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/tmp/faiss/IndexIVFPQ.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.
*/
// -*- c++ -*-
#include "IndexIVFPQ.h"
#include <cmath>
#include <cstdio>
#include <cassert>
#include <algorithm>
#include "Heap.h"
#include "utils.h"
#include "Clustering.h"
#include "IndexFlat.h"
#include "hamming.h"
#include "FaissAssert.h"
#include "AuxIndexStructures.h"
namespace faiss {
/*****************************************
* IndexIVFPQ implementation
******************************************/
IndexIVFPQ::IndexIVFPQ (Index * quantizer, size_t d, size_t nlist,
size_t M, size_t nbits_per_idx):
IndexIVF (quantizer, d, nlist, 0, METRIC_L2),
pq (d, M, nbits_per_idx)
{
FAISS_THROW_IF_NOT (nbits_per_idx <= 8);
code_size = pq.code_size;
invlists->code_size = code_size;
is_trained = false;
by_residual = true;
use_precomputed_table = 0;
scan_table_threshold = 0;
polysemous_training = nullptr;
do_polysemous_training = false;
polysemous_ht = 0;
}
/****************************************************************
* training */
void IndexIVFPQ::train_residual (idx_t n, const float *x)
{
train_residual_o (n, x, nullptr);
}
void IndexIVFPQ::train_residual_o (idx_t n, const float *x, float *residuals_2)
{
const float * x_in = x;
x = fvecs_maybe_subsample (
d, (size_t*)&n, pq.cp.max_points_per_centroid * pq.ksub,
x, verbose, pq.cp.seed);
ScopeDeleter<float> del_x (x_in == x ? nullptr : x);
const float *trainset;
ScopeDeleter<float> del_residuals;
if (by_residual) {
if(verbose) printf("computing residuals\n");
idx_t * assign = new idx_t [n]; // assignement to coarse centroids
ScopeDeleter<idx_t> del (assign);
quantizer->assign (n, x, assign);
float *residuals = new float [n * d];
del_residuals.set (residuals);
for (idx_t i = 0; i < n; i++)
quantizer->compute_residual (x + i * d, residuals+i*d, assign[i]);
trainset = residuals;
} else {
trainset = x;
}
if (verbose)
printf ("training %zdx%zd product quantizer on %ld vectors in %dD\n",
pq.M, pq.ksub, n, d);
pq.verbose = verbose;
pq.train (n, trainset);
if (do_polysemous_training) {
if (verbose)
printf("doing polysemous training for PQ\n");
PolysemousTraining default_pt;
PolysemousTraining *pt = polysemous_training;
if (!pt) pt = &default_pt;
pt->optimize_pq_for_hamming (pq, n, trainset);
}
// prepare second-level residuals for refine PQ
if (residuals_2) {
uint8_t *train_codes = new uint8_t [pq.code_size * n];
ScopeDeleter<uint8_t> del (train_codes);
pq.compute_codes (trainset, train_codes, n);
for (idx_t i = 0; i < n; i++) {
const float *xx = trainset + i * d;
float * res = residuals_2 + i * d;
pq.decode (train_codes + i * pq.code_size, res);
for (int j = 0; j < d; j++)
res[j] = xx[j] - res[j];
}
}
if (by_residual) {
precompute_table ();
}
}
/****************************************************************
* IVFPQ as codec */
/* produce a binary signature based on the residual vector */
void IndexIVFPQ::encode (long key, const float * x, uint8_t * code) const
{
if (by_residual) {
float residual_vec[d];
quantizer->compute_residual (x, residual_vec, key);
pq.compute_code (residual_vec, code);
}
else pq.compute_code (x, code);
}
void IndexIVFPQ::encode_multiple (size_t n, long *keys,
const float * x, uint8_t * xcodes,
bool compute_keys) const
{
if (compute_keys)
quantizer->assign (n, x, keys);
encode_vectors (n, x, keys, xcodes);
}
void IndexIVFPQ::decode_multiple (size_t n, const long *keys,
const uint8_t * xcodes, float * x) const
{
pq.decode (xcodes, x, n);
if (by_residual) {
std::vector<float> centroid (d);
for (size_t i = 0; i < n; i++) {
quantizer->reconstruct (keys[i], centroid.data());
float *xi = x + i * d;
for (size_t j = 0; j < d; j++) {
xi [j] += centroid [j];
}
}
}
}
/****************************************************************
* add */
void IndexIVFPQ::add_with_ids (idx_t n, const float * x, const long *xids)
{
add_core_o (n, x, xids, nullptr);
}
static float * compute_residuals (
const Index *quantizer,
Index::idx_t n, const float* x,
const Index::idx_t *list_nos)
{
size_t d = quantizer->d;
float *residuals = new float [n * d];
// TODO: parallelize?
for (size_t i = 0; i < n; i++) {
if (list_nos[i] < 0)
memset (residuals + i * d, 0, sizeof(*residuals) * d);
else
quantizer->compute_residual (
x + i * d, residuals + i * d, list_nos[i]);
}
return residuals;
}
void IndexIVFPQ::encode_vectors(idx_t n, const float* x,
const idx_t *list_nos,
uint8_t * codes) const
{
if (by_residual) {
float *to_encode = compute_residuals (quantizer, n, x, list_nos);
ScopeDeleter<float> del (to_encode);
pq.compute_codes (to_encode, codes, n);
} else {
pq.compute_codes (x, codes, n);
}
}
void IndexIVFPQ::add_core_o (idx_t n, const float * x, const long *xids,
float *residuals_2, const long *precomputed_idx)
{
idx_t bs = 32768;
if (n > bs) {
for (idx_t i0 = 0; i0 < n; i0 += bs) {
idx_t i1 = std::min(i0 + bs, n);
if (verbose) {
printf("IndexIVFPQ::add_core_o: adding %ld:%ld / %ld\n",
i0, i1, n);
}
add_core_o (i1 - i0, x + i0 * d,
xids ? xids + i0 : nullptr,
residuals_2 ? residuals_2 + i0 * d : nullptr,
precomputed_idx ? precomputed_idx + i0 : nullptr);
}
return;
}
FAISS_THROW_IF_NOT (is_trained);
double t0 = getmillisecs ();
const long * idx;
ScopeDeleter<long> del_idx;
if (precomputed_idx) {
idx = precomputed_idx;
} else {
long * idx0 = new long [n];
del_idx.set (idx0);
quantizer->assign (n, x, idx0);
idx = idx0;
}
double t1 = getmillisecs ();
uint8_t * xcodes = new uint8_t [n * code_size];
ScopeDeleter<uint8_t> del_xcodes (xcodes);
const float *to_encode = nullptr;
ScopeDeleter<float> del_to_encode;
if (by_residual) {
to_encode = compute_residuals (quantizer, n, x, idx);
del_to_encode.set (to_encode);
} else {
to_encode = x;
}
pq.compute_codes (to_encode, xcodes, n);
double t2 = getmillisecs ();
// TODO: parallelize?
