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/data/users/matthijs/github_faiss/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.
*/
#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>
#include <immintrin.h>
#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 {
/**************************************************************
* Auxiliary structures
**************************************************************/
/// set implementation optimized for fast access.
struct VisitedTable {
std::vector<uint8_t> visited;
int visno;
VisitedTable(int size):
visited(size), visno(1)
{}
/// set flog #no to true
void set(int no) {
visited[no] = visno;
}
/// get flag #no
bool get(int no) const {
return visited[no] == visno;
}
/// reset all flags to false
void advance() {
visno++;
if (visno == 250) {
// 250 rather than 255 because sometimes we use visno and visno+1
memset (visited.data(), 0, sizeof(visited[0]) * visited.size());
visno = 1;
}
}
};
namespace {
typedef HNSW::idx_t idx_t;
typedef HNSW::storage_idx_t storage_idx_t;
typedef HNSW::DistanceComputer DistanceComputer;
// typedef ::faiss::VisitedTable VisitedTable;
/// to sort pairs of (id, distance) from nearest to fathest or the reverse
struct NodeDistCloser {
float d;
int id;
NodeDistCloser(float d, int id): d(d), id(id) {}
bool operator<(const NodeDistCloser &obj1) const { return d < obj1.d; }
};
struct NodeDistFarther {
float d;
int id;
NodeDistFarther(float d, int id): d(d), id(id) {}
bool operator<(const NodeDistFarther &obj1) const { return d > obj1.d; }
};
/** Heap structure that allows fast */
struct MinimaxHeap {
int n;
int k;
int nvalid;
std::vector<storage_idx_t> ids;
std::vector<float> dis;
typedef faiss::CMax<float, storage_idx_t> HC;
explicit MinimaxHeap(int n): n(n), k(0), nvalid(0), ids(n), dis(n) {}
void push(storage_idx_t i, float v)
{
if (k == n) {
if (v >= dis[0]) return;
faiss::heap_pop<HC> (k--, dis.data(), ids.data());
nvalid--;
}
faiss::heap_push<HC> (++k, dis.data(), ids.data(), v, i);
nvalid++;
}
float max() const
{
return dis[0];
}
int size() const {return nvalid;}
void clear() {nvalid = k = 0; }
int pop_min(float *vmin_out = nullptr)
{
assert(k > 0);
// returns min. This is an O(n) operation
int i = k - 1;
while (i >= 0) {
if (ids[i] != -1) break;
i--;
}
if (i == -1) return -1;
int imin = i;
float vmin = dis[i];
i--;
while(i >= 0) {
if (ids[i] != -1 && dis[i] < vmin) {
vmin = dis[i];
imin = i;
}
i--;
}
if (vmin_out) *vmin_out = vmin;
int ret = ids[imin];
ids[imin] = -1;
nvalid --;
return ret;
}
int count_below(float thresh) {
float n_below = 0;
for(int i = 0; i < k; i++) {
if (dis[i] < thresh)
n_below++;
}
return n_below;
}
};
/**************************************************************
* Addition subroutines
**************************************************************/
/** Enumerate vertices from farthest to nearest from query, keep a
* neighbor only if there is no previous neighbor that is closer to
* that vertex than the query.
*/
void shrink_neighbor_list(DistanceComputer & qdis,
std::priority_queue<NodeDistFarther> &input,
std::vector<NodeDistFarther> &output,
int max_size)
{
while (input.size() > 0) {
NodeDistFarther v1 = input.top();
input.pop();
float dist_v1_q = v1.d;
bool good = true;
for (NodeDistFarther v2 : output) {
float dist_v1_v2 = qdis.symmetric_dis(v2.id, v1.id);
if (dist_v1_v2 < dist_v1_q) {
good = false;
break;
}
}
if (good) {
output.push_back(v1);
if (output.size() >= max_size)
return;
}
}
}
/// remove neighbors from the list to make it smaller than max_size
void shrink_neighbor_list(DistanceComputer & qdis,
std::priority_queue<NodeDistCloser> &resultSet1,
int max_size)
{
if (resultSet1.size() < max_size) {
return;
}
std::priority_queue<NodeDistFarther> resultSet;
std::vector<NodeDistFarther> returnlist;
while (resultSet1.size() > 0) {
resultSet.emplace(resultSet1.top().d, resultSet1.top().id);
resultSet1.pop();
}
shrink_neighbor_list (qdis, resultSet, returnlist, max_size);
for (NodeDistFarther curen2 : returnlist) {
resultSet1.emplace(curen2.d, curen2.id);
}
}
/// add a link between two elements, possibly shrinking the list
/// of links to make room for it.
