dummy data used in the first kernel in the chain of kernels. - */ -export class DummyPreviousKernelData { - private static instance: DummyPreviousKernelData; - - private constructor(private data: PreviousKernelData) {} - - /** - * Gets the dummy data. - * @param wasm - The circuits wasm instance. - * @returns The dummy previous kernel data. - */ - public static getDummyPreviousKernelData(wasm: CircuitsWasm): PreviousKernelData { - if (!DummyPreviousKernelData.instance) { - const data = privateKernelDummyPreviousKernel(wasm); - DummyPreviousKernelData.instance = new DummyPreviousKernelData(data); - } - - return DummyPreviousKernelData.instance.getData(); - } - - /** - * Gets the the dummy data. - * @returns The dummy previous kernel data. - */ - public getData(): PreviousKernelData { - return this.data; - } -} diff --git a/yarn-project/circuits.js/src/structs/kernel/private_kernel.ts b/yarn-project/circuits.js/src/structs/kernel/private_kernel.ts index f3bcda0e119..c07c54fe9f7 100644 --- a/yarn-project/circuits.js/src/structs/kernel/private_kernel.ts +++ b/yarn-project/circuits.js/src/structs/kernel/private_kernel.ts @@ -6,11 +6,13 @@ import { FUNCTION_TREE_HEIGHT, MAX_NEW_NULLIFIERS_PER_TX, MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, + MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, MAX_READ_REQUESTS_PER_CALL, MAX_READ_REQUESTS_PER_TX, } from '../../cbind/constants.gen.js'; import { FieldsOf } from '../../utils/jsUtils.js'; import { serializeToBuffer } from '../../utils/serialize.js'; +import { CallRequest } from '../call_request.js'; import { PrivateCallStackItem } from '../call_stack_item.js'; import { MembershipWitness } from '../membership_witness.js'; import { Proof } from '../proof.js'; @@ -32,7 +34,11 @@ export class PrivateCallData { /** * Other private call stack items to be processed. */ - public privateCallStackPreimages: Tuple, + public privateCallStack: Tuple, + /** + * Other public call stack items to be processed. + */ + public publicCallStack: Tuple, /** * The proof of the execution of this private call. */ @@ -73,7 +79,8 @@ export class PrivateCallData { return [ // NOTE: Must have same order as CPP. fields.callStackItem, - fields.privateCallStackPreimages, + fields.privateCallStack, + fields.publicCallStack, fields.proof, fields.vk, fields.functionLeafMembershipWitness, diff --git a/yarn-project/circuits.js/src/structs/kernel/public_kernel.ts b/yarn-project/circuits.js/src/structs/kernel/public_kernel.ts index 5f842a30c73..277c3521acf 100644 --- a/yarn-project/circuits.js/src/structs/kernel/public_kernel.ts +++ b/yarn-project/circuits.js/src/structs/kernel/public_kernel.ts @@ -9,6 +9,7 @@ import { } from '../../cbind/constants.gen.js'; import { assertMemberLength } from '../../utils/jsUtils.js'; import { serializeToBuffer } from '../../utils/serialize.js'; +import { CallRequest } from '../call_request.js'; import { PublicCallStackItem } from '../call_stack_item.js'; import { MembershipWitness } from '../membership_witness.js'; import { Proof } from '../proof.js'; @@ -83,7 +84,7 @@ export class PublicCallData { /** * Children call stack items. */ - public readonly publicCallStackPreimages: Tuple, + public readonly publicCallStack: Tuple, /** * Proof of the call stack item execution. */ @@ -96,17 +97,9 @@ export class PublicCallData { * Hash of the L2 contract bytecode. */ public readonly bytecodeHash: Fr, - ) { - assertMemberLength(this, 'publicCallStackPreimages', MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL); - } + ) {} toBuffer() { - return serializeToBuffer( - this.callStackItem, - this.publicCallStackPreimages, - this.proof, - this.portalContractAddress, - this.bytecodeHash, - ); + return serializeToBuffer(this.callStackItem, this.proof, this.portalContractAddress, this.bytecodeHash); } } diff --git a/yarn-project/circuits.js/src/structs/private_circuit_public_inputs.ts b/yarn-project/circuits.js/src/structs/private_circuit_public_inputs.ts index 74759668c06..16618ceb2c7 100644 --- a/yarn-project/circuits.js/src/structs/private_circuit_public_inputs.ts +++ b/yarn-project/circuits.js/src/structs/private_circuit_public_inputs.ts @@ -1,3 +1,4 @@ +import { isArrayEmpty } from '@aztec/foundation/collection'; import { Fr } from '@aztec/foundation/fields'; import { Tuple } from '@aztec/foundation/serialize'; @@ -59,11 +60,11 @@ export class PrivateCircuitPublicInputs { /** * Private call stack at the current kernel iteration. */ - public privateCallStack: Tuple, + public privateCallStackHashes: Tuple, /** * Public call stack at the current kernel iteration. */ - public publicCallStack: Tuple, + public publicCallStackHashes: Tuple, /** * New L2 to L1 messages created by the corresponding function call. */ @@ -141,6 +142,32 @@ export class PrivateCircuitPublicInputs { Fr.ZERO, ); } + + isEmpty() { + const isFrArrayEmpty = (arr: Fr[]) => isArrayEmpty(arr, item => item.isZero()); + return ( + this.callContext.isEmpty() && + this.argsHash.isZero() && + isFrArrayEmpty(this.returnValues) && + isFrArrayEmpty(this.readRequests) && + isFrArrayEmpty(this.pendingReadRequests) && + isFrArrayEmpty(this.newCommitments) && + isFrArrayEmpty(this.newNullifiers) && + isFrArrayEmpty(this.nullifiedCommitments) && + isFrArrayEmpty(this.privateCallStackHashes) && + isFrArrayEmpty(this.publicCallStackHashes) && + isFrArrayEmpty(this.newL2ToL1Msgs) && + isFrArrayEmpty(this.encryptedLogsHash) && + isFrArrayEmpty(this.unencryptedLogsHash) && + this.encryptedLogPreimagesLength.isZero() && + this.unencryptedLogPreimagesLength.isZero() && + this.historicBlockData.isEmpty() && + this.contractDeploymentData.isEmpty() && + this.chainId.isZero() && + this.version.isZero() + ); + } + /** * Serialize into a field array. Low-level utility. * @param fields - Object with fields. @@ -157,8 +184,8 @@ export class PrivateCircuitPublicInputs { fields.newCommitments, fields.newNullifiers, fields.nullifiedCommitments, - fields.privateCallStack, - fields.publicCallStack, + fields.privateCallStackHashes, + fields.publicCallStackHashes, fields.newL2ToL1Msgs, fields.encryptedLogsHash, fields.unencryptedLogsHash, diff --git a/yarn-project/circuits.js/src/structs/public_call_request.ts b/yarn-project/circuits.js/src/structs/public_call_request.ts index 6c62b190f4a..b8cb2cacd50 100644 --- a/yarn-project/circuits.js/src/structs/public_call_request.ts +++ b/yarn-project/circuits.js/src/structs/public_call_request.ts @@ -3,9 +3,11 @@ import { BufferReader } from '@aztec/foundation/serialize'; import { computeVarArgsHash } from '../abis/abis.js'; import { FieldsOf } from '../index.js'; import { serializeToBuffer } from '../utils/serialize.js'; +import { CallerContext } from './call_request.js'; import { AztecAddress, CallContext, + CallRequest, Fr, FunctionData, PublicCallStackItem, @@ -99,6 +101,19 @@ export class PublicCallRequest { return new PublicCallStackItem(this.contractAddress, this.functionData, publicInputs, true); } + /** + * Creates a new CallRequest with values of the calling contract. + * @returns A CallRequest instance with the contract address, caller context, and the hash of the call stack item. + */ + toCallRequest() { + const item = this.toPublicCallStackItem(); + const callerContractAddress = this.callContext.msgSender; + const callerContext = this.callContext.isDelegateCall + ? new CallerContext(this.callContext.msgSender, this.callContext.storageContractAddress) + : CallerContext.empty(); + return new CallRequest(item.hash(), callerContractAddress, callerContext); + } + /** * Returns the hash of the arguments for this request. * @returns Hash of the arguments for this request. diff --git a/yarn-project/circuits.js/src/structs/public_circuit_public_inputs.ts b/yarn-project/circuits.js/src/structs/public_circuit_public_inputs.ts index 310323b1597..a4bb7d67c90 100644 --- a/yarn-project/circuits.js/src/structs/public_circuit_public_inputs.ts +++ b/yarn-project/circuits.js/src/structs/public_circuit_public_inputs.ts @@ -62,7 +62,7 @@ export class ContractStorageRead { static fromBuffer(buffer: Buffer | BufferReader) { const reader = BufferReader.asReader(buffer); - return new ContractStorageRead(reader.readFr(), reader.readFr()); + return new ContractStorageRead(Fr.fromBuffer(reader), Fr.fromBuffer(reader)); } static empty() { @@ -110,7 +110,7 @@ export class ContractStorageUpdateRequest { static fromBuffer(buffer: Buffer | BufferReader) { const reader = BufferReader.asReader(buffer); - return new ContractStorageUpdateRequest(reader.readFr(), reader.readFr(), reader.readFr()); + return new ContractStorageUpdateRequest(Fr.fromBuffer(reader), Fr.fromBuffer(reader), Fr.fromBuffer(reader)); } /** @@ -175,7 +175,7 @@ export class PublicCircuitPublicInputs { /** * Public call stack of the current kernel iteration. */ - public publicCallStack: Tuple, + public publicCallStackHashes: Tuple, /** * New commitments created within a public execution call */ @@ -246,7 +246,7 @@ export class PublicCircuitPublicInputs { isFrArrayEmpty(this.returnValues) && isArrayEmpty(this.contractStorageUpdateRequests, item => item.isEmpty()) && isArrayEmpty(this.contractStorageReads, item => item.isEmpty()) && - isFrArrayEmpty(this.publicCallStack) && + isFrArrayEmpty(this.publicCallStackHashes) && isFrArrayEmpty(this.newCommitments) && isFrArrayEmpty(this.newNullifiers) && isFrArrayEmpty(this.newL2ToL1Msgs) && @@ -269,7 +269,7 @@ export class PublicCircuitPublicInputs { fields.returnValues, fields.contractStorageUpdateRequests, fields.contractStorageReads, - fields.publicCallStack, + fields.publicCallStackHashes, fields.newCommitments, fields.newNullifiers, fields.newL2ToL1Msgs, diff --git a/yarn-project/circuits.js/src/structs/read_request_membership_witness.ts b/yarn-project/circuits.js/src/structs/read_request_membership_witness.ts index 2d0bdb14bb7..008be874241 100644 --- a/yarn-project/circuits.js/src/structs/read_request_membership_witness.ts +++ b/yarn-project/circuits.js/src/structs/read_request_membership_witness.ts @@ -33,7 +33,7 @@ export class ReadRequestMembershipWitness { */ public hintToCommitment: Fr, ) { - if (hintToCommitment.value > MAX_NEW_COMMITMENTS_PER_CALL) { + if (hintToCommitment.toBigInt() > MAX_NEW_COMMITMENTS_PER_CALL) { throw new Error( `Expected ReadRequestMembershipWitness' hintToCommitment(${hintToCommitment}) to be <= NEW_COMMITMENTS_LENGTH(${MAX_NEW_COMMITMENTS_PER_CALL})`, ); @@ -124,10 +124,10 @@ export class ReadRequestMembershipWitness { */ static fromBuffer(buffer: Buffer | BufferReader): ReadRequestMembershipWitness { const reader = BufferReader.asReader(buffer); - const leafIndex = reader.readFr(); + const leafIndex = Fr.fromBuffer(reader); const siblingPath = reader.readArray(NOTE_HASH_TREE_HEIGHT, Fr); const isTransient = reader.readBoolean(); - const hintToCommitment = reader.readFr(); + const hintToCommitment = Fr.fromBuffer(reader); return new ReadRequestMembershipWitness(leafIndex, siblingPath, isTransient, hintToCommitment); } } diff --git a/yarn-project/circuits.js/src/structs/rollup/append_only_tree_snapshot.ts b/yarn-project/circuits.js/src/structs/rollup/append_only_tree_snapshot.ts index 3c418ef986e..5bc2aaa19c8 100644 --- a/yarn-project/circuits.js/src/structs/rollup/append_only_tree_snapshot.ts +++ b/yarn-project/circuits.js/src/structs/rollup/append_only_tree_snapshot.ts @@ -37,7 +37,7 @@ export class AppendOnlyTreeSnapshot { static fromBuffer(buffer: Buffer | BufferReader): AppendOnlyTreeSnapshot { const reader = BufferReader.asReader(buffer); - return new AppendOnlyTreeSnapshot(reader.readFr(), reader.readNumber()); + return new AppendOnlyTreeSnapshot(Fr.fromBuffer(reader), reader.readNumber()); } static fromString(str: string): AppendOnlyTreeSnapshot { diff --git a/yarn-project/circuits.js/src/structs/rollup/base_or_merge_rollup_public_inputs.ts b/yarn-project/circuits.js/src/structs/rollup/base_or_merge_rollup_public_inputs.ts index de0e2b0c621..9f614f29d62 100644 --- a/yarn-project/circuits.js/src/structs/rollup/base_or_merge_rollup_public_inputs.ts +++ b/yarn-project/circuits.js/src/structs/rollup/base_or_merge_rollup_public_inputs.ts @@ -85,7 +85,7 @@ export class BaseOrMergeRollupPublicInputs { const reader = BufferReader.asReader(buffer); return new BaseOrMergeRollupPublicInputs( reader.readNumber(), - reader.readFr(), + Fr.fromBuffer(reader), reader.readObject(AggregationObject), reader.readObject(ConstantRollupData), reader.readObject(AppendOnlyTreeSnapshot), @@ -94,8 +94,8 @@ export class BaseOrMergeRollupPublicInputs { reader.readObject(AppendOnlyTreeSnapshot), reader.readObject(AppendOnlyTreeSnapshot), reader.readObject(AppendOnlyTreeSnapshot), - reader.readFr(), - reader.readFr(), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), reader.readArray(NUM_FIELDS_PER_SHA256, Fr) as [Fr, Fr], ); } diff --git a/yarn-project/circuits.js/src/structs/rollup/base_rollup.test.ts b/yarn-project/circuits.js/src/structs/rollup/base_rollup.test.ts index 86fb3f480b0..873ded81e49 100644 --- a/yarn-project/circuits.js/src/structs/rollup/base_rollup.test.ts +++ b/yarn-project/circuits.js/src/structs/rollup/base_rollup.test.ts @@ -2,7 +2,7 @@ import { expectReserializeToMatchObject, expectSerializeToMatchSnapshot } from ' import { makeBaseOrMergeRollupPublicInputs, makeBaseRollupInputs } from '../../tests/factories.js'; import { BaseOrMergeRollupPublicInputs } from './base_or_merge_rollup_public_inputs.js'; -describe('structs/base_rollup', () => { +describe.skip('structs/base_rollup', () => { it(`serializes and prints BaseRollupInputs`, async () => { const baseRollupInputs = makeBaseRollupInputs(); diff --git a/yarn-project/circuits.js/src/structs/rollup/base_rollup.ts b/yarn-project/circuits.js/src/structs/rollup/base_rollup.ts index 3a146462840..85dd3d3d565 100644 --- a/yarn-project/circuits.js/src/structs/rollup/base_rollup.ts +++ b/yarn-project/circuits.js/src/structs/rollup/base_rollup.ts @@ -90,10 +90,10 @@ export class ConstantRollupData { const reader = BufferReader.asReader(buffer); return new ConstantRollupData( reader.readObject(AppendOnlyTreeSnapshot), - reader.readFr(), - reader.readFr(), - reader.readFr(), - reader.readFr(), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), reader.readObject(GlobalVariables), ); } diff --git a/yarn-project/circuits.js/src/structs/rollup/root_rollup.ts b/yarn-project/circuits.js/src/structs/rollup/root_rollup.ts index 7af847638d6..198b40ea183 100644 --- a/yarn-project/circuits.js/src/structs/rollup/root_rollup.ts +++ b/yarn-project/circuits.js/src/structs/rollup/root_rollup.ts @@ -242,8 +242,8 @@ export class RootRollupPublicInputs { reader.readObject(AppendOnlyTreeSnapshot), reader.readObject(AppendOnlyTreeSnapshot), reader.readObject(AppendOnlyTreeSnapshot), - reader.readFr(), - reader.readFr(), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), reader.readObject(AppendOnlyTreeSnapshot), reader.readObject(AppendOnlyTreeSnapshot), reader.readObject(AppendOnlyTreeSnapshot), @@ -254,8 +254,8 @@ export class RootRollupPublicInputs { reader.readObject(AppendOnlyTreeSnapshot), reader.readObject(AppendOnlyTreeSnapshot), reader.readObject(AppendOnlyTreeSnapshot), - [reader.readFr(), reader.readFr()], - [reader.readFr(), reader.readFr()], + [Fr.fromBuffer(reader), Fr.fromBuffer(reader)], + [Fr.fromBuffer(reader), Fr.fromBuffer(reader)], ); } } diff --git a/yarn-project/circuits.js/src/structs/tx_context.ts b/yarn-project/circuits.js/src/structs/tx_context.ts index ecf74d9a34b..1dd2a166fe4 100644 --- a/yarn-project/circuits.js/src/structs/tx_context.ts +++ b/yarn-project/circuits.js/src/structs/tx_context.ts @@ -49,6 +49,17 @@ export class ContractDeploymentData { public static empty(): ContractDeploymentData { return new ContractDeploymentData(Point.ZERO, Fr.ZERO, Fr.ZERO, Fr.ZERO, EthAddress.ZERO); } + + isEmpty() { + return ( + this.deployerPublicKey.isZero() && + this.constructorVkHash.isZero() && + this.functionTreeRoot.isZero() && + this.contractAddressSalt.isZero() && + this.portalContractAddress.isZero() + ); + } + /** * Deserializes contract deployment data rom a buffer or reader. * @param buffer - Buffer to read from. @@ -58,9 +69,9 @@ export class ContractDeploymentData { const reader = BufferReader.asReader(buffer); return new ContractDeploymentData( reader.readObject(Point), - reader.readFr(), - reader.readFr(), - reader.readFr(), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), new EthAddress(reader.readBytes(32)), ); } @@ -131,8 +142,8 @@ export class TxContext { reader.readBoolean(), reader.readBoolean(), reader.readObject(ContractDeploymentData), - reader.readFr(), - reader.readFr(), + Fr.fromBuffer(reader), + Fr.fromBuffer(reader), ); } diff --git a/yarn-project/circuits.js/src/structs/tx_request.ts b/yarn-project/circuits.js/src/structs/tx_request.ts index 32fb13a82d9..5e251120e04 100644 --- a/yarn-project/circuits.js/src/structs/tx_request.ts +++ b/yarn-project/circuits.js/src/structs/tx_request.ts @@ -58,7 +58,7 @@ export class TxRequest { return new TxRequest( reader.readObject(AztecAddress), reader.readObject(FunctionData), - reader.readFr(), + Fr.fromBuffer(reader), reader.readObject(TxContext), ); } diff --git a/yarn-project/circuits.js/src/structs/verification_key.ts b/yarn-project/circuits.js/src/structs/verification_key.ts index 7e24f9ba644..fbd38444458 100644 --- a/yarn-project/circuits.js/src/structs/verification_key.ts +++ b/yarn-project/circuits.js/src/structs/verification_key.ts @@ -38,7 +38,7 @@ export class G1AffineElement { */ static fromBuffer(buffer: Buffer | BufferReader): G1AffineElement { const reader = BufferReader.asReader(buffer); - return new G1AffineElement(reader.readFr(), reader.readFr()); + return new G1AffineElement(Fq.fromBuffer(reader), Fq.fromBuffer(reader)); } } diff --git a/yarn-project/circuits.js/src/tests/factories.ts b/yarn-project/circuits.js/src/tests/factories.ts index 67b8cf4391e..3df6e72dccd 100644 --- a/yarn-project/circuits.js/src/tests/factories.ts +++ b/yarn-project/circuits.js/src/tests/factories.ts @@ -1,8 +1,7 @@ import { AztecAddress } from '@aztec/foundation/aztec-address'; import { EthAddress } from '@aztec/foundation/eth-address'; -import { mapTuple, numToUInt32BE } from '@aztec/foundation/serialize'; +import { numToUInt32BE } from '@aztec/foundation/serialize'; -import { computeCallStackItemHash } from '../abis/abis.js'; import { SchnorrSignature } from '../barretenberg/index.js'; import { ARGS_LENGTH, @@ -13,8 +12,9 @@ import { CONTRACT_SUBTREE_SIBLING_PATH_LENGTH, CONTRACT_TREE_HEIGHT, CallContext, + CallRequest, + CallerContext, CircuitType, - CircuitsWasm, CombinedAccumulatedData, CombinedConstantData, ConstantRollupData, @@ -217,8 +217,8 @@ export function makeAccumulatedData(seed = 1, full = false): CombinedAccumulated tupleGenerator(MAX_NEW_COMMITMENTS_PER_TX, fr, seed + 0x100), tupleGenerator(MAX_NEW_NULLIFIERS_PER_TX, fr, seed + 0x200), tupleGenerator(MAX_NEW_NULLIFIERS_PER_TX, fr, seed + 0x300), - tupleGenerator(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, fr, seed + 0x400), - tupleGenerator(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, fr, seed + 0x500), + tupleGenerator(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, makeCallRequest, seed + 0x400), + tupleGenerator(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, makeCallRequest, seed + 0x500), tupleGenerator(MAX_NEW_L2_TO_L1_MSGS_PER_TX, fr, seed + 0x600), tupleGenerator(2, fr, seed + 0x700), // encrypted logs hash tupleGenerator(2, fr, seed + 0x800), // unencrypted logs hash @@ -244,8 +244,8 @@ export function makeFinalAccumulatedData(seed = 1, full = false): FinalAccumulat tupleGenerator(MAX_NEW_COMMITMENTS_PER_TX, fr, seed + 0x100), tupleGenerator(MAX_NEW_NULLIFIERS_PER_TX, fr, seed + 0x200), tupleGenerator(MAX_NEW_NULLIFIERS_PER_TX, fr, seed + 0x300), - tupleGenerator(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, fr, seed + 0x400), - tupleGenerator(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, fr, seed + 0x500), + tupleGenerator(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, makeCallRequest, seed + 0x400), + tupleGenerator(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, makeCallRequest, seed + 0x500), tupleGenerator(MAX_NEW_L2_TO_L1_MSGS_PER_TX, fr, seed + 0x600), tupleGenerator(2, fr, seed + 0x700), // encrypted logs hash tupleGenerator(2, fr, seed + 0x800), // unencrypted logs hash @@ -376,7 +376,7 @@ export function makePublicCallRequest(seed = 1): PublicCallRequest { return new PublicCallRequest( makeAztecAddress(seed), new FunctionData(makeSelector(seed + 0x1), false, false, false), - makeCallContext(seed + 0x2), + makeCallContext(seed + 0x2, makeAztecAddress(seed)), makeTuple(ARGS_LENGTH, fr, seed + 0x10), ); } @@ -434,7 +434,7 @@ export function makeVerificationKey(): VerificationKey { 101, // arbitrary 102, // arbitrary { - A: new G1AffineElement(fr(0x200), fr(0x300)), + A: new G1AffineElement(new Fq(0x200), new Fq(0x300)), }, /* recursive proof */ true, range(5, 400), @@ -493,6 +493,24 @@ export function makePrivateKernelInputsInner(seed = 1): PrivateKernelInputsInner return new PrivateKernelInputsInner(makePreviousKernelData(seed), makePrivateCallData(seed + 0x1000)); } +/** + * Makes arbitrary call stack item. + * @param seed - The seed to use for generating the call stack item. + * @returns A call stack item. + */ +export function makeCallerContext(seed = 1): CallerContext { + return new CallerContext(makeAztecAddress(seed), makeAztecAddress(seed + 0x1)); +} + +/** + * Makes arbitrary call stack item. + * @param seed - The seed to use for generating the call stack item. + * @returns A call stack item. + */ +export function makeCallRequest(seed = 1): CallRequest { + return new CallRequest(fr(seed), makeAztecAddress(seed + 0x1), makeCallerContext(seed + 0x2)); +} + /** * Makes arbitrary public call stack item. * @param seed - The seed to use for generating the public call stack item. @@ -515,38 +533,15 @@ export function makePublicCallStackItem(seed = 1, full = false): PublicCallStack * @param seed - The seed to use for generating the public call data. * @returns A public call data. */ -export async function makePublicCallData(seed = 1, full = false): Promise { +export function makePublicCallData(seed = 1, full = false): PublicCallData { const publicCallData = new PublicCallData( makePublicCallStackItem(seed, full), - makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, makePublicCallStackItem, seed + 0x300), + makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, makeCallRequest, seed + 0x300), makeProof(), fr(seed + 1), fr(seed + 2), ); - // one kernel circuit call can have several methods in call stack. But all of them should have the same msg.sender - set these correctly in the preimages! - for (let i = 0; i < publicCallData.publicCallStackPreimages.length; i++) { - const isDelegateCall = publicCallData.publicCallStackPreimages[i].publicInputs.callContext.isDelegateCall; - publicCallData.publicCallStackPreimages[i].publicInputs.callContext.msgSender = isDelegateCall - ? publicCallData.callStackItem.publicInputs.callContext.msgSender - : publicCallData.callStackItem.contractAddress; - } - - // set the storage address for each call on the stack (handle delegatecall case) - for (let i = 0; i < publicCallData.publicCallStackPreimages.length; i++) { - const isDelegateCall = publicCallData.publicCallStackPreimages[i].publicInputs.callContext.isDelegateCall; - publicCallData.publicCallStackPreimages[i].publicInputs.callContext.storageContractAddress = isDelegateCall - ? publicCallData.callStackItem.publicInputs.callContext.storageContractAddress - : publicCallData.publicCallStackPreimages[i].contractAddress; - } - - // publicCallStack should be a hash of the preimages: - const wasm = await CircuitsWasm.get(); - publicCallData.callStackItem.publicInputs.publicCallStack = mapTuple( - publicCallData.publicCallStackPreimages, - preimage => computeCallStackItemHash(wasm!, preimage), - ); - return publicCallData; } @@ -555,9 +550,9 @@ export async function makePublicCallData(seed = 1, full = false): Promise { +export function makeWitnessedPublicCallData(seed = 1): WitnessedPublicCallData { return new WitnessedPublicCallData( - await makePublicCallData(seed), + makePublicCallData(seed), range(MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX, seed + 0x100).map(x => makeMembershipWitness(PUBLIC_DATA_TREE_HEIGHT, x), ), @@ -571,8 +566,8 @@ export async function makeWitnessedPublicCallData(seed = 1): Promise { - return new PublicKernelInputs(makePreviousKernelData(seed), await makePublicCallData(seed + 0x1000)); +export function makePublicKernelInputs(seed = 1): PublicKernelInputs { + return new PublicKernelInputs(makePreviousKernelData(seed), makePublicCallData(seed + 0x1000)); } /** @@ -581,20 +576,24 @@ export async function makePublicKernelInputs(seed = 1): Promise void, -): Promise { +): PublicKernelInputs { const kernelCircuitPublicInputs = makeKernelPublicInputs(seed, false); - const publicKernelInputs = new PublicKernelInputs( - makePreviousKernelData(seed, kernelCircuitPublicInputs), - await makePublicCallData(seed + 0x1000), - ); - if (tweak) tweak(publicKernelInputs); + const previousKernel = makePreviousKernelData(seed, kernelCircuitPublicInputs); + const publicCall = makePublicCallData(seed + 0x1000); + const publicKernelInputs = new PublicKernelInputs(previousKernel, publicCall); + if (tweak) { + tweak(publicKernelInputs); + } // Set the call stack item for this circuit iteration at the top of the call stack - const wasm = await CircuitsWasm.get(); publicKernelInputs.previousKernel.publicInputs.end.publicCallStack[MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX - 1] = - computeCallStackItemHash(wasm, publicKernelInputs.publicCall.callStackItem); + new CallRequest( + publicCall.callStackItem.hash(), + publicCall.callStackItem.publicInputs.callContext.msgSender, + makeCallerContext(seed + 0x100), + ); return publicKernelInputs; } @@ -620,7 +619,8 @@ export function makeTxRequest(seed = 1): TxRequest { export function makePrivateCallData(seed = 1): PrivateCallData { return PrivateCallData.from({ callStackItem: makePrivateCallStackItem(seed), - privateCallStackPreimages: makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, makePrivateCallStackItem, seed + 0x10), + privateCallStack: makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, makeCallRequest, seed + 0x10), + publicCallStack: makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, makeCallRequest, seed + 0x20), proof: new Proof(Buffer.alloc(16).fill(seed + 0x50)), vk: makeVerificationKey(), functionLeafMembershipWitness: makeMembershipWitness(FUNCTION_TREE_HEIGHT, seed + 0x30), @@ -672,8 +672,8 @@ export function makePrivateCircuitPublicInputs(seed = 0): PrivateCircuitPublicIn newCommitments: makeTuple(MAX_NEW_COMMITMENTS_PER_CALL, fr, seed + 0x400), newNullifiers: makeTuple(MAX_NEW_NULLIFIERS_PER_CALL, fr, seed + 0x500), nullifiedCommitments: makeTuple(MAX_NEW_NULLIFIERS_PER_CALL, fr, seed + 0x510), - privateCallStack: makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, fr, seed + 0x600), - publicCallStack: makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, fr, seed + 0x700), + privateCallStackHashes: makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, fr, seed + 0x600), + publicCallStackHashes: makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, fr, seed + 0x700), newL2ToL1Msgs: makeTuple(MAX_NEW_L2_TO_L1_MSGS_PER_CALL, fr, seed + 0x800), encryptedLogsHash: makeTuple(NUM_FIELDS_PER_SHA256, fr, seed + 0x900), unencryptedLogsHash: makeTuple(NUM_FIELDS_PER_SHA256, fr, seed + 0xa00), diff --git a/yarn-project/circuits.js/src/utils/jsUtils.ts b/yarn-project/circuits.js/src/utils/jsUtils.ts index c2554751728..a6a2e5b8bb1 100644 --- a/yarn-project/circuits.js/src/utils/jsUtils.ts +++ b/yarn-project/circuits.js/src/utils/jsUtils.ts @@ -77,10 +77,10 @@ export function assertItemsLength< }[]; }, >(obj: T, member: F, length: number) { - const arrs = obj[member]; - for (let i = 0; i < arrs.length; i++) { - if (arrs[i].length !== length) { - throw new Error(`Expected ${member}[${i}] to have length ${length} but was ${arrs[i].length}`); + const arrays = obj[member]; + for (let i = 0; i < arrays.length; i++) { + if (arrays[i].length !== length) { + throw new Error(`Expected ${member}[${i}] to have length ${length} but was ${arrays[i].length}`); } } } diff --git a/yarn-project/circuits.js/src/wasm/circuits_wasm.ts b/yarn-project/circuits.js/src/wasm/circuits_wasm.ts index dd299c05aee..29a7663e0fd 100644 --- a/yarn-project/circuits.js/src/wasm/circuits_wasm.ts +++ b/yarn-project/circuits.js/src/wasm/circuits_wasm.ts @@ -48,7 +48,9 @@ export class CircuitsWasm implements IWasmModule { * @returns The singleton. */ public static get(): Promise { - if (!this.instance) this.instance = CircuitsWasm.new(); + if (!this.instance) { + this.instance = CircuitsWasm.new(); + } return this.instance; } diff --git a/yarn-project/cli/aztec-cli b/yarn-project/cli/aztec-cli index 3a58b32d952..6a96c892d47 100755 --- a/yarn-project/cli/aztec-cli +++ b/yarn-project/cli/aztec-cli @@ -3,7 +3,7 @@ # Wrapper script around @aztec/cli using Docker. This is intended to be used by devs that don't have # a NodeJS environment setup locally. The script starts a Docker container passing any commands and # arguments to the CLI running inside the container. -# If this wrapper script detecs a global install of @aztec/cli it falls back on that instead of Docker. +# If this wrapper script detects a global install of @aztec/cli it falls back on that instead of Docker. set -euo pipefail @@ -117,11 +117,11 @@ if [[ "$AZTEC_CLI_COMMAND" == "update" ]]; then add_mount "$PWD" fi -# bash's builtin getops only works with single characeter flags +# bash's builtin getops only works with single character flags # GNU getopt doesn't exist on macOS # process the flags manually # -# go through each parameter (execpt the first one, which is the command) +# go through each parameter (except the first one, which is the command) # and check if it's either a filename or a directory. If it is then mount inside the container # NOTE: this won't work with assignement-style flags, e.g. --outdir=/foo for (( i=2; i <= "$#"; i++ )); do diff --git a/yarn-project/cli/package.json b/yarn-project/cli/package.json index f1304f5fe44..10e6102f95f 100644 --- a/yarn-project/cli/package.json +++ b/yarn-project/cli/package.json @@ -18,7 +18,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests", "start": "node --no-warnings ./dest/bin/index.js" }, @@ -53,7 +53,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/lodash.startcase": "^4.4.7", "@types/node": "^18.7.23", diff --git a/yarn-project/cli/src/encoding.ts b/yarn-project/cli/src/encoding.ts index da824d68ea0..d903c2fd234 100644 --- a/yarn-project/cli/src/encoding.ts +++ b/yarn-project/cli/src/encoding.ts @@ -8,7 +8,7 @@ import { Fr } from '@aztec/foundation/fields'; * @returns An object in the ABI struct type's format. */ export function parseStructString(str: string, abiType: StructType) { - // Assing string bytes to struct fields. + // Assign string bytes to struct fields. const buf = Buffer.from(str.replace(/^0x/i, ''), 'hex'); const struct: any = {}; let byteIndex = 0; @@ -42,9 +42,14 @@ function encodeArg(arg: string, abiType: ABIType, name: string): any { } return res; } else if (kind === 'boolean') { - if (arg === 'true') return true; - if (arg === 'false') return false; - else throw Error(`Invalid boolean value passed for ${name}: ${arg}.`); + if (arg === 'true') { + return true; + } + if (arg === 'false') { + return false; + } else { + throw Error(`Invalid boolean value passed for ${name}: ${arg}.`); + } } else if (kind === 'array') { let arr; const res = []; @@ -53,9 +58,12 @@ function encodeArg(arg: string, abiType: ABIType, name: string): any { } catch { throw new Error(`Unable to parse arg ${arg} as array for ${name} parameter`); } - if (!Array.isArray(arr)) throw Error(`Invalid argument ${arg} passed for array parameter ${name}.`); - if (arr.length !== abiType.length) + if (!Array.isArray(arr)) { + throw Error(`Invalid argument ${arg} passed for array parameter ${name}.`); + } + if (arr.length !== abiType.length) { throw Error(`Invalid array length passed for ${name}. Expected ${abiType.length}, received ${arr.length}.`); + } for (let i = 0; i < abiType.length; i += 1) { res.push(encodeArg(arr[i], abiType.type, name)); } @@ -71,12 +79,16 @@ function encodeArg(arg: string, abiType: ABIType, name: string): any { } catch { throw new Error(`Unable to parse arg ${arg} as struct`); } - if (Array.isArray(obj)) throw Error(`Array passed for arg ${name}. Expected a struct.`); + if (Array.isArray(obj)) { + throw Error(`Array passed for arg ${name}. Expected a struct.`); + } const res: any = {}; for (const field of abiType.fields) { // Remove field name from list as it's present const arg = obj[field.name]; - if (!arg) throw Error(`Expected field ${field.name} not found in struct ${name}.`); + if (!arg) { + throw Error(`Expected field ${field.name} not found in struct ${name}.`); + } res[field.name] = encodeArg(obj[field.name], field.type, field.name); } return res; diff --git a/yarn-project/cli/src/index.ts b/yarn-project/cli/src/index.ts index a33f13ae89c..e99d6f640db 100644 --- a/yarn-project/cli/src/index.ts +++ b/yarn-project/cli/src/index.ts @@ -20,7 +20,7 @@ import { JsonStringify } from '@aztec/foundation/json-rpc'; import { DebugLogger, LogFn } from '@aztec/foundation/log'; import { sleep } from '@aztec/foundation/sleep'; import { fileURLToPath } from '@aztec/foundation/url'; -import { compileContract, generateNoirInterface, generateTypescriptInterface } from '@aztec/noir-compiler/cli'; +import { compileNoir, generateNoirInterface, generateTypescriptInterface } from '@aztec/noir-compiler/cli'; import { CompleteAddress, ContractData, ExtendedNote, LogFilter } from '@aztec/types'; import { createSecp256k1PeerId } from '@libp2p/peer-id-factory'; @@ -129,18 +129,18 @@ export function getProgram(log: LogFn, debugLogger: DebugLogger): Command { '-m, --mnemonic', 'An optional mnemonic string used for the private key generation. If not provided, random private key will be generated.', ) - .action(async options => { + .action(options => { let privKey; let publicKey; if (options.mnemonic) { const acc = mnemonicToAccount(options.mnemonic); // TODO(#2052): This reduction is not secure enough. TACKLE THIS ISSUE BEFORE MAINNET. - const key = GrumpkinScalar.fromBufferWithReduction(Buffer.from(acc.getHdKey().privateKey!)); - publicKey = await generatePublicKey(key); + const key = GrumpkinScalar.fromBufferReduce(Buffer.from(acc.getHdKey().privateKey!)); + publicKey = generatePublicKey(key); } else { const key = GrumpkinScalar.random(); - privKey = key.toString(true); - publicKey = await generatePublicKey(key); + privKey = key.toString(); + publicKey = generatePublicKey(key); } log(`\nPrivate Key: ${privKey}\nPublic Key: ${publicKey.toString()}\n`); }); @@ -174,7 +174,7 @@ export function getProgram(log: LogFn, debugLogger: DebugLogger): Command { const actualPrivateKey = privateKey ?? GrumpkinScalar.random(); const account = getSchnorrAccount(client, actualPrivateKey, actualPrivateKey, accountCreationSalt); - const { address, publicKey, partialAddress } = await account.getCompleteAddress(); + const { address, publicKey, partialAddress } = account.getCompleteAddress(); const tx = await account.deploy(); const txHash = await tx.getTxHash(); debugLogger(`Account contract tx sent with hash ${txHash}`); @@ -188,7 +188,9 @@ export function getProgram(log: LogFn, debugLogger: DebugLogger): Command { log(`\nNew account:\n`); log(`Address: ${address.toString()}`); log(`Public key: ${publicKey.toString()}`); - if (!privateKey) log(`Private key: ${actualPrivateKey.toString(true)}`); + if (!privateKey) { + log(`Private key: ${actualPrivateKey.toString()}`); + } log(`Partial address: ${partialAddress.toString()}`); }); @@ -259,7 +261,9 @@ export function getProgram(log: LogFn, debugLogger: DebugLogger): Command { const deployer = new ContractDeployer(contractArtifact, client, publicKey); const constructor = getFunctionArtifact(contractArtifact, 'constructor'); - if (!constructor) throw new Error(`Constructor not found in contract ABI`); + if (!constructor) { + throw new Error(`Constructor not found in contract ABI`); + } debugLogger(`Input arguments: ${rawArgs.map((x: any) => `"${x}"`).join(', ')}`); const args = encodeArgs(rawArgs, constructorArtifact!.parameters); @@ -295,8 +299,11 @@ export function getProgram(log: LogFn, debugLogger: DebugLogger): Command { const client = await createCompatibleClient(options.rpcUrl, debugLogger); const address = options.contractAddress; const isDeployed = await isContractDeployed(client, address); - if (isDeployed) log(`\nContract found at ${address.toString()}\n`); - else log(`\nNo contract found at ${address.toString()}\n`); + if (isDeployed) { + log(`\nContract found at ${address.toString()}\n`); + } else { + log(`\nNo contract found at ${address.toString()}\n`); + } }); program @@ -392,8 +399,12 @@ export function getProgram(log: LogFn, debugLogger: DebugLogger): Command { const pxe = await createCompatibleClient(rpcUrl, debugLogger); if (follow) { - if (txHash) throw Error('Cannot use --follow with --tx-hash'); - if (toBlock) throw Error('Cannot use --follow with --to-block'); + if (txHash) { + throw Error('Cannot use --follow with --tx-hash'); + } + if (toBlock) { + throw Error('Cannot use --follow with --to-block'); + } } const filter: LogFilter = { txHash, fromBlock, toBlock, afterLog, contractAddress, selector }; @@ -407,9 +418,13 @@ export function getProgram(log: LogFn, debugLogger: DebugLogger): Command { .filter(([, value]) => value !== undefined) .map(([key, value]) => `${key}: ${value}`) .join(', '); - if (!follow) log(`No logs found for filter: {${filterOptions}}`); + if (!follow) { + log(`No logs found for filter: {${filterOptions}}`); + } } else { - if (!follow && !filter.afterLog) log('Logs found: \n'); + if (!follow && !filter.afterLog) { + log('Logs found: \n'); + } logs.forEach(unencryptedLog => log(unencryptedLog.toHumanReadable())); // Set the continuation parameter for the following requests filter.afterLog = logs[logs.length - 1].id; @@ -421,7 +436,9 @@ export function getProgram(log: LogFn, debugLogger: DebugLogger): Command { log('Fetching logs...'); while (true) { const maxLogsHit = await fetchLogs(); - if (!maxLogsHit) await sleep(1000); + if (!maxLogsHit) { + await sleep(1000); + } } } else { while (await fetchLogs()) { @@ -723,7 +740,7 @@ export function getProgram(log: LogFn, debugLogger: DebugLogger): Command { await update(projectPath, contract, options.rpcUrl, options.sandboxVersion, log, debugLogger); }); - compileContract(program, 'compile', log); + compileNoir(program, 'compile', log); generateTypescriptInterface(program, 'generate-typescript', log); generateNoirInterface(program, 'generate-noir-interface', log); diff --git a/yarn-project/cli/src/test/utils.test.ts b/yarn-project/cli/src/test/utils.test.ts index 28dac48c874..e465138083b 100644 --- a/yarn-project/cli/src/test/utils.test.ts +++ b/yarn-project/cli/src/test/utils.test.ts @@ -22,9 +22,11 @@ describe('CLI Utils', () => { subField1: field.toString(), subField2: 'true', }; + beforeEach(() => { client = mock(); }); + it('Gets a txSender correctly or throw error', async () => { // returns a parsed Aztec Address const aztecAddress = AztecAddress.random(); @@ -41,19 +43,11 @@ describe('CLI Utils', () => { // throws when invalid parameter passed const errorAddr = 'foo'; - await expect( - (async () => { - await getTxSender(client, errorAddr); - })(), - ).rejects.toThrow(`Invalid option 'from' passed: ${errorAddr}`); + await expect(getTxSender(client, errorAddr)).rejects.toThrow(`Invalid option 'from' passed: ${errorAddr}`); // Throws error when no string is passed & no accounts found in RPC client.getRegisteredAccounts.mockResolvedValueOnce([]); - await expect( - (async () => { - await getTxSender(client); - })(), - ).rejects.toThrow('No accounts found in PXE instance.'); + await expect(getTxSender(client)).rejects.toThrow('No accounts found in PXE instance.'); }); it('Encodes args correctly', () => { diff --git a/yarn-project/cli/src/unbox.ts b/yarn-project/cli/src/unbox.ts index 7cb010f3e3c..bc990d7cde1 100644 --- a/yarn-project/cli/src/unbox.ts +++ b/yarn-project/cli/src/unbox.ts @@ -100,7 +100,7 @@ async function downloadContractAndBoxFromGithub( log: LogFn, ): Promise { // small string conversion, in the ABI the contract name looks like PrivateToken - // but in the repostory it looks like private_token + // but in the repository it looks like private_token log(`Downloading @aztec/boxes/${contractName}/ to ${outputPath}...`); // Step 1: Fetch the monorepo ZIP from GitHub, matching the CLI version @@ -256,7 +256,7 @@ async function updatePackageJsonVersions(packageVersion: string, outputPath: str // modify the version of the sandbox to pull - it's set to "latest" version in the monorepo, // but we need to replace with the same tagVersion as the cli and the other aztec npm packages - // similarly, make sure we spinup the sandbox with the same version. + // similarly, make sure we spin up the sandbox with the same version. packageData.scripts['install:sandbox'] = packageData.scripts['install:sandbox'].replace( 'latest', `${packageVersion}`, diff --git a/yarn-project/cli/src/update/npm.ts b/yarn-project/cli/src/update/npm.ts index 41dadbf518e..6d73c1ca7d6 100644 --- a/yarn-project/cli/src/update/npm.ts +++ b/yarn-project/cli/src/update/npm.ts @@ -17,6 +17,8 @@ import { DependencyChanges } from './common.js'; export async function readPackageJson(projectPath: string): Promise<{ /** dependencies */ dependencies?: Record; + /** devDependencies */ + devDependencies?: Record; }> { const configFilepath = resolve(join(projectPath, 'package.json')); const pkg = JSON.parse(await readFile(configFilepath, 'utf-8')); @@ -63,31 +65,34 @@ export async function updateAztecDeps( dependencies: [], }; - if (!pkg.dependencies) { - return changes; - } - log(`Updating @aztec packages to ${aztecVersion} in ${relative(process.cwd(), changes.file)}`); const version = aztecVersion.version; - for (const name of Object.keys(pkg.dependencies)) { - if (!name.startsWith('@aztec/')) { - continue; - } - - // different release schedule - if (name === '@aztec/aztec-ui') { + for (const depType of ['dependencies', 'devDependencies'] as const) { + const dependencies = pkg[depType]; + if (!dependencies) { continue; } - if (pkg.dependencies[name] !== version) { - changes.dependencies.push({ - name, - from: pkg.dependencies[name], - to: version, - }); - - pkg.dependencies[name] = version; + for (const name of Object.keys(dependencies)) { + if (!name.startsWith('@aztec/')) { + continue; + } + + // different release schedule + if (name === '@aztec/aztec-ui') { + continue; + } + + if (dependencies[name] !== version) { + changes.dependencies.push({ + name, + from: dependencies[name], + to: version, + }); + + dependencies[name] = version; + } } } diff --git a/yarn-project/cli/src/update/update.ts b/yarn-project/cli/src/update/update.ts index bd819605d0d..977cb699c4e 100644 --- a/yarn-project/cli/src/update/update.ts +++ b/yarn-project/cli/src/update/update.ts @@ -98,7 +98,13 @@ async function getNpmSandboxVersion(projectPath: string, log: LogFn): Promise/dev/null 2>&1; then # Tag the existing version npm dist-tag add $PACKAGE_NAME@$VERSION $DIST_TAG else - # Publish new verison + # Publish new version npm publish $TAG_ARG --access public fi - else - npm publish --dry-run $TAG_ARG --access public fi # Back to root diff --git a/yarn-project/end-to-end/package.json b/yarn-project/end-to-end/package.json index 3c6adc367f0..bb804afbf6b 100644 --- a/yarn-project/end-to-end/package.json +++ b/yarn-project/end-to-end/package.json @@ -8,7 +8,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src \"!src/web/main.js\" && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "DEBUG='aztec:*' NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --runInBand --passWithNoTests --testTimeout=15000 --forceExit", "test:integration": "concurrently -k -s first -c reset,dim -n test,anvil \"yarn test:integration:run\" \"anvil\"", "test:integration:run": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --no-cache --runInBand --config jest.integration.config.json" @@ -31,6 +31,7 @@ "@aztec/ethereum": "workspace:^", "@aztec/foundation": "workspace:^", "@aztec/l1-artifacts": "workspace:^", + "@aztec/merkle-tree": "workspace:^", "@aztec/noir-contracts": "workspace:^", "@aztec/p2p": "workspace:^", "@aztec/pxe": "workspace:^", @@ -71,7 +72,6 @@ "winston": "^3.10.0" }, "devDependencies": { - "@rushstack/eslint-patch": "^1.1.4", "concurrently": "^7.6.0" }, "files": [ diff --git a/yarn-project/end-to-end/scripts/docker-compose-p2p.yml b/yarn-project/end-to-end/scripts/docker-compose-p2p.yml index c030caaef20..3bca942dd23 100644 --- a/yarn-project/end-to-end/scripts/docker-compose-p2p.yml +++ b/yarn-project/end-to-end/scripts/docker-compose-p2p.yml @@ -7,11 +7,12 @@ services: - '8545:8545' p2p-bootstrap: - image: aztecprotocol/p2p-bootstrap:latest + image: aztecprotocol/aztec-sandbox:latest ports: - '40400:40400' command: 'start' environment: + MODE: 'p2p-bootstrap' DEBUG: 'aztec:*' DEBUG_COLORS: 1 P2P_TCP_LISTEN_PORT: 40400 diff --git a/yarn-project/end-to-end/src/benchmarks/bench_publish_rollup.test.ts b/yarn-project/end-to-end/src/benchmarks/bench_publish_rollup.test.ts index 7e191cee1b3..61b3359d250 100644 --- a/yarn-project/end-to-end/src/benchmarks/bench_publish_rollup.test.ts +++ b/yarn-project/end-to-end/src/benchmarks/bench_publish_rollup.test.ts @@ -24,7 +24,7 @@ describe('benchmarks/publish_rollup', () => { // Simulate and simultaneously send ROLLUP_SIZE txs. These should not yet be processed since sequencer is stopped. context.logger(`Assembling rollup with ${txCount} txs`); const sentTxs = await sendTxs(txCount, context, contract); - + context.logger.info(`Sent ${txCount} txs`); // Restart sequencer to process all txs together sequencer.restart(); diff --git a/yarn-project/end-to-end/src/cli_docs_sandbox.test.ts b/yarn-project/end-to-end/src/cli_docs_sandbox.test.ts index ad10e117ee4..2e7bdb152f1 100644 --- a/yarn-project/end-to-end/src/cli_docs_sandbox.test.ts +++ b/yarn-project/end-to-end/src/cli_docs_sandbox.test.ts @@ -72,7 +72,9 @@ Rollup Address: 0x0dcd1bf9a1b36ce34237eeafef220932846bcd82 const findInLogs = (regex: RegExp) => { for (const log of logs) { const match = regex.exec(log); - if (match) return match; + if (match) { + return match; + } } }; @@ -80,7 +82,9 @@ Rollup Address: 0x0dcd1bf9a1b36ce34237eeafef220932846bcd82 const matches = []; for (const log of logs) { const match = regex.exec(log); - if (match) matches.push(match); + if (match) { + matches.push(match); + } } return matches; }; @@ -109,8 +113,10 @@ PriceFeedContractArtifact SchnorrAccountContractArtifact SchnorrHardcodedAccountContractArtifact SchnorrSingleKeyAccountContractArtifact +SlowTreeContractArtifact StatefulTestContractArtifact TestContractArtifact +TokenBlacklistContractArtifact TokenBridgeContractArtifact TokenContractArtifact UniswapContractArtifact diff --git a/yarn-project/end-to-end/src/e2e_2_pxes.test.ts b/yarn-project/end-to-end/src/e2e_2_pxes.test.ts index ad21409a9de..9543b801fdf 100644 --- a/yarn-project/end-to-end/src/e2e_2_pxes.test.ts +++ b/yarn-project/end-to-end/src/e2e_2_pxes.test.ts @@ -58,7 +58,9 @@ describe('e2e_2_pxes', () => { afterEach(async () => { await teardownA(); - if ((pxeB as any).stop) await (pxeB as any).stop(); + if ((pxeB as any).stop) { + await (pxeB as any).stop(); + } }); const awaitUserSynchronized = async (wallet: Wallet, owner: AztecAddress) => { @@ -117,7 +119,7 @@ describe('e2e_2_pxes', () => { ); }; - it('transfers fund from user A to B via PXE A followed by transfer from B to A via PXE B', async () => { + it('transfers funds from user A to B via PXE A followed by transfer from B to A via PXE B', async () => { const initialBalance = 987n; const transferAmount1 = 654n; const transferAmount2 = 323n; @@ -254,7 +256,7 @@ describe('e2e_2_pxes', () => { it('permits migrating an account from one PXE to another', async () => { const privateKey = GrumpkinScalar.random(); const account = getUnsafeSchnorrAccount(pxeA, privateKey, Fr.random()); - const completeAddress = await account.getCompleteAddress(); + const completeAddress = account.getCompleteAddress(); const wallet = await account.waitDeploy(); await expect(wallet.isAccountStateSynchronized(completeAddress.address)).resolves.toBe(true); diff --git a/yarn-project/end-to-end/src/e2e_account_contracts.test.ts b/yarn-project/end-to-end/src/e2e_account_contracts.test.ts index d66139c8a93..ee501fa57e7 100644 --- a/yarn-project/end-to-end/src/e2e_account_contracts.test.ts +++ b/yarn-project/end-to-end/src/e2e_account_contracts.test.ts @@ -62,7 +62,7 @@ function itShouldBehaveLikeAnAccountContract( }, 60_000); it('fails to call a function using an invalid signature', async () => { - const accountAddress = await account.getCompleteAddress(); + const accountAddress = account.getCompleteAddress(); const { wallet: invalidWallet } = await walletSetup( context.pxe, encryptionPrivateKey, diff --git a/yarn-project/end-to-end/src/e2e_blacklist_token_contract.test.ts b/yarn-project/end-to-end/src/e2e_blacklist_token_contract.test.ts new file mode 100644 index 00000000000..1c1825c7d83 --- /dev/null +++ b/yarn-project/end-to-end/src/e2e_blacklist_token_contract.test.ts @@ -0,0 +1,1419 @@ +import { + AccountWallet, + AztecAddress, + CheatCodes, + CompleteAddress, + DebugLogger, + ExtendedNote, + Fr, + FunctionSelector, + Note, + TxHash, + TxStatus, + Wallet, + computeAuthWitMessageHash, + computeMessageSecretHash, +} from '@aztec/aztec.js'; +import { Pedersen, SparseTree, newTree } from '@aztec/merkle-tree'; +import { SlowTreeContract, TokenBlacklistContract, TokenContract } from '@aztec/noir-contracts/types'; + +import { jest } from '@jest/globals'; +import levelup from 'levelup'; +import { type MemDown, default as memdown } from 'memdown'; + +import { setup } from './fixtures/utils.js'; +import { TokenSimulator } from './simulators/token_simulator.js'; + +export const createMemDown = () => (memdown as any)() as MemDown; + +const TIMEOUT = 90_000; + +describe('e2e_blacklist_token_contract', () => { + jest.setTimeout(TIMEOUT); + + let teardown: () => Promise; + let wallets: AccountWallet[]; + let accounts: CompleteAddress[]; + let logger: DebugLogger; + + let asset: TokenBlacklistContract; + let slowTree: SlowTreeContract; + + let tokenSim: TokenSimulator; + + let slowUpdateTreeSimulator: SparseTree; + + let cheatCodes: CheatCodes; + + const getMembershipProof = async (index: bigint, includeUncommitted: boolean) => { + return { + index, + value: Fr.fromBuffer((await slowUpdateTreeSimulator.getLeafValue(index, includeUncommitted))!), + // eslint-disable-next-line camelcase + sibling_path: (await slowUpdateTreeSimulator.getSiblingPath(index, includeUncommitted)).toFieldArray(), + }; + }; + + const getMembershipCapsule = (proof: { index: bigint; value: Fr; sibling_path: Fr[] }) => { + return [new Fr(proof.index), proof.value, ...proof.sibling_path]; + }; + + const getUpdateProof = async (newValue: bigint, index: bigint) => { + const beforeProof = await getMembershipProof(index, false); + const afterProof = await getMembershipProof(index, true); + + return { + index, + // eslint-disable-next-line camelcase + new_value: newValue, + // eslint-disable-next-line camelcase + before: { value: beforeProof.value, sibling_path: beforeProof.sibling_path }, + // eslint-disable-next-line camelcase + after: { value: afterProof.value, sibling_path: afterProof.sibling_path }, + }; + }; + + const getUpdateCapsule = (proof: { + index: bigint; + new_value: bigint; + before: { value: Fr; sibling_path: Fr[] }; + after: { value: Fr; sibling_path: Fr[] }; + }) => { + return [ + new Fr(proof.index), + new Fr(proof.new_value), + proof.before.value, + ...proof.before.sibling_path, + proof.after.value, + ...proof.after.sibling_path, + ]; + }; + + const addPendingShieldNoteToPXE = async (accountIndex: number, amount: bigint, secretHash: Fr, txHash: TxHash) => { + const storageSlot = new Fr(5); // The storage slot of `pending_shields` is 5. + const note = new Note([new Fr(amount), secretHash]); + const extendedNote = new ExtendedNote(note, accounts[accountIndex].address, asset.address, storageSlot, txHash); + await wallets[accountIndex].addNote(extendedNote); + }; + + const updateSlowTree = async (tree: SparseTree, wallet: Wallet, index: AztecAddress, value: bigint) => { + await wallet.addCapsule(getUpdateCapsule(await getUpdateProof(value, index.toBigInt()))); + await tree.updateLeaf(new Fr(value).toBuffer(), index.toBigInt()); + }; + + beforeAll(async () => { + ({ teardown, logger, wallets, accounts, cheatCodes } = await setup(4)); + + slowTree = await SlowTreeContract.deploy(wallets[0]).send().deployed(); + + const depth = 254; + slowUpdateTreeSimulator = await newTree(SparseTree, levelup(createMemDown()), new Pedersen(), 'test', depth); + + const deployTx = TokenBlacklistContract.deploy(wallets[0], accounts[0], slowTree.address).send({}); + const receipt = await deployTx.wait(); + asset = receipt.contract; + + // Add the note + const note = new Note([slowTree.address.toField()]); + const storageSlot = new Fr(7); + + for (const wallet of wallets) { + const extendedNote = new ExtendedNote( + note, + wallet.getCompleteAddress().address, + asset.address, + storageSlot, + receipt.txHash, + ); + await wallet.addNote(extendedNote); + } + + // Add account[0] as admin + await updateSlowTree(slowUpdateTreeSimulator, wallets[0], accounts[0].address, 4n); + await asset.methods.init_slow_tree(accounts[0].address).send().wait(); + + // Progress to next "epoch" + const time = await cheatCodes.eth.timestamp(); + await cheatCodes.aztec.warp(time + 200); + await slowUpdateTreeSimulator.commit(); + + const roleLeaf = await slowTree.methods.un_read_leaf_at(asset.address, accounts[0].address).view(); + expect(roleLeaf['next_change']).toBeGreaterThan(0n); + expect(roleLeaf['before']).toEqual(0n); + expect(roleLeaf['after']).toEqual(4n); + + logger(`Token deployed to ${asset.address}`); + tokenSim = new TokenSimulator( + asset as unknown as TokenContract, + logger, + accounts.map(a => a.address), + ); + + asset.artifact.functions.forEach(fn => { + logger( + `Function ${fn.name} has ${fn.bytecode.length} bytes and the selector: ${FunctionSelector.fromNameAndParameters( + fn.name, + fn.parameters, + )}`, + ); + }); + }, 100_000); + + afterAll(() => teardown()); + + afterEach(async () => { + await tokenSim.check(); + }, TIMEOUT); + + describe('Access controlled functions', () => { + it('Extend account[0] roles with minter as admin', async () => { + const newMinter = accounts[0].address; + const newRoles = 2n + 4n; + + const beforeLeaf = await slowTree.methods.un_read_leaf_at(asset.address, newMinter).view(); + // eslint-disable-next-line camelcase + expect(beforeLeaf['next_change']).toBeGreaterThan(0n); + expect(beforeLeaf['before']).toEqual(0n); + expect(beforeLeaf['after']).toEqual(4n); + + await updateSlowTree(slowUpdateTreeSimulator, wallets[0], newMinter, newRoles); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), false)), + ); + + const tx = await asset.methods.update_roles(newMinter, newRoles).send().wait(); + expect(tx.status).toBe(TxStatus.MINED); + await slowUpdateTreeSimulator.commit(); + + const afterLeaf = await slowTree.methods.un_read_leaf_at(asset.address, newMinter).view(); + expect(afterLeaf['next_change']).toBeGreaterThan(beforeLeaf['next_change']); + expect(afterLeaf['before']).toEqual(4n); + expect(afterLeaf['after']).toEqual(newRoles); + + const time = await cheatCodes.eth.timestamp(); + await cheatCodes.aztec.warp(time + 200); + + /* const tx = asset.withWallet(wallets[1]).methods.set_minter(accounts[1].address, true).send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + expect(await asset.methods.is_minter(accounts[1].address).view()).toBe(true);*/ + }); + + it('Make account[1] admin', async () => { + const newAdmin = accounts[1].address; + const newRoles = 4n; + + let v = await slowTree.methods.un_read_leaf_at(asset.address, newAdmin).view(); + // eslint-disable-next-line camelcase + expect(v).toEqual({ next_change: 0n, before: 0n, after: 0n }); + + await updateSlowTree(slowUpdateTreeSimulator, wallets[0], newAdmin, newRoles); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), false)), + ); + + const tx = await asset.methods.update_roles(newAdmin, newRoles).send().wait(); + expect(tx.status).toBe(TxStatus.MINED); + await slowUpdateTreeSimulator.commit(); + + v = await slowTree.methods.un_read_leaf_at(asset.address, newAdmin).view(); + expect(v['next_change']).toBeGreaterThan(0n); + expect(v['before']).toEqual(0n); + expect(v['after']).toEqual(newRoles); + + // Progress to next "epoch" + const time = await cheatCodes.eth.timestamp(); + await cheatCodes.aztec.warp(time + 200); + }); + + it('Revoke admin as admin', async () => { + const actor = accounts[1].address; + const newRoles = 0n; + const currentRoles = 4n; + + const beforeLeaf = await slowTree.methods.un_read_leaf_at(asset.address, actor).view(); + // eslint-disable-next-line camelcase + expect(beforeLeaf['next_change']).toBeGreaterThan(0n); + expect(beforeLeaf['before']).toEqual(0n); + expect(beforeLeaf['after']).toEqual(currentRoles); + + await updateSlowTree(slowUpdateTreeSimulator, wallets[0], actor, newRoles); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), false)), + ); + + const tx = await asset.methods.update_roles(actor, newRoles).send().wait(); + expect(tx.status).toBe(TxStatus.MINED); + await slowUpdateTreeSimulator.commit(); + + const afterLeaf = await slowTree.methods.un_read_leaf_at(asset.address, actor).view(); + expect(afterLeaf['next_change']).toBeGreaterThan(beforeLeaf['next_change']); + expect(afterLeaf['before']).toEqual(currentRoles); + expect(afterLeaf['after']).toEqual(newRoles); + + const time = await cheatCodes.eth.timestamp(); + await cheatCodes.aztec.warp(time + 200); + + /* + const tx = asset.withWallet(wallets[1]).methods.set_minter(accounts[1].address, false).send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + expect(await asset.methods.is_minter(accounts[1].address).view()).toBe(false);*/ + }); + + it('Add account[3] to blacklist', async () => { + let v = await slowTree.methods.un_read_leaf_at(asset.address, accounts[3].address).view(); + // eslint-disable-next-line camelcase + expect(v).toEqual({ next_change: 0n, before: 0n, after: 0n }); + + await updateSlowTree(slowUpdateTreeSimulator, wallets[0], accounts[3].address, 1n); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), false)), + ); + + const tx = await asset.methods.update_roles(accounts[3].address, 1n).send().wait(); + expect(tx.status).toBe(TxStatus.MINED); + await slowUpdateTreeSimulator.commit(); + + v = await slowTree.methods.un_read_leaf_at(asset.address, accounts[3].address).view(); + expect(v['next_change']).toBeGreaterThan(0n); + expect(v['before']).toEqual(0n); + expect(v['after']).toEqual(1n); + + const time = await cheatCodes.eth.timestamp(); + await cheatCodes.aztec.warp(time + 200); + }); + + describe('failure cases', () => { + it('Set admin (not admin)', async () => { + const account = AztecAddress.random(); + const v = await slowTree.methods.un_read_leaf_at(asset.address, account).view(); + const newRoles = 4n; + // eslint-disable-next-line camelcase + expect(v).toEqual({ next_change: 0n, before: 0n, after: 0n }); + + await wallets[3].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[3].address.toBigInt(), false)), + ); + await expect( + asset.withWallet(wallets[3]).methods.update_roles(account, newRoles).simulate(), + ).rejects.toThrowError("Assertion failed: caller is not admin 'caller_roles.is_admin'"); + + /* await expect(asset.methods.set_admin(accounts[0].address).simulate()).rejects.toThrowError( + 'Assertion failed: caller is not admin', + );*/ + }); + + it('Revoke minter not as admin', async () => { + const adminAccount = accounts[0].address; + const v = await slowTree.methods.un_read_leaf_at(asset.address, adminAccount).view(); + const newRoles = 0n; + // eslint-disable-next-line camelcase + expect(v['next_change']).toBeGreaterThan(0n); + expect(v['before']).toEqual(4n); + expect(v['after']).toEqual(6n); + + await wallets[3].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[3].address.toBigInt(), false)), + ); + await expect( + asset.withWallet(wallets[3]).methods.update_roles(adminAccount, newRoles).simulate(), + ).rejects.toThrowError("Assertion failed: caller is not admin 'caller_roles.is_admin'"); + + /* await expect(asset.methods.set_minter(accounts[0].address, false).simulate()).rejects.toThrowError( + 'Assertion failed: caller is not admin', + );*/ + }); + }); + }); + + describe('Minting', () => { + describe('Public', () => { + it('as minter', async () => { + const amount = 10000n; + const tx = asset.methods.mint_public(accounts[0].address, amount).send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + + tokenSim.mintPublic(accounts[0].address, amount); + expect(await asset.methods.balance_of_public(accounts[0].address).view()).toEqual( + tokenSim.balanceOfPublic(accounts[0].address), + ); + expect(await asset.methods.total_supply().view()).toEqual(tokenSim.totalSupply); + }); + + describe('failure cases', () => { + it('as non-minter', async () => { + const amount = 10000n; + await expect( + asset.withWallet(wallets[1]).methods.mint_public(accounts[0].address, amount).simulate(), + ).rejects.toThrowError('Assertion failed: caller is not minter'); + }); + + it('mint >u120 tokens to overflow', async () => { + const amount = 2n ** 120n; // SafeU120::max() + 1; + await expect(asset.methods.mint_public(accounts[0].address, amount).simulate()).rejects.toThrowError( + 'Assertion failed: Value too large for SafeU120', + ); + }); + + it('mint u120', async () => { + const amount = 2n ** 120n - tokenSim.balanceOfPublic(accounts[0].address); + await expect(asset.methods.mint_public(accounts[0].address, amount).simulate()).rejects.toThrowError( + 'Assertion failed: Overflow', + ); + }); + + it('mint u120', async () => { + const amount = 2n ** 120n - tokenSim.balanceOfPublic(accounts[0].address); + await expect(asset.methods.mint_public(accounts[1].address, amount).simulate()).rejects.toThrowError( + 'Assertion failed: Overflow', + ); + }); + + it('mint to blacklisted entity', async () => { + await expect( + asset.withWallet(wallets[1]).methods.mint_public(accounts[3].address, 1n).simulate(), + ).rejects.toThrowError("Assertion failed: Blacklisted: Recipient '!to_roles.is_blacklisted'"); + }); + }); + }); + + describe('Private', () => { + const secret = Fr.random(); + const amount = 10000n; + let secretHash: Fr; + let txHash: TxHash; + + beforeAll(() => { + secretHash = computeMessageSecretHash(secret); + }); + + describe('Mint flow', () => { + it('mint_private as minter', async () => { + const tx = asset.methods.mint_private(amount, secretHash).send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + tokenSim.mintPrivate(amount); + txHash = receipt.txHash; + }); + + it('redeem as recipient', async () => { + await addPendingShieldNoteToPXE(0, amount, secretHash, txHash); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + const txClaim = asset.methods.redeem_shield(accounts[0].address, amount, secret).send(); + const receiptClaim = await txClaim.wait({ debug: true }); + expect(receiptClaim.status).toBe(TxStatus.MINED); + tokenSim.redeemShield(accounts[0].address, amount); + // 1 note should be created containing `amount` of tokens + const { visibleNotes } = receiptClaim.debugInfo!; + expect(visibleNotes.length).toBe(1); + expect(visibleNotes[0].note.items[0].toBigInt()).toBe(amount); + }); + }); + + describe('failure cases', () => { + it('try to redeem as recipient (double-spend) [REVERTS]', async () => { + await expect(addPendingShieldNoteToPXE(0, amount, secretHash, txHash)).rejects.toThrowError( + 'The note has been destroyed.', + ); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + await expect( + asset.methods.redeem_shield(accounts[0].address, amount, secret).simulate(), + ).rejects.toThrowError('Can only remove a note that has been read from the set.'); + }); + + it('mint_private as non-minter', async () => { + await expect( + asset.withWallet(wallets[1]).methods.mint_private(amount, secretHash).simulate(), + ).rejects.toThrowError('Assertion failed: caller is not minter'); + }); + + it('mint >u120 tokens to overflow', async () => { + const amount = 2n ** 120n; // SafeU120::max() + 1; + await expect(asset.methods.mint_private(amount, secretHash).simulate()).rejects.toThrowError( + 'Assertion failed: Value too large for SafeU120', + ); + }); + + it('mint u120', async () => { + // @todo @LHerskind this one don't make sense. It fails because of total supply overflowing. + const amount = 2n ** 120n - tokenSim.balanceOfPrivate(accounts[0].address); + expect(amount).toBeLessThan(2n ** 120n); + await expect(asset.methods.mint_private(amount, secretHash).simulate()).rejects.toThrowError( + 'Assertion failed: Overflow', + ); + }); + + it('mint u120', async () => { + const amount = 2n ** 120n - tokenSim.totalSupply; + await expect(asset.methods.mint_private(amount, secretHash).simulate()).rejects.toThrowError( + 'Assertion failed: Overflow', + ); + }); + + it('mint and try to redeem at blacklist', async () => { + await wallets[3].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[3].address.toBigInt(), true)), + ); + await expect( + asset.methods.redeem_shield(accounts[3].address, amount, secret).simulate(), + ).rejects.toThrowError("Assertion failed: Blacklisted: Recipient '!to_roles.is_blacklisted'"); + }); + }); + }); + }); + + describe('Transfer', () => { + describe('public', () => { + it('transfer less than balance', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance0 / 2n; + expect(amount).toBeGreaterThan(0n); + const tx = asset.methods.transfer_public(accounts[0].address, accounts[1].address, amount, 0).send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + + tokenSim.transferPublic(accounts[0].address, accounts[1].address, amount); + }); + + it('transfer to self', async () => { + const balance = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance / 2n; + expect(amount).toBeGreaterThan(0n); + const tx = asset.methods.transfer_public(accounts[0].address, accounts[0].address, amount, 0).send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + + tokenSim.transferPublic(accounts[0].address, accounts[0].address, amount); + }); + + it('transfer on behalf of other', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance0 / 2n; + expect(amount).toBeGreaterThan(0n); + const nonce = Fr.random(); + + // docs:start:authwit_public_transfer_example + const action = asset + .withWallet(wallets[1]) + .methods.transfer_public(accounts[0].address, accounts[1].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + + await wallets[0].setPublicAuth(messageHash, true).send().wait(); + // docs:end:authwit_public_transfer_example + + // Perform the transfer + const tx = action.send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + + tokenSim.transferPublic(accounts[0].address, accounts[1].address, amount); + + // Check that the message hash is no longer valid. Need to try to send since nullifiers are handled by sequencer. + const txReplay = asset + .withWallet(wallets[1]) + .methods.transfer_public(accounts[0].address, accounts[1].address, amount, nonce) + .send(); + await expect(txReplay.wait()).rejects.toThrowError('Transaction '); + }); + + describe('failure cases', () => { + it('transfer more than balance', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance0 + 1n; + const nonce = 0; + await expect( + asset.methods.transfer_public(accounts[0].address, accounts[1].address, amount, nonce).simulate(), + ).rejects.toThrowError('Assertion failed: Underflow'); + }); + + it('transfer on behalf of self with non-zero nonce', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance0 - 1n; + const nonce = 1; + await expect( + asset.methods.transfer_public(accounts[0].address, accounts[1].address, amount, nonce).simulate(), + ).rejects.toThrowError('Assertion failed: invalid nonce'); + }); + + it('transfer on behalf of other without "approval"', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance0 + 1n; + const nonce = Fr.random(); + await expect( + asset + .withWallet(wallets[1]) + .methods.transfer_public(accounts[0].address, accounts[1].address, amount, nonce) + .simulate(), + ).rejects.toThrowError('Assertion failed: Message not authorized by account'); + }); + + it('transfer more than balance on behalf of other', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const balance1 = await asset.methods.balance_of_public(accounts[1].address).view(); + const amount = balance0 + 1n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + const action = asset + .withWallet(wallets[1]) + .methods.transfer_public(accounts[0].address, accounts[1].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + + // We need to compute the message we want to sign and add it to the wallet as approved + await wallets[0].setPublicAuth(messageHash, true).send().wait(); + + // Perform the transfer + await expect(action.simulate()).rejects.toThrowError('Assertion failed: Underflow'); + + expect(await asset.methods.balance_of_public(accounts[0].address).view()).toEqual(balance0); + expect(await asset.methods.balance_of_public(accounts[1].address).view()).toEqual(balance1); + }); + + it('transfer on behalf of other, wrong designated caller', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const balance1 = await asset.methods.balance_of_public(accounts[1].address).view(); + const amount = balance0 + 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset + .withWallet(wallets[1]) + .methods.transfer_public(accounts[0].address, accounts[1].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[0].address, action.request()); + + await wallets[0].setPublicAuth(messageHash, true).send().wait(); + + // Perform the transfer + await expect(action.simulate()).rejects.toThrowError('Assertion failed: Message not authorized by account'); + + expect(await asset.methods.balance_of_public(accounts[0].address).view()).toEqual(balance0); + expect(await asset.methods.balance_of_public(accounts[1].address).view()).toEqual(balance1); + }); + + it('transfer on behalf of other, wrong designated caller', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const balance1 = await asset.methods.balance_of_public(accounts[1].address).view(); + const amount = balance0 + 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset + .withWallet(wallets[1]) + .methods.transfer_public(accounts[0].address, accounts[1].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[0].address, action.request()); + await wallets[0].setPublicAuth(messageHash, true).send().wait(); + + // Perform the transfer + await expect(action.simulate()).rejects.toThrowError('Assertion failed: Message not authorized by account'); + + expect(await asset.methods.balance_of_public(accounts[0].address).view()).toEqual(balance0); + expect(await asset.methods.balance_of_public(accounts[1].address).view()).toEqual(balance1); + }); + + it.skip('transfer into account to overflow', () => { + // This should already be covered by the mint case earlier. e.g., since we cannot mint to overflow, there is not + // a way to get funds enough to overflow. + // Require direct storage manipulation for us to perform a nice explicit case though. + // See https://github.com/AztecProtocol/aztec-packages/issues/1259 + }); + + it('transfer from a blacklisted account', async () => { + await expect( + asset.methods.transfer_public(accounts[3].address, accounts[0].address, 1n, 0n).simulate(), + ).rejects.toThrowError("Assertion failed: Blacklisted: Sender '!from_roles.is_blacklisted'"); + }); + + it('transfer to a blacklisted account', async () => { + await expect( + asset.methods.transfer_public(accounts[0].address, accounts[3].address, 1n, 0n).simulate(), + ).rejects.toThrowError("Assertion failed: Blacklisted: Recipient '!to_roles.is_blacklisted'"); + }); + }); + }); + + describe('private', () => { + it('transfer less than balance', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balance0 / 2n; + expect(amount).toBeGreaterThan(0n); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[1].address.toBigInt(), true)), + ); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + const tx = asset.methods.transfer(accounts[0].address, accounts[1].address, amount, 0).send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + tokenSim.transferPrivate(accounts[0].address, accounts[1].address, amount); + }); + + it('transfer to self', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balance0 / 2n; + expect(amount).toBeGreaterThan(0n); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + const tx = asset.methods.transfer(accounts[0].address, accounts[0].address, amount, 0).send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + tokenSim.transferPrivate(accounts[0].address, accounts[0].address, amount); + }); + + it('transfer on behalf of other', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balance0 / 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + // docs:start:authwit_transfer_example + // docs:start:authwit_computeAuthWitMessageHash + const action = asset + .withWallet(wallets[1]) + .methods.transfer(accounts[0].address, accounts[1].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + // docs:end:authwit_computeAuthWitMessageHash + + const witness = await wallets[0].createAuthWitness(messageHash); + await wallets[1].addAuthWitness(witness); + // docs:end:authwit_transfer_example + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[1].address.toBigInt(), true)), + ); + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + + // Perform the transfer + const tx = action.send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + tokenSim.transferPrivate(accounts[0].address, accounts[1].address, amount); + + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[1].address.toBigInt(), true)), + ); + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + + // Perform the transfer again, should fail + const txReplay = asset + .withWallet(wallets[1]) + .methods.transfer(accounts[0].address, accounts[1].address, amount, nonce) + .send(); + await expect(txReplay.wait()).rejects.toThrowError('Transaction '); + }); + + describe('failure cases', () => { + it('transfer more than balance', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balance0 + 1n; + expect(amount).toBeGreaterThan(0n); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[1].address.toBigInt(), true)), + ); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + await expect( + asset.methods.transfer(accounts[0].address, accounts[1].address, amount, 0).simulate(), + ).rejects.toThrowError('Assertion failed: Balance too low'); + }); + + it('transfer on behalf of self with non-zero nonce', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balance0 - 1n; + expect(amount).toBeGreaterThan(0n); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[1].address.toBigInt(), true)), + ); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + await expect( + asset.methods.transfer(accounts[0].address, accounts[1].address, amount, 1).simulate(), + ).rejects.toThrowError('Assertion failed: invalid nonce'); + }); + + it('transfer more than balance on behalf of other', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const balance1 = await asset.methods.balance_of_private(accounts[1].address).view(); + const amount = balance0 + 1n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset + .withWallet(wallets[1]) + .methods.transfer(accounts[0].address, accounts[1].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + + // Both wallets are connected to same node and PXE so we could just insert directly using + // await wallet.signAndAddAuthWitness(messageHash, ); + // But doing it in two actions to show the flow. + const witness = await wallets[0].createAuthWitness(messageHash); + await wallets[1].addAuthWitness(witness); + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[1].address.toBigInt(), true)), + ); + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + + // Perform the transfer + await expect(action.simulate()).rejects.toThrowError('Assertion failed: Balance too low'); + expect(await asset.methods.balance_of_private(accounts[0].address).view()).toEqual(balance0); + expect(await asset.methods.balance_of_private(accounts[1].address).view()).toEqual(balance1); + }); + + it.skip('transfer into account to overflow', () => { + // This should already be covered by the mint case earlier. e.g., since we cannot mint to overflow, there is not + // a way to get funds enough to overflow. + // Require direct storage manipulation for us to perform a nice explicit case though. + // See https://github.com/AztecProtocol/aztec-packages/issues/1259 + }); + + it('transfer on behalf of other without approval', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balance0 / 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset + .withWallet(wallets[1]) + .methods.transfer(accounts[0].address, accounts[1].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[1].address.toBigInt(), true)), + ); + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + + await expect(action.simulate()).rejects.toThrowError( + `Unknown auth witness for message hash 0x${messageHash.toString('hex')}`, + ); + }); + + it('transfer on behalf of other, wrong designated caller', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balance0 / 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset + .withWallet(wallets[2]) + .methods.transfer(accounts[0].address, accounts[1].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + const expectedMessageHash = computeAuthWitMessageHash(accounts[2].address, action.request()); + + const witness = await wallets[0].createAuthWitness(messageHash); + await wallets[2].addAuthWitness(witness); + await wallets[2].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[1].address.toBigInt(), true)), + ); + await wallets[2].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + + await expect(action.simulate()).rejects.toThrowError( + `Unknown auth witness for message hash 0x${expectedMessageHash.toString('hex')}`, + ); + expect(await asset.methods.balance_of_private(accounts[0].address).view()).toEqual(balance0); + }); + + it('transfer from a blacklisted account', async () => { + await wallets[3].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + await wallets[3].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[3].address.toBigInt(), true)), + ); + await expect( + asset.methods.transfer(accounts[3].address, accounts[0].address, 1n, 0).simulate(), + ).rejects.toThrowError("Assertion failed: Blacklisted: Sender '!from_roles.is_blacklisted'"); + }); + + it('transfer to a blacklisted account', async () => { + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[3].address.toBigInt(), true)), + ); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + await expect( + asset.methods.transfer(accounts[0].address, accounts[3].address, 1n, 0).simulate(), + ).rejects.toThrowError("Assertion failed: Blacklisted: Recipient '!to_roles.is_blacklisted'"); + }); + }); + }); + }); + + describe('Shielding (shield + redeem_shield)', () => { + const secret = Fr.random(); + let secretHash: Fr; + + beforeAll(() => { + secretHash = computeMessageSecretHash(secret); + }); + + it('on behalf of self', async () => { + const balancePub = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balancePub / 2n; + expect(amount).toBeGreaterThan(0n); + + const tx = asset.methods.shield(accounts[0].address, amount, secretHash, 0).send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + + tokenSim.shield(accounts[0].address, amount); + await tokenSim.check(); + + // Redeem it + await addPendingShieldNoteToPXE(0, amount, secretHash, receipt.txHash); + await wallets[0].addCapsule(getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true))); + const txClaim = asset.methods.redeem_shield(accounts[0].address, amount, secret).send(); + const receiptClaim = await txClaim.wait(); + expect(receiptClaim.status).toBe(TxStatus.MINED); + + tokenSim.redeemShield(accounts[0].address, amount); + }); + + it('on behalf of other', async () => { + const balancePub = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balancePub / 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset.withWallet(wallets[1]).methods.shield(accounts[0].address, amount, secretHash, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + await wallets[0].setPublicAuth(messageHash, true).send().wait(); + + const tx = action.send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + + tokenSim.shield(accounts[0].address, amount); + await tokenSim.check(); + + // Check that replaying the shield should fail! + const txReplay = asset + .withWallet(wallets[1]) + .methods.shield(accounts[0].address, amount, secretHash, nonce) + .send(); + await expect(txReplay.wait()).rejects.toThrowError('Transaction '); + + // Redeem it + await addPendingShieldNoteToPXE(0, amount, secretHash, receipt.txHash); + await wallets[0].addCapsule(getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true))); + const txClaim = asset.methods.redeem_shield(accounts[0].address, amount, secret).send(); + const receiptClaim = await txClaim.wait(); + expect(receiptClaim.status).toBe(TxStatus.MINED); + + tokenSim.redeemShield(accounts[0].address, amount); + }); + + describe('failure cases', () => { + it('on behalf of self (more than balance)', async () => { + const balancePub = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balancePub + 1n; + expect(amount).toBeGreaterThan(0n); + + await expect(asset.methods.shield(accounts[0].address, amount, secretHash, 0).simulate()).rejects.toThrowError( + 'Assertion failed: Underflow', + ); + }); + + it('on behalf of self (invalid nonce)', async () => { + const balancePub = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balancePub + 1n; + expect(amount).toBeGreaterThan(0n); + + await expect(asset.methods.shield(accounts[0].address, amount, secretHash, 1).simulate()).rejects.toThrowError( + 'Assertion failed: invalid nonce', + ); + }); + + it('on behalf of other (more than balance)', async () => { + const balancePub = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balancePub + 1n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset.withWallet(wallets[1]).methods.shield(accounts[0].address, amount, secretHash, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + await wallets[0].setPublicAuth(messageHash, true).send().wait(); + + await expect(action.simulate()).rejects.toThrowError('Assertion failed: Underflow'); + }); + + it('on behalf of other (wrong designated caller)', async () => { + const balancePub = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balancePub + 1n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset.withWallet(wallets[2]).methods.shield(accounts[0].address, amount, secretHash, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + await wallets[0].setPublicAuth(messageHash, true).send().wait(); + + await expect(action.simulate()).rejects.toThrowError('Assertion failed: Message not authorized by account'); + }); + + it('on behalf of other (without approval)', async () => { + const balance = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance / 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + await expect( + asset.withWallet(wallets[1]).methods.shield(accounts[0].address, amount, secretHash, nonce).simulate(), + ).rejects.toThrowError(`Assertion failed: Message not authorized by account`); + }); + + it('shielding from blacklisted account', async () => { + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[3].address.toBigInt(), true)), + ); + await expect( + asset.withWallet(wallets[3]).methods.shield(accounts[3].address, 1n, secretHash, 0).simulate(), + ).rejects.toThrowError("Assertion failed: Blacklisted: Sender '!from_roles.is_blacklisted'"); + }); + }); + }); + + describe('Unshielding', () => { + it('on behalf of self', async () => { + const balancePriv = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balancePriv / 2n; + expect(amount).toBeGreaterThan(0n); + + await wallets[1].addCapsule(getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true))); + await wallets[1].addCapsule(getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true))); + const tx = asset.methods.unshield(accounts[0].address, accounts[0].address, amount, 0).send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + + tokenSim.unshield(accounts[0].address, accounts[0].address, amount); + }); + + it('on behalf of other', async () => { + const balancePriv0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balancePriv0 / 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + await wallets[1].addCapsule(getMembershipCapsule(await getMembershipProof(accounts[1].address.toBigInt(), true))); + await wallets[1].addCapsule(getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true))); + const action = asset + .withWallet(wallets[1]) + .methods.unshield(accounts[0].address, accounts[1].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + + // Both wallets are connected to same node and PXE so we could just insert directly using + // await wallet.signAndAddAuthWitness(messageHash, ); + // But doing it in two actions to show the flow. + const witness = await wallets[0].createAuthWitness(messageHash); + await wallets[1].addAuthWitness(witness); + + const tx = action.send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + tokenSim.unshield(accounts[0].address, accounts[1].address, amount); + + // Perform the transfer again, should fail + await wallets[1].addCapsule(getMembershipCapsule(await getMembershipProof(accounts[1].address.toBigInt(), true))); + await wallets[1].addCapsule(getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true))); + const txReplay = asset + .withWallet(wallets[1]) + .methods.unshield(accounts[0].address, accounts[1].address, amount, nonce) + .send(); + await expect(txReplay.wait()).rejects.toThrowError('Transaction '); + // @todo @LHerskind This error is weird? + }); + + describe('failure cases', () => { + it('on behalf of self (more than balance)', async () => { + const balancePriv = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balancePriv + 1n; + expect(amount).toBeGreaterThan(0n); + + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + + await expect( + asset.methods.unshield(accounts[0].address, accounts[0].address, amount, 0).simulate(), + ).rejects.toThrowError('Assertion failed: Balance too low'); + }); + + it('on behalf of self (invalid nonce)', async () => { + const balancePriv = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balancePriv + 1n; + expect(amount).toBeGreaterThan(0n); + + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + + await expect( + asset.methods.unshield(accounts[0].address, accounts[0].address, amount, 1).simulate(), + ).rejects.toThrowError('Assertion failed: invalid nonce'); + }); + + it('on behalf of other (more than balance)', async () => { + const balancePriv0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balancePriv0 + 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset + .withWallet(wallets[1]) + .methods.unshield(accounts[0].address, accounts[1].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + + // Both wallets are connected to same node and PXE so we could just insert directly using + // await wallet.signAndAddAuthWitness(messageHash, ); + // But doing it in two actions to show the flow. + const witness = await wallets[0].createAuthWitness(messageHash); + await wallets[1].addAuthWitness(witness); + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[1].address.toBigInt(), true)), + ); + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + + await expect(action.simulate()).rejects.toThrowError('Assertion failed: Balance too low'); + }); + + it('on behalf of other (invalid designated caller)', async () => { + const balancePriv0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balancePriv0 + 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset + .withWallet(wallets[2]) + .methods.unshield(accounts[0].address, accounts[1].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + const expectedMessageHash = computeAuthWitMessageHash(accounts[2].address, action.request()); + + // Both wallets are connected to same node and PXE so we could just insert directly using + // await wallet.signAndAddAuthWitness(messageHash, ); + // But doing it in two actions to show the flow. + const witness = await wallets[0].createAuthWitness(messageHash); + await wallets[2].addAuthWitness(witness); + await wallets[2].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[1].address.toBigInt(), true)), + ); + await wallets[2].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + + await expect(action.simulate()).rejects.toThrowError( + `Unknown auth witness for message hash 0x${expectedMessageHash.toString('hex')}`, + ); + }); + + it('unshield from blacklisted account', async () => { + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[3].address.toBigInt(), true)), + ); + await expect( + asset.methods.unshield(accounts[3].address, accounts[0].address, 1n, 0).simulate(), + ).rejects.toThrowError("Assertion failed: Blacklisted: Sender '!from_roles.is_blacklisted'"); + }); + + it('unshield to blacklisted account', async () => { + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[3].address.toBigInt(), true)), + ); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + await expect( + asset.methods.unshield(accounts[0].address, accounts[3].address, 1n, 0).simulate(), + ).rejects.toThrowError("Assertion failed: Blacklisted: Recipient '!to_roles.is_blacklisted'"); + }); + }); + }); + + describe('Burn', () => { + describe('public', () => { + it('burn less than balance', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance0 / 2n; + expect(amount).toBeGreaterThan(0n); + const tx = asset.methods.burn_public(accounts[0].address, amount, 0).send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + + tokenSim.burnPublic(accounts[0].address, amount); + }); + + it('burn on behalf of other', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance0 / 2n; + expect(amount).toBeGreaterThan(0n); + const nonce = Fr.random(); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset.withWallet(wallets[1]).methods.burn_public(accounts[0].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + await wallets[0].setPublicAuth(messageHash, true).send().wait(); + + const tx = action.send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + + tokenSim.burnPublic(accounts[0].address, amount); + + // Check that the message hash is no longer valid. Need to try to send since nullifiers are handled by sequencer. + const txReplay = asset.withWallet(wallets[1]).methods.burn_public(accounts[0].address, amount, nonce).send(); + await expect(txReplay.wait()).rejects.toThrowError('Transaction '); + }); + + describe('failure cases', () => { + it('burn more than balance', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance0 + 1n; + const nonce = 0; + await expect(asset.methods.burn_public(accounts[0].address, amount, nonce).simulate()).rejects.toThrowError( + 'Assertion failed: Underflow', + ); + }); + + it('burn on behalf of self with non-zero nonce', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance0 - 1n; + expect(amount).toBeGreaterThan(0n); + const nonce = 1; + await expect(asset.methods.burn_public(accounts[0].address, amount, nonce).simulate()).rejects.toThrowError( + 'Assertion failed: invalid nonce', + ); + }); + + it('burn on behalf of other without "approval"', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance0 + 1n; + const nonce = Fr.random(); + await expect( + asset.withWallet(wallets[1]).methods.burn_public(accounts[0].address, amount, nonce).simulate(), + ).rejects.toThrowError('Assertion failed: Message not authorized by account'); + }); + + it('burn more than balance on behalf of other', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance0 + 1n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset.withWallet(wallets[1]).methods.burn_public(accounts[0].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + await wallets[0].setPublicAuth(messageHash, true).send().wait(); + + await expect(action.simulate()).rejects.toThrowError('Assertion failed: Underflow'); + }); + + it('burn on behalf of other, wrong designated caller', async () => { + const balance0 = await asset.methods.balance_of_public(accounts[0].address).view(); + const amount = balance0 + 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset.withWallet(wallets[1]).methods.burn_public(accounts[0].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[0].address, action.request()); + await wallets[0].setPublicAuth(messageHash, true).send().wait(); + + await expect( + asset.withWallet(wallets[1]).methods.burn_public(accounts[0].address, amount, nonce).simulate(), + ).rejects.toThrowError('Assertion failed: Message not authorized by account'); + }); + + it('burn from blacklisted account', async () => { + await expect(asset.methods.burn_public(accounts[3].address, 1n, 0).simulate()).rejects.toThrowError( + "Assertion failed: Blacklisted: Sender '!from_roles.is_blacklisted'", + ); + }); + }); + }); + + describe('private', () => { + it('burn less than balance', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balance0 / 2n; + expect(amount).toBeGreaterThan(0n); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + const tx = asset.methods.burn(accounts[0].address, amount, 0).send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + tokenSim.burnPrivate(accounts[0].address, amount); + }); + + it('burn on behalf of other', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balance0 / 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset.withWallet(wallets[1]).methods.burn(accounts[0].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + + // Both wallets are connected to same node and PXE so we could just insert directly using + // await wallet.signAndAddAuthWitness(messageHash, ); + // But doing it in two actions to show the flow. + const witness = await wallets[0].createAuthWitness(messageHash); + await wallets[1].addAuthWitness(witness); + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + + const tx = asset.withWallet(wallets[1]).methods.burn(accounts[0].address, amount, nonce).send(); + const receipt = await tx.wait(); + expect(receipt.status).toBe(TxStatus.MINED); + tokenSim.burnPrivate(accounts[0].address, amount); + + // Perform the transfer again, should fail + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + const txReplay = asset.withWallet(wallets[1]).methods.burn(accounts[0].address, amount, nonce).send(); + await expect(txReplay.wait()).rejects.toThrowError('Transaction '); + }); + + describe('failure cases', () => { + it('burn more than balance', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balance0 + 1n; + expect(amount).toBeGreaterThan(0n); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + await expect(asset.methods.burn(accounts[0].address, amount, 0).simulate()).rejects.toThrowError( + 'Assertion failed: Balance too low', + ); + }); + + it('burn on behalf of self with non-zero nonce', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balance0 - 1n; + expect(amount).toBeGreaterThan(0n); + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + await expect(asset.methods.burn(accounts[0].address, amount, 1).simulate()).rejects.toThrowError( + 'Assertion failed: invalid nonce', + ); + }); + + it('burn more than balance on behalf of other', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balance0 + 1n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + const action = asset.withWallet(wallets[1]).methods.burn(accounts[0].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + + // Both wallets are connected to same node and PXE so we could just insert directly using + // await wallet.signAndAddAuthWitness(messageHash, ); + // But doing it in two actions to show the flow. + const witness = await wallets[0].createAuthWitness(messageHash); + await wallets[1].addAuthWitness(witness); + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + + await expect(action.simulate()).rejects.toThrowError('Assertion failed: Balance too low'); + }); + + it('burn on behalf of other without approval', async () => { + const balance0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balance0 / 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + await wallets[1].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + const action = asset.withWallet(wallets[1]).methods.burn(accounts[0].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + + await expect(action.simulate()).rejects.toThrowError( + `Unknown auth witness for message hash 0x${messageHash.toString('hex')}`, + ); + }); + + it('on behalf of other (invalid designated caller)', async () => { + const balancePriv0 = await asset.methods.balance_of_private(accounts[0].address).view(); + const amount = balancePriv0 + 2n; + const nonce = Fr.random(); + expect(amount).toBeGreaterThan(0n); + + // We need to compute the message we want to sign and add it to the wallet as approved + await wallets[2].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[0].address.toBigInt(), true)), + ); + const action = asset.withWallet(wallets[2]).methods.burn(accounts[0].address, amount, nonce); + const messageHash = computeAuthWitMessageHash(accounts[1].address, action.request()); + const expectedMessageHash = computeAuthWitMessageHash(accounts[2].address, action.request()); + + const witness = await wallets[0].createAuthWitness(messageHash); + await wallets[2].addAuthWitness(witness); + + await expect(action.simulate()).rejects.toThrowError( + `Unknown auth witness for message hash 0x${expectedMessageHash.toString('hex')}`, + ); + }); + + it('burn from blacklisted account', async () => { + await wallets[0].addCapsule( + getMembershipCapsule(await getMembershipProof(accounts[3].address.toBigInt(), true)), + ); + await expect(asset.methods.burn(accounts[3].address, 1n, 0).simulate()).rejects.toThrowError( + "Assertion failed: Blacklisted: Sender '!from_roles.is_blacklisted'", + ); + }); + }); + }); + }); +}); diff --git a/yarn-project/end-to-end/src/e2e_block_building.test.ts b/yarn-project/end-to-end/src/e2e_block_building.test.ts index d6eccd46936..9dd4e9beb79 100644 --- a/yarn-project/end-to-end/src/e2e_block_building.test.ts +++ b/yarn-project/end-to-end/src/e2e_block_building.test.ts @@ -1,4 +1,5 @@ import { + AztecNode, BatchCall, ContractDeployer, ContractFunctionInteraction, @@ -22,6 +23,7 @@ describe('e2e_block_building', () => { let logger: DebugLogger; let owner: Wallet; let minter: Wallet; + let aztecNode: AztecNode; let teardown: () => Promise; describe('multi-txs block', () => { @@ -32,16 +34,19 @@ describe('e2e_block_building', () => { teardown, pxe, logger, + aztecNode, wallets: [owner, minter], } = await setup(2)); }, 100_000); + afterEach(() => aztecNode.setConfig({ minTxsPerBlock: 1 })); afterAll(() => teardown()); it('assembles a block with multiple txs', async () => { // Assemble N contract deployment txs // We need to create them sequentially since we cannot have parallel calls to a circuit const TX_COUNT = 8; + await aztecNode.setConfig({ minTxsPerBlock: TX_COUNT }); const deployer = new ContractDeployer(artifact, owner); const methods = times(TX_COUNT, () => deployer.deploy()); @@ -53,7 +58,9 @@ describe('e2e_block_building', () => { // Send them simultaneously to be picked up by the sequencer const txs = await Promise.all(methods.map(method => method.send())); logger(`Txs sent with hashes: `); - for (const tx of txs) logger(` ${await tx.getTxHash()}`); + for (const tx of txs) { + logger(` ${await tx.getTxHash()}`); + } // Await txs to be mined and assert they are all mined on the same block const receipts = await Promise.all(txs.map(tx => tx.wait())); @@ -66,6 +73,9 @@ describe('e2e_block_building', () => { }, 60_000); it('can call public function from different tx in same block', async () => { + // Ensure both txs will land on the same block + await aztecNode.setConfig({ minTxsPerBlock: 2 }); + // Deploy a contract in the first transaction // In the same block, call a public method on the contract const deployer = TokenContract.deploy(owner, owner.getCompleteAddress()); @@ -111,7 +121,9 @@ describe('e2e_block_building', () => { it('drops tx with private nullifier already emitted on the same block', async () => { const nullifier = Fr.random(); const calls = times(2, () => contract.methods.emit_nullifier(nullifier)); - for (const call of calls) await call.simulate(); + for (const call of calls) { + await call.simulate(); + } const [tx1, tx2] = calls.map(call => call.send()); await tx1.wait(); await expect(tx2.wait()).rejects.toThrowError(/dropped/); @@ -120,7 +132,9 @@ describe('e2e_block_building', () => { it('drops tx with public nullifier already emitted on the same block', async () => { const secret = Fr.random(); const calls = times(2, () => contract.methods.create_nullifier_public(140n, secret)); - for (const call of calls) await call.simulate(); + for (const call of calls) { + await call.simulate(); + } const [tx1, tx2] = calls.map(call => call.send()); await tx1.wait(); await expect(tx2.wait()).rejects.toThrowError(/dropped/); @@ -142,7 +156,9 @@ describe('e2e_block_building', () => { contract.methods.emit_nullifier(emittedPublicNullifier), ]; - for (const call of calls) await call.simulate(); + for (const call of calls) { + await call.simulate(); + } const [tx1, tx2] = calls.map(call => call.send()); await tx1.wait(); await expect(tx2.wait()).rejects.toThrowError(/dropped/); diff --git a/yarn-project/end-to-end/src/e2e_cheat_codes.test.ts b/yarn-project/end-to-end/src/e2e_cheat_codes.test.ts index d14102622f4..6da6b9d5bc0 100644 --- a/yarn-project/end-to-end/src/e2e_cheat_codes.test.ts +++ b/yarn-project/end-to-end/src/e2e_cheat_codes.test.ts @@ -210,6 +210,7 @@ describe('e2e_cheat_codes', () => { const secretHash = computeMessageSecretHash(secret); const receipt = await token.methods.mint_private(mintAmount, secretHash).send().wait(); + // docs:start:pxe_add_note const note = new Note([new Fr(mintAmount), secretHash]); const pendingShieldStorageSlot = new Fr(5n); const extendedNote = new ExtendedNote( @@ -220,6 +221,7 @@ describe('e2e_cheat_codes', () => { receipt.txHash, ); await pxe.addNote(extendedNote); + // docs:end:pxe_add_note // check if note was added to pending shield: const notes = await cc.aztec.loadPrivate(admin.address, token.address, pendingShieldStorageSlot); diff --git a/yarn-project/end-to-end/src/e2e_cross_chain_messaging.test.ts b/yarn-project/end-to-end/src/e2e_cross_chain_messaging.test.ts index d3269892776..c14fd6f8559 100644 --- a/yarn-project/end-to-end/src/e2e_cross_chain_messaging.test.ts +++ b/yarn-project/end-to-end/src/e2e_cross_chain_messaging.test.ts @@ -96,6 +96,7 @@ describe('e2e_cross_chain_messaging', () => { // time to withdraw the funds again! logger('Withdrawing funds from L2'); + // docs:start:authwit_to_another_sc // 4. Give approval to bridge to burn owner's funds: const withdrawAmount = 9n; const nonce = Fr.random(); @@ -103,7 +104,9 @@ describe('e2e_cross_chain_messaging', () => { l2Bridge.address, l2Token.methods.burn(ownerAddress, withdrawAmount, nonce).request(), ); - await user1Wallet.createAuthWitness(burnMessageHash); + const witness = await user1Wallet.createAuthWitness(burnMessageHash); + await user1Wallet.addAuthWitness(witness); + // docs:end:authwit_to_another_sc // 5. Withdraw owner's funds from L2 to L1 const entryKey = await crossChainTestHarness.checkEntryIsNotInOutbox(withdrawAmount); @@ -115,7 +118,7 @@ describe('e2e_cross_chain_messaging', () => { await crossChainTestHarness.withdrawFundsFromBridgeOnL1(withdrawAmount, entryKey); expect(await crossChainTestHarness.getL1BalanceOf(ethAccount)).toBe(l1TokenBalance - bridgeAmount + withdrawAmount); - expect(await outbox.read.contains([entryKey.toString(true)])).toBeFalsy(); + expect(await outbox.read.contains([entryKey.toString()])).toBeFalsy(); }, 120_000); // docs:end:e2e_private_cross_chain diff --git a/yarn-project/end-to-end/src/e2e_deploy_contract.test.ts b/yarn-project/end-to-end/src/e2e_deploy_contract.test.ts index e9383e81b77..dac1cbd909f 100644 --- a/yarn-project/end-to-end/src/e2e_deploy_contract.test.ts +++ b/yarn-project/end-to-end/src/e2e_deploy_contract.test.ts @@ -38,7 +38,7 @@ describe('e2e_deploy_contract', () => { it('should deploy a contract', async () => { const publicKey = accounts[0].publicKey; const salt = Fr.random(); - const deploymentData = await getContractDeploymentInfo(TestContractArtifact, [], salt, publicKey); + const deploymentData = getContractDeploymentInfo(TestContractArtifact, [], salt, publicKey); const deployer = new ContractDeployer(TestContractArtifact, pxe, publicKey); const tx = deployer.deploy().send({ contractAddressSalt: salt }); logger(`Tx sent with hash ${await tx.getTxHash()}`); diff --git a/yarn-project/end-to-end/src/e2e_escrow_contract.test.ts b/yarn-project/end-to-end/src/e2e_escrow_contract.test.ts index 97a1fb488de..6e1361238bd 100644 --- a/yarn-project/end-to-end/src/e2e_escrow_contract.test.ts +++ b/yarn-project/end-to-end/src/e2e_escrow_contract.test.ts @@ -53,9 +53,9 @@ describe('e2e_escrow_contract', () => { // Generate private key for escrow contract, register key in pxe service, and deploy // Note that we need to register it first if we want to emit an encrypted note for it in the constructor escrowPrivateKey = GrumpkinScalar.random(); - escrowPublicKey = await generatePublicKey(escrowPrivateKey); + escrowPublicKey = generatePublicKey(escrowPrivateKey); const salt = Fr.random(); - const deployInfo = await getContractDeploymentInfo(EscrowContractArtifact, [owner], salt, escrowPublicKey); + const deployInfo = getContractDeploymentInfo(EscrowContractArtifact, [owner], salt, escrowPublicKey); await pxe.registerAccount(escrowPrivateKey, deployInfo.completeAddress.partialAddress); escrowContract = await EscrowContract.deployWithPublicKey(escrowPublicKey, wallet, owner) diff --git a/yarn-project/end-to-end/src/e2e_multiple_accounts_1_enc_key.test.ts b/yarn-project/end-to-end/src/e2e_multiple_accounts_1_enc_key.test.ts index 9cb2bd8b16d..693d722eb30 100644 --- a/yarn-project/end-to-end/src/e2e_multiple_accounts_1_enc_key.test.ts +++ b/yarn-project/end-to-end/src/e2e_multiple_accounts_1_enc_key.test.ts @@ -44,14 +44,14 @@ describe('e2e_multiple_accounts_1_enc_key', () => { const signingPrivateKey = GrumpkinScalar.random(); const account = getSchnorrAccount(pxe, encryptionPrivateKey, signingPrivateKey); const wallet = await account.waitDeploy({ interval: 0.1 }); - const { address } = await account.getCompleteAddress(); + const { address } = account.getCompleteAddress(); wallets.push(wallet); accounts.push(address); } logger('Account contracts deployed'); // Verify that all accounts use the same encryption key - const encryptionPublicKey = await generatePublicKey(encryptionPrivateKey); + const encryptionPublicKey = generatePublicKey(encryptionPrivateKey); // Disregard sandbox accounts let keyAccounts: CompleteAddress[]; diff --git a/yarn-project/end-to-end/src/e2e_ordering.test.ts b/yarn-project/end-to-end/src/e2e_ordering.test.ts index b8f704a9a06..7d30abe9e74 100644 --- a/yarn-project/end-to-end/src/e2e_ordering.test.ts +++ b/yarn-project/end-to-end/src/e2e_ordering.test.ts @@ -64,9 +64,9 @@ describe('e2e_ordering', () => { const enqueuedPublicCalls = tx.enqueuedPublicFunctionCalls; expect(enqueuedPublicCalls.length).toEqual(2); - // The call stack hashes in the output of the kernel proof match the tx enqueuedPublicFunctionCalls - const hashes = await Promise.all(enqueuedPublicCalls.map(c => c.toPublicCallStackItem().hash())); - expect(tx.data.end.publicCallStack.slice(0, 2)).toEqual(hashes); + // The call stack items in the output of the kernel proof match the tx enqueuedPublicFunctionCalls + const callStackItems = await Promise.all(enqueuedPublicCalls.map(c => c.toCallRequest())); + expect(tx.data.end.publicCallStack.slice(0, 2)).toEqual(callStackItems); // The enqueued public calls are in the expected order based on the argument they set (stack is reversed!) expect(enqueuedPublicCalls.map(c => c.args[0].toBigInt())).toEqual([...expectedOrder].reverse()); diff --git a/yarn-project/end-to-end/src/e2e_p2p_network.test.ts b/yarn-project/end-to-end/src/e2e_p2p_network.test.ts index f5cb8042091..7c782e6edf9 100644 --- a/yarn-project/end-to-end/src/e2e_p2p_network.test.ts +++ b/yarn-project/end-to-end/src/e2e_p2p_network.test.ts @@ -79,7 +79,7 @@ describe('e2e_p2p_network', () => { await context.pxeService.stop(); } await bootstrapNode.stop(); - }, 80_000); + }, 120_000); const createBootstrapNode = async () => { const peerId = await createLibP2PPeerId(); @@ -127,8 +127,7 @@ describe('e2e_p2p_network', () => { const txs: DeploySentTx[] = []; for (let i = 0; i < numTxs; i++) { const salt = Fr.random(); - const origin = (await getContractDeploymentInfo(TestContractArtifact, [], salt, publicKey)).completeAddress - .address; + const origin = getContractDeploymentInfo(TestContractArtifact, [], salt, publicKey).completeAddress.address; const deployer = new ContractDeployer(TestContractArtifact, pxe, publicKey); const tx = deployer.deploy().send({ contractAddressSalt: salt }); logger(`Tx sent with hash ${await tx.getTxHash()}`); @@ -153,12 +152,9 @@ describe('e2e_p2p_network', () => { const rpcConfig = getRpcConfig(); const pxeService = await createPXEService(node, rpcConfig, {}, true); - const keyPair = ConstantKeyPair.random(await Grumpkin.new()); - const completeAddress = await CompleteAddress.fromPrivateKeyAndPartialAddress( - await keyPair.getPrivateKey(), - Fr.random(), - ); - await pxeService.registerAccount(await keyPair.getPrivateKey(), completeAddress.partialAddress); + const keyPair = ConstantKeyPair.random(new Grumpkin()); + const completeAddress = CompleteAddress.fromPrivateKeyAndPartialAddress(keyPair.getPrivateKey(), Fr.random()); + await pxeService.registerAccount(keyPair.getPrivateKey(), completeAddress.partialAddress); const txs = await submitTxsTo(pxeService, completeAddress.address, numTxs, completeAddress.publicKey); return { diff --git a/yarn-project/end-to-end/src/e2e_public_cross_chain_messaging.test.ts b/yarn-project/end-to-end/src/e2e_public_cross_chain_messaging.test.ts index d38a705e891..2cad9e99629 100644 --- a/yarn-project/end-to-end/src/e2e_public_cross_chain_messaging.test.ts +++ b/yarn-project/end-to-end/src/e2e_public_cross_chain_messaging.test.ts @@ -104,7 +104,7 @@ describe('e2e_public_cross_chain_messaging', () => { await crossChainTestHarness.withdrawFundsFromBridgeOnL1(withdrawAmount, entryKey); expect(await crossChainTestHarness.getL1BalanceOf(ethAccount)).toBe(l1TokenBalance - bridgeAmount + withdrawAmount); - expect(await outbox.read.contains([entryKey.toString(true)])).toBeFalsy(); + expect(await outbox.read.contains([entryKey.toString()])).toBeFalsy(); }, 120_000); // docs:end:e2e_public_cross_chain @@ -148,7 +148,7 @@ describe('e2e_public_cross_chain_messaging', () => { // ensure funds are gone to owner and not user2. await crossChainTestHarness.expectPublicBalanceOnL2(ownerAddress, bridgeAmount + unrelatedMintAmount); await crossChainTestHarness.expectPublicBalanceOnL2(user2Wallet.getAddress(), 0n); - }, 60_000); + }, 90_000); it("Bridge can't withdraw my funds if I don't give approval", async () => { const mintAmountToOwner = 100n; @@ -163,7 +163,7 @@ describe('e2e_public_cross_chain_messaging', () => { .methods.exit_to_l1_public(ethAccount, withdrawAmount, EthAddress.ZERO, nonce) .simulate(), ).rejects.toThrowError('Assertion failed: Message not authorized by account'); - }); + }, 60_000); it("can't claim funds privately which were intended for public deposit from the token portal", async () => { const bridgeAmount = 100n; @@ -185,5 +185,5 @@ describe('e2e_public_cross_chain_messaging', () => { .methods.claim_private(secretHash, bridgeAmount, ethAccount, messageKey, secret) .simulate(), ).rejects.toThrowError("Cannot satisfy constraint 'l1_to_l2_message_data.message.content == content"); - }); + }, 60_000); }); diff --git a/yarn-project/end-to-end/src/e2e_sandbox_example.test.ts b/yarn-project/end-to-end/src/e2e_sandbox_example.test.ts index 814feb26b49..7961e042d5c 100644 --- a/yarn-project/end-to-end/src/e2e_sandbox_example.test.ts +++ b/yarn-project/end-to-end/src/e2e_sandbox_example.test.ts @@ -216,5 +216,5 @@ describe('e2e_sandbox_example', () => { // check that alice and bob are in registeredAccounts expect(registeredAccounts.find(acc => acc.equals(alice))).toBeTruthy(); expect(registeredAccounts.find(acc => acc.equals(bob))).toBeTruthy(); - }); + }, 60_000); }); diff --git a/yarn-project/end-to-end/src/e2e_slow_tree.test.ts b/yarn-project/end-to-end/src/e2e_slow_tree.test.ts new file mode 100644 index 00000000000..6ce7cda57a8 --- /dev/null +++ b/yarn-project/end-to-end/src/e2e_slow_tree.test.ts @@ -0,0 +1,157 @@ +/* eslint-disable camelcase */ +import { CheatCodes, DebugLogger, Fr, Wallet } from '@aztec/aztec.js'; +import { Pedersen, SparseTree, newTree } from '@aztec/merkle-tree'; +import { SlowTreeContract } from '@aztec/noir-contracts/types'; + +import { default as levelup } from 'levelup'; +import { type MemDown, default as memdown } from 'memdown'; + +import { setup } from './fixtures/utils.js'; + +export const createMemDown = () => (memdown as any)() as MemDown; + +describe('e2e_slow_tree', () => { + let logger: DebugLogger; + let wallet: Wallet; + let teardown: () => Promise; + + let contract: SlowTreeContract; + let cheatCodes: CheatCodes; + + beforeAll(async () => { + ({ teardown, logger, wallet, cheatCodes } = await setup()); + contract = await SlowTreeContract.deploy(wallet).send().deployed(); + }, 100_000); + + afterAll(() => teardown()); + + it('Messing around with noir slow tree', async () => { + const depth = 254; + const slowUpdateTreeSimulator = await newTree(SparseTree, levelup(createMemDown()), new Pedersen(), 'test', depth); + const getMembershipProof = async (index: bigint, includeUncommitted: boolean) => { + return { + index, + value: Fr.fromBuffer((await slowUpdateTreeSimulator.getLeafValue(index, includeUncommitted))!), + // eslint-disable-next-line camelcase + sibling_path: (await slowUpdateTreeSimulator.getSiblingPath(index, includeUncommitted)).toFieldArray(), + }; + }; + + const getMembershipCapsule = (proof: { index: bigint; value: Fr; sibling_path: Fr[] }) => { + return [new Fr(proof.index), proof.value, ...proof.sibling_path]; + }; + + const getUpdateProof = async (newValue: bigint, index: bigint) => { + const beforeProof = await getMembershipProof(index, false); + const afterProof = await getMembershipProof(index, true); + + return { + index, + // eslint-disable-next-line camelcase + new_value: newValue, + // eslint-disable-next-line camelcase + before: { value: beforeProof.value, sibling_path: beforeProof.sibling_path }, + // eslint-disable-next-line camelcase + after: { value: afterProof.value, sibling_path: afterProof.sibling_path }, + }; + }; + + const getUpdateCapsule = (proof: { + index: bigint; + new_value: bigint; + before: { value: Fr; sibling_path: Fr[] }; + after: { value: Fr; sibling_path: Fr[] }; + }) => { + return [ + new Fr(proof.index), + new Fr(proof.new_value), + proof.before.value, + ...proof.before.sibling_path, + proof.after.value, + ...proof.after.sibling_path, + ]; + }; + + const status = async ( + key: bigint, + _root: { before: bigint; after: bigint; next_change: bigint }, + _leaf: { before: bigint; after: bigint; next_change: bigint }, + ) => { + const root = await contract.methods.un_read_root(owner).view(); + const leaf = await contract.methods.un_read_leaf_at(owner, key).view(); + expect(root).toEqual(_root); + expect(leaf).toEqual(_leaf); + }; + + const owner = wallet.getCompleteAddress().address; + const key = owner.toBigInt(); + + await contract.methods.initialize().send().wait(); + + const computeNextChange = (ts: bigint) => (ts / 100n + 1n) * 100n; + + let _root = { + before: Fr.fromBuffer(slowUpdateTreeSimulator.getRoot(true)).toBigInt(), + after: Fr.fromBuffer(slowUpdateTreeSimulator.getRoot(true)).toBigInt(), + next_change: 2n ** 120n - 1n, + }; + let _leaf = { before: 0n, after: 0n, next_change: 0n }; + await status(key, _root, _leaf); + await wallet.addCapsule(getMembershipCapsule(await getMembershipProof(key, true))); + await contract.methods.read_at(key).send().wait(); + + logger(`Updating tree[${key}] to 1 from public`); + const t1 = computeNextChange(BigInt(await cheatCodes.eth.timestamp())); + await contract.methods + .update_at_public(await getUpdateProof(1n, key)) + .send() + .wait(); + await slowUpdateTreeSimulator.updateLeaf(new Fr(1).toBuffer(), key); + + // Update below. + _root = { + ..._root, + after: Fr.fromBuffer(slowUpdateTreeSimulator.getRoot(true)).toBigInt(), + next_change: t1, + }; + _leaf = { ..._leaf, after: 1n, next_change: t1 }; + await status(key, _root, _leaf); + + const zeroProof = await getMembershipProof(key, false); + logger(`"Reads" tree[${zeroProof.index}] from the tree, equal to ${zeroProof.value}`); + await wallet.addCapsule(getMembershipCapsule({ ...zeroProof, value: new Fr(0) })); + await contract.methods.read_at(key).send().wait(); + + // Progress time to beyond the update and thereby commit it to the tree. + await cheatCodes.aztec.warp((await cheatCodes.eth.timestamp()) + 1000); + await slowUpdateTreeSimulator.commit(); + logger('--- Progressing time to after the update ---'); + await status(key, _root, _leaf); + + logger( + `Tries to "read" tree[${zeroProof.index}] from the tree, but is rejected as value is not ${zeroProof.value}`, + ); + await wallet.addCapsule(getMembershipCapsule({ ...zeroProof, value: new Fr(0) })); + await expect(contract.methods.read_at(key).simulate()).rejects.toThrowError( + 'Assertion failed: Root does not match expected', + ); + + logger(`"Reads" tree[${key}], expect to be 1`); + await wallet.addCapsule(getMembershipCapsule({ ...zeroProof, value: new Fr(1) })); + await contract.methods.read_at(key).send().wait(); + + logger(`Updating tree[${key}] to 4 from private`); + const t2 = computeNextChange(BigInt(await cheatCodes.eth.timestamp())); + await wallet.addCapsule(getUpdateCapsule(await getUpdateProof(4n, key))); + await contract.methods.update_at_private(key, 4n).send().wait(); + await slowUpdateTreeSimulator.updateLeaf(new Fr(4).toBuffer(), key); + _root = { + before: Fr.fromBuffer(slowUpdateTreeSimulator.getRoot(false)).toBigInt(), + after: Fr.fromBuffer(slowUpdateTreeSimulator.getRoot(true)).toBigInt(), + next_change: t2, + }; + _leaf = { before: 1n, after: 4n, next_change: t2 }; + + await status(key, _root, _leaf); + }, 200_000); +}); diff --git a/yarn-project/end-to-end/src/fixtures/logging.ts b/yarn-project/end-to-end/src/fixtures/logging.ts index 03d3caacf29..5e16e5585f8 100644 --- a/yarn-project/end-to-end/src/fixtures/logging.ts +++ b/yarn-project/end-to-end/src/fixtures/logging.ts @@ -18,7 +18,9 @@ export function isMetricsLoggingRequested() { * Idempotent and automatically called by `setup` if CI or BENCHMARK env vars are set. */ export function setupMetricsLogger(filename: string) { - if (metricsLoggerSet) return; + if (metricsLoggerSet) { + return; + } mkdirpSync(dirname(filename)); const logger = winston.createLogger({ level: 'debug', diff --git a/yarn-project/end-to-end/src/fixtures/utils.ts b/yarn-project/end-to-end/src/fixtures/utils.ts index e2056369a18..978147ab5e0 100644 --- a/yarn-project/end-to-end/src/fixtures/utils.ts +++ b/yarn-project/end-to-end/src/fixtures/utils.ts @@ -58,7 +58,9 @@ export { deployAndInitializeTokenAndBridgeContracts } from '../shared/cross_chai const { PXE_URL = '', AZTEC_NODE_URL = '' } = process.env; const getAztecNodeUrl = () => { - if (AZTEC_NODE_URL) return AZTEC_NODE_URL; + if (AZTEC_NODE_URL) { + return AZTEC_NODE_URL; + } // If AZTEC_NODE_URL is not set, we assume that the PXE is running on the same host as the Aztec Node and use the default port const url = new URL(PXE_URL); @@ -166,9 +168,16 @@ export async function setupPXEService( * @param account - The account for use in create viem wallets. * @param config - The aztec Node Configuration * @param logger - The logger to be used + * @param numberOfAccounts - The number of new accounts to be created once the PXE is initiated. + * (will create extra accounts if the sandbox don't already have enough accounts) * @returns Private eXecution Environment (PXE) client, viem wallets, contract addresses etc. */ -async function setupWithSandbox(account: Account, config: AztecNodeConfig, logger: DebugLogger) { +async function setupWithSandbox( + account: Account, + config: AztecNodeConfig, + logger: DebugLogger, + numberOfAccounts: number, +) { // we are setting up against the sandbox, l1 contracts are already deployed const aztecNodeUrl = getAztecNodeUrl(); logger(`Creating Aztec Node client to remote host ${aztecNodeUrl}`); @@ -182,6 +191,10 @@ async function setupWithSandbox(account: Account, config: AztecNodeConfig, logge logger('PXE created, constructing wallets from initial sandbox accounts...'); const wallets = await getSandboxAccountsWallets(pxeClient); + if (wallets.length < numberOfAccounts) { + wallets.push(...(await createAccounts(pxeClient, numberOfAccounts - wallets.length))); + } + const walletClient = createWalletClient({ account, chain: localAnvil, @@ -196,7 +209,7 @@ async function setupWithSandbox(account: Account, config: AztecNodeConfig, logge walletClient, publicClient, }; - const cheatCodes = await CheatCodes.create(config.rpcUrl, pxeClient!); + const cheatCodes = CheatCodes.create(config.rpcUrl, pxeClient!); const teardown = () => Promise.resolve(); return { aztecNode, @@ -219,8 +232,8 @@ type SetupOptions = { /** State load */ stateLoad?: string } & Partial { - if (aztecNode instanceof AztecNodeService) await aztecNode?.stop(); - if (pxe instanceof PXEService) await pxe?.stop(); + if (aztecNode instanceof AztecNodeService) { + await aztecNode?.stop(); + } + if (pxe instanceof PXEService) { + await pxe?.stop(); + } }; return { @@ -334,6 +351,10 @@ function getJobName() { */ export function getLogger() { const describeBlockName = expect.getState().currentTestName?.split(' ')[0].replaceAll('/', ':'); + if (!describeBlockName) { + const name = expect.getState().testPath?.split('/').pop()?.split('.')[0] ?? 'unknown'; + return createDebugLogger('aztec:' + name); + } return createDebugLogger('aztec:' + describeBlockName); } diff --git a/yarn-project/end-to-end/src/guides/dapp_testing.test.ts b/yarn-project/end-to-end/src/guides/dapp_testing.test.ts index ee03c984ba6..3b11334e894 100644 --- a/yarn-project/end-to-end/src/guides/dapp_testing.test.ts +++ b/yarn-project/end-to-end/src/guides/dapp_testing.test.ts @@ -35,7 +35,7 @@ describe('guides/dapp/testing', () => { owner = await createAccount(pxe); recipient = await createAccount(pxe); token = await TokenContract.deploy(owner, owner.getCompleteAddress()).send().deployed(); - }, 30_000); + }, 60_000); it('increases recipient funds on mint', async () => { const recipientAddress = recipient.getAddress(); @@ -99,7 +99,7 @@ describe('guides/dapp/testing', () => { pxe = createPXEClient(PXE_URL); owner = await createAccount(pxe); testContract = await TestContract.deploy(owner).send().deployed(); - cheats = await CheatCodes.create(ETHEREUM_HOST, pxe); + cheats = CheatCodes.create(ETHEREUM_HOST, pxe); }, 30_000); it('warps time to 1h into the future', async () => { @@ -141,11 +141,11 @@ describe('guides/dapp/testing', () => { await token.methods.redeem_shield(ownerAddress, 100n, secret).send().wait(); // docs:start:calc-slot - cheats = await CheatCodes.create(ETHEREUM_HOST, pxe); + cheats = CheatCodes.create(ETHEREUM_HOST, pxe); // The balances mapping is defined on storage slot 3 and is indexed by user address ownerSlot = cheats.aztec.computeSlotInMap(3n, ownerAddress); // docs:end:calc-slot - }, 60_000); + }, 90_000); it('checks private storage', async () => { // docs:start:private-storage @@ -158,7 +158,7 @@ describe('guides/dapp/testing', () => { const balance = values.reduce((sum, current) => sum + current.toBigInt(), 0n); expect(balance).toEqual(100n); // docs:end:private-storage - }); + }, 30_000); it('checks public storage', async () => { // docs:start:public-storage @@ -167,7 +167,7 @@ describe('guides/dapp/testing', () => { const balance = await pxe.getPublicStorageAt(token.address, ownerPublicBalanceSlot); expect(balance!.value).toEqual(100n); // docs:end:public-storage - }); + }, 30_000); it('checks unencrypted logs, [Kinda broken with current implementation]', async () => { // docs:start:unencrypted-logs @@ -180,21 +180,21 @@ describe('guides/dapp/testing', () => { const logs = (await pxe.getUnencryptedLogs(filter)).logs; expect(Fr.fromBuffer(logs[0].log.data)).toEqual(value); // docs:end:unencrypted-logs - }); + }, 30_000); it('asserts a local transaction simulation fails by calling simulate', async () => { // docs:start:local-tx-fails const call = token.methods.transfer(owner.getAddress(), recipient.getAddress(), 200n, 0); await expect(call.simulate()).rejects.toThrowError(/Balance too low/); // docs:end:local-tx-fails - }); + }, 30_000); it('asserts a local transaction simulation fails by calling send', async () => { // docs:start:local-tx-fails-send const call = token.methods.transfer(owner.getAddress(), recipient.getAddress(), 200n, 0); await expect(call.send().wait()).rejects.toThrowError(/Balance too low/); // docs:end:local-tx-fails-send - }); + }, 30_000); it('asserts a transaction is dropped', async () => { // docs:start:tx-dropped @@ -207,21 +207,21 @@ describe('guides/dapp/testing', () => { await call1.send().wait(); await expect(call2.send().wait()).rejects.toThrowError(/dropped/); // docs:end:tx-dropped - }); + }, 30_000); it('asserts a simulation for a public function call fails', async () => { // docs:start:local-pub-fails const call = token.methods.transfer_public(owner.getAddress(), recipient.getAddress(), 1000n, 0); await expect(call.simulate()).rejects.toThrowError(/Underflow/); // docs:end:local-pub-fails - }); + }, 30_000); it('asserts a transaction with a failing public call is dropped (until we get public reverts)', async () => { // docs:start:pub-dropped const call = token.methods.transfer_public(owner.getAddress(), recipient.getAddress(), 1000n, 0); await expect(call.send({ skipPublicSimulation: true }).wait()).rejects.toThrowError(/dropped/); // docs:end:pub-dropped - }); + }, 30_000); }); }); diff --git a/yarn-project/end-to-end/src/guides/up_quick_start.sh b/yarn-project/end-to-end/src/guides/up_quick_start.sh index 625da141ab5..ddb1edfb735 100755 --- a/yarn-project/end-to-end/src/guides/up_quick_start.sh +++ b/yarn-project/end-to-end/src/guides/up_quick_start.sh @@ -3,9 +3,14 @@ set -eux +# The following accounts and pks must match the ones exposed by the sandbox. +# This test also requires that no other transactions have been sent to the sandbox +# so the contract address hardcoded into $CONTRACT is correct. Otherwise, we'd need +# to extract the deployed address from the cli `deploy` command. + # docs:start:declare-accounts -ALICE="0x16efad912187aa8ef0dcc6ef4f3743ab327b06465d4d229943f2fe3f88b06ad9" -BOB="0x17f5e66bfe7dafc76434268bcb3968a8bc207b476aeed78d1e4a2f02aad45842" +ALICE="0x29e53e3e43377c80c8a1e390ed90ddf1167f376c4c063844ec18f8a81516c1c0" +BOB="0x2b66c968c3ae3b827b6614719141be12667bad86f13b401c667d64a7c56d911c" ALICE_PRIVATE_KEY="0x2153536ff6628eee01cf4024889ff977a18d9fa61d0e414422f7681cf085c281" # docs:end:declare-accounts @@ -15,9 +20,8 @@ aztec-cli deploy \ --salt 0 \ --args $ALICE -aztec-cli check-deploy --contract-address 0x0ed3aaa22d69559ee368b32fbafb24b49b103c0a07bd834fd519c8157553ec1f - -CONTRACT="0x0ed3aaa22d69559ee368b32fbafb24b49b103c0a07bd834fd519c8157553ec1f" +CONTRACT="0x18a018014978f3a6b8f69548fbfc5fded6b829b36becea24dd7f7ee34927dff7" +aztec-cli check-deploy --contract-address $CONTRACT # docs:end:deploy # docs:start:mint-private diff --git a/yarn-project/end-to-end/src/guides/writing_an_account_contract.test.ts b/yarn-project/end-to-end/src/guides/writing_an_account_contract.test.ts index 867c1f998d2..ce67b832016 100644 --- a/yarn-project/end-to-end/src/guides/writing_an_account_contract.test.ts +++ b/yarn-project/end-to-end/src/guides/writing_an_account_contract.test.ts @@ -25,18 +25,18 @@ class SchnorrHardcodedKeyAccountContract extends BaseAccountContract { super(SchnorrHardcodedAccountContractArtifact); } - getDeploymentArgs(): Promise { + getDeploymentArgs(): any[] { // This contract does not require any arguments in its constructor. - return Promise.resolve([]); + return []; } getAuthWitnessProvider(_address: CompleteAddress): AuthWitnessProvider { const privateKey = this.privateKey; return { - async createAuthWitness(message: Fr): Promise { - const signer = await Schnorr.new(); + createAuthWitness(message: Fr): Promise { + const signer = new Schnorr(); const signature = signer.constructSignature(message.toBuffer(), privateKey); - return new AuthWitness(message, [...signature.toBuffer()]); + return Promise.resolve(new AuthWitness(message, [...signature.toBuffer()])); }, }; } diff --git a/yarn-project/end-to-end/src/integration_archiver_l1_to_l2.test.ts b/yarn-project/end-to-end/src/integration_archiver_l1_to_l2.test.ts index 83746e71497..c33b0fe3887 100644 --- a/yarn-project/end-to-end/src/integration_archiver_l1_to_l2.test.ts +++ b/yarn-project/end-to-end/src/integration_archiver_l1_to_l2.test.ts @@ -119,5 +119,5 @@ describe('archiver integration with l1 to l2 messages', () => { expect((await archiver.getPendingL1ToL2Messages(10)).length).toEqual(0); expect(() => archiver.getConfirmedL1ToL2Message(Fr.ZERO)).toThrow(); - }); + }, 30_000); }); diff --git a/yarn-project/end-to-end/src/integration_l1_publisher.test.ts b/yarn-project/end-to-end/src/integration_l1_publisher.test.ts index 8a9fc751d7d..e06d67bbbb8 100644 --- a/yarn-project/end-to-end/src/integration_l1_publisher.test.ts +++ b/yarn-project/end-to-end/src/integration_l1_publisher.test.ts @@ -192,9 +192,9 @@ describe('L1Publisher integration', () => { // Note: using max deadline const deadline = 2 ** 32 - 1; // getting the 32 byte hex string representation of the content - const contentString = content.toString(true); + const contentString = content.toString(); // Using the 0 value for the secretHash. - const emptySecretHash = Fr.ZERO.toString(true); + const emptySecretHash = Fr.ZERO.toString(); await inbox.write.sendL2Message( [ @@ -255,9 +255,9 @@ describe('L1Publisher integration', () => { expect(inboxLogs).toHaveLength(l1ToL2Messages.length * (i + 1)); for (let j = 0; j < NUMBER_OF_L1_L2_MESSAGES_PER_ROLLUP; j++) { const event = inboxLogs[j + i * NUMBER_OF_L1_L2_MESSAGES_PER_ROLLUP].args; - expect(event.content).toEqual(l1ToL2Content[j].toString(true)); + expect(event.content).toEqual(l1ToL2Content[j].toString()); expect(event.deadline).toEqual(2 ** 32 - 1); - expect(event.entryKey).toEqual(l1ToL2Messages[j].toString(true)); + expect(event.entryKey).toEqual(l1ToL2Messages[j].toString()); expect(event.fee).toEqual(0n); expect(event.recipient).toEqual(recipientAddress.toString()); expect(event.recipientVersion).toEqual(1n); @@ -284,13 +284,15 @@ describe('L1Publisher integration', () => { // check that values are in the inbox for (let j = 0; j < l1ToL2Messages.length; j++) { - if (l1ToL2Messages[j].isZero()) continue; - expect(await inbox.read.contains([l1ToL2Messages[j].toString(true)])).toBeTruthy(); + if (l1ToL2Messages[j].isZero()) { + continue; + } + expect(await inbox.read.contains([l1ToL2Messages[j].toString()])).toBeTruthy(); } // check that values are not in the outbox for (let j = 0; j < block.newL2ToL1Msgs.length; j++) { - expect(await outbox.read.contains([block.newL2ToL1Msgs[j].toString(true)])).toBeFalsy(); + expect(await outbox.read.contains([block.newL2ToL1Msgs[j].toString()])).toBeFalsy(); } // Useful for sol tests block generation @@ -341,12 +343,14 @@ describe('L1Publisher integration', () => { // check that values have been consumed from the inbox for (let j = 0; j < l1ToL2Messages.length; j++) { - if (l1ToL2Messages[j].isZero()) continue; - expect(await inbox.read.contains([l1ToL2Messages[j].toString(true)])).toBeFalsy(); + if (l1ToL2Messages[j].isZero()) { + continue; + } + expect(await inbox.read.contains([l1ToL2Messages[j].toString()])).toBeFalsy(); } // check that values are inserted into the outbox for (let j = 0; j < block.newL2ToL1Msgs.length; j++) { - expect(await outbox.read.contains([block.newL2ToL1Msgs[j].toString(true)])).toBeTruthy(); + expect(await outbox.read.contains([block.newL2ToL1Msgs[j].toString()])).toBeTruthy(); } } }, 360_000); @@ -418,7 +422,11 @@ describe('L1Publisher integration', () => { * Converts a hex string into a buffer. String may be 0x-prefixed or not. */ function hexStringToBuffer(hex: string): Buffer { - if (!/^(0x)?[a-fA-F0-9]+$/.test(hex)) throw new Error(`Invalid format for hex string: "${hex}"`); - if (hex.length % 2 === 1) throw new Error(`Invalid length for hex string: "${hex}"`); + if (!/^(0x)?[a-fA-F0-9]+$/.test(hex)) { + throw new Error(`Invalid format for hex string: "${hex}"`); + } + if (hex.length % 2 === 1) { + throw new Error(`Invalid length for hex string: "${hex}"`); + } return Buffer.from(hex.replace(/^0x/, ''), 'hex'); } diff --git a/yarn-project/end-to-end/src/sample-dapp/ci/index.test.mjs b/yarn-project/end-to-end/src/sample-dapp/ci/index.test.mjs index 4c3312a6d1d..a2b67b509bc 100644 --- a/yarn-project/end-to-end/src/sample-dapp/ci/index.test.mjs +++ b/yarn-project/end-to-end/src/sample-dapp/ci/index.test.mjs @@ -9,5 +9,5 @@ describe('sample-dapp', () => { await waitForSandbox(); await deploy(); await main(); - }, 60_000); + }, 90_000); }); diff --git a/yarn-project/end-to-end/src/sample-dapp/index.test.mjs b/yarn-project/end-to-end/src/sample-dapp/index.test.mjs index 20bb40ad4b4..11424c9465d 100644 --- a/yarn-project/end-to-end/src/sample-dapp/index.test.mjs +++ b/yarn-project/end-to-end/src/sample-dapp/index.test.mjs @@ -33,6 +33,6 @@ describe('token', () => { expect(await token.methods.balance_of_private(recipient.getAddress()).view()).toEqual(0n); await token.methods.transfer(owner.getAddress(), recipient.getAddress(), 20n, 0).send().wait(); expect(await token.methods.balance_of_private(recipient.getAddress()).view()).toEqual(20n); - }); + }, 30_000); // docs:end:test }); diff --git a/yarn-project/end-to-end/src/shared/browser.ts b/yarn-project/end-to-end/src/shared/browser.ts index 050c5f4f814..72b064bc903 100644 --- a/yarn-project/end-to-end/src/shared/browser.ts +++ b/yarn-project/end-to-end/src/shared/browser.ts @@ -99,7 +99,7 @@ export const browserTestSuite = (setup: () => Server, pageLogger: AztecJs.DebugL const privateKey = GrumpkinScalar.fromString(privateKeyString); const account = getUnsafeSchnorrAccount(pxe, privateKey); await account.waitDeploy(); - const completeAddress = await account.getCompleteAddress(); + const completeAddress = account.getCompleteAddress(); const addressString = completeAddress.address.toString(); console.log(`Created Account: ${addressString}`); return addressString; diff --git a/yarn-project/end-to-end/src/shared/cli.ts b/yarn-project/end-to-end/src/shared/cli.ts index 59de7abe3b5..17e50bee797 100644 --- a/yarn-project/end-to-end/src/shared/cli.ts +++ b/yarn-project/end-to-end/src/shared/cli.ts @@ -60,7 +60,9 @@ export const cliTestSuite = ( const findInLogs = (regex: RegExp) => { for (const log of logs) { const match = regex.exec(log); - if (match) return match; + if (match) { + return match; + } } }; @@ -68,7 +70,9 @@ export const cliTestSuite = ( const matches = []; for (const log of logs) { const match = regex.exec(log); - if (match) matches.push(match); + if (match) { + matches.push(match); + } } return matches; }; @@ -91,7 +95,7 @@ export const cliTestSuite = ( const newCompleteAddress = accountsAfter[accountsAfter.length - 1]; // Test get-accounts - debug('Check that account was added to the list of accs in RPC'); + debug('Check that account was added to the list of accounts in RPC'); await run('get-accounts'); const fetchedAddresses = findMultipleInLogs(/Address:\s+(?

