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faiss/IndexIVF.cpp

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/**
* 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.
*/
// -*- c++ -*-
#include "IndexIVF.h"
#include <cstdio>
#include "utils.h"
#include "hamming.h"
#include "FaissAssert.h"
#include "IndexFlat.h"
#include "AuxIndexStructures.h"
namespace faiss {
using ScopedIds = InvertedLists::ScopedIds;
using ScopedCodes = InvertedLists::ScopedCodes;
/*****************************************
* Level1Quantizer implementation
******************************************/
Level1Quantizer::Level1Quantizer (Index * quantizer, size_t nlist):
quantizer (quantizer),
nlist (nlist),
quantizer_trains_alone (0),
own_fields (false),
clustering_index (nullptr)
{
// here we set a low # iterations because this is typically used
// for large clusterings (nb this is not used for the MultiIndex,
// for which quantizer_trains_alone = true)
cp.niter = 10;
}
Level1Quantizer::Level1Quantizer ():
quantizer (nullptr),
nlist (0),
quantizer_trains_alone (0), own_fields (false),
clustering_index (nullptr)
{}
Level1Quantizer::~Level1Quantizer ()
{
if (own_fields) delete quantizer;
}
void Level1Quantizer::train_q1 (size_t n, const float *x, bool verbose, MetricType metric_type)
{
size_t d = quantizer->d;
if (quantizer->is_trained && (quantizer->ntotal == nlist)) {
if (verbose)
printf ("IVF quantizer does not need training.\n");
} else if (quantizer_trains_alone == 1) {
if (verbose)
printf ("IVF quantizer trains alone...\n");
quantizer->train (n, x);
quantizer->verbose = verbose;
FAISS_THROW_IF_NOT_MSG (quantizer->ntotal == nlist,
"nlist not consistent with quantizer size");
} else if (quantizer_trains_alone == 0) {
if (verbose)
printf ("Training level-1 quantizer on %ld vectors in %ldD\n",
n, d);
Clustering clus (d, nlist, cp);
quantizer->reset();
if (clustering_index) {
clus.train (n, x, *clustering_index);
quantizer->add (nlist, clus.centroids.data());
} else {
clus.train (n, x, *quantizer);
}
quantizer->is_trained = true;
} else if (quantizer_trains_alone == 2) {
if (verbose)
printf (
"Training L2 quantizer on %ld vectors in %ldD%s\n",
n, d,
clustering_index ? "(user provided index)" : "");
FAISS_THROW_IF_NOT (metric_type == METRIC_L2);
Clustering clus (d, nlist, cp);
if (!clustering_index) {
IndexFlatL2 assigner (d);
clus.train(n, x, assigner);
} else {
clus.train(n, x, *clustering_index);
}
if (verbose)
printf ("Adding centroids to quantizer\n");
quantizer->add (nlist, clus.centroids.data());
}
}
/*****************************************
* IndexIVF implementation
******************************************/
IndexIVF::IndexIVF (Index * quantizer, size_t d,
size_t nlist, size_t code_size,
MetricType metric):
Index (d, metric),
Level1Quantizer (quantizer, nlist),
invlists (new ArrayInvertedLists (nlist, code_size)),
own_invlists (true),
code_size (code_size),
nprobe (1),
max_codes (0),
maintain_direct_map (false)
{
FAISS_THROW_IF_NOT (d == quantizer->d);
is_trained = quantizer->is_trained && (quantizer->ntotal == nlist);
// Spherical by default if the metric is inner_product
if (metric_type == METRIC_INNER_PRODUCT) {
cp.spherical = true;
}
}
IndexIVF::IndexIVF ():
invlists (nullptr), own_invlists (false),
code_size (0),
nprobe (1), max_codes (0),
maintain_direct_map (false)
{}
void IndexIVF::add (idx_t n, const float * x)
{
add_with_ids (n, x, nullptr);
}
void IndexIVF::make_direct_map (bool new_maintain_direct_map)
{
// nothing to do
if (new_maintain_direct_map == maintain_direct_map)
return;
if (new_maintain_direct_map) {
direct_map.resize (ntotal, -1);
for (size_t key = 0; key < nlist; key++) {
