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/tmp/faiss/IndexHNSW.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 "IndexHNSW.h"
#include <cstdlib>
#include <cassert>
#include <cstring>
#include <cstdio>
#include <cmath>
#include <omp.h>
#include <unordered_set>
#include <queue>
#include <sys/types.h>
#include <sys/stat.h>
#include <unistd.h>
#include <stdint.h>
#ifdef __SSE__
#include <immintrin.h>
#endif
#include "utils.h"
#include "Heap.h"
#include "FaissAssert.h"
#include "IndexFlat.h"
#include "IndexIVFPQ.h"
extern "C" {
/* declare BLAS functions, see http://www.netlib.org/clapack/cblas/ */
int sgemm_ (const char *transa, const char *transb, FINTEGER *m, FINTEGER *
n, FINTEGER *k, const float *alpha, const float *a,
FINTEGER *lda, const float *b, FINTEGER *
ldb, float *beta, float *c, FINTEGER *ldc);
}
namespace faiss {
using idx_t = Index::idx_t;
using MinimaxHeap = HNSW::MinimaxHeap;
using storage_idx_t = HNSW::storage_idx_t;
using NodeDistCloser = HNSW::NodeDistCloser;
using NodeDistFarther = HNSW::NodeDistFarther;
using DistanceComputer = HNSW::DistanceComputer;
HNSWStats hnsw_stats;
/**************************************************************
* add / search blocks of descriptors
**************************************************************/
namespace {
void hnsw_add_vertices(IndexHNSW &index_hnsw,
size_t n0,
size_t n, const float *x,
bool verbose,
bool preset_levels = false) {
HNSW & hnsw = index_hnsw.hnsw;
size_t ntotal = n0 + n;
double t0 = getmillisecs();
if (verbose) {
printf("hnsw_add_vertices: adding %ld elements on top of %ld "
"(preset_levels=%d)\n",
n, n0, int(preset_levels));
}
int max_level = hnsw.prepare_level_tab(n, preset_levels);
if (verbose) {
printf(" max_level = %d\n", max_level);
}
std::vector<omp_lock_t> locks(ntotal);
for(int i = 0; i < ntotal; i++)
omp_init_lock(&locks[i]);
// add vectors from highest to lowest level
std::vector<int> hist;
std::vector<int> order(n);
{ // make buckets with vectors of the same level
// build histogram
for (int i = 0; i < n; i++) {
storage_idx_t pt_id = i + n0;
int pt_level = hnsw.levels[pt_id] - 1;
while (pt_level >= hist.size())
hist.push_back(0);
hist[pt_level] ++;
}
// accumulate
std::vector<int> offsets(hist.size() + 1, 0);
for (int i = 0; i < hist.size() - 1; i++) {
offsets[i + 1] = offsets[i] + hist[i];
}
// bucket sort
for (int i = 0; i < n; i++) {
storage_idx_t pt_id = i + n0;
int pt_level = hnsw.levels[pt_id] - 1;
order[offsets[pt_level]++] = pt_id;
}
}
{ // perform add
RandomGenerator rng2(789);
int i1 = n;
for (int pt_level = hist.size() - 1; pt_level >= 0; pt_level--) {
int i0 = i1 - hist[pt_level];
if (verbose) {
printf("Adding %d elements at level %d\n",
i1 - i0, pt_level);
}
// random permutation to get rid of dataset order bias
for (int j = i0; j < i1; j++)
std::swap(order[j], order[j + rng2.rand_int(i1 - j)]);
#pragma omp parallel
{
VisitedTable vt (ntotal);
DistanceComputer *dis = index_hnsw.get_distance_computer();
ScopeDeleter1<DistanceComputer> del(dis);
int prev_display = verbose && omp_get_thread_num() == 0 ? 0 : -1;
#pragma omp for schedule(dynamic)
for (int i = i0; i < i1; i++) {
storage_idx_t pt_id = order[i];
dis->set_query (x + (pt_id - n0) * dis->d);
hnsw.add_with_locks(*dis, pt_level, pt_id, locks, vt);
if (prev_display >= 0 && i - i0 > prev_display + 10000) {
prev_display = i - i0;
printf(" %d / %d\r", i - i0, i1 - i0);
fflush(stdout);
}
}
}
i1 = i0;
}
FAISS_ASSERT(i1 == 0);
}
if (verbose) {
printf("Done in %.3f ms\n", getmillisecs() - t0);
}
for(int i = 0; i < ntotal; i++) {
omp_destroy_lock(&locks[i]);
}
}
} // namespace
/**************************************************************
* IndexHNSW implementation
**************************************************************/
IndexHNSW::IndexHNSW(int d, int M):
Index(d, METRIC_L2),
hnsw(M),
own_fields(false),
storage(nullptr),
reconstruct_from_neighbors(nullptr)
{}
IndexHNSW::IndexHNSW(Index *storage, int M):
Index(storage->d, METRIC_L2),
hnsw(M),
own_fields(false),
storage(storage),
reconstruct_from_neighbors(nullptr)
{}
IndexHNSW::~IndexHNSW() {
if (own_fields) {
delete storage;
}
}
void IndexHNSW::train(idx_t n, const float* x)
{
// hnsw structure does not require training