size_t n_ignore = 0;
for (size_t i = 0; i < n; i++) {
idx_t key = idx[i];
if (key < 0) {
n_ignore ++;
if (residuals_2)
memset (residuals_2, 0, sizeof(*residuals_2) * d);
continue;
}
idx_t id = xids ? xids[i] : ntotal + i;
uint8_t *code = xcodes + i * code_size;
size_t offset = invlists->add_entry (key, id, code);
if (residuals_2) {
float *res2 = residuals_2 + i * d;
const float *xi = to_encode + i * d;
pq.decode (code, res2);
for (int j = 0; j < d; j++)
res2[j] = xi[j] - res2[j];
}
if (maintain_direct_map)
direct_map.push_back (key << 32 | offset);
}
double t3 = getmillisecs ();
if(verbose) {
char comment[100] = {0};
if (n_ignore > 0)
snprintf (comment, 100, "(%ld vectors ignored)", n_ignore);
printf(" add_core times: %.3f %.3f %.3f %s\n",
t1 - t0, t2 - t1, t3 - t2, comment);
}
ntotal += n;
}
void IndexIVFPQ::reconstruct_from_offset (long list_no, long offset,
float* recons) const
{
const uint8_t* code = invlists->get_single_code (list_no, offset);
if (by_residual) {
std::vector<float> centroid(d);
quantizer->reconstruct (list_no, centroid.data());
pq.decode (code, recons);
for (int i = 0; i < d; ++i) {
recons[i] += centroid[i];
}
} else {
pq.decode (code, recons);
}
}
/// 2G by default, accommodates tables up to PQ32 w/ 65536 centroids
size_t IndexIVFPQ::precomputed_table_max_bytes = ((size_t)1) << 31;
/** Precomputed tables for residuals
*
* During IVFPQ search with by_residual, we compute
*
* d = || x - y_C - y_R ||^2
*
* where x is the query vector, y_C the coarse centroid, y_R the
* refined PQ centroid. The expression can be decomposed as:
*
* d = || x - y_C ||^2 + || y_R ||^2 + 2 * (y_C|y_R) - 2 * (x|y_R)
* --------------- --------------------------- -------
* term 1 term 2 term 3
*
* When using multiprobe, we use the following decomposition:
* - term 1 is the distance to the coarse centroid, that is computed
* during the 1st stage search.
* - term 2 can be precomputed, as it does not involve x. However,
* because of the PQ, it needs nlist * M * ksub storage. This is why
* use_precomputed_table is off by default
* - term 3 is the classical non-residual distance table.
*
* Since y_R defined by a product quantizer, it is split across
* subvectors and stored separately for each subvector. If the coarse
* quantizer is a MultiIndexQuantizer then the table can be stored
* more compactly.
*
* At search time, the tables for term 2 and term 3 are added up. This
* is faster when the length of the lists is > ksub * M.
*/
void IndexIVFPQ::precompute_table ()
{
if (use_precomputed_table == -1)
return;
if (use_precomputed_table == 0) { // then choose the type of table
if (quantizer->metric_type == METRIC_INNER_PRODUCT) {
if (verbose) {
printf("IndexIVFPQ::precompute_table: precomputed "
"tables not needed for inner product quantizers\n");
}
return;
}
const MultiIndexQuantizer *miq =
dynamic_cast<const MultiIndexQuantizer *> (quantizer);
if (miq && pq.M % miq->pq.M == 0)
use_precomputed_table = 2;
else {
size_t table_size = pq.M * pq.ksub * nlist * sizeof(float);
if (table_size > precomputed_table_max_bytes) {
if (verbose) {
printf(
"IndexIVFPQ::precompute_table: not precomputing table, "
"it would be too big: %ld bytes (max %ld)\n",
table_size, precomputed_table_max_bytes);
use_precomputed_table = 0;
}
return;
}
use_precomputed_table = 1;
}
} // otherwise assume user has set appropriate flag on input
if (verbose) {
printf ("precomputing IVFPQ tables type %d\n",
use_precomputed_table);
}
// squared norms of the PQ centroids
std::vector<float> r_norms (pq.M * pq.ksub, NAN);
for (int m = 0; m < pq.M; m++)
for (int j = 0; j < pq.ksub; j++)
r_norms [m * pq.ksub + j] =
fvec_norm_L2sqr (pq.get_centroids (m, j), pq.dsub);
if (use_precomputed_table == 1) {
precomputed_table.resize (nlist * pq.M * pq.ksub);
std::vector<float> centroid (d);
for (size_t i = 0; i < nlist; i++) {
quantizer->reconstruct (i, centroid.data());
float *tab = &precomputed_table[i * pq.M * pq.ksub];
pq.compute_inner_prod_table (centroid.data(), tab);
fvec_madd (pq.M * pq.ksub, r_norms.data(), 2.0, tab, tab);
}
} else if (use_precomputed_table == 2) {
const MultiIndexQuantizer *miq =
dynamic_cast<const MultiIndexQuantizer *> (quantizer);
FAISS_THROW_IF_NOT (miq);
const ProductQuantizer &cpq = miq->pq;
FAISS_THROW_IF_NOT (pq.M % cpq.M == 0);
precomputed_table.resize(cpq.ksub * pq.M * pq.ksub);
// reorder PQ centroid table
std::vector<float> centroids (d * cpq.ksub, NAN);
for (int m = 0; m < cpq.M; m++) {
for (size_t i = 0; i < cpq.ksub; i++) {
memcpy (centroids.data() + i * d + m * cpq.dsub,
cpq.get_centroids (m, i),
sizeof (*centroids.data()) * cpq.dsub);
}
}
pq.compute_inner_prod_tables (cpq.ksub, centroids.data (),
precomputed_table.data ());
for (size_t i = 0; i < cpq.ksub; i++) {
float *tab = &precomputed_table[i * pq.M * pq.ksub];
fvec_madd (pq.M * pq.ksub, r_norms.data(), 2.0, tab, tab);
}
}
}
namespace {
using idx_t = Index::idx_t;
static uint64_t get_cycles () {
#ifdef __x86_64__
uint32_t high, low;
asm volatile("rdtsc \n\t"
: "=a" (low),
"=d" (high));
return ((uint64_t)high << 32) | (low);
#else
return 0;
#endif
}
#define TIC t0 = get_cycles()
#define TOC get_cycles () - t0
/** QueryTables manages the various ways of searching an
* IndexIVFPQ. The code contains a lot of branches, depending on:
* - metric_type: are we computing L2 or Inner product similarity?
* - by_residual: do we encode raw vectors or residuals?
* - use_precomputed_table: are x_R|x_C tables precomputed?
* - polysemous_ht: are we filtering with polysemous codes?
