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GpuIndexFlat.cu
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 // Copyright 2004-present Facebook. All Rights Reserved.
10
11 #include "GpuIndexFlat.h"
12 #include "../IndexFlat.h"
13 #include "GpuResources.h"
14 #include "impl/FlatIndex.cuh"
15 #include "utils/CopyUtils.cuh"
16 #include "utils/DeviceUtils.h"
17 #include "utils/Float16.cuh"
18 #include "utils/StaticUtils.h"
19
20 #include <thrust/execution_policy.h>
21 #include <thrust/transform.h>
22 #include <limits>
23
24 namespace faiss { namespace gpu {
25
26 /// Default CPU search size for which we use paged copies
27 constexpr size_t kMinPageSize = (size_t) 256 * 1024 * 1024;
28
29 /// Size above which we page copies from the CPU to GPU (non-paged
30 /// memory usage)
31 constexpr size_t kNonPinnedPageSize = (size_t) 256 * 1024 * 1024;
32
33 GpuIndexFlat::GpuIndexFlat(GpuResources* resources,
34 const faiss::IndexFlat* index,
35 GpuIndexFlatConfig config) :
36 GpuIndex(resources, index->d, index->metric_type, config),
37 minPagedSize_(kMinPageSize),
38 config_(config),
39 data_(nullptr) {
40 verifySettings_();
41
42 // Flat index doesn't need training
43 this->is_trained = true;
44
45 copyFrom(index);
46 }
47
48 GpuIndexFlat::GpuIndexFlat(GpuResources* resources,
49 int dims,
50 faiss::MetricType metric,
51 GpuIndexFlatConfig config) :
52 GpuIndex(resources, dims, metric, config),
53 minPagedSize_(kMinPageSize),
54 config_(config),
55 data_(nullptr) {
56 verifySettings_();
57
58 // Flat index doesn't need training
59 this->is_trained = true;
60
61 // Construct index
62 DeviceScope scope(device_);
63 data_ = new FlatIndex(resources,
64 dims,
65 metric == faiss::METRIC_L2,
66 config_.useFloat16,
67 config_.useFloat16Accumulator,
68 config_.storeTransposed,
69 memorySpace_);
70 }
71
72 GpuIndexFlat::~GpuIndexFlat() {
73 delete data_;
74 }
75
76 void
77 GpuIndexFlat::setMinPagingSize(size_t size) {
78 minPagedSize_ = size;
79 }
80
81 size_t
82 GpuIndexFlat::getMinPagingSize() const {
83 return minPagedSize_;
84 }
85
86 void
87 GpuIndexFlat::copyFrom(const faiss::IndexFlat* index) {
88 DeviceScope scope(device_);
89
90 this->d = index->d;
91 this->metric_type = index->metric_type;
92
93 // GPU code has 32 bit indices
94 FAISS_THROW_IF_NOT_FMT(index->ntotal <=
95 (faiss::Index::idx_t) std::numeric_limits<int>::max(),
96 "GPU index only supports up to %zu indices; "
97 "attempting to copy CPU index with %zu parameters",
98 (size_t) std::numeric_limits<int>::max(),
99 (size_t) index->ntotal);
100 this->ntotal = index->ntotal;
101
102 delete data_;
103 data_ = new FlatIndex(resources_,
104 this->d,
105 index->metric_type == faiss::METRIC_L2,
106 config_.useFloat16,
107 config_.useFloat16Accumulator,
108 config_.storeTransposed,
109 memorySpace_);
110
111 // The index could be empty
112 if (index->ntotal > 0) {
113 data_->add(index->xb.data(),
114 index->ntotal,
115 resources_->getDefaultStream(device_));
116 }
117 }
118
119 void
120 GpuIndexFlat::copyTo(faiss::IndexFlat* index) const {
121 DeviceScope scope(device_);
122
123 index->d = this->d;
124 index->ntotal = this->ntotal;
125 index->metric_type = this->metric_type;
126
127 FAISS_ASSERT(data_->getSize() == this->ntotal);
128 index->xb.resize(this->ntotal * this->d);
129
130 auto stream = resources_->getDefaultStream(device_);
131
132 if (this->ntotal > 0) {
133 if (config_.useFloat16) {
134 auto vecFloat32 = data_->getVectorsFloat32Copy(stream);
135 fromDevice(vecFloat32, index->xb.data(), stream);
136 } else {
137 fromDevice(data_->getVectorsFloat32Ref(), index->xb.data(), stream);
138 }
139 }
140 }
141
142 size_t
143 GpuIndexFlat::getNumVecs() const {
144 return this->ntotal;
145 }
146
147 void
148 GpuIndexFlat::reset() {
149 DeviceScope scope(device_);
150
151 // Free the underlying memory
152 data_->reset();
153 this->ntotal = 0;
154 }
155
156 void
157 GpuIndexFlat::train(Index::idx_t n, const float* x) {
158 // nothing to do
159 }
160
161 void
162 GpuIndexFlat::add(Index::idx_t n, const float* x) {
163 DeviceScope scope(device_);
164
165 // To avoid multiple re-allocations, ensure we have enough storage
166 // available
167 data_->reserve(n, resources_->getDefaultStream(device_));
168
169 // If we're not operating in float16 mode, we don't need the input
170 // data to be resident on our device; we can add directly.