storage->train (n, x);
is_trained = true;
}
void IndexHNSW::search (idx_t n, const float *x, idx_t k,
float *distances, idx_t *labels) const
{
#pragma omp parallel
{
VisitedTable vt (ntotal);
DistanceComputer *dis = get_distance_computer();
ScopeDeleter1<DistanceComputer> del(dis);
size_t nreorder = 0;
#pragma omp for
for(idx_t i = 0; i < n; i++) {
idx_t * idxi = labels + i * k;
float * simi = distances + i * k;
dis->set_query(x + i * d);
maxheap_heapify (k, simi, idxi);
hnsw.search(*dis, k, idxi, simi, vt);
maxheap_reorder (k, simi, idxi);
if (reconstruct_from_neighbors &&
reconstruct_from_neighbors->k_reorder != 0) {
int k_reorder = reconstruct_from_neighbors->k_reorder;
if (k_reorder == -1 || k_reorder > k) k_reorder = k;
nreorder += reconstruct_from_neighbors->compute_distances(
k_reorder, idxi, x + i * d, simi);
// sort top k_reorder
maxheap_heapify (k_reorder, simi, idxi, simi, idxi, k_reorder);
maxheap_reorder (k_reorder, simi, idxi);
}
}
#pragma omp critical
{
hnsw_stats.nreorder += nreorder;
}
}
}
void IndexHNSW::add(idx_t n, const float *x)
{
FAISS_THROW_IF_NOT(is_trained);
int n0 = ntotal;
storage->add(n, x);
ntotal = storage->ntotal;
hnsw_add_vertices (*this, n0, n, x, verbose,
hnsw.levels.size() == ntotal);
}
void IndexHNSW::reset()
{
hnsw.reset();
storage->reset();
ntotal = 0;
}
void IndexHNSW::reconstruct (idx_t key, float* recons) const
{
storage->reconstruct(key, recons);
}
void IndexHNSW::shrink_level_0_neighbors(int new_size)
{
#pragma omp parallel
{
DistanceComputer *dis = get_distance_computer();
ScopeDeleter1<DistanceComputer> del(dis);
#pragma omp for
for (idx_t i = 0; i < ntotal; i++) {
size_t begin, end;
hnsw.neighbor_range(i, 0, &begin, &end);
std::priority_queue<NodeDistFarther> initial_list;
for (size_t j = begin; j < end; j++) {
int v1 = hnsw.neighbors[j];
if (v1 < 0) break;
initial_list.emplace(dis->symmetric_dis(i, v1), v1);
// initial_list.emplace(qdis(v1), v1);
}
std::vector<NodeDistFarther> shrunk_list;
HNSW::shrink_neighbor_list(*dis, initial_list,
shrunk_list, new_size);
for (size_t j = begin; j < end; j++) {
if (j - begin < shrunk_list.size())
hnsw.neighbors[j] = shrunk_list[j - begin].id;
else
hnsw.neighbors[j] = -1;
}
}
}
}
void IndexHNSW::search_level_0(
idx_t n, const float *x, idx_t k,
const storage_idx_t *nearest, const float *nearest_d,
float *distances, idx_t *labels, int nprobe,
int search_type) const
{
storage_idx_t ntotal = hnsw.levels.size();
#pragma omp parallel
{
DistanceComputer *qdis = get_distance_computer();
ScopeDeleter1<DistanceComputer> del(qdis);
VisitedTable vt (ntotal);
#pragma omp for
for(idx_t i = 0; i < n; i++) {
idx_t * idxi = labels + i * k;
float * simi = distances + i * k;
qdis->set_query(x + i * d);
maxheap_heapify (k, simi, idxi);
if (search_type == 1) {
int nres = 0;
for(int j = 0; j < nprobe; j++) {
storage_idx_t cj = nearest[i * nprobe + j];
if (cj < 0) break;
if (vt.get(cj)) continue;
int candidates_size = std::max(hnsw.efSearch, int(k));
MinimaxHeap candidates(candidates_size);
candidates.push(cj, nearest_d[i * nprobe + j]);
nres = hnsw.search_from_candidates(
*qdis, k, idxi, simi,
candidates, vt, 0, nres
);
}
} else if (search_type == 2) {
int candidates_size = std::max(hnsw.efSearch, int(k));
candidates_size = std::max(candidates_size, nprobe);
MinimaxHeap candidates(candidates_size);
for(int j = 0; j < nprobe; j++) {
storage_idx_t cj = nearest[i * nprobe + j];
if (cj < 0) break;
candidates.push(cj, nearest_d[i * nprobe + j]);
}
hnsw.search_from_candidates(
*qdis, k, idxi, simi,
candidates, vt, 0
);
}
vt.advance();
maxheap_reorder (k, simi, idxi);
}
}
}
void IndexHNSW::init_level_0_from_knngraph(
int k, const float *D, const idx_t *I)
{
int dest_size = hnsw.nb_neighbors (0);
#pragma omp parallel for
for (idx_t i = 0; i < ntotal; i++) {
DistanceComputer *qdis = get_distance_computer();
float vec[d];
storage->reconstruct(i, vec);
qdis->set_query(vec);
std::priority_queue<NodeDistFarther> initial_list;
for (size_t j = 0; j < k; j++) {
int v1 = I[i * k + j];
if (v1 == i) continue;
if (v1 < 0) break;
initial_list.emplace(D[i * k + j], v1);
}
std::vector<NodeDistFarther> shrunk_list;
HNSW::shrink_neighbor_list(*qdis, initial_list, shrunk_list, dest_size);
size_t begin, end;
