Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3455
Code quality control by reducing the number of prints
Reviewed By: junjieqi
Differential Revision: D57502194
fbshipit-source-id: a6cd65ed4cc49590ce73d2978d41b640b5259c17
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3452
Delete all remaining print within the Tests to improve the readability and effectiveness of the codebase.
Reviewed By: junjieqi
Differential Revision: D57466393
fbshipit-source-id: 6ebd66ae2e769894d810d4ba7a5f69fc865b797d
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2562
Introduce a table of transposed centroids in ProductQuantizer that significantly speeds up ProductQuantizer::compute_codes() call for certain PQ parameters, so speeds up search queries.
* ::sync_tranposed_centroids() call is used to fill the table
* ::clear_transposed_centroids() call clear the table, so that the original baseline code is used for ::compute_codes()
Reviewed By: mdouze
Differential Revision: D40763338
fbshipit-source-id: 87b40e5dd2f8c3cadeb94c1cd9e8a4a5b6ffa97d
Summary:
The tests TestPQTables are very slow in dev mode with BLAS. This seems to be due to the training operation of the PQ. However, since it does not matter if the training is accurate or not, we can just reduce the nb of training iterations from the default 25 to 4.
Still unclear why this happens, because the runtime is spent in BLAS, which should be independend of mode/opt or mode/dev.
Reviewed By: wickedfoo
Differential Revision: D24783752
fbshipit-source-id: 38077709eb9a6432210c11c3040765e139353ae8
Summary:
This diff streamlines the code that collects results for brute force distance computations for the L2 / IP and range search / knn search combinations.
It introduces a `ResultHandler` template class that abstracts what happens with the computed distances and ids. In addition to the heap result handler and the range search result handler, it introduces a reservoir result handler that improves the search speed for large k (>=100).
Benchmark results (https://fb.quip.com/y0g1ACLEqJXx#OCaACA2Gm45) show that on small datasets (10k) search is 10-50% faster (improvements are larger for small k). There is room for improvement in the reservoir implementation, whose implementation is quite naive currently, but the diff is already useful in its current form.
Experiments on precomputed db vector norms for L2 distance computations were not very concluding performance-wise, so the implementation is removed from IndexFlatL2.
This diff also removes IndexL2BaseShift, which was never used.
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1502
Test Plan:
```
buck test //faiss/tests/:test_product_quantizer
buck test //faiss/tests/:test_index -- TestIndexFlat
```
Reviewed By: wickedfoo
Differential Revision: D24705464
Pulled By: mdouze
fbshipit-source-id: 270e10b19f3c89ed7b607ec30549aca0ac5027fe
