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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 <cstdio>
#include <cstdlib>
#include <memory>
#include <random>
#include <vector>
#include <gtest/gtest.h>
#include <faiss/AutoTune.h>
#include <faiss/IVFlib.h>
#include <faiss/IndexIVF.h>
#include <faiss/clone_index.h>
#include <faiss/index_factory.h>
using namespace faiss;
// dimension of the vectors to index
int d = 32;
// nb of training vectors
size_t nt = 5000;
// size of the database points per window step
size_t nb = 1000;
// nb of queries
size_t nq = 200;
int total_size = 40;
int window_size = 10;
std::vector<float> make_data(size_t n) {
std::vector<float> database(n * d);
std::mt19937 rng;
std::uniform_real_distribution<> distrib;
for (size_t i = 0; i < n * d; i++) {
database[i] = distrib(rng);
}
return database;
}
std::unique_ptr<Index> make_trained_index(const char* index_type) {
auto index = std::unique_ptr<Index>(index_factory(d, index_type));
auto xt = make_data(nt * d);
index->train(nt, xt.data());
ParameterSpace().set_index_parameter(index.get(), "nprobe", 4);
return index;
}
std::vector<idx_t> search_index(Index* index, const float* xq) {
int k = 10;
std::vector<idx_t> I(k * nq);
std::vector<float> D(k * nq);
index->search(nq, xq, k, D.data(), I.data());
return I;
}
/*************************************************************
* Test functions for a given index type
*************************************************************/
// make a few slices of indexes that can be merged
void make_index_slices(
const Index* trained_index,
std::vector<std::unique_ptr<Index>>& sub_indexes) {
for (int i = 0; i < total_size; i++) {
sub_indexes.emplace_back(clone_index(trained_index));
Index* index = sub_indexes.back().get();
auto xb = make_data(nb * d);
std::vector<faiss::idx_t> ids(nb);
std::mt19937 rng;
std::uniform_int_distribution<> distrib;
for (int j = 0; j < nb; j++) {
ids[j] = distrib(rng);
}
index->add_with_ids(nb, xb.data(), ids.data());
}
}
// build merged index explicitly at sliding window position i
Index* make_merged_index(
const Index* trained_index,
const std::vector<std::unique_ptr<Index>>& sub_indexes,
int i) {
Index* merged_index = clone_index(trained_index);
for (int j = i - window_size + 1; j <= i; j++) {
if (j < 0 || j >= total_size) {
continue;
}
std::unique_ptr<Index> sub_index(clone_index(sub_indexes[j].get()));
IndexIVF* ivf0 = ivflib::extract_index_ivf(merged_index);
IndexIVF* ivf1 = ivflib::extract_index_ivf(sub_index.get());
ivf0->merge_from(*ivf1, 0);
merged_index->ntotal = ivf0->ntotal;
}
return merged_index;
}
int test_sliding_window(const char* index_key) {
std::unique_ptr<Index> trained_index = make_trained_index(index_key);
// make the index slices
std::vector<std::unique_ptr<Index>> sub_indexes;
make_index_slices(trained_index.get(), sub_indexes);
// now slide over the windows
std::unique_ptr<Index> index(clone_index(trained_index.get()));
ivflib::SlidingIndexWindow window(index.get());
auto xq = make_data(nq * d);
for (int i = 0; i < total_size + window_size; i++) {
// update the index
window.step(
i < total_size ? sub_indexes[i].get() : nullptr,
i >= window_size);
auto new_res = search_index(index.get(), xq.data());
std::unique_ptr<Index> merged_index(
make_merged_index(trained_index.get(), sub_indexes, i));
auto ref_res = search_index(merged_index.get(), xq.data());
EXPECT_EQ(ref_res.size(), new_res.size());
EXPECT_EQ(ref_res, new_res);
}
return 0;
}
int test_sliding_invlists(const char* index_key) {
std::unique_ptr<Index> trained_index = make_trained_index(index_key);
// make the index slices
std::vector<std::unique_ptr<Index>> sub_indexes;
make_index_slices(trained_index.get(), sub_indexes);
// now slide over the windows
std::unique_ptr<Index> index(clone_index(trained_index.get()));
IndexIVF* index_ivf = ivflib::extract_index_ivf(index.get());
auto xq = make_data(nq * d);
for (int i = 0; i < total_size + window_size; i++) {
// update the index
std::vector<const InvertedLists*> ils;
for (int j = i - window_size + 1; j <= i; j++) {
if (j < 0 || j >= total_size) {
continue;
}
ils.push_back(
ivflib::extract_index_ivf(sub_indexes[j].get())->invlists);
}
if (ils.size() == 0) {
continue;
}
ConcatenatedInvertedLists* ci =
new ConcatenatedInvertedLists(ils.size(), ils.data());
// will be deleted by the index
index_ivf->replace_invlists(ci, true);
auto new_res = search_index(index.get(), xq.data());
std::unique_ptr<Index> merged_index(
make_merged_index(trained_index.get(), sub_indexes, i));
auto ref_res = search_index(merged_index.get(), xq.data());
EXPECT_EQ(ref_res.size(), new_res.size());
EXPECT_EQ(ref_res, new_res);
}
return 0;
}
/*************************************************************
* Test entry points
*************************************************************/
TEST(SlidingWindow, IVFFlat) {
test_sliding_window("IVF32,Flat");
}
TEST(SlidingWindow, PCAIVFFlat) {
test_sliding_window("PCA24,IVF32,Flat");
}
TEST(SlidingInvlists, IVFFlat) {
test_sliding_invlists("IVF32,Flat");
}
TEST(SlidingInvlists, PCAIVFFlat) {
test_sliding_invlists("PCA24,IVF32,Flat");
}
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