171 if (!config_.useFloat16) {
172 addImpl_(n, x, nullptr);
173 } else {
174 // Otherwise, perform the paging
175 GpuIndex::add(n, x);
176 }
177 }
178
179 void
180 GpuIndexFlat::addImpl_(Index::idx_t n,
181 const float* x,
182 const Index::idx_t* ids) {
183 // Device is already set in GpuIndex::addInternal_
184
185 // We do not support add_with_ids
186 FAISS_THROW_IF_NOT_MSG(!ids, "add_with_ids not supported");
187 FAISS_THROW_IF_NOT(n > 0);
188
189 // Due to GPU indexing in int32, we can't store more than this
190 // number of vectors on a GPU
191 FAISS_THROW_IF_NOT_FMT(this->ntotal + n <=
192 (faiss::Index::idx_t) std::numeric_limits<int>::max(),
193 "GPU index only supports up to %zu indices",
194 (size_t) std::numeric_limits<int>::max());
195
196 data_->add(x, n, resources_->getDefaultStream(device_));
197 this->ntotal += n;
198 }
199
200 struct IntToLong {
201 __device__ long operator()(int v) const { return (long) v; }
202 };
203
204 void
205 GpuIndexFlat::search(faiss::Index::idx_t n,
206 const float* x,
207 faiss::Index::idx_t k,
208 float* distances,
209 faiss::Index::idx_t* labels) const {
210 if (n == 0) {
211 return;
212 }
213
214 // For now, only support <= max int results
215 FAISS_THROW_IF_NOT_FMT(n <=
216 (faiss::Index::idx_t) std::numeric_limits<int>::max(),
217 "GPU index only supports up to %zu indices",
218 (size_t) std::numeric_limits<int>::max());
219 FAISS_THROW_IF_NOT_FMT(k <= 1024,
220 "GPU only supports k <= 1024 (requested %d)",
221 (int) k); // select limitation
222
223 DeviceScope scope(device_);
224 auto stream = resources_->getDefaultStream(device_);
225
226 // The input vectors may be too large for the GPU, but we still
227 // assume that the output distances and labels are not.
228 // Go ahead and make space for output distances and labels on the
229 // GPU.
230 // If we reach a point where all inputs are too big, we can add
231 // another level of tiling.
232 auto outDistances = toDevice<float, 2>(resources_,
233 device_,
234 distances,
235 stream,
236 {(int) n, (int) k});
237
238 // FlatIndex only supports an interface returning int indices
239 DeviceTensor<int, 2, true> outIntIndices(
240 resources_->getMemoryManagerCurrentDevice(),
241 {(int) n, (int) k}, stream);
242
243 bool usePaged = false;
244
245 if (getDeviceForAddress(x) == -1) {
246 // It is possible that the user is querying for a vector set size
247 // `x` that won't fit on the GPU.
248 // In this case, we will have to handle paging of the data from CPU
249 // -> GPU.
250 // Currently, we don't handle the case where the output data won't
251 // fit on the GPU (e.g., n * k is too large for the GPU memory).
