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GpuIndexIVF.cu
/**
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "GpuIndexIVF.h"
#include "../FaissAssert.h"
#include "../IndexFlat.h"
#include "../IndexIVF.h"
#include "GpuIndexFlat.h"
#include "utils/DeviceUtils.h"
#include "utils/Float16.cuh"
namespace faiss { namespace gpu {
GpuIndexIVF::GpuIndexIVF(GpuResources* resources,
int dims,
faiss::MetricType metric,
int nlist,
GpuIndexIVFConfig config) :
GpuIndex(resources, dims, metric, config),
ivfConfig_(std::move(config)),
nlist_(nlist),
nprobe_(1),
quantizer_(nullptr) {
#ifndef FAISS_USE_FLOAT16
FAISS_THROW_IF_NOT_MSG(!ivfConfig_.flatConfig.useFloat16 &&
!ivfConfig_.flatConfig.useFloat16Accumulator,
"float16 unsupported; need CUDA SDK >= 7.5");
#endif
init_();
}
void
GpuIndexIVF::init_() {
FAISS_ASSERT(nlist_ > 0);
// Spherical by default if the metric is inner_product
if (this->metric_type == faiss::METRIC_INNER_PRODUCT) {
this->cp.spherical = true;
}
// here we set a low # iterations because this is typically used
// for large clusterings
this->cp.niter = 10;
this->cp.verbose = this->verbose;
if (!quantizer_) {
// Construct an empty quantizer
GpuIndexFlatConfig config = ivfConfig_.flatConfig;
// FIXME: inherit our same device
config.device = device_;
if (this->metric_type == faiss::METRIC_L2) {
quantizer_ = new GpuIndexFlatL2(resources_, this->d, config);
} else if (this->metric_type == faiss::METRIC_INNER_PRODUCT) {
quantizer_ = new GpuIndexFlatIP(resources_, this->d, config);
} else {
// unknown metric type
FAISS_ASSERT_MSG(false, "unknown metric type");
}
}
}
GpuIndexIVF::~GpuIndexIVF() {
delete quantizer_;
}
GpuIndexFlat*
GpuIndexIVF::getQuantizer() {
return quantizer_;
}
void
GpuIndexIVF::copyFrom(const faiss::IndexIVF* index) {
DeviceScope scope(device_);
this->d = index->d;
this->metric_type = index->metric_type;
FAISS_ASSERT(index->nlist > 0);
FAISS_THROW_IF_NOT_FMT(index->nlist <=
(faiss::Index::idx_t) std::numeric_limits<int>::max(),
"GPU index only supports %zu inverted lists",
(size_t) std::numeric_limits<int>::max());
nlist_ = index->nlist;
FAISS_THROW_IF_NOT_FMT(index->nprobe > 0 &&
index->nprobe <= getMaxKSelection(),
"GPU index only supports nprobe <= %zu; passed %zu",
(size_t) getMaxKSelection(),
index->nprobe);
nprobe_ = index->nprobe;
// The metric type may have changed as well, so we might have to
// change our quantizer
delete quantizer_;
quantizer_ = nullptr;
// Construct an empty quantizer
GpuIndexFlatConfig config = ivfConfig_.flatConfig;
// FIXME: inherit our same device
config.device = device_;
if (index->metric_type == faiss::METRIC_L2) {
// FIXME: 2 different float16 options?
quantizer_ = new GpuIndexFlatL2(resources_, this->d, config);
} else if (index->metric_type == faiss::METRIC_INNER_PRODUCT) {
// FIXME: 2 different float16 options?
quantizer_ = new GpuIndexFlatIP(resources_, this->d, config);
} else {
// unknown metric type
FAISS_ASSERT(false);
}
if (!index->is_trained) {
this->is_trained = false;
this->ntotal = 0;
return;
}
// Otherwise, we can populate ourselves from the other index
this->is_trained = true;
// ntotal can exceed max int, but the number of vectors per inverted
// list cannot exceed this. We check this in the subclasses.