hnsw.neighbor_range(i, 0, &begin, &end);
for (size_t j = begin; j < end; j++) {
if (j - begin < shrunk_list.size())
hnsw.neighbors[j] = shrunk_list[j - begin].id;
else
hnsw.neighbors[j] = -1;
}
}
}
void IndexHNSW::init_level_0_from_entry_points(
int n, const storage_idx_t *points,
const storage_idx_t *nearests)
{
std::vector<omp_lock_t> locks(ntotal);
for(int i = 0; i < ntotal; i++)
omp_init_lock(&locks[i]);
#pragma omp parallel
{
VisitedTable vt (ntotal);
DistanceComputer *dis = get_distance_computer();
ScopeDeleter1<DistanceComputer> del(dis);
float vec[storage->d];
#pragma omp for schedule(dynamic)
for (int i = 0; i < n; i++) {
storage_idx_t pt_id = points[i];
storage_idx_t nearest = nearests[i];
storage->reconstruct (pt_id, vec);
dis->set_query (vec);
hnsw.add_links_starting_from(*dis, pt_id,
nearest, (*dis)(nearest),
0, locks.data(), vt);
if (verbose && i % 10000 == 0) {
printf(" %d / %d\r", i, n);
fflush(stdout);
}
}
}
if (verbose) {
printf("\n");
}
for(int i = 0; i < ntotal; i++)
omp_destroy_lock(&locks[i]);
}
void IndexHNSW::reorder_links()
{
int M = hnsw.nb_neighbors(0);
#pragma omp parallel
{
std::vector<float> distances (M);
std::vector<size_t> order (M);
std::vector<storage_idx_t> tmp (M);
DistanceComputer *dis = get_distance_computer();
ScopeDeleter1<DistanceComputer> del(dis);
#pragma omp for
for(storage_idx_t i = 0; i < ntotal; i++) {
size_t begin, end;
hnsw.neighbor_range(i, 0, &begin, &end);
for (size_t j = begin; j < end; j++) {
storage_idx_t nj = hnsw.neighbors[j];
if (nj < 0) {
end = j;
break;
}
distances[j - begin] = dis->symmetric_dis(i, nj);
tmp [j - begin] = nj;
}
fvec_argsort (end - begin, distances.data(), order.data());
for (size_t j = begin; j < end; j++) {
hnsw.neighbors[j] = tmp[order[j - begin]];
}
}
}
}
void IndexHNSW::link_singletons()
{
printf("search for singletons\n");
std::vector<bool> seen(ntotal);
for (size_t i = 0; i < ntotal; i++) {
size_t begin, end;
hnsw.neighbor_range(i, 0, &begin, &end);
for (size_t j = begin; j < end; j++) {
storage_idx_t ni = hnsw.neighbors[j];
if (ni >= 0) seen[ni] = true;
}
}
int n_sing = 0, n_sing_l1 = 0;
std::vector<storage_idx_t> singletons;
for (storage_idx_t i = 0; i < ntotal; i++) {
if (!seen[i]) {
singletons.push_back(i);
n_sing++;
if (hnsw.levels[i] > 1)
n_sing_l1++;
}
}
printf(" Found %d / %ld singletons (%d appear in a level above)\n",
n_sing, ntotal, n_sing_l1);
std::vector<float>recons(singletons.size() * d);
for (int i = 0; i < singletons.size(); i++) {
FAISS_ASSERT(!"not implemented");
}
}
namespace {
// storage that explicitly reconstructs vectors before computing distances
struct GenericDistanceComputer: DistanceComputer {
const Index & storage;
std::vector<float> buf;
const float *q;
GenericDistanceComputer(const Index & storage): storage(storage)
{
d = storage.d;
buf.resize(d * 2);
}
float operator () (storage_idx_t i) override
{
storage.reconstruct(i, buf.data());
return fvec_L2sqr(q, buf.data(), d);
}
float symmetric_dis(storage_idx_t i, storage_idx_t j) override
{
storage.reconstruct(i, buf.data());
storage.reconstruct(j, buf.data() + d);
return fvec_L2sqr(buf.data() + d, buf.data(), d);
}
void set_query(const float *x) override {
q = x;
}
};
} // namespace
DistanceComputer * IndexHNSW::get_distance_computer () const
{
return new GenericDistanceComputer (*storage);
}
/**************************************************************
* ReconstructFromNeighbors implementation
**************************************************************/
ReconstructFromNeighbors::ReconstructFromNeighbors(
const IndexHNSW & index, size_t k, size_t nsq):
index(index), k(k), nsq(nsq) {
M = index.hnsw.nb_neighbors(0);
FAISS_ASSERT(k <= 256);
code_size = k == 1 ? 0 : nsq;
ntotal = 0;
d = index.d;
FAISS_ASSERT(d % nsq == 0);
dsub = d / nsq;
k_reorder = -1;
}
void ReconstructFromNeighbors::reconstruct(storage_idx_t i, float *x, float *tmp) const
{
const HNSW & hnsw = index.hnsw;
size_t begin, end;
hnsw.neighbor_range(i, 0, &begin, &end);
if (k == 1 || nsq == 1) {
const float * beta;
if (k == 1) {
beta = codebook.data();
} else {
int idx = codes[i];
beta = codebook.data() + idx * (M + 1);
}
float w0 = beta[0]; // weight of image itself
index.storage->reconstruct(i, tmp);
for (int l = 0; l < d; l++)