252 size_t dataSize = (size_t) n * this->d * sizeof(float);
253
254 if (dataSize >= minPagedSize_) {
255 searchFromCpuPaged_(n, x, k,
256 outDistances.data(),
257 outIntIndices.data());
258 usePaged = true;
259 }
260 }
261
262 if (!usePaged) {
263 searchNonPaged_(n, x, k,
264 outDistances.data(),
265 outIntIndices.data());
266 }
267
268 // Convert and copy int indices out
269 auto outIndices = toDevice<faiss::Index::idx_t, 2>(resources_,
270 device_,
271 labels,
272 stream,
273 {(int) n, (int) k});
274
275 // Convert int to long
276 thrust::transform(thrust::cuda::par.on(stream),
277 outIntIndices.data(),
278 outIntIndices.end(),
279 outIndices.data(),
280 IntToLong());
281
282 // Copy back if necessary
283 fromDevice<float, 2>(outDistances, distances, stream);
284 fromDevice<faiss::Index::idx_t, 2>(outIndices, labels, stream);
285 }
286
287 void
288 GpuIndexFlat::searchImpl_(faiss::Index::idx_t n,
289 const float* x,
290 faiss::Index::idx_t k,
291 float* distances,
292 faiss::Index::idx_t* labels) const {
293 FAISS_ASSERT_MSG(false, "Should not be called");
294 }
295
296 void
297 GpuIndexFlat::searchNonPaged_(int n,
298 const float* x,
299 int k,
300 float* outDistancesData,
301 int* outIndicesData) const {
302 Tensor<float, 2, true> outDistances(outDistancesData, {n, k});
303 Tensor<int, 2, true> outIndices(outIndicesData, {n, k});
304
305 auto stream = resources_->getDefaultStream(device_);
306
307 // Make sure arguments are on the device we desire; use temporary
308 // memory allocations to move it if necessary
309 auto vecs = toDevice<float, 2>(resources_,
310 device_,
311 const_cast<float*>(x),
312 stream,
313 {n, (int) this->d});
314
315 data_->query(vecs, k, outDistances, outIndices, true);
316 }
317
318 void
319 GpuIndexFlat::searchFromCpuPaged_(int n,
320 const float* x,
321 int k,
322 float* outDistancesData,
323 int* outIndicesData) const {
324 Tensor<float, 2, true> outDistances(outDistancesData, {n, k});
325 Tensor<int, 2, true> outIndices(outIndicesData, {n, k});
326
327 // Is pinned memory available?
328 auto pinnedAlloc = resources_->getPinnedMemory();
329 int pageSizeInVecs =
330 (int) ((pinnedAlloc.second / 2) / (sizeof(float) * this->d));
331
332 if (!pinnedAlloc.first || pageSizeInVecs < 1) {
333 // Just page without overlapping copy with compute
334 int batchSize = utils::nextHighestPowerOf2(
335 (int) ((size_t) kNonPinnedPageSize /
336 (sizeof(float) * this->d)));
337
338 for (int cur = 0; cur < n; cur += batchSize) {
339 int num = std::min(batchSize, n - cur);
340
341 auto outDistancesSlice = outDistances.narrowOutermost(cur, num);
342 auto outIndicesSlice = outIndices.narrowOutermost(cur, num);
343
344 searchNonPaged_(num,
345 x + (size_t) cur * this->d,
346 k,
347 outDistancesSlice.data(),
348 outIndicesSlice.data());
349 }
350
351 return;
352 }
353
354 //
355 // Pinned memory is available, so we can overlap copy with compute.
