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/data/users/matthijs/github_faiss/faiss/IndexHNSW.cpp
1 /**
2 * Copyright (c) 2015-present, Facebook, Inc.
3 * All rights reserved.
4 *
5 * This source code is licensed under the BSD+Patents license found in the
6 * LICENSE file in the root directory of this source tree.
7 */
8
9 #include "IndexHNSW.h"
10
11
12 #include <cstdlib>
13 #include <cassert>
14 #include <cstring>
15 #include <cstdio>
16 #include <cmath>
17 #include <omp.h>
18
19 #include <unordered_set>
20 #include <queue>
21
22 #include <sys/types.h>
23 #include <sys/stat.h>
24 #include <unistd.h>
25 #include <stdint.h>
26
27 #include <immintrin.h>
28
29 #include "utils.h"
30 #include "Heap.h"
31 #include "FaissAssert.h"
32 #include "IndexFlat.h"
33 #include "IndexIVFPQ.h"
34
35
36 extern "C" {
37
38 /* declare BLAS functions, see http://www.netlib.org/clapack/cblas/ */
39
40 int sgemm_ (const char *transa, const char *transb, FINTEGER *m, FINTEGER *
41 n, FINTEGER *k, const float *alpha, const float *a,
42 FINTEGER *lda, const float *b, FINTEGER *
43 ldb, float *beta, float *c, FINTEGER *ldc);
44
45 }
46
47 namespace faiss {
48
49 /**************************************************************
50 * Auxiliary structures
51 **************************************************************/
52
53 /// set implementation optimized for fast access.
54 struct VisitedTable {
55 std::vector<uint8_t> visited;
56 int visno;
57
58 VisitedTable(int size):
59 visited(size), visno(1)
60 {}
61 /// set flog #no to true
62 void set(int no) {
63 visited[no] = visno;
64 }
65 /// get flag #no
66 bool get(int no) const {
67 return visited[no] == visno;
68 }
69 /// reset all flags to false
70 void advance() {
71 visno++;
72 if (visno == 250) {
73 // 250 rather than 255 because sometimes we use visno and visno+1
74 memset (visited.data(), 0, sizeof(visited[0]) * visited.size());
75 visno = 1;
76 }
77 }
78 };
79
80
81 namespace {
82
83 typedef HNSW::idx_t idx_t;
84 typedef HNSW::storage_idx_t storage_idx_t;
85 typedef HNSW::DistanceComputer DistanceComputer;
86 // typedef ::faiss::VisitedTable VisitedTable;
87
88 /// to sort pairs of (id, distance) from nearest to fathest or the reverse
89 struct NodeDistCloser {
90 float d;
91 int id;
92 NodeDistCloser(float d, int id): d(d), id(id) {}
93 bool operator<(const NodeDistCloser &obj1) const { return d < obj1.d; }
94 };
95
96 struct NodeDistFarther {
97 float d;
98 int id;
99 NodeDistFarther(float d, int id): d(d), id(id) {}
100 bool operator<(const NodeDistFarther &obj1) const { return d > obj1.d; }
101 };
102
103
104 /** Heap structure that allows fast */
105
106 struct MinimaxHeap {
107 int n;
108 int k;
109 int nvalid;
110
111 std::vector<storage_idx_t> ids;
112 std::vector<float> dis;
113 typedef faiss::CMax<float, storage_idx_t> HC;
114
115 explicit MinimaxHeap(int n): n(n), k(0), nvalid(0), ids(n), dis(n) {}
116
117 void push(storage_idx_t i, float v)
118 {
119 if (k == n) {
120 if (v >= dis[0]) return;
121 faiss::heap_pop<HC> (k--, dis.data(), ids.data());
122 nvalid--;
123 }
124 faiss::heap_push<HC> (++k, dis.data(), ids.data(), v, i);
125 nvalid++;
126 }
127
128 float max() const
129 {
130 return dis[0];
131 }
132
133 int size() const {return nvalid;}
134
135 void clear() {nvalid = k = 0; }
136
137 int pop_min(float *vmin_out = nullptr)
138 {
139 assert(k > 0);
140 // returns min. This is an O(n) operation
141 int i = k - 1;
142 while (i >= 0) {
143 if (ids[i] != -1) break;
144 i--;
145 }
146 if (i == -1) return -1;
147 int imin = i;
148 float vmin = dis[i];
149 i--;
150 while(i >= 0) {
151 if (ids[i] != -1 && dis[i] < vmin) {
152 vmin = dis[i];
153 imin = i;
154 }
155 i--;
156 }
157 if (vmin_out) *vmin_out = vmin;
158 int ret = ids[imin];
159 ids[imin] = -1;
160 nvalid --;
161 return ret;
162 }
163
164 int count_below(float thresh) {
165 float n_below = 0;
166 for(int i = 0; i < k; i++) {
167 if (dis[i] < thresh)
168 n_below++;
169 }
170 return n_below;
171 }
172
173 };
174
175
176 /**************************************************************
177 * Addition subroutines
178 **************************************************************/
179
180 /** Enumerate vertices from farthest to nearest from query, keep a
181 * neighbor only if there is no previous neighbor that is closer to
182 * that vertex than the query.
183 */
184 void shrink_neighbor_list(DistanceComputer & qdis,
185 std::priority_queue<NodeDistFarther> &input,
186 std::vector<NodeDistFarther> &output,
187 int max_size)
188 {
189 while (input.size() > 0) {
190 NodeDistFarther v1 = input.top();
191 input.pop();
192 float dist_v1_q = v1.d;
193
194 bool good = true;
195 for (NodeDistFarther v2 : output) {
196 float dist_v1_v2 = qdis.symmetric_dis(v2.id, v1.id);
197
198 if (dist_v1_v2 < dist_v1_q) {
199 good = false;
200 break;
201 }
202 }
203 if (good) {
204 output.push_back(v1);
205 if (output.size() >= max_size)
206 return;
207 }
208 }
209 }
210
211
212
213
214 /// remove neighbors from the list to make it smaller than max_size
215 void shrink_neighbor_list(DistanceComputer & qdis,
216 std::priority_queue<NodeDistCloser> &resultSet1,
217 int max_size)
218 {
219
220 if (resultSet1.size() < max_size) {
221 return;
222 }
223 std::priority_queue<NodeDistFarther> resultSet;
224 std::vector<NodeDistFarther> returnlist;
225
226 while (resultSet1.size() > 0) {
227 resultSet.emplace(resultSet1.top().d, resultSet1.top().id);
228 resultSet1.pop();
229 }
230
231 shrink_neighbor_list (qdis, resultSet, returnlist, max_size);
232
233 for (NodeDistFarther curen2 : returnlist) {
234 resultSet1.emplace(curen2.d, curen2.id);
235 }
236
237 }
238
239
240 /// add a link between two elements, possibly shrinking the list
241 /// of links to make room for it.
242 void add_link(HNSW & hnsw,
243 DistanceComputer & qdis,
244 storage_idx_t src, storage_idx_t dest,
245 int level)
246 {
247 size_t begin, end;
248 hnsw.neighbor_range(src, level, &begin, &end);
249 if (hnsw.neighbors[end - 1] == -1) {
250 // there is enough room, find a slot to add it
251 size_t i = end;
252 while(i > begin) {
253 if (hnsw.neighbors[i - 1] != -1) break;
254 i--;
255 }
256 hnsw.neighbors[i] = dest;
257 return;
258 }
259
260 // otherwise we let them fight out which to keep
261
262 // copy to resultSet...
263 std::priority_queue<NodeDistCloser> resultSet;
264 resultSet.emplace(qdis.symmetric_dis(src, dest), dest);
265 for (size_t i = begin; i < end; i++) { // HERE WAS THE BUG
266 storage_idx_t neigh = hnsw.neighbors[i];
267 resultSet.emplace(qdis.symmetric_dis(src, neigh), neigh);
268 }
269
270 shrink_neighbor_list(qdis, resultSet, end - begin);
271
272 // ...and back
273 size_t i = begin;
274 while (resultSet.size()) {
275 hnsw.neighbors[i++] = resultSet.top().id;
276 resultSet.pop();
277 }
278 // they may have shrunk more than just by 1 element
279 while(i < end) {
280 hnsw.neighbors[i++] = -1;
281 }
282 }
283
284 /// search neighbors on a single level, starting from an entry point
285 void search_neighbors_to_add(HNSW & hnsw,
286 DistanceComputer &qdis,
287 std::priority_queue<NodeDistCloser> &results,
288 int entry_point,
289 float d_entry_point,
290 int level,
291 VisitedTable &vt)
292 {
293 // top is nearest candidate
294 std::priority_queue<NodeDistFarther> candidates;
295
296 NodeDistFarther ev(d_entry_point, entry_point);
297 candidates.push(ev);
298 results.emplace(d_entry_point, entry_point);
299 vt.set(entry_point);
300
301 while (!candidates.empty()) {
302 // get nearest
303 const NodeDistFarther &currEv = candidates.top();
304
305 if (currEv.d > results.top().d) {
306 break;
307 }
308 int currNode = currEv.id;
309 candidates.pop();
310
311 // loop over neighbors
312 size_t begin, end;
313 hnsw.neighbor_range(currNode, level, &begin, &end);
314 for(size_t i = begin; i < end; i++) {
315 storage_idx_t nodeId = hnsw.neighbors[i];
316 if (nodeId < 0) break;
317 if (vt.get(nodeId)) continue;
318 vt.set(nodeId);
319
320 float dis = qdis(nodeId);
321 NodeDistFarther evE1(dis, nodeId);
322
323 if (results.size() < hnsw.efConstruction ||
324 results.top().d > dis) {
325
326 results.emplace(dis, nodeId);
327 candidates.emplace(dis, nodeId);
328 if (results.size() > hnsw.efConstruction) {
329 results.pop();
330 }
331 }
332 }
333 }
334 vt.advance();
335 }
336
337
338 /// Finds neighbors and builds links with them, starting from an entry
339 /// point. The own neighbor list is assumed to be locked.
