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test_lowlevel_ivf.cpp
/**
* Copyright (c) 2015-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the BSD+Patents license found in the
* LICENSE file in the root directory of this source tree.
*/
#include <cstdio>
#include <cstdlib>
#include <memory>
#include <vector>
#include <thread>
#include <gtest/gtest.h>
#include <faiss/IndexIVF.h>
#include <faiss/IndexBinaryIVF.h>
#include <faiss/AutoTune.h>
#include <faiss/index_io.h>
#include <faiss/IVFlib.h>
#include <faiss/VectorTransform.h>
using namespace faiss;
namespace {
typedef Index::idx_t idx_t;
// 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 k = 10;
std::vector<float> make_data(size_t n)
{
std::vector <float> database (n * d);
for (size_t i = 0; i < n * d; i++) {
database[i] = drand48();
}
return database;
}
std::unique_ptr<Index> make_trained_index(const char *index_type,
MetricType metric_type)
{
auto index = std::unique_ptr<Index>(index_factory(
d, index_type, metric_type));
auto xt = make_data(nt);
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) {
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
*************************************************************/
void test_lowlevel_access (const char *index_key, MetricType metric) {
std::unique_ptr<Index> index = make_trained_index(index_key, metric);
auto xb = make_data (nb);
index->add(nb, xb.data());
/** handle the case if we have a preprocessor */
const IndexPreTransform *index_pt =
dynamic_cast<const IndexPreTransform*> (index.get());
int dt = index->d;
const float * xbt = xb.data();
std::unique_ptr<float []> del_xbt;
if (index_pt) {
dt = index_pt->index->d;
xbt = index_pt->apply_chain (nb, xb.data());
if (xbt != xb.data()) {
del_xbt.reset((float*)xbt);
}
}
IndexIVF * index_ivf = ivflib::extract_index_ivf (index.get());
/** Test independent encoding
*
* Makes it possible to do additions on a custom inverted list
* implementation. From a set of vectors, computes the inverted
* list ids + the codes corresponding to each vector.
*/
std::vector<idx_t> list_nos (nb);
std::vector<uint8_t> codes (index_ivf->code_size * nb);
index_ivf->quantizer->assign(nb, xbt, list_nos.data());
index_ivf->encode_vectors (nb, xbt, list_nos.data(), codes.data());
// compare with normal IVF addition
const InvertedLists *il = index_ivf->invlists;
for (int list_no = 0; list_no < index_ivf->nlist; list_no++) {
InvertedLists::ScopedCodes ivf_codes (il, list_no);
InvertedLists::ScopedIds ivf_ids (il, list_no);
size_t list_size = il->list_size (list_no);
for (int i = 0; i < list_size; i++) {
const uint8_t *ref_code = ivf_codes.get() + i * il->code_size;
const uint8_t *new_code =
codes.data() + ivf_ids[i] * il->code_size;
EXPECT_EQ (memcmp(ref_code, new_code, il->code_size), 0);
}
}
/** Test independent search
*
* Manually scans through inverted lists, computing distances and
* ordering results organized in a heap.
*/
// sample some example queries and get reference search results.
auto xq = make_data (nq);
auto ref_I = search_index (index.get(), xq.data());
// handle preprocessing
const float * xqt = xq.data();
std::unique_ptr<float []> del_xqt;
if (index_pt) {
xqt = index_pt->apply_chain (nq, xq.data());
if (xqt != xq.data()) {
del_xqt.reset((float*)xqt);
}
}
// quantize the queries to get the inverted list ids to visit.
int nprobe = index_ivf->nprobe;
std::vector<idx_t> q_lists (nq * nprobe);
std::vector<float> q_dis (nq * nprobe);
index_ivf->quantizer->search (nq, xqt, nprobe,
q_dis.data(), q_lists.data());
// object that does the scanning and distance computations.
