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TestGpuIndexIVFFlat.cpp
1 /**
2 * Copyright (c) 2015-present, Facebook, Inc.
3 * All rights reserved.
4 *
5 * This source code is licensed under the CC-by-NC 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 "../../IndexFlat.h"
12 #include "../../IndexIVF.h"
13 #include "../GpuIndexIVFFlat.h"
14 #include "../StandardGpuResources.h"
15 #include "../utils/DeviceUtils.h"
16 #include "../test/TestUtils.h"
17 #include <gtest/gtest.h>
18 #include <glog/logging.h>
19 #include <sstream>
20 #include <vector>
21
22 // FIXME: figure out a better way to test fp16
23 constexpr float kF16MaxRelErr = 0.3f;
24 constexpr float kF32MaxRelErr = 0.03f;
25
26
27 struct Options {
28 Options() {
29 numAdd = faiss::gpu::randVal(4000, 20000);
30 dim = faiss::gpu::randVal(64, 200);
31
32 numCentroids = std::sqrt((float) numAdd);
33 numTrain = numCentroids * 40;
34 nprobe = faiss::gpu::randVal(10, numCentroids);
35 numQuery = faiss::gpu::randVal(32, 100);
36 k = std::min(faiss::gpu::randVal(10, 30), numAdd / 40);
37 indicesOpt = faiss::gpu::randSelect({
38 faiss::gpu::INDICES_CPU,
39 faiss::gpu::INDICES_32_BIT,
40 faiss::gpu::INDICES_64_BIT});
41
42 device = faiss::gpu::randVal(0, faiss::gpu::getNumDevices() - 1);
43 }
44
45 std::string toString() const {
46 std::stringstream str;
47 str << "IVFFlat device " << device
48 << " numVecs " << numAdd
49 << " dim " << dim
50 << " numCentroids " << numCentroids
51 << " nprobe " << nprobe
52 << " numQuery " << numQuery
53 << " k " << k
54 << " indicesOpt " << indicesOpt;
55
56 return str.str();
57 }
58
59 int numAdd;
60 int dim;
61 int numCentroids;
62 int numTrain;
63 int nprobe;
64 int numQuery;
65 int k;
66 int device;
67 faiss::gpu::IndicesOptions indicesOpt;
68 };
69
70 void queryTest(faiss::MetricType metricType,
71 bool useFloat16CoarseQuantizer,
72 bool useFloat16,
73 int dimOverride = -1) {
74 for (int tries = 0; tries < 3; ++tries) {
75 faiss::gpu::newTestSeed();
76
77 Options opt;
78 opt.dim = dimOverride != -1 ? dimOverride : opt.dim;
79
80 std::vector<float> trainVecs = faiss::gpu::randVecs(opt.numTrain, opt.dim);
81 std::vector<float> addVecs = faiss::gpu::randVecs(opt.numAdd, opt.dim);
82
83 faiss::IndexFlatL2 quantizerL2(opt.dim);
84 faiss::IndexFlatIP quantizerIP(opt.dim);
85 faiss::Index* quantizer =
86 metricType == faiss::METRIC_L2 ?