340 void add_links_starting_from(HNSW & hnsw,
341 DistanceComputer &ptdis,
342 storage_idx_t pt_id,
343 storage_idx_t nearest,
344 float d_nearest,
345 int level,
346 omp_lock_t * locks,
347 VisitedTable &vt)
348 {
349
350 std::priority_queue<NodeDistCloser> link_targets;
351
352 search_neighbors_to_add(
353 hnsw, ptdis, link_targets, nearest, d_nearest,
354 level, vt);
355
356 // but we can afford only this many neighbors
357 int M = hnsw.nb_neighbors(level);
358
359 shrink_neighbor_list(ptdis, link_targets, M);
360
361 while (!link_targets.empty()) {
362 int other_id = link_targets.top().id;
363
364 omp_set_lock(&locks[other_id]);
365 add_link(hnsw, ptdis, other_id, pt_id, level);
366 omp_unset_lock(&locks[other_id]);
367
368 add_link(hnsw, ptdis, pt_id, other_id, level);
369
370 link_targets.pop();
371 }
372 }
373
374 /**************************************************************
375 * Searching subroutines
376 **************************************************************/
377
378 /// greedily update a nearest vector at a given level
379 void greedy_update_nearest(const HNSW & hnsw,
380 DistanceComputer & qdis,
381 int level,
382 storage_idx_t & nearest,
383 float & d_nearest)
384 {
385 for(;;) {
386 storage_idx_t prev_nearest = nearest;
387
388 size_t begin, end;
389 hnsw.neighbor_range(nearest, level, &begin, &end);
390 for(size_t i = begin; i < end; i++) {
391 storage_idx_t v = hnsw.neighbors[i];
392 if (v < 0) break;
393 float dis = qdis(v);
394 if (dis < d_nearest) {
395 nearest = v;
396 d_nearest = dis;
397 }
398 }
399 if (nearest == prev_nearest) {
400 return;
401 }
402 }
403
404 }
405
406
407 /** Do a BFS on the candidates list */
408
409 int search_from_candidates(const HNSW & hnsw,
410 DistanceComputer & qdis, int k,
411 idx_t *I, float * D,
412 MinimaxHeap &candidates,
413 VisitedTable &vt,
414 int level, int nres_in = 0)
415 {
416 int nres = nres_in;
417 int ndis = 0;
418 for (int i = 0; i < candidates.size(); i++) {
419 idx_t v1 = candidates.ids[i];
420 float d = candidates.dis[i];
421 FAISS_ASSERT(v1 >= 0);
422 if (nres < k) {
423 faiss::maxheap_push (++nres, D, I, d, v1);
424 } else if (d < D[0]) {
425 faiss::maxheap_pop (nres--, D, I);
426 faiss::maxheap_push (++nres, D, I, d, v1);
427 }
428 vt.set(v1);
429 }
430
431 bool do_dis_check = hnsw.check_relative_distance;
432 int nstep = 0;
433
434 while (candidates.size() > 0) {
435 float d0 = 0;
436 int v0 = candidates.pop_min(&d0);
437
438 if (do_dis_check) {
439 // tricky stopping condition: there are more that ef
440 // distances that are processed already that are smaller
441 // than d0
442
443 int n_dis_below = candidates.count_below(d0);
444 if(n_dis_below >= hnsw.efSearch) {
445 break;
446 }
447 }
448 size_t begin, end;
449 hnsw.neighbor_range(v0, level, &begin, &end);
450
451 for (size_t j = begin; j < end; j++) {
452 int v1 = hnsw.neighbors[j];
453 if (v1 < 0) break;
454 if (vt.get(v1)) {
455 continue;
456 }
457 vt.set(v1);
458 ndis++;
459 float d = qdis(v1);
460 if (nres < k) {
461 faiss::maxheap_push (++nres, D, I, d, v1);
462 } else if (d < D[0]) {
463 faiss::maxheap_pop (nres--, D, I);
464 faiss::maxheap_push (++nres, D, I, d, v1);
465 }
466 candidates.push(v1, d);
467 }
468
469 nstep++;
470 if (!do_dis_check && nstep > hnsw.efSearch) {
471 break;
472 }
473 }
474
475 if (level == 0) {
476 #pragma omp critical
477 {
478 hnsw_stats.n1 ++;
479 if (candidates.size() == 0)
480 hnsw_stats.n2 ++;
481 hnsw_stats.n3 += ndis;
482 }
483 }
484
485 return nres;
486 }
487
488
489 } // anonymous namespace
490
491
492 /**************************************************************
493 * HNSW structure implementation
494 **************************************************************/
495
496
497 int HNSW::nb_neighbors(int layer_no) const
498 {
499 return cum_nneighbor_per_level[layer_no + 1] -
500 cum_nneighbor_per_level[layer_no];
501 }
502
503 void HNSW::set_nb_neighbors(int level_no, int n)
504 {
505 FAISS_THROW_IF_NOT(levels.size() == 0);
506 int cur_n = nb_neighbors(level_no);
507 for (int i = level_no + 1; i < cum_nneighbor_per_level.size(); i++) {
508 cum_nneighbor_per_level[i] += n - cur_n;
509 }
510 }
511
512 int HNSW::cum_nb_neighbors(int layer_no) const
513 {
514 return cum_nneighbor_per_level[layer_no];
515 }
516
517 void HNSW::neighbor_range(idx_t no, int layer_no,
518 size_t * begin, size_t * end) const
519 {
520 size_t o = offsets[no];
521 *begin = o + cum_nb_neighbors(layer_no);
522 *end = o + cum_nb_neighbors(layer_no + 1);
523 }
524
525
526
527 HNSW::HNSW(int M): rng(12345) {
528 set_default_probas(M, 1.0 / log(M));
529 max_level = -1;
530 entry_point = -1;
531 efSearch = 16;
532 check_relative_distance = true;
533 efConstruction = 40;
534 upper_beam = 1;
535 offsets.push_back(0);
536 }
537
538
539 int HNSW::random_level()
540 {
541 double f = rng.rand_float();
542 // could be a bit faster with bissection
543 for (int level = 0; level < assign_probas.size(); level++) {
544 if (f < assign_probas[level]) {
545 return level;
546 }
547 f -= assign_probas[level];
548 }
549 // happens with exponentially low probability
550 return assign_probas.size() - 1;
551 }
552
553 void HNSW::set_default_probas(int M, float levelMult)
554 {
555 int nn = 0;
556 cum_nneighbor_per_level.push_back (0);
557 for (int level = 0; ;level++) {
558 float proba = exp(-level / levelMult) * (1 - exp(-1 / levelMult));
559 if (proba < 1e-9) break;
560 assign_probas.push_back(proba);
561 nn += level == 0 ? M * 2 : M;
562 cum_nneighbor_per_level.push_back (nn);
563 }
564 }
565
566 void HNSW::clear_neighbor_tables(int level)
567 {
568 for (int i = 0; i < levels.size(); i++) {
569 size_t begin, end;
570 neighbor_range(i, level, &begin, &end);
571 for (size_t j = begin; j < end; j++)
572 neighbors[j] = -1;
573 }
574 }
575
576
577 void HNSW::reset() {
578 max_level = -1;
579 entry_point = -1;
580 offsets.clear();
581 offsets.push_back(0);
582 levels.clear();
583 neighbors.clear();
584 }
585
586
587
588 void HNSW::print_neighbor_stats(int level) const
589 {
590 FAISS_THROW_IF_NOT (level < cum_nneighbor_per_level.size());
591 printf("stats on level %d, max %d neighbors per vertex:\n",
592 level, nb_neighbors(level));
593 size_t tot_neigh = 0, tot_common = 0, tot_reciprocal = 0, n_node = 0;
594 #pragma omp parallel for reduction(+: tot_neigh) reduction(+: tot_common) \
595 reduction(+: tot_reciprocal) reduction(+: n_node)
596 for (int i = 0; i < levels.size(); i++) {
597 if (levels[i] > level) {
598 n_node++;
599 size_t begin, end;
600 neighbor_range(i, level, &begin, &end);
601 std::unordered_set<int> neighset;
602 for (size_t j = begin; j < end; j++) {
603 if (neighbors [j] < 0) break;
604 neighset.insert(neighbors[j]);
605 }
606 int n_neigh = neighset.size();
607 int n_common = 0;
608 int n_reciprocal = 0;
609 for (size_t j = begin; j < end; j++) {
