147 lines
3.9 KiB
C++
147 lines
3.9 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 <cmath>
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#include <cstdio>
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#include <cstdlib>
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#include <random>
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#include <sys/time.h>
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#include <faiss/IndexFlat.h>
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#include <faiss/IndexIVFPQ.h>
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#include <faiss/index_io.h>
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double elapsed() {
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struct timeval tv;
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gettimeofday(&tv, nullptr);
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return tv.tv_sec + tv.tv_usec * 1e-6;
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}
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int main() {
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double t0 = elapsed();
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// dimension of the vectors to index
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int d = 128;
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// size of the database we plan to index
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size_t nb = 200 * 1000;
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// make a set of nt training vectors in the unit cube
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// (could be the database)
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size_t nt = 100 * 1000;
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// make the index object and train it
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faiss::IndexFlatL2 coarse_quantizer(d);
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// a reasonable number of centroids to index nb vectors
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int ncentroids = int(4 * sqrt(nb));
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// the coarse quantizer should not be dealloced before the index
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// 4 = nb of bytes per code (d must be a multiple of this)
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// 8 = nb of bits per sub-code (almost always 8)
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faiss::IndexIVFPQ index(&coarse_quantizer, d, ncentroids, 4, 8);
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std::mt19937 rng;
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{ // training
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printf("[%.3f s] Generating %ld vectors in %dD for training\n",
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elapsed() - t0,
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nt,
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d);
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std::vector<float> trainvecs(nt * d);
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std::uniform_real_distribution<> distrib;
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for (size_t i = 0; i < nt * d; i++) {
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trainvecs[i] = distrib(rng);
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}
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printf("[%.3f s] Training the index\n", elapsed() - t0);
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index.verbose = true;
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index.train(nt, trainvecs.data());
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}
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{ // I/O demo
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const char* outfilename = "/tmp/index_trained.faissindex";
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printf("[%.3f s] storing the pre-trained index to %s\n",
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elapsed() - t0,
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outfilename);
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write_index(&index, outfilename);
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}
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size_t nq;
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std::vector<float> queries;
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{ // populating the database
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printf("[%.3f s] Building a dataset of %ld vectors to index\n",
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elapsed() - t0,
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nb);
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std::vector<float> database(nb * d);
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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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printf("[%.3f s] Adding the vectors to the index\n", elapsed() - t0);
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index.add(nb, database.data());
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printf("[%.3f s] imbalance factor: %g\n",
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elapsed() - t0,
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index.invlists->imbalance_factor());
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// remember a few elements from the database as queries
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int i0 = 1234;
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int i1 = 1243;
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nq = i1 - i0;
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queries.resize(nq * d);
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for (int i = i0; i < i1; i++) {
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for (int j = 0; j < d; j++) {
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queries[(i - i0) * d + j] = database[i * d + j];
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}
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}
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}
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{ // searching the database
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int k = 5;
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printf("[%.3f s] Searching the %d nearest neighbors "
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"of %ld vectors in the index\n",
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elapsed() - t0,
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k,
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nq);
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std::vector<faiss::idx_t> nns(k * nq);
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std::vector<float> dis(k * nq);
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index.search(nq, queries.data(), k, dis.data(), nns.data());
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printf("[%.3f s] Query results (vector ids, then distances):\n",
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elapsed() - t0);
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for (int i = 0; i < nq; i++) {
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printf("query %2d: ", i);
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for (int j = 0; j < k; j++) {
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printf("%7ld ", nns[j + i * k]);
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}
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printf("\n dis: ");
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for (int j = 0; j < k; j++) {
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printf("%7g ", dis[j + i * k]);
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}
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printf("\n");
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}
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printf("note that the nearest neighbor is not at "
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"distance 0 due to quantization errors\n");
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}
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return 0;
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}
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