x[l] = w0 * tmp[l];
for (size_t j = begin; j < end; j++) {
storage_idx_t ji = hnsw.neighbors[j];
if (ji < 0) ji = i;
float w = beta[j - begin + 1];
index.storage->reconstruct(ji, tmp);
for (int l = 0; l < d; l++)
x[l] += w * tmp[l];
}
} else if (nsq == 2) {
int idx0 = codes[2 * i];
int idx1 = codes[2 * i + 1];
const float *beta0 = codebook.data() + idx0 * (M + 1);
const float *beta1 = codebook.data() + (idx1 + k) * (M + 1);
index.storage->reconstruct(i, tmp);
float w0;
w0 = beta0[0];
for (int l = 0; l < dsub; l++)
x[l] = w0 * tmp[l];
w0 = beta1[0];
for (int l = dsub; l < d; l++)
x[l] = w0 * tmp[l];
for (size_t j = begin; j < end; j++) {
storage_idx_t ji = hnsw.neighbors[j];
if (ji < 0) ji = i;
index.storage->reconstruct(ji, tmp);
float w;
w = beta0[j - begin + 1];
for (int l = 0; l < dsub; l++)
x[l] += w * tmp[l];
w = beta1[j - begin + 1];
for (int l = dsub; l < d; l++)
x[l] += w * tmp[l];
}
} else {
const float *betas[nsq];
{
const float *b = codebook.data();
const uint8_t *c = &codes[i * code_size];
for (int sq = 0; sq < nsq; sq++) {
betas[sq] = b + (*c++) * (M + 1);
b += (M + 1) * k;
}
}
index.storage->reconstruct(i, tmp);
{
int d0 = 0;
for (int sq = 0; sq < nsq; sq++) {
float w = *(betas[sq]++);
int d1 = d0 + dsub;
for (int l = d0; l < d1; l++) {
x[l] = w * tmp[l];
}
d0 = d1;
}
}
for (size_t j = begin; j < end; j++) {
storage_idx_t ji = hnsw.neighbors[j];
if (ji < 0) ji = i;
index.storage->reconstruct(ji, tmp);
int d0 = 0;
for (int sq = 0; sq < nsq; sq++) {
float w = *(betas[sq]++);
int d1 = d0 + dsub;
for (int l = d0; l < d1; l++) {
x[l] += w * tmp[l];
}
d0 = d1;
}
}
}
}
void ReconstructFromNeighbors::reconstruct_n(storage_idx_t n0,
storage_idx_t ni,
float *x) const
{
#pragma omp parallel
{
std::vector<float> tmp(index.d);
#pragma omp for
for (storage_idx_t i = 0; i < ni; i++) {
reconstruct(n0 + i, x + i * index.d, tmp.data());
}
}
}
size_t ReconstructFromNeighbors::compute_distances(
size_t n, const idx_t *shortlist,
const float *query, float *distances) const
{
std::vector<float> tmp(2 * index.d);
size_t ncomp = 0;
for (int i = 0; i < n; i++) {
if (shortlist[i] < 0) break;
reconstruct(shortlist[i], tmp.data(), tmp.data() + index.d);
distances[i] = fvec_L2sqr(query, tmp.data(), index.d);
ncomp++;
}
return ncomp;
}
void ReconstructFromNeighbors::get_neighbor_table(storage_idx_t i, float *tmp1) const
{
const HNSW & hnsw = index.hnsw;
size_t begin, end;
hnsw.neighbor_range(i, 0, &begin, &end);
size_t d = index.d;
index.storage->reconstruct(i, tmp1);
for (size_t j = begin; j < end; j++) {
storage_idx_t ji = hnsw.neighbors[j];
if (ji < 0) ji = i;
index.storage->reconstruct(ji, tmp1 + (j - begin + 1) * d);
}
}
/// called by add_codes
void ReconstructFromNeighbors::estimate_code(
const float *x, storage_idx_t i, uint8_t *code) const
{
// fill in tmp table with the neighbor values
float *tmp1 = new float[d * (M + 1) + (d * k)];
float *tmp2 = tmp1 + d * (M + 1);
ScopeDeleter<float> del(tmp1);
// collect coordinates of base
get_neighbor_table (i, tmp1);
for (size_t sq = 0; sq < nsq; sq++) {
int d0 = sq * dsub;
{
FINTEGER ki = k, di = d, m1 = M + 1;
FINTEGER dsubi = dsub;
float zero = 0, one = 1;
sgemm_ ("N", "N", &dsubi, &ki, &m1, &one,
tmp1 + d0, &di,
codebook.data() + sq * (m1 * k), &m1,
&zero, tmp2, &dsubi);
}
float min = HUGE_VAL;
int argmin = -1;
for (size_t j = 0; j < k; j++) {
float dis = fvec_L2sqr(x + d0, tmp2 + j * dsub, dsub);
if (dis < min) {
min = dis;
argmin = j;
}
}
code[sq] = argmin;
}
}
void ReconstructFromNeighbors::add_codes(size_t n, const float *x)
{
if (k == 1) { // nothing to encode
ntotal += n;
return;
}
codes.resize(codes.size() + code_size * n);
#pragma omp parallel for
for (int i = 0; i < n; i++) {
estimate_code(x + i * index.d, ntotal + i,
codes.data() + (ntotal + i) * code_size);
}
ntotal += n;
FAISS_ASSERT (codes.size() == ntotal * code_size);
}
/**************************************************************
* IndexHNSWFlat implementation
**************************************************************/
namespace {
struct FlatL2Dis: DistanceComputer {
Index::idx_t nb;
const float *q;
const float *b;
size_t ndis;
float operator () (storage_idx_t i) override
{
ndis++;
return (fvec_L2sqr(q, b + i * d, d));
}