610 storage_idx_t i2 = neighbors[j];
611 if (i2 < 0) break;
612 FAISS_ASSERT(i2 != i);
613 size_t begin2, end2;
614 neighbor_range(i2, level, &begin2, &end2);
615 for (size_t j2 = begin2; j2 < end2; j2++) {
616 storage_idx_t i3 = neighbors[j2];
617 if (i3 < 0) break;
618 if (i3 == i) {
619 n_reciprocal++;
620 continue;
621 }
622 if (neighset.count(i3)) {
623 neighset.erase(i3);
624 n_common++;
625 }
626 }
627 }
628 tot_neigh += n_neigh;
629 tot_common += n_common;
630 tot_reciprocal += n_reciprocal;
631 }
632 }
633 float normalizer = n_node;
634 printf(" nb of nodes at that level %ld\n", n_node);
635 printf(" neighbors per node: %.2f (%ld)\n", tot_neigh / normalizer, tot_neigh);
636 printf(" nb of reciprocal neighbors: %.2f\n", tot_reciprocal / normalizer);
637 printf(" nb of neighbors that are also neighbor-of-neighbors: %.2f (%ld)\n",
638 tot_common / normalizer, tot_common);
639
640
641
642 }
643
644 HNSWStats hnsw_stats;
645
646 void HNSWStats::reset ()
647 {
648 memset(this, 0, sizeof(*this));
649 }
650
651
652
653 /**************************************************************
654 * Building, parallel
655 **************************************************************/
656
657 void HNSW::add_with_locks(
658 DistanceComputer & ptdis, int pt_level, int pt_id,
659 std::vector<omp_lock_t> & locks,
660 VisitedTable &vt)
661 {
662 // greedy search on upper levels
663
664 storage_idx_t nearest;
665 #pragma omp critical
666 {
667 nearest = entry_point;
668
669 if (nearest == -1) {
670 max_level = pt_level;
671 entry_point = pt_id;
672 }
673 }
674
675 if (nearest < 0) {
676 return;
677 }
678
679 omp_set_lock(&locks[pt_id]);
680
681 int level = max_level; // level at which we start adding neighbors
682 float d_nearest = ptdis(nearest);
683
684 for(; level > pt_level; level--) {
685 greedy_update_nearest(*this, ptdis, level, nearest, d_nearest);
686 }
687
688 for(; level >= 0; level--) {
689 add_links_starting_from(*this, ptdis, pt_id, nearest, d_nearest,
690 level, locks.data(), vt);
691 }
692
693 omp_unset_lock(&locks[pt_id]);
694
695 if (pt_level > max_level) {
696 max_level = pt_level;
697 entry_point = pt_id;
698 }
699
700 }
701
702
703
704 /**************************************************************
705 * Searching
706 **************************************************************/
707
708
709
710
711 void HNSW::search(DistanceComputer & qdis,
712 int k, idx_t *I, float * D,
713 VisitedTable &vt) const
714 {
715
716 if (upper_beam == 1) {
717
718 // greedy search on upper levels
719 storage_idx_t nearest = entry_point;
720 float d_nearest = qdis(nearest);
721
722 for(int level = max_level; level >= 1; level--) {
723 greedy_update_nearest(*this, qdis, level, nearest, d_nearest);
724 }
725
726 int candidates_size = std::max(efSearch, k);
727 MinimaxHeap candidates(candidates_size);
728
729 candidates.push(nearest, d_nearest);
730
731 search_from_candidates (
732 *this, qdis, k, I, D, candidates, vt, 0);
733 vt.advance();
734
735 } else {
736
737 int candidates_size = upper_beam;
738 MinimaxHeap candidates(candidates_size);
739
740 std::vector<idx_t> I_to_next(candidates_size);
741 std::vector<float> D_to_next(candidates_size);
742
743 int nres = 1;
744 I_to_next[0] = entry_point;
745 D_to_next[0] = qdis(entry_point);
746
747 for(int level = max_level; level >= 0; level--) {
748
749 // copy I, D -> candidates
750
751 candidates.clear();
752
753 for (int i = 0; i < nres; i++) {
754 candidates.push(I_to_next[i], D_to_next[i]);
755 }
756
757 if (level == 0) {
758 nres = search_from_candidates (
759 *this, qdis, k, I, D, candidates, vt, 0);
760 } else {
761 nres = search_from_candidates (
762 *this, qdis, candidates_size,
763 I_to_next.data(), D_to_next.data(),
764 candidates, vt, level);
765 }
766 vt.advance();
767 }
768
769 }
770 }
771
772 /**************************************************************
773 * add / search blocks of descriptors
774 **************************************************************/
775
776 namespace {
777
778 int prepare_level_tab (HNSW & hnsw, size_t n, bool preset_levels = false)
779 {
780 size_t n0 = hnsw.offsets.size() - 1;
781
782 if (preset_levels) {
783 FAISS_ASSERT (n0 + n == hnsw.levels.size());
784 } else {
785 FAISS_ASSERT (n0 == hnsw.levels.size());
786 for (int i = 0; i < n; i++) {
787 int pt_level = hnsw.random_level();
788 hnsw.levels.push_back(pt_level + 1);
789 }
790 }
791
792 int max_level = 0;
793 for (int i = 0; i < n; i++) {
794 int pt_level = hnsw.levels[i + n0] - 1;
795 if (pt_level > max_level) max_level = pt_level;
796 hnsw.offsets.push_back(hnsw.offsets.back() +
797 hnsw.cum_nb_neighbors(pt_level + 1));
798 hnsw.neighbors.resize(hnsw.offsets.back(), -1);
799 }
800 return max_level;
801 }
802
803 void hnsw_add_vertices(IndexHNSW &index_hnsw,
804 size_t n0,
805 size_t n, const float *x,
806 bool verbose,
807 bool preset_levels = false) {
808 HNSW & hnsw = index_hnsw.hnsw;
809 size_t ntotal = n0 + n;
810 double t0 = getmillisecs();
811 if (verbose) {
812 printf("hnsw_add_vertices: adding %ld elements on top of %ld "
813 "(preset_levels=%d)\n",
814 n, n0, int(preset_levels));
815 }
816
817 int max_level = prepare_level_tab (index_hnsw.hnsw, n, preset_levels);
818
819 if (verbose) {
820 printf(" max_level = %d\n", max_level);
821 }
822
823 std::vector<omp_lock_t> locks(ntotal);
824 for(int i = 0; i < ntotal; i++)
825 omp_init_lock(&locks[i]);
826
827 // add vectors from highest to lowest level
828 std::vector<int> hist;
829 std::vector<int> order(n);
830
831 { // make buckets with vectors of the same level
832
833 // build histogram
834 for (int i = 0; i < n; i++) {
835 storage_idx_t pt_id = i + n0;
836 int pt_level = hnsw.levels[pt_id] - 1;
837 while (pt_level >= hist.size())
838 hist.push_back(0);
839 hist[pt_level] ++;
840 }
841
842 // accumulate
843 std::vector<int> offsets(hist.size() + 1, 0);
844 for (int i = 0; i < hist.size() - 1; i++) {
845 offsets[i + 1] = offsets[i] + hist[i];
846 }
847
848 // bucket sort
849 for (int i = 0; i < n; i++) {
850 storage_idx_t pt_id = i + n0;
851 int pt_level = hnsw.levels[pt_id] - 1;
852 order[offsets[pt_level]++] = pt_id;
853 }
854 }
855
856 { // perform add
857 RandomGenerator rng2(789);
858
859 int i1 = n;
860
861 for (int pt_level = hist.size() - 1; pt_level >= 0; pt_level--) {
862 int i0 = i1 - hist[pt_level];
863
864 if (verbose) {
865 printf("Adding %d elements at level %d\n",
866 i1 - i0, pt_level);
867 }
868
869 // random permutation to get rid of dataset order bias
870 for (int j = i0; j < i1; j++)
871 std::swap(order[j], order[j + rng2.rand_int(i1 - j)]);
872
873 #pragma omp parallel
874 {
875 VisitedTable vt (ntotal);
876
877 DistanceComputer *dis = index_hnsw.get_distance_computer();
878 ScopeDeleter1<DistanceComputer> del(dis);
879 int prev_display = verbose && omp_get_thread_num() == 0 ? 0 : -1;