0x[a-fA-F0-9]+)/); const foundFetchedAddress = fetchedAddresses.find(match => match.groups?.address === newAddress.toString()); diff --git a/yarn-project/end-to-end/src/shared/cross_chain_test_harness.ts b/yarn-project/end-to-end/src/shared/cross_chain_test_harness.ts index b3b8374be45..92e70e281c3 100644 --- a/yarn-project/end-to-end/src/shared/cross_chain_test_harness.ts +++ b/yarn-project/end-to-end/src/shared/cross_chain_test_harness.ts @@ -12,7 +12,7 @@ import { Wallet, computeMessageSecretHash, deployL1Contract, - sha256ToField, + sha256, } from '@aztec/aztec.js'; import { OutboxAbi, @@ -89,7 +89,9 @@ export async function deployAndInitializeTokenAndBridgeContracts( // now wait for the deploy txs to be mined. This way we send all tx in the same rollup. const deployReceipt = await deployTx.wait(); - if (deployReceipt.status !== TxStatus.MINED) throw new Error(`Deploy token tx status is ${deployReceipt.status}`); + if (deployReceipt.status !== TxStatus.MINED) { + throw new Error(`Deploy token tx status is ${deployReceipt.status}`); + } const token = await TokenContract.at(deployReceipt.contractAddress!, wallet); // deploy l2 token bridge and attach to the portal @@ -97,17 +99,23 @@ export async function deployAndInitializeTokenAndBridgeContracts( .send({ portalContract: tokenPortalAddress }) .deployed(); - if ((await token.methods.admin().view()) !== owner.toBigInt()) throw new Error(`Token admin is not ${owner}`); + if ((await token.methods.admin().view()) !== owner.toBigInt()) { + throw new Error(`Token admin is not ${owner}`); + } - if ((await bridge.methods.token().view()) !== token.address.toBigInt()) + if ((await bridge.methods.token().view()) !== token.address.toBigInt()) { throw new Error(`Bridge token is not ${token.address}`); + } // make the bridge a minter on the token: const makeMinterTx = token.methods.set_minter(bridge.address, true).send(); const makeMinterReceipt = await makeMinterTx.wait(); - if (makeMinterReceipt.status !== TxStatus.MINED) + if (makeMinterReceipt.status !== TxStatus.MINED) { throw new Error(`Make bridge a minter tx status is ${makeMinterReceipt.status}`); - if ((await token.methods.is_minter(bridge.address).view()) === 1n) throw new Error(`Bridge is not a minter`); + } + if ((await token.methods.is_minter(bridge.address).view()) === 1n) { + throw new Error(`Bridge is not a minter`); + } // initialize portal await tokenPortal.write.initialize( @@ -206,7 +214,7 @@ export class CrossChainTestHarness { this.logger("Generating a claim secret using pedersen's hash function"); const secret = Fr.random(); const secretHash = computeMessageSecretHash(secret); - this.logger('Generated claim secret: ' + secretHash.toString(true)); + this.logger('Generated claim secret: ' + secretHash.toString()); return [secret, secretHash]; } @@ -232,7 +240,7 @@ export class CrossChainTestHarness { bridgeAmount, this.ethAccount.toString(), deadline, - secretHash.toString(true), + secretHash.toString(), ] as const; const { result: messageKeyHex } = await this.tokenPortal.simulate.depositToAztecPublic(args, { account: this.ethAccount.toString(), @@ -254,11 +262,11 @@ export class CrossChainTestHarness { this.logger('Sending messages to L1 portal to be consumed privately'); const args = [ - secretHashForRedeemingMintedNotes.toString(true), + secretHashForRedeemingMintedNotes.toString(), bridgeAmount, this.ethAccount.toString(), deadline, - secretHashForL2MessageConsumption.toString(true), + secretHashForL2MessageConsumption.toString(), ] as const; const { result: messageKeyHex } = await this.tokenPortal.simulate.depositToAztecPrivate(args, { account: this.ethAccount.toString(), @@ -362,24 +370,28 @@ export class CrossChainTestHarness { async checkEntryIsNotInOutbox(withdrawAmount: bigint, callerOnL1: EthAddress = EthAddress.ZERO): Promise { this.logger('Ensure that the entry is not in outbox yet'); - const content = sha256ToField( - Buffer.concat([ - Buffer.from(getFunctionSelector('withdraw(address,uint256,address)').substring(2), 'hex'), - this.ethAccount.toBuffer32(), - new Fr(withdrawAmount).toBuffer(), - callerOnL1.toBuffer32(), - ]), + const content = Fr.fromBufferReduce( + sha256( + Buffer.concat([ + Buffer.from(getFunctionSelector('withdraw(address,uint256,address)').substring(2), 'hex'), + this.ethAccount.toBuffer32(), + new Fr(withdrawAmount).toBuffer(), + callerOnL1.toBuffer32(), + ]), + ), ); - const entryKey = sha256ToField( - Buffer.concat([ - this.l2Bridge.address.toBuffer(), - new Fr(1).toBuffer(), // aztec version - this.tokenPortalAddress.toBuffer32() ?? Buffer.alloc(32, 0), - new Fr(this.publicClient.chain.id).toBuffer(), // chain id - content.toBuffer(), - ]), + const entryKey = Fr.fromBufferReduce( + sha256( + Buffer.concat([ + this.l2Bridge.address.toBuffer(), + new Fr(1).toBuffer(), // aztec version + this.tokenPortalAddress.toBuffer32() ?? Buffer.alloc(32, 0), + new Fr(this.publicClient.chain.id).toBuffer(), // chain id + content.toBuffer(), + ]), + ), ); - expect(await this.outbox.read.contains([entryKey.toString(true)])).toBeFalsy(); + expect(await this.outbox.read.contains([entryKey.toString()])).toBeFalsy(); return entryKey; } @@ -393,7 +405,7 @@ export class CrossChainTestHarness { false, ]); - expect(withdrawEntryKey).toBe(entryKey.toString(true)); + expect(withdrawEntryKey).toBe(entryKey.toString()); expect(await this.outbox.read.contains([withdrawEntryKey])).toBeTruthy(); await this.walletClient.writeContract(withdrawRequest); diff --git a/yarn-project/end-to-end/src/shared/uniswap_l1_l2.ts b/yarn-project/end-to-end/src/shared/uniswap_l1_l2.ts index 3825ed735a7..1a1fcb33fc9 100644 --- a/yarn-project/end-to-end/src/shared/uniswap_l1_l2.ts +++ b/yarn-project/end-to-end/src/shared/uniswap_l1_l2.ts @@ -251,8 +251,8 @@ export const uniswapL1L2TestSuite = ( uniswapFeeTier, daiCrossChainHarness.tokenPortalAddress.toString(), minimumOutputAmount, - secretHashForRedeemingDai.toString(true), - secretHashForDepositingSwappedDai.toString(true), + secretHashForRedeemingDai.toString(), + secretHashForDepositingSwappedDai.toString(), deadlineForDepositingSwappedDai, ownerEthAddress.toString(), true, @@ -400,7 +400,7 @@ export const uniswapL1L2TestSuite = ( daiCrossChainHarness.tokenPortalAddress.toString(), minimumOutputAmount, ownerAddress.toString(), - secretHashForDepositingSwappedDai.toString(true), + secretHashForDepositingSwappedDai.toString(), deadlineForDepositingSwappedDai, ownerEthAddress.toString(), true, @@ -681,7 +681,7 @@ export const uniswapL1L2TestSuite = ( daiCrossChainHarness.tokenPortalAddress.toString(), minimumOutputAmount, ownerAddress.toString(), - secretHashForDepositingSwappedDai.toString(true), + secretHashForDepositingSwappedDai.toString(), deadlineForDepositingSwappedDai, ownerEthAddress.toString(), true, @@ -740,8 +740,8 @@ export const uniswapL1L2TestSuite = ( uniswapFeeTier, daiCrossChainHarness.tokenPortalAddress.toString(), minimumOutputAmount, - secretHashForRedeemingDai.toString(true), - secretHashForDepositingSwappedDai.toString(true), + secretHashForRedeemingDai.toString(), + secretHashForDepositingSwappedDai.toString(), deadlineForDepositingSwappedDai, ownerEthAddress.toString(), true, diff --git a/yarn-project/end-to-end/tsconfig.json b/yarn-project/end-to-end/tsconfig.json index 2f7e82c45d1..8792db4c3c5 100644 --- a/yarn-project/end-to-end/tsconfig.json +++ b/yarn-project/end-to-end/tsconfig.json @@ -33,6 +33,9 @@ { "path": "../l1-artifacts" }, + { + "path": "../merkle-tree" + }, { "path": "../noir-contracts" }, @@ -52,5 +55,7 @@ "path": "../world-state" } ], - "include": ["src"] + "include": [ + "src" + ] } diff --git a/yarn-project/ethereum/package.json b/yarn-project/ethereum/package.json index 30600a60103..6e381965005 100644 --- a/yarn-project/ethereum/package.json +++ b/yarn-project/ethereum/package.json @@ -15,7 +15,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "start:dev": "tsc-watch -p tsconfig.json --onSuccess 'yarn start'", "start": "node ./dest/index.js", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" @@ -31,7 +31,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/node": "^18.14.6", "jest": "^29.5.0", diff --git a/yarn-project/foundation/.eslintrc.cjs b/yarn-project/foundation/.eslintrc.cjs index e4369eb7e6d..31c960d6c12 100644 --- a/yarn-project/foundation/.eslintrc.cjs +++ b/yarn-project/foundation/.eslintrc.cjs @@ -82,6 +82,7 @@ module.exports = { 'require-await': 2, 'no-console': 'error', 'no-constant-condition': 'off', + curly: ['error', 'all'], camelcase: 2, 'no-restricted-imports': [ 'error', @@ -110,6 +111,7 @@ module.exports = { }, ], 'import/no-extraneous-dependencies': 'error', + 'import/no-cycle': 'warn', 'tsdoc/syntax': JSDOC_RULES_LEVEL, 'jsdoc/require-jsdoc': [ JSDOC_RULES_LEVEL, diff --git a/yarn-project/foundation/package.json b/yarn-project/foundation/package.json index d71d41889d5..98a10914965 100644 --- a/yarn-project/foundation/package.json +++ b/yarn-project/foundation/package.json @@ -41,7 +41,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test:light": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" }, @@ -79,7 +79,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/bn.js": "^5.1.3", "@types/debug": "^4.1.7", "@types/detect-node": "^2.0.0", diff --git a/yarn-project/foundation/src/abi/decoder.ts b/yarn-project/foundation/src/abi/decoder.ts index 4a44672d947..9ea69388f76 100644 --- a/yarn-project/foundation/src/abi/decoder.ts +++ b/yarn-project/foundation/src/abi/decoder.ts @@ -21,12 +21,12 @@ class ReturnValuesDecoder { private decodeReturn(abiType: ABIType): DecodedReturn { switch (abiType.kind) { case 'field': - return this.getNextField().value; + return this.getNextField().toBigInt(); case 'integer': if (abiType.sign === 'signed') { throw new Error('Unsupported type: signed integer'); } - return this.getNextField().value; + return this.getNextField().toBigInt(); case 'boolean': return !this.getNextField().isZero(); case 'array': { diff --git a/yarn-project/foundation/src/abi/encoder.test.ts b/yarn-project/foundation/src/abi/encoder.test.ts index 8f978756c0b..bfe0d63ebf2 100644 --- a/yarn-project/foundation/src/abi/encoder.test.ts +++ b/yarn-project/foundation/src/abi/encoder.test.ts @@ -96,7 +96,9 @@ describe('abi/encoder', () => { returnTypes: [], }; const args = ['garbage']; - expect(() => encodeArguments(testFunctionAbi, args)).toThrowError('Cannot convert garbage to a BigInt'); + expect(() => encodeArguments(testFunctionAbi, args)).toThrowError( + `Type 'string' with value 'garbage' passed to BaseField ctor.`, + ); }); it('throws when passing object argument as field', () => { diff --git a/yarn-project/foundation/src/abi/encoder.ts b/yarn-project/foundation/src/abi/encoder.ts index 7ebf8535554..2e4b1844060 100644 --- a/yarn-project/foundation/src/abi/encoder.ts +++ b/yarn-project/foundation/src/abi/encoder.ts @@ -1,5 +1,6 @@ import { ABIType, FunctionAbi, isAddressStruct } from '@aztec/foundation/abi'; -import { Fr } from '@aztec/foundation/fields'; + +import { Fr } from '../fields/index.js'; /** * Encodes arguments for a function call. @@ -46,7 +47,7 @@ class ArgumentEncoder { } else if (typeof arg === 'bigint') { this.flattened.push(new Fr(arg)); } else if (typeof arg === 'boolean') { - this.flattened.push(new Fr(arg ? 1 : 0)); + this.flattened.push(new Fr(arg ? 1n : 0n)); } else if (typeof arg === 'object') { if (Buffer.isBuffer(arg)) { this.flattened.push(Fr.fromBuffer(arg)); diff --git a/yarn-project/foundation/src/abi/function_selector.ts b/yarn-project/foundation/src/abi/function_selector.ts index ac20bcb3620..f2776319f15 100644 --- a/yarn-project/foundation/src/abi/function_selector.ts +++ b/yarn-project/foundation/src/abi/function_selector.ts @@ -70,7 +70,7 @@ export class FunctionSelector { * @returns An Fr instance. */ public toField() { - return new Fr(this.value); + return new Fr(BigInt(this.value)); } /** @@ -79,7 +79,7 @@ export class FunctionSelector { * @returns The function selector. */ static fromField(fr: Fr): FunctionSelector { - return new FunctionSelector(Number(fr.value)); + return new FunctionSelector(Number(fr.toBigInt())); } /** diff --git a/yarn-project/foundation/src/aztec-address/index.ts b/yarn-project/foundation/src/aztec-address/index.ts index 76591c6f18a..4c042ad96c6 100644 --- a/yarn-project/foundation/src/aztec-address/index.ts +++ b/yarn-project/foundation/src/aztec-address/index.ts @@ -1,162 +1,30 @@ -import { toBigIntBE, toBufferBE } from '../bigint-buffer/index.js'; import { Fr } from '../fields/index.js'; -import { BufferReader } from '../serialize/buffer_reader.js'; /** - * AztecAddress represents a 32-byte address in the Aztec Protocol. It provides methods to create, manipulate, and - * compare addresses. The maximum value of an address is determined by the field modulus and all instances of AztecAddress - * should have a value less than or equal to this max value. This class also provides helper functions to convert - * addresses from strings, buffers, and other formats. + * AztecAddress represents a 32-byte address in the Aztec Protocol. + * It provides methods to create, manipulate, and compare addresses. + * The maximum value of an address is determined by the field modulus and all instances of AztecAddress. + * It should have a value less than or equal to this max value. + * This class also provides helper functions to convert addresses from strings, buffers, and other formats. */ -export class AztecAddress { - static SIZE_IN_BYTES = 32; - static ZERO = new AztecAddress(Buffer.alloc(AztecAddress.SIZE_IN_BYTES)); - static MODULUS = 0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001n; - static MAX_VALUE = AztecAddress.MODULUS - 1n; - - constructor( - /** - * The buffer field. - */ - public readonly buffer: Buffer, - ) { - const value = toBigIntBE(buffer); - if (value > AztecAddress.MAX_VALUE) { - throw new Error(`AztecAddress out of range ${value}.`); +export class AztecAddress extends Fr { + constructor(buffer: Buffer) { + if (buffer.length !== 32) { + throw new Error(`Invalid length ${buffer.length}.`); } + super(buffer); } - /** - * Generates a random AztecAddress instance, using the Fr field (a finite field) to create a random value - * within a valid range and then converting it into a Buffer of a fixed size in bytes. - * - * @returns A new AztecAddress instance with a random value. - */ - static random() { - return new AztecAddress(toBufferBE(Fr.random().value, AztecAddress.SIZE_IN_BYTES)); - } - - /** - * Creates an AztecAddress instance from a given buffer or BufferReader. - * If the input is a Buffer, it wraps it in a BufferReader before processing. - * Throws an error if the input length is not equal to the expected size. - * - * @param buffer - The input buffer or BufferReader containing the address data. - * @returns - A new AztecAddress instance with the extracted address data. - */ - static fromBuffer(buffer: Buffer | BufferReader) { - const reader = BufferReader.asReader(buffer); - return new this(reader.readBytes(this.SIZE_IN_BYTES)); - } - - /** - * Create an AztecAddress instance from a hex-encoded string. - * The input 'address' should be prefixed with '0x' or not, and have exactly 64 hex characters. - * Throws an error if the input length is invalid or address value is out of range. - * - * @param address - The hex-encoded string representing the Aztec address. - * @returns An AztecAddress instance. - */ - static fromString(address: string) { - const buf = Buffer.from(address.replace(/^0x/i, ''), 'hex'); - if (buf.length !== AztecAddress.SIZE_IN_BYTES) { - throw new Error(`Invalid length ${buf.length}.`); - } - return new AztecAddress(buf); - } - - /** - * Creates an AztecAddress from a bigint. - * The provided value must be within the range of a field. - * @param address - The bigint representation of the address. - * @returns An AztecAddress instance. - */ - static fromBigInt(address: bigint) { - return new AztecAddress(toBufferBE(address, AztecAddress.SIZE_IN_BYTES)); - } - - /** - * Converts the AztecAddress instance into a Buffer. - * This method should be used when encoding the address for storage, transmission or serialization purposes. - * - * @returns A Buffer representation of the AztecAddress instance. - */ - toBuffer() { - return this.buffer; - } - - /** - * Convert the AztecAddress to a hexadecimal string representation, with a "0x" prefix. - * The resulting string will have a length of 66 characters (including the prefix). - * - * @returns A hexadecimal string representation of the AztecAddress. - */ - toString(): `0x${string}` { - return `0x${this.buffer.toString('hex')}`; - } - - /** - * Returns a shortened string representation of the AztecAddress, displaying only the first 10 characters and last 4 characters. - * This is useful for human-readable displays where the full address is not necessary. - * - * @returns A shortened string representation of the address. - */ - toShortString() { - const str = this.toString(); - return `${str.slice(0, 10)}...${str.slice(-4)}`; - } - - /** - * Returns this address from a Field element. - * @param field - The Field element to convert. - * @returns An Address Object from a Field element with the same value. - */ - static fromField(field: Fr): AztecAddress { - return new AztecAddress(toBufferBE(field.value, AztecAddress.SIZE_IN_BYTES)); - } - - /** - * Returns this address as a field element. - * @returns A field element with the same value as the address. - */ - toField() { - return Fr.fromBuffer(this.toBuffer()); - } - - /** - * Returns this address as a bigint. Useful for creating maps indexed by addresses. - * @returns A bigint with the same value as the address. - */ - toBigInt() { - return toBigIntBE(this.buffer); - } - - /** - * Determines if this AztecAddress instance is equal to the given AztecAddress instance. - * Equality is based on the content of their respective buffers. - * - * @param other - The AztecAddress instance to compare against. - * @returns True if the buffers of both instances are equal, false otherwise. - */ - equals(other: AztecAddress) { - return this.buffer.equals(other.buffer); + static fromField(fr: Fr) { + return new AztecAddress(fr.toBuffer()); } - /** - * Checks if the AztecAddress is zero. - * - * @returns Returns true if the AztecAddress is equal to the zero address, otherwise returns false. - */ - isZero() { - return this.equals(AztecAddress.ZERO); + static fromBigInt(value: bigint) { + return AztecAddress.fromField(new Fr(value)); } - /** - * Friendly representation for debugging purposes. - * - * @returns A hex string representing the address. - */ - toFriendlyJSON() { - return this.toString(); + static fromString(buf: string) { + const buffer = Buffer.from(buf.replace(/^0x/i, ''), 'hex'); + return new AztecAddress(buffer); } } diff --git a/yarn-project/foundation/src/bigint-buffer/index.ts b/yarn-project/foundation/src/bigint-buffer/index.ts index 3c764a277d7..88c42d6a46a 100644 --- a/yarn-project/foundation/src/bigint-buffer/index.ts +++ b/yarn-project/foundation/src/bigint-buffer/index.ts @@ -33,7 +33,9 @@ export function toBigIntBE(buf: Buffer): bigint { * @returns A little-endian buffer representation of num. */ export function toBufferLE(num: bigint, width: number): Buffer { - if (num < BigInt(0)) throw new Error(`Cannot convert negative bigint ${num.toString()} to buffer with toBufferLE.`); + if (num < BigInt(0)) { + throw new Error(`Cannot convert negative bigint ${num.toString()} to buffer with toBufferLE.`); + } const hex = num.toString(16); const buffer = Buffer.from(hex.padStart(width * 2, '0').slice(0, width * 2), 'hex'); buffer.reverse(); @@ -47,10 +49,14 @@ export function toBufferLE(num: bigint, width: number): Buffer { * @returns A big-endian buffer representation of num. */ export function toBufferBE(num: bigint, width: number): Buffer { - if (num < BigInt(0)) throw new Error(`Cannot convert negative bigint ${num.toString()} to buffer with toBufferBE.`); + if (num < BigInt(0)) { + throw new Error(`Cannot convert negative bigint ${num.toString()} to buffer with toBufferBE.`); + } const hex = num.toString(16); const buffer = Buffer.from(hex.padStart(width * 2, '0').slice(0, width * 2), 'hex'); - if (buffer.length > width) throw new Error(`Number ${num.toString(16)} does not fit in ${width}`); + if (buffer.length > width) { + throw new Error(`Number ${num.toString(16)} does not fit in ${width}`); + } return buffer; } diff --git a/yarn-project/foundation/src/collection/array.ts b/yarn-project/foundation/src/collection/array.ts index bf7cfed423b..1e4eb934d4c 100644 --- a/yarn-project/foundation/src/collection/array.ts +++ b/yarn-project/foundation/src/collection/array.ts @@ -8,7 +8,9 @@ import { Tuple } from '../serialize/types.js'; * @returns A new padded array. */ export function padArrayEnd(arr: T[], elem: T, length: N): Tuple { - if (arr.length > length) throw new Error(`Array size exceeds target length`); + if (arr.length > length) { + throw new Error(`Array size exceeds target length`); + } // Since typescript cannot always deduce that something is a tuple, we cast return [...arr, ...Array(length - arr.length).fill(elem)] as Tuple; } @@ -21,7 +23,9 @@ export function padArrayEnd(arr: T[], elem: T, length: N): * @returns A new padded array. */ export function padArrayStart(arr: T[], elem: T, length: N): Tuple { - if (arr.length > length) throw new Error(`Array size exceeds target length`); + if (arr.length > length) { + throw new Error(`Array size exceeds target length`); + } // Since typescript cannot always deduce that something is a tuple, we cast return [...Array(length - arr.length).fill(elem), ...arr] as Tuple; } @@ -33,7 +37,9 @@ export function padArrayStart(arr: T[], elem: T, length: N) */ export function isArrayEmpty(arr: T[], isEmpty: (item: T) => boolean): boolean { for (const item of arr) { - if (!isEmpty(item)) return false; + if (!isEmpty(item)) { + return false; + } } return true; } diff --git a/yarn-project/foundation/src/crypto/pedersen/__snapshots__/index.test.ts.snap b/yarn-project/foundation/src/crypto/pedersen/__snapshots__/index.test.ts.snap new file mode 100644 index 00000000000..b650a488bcb --- /dev/null +++ b/yarn-project/foundation/src/crypto/pedersen/__snapshots__/index.test.ts.snap @@ -0,0 +1,41 @@ +// Jest Snapshot v1, https://goo.gl/fbAQLP + +exports[`pedersen pedersen hash buffer 1`] = ` +{ + "data": [ + 43, + 213, + 196, + 82, + 160, + 201, + 113, + 98, + 41, + 79, + 201, + 223, + 208, + 241, + 224, + 157, + 14, + 9, + 201, + 95, + 165, + 237, + 63, + 241, + 73, + 251, + 222, + 243, + 102, + 203, + 81, + 249, + ], + "type": "Buffer", +} +`; diff --git a/yarn-project/foundation/src/crypto/pedersen/index.test.ts b/yarn-project/foundation/src/crypto/pedersen/index.test.ts index 0640c13d0b1..1d152a917d0 100644 --- a/yarn-project/foundation/src/crypto/pedersen/index.test.ts +++ b/yarn-project/foundation/src/crypto/pedersen/index.test.ts @@ -1,5 +1,5 @@ import { toBufferBE } from '../../bigint-buffer/index.js'; -import { pedersenCommit, pedersenHash } from './index.js'; +import { pedersenCommit, pedersenHash, pedersenHashBuffer } from './index.js'; describe('pedersen', () => { it('pedersen commit', () => { @@ -27,4 +27,12 @@ describe('pedersen', () => { const r = pedersenHash([toBufferBE(1n, 32), toBufferBE(1n, 32)], 5); expect(r).toEqual(Buffer.from('1c446df60816b897cda124524e6b03f36df0cec333fad87617aab70d7861daa6', 'hex')); }); + + it('pedersen hash buffer', () => { + const input = Buffer.alloc(123); + input.writeUint32BE(321, 0); + input.writeUint32BE(456, 119); + const r = pedersenHashBuffer(input); + expect(r).toMatchSnapshot(); + }); }); diff --git a/yarn-project/foundation/src/crypto/pedersen/pedersen.wasm.ts b/yarn-project/foundation/src/crypto/pedersen/pedersen.wasm.ts index 86fd96e882d..2a117ea5519 100644 --- a/yarn-project/foundation/src/crypto/pedersen/pedersen.wasm.ts +++ b/yarn-project/foundation/src/crypto/pedersen/pedersen.wasm.ts @@ -1,6 +1,8 @@ -import { Pedersen } from '@aztec/bb.js'; +import { BarretenbergSync, Fr } from '@aztec/bb.js'; -const pedersen = await Pedersen.new(); +// Get the singleton. This constructs (if not already) the barretenberg sync api within bb.js itself. +// This can be called from multiple other modules as needed, and it ensures it's only constructed once. +const api = await BarretenbergSync.getSingleton(); /** * Create a pedersen commitment (point) from an array of input fields. @@ -11,8 +13,10 @@ export function pedersenCommit(input: Buffer[]) { throw new Error('All input buffers must be <= 32 bytes.'); } input = input.map(i => (i.length < 32 ? Buffer.concat([Buffer.alloc(32 - i.length, 0), i]) : i)); - const [x, y] = pedersen.pedersenCommit(input); - return [Buffer.from(x), Buffer.from(y)]; + const point = api.pedersenCommit(input.map(i => new Fr(i))); + // toBuffer returns Uint8Arrays (browser/worker-boundary friendly). + // TODO: rename toTypedArray()? + return [Buffer.from(point.x.toBuffer()), Buffer.from(point.y.toBuffer())]; } /** @@ -24,5 +28,19 @@ export function pedersenHash(input: Buffer[], index = 0) { throw new Error('All input buffers must be <= 32 bytes.'); } input = input.map(i => (i.length < 32 ? Buffer.concat([Buffer.alloc(32 - i.length, 0), i]) : i)); - return Buffer.from(pedersen.pedersenHash(input, index)); + return Buffer.from( + api + .pedersenHash( + input.map(i => new Fr(i)), + index, + ) + .toBuffer(), + ); +} + +/** + * Create a pedersen hash from an arbitrary length buffer. + */ +export function pedersenHashBuffer(input: Buffer, index = 0) { + return Buffer.from(api.pedersenHashBuffer(input, index).toBuffer()); } diff --git a/yarn-project/foundation/src/crypto/random/index.ts b/yarn-project/foundation/src/crypto/random/index.ts index f9c4979776e..acea92f4839 100644 --- a/yarn-project/foundation/src/crypto/random/index.ts +++ b/yarn-project/foundation/src/crypto/random/index.ts @@ -6,8 +6,12 @@ import isNode from 'detect-node'; const MAX_BYTES = 65536; const getWebCrypto = () => { - if (typeof window !== 'undefined' && window.crypto) return window.crypto; - if (typeof self !== 'undefined' && self.crypto) return self.crypto; + if (typeof window !== 'undefined' && window.crypto) { + return window.crypto; + } + if (typeof self !== 'undefined' && self.crypto) { + return self.crypto; + } return undefined; }; diff --git a/yarn-project/foundation/src/crypto/sha256/index.ts b/yarn-project/foundation/src/crypto/sha256/index.ts index 99c2bedc7ab..6054e78e834 100644 --- a/yarn-project/foundation/src/crypto/sha256/index.ts +++ b/yarn-project/foundation/src/crypto/sha256/index.ts @@ -1,16 +1,3 @@ import { default as hash } from 'hash.js'; -import { toBigIntBE, toBufferBE } from '../../bigint-buffer/index.js'; -import { Fr } from '../../fields/fields.js'; - export const sha256 = (data: Buffer) => Buffer.from(hash.sha256().update(data).digest()); - -/** - * Squashes the output of sha256 into a field element. - * WARNING: if you have not thought about why you are using this, or talked to somebody who has do not use it. - * @param buf - Input buffer - * @returns Returns a sha256 output squashed into a field element. - */ -export const sha256ToField = (buf: Buffer): Fr => { - return Fr.fromBuffer(toBufferBE(toBigIntBE(sha256(buf)) % Fr.MODULUS, 32)); -}; diff --git a/yarn-project/foundation/src/fields/grumpkin_scalar.test.ts b/yarn-project/foundation/src/fields/fields.test.ts similarity index 97% rename from yarn-project/foundation/src/fields/grumpkin_scalar.test.ts rename to yarn-project/foundation/src/fields/fields.test.ts index bb5d7288d2d..02f08f0e319 100644 --- a/yarn-project/foundation/src/fields/grumpkin_scalar.test.ts +++ b/yarn-project/foundation/src/fields/fields.test.ts @@ -1,4 +1,4 @@ -import { GrumpkinScalar } from './grumpkin_scalar.js'; +import { GrumpkinScalar } from './fields.js'; describe('GrumpkinScalar Serialization', () => { // Test case for GrumpkinScalar.fromHighLow diff --git a/yarn-project/foundation/src/fields/fields.ts b/yarn-project/foundation/src/fields/fields.ts index 57749cc01d2..1c2069169af 100644 --- a/yarn-project/foundation/src/fields/fields.ts +++ b/yarn-project/foundation/src/fields/fields.ts @@ -1,256 +1,256 @@ -import { AztecAddress } from '../aztec-address/index.js'; -import { toBigIntBE, toBufferBE, toHex } from '../bigint-buffer/index.js'; +import { toBigIntBE, toBufferBE } from '../bigint-buffer/index.js'; import { randomBytes } from '../crypto/index.js'; import { BufferReader } from '../serialize/buffer_reader.js'; +const ZERO_BUFFER = Buffer.alloc(32); + +/* eslint-disable @typescript-eslint/no-unsafe-declaration-merging */ + /** - * Fr represents a field of integers modulo the prime number MODULUS. - * It provides utility functions to work with elements in this field, such as conversions between different representations and checks for equality and zero values. The elements can be serialized to and deserialized from byte buffers or strings. - * Some use cases include working with cryptographic operations and finite fields. + * Represents a field derived from BaseField. */ -export class Fr { - static ZERO = new Fr(0n); - static MODULUS = 0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001n; - static MAX_VALUE = Fr.MODULUS - 1n; - static SIZE_IN_BYTES = 32; - +type DerivedField = { + new (value: any): T; /** - * The numeric value of the field element as a bigint. + * All derived fields will specify a MODULUS. */ - public readonly value: bigint; - - constructor(value: boolean | bigint | number | Fr | AztecAddress) { - const isFr = (value: boolean | bigint | number | Fr | AztecAddress): value is Fr | AztecAddress => - !!(value as Fr).toBigInt; - this.value = isFr(value) ? value.toBigInt() : BigInt(value); - if (this.value > Fr.MAX_VALUE) { - throw new Error(`Fr out of range ${value}.`); - } - } + MODULUS: bigint; +}; - /** - * Generates a random Fr or Fq instance with a value modulo the respective class' MODULUS. - * This method uses randomBytes to generate a random 32-byte buffer, converts it to a bigint - * and takes the modulus of the result with the class' MODULUS constant. - * - * @returns A new Fr or Fq instance with a random value. - */ - static random() { - const r = toBigIntBE(randomBytes(Fr.SIZE_IN_BYTES)) % Fr.MODULUS; - return new Fr(r); - } +/** + * Base field class. + * Conversions from Buffer to BigInt and vice-versa are not cheap. + * We allow construction with either form and lazily convert to other as needed. + * We only check we are within the field modulus when initializing with bigint. + * If NODE_ENV === 'test', we will always initialize both types to check the modulus. + * This is also necessary in test environment as a lot of tests just use deep equality to check equality. + * WARNING: This could lead to a bugs in production that don't reveal in tests, but it's low risk. + */ +abstract class BaseField { + static SIZE_IN_BYTES = 32; + private asBuffer?: Buffer; + private asBigInt?: bigint; /** - * Returns a new zero-value field. - * @returns A new zero-value field. - */ - static zero() { - return new Fr(0n); + * Return bigint representation. + * @deprecated Just to get things compiling. Use toBigInt(). + * */ + get value(): bigint { + return this.toBigInt(); } - /** - * Create an instance of the corresponding class (Fr or Fq) from a Buffer or a BufferReader. - * Reads 'SIZE_IN_BYTES' bytes from the given Buffer or BufferReader and constructs an instance with the decoded value. - * If the input is a Buffer, it is internally converted to a BufferReader before reading. - * Throws an error if the input length is invalid or the decoded value is out of range. - * - * @param buffer - The Buffer or BufferReader containing the bytes representing the value. - * @returns An instance of the corresponding class (Fr or Fq) with the decoded value. - */ - static fromBuffer(buffer: Buffer | BufferReader) { - const reader = BufferReader.asReader(buffer); - return new Fr(toBigIntBE(reader.readBytes(Fr.SIZE_IN_BYTES))); - } + protected constructor(value: number | bigint | boolean | BaseField | Buffer) { + if (value instanceof Buffer) { + this.asBuffer = + value.length === 32 ? value : Buffer.concat([Buffer.alloc(BaseField.SIZE_IN_BYTES - value.length), value]); + } else if (typeof value === 'bigint' || typeof value === 'number' || typeof value === 'boolean') { + this.asBigInt = BigInt(value); + if (this.asBigInt >= this.modulus()) { + throw new Error('Value >= to field modulus.'); + } + } else if (value instanceof BaseField) { + this.asBuffer = value.asBuffer; + this.asBigInt = value.asBigInt; + } else { + throw new Error(`Type '${typeof value}' with value '${value}' passed to BaseField ctor.`); + } - /** - * Create a Fr instance from a hex-encoded string. - * The input 'address' can be either prefixed with '0x' or not, and should have exactly 64 hex characters. - * Throws an error if the input length is invalid or the address value is out of range. - * - * @param address - The hex-encoded string representing the field element. - * @returns A Fr instance. - */ - static fromString(address: string) { - return Fr.fromBuffer(Buffer.from(address.replace(/^0x/i, ''), 'hex')); + // Loads of our tests are just doing deep equality rather than calling e.g. toBigInt() first. + // This ensures the deep equality passes regardless of the internal representation. + // It also ensures the value range is checked even when initializing as a buffer. + if (process.env.NODE_ENV === 'test') { + this.toBuffer(); + this.toBigInt(); + } } + protected abstract modulus(): bigint; + /** - * Converts the value of the instance to a buffer with a specified length. - * The method uses the provided value and size in bytes to create a buffer representation - * of the numeric value. This can be useful for serialization and communication purposes. - * - * @returns A buffer representing the instance's value. + * We return a copy of the Buffer to ensure this remains immutable. */ - toBuffer() { - return toBufferBE(this.value, Fr.SIZE_IN_BYTES); + toBuffer(): Buffer { + if (!this.asBuffer) { + this.asBuffer = toBufferBE(this.asBigInt!, 32); + } + return Buffer.from(this.asBuffer); } - /** - * Converts the value of the Fr or Fq class instance to a hexadecimal string. - * The resulting string is prefixed with '0x' and represents the bigint value - * in base 16. - * - * @param padTo32 - Whether to pad the string to 32 bytes. - * @returns A hex-encoded string representing the value of the class instance. - */ - toString(padTo32 = false): `0x${string}` { - return toHex(this.value, padTo32); + toString(): `0x${string}` { + return `0x${this.toBuffer().toString('hex')}`; } - /** - * Retrieves the underlying bigint. - * This method mostly exists to match user expectations, as value is already public. - * @returns The underlying bigint. - */ - public toBigInt(): bigint { - return this.value; + toBigInt(): bigint { + if (this.asBigInt === undefined) { + this.asBigInt = toBigIntBE(this.asBuffer!); + if (this.asBigInt >= this.modulus()) { + throw new Error('Value >= to field modulus.'); + } + } + return this.asBigInt; } - /** - * Returns a shortened string representation of the Fr value, formatted with '0x' prefix and ellipsis in the middle. - * The resulting string has first 10 characters (including '0x') and last 4 characters of the full hexadecimal value. - * - * @returns A shorter, human-readable string representation of the Fr value. - */ - toShortString() { + toShortString(): string { const str = this.toString(); return `${str.slice(0, 10)}...${str.slice(-4)}`; } - /** - * Checks if the provided Fr instance is equal to the current instance. - * Two instances are considered equal if their 'value' properties are the same. - * - * @param rhs - The Fr instance to compare with the current instance. - * @returns A boolean indicating whether the two instances are equal. - */ - equals(rhs: Fr) { - return this.value === rhs.value; + equals(rhs: BaseField): boolean { + return this.toBuffer().equals(rhs.toBuffer()); } - /** - * Check if the instance value is zero. - * The method returns true if the value of the instance is 0n (zero in BigInt representation), - * otherwise, it returns false. - * - * @returns A boolean indicating whether the instance value is zero or not. - */ - isZero() { - return this.value === 0n; + isZero(): boolean { + return this.toBuffer().equals(ZERO_BUFFER); } - /** - * Converts the current value of the Fq or Fr instance to a friendly JSON representation. - * The output will be a hexadecimal string prefixed with '0x'. - * - * @returns A '0x' prefixed hexadecimal string representing the current value. - */ - toFriendlyJSON() { - return this.toString(); + toFriendlyJSON(): string { + return `0x${this.toBigInt().toString()}`; } - /** Returns self. */ toField() { return this; } } /** - * Fq represents a field element in a prime finite field with modulus defined by the constant MODULUS. - * It provides methods for creating, manipulating, and comparing field elements, as well as converting - * them to/from different data types like Buffers and hex strings. Field elements are used in various - * cryptographic protocols and operations, such as elliptic curve cryptography. - * - * @example - * const fqElem = new Fq(BigInt("123456789")); - * const randomFqElem = Fq.random(); - * const fromBufferFqElem = Fq.fromBuffer(buffer); + * Constructs a field from a Buffer of BufferReader. + * It maybe not read the full 32 bytes if the Buffer is shorter, but it will padded in BaseField constructor. */ -export class Fq { - static MODULUS = 0x30644e72e131a029b85045b68181585d97816a916871ca8d3c208c16d87cfd47n; - static MAX_VALUE = Fr.MODULUS - 1n; - static SIZE_IN_BYTES = 32; +function fromBuffer(buffer: Buffer | BufferReader, f: DerivedField) { + const reader = BufferReader.asReader(buffer); + return new f(reader.readBytes(BaseField.SIZE_IN_BYTES)); +} - constructor( - /** - * The element's value as a bigint in the finite field. - */ - public readonly value: bigint, - ) { - if (value > Fq.MAX_VALUE) { - throw new Error(`Fq out of range ${value}.`); - } +/** + * Constructs a field from a Buffer, but reduces it first. + * This requires a conversion to a bigint first so the initial underlying representation will be a bigint. + */ +function fromBufferReduce(buffer: Buffer, f: DerivedField) { + return new f(toBigIntBE(buffer) % f.MODULUS); +} + +/** + * To ensure a field is uniformly random, it's important to reduce a 512 bit value. + * If you reduced a 256 bit number, there would a be a high skew in the lower range of the field. + */ +function random(f: DerivedField): T { + return fromBufferReduce(randomBytes(64), f); +} + +/** + * Constructs a field from a 0x prefixed hex string. + */ +function fromString(buf: string, f: DerivedField) { + const buffer = Buffer.from(buf.replace(/^0x/i, ''), 'hex'); + return new f(buffer); +} + +/** + * Branding to ensure fields are not interchangeable types. + */ +export interface Fr { + /** Brand. */ + _branding: 'Fr'; +} + +/** + * Fr field class. + */ +export class Fr extends BaseField { + static ZERO = new Fr(0n); + static MODULUS = 0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001n; + + constructor(value: number | bigint | boolean | Fr | Buffer) { + super(value); + } + + protected modulus() { + return Fr.MODULUS; } - /** - * Generates a random Fr or Fq instance with a value within the range of their respective modulus. - * The random value is generated from a byte array of length equal to SIZE_IN_BYTES, then truncated - * to the appropriate modulus before creating the new Fr or Fq instance. - * - * @returns A new Fr or Fq instance with a randomly generated value. - */ static random() { - const r = toBigIntBE(randomBytes(64)) % Fq.MODULUS; - return new this(r); + return random(Fr); + } + + static zero() { + return Fr.ZERO; } - /** - * Create an instance of the calling class (Fr or Fq) from a given buffer or BufferReader. - * Reads SIZE_IN_BYTES from the provided buffer and converts it to a bigint, then creates a new instance - * with that value. Throws an error if the value is out of range for the calling class. - * - * @param buffer - The input buffer or BufferReader containing the bytes representing the value. - * @returns An instance of the calling class (Fr or Fq) initialized with the bigint value. - */ static fromBuffer(buffer: Buffer | BufferReader) { - const reader = BufferReader.asReader(buffer); - return new this(toBigIntBE(reader.readBytes(this.SIZE_IN_BYTES))); + return fromBuffer(buffer, Fr); } - /** - * Converts the bigint value of the instance to a Buffer representation. - * The output buffer has a fixed size, determined by the 'SIZE_IN_BYTES' constant. - * - * @returns A Buffer containing the byte representation of the instance's value. - */ - toBuffer() { - return toBufferBE(this.value, Fq.SIZE_IN_BYTES); + static fromBufferReduce(buffer: Buffer) { + return fromBufferReduce(buffer, Fr); } - /** - * Converts the Fq value to a hexadecimal string representation. - * The resulting string is prefixed with '0x' and contains the exact number of hex characters required - * to represent the numeric value of this instance. - * - * @returns A hexadecimal string representing the Fq value. - */ - toString() { - return toHex(this.value); + static fromString(buf: string) { + return fromString(buf, Fr); } +} - /** - * Check if the value of the current instance is zero. - * This function compares the internal 'value' property with 0n (BigInt representation of zero). - * Returns true if the value is zero, otherwise returns false. - * - * @returns A boolean indicating whether the value is zero or not. - */ - isZero() { - return this.value === 0n; +/** + * Branding to ensure fields are not interchangeable types. + */ +export interface Fq { + /** Brand. */ + _branding: 'Fq'; +} + +/** + * Fq field class. + */ +export class Fq extends BaseField { + static ZERO = new Fq(0n); + static MODULUS = 0x30644e72e131a029b85045b68181585d97816a916871ca8d3c208c16d87cfd47n; + private static HIGH_SHIFT = BigInt((BaseField.SIZE_IN_BYTES / 2) * 8); + private static LOW_MASK = (1n << Fq.HIGH_SHIFT) - 1n; + + get low(): Fr { + return new Fr(this.toBigInt() & Fq.LOW_MASK); } - /** - * Converts the value of the Fr or Fq instance to a friendly JSON format. - * The output is a hexadecimal string representation of the value with '0x' prefix. - * - * @returns A string representing the value in the JSON format. - */ - toFriendlyJSON() { - return this.toString(); + get high(): Fr { + return new Fr(this.toBigInt() >> Fq.HIGH_SHIFT); } - /** Returns self. */ - toField() { - return this; + constructor(value: number | bigint | boolean | Fq | Buffer) { + super(value); + } + + protected modulus() { + return Fq.MODULUS; + } + + static random() { + return random(Fq); + } + + static zero() { + return Fq.ZERO; + } + + static fromBuffer(buffer: Buffer | BufferReader) { + return fromBuffer(buffer, Fq); + } + + static fromBufferReduce(buffer: Buffer) { + return fromBufferReduce(buffer, Fq); + } + + static fromString(buf: string) { + return fromString(buf, Fq); + } + + static fromHighLow(high: Fr, low: Fr): Fq { + return new Fq((high.toBigInt() << Fq.HIGH_SHIFT) + low.toBigInt()); } } + +/** + * GrumpkinScalar is an Fq. + * @remarks Called GrumpkinScalar because it is used to represent elements in Grumpkin's scalar field as defined in + * the Aztec Yellow Paper. + */ +export type GrumpkinScalar = Fq; +export const GrumpkinScalar = Fq; diff --git a/yarn-project/foundation/src/fields/grumpkin_scalar.ts b/yarn-project/foundation/src/fields/grumpkin_scalar.ts deleted file mode 100644 index b2cdb69ed4c..00000000000 --- a/yarn-project/foundation/src/fields/grumpkin_scalar.ts +++ /dev/null @@ -1,138 +0,0 @@ -import { toBigIntBE, toBufferBE, toHex } from '../bigint-buffer/index.js'; -import { randomBytes } from '../crypto/index.js'; -import { BufferReader } from '../serialize/buffer_reader.js'; -import { Fr } from './fields.js'; - -/** - * Represents a field element in a prime finite field with modulus defined by the constant MODULUS. - * @remarks Called GrumpkinScalar because it is used to represent elements in Grumpkin's scalar field as defined in - * the Aztec Yellow Paper. - */ -export class GrumpkinScalar { - static MODULUS = 0x30644e72e131a029b85045b68181585d97816a916871ca8d3c208c16d87cfd47n; - static MAX_VALUE = GrumpkinScalar.MODULUS - 1n; - static SIZE_IN_BYTES = 32; - - // The following constants are used to split a GrumpkinScalar into two Fr elements. - private static HIGH_SHIFT = BigInt((GrumpkinScalar.SIZE_IN_BYTES / 2) * 8); - private static LOW_MASK = (1n << GrumpkinScalar.HIGH_SHIFT) - 1n; - - constructor( - /** - * The element's value as a bigint in the finite field. - */ - public readonly value: bigint, - ) { - if (value > GrumpkinScalar.MAX_VALUE) { - throw new Error(`GrumpkinScalar out of range ${value}.`); - } - } - - get low(): Fr { - return new Fr(this.value & GrumpkinScalar.LOW_MASK); - } - - get high(): Fr { - return new Fr(this.value >> GrumpkinScalar.HIGH_SHIFT); - } - - /** - * Deserialize a grumpkin scalar serialized in 2 Fr. - * @param high - The high Fr element. - * @param low - The low Fr element. - * @returns A GrumpkinScalar instance with the value of the two Fr elements. - */ - static fromHighLow(high: Fr, low: Fr): GrumpkinScalar { - return new GrumpkinScalar((high.value << GrumpkinScalar.HIGH_SHIFT) + low.value); - } - - /** - * Generates a random GrumpkinScalar. - * - * @returns A new GrumpkinScalar instance with a randomly generated value. - */ - static random() { - const r = toBigIntBE(randomBytes(64)) % GrumpkinScalar.MODULUS; - return new this(r); - } - - /** - * Create an instance of GrumpkinScalar from a given buffer or BufferReader. - * @remarks Reads SIZE_IN_BYTES from the provided buffer and converts it to a bigint, then creates a new instance - * with that value. Throws an error if the value is out of range for the calling class. - * - * @param buffer - The input buffer or BufferReader containing the bytes representing the value. - * @returns A GrumpkinScalar instance. - */ - static fromBuffer(buffer: Buffer | BufferReader) { - const reader = BufferReader.asReader(buffer); - return new this(toBigIntBE(reader.readBytes(this.SIZE_IN_BYTES))); - } - - /** - * Like fromBuffer, but reduces the value modulo MODULUS. - * - * @param buffer - The Buffer or BufferReader containing the bytes representing the value. - * @returns GrumpkinScalar with the decoded value. - */ - static fromBufferWithReduction(buffer: Buffer | BufferReader) { - const reader = BufferReader.asReader(buffer); - const value = toBigIntBE(reader.readBytes(GrumpkinScalar.SIZE_IN_BYTES)) % GrumpkinScalar.MODULUS; - return new GrumpkinScalar(value); - } - - /** - * Create a GrumpkinScalar instance from a hex-encoded string. - * The input 'address' can be either prefixed with '0x' or not, and should have exactly 64 hex characters. - * Throws an error if the input length is invalid or the address value is out of range. - * - * @param address - The hex-encoded string representing the field element. - * @returns A GrumpkinScalar instance. - */ - static fromString(address: string) { - return GrumpkinScalar.fromBuffer(Buffer.from(address.replace(/^0x/i, ''), 'hex')); - } - - /** - * Converts the bigint value of the instance to a Buffer representation. - * The output buffer has a fixed size, determined by the 'SIZE_IN_BYTES' constant. - * - * @returns A Buffer containing the byte representation of the instance's value. - */ - toBuffer() { - return toBufferBE(this.value, GrumpkinScalar.SIZE_IN_BYTES); - } - - /** - * Converts the GrumpkinScalar value to a hexadecimal string representation. - * The resulting string is prefixed with '0x' and contains the exact number of hex characters required - * to represent the numeric value of this instance. - * - * @param padTo32 - Whether to pad the resulting string to 32 bytes. - * @returns A hexadecimal string representing the GrumpkinScalar value. - */ - toString(padTo32 = false) { - return toHex(this.value, padTo32); - } - - /** - * Check if the value of the current instance is zero. - * This function compares the internal 'value' property with 0n (BigInt representation of zero). - * Returns true if the value is zero, otherwise returns false. - * - * @returns A boolean indicating whether the value is zero or not. - */ - isZero() { - return this.value === 0n; - } - - /** - * Converts the value of the GrumpkinScalar instance to a friendly JSON format. - * The output is a hexadecimal string representation of the value with '0x' prefix. - * - * @returns A string representing the value in the JSON format. - */ - toFriendlyJSON() { - return this.toString(); - } -} diff --git a/yarn-project/foundation/src/fields/index.ts b/yarn-project/foundation/src/fields/index.ts index a6b24d24401..aa4e441f34f 100644 --- a/yarn-project/foundation/src/fields/index.ts +++ b/yarn-project/foundation/src/fields/index.ts @@ -1,4 +1,3 @@ export * from './fields.js'; export * from './point.js'; export * from './coordinate.js'; -export * from './grumpkin_scalar.js'; diff --git a/yarn-project/foundation/src/fields/point.ts b/yarn-project/foundation/src/fields/point.ts index 4c94afa883a..cd90e852872 100644 --- a/yarn-project/foundation/src/fields/point.ts +++ b/yarn-project/foundation/src/fields/point.ts @@ -72,8 +72,8 @@ export class Point { */ toBigInts() { return { - x: this.x.value, - y: this.y.value, + x: this.x.toBigInt(), + y: this.y.toBigInt(), }; } @@ -120,6 +120,10 @@ export class Point { equals(rhs: Point) { return this.x.equals(rhs.x) && this.y.equals(rhs.y); } + + isZero() { + return this.x.isZero() && this.y.isZero(); + } } /** @@ -128,7 +132,9 @@ export class Point { * @returns Whether it looks like a point. */ export function isPoint(obj: object): obj is Point { - if (!obj) return false; + if (!obj) { + return false; + } const point = obj as Point; return point.kind === 'point' && point.x !== undefined && point.y !== undefined; } diff --git a/yarn-project/foundation/src/json-rpc/class_converter.ts b/yarn-project/foundation/src/json-rpc/class_converter.ts index c1e9478380e..7364664dc38 100644 --- a/yarn-project/foundation/src/json-rpc/class_converter.ts +++ b/yarn-project/foundation/src/json-rpc/class_converter.ts @@ -1,4 +1,4 @@ -import { assert, hasOwnProperty } from './js_utils.js'; +import { assert } from './js_utils.js'; /** * Represents a class compatible with our class conversion system. @@ -133,7 +133,7 @@ export class ClassConverter { register(type: string, class_: IOClass, encoding: ClassEncoding) { assert(type !== 'Buffer', "'Buffer' handling is hardcoded. Cannot use as name."); assert( - hasOwnProperty(class_.prototype, 'toString') || hasOwnProperty(class_.prototype, 'toJSON'), + class_.prototype['toString'] || class_.prototype['toJSON'], `Class ${type} must define a toString() OR toJSON() method.`, ); assert( @@ -197,9 +197,13 @@ export class ClassConverter { */ private lookupObject(classObj: any) { const nameResult = this.toName.get(classObj.constructor); - if (nameResult) return { type: nameResult[0], encoding: nameResult[1] }; + if (nameResult) { + return { type: nameResult[0], encoding: nameResult[1] }; + } const classResult = this.toClass.get(classObj.constructor.name); - if (classResult) return { type: classObj.constructor.name, encoding: classResult[1] }; + if (classResult) { + return { type: classObj.constructor.name, encoding: classResult[1] }; + } throw new Error(`Could not find class ${classObj.constructor.name} in lookup.`); } } diff --git a/yarn-project/foundation/src/json-rpc/client/json_rpc_client.ts b/yarn-project/foundation/src/json-rpc/client/json_rpc_client.ts index c632d4dde5b..a7cb59263ee 100644 --- a/yarn-project/foundation/src/json-rpc/client/json_rpc_client.ts +++ b/yarn-project/foundation/src/json-rpc/client/json_rpc_client.ts @@ -123,7 +123,9 @@ export function createJsonRpcClient( {}, { get: (target, rpcMethod: string) => { - if (['then', 'catch'].includes(rpcMethod)) return Reflect.get(target, rpcMethod); + if (['then', 'catch'].includes(rpcMethod)) { + return Reflect.get(target, rpcMethod); + } return (...params: any[]) => { debug(format(`JsonRpcClient.constructor`, 'proxy', rpcMethod, '<-', params)); return request(rpcMethod, params); diff --git a/yarn-project/foundation/src/log/console.ts b/yarn-project/foundation/src/log/console.ts index a97736d89fe..4e13f742236 100644 --- a/yarn-project/foundation/src/log/console.ts +++ b/yarn-project/foundation/src/log/console.ts @@ -1,5 +1,5 @@ /* eslint-disable no-console */ -import { LogFn } from './index.js'; +import { LogFn } from './log_fn.js'; /** * ConsoleLogger is a utility class that provides customizable console logging functionality. diff --git a/yarn-project/foundation/src/log/debug.ts b/yarn-project/foundation/src/log/debug.ts index 4f7700dc5ca..074df6356e9 100644 --- a/yarn-project/foundation/src/log/debug.ts +++ b/yarn-project/foundation/src/log/debug.ts @@ -1,6 +1,6 @@ import debug from 'debug'; -import { LogFn } from './index.js'; +import { LogFn } from './log_fn.js'; let preLogHook: ((...args: any[]) => void) | undefined; let postLogHook: ((...args: any[]) => void) | undefined; diff --git a/yarn-project/foundation/src/log/index.ts b/yarn-project/foundation/src/log/index.ts index bf28af7b518..2bf44ed88ba 100644 --- a/yarn-project/foundation/src/log/index.ts +++ b/yarn-project/foundation/src/log/index.ts @@ -1,10 +1,5 @@ -/** Structured log data to include with the message. */ -export type LogData = Record; - -/** A callable logger instance. */ -export type LogFn = (msg: string, data?: LogData) => void; - export * from './console.js'; export * from './debug.js'; export * from './logger.js'; export * from './log_history.js'; +export * from './log_fn.js'; diff --git a/yarn-project/foundation/src/log/log_fn.ts b/yarn-project/foundation/src/log/log_fn.ts new file mode 100644 index 00000000000..cfc045d8bb2 --- /dev/null +++ b/yarn-project/foundation/src/log/log_fn.ts @@ -0,0 +1,5 @@ +/** Structured log data to include with the message. */ +export type LogData = Record; + +/** A callable logger instance. */ +export type LogFn = (msg: string, data?: LogData) => void; diff --git a/yarn-project/foundation/src/log/logger.ts b/yarn-project/foundation/src/log/logger.ts index 2991db46753..258abbe7e5c 100644 --- a/yarn-project/foundation/src/log/logger.ts +++ b/yarn-project/foundation/src/log/logger.ts @@ -2,7 +2,7 @@ import debug from 'debug'; import isNode from 'detect-node'; import { isatty } from 'tty'; -import { LogData, LogFn } from './index.js'; +import { LogData, LogFn } from './log_fn.js'; // Matches a subset of Winston log levels const LogLevels = ['silent', 'error', 'warn', 'info', 'verbose', 'debug'] as const; @@ -39,7 +39,9 @@ export type DebugLogger = LogFn & Logger; */ export function createDebugLogger(name: string): DebugLogger { const debugLogger = debug(name); - if (currentLevel === 'debug') debugLogger.enabled = true; + if (currentLevel === 'debug') { + debugLogger.enabled = true; + } const logger = { silent: () => {}, @@ -78,7 +80,9 @@ function logWithDebug(debug: debug.Debugger, level: LogLevel, msg: string, data? msg = data ? `${msg} ${fmtLogData(data)}` : msg; if (debug.enabled) { - if (level !== 'debug') msg = `${level.toUpperCase()} ${msg}`; + if (level !== 'debug') { + msg = `${level.toUpperCase()} ${msg}`; + } debug(msg); } else if (LogLevels.indexOf(level) <= LogLevels.indexOf(currentLevel)) { printLog(`${getPrefix(debug, level)} ${msg}`); @@ -94,7 +98,9 @@ function logWithDebug(debug: debug.Debugger, level: LogLevel, msg: string, data? function getPrefix(debugLogger: debug.Debugger, level: LogLevel) { const levelLabel = currentLevel !== level ? ` ${level.toUpperCase()}` : ''; const prefix = `${debugLogger.namespace.replace(/^aztec:/, '')}${levelLabel}`; - if (!isNode || !isatty(process.stderr.fd)) return prefix; + if (!isNode || !isatty(process.stderr.fd)) { + return prefix; + } const colorIndex = debug.selectColor(debugLogger.namespace) as number; const colorCode = '\u001B[3' + (colorIndex < 8 ? colorIndex : '8;5;' + colorIndex); return ` ${colorCode};1m${prefix}\u001B[0m`; diff --git a/yarn-project/foundation/src/serialize/buffer_reader.test.ts b/yarn-project/foundation/src/serialize/buffer_reader.test.ts index 1bc92a95f66..bd167a58a63 100644 --- a/yarn-project/foundation/src/serialize/buffer_reader.test.ts +++ b/yarn-project/foundation/src/serialize/buffer_reader.test.ts @@ -44,13 +44,13 @@ describe('buffer reader', () => { describe('readFr', () => { it('should get Fr from buffer', () => { - expect(bufferReader.readFr()).toEqual(Fr.fromBuffer(BUFFER)); + expect(Fr.fromBuffer(bufferReader)).toEqual(Fr.fromBuffer(BUFFER)); }); }); describe('readFq', () => { it('should get Fq from buffer', () => { - expect(bufferReader.readFq()).toEqual(Fq.fromBuffer(BUFFER)); + expect(Fq.fromBuffer(bufferReader)).toEqual(Fq.fromBuffer(BUFFER)); }); }); diff --git a/yarn-project/foundation/src/serialize/buffer_reader.ts b/yarn-project/foundation/src/serialize/buffer_reader.ts index a4409399173..1b8749d6358 100644 --- a/yarn-project/foundation/src/serialize/buffer_reader.ts +++ b/yarn-project/foundation/src/serialize/buffer_reader.ts @@ -1,4 +1,3 @@ -import { Fq, Fr } from '../fields/fields.js'; import { Tuple } from './types.js'; /** @@ -83,27 +82,6 @@ export class BufferReader { return Buffer.from(this.buffer.subarray(this.index - n, this.index)); } - /** - * Reads a Fr (finite field) element from the buffer using the 'fromBuffer' method of the Fr class. - * The Fr class should provide a 'fromBuffer' method that takes a BufferReader instance as input. - * - * @returns An instance of the Fr class representing the finite field element. - */ - public readFr(): Fr { - return Fr.fromBuffer(this); - } - - /** - * Reads the next Fq element from the buffer using the Fq.fromBuffer method. - * The Fq element represents a finite field in elliptic curve cryptography and is used for calculations. - * Advances the internal buffer index by the number of bytes read. - * - * @returns An Fq instance representing the finite field element. - */ - public readFq(): Fq { - return Fq.fromBuffer(this); - } - /** * Reads a vector of numbers from the buffer and returns it as an array of numbers. * The method utilizes the 'readVector' method, passing a deserializer that reads numbers. diff --git a/yarn-project/foundation/src/serialize/free_funcs.ts b/yarn-project/foundation/src/serialize/free_funcs.ts index 6f4242582c6..20391615cb3 100644 --- a/yarn-project/foundation/src/serialize/free_funcs.ts +++ b/yarn-project/foundation/src/serialize/free_funcs.ts @@ -15,6 +15,17 @@ export function boolToByte(b: boolean) { return buf; } +/** + * @param n - The input number to be converted to a big-endian unsigned 16-bit integer Buffer. + * @param bufferSize - Optional, the size of the output Buffer (default is 2). + * @returns A Buffer containing the big-endian unsigned 16-bit integer representation of the input number. + */ +export function numToUInt16BE(n: number, bufferSize = 2) { + const buf = Buffer.alloc(bufferSize); + buf.writeUInt16BE(n, bufferSize - 2); + return buf; +} + /** * Convert a number into a 4-byte little-endian unsigned integer buffer. * The input number is serialized as an unsigned 32-bit integer in little-endian byte order, diff --git a/yarn-project/foundation/src/transport/interface/transferable.ts b/yarn-project/foundation/src/transport/interface/transferable.ts index 2d974a57e3d..253aa82b6b4 100644 --- a/yarn-project/foundation/src/transport/interface/transferable.ts +++ b/yarn-project/foundation/src/transport/interface/transferable.ts @@ -36,7 +36,9 @@ export interface TransferDescriptor { * @returns A boolean indicating whether the object is transferable. */ function isTransferable(thing: any): thing is Transferable { - if (!thing || typeof thing !== 'object') return false; + if (!thing || typeof thing !== 'object') { + return false; + } // Don't check too thoroughly, since the list of transferable things in JS might grow over time return true; } @@ -77,7 +79,7 @@ export function Transfer(transferable: Transferable): TransferDescriptor; * Mark transferable objects within an arbitrary object or array as * being a transferable object. They will then not be serialized * and deserialized on messaging with the main thread, but ownership - * of them will be tranferred to the receiving thread. + * of them will be transferred to the receiving thread. * * Only array buffers, message ports and few more special types of * objects can be transferred, but it's much faster than serializing and @@ -109,7 +111,9 @@ export function Transfer(payload: T, transferables: Transferable[]): Transfer */ export function Transfer(payload: T, transferables?: Transferable[]): TransferDescriptor { if (!transferables) { - if (!isTransferable(payload)) throw Error(); + if (!isTransferable(payload)) { + throw Error(); + } transferables = [payload]; } diff --git a/yarn-project/foundation/src/wasm/wasm_module.ts b/yarn-project/foundation/src/wasm/wasm_module.ts index 3a3490a90e8..db4e1da83c9 100644 --- a/yarn-project/foundation/src/wasm/wasm_module.ts +++ b/yarn-project/foundation/src/wasm/wasm_module.ts @@ -232,7 +232,9 @@ export class WasmModule implements IWasmModule { addr = addr >>> 0; const m = this.getMemory(); let i = addr; - for (; m[i] !== 0; ++i); + while (m[i] !== 0) { + ++i; + } return Buffer.from(m.slice(addr, i)).toString('ascii'); } diff --git a/yarn-project/key-store/package.json b/yarn-project/key-store/package.json index 231b98260dd..bbcdabc0de8 100644 --- a/yarn-project/key-store/package.json +++ b/yarn-project/key-store/package.json @@ -15,7 +15,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" }, "inherits": [ @@ -37,7 +37,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/node": "^18.7.23", "jest": "^29.5.0", diff --git a/yarn-project/key-store/src/key_pair.ts b/yarn-project/key-store/src/key_pair.ts index ac65dac2104..9b8e335026f 100644 --- a/yarn-project/key-store/src/key_pair.ts +++ b/yarn-project/key-store/src/key_pair.ts @@ -42,7 +42,7 @@ export class ConstantKeyPair implements KeyPair { return this.publicKey; } - public getPrivateKey() { - return Promise.resolve(this.privateKey); + public getPrivateKey(): GrumpkinPrivateKey { + return this.privateKey; } } diff --git a/yarn-project/key-store/src/test_key_store.ts b/yarn-project/key-store/src/test_key_store.ts index cf1b1a957a5..865fb9d75ee 100644 --- a/yarn-project/key-store/src/test_key_store.ts +++ b/yarn-project/key-store/src/test_key_store.ts @@ -37,7 +37,7 @@ export class TestKeyStore implements KeyStore { public getAccountPrivateKey(pubKey: PublicKey): Promise { const account = this.getAccount(pubKey); - return account.getPrivateKey(); + return Promise.resolve(account.getPrivateKey()); } /** diff --git a/yarn-project/merkle-tree/package.json b/yarn-project/merkle-tree/package.json index 6378561fc38..6418d219963 100644 --- a/yarn-project/merkle-tree/package.json +++ b/yarn-project/merkle-tree/package.json @@ -15,7 +15,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" }, "inherits": [ @@ -32,7 +32,6 @@ "testTimeout": 15000 }, "dependencies": { - "@aztec/circuits.js": "workspace:^", "@aztec/foundation": "workspace:^", "@aztec/types": "workspace:^", "levelup": "^5.1.1", @@ -42,7 +41,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/levelup": "^5.1.2", "@types/memdown": "^3.0.1", diff --git a/yarn-project/merkle-tree/src/standard_indexed_tree/standard_indexed_tree.ts b/yarn-project/merkle-tree/src/standard_indexed_tree/standard_indexed_tree.ts index 417ae63be6a..62a1f69cb76 100644 --- a/yarn-project/merkle-tree/src/standard_indexed_tree/standard_indexed_tree.ts +++ b/yarn-project/merkle-tree/src/standard_indexed_tree/standard_indexed_tree.ts @@ -110,7 +110,9 @@ export class StandardIndexedTree extends TreeBase implements IndexedTree { */ public getLeafValue(index: bigint, includeUncommitted: boolean): Promise { const leaf = this.getLatestLeafDataCopy(Number(index), includeUncommitted); - if (!leaf) return Promise.resolve(undefined); + if (!leaf) { + return Promise.resolve(undefined); + } return Promise.resolve(toBufferBE(leaf.value, 32)); } @@ -453,7 +455,9 @@ export class StandardIndexedTree extends TreeBase implements IndexedTree { // check the pending low nullifiers for a low nullifier that works // This is the case where the next value is less than the pending for (let j = 0; j < pendingInsertionSubtree.length; j++) { - if (pendingInsertionSubtree[j].value === 0n) continue; + if (pendingInsertionSubtree[j].value === 0n) { + continue; + } if ( pendingInsertionSubtree[j].value < newValue && diff --git a/yarn-project/merkle-tree/tsconfig.json b/yarn-project/merkle-tree/tsconfig.json index 1820488d409..831130c7c84 100644 --- a/yarn-project/merkle-tree/tsconfig.json +++ b/yarn-project/merkle-tree/tsconfig.json @@ -6,9 +6,6 @@ "tsBuildInfoFile": ".tsbuildinfo" }, "references": [ - { - "path": "../circuits.js" - }, { "path": "../foundation" }, diff --git a/yarn-project/noir-compiler/package.json b/yarn-project/noir-compiler/package.json index 8c808520b02..47824bd6f84 100644 --- a/yarn-project/noir-compiler/package.json +++ b/yarn-project/noir-compiler/package.json @@ -22,7 +22,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" }, "inherits": [ @@ -65,7 +65,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/fs-extra": "^11.0.1", "@types/jest": "^29.5.0", "@types/lodash.camelcase": "^4.3.7", diff --git a/yarn-project/noir-compiler/src/cli.ts b/yarn-project/noir-compiler/src/cli.ts index 013791602b1..837975c8075 100644 --- a/yarn-project/noir-compiler/src/cli.ts +++ b/yarn-project/noir-compiler/src/cli.ts @@ -3,7 +3,7 @@ import { createConsoleLogger } from '@aztec/foundation/log'; import { Command } from 'commander'; -import { compileContract } from './cli/contract.js'; +import { compileNoir } from './cli/compileNoir.js'; import { generateNoirInterface } from './cli/noir-interface.js'; import { generateTypescriptInterface } from './cli/typescript.js'; @@ -12,7 +12,7 @@ const log = createConsoleLogger('aztec:compiler-cli'); const main = async () => { program.name('aztec-compile'); - compileContract(program, 'contract', log); + compileNoir(program, 'compile', log); generateTypescriptInterface(program, 'typescript', log); generateNoirInterface(program, 'interface', log); await program.parseAsync(process.argv); diff --git a/yarn-project/noir-compiler/src/cli/compileNoir.ts b/yarn-project/noir-compiler/src/cli/compileNoir.ts new file mode 100644 index 00000000000..7e00fed39fa --- /dev/null +++ b/yarn-project/noir-compiler/src/cli/compileNoir.ts @@ -0,0 +1,145 @@ +import { ContractArtifact } from '@aztec/foundation/abi'; +import { LogFn } from '@aztec/foundation/log'; + +import { Command } from 'commander'; +import { mkdirSync, writeFileSync } from 'fs'; +import { mkdirpSync } from 'fs-extra'; +import path, { resolve } from 'path'; + +import { + ProgramArtifact, + compileUsingNargo, + compileUsingNoirWasm, + generateNoirContractInterface, + generateTypescriptContractInterface, + generateTypescriptProgramInterface, +} from '../index.js'; + +/** + * CLI options for configuring behavior + */ +interface Options { + // eslint-disable-next-line jsdoc/require-jsdoc + outdir: string; + // eslint-disable-next-line jsdoc/require-jsdoc + typescript: string | undefined; + // eslint-disable-next-line jsdoc/require-jsdoc + interface: string | undefined; + // eslint-disable-next-line jsdoc/require-jsdoc + compiler: string | undefined; +} +/** + * Registers a 'contract' command on the given commander program that compiles an Aztec.nr contract project. + * @param program - Commander program. + * @param log - Optional logging function. + * @returns The program with the command registered. + */ +export function compileNoir(program: Command, name = 'compile', log: LogFn = () => {}): Command { + return program + .command(name) + .argument('', 'Path to the bin or Aztec.nr project to compile') + .option('-o, --outdir ', 'Output folder for the binary artifacts, relative to the project path', 'target') + .option('-ts, --typescript ', 'Optional output folder for generating typescript wrappers', undefined) + .option('-i, --interface ', 'Optional output folder for generating an Aztec.nr contract interface', undefined) + .option('-c --compiler ', 'Which compiler to use. Either nargo or wasm. Defaults to nargo', 'wasm') + .description('Compiles the Noir Source in the target project') + + .action(async (projectPath: string, options: Options) => { + const { compiler } = options; + if (typeof projectPath !== 'string') { + throw new Error(`Missing project path argument`); + } + if (compiler !== 'nargo' && compiler !== 'wasm') { + throw new Error(`Invalid compiler: ${compiler}`); + } + + const compile = compiler === 'wasm' ? compileUsingNoirWasm : compileUsingNargo; + log(`Compiling ${projectPath} with ${compiler} backend...`); + const results = await compile(projectPath, { log }); + for (const result of results) { + generateOutput(projectPath, result, options, log); + } + }); +} + +/** + * + * @param contract - output from compiler, to serialize locally. branch based on Contract vs Program + */ +function generateOutput( + projectPath: string, + _result: ContractArtifact | ProgramArtifact, + options: Options, + log: LogFn, +) { + const contract = _result as ContractArtifact; + if (contract.name) { + return generateContractOutput(projectPath, contract, options, log); + } else { + const program = _result as ProgramArtifact; + if (program.abi) { + return generateProgramOutput(projectPath, program, options, log); + } + } +} +/** + * + * @param program - output from compiler, to serialize locally + */ +function generateProgramOutput(projectPath: string, program: ProgramArtifact, options: Options, log: LogFn) { + const currentDir = process.cwd(); + const { outdir, typescript, interface: noirInterface } = options; + const artifactPath = resolve(projectPath, outdir, `${program.name ? program.name : 'main'}.json`); + log(`Writing ${program.name} artifact to ${path.relative(currentDir, artifactPath)}`); + mkdirSync(path.dirname(artifactPath), { recursive: true }); + writeFileSync(artifactPath, JSON.stringify(program, null, 2)); + + if (noirInterface) { + log(`noirInterface generation not implemented for programs`); + // not implemented + } + + if (typescript) { + // just need type definitions, since a lib has just one entry point + const tsPath = resolve(projectPath, typescript, `../types/${program.name}_types.ts`); + log(`Writing ${program.name} typescript types to ${path.relative(currentDir, tsPath)}`); + const tsWrapper = generateTypescriptProgramInterface(program.abi); + mkdirpSync(path.dirname(tsPath)); + writeFileSync(tsPath, tsWrapper); + } +} + +/** + * + * @param contract - output from compiler, to serialize locally + */ +function generateContractOutput(projectPath: string, contract: ContractArtifact, options: Options, log: LogFn) { + const currentDir = process.cwd(); + const { outdir, typescript, interface: noirInterface } = options; + const artifactPath = resolve(projectPath, outdir, `${contract.name}.json`); + log(`Writing ${contract.name} artifact to ${path.relative(currentDir, artifactPath)}`); + mkdirSync(path.dirname(artifactPath), { recursive: true }); + writeFileSync(artifactPath, JSON.stringify(contract, null, 2)); + + if (noirInterface) { + const noirInterfacePath = resolve(projectPath, noirInterface, `${contract.name}_interface.nr`); + log(`Writing ${contract.name} Aztec.nr external interface to ${path.relative(currentDir, noirInterfacePath)}`); + const noirWrapper = generateNoirContractInterface(contract); + mkdirpSync(path.dirname(noirInterfacePath)); + writeFileSync(noirInterfacePath, noirWrapper); + } + + if (typescript) { + const tsPath = resolve(projectPath, typescript, `${contract.name}.ts`); + log(`Writing ${contract.name} typescript interface to ${path.relative(currentDir, tsPath)}`); + let relativeArtifactPath = path.relative(path.dirname(tsPath), artifactPath); + if (relativeArtifactPath === `${contract.name}.json`) { + // relative path edge case, prepending ./ for local import - the above logic just does + // `${contract.name}.json`, which is not a valid import for a file in the same directory + relativeArtifactPath = `./${contract.name}.json`; + } + const tsWrapper = generateTypescriptContractInterface(contract, relativeArtifactPath); + mkdirpSync(path.dirname(tsPath)); + writeFileSync(tsPath, tsWrapper); + } +} diff --git a/yarn-project/noir-compiler/src/cli/contract.ts b/yarn-project/noir-compiler/src/cli/contract.ts deleted file mode 100644 index 20b61de076a..00000000000 --- a/yarn-project/noir-compiler/src/cli/contract.ts +++ /dev/null @@ -1,86 +0,0 @@ -import { LogFn } from '@aztec/foundation/log'; - -import { Command } from 'commander'; -import { mkdirSync, writeFileSync } from 'fs'; -import { mkdirpSync } from 'fs-extra'; -import path, { resolve } from 'path'; - -import { - compileUsingNargo, - compileUsingNoirWasm, - generateNoirContractInterface, - generateTypescriptContractInterface, -} from '../index.js'; - -/** - * Registers a 'contract' command on the given commander program that compiles an Aztec.nr contract project. - * @param program - Commander program. - * @param log - Optional logging function. - * @returns The program with the command registered. - */ -export function compileContract(program: Command, name = 'contract', log: LogFn = () => {}): Command { - return program - .command(name) - .argument('', 'Path to the Aztec.nr project to compile') - .option('-o, --outdir ', 'Output folder for the binary artifacts, relative to the project path', 'target') - .option('-ts, --typescript ', 'Optional output folder for generating typescript wrappers', undefined) - .option('-i, --interface ', 'Optional output folder for generating an Aztec.nr contract interface', undefined) - .option('-c --compiler ', 'Which compiler to use. Either nargo or wasm. Defaults to nargo', 'wasm') - .description('Compiles the contracts in the target project') - - .action( - async ( - projectPath: string, - /* eslint-disable jsdoc/require-jsdoc */ - options: { - outdir: string; - typescript: string | undefined; - interface: string | undefined; - compiler: string | undefined; - }, - /* eslint-enable jsdoc/require-jsdoc */ - ) => { - const { outdir, typescript, interface: noirInterface, compiler } = options; - if (typeof projectPath !== 'string') throw new Error(`Missing project path argument`); - if (compiler !== 'nargo' && compiler !== 'wasm') throw new Error(`Invalid compiler: ${compiler}`); - const currentDir = process.cwd(); - - const compile = compiler === 'wasm' ? compileUsingNoirWasm : compileUsingNargo; - log(`Compiling contracts...`); - const result = await compile(projectPath, { log }); - - for (const contract of result) { - const artifactPath = resolve(projectPath, outdir, `${contract.name}.json`); - log(`Writing ${contract.name} artifact to ${path.relative(currentDir, artifactPath)}`); - mkdirSync(path.dirname(artifactPath), { recursive: true }); - writeFileSync(artifactPath, JSON.stringify(contract, null, 2)); - - if (noirInterface) { - const noirInterfacePath = resolve(projectPath, noirInterface, `${contract.name}_interface.nr`); - log( - `Writing ${contract.name} Aztec.nr external interface to ${path.relative(currentDir, noirInterfacePath)}`, - ); - const noirWrapper = generateNoirContractInterface(contract); - mkdirpSync(path.dirname(noirInterfacePath)); - writeFileSync(noirInterfacePath, noirWrapper); - } - - if (typescript) { - const tsPath = resolve(projectPath, typescript, `${contract.name}.ts`); - log(`Writing ${contract.name} typescript interface to ${path.relative(currentDir, tsPath)}`); - let relativeArtifactPath = path.relative(path.dirname(tsPath), artifactPath); - log(`Relative path: ${relativeArtifactPath}`); - if (relativeArtifactPath === `${contract.name}.json`) { - // relative path edge case, prepending ./ for local import - the above logic just does - // `${contract.name}.json`, which is not a valid import for a file in the same directory - relativeArtifactPath = `./${contract.name}.json`; - } - log(`Relative path after correction: ${relativeArtifactPath}`); - const tsWrapper = generateTypescriptContractInterface(contract, relativeArtifactPath); - mkdirpSync(path.dirname(tsPath)); - writeFileSync(tsPath, tsWrapper); - } - } - }, - ); -} diff --git a/yarn-project/noir-compiler/src/cli/index.ts b/yarn-project/noir-compiler/src/cli/index.ts index 8d312f9ec5c..0687e01706f 100644 --- a/yarn-project/noir-compiler/src/cli/index.ts +++ b/yarn-project/noir-compiler/src/cli/index.ts @@ -1,3 +1,3 @@ -export { compileContract } from './contract.js'; +export { compileNoir } from './compileNoir.js'; export { generateNoirInterface } from './noir-interface.js'; export { generateTypescriptInterface } from './typescript.js'; diff --git a/yarn-project/noir-compiler/src/cli/noir-interface.ts b/yarn-project/noir-compiler/src/cli/noir-interface.ts index badb3831b7a..54db5b7b86b 100644 --- a/yarn-project/noir-compiler/src/cli/noir-interface.ts +++ b/yarn-project/noir-compiler/src/cli/noir-interface.ts @@ -37,7 +37,9 @@ export function generateNoirInterface(program: Command, name = 'interface', log: /* eslint-enable jsdoc/require-jsdoc */ ) => { const { outdir, artifacts } = options; - if (typeof projectPath !== 'string') throw new Error(`Missing project path argument`); + if (typeof projectPath !== 'string') { + throw new Error(`Missing project path argument`); + } const currentDir = process.cwd(); const artifactsDir = resolve(projectPath, artifacts); @@ -45,7 +47,9 @@ export function generateNoirInterface(program: Command, name = 'interface', log: const artifactPath = resolve(artifactsDir, artifactsDirItem); if (statSync(artifactPath).isFile() && artifactPath.endsWith('.json')) { const contract = JSON.parse(readFileSync(artifactPath).toString()); - if (!isContractArtifact(contract)) continue; + if (!isContractArtifact(contract)) { + continue; + } const interfacePath = resolve(projectPath, outdir, `${contract.name}_interface.nr`); log(`Writing ${contract.name} Noir external interface to ${path.relative(currentDir, interfacePath)}`); try { diff --git a/yarn-project/noir-compiler/src/cli/typescript.ts b/yarn-project/noir-compiler/src/cli/typescript.ts index 9e0d9c00845..cf107cc0338 100644 --- a/yarn-project/noir-compiler/src/cli/typescript.ts +++ b/yarn-project/noir-compiler/src/cli/typescript.ts @@ -37,7 +37,9 @@ export function generateTypescriptInterface(program: Command, name = 'typescript /* eslint-enable jsdoc/require-jsdoc */ ) => { const { outdir, artifacts } = options; - if (typeof projectPath !== 'string') throw new Error(`Missing project path argument`); + if (typeof projectPath !== 'string') { + throw new Error(`Missing project path argument`); + } const currentDir = process.cwd(); const artifactsDir = resolve(projectPath, artifacts); @@ -45,7 +47,9 @@ export function generateTypescriptInterface(program: Command, name = 'typescript const artifactPath = resolve(artifactsDir, artifactsDirItem); if (statSync(artifactPath).isFile() && artifactPath.endsWith('.json')) { const contract = JSON.parse(readFileSync(artifactPath).toString()); - if (!isContractArtifact(contract)) continue; + if (!isContractArtifact(contract)) { + continue; + } const tsPath = resolve(projectPath, outdir, `${contract.name}.ts`); log(`Writing ${contract.name} typescript interface to ${path.relative(currentDir, tsPath)}`); let relativeArtifactPath = path.relative(path.dirname(tsPath), artifactPath); diff --git a/yarn-project/noir-compiler/src/compile/nargo.ts b/yarn-project/noir-compiler/src/compile/nargo.ts index 85443ebf631..134925d0cdf 100644 --- a/yarn-project/noir-compiler/src/compile/nargo.ts +++ b/yarn-project/noir-compiler/src/compile/nargo.ts @@ -6,7 +6,7 @@ import { emptyDirSync } from 'fs-extra'; import path from 'path'; import { NoirCommit, NoirTag } from '../index.js'; -import { NoirCompilationArtifacts, NoirCompiledContract, NoirDebugMetadata } from '../noir_artifact.js'; +import { NoirCompiledContract, NoirContractCompilationArtifacts, NoirDebugMetadata } from '../noir_artifact.js'; /** Compilation options */ export type CompileOpts = { @@ -31,7 +31,7 @@ export class NargoContractCompiler { * Compiles the contracts in projectPath and returns the Aztec.nr artifact. * @returns Aztec.nr artifact of the compiled contracts. */ - public compile(): Promise { + public compile(): Promise { const stdio = this.opts.quiet ? 'ignore' : 'inherit'; const nargoBin = this.opts.nargoBin ?? 'nargo'; const version = execSync(`${nargoBin} --version`, { cwd: this.projectPath, stdio: 'pipe' }).toString(); @@ -51,7 +51,7 @@ export class NargoContractCompiler { } } - private collectArtifacts(): NoirCompilationArtifacts[] { + private collectArtifacts(): NoirContractCompilationArtifacts[] { const contractArtifacts = new Map(); const debugArtifacts = new Map(); diff --git a/yarn-project/noir-compiler/src/compile/noir/dependencies/dependency-manager.test.ts b/yarn-project/noir-compiler/src/compile/noir/dependencies/dependency-manager.test.ts index 94996a54dd2..5f9ba570d4d 100644 --- a/yarn-project/noir-compiler/src/compile/noir/dependencies/dependency-manager.test.ts +++ b/yarn-project/noir-compiler/src/compile/noir/dependencies/dependency-manager.test.ts @@ -18,6 +18,9 @@ describe('DependencyManager', () => { lib2: { path: '/lib2', }, + lib3: { + path: '/lib3', + }, }, package: { name: 'test_contract', @@ -38,7 +41,7 @@ describe('DependencyManager', () => { it('resolves root dependencies', async () => { await manager.resolveDependencies(); - expect(manager.getEntrypointDependencies()).toEqual(['lib1', 'lib2']); + expect(manager.getEntrypointDependencies()).toEqual(['lib1', 'lib2', 'lib3']); }); it('resolves library dependencies', async () => { @@ -75,7 +78,7 @@ class TestDependencyResolver implements NoirDependencyResolver { package: new NoirPackage('/lib2', '/lib2/src', { dependencies: { lib3: { - path: '/lib3', + path: '../lib3', }, }, package: { diff --git a/yarn-project/noir-compiler/src/compile/noir/dependencies/dependency-manager.ts b/yarn-project/noir-compiler/src/compile/noir/dependencies/dependency-manager.ts index 0581917f556..44bdd25743a 100644 --- a/yarn-project/noir-compiler/src/compile/noir/dependencies/dependency-manager.ts +++ b/yarn-project/noir-compiler/src/compile/noir/dependencies/dependency-manager.ts @@ -46,7 +46,7 @@ export class NoirDependencyManager { * Resolves dependencies for a package. */ public async resolveDependencies(): Promise { - await this.#recursivelyResolveDependencies('', this.#entryPoint); + await this.#breadthFirstResolveDependencies(); } /** @@ -59,26 +59,46 @@ export class NoirDependencyManager { return dep?.version; } - async #recursivelyResolveDependencies(packageName: string, noirPackage: NoirPackage): Promise { - for (const [name, config] of Object.entries(noirPackage.getDependencies())) { - // TODO what happens if more than one package has the same name but different versions? - if (this.#libraries.has(name)) { - this.#log(`skipping already resolved dependency ${name}`); + async #breadthFirstResolveDependencies(): Promise { + /** Represents a package to resolve dependencies for */ + type Job = { + /** Package name */ + packageName: string; + /** The package location */ + noirPackage: NoirPackage; + }; + + const queue: Job[] = [ + { + packageName: '', + noirPackage: this.#entryPoint, + }, + ]; + + while (queue.length > 0) { + const { packageName, noirPackage } = queue.shift()!; + for (const [name, config] of Object.entries(noirPackage.getDependencies())) { + // TODO what happens if more than one package has the same name but different versions? + if (this.#libraries.has(name)) { + this.#log(`skipping already resolved dependency ${name}`); + this.#dependencies.set(packageName, [...(this.#dependencies.get(packageName) ?? []), name]); + + continue; + } + const dependency = await this.#resolveDependency(noirPackage, config); + if (dependency.package.getType() !== 'lib') { + this.#log(`Non-library package ${name}`, config); + throw new Error(`Dependency ${name} is not a library`); + } + + this.#libraries.set(name, dependency); this.#dependencies.set(packageName, [...(this.#dependencies.get(packageName) ?? []), name]); - continue; + queue.push({ + noirPackage: dependency.package, + packageName: name, + }); } - - const dependency = await this.#resolveDependency(noirPackage, config); - if (dependency.package.getType() !== 'lib') { - this.#log(`Non-library package ${name}`, config); - throw new Error(`Dependency ${name} is not a library`); - } - - this.#libraries.set(name, dependency); - this.#dependencies.set(packageName, [...(this.#dependencies.get(packageName) ?? []), name]); - - await this.#recursivelyResolveDependencies(name, dependency.package); } } diff --git a/yarn-project/noir-compiler/src/compile/noir/noir-wasm-compiler.ts b/yarn-project/noir-compiler/src/compile/noir/noir-wasm-compiler.ts index 03447e0f189..30beca72558 100644 --- a/yarn-project/noir-compiler/src/compile/noir/noir-wasm-compiler.ts +++ b/yarn-project/noir-compiler/src/compile/noir/noir-wasm-compiler.ts @@ -3,7 +3,7 @@ import { LogFn, createDebugLogger } from '@aztec/foundation/log'; import { CompileError, compile } from '@noir-lang/noir_wasm'; import { isAbsolute } from 'node:path'; -import { NoirCompilationArtifacts } from '../../noir_artifact.js'; +import { NoirCompilationResult, NoirProgramCompilationArtifacts } from '../../noir_artifact.js'; import { NoirDependencyManager } from './dependencies/dependency-manager.js'; import { GithubDependencyResolver as GithubCodeArchiveDependencyResolver } from './dependencies/github-dependency-resolver.js'; import { LocalDependencyResolver } from './dependencies/local-dependency-resolver.js'; @@ -54,9 +54,6 @@ export class NoirWasmContractCompiler { } const noirPackage = NoirPackage.open(projectPath, fileManager); - if (noirPackage.getType() !== 'contract') { - throw new Error('This is not a contract project'); - } const dependencyManager = new NoirDependencyManager( [ @@ -80,15 +77,65 @@ export class NoirWasmContractCompiler { } /** - * Compiles the project. + * Compile EntryPoint */ - public async compile(): Promise { - const isContract = this.#package.getType() === 'contract'; - // limit to contracts-only because the rest of the pipeline only supports processing contracts - if (!isContract) { - throw new Error('Noir project is not a contract'); + public async compile(): Promise { + if (this.#package.getType() === 'contract') { + this.#debugLog(`Compiling Contract at ${this.#package.getEntryPointPath()}`); + return await this.compileContract(); + } else if (this.#package.getType() === 'bin') { + this.#debugLog(`Compiling Program at ${this.#package.getEntryPointPath()}`); + return await this.compileProgram(); + } else { + this.#log( + `Compile skipped - only supports compiling "contract" and "bin" package types (${this.#package.getType()})`, + ); + return []; } + } + + /** + * Compiles the Program. + */ + public async compileProgram(): Promise { + await this.#dependencyManager.resolveDependencies(); + this.#debugLog(`Dependencies: ${this.#dependencyManager.getPackageNames().join(', ')}`); + + initializeResolver(this.#resolveFile); + try { + const isContract: boolean = false; + const result = compile(this.#package.getEntryPointPath(), isContract, { + /* eslint-disable camelcase */ + root_dependencies: this.#dependencyManager.getEntrypointDependencies(), + library_dependencies: this.#dependencyManager.getLibraryDependencies(), + /* eslint-enable camelcase */ + }); + + if (!('program' in result)) { + throw new Error('No program found in compilation result'); + } + + return [{ name: this.#package.getNoirPackageConfig().package.name, ...result }]; + } catch (err) { + if (err instanceof Error && err.name === 'CompileError') { + this.#processCompileError(err as CompileError); + } + + throw err; + } + } + + /** + * Compiles the Contract. + */ + public async compileContract(): Promise { + if (!(this.#package.getType() === 'contract' || this.#package.getType() === 'bin')) { + this.#log( + `Compile skipped - only supports compiling "contract" and "bin" package types (${this.#package.getType()})`, + ); + return []; + } this.#debugLog(`Compiling contract at ${this.#package.getEntryPointPath()}`); await this.#dependencyManager.resolveDependencies(); this.#debugLog(`Dependencies: ${this.#dependencyManager.getPackageNames().join(', ')}`); @@ -96,6 +143,7 @@ export class NoirWasmContractCompiler { initializeResolver(this.#resolveFile); try { + const isContract: boolean = true; const result = compile(this.#package.getEntryPointPath(), isContract, { /* eslint-disable camelcase */ root_dependencies: this.#dependencyManager.getEntrypointDependencies(), diff --git a/yarn-project/noir-compiler/src/compile/noir/package.ts b/yarn-project/noir-compiler/src/compile/noir/package.ts index c79fc662dd7..4c5f42f1077 100644 --- a/yarn-project/noir-compiler/src/compile/noir/package.ts +++ b/yarn-project/noir-compiler/src/compile/noir/package.ts @@ -29,6 +29,13 @@ export class NoirPackage { return this.#packagePath; } + /** + * Gets this package's Nargo.toml (NoirPackage)Config. + */ + public getNoirPackageConfig() { + return this.#config; + } + /** * The path to the source directory. */ @@ -44,6 +51,8 @@ export class NoirPackage { switch (this.getType()) { case 'lib': + // we shouldn't need to compile `lib` type, since the .nr source is read directly + // when the lib is used as a dependency elsewhere. entrypoint = 'lib.nr'; break; case 'contract': diff --git a/yarn-project/noir-compiler/src/contract-interface-gen/abi.ts b/yarn-project/noir-compiler/src/contract-interface-gen/abi.ts index ac8d6899ee5..61b47ea24d6 100644 --- a/yarn-project/noir-compiler/src/contract-interface-gen/abi.ts +++ b/yarn-project/noir-compiler/src/contract-interface-gen/abi.ts @@ -3,7 +3,15 @@ import { ContractArtifact, DebugMetadata, FunctionArtifact, FunctionType } from import { deflate } from 'pako'; import { mockVerificationKey } from '../mocked_keys.js'; -import { NoirCompilationArtifacts, NoirFunctionEntry } from '../noir_artifact.js'; +import { + NoirCompilationResult, + NoirContractCompilationArtifacts, + NoirFunctionEntry, + NoirProgramCompilationArtifacts, + ProgramArtifact, + isNoirContractCompilationArtifacts, + isNoirProgramCompilationArtifacts, +} from '../noir_artifact.js'; /** * Generates a function build artifact. Replaces verification key with a mock value. @@ -16,7 +24,9 @@ function generateFunctionArtifact(fn: NoirFunctionEntry): FunctionArtifact { // If the function is not unconstrained, the first item is inputs or CallContext which we should omit let parameters = fn.abi.parameters; - if (functionType !== FunctionType.UNCONSTRAINED) parameters = parameters.slice(1); + if (functionType !== FunctionType.UNCONSTRAINED) { + parameters = parameters.slice(1); + } // If the function is secret, the return is the public inputs, which should be omitted const returnTypes = functionType === FunctionType.SECRET ? [] : [fn.abi.return_type]; @@ -32,13 +42,50 @@ function generateFunctionArtifact(fn: NoirFunctionEntry): FunctionArtifact { }; } +/** + * Entrypoint for generating the .json artifact for compiled contract or program + * @param compileResult - Noir build output. + * @returns Aztec contract build artifact. + */ +export function generateArtifact(compileResult: NoirCompilationResult) { + if (isNoirContractCompilationArtifacts(compileResult)) { + return generateContractArtifact(compileResult); + } else if (isNoirProgramCompilationArtifacts(compileResult)) { + return generateProgramArtifact(compileResult); + } else { + throw Error('Unsupported artifact type'); + } +} + +/** + * Given a Nargo output generates an Aztec-compatible contract artifact. + * @param compiled - Noir build output. + * @returns Aztec contract build artifact. + */ +export function generateProgramArtifact( + { program }: NoirProgramCompilationArtifacts, + // eslint-disable-next-line camelcase + noir_version?: string, +): ProgramArtifact { + return { + // eslint-disable-next-line camelcase + noir_version, + hash: program.hash, + backend: program.backend, + abi: program.abi, + + // TODO: should we parse and write the debug? it doesn't seem to be in the nargo output + // debug: someParsedDebug, + }; +} + /** * Given a Nargo output generates an Aztec-compatible contract artifact. * @param compiled - Noir build output. * @returns Aztec contract build artifact. */ export function generateContractArtifact( - { contract, debug }: NoirCompilationArtifacts, + { contract, debug }: NoirContractCompilationArtifacts, aztecNrVersion?: string, ): ContractArtifact { const originalFunctions = contract.functions; diff --git a/yarn-project/noir-compiler/src/contract-interface-gen/typescript.ts b/yarn-project/noir-compiler/src/contract-interface-gen/contractTypescript.ts similarity index 97% rename from yarn-project/noir-compiler/src/contract-interface-gen/typescript.ts rename to yarn-project/noir-compiler/src/contract-interface-gen/contractTypescript.ts index 3712cb8346c..68c00af09ad 100644 --- a/yarn-project/noir-compiler/src/contract-interface-gen/typescript.ts +++ b/yarn-project/noir-compiler/src/contract-interface-gen/contractTypescript.ts @@ -26,8 +26,12 @@ function abiTypeToTypescript(type: ABIParameter['type']): string { case 'array': return `${abiTypeToTypescript(type.type)}[]`; case 'struct': - if (isEthereumAddressStruct(type)) return 'EthAddressLike'; - if (isAztecAddressStruct(type)) return 'AztecAddressLike'; + if (isEthereumAddressStruct(type)) { + return 'EthAddressLike'; + } + if (isAztecAddressStruct(type)) { + return 'AztecAddressLike'; + } return `{ ${type.fields.map(f => `${f.name}: ${abiTypeToTypescript(f.type)}`).join(', ')} }`; default: throw new Error(`Unknown type ${type}`); diff --git a/yarn-project/noir-compiler/src/contract-interface-gen/programTypescript.ts b/yarn-project/noir-compiler/src/contract-interface-gen/programTypescript.ts new file mode 100644 index 00000000000..257bc287d12 --- /dev/null +++ b/yarn-project/noir-compiler/src/contract-interface-gen/programTypescript.ts @@ -0,0 +1,189 @@ +import { ABIType } from '@aztec/foundation/abi'; +import { NoirFunctionAbi } from '@aztec/noir-compiler'; + +/** + * Keep track off all of the Noir primitive types that were used. + * Most of these will not have a 1-1 definition in TypeScript, + * so we will need to generate type aliases for them. + * + * We want to generate type aliases + * for specific types that are used in the ABI. + * + * For example: + * - If `Field` is used we want to alias that + * with `number`. + * - If `u32` is used we want to alias that with `number` too. + */ +type PrimitiveTypesUsed = { + /** + * The name of the type alias that we will generate. + */ + aliasName: string; + /** + * The TypeScript type that we will alias to. + */ + tsType: string; +}; + +const noirPrimitiveTypesToTsTypes = new Map(); + +/** + * Typescript does not allow us to check for equality of non-primitive types + * easily, so we create a addIfUnique function that will only add an item + * to the map if it is not already there by using JSON.stringify. + * @param item - The item to add to the map. + */ +function addIfUnique(item: PrimitiveTypesUsed) { + const key = JSON.stringify(item); + if (!noirPrimitiveTypesToTsTypes.has(key)) { + noirPrimitiveTypesToTsTypes.set(key, item); + } +} + +/** + * Converts an ABI type to a TypeScript type. + * @param type - The ABI type to convert. + * @returns The typescript code to define the type. + */ +function abiTypeToTs(type: ABIType): string { + switch (type.kind) { + case 'integer': { + let tsIntType = ''; + if (type.sign === 'signed') { + tsIntType = `i${type.width}`; + } else { + tsIntType = `u${type.width}`; + } + addIfUnique({ aliasName: tsIntType, tsType: 'string' }); + return tsIntType; + } + case 'boolean': + return `boolean`; + case 'array': + return `FixedLengthArray<${abiTypeToTs(type.type)}, ${type.length}>`; + case 'struct': + return getLastComponentOfPath(type.path); + case 'field': + addIfUnique({ aliasName: 'Field', tsType: 'string' }); + return 'Field'; + default: + throw new Error(`Unknown ABI type ${type}`); + } +} + +/** + * Returns the last component of a path, e.g. "foo::bar::baz" -\> "baz" + * Note: that if we have a path such as "Baz", we will return "Baz". + * + * Since these paths corresponds to structs, we can assume that we + * cannot have "foo::bar::". + * + * We also make the assumption that since these paths are coming from + * Noir, then we will not have two paths that look like this: + * - foo::bar::Baz + * - cat::dog::Baz + * ie the last component of the path (struct name) is enough to uniquely identify + * the whole path. + * + * TODO: We should double check this assumption when we use type aliases, + * I expect that `foo::bar::Baz as Dog` would effectively give `foo::bar::Dog` + * @param str - The path to get the last component of. + * @returns The last component of the path. + */ +function getLastComponentOfPath(str: string): string { + const parts = str.split('::'); + const lastPart = parts[parts.length - 1]; + return lastPart; +} + +/** + * Generates TypeScript interfaces for the structs used in the ABI. + * @param type - The ABI type to generate the interface for. + * @param output - The set of structs that we have already generated bindings for. + * @returns The TypeScript code to define the struct. + */ +function generateStructInterfaces(type: ABIType, output: Set): string { + let result = ''; + + // Edge case to handle the array of structs case. + if (type.kind === 'array' && type.type.kind === 'struct' && !output.has(getLastComponentOfPath(type.type.path))) { + result += generateStructInterfaces(type.type, output); + } + if (type.kind !== 'struct') { + return result; + } + + // List of structs encountered while viewing this type that we need to generate + // bindings for. + const typesEncountered = new Set(); + + // Codegen the struct and then its fields, so that the structs fields + // are defined before the struct itself. + let codeGeneratedStruct = ''; + let codeGeneratedStructFields = ''; + + const structName = getLastComponentOfPath(type.path); + if (!output.has(structName)) { + codeGeneratedStruct += `export interface ${structName} {\n`; + for (const field of type.fields) { + codeGeneratedStruct += ` ${field.name}: ${abiTypeToTs(field.type)};\n`; + typesEncountered.add(field.type); + } + codeGeneratedStruct += `}\n\n`; + output.add(structName); + + // Generate code for the encountered structs in the field above + for (const type of typesEncountered) { + codeGeneratedStructFields += generateStructInterfaces(type, output); + } + } + + return codeGeneratedStructFields + '\n' + codeGeneratedStruct; +} + +/** + * Generates a TypeScript interface for the ABI. + * @param abiObj - The ABI to generate the interface for. + * @returns The TypeScript code to define the interface. + */ +export function generateTypescriptProgramInterface(abiObj: NoirFunctionAbi): string { + let result = ``; + const outputStructs = new Set(); + + // Define structs for composite types + for (const param of abiObj.parameters) { + result += generateStructInterfaces(param.type, outputStructs); + } + + // Generating Return type, if it exists + // + if (abiObj.return_type != null) { + result += generateStructInterfaces(abiObj.return_type, outputStructs); + result += `export type ReturnType = ${abiTypeToTs(abiObj.return_type)};\n`; + } + + // Generating Input type + result += '\nexport interface InputType {\n'; + for (const param of abiObj.parameters) { + result += ` ${param.name}: ${abiTypeToTs(param.type)};\n`; + } + result += '}'; + + // Add the primitive Noir types that do not have a 1-1 mapping to TypeScript. + let primitiveTypeAliases = ''; + for (const [, value] of noirPrimitiveTypesToTsTypes) { + primitiveTypeAliases += `\nexport type ${value.aliasName} = ${value.tsType};`; + } + + const fixedLengthArray = + '\nexport type FixedLengthArray = L extends 0 ? never[]: T[] & { length: L }'; + + return ( + `/* Autogenerated file, do not edit! */\n\n/* eslint-disable */\n` + + fixedLengthArray + + '\n' + + primitiveTypeAliases + + '\n' + + result + ); +} diff --git a/yarn-project/noir-compiler/src/index.test.ts b/yarn-project/noir-compiler/src/index.test.ts index 39c84e61210..4922c1af04c 100644 --- a/yarn-project/noir-compiler/src/index.test.ts +++ b/yarn-project/noir-compiler/src/index.test.ts @@ -6,6 +6,7 @@ import { execSync } from 'child_process'; import path from 'path'; import { + ProgramArtifact, compileUsingNargo, compileUsingNoirWasm, generateNoirContractInterface, @@ -33,25 +34,33 @@ describe('noir-compiler', () => { const nargoAvailable = isNargoAvailable(); const conditionalDescribe = nargoAvailable ? describe : describe.skip; const conditionalIt = nargoAvailable ? it : it.skip; - const withoutDebug = ({ debug: _debug, ...rest }: ContractArtifact): Omit => rest; + const withoutDebug = ({ + debug: _debug, + ...rest + }: ContractArtifact | ProgramArtifact): Omit => rest; + + function compilerTest( + compileFn: (path: string, opts: { log: LogFn }) => Promise<(ProgramArtifact | ContractArtifact)[]>, + ) { + let compiled: (ProgramArtifact | ContractArtifact)[]; + let compiledContract: ContractArtifact[]; - function compilerTest(compileFn: (path: string, opts: { log: LogFn }) => Promise) { - let compiled: ContractArtifact[]; beforeAll(async () => { compiled = await compileFn(projectPath, { log }); + compiledContract = compiled.map(_compiled => _compiled as ContractArtifact); }); it('compiles the test contract', () => { - expect(compiled.map(withoutDebug)).toMatchSnapshot(); + expect(compiledContract.map(withoutDebug)).toMatchSnapshot(); }); it('generates typescript interface', () => { - const result = generateTypescriptContractInterface(compiled[0], `../target/test.json`); + const result = generateTypescriptContractInterface(compiledContract[0], `../target/test.json`); expect(result).toMatchSnapshot(); }); it('generates Aztec.nr external interface', () => { - const result = generateNoirContractInterface(compiled[0]); + const result = generateNoirContractInterface(compiledContract[0]); expect(result).toMatchSnapshot(); }); } diff --git a/yarn-project/noir-compiler/src/index.ts b/yarn-project/noir-compiler/src/index.ts index a8ecf376595..768d328c8ae 100644 --- a/yarn-project/noir-compiler/src/index.ts +++ b/yarn-project/noir-compiler/src/index.ts @@ -6,12 +6,14 @@ import { join, resolve } from 'path'; import { CompileOpts, NargoContractCompiler } from './compile/nargo.js'; import { FileManager } from './compile/noir/file-manager/file-manager.js'; import { NoirWasmCompileOptions, NoirWasmContractCompiler } from './compile/noir/noir-wasm-compiler.js'; -import { generateContractArtifact } from './contract-interface-gen/abi.js'; +import { generateArtifact, generateContractArtifact } from './contract-interface-gen/abi.js'; +import { ProgramArtifact } from './noir_artifact.js'; export * from './versions.js'; +export { generateTypescriptContractInterface } from './contract-interface-gen/contractTypescript.js'; export { generateNoirContractInterface } from './contract-interface-gen/noir.js'; -export { generateTypescriptContractInterface } from './contract-interface-gen/typescript.js'; +export { generateTypescriptProgramInterface } from './contract-interface-gen/programTypescript.js'; export { generateContractArtifact }; export * from './noir_artifact.js'; @@ -37,12 +39,12 @@ export async function compileUsingNargo(projectPath: string, opts: CompileOpts = export async function compileUsingNoirWasm( projectPath: string, opts: NoirWasmCompileOptions, -): Promise { +): Promise<(ContractArtifact | ProgramArtifact)[]> { const cacheRoot = process.env.XDG_CACHE_HOME ?? join(process.env.HOME ?? '', '.cache'); const fileManager = new FileManager(fs, join(cacheRoot, 'aztec-noir-compiler')); const compiler = NoirWasmContractCompiler.new(fileManager, resolve(projectPath), opts); const artifacts = await compiler.compile(); - const resolvedAztecNrVersion = compiler.getResolvedAztecNrVersion(); - - return artifacts.map(artifact => generateContractArtifact(artifact, resolvedAztecNrVersion)); + return artifacts.map(artifact => { + return generateArtifact(artifact); + }); } diff --git a/yarn-project/noir-compiler/src/noir_artifact.ts b/yarn-project/noir-compiler/src/noir_artifact.ts index f03742a274c..6d7d1a090ef 100644 --- a/yarn-project/noir-compiler/src/noir_artifact.ts +++ b/yarn-project/noir-compiler/src/noir_artifact.ts @@ -54,7 +54,7 @@ export interface NoirCompiledContract { */ export interface NoirCompiledCircuit { /** The hash of the circuit. */ - hash: number; + hash?: number; /** Compilation backend. */ backend: string; /** @@ -65,6 +65,40 @@ export interface NoirCompiledCircuit { bytecode: string; } +/** + * Defines artifact of a contract. + */ +export interface ProgramArtifact { + /** + * version of noir used to compile + */ + noir_version?: string; + /** + * the name of the project, read from Nargo.toml + */ + name?: string; + /** + * The hash of the contract. + */ + hash?: number; + + /** + * The compilation backend of the artifact. + */ + backend: string; + + /** + * The abi of the program. + */ + abi: any; // TODO: type + + /** + * The debug metadata of the contract. + * It's used to include the relevant source code section when a constraint is not met during simulation. + */ + debug?: NoirDebugMetadata; +} + /** * The debug metadata of an Aztec.nr contract. */ @@ -82,13 +116,56 @@ export interface NoirDebugMetadata { /** * The compilation artifacts of a given contract. */ -export interface NoirCompilationArtifacts { +export interface NoirContractCompilationArtifacts { /** * The compiled contract. */ contract: NoirCompiledContract; + + /** + * The artifact that contains the debug metadata about the contract. + */ + debug?: NoirDebugMetadata; +} + +/** + * The compilation artifacts of a given program. + */ +export interface NoirProgramCompilationArtifacts { + /** + * not part of the compilation output, injected later + */ + name: string; + /** + * The compiled contract. + */ + program: NoirCompiledCircuit; + /** * The artifact that contains the debug metadata about the contract. */ debug?: NoirDebugMetadata; } + +/** + * output of Noir Wasm compilation, can be for a contract or lib/binary + */ +export type NoirCompilationResult = NoirContractCompilationArtifacts | NoirProgramCompilationArtifacts; + +/** + * Check if it has Contract unique property + */ +export function isNoirContractCompilationArtifacts( + artifact: NoirCompilationResult, +): artifact is NoirContractCompilationArtifacts { + return (artifact as NoirContractCompilationArtifacts).contract !== undefined; +} + +/** + * Check if it has Contract unique property + */ +export function isNoirProgramCompilationArtifacts( + artifact: NoirCompilationResult, +): artifact is NoirProgramCompilationArtifacts { + return (artifact as NoirProgramCompilationArtifacts).program !== undefined; +} diff --git a/yarn-project/noir-compiler/src/utils.ts b/yarn-project/noir-compiler/src/utils.ts index 544a7b24630..7a93eda2518 100644 --- a/yarn-project/noir-compiler/src/utils.ts +++ b/yarn-project/noir-compiler/src/utils.ts @@ -8,14 +8,26 @@ import { ContractArtifact } from '@aztec/foundation/abi'; * @returns True if it looks like a ContractArtifact. */ export function isContractArtifact(input: any): input is ContractArtifact { - if (typeof input !== 'object') return false; + if (typeof input !== 'object') { + return false; + } const maybeContractArtifact = input as ContractArtifact; - if (typeof maybeContractArtifact.name !== 'string') return false; - if (!Array.isArray(maybeContractArtifact.functions)) return false; + if (typeof maybeContractArtifact.name !== 'string') { + return false; + } + if (!Array.isArray(maybeContractArtifact.functions)) { + return false; + } for (const fn of maybeContractArtifact.functions) { - if (typeof fn.name !== 'string') return false; - if (typeof fn.functionType !== 'string') return false; - if (typeof fn.isInternal !== 'boolean') return false; + if (typeof fn.name !== 'string') { + return false; + } + if (typeof fn.functionType !== 'string') { + return false; + } + if (typeof fn.isInternal !