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IVFPQ.cu
1
2 /**
3 * Copyright (c) 2015-present, Facebook, Inc.
4 * All rights reserved.
5 *
6 * This source code is licensed under the CC-by-NC license found in the
7 * LICENSE file in the root directory of this source tree.
8 */
9
10 // Copyright 2004-present Facebook. All Rights Reserved.
11
12 #include "IVFPQ.cuh"
13 #include "../GpuResources.h"
14 #include "BroadcastSum.cuh"
15 #include "Distance.cuh"
16 #include "FlatIndex.cuh"
17 #include "InvertedListAppend.cuh"
18 #include "L2Norm.cuh"
19 #include "PQCodeDistances.cuh"
20 #include "PQScanMultiPassNoPrecomputed.cuh"
21 #include "PQScanMultiPassPrecomputed.cuh"
22 #include "RemapIndices.h"
23 #include "VectorResidual.cuh"
24 #include "../utils/DeviceDefs.cuh"
25 #include "../utils/DeviceUtils.h"
26 #include "../utils/HostTensor.cuh"
27 #include "../utils/MatrixMult.cuh"
28 #include "../utils/NoTypeTensor.cuh"
29 #include "../utils/Transpose.cuh"
30 #include <limits>
31 #include <thrust/host_vector.h>
32 #include <unordered_map>
33
34 namespace faiss { namespace gpu {
35
36 IVFPQ::IVFPQ(GpuResources* resources,
37 FlatIndex* quantizer,
38 int numSubQuantizers,
39 int bitsPerSubQuantizer,
40 float* pqCentroidData,
41 IndicesOptions indicesOptions,
42 bool useFloat16LookupTables) :
43 IVFBase(resources,
44 quantizer,
45 numSubQuantizers,
46 indicesOptions),
47 numSubQuantizers_(numSubQuantizers),
48 bitsPerSubQuantizer_(bitsPerSubQuantizer),
49 numSubQuantizerCodes_(utils::pow2(bitsPerSubQuantizer_)),
50 dimPerSubQuantizer_(dim_ / numSubQuantizers),
51 precomputedCodes_(false),
52 useFloat16LookupTables_(useFloat16LookupTables) {
53 FAISS_ASSERT(pqCentroidData);
54
55 FAISS_ASSERT(bitsPerSubQuantizer_ <= 8);
56 FAISS_ASSERT(dim_ % numSubQuantizers_ == 0);
57 FAISS_ASSERT(isSupportedPQCodeLength(bytesPerVector_));
58
59 #ifndef FAISS_USE_FLOAT16
60 FAISS_ASSERT(!useFloat16LookupTables_);
61 #endif
62
63 setPQCentroids_(pqCentroidData);
64 }
65
66 IVFPQ::~IVFPQ() {
67 }
68
69
70 bool
71 IVFPQ::isSupportedPQCodeLength(int size) {
72 switch (size) {
73 case 1:
74 case 2:
75 case 3:
76 case 4:
77 case 8:
78 case 12:
79 case 16:
80 case 20:
81 case 24:
82 case 28:
83 case 32:
84 case 40:
85 case 48:
86 case 56: // only supported with float16
87 case 64: // only supported with float16
88 return true;
89 default:
90 return false;
91 }
92 }
93
94 bool
95 IVFPQ::isSupportedNoPrecomputedSubDimSize(int dims) {
96 return faiss::gpu::isSupportedNoPrecomputedSubDimSize(dims);
97 }
98
99 void
100 IVFPQ::setPrecomputedCodes(bool enable) {
101 if (precomputedCodes_ != enable) {
102 precomputedCodes_ = enable;
103
104 if (precomputedCodes_) {
105 precomputeCodes_();
106 } else {
107 // Clear out old precomputed code data
108 precomputedCode_ = std::move(DeviceTensor<float, 3, true>());
109
110 #ifdef FAISS_USE_FLOAT16
111 precomputedCodeHalf_ = std::move(DeviceTensor<half, 3, true>());
112 #endif
113 }
114 }
115 }
116
117 int
118 IVFPQ::classifyAndAddVectors(Tensor<float, 2, true>& vecs,
119 Tensor<long, 1, true>& indices) {
120 FAISS_ASSERT(vecs.getSize(0) == indices.getSize(0));