356 // We use two pinned memory buffers, and triple-buffer the
357 // procedure:
358 //
359 // 1 CPU copy -> pinned
360 // 2 pinned copy -> GPU
361 // 3 GPU compute
362 //
363 // 1 2 3 1 2 3 ... (pinned buf A)
364 // 1 2 3 1 2 ... (pinned buf B)
365 // 1 2 3 1 ... (pinned buf A)
366 // time ->
367 //
368 auto defaultStream = resources_->getDefaultStream(device_);
369 auto copyStream = resources_->getAsyncCopyStream(device_);
370
371 FAISS_ASSERT((size_t) pageSizeInVecs * this->d <=
372 (size_t) std::numeric_limits<int>::max());
373
374 float* bufPinnedA = (float*) pinnedAlloc.first;
375 float* bufPinnedB = bufPinnedA + (size_t) pageSizeInVecs * this->d;
376 float* bufPinned[2] = {bufPinnedA, bufPinnedB};
377
378 // Reserve space on the GPU for the destination of the pinned buffer
379 // copy
380 DeviceTensor<float, 2, true> bufGpuA(
381 resources_->getMemoryManagerCurrentDevice(),
382 {(int) pageSizeInVecs, (int) this->d},
383 defaultStream);
384 DeviceTensor<float, 2, true> bufGpuB(
385 resources_->getMemoryManagerCurrentDevice(),
386 {(int) pageSizeInVecs, (int) this->d},
387 defaultStream);
388 DeviceTensor<float, 2, true>* bufGpus[2] = {&bufGpuA, &bufGpuB};
389
390 // Copy completion events for the pinned buffers
391 std::unique_ptr<CudaEvent> eventPinnedCopyDone[2];
392
393 // Execute completion events for the GPU buffers
394 std::unique_ptr<CudaEvent> eventGpuExecuteDone[2];
395
396 // All offsets are in terms of number of vectors; they remain within
397 // int bounds (as this function only handles max in vectors)
398
399 // Current start offset for buffer 1
400 int cur1 = 0;
401 int cur1BufIndex = 0;
402
403 // Current start offset for buffer 2
404 int cur2 = -1;
405 int cur2BufIndex = 0;
406
407 // Current start offset for buffer 3
408 int cur3 = -1;
409 int cur3BufIndex = 0;
410
411 while (cur3 < n) {
412 // Start async pinned -> GPU copy first (buf 2)
413 if (cur2 != -1 && cur2 < n) {
414 // Copy pinned to GPU
415 int numToCopy = std::min(pageSizeInVecs, n - cur2);
416
417 // Make sure any previous execution has completed before continuing
418 auto& eventPrev = eventGpuExecuteDone[cur2BufIndex];
419 if (eventPrev.get()) {
420 eventPrev->streamWaitOnEvent(copyStream);
421 }
422
423 CUDA_VERIFY(cudaMemcpyAsync(bufGpus[cur2BufIndex]->data(),
424 bufPinned[cur2BufIndex],
425 (size_t) numToCopy * this->d * sizeof(float),
426 cudaMemcpyHostToDevice,
427 copyStream));
428
429 // Mark a completion event in this stream
430 eventPinnedCopyDone[cur2BufIndex] =
431 std::move(std::unique_ptr<CudaEvent>(new CudaEvent(copyStream)));
432
433 // We pick up from here
434 cur3 = cur2;
435 cur2 += numToCopy;
436 cur2BufIndex = (cur2BufIndex == 0) ? 1 : 0;
437 }
438
439 if (cur3 != -1 && cur3 < n) {
440 // Process on GPU
441 int numToProcess = std::min(pageSizeInVecs, n - cur3);
442
443 // Make sure the previous copy has completed before continuing
444 auto& eventPrev = eventPinnedCopyDone[cur3BufIndex];
445 FAISS_ASSERT(eventPrev.get());
446
447 eventPrev->streamWaitOnEvent(defaultStream);
448
449 // Create tensor wrappers
450 DeviceTensor<float, 2, true> input(bufGpus[cur3BufIndex]->data(),
451 {numToProcess, this->d});
452 auto outDistancesSlice = outDistances.narrowOutermost(cur3, numToProcess);
453 auto outIndicesSlice = outIndices.narrowOutermost(cur3, numToProcess);
454
455 data_->query(input, k,
456 outDistancesSlice,
457 outIndicesSlice, true);
458
459 // Create completion event
460 eventGpuExecuteDone[cur3BufIndex] =
461 std::move(std::unique_ptr<CudaEvent>(new CudaEvent(defaultStream)));
462
463 // We pick up from here
464 cur3BufIndex = (cur3BufIndex == 0) ? 1 : 0;
465 cur3 += numToProcess;
466 }
467
468 if (cur1 < n) {
469 // Copy CPU mem to CPU pinned
470 int numToCopy = std::min(pageSizeInVecs, n - cur1);
471
472 // Make sure any previous copy has completed before continuing
473 auto& eventPrev = eventPinnedCopyDone[cur1BufIndex];
474 if (eventPrev.get()) {
475 eventPrev->cpuWaitOnEvent();
476 }
477
478 memcpy(bufPinned[cur1BufIndex],