void add_link(HNSW & hnsw,
DistanceComputer & qdis,
storage_idx_t src, storage_idx_t dest,
int level)
{
size_t begin, end;
hnsw.neighbor_range(src, level, &begin, &end);
if (hnsw.neighbors[end - 1] == -1) {
// there is enough room, find a slot to add it
size_t i = end;
while(i > begin) {
if (hnsw.neighbors[i - 1] != -1) break;
i--;
}
hnsw.neighbors[i] = dest;
return;
}
// otherwise we let them fight out which to keep
// copy to resultSet...
std::priority_queue<NodeDistCloser> resultSet;
resultSet.emplace(qdis.symmetric_dis(src, dest), dest);
for (size_t i = begin; i < end; i++) { // HERE WAS THE BUG
storage_idx_t neigh = hnsw.neighbors[i];
resultSet.emplace(qdis.symmetric_dis(src, neigh), neigh);
}
shrink_neighbor_list(qdis, resultSet, end - begin);
// ...and back
size_t i = begin;
while (resultSet.size()) {
hnsw.neighbors[i++] = resultSet.top().id;
resultSet.pop();
}
// they may have shrunk more than just by 1 element
while(i < end) {
hnsw.neighbors[i++] = -1;
}
}
/// search neighbors on a single level, starting from an entry point
void search_neighbors_to_add(HNSW & hnsw,
DistanceComputer &qdis,
std::priority_queue<NodeDistCloser> &results,
int entry_point,
float d_entry_point,
int level,
VisitedTable &vt)
{
// top is nearest candidate
std::priority_queue<NodeDistFarther> candidates;
NodeDistFarther ev(d_entry_point, entry_point);
candidates.push(ev);
results.emplace(d_entry_point, entry_point);
vt.set(entry_point);
while (!candidates.empty()) {
// get nearest
const NodeDistFarther &currEv = candidates.top();
if (currEv.d > results.top().d) {
break;
}
int currNode = currEv.id;
candidates.pop();
// loop over neighbors
size_t begin, end;
hnsw.neighbor_range(currNode, level, &begin, &end);
for(size_t i = begin; i < end; i++) {
storage_idx_t nodeId = hnsw.neighbors[i];
if (nodeId < 0) break;
if (vt.get(nodeId)) continue;
vt.set(nodeId);
float dis = qdis(nodeId);
NodeDistFarther evE1(dis, nodeId);
if (results.size() < hnsw.efConstruction ||
results.top().d > dis) {
results.emplace(dis, nodeId);
candidates.emplace(dis, nodeId);
if (results.size() > hnsw.efConstruction) {
results.pop();
}
}
}
}
vt.advance();
}
/// Finds neighbors and builds links with them, starting from an entry
/// point. The own neighbor list is assumed to be locked.
void add_links_starting_from(HNSW & hnsw,
DistanceComputer &ptdis,
storage_idx_t pt_id,
storage_idx_t nearest,
float d_nearest,
int level,
omp_lock_t * locks,
VisitedTable &vt)
{
std::priority_queue<NodeDistCloser> link_targets;
search_neighbors_to_add(
hnsw, ptdis, link_targets, nearest, d_nearest,
level, vt);
// but we can afford only this many neighbors
int M = hnsw.nb_neighbors(level);
shrink_neighbor_list(ptdis, link_targets, M);
while (!link_targets.empty()) {
int other_id = link_targets.top().id;
omp_set_lock(&locks[other_id]);
add_link(hnsw, ptdis, other_id, pt_id, level);
omp_unset_lock(&locks[other_id]);
add_link(hnsw, ptdis, pt_id, other_id, level);
link_targets.pop();
}
}
/**************************************************************
* Searching subroutines
**************************************************************/
/// greedily update a nearest vector at a given level
void greedy_update_nearest(const HNSW & hnsw,
DistanceComputer & qdis,
int level,
storage_idx_t & nearest,
float & d_nearest)
{
for(;;) {
storage_idx_t prev_nearest = nearest;
size_t begin, end;
hnsw.neighbor_range(nearest, level, &begin, &end);
for(size_t i = begin; i < end; i++) {
storage_idx_t v = hnsw.neighbors[i];
if (v < 0) break;
float dis = qdis(v);
if (dis < d_nearest) {
nearest = v;
d_nearest = dis;
}
}
if (nearest == prev_nearest) {
return;
}
}
}
/** Do a BFS on the candidates list */
int search_from_candidates(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];
float d = candidates.dis[i];
FAISS_ASSERT(v1 >= 0);
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.set(v1);
}
bool do_dis_check = hnsw.check_relative_distance;
int nstep = 0;
while (candidates.size() > 0) {
float d0 = 0;
int v0 = candidates.pop_min(&d0);
if (do_dis_check) {
// tricky stopping condition: there are more that ef
// distances that are processed already that are smaller
// than d0