121 FAISS_ASSERT(vecs.getSize(1) == dim_);
122
123 FAISS_ASSERT(!quantizer_->getUseFloat16());
124 auto& coarseCentroids = quantizer_->getVectorsFloat32Ref();
125 auto& mem = resources_->getMemoryManagerCurrentDevice();
126 auto stream = resources_->getDefaultStreamCurrentDevice();
127
128 // Number of valid vectors that we actually add; we return this
129 int numAdded = 0;
130
131 // We don't actually need this
132 DeviceTensor<float, 2, true> listDistance(mem, {vecs.getSize(0), 1}, stream);
133 // We use this
134 DeviceTensor<int, 2, true> listIds2d(mem, {vecs.getSize(0), 1}, stream);
135 auto listIds = listIds2d.view<1>({vecs.getSize(0)});
136
137 quantizer_->query(vecs, 1, listDistance, listIds2d, false);
138
139 // Copy the lists that we wish to append to back to the CPU
140 // FIXME: really this can be into pinned memory and a true async
141 // copy on a different stream; we can start the copy early, but it's
142 // tiny
143 HostTensor<int, 1, true> listIdsHost(listIds, stream);
144
145 // Calculate the residual for each closest centroid
146 DeviceTensor<float, 2, true> residuals(
147 mem, {vecs.getSize(0), vecs.getSize(1)}, stream);
148
149 runCalcResidual(vecs, coarseCentroids, listIds, residuals, stream);
150
151 // Residuals are in the form
152 // (vec x numSubQuantizer x dimPerSubQuantizer)
153 // transpose to
154 // (numSubQuantizer x vec x dimPerSubQuantizer)
155 auto residualsView = residuals.view<3>(
156 {residuals.getSize(0), numSubQuantizers_, dimPerSubQuantizer_});
157
158 DeviceTensor<float, 3, true> residualsTranspose(
159 mem,
160 {numSubQuantizers_, residuals.getSize(0), dimPerSubQuantizer_},
161 stream);
162
163 runTransposeAny(residualsView, 0, 1, residualsTranspose, stream);
164
165 // Get the product quantizer centroids in the form
166 // (numSubQuantizer x numSubQuantizerCodes x dimPerSubQuantizer)
167 // which is pqCentroidsMiddleCode_
168
169 // We now have a batch operation to find the top-1 distances:
170 // batch size: numSubQuantizer
171 // centroids: (numSubQuantizerCodes x dimPerSubQuantizer)
172 // residuals: (vec x dimPerSubQuantizer)
173 // => (numSubQuantizer x vec x 1)
174
175 DeviceTensor<float, 3, true> closestSubQDistance(
176 mem, {numSubQuantizers_, residuals.getSize(0), 1}, stream);
177 DeviceTensor<int, 3, true> closestSubQIndex(
178 mem, {numSubQuantizers_, residuals.getSize(0), 1}, stream);
179
180 for (int subQ = 0; subQ < numSubQuantizers_; ++subQ) {
181 auto closestSubQDistanceView = closestSubQDistance[subQ].view();
182 auto closestSubQIndexView = closestSubQIndex[subQ].view();
183
184 auto pqCentroidsMiddleCodeView = pqCentroidsMiddleCode_[subQ].view();
185 auto residualsTransposeView = residualsTranspose[subQ].view();
186
187 runL2Distance(resources_,
188 pqCentroidsMiddleCodeView,
189 nullptr, // no precomputed norms
190 residualsTransposeView,
191 1,
192 closestSubQDistanceView,
193 closestSubQIndexView,
194 // We don't care about distances
195 true,
196 // Much larger tile size, since these vectors are a
197 // lot smaller than query vectors
198 1024);
199 }
200
201 // Now, we have the nearest sub-q centroid for each slice of the
202 // residual vector.