std::unique_ptr<InvertedListScanner> scanner (
index_ivf->get_InvertedListScanner());
for (int i = 0; i < nq; i++) {
std::vector<idx_t> I (k, -1);
float default_dis = metric == METRIC_L2 ? HUGE_VAL : -HUGE_VAL;
std::vector<float> D (k, default_dis);
scanner->set_query (xqt + i * dt);
for (int j = 0; j < nprobe; j++) {
int list_no = q_lists[i * nprobe + j];
if (list_no < 0) continue;
scanner->set_list (list_no, q_dis[i * nprobe + j]);
// here we get the inverted lists from the InvertedLists
// object but they could come from anywhere
scanner->scan_codes (
il->list_size (list_no),
InvertedLists::ScopedCodes(il, list_no).get(),
InvertedLists::ScopedIds(il, list_no).get(),
D.data(), I.data(), k);
if (j == 0) {
// all results so far come from list_no, so let's check if
// the distance function works
for (int jj = 0; jj < k; jj++) {
int vno = I[jj];
if (vno < 0) break; // heap is not full yet
// we have the codes from the addition test
float computed_D = scanner->distance_to_code (
codes.data() + vno * il->code_size);
EXPECT_EQ (computed_D, D[jj]);
}
}
}
// re-order heap
if (metric == METRIC_L2) {
maxheap_reorder (k, D.data(), I.data());
} else {
minheap_reorder (k, D.data(), I.data());
}
// check that we have the same results as the reference search
for (int j = 0; j < k; j++) {
EXPECT_EQ (I[j], ref_I[i * k + j]);
}
}
}
} // anonymous namespace
/*************************************************************
* Test entry points
*************************************************************/
TEST(TestLowLevelIVF, IVFFlatL2) {
test_lowlevel_access ("IVF32,Flat", METRIC_L2);
}
TEST(TestLowLevelIVF, PCAIVFFlatL2) {
test_lowlevel_access ("PCAR16,IVF32,Flat", METRIC_L2);
}
TEST(TestLowLevelIVF, IVFFlatIP) {
test_lowlevel_access ("IVF32,Flat", METRIC_INNER_PRODUCT);
}
TEST(TestLowLevelIVF, IVFSQL2) {
test_lowlevel_access ("IVF32,SQ8", METRIC_L2);
}
TEST(TestLowLevelIVF, IVFSQIP) {
test_lowlevel_access ("IVF32,SQ8", METRIC_INNER_PRODUCT);
}
TEST(TestLowLevelIVF, IVFPQL2) {
test_lowlevel_access ("IVF32,PQ4np", METRIC_L2);
}
TEST(TestLowLevelIVF, IVFPQIP) {
test_lowlevel_access ("IVF32,PQ4np", METRIC_INNER_PRODUCT);
}
/*************************************************************
* Same for binary (a bit simpler)
*************************************************************/
namespace {
int nbit = 256;
// here d is used the number of ints -> d=32 means 128 bits
std::vector<uint8_t> make_data_binary(size_t n)
{
std::vector <uint8_t> database (n * nbit / 8);
for (size_t i = 0; i < n * d; i++) {
database[i] = lrand48();
}
return database;
}
std::unique_ptr<IndexBinary> make_trained_index_binary(const char *index_type)
{
auto index = std::unique_ptr<IndexBinary>(index_binary_factory(
nbit, index_type));
auto xt = make_data_binary (nt);
index->train(nt, xt.data());
return index;
}
void test_lowlevel_access_binary (const char *index_key) {
std::unique_ptr<IndexBinary> index =
make_trained_index_binary (index_key);
IndexBinaryIVF * index_ivf = dynamic_cast<IndexBinaryIVF*>
(index.get());
assert (index_ivf);
index_ivf->nprobe = 4;
auto xb = make_data_binary (nb);
index->add(nb, xb.data());
std::vector<idx_t> list_nos (nb);
index_ivf->quantizer->assign(nb, xb.data(), list_nos.data());
/* For binary there is no test for encoding because binary vectors
* are copied verbatim to the inverted lists */
const InvertedLists *il = index_ivf->invlists;
/** Test independent search
*
* Manually scans through inverted lists, computing distances and
* ordering results organized in a heap.
*/
// sample some example queries and get reference search results.
auto xq = make_data_binary (nq);
std::vector<idx_t> I_ref(k * nq);
std::vector<int32_t> D_ref(k * nq);
index->search (nq, xq.data(), k, D_ref.data(), I_ref.data());
// quantize the queries to get the inverted list ids to visit.
int nprobe = index_ivf->nprobe;
std::vector<idx_t> q_lists (nq * nprobe);
std::vector<int32_t> q_dis (nq * nprobe);
// quantize queries
index_ivf->quantizer->search (nq, xq.data(), nprobe,
q_dis.data(), q_lists.data());
// object that does the scanning and distance computations.