int n_dis_below = candidates.count_below(d0);
if(n_dis_below >= hnsw.efSearch) {
break;
}
}
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.get(v1)) {
continue;
}
vt.set(v1);
ndis++;
float d = qdis(v1);
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);
}
candidates.push(v1, d);
}
nstep++;
if (!do_dis_check && nstep > hnsw.efSearch) {
break;
}
}
if (level == 0) {
#pragma omp critical
{
hnsw_stats.n1 ++;
if (candidates.size() == 0)
hnsw_stats.n2 ++;
hnsw_stats.n3 += ndis;
}
}
return nres;
}
} // anonymous namespace
/**************************************************************
* HNSW structure implementation
**************************************************************/
int HNSW::nb_neighbors(int layer_no) const
{
return cum_nneighbor_per_level[layer_no + 1] -
cum_nneighbor_per_level[layer_no];
}
void HNSW::set_nb_neighbors(int level_no, int n)
{
FAISS_THROW_IF_NOT(levels.size() == 0);
int cur_n = nb_neighbors(level_no);
for (int i = level_no + 1; i < cum_nneighbor_per_level.size(); i++) {
cum_nneighbor_per_level[i] += n - cur_n;
}
}
int HNSW::cum_nb_neighbors(int layer_no) const
{
return cum_nneighbor_per_level[layer_no];
}
void HNSW::neighbor_range(idx_t no, int layer_no,
size_t * begin, size_t * end) const
{
size_t o = offsets[no];
*begin = o + cum_nb_neighbors(layer_no);
*end = o + cum_nb_neighbors(layer_no + 1);
}
HNSW::HNSW(int M): rng(12345) {
set_default_probas(M, 1.0 / log(M));
max_level = -1;
entry_point = -1;
efSearch = 16;
check_relative_distance = true;
efConstruction = 40;
upper_beam = 1;
offsets.push_back(0);
}
int HNSW::random_level()
{
double f = rng.rand_float();
// could be a bit faster with bissection
for (int level = 0; level < assign_probas.size(); level++) {
if (f < assign_probas[level]) {
return level;
}
f -= assign_probas[level];
}
// happens with exponentially low probability
return assign_probas.size() - 1;
}
void HNSW::set_default_probas(int M, float levelMult)
{
int nn = 0;
cum_nneighbor_per_level.push_back (0);
for (int level = 0; ;level++) {
float proba = exp(-level / levelMult) * (1 - exp(-1 / levelMult));
if (proba < 1e-9) break;
assign_probas.push_back(proba);
nn += level == 0 ? M * 2 : M;
cum_nneighbor_per_level.push_back (nn);
}
}
void HNSW::clear_neighbor_tables(int level)
{
for (int i = 0; i < levels.size(); i++) {
size_t begin, end;
neighbor_range(i, level, &begin, &end);
for (size_t j = begin; j < end; j++)
neighbors[j] = -1;
}
}
void HNSW::reset() {
max_level = -1;
entry_point = -1;
offsets.clear();
offsets.push_back(0);
levels.clear();
neighbors.clear();
}
void HNSW::print_neighbor_stats(int level) const
{
FAISS_THROW_IF_NOT (level < cum_nneighbor_per_level.size());
printf("stats on level %d, max %d neighbors per vertex:\n",
level, nb_neighbors(level));
size_t tot_neigh = 0, tot_common = 0, tot_reciprocal = 0, n_node = 0;
#pragma omp parallel for reduction(+: tot_neigh) reduction(+: tot_common) \
reduction(+: tot_reciprocal) reduction(+: n_node)
for (int i = 0; i < levels.size(); i++) {
if (levels[i] > level) {
n_node++;
size_t begin, end;
neighbor_range(i, level, &begin, &end);
std::unordered_set<int> neighset;
for (size_t j = begin; j < end; j++) {
if (neighbors [j] < 0) break;
neighset.insert(neighbors[j]);
}
int n_neigh = neighset.size();
int n_common = 0;
int n_reciprocal = 0;
for (size_t j = begin; j < end; j++) {
storage_idx_t i2 = neighbors[j];
if (i2 < 0) break;
FAISS_ASSERT(i2 != i);
size_t begin2, end2;
neighbor_range(i2, level, &begin2, &end2);
for (size_t j2 = begin2; j2 < end2; j2++) {
storage_idx_t i3 = neighbors[j2];
if (i3 < 0) break;
if (i3 == i) {
n_reciprocal++;
continue;
}
if (neighset.count(i3)) {
neighset.erase(i3);
n_common++;
}
}
}
tot_neigh += n_neigh;
tot_common += n_common;
tot_reciprocal += n_reciprocal;
}
}
float normalizer = n_node;
printf(" nb of nodes at that level %ld\n", n_node);
printf(" neighbors per node: %.2f (%ld)\n", tot_neigh / normalizer, tot_neigh);
printf(" nb of reciprocal neighbors: %.2f\n", tot_reciprocal / normalizer);
printf(" nb of neighbors that are also neighbor-of-neighbors: %.2f (%ld)\n",
tot_common / normalizer, tot_common);
}
HNSWStats hnsw_stats;
void HNSWStats::reset ()
{
memset(this, 0, sizeof(*this));
