File IndexAdditiveQuantizerFastScan.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.
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.
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. Implements a few neural net layers, mainly to support QINCo
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 IndexAdditiveQuantizerFastScan : public faiss::IndexFastScan
- #include <IndexAdditiveQuantizerFastScan.h>
Fast scan version of IndexAQ. Works for 4-bit AQ for now.
The codes are not stored sequentially but grouped in blocks of size bbs. This makes it possible to compute distances quickly with SIMD instructions.
Implementations: 12: blocked loop with internal loop on Q with qbs 13: same with reservoir accumulator to store results 14: no qbs with heap accumulator 15: no qbs with reservoir accumulator
Subclassed by faiss::IndexLocalSearchQuantizerFastScan, faiss::IndexProductLocalSearchQuantizerFastScan, faiss::IndexProductResidualQuantizerFastScan, faiss::IndexResidualQuantizerFastScan
Public Types
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using Search_type_t = AdditiveQuantizer::Search_type_t
Public Functions
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explicit IndexAdditiveQuantizerFastScan(AdditiveQuantizer *aq, MetricType metric = METRIC_L2, int bbs = 32)
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void init(AdditiveQuantizer *aq, MetricType metric = METRIC_L2, int bbs = 32)
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IndexAdditiveQuantizerFastScan()
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~IndexAdditiveQuantizerFastScan() override
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explicit IndexAdditiveQuantizerFastScan(const IndexAdditiveQuantizer &orig, int bbs = 32)
build from an existing IndexAQ
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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
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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:
n – number of vectors
x – input vectors to search, size n * d
k – number of extracted vectors
distances – output pairwise distances, size n*k
labels – output labels of the NNs, size n*k
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virtual void sa_decode(idx_t n, const uint8_t *bytes, float *x) const override
Decode a set of vectors.
NOTE: The codes in the IndexAdditiveQuantizerFastScan object are non- contiguous. But this method requires a contiguous representation.
- Parameters:
n – number of vectors
bytes – input encoded vectors, size n * 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 IndexResidualQuantizerFastScan : public faiss::IndexAdditiveQuantizerFastScan
- #include <IndexAdditiveQuantizerFastScan.h>
Index based on a residual quantizer. Stored vectors are approximated by residual quantization codes. Can also be used as a codec
Public Types
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using Search_type_t = AdditiveQuantizer::Search_type_t
Public Functions
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IndexResidualQuantizerFastScan(int d, size_t M, size_t nbits, MetricType metric = METRIC_L2, Search_type_t search_type = AdditiveQuantizer::ST_norm_rq2x4, int bbs = 32)
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
metric – metric type
search_type – AQ search type
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IndexResidualQuantizerFastScan()
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void init(AdditiveQuantizer *aq, MetricType metric = METRIC_L2, int bbs = 32)
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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
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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:
n – number of vectors
x – input vectors to search, size n * d
k – number of extracted vectors
distances – output pairwise distances, size n*k
labels – output labels of the NNs, size n*k
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virtual void sa_decode(idx_t n, const uint8_t *bytes, float *x) const override
Decode a set of vectors.
NOTE: The codes in the IndexAdditiveQuantizerFastScan object are non- contiguous. But this method requires a contiguous representation.
- Parameters:
n – number of vectors
bytes – input encoded vectors, size n * code_size
x – output vectors, size n * d
Public Members
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ResidualQuantizer rq
The residual quantizer used to encode the vectors.
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bool rescale_norm = true
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int norm_scale = 1
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size_t max_train_points = 0
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using Search_type_t = AdditiveQuantizer::Search_type_t
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struct IndexLocalSearchQuantizerFastScan : public faiss::IndexAdditiveQuantizerFastScan
- #include <IndexAdditiveQuantizerFastScan.h>
Index based on a local search quantizer. Stored vectors are approximated by local search quantization codes. Can also be used as a codec
Public Types
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using Search_type_t = AdditiveQuantizer::Search_type_t
Public Functions
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IndexLocalSearchQuantizerFastScan(int d, size_t M, size_t nbits, MetricType metric = METRIC_L2, Search_type_t search_type = AdditiveQuantizer::ST_norm_lsq2x4, int bbs = 32)
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
metric – metric type
search_type – AQ search type
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IndexLocalSearchQuantizerFastScan()
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void init(AdditiveQuantizer *aq, MetricType metric = METRIC_L2, int bbs = 32)
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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
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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:
n – number of vectors
x – input vectors to search, size n * d
k – number of extracted vectors
distances – output pairwise distances, size n*k
labels – output labels of the NNs, size n*k
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virtual void sa_decode(idx_t n, const uint8_t *bytes, float *x) const override
Decode a set of vectors.