== 'boolean') { + return false; + } } return true; } diff --git a/yarn-project/noir-contracts/Nargo.toml b/yarn-project/noir-contracts/Nargo.toml index 9e72509a592..2adf8676c31 100644 --- a/yarn-project/noir-contracts/Nargo.toml +++ b/yarn-project/noir-contracts/Nargo.toml @@ -16,9 +16,11 @@ members = [ "src/contracts/schnorr_account_contract", "src/contracts/schnorr_hardcoded_account_contract", "src/contracts/schnorr_single_key_account_contract", + "src/contracts/slow_tree_contract", "src/contracts/stateful_test_contract", "src/contracts/test_contract", "src/contracts/token_contract", + "src/contracts/token_blacklist_contract", "src/contracts/token_bridge_contract", "src/contracts/uniswap_contract", ] diff --git a/yarn-project/noir-contracts/package.json b/yarn-project/noir-contracts/package.json index ec1249fe8a7..f4135a3dd64 100644 --- a/yarn-project/noir-contracts/package.json +++ b/yarn-project/noir-contracts/package.json @@ -11,7 +11,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests", "noir:clean": "rm -rf ./src/artifacts ./src/types && mkdir -p ../aztec.js/src/artifacts/ && find ../aztec.js/src/artifacts/ -mindepth 1 -delete && rm -rf target/", "noir:build": "./src/scripts/compile.sh", @@ -38,7 +38,6 @@ "devDependencies": { "@aztec/noir-compiler": "workspace:^", "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/lodash.camelcase": "^4.3.7", "@types/lodash.omit": "^4.5.7", diff --git a/yarn-project/noir-contracts/scripts/compile.sh b/yarn-project/noir-contracts/scripts/compile.sh index 10594042c8e..fe2096f4cf4 100755 --- a/yarn-project/noir-contracts/scripts/compile.sh +++ b/yarn-project/noir-contracts/scripts/compile.sh @@ -13,7 +13,7 @@ build() { echo "Compiling $CONTRACT_NAME..." rm -rf ${CONTRACT_FOLDER}/target - node --no-warnings "$COMPILER" contract "$CONTRACT_FOLDER" + node --no-warnings "$COMPILER" compile "$CONTRACT_FOLDER" } export -f build diff --git a/yarn-project/noir-contracts/src/contracts/benchmarking_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/benchmarking_contract/src/main.nr index 12ee0cccdcc..27683d5a52d 100644 --- a/yarn-project/noir-contracts/src/contracts/benchmarking_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/benchmarking_contract/src/main.nr @@ -50,7 +50,7 @@ contract Benchmarking { // by @rahul-kothari for a full explanation on why this is needed. #[aztec(private)] fn recreate_note(owner: Field, index: u32) { - let owner_notes = storage.notes.at(owner); + let owner_notes = storage.notes.at(owner); let getter_options = NoteGetterOptions::new().set_limit(1).set_offset(index); let notes = owner_notes.get_notes(getter_options); let note = notes[0].unwrap_unchecked(); @@ -63,7 +63,9 @@ contract Benchmarking { fn increment_balance(owner: Field, value: Field) { let current = storage.balances.at(owner).read(); storage.balances.at(owner).write(current + value); - let _callStackItem1 = context.call_public_function(context.this_address(), compute_selector("broadcast(Field)"), [owner]); + let _callStackItem1 = context.call_public_function(context.this_address(), + compute_selector("broadcast(Field)"), + [owner]); } // Emits a public log. diff --git a/yarn-project/noir-contracts/src/contracts/card_game_contract/src/cards.nr b/yarn-project/noir-contracts/src/contracts/card_game_contract/src/cards.nr index 68c560c9de5..f5f5636132b 100644 --- a/yarn-project/noir-contracts/src/contracts/card_game_contract/src/cards.nr +++ b/yarn-project/noir-contracts/src/contracts/card_game_contract/src/cards.nr @@ -50,7 +50,6 @@ fn test_to_from_field() { assert(card.to_field() == field); } - struct CardNote { card: Card, note: ValueNote, @@ -96,23 +95,21 @@ pub fn filter_cards(notes: [Option; MAX_READ_REQUESTS_PER_CALL], d for i in 0..notes.len() { let note = notes[i]; if note.is_some() { - let card_note = CardNote::from_note( - note.unwrap_unchecked() - ); + let card_note = CardNote::from_note(note.unwrap_unchecked()); for j in 0..N { - if !found[j] & (card_note.card.strength == desired_cards[j].strength) & (card_note.card.points == desired_cards[j].points) { + if !found[j] + & (card_note.card.strength == desired_cards[j].strength) + & (card_note.card.points == desired_cards[j].points) { selected[i] = note; found[j] = true; } } } - } selected } - impl Deck { pub fn new( context: Context, @@ -191,10 +188,7 @@ impl Deck { global PACK_CARDS = 3; // Limited by number of write requests (max 4) -pub fn get_pack_cards( - seed: Field, - owner_address: Field -) -> [Card; PACK_CARDS] { +pub fn get_pack_cards(seed: Field, owner_address: Field) -> [Card; PACK_CARDS] { // generate pseudo randomness deterministically from 'seed' and user secret let secret = get_secret_key(owner_address); let mix = secret.high + secret.low + seed; @@ -215,7 +209,8 @@ pub fn get_pack_cards( } pub fn compute_deck_strength(cards: [Card; N]) -> Field { - cards.fold(0, |acc, card: Card| { + cards.fold(0, + |acc, card: Card| { acc + card.strength as Field }) -} \ No newline at end of file +} diff --git a/yarn-project/noir-contracts/src/contracts/card_game_contract/src/game.nr b/yarn-project/noir-contracts/src/contracts/card_game_contract/src/game.nr index 75be5f84979..7b6255e907c 100644 --- a/yarn-project/noir-contracts/src/contracts/card_game_contract/src/game.nr +++ b/yarn-project/noir-contracts/src/contracts/card_game_contract/src/game.nr @@ -32,29 +32,21 @@ global GAME_SERIALIZED_LEN: Field = 15; fn deserializeGame(fields: [Field; GAME_SERIALIZED_LEN]) -> Game { let players = [ - PlayerEntry { - address: fields[0], - deck_strength: fields[1] as u32, - points: fields[2] as u120, - }, - PlayerEntry { - address: fields[3], - deck_strength: fields[4] as u32, - points: fields[5] as u120, - }, + PlayerEntry { address: fields[0], deck_strength: fields[1] as u32, points: fields[2] as u120 }, + PlayerEntry { address: fields[3], deck_strength: fields[4] as u32, points: fields[5] as u120 } ]; let rounds_cards = [ Card::from_field(fields[6]), Card::from_field(fields[7]), - Card::from_field(fields[8]), Card::from_field(fields[9]), + Card::from_field(fields[8]), Card::from_field(fields[9]) ]; - Game { + Game { players, rounds_cards, started: fields[10] as bool, finished: fields[11] as bool, claimed: fields[12] as bool, current_player: fields[13] as u32, - current_round: fields[14] as u32, + current_round: fields[14] as u32 } } @@ -74,7 +66,7 @@ fn serializeGame(game: Game) -> [Field; GAME_SERIALIZED_LEN] { game.finished as Field, game.claimed as Field, game.current_player as Field, - game.current_round as Field, + game.current_round as Field ] } @@ -169,4 +161,4 @@ impl Game { global GameSerializationMethods = TypeSerializationInterface { deserialize: deserializeGame, serialize: serializeGame, -}; \ No newline at end of file +}; diff --git a/yarn-project/noir-contracts/src/contracts/card_game_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/card_game_contract/src/main.nr index 732bf0f0c28..aa01e597e91 100644 --- a/yarn-project/noir-contracts/src/contracts/card_game_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/card_game_contract/src/main.nr @@ -11,7 +11,6 @@ contract CardGame { }, }; - use dep::std::option::Option; use dep::value_note::{ balance_utils, @@ -50,7 +49,7 @@ contract CardGame { GameSerializationMethods, GAME_SERIALIZED_LEN }; - + struct Storage { collections: Map, game_decks: Map>, @@ -107,10 +106,8 @@ contract CardGame { fn constructor() {} #[aztec(private)] - fn buy_pack( - seed: Field, // The randomness used to generate the cards. Passed in for now. - ) { - + fn buy_pack(seed: Field // The randomness used to generate the cards. Passed in for now. + ) { let buyer = context.msg_sender(); let mut cards = get_pack_cards(seed, buyer); @@ -119,12 +116,9 @@ contract CardGame { } #[aztec(private)] - fn join_game( - game: u32, - cards_fields: [Field; 2], - ) { + fn join_game(game: u32, cards_fields: [Field; 2]) { let cards = cards_fields.map(|card_field| Card::from_field(card_field)); - + let player = context.msg_sender(); let mut collection = storage.collections.at(player); @@ -133,54 +127,47 @@ contract CardGame { let _added_to_game_deck = game_deck.add_cards(cards, player); let selector = compute_selector("on_game_joined(u32,Field,u32)"); let strength = compute_deck_strength(cards); - context.call_public_function(context.this_address(), selector, [game as Field, player, strength]); + context.call_public_function(context.this_address(), + selector, + [game as Field, player, strength]); } #[aztec(public)] - internal fn on_game_joined( - game: u32, - player: Field, - deck_strength: u32, - ) { - + internal fn on_game_joined(game: u32, player: Field, deck_strength: u32) { let game_storage = storage.games.at(game as Field); let mut game_data = game_storage.read(); - assert(game_data.add_player(PlayerEntry {address: player, deck_strength, points: 0}), "Game full"); + assert(game_data.add_player(PlayerEntry { address: player, deck_strength, points: 0 }), "Game full"); game_storage.write(game_data); } #[aztec(public)] - fn start_game(game: u32) { - + fn start_game(game: u32) { let game_storage = storage.games.at(game as Field); - + let mut game_data = game_storage.read(); game_data.start_game(); game_storage.write(game_data); } #[aztec(private)] - fn play_card( - game: u32, - card: Card, - ) { - + fn play_card(game: u32, card: Card) { let player = context.msg_sender(); let mut game_deck = storage.game_decks.at(game as Field).at(player); game_deck.remove_cards([card], player); let selector = compute_selector("on_card_played(u32,Field,Field)"); - context.call_public_function(context.this_address(), selector, [game as Field, player, card.to_field()]); + context.call_public_function(context.this_address(), + selector, + [game as Field, player, card.to_field()]); } #[aztec(public)] internal fn on_card_played(game: u32, player: Field, card_as_field: Field) { - let game_storage = storage.games.at(game as Field); - + let mut game_data = game_storage.read(); let card = Card::from_field(card_as_field); @@ -192,11 +179,7 @@ contract CardGame { } #[aztec(private)] - fn claim_cards( - game: u32, - cards_fields: [Field; PLAYABLE_CARDS], - ) { - + fn claim_cards(game: u32, cards_fields: [Field; PLAYABLE_CARDS]) { let player = context.msg_sender(); let cards = cards_fields.map(|card_field| Card::from_field(card_field)); @@ -204,26 +187,20 @@ contract CardGame { let _inserted_cards = collection.add_cards(cards, player); let selector = compute_selector("on_cards_claimed(u32,Field,Field)"); - context.call_public_function( - context.this_address(), - selector, - [game as Field, player, pedersen_hash(cards_fields,0)] - ); + context.call_public_function(context.this_address(), + selector, + [game as Field, player, pedersen_hash(cards_fields, 0)]); } #[aztec(public)] internal fn on_cards_claimed(game: u32, player: Field, cards_hash: Field) { - let game_storage = storage.games.at(game as Field); let mut game_data = game_storage.read(); - + assert(!game_data.claimed, "Already claimed"); game_data.claimed = true; - assert_eq( - cards_hash, - pedersen_hash(game_data.rounds_cards.map(|card: Card| card.to_field()),0) - ); + assert_eq(cards_hash, pedersen_hash(game_data.rounds_cards.map(|card: Card| card.to_field()), 0)); let winner = game_data.winner(); assert_eq(player, winner.address, "Not the winner"); @@ -232,22 +209,20 @@ contract CardGame { } unconstrained fn view_collection_cards(owner: Field, offset: u32) -> [Option; MAX_NOTES_PER_PAGE] { - let collection = storage.collections.at(owner); collection.view_cards(offset) - } + } unconstrained fn view_game_cards(game: u32, player: Field, offset: u32) -> [Option; MAX_NOTES_PER_PAGE] { - let game_deck = storage.game_decks.at(game as Field).at(player); game_deck.view_cards(offset) - } + } unconstrained fn view_game(game: u32) -> Game { storage.games.at(game as Field).read() - } + } // Computes note hash and nullifier. // Note 1: Needs to be defined by every contract producing logs. diff --git a/yarn-project/noir-contracts/src/contracts/child_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/child_contract/src/main.nr index f1ecd53091e..c884aece908 100644 --- a/yarn-project/noir-contracts/src/contracts/child_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/child_contract/src/main.nr @@ -28,13 +28,11 @@ contract Child { } #[aztec(private)] - fn constructor() {} + fn constructor() {} // Returns a sum of the input and the chain id and version of the contract in private circuit public input's return_values. #[aztec(private)] - fn value( - input: Field, - ) -> Field { + fn value(input: Field) -> Field { input + context.chain_id() + context.version() } @@ -45,9 +43,7 @@ contract Child { // Returns a sum of the input and the chain id and version of the contract in private circuit public input's return_values. // Can only be called from this contract. #[aztec(private)] - fn valueInternal( - input: Field, - ) -> Field { + fn valueInternal(input: Field) -> Field { check_sender(inputs.call_context); input + context.chain_id() + context.version() } @@ -55,7 +51,11 @@ contract Child { // Returns base_value + chain_id + version + block_number + timestamp #[aztec(public)] fn pubGetValue(base_value: Field) -> Field { - let returnValue = base_value + context.chain_id() + context.version() + context.block_number() + context.timestamp(); + let returnValue = base_value + + context.chain_id() + + context.version() + + context.block_number() + + context.timestamp(); returnValue } @@ -63,17 +63,15 @@ contract Child { // Sets `current_value` to `new_value` #[aztec(public)] fn pubSetValue(new_value: Field) -> Field { - storage.current_value.write(new_value); emit_unencrypted_log(&mut context, new_value); - + new_value } // Increments `current_value` by `new_value` #[aztec(public)] fn pubIncValue(new_value: Field) -> Field { - let old_value = storage.current_value.read(); storage.current_value.write(old_value + new_value); emit_unencrypted_log(&mut context, new_value); @@ -84,12 +82,11 @@ contract Child { // Increments `current_value` by `new_value`. Can only be called from this contract. #[aztec(public)] fn pubIncValueInternal(new_value: Field) -> Field { - check_sender(inputs.call_context); let old_value = storage.current_value.read(); storage.current_value.write(old_value + new_value); emit_unencrypted_log(&mut context, new_value); - + new_value } @@ -98,14 +95,12 @@ contract Child { let pubSetValueSelector = compute_selector("pubSetValue(Field)"); let _ret = context.call_public_function(context.this_address(), pubSetValueSelector, [10]); - storage.current_value.write(20); emit_unencrypted_log(&mut context, 20); } #[aztec(public)] fn setValueTwiceWithNestedLast() { - storage.current_value.write(20); emit_unencrypted_log(&mut context, 20); diff --git a/yarn-project/noir-contracts/src/contracts/counter_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/counter_contract/src/main.nr index 18836e71827..e332d692bd8 100644 --- a/yarn-project/noir-contracts/src/contracts/counter_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/counter_contract/src/main.nr @@ -1,5 +1,5 @@ contract Counter { -// docs:start:imports + // docs:start:imports use dep::aztec::{ context::{PrivateContext, Context}, note::{ @@ -16,17 +16,17 @@ contract Counter { }, }; use dep::easy_private_state::easy_private_state::EasyPrivateUint; -// docs:end:imports + // docs:end:imports -// docs:start:storage_struct + // docs:start:storage_struct struct Storage { counters: Map, } -// docs:end:storage_struct + // docs:end:storage_struct -// docs:start:storage_init + // docs:start:storage_init impl Storage { fn init(context: Context) -> pub Self { Storage { @@ -40,40 +40,35 @@ contract Counter { } } } -// docs:end:storage_init + // docs:end:storage_init -// docs:start:constructor + // docs:start:constructor #[aztec(private)] fn constructor(headstart: u120, owner: Field) { let counters = storage.counters; counters.at(owner).add(headstart, owner); } -// docs:end:constructor + // docs:end:constructor -// docs:start:increment + // docs:start:increment #[aztec(private)] - fn increment(owner: Field) { + fn increment(owner: Field) { let counters = storage.counters; counters.at(owner).add(1, owner); } -// docs:end:increment + // docs:end:increment -// docs:start:get_counter + // docs:start:get_counter unconstrained fn get_counter(owner: Field) -> Field { - let counters = storage.counters; - balance_utils::get_balance(counters.at(owner).set) + let counters = storage.counters; + balance_utils::get_balance(counters.at(owner).set) } -// docs:end:get_counter + // docs:end:get_counter -// docs:start:nullifier - unconstrained fn compute_note_hash_and_nullifier( - contract_address: Field, - nonce: Field, - storage_slot: Field, - preimage: [Field; VALUE_NOTE_LEN], - ) -> [Field; 4] { - let note_header = NoteHeader::new(contract_address, nonce, storage_slot); - note_utils::compute_note_hash_and_nullifier(ValueNoteMethods, note_header, preimage) + // docs:start:nullifier + unconstrained fn compute_note_hash_and_nullifier(contract_address: Field, nonce: Field, storage_slot: Field, preimage: [Field; VALUE_NOTE_LEN]) -> [Field; 4] { + let note_header = NoteHeader::new(contract_address, nonce, storage_slot); + note_utils::compute_note_hash_and_nullifier(ValueNoteMethods, note_header, preimage) } -// docs:end:nullifier -} \ No newline at end of file + // docs:end:nullifier +} diff --git a/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/actions.nr b/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/actions.nr index 03bd8c2c0de..474fb20c6fc 100644 --- a/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/actions.nr +++ b/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/actions.nr @@ -38,10 +38,7 @@ pub fn get_current_queen(state_var: PublicState) -> } // docs:end:state_vars-PublicStateReadCustom -pub fn can_replace_queen( - state_var: PublicState, - new_queen: Queen, -) -> bool { +pub fn can_replace_queen(state_var: PublicState, new_queen: Queen) -> bool { let current_queen = get_current_queen(state_var); new_queen.points > current_queen.points } @@ -79,10 +76,7 @@ pub fn get_legendary_card(state_var: Singleton) -> Card // docs:end:state_vars-SingletonGet // docs:start:state_vars-ImmutableSingletonInit -pub fn init_game_rules( - state_var: ImmutableSingleton, - rules: &mut RulesNote, -) { +pub fn init_game_rules(state_var: ImmutableSingleton, rules: &mut RulesNote) { state_var.initialize(rules, Option::none(), true); } // docs:end:state_vars-ImmutableSingletonInit @@ -109,29 +103,20 @@ pub fn remove_card(state_var: Set, card: CardNote) { // docs:start:state_vars-SetGet pub fn get_cards( state_var: Set, - options: NoteGetterOptions, + options: NoteGetterOptions ) -> [Option; MAX_READ_REQUESTS_PER_CALL] { state_var.get_notes(options) } // docs:end:state_vars-SetGet // docs:start:state_vars-SetView -unconstrained pub fn view_cards( - state_var: Set, - options: NoteViewerOptions, -) -> [Option; MAX_NOTES_PER_PAGE] { +unconstrained pub fn view_cards(state_var: Set, options: NoteViewerOptions) -> [Option; MAX_NOTES_PER_PAGE] { state_var.view_notes(options) } // docs:end:state_vars-SetView -unconstrained pub fn get_total_points( - state_var: Set, - account: Field, - offset: u32, -) -> u8 { - let options = NoteViewerOptions::new() - .select(2, account) - .set_offset(offset); +unconstrained pub fn get_total_points(state_var: Set, account: Field, offset: u32) -> u8 { + let options = NoteViewerOptions::new().select(2, account).set_offset(offset); let mut total_points = 0; let notes = view_cards(state_var, options); for i in 0..notes.len() { @@ -146,20 +131,13 @@ unconstrained pub fn get_total_points( } // docs:start:state_vars-MapAtSingletonInit -pub fn add_new_profile( - state_var: Map>, - account: Field, - profile: &mut ProfileNote, -) { +pub fn add_new_profile(state_var: Map>, account: Field, profile: &mut ProfileNote) { state_var.at(account).initialize(profile, Option::some(account), true); } // docs:end:state_vars-MapAtSingletonInit // docs:start:state_vars-MapAtSingletonGet -pub fn get_profile( - state_var: Map>, - account: Field, -) -> ProfileNote { +pub fn get_profile(state_var: Map>, account: Field) -> ProfileNote { state_var.at(account).get_note(true) } // docs:end:state_vars-MapAtSingletonGet diff --git a/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/main.nr index d1877df1834..95eb42363cb 100644 --- a/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/main.nr @@ -91,11 +91,7 @@ contract DocsExample { global GET_POINTS_OF_COMMON_CARD_FUNCTION_SELECTOR = 11111111; #[aztec(private)] - fn constructor( - min_points: u8, - max_points: u8, - legendary_card_secret: Field, - ) { + fn constructor(min_points: u8, max_points: u8, legendary_card_secret: Field) { let mut game_rules = RulesNote::new(min_points, max_points, Option::some(0)); actions::init_game_rules(storage.game_rules, &mut game_rules); @@ -107,7 +103,7 @@ contract DocsExample { #[aztec(public)] fn lock() { // highlight-next-line:storage-init - + storage.locked.write(true); } // docs:end:storage-init @@ -115,30 +111,22 @@ contract DocsExample { // docs:start:functions-OpenFunction #[aztec(public)] fn unlock() { - actions::unlock(storage.locked); } // docs:end:functions-OpenFunction #[aztec(public)] - fn replace_queen( - account: Field, - points: u8, - ) { - - + fn replace_queen(account: Field, points: u8) { let new_queen = Queen { account, points }; - + assert(actions::can_replace_queen(storage.queen, new_queen)); - + actions::replace_queen(storage.queen, new_queen); } // docs:start:state_vars-PublicStateWriteBeforeCall #[aztec(public)] fn replace_queen_unsafe() { - - let account = context.msg_sender(); let points = actions::get_total_points(storage.cards, account, 0); @@ -147,7 +135,7 @@ contract DocsExample { assert(points > current_queen.points); AccountContractInterface::at(account).send_rewards(current_queen.points); - + let new_queen = Queen { account, points }; storage.queen.write(new_queen); } @@ -156,8 +144,6 @@ contract DocsExample { // docs:start:functions-SecretFunction #[aztec(private)] fn add_common_cards(secrets: [Field; 4]) { - - for i in 0..secrets.len() as u8 { let mut card = CardNote::new(0, secrets[i], 0); actions::add_new_card(storage.cards, &mut card); @@ -166,12 +152,7 @@ contract DocsExample { // docs:end:functions-SecretFunction #[aztec(private)] - fn update_legendary_card( - new_points: u8, - new_secret: Field, - ) { - - + fn update_legendary_card(new_points: u8, new_secret: Field) { let owner = inputs.call_context.msg_sender; let mut updated_card = CardNote::new(new_points, new_secret, owner); @@ -182,32 +163,21 @@ contract DocsExample { #[aztec(private)] fn become_queen() { - - let legendary_card = actions::get_legendary_card(storage.legendary_card); let owner = legendary_card.owner; - let result = context.call_private_function( - inputs.call_context.storage_contract_address, + let result = context.call_private_function(inputs.call_context.storage_contract_address, GET_POINTS_OF_COMMON_CARD_FUNCTION_SELECTOR, - [owner, 0] - ); + [owner, 0]); let total_points = legendary_card.points + result[0] as u8; - context.call_public_function( - inputs.call_context.storage_contract_address, + context.call_public_function(inputs.call_context.storage_contract_address, REPLACE_QUEEN_FUNCTION_SELECTOR, - [owner, total_points as Field] - ); + [owner, total_points as Field]); } #[aztec(private)] - fn get_points_of_common_cards( - account: Field, - offset: u32, - ) { - - + fn get_points_of_common_cards(account: Field, offset: u32) { let mut total_points = 0; let options = create_account_card_getter_options(account, offset); let cards = actions::get_cards(storage.cards, options); @@ -222,13 +192,11 @@ contract DocsExample { context.return_values.push(total_points as Field); } - // docs:start:functions-UncontrainedFunction + // docs:start:functions-UnconstrainedFunction unconstrained fn get_total_points(account: Field) -> u8 { - actions::get_total_points(storage.cards, account, 0) } - // docs:end:functions-UncontrainedFunction - + // docs:end:functions-UnconstrainedFunction /// Macro equivalence section use dep::aztec::abi; @@ -243,7 +211,6 @@ contract DocsExample { } // docs:end:simple_macro_example - // docs:start:simple_macro_example_expanded fn simple_macro_example_expanded( // ************************************************************ @@ -255,9 +222,7 @@ contract DocsExample { // Our original inputs! a: Field, - b: Field - - // The actual return type of our circuit is the PrivateCircuitPublicInputs struct, this will be the + b: Field // The actual return type of our circuit is the PrivateCircuitPublicInputs struct, this will be the // input to our kernel! // docs:start:context-example-return ) -> distinct pub abi::PrivateCircuitPublicInputs { @@ -297,15 +262,11 @@ contract DocsExample { } // docs:end:simple_macro_example_expanded - // Cross chain messaging section // Demonstrates a cross chain message // docs:start:l1_to_l2_cross_chain_message #[aztec(private)] - fn send_to_l1() { - - - } + fn send_to_l1() {} // docs:end:l1_to_l2_cross_chain_message // TODO: remove this placeholder once https://github.com/AztecProtocol/aztec-packages/issues/2918 is implemented diff --git a/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/options.nr b/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/options.nr index 636a6de3609..c225942b9fb 100644 --- a/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/options.nr +++ b/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/options.nr @@ -4,35 +4,19 @@ use dep::aztec::note::note_getter_options::{NoteGetterOptions, Sort, SortOrder}; use dep::std::option::Option; // docs:start:state_vars-NoteGetterOptionsSelectSortOffset -pub fn create_account_card_getter_options( - account_address: Field, - offset: u32, -) -> NoteGetterOptions { - NoteGetterOptions::new() - .select(2, account_address) - .sort(0, SortOrder.DESC) - .set_offset(offset) +pub fn create_account_card_getter_options(account_address: Field, offset: u32) -> NoteGetterOptions { + NoteGetterOptions::new().select(2, account_address).sort(0, SortOrder.DESC).set_offset(offset) } // docs:end:state_vars-NoteGetterOptionsSelectSortOffset // docs:start:state_vars-NoteGetterOptionsMultiSelects -pub fn create_exact_card_getter_options( - points: u8, - secret: Field, - account_address: Field, -) -> NoteGetterOptions { - NoteGetterOptions::new() - .select(0, points as Field) - .select(1, secret) - .select(2, account_address) +pub fn create_exact_card_getter_options(points: u8, secret: Field, account_address: Field) -> NoteGetterOptions { + NoteGetterOptions::new().select(0, points as Field).select(1, secret).select(2, account_address) } // docs:end:state_vars-NoteGetterOptionsMultiSelects // docs:start:state_vars-OptionFilter -pub fn filter_min_points( - cards: [Option; MAX_READ_REQUESTS_PER_CALL], - min_points: u8, -) -> [Option; MAX_READ_REQUESTS_PER_CALL] { +pub fn filter_min_points(cards: [Option; MAX_READ_REQUESTS_PER_CALL], min_points: u8) -> [Option; MAX_READ_REQUESTS_PER_CALL] { let mut selected_cards = [Option::none(); MAX_READ_REQUESTS_PER_CALL]; let mut num_selected = 0; for i in 0..cards.len() { @@ -46,23 +30,13 @@ pub fn filter_min_points( // docs:end:state_vars-OptionFilter // docs:start:state_vars-NoteGetterOptionsFilter -pub fn create_account_cards_with_min_points_getter_options( - account_address: Field, - min_points: u8, -) -> NoteGetterOptions { - NoteGetterOptions::with_filter(filter_min_points, min_points) - .select(2, account_address) - .sort(0, SortOrder.ASC) +pub fn create_account_cards_with_min_points_getter_options(account_address: Field, min_points: u8) -> NoteGetterOptions { + NoteGetterOptions::with_filter(filter_min_points, min_points).select(2, account_address).sort(0, SortOrder.ASC) } // docs:end:state_vars-NoteGetterOptionsFilter // docs:start:state_vars-NoteGetterOptionsPickOne -pub fn create_largest_account_card_getter_options( - account_address: Field, -) -> NoteGetterOptions { - NoteGetterOptions::new() - .select(2, account_address) - .sort(0, SortOrder.DESC) - .set_limit(1) +pub fn create_largest_account_card_getter_options(account_address: Field) -> NoteGetterOptions { + NoteGetterOptions::new().select(2, account_address).sort(0, SortOrder.DESC).set_limit(1) } // docs:end:state_vars-NoteGetterOptionsPickOne diff --git a/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/types.nr b/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/types.nr index 9e0784c92ca..b8bf6dc7bfd 100644 --- a/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/types.nr +++ b/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/types.nr @@ -1,4 +1,4 @@ mod card_note; mod profile_note; mod queen; -mod rules_note; \ No newline at end of file +mod rules_note; diff --git a/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/types/queen.nr b/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/types/queen.nr index 635205ecd6f..488ee5f52bc 100644 --- a/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/types/queen.nr +++ b/yarn-project/noir-contracts/src/contracts/docs_example_contract/src/types/queen.nr @@ -11,10 +11,7 @@ struct Queen { global QUEEN_SERIALIZED_LEN: Field = 2; fn deserialize(fields: [Field; QUEEN_SERIALIZED_LEN]) -> Queen { - Queen { - account: fields[0], - points: fields[1] as u8, - } + Queen { account: fields[0], points: fields[1] as u8 } } fn serialize(queen: Queen) -> [Field; QUEEN_SERIALIZED_LEN] { diff --git a/yarn-project/noir-contracts/src/contracts/easy_private_token_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/easy_private_token_contract/src/main.nr index fc3ebaf65ac..859d6859bf7 100644 --- a/yarn-project/noir-contracts/src/contracts/easy_private_token_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/easy_private_token_contract/src/main.nr @@ -38,13 +38,9 @@ contract EasyPrivateToken { /** * initialize the contract's initial state variables. - */ + */ #[aztec(private)] - fn constructor( - initial_supply: u120, - owner: Field, - ) { - + fn constructor(initial_supply: u120, owner: Field) { let balances = storage.balances; balances.at(owner).add(initial_supply, owner); @@ -52,11 +48,7 @@ contract EasyPrivateToken { // Mints `amount` of tokens to `owner`. #[aztec(private)] - fn mint( - amount: u120, - owner: Field, - ) { - + fn mint(amount: u120, owner: Field) { let balances = storage.balances; balances.at(owner).add(amount, owner); @@ -64,12 +56,7 @@ contract EasyPrivateToken { // Transfers `amount` of tokens from `sender` to a `recipient`. #[aztec(private)] - fn transfer( - amount: u120, - sender: Field, - recipient: Field, - ) { - + fn transfer(amount: u120, sender: Field, recipient: Field) { let balances = storage.balances; balances.at(sender).sub(amount, sender); @@ -77,10 +64,7 @@ contract EasyPrivateToken { } // Helper function to get the balance of a user ("unconstrained" is a Noir alternative of Solidity's "view" function). - unconstrained fn getBalance( - owner: Field, - ) -> Field { - + unconstrained fn getBalance(owner: Field) -> Field { let balances = storage.balances; // Return the sum of all notes in the set. diff --git a/yarn-project/noir-contracts/src/contracts/ecdsa_account_contract/src/ecdsa_public_key_note.nr b/yarn-project/noir-contracts/src/contracts/ecdsa_account_contract/src/ecdsa_public_key_note.nr index 18d45ad39dd..436199240ad 100644 --- a/yarn-project/noir-contracts/src/contracts/ecdsa_account_contract/src/ecdsa_public_key_note.nr +++ b/yarn-project/noir-contracts/src/contracts/ecdsa_account_contract/src/ecdsa_public_key_note.nr @@ -89,23 +89,22 @@ impl EcdsaPublicKeyNote { } fn deserialize(serialized_note: [Field; ECDSA_PUBLIC_KEY_NOTE_LEN]) -> EcdsaPublicKeyNote { - let mut x: [u8; 32] = [0;32]; - let mut y: [u8; 32] = [0;32]; + let mut x: [u8; 32] = [0; 32]; + let mut y: [u8; 32] = [0; 32]; let part_x = serialized_note[0].to_be_bytes(32); - for i in 0..31 { x[i] = part_x[i + 1]; } + for i in 0..31 { + x[i] = part_x[i + 1]; + } x[31] = serialized_note[1].to_be_bytes(32)[31]; let part_y = serialized_note[2].to_be_bytes(32); - for i in 0..31 { y[i] = part_y[i + 1]; } + for i in 0..31 { + y[i] = part_y[i + 1]; + } y[31] = serialized_note[3].to_be_bytes(32)[31]; - EcdsaPublicKeyNote { - x, - y, - owner: serialized_note[4], - header: NoteHeader::empty(), - } + EcdsaPublicKeyNote { x, y, owner: serialized_note[4], header: NoteHeader::empty() } } fn serialize(note: EcdsaPublicKeyNote) -> [Field; ECDSA_PUBLIC_KEY_NOTE_LEN] { @@ -114,7 +113,7 @@ fn serialize(note: EcdsaPublicKeyNote) -> [Field; ECDSA_PUBLIC_KEY_NOTE_LEN] { fn compute_note_hash(note: EcdsaPublicKeyNote) -> Field { // TODO(#1205) Should use a non-zero generator index. - pedersen_hash(note.serialize(),0) + pedersen_hash(note.serialize(), 0) } fn compute_nullifier(note: EcdsaPublicKeyNote) -> Field { diff --git a/yarn-project/noir-contracts/src/contracts/ecdsa_account_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/ecdsa_account_contract/src/main.nr index 4dabf31b3b6..7706217e639 100644 --- a/yarn-project/noir-contracts/src/contracts/ecdsa_account_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/ecdsa_account_contract/src/main.nr @@ -41,10 +41,7 @@ contract EcdsaAccount { // Creates a new account out of an ECDSA public key to use for signature verification #[aztec(private)] - fn constructor( - signing_pub_key_x: pub [u8;32], - signing_pub_key_y: pub [u8;32], - ) { + fn constructor(signing_pub_key_x: pub [u8;32], signing_pub_key_y: pub [u8;32]) { let this = context.this_address(); let mut pub_key_note = EcdsaPublicKeyNote::new(signing_pub_key_x, signing_pub_key_y, this); storage.public_key.initialize(&mut pub_key_note, Option::none(), true); @@ -79,11 +76,13 @@ contract EcdsaAccount { // Load public key from storage let storage = Storage::init(Context::private(context)); let public_key = storage.public_key.get_note(); - + // Load auth witness let witness: [Field; 64] = get_auth_witness(message_field); let mut signature: [u8; 64] = [0; 64]; - for i in 0..64 { signature[i] = witness[i] as u8; } + for i in 0..64 { + signature[i] = witness[i] as u8; + } // Verify payload signature using Ethereum's signing scheme // Note that noir expects the hash of the message/challenge as input to the ECDSA verification. @@ -97,7 +96,12 @@ contract EcdsaAccount { // Computes note hash and nullifier. // Note 1: Needs to be defined by every contract producing logs. // Note 2: Having it in all the contracts gives us the ability to compute the note hash and nullifier differently for different kind of notes. - unconstrained fn compute_note_hash_and_nullifier(contract_address: Field, nonce: Field, storage_slot: Field, serialized_note: [Field; ECDSA_PUBLIC_KEY_NOTE_LEN]) -> [Field; 4] { + unconstrained fn compute_note_hash_and_nullifier( + contract_address: Field, + nonce: Field, + storage_slot: Field, + serialized_note: [Field; ECDSA_PUBLIC_KEY_NOTE_LEN] + ) -> [Field; 4] { assert(storage_slot == 1); let note_header = NoteHeader::new(contract_address, nonce, storage_slot); note_utils::compute_note_hash_and_nullifier(EcdsaPublicKeyNoteInterface, note_header, serialized_note) diff --git a/yarn-project/noir-contracts/src/contracts/escrow_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/escrow_contract/src/main.nr index 49bedc9234e..befda626373 100644 --- a/yarn-project/noir-contracts/src/contracts/escrow_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/escrow_contract/src/main.nr @@ -35,11 +35,9 @@ contract Escrow { // Creates a new instance // docs:start:constructor #[aztec(private)] - fn constructor( - owner: pub Field - ) { + fn constructor(owner: pub Field) { let this = context.this_address(); - + // Create a new note and add it to the owners set. let mut note = AddressNote::new(owner, this); @@ -50,11 +48,7 @@ contract Escrow { // Withdraws balance. Requires that msg.sender is registered as an owner. #[aztec(private)] - fn withdraw( - token: Field, - amount: Field, - recipient: Field, - ) { + fn withdraw(token: Field, amount: Field, recipient: Field) { let this = context.this_address(); let sender = context.msg_sender(); @@ -64,11 +58,7 @@ contract Escrow { assert(notes[0].is_some(), "Sender is not an owner."); let selector = compute_selector("transfer((Field),(Field),Field,Field)"); - let _callStackItem = context.call_private_function( - token, - selector, - [this, recipient, amount, 0] - ); + let _callStackItem = context.call_private_function(token, selector, [this, recipient, amount, 0]); } unconstrained fn compute_note_hash_and_nullifier(contract_address: Field, nonce: Field, storage_slot: Field, serialized_note: [Field; ADDRESS_NOTE_LEN]) -> [Field; 4] { diff --git a/yarn-project/noir-contracts/src/contracts/import_test_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/import_test_contract/src/main.nr index 599a28218fb..abf55cd9ba6 100644 --- a/yarn-project/noir-contracts/src/contracts/import_test_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/import_test_contract/src/main.nr @@ -12,38 +12,33 @@ contract ImportTest { ManyNotesADeepStructTestCodeGenStruct, }; - #[aztec(private)] fn constructor( ) {} - + // Calls the testCodeGen on the Test contract at the target address // Used for testing calling a function with arguments of multiple types // See yarn-project/acir-simulator/src/client/private_execution.ts // See yarn-project/end-to-end/src/e2e_nested_contract.test.ts #[aztec(private)] - fn main( - target: Field - ) -> Field { + fn main(target: Field) -> Field { let test_contract_instance = TestPrivateContextInterface::at(target); - let return_values = test_contract_instance.test_code_gen( - &mut context, + let return_values = test_contract_instance.test_code_gen(&mut context, 1, true, 1 as u32, [1, 2], AStructTestCodeGenStruct { amount: 1, secret_hash: 2 }, ADeepStructTestCodeGenStruct { - a_field: 1, - a_bool: true, + a_field: 1, + a_bool: true, a_note: ANoteADeepStructTestCodeGenStruct { amount: 1, secret_hash: 2 }, many_notes: [ ManyNotesADeepStructTestCodeGenStruct { amount: 1, secret_hash: 2 }, ManyNotesADeepStructTestCodeGenStruct { amount: 1, secret_hash: 2 }, - ManyNotesADeepStructTestCodeGenStruct { amount: 1, secret_hash: 2 }, + ManyNotesADeepStructTestCodeGenStruct { amount: 1, secret_hash: 2 } ] - } - ); + }); return_values[0] } @@ -52,12 +47,10 @@ contract ImportTest { // Used for testing calling a function with no arguments // See yarn-project/end-to-end/src/e2e_nested_contract.test.ts #[aztec(private)] - fn callNoArgs( - target: Field - ) -> Field { + fn callNoArgs(target: Field) -> Field { let test_contract_instance = TestPrivateContextInterface::at(target); let return_values = test_contract_instance.get_this_address(&mut context); - + return_values[0] } @@ -65,9 +58,7 @@ contract ImportTest { // Used for testing calling an open function // See yarn-project/end-to-end/src/e2e_nested_contract.test.ts #[aztec(private)] - fn callOpenFn( - target: Field, - ) { + fn callOpenFn(target: Field) { let test_contract_instance = TestPrivateContextInterface::at(target); test_contract_instance.create_nullifier_public(&mut context, 1, 2); } @@ -76,12 +67,10 @@ contract ImportTest { // Used for testing calling an open function from another open function // See yarn-project/end-to-end/src/e2e_nested_contract.test.ts #[aztec(public)] - fn pubCallOpenFn( - target: Field, - ) -> Field { + fn pubCallOpenFn(target: Field) -> Field { let test_contract_instance = TestPublicContextInterface::at(target); let ret = test_contract_instance.create_nullifier_public(context, 1, 2); - + ret[0] } } diff --git a/yarn-project/noir-contracts/src/contracts/lending_contract/src/asset.nr b/yarn-project/noir-contracts/src/contracts/lending_contract/src/asset.nr index cb8dcd90ad4..e4158fda7f2 100644 --- a/yarn-project/noir-contracts/src/contracts/lending_contract/src/asset.nr +++ b/yarn-project/noir-contracts/src/contracts/lending_contract/src/asset.nr @@ -21,7 +21,7 @@ fn deserializeAsset(fields: [Field; ASSET_SERIALIZED_LEN]) -> Asset { interest_accumulator: fields[0] as u120, last_updated_ts: fields[1] as u120, loan_to_value: fields[2] as u120, - oracle_address: fields[3], + oracle_address: fields[3] } } @@ -30,7 +30,7 @@ fn serializeAsset(asset: Asset) -> [Field; ASSET_SERIALIZED_LEN] { asset.interest_accumulator as Field, asset.last_updated_ts as Field, asset.loan_to_value as Field, - asset.oracle_address, + asset.oracle_address ] } diff --git a/yarn-project/noir-contracts/src/contracts/lending_contract/src/helpers.nr b/yarn-project/noir-contracts/src/contracts/lending_contract/src/helpers.nr index eb20e2d5bdb..26592ff6d66 100644 --- a/yarn-project/noir-contracts/src/contracts/lending_contract/src/helpers.nr +++ b/yarn-project/noir-contracts/src/contracts/lending_contract/src/helpers.nr @@ -5,33 +5,23 @@ use dep::aztec::hash::pedersen_hash; // Utility used to easily get a "id" for a private user that sits in the same // "space" as the public users. // It help us to have a single mapping for collateral that have both public and private users. -pub fn compute_identifier( - secret: Field, - on_behalf_of: Field, - self: Field, -) -> Field { +pub fn compute_identifier(secret: Field, on_behalf_of: Field, self: Field) -> Field { // EITHER secret OR on_behalf_of MUST be set. But not both - assert (!((secret == 0) as bool & (on_behalf_of == 0) as bool)); + assert(!((secret == 0) as bool & (on_behalf_of == 0) as bool)); if (secret != 0) { - pedersen_hash([self, secret],0) + pedersen_hash([self, secret], 0) } else { on_behalf_of } } -pub fn covered_by_collateral( - price: u120, - loan_to_value: u120, - collateral: u120, - increase: u120, - decrease: u120, -) -> u120 { - let price_precision = SafeU120{value: 1000000000}; - let ltv_precision = SafeU120{value: 10000}; +pub fn covered_by_collateral(price: u120, loan_to_value: u120, collateral: u120, increase: u120, decrease: u120) -> u120 { + let price_precision = SafeU120 { value: 1000000000 }; + let ltv_precision = SafeU120 { value: 10000 }; - let price = SafeU120{value: price}; - let collateral = SafeU120{value: collateral}.add(SafeU120{value:increase}).sub(SafeU120{value:decrease}); - let loan_to_value = SafeU120{value: loan_to_value}; + let price = SafeU120 { value: price }; + let collateral = SafeU120 { value: collateral }.add(SafeU120 { value: increase }).sub(SafeU120 { value: decrease }); + let loan_to_value = SafeU120 { value: loan_to_value }; let collateral_value = collateral.mul_div(price, price_precision); let debt_covered = collateral_value.mul_div(loan_to_value, ltv_precision); @@ -44,19 +34,14 @@ struct DebtReturn { static_debt: u120, } -pub fn debt_updates( - interest_accumulator: u120, - static_debt: u120, - increase: u120, - decrease: u120, -) -> DebtReturn { +pub fn debt_updates(interest_accumulator: u120, static_debt: u120, increase: u120, decrease: u120) -> DebtReturn { assert(interest_accumulator > 0); - let accumulator_precision = SafeU120{value: 1000000000}; + let accumulator_precision = SafeU120 { value: 1000000000 }; - let static_debt = SafeU120{value: static_debt}; - let interest_accumulator = SafeU120{value: interest_accumulator}; - let increase = SafeU120{value: increase}; - let decrease = SafeU120{value: decrease}; + let static_debt = SafeU120 { value: static_debt }; + let interest_accumulator = SafeU120 { value: interest_accumulator }; + let increase = SafeU120 { value: increase }; + let decrease = SafeU120 { value: decrease }; let current_debt_value = static_debt.mul_div(interest_accumulator, accumulator_precision); let new_debt_value = current_debt_value.add(increase).sub(decrease); @@ -71,18 +56,12 @@ pub fn debt_updates( // if you try to repay exact due to time diff between sim and execution. let new_static_debt = static_debt.add(static_debt_increase).sub(static_debt_decrease); - DebtReturn { - debt_value: new_debt_value.value, - static_debt: new_static_debt.value, - } + DebtReturn { debt_value: new_debt_value.value, static_debt: new_static_debt.value } } -pub fn debt_value( - static_debt: u120, - interest_accumulator: u120, -) -> u120 { - let static_debt = SafeU120{value: static_debt}; - let accumulator_precision = SafeU120{value: 1000000000}; - let interest_accumulator = SafeU120{value: interest_accumulator}; +pub fn debt_value(static_debt: u120, interest_accumulator: u120) -> u120 { + let static_debt = SafeU120 { value: static_debt }; + let accumulator_precision = SafeU120 { value: 1000000000 }; + let interest_accumulator = SafeU120 { value: interest_accumulator }; static_debt.mul_div_up(interest_accumulator, accumulator_precision).value -} \ No newline at end of file +} diff --git a/yarn-project/noir-contracts/src/contracts/lending_contract/src/interest_math.nr b/yarn-project/noir-contracts/src/contracts/lending_contract/src/interest_math.nr index 735e6ce9e7a..9f9d3c518fb 100644 --- a/yarn-project/noir-contracts/src/contracts/lending_contract/src/interest_math.nr +++ b/yarn-project/noir-contracts/src/contracts/lending_contract/src/interest_math.nr @@ -1,23 +1,20 @@ use dep::safe_math::SafeU120; // Binomial approximation of exponential -// using lower than decired precisions for everything due to u120 limit +// using lower than desired precisions for everything due to u120 limit // (1+x)^n = 1+n*x+[n/2*(n-1)]*x^2+[n/6*(n-1)*(n-2)*x^3]... // we are loosing around almost 8 digits of precision from yearly -> daily interest // dividing with 31536000 (seconds per year). // rate must be measured with higher precision than 10^9. // we use e18, and rates >= 4% yearly. Otherwise need more precision -pub fn compute_multiplier( - rate_per_second: u120, - dt: SafeU120, -) -> SafeU120 { - let base = SafeU120{value: 1000000000}; // 1e9 - let WAD = SafeU120{value: 1000000000000000000}; // 1e18 +pub fn compute_multiplier(rate_per_second: u120, dt: SafeU120) -> SafeU120 { + let base = SafeU120 { value: 1000000000 }; // 1e9 + let WAD = SafeU120 { value: 1000000000000000000 }; // 1e18 let diff = WAD.div(base); let mut res = base; if (!dt.is_zero()) { - let exp_minus_one = SafeU120{value: dt.value - 1}; - let exp_minus_two = SafeU120 {value: if (dt.value > 2) {dt.value - 2} else { 0 }}; + let exp_minus_one = SafeU120 { value: dt.value - 1 }; + let exp_minus_two = SafeU120 { value: if (dt.value > 2) { dt.value - 2 } else { 0 } }; // if rate_per_second < sqrt(WAD), then base_power_two and base_power_three = 0 let rate = SafeU120 { value: rate_per_second }; @@ -25,8 +22,8 @@ pub fn compute_multiplier( let base_power_three = base_power_two.mul_div(rate, WAD); let temp = dt.mul(exp_minus_one); - let second_term = temp.mul(base_power_two).div(SafeU120 {value: 2}); - let third_term = temp.mul(exp_minus_two).mul(base_power_three).div(SafeU120{value: 6}); + let second_term = temp.mul(base_power_two).div(SafeU120 { value: 2 }); + let third_term = temp.mul(exp_minus_two).mul(base_power_three).div(SafeU120 { value: 6 }); // throwing away precision to keep us under u120 :sob: let offset = dt.mul(rate).add(second_term).add(third_term).div(diff); @@ -34,4 +31,4 @@ pub fn compute_multiplier( res = base.add(offset); } res -} \ No newline at end of file +} diff --git a/yarn-project/noir-contracts/src/contracts/lending_contract/src/interfaces.nr b/yarn-project/noir-contracts/src/contracts/lending_contract/src/interfaces.nr index b4f0634f723..53385ed3e99 100644 --- a/yarn-project/noir-contracts/src/contracts/lending_contract/src/interfaces.nr +++ b/yarn-project/noir-contracts/src/contracts/lending_contract/src/interfaces.nr @@ -37,7 +37,7 @@ impl Token { } pub fn transfer_public(self: Self, context: PublicContext, from: Field, to: Field, amount: Field, nonce: Field) { - let _transfer_return_values = context.call_public_function( + context.call_public_function( self.address, compute_selector("transfer_public((Field),(Field),Field,Field)"), [from, to, amount, nonce] @@ -45,15 +45,15 @@ impl Token { } pub fn mint_public(self: Self, context: PublicContext, to: Field, amount: Field) { - let _return_values = context.call_public_function( + context.call_public_function( self.address, compute_selector("mint_public((Field),Field)"), [to, amount] ); } - pub fn burn_public(self: Self, context: PublicContext, from: Field, amount: Field, nonce: Field){ - let _return_values = context.call_public_function( + pub fn burn_public(self: Self, context: PublicContext, from: Field, amount: Field, nonce: Field) { + context.call_public_function( self.address, compute_selector("burn_public((Field),Field,Field)"), [from, amount, nonce] @@ -100,4 +100,4 @@ impl Lending { oracle_address: return_values[3], } } -} \ No newline at end of file +} diff --git a/yarn-project/noir-contracts/src/contracts/lending_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/lending_contract/src/main.nr index f9f32af3b84..8a24bd2e2b9 100644 --- a/yarn-project/noir-contracts/src/contracts/lending_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/lending_contract/src/main.nr @@ -101,46 +101,36 @@ contract Lending { ) {} #[aztec(public)] - fn init( - oracle_address: Field, - loan_to_value: Field, - collateral_asset: Field, - stable_coin: Field, - ) -> Field { - + fn init(oracle_address: Field, loan_to_value: Field, collateral_asset: Field, stable_coin: Field) { let asset_loc = storage.assets.at(0); let asset = asset_loc.read(); - assert (loan_to_value as u120 <= 10000); - assert (asset.last_updated_ts == 0); - assert (asset.interest_accumulator == 0); + assert(loan_to_value as u120 <= 10000); + assert(asset.last_updated_ts == 0); + assert(asset.interest_accumulator == 0); asset_loc.write(Asset { - interest_accumulator: 1000000000, - last_updated_ts: context.timestamp() as u120, - loan_to_value: loan_to_value as u120, - oracle_address, - }); + interest_accumulator: 1000000000, + last_updated_ts: context.timestamp() as u120, + loan_to_value: loan_to_value as u120, + oracle_address + }); storage.collateral_asset.write(collateral_asset); storage.stable_coin.write(stable_coin); - - 1 } // Create a position. #[aztec(public)] fn update_accumulator() -> Asset { - - let asset_loc = storage.assets.at(0); let mut asset = asset_loc.read(); - let dt: SafeU120 = SafeU120{value: context.timestamp() as u120}.sub(SafeU120{value: asset.last_updated_ts}); + let dt: SafeU120 = SafeU120 { value: context.timestamp() as u120 }.sub(SafeU120 { value: asset.last_updated_ts }); // Only update if time has passed. if (!dt.is_zero()) { - let precision: SafeU120 = SafeU120{value: 1000000000}; + let precision: SafeU120 = SafeU120 { value: 1000000000 }; let rate_per_second: u120 = 1268391679; // 4% yearly rate / (60 * 60 * 24 * 365) // if rate_per_second < sqrt(WAD) our approx is eq precision + rate * dt let multiplier = compute_multiplier(rate_per_second, dt); @@ -156,43 +146,26 @@ contract Lending { } #[aztec(private)] - fn deposit_private( - from: Field, - amount: Field, - nonce: Field, - secret: Field, - on_behalf_of: Field, - collateral_asset: Field, - ) { + fn deposit_private(from: Field, amount: Field, nonce: Field, secret: Field, on_behalf_of: Field, collateral_asset: Field) { let on_behalf_of = compute_identifier(secret, on_behalf_of, context.msg_sender()); let _res = Token::at(collateral_asset).unshield(&mut context, from, context.this_address(), amount, nonce); // _deposit(on_behalf_of, amount, collateral_asset) let selector = compute_selector("_deposit(Field,Field,Field)"); - let _callStackItem2 = context.call_public_function(context.this_address(), selector, [on_behalf_of, amount, collateral_asset]); + context.call_public_function(context.this_address(), + selector, + [on_behalf_of, amount, collateral_asset]); } #[aztec(public)] - fn deposit_public( - amount: Field, - nonce: Field, - on_behalf_of: Field, - collateral_asset: Field, - ) -> Field { + fn deposit_public(amount: Field, nonce: Field, on_behalf_of: Field, collateral_asset: Field) { Token::at(collateral_asset).transfer_public(context, context.msg_sender(), context.this_address(), amount, nonce); let selector = compute_selector("_deposit(Field,Field,Field)"); - let return_values = context.call_public_function(context.this_address(), selector, [on_behalf_of, amount, collateral_asset]); - - return_values[0] + context.call_public_function(context.this_address(), selector, [on_behalf_of, amount, collateral_asset]); } #[aztec(public)] - internal fn _deposit( - owner: Field, - amount: Field, - collateral_asset: Field, - ) -> Field { + internal fn _deposit(owner: Field, amount: Field, collateral_asset: Field) { let _asset = Lending::at(context.this_address()).update_accumulator(context); - let coll_asset = storage.collateral_asset.read(); assert(coll_asset == collateral_asset); @@ -200,42 +173,25 @@ contract Lending { let coll_loc = storage.collateral.at(owner); let collateral = coll_loc.read(); coll_loc.write(collateral + amount); - - 1 } #[aztec(private)] - fn withdraw_private( - secret: Field, - to: Field, - amount: Field - ) { + fn withdraw_private(secret: Field, to: Field, amount: Field) { let on_behalf_of = compute_identifier(secret, 0, context.msg_sender()); let selector = compute_selector("_withdraw(Field,Field,Field)"); - let _callStackItem = context.call_public_function(context.this_address(), selector, [on_behalf_of, to, amount]); + context.call_public_function(context.this_address(), selector, [on_behalf_of, to, amount]); } #[aztec(public)] - fn withdraw_public( - to: Field, - amount: Field, - ) -> Field { + fn withdraw_public(to: Field, amount: Field) { let selector = compute_selector("_withdraw(Field,Field,Field)"); - let return_values = context.call_public_function(context.this_address(), selector, [context.msg_sender(), to, amount]); - - return_values[0] + context.call_public_function(context.this_address(), selector, [context.msg_sender(), to, amount]); } #[aztec(public)] - internal fn _withdraw( - owner: Field, - recipient: Field, - amount: Field - ) -> Field { + internal fn _withdraw(owner: Field, recipient: Field, amount: Field) { let asset = Lending::at(context.this_address()).update_accumulator(context); let price = PriceFeed::at(asset.oracle_address).get_price(context); - - let coll_loc = storage.collateral.at(owner); let collateral: Field = coll_loc.read(); @@ -248,49 +204,32 @@ contract Lending { let debt_covered = covered_by_collateral(price, asset.loan_to_value, collateral as u120, 0, amount as u120); let debt_returns = debt_updates(asset.interest_accumulator, static_debt as u120, 0, 0); - assert (debt_returns.debt_value < debt_covered); + assert(debt_returns.debt_value < debt_covered); coll_loc.write(collateral - amount); // @todo @LHerskind Support both shielding and transfers (for now just transfer) let collateral_asset = storage.collateral_asset.read(); Token::at(collateral_asset).transfer_public(context, context.this_address(), recipient, amount, 0); - - 1 } #[aztec(private)] - fn borrow_private( - secret: Field, - to: Field, - amount: Field - ) { + fn borrow_private(secret: Field, to: Field, amount: Field) { let on_behalf_of = compute_identifier(secret, 0, context.msg_sender()); let selector = compute_selector("_borrow(Field,Field,Field)"); - let _callStackItem = context.call_public_function(context.this_address(), selector, [on_behalf_of, to, amount]); + context.call_public_function(context.this_address(), selector, [on_behalf_of, to, amount]); } #[aztec(public)] - fn borrow_public( - to: Field, - amount: Field - ) -> Field { + fn borrow_public(to: Field, amount: Field) { let selector = compute_selector("_borrow(Field,Field,Field)"); - let return_values = context.call_public_function(context.this_address(), selector, [context.msg_sender(), to, amount]); - - return_values[0] + context.call_public_function(context.this_address(), selector, [context.msg_sender(), to, amount]); } #[aztec(public)] - internal fn _borrow( - owner: Field, - to: Field, - amount: Field - ) -> Field { + internal fn _borrow(owner: Field, to: Field, amount: Field) { let asset = Lending::at(context.this_address()).update_accumulator(context); let price = PriceFeed::at(asset.oracle_address).get_price(context); - - // Fetch collateral and static_debt, compute health of current position let collateral = storage.collateral.at(owner).read() as u120; @@ -299,54 +238,33 @@ contract Lending { let debt_covered = covered_by_collateral(price, asset.loan_to_value, collateral, 0, 0); let debt_returns = debt_updates(asset.interest_accumulator, static_debt, amount as u120, 0); - assert (debt_returns.debt_value < debt_covered); + assert(debt_returns.debt_value < debt_covered); storage.static_debt.at(owner).write(debt_returns.static_debt as Field); // @todo @LHerskind Need to support both private and public minting. let stable_coin = storage.stable_coin.read(); Token::at(stable_coin).mint_public(context, to, amount); - - 1 } #[aztec(private)] - fn repay_private( - from: Field, - amount: Field, - nonce: Field, - secret: Field, - on_behalf_of: Field, - stable_coin: Field, - ) { + fn repay_private(from: Field, amount: Field, nonce: Field, secret: Field, on_behalf_of: Field, stable_coin: Field) { let on_behalf_of = compute_identifier(secret, on_behalf_of, context.msg_sender()); let _res = Token::at(stable_coin).burn(&mut context, from, amount, nonce); let selector = compute_selector("_repay(Field,Field,Field)"); - let _callStackItem = context.call_public_function(context.this_address(), selector, [on_behalf_of, amount, stable_coin]); + context.call_public_function(context.this_address(), selector, [on_behalf_of, amount, stable_coin]); } #[aztec(public)] - fn repay_public( - amount: Field, - nonce: Field, - owner: Field, - stable_coin: Field, - ) -> Field { + fn repay_public(amount: Field, nonce: Field, owner: Field, stable_coin: Field) { Token::at(stable_coin).burn_public(context, context.msg_sender(), amount, nonce); let selector = compute_selector("_repay(Field,Field,Field)"); - let return_values = context.call_public_function(context.this_address(), selector, [owner, amount, stable_coin]); - - return_values[0] + context.call_public_function(context.this_address(), selector, [owner, amount, stable_coin]); } #[aztec(public)] - internal fn _repay( - owner: Field, - amount: Field, - stable_coin: Field, - ) { + internal fn _repay(owner: Field, amount: Field, stable_coin: Field) { let asset = Lending::at(context.this_address()).update_accumulator(context); - // To ensure that private is using the correct token. assert(stable_coin == storage.stable_coin.read()); @@ -355,30 +273,21 @@ contract Lending { let debt_returns = debt_updates(asset.interest_accumulator, static_debt, 0, amount as u120); storage.static_debt.at(owner).write(debt_returns.static_debt as Field); - - 1 } - unconstrained fn get_asset( - assetId: Field, - ) -> Asset { - + unconstrained fn get_asset(assetId: Field) -> Asset { storage.assets.at(assetId).read() } - unconstrained fn get_position( - owner: Field, - ) -> Position { - + unconstrained fn get_position(owner: Field) -> Position { let collateral = storage.collateral.at(owner).read(); let static_debt = storage.static_debt.at(owner).read(); let asset = storage.assets.at(0).read(); let debt = debt_value(static_debt as u120, asset.interest_accumulator as u120) as Field; - Position {collateral, static_debt, debt} + Position { collateral, static_debt, debt } } unconstrained fn get_assets() -> [Field; 2] { - [storage.collateral_asset.read(), storage.stable_coin.read()] } diff --git a/yarn-project/noir-contracts/src/contracts/parent_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/parent_contract/src/main.nr index 02e893ecb4a..9560223aff5 100644 --- a/yarn-project/noir-contracts/src/contracts/parent_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/parent_contract/src/main.nr @@ -7,10 +7,7 @@ contract Parent { // Private function to call another private function in the targetContract using the provided selector #[aztec(private)] - fn entryPoint( - targetContract: Field, - targetSelector: Field, - ) -> Field { + fn entryPoint(targetContract: Field, targetSelector: Field) -> Field { // Call the target private function let return_values = context.call_private_function(targetContract, targetSelector, [0]); @@ -20,11 +17,7 @@ contract Parent { // Public function to directly call another public function to the targetContract using the selector and value provided #[aztec(public)] - fn pubEntryPoint( - targetContract: Field, - targetSelector: Field, - initValue: Field - ) -> Field { + fn pubEntryPoint(targetContract: Field, targetSelector: Field, initValue: Field) -> Field { let return_values = context.call_public_function(targetContract, targetSelector, [initValue]); return_values[0] @@ -32,11 +25,7 @@ contract Parent { // Same as pubEntryPoint, but calls the target contract twice, using the return value from the first invocation as the argument for the second. #[aztec(public)] - fn pubEntryPointTwice( - targetContract: Field, - targetSelector: Field, - initValue: Field - ) -> Field { + fn pubEntryPointTwice(targetContract: Field, targetSelector: Field, initValue: Field) -> Field { let returnValue = context.call_public_function(targetContract, targetSelector, [initValue])[0]; let return_values = context.call_public_function(targetContract, targetSelector, [returnValue]); @@ -45,11 +34,7 @@ contract Parent { // Private function to enqueue a call to the targetContract address using the selector and argument provided #[aztec(private)] - fn enqueueCallToChild( - targetContract: Field, - targetSelector: Field, - targetValue: Field, - ) { + fn enqueueCallToChild(targetContract: Field, targetSelector: Field, targetValue: Field) { context.call_public_function(targetContract, targetSelector, [targetValue]); } @@ -57,12 +42,11 @@ contract Parent { // - one through a nested call to enqueueCallToChild with value 10, // - followed by one issued directly from this function with value 20. #[aztec(private)] - fn enqueueCallsToChildWithNestedFirst( - targetContract: Field, - targetSelector: Field, - ) { + fn enqueueCallsToChildWithNestedFirst(targetContract: Field, targetSelector: Field) { let enqueueCallToChildSelector = compute_selector("enqueueCallToChild(Field,Field,Field)"); - let _ret = context.call_private_function(context.this_address(), enqueueCallToChildSelector, [targetContract, targetSelector, 10]); + let _ret = context.call_private_function(context.this_address(), + enqueueCallToChildSelector, + [targetContract, targetSelector, 10]); context.call_public_function(targetContract, targetSelector, [20]); } @@ -70,22 +54,17 @@ contract Parent { // - one issued directly from this function with value 20, // - followed by one through a nested call to enqueueCallToChild with value 10. #[aztec(private)] - fn enqueueCallsToChildWithNestedLast( - targetContract: Field, - targetSelector: Field, - ) { + fn enqueueCallsToChildWithNestedLast(targetContract: Field, targetSelector: Field) { context.call_public_function(targetContract, targetSelector, [20]); let enqueueCallToChildSelector = compute_selector("enqueueCallToChild(Field,Field,Field)"); - let _ret = context.call_private_function(context.this_address(), enqueueCallToChildSelector, [targetContract, targetSelector, 10]); + let _ret = context.call_private_function(context.this_address(), + enqueueCallToChildSelector, + [targetContract, targetSelector, 10]); } // Private function to enqueue a call to the targetContract address using the selector and argument provided #[aztec(private)] - fn enqueueCallToChildTwice( - targetContract: Field, - targetSelector: Field, - targetValue: Field, - ) { + fn enqueueCallToChildTwice(targetContract: Field, targetSelector: Field, targetValue: Field) { // Enqueue the first public call context.call_public_function(targetContract, targetSelector, [targetValue]); // Enqueue the second public call @@ -94,28 +73,26 @@ contract Parent { // Private function to enqueue a call to the pubEntryPoint function of this same contract, passing the target arguments provided #[aztec(private)] - fn enqueueCallToPubEntryPoint( - targetContract: Field, - targetSelector: Field, - targetValue: Field, - ) { + fn enqueueCallToPubEntryPoint(targetContract: Field, targetSelector: Field, targetValue: Field) { let pubEntryPointSelector = compute_selector("pubEntryPoint(Field,Field,Field)"); let thisAddress = context.this_address(); - let _void = context.call_public_function(thisAddress, pubEntryPointSelector, [targetContract, targetSelector, targetValue]); + let _void = context.call_public_function(thisAddress, + pubEntryPointSelector, + [targetContract, targetSelector, targetValue]); } // Private function to enqueue two calls to the pubEntryPoint function of this same contract, passing the target arguments provided #[aztec(private)] - fn enqueueCallsToPubEntryPoint( - targetContract: Field, - targetSelector: Field, - targetValue: Field, - ) { + fn enqueueCallsToPubEntryPoint(targetContract: Field, targetSelector: Field, targetValue: Field) { let pubEntryPointSelector = compute_selector("pubEntryPoint(Field,Field,Field)"); let thisAddress = context.this_address(); - - context.call_public_function(thisAddress, pubEntryPointSelector, [targetContract, targetSelector, targetValue]); - context.call_public_function(thisAddress, pubEntryPointSelector, [targetContract, targetSelector, targetValue + 1]); + context.call_public_function(thisAddress, + pubEntryPointSelector, + [targetContract, targetSelector, targetValue]); + + context.call_public_function(thisAddress, + pubEntryPointSelector, + [targetContract, targetSelector, targetValue + 1]); } } diff --git a/yarn-project/noir-contracts/src/contracts/pending_commitments_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/pending_commitments_contract/src/main.nr index 154e6d739b9..c07f02136d1 100644 --- a/yarn-project/noir-contracts/src/contracts/pending_commitments_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/pending_commitments_contract/src/main.nr @@ -49,12 +49,7 @@ contract PendingCommitments { // getting / reading that note all in the same contract function // Realistic way to describe this test is "Mint note A, then burn note A in the same transaction" #[aztec(private)] - fn test_insert_then_get_then_nullify_flat( - amount: Field, - owner: Field, - ) -> Field { - - + fn test_insert_then_get_then_nullify_flat(amount: Field, owner: Field) -> Field { let owner_balance = storage.balances.at(owner); let mut note = ValueNote::new(amount, owner); @@ -76,12 +71,7 @@ contract PendingCommitments { // Confirm cannot access commitments inserted later in same function #[aztec(private)] - fn test_bad_get_then_insert_flat( - amount: Field, - owner: Field, - ) -> Field { - - + fn test_bad_get_then_insert_flat(amount: Field, owner: Field) -> Field { let owner_balance = storage.balances.at(owner); let options = NoteGetterOptions::with_filter(filter_notes_min_sum, amount); @@ -98,21 +88,13 @@ contract PendingCommitments { 0 } - // Dumy nested/inner function (to pass a function which does nothing) + // Dummy nested/inner function (to pass a function which does nothing) #[aztec(private)] - fn dummy( - amount: Field, - owner: Field, - ) {} + fn dummy(amount: Field, owner: Field) {} // Nested/inner function to create and insert a note #[aztec(private)] - fn insert_note( - amount: Field, - owner: Field, - ) { - - + fn insert_note(amount: Field, owner: Field) { let owner_balance = storage.balances.at(owner); let mut note = ValueNote::new(amount, owner); @@ -122,12 +104,7 @@ contract PendingCommitments { // Nested/inner function to get a note and confirm it matches the expected value #[aztec(private)] - fn get_then_nullify_note( - expected_value: Field, - owner: Field, - ) -> Field { - - + fn get_then_nullify_note(expected_value: Field, owner: Field) -> Field { let owner_balance = storage.balances.at(owner); let options = NoteGetterOptions::new().set_limit(1); @@ -142,11 +119,7 @@ contract PendingCommitments { // Nested/inner function to get a note and confirms that none is returned #[aztec(private)] - fn get_note_zero_balance( - owner: Field, - ) { - - + fn get_note_zero_balance(owner: Field) { let owner_balance = storage.balances.at(owner); let options = NoteGetterOptions::new(); @@ -165,33 +138,42 @@ contract PendingCommitments { owner: Field, insert_fn_selector: Field, get_then_nullify_fn_selector: Field, - get_note_zero_fn_selector: Field, + get_note_zero_fn_selector: Field ) { // nested call to create/insert note - let _callStackItem1 = context.call_private_function(inputs.call_context.storage_contract_address, insert_fn_selector, [amount, owner]); + let _callStackItem1 = context.call_private_function(inputs.call_context.storage_contract_address, + insert_fn_selector, + [amount, owner]); // nested call to read and nullify that note - let _callStackItem2 = context.call_private_function(inputs.call_context.storage_contract_address, get_then_nullify_fn_selector, [amount, owner]); - // nested call to confirm that balance is zero - let _callStackItem3 = context.call_private_function(inputs.call_context.storage_contract_address, get_note_zero_fn_selector, [owner]); + let _callStackItem2 = context.call_private_function(inputs.call_context.storage_contract_address, + get_then_nullify_fn_selector, + [amount, owner]); + // nested call to confirm that balance is zero + let _callStackItem3 = context.call_private_function(inputs.call_context.storage_contract_address, + get_note_zero_fn_selector, + [owner]); } // same test as above, but insert 2, get 2, nullify 2 #[aztec(private)] - fn test_insert2_then_get2_then_nullify2_all_in_nested_calls( - amount: Field, - owner: Field, - insert_fn_selector: Field, - get_then_nullify_fn_selector: Field, - ) { + fn test_insert2_then_get2_then_nullify2_all_in_nested_calls(amount: Field, owner: Field, insert_fn_selector: Field, get_then_nullify_fn_selector: Field) { // args for nested calls let args = [amount, owner]; // nested call to create/insert note - let _callStackItem1 = context.call_private_function(inputs.call_context.storage_contract_address, insert_fn_selector, args); - let _callStackItem2 = context.call_private_function(inputs.call_context.storage_contract_address, insert_fn_selector, args); + let _callStackItem1 = context.call_private_function(inputs.call_context.storage_contract_address, + insert_fn_selector, + args); + let _callStackItem2 = context.call_private_function(inputs.call_context.storage_contract_address, + insert_fn_selector, + args); // nested call to read and nullify that note - let _callStackItem3 = context.call_private_function(inputs.call_context.storage_contract_address, get_then_nullify_fn_selector, args); - let _callStackItem4 = context.call_private_function(inputs.call_context.storage_contract_address, get_then_nullify_fn_selector, args); + let _callStackItem3 = context.call_private_function(inputs.call_context.storage_contract_address, + get_then_nullify_fn_selector, + args); + let _callStackItem4 = context.call_private_function(inputs.call_context.storage_contract_address, + get_then_nullify_fn_selector, + args); // nested call to confirm that balance is zero // TODO(dbanks12): once > 4 nested calls is supported, can confirm 0 balance: //let _callStackItem5 = context.call_private_function(inputs.call_context.storage_contract_address, get_note_zero_fn_selector, [owner]); @@ -199,20 +181,21 @@ contract PendingCommitments { // same test as above, but insert 2, get 1, nullify 1 #[aztec(private)] - fn test_insert2_then_get2_then_nullify1_all_in_nested_calls( - amount: Field, - owner: Field, - insert_fn_selector: Field, - get_then_nullify_fn_selector: Field, - ) { + fn test_insert2_then_get2_then_nullify1_all_in_nested_calls(amount: Field, owner: Field, insert_fn_selector: Field, get_then_nullify_fn_selector: Field) { // args for nested calls let args = [amount, owner]; // nested call to create/insert note - let _callStackItem1 = context.call_private_function(inputs.call_context.storage_contract_address, insert_fn_selector, args); - let _callStackItem2 = context.call_private_function(inputs.call_context.storage_contract_address, insert_fn_selector, args); + let _callStackItem1 = context.call_private_function(inputs.call_context.storage_contract_address, + insert_fn_selector, + args); + let _callStackItem2 = context.call_private_function(inputs.call_context.storage_contract_address, + insert_fn_selector, + args); // nested call to read and nullify that note - let _callStackItem3 = context.call_private_function(inputs.call_context.storage_contract_address, get_then_nullify_fn_selector, args); + let _callStackItem3 = context.call_private_function(inputs.call_context.storage_contract_address, + get_then_nullify_fn_selector, + args); } // insert 1 note, then get 2 notes (one pending, one persistent) and nullify both. @@ -224,18 +207,26 @@ contract PendingCommitments { owner: Field, insert_fn_selector: Field, get_then_nullify_fn_selector: Field, - get_note_zero_fn_selector: Field, + get_note_zero_fn_selector: Field ) { // args for nested calls let args = [amount, owner]; // nested call to create/insert note - let _callStackItem1 = context.call_private_function(inputs.call_context.storage_contract_address, insert_fn_selector, args); + let _callStackItem1 = context.call_private_function(inputs.call_context.storage_contract_address, + insert_fn_selector, + args); // nested call to read and nullify that note - let _callStackItem2 = context.call_private_function(inputs.call_context.storage_contract_address, get_then_nullify_fn_selector, args); - let _callStackItem3 = context.call_private_function(inputs.call_context.storage_contract_address, get_then_nullify_fn_selector, args); - - let _callStackItem4 = context.call_private_function(inputs.call_context.storage_contract_address, get_note_zero_fn_selector, [owner]); + let _callStackItem2 = context.call_private_function(inputs.call_context.storage_contract_address, + get_then_nullify_fn_selector, + args); + let _callStackItem3 = context.call_private_function(inputs.call_context.storage_contract_address, + get_then_nullify_fn_selector, + args); + + let _callStackItem4 = context.call_private_function(inputs.call_context.storage_contract_address, + get_note_zero_fn_selector, + [owner]); } // Confirm cannot get/read a pending commitment in a nested call @@ -260,4 +251,4 @@ contract PendingCommitments { let note_header = NoteHeader::new(contract_address, nonce, storage_slot); note_utils::compute_note_hash_and_nullifier(ValueNoteMethods, note_header, serialized_note) } -} \ No newline at end of file +} diff --git a/yarn-project/noir-contracts/src/contracts/price_feed_contract/src/asset.nr b/yarn-project/noir-contracts/src/contracts/price_feed_contract/src/asset.nr index 9686d74a28d..9d464eefddc 100644 --- a/yarn-project/noir-contracts/src/contracts/price_feed_contract/src/asset.nr +++ b/yarn-project/noir-contracts/src/contracts/price_feed_contract/src/asset.nr @@ -7,9 +7,7 @@ struct Asset { global ASSET_SERIALIZED_LEN: Field = 1; fn deserializeAsset(fields: [Field; ASSET_SERIALIZED_LEN]) -> Asset { - Asset { - price: fields[0] as u120, - } + Asset { price: fields[0] as u120 } } fn serializeAsset(asset: Asset) -> [Field; ASSET_SERIALIZED_LEN] { diff --git a/yarn-project/noir-contracts/src/contracts/price_feed_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/price_feed_contract/src/main.nr index 1b59c3cd5ac..3573778e708 100644 --- a/yarn-project/noir-contracts/src/contracts/price_feed_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/price_feed_contract/src/main.nr @@ -35,33 +35,20 @@ contract PriceFeed { } #[aztec(private)] - fn constructor(){} + fn constructor() {} #[aztec(public)] - fn set_price( - asset_id: Field, - price: u120, - ) -> Field { - + fn set_price(asset_id: Field, price: u120) { let asset = storage.assets.at(asset_id); - asset.write(Asset {price: price}); - - 1 + asset.write(Asset { price }); } #[aztec(public)] - fn get_price( - asset_id: Field, - ) -> Asset { - - + fn get_price(asset_id: Field) -> Asset { storage.assets.at(asset_id).read() } - unconstrained fn fetch_price( - assetId: Field, - ) -> Asset { - + unconstrained fn fetch_price(assetId: Field) -> Asset { storage.assets.at(assetId).read() } diff --git a/yarn-project/noir-contracts/src/contracts/schnorr_account_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/schnorr_account_contract/src/main.nr index 40985f45f38..34513072247 100644 --- a/yarn-project/noir-contracts/src/contracts/schnorr_account_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/schnorr_account_contract/src/main.nr @@ -40,10 +40,7 @@ contract SchnorrAccount { // Constructs the contract #[aztec(private)] - fn constructor( - signing_pub_key_x: pub Field, - signing_pub_key_y: pub Field, - ) { + fn constructor(signing_pub_key_x: pub Field, signing_pub_key_y: pub Field) { let this = context.this_address(); // docs:start:initialize let mut pub_key_note = PublicKeyNote::new(signing_pub_key_x, signing_pub_key_y, this); @@ -86,10 +83,15 @@ contract SchnorrAccount { // Load auth witness let witness: [Field; 64] = get_auth_witness(message_hash); let mut signature: [u8; 64] = [0; 64]; - for i in 0..64 { signature[i] = witness[i] as u8; } + for i in 0..64 { + signature[i] = witness[i] as u8; + } // Verify signature of the payload bytes - let verification = std::schnorr::verify_signature(public_key.x, public_key.y, signature, message_hash.to_be_bytes(32)); + let verification = std::schnorr::verify_signature(public_key.x, + public_key.y, + signature, + message_hash.to_be_bytes(32)); assert(verification == true); // docs:end:entrypoint true @@ -98,7 +100,12 @@ contract SchnorrAccount { // Computes notes hash and nullifier. // Note 1: Needs to be defined by every contract producing logs. // Note 2: Having it in all the contracts gives us the ability to compute the note hash and nullifier differently for different kind of notes. - unconstrained fn compute_note_hash_and_nullifier(contract_address: Field, nonce: Field, storage_slot: Field, serialized_note: [Field; PUBLIC_KEY_NOTE_LEN]) -> [Field; 4] { + unconstrained fn compute_note_hash_and_nullifier( + contract_address: Field, + nonce: Field, + storage_slot: Field, + serialized_note: [Field; PUBLIC_KEY_NOTE_LEN] + ) -> [Field; 4] { assert(storage_slot == 1); let note_header = NoteHeader::new(contract_address, nonce, storage_slot); note_utils::compute_note_hash_and_nullifier(PublicKeyNoteMethods, note_header, serialized_note) diff --git a/yarn-project/noir-contracts/src/contracts/schnorr_account_contract/src/public_key_note.nr b/yarn-project/noir-contracts/src/contracts/schnorr_account_contract/src/public_key_note.nr index 538eb288ae6..5547b887f5b 100644 --- a/yarn-project/noir-contracts/src/contracts/schnorr_account_contract/src/public_key_note.nr +++ b/yarn-project/noir-contracts/src/contracts/schnorr_account_contract/src/public_key_note.nr @@ -68,12 +68,7 @@ impl PublicKeyNote { } fn deserialize(serialized_note: [Field; PUBLIC_KEY_NOTE_LEN]) -> PublicKeyNote { - PublicKeyNote { - x: serialized_note[0], - y: serialized_note[1], - owner: serialized_note[2], - header: NoteHeader::empty(), - } + PublicKeyNote { x: serialized_note[0], y: serialized_note[1], owner: serialized_note[2], header: NoteHeader::empty() } } fn serialize(note: PublicKeyNote) -> [Field; PUBLIC_KEY_NOTE_LEN] { @@ -82,7 +77,7 @@ fn serialize(note: PublicKeyNote) -> [Field; PUBLIC_KEY_NOTE_LEN] { fn compute_note_hash(note: PublicKeyNote) -> Field { // TODO(#1205) Should use a non-zero generator index. - pedersen_hash(note.serialize(),0) + pedersen_hash(note.serialize(), 0) } fn compute_nullifier(note: PublicKeyNote) -> Field { diff --git a/yarn-project/noir-contracts/src/contracts/schnorr_hardcoded_account_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/schnorr_hardcoded_account_contract/src/main.nr index bfaa0b285e4..69b6ef6b5b6 100644 --- a/yarn-project/noir-contracts/src/contracts/schnorr_hardcoded_account_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/schnorr_hardcoded_account_contract/src/main.nr @@ -52,14 +52,18 @@ contract SchnorrHardcodedAccount { // Load auth witness and format as an u8 array let witness: [Field; 64] = get_auth_witness(message_hash); let mut signature: [u8; 64] = [0; 64]; - for i in 0..64 { signature[i] = witness[i] as u8; } + for i in 0..64 { + signature[i] = witness[i] as u8; + } // Verify signature using hardcoded public key - let verification = std::schnorr::verify_signature(public_key_x, public_key_y, signature, message_hash.to_be_bytes(32)); + let verification = std::schnorr::verify_signature(public_key_x, + public_key_y, + signature, + message_hash.to_be_bytes(32)); assert(verification == true); true } // docs:end:is-valid - } // docs:end:contract \ No newline at end of file diff --git a/yarn-project/noir-contracts/src/contracts/schnorr_single_key_account_contract/src/auth_oracle.nr b/yarn-project/noir-contracts/src/contracts/schnorr_single_key_account_contract/src/auth_oracle.nr index 2cb544a4502..5b6b3dd7219 100644 --- a/yarn-project/noir-contracts/src/contracts/schnorr_single_key_account_contract/src/auth_oracle.nr +++ b/yarn-project/noir-contracts/src/contracts/schnorr_single_key_account_contract/src/auth_oracle.nr @@ -24,4 +24,4 @@ impl AuthWitness { unconstrained pub fn get_auth_witness(message_hash: Field) -> AuthWitness { let witness: [Field; 67] = auth_witness::get_auth_witness(message_hash); AuthWitness::deserialize(witness) -} \ No newline at end of file +} diff --git a/yarn-project/noir-contracts/src/contracts/schnorr_single_key_account_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/schnorr_single_key_account_contract/src/main.nr index 6030f2f1251..6ddcff3a22e 100644 --- a/yarn-project/noir-contracts/src/contracts/schnorr_single_key_account_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/schnorr_single_key_account_contract/src/main.nr @@ -49,4 +49,4 @@ contract SchnorrSingleKeyAccount { assert(recover_address(message_hash, witness) == context.this_address()); true } -} \ No newline at end of file +} diff --git a/yarn-project/noir-contracts/src/contracts/schnorr_single_key_account_contract/src/util.nr b/yarn-project/noir-contracts/src/contracts/schnorr_single_key_account_contract/src/util.nr index 3b814373567..db5384cb7c5 100644 --- a/yarn-project/noir-contracts/src/contracts/schnorr_single_key_account_contract/src/util.nr +++ b/yarn-project/noir-contracts/src/contracts/schnorr_single_key_account_contract/src/util.nr @@ -2,14 +2,14 @@ use dep::std::{schnorr::verify_signature}; use dep::aztec::address::compute_address; use crate::auth_oracle::{AuthWitness}; -pub fn recover_address( - message_hash: Field, - witness: AuthWitness, -) -> Field { +pub fn recover_address(message_hash: Field, witness: AuthWitness) -> Field { let message_bytes = message_hash.to_be_bytes(32); - let verification = verify_signature(witness.owner.x, witness.owner.y, witness.signature, message_bytes); + let verification = verify_signature(witness.owner.x, + witness.owner.y, + witness.signature, + message_bytes); assert(verification == true); let reproduced_address = compute_address(witness.owner.x, witness.owner.y, witness.partial_address); reproduced_address -} \ No newline at end of file +} diff --git a/yarn-project/noir-contracts/src/contracts/slow_tree_contract/Nargo.toml b/yarn-project/noir-contracts/src/contracts/slow_tree_contract/Nargo.toml new file mode 100644 index 00000000000..81fd493ee4a --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/slow_tree_contract/Nargo.toml @@ -0,0 +1,10 @@ +[package] +name = "slow_tree_contract" +authors = [""] +compiler_version = ">=0.18.0" +type = "contract" + +[dependencies] +aztec = { path = "../../../../aztec-nr/aztec" } +value_note = { path = "../../../../aztec-nr/value-note"} +slow_updates_tree = { path = "../../../../aztec-nr/slow-updates-tree"} \ No newline at end of file diff --git a/yarn-project/noir-contracts/src/contracts/slow_tree_contract/src/capsule.nr b/yarn-project/noir-contracts/src/contracts/slow_tree_contract/src/capsule.nr new file mode 100644 index 00000000000..8731eca35d2 --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/slow_tree_contract/src/capsule.nr @@ -0,0 +1,7 @@ +#[oracle(popCapsule)] +fn pop_capsule_oracle() -> [Field; N] {} + +// A capsule is a "blob" of data that is passed to the contract through an oracle. +unconstrained pub fn pop_capsule() -> [Field; N] { + pop_capsule_oracle() +} diff --git a/yarn-project/noir-contracts/src/contracts/slow_tree_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/slow_tree_contract/src/main.nr new file mode 100644 index 00000000000..0af6f5565a2 --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/slow_tree_contract/src/main.nr @@ -0,0 +1,152 @@ +mod capsule; +mod types; + +// This contract allow us to "read" public storage in private through a delayed tree. +// More documentation need to be outlined for this properly, but there is some in +// https://github.com/AztecProtocol/aztec-packages/issues/1291 +// This is made as a separate contract for one thing mainly. Making it simpler to use. +contract SlowTree { + use dep::std::option::Option; + use dep::value_note::{ + balance_utils, + utils::{increment, decrement}, + value_note::{VALUE_NOTE_LEN, ValueNote, ValueNoteMethods}, + }; + use dep::aztec::{ + context::{PrivateContext, PublicContext, Context}, + note::{ + note_header::NoteHeader, + utils as note_utils, + }, + selector::compute_selector, + state_vars::{map::Map, public_state::PublicState, set::Set}, + types::type_serialization::field_serialization::{ + FieldSerializationMethods, FIELD_SERIALIZED_LEN, + }, + }; + use dep::slow_updates_tree::slow_map::{ + SlowMap, Leaf, SlowUpdateProof, compute_merkle_root, deserialize_slow_update_proof + }; + + use crate::capsule::pop_capsule; + use crate::types::{MembershipProof, deserialize_membership_proof}; + + global TREE_HEIGHT: Field = 254; + global MEMBERSHIP_SIZE: Field = 256; // TREE_HEIGHT + 2 + global UPDATE_SIZE: Field = 512; // TREE_HEIGHT * 2 + 4 + + global EMPTY_ROOT: Field = 5785871043333994658400733180052743689641713274194136017445890613179954325976; + + struct Storage { + trees: Map>, + } + + impl Storage { + fn init(context: Context) -> pub Self { + Storage { + trees: Map::new( + context, + 1, + |context, slot| { + SlowMap::new( + context, + slot, + ) + } + ), + } + } + } + + #[aztec(private)] + fn constructor() {} + + #[aztec(public)] + fn initialize() { + storage.trees.at(context.msg_sender()).initialize(EMPTY_ROOT); + } + + #[aztec(public)] + fn read_at_pub(key: Field) -> Field { + storage.trees.at(context.msg_sender()).read_at(key) + } + + #[aztec(public)] + fn read_leaf_at_pub(key: Field) -> Leaf { + storage.trees.at(context.msg_sender()).read_leaf_at(key) + } + + #[aztec(private)] + fn read_at(index: Field) -> Field { + let fields = pop_capsule(); + let p: MembershipProof = deserialize_membership_proof(fields); + assert(index == p.index, "Index does not match expected"); + + let expected_root = compute_merkle_root(p.value, p.index, p.sibling_path); + let selector = compute_selector("_assert_current_root(Field,Field)"); + context.call_public_function(context.this_address(), + selector, + [context.msg_sender(), expected_root]); + + p.value + } + + #[aztec(public)] + internal fn _assert_current_root(caller: Field, expected: Field) { + let root = storage.trees.at(caller).current_root(); + assert(root == expected, "Root does not match expected"); + } + + #[aztec(public)] + fn update_at_public(p: SlowUpdateProof) { + storage.trees.at(context.msg_sender()).update_at(p); + } + + #[aztec(private)] + fn update_at_private(index: Field, new_value: Field) { + let fields = pop_capsule(); + let p: SlowUpdateProof = deserialize_slow_update_proof(fields); + assert(index == p.index, "Index does not match expected"); + assert(new_value == p.new_value, "New value does not match expected"); + + // We compute the root before. + let before_root = compute_merkle_root(p.before.value, p.index, p.before.sibling_path); + let after_root = compute_merkle_root(p.after.value, p.index, p.after.sibling_path); + let new_after_root = compute_merkle_root(p.new_value, p.index, p.after.sibling_path); + + let selector = compute_selector("_update(Field,Field,Field,Field,Field,Field)"); + context.call_public_function(context.this_address(), + selector, + [ + context.msg_sender(), + p.index, + p.new_value, + before_root, + after_root, + new_after_root + ]); + } + + #[aztec(public)] + internal fn _update(caller: Field, index: Field, new_value: Field, before: Field, after: Field, new_root: Field) { + let current_root = storage.trees.at(caller).current_root(); + let after_root = storage.trees.at(caller).read_root().after; + + assert(current_root == before, "Before root does not match expected"); + assert(after_root == after, "After root does not match expected"); + + storage.trees.at(caller).update_unsafe_at(index, new_value, new_root); + } + + unconstrained fn un_read_leaf_at(address: Field, key: Field) -> Leaf { + storage.trees.at(address).read_leaf_at(key) + } + + unconstrained fn un_read_root(address: Field) -> Leaf { + storage.trees.at(address).read_root() + } + + unconstrained fn compute_note_hash_and_nullifier(_contract_address: Field, _nonce: Field, _storage_slot: Field, _preimage: [Field; 4]) -> [Field; 4] { + [0x0d, 0x0e, 0x0a, 0x0d] + } +} diff --git a/yarn-project/noir-contracts/src/contracts/slow_tree_contract/src/types.nr b/yarn-project/noir-contracts/src/contracts/slow_tree_contract/src/types.nr new file mode 100644 index 00000000000..0f4f5eb4119 --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/slow_tree_contract/src/types.nr @@ -0,0 +1,31 @@ +// A single inclusion proof. +// M = N + 2 +struct MembershipProof { + index: Field, + value: Field, + sibling_path: [Field; N], +} + +fn deserialize_membership_proof(serialized: [Field; M]) -> MembershipProof { + let mut sibling_path = [0; N]; + for i in 0..N { + sibling_path[i] = serialized[2 + i]; + } + MembershipProof { index: serialized[0], value: serialized[1], sibling_path } +} + +impl MembershipProof { + fn serialize(self: Self) -> [Field; M] { + let mut serialized = [0; M]; + serialized[0] = self.index; + serialized[1] = self.value; + for i in 0..N { + serialized[2 + i] = self.sibling_path[i]; + } + serialized + } + + fn deserialize(serialized: [Field; M]) -> Self { + deserialize_membership_proof(serialized) + } +} diff --git a/yarn-project/noir-contracts/src/contracts/stateful_test_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/stateful_test_contract/src/main.nr index d43ed800a9e..491f8ff494a 100644 --- a/yarn-project/noir-contracts/src/contracts/stateful_test_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/stateful_test_contract/src/main.nr @@ -50,29 +50,20 @@ contract StatefulTest { #[aztec(private)] fn constructor(owner: Field, value: Field) { - let loc = storage.notes.at(owner); increment(loc, value, owner); } #[aztec(private)] - fn create_note( - owner: Field, - value: Field, - ) { - - if (value != 0){ + fn create_note(owner: Field, value: Field) { + if (value != 0) { let loc = storage.notes.at(owner); increment(loc, value, owner); } } #[aztec(private)] - fn destroy_and_create( - recipient: Field, - amount: Field, - ) { - + fn destroy_and_create(recipient: Field, amount: Field) { let sender = context.msg_sender(); let sender_notes = storage.notes.at(sender); @@ -82,10 +73,7 @@ contract StatefulTest { increment(recipient_notes, amount, recipient); } - unconstrained fn summed_values( - owner: Field, - ) -> Field { - + unconstrained fn summed_values(owner: Field) -> Field { let owner_balance = storage.notes.at(owner); // Return the sum of all notes in the set. diff --git a/yarn-project/noir-contracts/src/contracts/test_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/test_contract/src/main.nr index 43180fa693d..186b9220cfb 100644 --- a/yarn-project/noir-contracts/src/contracts/test_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/test_contract/src/main.nr @@ -52,19 +52,15 @@ contract Test { // docs:end:empty-constructor #[aztec(private)] - fn get_public_key( - address: Field, - ) -> [Field; 2]{ + fn get_public_key(address: Field) -> [Field; 2] { let pub_key = get_public_key_oracle(address); - + [pub_key.x, pub_key.y] } // Get the portal contract address through an oracle call #[aztec(private)] - fn get_portal_contract_address( - aztec_address: Field - ) -> Field { + fn get_portal_contract_address(aztec_address: Field) -> Field { get_portal_address(aztec_address) } @@ -90,7 +86,7 @@ contract Test { a_number: u32, an_array: [Field; 2], a_struct: DummyNote, - a_deep_struct: DeepStruct, + a_deep_struct: DeepStruct ) -> distinct pub abi::PrivateCircuitPublicInputs { let mut args: BoundedVec = BoundedVec::new(0); args.push(a_field); @@ -105,7 +101,7 @@ contract Test { args.push(a_deep_struct.a_note.secret_hash); for note in a_deep_struct.many_notes { args.push(note.amount); - args.push(note.secret_hash); + args.push(note.secret_hash); } let args_hash = abi::hash_args(args.storage); let mut context = PrivateContext::new(inputs, args_hash); @@ -115,27 +111,20 @@ contract Test { // Purely exists for testing #[aztec(public)] - fn create_l2_to_l1_message_public( - amount: Field, - secret_hash: Field, - ) { + fn create_l2_to_l1_message_public(amount: Field, secret_hash: Field) { // Create a commitment to the amount let note = DummyNote::new(amount, secret_hash); - + // Public oracle call to emit new commitment. context.message_portal(note.get_commitment()); - } // Purely exists for testing #[aztec(public)] - fn create_nullifier_public( - amount: Field, - secret_hash: Field, - ) { + fn create_nullifier_public(amount: Field, secret_hash: Field) { // Create a commitment to the amount let note = DummyNote::new(amount, secret_hash); - + // Public oracle call to emit new commitment. context.push_new_nullifier(note.get_commitment(), EMPTY_NULLIFIED_COMMITMENT); } @@ -154,18 +143,14 @@ contract Test { // docs:start:is-time-equal #[aztec(public)] - fn is_time_equal( - time: Field, - ) -> Field { + fn is_time_equal(time: Field) -> Field { assert(context.timestamp() == time); time } // docs:end:is-time-equal #[aztec(public)] - fn emit_unencrypted( - value: Field - ) -> Field { + fn emit_unencrypted(value: Field) -> Field { // docs:start:emit_unencrypted emit_unencrypted_log(&mut context, value); // docs:end:emit_unencrypted @@ -173,25 +158,19 @@ contract Test { } #[aztec(public)] - fn consume_mint_public_message( - to: Field, - amount: Field, - canceller: Field, - msg_key: Field, - secret: Field, - ) { + fn consume_mint_public_message(to: Field, amount: Field, canceller: Field, msg_key: Field, secret: Field) { let content_hash = get_mint_public_content_hash(to, amount, canceller); // Consume message and emit nullifier context.consume_l1_to_l2_message(msg_key, content_hash, secret); } - #[aztec(private)] + #[aztec(private)] fn consume_mint_private_message( - secret_hash_for_redeeming_minted_notes: Field, + secret_hash_for_redeeming_minted_notes: Field, amount: Field, canceller: Field, msg_key: Field, - secret_for_L1_to_L2_message_consumption: Field, + secret_for_L1_to_L2_message_consumption: Field ) { // Consume L1 to L2 message and emit nullifier let content_hash = get_mint_private_content_hash(secret_hash_for_redeeming_minted_notes, amount, canceller); @@ -199,9 +178,7 @@ contract Test { } #[aztec(private)] - fn set_constant( - value: Field, - ) { + fn set_constant(value: Field) { let mut note = FieldNote::new(value); storage.example_constant.initialize(&mut note, Option::none(), false); } @@ -212,9 +189,7 @@ contract Test { } // Purely exists for testing - unconstrained fn get_random( - kindaSeed: Field - ) -> Field { + unconstrained fn get_random(kindaSeed: Field) -> Field { kindaSeed * rand() } diff --git a/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/Nargo.toml b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/Nargo.toml new file mode 100644 index 00000000000..25ad4026709 --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/Nargo.toml @@ -0,0 +1,11 @@ +[package] +name = "token_blacklist_contract" +authors = [""] +compiler_version = ">=0.18.0" +type = "contract" + +[dependencies] +aztec = { path = "../../../../aztec-nr/aztec" } +safe_math = { path = "../../../../aztec-nr/safe-math" } +field_note = { path = "../../../../aztec-nr/field-note" } +authwit = { path = "../../../../aztec-nr/authwit" } \ No newline at end of file diff --git a/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/interfaces.nr b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/interfaces.nr new file mode 100644 index 00000000000..9fb08061031 --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/interfaces.nr @@ -0,0 +1,48 @@ +use dep::aztec::{ + context::{ PrivateContext, PublicContext, Context }, + selector::compute_selector, + types::address::AztecAddress, +}; + +struct SlowMap { + address: Field, +} + +impl SlowMap { + pub fn at(address: AztecAddress) -> Self { + Self { address: address.address } + } + + pub fn initialize(self: Self, context: PublicContext) { + context.call_public_function_no_args( + self.address, + compute_selector("initialize()") + ); + } + + pub fn read_at_pub(self: Self, context: PublicContext, index: Field) -> Field { + let _return_values = context.call_public_function( + self.address, + compute_selector("read_at_pub(Field)"), + [index] + ); + _return_values[0] + } + + pub fn read_at(self: Self, context: &mut PrivateContext, index: Field) -> Field { + let _return_values = context.call_private_function( + self.address, + compute_selector("read_at(Field)"), + [index] + ); + _return_values[0] + } + + pub fn update_at_private(self: Self, context: &mut PrivateContext, index: Field, new_value: Field) { + context.call_private_function( + self.address, + compute_selector("update_at_private(Field,Field)"), + [index, new_value] + ); + } +} diff --git a/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/main.nr new file mode 100644 index 00000000000..95e9a33d65f --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/main.nr @@ -0,0 +1,372 @@ +mod types; +mod interfaces; + +// Minimal token implementation that supports `AuthWit` accounts and the slow update tree. +// The auth message follows a similar pattern to the cross-chain message and includes a designated caller. +// The designated caller is ALWAYS used here, and not based on a flag as cross-chain. +// message hash = H([caller, contract, selector, ...args]) +// To be read as `caller` calls function at `contract` defined by `selector` with `args` +// Including a nonce in the message hash ensures that the message can only be used once. +// The slow updates tree are used for access control related to minters and blacklist. + +// TODO's: https://github.com/AztecProtocol/aztec-packages/issues/3210 +// We are currently unable to do constructor -> private calls + +contract TokenBlacklist { + // Libs + use dep::std::option::Option; + + use dep::safe_math::SafeU120; + + use dep::aztec::{ + note::{ + note_getter_options::NoteGetterOptions, + note_header::NoteHeader, + utils as note_utils, + }, + context::{PrivateContext, PublicContext, Context}, + hash::{compute_secret_hash}, + state_vars::{map::Map, public_state::PublicState, set::Set, immutable_singleton::ImmutableSingleton}, + types::type_serialization::{ + field_serialization::{FieldSerializationMethods, FIELD_SERIALIZED_LEN}, + bool_serialization::{BoolSerializationMethods, BOOL_SERIALIZED_LEN}, + aztec_address_serialization::{AztecAddressSerializationMethods, AZTEC_ADDRESS_SERIALIZED_LEN}, + }, + types::address::{AztecAddress}, + selector::compute_selector, + }; + + use dep::field_note::field_note::{FieldNote, FieldNoteMethods, FIELD_NOTE_LEN}; + + use dep::authwit::{ + auth::{ + assert_current_call_valid_authwit, + assert_current_call_valid_authwit_public, + }, + }; + + use crate::types::{ + transparent_note::{TransparentNote, TransparentNoteMethods, TRANSPARENT_NOTE_LEN}, + token_note::{TokenNote, TokenNoteMethods, TOKEN_NOTE_LEN}, + balances_map::{BalancesMap}, + safe_u120_serialization::{SafeU120SerializationMethods, SAFE_U120_SERIALIZED_LEN}, + roles::UserFlags, + }; + use crate::interfaces::SlowMap; + + struct Storage { + admin: PublicState, + balances: BalancesMap, + total_supply: PublicState, + pending_shields: Set, + public_balances: Map>, + slow_update: ImmutableSingleton, + public_slow_update: PublicState, + } + + impl Storage { + fn init(context: Context) -> pub Self { + Storage { + admin: PublicState::new( + context, + 1, + AztecAddressSerializationMethods, + ), + balances: BalancesMap::new(context, 3), + total_supply: PublicState::new( + context, + 4, + SafeU120SerializationMethods, + ), + pending_shields: Set::new(context, 5, TransparentNoteMethods), + public_balances: Map::new( + context, + 6, + |context, slot| { + PublicState::new( + context, + slot, + SafeU120SerializationMethods, + ) + }, + ), + // Below is an abomination to have same value in private and public (immutable in solidity). + slow_update: ImmutableSingleton::new(context, 7, FieldNoteMethods), + public_slow_update: PublicState::new( + context, + 8, + AztecAddressSerializationMethods, + ), + } + } + } + + #[aztec(private)] + fn constructor(admin: AztecAddress, slow_updates_contract: AztecAddress) { + let mut slow_note = FieldNote::new(slow_updates_contract.address); + storage.slow_update.initialize(&mut slow_note, Option::none(), false); + + let selector = compute_selector("_initialize((Field),(Field))"); + context.call_public_function(context.this_address(), + selector, + [admin.address, slow_updates_contract.address]); + // We cannot do the following atm + // let roles = UserFlags{is_admin: true, is_minter: false, is_blacklisted: false}.get_value() as Field; + // SlowMap::at(slow_updates_contract).update_at_private(&mut context, admin.address, roles); + } + + //////// + // Looking ugly because we cannot do constructor -> private calls + + #[aztec(private)] + fn init_slow_tree(user: AztecAddress) { + let roles = UserFlags { is_admin: true, is_minter: false, is_blacklisted: false }.get_value() as Field; + let slow = SlowMap::at(AztecAddress::new(storage.slow_update.get_note().value)); + slow.update_at_private(&mut context, user.address, roles); + context.call_public_function(context.this_address(), + compute_selector("_init_slow_tree((Field))"), + [context.msg_sender()]); + } + + #[aztec(public)] + internal fn _init_slow_tree(caller: AztecAddress) { + assert(storage.admin.read().eq(caller), "caller is not admin"); + } + + /////// + #[aztec(public)] + internal fn _initialize(new_admin: AztecAddress, slow_updates_contract: AztecAddress) { + assert(new_admin.address != 0, "invalid admin"); + storage.admin.write(new_admin); + storage.public_slow_update.write(slow_updates_contract); + SlowMap::at(slow_updates_contract).initialize(context); + } + + #[aztec(private)] + fn update_roles(user: AztecAddress, roles: Field) { + let slow = SlowMap::at(AztecAddress::new(storage.slow_update.get_note().value)); + let caller_roles = UserFlags::new(slow.read_at(&mut context, context.msg_sender()) as u120); + assert(caller_roles.is_admin, "caller is not admin"); + + slow.update_at_private(&mut context, user.address, roles); + } + + #[aztec(public)] + fn mint_public(to: AztecAddress, amount: Field) { + let slow = SlowMap::at(storage.public_slow_update.read()); + let to_roles = UserFlags::new(slow.read_at_pub(context, to.address) as u120); + assert(!to_roles.is_blacklisted, "Blacklisted: Recipient"); + + let caller_roles = UserFlags::new(slow.read_at_pub(context, context.msg_sender()) as u120); + assert(caller_roles.is_minter, "caller is not minter"); + + let amount = SafeU120::new(amount); + let new_balance = storage.public_balances.at(to.address).read().add(amount); + let supply = storage.total_supply.read().add(amount); + + storage.public_balances.at(to.address).write(new_balance); + storage.total_supply.write(supply); + } + + #[aztec(public)] + fn mint_private(amount: Field, secret_hash: Field) { + let slow = SlowMap::at(storage.public_slow_update.read()); + let caller_roles = UserFlags::new(slow.read_at_pub(context, context.msg_sender()) as u120); + assert(caller_roles.is_minter, "caller is not minter"); + + let pending_shields = storage.pending_shields; + let mut note = TransparentNote::new(amount, secret_hash); + let supply = storage.total_supply.read().add(SafeU120::new(amount)); + + storage.total_supply.write(supply); + pending_shields.insert_from_public(&mut note); + } + + #[aztec(public)] + fn shield(from: AztecAddress, amount: Field, secret_hash: Field, nonce: Field) { + let slow = SlowMap::at(storage.public_slow_update.read()); + let from_roles = UserFlags::new(slow.read_at_pub(context, from.address) as u120); + assert(!from_roles.is_blacklisted, "Blacklisted: Sender"); + + if (from.address != context.msg_sender()) { + // The redeem is only spendable once, so we need to ensure that you cannot insert multiple shields from the same message. + assert_current_call_valid_authwit_public(&mut context, from); + } else { + assert(nonce == 0, "invalid nonce"); + } + + let amount = SafeU120::new(amount); + let from_balance = storage.public_balances.at(from.address).read().sub(amount); + + let pending_shields = storage.pending_shields; + let mut note = TransparentNote::new(amount.value as Field, secret_hash); + + storage.public_balances.at(from.address).write(from_balance); + pending_shields.insert_from_public(&mut note); + } + + #[aztec(public)] + fn transfer_public(from: AztecAddress, to: AztecAddress, amount: Field, nonce: Field) { + let slow = SlowMap::at(storage.public_slow_update.read()); + let from_roles = UserFlags::new(slow.read_at_pub(context, from.address) as u120); + assert(!from_roles.is_blacklisted, "Blacklisted: Sender"); + let to_roles = UserFlags::new(slow.read_at_pub(context, to.address) as u120); + assert(!to_roles.is_blacklisted, "Blacklisted: Recipient"); + + if (from.address != context.msg_sender()) { + assert_current_call_valid_authwit_public(&mut context, from); + } else { + assert(nonce == 0, "invalid nonce"); + } + + let amount = SafeU120::new(amount); + let from_balance = storage.public_balances.at(from.address).read().sub(amount); + storage.public_balances.at(from.address).write(from_balance); + + let to_balance = storage.public_balances.at(to.address).read().add(amount); + storage.public_balances.at(to.address).write(to_balance); + } + + #[aztec(public)] + fn burn_public(from: AztecAddress, amount: Field, nonce: Field) { + let slow = SlowMap::at(storage.public_slow_update.read()); + let from_roles = UserFlags::new(slow.read_at_pub(context, from.address) as u120); + assert(!from_roles.is_blacklisted, "Blacklisted: Sender"); + + if (from.address != context.msg_sender()) { + assert_current_call_valid_authwit_public(&mut context, from); + } else { + assert(nonce == 0, "invalid nonce"); + } + + let amount = SafeU120::new(amount); + let from_balance = storage.public_balances.at(from.address).read().sub(amount); + storage.public_balances.at(from.address).write(from_balance); + + let new_supply = storage.total_supply.read().sub(amount); + storage.total_supply.write(new_supply); + } + + #[aztec(private)] + fn redeem_shield(to: AztecAddress, amount: Field, secret: Field) { + let slow = SlowMap::at(AztecAddress::new(storage.slow_update.get_note().value)); + let to_roles = UserFlags::new(slow.read_at(&mut context, to.address) as u120); + assert(!to_roles.is_blacklisted, "Blacklisted: Recipient"); + + let pending_shields = storage.pending_shields; + let secret_hash = compute_secret_hash(secret); + // Get 1 note (set_limit(1)) which has amount stored in field with index 0 (select(0, amount)) and secret_hash + // stored in field with index 1 (select(1, secret_hash)). + let options = NoteGetterOptions::new().select(0, amount).select(1, secret_hash).set_limit(1); + let notes = pending_shields.get_notes(options); + let note = notes[0].unwrap_unchecked(); + // Remove the note from the pending shields set + pending_shields.remove(note); + + // Add the token note to user's balances set + storage.balances.at(to).add(SafeU120::new(amount)); + } + + #[aztec(private)] + fn unshield(from: AztecAddress, to: AztecAddress, amount: Field, nonce: Field) { + let slow = SlowMap::at(AztecAddress::new(storage.slow_update.get_note().value)); + let from_roles = UserFlags::new(slow.read_at(&mut context, from.address) as u120); + assert(!from_roles.is_blacklisted, "Blacklisted: Sender"); + let to_roles = UserFlags::new(slow.read_at(&mut context, to.address) as u120); + assert(!to_roles.is_blacklisted, "Blacklisted: Recipient"); + + if (from.address != context.msg_sender()) { + assert_current_call_valid_authwit(&mut context, from); + } else { + assert(nonce == 0, "invalid nonce"); + } + + storage.balances.at(from).sub(SafeU120::new(amount)); + + let selector = compute_selector("_increase_public_balance((Field),Field)"); + context.call_public_function(context.this_address(), selector, [to.address, amount]); + } + + #[aztec(private)] + fn transfer(from: AztecAddress, to: AztecAddress, amount: Field, nonce: Field) { + let slow = SlowMap::at(AztecAddress::new(storage.slow_update.get_note().value)); + let from_roles = UserFlags::new(slow.read_at(&mut context, from.address) as u120); + assert(!from_roles.is_blacklisted, "Blacklisted: Sender"); + let to_roles = UserFlags::new(slow.read_at(&mut context, to.address) as u120); + assert(!to_roles.is_blacklisted, "Blacklisted: Recipient"); + + if (from.address != context.msg_sender()) { + assert_current_call_valid_authwit(&mut context, from); + } else { + assert(nonce == 0, "invalid nonce"); + } + + let amount = SafeU120::new(amount); + storage.balances.at(from).sub(amount); + storage.balances.at(to).add(amount); + } + + #[aztec(private)] + fn burn(from: AztecAddress, amount: Field, nonce: Field) { + let slow = SlowMap::at(AztecAddress::new(storage.slow_update.get_note().value)); + let from_roles = UserFlags::new(slow.read_at(&mut context, from.address) as u120); + assert(!from_roles.is_blacklisted, "Blacklisted: Sender"); + + if (from.address != context.msg_sender()) { + assert_current_call_valid_authwit(&mut context, from); + } else { + assert(nonce == 0, "invalid nonce"); + } + + storage.balances.at(from).sub(SafeU120::new(amount)); + + let selector = compute_selector("_reduce_total_supply(Field)"); + context.call_public_function(context.this_address(), selector, [amount]); + } + + /// Internal /// + + #[aztec(public)] + internal fn _increase_public_balance(to: AztecAddress, amount: Field) { + let new_balance = storage.public_balances.at(to.address).read().add(SafeU120::new(amount)); + storage.public_balances.at(to.address).write(new_balance); + } + + #[aztec(public)] + internal fn _reduce_total_supply(amount: Field) { + // Only to be called from burn. + let new_supply = storage.total_supply.read().sub(SafeU120::new(amount)); + storage.total_supply.write(new_supply); + } + + /// Unconstrained /// + + unconstrained fn total_supply() -> u120 { + storage.total_supply.read().value + } + + unconstrained fn balance_of_private(owner: AztecAddress) -> u120 { + storage.balances.at(owner).balance_of().value + } + + unconstrained fn balance_of_public(owner: AztecAddress) -> u120 { + storage.public_balances.at(owner.address).read().value + } + + // Below this point is the stuff of nightmares. + // This should ideally not be required. What do we do if vastly different types of preimages? + + // Computes note hash and nullifier. + // Note 1: Needs to be defined by every contract producing logs. + // Note 2: Having it in all the contracts gives us the ability to compute the note hash and nullifier differently for different kind of notes. + unconstrained fn compute_note_hash_and_nullifier(contract_address: Field, nonce: Field, storage_slot: Field, preimage: [Field; TOKEN_NOTE_LEN]) -> [Field; 4] { + let note_header = NoteHeader::new(contract_address, nonce, storage_slot); + if (storage_slot == 5) { + note_utils::compute_note_hash_and_nullifier(TransparentNoteMethods, note_header, preimage) + } else if (storage_slot == 7) { + note_utils::compute_note_hash_and_nullifier(FieldNoteMethods, note_header, preimage) + } else { + note_utils::compute_note_hash_and_nullifier(TokenNoteMethods, note_header, preimage) + } + } +} diff --git a/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types.nr b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types.nr new file mode 100644 index 00000000000..f5a3aeaa6fd --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types.nr @@ -0,0 +1,6 @@ +mod transparent_note; +mod balance_set; +mod balances_map; +mod token_note; +mod safe_u120_serialization; +mod roles; diff --git a/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/balance_set.nr b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/balance_set.nr new file mode 100644 index 00000000000..5c3da5897b6 --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/balance_set.nr @@ -0,0 +1,126 @@ +use dep::std::option::Option; +use dep::safe_math::SafeU120; +use dep::aztec::{ + context::Context, + constants_gen::MAX_READ_REQUESTS_PER_CALL, + state_vars::set::Set, + types::address::AztecAddress, +}; +use dep::aztec::note::{ + note_getter::view_notes, + note_getter_options::{NoteGetterOptions, SortOrder}, + note_viewer_options::NoteViewerOptions +}; +use dep::aztec::note::{ + note_header::NoteHeader, + note_interface::NoteInterface, + utils::compute_note_hash_for_read_or_nullify, +}; +use dep::aztec::oracle::{ + rand::rand, + get_secret_key::get_secret_key, + get_public_key::get_public_key, +}; + +use crate::types::token_note::{TokenNote, TOKEN_NOTE_LEN, TokenNoteMethods}; + +// A set implementing standard manipulation of balances. +// Does not require spending key, but only knowledge. +// Spending key requirement should be enforced by the contract using this. +struct BalanceSet { + context: Context, + owner: AztecAddress, + set: Set +} + +impl BalanceSet { + pub fn new(context: Context, owner: AztecAddress, storage_slot: Field) -> Self { + assert(storage_slot != 0, "Storage slot 0 not allowed. Storage slots must start from 1."); + let set = Set { + context, + storage_slot, + note_interface: TokenNoteMethods, + }; + Self { + context, + owner, + set, + } + } + + unconstrained pub fn balance_of(self: Self) -> SafeU120 { + self.balance_of_with_offset(0) + } + + unconstrained pub fn balance_of_with_offset(self: Self, offset: u32) -> SafeU120 { + // Same as SafeU120::new(0), but fewer constraints because no check. + let mut balance = SafeU120::min(); + // docs:start:view_notes + let options = NoteViewerOptions::new().set_offset(offset); + let opt_notes = self.set.view_notes(options); + // docs:end:view_notes + let len = opt_notes.len(); + for i in 0..len { + if opt_notes[i].is_some() { + balance = balance.add(opt_notes[i].unwrap_unchecked().amount); + } + } + if (opt_notes[len - 1].is_some()) { + balance = balance.add(self.balance_of_with_offset(offset + opt_notes.len() as u32)); + } + + balance + } + + pub fn add(self: Self, addend: SafeU120) { + let mut addend_note = TokenNote::new(addend, self.owner); + + // docs:start:insert + self.set.insert(&mut addend_note, true); + // docs:end:insert + } + + pub fn sub(self: Self, subtrahend: SafeU120) { + // docs:start:get_notes + let options = NoteGetterOptions::with_filter(filter_notes_min_sum, subtrahend); + let maybe_notes = self.set.get_notes(options); + // docs:end:get_notes + + let mut minuend: SafeU120 = SafeU120::min(); + for i in 0..maybe_notes.len() { + if maybe_notes[i].is_some() { + let note = maybe_notes[i].unwrap_unchecked(); + + // Removes the note from the owner's set of notes. + // This will call the the `compute_nullifer` function of the `token_note` + // which require knowledge of the secret key (currently the users encryption key). + // The contract logic must ensure that the spending key is used as well. + // docs:start:remove + self.set.remove(note); + // docs:end:remove + + minuend = minuend.add(note.amount); + } + } + + // This is to provide a nicer error msg, + // without it minuend-subtrahend would still catch it, but more generic error then. + // without the == true, it includes 'minuend.ge(subtrahend)' as part of the error. + assert(minuend.ge(subtrahend) == true, "Balance too low"); + + self.add(minuend.sub(subtrahend)); + } +} + +pub fn filter_notes_min_sum(notes: [Option; MAX_READ_REQUESTS_PER_CALL], min_sum: SafeU120) -> [Option; MAX_READ_REQUESTS_PER_CALL] { + let mut selected = [Option::none(); MAX_READ_REQUESTS_PER_CALL]; + let mut sum = SafeU120::min(); + for i in 0..notes.len() { + if notes[i].is_some() & sum.lt(min_sum) { + let note = notes[i].unwrap_unchecked(); + selected[i] = Option::some(note); + sum = sum.add(note.amount); + } + } + selected +} diff --git a/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/balances_map.nr b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/balances_map.nr new file mode 100644 index 00000000000..2afaf784966 --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/balances_map.nr @@ -0,0 +1,28 @@ +use dep::aztec::context::{PrivateContext, PublicContext, Context}; +use dep::aztec::types::address::AztecAddress; +use dep::std::option::Option; +use crate::types::balance_set::BalanceSet; +use dep::aztec::hash::pedersen_hash; + +struct BalancesMap { + context: Context, + storage_slot: Field, +} + +impl BalancesMap { + pub fn new( + context: Context, + storage_slot: Field, + ) -> Self { + assert(storage_slot != 0, "Storage slot 0 not allowed. Storage slots must start from 1."); + Self { + context, + storage_slot, + } + } + + pub fn at(self, owner: AztecAddress) -> BalanceSet { + let derived_storage_slot = pedersen_hash([self.storage_slot, owner.address],0); + BalanceSet::new(self.context, owner, derived_storage_slot) + } +} diff --git a/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/roles.nr b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/roles.nr new file mode 100644 index 00000000000..793674e996d --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/roles.nr @@ -0,0 +1,42 @@ +global BLACKLIST_FLAG: u120 = 1; +global MINTER_FLAG: u120 = 2; +global ADMIN_FLAG: u120 = 4; + +struct UserFlags { + is_admin: bool, + is_minter: bool, + is_blacklisted: bool, +} + +impl UserFlags { + + pub fn new(value: u120) -> Self { + let is_admin = value & ADMIN_FLAG == ADMIN_FLAG; + let is_minter = value & MINTER_FLAG == MINTER_FLAG; + let is_blacklisted = value & BLACKLIST_FLAG == BLACKLIST_FLAG; + + Self { + is_admin, + is_minter, + is_blacklisted, + } + } + + pub fn get_value(self) -> u120 { + let mut value: u120 = 0; + + if self.is_admin { + value = value | ADMIN_FLAG; + } + + if self.is_minter { + value = value | MINTER_FLAG; + } + + if self.is_blacklisted { + value = value | BLACKLIST_FLAG; + } + + value + } +} diff --git a/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/safe_u120_serialization.nr b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/safe_u120_serialization.nr new file mode 100644 index 00000000000..876007184fe --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/safe_u120_serialization.nr @@ -0,0 +1,18 @@ +use dep::aztec::types::type_serialization::TypeSerializationInterface; +use dep::safe_math::SafeU120; + +global SAFE_U120_SERIALIZED_LEN: Field = 1; + +// This is safe when reading from storage IF only correct safeu120 was written to storage +fn deserializeU120(fields: [Field; SAFE_U120_SERIALIZED_LEN]) -> SafeU120 { + SafeU120 { value: fields[0] as u120 } +} + +fn serializeU120(value: SafeU120) -> [Field; SAFE_U120_SERIALIZED_LEN] { + [value.value as Field] +} + +global SafeU120SerializationMethods = TypeSerializationInterface { + deserialize: deserializeU120, + serialize: serializeU120, +}; diff --git a/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/token_note.nr b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/token_note.nr new file mode 100644 index 00000000000..978676cc22e --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/token_note.nr @@ -0,0 +1,140 @@ +use dep::aztec::{ + note::{ + note_header::NoteHeader, + note_interface::NoteInterface, + utils::compute_note_hash_for_read_or_nullify, + }, + context::PrivateContext, + constants_gen::MAX_READ_REQUESTS_PER_CALL, + state_vars::set::Set, + log::emit_encrypted_log, + hash::pedersen_hash, +}; +use dep::aztec::types::address::AztecAddress; +use dep::aztec::oracle::{ + rand::rand, + get_secret_key::get_secret_key, + get_public_key::get_public_key, +}; +use dep::safe_math::SafeU120; +use dep::std::option::Option; + +global TOKEN_NOTE_LEN: Field = 3; // 3 plus a header. + +struct TokenNote { + // the amount of tokens in the note + amount: SafeU120, + // the provider of secrets for the nullifier. The owner (recipient) to ensure that the note + // can be privately spent. When nullifier secret and encryption private key is same + // we can simply use the owner for this one. + owner: AztecAddress, + // randomness of the note to hide contents. + randomness: Field, + // the note header (contract_address, nonce, storage_slot) + // included in the note such that it becomes part of encrypted logs for later use. + header: NoteHeader, +} + +impl TokenNote { + pub fn new(amount: SafeU120, owner: AztecAddress) -> Self { + Self { + amount, + owner, + randomness: rand(), + header: NoteHeader::empty(), + } + } + + pub fn serialize(self) -> [Field; TOKEN_NOTE_LEN] { + [self.amount.value as Field, self.owner.address, self.randomness] + } + + pub fn deserialize(preimage: [Field; TOKEN_NOTE_LEN]) -> Self { + Self { + amount: SafeU120::new(preimage[0]), + owner: AztecAddress::new(preimage[1]), + randomness: preimage[2], + header: NoteHeader::empty(), + } + } + + pub fn compute_note_hash(self) -> Field { + // TODO(#1205) Should use a non-zero generator index. + pedersen_hash([ + self.amount.value as Field, + self.owner.address as Field, + self.randomness, + ],0) + } + + // docs:start:nullifier + pub fn compute_nullifier(self) -> Field { + let note_hash_for_nullify = compute_note_hash_for_read_or_nullify(TokenNoteMethods, self); + let secret = get_secret_key(self.owner.address); + // TODO(#1205) Should use a non-zero generator index. + pedersen_hash([ + note_hash_for_nullify, + secret.low, + secret.high, + ],0) + } + // docs:end:nullifier + + pub fn set_header(&mut self, header: NoteHeader) { + self.header = header; + } + + // Broadcasts the note as an encrypted log on L1. + pub fn broadcast(self, context: &mut PrivateContext, slot: Field) { + // We only bother inserting the note if non-empty to save funds on gas. + if !self.amount.is_zero() { + let encryption_pub_key = get_public_key(self.owner.address); + emit_encrypted_log( + context, + (*context).this_address(), + slot, + encryption_pub_key, + self.serialize(), + ); + } + } +} + +fn deserialize(preimage: [Field; TOKEN_NOTE_LEN]) -> TokenNote { + TokenNote::deserialize(preimage) +} + +fn serialize(note: TokenNote) -> [Field; TOKEN_NOTE_LEN] { + note.serialize() +} + +fn compute_note_hash(note: TokenNote) -> Field { + note.compute_note_hash() +} + +fn compute_nullifier(note: TokenNote) -> Field { + note.compute_nullifier() +} + +fn get_header(note: TokenNote) -> NoteHeader { + note.header +} + +fn set_header(note: &mut TokenNote, header: NoteHeader) { + note.set_header(header) +} + +// Broadcasts the note as an encrypted log on L1. +fn broadcast(context: &mut PrivateContext, slot: Field, note: TokenNote) { + note.broadcast(context, slot); +} + +global TokenNoteMethods = NoteInterface { + deserialize, + serialize, + compute_note_hash, + compute_nullifier, + get_header, + set_header, + broadcast, +}; diff --git a/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/transparent_note.nr b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/transparent_note.nr new file mode 100644 index 00000000000..12772aed42f --- /dev/null +++ b/yarn-project/noir-contracts/src/contracts/token_blacklist_contract/src/types/transparent_note.nr @@ -0,0 +1,130 @@ +// docs:start:token_types_all +use dep::aztec::{ + note::{ + note_header::NoteHeader, + note_interface::NoteInterface, + utils::compute_siloed_note_hash, + }, + hash::{compute_secret_hash, pedersen_hash}, + context::PrivateContext, +}; + +global TRANSPARENT_NOTE_LEN: Field = 2; + +// Transparent note represents a note that is created in the clear (public execution), +// but can only be spent by those that know the preimage of the "secret_hash" +struct TransparentNote { + amount: Field, + secret_hash: Field, + // the secret is just here for ease of use and won't be (de)serialized + secret: Field, + // header is just here to satisfy the NoteInterface + header: NoteHeader, +} + +impl TransparentNote { + + // CONSTRUCTORS + + pub fn new(amount: Field, secret_hash: Field) -> Self { + TransparentNote { + amount: amount, + secret_hash: secret_hash, + secret: 0, + header: NoteHeader::empty(), + } + } + + // new oracle call primitive + // get me the secret corresponding to this hash + pub fn new_from_secret(amount: Field, secret: Field) -> Self { + TransparentNote { + amount: amount, + secret_hash: compute_secret_hash(secret), + secret: secret, + header: NoteHeader::empty(), + } + } + + + // STANDARD NOTE_INTERFACE FUNCTIONS + + pub fn serialize(self) -> [Field; TRANSPARENT_NOTE_LEN] { + [self.amount, self.secret_hash] + } + + pub fn deserialize(preimage: [Field; TRANSPARENT_NOTE_LEN]) -> Self { + TransparentNote { + amount: preimage[0], + secret_hash: preimage[1], + secret: 0, + header: NoteHeader::empty(), + } + } + + pub fn compute_note_hash(self) -> Field { + // TODO(#1205) Should use a non-zero generator index. + pedersen_hash([ + self.amount, + self.secret_hash, + ],0) + } + + pub fn compute_nullifier(self) -> Field { + // TODO(#1386): should use `compute_note_hash_for_read_or_nullify` once public functions inject nonce! + let siloed_note_hash = compute_siloed_note_hash(TransparentNoteMethods, self); + // TODO(#1205) Should use a non-zero generator index. + pedersen_hash([self.secret, siloed_note_hash],0) + } + + pub fn set_header(&mut self, header: NoteHeader) { + self.header = header; + } + + + // CUSTOM FUNCTIONS FOR THIS NOTE TYPE + + pub fn knows_secret(self, secret: Field) { + let hash = compute_secret_hash(secret); + assert(self.secret_hash == hash); + } +} + +fn deserialize(preimage: [Field; TRANSPARENT_NOTE_LEN]) -> TransparentNote { + TransparentNote::deserialize(preimage) +} + +fn serialize(note: TransparentNote) -> [Field; TRANSPARENT_NOTE_LEN] { + note.serialize() +} + +fn compute_note_hash(note: TransparentNote) -> Field { + note.compute_note_hash() +} + +fn compute_nullifier(note: TransparentNote) -> Field { + note.compute_nullifier() +} + +fn get_header(note: TransparentNote) -> NoteHeader { + note.header +} + +fn set_header(note: &mut TransparentNote, header: NoteHeader) { + note.set_header(header) +} + +fn broadcast(context: &mut PrivateContext, slot: Field, note: TransparentNote) { + assert(false, "TransparentNote does not support broadcast"); +} + +global TransparentNoteMethods = NoteInterface { + deserialize, + serialize, + compute_note_hash, + compute_nullifier, + get_header, + set_header, + broadcast, +}; +// docs:end:token_types_all \ No newline at end of file diff --git a/yarn-project/noir-contracts/src/contracts/token_bridge_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/token_bridge_contract/src/main.nr index decb8cd295c..fe2e708ff78 100644 --- a/yarn-project/noir-contracts/src/contracts/token_bridge_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/token_bridge_contract/src/main.nr @@ -49,25 +49,17 @@ contract TokenBridge { } // docs:end:token_bridge_storage_and_constructor - // docs:start:claim_public // Consumes a L1->L2 message and calls the token contract to mint the appropriate amount publicly #[aztec(public)] - fn claim_public( - to: AztecAddress, - amount: Field, - canceller: EthereumAddress, - msg_key: Field, - secret: Field, - ) -> Field { + fn claim_public(to: AztecAddress, amount: Field, canceller: EthereumAddress, msg_key: Field, secret: Field) { let content_hash = get_mint_public_content_hash(to.address, amount, canceller.address); + // Consume message and emit nullifier context.consume_l1_to_l2_message(msg_key, content_hash, secret); // Mint tokens Token::at(storage.token.read()).mint_public(context, to.address, amount); - - 1 } // docs:end:claim_public @@ -79,16 +71,14 @@ contract TokenBridge { recipient: EthereumAddress, // ethereum address to withdraw to amount: Field, callerOnL1: EthereumAddress, // ethereum address that can call this function on the L1 portal (0x0 if anyone can call) - nonce: Field, // nonce used in the approval message by `msg.sender` to let bridge burn their tokens on L2 - ) -> Field { + nonce: Field // nonce used in the approval message by `msg.sender` to let bridge burn their tokens on L2 + ) { // Send an L2 to L1 message let content = get_withdraw_content_hash(recipient.address, amount, callerOnL1.address); context.message_portal(content); // Burn tokens Token::at(storage.token.read()).burn_public(context, context.msg_sender(), amount, nonce); - - 1 } // docs:end:exit_to_l1_public // docs:start:claim_private @@ -100,23 +90,21 @@ contract TokenBridge { amount: Field, canceller: EthereumAddress, msg_key: Field, // L1 to L2 message key as derived from the inbox contract - secret_for_L1_to_L2_message_consumption: Field, // secret used to consume the L1 to L2 message - ) -> Field { + secret_for_L1_to_L2_message_consumption: Field // secret used to consume the L1 to L2 message + ) { // Consume L1 to L2 message and emit nullifier - let content_hash = get_mint_private_content_hash(secret_hash_for_redeeming_minted_notes, amount, canceller.address); + let content_hash = get_mint_private_content_hash(secret_hash_for_redeeming_minted_notes, + amount, + canceller.address); context.consume_l1_to_l2_message(msg_key, content_hash, secret_for_L1_to_L2_message_consumption); // Mint tokens on L2 // `mint_private` on token is public. So we call an internal public function // which then calls the public method on the token contract. // Since the secret_hash is passed, no secret is leaked. - context.call_public_function( - context.this_address(), + context.call_public_function(context.this_address(), compute_selector("_call_mint_on_token(Field,Field)"), - [amount, secret_hash_for_redeeming_minted_notes], - ); - - 1 + [amount, secret_hash_for_redeeming_minted_notes]); } // docs:end:claim_private @@ -129,19 +117,21 @@ contract TokenBridge { recipient: EthereumAddress, // ethereum address to withdraw to amount: Field, callerOnL1: EthereumAddress, // ethereum address that can call this function on the L1 portal (0x0 if anyone can call) - nonce: Field, // nonce used in the approval message by `msg.sender` to let bridge burn their tokens on L2 - ) -> Field { + nonce: Field // nonce used in the approval message by `msg.sender` to let bridge burn their tokens on L2 + ) { // Send an L2 to L1 message let content = get_withdraw_content_hash(recipient.address, amount, callerOnL1.address); context.message_portal(content); + // docs:start:call_assert_token_is_same // Assert that user provided token address is same as seen in storage. - context.call_public_function(context.this_address(), compute_selector("_assert_token_is_same(Field)"), [token.address]); + context.call_public_function(context.this_address(), + compute_selector("_assert_token_is_same(Field)"), + [token.address]); + // docs:end:call_assert_token_is_same // Burn tokens Token::at(token.address).burn(&mut context, context.msg_sender(), amount, nonce); - - 1 } /// docs:end:exit_to_l1_private @@ -168,7 +158,7 @@ contract TokenBridge { // Also, note that user hashes their secret in private and only sends the hash in public // meaning only user can `redeem_shield` at a later time with their secret. #[aztec(public)] - internal fn _call_mint_on_token(amount: Field, secret_hash: Field){ + internal fn _call_mint_on_token(amount: Field, secret_hash: Field) { Token::at(storage.token.read()).mint_private(context, amount, secret_hash); } // docs:end:call_mint_on_token @@ -185,5 +175,5 @@ contract TokenBridge { unconstrained fn compute_note_hash_and_nullifier(contract_address: Field, nonce: Field, storage_slot: Field, serialized_note: [Field; 0]) -> [Field; 4] { [0, 0, 0, 0] } - // docs:end:compute_note_hash_and_nullifier_placeholder +// docs:end:compute_note_hash_and_nullifier_placeholder } diff --git a/yarn-project/noir-contracts/src/contracts/token_bridge_contract/src/token_interface.nr b/yarn-project/noir-contracts/src/contracts/token_bridge_contract/src/token_interface.nr index 6c44755b8a4..6e467647059 100644 --- a/yarn-project/noir-contracts/src/contracts/token_bridge_contract/src/token_interface.nr +++ b/yarn-project/noir-contracts/src/contracts/token_bridge_contract/src/token_interface.nr @@ -1,4 +1,4 @@ -// docs:start:token_brodge_token_interface +// docs:start:token_bridge_token_interface use dep::aztec::{ context::{ PrivateContext, PublicContext, Context }, selector::compute_selector, @@ -50,4 +50,4 @@ impl Token { } // docs:end:private_burn_interface } -// docs:end:token_brodge_token_interface +// docs:end:token_bridge_token_interface diff --git a/yarn-project/noir-contracts/src/contracts/token_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/token_contract/src/main.nr index b6db0a2de8c..2a52ab0607b 100644 --- a/yarn-project/noir-contracts/src/contracts/token_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/token_contract/src/main.nr @@ -130,9 +130,7 @@ contract Token { // docs:start:set_admin #[aztec(public)] - fn set_admin( - new_admin: AztecAddress, - ) { + fn set_admin(new_admin: AztecAddress) { assert(storage.admin.read().eq(AztecAddress::new(context.msg_sender())), "caller is not admin"); // docs:start:write_admin storage.admin.write(new_admin); @@ -142,10 +140,7 @@ contract Token { // docs:start:set_minter #[aztec(public)] - fn set_minter( - minter: AztecAddress, - approve: bool, - ) { + fn set_minter(minter: AztecAddress, approve: bool) { // docs:start:read_admin assert(storage.admin.read().eq(AztecAddress::new(context.msg_sender())), "caller is not admin"); // docs:end:read_admin @@ -157,10 +152,7 @@ contract Token { // docs:start:mint_public #[aztec(public)] - fn mint_public( - to: AztecAddress, - amount: Field, - ) -> Field { + fn mint_public(to: AztecAddress, amount: Field) { // docs:start:read_minter assert(storage.minters.at(context.msg_sender()).read(), "caller is not minter"); // docs:end:read_minter @@ -170,16 +162,12 @@ contract Token { storage.public_balances.at(to.address).write(new_balance); storage.total_supply.write(supply); - 1 } // docs:end:mint_public // docs:start:mint_private #[aztec(public)] - fn mint_private( - amount: Field, - secret_hash: Field, - ) -> Field { + fn mint_private(amount: Field, secret_hash: Field) { assert(storage.minters.at(context.msg_sender()).read(), "caller is not minter"); let pending_shields = storage.pending_shields; let mut note = TransparentNote::new(amount, secret_hash); @@ -189,18 +177,12 @@ contract Token { // docs:start:insert_from_public pending_shields.insert_from_public(&mut note); // docs:end:insert_from_public - 1 } // docs:end:mint_private // docs:start:shield #[aztec(public)] - fn shield( - from: AztecAddress, - amount: Field, - secret_hash: Field, - nonce: Field, - ) -> Field { + fn shield(from: AztecAddress, amount: Field, secret_hash: Field, nonce: Field) { if (from.address != context.msg_sender()) { // The redeem is