mirror of
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254 lines
6.8 KiB
C++
254 lines
6.8 KiB
C++
/*******************************************************************************
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* Copyright 上海赜睿信息科技有限公司(Zilliz) - All Rights Reserved
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* Unauthorized copying of this file, via any medium is strictly prohibited.
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* Proprietary and confidential.
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******************************************************************************/
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#include "FaissTest.h"
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#include "utils/TimeRecorder.h"
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#include <faiss/IndexFlat.h>
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#include <faiss/MetaIndexes.h>
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#include <faiss/index_io.h>
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#include <faiss/AutoTune.h>
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#include <faiss/gpu/GpuIndexFlat.h>
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#include <faiss/gpu/GpuIndexIVFFlat.h>
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#include <faiss/gpu/StandardGpuResources.h>
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#include <assert.h>
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namespace zilliz {
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namespace vecwise {
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namespace client {
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namespace {
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void test_flat() {
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zilliz::vecwise::server::TimeRecorder recorder("test_flat");
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int d = 64; // dimension
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int nb = 100000; // database size
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int nq = 10000; // nb of queries
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float *xb = new float[d * nb];
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float *xq = new float[d * nq];
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for (int i = 0; i < nb; i++) {
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for (int j = 0; j < d; j++)
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xb[d * i + j] = drand48();
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xb[d * i] += i / 1000.;
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}
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for (int i = 0; i < nq; i++) {
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for (int j = 0; j < d; j++)
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xq[d * i + j] = drand48();
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xq[d * i] += i / 1000.;
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}
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recorder.Record("prepare data");
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faiss::IndexFlatL2 index(d); // call constructor
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recorder.Record("declare index");
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printf("is_trained = %s\n", index.is_trained ? "true" : "false");
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index.add(nb, xb); // add vectors to the index
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printf("ntotal = %ld\n", index.ntotal);
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recorder.Record("add index");
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int k = 4;
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{ // sanity check: search 5 first vectors of xb
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long *I = new long[k * 5];
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float *D = new float[k * 5];
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index.search(5, xb, k, D, I);
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// print results
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printf("I=\n");
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for (int i = 0; i < 5; i++) {
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for (int j = 0; j < k; j++)
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printf("%5ld ", I[i * k + j]);
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printf("\n");
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}
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printf("D=\n");
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for (int i = 0; i < 5; i++) {
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for (int j = 0; j < k; j++)
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printf("%7g ", D[i * k + j]);
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printf("\n");
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}
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delete[] I;
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delete[] D;
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}
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recorder.Record("search top 4");
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{ // search xq
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long *I = new long[k * nq];
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float *D = new float[k * nq];
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index.search(nq, xq, k, D, I);
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// print results
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printf("I (5 first results)=\n");
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for (int i = 0; i < 5; i++) {
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for (int j = 0; j < k; j++)
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printf("%5ld ", I[i * k + j]);
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printf("\n");
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}
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printf("I (5 last results)=\n");
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for (int i = nq - 5; i < nq; i++) {
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for (int j = 0; j < k; j++)
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printf("%5ld ", I[i * k + j]);
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printf("\n");
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}
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delete[] I;
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delete[] D;
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}
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recorder.Record("search xq");
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delete[] xb;
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delete[] xq;
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recorder.Record("delete data");
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}
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void test_gpu() {
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zilliz::vecwise::server::TimeRecorder recorder("test_gpu");
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int d = 64; // dimension
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int nb = 100000; // database size
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int nq = 10000; // nb of queries
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float *xb = new float[d * nb];
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float *xq = new float[d * nq];
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for (int i = 0; i < nb; i++) {
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for (int j = 0; j < d; j++)
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xb[d * i + j] = drand48();
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xb[d * i] += i / 1000.;
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}
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for (int i = 0; i < nq; i++) {
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for (int j = 0; j < d; j++)
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xq[d * i + j] = drand48();
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xq[d * i] += i / 1000.;
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}
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recorder.Record("prepare data");
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faiss::gpu::StandardGpuResources res;
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// Using a flat index
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faiss::gpu::GpuIndexFlatL2 index_flat(&res, d);
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recorder.Record("declare index");
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printf("is_trained = %s\n", index_flat.is_trained ? "true" : "false");
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index_flat.add(nb, xb); // add vectors to the index
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printf("ntotal = %ld\n", index_flat.ntotal);
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recorder.Record("add index");
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int k = 4;
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{ // search xq
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long *I = new long[k * nq];
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float *D = new float[k * nq];
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index_flat.search(nq, xq, k, D, I);
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// print results
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printf("I (5 first results)=\n");
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for (int i = 0; i < 5; i++) {
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for (int j = 0; j < k; j++)
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printf("%5ld ", I[i * k + j]);
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printf("\n");
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}
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printf("I (5 last results)=\n");
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for (int i = nq - 5; i < nq; i++) {
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for (int j = 0; j < k; j++)
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printf("%5ld ", I[i * k + j]);
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printf("\n");
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}
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delete[] I;
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delete[] D;
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}
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recorder.Record("search top 4");
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// Using an IVF index
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int nlist = 100;
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faiss::gpu::GpuIndexIVFFlat index_ivf(&res, d, nlist, faiss::METRIC_L2);
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// here we specify METRIC_L2, by default it performs inner-product search
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recorder.Record("declare index");
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assert(!index_ivf.is_trained);
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index_ivf.train(nb, xb);
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assert(index_ivf.is_trained);
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recorder.Record("train index");
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index_ivf.add(nb, xb); // add vectors to the index
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recorder.Record("add index");
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printf("is_trained = %s\n", index_ivf.is_trained ? "true" : "false");
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printf("ntotal = %ld\n", index_ivf.ntotal);
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{ // search xq
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long *I = new long[k * nq];
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float *D = new float[k * nq];
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index_ivf.search(nq, xq, k, D, I);
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// print results
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printf("I (5 first results)=\n");
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for (int i = 0; i < 5; i++) {
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for (int j = 0; j < k; j++)
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printf("%5ld ", I[i * k + j]);
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printf("\n");
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}
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printf("I (5 last results)=\n");
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for (int i = nq - 5; i < nq; i++) {
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for (int j = 0; j < k; j++)
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printf("%5ld ", I[i * k + j]);
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printf("\n");
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}
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delete[] I;
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delete[] D;
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}
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recorder.Record("search xq");
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delete[] xb;
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delete[] xq;
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recorder.Record("delete data");
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}
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}
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void FaissTest::test() {
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int ngpus = faiss::gpu::getNumDevices();
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printf("Number of GPUs: %d\n", ngpus);
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test_flat();
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test_gpu();
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}
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}
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}
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} |