this->ntotal = index->ntotal;
// Since we're trained, the quantizer must have data
FAISS_ASSERT(index->quantizer->ntotal > 0);
if (index->metric_type == faiss::METRIC_L2) {
auto q = dynamic_cast< faiss::IndexFlatL2*>(index->quantizer);
FAISS_ASSERT(q);
quantizer_->copyFrom(q);
} else if (index->metric_type == faiss::METRIC_INNER_PRODUCT) {
auto q = dynamic_cast< faiss::IndexFlatIP*>(index->quantizer);
FAISS_ASSERT(q);
quantizer_->copyFrom(q);
} else {
// unknown metric type
FAISS_ASSERT(false);
}
}
void
GpuIndexIVF::copyTo(faiss::IndexIVF* index) const {
DeviceScope scope(device_);
//
// Index information
//
index->ntotal = this->ntotal;
index->d = this->d;
index->metric_type = this->metric_type;
index->is_trained = this->is_trained;
//
// IndexIVF information
//
index->nlist = nlist_;
index->nprobe = nprobe_;
// Construct and copy the appropriate quantizer
faiss::IndexFlat* q = nullptr;
if (this->metric_type == faiss::METRIC_L2) {
q = new faiss::IndexFlatL2(this->d);
} else if (this->metric_type == faiss::METRIC_INNER_PRODUCT) {
q = new faiss::IndexFlatIP(this->d);
} else {
// unknown metric type
FAISS_ASSERT(false);
}
FAISS_ASSERT(quantizer_);
quantizer_->copyTo(q);
if (index->own_fields) {
delete index->quantizer;
}
index->quantizer = q;
index->quantizer_trains_alone = 0;
index->own_fields = true;
index->cp = this->cp;
index->maintain_direct_map = false;
index->direct_map.clear();
}
int
GpuIndexIVF::getNumLists() const {
return nlist_;
}
void
GpuIndexIVF::setNumProbes(int nprobe) {
FAISS_THROW_IF_NOT_FMT(nprobe > 0 && nprobe <= getMaxKSelection(),
"GPU index only supports nprobe <= %d; passed %d",
getMaxKSelection(),
nprobe);
nprobe_ = nprobe;
}
int
GpuIndexIVF::getNumProbes() const {
return nprobe_;
}
bool
GpuIndexIVF::addImplRequiresIDs_() const {
// All IVF indices have storage for IDs
return true;
}
void
GpuIndexIVF::trainQuantizer_(faiss::Index::idx_t n, const float* x) {
if (n == 0) {
// nothing to do
return;
}
if (quantizer_->is_trained && (quantizer_->ntotal == nlist_)) {
if (this->verbose) {
printf ("IVF quantizer does not need training.\n");
}
return;
}
if (this->verbose) {
printf ("Training IVF quantizer on %ld vectors in %dD\n", n, d);
}
DeviceScope scope(device_);
// leverage the CPU-side k-means code, which works for the GPU
// flat index as well
quantizer_->reset();
Clustering clus(this->d, nlist_, this->cp);
clus.verbose = verbose;
clus.train(n, x, *quantizer_);
quantizer_->is_trained = true;
FAISS_ASSERT(quantizer_->ntotal == nlist_);
}
} } // namespace
faiss::IndexIVF
Definition: IndexIVF.h:90
faiss::gpu::GpuIndexIVF::getNumProbes
int getNumProbes() const
Returns our current number of list probes per query.
Definition: GpuIndexIVF.cu:214
faiss::IndexFlat
Definition: IndexFlat.h:21
faiss::gpu::GpuIndexIVF::setNumProbes
void setNumProbes(int nprobe)
Sets the number of list probes per query.
Definition: GpuIndexIVF.cu:205
faiss::ClusteringParameters::niter
int niter
clustering iterations
Definition: Clustering.h:23
faiss::gpu::GpuIndexIVF::getNumLists
int getNumLists() const
Returns the number of inverted lists we&#39;re managing.
Definition: GpuIndexIVF.cu:200
faiss::gpu::GpuIndexFlat::copyTo
void copyTo(faiss::IndexFlat *index) const
Definition: GpuIndexFlat.cu:100
faiss::IndexIVF::nprobe
size_t nprobe
number of probes at query time
Definition: IndexIVF.h:97
faiss::IndexFlatL2
Definition: IndexFlat.h:77
faiss::IndexFlatIP
Definition: IndexFlat.h:71
faiss::gpu::GpuIndexConfig::device
int device
GPU device on which the index is resident.