203 auto closestSubQIndexView = closestSubQIndex.view<2>(
204 {numSubQuantizers_, residuals.getSize(0)});
205
206 // Transpose this for easy use
207 DeviceTensor<int, 2, true> encodings(
208 mem, {residuals.getSize(0), numSubQuantizers_}, stream);
209
210 runTransposeAny(closestSubQIndexView, 0, 1, encodings, stream);
211
212 // Now we add the encoded vectors to the individual lists
213 // First, make sure that there is space available for adding the new
214 // encoded vectors and indices
215
216 // list id -> # being added
217 std::unordered_map<int, int> assignCounts;
218
219 // vector id -> offset in list
220 // (we already have vector id -> list id in listIds)
221 HostTensor<int, 1, true> listOffsetHost({listIdsHost.getSize(0)});
222
223 for (int i = 0; i < listIdsHost.getSize(0); ++i) {
224 int listId = listIdsHost[i];
225
226 // Add vector could be invalid (contains NaNs etc)
227 if (listId < 0) {
228 listOffsetHost[i] = -1;
229 continue;
230 }
231
232 FAISS_ASSERT(listId < numLists_);
233 ++numAdded;
234
235 int offset = deviceListData_[listId]->size() / bytesPerVector_;
236
237 auto it = assignCounts.find(listId);
238 if (it != assignCounts.end()) {
239 offset += it->second;
240 it->second++;
241 } else {
242 assignCounts[listId] = 1;
243 }
244
245 listOffsetHost[i] = offset;
246 }
247
248 // If we didn't add anything (all invalid vectors), no need to
249 // continue
250 if (numAdded == 0) {
251 return 0;
252 }
253
254 // We need to resize the data structures for the inverted lists on
255 // the GPUs, which means that they might need reallocation, which
256 // means that their base address may change. Figure out the new base
257 // addresses, and update those in a batch on the device
258 {
259 // Resize all of the lists that we are appending to
260 for (auto& counts : assignCounts) {
261 auto& codes = deviceListData_[counts.first];
262 codes->resize(codes->size() + counts.second * bytesPerVector_,
263 stream);
264 int newNumVecs = (int) (codes->size() / bytesPerVector_);
265
266 auto& indices = deviceListIndices_[counts.first];
267 if ((indicesOptions_ == INDICES_32_BIT) ||
268 (indicesOptions_ == INDICES_64_BIT)) {
269 size_t indexSize =
270 (indicesOptions_ == INDICES_32_BIT) ? sizeof(int) : sizeof(long);
271
272 indices->resize(indices->size() + counts.second * indexSize, stream);
273 } else if (indicesOptions_ == INDICES_CPU) {
274 // indices are stored on the CPU side
275 FAISS_ASSERT(counts.first < listOffsetToUserIndex_.size());
276
277 auto& userIndices = listOffsetToUserIndex_[counts.first];
278 userIndices.resize(newNumVecs);
279 } else {
280 // indices are not stored on the GPU or CPU side
281 FAISS_ASSERT(indicesOptions_ == INDICES_IVF);
282 }
283
284 // This is used by the multi-pass query to decide how much scratch
285 // space to allocate for intermediate results
286 maxListLength_ = std::max(maxListLength_, newNumVecs);
287 }
288
289 // Update all pointers and sizes on the device for lists that we
290 // appended to
291 {
292 std::vector<int> listIds(assignCounts.size());
293 int i = 0;
294 for (auto& counts : assignCounts) {
295 listIds[i++] = counts.first;
296 }
297
298 updateDeviceListInfo_(listIds, stream);
299 }
300 }
301
302 // If we're maintaining the indices on the CPU side, update our
303 // map. We already resized our map above.