*/
struct QueryTables {
/*****************************************************
* General data from the IVFPQ
*****************************************************/
const IndexIVFPQ & ivfpq;
const IVFSearchParameters *params;
// copied from IndexIVFPQ for easier access
int d;
const ProductQuantizer & pq;
MetricType metric_type;
bool by_residual;
int use_precomputed_table;
int polysemous_ht;
// pre-allocated data buffers
float * sim_table, * sim_table_2;
float * residual_vec, *decoded_vec;
// single data buffer
std::vector<float> mem;
// for table pointers
std::vector<const float *> sim_table_ptrs;
explicit QueryTables (const IndexIVFPQ & ivfpq,
const IVFSearchParameters *params):
ivfpq(ivfpq),
d(ivfpq.d),
pq (ivfpq.pq),
metric_type (ivfpq.metric_type),
by_residual (ivfpq.by_residual),
use_precomputed_table (ivfpq.use_precomputed_table)
{
mem.resize (pq.ksub * pq.M * 2 + d * 2);
sim_table = mem.data ();
sim_table_2 = sim_table + pq.ksub * pq.M;
residual_vec = sim_table_2 + pq.ksub * pq.M;
decoded_vec = residual_vec + d;
// for polysemous
polysemous_ht = ivfpq.polysemous_ht;
if (auto ivfpq_params =
dynamic_cast<const IVFPQSearchParameters *>(params)) {
polysemous_ht = ivfpq_params->polysemous_ht;
}
if (polysemous_ht != 0) {
q_code.resize (pq.code_size);
}
init_list_cycles = 0;
sim_table_ptrs.resize (pq.M);
}
/*****************************************************
* What we do when query is known
*****************************************************/
// field specific to query
const float * qi;
// query-specific intialization
void init_query (const float * qi) {
this->qi = qi;
if (metric_type == METRIC_INNER_PRODUCT)
init_query_IP ();
else
init_query_L2 ();
if (!by_residual && polysemous_ht != 0)
pq.compute_code (qi, q_code.data());
}
void init_query_IP () {
// precompute some tables specific to the query qi
pq.compute_inner_prod_table (qi, sim_table);
}
void init_query_L2 () {
if (!by_residual) {
pq.compute_distance_table (qi, sim_table);
} else if (use_precomputed_table) {
pq.compute_inner_prod_table (qi, sim_table_2);
}
}
/*****************************************************
* When inverted list is known: prepare computations
*****************************************************/
// fields specific to list
Index::idx_t key;
float coarse_dis;
std::vector<uint8_t> q_code;
uint64_t init_list_cycles;
/// once we know the query and the centroid, we can prepare the
/// sim_table that will be used for accumulation
/// and dis0, the initial value
float precompute_list_tables () {
float dis0 = 0;
uint64_t t0; TIC;
if (by_residual) {
if (metric_type == METRIC_INNER_PRODUCT)
dis0 = precompute_list_tables_IP ();
else
dis0 = precompute_list_tables_L2 ();
}
init_list_cycles += TOC;
return dis0;
}
float precompute_list_table_pointers () {
float dis0 = 0;
uint64_t t0; TIC;
if (by_residual) {
if (metric_type == METRIC_INNER_PRODUCT)
FAISS_THROW_MSG ("not implemented");
else
dis0 = precompute_list_table_pointers_L2 ();
}
init_list_cycles += TOC;
return dis0;
}
/*****************************************************
* compute tables for inner prod
*****************************************************/
float precompute_list_tables_IP ()
{
// prepare the sim_table that will be used for accumulation
// and dis0, the initial value
ivfpq.quantizer->reconstruct (key, decoded_vec);
// decoded_vec = centroid
float dis0 = fvec_inner_product (qi, decoded_vec, d);
if (polysemous_ht) {
for (int i = 0; i < d; i++) {
residual_vec [i] = qi[i] - decoded_vec[i];
}
pq.compute_code (residual_vec, q_code.data());
}
return dis0;
}
/*****************************************************
* compute tables for L2 distance
*****************************************************/
float precompute_list_tables_L2 ()
{
float dis0 = 0;
if (use_precomputed_table == 0 || use_precomputed_table == -1) {
ivfpq.quantizer->compute_residual (qi, residual_vec, key);
pq.compute_distance_table (residual_vec, sim_table);
} else if (use_precomputed_table == 1) {
dis0 = coarse_dis;
fvec_madd (pq.M * pq.ksub,
&ivfpq.precomputed_table [key * pq.ksub * pq.M],
-2.0, sim_table_2,
sim_table);
} else if (use_precomputed_table == 2) {
dis0 = coarse_dis;
const MultiIndexQuantizer *miq =
dynamic_cast<const MultiIndexQuantizer *> (ivfpq.quantizer);
FAISS_THROW_IF_NOT (miq);
const ProductQuantizer &cpq = miq->pq;
int Mf = pq.M / cpq.M;
const float *qtab = sim_table_2; // query-specific table
float *ltab = sim_table; // (output) list-specific table
long k = key;
for (int cm = 0; cm < cpq.M; cm++) {
// compute PQ index
int ki = k & ((uint64_t(1) << cpq.nbits) - 1);
k >>= cpq.nbits;
// get corresponding table
const float *pc = &ivfpq.precomputed_table
[(ki * pq.M + cm * Mf) * pq.ksub];
if (polysemous_ht == 0) {
// sum up with query-specific table
fvec_madd (Mf * pq.ksub,
pc,
-2.0, qtab,
ltab);
ltab += Mf * pq.ksub;
qtab += Mf * pq.ksub;
} else {
for (int m = cm * Mf; m < (cm + 1) * Mf; m++) {
q_code[m] = fvec_madd_and_argmin
(pq.ksub, pc, -2, qtab, ltab);
pc += pq.ksub;
ltab += pq.ksub;
qtab += pq.ksub;
}
}
}
}
return dis0;
}
float precompute_list_table_pointers_L2 ()
{
float dis0 = 0;
if (use_precomputed_table == 1) {
dis0 = coarse_dis;
const float * s = &ivfpq.precomputed_table [key * pq.ksub * pq.M];
for (int m = 0; m < pq.M; m++) {
sim_table_ptrs [m] = s;
s += pq.ksub;
}
} else if (use_precomputed_table == 2) {
dis0 = coarse_dis;
const MultiIndexQuantizer *miq =
dynamic_cast<const MultiIndexQuantizer *> (ivfpq.quantizer);
FAISS_THROW_IF_NOT (miq);
const ProductQuantizer &cpq = miq->pq;
int Mf = pq.M / cpq.M;
long k = key;
int m0 = 0;
for (int cm = 0; cm < cpq.M; cm++) {
int ki = k & ((uint64_t(1) << cpq.nbits) - 1);
k >>= cpq.nbits;
const float *pc = &ivfpq.precomputed_table
[(ki * pq.M + cm * Mf) * pq.ksub];
for (int m = m0; m < m0 + Mf; m++) {
sim_table_ptrs [m] = pc;
pc += pq.ksub;
}
m0 += Mf;
}
} else {
FAISS_THROW_MSG ("need precomputed tables");
}
if (polysemous_ht) {
FAISS_THROW_MSG ("not implemented");
// Not clear that it makes sense to implemente this,
// because it costs M * ksub, which is what we wanted to
// avoid with the tables pointers.
}
return dis0;
}
};
/*****************************************************
* Scaning the codes.
* The scanning functions call their favorite precompute_*
* function to precompute the tables they need.
