File IndexPQ.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.

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.

Variables

FAISS_API IndexPQStats indexPQ_stats
FAISS_API int multi_index_quantizer_search_bs
struct IndexPQ : public faiss::IndexFlatCodes
#include <IndexPQ.h>

Index based on a product quantizer. Stored vectors are approximated by PQ codes.

Public Types

enum Search_type_t

how to perform the search in search_core

Values:

enumerator ST_PQ

asymmetric product quantizer (default)

enumerator ST_HE

Hamming distance on codes.

enumerator ST_generalized_HE

nb of same codes

enumerator ST_SDC

symmetric product quantizer (SDC)

enumerator ST_polysemous

HE filter (using ht) + PQ combination.

enumerator ST_polysemous_generalize

Filter on generalized Hamming.

Public Functions

IndexPQ(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

IndexPQ()
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

Search implemented by decoding

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

The default implementation explicitly decodes the vector with sa_decode.

void search_core_polysemous(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, int polysemous_ht, bool generalized_hamming) const
void hamming_distance_histogram(idx_t n, const float *x, idx_t nb, const float *xb, int64_t *dist_histogram)

prepare query for a polysemous search, but instead of computing the result, just get the histogram of Hamming distances. May be computed on a provided dataset if xb != NULL

Parameters:

dist_histogram – (M * nbits + 1)

void hamming_distance_table(idx_t n, const float *x, int32_t *dis) const

compute pairwise distances between queries and database

Parameters:

  • n – nb of query vectors

  • x – query vector, size n * d

  • dis – output distances, size n * ntotal

Public Members

ProductQuantizer pq

The product quantizer used to encode the vectors.

bool do_polysemous_training

false = standard PQ

PolysemousTraining polysemous_training

parameters used for the polysemous training

Search_type_t search_type
bool encode_signs
int polysemous_ht

Hamming threshold used for polysemy.

struct SearchParametersPQ : public faiss::SearchParameters
#include <IndexPQ.h>

override search parameters from the class

Public Members

IndexPQ::Search_type_t search_type
int polysemous_ht
struct IndexPQStats
#include <IndexPQ.h>

statistics are robust to internal threading, but not if IndexPQ::search is called by multiple threads

Public Functions

inline IndexPQStats()
void reset()

Public Members

size_t nq
size_t ncode
size_t n_hamming_pass
struct MultiIndexQuantizer : public faiss::Index
#include <IndexPQ.h>

Quantizer where centroids are virtual: they are the Cartesian product of sub-centroids.

Subclassed by faiss::MultiIndexQuantizer2

Public Functions

MultiIndexQuantizer(int d, size_t M, size_t nbits)

number of bit per subvector index

Parameters:

  • d – dimension of the input vectors

  • M – number of subquantizers

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:

  • 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

virtual void add(idx_t n, const float *x) override

add and reset will crash at runtime

virtual void reset() override

removes all elements from the database.

inline MultiIndexQuantizer()
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)

Public Members

ProductQuantizer pq
struct MultiIndexQuantizer2 : public faiss::MultiIndexQuantizer
#include <IndexPQ.h>

MultiIndexQuantizer where the PQ assignmnet is performed by sub-indexes

Public Functions

MultiIndexQuantizer2(int d, size_t M, size_t nbits, Index **indexes)
MultiIndexQuantizer2(int d, size_t nbits, Index *assign_index_0, Index *assign_index_1)
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:

  • 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

virtual void add(idx_t n, const float *x) override

add and reset will crash at runtime

virtual void reset() override

removes all elements from the database.

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)

Public Members

std::vector<Index*> assign_indexes

M Indexes on d / M dimensions.

bool own_fields
ProductQuantizer pq