Faiss

- Main Page
- Namespaces
- Classes
- Files

- File List

All Classes Namespaces Functions Variables Typedefs Enumerations Enumerator Friends
/data/users/hoss/faiss/IndexHNSW.cpp
1 /**
2 * Copyright (c) Facebook, Inc. and its affiliates.
3 *
4 * This source code is licensed under the MIT license found in the
5 * LICENSE file in the root directory of this source tree.
6 */
7
8 // -*- c++ -*-
9
10 #include "IndexHNSW.h"
11
12
13 #include <cstdlib>
14 #include <cassert>
15 #include <cstring>
16 #include <cstdio>
17 #include <cmath>
18 #include <omp.h>
19
20 #include <unordered_set>
21 #include <queue>
22
23 #include <sys/types.h>
24 #include <sys/stat.h>
25 #include <unistd.h>
26 #include <stdint.h>
27
28 #ifdef __SSE__
29 #include <immintrin.h>
30 #endif
31
32 #include "utils.h"
33 #include "Heap.h"
34 #include "FaissAssert.h"
35 #include "IndexFlat.h"
36 #include "IndexIVFPQ.h"
37 #include "AuxIndexStructures.h"
38
39
40 extern "C" {
41
42 /* declare BLAS functions, see http://www.netlib.org/clapack/cblas/ */
43
44 int sgemm_ (const char *transa, const char *transb, FINTEGER *m, FINTEGER *
45 n, FINTEGER *k, const float *alpha, const float *a,
46 FINTEGER *lda, const float *b, FINTEGER *
47 ldb, float *beta, float *c, FINTEGER *ldc);
48
49 }
50
51 namespace faiss {
52
53 using idx_t = Index::idx_t;
54 using MinimaxHeap = HNSW::MinimaxHeap;
55 using storage_idx_t = HNSW::storage_idx_t;
56 using NodeDistCloser = HNSW::NodeDistCloser;
57 using NodeDistFarther = HNSW::NodeDistFarther;
58
59 HNSWStats hnsw_stats;
60
61 /**************************************************************
62 * add / search blocks of descriptors
63 **************************************************************/
64
65 namespace {
66
67
68 void hnsw_add_vertices(IndexHNSW &index_hnsw,
69 size_t n0,
70 size_t n, const float *x,
71 bool verbose,
72 bool preset_levels = false) {
73 size_t d = index_hnsw.d;
74 HNSW & hnsw = index_hnsw.hnsw;
75 size_t ntotal = n0 + n;
76 double t0 = getmillisecs();
77 if (verbose) {
78 printf("hnsw_add_vertices: adding %ld elements on top of %ld "
79 "(preset_levels=%d)\n",
80 n, n0, int(preset_levels));
81 }
82
83 if (n == 0) {
84 return;
85 }
86
87 int max_level = hnsw.prepare_level_tab(n, preset_levels);
88
89 if (verbose) {
90 printf(" max_level = %d\n", max_level);
91 }
92
93 std::vector<omp_lock_t> locks(ntotal);
94 for(int i = 0; i < ntotal; i++)
95 omp_init_lock(&locks[i]);
96
97 // add vectors from highest to lowest level
98 std::vector<int> hist;
99 std::vector<int> order(n);
100
101 { // make buckets with vectors of the same level
102
103 // build histogram
104 for (int i = 0; i < n; i++) {
105 storage_idx_t pt_id = i + n0;
106 int pt_level = hnsw.levels[pt_id] - 1;
107 while (pt_level >= hist.size())
108 hist.push_back(0);
109 hist[pt_level] ++;
110 }
111
112 // accumulate
113 std::vector<int> offsets(hist.size() + 1, 0);
114 for (int i = 0; i < hist.size() - 1; i++) {
115 offsets[i + 1] = offsets[i] + hist[i];
116 }
117
118 // bucket sort
119 for (int i = 0; i < n; i++) {
120 storage_idx_t pt_id = i + n0;
121 int pt_level = hnsw.levels[pt_id] - 1;
122 order[offsets[pt_level]++] = pt_id;
123 }
124 }
125
126
127 idx_t check_period = InterruptCallback::get_period_hint
128 (max_level * index_hnsw.d * hnsw.efConstruction);
129
130 { // perform add
131 RandomGenerator rng2(789);
132
133 int i1 = n;
134
135 for (int pt_level = hist.size() - 1; pt_level >= 0; pt_level--) {
136 int i0 = i1 - hist[pt_level];
137
138 if (verbose) {
139 printf("Adding %d elements at level %d\n",
140 i1 - i0, pt_level);
141 }
142
143 // random permutation to get rid of dataset order bias
144 for (int j = i0; j < i1; j++)
145 std::swap(order[j], order[j + rng2.rand_int(i1 - j)]);
146
147 bool interrupt = false;
148
149 #pragma omp parallel if(i1 > i0 + 100)
150 {
151 VisitedTable vt (ntotal);
152
153 DistanceComputer *dis = index_hnsw.get_distance_computer();
154 ScopeDeleter1<DistanceComputer> del(dis);
155 int prev_display = verbose && omp_get_thread_num() == 0 ? 0 : -1;
156 size_t counter = 0;
157
158 #pragma omp for schedule(dynamic)
159 for (int i = i0; i < i1; i++) {
160 storage_idx_t pt_id = order[i];
161 dis->set_query (x + (pt_id - n0) * d);
162
163 // cannot break
164 if (interrupt) {
165 continue;
166 }
167
168 hnsw.add_with_locks(*dis, pt_level, pt_id, locks, vt);
169
170 if (prev_display >= 0 && i - i0 > prev_display + 10000) {
171 prev_display = i - i0;
172 printf(" %d / %d\r", i - i0, i1 - i0);
173 fflush(stdout);
174 }
175
176 if (counter % check_period == 0) {
177 #pragma omp critical
178 {
179 if (InterruptCallback::is_interrupted ()) {
180 interrupt = true;
181 }
182 }
183 }
184 counter++;
185 }
186
187 }
188 if (interrupt) {
189 FAISS_THROW_MSG ("computation interrupted");
190 }
191 i1 = i0;
192 }
193 FAISS_ASSERT(i1 == 0);
194 }
195 if (verbose) {
196 printf("Done in %.3f ms\n", getmillisecs() - t0);
197 }
198
199 for(int i = 0; i < ntotal; i++) {
200 omp_destroy_lock(&locks[i]);
201 }
202 }
203
204
205 } // namespace
206
207
208
209
210 /**************************************************************
211 * IndexHNSW implementation
212 **************************************************************/