}
/**************************************************************
* Building, parallel
**************************************************************/
void HNSW::add_with_locks(
DistanceComputer & ptdis, int pt_level, int pt_id,
std::vector<omp_lock_t> & locks,
VisitedTable &vt)
{
// greedy search on upper levels
storage_idx_t nearest;
#pragma omp critical
{
nearest = entry_point;
if (nearest == -1) {
max_level = pt_level;
entry_point = pt_id;
}
}
if (nearest < 0) {
return;
}
omp_set_lock(&locks[pt_id]);
int level = max_level; // level at which we start adding neighbors
float d_nearest = ptdis(nearest);
for(; level > pt_level; level--) {
greedy_update_nearest(*this, ptdis, level, nearest, d_nearest);
}
for(; level >= 0; level--) {
add_links_starting_from(*this, ptdis, pt_id, nearest, d_nearest,
level, locks.data(), vt);
}
omp_unset_lock(&locks[pt_id]);
if (pt_level > max_level) {
max_level = pt_level;
entry_point = pt_id;
}
}
/**************************************************************
* Searching
**************************************************************/
void HNSW::search(DistanceComputer & qdis,
int k, idx_t *I, float * D,
VisitedTable &vt) const
{
if (upper_beam == 1) {
// greedy search on upper levels
storage_idx_t nearest = entry_point;
float d_nearest = qdis(nearest);
for(int level = max_level; level >= 1; level--) {
greedy_update_nearest(*this, qdis, level, nearest, d_nearest);
}
int candidates_size = std::max(efSearch, k);
MinimaxHeap candidates(candidates_size);
candidates.push(nearest, d_nearest);
search_from_candidates (
*this, qdis, k, I, D, candidates, vt, 0);
vt.advance();
} else {
int candidates_size = upper_beam;
MinimaxHeap candidates(candidates_size);
std::vector<idx_t> I_to_next(candidates_size);
std::vector<float> D_to_next(candidates_size);
int nres = 1;
I_to_next[0] = entry_point;
D_to_next[0] = qdis(entry_point);
for(int level = max_level; level >= 0; level--) {
// copy I, D -> candidates
candidates.clear();
for (int i = 0; i < nres; i++) {
candidates.push(I_to_next[i], D_to_next[i]);
}
if (level == 0) {
nres = search_from_candidates (
*this, qdis, k, I, D, candidates, vt, 0);
} else {
nres = search_from_candidates (
*this, qdis, candidates_size,
I_to_next.data(), D_to_next.data(),
candidates, vt, level);
}
vt.advance();
}
}
}
/**************************************************************
* add / search blocks of descriptors
**************************************************************/
namespace {
int prepare_level_tab (HNSW & hnsw, size_t n, bool preset_levels = false)
{
size_t n0 = hnsw.offsets.size() - 1;
if (preset_levels) {
FAISS_ASSERT (n0 + n == hnsw.levels.size());
} else {
FAISS_ASSERT (n0 == hnsw.levels.size());
for (int i = 0; i < n; i++) {
int pt_level = hnsw.random_level();
hnsw.levels.push_back(pt_level + 1);
}
}
int max_level = 0;
for (int i = 0; i < n; i++) {
int pt_level = hnsw.levels[i + n0] - 1;
if (pt_level > max_level) max_level = pt_level;
hnsw.offsets.push_back(hnsw.offsets.back() +
hnsw.cum_nb_neighbors(pt_level + 1));
hnsw.neighbors.resize(hnsw.offsets.back(), -1);
}
return max_level;
}
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 = prepare_level_tab (index_hnsw.hnsw, 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]);
}
} // anonymous namespace
void HNSW::fill_with_random_links(size_t n)
{
int max_level = prepare_level_tab (*this, n);
RandomGenerator rng2(456);
for (int level = max_level - 1; level >= 0; level++) {
std::vector<int> elts;
for (int i = 0; i < n; i++) {
if (levels[i] > level) {
elts.push_back(i);
}
}
printf ("linking %ld elements in level %d\n",
elts.size(), level);
if (elts.size() == 1) continue;
for (int ii = 0; ii < elts.size(); ii++) {
int i = elts[ii];
size_t begin, end;
neighbor_range(i, 0, &begin, &end);
for (size_t j = begin; j < end; j++) {
int other = 0;
do {
other = elts[rng2.rand_int(elts.size())];
} while(other == i);
neighbors[j] = other;
}
}
}
}
/**************************************************************
* 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(int 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;
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 = search_from_candidates (