304 if (indicesOptions_ == INDICES_CPU) {
305 // We need to maintain the indices on the CPU side
306 HostTensor<long, 1, true> hostIndices(indices, stream);
307
308 for (int i = 0; i < hostIndices.getSize(0); ++i) {
309 int listId = listIdsHost[i];
310
311 // Add vector could be invalid (contains NaNs etc)
312 if (listId < 0) {
313 continue;
314 }
315
316 int offset = listOffsetHost[i];
317
318 FAISS_ASSERT(listId < listOffsetToUserIndex_.size());
319 auto& userIndices = listOffsetToUserIndex_[listId];
320
321 FAISS_ASSERT(offset < userIndices.size());
322 userIndices[offset] = hostIndices[i];
323 }
324 }
325
326 // We similarly need to actually append the new encoded vectors
327 {
328 DeviceTensor<int, 1, true> listOffset(mem, listOffsetHost, stream);
329
330 // This kernel will handle appending each encoded vector + index to
331 // the appropriate list
332 runIVFPQInvertedListAppend(listIds,
333 listOffset,
334 encodings,
335 indices,
336 deviceListDataPointers_,
337 deviceListIndexPointers_,
338 indicesOptions_,
339 stream);
340 }
341
342 return numAdded;
343 }
344
345 void
346 IVFPQ::addCodeVectorsFromCpu(int listId,
347 const void* codes,
348 const long* indices,
349 size_t numVecs) {
350 // This list must already exist
351 FAISS_ASSERT(listId < deviceListData_.size());
352 auto stream = resources_->getDefaultStreamCurrentDevice();
353
354 // If there's nothing to add, then there's nothing we have to do
355 if (numVecs == 0) {
356 return;
357 }
358
359 size_t lengthInBytes = numVecs * bytesPerVector_;
360
361 auto& listCodes = deviceListData_[listId];
362 auto prevCodeData = listCodes->data();
363
364 // We only have int32 length representations on the GPU per each
365 // list; the length is in sizeof(char)
366 FAISS_ASSERT(listCodes->size() % bytesPerVector_ == 0);
367 FAISS_ASSERT(listCodes->size() + lengthInBytes <=
368 (size_t) std::numeric_limits<int>::max());
369
370 listCodes->append((unsigned char*) codes,
371 lengthInBytes,
372 stream,
373 true /* exact reserved size */);
374
375 // Handle the indices as well
376 addIndicesFromCpu_(listId, indices, numVecs);
377
378 // This list address may have changed due to vector resizing, but
379 // only bother updating it on the device if it has changed
380 if (prevCodeData != listCodes->data()) {
381 deviceListDataPointers_[listId] = listCodes->data();
382 }
383
384 // And our size has changed too
385 int listLength = listCodes->size() / bytesPerVector_;
386 deviceListLengths_[listId] = listLength;
387
388 // We update this as well, since the multi-pass algorithm uses it
389 maxListLength_ = std::max(maxListLength_, listLength);
390
391 // device_vector add is potentially happening on a different stream
392 // than our default stream
393 if (resources_->getDefaultStreamCurrentDevice() != 0) {
394 streamWait({stream}, {0});
395 }
396 }
397
398 void
399 IVFPQ::setPQCentroids_(float* data) {
400 size_t pqSize =
401 numSubQuantizers_ * numSubQuantizerCodes_ * dimPerSubQuantizer_;
402
403 // Make sure the data is on the host
404 // FIXME: why are we doing this?
405 thrust::host_vector<float> hostMemory;
406 hostMemory.insert(hostMemory.end(), data, data + pqSize);
407
408 HostTensor<float, 3, true> pqHost(
409 hostMemory.data(),
410 {numSubQuantizers_, numSubQuantizerCodes_, dimPerSubQuantizer_});
411 DeviceTensor<float, 3, true> pqDevice(
412 pqHost,
413 resources_->getDefaultStreamCurrentDevice());
414
415 DeviceTensor<float, 3, true> pqDeviceTranspose(
416 {numSubQuantizers_, dimPerSubQuantizer_, numSubQuantizerCodes_});
417 runTransposeAny(pqDevice, 1, 2, pqDeviceTranspose,
418 resources_->getDefaultStreamCurrentDevice());
419
420 pqCentroidsInnermostCode_ = std::move(pqDeviceTranspose);
421
422 // Also maintain the PQ centroids in the form
423 // (sub q)(code id)(sub dim)
424 DeviceTensor<float, 3, true> pqCentroidsMiddleCode(
425 {numSubQuantizers_, numSubQuantizerCodes_, dimPerSubQuantizer_});
426 runTransposeAny(pqCentroidsInnermostCode_, 1, 2, pqCentroidsMiddleCode,
427 resources_->getDefaultStreamCurrentDevice());
428
429 pqCentroidsMiddleCode_ = std::move(pqCentroidsMiddleCode);
430 }
431
432 void
433 IVFPQ::precomputeCodes_() {
434 //
435 // d = || x - y_C ||^2 + || y_R ||^2 + 2 * (y_C|y_R) - 2 * (x|y_R)
436 // --------------- --------------------------- -------
437 // term 1 term 2 term 3
438 //
439
440 // Terms 1 and 3 are available only at query time. We compute term 2
441 // here.