*****************************************************/
template <typename IDType, bool store_pairs, class C, MetricType METRIC_TYPE>
struct IVFPQScannerT: QueryTables {
const uint8_t * list_codes;
const IDType * list_ids;
size_t list_size;
explicit IVFPQScannerT (const IndexIVFPQ & ivfpq,
const IVFSearchParameters *params):
QueryTables (ivfpq, params)
{
FAISS_THROW_IF_NOT (pq.byte_per_idx == 1);
assert(METRIC_TYPE == metric_type);
}
float dis0;
void init_list (idx_t list_no, float coarse_dis,
int mode) {
this->key = list_no;
this->coarse_dis = coarse_dis;
if (mode == 2) {
dis0 = precompute_list_tables ();
} else if (mode == 1) {
dis0 = precompute_list_table_pointers ();
}
}
/*****************************************************
* Scaning the codes: simple PQ scan.
*****************************************************/
/// version of the scan where we use precomputed tables
size_t scan_list_with_table (
size_t ncode, const uint8_t *codes, const idx_t *ids,
size_t k, float * heap_sim, long * heap_ids) const
{
int nup = 0;
for (size_t j = 0; j < ncode; j++) {
float dis = dis0;
const float *tab = sim_table;
for (size_t m = 0; m < pq.M; m++) {
dis += tab[*codes++];
tab += pq.ksub;
}
if (C::cmp (heap_sim[0], dis)) {
heap_pop<C> (k, heap_sim, heap_ids);
long id = store_pairs ? (key << 32 | j) : ids[j];
heap_push<C> (k, heap_sim, heap_ids, dis, id);
nup++;
}
}
return nup;
}
/// tables are not precomputed, but pointers are provided to the
/// relevant X_c|x_r tables
size_t scan_list_with_pointer (
size_t ncode, const uint8_t *codes, const idx_t *ids,
size_t k, float * heap_sim, long * heap_ids) const
{
size_t nup = 0;
for (size_t j = 0; j < ncode; j++) {
float dis = dis0;
const float *tab = sim_table_2;
for (size_t m = 0; m < pq.M; m++) {
int ci = *codes++;
dis += sim_table_ptrs [m][ci] - 2 * tab [ci];
tab += pq.ksub;
}
if (C::cmp (heap_sim[0], dis)) {
heap_pop<C> (k, heap_sim, heap_ids);
long id = store_pairs ? (key << 32 | j) : ids[j];
heap_push<C> (k, heap_sim, heap_ids, dis, id);
nup++;
}
}
return nup;
}
/// nothing is precomputed: access residuals on-the-fly
size_t scan_on_the_fly_dist (
size_t ncode, const uint8_t *codes, const idx_t *ids,
size_t k, float * heap_sim, long * heap_ids) const
{
const float *dvec;
float dis0 = 0;
size_t nup = 0;
if (by_residual) {
if (METRIC_TYPE == METRIC_INNER_PRODUCT) {
ivfpq.quantizer->reconstruct (key, residual_vec);
dis0 = fvec_inner_product (residual_vec, qi, d);
} else {
ivfpq.quantizer->compute_residual (qi, residual_vec, key);
}
dvec = residual_vec;
} else {
dvec = qi;
dis0 = 0;
}
for (size_t j = 0; j < ncode; j++) {
pq.decode (codes, decoded_vec);
codes += pq.code_size;
float dis;
if (METRIC_TYPE == METRIC_INNER_PRODUCT) {
dis = dis0 + fvec_inner_product (decoded_vec, qi, d);
} else {
dis = fvec_L2sqr (decoded_vec, dvec, d);
}
if (C::cmp (heap_sim[0], dis)) {
heap_pop<C> (k, heap_sim, heap_ids);
long id = store_pairs ? (key << 32 | j) : ids[j];
heap_push<C> (k, heap_sim, heap_ids, dis, id);
nup++;
}
}
return nup;
}
/*****************************************************
* Scanning codes with polysemous filtering
*****************************************************/
template <class HammingComputer>
size_t scan_list_polysemous_hc (
size_t ncode, const uint8_t *codes, const idx_t *ids,
size_t k, float * heap_sim, long * heap_ids) const
{
int ht = ivfpq.polysemous_ht;
size_t n_hamming_pass = 0, nup = 0;
int code_size = pq.code_size;
HammingComputer hc (q_code.data(), code_size);
for (size_t j = 0; j < ncode; j++) {
const uint8_t *b_code = codes;
int hd = hc.hamming (b_code);
if (hd < ht) {
n_hamming_pass ++;
float dis = dis0;
const float *tab = sim_table;
for (size_t m = 0; m < pq.M; m++) {
dis += tab[*b_code++];
tab += pq.ksub;
}
if (C::cmp (heap_sim[0], dis)) {
heap_pop<C> (k, heap_sim, heap_ids);
long id = store_pairs ? (key << 32 | j) : ids[j];
heap_push<C> (k, heap_sim, heap_ids, dis, id);
nup++;
}
}
codes += code_size;
}
#pragma omp critical
{
indexIVFPQ_stats.n_hamming_pass += n_hamming_pass;
}
return nup;
}
size_t scan_list_polysemous (
size_t ncode, const uint8_t *codes, const idx_t *ids,
size_t k, float * heap_sim, long * heap_ids) const
{
switch (pq.code_size) {
#define HANDLE_CODE_SIZE(cs) \
case cs: \
return scan_list_polysemous_hc <HammingComputer ## cs> \
(ncode, codes, ids, k, heap_sim, heap_ids); \
break
HANDLE_CODE_SIZE(4);
HANDLE_CODE_SIZE(8);
HANDLE_CODE_SIZE(16);
HANDLE_CODE_SIZE(20);
HANDLE_CODE_SIZE(32);
HANDLE_CODE_SIZE(64);
#undef HANDLE_CODE_SIZE
default:
if (pq.code_size % 8 == 0)
return scan_list_polysemous_hc <HammingComputerM8>
(ncode, codes, ids, k, heap_sim, heap_ids);
else
return scan_list_polysemous_hc <HammingComputerM4>
(ncode, codes, ids, k, heap_sim, heap_ids);
break;
}
}
};
/* We put as many parameters as possible in template. Hopefully the
* gain in runtime is worth the code bloat. C is the comparator < or
* >, it is directly related to METRIC_TYPE. precompute_mode is how
* much we precompute (2 = precompute distance tables, 1 = precompute
* pointers to distances, 0 = compute distances one by one). Currently
* only 2 is supported. */
template<MetricType METRIC_TYPE, bool store_pairs, class C,