213
214 IndexHNSW::IndexHNSW(int d, int M):
215 Index(d, METRIC_L2),
216 hnsw(M),
217 own_fields(false),
218 storage(nullptr),
219 reconstruct_from_neighbors(nullptr)
220 {}
221
222 IndexHNSW::IndexHNSW(Index *storage, int M):
223 Index(storage->d, METRIC_L2),
224 hnsw(M),
225 own_fields(false),
226 storage(storage),
227 reconstruct_from_neighbors(nullptr)
228 {}
229
230 IndexHNSW::~IndexHNSW() {
231 if (own_fields) {
232 delete storage;
233 }
234 }
235
236 void IndexHNSW::train(idx_t n, const float* x)
237 {
238 // hnsw structure does not require training
239 storage->train (n, x);
240 is_trained = true;
241 }
242
243 void IndexHNSW::search (idx_t n, const float *x, idx_t k,
244 float *distances, idx_t *labels) const
245
246 {
247 size_t nreorder = 0;
248
249 idx_t check_period = InterruptCallback::get_period_hint (
250 hnsw.max_level * d * hnsw.efSearch);
251
252 for (idx_t i0 = 0; i0 < n; i0 += check_period) {
253 idx_t i1 = std::min(i0 + check_period, n);
254
255 #pragma omp parallel reduction(+ : nreorder)
256 {
257 VisitedTable vt (ntotal);
258 DistanceComputer *dis = get_distance_computer();
259 ScopeDeleter1<DistanceComputer> del(dis);
260
261 #pragma omp for
262 for(idx_t i = i0; i < i1; i++) {
263 idx_t * idxi = labels + i * k;
264 float * simi = distances + i * k;
265 dis->set_query(x + i * d);
266
267 maxheap_heapify (k, simi, idxi);
268 hnsw.search(*dis, k, idxi, simi, vt);
269
270 maxheap_reorder (k, simi, idxi);
271
272 if (reconstruct_from_neighbors &&
273 reconstruct_from_neighbors->k_reorder != 0) {
274 int k_reorder = reconstruct_from_neighbors->k_reorder;
275 if (k_reorder == -1 || k_reorder > k) k_reorder = k;
276
277 nreorder += reconstruct_from_neighbors->compute_distances(
278 k_reorder, idxi, x + i * d, simi);
279
280 // sort top k_reorder
281 maxheap_heapify (k_reorder, simi, idxi, simi, idxi, k_reorder);
282 maxheap_reorder (k_reorder, simi, idxi);
283 }
284
285 }
286
287 }
288 InterruptCallback::check ();
289 }
290 hnsw_stats.nreorder += nreorder;
291 }
292
293
294 void IndexHNSW::add(idx_t n, const float *x)
295 {
296 FAISS_THROW_IF_NOT(is_trained);
297 int n0 = ntotal;
298 storage->add(n, x);
299 ntotal = storage->ntotal;
300
301 hnsw_add_vertices (*this, n0, n, x, verbose,
302 hnsw.levels.size() == ntotal);
303 }
304
305 void IndexHNSW::reset()
306 {
307 hnsw.reset();
308 storage->reset();
309 ntotal = 0;
310 }
311
312 void IndexHNSW::reconstruct (idx_t key, float* recons) const
313 {
314 storage->reconstruct(key, recons);
315 }
316
317 void IndexHNSW::shrink_level_0_neighbors(int new_size)
318 {
319 #pragma omp parallel
320 {
321 DistanceComputer *dis = get_distance_computer();
322 ScopeDeleter1<DistanceComputer> del(dis);
323
324 #pragma omp for
325 for (idx_t i = 0; i < ntotal; i++) {
326
327 size_t begin, end;
328 hnsw.neighbor_range(i, 0, &begin, &end);
329
330 std::priority_queue<NodeDistFarther> initial_list;
331
332 for (size_t j = begin; j < end; j++) {
333 int v1 = hnsw.neighbors[j];
334 if (v1 < 0) break;
335 initial_list.emplace(dis->symmetric_dis(i, v1), v1);
336
337 // initial_list.emplace(qdis(v1), v1);
338 }
339
340 std::vector<NodeDistFarther> shrunk_list;
341 HNSW::shrink_neighbor_list(*dis, initial_list,
342 shrunk_list, new_size);
343
344 for (size_t j = begin; j < end; j++) {
345 if (j - begin < shrunk_list.size())
346 hnsw.neighbors[j] = shrunk_list[j - begin].id;
347 else
348 hnsw.neighbors[j] = -1;
349 }
350 }
351 }
352
353 }
354
355 void IndexHNSW::search_level_0(
356 idx_t n, const float *x, idx_t k,
357 const storage_idx_t *nearest, const float *nearest_d,
358 float *distances, idx_t *labels, int nprobe,
359 int search_type) const
360 {
361
362 storage_idx_t ntotal = hnsw.levels.size();
363 #pragma omp parallel
364 {
365 DistanceComputer *qdis = get_distance_computer();
366 ScopeDeleter1<DistanceComputer> del(qdis);
367
368 VisitedTable vt (ntotal);
369
370 #pragma omp for
371 for(idx_t i = 0; i < n; i++) {
372 idx_t * idxi = labels + i * k;
373 float * simi = distances + i * k;
374
375 qdis->set_query(x + i * d);
376 maxheap_heapify (k, simi, idxi);
377
378 if (search_type == 1) {
379
380 int nres = 0;
381
382 for(int j = 0; j < nprobe; j++) {
383 storage_idx_t cj = nearest[i * nprobe + j];
384
385 if (cj < 0) break;
386
387 if (vt.get(cj)) continue;
388
389 int candidates_size = std::max(hnsw.efSearch, int(k));
390 MinimaxHeap candidates(candidates_size);
391
392 candidates.push(cj, nearest_d[i * nprobe + j]);
393
394 nres = hnsw.search_from_candidates(
395 *qdis, k, idxi, simi,
396 candidates, vt, 0, nres
397 );
398 }
399 } else if (search_type == 2) {
400
401 int candidates_size = std::max(hnsw.efSearch, int(k));
402 candidates_size = std::max(candidates_size, nprobe);
403
404 MinimaxHeap candidates(candidates_size);
405 for(int j = 0; j < nprobe; j++) {
406 storage_idx_t cj = nearest[i * nprobe + j];
407
408 if (cj < 0) break;
409 candidates.push(cj, nearest_d[i * nprobe + j]);
410 }
411 hnsw.search_from_candidates(
412 *qdis, k, idxi, simi,
413 candidates, vt, 0
414 );
415
416 }
417 vt.advance();
418
419 maxheap_reorder (k, simi, idxi);
420
421 }
422 }
423
424
425 }
426
427 void IndexHNSW::init_level_0_from_knngraph(
