File ProductAdditiveQuantizer.h
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namespace faiss
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)
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.
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.
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.
Definition of inverted lists + a few common classes that implement the interface.
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.
In this file are the implementations of extra metrics beyond L2 and inner product
Implements a few neural net layers, mainly to support QINCo
Defines a few objects that apply transformations to a set of vectors Often these are pre-processing steps.
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struct ProductAdditiveQuantizer : public faiss::AdditiveQuantizer
- #include <ProductAdditiveQuantizer.h>
Product Additive Quantizers
The product additive quantizer is a variant of AQ and PQ. It first splits the vector space into multiple orthogonal sub-spaces just like PQ does. And then it quantizes each sub-space by an independent additive quantizer.
Subclassed by faiss::ProductLocalSearchQuantizer, faiss::ProductResidualQuantizer
Public Functions
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ProductAdditiveQuantizer(size_t d, const std::vector<AdditiveQuantizer*> &aqs, Search_type_t search_type = ST_decompress)
Construct a product additive quantizer.
The additive quantizers passed in will be cloned into the ProductAdditiveQuantizer object.
- Parameters:
d – dimensionality of the input vectors
aqs – sub-additive quantizers
search_type – AQ search type
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ProductAdditiveQuantizer()
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virtual ~ProductAdditiveQuantizer()
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void init(size_t d, const std::vector<AdditiveQuantizer*> &aqs, Search_type_t search_type)
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AdditiveQuantizer *subquantizer(size_t m) const
Train the product additive quantizer.
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virtual void train(size_t n, const float *x) override
Train the quantizer
- Parameters:
x – training vectors, size n * d
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virtual void compute_codes_add_centroids(const float *x, uint8_t *codes, size_t n, const float *centroids = nullptr) const override
Encode a set of vectors
- Parameters:
x – vectors to encode, size n * d
codes – output codes, size n * code_size
centroids – centroids to be added to x, size n * d
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void compute_unpacked_codes(const float *x, int32_t *codes, size_t n, const float *centroids = nullptr) const
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virtual void decode_unpacked(const int32_t *codes, float *x, size_t n, int64_t ld_codes = -1) const override
Decode a set of vectors in non-packed format
- Parameters:
codes – codes to decode, size n * ld_codes
x – output vectors, size n * d
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virtual void decode(const uint8_t *codes, float *x, size_t n) const override
Decode a set of vectors
- Parameters:
codes – codes to decode, size n * code_size
x – output vectors, size n * d
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virtual void compute_LUT(size_t n, const float *xq, float *LUT, float alpha = 1.0f, long ld_lut = -1) const override
Compute inner-product look-up tables. Used in the search functions.
- Parameters:
xq – query vector, size (n, d)
LUT – look-up table, size (n, total_codebook_size)
alpha – compute alpha * inner-product
ld_lut – leading dimension of LUT
Public Members
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size_t nsplits
number of sub-vectors we split a vector into
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std::vector<AdditiveQuantizer*> quantizers
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ProductAdditiveQuantizer(size_t d, const std::vector<AdditiveQuantizer*> &aqs, Search_type_t search_type = ST_decompress)
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struct ProductLocalSearchQuantizer : public faiss::ProductAdditiveQuantizer
- #include <ProductAdditiveQuantizer.h>
Product Local Search Quantizer
Public Functions
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ProductLocalSearchQuantizer(size_t d, size_t nsplits, size_t Msub, size_t nbits, Search_type_t search_type = ST_decompress)
Construct a product LSQ object.
- Parameters:
d – dimensionality of the input vectors
nsplits – number of sub-vectors we split a vector into
Msub – number of codebooks of each LSQ
nbits – bits for each step
search_type – AQ search type
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ProductLocalSearchQuantizer()
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void init(size_t d, const std::vector<AdditiveQuantizer*> &aqs, Search_type_t search_type)
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AdditiveQuantizer *subquantizer(size_t m) const
Train the product additive quantizer.