int precompute_mode>
struct IVFPQScanner:
IVFPQScannerT<Index::idx_t, store_pairs, C, METRIC_TYPE>,
InvertedListScanner
{
IVFPQScanner(const IndexIVFPQ & ivfpq):
IVFPQScannerT<Index::idx_t, store_pairs, C, METRIC_TYPE>(ivfpq, nullptr)
{
}
void set_query (const float *query) override {
this->init_query (query);
}
void set_list (idx_t list_no, float coarse_dis) override {
this->init_list (list_no, coarse_dis, precompute_mode);
}
float distance_to_code (const uint8_t *code) const override {
assert(precompute_mode == 2);
float dis = this->dis0;
const float *tab = this->sim_table;
for (size_t m = 0; m < this->pq.M; m++) {
dis += tab[*code++];
tab += this->pq.ksub;
}
return dis;
}
size_t scan_codes (size_t ncode,
const uint8_t *codes,
const idx_t *ids,
float *heap_sim, idx_t *heap_ids,
size_t k) const override
{
if (this->polysemous_ht > 0) {
assert(precompute_mode == 2);
this->scan_list_polysemous
(ncode, codes, ids, k, heap_sim, heap_ids);
} else if (precompute_mode == 2) {
this->scan_list_with_table
(ncode, codes, ids, k, heap_sim, heap_ids);
} else if (precompute_mode == 1) {
this->scan_list_with_pointer
(ncode, codes, ids, k, heap_sim, heap_ids);
} else if (precompute_mode == 0) {
this->scan_on_the_fly_dist
(ncode, codes, ids, k, heap_sim, heap_ids);
} else {
FAISS_THROW_MSG("bad precomp mode");
}
return 0;
}
};
} // anonymous namespace
InvertedListScanner *
IndexIVFPQ::get_InvertedListScanner (bool store_pairs) const
{
if (metric_type == METRIC_INNER_PRODUCT) {
if (store_pairs) {
return new IVFPQScanner<
METRIC_INNER_PRODUCT, true, CMin<float, long>, 2> (*this);
} else {
return new IVFPQScanner<
METRIC_INNER_PRODUCT, false, CMin<float, long>, 2>(*this);
}
} else if (metric_type == METRIC_L2) {
if (store_pairs) {
return new IVFPQScanner<
METRIC_L2, true, CMax<float, long>, 2> (*this);
} else {
return new IVFPQScanner<
METRIC_L2, false, CMax<float, long>, 2>(*this);
}
}
return nullptr;
}
IndexIVFPQStats indexIVFPQ_stats;
void IndexIVFPQStats::reset () {
memset (this, 0, sizeof (*this));
}
IndexIVFPQ::IndexIVFPQ ()
{
// initialize some runtime values
use_precomputed_table = 0;
scan_table_threshold = 0;
do_polysemous_training = false;
polysemous_ht = 0;
polysemous_training = nullptr;
}
struct CodeCmp {
const uint8_t *tab;
size_t code_size;
bool operator () (int a, int b) const {
return cmp (a, b) > 0;
}
int cmp (int a, int b) const {
return memcmp (tab + a * code_size, tab + b * code_size,
code_size);
}
};
size_t IndexIVFPQ::find_duplicates (idx_t *dup_ids, size_t *lims) const
{
size_t ngroup = 0;
lims[0] = 0;
for (size_t list_no = 0; list_no < nlist; list_no++) {
size_t n = invlists->list_size (list_no);
std::vector<int> ord (n);
for (int i = 0; i < n; i++) ord[i] = i;
InvertedLists::ScopedCodes codes (invlists, list_no);
CodeCmp cs = { codes.get(), code_size };
std::sort (ord.begin(), ord.end(), cs);
InvertedLists::ScopedIds list_ids (invlists, list_no);
int prev = -1; // all elements from prev to i-1 are equal
for (int i = 0; i < n; i++) {
if (prev >= 0 && cs.cmp (ord [prev], ord [i]) == 0) {
// same as previous => remember
if (prev + 1 == i) { // start new group
ngroup++;
lims[ngroup] = lims[ngroup - 1];
dup_ids [lims [ngroup]++] = list_ids [ord [prev]];
}
dup_ids [lims [ngroup]++] = list_ids [ord [i]];
} else { // not same as previous.
prev = i;
}
}
}
return ngroup;
}
/*****************************************
* IndexIVFPQR implementation
******************************************/
IndexIVFPQR::IndexIVFPQR (
Index * quantizer, size_t d, size_t nlist,
size_t M, size_t nbits_per_idx,
size_t M_refine, size_t nbits_per_idx_refine):
IndexIVFPQ (quantizer, d, nlist, M, nbits_per_idx),
refine_pq (d, M_refine, nbits_per_idx_refine),
k_factor (4)
{
by_residual = true;
}
IndexIVFPQR::IndexIVFPQR ():
k_factor (1)
{
by_residual = true;
}
void IndexIVFPQR::reset()
{
IndexIVFPQ::reset();
refine_codes.clear();
}
void IndexIVFPQR::train_residual (idx_t n, const float *x)
{
float * residual_2 = new float [n * d];
ScopeDeleter <float> del(residual_2);
train_residual_o (n, x, residual_2);
if (verbose)
printf ("training %zdx%zd 2nd level PQ quantizer on %ld %dD-vectors\n",
refine_pq.M, refine_pq.ksub, n, d);
refine_pq.cp.max_points_per_centroid = 1000;
refine_pq.cp.verbose = verbose;
refine_pq.train (n, residual_2);
}
void IndexIVFPQR::add_with_ids (idx_t n, const float *x, const long *xids) {
add_core (n, x, xids, nullptr);
}
void IndexIVFPQR::add_core (idx_t n, const float *x, const long *xids,
const long *precomputed_idx) {
float * residual_2 = new float [n * d];
ScopeDeleter <float> del(residual_2);
idx_t n0 = ntotal;
add_core_o (n, x, xids, residual_2, precomputed_idx);
refine_codes.resize (ntotal * refine_pq.code_size);
refine_pq.compute_codes (
residual_2, &refine_codes[n0 * refine_pq.code_size], n);
}
void IndexIVFPQR::search_preassigned (idx_t n, const float *x, idx_t k,
const idx_t *idx,
const float *L1_dis,
float *distances, idx_t *labels,
bool store_pairs,
const IVFSearchParameters *params
) const
{
uint64_t t0;
TIC;
size_t k_coarse = long(k * k_factor);
idx_t *coarse_labels = new idx_t [k_coarse * n];
ScopeDeleter<idx_t> del1 (coarse_labels);
{ // query with quantizer levels 1 and 2.
