File IndexReplicas.h
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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.
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. 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.
Typedefs
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using IndexReplicas = IndexReplicasTemplate<Index>
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using IndexBinaryReplicas = IndexReplicasTemplate<IndexBinary>
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template<typename IndexT>
class IndexReplicasTemplate : public faiss::ThreadedIndex<IndexT> - #include <IndexReplicas.h>
Takes individual faiss::Index instances, and splits queries for sending to each Index instance, and joins the results together when done. Each index is managed by a separate CPU thread.
Public Types
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using component_t = typename IndexT::component_t
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using distance_t = typename IndexT::distance_t
Public Functions
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explicit IndexReplicasTemplate(bool threaded = true)
The dimension that all sub-indices must share will be the dimension of the first sub-index added
- Parameters:
threaded – do we use one thread per sub-index or do queries sequentially?
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explicit IndexReplicasTemplate(idx_t d, bool threaded = true)
- Parameters:
d – the dimension that all sub-indices must share
threaded – do we use one thread per sub index or do queries sequentially?
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explicit IndexReplicasTemplate(int d, bool threaded = true)
int version due to the implicit bool conversion ambiguity of int as dimension
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inline void add_replica(IndexT *index)
Alias for addIndex()
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inline void remove_replica(IndexT *index)
Alias for removeIndex()
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void train(idx_t n, const component_t *x) override
faiss::Index API All indices receive the same call
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void add(idx_t n, const component_t *x) override
faiss::Index API All indices receive the same call
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void search(idx_t n, const component_t *x, idx_t k, distance_t *distances, idx_t *labels, const SearchParameters *params = nullptr) const override
faiss::Index API Query is partitioned into a slice for each sub-index split by ceil(n / #indices) for our sub-indices
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void reconstruct(idx_t, component_t *v) const override
reconstructs from the first index
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void syncWithSubIndexes()
Synchronize the top-level index (IndexShards) with data in the sub-indices
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using component_t = typename IndexT::component_t