File IndexAdditiveQuantizer.h

namespace faiss

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.

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. Implementation of k-means clustering with many variants.

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. IDSelector is intended to define a subset of vectors to handle (for removal or as subset to search)

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. PQ4 SIMD packing and accumulation functions

The basic kernel accumulates nq query vectors with bbs = nb * 2 * 16 vectors and produces an output matrix for that. It is interesting for nq * nb <= 4, otherwise register spilling becomes too large.

The implementation of these functions is spread over 3 cpp files to reduce parallel compile times. Templates are instantiated explicitly.

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. This file contains callbacks for kernels that compute distances.

The SIMDResultHandler object is intended to be templated and inlined. Methods:

  • handle(): called when 32 distances are computed and provided in two simd16uint16. (q, b) indicate which entry it is in the block.

  • set_block_origin(): set the sub-matrix that is being computed

Throughout the library, vectors are provided as float * pointers. Most algorithms can be optimized when several vectors are processed (added/searched) together in a batch. In this case, they are passed in as a matrix. When n vectors of size d are provided as float * x, component j of vector i is

x[ i * d + j ]

where 0 <= i < n and 0 <= j < d. In other words, matrices are always compact. When specifying the size of the matrix, we call it an n*d matrix, which implies a row-major storage.

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. I/O functions can read/write to a filename, a file handle or to an object that abstracts the medium.

The read functions return objects that should be deallocated with delete. All references within these objectes are owned by the object.

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. Definition of inverted lists + a few common classes that implement the interface.

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. Since IVF (inverted file) indexes are of so much use for large-scale use cases, we group a few functions related to them in this small library. Most functions work both on IndexIVFs and IndexIVFs embedded within an IndexPreTransform.

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. In this file are the implementations of extra metrics beyond L2 and inner product

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. Defines a few objects that apply transformations to a set of vectors Often these are pre-processing steps.

struct IndexAdditiveQuantizer : public faiss::IndexFlatCodes
#include <IndexAdditiveQuantizer.h>

Abstract class for additive quantizers. The search functions are in common.

Subclassed by faiss::IndexLocalSearchQuantizer, faiss::IndexProductLocalSearchQuantizer, faiss::IndexProductResidualQuantizer, faiss::IndexResidualQuantizer

Public Types

using Search_type_t = AdditiveQuantizer::Search_type_t

Public Functions

explicit IndexAdditiveQuantizer(idx_t d, AdditiveQuantizer *aq, MetricType metric = METRIC_L2)
virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, const SearchParameters *params = nullptr) const override

query n vectors of dimension d to the index.

return at most k vectors. If there are not enough results for a query, the result array is padded with -1s.

Parameters:

  • x – input vectors to search, size n * d

  • labels – output labels of the NNs, size n*k

  • distances – output pairwise distances, size n*k

virtual void sa_encode(idx_t n, const float *x, uint8_t *bytes) const override

encode a set of vectors

Parameters:

  • n – number of vectors

  • x – input vectors, size n * d

  • bytes – output encoded vectors, size n * sa_code_size()

virtual void sa_decode(idx_t n, const uint8_t *bytes, float *x) const override

decode a set of vectors

Parameters:

  • n – number of vectors

  • bytes – input encoded vectors, size n * sa_code_size()

  • x – output vectors, size n * d

virtual FlatCodesDistanceComputer *get_FlatCodesDistanceComputer() const override

a FlatCodesDistanceComputer offers a distance_to_code method

Public Members

AdditiveQuantizer *aq
struct IndexResidualQuantizer : public faiss::IndexAdditiveQuantizer
#include <IndexAdditiveQuantizer.h>

Index based on a residual quantizer. Stored vectors are approximated by residual quantization codes. Can also be used as a codec

Public Types

using Search_type_t = AdditiveQuantizer::Search_type_t

Public Functions

IndexResidualQuantizer(int d, size_t M, size_t nbits, MetricType metric = METRIC_L2, Search_type_t search_type = AdditiveQuantizer::ST_decompress)

Constructor.