float symmetric_dis(storage_idx_t i, storage_idx_t j) override
{
return (fvec_L2sqr(b + j * d, b + i * d, d));
}
FlatL2Dis(const IndexFlatL2 & storage, const float *q = nullptr):
q(q)
{
nb = storage.ntotal;
d = storage.d;
b = storage.xb.data();
ndis = 0;
}
void set_query(const float *x) override {
q = x;
}
virtual ~FlatL2Dis () {
#pragma omp critical
{
hnsw_stats.ndis += ndis;
}
}
};
} // namespace
IndexHNSWFlat::IndexHNSWFlat()
{
is_trained = true;
}
IndexHNSWFlat::IndexHNSWFlat(int d, int M):
IndexHNSW(new IndexFlatL2(d), M)
{
own_fields = true;
is_trained = true;
}
DistanceComputer * IndexHNSWFlat::get_distance_computer () const
{
return new FlatL2Dis (*dynamic_cast<IndexFlatL2*> (storage));
}
/**************************************************************
* IndexHNSWPQ implementation
**************************************************************/
namespace {
struct PQDis: DistanceComputer {
Index::idx_t nb;
const uint8_t *codes;
size_t code_size;
const ProductQuantizer & pq;
const float *sdc;
std::vector<float> precomputed_table;
size_t ndis;
float operator () (storage_idx_t i) override
{
const uint8_t *code = codes + i * code_size;
const float *dt = precomputed_table.data();
float accu = 0;
for (int j = 0; j < pq.M; j++) {
accu += dt[*code++];
dt += 256;
}
ndis++;
return accu;
}
float symmetric_dis(storage_idx_t i, storage_idx_t j) override
{
const float * sdci = sdc;
float accu = 0;
const uint8_t *codei = codes + i * code_size;
const uint8_t *codej = codes + j * code_size;
for (int l = 0; l < pq.M; l++) {
accu += sdci[(*codei++) + (*codej++) * 256];
sdci += 256 * 256;
}
return accu;
}
PQDis(const IndexPQ& storage, const float* /*q*/ = nullptr)
: pq(storage.pq) {
precomputed_table.resize(pq.M * pq.ksub);
nb = storage.ntotal;
d = storage.d;
codes = storage.codes.data();
code_size = pq.code_size;
FAISS_ASSERT(pq.ksub == 256);
FAISS_ASSERT(pq.sdc_table.size() == pq.ksub * pq.ksub * pq.M);
sdc = pq.sdc_table.data();
ndis = 0;
}
void set_query(const float *x) override {
pq.compute_distance_table(x, precomputed_table.data());
}
virtual ~PQDis () {
#pragma omp critical
{
hnsw_stats.ndis += ndis;
}
}
};
} // namespace
IndexHNSWPQ::IndexHNSWPQ() {}
IndexHNSWPQ::IndexHNSWPQ(int d, int pq_m, int M):
IndexHNSW(new IndexPQ(d, pq_m, 8), M)
{
own_fields = true;
is_trained = false;
}
void IndexHNSWPQ::train(idx_t n, const float* x)
{
IndexHNSW::train (n, x);
(dynamic_cast<IndexPQ*> (storage))->pq.compute_sdc_table();
}
DistanceComputer * IndexHNSWPQ::get_distance_computer () const
{
return new PQDis (*dynamic_cast<IndexPQ*> (storage));
}
/**************************************************************
* IndexHNSWSQ implementation
**************************************************************/
namespace {
struct SQDis: DistanceComputer {
Index::idx_t nb;
const uint8_t *codes;
size_t code_size;
const ScalarQuantizer & sq;
const float *q;
ScalarQuantizer::DistanceComputer * dc;
float operator () (storage_idx_t i) override
{
const uint8_t *code = codes + i * code_size;
return dc->compute_distance (q, code);
}
float symmetric_dis(storage_idx_t i, storage_idx_t j) override
{
const uint8_t *codei = codes + i * code_size;
const uint8_t *codej = codes + j * code_size;
return dc->compute_code_distance (codei, codej);
}
SQDis(const IndexScalarQuantizer& storage, const float* /*q*/ = nullptr)
: sq(storage.sq) {
nb = storage.ntotal;
d = storage.d;
codes = storage.codes.data();
code_size = sq.code_size;
dc = sq.get_distance_computer();
}
void set_query(const float *x) override {
q = x;
}
virtual ~SQDis () {
delete dc;
}
};
} // namespace
IndexHNSWSQ::IndexHNSWSQ(int d, ScalarQuantizer::QuantizerType qtype, int M):
IndexHNSW (new IndexScalarQuantizer (d, qtype), M)
{
own_fields = true;
}
IndexHNSWSQ::IndexHNSWSQ() {}
DistanceComputer * IndexHNSWSQ::get_distance_computer () const
{
return new SQDis (*dynamic_cast<IndexScalarQuantizer*> (storage));
}
/**************************************************************
* IndexHNSW2Level implementation
**************************************************************/
IndexHNSW2Level::IndexHNSW2Level(Index *quantizer, size_t nlist, int m_pq, int M):
IndexHNSW (new Index2Layer (quantizer, nlist, m_pq), M)
{
own_fields = true;