880
881 #pragma omp for schedule(dynamic)
882 for (int i = i0; i < i1; i++) {
883 storage_idx_t pt_id = order[i];
884 dis->set_query (x + (pt_id - n0) * dis->d);
885
886 hnsw.add_with_locks (
887 *dis, pt_level, pt_id, locks,
888 vt);
889
890 if (prev_display >= 0 && i - i0 > prev_display + 10000) {
891 prev_display = i - i0;
892 printf(" %d / %d\r", i - i0, i1 - i0);
893 fflush(stdout);
894 }
895 }
896 }
897 i1 = i0;
898 }
899 FAISS_ASSERT(i1 == 0);
900 }
901 if (verbose)
902 printf("Done in %.3f ms\n", getmillisecs() - t0);
903
904 for(int i = 0; i < ntotal; i++)
905 omp_destroy_lock(&locks[i]);
906
907 }
908
909
910 } // anonymous namespace
911
912
913 void HNSW::fill_with_random_links(size_t n)
914 {
915 int max_level = prepare_level_tab (*this, n);
916 RandomGenerator rng2(456);
917
918 for (int level = max_level - 1; level >= 0; level++) {
919 std::vector<int> elts;
920 for (int i = 0; i < n; i++) {
921 if (levels[i] > level) {
922 elts.push_back(i);
923 }
924 }
925 printf ("linking %ld elements in level %d\n",
926 elts.size(), level);
927
928 if (elts.size() == 1) continue;
929
930 for (int ii = 0; ii < elts.size(); ii++) {
931 int i = elts[ii];
932 size_t begin, end;
933 neighbor_range(i, 0, &begin, &end);
934 for (size_t j = begin; j < end; j++) {
935 int other = 0;
936 do {
937 other = elts[rng2.rand_int(elts.size())];
938 } while(other == i);
939
940 neighbors[j] = other;
941 }
942
943 }
944
945 }
946
947 }
948
949
950
951
952 /**************************************************************
953 * IndexHNSW implementation
954 **************************************************************/
955
956 IndexHNSW::IndexHNSW(int d, int M):
957 Index(d, METRIC_L2),
958 hnsw(M),
959 own_fields(false),
960 storage(nullptr),
961 reconstruct_from_neighbors(nullptr)
962 {}
963
964 IndexHNSW::IndexHNSW(Index *storage, int M):
965 Index(storage->d, METRIC_L2),
966 hnsw(M),
967 own_fields(false),
968 storage(storage),
969 reconstruct_from_neighbors(nullptr)
970 {}
971
972 IndexHNSW::~IndexHNSW() {
973 if (own_fields) {
974 delete storage;
975 }
976 }
977
978 void IndexHNSW::train(idx_t n, const float* x)
979 {
980 // hnsw structure does not require training
981 storage->train (n, x);
982 is_trained = true;
983 }
984
985 void IndexHNSW::search (idx_t n, const float *x, idx_t k,
986 float *distances, idx_t *labels) const
987
988 {
989
990 #pragma omp parallel
991 {
992 VisitedTable vt (ntotal);
993 DistanceComputer *dis = get_distance_computer();
994 ScopeDeleter1<DistanceComputer> del(dis);
995 size_t nreorder = 0;
996
997 #pragma omp for
998 for(int i = 0; i < n; i++) {
999 idx_t * idxi = labels + i * k;
1000 float * simi = distances + i * k;
1001 dis->set_query(x + i * d);
1002
1003 maxheap_heapify (k, simi, idxi);
1004 hnsw.search (*dis, k, idxi, simi, vt);
1005
1006 maxheap_reorder (k, simi, idxi);
1007
1008 if (reconstruct_from_neighbors &&
1009 reconstruct_from_neighbors->k_reorder != 0) {
1010 int k_reorder = reconstruct_from_neighbors->k_reorder;
1011 if (k_reorder == -1 || k_reorder > k) k_reorder = k;
1012
1013 nreorder += reconstruct_from_neighbors->compute_distances(
1014 k_reorder, idxi, x + i * d, simi);
1015
1016 // sort top k_reorder
1017 maxheap_heapify (k_reorder, simi, idxi, simi, idxi, k_reorder);
1018 maxheap_reorder (k_reorder, simi, idxi);
1019 }
1020 }
1021 #pragma omp critical
1022 {
1023 hnsw_stats.nreorder += nreorder;
1024 }
1025 }
1026
1027
1028 }
1029
1030
1031 void IndexHNSW::add(idx_t n, const float *x)
1032 {
1033 FAISS_THROW_IF_NOT(is_trained);
1034 int n0 = ntotal;
1035 storage->add(n, x);
1036 ntotal = storage->ntotal;
1037
1038 hnsw_add_vertices (*this, n0, n, x, verbose,
1039 hnsw.levels.size() == ntotal);
1040
1041 }
1042
1043 void IndexHNSW::reset()
1044 {
1045 hnsw.reset();
1046 storage->reset();
1047 ntotal = 0;
1048 }
1049
1050 void IndexHNSW::reconstruct (idx_t key, float* recons) const
1051 {
1052 storage->reconstruct(key, recons);
1053 }
1054
1055 void IndexHNSW::shrink_level_0_neighbors(int new_size)
1056 {
1057 #pragma omp parallel
1058 {
1059 DistanceComputer *dis = get_distance_computer();
1060 ScopeDeleter1<DistanceComputer> del(dis);
1061
1062 #pragma omp for
1063 for (idx_t i = 0; i < ntotal; i++) {
1064
1065 size_t begin, end;
1066 hnsw.neighbor_range(i, 0, &begin, &end);
1067
1068 std::priority_queue<NodeDistFarther> initial_list;
1069
1070 for (size_t j = begin; j < end; j++) {
1071 int v1 = hnsw.neighbors[j];
1072 if (v1 < 0) break;
1073 initial_list.emplace(dis->symmetric_dis(i, v1), v1);
1074
1075 // initial_list.emplace(qdis(v1), v1);
1076 }
1077
1078 std::vector<NodeDistFarther> shrunk_list;
1079 shrink_neighbor_list (*dis, initial_list, shrunk_list, new_size);
1080
1081 for (size_t j = begin; j < end; j++) {
1082 if (j - begin < shrunk_list.size())
1083 hnsw.neighbors[j] = shrunk_list[j - begin].id;
1084 else
1085 hnsw.neighbors[j] = -1;
1086 }
1087 }
1088 }
1089
1090 }
1091
1092 void IndexHNSW::search_level_0(
1093 idx_t n, const float *x, idx_t k,
1094 const storage_idx_t *nearest, const float *nearest_d,
1095 float *distances, idx_t *labels, int nprobe,
1096 int search_type) const
1097 {
1098
1099 storage_idx_t ntotal = hnsw.levels.size();
1100 #pragma omp parallel
1101 {
1102 DistanceComputer *qdis = get_distance_computer();
1103 ScopeDeleter1<DistanceComputer> del(qdis);
1104
1105 VisitedTable vt (ntotal);
1106
1107 #pragma omp for
1108 for(idx_t i = 0; i < n; i++) {
1109 idx_t * idxi = labels + i * k;
1110 float * simi = distances + i * k;
1111
1112 qdis->set_query(x + i * d);
1113 maxheap_heapify (k, simi, idxi);
1114
1115 if (search_type == 1) {
1116
1117 int nres = 0;
1118
1119 for(int j = 0; j < nprobe; j++) {
1120 storage_idx_t cj = nearest[i * nprobe + j];
1121
1122 if (cj < 0) break;
1123
1124 if (vt.get(cj)) continue;
1125
1126 int candidates_size = std::max(hnsw.efSearch, int(k));
1127 MinimaxHeap candidates(candidates_size);
1128
1129 candidates.push(cj, nearest_d[i * nprobe + j]);
1130
1131 nres = search_from_candidates (
1132 hnsw, *qdis, k, idxi, simi,
1133 candidates, vt, 0, nres);
1134 }
1135 } else if (search_type == 2) {
1136
1137 int candidates_size = std::max(hnsw.efSearch, int(k));
1138 candidates_size = std::max(candidates_size, nprobe);
1139
1140 MinimaxHeap candidates(candidates_size);
1141 for(int j = 0; j < nprobe; j++) {
1142 storage_idx_t cj = nearest[i * nprobe + j];
1143
1144 if (cj < 0) break;
1145 candidates.push(cj, nearest_d[i * nprobe + j]);
1146 }
1147 search_from_candidates (
1148 hnsw, *qdis, k, idxi, simi,
1149 candidates, vt, 0);
1150
1151 }
1152 vt.advance();
1153
1154 maxheap_reorder (k, simi, idxi);
1155
1156 }
1157 }
1158
1159
1160 }
1161
1162 void IndexHNSW::init_level_0_from_knngraph(
1163 int k, const float *D, const idx_t *I)
1164 {
1165 int dest_size = hnsw.nb_neighbors (0);
1166
