File Index2Layer.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.

Variables

FAISS_API int index2layer_sa_encode_bs
struct Index2Layer : public faiss::IndexFlatCodes
#include <Index2Layer.h>

Same as an IndexIVFPQ without the inverted lists: codes are stored sequentially

The class is mainly inteded to store encoded vectors that can be accessed randomly, the search function is not implemented.

Public Functions

Index2Layer(Index *quantizer, size_t nlist, int M, int nbit = 8, MetricType metric = METRIC_L2)
Index2Layer()
~Index2Layer()
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

not implemented

virtual DistanceComputer *get_distance_computer() const override

Get a DistanceComputer (defined in AuxIndexStructures) object for this kind of index.

DistanceComputer is implemented for indexes that support random access of their vectors.

void transfer_to_IVFPQ(IndexIVFPQ &other) const

transfer the flat codes to an IVFPQ index

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

Level1Quantizer q1

first level quantizer

ProductQuantizer pq

second level quantizer is always a PQ

size_t code_size_1

size of the code for the first level (ceil(log8(q1.nlist)))

size_t code_size_2

size of the code for the second level