is_trained = false;
}
IndexHNSW2Level::IndexHNSW2Level() {}
namespace {
struct Distance2Level: DistanceComputer {
const Index2Layer & storage;
std::vector<float> buf;
const float *q;
const float *pq_l1_tab, *pq_l2_tab;
Distance2Level(const Index2Layer & storage): storage(storage)
{
d = storage.d;
FAISS_ASSERT(storage.pq.dsub == 4);
pq_l2_tab = storage.pq.centroids.data();
buf.resize(2 * d);
}
float symmetric_dis(storage_idx_t i, storage_idx_t j) override
{
storage.reconstruct(i, buf.data());
storage.reconstruct(j, buf.data() + d);
return fvec_L2sqr(buf.data() + d, buf.data(), d);
}
void set_query(const float *x) override {
q = x;
}
};
// well optimized for xNN+PQNN
struct DistanceXPQ4: Distance2Level {
int M, k;
DistanceXPQ4(const Index2Layer & storage):
Distance2Level (storage)
{
const IndexFlat *quantizer =
dynamic_cast<IndexFlat*> (storage.q1.quantizer);
FAISS_ASSERT(quantizer);
M = storage.pq.M;
pq_l1_tab = quantizer->xb.data();
}
float operator () (storage_idx_t i) override
{
#ifdef __SSE__
const uint8_t *code = storage.codes.data() + i * storage.code_size;
long key = 0;
memcpy (&key, code, storage.code_size_1);
code += storage.code_size_1;
// walking pointers
const float *qa = q;
const __m128 *l1_t = (const __m128 *)(pq_l1_tab + d * key);
const __m128 *pq_l2_t = (const __m128 *)pq_l2_tab;
__m128 accu = _mm_setzero_ps();
for (int m = 0; m < M; m++) {
__m128 qi = _mm_loadu_ps(qa);
__m128 recons = l1_t[m] + pq_l2_t[*code++];
__m128 diff = qi - recons;
accu += diff * diff;
pq_l2_t += 256;
qa += 4;
}
accu = _mm_hadd_ps (accu, accu);
accu = _mm_hadd_ps (accu, accu);
return _mm_cvtss_f32 (accu);
#else
FAISS_THROW_MSG("not implemented for non-x64 platforms");
#endif
}
};
// well optimized for 2xNN+PQNN
struct Distance2xXPQ4: Distance2Level {
int M_2, mi_nbits;
Distance2xXPQ4(const Index2Layer & storage):
Distance2Level (storage)
{
const MultiIndexQuantizer *mi =
dynamic_cast<MultiIndexQuantizer*> (storage.q1.quantizer);
FAISS_ASSERT(mi);
FAISS_ASSERT(storage.pq.M % 2 == 0);
M_2 = storage.pq.M / 2;
mi_nbits = mi->pq.nbits;
pq_l1_tab = mi->pq.centroids.data();
}
float operator () (storage_idx_t i) override
{
const uint8_t *code = storage.codes.data() + i * storage.code_size;
long key01 = 0;
memcpy (&key01, code, storage.code_size_1);
code += storage.code_size_1;
#ifdef __SSE__
// walking pointers
const float *qa = q;
const __m128 *pq_l1_t = (const __m128 *)pq_l1_tab;
const __m128 *pq_l2_t = (const __m128 *)pq_l2_tab;
__m128 accu = _mm_setzero_ps();
for (int mi_m = 0; mi_m < 2; mi_m++) {
long l1_idx = key01 & ((1L << mi_nbits) - 1);
const __m128 * pq_l1 = pq_l1_t + M_2 * l1_idx;
for (int m = 0; m < M_2; m++) {
__m128 qi = _mm_loadu_ps(qa);
__m128 recons = pq_l1[m] + pq_l2_t[*code++];
__m128 diff = qi - recons;
accu += diff * diff;
pq_l2_t += 256;
qa += 4;
}
pq_l1_t += M_2 << mi_nbits;
key01 >>= mi_nbits;
}
accu = _mm_hadd_ps (accu, accu);
accu = _mm_hadd_ps (accu, accu);
return _mm_cvtss_f32 (accu);
#else
FAISS_THROW_MSG("not implemented for non-x64 platforms");
#endif
}
};
} // namespace
DistanceComputer * IndexHNSW2Level::get_distance_computer () const
{
const Index2Layer *storage2l =
dynamic_cast<Index2Layer*>(storage);
if (storage2l) {
#ifdef __SSE__
const MultiIndexQuantizer *mi =
dynamic_cast<MultiIndexQuantizer*> (storage2l->q1.quantizer);
if (mi && storage2l->pq.M % 2 == 0 && storage2l->pq.dsub == 4) {
return new Distance2xXPQ4(*storage2l);
}
const IndexFlat *fl =
dynamic_cast<IndexFlat*> (storage2l->q1.quantizer);
if (fl && storage2l->pq.dsub == 4) {
return new DistanceXPQ4(*storage2l);
}
#endif
}
// IVFPQ and cases not handled above
return new GenericDistanceComputer (*storage);
}
namespace {
// same as search_from_candidates but uses v
// visno -> is in result list
// visno + 1 -> in result list + in candidates
int search_from_candidates_2(const HNSW & hnsw,
DistanceComputer & qdis, int k,
idx_t *I, float * D,
MinimaxHeap &candidates,
VisitedTable &vt,
int level, int nres_in = 0)
{
int nres = nres_in;
int ndis = 0;
for (int i = 0; i < candidates.size(); i++) {
idx_t v1 = candidates.ids[i];
FAISS_ASSERT(v1 >= 0);
vt.visited[v1] = vt.visno + 1;
}
int nstep = 0;
while (candidates.size() > 0) {
float d0 = 0;
int v0 = candidates.pop_min(&d0);