1167 #pragma omp parallel for
1168 for (idx_t i = 0; i < ntotal; i++) {
1169 DistanceComputer *qdis = get_distance_computer();
1170 float vec[d];
1171 storage->reconstruct(i, vec);
1172 qdis->set_query(vec);
1173
1174 std::priority_queue<NodeDistFarther> initial_list;
1175
1176 for (size_t j = 0; j < k; j++) {
1177 int v1 = I[i * k + j];
1178 if (v1 == i) continue;
1179 if (v1 < 0) break;
1180 initial_list.emplace(D[i * k + j], v1);
1181 }
1182
1183 std::vector<NodeDistFarther> shrunk_list;
1184 shrink_neighbor_list (*qdis, initial_list, shrunk_list, dest_size);
1185
1186 size_t begin, end;
1187 hnsw.neighbor_range(i, 0, &begin, &end);
1188
1189 for (size_t j = begin; j < end; j++) {
1190 if (j - begin < shrunk_list.size())
1191 hnsw.neighbors[j] = shrunk_list[j - begin].id;
1192 else
1193 hnsw.neighbors[j] = -1;
1194 }
1195 }
1196 }
1197
1198
1199
1200 void IndexHNSW::init_level_0_from_entry_points(
1201 int n, const storage_idx_t *points,
1202 const storage_idx_t *nearests)
1203 {
1204
1205 std::vector<omp_lock_t> locks(ntotal);
1206 for(int i = 0; i < ntotal; i++)
1207 omp_init_lock(&locks[i]);
1208
1209 #pragma omp parallel
1210 {
1211 VisitedTable vt (ntotal);
1212
1213 DistanceComputer *dis = get_distance_computer();
1214 ScopeDeleter1<DistanceComputer> del(dis);
1215 float vec[storage->d];
1216
1217 #pragma omp for schedule(dynamic)
1218 for (int i = 0; i < n; i++) {
1219 storage_idx_t pt_id = points[i];
1220 storage_idx_t nearest = nearests[i];
1221 storage->reconstruct (pt_id, vec);
1222 dis->set_query (vec);
1223
1224 add_links_starting_from(hnsw, *dis, pt_id, nearest, (*dis)(nearest),
1225 0, locks.data(), vt);
1226
1227 if (verbose && i % 10000 == 0) {
1228 printf(" %d / %d\r", i, n);
1229 fflush(stdout);
1230 }
1231 }
1232 }
1233 if (verbose) {
1234 printf("\n");
1235 }
1236
1237 for(int i = 0; i < ntotal; i++)
1238 omp_destroy_lock(&locks[i]);
1239 }
1240
1241 void IndexHNSW::reorder_links()
1242 {
1243 int M = hnsw.nb_neighbors(0);
1244
1245 #pragma omp parallel
1246 {
1247 std::vector<float> distances (M);
1248 std::vector<size_t> order (M);
1249 std::vector<storage_idx_t> tmp (M);
1250 DistanceComputer *dis = get_distance_computer();
1251 ScopeDeleter1<DistanceComputer> del(dis);
1252
1253 #pragma omp for
1254 for(storage_idx_t i = 0; i < ntotal; i++) {
1255
1256 size_t begin, end;
1257 hnsw.neighbor_range(i, 0, &begin, &end);
1258
1259 for (size_t j = begin; j < end; j++) {
1260 storage_idx_t nj = hnsw.neighbors[j];
1261 if (nj < 0) {
1262 end = j;
1263 break;
1264 }
1265 distances[j - begin] = dis->symmetric_dis(i, nj);
1266 tmp [j - begin] = nj;
1267 }
1268
1269 fvec_argsort (end - begin, distances.data(), order.data());
1270 for (size_t j = begin; j < end; j++) {
1271 hnsw.neighbors[j] = tmp[order[j - begin]];
1272 }
1273 }
1274
1275 }
1276 }
1277
1278
1279 void IndexHNSW::link_singletons()
1280 {
1281 printf("search for singletons\n");
1282
1283 std::vector<bool> seen(ntotal);
1284
1285 for (size_t i = 0; i < ntotal; i++) {
1286 size_t begin, end;
1287 hnsw.neighbor_range(i, 0, &begin, &end);
1288 for (size_t j = begin; j < end; j++) {
1289 storage_idx_t ni = hnsw.neighbors[j];
1290 if (ni >= 0) seen[ni] = true;
1291 }
1292 }
1293
1294 int n_sing = 0, n_sing_l1 = 0;
1295 std::vector<storage_idx_t> singletons;
1296 for (storage_idx_t i = 0; i < ntotal; i++) {
1297 if (!seen[i]) {
1298 singletons.push_back(i);
1299 n_sing++;
1300 if (hnsw.levels[i] > 1)
1301 n_sing_l1++;
1302 }
1303 }
1304
1305 printf(" Found %d / %ld singletons (%d appear in a level above)\n",
1306 n_sing, ntotal, n_sing_l1);
1307
1308 std::vector<float>recons(singletons.size() * d);
1309 for (int i = 0; i < singletons.size(); i++) {
1310
1311 FAISS_ASSERT(!"not implemented");
1312
1313 }
1314
1315
1316 }
1317
1318
1319
1320
1321 // storage that explicitly reconstructs vectors before computing distances
1322 struct GenericDistanceComputer: HNSW::DistanceComputer {
1323
1324 const Index & storage;
1325 std::vector<float> buf;
1326 const float *q;
1327
1328 GenericDistanceComputer(const Index & storage): storage(storage)
1329 {
1330 d = storage.d;
1331 buf.resize(d * 2);
1332 }
1333
1334 float operator () (storage_idx_t i) override
1335 {
1336 storage.reconstruct(i, buf.data());
1337 return fvec_L2sqr(q, buf.data(), d);
1338 }
1339
1340 float symmetric_dis(storage_idx_t i, storage_idx_t j) override
1341 {
1342 storage.reconstruct(i, buf.data());
1343 storage.reconstruct(j, buf.data() + d);
1344 return fvec_L2sqr(buf.data() + d, buf.data(), d);
1345 }
1346
1347 void set_query(const float *x) override {
1348 q = x;
1349 }
1350
1351
1352 };
1353
1354 HNSW::DistanceComputer * IndexHNSW::get_distance_computer () const
1355 {
1356 return new GenericDistanceComputer (*storage);
1357 }
1358
1359
1360 /**************************************************************
1361 * ReconstructFromNeighbors implementation
1362 **************************************************************/
1363
1364
1365 ReconstructFromNeighbors::ReconstructFromNeighbors(
1366 const IndexHNSW & index, size_t k, size_t nsq):
1367 index(index), k(k), nsq(nsq) {
1368 M = index.hnsw.nb_neighbors(0);
1369 FAISS_ASSERT(k <= 256);
1370 code_size = k == 1 ? 0 : nsq;
1371 ntotal = 0;
1372 d = index.d;
1373 FAISS_ASSERT(d % nsq == 0);
1374 dsub = d / nsq;
1375 k_reorder = -1;
1376 }
1377
1378 void ReconstructFromNeighbors::reconstruct(storage_idx_t i, float *x, float *tmp) const
1379 {
1380
1381
1382 const HNSW & hnsw = index.hnsw;
1383 size_t begin, end;
1384 hnsw.neighbor_range(i, 0, &begin, &end);
1385
1386 if (k == 1 || nsq == 1) {
1387 const float * beta;
1388 if (k == 1) {
1389 beta = codebook.data();
1390 } else {
1391 int idx = codes[i];
1392 beta = codebook.data() + idx * (M + 1);
1393 }
1394
1395 float w0 = beta[0]; // weight of image itself
1396 index.storage->reconstruct(i, tmp);
1397
1398 for (int l = 0; l < d; l++)
1399 x[l] = w0 * tmp[l];
1400
1401 for (size_t j = begin; j < end; j++) {
1402
1403 storage_idx_t ji = hnsw.neighbors[j];
1404 if (ji < 0) ji = i;
1405 float w = beta[j - begin + 1];
1406 index.storage->reconstruct(ji, tmp);
1407 for (int l = 0; l < d; l++)
1408 x[l] += w * tmp[l];
1409 }
1410 } else if (nsq == 2) {
1411 int idx0 = codes[2 * i];
1412 int idx1 = codes[2 * i + 1];
1413
1414 const float *beta0 = codebook.data() + idx0 * (M + 1);
1415 const float *beta1 = codebook.data() + (idx1 + k) * (M + 1);
1416
1417 index.storage->reconstruct(i, tmp);
1418
1419 float w0;
1420
1421 w0 = beta0[0];
1422 for (int l = 0; l < dsub; l++)
1423 x[l] = w0 * tmp[l];
1424
1425 w0 = beta1[0];
1426 for (int l = dsub; l < d; l++)
1427 x[l] = w0 * tmp[l];
1428
1429 for (size_t j = begin; j < end; j++) {
1430 storage_idx_t ji = hnsw.neighbors[j];
1431 if (ji < 0) ji = i;
1432 index.storage->reconstruct(ji, tmp);
1433 float w;
1434 w = beta0[j - begin + 1];
