All Classes Namespaces Functions Variables Typedefs Enumerations Enumerator Friends
TestGpuIndexFlat.cpp
/**
* Copyright (c) 2015-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the CC-by-NC license found in the
* LICENSE file in the root directory of this source tree.
*/
// Copyright 2004-present Facebook. All Rights Reserved.
#include "../../IndexFlat.h"
#include "../GpuIndexFlat.h"
#include "../StandardGpuResources.h"
#include "../utils/DeviceUtils.h"
#include "../test/TestUtils.h"
#include <gtest/gtest.h>
#include <sstream>
#include <vector>
// FIXME: figure out a better way to test fp16
constexpr float kF16MaxRelErr = 0.07f;
constexpr float kF32MaxRelErr = 6e-3f;
void testFlat(bool useL2,
bool useFloat16,
bool useTransposed,
int kOverride = -1) {
int numVecs = faiss::gpu::randVal(1000, 20000);
int dim = faiss::gpu::randVal(50, 800);
int numQuery = faiss::gpu::randVal(1, 512);
// Due to loss of precision in a float16 accumulator, for large k,
// the number of differences is pretty huge. Restrict ourselves to a
// fairly small `k` for float16
int k = useFloat16 ?
std::min(faiss::gpu::randVal(1, 50), numVecs) :
std::min(faiss::gpu::randVal(1, 1024), numVecs);
if (kOverride > 0) {
k = kOverride;
}
faiss::IndexFlatIP cpuIndexIP(dim);
faiss::IndexFlatL2 cpuIndexL2(dim);
faiss::IndexFlat* cpuIndex =
useL2 ? (faiss::IndexFlat*) &cpuIndexL2 : (faiss::IndexFlat*) &cpuIndexIP;
// Construct on a random device to test multi-device, if we have
// multiple devices
int device = faiss::gpu::randVal(0, faiss::gpu::getNumDevices() - 1);
faiss::gpu::StandardGpuResources res;
res.noTempMemory();
faiss::gpu::GpuIndexFlatConfig config;
config.device = device;
config.useFloat16 = useFloat16;
config.storeTransposed = useTransposed;
faiss::gpu::GpuIndexFlatIP gpuIndexIP(&res, dim, config);
faiss::gpu::GpuIndexFlatL2 gpuIndexL2(&res, dim, config);
faiss::gpu::GpuIndexFlat* gpuIndex =
useL2 ? (faiss::gpu::GpuIndexFlat*) &gpuIndexL2 :
(faiss::gpu::GpuIndexFlat*) &gpuIndexIP;
std::vector<float> vecs = faiss::gpu::randVecs(numVecs, dim);
cpuIndex->add(numVecs, vecs.data());
gpuIndex->add(numVecs, vecs.data());
std::stringstream str;
str << (useL2 ? "L2" : "IP") << " numVecs " << numVecs
<< " dim " << dim
<< " useFloat16 " << useFloat16
<< " transposed " << useTransposed
<< " numQuery " << numQuery
<< " k " << k;
// To some extent, we depend upon the relative error for the test
// for float16
faiss::gpu::compareIndices(*cpuIndex, *gpuIndex, numQuery, dim, k, str.str(),
useFloat16 ? kF16MaxRelErr : kF32MaxRelErr,
// FIXME: the fp16 bounds are
// useless when math (the accumulator) is
// in fp16. Figure out another way to test
useFloat16 ? 0.99f : 0.1f,
useFloat16 ? 0.65f : 0.015f);
}
TEST(TestGpuIndexFlat, IP_Float32) {
for (int tries = 0; tries < 5; ++tries) {
faiss::gpu::newTestSeed();
testFlat(false, false, false);
testFlat(false, false, true);
}
}
TEST(TestGpuIndexFlat, L2_Float32) {
for (int tries = 0; tries < 5; ++tries) {
faiss::gpu::newTestSeed();
testFlat(true, false, false);
testFlat(true, false, true);
}
}
