File IndexNeuralNetCodec.h
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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.
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struct IndexNeuralNetCodec : public faiss::IndexFlatCodes
Subclassed by faiss::IndexQINCo
Public Functions
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explicit IndexNeuralNetCodec(int d = 0, int M = 0, int nbits = 0, MetricType metric = METRIC_L2)
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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
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virtual void sa_encode(idx_t n, const float *x, uint8_t *codes) 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()
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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
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inline ~IndexNeuralNetCodec()
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explicit IndexNeuralNetCodec(int d = 0, int M = 0, int nbits = 0, MetricType metric = METRIC_L2)
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struct IndexQINCo : public faiss::IndexNeuralNetCodec
Public Functions
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IndexQINCo(int d, int M, int nbits, int L, int h, MetricType metric = METRIC_L2)
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inline ~IndexQINCo()
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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
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virtual void sa_encode(idx_t n, const float *x, uint8_t *codes) 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()
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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
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IndexQINCo(int d, int M, int nbits, int L, int h, MetricType metric = METRIC_L2)
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struct IndexNeuralNetCodec : public faiss::IndexFlatCodes