428 int k, const float *D, const idx_t *I)
429 {
430 int dest_size = hnsw.nb_neighbors (0);
431
432 #pragma omp parallel for
433 for (idx_t i = 0; i < ntotal; i++) {
434 DistanceComputer *qdis = get_distance_computer();
435 float vec[d];
436 storage->reconstruct(i, vec);
437 qdis->set_query(vec);
438
439 std::priority_queue<NodeDistFarther> initial_list;
440
441 for (size_t j = 0; j < k; j++) {
442 int v1 = I[i * k + j];
443 if (v1 == i) continue;
444 if (v1 < 0) break;
445 initial_list.emplace(D[i * k + j], v1);
446 }
447
448 std::vector<NodeDistFarther> shrunk_list;
449 HNSW::shrink_neighbor_list(*qdis, initial_list, shrunk_list, dest_size);
450
451 size_t begin, end;
452 hnsw.neighbor_range(i, 0, &begin, &end);
453
454 for (size_t j = begin; j < end; j++) {
455 if (j - begin < shrunk_list.size())
456 hnsw.neighbors[j] = shrunk_list[j - begin].id;
457 else
458 hnsw.neighbors[j] = -1;
459 }
460 }
461 }
462
463
464
465 void IndexHNSW::init_level_0_from_entry_points(
466 int n, const storage_idx_t *points,
467 const storage_idx_t *nearests)
468 {
469
470 std::vector<omp_lock_t> locks(ntotal);
471 for(int i = 0; i < ntotal; i++)
472 omp_init_lock(&locks[i]);
473
474 #pragma omp parallel
475 {
476 VisitedTable vt (ntotal);
477
478 DistanceComputer *dis = get_distance_computer();
479 ScopeDeleter1<DistanceComputer> del(dis);
480 float vec[storage->d];
481
482 #pragma omp for schedule(dynamic)
483 for (int i = 0; i < n; i++) {
484 storage_idx_t pt_id = points[i];
485 storage_idx_t nearest = nearests[i];
486 storage->reconstruct (pt_id, vec);
487 dis->set_query (vec);
488
489 hnsw.add_links_starting_from(*dis, pt_id,
490 nearest, (*dis)(nearest),
491 0, locks.data(), vt);
492
493 if (verbose && i % 10000 == 0) {
494 printf(" %d / %d\r", i, n);
495 fflush(stdout);
496 }
497 }
498 }
499 if (verbose) {
500 printf("\n");
501 }
502
503 for(int i = 0; i < ntotal; i++)
504 omp_destroy_lock(&locks[i]);
505 }
506
507 void IndexHNSW::reorder_links()
508 {
509 int M = hnsw.nb_neighbors(0);
510
511 #pragma omp parallel
512 {
513 std::vector<float> distances (M);
514 std::vector<size_t> order (M);
515 std::vector<storage_idx_t> tmp (M);
516 DistanceComputer *dis = get_distance_computer();
517 ScopeDeleter1<DistanceComputer> del(dis);
518
519 #pragma omp for
520 for(storage_idx_t i = 0; i < ntotal; i++) {
521
522 size_t begin, end;
523 hnsw.neighbor_range(i, 0, &begin, &end);
524
525 for (size_t j = begin; j < end; j++) {
526 storage_idx_t nj = hnsw.neighbors[j];
527 if (nj < 0) {
528 end = j;
529 break;
530 }
531 distances[j - begin] = dis->symmetric_dis(i, nj);
532 tmp [j - begin] = nj;
533 }
534
535 fvec_argsort (end - begin, distances.data(), order.data());
536 for (size_t j = begin; j < end; j++) {
537 hnsw.neighbors[j] = tmp[order[j - begin]];
538 }
539 }
540
541 }
542 }
543
544
545 void IndexHNSW::link_singletons()
546 {
547 printf("search for singletons\n");
548
549 std::vector<bool> seen(ntotal);
550
551 for (size_t i = 0; i < ntotal; i++) {
552 size_t begin, end;
553 hnsw.neighbor_range(i, 0, &begin, &end);
554 for (size_t j = begin; j < end; j++) {
555 storage_idx_t ni = hnsw.neighbors[j];
556 if (ni >= 0) seen[ni] = true;
557 }
558 }
559
560 int n_sing = 0, n_sing_l1 = 0;
561 std::vector<storage_idx_t> singletons;
562 for (storage_idx_t i = 0; i < ntotal; i++) {
563 if (!seen[i]) {
564 singletons.push_back(i);
565 n_sing++;
566 if (hnsw.levels[i] > 1)
567 n_sing_l1++;
568 }
569 }
570
571 printf(" Found %d / %ld singletons (%d appear in a level above)\n",
572 n_sing, ntotal, n_sing_l1);
573
574 std::vector<float>recons(singletons.size() * d);
575 for (int i = 0; i < singletons.size(); i++) {
576
577 FAISS_ASSERT(!"not implemented");
578
579 }
580
581
582 }
583
584
585 namespace {
586
587
588 // storage that explicitly reconstructs vectors before computing distances
589 struct GenericDistanceComputer: DistanceComputer {
590 size_t d;
591 const Index & storage;
592 std::vector<float> buf;
593 const float *q;
594
595 GenericDistanceComputer(const Index & storage): storage(storage)
596 {
597 d = storage.d;
598 buf.resize(d * 2);
599 }
600
601 float operator () (idx_t i) override
602 {
603 storage.reconstruct(i, buf.data());
604 return fvec_L2sqr(q, buf.data(), d);
605 }
606
607 float symmetric_dis(idx_t i, idx_t j) override
608 {
609 storage.reconstruct(i, buf.data());
610 storage.reconstruct(j, buf.data() + d);
611 return fvec_L2sqr(buf.data() + d, buf.data(), d);
612 }
613
614 void set_query(const float *x) override {
615 q = x;
616 }
617
618
619 };
620
621
622 } // namespace
623
624 DistanceComputer * IndexHNSW::get_distance_computer () const
625 {
626 return new GenericDistanceComputer (*storage);
627 }
628
629
630 /**************************************************************
631 * ReconstructFromNeighbors implementation
632 **************************************************************/
633
634
635 ReconstructFromNeighbors::ReconstructFromNeighbors(
636 const IndexHNSW & index, size_t k, size_t nsq):
637 index(index), k(k), nsq(nsq) {
638 M = index.hnsw.nb_neighbors(0);
639 FAISS_ASSERT(k <= 256);
640 code_size = k == 1 ? 0 : nsq;
641 ntotal = 0;
642 d = index.d;