479 x + (size_t) cur1 * this->d,
480 (size_t) numToCopy * this->d * sizeof(float));
481
482 // We pick up from here
483 cur2 = cur1;
484 cur1 += numToCopy;
485 cur1BufIndex = (cur1BufIndex == 0) ? 1 : 0;
486 }
487 }
488 }
489
490 void
491 GpuIndexFlat::reconstruct(faiss::Index::idx_t key,
492 float* out) const {
493 DeviceScope scope(device_);
494
495 FAISS_THROW_IF_NOT_MSG(key < this->ntotal, "index out of bounds");
496 auto stream = resources_->getDefaultStream(device_);
497
498 if (config_.useFloat16) {
499 auto vec = data_->getVectorsFloat32Copy(key, 1, stream);
500 fromDevice(vec.data(), out, this->d, stream);
501 } else {
502 auto vec = data_->getVectorsFloat32Ref()[key];
503 fromDevice(vec.data(), out, this->d, stream);
504 }
505 }
506
507 void
508 GpuIndexFlat::reconstruct_n(faiss::Index::idx_t i0,
509 faiss::Index::idx_t num,
510 float* out) const {
511 DeviceScope scope(device_);
512
513 FAISS_THROW_IF_NOT_MSG(i0 < this->ntotal, "index out of bounds");
514 FAISS_THROW_IF_NOT_MSG(i0 + num - 1 < this->ntotal, "num out of bounds");
515 auto stream = resources_->getDefaultStream(device_);
516
517 if (config_.useFloat16) {
518 auto vec = data_->getVectorsFloat32Copy(i0, num, stream);
519 fromDevice(vec.data(), out, num * this->d, stream);
520 } else {
521 auto vec = data_->getVectorsFloat32Ref()[i0];
522 fromDevice(vec.data(), out, this->d * num, stream);
523 }
524 }
525
526 void
527 GpuIndexFlat::verifySettings_() const {
528 // If we want Hgemm, ensure that it is supported on this device
529 if (config_.useFloat16Accumulator) {
530 #ifdef FAISS_USE_FLOAT16
531 FAISS_THROW_IF_NOT_MSG(config_.useFloat16,
532 "useFloat16Accumulator can only be enabled "
533 "with useFloat16");
534
535 FAISS_THROW_IF_NOT_FMT(getDeviceSupportsFloat16Math(config_.device),
536 "Device %d does not support Hgemm "
537 "(useFloat16Accumulator)",
538 config_.device);
539 #else
540 FAISS_THROW_IF_NOT_MSG(false, "not compiled with float16 support");
541 #endif
542 }
543 }
544
545 //
546 // GpuIndexFlatL2
547 //
548
549 GpuIndexFlatL2::GpuIndexFlatL2(GpuResources* resources,
550 faiss::IndexFlatL2* index,
551 GpuIndexFlatConfig config) :
552 GpuIndexFlat(resources, index, config) {
553 }
554
555 GpuIndexFlatL2::GpuIndexFlatL2(GpuResources* resources,
556 int dims,
557 GpuIndexFlatConfig config) :
558 GpuIndexFlat(resources, dims, faiss::METRIC_L2, config) {
559 }
560
561 void
562 GpuIndexFlatL2::copyFrom(faiss::IndexFlatL2* index) {
563 GpuIndexFlat::copyFrom(index);
564 }
565
566 void
567 GpuIndexFlatL2::copyTo(faiss::IndexFlatL2* index) {
568 GpuIndexFlat::copyTo(index);
569 }
570
571 //
572 // GpuIndexFlatIP
573 //
574
575 GpuIndexFlatIP::GpuIndexFlatIP(GpuResources* resources,
576 faiss::IndexFlatIP* index,
577 GpuIndexFlatConfig config) :
578 GpuIndexFlat(resources, index, config) {
579 }
580
581 GpuIndexFlatIP::GpuIndexFlatIP(GpuResources* resources,
582 int dims,
583 GpuIndexFlatConfig config) :
584 GpuIndexFlat(resources, dims, faiss::METRIC_INNER_PRODUCT, config) {
585 }
586
587 void
588 GpuIndexFlatIP::copyFrom(faiss::IndexFlatIP* index) {
589 GpuIndexFlat::copyFrom(index);
590 }
591
592 void
593 GpuIndexFlatIP::copyTo(faiss::IndexFlatIP* index) {
594 GpuIndexFlat::copyTo(index);
595 }
596
597 } } // namespace
faiss::gpu::FlatIndex::getVectorsFloat32Copy
DeviceTensor< float, 2, true > getVectorsFloat32Copy(cudaStream_t stream)
Definition: FlatIndex.cu:91
faiss::IndexFlat
Definition: IndexFlat.h:23
faiss::gpu::GpuIndexFlatConfig::useFloat16Accumulator
bool useFloat16Accumulator
Definition: GpuIndexFlat.h:40
faiss::gpu::GpuIndexFlatConfig::storeTransposed
bool storeTransposed
Definition: GpuIndexFlat.h:48
faiss::gpu::GpuIndexFlatL2::copyFrom
void copyFrom(faiss::IndexFlatL2 *index)
Definition: GpuIndexFlat.cu:562
faiss::gpu::GpuIndexFlat::copyTo
void copyTo(faiss::IndexFlat *index) const
Definition: GpuIndexFlat.cu:120
faiss::gpu::GpuIndexFlat::reconstruct_n
void reconstruct_n(faiss::Index::idx_t i0, faiss::Index::idx_t num, float *out) const override
Batch reconstruction method.