hnsw, *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]);
}
search_from_candidates (
hnsw, *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;
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);
add_links_starting_from(hnsw, *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");
}
}
// storage that explicitly reconstructs vectors before computing distances
struct GenericDistanceComputer: HNSW::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;
}
};
HNSW::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 (int sq = 0; sq < nsq; sq++) {
int d0 = sq * dsub;
int d1 = d0 + 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 (int 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
**************************************************************/
struct FlatL2Dis: HNSW::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;
}
}
};
IndexHNSWFlat::IndexHNSWFlat()
{
is_trained = true;
}
IndexHNSWFlat::IndexHNSWFlat(int d, int M):
IndexHNSW(new IndexFlatL2(d), M)
{
own_fields = true;
is_trained = true;
}
HNSW::DistanceComputer * IndexHNSWFlat::get_distance_computer () const
{
return new FlatL2Dis (*dynamic_cast<IndexFlatL2*> (storage));
}
/**************************************************************
* IndexHNSWPQ implementation
**************************************************************/
struct PQDis: HNSW::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;
}
}
};
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();
}
HNSW::DistanceComputer * IndexHNSWPQ::get_distance_computer () const
{
return new PQDis (*dynamic_cast<IndexPQ*> (storage));
}
/**************************************************************
* IndexHNSWSQ implementation
**************************************************************/
struct SQDis: HNSW::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;
}
};
IndexHNSWSQ::IndexHNSWSQ(int d, ScalarQuantizer::QuantizerType qtype, int M):
IndexHNSW (new IndexScalarQuantizer (d, qtype), M)
{
own_fields = true;
}
IndexHNSWSQ::IndexHNSWSQ() {}
HNSW::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() {}
struct Distance2Level: HNSW::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
{
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);
}
};
// 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;
// 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);
}
};
HNSW::DistanceComputer * IndexHNSW2Level::get_distance_computer () const
{
const Index2Layer *storage2l =
dynamic_cast< Index2Layer*>(storage);
if (storage2l) {
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);
}
}
// 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];
float d = candidates.dis[i];
FAISS_ASSERT(v1 >= 0);
vt.visited[v1] = vt.visno + 1;
}
bool do_dis_check = hnsw.check_relative_distance;
int nstep = 0;
while (candidates.size() > 0) {
float d0 = 0;
int v0 = candidates.pop_min(&d0);
if (do_dis_check) {
// tricky stopping condition: there are more that ef
// distances that are processed already that are smaller
// than d0
int n_dis_below = candidates.count_below(d0);
if(n_dis_below >= hnsw.efSearch) {
break;
}
}
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 (!do_dis_check && nstep > hnsw.efSearch) {
break;
}
}
if (level == 0) {
#pragma omp critical
{
hnsw_stats.n1 ++;
if (candidates.size() == 0)
hnsw_stats.n2 ++;
}
}
return nres;
}
} // anonymous 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(int 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;
const std::vector<idx_t> & ids = index_ivfpq->ids[key];
for (int jj = 0; jj < ids.size(); 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);
search_from_candidates (
hnsw, *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::RandomGenerator
random generator that can be used in multithreaded contexts
Definition: utils.h:48
faiss::IndexIVFPQ::precompute_table
void precompute_table()
build precomputed table
Definition: IndexIVFPQ.cpp:344
faiss::HNSW::add_with_locks
void add_with_locks(DistanceComputer &ptdis, int pt_level, int pt_id, std::vector< omp_lock_t > &locks, VisitedTable &vt)
Definition: IndexHNSW.cpp:657
faiss::IndexPQ::codes
std::vector< uint8_t > codes
Codes. Size ntotal * pq.code_size.