Parameters:

  • d – dimensionality of the input vectors

  • M – number of subquantizers

  • nbits – number of bit per subvector index

  • d – dimensionality of the input vectors

  • M – number of subquantizers

  • nbits – number of bit per subvector index

IndexResidualQuantizer(int d, const std::vector<size_t> &nbits, MetricType metric = METRIC_L2, Search_type_t search_type = AdditiveQuantizer::ST_decompress)
IndexResidualQuantizer()
virtual void train(idx_t n, const float *x) override

Perform training on a representative set of vectors

Parameters:

  • n – nb of training vectors

  • x – training vecors, size n * d

virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, const SearchParameters *params = nullptr) const override

query n vectors of dimension d to the index.

return at most k vectors. If there are not enough results for a query, the result array is padded with -1s.

Parameters:

  • x – input vectors to search, size n * d

  • labels – output labels of the NNs, size n*k

  • distances – output pairwise distances, size n*k

virtual void sa_encode(idx_t n, const float *x, uint8_t *bytes) const override

encode a set of vectors

Parameters:

  • n – number of vectors

  • x – input vectors, size n * d

  • bytes – output encoded vectors, size n * sa_code_size()

virtual void sa_decode(idx_t n, const uint8_t *bytes, float *x) const override

decode a set of vectors

Parameters:

  • n – number of vectors

  • bytes – input encoded vectors, size n * sa_code_size()

  • x – output vectors, size n * d

virtual FlatCodesDistanceComputer *get_FlatCodesDistanceComputer() const override

a FlatCodesDistanceComputer offers a distance_to_code method

Public Members

ResidualQuantizer rq

The residual quantizer used to encode the vectors.

AdditiveQuantizer *aq
struct IndexLocalSearchQuantizer : public faiss::IndexAdditiveQuantizer

Public Types

using Search_type_t = AdditiveQuantizer::Search_type_t

Public Functions

IndexLocalSearchQuantizer(int d, size_t M, size_t nbits, MetricType metric = METRIC_L2, Search_type_t search_type = AdditiveQuantizer::ST_decompress)

Constructor.

Parameters:

  • d – dimensionality of the input vectors

  • M – number of subquantizers

  • nbits – number of bit per subvector index

  • d – dimensionality of the input vectors

  • M – number of subquantizers

  • nbits – number of bit per subvector index

IndexLocalSearchQuantizer()
virtual void train(idx_t n, const float *x) override

Perform training on a representative set of vectors

Parameters:

  • n – nb of training vectors

  • x – training vecors, size n * d

virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, const SearchParameters *params = nullptr) const override

query n vectors of dimension d to the index.

return at most k vectors. If there are not enough results for a query, the result array is padded with -1s.

Parameters:

  • x – input vectors to search, size n * d

  • labels – output labels of the NNs, size n*k

  • distances – output pairwise distances, size n*k

virtual void sa_encode(idx_t n, const float *x, uint8_t *bytes) const override

encode a set of vectors

Parameters:

  • n – number of vectors

  • x – input vectors, size n * d

  • bytes – output encoded vectors, size n * sa_code_size()

virtual void sa_decode(idx_t n, const uint8_t *bytes, float *x) const override

decode a set of vectors

Parameters:

  • n – number of vectors

  • bytes – input encoded vectors, size n * sa_code_size()

  • x – output vectors, size n * d

virtual FlatCodesDistanceComputer *get_FlatCodesDistanceComputer() const override

a FlatCodesDistanceComputer offers a distance_to_code method

Public Members

LocalSearchQuantizer lsq
AdditiveQuantizer *aq
struct IndexProductResidualQuantizer : public faiss::IndexAdditiveQuantizer
#include <IndexAdditiveQuantizer.h>

Index based on a product residual quantizer.

Public Types

using Search_type_t = AdditiveQuantizer::Search_type_t

Public Functions

IndexProductResidualQuantizer(int d, size_t nsplits, size_t Msub, size_t nbits, MetricType metric = METRIC_L2, Search_type_t search_type = AdditiveQuantizer::ST_decompress)

Constructor.

Parameters:

  • d – dimensionality of the input vectors

  • nsplits – number of residual quantizers

  • Msub – number of subquantizers per RQ

  • nbits – number of bit per subvector index

  • d – dimensionality of the input vectors

  • nsplits – number of residual quantizers

  • Msub – number of subquantizers per RQ

  • nbits – number of bit per subvector index

IndexProductResidualQuantizer()
virtual void train(idx_t n, const float *x) override

Perform training on a representative set of vectors

Parameters:

  • n – nb of training vectors

  • x – training vecors, size n * d

virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, const SearchParameters *params = nullptr) const override

query n vectors of dimension d to the index.

return at most k vectors. If there are not enough results for a query, the result array is padded with -1s.