442 FAISS_ASSERT(!quantizer_->getUseFloat16());
443 auto& coarseCentroids = quantizer_->getVectorsFloat32Ref();
444
445 // Compute ||y_R||^2 by treating
446 // (sub q)(code id)(sub dim) as (sub q * code id)(sub dim)
447 auto pqCentroidsMiddleCodeView =
448 pqCentroidsMiddleCode_.view<2>(
449 {numSubQuantizers_ * numSubQuantizerCodes_, dimPerSubQuantizer_});
450 DeviceTensor<float, 1, true> subQuantizerNorms(
451 {numSubQuantizers_ * numSubQuantizerCodes_});
452
453 runL2Norm(pqCentroidsMiddleCodeView, subQuantizerNorms, true,
454 resources_->getDefaultStreamCurrentDevice());
455
456 // Compute 2 * (y_C|y_R) via batch matrix multiplication
457 // batch size (sub q) x {(centroid id)(sub dim) x (code id)(sub dim)'}
458 // => (sub q) x {(centroid id)(code id)}
459 // => (sub q)(centroid id)(code id)
460
461 // View (centroid id)(dim) as
462 // (centroid id)(sub q)(dim)
463 // Transpose (centroid id)(sub q)(sub dim) to
464 // (sub q)(centroid id)(sub dim)
465 auto centroidView = coarseCentroids.view<3>(
466 {coarseCentroids.getSize(0), numSubQuantizers_, dimPerSubQuantizer_});
467 DeviceTensor<float, 3, true> centroidsTransposed(
468 {numSubQuantizers_, coarseCentroids.getSize(0), dimPerSubQuantizer_});
469
470 runTransposeAny(centroidView, 0, 1, centroidsTransposed,
471 resources_->getDefaultStreamCurrentDevice());
472
473 DeviceTensor<float, 3, true> coarsePQProduct(
474 {numSubQuantizers_, coarseCentroids.getSize(0), numSubQuantizerCodes_});
475
476 runIteratedMatrixMult(coarsePQProduct, false,
477 centroidsTransposed, false,
478 pqCentroidsMiddleCode_, true,
479 2.0f, 0.0f,
480 resources_->getBlasHandleCurrentDevice(),
481 resources_->getDefaultStreamCurrentDevice());
482
483 // Transpose (sub q)(centroid id)(code id) to
484 // (centroid id)(sub q)(code id)
485 DeviceTensor<float, 3, true> coarsePQProductTransposed(
486 {coarseCentroids.getSize(0), numSubQuantizers_, numSubQuantizerCodes_});
487 runTransposeAny(coarsePQProduct, 0, 1, coarsePQProductTransposed,
488 resources_->getDefaultStreamCurrentDevice());
489
490 // View (centroid id)(sub q)(code id) as
491 // (centroid id)(sub q * code id)
492 auto coarsePQProductTransposedView = coarsePQProductTransposed.view<2>(
493 {coarseCentroids.getSize(0), numSubQuantizers_ * numSubQuantizerCodes_});
494
495 // Sum || y_R ||^2 + 2 * (y_C|y_R)
496 // i.e., add norms (sub q * code id)
497 // along columns of inner product (centroid id)(sub q * code id)
498 runSumAlongColumns(subQuantizerNorms, coarsePQProductTransposedView,
499 resources_->getDefaultStreamCurrentDevice());
500
501 #ifdef FAISS_USE_FLOAT16
502 if (useFloat16LookupTables_) {
503 precomputedCodeHalf_ = toHalf(resources_,
504 resources_->getDefaultStreamCurrentDevice(),
505 coarsePQProductTransposed);
506 return;
507 }
508 #endif
509
510 // We added into the view, so `coarsePQProductTransposed` is now our
511 // precomputed term 2.