87 (faiss::Index*) &quantizerL2 : (faiss::Index*) &quantizerIP;
88
89 faiss::IndexIVFFlat cpuIndex(quantizer,
90 opt.dim, opt.numCentroids, metricType);
91 cpuIndex.train(opt.numTrain, trainVecs.data());
92 cpuIndex.add(opt.numAdd, addVecs.data());
93 cpuIndex.nprobe = opt.nprobe;
94
95 faiss::gpu::StandardGpuResources res;
96 res.noTempMemory();
97
98 faiss::gpu::GpuIndexIVFFlatConfig config;
99 config.device = opt.device;
100 config.indicesOptions = opt.indicesOpt;
101 config.flatConfig.useFloat16 = useFloat16CoarseQuantizer;
102 config.useFloat16IVFStorage = useFloat16;
103
104 faiss::gpu::GpuIndexIVFFlat gpuIndex(&res,
105 cpuIndex.d,
106 cpuIndex.nlist,
107 cpuIndex.metric_type,
108 config);
109 gpuIndex.copyFrom(&cpuIndex);
110 gpuIndex.setNumProbes(opt.nprobe);
111
112 bool compFloat16 = useFloat16CoarseQuantizer || useFloat16;
113 faiss::gpu::compareIndices(cpuIndex, gpuIndex,
114 opt.numQuery, opt.dim, opt.k, opt.toString(),
115 compFloat16 ? kF16MaxRelErr : kF32MaxRelErr,
116 // FIXME: the fp16 bounds are
117 // useless when math (the accumulator) is
118 // in fp16. Figure out another way to test
119 compFloat16 ? 0.99f : 0.1f,
120 compFloat16 ? 0.65f : 0.015f);
121 }
122 }
123
124 void addTest(faiss::MetricType metricType,
125 bool useFloat16CoarseQuantizer,
126 bool useFloat16) {
127 for (int tries = 0; tries < 5; ++tries) {
128 faiss::gpu::newTestSeed();
129
130 Options opt;
131
132 std::vector<float> trainVecs = faiss::gpu::randVecs(opt.numTrain, opt.dim);
133 std::vector<float> addVecs = faiss::gpu::randVecs(opt.numAdd, opt.dim);
134
135 faiss::IndexFlatL2 quantizerL2(opt.dim);
136 faiss::IndexFlatIP quantizerIP(opt.dim);
137 faiss::Index* quantizer =
138 metricType == faiss::METRIC_L2 ?
139 (faiss::Index*) &quantizerL2 : (faiss::Index*) &quantizerIP;
140
141 faiss::IndexIVFFlat cpuIndex(quantizer,
142 opt.dim,
143 opt.numCentroids,
144 metricType);
145 cpuIndex.train(opt.numTrain, trainVecs.data());
146 cpuIndex.nprobe = opt.nprobe;
147
148 faiss::gpu::StandardGpuResources res;
149 res.noTempMemory();
150
151 faiss::gpu::GpuIndexIVFFlatConfig config;
152 config.device = opt.device;
153 config.indicesOptions = opt.indicesOpt;
154 config.flatConfig.useFloat16 = useFloat16CoarseQuantizer;
155 config.useFloat16IVFStorage = useFloat16;
156
157 faiss::gpu::GpuIndexIVFFlat gpuIndex(&res,
158 cpuIndex.d,
159 cpuIndex.nlist,
160 cpuIndex.metric_type,
161 config);
162 gpuIndex.copyFrom(&cpuIndex);
163 gpuIndex.setNumProbes(opt.nprobe);
164
165 cpuIndex.add(opt.numAdd, addVecs.data());
166 gpuIndex.add(opt.numAdd, addVecs.data());
167
168 bool compFloat16 = useFloat16CoarseQuantizer || useFloat16;
169 faiss::gpu::compareIndices(cpuIndex, gpuIndex,
170 opt.numQuery, opt.dim, opt.k, opt.toString(),
171 compFloat16 ? kF16MaxRelErr : kF32MaxRelErr,
172 compFloat16 ? 0.70f : 0.1f,
173 compFloat16 ? 0.30f : 0.015f);
174 }
175 }
176
177 void copyToTest(bool useFloat16CoarseQuantizer,
178 bool useFloat16) {
179 faiss::gpu::newTestSeed();
180
181 Options opt;