// test specialized k == 1 codepath
TEST(TestGpuIndexFlat, L2_Float32_K1) {
for (int tries = 0; tries < 5; ++tries) {
faiss::gpu::newTestSeed();
testFlat(true, false, false, 1);
testFlat(true, false, true, 1);
}
}
TEST(TestGpuIndexFlat, IP_Float16) {
for (int tries = 0; tries < 5; ++tries) {
faiss::gpu::newTestSeed();
testFlat(false, true, false);
testFlat(false, true, false);
}
}
TEST(TestGpuIndexFlat, L2_Float16) {
for (int tries = 0; tries < 5; ++tries) {
faiss::gpu::newTestSeed();
testFlat(true, true, false);
testFlat(true, true, true);
}
}
// test specialized k == 1 codepath
TEST(TestGpuIndexFlat, L2_Float16_K1) {
for (int tries = 0; tries < 5; ++tries) {
faiss::gpu::newTestSeed();
testFlat(true, true, false, 1);
testFlat(true, true, true, 1);
}
}
TEST(TestGpuIndexFlat, QueryEmpty) {
faiss::gpu::StandardGpuResources res;
res.noTempMemory();
faiss::gpu::GpuIndexFlatConfig config;
config.device = 0;
config.useFloat16 = false;
config.storeTransposed = false;
int dim = 128;
faiss::gpu::GpuIndexFlatL2 gpuIndex(&res, dim, config);
// Querying an empty index should not blow up, and just return
// (FLT_MAX, -1)
int numQuery = 10;
int k = 50;
std::vector<float> queries(numQuery * dim, 1.0f);
std::vector<float> dist(numQuery * k, 0);
std::vector<faiss::Index::idx_t> ind(numQuery * k);
gpuIndex.search(numQuery, queries.data(), k, dist.data(), ind.data());
for (auto d : dist) {
EXPECT_EQ(d, std::numeric_limits<float>::max());
}
for (auto i : ind) {
EXPECT_EQ(i, -1);
}
}
TEST(TestGpuIndexFlat, CopyFrom) {
faiss::gpu::newTestSeed();
int numVecs = faiss::gpu::randVal(100, 200);
int dim = faiss::gpu::randVal(1, 1000);
faiss::IndexFlatL2 cpuIndex(dim);
std::vector<float> vecs = faiss::gpu::randVecs(numVecs, dim);
cpuIndex.add(numVecs, vecs.data());
faiss::gpu::StandardGpuResources res;
res.noTempMemory();
// Fill with garbage values
int device = faiss::gpu::randVal(0, faiss::gpu::getNumDevices() - 1);
faiss::gpu::GpuIndexFlatConfig config;
config.device = 0;
config.useFloat16 = false;
config.storeTransposed = false;
faiss::gpu::GpuIndexFlatL2 gpuIndex(&res, 2000, config);
gpuIndex.copyFrom(&cpuIndex);
EXPECT_EQ(cpuIndex.ntotal, gpuIndex.ntotal);
EXPECT_EQ(gpuIndex.ntotal, numVecs);
EXPECT_EQ(cpuIndex.d, gpuIndex.d);
EXPECT_EQ(cpuIndex.d, dim);
int idx = faiss::gpu::randVal(0, numVecs - 1);
std::vector<float> gpuVals(dim);
gpuIndex.reconstruct(idx, gpuVals.data());
std::vector<float> cpuVals(dim);
cpuIndex.reconstruct(idx, cpuVals.data());
EXPECT_EQ(gpuVals, cpuVals);
}
TEST(TestGpuIndexFlat, CopyTo) {
faiss::gpu::newTestSeed();
faiss::gpu::StandardGpuResources res;
res.noTempMemory();
int numVecs = faiss::gpu::randVal(100, 200);
int dim = faiss::gpu::randVal(1, 1000);
int device = faiss::gpu::randVal(0, faiss::gpu::getNumDevices() - 1);
faiss::gpu::GpuIndexFlatConfig config;
config.device = device;
config.useFloat16 = false;
config.storeTransposed = false;
faiss::gpu::GpuIndexFlatL2 gpuIndex(&res, dim, config);
std::vector<float> vecs = faiss::gpu::randVecs(numVecs, dim);
gpuIndex.add(numVecs, vecs.data());
// Fill with garbage values
