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.
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¶
-
using Search_type_t = AdditiveQuantizer::Search_type_t¶
-
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
-
using Search_type_t = AdditiveQuantizer::Search_type_t¶
-
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
-
using Search_type_t = AdditiveQuantizer::Search_type_t¶
-
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
-
using Search_type_t = AdditiveQuantizer::Search_type_t¶
-
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
-
using Search_type_t = AdditiveQuantizer::Search_type_t¶
-
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 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.
-
explicit AdditiveCoarseQuantizer(idx_t d = 0, AdditiveQuantizer *aq = nullptr, MetricType metric = METRIC_L2)¶
-
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
-
ResidualCoarseQuantizer()¶
-
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.
-
void set_beam_factor(float new_beam_factor)¶
-
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 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.
-
LocalSearchCoarseQuantizer(int d, size_t M, size_t nbits, MetricType metric = METRIC_L2)¶
-
struct IndexAdditiveQuantizer : public faiss::IndexFlatCodes¶