182 std::vector<float> trainVecs = faiss::gpu::randVecs(opt.numTrain, opt.dim);
183 std::vector<float> addVecs = faiss::gpu::randVecs(opt.numAdd, opt.dim);
184
185 faiss::gpu::StandardGpuResources res;
186 res.noTempMemory();
187
188 faiss::gpu::GpuIndexIVFFlatConfig config;
189 config.device = opt.device;
190 config.indicesOptions = opt.indicesOpt;
191 config.flatConfig.useFloat16 = useFloat16CoarseQuantizer;
192 config.useFloat16IVFStorage = useFloat16;
193
194 faiss::gpu::GpuIndexIVFFlat gpuIndex(&res,
195 opt.dim,
196 opt.numCentroids,
197 faiss::METRIC_L2,
198 config);
199 gpuIndex.train(opt.numTrain, trainVecs.data());
200 gpuIndex.add(opt.numAdd, addVecs.data());
201 gpuIndex.setNumProbes(opt.nprobe);
202
203 // use garbage values to see if we overwrite then
204 faiss::IndexFlatL2 cpuQuantizer(1);
205 faiss::IndexIVFFlat cpuIndex(&cpuQuantizer, 1, 1, faiss::METRIC_L2);
206 cpuIndex.nprobe = 1;
207
208 gpuIndex.copyTo(&cpuIndex);
209
210 EXPECT_EQ(cpuIndex.ntotal, gpuIndex.ntotal);
211 EXPECT_EQ(gpuIndex.ntotal, opt.numAdd);
212
213 EXPECT_EQ(cpuIndex.d, gpuIndex.d);
214 EXPECT_EQ(cpuIndex.d, opt.dim);
215 EXPECT_EQ(cpuIndex.nlist, gpuIndex.getNumLists());
216 EXPECT_EQ(cpuIndex.nprobe, gpuIndex.getNumProbes());
217
218 // Query both objects; results should be equivalent
219 bool compFloat16 = useFloat16CoarseQuantizer || useFloat16;
220 faiss::gpu::compareIndices(cpuIndex, gpuIndex,
221 opt.numQuery, opt.dim, opt.k, opt.toString(),
222 compFloat16 ? kF16MaxRelErr : kF32MaxRelErr,
223 compFloat16 ? 0.70f : 0.1f,
224 compFloat16 ? 0.30f : 0.015f);
225 }
226
227 void copyFromTest(bool useFloat16CoarseQuantizer,
228 bool useFloat16) {
229 faiss::gpu::newTestSeed();
230
231 Options opt;
232 std::vector<float> trainVecs = faiss::gpu::randVecs(opt.numTrain, opt.dim);
233 std::vector<float> addVecs = faiss::gpu::randVecs(opt.numAdd, opt.dim);
234
235 faiss::IndexFlatL2 cpuQuantizer(opt.dim);
236 faiss::IndexIVFFlat cpuIndex(&cpuQuantizer,
237 opt.dim,
238 opt.numCentroids,
239 faiss::METRIC_L2);
240 cpuIndex.nprobe = opt.nprobe;
241 cpuIndex.train(opt.numTrain, trainVecs.data());
242 cpuIndex.add(opt.numAdd, addVecs.data());
243
244 // use garbage values to see if we overwrite then
245 faiss::gpu::StandardGpuResources res;
246 res.noTempMemory();
247
248 faiss::gpu::GpuIndexIVFFlatConfig config;
249 config.device = opt.device;
250 config.indicesOptions = opt.indicesOpt;
251 config.flatConfig.useFloat16 = useFloat16CoarseQuantizer;
252 config.useFloat16IVFStorage = useFloat16;
253
254 faiss::gpu::GpuIndexIVFFlat gpuIndex(&res,
255 1,
256 1,
257 faiss::METRIC_L2,
258 config);
259 gpuIndex.setNumProbes(1);
260
261 gpuIndex.copyFrom(&cpuIndex);
262
263 EXPECT_EQ(cpuIndex.ntotal, gpuIndex.ntotal);
264 EXPECT_EQ(gpuIndex.ntotal, opt.numAdd);
265
266 EXPECT_EQ(cpuIndex.d, gpuIndex.d);
267 EXPECT_EQ(cpuIndex.d, opt.dim);
268 EXPECT_EQ(cpuIndex.nlist, gpuIndex.getNumLists());
269 EXPECT_EQ(cpuIndex.nprobe, gpuIndex.getNumProbes());
270
271 // Query both objects; results should be equivalent
272 bool compFloat16 = useFloat16CoarseQuantizer || useFloat16;
