File IndexIVFPQFastScan.h

namespace faiss

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.

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. 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)

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. 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.

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This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. This file contains callbacks for kernels that compute distances.

The SIMDResultHandler object is intended to be templated and inlined. Methods:

  • handle(): called when 32 distances are computed and provided in two simd16uint16. (q, b) indicate which entry it is in the block.

  • set_block_origin(): set the sub-matrix that is being computed

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.

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. 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.

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. Definition of inverted lists + a few common classes that implement the interface.

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. 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.

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. In this file are the implementations of extra metrics beyond L2 and inner product

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. Defines a few objects that apply transformations to a set of vectors Often these are pre-processing steps.

struct IndexIVFPQFastScan : public faiss::IndexIVFFastScan
#include <IndexIVFPQFastScan.h>

Fast scan version of IVFPQ. Works for 4-bit PQ for now.

The codes in the inverted lists are not stored sequentially but grouped in blocks of size bbs. This makes it possible to very quickly compute distances with SIMD instructions.

Implementations (implem): 0: auto-select implementation (default) 1: orig’s search, re-implemented 2: orig’s search, re-ordered by invlist 10: optimizer int16 search, collect results in heap, no qbs 11: idem, collect results in reservoir 12: optimizer int16 search, collect results in heap, uses qbs 13: idem, collect results in reservoir

Public Functions

IndexIVFPQFastScan(Index *quantizer, size_t d, size_t nlist, size_t M, size_t nbits, MetricType metric = METRIC_L2, int bbs = 32)
IndexIVFPQFastScan()
explicit IndexIVFPQFastScan(const IndexIVFPQ &orig, int bbs = 32)
virtual void train_encoder(idx_t n, const float *x, const idx_t *assign) override

Train the encoder for the vectors.

If by_residual then it is called with residuals and corresponding assign array, otherwise x is the raw training vectors and assign=nullptr

virtual idx_t train_encoder_num_vectors() const override

can be redefined by subclasses to indicate how many training vectors they need

void precompute_table()

build precomputed table, possibly updating use_precomputed_table

virtual void encode_vectors(idx_t n, const float *x, const idx_t *list_nos, uint8_t *codes, bool include_listno = false) const override

same as the regular IVFPQ encoder. The codes are not reorganized by blocks a that point

virtual bool lookup_table_is_3d() const override
virtual void compute_LUT(size_t n, const float *x, const idx_t *coarse_ids, const float *coarse_dis, AlignedTable<float> &dis_tables, AlignedTable<float> &biases) const override
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

ProductQuantizer pq

produces the codes

int use_precomputed_table = 0

precomputed tables management

AlignedTable<float> precomputed_table

if use_precompute_table size (nlist, pq.M, pq.ksub)