Definition: IndexPQ.h:34
faiss::Index2Layer::transfer_to_IVFPQ
void transfer_to_IVFPQ(IndexIVFPQ &other) const
transfer the flat codes to an IVFPQ index
Definition: IndexIVFPQ.cpp:1416
faiss::ScalarQuantizer::code_size
size_t code_size
bytes per vector
Definition: IndexScalarQuantizer.h:60
faiss::ScalarQuantizer::QuantizerType
QuantizerType
Definition: IndexScalarQuantizer.h:32
faiss::IndexFlat
Definition: IndexFlat.h:23
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: IndexHNSW.cpp:517
faiss::ProductQuantizer::nbits
size_t nbits
number of bits per quantization index
Definition: ProductQuantizer.h:29
faiss::VisitedTable::get
bool get(int no) const
get flag #no
Definition: IndexHNSW.cpp:66
faiss::fvec_L2sqr
float fvec_L2sqr(const float *x, const float *y, size_t d)
Squared L2 distance between two vectors.
Definition: utils.cpp:574
faiss::SQDis
Definition: IndexHNSW.cpp:1752
faiss::IndexHNSWPQ::train
void train(idx_t n, const float *x) override
Trains the storage if needed.
Definition: IndexHNSW.cpp:1733
faiss::CMax
Definition: Heap.h:48
faiss::HNSW::nb_neighbors
int nb_neighbors(int layer_no) const
nb of neighbors for this level
Definition: IndexHNSW.cpp:497
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:2071
faiss::IndexScalarQuantizer
Definition: IndexScalarQuantizer.h:99
faiss::Index2Layer::code_size
size_t code_size
code_size_1 + code_size_2
Definition: IndexIVFPQ.h:256
faiss::Index::reset
virtual void reset()=0
removes all elements from the database.
faiss::HNSW::entry_point
storage_idx_t entry_point
entry point in the search structure (one of the points with maximum level
Definition: IndexHNSW.h:88
faiss::HNSW::cum_nb_neighbors
int cum_nb_neighbors(int layer_no) const
cumumlative nb up to (and excluding) this level
Definition: IndexHNSW.cpp:512
faiss::PQDis
Definition: IndexHNSW.cpp:1661
faiss::ScalarQuantizer::DistanceComputer::compute_code_distance
virtual float compute_code_distance(const uint8_t *code1, const uint8_t *code2)=0
code-to-code distance computation
faiss::IndexPQ
Definition: IndexPQ.h:28
faiss::IndexIVF::nprobe
size_t nprobe
number of probes at query time
Definition: IndexIVF.h:78
faiss::IndexFlatL2
Definition: IndexFlat.h:79
faiss::HNSW::assign_probas
std::vector< double > assign_probas
assignment probability to each layer (sum=1)
Definition: IndexHNSW.h:70
faiss::ProductQuantizer::sdc_table
std::vector< float > sdc_table
Symmetric Distance Table.
Definition: ProductQuantizer.h:155
faiss::Index::train
virtual void train(idx_t n, const float *x)
Definition: Index.cpp:23
faiss::DistanceXPQ4
Definition: IndexHNSW.cpp:1857
faiss::RandomGenerator::rand_float
float rand_float()
between 0 and 1
Definition: utils.cpp:211
faiss::ScopeDeleter1
Definition: FaissException.h:52
faiss::HNSW::cum_nneighbor_per_level
std::vector< int > cum_nneighbor_per_level
Definition: IndexHNSW.h:74
faiss::ScopeDeleter
Definition: FaissException.h:39
faiss::Distance2Level
Definition: IndexHNSW.cpp:1825
faiss::ProductQuantizer::dsub
size_t dsub
dimensionality of each subvector
Definition: ProductQuantizer.h:32
faiss::Index2Layer::q1
Level1Quantizer q1
first level quantizer
Definition: IndexIVFPQ.h:241
faiss::Index2Layer::codes
std::vector< uint8_t > codes
Codes. Size ntotal * code_size.
