File residual_quantizer_encode_steps.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.
Functions
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void beam_search_encode_step(size_t d, size_t K, const float *cent, size_t n, size_t beam_size, const float *residuals, size_t m, const int32_t *codes, size_t new_beam_size, int32_t *new_codes, float *new_residuals, float *new_distances, Index *assign_index = nullptr, ApproxTopK_mode_t approx_topk = ApproxTopK_mode_t::EXACT_TOPK)
Encode a residual by sampling from a centroid table.
This is a single encoding step the residual quantizer. It allows low-level access to the encoding function, exposed mainly for unit tests.
- Parameters:
n – number of vectors to handle
residuals – vectors to encode, size (n, beam_size, d)
cent – centroids, size (K, d)
beam_size – input beam size
m – size of the codes for the previous encoding steps
codes – code array for the previous steps of the beam (n, beam_size, m)
new_beam_size – output beam size (should be <= K * beam_size)
new_codes – output codes, size (n, new_beam_size, m + 1)
new_residuals – output residuals, size (n, new_beam_size, d)
new_distances – output distances, size (n, new_beam_size)
assign_index – if non-NULL, will be used to perform assignment
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void beam_search_encode_step_tab(size_t K, size_t n, size_t beam_size, const float *codebook_cross_norms, size_t ldc, const uint64_t *codebook_offsets, const float *query_cp, size_t ldqc, const float *cent_norms_i, size_t m, const int32_t *codes, const float *distances, size_t new_beam_size, int32_t *new_codes, float *new_distances, ApproxTopK_mode_t approx_topk = ApproxTopK_mode_t::EXACT_TOPK)
Encode a set of vectors using their dot products with the codebooks
- Parameters:
K – number of vectors in the codebook
n – nb of vectors to encode
beam_size – input beam size
codebook_cross_norms – inner product of this codebook with the m previously encoded codebooks
codebook_offsets – offsets into codebook_cross_norms for each previous codebook
query_cp – dot products of query vectors with ???
cent_norms_i – norms of centroids
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namespace rq_encode_steps
Functions
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void refine_beam_mp(const ResidualQuantizer &rq, size_t n, size_t beam_size, const float *x, int out_beam_size, int32_t *out_codes, float *out_residuals, float *out_distances, RefineBeamMemoryPool &pool)
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void refine_beam_LUT_mp(const ResidualQuantizer &rq, size_t n, const float *query_norms, const float *query_cp, int out_beam_size, int32_t *out_codes, float *out_distances, RefineBeamLUTMemoryPool &pool)
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void compute_codes_add_centroids_mp_lut0(const ResidualQuantizer &rq, const float *x, uint8_t *codes_out, size_t n, const float *centroids, ComputeCodesAddCentroidsLUT0MemoryPool &pool)
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void compute_codes_add_centroids_mp_lut1(const ResidualQuantizer &rq, const float *x, uint8_t *codes_out, size_t n, const float *centroids, ComputeCodesAddCentroidsLUT1MemoryPool &pool)
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struct RefineBeamMemoryPool
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struct RefineBeamLUTMemoryPool
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struct ComputeCodesAddCentroidsLUT0MemoryPool
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struct ComputeCodesAddCentroidsLUT1MemoryPool
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void refine_beam_mp(const ResidualQuantizer &rq, size_t n, size_t beam_size, const float *x, int out_beam_size, int32_t *out_codes, float *out_residuals, float *out_distances, RefineBeamMemoryPool &pool)
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void beam_search_encode_step(size_t d, size_t K, const float *cent, size_t n, size_t beam_size, const float *residuals, size_t m, const int32_t *codes, size_t new_beam_size, int32_t *new_codes, float *new_residuals, float *new_distances, Index *assign_index = nullptr, ApproxTopK_mode_t approx_topk = ApproxTopK_mode_t::EXACT_TOPK)