size_t list_size = invlists->list_size (key);
ScopedIds idlist (invlists, key);
for (long ofs = 0; ofs < list_size; ofs++) {
FAISS_THROW_IF_NOT_MSG (
0 <= idlist [ofs] && idlist[ofs] < ntotal,
"direct map supported only for seuquential ids");
direct_map [idlist [ofs]] = key << 32 | ofs;
}
}
} else {
direct_map.clear ();
}
maintain_direct_map = new_maintain_direct_map;
}
void IndexIVF::search (idx_t n, const float *x, idx_t k,
float *distances, idx_t *labels) const
{
long * idx = new long [n * nprobe];
ScopeDeleter<long> del (idx);
float * coarse_dis = new float [n * nprobe];
ScopeDeleter<float> del2 (coarse_dis);
quantizer->search (n, x, nprobe, coarse_dis, idx);
invlists->prefetch_lists (idx, n * nprobe);
search_preassigned (n, x, k, idx, coarse_dis,
distances, labels, false);
}
void IndexIVF::reconstruct (idx_t key, float* recons) const
{
FAISS_THROW_IF_NOT_MSG (direct_map.size() == ntotal,
"direct map is not initialized");
long list_no = direct_map[key] >> 32;
long offset = direct_map[key] & 0xffffffff;
reconstruct_from_offset (list_no, offset, recons);
}
void IndexIVF::reconstruct_n (idx_t i0, idx_t ni, float* recons) const
{
FAISS_THROW_IF_NOT (ni == 0 || (i0 >= 0 && i0 + ni <= ntotal));
for (long list_no = 0; list_no < nlist; list_no++) {
size_t list_size = invlists->list_size (list_no);
ScopedIds idlist (invlists, list_no);
for (long offset = 0; offset < list_size; offset++) {
long id = idlist[offset];
if (!(id >= i0 && id < i0 + ni)) {
continue;
}
float* reconstructed = recons + (id - i0) * d;
reconstruct_from_offset (list_no, offset, reconstructed);
}
}
}
void IndexIVF::search_and_reconstruct (idx_t n, const float *x, idx_t k,
float *distances, idx_t *labels,
float *recons) const
{
long * idx = new long [n * nprobe];
ScopeDeleter<long> del (idx);
float * coarse_dis = new float [n * nprobe];
ScopeDeleter<float> del2 (coarse_dis);
quantizer->search (n, x, nprobe, coarse_dis, idx);
invlists->prefetch_lists (idx, n * nprobe);
// search_preassigned() with `store_pairs` enabled to obtain the list_no
// and offset into `codes` for reconstruction
search_preassigned (n, x, k, idx, coarse_dis,
distances, labels, true /* store_pairs */);
for (idx_t i = 0; i < n; ++i) {
for (idx_t j = 0; j < k; ++j) {
idx_t ij = i * k + j;
idx_t key = labels[ij];
float* reconstructed = recons + ij * d;
if (key < 0) {
// Fill with NaNs
memset(reconstructed, -1, sizeof(*reconstructed) * d);
} else {
int list_no = key >> 32;
int offset = key & 0xffffffff;
// Update label to the actual id
labels[ij] = invlists->get_single_id (list_no, offset);
reconstruct_from_offset (list_no, offset, reconstructed);
}
}
}
}
void IndexIVF::reconstruct_from_offset(
long /*list_no*/,
long /*offset*/,
float* /*recons*/) const {
FAISS_THROW_MSG ("reconstruct_from_offset not implemented");
}
void IndexIVF::reset ()
{
direct_map.clear ();
invlists->reset ();
ntotal = 0;
}
long IndexIVF::remove_ids (const IDSelector & sel)
{
FAISS_THROW_IF_NOT_MSG (!maintain_direct_map,
"direct map remove not implemented");
std::vector<long> toremove(nlist);
#pragma omp parallel for
for (long i = 0; i < nlist; i++) {
long l0 = invlists->list_size (i), l = l0, j = 0;
ScopedIds idsi (invlists, i);
while (j < l) {
if (sel.is_member (idsi[j])) {
l--;
invlists->update_entry (
i, j,
invlists->get_single_id (i, l),
ScopedCodes (invlists, i, l).get());
} else {
j++;
}
}
toremove[i] = l0 - l;
}
// this will not run well in parallel on ondisk because of possible shrinks
long nremove = 0;
for (long i = 0; i < nlist; i++) {
if (toremove[i] > 0) {
nremove += toremove[i];
invlists->resize(
i, invlists->list_size(i) - toremove[i]);
}
}
ntotal -= nremove;
return nremove;