Definition: GpuIndex.h:25
faiss::gpu::GpuIndexIVF::addImplRequiresIDs_
bool addImplRequiresIDs_() const override
Definition: GpuIndexIVF.cu:219
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::gpu::GpuIndexFlatConfig
Definition: GpuIndexFlat.h:25
faiss::gpu::GpuIndexIVFConfig::flatConfig
GpuIndexFlatConfig flatConfig
Configuration for the coarse quantizer object.
Definition: GpuIndexIVF.h:32
faiss::gpu::GpuIndexIVF::getQuantizer
GpuIndexFlat * getQuantizer()
Return the quantizer we&#39;re using.
Definition: GpuIndexIVF.cu:74
faiss::gpu::GpuIndexIVF::nprobe_
int nprobe_
Number of inverted list probes per query.
Definition: GpuIndexIVF.h:84
faiss::Level1Quantizer::quantizer_trains_alone
char quantizer_trains_alone
Definition: IndexIVF.h:40
faiss::gpu::GpuIndex::device_
const int device_
The GPU device we are resident on.
Definition: GpuIndex.h:126
faiss::gpu::GpuIndex::resources_
GpuResources * resources_
Manages streams, cuBLAS handles and scratch memory for devices.
Definition: GpuIndex.h:123
faiss::gpu::GpuIndexIVF::copyTo
void copyTo(faiss::IndexIVF *index) const
Copy what we have to the CPU equivalent.
Definition: GpuIndexIVF.cu:153
faiss::Level1Quantizer::cp
ClusteringParameters cp
to override default clustering params
Definition: IndexIVF.h:43
faiss::gpu::GpuIndexIVF::nlist_
int nlist_
Number of inverted lists that we manage.
Definition: GpuIndexIVF.h:81
faiss::Index::ntotal
idx_t ntotal
total nb of indexed vectors
Definition: Index.h:67
faiss::Index::verbose
bool verbose
verbosity level
Definition: Index.h:68
faiss::gpu::GpuIndexFlatIP
Definition: GpuIndexFlat.h:158
faiss::gpu::GpuIndexFlat::copyFrom
void copyFrom(const faiss::IndexFlat *index)
Definition: GpuIndexFlat.cu:67
faiss::gpu::DeviceScope
Definition: DeviceUtils.h:68
faiss::gpu::GpuIndexIVF::quantizer_
GpuIndexFlat * quantizer_
Quantizer for inverted lists.
Definition: GpuIndexIVF.h:87
faiss::gpu::GpuIndexFlatL2
Definition: GpuIndexFlat.h:133
faiss::Index::metric_type
MetricType metric_type
type of metric this index uses for search
Definition: Index.h:74
faiss::gpu::GpuIndexFlat::reset
void reset() override
Clears all vectors from this index.
Definition: GpuIndexFlat.cu:129
faiss::Level1Quantizer::quantizer
Index * quantizer
quantizer that maps vectors to inverted lists
Definition: IndexIVF.h:32
faiss::gpu::GpuIndexIVF::copyFrom
void copyFrom(const faiss::IndexIVF *index)
Copy what we need from the CPU equivalent.
Definition: GpuIndexIVF.cu:79
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::IndexIVF::maintain_direct_map
bool maintain_direct_map
map for direct access to the elements. Enables reconstruct().
Definition: IndexIVF.h:109
faiss::ClusteringParameters::spherical
bool spherical
do we want normalized centroids?
Definition: Clustering.h:27
faiss::Level1Quantizer::own_fields
bool own_fields
whether object owns the quantizer
Definition: IndexIVF.h:41
faiss::gpu::GpuIndexIVF::cp
ClusteringParameters cp
Definition: GpuIndexIVF.h:75
faiss::Level1Quantizer::nlist
size_t nlist
number of possible key values
Definition: IndexIVF.h:33
faiss::MetricType
MetricType
Some algorithms support both an inner product version and a L2 search version.
Definition: Index.h:44