float *coarse_distances = new float [k_coarse * n];
ScopeDeleter<float> del(coarse_distances);
IndexIVFPQ::search_preassigned (
n, x, k_coarse,
idx, L1_dis, coarse_distances, coarse_labels,
true, params);
}
indexIVFPQ_stats.search_cycles += TOC;
TIC;
// 3rd level refinement
size_t n_refine = 0;
#pragma omp parallel reduction(+ : n_refine)
{
// tmp buffers
float *residual_1 = new float [2 * d];
ScopeDeleter<float> del (residual_1);
float *residual_2 = residual_1 + d;
#pragma omp for
for (idx_t i = 0; i < n; i++) {
const float *xq = x + i * d;
const long * shortlist = coarse_labels + k_coarse * i;
float * heap_sim = distances + k * i;
long * heap_ids = labels + k * i;
maxheap_heapify (k, heap_sim, heap_ids);
for (int j = 0; j < k_coarse; j++) {
long sl = shortlist[j];
if (sl == -1) continue;
int list_no = sl >> 32;
int ofs = sl & 0xffffffff;
assert (list_no >= 0 && list_no < nlist);
assert (ofs >= 0 && ofs < invlists->list_size (list_no));
// 1st level residual
quantizer->compute_residual (xq, residual_1, list_no);
// 2nd level residual
const uint8_t * l2code =
invlists->get_single_code (list_no, ofs);
pq.decode (l2code, residual_2);
for (int l = 0; l < d; l++)
residual_2[l] = residual_1[l] - residual_2[l];
// 3rd level residual's approximation
idx_t id = invlists->get_single_id (list_no, ofs);
assert (0 <= id && id < ntotal);
refine_pq.decode (&refine_codes [id * refine_pq.code_size],
residual_1);
float dis = fvec_L2sqr (residual_1, residual_2, d);
if (dis < heap_sim[0]) {
maxheap_pop (k, heap_sim, heap_ids);
long id_or_pair = store_pairs ? sl : id;
maxheap_push (k, heap_sim, heap_ids, dis, id_or_pair);
}
n_refine ++;
}
maxheap_reorder (k, heap_sim, heap_ids);
}
}
indexIVFPQ_stats.nrefine += n_refine;
indexIVFPQ_stats.refine_cycles += TOC;
}
void IndexIVFPQR::reconstruct_from_offset (long list_no, long offset,
float* recons) const
{
IndexIVFPQ::reconstruct_from_offset (list_no, offset, recons);
idx_t id = invlists->get_single_id (list_no, offset);
assert (0 <= id && id < ntotal);
std::vector<float> r3(d);
refine_pq.decode (&refine_codes [id * refine_pq.code_size], r3.data());
for (int i = 0; i < d; ++i) {
recons[i] += r3[i];
}
}
void IndexIVFPQR::merge_from (IndexIVF &other_in, idx_t add_id)
{
IndexIVFPQR *other = dynamic_cast< IndexIVFPQR *> (&other_in);
FAISS_THROW_IF_NOT(other);
IndexIVF::merge_from (other_in, add_id);
refine_codes.insert (refine_codes.end(),
other->refine_codes.begin(),
other->refine_codes.end());
other->refine_codes.clear();
}
long IndexIVFPQR::remove_ids(const IDSelector& /*sel*/) {
FAISS_THROW_MSG("not implemented");
return 0;
}
/*************************************
* Index2Layer implementation
*************************************/
Index2Layer::Index2Layer (Index * quantizer, size_t nlist,
int M,
MetricType metric):
Index (quantizer->d, metric),
q1 (quantizer, nlist),
pq (quantizer->d, M, 8)
{
is_trained = false;
for (int nbyte = 0; nbyte < 7; nbyte++) {
if ((1L << (8 * nbyte)) >= nlist) {
code_size_1 = nbyte;
break;
}
}
code_size_2 = pq.code_size;
code_size = code_size_1 + code_size_2;
}
Index2Layer::Index2Layer ()
{
code_size = code_size_1 = code_size_2 = 0;
}
Index2Layer::~Index2Layer ()
{}
void Index2Layer::train(idx_t n, const float* x)
{
if (verbose) {
printf ("training level-1 quantizer %ld vectors in %dD\n",
n, d);
}
q1.train_q1 (n, x, verbose, metric_type);
if (verbose) {
printf("computing residuals\n");
}
const float * x_in = x;
x = fvecs_maybe_subsample (
d, (size_t*)&n, pq.cp.max_points_per_centroid * pq.ksub,
x, verbose, pq.cp.seed);
ScopeDeleter<float> del_x (x_in == x ? nullptr : x);
std::vector<idx_t> assign(n); // assignement to coarse centroids
q1.quantizer->assign (n, x, assign.data());
std::vector<float> residuals(n * d);
for (idx_t i = 0; i < n; i++) {
q1.quantizer->compute_residual (
x + i * d, residuals.data() + i * d, assign[i]);
}
if (verbose)
printf ("training %zdx%zd product quantizer on %ld vectors in %dD\n",
pq.M, pq.ksub, n, d);
pq.verbose = verbose;
pq.train (n, residuals.data());
is_trained = true;
}
void Index2Layer::add(idx_t n, const float* x)
{
idx_t bs = 32768;
if (n > bs) {
for (idx_t i0 = 0; i0 < n; i0 += bs) {
idx_t i1 = std::min(i0 + bs, n);
if (verbose) {
printf("Index2Layer::add: adding %ld:%ld / %ld\n",
i0, i1, n);
}
add (i1 - i0, x + i0 * d);
}
return;
}
std::vector<idx_t> codes1 (n);
q1.quantizer->assign (n, x, codes1.data());
std::vector<float> residuals(n * d);
for (idx_t i = 0; i < n; i++) {
q1.quantizer->compute_residual (
x + i * d, residuals.data() + i * d, codes1[i]);
}
std::vector<uint8_t> codes2 (n * code_size_2);
pq.compute_codes (residuals.data(), codes2.data(), n);
codes.resize ((ntotal + n) * code_size);
uint8_t *wp = &codes[ntotal * code_size];
{
int i = 0x11223344;
const char *ip = (char*)&i;
FAISS_THROW_IF_NOT_MSG (ip[0] == 0x44,
"works only on a little-endian CPU");
}
// copy to output table
for (idx_t i = 0; i < n; i++) {
memcpy (wp, &codes1[i], code_size_1);
wp += code_size_1;
memcpy (wp, &codes2[i * code_size_2], code_size_2);
wp += code_size_2;
}
ntotal += n;
}
void Index2Layer::search(
idx_t /*n*/,
const float* /*x*/,
idx_t /*k*/,
float* /*distances*/,
idx_t* /*labels*/) const {
FAISS_THROW_MSG("not implemented");
}
void Index2Layer::reconstruct_n(idx_t i0, idx_t ni, float* recons) const
{
float recons1[d];
FAISS_THROW_IF_NOT (i0 >= 0 && i0 + ni <= ntotal);
const uint8_t *rp = &codes[i0 * code_size];
for (idx_t i = 0; i < ni; i++) {
idx_t key = 0;
memcpy (&key, rp, code_size_1);
q1.quantizer->reconstruct (key, recons1);
rp += code_size_1;
pq.decode (rp, recons);
for (idx_t j = 0; j < d; j++) {
recons[j] += recons1[j];
}
rp += code_size_2;
recons += d;
}
}
void Index2Layer::transfer_to_IVFPQ (IndexIVFPQ & other) const
{
FAISS_THROW_IF_NOT (other.nlist == q1.nlist);
FAISS_THROW_IF_NOT (other.code_size == code_size_2);
FAISS_THROW_IF_NOT (other.ntotal == 0);
const uint8_t *rp = codes.data();
for (idx_t i = 0; i < ntotal; i++) {
idx_t key = 0;
memcpy (&key, rp, code_size_1);
rp += code_size_1;
other.invlists->add_entry (key, i, rp);
rp += code_size_2;
}
other.ntotal = ntotal;
}
void Index2Layer::reconstruct(idx_t key, float* recons) const
{
reconstruct_n (key, 1, recons);
}
void Index2Layer::reset()
{
ntotal = 0;
codes.clear ();
}
} // namespace faiss
faiss::IndexIVF
Definition: IndexIVF.h:91
faiss::IndexIVFPQ::precompute_table
void precompute_table()
build precomputed table
Definition: IndexIVFPQ.cpp:364
faiss::IndexIVFPQR::merge_from
void merge_from(IndexIVF &other, idx_t add_id) override
Definition: IndexIVFPQ.cpp:1301
faiss::Index2Layer::transfer_to_IVFPQ
void transfer_to_IVFPQ(IndexIVFPQ &other) const
transfer the flat codes to an IVFPQ index
Definition: IndexIVFPQ.cpp:1466
faiss::Index2Layer::code_size_2
size_t code_size_2
size of the code for the second level
Definition: IndexIVFPQ.h:219
faiss::IndexIVFPQR::reconstruct_from_offset
void reconstruct_from_offset(long list_no, long offset, float *recons) const override
Definition: IndexIVFPQ.cpp:1286
faiss::IndexIVF::search_preassigned
virtual void search_preassigned(idx_t n, const float *x, idx_t k, const idx_t *assign, const float *centroid_dis, float *distances, idx_t *labels, bool store_pairs, const IVFSearchParameters *params=nullptr) const
Definition: IndexIVF.cpp:189
faiss::IndexIVFPQR
Definition: IndexIVFPQ.h:156
faiss::ProductQuantizer::decode
void decode(const uint8_t *code, float *x) const
decode a vector from a given code (or n vectors if third argument)
Definition: ProductQuantizer.cpp:335
faiss::IndexIVFPQR::refine_pq
ProductQuantizer refine_pq
3rd level quantizer
Definition: IndexIVFPQ.h:157
faiss::fvec_L2sqr
float fvec_L2sqr(const float *x, const float *y, size_t d)
Squared L2 distance between two vectors.