size_t begin, end;
hnsw.neighbor_range(v0, level, &begin, &end);
for (size_t j = begin; j < end; j++) {
int v1 = hnsw.neighbors[j];
if (v1 < 0) break;
if (vt.visited[v1] == vt.visno + 1) {
// nothing to do
} else {
ndis++;
float d = qdis(v1);
candidates.push(v1, d);
// never seen before --> add to heap
if (vt.visited[v1] < vt.visno) {
if (nres < k) {
faiss::maxheap_push (++nres, D, I, d, v1);
} else if (d < D[0]) {
faiss::maxheap_pop (nres--, D, I);
faiss::maxheap_push (++nres, D, I, d, v1);
}
}
vt.visited[v1] = vt.visno + 1;
}
}
nstep++;
if (nstep > hnsw.efSearch) {
break;
}
}
if (level == 0) {
#pragma omp critical
{
hnsw_stats.n1 ++;
if (candidates.size() == 0)
hnsw_stats.n2 ++;
}
}
return nres;
}
} // namespace
void IndexHNSW2Level::search (idx_t n, const float *x, idx_t k,
float *distances, idx_t *labels) const
{
if (dynamic_cast<const Index2Layer*>(storage)) {
IndexHNSW::search (n, x, k, distances, labels);
} else { // "mixed" search
const IndexIVFPQ *index_ivfpq =
dynamic_cast<const IndexIVFPQ*>(storage);
int nprobe = index_ivfpq->nprobe;
long * coarse_assign = new long [n * nprobe];
ScopeDeleter<long> del (coarse_assign);
float * coarse_dis = new float [n * nprobe];
ScopeDeleter<float> del2 (coarse_dis);
index_ivfpq->quantizer->search (n, x, nprobe, coarse_dis, coarse_assign);
index_ivfpq->search_preassigned (
n, x, k, coarse_assign, coarse_dis, distances, labels, false);
#pragma omp parallel
{
VisitedTable vt (ntotal);
DistanceComputer *dis = get_distance_computer();
ScopeDeleter1<DistanceComputer> del(dis);
int candidates_size = hnsw.upper_beam;
MinimaxHeap candidates(candidates_size);
#pragma omp for
for(idx_t i = 0; i < n; i++) {
idx_t * idxi = labels + i * k;
float * simi = distances + i * k;
dis->set_query(x + i * d);
// mark all inverted list elements as visited
for (int j = 0; j < nprobe; j++) {
idx_t key = coarse_assign[j + i * nprobe];
if (key < 0) break;
size_t list_length = index_ivfpq->get_list_size (key);
const idx_t * ids = index_ivfpq->invlists->get_ids (key);
for (int jj = 0; jj < list_length; jj++) {
vt.set (ids[jj]);
}
}
candidates.clear();
// copy the upper_beam elements to candidates list
int search_policy = 2;
if (search_policy == 1) {
for (int j = 0 ; j < hnsw.upper_beam && j < k; j++) {
if (idxi[j] < 0) break;
candidates.push (idxi[j], simi[j]);
// search_from_candidates adds them back
idxi[j] = -1;
simi[j] = HUGE_VAL;
}
// reorder from sorted to heap
maxheap_heapify (k, simi, idxi, simi, idxi, k);
hnsw.search_from_candidates(
*dis, k, idxi, simi,
candidates, vt, 0, k
);
vt.advance();
} else if (search_policy == 2) {
for (int j = 0 ; j < hnsw.upper_beam && j < k; j++) {
if (idxi[j] < 0) break;
candidates.push (idxi[j], simi[j]);
}
// reorder from sorted to heap
maxheap_heapify (k, simi, idxi, simi, idxi, k);
search_from_candidates_2 (
hnsw, *dis, k, idxi, simi,
candidates, vt, 0, k);
vt.advance ();
vt.advance ();
}
maxheap_reorder (k, simi, idxi);
}
}
}
}
void IndexHNSW2Level::flip_to_ivf ()
{
Index2Layer *storage2l =
dynamic_cast<Index2Layer*>(storage);
FAISS_THROW_IF_NOT (storage2l);
IndexIVFPQ * index_ivfpq =
new IndexIVFPQ (storage2l->q1.quantizer,
d, storage2l->q1.nlist,
storage2l->pq.M, 8);
index_ivfpq->pq = storage2l->pq;
index_ivfpq->is_trained = storage2l->is_trained;
index_ivfpq->precompute_table();
index_ivfpq->own_fields = storage2l->q1.own_fields;
storage2l->transfer_to_IVFPQ(*index_ivfpq);
index_ivfpq->make_direct_map (true);
storage = index_ivfpq;
delete storage2l;
}
} // namespace faiss
faiss::IndexIVFPQ::precompute_table
void precompute_table()
build precomputed table
Definition: IndexIVFPQ.cpp:364
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::ScalarQuantizer::QuantizerType
QuantizerType
Definition: IndexScalarQuantizer.h:34
faiss::HNSW::neighbor_range
void neighbor_range(idx_t no, int layer_no, size_t *begin, size_t *end) const
range of entries in the neighbors table of vertex no at layer_no
Definition: HNSW.cpp:43
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::VisitedTable::get
bool get(int no) const
get flag #no
Definition: HNSW.h:248
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::IndexHNSWPQ::train
void train(idx_t n, const float *x) override
Trains the storage if needed.
