File approx_topk_hamming.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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template<typename C, uint32_t NBUCKETS, uint32_t N, typename HammingComputerT>
struct HeapWithBucketsForHamming32
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template<uint32_t NBUCKETS, uint32_t N, typename HammingComputerT>
struct HeapWithBucketsForHamming32<CMax<int, int64_t>, NBUCKETS, N, HammingComputerT> Public Static Functions
- static inline void addn (const uint32_t n, const HammingComputerT &hc, const uint8_t *const __restrict binaryVectors, const uint32_t k, int *const __restrict bh_val, int64_t *const __restrict bh_ids)
- static inline void bs_addn (const uint32_t beam_size, const uint32_t n_per_beam, const HammingComputerT &hc, const uint8_t *const __restrict binary_vectors, const uint32_t k, int *const __restrict bh_val, int64_t *const __restrict bh_ids)
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template<typename C, uint32_t NBUCKETS, uint32_t N, typename HammingComputerT>
struct HeapWithBucketsForHamming16
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template<uint32_t NBUCKETS, uint32_t N, typename HammingComputerT>
struct HeapWithBucketsForHamming16<CMax<int, int64_t>, NBUCKETS, N, HammingComputerT> Public Static Functions
- static inline void addn (const uint32_t n, const HammingComputerT &hc, const uint8_t *const __restrict binaryVectors, const uint32_t k, int *const __restrict bh_val, int64_t *const __restrict bh_ids)
- static inline void bs_addn (const uint32_t beam_size, const uint32_t n_per_beam, const HammingComputerT &hc, const uint8_t *const __restrict binary_vectors, const uint32_t k, int *const __restrict bh_val, int64_t *const __restrict bh_ids)
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template<typename C, uint32_t NBUCKETS, uint32_t N, typename HammingComputerT>