Definition: GpuIndexFlat.cu:508
faiss::gpu::GpuIndexFlat::getMinPagingSize
size_t getMinPagingSize() const
Returns the current minimum data size for paged searches.
Definition: GpuIndexFlat.cu:82
faiss::gpu::FlatIndex::getSize
int getSize() const
Returns the number of vectors we contain.
Definition: FlatIndex.cu:47
faiss::gpu::FlatIndex
Holder of GPU resources for a particular flat index.
Definition: FlatIndex.cuh:23
faiss::gpu::GpuIndexFlatL2::copyTo
void copyTo(faiss::IndexFlatL2 *index)
Definition: GpuIndexFlat.cu:567
faiss::IndexFlatL2
Definition: IndexFlat.h:79
faiss::gpu::GpuIndexFlat::searchFromCpuPaged_
void searchFromCpuPaged_(int n, const float *x, int k, float *outDistancesData, int *outIndicesData) const
Definition: GpuIndexFlat.cu:319
faiss::gpu::GpuIndexFlat::addImpl_
void addImpl_(faiss::Index::idx_t n, const float *x, const faiss::Index::idx_t *ids) override
Called from GpuIndex for add.
Definition: GpuIndexFlat.cu:180
faiss::gpu::Tensor::narrowOutermost
__host__ __device__ Tensor< T, Dim, Contig, IndexT, PtrTraits > narrowOutermost(IndexT start, IndexT size)
Definition: Tensor-inl.cuh:605
faiss::gpu::GpuIndexFlat::searchImpl_
void searchImpl_(faiss::Index::idx_t n, const float *x, faiss::Index::idx_t k, float *distances, faiss::Index::idx_t *labels) const override
Should not be called (we have our own implementation)
Definition: GpuIndexFlat.cu:288
faiss::gpu::GpuIndexFlat::getNumVecs
size_t getNumVecs() const
Returns the number of vectors we contain.
Definition: GpuIndexFlat.cu:143
faiss::gpu::GpuResources
Definition: GpuResources.h:23
faiss::gpu::GpuIndexFlat::GpuIndexFlat
GpuIndexFlat(GpuResources *resources, const faiss::IndexFlat *index, GpuIndexFlatConfig config=GpuIndexFlatConfig())
Definition: GpuIndexFlat.cu:33
faiss::IndexFlatIP
Definition: IndexFlat.h:73
faiss::gpu::GpuIndexFlat::setMinPagingSize
void setMinPagingSize(size_t size)
Definition: GpuIndexFlat.cu:77
faiss::gpu::GpuIndexFlatConfig::useFloat16
bool useFloat16
Whether or not data is stored as float16.
Definition: GpuIndexFlat.h:35
faiss::gpu::GpuIndexConfig::device
int device
GPU device on which the index is resident.
Definition: GpuIndex.h:27
faiss::gpu::GpuIndex
Definition: GpuIndex.h:35
faiss::gpu::GpuIndexFlatL2::GpuIndexFlatL2
GpuIndexFlatL2(GpuResources *resources, faiss::IndexFlatL2 *index, GpuIndexFlatConfig config=GpuIndexFlatConfig())
Definition: GpuIndexFlat.cu:549
faiss::gpu::GpuResources::getDefaultStream
virtual cudaStream_t getDefaultStream(int device)=0
faiss::Index::d
int d
vector dimension
Definition: Index.h:64
faiss::gpu::GpuIndexFlatConfig
Definition: GpuIndexFlat.h:27
faiss::gpu::IntToLong
Definition: GpuIndexFlat.cu:200
faiss::gpu::GpuIndexFlat::reconstruct
void reconstruct(faiss::Index::idx_t key, float *out) const override
Definition: GpuIndexFlat.cu:491
faiss::gpu::GpuIndex::device_
const int device_
The GPU device we are resident on.