512 precomputedCode_ = std::move(coarsePQProductTransposed);
513 }
514
515 void
516 IVFPQ::query(Tensor<float, 2, true>& queries,
517 int nprobe,
518 int k,
519 Tensor<float, 2, true>& outDistances,
520 Tensor<long, 2, true>& outIndices) {
521 // Validate these at a top level
522 FAISS_ASSERT(nprobe <= 1024);
523 FAISS_ASSERT(k <= 1024);
524
525 auto& mem = resources_->getMemoryManagerCurrentDevice();
526 auto stream = resources_->getDefaultStreamCurrentDevice();
527 nprobe = std::min(nprobe, quantizer_->getSize());
528
529 FAISS_ASSERT(queries.getSize(1) == dim_);
530 FAISS_ASSERT(outDistances.getSize(0) == queries.getSize(0));
531 FAISS_ASSERT(outIndices.getSize(0) == queries.getSize(0));
532
533 // Reserve space for the closest coarse centroids
534 DeviceTensor<float, 2, true>
535 coarseDistances(mem, {queries.getSize(0), nprobe}, stream);
536 DeviceTensor<int, 2, true>
537 coarseIndices(mem, {queries.getSize(0), nprobe}, stream);
538
539 // Find the `nprobe` closest coarse centroids; we can use int
540 // indices both internally and externally
541 quantizer_->query(queries,
542 nprobe,
543 coarseDistances,
544 coarseIndices,
545 true);
546
547 if (precomputedCodes_) {
548 runPQPrecomputedCodes_(queries,
549 coarseDistances,
550 coarseIndices,
551 k,
552 outDistances,
553 outIndices);
554 } else {
555 runPQNoPrecomputedCodes_(queries,
556 coarseDistances,
557 coarseIndices,
558 k,
559 outDistances,
560 outIndices);
561 }
562
563 // If the GPU isn't storing indices (they are on the CPU side), we
564 // need to perform the re-mapping here
565 // FIXME: we might ultimately be calling this function with inputs
566 // from the CPU, these are unnecessary copies
567 if (indicesOptions_ == INDICES_CPU) {
568 HostTensor<long, 2, true> hostOutIndices(outIndices, stream);
569
570 ivfOffsetToUserIndex(hostOutIndices.data(),
571 numLists_,
572 hostOutIndices.getSize(0),
573 hostOutIndices.getSize(1),
574 listOffsetToUserIndex_);
575
576 // Copy back to GPU, since the input to this function is on the
577 // GPU
578 outIndices.copyFrom(hostOutIndices, stream);
579 }
580 }
581
582 std::vector<unsigned char>
583 IVFPQ::getListCodes(int listId) const {
584 FAISS_ASSERT(listId < deviceListData_.size());
585
586 return deviceListData_[listId]->copyToHost<unsigned char>(
587 resources_->getDefaultStreamCurrentDevice());
588 }
589
590 Tensor<float, 3, true>
591 IVFPQ::getPQCentroids() {
592 return pqCentroidsMiddleCode_;
593 }
594
595 void
596 IVFPQ::runPQPrecomputedCodes_(
597 Tensor<float, 2, true>& queries,
598 DeviceTensor<float, 2, true>& coarseDistances,
599 DeviceTensor<int, 2, true>& coarseIndices,
600 int k,
601 Tensor<float, 2, true>& outDistances,
602 Tensor<long, 2, true>& outIndices) {
603 auto& mem = resources_->getMemoryManagerCurrentDevice();
604 auto stream = resources_->getDefaultStreamCurrentDevice();
605
606 // Compute precomputed code term 3, - 2 * (x|y_R)
607 // This is done via batch MM
608 // {sub q} x {(query id)(sub dim) * (code id)(sub dim)'} =>
609 // {sub q} x {(query id)(code id)}
610 DeviceTensor<float, 3, true> term3Transposed(
611 mem,
612 {queries.getSize(0), numSubQuantizers_, numSubQuantizerCodes_},
613 stream);
614
615 // These allocations within are only temporary, so release them when
616 // we're done to maximize free space
617 {
618 auto querySubQuantizerView = queries.view<3>(
619 {queries.getSize(0), numSubQuantizers_, dimPerSubQuantizer_});
620 DeviceTensor<float, 3, true> queriesTransposed(
621 mem,
622 {numSubQuantizers_, queries.getSize(0), dimPerSubQuantizer_},
623 stream);
624 runTransposeAny(querySubQuantizerView, 0, 1, queriesTransposed, stream);
625