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virtual void train(size_t n, const float *x) override
Train the quantizer
- Parameters:
x – training vectors, size n * d
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virtual void compute_codes_add_centroids(const float *x, uint8_t *codes, size_t n, const float *centroids = nullptr) const override
Encode a set of vectors
- Parameters:
x – vectors to encode, size n * d
codes – output codes, size n * code_size
centroids – centroids to be added to x, size n * d
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void compute_unpacked_codes(const float *x, int32_t *codes, size_t n, const float *centroids = nullptr) const
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virtual void decode_unpacked(const int32_t *codes, float *x, size_t n, int64_t ld_codes = -1) const override
Decode a set of vectors in non-packed format
- Parameters:
codes – codes to decode, size n * ld_codes
x – output vectors, size n * d
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virtual void decode(const uint8_t *codes, float *x, size_t n) const override
Decode a set of vectors
- Parameters:
codes – codes to decode, size n * code_size
x – output vectors, size n * d
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virtual void compute_LUT(size_t n, const float *xq, float *LUT, float alpha = 1.0f, long ld_lut = -1) const override
Compute inner-product look-up tables. Used in the search functions.
- Parameters:
xq – query vector, size (n, d)
LUT – look-up table, size (n, total_codebook_size)
alpha – compute alpha * inner-product
ld_lut – leading dimension of LUT
Public Members
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size_t nsplits
number of sub-vectors we split a vector into
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std::vector<AdditiveQuantizer*> quantizers
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ProductLocalSearchQuantizer(size_t d, size_t nsplits, size_t Msub, size_t nbits, Search_type_t search_type = ST_decompress)
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struct ProductResidualQuantizer : public faiss::ProductAdditiveQuantizer
- #include <ProductAdditiveQuantizer.h>
Product Residual Quantizer
Public Functions
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ProductResidualQuantizer(size_t d, size_t nsplits, size_t Msub, size_t nbits, Search_type_t search_type = ST_decompress)
Construct a product RQ object.
- Parameters:
d – dimensionality of the input vectors
nsplits – number of sub-vectors we split a vector into
Msub – number of codebooks of each RQ
nbits – bits for each step
search_type – AQ search type
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ProductResidualQuantizer()
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void init(size_t d, const std::vector<AdditiveQuantizer*> &aqs, Search_type_t search_type)
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AdditiveQuantizer *subquantizer(size_t m) const
Train the product additive quantizer.
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virtual void train(size_t n, const float *x) override
Train the quantizer
- Parameters:
x – training vectors, size n * d
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virtual void compute_codes_add_centroids(const float *x, uint8_t *codes, size_t n, const float *centroids = nullptr) const override
Encode a set of vectors
- Parameters:
x – vectors to encode, size n * d
codes – output codes, size n * code_size
centroids – centroids to be added to x, size n * d
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void compute_unpacked_codes(const float *x, int32_t *codes, size_t n, const float *centroids = nullptr) const
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virtual void decode_unpacked(const int32_t *codes, float *x, size_t n, int64_t ld_codes = -1) const override
Decode a set of vectors in non-packed format
- Parameters:
codes – codes to decode, size n * ld_codes
x – output vectors, size n * d
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virtual void decode(const uint8_t *codes, float *x, size_t n) const override
Decode a set of vectors
- Parameters:
codes – codes to decode, size n * code_size
x – output vectors, size n * d
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virtual void compute_LUT(size_t n, const float *xq, float *LUT, float alpha = 1.0f, long ld_lut = -1) const override
Compute inner-product look-up tables. Used in the search functions.
- Parameters:
xq – query vector, size (n, d)
LUT – look-up table, size (n, total_codebook_size)
alpha – compute alpha * inner-product
ld_lut – leading dimension of LUT
Public Members
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size_t nsplits
number of sub-vectors we split a vector into
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std::vector<AdditiveQuantizer*> quantizers
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ProductResidualQuantizer(size_t d, size_t nsplits, size_t Msub, size_t nbits, Search_type_t search_type = ST_decompress)
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struct ProductAdditiveQuantizer : public faiss::AdditiveQuantizer