Definition: IndexIVFPQ.h:247
faiss::ScalarQuantizer
Definition: IndexScalarQuantizer.h:30
faiss::VisitedTable::advance
void advance()
reset all flags to false
Definition: IndexHNSW.cpp:70
faiss::Index2Layer
Definition: IndexIVFPQ.h:239
faiss::IndexIVF::ids
std::vector< std::vector< long > > ids
Inverted lists for indexes.
Definition: IndexIVF.h:81
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:1512
faiss::Distance2Level::set_query
void set_query(const float *x) override
called before computing distances
Definition: IndexHNSW.cpp:1848
faiss::IndexHNSW::train
void train(idx_t n, const float *x) override
Trains the storage if needed.
Definition: IndexHNSW.cpp:978
faiss::Index::d
int d
vector dimension
Definition: Index.h:64
faiss::ProductQuantizer::compute_distance_table
void compute_distance_table(const float *x, float *dis_table) const
Definition: ProductQuantizer.cpp:410
faiss::ProductQuantizer::code_size
size_t code_size
byte per indexed vector
Definition: ProductQuantizer.h:34
faiss::HNSW::offsets
std::vector< size_t > offsets
Definition: IndexHNSW.h:81
faiss::IndexScalarQuantizer::codes
std::vector< uint8_t > codes
Codes. Size ntotal * pq.code_size.
Definition: IndexScalarQuantizer.h:104
faiss::VisitedTable
set implementation optimized for fast access.
Definition: IndexHNSW.cpp:54
faiss::IndexIVFPQ
Definition: IndexIVFPQ.h:29
faiss::IndexHNSW::add
void add(idx_t n, const float *x) override
Definition: IndexHNSW.cpp:1031
faiss::SQDis::set_query
void set_query(const float *x) override
called before computing distances
Definition: IndexHNSW.cpp:1785
faiss::ScalarQuantizer::DistanceComputer
Definition: IndexScalarQuantizer.h:81
faiss::ReconstructFromNeighbors::reconstruct
void reconstruct(storage_idx_t i, float *x, float *tmp) const
called by compute_distances
Definition: IndexHNSW.cpp:1378
faiss::Index::add
virtual void add(idx_t n, const float *x)=0
faiss::RandomGenerator::rand_int
int rand_int()
random 31-bit positive integer
faiss::IndexScalarQuantizer::sq
ScalarQuantizer sq
Used to encode the vectors.
Definition: IndexScalarQuantizer.h:101
faiss::ProductQuantizer::ksub
size_t ksub
number of centroids for each subquantizer
Definition: ProductQuantizer.h:35
faiss::HNSW::efSearch
int efSearch
expansion factor at search time
Definition: IndexHNSW.h:99
faiss::Index::idx_t
long idx_t
all indices are this type
Definition: Index.h:62
faiss::HNSW::check_relative_distance
bool check_relative_distance
during search: do we check whether the next best distance is good enough?
Definition: IndexHNSW.h:102
faiss::HNSW::HNSW
HNSW(int M=32)
only mandatory parameter: nb of neighbors
Definition: IndexHNSW.cpp:527
faiss::IndexPQ::pq
ProductQuantizer pq
The product quantizer used to encode the vectors.
Definition: IndexPQ.h:31
faiss::Index::ntotal
idx_t ntotal
total nb of indexed vectors
Definition: Index.h:65
faiss::HNSW::upper_beam
int upper_beam
number of entry points in levels &gt; 0.
Definition: IndexHNSW.h:105
faiss::getmillisecs
double getmillisecs()
ms elapsed since some arbitrary epoch
Definition: utils.cpp:74
faiss::HNSW::set_nb_neighbors
void set_nb_neighbors(int level_no, int n)
set nb of neighbors for this level (before adding anything)
Definition: IndexHNSW.cpp:503
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:1531
faiss::Index::search
virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const =0
faiss::MultiIndexQuantizer
Definition: IndexPQ.h:135
faiss::HNSW
Definition: IndexHNSW.h:40
faiss::HNSW::idx_t
faiss::Index::idx_t idx_t
Faiss results are 64-bit.