Definition: utils_simd.cpp:502
faiss::IndexIVFPQ::polysemous_training
PolysemousTraining * polysemous_training
if NULL, use default
Definition: IndexIVFPQ.h:41
faiss::CMax
Definition: Heap.h:48
faiss::Index2Layer::reconstruct_n
void reconstruct_n(idx_t i0, idx_t ni, float *recons) const override
Definition: IndexIVFPQ.cpp:1446
faiss::Index2Layer::code_size
size_t code_size
code_size_1 + code_size_2
Definition: IndexIVFPQ.h:222
faiss::IndexIVFPQStats
Definition: IndexIVFPQ.h:135
faiss::fvecs_maybe_subsample
const float * fvecs_maybe_subsample(size_t d, size_t *n, size_t nmax, const float *x, bool verbose, long seed)
Definition: utils.cpp:1528
faiss::Index2Layer::reset
void reset() override
removes all elements from the database.
Definition: IndexIVFPQ.cpp:1493
faiss::Index::assign
void assign(idx_t n, const float *x, idx_t *labels, idx_t k=1)
Definition: Index.cpp:35
faiss::IDSelector
Definition: AuxIndexStructures.h:54
faiss::InvertedLists::list_size
virtual size_t list_size(size_t list_no) const =0
get the size of a list
faiss::IndexIVFPQ::decode_multiple
void decode_multiple(size_t n, const long *keys, const uint8_t *xcodes, float *x) const
inverse of encode_multiple
Definition: IndexIVFPQ.cpp:160
faiss::IndexIVFPQ::train_residual_o
void train_residual_o(idx_t n, const float *x, float *residuals_2)
same as train_residual, also output 2nd level residuals
Definition: IndexIVFPQ.cpp:67
faiss::IndexIVFPQ::do_polysemous_training
bool do_polysemous_training
reorder PQ centroids after training?
Definition: IndexIVFPQ.h:40
faiss::IndexIVFPQ::scan_table_threshold
size_t scan_table_threshold
use table computation or on-the-fly?
Definition: IndexIVFPQ.h:44
faiss::IndexIVFPQR::train_residual
void train_residual(idx_t n, const float *x) override
trains the two product quantizers
Definition: IndexIVFPQ.cpp:1155
faiss::IndexIVFPQR::add_core
void add_core(idx_t n, const float *x, const long *xids, const long *precomputed_idx=nullptr)
same as add_with_ids, but optionally use the precomputed list ids
Definition: IndexIVFPQ.cpp:1179
faiss::ScopeDeleter
Definition: FaissException.h:39
faiss::ProductQuantizer::dsub
size_t dsub
dimensionality of each subvector
Definition: ProductQuantizer.h:31
faiss::ClusteringParameters::seed
int seed
seed for the random number generator
Definition: Clustering.h:35
faiss::IndexIVFPQ::precomputed_table
std::vector< float > precomputed_table
Definition: IndexIVFPQ.h:60
faiss::Index2Layer::q1
Level1Quantizer q1
first level quantizer
Definition: IndexIVFPQ.h:207
faiss::fvec_madd
void fvec_madd(size_t n, const float *a, float bf, const float *b, float *c)
Definition: utils_simd.cpp:589
faiss::IndexIVFPQ::encode_vectors
void encode_vectors(idx_t n, const float *x, const idx_t *list_nos, uint8_t *codes) const override
Definition: IndexIVFPQ.cpp:207
faiss::IndexIVFPQ::polysemous_ht
int polysemous_ht
Hamming thresh for polysemous filtering.
Definition: IndexIVFPQ.h:45
faiss::IndexIVFPQ::get_InvertedListScanner
InvertedListScanner * get_InvertedListScanner(bool store_pairs) const override
get a scanner for this index (store_pairs means ignore labels)
Definition: IndexIVFPQ.cpp:1032
faiss::IndexIVFPQR::reset
void reset() override
removes all elements from the database.
Definition: IndexIVFPQ.cpp:1146
faiss::IndexIVFPQ::add_with_ids
void add_with_ids(idx_t n, const float *x, const long *xids=nullptr) override
Definition: IndexIVFPQ.cpp:183
faiss::IVFSearchParameters
Definition: IndexIVF.h:61
faiss::ProductQuantizer::compute_codes
void compute_codes(const float *x, uint8_t *codes, size_t n) const
same as compute_code for several vectors
Definition: ProductQuantizer.cpp:382
faiss::InvertedLists::get_single_id
virtual idx_t get_single_id(size_t list_no, size_t offset) const
Definition: InvertedLists.cpp:36
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::CodeCmp
Definition: IndexIVFPQ.cpp:1076
faiss::InvertedLists::get_single_code
virtual const uint8_t * get_single_code(size_t list_no, size_t offset) const
Definition: InvertedLists.cpp:53
faiss::IndexIVFPQR::refine_codes
std::vector< uint8_t > refine_codes
corresponding codes
Definition: IndexIVFPQ.h:158
faiss::ProductQuantizer::code_size
size_t code_size
byte per indexed vector
Definition: ProductQuantizer.h:33
faiss::IndexIVFPQ::train_residual
void train_residual(idx_t n, const float *x) override
trains the product quantizer
Definition: IndexIVFPQ.cpp:61
faiss::IndexIVFPQ::precomputed_table_max_bytes
static size_t precomputed_table_max_bytes
2G by default, accommodates tables up to PQ32 w/ 65536 centroids
Definition: IndexIVFPQ.h:56
faiss::IndexIVFPQ::reconstruct_from_offset
void reconstruct_from_offset(long list_no, long offset, float *recons) const override
Definition: IndexIVFPQ.cpp:311
faiss::IndexIVFPQ
Definition: IndexIVFPQ.h:35
faiss::IndexIVFPQ::encode_multiple
void encode_multiple(size_t n, long *keys, const float *x, uint8_t *codes, bool compute_keys=false) const
Definition: IndexIVFPQ.cpp:150
faiss::InvertedLists::add_entry
virtual size_t add_entry(size_t list_no, idx_t theid, const uint8_t *code)
add one entry to an inverted list
Definition: InvertedLists.cpp:60
faiss::ProductQuantizer::ksub
size_t ksub
number of centroids for each subquantizer
Definition: ProductQuantizer.h:34
faiss::Index::idx_t
long idx_t
all indices are this type
Definition: Index.h:64
faiss::ProductQuantizer::compute_code
void compute_code(const float *x, uint8_t *code) const
Quantize one vector with the product quantizer.