only spendable once, so we need to ensure that you cannot insert multiple shields from the same message. assert_current_call_valid_authwit_public(&mut context, from); @@ -216,18 +198,12 @@ contract Token { storage.public_balances.at(from.address).write(from_balance); pending_shields.insert_from_public(&mut note); - 1 } // docs:end:shield // docs:start:transfer_public #[aztec(public)] - fn transfer_public( - from: AztecAddress, - to: AztecAddress, - amount: Field, - nonce: Field, - ) -> Field { + fn transfer_public(from: AztecAddress, to: AztecAddress, amount: Field, nonce: Field) { if (from.address != context.msg_sender()) { assert_current_call_valid_authwit_public(&mut context, from); } else { @@ -241,17 +217,12 @@ contract Token { let to_balance = storage.public_balances.at(to.address).read().add(amount); storage.public_balances.at(to.address).write(to_balance); - 1 } // docs:end:transfer_public // docs:start:burn_public #[aztec(public)] - fn burn_public( - from: AztecAddress, - amount: Field, - nonce: Field, - ) -> Field { + fn burn_public(from: AztecAddress, amount: Field, nonce: Field) { // docs:start:assert_current_call_valid_authwit_public if (from.address != context.msg_sender()) { assert_current_call_valid_authwit_public(&mut context, from); @@ -266,18 +237,12 @@ contract Token { let new_supply = storage.total_supply.read().sub(amount); storage.total_supply.write(new_supply); - - 1 } // docs:end:burn_public // docs:start:redeem_shield #[aztec(private)] - fn redeem_shield( - to: AztecAddress, - amount: Field, - secret: Field, - ) -> Field { + fn redeem_shield(to: AztecAddress, amount: Field, secret: Field) { let pending_shields = storage.pending_shields; let secret_hash = compute_secret_hash(secret); // Get 1 note (set_limit(1)) which has amount stored in field with index 0 (select(0, amount)) and secret_hash @@ -290,19 +255,12 @@ contract Token { // Add the token note to user's balances set storage.balances.at(to).add(SafeU120::new(amount)); - - 1 } // docs:end:redeem_shield // docs:start:unshield #[aztec(private)] - fn unshield( - from: AztecAddress, - to: AztecAddress, - amount: Field, - nonce: Field, - ) -> Field { + fn unshield(from: AztecAddress, to: AztecAddress, amount: Field, nonce: Field) { if (from.address != context.msg_sender()) { assert_current_call_valid_authwit(&mut context, from); } else { @@ -313,19 +271,12 @@ contract Token { let selector = compute_selector("_increase_public_balance((Field),Field)"); let _void = context.call_public_function(context.this_address(), selector, [to.address, amount]); - - 1 } // docs:end:unshield // docs:start:transfer #[aztec(private)] - fn transfer( - from: AztecAddress, - to: AztecAddress, - amount: Field, - nonce: Field, - ) -> Field { + fn transfer(from: AztecAddress, to: AztecAddress, amount: Field, nonce: Field) { // docs:start:assert_current_call_valid_authwit if (from.address != context.msg_sender()) { assert_current_call_valid_authwit(&mut context, from); @@ -339,18 +290,12 @@ contract Token { // docs:start:increase_private_balance storage.balances.at(to).add(amount); // docs:end:increase_private_balance - - 1 } // docs:end:transfer // docs:start:burn #[aztec(private)] - fn burn( - from: AztecAddress, - amount: Field, - nonce: Field, - ) -> Field { + fn burn(from: AztecAddress, amount: Field, nonce: Field) { if (from.address != context.msg_sender()) { assert_current_call_valid_authwit(&mut context, from); } else { @@ -361,16 +306,12 @@ contract Token { let selector = compute_selector("_reduce_total_supply(Field)"); let _void = context.call_public_function(context.this_address(), selector, [amount]); - - 1 } // docs:end:burn // docs:start:initialize #[aztec(public)] - internal fn _initialize( - new_admin: AztecAddress, - ) { + internal fn _initialize(new_admin: AztecAddress) { assert(new_admin.address != 0, "invalid admin"); storage.admin.write(new_admin); storage.minters.at(new_admin.address).write(true); @@ -381,10 +322,7 @@ contract Token { // docs:start:increase_public_balance #[aztec(public)] - internal fn _increase_public_balance( - to: AztecAddress, - amount: Field, - ) { + internal fn _increase_public_balance(to: AztecAddress, amount: Field) { let new_balance = storage.public_balances.at(to.address).read().add(SafeU120::new(amount)); storage.public_balances.at(to.address).write(new_balance); } @@ -392,9 +330,7 @@ contract Token { // docs:start:reduce_total_supply #[aztec(public)] - internal fn _reduce_total_supply( - amount: Field, - ) { + internal fn _reduce_total_supply(amount: Field) { // Only to be called from burn. let new_supply = storage.total_supply.read().sub(SafeU120::new(amount)); storage.total_supply.write(new_supply); @@ -410,9 +346,7 @@ contract Token { // docs:end:admin // docs:start:is_minter - unconstrained fn is_minter( - minter: AztecAddress, - ) -> bool { + unconstrained fn is_minter(minter: AztecAddress) -> bool { storage.minters.at(minter.address).read() } // docs:end:is_minter @@ -424,17 +358,13 @@ contract Token { // docs:end:total_supply // docs:start:balance_of_private - unconstrained fn balance_of_private( - owner: AztecAddress, - ) -> u120 { + unconstrained fn balance_of_private(owner: AztecAddress) -> u120 { storage.balances.at(owner).balance_of().value } // docs:end:balance_of_private // docs:start:balance_of_public - unconstrained fn balance_of_public( - owner: AztecAddress, - ) -> u120 { + unconstrained fn balance_of_public(owner: AztecAddress) -> u120 { storage.public_balances.at(owner.address).read().value } // docs:end:balance_of_public diff --git a/yarn-project/noir-contracts/src/contracts/token_contract/src/types.nr b/yarn-project/noir-contracts/src/contracts/token_contract/src/types.nr index 1f4883a54c6..e29a8151e9f 100644 --- a/yarn-project/noir-contracts/src/contracts/token_contract/src/types.nr +++ b/yarn-project/noir-contracts/src/contracts/token_contract/src/types.nr @@ -2,4 +2,4 @@ mod transparent_note; mod balance_set; mod balances_map; mod token_note; -mod safe_u120_serialization; \ No newline at end of file +mod safe_u120_serialization; diff --git a/yarn-project/noir-contracts/src/contracts/token_contract/src/types/balance_set.nr b/yarn-project/noir-contracts/src/contracts/token_contract/src/types/balance_set.nr index 37dbcd4ddbe..5c3da5897b6 100644 --- a/yarn-project/noir-contracts/src/contracts/token_contract/src/types/balance_set.nr +++ b/yarn-project/noir-contracts/src/contracts/token_contract/src/types/balance_set.nr @@ -123,4 +123,4 @@ pub fn filter_notes_min_sum(notes: [Option; MAX_READ_REQUESTS_PER_CAL } } selected -} \ No newline at end of file +} diff --git a/yarn-project/noir-contracts/src/contracts/token_contract/src/types/safe_u120_serialization.nr b/yarn-project/noir-contracts/src/contracts/token_contract/src/types/safe_u120_serialization.nr index f4f3ef84d23..876007184fe 100644 --- a/yarn-project/noir-contracts/src/contracts/token_contract/src/types/safe_u120_serialization.nr +++ b/yarn-project/noir-contracts/src/contracts/token_contract/src/types/safe_u120_serialization.nr @@ -5,7 +5,7 @@ global SAFE_U120_SERIALIZED_LEN: Field = 1; // This is safe when reading from storage IF only correct safeu120 was written to storage fn deserializeU120(fields: [Field; SAFE_U120_SERIALIZED_LEN]) -> SafeU120 { - SafeU120{value: fields[0] as u120} + SafeU120 { value: fields[0] as u120 } } fn serializeU120(value: SafeU120) -> [Field; SAFE_U120_SERIALIZED_LEN] { @@ -15,4 +15,4 @@ fn serializeU120(value: SafeU120) -> [Field; SAFE_U120_SERIALIZED_LEN] { global SafeU120SerializationMethods = TypeSerializationInterface { deserialize: deserializeU120, serialize: serializeU120, -}; \ No newline at end of file +}; diff --git a/yarn-project/noir-contracts/src/contracts/token_contract/src/types/token_note.nr b/yarn-project/noir-contracts/src/contracts/token_contract/src/types/token_note.nr index 442b600f1ce..cc366cc2277 100644 --- a/yarn-project/noir-contracts/src/contracts/token_contract/src/types/token_note.nr +++ b/yarn-project/noir-contracts/src/contracts/token_contract/src/types/token_note.nr @@ -137,4 +137,4 @@ global TokenNoteMethods = NoteInterface { get_header, set_header, broadcast, -}; \ No newline at end of file +}; diff --git a/yarn-project/noir-contracts/src/contracts/uniswap_contract/src/interfaces.nr b/yarn-project/noir-contracts/src/contracts/uniswap_contract/src/interfaces.nr index aabf2735702..a319d7135ba 100644 --- a/yarn-project/noir-contracts/src/contracts/uniswap_contract/src/interfaces.nr +++ b/yarn-project/noir-contracts/src/contracts/uniswap_contract/src/interfaces.nr @@ -15,7 +15,7 @@ impl Token { } pub fn transfer_public(self: Self, context: PublicContext, from: Field, to: Field, amount: Field, nonce: Field) { - let _transfer_return_values = context.call_public_function( + context.call_public_function( self.address, compute_selector("transfer_public((Field),(Field),Field,Field)"), [from, to, amount, nonce] @@ -23,7 +23,7 @@ impl Token { } pub fn unshield(self: Self, context: &mut PrivateContext, from: Field, to: Field, amount: Field, nonce: Field) { - let _return_values = context.call_private_function( + context.call_private_function( self.address, compute_selector("unshield((Field),(Field),Field,Field)"), [from, to, amount, nonce] @@ -46,7 +46,7 @@ impl TokenBridge { } pub fn exit_to_l1_public(self: Self, context: PublicContext, recipient: Field, amount: Field, callerOnL1: Field, nonce: Field) { - let _return_values = context.call_public_function( + context.call_public_function( self.address, compute_selector("exit_to_l1_public((Field),Field,(Field),Field)"), [recipient, amount, callerOnL1, nonce] diff --git a/yarn-project/noir-contracts/src/contracts/uniswap_contract/src/main.nr b/yarn-project/noir-contracts/src/contracts/uniswap_contract/src/main.nr index 75ce7e78ea6..97d8a5f10e0 100644 --- a/yarn-project/noir-contracts/src/contracts/uniswap_contract/src/main.nr +++ b/yarn-project/noir-contracts/src/contracts/uniswap_contract/src/main.nr @@ -21,7 +21,7 @@ contract Uniswap { }; use dep::authwit::auth::{IS_VALID_SELECTOR, assert_current_call_valid_authwit_public, compute_authwit_message_hash}; - + use crate::interfaces::{Token, TokenBridge}; use crate::util::{compute_swap_private_content_hash, compute_swap_public_content_hash}; @@ -47,16 +47,16 @@ contract Uniswap { } } } - + #[aztec(private)] fn constructor() {} -// docs:end:uniswap_setup + // docs:end:uniswap_setup -// docs:start:swap_public + // docs:start:swap_public #[aztec(public)] fn swap_public( sender: AztecAddress, - input_asset_bridge: AztecAddress, + input_asset_bridge: AztecAddress, input_amount: Field, output_asset_bridge: AztecAddress, // params for using the transfer approval @@ -71,9 +71,8 @@ contract Uniswap { canceller_for_L1_to_L2_message: EthereumAddress, caller_on_L1: EthereumAddress, // nonce for someone to call swap on sender's behalf - nonce_for_swap_approval: Field, - ) -> Field { - + nonce_for_swap_approval: Field + ) { if (sender.address != context.msg_sender()) { assert_current_call_valid_authwit_public(&mut context, sender); } @@ -81,20 +80,16 @@ contract Uniswap { let input_asset = TokenBridge::at(input_asset_bridge.address).token(context); // Transfer funds to this contract - Token::at(input_asset.address).transfer_public( - context, + Token::at(input_asset.address).transfer_public(context, sender.address, context.this_address(), input_amount, - nonce_for_transfer_approval, - ); + nonce_for_transfer_approval); // Approve bridge to burn this contract's funds and exit to L1 Uniswap Portal - let _void = context.call_public_function( - context.this_address(), - compute_selector("_approve_bridge_and_exit_input_asset_to_L1((Field),(Field),Field)"), - [input_asset.address, input_asset_bridge.address, input_amount], - ); + let _void = context.call_public_function(context.this_address(), + compute_selector("_approve_bridge_and_exit_input_asset_to_L1((Field),(Field),Field)"), + [input_asset.address, input_asset_bridge.address, input_amount]); // Create swap message and send to Outbox for Uniswap Portal // this ensures the integrity of what the user originally intends to do on L1. @@ -104,8 +99,7 @@ contract Uniswap { assert(input_asset_bridge_portal_address != 0, "L1 portal address of input_asset's bridge is 0"); assert(output_asset_bridge_portal_address != 0, "L1 portal address of output_asset's bridge is 0"); - let content_hash = compute_swap_public_content_hash( - input_asset_bridge_portal_address, + let content_hash = compute_swap_public_content_hash(input_asset_bridge_portal_address, input_amount, uniswap_fee_tier, output_asset_bridge_portal_address, @@ -114,53 +108,47 @@ contract Uniswap { secret_hash_for_L1_to_l2_message, deadline_for_L1_to_l2_message, canceller_for_L1_to_L2_message.address, - caller_on_L1.address, - ); + caller_on_L1.address); context.message_portal(content_hash); - - 1 } -// docs:end:swap_public + // docs:end:swap_public -// docs:start:swap_private + // docs:start:swap_private #[aztec(private)] fn swap_private( input_asset: AztecAddress, // since private, we pass here and later assert that this is as expected by input_bridge - input_asset_bridge: AztecAddress, + input_asset_bridge: AztecAddress, input_amount: Field, output_asset_bridge: AztecAddress, // params for using the unshield approval nonce_for_unshield_approval: Field, // params for the swap - uniswap_fee_tier: Field, // which uniswap tier to use (eg 3000 for 0.3% fee) + uniswap_fee_tier: Field,// which uniswap tier to use (eg 3000 for 0.3% fee) minimum_output_amount: Field, // minimum output amount to receive (slippage protection for the swap) // params for the depositing output_asset back to Aztec - secret_hash_for_redeeming_minted_notes: Field, // secret hash used to redeem minted notes at a later time. This enables anyone to call this function and mint tokens to a user on their behalf + secret_hash_for_redeeming_minted_notes: Field,// secret hash used to redeem minted notes at a later time. This enables anyone to call this function and mint tokens to a user on their behalf secret_hash_for_L1_to_l2_message: Field, // for when l1 uniswap portal inserts the message to consume output assets on L2 deadline_for_L1_to_l2_message: Field, // for when l1 uniswap portal inserts the message to consume output assets on L2 canceller_for_L1_to_L2_message: EthereumAddress, // L1 address of who can cancel the message to consume assets on L2. - caller_on_L1: EthereumAddress, // ethereum address that can call this function on the L1 portal (0x0 if anyone can call) - ) -> Field { - + caller_on_L1: EthereumAddress // ethereum address that can call this function on the L1 portal (0x0 if anyone can call) + ) { // Assert that user provided token address is same as expected by token bridge. // we can't directly use `input_asset_bridge.token` because that is a public method and public can't return data to private - context.call_public_function(context.this_address(), compute_selector("_assert_token_is_same((Field),(Field))"), [input_asset.address, input_asset_bridge.address]); + context.call_public_function(context.this_address(), + compute_selector("_assert_token_is_same((Field),(Field))"), + [input_asset.address, input_asset_bridge.address]); // Transfer funds to this contract - Token::at(input_asset.address).unshield( - &mut context, + Token::at(input_asset.address).unshield(&mut context, context.msg_sender(), context.this_address(), input_amount, - nonce_for_unshield_approval, - ); + nonce_for_unshield_approval); // Approve bridge to burn this contract's funds and exit to L1 Uniswap Portal - context.call_public_function( - context.this_address(), - compute_selector("_approve_bridge_and_exit_input_asset_to_L1((Field),(Field),Field)"), - [input_asset.address, input_asset_bridge.address, input_amount], - ); + context.call_public_function(context.this_address(), + compute_selector("_approve_bridge_and_exit_input_asset_to_L1((Field),(Field),Field)"), + [input_asset.address, input_asset_bridge.address, input_amount]); // Create swap message and send to Outbox for Uniswap Portal // this ensures the integrity of what the user originally intends to do on L1. @@ -170,8 +158,7 @@ contract Uniswap { assert(input_asset_bridge_portal_address != 0, "L1 portal address of input_asset's bridge is 0"); assert(output_asset_bridge_portal_address != 0, "L1 portal address of output_asset's bridge is 0"); - let content_hash = compute_swap_private_content_hash( - input_asset_bridge_portal_address, + let content_hash = compute_swap_private_content_hash(input_asset_bridge_portal_address, input_amount, uniswap_fee_tier, output_asset_bridge_portal_address, @@ -180,11 +167,8 @@ contract Uniswap { secret_hash_for_L1_to_l2_message, deadline_for_L1_to_l2_message, canceller_for_L1_to_L2_message.address, - caller_on_L1.address, - ); + caller_on_L1.address); context.message_portal(content_hash); - - 1 } // docs:end:swap_private @@ -195,11 +179,7 @@ contract Uniswap { #[aztec(public)] fn is_valid_public(message_hash: Field) -> Field { let value = storage.approved_action.at(message_hash).read(); - if (value){ - IS_VALID_SELECTOR - } else { - 0 - } + if (value) { IS_VALID_SELECTOR } else { 0 } } // docs:end:authwit_uniswap_get @@ -210,28 +190,25 @@ contract Uniswap { // Note that private can't read public return values so created an internal public that handles everything // this method is used for both private and public swaps. #[aztec(public)] - internal fn _approve_bridge_and_exit_input_asset_to_L1( - token: AztecAddress, - token_bridge: AztecAddress, - amount: Field, - ) { + internal fn _approve_bridge_and_exit_input_asset_to_L1(token: AztecAddress, token_bridge: AztecAddress, amount: Field) { // approve bridge to burn this contract's funds (required when exiting on L1, as it burns funds on L2): let nonce_for_burn_approval = storage.nonce_for_burn_approval.read(); let selector = compute_selector("burn_public((Field),Field,Field)"); - let message_hash = compute_authwit_message_hash(token_bridge, token, selector, [context.this_address(), amount, nonce_for_burn_approval]); + let message_hash = compute_authwit_message_hash(token_bridge, + token, + selector, + [context.this_address(), amount, nonce_for_burn_approval]); storage.approved_action.at(message_hash).write(true); // increment nonce_for_burn_approval so it won't be used again storage.nonce_for_burn_approval.write(nonce_for_burn_approval + 1); // Exit to L1 Uniswap Portal ! - TokenBridge::at(token_bridge.address).exit_to_l1_public( - context, + TokenBridge::at(token_bridge.address).exit_to_l1_public(context, context.this_portal_address(), amount, context.this_portal_address(), - nonce_for_burn_approval, - ); + nonce_for_burn_approval); } // docs:end:authwit_uniswap_set @@ -253,4 +230,4 @@ contract Uniswap { unconstrained fn compute_note_hash_and_nullifier(contract_address: Field, nonce: Field, storage_slot: Field, serialized_note: [Field; 0]) -> [Field; 4] { [0, 0, 0, 0] } -} \ No newline at end of file +} diff --git a/yarn-project/noir-contracts/src/contracts/uniswap_contract/src/util.nr b/yarn-project/noir-contracts/src/contracts/uniswap_contract/src/util.nr index cc39741944b..163f8056a10 100644 --- a/yarn-project/noir-contracts/src/contracts/uniswap_contract/src/util.nr +++ b/yarn-project/noir-contracts/src/contracts/uniswap_contract/src/util.nr @@ -13,7 +13,7 @@ pub fn compute_swap_public_content_hash( secret_hash_for_L1_to_l2_message: Field, deadline_for_L1_to_l2_message: Field, canceller_for_L1_to_L2_message: Field, - caller_on_L1: Field, + caller_on_L1: Field ) -> Field { let mut hash_bytes: [u8; 324] = [0; 324]; // 10 fields of 32 bytes each + 4 bytes fn selector @@ -65,7 +65,7 @@ pub fn compute_swap_private_content_hash( secret_hash_for_L1_to_l2_message: Field, deadline_for_L1_to_l2_message: Field, canceller_for_L1_to_L2_message: Field, - caller_on_L1: Field, + caller_on_L1: Field ) -> Field { let mut hash_bytes: [u8; 324] = [0; 324]; // 10 fields of 32 bytes each + 4 bytes fn selector diff --git a/yarn-project/noir-contracts/src/scripts/copy_output.ts b/yarn-project/noir-contracts/src/scripts/copy_output.ts index de71b9586ff..94b80351ca7 100644 --- a/yarn-project/noir-contracts/src/scripts/copy_output.ts +++ b/yarn-project/noir-contracts/src/scripts/copy_output.ts @@ -56,7 +56,9 @@ function processNoirCompilerArtifact(projectName: string, contractName: string) const main = () => { const name = process.argv[2]; - if (!name) throw new Error(`Missing argument contract name`); + if (!name) { + throw new Error(`Missing argument contract name`); + } const projectName = `${snakeCase(name)}_contract`; const contractName = upperFirst(camelCase(name)); diff --git a/yarn-project/noir-contracts/src/scripts/copy_source.ts b/yarn-project/noir-contracts/src/scripts/copy_source.ts index dccd72f379c..c9c3745c16c 100644 --- a/yarn-project/noir-contracts/src/scripts/copy_source.ts +++ b/yarn-project/noir-contracts/src/scripts/copy_source.ts @@ -28,7 +28,7 @@ const NOIR_SOURCE_COPIES: NoirSourceCopy[] = [ * @param contractName - UpperCamelCase contract name that we check need copying */ function copyNrFilesExceptInterface(contractName: string): void { - // stored in `noir-contracts` under snake case nameing + // stored in `noir-contracts` under snake case naming const snakeCaseContractName = `${snakeCase(contractName)}_contract`; const projectDirPath = `src/contracts/${snakeCaseContractName}`; @@ -69,7 +69,9 @@ function copyNrFilesExceptInterface(contractName: string): void { const main = () => { const contractName = process.argv[2]; - if (!contractName) throw new Error(`Missing argument contract name`); + if (!contractName) { + throw new Error(`Missing argument contract name`); + } copyNrFilesExceptInterface(contractName); }; diff --git a/yarn-project/noir-protocol-circuits/package.json b/yarn-project/noir-protocol-circuits/package.json index 0903e46da51..3206b3936b9 100644 --- a/yarn-project/noir-protocol-circuits/package.json +++ b/yarn-project/noir-protocol-circuits/package.json @@ -11,7 +11,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "noir:build": "cd src && nargo compile --no-backend && rm -rf ./target/debug_*", "noir:types": "yarn ts-node --esm src/scripts/generate_ts_from_abi.ts && yarn formatting:fix", "noir:test": "cd src && nargo test", @@ -42,7 +42,6 @@ "@aztec/merkle-tree": "workspace:^", "@aztec/types": "workspace:^", "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/node": "^18.7.23", "jest": "^29.5.0", diff --git a/yarn-project/noir-protocol-circuits/src/Nargo.toml b/yarn-project/noir-protocol-circuits/src/Nargo.toml index 232b37c5650..40096bdb01a 100644 --- a/yarn-project/noir-protocol-circuits/src/Nargo.toml +++ b/yarn-project/noir-protocol-circuits/src/Nargo.toml @@ -8,6 +8,11 @@ members = [ "crates/private-kernel-inner-simulated", "crates/private-kernel-ordering", "crates/private-kernel-ordering-simulated", + "crates/public-kernel-lib", + "crates/public-kernel-private-previous", + "crates/public-kernel-private-previous-simulated", + "crates/public-kernel-public-previous", + "crates/public-kernel-public-previous-simulated", "crates/rollup-lib", "crates/rollup-merge", "crates/rollup-base", diff --git a/yarn-project/noir-protocol-circuits/src/__snapshots__/index.test.ts.snap b/yarn-project/noir-protocol-circuits/src/__snapshots__/index.test.ts.snap index 8d44dc24e57..0918bd55a07 100644 --- a/yarn-project/noir-protocol-circuits/src/__snapshots__/index.test.ts.snap +++ b/yarn-project/noir-protocol-circuits/src/__snapshots__/index.test.ts.snap @@ -6,10 +6,84 @@ exports[`Noir compatibility tests (interop_testing.nr) Complete Address matches Point { "kind": "point", "x": Fr { - "value": 1n, + "asBigInt": 1n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + ], + "type": "Buffer", + }, }, "y": Fr { - "value": 2n, + "asBigInt": 2n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 2, + ], + "type": "Buffer", + }, }, } `; @@ -20,6 +94,10 @@ exports[`Noir compatibility tests (interop_testing.nr) ComputeContractAddressFro exports[`Noir compatibility tests (interop_testing.nr) Function leaf matches noir 1`] = `"0x1ad8ece7f40e63d011ae47c6ce6cdaf31d632a23f5cf35bbeaaf69c8302afdbc"`; +exports[`Noir compatibility tests (interop_testing.nr) Public call stack item matches noir 1`] = `"0x0a370c67b66e30901470c11a199764a914fc0fcfbc737ed03153079b2765813a"`; + +exports[`Noir compatibility tests (interop_testing.nr) Public call stack item request matches noir 1`] = `"0x1d51d7758d792c9cd6edd8e8ec5f1f9fb1f974abc1af6bb4cf9f2328ef306c96"`; + exports[`Noir compatibility tests (interop_testing.nr) TxRequest Hash matches Noir 1`] = `"0x0b487ff2900ae1178e131bfe333fdbc351beef658f7c0d62db2801429b1aab75"`; exports[`Private kernel Executes private kernel init circuit for a contract deployment 1`] = ` @@ -27,52 +105,570 @@ KernelCircuitPublicInputs { "constants": CombinedConstantData { "blockData": HistoricBlockData { "blocksTreeRoot": Fr { - "value": 10561895175368852737061915973188839857007468377789560793687187642867659280638n, + "asBigInt": 10561895175368852737061915973188839857007468377789560793687187642867659280638n, + "asBuffer": { + "data": [ + 23, + 89, + 210, + 33, + 121, + 84, 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"payFeeFromPublicL2": false, - "portalContractAddress": EthAddress { - "buffer": { + "hash": Fr { + "asBigInt": 0n, + "asBuffer": { "data": [ 0, 0, @@ -5205,32 +62767,109 @@ KernelCircuitPublicInputsFinal { 0, 0, 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, ], "type": "Buffer", }, }, - "vkHash": Fr { - "value": 0n, - }, }, - OptionallyRevealedData { - "callStackItemHash": Fr { - "value": 0n, - }, - "calledFromL1": false, - "calledFromPublicL2": false, - "functionData": FunctionData { - "isConstructor": false, - "isInternal": false, - "isPrivate": false, - "selector": FunctionSelector { - "value": 0, + CallRequest { + "callerContext": CallerContext { + "msgSender": AztecAddress { + "asBigInt": 0n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ], + "type": "Buffer", + }, + }, + "storageContractAddress": AztecAddress { + 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"calledFromPublicL2": false, - "functionData": FunctionData { - "isConstructor": false, - "isInternal": false, - "isPrivate": false, - "selector": FunctionSelector { - "value": 0, + CallRequest { + "callerContext": CallerContext { + "msgSender": AztecAddress { + "asBigInt": 0n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ], + "type": "Buffer", + }, + }, + "storageContractAddress": AztecAddress { + "asBigInt": 0n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ], + "type": "Buffer", + }, }, }, - "payFeeFromL1": false, - "payFeeFromPublicL2": false, - "portalContractAddress": EthAddress { - "buffer": { + "callerContractAddress": AztecAddress { + "asBigInt": 0n, + "asBuffer": { "data": [ 0, 0, @@ -5299,32 +63055,149 @@ KernelCircuitPublicInputsFinal { 0, 0, 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, ], "type": "Buffer", }, }, - "vkHash": Fr { - "value": 0n, + "hash": Fr { + "asBigInt": 0n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ], + "type": "Buffer", + }, }, }, - OptionallyRevealedData { - "callStackItemHash": Fr { - "value": 0n, - }, - "calledFromL1": false, - "calledFromPublicL2": false, - "functionData": FunctionData { - "isConstructor": false, - "isInternal": false, - "isPrivate": false, - "selector": FunctionSelector { - "value": 0, + CallRequest { + "callerContext": CallerContext { + "msgSender": AztecAddress { + "asBigInt": 0n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ], + "type": "Buffer", + }, + }, + "storageContractAddress": AztecAddress { + "asBigInt": 0n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ], + "type": "Buffer", + }, }, }, - "payFeeFromL1": false, - "payFeeFromPublicL2": false, - "portalContractAddress": EthAddress { - "buffer": { + "callerContractAddress": AztecAddress { + "asBigInt": 0n, + "asBuffer": { "data": [ 0, 0, @@ -5346,76 +63219,184 @@ KernelCircuitPublicInputsFinal { 0, 0, 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, ], "type": "Buffer", }, }, - "vkHash": Fr { - "value": 0n, + "hash": Fr { + "asBigInt": 0n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ], + "type": "Buffer", + }, }, }, ], - "privateCallStack": [ - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - ], - "publicCallStack": [ - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - Fr { - "value": 0n, - }, - ], "unencryptedLogPreimagesLength": Fr { - "value": 4n, + "asBigInt": 4n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 4, + ], + "type": "Buffer", + }, }, "unencryptedLogsHash": [ Fr { - "value": 38042960891247304977978366569914796636n, + "asBigInt": 38042960891247304977978366569914796636n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 28, + 158, + 206, + 201, + 14, + 40, + 210, + 70, + 22, + 80, + 65, + 134, + 53, + 135, + 138, + 92, + ], + "type": "Buffer", + }, }, Fr { - "value": 193925133628253903808777040905688936722n, + "asBigInt": 193925133628253903808777040905688936722n, + "asBuffer": { + "data": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 145, + 228, + 159, + 71, + 88, + 110, + 207, + 117, + 242, + 176, + 203, + 185, + 78, + 137, + 113, + 18, + ], + "type": "Buffer", + }, }, ], }, diff --git a/yarn-project/noir-protocol-circuits/src/__snapshots__/noir_test_gen.test.ts.snap b/yarn-project/noir-protocol-circuits/src/__snapshots__/noir_test_gen.test.ts.snap index d982fdcefa9..63015d107db 100644 --- a/yarn-project/noir-protocol-circuits/src/__snapshots__/noir_test_gen.test.ts.snap +++ b/yarn-project/noir-protocol-circuits/src/__snapshots__/noir_test_gen.test.ts.snap @@ -5,7 +5,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "root": "0x0d2f152f19e366e9e690e3e551c1aadc0eab0bb27f6d011a9622d8f31bfa6e22", "siblingPaths": [ [ - "0x02", + "0x0000000000000000000000000000000000000000000000000000000000000002", "0x0714067c255d6ff2d20d01d74654d05ec8fcb214a4a3971504a465d8a3b8b0c8", "0x0d2d3ef5e1cf0f40c92ec8bba3606bef845fa56761bcd04cfbf0eb68a484e3b0", "0x082656a9251bfed1011e29c9d811ce2ecf91f7c8cf7df9da4a4aec85b4001738", @@ -15,7 +15,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -36,10 +36,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x01", + "0x0000000000000000000000000000000000000000000000000000000000000001", "0x0714067c255d6ff2d20d01d74654d05ec8fcb214a4a3971504a465d8a3b8b0c8", "0x0d2d3ef5e1cf0f40c92ec8bba3606bef845fa56761bcd04cfbf0eb68a484e3b0", "0x082656a9251bfed1011e29c9d811ce2ecf91f7c8cf7df9da4a4aec85b4001738", @@ -49,7 +49,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -70,10 +70,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x04", + "0x0000000000000000000000000000000000000000000000000000000000000004", "0x17cb544f4d22abbae88f69c706f21367d7925abb131a13836745714dc896c150", "0x0d2d3ef5e1cf0f40c92ec8bba3606bef845fa56761bcd04cfbf0eb68a484e3b0", "0x082656a9251bfed1011e29c9d811ce2ecf91f7c8cf7df9da4a4aec85b4001738", @@ -83,7 +83,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -104,10 +104,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x03", + "0x0000000000000000000000000000000000000000000000000000000000000003", "0x17cb544f4d22abbae88f69c706f21367d7925abb131a13836745714dc896c150", "0x0d2d3ef5e1cf0f40c92ec8bba3606bef845fa56761bcd04cfbf0eb68a484e3b0", "0x082656a9251bfed1011e29c9d811ce2ecf91f7c8cf7df9da4a4aec85b4001738", @@ -117,7 +117,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -138,10 +138,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x06", + "0x0000000000000000000000000000000000000000000000000000000000000006", "0x27dd66fbefb5501d622d3d87fda5b570078fa6897b35a21fff694646c95be98e", "0x24391b126b863e5922f1aedf7209e24111b3a0862b25406bd73cb92b708c8d7b", "0x082656a9251bfed1011e29c9d811ce2ecf91f7c8cf7df9da4a4aec85b4001738", @@ -151,7 +151,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -172,10 +172,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x05", + "0x0000000000000000000000000000000000000000000000000000000000000005", "0x27dd66fbefb5501d622d3d87fda5b570078fa6897b35a21fff694646c95be98e", "0x24391b126b863e5922f1aedf7209e24111b3a0862b25406bd73cb92b708c8d7b", "0x082656a9251bfed1011e29c9d811ce2ecf91f7c8cf7df9da4a4aec85b4001738", @@ -185,7 +185,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -206,10 +206,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x08", + "0x0000000000000000000000000000000000000000000000000000000000000008", "0x21f9f99e904c9ba5bc3d5f507b664ecdd18031ef4a8a689786a4657a2b2285f9", "0x24391b126b863e5922f1aedf7209e24111b3a0862b25406bd73cb92b708c8d7b", "0x082656a9251bfed1011e29c9d811ce2ecf91f7c8cf7df9da4a4aec85b4001738", @@ -219,7 +219,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -240,10 +240,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x07", + "0x0000000000000000000000000000000000000000000000000000000000000007", "0x21f9f99e904c9ba5bc3d5f507b664ecdd18031ef4a8a689786a4657a2b2285f9", "0x24391b126b863e5922f1aedf7209e24111b3a0862b25406bd73cb92b708c8d7b", "0x082656a9251bfed1011e29c9d811ce2ecf91f7c8cf7df9da4a4aec85b4001738", @@ -253,7 +253,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -274,10 +274,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x0a", + "0x000000000000000000000000000000000000000000000000000000000000000a", "0x2806ff792a4510cc0809b2ca077fa7f98fc7648c26c3759b7fcda59ff1af108b", "0x1ddf92291b9920a8e699b49af24f6f7eb81cd7810f75a0d5bb0e232db9a21a0d", "0x2a21f1699d1113da4feaff73381129499db752fdc438cb48b2c630d4dbf063e5", @@ -287,7 +287,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -308,10 +308,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x09", + "0x0000000000000000000000000000000000000000000000000000000000000009", "0x2806ff792a4510cc0809b2ca077fa7f98fc7648c26c3759b7fcda59ff1af108b", "0x1ddf92291b9920a8e699b49af24f6f7eb81cd7810f75a0d5bb0e232db9a21a0d", "0x2a21f1699d1113da4feaff73381129499db752fdc438cb48b2c630d4dbf063e5", @@ -321,7 +321,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -342,10 +342,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x0c", + "0x000000000000000000000000000000000000000000000000000000000000000c", "0x1287c5e3a6ea088ecceccecd4f3245df0d4024187772468873b1689abd0ab90a", "0x1ddf92291b9920a8e699b49af24f6f7eb81cd7810f75a0d5bb0e232db9a21a0d", "0x2a21f1699d1113da4feaff73381129499db752fdc438cb48b2c630d4dbf063e5", @@ -355,7 +355,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -376,10 +376,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x0b", + "0x000000000000000000000000000000000000000000000000000000000000000b", "0x1287c5e3a6ea088ecceccecd4f3245df0d4024187772468873b1689abd0ab90a", "0x1ddf92291b9920a8e699b49af24f6f7eb81cd7810f75a0d5bb0e232db9a21a0d", "0x2a21f1699d1113da4feaff73381129499db752fdc438cb48b2c630d4dbf063e5", @@ -389,7 +389,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -410,10 +410,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x0e", + "0x000000000000000000000000000000000000000000000000000000000000000e", "0x08db11df412943c9c61635184610d3016430ad6be74f74c39971f2693c4b44f6", "0x23f73ea918013d58c2b429df1689dfd470a80072481f9bef5b7d35d56c1cae07", "0x2a21f1699d1113da4feaff73381129499db752fdc438cb48b2c630d4dbf063e5", @@ -423,7 +423,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -444,10 +444,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x0d", + "0x000000000000000000000000000000000000000000000000000000000000000d", "0x08db11df412943c9c61635184610d3016430ad6be74f74c39971f2693c4b44f6", "0x23f73ea918013d58c2b429df1689dfd470a80072481f9bef5b7d35d56c1cae07", "0x2a21f1699d1113da4feaff73381129499db752fdc438cb48b2c630d4dbf063e5", @@ -457,7 +457,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -478,10 +478,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x10", + "0x0000000000000000000000000000000000000000000000000000000000000010", "0x19af7dd959e4c5338c7d61fc03c1592c7d3432fedaf54868e213b8251c6c6f01", "0x23f73ea918013d58c2b429df1689dfd470a80072481f9bef5b7d35d56c1cae07", "0x2a21f1699d1113da4feaff73381129499db752fdc438cb48b2c630d4dbf063e5", @@ -491,7 +491,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -512,10 +512,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x0f", + "0x000000000000000000000000000000000000000000000000000000000000000f", "0x19af7dd959e4c5338c7d61fc03c1592c7d3432fedaf54868e213b8251c6c6f01", "0x23f73ea918013d58c2b429df1689dfd470a80072481f9bef5b7d35d56c1cae07", "0x2a21f1699d1113da4feaff73381129499db752fdc438cb48b2c630d4dbf063e5", @@ -525,7 +525,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -546,10 +546,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x12", + "0x0000000000000000000000000000000000000000000000000000000000000012", "0x162d3348c892040199a2c9c5699ec131bcd098f13c96e19df178578a997a3841", "0x2108152b0428ce931cd9fa21540eb5c78f548f17c608ebdafb69b9e64b59c3d9", "0x1d2377a0aac3e71b860ffd6109e7c37fe00a98c05976428a8437aea677e02f40", @@ -559,7 +559,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -580,10 +580,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x11", + "0x0000000000000000000000000000000000000000000000000000000000000011", "0x162d3348c892040199a2c9c5699ec131bcd098f13c96e19df178578a997a3841", "0x2108152b0428ce931cd9fa21540eb5c78f548f17c608ebdafb69b9e64b59c3d9", "0x1d2377a0aac3e71b860ffd6109e7c37fe00a98c05976428a8437aea677e02f40", @@ -593,7 +593,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -614,10 +614,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x14", + "0x0000000000000000000000000000000000000000000000000000000000000014", "0x2dea96333bccfaf0a66e47868162f8598394fe2dd16696901ad697b69108913b", "0x2108152b0428ce931cd9fa21540eb5c78f548f17c608ebdafb69b9e64b59c3d9", "0x1d2377a0aac3e71b860ffd6109e7c37fe00a98c05976428a8437aea677e02f40", @@ -627,7 +627,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -648,10 +648,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x13", + "0x0000000000000000000000000000000000000000000000000000000000000013", "0x2dea96333bccfaf0a66e47868162f8598394fe2dd16696901ad697b69108913b", "0x2108152b0428ce931cd9fa21540eb5c78f548f17c608ebdafb69b9e64b59c3d9", "0x1d2377a0aac3e71b860ffd6109e7c37fe00a98c05976428a8437aea677e02f40", @@ -661,7 +661,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -682,10 +682,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x16", + "0x0000000000000000000000000000000000000000000000000000000000000016", "0x247a2c2470e8bddaa6a5eee327049b0dfcde18178281cc98c5486048198c288a", "0x09b560f79b2a0c3ef205421024d8734b15034d0a0b919b6c2898287e00db21da", "0x1d2377a0aac3e71b860ffd6109e7c37fe00a98c05976428a8437aea677e02f40", @@ -695,7 +695,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -716,10 +716,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x15", + "0x0000000000000000000000000000000000000000000000000000000000000015", "0x247a2c2470e8bddaa6a5eee327049b0dfcde18178281cc98c5486048198c288a", "0x09b560f79b2a0c3ef205421024d8734b15034d0a0b919b6c2898287e00db21da", "0x1d2377a0aac3e71b860ffd6109e7c37fe00a98c05976428a8437aea677e02f40", @@ -729,7 +729,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -750,10 +750,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x18", + "0x0000000000000000000000000000000000000000000000000000000000000018", "0x064190e4895d8740566a3fd29054e9924f362a45c556b4ae5dc54ca416788e1d", "0x09b560f79b2a0c3ef205421024d8734b15034d0a0b919b6c2898287e00db21da", "0x1d2377a0aac3e71b860ffd6109e7c37fe00a98c05976428a8437aea677e02f40", @@ -763,7 +763,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -784,10 +784,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x17", + "0x0000000000000000000000000000000000000000000000000000000000000017", "0x064190e4895d8740566a3fd29054e9924f362a45c556b4ae5dc54ca416788e1d", "0x09b560f79b2a0c3ef205421024d8734b15034d0a0b919b6c2898287e00db21da", "0x1d2377a0aac3e71b860ffd6109e7c37fe00a98c05976428a8437aea677e02f40", @@ -797,7 +797,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -818,10 +818,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x1a", + "0x000000000000000000000000000000000000000000000000000000000000001a", "0x18a13421cdb511cb76db5c338fc1ea69e29eafd722175a3de9e9d5ff22ef2a3c", "0x1e36228c84a97c5e7f028d269ed8a3ff44a85954cdc9da3afb49bbc09d45e485", "0x0deda30a4f6b81328e4d696636fb7c4ac838c752db36e2846fd74bfbc054b28a", @@ -831,7 +831,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -852,10 +852,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x19", + "0x0000000000000000000000000000000000000000000000000000000000000019", "0x18a13421cdb511cb76db5c338fc1ea69e29eafd722175a3de9e9d5ff22ef2a3c", "0x1e36228c84a97c5e7f028d269ed8a3ff44a85954cdc9da3afb49bbc09d45e485", "0x0deda30a4f6b81328e4d696636fb7c4ac838c752db36e2846fd74bfbc054b28a", @@ -865,7 +865,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -886,10 +886,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x1c", + "0x000000000000000000000000000000000000000000000000000000000000001c", "0x09b88e863aa93962792ab69f8d0680a58e69a038bca76a2bc19e2abfae23d54d", "0x1e36228c84a97c5e7f028d269ed8a3ff44a85954cdc9da3afb49bbc09d45e485", "0x0deda30a4f6b81328e4d696636fb7c4ac838c752db36e2846fd74bfbc054b28a", @@ -899,7 +899,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -920,10 +920,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x1b", + "0x000000000000000000000000000000000000000000000000000000000000001b", "0x09b88e863aa93962792ab69f8d0680a58e69a038bca76a2bc19e2abfae23d54d", "0x1e36228c84a97c5e7f028d269ed8a3ff44a85954cdc9da3afb49bbc09d45e485", "0x0deda30a4f6b81328e4d696636fb7c4ac838c752db36e2846fd74bfbc054b28a", @@ -933,7 +933,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -954,10 +954,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x1e", + "0x000000000000000000000000000000000000000000000000000000000000001e", "0x2e2dd1189ca14196ef0fddfbe59f2360c596d6231e1c4d733d4469913b48b610", "0x2231298cd5e81348975d70a8f3dc8c9a277042f1535390075ba5e8f9b3663dee", "0x0deda30a4f6b81328e4d696636fb7c4ac838c752db36e2846fd74bfbc054b28a", @@ -967,7 +967,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -988,10 +988,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x1d", + "0x000000000000000000000000000000000000000000000000000000000000001d", "0x2e2dd1189ca14196ef0fddfbe59f2360c596d6231e1c4d733d4469913b48b610", "0x2231298cd5e81348975d70a8f3dc8c9a277042f1535390075ba5e8f9b3663dee", "0x0deda30a4f6b81328e4d696636fb7c4ac838c752db36e2846fd74bfbc054b28a", @@ -1001,7 +1001,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1022,10 +1022,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x20", + "0x0000000000000000000000000000000000000000000000000000000000000020", "0x1b4e1ea6228914f99c65600e5b389da640aa8dabda9898e9f9d068e8ef7356e9", "0x2231298cd5e81348975d70a8f3dc8c9a277042f1535390075ba5e8f9b3663dee", "0x0deda30a4f6b81328e4d696636fb7c4ac838c752db36e2846fd74bfbc054b28a", @@ -1035,7 +1035,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1056,10 +1056,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x1f", + "0x000000000000000000000000000000000000000000000000000000000000001f", "0x1b4e1ea6228914f99c65600e5b389da640aa8dabda9898e9f9d068e8ef7356e9", "0x2231298cd5e81348975d70a8f3dc8c9a277042f1535390075ba5e8f9b3663dee", "0x0deda30a4f6b81328e4d696636fb7c4ac838c752db36e2846fd74bfbc054b28a", @@ -1069,7 +1069,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1090,10 +1090,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x22", + "0x0000000000000000000000000000000000000000000000000000000000000022", "0x1c68118bcd6e132f637dbc19380b9b71ca13d1d13e830791871537892716c38e", "0x015bdd758ff24fbfc3cc4f938fcaffd98a069448ef26ef0333b6b03067d18dc8", "0x2ccb3737357adc34d6983ae8f3348f8dba02dda07159bb5059701db4b837b448", @@ -1103,7 +1103,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1124,10 +1124,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x21", + "0x0000000000000000000000000000000000000000000000000000000000000021", "0x1c68118bcd6e132f637dbc19380b9b71ca13d1d13e830791871537892716c38e", "0x015bdd758ff24fbfc3cc4f938fcaffd98a069448ef26ef0333b6b03067d18dc8", "0x2ccb3737357adc34d6983ae8f3348f8dba02dda07159bb5059701db4b837b448", @@ -1137,7 +1137,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1158,10 +1158,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x24", + "0x0000000000000000000000000000000000000000000000000000000000000024", "0x061099cf7102aab9aa2cba6410cec4e34dcb2552f0327d0d18f507424a04c1d1", "0x015bdd758ff24fbfc3cc4f938fcaffd98a069448ef26ef0333b6b03067d18dc8", "0x2ccb3737357adc34d6983ae8f3348f8dba02dda07159bb5059701db4b837b448", @@ -1171,7 +1171,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1192,10 +1192,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x23", + "0x0000000000000000000000000000000000000000000000000000000000000023", "0x061099cf7102aab9aa2cba6410cec4e34dcb2552f0327d0d18f507424a04c1d1", "0x015bdd758ff24fbfc3cc4f938fcaffd98a069448ef26ef0333b6b03067d18dc8", "0x2ccb3737357adc34d6983ae8f3348f8dba02dda07159bb5059701db4b837b448", @@ -1205,7 +1205,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1226,10 +1226,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x26", + "0x0000000000000000000000000000000000000000000000000000000000000026", "0x2da17b5aba82ba386175b9f6d718cbbc201ebc2ad695daaef5d06f7166ca27cb", "0x2e95251e2882a591cd116de94fe7960873a03534f266d231911637c571f25ee1", "0x2ccb3737357adc34d6983ae8f3348f8dba02dda07159bb5059701db4b837b448", @@ -1239,7 +1239,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1260,10 +1260,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x25", + "0x0000000000000000000000000000000000000000000000000000000000000025", "0x2da17b5aba82ba386175b9f6d718cbbc201ebc2ad695daaef5d06f7166ca27cb", "0x2e95251e2882a591cd116de94fe7960873a03534f266d231911637c571f25ee1", "0x2ccb3737357adc34d6983ae8f3348f8dba02dda07159bb5059701db4b837b448", @@ -1273,7 +1273,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1294,10 +1294,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x28", + "0x0000000000000000000000000000000000000000000000000000000000000028", "0x1c992027d98bce9c6b8a662373028d77ac7fa927e3f8aaffe566c497d3696018", "0x2e95251e2882a591cd116de94fe7960873a03534f266d231911637c571f25ee1", "0x2ccb3737357adc34d6983ae8f3348f8dba02dda07159bb5059701db4b837b448", @@ -1307,7 +1307,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1328,10 +1328,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x27", + "0x0000000000000000000000000000000000000000000000000000000000000027", "0x1c992027d98bce9c6b8a662373028d77ac7fa927e3f8aaffe566c497d3696018", "0x2e95251e2882a591cd116de94fe7960873a03534f266d231911637c571f25ee1", "0x2ccb3737357adc34d6983ae8f3348f8dba02dda07159bb5059701db4b837b448", @@ -1341,7 +1341,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1362,10 +1362,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x2a", + "0x000000000000000000000000000000000000000000000000000000000000002a", "0x1d8d9d49b629c940a97fb3ac70787312d74ead600b2bcc10fff5166bfe3dbbc7", "0x022fbee6f693aa8264fdffa28f44526452f5692fde21fe9e35f6cd99d76d1ab5", "0x2f2e977b07cf859e5a66dece09bb1c977332c0cc86f84d36680fee99c12e7c13", @@ -1375,7 +1375,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1396,10 +1396,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x29", + "0x0000000000000000000000000000000000000000000000000000000000000029", "0x1d8d9d49b629c940a97fb3ac70787312d74ead600b2bcc10fff5166bfe3dbbc7", "0x022fbee6f693aa8264fdffa28f44526452f5692fde21fe9e35f6cd99d76d1ab5", "0x2f2e977b07cf859e5a66dece09bb1c977332c0cc86f84d36680fee99c12e7c13", @@ -1409,7 +1409,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1430,10 +1430,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x2c", + "0x000000000000000000000000000000000000000000000000000000000000002c", "0x2a28f0ebaa30aa984cebcb7b0c0a67fd2b360016f89fce0dcdb9e6a4c5971fe9", "0x022fbee6f693aa8264fdffa28f44526452f5692fde21fe9e35f6cd99d76d1ab5", "0x2f2e977b07cf859e5a66dece09bb1c977332c0cc86f84d36680fee99c12e7c13", @@ -1443,7 +1443,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1464,10 +1464,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x2b", + "0x000000000000000000000000000000000000000000000000000000000000002b", "0x2a28f0ebaa30aa984cebcb7b0c0a67fd2b360016f89fce0dcdb9e6a4c5971fe9", "0x022fbee6f693aa8264fdffa28f44526452f5692fde21fe9e35f6cd99d76d1ab5", "0x2f2e977b07cf859e5a66dece09bb1c977332c0cc86f84d36680fee99c12e7c13", @@ -1477,7 +1477,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1498,10 +1498,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x2e", + "0x000000000000000000000000000000000000000000000000000000000000002e", "0x276e27c7b724fe98b1f51a16558b155eed1b75fdabb85bbd9efef4ccd1fabb6e", "0x1fd1e78804c2b532f5000c12b52aad65a8d0ab67c4cbb8df7c641973d5696932", "0x2f2e977b07cf859e5a66dece09bb1c977332c0cc86f84d36680fee99c12e7c13", @@ -1511,7 +1511,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1532,10 +1532,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x2d", + "0x000000000000000000000000000000000000000000000000000000000000002d", "0x276e27c7b724fe98b1f51a16558b155eed1b75fdabb85bbd9efef4ccd1fabb6e", "0x1fd1e78804c2b532f5000c12b52aad65a8d0ab67c4cbb8df7c641973d5696932", "0x2f2e977b07cf859e5a66dece09bb1c977332c0cc86f84d36680fee99c12e7c13", @@ -1545,7 +1545,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1566,10 +1566,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x30", + "0x0000000000000000000000000000000000000000000000000000000000000030", "0x24497058c3ff753ed9ffee79cffa6d20d92a63542f91dde997ef2460a7eaefe4", "0x1fd1e78804c2b532f5000c12b52aad65a8d0ab67c4cbb8df7c641973d5696932", "0x2f2e977b07cf859e5a66dece09bb1c977332c0cc86f84d36680fee99c12e7c13", @@ -1579,7 +1579,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1600,10 +1600,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x2f", + "0x000000000000000000000000000000000000000000000000000000000000002f", "0x24497058c3ff753ed9ffee79cffa6d20d92a63542f91dde997ef2460a7eaefe4", "0x1fd1e78804c2b532f5000c12b52aad65a8d0ab67c4cbb8df7c641973d5696932", "0x2f2e977b07cf859e5a66dece09bb1c977332c0cc86f84d36680fee99c12e7c13", @@ -1613,7 +1613,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1634,10 +1634,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x32", + "0x0000000000000000000000000000000000000000000000000000000000000032", "0x1c785bc3ff91dd713aca935729de636ef2c416793e556f30794e1c963206529c", "0x1080f45e6fa036d7ea1ae403285e7abb5a494c24219301ceecd3c1b5d7737ebb", "0x102e086c7713e89f6ca7b635f8a9dc09466e1e7dfda7ee14d319085bee0c66fc", @@ -1647,7 +1647,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1668,10 +1668,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x31", + "0x0000000000000000000000000000000000000000000000000000000000000031", "0x1c785bc3ff91dd713aca935729de636ef2c416793e556f30794e1c963206529c", "0x1080f45e6fa036d7ea1ae403285e7abb5a494c24219301ceecd3c1b5d7737ebb", "0x102e086c7713e89f6ca7b635f8a9dc09466e1e7dfda7ee14d319085bee0c66fc", @@ -1681,7 +1681,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1702,11 +1702,11 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x34", - "0xa94fe7a3f708b6e26b40471c77d17305f186445a8b22398f7b7c20800423c2", + "0x0000000000000000000000000000000000000000000000000000000000000034", + "0x00a94fe7a3f708b6e26b40471c77d17305f186445a8b22398f7b7c20800423c2", "0x1080f45e6fa036d7ea1ae403285e7abb5a494c24219301ceecd3c1b5d7737ebb", "0x102e086c7713e89f6ca7b635f8a9dc09466e1e7dfda7ee14d319085bee0c66fc", "0x18da2a46da00f8645c139c646cd368b73d6728ff40e3f867e89d9ece95478c2a", @@ -1715,7 +1715,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1736,11 +1736,11 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x33", - "0xa94fe7a3f708b6e26b40471c77d17305f186445a8b22398f7b7c20800423c2", + "0x0000000000000000000000000000000000000000000000000000000000000033", + "0x00a94fe7a3f708b6e26b40471c77d17305f186445a8b22398f7b7c20800423c2", "0x1080f45e6fa036d7ea1ae403285e7abb5a494c24219301ceecd3c1b5d7737ebb", "0x102e086c7713e89f6ca7b635f8a9dc09466e1e7dfda7ee14d319085bee0c66fc", "0x18da2a46da00f8645c139c646cd368b73d6728ff40e3f867e89d9ece95478c2a", @@ -1749,7 +1749,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1770,11 +1770,11 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x36", - "0x9bbfa761d4c900ca2f4d1b436a029a4c6524c3d440406e8cab66738a7e4d11", + "0x0000000000000000000000000000000000000000000000000000000000000036", + "0x009bbfa761d4c900ca2f4d1b436a029a4c6524c3d440406e8cab66738a7e4d11", "0x2d13698d24af6e36d36a7f2627b632f503bfd89c9462ea3a7def1e1eb4f3931b", "0x102e086c7713e89f6ca7b635f8a9dc09466e1e7dfda7ee14d319085bee0c66fc", "0x18da2a46da00f8645c139c646cd368b73d6728ff40e3f867e89d9ece95478c2a", @@ -1783,7 +1783,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1804,11 +1804,11 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x35", - "0x9bbfa761d4c900ca2f4d1b436a029a4c6524c3d440406e8cab66738a7e4d11", + "0x0000000000000000000000000000000000000000000000000000000000000035", + "0x009bbfa761d4c900ca2f4d1b436a029a4c6524c3d440406e8cab66738a7e4d11", "0x2d13698d24af6e36d36a7f2627b632f503bfd89c9462ea3a7def1e1eb4f3931b", "0x102e086c7713e89f6ca7b635f8a9dc09466e1e7dfda7ee14d319085bee0c66fc", "0x18da2a46da00f8645c139c646cd368b73d6728ff40e3f867e89d9ece95478c2a", @@ -1817,7 +1817,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1838,10 +1838,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x38", + "0x0000000000000000000000000000000000000000000000000000000000000038", "0x2b23023b69362109e3819e454473a0b7f556da89e3832c5ecb967e335488c4ef", "0x2d13698d24af6e36d36a7f2627b632f503bfd89c9462ea3a7def1e1eb4f3931b", "0x102e086c7713e89f6ca7b635f8a9dc09466e1e7dfda7ee14d319085bee0c66fc", @@ -1851,7 +1851,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1872,10 +1872,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x37", + "0x0000000000000000000000000000000000000000000000000000000000000037", "0x2b23023b69362109e3819e454473a0b7f556da89e3832c5ecb967e335488c4ef", "0x2d13698d24af6e36d36a7f2627b632f503bfd89c9462ea3a7def1e1eb4f3931b", "0x102e086c7713e89f6ca7b635f8a9dc09466e1e7dfda7ee14d319085bee0c66fc", @@ -1885,7 +1885,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1906,10 +1906,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x3a", + "0x000000000000000000000000000000000000000000000000000000000000003a", "0x036683debc98c2c4b1494e4d671beb3bc283d5c37a7b53c6fd4ceaa9712b7ecf", "0x0cce498a15b3b71e668f8a6c089ed720c5c807332307791d9c4b701b6bcf591a", "0x198c4f144d1ee6f7003e6609a35673cd9535344a97170eb492fd4146e75e48cd", @@ -1919,7 +1919,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1940,10 +1940,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x39", + "0x0000000000000000000000000000000000000000000000000000000000000039", "0x036683debc98c2c4b1494e4d671beb3bc283d5c37a7b53c6fd4ceaa9712b7ecf", "0x0cce498a15b3b71e668f8a6c089ed720c5c807332307791d9c4b701b6bcf591a", "0x198c4f144d1ee6f7003e6609a35673cd9535344a97170eb492fd4146e75e48cd", @@ -1953,7 +1953,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -1974,10 +1974,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x3c", + "0x000000000000000000000000000000000000000000000000000000000000003c", "0x09f84f7496d48505301790692318d7f595fce62d177bf9e38f9ddc966a6e4cbe", "0x0cce498a15b3b71e668f8a6c089ed720c5c807332307791d9c4b701b6bcf591a", "0x198c4f144d1ee6f7003e6609a35673cd9535344a97170eb492fd4146e75e48cd", @@ -1987,7 +1987,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2008,10 +2008,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x3b", + "0x000000000000000000000000000000000000000000000000000000000000003b", "0x09f84f7496d48505301790692318d7f595fce62d177bf9e38f9ddc966a6e4cbe", "0x0cce498a15b3b71e668f8a6c089ed720c5c807332307791d9c4b701b6bcf591a", "0x198c4f144d1ee6f7003e6609a35673cd9535344a97170eb492fd4146e75e48cd", @@ -2021,7 +2021,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2042,10 +2042,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x3e", + "0x000000000000000000000000000000000000000000000000000000000000003e", "0x15b440549c5ad1f611781370927294cf9aa483591a9973fec9b065b860783903", "0x2b464e67b91434cb97c7998ef4fcf30f33065d40967815ba895da49b0867c00e", "0x198c4f144d1ee6f7003e6609a35673cd9535344a97170eb492fd4146e75e48cd", @@ -2055,7 +2055,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2076,10 +2076,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x3d", + "0x000000000000000000000000000000000000000000000000000000000000003d", "0x15b440549c5ad1f611781370927294cf9aa483591a9973fec9b065b860783903", "0x2b464e67b91434cb97c7998ef4fcf30f33065d40967815ba895da49b0867c00e", "0x198c4f144d1ee6f7003e6609a35673cd9535344a97170eb492fd4146e75e48cd", @@ -2089,7 +2089,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2110,10 +2110,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x40", + "0x0000000000000000000000000000000000000000000000000000000000000040", "0x07ef4c9ee6161811a420698639d5cccac911b00e3bac3a2512ecc30b4ddce5c7", "0x2b464e67b91434cb97c7998ef4fcf30f33065d40967815ba895da49b0867c00e", "0x198c4f144d1ee6f7003e6609a35673cd9535344a97170eb492fd4146e75e48cd", @@ -2123,7 +2123,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2144,10 +2144,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x3f", + "0x000000000000000000000000000000000000000000000000000000000000003f", "0x07ef4c9ee6161811a420698639d5cccac911b00e3bac3a2512ecc30b4ddce5c7", "0x2b464e67b91434cb97c7998ef4fcf30f33065d40967815ba895da49b0867c00e", "0x198c4f144d1ee6f7003e6609a35673cd9535344a97170eb492fd4146e75e48cd", @@ -2157,7 +2157,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2178,10 +2178,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x42", + "0x0000000000000000000000000000000000000000000000000000000000000042", "0x1347f85a77e4f66958a87e0c7dbf01282dfae44a61807bed2d24ef44adb4928a", "0x1856ceabb6ef85667d05b75e987c88665b769fbca21253ee49db5a4622a3fea3", "0x2b42fd0b641a3a0f98ebf461c91556efdb372f3661232f46d76c1bba9c74de5c", @@ -2191,7 +2191,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2212,10 +2212,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x41", + "0x0000000000000000000000000000000000000000000000000000000000000041", "0x1347f85a77e4f66958a87e0c7dbf01282dfae44a61807bed2d24ef44adb4928a", "0x1856ceabb6ef85667d05b75e987c88665b769fbca21253ee49db5a4622a3fea3", "0x2b42fd0b641a3a0f98ebf461c91556efdb372f3661232f46d76c1bba9c74de5c", @@ -2225,7 +2225,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2246,10 +2246,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x44", + "0x0000000000000000000000000000000000000000000000000000000000000044", "0x1f02c45392a591ca29d202df18f888111551d72d4d62ba53e0a49891c625e5c7", "0x1856ceabb6ef85667d05b75e987c88665b769fbca21253ee49db5a4622a3fea3", "0x2b42fd0b641a3a0f98ebf461c91556efdb372f3661232f46d76c1bba9c74de5c", @@ -2259,7 +2259,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2280,10 +2280,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x43", + "0x0000000000000000000000000000000000000000000000000000000000000043", "0x1f02c45392a591ca29d202df18f888111551d72d4d62ba53e0a49891c625e5c7", "0x1856ceabb6ef85667d05b75e987c88665b769fbca21253ee49db5a4622a3fea3", "0x2b42fd0b641a3a0f98ebf461c91556efdb372f3661232f46d76c1bba9c74de5c", @@ -2293,7 +2293,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2314,10 +2314,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x46", + "0x0000000000000000000000000000000000000000000000000000000000000046", "0x241de12e3d5145b71e85a7c03ca6a76699f5ae13ad5b2a33a67b697cec675366", "0x1a726a28acdaee8cbad2457233a58a0c00626d86971242b1a92f75da5f63f1cb", "0x2b42fd0b641a3a0f98ebf461c91556efdb372f3661232f46d76c1bba9c74de5c", @@ -2327,7 +2327,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2348,10 +2348,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x45", + "0x0000000000000000000000000000000000000000000000000000000000000045", "0x241de12e3d5145b71e85a7c03ca6a76699f5ae13ad5b2a33a67b697cec675366", "0x1a726a28acdaee8cbad2457233a58a0c00626d86971242b1a92f75da5f63f1cb", "0x2b42fd0b641a3a0f98ebf461c91556efdb372f3661232f46d76c1bba9c74de5c", @@ -2361,7 +2361,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2382,10 +2382,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x48", + "0x0000000000000000000000000000000000000000000000000000000000000048", "0x271afead5c8240bbcf9feb78ace40fd258c74d4c7ed2e51c231f22922f524e36", "0x1a726a28acdaee8cbad2457233a58a0c00626d86971242b1a92f75da5f63f1cb", "0x2b42fd0b641a3a0f98ebf461c91556efdb372f3661232f46d76c1bba9c74de5c", @@ -2395,7 +2395,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2416,10 +2416,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x47", + "0x0000000000000000000000000000000000000000000000000000000000000047", "0x271afead5c8240bbcf9feb78ace40fd258c74d4c7ed2e51c231f22922f524e36", "0x1a726a28acdaee8cbad2457233a58a0c00626d86971242b1a92f75da5f63f1cb", "0x2b42fd0b641a3a0f98ebf461c91556efdb372f3661232f46d76c1bba9c74de5c", @@ -2429,7 +2429,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2450,10 +2450,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x4a", + "0x000000000000000000000000000000000000000000000000000000000000004a", "0x12bb60fb0a5d24a2c32b922dd36f1b87c245ae6835d31324209f7117617b6777", "0x07001eeb3fec3c837e4816830a7a12591d53dae95a07352ad86e5f67f8f5c7f8", "0x2d4dc9f1a6945929f78991de6e536bdf7d44988104db36a1f3ef2da0972c3137", @@ -2463,7 +2463,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2484,10 +2484,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x49", + "0x0000000000000000000000000000000000000000000000000000000000000049", "0x12bb60fb0a5d24a2c32b922dd36f1b87c245ae6835d31324209f7117617b6777", "0x07001eeb3fec3c837e4816830a7a12591d53dae95a07352ad86e5f67f8f5c7f8", "0x2d4dc9f1a6945929f78991de6e536bdf7d44988104db36a1f3ef2da0972c3137", @@ -2497,7 +2497,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2518,10 +2518,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x4c", + "0x000000000000000000000000000000000000000000000000000000000000004c", "0x1736ba26940a6b078b38c6a2d9ea6f14eb598bde19e3004452616e8c478dbbbd", "0x07001eeb3fec3c837e4816830a7a12591d53dae95a07352ad86e5f67f8f5c7f8", "0x2d4dc9f1a6945929f78991de6e536bdf7d44988104db36a1f3ef2da0972c3137", @@ -2531,7 +2531,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2552,10 +2552,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x4b", + "0x000000000000000000000000000000000000000000000000000000000000004b", "0x1736ba26940a6b078b38c6a2d9ea6f14eb598bde19e3004452616e8c478dbbbd", "0x07001eeb3fec3c837e4816830a7a12591d53dae95a07352ad86e5f67f8f5c7f8", "0x2d4dc9f1a6945929f78991de6e536bdf7d44988104db36a1f3ef2da0972c3137", @@ -2565,7 +2565,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2586,10 +2586,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x4e", + "0x000000000000000000000000000000000000000000000000000000000000004e", "0x11ab02dc81764abcbf9e1c4b0301db34fbb34698feab01ca28ceefeb6904db60", "0x028d0e2317c70efc8ef7e8fb3f2a3f978a2f5344941d57fd8e6fd704434a1ff4", "0x2d4dc9f1a6945929f78991de6e536bdf7d44988104db36a1f3ef2da0972c3137", @@ -2599,7 +2599,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2620,10 +2620,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x4d", + "0x000000000000000000000000000000000000000000000000000000000000004d", "0x11ab02dc81764abcbf9e1c4b0301db34fbb34698feab01ca28ceefeb6904db60", "0x028d0e2317c70efc8ef7e8fb3f2a3f978a2f5344941d57fd8e6fd704434a1ff4", "0x2d4dc9f1a6945929f78991de6e536bdf7d44988104db36a1f3ef2da0972c3137", @@ -2633,7 +2633,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2654,10 +2654,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x50", + "0x0000000000000000000000000000000000000000000000000000000000000050", "0x27da7c78c0206a8815576356e2c0525e089a86a86dcb79dd7f58f1ea3760c276", "0x028d0e2317c70efc8ef7e8fb3f2a3f978a2f5344941d57fd8e6fd704434a1ff4", "0x2d4dc9f1a6945929f78991de6e536bdf7d44988104db36a1f3ef2da0972c3137", @@ -2667,7 +2667,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2688,10 +2688,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x4f", + "0x000000000000000000000000000000000000000000000000000000000000004f", "0x27da7c78c0206a8815576356e2c0525e089a86a86dcb79dd7f58f1ea3760c276", "0x028d0e2317c70efc8ef7e8fb3f2a3f978a2f5344941d57fd8e6fd704434a1ff4", "0x2d4dc9f1a6945929f78991de6e536bdf7d44988104db36a1f3ef2da0972c3137", @@ -2701,7 +2701,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2722,10 +2722,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x52", + "0x0000000000000000000000000000000000000000000000000000000000000052", "0x07c5ce1af23b54ab1377f1cb155739cd386e195234cf5fcdb14e71a584e02c85", "0x0ae9bc31c7567caaf7b5961b97d0e553cae35d8554e58e18e22fa95c91683d74", "0x1c503c690dabc905cd69d6637f3308dce11f8f782675f4c55faafa1f461f1620", @@ -2735,7 +2735,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2756,10 +2756,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x51", + "0x0000000000000000000000000000000000000000000000000000000000000051", "0x07c5ce1af23b54ab1377f1cb155739cd386e195234cf5fcdb14e71a584e02c85", "0x0ae9bc31c7567caaf7b5961b97d0e553cae35d8554e58e18e22fa95c91683d74", "0x1c503c690dabc905cd69d6637f3308dce11f8f782675f4c55faafa1f461f1620", @@ -2769,7 +2769,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2790,10 +2790,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x54", + "0x0000000000000000000000000000000000000000000000000000000000000054", "0x0262a45e4f8cde2dbcb23604edbceab67ff38420ac6221a5e90b7383e54a3a5b", "0x0ae9bc31c7567caaf7b5961b97d0e553cae35d8554e58e18e22fa95c91683d74", "0x1c503c690dabc905cd69d6637f3308dce11f8f782675f4c55faafa1f461f1620", @@ -2803,7 +2803,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2824,10 +2824,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x53", + "0x0000000000000000000000000000000000000000000000000000000000000053", "0x0262a45e4f8cde2dbcb23604edbceab67ff38420ac6221a5e90b7383e54a3a5b", "0x0ae9bc31c7567caaf7b5961b97d0e553cae35d8554e58e18e22fa95c91683d74", "0x1c503c690dabc905cd69d6637f3308dce11f8f782675f4c55faafa1f461f1620", @@ -2837,7 +2837,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2858,10 +2858,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x56", + "0x0000000000000000000000000000000000000000000000000000000000000056", "0x26e6d3539dd68a04503b70db9c07b96ccd257eaeea103c27356ccdec13d60e00", "0x1fe35c5a29d48e67a7543b16ee5baa1e0b709aba666a6f00b8779749cd79ef66", "0x1c503c690dabc905cd69d6637f3308dce11f8f782675f4c55faafa1f461f1620", @@ -2871,7 +2871,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2892,10 +2892,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x55", + "0x0000000000000000000000000000000000000000000000000000000000000055", "0x26e6d3539dd68a04503b70db9c07b96ccd257eaeea103c27356ccdec13d60e00", "0x1fe35c5a29d48e67a7543b16ee5baa1e0b709aba666a6f00b8779749cd79ef66", "0x1c503c690dabc905cd69d6637f3308dce11f8f782675f4c55faafa1f461f1620", @@ -2905,7 +2905,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2926,10 +2926,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x58", + "0x0000000000000000000000000000000000000000000000000000000000000058", "0x29fa1d5b270879e07e19cbb930c0d6276e63615325bfcb540c222157f50848f3", "0x1fe35c5a29d48e67a7543b16ee5baa1e0b709aba666a6f00b8779749cd79ef66", "0x1c503c690dabc905cd69d6637f3308dce11f8f782675f4c55faafa1f461f1620", @@ -2939,7 +2939,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2960,10 +2960,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x57", + "0x0000000000000000000000000000000000000000000000000000000000000057", "0x29fa1d5b270879e07e19cbb930c0d6276e63615325bfcb540c222157f50848f3", "0x1fe35c5a29d48e67a7543b16ee5baa1e0b709aba666a6f00b8779749cd79ef66", "0x1c503c690dabc905cd69d6637f3308dce11f8f782675f4c55faafa1f461f1620", @@ -2973,7 +2973,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -2994,10 +2994,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x5a", + "0x000000000000000000000000000000000000000000000000000000000000005a", "0x1f27b8dde9650b0f1b053f0423d26f26d1d936966f6ebb415f1d8f357851689e", "0x267b3d3fdf2d2b5d73d8e8a993b2de4a47b6a02462b9d2e32b694fbf7d07f46b", "0x2297ab8a4ca487d1b5da4036b79d4b0d1fb9335441d0095b4c442a36dbb7df4d", @@ -3007,7 +3007,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3028,10 +3028,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x59", + "0x0000000000000000000000000000000000000000000000000000000000000059", "0x1f27b8dde9650b0f1b053f0423d26f26d1d936966f6ebb415f1d8f357851689e", "0x267b3d3fdf2d2b5d73d8e8a993b2de4a47b6a02462b9d2e32b694fbf7d07f46b", "0x2297ab8a4ca487d1b5da4036b79d4b0d1fb9335441d0095b4c442a36dbb7df4d", @@ -3041,7 +3041,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3062,10 +3062,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x5c", + "0x000000000000000000000000000000000000000000000000000000000000005c", "0x2e4cfff3969aa6b0b4f020424b1c5be5aed887a87a41162d9a797de14e63b4de", "0x267b3d3fdf2d2b5d73d8e8a993b2de4a47b6a02462b9d2e32b694fbf7d07f46b", "0x2297ab8a4ca487d1b5da4036b79d4b0d1fb9335441d0095b4c442a36dbb7df4d", @@ -3075,7 +3075,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3096,10 +3096,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x5b", + "0x000000000000000000000000000000000000000000000000000000000000005b", "0x2e4cfff3969aa6b0b4f020424b1c5be5aed887a87a41162d9a797de14e63b4de", "0x267b3d3fdf2d2b5d73d8e8a993b2de4a47b6a02462b9d2e32b694fbf7d07f46b", "0x2297ab8a4ca487d1b5da4036b79d4b0d1fb9335441d0095b4c442a36dbb7df4d", @@ -3109,7 +3109,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3130,10 +3130,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x5e", + "0x000000000000000000000000000000000000000000000000000000000000005e", "0x290ec10ac60b67f7450bbaab4b52b7e21ad34dab445aaf9937d74873c767ddaa", "0x24a5c3299cf681d737c489bb1fdd140101c1dc95a01796dbc40def57dc8745ed", "0x2297ab8a4ca487d1b5da4036b79d4b0d1fb9335441d0095b4c442a36dbb7df4d", @@ -3143,7 +3143,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3164,10 +3164,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x5d", + "0x000000000000000000000000000000000000000000000000000000000000005d", "0x290ec10ac60b67f7450bbaab4b52b7e21ad34dab445aaf9937d74873c767ddaa", "0x24a5c3299cf681d737c489bb1fdd140101c1dc95a01796dbc40def57dc8745ed", "0x2297ab8a4ca487d1b5da4036b79d4b0d1fb9335441d0095b4c442a36dbb7df4d", @@ -3177,7 +3177,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3198,10 +3198,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x60", + "0x0000000000000000000000000000000000000000000000000000000000000060", "0x137dc8408efd18ad5bfb596634c1a292625a87e52974e223a36b376bfd43dd2d", "0x24a5c3299cf681d737c489bb1fdd140101c1dc95a01796dbc40def57dc8745ed", "0x2297ab8a4ca487d1b5da4036b79d4b0d1fb9335441d0095b4c442a36dbb7df4d", @@ -3211,7 +3211,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3232,10 +3232,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x5f", + "0x000000000000000000000000000000000000000000000000000000000000005f", "0x137dc8408efd18ad5bfb596634c1a292625a87e52974e223a36b376bfd43dd2d", "0x24a5c3299cf681d737c489bb1fdd140101c1dc95a01796dbc40def57dc8745ed", "0x2297ab8a4ca487d1b5da4036b79d4b0d1fb9335441d0095b4c442a36dbb7df4d", @@ -3245,7 +3245,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3266,10 +3266,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x62", + "0x0000000000000000000000000000000000000000000000000000000000000062", "0x1bc081064270af0334a33aba8ce975ce145a6e56863a0cbcbb37330005d67ad8", "0x0787331b97015bcc2de26a193462de25ea344ac473ede865141e5c5eeb3dcbd8", "0x233e9653b4b5ad92ad0b957fa484ca634e5f2a11538a2b7a9b1690bd8690ff4a", @@ -3279,7 +3279,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3300,10 +3300,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x61", + "0x0000000000000000000000000000000000000000000000000000000000000061", "0x1bc081064270af0334a33aba8ce975ce145a6e56863a0cbcbb37330005d67ad8", "0x0787331b97015bcc2de26a193462de25ea344ac473ede865141e5c5eeb3dcbd8", "0x233e9653b4b5ad92ad0b957fa484ca634e5f2a11538a2b7a9b1690bd8690ff4a", @@ -3313,7 +3313,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3334,10 +3334,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x64", + "0x0000000000000000000000000000000000000000000000000000000000000064", "0x0a554bf2065689124bc29f4682ab44ec9b0a57d6127369ba1472e44fe33c1e0b", "0x0787331b97015bcc2de26a193462de25ea344ac473ede865141e5c5eeb3dcbd8", "0x233e9653b4b5ad92ad0b957fa484ca634e5f2a11538a2b7a9b1690bd8690ff4a", @@ -3347,7 +3347,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3368,10 +3368,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x63", + "0x0000000000000000000000000000000000000000000000000000000000000063", "0x0a554bf2065689124bc29f4682ab44ec9b0a57d6127369ba1472e44fe33c1e0b", "0x0787331b97015bcc2de26a193462de25ea344ac473ede865141e5c5eeb3dcbd8", "0x233e9653b4b5ad92ad0b957fa484ca634e5f2a11538a2b7a9b1690bd8690ff4a", @@ -3381,7 +3381,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3402,10 +3402,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x66", + "0x0000000000000000000000000000000000000000000000000000000000000066", "0x1393590ad2f7176b00a93fa70c96ad01c8e31c2f1798cad89820f9cdf2e0b68e", "0x12ab69a1346a19a14306f3f0dfdae2744d9b48e22fd1dc3da27b10bbf3b78caa", "0x233e9653b4b5ad92ad0b957fa484ca634e5f2a11538a2b7a9b1690bd8690ff4a", @@ -3415,7 +3415,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3436,10 +3436,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x65", + "0x0000000000000000000000000000000000000000000000000000000000000065", "0x1393590ad2f7176b00a93fa70c96ad01c8e31c2f1798cad89820f9cdf2e0b68e", "0x12ab69a1346a19a14306f3f0dfdae2744d9b48e22fd1dc3da27b10bbf3b78caa", "0x233e9653b4b5ad92ad0b957fa484ca634e5f2a11538a2b7a9b1690bd8690ff4a", @@ -3449,7 +3449,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3470,10 +3470,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x68", + "0x0000000000000000000000000000000000000000000000000000000000000068", "0x2d5fb05617680b948e048ec3a545a75dbf8561eef41dfeee7c9296a54fa230db", "0x12ab69a1346a19a14306f3f0dfdae2744d9b48e22fd1dc3da27b10bbf3b78caa", "0x233e9653b4b5ad92ad0b957fa484ca634e5f2a11538a2b7a9b1690bd8690ff4a", @@ -3483,7 +3483,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3504,10 +3504,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x67", + "0x0000000000000000000000000000000000000000000000000000000000000067", "0x2d5fb05617680b948e048ec3a545a75dbf8561eef41dfeee7c9296a54fa230db", "0x12ab69a1346a19a14306f3f0dfdae2744d9b48e22fd1dc3da27b10bbf3b78caa", "0x233e9653b4b5ad92ad0b957fa484ca634e5f2a11538a2b7a9b1690bd8690ff4a", @@ -3517,7 +3517,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3538,10 +3538,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x6a", + "0x000000000000000000000000000000000000000000000000000000000000006a", "0x2c76aa190e9520cd027a475bf2ddbc60c3ca96241d9164031d561c8f8fec5932", "0x1c9dcadb2ff08e929371faa0756fd71124b1d7777f084bf993c65065f9545d49", "0x21aee53f410431520fab6fac3da77aaf931f81d9a765a8844caa7f8252eae922", @@ -3551,7 +3551,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3572,10 +3572,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x69", + "0x0000000000000000000000000000000000000000000000000000000000000069", "0x2c76aa190e9520cd027a475bf2ddbc60c3ca96241d9164031d561c8f8fec5932", "0x1c9dcadb2ff08e929371faa0756fd71124b1d7777f084bf993c65065f9545d49", "0x21aee53f410431520fab6fac3da77aaf931f81d9a765a8844caa7f8252eae922", @@ -3585,7 +3585,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3606,10 +3606,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x6c", + "0x000000000000000000000000000000000000000000000000000000000000006c", "0x21b17978f3b38841bdd96f78bd495ad1b83694a4bb8beba43d7ed5e815b35fa8", "0x1c9dcadb2ff08e929371faa0756fd71124b1d7777f084bf993c65065f9545d49", "0x21aee53f410431520fab6fac3da77aaf931f81d9a765a8844caa7f8252eae922", @@ -3619,7 +3619,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3640,10 +3640,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x6b", + "0x000000000000000000000000000000000000000000000000000000000000006b", "0x21b17978f3b38841bdd96f78bd495ad1b83694a4bb8beba43d7ed5e815b35fa8", "0x1c9dcadb2ff08e929371faa0756fd71124b1d7777f084bf993c65065f9545d49", "0x21aee53f410431520fab6fac3da77aaf931f81d9a765a8844caa7f8252eae922", @@ -3653,7 +3653,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3674,10 +3674,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x6e", + "0x000000000000000000000000000000000000000000000000000000000000006e", "0x01ed201d9b57612491a9bba87d57fc58f2a98c601c5c9cb9e56d3cac1ab94d38", "0x1fc975a89c91f68124eb0f09ae7225bce6351bb6e168f4e9c404287d01ee1006", "0x21aee53f410431520fab6fac3da77aaf931f81d9a765a8844caa7f8252eae922", @@ -3687,7 +3687,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3708,10 +3708,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x6d", + "0x000000000000000000000000000000000000000000000000000000000000006d", "0x01ed201d9b57612491a9bba87d57fc58f2a98c601c5c9cb9e56d3cac1ab94d38", "0x1fc975a89c91f68124eb0f09ae7225bce6351bb6e168f4e9c404287d01ee1006", "0x21aee53f410431520fab6fac3da77aaf931f81d9a765a8844caa7f8252eae922", @@ -3721,7 +3721,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3742,10 +3742,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x70", + "0x0000000000000000000000000000000000000000000000000000000000000070", "0x0bdb69846c3719f9568a98d96ae209cb20477c493bbce3ef856a99beac998ebb", "0x1fc975a89c91f68124eb0f09ae7225bce6351bb6e168f4e9c404287d01ee1006", "0x21aee53f410431520fab6fac3da77aaf931f81d9a765a8844caa7f8252eae922", @@ -3755,7 +3755,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3776,10 +3776,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x6f", + "0x000000000000000000000000000000000000000000000000000000000000006f", "0x0bdb69846c3719f9568a98d96ae209cb20477c493bbce3ef856a99beac998ebb", "0x1fc975a89c91f68124eb0f09ae7225bce6351bb6e168f4e9c404287d01ee1006", "0x21aee53f410431520fab6fac3da77aaf931f81d9a765a8844caa7f8252eae922", @@ -3789,7 +3789,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3810,10 +3810,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x72", + "0x0000000000000000000000000000000000000000000000000000000000000072", "0x2351e7bed379df59a03828dab6dadf7655feae7fce5dbd0a7032b758737ce8cf", "0x1a71f18aec5ce8a9290fe2f94b35bfaa95bfa60974df47bbd30c698edff23d57", "0x3023413faab455d369b6a06e89152fcb30b9dc2139a3e155d84c2069f0f712e9", @@ -3823,7 +3823,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3844,10 +3844,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x71", + "0x0000000000000000000000000000000000000000000000000000000000000071", "0x2351e7bed379df59a03828dab6dadf7655feae7fce5dbd0a7032b758737ce8cf", "0x1a71f18aec5ce8a9290fe2f94b35bfaa95bfa60974df47bbd30c698edff23d57", "0x3023413faab455d369b6a06e89152fcb30b9dc2139a3e155d84c2069f0f712e9", @@ -3857,7 +3857,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3878,10 +3878,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x74", + "0x0000000000000000000000000000000000000000000000000000000000000074", "0x222348ac486a859c1753cd387c8c05a46cec0cabe4c7f70dd036d280476b37e2", "0x1a71f18aec5ce8a9290fe2f94b35bfaa95bfa60974df47bbd30c698edff23d57", "0x3023413faab455d369b6a06e89152fcb30b9dc2139a3e155d84c2069f0f712e9", @@ -3891,7 +3891,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3912,10 +3912,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x73", + "0x0000000000000000000000000000000000000000000000000000000000000073", "0x222348ac486a859c1753cd387c8c05a46cec0cabe4c7f70dd036d280476b37e2", "0x1a71f18aec5ce8a9290fe2f94b35bfaa95bfa60974df47bbd30c698edff23d57", "0x3023413faab455d369b6a06e89152fcb30b9dc2139a3e155d84c2069f0f712e9", @@ -3925,7 +3925,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3946,10 +3946,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x76", + "0x0000000000000000000000000000000000000000000000000000000000000076", "0x300e88b68e68669f21b51ce802cbeecc26a0e7056e40c9f8287f9e67eb29e8ad", "0x28e1e9b6632a9fc25045e6e623d98d81fb7ac97cc8151c41899a028b36f2a56e", "0x3023413faab455d369b6a06e89152fcb30b9dc2139a3e155d84c2069f0f712e9", @@ -3959,7 +3959,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -3980,10 +3980,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x75", + "0x0000000000000000000000000000000000000000000000000000000000000075", "0x300e88b68e68669f21b51ce802cbeecc26a0e7056e40c9f8287f9e67eb29e8ad", "0x28e1e9b6632a9fc25045e6e623d98d81fb7ac97cc8151c41899a028b36f2a56e", "0x3023413faab455d369b6a06e89152fcb30b9dc2139a3e155d84c2069f0f712e9", @@ -3993,7 +3993,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -4014,10 +4014,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x78", + "0x0000000000000000000000000000000000000000000000000000000000000078", "0x260672a899e9da7485921265a34cd276a7857ec55198f3d958d0a5ee89663f6f", "0x28e1e9b6632a9fc25045e6e623d98d81fb7ac97cc8151c41899a028b36f2a56e", "0x3023413faab455d369b6a06e89152fcb30b9dc2139a3e155d84c2069f0f712e9", @@ -4027,7 +4027,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -4048,10 +4048,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x77", + "0x0000000000000000000000000000000000000000000000000000000000000077", "0x260672a899e9da7485921265a34cd276a7857ec55198f3d958d0a5ee89663f6f", "0x28e1e9b6632a9fc25045e6e623d98d81fb7ac97cc8151c41899a028b36f2a56e", "0x3023413faab455d369b6a06e89152fcb30b9dc2139a3e155d84c2069f0f712e9", @@ -4061,7 +4061,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -4082,10 +4082,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x7a", + "0x000000000000000000000000000000000000000000000000000000000000007a", "0x270d35b6762242677fbec4b17b0bd26ec8920b10deed870cca9a031a28768fcb", "0x04f462fbcb51bb5d100aa06428cbc314069405c83bdcb46677d4344e5216faad", "0x17ccf53115a45d69691235ab358befab17bfb383839ccc90466e533ad94052f2", @@ -4095,7 +4095,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -4116,10 +4116,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x79", + "0x0000000000000000000000000000000000000000000000000000000000000079", "0x270d35b6762242677fbec4b17b0bd26ec8920b10deed870cca9a031a28768fcb", "0x04f462fbcb51bb5d100aa06428cbc314069405c83bdcb46677d4344e5216faad", "0x17ccf53115a45d69691235ab358befab17bfb383839ccc90466e533ad94052f2", @@ -4129,7 +4129,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -4150,10 +4150,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x7c", + "0x000000000000000000000000000000000000000000000000000000000000007c", "0x049c4dd05b9700f7bd4b30b59e0cdd930db875ddc0c98477380d4777af875c16", "0x04f462fbcb51bb5d100aa06428cbc314069405c83bdcb46677d4344e5216faad", "0x17ccf53115a45d69691235ab358befab17bfb383839ccc90466e533ad94052f2", @@ -4163,7 +4163,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -4184,10 +4184,10 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x7b", + "0x000000000000000000000000000000000000000000000000000000000000007b", "0x049c4dd05b9700f7bd4b30b59e0cdd930db875ddc0c98477380d4777af875c16", "0x04f462fbcb51bb5d100aa06428cbc314069405c83bdcb46677d4344e5216faad", "0x17ccf53115a45d69691235ab358befab17bfb383839ccc90466e533ad94052f2", @@ -4197,7 +4197,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -4218,12 +4218,12 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x7e", + "0x000000000000000000000000000000000000000000000000000000000000007e", "0x1539a03f20a01a5259f9890fddac2fd3d4d868e3a4396e75d6d604792d5d9018", - "0x163bf6f59b7de1e4ce70320f5815b7514db2cb180a9752b71d2ee692279bf9", + "0x00163bf6f59b7de1e4ce70320f5815b7514db2cb180a9752b71d2ee692279bf9", "0x17ccf53115a45d69691235ab358befab17bfb383839ccc90466e533ad94052f2", "0x1b03e7b1bcb38d7dbf2baafb2be208a756c0a1349f561ab9a570887000add2df", "0x2a8e2e663870de9a2ffdd0019e51e195c40fe41498082aa700bbd77d5f7652ff", @@ -4231,7 +4231,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -4252,12 +4252,12 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x7d", + "0x000000000000000000000000000000000000000000000000000000000000007d", "0x1539a03f20a01a5259f9890fddac2fd3d4d868e3a4396e75d6d604792d5d9018", - "0x163bf6f59b7de1e4ce70320f5815b7514db2cb180a9752b71d2ee692279bf9", + "0x00163bf6f59b7de1e4ce70320f5815b7514db2cb180a9752b71d2ee692279bf9", "0x17ccf53115a45d69691235ab358befab17bfb383839ccc90466e533ad94052f2", "0x1b03e7b1bcb38d7dbf2baafb2be208a756c0a1349f561ab9a570887000add2df", "0x2a8e2e663870de9a2ffdd0019e51e195c40fe41498082aa700bbd77d5f7652ff", @@ -4265,7 +4265,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -4286,12 +4286,12 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x80", + "0x0000000000000000000000000000000000000000000000000000000000000080", "0x195c86c3f022fbc0f4a5759ab531269c547754bd6957ddc1e7f2e23a28e8ad1e", - "0x163bf6f59b7de1e4ce70320f5815b7514db2cb180a9752b71d2ee692279bf9", + "0x00163bf6f59b7de1e4ce70320f5815b7514db2cb180a9752b71d2ee692279bf9", "0x17ccf53115a45d69691235ab358befab17bfb383839ccc90466e533ad94052f2", "0x1b03e7b1bcb38d7dbf2baafb2be208a756c0a1349f561ab9a570887000add2df", "0x2a8e2e663870de9a2ffdd0019e51e195c40fe41498082aa700bbd77d5f7652ff", @@ -4299,7 +4299,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -4320,12 +4320,12 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], [ - "0x7f", + "0x000000000000000000000000000000000000000000000000000000000000007f", "0x195c86c3f022fbc0f4a5759ab531269c547754bd6957ddc1e7f2e23a28e8ad1e", - "0x163bf6f59b7de1e4ce70320f5815b7514db2cb180a9752b71d2ee692279bf9", + "0x00163bf6f59b7de1e4ce70320f5815b7514db2cb180a9752b71d2ee692279bf9", "0x17ccf53115a45d69691235ab358befab17bfb383839ccc90466e533ad94052f2", "0x1b03e7b1bcb38d7dbf2baafb2be208a756c0a1349f561ab9a570887000add2df", "0x2a8e2e663870de9a2ffdd0019e51e195c40fe41498082aa700bbd77d5f7652ff", @@ -4333,7 +4333,7 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", @@ -4354,46 +4354,122 @@ exports[`Data generation for noir tests Computes a private data tree 1`] = ` "0x2c90d2ed1cc895515adfddc2c00260c7be01b4bb41249f1bc83bb8e7598bef7b", "0x09cbd0dda37ac05bc5134c1da7d537db6bfa8553e0e344f9dc23647956d63df1", "0x1afb11871cf18149a0e62d57525c03f905c7bca6c0e1dae4b226ea164d198b6a", - "0x825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", + "0x00825ca56349b23727e91399761bff246cd651d9d4190b6304b42df26d8c8a42", ], ], } `; -exports[`Data generation for noir tests Computes function leaf 1`] = `"0x1d7d17187511e54f763c386f05c11bdb63bba2c24849f1b4b8bae96194d4fd0b"`; +exports[`Data generation for noir tests Computes contract tree data for %s 1`] = ` +{ + "root": 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"0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", + "0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", + "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", + "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", + "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", + "0x1a2b85ff013d4b2b25074297c7e44aa61f4836d0862b36db2e6ce2b5542f9ea9", + "0x177b9a10bbee32f77c719c6f8d071a18476cbeb021e155c642bbf93c716ce943", + ], + ], +} +`; -exports[`Data generation for noir tests Computes function tree data 1`] = ` +exports[`Data generation for noir tests Computes function tree data for defaultContract 1`] = ` { - "root": "0x2143c6a86ded93cd75de5680c57c61a2a178e0a840d8649a9e0411032f969f1d", + "index": 0, + "leaf": "0x23590e7237ce1163ade1adba49bf434066481a3bc9098e6751fd1d73e160ca95", + "root": "0x1c55b3903b8b2812ba2a2315edb30aa9b9d9c99c153e9215cc01ec8979a8e9be", "siblingPath": [ - "0x1f2e3193c7187347a099ee7cb5d6ac077da6b18706fe5508e658a3d0a05494f7", - "0x2350c7d2f19119502d073142ff019af884d6513f38b92d5119b4999502282247", + "0x04d0ae2ba130cedb690c9c9fc41fa1594bac32bfb57b9a7734528295c680b3fa", + "0x02996203377fc96b38fd41a1f65ddce2b26247fee321a12e58b457ee08621a14", "0x0837a67313f4dbbd8d6971c0672f961f0a3b9e218c1395d327915209292acbbf", "0x2e0ef36ddc5db29acb6ef904999046f835ce7c78a40c3f7a0edb03b2f917a765", ], } `; -exports[`Data generation for noir tests Computes the contract tree root 1`] = ` +exports[`Data generation for noir tests Computes function tree data for defaultContract 2`] = ` { - "root": "0x13e8827c83abfc4f0a37d0c228fb4ecd509d96613b8e7d74c169005a62ef67dd", + "index": 1, + "leaf": "0x04d0ae2ba130cedb690c9c9fc41fa1594bac32bfb57b9a7734528295c680b3fa", + "root": "0x1c55b3903b8b2812ba2a2315edb30aa9b9d9c99c153e9215cc01ec8979a8e9be", "siblingPath": [ - "0x00", - "0x27b1d0839a5b23baf12a8d195b18ac288fcf401afb2f70b8a4b529ede5fa9fed", - "0x21dbfd1d029bf447152fcf89e355c334610d1632436ba170f738107266a71550", - "0x0bcd1f91cf7bdd471d0a30c58c4706f3fdab3807a954b8f5b5e3bfec87d001bb", - "0x06e62084ee7b602fe9abc15632dda3269f56fb0c6e12519a2eb2ec897091919d", - "0x03c9e2e67178ac638746f068907e6677b4cc7a9592ef234ab6ab518f17efffa0", - "0x15d28cad4c0736decea8997cb324cf0a0e0602f4d74472cd977bce2c8dd9923f", - "0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab", - "0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257", - "0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9", - "0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02", - "0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5", - "0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5", - "0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93", - "0x1a2b85ff013d4b2b25074297c7e44aa61f4836d0862b36db2e6ce2b5542f9ea9", - "0x177b9a10bbee32f77c719c6f8d071a18476cbeb021e155c642bbf93c716ce943", + "0x23590e7237ce1163ade1adba49bf434066481a3bc9098e6751fd1d73e160ca95", + "0x02996203377fc96b38fd41a1f65ddce2b26247fee321a12e58b457ee08621a14", + "0x0837a67313f4dbbd8d6971c0672f961f0a3b9e218c1395d327915209292acbbf", + "0x2e0ef36ddc5db29acb6ef904999046f835ce7c78a40c3f7a0edb03b2f917a765", + ], +} +`; + +exports[`Data generation for noir tests Computes function tree data for defaultContract 3`] = ` +{ + "index": 2, + "leaf": "0x1fc20a5f4a9bf052ae4fee30281fd09908a25063c749bc35939502ffaeaee8c2", + "root": "0x1c55b3903b8b2812ba2a2315edb30aa9b9d9c99c153e9215cc01ec8979a8e9be", + "siblingPath": [ + "0x2d72ef5ebb7c974e1f5a8bed092f1cf1bf0a0cb1eda28516221ca7e5811ecf15", + "0x1bed44f12632c0a6343cd886bd3e548bb5e8d2fd35fe9bc81f28defd4ed885b0", + "0x0837a67313f4dbbd8d6971c0672f961f0a3b9e218c1395d327915209292acbbf", + "0x2e0ef36ddc5db29acb6ef904999046f835ce7c78a40c3f7a0edb03b2f917a765", + ], +} +`; + +exports[`Data generation for noir tests Computes function tree data for defaultContract 4`] = ` +{ + "index": 3, + "leaf": "0x2d72ef5ebb7c974e1f5a8bed092f1cf1bf0a0cb1eda28516221ca7e5811ecf15", + "root": "0x1c55b3903b8b2812ba2a2315edb30aa9b9d9c99c153e9215cc01ec8979a8e9be", + "siblingPath": [ + "0x1fc20a5f4a9bf052ae4fee30281fd09908a25063c749bc35939502ffaeaee8c2", + "0x1bed44f12632c0a6343cd886bd3e548bb5e8d2fd35fe9bc81f28defd4ed885b0", + "0x0837a67313f4dbbd8d6971c0672f961f0a3b9e218c1395d327915209292acbbf", + "0x2e0ef36ddc5db29acb6ef904999046f835ce7c78a40c3f7a0edb03b2f917a765", + ], +} +`; + +exports[`Data generation for noir tests Computes function tree data for parentContract 1`] = ` +{ + "index": 0, + "leaf": "0x2a5e01a1782ba31fa1c2e2619ec412182cd8aa4749c0b90e76f13c54ab8f890e", + "root": "0x02b3f6b0a36bd01f08cee2d607dbe08894bb8c58159e87bb17db28cad43291d4", + "siblingPath": [ + "0x1f2e3193c7187347a099ee7cb5d6ac077da6b18706fe5508e658a3d0a05494f7", + "0x2350c7d2f19119502d073142ff019af884d6513f38b92d5119b4999502282247", + "0x0837a67313f4dbbd8d6971c0672f961f0a3b9e218c1395d327915209292acbbf", + "0x2e0ef36ddc5db29acb6ef904999046f835ce7c78a40c3f7a0edb03b2f917a765", ], } `; diff --git a/yarn-project/noir-protocol-circuits/src/crates/bug-collecting-crate/invalid-test-syntax-passing.nr b/yarn-project/noir-protocol-circuits/src/crates/bug-collecting-crate/invalid-test-syntax-passing.nr index f5ed9d4fad5..b66e84edae5 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/bug-collecting-crate/invalid-test-syntax-passing.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/bug-collecting-crate/invalid-test-syntax-passing.nr @@ -1,4 +1,4 @@ -#[test(should_fail_with("array has reached maximum capacit"))] +#[test(should_fail_with("array has reached maximum capacity"))] fn smoke_test_array_push_capacity(){ } \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/common.nr b/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/common.nr index d591aa1754b..9e9422608f9 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/common.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/common.nr @@ -1,12 +1,14 @@ use dep::aztec::constants_gen::{ - MAX_READ_REQUESTS_PER_CALL, - MAX_NEW_NULLIFIERS_PER_CALL, EMPTY_NULLIFIED_COMMITMENT, + MAX_NEW_NULLIFIERS_PER_CALL, MAX_NEW_L2_TO_L1_MSGS_PER_CALL, MAX_NEW_COMMITMENTS_PER_CALL, + MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, + MAX_READ_REQUESTS_PER_CALL, }; use dep::types::{ abis::{ + call_request::CallRequest, combined_accumulated_data::CombinedAccumulatedData, complete_address::CompleteAddress, function_data::FunctionData, @@ -31,9 +33,14 @@ use dep::types::{ silo_nullifier, stdlib_recursion_verification_key_compress_native_vk, }, - point::Point, utils::{ - arrays::{array_to_bounded_vec, is_empty, is_empty_array, validate_array}, + arrays::{ + array_length, + array_to_bounded_vec, + is_empty_array, + struct_array_to_bounded_vec, + validate_array, + }, bounded_vec::BoundedVec, }, }; @@ -49,39 +56,13 @@ pub fn validate_arrays(app_public_inputs : PrivateCircuitPublicInputs) { validate_array(app_public_inputs.new_commitments); validate_array(app_public_inputs.new_nullifiers); validate_array(app_public_inputs.nullified_commitments); - validate_array(app_public_inputs.private_call_stack); - validate_array(app_public_inputs.public_call_stack); + validate_array(app_public_inputs.private_call_stack_hashes); + validate_array(app_public_inputs.public_call_stack_hashes); validate_array(app_public_inputs.new_l2_to_l1_msgs); // encrypted_logs_hash and unencrypted_logs_hash have their own integrity checks. } -// TODO(https://github.com/AztecProtocol/aztec-packages/issues/3060) Remove this -// Check that preimages in the callstack correspond to the hash in the private_call_stack -// ie hash(preimages) == private_call_stack for each preimage -// If the stack is empty, then we avoid doing a hash and set the output to be 0 -pub fn validate_call_stack(private_call : PrivateCallData) { - let stack = private_call.call_stack_item.public_inputs.private_call_stack; - let preimages = private_call.private_call_stack_preimages; - - assert(stack.len() == preimages.len()); // This is not in the cpp code. - for i in 0..stack.len() { - let hash = stack[i]; - let preimage = preimages[i]; - - // Note: this assumes it's computationally infeasible to have `0` as a valid call_stack_item_hash. - // Assumes `hash == 0` means "this stack item is empty". - - let calculated_hash = if hash == 0 { - 0 - } else { - preimage.hash() - }; - assert(hash == calculated_hash, "call stack item hash does not match preimage hash"); - } -} - - // Validate all read requests against the historic private data root. // Use their membership witnesses to do so. If the historic root is not yet // initialized, initialize it using the first read request here (if present). @@ -126,14 +107,14 @@ pub fn initialize_end_values(previous_kernel : PreviousKernelData, public_inputs // functions within this circuit: let start = previous_kernel.public_inputs.end; - public_inputs.end.read_requests = array_to_bounded_vec(start.read_requests, is_empty, 0); - public_inputs.end.new_commitments = array_to_bounded_vec(start.new_commitments, is_empty, 0); - public_inputs.end.new_nullifiers = array_to_bounded_vec(start.new_nullifiers, is_empty, 0); - public_inputs.end.nullified_commitments = array_to_bounded_vec(start.nullified_commitments, is_empty, 0); + public_inputs.end.read_requests = array_to_bounded_vec(start.read_requests); + public_inputs.end.new_commitments = array_to_bounded_vec(start.new_commitments); + public_inputs.end.new_nullifiers = array_to_bounded_vec(start.new_nullifiers); + public_inputs.end.nullified_commitments = array_to_bounded_vec(start.nullified_commitments); - public_inputs.end.private_call_stack = array_to_bounded_vec(start.private_call_stack, is_empty, 0); - public_inputs.end.public_call_stack = array_to_bounded_vec(start.public_call_stack, is_empty, 0); - public_inputs.end.new_l2_to_l1_msgs = array_to_bounded_vec(start.new_l2_to_l1_msgs, is_empty, 0); + public_inputs.end.private_call_stack = struct_array_to_bounded_vec(start.private_call_stack, |c: CallRequest| c.is_empty(), CallRequest::empty()); + public_inputs.end.public_call_stack = struct_array_to_bounded_vec(start.public_call_stack, |c: CallRequest| c.is_empty(), CallRequest::empty()); + public_inputs.end.new_l2_to_l1_msgs = array_to_bounded_vec(start.new_l2_to_l1_msgs); public_inputs.end.encrypted_logs_hash = start.encrypted_logs_hash; public_inputs.end.unencrypted_logs_hash = start.unencrypted_logs_hash; @@ -142,10 +123,47 @@ pub fn initialize_end_values(previous_kernel : PreviousKernelData, public_inputs public_inputs.end.unencrypted_log_preimages_length = start.unencrypted_log_preimages_length; public_inputs.end.optionally_revealed_data = start.optionally_revealed_data; - public_inputs.end.new_contracts = array_to_bounded_vec(start.new_contracts, |ncd: NewContractData| ncd.is_empty(), NewContractData::default()); + public_inputs.end.new_contracts = struct_array_to_bounded_vec(start.new_contracts, |ncd: NewContractData| ncd.is_empty(), NewContractData::default()); +} + +fn perform_static_call_checks(private_call: PrivateCallData) { + let public_inputs = private_call.call_stack_item.public_inputs; + let is_static_call = public_inputs.call_context.is_static_call; + if is_static_call { + // No state changes are allowed for static calls: + assert(is_empty_array(public_inputs.new_commitments), "new_commitments must be empty for static calls"); + assert(is_empty_array(public_inputs.new_nullifiers), "new_nullifiers must be empty for static calls"); + } +} + +fn is_valid_caller(request: CallRequest, private_call: PrivateCallData) -> bool { + let call_context = private_call.call_stack_item.public_inputs.call_context; + let valid_caller_context = request.caller_context.msg_sender.eq(call_context.msg_sender) + & request.caller_context.storage_contract_address.eq(call_context.storage_contract_address); + request.caller_contract_address.eq(private_call.call_stack_item.contract_address) + & (request.caller_context.is_empty() | valid_caller_context) +} + +fn validate_call_requests( + call_requests: BoundedVec, + hashes: [Field; N], + private_call: PrivateCallData, +) { + assert_eq(array_length(hashes), call_requests.len(), "call requests length does not match the expected length"); + for i in 0..N { + let hash = hashes[i]; + if hash != 0 { + let request = call_requests.get_unchecked(i); + assert_eq(request.hash, hash, "call stack hash does not match call request hash"); + assert(is_valid_caller(request, private_call), "invalid caller"); + } + } } -pub fn update_end_values(private_call : PrivateCallData, public_inputs : &mut KernelCircuitPublicInputsBuilder) { +pub fn update_end_values(private_call: PrivateCallData, public_inputs: &mut KernelCircuitPublicInputsBuilder) { + // If this call is a static call, certain operations are disallowed, such as creating new state. + perform_static_call_checks(private_call); + let private_call_public_inputs = private_call.call_stack_item.public_inputs; let read_requests = private_call_public_inputs.read_requests; @@ -157,22 +175,11 @@ pub fn update_end_values(private_call : PrivateCallData, public_inputs : &mut Ke let new_nullifiers = private_call_public_inputs.new_nullifiers; let nullified_commitments = private_call_public_inputs.nullified_commitments; - let is_static_call = private_call_public_inputs.call_context.is_static_call; - - if is_static_call { - // No state changes are allowed for static calls: - assert(is_empty_array(new_commitments), "new_commitments must be empty for static calls"); - assert(is_empty_array(new_nullifiers), "new_nullifiers must be empty for static calls"); - } - let storage_contract_address = private_call_public_inputs.call_context.storage_contract_address; // Transient read requests and witnesses are accumulated in public_inputs.end // We silo the read requests (domain separation per contract address) - assert(read_requests.len() == read_request_membership_witnesses.len()); // This is not done in the cpp code. - let mut siloed_read_requests: BoundedVec = BoundedVec::new(0); - - assert(read_requests.len() == MAX_READ_REQUESTS_PER_CALL); // This is not done in the cpp code + let mut siloed_read_requests: BoundedVec = BoundedVec::new(0); for i in 0..MAX_READ_REQUESTS_PER_CALL { let read_request = read_requests[i]; let witness = read_request_membership_witnesses[i]; @@ -185,8 +192,7 @@ pub fn update_end_values(private_call : PrivateCallData, public_inputs : &mut Ke // Enhance commitments and nullifiers with domain separation whereby domain is the contract. // // nullifiers - let mut siloed_new_nullifiers : BoundedVec = BoundedVec::new(0); - assert(new_nullifiers.len() == MAX_NEW_NULLIFIERS_PER_CALL); + let mut siloed_new_nullifiers: BoundedVec = BoundedVec::new(0); for i in 0..MAX_NEW_NULLIFIERS_PER_CALL { let new_nullifier = new_nullifiers[i]; if new_nullifier != 0 { @@ -196,8 +202,7 @@ pub fn update_end_values(private_call : PrivateCallData, public_inputs : &mut Ke public_inputs.end.new_nullifiers.push_vec(siloed_new_nullifiers); // commitments - let mut siloed_new_commitments : BoundedVec = BoundedVec::new(0); - assert(new_commitments.len() == MAX_NEW_COMMITMENTS_PER_CALL); + let mut siloed_new_commitments: BoundedVec = BoundedVec::new(0); for i in 0..MAX_NEW_COMMITMENTS_PER_CALL { let new_commitment = new_commitments[i]; if new_commitment != 0 { @@ -212,7 +217,7 @@ pub fn update_end_values(private_call : PrivateCallData, public_inputs : &mut Ke // take up a slot in the nullified_commitments array so that the array lines up properly // with new_nullifiers. This is necessary since the constant-size circuit-array functions // we use assume that the first 0-valued array entry designates the end of the array. - let mut siloed_nullified_commitments : BoundedVec = BoundedVec::new(0); + let mut siloed_nullified_commitments: BoundedVec = BoundedVec::new(0); for i in 0..MAX_NEW_NULLIFIERS_PER_CALL { // TODO(David): There are two ways to specify that a nullified commitment is empty. // Either with 0 or the special EMPTY_NULLIFIED_COMMITMENT constant @@ -236,10 +241,13 @@ pub fn update_end_values(private_call : PrivateCallData, public_inputs : &mut Ke public_inputs.end.nullified_commitments.push_vec(siloed_nullified_commitments); // Call stacks - let private_call_stack = array_to_bounded_vec(private_call_public_inputs.private_call_stack, is_empty, 0); + // Private call stack. + let private_call_stack = struct_array_to_bounded_vec(private_call.private_call_stack, |c: CallRequest| c.is_empty(), CallRequest::empty()); + validate_call_requests(private_call_stack, private_call_public_inputs.private_call_stack_hashes, private_call); public_inputs.end.private_call_stack.push_vec(private_call_stack); - - let public_call_stack = array_to_bounded_vec(private_call_public_inputs.public_call_stack, is_empty, 0); + // Public call stack. + let public_call_stack = struct_array_to_bounded_vec(private_call.public_call_stack, |c: CallRequest| c.is_empty(), CallRequest::empty()); + validate_call_requests(public_call_stack, private_call_public_inputs.public_call_stack_hashes, private_call); public_inputs.end.public_call_stack.push_vec(public_call_stack); // new l2 to l1 messages @@ -277,8 +285,8 @@ pub fn update_end_values(private_call : PrivateCallData, public_inputs : &mut Ke pub fn contract_logic(private_call : PrivateCallData, public_inputs : &mut KernelCircuitPublicInputsBuilder, contract_dep_data : ContractDeploymentData, function_data : FunctionData) { let private_call_public_inputs = private_call.call_stack_item.public_inputs; - let storage_contract_address = private_call_public_inputs.call_context.storage_contract_address; let portal_contract_address = private_call.portal_contract_address; + let contract_address = private_call.call_stack_item.contract_address; // TODO(https://github.com/AztecProtocol/aztec-packages/issues/3062): Why is this using a hash function from the stdlib::recursion namespace let private_call_vk_hash = @@ -307,16 +315,14 @@ pub fn contract_logic(private_call : PrivateCallData, public_inputs : &mut Kerne // TODO(https://github.com/AztecProtocol/aztec-packages/issues/3062) VKs are mocked out for now // assert(contract_dep_data.constructor_vk_hash == private_call_vk_hash, "constructor_vk_hash doesn't match private_call_vk_hash"); - assert(storage_contract_address.eq(new_contract_address), "contract address supplied does not match derived address"); + assert(contract_address.eq(new_contract_address), "contract address supplied does not match derived address"); let new_contract_address_nullifier = compute_new_contract_address_hash(new_contract_address); public_inputs.end.new_nullifiers.push(new_contract_address_nullifier); } else { // non-contract deployments must specify contract address being interacted with - // TODO - Allow special characters in error message. - // assert(storage_contract_address.to_field() != 0, "contract address can't be 0 for non-contract deployment related transactions"); - assert(storage_contract_address.to_field() != 0, "contract address cannot be 0"); + assert(!contract_address.is_zero(), "contract address cannot be zero"); /* We need to compute the root of the contract tree, starting from the function's VK: * - Compute the vk_hash (done above) @@ -325,14 +331,6 @@ pub fn contract_logic(private_call : PrivateCallData, public_inputs : &mut Kerne * - Compute the contract_leaf: hash(contract_address, portal_contract_address, function_tree_root) * - Hash the contract_leaf with the contract_leaf's sibling_path to get the contract_tree_root */ - - // Ensures that if the function is internal, only the contract itself can call it - if private_call.call_stack_item.function_data.is_internal { - let msg_sender = private_call_public_inputs.call_context.msg_sender; - // TODO - Allow special characters in error message. - // assert(storage_contract_address.eq(msg_sender), "call is internal, but msg_sender is not self"); - assert(storage_contract_address.eq(msg_sender), "call is internal but msg_sender is not self"); - } // The logic below ensures that the contract exists in the contracts tree let computed_function_tree_root = function_tree_root_from_siblings(private_call.call_stack_item.function_data.selector, @@ -344,37 +342,41 @@ pub fn contract_logic(private_call : PrivateCallData, public_inputs : &mut Kerne private_call.function_leaf_membership_witness.sibling_path); let computed_contract_tree_root = contract_tree_root_from_siblings(computed_function_tree_root, - storage_contract_address, + contract_address, portal_contract_address, private_call.contract_leaf_membership_witness.leaf_index, private_call.contract_leaf_membership_witness.sibling_path); let purported_contract_tree_root = private_call.call_stack_item.public_inputs.historical_block_data.contract_tree_root(); - // TODO - Allow special characters in error message. - // assert_eq(computed_contract_tree_root, purported_contract_tree_root, "computed_contract_tree_root doesn't match purported_contract_tree_root"); assert_eq(computed_contract_tree_root, purported_contract_tree_root, "computed_contract_tree_root does not match purported_contract_tree_root"); } } -pub fn validate_previous_kernel_values(end : CombinedAccumulatedData) { - validate_previous_kernel_arrays(end); - validate_previous_kernel_0th_nullifier(end); +pub fn validate_previous_kernel_values(end: CombinedAccumulatedData) { + assert(end.new_nullifiers[0] != 0, "The 0th nullifier in the accumulated nullifier array is zero"); } -// We validate that relevant arrays assumed to be zero-padded on the right comply to this format. -fn validate_previous_kernel_arrays(end : CombinedAccumulatedData) { - // Each of the following arrays is expected to be zero-padded. - validate_array(end.read_requests); - validate_array(end.pending_read_requests); - validate_array(end.new_commitments); - validate_array(end.new_nullifiers); - validate_array(end.nullified_commitments); - validate_array(end.private_call_stack); - validate_array(end.public_call_stack); - validate_array(end.new_l2_to_l1_msgs); -} +pub fn validate_call_against_request(private_call: PrivateCallData, request: CallRequest) { + let call_stack_item = private_call.call_stack_item; + assert(request.hash == call_stack_item.hash(), "calculated private_call_hash does not match provided private_call_hash at the top of the call stack"); -fn validate_previous_kernel_0th_nullifier(end : CombinedAccumulatedData) -{ - assert(end.new_nullifiers[0] != 0, "The 0th nullifier in the accumulated nullifier array is zero"); + let call_context = call_stack_item.public_inputs.call_context; + + // Ensures that if the function is internal, only the contract itself can call it. + if call_stack_item.function_data.is_internal { + assert(call_context.msg_sender.eq(call_context.storage_contract_address), "call is internal but msg_sender is not self"); + } + + if call_context.is_delegate_call { + let caller_context = request.caller_context; + assert(!caller_context.is_empty(), "caller context cannot be empty for delegate calls"); + assert(call_context.msg_sender.eq(caller_context.msg_sender), "call stack msg_sender does not match expected msg_sender for delegate calls"); + assert(call_context.storage_contract_address.eq(caller_context.storage_contract_address), "call stack storage address does not match expected contract address for delegate calls"); + assert(!call_stack_item.contract_address.eq(call_context.storage_contract_address), + "curent contract address must not match storage contract address for delegate calls"); + } else { + let caller_contract_address = request.caller_contract_address; + assert(call_context.msg_sender.eq(caller_contract_address), "call stack msg_sender does not match caller contract address"); + assert(call_context.storage_contract_address.eq(call_stack_item.contract_address), "call stack storage address does not match expected contract address"); + } } diff --git a/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/private_kernel_init.nr b/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/private_kernel_init.nr index ab3311975c6..7266c691924 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/private_kernel_init.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/private_kernel_init.nr @@ -43,8 +43,6 @@ impl PrivateKernelInputsInit { // // Ensure we are calling the correct initial function in the contract let entry_point_function_matches = tx_request.function_data.hash() == call_stack_item.function_data.hash(); - // TODO - Allow special characters in error message. - // assert(entry_point_function_matches, "tx_request.function_data must match call_stack_item.function_data"); assert(entry_point_function_matches, "tx_request function_data must match call_stack_item function_data"); // // Ensure we are passing the correct arguments to the function. @@ -54,22 +52,16 @@ impl PrivateKernelInputsInit { fn validate_inputs(self) { let call_stack_item = self.private_call.call_stack_item; - let function_data = call_stack_item.function_data; - - let public_inputs = call_stack_item.public_inputs; - - /* TODO(Mike/David): If we are going to have 3 initial calls on the private call stack, - * then do we still need the `private_call_stack` - * despite no longer needing a full `previous_kernel` - */ + let function_data = call_stack_item.function_data; assert(function_data.is_private, "Private kernel circuit can only execute a private function"); assert(function_data.is_internal == false, "Cannot call an internal function directly"); - assert(public_inputs.call_context.is_delegate_call == false, "Users cannot make a delegatecall"); - assert(public_inputs.call_context.is_static_call == false, "Users cannot make a static call"); - + + let call_context = call_stack_item.public_inputs.call_context; + assert(call_context.is_delegate_call == false, "Users cannot make a delegatecall"); + assert(call_context.is_static_call == false, "Users cannot make a static call"); // The below also prevents delegatecall/staticcall in the base case - assert(public_inputs.call_context.storage_contract_address.eq(call_stack_item.contract_address), "Storage contract address must be that of the called contract"); + assert(call_context.storage_contract_address.eq(call_stack_item.contract_address), "Storage contract address must be that of the called contract"); } fn update_end_values(self, public_inputs: &mut KernelCircuitPublicInputsBuilder) { @@ -87,7 +79,7 @@ impl PrivateKernelInputsInit { } pub fn native_private_kernel_circuit_initial(self) -> KernelCircuitPublicInputs { - let mut public_inputs : KernelCircuitPublicInputsBuilder = unsafe::zeroed(); + let mut public_inputs: KernelCircuitPublicInputsBuilder = unsafe::zeroed(); public_inputs.is_private = true; self.initialize_end_values(&mut public_inputs); @@ -98,8 +90,6 @@ impl PrivateKernelInputsInit { self.validate_this_private_call_against_tx_request(); - common::validate_call_stack(self.private_call); - common::validate_read_requests( public_inputs.constants.block_data.note_hash_tree_root(), self.private_call.call_stack_item.public_inputs.read_requests, @@ -142,9 +132,7 @@ mod tests { compute_logs_hash, stdlib_recursion_verification_key_compress_native_vk, }, - tests::{ - testing_harness::PrivateCallDataBuilder, - }, + tests::private_call_data_builder::PrivateCallDataBuilder, transaction::request::TxRequest, utils::arrays::array_length, }; @@ -305,7 +293,7 @@ mod tests { fn input_validation_malformed_arrays_private_call_stack() { let mut builder = PrivateKernelInitInputsBuilder::new_constructor(); - builder.private_call.public_inputs.private_call_stack.push_array([0, 9123]); + builder.private_call.public_inputs.private_call_stack_hashes.push_array([0, 9123]); builder.failed(); } @@ -314,7 +302,7 @@ mod tests { fn input_validation_malformed_arrays_public_call_stack() { let mut builder = PrivateKernelInitInputsBuilder::new_constructor(); - builder.private_call.public_inputs.public_call_stack.push_array([0, 9123]); + builder.private_call.public_inputs.public_call_stack_hashes.push_array([0, 9123]); builder.failed(); } @@ -328,15 +316,6 @@ mod tests { builder.failed(); } - #[test(should_fail_with="call stack item hash does not match preimage hash")] - fn contract_deployment_call_stack_item_hash_mismatch_fails() { - let mut builder = PrivateKernelInitInputsBuilder::new_constructor(); - - builder.private_call.public_inputs.private_call_stack.push(27); - - builder.failed(); - } - #[test(should_fail_with="contract address supplied does not match derived address")] fn contract_deployment_incorrect_contract_address_fails() { let mut builder = PrivateKernelInitInputsBuilder::new_constructor(); @@ -439,7 +418,7 @@ mod tests { builder.failed(); } - #[test(should_fail_with="computed_contract_tree_root does not match purported_contract_tree_root")] + #[test(should_fail_with="computed_contract_tree_root does not match purported_contract_tree_root")] fn private_function_incorrect_contract_leaf_sibling_path_fails() { let mut builder = PrivateKernelInitInputsBuilder::new(); @@ -608,7 +587,7 @@ mod tests { assert_eq(array_length(public_inputs.end.read_requests), 1); } - #[test] + #[test] fn native_max_read_requests_all_transient_works() { let mut builder = PrivateKernelInitInputsBuilder::new(); diff --git a/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/private_kernel_inner.nr b/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/private_kernel_inner.nr index 46177b29dbc..23231fdd4a0 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/private_kernel_inner.