NOTE: The codes in the IndexAdditiveQuantizerFastScan object are non- contiguous. But this method requires a contiguous representation.
- Parameters:
n – number of vectors
bytes – input encoded vectors, size n * 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 IndexProductResidualQuantizerFastScan : public faiss::IndexAdditiveQuantizerFastScan
- #include <IndexAdditiveQuantizerFastScan.h>
Index based on a product residual quantizer. Stored vectors are approximated by product residual quantization codes. Can also be used as a codec
Public Types
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using Search_type_t = AdditiveQuantizer::Search_type_t
Public Functions
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IndexProductResidualQuantizerFastScan(int d, size_t nsplits, size_t Msub, size_t nbits, MetricType metric = METRIC_L2, Search_type_t search_type = AdditiveQuantizer::ST_norm_rq2x4, int bbs = 32)
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
metric – metric type
search_type – AQ search type
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IndexProductResidualQuantizerFastScan()
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void init(AdditiveQuantizer *aq, MetricType metric = METRIC_L2, int bbs = 32)
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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
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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:
n – number of vectors
x – input vectors to search, size n * d
k – number of extracted vectors
distances – output pairwise distances, size n*k
labels – output labels of the NNs, size n*k
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virtual void sa_decode(idx_t n, const uint8_t *bytes, float *x) const override
Decode a set of vectors.
NOTE: The codes in the IndexAdditiveQuantizerFastScan object are non- contiguous. But this method requires a contiguous representation.
- Parameters:
n – number of vectors
bytes – input encoded vectors, size n * code_size
x – output vectors, size n * d
Public Members
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ProductResidualQuantizer prq
The product residual quantizer used to encode the vectors.
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bool rescale_norm = true
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int norm_scale = 1
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size_t max_train_points = 0
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using Search_type_t = AdditiveQuantizer::Search_type_t
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struct IndexProductLocalSearchQuantizerFastScan : public faiss::IndexAdditiveQuantizerFastScan
- #include <IndexAdditiveQuantizerFastScan.h>
Index based on a product local search quantizer. Stored vectors are approximated by product local search quantization codes. Can also be used as a codec
Public Types
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using Search_type_t = AdditiveQuantizer::Search_type_t
Public Functions
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IndexProductLocalSearchQuantizerFastScan(int d, size_t nsplits, size_t Msub, size_t nbits, MetricType metric = METRIC_L2, Search_type_t search_type = AdditiveQuantizer::ST_norm_rq2x4, int bbs = 32)
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
metric – metric type
search_type – AQ search type
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IndexProductLocalSearchQuantizerFastScan()
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void init(AdditiveQuantizer *aq, MetricType metric = METRIC_L2, int bbs = 32)
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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
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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:
n – number of vectors
x – input vectors to search, size n * d
k – number of extracted vectors
distances – output pairwise distances, size n*k
labels – output labels of the NNs, size n*k
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virtual void sa_decode(idx_t n, const uint8_t *bytes, float *x) const override
Decode a set of vectors.
NOTE: The codes in the IndexAdditiveQuantizerFastScan object are non- contiguous. But this method requires a contiguous representation.
- Parameters:
n – number of vectors
bytes – input encoded vectors, size n * code_size
x – output vectors, size n * d
Public Members
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ProductLocalSearchQuantizer plsq
The product local search quantizer used to encode the vectors.
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bool rescale_norm = true
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int norm_scale = 1
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size_t max_train_points = 0
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using Search_type_t = AdditiveQuantizer::Search_type_t
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struct IndexAdditiveQuantizerFastScan : public faiss::IndexFastScan