Parameters:

  • x – input vectors to search, size n * d

  • labels – output labels of the NNs, size n*k

  • distances – output pairwise distances, size n*k

virtual void sa_encode(idx_t n, const float *x, uint8_t *bytes) const override

encode a set of vectors

Parameters:

  • n – number of vectors

  • x – input vectors, size n * d

  • bytes – output encoded vectors, size n * sa_code_size()

virtual void sa_decode(idx_t n, const uint8_t *bytes, float *x) const override

decode a set of vectors

Parameters:

  • n – number of vectors

  • bytes – input encoded vectors, size n * sa_code_size()

  • x – output vectors, size n * d

virtual FlatCodesDistanceComputer *get_FlatCodesDistanceComputer() const override

a FlatCodesDistanceComputer offers a distance_to_code method

Public Members

ProductResidualQuantizer prq

The product residual quantizer used to encode the vectors.

AdditiveQuantizer *aq
struct IndexProductLocalSearchQuantizer : public faiss::IndexAdditiveQuantizer
#include <IndexAdditiveQuantizer.h>

Index based on a product local search quantizer.

Public Types

using Search_type_t = AdditiveQuantizer::Search_type_t

Public Functions

IndexProductLocalSearchQuantizer(int d, size_t nsplits, size_t Msub, size_t nbits, MetricType metric = METRIC_L2, Search_type_t search_type = AdditiveQuantizer::ST_decompress)

Constructor.

Parameters:

  • d – dimensionality of the input vectors

  • nsplits – number of local search quantizers

  • Msub – number of subquantizers per LSQ

  • nbits – number of bit per subvector index

  • d – dimensionality of the input vectors

  • nsplits – number of local search quantizers

  • Msub – number of subquantizers per LSQ

  • nbits – number of bit per subvector index

IndexProductLocalSearchQuantizer()
virtual void train(idx_t n, const float *x) override

Perform training on a representative set of vectors

Parameters:

  • n – nb of training vectors

  • x – training vecors, size n * d

virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, const SearchParameters *params = nullptr) const override

query n vectors of dimension d to the index.

return at most k vectors. If there are not enough results for a query, the result array is padded with -1s.

Parameters:

  • x – input vectors to search, size n * d

  • labels – output labels of the NNs, size n*k

  • distances – output pairwise distances, size n*k

virtual void sa_encode(idx_t n, const float *x, uint8_t *bytes) const override

encode a set of vectors

Parameters:

  • n – number of vectors

  • x – input vectors, size n * d

  • bytes – output encoded vectors, size n * sa_code_size()

virtual void sa_decode(idx_t n, const uint8_t *bytes, float *x) const override

decode a set of vectors

Parameters:

  • n – number of vectors

  • bytes – input encoded vectors, size n * sa_code_size()

  • x – output vectors, size n * d

virtual FlatCodesDistanceComputer *get_FlatCodesDistanceComputer() const override

a FlatCodesDistanceComputer offers a distance_to_code method

Public Members

ProductLocalSearchQuantizer plsq

The product local search quantizer used to encode the vectors.

AdditiveQuantizer *aq
struct AdditiveCoarseQuantizer : public faiss::Index
#include <IndexAdditiveQuantizer.h>

A “virtual” index where the elements are the residual quantizer centroids.

Intended for use as a coarse quantizer in an IndexIVF.

Subclassed by faiss::LocalSearchCoarseQuantizer, faiss::ResidualCoarseQuantizer

Public Functions

explicit AdditiveCoarseQuantizer(idx_t d = 0, AdditiveQuantizer *aq = nullptr, MetricType metric = METRIC_L2)
virtual void add(idx_t n, const float *x) override

N/A.

virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, const SearchParameters *params = nullptr) const override

query n vectors of dimension d to the index.

return at most k vectors. If there are not enough results for a query, the result array is padded with -1s.

Parameters:

  • x – input vectors to search, size n * d

  • labels – output labels of the NNs, size n*k

  • distances – output pairwise distances, size n*k

virtual void reconstruct(idx_t key, float *recons) const override

Reconstruct a stored vector (or an approximation if lossy coding)

this function may not be defined for some indexes

Parameters:

  • key – id of the vector to reconstruct

  • recons – reconstucted vector (size d)

virtual void train(idx_t n, const float *x) override

Perform training on a representative set of vectors

Parameters:

  • n – nb of training vectors

  • x – training vecors, size n * d

virtual void reset() override

N/A.

