mirror of
https://github.com/facebookresearch/faiss.git
synced 2026-10-11 22:50:00 +00:00
Matthijs Douze
8d1b964c11
Summary: Pull Request resolved: https://github.com/facebookresearch/faiss/pull/5127 Attempt to enable FAISS dynamic dispatch (DD) mode on Windows/MSVC. Changes: - CMakeLists.txt: Remove if(NOT WIN32) guard from DD section, add MSVC per-file SIMD flags (/arch:AVX2, /arch:AVX512, /bigobj) alongside existing GCC/Clang flags - build-pull-request.yml: Add windows-x86_64-DD-cmake job that runs immediately (no dependency on linux-x86_64-cmake), builds with MSVC and FAISS_OPT_LEVEL=dd, runs C++ tests and Python tests This diff is expected to fail on Windows due to MSVC requiring explicit template specialization declarations (C++ §17.8.3) which GCC/Clang don't enforce. The CI failure will surface the exact errors to guide the fix. UPDATE: in fact, MSVC does not seem to require this. After fixing build scripts and a few classical Windows error, the C++ and python tests pass. Reviewed By: algoriddle Differential Revision: D101649751 fbshipit-source-id: 765ef8483d02652ce58625cd22baab8870acf718
68 lines
1.9 KiB
C++
68 lines
1.9 KiB
C++
/*
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* Copyright (c) Meta Platforms, Inc. and affiliates.
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*
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* This source code is licensed under the MIT license found in the
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* LICENSE file in the root directory of this source tree.
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*/
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#include <gtest/gtest.h>
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#include <chrono>
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#include <cstddef>
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#include <cstdint>
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#include <memory>
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#include <random>
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#include <vector>
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#include <omp.h>
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#include <faiss/IndexFlat.h>
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#include <faiss/IndexIVFPQFastScan.h>
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#include <faiss/impl/AuxIndexStructures.h>
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TEST(TestFastScan, knnVSrange) {
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// small vectors and database
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int d = 64;
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size_t nb = 4000;
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// ivf centroids
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size_t nlist = 4;
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// more than 2 threads to surface
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// problems related to multi-threading
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omp_set_num_threads(8);
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// random database, also used as queries
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std::vector<float> database(nb * d);
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std::mt19937 rng;
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std::uniform_real_distribution<> distrib;
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for (size_t i = 0; i < nb * d; i++) {
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database[i] = distrib(rng);
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}
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// build index
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faiss::IndexFlatL2 coarse_quantizer(d);
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faiss::IndexIVFPQFastScan index(
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&coarse_quantizer, d, nlist, d / 2, 4, faiss::METRIC_L2, 32);
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index.pq.cp.niter = 10; // speed up train
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index.nprobe = nlist;
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index.train(nb, database.data());
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index.add(nb, database.data());
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std::vector<float> distances(nb);
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std::vector<faiss::idx_t> labels(nb);
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auto t = std::chrono::high_resolution_clock::now();
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index.search(nb, database.data(), 1, distances.data(), labels.data());
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auto knn_time = std::chrono::high_resolution_clock::now() - t;
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faiss::RangeSearchResult rsr(nb);
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t = std::chrono::high_resolution_clock::now();
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index.range_search(nb, database.data(), 1.0, &rsr);
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auto range_time = std::chrono::high_resolution_clock::now() - t;
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// we expect the perf of knn and range search
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// to be similar, at least within a factor of 4
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ASSERT_LE(range_time, knn_time * 4);
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ASSERT_LE(knn_time, range_time * 4);
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}
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