Merge branch 'facebookresearch:main' into convert-circleci-to-github-…

…actions

.circleci/config.yml

@@ -168,7 +168,7 @@ jobs:
                 command: |
                   cd conda
                   conda build faiss-gpu-raft --variants '{ "cudatoolkit": "<<parameters.cuda>>", "c_compiler_version": "<<parameters.compiler_version>>", "cxx_compiler_version": "<<parameters.compiler_version>>" }' \
-                      -c pytorch -c nvidia/label/cuda-<<parameters.cuda>> -c nvidia -c rapidsai -c conda-forge
+                      -c pytorch -c nvidia/label/cuda-<<parameters.cuda>> -c nvidia -c rapidsai -c rapidsai-nightly -c conda-forge
       - when:
           condition:
             and:
@@ -182,7 +182,7 @@ jobs:
                 command: |
                   cd conda
                   conda build faiss-gpu-raft --variants '{ "cudatoolkit": "<<parameters.cuda>>", "c_compiler_version": "<<parameters.compiler_version>>", "cxx_compiler_version": "<<parameters.compiler_version>>" }' \
-                      --user pytorch --label <<parameters.label>> -c pytorch -c nvidia/label/cuda-<<parameters.cuda>> -c nvidia -c rapidsai -c conda-forge
+                      --user pytorch --label <<parameters.label>> -c pytorch -c nvidia/label/cuda-<<parameters.cuda>> -c nvidia -c rapidsai -c rapidsai-nightly -c conda-forge
 
   build_cmake:
     parameters:

benchs/link_and_code/README.md

@@ -21,138 +21,5 @@ graph to improve the reconstruction. It is described in
 
 ArXiV [here](https://arxiv.org/abs/1804.09996)
 
-Code structure
---------------
-
-The test runs with 3 files:
-
-- `bench_link_and_code.py`: driver script
-
-- `datasets.py`: code to load the datasets. The example code runs on the
-  deep1b and bigann datasets. See the [toplevel README](../README.md)
-  on how to download them. They should be put in a directory, edit
-  datasets.py to set the path.
-
-- `neighbor_codec.py`: this is where the representation is trained.
-
-The code runs on top of Faiss. The HNSW index can be extended with a
-`ReconstructFromNeighbors` C++ object that refines the distances. The
-training is implemented in Python.
-
-Update: 2023-12-28: the current Faiss dropped support for reconstruction with
-this method.
-
-Reproducing Table 2 in the paper
---------------------------------
-
-The results of table 2 (accuracy on deep100M) in the paper can be
-obtained with:
-
-```bash
-python bench_link_and_code.py \
-   --db deep100M \
-   --M0 6 \
-   --indexkey OPQ36_144,HNSW32_PQ36 \
-   --indexfile $bdir/deep100M_PQ36_L6.index \
-   --beta_nsq 4  \
-   --beta_centroids $bdir/deep100M_PQ36_L6_nsq4.npy \
-   --neigh_recons_codes $bdir/deep100M_PQ36_L6_nsq4_codes.npy \
-   --k_reorder 0,5 --efSearch 1,1024
-```
-
-Set `bdir` to a scratch directory.
-
-Explanation of the flags:
-
-- `--db deep1M`: dataset to process
-
-- `--M0 6`: number of links on the base level (L6)
-
-- `--indexkey OPQ36_144,HNSW32_PQ36`: Faiss index key to construct the
-  HNSW structure. It means that vectors are transformed by OPQ and
-  encoded with PQ 36x8 (with an intermediate size of 144D). The HNSW
-  level>0 nodes have 32 links (theses ones are "cheap" to store
-  because there are fewer nodes in the upper levels.
-
-- `--indexfile $bdir/deep1M_PQ36_M6.index`: name of the index file
-  (without information for the L&C extension)
-
-- `--beta_nsq 4`: number of bytes to allocate for the codes (M in the
-  paper)
-
-- `--beta_centroids $bdir/deep1M_PQ36_M6_nsq4.npy`: filename to store
-  the trained beta centroids
-
-- `--neigh_recons_codes $bdir/deep1M_PQ36_M6_nsq4_codes.npy`: filename
-  for the encoded weights (beta) of the combination
-
-- `--k_reorder 0,5`: number of results to reorder. 0 = baseline
-  without reordering, 5 = value used throughout the paper
-
-- `--efSearch 1,1024`: number of nodes to visit (T in the paper)
-
-The script will proceed with the following steps:
-
-0. load dataset (and possibly compute the ground-truth if the
-ground-truth file is not provided)
-
-1. train the OPQ encoder
-
-2. build the index and store it
-
-3. compute the residuals and train the beta vocabulary to do the reconstruction
-
-4. encode the vertices
-
-5. search and evaluate the search results.
-
-With option `--exhaustive` the results of the exhaustive column can be
-obtained.
-
-The run above should output:
-```bash
-...
-setting k_reorder=5
-...
-efSearch=1024      0.3132 ms per query,  R@1: 0.4283 R@10: 0.6337 R@100: 0.6520 ndis 40941919 nreorder 50000
-
-```
-which matches the paper's table 2.
-
-Note that in multi-threaded mode, the building of the HNSW structure
-is not deterministic. Therefore, the results across runs may not be exactly the same.
-
-Reproducing Figure 5 in the paper
----------------------------------
-
-Figure 5 just evaluates the combination of HNSW and PQ. For example,
-the operating point L6&OPQ40 can be obtained with
-
-```bash
-python bench_link_and_code.py \
-   --db deep1M \
-   --M0 6 \
-   --indexkey OPQ40_160,HNSW32_PQ40 \
-   --indexfile $bdir/deep1M_PQ40_M6.index \
-   --beta_nsq 1 --beta_k 1  \
-   --beta_centroids $bdir/deep1M_PQ40_M6_nsq0.npy \
-   --neigh_recons_codes $bdir/deep1M_PQ36_M6_nsq0_codes.npy \
-   --k_reorder 0 --efSearch 16,64,256,1024
-```
-
-The arguments are similar to the previous table. Note that nsq = 0 is
-simulated by setting beta_nsq = 1 and beta_k = 1 (ie a code with a single
-reproduction value).
-
-The output should look like:
-
-```bash
-setting k_reorder=0
-efSearch=16        0.0147 ms per query,  R@1: 0.3409 R@10: 0.4388 R@100: 0.4394 ndis 2629735 nreorder 0
-efSearch=64        0.0122 ms per query,  R@1: 0.4836 R@10: 0.6490 R@100: 0.6509 ndis 4623221 nreorder 0
-efSearch=256       0.0344 ms per query,  R@1: 0.5730 R@10: 0.7915 R@100: 0.7951 ndis 11090176 nreorder 0
-efSearch=1024      0.2656 ms per query,  R@1: 0.6212 R@10: 0.8722 R@100: 0.8765 ndis 33501951 nreorder 0
-```
-
-The results with k_reorder=5 are not reported in the paper, they
-represent the performance of a "free coding" version of the algorithm.
+The necessary code for this paper was removed from Faiss in version 1.8.0.
+For a functioning verinsion, use Faiss 1.7.4.