Public Members

AdditiveQuantizer *aq
std::vector<float> centroid_norms

norms of centroids, useful for knn-search

struct SearchParametersResidualCoarseQuantizer : public faiss::SearchParameters

Public Functions

inline ~SearchParametersResidualCoarseQuantizer()

Public Members

float beam_factor = 4.0f
struct ResidualCoarseQuantizer : public faiss::AdditiveCoarseQuantizer
#include <IndexAdditiveQuantizer.h>

The ResidualCoarseQuantizer is a bit specialized compared to the default AdditiveCoarseQuantizer because it can use a beam search at search time (slow but may be useful for very large vocabularies)

Public Functions

void set_beam_factor(float new_beam_factor)

computes centroid norms if required

ResidualCoarseQuantizer(int d, size_t M, size_t nbits, MetricType metric = METRIC_L2)

Constructor.

Parameters:

  • d – dimensionality of the input vectors

  • M – number of subquantizers

  • nbits – number of bit per subvector index

  • d – dimensionality of the input vectors

  • M – number of subquantizers

  • nbits – number of bit per subvector index

ResidualCoarseQuantizer(int d, const std::vector<size_t> &nbits, MetricType metric = METRIC_L2)
virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, const SearchParameters *params = nullptr) const override

query n vectors of dimension d to the index.

return at most k vectors. If there are not enough results for a query, the result array is padded with -1s.

Parameters:

  • x – input vectors to search, size n * d

  • labels – output labels of the NNs, size n*k

  • distances – output pairwise distances, size n*k

void initialize_from(const ResidualCoarseQuantizer &other)

Copy the M first codebook levels from other. Useful to crop a ResidualQuantizer to its first M quantizers.

ResidualCoarseQuantizer()
virtual void add(idx_t n, const float *x) override

N/A.

virtual void reconstruct(idx_t key, float *recons) const override

Reconstruct a stored vector (or an approximation if lossy coding)

this function may not be defined for some indexes

Parameters:

  • key – id of the vector to reconstruct

  • recons – reconstucted vector (size d)

virtual void train(idx_t n, const float *x) override

Perform training on a representative set of vectors

Parameters:

  • n – nb of training vectors

  • x – training vecors, size n * d

virtual void reset() override

N/A.

Public Members

ResidualQuantizer rq

The residual quantizer used to encode the vectors.

float beam_factor = 4.0f

factor between the beam size and the search k if negative, use exact search-to-centroid

AdditiveQuantizer *aq
std::vector<float> centroid_norms

norms of centroids, useful for knn-search

struct LocalSearchCoarseQuantizer : public faiss::AdditiveCoarseQuantizer

Public Functions

LocalSearchCoarseQuantizer(int d, size_t M, size_t nbits, MetricType metric = METRIC_L2)

Constructor.

Parameters:

  • d – dimensionality of the input vectors

  • M – number of subquantizers

  • nbits – number of bit per subvector index

  • d – dimensionality of the input vectors

  • M – number of subquantizers

  • nbits – number of bit per subvector index

LocalSearchCoarseQuantizer()
virtual void add(idx_t n, const float *x) override

N/A.

virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, const SearchParameters *params = nullptr) const override

query n vectors of dimension d to the index.

return at most k vectors. If there are not enough results for a query, the result array is padded with -1s.

Parameters:

  • x – input vectors to search, size n * d

  • labels – output labels of the NNs, size n*k

  • distances – output pairwise distances, size n*k

virtual void reconstruct(idx_t key, float *recons) const override

Reconstruct a stored vector (or an approximation if lossy coding)

this function may not be defined for some indexes

Parameters:

  • key – id of the vector to reconstruct

  • recons – reconstucted vector (size d)

virtual void train(idx_t n, const float *x) override

Perform training on a representative set of vectors

Parameters:

  • n – nb of training vectors

  • x – training vecors, size n * d

virtual void reset() override

N/A.

Public Members

LocalSearchQuantizer lsq

The residual quantizer used to encode the vectors.

AdditiveQuantizer *aq
std::vector<float> centroid_norms

norms of centroids, useful for knn-search