643 FAISS_ASSERT(d % nsq == 0);
644 dsub = d / nsq;
645 k_reorder = -1;
646 }
647
648 void ReconstructFromNeighbors::reconstruct(storage_idx_t i, float *x, float *tmp) const
649 {
650
651
652 const HNSW & hnsw = index.hnsw;
653 size_t begin, end;
654 hnsw.neighbor_range(i, 0, &begin, &end);
655
656 if (k == 1 || nsq == 1) {
657 const float * beta;
658 if (k == 1) {
659 beta = codebook.data();
660 } else {
661 int idx = codes[i];
662 beta = codebook.data() + idx * (M + 1);
663 }
664
665 float w0 = beta[0]; // weight of image itself
666 index.storage->reconstruct(i, tmp);
667
668 for (int l = 0; l < d; l++)
669 x[l] = w0 * tmp[l];
670
671 for (size_t j = begin; j < end; j++) {
672
673 storage_idx_t ji = hnsw.neighbors[j];
674 if (ji < 0) ji = i;
675 float w = beta[j - begin + 1];
676 index.storage->reconstruct(ji, tmp);
677 for (int l = 0; l < d; l++)
678 x[l] += w * tmp[l];
679 }
680 } else if (nsq == 2) {
681 int idx0 = codes[2 * i];
682 int idx1 = codes[2 * i + 1];
683
684 const float *beta0 = codebook.data() + idx0 * (M + 1);
685 const float *beta1 = codebook.data() + (idx1 + k) * (M + 1);
686
687 index.storage->reconstruct(i, tmp);
688
689 float w0;
690
691 w0 = beta0[0];
692 for (int l = 0; l < dsub; l++)
693 x[l] = w0 * tmp[l];
694
695 w0 = beta1[0];
696 for (int l = dsub; l < d; l++)
697 x[l] = w0 * tmp[l];
698
699 for (size_t j = begin; j < end; j++) {
700 storage_idx_t ji = hnsw.neighbors[j];
701 if (ji < 0) ji = i;
702 index.storage->reconstruct(ji, tmp);
703 float w;
704 w = beta0[j - begin + 1];
705 for (int l = 0; l < dsub; l++)
706 x[l] += w * tmp[l];
707
708 w = beta1[j - begin + 1];
709 for (int l = dsub; l < d; l++)
710 x[l] += w * tmp[l];
711 }
712 } else {
713 const float *betas[nsq];
714 {
715 const float *b = codebook.data();
716 const uint8_t *c = &codes[i * code_size];
717 for (int sq = 0; sq < nsq; sq++) {
718 betas[sq] = b + (*c++) * (M + 1);
719 b += (M + 1) * k;
720 }
721 }
722
723 index.storage->reconstruct(i, tmp);
724 {
725 int d0 = 0;
726 for (int sq = 0; sq < nsq; sq++) {
727 float w = *(betas[sq]++);
728 int d1 = d0 + dsub;
729 for (int l = d0; l < d1; l++) {
730 x[l] = w * tmp[l];
731 }
732 d0 = d1;
733 }
734 }
735
736 for (size_t j = begin; j < end; j++) {
737 storage_idx_t ji = hnsw.neighbors[j];
738 if (ji < 0) ji = i;
739
740 index.storage->reconstruct(ji, tmp);
741 int d0 = 0;
742 for (int sq = 0; sq < nsq; sq++) {
743 float w = *(betas[sq]++);
744 int d1 = d0 + dsub;
745 for (int l = d0; l < d1; l++) {
746 x[l] += w * tmp[l];
747 }
748 d0 = d1;
749 }
750 }
751 }
752 }
753
754 void ReconstructFromNeighbors::reconstruct_n(storage_idx_t n0,
755 storage_idx_t ni,
756 float *x) const
757 {
758 #pragma omp parallel
759 {
760 std::vector<float> tmp(index.d);
761 #pragma omp for
762 for (storage_idx_t i = 0; i < ni; i++) {
763 reconstruct(n0 + i, x + i * index.d, tmp.data());
764 }
765 }
766 }
767
768 size_t ReconstructFromNeighbors::compute_distances(
769 size_t n, const idx_t *shortlist,
770 const float *query, float *distances) const
771 {
772 std::vector<float> tmp(2 * index.d);
773 size_t ncomp = 0;
774 for (int i = 0; i < n; i++) {
775 if (shortlist[i] < 0) break;
776 reconstruct(shortlist[i], tmp.data(), tmp.data() + index.d);
777 distances[i] = fvec_L2sqr(query, tmp.data(), index.d);
778 ncomp++;
779 }
780 return ncomp;
781 }
782
783 void ReconstructFromNeighbors::get_neighbor_table(storage_idx_t i, float *tmp1) const
784 {
785 const HNSW & hnsw = index.hnsw;
786 size_t begin, end;
787 hnsw.neighbor_range(i, 0, &begin, &end);
788 size_t d = index.d;
789
790 index.storage->reconstruct(i, tmp1);
791
792 for (size_t j = begin; j < end; j++) {
793 storage_idx_t ji = hnsw.neighbors[j];
794 if (ji < 0) ji = i;
795 index.storage->reconstruct(ji, tmp1 + (j - begin + 1) * d);
796 }
797
798 }
799
800
801 /// called by add_codes
802 void ReconstructFromNeighbors::estimate_code(
803 const float *x, storage_idx_t i, uint8_t *code) const
804 {
805
806 // fill in tmp table with the neighbor values
807 float *tmp1 = new float[d * (M + 1) + (d * k)];
808 float *tmp2 = tmp1 + d * (M + 1);
809 ScopeDeleter<float> del(tmp1);
810
811 // collect coordinates of base
812 get_neighbor_table (i, tmp1);
813
814 for (size_t sq = 0; sq < nsq; sq++) {
815 int d0 = sq * dsub;
816
817 {
818 FINTEGER ki = k, di = d, m1 = M + 1;
819 FINTEGER dsubi = dsub;
820 float zero = 0, one = 1;
821
822 sgemm_ ("N", "N", &dsubi, &ki, &m1, &one,
823 tmp1 + d0, &di,
824 codebook.data() + sq * (m1 * k), &m1,
825 &zero, tmp2, &dsubi);
826 }
827
828 float min = HUGE_VAL;
829 int argmin = -1;
830 for (size_t j = 0; j < k; j++) {
831 float dis = fvec_L2sqr(x + d0, tmp2 + j * dsub, dsub);
832 if (dis < min) {
833 min = dis;
834 argmin = j;
835 }
836 }
837 code[sq] = argmin;
838 }
839
840 }
841
842 void ReconstructFromNeighbors::add_codes(size_t n, const float *x)
843 {
844 if (k == 1) { // nothing to encode
845 ntotal += n;
846 return;
847 }
848 codes.resize(codes.size() + code_size * n);
849 #pragma omp parallel for
850 for (int i = 0; i < n; i++) {
851 estimate_code(x + i * index.d, ntotal + i,
852 codes.data() + (ntotal + i) * code_size);
853 }
854 ntotal += n;
855 FAISS_ASSERT (codes.size() == ntotal * code_size);