626 DeviceTensor<float, 3, true> term3(
627 mem,
628 {numSubQuantizers_, queries.getSize(0), numSubQuantizerCodes_},
629 stream);
630
631 runIteratedMatrixMult(term3, false,
632 queriesTransposed, false,
633 pqCentroidsMiddleCode_, true,
634 -2.0f, 0.0f,
635 resources_->getBlasHandleCurrentDevice(),
636 stream);
637
638 runTransposeAny(term3, 0, 1, term3Transposed, stream);
639 }
640
641 NoTypeTensor<3, true> term2;
642 NoTypeTensor<3, true> term3;
643 #ifdef FAISS_USE_FLOAT16
644 DeviceTensor<half, 3, true> term3Half;
645
646 if (useFloat16LookupTables_) {
647 term3Half = toHalf(resources_, stream, term3Transposed);
648 term2 = NoTypeTensor<3, true>(precomputedCodeHalf_);
649 term3 = NoTypeTensor<3, true>(term3Half);
650 }
651 #endif
652
653 if (!useFloat16LookupTables_) {
654 term2 = NoTypeTensor<3, true>(precomputedCode_);
655 term3 = NoTypeTensor<3, true>(term3Transposed);
656 }
657
658 runPQScanMultiPassPrecomputed(queries,
659 coarseDistances, // term 1
660 term2, // term 2
661 term3, // term 3
662 coarseIndices,
663 useFloat16LookupTables_,
664 bytesPerVector_,
665 numSubQuantizers_,
666 numSubQuantizerCodes_,
667 deviceListDataPointers_,
668 deviceListIndexPointers_,
669 indicesOptions_,
670 deviceListLengths_,
671 maxListLength_,
672 k,
673 outDistances,
674 outIndices,
675 resources_);
676 }
677
678 void
679 IVFPQ::runPQNoPrecomputedCodes_(
680 Tensor<float, 2, true>& queries,
681 DeviceTensor<float, 2, true>& coarseDistances,
682 DeviceTensor<int, 2, true>& coarseIndices,
683 int k,
684 Tensor<float, 2, true>& outDistances,
685 Tensor<long, 2, true>& outIndices) {
686 FAISS_ASSERT(!quantizer_->getUseFloat16());
687 auto& coarseCentroids = quantizer_->getVectorsFloat32Ref();
688
689 runPQScanMultiPassNoPrecomputed(queries,
690 coarseCentroids,
691 pqCentroidsInnermostCode_,
692 coarseIndices,
693 useFloat16LookupTables_,
694 bytesPerVector_,
695 numSubQuantizers_,
696 numSubQuantizerCodes_,
697 deviceListDataPointers_,
698 deviceListIndexPointers_,
699 indicesOptions_,
700 deviceListLengths_,
701 maxListLength_,
702 k,
703 outDistances,
704 outIndices,
705 resources_);
706 }
707
708 } } // namespace
faiss::gpu::IVFBase::numLists_
const int numLists_
Number of inverted lists we maintain.
Definition: IVFBase.cuh:90
faiss::gpu::IVFBase::maxListLength_
int maxListLength_
Maximum list length seen.
Definition: IVFBase.cuh:111
faiss::gpu::IVFPQ::addCodeVectorsFromCpu
void addCodeVectorsFromCpu(int listId, const void *codes, const long *indices, size_t numVecs)
Definition: IVFPQ.cu:346
faiss::gpu::FlatIndex::getSize
int getSize() const
Returns the number of vectors we contain.
Definition: FlatIndex.cu:40
faiss::gpu::IVFBase::listOffsetToUserIndex_
std::vector< std::vector< long > > listOffsetToUserIndex_
Definition: IVFBase.cuh:123
faiss::gpu::FlatIndex
Holder of GPU resources for a particular flat index.
Definition: FlatIndex.cuh:23
faiss::gpu::Tensor::data
__host__ __device__ DataPtrType data()
Returns a raw pointer to the start of our data.
Definition: Tensor.cuh:162
faiss::gpu::IVFBase
Base inverted list functionality for IVFFlat and IVFPQ.
Definition: IVFBase.cuh:27
faiss::gpu::GpuResources
Definition: GpuResources.h:24
faiss::gpu::IVFPQ::IVFPQ
IVFPQ(GpuResources *resources, FlatIndex *quantizer, int numSubQuantizers, int bitsPerSubQuantizer, float *pqCentroidData, IndicesOptions indicesOptions, bool useFloat16LookupTables)
Definition: IVFPQ.cu:36
faiss::gpu::IVFBase::deviceListLengths_
thrust::device_vector< int > deviceListLengths_
Definition: IVFBase.cuh:108
faiss::gpu::IVFPQ::isSupportedPQCodeLength
static bool isSupportedPQCodeLength(int size)
Returns true if we support PQ in this size.
