File IndexFlat.h

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.

struct IndexFlat : public faiss::IndexFlatCodes
#include <IndexFlat.h>

Index that stores the full vectors and performs exhaustive search

Subclassed by faiss::IndexFlatIP, faiss::IndexFlatL2

Public Functions

explicit IndexFlat(idx_t d, MetricType metric = METRIC_L2)
Parameters:

d – dimensionality of the input vectors

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 range_search(idx_t n, const float *x, float radius, RangeSearchResult *result, const SearchParameters *params = nullptr) const override

query n vectors of dimension d to the index.

return all vectors with distance < radius. Note that many indexes do not implement the range_search (only the k-NN search is mandatory).

Parameters:

  • n – number of vectors

  • x – input vectors to search, size n * d

  • radius – search radius

  • result – result table

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)

void compute_distance_subset(idx_t n, const float *x, idx_t k, float *distances, const idx_t *labels) const

compute distance with a subset of vectors

Parameters:

  • x – query vectors, size n * d

  • labels – indices of the vectors that should be compared for each query vector, size n * k

  • distances – corresponding output distances, size n * k

inline float *get_xb()
inline const float *get_xb() const
inline IndexFlat()
virtual FlatCodesDistanceComputer *get_FlatCodesDistanceComputer() const override

a FlatCodesDistanceComputer offers a distance_to_code method

The default implementation explicitly decodes the vector with sa_decode.

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

struct IndexFlatIP : public faiss::IndexFlat

Public Functions

inline explicit IndexFlatIP(idx_t d)
inline IndexFlatIP()
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 range_search(idx_t n, const float *x, float radius, RangeSearchResult *result, const SearchParameters *params = nullptr) const override

query n vectors of dimension d to the index.

return all vectors with distance < radius. Note that many indexes do not implement the range_search (only the k-NN search is mandatory).

Parameters:

  • n – number of vectors

  • x – input vectors to search, size n * d

  • radius – search radius

  • result – result table

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)

void compute_distance_subset(idx_t n, const float *x, idx_t k, float *distances, const idx_t *labels) const

compute distance with a subset of vectors

Parameters:

  • x – query vectors, size n * d

  • labels – indices of the vectors that should be compared for each query vector, size n * k

  • distances – corresponding output distances, size n * k

inline float *get_xb()
inline const float *get_xb() const
virtual FlatCodesDistanceComputer *get_FlatCodesDistanceComputer() const override

a FlatCodesDistanceComputer offers a distance_to_code method

The default implementation explicitly decodes the vector with sa_decode.

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

struct IndexFlatL2 : public faiss::IndexFlat

Subclassed by faiss::IndexFlat1D

Public Functions

inline explicit IndexFlatL2(idx_t d)
Parameters:

d – dimensionality of the input vectors

inline IndexFlatL2()
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 sync_l2norms()
void clear_l2norms()
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 range_search(idx_t n, const float *x, float radius, RangeSearchResult *result, const SearchParameters *params = nullptr) const override

query n vectors of dimension d to the index.

return all vectors with distance < radius. Note that many indexes do not implement the range_search (only the k-NN search is mandatory).

Parameters:

  • n – number of vectors

  • x – input vectors to search, size n * d

  • radius – search radius

  • result – result table

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)

void compute_distance_subset(idx_t n, const float *x, idx_t k, float *distances, const idx_t *labels) const

compute distance with a subset of vectors

Parameters:

  • x – query vectors, size n * d

  • labels – indices of the vectors that should be compared for each query vector, size n * k

  • distances – corresponding output distances, size n * k

inline float *get_xb()
inline const float *get_xb() const
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

Public Members

std::vector<float> cached_l2norms
struct IndexFlat1D : public faiss::IndexFlatL2
#include <IndexFlat.h>

optimized version for 1D “vectors”.

Public Functions

explicit IndexFlat1D(bool continuous_update = true)
void update_permutation()

if not continuous_update, call this between the last add and the first search

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

Add n vectors of dimension d to the index.

Vectors are implicitly assigned labels ntotal .. ntotal + n - 1 This function slices the input vectors in chunks smaller than blocksize_add and calls add_core.

Parameters:

  • n – number of vectors

  • x – input matrix, size n * d

virtual void reset() override

removes all elements from the database.

virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, const SearchParameters *params = nullptr) const override

Warn: the distances returned are L1 not L2.

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 sync_l2norms()
void clear_l2norms()
virtual void range_search(idx_t n, const float *x, float radius, RangeSearchResult *result, const SearchParameters *params = nullptr) const override

query n vectors of dimension d to the index.

return all vectors with distance < radius. Note that many indexes do not implement the range_search (only the k-NN search is mandatory).

Parameters:

  • n – number of vectors

  • x – input vectors to search, size n * d

  • radius – search radius

  • result – result table

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)

void compute_distance_subset(idx_t n, const float *x, idx_t k, float *distances, const idx_t *labels) const

compute distance with a subset of vectors

Parameters:

  • x – query vectors, size n * d

  • labels – indices of the vectors that should be compared for each query vector, size n * k

  • distances – corresponding output distances, size n * k

inline float *get_xb()
inline const float *get_xb() const
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

Public Members

bool continuous_update = true

is the permutation updated continuously?

std::vector<idx_t> perm

sorted database indices

std::vector<float> cached_l2norms