1435 for (int l = 0; l < dsub; l++)
1436 x[l] += w * tmp[l];
1437
1438 w = beta1[j - begin + 1];
1439 for (int l = dsub; l < d; l++)
1440 x[l] += w * tmp[l];
1441 }
1442 } else {
1443 const float *betas[nsq];
1444 {
1445 const float *b = codebook.data();
1446 const uint8_t *c = &codes[i * code_size];
1447 for (int sq = 0; sq < nsq; sq++) {
1448 betas[sq] = b + (*c++) * (M + 1);
1449 b += (M + 1) * k;
1450 }
1451 }
1452
1453 index.storage->reconstruct(i, tmp);
1454 {
1455 int d0 = 0;
1456 for (int sq = 0; sq < nsq; sq++) {
1457 float w = *(betas[sq]++);
1458 int d1 = d0 + dsub;
1459 for (int l = d0; l < d1; l++) {
1460 x[l] = w * tmp[l];
1461 }
1462 d0 = d1;
1463 }
1464 }
1465
1466 for (size_t j = begin; j < end; j++) {
1467 storage_idx_t ji = hnsw.neighbors[j];
1468 if (ji < 0) ji = i;
1469
1470 index.storage->reconstruct(ji, tmp);
1471 int d0 = 0;
1472 for (int sq = 0; sq < nsq; sq++) {
1473 float w = *(betas[sq]++);
1474 int d1 = d0 + dsub;
1475 for (int l = d0; l < d1; l++) {
1476 x[l] += w * tmp[l];
1477 }
1478 d0 = d1;
1479 }
1480 }
1481 }
1482 }
1483
1484 void ReconstructFromNeighbors::reconstruct_n(storage_idx_t n0,
1485 storage_idx_t ni,
1486 float *x) const
1487 {
1488 #pragma omp parallel
1489 {
1490 std::vector<float> tmp(index.d);
1491 #pragma omp for
1492 for (storage_idx_t i = 0; i < ni; i++) {
1493 reconstruct(n0 + i, x + i * index.d, tmp.data());
1494 }
1495 }
1496 }
1497
1498 size_t ReconstructFromNeighbors::compute_distances(size_t n, const idx_t *shortlist,
1499 const float *query, float *distances) const
1500 {
1501 std::vector<float> tmp(2 * index.d);
1502 size_t ncomp = 0;
1503 for (int i = 0; i < n; i++) {
1504 if (shortlist[i] < 0) break;
1505 reconstruct(shortlist[i], tmp.data(), tmp.data() + index.d);
1506 distances[i] = fvec_L2sqr(query, tmp.data(), index.d);
1507 ncomp++;
1508 }
1509 return ncomp;
1510 }
1511
1512 void ReconstructFromNeighbors::get_neighbor_table(storage_idx_t i, float *tmp1) const
1513 {
1514 const HNSW & hnsw = index.hnsw;
1515 size_t begin, end;
1516 hnsw.neighbor_range(i, 0, &begin, &end);
1517 size_t d = index.d;
1518
1519 index.storage->reconstruct(i, tmp1);
1520
1521 for (size_t j = begin; j < end; j++) {
1522 storage_idx_t ji = hnsw.neighbors[j];
1523 if (ji < 0) ji = i;
1524 index.storage->reconstruct(ji, tmp1 + (j - begin + 1) * d);
1525 }
1526
1527 }
1528
1529
1530 /// called by add_codes
1531 void ReconstructFromNeighbors::estimate_code(
1532 const float *x, storage_idx_t i, uint8_t *code) const
1533 {
1534
1535 // fill in tmp table with the neighbor values
1536 float *tmp1 = new float[d * (M + 1) + (d * k)];
1537 float *tmp2 = tmp1 + d * (M + 1);
1538 ScopeDeleter<float> del(tmp1);
1539
1540 // collect coordinates of base
1541 get_neighbor_table (i, tmp1);
1542
1543 for (int sq = 0; sq < nsq; sq++) {
1544 int d0 = sq * dsub;
1545 int d1 = d0 + dsub;
1546
1547 {
1548 FINTEGER ki = k, di = d, m1 = M + 1;
1549 FINTEGER dsubi = dsub;
1550 float zero = 0, one = 1;
1551
1552 sgemm_ ("N", "N", &dsubi, &ki, &m1, &one,
1553 tmp1 + d0, &di,
1554 codebook.data() + sq * (m1 * k), &m1,
1555 &zero, tmp2, &dsubi);
1556 }
1557
1558 float min = HUGE_VAL;
1559 int argmin = -1;
1560 for (int j = 0; j < k; j++) {
1561 float dis = fvec_L2sqr(x + d0, tmp2 + j * dsub, dsub);
1562 if (dis < min) {
1563 min = dis;
1564 argmin = j;
1565 }
1566 }
1567 code[sq] = argmin;
1568 }
1569
1570 }
1571
1572 void ReconstructFromNeighbors::add_codes(size_t n, const float *x)
1573 {
1574 if (k == 1) { // nothing to encode
1575 ntotal += n;
1576 return;
1577 }
1578 codes.resize(codes.size() + code_size * n);
1579 #pragma omp parallel for
1580 for (int i = 0; i < n; i++) {
1581 estimate_code(x + i * index.d, ntotal + i,
1582 codes.data() + (ntotal + i) * code_size);
1583 }
1584 ntotal += n;
1585 FAISS_ASSERT (codes.size() == ntotal * code_size);
1586 }
1587
1588
1589 /**************************************************************
1590 * IndexHNSWFlat implementation
1591 **************************************************************/
1592
1593
1594 struct FlatL2Dis: HNSW::DistanceComputer {
1595 Index::idx_t nb;
1596 const float *q;
1597 const float *b;
1598 size_t ndis;
1599
1600 float operator () (storage_idx_t i) override
1601 {
1602 ndis++;
1603 return (fvec_L2sqr(q, b + i * d, d));
1604 }
1605
1606 float symmetric_dis(storage_idx_t i, storage_idx_t j) override
1607 {
1608 return (fvec_L2sqr(b + j * d, b + i * d, d));
1609 }
1610
1611
1612 FlatL2Dis(const IndexFlatL2 & storage, const float *q = nullptr):
1613 q(q)
1614 {
1615 nb = storage.ntotal;
1616 d = storage.d;
1617 b = storage.xb.data();
1618 ndis = 0;
1619 }
1620
1621 void set_query(const float *x) override {
1622 q = x;
1623 }
1624
1625 virtual ~FlatL2Dis () {
1626 #pragma omp critical
1627 {
1628 hnsw_stats.ndis += ndis;
1629 }
1630 }
1631 };
1632
1633
1634 IndexHNSWFlat::IndexHNSWFlat()
1635 {
1636 is_trained = true;
1637 }
1638
1639
1640 IndexHNSWFlat::IndexHNSWFlat(int d, int M):
1641 IndexHNSW(new IndexFlatL2(d), M)
1642 {
1643 own_fields = true;
1644 is_trained = true;
1645 }
1646
1647
1648 HNSW::DistanceComputer * IndexHNSWFlat::get_distance_computer () const
1649 {
1650 return new FlatL2Dis (*dynamic_cast<IndexFlatL2*> (storage));
1651 }
1652
1653
1654
1655
1656 /**************************************************************
1657 * IndexHNSWPQ implementation
1658 **************************************************************/
1659
1660
1661 struct PQDis: HNSW::DistanceComputer {
1662 Index::idx_t nb;
1663 const uint8_t *codes;
1664 size_t code_size;
1665 const ProductQuantizer & pq;
1666 const float *sdc;
1667 std::vector<float> precomputed_table;
1668 size_t ndis;
1669
1670 float operator () (storage_idx_t i) override
1671 {
1672 const uint8_t *code = codes + i * code_size;
1673 const float *dt = precomputed_table.data();
1674 float accu = 0;
1675 for (int j = 0; j < pq.M; j++) {
1676 accu += dt[*code++];
1677 dt += 256;
1678 }
1679 ndis++;
1680 return accu;
1681 }
1682
1683 float symmetric_dis(storage_idx_t i, storage_idx_t j) override
1684 {
1685 const float * sdci = sdc;
1686 float accu = 0;
1687 const uint8_t *codei = codes + i * code_size;
1688 const uint8_t *codej = codes + j * code_size;
1689
1690 for (int l = 0; l < pq.M; l++) {
1691 accu += sdci[(*codei++) + (*codej++) * 256];
1692 sdci += 256 * 256;
1693 }
1694 return accu;
1695 }
1696
1697
1698 PQDis(const IndexPQ & storage, const float *q = nullptr):
1699 pq(storage.pq)
1700 {
1701 precomputed_table.resize(pq.M * pq.ksub);
1702 nb = storage.ntotal;
1703 d = storage.d;
1704 codes = storage.codes.data();
1705 code_size = pq.code_size;
1706 FAISS_ASSERT(pq.ksub == 256);
1707 FAISS_ASSERT(pq.sdc_table.size() == pq.ksub * pq.ksub * pq.M);
1708 sdc = pq.sdc_table.data();
1709 ndis = 0;
1710 }
1711
1712 void set_query(const float *x) override {