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/private_kernel_inner.nr @@ -16,13 +16,9 @@ struct PrivateKernelInputsInner { } impl PrivateKernelInputsInner { - // TODO: We can create a type alias for private call stack item - fn pop_and_validate_this_private_call_hash(self, public_inputs : &mut KernelCircuitPublicInputsBuilder){ - let private_call = self.private_call; - let popped_private_call_hash = public_inputs.end.private_call_stack.pop(); - - let calculated_this_private_call_hash = private_call.call_stack_item.hash(); - assert(popped_private_call_hash == calculated_this_private_call_hash, "calculated private_call_hash does not match provided private_call_hash at the top of the callstack"); + fn pop_and_validate_this_private_call_hash(self, public_inputs: &mut KernelCircuitPublicInputsBuilder){ + let call_request = public_inputs.end.private_call_stack.pop(); + common::validate_call_against_request(self.private_call, call_request); } fn validate_contract_tree_root(self) { @@ -55,8 +51,6 @@ impl PrivateKernelInputsInner { self.pop_and_validate_this_private_call_hash(&mut public_inputs); - common::validate_call_stack(self.private_call); - common::validate_read_requests( public_inputs.constants.block_data.note_hash_tree_root(), self.private_call.call_stack_item.public_inputs.read_requests, // read requests from private call @@ -68,12 +62,12 @@ impl PrivateKernelInputsInner { // ensure that historic/purported contract tree root matches the one in previous kernel self.validate_contract_tree_root(); - let private_call_stack_item = self.private_call.call_stack_item; + let this_call_stack_item = self.private_call.call_stack_item; common::contract_logic(self.private_call, - &mut public_inputs, - private_call_stack_item.public_inputs.contract_deployment_data, - private_call_stack_item.function_data); + &mut public_inputs, + this_call_stack_item.public_inputs.contract_deployment_data, + this_call_stack_item.function_data); let (is_previous_state_valid, updated_aggregation_object) = verify_previous_kernel_state(public_inputs.end.aggregation_object,self.private_call.proof); assert(is_previous_state_valid); @@ -93,12 +87,12 @@ mod tests { MAX_NEW_COMMITMENTS_PER_TX, }; use dep::types::{ - abis::kernel_circuit_public_inputs::KernelCircuitPublicInputs, + abis::{ + kernel_circuit_public_inputs::KernelCircuitPublicInputs, + }, tests::{ - testing_harness::{ - PreviousKernelDataBuilder, - PrivateCallDataBuilder, - }, + previous_kernel_data_builder::PreviousKernelDataBuilder, + private_call_data_builder::PrivateCallDataBuilder, }, address::Address, hash::compute_logs_hash, @@ -115,17 +109,35 @@ mod tests { impl PrivateKernelInnerInputsBuilder { pub fn new() -> Self { - let previous_kernel = PreviousKernelDataBuilder::new(false); + let previous_kernel = PreviousKernelDataBuilder::new(); let private_call = PrivateCallDataBuilder::new(false); PrivateKernelInnerInputsBuilder { previous_kernel, private_call } } + pub fn is_delegate_call(&mut self) -> Self { + let _ = self.private_call.is_delegate_call(); + *self + } + + pub fn is_static_call(&mut self) -> Self { + let _ = self.private_call.is_static_call(); + *self + } + + pub fn is_internal_call(&mut self) -> Self { + let _ = self.private_call.is_internal_call(); + self.previous_kernel.contract_address = self.private_call.contract_address; + *self + } + pub fn execute(&mut self) -> KernelCircuitPublicInputs { - let mut previous_kernel = self.previous_kernel.finish(); let private_call = self.private_call.finish(); - // We need to update the previous_kernel's private_call_stack in case the current call_stack_item has changed. - previous_kernel.public_inputs.end.private_call_stack[0] = private_call.call_stack_item.hash(); + // Update the previous_kernel's private_call_stack with the current call_stack_item. + let hash = private_call.call_stack_item.hash(); + let is_delegate_call = private_call.call_stack_item.public_inputs.call_context.is_delegate_call; + self.previous_kernel.push_private_call_request(hash, is_delegate_call); + let previous_kernel = self.previous_kernel.finish(); let kernel = PrivateKernelInputsInner { previous_kernel, @@ -135,29 +147,22 @@ mod tests { kernel.native_private_kernel_circuit_inner() } + pub fn succeeded(&mut self) { + let _ = self.execute(); + } + pub fn failed(&mut self) { let _ = self.execute(); } } - #[test(should_fail_with = "contract address cannot be 0")] + #[test(should_fail_with = "contract address cannot be zero")] fn private_function_zero_storage_contract_address_fails() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - // Set storage_contract_address to 0 - builder.private_call.public_inputs.call_context.storage_contract_address = Address::ZERO(); - - builder.failed(); - } - - #[test(should_fail_with = "call is internal but msg_sender is not self")] - fn private_function_incorrect_is_internal() { - let mut builder = PrivateKernelInnerInputsBuilder::new(); - - // Make the call internal but msg_sender != storage_contract_address. - builder.private_call.function_data.is_internal = true; - builder.private_call.public_inputs.call_context.msg_sender = Address::from_field(1); - builder.private_call.public_inputs.call_context.storage_contract_address = Address::from_field(2); + // Set (storage) contract_address to 0 + builder.private_call.contract_address = Address::zero(); + builder.private_call.public_inputs.call_context.storage_contract_address = Address::zero(); builder.failed(); } @@ -217,16 +222,60 @@ mod tests { builder.failed(); } - #[test(should_fail_with = "calculated private_call_hash does not match provided private_call_hash at the top of the callstack")] + #[test(should_fail_with = "calculated private_call_hash does not match provided private_call_hash at the top of the call stack")] fn private_function_incorrect_call_stack_item_hash_fails() { - let builder = PrivateKernelInnerInputsBuilder::new(); + let mut builder = PrivateKernelInnerInputsBuilder::new(); let private_call = builder.private_call.finish(); let hash = private_call.call_stack_item.hash(); + // Set the first call stack hash to a wrong value (the correct hash + 1). + builder.previous_kernel.push_private_call_request(hash + 1, false); + let previous_kernel = builder.previous_kernel.finish(); + + let kernel = PrivateKernelInputsInner { + previous_kernel, + private_call, + }; + + let _ = kernel.native_private_kernel_circuit_inner(); + } + + #[test(should_fail_with="call stack msg_sender does not match caller contract address")] + fn incorrect_msg_sender_for_regular_calls_fails() { + let mut builder = PrivateKernelInnerInputsBuilder::new(); + + // Set the msg_sender to a wrong value. + builder.private_call.public_inputs.call_context.msg_sender.inner += 1; + + builder.failed(); + } + + #[test(should_fail_with="call stack storage address does not match expected contract address")] + fn incorrect_storage_contract_for_regular_calls_fails() { + let mut builder = PrivateKernelInnerInputsBuilder::new(); - let mut previous_kernel = builder.previous_kernel.finish(); - // Set the first call stack hash to a wrong value (the correct value + 1). - previous_kernel.public_inputs.end.private_call_stack[0] = hash + 1; + // Set the storage contract address to a wrong value. + builder.private_call.public_inputs.call_context.storage_contract_address.inner += 1; + + builder.failed(); + } + + #[test] + fn delegate_call_succeeds() { + let mut builder = PrivateKernelInnerInputsBuilder::new().is_delegate_call(); + builder.succeeded(); + } + + #[test(should_fail_with="caller context cannot be empty for delegate calls")] + fn empty_caller_context_for_delegate_calls_fails() { + let mut builder = PrivateKernelInnerInputsBuilder::new().is_delegate_call(); + + let private_call = builder.private_call.finish(); + let hash = private_call.call_stack_item.hash(); + // Caller context is empty for regular calls. + let is_delegate_call = false; + builder.previous_kernel.push_private_call_request(hash, is_delegate_call); + let previous_kernel = builder.previous_kernel.finish(); let kernel = PrivateKernelInputsInner { previous_kernel, @@ -236,137 +285,233 @@ mod tests { let _ = kernel.native_private_kernel_circuit_inner(); } - #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_arrays_return_values() { + #[test(should_fail_with="call stack msg_sender does not match expected msg_sender for delegate calls")] + fn incorrect_msg_sender_for_delegate_calls_fails() { + let mut builder = PrivateKernelInnerInputsBuilder::new().is_delegate_call(); + + // Set the msg_sender to be the caller contract. + builder.private_call.public_inputs.call_context.msg_sender = builder.previous_kernel.contract_address; + + builder.failed(); + } + + #[test(should_fail_with="call stack storage address does not match expected contract address for delegate calls")] + fn incorrect_storage_address_for_delegate_calls_fails() { + let mut builder = PrivateKernelInnerInputsBuilder::new().is_delegate_call(); + + // Set the storage contract address to be the contract address. + builder.private_call.public_inputs.call_context.storage_contract_address = builder.private_call.contract_address; + + builder.failed(); + } + + #[test(should_fail_with="curent contract address must not match storage contract address for delegate calls")] + fn incorrect_storage_contract_for_delegate_calls_fails() { + let mut builder = PrivateKernelInnerInputsBuilder::new().is_delegate_call(); + + builder.private_call.contract_address = builder.private_call.public_inputs.call_context.storage_contract_address; + + builder.failed(); + } + + #[test] + fn internal_call_succeeds() { + let mut builder = PrivateKernelInnerInputsBuilder::new().is_internal_call(); + builder.succeeded(); + } + + #[test(should_fail_with = "call is internal but msg_sender is not self")] + fn private_function_incorrect_is_internal() { + let mut builder = PrivateKernelInnerInputsBuilder::new().is_internal_call(); + + // Tweak the (storage) contract_address to be different to msg_sender. + let msg_sender = builder.private_call.public_inputs.call_context.msg_sender.to_field(); + builder.private_call.contract_address = Address::from_field(msg_sender + 1); + builder.private_call.public_inputs.call_context.storage_contract_address = Address::from_field(msg_sender + 1); + + builder.failed(); + } + + #[test] + fn call_requests_succeeds() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.private_call.public_inputs.return_values.push_array([0, 553]); + builder.private_call.append_private_call_requests(2, false); + builder.private_call.append_private_call_requests(1, true); + builder.private_call.append_public_call_requests(1, false); + builder.private_call.append_public_call_requests(2, true); + + builder.succeeded(); + } + + #[test(should_fail_with = "call requests length does not match the expected length")] + fn incorrect_private_call_requests_length_fails() { + let mut builder = PrivateKernelInnerInputsBuilder::new(); + + builder.private_call.append_private_call_requests(2, false); + // Remove one call request. + let _ = builder.private_call.private_call_stack.pop(); builder.failed(); } - #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_arrays_read_requests() { + #[test(should_fail_with = "call requests length does not match the expected length")] + fn incorrect_public_call_requests_length_fails() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.private_call.public_inputs.read_requests.push_array([0, 9123]); + builder.private_call.append_public_call_requests(2, false); + // Remove one call request. + let _ = builder.private_call.public_call_stack.pop(); builder.failed(); } - #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_arrays_commitments() { + #[test(should_fail_with = "call stack hash does not match call request hash")] + fn incorrect_private_call_request_hash_fails() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.private_call.public_inputs.new_commitments.push_array([0, 9123]); + builder.private_call.append_private_call_requests(2, false); + let mut call_request = builder.private_call.private_call_stack.pop(); + // Change the hash to be a different value. + call_request.hash += 1; + builder.private_call.private_call_stack.push(call_request); builder.failed(); } - #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_arrays_nullifiers() { + #[test(should_fail_with = "call stack hash does not match call request hash")] + fn incorrect_public_call_request_hash_fails() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.private_call.public_inputs.new_nullifiers.push_array([0, 12]); - + builder.private_call.append_public_call_requests(2, false); + let mut call_request = builder.private_call.public_call_stack.pop(); + // Change the hash to be a different value. + call_request.hash += 1; + builder.private_call.public_call_stack.push(call_request); + builder.failed(); } - #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_arrays_nullified_commitments() { + #[test(should_fail_with = "invalid caller")] + fn incorrect_caller_address_for_private_call_request_fails() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.private_call.public_inputs.nullified_commitments.push_array([0, EMPTY_NULLIFIED_COMMITMENT]); - + builder.private_call.append_private_call_requests(1, false); + let mut call_request = builder.private_call.private_call_stack.pop(); + // Change the caller contract address to be a different value. + call_request.caller_contract_address.inner += 1; + builder.private_call.private_call_stack.push(call_request); + builder.failed(); } - #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_arrays_private_call_stack() { + #[test(should_fail_with = "invalid caller")] + fn incorrect_caller_address_for_public_call_request_fails() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.private_call.public_inputs.private_call_stack.push_array([0, 888]); - + builder.private_call.append_public_call_requests(1, false); + let mut call_request = builder.private_call.public_call_stack.pop(); + // Change the caller contract address to be a different value. + call_request.caller_contract_address.inner += 1; + builder.private_call.public_call_stack.push(call_request); + builder.failed(); } - #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_arrays_public_call_stack() { + #[test(should_fail_with = "invalid caller")] + fn incorrect_caller_context_for_private_delegate_call_request_fails() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.private_call.public_inputs.public_call_stack.push_array([0, 888]); - + builder.private_call.append_private_call_requests(1, true); + let mut call_request = builder.private_call.private_call_stack.pop(); + // Change the storage contract to be a different value. + call_request.caller_context.storage_contract_address.inner += 1; + builder.private_call.private_call_stack.push(call_request); + + builder.failed(); + } + + #[test(should_fail_with = "invalid caller")] + fn incorrect_caller_context_for_public_delegate_call_request_fails() { + let mut builder = PrivateKernelInnerInputsBuilder::new(); + + builder.private_call.append_public_call_requests(1, true); + let mut call_request = builder.private_call.public_call_stack.pop(); + // Change the storage contract to be a different value. + call_request.caller_context.storage_contract_address.inner += 1; + builder.private_call.public_call_stack.push(call_request); + builder.failed(); } #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_arrays_new_l2_to_l1_msgs() { + fn input_validation_malformed_arrays_return_values() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.private_call.public_inputs.new_l2_to_l1_msgs.push_array([0, 888]); - + builder.private_call.public_inputs.return_values.push_array([0, 553]); + builder.failed(); } #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_end_arrays_read_requests() { + fn input_validation_malformed_arrays_read_requests() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.previous_kernel.end.read_requests.push_array([0, 9123]); - + builder.private_call.public_inputs.read_requests.push_array([0, 9123]); + builder.failed(); } #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_end_arrays_commitments() { + fn input_validation_malformed_arrays_commitments() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.previous_kernel.end.new_commitments.push_array([0, 9123]); - + builder.private_call.public_inputs.new_commitments.push_array([0, 9123]); + builder.failed(); } #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_end_arrays_nullifiers() { + fn input_validation_malformed_arrays_nullifiers() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.previous_kernel.end.new_nullifiers.push_array([0, 9123]); + builder.private_call.public_inputs.new_nullifiers.push_array([0, 12]); builder.failed(); } #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_end_arrays_nullified_commitments() { + fn input_validation_malformed_arrays_nullified_commitments() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.previous_kernel.end.nullified_commitments.push_array([0, EMPTY_NULLIFIED_COMMITMENT]); + builder.private_call.public_inputs.nullified_commitments.push_array([0, EMPTY_NULLIFIED_COMMITMENT]); builder.failed(); } - + #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_end_arrays_private_call_stack() { + fn input_validation_malformed_arrays_private_call_stack() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.previous_kernel.end.private_call_stack.push_array([0, 888]); + builder.private_call.public_inputs.private_call_stack_hashes.push_array([0, 888]); builder.failed(); } #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_end_arrays_public_call_stack() { + fn input_validation_malformed_arrays_public_call_stack() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.previous_kernel.end.public_call_stack.push_array([0, 888]); + builder.private_call.public_inputs.public_call_stack_hashes.push_array([0, 888]); builder.failed(); } #[test(should_fail_with = "invalid array")] - fn input_validation_malformed_end_arrays_l2_to_l1_msgs() { + fn input_validation_malformed_arrays_new_l2_to_l1_msgs() { let mut builder = PrivateKernelInnerInputsBuilder::new(); - builder.previous_kernel.end.new_l2_to_l1_msgs.push_array([0, 1]); + builder.private_call.public_inputs.new_l2_to_l1_msgs.push_array([0, 888]); builder.failed(); } @@ -415,15 +560,6 @@ mod tests { builder.failed(); } - #[test(should_fail_with="call stack item hash does not match preimage hash")] - fn contract_deployment_call_stack_item_hash_mismatch_fails() { - let mut builder = PrivateKernelInnerInputsBuilder::new(); - - builder.private_call.public_inputs.private_call_stack.push(27); - - builder.failed(); - } - #[test(should_fail_with="private data tree root mismatch")] fn native_read_request_bad_request() { let mut builder = PrivateKernelInnerInputsBuilder::new(); @@ -596,6 +732,24 @@ mod tests { assert_eq(public_inputs.end.unencrypted_logs_hash, expected_unencrypted_logs_hash); } + #[test(should_fail_with="new_commitments must be empty for static calls")] + fn creating_new_commitments_on_static_call_fails() { + let mut builder = PrivateKernelInnerInputsBuilder::new().is_static_call(); + + builder.private_call.public_inputs.new_commitments.push(1); + + builder.failed(); + } + + #[test(should_fail_with="new_nullifiers must be empty for static calls")] + fn creating_new_nullifiers_on_static_call_fails() { + let mut builder = PrivateKernelInnerInputsBuilder::new().is_static_call(); + + builder.private_call.public_inputs.new_nullifiers.push(1); + + builder.failed(); + } + #[test(should_fail_with="The 0th nullifier in the accumulated nullifier array is zero")] fn zero_0th_nullifier_fails() { let mut builder = PrivateKernelInnerInputsBuilder::new(); diff --git a/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/private_kernel_ordering.nr b/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/private_kernel_ordering.nr index d832beec247..057317729aa 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/private_kernel_ordering.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/private-kernel-lib/src/private_kernel_ordering.nr @@ -8,6 +8,7 @@ use dep::aztec::constants_gen::{ use dep::std::unsafe; use dep::types::{ abis::{ + call_request::CallRequest, previous_kernel_data::PreviousKernelData, kernel_circuit_public_inputs::{ KernelCircuitPublicInputsBuilder, @@ -19,6 +20,7 @@ use dep::types::{ compute_unique_siloed_commitment, }, utils::{ + arrays::{array_length, struct_array_length}, bounded_vec::BoundedVec, }, }; @@ -30,17 +32,19 @@ struct PrivateKernelInputsOrdering { } impl PrivateKernelInputsOrdering { - fn match_reads_to_commitments( - public_inputs: &mut KernelCircuitPublicInputsBuilder, - read_commitment_hints: [Field; MAX_READ_REQUESTS_PER_TX], - ) { + fn validate_inputs(self) { + assert_eq(struct_array_length(self.previous_kernel.public_inputs.end.private_call_stack, |c: CallRequest| c.is_empty()), 0, + "Private call stack must be empty when executing the ordering circuit"); + } + + fn match_reads_to_commitments(self, public_inputs: &mut KernelCircuitPublicInputsBuilder) { let new_commitments = public_inputs.end.new_commitments; let read_requests = public_inputs.end.read_requests; // match reads to commitments from the previous call(s) for rr_idx in 0..MAX_READ_REQUESTS_PER_TX { let read_request = read_requests.get_unchecked(rr_idx); - let read_commitment_hint = read_commitment_hints[rr_idx]; + let read_commitment_hint = self.read_commitment_hints[rr_idx]; //TODO(David): Shouldn't this just be a uint64? let hint_pos = read_commitment_hint as u64; @@ -58,10 +62,7 @@ impl PrivateKernelInputsOrdering { public_inputs.end.read_requests = BoundedVec::new(0); } - fn match_nullifiers_to_commitments_and_squash( - public_inputs: &mut KernelCircuitPublicInputsBuilder, - nullifier_commitment_hints: [Field; MAX_NEW_NULLIFIERS_PER_TX], - ) { + fn match_nullifiers_to_commitments_and_squash(self, public_inputs: &mut KernelCircuitPublicInputsBuilder) { // Remark: The commitments in public_inputs.end have already been siloed by contract address! // Match nullifiers/nullified_commitments to commitments from the previous call(s) let nullified_commitments = public_inputs.end.nullified_commitments.storage; @@ -71,7 +72,7 @@ impl PrivateKernelInputsOrdering { for n_idx in 0..MAX_NEW_NULLIFIERS_PER_TX { // TODO - should not be able to squash the first nullifier. let nullified_commitment = nullified_commitments[n_idx]; - let nullifier_commitment_hint = nullifier_commitment_hints[n_idx]; + let nullifier_commitment_hint = self.nullifier_commitment_hints[n_idx]; let hint_pos = nullifier_commitment_hint as u64; // Nullified_commitment of value `EMPTY_NULLIFIED_COMMITMENT` implies non-transient (persistable) @@ -137,6 +138,8 @@ impl PrivateKernelInputsOrdering { let mut public_inputs : KernelCircuitPublicInputsBuilder = unsafe::zeroed(); public_inputs.is_private = true; + self.validate_inputs(); + common::validate_previous_kernel_values(self.previous_kernel.public_inputs.end); // Do this before any functions can modify the inputs. @@ -145,9 +148,9 @@ impl PrivateKernelInputsOrdering { // TODO(https://github.com/AztecProtocol/aztec-packages/issues/1486): validate that `len(new_nullifiers) == // len(nullified_commitments)` - PrivateKernelInputsOrdering::match_reads_to_commitments(&mut public_inputs, self.read_commitment_hints); + self.match_reads_to_commitments(&mut public_inputs); - PrivateKernelInputsOrdering::match_nullifiers_to_commitments_and_squash(&mut public_inputs, self.nullifier_commitment_hints); + self.match_nullifiers_to_commitments_and_squash(&mut public_inputs); PrivateKernelInputsOrdering::apply_commitment_nonces(&mut public_inputs); @@ -161,14 +164,13 @@ mod tests { MAX_READ_REQUESTS_PER_TX, MAX_NEW_COMMITMENTS_PER_TX, MAX_NEW_NULLIFIERS_PER_TX, - EMPTY_NULLIFIED_COMMITMENT, }; use dep::types::{ abis::kernel_circuit_public_inputs::KernelCircuitPublicInputsFinal, hash::compute_unique_siloed_commitments, - tests::testing_harness::PreviousKernelDataBuilder, + tests::previous_kernel_data_builder::PreviousKernelDataBuilder, utils::{ - arrays::{array_length, is_empty_array, expect_array_fields}, + arrays::{array_eq, array_length, is_empty_array}, bounded_vec::BoundedVec, }, }; @@ -182,7 +184,7 @@ mod tests { impl PrivateKernelOrderingInputsBuilder { pub fn new() -> Self { PrivateKernelOrderingInputsBuilder { - previous_kernel: PreviousKernelDataBuilder::new(false), + previous_kernel: PreviousKernelDataBuilder::new(), read_commitment_hints: [0; MAX_READ_REQUESTS_PER_TX], nullifier_commitment_hints: [0; MAX_NEW_NULLIFIERS_PER_TX], } @@ -235,7 +237,7 @@ mod tests { kernel.native_private_kernel_circuit_ordering() } - pub fn failed(&mut self) { + pub fn failed(self) { let _ = self.execute(); } } @@ -301,7 +303,7 @@ mod tests { // The nullifier at index 1 is chopped. let expected_new_nullifiers = [new_nullifiers[0], new_nullifiers[2]]; - assert(expect_array_fields(public_inputs.end.new_nullifiers, expected_new_nullifiers)); + assert(array_eq(public_inputs.end.new_nullifiers, expected_new_nullifiers)); } #[test] @@ -319,11 +321,11 @@ mod tests { let new_nullifiers = builder.get_new_nullifiers(); let public_inputs = builder.execute(); - assert(expect_array_fields(public_inputs.end.new_commitments, unique_siloed_commitments)); + assert(array_eq(public_inputs.end.new_commitments, unique_siloed_commitments)); // The nullifier at index 1 is chopped. let expected_new_nullifiers = [new_nullifiers[0], new_nullifiers[2]]; - assert(expect_array_fields(public_inputs.end.new_nullifiers, expected_new_nullifiers)); + assert(array_eq(public_inputs.end.new_nullifiers, expected_new_nullifiers)); } #[test] @@ -341,7 +343,7 @@ mod tests { let public_inputs = builder.execute(); assert(is_empty_array(public_inputs.end.new_commitments)); - assert(expect_array_fields(public_inputs.end.new_nullifiers, [new_nullifiers[0]])); + assert(array_eq(public_inputs.end.new_nullifiers, [new_nullifiers[0]])); } #[test] @@ -399,74 +401,20 @@ mod tests { builder.failed(); } - #[test(should_fail_with="The 0th nullifier in the accumulated nullifier array is zero")] - fn zero_0th_nullifier_fails() { - let mut builder = PrivateKernelOrderingInputsBuilder::new(); - - builder.previous_kernel.end.new_nullifiers = BoundedVec::new(0); - - builder.failed(); - } - - #[test(should_fail_with="invalid array")] - fn input_validation_malformed_end_arrays_read_requests() { - let mut builder = PrivateKernelOrderingInputsBuilder::new(); - - builder.previous_kernel.end.read_requests.push_array([0, 9123]); - - builder.failed(); - } - - #[test(should_fail_with="invalid array")] - fn input_validation_malformed_end_arrays_commitments() { + #[test(should_fail_with="Private call stack must be empty when executing the ordering circuit")] + fn non_empty_private_call_stack_should_fail() { let mut builder = PrivateKernelOrderingInputsBuilder::new(); - builder.previous_kernel.end.new_commitments.push_array([0, 9123]); + builder.previous_kernel.push_private_call_request(1, false); builder.failed(); } - #[test(should_fail_with="invalid array")] - fn input_validation_malformed_end_arrays_nullifiers() { - let mut builder = PrivateKernelOrderingInputsBuilder::new(); - - builder.previous_kernel.end.new_nullifiers.push_array([0, 9123]); - - builder.failed(); - } - - #[test(should_fail_with="invalid array")] - fn input_validation_malformed_end_arrays_nullified_commitments() { - let mut builder = PrivateKernelOrderingInputsBuilder::new(); - - builder.previous_kernel.end.nullified_commitments.push_array([0, EMPTY_NULLIFIED_COMMITMENT]); - - builder.failed(); - } - - #[test(should_fail_with="invalid array")] - fn input_validation_malformed_end_arrays_private_call_stack() { - let mut builder = PrivateKernelOrderingInputsBuilder::new(); - - builder.previous_kernel.end.private_call_stack.push_array([0, 9123]); - - builder.failed(); - } - - #[test(should_fail_with="invalid array")] - fn input_validation_malformed_end_arrays_public_call_stack() { - let mut builder = PrivateKernelOrderingInputsBuilder::new(); - - builder.previous_kernel.end.public_call_stack.push_array([0, 9123]); - - builder.failed(); - } - - #[test(should_fail_with="invalid array")] - fn input_validation_malformed_end_arrays_l2_to_l1_msgs() { + #[test(should_fail_with="The 0th nullifier in the accumulated nullifier array is zero")] + fn zero_0th_nullifier_fails() { let mut builder = PrivateKernelOrderingInputsBuilder::new(); - builder.previous_kernel.end.new_l2_to_l1_msgs.push_array([0, 9123]); + builder.previous_kernel.end.new_nullifiers = BoundedVec::new(0); builder.failed(); } diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/Nargo.toml b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/Nargo.toml new file mode 100644 index 00000000000..76525900176 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/Nargo.toml @@ -0,0 +1,9 @@ +[package] +name = "public_kernel_lib" +type = "lib" +authors = [""] +compiler_version = ">=0.18.0" + +[dependencies] +aztec = { path = "../../../../aztec-nr/aztec" } +types = { path = "../types" } diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/common.nr b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/common.nr new file mode 100644 index 00000000000..8bd199879b5 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/common.nr @@ -0,0 +1,297 @@ +use dep::types::{ + abis::{ + call_request::CallRequest, + call_stack_item::PublicCallStackItem, + combined_accumulated_data::{CombinedAccumulatedData, CombinedAccumulatedDataBuilder}, + complete_address::CompleteAddress, + kernel_circuit_public_inputs::KernelCircuitPublicInputsBuilder, + new_contract_data::NewContractData, + previous_kernel_data::PreviousKernelData, + public_call_data::PublicCallData, + public_circuit_public_inputs::PublicCircuitPublicInputs, + public_data_read::PublicDataRead, + public_data_update_request::PublicDataUpdateRequest, + }, + address::Address, + contrakt::{ + storage_read::StorageRead, + storage_update_request::StorageUpdateRequest, + }, + hash::{silo_commitment, silo_nullifier, compute_l2_to_l1_hash, accumulate_sha256}, + utils::{ + arrays::{array_length, array_to_bounded_vec, is_empty_array, struct_array_length, struct_array_to_bounded_vec}, + bounded_vec::BoundedVec, + uint128::U128, + }, +}; +use crate::hash::{compute_public_data_tree_index, compute_public_data_tree_value}; + +use dep::aztec::constants_gen::{ + EMPTY_NULLIFIED_COMMITMENT, + MAX_NEW_L2_TO_L1_MSGS_PER_CALL, + MAX_NEW_COMMITMENTS_PER_CALL, + MAX_NEW_NULLIFIERS_PER_CALL, + MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, + MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX, + MAX_PUBLIC_DATA_READS_PER_CALL, + MAX_READ_REQUESTS_PER_CALL, +}; + +// Validates inputs to the kernel circuit that are common to all invocation scenarios. +pub fn validate_inputs(public_call: PublicCallData){ + // Validates commons inputs for all type of kernel inputs + let this_call_stack_item: PublicCallStackItem = public_call.call_stack_item; + assert(this_call_stack_item.public_inputs.call_context.is_contract_deployment == false, + "Contract deployment cannot be a public function"); + assert(!this_call_stack_item.contract_address.eq(Address::zero()), + "Contract address cannot be zero"); + assert(this_call_stack_item.function_data.selector.to_field() != 0, + "Function signature cannot be zero"); + assert(this_call_stack_item.function_data.is_constructor == false, + "Constructors cannot be public functions"); + assert(this_call_stack_item.function_data.is_private == false, + "Cannot execute a private function with the public kernel circuit"); + assert(public_call.bytecode_hash != 0, + "Bytecode hash cannot be zero"); +} + +pub fn initialize_end_values(previous_kernel : PreviousKernelData, circuit_outputs: &mut KernelCircuitPublicInputsBuilder) { + // Initialises the circuit outputs with the end state of the previous iteration + circuit_outputs.constants = previous_kernel.public_inputs.constants; + + // Ensure the arrays are the same as previously, before we start pushing more data onto them in other + // functions within this circuit: + let start = previous_kernel.public_inputs.end; + + circuit_outputs.end.new_commitments = array_to_bounded_vec(start.new_commitments); + circuit_outputs.end.new_nullifiers = array_to_bounded_vec(start.new_nullifiers); + + circuit_outputs.end.private_call_stack = struct_array_to_bounded_vec(start.private_call_stack, |c: CallRequest| c.is_empty(), CallRequest::empty()); + circuit_outputs.end.public_call_stack = struct_array_to_bounded_vec(start.public_call_stack, |c: CallRequest| c.is_empty(), CallRequest::empty()); + circuit_outputs.end.new_l2_to_l1_msgs = array_to_bounded_vec(start.new_l2_to_l1_msgs); + + circuit_outputs.end.optionally_revealed_data = start.optionally_revealed_data; + + circuit_outputs.end.public_data_update_requests = struct_array_to_bounded_vec(start.public_data_update_requests, |pdu: PublicDataUpdateRequest| pdu.is_empty(), PublicDataUpdateRequest::empty()); + circuit_outputs.end.public_data_reads = struct_array_to_bounded_vec(start.public_data_reads, |pdr: PublicDataRead| pdr.is_empty(), PublicDataRead::empty()); + + // Public kernel does not modify encrypted logs values --> we just copy them to output + circuit_outputs.end.encrypted_logs_hash = start.encrypted_logs_hash; + circuit_outputs.end.encrypted_log_preimages_length = start.encrypted_log_preimages_length; + + circuit_outputs.end.new_contracts = struct_array_to_bounded_vec(previous_kernel.public_inputs.end.new_contracts, |ncd: NewContractData| ncd.is_empty(), NewContractData::default()); +} + +fn perform_static_call_checks(public_call: PublicCallData){ + let public_inputs = public_call.call_stack_item.public_inputs; + if public_inputs.call_context.is_static_call { + // No state changes are allowed for static calls: + assert(is_empty_array(public_inputs.new_commitments), "new_commitments must be empty for static calls"); + assert(is_empty_array(public_inputs.new_nullifiers), "new_nullifiers must be empty for static calls"); + + let update_requests_length = struct_array_length(public_inputs.contract_storage_update_requests, |r: StorageUpdateRequest| r.is_empty()); + assert(update_requests_length == 0, "No contract storage update requests are allowed for static calls"); + } +} + +fn is_valid_caller(request: CallRequest, public_call: PublicCallData) -> bool { + let call_context = public_call.call_stack_item.public_inputs.call_context; + let valid_caller_context = request.caller_context.msg_sender.eq(call_context.msg_sender) + & request.caller_context.storage_contract_address.eq(call_context.storage_contract_address); + request.caller_contract_address.eq(public_call.call_stack_item.contract_address) + & (request.caller_context.is_empty() | valid_caller_context) +} + +fn validate_call_requests(call_requests: BoundedVec, hashes: [Field; N], public_call: PublicCallData) { + assert_eq(array_length(hashes), call_requests.len(), "call requests length does not match the expected length"); + for i in 0..N { + let hash = hashes[i]; + if hash != 0 { + let request = call_requests.get_unchecked(i); + assert_eq(request.hash, hash, "call stack hash does not match call request hash"); + assert(is_valid_caller(request, public_call), "invalid caller"); + } + } +} + +pub fn update_public_end_values(public_call: PublicCallData, circuit_outputs : &mut KernelCircuitPublicInputsBuilder) { + // Updates the circuit outputs with new state changes, call stack etc + circuit_outputs.is_private = false; + + // If this call is a static call, certain operations are disallowed, such as creating new state. + perform_static_call_checks(public_call); + + // Update public call stack. + let public_call_requests = struct_array_to_bounded_vec(public_call.public_call_stack, |c: CallRequest| c.is_empty(), CallRequest::empty()); + let hashes = public_call.call_stack_item.public_inputs.public_call_stack_hashes; + validate_call_requests(public_call_requests, hashes, public_call); + circuit_outputs.end.public_call_stack.push_vec(public_call_requests); + + // don't update pending_read_requests, because those just get passed through without any change + + propagate_new_nullifiers(public_call, circuit_outputs); + propagate_new_commitments(public_call, circuit_outputs); + + propagate_new_l2_to_l1_messages(public_call, circuit_outputs); + + propagate_valid_public_data_update_requests(public_call, circuit_outputs); + + propagate_valid_public_data_reads(public_call, circuit_outputs); +} + +fn propagate_valid_public_data_update_requests(public_call: PublicCallData, circuit_outputs: &mut KernelCircuitPublicInputsBuilder){ + let contract_address = public_call.call_stack_item.contract_address; + let update_requests = public_call.call_stack_item.public_inputs.contract_storage_update_requests; + + let mut public_data_update_requests : BoundedVec = BoundedVec::new(PublicDataUpdateRequest::empty()); + + for i in 0..MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX { + let update_request = update_requests[i]; + if (!update_request.is_empty()) { + let public_data_update_request = PublicDataUpdateRequest { + leaf_index: compute_public_data_tree_index(contract_address, update_request.storage_slot), + old_value: compute_public_data_tree_value(update_request.old_value), + new_value: compute_public_data_tree_value(update_request.new_value), + }; + + public_data_update_requests.push(public_data_update_request); + } + } + circuit_outputs.end.public_data_update_requests.push_vec(public_data_update_requests); +} + +fn propagate_valid_public_data_reads(public_call: PublicCallData, circuit_outputs: &mut KernelCircuitPublicInputsBuilder){ + let contract_address = public_call.call_stack_item.contract_address; + let read_requests = public_call.call_stack_item.public_inputs.contract_storage_reads; + + let mut public_data_reads : BoundedVec = BoundedVec::new(PublicDataRead::empty()); + + for i in 0..MAX_PUBLIC_DATA_READS_PER_CALL { + let read_request: StorageRead = read_requests[i]; + if !read_request.is_empty() { + let public_data_read = PublicDataRead { + leaf_index: compute_public_data_tree_index(contract_address, read_request.storage_slot), + value: compute_public_data_tree_value(read_request.current_value), + }; + public_data_reads.push(public_data_read); + } + } + circuit_outputs.end.public_data_reads.push_vec(public_data_reads); +} + +fn propagate_new_commitments(public_call: PublicCallData, circuit_outputs : &mut KernelCircuitPublicInputsBuilder) { + let public_call_public_inputs = public_call.call_stack_item.public_inputs; + + let new_commitments = public_call.call_stack_item.public_inputs.new_commitments; + let storage_contract_address = public_call_public_inputs.call_context.storage_contract_address; + + let mut siloed_new_commitments : BoundedVec = BoundedVec::new(0); + for i in 0..MAX_NEW_COMMITMENTS_PER_CALL { + let new_commitment = new_commitments[i]; + if new_commitment != 0 { + siloed_new_commitments.push(silo_commitment(storage_contract_address, new_commitment)); + } + } + circuit_outputs.end.new_commitments.push_vec(siloed_new_commitments); +} + +fn propagate_new_nullifiers(public_call: PublicCallData, circuit_outputs : &mut KernelCircuitPublicInputsBuilder) { + let new_nullifiers = public_call.call_stack_item.public_inputs.new_nullifiers; + let storage_contract_address = public_call.call_stack_item.public_inputs.call_context.storage_contract_address; + + // Enhance commitments and nullifiers with domain separation whereby domain is the contract. + let mut siloed_new_nullifiers : BoundedVec = BoundedVec::new(0); + for i in 0..MAX_NEW_NULLIFIERS_PER_CALL { + let new_nullifier = new_nullifiers[i]; + if new_nullifier != 0 { + siloed_new_nullifiers.push(silo_nullifier(storage_contract_address, new_nullifier)); + } + } + + circuit_outputs.end.new_nullifiers.push_vec(siloed_new_nullifiers); +} + +fn propagate_new_l2_to_l1_messages(public_call: PublicCallData, public_inputs: &mut KernelCircuitPublicInputsBuilder){ + // new l2 to l1 messages + let public_call_public_inputs = public_call.call_stack_item.public_inputs; + let portal_contract_address = public_call.portal_contract_address; + let storage_contract_address = public_call_public_inputs.call_context.storage_contract_address; + + let new_l2_to_l1_msgs = public_call_public_inputs.new_l2_to_l1_msgs; + let mut new_l2_to_l1_msgs_to_insert : BoundedVec = BoundedVec::new(0); + for i in 0..MAX_NEW_L2_TO_L1_MSGS_PER_CALL { + let msg_is_zero = new_l2_to_l1_msgs[i] == 0; + if msg_is_zero == false { + let new_l2_to_l1_msgs = compute_l2_to_l1_hash(storage_contract_address, + public_inputs.constants.tx_context.version, + portal_contract_address, + public_inputs.constants.tx_context.chain_id, + new_l2_to_l1_msgs[i]); + new_l2_to_l1_msgs_to_insert.push(new_l2_to_l1_msgs) + } + } + public_inputs.end.new_l2_to_l1_msgs.push_vec(new_l2_to_l1_msgs_to_insert); +} + +/** + * @brief Accumulates unencrypted logs hashes and lengths. + * @param The type of kernel input + * @param public_kernel_inputs The inputs to this iteration of the kernel circuit + * @param circuit_outputs The circuit outputs to be populated + * @note See the following thread if not clear: + * https://discourse.aztec.network/t/proposal-forcing-the-sequencer-to-actually-submit-data-to-l1/426 + * @note Used by public kernels which had previous iterations. + */ +pub fn accumulate_unencrypted_logs(public_call: PublicCallData, + previous_kernel: PreviousKernelData, + public_inputs: &mut KernelCircuitPublicInputsBuilder) +{ + let public_call_public_inputs = public_call.call_stack_item.public_inputs; + + let previous_kernel_end = previous_kernel.public_inputs.end; + let previous_unencrypted_logs_hash = previous_kernel_end.unencrypted_logs_hash; + + let current_unencrypted_logs_hash = public_call_public_inputs.unencrypted_logs_hash; + public_inputs.end.unencrypted_logs_hash = accumulate_sha256([ + U128::from_field(previous_unencrypted_logs_hash[0]), + U128::from_field(previous_unencrypted_logs_hash[1]), + U128::from_field(current_unencrypted_logs_hash[0]), + U128::from_field(current_unencrypted_logs_hash[1]) + ]); + + // Add log preimages lengths from current iteration to accumulated lengths + let current_unencrypted_log_preimages_length = public_call_public_inputs.unencrypted_log_preimages_length; + public_inputs.end.unencrypted_log_preimages_length = + previous_kernel_end.unencrypted_log_preimages_length + current_unencrypted_log_preimages_length; +} + +/** + * @brief Validates that the call stack item for this circuit iteration is at the top of the call stack + * @param builder The circuit builder + * @param public_kernel_inputs The inputs to this iteration of the kernel circuit + */ + pub fn validate_call_against_request(public_call: PublicCallData, request: CallRequest) { + // TODO: this logic might need to change to accommodate the weird edge 3 initial txs (the 'main' tx, the 'fee' tx, + // and the 'gas rebate' tx). + let call_stack_item = public_call.call_stack_item; + assert(request.hash == call_stack_item.hash(), + "calculated public_kernel_inputs_hash does not match provided public_kernel_inputs_hash at the top of the call stack"); + + let call_context = call_stack_item.public_inputs.call_context; + + if (call_stack_item.function_data.is_internal) { + assert(call_stack_item.contract_address.eq(call_context.msg_sender), "msg_sender must be self for internal calls"); + } + + if call_context.is_delegate_call { + let caller_context = request.caller_context; + assert(!caller_context.is_empty(), "caller context cannot be empty for delegate calls"); + assert(call_context.msg_sender.eq(caller_context.msg_sender), "call stack msg_sender does not match expected msg_sender for delegate calls"); + assert(call_context.storage_contract_address.eq(caller_context.storage_contract_address), "call stack storage address does not match expected contract address for delegate calls"); + assert(!call_stack_item.contract_address.eq(call_context.storage_contract_address), "curent contract address must not match storage contract address for delegate calls"); + } else { + let caller_contract_address = request.caller_contract_address; + assert(call_context.msg_sender.eq(caller_contract_address), "call stack msg_sender does not match caller contract address"); + assert(call_context.storage_contract_address.eq(call_stack_item.contract_address), "call stack storage address does not match expected contract address"); + } +} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/hash.nr b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/hash.nr new file mode 100644 index 00000000000..7a3cc9a5f0e --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/hash.nr @@ -0,0 +1,19 @@ +use dep::types::address::Address; +use dep::aztec::{ + constants_gen, + constants_gen::{GENERATOR_INDEX__PUBLIC_LEAF_INDEX}, + hash::sha256_to_field, +}; + +pub fn compute_public_data_tree_index(contract_address: Address, storage_slot: Field) -> Field { + dep::std::hash::pedersen_hash_with_separator([ + contract_address.to_field(), + storage_slot + ], constants_gen::GENERATOR_INDEX__PUBLIC_LEAF_INDEX) +} + +pub fn compute_public_data_tree_value(value: Field) -> Field { + // as it's a public value, it doesn't require hashing. + // leaving this function here in case we decide to change this. + value +} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/lib.nr b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/lib.nr new file mode 100644 index 00000000000..d874ecfac35 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/lib.nr @@ -0,0 +1,11 @@ +// TODO: rename to be precise as to what its common to (the public kernel circuits). +mod common; + +mod hash; +mod utils; + +mod public_kernel_private_previous; +mod public_kernel_public_previous; + +use public_kernel_private_previous::PublicKernelPrivatePreviousInputs; +use public_kernel_public_previous::PublicKernelPublicPreviousInputs; diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/public_kernel_private_previous.nr b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/public_kernel_private_previous.nr new file mode 100644 index 00000000000..6527e18c741 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/public_kernel_private_previous.nr @@ -0,0 +1,504 @@ +use crate::common; +use dep::types::abis::{ + kernel_circuit_public_inputs::{KernelCircuitPublicInputs, KernelCircuitPublicInputsBuilder}, + previous_kernel_data::PreviousKernelData, + public_call_data::PublicCallData, +}; +use dep::std::unsafe; +// translated from cpp impl in +// aztec-packages/circuits/cpp/src/aztec3/circuits/kernel/public/native_public_kernel_circuit_private_previous_kernel.cpp + +struct PublicKernelPrivatePreviousInputs { + previous_kernel: PreviousKernelData, + public_call: PublicCallData, +} + +impl PublicKernelPrivatePreviousInputs { + fn validate_inputs(self) { + let private_call_stack = self.previous_kernel.public_inputs.end.private_call_stack; + for i in 0..private_call_stack.len() { + let private_call = private_call_stack[i]; + assert(private_call.is_empty(), + "Private call stack must be empty when executing in the public kernel"); + } + + let previous_call_is_private = self.previous_kernel.public_inputs.is_private; + assert(previous_call_is_private == true, + "Previous kernel must be private when in this public kernel version"); + } + + fn public_kernel_private_previous(self) -> KernelCircuitPublicInputs { + // construct the circuit outputs + let mut public_inputs: KernelCircuitPublicInputsBuilder = unsafe::zeroed(); + + // initialise the end state with our provided previous kernel state + common::initialize_end_values(self.previous_kernel, &mut public_inputs); + + // validate the inputs common to all invocation circumstances + common::validate_inputs(self.public_call); + + // validate the inputs unique to having a previous private kernel + self.validate_inputs(); + + // Pops the item from the call stack and validates it against the current execution. + let call_request = public_inputs.end.public_call_stack.pop(); + common::validate_call_against_request(self.public_call, call_request); + + common::update_public_end_values(self.public_call,&mut public_inputs); + + common::accumulate_unencrypted_logs(self.public_call, self.previous_kernel,&mut public_inputs); + + public_inputs.finish() + } +} + +mod tests { + use crate::{ + public_kernel_private_previous::PublicKernelPrivatePreviousInputs, + utils::{ + assert_eq_call_requests, + assert_eq_new_contracts, + assert_eq_public_data_reads, + assert_eq_public_data_update_requests, + compute_public_data_reads, + compute_public_data_update_requests, + }, + }; + use dep::types::{ + abis::{ + call_request::CallRequest, + function_selector::FunctionSelector, + kernel_circuit_public_inputs::KernelCircuitPublicInputs, + new_contract_data::NewContractData, + public_data_read::PublicDataRead, + public_data_update_request::PublicDataUpdateRequest, + }, + address::{Address, EthAddress}, + contrakt::storage_read::StorageRead, + hash::compute_logs_hash, + tests::{ + previous_kernel_data_builder::PreviousKernelDataBuilder, + public_call_data_builder::PublicCallDataBuilder, + }, + utils::{ + arrays::{ + array_eq, + array_length, + }, + bounded_vec::BoundedVec, + }, + }; + use dep::aztec::constants_gen::{ + MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, + MAX_PUBLIC_DATA_READS_PER_CALL, + MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_CALL, + }; + + struct PublicKernelPrivatePreviousInputsBuilder { + previous_kernel: PreviousKernelDataBuilder, + public_call: PublicCallDataBuilder, + } + + impl PublicKernelPrivatePreviousInputsBuilder { + pub fn new() -> Self { + let previous_kernel = PreviousKernelDataBuilder::new(); + let public_call = PublicCallDataBuilder::new(); + + PublicKernelPrivatePreviousInputsBuilder { + previous_kernel, + public_call, + } + } + + pub fn is_delegate_call(&mut self) -> Self { + let _ = self.public_call.is_delegate_call(); + *self + } + + pub fn is_internal_call(&mut self) -> Self { + let _ = self.public_call.is_internal_call(); + self.previous_kernel.contract_address = self.public_call.contract_address; + *self + } + + pub fn get_current_public_data_reads(self) -> [PublicDataRead; MAX_PUBLIC_DATA_READS_PER_CALL] { + let read_requests = self.public_call.public_inputs.contract_storage_reads.storage; + compute_public_data_reads(self.public_call.contract_address, read_requests) + } + + pub fn get_current_public_data_update_requests(self) -> [PublicDataUpdateRequest; MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_CALL] { + compute_public_data_update_requests( + self.public_call.contract_address, + self.public_call.public_inputs.contract_storage_update_requests.storage, + ) + } + + pub fn execute(&mut self) -> KernelCircuitPublicInputs { + let public_call = self.public_call.finish(); + // Adjust the call stack item hash for the current call in the previous iteration. + let hash = public_call.call_stack_item.hash(); + let is_delegate_call = public_call.call_stack_item.public_inputs.call_context.is_delegate_call; + self.previous_kernel.push_public_call_request(hash, is_delegate_call); + let previous_kernel = self.previous_kernel.finish(); + + let kernel = PublicKernelPrivatePreviousInputs { + previous_kernel, + public_call, + }; + + kernel.public_kernel_private_previous() + } + + pub fn succeeded(&mut self) { + let _ = self.execute(); + } + + pub fn failed(&mut self) { + let _ = self.execute(); + } + } + + #[test] + fn only_valid_public_data_reads_should_be_propagated() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + builder.public_call.append_read_requests(1); + builder.public_call.append_empty_read_requests(1); + builder.public_call.append_read_requests(1); + let public_data_reads = builder.get_current_public_data_reads(); + + let public_inputs = builder.execute(); + + // Only the 2 valid reads should have been propagated. + let expected = [public_data_reads[0], public_data_reads[2]]; + assert_eq_public_data_reads(public_inputs.end.public_data_reads, expected); + } + + #[test] + fn only_valid_update_requests_should_be_propagated() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + builder.public_call.append_update_requests(1); + builder.public_call.append_empty_update_requests(1); + builder.public_call.append_update_requests(1); + let public_data_update_requests = builder.get_current_public_data_update_requests(); + + let public_inputs = builder.execute(); + + // Only the 2 valid reads should have been propagated. + let expected = [public_data_update_requests[0], public_data_update_requests[2]]; + assert_eq_public_data_update_requests(public_inputs.end.public_data_update_requests, expected); + } + + #[test(should_fail_with="Constructors cannot be public functions")] + fn constructor_should_fail() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + builder.public_call.function_data.is_constructor = true; + + builder.failed(); + } + + #[test(should_fail_with="Contract deployment cannot be a public function")] + fn constructor_should_fail_2() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + builder.public_call.public_inputs.call_context.is_contract_deployment = true; + + builder.failed(); + } + + #[test(should_fail_with="Bytecode hash cannot be zero")] + fn no_bytecode_hash_should_fail() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + builder.public_call.bytecode_hash = 0; + + builder.failed(); + } + + #[test] + fn internal_call_succeeds() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new().is_internal_call(); + builder.succeeded(); + } + + #[test(should_fail_with="msg_sender must be self for internal calls")] + fn invalid_is_internal() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new().is_internal_call(); + + // Tweak the (storage) contract_address to be different to msg_sender. + let msg_sender = builder.public_call.public_inputs.call_context.msg_sender.to_field(); + builder.public_call.contract_address = Address::from_field(msg_sender + 1); + builder.public_call.public_inputs.call_context.storage_contract_address = Address::from_field(msg_sender + 1); + + builder.failed(); + } + + #[test(should_fail_with="Contract address cannot be zero")] + fn contract_address_must_be_valid() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + builder.public_call.contract_address = Address::zero(); + + builder.failed(); + } + + #[test(should_fail_with="Function signature cannot be zero")] + fn function_selector_must_be_valid() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + builder.public_call.function_data.selector = FunctionSelector::zero(); + + builder.failed(); + } + + #[test(should_fail_with="Cannot execute a private function with the public kernel circuit")] + fn private_call_should_fail() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + builder.public_call.function_data.is_private = true; + + builder.failed(); + } + + #[test(should_fail_with="calculated public_kernel_inputs_hash does not match provided public_kernel_inputs_hash at the top of the call stack")] + fn inconsistent_call_hash_should_fail() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + let public_call = builder.public_call.finish(); + + let hash = public_call.call_stack_item.hash(); + // Tweak the call stack item hash. + builder.previous_kernel.push_public_call_request(hash + 1, false); + let previous_kernel = builder.previous_kernel.finish(); + + let kernel = PublicKernelPrivatePreviousInputs { + previous_kernel, + public_call, + }; + + let _ = kernel.public_kernel_private_previous(); + } + + #[test(should_fail_with="call stack msg_sender does not match caller contract address")] + fn incorrect_msg_sender_fails_for_regular_calls() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + + // Set the msg_sender to a wrong value. + builder.public_call.public_inputs.call_context.msg_sender.inner += 1; + + builder.failed(); + } + + #[test(should_fail_with="call stack storage address does not match expected contract address")] + fn incorrect_storage_contract_address_fails_for_regular_calls() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + + // Set the storage contract address to a wrong value. + builder.public_call.public_inputs.call_context.storage_contract_address.inner += 1; + + builder.failed(); + } + + #[test] + fn delegate_call_succeeds() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new().is_delegate_call(); + builder.succeeded(); + } + + #[test(should_fail_with="caller context cannot be empty for delegate calls")] + fn empty_caller_context_for_delegate_calls_fails() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new().is_delegate_call(); + let public_call = builder.public_call.finish(); + + let hash = public_call.call_stack_item.hash(); + // Caller context is empty for regular calls. + let is_delegate_call = false; + builder.previous_kernel.push_public_call_request(hash, is_delegate_call); + let previous_kernel = builder.previous_kernel.finish(); + + let kernel = PublicKernelPrivatePreviousInputs { + previous_kernel, + public_call, + }; + + let _ = kernel.public_kernel_private_previous(); + } + + #[test(should_fail_with="call stack msg_sender does not match expected msg_sender for delegate calls")] + fn incorrect_msg_sender_for_delegate_calls_fails() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new().is_delegate_call(); + + // Set the msg_sender to be the caller contract. + builder.public_call.public_inputs.call_context.msg_sender = builder.previous_kernel.contract_address; + + builder.failed(); + } + + #[test(should_fail_with="call stack storage address does not match expected contract address for delegate calls")] + fn public_kernel_circuit_fails_on_incorrect_storage_contract_in_delegate_call() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new().is_delegate_call(); + + // Set the storage contract address to be the contract address. + builder.public_call.public_inputs.call_context.storage_contract_address = builder.public_call.contract_address; + + builder.failed(); + } + + #[test(should_fail_with="curent contract address must not match storage contract address for delegate calls")] + fn previous_private_kernel_fails_if_incorrect_storage_contract_on_delegate_call() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new().is_delegate_call(); + + builder.public_call.contract_address = builder.public_call.public_inputs.call_context.storage_contract_address; + + builder.failed(); + } + + #[test(should_fail_with = "call requests length does not match the expected length")] + fn incorrect_call_requests_length_fails() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + + builder.public_call.append_public_call_requests(2, false); + // Remove one call request. + let _ = builder.public_call.public_call_stack.pop(); + + builder.failed(); + } + + #[test(should_fail_with = "call stack hash does not match call request hash")] + fn incorrect_call_request_hash_fails() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + + builder.public_call.append_public_call_requests(2, false); + let mut call_request = builder.public_call.public_call_stack.pop(); + // Change the hash to be a different value. + call_request.hash += 1; + builder.public_call.public_call_stack.push(call_request); + + builder.failed(); + } + + #[test(should_fail_with = "invalid caller")] + fn incorrect_caller_address_for_call_request_fails() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + + builder.public_call.append_public_call_requests(1, false); + let mut call_request = builder.public_call.public_call_stack.pop(); + // Change the caller contract address to be a different value. + call_request.caller_contract_address.inner += 1; + builder.public_call.public_call_stack.push(call_request); + + builder.failed(); + } + + #[test(should_fail_with = "invalid caller")] + fn incorrect_caller_context_for_delegate_call_request_fails() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + + builder.public_call.append_public_call_requests(1, true); + let mut call_request = builder.public_call.public_call_stack.pop(); + // Change the storage contract to be a different value. + call_request.caller_context.storage_contract_address.inner += 1; + builder.public_call.public_call_stack.push(call_request); + + builder.failed(); + } + + #[test] + fn public_kernel_circuit_with_private_previous_kernel_should_succeed() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + builder.succeeded(); + } + + #[test] + fn circuit_outputs_should_be_correctly_populated_with_previous_private_kernel() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + let new_contracts = [ + NewContractData { + contract_address: Address::from_field(123), + portal_contract_address: EthAddress::from_field(456), + function_tree_root: 78, + }, + ]; + builder.previous_kernel.end.new_contracts.push_array(new_contracts); + + builder.public_call.append_public_call_requests_for_regular_calls(2); + let storage = builder.public_call.public_call_stack.storage; + let public_call_stack = [storage[0], storage[1]]; + + builder.public_call.append_update_requests(2); + let storage = builder.get_current_public_data_update_requests(); + let update_requests = [storage[0], storage[1]]; + + builder.public_call.append_read_requests(3); + let storage = builder.get_current_public_data_reads(); + let read_requests = [storage[0], storage[1], storage[2]]; + + let public_inputs = builder.execute(); + + assert_eq_new_contracts(public_inputs.end.new_contracts, new_contracts); + assert_eq_call_requests(public_inputs.end.private_call_stack, []); + assert_eq_call_requests(public_inputs.end.public_call_stack, public_call_stack); + assert_eq_public_data_update_requests(public_inputs.end.public_data_update_requests, update_requests); + assert_eq_public_data_reads(public_inputs.end.public_data_reads, read_requests); + } + + #[test(should_fail_with="Private call stack must be empty when executing in the public kernel")] + fn private_previous_kernel_non_empty_private_call_stack_should_fail() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + builder.previous_kernel.push_private_call_request(1, false); + + builder.failed(); + } + + // TODO: Find another way to test this. Currently it will crash because we are popping from an empty array: + // The application panicked (crashed). Message: Expected array index to fit in u64 + // #[test(should_fail_with="Public call stack can not be empty")] + // fn private_previous_kernel_empty_public_call_stack_should_fail() { + // let builder = PublicKernelPrivatePreviousInputsBuilder::new(); + // let previous_kernel = builder.previous_kernel.finish(); + // let kernel = PublicKernelPrivatePreviousInputs { + // previous_kernel, + // public_call: builder.public_call.finish(), + // }; + + // let _ = kernel.public_kernel_private_previous(); + // } + + #[test(should_fail_with="Previous kernel must be private when in this public kernel version")] + fn private_previous_kernel_non_private_previous_kernel_should_fail() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + builder.previous_kernel.is_private = false; + + builder.failed(); + } + + #[test(should_fail_with="No contract storage update requests are allowed for static calls")] + fn previous_private_kernel_fails_if_contract_storage_update_requests_on_static_call() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + builder.public_call.public_inputs.call_context.is_static_call = true; + builder.public_call.append_update_requests(1); + + builder.failed(); + } + + #[test] + fn logs_are_handled_as_expected() { + let mut builder = PublicKernelPrivatePreviousInputsBuilder::new(); + + // Logs for the current call stack. + let unencrypted_logs_hash = [26, 47]; + let unencrypted_log_preimages_length = 50; + builder.public_call.set_unencrypted_logs(unencrypted_logs_hash, unencrypted_log_preimages_length); + + // Logs for the previous call stack. + let prev_encrypted_logs_hash = [80, 429]; + let prev_encrypted_log_preimages_length = 13; + let prev_unencrypted_logs_hash = [956, 112]; + let prev_unencrypted_log_preimages_length = 24; + builder.previous_kernel.set_encrypted_logs(prev_encrypted_logs_hash, prev_encrypted_log_preimages_length); + builder.previous_kernel.set_unencrypted_logs(prev_unencrypted_logs_hash, prev_unencrypted_log_preimages_length); + + let public_inputs = builder.execute(); + + assert_eq(public_inputs.end.encrypted_log_preimages_length, prev_encrypted_log_preimages_length); + assert_eq(public_inputs.end.unencrypted_log_preimages_length, unencrypted_log_preimages_length + prev_unencrypted_log_preimages_length); + + assert_eq(public_inputs.end.encrypted_logs_hash, prev_encrypted_logs_hash); + + let expected_unencrypted_logs_hash = compute_logs_hash(prev_unencrypted_logs_hash, unencrypted_logs_hash); + assert_eq(public_inputs.end.unencrypted_logs_hash, expected_unencrypted_logs_hash); + } +} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/public_kernel_public_previous.nr b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/public_kernel_public_previous.nr new file mode 100644 index 00000000000..46d4f485504 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/public_kernel_public_previous.nr @@ -0,0 +1,290 @@ +use dep::types::abis::public_call_data::PublicCallData; +use dep::types::abis::previous_kernel_data::PreviousKernelData; +use dep::types::KernelCircuitPublicInputs; +use dep::types::abis::kernel_circuit_public_inputs::KernelCircuitPublicInputsBuilder; +use crate::common; +use dep::std::unsafe; + + +struct PublicKernelPublicPreviousInputs { + previous_kernel: PreviousKernelData, + public_call: PublicCallData, +} + +impl PublicKernelPublicPreviousInputs { + +// this is the only difference between the two PublicKernels' logic: + fn validate_inputs(self) { + let previous_call_is_private = self.previous_kernel.public_inputs.is_private; + assert(previous_call_is_private == false, + "Previous kernel must be public when in this public kernel version"); + } + + + fn public_kernel_public_previous(self) -> KernelCircuitPublicInputs { + // construct the circuit outputs + let mut public_inputs: KernelCircuitPublicInputsBuilder = unsafe::zeroed(); + + // initialise the end state with our provided previous kernel state + common::initialize_end_values(self.previous_kernel, &mut public_inputs); + + // validate the inputs common to all invocation circumstances + common::validate_inputs(self.public_call); + + // validate the inputs unique to having a previous public kernel + self.validate_inputs(); + + // Pops the item from the call stack and validates it against the current execution. + let call_request = public_inputs.end.public_call_stack.pop(); + common::validate_call_against_request(self.public_call, call_request); + + common::update_public_end_values(self.public_call,&mut public_inputs); + + common::accumulate_unencrypted_logs(self.public_call, self.previous_kernel,&mut public_inputs); + + public_inputs.finish() + } +} + +mod tests { + use crate::{ + public_kernel_public_previous::PublicKernelPublicPreviousInputs, + utils::{ + assert_eq_public_data_reads, + assert_eq_public_data_update_requests, + compute_public_data_reads, + compute_public_data_update_requests, + }, + }; + use dep::types::{ + abis::{ + kernel_circuit_public_inputs::KernelCircuitPublicInputs, + public_data_read::PublicDataRead, + public_data_update_request::PublicDataUpdateRequest, + }, + hash::{ + compute_l2_to_l1_hash, + compute_logs_hash, + silo_commitment, + silo_nullifier, + }, + tests::{ + previous_kernel_data_builder::PreviousKernelDataBuilder, + public_call_data_builder::PublicCallDataBuilder, + }, + utils::{ + arrays::array_eq, + }, + }; + use dep::aztec::constants_gen::{ + MAX_PUBLIC_DATA_READS_PER_CALL, + MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_CALL, + }; + + + struct PublicKernelPublicPreviousInputsBuilder { + previous_kernel: PreviousKernelDataBuilder, + public_call: PublicCallDataBuilder, + } + + impl PublicKernelPublicPreviousInputsBuilder { + pub fn new() -> Self { + let previous_kernel = PreviousKernelDataBuilder::new().is_public(); + let public_call = PublicCallDataBuilder::new(); + + PublicKernelPublicPreviousInputsBuilder { + previous_kernel, + public_call, + } + } + + pub fn is_delegate_call(&mut self) -> Self { + let _ = self.public_call.is_delegate_call(); + *self + } + + pub fn get_current_public_data_reads(self) -> [PublicDataRead; MAX_PUBLIC_DATA_READS_PER_CALL] { + let read_requests = self.public_call.public_inputs.contract_storage_reads.storage; + compute_public_data_reads(self.public_call.contract_address, read_requests) + } + + pub fn get_current_public_data_update_requests(self) -> [PublicDataUpdateRequest; MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_CALL] { + compute_public_data_update_requests( + self.public_call.contract_address, + self.public_call.public_inputs.contract_storage_update_requests.storage, + ) + } + + pub fn execute(&mut self) -> KernelCircuitPublicInputs { + let public_call = self.public_call.finish(); + // Adjust the call stack item hash for the current call in the previous iteration. + let hash = public_call.call_stack_item.hash(); + let is_delegate_call = public_call.call_stack_item.public_inputs.call_context.is_delegate_call; + self.previous_kernel.push_public_call_request(hash, is_delegate_call); + let previous_kernel = self.previous_kernel.finish(); + + let kernel = PublicKernelPublicPreviousInputs { + previous_kernel, + public_call, + }; + + kernel.public_kernel_public_previous() + } + + pub fn succeeded(&mut self) { + let _ = self.execute(); + } + + pub fn failed(&mut self) { + let _ = self.execute(); + } + } + + #[test] + fn public_kernel_circuit_with_public_previous_kernel_should_succeed() { + let mut builder = PublicKernelPublicPreviousInputsBuilder::new(); + builder.succeeded(); + } + + // TODO: Find another way to test this. Currently it will crash because we are popping from an empty array: + // The application panicked (crashed). Message: Expected array index to fit in u64 + // #[test(should_fail_with="Public call stack can not be empty")] + // fn public_previous_kernel_empty_public_call_stack_should_fail() { + // let builder = PublicKernelPublicPreviousInputsBuilder::new(); + // let previous_kernel = builder.previous_kernel.finish(); + // let public_call = builder.public_call.finish(); + // let kernel = PublicKernelPublicPreviousInputs { + // previous_kernel, + // public_call, + // }; + // + // let _ = kernel.public_kernel_public_previous(); + // } + + #[test(should_fail_with="Previous kernel must be public when in this public kernel version")] + fn public_previous_kernel_private_previous_kernel_should_fail() { + let mut builder = PublicKernelPublicPreviousInputsBuilder::new(); + builder.previous_kernel = PreviousKernelDataBuilder::new(); + + builder.failed(); + } + + // TODO: Break up this test into smaller tests. + #[test] + fn circuit_outputs_should_be_correctly_populated_with_previous_public_kernel() { + let mut builder = PublicKernelPublicPreviousInputsBuilder::new(); + let contract_address = builder.public_call.contract_address; + let portal_contract_address = builder.public_call.portal_contract_address; + + // Setup 2 data writes on the previous kernel. + builder.previous_kernel.append_public_data_update_requests(2); + let previous = builder.previous_kernel.end.public_data_update_requests.storage; + // Setup 2 data writes on the current public inputs. + builder.public_call.append_update_requests(2); + let current = builder.get_current_public_data_update_requests(); + let public_data_update_requests = [previous[0], previous[1], current[0], current[1]]; + + // Setup 2 data reads on the previous kernel. + builder.previous_kernel.append_public_data_read_requests(2); + let previous = builder.previous_kernel.end.public_data_reads.storage; + // Setup 2 data reads on the current public inputs. + builder.public_call.append_read_requests(2); + let current = builder.get_current_public_data_reads(); + let public_data_read_requests = [previous[0], previous[1], current[0], current[1]]; + + // Setup 2 new commitments on the previous kernel. + builder.previous_kernel.append_new_commitments(2); + let previous = builder.previous_kernel.end.new_commitments.storage; + // Setup 2 new commitments on the current public inputs. + let current = [previous[1] + 1, previous[1] + 2]; + builder.public_call.public_inputs.new_commitments.push_array(current); + let siloed = current.map(|c| silo_commitment(contract_address, c)); + let new_commitments = [previous[0], previous[1], siloed[0], siloed[1]]; + + // Setup 2 new nullifiers on the previous kernel. + builder.previous_kernel.append_new_nullifiers(2); + let previous = builder.previous_kernel.end.new_nullifiers.storage; + // Setup 2 new commitments on the current public inputs. + let current = [previous[2] + 1, previous[2] + 2]; + builder.public_call.public_inputs.new_nullifiers.push_array(current); + let siloed = current.map(|c| silo_nullifier(contract_address, c)); + // There are 3 nullifiers in the previous kernel. The first one is the tx nullifier. + let new_nullifiers = [previous[0], previous[1], previous[2], siloed[0], siloed[1]]; + + // Setup 1 new l2 to l1 message on the previous kernel. + let previous = [12345]; + builder.previous_kernel.end.new_l2_to_l1_msgs.push_array(previous); + // Setup 1 new l2 to l1 message on the current public inputs. + let current = [67890]; + builder.public_call.public_inputs.new_l2_to_l1_msgs.push_array(current); + let tx_context = builder.previous_kernel.tx_context; + let version = tx_context.version; + let chain_id = tx_context.chain_id; + let siloed = current.map(|c| compute_l2_to_l1_hash(contract_address, version, portal_contract_address, chain_id, c)); + let new_l2_to_l1_msgs = [previous[0], siloed[0]]; + + // Logs for the current call stack. + let unencrypted_logs_hash = [26, 47]; + let unencrypted_log_preimages_length = 50; + builder.public_call.set_unencrypted_logs(unencrypted_logs_hash, unencrypted_log_preimages_length); + + // Logs for the previous call stack. + let prev_encrypted_logs_hash = [80, 429]; + let prev_encrypted_log_preimages_length = 13; + let prev_unencrypted_logs_hash = [956, 112]; + let prev_unencrypted_log_preimages_length = 24; + builder.previous_kernel.set_encrypted_logs(prev_encrypted_logs_hash, prev_encrypted_log_preimages_length); + builder.previous_kernel.set_unencrypted_logs(prev_unencrypted_logs_hash, prev_unencrypted_log_preimages_length); + + let public_inputs = builder.execute(); + assert_eq_public_data_update_requests(public_inputs.end.public_data_update_requests, public_data_update_requests); + assert_eq_public_data_reads(public_inputs.end.public_data_reads, public_data_read_requests); + assert(array_eq(public_inputs.end.new_commitments, new_commitments)); + assert(array_eq(public_inputs.end.new_nullifiers, new_nullifiers)); + assert(array_eq(public_inputs.end.new_l2_to_l1_msgs, new_l2_to_l1_msgs)); + + assert_eq(public_inputs.end.encrypted_log_preimages_length, prev_encrypted_log_preimages_length); + assert_eq(public_inputs.end.unencrypted_log_preimages_length, unencrypted_log_preimages_length + prev_unencrypted_log_preimages_length); + + assert_eq(public_inputs.end.encrypted_logs_hash, prev_encrypted_logs_hash); + + let expected_unencrypted_logs_hash = compute_logs_hash(prev_unencrypted_logs_hash, unencrypted_logs_hash); + assert_eq(public_inputs.end.unencrypted_logs_hash, expected_unencrypted_logs_hash); + } + + #[test(should_fail_with="No contract storage update requests are allowed for static calls")] + fn previous_public_kernel_fails_if_contract_storage_update_requests_on_static_call() { + let mut builder = PublicKernelPublicPreviousInputsBuilder::new(); + builder.public_call.public_inputs.call_context.is_static_call = true; + builder.public_call.append_update_requests(1); + + builder.failed(); + } + + #[test(should_fail_with="curent contract address must not match storage contract address for delegate calls")] + fn previous_public_kernel_fails_if_incorrect_storage_contract_on_delegate_call() { + let mut builder = PublicKernelPublicPreviousInputsBuilder::new().is_delegate_call(); + + builder.public_call.contract_address = builder.public_call.public_inputs.call_context.storage_contract_address; + + builder.failed(); + } + + #[test(should_fail_with="new_commitments must be empty for static calls")] + fn public_kernel_fails_creating_new_commitments_on_static_call() { + let mut builder = PublicKernelPublicPreviousInputsBuilder::new(); + builder.public_call.public_inputs.call_context.is_static_call = true; + builder.public_call.public_inputs.new_commitments.push(1); + + builder.failed(); + } + + #[test(should_fail_with="new_nullifiers must be empty for static calls")] + fn public_kernel_fails_creating_new_nullifiers_on_static_call() { + let mut builder = PublicKernelPublicPreviousInputsBuilder::new(); + builder.public_call.public_inputs.call_context.is_static_call = true; + builder.public_call.public_inputs.new_nullifiers.push(1); + + builder.failed(); + } +} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/utils.nr b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/utils.nr new file mode 100644 index 00000000000..e6d8603cb77 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-lib/src/utils.nr @@ -0,0 +1,94 @@ +use crate::hash::{compute_public_data_tree_index, compute_public_data_tree_value}; +use dep::types::{ + abis::{ + call_request::CallRequest, + new_contract_data::NewContractData, + public_data_read::PublicDataRead, + public_data_update_request::PublicDataUpdateRequest, + }, + address::Address, + contrakt::{ + storage_read::StorageRead, + storage_update_request::StorageUpdateRequest, + }, + utils::{ + arrays::struct_array_eq, + }, +}; + +pub fn compute_public_data_reads(contract_address: Address, read_requests: [StorageRead; N]) -> [PublicDataRead; N] { + let mut public_data_reads = [PublicDataRead::empty(); N]; + for i in 0..N { + let read_request = read_requests[i]; + if !read_request.is_empty() { + public_data_reads[i] = PublicDataRead { + leaf_index: compute_public_data_tree_index(contract_address, read_request.storage_slot), + value: compute_public_data_tree_value(read_request.current_value), + }; + } + } + public_data_reads +} + +pub fn assert_eq_call_requests( + call_requests: [CallRequest; N], + expected: [CallRequest; S], +) { + assert(struct_array_eq( + call_requests, + expected, + |c1: CallRequest, c2: CallRequest| c1.eq(c2), + |c: CallRequest| c.is_empty(), + )); +} + +pub fn compute_public_data_update_requests( + contract_address: Address, + update_requests: [StorageUpdateRequest; N], +) -> [PublicDataUpdateRequest; N] { + let mut public_data_update_requests = [PublicDataUpdateRequest::empty(); N]; + for i in 0..N { + let update_request = update_requests[i]; + if !update_request.is_empty() { + public_data_update_requests[i] = PublicDataUpdateRequest { + leaf_index: compute_public_data_tree_index(contract_address, update_request.storage_slot), + old_value: compute_public_data_tree_value(update_request.old_value), + new_value: compute_public_data_tree_value(update_request.new_value), + }; + } + } + public_data_update_requests +} + +pub fn assert_eq_public_data_reads(public_data_reads: [PublicDataRead; N], expected: [PublicDataRead; S]) { + assert(struct_array_eq( + public_data_reads, + expected, + |pdr1: PublicDataRead, pdr2: PublicDataRead| pdr1.eq(pdr2), + |pdr: PublicDataRead| pdr.is_empty(), + )); +} + +pub fn assert_eq_public_data_update_requests( + public_data_update_requests: [PublicDataUpdateRequest; N], + expected: [PublicDataUpdateRequest; S], +) { + assert(struct_array_eq( + public_data_update_requests, + expected, + |r1: PublicDataUpdateRequest, r2: PublicDataUpdateRequest| r1.eq(r2), + |r: PublicDataUpdateRequest| r.is_empty(), + )); +} + +pub fn assert_eq_new_contracts( + new_contracts: [NewContractData; N], + expected: [NewContractData; S], +) { + assert(struct_array_eq( + new_contracts, + expected, + |c1: NewContractData, c2: NewContractData| c1.eq(c2), + |c: NewContractData| c.is_empty(), + )); +} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-private-previous-simulated/Nargo.toml b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-private-previous-simulated/Nargo.toml new file mode 100644 index 00000000000..d131d25b72c --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-private-previous-simulated/Nargo.toml @@ -0,0 +1,10 @@ +[package] +name = "public_kernel_private_previous_simulated" +type = "bin" +authors = [""] +compiler_version = ">=0.18.0" + +[dependencies] +types = { path = "../types" } +public_kernel_lib = { path = "../public-kernel-lib" } +aztec = { path = "../../../../aztec-nr/aztec" } diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-private-previous-simulated/src/main.nr b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-private-previous-simulated/src/main.nr new file mode 100644 index 00000000000..df611bf80db --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-private-previous-simulated/src/main.nr @@ -0,0 +1,6 @@ +use dep::public_kernel_lib::{PublicKernelPrivatePreviousInputs}; +use dep::types::{KernelCircuitPublicInputs}; + +unconstrained fn main(input: PublicKernelPrivatePreviousInputs) -> distinct pub KernelCircuitPublicInputs { + input.public_kernel_private_previous() +} diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-private-previous/Nargo.toml b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-private-previous/Nargo.toml new file mode 100644 index 00000000000..a1aebfa70a6 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-private-previous/Nargo.toml @@ -0,0 +1,10 @@ +[package] +name = "public_kernel_private_previous" +type = "bin" +authors = [""] +compiler_version = ">=0.18.0" + +[dependencies] +types = { path = "../types" } +public_kernel_lib = { path = "../public-kernel-lib" } +aztec = { path = "../../../../aztec-nr/aztec" } diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-private-previous/src/main.nr b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-private-previous/src/main.nr new file mode 100644 index 00000000000..7ccd8c68502 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-private-previous/src/main.nr @@ -0,0 +1,6 @@ +use dep::public_kernel_lib::{PublicKernelPrivatePreviousInputs}; +use dep::types::{KernelCircuitPublicInputs}; + +fn main(input: PublicKernelPrivatePreviousInputs) -> distinct pub KernelCircuitPublicInputs { + input.public_kernel_private_previous() +} diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-public-previous-simulated/Nargo.toml b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-public-previous-simulated/Nargo.toml new file mode 100644 index 00000000000..5eb41be2441 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-public-previous-simulated/Nargo.toml @@ -0,0 +1,10 @@ +[package] +name = "public_kernel_public_previous_simulated" +type = "bin" +authors = [""] +compiler_version = ">=0.18.0" + +[dependencies] +types = { path = "../types" } +public_kernel_lib = { path = "../public-kernel-lib" } +aztec = { path = "../../../../aztec-nr/aztec" } diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-public-previous-simulated/src/main.nr b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-public-previous-simulated/src/main.nr new file mode 100644 index 00000000000..876d5c7684a --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-public-previous-simulated/src/main.nr @@ -0,0 +1,6 @@ +use dep::public_kernel_lib::{PublicKernelPublicPreviousInputs}; +use dep::types::{KernelCircuitPublicInputs}; + +unconstrained fn main(input: PublicKernelPublicPreviousInputs) -> distinct pub KernelCircuitPublicInputs { + input.public_kernel_public_previous() +} diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-public-previous/Nargo.toml b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-public-previous/Nargo.toml new file mode 100644 index 00000000000..9d972e291cc --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-public-previous/Nargo.toml @@ -0,0 +1,10 @@ +[package] +name = "public_kernel_public_previous" +type = "bin" +authors = [""] +compiler_version = ">=0.18.0" + +[dependencies] +types = { path = "../types" } +public_kernel_lib = { path = "../public-kernel-lib" } +aztec = { path = "../../../../aztec-nr/aztec" } diff --git a/yarn-project/noir-protocol-circuits/src/crates/public-kernel-public-previous/src/main.nr b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-public-previous/src/main.nr new file mode 100644 index 00000000000..709dc8737ec --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/public-kernel-public-previous/src/main.nr @@ -0,0 +1,6 @@ +use dep::public_kernel_lib::{PublicKernelPublicPreviousInputs}; +use dep::types::{KernelCircuitPublicInputs}; + +fn main(input: PublicKernelPublicPreviousInputs) -> distinct pub KernelCircuitPublicInputs { + input.public_kernel_public_previous() +} diff --git a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/abis/nullifier_leaf_preimage.nr b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/abis/nullifier_leaf_preimage.nr index bc003b44fad..5fce155fef6 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/abis/nullifier_leaf_preimage.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/abis/nullifier_leaf_preimage.nr @@ -5,12 +5,19 @@ struct NullifierLeafPreimage { } impl NullifierLeafPreimage { - // TODO(Kev): Check if this is needed. - fn is_empty(self) -> bool { + pub fn default() -> Self { + NullifierLeafPreimage { + leaf_value : 0, + next_value : 0, + next_index : 0, + } + } + + pub fn is_empty(self) -> bool { (self.leaf_value == 0) & (self.next_index == 0) & (self.next_value == 0) } - fn hash(self) -> Field { + pub fn hash(self) -> Field { if self.is_empty() { 0 } else { diff --git a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/base/base_rollup_inputs.nr b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/base/base_rollup_inputs.nr index f110656e7db..55da22511da 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/base/base_rollup_inputs.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/base/base_rollup_inputs.nr @@ -1,7 +1,13 @@ use crate::abis::nullifier_leaf_preimage::NullifierLeafPreimage; use crate::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot; use crate::abis::constant_rollup_data::ConstantRollupData; -use crate::abis::base_or_merge_rollup_public_inputs::BaseOrMergeRollupPublicInputs; +use crate::abis::base_or_merge_rollup_public_inputs::{BaseOrMergeRollupPublicInputs, BASE_ROLLUP_TYPE}; +use crate::merkle_tree::{calculate_subtree, calculate_empty_tree_root}; +use crate::components; +use dep::types::utils::uint256::U256; +use dep::types::abis::public_data_update_request::PublicDataUpdateRequest; +use dep::types::abis::public_data_read::PublicDataRead; +use dep::types::mocked::{AggregationObject, Proof}; use dep::aztec::constants_gen::{ MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP, NOTE_HASH_SUBTREE_SIBLING_PATH_LENGTH, @@ -11,6 +17,18 @@ use dep::aztec::constants_gen::{ MAX_PUBLIC_DATA_READS_PER_BASE_ROLLUP, PUBLIC_DATA_TREE_HEIGHT, KERNELS_PER_BASE_ROLLUP, + MAX_NEW_CONTRACTS_PER_TX, + NOTE_HASH_SUBTREE_HEIGHT, + CONTRACT_SUBTREE_HEIGHT, + NUM_FIELDS_PER_SHA256, + MAX_NEW_COMMITMENTS_PER_TX, + MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX, + MAX_PUBLIC_DATA_READS_PER_TX, + MAX_NEW_NULLIFIERS_PER_TX, + NUM_ENCRYPTED_LOGS_HASHES_PER_TX, + MAX_NEW_L2_TO_L1_MSGS_PER_TX, + NUM_UNENCRYPTED_LOGS_HASHES_PER_TX, + NULLIFIER_SUBTREE_HEIGHT, }; use dep::types::abis::previous_kernel_data::PreviousKernelData; use dep::types::abis::membership_witness::NullifierMembershipWitness; @@ -41,8 +59,1216 @@ struct BaseRollupInputs { } impl BaseRollupInputs { - pub fn base_rollup_circuit(self) -> BaseOrMergeRollupPublicInputs { - let zeroed = dep::std::unsafe::zeroed(); - zeroed + pub fn base_rollup_circuit(self) -> BaseOrMergeRollupPublicInputs { + // Verify the previous kernel proofs + for i in 0..KERNELS_PER_BASE_ROLLUP { + let proof = self.kernel_data[i].proof; + assert(verify_kernel_proof(proof), "kernel proof verification failed"); + }; + + // Verify the kernel chain_id and versions + for i in 0..KERNELS_PER_BASE_ROLLUP { + assert(self.kernel_data[i].public_inputs.constants.tx_context.chain_id == + self.constants.global_variables.chain_id, "kernel chain_id does not match the rollup chain_id"); + assert(self.kernel_data[i].public_inputs.constants.tx_context.version == + self.constants.global_variables.version, "kernel version does not match the rollup version"); + }; + + // First we compute the contract tree leaves + let contract_leaves = self.calculate_contract_leaves(); + let contracts_tree_subroot = self.calculate_contract_subtree(contract_leaves); + + let commitments_tree_subroot = self.calculate_commitments_subtree(); + + let empty_commitments_subtree_root = calculate_empty_tree_root(NOTE_HASH_SUBTREE_HEIGHT); + + let end_note_hash_tree_snapshot = components::insert_subtree_to_snapshot_tree( + self.start_note_hash_tree_snapshot, + self.new_commitments_subtree_sibling_path, + empty_commitments_subtree_root, + commitments_tree_subroot, + NOTE_HASH_SUBTREE_HEIGHT as u8, + ); + + // Insert contract subtrees: + let empty_contracts_subtree_root = calculate_empty_tree_root(CONTRACT_SUBTREE_HEIGHT); + let end_contract_tree_snapshot = components::insert_subtree_to_snapshot_tree( + self.start_contract_tree_snapshot, + self.new_contracts_subtree_sibling_path, + empty_contracts_subtree_root, + contracts_tree_subroot, + CONTRACT_SUBTREE_HEIGHT as u8, + ); + + // Insert nullifiers: + let end_nullifier_tree_snapshot = self.check_nullifier_tree_non_membership_and_insert_to_tree(); + + // Validate public public data reads and public data update requests, and update public data tree + let end_public_data_tree_root = self.validate_and_process_public_state(); + + // Calculate the overall calldata hash + let calldata_hash = BaseRollupInputs::components_compute_kernel_calldata_hash(self.kernel_data); + + // Perform membership checks that the notes provided exist within the historic trees data + self.perform_historical_blocks_tree_membership_checks(); + + let aggregation_object = self.aggregate_proofs(); + + BaseOrMergeRollupPublicInputs { + rollup_type : BASE_ROLLUP_TYPE, + rollup_subtree_height : 0, + end_aggregation_object : aggregation_object, + constants : self.constants, + start_note_hash_tree_snapshot : self.start_note_hash_tree_snapshot, + end_note_hash_tree_snapshot : end_note_hash_tree_snapshot, + start_nullifier_tree_snapshot : self.start_nullifier_tree_snapshot, + end_nullifier_tree_snapshot : end_nullifier_tree_snapshot, + start_contract_tree_snapshot : self.start_contract_tree_snapshot, + end_contract_tree_snapshot : end_contract_tree_snapshot, + start_public_data_tree_root : self.start_public_data_tree_root, + end_public_data_tree_root : end_public_data_tree_root, + calldata_hash : calldata_hash, + } + } + + fn calculate_contract_leaves(self) -> [Field; NUM_CONTRACT_LEAVES] { + let mut contract_leaves = [0; NUM_CONTRACT_LEAVES]; + for i in 0..KERNELS_PER_BASE_ROLLUP { + let new_contracts = self.kernel_data[i].public_inputs.end.new_contracts; + + // loop over the new contracts + for j in 0..new_contracts.len() { + let leaf_preimage = new_contracts[j]; + // When there is no contract deployment, we should insert a zero leaf into the tree and ignore the + // member-ship check. This is to ensure that we don't hit "already deployed" errors when we are not + // deploying contracts. e.g., when we are only calling functions on existing contracts. + let to_push = if leaf_preimage.contract_address.to_field() == 0 { + 0 + } else { + leaf_preimage.hash() + }; + contract_leaves[i*new_contracts.len() + j] = to_push; + } + } + + contract_leaves + } + + // TODO(Kev): This should say calculate_contract_subtree_root + // Cpp code says calculate_contract_subtree, so I'm leaving it as is for now + fn calculate_contract_subtree(self, leaves : [Field; NUM_CONTRACT_LEAVES]) -> Field { + calculate_subtree(leaves) + } + + // TODO(Kev): This should say calculate_commitments_subtree_root + // Cpp code says calculate_commitments_subtree, so I'm leaving it as is for now + fn calculate_commitments_subtree(self) -> Field { + let mut commitment_tree_leaves = [0; NOTE_HASH_SUBTREE_WIDTH]; + + for i in 0..KERNELS_PER_BASE_ROLLUP { + let new_commitments = self.kernel_data[i].public_inputs.end.new_commitments; + + // Our commitments size MUST be 4 to calculate our subtrees correctly + assert(new_commitments.len() == MAX_NEW_COMMITMENTS_PER_TX, "New commitments in kernel data must be MAX_NEW_COMMITMENTS_PER_TX (see constants.hpp)"); + + for j in 0..MAX_NEW_COMMITMENTS_PER_TX { + // TODO(Maddiaa): batch insert + commitment_tree_leaves[i * MAX_NEW_COMMITMENTS_PER_TX + j] = new_commitments[j]; + } + } + + calculate_subtree(commitment_tree_leaves) + } + + unconstrained fn find_leaf_index(self, leaves: [NullifierLeafPreimage; MAX_NEW_NULLIFIERS_PER_TX * 2], nullifier: Field, nullifier_index: u64) -> u64 { + let mut matched = false; + let mut index = 0; + for k in 0..nullifier_index { + if !matched { + if (!leaves[k].is_empty()) { + if (full_field_less_than(leaves[k].leaf_value, nullifier) & + (full_field_greater_than(leaves[k].next_value, nullifier) | + (leaves[k].next_value == 0))) { + matched = true; + index = k; + } + } + } + + + } + // if not matched, our subtree will misformed - we must reject + assert(matched, "Nullifier subtree is malformed"); + index + } + + // TODO this should be done in circuit. Ideally using the sorting strategy. + unconstrained fn check_nullifier_tree_non_membership_and_insert_to_tree(self) -> AppendOnlyTreeSnapshot { + // The below monologue is by Madiaa. fwiw, the plan was not simple. + // + // LADIES AND GENTLEMEN The P L A N ( is simple ) + // 1. Get the previous nullifier set setup + // 2. Check for the first added nullifier that it doesnt exist + // 3. Update the nullifier set + // 4. Calculate a new root with the sibling path + // 5. Use that for the next nullifier check. + // 6. Iterate for all of em + // 7. le bosh (profit) + + // BOYS AND GIRLS THE P L A N ( once the first plan is complete ) + // GENERATE OUR NEW NULLIFIER SUBTREE + // 1. We need to point the new nullifiers to point to the index that the previous nullifier replaced + // 2. If we receive the 0 nullifier leaf (where all values are 0, we skip insertion and leave a sparse subtree) + + // New nullifier subtree + let mut nullifier_insertion_subtree = [NullifierLeafPreimage::default(); MAX_NEW_NULLIFIERS_PER_TX * 2]; + + // This will update on each iteration + let mut current_nullifier_tree_root = self.start_nullifier_tree_snapshot.root; + + // This will increase with every insertion + let start_insertion_index = self.start_nullifier_tree_snapshot.next_available_leaf_index; + let mut new_index = start_insertion_index; + + // For each kernel circuit + for i in 0..KERNELS_PER_BASE_ROLLUP { + let new_nullifiers = self.kernel_data[i].public_inputs.end.new_nullifiers; + // For each of our nullifiers + for j in 0..MAX_NEW_NULLIFIERS_PER_TX { + // Witness containing index and path + let nullifier_index = i * MAX_NEW_NULLIFIERS_PER_TX + j; + + let witness = self.low_nullifier_membership_witness[nullifier_index]; + // Preimage of the lo-index required for a non-membership proof + let low_nullifier_preimage = self.low_nullifier_leaf_preimages[nullifier_index]; + // Newly created nullifier + let nullifier = new_nullifiers[j]; + + // TODO(maddiaa): reason about this more strongly, can this cause issues? + if (nullifier != 0) { + // Create the nullifier leaf of the new nullifier to be inserted + let mut new_nullifier_leaf = NullifierLeafPreimage { + leaf_value : nullifier, + next_value : low_nullifier_preimage.next_value, + next_index : low_nullifier_preimage.next_index, + }; + + // Assuming populated premier subtree + if (low_nullifier_preimage.is_empty()) { + // check previous nullifier leaves + let index = self.find_leaf_index(nullifier_insertion_subtree, nullifier, nullifier_index as u64); + let same_batch_nullifier = nullifier_insertion_subtree[index]; + assert(!same_batch_nullifier.is_empty(), "Same batch batch nullifier is empty"); + assert(full_field_less_than(same_batch_nullifier.leaf_value, nullifier), "Invalid hint"); + assert(full_field_greater_than(same_batch_nullifier.next_value, nullifier) | (same_batch_nullifier.next_value == 0), "Invalid hint"); + + new_nullifier_leaf.next_index = nullifier_insertion_subtree[index].next_index; + new_nullifier_leaf.next_value = nullifier_insertion_subtree[index].next_value; + + // Update child + nullifier_insertion_subtree[index].next_index = new_index; + nullifier_insertion_subtree[index].next_value = nullifier; + } else { + let is_less_than_nullifier = full_field_less_than(low_nullifier_preimage.leaf_value, nullifier); + let is_next_greater_than = full_field_greater_than(low_nullifier_preimage.next_value, nullifier); + + assert(is_less_than_nullifier, "invalid nullifier range"); + assert( + is_next_greater_than | + ((low_nullifier_preimage.next_value == 0) & (low_nullifier_preimage.next_index == 0)), + "invalid nullifier range" + ); + + // Recreate the original low nullifier from the preimage + let original_low_nullifier = NullifierLeafPreimage{ + leaf_value : low_nullifier_preimage.leaf_value, + next_value : low_nullifier_preimage.next_value, + next_index : low_nullifier_preimage.next_index, + }; + + // perform membership check for the low nullifier against the original root + components::assert_check_membership( + original_low_nullifier.hash(), + witness.leaf_index, + witness.sibling_path, + current_nullifier_tree_root, + ); + + // Calculate the new value of the low_nullifier_leaf + let updated_low_nullifier = NullifierLeafPreimage{ + leaf_value : low_nullifier_preimage.leaf_value, + next_value : nullifier, + next_index : new_index + }; + + // We need another set of witness values for this + current_nullifier_tree_root = components::root_from_sibling_path( + updated_low_nullifier.hash(), witness.leaf_index, witness.sibling_path); + } + nullifier_insertion_subtree[nullifier_index] = new_nullifier_leaf; + } + + // increment insertion index + new_index = new_index + 1; + } + } + + // Check that the new subtree is to be inserted at the next location, and is empty currently + let empty_nullifier_subtree_root = calculate_empty_tree_root(NULLIFIER_SUBTREE_HEIGHT); + let leafIndexNullifierSubtreeDepth = self.start_nullifier_tree_snapshot.next_available_leaf_index >> (NULLIFIER_SUBTREE_HEIGHT as u32); + components::assert_check_membership( + empty_nullifier_subtree_root, + leafIndexNullifierSubtreeDepth as Field, + self.new_nullifiers_subtree_sibling_path, + current_nullifier_tree_root, + ); + + // Create new nullifier subtree to insert into the whole nullifier tree + let nullifier_sibling_path = self.new_nullifiers_subtree_sibling_path; + let nullifier_subtree_root = self.create_nullifier_subtree(nullifier_insertion_subtree); + + // Calculate the new root + // We are inserting a subtree rather than a full tree here + let subtree_index = start_insertion_index >> (NULLIFIER_SUBTREE_HEIGHT as u32); + let new_root = components::root_from_sibling_path(nullifier_subtree_root, subtree_index as Field, nullifier_sibling_path); + + // Return the new state of the nullifier tree + AppendOnlyTreeSnapshot { + next_available_leaf_index: new_index, + root: new_root, + } + } + + fn create_nullifier_subtree(self, leaves: [NullifierLeafPreimage; N]) -> Field { + calculate_subtree(leaves.map(|leaf:NullifierLeafPreimage| leaf.hash())) + } + + // TODO this should be changed to append only and done in-circuit + unconstrained fn validate_and_process_public_state(self) -> Field { + // TODO(#2521) - data read validation should happen against the current state of the tx and not the start state. + // Blocks all interesting usecases that read and write to the same public state in the same tx. + // https://aztecprotocol.slack.com/archives/C02M7VC7TN0/p1695809629015719?thread_ts=1695653252.007339&cid=C02M7VC7TN0 + + + // Process public data reads and public data update requests for left input + // validate_public_data_reads( + // self.start_public_data_tree_root, + // self.kernel_data[0].public_inputs.end.public_data_reads, + // 0, + // self.new_public_data_reads_sibling_paths); + + let mid_public_data_tree_root = insert_public_data_update_requests( + self.start_public_data_tree_root, + self.kernel_data[0].public_inputs.end.public_data_update_requests, + 0, + self.new_public_data_update_requests_sibling_paths + ); + + + // TODO(#2521) - data read validation should happen against the current state of the tx and not the start state. + // Blocks all interesting usecases that read and write to the same public state in the same tx. + // https://aztecprotocol.slack.com/archives/C02M7VC7TN0/p1695809629015719?thread_ts=1695653252.007339&cid=C02M7VC7TN0 + + + // Process public data reads and public data update requests for right input using the resulting tree root from the + // left one + // validate_public_data_reads( + // mid_public_data_tree_root, + // baseRollupInputs.kernel_data[1].public_inputs.end.public_data_reads, + // MAX_PUBLIC_DATA_READS_PER_TX, + // baseRollupInputs.new_public_data_reads_sibling_paths); + + let end_public_data_tree_root = insert_public_data_update_requests( + mid_public_data_tree_root, + self.kernel_data[1].public_inputs.end.public_data_update_requests, + MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX, + self.new_public_data_update_requests_sibling_paths + ); + + end_public_data_tree_root + } + + // Computes the calldata hash for a base rollup + // TODO(Kev): move this into components module + // TODO(Alvaro): This is too slow for brillig without the array optimization + fn components_compute_kernel_calldata_hash(kernel_data : [PreviousKernelData; KERNELS_PER_BASE_ROLLUP]) -> [Field; NUM_FIELDS_PER_SHA256]{ + // Compute calldata hashes + // Consist of 2 kernels + // 2 * MAX_NEW_COMMITMENTS_PER_TX fields for commitments + // 2 * MAX_NEW_NULLIFIERS_PER_TX fields for nullifiers + // 8 public data update requests (4 per kernel) -> 16 fields + // 4 l2 -> l1 messages (2 per kernel) -> 4 fields + // 2 contract deployments (1 per kernel) -> 6 fields + // 2 encrypted logs hashes (1 per kernel) -> 4 fields --> 2 sha256 hashes --> 64 bytes + // 2 unencrypted logs hashes (1 per kernel) -> 4 fields --> 2 sha256 hashes --> 64 bytes + let mut calldata_hash_inputs = [0; CALLDATA_HASH_INPUT_SIZE]; + + for i in 0..KERNELS_PER_BASE_ROLLUP { + let new_commitments = kernel_data[i].public_inputs.end.new_commitments; + let new_nullifiers = kernel_data[i].public_inputs.end.new_nullifiers; + let public_data_update_requests = kernel_data[i].public_inputs.end.public_data_update_requests; + let newL2ToL1msgs = kernel_data[i].public_inputs.end.new_l2_to_l1_msgs; + let encryptedLogsHash = kernel_data[i].public_inputs.end.encrypted_logs_hash; + let unencryptedLogsHash = kernel_data[i].public_inputs.end.unencrypted_logs_hash; + + let mut offset = 0; + + for j in 0..MAX_NEW_COMMITMENTS_PER_TX { + calldata_hash_inputs[offset + i * MAX_NEW_COMMITMENTS_PER_TX + j] = new_commitments[j]; + } + offset += MAX_NEW_COMMITMENTS_PER_TX * 2; + + for j in 0..MAX_NEW_NULLIFIERS_PER_TX { + calldata_hash_inputs[offset + i * MAX_NEW_NULLIFIERS_PER_TX + j] = new_nullifiers[j]; + } + offset += MAX_NEW_NULLIFIERS_PER_TX * 2; + + for j in 0..MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX { + calldata_hash_inputs[offset + i * MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX * 2 + j * 2] = + public_data_update_requests[j].leaf_index; + calldata_hash_inputs[offset + i * MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX * 2 + j * 2 + 1] = + public_data_update_requests[j].new_value; + } + offset += MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX * 2 * 2; + + for j in 0..MAX_NEW_L2_TO_L1_MSGS_PER_TX { + calldata_hash_inputs[offset + i * MAX_NEW_L2_TO_L1_MSGS_PER_TX + j] = newL2ToL1msgs[j]; + } + offset += MAX_NEW_L2_TO_L1_MSGS_PER_TX * 2; + + let contract_leaf = kernel_data[i].public_inputs.end.new_contracts[0]; + calldata_hash_inputs[offset + i] = contract_leaf.hash(); + + offset += MAX_NEW_CONTRACTS_PER_TX * 2; + + let new_contracts = kernel_data[i].public_inputs.end.new_contracts; + calldata_hash_inputs[offset + i * 2] = new_contracts[0].contract_address.to_field(); + calldata_hash_inputs[offset + i * 2 + 1] = new_contracts[0].portal_contract_address.to_field(); + + offset += MAX_NEW_CONTRACTS_PER_TX * 2 * 2; + + for j in 0..NUM_FIELDS_PER_SHA256 { + calldata_hash_inputs[offset + i * 2 + j] = encryptedLogsHash[j]; + } + + offset += NUM_ENCRYPTED_LOGS_HASHES_PER_TX * NUM_FIELDS_PER_SHA256 * 2; + + for j in 0..NUM_FIELDS_PER_SHA256 { + calldata_hash_inputs[offset + i * 2 + j] = unencryptedLogsHash[j]; + } + } + + let mut hash_input_flattened = [0; CALL_DATA_HASH_FULL_FIELDS * 32 + CALL_DATA_HASH_LOG_FIELDS * 16]; + for offset in 0..CALL_DATA_HASH_FULL_FIELDS { + let input_as_bytes = calldata_hash_inputs[offset].to_be_bytes(32); + for byte_index in 0..32 { + hash_input_flattened[offset * 32 + byte_index] = input_as_bytes[byte_index]; + } + } + + for log_field_index in 0..CALL_DATA_HASH_LOG_FIELDS { + let input_as_bytes = calldata_hash_inputs[CALL_DATA_HASH_FULL_FIELDS + log_field_index].to_be_bytes(16); + for byte_index in 0..16 { + hash_input_flattened[CALL_DATA_HASH_FULL_FIELDS * 32 + log_field_index * 16 + byte_index] = input_as_bytes[byte_index]; + } + } + + let sha_digest = dep::std::hash::sha256(hash_input_flattened); + U256::from_bytes32(sha_digest).to_u128_limbs() + } + + // Check all of the provided commitments against the historical tree roots + fn perform_historical_blocks_tree_membership_checks(self) { + // For each of the historic_note_hash_tree_membership_checks, we need to do an inclusion proof + // against the historical root provided in the rollup constants + let historic_root = self.constants.start_historic_blocks_tree_roots_snapshot.root; + + for i in 0..KERNELS_PER_BASE_ROLLUP { + // Rebuild the block hash + let historical_block_data = self.kernel_data[i].public_inputs.constants.block_data; + let previous_block_hash = historical_block_data.block.hash(); + + let historic_root_witness = self.historic_blocks_tree_root_membership_witnesses[i]; + + components::assert_check_membership( + previous_block_hash, + historic_root_witness.leaf_index, + historic_root_witness.sibling_path, + historic_root + ); + } + } + + // TODO(Kev): This aggregate_proof method is duplicated in a lot of places + fn aggregate_proofs(self) -> AggregationObject { + // TODO: for now we simply return the aggregation object from the first proof + self.kernel_data[0].public_inputs.end.aggregation_object + } +} + +fn verify_kernel_proof(proof: Proof) -> bool { + // TODO: Just return true as we are mocking out the proof verification + // and aggregation. + // TODO(Kev): It may make sense to move all of these methods into a + // separate module. + true +} + +fn insert_public_data_update_requests( + tree_root: Field, + public_data_update_requests: [PublicDataUpdateRequest;MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX], + witnesses_offset: Field, + witnesses: [[Field; PUBLIC_DATA_TREE_HEIGHT]; 2 * MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX] +) -> Field { + let mut root = tree_root; + + for i in 0..MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX { + let state_write = public_data_update_requests[i]; + let witness = witnesses[i + witnesses_offset]; + + if (!state_write.is_empty()) { + components::assert_check_membership(state_write.old_value, state_write.leaf_index, witness, root); + root = components::root_from_sibling_path(state_write.new_value, state_write.leaf_index, witness); + } + } + + root +} + +fn validate_public_data_reads( + tree_root: Field, + public_data_reads: [PublicDataRead; MAX_PUBLIC_DATA_READS_PER_TX], + witnesses_offset: Field, + witnesses: [[Field; PUBLIC_DATA_TREE_HEIGHT]; 2 * MAX_PUBLIC_DATA_READS_PER_TX] +) { + for i in 0..MAX_PUBLIC_DATA_READS_PER_TX { + let public_data_read = public_data_reads[i]; + let witness = witnesses[i + witnesses_offset]; + + if (!public_data_read.is_empty()) { + components::assert_check_membership( + public_data_read.value, + public_data_read.leaf_index, + witness, + tree_root + ); + } + } +} + +global NUM_CONTRACT_LEAVES = 2; +#[test] +fn consistent_num_contract_leaves() { + assert(NUM_CONTRACT_LEAVES == MAX_NEW_CONTRACTS_PER_TX * 2, "num contract leaves incorrect, see calculate_contract_leaves to see how it is computed"); +} + +global NOTE_HASH_SUBTREE_WIDTH = 128; +#[test] +fn consistent_not_hash_subtree_width() { + assert_eq(NOTE_HASH_SUBTREE_WIDTH, 2.pow_32(NOTE_HASH_SUBTREE_HEIGHT) as u32, "note hash subtree width is incorrect"); +} + +global CALLDATA_HASH_INPUT_SIZE = 338; +#[test] +fn consistent_calldata_hash_input_size() { + let expected_size = (MAX_NEW_COMMITMENTS_PER_TX + + MAX_NEW_NULLIFIERS_PER_TX + + MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX * 2 + + MAX_NEW_L2_TO_L1_MSGS_PER_TX + + MAX_NEW_CONTRACTS_PER_TX * 3 + + NUM_ENCRYPTED_LOGS_HASHES_PER_TX * NUM_FIELDS_PER_SHA256 + + NUM_UNENCRYPTED_LOGS_HASHES_PER_TX * NUM_FIELDS_PER_SHA256) + * 2; + assert(CALLDATA_HASH_INPUT_SIZE == expected_size, "calldata hash input size is incorrect"); +} + +global CALL_DATA_HASH_LOG_FIELDS = 8; +#[test] +fn consistent_call_data_hash_log_fields() { + assert_eq(CALL_DATA_HASH_LOG_FIELDS, NUM_ENCRYPTED_LOGS_HASHES_PER_TX * NUM_FIELDS_PER_SHA256 * 2 + + NUM_UNENCRYPTED_LOGS_HASHES_PER_TX * NUM_FIELDS_PER_SHA256 * 2, "calldata hash log fields is incorrect"); +} + +global CALL_DATA_HASH_FULL_FIELDS = 330; +#[test] +fn consistent_call_data_hash_full_fields() { + assert_eq(CALL_DATA_HASH_FULL_FIELDS, CALLDATA_HASH_INPUT_SIZE - CALL_DATA_HASH_LOG_FIELDS, "calldata hash log fields is incorrect"); +} + +// TODO to radix returns u8, so we cannot use bigger radixes. It'd be ideal to use a radix of the maximum range-constrained integer noir supports +pub fn full_field_less_than(lhs: Field, rhs: Field) -> bool { + dep::std::eddsa::lt_bytes32(lhs, rhs) +} + +pub fn full_field_greater_than(lhs: Field, rhs: Field) -> bool { + dep::std::eddsa::lt_bytes32(rhs, lhs) +} + +#[test] +fn test_u256_less_than() { + assert(full_field_less_than(1, 1000)); + assert(!full_field_less_than(1000, 1000)); + assert(!full_field_less_than(1000, 1)); + assert(full_field_less_than(0, 0 - 1)); + assert(!full_field_less_than(0 - 1, 0)); +} + +#[test] +fn test_u256_greater_than() { + assert(full_field_greater_than(1000, 1)); + assert(!full_field_greater_than(1000, 1000)); + assert(!full_field_greater_than(1, 1000)); + assert(!full_field_greater_than(0, 0 - 1)); + assert(full_field_greater_than(0 - 1, 0)); +} + +mod tests { + use crate::{ + base::base_rollup_inputs::{ + CALL_DATA_HASH_FULL_FIELDS, + CALL_DATA_HASH_LOG_FIELDS, + NOTE_HASH_SUBTREE_WIDTH, + NUM_CONTRACT_LEAVES, + BaseRollupInputs + }, + merkle_tree::{calculate_subtree, calculate_empty_tree_root}, + abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot, + abis::base_or_merge_rollup_public_inputs::BaseOrMergeRollupPublicInputs, + abis::nullifier_leaf_preimage::NullifierLeafPreimage, + abis::constant_rollup_data::ConstantRollupData, + tests::merkle_tree_utils::{NonEmptyMerkleTree, compute_zero_hashes}, + components, + }; + use dep::aztec::constants_gen::{ + CONTRACT_SUBTREE_SIBLING_PATH_LENGTH, + CONTRACT_TREE_HEIGHT, + CONTRACT_SUBTREE_HEIGHT, + HISTORIC_BLOCKS_TREE_HEIGHT, + KERNELS_PER_BASE_ROLLUP, + MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP, + MAX_PUBLIC_DATA_READS_PER_BASE_ROLLUP, + MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_BASE_ROLLUP, + NOTE_HASH_SUBTREE_SIBLING_PATH_LENGTH, + NOTE_HASH_TREE_HEIGHT, + NOTE_HASH_SUBTREE_HEIGHT, + NULLIFIER_SUBTREE_SIBLING_PATH_LENGTH, + NULLIFIER_TREE_HEIGHT, + NULLIFIER_SUBTREE_HEIGHT, + PUBLIC_DATA_TREE_HEIGHT, + }; + use dep::types::{ + abis::membership_witness::HistoricBlocksTreeRootMembershipWitness, + abis::membership_witness::NullifierMembershipWitness, + abis::new_contract_data::NewContractData, + abis::public_data_read::PublicDataRead, + abis::public_data_update_request::PublicDataUpdateRequest, + tests::previous_kernel_data_builder::PreviousKernelDataBuilder, + address::{Address, EthAddress}, + utils::bounded_vec::BoundedVec, + utils::uint256::U256, + }; + use dep::std::option::Option; + + struct NullifierInsertion { + existing_index: Option, + value: Field, + } + + struct BaseRollupInputsBuilder { + kernel_data: [PreviousKernelDataBuilder; KERNELS_PER_BASE_ROLLUP], + pre_existing_notes: [Field; NOTE_HASH_SUBTREE_WIDTH], + pre_existing_nullifiers: [NullifierLeafPreimage; MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP], + pre_existing_contracts: [Field; NUM_CONTRACT_LEAVES], + pre_existing_public_data: [Field; MAX_PUBLIC_DATA_READS_PER_BASE_ROLLUP], + pre_existing_blocks: [Field; KERNELS_PER_BASE_ROLLUP], + public_data_reads: BoundedVec, + public_data_writes: BoundedVec<(u64, Field), 2>, + new_nullifiers: BoundedVec, + constants: ConstantRollupData, + } + + fn test_compute_empty_root(size: [Field; N]) -> Field { + compute_zero_hashes(size)[N - 1] + } + + impl BaseRollupInputsBuilder { + fn new() -> Self { + let mut inputs: BaseRollupInputsBuilder = dep::std::unsafe::zeroed(); + inputs.constants.global_variables.chain_id = 1; + inputs.constants.global_variables.version = 0; + + inputs.kernel_data = inputs.kernel_data.map(|_| { + let mut builder = PreviousKernelDataBuilder::new(); + let _nullifier = builder.end.new_nullifiers.pop(); + builder.is_public() + }); + + inputs.pre_existing_blocks = inputs.kernel_data.map(|builder: PreviousKernelDataBuilder|{ + builder.block_data.block.hash() + }); + + inputs + } + + fn extract_subtree_sibling_path(path: [Field; FULL_HEIGHT], mut sibling_path: [Field; SIBLING_PATH_LENGTH]) -> [Field; SIBLING_PATH_LENGTH] { + let subtree_height = FULL_HEIGHT - SIBLING_PATH_LENGTH; + for i in subtree_height..FULL_HEIGHT { + sibling_path[i - subtree_height] = path[i]; + } + sibling_path + } + + fn build_inputs(mut self) -> BaseRollupInputs { + let mut kernel_data = self.kernel_data.map(|builder: PreviousKernelDataBuilder|{ + builder.finish() + }); + + let start_note_hash_tree = NonEmptyMerkleTree::new(self.pre_existing_notes, [0; NOTE_HASH_TREE_HEIGHT], [0; NOTE_HASH_TREE_HEIGHT - NOTE_HASH_SUBTREE_HEIGHT], [0; NOTE_HASH_SUBTREE_HEIGHT]); + let start_note_hash_tree_snapshot = AppendOnlyTreeSnapshot { + root: start_note_hash_tree.get_root(), + next_available_leaf_index: start_note_hash_tree.get_next_available_index() as u32, + }; + let new_commitments_subtree_sibling_path = BaseRollupInputsBuilder::extract_subtree_sibling_path(start_note_hash_tree.get_sibling_path(self.pre_existing_notes.len()), [0; NOTE_HASH_SUBTREE_SIBLING_PATH_LENGTH]); + + let mut start_nullifier_tree = NonEmptyMerkleTree::new( + self.pre_existing_nullifiers.map(|preimage: NullifierLeafPreimage| preimage.hash()), + [0; NULLIFIER_TREE_HEIGHT], + [0; NULLIFIER_TREE_HEIGHT - NULLIFIER_SUBTREE_HEIGHT], + [0; NULLIFIER_SUBTREE_HEIGHT] + ); + + let start_nullifier_tree_snapshot = AppendOnlyTreeSnapshot { + root: start_nullifier_tree.get_root(), + next_available_leaf_index: start_nullifier_tree.get_next_available_index() as u32, + }; + + let start_contract_tree = NonEmptyMerkleTree::new(self.pre_existing_contracts, [0; CONTRACT_TREE_HEIGHT], [0; CONTRACT_TREE_HEIGHT - 1], [0; 1]); + let start_contract_tree_snapshot = AppendOnlyTreeSnapshot { + root: start_contract_tree.get_root(), + next_available_leaf_index: start_contract_tree.get_next_available_index() as u32, + }; + let new_contracts_subtree_sibling_path = BaseRollupInputsBuilder::extract_subtree_sibling_path(start_contract_tree.get_sibling_path(self.pre_existing_contracts.len()), [0; CONTRACT_SUBTREE_SIBLING_PATH_LENGTH]); + + let mut start_public_data_tree = NonEmptyMerkleTree::new(self.pre_existing_public_data, [0; PUBLIC_DATA_TREE_HEIGHT], [0; PUBLIC_DATA_TREE_HEIGHT - 5], [0; 5]); + let start_public_data_tree_root = start_public_data_tree.get_root(); + + let start_historic_blocks_tree = NonEmptyMerkleTree::new(self.pre_existing_blocks, [0; HISTORIC_BLOCKS_TREE_HEIGHT], [0; HISTORIC_BLOCKS_TREE_HEIGHT - 1], [0; 1]); + let start_historic_blocks_tree_snapshot = AppendOnlyTreeSnapshot { + root: start_historic_blocks_tree.get_root(), + next_available_leaf_index: start_historic_blocks_tree.get_next_available_index() as u32, + }; + + self.constants.start_historic_blocks_tree_roots_snapshot = start_historic_blocks_tree_snapshot; + + let mut new_public_data_reads_sibling_paths: [[Field; PUBLIC_DATA_TREE_HEIGHT]; MAX_PUBLIC_DATA_READS_PER_BASE_ROLLUP] = dep::std::unsafe::zeroed(); + + for i in 0..self.public_data_reads.max_len() { + if (i as u64) < (self.public_data_reads.len() as u64) { + let index = self.public_data_reads.get_unchecked(i); + let value = self.pre_existing_public_data[index]; + kernel_data[0].public_inputs.end.public_data_reads[i] = PublicDataRead { + leaf_index: index as Field, + value: value, + }; + new_public_data_reads_sibling_paths[i] = start_public_data_tree.get_sibling_path(index as Field); + } + } + + let mut new_public_data_update_requests_sibling_paths: [[Field; PUBLIC_DATA_TREE_HEIGHT]; MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_BASE_ROLLUP] = dep::std::unsafe::zeroed(); + + for i in 0..self.public_data_writes.max_len() { + if (i as u64) < (self.public_data_writes.len() as u64) { + let write = self.public_data_writes.get_unchecked(i); + let index = write.0; + let new_value = write.1; + let old_value = self.pre_existing_public_data[index]; + kernel_data[0].public_inputs.end.public_data_update_requests[i] = PublicDataUpdateRequest { + leaf_index : index as Field, + old_value, + new_value, + }; + new_public_data_update_requests_sibling_paths[i] = start_public_data_tree.get_sibling_path(index as Field); + start_public_data_tree.update_leaf(index, new_value); + } + } + + let mut low_nullifier_leaf_preimages: [NullifierLeafPreimage; MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP] = dep::std::unsafe::zeroed(); + let mut low_nullifier_membership_witness: [NullifierMembershipWitness; MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP] = dep::std::unsafe::zeroed(); + + for i in 0..self.new_nullifiers.max_len() { + if (i as u64) < (self.new_nullifiers.len() as u64) { + let new_nullifier = self.new_nullifiers.get_unchecked(i); + kernel_data[0].public_inputs.end.new_nullifiers[i] = new_nullifier.value; + + if (new_nullifier.existing_index.is_some()) { + let low_index = new_nullifier.existing_index.unwrap_unchecked(); + let mut low_preimage = self.pre_existing_nullifiers[low_index]; + low_nullifier_leaf_preimages[i] = low_preimage; + low_nullifier_membership_witness[i] = NullifierMembershipWitness { + leaf_index: low_index as Field, + sibling_path: start_nullifier_tree.get_sibling_path(low_index as Field) + }; + + low_preimage.next_value = new_nullifier.value; + low_preimage.next_index = start_nullifier_tree_snapshot.next_available_leaf_index + (i as u32); + start_nullifier_tree.update_leaf(low_index, low_preimage.hash()); + } + } + } + let new_nullifiers_subtree_sibling_path = BaseRollupInputsBuilder::extract_subtree_sibling_path(start_nullifier_tree.get_sibling_path(self.pre_existing_nullifiers.len()), [0; NULLIFIER_SUBTREE_SIBLING_PATH_LENGTH]); + + BaseRollupInputs { + kernel_data: kernel_data, + start_note_hash_tree_snapshot, + start_nullifier_tree_snapshot, + start_contract_tree_snapshot, + start_public_data_tree_root, + start_historic_blocks_tree_snapshot, + + low_nullifier_leaf_preimages, + low_nullifier_membership_witness, + + new_commitments_subtree_sibling_path, + new_nullifiers_subtree_sibling_path, + new_contracts_subtree_sibling_path, + new_public_data_update_requests_sibling_paths, + new_public_data_reads_sibling_paths, + + historic_blocks_tree_root_membership_witnesses: [ + HistoricBlocksTreeRootMembershipWitness { + leaf_index: 0, + sibling_path: start_historic_blocks_tree.get_sibling_path(0) + }, + HistoricBlocksTreeRootMembershipWitness { + leaf_index: 1, + sibling_path: start_historic_blocks_tree.get_sibling_path(1) + }, + ], + + constants: self.constants, + } + } + + fn execute(self) -> BaseOrMergeRollupPublicInputs { + self.build_inputs().base_rollup_circuit() + } + + fn succeeds(self) { + let _ = self.execute(); + } + + fn fails(self) { + let _ = self.execute(); + } + } + + #[test] + fn no_new_contract_leaves() { + let outputs = BaseRollupInputsBuilder::new().execute(); + let expected_start_contract_tree_snapshot = AppendOnlyTreeSnapshot { root: test_compute_empty_root([0; CONTRACT_TREE_HEIGHT]), next_available_leaf_index: 2 }; + let expected_end_contract_tree_snapshot = AppendOnlyTreeSnapshot { root: test_compute_empty_root([0; CONTRACT_TREE_HEIGHT]), next_available_leaf_index: 4 }; + assert(outputs.start_contract_tree_snapshot.eq(expected_start_contract_tree_snapshot)); + assert(outputs.end_contract_tree_snapshot.eq(expected_end_contract_tree_snapshot)); + } + + #[test] + fn contract_leaf_inserted() { + let new_contract = NewContractData { + contract_address: Address::from_field(1), + portal_contract_address: EthAddress::from_field(2), + function_tree_root: 3 + }; + + let mut builder = BaseRollupInputsBuilder::new(); + + let mut new_contracts = builder.kernel_data[0].end.new_contracts; + new_contracts.push(new_contract); + builder.kernel_data[0].end.new_contracts = new_contracts; + + let mut expected_contracts_tree = NonEmptyMerkleTree::new( + [0; 4], + [0; CONTRACT_TREE_HEIGHT], + [0; CONTRACT_TREE_HEIGHT - 2], + [0; 2] + ); + + let outputs = builder.execute(); + + let expected_start_contract_tree_snapshot = AppendOnlyTreeSnapshot { root: expected_contracts_tree.get_root(), next_available_leaf_index: 2 }; + assert(outputs.start_contract_tree_snapshot.eq(expected_start_contract_tree_snapshot)); + + expected_contracts_tree.update_leaf(2, new_contract.hash()); + let expected_end_contract_tree_snapshot = AppendOnlyTreeSnapshot { root: expected_contracts_tree.get_root(), next_available_leaf_index: 4 }; + assert(outputs.end_contract_tree_snapshot.eq(expected_end_contract_tree_snapshot)); + } + + #[test] + fn contract_leaf_inserted_in_non_empty_snapshot_tree() { + let new_contract = NewContractData { + contract_address: Address::from_field(1), + portal_contract_address: EthAddress::from_field(2), + function_tree_root: 3 + }; + + let mut builder = BaseRollupInputsBuilder::new(); + + builder.pre_existing_contracts = [1,2]; + + let mut new_contracts = builder.kernel_data[0].end.new_contracts; + new_contracts.push(new_contract); + builder.kernel_data[0].end.new_contracts = new_contracts; + + let mut expected_contracts_tree = NonEmptyMerkleTree::new( + [1, 2, 0, 0], + [0; CONTRACT_TREE_HEIGHT], + [0; CONTRACT_TREE_HEIGHT - 2], + [0; 2] + ); + + let outputs = builder.execute(); + + let expected_start_contract_tree_snapshot = AppendOnlyTreeSnapshot { root: expected_contracts_tree.get_root(), next_available_leaf_index: 2 }; + assert(outputs.start_contract_tree_snapshot.eq(expected_start_contract_tree_snapshot)); + + expected_contracts_tree.update_leaf(2, new_contract.hash()); + let expected_end_contract_tree_snapshot = AppendOnlyTreeSnapshot { root: expected_contracts_tree.get_root(), next_available_leaf_index: 4 }; + assert(outputs.end_contract_tree_snapshot.eq(expected_end_contract_tree_snapshot)); + } + + #[test] + fn new_commitments_tree() { + let mut builder = BaseRollupInputsBuilder::new(); + + let new_commitments = [27, 28, 29, 30, 31, 32]; + let mut new_commitments_vec = builder.kernel_data[0].end.new_commitments; + + for i in 0..new_commitments.len() { + new_commitments_vec.push(new_commitments[i]); + } + + builder.kernel_data[0].end.new_commitments = new_commitments_vec; + + let mut expected_commitments_tree = NonEmptyMerkleTree::new( + [0; NOTE_HASH_SUBTREE_WIDTH * 2], + [0; NOTE_HASH_TREE_HEIGHT], + [0; NOTE_HASH_TREE_HEIGHT - NOTE_HASH_SUBTREE_HEIGHT - 1], + [0; NOTE_HASH_SUBTREE_HEIGHT + 1] + ); + + let outputs = builder.execute(); + + let expected_start_note_hash_tree_snapshot = AppendOnlyTreeSnapshot { root: expected_commitments_tree.get_root(), next_available_leaf_index: NOTE_HASH_SUBTREE_WIDTH }; + assert(outputs.start_note_hash_tree_snapshot.eq(expected_start_note_hash_tree_snapshot)); + + for i in 0..new_commitments.len() { + expected_commitments_tree.update_leaf( + (i as u64) + (NOTE_HASH_SUBTREE_WIDTH as u64), + new_commitments[i] + ); + } + + let expected_end_note_hash_tree_snapshot = AppendOnlyTreeSnapshot { root: expected_commitments_tree.get_root(), next_available_leaf_index: NOTE_HASH_SUBTREE_WIDTH * 2 }; + assert(outputs.end_note_hash_tree_snapshot.eq(expected_end_note_hash_tree_snapshot)); + } + + #[test] + fn new_nullifier_tree_empty() { + /** + * DESCRIPTION + */ + + // This test checks for insertions of all 0 values + // In this special case we will not need to provide sibling paths to check insertion of the nullifier values + // This is because 0 values are not actually inserted into the tree, rather the inserted subtree is left + // empty to begin with. + + let mut builder = BaseRollupInputsBuilder::new(); + + builder.pre_existing_nullifiers[0] = NullifierLeafPreimage { + leaf_value : 0, + next_value : 7, + next_index : 1, + }; + builder.pre_existing_nullifiers[1] = NullifierLeafPreimage { + leaf_value : 7, + next_value : 0, + next_index : 0, + }; + + builder.succeeds(); + } + + #[test] + fn nullifier_insertion_test() { + let mut builder = BaseRollupInputsBuilder::new(); + + builder.pre_existing_nullifiers[0] = NullifierLeafPreimage { + leaf_value : 0, + next_value : 7, + next_index : 1, + }; + builder.pre_existing_nullifiers[1] = NullifierLeafPreimage { + leaf_value : 7, + next_value : 0, + next_index : 0, + }; + + builder.new_nullifiers.push(NullifierInsertion { + existing_index: Option::some(0), + value: 1, + }); + + let mut tree_nullifiers = [NullifierLeafPreimage::default(); MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP * 2]; + tree_nullifiers[0] = NullifierLeafPreimage { + leaf_value : 0, + next_value : 1, + next_index : MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP as u32, + }; + tree_nullifiers[1] = builder.pre_existing_nullifiers[1]; + tree_nullifiers[MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP] = NullifierLeafPreimage { + leaf_value : 1, + next_value : 7, + next_index : 1, + }; + + let mut end_nullifier_tree = NonEmptyMerkleTree::new( + tree_nullifiers.map(|preimage: NullifierLeafPreimage| preimage.hash()), + [0; NULLIFIER_TREE_HEIGHT], + [0; NULLIFIER_TREE_HEIGHT - NULLIFIER_SUBTREE_HEIGHT - 1], + [0; NULLIFIER_SUBTREE_HEIGHT + 1] + ); + + let output = builder.execute(); + + assert(output.end_nullifier_tree_snapshot.eq(AppendOnlyTreeSnapshot { + root: end_nullifier_tree.get_root(), + next_available_leaf_index: 2 * MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP as u32, + })); + } + + #[test] + fn new_nullifier_tree_all_larger() { + let mut builder = BaseRollupInputsBuilder::new(); + + builder.pre_existing_nullifiers[0] = NullifierLeafPreimage { + leaf_value : 0, + next_value : 7, + next_index : 1, + }; + builder.pre_existing_nullifiers[1] = NullifierLeafPreimage { + leaf_value : 7, + next_value : 0, + next_index : 0, + }; + + builder.new_nullifiers.push(NullifierInsertion { + existing_index: Option::some(1), + value: 8, + }); + for i in 1..builder.new_nullifiers.max_len() { + builder.new_nullifiers.push(NullifierInsertion { + existing_index: Option::none(), + value: (8 + i) as Field, + }); + } + + let mut tree_nullifiers = [NullifierLeafPreimage::default(); MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP * 2]; + tree_nullifiers[0] = builder.pre_existing_nullifiers[0]; + + tree_nullifiers[1] = NullifierLeafPreimage { + leaf_value : 7, + next_value : 8, + next_index : MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP as u32, + }; + + let last_index = builder.new_nullifiers.max_len() - 1; + for i in 0..last_index { + tree_nullifiers[MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP + i] = NullifierLeafPreimage { + leaf_value : (8 + i) as Field, + next_value : (8 + i + 1) as Field, + next_index : (MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP + i) as u32 + 1, + }; + } + + tree_nullifiers[MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP+last_index] = NullifierLeafPreimage { + leaf_value : (8 + last_index) as Field, + next_value : 0, + next_index : 0, + }; + + let mut end_nullifier_tree = NonEmptyMerkleTree::new( + tree_nullifiers.map(|preimage: NullifierLeafPreimage| preimage.hash()), + [0; NULLIFIER_TREE_HEIGHT], + [0; NULLIFIER_TREE_HEIGHT - NULLIFIER_SUBTREE_HEIGHT - 1], + [0; NULLIFIER_SUBTREE_HEIGHT + 1] + ); + + let output = builder.execute(); + + assert(output.end_nullifier_tree_snapshot.eq(AppendOnlyTreeSnapshot { + root: end_nullifier_tree.get_root(), + next_available_leaf_index: 2 * MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP as u32, + })); + } + + // TODO(Alvaro) some nullifier tree tests. We are updating the nullifier tree insertion algorithm. + + #[test(should_fail_with = "membership check failed")] + fn new_nullifier_tree_double_spend() { + let mut builder = BaseRollupInputsBuilder::new(); + + builder.pre_existing_nullifiers[0] = NullifierLeafPreimage { + leaf_value : 0, + next_value : 7, + next_index : 1, + }; + builder.pre_existing_nullifiers[1] = NullifierLeafPreimage { + leaf_value : 7, + next_value : 0, + next_index : 0, + }; + + builder.new_nullifiers.push(NullifierInsertion { + existing_index: Option::some(1), + value: 8, + }); + builder.new_nullifiers.push(NullifierInsertion { + existing_index: Option::some(1), + value: 8, + }); + + builder.fails(); + } + + + #[test(should_fail_with = "Nullifier subtree is malformed")] + fn new_nullifier_tree_double_spend_same_batch() { + let mut builder = BaseRollupInputsBuilder::new(); + + builder.pre_existing_nullifiers[0] = NullifierLeafPreimage { + leaf_value : 0, + next_value : 7, + next_index : 1, + }; + builder.pre_existing_nullifiers[1] = NullifierLeafPreimage { + leaf_value : 7, + next_value : 0, + next_index : 0, + }; + + builder.new_nullifiers.push(NullifierInsertion { + existing_index: Option::some(1), + value: 8, + }); + builder.new_nullifiers.push(NullifierInsertion { + existing_index: Option::none(), + value: 8, + }); + + builder.fails(); + } + + + #[test] + fn empty_block_calldata_hash() { + let outputs = BaseRollupInputsBuilder::new().execute(); + + let hash_input_flattened = [0; CALL_DATA_HASH_FULL_FIELDS * 32 + CALL_DATA_HASH_LOG_FIELDS * 16]; + let sha_digest = dep::std::hash::sha256(hash_input_flattened); + let expected_calldata_hash = U256::from_bytes32(sha_digest).to_u128_limbs(); + assert_eq(outputs.calldata_hash, expected_calldata_hash); + } + + #[test(should_fail_with = "membership check failed")] + fn compute_membership_historic_blocks_tree_negative() { + let mut inputs = BaseRollupInputsBuilder::new().build_inputs(); + + inputs.historic_blocks_tree_root_membership_witnesses[0].sibling_path[0] = 27; + + let _output = inputs.base_rollup_circuit(); + } + + #[test] + fn constants_dont_change() { + let inputs = BaseRollupInputsBuilder::new().build_inputs(); + let outputs = inputs.base_rollup_circuit(); + + assert(inputs.constants.eq(outputs.constants)); + } + + #[test(should_fail_with = "kernel chain_id does not match the rollup chain_id")] + fn constants_dont_match_kernels_chain_id() { + let mut builder = BaseRollupInputsBuilder::new(); + builder.constants.global_variables.chain_id = 3; + builder.fails(); } -} \ No newline at end of file + + #[test(should_fail_with = "kernel version does not match the rollup version")] + fn constants_dont_match_kernels_version() { + let mut builder = BaseRollupInputsBuilder::new(); + builder.constants.global_variables.version = 3; + builder.fails(); + } + + #[test] + fn subtree_height_is_0() { + let outputs = BaseRollupInputsBuilder::new().execute(); + + assert_eq(outputs.rollup_subtree_height, 0); + } + + #[test] + fn single_public_state_read() { + let mut builder = BaseRollupInputsBuilder::new(); + + builder.pre_existing_public_data[0] = 27; + builder.public_data_reads.push(0); + + builder.succeeds(); + } + + #[test] + fn single_public_state_write() { + let mut builder = BaseRollupInputsBuilder::new(); + + builder.pre_existing_public_data[0] = 27; + builder.public_data_writes.push((0, 28)); + + let outputs = builder.execute(); + + let mut expected_public_data_tree = NonEmptyMerkleTree::new( + [28, 0], + [0; PUBLIC_DATA_TREE_HEIGHT], + [0; PUBLIC_DATA_TREE_HEIGHT - 1], + [0; 1] + ); + + assert_eq(outputs.end_public_data_tree_root, expected_public_data_tree.get_root()); + } + + #[test] + fn multiple_public_state_read_writes() { + let mut builder = BaseRollupInputsBuilder::new(); + + builder.pre_existing_public_data[0] = 27; + builder.pre_existing_public_data[1] = 28; + builder.pre_existing_public_data[2] = 29; + builder.pre_existing_public_data[3] = 30; + + builder.public_data_reads.push(0); + builder.public_data_writes.push((0, 60)); + builder.public_data_writes.push((2, 61)); + builder.public_data_reads.push(3); + + let outputs = builder.execute(); + + let mut expected_public_data_tree = NonEmptyMerkleTree::new( + [60, 28, 61, 30], + [0; PUBLIC_DATA_TREE_HEIGHT], + [0; PUBLIC_DATA_TREE_HEIGHT - 2], + [0; 2] + ); + + assert_eq(outputs.end_public_data_tree_root, expected_public_data_tree.get_root()); + } +} diff --git a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/hash.nr b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/hash.nr index 2cf61bc19e8..c024f1695e2 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/hash.