File IndexIVFAdditiveQuantizer.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 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

using Search_type_t = AdditiveQuantizer::Search_type_t

Public Functions

IndexIVFAdditiveQuantizer(AdditiveQuantizer *aq, Index *quantizer, size_t d, size_t nlist, MetricType metric = METRIC_L2)
explicit IndexIVFAdditiveQuantizer(AdditiveQuantizer *aq)
virtual void train_encoder(idx_t n, const float *x, const idx_t *assign) override

Train the encoder for the vectors.

If by_residual then it is called with residuals and corresponding assign array, otherwise x is the raw training vectors and assign=nullptr

virtual idx_t train_encoder_num_vectors() const override

can be redefined by subclasses to indicate how many training vectors they need

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)

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

~IndexIVFAdditiveQuantizer() override

Public Members

AdditiveQuantizer *aq
int use_precomputed_table = 0
struct IndexIVFResidualQuantizer : public faiss::IndexIVFAdditiveQuantizer
#include <IndexIVFAdditiveQuantizer.h>

IndexIVF based on a residual quantizer. Stored vectors are approximated by residual quantization codes.

Public Types

using Search_type_t = AdditiveQuantizer::Search_type_t

Public Functions

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

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)
IndexIVFResidualQuantizer()
virtual ~IndexIVFResidualQuantizer()
virtual void train_encoder(idx_t n, const float *x, const idx_t *assign) override

Train the encoder for the vectors.

If by_residual then it is called with residuals and corresponding assign array, otherwise x is the raw training vectors and assign=nullptr

virtual idx_t train_encoder_num_vectors() const override

can be redefined by subclasses to indicate how many training vectors they need

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)

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

Public Members

ResidualQuantizer rq

The residual quantizer used to encode the vectors.

AdditiveQuantizer *aq
int use_precomputed_table = 0
struct IndexIVFLocalSearchQuantizer : public faiss::IndexIVFAdditiveQuantizer
#include <IndexIVFAdditiveQuantizer.h>

IndexIVF based on a residual quantizer. Stored vectors are approximated by residual quantization codes.

Public Types

using Search_type_t = AdditiveQuantizer::Search_type_t

Public Functions

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

IndexIVFLocalSearchQuantizer()
virtual ~IndexIVFLocalSearchQuantizer()
virtual void train_encoder(idx_t n, const float *x, const idx_t *assign) override

Train the encoder for the vectors.

If by_residual then it is called with residuals and corresponding assign array, otherwise x is the raw training vectors and assign=nullptr

virtual idx_t train_encoder_num_vectors() const override

can be redefined by subclasses to indicate how many training vectors they need

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)

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

Public Members

LocalSearchQuantizer lsq

The LSQ quantizer used to encode the vectors.

AdditiveQuantizer *aq
int use_precomputed_table = 0
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

using Search_type_t = AdditiveQuantizer::Search_type_t

Public Functions

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

IndexIVFProductResidualQuantizer()
virtual ~IndexIVFProductResidualQuantizer()
virtual void train_encoder(idx_t n, const float *x, const idx_t *assign) override

Train the encoder for the vectors.

If by_residual then it is called with residuals and corresponding assign array, otherwise x is the raw training vectors and assign=nullptr

virtual idx_t train_encoder_num_vectors() const override

can be redefined by subclasses to indicate how many training vectors they need

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)

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

Public Members

ProductResidualQuantizer prq

The product residual quantizer used to encode the vectors.

AdditiveQuantizer *aq
int use_precomputed_table = 0
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

using Search_type_t = AdditiveQuantizer::Search_type_t

Public Functions

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

IndexIVFProductLocalSearchQuantizer()
virtual ~IndexIVFProductLocalSearchQuantizer()
virtual void train_encoder(idx_t n, const float *x, const idx_t *assign) override

Train the encoder for the vectors.

If by_residual then it is called with residuals and corresponding assign array, otherwise x is the raw training vectors and assign=nullptr

virtual idx_t train_encoder_num_vectors() const override

can be redefined by subclasses to indicate how many training vectors they need

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)

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

Public Members

ProductLocalSearchQuantizer plsq

The product local search quantizer used to encode the vectors.

AdditiveQuantizer *aq
int use_precomputed_table = 0