File IndexBinary.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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struct IndexBinary
- #include <IndexBinary.h>
Abstract structure for a binary index.
Supports adding vertices and searching them.
All queries are symmetric because there is no distinction between codes and vectors.
Subclassed by faiss::IndexBinaryFlat, faiss::IndexBinaryFromFloat, faiss::IndexBinaryHNSW, faiss::IndexBinaryHash, faiss::IndexBinaryIVF, faiss::IndexBinaryMultiHash, faiss::gpu::GpuIndexBinaryFlat
Public Functions
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explicit IndexBinary(idx_t d = 0, MetricType metric = METRIC_L2)
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virtual ~IndexBinary()
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virtual void train(idx_t n, const uint8_t *x)
Perform training on a representative set of vectors.
- Parameters:
n – nb of training vectors
x – training vecors, size n * d / 8
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virtual void add(idx_t n, const uint8_t *x) = 0
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)
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 search(idx_t n, const uint8_t *x, idx_t k, int32_t *distances, idx_t *labels, const SearchParameters *params = nullptr) const = 0
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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virtual void range_search(idx_t n, const uint8_t *x, int radius, RangeSearchResult *result, const SearchParameters *params = nullptr) const
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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void assign(idx_t n, const uint8_t *x, idx_t *labels, idx_t k = 1) const
Return the indexes of the k vectors closest to the query x.
This function is identical to search but only returns labels of neighbors.
- Parameters:
x – input vectors to search, size n * d / 8
labels – output labels of the NNs, size n*k
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virtual void reset() = 0
Removes all elements from the database.
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virtual size_t remove_ids(const IDSelector &sel)
Removes IDs from the index. Not supported by all indexes.
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virtual void reconstruct(idx_t key, uint8_t *recons) const
Reconstruct a stored vector.
This function may not be defined for some indexes.
- Parameters:
key – id of the vector to reconstruct
recons – reconstucted vector (size d / 8)
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virtual void reconstruct_n(idx_t i0, idx_t ni, uint8_t *recons) const
Reconstruct vectors i0 to i0 + ni - 1.
This function may not be defined for some indexes.
- Parameters:
recons – reconstucted vectors (size ni * d / 8)
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virtual void search_and_reconstruct(idx_t n, const uint8_t *x, idx_t k, int32_t *distances, idx_t *labels, uint8_t *recons, const SearchParameters *params = nullptr) const
Similar to search, but also reconstructs the stored vectors (or an approximation in the case of lossy coding) for the search results.
If there are not enough results for a query, the resulting array is padded with -1s.
- Parameters:
recons – reconstructed vectors size (n, k, d)
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void display() const
Display the actual class name and some more info.
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virtual void merge_from(IndexBinary &otherIndex, idx_t add_id = 0)
moves the entries from another dataset to self. On output, other is empty. add_id is added to all moved ids (for sequential ids, this would be this->ntotal)
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virtual void check_compatible_for_merge(const IndexBinary &otherIndex) const
check that the two indexes are compatible (ie, they are trained in the same way and have the same parameters). Otherwise throw.
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virtual size_t sa_code_size() const
size of the produced codes in bytes
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virtual void add_sa_codes(idx_t n, const uint8_t *codes, const idx_t *xids)
Same as add_with_ids for IndexBinary.
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explicit IndexBinary(idx_t d = 0, MetricType metric = METRIC_L2)
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struct IndexBinary