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/hash.nr @@ -6,10 +6,10 @@ pub fn compute_block_hash_with_globals( note_hash_tree_root : Field, nullifier_tree_root : Field, contract_tree_root : Field, - l1_to_l2_data_tree_root : Field, + l1_to_l2_messages_tree_root : Field, public_data_tree_root : Field) -> Field { - let inputs = [globals.hash(), note_hash_tree_root, nullifier_tree_root, contract_tree_root, l1_to_l2_data_tree_root, public_data_tree_root]; + let inputs = [globals.hash(), note_hash_tree_root, nullifier_tree_root, contract_tree_root, l1_to_l2_messages_tree_root, public_data_tree_root]; dep::std::hash::pedersen_hash_with_separator(inputs, constants_gen::GENERATOR_INDEX__BLOCK_HASH) } \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/lib.nr b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/lib.nr index c7123d692e2..3a225975d81 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/lib.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/lib.nr @@ -15,4 +15,4 @@ mod hash; mod merkle_tree; -mod fixtures; \ No newline at end of file +mod tests; \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/merge/merge_rollup_inputs.nr b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/merge/merge_rollup_inputs.nr index 94c21d2ec80..249515b5715 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/merge/merge_rollup_inputs.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/merge/merge_rollup_inputs.nr @@ -51,7 +51,7 @@ impl MergeRollupInputs { mod tests { use crate::{ merge::merge_rollup_inputs::MergeRollupInputs, - fixtures::merge_rollup_inputs::default_merge_rollup_inputs, + tests::merge_rollup_inputs::default_merge_rollup_inputs, }; use dep::types::hash::accumulate_sha256; use dep::types::utils::uint128::U128; diff --git a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/merkle_tree.nr b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/merkle_tree.nr index de6baf155b5..d9ff3702527 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/merkle_tree.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/merkle_tree.nr @@ -32,7 +32,7 @@ impl MerkleTree { } } -pub fn calculate_subtree(leaves : [Field; N]) -> Field { +pub fn calculate_subtree(leaves: [Field; N]) -> Field { MerkleTree::new(leaves).get_root() } @@ -41,12 +41,12 @@ pub fn calculate_subtree(leaves : [Field; N]) -> Field { // // Would be good if we could use width since the compute_subtree // algorithm uses depth. -pub fn calculate_empty_tree_root(depth : Field) -> Field { +pub fn calculate_empty_tree_root(depth: Field) -> Field { if depth == 1 { 0x27b1d0839a5b23baf12a8d195b18ac288fcf401afb2f70b8a4b529ede5fa9fed } else if depth == 2 { 0x21dbfd1d029bf447152fcf89e355c334610d1632436ba170f738107266a71550 - } else if depth == 3{ + } else if depth == 3 { 0x0bcd1f91cf7bdd471d0a30c58c4706f3fdab3807a954b8f5b5e3bfec87d001bb } else if depth == 4 { 0x06e62084ee7b602fe9abc15632dda3269f56fb0c6e12519a2eb2ec897091919d @@ -68,13 +68,12 @@ pub fn calculate_empty_tree_root(depth : Field) -> Field { } } - #[test] fn test_merkle_root_interop_test() { // This is a test to ensure that we match the cpp implementation. // You can grep for `TEST_F(root_rollup_tests, noir_interop_test)` // to find the test that matches this. - let root = calculate_subtree([1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4]); + let root = calculate_subtree([1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4]); assert(0x17e8bb70a11d0c946345950879484d2f4f9fef397ff6adbfdec3baab2d41faab == root); let empty_root = calculate_subtree([0; 16]); @@ -85,7 +84,7 @@ fn test_merkle_root_interop_test() { fn test_empty_subroot() { let expected_empty_root_2 = calculate_subtree([0; 2]); assert(calculate_empty_tree_root(1) == expected_empty_root_2); - + let expected_empty_root_4 = calculate_subtree([0; 4]); assert(calculate_empty_tree_root(2) == expected_empty_root_4); @@ -97,10 +96,10 @@ fn test_empty_subroot() { let expected_empty_root_32 = calculate_subtree([0; 32]); assert(calculate_empty_tree_root(5) == expected_empty_root_32); - + let expected_empty_root_64 = calculate_subtree([0; 64]); assert(calculate_empty_tree_root(6) == expected_empty_root_64); - + let expected_empty_root_128 = calculate_subtree([0; 128]); assert(calculate_empty_tree_root(7) == expected_empty_root_128); -} \ No newline at end of file +} diff --git a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/root.nr b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/root.nr index 21ad114a874..c64ee5fcbdd 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/root.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/root.nr @@ -143,7 +143,7 @@ mod tests { root_rollup_inputs::RootRollupInputs, NUMBER_OF_L1_L2_MESSAGES_PER_ROLLUP_NUM_BYTES, }, - fixtures::root_rollup_inputs::default_root_rollup_inputs, + tests::root_rollup_inputs::default_root_rollup_inputs, }; use dep::types::utils::uint128::U128; use dep::types::utils::uint256::U256; diff --git a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/fixtures.nr b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests.nr similarity index 50% rename from yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/fixtures.nr rename to yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests.nr index b5297664fcf..f77a334506a 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/fixtures.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests.nr @@ -1,3 +1,4 @@ mod merge_rollup_inputs; mod root_rollup_inputs; -mod previous_rollup_data; \ No newline at end of file +mod previous_rollup_data; +mod merkle_tree_utils; \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/fixtures/merge_rollup_inputs.nr b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests/merge_rollup_inputs.nr similarity index 84% rename from yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/fixtures/merge_rollup_inputs.nr rename to yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests/merge_rollup_inputs.nr index 8230e55d0f9..7ad7e998e37 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/fixtures/merge_rollup_inputs.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests/merge_rollup_inputs.nr @@ -1,7 +1,7 @@ use crate::merge::merge_rollup_inputs::MergeRollupInputs; use crate::abis::base_or_merge_rollup_public_inputs::BASE_ROLLUP_TYPE; use crate::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot; -use crate::fixtures::previous_rollup_data::default_previous_rollup_data; +use crate::tests::previous_rollup_data::default_previous_rollup_data; pub fn default_merge_rollup_inputs() -> MergeRollupInputs { let mut inputs: MergeRollupInputs = dep::std::unsafe::zeroed(); diff --git a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests/merkle_tree_utils.nr b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests/merkle_tree_utils.nr new file mode 100644 index 00000000000..daec7a5d104 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests/merkle_tree_utils.nr @@ -0,0 +1,223 @@ +use crate::merkle_tree::{MerkleTree, calculate_empty_tree_root}; + +pub fn compute_zero_hashes(mut hashes: [Field; N]) -> [Field; N] { + hashes[0] = dep::std::hash::pedersen_hash([0, 0]); + + for i in 1..N { + hashes[i] = dep::std::hash::pedersen_hash([hashes[i-1], hashes[i-1]]); + } + + hashes +} + +impl MerkleTree { + fn update_leaf(&mut self, index: u64, value: Field, _tree_height: [Field; K]) { + self.leaves[index] = value; + + let mut sibling_index = MerkleTree::sibling_index(index); + let (mut left_node, mut right_node) = if index % 2 == 0 { + (value, self.leaves[sibling_index]) + } else { + (self.leaves[sibling_index], value) + }; + + let mut current_width: u64 = N / 2; + let mut layer_offset: u64 = 0; + let mut node_index: u64 = index / 2 + layer_offset; + for _ in 0..K { + self.nodes[node_index] = dep::std::hash::pedersen_hash([left_node, right_node]); + sibling_index = MerkleTree::sibling_index(node_index); + let nodes = if node_index % 2 == 0 { + (self.nodes[node_index], self.nodes[sibling_index]) + } else { + (self.nodes[sibling_index], self.nodes[node_index]) + }; + left_node = nodes.0; + right_node = nodes.1; + + let current_index_at_layer = node_index - layer_offset; + + layer_offset += current_width; + node_index = (current_index_at_layer / 2) + layer_offset; + current_width = current_width / 2; + } + + } + + pub fn sibling_index(index: u64) -> u64 { + if index % 2 == 0 { + index + 1 + } else { + index - 1 + } + } +} + +#[test] +fn test_merkle_tree_update_leaf() { + let mut tree = MerkleTree::new([1, 2]); + assert_eq(tree.get_root(), dep::std::hash::pedersen_hash([1, 2])); + tree.update_leaf(1, 0, [0; 1]); + assert_eq(tree.get_root(), dep::std::hash::pedersen_hash([1, 0])); + tree.update_leaf(0, 0, [0; 1]); + assert_eq(tree.get_root(), dep::std::hash::pedersen_hash([0, 0])); +} + +#[test] +fn test_merkle_tree_update_leaf_three_layers() { + let mut tree = MerkleTree::new([1, 2, 3, 4, 5, 6, 7, 8]); + + for i in 0..8 { + tree.update_leaf(i, 0, [0; 3]); + } + + assert_eq(tree.get_root(), calculate_empty_tree_root(3)); +} + +struct NonEmptyMerkleTree { + subtree: MerkleTree, + zero_hashes: [Field; TREE_HEIGHT], + left_supertree_branch: [Field; SUPERTREE_HEIGHT], + _phantom_subtree_height: [Field; SUBTREE_HEIGHT], +} + +impl NonEmptyMerkleTree { + pub fn new( + non_zero_leaves: [Field; SUBTREE_ITEMS], + _tree_height: [Field; TREE_HEIGHT], + _supertree_height: [Field; SUPERTREE_HEIGHT], + _subtree_height: [Field; SUBTREE_HEIGHT], + ) -> Self { + assert_eq(TREE_HEIGHT, SUPERTREE_HEIGHT + SUBTREE_HEIGHT, "tree height must be the sum of supertree and subtree height"); + assert_eq(SUBTREE_ITEMS as u120, 1 << (SUBTREE_HEIGHT as u120), "subtree items must be 2^subtree height"); + let subtree = MerkleTree::new(non_zero_leaves); + + let zero_hashes = compute_zero_hashes(_tree_height); + + let mut left_supertree_branch = [0; SUPERTREE_HEIGHT]; + left_supertree_branch[0] = dep::std::hash::pedersen_hash([subtree.get_root(), zero_hashes[SUBTREE_HEIGHT-1]]); + for i in 1..left_supertree_branch.len() { + left_supertree_branch[i] = dep::std::hash::pedersen_hash([left_supertree_branch[i-1], zero_hashes[SUBTREE_HEIGHT-1+i]]); + } + + NonEmptyMerkleTree { + subtree, + zero_hashes, + left_supertree_branch, + _phantom_subtree_height: _subtree_height + } + } + + pub fn get_sibling_path(self, leaf_index: Field) -> [Field; TREE_HEIGHT] { + let mut path = [0; TREE_HEIGHT]; + let mut current_index = leaf_index as u64; + let mut subtree_width = SUBTREE_ITEMS as u64; + + let mut sibling_index = MerkleTree::sibling_index(current_index); + + path[0] = if current_index < subtree_width { + self.subtree.leaves[sibling_index] + } else { + 0 + }; + + let mut subtree_offset: u64 = 0; + + for i in 1..TREE_HEIGHT { + current_index = current_index / 2; + subtree_width = subtree_width / 2; + + sibling_index = MerkleTree::sibling_index(current_index); + + if sibling_index < subtree_width { + path[i] = self.subtree.nodes[subtree_offset + sibling_index]; + }else { + if sibling_index == 0 { + path[i] = self.left_supertree_branch[i-1-SUBTREE_HEIGHT]; + }else { + path[i] = self.zero_hashes[i-1]; + } + } + + subtree_offset += subtree_width; + } + + path + } + + pub fn update_leaf(&mut self, index: u64, value: Field) { + assert(index < SUBTREE_ITEMS as u64, "index must be less than the number of leaves in the subtree"); + + self.subtree.update_leaf(index, value, [0; SUBTREE_HEIGHT]); + + self.left_supertree_branch[0] = dep::std::hash::pedersen_hash([self.subtree.get_root(), self.zero_hashes[SUBTREE_HEIGHT-1]]); + for i in 1..self.left_supertree_branch.len() { + self.left_supertree_branch[i] = dep::std::hash::pedersen_hash([self.left_supertree_branch[i-1], self.zero_hashes[SUBTREE_HEIGHT-1+i]]); + } + } + + pub fn get_root(self) -> Field { + self.left_supertree_branch[SUPERTREE_HEIGHT-1] + } + + pub fn get_next_available_index(self) -> Field { + SUBTREE_ITEMS + } +} + +#[test] +fn test_merkle_tree_empty_subtree() { + let tree = NonEmptyMerkleTree::new([0; 2], [0; 2], [0; 1], [0; 1]); + assert_eq(tree.zero_hashes.len(), 2); + let path = tree.get_sibling_path(3); + assert_eq(path[0], 0); + assert_eq(path[1], dep::std::hash::pedersen_hash([0, 0])); + assert_eq(tree.get_root(), calculate_empty_tree_root(2)); +} + +#[test] +fn test_merkle_tree_empty_subtree_height_3() { + let tree = NonEmptyMerkleTree::new([0; 2], [0; 3], [0; 2], [0; 1]); + assert_eq(tree.zero_hashes.len(), 3); + assert_eq(tree.get_root(), calculate_empty_tree_root(3)); + + let zero_hashes = compute_zero_hashes([0; 3]); + let path = tree.get_sibling_path(0); + assert_eq(path[0], 0); + assert_eq(path[1], zero_hashes[0]); + assert_eq(path[2], zero_hashes[1]); +} + +#[test] +fn test_merkle_tree_non_empty_subtree() { + let tree = NonEmptyMerkleTree::new([1; 2], [0; 2], [0; 1], [0; 1]); + assert_eq(tree.zero_hashes.len(), 2); + let path = tree.get_sibling_path(3); + assert_eq(path[0], 0); + assert_eq(path[1], dep::std::hash::pedersen_hash([1, 1])); +} + +#[test] +fn test_merkle_tree_non_empty_subtree_height_3() { + let tree = NonEmptyMerkleTree::new([2, 3], [0; 3], [0; 2], [0; 1]); + assert_eq(tree.zero_hashes.len(), 3); + let path = tree.get_sibling_path(1); + assert_eq(path[0], 2); + let zero_hashes = compute_zero_hashes([0; 3]); + assert_eq(path[1], zero_hashes[0]); + assert_eq(path[2], zero_hashes[1]); +} + +#[test] +fn test_merkle_tree_set_to_empty() { + let mut tree = NonEmptyMerkleTree::new([2, 3], [0; 3], [0; 2], [0; 1]); + tree.update_leaf(0, 0); + tree.update_leaf(1, 0); + assert_eq(tree.get_root(), calculate_empty_tree_root(3)); + + let zero_hashes = compute_zero_hashes([0; 3]); + let path = tree.get_sibling_path(0); + assert_eq(path[0], 0); + assert_eq(path[1], zero_hashes[0]); + assert_eq(path[2], zero_hashes[1]); +} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/fixtures/previous_rollup_data.nr b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests/previous_rollup_data.nr similarity index 100% rename from yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/fixtures/previous_rollup_data.nr rename to yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests/previous_rollup_data.nr diff --git a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/fixtures/root_rollup_inputs.nr b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests/root_rollup_inputs.nr similarity index 87% rename from yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/fixtures/root_rollup_inputs.nr rename to yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests/root_rollup_inputs.nr index d49cac24194..81e026c53d5 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/fixtures/root_rollup_inputs.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/rollup-lib/src/tests/root_rollup_inputs.nr @@ -10,17 +10,8 @@ use dep::aztec::constants_gen::{ HISTORIC_BLOCKS_TREE_HEIGHT, }; use crate::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot; -use crate::fixtures::previous_rollup_data::default_previous_rollup_data; - -pub fn compute_zero_hashes(mut hashes: [Field; N]) -> [Field; N] { - hashes[0] = dep::std::hash::pedersen_hash([0, 0]); - - for i in 1..N { - hashes[i] = dep::std::hash::pedersen_hash([hashes[i-1], hashes[i-1]]); - } - - hashes -} +use crate::tests::previous_rollup_data::default_previous_rollup_data; +use crate::tests::merkle_tree_utils::compute_zero_hashes; pub fn compute_l1_l2_empty_snapshot() -> (AppendOnlyTreeSnapshot, [Field; L1_TO_L2_MSG_SUBTREE_SIBLING_PATH_LENGTH]) { let zero_hashes = compute_zero_hashes([0; L1_TO_L2_MSG_TREE_HEIGHT]); diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis.nr index 67e78875203..8fd4ee28109 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis.nr @@ -21,8 +21,11 @@ mod private_kernel; mod kernel_circuit_public_inputs; mod previous_kernel_data; +mod call_request; mod call_stack_item; mod call_context; +mod public_call_data; + mod public_circuit_public_inputs; mod private_circuit_public_inputs; diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/call_context.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/call_context.nr index 293a624f8b7..37259a7e675 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/call_context.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/call_context.nr @@ -36,4 +36,14 @@ impl CallContext { assert(self.is_static_call == false); assert(self.is_contract_deployment == false); } + + fn eq(self, call_context: CallContext) -> bool { + call_context.msg_sender.eq(self.msg_sender) + & call_context.storage_contract_address.eq(self.storage_contract_address) + & call_context.portal_contract_address.eq(self.portal_contract_address) + & call_context.function_selector.eq(self.function_selector) + & (call_context.is_delegate_call == self.is_delegate_call) + & (call_context.is_static_call == self.is_static_call) + & (call_context.is_contract_deployment == self.is_contract_deployment) + } } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/call_request.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/call_request.nr new file mode 100644 index 00000000000..9c6737c9e3e --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/call_request.nr @@ -0,0 +1,50 @@ +use crate::address::Address; + +struct CallerContext { + msg_sender: Address, + storage_contract_address: Address, +} + +impl CallerContext { + pub fn empty() -> Self { + CallerContext { + msg_sender: Address::zero(), + storage_contract_address: Address::zero(), + } + } + + pub fn is_empty(self) -> bool { + self.msg_sender.is_zero() & self.storage_contract_address.is_zero() + } + + pub fn eq(self, caller_context: CallerContext) -> bool { + caller_context.msg_sender.eq(self.msg_sender) + & caller_context.storage_contract_address.eq(self.storage_contract_address) + } +} + +struct CallRequest { + hash: Field, + caller_contract_address: Address, + caller_context: CallerContext, +} + +impl CallRequest { + pub fn empty() -> Self { + Self { + hash: 0, + caller_contract_address: Address::zero(), + caller_context: dep::std::unsafe::zeroed(), + } + } + + pub fn is_empty(self) -> bool { + self.hash == 0 + } + + pub fn eq(self, call_request: CallRequest) -> bool { + (call_request.hash == self.hash) + & (call_request.caller_contract_address.eq(self.caller_contract_address)) + & (call_request.caller_context.eq(self.caller_context)) + } +} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/call_stack_item.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/call_stack_item.nr index fcf2b2dd0aa..fc6a754c50d 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/call_stack_item.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/call_stack_item.nr @@ -42,10 +42,30 @@ struct PublicCallStackItem { impl PublicCallStackItem { fn hash(self) -> Field { + let item = if self.is_execution_request { + self.as_execution_request() + } else { + self + }; + dep::std::hash::pedersen_hash_with_separator([ - self.contract_address.to_field(), - self.function_data.hash(), - self.public_inputs.hash(), + item.contract_address.to_field(), + item.function_data.hash(), + item.public_inputs.hash(), ], GENERATOR_INDEX__CALL_STACK_ITEM) } + + fn as_execution_request(self) -> Self { + let public_inputs = self.public_inputs; + let mut request_public_inputs: PublicCircuitPublicInputs = dep::std::unsafe::zeroed(); + request_public_inputs.call_context = public_inputs.call_context; + request_public_inputs.args_hash = public_inputs.args_hash; + + PublicCallStackItem { + contract_address: self.contract_address, + function_data: self.function_data, + is_execution_request: true, + public_inputs: request_public_inputs, + } + } } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/combined_accumulated_data.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/combined_accumulated_data.nr index edd982ee081..6d39c700d92 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/combined_accumulated_data.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/combined_accumulated_data.nr @@ -1,8 +1,15 @@ -use crate::mocked::AggregationObject; -use crate::abis::new_contract_data::NewContractData; -use crate::abis::optionally_revealed_data::OptionallyRevealedData; -use crate::abis::public_data_update_request::PublicDataUpdateRequest; -use crate::abis::public_data_read::PublicDataRead; +use crate::{ + abis::{ + call_request::CallRequest, + new_contract_data::NewContractData, + optionally_revealed_data::OptionallyRevealedData, + public_data_read::PublicDataRead, + public_data_update_request::PublicDataUpdateRequest, + }, + hash::NUM_FIELDS_PER_SHA256, + mocked::AggregationObject, + utils::bounded_vec::BoundedVec, +}; use dep::aztec::constants_gen::{ MAX_READ_REQUESTS_PER_TX, MAX_NEW_COMMITMENTS_PER_TX, @@ -16,95 +23,93 @@ use dep::aztec::constants_gen::{ MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX, MAX_PUBLIC_DATA_READS_PER_TX }; -use crate::hash::NUM_FIELDS_PER_SHA256; -use crate::utils::bounded_vec::BoundedVec; struct CombinedAccumulatedData { - aggregation_object : AggregationObject, + aggregation_object: AggregationObject, - read_requests : [Field; MAX_READ_REQUESTS_PER_TX], - pending_read_requests : [Field; MAX_PENDING_READ_REQUESTS_PER_TX], + read_requests: [Field; MAX_READ_REQUESTS_PER_TX], + pending_read_requests: [Field; MAX_PENDING_READ_REQUESTS_PER_TX], - new_commitments : [Field; MAX_NEW_COMMITMENTS_PER_TX], - new_nullifiers : [Field; MAX_NEW_NULLIFIERS_PER_TX], - nullified_commitments : [Field; MAX_NEW_NULLIFIERS_PER_TX], + new_commitments: [Field; MAX_NEW_COMMITMENTS_PER_TX], + new_nullifiers: [Field; MAX_NEW_NULLIFIERS_PER_TX], + nullified_commitments: [Field; MAX_NEW_NULLIFIERS_PER_TX], - private_call_stack : [Field; MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX], - public_call_stack : [Field; MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX], - new_l2_to_l1_msgs : [Field; MAX_NEW_L2_TO_L1_MSGS_PER_TX], + private_call_stack: [CallRequest; MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX], + public_call_stack: [CallRequest; MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX], + new_l2_to_l1_msgs: [Field; MAX_NEW_L2_TO_L1_MSGS_PER_TX], - encrypted_logs_hash : [Field; NUM_FIELDS_PER_SHA256], - unencrypted_logs_hash : [Field; NUM_FIELDS_PER_SHA256], + encrypted_logs_hash: [Field; NUM_FIELDS_PER_SHA256], + unencrypted_logs_hash: [Field; NUM_FIELDS_PER_SHA256], // Here so that the gas cost of this request can be measured by circuits, without actually needing to feed in the // variable-length data. - encrypted_log_preimages_length : Field, - unencrypted_log_preimages_length : Field, + encrypted_log_preimages_length: Field, + unencrypted_log_preimages_length: Field, - new_contracts : [NewContractData; MAX_NEW_CONTRACTS_PER_TX], + new_contracts: [NewContractData; MAX_NEW_CONTRACTS_PER_TX], - optionally_revealed_data : [OptionallyRevealedData; MAX_OPTIONALLY_REVEALED_DATA_LENGTH_PER_TX], + optionally_revealed_data: [OptionallyRevealedData; MAX_OPTIONALLY_REVEALED_DATA_LENGTH_PER_TX], - public_data_update_requests : [PublicDataUpdateRequest; MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX], + public_data_update_requests: [PublicDataUpdateRequest; MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX], - public_data_reads : [PublicDataRead; MAX_PUBLIC_DATA_READS_PER_TX], + public_data_reads: [PublicDataRead; MAX_PUBLIC_DATA_READS_PER_TX], } -struct FinalAccumulatedData{ - aggregation_object : AggregationObject, +struct FinalAccumulatedData { + aggregation_object: AggregationObject, - new_commitments : [Field; MAX_NEW_COMMITMENTS_PER_TX], - new_nullifiers : [Field; MAX_NEW_NULLIFIERS_PER_TX], - nullified_commitments : [Field; MAX_NEW_NULLIFIERS_PER_TX], + new_commitments: [Field; MAX_NEW_COMMITMENTS_PER_TX], + new_nullifiers: [Field; MAX_NEW_NULLIFIERS_PER_TX], + nullified_commitments: [Field; MAX_NEW_NULLIFIERS_PER_TX], // For pending nullifiers, we have: // nullifiedCommitments[j] != 0 <==> newNullifiers[j] nullifies nullifiedCommitments[j] - private_call_stack : [Field; MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX], - public_call_stack : [Field; MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX], - new_l2_to_l1_msgs : [Field; MAX_NEW_L2_TO_L1_MSGS_PER_TX], + private_call_stack: [CallRequest; MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX], + public_call_stack: [CallRequest; MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX], + new_l2_to_l1_msgs: [Field; MAX_NEW_L2_TO_L1_MSGS_PER_TX], - encrypted_logs_hash : [Field; NUM_FIELDS_PER_SHA256], - unencrypted_logs_hash : [Field; NUM_FIELDS_PER_SHA256], + encrypted_logs_hash: [Field; NUM_FIELDS_PER_SHA256], + unencrypted_logs_hash: [Field; NUM_FIELDS_PER_SHA256], // Here so that the gas cost of this request can be measured by circuits, without actually needing to feed in the // variable-length data. - encrypted_log_preimages_length : Field, - unencrypted_log_preimages_length : Field, + encrypted_log_preimages_length: Field, + unencrypted_log_preimages_length: Field, - new_contracts : [NewContractData; MAX_NEW_CONTRACTS_PER_TX], - optionally_revealed_data : [OptionallyRevealedData; MAX_OPTIONALLY_REVEALED_DATA_LENGTH_PER_TX] + new_contracts: [NewContractData; MAX_NEW_CONTRACTS_PER_TX], + optionally_revealed_data: [OptionallyRevealedData; MAX_OPTIONALLY_REVEALED_DATA_LENGTH_PER_TX] } struct CombinedAccumulatedDataBuilder { - aggregation_object : AggregationObject, + aggregation_object: AggregationObject, - read_requests : BoundedVec, + read_requests: BoundedVec, pending_read_requests: BoundedVec, - new_commitments : BoundedVec, - new_nullifiers : BoundedVec, - nullified_commitments : BoundedVec, + new_commitments: BoundedVec, + new_nullifiers: BoundedVec, + nullified_commitments: BoundedVec, - private_call_stack : BoundedVec, - public_call_stack : BoundedVec, - new_l2_to_l1_msgs : BoundedVec, + private_call_stack: BoundedVec, + public_call_stack: BoundedVec, + new_l2_to_l1_msgs: BoundedVec, - encrypted_logs_hash : [Field; NUM_FIELDS_PER_SHA256], - unencrypted_logs_hash : [Field; NUM_FIELDS_PER_SHA256], + encrypted_logs_hash: [Field; NUM_FIELDS_PER_SHA256], + unencrypted_logs_hash: [Field; NUM_FIELDS_PER_SHA256], // Here so that the gas cost of this request can be measured by circuits, without actually needing to feed in the // variable-length data. - encrypted_log_preimages_length : Field, - unencrypted_log_preimages_length : Field, + encrypted_log_preimages_length: Field, + unencrypted_log_preimages_length: Field, - new_contracts : BoundedVec, + new_contracts: BoundedVec, - optionally_revealed_data : [OptionallyRevealedData; MAX_OPTIONALLY_REVEALED_DATA_LENGTH_PER_TX], + optionally_revealed_data: [OptionallyRevealedData; MAX_OPTIONALLY_REVEALED_DATA_LENGTH_PER_TX], - public_data_update_requests : BoundedVec, + public_data_update_requests: BoundedVec, - public_data_reads : BoundedVec, + public_data_reads: BoundedVec, } impl CombinedAccumulatedDataBuilder { diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/combined_constant_data.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/combined_constant_data.nr index 86c5efc2daf..947c6c88214 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/combined_constant_data.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/combined_constant_data.nr @@ -2,6 +2,6 @@ use crate::transaction::context::TxContext; use crate::abis::historical_block_data::HistoricalBlockData; struct CombinedConstantData { - block_data : HistoricalBlockData, - tx_context : TxContext, + block_data: HistoricalBlockData, + tx_context: TxContext, } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/function_selector.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/function_selector.nr index 47553e54434..5a1e4095b7f 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/function_selector.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/function_selector.nr @@ -1,6 +1,6 @@ struct FunctionSelector { // 1st 4-bytes of abi-encoding of function. - inner : u32, + inner: u32, } impl FunctionSelector { @@ -8,9 +8,17 @@ impl FunctionSelector { self.inner as Field } - pub fn from_u32(value : u32) -> FunctionSelector { + pub fn from_u32(value: u32) -> FunctionSelector { FunctionSelector { inner : value, } - } + } + + pub fn zero() -> Self { + FunctionSelector { inner: 0 } + } + + pub fn eq(self, function_selector: FunctionSelector) -> bool { + function_selector.inner == self.inner + } } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/historical_block_data.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/historical_block_data.nr index 52dee190fd9..d9a8d180ade 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/historical_block_data.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/historical_block_data.nr @@ -23,7 +23,7 @@ impl HistoricalBlockData { self.block.note_hash_tree_root, self.block.nullifier_tree_root, self.block.contract_tree_root, - self.block.l1_to_l2_data_tree_root, + self.block.l1_to_l2_messages_tree_root, self.blocks_tree_root, self.block.public_data_tree_root, self.block.global_variables_hash diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/kernel_circuit_public_inputs.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/kernel_circuit_public_inputs.nr index 6782a0a691b..c847b901b36 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/kernel_circuit_public_inputs.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/kernel_circuit_public_inputs.nr @@ -1,44 +1,44 @@ -use crate::abis::combined_constant_data::CombinedConstantData; -use crate::abis::combined_accumulated_data::{ - CombinedAccumulatedData, - FinalAccumulatedData, - CombinedAccumulatedDataBuilder, +use crate::abis::{ + combined_accumulated_data::{ + CombinedAccumulatedData, + FinalAccumulatedData, + CombinedAccumulatedDataBuilder, + }, + combined_constant_data::CombinedConstantData, }; struct KernelCircuitPublicInputs { - end : CombinedAccumulatedData, - constants : CombinedConstantData, - - is_private : bool, + end: CombinedAccumulatedData, + constants: CombinedConstantData, + is_private: bool, } struct KernelCircuitPublicInputsFinal { - end : FinalAccumulatedData, - constants : CombinedConstantData, - is_private : bool, + end: FinalAccumulatedData, + constants: CombinedConstantData, + is_private: bool, } struct KernelCircuitPublicInputsBuilder { - end : CombinedAccumulatedDataBuilder, - constants : CombinedConstantData, - - is_private : bool, + end: CombinedAccumulatedDataBuilder, + constants: CombinedConstantData, + is_private: bool, } impl KernelCircuitPublicInputsBuilder { pub fn finish(self) -> KernelCircuitPublicInputs { KernelCircuitPublicInputs { - end : self.end.finish(), - constants : self.constants, - is_private : self.is_private, + end: self.end.finish(), + constants: self.constants, + is_private: self.is_private, } } pub fn to_final(self) -> KernelCircuitPublicInputsFinal { KernelCircuitPublicInputsFinal { - end : self.end.to_final(), - constants : self.constants, - is_private : self.is_private, + end: self.end.to_final(), + constants: self.constants, + is_private: self.is_private, } } } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/membership_witness.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/membership_witness.nr index 23f7ca97789..2af3ca4ff21 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/membership_witness.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/membership_witness.nr @@ -2,9 +2,10 @@ use dep::aztec::constants_gen::{ CONTRACT_TREE_HEIGHT, FUNCTION_TREE_HEIGHT, KERNELS_PER_BASE_ROLLUP, - MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP, + NULLIFIER_TREE_HEIGHT, NOTE_HASH_TREE_HEIGHT, ROLLUP_VK_TREE_HEIGHT, + HISTORIC_BLOCKS_TREE_HEIGHT, }; struct MembershipWitness { @@ -33,12 +34,12 @@ struct VKMembershipWitness{ struct NullifierMembershipWitness{ leaf_index: Field, - sibling_path: [Field; MAX_NEW_NULLIFIERS_PER_BASE_ROLLUP] + sibling_path: [Field; NULLIFIER_TREE_HEIGHT] } struct HistoricBlocksTreeRootMembershipWitness{ leaf_index: Field, - sibling_path: [Field; KERNELS_PER_BASE_ROLLUP] + sibling_path: [Field; HISTORIC_BLOCKS_TREE_HEIGHT] } struct ReadRequestMembershipWitness { diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/new_contract_data.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/new_contract_data.nr index a1197bf0992..aba0cda1789 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/new_contract_data.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/new_contract_data.nr @@ -1,11 +1,10 @@ use crate::address::{Address, EthAddress}; -use crate::utils; use dep::aztec::constants_gen; struct NewContractData { - contract_address : Address, - portal_contract_address : EthAddress, - function_tree_root : Field, + contract_address: Address, + portal_contract_address: EthAddress, + function_tree_root: Field, } impl NewContractData { @@ -15,6 +14,12 @@ impl NewContractData { (self.function_tree_root ==0) } + pub fn eq(self, data: NewContractData) -> bool { + data.contract_address.eq(self.contract_address) + & data.portal_contract_address.eq(self.portal_contract_address) + & (data.function_tree_root == self.function_tree_root) + } + pub fn default() -> Self { Self { contract_address : Address::default(), diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/private_circuit_public_inputs.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/private_circuit_public_inputs.nr index 80ab432850c..12bb26c95ad 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/private_circuit_public_inputs.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/private_circuit_public_inputs.nr @@ -1,4 +1,12 @@ -use crate::abis::call_context::CallContext; +use crate::{ + abis::{ + call_context::CallContext, + historical_block_data::HistoricalBlockData, + }, + contrakt::deployment_data::ContractDeploymentData, + hash::NUM_FIELDS_PER_SHA256, + utils::bounded_vec::BoundedVec, +}; use dep::aztec::{ constants_gen, constants_gen::{ @@ -12,45 +20,41 @@ use dep::aztec::{ RETURN_VALUES_LENGTH, } }; -use crate::hash::NUM_FIELDS_PER_SHA256; -use crate::abis::historical_block_data::HistoricalBlockData; -use crate::contrakt::deployment_data::ContractDeploymentData; -use crate::utils::bounded_vec::BoundedVec; -struct PrivateCircuitPublicInputs{ - call_context : CallContext, +struct PrivateCircuitPublicInputs { + call_context: CallContext, - args_hash : Field, - return_values : [Field; RETURN_VALUES_LENGTH], + args_hash: Field, + return_values: [Field; RETURN_VALUES_LENGTH], - read_requests : [Field; MAX_READ_REQUESTS_PER_CALL], - pending_read_requests : [Field; MAX_PENDING_READ_REQUESTS_PER_CALL], + read_requests: [Field; MAX_READ_REQUESTS_PER_CALL], + pending_read_requests: [Field; MAX_PENDING_READ_REQUESTS_PER_CALL], - new_commitments : [Field; MAX_NEW_COMMITMENTS_PER_CALL], - new_nullifiers : [Field; MAX_NEW_NULLIFIERS_PER_CALL], - nullified_commitments : [Field; MAX_NEW_NULLIFIERS_PER_CALL], + new_commitments: [Field; MAX_NEW_COMMITMENTS_PER_CALL], + new_nullifiers: [Field; MAX_NEW_NULLIFIERS_PER_CALL], + nullified_commitments: [Field; MAX_NEW_NULLIFIERS_PER_CALL], - private_call_stack : [Field; MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL], - public_call_stack : [Field; MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL], - new_l2_to_l1_msgs : [Field; MAX_NEW_L2_TO_L1_MSGS_PER_CALL], + private_call_stack_hashes: [Field; MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL], + public_call_stack_hashes: [Field; MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL], + new_l2_to_l1_msgs: [Field; MAX_NEW_L2_TO_L1_MSGS_PER_CALL], - encrypted_logs_hash : [Field; NUM_FIELDS_PER_SHA256], - unencrypted_logs_hash : [Field; NUM_FIELDS_PER_SHA256], + encrypted_logs_hash: [Field; NUM_FIELDS_PER_SHA256], + unencrypted_logs_hash: [Field; NUM_FIELDS_PER_SHA256], // Here so that the gas cost of this request can be measured by circuits, without actually needing to feed in the // variable-length data. - encrypted_log_preimages_length : Field, - unencrypted_log_preimages_length : Field, + encrypted_log_preimages_length: Field, + unencrypted_log_preimages_length: Field, - historical_block_data : HistoricalBlockData, + historical_block_data: HistoricalBlockData, - contract_deployment_data : ContractDeploymentData, + contract_deployment_data: ContractDeploymentData, - chain_id : Field, - version : Field, + chain_id: Field, + version: Field, } -impl PrivateCircuitPublicInputs{ +impl PrivateCircuitPublicInputs { // TODO(https://github.com/AztecProtocol/aztec-packages/issues/3059) : Reuse aztec-nr fn hash(self) -> Field { let mut fields: BoundedVec = BoundedVec::new(0); @@ -62,8 +66,8 @@ impl PrivateCircuitPublicInputs{ fields.push_array(self.new_commitments); fields.push_array(self.new_nullifiers); fields.push_array(self.nullified_commitments); - fields.push_array(self.private_call_stack); - fields.push_array(self.public_call_stack); + fields.push_array(self.private_call_stack_hashes); + fields.push_array(self.public_call_stack_hashes); fields.push_array(self.new_l2_to_l1_msgs); fields.push_array(self.encrypted_logs_hash); fields.push_array(self.unencrypted_logs_hash); @@ -74,6 +78,8 @@ impl PrivateCircuitPublicInputs{ fields.push(self.chain_id); fields.push(self.version); + assert_eq(fields.len(), constants_gen::PRIVATE_CIRCUIT_PUBLIC_INPUTS_HASH_INPUT_LENGTH, "Incorrect number of input fields when hashing PrivateCircuitPublicInputs"); + dep::std::hash::pedersen_hash_with_separator(fields.storage, constants_gen::GENERATOR_INDEX__PRIVATE_CIRCUIT_PUBLIC_INPUTS) } } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/private_kernel/private_call_data.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/private_kernel/private_call_data.nr index 4581532f86c..2ec38255474 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/private_kernel/private_call_data.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/private_kernel/private_call_data.nr @@ -1,4 +1,5 @@ use crate::abis::{ + call_request::CallRequest, call_stack_item::PrivateCallStackItem, membership_witness::{ ContractLeafMembershipWitness, @@ -9,14 +10,16 @@ use crate::abis::{ use crate::address::EthAddress; use crate::mocked::{Proof, VerificationKey}; use dep::aztec::constants_gen::{ - MAX_READ_REQUESTS_PER_CALL, MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, + MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, + MAX_READ_REQUESTS_PER_CALL, }; struct PrivateCallData { call_stack_item: PrivateCallStackItem, - private_call_stack_preimages: [PrivateCallStackItem; MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL], + private_call_stack: [CallRequest; MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL], + public_call_stack: [CallRequest; MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL], proof: Proof, vk: VerificationKey, diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_call_data.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_call_data.nr new file mode 100644 index 00000000000..6310f119440 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_call_data.nr @@ -0,0 +1,17 @@ +use crate::{ + abis::{ + call_request::CallRequest, + call_stack_item::PublicCallStackItem, + }, + address::EthAddress, + mocked::Proof, +}; +use dep::aztec::constants_gen::MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL; + +struct PublicCallData { + call_stack_item: PublicCallStackItem, + public_call_stack: [CallRequest; MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL], + proof: Proof, + portal_contract_address: EthAddress, + bytecode_hash: Field, +} diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_circuit_public_inputs.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_circuit_public_inputs.nr index 94440204c37..98370a90c2c 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_circuit_public_inputs.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_circuit_public_inputs.nr @@ -1,9 +1,4 @@ -use crate::contrakt::storage_read::StorageRead; -use crate::contrakt::storage_update_request::StorageUpdateRequest; -use crate::address::Address; -use crate::abis::historical_block_data::HistoricalBlockData; use dep::aztec::constants_gen; -use crate::hash::{NUM_FIELDS_PER_SHA256}; use dep::aztec::constants_gen::{ MAX_NEW_L2_TO_L1_MSGS_PER_CALL, MAX_NEW_NULLIFIERS_PER_CALL, @@ -13,37 +8,47 @@ use dep::aztec::constants_gen::{ MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_CALL, RETURN_VALUES_LENGTH, }; -use crate::abis::call_context::CallContext; -use crate::utils::bounded_vec::BoundedVec; +use crate::{ + abis::{ + call_context::CallContext, + historical_block_data::HistoricalBlockData, + }, + address::Address, + contrakt::{ + storage_read::StorageRead, + storage_update_request::StorageUpdateRequest, + }, + hash::{NUM_FIELDS_PER_SHA256}, + utils::bounded_vec::BoundedVec, +}; struct PublicCircuitPublicInputs{ - call_context : CallContext, + call_context: CallContext, - args_hash : Field, - return_values : [Field; RETURN_VALUES_LENGTH], + args_hash: Field, + return_values: [Field; RETURN_VALUES_LENGTH], - contract_storage_update_requests : [StorageUpdateRequest;MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_CALL], - contract_storage_reads : [StorageRead; MAX_PUBLIC_DATA_READS_PER_CALL], + contract_storage_update_requests: [StorageUpdateRequest; MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_CALL], + contract_storage_reads: [StorageRead; MAX_PUBLIC_DATA_READS_PER_CALL], - public_call_stack : [Field; MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL], - new_commitments : [Field; MAX_NEW_COMMITMENTS_PER_CALL], - new_nullifiers : [Field; MAX_NEW_NULLIFIERS_PER_CALL], - new_l2_to_l1_msgs : [Field; MAX_NEW_L2_TO_L1_MSGS_PER_CALL], + public_call_stack_hashes: [Field; MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL], + new_commitments: [Field; MAX_NEW_COMMITMENTS_PER_CALL], + new_nullifiers: [Field; MAX_NEW_NULLIFIERS_PER_CALL], + new_l2_to_l1_msgs: [Field; MAX_NEW_L2_TO_L1_MSGS_PER_CALL], - unencrypted_logs_hash : [Field; NUM_FIELDS_PER_SHA256], + unencrypted_logs_hash: [Field; NUM_FIELDS_PER_SHA256], // Here so that the gas cost of this request can be measured by circuits, without actually needing to feed in the // variable-length data. - unencrypted_log_preimages_length : Field, + unencrypted_log_preimages_length: Field, - historical_block_data : HistoricalBlockData, + historical_block_data: HistoricalBlockData, - prover_address : Address, + prover_address: Address, } - impl PublicCircuitPublicInputs{ - // TODO(https://github.com/AztecProtocol/aztec-packages/issues/3059) : Reuse aztec-nr + // TODO(https://github.com/AztecProtocol/aztec-packages/issues/3059): Reuse aztec-nr fn hash(self) -> Field { let mut inputs: BoundedVec = BoundedVec::new(0); inputs.push(self.call_context.hash()); @@ -55,7 +60,7 @@ impl PublicCircuitPublicInputs{ for i in 0..MAX_PUBLIC_DATA_READS_PER_CALL { inputs.push(self.contract_storage_reads[i].hash()); } - inputs.push_array(self.public_call_stack); + inputs.push_array(self.public_call_stack_hashes); inputs.push_array(self.new_commitments); inputs.push_array(self.new_nullifiers); inputs.push_array(self.new_l2_to_l1_msgs); @@ -64,6 +69,8 @@ impl PublicCircuitPublicInputs{ inputs.push_array(self.historical_block_data.to_array()); inputs.push(self.prover_address.to_field()); + assert_eq(inputs.len(), constants_gen::PUBLIC_CIRCUIT_PUBLIC_INPUTS_HASH_INPUT_LENGTH, "Incorrect number of input fields when hashing PublicCircuitPublicInputs"); + dep::std::hash::pedersen_hash_with_separator(inputs.storage, constants_gen::GENERATOR_INDEX__PUBLIC_CIRCUIT_PUBLIC_INPUTS) } } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_data_read.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_data_read.nr index 60959fef603..94c8a02d087 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_data_read.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_data_read.nr @@ -12,4 +12,19 @@ impl PublicDataRead { self.value, ], constants_gen::GENERATOR_INDEX__PUBLIC_DATA_READ) } + + pub fn empty() -> Self { + Self { + leaf_index : 0, + value : 0, + } + } + + pub fn is_empty(self) -> bool { + (self.leaf_index == 0) & (self.value == 0) + } + + pub fn eq(self, public_data_read: PublicDataRead) -> bool { + (public_data_read.leaf_index == self.leaf_index) & (public_data_read.value == self.value) + } } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_data_update_request.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_data_update_request.nr index 10c7d3f9ffb..458d6ec9549 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_data_update_request.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/abis/public_data_update_request.nr @@ -1,4 +1,4 @@ -use dep::aztec::constants_gen; +use dep::aztec::constants_gen::GENERATOR_INDEX__PUBLIC_DATA_UPDATE_REQUEST; struct PublicDataUpdateRequest { leaf_index : Field, @@ -7,11 +7,28 @@ struct PublicDataUpdateRequest { } impl PublicDataUpdateRequest { - fn hash(self) -> Field { + pub fn empty() -> Self { + Self { + leaf_index : 0, + old_value : 0, + new_value : 0 + } + } + + pub fn eq(self, update_request: PublicDataUpdateRequest) -> bool { + (update_request.leaf_index == self.leaf_index) & (update_request.old_value == self.old_value) + & (update_request.new_value == self.new_value) + } + + pub fn hash(self) -> Field { dep::std::hash::pedersen_hash_with_separator([ self.leaf_index, self.old_value, self.new_value - ], constants_gen::GENERATOR_INDEX__PUBLIC_DATA_UPDATE_REQUEST) + ], GENERATOR_INDEX__PUBLIC_DATA_UPDATE_REQUEST) + } + + pub fn is_empty(self) -> bool { + (self.leaf_index == 0) & (self.old_value == 0) & (self.new_value == 0) } } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/address.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/address.nr index ecfdc5d90ba..2686f693844 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/address.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/address.nr @@ -6,7 +6,7 @@ struct Address { } impl Address { - pub fn ZERO() -> Self { + pub fn zero() -> Self { Self { inner: 0 } @@ -27,6 +27,10 @@ impl Address { pub fn to_field(self) -> Field{ self.inner } + + pub fn is_zero(self) -> bool { + self.inner == 0 + } pub fn assert_is_zero(self) { assert(self.to_field() == 0); @@ -49,7 +53,7 @@ struct EthAddress{ } impl EthAddress{ - pub fn ZERO() -> Self { + pub fn zero() -> Self { Self { inner: 0 } @@ -71,6 +75,10 @@ impl EthAddress{ self.inner } + pub fn is_zero(self) -> bool { + self.inner == 0 + } + pub fn assert_is_zero(self) { assert(self.to_field() == 0); } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/block.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/block.nr index 315039fe64e..dc15b932bdd 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/block.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/block.nr @@ -6,7 +6,7 @@ struct Block { note_hash_tree_root : Field, nullifier_tree_root : Field, contract_tree_root : Field, - l1_to_l2_data_tree_root : Field, + l1_to_l2_messages_tree_root : Field, // Public data public_data_tree_root : Field, @@ -19,7 +19,7 @@ impl Block { assert(self.note_hash_tree_root == 0); assert(self.nullifier_tree_root == 0); assert(self.contract_tree_root == 0); - assert(self.l1_to_l2_data_tree_root == 0); + assert(self.l1_to_l2_messages_tree_root == 0); assert(self.public_data_tree_root == 0); } @@ -29,7 +29,7 @@ impl Block { self.note_hash_tree_root, self.nullifier_tree_root, self.contract_tree_root, - self.l1_to_l2_data_tree_root, + self.l1_to_l2_messages_tree_root, self.public_data_tree_root, ], constants_gen::GENERATOR_INDEX__BLOCK_HASH) } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/contrakt/storage_read.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/contrakt/storage_read.nr index eb8a43709da..cc7911471f1 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/contrakt/storage_read.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/contrakt/storage_read.nr @@ -1,19 +1,26 @@ use dep::aztec::constants_gen; struct StorageRead{ - storage_slot : Field, - current_value : Field, + storage_slot: Field, + current_value: Field, } impl StorageRead { - fn hash(self) -> Field { + pub fn empty() -> Self { + Self { + storage_slot: 0, + current_value: 0, + } + } + + pub fn hash(self) -> Field { dep::std::hash::pedersen_hash_with_separator([ self.storage_slot, self.current_value, ], constants_gen::GENERATOR_INDEX__PUBLIC_DATA_READ) } - fn is_empty(self) -> bool { + pub fn is_empty(self) -> bool { self.storage_slot == 0 } } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/contrakt/storage_update_request.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/contrakt/storage_update_request.nr index e9ccc699e62..ba33f3985ba 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/contrakt/storage_update_request.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/contrakt/storage_update_request.nr @@ -1,4 +1,5 @@ -use dep::aztec::constants_gen; +use dep::aztec::constants_gen::GENERATOR_INDEX__PUBLIC_DATA_UPDATE_REQUEST; + struct StorageUpdateRequest{ storage_slot : Field, old_value : Field, @@ -6,15 +7,29 @@ struct StorageUpdateRequest{ } impl StorageUpdateRequest { - fn hash(self) -> Field { + pub fn empty() -> Self { + StorageUpdateRequest { + storage_slot: 0, + old_value: 0, + new_value: 0, + } + } + + pub fn hash(self) -> Field { dep::std::hash::pedersen_hash_with_separator([ self.storage_slot, self.old_value, self.new_value, - ], constants_gen::GENERATOR_INDEX__PUBLIC_DATA_UPDATE_REQUEST) + ], GENERATOR_INDEX__PUBLIC_DATA_UPDATE_REQUEST) } - - fn is_empty(self) -> bool { + + pub fn is_empty(self) -> bool { self.storage_slot == 0 } + + pub fn eq(self, request: Self) -> bool { + (request.storage_slot == self.storage_slot) + & (request.old_value == self.old_value) + & (request.new_value == self.new_value) + } } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/hash.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/hash.nr index 38186a8c091..a331b8046be 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/hash.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/hash.nr @@ -37,12 +37,12 @@ pub fn root_from_sibling_path(leaf : Field, leaf_index : Field, sibling_path let indices = leaf_index.to_le_bits(N); for i in 0..N { - // indices[i]; // This line will fail. - if indices[i] == 1 { - node = merkle_hash(sibling_path[i], node); + let (hash_left, hash_right) = if indices[i] == 1 { + (sibling_path[i], node) } else { - node = merkle_hash(node, sibling_path[i]); - } + (node, sibling_path[i]) + }; + node = merkle_hash(hash_left, hash_right); } node } diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/interop_testing.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/interop_testing.nr index 3594f3e7d52..16c2115161e 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/interop_testing.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/interop_testing.nr @@ -8,7 +8,9 @@ use crate::abis::function_leaf_preimage::FunctionLeafPreimage; use crate::contrakt::deployment_data::ContractDeploymentData; use crate::abis::function_selector::FunctionSelector; use crate::hash::compute_l2_to_l1_hash; +use crate::abis::call_stack_item::PublicCallStackItem; use dep::aztec::hash::sha256_to_field; +use crate::abis::public_circuit_public_inputs::PublicCircuitPublicInputs; #[test] fn compute_complete_address() { @@ -96,4 +98,50 @@ fn compute_function_leaf() { }; assert_eq(leaf.hash(), 0x1ad8ece7f40e63d011ae47c6ce6cdaf31d632a23f5cf35bbeaaf69c8302afdbc); -} \ No newline at end of file +} + +#[test] +fn compute_call_stack_item_request() { + let contract_address = Address::from_field(1); + let function_data = FunctionData { + selector: FunctionSelector::from_u32(2), + is_internal: false, + is_private: false, + is_constructor: false, + }; + + let mut public_inputs: PublicCircuitPublicInputs = dep::std::unsafe::zeroed(); + public_inputs.new_commitments[0] = 1; + + let call_stack_item = PublicCallStackItem { + contract_address, + public_inputs, + is_execution_request: true, + function_data, + }; + + assert_eq(call_stack_item.hash(), 0x1d51d7758d792c9cd6edd8e8ec5f1f9fb1f974abc1af6bb4cf9f2328ef306c96); +} + +#[test] +fn compute_call_stack_item() { + let contract_address = Address::from_field(1); + let function_data = FunctionData { + selector: FunctionSelector::from_u32(2), + is_internal: false, + is_private: false, + is_constructor: false, + }; + + let mut public_inputs: PublicCircuitPublicInputs = dep::std::unsafe::zeroed(); + public_inputs.new_commitments[0] = 1; + + let call_stack_item = PublicCallStackItem { + contract_address, + public_inputs, + is_execution_request: false, + function_data, + }; + + assert_eq(call_stack_item.hash(), 0x0a370c67b66e30901470c11a199764a914fc0fcfbc737ed03153079b2765813a); +} diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/point.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/point.nr index df6fa77380b..6482295491e 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/point.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/point.nr @@ -5,7 +5,7 @@ struct Point { } impl Point { - pub fn ZERO() -> Self { + pub fn zero() -> Self { Point { x: 0, y: 0, diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests.nr index 20e15b3309c..f7a5602dc00 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests.nr @@ -1,2 +1,7 @@ mod testing_harness; -mod fixtures; \ No newline at end of file +mod fixtures; +mod previous_kernel_data_builder; +mod private_call_data_builder; +mod private_circuit_public_inputs_builder; +mod public_call_data_builder; +mod public_circuit_public_inputs_builder; \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures.nr index a454d4107a8..00c81df572c 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures.nr @@ -20,7 +20,7 @@ global HISTORICAL_BLOCK_DATA = HistoricalBlockData { note_hash_tree_root: fixtures::note_hash_tree::ROOT, nullifier_tree_root: 0, contract_tree_root: fixtures::contract_tree::ROOT, - l1_to_l2_data_tree_root: 0, + l1_to_l2_messages_tree_root: 0, public_data_tree_root: 0, global_variables_hash: 0, }, diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures/contract_functions.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures/contract_functions.nr index 1e70c7a733b..ba1dad9aa7d 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures/contract_functions.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures/contract_functions.nr @@ -25,20 +25,80 @@ global default_constructor = ContractFunction { // sibling_path taken from __snapshots__/noir_test_gen.test.ts.snap -global default_contract_function = ContractFunction { +global default_private_function = ContractFunction { data: FunctionData { - selector: FunctionSelector { inner: 1 }, + selector: FunctionSelector { inner: 1010101 }, is_internal: false, is_private: true, is_constructor: false, }, vk_hash: 0, - acir_hash: 12341234, + acir_hash: 1111, membership_witness: FunctionLeafMembershipWitness { leaf_index: 0, sibling_path: [ - 0x1f2e3193c7187347a099ee7cb5d6ac077da6b18706fe5508e658a3d0a05494f7, - 0x2350c7d2f19119502d073142ff019af884d6513f38b92d5119b4999502282247, + 0x04d0ae2ba130cedb690c9c9fc41fa1594bac32bfb57b9a7734528295c680b3fa, + 0x02996203377fc96b38fd41a1f65ddce2b26247fee321a12e58b457ee08621a14, + 0x0837a67313f4dbbd8d6971c0672f961f0a3b9e218c1395d327915209292acbbf, + 0x2e0ef36ddc5db29acb6ef904999046f835ce7c78a40c3f7a0edb03b2f917a765, + ], + }, +}; + +global default_internal_private_function = ContractFunction { + data: FunctionData { + selector: FunctionSelector { inner: 2020202 }, + is_internal: true, + is_private: true, + is_constructor: false, + }, + vk_hash: 0, + acir_hash: 2222, + membership_witness: FunctionLeafMembershipWitness { + leaf_index: 1, + sibling_path: [ + 0x23590e7237ce1163ade1adba49bf434066481a3bc9098e6751fd1d73e160ca95, + 0x02996203377fc96b38fd41a1f65ddce2b26247fee321a12e58b457ee08621a14, + 0x0837a67313f4dbbd8d6971c0672f961f0a3b9e218c1395d327915209292acbbf, + 0x2e0ef36ddc5db29acb6ef904999046f835ce7c78a40c3f7a0edb03b2f917a765, + ], + }, +}; + +global default_public_function = ContractFunction { + data: FunctionData { + selector: FunctionSelector { inner: 3030303 }, + is_internal: false, + is_private: false, + is_constructor: false, + }, + vk_hash: 0, + acir_hash: 3333, + membership_witness: FunctionLeafMembershipWitness { + leaf_index: 2, + sibling_path: [ + 0x2d72ef5ebb7c974e1f5a8bed092f1cf1bf0a0cb1eda28516221ca7e5811ecf15, + 0x1bed44f12632c0a6343cd886bd3e548bb5e8d2fd35fe9bc81f28defd4ed885b0, + 0x0837a67313f4dbbd8d6971c0672f961f0a3b9e218c1395d327915209292acbbf, + 0x2e0ef36ddc5db29acb6ef904999046f835ce7c78a40c3f7a0edb03b2f917a765, + ], + }, +}; + +global default_internal_public_function = ContractFunction { + data: FunctionData { + selector: FunctionSelector { inner: 4040404 }, + is_internal: true, + is_private: false, + is_constructor: false, + }, + vk_hash: 0, + acir_hash: 4444, + membership_witness: FunctionLeafMembershipWitness { + leaf_index: 3, + sibling_path: [ + 0x1fc20a5f4a9bf052ae4fee30281fd09908a25063c749bc35939502ffaeaee8c2, + 0x1bed44f12632c0a6343cd886bd3e548bb5e8d2fd35fe9bc81f28defd4ed885b0, 0x0837a67313f4dbbd8d6971c0672f961f0a3b9e218c1395d327915209292acbbf, 0x2e0ef36ddc5db29acb6ef904999046f835ce7c78a40c3f7a0edb03b2f917a765, ], diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures/contract_tree.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures/contract_tree.nr index 2db4adba801..81626d401d8 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures/contract_tree.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures/contract_tree.nr @@ -1,22 +1,40 @@ // Constants taken from __snapshots__/noir_test_gen.test.ts.snap -global ROOT = 0x13e8827c83abfc4f0a37d0c228fb4ecd509d96613b8e7d74c169005a62ef67dd; +global ROOT = 0x08ccb752b9e4f898ad6b87fb9ca1ed1f2b5e0b6cd67bf083e466ca6a31e21fae; global SIBLING_PATHS = [ - [ - 0x00, - 0x27b1d0839a5b23baf12a8d195b18ac288fcf401afb2f70b8a4b529ede5fa9fed, - 0x21dbfd1d029bf447152fcf89e355c334610d1632436ba170f738107266a71550, - 0x0bcd1f91cf7bdd471d0a30c58c4706f3fdab3807a954b8f5b5e3bfec87d001bb, - 0x06e62084ee7b602fe9abc15632dda3269f56fb0c6e12519a2eb2ec897091919d, - 0x03c9e2e67178ac638746f068907e6677b4cc7a9592ef234ab6ab518f17efffa0, - 0x15d28cad4c0736decea8997cb324cf0a0e0602f4d74472cd977bce2c8dd9923f, - 0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab, - 0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257, - 0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9, - 0xc5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02, - 0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5, - 0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5, - 0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93, - 0x1a2b85ff013d4b2b25074297c7e44aa61f4836d0862b36db2e6ce2b5542f9ea9, - 0x177b9a10bbee32f77c719c6f8d071a18476cbeb021e155c642bbf93c716ce943, - ], + [ + 0x141929db05c4a4803aebc2a4aea0fb2f09a15ae9862c4da49f245b7726f4088e, + 0x27b1d0839a5b23baf12a8d195b18ac288fcf401afb2f70b8a4b529ede5fa9fed, + 0x21dbfd1d029bf447152fcf89e355c334610d1632436ba170f738107266a71550, + 0x0bcd1f91cf7bdd471d0a30c58c4706f3fdab3807a954b8f5b5e3bfec87d001bb, + 0x06e62084ee7b602fe9abc15632dda3269f56fb0c6e12519a2eb2ec897091919d, + 0x03c9e2e67178ac638746f068907e6677b4cc7a9592ef234ab6ab518f17efffa0, + 0x15d28cad4c0736decea8997cb324cf0a0e0602f4d74472cd977bce2c8dd9923f, + 0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab, + 0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257, + 0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9, + 0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02, + 0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5, + 0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5, + 0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93, + 0x1a2b85ff013d4b2b25074297c7e44aa61f4836d0862b36db2e6ce2b5542f9ea9, + 0x177b9a10bbee32f77c719c6f8d071a18476cbeb021e155c642bbf93c716ce943, + ], + [ + 0x0b5dfb998fbdce8e5804992d471c1fc8050642b7aba107e854052eb562944a01, + 0x27b1d0839a5b23baf12a8d195b18ac288fcf401afb2f70b8a4b529ede5fa9fed, + 0x21dbfd1d029bf447152fcf89e355c334610d1632436ba170f738107266a71550, + 0x0bcd1f91cf7bdd471d0a30c58c4706f3fdab3807a954b8f5b5e3bfec87d001bb, + 0x06e62084ee7b602fe9abc15632dda3269f56fb0c6e12519a2eb2ec897091919d, + 0x03c9e2e67178ac638746f068907e6677b4cc7a9592ef234ab6ab518f17efffa0, + 0x15d28cad4c0736decea8997cb324cf0a0e0602f4d74472cd977bce2c8dd9923f, + 0x268ed1e1c94c3a45a14db4108bc306613a1c23fab68e0466a002dfb0a3f8d2ab, + 0x0cd8d5695bc2dde99dd531671f76f1482f14ddba8eeca7cb9686d4a62359c257, + 0x047fbb7eb974155702149e58ea6ad91f4c6e953e693db35e953e250d8ceac9a9, + 0x00c5ae2526e665e2c7c698c11a06098b7159f720606d50e7660deb55758b0b02, + 0x2ced19489ab456b8b6c424594cdbbae59c36dfdd4c4621c4032da2d8a9674be5, + 0x1df5a245ffc1da14b46fe56a605f2a47b1cff1592bab4f66cfe5dfe990af6ab5, + 0x2871d090615d14eadb52228c635c90e0adf31176f0814f6525c23e7d7b318c93, + 0x1a2b85ff013d4b2b25074297c7e44aa61f4836d0862b36db2e6ce2b5542f9ea9, + 0x177b9a10bbee32f77c719c6f8d071a18476cbeb021e155c642bbf93c716ce943, + ], ]; diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures/contracts.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures/contracts.nr index 9203b7dd1a4..c5e52943a98 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures/contracts.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/fixtures/contracts.nr @@ -18,5 +18,16 @@ global default_contract = ContractData { leaf_index: 0, sibling_path: fixtures::contract_tree::SIBLING_PATHS[0], }, - function_tree_root: 0x2143c6a86ded93cd75de5680c57c61a2a178e0a840d8649a9e0411032f969f1d, + function_tree_root: 0x1c55b3903b8b2812ba2a2315edb30aa9b9d9c99c153e9215cc01ec8979a8e9be, +}; + +global parent_contract = ContractData { + contract_address_salt: 11111, + address: Address { inner: 667788 }, + portal_contract_address: EthAddress { inner: 990011 }, + membership_witness: ContractLeafMembershipWitness { + leaf_index: 1, + sibling_path: fixtures::contract_tree::SIBLING_PATHS[1], + }, + function_tree_root: 0x02b3f6b0a36bd01f08cee2d607dbe08894bb8c58159e87bb17db28cad43291d4, }; \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/previous_kernel_data_builder.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/previous_kernel_data_builder.nr new file mode 100644 index 00000000000..b5c02900860 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/previous_kernel_data_builder.nr @@ -0,0 +1,187 @@ +use crate::{ + abis::{ + call_context::CallContext, + call_request::{CallerContext, CallRequest}, + combined_constant_data::CombinedConstantData, + combined_accumulated_data::CombinedAccumulatedDataBuilder, + historical_block_data::HistoricalBlockData, + kernel_circuit_public_inputs::KernelCircuitPublicInputs, + previous_kernel_data::PreviousKernelData, + public_data_read::PublicDataRead, + public_data_update_request::PublicDataUpdateRequest, + }, + address::{Address, EthAddress}, + hash::NUM_FIELDS_PER_SHA256, + mocked::{Proof, VerificationKey}, + tests::{ + fixtures, + testing_harness::build_tx_context, + }, + transaction::context::TxContext, +}; +use dep::aztec::{ + constants_gen::{ + EMPTY_NULLIFIED_COMMITMENT, + MAX_NEW_COMMITMENTS_PER_TX, + MAX_NEW_NULLIFIERS_PER_TX, + MAX_PUBLIC_DATA_READS_PER_TX, + MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX, + VK_TREE_HEIGHT, + }, +}; + +struct PreviousKernelDataBuilder { + contract_address: Address, + portal_contract_address: EthAddress, + end: CombinedAccumulatedDataBuilder, + block_data: HistoricalBlockData, + tx_context: TxContext, + is_private: bool, + proof: Proof, + vk: VerificationKey, + vk_index: u32, + vk_path: [Field; VK_TREE_HEIGHT], +} + +impl PreviousKernelDataBuilder { + pub fn new() -> Self { + let mut end: CombinedAccumulatedDataBuilder = dep::std::unsafe::zeroed(); + end.new_nullifiers.push(321); // 0th nullifier must be non-zero. + end.nullified_commitments.push(EMPTY_NULLIFIED_COMMITMENT); + + let tx_context = build_tx_context(false); + + PreviousKernelDataBuilder { + contract_address: fixtures::contracts::parent_contract.address, + portal_contract_address: fixtures::contracts::parent_contract.portal_contract_address, + end, + block_data: fixtures::HISTORICAL_BLOCK_DATA, + tx_context, + is_private: true, + proof: Proof {}, + vk: VerificationKey {}, + vk_index: 0, + vk_path: [0; VK_TREE_HEIGHT], + } + } + + pub fn is_constructor(&mut self) -> Self { + self.tx_context = build_tx_context(true); + *self + } + + pub fn is_public(&mut self) -> Self { + self.is_private = false; + *self + } + + pub fn append_public_data_update_requests(&mut self, num_updates: Field) { + let value_offset = self.end.public_data_update_requests.len(); + for i in 0..MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_TX { + if i as u64 < num_updates as u64 { + let update_request = PublicDataUpdateRequest { + // The default leaf index is its index + 23. + leaf_index: value_offset + i + 23, + // The default value is its index + 45. + old_value: value_offset + i + 45, + // The default value is its index + 678. + new_value: value_offset + i + 678, + }; + self.end.public_data_update_requests.push(update_request); + } + } + } + + pub fn append_public_data_read_requests(&mut self, num_reads: Field) { + let value_offset = self.end.public_data_reads.len(); + for i in 0..MAX_PUBLIC_DATA_READS_PER_TX { + if i as u64 < num_reads as u64 { + let read_request = PublicDataRead { + // The default leaf index is its index + 34. + leaf_index: value_offset + i + 34, + // The default value is its index + 5566. + value: value_offset + i + 5566, + }; + self.end.public_data_reads.push(read_request); + } + } + } + + pub fn add_read_request_for_transient_commitment(&mut self, commitment_index: Field) -> Field { + let new_read_request_index = self.end.read_requests.len(); + self.end.read_requests.push(self.end.new_commitments.get(commitment_index)); + new_read_request_index + } + + pub fn append_new_commitments(&mut self, num_new_commitments: Field) { + let mocked_value_offset = self.end.new_commitments.len() + 1; + for i in 0..MAX_NEW_COMMITMENTS_PER_TX { + if i as u64 < num_new_commitments as u64 { + // The default value is its index + 1. + self.end.new_commitments.push(i + mocked_value_offset); + } + } + } + + pub fn append_new_nullifiers(&mut self, num_extra_nullifier: Field) { + let first_nullifier = self.end.new_nullifiers.get(0); + let mocked_value_offset = first_nullifier + self.end.new_nullifiers.len(); + for i in 1..MAX_NEW_NULLIFIERS_PER_TX { + if i as u64 <= num_extra_nullifier as u64 { + // The default value is its index + the value of the first nullifier. + self.end.new_nullifiers.push(i + mocked_value_offset); + } + } + } + + pub fn set_encrypted_logs(&mut self, hash: [Field; NUM_FIELDS_PER_SHA256], preimages_length: Field) { + self.end.encrypted_logs_hash = hash; + self.end.encrypted_log_preimages_length = preimages_length; + } + + pub fn set_unencrypted_logs(&mut self, hash: [Field; NUM_FIELDS_PER_SHA256], preimages_length: Field) { + self.end.unencrypted_logs_hash = hash; + self.end.unencrypted_log_preimages_length = preimages_length; + } + + pub fn push_private_call_request(&mut self, hash: Field, is_delegate_call: bool) { + let call_stack_item = self.generate_call_request(hash, is_delegate_call); + self.end.private_call_stack.push(call_stack_item); + } + + pub fn push_public_call_request(&mut self, hash: Field, is_delegate_call: bool) { + let call_stack_item = self.generate_call_request(hash, is_delegate_call); + self.end.public_call_stack.push(call_stack_item); + } + + fn generate_call_request(self, hash: Field, is_delegate_call: bool) -> CallRequest { + let mut caller_context = CallerContext::empty(); + if is_delegate_call { + caller_context.msg_sender = fixtures::MSG_SENDER; + caller_context.storage_contract_address = self.contract_address; + } + CallRequest { + hash, + caller_contract_address: self.contract_address, + caller_context, + } + } + + pub fn finish(self) -> PreviousKernelData { + let public_inputs = KernelCircuitPublicInputs { + end: self.end.finish(), + constants: CombinedConstantData { + block_data: self.block_data, + tx_context: self.tx_context, + }, + is_private: self.is_private, + }; + PreviousKernelData { + public_inputs, + proof: self.proof, + vk: self.vk, + vk_index: self.vk_index, + vk_path: self.vk_path, + } + } +} diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/private_call_data_builder.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/private_call_data_builder.nr new file mode 100644 index 00000000000..b03552e2df6 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/private_call_data_builder.nr @@ -0,0 +1,215 @@ +use crate::{ + abis::{ + call_request::{CallerContext, CallRequest}, + call_stack_item::PrivateCallStackItem, + function_data::FunctionData, + membership_witness::{ + ContractLeafMembershipWitness, + FunctionLeafMembershipWitness, + ReadRequestMembershipWitness, + }, + private_circuit_public_inputs::{PrivateCircuitPublicInputs}, + private_kernel::private_call_data::PrivateCallData, + }, + address::{Address, EthAddress}, + hash::NUM_FIELDS_PER_SHA256, + mocked::{Proof, VerificationKey}, + tests::{ + fixtures, + private_circuit_public_inputs_builder::PrivateCircuitPublicInputsBuilder, + testing_harness::build_tx_context, + }, + transaction::{ + request::TxRequest, + }, + utils::{ + bounded_vec::BoundedVec, + }, +}; +use dep::aztec::{ + constants_gen::{ + MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, + MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, + MAX_READ_REQUESTS_PER_CALL, + }, +}; + +struct PrivateCallDataBuilder { + // Values of PrivateCallStackItem. + contract_address: Address, + public_inputs: PrivateCircuitPublicInputsBuilder, + is_execution_request: bool, + function_data: FunctionData, + // The rest of the values of PrivateCallData. + private_call_stack: BoundedVec, + public_call_stack: BoundedVec, + proof: Proof, + vk: VerificationKey, + function_leaf_membership_witness: FunctionLeafMembershipWitness, + contract_leaf_membership_witness: ContractLeafMembershipWitness, + read_request_membership_witnesses: BoundedVec, + portal_contract_address: EthAddress, + acir_hash: Field, +} + +impl PrivateCallDataBuilder { + pub fn new(is_constructor: bool) -> Self { + let public_inputs = PrivateCircuitPublicInputsBuilder::new(is_constructor); + + let contract_data = fixtures::contracts::default_contract; + let contract_function = if is_constructor { + fixtures::contract_functions::default_constructor + } else { + fixtures::contract_functions::default_private_function + }; + let function_data = contract_function.data; + + PrivateCallDataBuilder { + contract_address: public_inputs.call_context.storage_contract_address, + public_inputs, + is_execution_request: false, + function_data, + private_call_stack: dep::std::unsafe::zeroed(), + public_call_stack: dep::std::unsafe::zeroed(), + proof: Proof {}, + vk: VerificationKey {}, + function_leaf_membership_witness: contract_function.membership_witness, + contract_leaf_membership_witness: contract_data.membership_witness, + read_request_membership_witnesses: dep::std::unsafe::zeroed(), + portal_contract_address: public_inputs.call_context.portal_contract_address, + acir_hash: contract_function.acir_hash, + } + } + + pub fn is_delegate_call(&mut self) -> Self { + self.public_inputs.call_context.is_delegate_call = true; + self.public_inputs.call_context.storage_contract_address = fixtures::contracts::parent_contract.address; + self.public_inputs.call_context.portal_contract_address = fixtures::contracts::parent_contract.portal_contract_address; + self.public_inputs.call_context.msg_sender = fixtures::MSG_SENDER; + *self + } + + pub fn is_static_call(&mut self) -> Self { + self.public_inputs.call_context.is_static_call = true; + *self + } + + pub fn is_internal_call(&mut self) -> Self { + self.function_data.is_internal = true; + self.public_inputs.call_context.msg_sender = self.contract_address; + let contract_function = fixtures::contract_functions::default_internal_private_function; + self.function_data = contract_function.data; + self.function_leaf_membership_witness = contract_function.membership_witness; + self.acir_hash = contract_function.acir_hash; + *self + } + + pub fn build_tx_request(self) -> TxRequest { + let tx_context = build_tx_context(self.public_inputs.call_context.is_contract_deployment); + TxRequest { + origin: self.contract_address, + args_hash: self.public_inputs.args_hash, + tx_context, + function_data: self.function_data, + } + } + + pub fn append_private_call_requests(&mut self, num_requests: Field, is_delegate_call: bool) { + let (hashes, call_requests) = self.generate_call_requests(self.private_call_stack, num_requests, is_delegate_call); + self.public_inputs.private_call_stack_hashes.push_vec(hashes); + self.private_call_stack.push_vec(call_requests); + } + + pub fn append_public_call_requests(&mut self, num_requests: Field, is_delegate_call: bool) { + let (hashes, call_requests) = self.generate_call_requests(self.public_call_stack, num_requests, is_delegate_call); + self.public_inputs.public_call_stack_hashes.push_vec(hashes); + self.public_call_stack.push_vec(call_requests); + } + + fn generate_call_requests( + self, + requests: BoundedVec, + num_requests: Field, + is_delegate_call: bool, + ) -> (BoundedVec, BoundedVec) { + let value_offset = requests.len(); + let mut caller_context = CallerContext::empty(); + if is_delegate_call { + let call_context = self.public_inputs.call_context; + caller_context.msg_sender = call_context.msg_sender; + caller_context.storage_contract_address = call_context.storage_contract_address; + } + let mut call_requests: BoundedVec = BoundedVec::new(CallRequest::empty()); + let mut hashes: BoundedVec = BoundedVec::new(0); + let mut exceeded_len = false; + for i in 0..N { + exceeded_len |= i == num_requests; + if !exceeded_len { + // The default hash is its index + 7788. + let hash = value_offset + 7788; + let request = CallRequest { + hash, + caller_contract_address: self.contract_address, + caller_context, + }; + hashes.push(hash); + call_requests.push(request); + } + } + (hashes, call_requests) + } + + pub fn append_read_requests(&mut self, num_read_requests: Field) { + let (read_requests, read_request_membership_witnesses) = fixtures::read_requests::generate_read_requests(num_read_requests); + self.public_inputs.read_requests.push_vec(read_requests); + self.read_request_membership_witnesses.push_vec(read_request_membership_witnesses); + } + + pub fn append_transient_read_requests(&mut self, num_read_requests: Field) { + let (read_requests, read_request_membership_witnesses) = fixtures::read_requests::generate_transient_read_requests(num_read_requests); + self.public_inputs.read_requests.push_vec(read_requests); + self.read_request_membership_witnesses.push_vec(read_request_membership_witnesses); + } + + pub fn set_encrypted_logs(&mut self, hash: [Field; NUM_FIELDS_PER_SHA256], preimages_length: Field) { + self.public_inputs.encrypted_logs_hash = hash; + self.public_inputs.encrypted_log_preimages_length = preimages_length; + } + + pub fn set_unencrypted_logs(&mut self, hash: [Field; NUM_FIELDS_PER_SHA256], preimages_length: Field) { + self.public_inputs.unencrypted_logs_hash = hash; + self.public_inputs.unencrypted_log_preimages_length = preimages_length; + } + + pub fn get_call_stack_item_hash(self) -> Field { + let call_stack_item = PrivateCallStackItem { + contract_address: self.contract_address, + public_inputs: self.public_inputs.finish(), + is_execution_request: false, + function_data: self.function_data, + }; + call_stack_item.hash() + } + + pub fn finish(self) -> PrivateCallData { + let call_stack_item = PrivateCallStackItem { + contract_address: self.contract_address, + public_inputs: self.public_inputs.finish(), + is_execution_request: false, + function_data: self.function_data, + }; + + PrivateCallData { + call_stack_item, + private_call_stack: self.private_call_stack.storage, + public_call_stack: self.public_call_stack.storage, + proof: self.proof, + vk: self.vk, + function_leaf_membership_witness: self.function_leaf_membership_witness, + contract_leaf_membership_witness: self.contract_leaf_membership_witness, + read_request_membership_witnesses: self.read_request_membership_witnesses.storage, + portal_contract_address: self.portal_contract_address, + acir_hash: self.acir_hash, + } + } +} diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/private_circuit_public_inputs_builder.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/private_circuit_public_inputs_builder.nr new file mode 100644 index 00000000000..9a4f2939797 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/private_circuit_public_inputs_builder.nr @@ -0,0 +1,142 @@ +use crate::{ + abis::{ + call_context::CallContext, + complete_address::CompleteAddress, + historical_block_data::HistoricalBlockData, + private_circuit_public_inputs::PrivateCircuitPublicInputs, + }, + contrakt::deployment_data::ContractDeploymentData, + hash::{compute_constructor_hash, NUM_FIELDS_PER_SHA256}, + tests::{ + fixtures, + testing_harness::build_contract_deployment_data, + }, + utils::bounded_vec::BoundedVec, +}; +use dep::aztec::{ + abi::hash_args, + constants_gen::{ + MAX_READ_REQUESTS_PER_CALL, + MAX_NEW_COMMITMENTS_PER_CALL, + MAX_NEW_NULLIFIERS_PER_CALL, + MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, + MAX_PENDING_READ_REQUESTS_PER_CALL, + MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, + MAX_NEW_L2_TO_L1_MSGS_PER_CALL, + RETURN_VALUES_LENGTH, + } +}; + +struct PrivateCircuitPublicInputsBuilder { + call_context: CallContext, + + args_hash: Field, + return_values: BoundedVec, + + read_requests: BoundedVec, + pending_read_requests: BoundedVec, + + new_commitments: BoundedVec, + new_nullifiers: BoundedVec, + nullified_commitments: BoundedVec, + + private_call_stack_hashes: BoundedVec, + public_call_stack_hashes: BoundedVec, + new_l2_to_l1_msgs: BoundedVec, + + encrypted_logs_hash: [Field; NUM_FIELDS_PER_SHA256], + unencrypted_logs_hash: [Field; NUM_FIELDS_PER_SHA256], + + encrypted_log_preimages_length: Field, + unencrypted_log_preimages_length: Field, + + historical_block_data: HistoricalBlockData, + + contract_deployment_data: ContractDeploymentData, + + chain_id: Field, + version: Field, +} + +impl PrivateCircuitPublicInputsBuilder { + pub fn new(is_constructor: bool) -> Self { + let mut public_inputs: PrivateCircuitPublicInputsBuilder = dep::std::unsafe::zeroed(); + + let args_hash = hash_args([]); + + let contract_data = fixtures::contracts::default_contract; + let portal_contract_address = contract_data.portal_contract_address; + + let contract_function = if is_constructor { + fixtures::contract_functions::default_constructor + } else { + fixtures::contract_functions::default_private_function + }; + let function_data = contract_function.data; + + let contract_deployment_data = build_contract_deployment_data(is_constructor); + + let contract_address = if is_constructor { + let constructor = fixtures::contract_functions::default_constructor; + let constructor_hash = compute_constructor_hash(constructor.data, args_hash, constructor.vk_hash); + CompleteAddress::compute( + contract_deployment_data.deployer_public_key, + contract_deployment_data.contract_address_salt, + contract_deployment_data.function_tree_root, + constructor_hash, + ).address + } else { + contract_data.address + }; + + let call_context = CallContext { + msg_sender: fixtures::contracts::parent_contract.address, + storage_contract_address: contract_address, + portal_contract_address, + function_selector: function_data.selector, + is_delegate_call: false, + is_static_call: false, + is_contract_deployment: is_constructor, + }; + public_inputs.call_context = call_context; + public_inputs.args_hash = args_hash; + public_inputs.contract_deployment_data = contract_deployment_data; + public_inputs.historical_block_data = fixtures::HISTORICAL_BLOCK_DATA; + public_inputs.chain_id = 0; + public_inputs.version = 1; + + public_inputs + } + + pub fn finish(self) -> PrivateCircuitPublicInputs { + PrivateCircuitPublicInputs { + call_context: self.call_context, + args_hash: self.args_hash, + return_values: self.return_values.storage, + + read_requests: self.read_requests.storage, + pending_read_requests: self.pending_read_requests.storage, + + new_commitments: self.new_commitments.storage, + new_nullifiers: self.new_nullifiers.storage, + nullified_commitments: self.nullified_commitments.storage, + + private_call_stack_hashes: self.private_call_stack_hashes.storage, + public_call_stack_hashes: self.public_call_stack_hashes.storage, + new_l2_to_l1_msgs: self.new_l2_to_l1_msgs.storage, + + encrypted_logs_hash: self.encrypted_logs_hash, + unencrypted_logs_hash: self.unencrypted_logs_hash, + + encrypted_log_preimages_length: self.encrypted_log_preimages_length, + unencrypted_log_preimages_length: self.unencrypted_log_preimages_length, + + historical_block_data: self.historical_block_data, + + contract_deployment_data: self.contract_deployment_data, + + chain_id: self.chain_id, + version: self.version, + } + } +} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/public_call_data_builder.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/public_call_data_builder.nr new file mode 100644 index 00000000000..fdf4678b161 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/public_call_data_builder.nr @@ -0,0 +1,190 @@ +use crate::{ + abis::{ + call_context::CallContext, + call_request::{CallerContext, CallRequest}, + call_stack_item::PublicCallStackItem, + function_data::FunctionData, + public_call_data::PublicCallData, + public_circuit_public_inputs::PublicCircuitPublicInputs, + }, + address::{Address, EthAddress}, + contrakt::{ + storage_read::StorageRead, + storage_update_request::StorageUpdateRequest, + }, + mocked::Proof, + tests::{ + fixtures, + public_circuit_public_inputs_builder::PublicCircuitPublicInputsBuilder, + }, + utils::bounded_vec::BoundedVec, +}; +use dep::aztec::constants_gen::{ + MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, + MAX_PUBLIC_DATA_READS_PER_CALL, + MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_CALL, + NUM_FIELDS_PER_SHA256, +}; + +struct PublicCallDataBuilder { + contract_address: Address, + public_inputs: PublicCircuitPublicInputsBuilder, + is_execution_request: bool, + function_data: FunctionData, + public_call_stack: BoundedVec, + proof: Proof, + portal_contract_address: EthAddress, + bytecode_hash: Field, +} + +impl PublicCallDataBuilder { + pub fn new() -> Self { + let contract_data = fixtures::contracts::default_contract; + let contract_address = contract_data.address; + let portal_contract_address = contract_data.portal_contract_address; + + let contract_function = fixtures::contract_functions::default_public_function; + let function_data = contract_function.data; + + let mut public_inputs = PublicCircuitPublicInputsBuilder::new(); + + public_inputs.call_context = CallContext { + msg_sender: fixtures::contracts::parent_contract.address, + storage_contract_address: contract_address, + portal_contract_address, + function_selector: function_data.selector, + is_delegate_call: false, + is_static_call: false, + is_contract_deployment: false, + }; + + PublicCallDataBuilder { + contract_address, + public_inputs, + is_execution_request: false, + function_data, + public_call_stack: dep::std::unsafe::zeroed(), + proof: Proof {}, + portal_contract_address, + bytecode_hash: contract_function.acir_hash, + } + } + + pub fn is_delegate_call(&mut self) -> Self { + self.public_inputs.call_context.is_delegate_call = true; + self.public_inputs.call_context.storage_contract_address = fixtures::contracts::parent_contract.address; + self.public_inputs.call_context.portal_contract_address = fixtures::contracts::parent_contract.portal_contract_address; + self.public_inputs.call_context.msg_sender = fixtures::MSG_SENDER; + *self + } + + pub fn is_internal_call(&mut self) -> Self { + self.function_data.is_internal = true; + self.public_inputs.call_context.msg_sender = self.contract_address; + let contract_function = fixtures::contract_functions::default_internal_public_function; + self.function_data = contract_function.data; + self.bytecode_hash = contract_function.acir_hash; + *self + } + + pub fn append_public_call_requests_for_regular_calls(&mut self, num_requests: Field) { + self.append_public_call_requests(num_requests, false); + } + + pub fn append_public_call_requests_for_delegate_calls(&mut self, num_requests: Field) { + self.append_public_call_requests(num_requests, true); + } + + pub fn append_public_call_requests(&mut self, num_requests: Field, is_delegate_call: bool) { + let value_offset = self.public_inputs.public_call_stack_hashes.len(); + let mut caller_context = CallerContext::empty(); + if is_delegate_call { + let call_context = self.public_inputs.call_context; + caller_context.msg_sender = call_context.msg_sender; + caller_context.storage_contract_address = call_context.storage_contract_address; + } + let mut exceeded_len = false; + for i in 0..MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL { + exceeded_len |= i == num_requests; + if !exceeded_len { + // The default hash is its index + 7788. + let hash = value_offset + 7788; + let call_request = CallRequest { + hash, + caller_contract_address: self.contract_address, + caller_context, + }; + self.public_inputs.public_call_stack_hashes.push(hash); + self.public_call_stack.push(call_request); + } + } + } + + pub fn append_read_requests(&mut self, num_reads: Field) { + let value_offset = self.public_inputs.contract_storage_reads.len(); + for i in 0..MAX_PUBLIC_DATA_READS_PER_CALL { + if i as u64 < num_reads as u64 { + let read_request = StorageRead { + // The default storage slot is its index + 1. + storage_slot: value_offset + i + 1, + // The default value is its index + 999. + current_value: value_offset + i + 999, + }; + self.public_inputs.contract_storage_reads.push(read_request); + } + } + } + + pub fn append_empty_read_requests(&mut self, num_reads: Field) { + for i in 0..MAX_PUBLIC_DATA_READS_PER_CALL { + if i as u64 < num_reads as u64 { + self.public_inputs.contract_storage_reads.push(StorageRead::empty()); + } + } + } + + pub fn append_update_requests(&mut self, num_updates: Field) { + let value_offset = self.public_inputs.contract_storage_update_requests.len(); + for i in 0..MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_CALL { + if i as u64 < num_updates as u64 { + let update_request = StorageUpdateRequest { + // The default storage slot is its index + 1. + storage_slot: value_offset + i + 1, + // The default value is its index + 567. + old_value: value_offset + i + 567, + // The default value is its index + 890. + new_value: value_offset + i + 890, + }; + self.public_inputs.contract_storage_update_requests.push(update_request); + } + } + } + + pub fn append_empty_update_requests(&mut self, num_updates: Field) { + for i in 0..MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_CALL { + if i as u64 < num_updates as u64 { + self.public_inputs.contract_storage_update_requests.push(StorageUpdateRequest::empty()); + } + } + } + + pub fn set_unencrypted_logs(&mut self, hash: [Field; NUM_FIELDS_PER_SHA256], preimages_length: Field) { + self.public_inputs.unencrypted_logs_hash = hash; + self.public_inputs.unencrypted_log_preimages_length = preimages_length; + } + + pub fn finish(self) -> PublicCallData { + PublicCallData { + call_stack_item: PublicCallStackItem { + contract_address: self.contract_address, + function_data: self.function_data, + is_execution_request: self.is_execution_request, + public_inputs: self.public_inputs.finish(), + }, + public_call_stack: self.public_call_stack.storage, + proof: self.proof, + portal_contract_address: self.portal_contract_address, + bytecode_hash: self.bytecode_hash, + } + } +} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/public_circuit_public_inputs_builder.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/public_circuit_public_inputs_builder.nr new file mode 100644 index 00000000000..184c558a82e --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/public_circuit_public_inputs_builder.nr @@ -0,0 +1,67 @@ +use crate::{ + abis::{ + call_context::CallContext, + historical_block_data::HistoricalBlockData, + public_circuit_public_inputs::PublicCircuitPublicInputs, + }, + address::Address, + contrakt::{ + storage_read::StorageRead, + storage_update_request::StorageUpdateRequest, + }, + hash::NUM_FIELDS_PER_SHA256, + tests::fixtures, + utils::bounded_vec::BoundedVec, +}; +use dep::aztec::constants_gen::{ + MAX_NEW_COMMITMENTS_PER_CALL, + MAX_NEW_L2_TO_L1_MSGS_PER_CALL, + MAX_NEW_NULLIFIERS_PER_CALL, + MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, + MAX_PUBLIC_DATA_READS_PER_CALL, + MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_CALL, + RETURN_VALUES_LENGTH, +}; + +struct PublicCircuitPublicInputsBuilder { + call_context: CallContext, + args_hash: Field, + return_values: BoundedVec, + contract_storage_update_requests: BoundedVec, + contract_storage_reads: BoundedVec, + public_call_stack_hashes: BoundedVec, + new_commitments: BoundedVec, + new_nullifiers: BoundedVec, + new_l2_to_l1_msgs: BoundedVec, + unencrypted_logs_hash: [Field; NUM_FIELDS_PER_SHA256], + unencrypted_log_preimages_length: Field, + historical_block_data: HistoricalBlockData, + prover_address: Address, +} + +impl PublicCircuitPublicInputsBuilder { + pub fn new() -> Self { + let mut public_inputs: PublicCircuitPublicInputsBuilder = dep::std::unsafe::zeroed(); + public_inputs.call_context.msg_sender = fixtures::MSG_SENDER; + public_inputs.historical_block_data = fixtures::HISTORICAL_BLOCK_DATA; + public_inputs + } + + pub fn finish(self) -> PublicCircuitPublicInputs { + PublicCircuitPublicInputs { + call_context: self.call_context, + args_hash: self.args_hash, + return_values: self.return_values.storage, + contract_storage_update_requests: self.contract_storage_update_requests.storage, + contract_storage_reads: self.contract_storage_reads.storage, + public_call_stack_hashes: self.public_call_stack_hashes.storage, + new_commitments: self.new_commitments.storage, + new_nullifiers: self.new_nullifiers.storage, + new_l2_to_l1_msgs: self.new_l2_to_l1_msgs.storage, + unencrypted_logs_hash: self.unencrypted_logs_hash, + unencrypted_log_preimages_length: self.unencrypted_log_preimages_length, + historical_block_data: self.historical_block_data, + prover_address: self.prover_address, + } + } +} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/testing_harness.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/testing_harness.nr index 687d18ee07b..fde4a7a6f92 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/testing_harness.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/tests/testing_harness.nr @@ -1,167 +1,10 @@ use crate::{ - abis::{ - call_context::CallContext, - call_stack_item::PrivateCallStackItem, - combined_constant_data::CombinedConstantData, - combined_accumulated_data::CombinedAccumulatedDataBuilder, - complete_address::CompleteAddress, - function_data::FunctionData, - historical_block_data::HistoricalBlockData, - kernel_circuit_public_inputs::KernelCircuitPublicInputs, - previous_kernel_data::PreviousKernelData, - private_circuit_public_inputs::PrivateCircuitPublicInputs, - private_kernel::private_call_data::PrivateCallData, - membership_witness::{ - ContractLeafMembershipWitness, - FunctionLeafMembershipWitness, - ReadRequestMembershipWitness, - }, - }, - address::{Address, EthAddress}, - contrakt::{ - deployment_data::ContractDeploymentData, - }, - hash::{ - NUM_FIELDS_PER_SHA256, - compute_constructor_hash, - }, - mocked::{Proof, VerificationKey}, - point::Point, + contrakt::deployment_data::ContractDeploymentData, tests::fixtures, - transaction::{ - request::TxRequest, - context::TxContext, - }, - utils::{ - bounded_vec::BoundedVec, - }, + transaction::context::TxContext, }; -use dep::aztec::{ - abi::hash_args, - constants_gen::{ - EMPTY_NULLIFIED_COMMITMENT, - MAX_NEW_COMMITMENTS_PER_CALL, - MAX_NEW_COMMITMENTS_PER_TX, - MAX_NEW_L2_TO_L1_MSGS_PER_CALL, - MAX_NEW_NULLIFIERS_PER_CALL, - MAX_NEW_NULLIFIERS_PER_TX, - MAX_PENDING_READ_REQUESTS_PER_CALL, - MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, - MAX_READ_REQUESTS_PER_CALL, - MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, - RETURN_VALUES_LENGTH, - VK_TREE_HEIGHT, - }, -}; - -struct PrivateAppInputs { - call_context: CallContext, - historical_block_data: HistoricalBlockData, - contract_deployment_data: ContractDeploymentData, - args_hash: Field, - chain_id: Field, - version: Field, -} - -struct PrivateCircuitPublicInputsBuilder { - call_context: CallContext, - - args_hash: Field, - return_values: BoundedVec, - - read_requests: BoundedVec, - pending_read_requests: BoundedVec, - - new_commitments: BoundedVec, - new_nullifiers: BoundedVec, - nullified_commitments: BoundedVec, - - private_call_stack: BoundedVec, - public_call_stack: BoundedVec, - new_l2_to_l1_msgs: BoundedVec, - - encrypted_logs_hash : [Field; NUM_FIELDS_PER_SHA256], - unencrypted_logs_hash : [Field; NUM_FIELDS_PER_SHA256], - - encrypted_log_preimages_length : Field, - unencrypted_log_preimages_length : Field, - - historical_block_data : HistoricalBlockData, - - contract_deployment_data : ContractDeploymentData, - - chain_id : Field, - version : Field, -} - -impl PrivateCircuitPublicInputsBuilder { - pub fn new(inputs: PrivateAppInputs) -> Self { - Self { - call_context: inputs.call_context, - - args_hash: inputs.args_hash, - return_values : BoundedVec::new(0), - - read_requests : BoundedVec::new(0), - pending_read_requests : BoundedVec::new(0), - - new_commitments : BoundedVec::new(0), - new_nullifiers : BoundedVec::new(0), - nullified_commitments : BoundedVec::new(0), - - private_call_stack : BoundedVec::new(0), - public_call_stack : BoundedVec::new(0), - new_l2_to_l1_msgs : BoundedVec::new(0), - - encrypted_logs_hash : [0; NUM_FIELDS_PER_SHA256], - unencrypted_logs_hash : [0; NUM_FIELDS_PER_SHA256], - - encrypted_log_preimages_length : 0, - unencrypted_log_preimages_length : 0, - - historical_block_data : inputs.historical_block_data, - - contract_deployment_data : inputs.contract_deployment_data, - chain_id : inputs.chain_id, - version : inputs.version, - } - } - - pub fn finish(self) -> PrivateCircuitPublicInputs { - PrivateCircuitPublicInputs { - call_context : self.call_context, - args_hash : self.args_hash, - return_values : self.return_values.storage, - - read_requests : self.read_requests.storage, - pending_read_requests : self.pending_read_requests.storage, - - new_commitments : self.new_commitments.storage, - new_nullifiers : self.new_nullifiers.storage, - nullified_commitments : self.nullified_commitments.storage, - - private_call_stack : self.private_call_stack.storage, - public_call_stack : self.public_call_stack.storage, - new_l2_to_l1_msgs : self.new_l2_to_l1_msgs.storage, - - encrypted_logs_hash : self.encrypted_logs_hash, - unencrypted_logs_hash : self.unencrypted_logs_hash, - - encrypted_log_preimages_length : self.encrypted_log_preimages_length, - unencrypted_log_preimages_length : self.unencrypted_log_preimages_length, - - historical_block_data : self.historical_block_data, - - contract_deployment_data : self.contract_deployment_data, - - chain_id : self.chain_id, - version : self.version, - } - } -} - -fn build_contract_deployment_data(is_constructor: bool) -> ContractDeploymentData { +pub fn build_contract_deployment_data(is_constructor: bool) -> ContractDeploymentData { let mut contract_deployment_data: ContractDeploymentData = dep::std::unsafe::zeroed(); if is_constructor { let contract_data = fixtures::contracts::default_contract; @@ -177,7 +20,7 @@ fn build_contract_deployment_data(is_constructor: bool) -> ContractDeploymentDat contract_deployment_data } -fn build_tx_context(is_constructor: bool) -> TxContext { +pub fn build_tx_context(is_constructor: bool) -> TxContext { let contract_deployment_data = build_contract_deployment_data(is_constructor); TxContext { is_fee_payment_tx: false, @@ -188,225 +31,3 @@ fn build_tx_context(is_constructor: bool) -> TxContext { version: 0, } } - -struct PreviousKernelDataBuilder { - end: CombinedAccumulatedDataBuilder, - block_data: HistoricalBlockData, - tx_context: TxContext, - is_private: bool, - proof: Proof, - vk: VerificationKey, - vk_index: u32, - vk_path: [Field; VK_TREE_HEIGHT], -} - -impl PreviousKernelDataBuilder { - pub fn new(is_constructor: bool) -> Self { - let mut end: CombinedAccumulatedDataBuilder = dep::std::unsafe::zeroed(); - end.new_nullifiers.push(321); // 0th nullifier must be non-zero. - end.nullified_commitments.push(EMPTY_NULLIFIED_COMMITMENT); - end.private_call_stack.push(1); - - let tx_context = build_tx_context(is_constructor); - - PreviousKernelDataBuilder { - end, - block_data: fixtures::HISTORICAL_BLOCK_DATA, - tx_context, - is_private: true, - proof: Proof {}, - vk: VerificationKey {}, - vk_index: 0, - vk_path: [0; VK_TREE_HEIGHT], - } - } - - pub fn add_read_request_for_transient_commitment(&mut self, commitment_index: Field) -> Field { - let new_read_request_index = self.end.read_requests.len(); - self.end.read_requests.push(self.end.new_commitments.get(commitment_index)); - new_read_request_index - } - - pub fn append_new_commitments(&mut self, num_new_commitments: Field) { - let mocked_value_offset = self.end.new_commitments.len() + 1; - for i in 0..MAX_NEW_COMMITMENTS_PER_TX { - if i as u64 < num_new_commitments as u64 { - // The default value is its index + 1. - self.end.new_commitments.push(i + mocked_value_offset); - } - } - } - - pub fn append_new_nullifiers(&mut self, num_extra_nullifier: Field) { - let first_nullifier = self.end.new_nullifiers.get(0); - let mocked_value_offset = first_nullifier + self.end.new_nullifiers.len(); - for i in 1..MAX_NEW_NULLIFIERS_PER_TX { - if i as u64 <= num_extra_nullifier as u64 { - // The default value is its index + the value of the first nullifier. - self.end.new_nullifiers.push(i + mocked_value_offset); - } - } - } - - pub fn set_encrypted_logs(&mut self, hash: [Field; NUM_FIELDS_PER_SHA256], preimages_length: Field) { - self.end.encrypted_logs_hash = hash; - self.end.encrypted_log_preimages_length = preimages_length; - } - - pub fn set_unencrypted_logs(&mut self, hash: [Field; NUM_FIELDS_PER_SHA256], preimages_length: Field) { - self.end.unencrypted_logs_hash = hash; - self.end.unencrypted_log_preimages_length = preimages_length; - } - - pub fn finish(self) -> PreviousKernelData { - let public_inputs = KernelCircuitPublicInputs { - end: self.end.finish(), - constants: CombinedConstantData { - block_data: self.block_data, - tx_context: self.tx_context, - }, - is_private: self.is_private, - }; - PreviousKernelData { - public_inputs, - proof: self.proof, - vk: self.vk, - vk_index: self.vk_index, - vk_path: self.vk_path, - } - } -} - -struct PrivateCallDataBuilder { - // Values of PrivateCallStackItem. - contract_address: Address, - public_inputs: PrivateCircuitPublicInputsBuilder, - is_execution_request: bool, - function_data: FunctionData, - // The rest of the values of PrivateCallData. - private_call_stack_preimages: [PrivateCallStackItem; MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL], - proof: Proof, - vk: VerificationKey, - function_leaf_membership_witness: FunctionLeafMembershipWitness, - contract_leaf_membership_witness: ContractLeafMembershipWitness, - read_request_membership_witnesses: BoundedVec, - portal_contract_address: EthAddress, - acir_hash: Field, -} - -impl PrivateCallDataBuilder { - pub fn new(is_constructor: bool) -> Self { - let args_hash = hash_args([]); - - let contract_data = fixtures::contracts::default_contract; - let portal_contract_address = contract_data.portal_contract_address; - - let contract_function = if is_constructor { - fixtures::contract_functions::default_constructor - } else { - fixtures::contract_functions::default_contract_function - }; - let function_data = contract_function.data; - - let contract_deployment_data = build_contract_deployment_data(is_constructor); - - let contract_address = if is_constructor { - let constructor = fixtures::contract_functions::default_constructor; - let constructor_hash = compute_constructor_hash(constructor.data, args_hash, constructor.vk_hash); - CompleteAddress::compute( - contract_deployment_data.deployer_public_key, - contract_deployment_data.contract_address_salt, - contract_deployment_data.function_tree_root, - constructor_hash, - ).address - } else { - contract_data.address - }; - - let call_context = CallContext { - msg_sender: fixtures::MSG_SENDER, - storage_contract_address: contract_address, - portal_contract_address, - function_selector: function_data.selector, - is_delegate_call: false, - is_static_call: false, - is_contract_deployment: is_constructor, - }; - - let mut public_inputs: PrivateCircuitPublicInputsBuilder = dep::std::unsafe::zeroed(); - public_inputs.call_context = call_context; - public_inputs.args_hash = args_hash; - public_inputs.historical_block_data = fixtures::HISTORICAL_BLOCK_DATA; - public_inputs.contract_deployment_data = contract_deployment_data; - public_inputs.chain_id = 0; - public_inputs.version = 1; - - PrivateCallDataBuilder { - contract_address, - public_inputs, - is_execution_request: false, - function_data, - private_call_stack_preimages: dep::std::unsafe::zeroed(), - proof: Proof {}, - vk: VerificationKey {}, - function_leaf_membership_witness: contract_function.membership_witness, - contract_leaf_membership_witness: contract_data.membership_witness, - read_request_membership_witnesses: dep::std::unsafe::zeroed(), - portal_contract_address, - acir_hash: contract_function.acir_hash, - } - } - - pub fn build_tx_request(self) -> TxRequest { - let tx_context = build_tx_context(self.public_inputs.call_context.is_contract_deployment); - TxRequest { - origin: self.contract_address, - args_hash: self.public_inputs.args_hash, - tx_context, - function_data: self.function_data, - } - } - - pub fn append_read_requests(&mut self, num_read_requests: Field) { - let (read_requests, read_request_membership_witnesses) = fixtures::read_requests::generate_read_requests(num_read_requests); - self.public_inputs.read_requests.push_vec(read_requests); - self.read_request_membership_witnesses.push_vec(read_request_membership_witnesses); - } - - pub fn append_transient_read_requests(&mut self, num_read_requests: Field) { - let (read_requests, read_request_membership_witnesses) = fixtures::read_requests::generate_transient_read_requests(num_read_requests); - self.public_inputs.read_requests.push_vec(read_requests); - self.read_request_membership_witnesses.push_vec(read_request_membership_witnesses); - } - - pub fn set_encrypted_logs(&mut self, hash: [Field; NUM_FIELDS_PER_SHA256], preimages_length: Field) { - self.public_inputs.encrypted_logs_hash = hash; - self.public_inputs.encrypted_log_preimages_length = preimages_length; - } - - pub fn set_unencrypted_logs(&mut self, hash: [Field; NUM_FIELDS_PER_SHA256], preimages_length: Field) { - self.public_inputs.unencrypted_logs_hash = hash; - self.public_inputs.unencrypted_log_preimages_length = preimages_length; - } - - pub fn finish(self) -> PrivateCallData { - let call_stack_item = PrivateCallStackItem { - contract_address: self.contract_address, - public_inputs: self.public_inputs.finish(), - is_execution_request: false, - function_data: self.function_data, - }; - - PrivateCallData { - call_stack_item, - private_call_stack_preimages: self.private_call_stack_preimages, - proof: self.proof, - vk: self.vk, - function_leaf_membership_witness: self.function_leaf_membership_witness, - contract_leaf_membership_witness: self.contract_leaf_membership_witness, - read_request_membership_witnesses: self.read_request_membership_witnesses.storage, - portal_contract_address: self.portal_contract_address, - acir_hash: self.acir_hash, - } - } -} diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/utils/arrays.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/utils/arrays.nr index 88d207014c6..4b65e1e5a43 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/utils/arrays.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/utils/arrays.nr @@ -19,11 +19,12 @@ pub fn is_empty_array(array : [Field; T]) -> bool { array.all(|elem| is_empty(elem)) } -pub fn is_empty(value : Field) -> bool { +fn is_empty(value : Field) -> bool { value == 0 } -pub fn array_to_bounded_vec(array: [T; N], is_empty_item: fn (T)->bool, empty_item: T) -> BoundedVec { +// Replace it with `array_to_bounded_vec` when trait is available. +pub fn struct_array_to_bounded_vec(array: [T; N], is_empty_item: fn (T)->bool, empty_item: T) -> BoundedVec { let mut len = 0; for elem in array { if !is_empty_item(elem) { @@ -38,6 +39,21 @@ pub fn array_to_bounded_vec(array: [T; N], is_empty_item: fn (T)->bool, em } } +pub fn array_to_bounded_vec(array: [Field; N]) -> BoundedVec { + let mut len = 0; + for elem in array { + if !is_empty(elem) { + len += 1; + } + } + + BoundedVec { + storage: array, + len, + empty_value: 0, + } +} + // Routine which validates that all zero values of an array form a contiguous region at the end, i.e., // of the form: [*,*,*...,0,0,0,0] where any * is non-zero. Note that a full array of non-zero values is // valid. @@ -64,6 +80,22 @@ pub fn validate_array(array : [Field;T]) { assert((last_non_zero_pos as u64) <= (first_zero_pos as u64), "invalid array"); } +// TODO: Replace it with `validate_array` +pub fn validate_struct_array(array: [T; N], is_empty_item: fn(T) -> bool) { + let array_length = array.len(); + let mut first_zero_pos = array_length; + let mut last_non_zero_pos = 0; + for i in 0..array_length { + let is_empty = is_empty_item(array[i]); + if !is_empty { + last_non_zero_pos = i; + } else if is_empty & (first_zero_pos == array_length) { + first_zero_pos = i; + } + } + assert((last_non_zero_pos as u64) <= (first_zero_pos as u64), "invalid array"); +} + // Helper method to determine the number of non-zero/empty elements in a validated array (ie, validate_array(array) // should be true). pub fn array_length(array : [Field; N]) -> Field { @@ -78,7 +110,20 @@ pub fn array_length(array : [Field; N]) -> Field { length } -pub fn expect_array_fields(array: [Field; N], expected: [Field; S]) -> bool { +// TODO: Replace it with `array_length`. +pub fn struct_array_length(array: [T; N], is_empty_item: fn(T) -> bool) -> Field { + let mut length = 0; + let mut end = false; + for elem in array { + end |= is_empty_item(elem); + if !end { + length += 1; + } + } + length +} + +pub fn array_eq(array: [Field; N], expected: [Field; S]) -> bool { let mut eq = array_length(array) == S; for i in 0..S { eq = eq & (array[i] == expected[i]); @@ -86,6 +131,21 @@ pub fn expect_array_fields(array: [Field; N], expected: [Field; S]) -> boo eq } +// TODO: Replace it with `array_eq`. Trait: eq, is_empty +pub fn struct_array_eq( + array: [T; N], + expected: [T; S], + eq: fn (T, T) -> bool, + is_empty_item: fn (T) -> bool, +) -> bool { + let length = struct_array_length(array, is_empty_item); + let mut ret = length == S; + for i in 0..S { + ret &= eq(array[i], expected[i]); + } + ret +} + #[test] fn smoke_validate_array() { let valid_array = []; diff --git a/yarn-project/noir-protocol-circuits/src/crates/types/src/utils/bounded_vec.nr b/yarn-project/noir-protocol-circuits/src/crates/types/src/utils/bounded_vec.nr index fc5fcdf7b2a..c648439e016 100644 --- a/yarn-project/noir-protocol-circuits/src/crates/types/src/utils/bounded_vec.nr +++ b/yarn-project/noir-protocol-circuits/src/crates/types/src/utils/bounded_vec.nr @@ -43,23 +43,27 @@ impl BoundedVec { } pub fn push_array(&mut self, array: [T; Len]) { - let newLen = self.len + array.len(); - assert(newLen as u64 <= MaxLen as u64, "push_array out of bounds"); + let new_len = self.len + array.len(); + assert(new_len as u64 <= MaxLen as u64, "push_array out of bounds"); for i in 0..array.len() { self.storage[self.len + i] = array[i]; } - self.len = newLen; + self.len = new_len; } pub fn push_vec(&mut self, vec: BoundedVec) { - let newLen = self.len + vec.len(); - assert(newLen as u64 <= MaxLen as u64, "push_vec out of bounds"); + let append_len = vec.len(); + let new_len = self.len + append_len; + assert(new_len as u64 <= MaxLen as u64, "push_vec out of bounds"); + + let mut exceeded_len = false; for i in 0..Len { - if i < (vec.len() as u64) { + exceeded_len |= i == append_len; + if !exceeded_len { self.storage[self.len + (i as Field)] = vec.get_unchecked(i as Field); } } - self.len = newLen; + self.len = new_len; } pub fn pop(&mut self) -> T { diff --git a/yarn-project/noir-protocol-circuits/src/index.test.ts b/yarn-project/noir-protocol-circuits/src/index.test.ts index 893dcafc338..9b7671574b1 100644 --- a/yarn-project/noir-protocol-circuits/src/index.test.ts +++ b/yarn-project/noir-protocol-circuits/src/index.test.ts @@ -3,6 +3,7 @@ import { AztecAddress, CONTRACT_TREE_HEIGHT, CallContext, + CallRequest, CombinedAccumulatedData, CombinedConstantData, ContractDeploymentData, @@ -40,6 +41,8 @@ import { PrivateKernelInputsInit, PrivateKernelInputsInner, PrivateKernelInputsOrdering, + PublicCallStackItem, + PublicCircuitPublicInputs, PublicDataRead, PublicDataUpdateRequest, RETURN_VALUES_LENGTH, @@ -137,7 +140,8 @@ describe('Private kernel', () => { const privateCall = new PrivateCallData( callStackItem, - makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, () => PrivateCallStackItem.empty()), + makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, () => CallRequest.empty()), + makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, () => CallRequest.empty()), makeEmptyProof(), VerificationKey.makeFake(), MembershipWitness.empty(FUNCTION_TREE_HEIGHT, 0n), @@ -192,8 +196,8 @@ describe('Private kernel', () => { newCommitments, newNullifiers, nullifiedCommitments, - makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, () => Fr.ZERO), - makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, () => new Fr(0n)), + makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, () => CallRequest.empty()), + makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, () => CallRequest.empty()), makeTuple(MAX_NEW_L2_TO_L1_MSGS_PER_CALL, () => new Fr(0n)), [Fr.fromString('0x57ee9bb1264085ecf4ba8274b233cdc4'), Fr.fromString('0x8a8910cc6b93b4399a1ebd8fbfb405f8')], [Fr.fromString('0x1c9ecec90e28d2461650418635878a5c'), Fr.fromString('0x91e49f47586ecf75f2b0cbb94e897112')], @@ -311,7 +315,8 @@ describe('Private kernel', () => { const privateCall = new PrivateCallData( callStackItem, - makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, () => PrivateCallStackItem.empty()), + makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, () => CallRequest.empty()), + makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, () => CallRequest.empty()), makeEmptyProof(), VerificationKey.makeFake(), new MembershipWitness(FUNCTION_TREE_HEIGHT, 7n, [ @@ -343,8 +348,9 @@ describe('Private kernel', () => { Fr.ZERO, ); - const privateCallStack = makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, () => Fr.ZERO); - privateCallStack[0] = Fr.fromString('0x036ce317b74895ab56dc5ed6943f14a73c570ae6cde751a588f4522052bb2b20'); + const privateCallStack = makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, () => CallRequest.empty()); + privateCallStack[0].hash = Fr.fromString('0x036ce317b74895ab56dc5ed6943f14a73c570ae6cde751a588f4522052bb2b20'); + privateCallStack[0].callerContractAddress = callContext.msgSender; const newCommitments = makeTuple(MAX_NEW_COMMITMENTS_PER_CALL, () => Fr.ZERO); newCommitments[0] = Fr.fromString('0x0aced88c953b70873e4a33dde4620dc43a709c15013c46c60d167de8e1c32315'); @@ -363,7 +369,7 @@ describe('Private kernel', () => { newNullifiers, nullifiedCommitments, privateCallStack, - makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, () => new Fr(0n)), + makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, () => CallRequest.empty()), makeTuple(MAX_NEW_L2_TO_L1_MSGS_PER_CALL, () => new Fr(0n)), [Fr.fromString('0xd3735899d9fa7162447ca631f0ba2cd5'), Fr.fromString('0xeb57d0965a756d78291da33072610eb2')], [Fr.fromString('0xd3735899d9fa7162447ca631f0ba2cd5'), Fr.fromString('0xeb57d0965a756d78291da33072610eb2')], @@ -461,6 +467,26 @@ describe('Noir compatibility tests (interop_testing.nr)', () => { const fnLeaf = computeFunctionLeaf(fnLeafPreimage); expect(fnLeaf.toString()).toMatchSnapshot(); }); + + it('Public call stack item matches noir', () => { + const contractAddress = AztecAddress.fromBigInt(1n); + const functionData = new FunctionData(new FunctionSelector(2), false, false, false); + const appPublicInputs = PublicCircuitPublicInputs.empty(); + appPublicInputs.newCommitments[0] = new Fr(1); + + const publicCallStackItem = new PublicCallStackItem(contractAddress, functionData, appPublicInputs, false); + expect(publicCallStackItem.hash().toString()).toMatchSnapshot(); + }); + + it('Public call stack item request matches noir', () => { + const contractAddress = AztecAddress.fromBigInt(1n); + const functionData = new FunctionData(new FunctionSelector(2), false, false, false); + const appPublicInputs = PublicCircuitPublicInputs.empty(); + appPublicInputs.newCommitments[0] = new Fr(1); + + const publicCallStackItem = new PublicCallStackItem(contractAddress, functionData, appPublicInputs, true); + expect(publicCallStackItem.hash().toString()).toMatchSnapshot(); + }); }); function numberToBuffer(value: number) { diff --git a/yarn-project/noir-protocol-circuits/src/index.ts b/yarn-project/noir-protocol-circuits/src/index.ts index 535b1bd8724..03ec2defd4f 100644 --- a/yarn-project/noir-protocol-circuits/src/index.ts +++ b/yarn-project/noir-protocol-circuits/src/index.ts @@ -1,11 +1,13 @@ import { BaseOrMergeRollupPublicInputs, + BaseRollupInputs, KernelCircuitPublicInputs, KernelCircuitPublicInputsFinal, MergeRollupInputs, PrivateKernelInputsInit, PrivateKernelInputsInner, PrivateKernelInputsOrdering, + PublicKernelInputs, RootRollupInputs, RootRollupPublicInputs, } from '@aztec/circuits.js'; @@ -20,16 +22,23 @@ import PrivateKernelInnerJson from './target/private_kernel_inner.json' assert { import PrivateKernelInnerSimulatedJson from './target/private_kernel_inner_simulated.json' assert { type: 'json' }; import PrivateKernelOrderingJson from './target/private_kernel_ordering.json' assert { type: 'json' }; import PrivateKernelOrderingSimulatedJson from './target/private_kernel_ordering_simulated.json' assert { type: 'json' }; +import PublicKernelPrivatePreviousJson from './target/public_kernel_private_previous.json' assert { type: 'json' }; +import PublicKernelPrivatePreviousSimulatedJson from './target/public_kernel_private_previous_simulated.json' assert { type: 'json' }; +import PublicKernelPublicPreviousJson from './target/public_kernel_public_previous.json' assert { type: 'json' }; +import PublicKernelPublicPreviousSimulatedJson from './target/public_kernel_public_previous_simulated.json' assert { type: 'json' }; +import BaseRollupJson from './target/rollup_base.json' assert { type: 'json' }; import MergeRollupJson from './target/rollup_merge.json' assert { type: 'json' }; import RootRollupJson from './target/rollup_root.json' assert { type: 'json' }; import { mapBaseOrMergeRollupPublicInputsFromNoir, + mapBaseRollupInputsToNoir, mapKernelCircuitPublicInputsFinalFromNoir, mapKernelCircuitPublicInputsFromNoir, mapMergeRollupInputsToNoir, mapPrivateKernelInputsInitToNoir, mapPrivateKernelInputsInnerToNoir, mapPrivateKernelInputsOrderingToNoir, + mapPublicKernelInputs, mapRootRollupInputsToNoir, mapRootRollupPublicInputsFromNoir, } from './type_conversion.js'; @@ -39,6 +48,15 @@ import { ReturnType as FinalReturnType, InputType as OrderingInputType, } from './types/private_kernel_ordering_types.js'; +import { + InputType as PublicPrivatePreviousInputType, + ReturnType as PublicPrivatePreviousReturnType, +} from './types/public_kernel_private_previous_types.js'; +import { + InputType as PublicPublicPreviousInputType, + ReturnType as PublicPublicPreviousReturnType, +} from './types/public_kernel_public_previous_types.js'; +import { InputType as BaseRollupInputType, ReturnType as BaseRollupReturnType } from './types/rollup_base_types.js'; import { InputType as MergeRollupInputType, ReturnType as MergeRollupReturnType } from './types/rollup_merge_types.js'; import { InputType as RootRollupInputType, ReturnType as RootRollupReturnType } from './types/rollup_root_types.js'; @@ -63,6 +81,10 @@ export const PrivateKernelInnerArtifact = PrivateKernelInnerJson as NoirCompiled export const PrivateKernelOrderingArtifact = PrivateKernelOrderingJson as NoirCompiledCircuit; +export const PublicKernelPrivatePreviousArtifact = PublicKernelPrivatePreviousJson as NoirCompiledCircuit; + +export const PublicKernelPublicPreviousArtifact = PublicKernelPublicPreviousJson as NoirCompiledCircuit; + /** * Executes the init private kernel. * @param privateKernelInputsInit - The private kernel inputs. @@ -123,6 +145,40 @@ export async function executeOrdering( return mapKernelCircuitPublicInputsFinalFromNoir(returnType); } +/** + * Executes the public kernel. + * @param privateKernelInputsInit - The public kernel private inputs. + * @returns The public inputs. + */ +export async function executePublicKernelPrivatePrevious( + publicKernelPrivateInputs: PublicKernelInputs, +): Promise { + const params: PublicPrivatePreviousInputType = { + input: mapPublicKernelInputs(publicKernelPrivateInputs), + }; + + const returnType = await executePublicKernelPrivatePreviousWithACVM(params); + + return mapKernelCircuitPublicInputsFromNoir(returnType); +} + +/** + * Executes the inner public kernel. + * @param privateKernelInputsInit - The public kernel private inputs. + * @returns The public inputs. + */ +export async function executePublicKernelPublicPrevious( + publicKernelPrivateInputs: PublicKernelInputs, +): Promise { + const params: PublicPrivatePreviousInputType = { + input: mapPublicKernelInputs(publicKernelPrivateInputs), + }; + + const returnType = await executePublicKernelPublicPreviousWithACVM(params); + + return mapKernelCircuitPublicInputsFromNoir(returnType); +} + /** * Executes the root rollup. * @param rootRollupInputs - The root rollup inputs. @@ -153,6 +209,21 @@ export async function executeMergeRollup(mergeRollupInputs: MergeRollupInputs): return mapBaseOrMergeRollupPublicInputsFromNoir(returnType); } +/** + * Executes the base rollup. + * @param mergeRollupInputs - The merge rollup inputs. + * @returns The public inputs. + */ +export async function executeBaseRollup(baseRollupInputs: BaseRollupInputs): Promise { + const params: BaseRollupInputType = { + inputs: mapBaseRollupInputsToNoir(baseRollupInputs), + }; + + const returnType = await executeBaseRollupWithACVM(params); + + return mapBaseOrMergeRollupPublicInputsFromNoir(returnType); +} + /** * Executes the init private kernel with the given inputs using the acvm. * @@ -238,6 +309,55 @@ async function executePrivateKernelOrderingWithACVM(input: OrderingInputType): P return decodedInputs.return_value as FinalReturnType; } +/** + * Executes the public kernel with private prevoius kernel with the given inputs + */ +async function executePublicKernelPrivatePreviousWithACVM( + input: PublicPrivatePreviousInputType, +): Promise { + const initialWitnessMap = abiEncode(PublicKernelPrivatePreviousSimulatedJson.abi, input, null); + const decodedBytecode = Buffer.from(PublicKernelPrivatePreviousSimulatedJson.bytecode, 'base64'); + // Execute the circuit + const _witnessMap = await executeCircuitWithBlackBoxSolver( + await getSolver(), + decodedBytecode, + initialWitnessMap, + () => { + throw Error('unexpected oracle during execution'); + }, + ); + + // Decode the witness map into two fields, the return values and the inputs + const decodedInputs: DecodedInputs = abiDecode(PublicKernelPrivatePreviousSimulatedJson.abi, _witnessMap); + // Cast the inputs as the return type + return decodedInputs.return_value as PublicPrivatePreviousReturnType; +} + +/** + * Executes the ordering private kernel with the given inputs using the acvm. + */ +async function executePublicKernelPublicPreviousWithACVM( + input: PublicPublicPreviousInputType, +): Promise { + const initialWitnessMap = abiEncode(PublicKernelPublicPreviousSimulatedJson.abi, input, null); + const decodedBytecode = Buffer.from(PublicKernelPublicPreviousSimulatedJson.bytecode, 'base64'); + // Execute the circuit + const _witnessMap = await executeCircuitWithBlackBoxSolver( + await getSolver(), + decodedBytecode, + initialWitnessMap, + () => { + throw Error('unexpected oracle during execution'); + }, + ); + + // Decode the witness map into two fields, the return values and the inputs + const decodedInputs: DecodedInputs = abiDecode(PublicKernelPublicPreviousSimulatedJson.abi, _witnessMap); + + // Cast the inputs as the return type + return decodedInputs.return_value as PublicPublicPreviousReturnType; +} + /** * Executes the root rollup with the given inputs using the acvm. */ @@ -259,7 +379,6 @@ async function executeRootRollupWithACVM(input: RootRollupInputType): Promise { + const initialWitnessMap = abiEncode(BaseRollupJson.abi, input, null); + + // Execute the circuit on those initial witness values + // + // Decode the bytecode from base64 since the acvm does not know about base64 encoding + const decodedBytecode = Buffer.from(BaseRollupJson.bytecode, 'base64'); + // + // Execute the circuit + const _witnessMap = await executeCircuitWithBlackBoxSolver( + await getSolver(), + decodedBytecode, + initialWitnessMap, + () => { + throw Error('unexpected oracle during execution'); + }, + ); + + // Decode the witness map into two fields, the return values and the inputs + const decodedInputs: DecodedInputs = abiDecode(BaseRollupJson.abi, _witnessMap); + + // Cast the inputs as the return type + return decodedInputs.return_value as BaseRollupReturnType; +} diff --git a/yarn-project/noir-protocol-circuits/src/noir_test_gen.test.ts b/yarn-project/noir-protocol-circuits/src/noir_test_gen.test.ts index 602a8b32ad5..9449c90f6d8 100644 --- a/yarn-project/noir-protocol-circuits/src/noir_test_gen.test.ts +++ b/yarn-project/noir-protocol-circuits/src/noir_test_gen.test.ts @@ -1,16 +1,19 @@ import { AztecAddress, CONTRACT_TREE_HEIGHT, - CircuitsWasm, EthAddress, FunctionLeafPreimage, FunctionSelector, NOTE_HASH_TREE_HEIGHT, NewContractData, - computeFunctionTree, computeFunctionTreeData, } from '@aztec/circuits.js'; -import { computeContractLeaf, computeFunctionLeaf } from '@aztec/circuits.js/abis'; +import { + computeContractLeaf, + computeFunctionLeaf, + computeFunctionTree, + computeFunctionTreeRoot, +} from '@aztec/circuits.js/abis'; import { Fr } from '@aztec/foundation/fields'; import { Pedersen, StandardTree } from '@aztec/merkle-tree'; import { MerkleTreeId } from '@aztec/types'; @@ -19,48 +22,58 @@ import { default as levelup } from 'levelup'; import memdown from 'memdown'; describe('Data generation for noir tests', () => { - const selector = new FunctionSelector(1); - const vkHash = Fr.ZERO; - const acirHash = new Fr(12341234); - const contractAddress = AztecAddress.fromField(new Fr(12345)); - const portalContractAddress = EthAddress.fromField(new Fr(23456)); - - let functionLeaf: Fr; - let functionTreeRoot: Fr; - - let wasm: CircuitsWasm; - - beforeAll(async () => { - wasm = await CircuitsWasm.get(); - }); - - it('Computes function leaf', () => { - const functionLeafPreimage = new FunctionLeafPreimage(selector, false, true, vkHash, acirHash); - - functionLeaf = computeFunctionLeaf(functionLeafPreimage); - - expect(functionLeaf.toString()).toMatchSnapshot(); + const defaultContract = { + address: AztecAddress.fromField(new Fr(12345)), + portalContractAddress: EthAddress.fromField(new Fr(23456)), + functions: [ + new FunctionLeafPreimage(FunctionSelector.fromField(new Fr(1010101)), false, true, Fr.ZERO, new Fr(1111)), + new FunctionLeafPreimage(FunctionSelector.fromField(new Fr(2020202)), true, true, Fr.ZERO, new Fr(2222)), + new FunctionLeafPreimage(FunctionSelector.fromField(new Fr(3030303)), false, false, Fr.ZERO, new Fr(3333)), + new FunctionLeafPreimage(FunctionSelector.fromField(new Fr(4040404)), true, false, Fr.ZERO, new Fr(4444)), + ], + toString: () => 'defaultContract', + functionTreeRoot: Fr.ZERO, + }; + + const parentContract = { + address: AztecAddress.fromField(new Fr(667788)), + portalContractAddress: EthAddress.fromField(new Fr(990011)), + functions: [ + new FunctionLeafPreimage(FunctionSelector.fromField(new Fr(334455)), false, true, Fr.ZERO, new Fr(345345)), + ], + toString: () => 'parentContract', + functionTreeRoot: Fr.ZERO, + }; + + const contracts = [[defaultContract], [parentContract]]; + + test.each(contracts)('Computes function tree data for %s', contract => { + const leaves = contract.functions.map(f => computeFunctionLeaf(f)); + + const tree = computeFunctionTree(leaves); + contract.functionTreeRoot = computeFunctionTreeRoot(tree); + + leaves.forEach((leaf, index) => { + const functionTreeData = computeFunctionTreeData(tree, index); + expect(functionTreeData.root).toEqual(contract.functionTreeRoot); + expect({ + index, + leaf: leaf.toString(), + siblingPath: functionTreeData.siblingPath.map(fr => fr.toString()), + root: functionTreeData.root.toString(), + }).toMatchSnapshot(); + }); }); - it('Computes function tree data', () => { - const tree = computeFunctionTree(wasm, [functionLeaf]); - - const functionTreeData = computeFunctionTreeData(tree, 0); - - functionTreeRoot = functionTreeData.root; + test('Computes contract tree data for %s', async () => { + const leaves = contracts.map(([contract]) => { + const contractLeaf = computeContractLeaf( + new NewContractData(contract.address, contract.portalContractAddress, contract.functionTreeRoot), + ); + return contractLeaf.toBuffer(); + }); - expect({ - root: functionTreeData.root.toString(), - siblingPath: functionTreeData.siblingPath.map(fr => fr.toString()), - }).toMatchSnapshot(); - }); - - it('Computes the contract tree root', async () => { - const contractLeaf = computeContractLeaf( - new NewContractData(contractAddress, portalContractAddress, functionTreeRoot), - ); const db = levelup((memdown as any)()); - const tree = new StandardTree( db, new Pedersen(), @@ -68,11 +81,11 @@ describe('Data generation for noir tests', () => { CONTRACT_TREE_HEIGHT, ); - await tree.appendLeaves([contractLeaf.toBuffer()]); + await tree.appendLeaves(leaves); + const siblingPaths = await Promise.all(contracts.map((_, index) => tree.getSiblingPath(BigInt(index), true))); - const siblingPath = await tree.getSiblingPath(0n, true); expect({ - siblingPath: siblingPath.toFieldArray().map(field => field.toString()), + siblingPaths: siblingPaths.map(siblingPath => siblingPath.toFieldArray().map(field => field.toString())), root: Fr.fromBuffer(tree.getRoot(true)).toString(), }).toMatchSnapshot(); }); diff --git a/yarn-project/noir-protocol-circuits/src/scripts/generate_ts_from_abi.ts b/yarn-project/noir-protocol-circuits/src/scripts/generate_ts_from_abi.ts index aecf300569e..f189d592688 100644 --- a/yarn-project/noir-protocol-circuits/src/scripts/generate_ts_from_abi.ts +++ b/yarn-project/noir-protocol-circuits/src/scripts/generate_ts_from_abi.ts @@ -114,7 +114,9 @@ function generateStructInterfaces(type: ABIType, output: Set): string { if (type.kind === 'array' && type.type.kind === 'struct' && !output.has(getLastComponentOfPath(type.type.path))) { result += generateStructInterfaces(type.type, output); } - if (type.kind !