File IndexIVFAdditiveQuantizer.h¶
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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.
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struct IndexIVFAdditiveQuantizer : public faiss::IndexIVF¶
- #include <IndexIVFAdditiveQuantizer.h>
Abstract class for IVF additive quantizers. The search functions are in common.
Subclassed by faiss::IndexIVFLocalSearchQuantizer, faiss::IndexIVFProductLocalSearchQuantizer, faiss::IndexIVFProductResidualQuantizer, faiss::IndexIVFResidualQuantizer
Public Types
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using Search_type_t = AdditiveQuantizer::Search_type_t¶
Public Functions
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IndexIVFAdditiveQuantizer(AdditiveQuantizer *aq, Index *quantizer, size_t d, size_t nlist, MetricType metric = METRIC_L2)¶
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explicit IndexIVFAdditiveQuantizer(AdditiveQuantizer *aq)¶
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virtual void train_residual(idx_t n, const float *x) override¶
Sub-classes that encode the residuals can train their encoders here does nothing by default
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virtual void encode_vectors(idx_t n, const float *x, const idx_t *list_nos, uint8_t *codes, bool include_listnos = false) const override¶
Encodes a set of vectors as they would appear in the inverted lists
- Parameters:
list_nos – inverted list ids as returned by the quantizer (size n). -1s are ignored.
codes – output codes, size n * code_size
include_listno – include the list ids in the code (in this case add ceil(log8(nlist)) to the code size)
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virtual InvertedListScanner *get_InvertedListScanner(bool store_pairs, const IDSelector *sel) const override¶
Get a scanner for this index (store_pairs means ignore labels)
The default search implementation uses this to compute the distances
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virtual void sa_decode(idx_t n, const uint8_t *codes, 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
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~IndexIVFAdditiveQuantizer() override¶
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using Search_type_t = AdditiveQuantizer::Search_type_t¶
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struct IndexIVFResidualQuantizer : public faiss::IndexIVFAdditiveQuantizer¶
- #include <IndexIVFAdditiveQuantizer.h>
IndexIVF based on a residual quantizer. Stored vectors are approximated by residual quantization codes.
Public Types
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using Search_type_t = AdditiveQuantizer::Search_type_t¶
Public Functions
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IndexIVFResidualQuantizer(Index *quantizer, size_t d, size_t nlist, const std::vector<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
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IndexIVFResidualQuantizer(Index *quantizer, size_t d, size_t nlist, size_t M, size_t nbits, MetricType metric = METRIC_L2, Search_type_t search_type = AdditiveQuantizer::ST_decompress)¶
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IndexIVFResidualQuantizer()¶
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virtual ~IndexIVFResidualQuantizer()¶
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virtual void train_residual(idx_t n, const float *x) override¶
Sub-classes that encode the residuals can train their encoders here does nothing by default
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virtual void encode_vectors(idx_t n, const float *x, const idx_t *list_nos, uint8_t *codes, bool include_listnos = false) const override¶
Encodes a set of vectors as they would appear in the inverted lists
- Parameters:
list_nos – inverted list ids as returned by the quantizer (size n). -1s are ignored.
codes – output codes, size n * code_size
include_listno – include the list ids in the code (in this case add ceil(log8(nlist)) to the code size)
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virtual InvertedListScanner *get_InvertedListScanner(bool store_pairs, const IDSelector *sel) const override¶
Get a scanner for this index (store_pairs means ignore labels)
The default search implementation uses this to compute the distances
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virtual void sa_decode(idx_t n, const uint8_t *codes, 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
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using Search_type_t = AdditiveQuantizer::Search_type_t¶
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struct IndexIVFLocalSearchQuantizer : public faiss::IndexIVFAdditiveQuantizer¶
- #include <IndexIVFAdditiveQuantizer.h>
IndexIVF based on a residual quantizer. Stored vectors are approximated by residual quantization codes.
