File IndexBinaryHash.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.
Variables
- FAISS_API IndexBinaryHashStats indexBinaryHash_stats
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struct IndexBinaryHash : public faiss::IndexBinary
- #include <IndexBinaryHash.h>
just uses the b first bits as a hash value
Public Types
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using InvertedListMap = std::unordered_map<idx_t, InvertedList>
Public Functions
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IndexBinaryHash(int d, int b)
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IndexBinaryHash()
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virtual void reset() override
Removes all elements from the database.
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virtual void add(idx_t n, const uint8_t *x) override
Add n vectors of dimension d to the index.
Vectors are implicitly assigned labels ntotal .. ntotal + n - 1
- Parameters:
x – input matrix, size n * d / 8
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virtual void add_with_ids(idx_t n, const uint8_t *x, const idx_t *xids) override
Same as add, but stores xids instead of sequential ids.
The default implementation fails with an assertion, as it is not supported by all indexes.
- Parameters:
xids – if non-null, ids to store for the vectors (size n)
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virtual void range_search(idx_t n, const uint8_t *x, int radius, RangeSearchResult *result, const SearchParameters *params = nullptr) const override
Query n vectors of dimension d to the index.
return all vectors with distance < radius. Note that many indexes do not implement the range_search (only the k-NN search is mandatory). The distances are converted to float to reuse the RangeSearchResult structure, but they are integer. By convention, only distances < radius (strict comparison) are returned, ie. radius = 0 does not return any result and 1 returns only exact same vectors.
- Parameters:
x – input vectors to search, size n * d / 8
radius – search radius
result – result table
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virtual void search(idx_t n, const uint8_t *x, idx_t k, int32_t *distances, idx_t *labels, const SearchParameters *params = nullptr) const override
Query n vectors of dimension d to the index.
return at most k vectors. If there are not enough results for a query, the result array is padded with -1s.
- Parameters:
x – input vectors to search, size n * d / 8
labels – output labels of the NNs, size n*k
distances – output pairwise distances, size n*k
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void display() const
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size_t hashtable_size() const
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struct InvertedList
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using InvertedListMap = std::unordered_map<idx_t, InvertedList>
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struct IndexBinaryHashStats
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struct IndexBinaryMultiHash : public faiss::IndexBinary
- #include <IndexBinaryHash.h>
just uses the b first bits as a hash value
Public Functions
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IndexBinaryMultiHash(int d, int nhash, int b)
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IndexBinaryMultiHash()
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~IndexBinaryMultiHash()
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virtual void reset() override
Removes all elements from the database.
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virtual void add(idx_t n, const uint8_t *x) override
Add n vectors of dimension d to the index.
Vectors are implicitly assigned labels ntotal .. ntotal + n - 1
- Parameters:
x – input matrix, size n * d / 8
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virtual void range_search(idx_t n, const uint8_t *x, int radius, RangeSearchResult *result, const SearchParameters *params = nullptr) const override
Query n vectors of dimension d to the index.
return all vectors with distance < radius. Note that many indexes do not implement the range_search (only the k-NN search is mandatory). The distances are converted to float to reuse the RangeSearchResult structure, but they are integer. By convention, only distances < radius (strict comparison) are returned, ie. radius = 0 does not return any result and 1 returns only exact same vectors.
- Parameters:
x – input vectors to search, size n * d / 8
radius – search radius
result – result table
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virtual void search(idx_t n, const uint8_t *x, idx_t k, int32_t *distances, idx_t *labels, const SearchParameters *params = nullptr) const override
Query n vectors of dimension d to the index.
return at most k vectors. If there are not enough results for a query, the result array is padded with -1s.
- Parameters:
x – input vectors to search, size n * d / 8
labels – output labels of the NNs, size n*k
distances – output pairwise distances, size n*k
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size_t hashtable_size() const
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IndexBinaryMultiHash(int d, int nhash, int b)