856 }
857
858
859 /**************************************************************
860 * IndexHNSWFlat implementation
861 **************************************************************/
862
863
864 namespace {
865
866
867 struct FlatL2Dis: DistanceComputer {
868 size_t d;
869 Index::idx_t nb;
870 const float *q;
871 const float *b;
872 size_t ndis;
873
874 float operator () (idx_t i) override
875 {
876 ndis++;
877 return (fvec_L2sqr(q, b + i * d, d));
878 }
879
880 float symmetric_dis(idx_t i, idx_t j) override
881 {
882 return (fvec_L2sqr(b + j * d, b + i * d, d));
883 }
884
885
886 FlatL2Dis(const IndexFlatL2 & storage, const float *q = nullptr):
887 q(q)
888 {
889 nb = storage.ntotal;
890 d = storage.d;
891 b = storage.xb.data();
892 ndis = 0;
893 }
894
895 void set_query(const float *x) override {
896 q = x;
897 }
898
899 ~FlatL2Dis() override {
900 #pragma omp critical
901 {
902 hnsw_stats.ndis += ndis;
903 }
904 }
905 };
906
907
908 } // namespace
909
910
911 IndexHNSWFlat::IndexHNSWFlat()
912 {
913 is_trained = true;
914 }
915
916
917 IndexHNSWFlat::IndexHNSWFlat(int d, int M):
918 IndexHNSW(new IndexFlatL2(d), M)
919 {
920 own_fields = true;
921 is_trained = true;
922 }
923
924
925 DistanceComputer * IndexHNSWFlat::get_distance_computer () const
926 {
927 return new FlatL2Dis (*dynamic_cast<IndexFlatL2*> (storage));
928 }
929
930
931
932
933 /**************************************************************
934 * IndexHNSWPQ implementation
935 **************************************************************/
936
937
938 namespace {
939
940
941 struct PQDis: DistanceComputer {
942 size_t d;
943 Index::idx_t nb;
944 const uint8_t *codes;
945 size_t code_size;
946 const ProductQuantizer & pq;
947 const float *sdc;
948 std::vector<float> precomputed_table;
949 size_t ndis;
950
951 float operator () (idx_t i) override
952 {
953 const uint8_t *code = codes + i * code_size;
954 const float *dt = precomputed_table.data();
955 float accu = 0;
956 for (int j = 0; j < pq.M; j++) {
957 accu += dt[*code++];
958 dt += 256;
959 }
960 ndis++;
961 return accu;
962 }
963
964 float symmetric_dis(idx_t i, idx_t j) override
965 {
966 const float * sdci = sdc;
967 float accu = 0;
968 const uint8_t *codei = codes + i * code_size;
969 const uint8_t *codej = codes + j * code_size;
970
971 for (int l = 0; l < pq.M; l++) {
972 accu += sdci[(*codei++) + (*codej++) * 256];
973 sdci += 256 * 256;
974 }
975 return accu;
976 }
977
978 PQDis(const IndexPQ& storage, const float* /*q*/ = nullptr)
979 : pq(storage.pq) {
980 precomputed_table.resize(pq.M * pq.ksub);
981 nb = storage.ntotal;
982 d = storage.d;
983 codes = storage.codes.data();
984 code_size = pq.code_size;
985 FAISS_ASSERT(pq.ksub == 256);
986 FAISS_ASSERT(pq.sdc_table.size() == pq.ksub * pq.ksub * pq.M);
987 sdc = pq.sdc_table.data();
988 ndis = 0;
989 }
990
991 void set_query(const float *x) override {
992 pq.compute_distance_table(x, precomputed_table.data());
993 }
994
995 ~PQDis() override {
996 #pragma omp critical
997 {
998 hnsw_stats.ndis += ndis;
999 }
1000 }
1001 };
1002
1003
1004 } // namespace
1005
1006
1007 IndexHNSWPQ::IndexHNSWPQ() {}
1008
1009 IndexHNSWPQ::IndexHNSWPQ(int d, int pq_m, int M):
1010 IndexHNSW(new IndexPQ(d, pq_m, 8), M)
1011 {
1012 own_fields = true;
1013 is_trained = false;
1014 }
1015
1016 void IndexHNSWPQ::train(idx_t n, const float* x)
1017 {
1018 IndexHNSW::train (n, x);
1019 (dynamic_cast<IndexPQ*> (storage))->pq.compute_sdc_table();
1020 }
1021
1022
1023
1024 DistanceComputer * IndexHNSWPQ::get_distance_computer () const
1025 {
1026 return new PQDis (*dynamic_cast<IndexPQ*> (storage));
1027 }
1028
1029
1030 /**************************************************************
1031 * IndexHNSWSQ implementation
1032 **************************************************************/
1033
1034
1035 IndexHNSWSQ::IndexHNSWSQ(int d, ScalarQuantizer::QuantizerType qtype, int M):
1036 IndexHNSW (new IndexScalarQuantizer (d, qtype), M)
1037 {
1038 is_trained = false;
1039 own_fields = true;
1040 }
1041
1042 IndexHNSWSQ::IndexHNSWSQ() {}
1043
1044 DistanceComputer * IndexHNSWSQ::get_distance_computer () const
1045 {
1046 return (dynamic_cast<const IndexScalarQuantizer*> (storage))->
1047 get_distance_computer ();
1048 }
1049
1050
1051
1052
1053 /**************************************************************
1054 * IndexHNSW2Level implementation
1055 **************************************************************/
1056
1057
1058
1059 IndexHNSW2Level::IndexHNSW2Level(Index *quantizer, size_t nlist, int m_pq, int M):
1060 IndexHNSW (new Index2Layer (quantizer, nlist, m_pq), M)
1061 {
1062 own_fields = true;
1063 is_trained = false;
1064 }
1065
1066 IndexHNSW2Level::IndexHNSW2Level() {}
1067
1068
1069 namespace {
1070
1071
1072 struct Distance2Level: DistanceComputer {
1073 size_t d;
1074 const Index2Layer & storage;
1075 std::vector<float> buf;
1076 const float *q;
1077
1078 const float *pq_l1_tab, *pq_l2_tab;
1079
1080 Distance2Level(const Index2Layer & storage): storage(storage)
1081 {
1082 d = storage.d;
1083 FAISS_ASSERT(storage.pq.dsub == 4);
1084 pq_l2_tab = storage.pq.centroids.data();
1085 buf.resize(2 * d);
1086 }