std::unique_ptr<BinaryInvertedListScanner> scanner (
index_ivf->get_InvertedListScanner());
for (int i = 0; i < nq; i++) {
std::vector<idx_t> I (k, -1);
uint32_t default_dis = 1 << 30;
std::vector<int32_t> D (k, default_dis);
scanner->set_query (xq.data() + i * index_ivf->code_size);
for (int j = 0; j < nprobe; j++) {
int list_no = q_lists[i * nprobe + j];
if (list_no < 0) continue;
scanner->set_list (list_no, q_dis[i * nprobe + j]);
// here we get the inverted lists from the InvertedLists
// object but they could come from anywhere
scanner->scan_codes (
il->list_size (list_no),
InvertedLists::ScopedCodes(il, list_no).get(),
InvertedLists::ScopedIds(il, list_no).get(),
D.data(), I.data(), k);
if (j == 0) {
// all results so far come from list_no, so let's check if
// the distance function works
for (int jj = 0; jj < k; jj++) {
int vno = I[jj];
if (vno < 0) break; // heap is not full yet
// we have the codes from the addition test
float computed_D = scanner->distance_to_code (
xb.data() + vno * il->code_size);
EXPECT_EQ (computed_D, D[jj]);
}
}
}
printf("new before reroder: [");
for (int j = 0; j < k; j++)
printf("%ld,%d ", I[j], D[j]);
printf("]\n");
// re-order heap
heap_reorder<CMax<int32_t, int64_t> > (k, D.data(), I.data());
printf("ref: [");
for (int j = 0; j < k; j++)
printf("%ld,%d ", I_ref[j], D_ref[j]);
printf("]\nnew: [");
for (int j = 0; j < k; j++)
printf("%ld,%d ", I[j], D[j]);
printf("]\n");
// check that we have the same results as the reference search
for (int j = 0; j < k; j++) {
// here the order is not guaranteed to be the same
// so we scan through ref results
// EXPECT_EQ (I[j], I_ref[i * k + j]);
EXPECT_LE (D[j], D_ref[i * k + k - 1]);
if (D[j] < D_ref[i * k + k - 1]) {
int j2 = 0;
while (j2 < k) {
if (I[j] == I_ref[i * k + j2]) break;
j2++;
}
EXPECT_LT(j2, k); // it was found
if (j2 < k) {
EXPECT_EQ(D[j], D_ref[i * k + j2]);
}
}
}
}
}
} // anonymous namespace
TEST(TestLowLevelIVF, IVFBinary) {
test_lowlevel_access_binary ("BIVF32");
}
namespace {
void test_threaded_search (const char *index_key, MetricType metric) {
std::unique_ptr<Index> index = make_trained_index(index_key, metric);
auto xb = make_data (nb);
index->add(nb, xb.data());
/** handle the case if we have a preprocessor */
const IndexPreTransform *index_pt =
dynamic_cast<const IndexPreTransform*> (index.get());
int dt = index->d;
const float * xbt = xb.data();
std::unique_ptr<float []> del_xbt;
if (index_pt) {
dt = index_pt->index->d;
xbt = index_pt->apply_chain (nb, xb.data());
if (xbt != xb.data()) {
del_xbt.reset((float*)xbt);
}
}
IndexIVF * index_ivf = ivflib::extract_index_ivf (index.get());
/** Test independent search
*
* Manually scans through inverted lists, computing distances and
* ordering results organized in a heap.
*/
// sample some example queries and get reference search results.
auto xq = make_data (nq);
auto ref_I = search_index (index.get(), xq.data());
// handle preprocessing
const float * xqt = xq.data();
std::unique_ptr<float []> del_xqt;
if (index_pt) {
xqt = index_pt->apply_chain (nq, xq.data());
if (xqt != xq.data()) {
del_xqt.reset((float*)xqt);
}
}
// quantize the queries to get the inverted list ids to visit.
int nprobe = index_ivf->nprobe;
std::vector<idx_t> q_lists (nq * nprobe);
std::vector<float> q_dis (nq * nprobe);
index_ivf->quantizer->search (nq, xqt, nprobe,
q_dis.data(), q_lists.data());
// now run search in this many threads
int nproc = 3;
for (int i = 0; i < nq; i++) {
// one result table per thread
std::vector<idx_t> I (k * nproc, -1);
float default_dis = metric == METRIC_L2 ? HUGE_VAL : -HUGE_VAL;
std::vector<float> D (k * nproc, default_dis);
auto search_function = [index_ivf, &I, &D, dt, i, nproc,
xqt, nprobe, &q_dis, &q_lists]
(int rank) {
const InvertedLists *il = index_ivf->invlists;
// object that does the scanning and distance computations.
std::unique_ptr<InvertedListScanner> scanner (
index_ivf->get_InvertedListScanner());
idx_t *local_I = I.data() + rank * k;
float *local_D = D.data() + rank * k;
scanner->set_query (xqt + i * dt);
for (int j = rank; j < nprobe; j += nproc) {
int list_no = q_lists[i * nprobe + j];
if (list_no < 0) continue;
scanner->set_list (list_no, q_dis[i * nprobe + j]);
scanner->scan_codes (
il->list_size (list_no),
InvertedLists::ScopedCodes(il, list_no).get(),
InvertedLists::ScopedIds(il, list_no).get(),
local_D, local_I, k);
}
};
// start the threads. Threads are numbered rank=0..nproc-1 (a la MPI)
// thread rank takes care of inverted lists
// rank, rank+nproc, rank+2*nproc,...
