File IndexHNSW.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.
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struct ReconstructFromNeighbors
Public Types
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typedef HNSW::storage_idx_t storage_idx_t
Public Functions
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void add_codes(size_t n, const float *x)
codes must be added in the correct order and the IndexHNSW must be populated and sorted
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size_t compute_distances(size_t n, const idx_t *shortlist, const float *query, float *distances) const
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void estimate_code(const float *x, storage_idx_t i, uint8_t *code) const
called by add_codes
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void reconstruct(storage_idx_t i, float *x, float *tmp) const
called by compute_distances
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void reconstruct_n(storage_idx_t n0, storage_idx_t ni, float *x) const
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void get_neighbor_table(storage_idx_t i, float *out) const
get the M+1 -by-d table for neighbor coordinates for vector i
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typedef HNSW::storage_idx_t storage_idx_t
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struct IndexHNSW : public faiss::Index
- #include <IndexHNSW.h>
The HNSW index is a normal random-access index with a HNSW link structure built on top
Subclassed by faiss::IndexHNSW2Level, faiss::IndexHNSWFlat, faiss::IndexHNSWPQ, faiss::IndexHNSWSQ
Public Types
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typedef HNSW::storage_idx_t storage_idx_t
Public Functions
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explicit IndexHNSW(int d = 0, int M = 32, MetricType metric = METRIC_L2)
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~IndexHNSW() 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:
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
entry point for search
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virtual void reconstruct(idx_t key, float *recons) const override
Reconstruct a stored vector (or an approximation if lossy coding)
this function may not be defined for some indexes
- Parameters:
key – id of the vector to reconstruct
recons – reconstucted vector (size d)
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virtual void reset() override
removes all elements from the database.
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void shrink_level_0_neighbors(int size)
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void search_level_0(idx_t n, const float *x, idx_t k, const storage_idx_t *nearest, const float *nearest_d, float *distances, idx_t *labels, int nprobe = 1, int search_type = 1) const
Perform search only on level 0, given the starting points for each vertex.
- Parameters:
search_type – 1:perform one search per nprobe, 2: enqueue all entry points
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void init_level_0_from_entry_points(int npt, const storage_idx_t *points, const storage_idx_t *nearests)
alternative graph building
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void reorder_links()
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void link_singletons()
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typedef HNSW::storage_idx_t storage_idx_t
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struct IndexHNSWFlat : public faiss::IndexHNSW
- #include <IndexHNSW.h>
Flat index topped with with a HNSW structure to access elements more efficiently.
Public Types
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typedef HNSW::storage_idx_t storage_idx_t
Public Functions
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IndexHNSWFlat()
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IndexHNSWFlat(int d, int M, MetricType metric = METRIC_L2)
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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:
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
entry point for search
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virtual void reconstruct(idx_t key, float *recons) const override
Reconstruct a stored vector (or an approximation if lossy coding)
this function may not be defined for some indexes
- Parameters:
key – id of the vector to reconstruct
recons – reconstucted vector (size d)
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virtual void reset() override
removes all elements from the database.
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void shrink_level_0_neighbors(int size)
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void search_level_0(idx_t n, const float *x, idx_t k, const storage_idx_t *nearest, const float *nearest_d, float *distances, idx_t *labels, int nprobe = 1, int search_type = 1) const
Perform search only on level 0, given the starting points for each vertex.
- Parameters:
search_type – 1:perform one search per nprobe, 2: enqueue all entry points
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void init_level_0_from_entry_points(int npt, const storage_idx_t *points, const storage_idx_t *nearests)
alternative graph building
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void reorder_links()
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void link_singletons()
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typedef HNSW::storage_idx_t storage_idx_t
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struct IndexHNSWPQ : public faiss::IndexHNSW
- #include <IndexHNSW.h>
PQ index topped with with a HNSW structure to access elements more efficiently.
Public Types
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typedef HNSW::storage_idx_t storage_idx_t
Public Functions
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IndexHNSWPQ()
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IndexHNSWPQ(int d, int pq_m, int M, int pq_nbits = 8)
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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:
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
entry point for search
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virtual void reconstruct(idx_t key, float *recons) const override
Reconstruct a stored vector (or an approximation if lossy coding)
this function may not be defined for some indexes
- Parameters:
key – id of the vector to reconstruct
recons – reconstucted vector (size d)
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virtual void reset() override
removes all elements from the database.
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void shrink_level_0_neighbors(int size)
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void search_level_0(idx_t n, const float *x, idx_t k, const storage_idx_t *nearest, const float *nearest_d, float *distances, idx_t *labels, int nprobe = 1, int search_type = 1) const
Perform search only on level 0, given the starting points for each vertex.
- Parameters:
search_type – 1:perform one search per nprobe, 2: enqueue all entry points
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void init_level_0_from_entry_points(int npt, const storage_idx_t *points, const storage_idx_t *nearests)
alternative graph building
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void reorder_links()
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void link_singletons()
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typedef HNSW::storage_idx_t storage_idx_t
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struct IndexHNSWSQ : public faiss::IndexHNSW
- #include <IndexHNSW.h>
SQ index topped with with a HNSW structure to access elements more efficiently.
Public Types
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typedef HNSW::storage_idx_t storage_idx_t
Public Functions
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IndexHNSWSQ()
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IndexHNSWSQ(int d, ScalarQuantizer::QuantizerType qtype, int M, MetricType metric = METRIC_L2)
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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:
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
entry point for search
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virtual void reconstruct(idx_t key, float *recons) const override
Reconstruct a stored vector (or an approximation if lossy coding)
this function may not be defined for some indexes
- Parameters:
key – id of the vector to reconstruct
recons – reconstucted vector (size d)
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virtual void reset() override
removes all elements from the database.
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void shrink_level_0_neighbors(int size)
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void search_level_0(idx_t n, const float *x, idx_t k, const storage_idx_t *nearest, const float *nearest_d, float *distances, idx_t *labels, int nprobe = 1, int search_type = 1) const
Perform search only on level 0, given the starting points for each vertex.
- Parameters:
search_type – 1:perform one search per nprobe, 2: enqueue all entry points
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void init_level_0_from_entry_points(int npt, const storage_idx_t *points, const storage_idx_t *nearests)
alternative graph building
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void reorder_links()
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void link_singletons()
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typedef HNSW::storage_idx_t storage_idx_t
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struct IndexHNSW2Level : public faiss::IndexHNSW
- #include <IndexHNSW.h>
2-level code structure with fast random access
Public Types
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typedef HNSW::storage_idx_t storage_idx_t
Public Functions
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IndexHNSW2Level()
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void flip_to_ivf()
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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
entry point for search
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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:
x – input matrix, size n * d
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virtual void reconstruct(idx_t key, float *recons) const override
Reconstruct a stored vector (or an approximation if lossy coding)
this function may not be defined for some indexes
- Parameters:
key – id of the vector to reconstruct
recons – reconstucted vector (size d)
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virtual void reset() override
removes all elements from the database.
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void shrink_level_0_neighbors(int size)
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void search_level_0(idx_t n, const float *x, idx_t k, const storage_idx_t *nearest, const float *nearest_d, float *distances, idx_t *labels, int nprobe = 1, int search_type = 1) const
Perform search only on level 0, given the starting points for each vertex.
- Parameters:
search_type – 1:perform one search per nprobe, 2: enqueue all entry points
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void init_level_0_from_entry_points(int npt, const storage_idx_t *points, const storage_idx_t *nearests)
alternative graph building
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void reorder_links()
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void link_singletons()
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typedef HNSW::storage_idx_t storage_idx_t