1087
1088 float symmetric_dis(idx_t i, idx_t j) override
1089 {
1090 storage.reconstruct(i, buf.data());
1091 storage.reconstruct(j, buf.data() + d);
1092 return fvec_L2sqr(buf.data() + d, buf.data(), d);
1093 }
1094
1095 void set_query(const float *x) override {
1096 q = x;
1097 }
1098 };
1099
1100
1101 // well optimized for xNN+PQNN
1102 struct DistanceXPQ4: Distance2Level {
1103
1104 int M, k;
1105
1106 DistanceXPQ4(const Index2Layer & storage):
1107 Distance2Level (storage)
1108 {
1109 const IndexFlat *quantizer =
1110 dynamic_cast<IndexFlat*> (storage.q1.quantizer);
1111
1112 FAISS_ASSERT(quantizer);
1113 M = storage.pq.M;
1114 pq_l1_tab = quantizer->xb.data();
1115 }
1116
1117 float operator () (idx_t i) override
1118 {
1119 #ifdef __SSE__
1120 const uint8_t *code = storage.codes.data() + i * storage.code_size;
1121 long key = 0;
1122 memcpy (&key, code, storage.code_size_1);
1123 code += storage.code_size_1;
1124
1125 // walking pointers
1126 const float *qa = q;
1127 const __m128 *l1_t = (const __m128 *)(pq_l1_tab + d * key);
1128 const __m128 *pq_l2_t = (const __m128 *)pq_l2_tab;
1129 __m128 accu = _mm_setzero_ps();
1130
1131 for (int m = 0; m < M; m++) {
1132 __m128 qi = _mm_loadu_ps(qa);
1133 __m128 recons = l1_t[m] + pq_l2_t[*code++];
1134 __m128 diff = qi - recons;
1135 accu += diff * diff;
1136 pq_l2_t += 256;
1137 qa += 4;
1138 }
1139
1140 accu = _mm_hadd_ps (accu, accu);
1141 accu = _mm_hadd_ps (accu, accu);
1142 return _mm_cvtss_f32 (accu);
1143 #else
1144 FAISS_THROW_MSG("not implemented for non-x64 platforms");
1145 #endif
1146 }
1147
1148 };
1149
1150 // well optimized for 2xNN+PQNN
1151 struct Distance2xXPQ4: Distance2Level {
1152
1153 int M_2, mi_nbits;
1154
1155 Distance2xXPQ4(const Index2Layer & storage):
1156 Distance2Level (storage)
1157 {
1158 const MultiIndexQuantizer *mi =
1159 dynamic_cast<MultiIndexQuantizer*> (storage.q1.quantizer);
1160
1161 FAISS_ASSERT(mi);
1162 FAISS_ASSERT(storage.pq.M % 2 == 0);
1163 M_2 = storage.pq.M / 2;
1164 mi_nbits = mi->pq.nbits;
1165 pq_l1_tab = mi->pq.centroids.data();
1166 }
1167
1168 float operator () (idx_t i) override
1169 {
1170 const uint8_t *code = storage.codes.data() + i * storage.code_size;
1171 long key01 = 0;
1172 memcpy (&key01, code, storage.code_size_1);
1173 code += storage.code_size_1;
1174 #ifdef __SSE__
1175
1176 // walking pointers
1177 const float *qa = q;
1178 const __m128 *pq_l1_t = (const __m128 *)pq_l1_tab;
1179 const __m128 *pq_l2_t = (const __m128 *)pq_l2_tab;
1180 __m128 accu = _mm_setzero_ps();
1181
1182 for (int mi_m = 0; mi_m < 2; mi_m++) {
1183 long l1_idx = key01 & ((1L << mi_nbits) - 1);
1184 const __m128 * pq_l1 = pq_l1_t + M_2 * l1_idx;
1185
1186 for (int m = 0; m < M_2; m++) {
1187 __m128 qi = _mm_loadu_ps(qa);
1188 __m128 recons = pq_l1[m] + pq_l2_t[*code++];
1189 __m128 diff = qi - recons;
1190 accu += diff * diff;
1191 pq_l2_t += 256;
1192 qa += 4;
1193 }
1194 pq_l1_t += M_2 << mi_nbits;
1195 key01 >>= mi_nbits;
1196 }
1197 accu = _mm_hadd_ps (accu, accu);
1198 accu = _mm_hadd_ps (accu, accu);
1199 return _mm_cvtss_f32 (accu);
1200 #else
1201 FAISS_THROW_MSG("not implemented for non-x64 platforms");
1202 #endif
1203 }
1204
1205 };
1206
1207
1208 } // namespace
1209
1210
1211 DistanceComputer * IndexHNSW2Level::get_distance_computer () const
1212 {
1213 const Index2Layer *storage2l =
1214 dynamic_cast<Index2Layer*>(storage);
1215
1216 if (storage2l) {
1217 #ifdef __SSE__
1218
1219 const MultiIndexQuantizer *mi =
1220 dynamic_cast<MultiIndexQuantizer*> (storage2l->q1.quantizer);
1221
1222 if (mi && storage2l->pq.M % 2 == 0 && storage2l->pq.dsub == 4) {
1223 return new Distance2xXPQ4(*storage2l);
1224 }
1225
1226 const IndexFlat *fl =
1227 dynamic_cast<IndexFlat*> (storage2l->q1.quantizer);
1228
1229 if (fl && storage2l->pq.dsub == 4) {
1230 return new DistanceXPQ4(*storage2l);
1231 }
1232 #endif
1233 }
1234
1235 // IVFPQ and cases not handled above
1236 return new GenericDistanceComputer (*storage);
1237
1238 }
1239
1240
1241 namespace {
1242
1243
1244 // same as search_from_candidates but uses v
1245 // visno -> is in result list
1246 // visno + 1 -> in result list + in candidates
1247 int search_from_candidates_2(const HNSW & hnsw,
1248 DistanceComputer & qdis, int k,
1249 idx_t *I, float * D,
1250 MinimaxHeap &candidates,
1251 VisitedTable &vt,
1252 int level, int nres_in = 0)
1253 {
1254 int nres = nres_in;
1255 int ndis = 0;
1256 for (int i = 0; i < candidates.size(); i++) {
1257 idx_t v1 = candidates.ids[i];
1258 FAISS_ASSERT(v1 >= 0);
1259 vt.visited[v1] = vt.visno + 1;
1260 }
1261
1262 int nstep = 0;
1263
1264 while (candidates.size() > 0) {
1265 float d0 = 0;
1266 int v0 = candidates.pop_min(&d0);
1267
1268 size_t begin, end;
1269 hnsw.neighbor_range(v0, level, &begin, &end);
1270
1271 for (size_t j = begin; j < end; j++) {
1272 int v1 = hnsw.neighbors[j];
1273 if (v1 < 0) break;
1274 if (vt.visited[v1] == vt.visno + 1) {
1275 // nothing to do
1276 } else {
1277 ndis++;
1278 float d = qdis(v1);
1279 candidates.push(v1, d);
1280
1281 // never seen before --> add to heap
1282 if (vt.visited[v1] < vt.visno) {
1283 if (nres < k) {
1284 faiss::maxheap_push (++nres, D, I, d, v1);