273 faiss::gpu::compareIndices(cpuIndex, gpuIndex,
274 opt.numQuery, opt.dim, opt.k, opt.toString(),
275 compFloat16 ? kF16MaxRelErr : kF32MaxRelErr,
276 compFloat16 ? 0.70f : 0.1f,
277 compFloat16 ? 0.30f : 0.015f);
278 }
279
280 TEST(TestGpuIndexIVFFlat, Float32_32_Add_L2) {
281 addTest(faiss::METRIC_L2, false, false);
282 }
283
284 TEST(TestGpuIndexIVFFlat, Float32_32_Add_IP) {
285 addTest(faiss::METRIC_INNER_PRODUCT, false, false);
286 }
287
288 TEST(TestGpuIndexIVFFlat, Float32_16_Add_L2) {
289 addTest(faiss::METRIC_L2, false, true);
290 }
291
292 TEST(TestGpuIndexIVFFlat, Float32_16_Add_IP) {
293 addTest(faiss::METRIC_INNER_PRODUCT, false, true);
294 }
295
296 TEST(TestGpuIndexIVFFlat, Float16_32_Add_L2) {
297 addTest(faiss::METRIC_L2, true, false);
298 }
299
300 TEST(TestGpuIndexIVFFlat, Float16_32_Add_IP) {
301 addTest(faiss::METRIC_INNER_PRODUCT, true, false);
302 }
303
304 //
305 // General query tests
306 //
307
308 TEST(TestGpuIndexIVFFlat, Float32_Query_L2) {
309 queryTest(faiss::METRIC_L2, false, false);
310 }
311
312 TEST(TestGpuIndexIVFFlat, Float32_Query_IP) {
313 queryTest(faiss::METRIC_INNER_PRODUCT, false, false);
314 }
315
316 TEST(TestGpuIndexIVFFlat, Float16_Query_L2) {
317 queryTest(faiss::METRIC_L2, false, true);
318 }
319
320 TEST(TestGpuIndexIVFFlat, Float16_Query_IP) {
321 queryTest(faiss::METRIC_INNER_PRODUCT, false, true);
322 }
323
324 // float16 coarse quantizer
325
326 TEST(TestGpuIndexIVFFlat, Float16_32_Query_L2) {
327 queryTest(faiss::METRIC_L2, true, false);
328 }
329
330 TEST(TestGpuIndexIVFFlat, Float16_32_Query_IP) {
331 queryTest(faiss::METRIC_INNER_PRODUCT, true, false);
332 }
333
334 //
335 // There are IVF list scanning specializations for 64-d and 128-d that we
336 // make sure we explicitly test here
337 //
338
339 TEST(TestGpuIndexIVFFlat, Float32_Query_L2_64) {
340 queryTest(faiss::METRIC_L2, false, false, 64);
341 }
342
343 TEST(TestGpuIndexIVFFlat, Float32_Query_IP_64) {
344 queryTest(faiss::METRIC_INNER_PRODUCT, false, false, 64);
345 }
346
347 TEST(TestGpuIndexIVFFlat, Float16_Query_L2_64) {
348 queryTest(faiss::METRIC_L2, false, true, 64);
349 }
350
351 TEST(TestGpuIndexIVFFlat, Float16_Query_IP_64) {
352 queryTest(faiss::METRIC_INNER_PRODUCT, false, true, 64);
353 }
354
355 TEST(TestGpuIndexIVFFlat, Float32_Query_L2_128) {
356 queryTest(faiss::METRIC_L2, false, false, 128);
357 }
358
359 TEST(TestGpuIndexIVFFlat, Float32_Query_IP_128) {
360 queryTest(faiss::METRIC_INNER_PRODUCT, false, false, 128);
361 }
362
363 TEST(TestGpuIndexIVFFlat, Float16_Query_L2_128) {
364 queryTest(faiss::METRIC_L2, false, true, 128);
365 }
366
367 TEST(TestGpuIndexIVFFlat, Float16_Query_IP_128) {
368 queryTest(faiss::METRIC_INNER_PRODUCT, false, true, 128);
369 }
370
371 // For 256-d, only float16 is specialized
372
373 TEST(TestGpuIndexIVFFlat, Float16_Query_L2_256) {
374 queryTest(faiss::METRIC_L2, false, true, 256);
375 }
376
377 TEST(TestGpuIndexIVFFlat, Float16_Query_IP_256) {
378 queryTest(faiss::METRIC_INNER_PRODUCT, false, true, 256);
379 }