Definition: IndexHNSW.h:46
faiss::ReconstructFromNeighbors::add_codes
void add_codes(size_t n, const float *x)
Definition: IndexHNSW.cpp:1572
faiss::IndexIVF::make_direct_map
void make_direct_map(bool new_maintain_direct_map=true)
Definition: IndexIVF.cpp:139
faiss::GenericDistanceComputer::set_query
void set_query(const float *x) override
called before computing distances
Definition: IndexHNSW.cpp:1347
faiss::HNSW::random_level
int random_level()
pick a random level for a new point
Definition: IndexHNSW.cpp:539
faiss::HNSW::set_default_probas
void set_default_probas(int M, float levelMult)
Definition: IndexHNSW.cpp:553
faiss::IndexIVFPQ::pq
ProductQuantizer pq
produces the codes
Definition: IndexIVFPQ.h:32
faiss::IndexHNSW::reset
void reset() override
removes all elements from the database.
Definition: IndexHNSW.cpp:1043
faiss::ProductQuantizer::M
size_t M
number of subquantizers
Definition: ProductQuantizer.h:28
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:250
faiss::HNSW::search
void search(DistanceComputer &qdis, int k, idx_t *I, float *D, VisitedTable &vt) const
search interface
Definition: IndexHNSW.cpp:711
faiss::Index
Definition: Index.h:60
faiss::GenericDistanceComputer
Definition: IndexHNSW.cpp:1322
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:1200
faiss::FlatL2Dis::set_query
void set_query(const float *x) override
called before computing distances
Definition: IndexHNSW.cpp:1621
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:985
faiss::IndexIVFPQ::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 override
Definition: IndexIVFPQ.cpp:919
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:1162
faiss::ProductQuantizer
Definition: ProductQuantizer.h:25
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:1092
faiss::HNSW::fill_with_random_links
void fill_with_random_links(size_t n)
add n random levels to table (for debugging...)
Definition: IndexHNSW.cpp:913
faiss::Index::is_trained
bool is_trained
set if the Index does not require training, or if training is done already
Definition: Index.h:69
faiss::HNSW::neighbors
std::vector< storage_idx_t > neighbors
Definition: IndexHNSW.h:85
faiss::IndexHNSW::reconstruct
void reconstruct(idx_t key, float *recons) const override
Definition: IndexHNSW.cpp:1050
faiss::FlatL2Dis
Definition: IndexHNSW.cpp:1594
faiss::Index::reconstruct
virtual void reconstruct(idx_t key, float *recons) const
Definition: Index.cpp:54
faiss::Index2Layer::pq
ProductQuantizer pq
second level quantizer is always a PQ
Definition: IndexIVFPQ.h:244
faiss::HNSW::efConstruction
int efConstruction
expansion factor at construction time
Definition: IndexHNSW.h:96
faiss::HNSW::storage_idx_t
int storage_idx_t
internal storage of vectors (32 bits: this is expensive)
Definition: IndexHNSW.h:43
faiss::HNSW::DistanceComputer
Definition: IndexHNSW.h:52
faiss::IndexFlat::xb
std::vector< float > xb
database vectors, size ntotal * d
Definition: IndexFlat.h:25
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: IndexHNSW.cpp:62
faiss::Distance2xXPQ4
Definition: IndexHNSW.cpp:1902
faiss::PQDis::set_query
void set_query(const float *x) override
called before computing distances
Definition: IndexHNSW.cpp:1712
faiss::Index2Layer::reconstruct
void reconstruct(idx_t key, float *recons) const override
Definition: IndexIVFPQ.cpp:1442
faiss::HNSW::levels
std::vector< int > levels
level of each vector (base level = 1), size = ntotal
Definition: IndexHNSW.h:77
faiss::HNSW::max_level
int max_level
maximum level
Definition: IndexHNSW.h:93
faiss::Level1Quantizer::nlist
size_t nlist
number of possible key values
Definition: IndexIVF.h:34
faiss::ScalarQuantizer::DistanceComputer::compute_distance
virtual float compute_distance(const float *x, const uint8_t *code)=0
vector-to-code distance computation
faiss::ProductQuantizer::centroids
std::vector< float > centroids
Centroid table, size M * ksub * dsub.
Definition: ProductQuantizer.h:52