1713 pq.compute_distance_table(x, precomputed_table.data());
1714 }
1715
1716 virtual ~PQDis () {
1717 #pragma omp critical
1718 {
1719 hnsw_stats.ndis += ndis;
1720 }
1721 }
1722 };
1723
1724 IndexHNSWPQ::IndexHNSWPQ() {}
1725
1726 IndexHNSWPQ::IndexHNSWPQ(int d, int pq_m, int M):
1727 IndexHNSW(new IndexPQ(d, pq_m, 8), M)
1728 {
1729 own_fields = true;
1730 is_trained = false;
1731 }
1732
1733 void IndexHNSWPQ::train(idx_t n, const float* x)
1734 {
1735 IndexHNSW::train (n, x);
1736 (dynamic_cast<IndexPQ*> (storage))->pq.compute_sdc_table();
1737 }
1738
1739
1740
1741 HNSW::DistanceComputer * IndexHNSWPQ::get_distance_computer () const
1742 {
1743 return new PQDis (*dynamic_cast<IndexPQ*> (storage));
1744 }
1745
1746
1747 /**************************************************************
1748 * IndexHNSWSQ implementation
1749 **************************************************************/
1750
1751
1752 struct SQDis: HNSW::DistanceComputer {
1753 Index::idx_t nb;
1754 const uint8_t *codes;
1755 size_t code_size;
1756 const ScalarQuantizer & sq;
1757 const float *q;
1758 ScalarQuantizer::DistanceComputer * dc;
1759
1760 float operator () (storage_idx_t i) override
1761 {
1762 const uint8_t *code = codes + i * code_size;
1763
1764 return dc->compute_distance (q, code);
1765 }
1766
1767 float symmetric_dis(storage_idx_t i, storage_idx_t j) override
1768 {
1769 const uint8_t *codei = codes + i * code_size;
1770 const uint8_t *codej = codes + j * code_size;
1771 return dc->compute_code_distance (codei, codej);
1772 }
1773
1774
1775 SQDis(const IndexScalarQuantizer & storage, const float *q = nullptr):
1776 sq(storage.sq)
1777 {
1778 nb = storage.ntotal;
1779 d = storage.d;
1780 codes = storage.codes.data();
1781 code_size = sq.code_size;
1782 dc = sq.get_distance_computer();
1783 }
1784
1785 void set_query(const float *x) override {
1786 q = x;
1787 }
1788
1789 virtual ~SQDis () {
1790 delete dc;
1791 }
1792 };
1793
1794 IndexHNSWSQ::IndexHNSWSQ(int d, ScalarQuantizer::QuantizerType qtype, int M):
1795 IndexHNSW (new IndexScalarQuantizer (d, qtype), M)
1796 {
1797 own_fields = true;
1798 }
1799
1800 IndexHNSWSQ::IndexHNSWSQ() {}
1801
1802 HNSW::DistanceComputer * IndexHNSWSQ::get_distance_computer () const
1803 {
1804 return new SQDis (*dynamic_cast<IndexScalarQuantizer*> (storage));
1805 }
1806
1807
1808
1809
1810 /**************************************************************
1811 * IndexHNSW2Level implementation
1812 **************************************************************/
1813
1814
1815
1816 IndexHNSW2Level::IndexHNSW2Level(Index *quantizer, size_t nlist, int m_pq, int M):
1817 IndexHNSW (new Index2Layer (quantizer, nlist, m_pq), M)
1818 {
1819 own_fields = true;
1820 is_trained = false;
1821 }
1822
1823 IndexHNSW2Level::IndexHNSW2Level() {}
1824
1825 struct Distance2Level: HNSW::DistanceComputer {
1826
1827 const Index2Layer & storage;
1828 std::vector<float> buf;
1829 const float *q;
1830
1831 const float *pq_l1_tab, *pq_l2_tab;
1832
1833 Distance2Level(const Index2Layer & storage): storage(storage)
1834 {
1835 d = storage.d;
1836 FAISS_ASSERT(storage.pq.dsub == 4);
1837 pq_l2_tab = storage.pq.centroids.data();
1838 buf.resize(2 * d);
1839 }
1840
1841 float symmetric_dis(storage_idx_t i, storage_idx_t j) override
1842 {
1843 storage.reconstruct(i, buf.data());
1844 storage.reconstruct(j, buf.data() + d);
1845 return fvec_L2sqr(buf.data() + d, buf.data(), d);
1846 }
1847
1848 void set_query(const float *x) override {
1849 q = x;
1850 }
1851
1852
1853 };
1854
1855
1856 // well optimized for xNN+PQNN
1857 struct DistanceXPQ4: Distance2Level {
1858
1859 int M, k;
1860
1861 DistanceXPQ4(const Index2Layer & storage):
1862 Distance2Level (storage)
1863 {
1864 const IndexFlat *quantizer =
1865 dynamic_cast<IndexFlat*> (storage.q1.quantizer);
1866
1867 FAISS_ASSERT(quantizer);
1868 M = storage.pq.M;
1869 pq_l1_tab = quantizer->xb.data();
1870 }
1871
1872 float operator () (storage_idx_t i) override
1873 {
1874 const uint8_t *code = storage.codes.data() + i * storage.code_size;
1875 long key = 0;
1876 memcpy (&key, code, storage.code_size_1);
1877 code += storage.code_size_1;
1878
1879 // walking pointers
1880 const float *qa = q;
1881 const __m128 *l1_t = (const __m128 *)(pq_l1_tab + d * key);
1882 const __m128 *pq_l2_t = (const __m128 *)pq_l2_tab;
1883 __m128 accu = _mm_setzero_ps();
1884
1885 for (int m = 0; m < M; m++) {
1886 __m128 qi = _mm_loadu_ps(qa);
1887 __m128 recons = l1_t[m] + pq_l2_t[*code++];
1888 __m128 diff = qi - recons;
1889 accu += diff * diff;
1890 pq_l2_t += 256;
1891 qa += 4;
1892 }
1893
1894 accu = _mm_hadd_ps (accu, accu);
1895 accu = _mm_hadd_ps (accu, accu);
1896 return _mm_cvtss_f32 (accu);
1897 }
1898
1899 };
1900
1901 // well optimized for 2xNN+PQNN
1902 struct Distance2xXPQ4: Distance2Level {
1903
1904 int M_2, mi_nbits;
1905
1906 Distance2xXPQ4(const Index2Layer & storage):
1907 Distance2Level (storage)
1908 {
1909 const MultiIndexQuantizer *mi =
1910 dynamic_cast<MultiIndexQuantizer*> (storage.q1.quantizer);
1911
1912 FAISS_ASSERT(mi);
1913 FAISS_ASSERT(storage.pq.M % 2 == 0);
1914 M_2 = storage.pq.M / 2;
1915 mi_nbits = mi->pq.nbits;
1916 pq_l1_tab = mi->pq.centroids.data();
1917 }
1918
1919 float operator () (storage_idx_t i) override
1920 {
1921 const uint8_t *code = storage.codes.data() + i * storage.code_size;
1922 long key01 = 0;
1923 memcpy (&key01, code, storage.code_size_1);
1924 code += storage.code_size_1;
1925
1926 // walking pointers
1927 const float *qa = q;
1928 const __m128 *pq_l1_t = (const __m128 *)pq_l1_tab;
1929 const __m128 *pq_l2_t = (const __m128 *)pq_l2_tab;
1930 __m128 accu = _mm_setzero_ps();
1931
1932 for (int mi_m = 0; mi_m < 2; mi_m++) {
1933 long l1_idx = key01 & ((1L << mi_nbits) - 1);
1934 const __m128 * pq_l1 = pq_l1_t + M_2 * l1_idx;
1935
1936 for (int m = 0; m < M_2; m++) {
1937 __m128 qi = _mm_loadu_ps(qa);
1938 __m128 recons = pq_l1[m] + pq_l2_t[*code++];
1939 __m128 diff = qi - recons;
1940 accu += diff * diff;
1941 pq_l2_t += 256;
1942 qa += 4;
1943 }
1944 pq_l1_t += M_2 << mi_nbits;
1945 key01 >>= mi_nbits;
1946 }
1947 accu = _mm_hadd_ps (accu, accu);
1948 accu = _mm_hadd_ps (accu, accu);
1949 return _mm_cvtss_f32 (accu);
1950 }
1951
1952 };
1953
1954
1955
1956 HNSW::DistanceComputer * IndexHNSW2Level::get_distance_computer () const
1957 {
1958 const Index2Layer *storage2l =
1959 dynamic_cast<Index2Layer*>(storage);
1960
1961 if (storage2l) {
1962
1963 const MultiIndexQuantizer *mi =
1964 dynamic_cast<MultiIndexQuantizer*> (storage2l->q1.quantizer);
1965
1966 if (mi && storage2l->pq.M % 2 == 0 && storage2l->pq.dsub == 4) {
1967 return new Distance2xXPQ4(*storage2l);
1968 }
1969
1970 const IndexFlat *fl =
1971 dynamic_cast<IndexFlat*> (storage2l->q1.quantizer);
1972
1973 if (fl && storage2l->pq.dsub == 4) {
1974 return new DistanceXPQ4(*storage2l);