Public Types
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using Search_type_t = AdditiveQuantizer::Search_type_t¶
Public Functions
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IndexIVFLocalSearchQuantizer(Index *quantizer, size_t d, size_t nlist, 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
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IndexIVFLocalSearchQuantizer()¶
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virtual ~IndexIVFLocalSearchQuantizer()¶
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virtual void train_residual(idx_t n, const float *x) override¶
Sub-classes that encode the residuals can train their encoders here does nothing by default
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virtual void encode_vectors(idx_t n, const float *x, const idx_t *list_nos, uint8_t *codes, bool include_listnos = false) const override¶
Encodes a set of vectors as they would appear in the inverted lists
- Parameters:
list_nos – inverted list ids as returned by the quantizer (size n). -1s are ignored.
codes – output codes, size n * code_size
include_listno – include the list ids in the code (in this case add ceil(log8(nlist)) to the code size)
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virtual InvertedListScanner *get_InvertedListScanner(bool store_pairs, const IDSelector *sel) const override¶
Get a scanner for this index (store_pairs means ignore labels)
The default search implementation uses this to compute the distances
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virtual void sa_decode(idx_t n, const uint8_t *codes, 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
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using Search_type_t = AdditiveQuantizer::Search_type_t¶
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struct IndexIVFProductResidualQuantizer : public faiss::IndexIVFAdditiveQuantizer¶
- #include <IndexIVFAdditiveQuantizer.h>
IndexIVF based on a product residual quantizer. Stored vectors are approximated by product residual quantization codes.
Public Types
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using Search_type_t = AdditiveQuantizer::Search_type_t¶
Public Functions
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IndexIVFProductResidualQuantizer(Index *quantizer, size_t d, size_t nlist, 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
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IndexIVFProductResidualQuantizer()¶
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virtual ~IndexIVFProductResidualQuantizer()¶
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virtual void train_residual(idx_t n, const float *x) override¶
Sub-classes that encode the residuals can train their encoders here does nothing by default
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virtual void encode_vectors(idx_t n, const float *x, const idx_t *list_nos, uint8_t *codes, bool include_listnos = false) const override¶
Encodes a set of vectors as they would appear in the inverted lists
- Parameters:
list_nos – inverted list ids as returned by the quantizer (size n). -1s are ignored.
codes – output codes, size n * code_size
include_listno – include the list ids in the code (in this case add ceil(log8(nlist)) to the code size)
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virtual InvertedListScanner *get_InvertedListScanner(bool store_pairs, const IDSelector *sel) const override¶
Get a scanner for this index (store_pairs means ignore labels)
The default search implementation uses this to compute the distances
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virtual void sa_decode(idx_t n, const uint8_t *codes, 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
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using Search_type_t = AdditiveQuantizer::Search_type_t¶
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struct IndexIVFProductLocalSearchQuantizer : public faiss::IndexIVFAdditiveQuantizer¶
- #include <IndexIVFAdditiveQuantizer.h>
IndexIVF based on a product local search quantizer. Stored vectors are approximated by product local search quantization codes.
Public Types
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using Search_type_t = AdditiveQuantizer::Search_type_t¶
Public Functions
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IndexIVFProductLocalSearchQuantizer(Index *quantizer, size_t d, size_t nlist, 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
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IndexIVFProductLocalSearchQuantizer()¶
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virtual ~IndexIVFProductLocalSearchQuantizer()¶
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virtual void train_residual(idx_t n, const float *x) override¶
Sub-classes that encode the residuals can train their encoders here does nothing by default
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virtual void encode_vectors(idx_t n, const float *x, const idx_t *list_nos, uint8_t *codes, bool include_listnos = false) const override¶
Encodes a set of vectors as they would appear in the inverted lists
- Parameters:
list_nos – inverted list ids as returned by the quantizer (size n). -1s are ignored.
codes – output codes, size n * code_size
include_listno – include the list ids in the code (in this case add ceil(log8(nlist)) to the code size)
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virtual InvertedListScanner *get_InvertedListScanner(bool store_pairs, const IDSelector *sel) const override¶
Get a scanner for this index (store_pairs means ignore labels)
The default search implementation uses this to compute the distances
-
virtual void sa_decode(idx_t n, const uint8_t *codes, 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
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using Search_type_t = AdditiveQuantizer::Search_type_t¶
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struct IndexIVFAdditiveQuantizer : public faiss::IndexIVF¶