faiss::IndexFlatL2 cpuIndex(2000);
gpuIndex.copyTo(&cpuIndex);
EXPECT_EQ(cpuIndex.ntotal, gpuIndex.ntotal);
EXPECT_EQ(gpuIndex.ntotal, numVecs);
EXPECT_EQ(cpuIndex.d, gpuIndex.d);
EXPECT_EQ(cpuIndex.d, dim);
int idx = faiss::gpu::randVal(0, numVecs - 1);
std::vector<float> gpuVals(dim);
gpuIndex.reconstruct(idx, gpuVals.data());
std::vector<float> cpuVals(dim);
cpuIndex.reconstruct(idx, cpuVals.data());
EXPECT_EQ(gpuVals, cpuVals);
}
TEST(TestGpuIndexFlat, UnifiedMemory) {
// Construct on a random device to test multi-device, if we have
// multiple devices
int device = faiss::gpu::randVal(0, faiss::gpu::getNumDevices() - 1);
if (!faiss::gpu::getFullUnifiedMemSupport(device)) {
return;
}
int dim = 256;
// FIXME: GpuIndexFlat doesn't support > 2^31 (vecs * dims) due to
// kernel indexing, so we can't test unified memory for memory
// oversubscription.
size_t numVecs = 50000;
int numQuery = 10;
int k = 10;
faiss::IndexFlatL2 cpuIndexL2(dim);
faiss::gpu::StandardGpuResources res;
res.noTempMemory();
faiss::gpu::GpuIndexFlatConfig config;
config.device = device;
config.memorySpace = faiss::gpu::MemorySpace::Unified;
faiss::gpu::GpuIndexFlatL2 gpuIndexL2(&res, dim, config);
std::vector<float> vecs = faiss::gpu::randVecs(numVecs, dim);
cpuIndexL2.add(numVecs, vecs.data());
gpuIndexL2.add(numVecs, vecs.data());
// To some extent, we depend upon the relative error for the test
// for float16
faiss::gpu::compareIndices(cpuIndexL2, gpuIndexL2,
numQuery, dim, k, "Unified Memory",
kF32MaxRelErr,
0.1f,
0.015f);
}
faiss::IndexFlat
Definition: IndexFlat.h:23
faiss::gpu::GpuIndexFlatConfig::storeTransposed
bool storeTransposed
Definition: GpuIndexFlat.h:48
faiss::gpu::GpuIndexFlat::copyTo
void copyTo(faiss::IndexFlat *index) const
Definition: GpuIndexFlat.cu:120
faiss::gpu::StandardGpuResources::noTempMemory
void noTempMemory()
Definition: StandardGpuResources.cpp:71
faiss::IndexFlatL2
Definition: IndexFlat.h:79
faiss::IndexFlat::reconstruct
void reconstruct(idx_t key, float *recons) const override
Definition: IndexFlat.cpp:118
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::Index::d
int d
vector dimension
Definition: Index.h:64
faiss::gpu::GpuIndexFlatConfig
Definition: GpuIndexFlat.h:27
faiss::gpu::GpuIndexConfig::memorySpace
MemorySpace memorySpace
Definition: GpuIndex.h:32
faiss::gpu::StandardGpuResources
Definition: StandardGpuResources.h:23
faiss::gpu::GpuIndexFlat::reconstruct
void reconstruct(faiss::Index::idx_t key, float *out) const override
Definition: GpuIndexFlat.cu:491
faiss::Index::ntotal
idx_t ntotal
total nb of indexed vectors
Definition: Index.h:65
faiss::IndexFlat::add
void add(idx_t n, const float *x) override
Definition: IndexFlat.cpp:30
faiss::gpu::GpuIndexFlatIP
Definition: GpuIndexFlat.h:194
faiss::gpu::GpuIndexFlat::copyFrom
void copyFrom(const faiss::IndexFlat *index)
Definition: GpuIndexFlat.cu:87
faiss::gpu::GpuIndexFlatL2
Definition: GpuIndexFlat.h:169
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::GpuIndexFlat
Definition: GpuIndexFlat.h:54
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