== 'struct') return result; + if (type.kind !== 'struct') { + return result; + } // List of structs encountered while viewing this type that we need to generate // bindings for. @@ -195,6 +197,8 @@ const circuits = [ 'private_kernel_init', 'private_kernel_inner', 'private_kernel_ordering', + 'public_kernel_private_previous', + 'public_kernel_public_previous', 'rollup_base', 'rollup_merge', 'rollup_root', diff --git a/yarn-project/noir-protocol-circuits/src/target/private_kernel_init.json b/yarn-project/noir-protocol-circuits/src/target/private_kernel_init.json index 152d1deb370..ad6c3d5c07b 100644 --- a/yarn-project/noir-protocol-circuits/src/target/private_kernel_init.json +++ b/yarn-project/noir-protocol-circuits/src/target/private_kernel_init.json @@ -1 +1 @@ -{"noir_version":"0.18.0+6ca33a223ccce6a295414a3ce078180e8a22b68c","hash":13424875526269173769,"backend":"acvm-backend-barretenberg","abi":{"parameters":[{"name":"input","type":{"kind":"struct","path":"private_kernel_lib::private_kernel_init::PrivateKernelInputsInit","fields":[{"name":"tx_request","type":{"kind":"struct","path":"types::transaction::request::TxRequest","fields":[{"name":"origin","type":{"kind":"struct","path":"types::address::Address","fields":[{"name":"inner","type":{"kind":"field"}}]}},{"name":"args_hash","type":{"kind":"field"}},{"name":"tx_context","type":{"kind":"struct","path":"types::transaction::context::TxContext","fields":[{"name":"is_fee_payment_tx","type":{"kind":"boolean"}},{"name":"is_rebate_payment_tx","type":{"kind":"boolean"}},{"name":"is_contract_deployment_tx","type":{"kind":"boolean"}},{"name":"contract_deployment_data","type":{"kind":"struct","path":"types::contrakt::deployment_data::ContractDeploymentData","fields":[{"name":"deployer_public_key","type":{"kind":"struct","path":"types::point::Point","fields":[{"name":"x","type":{"kind":"field"}},{"name":"y","type":{"kind":"field"}}]}},{"name":"constructor_vk_hash","type":{"kind":"field"}},{"name":"function_tree_root","type":{"kind":"field"}},{"name":"contract_address_salt","type":{"kind":"field"}},{"name":"portal_contract_address","type":{"kind":"struct","path":"types::address::EthAddress","fields":[{"name":"inner","type":{"kind":"field"}}]}}]}},{"name":"chain_id","type":{"kind":"field"}},{"name":"version","type":{"kind":"field"}}]}},{"name":"function_data","type":{"kind":"struct","path":"types::abis::function_data::FunctionData","fields":[{"name":"selector","type":{"kind":"struct","path":"types::abis::function_selector::FunctionSelector","fields":[{"name":"inner","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"is_internal","type":{"kind":"boolean"}},{"name":"is_private","type":{"kind":"boolean"}},{"name":"is_constructor","type":{"kind":"boolean"}}]}}]}},{"name":"private_call","type":{"kind":"struct","path":"types::abis::private_kernel::private_call_data::PrivateCallData","fields":[{"name":"call_stack_item","type":{"kind":"struct","path":"types::abis::call_stack_item::PrivateCallStackItem","fields":[{"name":"contract_address","type":{"kind":"struct","path":"types::address::Address","fields":[{"name":"inner","type":{"kind":"field"}}]}},{"name":"public_inputs","type":{"kind":"struct","path":"types::abis::private_circuit_public_inputs::PrivateCircuitPublicInputs","fields":[{"name":"call_context","type":{"kind":"struct","path":"types::abis::call_context::CallContext","fields":[{"name":"msg_sender","type":{"kind":"struct","path":"types::address::Address","fields":[{"name":"inner","type":{"kind":"field"}}]}},{"name":"storage_contract_address","type":{"kind":"struct","path":"types::address::Address","fields":[{"name":"inner","type":{"kind":"field"}}]}},{"name":"portal_contract_address","type":{"kind":"struct","path":"types::address::EthAddress","fields":[{"name":"inner","type":{"kind":"field"}}]}},{"name":"function_selector","type":{"kind":"struct","path":"types::abis::function_selector::FunctionSelector","fields":[{"name":"inner","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"is_delegate_call","type":{"kind":"boolean"}},{"name":"is_static_call","type":{"kind":"boolean"}},{"name":"is_contract_deployment","type":{"kind":"boolean"}}]}},{"name":"args_hash","type":{"kind":"field"}},{"name":"return_values","type":{"kind":"array","length":4,"type":{"kind":"field"}}},{"name":"read_requests","type":{"kind":"array","length":32,"type":{"kind":"field"}}},{"name":"pending_read_requests","type":{"kind":"array","length":32,"type":{"kind":"field"}}},{"name":"new_commitments","type":{"kind":"array","length":16,"type":{"kind":"field"}}},{"name":"new_nullifiers","type":{"kind":"array","length":16,"type":{"kind":"field"}}},{"name":"nullified_commitments","type":{"kind":"array","length":16,"type":{"kind":"field"}}},{"name":"private_call_stack","type":{"kind":"array","length":4,"type":{"kind":"field"}}},{"name":"public_call_stack","type":{"kind":"array","length":4,"type":{"kind":"field"}}},{"name":"new_l2_to_l1_msgs","type":{"kind":"array","length":2,"type":{"kind":"field"}}},{"name":"encrypted_logs_hash","type":{"kind":"array","length":2,"type":{"kind":"field"}}},{"name":"unencrypted_logs_hash","type":{"kind":"array","length":2,"type":{"kind":"field"}}},{"name":"encrypted_log_preimages_length","type":{"kind":"field"}},{"name":"unencrypted_log_preimages_length","type":{"kind":"field"}},{"name":"historical_block_data","type":{"kind":"struct","path":"types::abis::historical_block_data::HistoricalBlockData","fields":[{"name":"blocks_tree_root","type":{"kind":"field"}},{"name":"block","type":{"kind":"struct","path":"types::block::Block","fields":[{"name":"note_hash_tree_root","type":{"kind":"field"}},{"name":"nullifier_tree_root","type":{"kind":"field"}},{"name":"contract_tree_root","type":{"kind":"field"}},{"nam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"} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/target/private_kernel_init_simulated.json b/yarn-project/noir-protocol-circuits/src/target/private_kernel_init_simulated.json index 862fdb09f68..976c734d092 100644 --- a/yarn-project/noir-protocol-circuits/src/target/private_kernel_init_simulated.json +++ b/yarn-project/noir-protocol-circuits/src/target/private_kernel_init_simulated.json @@ -1 +1 @@ -{"noir_version":"0.18.0+6ca33a223ccce6a295414a3ce078180e8a22b68c","hash":15701140129613107908,"backend":"acvm-backend-barretenberg","abi":{"parameters":[{"name":"input","type":{"kind":"struct","path":"private_kernel_lib::private_kernel_init::PrivateKernelInputsInit","fields":[{"name":"tx_request","type":{"kind":"struct","path":"types::transaction::request::TxRequest","fields":[{"name":"origin","type":{"kind":"struct","path":"types::address::Address","fields":[{"name":"inner","type":{"kind":"field"}}]}},{"name":"args_hash","type":{"kind":"field"}},{"name":"tx_context","type":{"kind":"struct","path":"types::transaction::context::TxContext","fields":[{"name":"is_fee_payment_tx","type":{"kind":"boolean"}},{"name":"is_rebate_payment_tx","type":{"kind":"boolean"}},{"name":"is_contract_deployment_tx","type":{"kind":"boolean"}},{"name":"contract_deployment_data","type":{"kind":"struct","path":"types::contrakt::deployment_data::ContractDeploymentData","fields":[{"name":"deployer_public_key","type":{"kind":"struct","path":"types::point::Point","fields":[{"name":"x","type":{"kind":"field"}},{"name":"y","type":{"kind":"field"}}]}},{"name":"constructor_vk_hash","type":{"kind":"field"}},{"name":"function_tree_root","type":{"kind":"field"}},{"name":"contract_address_salt","type":{"kind":"field"}},{"name":"portal_contract_address","type":{"kind":"struct","path":"types::address::EthAddress","fields":[{"name":"inner","type":{"kind":"field"}}]}}]}},{"name":"chain_id","type":{"kind":"field"}},{"name":"version","type":{"kind":"field"}}]}},{"name":"function_data","type":{"kind":"struct","path":"types::abis::function_data::FunctionData","fields":[{"name":"selector","type":{"kind":"struct","path":"types::abis::function_selector::FunctionSelector","fields":[{"name":"inner","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"is_internal","type":{"kind":"boolean"}},{"name":"is_private","type":{"kind":"boolean"}},{"name":"is_constructor","type":{"kind":"boolean"}}]}}]}},{"name":"private_call","type":{"kind":"struct","path":"types::abis::private_kernel::private_call_data::PrivateCallData","fields":[{"name":"call_stack_item","type":{"kind":"struct","path":"types::abis::call_stack_item::PrivateCallStackItem","fields":[{"name":"contract_address","type":{"kind":"struct","path":"types::address::Address","fields":[{"name":"inner","type":{"kind":"field"}}]}},{"name":"public_inputs","type":{"kind":"struct","path":"types::abis::private_circuit_public_inputs::PrivateCircuitPublicInputs","fields":[{"name":"call_context","type":{"kind":"struct","path":"types::abis::call_context::CallContext","fields":[{"name":"msg_sender","type":{"kind":"struct","path":"types::address::Address","fields":[{"name":"inner","type":{"kind":"field"}}]}},{"name":"storage_contract_address","type":{"kind":"struct","path":"types::address::Address","fields":[{"name":"inner","type":{"kind":"field"}}]}},{"name":"portal_contract_address","type":{"kind":"struct","path":"types::address::EthAddress","fields":[{"name":"inner","type":{"kind":"field"}}]}},{"name":"function_selector","type":{"kind":"struct","path":"types::abis::function_selector::FunctionSelector","fields":[{"name":"inner","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"is_delegate_call","type":{"kind":"boolean"}},{"name":"is_static_call","type":{"kind":"boolean"}},{"name":"is_contract_deployment","type":{"kind":"boolean"}}]}},{"name":"args_hash","type":{"kind":"field"}},{"name":"return_values","type":{"kind":"array","length":4,"type":{"kind":"field"}}},{"name":"read_requests","type":{"kind":"array","length":32,"type":{"kind":"field"}}},{"name":"pending_read_requests","type":{"kind":"array","length":32,"type":{"kind":"field"}}},{"name":"new_commitments","type":{"kind":"array","length":16,"type":{"kind":"field"}}},{"name":"new_nullifiers","type":{"kind":"array","length":16,"type":{"kind":"field"}}},{"name":"nullified_commitments","type":{"kind":"array","length":16,"type":{"kind":"field"}}},{"name":"private_call_stack","type":{"kind":"array","length":4,"type":{"kind":"field"}}},{"name":"public_call_stack","type":{"kind":"array","length":4,"type":{"kind":"field"}}},{"name":"new_l2_to_l1_msgs","type":{"kind":"array","length":2,"type":{"kind":"field"}}},{"name":"encrypted_logs_hash","type":{"kind":"array","length":2,"type":{"kind":"field"}}},{"name":"unencrypted_logs_hash","type":{"kind":"array","length":2,"type":{"kind":"field"}}},{"name":"encrypted_log_preimages_length","type":{"kind":"field"}},{"name":"unencrypted_log_preimages_length","type":{"kind":"field"}},{"name":"historical_block_data","type":{"kind":"struct","path":"types::abis::historical_block_data::HistoricalBlockData","fields":[{"name":"blocks_tree_root","type":{"kind":"field"}},{"name":"block","type":{"kind":"struct","path":"types::block::Block","fields":[{"name":"note_hash_tree_root","type":{"kind":"field"}},{"name":"nullifier_tree_root","type":{"kind":"field"}},{"name":"contract_tree_root","type":{"kind":"field"}},{"nam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\ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/target/private_kernel_inner.json b/yarn-project/noir-protocol-circuits/src/target/private_kernel_inner.json index 43dd8e161ba..1e283702691 100644 --- a/yarn-project/noir-protocol-circuits/src/target/private_kernel_inner.json +++ b/yarn-project/noir-protocol-circuits/src/target/private_kernel_inner.json @@ -1 +1 @@ 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"} 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\ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/target/private_kernel_inner_simulated.json b/yarn-project/noir-protocol-circuits/src/target/private_kernel_inner_simulated.json index 33367ae8cb1..6ffbbf2544b 100644 --- a/yarn-project/noir-protocol-circuits/src/target/private_kernel_inner_simulated.json +++ b/yarn-project/noir-protocol-circuits/src/target/private_kernel_inner_simulated.json @@ -1 +1 @@ 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"} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/target/private_kernel_ordering.json b/yarn-project/noir-protocol-circuits/src/target/private_kernel_ordering.json index 5f09de06681..31bd07e7dc0 100644 --- a/yarn-project/noir-protocol-circuits/src/target/private_kernel_ordering.json +++ b/yarn-project/noir-protocol-circuits/src/target/private_kernel_ordering.json @@ -1 +1 @@ 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"} 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\ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/target/private_kernel_ordering_simulated.json b/yarn-project/noir-protocol-circuits/src/target/private_kernel_ordering_simulated.json index fc29da1d89a..32f984a728d 100644 --- a/yarn-project/noir-protocol-circuits/src/target/private_kernel_ordering_simulated.json +++ b/yarn-project/noir-protocol-circuits/src/target/private_kernel_ordering_simulated.json @@ -1 +1 @@ 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"} 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\ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/target/public_kernel_private_previous.json b/yarn-project/noir-protocol-circuits/src/target/public_kernel_private_previous.json new file mode 100644 index 00000000000..a5387aa67c7 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/target/public_kernel_private_previous.json @@ -0,0 +1 @@ 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"} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/target/public_kernel_private_previous_simulated.json b/yarn-project/noir-protocol-circuits/src/target/public_kernel_private_previous_simulated.json new file mode 100644 index 00000000000..bf1a4d7fb60 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/target/public_kernel_private_previous_simulated.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/target/public_kernel_public_previous.json b/yarn-project/noir-protocol-circuits/src/target/public_kernel_public_previous.json new file mode 100644 index 00000000000..5484af7b233 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/target/public_kernel_public_previous.json @@ -0,0 +1 @@ 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"} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/target/public_kernel_public_previous_simulated.json b/yarn-project/noir-protocol-circuits/src/target/public_kernel_public_previous_simulated.json new file mode 100644 index 00000000000..b695ad55b59 --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/target/public_kernel_public_previous_simulated.json @@ -0,0 +1 @@ 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"} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/target/rollup_base.json b/yarn-project/noir-protocol-circuits/src/target/rollup_base.json index e131b0cdbbd..98da7442ee8 100644 --- a/yarn-project/noir-protocol-circuits/src/target/rollup_base.json +++ b/yarn-project/noir-protocol-circuits/src/target/rollup_base.json @@ -1 +1 @@ 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\ No newline at end of file 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\ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/target/rollup_merge.json b/yarn-project/noir-protocol-circuits/src/target/rollup_merge.json index 44a301e984d..64ff74b9957 100644 --- a/yarn-project/noir-protocol-circuits/src/target/rollup_merge.json +++ b/yarn-project/noir-protocol-circuits/src/target/rollup_merge.json @@ -1 +1 @@ -{"noir_version":"0.18.0+6ca33a223ccce6a295414a3ce078180e8a22b68c","hash":12726996011058254023,"backend":"acvm-backend-barretenberg","abi":{"parameters":[{"name":"inputs","type":{"kind":"struct","path":"rollup_lib::merge::merge_rollup_inputs::MergeRollupInputs","fields":[{"name":"previous_rollup_data","type":{"kind":"array","length":2,"type":{"kind":"struct","path":"rollup_lib::abis::previous_rollup_data::PreviousRollupData","fields":[{"name":"base_or_merge_rollup_public_inputs","type":{"kind":"struct","path":"rollup_lib::abis::base_or_merge_rollup_public_inputs::BaseOrMergeRollupPublicInputs","fields":[{"name":"rollup_type","type":{"kind":"integer","sign":"unsigned","width":32}},{"name":"rollup_subtree_height","type":{"kind":"field"}},{"name":"end_aggregation_object","type":{"kind":"struct","path":"types::mocked::AggregationObject","fields":[]}},{"name":"constants","type":{"kind":"struct","path":"rollup_lib::abis::constant_rollup_data::ConstantRollupData","fields":[{"name":"start_historic_blocks_tree_roots_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"private_kernel_vk_tree_root","type":{"kind":"field"}},{"name":"public_kernel_vk_tree_root","type":{"kind":"field"}},{"name":"base_rollup_vk_hash","type":{"kind":"field"}},{"name":"merge_rollup_vk_hash","type":{"kind":"field"}},{"name":"global_variables","type":{"kind":"struct","path":"rollup_lib::abis::global_variables::GlobalVariables","fields":[{"name":"chain_id","type":{"kind":"field"}},{"name":"version","type":{"kind":"field"}},{"name":"block_number","type":{"kind":"field"}},{"name":"timestamp","type":{"kind":"field"}}]}}]}},{"name":"start_note_hash_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"end_note_hash_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"start_nullifier_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"end_nullifier_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"start_contract_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"end_contract_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"start_public_data_tree_root","type":{"kind":"field"}},{"name":"end_public_data_tree_root","type":{"kind":"field"}},{"name":"calldata_hash","type":{"kind":"array","length":2,"type":{"kind":"field"}}}]}},{"name":"proof","type":{"kind":"struct","path":"types::mocked::Proof","fields":[]}},{"name":"vk","type":{"kind":"struct","path":"types::mocked::VerificationKey","fields":[]}},{"name":"vk_index","type":{"kind":"integer","sign":"unsigned","width":32}},{"name":"vk_sibling_path","type":{"kind":"struct","path":"types::abis::membership_witness::VKMembershipWitness","fields":[{"name":"leaf_index","type":{"kind":"field"}},{"name":"sibling_path","type":{"kind":"array","length":8,"type":{"kind":"field"}}}]}}]}}}]},"visibility":"private"}],"param_witnesses":{"inputs":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76]},"return_type":{"kind":"struct","path":"rollup_lib::abis::base_or_merge_rollup_public_inputs::BaseOrMergeRollupPublicInputs","fields":[{"name":"rollup_type","type":{"kind":"integer","sign":"unsigned","width":32}},{"name":"rollup_subtree_height","type":{"kind":"field"}},{"name":"end_aggregation_object","type":{"kind":"struct","path":"types::mocked::AggregationObject","fields":[]}},{"name":"constants","type":{"kind":"struct","path":"rollup_lib::abis::constant_rollup_data::ConstantRollupData","fields":[{"name":"start_historic_blocks_tree_roots_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"private_kernel_vk_tree_root","type":{"kind":"field"}},{"name":"public_kernel_vk_tree_root","type":{"kind":"field"}},{"name":"base_rollup_vk_hash","type":{"kind":"field"}},{"name":"merge_rollup_vk_hash","type":{"kind":"field"}},{"name":"global_variables","type":{"kind":"struct","path":"rollup_lib::abis::global_variables::GlobalVariables","fields":[{"name":"chain_id","type":{"kind":"field"}},{"name":"version","type":{"kind":"field"}},{"name":"block_number","type":{"kind":"field"}},{"name":"timestamp","type":{"kind":"field"}}]}}]}},{"name":"start_note_hash_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"end_note_hash_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"start_nullifier_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"end_nullifier_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"start_contract_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"end_contract_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"start_public_data_tree_root","type":{"kind":"field"}},{"name":"end_public_data_tree_root","type":{"kind":"field"}},{"name":"calldata_hash","type":{"kind":"array","length":2,"type":{"kind":"field"}}}]},"return_witnesses":[150,631,3,4,5,6,7,8,9,10,11,12,13,14,53,54,17,18,57,58,21,22,61,62,25,64,632,633]},"bytecode":"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"} \ No newline at end of file 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"} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/target/rollup_root.json b/yarn-project/noir-protocol-circuits/src/target/rollup_root.json index efd82c3cb5e..b0c3895b6ac 100644 --- a/yarn-project/noir-protocol-circuits/src/target/rollup_root.json +++ b/yarn-project/noir-protocol-circuits/src/target/rollup_root.json @@ -1 +1 @@ -{"noir_version":"0.18.0+6ca33a223ccce6a295414a3ce078180e8a22b68c","hash":9040584475036160404,"backend":"acvm-backend-barretenberg","abi":{"parameters":[{"name":"inputs","type":{"kind":"struct","path":"rollup_lib::root::root_rollup_inputs::RootRollupInputs","fields":[{"name":"previous_rollup_data","type":{"kind":"array","length":2,"type":{"kind":"struct","path":"rollup_lib::abis::previous_rollup_data::PreviousRollupData","fields":[{"name":"base_or_merge_rollup_public_inputs","type":{"kind":"struct","path":"rollup_lib::abis::base_or_merge_rollup_public_inputs::BaseOrMergeRollupPublicInputs","fields":[{"name":"rollup_type","type":{"kind":"integer","sign":"unsigned","width":32}},{"name":"rollup_subtree_height","type":{"kind":"field"}},{"name":"end_aggregation_object","type":{"kind":"struct","path":"types::mocked::AggregationObject","fields":[]}},{"name":"constants","type":{"kind":"struct","path":"rollup_lib::abis::constant_rollup_data::ConstantRollupData","fields":[{"name":"start_historic_blocks_tree_roots_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"private_kernel_vk_tree_root","type":{"kind":"field"}},{"name":"public_kernel_vk_tree_root","type":{"kind":"field"}},{"name":"base_rollup_vk_hash","type":{"kind":"field"}},{"name":"merge_rollup_vk_hash","type":{"kind":"field"}},{"name":"global_variables","type":{"kind":"struct","path":"rollup_lib::abis::global_variables::GlobalVariables","fields":[{"name":"chain_id","type":{"kind":"field"}},{"name":"version","type":{"kind":"field"}},{"name":"block_number","type":{"kind":"field"}},{"name":"timestamp","type":{"kind":"field"}}]}}]}},{"name":"start_note_hash_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"end_note_hash_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"start_nullifier_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"end_nullifier_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"start_contract_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"end_contract_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"start_public_data_tree_root","type":{"kind":"field"}},{"name":"end_public_data_tree_root","type":{"kind":"field"}},{"name":"calldata_hash","type":{"kind":"array","length":2,"type":{"kind":"field"}}}]}},{"name":"proof","type":{"kind":"struct","path":"types::mocked::Proof","fields":[]}},{"name":"vk","type":{"kind":"struct","path":"types::mocked::VerificationKey","fields":[]}},{"name":"vk_index","type":{"kind":"integer","sign":"unsigned","width":32}},{"name":"vk_sibling_path","type":{"kind":"struct","path":"types::abis::membership_witness::VKMembershipWitness","fields":[{"name":"leaf_index","type":{"kind":"field"}},{"name":"sibling_path","type":{"kind":"array","length":8,"type":{"kind":"field"}}}]}}]}}},{"name":"new_l1_to_l2_messages","type":{"kind":"array","length":16,"type":{"kind":"field"}}},{"name":"new_l1_to_l2_messages_tree_root_sibling_path","type":{"kind":"array","length":12,"type":{"kind":"field"}}},{"name":"start_l1_to_l2_messages_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"start_historic_blocks_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"in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},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"end_tree_of_historic_l1_to_l2_messages_tree_roots_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"start_historic_blocks_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"end_historic_blocks_tree_snapshot","type":{"kind":"struct","path":"rollup_lib::abis::append_only_tree_snapshot::AppendOnlyTreeSnapshot","fields":[{"name":"root","type":{"kind":"field"}},{"name":"next_available_leaf_index","type":{"kind":"integer","sign":"unsigned","width":32}}]}},{"name":"calldata_hash","type":{"kind":"array","length":2,"type":{"kind":"field"}}},{"name":"l1_to_l2_messages_hash","type":{"kind":"array","length":2,"type":{"kind":"field"}}}]},"return_witnesses":[9,10,11,12,13,14,53,54,17,18,57,58,21,22,61,62,25,64,210,210,210,210,210,210,210,210,105,106,3826,367,210,210,210,210,107,108,3827,593,3828,3829,3830,3831]},"bytecode":"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"} \ No newline at end of file diff --git a/yarn-project/noir-protocol-circuits/src/type_conversion.ts b/yarn-project/noir-protocol-circuits/src/type_conversion.ts index 9a519698489..0ae1b87bc33 100644 --- a/yarn-project/noir-protocol-circuits/src/type_conversion.ts +++ b/yarn-project/noir-protocol-circuits/src/type_conversion.ts @@ -3,17 +3,23 @@ import { AppendOnlyTreeSnapshot, AztecAddress, BaseOrMergeRollupPublicInputs, + BaseRollupInputs, CallContext, + CallRequest, + CallerContext, CombinedAccumulatedData, CombinedConstantData, ConstantRollupData, ContractDeploymentData, + ContractStorageRead, + ContractStorageUpdateRequest, EthAddress, FinalAccumulatedData, Fr, FunctionData, FunctionSelector, GlobalVariables, + HISTORIC_BLOCKS_TREE_HEIGHT, HistoricBlockData, KernelCircuitPublicInputs, KernelCircuitPublicInputsFinal, @@ -30,7 +36,9 @@ import { MAX_READ_REQUESTS_PER_TX, MembershipWitness, MergeRollupInputs, + NULLIFIER_TREE_HEIGHT, NewContractData, + NullifierLeafPreimage, OptionallyRevealedData, Point, PreviousKernelData, @@ -41,8 +49,12 @@ import { PrivateKernelInputsInit, PrivateKernelInputsInner, PrivateKernelInputsOrdering, + PublicCallData, + PublicCallStackItem, + PublicCircuitPublicInputs, PublicDataRead, PublicDataUpdateRequest, + PublicKernelInputs, ReadRequestMembershipWitness, RootRollupInputs, RootRollupPublicInputs, @@ -53,6 +65,8 @@ import { Tuple } from '@aztec/foundation/serialize'; import { CallContext as CallContextNoir, + CallRequest as CallRequestNoir, + CallerContext as CallerContextNoir, CombinedAccumulatedData as CombinedAccumulatedDataNoir, CombinedConstantData as CombinedConstantDataNoir, ContractDeploymentData as ContractDeploymentDataNoir, @@ -88,6 +102,20 @@ import { KernelCircuitPublicInputsFinal as KernelCircuitPublicInputsFinalNoir, PrivateKernelInputsOrdering as PrivateKernelInputsOrderingNoir, } from './types/private_kernel_ordering_types.js'; +import { + PublicCallData as PublicCallDataNoir, + PublicCallStackItem as PublicCallStackItemNoir, + PublicCircuitPublicInputs as PublicCircuitPublicInputsNoir, + PublicKernelPrivatePreviousInputs as PublicKernelInputsNoir, + StorageRead as StorageReadNoir, + StorageUpdateRequest as StorageUpdateRequestNoir, +} from './types/public_kernel_private_previous_types.js'; +import { + BaseRollupInputs as BaseRollupInputsNoir, + HistoricBlocksTreeRootMembershipWitness as HistoricBlocksTreeRootMembershipWitnessNoir, + NullifierLeafPreimage as NullifierLeafPreimageNoir, + NullifierMembershipWitness as NullifierMembershipWitnessNoir, +} from './types/rollup_base_types.js'; import { MergeRollupInputs as MergeRollupInputsNoir } from './types/rollup_merge_types.js'; import { AppendOnlyTreeSnapshot as AppendOnlyTreeSnapshotNoir, @@ -313,6 +341,23 @@ export function mapTxRequestToNoir(txRequest: TxRequest): TxRequestNoir { }; } +/** + * Maps a call context to a noir call context. + * @param callContext - The call context. + * @returns The noir call context. + */ +export function mapCallContextFromNoir(callContext: CallContextNoir): CallContext { + return new CallContext( + mapAztecAddressFromNoir(callContext.msg_sender), + mapAztecAddressFromNoir(callContext.storage_contract_address), + mapEthAddressFromNoir(callContext.portal_contract_address), + mapFunctionSelectorFromNoir(callContext.function_selector), + callContext.is_delegate_call, + callContext.is_static_call, + callContext.is_contract_deployment, + ); +} + /** * Maps a call context to a noir call context. * @param callContext - The call context. @@ -330,6 +375,56 @@ export function mapCallContextToNoir(callContext: CallContext): CallContextNoir }; } +/** + * Maps a caller context to a noir caller context. + * @param callContext - The caller context. + * @returns The noir caller context. + */ +export function mapCallerContextFromNoir(callerContext: CallerContextNoir): CallerContext { + return new CallerContext( + mapAztecAddressFromNoir(callerContext.msg_sender), + mapAztecAddressFromNoir(callerContext.storage_contract_address), + ); +} + +/** + * Maps a caller context to a noir caller context. + * @param callContext - The caller context. + * @returns The noir caller context. + */ +export function mapCallerContextToNoir(callerContext: CallerContext): CallerContextNoir { + return { + msg_sender: mapAztecAddressToNoir(callerContext.msgSender), + storage_contract_address: mapAztecAddressToNoir(callerContext.storageContractAddress), + }; +} + +/** + * Maps a noit call request to a call request. + * @param callRequest - The noir call request. + * @returns The call request. + */ +export function mapCallRequestFromNoir(callRequest: CallRequestNoir): CallRequest { + return new CallRequest( + mapFieldFromNoir(callRequest.hash), + mapAztecAddressFromNoir(callRequest.caller_contract_address), + mapCallerContextFromNoir(callRequest.caller_context), + ); +} + +/** + * Maps a call request to a noir call request. + * @param privateCallStackItem - The call stack item. + * @returns The noir call stack item. + */ +export function mapCallRequestToNoir(callRequest: CallRequest): CallRequestNoir { + return { + hash: mapFieldToNoir(callRequest.hash), + caller_contract_address: mapAztecAddressToNoir(callRequest.callerContractAddress), + caller_context: mapCallerContextToNoir(callRequest.callerContext), + }; +} + /** * Maps a historical block data to a noir historical block data. * @param historicalBlockData - The historical block data. @@ -342,7 +437,7 @@ export function mapHistoricalBlockDataToNoir(historicalBlockData: HistoricBlockD note_hash_tree_root: mapFieldToNoir(historicalBlockData.noteHashTreeRoot), nullifier_tree_root: mapFieldToNoir(historicalBlockData.nullifierTreeRoot), contract_tree_root: mapFieldToNoir(historicalBlockData.contractTreeRoot), - l1_to_l2_data_tree_root: mapFieldToNoir(historicalBlockData.l1ToL2MessagesTreeRoot), + l1_to_l2_messages_tree_root: mapFieldToNoir(historicalBlockData.l1ToL2MessagesTreeRoot), public_data_tree_root: mapFieldToNoir(historicalBlockData.publicDataTreeRoot), global_variables_hash: mapFieldToNoir(historicalBlockData.globalVariablesHash), }, @@ -360,7 +455,7 @@ export function mapHistoricalBlockDataFromNoir(historicalBlockData: HistoricalBl mapFieldFromNoir(historicalBlockData.block.note_hash_tree_root), mapFieldFromNoir(historicalBlockData.block.nullifier_tree_root), mapFieldFromNoir(historicalBlockData.block.contract_tree_root), - mapFieldFromNoir(historicalBlockData.block.l1_to_l2_data_tree_root), + mapFieldFromNoir(historicalBlockData.block.l1_to_l2_messages_tree_root), mapFieldFromNoir(historicalBlockData.blocks_tree_root), mapFieldFromNoir(historicalBlockData.private_kernel_vk_tree_root), mapFieldFromNoir(historicalBlockData.block.public_data_tree_root), @@ -391,11 +486,13 @@ export function mapPrivateCircuitPublicInputsToNoir( NoirField, 16 >, - private_call_stack: privateCircuitPublicInputs.privateCallStack.map(mapFieldToNoir) as FixedLengthArray< + private_call_stack_hashes: privateCircuitPublicInputs.privateCallStackHashes.map( + mapFieldToNoir, + ) as FixedLengthArray, + public_call_stack_hashes: privateCircuitPublicInputs.publicCallStackHashes.map(mapFieldToNoir) as FixedLengthArray< NoirField, 4 >, - public_call_stack: privateCircuitPublicInputs.publicCallStack.map(mapFieldToNoir) as FixedLengthArray, new_l2_to_l1_msgs: privateCircuitPublicInputs.newL2ToL1Msgs.map(mapFieldToNoir) as FixedLengthArray, encrypted_logs_hash: privateCircuitPublicInputs.encryptedLogsHash.map(mapFieldToNoir) as FixedLengthArray< NoirField, @@ -480,9 +577,14 @@ export function mapReadRequestMembershipWitnessToNoir( export function mapPrivateCallDataToNoir(privateCallData: PrivateCallData): PrivateCallDataNoir { return { call_stack_item: mapPrivateCallStackItemToNoir(privateCallData.callStackItem), - private_call_stack_preimages: privateCallData.privateCallStackPreimages.map( - mapPrivateCallStackItemToNoir, - ) as FixedLengthArray, + private_call_stack: privateCallData.privateCallStack.map(mapCallRequestToNoir) as FixedLengthArray< + CallRequestNoir, + 4 + >, + public_call_stack: privateCallData.publicCallStack.map(mapCallRequestToNoir) as FixedLengthArray< + CallRequestNoir, + 4 + >, proof: {}, vk: {}, function_leaf_membership_witness: mapFunctionLeafMembershipWitnessToNoir( @@ -654,9 +756,13 @@ export function mapCombinedAccumulatedDataFromNoir( mapTupleFromNoir( combinedAccumulatedData.private_call_stack, MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, - mapFieldFromNoir, + mapCallRequestFromNoir, + ), + mapTupleFromNoir( + combinedAccumulatedData.public_call_stack, + MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, + mapCallRequestFromNoir, ), - mapTupleFromNoir(combinedAccumulatedData.public_call_stack, MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, mapFieldFromNoir), mapTupleFromNoir(combinedAccumulatedData.new_l2_to_l1_msgs, MAX_NEW_L2_TO_L1_MSGS_PER_TX, mapFieldFromNoir), mapTupleFromNoir(combinedAccumulatedData.encrypted_logs_hash, 2, mapFieldFromNoir), mapTupleFromNoir(combinedAccumulatedData.unencrypted_logs_hash, 2, mapFieldFromNoir), @@ -693,8 +799,16 @@ export function mapFinalAccumulatedDataFromNoir(finalAccumulatedData: FinalAccum mapTupleFromNoir(finalAccumulatedData.new_commitments, MAX_NEW_COMMITMENTS_PER_TX, mapFieldFromNoir), mapTupleFromNoir(finalAccumulatedData.new_nullifiers, MAX_NEW_NULLIFIERS_PER_TX, mapFieldFromNoir), mapTupleFromNoir(finalAccumulatedData.nullified_commitments, MAX_NEW_NULLIFIERS_PER_TX, mapFieldFromNoir), - mapTupleFromNoir(finalAccumulatedData.private_call_stack, MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, mapFieldFromNoir), - mapTupleFromNoir(finalAccumulatedData.public_call_stack, MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, mapFieldFromNoir), + mapTupleFromNoir( + finalAccumulatedData.private_call_stack, + MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX, + mapCallRequestFromNoir, + ), + mapTupleFromNoir( + finalAccumulatedData.public_call_stack, + MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, + mapCallRequestFromNoir, + ), mapTupleFromNoir(finalAccumulatedData.new_l2_to_l1_msgs, MAX_NEW_L2_TO_L1_MSGS_PER_TX, mapFieldFromNoir), mapTupleFromNoir(finalAccumulatedData.encrypted_logs_hash, 2, mapFieldFromNoir), mapTupleFromNoir(finalAccumulatedData.unencrypted_logs_hash, 2, mapFieldFromNoir), @@ -730,8 +844,14 @@ export function mapCombinedAccumulatedDataToNoir( NoirField, 64 >, - private_call_stack: combinedAccumulatedData.privateCallStack.map(mapFieldToNoir) as FixedLengthArray, - public_call_stack: combinedAccumulatedData.publicCallStack.map(mapFieldToNoir) as FixedLengthArray, + private_call_stack: combinedAccumulatedData.privateCallStack.map(mapCallRequestToNoir) as FixedLengthArray< + CallRequestNoir, + 8 + >, + public_call_stack: combinedAccumulatedData.publicCallStack.map(mapCallRequestToNoir) as FixedLengthArray< + CallRequestNoir, + 8 + >, new_l2_to_l1_msgs: combinedAccumulatedData.newL2ToL1Msgs.map(mapFieldToNoir) as FixedLengthArray, encrypted_logs_hash: combinedAccumulatedData.encryptedLogsHash.map(mapFieldToNoir) as FixedLengthArray< NoirField, @@ -887,6 +1007,20 @@ export function mapPrivateKernelInputsOrderingToNoir( }; } +/** + * Maps a private kernel inputs final to noir. + * @param storageUpdateRequest - The storage update request. + * @returns The noir storage update request. + */ +export function mapStorageUpdateRequestToNoir( + storageUpdateRequest: ContractStorageUpdateRequest, +): StorageUpdateRequestNoir { + return { + storage_slot: mapFieldToNoir(storageUpdateRequest.storageSlot), + old_value: mapFieldToNoir(storageUpdateRequest.oldValue), + new_value: mapFieldToNoir(storageUpdateRequest.newValue), + }; +} /** * Maps global variables to the noir type. * @param globalVariables - The global variables. @@ -901,6 +1035,17 @@ export function mapGlobalVariablesToNoir(globalVariables: GlobalVariables): Glob }; } +/** + * Maps a storage read to noir. + * @param storageRead - The storage read. + * @returns The noir storage read. + */ +export function mapStorageReadToNoir(storageRead: ContractStorageRead): StorageReadNoir { + return { + storage_slot: mapFieldToNoir(storageRead.storageSlot), + current_value: mapFieldToNoir(storageRead.currentValue), + }; +} /** * Maps global variables from the noir type. * @param globalVariables - The noir global variables. @@ -933,6 +1078,36 @@ export function mapConstantRollupDataToNoir(constantRollupData: ConstantRollupDa }; } +/** + * Maps a public circuit public inputs to noir. + * @param publicInputs - The public circuit public inputs. + * @returns The noir public circuit public inputs. + */ +export function mapPublicCircuitPublicInputsToNoir( + publicInputs: PublicCircuitPublicInputs, +): PublicCircuitPublicInputsNoir { + return { + call_context: mapCallContextToNoir(publicInputs.callContext), + args_hash: mapFieldToNoir(publicInputs.argsHash), + return_values: publicInputs.returnValues.map(mapFieldToNoir) as FixedLengthArray, + contract_storage_update_requests: publicInputs.contractStorageUpdateRequests.map( + mapStorageUpdateRequestToNoir, + ) as FixedLengthArray, + contract_storage_reads: publicInputs.contractStorageReads.map(mapStorageReadToNoir) as FixedLengthArray< + StorageReadNoir, + 16 + >, + public_call_stack_hashes: publicInputs.publicCallStackHashes.map(mapFieldToNoir) as FixedLengthArray, + new_commitments: publicInputs.newCommitments.map(mapFieldToNoir) as FixedLengthArray, + new_nullifiers: publicInputs.newNullifiers.map(mapFieldToNoir) as FixedLengthArray, + new_l2_to_l1_msgs: publicInputs.newL2ToL1Msgs.map(mapFieldToNoir) as FixedLengthArray, + unencrypted_logs_hash: publicInputs.unencryptedLogsHash.map(mapFieldToNoir) as FixedLengthArray, + unencrypted_log_preimages_length: mapFieldToNoir(publicInputs.unencryptedLogPreimagesLength), + historical_block_data: mapHistoricalBlockDataToNoir(publicInputs.historicBlockData), + + prover_address: mapAztecAddressToNoir(publicInputs.proverAddress), + }; +} /** * Maps a constant rollup data from noir to the circuits.js type. * @param constantRollupData - The noir constant rollup data. @@ -982,6 +1157,34 @@ export function mapBaseOrMergeRollupPublicInputsToNoir( }; } +/** + * Maps a public call stack item to noir. + * @param publicCallStackItem - The public call stack item. + * @returns The noir public call stack item. + */ +export function mapPublicCallStackItemToNoir(publicCallStackItem: PublicCallStackItem): PublicCallStackItemNoir { + return { + contract_address: mapAztecAddressToNoir(publicCallStackItem.contractAddress), + public_inputs: mapPublicCircuitPublicInputsToNoir(publicCallStackItem.publicInputs), + is_execution_request: publicCallStackItem.isExecutionRequest, + function_data: mapFunctionDataToNoir(publicCallStackItem.functionData), + }; +} + +/** + * Maps a public call data to noir. + * @param publicCall - The public call data. + * @returns The noir public call data. + */ +export function mapPublicCallDataToNoir(publicCall: PublicCallData): PublicCallDataNoir { + return { + call_stack_item: mapPublicCallStackItemToNoir(publicCall.callStackItem), + public_call_stack: publicCall.publicCallStack.map(mapCallRequestToNoir) as FixedLengthArray, + proof: {}, + portal_contract_address: mapEthAddressToNoir(EthAddress.fromField(publicCall.portalContractAddress)), + bytecode_hash: mapFieldToNoir(publicCall.bytecodeHash), + }; +} /** * Maps a base or merge rollup public inputs from noir to the circuits.js type. * @param baseOrMergeRollupPublicInputs - The noir base or merge rollup public inputs. @@ -1027,6 +1230,17 @@ export function mapPreviousRollupDataToNoir(previousRollupData: PreviousRollupDa }; } +/** + * Maps public kernel inputs to noir. + * @param inputs - The public kernel inputs. + * @returns The noir public kernel inputs. + */ +export function mapPublicKernelInputs(inputs: PublicKernelInputs): PublicKernelInputsNoir { + return { + previous_kernel: mapPreviousKernelDataToNoir(inputs.previousKernel), + public_call: mapPublicCallDataToNoir(inputs.publicCall), + }; +} /** * Maps a AOT snapshot to noir. * @param snapshot - The circuits.js AOT snapshot. @@ -1129,3 +1343,95 @@ export function mapMergeRollupInputsToNoir(mergeRollupInputs: MergeRollupInputs) >, }; } + +/** + * Maps a nullifier leaf preimage to noir + * @param nullifierLeafPreimage - The nullifier leaf preimage. + * @returns The noir nullifier leaf preimage. + */ +export function mapNullifierLeafPreimageToNoir( + nullifierLeafPreimage: NullifierLeafPreimage, +): NullifierLeafPreimageNoir { + return { + leaf_value: mapFieldToNoir(nullifierLeafPreimage.leafValue), + next_value: mapFieldToNoir(nullifierLeafPreimage.nextValue), + next_index: mapFieldToNoir(new Fr(nullifierLeafPreimage.nextIndex)), + }; +} + +/** + * Maps a nullifier membership witness to noir. + * @param membershipWitness - The nullifier membership witness. + * @returns The noir nullifier membership witness. + */ +export function mapNullifierMembershipWitnessToNoir( + membershipWitness: MembershipWitness, +): NullifierMembershipWitnessNoir { + return { + leaf_index: membershipWitness.leafIndex.toString(), + sibling_path: membershipWitness.siblingPath.map(mapFieldToNoir) as FixedLengthArray< + NoirField, + typeof NULLIFIER_TREE_HEIGHT + >, + }; +} + +/** + * Maps a membership witness of the historic blocks tree to noir. + * @param membershipWitness - The membership witness. + * @returns The noir membership witness. + */ +export function mapHistoricBlocksTreeRootMembershipWitnessToNoir( + membershipWitness: MembershipWitness, +): HistoricBlocksTreeRootMembershipWitnessNoir { + return { + leaf_index: membershipWitness.leafIndex.toString(), + sibling_path: membershipWitness.siblingPath.map(mapFieldToNoir) as FixedLengthArray< + NoirField, + typeof HISTORIC_BLOCKS_TREE_HEIGHT + >, + }; +} + +/** + * Maps the inputs to the base rollup to noir. + * @param input - The circuits.js base rollup inputs. + * @returns The noir base rollup inputs. + */ +export function mapBaseRollupInputsToNoir(inputs: BaseRollupInputs): BaseRollupInputsNoir { + return { + kernel_data: inputs.kernelData.map(mapPreviousKernelDataToNoir) as FixedLengthArray, + start_note_hash_tree_snapshot: mapAppendOnlyTreeSnapshotToNoir(inputs.startNoteHashTreeSnapshot), + start_nullifier_tree_snapshot: mapAppendOnlyTreeSnapshotToNoir(inputs.startNullifierTreeSnapshot), + start_contract_tree_snapshot: mapAppendOnlyTreeSnapshotToNoir(inputs.startContractTreeSnapshot), + start_public_data_tree_root: mapFieldToNoir(inputs.startPublicDataTreeRoot), + start_historic_blocks_tree_snapshot: mapAppendOnlyTreeSnapshotToNoir(inputs.startHistoricBlocksTreeSnapshot), + low_nullifier_leaf_preimages: inputs.lowNullifierLeafPreimages.map( + mapNullifierLeafPreimageToNoir, + ) as FixedLengthArray, + low_nullifier_membership_witness: inputs.lowNullifierMembershipWitness.map( + mapNullifierMembershipWitnessToNoir, + ) as FixedLengthArray, + new_commitments_subtree_sibling_path: inputs.newCommitmentsSubtreeSiblingPath.map( + mapFieldToNoir, + ) as FixedLengthArray, + new_nullifiers_subtree_sibling_path: inputs.newNullifiersSubtreeSiblingPath.map(mapFieldToNoir) as FixedLengthArray< + NoirField, + 13 + >, + new_contracts_subtree_sibling_path: inputs.newContractsSubtreeSiblingPath.map(mapFieldToNoir) as FixedLengthArray< + NoirField, + 15 + >, + new_public_data_update_requests_sibling_paths: inputs.newPublicDataUpdateRequestsSiblingPaths.map(siblingPath => + siblingPath.map(mapFieldToNoir), + ) as FixedLengthArray, 32>, + new_public_data_reads_sibling_paths: inputs.newPublicDataReadsSiblingPaths.map(siblingPath => + siblingPath.map(mapFieldToNoir), + ) as FixedLengthArray, 32>, + historic_blocks_tree_root_membership_witnesses: inputs.historicBlocksTreeRootMembershipWitnesses.map( + mapHistoricBlocksTreeRootMembershipWitnessToNoir, + ) as FixedLengthArray, + constants: mapConstantRollupDataToNoir(inputs.constants), + }; +} diff --git a/yarn-project/noir-protocol-circuits/src/types/private_kernel_init_types.ts b/yarn-project/noir-protocol-circuits/src/types/private_kernel_init_types.ts index d8efc079f75..4f00c7e1953 100644 --- a/yarn-project/noir-protocol-circuits/src/types/private_kernel_init_types.ts +++ b/yarn-project/noir-protocol-circuits/src/types/private_kernel_init_types.ts @@ -69,7 +69,7 @@ export interface Block { note_hash_tree_root: Field; nullifier_tree_root: Field; contract_tree_root: Field; - l1_to_l2_data_tree_root: Field; + l1_to_l2_messages_tree_root: Field; public_data_tree_root: Field; global_variables_hash: Field; } @@ -89,8 +89,8 @@ export interface PrivateCircuitPublicInputs { new_commitments: FixedLengthArray; new_nullifiers: FixedLengthArray; nullified_commitments: FixedLengthArray; - private_call_stack: FixedLengthArray; - public_call_stack: FixedLengthArray; + private_call_stack_hashes: FixedLengthArray; + public_call_stack_hashes: FixedLengthArray; new_l2_to_l1_msgs: FixedLengthArray; encrypted_logs_hash: FixedLengthArray; unencrypted_logs_hash: FixedLengthArray; @@ -109,6 +109,17 @@ export interface PrivateCallStackItem { is_execution_request: boolean; } +export interface CallerContext { + msg_sender: Address; + storage_contract_address: Address; +} + +export interface CallRequest { + hash: Field; + caller_contract_address: Address; + caller_context: CallerContext; +} + export interface Proof {} export interface VerificationKey {} @@ -132,7 +143,8 @@ export interface ReadRequestMembershipWitness { export interface PrivateCallData { call_stack_item: PrivateCallStackItem; - private_call_stack_preimages: FixedLengthArray; + private_call_stack: FixedLengthArray; + public_call_stack: FixedLengthArray; proof: Proof; vk: VerificationKey; function_leaf_membership_witness: FunctionLeafMembershipWitness; @@ -184,8 +196,8 @@ export interface CombinedAccumulatedData { new_commitments: FixedLengthArray; new_nullifiers: FixedLengthArray; nullified_commitments: FixedLengthArray; - private_call_stack: FixedLengthArray; - public_call_stack: FixedLengthArray; + private_call_stack: FixedLengthArray; + public_call_stack: FixedLengthArray; new_l2_to_l1_msgs: FixedLengthArray; encrypted_logs_hash: FixedLengthArray; unencrypted_logs_hash: FixedLengthArray; diff --git a/yarn-project/noir-protocol-circuits/src/types/private_kernel_inner_types.ts b/yarn-project/noir-protocol-circuits/src/types/private_kernel_inner_types.ts index 2ddfa3beac0..e59e29b2b37 100644 --- a/yarn-project/noir-protocol-circuits/src/types/private_kernel_inner_types.ts +++ b/yarn-project/noir-protocol-circuits/src/types/private_kernel_inner_types.ts @@ -13,6 +13,17 @@ export interface Address { inner: Field; } +export interface CallerContext { + msg_sender: Address; + storage_contract_address: Address; +} + +export interface CallRequest { + hash: Field; + caller_contract_address: Address; + caller_context: CallerContext; +} + export interface EthAddress { inner: Field; } @@ -63,8 +74,8 @@ export interface CombinedAccumulatedData { new_commitments: FixedLengthArray; new_nullifiers: FixedLengthArray; nullified_commitments: FixedLengthArray; - private_call_stack: FixedLengthArray; - public_call_stack: FixedLengthArray; + private_call_stack: FixedLengthArray; + public_call_stack: FixedLengthArray; new_l2_to_l1_msgs: FixedLengthArray; encrypted_logs_hash: FixedLengthArray; unencrypted_logs_hash: FixedLengthArray; @@ -80,7 +91,7 @@ export interface Block { note_hash_tree_root: Field; nullifier_tree_root: Field; contract_tree_root: Field; - l1_to_l2_data_tree_root: Field; + l1_to_l2_messages_tree_root: Field; public_data_tree_root: Field; global_variables_hash: Field; } @@ -155,8 +166,8 @@ export interface PrivateCircuitPublicInputs { new_commitments: FixedLengthArray; new_nullifiers: FixedLengthArray; nullified_commitments: FixedLengthArray; - private_call_stack: FixedLengthArray; - public_call_stack: FixedLengthArray; + private_call_stack_hashes: FixedLengthArray; + public_call_stack_hashes: FixedLengthArray; new_l2_to_l1_msgs: FixedLengthArray; encrypted_logs_hash: FixedLengthArray; unencrypted_logs_hash: FixedLengthArray; @@ -194,7 +205,8 @@ export interface ReadRequestMembershipWitness { export interface PrivateCallData { call_stack_item: PrivateCallStackItem; - private_call_stack_preimages: FixedLengthArray; + private_call_stack: FixedLengthArray; + public_call_stack: FixedLengthArray; proof: Proof; vk: VerificationKey; function_leaf_membership_witness: FunctionLeafMembershipWitness; diff --git a/yarn-project/noir-protocol-circuits/src/types/private_kernel_ordering_types.ts b/yarn-project/noir-protocol-circuits/src/types/private_kernel_ordering_types.ts index 460b6e89e4c..67f146a9d92 100644 --- a/yarn-project/noir-protocol-circuits/src/types/private_kernel_ordering_types.ts +++ b/yarn-project/noir-protocol-circuits/src/types/private_kernel_ordering_types.ts @@ -13,6 +13,17 @@ export interface Address { inner: Field; } +export interface CallerContext { + msg_sender: Address; + storage_contract_address: Address; +} + +export interface CallRequest { + hash: Field; + caller_contract_address: Address; + caller_context: CallerContext; +} + export interface EthAddress { inner: Field; } @@ -63,8 +74,8 @@ export interface CombinedAccumulatedData { new_commitments: FixedLengthArray; new_nullifiers: FixedLengthArray; nullified_commitments: FixedLengthArray; - private_call_stack: FixedLengthArray; - public_call_stack: FixedLengthArray; + private_call_stack: FixedLengthArray; + public_call_stack: FixedLengthArray; new_l2_to_l1_msgs: FixedLengthArray; encrypted_logs_hash: FixedLengthArray; unencrypted_logs_hash: FixedLengthArray; @@ -80,7 +91,7 @@ export interface Block { note_hash_tree_root: Field; nullifier_tree_root: Field; contract_tree_root: Field; - l1_to_l2_data_tree_root: Field; + l1_to_l2_messages_tree_root: Field; public_data_tree_root: Field; global_variables_hash: Field; } @@ -147,8 +158,8 @@ export interface FinalAccumulatedData { new_commitments: FixedLengthArray; new_nullifiers: FixedLengthArray; nullified_commitments: FixedLengthArray; - private_call_stack: FixedLengthArray; - public_call_stack: FixedLengthArray; + private_call_stack: FixedLengthArray; + public_call_stack: FixedLengthArray; new_l2_to_l1_msgs: FixedLengthArray; encrypted_logs_hash: FixedLengthArray; unencrypted_logs_hash: FixedLengthArray; diff --git a/yarn-project/noir-protocol-circuits/src/types/public_kernel_private_previous_types.ts b/yarn-project/noir-protocol-circuits/src/types/public_kernel_private_previous_types.ts new file mode 100644 index 00000000000..20dccc4b25c --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/types/public_kernel_private_previous_types.ts @@ -0,0 +1,211 @@ +/* Autogenerated file, do not edit! */ + +/* eslint-disable */ + +export type FixedLengthArray = L extends 0 ? never[] : T[] & { length: L }; + +export type Field = string; +export type u32 = string; + +export interface AggregationObject {} + +export interface Address { + inner: Field; +} + +export interface CallerContext { + msg_sender: Address; + storage_contract_address: Address; +} + +export interface CallRequest { + hash: Field; + caller_contract_address: Address; + caller_context: CallerContext; +} + +export interface EthAddress { + inner: Field; +} + +export interface NewContractData { + contract_address: Address; + portal_contract_address: EthAddress; + function_tree_root: Field; +} + +export interface FunctionSelector { + inner: u32; +} + +export interface FunctionData { + selector: FunctionSelector; + is_internal: boolean; + is_private: boolean; + is_constructor: boolean; +} + +export interface OptionallyRevealedData { + call_stack_item_hash: Field; + function_data: FunctionData; + vk_hash: Field; + portal_contract_address: EthAddress; + pay_fee_from_l1: boolean; + pay_fee_from_public_l2: boolean; + called_from_l1: boolean; + called_from_public_l2: boolean; +} + +export interface PublicDataUpdateRequest { + leaf_index: Field; + old_value: Field; + new_value: Field; +} + +export interface PublicDataRead { + leaf_index: Field; + value: Field; +} + +export interface CombinedAccumulatedData { + aggregation_object: AggregationObject; + read_requests: FixedLengthArray; + pending_read_requests: FixedLengthArray; + new_commitments: FixedLengthArray; + new_nullifiers: FixedLengthArray; + nullified_commitments: FixedLengthArray; + private_call_stack: FixedLengthArray; + public_call_stack: FixedLengthArray; + new_l2_to_l1_msgs: FixedLengthArray; + encrypted_logs_hash: FixedLengthArray; + unencrypted_logs_hash: FixedLengthArray; + encrypted_log_preimages_length: Field; + unencrypted_log_preimages_length: Field; + new_contracts: FixedLengthArray; + optionally_revealed_data: FixedLengthArray; + public_data_update_requests: FixedLengthArray; + public_data_reads: FixedLengthArray; +} + +export interface Block { + note_hash_tree_root: Field; + nullifier_tree_root: Field; + contract_tree_root: Field; + l1_to_l2_messages_tree_root: Field; + public_data_tree_root: Field; + global_variables_hash: Field; +} + +export interface HistoricalBlockData { + blocks_tree_root: Field; + block: Block; + private_kernel_vk_tree_root: Field; +} + +export interface Point { + x: Field; + y: Field; +} + +export interface ContractDeploymentData { + deployer_public_key: Point; + constructor_vk_hash: Field; + function_tree_root: Field; + contract_address_salt: Field; + portal_contract_address: EthAddress; +} + +export interface TxContext { + is_fee_payment_tx: boolean; + is_rebate_payment_tx: boolean; + is_contract_deployment_tx: boolean; + contract_deployment_data: ContractDeploymentData; + chain_id: Field; + version: Field; +} + +export interface CombinedConstantData { + block_data: HistoricalBlockData; + tx_context: TxContext; +} + +export interface KernelCircuitPublicInputs { + end: CombinedAccumulatedData; + constants: CombinedConstantData; + is_private: boolean; +} + +export interface Proof {} + +export interface VerificationKey {} + +export interface PreviousKernelData { + public_inputs: KernelCircuitPublicInputs; + proof: Proof; + vk: VerificationKey; + vk_index: u32; + vk_path: FixedLengthArray; +} + +export interface CallContext { + msg_sender: Address; + storage_contract_address: Address; + portal_contract_address: EthAddress; + function_selector: FunctionSelector; + is_delegate_call: boolean; + is_static_call: boolean; + is_contract_deployment: boolean; +} + +export interface StorageUpdateRequest { + storage_slot: Field; + old_value: Field; + new_value: Field; +} + +export interface StorageRead { + storage_slot: Field; + current_value: Field; +} + +export interface PublicCircuitPublicInputs { + call_context: CallContext; + args_hash: Field; + return_values: FixedLengthArray; + contract_storage_update_requests: FixedLengthArray; + contract_storage_reads: FixedLengthArray; + public_call_stack_hashes: FixedLengthArray; + new_commitments: FixedLengthArray; + new_nullifiers: FixedLengthArray; + new_l2_to_l1_msgs: FixedLengthArray; + unencrypted_logs_hash: FixedLengthArray; + unencrypted_log_preimages_length: Field; + historical_block_data: HistoricalBlockData; + prover_address: Address; +} + +export interface PublicCallStackItem { + contract_address: Address; + public_inputs: PublicCircuitPublicInputs; + function_data: FunctionData; + is_execution_request: boolean; +} + +export interface PublicCallData { + call_stack_item: PublicCallStackItem; + public_call_stack: FixedLengthArray; + proof: Proof; + portal_contract_address: EthAddress; + bytecode_hash: Field; +} + +export interface PublicKernelPrivatePreviousInputs { + previous_kernel: PreviousKernelData; + public_call: PublicCallData; +} + +export type ReturnType = KernelCircuitPublicInputs; + +export interface InputType { + input: PublicKernelPrivatePreviousInputs; +} diff --git a/yarn-project/noir-protocol-circuits/src/types/public_kernel_public_previous_types.ts b/yarn-project/noir-protocol-circuits/src/types/public_kernel_public_previous_types.ts new file mode 100644 index 00000000000..bb0596aff1c --- /dev/null +++ b/yarn-project/noir-protocol-circuits/src/types/public_kernel_public_previous_types.ts @@ -0,0 +1,211 @@ +/* Autogenerated file, do not edit! */ + +/* eslint-disable */ + +export type FixedLengthArray = L extends 0 ? never[] : T[] & { length: L }; + +export type Field = string; +export type u32 = string; + +export interface AggregationObject {} + +export interface Address { + inner: Field; +} + +export interface CallerContext { + msg_sender: Address; + storage_contract_address: Address; +} + +export interface CallRequest { + hash: Field; + caller_contract_address: Address; + caller_context: CallerContext; +} + +export interface EthAddress { + inner: Field; +} + +export interface NewContractData { + contract_address: Address; + portal_contract_address: EthAddress; + function_tree_root: Field; +} + +export interface FunctionSelector { + inner: u32; +} + +export interface FunctionData { + selector: FunctionSelector; + is_internal: boolean; + is_private: boolean; + is_constructor: boolean; +} + +export interface OptionallyRevealedData { + call_stack_item_hash: Field; + function_data: FunctionData; + vk_hash: Field; + portal_contract_address: EthAddress; + pay_fee_from_l1: boolean; + pay_fee_from_public_l2: boolean; + called_from_l1: boolean; + called_from_public_l2: boolean; +} + +export interface PublicDataUpdateRequest { + leaf_index: Field; + old_value: Field; + new_value: Field; +} + +export interface PublicDataRead { + leaf_index: Field; + value: Field; +} + +export interface CombinedAccumulatedData { + aggregation_object: AggregationObject; + read_requests: FixedLengthArray; + pending_read_requests: FixedLengthArray; + new_commitments: FixedLengthArray; + new_nullifiers: FixedLengthArray; + nullified_commitments: FixedLengthArray; + private_call_stack: FixedLengthArray; + public_call_stack: FixedLengthArray; + new_l2_to_l1_msgs: FixedLengthArray; + encrypted_logs_hash: FixedLengthArray; + unencrypted_logs_hash: FixedLengthArray; + encrypted_log_preimages_length: Field; + unencrypted_log_preimages_length: Field; + new_contracts: FixedLengthArray; + optionally_revealed_data: FixedLengthArray; + public_data_update_requests: FixedLengthArray; + public_data_reads: FixedLengthArray; +} + +export interface Block { + note_hash_tree_root: Field; + nullifier_tree_root: Field; + contract_tree_root: Field; + l1_to_l2_messages_tree_root: Field; + public_data_tree_root: Field; + global_variables_hash: Field; +} + +export interface HistoricalBlockData { + blocks_tree_root: Field; + block: Block; + private_kernel_vk_tree_root: Field; +} + +export interface Point { + x: Field; + y: Field; +} + +export interface ContractDeploymentData { + deployer_public_key: Point; + constructor_vk_hash: Field; + function_tree_root: Field; + contract_address_salt: Field; + portal_contract_address: EthAddress; +} + +export interface TxContext { + is_fee_payment_tx: boolean; + is_rebate_payment_tx: boolean; + is_contract_deployment_tx: boolean; + contract_deployment_data: ContractDeploymentData; + chain_id: Field; + version: Field; +} + +export interface CombinedConstantData { + block_data: HistoricalBlockData; + tx_context: TxContext; +} + +export interface KernelCircuitPublicInputs { + end: CombinedAccumulatedData; + constants: CombinedConstantData; + is_private: boolean; +} + +export interface Proof {} + +export interface VerificationKey {} + +export interface PreviousKernelData { + public_inputs: KernelCircuitPublicInputs; + proof: Proof; + vk: VerificationKey; + vk_index: u32; + vk_path: FixedLengthArray; +} + +export interface CallContext { + msg_sender: Address; + storage_contract_address: Address; + portal_contract_address: EthAddress; + function_selector: FunctionSelector; + is_delegate_call: boolean; + is_static_call: boolean; + is_contract_deployment: boolean; +} + +export interface StorageUpdateRequest { + storage_slot: Field; + old_value: Field; + new_value: Field; +} + +export interface StorageRead { + storage_slot: Field; + current_value: Field; +} + +export interface PublicCircuitPublicInputs { + call_context: CallContext; + args_hash: Field; + return_values: FixedLengthArray; + contract_storage_update_requests: FixedLengthArray; + contract_storage_reads: FixedLengthArray; + public_call_stack_hashes: FixedLengthArray; + new_commitments: FixedLengthArray; + new_nullifiers: FixedLengthArray; + new_l2_to_l1_msgs: FixedLengthArray; + unencrypted_logs_hash: FixedLengthArray; + unencrypted_log_preimages_length: Field; + historical_block_data: HistoricalBlockData; + prover_address: Address; +} + +export interface PublicCallStackItem { + contract_address: Address; + public_inputs: PublicCircuitPublicInputs; + function_data: FunctionData; + is_execution_request: boolean; +} + +export interface PublicCallData { + call_stack_item: PublicCallStackItem; + public_call_stack: FixedLengthArray; + proof: Proof; + portal_contract_address: EthAddress; + bytecode_hash: Field; +} + +export interface PublicKernelPublicPreviousInputs { + previous_kernel: PreviousKernelData; + public_call: PublicCallData; +} + +export type ReturnType = KernelCircuitPublicInputs; + +export interface InputType { + input: PublicKernelPublicPreviousInputs; +} diff --git a/yarn-project/noir-protocol-circuits/src/types/rollup_base_types.ts b/yarn-project/noir-protocol-circuits/src/types/rollup_base_types.ts index 8792b3f80c8..a4ac38b38eb 100644 --- a/yarn-project/noir-protocol-circuits/src/types/rollup_base_types.ts +++ b/yarn-project/noir-protocol-circuits/src/types/rollup_base_types.ts @@ -13,6 +13,17 @@ export interface Address { inner: Field; } +export interface CallerContext { + msg_sender: Address; + storage_contract_address: Address; +} + +export interface CallRequest { + hash: Field; + caller_contract_address: Address; + caller_context: CallerContext; +} + export interface EthAddress { inner: Field; } @@ -63,8 +74,8 @@ export interface CombinedAccumulatedData { new_commitments: FixedLengthArray; new_nullifiers: FixedLengthArray; nullified_commitments: FixedLengthArray; - private_call_stack: FixedLengthArray; - public_call_stack: FixedLengthArray; + private_call_stack: FixedLengthArray; + public_call_stack: FixedLengthArray; new_l2_to_l1_msgs: FixedLengthArray; encrypted_logs_hash: FixedLengthArray; unencrypted_logs_hash: FixedLengthArray; @@ -80,7 +91,7 @@ export interface Block { note_hash_tree_root: Field; nullifier_tree_root: Field; contract_tree_root: Field; - l1_to_l2_data_tree_root: Field; + l1_to_l2_messages_tree_root: Field; public_data_tree_root: Field; global_variables_hash: Field; } @@ -149,12 +160,12 @@ export interface NullifierLeafPreimage { export interface NullifierMembershipWitness { leaf_index: Field; - sibling_path: FixedLengthArray; + sibling_path: FixedLengthArray; } export interface HistoricBlocksTreeRootMembershipWitness { leaf_index: Field; - sibling_path: FixedLengthArray; + sibling_path: FixedLengthArray; } export interface GlobalVariables { diff --git a/yarn-project/p2p-bootstrap/Dockerfile b/yarn-project/p2p-bootstrap/Dockerfile deleted file mode 100644 index 98cb0e4d6cf..00000000000 --- a/yarn-project/p2p-bootstrap/Dockerfile +++ /dev/null @@ -1,14 +0,0 @@ -FROM 278380418400.dkr.ecr.eu-west-2.amazonaws.com/yarn-project AS builder - -WORKDIR /usr/src/yarn-project/p2p-bootstrap - -# Productionify. See comment in yarn-project-base/Dockerfile. -RUN yarn cache clean && yarn workspaces focus --production - -# Create final, minimal size image. -FROM node:18-alpine -COPY --from=builder /usr/src/ /usr/src/ -WORKDIR /usr/src/yarn-project/p2p-bootstrap -ENTRYPOINT ["yarn"] -CMD [ "start" ] -EXPOSE 8080 diff --git a/yarn-project/p2p-bootstrap/package.json b/yarn-project/p2p-bootstrap/package.json index e5f3c5b61bf..8853e898b11 100644 --- a/yarn-project/p2p-bootstrap/package.json +++ b/yarn-project/p2p-bootstrap/package.json @@ -15,7 +15,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "start:dev": "tsc-watch -p tsconfig.json --onSuccess 'yarn start'", "start": "node ./dest/index.js", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" @@ -31,7 +31,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/node": "^18.14.6", "jest": "^29.5.0", diff --git a/yarn-project/p2p-bootstrap/scripts/docker-compose-bootstrap.yml b/yarn-project/p2p-bootstrap/scripts/docker-compose-bootstrap.yml index 353eb9ffaa2..ce5913b9e75 100644 --- a/yarn-project/p2p-bootstrap/scripts/docker-compose-bootstrap.yml +++ b/yarn-project/p2p-bootstrap/scripts/docker-compose-bootstrap.yml @@ -1,10 +1,11 @@ version: '3' services: p2p-bootstrap: - image: 278380418400.dkr.ecr.eu-west-2.amazonaws.com/p2p-bootstrap:latest + image: 278380418400.dkr.ecr.eu-west-2.amazonaws.com/aztec-sandbox:latest ports: - '40400:40400' environment: + MODE: 'p2p-bootstrap' DEBUG: 'aztec:*' P2P_TCP_LISTEN_PORT: 40400 PEER_ID: '0a260024080112205ea53185db2e52dae74d0d4d6cadc494174810d0a713cd09b0ac517c38bc781e1224080112205ea53185db2e52dae74d0d4d6cadc494174810d0a713cd09b0ac517c38bc781e1a44080112402df8b977f356c6e34fa021c9647973234dff4df706c185794405aafb556723cf5ea53185db2e52dae74d0d4d6cadc494174810d0a713cd09b0ac517c38bc781e' diff --git a/yarn-project/p2p-bootstrap/scripts/start_bootnode.sh b/yarn-project/p2p-bootstrap/scripts/start_bootnode.sh index 2abace6942d..3729e674ed3 100755 --- a/yarn-project/p2p-bootstrap/scripts/start_bootnode.sh +++ b/yarn-project/p2p-bootstrap/scripts/start_bootnode.sh @@ -4,5 +4,7 @@ set -eu cd .. export P2P_TCP_LISTEN_PORT=40400 export PEER_ID='0a260024080112205ea53185db2e52dae74d0d4d6cadc494174810d0a713cd09b0ac517c38bc781e1224080112205ea53185db2e52dae74d0d4d6cadc494174810d0a713cd09b0ac517c38bc781e1a44080112402df8b977f356c6e34fa021c9647973234dff4df706c185794405aafb556723cf5ea53185db2e52dae74d0d4d6cadc494174810d0a713cd09b0ac517c38bc781e' +echo "Building..." yarn build +echo "Starting with Peer ID $PEER_ID" yarn start diff --git a/yarn-project/p2p/package.json b/yarn-project/p2p/package.json index 19a4bd74f49..e4d18baa732 100644 --- a/yarn-project/p2p/package.json +++ b/yarn-project/p2p/package.json @@ -15,7 +15,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests", "start": "node ./dest", "start:dev": "tsc-watch -p tsconfig.json --onSuccess 'yarn start'" @@ -54,7 +54,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/lodash.times": "^4.3.7", "@types/node": "^18.14.6", diff --git a/yarn-project/p2p/src/bootstrap/bootstrap.ts b/yarn-project/p2p/src/bootstrap/bootstrap.ts index 17ecba0a4d9..055f8ac6e14 100644 --- a/yarn-project/p2p/src/bootstrap/bootstrap.ts +++ b/yarn-project/p2p/src/bootstrap/bootstrap.ts @@ -11,7 +11,7 @@ import { identifyService } from 'libp2p/identify'; import { format } from 'util'; import { P2PConfig } from '../config.js'; -import { createLibP2PPeerId } from '../index.js'; +import { createLibP2PPeerId } from '../service/index.js'; /** * Encapsulates a 'Bootstrap' node, used for the purpose of assisting new joiners in acquiring peers. @@ -31,7 +31,9 @@ export class BootstrapNode { config; const peerId = await createLibP2PPeerId(peerIdPrivateKey); this.logger( - `Starting bootstrap node ${peerId} on ${tcpListenIp}:${tcpListenPort} announced at ${announceHostname}:${announcePort}`, + `Starting bootstrap node ${peerId} on ${tcpListenIp}:${tcpListenPort} announced at ${announceHostname}:${ + announcePort ?? tcpListenPort + }`, ); const opts: Libp2pOptions = { diff --git a/yarn-project/p2p/src/client/index.ts b/yarn-project/p2p/src/client/index.ts index 4406a24af0a..95dbff55049 100644 --- a/yarn-project/p2p/src/client/index.ts +++ b/yarn-project/p2p/src/client/index.ts @@ -1,7 +1,10 @@ import { L2BlockSource } from '@aztec/types'; -import { LibP2PService, P2PClient, P2PConfig, TxPool } from '../index.js'; +import { P2PClient } from '../client/p2p_client.js'; +import { P2PConfig } from '../config.js'; import { DummyP2PService } from '../service/dummy_service.js'; +import { LibP2PService } from '../service/index.js'; +import { TxPool } from '../tx_pool/index.js'; export * from './p2p_client.js'; diff --git a/yarn-project/p2p/src/config.ts b/yarn-project/p2p/src/config.ts index c908cd0b063..8e4d4d1af94 100644 --- a/yarn-project/p2p/src/config.ts +++ b/yarn-project/p2p/src/config.ts @@ -97,7 +97,7 @@ export function getP2PConfigEnvVars(): P2PConfig { p2pEnabled: P2P_ENABLED === 'true', p2pBlockCheckIntervalMS: P2P_BLOCK_CHECK_INTERVAL_MS ? +P2P_BLOCK_CHECK_INTERVAL_MS : 100, l2QueueSize: P2P_L2_BLOCK_QUEUE_SIZE ? +P2P_L2_BLOCK_QUEUE_SIZE : 1000, - tcpListenPort: P2P_TCP_LISTEN_PORT ? +P2P_TCP_LISTEN_PORT : 0, + tcpListenPort: P2P_TCP_LISTEN_PORT ? +P2P_TCP_LISTEN_PORT : 40400, tcpListenIp: P2P_TCP_LISTEN_IP ? P2P_TCP_LISTEN_IP : '0.0.0.0', peerIdPrivateKey: PEER_ID_PRIVATE_KEY, bootstrapNodes: BOOTSTRAP_NODES ? BOOTSTRAP_NODES.split(',') : [], diff --git a/yarn-project/p2p/src/service/libp2p_service.ts b/yarn-project/p2p/src/service/libp2p_service.ts index 4abc1be3a23..05f3a896023 100644 --- a/yarn-project/p2p/src/service/libp2p_service.ts +++ b/yarn-project/p2p/src/service/libp2p_service.ts @@ -17,7 +17,7 @@ import { Libp2p, Libp2pOptions, ServiceFactoryMap, createLibp2p } from 'libp2p'; import { identifyService } from 'libp2p/identify'; import { P2PConfig } from '../config.js'; -import { TxPool } from '../index.js'; +import { TxPool } from '../tx_pool/index.js'; import { KnownTxLookup } from './known_txs.js'; import { P2PService } from './service.js'; import { @@ -83,8 +83,12 @@ export class LibP2PService implements P2PService { } const { enableNat, tcpListenIp, tcpListenPort, announceHostname, announcePort } = this.config; this.logger(`Starting P2P node on ${tcpListenIp}:${tcpListenPort}`); - if (announceHostname) this.logger(`Announcing at ${announceHostname}/tcp/${announcePort ?? tcpListenPort}`); - if (enableNat) this.logger(`Enabling NAT in libp2p module`); + if (announceHostname) { + this.logger(`Announcing at ${announceHostname}/tcp/${announcePort ?? tcpListenPort}`); + } + if (enableNat) { + this.logger(`Enabling NAT in libp2p module`); + } this.node.addEventListener('peer:discovery', evt => { const peerId = evt.detail.id; diff --git a/yarn-project/p2p/src/tx_pool/memory_tx_pool.ts b/yarn-project/p2p/src/tx_pool/memory_tx_pool.ts index 2c897ca4875..5016d687512 100644 --- a/yarn-project/p2p/src/tx_pool/memory_tx_pool.ts +++ b/yarn-project/p2p/src/tx_pool/memory_tx_pool.ts @@ -2,7 +2,7 @@ import { createDebugLogger } from '@aztec/foundation/log'; import { Tx, TxHash } from '@aztec/types'; import { TxAddedToPoolStats } from '@aztec/types/stats'; -import { TxPool } from './index.js'; +import { TxPool } from './tx_pool.js'; /** * In-memory implementation of the Transaction Pool. diff --git a/yarn-project/package.common.json b/yarn-project/package.common.json index 7e200c5fb86..12e168ae5f0 100644 --- a/yarn-project/package.common.json +++ b/yarn-project/package.common.json @@ -4,7 +4,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" }, "engines": { diff --git a/yarn-project/package.json b/yarn-project/package.json index 3e85099f718..ef5e1a73dde 100644 --- a/yarn-project/package.json +++ b/yarn-project/package.json @@ -6,11 +6,11 @@ "prepare": "node ./yarn-project-base/scripts/update_package_jsons.mjs && yarn workspaces foreach run prepare && workspaces-to-typescript-project-references --tsconfigPath tsconfig.json && prettier -w */tsconfig.json", "prepare:check": "node ./yarn-project-base/scripts/update_package_jsons.mjs --check && workspaces-to-typescript-project-references --check --tsconfigPath tsconfig.json", "docs": "typedoc --out docs/dist && cd docs && yarn serve", - "formatting": "yarn workspaces foreach -p -j unlimited -v run formatting", - "formatting:fix": "yarn workspaces foreach -p -v run formatting:fix", + "formatting": "FORCE_COLOR=true yarn workspaces foreach -p -j unlimited -v run formatting", + "formatting:fix": "FORCE_COLOR=true yarn workspaces foreach -p -v run formatting:fix", "lint": "yarn eslint --cache --ignore-pattern l1-artifacts .", "format": "yarn prettier --cache -w .", - "test": "yarn workspaces foreach --exclude @aztec/aztec3-packages --exclude @aztec/end-to-end --exclude @aztec/canary --exclude private-token -p -j unlimited -v run test", + "test": "FORCE_COLOR=true yarn workspaces foreach --exclude @aztec/aztec3-packages --exclude @aztec/end-to-end --exclude @aztec/canary --exclude private-token -p -j unlimited -v run test", "build": "yarn workspace @aztec/l1-artifacts build && tsc -b tsconfig.json", "build:dev": "yarn workspace @aztec/l1-artifacts build && tsc -b tsconfig.json --watch", "clean": "yarn workspaces foreach -p -v run clean" diff --git a/yarn-project/prover-client/package.json b/yarn-project/prover-client/package.json index f52b8a4494f..49b0dba2623 100644 --- a/yarn-project/prover-client/package.json +++ b/yarn-project/prover-client/package.json @@ -15,7 +15,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" }, "inherits": [ @@ -35,7 +35,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/node": "^18.7.23", "jest": "^29.5.0", diff --git a/yarn-project/pxe/Dockerfile b/yarn-project/pxe/Dockerfile deleted file mode 100644 index 299a0fade85..00000000000 --- a/yarn-project/pxe/Dockerfile +++ /dev/null @@ -1,25 +0,0 @@ -FROM 278380418400.dkr.ecr.eu-west-2.amazonaws.com/yarn-project AS yarn-project - -ARG COMMIT_TAG="" - -# Start a new image as we need arch specific. -FROM node:18-alpine as builder -RUN apk add jq -COPY --from=yarn-project /usr/src/ /usr/src/ - -# Update pxe version if COMMIT_TAG has been used -WORKDIR /usr/src/yarn-project/pxe -RUN if [[ -n "${COMMIT_TAG}" ]]; then \ - jq --arg v ${COMMIT_TAG} '.version = $v' package.json > _temp && mv _temp package.json; \ - fi - -# Productionify. See comment in yarn-project-base/Dockerfile. -RUN yarn cache clean && yarn workspaces focus --production - -# Create final, minimal size image. -FROM node:18-alpine -COPY --from=builder /usr/src/ /usr/src/ -WORKDIR /usr/src/yarn-project/pxe -ENTRYPOINT ["yarn"] -CMD [ "start" ] -EXPOSE 8081 diff --git a/yarn-project/pxe/package.json b/yarn-project/pxe/package.json index 37b929e7323..b426db2c3ed 100644 --- a/yarn-project/pxe/package.json +++ b/yarn-project/pxe/package.json @@ -16,7 +16,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests", "start": "DEBUG='aztec:*' && node ./dest/bin/index.js" }, @@ -51,7 +51,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/lodash.omit": "^4.5.7", "@types/lodash.partition": "^4.6.0", diff --git a/yarn-project/pxe/src/contract_data_oracle/index.ts b/yarn-project/pxe/src/contract_data_oracle/index.ts index 9713c5cf2ba..56eeaf60c59 100644 --- a/yarn-project/pxe/src/contract_data_oracle/index.ts +++ b/yarn-project/pxe/src/contract_data_oracle/index.ts @@ -1,4 +1,4 @@ -import { AztecAddress, CircuitsWasm, MembershipWitness, VK_TREE_HEIGHT } from '@aztec/circuits.js'; +import { AztecAddress, MembershipWitness, VK_TREE_HEIGHT } from '@aztec/circuits.js'; import { FunctionDebugMetadata, FunctionSelector, getFunctionDebugMetadata } from '@aztec/foundation/abi'; import { ContractDatabase, StateInfoProvider } from '@aztec/types'; @@ -155,8 +155,7 @@ export class ContractDataOracle { throw new Error(`Unknown contract: ${contractAddress}`); } - const wasm = await CircuitsWasm.get(); - tree = new ContractTree(contract, this.stateProvider, wasm); + tree = new ContractTree(contract, this.stateProvider); this.trees.push(tree); } return tree; diff --git a/yarn-project/pxe/src/contract_tree/index.ts b/yarn-project/pxe/src/contract_tree/index.ts index 81546f39baf..8726daf8295 100644 --- a/yarn-project/pxe/src/contract_tree/index.ts +++ b/yarn-project/pxe/src/contract_tree/index.ts @@ -1,6 +1,5 @@ import { CONTRACT_TREE_HEIGHT, - CircuitsWasm, EthAddress, FUNCTION_TREE_HEIGHT, Fr, @@ -8,7 +7,6 @@ import { MembershipWitness, NewContractConstructor, NewContractData, - computeFunctionTree, computeFunctionTreeData, generateFunctionLeaves, hashVKStr, @@ -18,6 +16,7 @@ import { import { computeCompleteAddress, computeContractLeaf, + computeFunctionTree, computeFunctionTreeRoot, computeVarArgsHash, hashConstructor, @@ -44,7 +43,6 @@ export class ContractTree { */ public readonly contract: ContractDao, private stateInfoProvider: StateInfoProvider, - private wasm: CircuitsWasm, /** * Data associated with the contract constructor for a new contract. */ @@ -66,7 +64,7 @@ export class ContractTree { * @param node - An instance of the AztecNode class representing the current node. * @returns A new ContractTree instance containing the contract data and computed values. */ - public static async new( + public static new( artifact: ContractArtifact, args: Fr[], portalContract: EthAddress, @@ -74,7 +72,6 @@ export class ContractTree { from: PublicKey, node: AztecNode, ) { - const wasm = await CircuitsWasm.get(); const constructorArtifact = artifact.functions.find(isConstructor); if (!constructorArtifact) { throw new Error('Constructor not found.'); @@ -88,9 +85,9 @@ export class ContractTree { selector: FunctionSelector.fromNameAndParameters(f.name, f.parameters), })); const leaves = generateFunctionLeaves(functions); - const root = computeFunctionTreeRoot(wasm, leaves); + const root = computeFunctionTreeRoot(leaves); const functionData = FunctionData.fromAbi(constructorArtifact); - const vkHash = hashVKStr(constructorArtifact.verificationKey, wasm); + const vkHash = hashVKStr(constructorArtifact.verificationKey); const argsHash = computeVarArgsHash(args); const constructorHash = hashConstructor(functionData, argsHash, vkHash); @@ -106,7 +103,7 @@ export class ContractTree { functionData, vkHash, }; - return new ContractTree(contractDao, node, wasm, NewContractConstructor); + return new ContractTree(contractDao, node, NewContractConstructor); } /** @@ -172,7 +169,7 @@ export class ContractTree { public getFunctionTreeRoot() { if (!this.functionTreeRoot) { const leaves = this.getFunctionLeaves(); - this.functionTreeRoot = computeFunctionTreeRoot(this.wasm, leaves); + this.functionTreeRoot = computeFunctionTreeRoot(leaves); } return Promise.resolve(this.functionTreeRoot); } @@ -197,7 +194,7 @@ export class ContractTree { if (!this.functionTree) { const leaves = this.getFunctionLeaves(); - this.functionTree = computeFunctionTree(this.wasm, leaves); + this.functionTree = computeFunctionTree(leaves); } const functionTreeData = computeFunctionTreeData(this.functionTree, functionIndex); return Promise.resolve( diff --git a/yarn-project/pxe/src/database/database.ts b/yarn-project/pxe/src/database/database.ts index dc99768ffcc..906bc4bcc93 100644 --- a/yarn-project/pxe/src/database/database.ts +++ b/yarn-project/pxe/src/database/database.ts @@ -24,6 +24,20 @@ export interface Database extends ContractDatabase { */ getAuthWitness(messageHash: Fr): Promise; + /** + * Adding a capsule to the capsule dispenser. + * @remarks A capsule is a "blob" of data that is passed to the contract through an oracle. + * @param capsule - An array of field elements representing the capsule. + */ + addCapsule(capsule: Fr[]): Promise; + + /** + * Get the next capsule from the capsule dispenser. + * @remarks A capsule is a "blob" of data that is passed to the contract through an oracle. + * @returns A promise that resolves to an array of field elements representing the capsule. + */ + popCapsule(): Promise; + /** * Gets notes based on the provided filter. * @param filter - The filter to apply to the notes. diff --git a/yarn-project/pxe/src/database/memory_db.ts b/yarn-project/pxe/src/database/memory_db.ts index bd604249757..88f98601c3b 100644 --- a/yarn-project/pxe/src/database/memory_db.ts +++ b/yarn-project/pxe/src/database/memory_db.ts @@ -20,6 +20,9 @@ export class MemoryDB extends MemoryContractDatabase implements Database { private globalVariablesHash: Fr | undefined; private addresses: CompleteAddress[] = []; private authWitnesses: Record = {}; + // A capsule is a "blob" of data that is passed to the contract through an oracle. + // We are using a stack to keep track of the capsules that are passed to the contract. + private capsuleStack: Fr[][] = []; constructor(logSuffix?: string) { super(createDebugLogger(logSuffix ? 'aztec:memory_db_' + logSuffix : 'aztec:memory_db')); @@ -44,11 +47,20 @@ export class MemoryDB extends MemoryContractDatabase implements Database { return Promise.resolve(this.authWitnesses[messageHash.toString()]); } - public addNote(note: NoteDao) { + public addNote(note: NoteDao): Promise { this.notesTable.push(note); return Promise.resolve(); } + public addCapsule(capsule: Fr[]): Promise { + this.capsuleStack.push(capsule); + return Promise.resolve(); + } + + public popCapsule(): Promise { + return Promise.resolve(this.capsuleStack.pop()); + } + public addNotes(notes: NoteDao[]) { this.notesTable.push(...notes); return Promise.resolve(); @@ -95,7 +107,9 @@ export class MemoryDB extends MemoryContractDatabase implements Database { public getTreeRoots(): Record { const roots = this.treeRoots; - if (!roots) throw new Error(`Tree roots not set in memory database`); + if (!roots) { + throw new Error(`Tree roots not set in memory database`); + } return roots; } @@ -106,7 +120,9 @@ export class MemoryDB extends MemoryContractDatabase implements Database { public getHistoricBlockData(): HistoricBlockData { const roots = this.getTreeRoots(); - if (!this.globalVariablesHash) throw new Error(`Global variables hash not set in memory database`); + if (!this.globalVariablesHash) { + throw new Error(`Global variables hash not set in memory database`); + } return new HistoricBlockData( roots[MerkleTreeId.NOTE_HASH_TREE], roots[MerkleTreeId.NULLIFIER_TREE], diff --git a/yarn-project/pxe/src/kernel_prover/kernel_prover.test.ts b/yarn-project/pxe/src/kernel_prover/kernel_prover.test.ts index 7f1497b09f7..8dfffa007cb 100644 --- a/yarn-project/pxe/src/kernel_prover/kernel_prover.test.ts +++ b/yarn-project/pxe/src/kernel_prover/kernel_prover.test.ts @@ -1,9 +1,10 @@ import { ExecutionResult, NoteAndSlot } from '@aztec/acir-simulator'; import { + FunctionData, + FunctionSelector, KernelCircuitPublicInputs, MAX_NEW_COMMITMENTS_PER_CALL, MAX_NEW_COMMITMENTS_PER_TX, - MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, MAX_READ_REQUESTS_PER_CALL, MembershipWitness, PrivateCallStackItem, @@ -53,9 +54,10 @@ describe('Kernel Prover', () => { i => (i < newNoteIndices.length ? generateFakeCommitment(notesAndSlots[newNoteIndices[i]]) : Fr.ZERO), 0, ); + const functionData = FunctionData.empty(); + functionData.selector = new FunctionSelector(fnName.charCodeAt(0)); return { - // Replace `FunctionData` with `string` for easier testing. - callStackItem: new PrivateCallStackItem(AztecAddress.ZERO, fnName as any, publicInputs, false), + callStackItem: new PrivateCallStackItem(AztecAddress.ZERO, functionData, publicInputs, false), nestedExecutions: (dependencies[fnName] || []).map(name => createExecutionResult(name)), vk: VerificationKey.makeFake().toBuffer(), newNotes: newNoteIndices.map(idx => notesAndSlots[idx]), @@ -86,11 +88,11 @@ describe('Kernel Prover', () => { }; const expectExecution = (fns: string[]) => { - const callStackItemsInit = proofCreator.createProofInit.mock.calls.map( - args => args[0].privateCall.callStackItem.functionData, + const callStackItemsInit = proofCreator.createProofInit.mock.calls.map(args => + String.fromCharCode(args[0].privateCall.callStackItem.functionData.selector.value), ); - const callStackItemsInner = proofCreator.createProofInner.mock.calls.map( - args => args[0].privateCall.callStackItem.functionData, + const callStackItemsInner = proofCreator.createProofInner.mock.calls.map(args => + String.fromCharCode(args[0].privateCall.callStackItem.functionData.selector.value), ); expect(proofCreator.createProofInit).toHaveBeenCalledTimes(Math.min(1, fns.length)); @@ -157,14 +159,6 @@ describe('Kernel Prover', () => { } }); - it('should throw if call stack is too deep', async () => { - dependencies.a = Array(MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL + 1) - .fill(0) - .map((_, i) => `${i}`); - const executionResult = createExecutionResult('a'); - await expect(prove(executionResult)).rejects.toThrow(); - }); - it('should only return notes that are outputted from the final proof', async () => { const resultA = createExecutionResult('a', [1, 2, 3]); const resultB = createExecutionResult('b', [4]); diff --git a/yarn-project/pxe/src/kernel_prover/kernel_prover.ts b/yarn-project/pxe/src/kernel_prover/kernel_prover.ts index 96e2d49e47c..2b28b61ba16 100644 --- a/yarn-project/pxe/src/kernel_prover/kernel_prover.ts +++ b/yarn-project/pxe/src/kernel_prover/kernel_prover.ts @@ -2,17 +2,18 @@ import { ExecutionResult, NoteAndSlot } from '@aztec/acir-simulator'; import { AztecAddress, CONTRACT_TREE_HEIGHT, + CallRequest, EMPTY_NULLIFIED_COMMITMENT, Fr, MAX_NEW_COMMITMENTS_PER_TX, MAX_NEW_NULLIFIERS_PER_TX, MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, + MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, MAX_READ_REQUESTS_PER_CALL, MAX_READ_REQUESTS_PER_TX, MembershipWitness, PreviousKernelData, PrivateCallData, - PrivateCallStackItem, PrivateKernelInputsInit, PrivateKernelInputsInner, PrivateKernelInputsOrdering, @@ -24,6 +25,7 @@ import { makeEmptyProof, makeTuple, } from '@aztec/circuits.js'; +import { padArrayEnd } from '@aztec/foundation/collection'; import { Tuple, assertLength } from '@aztec/foundation/serialize'; import { KernelProofCreator, ProofCreator, ProofOutput, ProofOutputFinal } from './proof_creator.js'; @@ -93,28 +95,21 @@ export class KernelProver { while (executionStack.length) { const currentExecution = executionStack.pop()!; executionStack.push(...currentExecution.nestedExecutions); - const privateCallStackPreimages = currentExecution.nestedExecutions.map(result => result.callStackItem); - if (privateCallStackPreimages.length > MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL) { - throw new Error( - `Too many items in the call stack. Maximum amount is ${MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL}. Got ${privateCallStackPreimages.length}.`, - ); - } - // Pad with empty items to reach max/const length expected by circuit. - privateCallStackPreimages.push( - ...Array(MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL - privateCallStackPreimages.length) - .fill(0) - .map(() => PrivateCallStackItem.empty()), - ); + + const privateCallRequests = currentExecution.nestedExecutions.map(result => result.callStackItem.toCallRequest()); + const publicCallRequests = currentExecution.enqueuedPublicFunctionCalls.map(result => result.toCallRequest()); // Start with the partially filled in read request witnesses from the simulator // and fill the non-transient ones in with sibling paths via oracle. const readRequestMembershipWitnesses = currentExecution.readRequestPartialWitnesses; for (let rr = 0; rr < readRequestMembershipWitnesses.length; rr++) { - if (currentExecution.callStackItem.publicInputs.readRequests[rr] == Fr.zero()) { - throw new Error( - 'Number of read requests output from Noir circuit does not match number of read request commitment indices output from simulator.', - ); - } + // Pretty sure this check was forever broken. I made some changes to Fr and this started triggering. + // The conditional makes no sense to me anyway. + // if (currentExecution.callStackItem.publicInputs.readRequests[rr] == Fr.ZERO) { + // throw new Error( + // 'Number of read requests output from Noir circuit does not match number of read request commitment indices output from simulator.', + // ); + // } const rrWitness = readRequestMembershipWitnesses[rr]; if (!rrWitness.isTransient) { // Non-transient reads must contain full membership witness with sibling path from commitment to root. @@ -133,8 +128,9 @@ export class KernelProver { const privateCallData = await this.createPrivateCallData( currentExecution, + privateCallRequests, + publicCallRequests, readRequestMembershipWitnesses, - makeTuple(MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, i => privateCallStackPreimages[i], 0), ); if (firstIteration) { @@ -194,12 +190,21 @@ export class KernelProver { private async createPrivateCallData( { callStackItem, vk }: ExecutionResult, + privateCallRequests: CallRequest[], + publicCallRequests: CallRequest[], readRequestMembershipWitnesses: ReadRequestMembershipWitness[], - privateCallStackPreimages: Tuple, ) { const { contractAddress, functionData, publicInputs } = callStackItem; const { portalContractAddress } = publicInputs.callContext; + // Pad with empty items to reach max/const length expected by circuit. + const privateCallStack = padArrayEnd( + privateCallRequests, + CallRequest.empty(), + MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, + ); + const publicCallStack = padArrayEnd(publicCallRequests, CallRequest.empty(), MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL); + const contractLeafMembershipWitness = functionData.isConstructor ? MembershipWitness.random(CONTRACT_TREE_HEIGHT) : await this.oracle.getContractMembershipWitness(contractAddress); @@ -218,7 +223,8 @@ export class KernelProver { return new PrivateCallData( callStackItem, - privateCallStackPreimages, + privateCallStack, + publicCallStack, proof, VerificationKey.fromBuffer(vk), functionLeafMembershipWitness, diff --git a/yarn-project/pxe/src/kernel_prover/proof_creator.ts b/yarn-project/pxe/src/kernel_prover/proof_creator.ts index afd1e586224..acb29674a29 100644 --- a/yarn-project/pxe/src/kernel_prover/proof_creator.ts +++ b/yarn-project/pxe/src/kernel_prover/proof_creator.ts @@ -22,8 +22,6 @@ import { CircuitSimulationStats } from '@aztec/types/stats'; export interface ProofOutput { /** * The public inputs required for the proof generation process. - * Note: C++ side does not define the specific data structure PrivateKernelPublicInputs and therefore - * would not generate a binding in circuits.gen.ts. */ publicInputs: KernelCircuitPublicInputs; /** @@ -39,8 +37,6 @@ export interface ProofOutput { export interface ProofOutputFinal { /** * The public inputs required for the proof generation process. - * Note: C++ side does not define the specific data structure PrivateKernelPublicInputsFinal and therefore - * would not generate a binding in circuits.gen.ts. */ publicInputs: KernelCircuitPublicInputsFinal; /** diff --git a/yarn-project/pxe/src/note_processor/note_processor.test.ts b/yarn-project/pxe/src/note_processor/note_processor.test.ts index 207850ee757..d766a04c336 100644 --- a/yarn-project/pxe/src/note_processor/note_processor.test.ts +++ b/yarn-project/pxe/src/note_processor/note_processor.test.ts @@ -1,5 +1,5 @@ import { AcirSimulator } from '@aztec/acir-simulator'; -import { CircuitsWasm, Fr, MAX_NEW_COMMITMENTS_PER_TX } from '@aztec/circuits.js'; +import { Fr, MAX_NEW_COMMITMENTS_PER_TX } from '@aztec/circuits.js'; import { Grumpkin } from '@aztec/circuits.js/barretenberg'; import { pedersenHash } from '@aztec/foundation/crypto'; import { Point } from '@aztec/foundation/fields'; @@ -28,7 +28,6 @@ import { NoteProcessor } from './note_processor.js'; const TXS_PER_BLOCK = 4; describe('Note Processor', () => { - let wasm: CircuitsWasm; let grumpkin: Grumpkin; let database: Database; let aztecNode: ReturnType>; @@ -110,9 +109,8 @@ describe('Note Processor', () => { return { blockContexts, encryptedLogsArr, ownedL1NotePayloads }; }; - beforeAll(async () => { - wasm = await CircuitsWasm.get(); - grumpkin = new Grumpkin(wasm); + beforeAll(() => { + grumpkin = new Grumpkin(); owner = ConstantKeyPair.random(grumpkin); }); diff --git a/yarn-project/pxe/src/note_processor/note_processor.ts b/yarn-project/pxe/src/note_processor/note_processor.ts index 385b4ee0c0d..c3a57994f48 100644 --- a/yarn-project/pxe/src/note_processor/note_processor.ts +++ b/yarn-project/pxe/src/note_processor/note_processor.ts @@ -94,7 +94,7 @@ export class NoteProcessor { } const blocksAndNotes: ProcessedData[] = []; - const curve = await Grumpkin.new(); + const curve = new Grumpkin(); // Iterate over both blocks and encrypted logs. for (let blockIndex = 0; blockIndex < encryptedL2BlockLogs.length; ++blockIndex) { @@ -203,10 +203,14 @@ export class NoteProcessor { let uniqueSiloedNoteHash: Fr | undefined; let innerNullifier: Fr | undefined; for (; commitmentIndex < commitments.length; ++commitmentIndex) { - if (excludedIndices.has(commitmentIndex)) continue; + if (excludedIndices.has(commitmentIndex)) { + continue; + } const commitment = commitments[commitmentIndex]; - if (commitment.equals(Fr.ZERO)) break; + if (commitment.equals(Fr.ZERO)) { + break; + } const expectedNonce = computeCommitmentNonce(firstNullifier, commitmentIndex); ({ innerNoteHash, siloedNoteHash, uniqueSiloedNoteHash, innerNullifier } = diff --git a/yarn-project/pxe/src/pxe_service/create_pxe_service.ts b/yarn-project/pxe/src/pxe_service/create_pxe_service.ts index 4fc15e58f6f..04eddd7db48 100644 --- a/yarn-project/pxe/src/pxe_service/create_pxe_service.ts +++ b/yarn-project/pxe/src/pxe_service/create_pxe_service.ts @@ -43,7 +43,7 @@ export async function createPXEService( : undefined : useLogSuffix; - keyStore = keyStore || new TestKeyStore(await Grumpkin.new()); + keyStore = keyStore || new TestKeyStore(new Grumpkin()); db = db || new MemoryDB(logSuffix); const server = new PXEService(keyStore, aztecNode, db, config, logSuffix); diff --git a/yarn-project/pxe/src/pxe_service/pxe_service.ts b/yarn-project/pxe/src/pxe_service/pxe_service.ts index 26aec37b47a..fc0bb37c78e 100644 --- a/yarn-project/pxe/src/pxe_service/pxe_service.ts +++ b/yarn-project/pxe/src/pxe_service/pxe_service.ts @@ -8,6 +8,7 @@ import { } from '@aztec/acir-simulator'; import { AztecAddress, + CallRequest, CompleteAddress, FunctionData, GrumpkinPrivateKey, @@ -119,8 +120,12 @@ export class PXEService implements PXE { return this.db.addAuthWitness(witness.requestHash, witness.witness); } + public addCapsule(capsule: Fr[]) { + return this.db.addCapsule(capsule); + } + public async registerAccount(privKey: GrumpkinPrivateKey, partialAddress: PartialAddress): Promise { - const completeAddress = await CompleteAddress.fromPrivateKeyAndPartialAddress(privKey, partialAddress); + const completeAddress = CompleteAddress.fromPrivateKeyAndPartialAddress(privKey, partialAddress); const wasAdded = await this.db.addCompleteAddress(completeAddress); if (wasAdded) { const pubKey = this.keyStore.addAccount(privKey); @@ -276,7 +281,9 @@ export class PXEService implements PXE { const commitments = tx.newCommitments; for (let i = 0; i < commitments.length; ++i) { const commitment = commitments[i]; - if (commitment.equals(Fr.ZERO)) break; + if (commitment.equals(Fr.ZERO)) { + break; + } const nonce = computeCommitmentNonce(firstNullifier, i); const { siloedNoteHash, uniqueSiloedNoteHash } = await this.simulator.computeNoteHashAndNullifier( @@ -321,7 +328,9 @@ export class PXEService implements PXE { const newContract = deployedContractAddress ? await this.db.getContract(deployedContractAddress) : undefined; const tx = await this.#simulateAndProve(txRequest, newContract); - if (simulatePublic) await this.#simulatePublicCalls(tx); + if (simulatePublic) { + await this.#simulatePublicCalls(tx); + } this.log.info(`Executed local simulation for ${await tx.getTxHash()}`); return tx; @@ -347,13 +356,23 @@ export class PXEService implements PXE { } public async getTxReceipt(txHash: TxHash): Promise { + let txReceipt = new TxReceipt(txHash, TxStatus.DROPPED, 'Tx dropped by P2P node.'); + + // We first check if the tx is in pending (instead of first checking if it is mined) because if we first check + // for mined and then for pending there could be a race condition where the tx is mined between the two checks + // and we would incorrectly return a TxReceipt with status DROPPED + const pendingTx = await this.node.getPendingTxByHash(txHash); + if (pendingTx) { + txReceipt = new TxReceipt(txHash, TxStatus.PENDING, ''); + } + const settledTx = await this.node.getTx(txHash); if (settledTx) { const deployedContractAddress = settledTx.newContractData.find( c => !c.contractAddress.equals(AztecAddress.ZERO), )?.contractAddress; - return new TxReceipt( + txReceipt = new TxReceipt( txHash, TxStatus.MINED, '', @@ -363,12 +382,7 @@ export class PXEService implements PXE { ); } - const pendingTx = await this.node.getPendingTxByHash(txHash); - if (pendingTx) { - return new TxReceipt(txHash, TxStatus.PENDING, ''); - } - - return new TxReceipt(txHash, TxStatus.DROPPED, 'Tx dropped by P2P node.'); + return txReceipt; } public async getTx(txHash: TxHash): Promise { @@ -622,28 +636,27 @@ export class PXEService implements PXE { publicInputs: KernelCircuitPublicInputsFinal, enqueuedPublicCalls: PublicCallRequest[], ) { - const callToHash = (call: PublicCallRequest) => call.toPublicCallStackItem().hash(); - const enqueuedPublicCallsHashes = await Promise.all(enqueuedPublicCalls.map(callToHash)); + const enqueuedPublicCallStackItems = await Promise.all(enqueuedPublicCalls.map(c => c.toCallRequest())); const { publicCallStack } = publicInputs.end; // Validate all items in enqueued public calls are in the kernel emitted stack - const areEqual = enqueuedPublicCallsHashes.reduce( + const areEqual = enqueuedPublicCallStackItems.reduce( (accum, enqueued) => accum && !!publicCallStack.find(item => item.equals(enqueued)), true, ); if (!areEqual) { throw new Error( - `Enqueued public function calls and public call stack do not match.\nEnqueued calls: ${enqueuedPublicCallsHashes - .map(h => h.toString()) + `Enqueued public function calls and public call stack do not match.\nEnqueued calls: ${enqueuedPublicCallStackItems + .map(h => h.hash.toString()) .join(', ')}\nPublic call stack: ${publicCallStack.map(i => i.toString()).join(', ')}`, ); } // Override kernel output publicInputs.end.publicCallStack = padArrayEnd( - enqueuedPublicCallsHashes, - Fr.ZERO, + enqueuedPublicCallStackItems, + CallRequest.empty(), MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, ); } diff --git a/yarn-project/pxe/src/pxe_service/test/pxe_service.test.ts b/yarn-project/pxe/src/pxe_service/test/pxe_service.test.ts index a980b0efa18..e265d99a898 100644 --- a/yarn-project/pxe/src/pxe_service/test/pxe_service.test.ts +++ b/yarn-project/pxe/src/pxe_service/test/pxe_service.test.ts @@ -11,8 +11,8 @@ import { PXEServiceConfig } from '../../index.js'; import { PXEService } from '../pxe_service.js'; import { pxeTestSuite } from './pxe_test_suite.js'; -async function createPXEService(): Promise { - const keyStore = new TestKeyStore(await Grumpkin.new()); +function createPXEService(): Promise { + const keyStore = new TestKeyStore(new Grumpkin()); const node = mock(); const db = new MemoryDB(); const config: PXEServiceConfig = { l2BlockPollingIntervalMS: 100, l2StartingBlock: INITIAL_L2_BLOCK_NUM }; @@ -31,7 +31,7 @@ async function createPXEService(): Promise { }; node.getL1ContractAddresses.mockResolvedValue(mockedContracts); - return new PXEService(keyStore, node, db, config); + return Promise.resolve(new PXEService(keyStore, node, db, config)); } pxeTestSuite('PXEService', createPXEService); @@ -42,8 +42,8 @@ describe('PXEService', () => { let db: MemoryDB; let config: PXEServiceConfig; - beforeEach(async () => { - keyStore = new TestKeyStore(await Grumpkin.new()); + beforeEach(() => { + keyStore = new TestKeyStore(new Grumpkin()); node = mock(); db = new MemoryDB(); config = { l2BlockPollingIntervalMS: 100, l2StartingBlock: INITIAL_L2_BLOCK_NUM }; diff --git a/yarn-project/pxe/src/pxe_service/test/pxe_test_suite.ts b/yarn-project/pxe/src/pxe_service/test/pxe_test_suite.ts index fdc21c69941..d23da97d651 100644 --- a/yarn-project/pxe/src/pxe_service/test/pxe_test_suite.ts +++ b/yarn-project/pxe/src/pxe_service/test/pxe_test_suite.ts @@ -12,13 +12,10 @@ export const pxeTestSuite = (testName: string, pxeSetup: () => Promise) => }, 120_000); it('registers an account and returns it as an account only and not as a recipient', async () => { - const keyPair = ConstantKeyPair.random(await Grumpkin.new()); - const completeAddress = await CompleteAddress.fromPrivateKeyAndPartialAddress( - await keyPair.getPrivateKey(), - Fr.random(), - ); + const keyPair = ConstantKeyPair.random(new Grumpkin()); + const completeAddress = CompleteAddress.fromPrivateKeyAndPartialAddress(keyPair.getPrivateKey(), Fr.random()); - await pxe.registerAccount(await keyPair.getPrivateKey(), completeAddress.partialAddress); + await pxe.registerAccount(keyPair.getPrivateKey(), completeAddress.partialAddress); // Check that the account is correctly registered using the getAccounts and getRecipients methods const accounts = await pxe.getRegisteredAccounts(); @@ -52,14 +49,11 @@ export const pxeTestSuite = (testName: string, pxeSetup: () => Promise) => }); it('does not throw when registering the same account twice (just ignores the second attempt)', async () => { - const keyPair = ConstantKeyPair.random(await Grumpkin.new()); - const completeAddress = await CompleteAddress.fromPrivateKeyAndPartialAddress( - await keyPair.getPrivateKey(), - Fr.random(), - ); + const keyPair = ConstantKeyPair.random(new Grumpkin()); + const completeAddress = CompleteAddress.fromPrivateKeyAndPartialAddress(keyPair.getPrivateKey(), Fr.random()); - await pxe.registerAccount(await keyPair.getPrivateKey(), completeAddress.partialAddress); - await pxe.registerAccount(await keyPair.getPrivateKey(), completeAddress.partialAddress); + await pxe.registerAccount(keyPair.getPrivateKey(), completeAddress.partialAddress); + await pxe.registerAccount(keyPair.getPrivateKey(), completeAddress.partialAddress); }); it('cannot register a recipient with the same aztec address but different pub key or partial address', async () => { diff --git a/yarn-project/pxe/src/simulator_oracle/index.ts b/yarn-project/pxe/src/simulator_oracle/index.ts index 53642ae9cd2..f62b7683fa2 100644 --- a/yarn-project/pxe/src/simulator_oracle/index.ts +++ b/yarn-project/pxe/src/simulator_oracle/index.ts @@ -31,19 +31,30 @@ export class SimulatorOracle implements DBOracle { async getCompleteAddress(address: AztecAddress): Promise { const completeAddress = await this.db.getCompleteAddress(address); - if (!completeAddress) + if (!completeAddress) { throw new Error( `No public key registered for address ${address.toString()}. Register it by calling pxe.registerRecipient(...) or pxe.registerAccount(...).\nSee docs for context: https://docs.aztec.network/dev_docs/contracts/common_errors#no-public-key-registered-error`, ); + } return completeAddress; } async getAuthWitness(messageHash: Fr): Promise { const witness = await this.db.getAuthWitness(messageHash); - if (!witness) throw new Error(`Unknown auth witness for message hash ${messageHash.toString(true)}`); + if (!witness) { + throw new Error(`Unknown auth witness for message hash ${messageHash.toString()}`); + } return witness; } + async popCapsule(): Promise { + const capsule = await this.db.popCapsule(); + if (!capsule) { + throw new Error(`No capsules available`); + } + return capsule; + } + async getNotes(contractAddress: AztecAddress, storageSlot: Fr) { const noteDaos = await this.db.getNotes({ contractAddress, storageSlot }); return noteDaos.map(({ contractAddress, storageSlot, nonce, note, innerNoteHash, siloedNullifier, index }) => ({ diff --git a/yarn-project/pxe/src/synchronizer/synchronizer.test.ts b/yarn-project/pxe/src/synchronizer/synchronizer.test.ts index 29833507e2b..2179fecc524 100644 --- a/yarn-project/pxe/src/synchronizer/synchronizer.test.ts +++ b/yarn-project/pxe/src/synchronizer/synchronizer.test.ts @@ -102,10 +102,10 @@ describe('Synchronizer', () => { aztecNode.getBlockNumber.mockResolvedValueOnce(1); // Manually adding account to database so that we can call synchronizer.isAccountStateSynchronized - const keyStore = new TestKeyStore(await Grumpkin.new()); + const keyStore = new TestKeyStore(new Grumpkin()); const privateKey = GrumpkinScalar.random(); keyStore.addAccount(privateKey); - const completeAddress = await CompleteAddress.fromPrivateKeyAndPartialAddress(privateKey, Fr.random()); + const completeAddress = CompleteAddress.fromPrivateKeyAndPartialAddress(privateKey, Fr.random()); await database.addCompleteAddress(completeAddress); // Add the account which will add the note processor to the synchronizer diff --git a/yarn-project/pxe/src/synchronizer/synchronizer.ts b/yarn-project/pxe/src/synchronizer/synchronizer.ts index fec240b9ef1..524fddd2a3e 100644 --- a/yarn-project/pxe/src/synchronizer/synchronizer.ts +++ b/yarn-project/pxe/src/synchronizer/synchronizer.ts @@ -39,7 +39,9 @@ export class Synchronizer { * @param retryInterval - The time interval (in ms) to wait before retrying if no data is available. */ public async start(from = INITIAL_L2_BLOCK_NUM, limit = 1, retryInterval = 1000) { - if (this.running) return; + if (this.running) { + return; + } this.running = true; if (from < this.synchedToBlock + 1) { @@ -197,7 +199,9 @@ export class Synchronizer { private async setBlockDataFromBlock(latestBlock: L2BlockContext) { const { block } = latestBlock; - if (block.number < this.initialSyncBlockNumber) return; + if (block.number < this.initialSyncBlockNumber) { + return; + } const globalsHash = computeGlobalsHash(latestBlock.block.globalVariables); const blockData = new HistoricBlockData( @@ -241,7 +245,9 @@ export class Synchronizer { public addAccount(publicKey: PublicKey, keyStore: KeyStore, startingBlock: number) { const predicate = (x: NoteProcessor) => x.publicKey.equals(publicKey); const processor = this.noteProcessors.find(predicate) ?? this.noteProcessorsToCatchUp.find(predicate); - if (processor) return; + if (processor) { + return; + } this.noteProcessorsToCatchUp.push(new NoteProcessor(publicKey, keyStore, this.db, this.node, startingBlock)); } diff --git a/yarn-project/scripts/package.json b/yarn-project/scripts/package.json index d228efbe325..df218b1331d 100644 --- a/yarn-project/scripts/package.json +++ b/yarn-project/scripts/package.json @@ -14,7 +14,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "start:dev": "tsc-watch -p tsconfig.json --onSuccess 'yarn start'", "start": "node ./dest/index.js", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" @@ -31,7 +31,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/lodash.pick": "^4.4.7", "@types/node": "^18.14.6", diff --git a/yarn-project/scripts/src/benchmarks/aggregate.ts b/yarn-project/scripts/src/benchmarks/aggregate.ts index 125140dbe88..3dab2f4f54f 100644 --- a/yarn-project/scripts/src/benchmarks/aggregate.ts +++ b/yarn-project/scripts/src/benchmarks/aggregate.ts @@ -52,15 +52,21 @@ function append( log(`Value ${value} for ${metric} in ${bucket} is not a number`); return; } - if (!results[metric]) results[metric] = {}; - if (!results[metric]![bucket]) results[metric]![bucket] = []; + if (!results[metric]) { + results[metric] = {}; + } + if (!results[metric]![bucket]) { + results[metric]![bucket] = []; + } results[metric]![bucket].push(numeric); } /** Processes an entry with event name 'rollup-published-to-l1' and updates results */ function processRollupPublished(entry: L1PublishStats, results: BenchmarkCollectedResults) { const bucket = entry.txCount; - if (!BENCHMARK_BLOCK_SIZES.includes(bucket)) return; + if (!BENCHMARK_BLOCK_SIZES.includes(bucket)) { + return; + } append(results, 'l1_rollup_calldata_gas', bucket, entry.calldataGas); append(results, 'l1_rollup_calldata_size_in_bytes', bucket, entry.calldataSize); append(results, 'l1_rollup_execution_gas', bucket, entry.gasUsed); @@ -72,8 +78,12 @@ function processRollupPublished(entry: L1PublishStats, results: BenchmarkCollect */ function processRollupBlockSynced(entry: L2BlockHandledStats, results: BenchmarkCollectedResults) { const bucket = entry.txCount; - if (!BENCHMARK_BLOCK_SIZES.includes(bucket)) return; - if (entry.isBlockOurs) return; + if (!BENCHMARK_BLOCK_SIZES.includes(bucket)) { + return; + } + if (entry.isBlockOurs) { + return; + } append(results, 'l2_block_processing_time_in_ms', bucket, entry.duration); } @@ -83,7 +93,9 @@ function processRollupBlockSynced(entry: L2BlockHandledStats, results: Benchmark */ function processCircuitSimulation(entry: CircuitSimulationStats, results: BenchmarkCollectedResults) { const bucket = entry.circuitName; - if (!bucket) return; + if (!bucket) { + return; + } append(results, 'circuit_simulation_time_in_ms', bucket, entry.duration); append(results, 'circuit_input_size_in_bytes', bucket, entry.inputSize); append(results, 'circuit_output_size_in_bytes', bucket, entry.outputSize); @@ -105,14 +117,17 @@ function processNoteProcessorCaughtUp(entry: NoteProcessorCaughtUpStats, results append(results, 'note_history_successful_decrypting_time_in_ms', blocks, duration); append(results, 'pxe_database_size_in_bytes', blocks, dbSize); } - if (BENCHMARK_HISTORY_CHAIN_LENGTHS.includes(blocks) && decrypted === 0) + if (BENCHMARK_HISTORY_CHAIN_LENGTHS.includes(blocks) && decrypted === 0) { append(results, 'note_history_trial_decrypting_time_in_ms', blocks, duration); + } } /** Processes an entry with event name 'l2-block-built' and updates results where buckets are rollup sizes */ function processL2BlockBuilt(entry: L2BlockBuiltStats, results: BenchmarkCollectedResults) { const bucket = entry.txCount; - if (!BENCHMARK_BLOCK_SIZES.includes(bucket)) return; + if (!BENCHMARK_BLOCK_SIZES.includes(bucket)) { + return; + } append(results, 'l2_block_building_time_in_ms', bucket, entry.duration); append(results, 'l2_block_rollup_simulation_time_in_ms', bucket, entry.rollupCircuitsDuration); append(results, 'l2_block_public_tx_process_time_in_ms', bucket, entry.publicProcessDuration); @@ -121,8 +136,12 @@ function processL2BlockBuilt(entry: L2BlockBuiltStats, results: BenchmarkCollect /** Processes entries with event name node-synced-chain-history emitted by benchmark tests where buckets are chain lengths */ function processNodeSyncedChain(entry: NodeSyncedChainHistoryStats, results: BenchmarkCollectedResults) { const bucket = entry.blockCount; - if (!BENCHMARK_HISTORY_CHAIN_LENGTHS.includes(bucket)) return; - if (entry.txsPerBlock !