Definition: GpuIndex.h:94
faiss::gpu::GpuIndexFlatIP::copyTo
void copyTo(faiss::IndexFlatIP *index)
Definition: GpuIndexFlat.cu:593
faiss::gpu::GpuIndexFlatIP::GpuIndexFlatIP
GpuIndexFlatIP(GpuResources *resources, faiss::IndexFlatIP *index, GpuIndexFlatConfig config=GpuIndexFlatConfig())
Definition: GpuIndexFlat.cu:575
faiss::gpu::GpuIndex::resources_
GpuResources * resources_
Manages streans, cuBLAS handles and scratch memory for devices.
Definition: GpuIndex.h:91
faiss::Index::idx_t
long idx_t
all indices are this type
Definition: Index.h:62
faiss::gpu::FlatIndex::reserve
void reserve(size_t numVecs, cudaStream_t stream)
Reserve storage that can contain at least this many vectors.
Definition: FlatIndex.cu:68
faiss::gpu::FlatIndex::add
void add(const float *data, int numVecs, cudaStream_t stream)
Definition: FlatIndex.cu:201
faiss::Index::ntotal
idx_t ntotal
total nb of indexed vectors
Definition: Index.h:65
faiss::gpu::GpuIndex::add
void add(faiss::Index::idx_t, const float *x) override
Definition: GpuIndex.cu:61
faiss::gpu::GpuIndexFlat::copyFrom
void copyFrom(const faiss::IndexFlat *index)
Definition: GpuIndexFlat.cu:87
faiss::gpu::Tensor
Our tensor type.
Definition: Tensor.cuh:30
faiss::gpu::DeviceScope
Definition: DeviceUtils.h:62
faiss::gpu::GpuIndex::memorySpace_
const MemorySpace memorySpace_
The memory space of our primary storage on the GPU.
Definition: GpuIndex.h:97
faiss::gpu::GpuIndexFlat::minPagedSize_
size_t minPagedSize_
Size above which we page copies from the CPU to GPU.
Definition: GpuIndexFlat.h:160
faiss::gpu::GpuIndexFlat::config_
const GpuIndexFlatConfig config_
Our config object.
Definition: GpuIndexFlat.h:157
faiss::Index::metric_type
MetricType metric_type
type of metric this index uses for search
Definition: Index.h:72
faiss::gpu::GpuIndexFlat::add
void add(faiss::Index::idx_t, const float *x) override
Overrides to avoid excessive copies.
Definition: GpuIndexFlat.cu:162
faiss::gpu::FlatIndex::getVectorsFloat32Ref
Tensor< float, 2, true > & getVectorsFloat32Ref()
Returns a reference to our vectors currently in use.
Definition: FlatIndex.cu:79
faiss::gpu::GpuIndexFlat
Definition: GpuIndexFlat.h:54
faiss::gpu::GpuIndexFlat::reset
void reset() override
Clears all vectors from this index.
Definition: GpuIndexFlat.cu:148
faiss::gpu::GpuIndexFlatIP::copyFrom
void copyFrom(faiss::IndexFlatIP *index)
Definition: GpuIndexFlat.cu:588
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::gpu::FlatIndex::reset
void reset()
Free all storage.
Definition: FlatIndex.cu:273
faiss::gpu::CudaEvent
Definition: DeviceUtils.h:84
faiss::gpu::GpuIndexFlat::search
void search(faiss::Index::idx_t n, const float *x, faiss::Index::idx_t k, float *distances, faiss::Index::idx_t *labels) const override
Definition: GpuIndexFlat.cu:205
faiss::gpu::DeviceTensor
Definition: DeviceTensor.cuh:24
faiss::IndexFlat::xb
std::vector< float > xb
database vectors, size ntotal * d
Definition: IndexFlat.h:25
faiss::gpu::GpuIndexFlat::train
void train(Index::idx_t n, const float *x) override
This index is not trained, so this does nothing.
Definition: GpuIndexFlat.cu:157
faiss::MetricType
MetricType
Some algorithms support both an inner product vetsion and a L2 search version.
Definition: Index.h:43
faiss::gpu::GpuIndexFlat::data_
FlatIndex * data_
Holds our GPU data containing the list of vectors.
Definition: GpuIndexFlat.h:163