File IndexRowwiseMinMax.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.
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.
Variables
- FAISS_API int rowwise_minmax_sa_encode_bs
block size for performing sa_encode and sa_decode
- FAISS_API int rowwise_minmax_sa_decode_bs
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struct IndexRowwiseMinMaxBase : public faiss::Index
- #include <IndexRowwiseMinMax.h>
Provides base functions for rowwise normalizing indices.
Index wrapper that performs rowwise normalization to [0,1], preserving the coefficients. This is a vector codec index only.
Basically, this index performs a rowwise scaling to [0,1] of every row in an input dataset before calling subindex::train() and subindex::sa_encode(). sa_encode() call stores the scaling coefficients (scaler and minv) in the very beginning of every output code. The format: [scaler][minv][subindex::sa_encode() output] The de-scaling in sa_decode() is done using: output_rescaled = scaler * output + minv
An additional ::train_inplace() function is provided in order to do an inplace scaling before calling subindex::train() and, thus, avoiding the cloning of the input dataset, but modifying the input dataset because of the scaling and the scaling back. It is up to user to call this function instead of ::train()
Derived classes provide different data types for scaling coefficients. Currently, versions with fp16 and fp32 scaling coefficients are available.
fp16 version adds 4 extra bytes per encoded vector
fp32 version adds 8 extra bytes per encoded vector
Subclassed by faiss::IndexRowwiseMinMax, faiss::IndexRowwiseMinMaxFP16
Public Functions
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IndexRowwiseMinMaxBase()
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~IndexRowwiseMinMaxBase() override
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virtual void add(idx_t n, const float *x) override
Add n vectors of dimension d to the index.
Vectors are implicitly assigned labels ntotal .. ntotal + n - 1 This function slices the input vectors in chunks smaller than blocksize_add and calls add_core.
- Parameters:
n – number of vectors
x – input matrix, size n * d
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virtual void search(idx_t n, const float *x, idx_t k, float *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:
n – number of vectors
x – input vectors to search, size n * d
k – number of extracted vectors
distances – output pairwise distances, size n*k
labels – output labels of the NNs, size n*k
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virtual void reset() override
removes all elements from the database.
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struct IndexRowwiseMinMaxFP16 : public faiss::IndexRowwiseMinMaxBase
- #include <IndexRowwiseMinMax.h>
Stores scaling coefficients as fp16 values.
Public Functions
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IndexRowwiseMinMaxFP16()
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virtual void train(idx_t n, const float *x) override
Perform training on a representative set of vectors
- Parameters:
n – nb of training vectors
x – training vecors, size n * d
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virtual size_t sa_code_size() const override
size of the produced codes in bytes
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virtual void sa_encode(idx_t n, const float *x, uint8_t *bytes) const override
encode a set of vectors
- Parameters:
n – number of vectors
x – input vectors, size n * d
bytes – output encoded vectors, size n * sa_code_size()
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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
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virtual void add(idx_t n, const float *x) override
Add n vectors of dimension d to the index.
Vectors are implicitly assigned labels ntotal .. ntotal + n - 1 This function slices the input vectors in chunks smaller than blocksize_add and calls add_core.
- Parameters:
n – number of vectors
x – input matrix, size n * d
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virtual void search(idx_t n, const float *x, idx_t k, float *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:
n – number of vectors
x – input vectors to search, size n * d
k – number of extracted vectors
distances – output pairwise distances, size n*k
labels – output labels of the NNs, size n*k
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virtual void reset() override
removes all elements from the database.
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IndexRowwiseMinMaxFP16()
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struct IndexRowwiseMinMax : public faiss::IndexRowwiseMinMaxBase
- #include <IndexRowwiseMinMax.h>
Stores scaling coefficients as fp32 values.
Public Functions
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IndexRowwiseMinMax()
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virtual void train(idx_t n, const float *x) override
Perform training on a representative set of vectors
- Parameters:
n – nb of training vectors
x – training vecors, size n * d
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virtual size_t sa_code_size() const override
size of the produced codes in bytes
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virtual void sa_encode(idx_t n, const float *x, uint8_t *bytes) const override
encode a set of vectors
- Parameters:
n – number of vectors
x – input vectors, size n * d
bytes – output encoded vectors, size n * sa_code_size()
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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
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virtual void add(idx_t n, const float *x) override
Add n vectors of dimension d to the index.
Vectors are implicitly assigned labels ntotal .. ntotal + n - 1 This function slices the input vectors in chunks smaller than blocksize_add and calls add_core.
- Parameters:
n – number of vectors
x – input matrix, size n * d
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virtual void search(idx_t n, const float *x, idx_t k, float *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:
n – number of vectors
x – input vectors to search, size n * d
k – number of extracted vectors
distances – output pairwise distances, size n*k
labels – output labels of the NNs, size n*k
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virtual void reset() override
removes all elements from the database.
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IndexRowwiseMinMax()