Definition: IndexHNSW.cpp:979
faiss::InvertedLists::get_ids
virtual const idx_t * get_ids(size_t list_no) const =0
faiss::IndexHNSW2Level::search
void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const override
entry point for search
Definition: IndexHNSW.cpp:1324
faiss::Index::reset
virtual void reset()=0
removes all elements from the database.
faiss::IndexPQ
Definition: IndexPQ.h:27
faiss::IndexIVF::nprobe
size_t nprobe
number of probes at query time
Definition: IndexIVF.h:98
faiss::HNSW::DistanceComputer::symmetric_dis
virtual float symmetric_dis(storage_idx_t i, storage_idx_t j)=0
compute distance between two stored vectors
faiss::Index::train
virtual void train(idx_t n, const float *x)
Definition: Index.cpp:24
faiss::ScopeDeleter1
Definition: FaissException.h:52
faiss::ScopeDeleter
Definition: FaissException.h:39
faiss::Index2Layer::q1
Level1Quantizer q1
first level quantizer
Definition: IndexIVFPQ.h:207
faiss::VisitedTable::advance
void advance()
reset all flags to false
Definition: HNSW.h:253
faiss::Index2Layer
Definition: IndexIVFPQ.h:205
faiss::ReconstructFromNeighbors::get_neighbor_table
void get_neighbor_table(storage_idx_t i, float *out) const
get the M+1 -by-d table for neighbor coordinates for vector i
Definition: IndexHNSW.cpp:748
faiss::IndexHNSW::train
void train(idx_t n, const float *x) override
Trains the storage if needed.
Definition: IndexHNSW.cpp:206
faiss::Index::d
int d
vector dimension
Definition: Index.h:66
faiss::VisitedTable
set implementation optimized for fast access.
Definition: HNSW.h:235
faiss::IndexIVFPQ
Definition: IndexIVFPQ.h:35
faiss::IndexHNSW::add
void add(idx_t n, const float *x) override
Definition: IndexHNSW.cpp:259
faiss::ReconstructFromNeighbors::reconstruct
void reconstruct(storage_idx_t i, float *x, float *tmp) const
called by compute_distances
Definition: IndexHNSW.cpp:613
faiss::Index::add
virtual void add(idx_t n, const float *x)=0
faiss::Index::idx_t
long idx_t
all indices are this type
Definition: Index.h:64
faiss::HNSW::DistanceComputer::set_query
virtual void set_query(const float *x)=0
called before computing distances
faiss::Index::ntotal
idx_t ntotal
total nb of indexed vectors
Definition: Index.h:67
faiss::getmillisecs
double getmillisecs()
ms elapsed since some arbitrary epoch
Definition: utils.cpp:70
faiss::ReconstructFromNeighbors::estimate_code
void estimate_code(const float *x, storage_idx_t i, uint8_t *code) const
called by add_codes
Definition: IndexHNSW.cpp:767
faiss::Index::search
virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const =0
faiss::HNSW
Definition: HNSW.h:47
faiss::ReconstructFromNeighbors::add_codes
void add_codes(size_t n, const float *x)
Definition: IndexHNSW.cpp:807
faiss::IndexIVF::make_direct_map
void make_direct_map(bool new_maintain_direct_map=true)
Definition: IndexIVF.cpp:144
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::IndexHNSW::reset
void reset() override
removes all elements from the database.
Definition: IndexHNSW.cpp:270
faiss::ProductQuantizer::M
size_t M
number of subquantizers
Definition: ProductQuantizer.h:27
faiss::IndexHNSW::init_level_0_from_entry_points
void init_level_0_from_entry_points(int npt, const storage_idx_t *points, const storage_idx_t *nearests)
alternative graph building
Definition: IndexHNSW.cpp:430
faiss::IndexHNSW::search
void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const override
entry point for search
Definition: IndexHNSW.cpp:213
faiss::Level1Quantizer::quantizer
Index * quantizer
quantizer that maps vectors to inverted lists
Definition: IndexIVF.h:33
faiss::IndexHNSW::init_level_0_from_knngraph
void init_level_0_from_knngraph(int k, const float *D, const idx_t *I)
alternative graph building
Definition: IndexHNSW.cpp:392
faiss::IndexHNSW::search_level_0
void search_level_0(idx_t n, const float *x, idx_t k, const storage_idx_t *nearest, const float *nearest_d, float *distances, idx_t *labels, int nprobe=1, int search_type=1) const
Definition: IndexHNSW.cpp:320
faiss::HNSW::MinimaxHeap
Definition: HNSW.h:78
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::HNSW::neighbors
std::vector< storage_idx_t > neighbors
Definition: HNSW.h:134
faiss::IndexHNSW::reconstruct
void reconstruct(idx_t key, float *recons) const override
Definition: IndexHNSW.cpp:277
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::HNSW::storage_idx_t
int storage_idx_t
internal storage of vectors (32 bits: this is expensive)
Definition: HNSW.h:49
faiss::HNSW::DistanceComputer
Definition: HNSW.h:60
faiss::Level1Quantizer::own_fields
bool own_fields
whether object owns the quantizer
Definition: IndexIVF.h:42
faiss::VisitedTable::set
void set(int no)
set flog #no to true
Definition: HNSW.h:243
faiss::Level1Quantizer::nlist
size_t nlist
number of possible key values
Definition: IndexIVF.h:34
faiss::HNSW::shrink_neighbor_list
static void shrink_neighbor_list(DistanceComputer &qdis, std::priority_queue< NodeDistFarther > &input, std::vector< NodeDistFarther > &output, int max_size)
Definition: HNSW.cpp:237