1285 } else if (d < D[0]) {
1286 faiss::maxheap_pop (nres--, D, I);
1287 faiss::maxheap_push (++nres, D, I, d, v1);
1288 }
1289 }
1290 vt.visited[v1] = vt.visno + 1;
1291 }
1292 }
1293
1294 nstep++;
1295 if (nstep > hnsw.efSearch) {
1296 break;
1297 }
1298 }
1299
1300 if (level == 0) {
1301 #pragma omp critical
1302 {
1303 hnsw_stats.n1 ++;
1304 if (candidates.size() == 0)
1305 hnsw_stats.n2 ++;
1306 }
1307 }
1308
1309
1310 return nres;
1311 }
1312
1313
1314 } // namespace
1315
1316 void IndexHNSW2Level::search (idx_t n, const float *x, idx_t k,
1317 float *distances, idx_t *labels) const
1318 {
1319 if (dynamic_cast<const Index2Layer*>(storage)) {
1320 IndexHNSW::search (n, x, k, distances, labels);
1321
1322 } else { // "mixed" search
1323
1324 const IndexIVFPQ *index_ivfpq =
1325 dynamic_cast<const IndexIVFPQ*>(storage);
1326
1327 int nprobe = index_ivfpq->nprobe;
1328
1329 long * coarse_assign = new long [n * nprobe];
1330 ScopeDeleter<long> del (coarse_assign);
1331 float * coarse_dis = new float [n * nprobe];
1332 ScopeDeleter<float> del2 (coarse_dis);
1333
1334 index_ivfpq->quantizer->search (n, x, nprobe, coarse_dis, coarse_assign);
1335
1336 index_ivfpq->search_preassigned (
1337 n, x, k, coarse_assign, coarse_dis, distances, labels, false);
1338
1339 #pragma omp parallel
1340 {
1341 VisitedTable vt (ntotal);
1342 DistanceComputer *dis = get_distance_computer();
1343 ScopeDeleter1<DistanceComputer> del(dis);
1344
1345 int candidates_size = hnsw.upper_beam;
1346 MinimaxHeap candidates(candidates_size);
1347
1348 #pragma omp for
1349 for(idx_t i = 0; i < n; i++) {
1350 idx_t * idxi = labels + i * k;
1351 float * simi = distances + i * k;
1352 dis->set_query(x + i * d);
1353
1354 // mark all inverted list elements as visited
1355
1356 for (int j = 0; j < nprobe; j++) {
1357 idx_t key = coarse_assign[j + i * nprobe];
1358 if (key < 0) break;
1359 size_t list_length = index_ivfpq->get_list_size (key);
1360 const idx_t * ids = index_ivfpq->invlists->get_ids (key);
1361
1362 for (int jj = 0; jj < list_length; jj++) {
1363 vt.set (ids[jj]);
1364 }
1365 }
1366
1367 candidates.clear();
1368 // copy the upper_beam elements to candidates list
1369
1370 int search_policy = 2;
1371
1372 if (search_policy == 1) {
1373
1374 for (int j = 0 ; j < hnsw.upper_beam && j < k; j++) {
1375 if (idxi[j] < 0) break;
1376 candidates.push (idxi[j], simi[j]);
1377 // search_from_candidates adds them back
1378 idxi[j] = -1;
1379 simi[j] = HUGE_VAL;
1380 }
1381
1382 // reorder from sorted to heap
1383 maxheap_heapify (k, simi, idxi, simi, idxi, k);
1384
1385 hnsw.search_from_candidates(
1386 *dis, k, idxi, simi,
1387 candidates, vt, 0, k
1388 );
1389
1390 vt.advance();
1391
1392 } else if (search_policy == 2) {
1393
1394 for (int j = 0 ; j < hnsw.upper_beam && j < k; j++) {
1395 if (idxi[j] < 0) break;
1396 candidates.push (idxi[j], simi[j]);
1397 }
1398
1399 // reorder from sorted to heap
1400 maxheap_heapify (k, simi, idxi, simi, idxi, k);
1401
1402 search_from_candidates_2 (
1403 hnsw, *dis, k, idxi, simi,
1404 candidates, vt, 0, k);
1405 vt.advance ();
1406 vt.advance ();
1407
1408 }
1409
1410 maxheap_reorder (k, simi, idxi);
1411 }
1412 }
1413 }
1414
1415
1416 }
1417
1418
1419 void IndexHNSW2Level::flip_to_ivf ()
1420 {
1421 Index2Layer *storage2l =
1422 dynamic_cast<Index2Layer*>(storage);
1423
1424 FAISS_THROW_IF_NOT (storage2l);
1425
1426 IndexIVFPQ * index_ivfpq =
1427 new IndexIVFPQ (storage2l->q1.quantizer,
1428 d, storage2l->q1.nlist,
1429 storage2l->pq.M, 8);
1430 index_ivfpq->pq = storage2l->pq;
1431 index_ivfpq->is_trained = storage2l->is_trained;
1432 index_ivfpq->precompute_table();
1433 index_ivfpq->own_fields = storage2l->q1.own_fields;
1434 storage2l->transfer_to_IVFPQ(*index_ivfpq);
1435 index_ivfpq->make_direct_map (true);
1436
1437 storage = index_ivfpq;
1438 delete storage2l;
1439
1440 }
1441
1442
1443 } // namespace faiss
faiss::IndexIVFPQ::precompute_table
void precompute_table()
build precomputed table
Definition: IndexIVFPQ.cpp:363
faiss::Index2Layer::transfer_to_IVFPQ
void transfer_to_IVFPQ(IndexIVFPQ &other) const
transfer the flat codes to an IVFPQ index
Definition: IndexIVFPQ.cpp:1518
faiss::DistanceComputer::symmetric_dis
virtual float symmetric_dis(idx_t i, idx_t j)=0
compute distance between two stored vectors
faiss::ScalarQuantizer::QuantizerType
QuantizerType
Definition: IndexScalarQuantizer.h:34
faiss::HNSW::neighbor_range
void neighbor_range(idx_t no, int layer_no, size_t *begin, size_t *end) const
range of entries in the neighbors table of vertex no at layer_no
Definition: HNSW.cpp:41
faiss::IndexIVF::search_preassigned
virtual void search_preassigned(idx_t n, const float *x, idx_t k, const idx_t *assign, const float *centroid_dis, float *distances, idx_t *labels, bool store_pairs, const IVFSearchParameters *params=nullptr) const
Definition: IndexIVF.cpp:250
faiss::DistanceComputer::set_query
virtual void set_query(const float *x)=0
called before computing distances
faiss::VisitedTable::get
bool get(int no) const
get flag #no
Definition: HNSW.h:236
faiss::fvec_L2sqr
float fvec_L2sqr(const float *x, const float *y, size_t d)
Squared L2 distance between two vectors.