std::vector<std::thread> threads;
for (int rank = 0; rank < nproc; rank++) {
threads.emplace_back(search_function, rank);
}
// join threads, merge heaps
for (int rank = 0; rank < nproc; rank++) {
threads[rank].join();
if (rank == 0) continue; // nothing to merge
// merge into first result
if (metric == METRIC_L2) {
maxheap_addn (k, D.data(), I.data(),
D.data() + rank * k,
I.data() + rank * k, k);
} else {
minheap_addn (k, D.data(), I.data(),
D.data() + rank * k,
I.data() + rank * k, k);
}
}
// re-order heap
if (metric == METRIC_L2) {
maxheap_reorder (k, D.data(), I.data());
} else {
minheap_reorder (k, D.data(), I.data());
}
// check that we have the same results as the reference search
for (int j = 0; j < k; j++) {
EXPECT_EQ (I[j], ref_I[i * k + j]);
}
}
}
} // anonymous namepace
TEST(TestLowLevelIVF, ThreadedSearch) {
test_threaded_search ("IVF32,Flat", METRIC_L2);
}
faiss::IndexIVF::encode_vectors
virtual void encode_vectors(idx_t n, const float *x, const idx_t *list_nos, uint8_t *codes) const =0
faiss::IndexShards::train
void train(idx_t n, const float *x) override
Definition: MetaIndexes.cpp:399
faiss::IndexIVF
Definition: IndexIVF.h:91
faiss::IndexBinary::search
virtual void search(idx_t n, const uint8_t *x, idx_t k, int32_t *distances, idx_t *labels) const =0
faiss::IndexBinaryIVF::nprobe
size_t nprobe
number of probes at query time
Definition: IndexBinaryIVF.h:41
faiss::IndexPreTransform::index
Index * index
! chain of tranforms
Definition: VectorTransform.h:256
faiss::IndexBinaryIVF
Definition: IndexBinaryIVF.h:36
faiss::IndexIVF::nprobe
size_t nprobe
number of probes at query time
Definition: IndexIVF.h:98
faiss::Index::assign
void assign(idx_t n, const float *x, idx_t *labels, idx_t k=1)
Definition: Index.cpp:35
faiss::InvertedLists::list_size
virtual size_t list_size(size_t list_no) const =0
get the size of a list
faiss::IndexPreTransform
Definition: VectorTransform.h:253
faiss::IndexBinaryIVF::quantizer
IndexBinary * quantizer
quantizer that maps vectors to inverted lists
Definition: IndexBinaryIVF.h:53
faiss::IndexBinary::code_size
int code_size
number of bytes per vector ( = d / 8 )
Definition: IndexBinary.h:41
faiss::InvertedLists
Definition: InvertedLists.h:32
faiss::IndexPreTransform::apply_chain
const float * apply_chain(idx_t n, const float *x) const
Definition: VectorTransform.cpp:899
faiss::Index::d
int d
vector dimension
Definition: Index.h:66
faiss::InvertedLists::code_size
size_t code_size
code size per vector in bytes
Definition: InvertedLists.h:36
faiss::IndexBinary::assign
void assign(idx_t n, const uint8_t *x, idx_t *labels, idx_t k=1)
Definition: IndexBinary.cpp:29
faiss::Index::idx_t
long idx_t
all indices are this type
Definition: Index.h:64
faiss::Index::search
virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const =0
faiss::ParameterSpace::set_index_parameter
virtual void set_index_parameter(Index *index, const std::string &name, double val) const
set one of the parameters
Definition: AutoTune.cpp:452
faiss::IndexIVF::invlists
InvertedLists * invlists
Acess to the actual data.
Definition: IndexIVF.h:93
faiss::ParameterSpace
Definition: AutoTune.h:132
faiss::Index
Definition: Index.h:62
faiss::Level1Quantizer::quantizer
Index * quantizer
quantizer that maps vectors to inverted lists
Definition: IndexIVF.h:33
faiss::InvertedLists::ScopedIds
Definition: InvertedLists.h:125
faiss::IndexIVF::get_InvertedListScanner
virtual InvertedListScanner * get_InvertedListScanner(bool store_pairs=false) const
get a scanner for this index (store_pairs means ignore labels)
Definition: IndexIVF.h:167
faiss::index_factory
Index * index_factory(int d, const char *description_in, MetricType metric)
Definition: AutoTune.cpp:722
faiss::InvertedLists::ScopedCodes
Definition: InvertedLists.h:144
faiss::Level1Quantizer::nlist
size_t nlist
number of possible key values
Definition: IndexIVF.h:34
faiss::IndexIVF::code_size
size_t code_size
code size per vector in bytes
Definition: IndexIVF.h:96
faiss::MetricType
MetricType
Some algorithms support both an inner product version and a L2 search version.
Definition: Index.h:45
faiss::IndexBinaryIVF::invlists
InvertedLists * invlists
Acess to the actual data.
Definition: IndexBinaryIVF.h:38