File ScalarQuantizer.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 ScalarQuantizer : public faiss::Quantizer
#include <ScalarQuantizer.h>

The uniform quantizer has a range [vmin, vmax]. The range can be the same for all dimensions (uniform) or specific per dimension (default).

Public Types

enum QuantizerType

Values:

enumerator QT_8bit

8 bits per component

enumerator QT_4bit

4 bits per component

enumerator QT_8bit_uniform

same, shared range for all dimensions

enumerator QT_4bit_uniform
enumerator QT_fp16
enumerator QT_8bit_direct

fast indexing of uint8s

enumerator QT_6bit

6 bits per component

enumerator QT_bf16
enumerator QT_8bit_direct_signed

fast indexing of signed int8s ranging from [-128 to 127]

enum RangeStat

The uniform encoder can estimate the range of representable values of the unform encoder using different statistics. Here rs = rangestat_arg

Values:

enumerator RS_minmax

[min - rs*(max-min), max + rs*(max-min)]

enumerator RS_meanstd

[mean - std * rs, mean + std * rs]

enumerator RS_quantiles

[Q(rs), Q(1-rs)]

enumerator RS_optim

alternate optimization of reconstruction error

Public Functions

ScalarQuantizer(size_t d, QuantizerType qtype)
ScalarQuantizer()
void set_derived_sizes()

updates internal values based on qtype and d

virtual void train(size_t n, const float *x) override

Train the quantizer

Parameters:

x – training vectors, size n * d

virtual void compute_codes(const float *x, uint8_t *codes, size_t n) const override

Encode a set of vectors

Parameters:

  • x – vectors to encode, size n * d

  • codes – output codes, size n * code_size

virtual void decode(const uint8_t *code, 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

SQuantizer *select_quantizer() const
SQDistanceComputer *get_distance_computer(MetricType metric = METRIC_L2) const
InvertedListScanner *select_InvertedListScanner(MetricType mt, const Index *quantizer, bool store_pairs, const IDSelector *sel, bool by_residual = false) const

Public Members

QuantizerType qtype = QT_8bit
RangeStat rangestat = RS_minmax
float rangestat_arg = 0
size_t bits = 0

bits per scalar code

std::vector<float> trained

trained values (including the range)

struct SQDistanceComputer : public faiss::FlatCodesDistanceComputer

Public Functions

inline SQDistanceComputer()
virtual float query_to_code(const uint8_t *code) const = 0
inline virtual float distance_to_code(const uint8_t *code) final

compute distance of current query to an encoded vector

Public Members

const float *q
struct SQuantizer

Public Functions

virtual void encode_vector(const float *x, uint8_t *code) const = 0
virtual void decode_vector(const uint8_t *code, float *x) const = 0
inline virtual ~SQuantizer()