1975 }
1976 }
1977
1978 // IVFPQ and cases not handled above
1979 return new GenericDistanceComputer (*storage);
1980
1981 }
1982
1983
1984 namespace {
1985
1986
1987 // same as search_from_candidates but uses v
1988 // visno -> is in result list
1989 // visno + 1 -> in result list + in candidates
1990 int search_from_candidates_2(const HNSW & hnsw,
1991 DistanceComputer & qdis, int k,
1992 idx_t *I, float * D,
1993 MinimaxHeap &candidates,
1994 VisitedTable &vt,
1995 int level, int nres_in = 0)
1996 {
1997 int nres = nres_in;
1998 int ndis = 0;
1999 for (int i = 0; i < candidates.size(); i++) {
2000 idx_t v1 = candidates.ids[i];
2001 float d = candidates.dis[i];
2002 FAISS_ASSERT(v1 >= 0);
2003 vt.visited[v1] = vt.visno + 1;
2004 }
2005
2006 bool do_dis_check = hnsw.check_relative_distance;
2007 int nstep = 0;
2008
2009 while (candidates.size() > 0) {
2010 float d0 = 0;
2011 int v0 = candidates.pop_min(&d0);
2012
2013 if (do_dis_check) {
2014 // tricky stopping condition: there are more that ef
2015 // distances that are processed already that are smaller
2016 // than d0
2017
2018 int n_dis_below = candidates.count_below(d0);
2019 if(n_dis_below >= hnsw.efSearch) {
2020 break;
2021 }
2022 }
2023 size_t begin, end;
2024 hnsw.neighbor_range(v0, level, &begin, &end);
2025
2026 for (size_t j = begin; j < end; j++) {
2027 int v1 = hnsw.neighbors[j];
2028 if (v1 < 0) break;
2029 if (vt.visited[v1] == vt.visno + 1) {
2030 // nothing to do
2031 } else {
2032 ndis++;
2033 float d = qdis(v1);
2034 candidates.push(v1, d);
2035
2036 // never seen before --> add to heap
2037 if (vt.visited[v1] < vt.visno) {
2038 if (nres < k) {
2039 faiss::maxheap_push (++nres, D, I, d, v1);
2040 } else if (d < D[0]) {
2041 faiss::maxheap_pop (nres--, D, I);
2042 faiss::maxheap_push (++nres, D, I, d, v1);
2043 }
2044 }
2045 vt.visited[v1] = vt.visno + 1;
2046 }
2047 }
2048
2049 nstep++;
2050 if (!do_dis_check && nstep > hnsw.efSearch) {
2051 break;
2052 }
2053 }
2054
2055 if (level == 0) {
2056 #pragma omp critical
2057 {
2058 hnsw_stats.n1 ++;
2059 if (candidates.size() == 0)
2060 hnsw_stats.n2 ++;
2061 }
2062 }
2063
2064
2065 return nres;
2066 }
2067
2068
2069 } // anonymous namespace
2070
2071 void IndexHNSW2Level::search (idx_t n, const float *x, idx_t k,
2072 float *distances, idx_t *labels) const
2073 {
2074 if (dynamic_cast<const Index2Layer*>(storage)) {
2075 IndexHNSW::search (n, x, k, distances, labels);
2076
2077 } else { // "mixed" search
2078
2079 const IndexIVFPQ *index_ivfpq =
2080 dynamic_cast<const IndexIVFPQ*>(storage);
2081
2082 int nprobe = index_ivfpq->nprobe;
2083
2084 long * coarse_assign = new long [n * nprobe];
2085 ScopeDeleter<long> del (coarse_assign);
2086 float * coarse_dis = new float [n * nprobe];
2087 ScopeDeleter<float> del2 (coarse_dis);
2088
2089 index_ivfpq->quantizer->search (n, x, nprobe, coarse_dis, coarse_assign);
2090
2091 index_ivfpq->search_preassigned (
2092 n, x, k, coarse_assign, coarse_dis, distances, labels, false);
2093
2094 #pragma omp parallel
2095 {
2096 VisitedTable vt (ntotal);
2097 DistanceComputer *dis = get_distance_computer();
2098 ScopeDeleter1<DistanceComputer> del(dis);
2099
2100 int candidates_size = hnsw.upper_beam;
2101 MinimaxHeap candidates(candidates_size);
2102
2103 #pragma omp for
2104 for(int i = 0; i < n; i++) {
2105 idx_t * idxi = labels + i * k;
2106 float * simi = distances + i * k;
2107 dis->set_query(x + i * d);
2108
2109 // mark all inverted list elements as visited
2110
2111 for (int j = 0; j < nprobe; j++) {
2112 idx_t key = coarse_assign[j + i * nprobe];
2113 if (key < 0) break;
2114 size_t list_length = index_ivfpq->get_list_size (key);
2115 const idx_t * ids = index_ivfpq->invlists->get_ids (key);
2116
2117 for (int jj = 0; jj < list_length; jj++) {
2118 vt.set (ids[jj]);
2119 }
2120 }
2121
2122 candidates.clear();
2123 // copy the upper_beam elements to candidates list
2124
2125 int search_policy = 2;
2126
2127 if (search_policy == 1) {
2128
2129 for (int j = 0 ; j < hnsw.upper_beam && j < k; j++) {
2130 if (idxi[j] < 0) break;
2131 candidates.push (idxi[j], simi[j]);
2132 // search_from_candidates adds them back
2133 idxi[j] = -1;
2134 simi[j] = HUGE_VAL;
2135 }
2136
2137 // reorder from sorted to heap
2138 maxheap_heapify (k, simi, idxi, simi, idxi, k);
2139
2140 search_from_candidates (
2141 hnsw, *dis, k, idxi, simi,
2142 candidates, vt, 0, k);
2143
2144 vt.advance();
2145
2146 } else if (search_policy == 2) {
2147
2148 for (int j = 0 ; j < hnsw.upper_beam && j < k; j++) {
2149 if (idxi[j] < 0) break;
2150 candidates.push (idxi[j], simi[j]);
2151 }
2152
2153 // reorder from sorted to heap
2154 maxheap_heapify (k, simi, idxi, simi, idxi, k);
2155
2156 search_from_candidates_2 (
2157 hnsw, *dis, k, idxi, simi,
2158 candidates, vt, 0, k);
2159 vt.advance ();
2160 vt.advance ();
2161
2162 }
2163
2164 maxheap_reorder (k, simi, idxi);
2165 }
2166 }
2167 }
2168
2169
2170 }
2171
2172
2173 void IndexHNSW2Level::flip_to_ivf ()
2174 {
2175 Index2Layer *storage2l =
2176 dynamic_cast<Index2Layer*>(storage);
2177
2178 FAISS_THROW_IF_NOT (storage2l);
2179
2180 IndexIVFPQ * index_ivfpq =
2181 new IndexIVFPQ (storage2l->q1.quantizer,
2182 d, storage2l->q1.nlist,
2183 storage2l->pq.M, 8);
2184 index_ivfpq->pq = storage2l->pq;
2185 index_ivfpq->is_trained = storage2l->is_trained;
2186 index_ivfpq->precompute_table();
2187 index_ivfpq->own_fields = storage2l->q1.own_fields;
2188 storage2l->transfer_to_IVFPQ(*index_ivfpq);
2189 index_ivfpq->make_direct_map (true);
2190
2191 storage = index_ivfpq;
2192 delete storage2l;
2193
2194 }
2195
2196
2197 } // 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:355
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:1425
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::InvertedLists::get_ids
virtual const idx_t * get_ids(size_t list_no) const =0
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:213
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:173
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:159
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:198
faiss::Index2Layer::codes
std::vector< uint8_t > codes
Codes. Size ntotal * code_size.
Definition: IndexIVFPQ.h:204
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:196
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:411
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:257
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::IndexIVF::invlists
InvertedLists * invlists
Acess to the actual data.
Definition: IndexIVF.h:168
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:207
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:930
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:201
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:1447
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:56