Definition: utils_simd.cpp:501
faiss::DistanceComputer
Definition: AuxIndexStructures.h:231
faiss::IndexHNSWPQ::train
void train(idx_t n, const float *x) override
Trains the storage if needed.
Definition: IndexHNSW.cpp:1016
faiss::InvertedLists::get_ids
virtual const idx_t * get_ids(size_t list_no) const =0
faiss::IndexHNSW2Level::search
void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const override
entry point for search
Definition: IndexHNSW.cpp:1316
faiss::Index::reset
virtual void reset()=0
removes all elements from the database.
faiss::IndexPQ
Definition: IndexPQ.h:26
faiss::IndexIVF::nprobe
size_t nprobe
number of probes at query time
Definition: IndexIVF.h:97
faiss::Index::train
virtual void train(idx_t n, const float *x)
Definition: Index.cpp:23
faiss::ScopeDeleter1
Definition: FaissException.h:58
faiss::ScopeDeleter
Definition: FaissException.h:45
faiss::Index2Layer::q1
Level1Quantizer q1
first level quantizer
Definition: IndexIVFPQ.h:206
faiss::VisitedTable::advance
void advance()
reset all flags to false
Definition: HNSW.h:241
faiss::Index2Layer
Definition: IndexIVFPQ.h:204
faiss::InterruptCallback::check
static void check()
Definition: AuxIndexStructures.cpp:307
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:783
faiss::IndexHNSW::train
void train(idx_t n, const float *x) override
Trains the storage if needed.
Definition: IndexHNSW.cpp:236
faiss::Index::d
int d
vector dimension
Definition: Index.h:66
faiss::Index::idx_t
long idx_t
all indices are this type
Definition: Index.h:62
faiss::VisitedTable
set implementation optimized for fast access.
Definition: HNSW.h:223
faiss::IndexIVFPQ
Definition: IndexIVFPQ.h:34
faiss::IndexHNSW::add
void add(idx_t n, const float *x) override
Definition: IndexHNSW.cpp:294
faiss::ReconstructFromNeighbors::reconstruct
void reconstruct(storage_idx_t i, float *x, float *tmp) const
called by compute_distances
Definition: IndexHNSW.cpp:648
faiss::Index::add
virtual void add(idx_t n, const float *x)=0
faiss::Index::ntotal
idx_t ntotal
total nb of indexed vectors
Definition: Index.h:67
faiss::getmillisecs
double getmillisecs()
ms elapsed since some arbitrary epoch
Definition: utils.cpp:69
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:802
faiss::Index::search
virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const =0
faiss::HNSW
Definition: HNSW.h:46
faiss::InterruptCallback::is_interrupted
static bool is_interrupted()
Definition: AuxIndexStructures.cpp:316
faiss::ReconstructFromNeighbors::add_codes
void add_codes(size_t n, const float *x)
Definition: IndexHNSW.cpp:842
faiss::IndexIVF::make_direct_map
void make_direct_map(bool new_maintain_direct_map=true)
Definition: IndexIVF.cpp:202
faiss::IndexIVFPQ::pq
ProductQuantizer pq
produces the codes
Definition: IndexIVFPQ.h:37
faiss::IndexIVF::invlists
InvertedLists * invlists
Acess to the actual data.
Definition: IndexIVF.h:92
faiss::IndexHNSW::reset
void reset() override
removes all elements from the database.
Definition: IndexHNSW.cpp:305
faiss::ProductQuantizer::M
size_t M
number of subquantizers
Definition: ProductQuantizer.h:28
faiss::InterruptCallback::get_period_hint
static size_t get_period_hint(size_t flops)
Definition: AuxIndexStructures.cpp:324
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:465
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:243
faiss::Level1Quantizer::quantizer
Index * quantizer
quantizer that maps vectors to inverted lists
Definition: IndexIVF.h:32
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:427
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:355
faiss::HNSW::MinimaxHeap
Definition: HNSW.h:57
faiss::Index::is_trained
bool is_trained
set if the Index does not require training, or if training is done already
Definition: Index.h:71
faiss::HNSW::neighbors
std::vector< storage_idx_t > neighbors
Definition: HNSW.h:114
faiss::IndexHNSW::reconstruct
void reconstruct(idx_t key, float *recons) const override
Definition: IndexHNSW.cpp:312
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:209
faiss::HNSW::storage_idx_t
int storage_idx_t
internal storage of vectors (32 bits: this is expensive)
Definition: HNSW.h:48
faiss::Level1Quantizer::own_fields
bool own_fields
whether object owns the quantizer
Definition: IndexIVF.h:41
faiss::VisitedTable::set
void set(int no)
set flog #no to true
Definition: HNSW.h:231
faiss::Level1Quantizer::nlist
size_t nlist
number of possible key values
Definition: IndexIVF.h:33
faiss::HNSW::shrink_neighbor_list
static void shrink_neighbor_list(DistanceComputer &qdis, std::priority_queue< NodeDistFarther > &input, std::vector< NodeDistFarther > &output, int max_size)
Definition: HNSW.cpp:235