refactor: CI toolchains for different platforms

Signed-off-by: Naren Dasan <[email protected]>

toolchains/ci_workspaces/WORKSPACE.sbsa

@@ -0,0 +1,147 @@
+workspace(name = "Torch-TensorRT")
+
+load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
+load("@bazel_tools//tools/build_defs/repo:git.bzl", "git_repository")
+
+http_archive(
+    name = "rules_python",
+    sha256 = "778197e26c5fbeb07ac2a2c5ae405b30f6cb7ad1f5510ea6fdac03bded96cc6f",
+    url = "https://github.com/bazelbuild/rules_python/releases/download/0.2.0/rules_python-0.2.0.tar.gz",
+)
+
+load("@rules_python//python:pip.bzl", "pip_install")
+
+http_archive(
+    name = "rules_pkg",
+    sha256 = "038f1caa773a7e35b3663865ffb003169c6a71dc995e39bf4815792f385d837d",
+    urls = [
+        "https://mirror.bazel.build/github.com/bazelbuild/rules_pkg/releases/download/0.4.0/rules_pkg-0.4.0.tar.gz",
+        "https://github.com/bazelbuild/rules_pkg/releases/download/0.4.0/rules_pkg-0.4.0.tar.gz",
+    ],
+)
+
+load("@rules_pkg//:deps.bzl", "rules_pkg_dependencies")
+
+rules_pkg_dependencies()
+
+git_repository(
+    name = "googletest",
+    commit = "703bd9caab50b139428cea1aaff9974ebee5742e",
+    remote = "https://github.com/google/googletest",
+    shallow_since = "1570114335 -0400",
+)
+
+# External dependency for torch_tensorrt if you already have precompiled binaries.
+local_repository(
+    name = "torch_tensorrt",
+    path = "/opt/circleci/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch_tensorrt"
+)
+
+# CUDA should be installed on the system locally
+new_local_repository(
+    name = "cuda",
+    build_file = "@//third_party/cuda:BUILD",
+    path = "/usr/local/cuda/",
+)
+
+new_local_repository(
+    name = "cublas",
+    build_file = "@//third_party/cublas:BUILD",
+    path = "/usr",
+)
+#############################################################################################################
+# Tarballs and fetched dependencies (default - use in cases when building from precompiled bin and tarballs)
+#############################################################################################################
+
+#http_archive(
+#    name = "libtorch",
+#    build_file = "@//third_party/libtorch:BUILD",
+#    sha256 = "8d9e829ce9478db4f35bdb7943308cf02e8a2f58cf9bb10f742462c1d57bf287",
+#    strip_prefix = "libtorch",
+#    urls = ["https://download.pytorch.org/libtorch/cu113/libtorch-cxx11-abi-shared-with-deps-1.11.0%2Bcu113.zip"],
+#)
+#
+#http_archive(
+#    name = "libtorch_pre_cxx11_abi",
+#    build_file = "@//third_party/libtorch:BUILD",
+#    sha256 = "90159ecce3ff451f3ef3f657493b6c7c96759c3b74bbd70c1695f2ea2f81e1ad",
+#    strip_prefix = "libtorch",
+#    urls = ["https://download.pytorch.org/libtorch/cu113/libtorch-shared-with-deps-1.11.0%2Bcu113.zip"],
+#)
+
+# Download these tarballs manually from the NVIDIA website
+# Either place them in the distdir directory in third_party and use the --distdir flag
+# or modify the urls to "file:///<PATH TO TARBALL>/<TARBALL NAME>.tar.gz
+
+#http_archive(
+#    name = "cudnn",
+#    build_file = "@//third_party/cudnn/archive:BUILD",
+#    sha256 = "0e5d2df890b9967efa6619da421310d97323565a79f05a1a8cb9b7165baad0d7",
+#    strip_prefix = "cuda",
+#    urls = [
+#        "https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.2.4/11.4_20210831/cudnn-11.4-linux-x64-v8.2.4.15.tgz",
+#    ],
+#)
+#
+#http_archive(
+#    name = "tensorrt",
+#    build_file = "@//third_party/tensorrt/archive:BUILD",
+#    sha256 = "826180eaaecdf9a7e76116855b9f1f3400ea9b06e66b06a3f6a0747ba6f863ad",
+#    strip_prefix = "TensorRT-8.2.4.2",
+#    urls = [
+#        "https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/8.2.4/tars/tensorrt-8.2.4.2.linux.x86_64-gnu.cuda-11.4.cudnn8.2.tar.gz",
+#    ],
+#)
+
+####################################################################################
+# Locally installed dependencies (use in cases of custom dependencies or aarch64)
+####################################################################################
+
+# NOTE: In the case you are using just the pre-cxx11-abi path or just the cxx11 abi path
+# with your local libtorch, just point deps at the same path to satisfy bazel.
+
+# NOTE: NVIDIA's aarch64 PyTorch (python) wheel file uses the CXX11 ABI unlike PyTorch's standard
+# x86_64 python distribution. If using NVIDIA's version just point to the root of the package
+# for both versions here and do not use --config=pre-cxx11-abi
+
+new_local_repository(
+    name = "libtorch",
+    path = "/opt/circleci/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch",
+    build_file = "third_party/libtorch/BUILD"
+)
+
+new_local_repository(
+    name = "libtorch_pre_cxx11_abi",
+    path = "/opt/circleci/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch",
+    build_file = "third_party/libtorch/BUILD"
+)
+
+new_local_repository(
+    name = "cudnn",
+    path = "/usr/",
+    build_file = "@//third_party/cudnn/local:BUILD"
+)
+
+new_local_repository(
+   name = "tensorrt",
+   path = "/usr/",
+   build_file = "@//third_party/tensorrt/local:BUILD"
+)
+
+# #########################################################################
+# # Testing Dependencies (optional - comment out on aarch64)
+# #########################################################################
+# pip_install(
+#     name = "torch_tensorrt_py_deps",
+#     requirements = "//py:requirements.txt",
+# )
+
+# pip_install(
+#     name = "py_test_deps",
+#     requirements = "//tests/py:requirements.txt",
+# )
+
+pip_install(
+    name = "pylinter_deps",
+    requirements = "//tools/linter:requirements.txt",
+)

WORKSPACE.ci → toolchains/ci_workspaces/WORKSPACE.x86_64

@@ -34,7 +34,7 @@ git_repository(
 # External dependency for torch_tensorrt if you already have precompiled binaries.
 local_repository(
     name = "torch_tensorrt",
-    path = "/opt/conda/lib/python3.8/site-packages/torch_tensorrt"
+    path = "/opt/circleci/.pyenv/versions/3.9.4/lib/python3.9/site-packages/torch_tensorrt"
 )
 
 # CUDA should be installed on the system locally