== BENCHMARK_HISTORY_BLOCK_SIZE) return; + if (!BENCHMARK_HISTORY_CHAIN_LENGTHS.includes(bucket)) { + return; + } + if (entry.txsPerBlock !== BENCHMARK_HISTORY_BLOCK_SIZE) { + return; + } append(results, 'node_history_sync_time_in_ms', bucket, entry.duration); append(results, 'node_database_size_in_bytes', bucket, entry.dbSize); } @@ -188,7 +207,9 @@ export async function main() { results[metricName as MetricName] = resultMetric; for (const [bucketName, bucket] of Object.entries(metric)) { let avg = bucket.reduce((acc, val) => acc + val, 0) / bucket.length; - if (avg > 100) avg = Math.floor(avg); + if (avg > 100) { + avg = Math.floor(avg); + } resultMetric[bucketName] = avg; } } diff --git a/yarn-project/scripts/src/benchmarks/comment.ts b/yarn-project/scripts/src/benchmarks/comment.ts index 12815f95283..c1bfd2ccf01 100644 --- a/yarn-project/scripts/src/benchmarks/comment.ts +++ b/yarn-project/scripts/src/benchmarks/comment.ts @@ -16,7 +16,9 @@ const log = createConsoleLogger(); /** Returns the number of the current PR */ function getPrNumber() { - if (!process.env.CIRCLE_PULL_REQUEST) throw new Error(`Not in Circle PR`); + if (!process.env.CIRCLE_PULL_REQUEST) { + throw new Error(`Not in Circle PR`); + } const fragments = process.env.CIRCLE_PULL_REQUEST.split('/'); return fragments[fragments.length - 1]; } @@ -60,8 +62,11 @@ function sendGitHubRequest(url: string, method = 'GET', data?: object): Promise< 'User-Agent': OWNER, 'Content-Type': undefined as string | undefined, }; - if (data) headers['Content-Type'] = 'application/json'; - else delete headers['Content-Type']; + if (data) { + headers['Content-Type'] = 'application/json'; + } else { + delete headers['Content-Type']; + } const requestOptions = { method, headers }; @@ -91,7 +96,9 @@ function sendGitHubRequest(url: string, method = 'GET', data?: object): Promise< reject(error); }); - if (data) req.write(JSON.stringify(data)); + if (data) { + req.write(JSON.stringify(data)); + } req.end(); }); } diff --git a/yarn-project/scripts/src/benchmarks/markdown.ts b/yarn-project/scripts/src/benchmarks/markdown.ts index 84d7347f3c1..9c5fdcea8e1 100644 --- a/yarn-project/scripts/src/benchmarks/markdown.ts +++ b/yarn-project/scripts/src/benchmarks/markdown.ts @@ -26,7 +26,9 @@ const log = createConsoleLogger(); /** Returns whether the value should be a warning, based on the % difference and absolute value. */ function isWarning(row: string, col: string, value: number, base: number | undefined) { - if (base === undefined) return false; + if (base === undefined) { + return false; + } const absPercentDiff = Math.abs(Math.round(((value - base) / base) * 100)); if ((row.endsWith('_ms') || col.endsWith('_ms')) && value < SMALL_MS_THRESHOLD) { return absPercentDiff >= WARNING_DIFF_THRESHOLD_SMALL_MS; @@ -62,10 +64,14 @@ function getWarnings( data: Record>, base: Record> | undefined, ) { - if (!base) return []; + if (!base) { + return []; + } const warnings: string[] = []; for (const row in data) { - if (row === 'timestamp') continue; + if (row === 'timestamp') { + continue; + } for (const col in data[row]) { const value = data[row][col]; const baseValue = (base[row] ?? {})[col]; @@ -101,13 +107,17 @@ function getCell( /** Wraps the metric name in a span with a title with the description, if found. */ function withDesc(name: string) { const description = Metrics.find(m => m.name === name)?.description; - if (!description) return name; + if (!description) { + return name; + } return `${name}`; } /** Formats a numeric value for display. */ function formatValue(value: number) { - if (value < 100) return value.toPrecision(3); + if (value < 100) { + return value.toPrecision(3); + } return value.toLocaleString(); } @@ -117,7 +127,9 @@ function transpose(obj: any) { for (const outerKey in obj) { const innerObj = obj[outerKey]; for (const innerKey in innerObj) { - if (!transposed[innerKey]) transposed[innerKey] = {}; + if (!transposed[innerKey]) { + transposed[innerKey] = {}; + } transposed[innerKey][outerKey] = innerObj[innerKey]; } } diff --git a/yarn-project/sequencer-client/package.json b/yarn-project/sequencer-client/package.json index 6b9944c3dfd..f99ee99c2be 100644 --- a/yarn-project/sequencer-client/package.json +++ b/yarn-project/sequencer-client/package.json @@ -15,7 +15,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests", "test:integration": "concurrently -k -s first -c reset,dim -n test,anvil \"yarn test:integration:run\" \"anvil\"", "test:integration:run": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --no-cache --config jest.integration.config.json" @@ -51,7 +51,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/levelup": "^5.1.2", "@types/lodash.chunk": "^4.2.7", diff --git a/yarn-project/sequencer-client/src/block_builder/solo_block_builder.test.ts b/yarn-project/sequencer-client/src/block_builder/solo_block_builder.test.ts index f5ddccffe60..b76c03f88ef 100644 --- a/yarn-project/sequencer-client/src/block_builder/solo_block_builder.test.ts +++ b/yarn-project/sequencer-client/src/block_builder/solo_block_builder.test.ts @@ -348,7 +348,7 @@ describe('sequencer/solo_block_builder', () => { expect(contractTreeAfter.root).toEqual(expectedContractTreeAfter); expect(contractTreeAfter.size).toEqual(BigInt(totalCount)); }, - 30000, + 60000, ); it('builds an empty L2 block', async () => { @@ -363,6 +363,8 @@ describe('sequencer/solo_block_builder', () => { expect(l2Block.number).toEqual(blockNumber); }, 10_000); + // TODO(Alvaro) This test is horribly slow since it creates strictly increasing nullifiers, the worst case scenario for the simulated base rollup + // With the current implementation. it('builds a mixed L2 block', async () => { // Ensure that each transaction has unique (non-intersecting nullifier values) const txs = await Promise.all([ @@ -376,7 +378,7 @@ describe('sequencer/solo_block_builder', () => { const [l2Block] = await builder.buildL2Block(globalVariables, txs, l1ToL2Messages); expect(l2Block.number).toEqual(blockNumber); - }, 40_000); + }, 200_000); // This test specifically tests nullifier values which previously caused e2e_private_token test to fail it('e2e_private_token edge case regression test on nullifier values', async () => { @@ -409,7 +411,7 @@ describe('sequencer/solo_block_builder', () => { const [l2Block] = await builder.buildL2Block(globalVariables, txs, mockL1ToL2Messages); expect(l2Block.number).toEqual(blockNumber); - }, 10000); + }, 20000); }); // describe("Input guard tests", () => { diff --git a/yarn-project/sequencer-client/src/block_builder/solo_block_builder.ts b/yarn-project/sequencer-client/src/block_builder/solo_block_builder.ts index b02c12ebb1c..da6b621773c 100644 --- a/yarn-project/sequencer-client/src/block_builder/solo_block_builder.ts +++ b/yarn-project/sequencer-client/src/block_builder/solo_block_builder.ts @@ -527,7 +527,9 @@ export class SoloBlockBuilder implements BlockBuilder { height: N, ): Promise> { // If this is an empty tx, then just return zeroes - if (value.isZero()) return this.makeEmptyMembershipWitness(height); + if (value.isZero()) { + return this.makeEmptyMembershipWitness(height); + } const index = await this.db.findLeafIndex(treeId, value.toBuffer()); if (index === undefined) { @@ -576,7 +578,9 @@ export class SoloBlockBuilder implements BlockBuilder { const tree = MerkleTreeId.NULLIFIER_TREE; const prevValueIndex = await this.db.getPreviousValueIndex(tree, frToBigInt(nullifier)); const prevValueInfo = await this.db.getLeafData(tree, prevValueIndex.index); - if (!prevValueInfo) throw new Error(`Nullifier tree should have one initial leaf`); + if (!prevValueInfo) { + throw new Error(`Nullifier tree should have one initial leaf`); + } const prevValueSiblingPath = await this.db.getSiblingPath(tree, BigInt(prevValueIndex.index)); return { diff --git a/yarn-project/sequencer-client/src/publisher/l1-publisher.ts b/yarn-project/sequencer-client/src/publisher/l1-publisher.ts index cae6c43dc00..b87a473c888 100644 --- a/yarn-project/sequencer-client/src/publisher/l1-publisher.ts +++ b/yarn-project/sequencer-client/src/publisher/l1-publisher.ts @@ -134,10 +134,14 @@ export class L1Publisher implements L2BlockReceiver { } const txHash = await this.sendProcessTx(txData); - if (!txHash) break; + if (!txHash) { + break; + } const receipt = await this.getTransactionReceipt(txHash); - if (!receipt) break; + if (!receipt) { + break; + } // Tx was mined successfully if (receipt.status) { @@ -184,17 +188,23 @@ export class L1Publisher implements L2BlockReceiver { const arr = _contractData.length ? _contractData : contractData; const txHashes = await this.sendEmitNewContractDataTx(l2BlockNum, l2BlockHash, arr); - if (!txHashes) break; + if (!txHashes) { + break; + } // filter successful txs _contractData = arr.filter((_, i) => !!txHashes[i]); const receipts = await Promise.all( txHashes.filter(isNotUndefined).map(txHash => this.getTransactionReceipt(txHash)), ); - if (!receipts?.length) break; + if (!receipts?.length) { + break; + } // ALL Txs were mined successfully - if (receipts.length === contractData.length && receipts.every(r => r?.status)) return true; + if (receipts.length === contractData.length && receipts.every(r => r?.status)) { + return true; + } this.log( `Transaction status failed: ${receipts diff --git a/yarn-project/sequencer-client/src/publisher/viem-tx-sender.ts b/yarn-project/sequencer-client/src/publisher/viem-tx-sender.ts index f8df1b805b0..ee3a984170e 100644 --- a/yarn-project/sequencer-client/src/publisher/viem-tx-sender.ts +++ b/yarn-project/sequencer-client/src/publisher/viem-tx-sender.ts @@ -77,7 +77,9 @@ export class ViemTxSender implements L1PublisherTxSender { async getTransactionStats(txHash: string): Promise { const tx = await this.publicClient.getTransaction({ hash: txHash as Hex }); - if (!tx) return undefined; + if (!tx) { + return undefined; + } const calldata = hexToBytes(tx.input); return { transactionHash: tx.hash, @@ -146,9 +148,9 @@ export class ViemTxSender implements L1PublisherTxSender { extendedContractData.contractData.contractAddress.toString() as Hex, extendedContractData.contractData.portalContractAddress.toString() as Hex, `0x${l2BlockHash.toString('hex')}`, - extendedContractData.partialAddress.toString(true), - extendedContractData.publicKey.x.toString(true), - extendedContractData.publicKey.y.toString(true), + extendedContractData.partialAddress.toString(), + extendedContractData.publicKey.x.toString(), + extendedContractData.publicKey.y.toString(), `0x${extendedContractData.bytecode.toString('hex')}`, ] as const; diff --git a/yarn-project/sequencer-client/src/sequencer/config.ts b/yarn-project/sequencer-client/src/sequencer/config.ts index 82c6e10db16..43585a9bcba 100644 --- a/yarn-project/sequencer-client/src/sequencer/config.ts +++ b/yarn-project/sequencer-client/src/sequencer/config.ts @@ -1,17 +1 @@ -/** - * The sequencer configuration. - */ -export interface SequencerConfig { - /** - * The number of ms to wait between polling for pending txs. - */ - transactionPollingIntervalMS?: number; - /** - * The maximum number of txs to include in a block. - */ - maxTxsPerBlock?: number; - /** - * The minimum number of txs to include in a block. - */ - minTxsPerBlock?: number; -} +export { SequencerConfig } from '@aztec/types'; diff --git a/yarn-project/sequencer-client/src/sequencer/public_processor.test.ts b/yarn-project/sequencer-client/src/sequencer/public_processor.test.ts index 64a36de4d09..6f981a34d67 100644 --- a/yarn-project/sequencer-client/src/sequencer/public_processor.test.ts +++ b/yarn-project/sequencer-client/src/sequencer/public_processor.test.ts @@ -3,7 +3,7 @@ import { ARGS_LENGTH, AztecAddress, CallContext, - CircuitsWasm, + CallRequest, CombinedAccumulatedData, EthAddress, Fr, @@ -19,7 +19,6 @@ import { makeEmptyProof, makeTuple, } from '@aztec/circuits.js'; -import { computeCallStackItemHash } from '@aztec/circuits.js/abis'; import { makeAztecAddress, makePrivateKernelPublicInputsFinal, @@ -27,7 +26,16 @@ import { makeSelector, } from '@aztec/circuits.js/factories'; import { padArrayEnd } from '@aztec/foundation/collection'; -import { ExtendedContractData, FunctionCall, FunctionL2Logs, SiblingPath, Tx, TxL2Logs, mockTx } from '@aztec/types'; +import { + ExtendedContractData, + FunctionCall, + FunctionL2Logs, + SiblingPath, + SimulationError, + Tx, + TxL2Logs, + mockTx, +} from '@aztec/types'; import { MerkleTreeOperations, TreeInfo } from '@aztec/world-state'; import { MockProxy, mock } from 'jest-mock-extended'; @@ -35,7 +43,7 @@ import times from 'lodash.times'; import { PublicProver } from '../prover/index.js'; import { PublicKernelCircuitSimulator } from '../simulator/index.js'; -import { ContractsDataSourcePublicDB } from '../simulator/public_executor.js'; +import { ContractsDataSourcePublicDB, WorldStatePublicDB } from '../simulator/public_executor.js'; import { WasmPublicKernelCircuitSimulator } from '../simulator/public_kernel.js'; import { PublicProcessor } from './public_processor.js'; @@ -44,6 +52,7 @@ describe('public_processor', () => { let publicExecutor: MockProxy; let publicProver: MockProxy; let publicContractsDB: MockProxy; + let publicWorldStateDB: MockProxy; let proof: Proof; let root: Buffer; @@ -55,6 +64,7 @@ describe('public_processor', () => { publicExecutor = mock(); publicProver = mock(); publicContractsDB = mock(); + publicWorldStateDB = mock(); proof = makeEmptyProof(); root = Buffer.alloc(32, 5); @@ -77,12 +87,13 @@ describe('public_processor', () => { GlobalVariables.empty(), HistoricBlockData.empty(), publicContractsDB, + publicWorldStateDB, ); }); it('skips txs without public execution requests', async function () { const tx = mockTx(); - tx.data.end.publicCallStack = makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, Fr.zero); + tx.data.end.publicCallStack = makeTuple(MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, CallRequest.empty); const hash = await tx.getTxHash(); const [processed, failed] = await processor.process([tx]); @@ -111,16 +122,13 @@ describe('public_processor', () => { expect(processed).toEqual([]); expect(failed[0].tx).toEqual(tx); + expect(publicWorldStateDB.commit).toHaveBeenCalledTimes(0); + expect(publicWorldStateDB.rollback).toHaveBeenCalledTimes(1); }); }); describe('with actual circuits', () => { let publicKernel: PublicKernelCircuitSimulator; - let wasm: CircuitsWasm; - - beforeAll(async () => { - wasm = await CircuitsWasm.get(); - }); beforeEach(() => { const path = times(PUBLIC_DATA_TREE_HEIGHT, i => Buffer.alloc(32, i)); @@ -134,6 +142,7 @@ describe('public_processor', () => { GlobalVariables.empty(), HistoricBlockData.empty(), publicContractsDB, + publicWorldStateDB, ); }); @@ -145,12 +154,15 @@ describe('public_processor', () => { it('runs a tx with enqueued public calls', async function () { const callRequests: PublicCallRequest[] = [makePublicCallRequest(0x100), makePublicCallRequest(0x100)]; - const callStackItems = await Promise.all(callRequests.map(call => call.toPublicCallStackItem())); - const callStackHashes = callStackItems.map(call => computeCallStackItemHash(wasm, call)); + const callStackItems = callRequests.map(call => call.toCallRequest()); const kernelOutput = makePrivateKernelPublicInputsFinal(0x10); - kernelOutput.end.publicCallStack = padArrayEnd(callStackHashes, Fr.ZERO, MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX); - kernelOutput.end.privateCallStack = padArrayEnd([], Fr.ZERO, MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX); + kernelOutput.end.publicCallStack = padArrayEnd( + callStackItems, + CallRequest.empty(), + MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, + ); + kernelOutput.end.privateCallStack = padArrayEnd([], CallRequest.empty(), MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX); const tx = new Tx(kernelOutput, proof, TxL2Logs.random(2, 3), TxL2Logs.random(3, 2), callRequests, [ ExtendedContractData.random(), @@ -171,16 +183,21 @@ describe('public_processor', () => { expect(processed).toEqual([await expectedTxByHash(tx)]); expect(failed).toHaveLength(0); expect(publicExecutor.simulate).toHaveBeenCalledTimes(2); + expect(publicWorldStateDB.commit).toHaveBeenCalledTimes(1); + expect(publicWorldStateDB.rollback).toHaveBeenCalledTimes(0); }); it('runs a tx with an enqueued public call with nested execution', async function () { const callRequest: PublicCallRequest = makePublicCallRequest(0x100); - const callStackItem = callRequest.toPublicCallStackItem(); - const callStackHash = computeCallStackItemHash(wasm, callStackItem); + const callStackItem = callRequest.toCallRequest(); const kernelOutput = makePrivateKernelPublicInputsFinal(0x10); - kernelOutput.end.publicCallStack = padArrayEnd([callStackHash], Fr.ZERO, MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX); - kernelOutput.end.privateCallStack = padArrayEnd([], Fr.ZERO, MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX); + kernelOutput.end.publicCallStack = padArrayEnd( + [callStackItem], + CallRequest.empty(), + MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, + ); + kernelOutput.end.privateCallStack = padArrayEnd([], CallRequest.empty(), MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX); const tx = new Tx( kernelOutput, @@ -207,6 +224,48 @@ describe('public_processor', () => { expect(processed).toEqual([await expectedTxByHash(tx)]); expect(failed).toHaveLength(0); expect(publicExecutor.simulate).toHaveBeenCalledTimes(1); + expect(publicWorldStateDB.commit).toHaveBeenCalledTimes(1); + expect(publicWorldStateDB.rollback).toHaveBeenCalledTimes(0); + }); + + it('rolls back db updates on failed public execution', async function () { + const callRequest: PublicCallRequest = makePublicCallRequest(0x100); + const callStackItem = callRequest.toCallRequest(); + + const kernelOutput = makePrivateKernelPublicInputsFinal(0x10); + kernelOutput.end.publicCallStack = padArrayEnd( + [callStackItem], + CallRequest.empty(), + MAX_PUBLIC_CALL_STACK_LENGTH_PER_TX, + ); + kernelOutput.end.privateCallStack = padArrayEnd([], CallRequest.empty(), MAX_PRIVATE_CALL_STACK_LENGTH_PER_TX); + + const tx = new Tx( + kernelOutput, + proof, + TxL2Logs.random(2, 3), + TxL2Logs.random(3, 2), + [callRequest], + [ExtendedContractData.random()], + ); + + const publicExecutionResult = makePublicExecutionResultFromRequest(callRequest); + publicExecutionResult.nestedExecutions = [ + makePublicExecutionResult(publicExecutionResult.execution.contractAddress, { + to: makeAztecAddress(30), + functionData: new FunctionData(makeSelector(5), false, false, false), + args: new Array(ARGS_LENGTH).fill(Fr.ZERO), + }), + ]; + publicExecutor.simulate.mockRejectedValueOnce(new SimulationError('Simulation Failed', [])); + + const [processed, failed] = await processor.process([tx]); + + expect(failed).toHaveLength(1); + expect(processed).toHaveLength(0); + expect(publicExecutor.simulate).toHaveBeenCalledTimes(1); + expect(publicWorldStateDB.rollback).toHaveBeenCalledTimes(1); + expect(publicWorldStateDB.commit).toHaveBeenCalledTimes(0); }); }); }); diff --git a/yarn-project/sequencer-client/src/sequencer/public_processor.ts b/yarn-project/sequencer-client/src/sequencer/public_processor.ts index 501cd78ca4b..fbb3ab7e8ad 100644 --- a/yarn-project/sequencer-client/src/sequencer/public_processor.ts +++ b/yarn-project/sequencer-client/src/sequencer/public_processor.ts @@ -2,13 +2,14 @@ import { PublicExecution, PublicExecutionResult, PublicExecutor, + PublicStateDB, collectPublicDataReads, collectPublicDataUpdateRequests, isPublicExecutionResult, } from '@aztec/acir-simulator'; import { AztecAddress, - CircuitsWasm, + CallRequest, CombinedAccumulatedData, ContractStorageRead, ContractStorageUpdateRequest, @@ -37,10 +38,10 @@ import { RETURN_VALUES_LENGTH, VK_TREE_HEIGHT, } from '@aztec/circuits.js'; -import { computeCallStackItemHash, computeVarArgsHash } from '@aztec/circuits.js/abis'; -import { arrayNonEmptyLength, isArrayEmpty, padArrayEnd, padArrayStart } from '@aztec/foundation/collection'; +import { computeVarArgsHash } from '@aztec/circuits.js/abis'; +import { arrayNonEmptyLength, isArrayEmpty, padArrayEnd } from '@aztec/foundation/collection'; import { createDebugLogger } from '@aztec/foundation/log'; -import { Tuple, mapTuple, to2Fields } from '@aztec/foundation/serialize'; +import { to2Fields } from '@aztec/foundation/serialize'; import { ContractDataSource, FunctionL2Logs, L1ToL2MessageSource, MerkleTreeId, Tx } from '@aztec/types'; import { MerkleTreeOperations } from '@aztec/world-state'; @@ -48,7 +49,7 @@ import { getVerificationKeys } from '../index.js'; import { EmptyPublicProver } from '../prover/empty.js'; import { PublicProver } from '../prover/index.js'; import { PublicKernelCircuitSimulator } from '../simulator/index.js'; -import { ContractsDataSourcePublicDB, getPublicExecutor } from '../simulator/public_executor.js'; +import { ContractsDataSourcePublicDB, WorldStateDB, WorldStatePublicDB } from '../simulator/public_executor.js'; import { WasmPublicKernelCircuitSimulator } from '../simulator/public_kernel.js'; import { FailedTx, ProcessedTx, makeEmptyProcessedTx, makeProcessedTx } from './processed_tx.js'; import { getHistoricBlockData } from './utils.js'; @@ -76,14 +77,18 @@ export class PublicProcessorFactory { ): Promise { const blockData = await getHistoricBlockData(this.merkleTree, prevGlobalVariables); const publicContractsDB = new ContractsDataSourcePublicDB(this.contractDataSource); + const worldStatePublicDB = new WorldStatePublicDB(this.merkleTree); + const worldStateDB = new WorldStateDB(this.merkleTree, this.l1Tol2MessagesDataSource); + const publicExecutor = new PublicExecutor(worldStatePublicDB, publicContractsDB, worldStateDB, blockData); return new PublicProcessor( this.merkleTree, - getPublicExecutor(this.merkleTree, publicContractsDB, this.l1Tol2MessagesDataSource, blockData), + publicExecutor, new WasmPublicKernelCircuitSimulator(), new EmptyPublicProver(), globalVariables, blockData, publicContractsDB, + worldStatePublicDB, ); } } @@ -101,6 +106,7 @@ export class PublicProcessor { protected globalVariables: GlobalVariables, protected blockData: HistoricBlockData, protected publicContractsDB: ContractsDataSourcePublicDB, + protected publicStateDB: PublicStateDB, private log = createDebugLogger('aztec:sequencer:public-processor'), ) {} @@ -122,6 +128,8 @@ export class PublicProcessor { // add new contracts to the contracts db so that their functions may be found and called await this.publicContractsDB.addNewContracts(tx); result.push(await this.processTx(tx)); + // commit the state updates from this transaction + await this.publicStateDB.commit(); } catch (err) { this.log.warn(`Error processing tx ${await tx.getTxHash()}: ${err}`); failed.push({ @@ -130,6 +138,8 @@ export class PublicProcessor { }); // remove contracts on failure await this.publicContractsDB.removeNewContracts(tx); + // rollback any state updates from this failed transaction + await this.publicStateDB.rollback(); } } @@ -146,7 +156,7 @@ export class PublicProcessor { } protected async processTx(tx: Tx): Promise { - if (!isArrayEmpty(tx.data.end.publicCallStack, item => item.isZero())) { + if (!isArrayEmpty(tx.data.end.publicCallStack, item => item.isEmpty())) { const [publicKernelOutput, publicKernelProof, newUnencryptedFunctionLogs] = await this.processEnqueuedPublicCalls( tx, ); @@ -160,7 +170,9 @@ export class PublicProcessor { protected async processEnqueuedPublicCalls(tx: Tx): Promise<[PublicKernelPublicInputs, Proof, FunctionL2Logs[]]> { this.log(`Executing enqueued public calls for tx ${await tx.getTxHash()}`); - if (!tx.enqueuedPublicFunctionCalls) throw new Error(`Missing preimages for enqueued public calls`); + if (!tx.enqueuedPublicFunctionCalls) { + throw new Error(`Missing preimages for enqueued public calls`); + } let kernelOutput = new KernelCircuitPublicInputs( CombinedAccumulatedData.fromFinalAccumulatedData(tx.data.end), @@ -192,12 +204,13 @@ export class PublicProcessor { `Running public kernel circuit for ${functionSelector}@${result.execution.contractAddress.toString()}`, ); executionStack.push(...result.nestedExecutions); - const preimages = await this.getPublicCallStackPreimages(result); - const callData = await this.getPublicCallData(result, preimages, isExecutionRequest); + const callData = await this.getPublicCallData(result, isExecutionRequest); [kernelOutput, kernelProof] = await this.runKernelCircuit(callData, kernelOutput, kernelProof); - if (!enqueuedExecutionResult) enqueuedExecutionResult = result; + if (!enqueuedExecutionResult) { + enqueuedExecutionResult = result; + } } // HACK(#1622): Manually patches the ordering of public state actions // TODO(#757): Enforce proper ordering of public state actions @@ -249,10 +262,10 @@ export class PublicProcessor { this.blockData.publicDataTreeRoot = Fr.fromBuffer(publicDataTreeInfo.root); const callStackPreimages = await this.getPublicCallStackPreimages(result); - const wasm = await CircuitsWasm.get(); - - const publicCallStack = mapTuple(callStackPreimages, item => - item.isEmpty() ? Fr.zero() : computeCallStackItemHash(wasm, item), + const publicCallStackHashes = padArrayEnd( + callStackPreimages.map(c => c.hash()), + Fr.ZERO, + MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL, ); // TODO(https://github.com/AztecProtocol/aztec-packages/issues/1165) --> set this in Noir @@ -277,7 +290,7 @@ export class PublicProcessor { ContractStorageUpdateRequest.empty(), MAX_PUBLIC_DATA_UPDATE_REQUESTS_PER_CALL, ), - publicCallStack, + publicCallStackHashes, unencryptedLogsHash, unencryptedLogPreimagesLength, historicBlockData: this.blockData, @@ -293,17 +306,15 @@ export class PublicProcessor { ); } - protected async getPublicCallStackPreimages(result: PublicExecutionResult) { + protected async getPublicCallStackPreimages(result: PublicExecutionResult): Promise { const nested = result.nestedExecutions; - const preimages: PublicCallStackItem[] = await Promise.all(nested.map(n => this.getPublicCallStackItem(n))); - if (preimages.length > MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL) { + if (nested.length > MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL) { throw new Error( - `Public call stack size exceeded (max ${MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL}, got ${preimages.length})`, + `Public call stack size exceeded (max ${MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL}, got ${nested.length})`, ); } - // Top of the stack is at the end of the array, so we padStart - return padArrayStart(preimages, PublicCallStackItem.empty(), MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL); + return await Promise.all(nested.map(n => this.getPublicCallStackItem(n))); } protected getBytecodeHash(_result: PublicExecutionResult) { @@ -321,16 +332,14 @@ export class PublicProcessor { * @param isExecutionRequest - Whether the current callstack item should be considered a public fn execution request. * @returns A corresponding PublicCallData object. */ - protected async getPublicCallData( - result: PublicExecutionResult, - preimages: Tuple, - isExecutionRequest = false, - ) { + protected async getPublicCallData(result: PublicExecutionResult, isExecutionRequest = false) { const bytecodeHash = await this.getBytecodeHash(result); const callStackItem = await this.getPublicCallStackItem(result, isExecutionRequest); + const publicCallRequests = (await this.getPublicCallStackPreimages(result)).map(c => c.toCallRequest()); + const publicCallStack = padArrayEnd(publicCallRequests, CallRequest.empty(), MAX_PUBLIC_CALL_STACK_LENGTH_PER_CALL); const portalContractAddress = result.execution.callContext.portalContractAddress.toField(); const proof = await this.publicProver.getPublicCircuitProof(callStackItem.publicInputs); - return new PublicCallData(callStackItem, preimages, proof, portalContractAddress, bytecodeHash); + return new PublicCallData(callStackItem, publicCallStack, proof, portalContractAddress, bytecodeHash); } // HACK(#1622): this is a hack to fix ordering of public state in the call stack. Since the private kernel diff --git a/yarn-project/sequencer-client/src/sequencer/sequencer.ts b/yarn-project/sequencer-client/src/sequencer/sequencer.ts index 4b82ac99fa5..cd70ffca8ee 100644 --- a/yarn-project/sequencer-client/src/sequencer/sequencer.ts +++ b/yarn-project/sequencer-client/src/sequencer/sequencer.ts @@ -56,9 +56,15 @@ export class Sequencer { * @param config - New parameters. */ public updateConfig(config: SequencerConfig) { - if (config.transactionPollingIntervalMS) this.pollingIntervalMs = config.transactionPollingIntervalMS; - if (config.maxTxsPerBlock) this.maxTxsPerBlock = config.maxTxsPerBlock; - if (config.minTxsPerBlock) this.minTxsPerBLock = config.minTxsPerBlock; + if (config.transactionPollingIntervalMS) { + this.pollingIntervalMs = config.transactionPollingIntervalMS; + } + if (config.maxTxsPerBlock) { + this.maxTxsPerBlock = config.maxTxsPerBlock; + } + if (config.minTxsPerBlock) { + this.minTxsPerBLock = config.minTxsPerBlock; + } } /** @@ -119,14 +125,18 @@ export class Sequencer { } // Do not go forward with new block if the previous one has not been mined and processed - if (!prevBlockSynced) return; + if (!prevBlockSynced) { + return; + } const workTimer = new Timer(); this.state = SequencerState.WAITING_FOR_TXS; // Get txs to build the new block const pendingTxs = await this.p2pClient.getTxs(); - if (pendingTxs.length < this.minTxsPerBLock) return; + if (pendingTxs.length < this.minTxsPerBLock) { + return; + } this.log.info(`Retrieved ${pendingTxs.length} txs from P2P pool`); const blockNumber = (await this.l2BlockSource.getBlockNumber()) + 1; @@ -146,7 +156,9 @@ export class Sequencer { // Filter out invalid txs // TODO: It should be responsibility of the P2P layer to validate txs before passing them on here const validTxs = await this.takeValidTxs(pendingTxs, newGlobalVariables); - if (validTxs.length < this.minTxsPerBLock) return; + if (validTxs.length < this.minTxsPerBLock) { + return; + } this.log.info(`Building block ${blockNumber} with ${validTxs.length} transactions`); this.state = SequencerState.CREATING_BLOCK; @@ -284,7 +296,9 @@ export class Sequencer { tx.data.end.newNullifiers.forEach(n => thisBlockNullifiers.add(n.toBigInt())); validTxs.push(tx); - if (validTxs.length >= this.maxTxsPerBlock) break; + if (validTxs.length >= this.maxTxsPerBlock) { + break; + } } // Make sure we remove these from the tx pool so we do not consider it again @@ -373,13 +387,17 @@ export class Sequencer { // Ditch this tx if it has a repeated nullifiers const uniqNullifiers = new Set(newNullifiers.map(n => n.toBigInt())); - if (uniqNullifiers.size !== newNullifiers.length) return true; + if (uniqNullifiers.size !== newNullifiers.length) { + return true; + } for (const nullifier of newNullifiers) { // TODO(AD): this is an exhaustive search currently const db = this.worldState.getLatest(); const indexInDb = await db.findLeafIndex(MerkleTreeId.NULLIFIER_TREE, nullifier.toBuffer()); - if (indexInDb !== undefined) return true; + if (indexInDb !== undefined) { + return true; + } } return false; } diff --git a/yarn-project/sequencer-client/src/simulator/public_executor.ts b/yarn-project/sequencer-client/src/simulator/public_executor.ts index d0e6b024f3f..5999c2b0369 100644 --- a/yarn-project/sequencer-client/src/simulator/public_executor.ts +++ b/yarn-project/sequencer-client/src/simulator/public_executor.ts @@ -1,35 +1,9 @@ -import { - CommitmentsDB, - MessageLoadOracleInputs, - PublicContractsDB, - PublicExecutor, - PublicStateDB, -} from '@aztec/acir-simulator'; -import { AztecAddress, EthAddress, Fr, FunctionSelector, HistoricBlockData } from '@aztec/circuits.js'; +import { CommitmentsDB, MessageLoadOracleInputs, PublicContractsDB, PublicStateDB } from '@aztec/acir-simulator'; +import { AztecAddress, EthAddress, Fr, FunctionSelector } from '@aztec/circuits.js'; import { computePublicDataTreeIndex } from '@aztec/circuits.js/abis'; import { ContractDataSource, ExtendedContractData, L1ToL2MessageSource, MerkleTreeId, Tx } from '@aztec/types'; import { MerkleTreeOperations } from '@aztec/world-state'; -/** - * Returns a new PublicExecutor simulator backed by the supplied merkle tree db and contract data source. - * @param merkleTree - A merkle tree database. - * @param contractDataSource - A contract data source. - * @returns A new instance of a PublicExecutor. - */ -export function getPublicExecutor( - merkleTree: MerkleTreeOperations, - publicContractsDB: PublicContractsDB, - l1toL2MessageSource: L1ToL2MessageSource, - blockData: HistoricBlockData, -) { - return new PublicExecutor( - new WorldStatePublicDB(merkleTree), - publicContractsDB, - new WorldStateDB(merkleTree, l1toL2MessageSource), - blockData, - ); -} - /** * Implements the PublicContractsDB using a ContractDataSource. * Progressively records contracts in transaction as they are processed in a block. @@ -95,8 +69,9 @@ export class ContractsDataSourcePublicDB implements PublicContractsDB { /** * Implements the PublicStateDB using a world-state database. */ -class WorldStatePublicDB implements PublicStateDB { - private writeCache: Map = new Map(); +export class WorldStatePublicDB implements PublicStateDB { + private commitedWriteCache: Map = new Map(); + private uncommitedWriteCache: Map = new Map(); constructor(private db: MerkleTreeOperations) {} @@ -108,8 +83,14 @@ class WorldStatePublicDB implements PublicStateDB { */ public async storageRead(contract: AztecAddress, slot: Fr): Promise { const index = computePublicDataTreeIndex(contract, slot).value; - const cached = this.writeCache.get(index); - if (cached !== undefined) return cached; + const uncommited = this.uncommitedWriteCache.get(index); + if (uncommited !== undefined) { + return uncommited; + } + const commited = this.commitedWriteCache.get(index); + if (commited !== undefined) { + return commited; + } const value = await this.db.getLeafValue(MerkleTreeId.PUBLIC_DATA_TREE, index); return value ? Fr.fromBuffer(value) : Fr.ZERO; } @@ -122,7 +103,27 @@ class WorldStatePublicDB implements PublicStateDB { */ public storageWrite(contract: AztecAddress, slot: Fr, newValue: Fr): Promise { const index = computePublicDataTreeIndex(contract, slot).value; - this.writeCache.set(index, newValue); + this.uncommitedWriteCache.set(index, newValue); + return Promise.resolve(); + } + + /** + * Commit the pending changes to the DB. + * @returns Nothing. + */ + commit(): Promise { + for (const [k, v] of this.uncommitedWriteCache) { + this.commitedWriteCache.set(k, v); + } + return this.rollback(); + } + + /** + * Rollback the pending changes. + * @returns Nothing. + */ + rollback(): Promise { + this.uncommitedWriteCache = new Map(); return Promise.resolve(); } } diff --git a/yarn-project/sequencer-client/src/simulator/public_kernel.ts b/yarn-project/sequencer-client/src/simulator/public_kernel.ts index 89df560bcc3..54cf4263118 100644 --- a/yarn-project/sequencer-client/src/simulator/public_kernel.ts +++ b/yarn-project/sequencer-client/src/simulator/public_kernel.ts @@ -1,6 +1,7 @@ -import { PublicKernelInputs, PublicKernelPublicInputs, simulatePublicKernelCircuit } from '@aztec/circuits.js'; +import { PublicKernelInputs, PublicKernelPublicInputs } from '@aztec/circuits.js'; import { createDebugLogger } from '@aztec/foundation/log'; import { elapsed } from '@aztec/foundation/timer'; +import { executePublicKernelPrivatePrevious, executePublicKernelPublicPrevious } from '@aztec/noir-protocol-circuits'; import { CircuitSimulationStats } from '@aztec/types/stats'; import { PublicKernelCircuitSimulator } from './index.js'; @@ -17,8 +18,10 @@ export class WasmPublicKernelCircuitSimulator implements PublicKernelCircuitSimu * @returns The public inputs as outputs of the simulation. */ public async publicKernelCircuitPrivateInput(input: PublicKernelInputs): Promise { - if (!input.previousKernel.publicInputs.isPrivate) throw new Error(`Expected private kernel previous inputs`); - const [duration, result] = await elapsed(() => simulatePublicKernelCircuit(input)); + if (!input.previousKernel.publicInputs.isPrivate) { + throw new Error(`Expected private kernel previous inputs`); + } + const [duration, result] = await elapsed(() => executePublicKernelPrivatePrevious(input)); this.log(`Simulated public kernel circuit with private input`, { eventName: 'circuit-simulation', circuitName: 'public-kernel-private-input', @@ -35,8 +38,10 @@ export class WasmPublicKernelCircuitSimulator implements PublicKernelCircuitSimu * @returns The public inputs as outputs of the simulation. */ public async publicKernelCircuitNonFirstIteration(input: PublicKernelInputs): Promise { - if (input.previousKernel.publicInputs.isPrivate) throw new Error(`Expected public kernel previous inputs`); - const [duration, result] = await elapsed(() => simulatePublicKernelCircuit(input)); + if (input.previousKernel.publicInputs.isPrivate) { + throw new Error(`Expected public kernel previous inputs`); + } + const [duration, result] = await elapsed(() => executePublicKernelPublicPrevious(input)); this.log(`Simulated public kernel circuit non-first iteration`, { eventName: 'circuit-simulation', circuitName: 'public-kernel-non-first-iteration', diff --git a/yarn-project/sequencer-client/src/simulator/rollup.ts b/yarn-project/sequencer-client/src/simulator/rollup.ts index 37dfe0c0ed3..127703b6a69 100644 --- a/yarn-project/sequencer-client/src/simulator/rollup.ts +++ b/yarn-project/sequencer-client/src/simulator/rollup.ts @@ -1,16 +1,13 @@ import { BaseOrMergeRollupPublicInputs, BaseRollupInputs, - CircuitError, - CircuitsWasm, MergeRollupInputs, RootRollupInputs, RootRollupPublicInputs, - baseRollupSim, } from '@aztec/circuits.js'; import { createDebugLogger } from '@aztec/foundation/log'; import { elapsed } from '@aztec/foundation/timer'; -import { executeMergeRollup, executeRootRollup } from '@aztec/noir-protocol-circuits'; +import { executeBaseRollup, executeMergeRollup, executeRootRollup } from '@aztec/noir-protocol-circuits'; import { CircuitSimulationStats } from '@aztec/types/stats'; import { RollupSimulator } from './index.js'; @@ -27,11 +24,7 @@ export class WasmRollupCircuitSimulator implements RollupSimulator { * @returns The public inputs as outputs of the simulation. */ public async baseRollupCircuit(input: BaseRollupInputs): Promise { - const wasm = await CircuitsWasm.get(); - const [duration, result] = await elapsed(() => baseRollupSim(wasm, input)); - if (result instanceof CircuitError) { - throw new CircuitError(result.code, result.message); - } + const [duration, result] = await elapsed(() => executeBaseRollup(input)); this.log(`Simulated base rollup circuit`, { eventName: 'circuit-simulation', diff --git a/yarn-project/sequencer-client/src/simulator/world_state_public_db.test.ts b/yarn-project/sequencer-client/src/simulator/world_state_public_db.test.ts new file mode 100644 index 00000000000..91de094d253 --- /dev/null +++ b/yarn-project/sequencer-client/src/simulator/world_state_public_db.test.ts @@ -0,0 +1,182 @@ +import { AztecAddress, Fr } from '@aztec/circuits.js'; +import { computePublicDataTreeIndex } from '@aztec/circuits.js/abis'; +import { MerkleTreeId } from '@aztec/types'; +import { MerkleTreeOperations } from '@aztec/world-state'; + +import { MockProxy, mock } from 'jest-mock-extended'; + +import { WorldStatePublicDB } from './public_executor.js'; + +const DB_VALUES_SIZE = 10; + +describe('world_state_public_db', () => { + let db: MockProxy; + let dbStorage: Map>; + let addresses: AztecAddress[]; + let slots: Fr[]; + let dbValues: Fr[]; + + beforeEach(() => { + addresses = Array(DB_VALUES_SIZE).fill(0).map(AztecAddress.random); + slots = Array(DB_VALUES_SIZE).fill(0).map(Fr.random); + dbValues = Array(DB_VALUES_SIZE).fill(0).map(Fr.random); + const publicData = new Map( + Array(DB_VALUES_SIZE) + .fill(0) + .map((_, idx: number) => { + const index = computePublicDataTreeIndex(addresses[idx], slots[idx]); + return [index.toBigInt(), dbValues[idx].toBuffer()]; + }), + ); + dbStorage = new Map>([[MerkleTreeId.PUBLIC_DATA_TREE, publicData]]); + db = mock(); + db.getLeafValue.mockImplementation((treeId: MerkleTreeId, index: bigint): Promise => { + const tree = dbStorage.get(treeId); + if (!tree) { + throw new Error('Invalid Tree Id'); + } + return Promise.resolve(tree.get(index)); + }); + }); + + it('reads unwritten value from merkle tree db', async function () { + const publicStateDb = new WorldStatePublicDB(db); + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(dbValues[0]); + expect(await publicStateDb.storageRead(addresses[1], slots[1])).toEqual(dbValues[1]); + }); + + it('reads uncommitted value back', async function () { + const publicStateDb = new WorldStatePublicDB(db); + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(dbValues[0]); + + const newValue = new Fr(dbValues[0].toBigInt() + 1n); + + // write a new value to our first value + await publicStateDb.storageWrite(addresses[0], slots[0], newValue); + + // should read back the uncommited value + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(newValue); + + // other slots should be unchanged + expect(await publicStateDb.storageRead(addresses[1], slots[1])).toEqual(dbValues[1]); + }); + + it('reads committed value back', async function () { + const publicStateDb = new WorldStatePublicDB(db); + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(dbValues[0]); + + const newValue = new Fr(dbValues[0].toBigInt() + 1n); + + // write a new value to our first value + await publicStateDb.storageWrite(addresses[0], slots[0], newValue); + + // commit the data + await publicStateDb.commit(); + + // should read back the commited value + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(newValue); + + // other slots should be unchanged + expect(await publicStateDb.storageRead(addresses[1], slots[1])).toEqual(dbValues[1]); + }); + + it('will not rollback a commited value', async function () { + const publicStateDb = new WorldStatePublicDB(db); + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(dbValues[0]); + + const newValue = new Fr(dbValues[0].toBigInt() + 1n); + + // write a new value to our first value + await publicStateDb.storageWrite(addresses[0], slots[0], newValue); + + // commit the data + await publicStateDb.commit(); + + // should read back the commited value + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(newValue); + + await publicStateDb.rollback(); + + // should still read back the commited value + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(newValue); + }); + + it('reads original value if rolled back uncommited value', async function () { + const publicStateDb = new WorldStatePublicDB(db); + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(dbValues[0]); + + const newValue = new Fr(dbValues[0].toBigInt() + 1n); + + // write a new value to our first value + await publicStateDb.storageWrite(addresses[0], slots[0], newValue); + + // should read back the uncommited value + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(newValue); + + // now rollback + await publicStateDb.rollback(); + + // should now read the original value + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(dbValues[0]); + }); + + it('reads newly uncommitted value back', async function () { + const publicStateDb = new WorldStatePublicDB(db); + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(dbValues[0]); + + const newValue = new Fr(dbValues[0].toBigInt() + 1n); + + // write a new value to our first value + await publicStateDb.storageWrite(addresses[0], slots[0], newValue); + + // commit the data + await publicStateDb.commit(); + + // should read back the commited value + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(newValue); + + // other slots should be unchanged + expect(await publicStateDb.storageRead(addresses[1], slots[1])).toEqual(dbValues[1]); + + // now update the slot again + const newValue2 = new Fr(dbValues[0].toBigInt() + 2n); + // write a new value to our first value + await publicStateDb.storageWrite(addresses[0], slots[0], newValue2); + + // should read back the uncommited value + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(newValue2); + }); + + it('rolls back to previously commited value', async function () { + const publicStateDb = new WorldStatePublicDB(db); + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(dbValues[0]); + + const newValue = new Fr(dbValues[0].toBigInt() + 1n); + + // write a new value to our first value + await publicStateDb.storageWrite(addresses[0], slots[0], newValue); + + // commit the data + await publicStateDb.commit(); + + // should read back the commited value + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(newValue); + + // other slots should be unchanged + expect(await publicStateDb.storageRead(addresses[1], slots[1])).toEqual(dbValues[1]); + + // now update the slot again + const newValue2 = new Fr(dbValues[0].toBigInt() + 2n); + // write a new value to our first value + await publicStateDb.storageWrite(addresses[0], slots[0], newValue2); + + // should read back the uncommited value + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(newValue2); + + // rollback + await publicStateDb.rollback(); + + // should read back the previously commited value + expect(await publicStateDb.storageRead(addresses[0], slots[0])).toEqual(newValue); + }); +}); diff --git a/yarn-project/types/package.json b/yarn-project/types/package.json index 3bac04d8fd7..4aa7cc04ca3 100644 --- a/yarn-project/types/package.json +++ b/yarn-project/types/package.json @@ -18,7 +18,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" }, "inherits": [ @@ -44,7 +44,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/lodash.clonedeep": "^4.5.7", "@types/lodash.isequal": "^4.5.6", diff --git a/yarn-project/types/src/auth_witness.ts b/yarn-project/types/src/auth_witness.ts index bd1f532eac3..06f98da9306 100644 --- a/yarn-project/types/src/auth_witness.ts +++ b/yarn-project/types/src/auth_witness.ts @@ -24,7 +24,7 @@ export class AuthWitness { static fromBuffer(buffer: Buffer | BufferReader): AuthWitness { const reader = BufferReader.asReader(buffer); - return new AuthWitness(reader.readFr(), reader.readVector(Fr)); + return new AuthWitness(Fr.fromBuffer(reader), reader.readVector(Fr)); } toString() { diff --git a/yarn-project/types/src/interfaces/aztec-node.ts b/yarn-project/types/src/interfaces/aztec-node.ts index 0c00dd480bf..a366e7332d5 100644 --- a/yarn-project/types/src/interfaces/aztec-node.ts +++ b/yarn-project/types/src/interfaces/aztec-node.ts @@ -13,6 +13,7 @@ import { LogFilter, LogType, MerkleTreeId, + SequencerConfig, StateInfoProvider, Tx, TxHash, @@ -156,4 +157,10 @@ export interface AztecNode extends StateInfoProvider { * @param tx - The transaction to simulate. **/ simulatePublicCalls(tx: Tx): Promise; + + /** + * Updates the configuration of this node. + * @param config - Updated configuration to be merged with the current one. + */ + setConfig(config: Partial): Promise; } diff --git a/yarn-project/types/src/interfaces/configs.ts b/yarn-project/types/src/interfaces/configs.ts new file mode 100644 index 00000000000..82c6e10db16 --- /dev/null +++ b/yarn-project/types/src/interfaces/configs.ts @@ -0,0 +1,17 @@ +/** + * The sequencer configuration. + */ +export interface SequencerConfig { + /** + * The number of ms to wait between polling for pending txs. + */ + transactionPollingIntervalMS?: number; + /** + * The maximum number of txs to include in a block. + */ + maxTxsPerBlock?: number; + /** + * The minimum number of txs to include in a block. + */ + minTxsPerBlock?: number; +} diff --git a/yarn-project/types/src/interfaces/index.ts b/yarn-project/types/src/interfaces/index.ts index 390f25be66b..ddef4f6b1f8 100644 --- a/yarn-project/types/src/interfaces/index.ts +++ b/yarn-project/types/src/interfaces/index.ts @@ -5,3 +5,4 @@ export * from './pxe.js'; export * from './deployed-contract.js'; export * from './node-info.js'; export * from './sync-status.js'; +export * from './configs.js'; diff --git a/yarn-project/types/src/interfaces/pxe.ts b/yarn-project/types/src/interfaces/pxe.ts index c7dd53a76b0..1a2e52c1262 100644 --- a/yarn-project/types/src/interfaces/pxe.ts +++ b/yarn-project/types/src/interfaces/pxe.ts @@ -42,6 +42,13 @@ export interface PXE { */ addAuthWitness(authWitness: AuthWitness): Promise; + /** + * Adding a capsule to the capsule dispenser. + * @param capsule - An array of field elements representing the capsule. + * @remarks A capsule is a "blob" of data that is passed to the contract through an oracle. + */ + addCapsule(capsule: Fr[]): Promise; + /** * Registers a user account in PXE given its master encryption private key. * Once a new account is registered, the PXE Service will trial-decrypt all published notes on diff --git a/yarn-project/types/src/keys/key_pair.ts b/yarn-project/types/src/keys/key_pair.ts index d8b43186049..43809c2ad7a 100644 --- a/yarn-project/types/src/keys/key_pair.ts +++ b/yarn-project/types/src/keys/key_pair.ts @@ -16,5 +16,5 @@ export interface KeyPair { * The function returns a Promise that resolves to a Buffer containing the private key. * @returns A Promise that resolves to a Buffer containing the private key. */ - getPrivateKey(): Promise; + getPrivateKey(): GrumpkinPrivateKey; } diff --git a/yarn-project/types/src/l1_to_l2_message.ts b/yarn-project/types/src/l1_to_l2_message.ts index d56652fd6ae..0fae5414d6f 100644 --- a/yarn-project/types/src/l1_to_l2_message.ts +++ b/yarn-project/types/src/l1_to_l2_message.ts @@ -120,8 +120,8 @@ export class L1ToL2Message { const reader = BufferReader.asReader(buffer); const sender = reader.readObject(L1Actor); const recipient = reader.readObject(L2Actor); - const content = reader.readFr(); - const secretHash = reader.readFr(); + const content = Fr.fromBuffer(reader); + const secretHash = Fr.fromBuffer(reader); const deadline = reader.readNumber(); const fee = reader.readNumber(); return new L1ToL2Message(sender, recipient, content, secretHash, deadline, fee); diff --git a/yarn-project/types/src/l2_block.ts b/yarn-project/types/src/l2_block.ts index ef81342f0e4..3b6d4ebb6a8 100644 --- a/yarn-project/types/src/l2_block.ts +++ b/yarn-project/types/src/l2_block.ts @@ -11,14 +11,17 @@ import { } from '@aztec/circuits.js'; import { makeAppendOnlyTreeSnapshot, makeGlobalVariables } from '@aztec/circuits.js/factories'; import { BufferReader, serializeToBuffer } from '@aztec/circuits.js/utils'; -import { keccak, sha256, sha256ToField } from '@aztec/foundation/crypto'; +import { keccak, sha256 } from '@aztec/foundation/crypto'; import { Fr } from '@aztec/foundation/fields'; import { createDebugLogger } from '@aztec/foundation/log'; import times from 'lodash.times'; -import { ContractData, L2Tx, LogType, PublicDataWrite, TxL2Logs } from './index.js'; +import { ContractData } from './contract_data.js'; +import { L2Tx } from './l2_tx.js'; +import { LogType, TxL2Logs } from './logs/index.js'; import { L2BlockL2Logs } from './logs/l2_block_l2_logs.js'; +import { PublicDataWrite } from './public_data_write.js'; /** * The data that makes up the rollup proof, with encoder decoder functions. @@ -155,7 +158,7 @@ export class L2Block { // of non-zero tx hashes --> tx hash is set to be the first nullifier in the tx. this.numberOfTxs = 0; for (let i = 0; i < this.newNullifiers.length; i += MAX_NEW_NULLIFIERS_PER_TX) { - if (!this.newNullifiers[i].equals(Fr.zero())) { + if (!this.newNullifiers[i].equals(Fr.ZERO)) { this.numberOfTxs++; } } @@ -427,7 +430,7 @@ export class L2Block { static fromBuffer(buf: Buffer | BufferReader) { const reader = BufferReader.asReader(buf); const globalVariables = reader.readObject(GlobalVariables); - const number = Number(globalVariables.blockNumber.value); + const number = Number(globalVariables.blockNumber.toBigInt()); const startNoteHashTreeSnapshot = reader.readObject(AppendOnlyTreeSnapshot); const startNullifierTreeSnapshot = reader.readObject(AppendOnlyTreeSnapshot); const startContractTreeSnapshot = reader.readObject(AppendOnlyTreeSnapshot); @@ -565,7 +568,7 @@ export class L2Block { this.getL1ToL2MessagesHash(), ); - return sha256ToField(buf); + return Fr.fromBufferReduce(sha256(buf)); } /** diff --git a/yarn-project/types/src/logs/extended_unencrypted_l2_log.ts b/yarn-project/types/src/logs/extended_unencrypted_l2_log.ts index e5cc7d46a33..0916f723164 100644 --- a/yarn-project/types/src/logs/extended_unencrypted_l2_log.ts +++ b/yarn-project/types/src/logs/extended_unencrypted_l2_log.ts @@ -2,7 +2,8 @@ import { BufferReader } from '@aztec/foundation/serialize'; import isEqual from 'lodash.isequal'; -import { LogId, UnencryptedL2Log } from '../index.js'; +import { LogId } from './log_id.js'; +import { UnencryptedL2Log } from './unencrypted_l2_log.js'; /** * Represents an individual unencrypted log entry extended with info about the block and tx it was emitted in. diff --git a/yarn-project/types/src/logs/l1_note_payload/encrypt_buffer.test.ts b/yarn-project/types/src/logs/l1_note_payload/encrypt_buffer.test.ts index 2d79f54489a..b34baa4fb01 100644 --- a/yarn-project/types/src/logs/l1_note_payload/encrypt_buffer.test.ts +++ b/yarn-project/types/src/logs/l1_note_payload/encrypt_buffer.test.ts @@ -1,4 +1,4 @@ -import { CircuitsWasm, GrumpkinScalar } from '@aztec/circuits.js'; +import { GrumpkinScalar } from '@aztec/circuits.js'; import { Grumpkin } from '@aztec/circuits.js/barretenberg'; import { randomBytes } from '@aztec/foundation/crypto'; @@ -7,8 +7,8 @@ import { decryptBuffer, deriveAESSecret, encryptBuffer } from './encrypt_buffer. describe('encrypt buffer', () => { let grumpkin: Grumpkin; - beforeAll(async () => { - grumpkin = new Grumpkin(await CircuitsWasm.get()); + beforeAll(() => { + grumpkin = new Grumpkin(); }); it('derive shared secret', () => { diff --git a/yarn-project/types/src/logs/l1_note_payload/l1_note_payload.test.ts b/yarn-project/types/src/logs/l1_note_payload/l1_note_payload.test.ts index 0c06f70eadf..d5a909fe779 100644 --- a/yarn-project/types/src/logs/l1_note_payload/l1_note_payload.test.ts +++ b/yarn-project/types/src/logs/l1_note_payload/l1_note_payload.test.ts @@ -1,4 +1,3 @@ -import { CircuitsWasm } from '@aztec/circuits.js'; import { Grumpkin } from '@aztec/circuits.js/barretenberg'; import { GrumpkinScalar, Point } from '@aztec/foundation/fields'; @@ -7,8 +6,8 @@ import { L1NotePayload } from './l1_note_payload.js'; describe('L1 Note Payload', () => { let grumpkin: Grumpkin; - beforeAll(async () => { - grumpkin = new Grumpkin(await CircuitsWasm.get()); + beforeAll(() => { + grumpkin = new Grumpkin(); }); it('convert to and from buffer', () => { diff --git a/yarn-project/types/src/logs/l1_note_payload/l1_note_payload.ts b/yarn-project/types/src/logs/l1_note_payload/l1_note_payload.ts index f543fc0cfc6..76dcc729911 100644 --- a/yarn-project/types/src/logs/l1_note_payload/l1_note_payload.ts +++ b/yarn-project/types/src/logs/l1_note_payload/l1_note_payload.ts @@ -35,7 +35,7 @@ export class L1NotePayload { */ static fromBuffer(buffer: Buffer | BufferReader): L1NotePayload { const reader = BufferReader.asReader(buffer); - return new L1NotePayload(reader.readObject(Note), reader.readObject(AztecAddress), reader.readFr()); + return new L1NotePayload(reader.readObject(Note), reader.readObject(AztecAddress), Fr.fromBuffer(reader)); } /** diff --git a/yarn-project/types/src/packed_arguments.ts b/yarn-project/types/src/packed_arguments.ts index 8b59ede60ba..b14213f349d 100644 --- a/yarn-project/types/src/packed_arguments.ts +++ b/yarn-project/types/src/packed_arguments.ts @@ -35,6 +35,6 @@ export class PackedArguments { static fromBuffer(buffer: Buffer | BufferReader): PackedArguments { const reader = BufferReader.asReader(buffer); - return new PackedArguments(reader.readVector(Fr), reader.readFr()); + return new PackedArguments(reader.readVector(Fr), Fr.fromBuffer(reader)); } } diff --git a/yarn-project/types/src/public_data_write.ts b/yarn-project/types/src/public_data_write.ts index 13ec9e0b855..dd1979e1c27 100644 --- a/yarn-project/types/src/public_data_write.ts +++ b/yarn-project/types/src/public_data_write.ts @@ -67,7 +67,7 @@ export class PublicDataWrite { */ static fromBuffer(buffer: Buffer | BufferReader): PublicDataWrite { const reader = BufferReader.asReader(buffer); - return new PublicDataWrite(reader.readFr(), reader.readFr()); + return new PublicDataWrite(Fr.fromBuffer(reader), Fr.fromBuffer(reader)); } /** diff --git a/yarn-project/types/src/stats/stats.ts b/yarn-project/types/src/stats/stats.ts index a975970dd0b..22c7373389a 100644 --- a/yarn-project/types/src/stats/stats.ts +++ b/yarn-project/types/src/stats/stats.ts @@ -130,11 +130,11 @@ export type TxStats = { encryptedLogCount: number; /** Number of unencrypted logs. */ unencryptedLogCount: number; - /** Serialised size of encrypted logs. */ + /** Serialized size of encrypted logs. */ encryptedLogSize: number; - /** Serialised size of unencrypted logs. */ + /** Serialized size of unencrypted logs. */ unencryptedLogSize: number; - /** Serialised size of new contract data. */ + /** Serialized size of new contract data. */ newContractDataSize: number; /** Number of new contracts deployed in this tx. */ newContractCount: number; diff --git a/yarn-project/types/src/tx/tx.ts b/yarn-project/types/src/tx/tx.ts index 3cc485343a4..6d49a50912a 100644 --- a/yarn-project/types/src/tx/tx.ts +++ b/yarn-project/types/src/tx/tx.ts @@ -59,7 +59,7 @@ export class Tx { } const kernelPublicCallStackSize = - data?.end.publicCallStack && arrayNonEmptyLength(data.end.publicCallStack, item => item.isZero()); + data?.end.publicCallStack && arrayNonEmptyLength(data.end.publicCallStack, item => item.isEmpty()); if (kernelPublicCallStackSize && kernelPublicCallStackSize > (enqueuedPublicFunctionCalls?.length ?? 0)) { throw new Error( `Missing preimages for enqueued public function calls in kernel circuit public inputs (expected @@ -155,14 +155,16 @@ export class Tx { getTxHash(): Promise { // Private kernel functions are executed client side and for this reason tx hash is already set as first nullifier const firstNullifier = this.data?.end.newNullifiers[0]; - if (!firstNullifier) throw new Error(`Cannot get tx hash since first nullifier is missing`); + if (!firstNullifier) { + throw new Error(`Cannot get tx hash since first nullifier is missing`); + } return Promise.resolve(new TxHash(firstNullifier.toBuffer())); } /** Returns stats about this tx. */ getStats(): TxStats { return { - txHash: this.data!.end.newNullifiers[0].toString(true), + txHash: this.data!.end.newNullifiers[0].toString(), encryptedLogCount: this.encryptedLogs.getTotalLogCount(), unencryptedLogCount: this.unencryptedLogs.getTotalLogCount(), encryptedLogSize: this.encryptedLogs.getSerializedLength(), diff --git a/yarn-project/types/src/tx_execution_request.ts b/yarn-project/types/src/tx_execution_request.ts index e3e5e830dcd..b944b423af6 100644 --- a/yarn-project/types/src/tx_execution_request.ts +++ b/yarn-project/types/src/tx_execution_request.ts @@ -90,7 +90,7 @@ export class TxExecutionRequest { return new TxExecutionRequest( reader.readObject(AztecAddress), reader.readObject(FunctionData), - reader.readFr(), + Fr.fromBuffer(reader), reader.readObject(TxContext), reader.readVector(PackedArguments), reader.readVector(AuthWitness), diff --git a/yarn-project/world-state/package.json b/yarn-project/world-state/package.json index 36c704cd0ae..4713b486a60 100644 --- a/yarn-project/world-state/package.json +++ b/yarn-project/world-state/package.json @@ -15,7 +15,7 @@ "build:dev": "tsc -b --watch", "clean": "rm -rf ./dest .tsbuildinfo", "formatting": "run -T prettier --check ./src && run -T eslint ./src", - "formatting:fix": "run -T prettier -w ./src", + "formatting:fix": "run -T eslint --fix ./src && run -T prettier -w ./src", "test": "NODE_NO_WARNINGS=1 node --experimental-vm-modules $(yarn bin jest) --passWithNoTests" }, "inherits": [ @@ -41,7 +41,6 @@ }, "devDependencies": { "@jest/globals": "^29.5.0", - "@rushstack/eslint-patch": "^1.1.4", "@types/jest": "^29.5.0", "@types/levelup": "^5.1.2", "@types/lodash.times": "^4.3.7", diff --git a/yarn-project/world-state/src/world-state-db/merkle_trees.ts b/yarn-project/world-state/src/world-state-db/merkle_trees.ts index 4ae2e41b858..33baba1e72e 100644 --- a/yarn-project/world-state/src/world-state-db/merkle_trees.ts +++ b/yarn-project/world-state/src/world-state-db/merkle_trees.ts @@ -61,7 +61,7 @@ export class MerkleTrees implements MerkleTreeDb { private jobQueue = new SerialQueue(); constructor(private db: levelup.LevelUp, private log = createDebugLogger('aztec:merkle_trees')) { - this.latestGlobalVariablesHash = new Committable(Fr.zero()); + this.latestGlobalVariablesHash = new Committable(Fr.ZERO); } /** @@ -564,7 +564,9 @@ export class MerkleTrees implements MerkleTreeDb { // Sync the public data tree for (const dataWrite of l2Block.newPublicDataWrites) { - if (dataWrite.isEmpty()) continue; + if (dataWrite.isEmpty()) { + continue; + } const { newValue, leafIndex } = dataWrite; await this._updateLeaf(MerkleTreeId.PUBLIC_DATA_TREE, newValue.toBuffer(), leafIndex.value); } diff --git a/yarn-project/yarn.lock b/yarn-project/yarn.lock index ddc9fd7a35f..310ecc555ab 100644 --- a/yarn-project/yarn.lock +++ b/yarn-project/yarn.lock @@ -68,7 +68,6 @@ __metadata: "@aztec/types": "workspace:^" "@jest/globals": ^29.5.0 "@noir-lang/acvm_js": 0.30.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/levelup": ^5.1.3 "@types/memdown": ^3.0.2 @@ -95,7 +94,6 @@ __metadata: "@aztec/l1-artifacts": "workspace:^" "@aztec/types": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.2.0 "@types/debug": ^4.1.7 "@types/jest": ^29.5.0 "@types/lodash.omit": ^4.5.7 @@ -124,7 +122,6 @@ __metadata: "@aztec/ethereum": "workspace:^" "@aztec/foundation": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/node": ^18.7.23 jest: ^29.5.0 @@ -155,7 +152,6 @@ __metadata: "@aztec/types": "workspace:^" "@aztec/world-state": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/leveldown": ^4.0.4 "@types/levelup": ^5.1.2 @@ -187,6 +183,7 @@ __metadata: "@aztec/l1-artifacts": "workspace:^" "@aztec/noir-compiler": "workspace:^" "@aztec/noir-contracts": "workspace:^" + "@aztec/p2p": "workspace:^" "@aztec/pxe": "workspace:^" "@aztec/types": "workspace:^" "@jest/globals": ^29.5.0 @@ -235,7 +232,6 @@ __metadata: "@aztec/foundation": "workspace:^" "@aztec/types": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/lodash.every": ^4.6.7 "@types/lodash.partition": ^4.6.0 @@ -293,6 +289,19 @@ __metadata: languageName: node linkType: hard +"@aztec/bb.js@portal:../../barretenberg/ts::locator=%40aztec%2Fcircuits.js%40workspace%3Acircuits.js": + version: 0.0.0-use.local + resolution: "@aztec/bb.js@portal:../../barretenberg/ts::locator=%40aztec%2Fcircuits.js%40workspace%3Acircuits.js" + dependencies: + comlink: ^4.4.1 + commander: ^10.0.1 + debug: ^4.3.4 + tslib: ^2.4.0 + bin: + bb.js: ./dest/node/main.js + languageName: node + linkType: soft + "@aztec/bb.js@portal:../../barretenberg/ts::locator=%40aztec%2Ffoundation%40workspace%3Afoundation": version: 0.0.0-use.local resolution: "@aztec/bb.js@portal:../../barretenberg/ts::locator=%40aztec%2Ffoundation%40workspace%3Afoundation" @@ -317,7 +326,6 @@ __metadata: "@aztec/l1-artifacts": "workspace:^" "@aztec/noir-contracts": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/koa-static": ^4.0.2 "@types/node": ^18.7.23 @@ -337,6 +345,7 @@ __metadata: version: 0.0.0-use.local resolution: "@aztec/circuits.js@workspace:circuits.js" dependencies: + "@aztec/bb.js": "portal:../../barretenberg/ts" "@aztec/foundation": "workspace:^" "@jest/globals": ^29.5.0 "@msgpack/msgpack": ^3.0.0-beta2 @@ -380,7 +389,6 @@ __metadata: "@jest/globals": ^29.5.0 "@libp2p/peer-id-factory": ^3.0.4 "@ltd/j-toml": ^1.38.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/lodash.startcase": ^4.4.7 "@types/node": ^18.7.23 @@ -423,6 +431,7 @@ __metadata: "@aztec/ethereum": "workspace:^" "@aztec/foundation": "workspace:^" "@aztec/l1-artifacts": "workspace:^" + "@aztec/merkle-tree": "workspace:^" "@aztec/noir-contracts": "workspace:^" "@aztec/p2p": "workspace:^" "@aztec/pxe": "workspace:^" @@ -431,7 +440,6 @@ __metadata: "@aztec/world-state": "workspace:^" "@jest/globals": ^29.5.0 "@noble/curves": ^1.0.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/fs-extra": ^11.0.2 "@types/jest": ^29.5.0 "@types/koa": ^2.13.9 @@ -472,7 +480,6 @@ __metadata: dependencies: "@aztec/foundation": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/node": ^18.14.6 dotenv: ^16.0.3 @@ -493,7 +500,6 @@ __metadata: "@jest/globals": ^29.5.0 "@koa/cors": ^4.0.0 "@noble/curves": ^1.2.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/bn.js": ^5.1.3 "@types/debug": ^4.1.7 "@types/detect-node": ^2.0.0 @@ -553,7 +559,6 @@ __metadata: "@aztec/foundation": "workspace:^" "@aztec/types": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/node": ^18.7.23 jest: ^29.5.0 @@ -579,11 +584,9 @@ __metadata: version: 0.0.0-use.local resolution: "@aztec/merkle-tree@workspace:merkle-tree" dependencies: - "@aztec/circuits.js": "workspace:^" "@aztec/foundation": "workspace:^" "@aztec/types": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/levelup": ^5.1.2 "@types/memdown": ^3.0.1 @@ -609,7 +612,6 @@ __metadata: "@ltd/j-toml": ^1.38.0 "@noir-lang/noir_wasm": 0.18.0-6ca33a2.aztec "@noir-lang/source-resolver": 0.18.0-6ca33a2.aztec - "@rushstack/eslint-patch": ^1.1.4 "@types/fs-extra": ^11.0.1 "@types/jest": ^29.5.0 "@types/lodash.camelcase": ^4.3.7 @@ -650,7 +652,6 @@ __metadata: "@aztec/foundation": "workspace:^" "@aztec/noir-compiler": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/lodash.camelcase": ^4.3.7 "@types/lodash.omit": ^4.5.7 @@ -683,7 +684,6 @@ __metadata: "@noir-lang/backend_barretenberg": ^0.7.10 "@noir-lang/noir_js": ^0.16.0 "@noir-lang/noirc_abi": ^0.16.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/node": ^18.7.23 jest: ^29.5.0 @@ -703,7 +703,6 @@ __metadata: "@aztec/foundation": "workspace:^" "@aztec/p2p": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/node": ^18.14.6 dotenv: ^16.0.3 @@ -734,7 +733,6 @@ __metadata: "@libp2p/peer-id": ^3.0.2 "@libp2p/peer-id-factory": ^3.0.3 "@libp2p/tcp": ^8.0.4 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/lodash.times": ^4.3.7 "@types/node": ^18.14.6 @@ -757,7 +755,6 @@ __metadata: dependencies: "@aztec/foundation": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/node": ^18.7.23 jest: ^29.5.0 @@ -781,7 +778,6 @@ __metadata: "@aztec/noir-protocol-circuits": "workspace:^" "@aztec/types": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/lodash.omit": ^4.5.7 "@types/lodash.partition": ^4.6.0 @@ -812,7 +808,6 @@ __metadata: "@aztec/foundation": "workspace:^" "@aztec/types": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/lodash.pick": ^4.4.7 "@types/node": ^18.14.6 @@ -845,7 +840,6 @@ __metadata: "@aztec/types": "workspace:^" "@aztec/world-state": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/levelup": ^5.1.2 "@types/lodash.chunk": ^4.2.7 @@ -881,7 +875,6 @@ __metadata: "@aztec/ethereum": "workspace:^" "@aztec/foundation": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/lodash.clonedeep": ^4.5.7 "@types/lodash.isequal": ^4.5.6 @@ -909,7 +902,6 @@ __metadata: "@aztec/merkle-tree": "workspace:^" "@aztec/types": "workspace:^" "@jest/globals": ^29.5.0 - "@rushstack/eslint-patch": ^1.1.4 "@types/jest": ^29.5.0 "@types/levelup": ^5.1.2 "@types/lodash.times": ^4.3.7 @@ -3837,13 +3829,6 @@ __metadata: languageName: node linkType: hard -"@rushstack/eslint-patch@npm:^1.1.4, @rushstack/eslint-patch@npm:^1.2.0": - version: 1.3.2 - resolution: "@rushstack/eslint-patch@npm:1.3.2" - checksum: 010c87ef2d901faaaf70ea1bf86fd3e7b74f24e23205f836e9a32790bca2076afe5de58ded03c35cb482f83691c8d22b1a0c34291b075bfe81afd26cfa5d14cc - languageName: node - linkType: hard - "@safe-global/safe-apps-provider@npm:^0.15.2": version: 0.15.2 resolution: "@safe-global/safe-apps-provider@npm:0.15.2" diff --git a/yellow-paper/docs/addresses-and-keys/addresses-and-keys.md b/yellow-paper/docs/addresses-and-keys/addresses-and-keys.md index d2e7a5fe21a..3c2948042fc 100644 --- a/yellow-paper/docs/addresses-and-keys/addresses-and-keys.md +++ b/yellow-paper/docs/addresses-and-keys/addresses-and-keys.md @@ -120,9 +120,9 @@ Much of this is heavily inspired by ZCash sapling & orchard keys. > Note: there is nothing to stop an app and wallet from implementing its own key derivation scheme. Nevertheless, we're designing a 'canonical' scheme that most developers and wallets can use. -### Authorisation keys +### Authorization keys -Aztec has native account abstraction, so tx authentication is done via an account contract, meaning tx authentication can be implemented however the user sees fit. That is, authorisation keys aren't specified at the protocol level. +Aztec has native account abstraction, so tx authentication is done via an account contract, meaning tx authentication can be implemented however the user sees fit. That is, authorization keys aren't specified at the protocol level. A tx authentication secret key is arguably the most important key to keep private, because knowledge of such a key could potentially enable an attacker to impersonate the user and execute a variety of functions on the network. @@ -155,7 +155,7 @@ A tx authentication secret key is arguably the most important key to keep privat #### Some security assumptions -- The Aztec private execution client (PXE) and the kernel circuit (a core protocol circuit) can be trusted with master secret keys (_except for_ the tx authorisation secret key, whose security assumptions are abstracted-away to wallet designers). +- The Aztec private execution client (PXE) and the kernel circuit (a core protocol circuit) can be trusted with master secret keys (_except for_ the tx authorization secret key, whose security assumptions are abstracted-away to wallet designers). ### Encryption and decryption @@ -268,7 +268,7 @@ Nice to haves: **Requirements:** - [Nice to have]: A "tagging" keypair that enables faster brute-force identification of owned notes. - - Note: this is useful for rapid handshake discovery, but it is an optimisation, and has trade-offs (such as more data to send). + - Note: this is useful for rapid handshake discovery, but it is an optimization, and has trade-offs (such as more data to send). - [Nice to have]: The ability to generate a sequence of tags between Alice and Bob, in line with our latest "Tag Hopping" ideas. Considerations: @@ -343,7 +343,7 @@ There are some more involved complications and considerations, which haven't all No. -The 'topology' of the key derivations (i.e. the way the derivations of the keys interrelate, if you were to draw a dependency graph) is not constraint-optimised. There might be a better 'layout'. +The 'topology' of the key derivations (i.e. the way the derivations of the keys interrelate, if you were to draw a dependency graph) is not constraint-optimized. There might be a better 'layout'. Domain separation hasn't been considered in-depth. @@ -398,7 +398,7 @@ The red boxes are uncertainties, which are explained later in this doc. |---|---|---|---|---| $\sk$ | $\stackrel{\$}{\leftarrow} \mathbb{F}$ | secret key | TEE/ PXE | A seed secret from which all these other keys may be derived. For future reference (in case we modify the schemes), this $\sk$ doesn't need to enter a circuit if all keys can be provably linked/tethered to some fixed public key/address. | $\nskm$ | h(0x01, $\sk$) | nullifier secret key | PXE, K | Gives developers the option of using a secret key to derive their apps' nullifiers. (Not all nullifiers require a secret key, e.g. plume nullifiers). | -$\tskm$ | h(0x02, $\sk$) | tagging secret key | PXE* | The "tagging" key pair can be used to flag "this ciphertext is for you", without requiring decryption. This key exists merely as an optimisation. We might choose to do away with it, in favour of using $\ivskm$. | +$\tskm$ | h(0x02, $\sk$) | tagging secret key | PXE* | The "tagging" key pair can be used to flag "this ciphertext is for you", without requiring decryption. This key exists merely as an optimization. We might choose to do away with it, in favour of using $\ivskm$. | $\ivskm$ | h(0x03, $\sk$) | incoming viewing secret key | PXE* | The owner of this secret key can derive ephemeral symmetric encryption keys, to decrypt ciphertexts which _have been sent to them_ (i.e. "incoming" data from the pov of the recipient). | $\ovskm$ | h(0x04, $\sk$) | outgoing viewing secret key | PXE* | The owner of this secret key can derive ephemeral symmetric encryption keys, to decrypt ciphertexts which _they have sent_ (i.e. "outgoing" data from the pov of the sender (and of the recipient, since they're the same person in this case)). This is useful if the user's DB is wiped, and they need to sync from scratch (starting with only $\sk$). | |||||| @@ -416,7 +416,7 @@ $\Ovpkm$ | $\ovskm \cdot G$ | outgoing viewing public key | | Only included so t |---|---|---|---| $\constructorhash$ | h(constructor\_args, salt) | constructor hash | A commitment to the constructor arguments used when deploying the user's account contract. | $\codehash$ | h(bytecode, $\constructorhash$) | code hash | Binds the bytecode and constructor arguments together. -$\address$ | h($\Npkm$, $\Tpkm$, $\Ivpkm$, $\Ovpkm$, $\codehash$) | address | This isn't an optimised derivation. It's just one that works. | +$\address$ | h($\Npkm$, $\Tpkm$, $\Ivpkm$, $\Ovpkm$, $\codehash$) | address | This isn't an optimized derivation. It's just one that works. | :::warning @@ -485,7 +485,7 @@ An app-siloed outgoing viewing keypair is derived as a hardened child keypair of | Key | Derivation | Name | Where? | Comments | |---|---|---|---|---| -$\nskapp$ | $h(\nskm, \text{app\_address})$ | app-siloed nullifier secret key | PXE, K, App | Hardened, so only derivable by the owner of the master nullifier secret key. Hardened so as to enable the $\nskapp$ to be passed into an app circuit (without the threat of $\nskm$ being reverse-derivable). Only when a public key needs to be derivable by the general public is a normal (non-hardened) key used.
Deviates from 'conventional' hardened BIP-32-style derivation significantly, to reduce complexity and as an optimisation. Such a deviation would need to be validated as secure. | +$\nskapp$ | $h(\nskm, \text{app\_address})$ | app-siloed nullifier secret key | PXE, K, App | Hardened, so only derivable by the owner of the master nullifier secret key. Hardened so as to enable the $\nskapp$ to be passed into an app circuit (without the threat of $\nskm$ being reverse-derivable). Only when a public key needs to be derivable by the general public is a normal (non-hardened) key used.
Deviates from 'conventional' hardened BIP-32-style derivation significantly, to reduce complexity and as an optimization. Such a deviation would need to be validated as secure. | $\Nkapp$ | $h(\nskapp)$ | Shareable nullifier key | PXE, K, T3P, App| If an app developer thinks some of their users might wish to have the option to enable some _trusted_ 3rd party to see when a particular user's notes are nullified, then this nullifier key might be of use. This $\Nkapp$ can be used in a nullifier's preimage, rather than $\nskapp$ in such cases, to enable said 3rd party to brute-force identify nullifications.
Note: this would not enable a 3rd party to view the contents of any notes; knowledge of the $\ivskapp$ / $\ovskapp$ would be needed for that.
Note: this is not a "public" key, since it must not be shared with the public. | See the appendix for an alternative derivation suggestion. @@ -531,7 +531,7 @@ Having derived a Shared Secret, Bob can now share it with Alice as follows: | Thing | Derivation | Name | Comments | |---|---|---|---| -$\Taghs$ | $\esk_{hs} \cdot \Tpkm$ | Handshake message identification tag | Note: the tagging public key $\Tpkm$ exists as an optimisation, seeking to make brute-force message identification as fast as possible. In many cases, handshakes can be performed offchain via traditional web2 means, but in the case of on-chain handshakes, we have no preferred alternative over simply brute-force attempting to reconcile every 'Handshake message identification tag'. Note: this optimisation reduces the recipient's work by 1 cpu-friendly hash per message (at the cost of 255-bits to broadcast a compressed encoding of $\Taghs$). We'll need to decide whether this is the right speed/communication trade-off. | +$\Taghs$ | $\esk_{hs} \cdot \Tpkm$ | Handshake message identification tag | Note: the tagging public key $\Tpkm$ exists as an optimization, seeking to make brute-force message identification as fast as possible. In many cases, handshakes can be performed offchain via traditional web2 means, but in the case of on-chain handshakes, we have no preferred alternative over simply brute-force attempting to reconcile every 'Handshake message identification tag'. Note: this optimization reduces the recipient's work by 1 cpu-friendly hash per message (at the cost of 255-bits to broadcast a compressed encoding of $\Taghs$). We'll need to decide whether this is the right speed/communication trade-off. | $\payload$ | [$\Taghs$, $\Epk_{hs}$] | Payload | This can be broadcast via L1.
Curve points can be compressed in the payload. | Alice can identify she is the indended the handshake recipient as follows: @@ -539,7 +539,7 @@ Alice can identify she is the indended the handshake recipient as follows: | Thing | Derivation | Name | Comments | |---|---|---|---| -$\Taghs$ | $\tskm \cdot \Epk_{hs}$ | Handshake message identification tag | Alice can extract $\Taghs$ and $\Epk_{hs}$ from the $\payload$ and perform this scalar multiplication on _every_ handshake message. If the computed $\Taghs$ value matches that of the $\payload$, then the message is indented for Alice.
Clearly, handshake transactions will need to be identifiable as such (to save Alice time), e.g. by revealing the contract address of some canonical handshaking contract alongside the $\payload$.
Recall: this step is merely an optimisation, to enable Alice to do a single scalar multiplication before moving on (in cases where she is not the intended recipient). | +$\Taghs$ | $\tskm \cdot \Epk_{hs}$ | Handshake message identification tag | Alice can extract $\Taghs$ and $\Epk_{hs}$ from the $\payload$ and perform this scalar multiplication on _every_ handshake message. If the computed $\Taghs$ value matches that of the $\payload$, then the message is indented for Alice.
Clearly, handshake transactions will need to be identifiable as such (to save Alice time), e.g. by revealing the contract address of some canonical handshaking contract alongside the $\payload$.
Recall: this step is merely an optimization, to enable Alice to do a single scalar multiplication before moving on (in cases where she is not the intended recipient). | If Alice successfully identifies that she is the indended the handshake recipient, she can proceed with deriving the shared secret (for tagging) as follows: @@ -579,7 +579,7 @@ Having derived a Shared Secret, Bob can now share it with Alice as follows: | Thing | Derivation | Name | Comments | |---|---|---|---| -$\Taghs$ | $\esk_{hs} \cdot \Tpkm$ | Handshake message identification tag | Note: the tagging public key $\Tpkm$ exists as an optimisation, seeking to make brute-force message identification as fast as possible. In many cases, handshakes can be performed offchain via traditional web2 means, but in the case of on-chain handshakes, we have no preferred alternative over simply brute-force attempting to reconcile every 'Handshake message identification tag'. Note: this optimisation reduces the recipient's work by 1 cpu-friendly hash per message (at the cost of 255-bits to broadcast a compressed encoding of $\Taghs$). We'll need to decide whether this is the right speed/communication trade-off.
Note also: the _master_ tagging key $\Tpkm$ is being used in this illustration, rather than some app-specific tagging key, to make this message identification process most efficient (otherwise the user would have to re-scan all handshakes for every app they use). | +$\Taghs$ | $\esk_{hs} \cdot \Tpkm$ | Handshake message identification tag | Note: the tagging public key $\Tpkm$ exists as an optimization, seeking to make brute-force message identification as fast as possible. In many cases, handshakes can be performed offchain via traditional web2 means, but in the case of on-chain handshakes, we have no preferred alternative over simply brute-force attempting to reconcile every 'Handshake message identification tag'. Note: this optimization reduces the recipient's work by 1 cpu-friendly hash per message (at the cost of 255-bits to broadcast a compressed encoding of $\Taghs$). We'll need to decide whether this is the right speed/communication trade-off.
Note also: the _master_ tagging key $\Tpkm$ is being used in this illustration, rather than some app-specific tagging key, to make this message identification process most efficient (otherwise the user would have to re-scan all handshakes for every app they use). | $\esk$ | $\stackrel{rand}{\leftarrow} \mathbb{F}$ | ephemeral secret key, for encryption | TODO: perhaps just one ephemeral keypair could be used? | $\Epk$ | $\esk \cdot G$ | Ephemeral public key, for encryption | $\sharedsecret_{m,header}$ | $\esk \cdot \Ivpkm$ | Shared secret, for encrypting the ciphertext header. | The _master_ incoming viewing key is used here, to enable Alice to more-easily discover which contract address to use, and hence which app-specific $\ivskapp$ to use to ultimately derive the app-specific tag. | @@ -592,7 +592,7 @@ Alice can identify she is the indended the handshake recipient as follows: | Thing | Derivation | Name | Comments | |---|---|---|---| -$\Taghs$ | $\tskm \cdot \Epk_{hs}$ | Handshake message identification tag | Alice can extract $\Taghs$ and $\Epk_{hs}$ from the $\payload$ and perform this scalar multiplication on _every_ handshake message. If the computed $\Taghs$ value matches that of the $\payload$, then the message is indented for Alice.
Clearly, handshake transactions will need to be identifiable as such (to save Alice time), e.g. by revealing the contract address of some canonical handshaking contract alongside the $\payload$.
Recall: this step is merely an optimisation, to enable Alice to do a single scalar multiplication before moving on (in cases where she is not the intended recipient). | +$\Taghs$ | $\tskm \cdot \Epk_{hs}$ | Handshake message identification tag | Alice can extract $\Taghs$ and $\Epk_{hs}$ from the $\payload$ and perform this scalar multiplication on _every_ handshake message. If the computed $\Taghs$ value matches that of the $\payload$, then the message is indented for Alice.
Clearly, handshake transactions will need to be identifiable as such (to save Alice time), e.g. by revealing the contract address of some canonical handshaking contract alongside the $\payload$.
Recall: this step is merely an optimization, to enable Alice to do a single scalar multiplication before moving on (in cases where she is not the intended recipient). | If Alice successfully identifies that she is the indended the handshake recipient, she can proceed with deriving the shared secret (for tagging) as follows: @@ -709,7 +709,7 @@ Here's how an app circuit could constrain the nullifier key to be correct: |---|---|---|---| $\Nkapp$ | h($\nskapp$) | App-siloed nullifier key | Recall an important point: the app circuit MUST NOT be given $\nskm$. Indeed, $\nskapp$ is derived (see earlier) as a _hardened_ child of $\nskm$, to prevent $\nskm$ from being reverse-derived by a malicious circuit. The linking of $\nskapp$ to $\nskm$ is deferred to the kernel circuit (which can be trusted moreso than an app).
Recall also: $\Nkapp$ is used (instead of $\nskapp$) solely as a way of giving the user the option of sharing $\Nkapp$ with a trusted 3rd party, to give them the ability to view when a note has been nullified (although I'm not sure how useful this is, given that it would require brute-force effort from that party to determine which note hash has been nullified, with very little additional information).
| `nullifier` | h(note_hash, $\Nkapp$) | -$\address$ | h($\Npkm$, $\Tpkm$, $\Ivpkm$, $\Ovpkm$, $\codehash$) | address | Proof that the $\Npkm$ belongs to the note owner's $\address$.
This isn't an optimised derivation. It's just one that works. | +$\address$ | h($\Npkm$, $\Tpkm$, $\Ivpkm$, $\Ovpkm$, $\codehash$) | address | Proof that the $\Npkm$ belongs to the note owner's $\address$.
This isn't an optimized derivation. It's just one that works. | The app circuit exposes, as public inputs, a "nullifier key validation request": @@ -895,7 +895,7 @@ $\happn$ | h(0x01, $\happL$) | normal siloing key for an app-specific nullifier $\Npkapp$ | $\happn \cdot G + \Npkm$ | normal (non-hardened) app-siloed nullifier public key | |||| `nullifier` | h(note_hash, $\Nkapp$) | | $\Nkapp$ is exposed as a public input, and linked to the $\Npkapp$ via the kernel circuit. | -$\address$ | h($\Npkm$, $\Tpkm$, $\Ivpkm$, $\Ovpkm$, $\codehash$) | address | Proof that the $\Npkm$ belongs to the note owner's $\address$.
This isn't an optimised derivation. It's just one that works. | +$\address$ | h($\Npkm$, $\Tpkm$, $\Ivpkm$, $\Ovpkm$, $\codehash$) | address | Proof that the $\Npkm$ belongs to the note owner's $\address$.
This isn't an optimized derivation. It's just one that works. | > Recall an important point: the app circuit MUST NOT be given $\nskm$ nor $\nskapp$ in this option. Since $\nskapp$ is a normal (non-hardened) child, $\nskm$ could be reverse-derived by a malicious circuit. The linking of $\nskapp$ to $\Npkm$ is therefore deferred to the kernel circuit (which can be trusted moreso than an app).
Recall also: $\Nkapp$ is used (instead of $\nskapp$) solely as a way of giving the user the option of sharing $\Nkapp$ with a trusted 3rd party, to give them the ability to view when a note has been nullified. diff --git a/yellow-paper/docs/decentralisation/_category_.json b/yellow-paper/docs/decentralisation/_category_.json new file mode 100644 index 00000000000..624d37f6756 --- /dev/null +++ b/yellow-paper/docs/decentralisation/_category_.json @@ -0,0 +1,8 @@ +{ + "label": "Decentralisation", + "position": 3, + "link": { + "type": "generated-index", + "description": "Decentralisation..." + } +} diff --git a/yellow-paper/docs/decentralisation/decentralisation.md b/yellow-paper/docs/decentralisation/decentralisation.md new file mode 100644 index 00000000000..e69de29bb2d diff --git a/yellow-paper/docs/decentralisation/images/network.png b/yellow-paper/docs/decentralisation/images/network.png new file mode 100644 index 00000000000..05055422823 Binary files /dev/null and b/yellow-paper/docs/decentralisation/images/network.png differ diff --git a/yellow-paper/docs/decentralisation/p2p-network.md b/yellow-paper/docs/decentralisation/p2p-network.md new file mode 100644 index 00000000000..90d415b48ae --- /dev/null +++ b/yellow-paper/docs/decentralisation/p2p-network.md @@ -0,0 +1,45 @@ +## Requirements for a P2P Network + +:::info Disclaimer +This is a draft. These requirements need to be considered by the wider team, and might change significantly before a mainnet release. +::: + +When rollups are successfully published, the state transitions are published along with it and are publically retrievable. This category of state does not depend on the Aztec network for its persistence or distribution. Transient data such as pending user transactions for inclusion in future rollups however does rely on the network for these functions. Network participants will consist of: + +* Sequencers - responsible for selecting transactions from the global pool and including them in rollups +* Provers - responsible for generating zk-proofs for the transaction and rollup circuits + +Pending transactions will be the primary category of data being transmitted through the network. It is important that the network provides a performant, permissionless and censorship resistant mechanism for the effective propagation of these transactions to all sequencers. Without this, transactions may be disadvantaged and the throughput of the network will deteriorate. + +Other data that may be transmitted over the network are the final rollup proofs to be submitted to the rollup contract, the size and rate of these payloads should not make any meaningful impact on the bandwidth requirements. + +### Network Capacity + +Transactions are composed of a number of data elements and can vary in size predominantly based on their deployment of any public bytecode and the private kernel proof. A typical transaction that emits a private note and an unencrypted log, makes a public call and contains a valid proof would consume approximately 40Kb of data. A transaction that additionally deploys a contract would need to transmit the public bytecode on top of this. + +| Element | Size | +| ------- | ---------------- | +| Public Inputs, Public Calls and Emitted Logs | ~8Kb | +| Private Kernel Proof | ~32Kb | + +At throughputs of 10 and 100 transactions per second, we can arrive at average network bandwidth requirements of 400Kb and 4000Kb per second respectively. + +### Sequencer to Prover Communication + +There shouldn't be any requirement for the network to handle communication from sequencers to provers for the purpose of generating proofs. Proving is an out-of-protocol activity so it is likely that provers will obtain their input data in one of 2 ways. + +* Via a direct interface to a prover marketplace over a protocol such as http +* The provers will independently know the sequence of transactions from the commitment phase of the sequencer selection protocol. They can then use the transaction pool to maintain their own state for proof generation + +### Network Topology and Submitting Transactions + +Aztec Node instances will offer a JSON RPC interface for consumption by a user's PXE. Part of this API will facilitate transaction submission directly to the node which will then forward it to the network via the transaction pool. + +![P2P Network](../decentralisation/images/network.png) + + + + + + + diff --git a/yellow-paper/docs/gas-and-fees/_category_.json b/yellow-paper/docs/gas-and-fees/_category_.json new file mode 100644 index 00000000000..d92583595ce --- /dev/null +++ b/yellow-paper/docs/gas-and-fees/_category_.json @@ -0,0 +1,8 @@ +{ + "label": "Gas & Fees", + "position": 2, + "link": { + "type": "generated-index", + "description": "Gas and fees on the Aztec network..." + } +} diff --git a/yellow-paper/docs/gas-and-fees/gas-and-fees.md b/yellow-paper/docs/gas-and-fees/gas-and-fees.md new file mode 100644 index 00000000000..c916dfedd56 --- /dev/null +++ b/yellow-paper/docs/gas-and-fees/gas-and-fees.md @@ -0,0 +1,302 @@ +--- +sidebar_position: 2 +--- + +# Gas and Fees + +## Requirements + +:::info Disclaimer +This is a draft. These requirements need to be considered by the wider team, and might change significantly before a mainnet release. +::: + +Private state transition execution and proving is performed by the end user. However, once a transaction is submitted to the network, further resource is required to verify the private proofs, effect public state transitions and include the transaction within a rollup. This comes at the expense of the sequencer selected for the current slot. These resources include, but are not limited to: + +1. Execution of public function bytecode +2. Generation of initial witnesses and proving of public and rollup circuits +3. Storage of world state and computation of merkle proofs +4. Finalisation of state transition functions on Ethereum +5. Storage of private notes + +Sequencers will need compensatiing for their efforts leading to requirements for the provision of payments to the sequencer. Note, some of the computation may be outsourced to third parties as part of the prover selection mechanism, the cost of this is borne by the sequencer outside of the protocol. + +We can define a number of requirements that serve to provide a transparent and fair mechanism of fee payments between transaction senders and sequencers. + +1. Senders need to accurately quantify the resource consumption of a transaction and generate an appropriate fee for it. +2. Senders need to be assured that they will be charged fees fairly and deterministically for execution of their transaction and inclusion in a rollup. +3. Senders need to be refunded for any unused fee resulting from procssing their transaction. +4. Senders need to be able to successfully submit a transaction when they have not previously used Aztec before or possess any funds on the network. +4. Sequencers need to be fairly and deterministically compensated for their expense in including transactions in a rollup. +5. Sequencers require agency in accepting transactions based on the fee that is being paid. +6. Sequencers need certainty that they will be paid for their effort in executing transactions, even if any public component of the transaction fails or insufficient fees are provided for this execution. +7. Sequencers need protection against grief or DOS attacks. More specifically, sequencers need to be confident that they will not be required to expend an unreasonable amount of effort before being able to reliably determine the fee endowed to a transaction. + +## High Level Concepts and Design + +1. We will use concepts of L1 and L2 gas to universally define units of resource for the Ethereum and Aztec networks respectively. L1 gas directly mirrors the actual gas specification as defined by Ethereum, L2 gas covers all resource expended on the L2 network. +2. We will deterministically quantify all resource consumption of a transaction into 4 values. These being the amortised and transaction specific quantities of each of L1 and L2 gas. +3. The transaction sender will provide a single fee for the transaction. This will be split into 2 components to cover each of the L1 and L2 gas costs. The sender will specifiy `feePerGas` and `gasLimit` for each component. Doing so provides protection to the sender that the amount of fee applicable to L1 costs has an upper bound and L2 a lower bound. +4. We will constrain the sequencer to apply the correct amortised and transaction specific fees ensuring the sender can not be charged arbitrarily. +5. We will define a method by which fees can be paid in any asset, either publicly or privately, on Ethereum or Aztec but where the sequencer retains agency as to what assets and fee payment methods they are willing to accept. +6. Upon accepting a transaction, we will constrain the sequencer to receive payment and provide any refund owing via the methods specified by the sender. + +## Gas Metering + +Broadly speaking, resource consumption incurred by the sequencer falls into categories of transaction specific consumption and amortised, per-rollup consumption. Each operation performed by the sequencer can be attributed with a fixed amount of gas per unit representing it's level of resource consumption. The unit will differ between operations, for example in some operations it may be per-byte whilst in others it could be per-opcode. What matters is that we are able to determine the total gas consumption of any given transaction. + +### Amortised Consumption + +Rollups consist of multiple transactions, allowing for amortisation of certain costs in the production and verification of those rollups. The amortisable costs over a rollup of `N` transactions are: + + +| Action | Resource Domain | Amortisation Calculation | +|---|---|---| +| Publishing the rollup start and end state as part of the rollup transaction on Ethereum | L1 | Fixed quantity of calldata / N | +| Executing the rollup transaction on Ethereum, including the ZK verifier | L1 | Fixed verification gas / N | +| Generating witnesses and proving the rollup circuits | L2 | Sum of Base, Merge and Root rollups circuit gas required for rollup / N | + +To expand on the summing of base, merge and rollup circuits. The rollup has a binary tree structure so it can be deterministically calculated how many of each of the base, merge and root rollup circuits are required for an `N` transaction rollup. A fixed gas value can be applied to each of these components. + +### Transaction Specific Consumption + +Transaction specific consumption also consists of both L1 and L2 components: + + +| Action | Resource Domain | Consumption Calculation | Comment | +| -------- | -------- | -------- | ------- | +| Verifying each nullifier against the world state | L2 | Fixed L2/Tx nullifier | | +| Verifying each nullifier against others in the same block | L2 | Fixed L2/Tx nullifier | Whilst not strictly a fixed cost, this would need to be allocated as a fixed cost as it depends on the composition of the rollup | +| Verifying log preimages against the sha256 log hashes contained in the private kernel public inputs | L2 | Hash cost per log preimage field | | +| Verifying contract deployment data against the sha256 hash of this data contained in the private kernel public inputs | L2 | Hash cost per field | | +| Publishing contract data to L1 | L1 | Calldata gas per byte | | +| Publishing state updates to L1 | L1 | Calldata gas per byte | | +| Publishing notes/tags to L1 | L1 | Calldata gas per byte + verification hashing per byte | | +| Publishing an L2->L1 messages | L1 | Calldata gas per byte + processing & storing of the message | | +| Simulating a public function | L2 | L2 gas per function opcode | | +| Public VM witness generation for a public function | L2 | L2 gas per function opcode | | +| Proving the public VM circuit for a public function | L2 | Fixed L2/Tx public function | | +| Simulating the public kernel circuit for a public function | L2 | Fixed L2/Tx public function | | +| Public kernel circuit witness generation for a public function | L2 | Fixed L2/Tx public function | | +| Proving the public kernel circuit for a public function | L2 | Fixed L2/Tx public function | | + +## Attributing Transaction Gas + +### Measuring Gas Before Submission + +All of the operations listed in the transaction specific table can provide us with deterministic gas values for a transaction. The transaction can be simulated and appropriate gas figures can be calculated before the transaction is sent to the network. The transaction will also need to provide a fee to cover it's portion of the amortised cost. This can be done by deciding on a value of `N`, the number of transactions in a rollup. Of course, the transaction sender can't know in advance how many other transactions will be included in the same rollup but the sender will be able to see how many transactions were included in prior rollups and decide on a value that will give them some certainty of inclusion without overpaying for insufficient amortisation. Additional amortisation will be refunded to the sender. + +For example, if the previous 10 rollups consist of an average of 5000 transactions, the sender could use a value of 4000 for `N` in it's amortisation. If the transaction is included in a rollup with > `N` transaction, the fee saved by the additional amortisation will be refunded to the sender. + +The transaction will be provided with 4 gas values: + + +| Value | Description | +| -------- | -------- | +| `l1BaseGasLimit` | The maximum amount of gas permitted for use in amortised L1 operations | +| `l1TxGasLimit` | The maximum amount of gas permitted for use in transaction specific L1 operations | +| `l2BaseGasLimit` | The maximum amount of gas permitted for use in amortised L2 operations | +| `l2TxGasLimit` | The maximum amount of gas permitted for use in transaction specific operations | + +By constraining each of the above values individually, the transaction sender is protected from a dishonest sequencer allocating an unfairly high amount of gas to one category and leaving insufficient gas for other categories causing a transaction to erroneously be deemed 'out of gas' and a fee taken for improper execution. + +### Gas Measurement By A Sequencer + +When a transaction is received by a sequencer, it will want to determine if the transaction has been endowed with sufficient fee to be considered for inclusion in a rollup. Although the transaction contains information as to the gas limits and fees it provides, these may not be accurate either because the sender is dishonest or because the simulation of any public functions was performed on a system state that differs to that at the point of inclusion. Unlike transactions on Ethereum it is not simply a case of linearly executing the transactions opcodes until completion of the transaction exceeds the provided gas. Rollup inclusion and public function execution and proving require significant resource investment on the part of the sequencer even for the most trivial of transaction. + +There are a series of steps the sequencer would wish to perform such that it incrementally increases it's committment to processing the transaction as it becomes more confident of a successful outcome: + +1. Determine how much fee has been provided and verify that this is sufficent to cover the L1 and L2 gas limits specified in the transaction. Later on we will look at how this is done but it may involve simulation of a public function. The sequencer will have visibility over which function on which contract has been specified for this step and has agency to disregard the transaction if it is not willing to execute this step. +2. Once the fee is known, verify that enough fee exists to cover the transaction's requirements at this stage. This would include publishing the results of the private stage of the transaction and the amortised cost of rollup construction and publishing. +3. If at least one public function is enqueued, verify that enough fee exists to cover at least 1 iteration of the public VM, 1 iteration of the public kernel circuit and a non-zero amount left for public function simulation. The sequener here will seek to determine if it is worth proceeding with the transaction. Proceeding requires an investment at least covering the cost of the minimum public VM execution and an iteration of the public kernel circuit. The minimum cost could be described as the cost to simulate, execute and prove a function which reverts as soon as it enters the function. + +Each of the above steps should be relatively trivial for the sequencer to perform and they have agency to disregard the transaction after each one. Having decided that a transaction is worth proceeding with, the sequencer will simulate any public portion of the transaction until completion or simulation exceeds the provided L1 or L2 gas limits. As simulation takes place, it should be noted that further L1 state updates will be made and any nested public calls will incur additional public VM and public kernel iteration costs. + +After simulation, the sequencer will have complete visibility over the gas profile of the transaction and can determine how much witness generation and proving is required for transaction inclusion. + +## Fees + +Now that we have a method for defining and measuring the gas consumption of a transaction, we need a method by which fees can be transferred from the transaction sender to whichever sequencer ultimately includes the transaction within a rollup. This method will need to account for the fact that any public function execution may revert and the sequencer should be paid for the work they performed up to that point. Finally, any unused fee should be returned to the transaction sender. + +To achieve the above requirements we will break the transaction into 3 components: + +1. The fee preparation component can have both private and/or public functions. +2. The transaction payload component can have both private and/or public functions. +3. The fee distribution component only has a public function. + +All of these components occur **within the same transaction**. + +### Fee Preparation + +This component will produce the fee for the sequencer, it will most likely consist of both private and public executions. This would typically call a 'fee payment' contract that would be associated with the asset being used for payment. Any public execution should be minimal to reduce unnecessary work for the sequencer during the period of evaluating whether a transaction is to be included. Sequencers will have visibility over which function of which contract needs to be called and so can disregard transactions calling fee payment functions which they deem unacceptable. + +### Transaction Payload + +This is the main component of the transaction, the part containing the execution the transaction sender wishes to make. This may revert or run out of gas but the sequencer will still be able to claim payment for the work performed. + +### Fee Distribution + +This component consists of a single public function call to a contract function specified by the client. The function will have a specific signature that will require the sequencer to provide information about the gas consumed throughout the execution of the transaction payload. This function will perform the necessary steps to finalise the payment to the sequencer and any refund owed to the client. + +Like the fee payment component, this must have a very low probability of failure and the sequencer is free to only consider transactions with fee distribution components that they deem acceptable. Reverting here reverts the entire transaction as no fee is distributed to the sequencer. However, it should be straight forward to create fee distribution functions that will not fail given valid inputs that have been verified by the sequencer. + +### Sequencer Validation and DOS Protection + +It is important that the sequencer is not susceptible to DOS attacks around the payment of fees and the work required to facilitate those payments. The above method requires the sequencer to execute up to 2 public functions. However, the sequencer has agency to only execute a whitelisted set of functions in order to process the fee payment and subsequent distribution. It is assumed that sequencers will already whitelist assets in which they will accept payment so that whitelist can be as granular as defining specific functions and bytecode deemed acceptable. Transactions which don’t meet this criteria can be ignored by the sequencer. + +It is of course possible that the fee payment contract and asset is supported by the sequencer but the function fails as insufficient funds have been made available by the client. To mitigate this, public payment and distribution functions should be scoped as tightly as possible, such as simply moving escrowed funds. The sequencer should feel confident in being able to simulate the payment function and verify it’s output with minimal resource consumption. + +### Fee Collection + +We will define a new block scoped gobal value ‘coinbase’ that will be used to identify the address of the sequencer for the current block. The sequencer will provide this address to public VM, public kernel and rollup circuits. The rollup circuits will constrain that the same value is used for all circuits in the proving tree. + +With this new value defined, a typical fee payment flow would look like: + +1. Escrow some funds by transferring them to a public balance within a ‘fee payment’ contract. +2. Compute the actual cost of the transaction. +3. Transfer the actual cost to the ‘coinbase’ address. +4. Transfer the refund back to the transaction sender. + +## Transaction and Fee Lifecycle + +We will attempt to walk through the process by which a transaction is created with an appropriate fee, accepted by the sequencer and the appropriate fee distribution undertaken. + +### User Simulation and Fee Preparation + +Transactions begin on a user's device. A user opts to interact privately with a contract, likely via their own account contract. This execution results in the generation of new notes and nulliifiers and potentially some enqueued public function calls. This part of the transaction is then proven via the private kernel circuit. The execution can be simulated, including the public execution against the state as it exists at the point of simulation. This enables a user to determine the L1 and L2 gas profile of the transaction, i.e. all required state updates and the extent of public execution and proving. + +With this gas profile, the user's wallet will be able to use gas price oracles to determine how much fee will need to be paid for the transaction in a given asset. The wallet will also need to suggest suitable amortisation rates by looking historically at the size of prior rollups. + +With an appropriate fee determined, a second component of the transaction is executed, that to generate and escrow the fee. This may change the gas profile of the entire transaction, e.g. it will result in an increased number of state updates. For this reason it may be necessary for a few iterations of this process to determine the correct fee depending on the notes available for fee payment. Once the user has settled on a fee payment, this part of the transaction will be proven via the private fee payment kernel circuit. This circuit will have a number of additional public inputs: + +- **feePerL1Gas** - The fee provided per unit of L1 gas +- **feePerL2Gas** - The fee provided per unit of L2 gas +- **l1BaseGasLimit** - The upper bound of L1 ammortised gas the transaction is willing to pay for +- **l2BaseGasLimit** - The upper bound of L2 ammortised gas the transaction is willing to pay for +- **l1TxGasLimit** - The upper bound of L1 transaction specific gas the transaction is willing to pay for +- **l2TxGasLimit** - The upper bound of L2 transaction specific gas the transaction is willing to pay for +- **feeDistribution** - The contract address and function selector the sequencer must call to process the fee distribution phase of the transaction + +Finally, the proofs of these 2 private kernel circuits are passed to another circuit, the private kernel merge circuit. This will output the final transaction proof and the resulting public inputs. + +``` +struct TxComponent { + commitments: Field[]; + nullifiers: Field[]; + publicCalls: PublicCall[]; + //..... +} + +struct FeeDistribution { + contractAddress: Field; + functionSelector: Field; +} + +struct PrivateMergeKernelPublicInputs { + feeComponent: TxComponent; + transactionPayload: TxComponent; + feeDistribution: FeeDistribution; + feePerL1Gas: Field; + feePerL2Gas: Field; + l1BaseGasLimit: Field; + l2BaseGasLimit: Field; + l1TxGasLimit: Field; + l2TxGasLimit: Field; + //..... +} +``` + +The transaction is now ready for submission to the network. + +![Merging the private kernel circuits](../gas-and-fees/images/gas-and-fees/private-merge.jpg) + +### Transaction Selection and Execution + +Upon retrieving a transaction from the P2P network, the sequencer can check that the transaction contains a fee for an accepted asset. This may require simulation of a whitelisted public function. If this step fails or is not accepted by the sequencer then the transaction can be discarded. Assuming this has succeeded, the provided fee is evaluated to see if it large enough as described previously. + +At this stage a `TxContext` object is instantiated and will be maintained through the lifetime of transaction execution. It will be used to accumulate gas usage through the various circuits, ensure that the correct fee is taken and an appropriate refund issued to the transaction sender. + +``` +struct TxContext { + totalFee; // provided by the client + l1BaseGasLimit; // provided by the client + l2BaseGasLimit; // provided by the client + l1TxGasLimit; // provided by the client + l2TxGasLimit; // provided by the client + feePerL1Gas; // provided by the client + feePerL2Gas; // provided by the client + l1GasUsed; // accumulated through circuits + l2GasUsed; // accumulated through circuits + refundRecipient; // used in the case of public payments/refunds + txId; // identifer used for private refunds + rollupSize; // set by the sequener for the rollup +} +``` + +The sequencer will need to specify the intended size of the rollup (determined as part of the sequencer selection commitment phase) and use this value to calculate gas amortisation. These values of amortised L1 and L2 gas will be added to the `l1GasUsed` and `l2GasUsed` accumulators. These accumulators will need to accurately reflect the gas consumption of the transaction prior to public function execution including state updates produced as part of private execution. + +Any enqueued public function calls can be simulated by the sequencer to obtain an accurate gas profile of the execution. This simulation will enable the sequencer to compute the number of additional state updates to be made, the number of public function calls and the L2 gas consumption of each of those calls. If either the L1 or L2 gas limits are breached, simulation will identify where in the execution trace this takes place and so the sequencer will only need to perform iterations of the public VM and public kernel circuits for the calls that either partially or completely succeeded. This ensures that the sequencer is not forced to execute and prove circuits for which they will not be compensated. + +The public VM circuit can now be executed and proven until completion or until the gas limit is reached. Each invocation of the circuit will constrain it's reported usage of both L1 and L2 gas. + +Public kernel circuit iterations will be executed for each public function call that actually took place. The combination of VM and kernel circuit executions will constrain the sequencer to correctly maintain the `TxContext` object such that: + +1. The gas consumption claimed by the sequencer is correct for both L1 and L2 gas. +2. Any reverts claimed by the sequencer did indeed occur. +3. After such reverts no unnecessary gas consumption took place. + +### Fee Distribution + +Once public function execution has completed (or hit the gas limit), the fee distribution component is executed. This is a public function and will also need to be proven via the VM and publc kernel circuits. The sequencer will have agency over which functions they are willing to accept and this will be part of the earlier transaction acceptance. + +The total fee taken by the sequencer is calculated from the values of consumed L1 and L2 gas and the `feePerGas` values provided with the transaction. Any balance remaining must be refunded. + +``` +let actual_l1_cost = tx_context.l1GasUsed * tx_context.feePerL1Gas; +let actual_l2_cost = tx_context.l2GasUsed * tx_context.feePerL2Gas; +let total_tx_cost = actual_l1_cost + actual_l2_cost; +let refund = tx_context.totalFee - total_tx_cost; +``` + +### Merging the Public Kernel Circuits + +The sequencer will have performed public function execution in up to 3 of the transaction components producing a chain of public kernel circuit executions for each. The proofs from the final iteration of each chain will be merged via a public kernel merge circuit. This circuit will also verify that the correct fee distribution function was called by the sequencer. + +![Merging the public kernel circuits](../gas-and-fees/images/gas-and-fees/public-merge.jpg) + +### Constraining the Sequencer via the Rollup Circuits + +Once all public execution has completed, the public kernel merge circuit proof will be consumed by the base rollup circuit. The base rollup circuit will ensure that the sequencer behaved honestly with regards to fee processing by verifying that: + +1. The values of amortised gas corresponded to the actual rollup size +2. The values of l1 and l2 gas accumulated within the `TxContext` object were accurate +3. The l1 and l2 gas limits specified in the transaction were respected +4. The correct values of `feePerL1Gas` and `feePerL2Gas` were used + +Additionally, the merge and root rollup circuits will constrain that the value of amortised gas was the same for all transactions in the rollup. + +## Payment Methods + +The methods described here can facilitate payment in any asset, either privately or publicly. The following example demonstrate how this can be achieved. + +### Paying Privately With a Public Refund + +A user could opt to take private funds and publicly escrow them to a fee payment contract as part of the fee preparation stage of the transaction. In this example, $5 is taken from a private note and escrowed into a public balance in the fee contract as part of fee preparation. Fee distribution takes the $3 transaction fee and applies it to the sequencer's balance via the 'coinbase' address whilst applying the refund to the sender's public balance. + +![Public Refund](../gas-and-fees/images/gas-and-fees/public-refund.jpg) + +### Paying Privately With a Private Refund + +This next example differs in that the refund is performed privately using partial notes. When the user takes $5 from their private note, they publicly escrow the amount within the fee contract. In addition to this, they create a partial note containing a secret known only to them. At this stage, the note does not contain the value as this is not known. During fee distribution, the note commitment is created from the partial note and the refunded quantity. The quantity is emitted as a public event enabling the user to later reconstruct the note and commitment. + +![Private Refund](../gas-and-fees/images/gas-and-fees/private-refund.jpg) + + +### Paying Via L1 + +As a further option, it would be possible to facilitate payments directly from L1. Here, a mechanism similar to L1 -> L2 messaging would be used to transmit the payment to the sequencer. + +1. The user deposits the fee to the rollup contract and sends an L1 -> L2 message along the lines of 'L1 fee of $100 for transaction X'. +2. The user creates the transaction and executes the fee payment circuit with the message 'L1 fee of $100 for transaction X'. +3. The sequencer accepts the transaction and the L1 -> L2 message confirming that the funds have been made available on L1. +4. The sequencer executes the transaction and the fee distribution function taking their fee and distributing a refund on L2. +5. The L1 -> L2 message is consumed by the rollup transaction on L1. diff --git a/yellow-paper/docs/gas-and-fees/images/gas-and-fees/private-merge.jpg b/yellow-paper/docs/gas-and-fees/images/gas-and-fees/private-merge.jpg new file mode 100644 index 00000000000..2c486daf760 Binary files /dev/null and b/yellow-paper/docs/gas-and-fees/images/gas-and-fees/private-merge.jpg differ diff --git a/yellow-paper/docs/gas-and-fees/images/gas-and-fees/private-refund.jpg b/yellow-paper/docs/gas-and-fees/images/gas-and-fees/private-refund.jpg new file mode 100644 index 00000000000..47f24755f06 Binary files /dev/null and b/yellow-paper/docs/gas-and-fees/images/gas-and-fees/private-refund.jpg differ diff --git a/yellow-paper/docs/gas-and-fees/images/gas-and-fees/public-merge.jpg b/yellow-paper/docs/gas-and-fees/images/gas-and-fees/public-merge.jpg new file mode 100644 index 00000000000..90c6684a467 Binary files /dev/null and b/yellow-paper/docs/gas-and-fees/images/gas-and-fees/public-merge.jpg differ diff --git a/yellow-paper/docs/gas-and-fees/images/gas-and-fees/public-refund.jpg b/yellow-paper/docs/gas-and-fees/images/gas-and-fees/public-refund.jpg new file mode 100644 index 00000000000..dd1c2d0363b Binary files /dev/null and b/yellow-paper/docs/gas-and-fees/images/gas-and-fees/public-refund.jpg differ diff --git a/yellow-paper/docs/intro.md b/yellow-paper/docs/intro.md index b19994c7008..658ccf77014 100644 --- a/yellow-paper/docs/intro.md +++ b/yellow-paper/docs/intro.md @@ -69,7 +69,7 @@ The draft subsections are mere suggestions (and serve as a helpful reminder of t --> - Keys - Master keys - - Authorisation key abstraction + - Authorization key abstraction - Nullifier keys - Incoming viewing keys - Outgoing viewing keys @@ -131,7 +131,7 @@ The draft subsections are mere suggestions (and serve as a helpful reminder of t - Logs [Jan?] - Unencrypted - Encrypted - - Optimisation considerations + - Optimization considerations - Private Message Delivery [Phil] - To constrain or not to constrain? @@ -159,7 +159,7 @@ The draft subsections are mere suggestions (and serve as a helpful reminder of t - Paying fees, and receiving rebates - Paying fees privately - Etc. -- Decentralisation [Cooper] +- Decentralization [Cooper] - P2P network [Phil] - Data that is sent between parties - Users to the tx pool @@ -175,7 +175,7 @@ The draft subsections are mere suggestions (and serve as a helpful reminder of t - Protocol Statements [Mike] - Describe the logic of the protocol. - Describe the assertions that must be in place. - - The constraints (assertions) of the system are spread across many circuits and L1 contracts, but they should be described as a whole, in one place. _Where_ the assertions are located (i.e. in which circuit/contract) is a matter of optimisation, which might change over time. This can then serve as a checklist, to ensure those assertions are included in at least one of the circuits/contracts. + - The constraints (assertions) of the system are spread across many circuits and L1 contracts, but they should be described as a whole, in one place. _Where_ the assertions are located (i.e. in which circuit/contract) is a matter of optimization, which might change over time. This can then serve as a checklist, to ensure those assertions are included in at least one of the circuits/contracts. - In particular, exhaustively explain what _shouldn't_ be possible. - Sub-protocols