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packages/leann-backend-hnsw/third_party/faiss/demos/demo_ivfpq_indexing.cpp

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