}
void IndexIVF::train (idx_t n, const float *x)
{
if (verbose)
printf ("Training level-1 quantizer\n");
train_q1 (n, x, verbose, metric_type);
if (verbose)
printf ("Training IVF residual\n");
train_residual (n, x);
is_trained = true;
}
void IndexIVF::train_residual(idx_t /*n*/, const float* /*x*/) {
if (verbose)
printf("IndexIVF: no residual training\n");
// does nothing by default
}
double IndexIVF::imbalance_factor () const
{
std::vector<int> hist (nlist);
for (int i = 0; i < nlist; i++) {
hist[i] = invlists->list_size(i);
}
return faiss::imbalance_factor (nlist, hist.data());
}
void IndexIVF::print_stats () const
{
std::vector<int> sizes(40);
for (int i = 0; i < nlist; i++) {
for (int j = 0; j < sizes.size(); j++) {
if ((invlists->list_size(i) >> j) == 0) {
sizes[j]++;
break;
}
}
}
for (int i = 0; i < sizes.size(); i++) {
if (sizes[i]) {
printf ("list size in < %d: %d instances\n",
1 << i, sizes[i]);
}
}
}
void IndexIVF::check_compatible_for_merge (const IndexIVF &other) const
{
// minimal sanity checks
FAISS_THROW_IF_NOT (other.d == d);
FAISS_THROW_IF_NOT (other.nlist == nlist);
FAISS_THROW_IF_NOT (other.code_size == code_size);
FAISS_THROW_IF_NOT_MSG (typeid (*this) == typeid (other),
"can only merge indexes of the same type");
}
void IndexIVF::merge_from (IndexIVF &other, idx_t add_id)
{
check_compatible_for_merge (other);
FAISS_THROW_IF_NOT_MSG ((!maintain_direct_map &&
!other.maintain_direct_map),
"direct map copy not implemented");
invlists->merge_from (other.invlists, add_id);
ntotal += other.ntotal;
other.ntotal = 0;
}
void IndexIVF::replace_invlists (InvertedLists *il, bool own)
{
//FAISS_THROW_IF_NOT (ntotal == 0);
FAISS_THROW_IF_NOT (il->nlist == nlist &&
il->code_size == code_size);
if (own_invlists) {
delete invlists;
}
invlists = il;
own_invlists = own;
}
void IndexIVF::copy_subset_to (IndexIVF & other, int subset_type,
long a1, long a2) const
{
FAISS_THROW_IF_NOT (nlist == other.nlist);
FAISS_THROW_IF_NOT (code_size == other.code_size);
FAISS_THROW_IF_NOT (!other.maintain_direct_map);
FAISS_THROW_IF_NOT_FMT (
subset_type == 0 || subset_type == 1 || subset_type == 2,
"subset type %d not implemented", subset_type);
size_t accu_n = 0;
size_t accu_a1 = 0;
size_t accu_a2 = 0;
InvertedLists *oivf = other.invlists;
for (long list_no = 0; list_no < nlist; list_no++) {
size_t n = invlists->list_size (list_no);
ScopedIds ids_in (invlists, list_no);
if (subset_type == 0) {
for (long i = 0; i < n; i++) {
idx_t id = ids_in[i];
if (a1 <= id && id < a2) {
oivf->add_entry (list_no,
invlists->get_single_id (list_no, i),
ScopedCodes (invlists, list_no, i).get());
other.ntotal++;
}
}
} else if (subset_type == 1) {
for (long i = 0; i < n; i++) {
idx_t id = ids_in[i];
if (id % a1 == a2) {
oivf->add_entry (list_no,
invlists->get_single_id (list_no, i),
ScopedCodes (invlists, list_no, i).get());
other.ntotal++;
}
}
} else if (subset_type == 2) {
// see what is allocated to a1 and to a2
size_t next_accu_n = accu_n + n;
size_t next_accu_a1 = next_accu_n * a1 / ntotal;
size_t i1 = next_accu_a1 - accu_a1;
size_t next_accu_a2 = next_accu_n * a2 / ntotal;
size_t i2 = next_accu_a2 - accu_a2;
for (long i = i1; i < i2; i++) {
oivf->add_entry (list_no,
invlists->get_single_id (list_no, i),
ScopedCodes (invlists, list_no, i).get());
}
other.ntotal += i2 - i1;
accu_a1 = next_accu_a1;
accu_a2 = next_accu_a2;
}
accu_n += n;
}
FAISS_ASSERT(accu_n == ntotal);
}
IndexIVF::~IndexIVF()
{
if (own_invlists) {
delete invlists;
}
}
void IndexIVFStats::reset()
{
memset ((void*)this, 0, sizeof (*this));
}
IndexIVFStats indexIVF_stats;
} // namespace faiss
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