380
381 //
382 // Copy tests
383 //
384
385 TEST(TestGpuIndexIVFFlat, Float32_16_CopyTo) {
386 copyToTest(false, true);
387 }
388
389 TEST(TestGpuIndexIVFFlat, Float32_32_CopyTo) {
390 copyToTest(false, false);
391 }
392
393 //
394 // NaN tests
395 //
396
397 TEST(TestGpuIndexIVFFlat, QueryNaN) {
398 faiss::gpu::newTestSeed();
399
400 Options opt;
401
402 std::vector<float> trainVecs = faiss::gpu::randVecs(opt.numTrain, opt.dim);
403 std::vector<float> addVecs = faiss::gpu::randVecs(opt.numAdd, opt.dim);
404
405 faiss::gpu::StandardGpuResources res;
406 res.noTempMemory();
407
408 faiss::gpu::GpuIndexIVFFlatConfig config;
409 config.device = opt.device;
410 config.indicesOptions = opt.indicesOpt;
411 config.flatConfig.useFloat16 = faiss::gpu::randBool();
412 config.useFloat16IVFStorage = faiss::gpu::randBool();
413
414 faiss::gpu::GpuIndexIVFFlat gpuIndex(&res,
415 opt.dim,
416 opt.numCentroids,
417 faiss::METRIC_L2,
418 config);
419 gpuIndex.setNumProbes(opt.nprobe);
420
421 gpuIndex.train(opt.numTrain, trainVecs.data());
422 gpuIndex.add(opt.numAdd, addVecs.data());
423
424 int numQuery = 10;
425 std::vector<float> nans(numQuery * opt.dim,
426 std::numeric_limits<float>::quiet_NaN());
427
428 std::vector<float> distances(numQuery * opt.k, 0);
429 std::vector<faiss::Index::idx_t> indices(numQuery * opt.k, 0);
430
431 gpuIndex.search(numQuery,
432 nans.data(),
433 opt.k,
434 distances.data(),
435 indices.data());
436
437 for (int q = 0; q < numQuery; ++q) {
438 for (int k = 0; k < opt.k; ++k) {
439 EXPECT_EQ(indices[q * opt.k + k], -1);
440 EXPECT_EQ(distances[q * opt.k + k], std::numeric_limits<float>::max());
441 }
442 }
443 }
444
445 TEST(TestGpuIndexIVFFlat, AddNaN) {
446 faiss::gpu::newTestSeed();
447
448 Options opt;
449
450 faiss::gpu::StandardGpuResources res;
451 res.noTempMemory();
452
453 faiss::gpu::GpuIndexIVFFlatConfig config;
454 config.device = opt.device;
455 config.indicesOptions = opt.indicesOpt;
456 config.flatConfig.useFloat16 = faiss::gpu::randBool();
457 config.useFloat16IVFStorage = faiss::gpu::randBool();
458
459 faiss::gpu::GpuIndexIVFFlat gpuIndex(&res,
460 opt.dim,
461 opt.numCentroids,
462 faiss::METRIC_L2,
463 config);
464 gpuIndex.setNumProbes(opt.nprobe);
465
466 int numNans = 10;
467 std::vector<float> nans(numNans * opt.dim,
468 std::numeric_limits<float>::quiet_NaN());
469
470 // Make one vector valid, which should actually add
471 for (int i = 0; i < opt.dim; ++i) {
472 nans[i] = 0.0f;
473 }
474
475 std::vector<float> trainVecs = faiss::gpu::randVecs(opt.numTrain, opt.dim);
476 gpuIndex.train(opt.numTrain, trainVecs.data());
477
478 // should not crash
479 EXPECT_EQ(gpuIndex.ntotal, 0);
480 gpuIndex.add(numNans, nans.data());
481
482 // Only the single valid vector should have added
483 EXPECT_EQ(gpuIndex.ntotal, 1);
484
485 std::vector<float> queryVecs = faiss::gpu::randVecs(opt.numQuery, opt.dim);
486 std::vector<float> distance(opt.numQuery * opt.k, 0);
487 std::vector<faiss::Index::idx_t> indices(opt.numQuery * opt.k, 0);
488
489 // should not crash