Definition: ProductQuantizer.cpp:308
faiss::Index::ntotal
idx_t ntotal
total nb of indexed vectors
Definition: Index.h:67
faiss::Index2Layer::train
void train(idx_t n, const float *x) override
Definition: IndexIVFPQ.cpp:1350
faiss::Index::verbose
bool verbose
verbosity level
Definition: Index.h:68
faiss::IndexIVF::reset
void reset() override
removes all elements from the database.
Definition: IndexIVF.cpp:360
faiss::getmillisecs
double getmillisecs()
ms elapsed since some arbitrary epoch
Definition: utils.cpp:70
faiss::PolysemousTraining
optimizes the order of indices in a ProductQuantizer
Definition: PolysemousTraining.h:122
faiss::Index2Layer::add
void add(idx_t n, const float *x) override
Definition: IndexIVFPQ.cpp:1388
faiss::MultiIndexQuantizer
Definition: IndexPQ.h:136
faiss::fvec_norm_L2sqr
float fvec_norm_L2sqr(const float *x, size_t d)
Definition: utils_simd.cpp:516
faiss::Level1Quantizer::train_q1
void train_q1(size_t n, const float *x, bool verbose, MetricType metric_type)
Trains the quantizer and calls train_residual to train sub-quantizers.
Definition: IndexIVF.cpp:57
faiss::ProductQuantizer::cp
ClusteringParameters cp
parameters used during clustering
Definition: ProductQuantizer.h:48
faiss::Index2Layer::search
void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const override
not implemented
Definition: IndexIVFPQ.cpp:1436
faiss::IndexIVFPQ::by_residual
bool by_residual
Encode residual or plain vector?
Definition: IndexIVFPQ.h:36
faiss::Index::metric_type
MetricType metric_type
type of metric this index uses for search
Definition: Index.h:74
faiss::IndexIVFPQ::pq
ProductQuantizer pq
produces the codes
Definition: IndexIVFPQ.h:38
faiss::IndexIVF::invlists
InvertedLists * invlists
Acess to the actual data.
Definition: IndexIVF.h:93
faiss::ProductQuantizer::M
size_t M
number of subquantizers
Definition: ProductQuantizer.h:27
faiss::Index2Layer::code_size_1
size_t code_size_1
size of the code for the first level (ceil(log8(q1.nlist)))
Definition: IndexIVFPQ.h:216
faiss::Index
Definition: Index.h:62
faiss::IndexIVFPQ::add_core_o
void add_core_o(idx_t n, const float *x, const long *xids, float *residuals_2, const long *precomputed_idx=nullptr)
Definition: IndexIVFPQ.cpp:221
faiss::fvec_madd_and_argmin
int fvec_madd_and_argmin(size_t n, const float *a, float bf, const float *b, float *c)
Definition: utils_simd.cpp:676
faiss::IndexIVFPQR::search_preassigned
void search_preassigned(idx_t n, const float *x, idx_t k, const idx_t *assign, const float *centroid_dis, float *distances, idx_t *labels, bool store_pairs, const IVFSearchParameters *params=nullptr) const override
Definition: IndexIVFPQ.cpp:1198
faiss::IndexIVFPQR::remove_ids
long remove_ids(const IDSelector &sel) override
Dataset manipulation functions.
Definition: IndexIVFPQ.cpp:1314
faiss::Level1Quantizer::quantizer
Index * quantizer
quantizer that maps vectors to inverted lists
Definition: IndexIVF.h:33
faiss::ProductQuantizer
Definition: ProductQuantizer.h:24
faiss::Index::is_trained
bool is_trained
set if the Index does not require training, or if training is done already
Definition: Index.h:71
faiss::Index::compute_residual
void compute_residual(const float *x, float *residual, idx_t key) const
Definition: Index.cpp:87
faiss::InvertedLists::ScopedIds
Definition: InvertedLists.h:125
faiss::ProductQuantizer::get_centroids
float * get_centroids(size_t m, size_t i)
return the centroids associated with subvector m
Definition: ProductQuantizer.h:58
faiss::Index::reconstruct
virtual void reconstruct(idx_t key, float *recons) const
Definition: Index.cpp:55
faiss::Index2Layer::pq
ProductQuantizer pq
second level quantizer is always a PQ
Definition: IndexIVFPQ.h:210
faiss::IndexIVF::maintain_direct_map
bool maintain_direct_map
map for direct access to the elements. Enables reconstruct().
Definition: IndexIVF.h:102
faiss::PolysemousTraining::optimize_pq_for_hamming
void optimize_pq_for_hamming(ProductQuantizer &pq, size_t n, const float *x) const
Definition: PolysemousTraining.cpp:935
faiss::ClusteringParameters::max_points_per_centroid
int max_points_per_centroid
to limit size of dataset
Definition: Clustering.h:33
faiss::ProductQuantizer::verbose
bool verbose
verbose during training?
Definition: ProductQuantizer.h:35
faiss::IndexIVFPQR::add_with_ids
void add_with_ids(idx_t n, const float *x, const long *xids) override
Definition: IndexIVFPQ.cpp:1175
faiss::Index2Layer::reconstruct
void reconstruct(idx_t key, float *recons) const override
Definition: IndexIVFPQ.cpp:1488
faiss::IndexIVF::merge_from
virtual void merge_from(IndexIVF &other, idx_t add_id)
Definition: IndexIVF.cpp:472
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::IndexIVFPQ::find_duplicates
size_t find_duplicates(idx_t *ids, size_t *lims) const
Definition: IndexIVFPQ.cpp:1089
faiss::MetricType
MetricType
Some algorithms support both an inner product version and a L2 search version.
Definition: Index.h:45
faiss::IndexIVFPQR::k_factor
float k_factor
factor between k requested in search and the k requested from the IVFPQ
Definition: IndexIVFPQ.h:161
faiss::CMin
Definition: Heap.h:53
faiss::IndexIVFPQ::use_precomputed_table
int use_precomputed_table
if by_residual, build precompute tables
Definition: IndexIVFPQ.h:55