200 lines
6.5 KiB
C++
200 lines
6.5 KiB
C++
/**
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* Copyright (c) Facebook, Inc. and its 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 <sys/time.h>
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#include <faiss/IndexPQ.h>
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#include <faiss/IndexIVFPQ.h>
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#include <faiss/IndexFlat.h>
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#include <faiss/index_io.h>
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double elapsed ()
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{
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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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{
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double t0 = elapsed();
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// dimension of the vectors to index
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int d = 64;
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// size of the database we plan to index
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size_t nb = 1000 * 1000;
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size_t add_bs = 10000; // # size of the blocks to add
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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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//---------------------------------------------------------------
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// Define the core quantizer
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// We choose a multiple inverted index for faster training with less data
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// and because it usually offers best accuracy/speed trade-offs
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//
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// We here assume that its lifespan of this coarse quantizer will cover the
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// lifespan of the inverted-file quantizer IndexIVFFlat below
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// With dynamic allocation, one may give the responsability to free the
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// quantizer to the inverted-file index (with attribute do_delete_quantizer)
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//
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// Note: a regular clustering algorithm would be defined as:
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// faiss::IndexFlatL2 coarse_quantizer (d);
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//
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// Use nhash=2 subquantizers used to define the product coarse quantizer
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// Number of bits: we will have 2^nbits_coarse centroids per subquantizer
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// meaning (2^12)^nhash distinct inverted lists
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//
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// The parameter bytes_per_code is determined by the memory
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// constraint, the dataset will use nb * (bytes_per_code + 8)
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// bytes.
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//
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// The parameter nbits_subq is determined by the size of the dataset to index.
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//
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size_t nhash = 2;
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size_t nbits_subq = 9;
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size_t ncentroids = 1 << (nhash * nbits_subq); // total # of centroids
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int bytes_per_code = 16;
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faiss::MultiIndexQuantizer coarse_quantizer (d, nhash, nbits_subq);
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printf ("IMI (%ld,%ld): %ld virtual centroids (target: %ld base vectors)",
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nhash, nbits_subq, ncentroids, 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::MetricType metric = faiss::METRIC_L2; // can be METRIC_INNER_PRODUCT
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faiss::IndexIVFPQ index (&coarse_quantizer, d, ncentroids, bytes_per_code, 8);
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index.quantizer_trains_alone = true;
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// define the number of probes. 2048 is for high-dim, overkill in practice
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// Use 4-1024 depending on the trade-off speed accuracy that you want
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index.nprobe = 2048;
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{ // training.
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// The distribution of the training vectors should be the same
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// as the database vectors. It could be a sub-sample of the
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// database vectors, if sampling is not biased. Here we just
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// randomly generate the vectors.
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printf ("[%.3f s] Generating %ld vectors in %dD for training\n",
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elapsed() - t0, nt, d);
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std::vector <float> trainvecs (nt * d);
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for (size_t i = 0; i < nt; i++) {
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for (size_t j = 0; j < d; j++) {
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trainvecs[i * d + j] = drand48();
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}
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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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// the index can be re-loaded later with
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// faiss::Index * idx = faiss::read_index("/tmp/trained_index.faissindex");
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faiss::write_index(&index, "/tmp/trained_index.faissindex");
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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, nb);
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std::vector <float> database (nb * d);
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std::vector <long> ids (nb);
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for (size_t i = 0; i < nb; i++) {
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for (size_t j = 0; j < d; j++) {
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database[i * d + j] = drand48();
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}
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ids[i] = 8760000000L + i;
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}
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printf ("[%.3f s] Adding the vectors to the index\n", elapsed() - t0);
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for (size_t begin = 0; begin < nb; begin += add_bs) {
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size_t end = std::min (begin + add_bs, nb);
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index.add_with_ids (end - begin,
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database.data() + d * begin,
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ids.data() + begin);
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}
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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 = 1244;
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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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// A few notes on the internal format of the index:
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//
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// - the positing lists for PQ codes are index.codes, which is a
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// std::vector < std::vector<uint8_t> >
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// if n is the length of posting list #i, codes[i] has length bytes_per_code * n
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//
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// - the corresponding ids are stored in index.ids
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//
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// - given a vector float *x, finding which k centroids are
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// closest to it (ie to find the nearest neighbors) can be done with
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//
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// long *centroid_ids = new long[k];
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// float *distances = new float[k];
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// index.quantizer->search (1, x, k, dis, centroids_ids);
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//
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faiss::write_index(&index, "/tmp/populated_index.faissindex");
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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, k, nq);
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std::vector<faiss::Index::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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}
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return 0;
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}
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