490 gpuIndex.search(opt.numQuery, queryVecs.data(), opt.k,
491 distance.data(), indices.data());
492
493 }
494
495 TEST(TestGpuIndexIVFFlat, UnifiedMemory) {
496 // Construct on a random device to test multi-device, if we have
497 // multiple devices
498 int device = faiss::gpu::randVal(0, faiss::gpu::getNumDevices() - 1);
499
500 if (!faiss::gpu::getFullUnifiedMemSupport(device)) {
501 return;
502 }
503
504 int dim = 256;
505
506 int numCentroids = 1024;
507 // 24 GB of vecs should be enough to test unified memory in
508 // oversubscription mode
509 size_t numAdd =
510 (size_t) 1024 * 1024 * 1024 * 24 / ((size_t) dim * sizeof(float));
511 size_t numTrain = numCentroids * 40;
512 int numQuery = 10;
513 int k = 10;
514 int nprobe = 8;
515
516 LOG(INFO) << "generating vecs";
517 std::vector<float> trainVecs = faiss::gpu::randVecs(numTrain, dim);
518 std::vector<float> addVecs = faiss::gpu::randVecs(numAdd, dim);
519
520 LOG(INFO) << "train CPU";
521 faiss::IndexFlatL2 quantizer(dim);
522 faiss::IndexIVFFlat cpuIndex(&quantizer, dim, numCentroids, faiss::METRIC_L2);
523 LOG(INFO) << "train CPU";
524 cpuIndex.train(numTrain, trainVecs.data());
525 LOG(INFO) << "add CPU";
526 cpuIndex.add(numAdd, addVecs.data());
527 cpuIndex.nprobe = nprobe;
528
529 faiss::gpu::StandardGpuResources res;
530 res.noTempMemory();
531
532 faiss::gpu::GpuIndexIVFFlatConfig config;
533 config.device = device;
534 config.memorySpace = faiss::gpu::MemorySpace::Unified;
535
536 faiss::gpu::GpuIndexIVFFlat gpuIndex(&res,
537 dim,
538 numCentroids,
539 faiss::METRIC_L2,
540 config);
541 LOG(INFO) << "copy from CPU";
542 gpuIndex.copyFrom(&cpuIndex);
543 gpuIndex.setNumProbes(nprobe);
544
545 LOG(INFO) << "compare";
546
547 faiss::gpu::compareIndices(cpuIndex, gpuIndex,
548 numQuery, dim, k, "Unified Memory",
549 kF32MaxRelErr,
550 0.1f,
551 0.015f);
552 }
faiss::gpu::StandardGpuResources::noTempMemory
void noTempMemory()
Definition: StandardGpuResources.cpp:71
faiss::IndexFlatL2
Definition: IndexFlat.h:79
faiss::gpu::GpuIndexIVFFlatConfig::useFloat16IVFStorage
bool useFloat16IVFStorage
Definition: GpuIndexIVFFlat.h:29
faiss::IndexIVFFlat
Definition: IndexIVF.h:132
faiss::IndexFlatIP
Definition: IndexFlat.h:73
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::GpuIndexIVFConfig::flatConfig
GpuIndexFlatConfig flatConfig
Configuration for the coarse quantizer object.
Definition: GpuIndexIVF.h:34
faiss::gpu::GpuIndexConfig::memorySpace
MemorySpace memorySpace
Definition: GpuIndex.h:32
faiss::gpu::StandardGpuResources
Definition: StandardGpuResources.h:23
faiss::Index
Definition: Index.h:60
faiss::gpu::GpuIndexIVFFlatConfig
Definition: GpuIndexIVFFlat.h:22
faiss::gpu::GpuIndexIVFConfig::indicesOptions
IndicesOptions indicesOptions
Index storage options for the GPU.
Definition: GpuIndexIVF.h:31
Options
Definition: TestGpuIndexIVFFlat.cpp:27
faiss::gpu::GpuIndexIVFFlat
Definition: GpuIndexIVFFlat.h:34
faiss::MetricType
MetricType
Some algorithms support both an inner product vetsion and a L2 search version.
Definition: Index.h:43