Definition: IVFPQ.cu:71
faiss::gpu::IVFBase::deviceListIndexPointers_
thrust::device_vector< void * > deviceListIndexPointers_
Definition: IVFBase.cuh:104
faiss::gpu::IVFPQ::classifyAndAddVectors
int classifyAndAddVectors(Tensor< float, 2, true > &vecs, Tensor< long, 1, true > &indices)
Definition: IVFPQ.cu:118
faiss::gpu::IVFPQ::query
void query(Tensor< float, 2, true > &queries, int nprobe, int k, Tensor< float, 2, true > &outDistances, Tensor< long, 2, true > &outIndices)
Definition: IVFPQ.cu:516
faiss::gpu::IVFPQ::getPQCentroids
Tensor< float, 3, true > getPQCentroids()
Definition: IVFPQ.cu:591
faiss::gpu::IVFBase::quantizer_
FlatIndex * quantizer_
Quantizer object.
Definition: IVFBase.cuh:84
faiss::gpu::IVFPQ::setPrecomputedCodes
void setPrecomputedCodes(bool enable)
Enable or disable pre-computed codes.
Definition: IVFPQ.cu:100
faiss::gpu::IVFPQ::getListCodes
std::vector< unsigned char > getListCodes(int listId) const
Return the list codes of a particular list back to the CPU.
Definition: IVFPQ.cu:583
faiss::gpu::IVFBase::deviceListDataPointers_
thrust::device_vector< void * > deviceListDataPointers_
Definition: IVFBase.cuh:100
faiss::gpu::IVFBase::resources_
GpuResources * resources_
Collection of GPU resources that we use.
Definition: IVFBase.cuh:81
faiss::gpu::Tensor
Our tensor type.
Definition: Tensor.cuh:31
faiss::gpu::Tensor::view
__host__ __device__ Tensor< T, SubDim, Contig, IndexT, PtrTraits > view(DataPtrType at)
Definition: Tensor-inl.cuh:526
faiss::gpu::IVFBase::bytesPerVector_
const int bytesPerVector_
Number of bytes per vector in the list.
Definition: IVFBase.cuh:93
faiss::gpu::IVFBase::updateDeviceListInfo_
void updateDeviceListInfo_(cudaStream_t stream)
Update all device-side list pointer and size information.
Definition: IVFBase.cu:136
faiss::gpu::Tensor::getSize
__host__ __device__ IndexT getSize(int i) const
Definition: Tensor.cuh:210
faiss::gpu::Tensor::copyFrom
__host__ void copyFrom(Tensor< T, Dim, Contig, IndexT, PtrTraits > &t, cudaStream_t stream)
Copies a tensor into ourselves; sizes must match.
Definition: Tensor-inl.cuh:101
faiss::gpu::FlatIndex::getVectorsFloat32Ref
Tensor< float, 2, true > & getVectorsFloat32Ref()
Returns a reference to our vectors currently in use.
Definition: FlatIndex.cu:61
faiss::gpu::IVFBase::indicesOptions_
const IndicesOptions indicesOptions_
How are user indices stored on the GPU?
Definition: IVFBase.cuh:96
faiss::gpu::HostTensor
Definition: HostTensor.cuh:23
faiss::gpu::IVFBase::deviceListData_
std::vector< std::unique_ptr< DeviceVector< unsigned char > > > deviceListData_
Definition: IVFBase.cuh:117
faiss::gpu::DeviceTensor< float, 3, true >
faiss::gpu::IVFBase::dim_
const int dim_
Expected dimensionality of the vectors.
Definition: IVFBase.cuh:87
faiss::gpu::IVFBase::addIndicesFromCpu_
void addIndicesFromCpu_(int listId, const long *indices, size_t numVecs)
Shared function to copy indices from CPU to GPU.
Definition: IVFBase.cu:243
faiss::gpu::IVFPQ::isSupportedNoPrecomputedSubDimSize
static bool isSupportedNoPrecomputedSubDimSize(int dims)
Definition: IVFPQ.cu:95