File PolysemousTraining.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. Implements a few neural net layers, mainly to support QINCo
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 SimulatedAnnealingParameters
- #include <PolysemousTraining.h>
parameters used for the simulated annealing method
Subclassed by faiss::PolysemousTraining, faiss::SimulatedAnnealingOptimizer
Public Functions
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inline SimulatedAnnealingParameters()
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inline SimulatedAnnealingParameters()
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struct PermutationObjective
- #include <PolysemousTraining.h>
abstract class for the loss function
Subclassed by faiss::ReproduceDistancesObjective
Public Functions
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virtual double compute_cost(const int *perm) const = 0
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virtual double cost_update(const int *perm, int iw, int jw) const
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inline virtual ~PermutationObjective()
Public Members
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int n
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virtual double compute_cost(const int *perm) const = 0
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struct ReproduceDistancesObjective : public faiss::PermutationObjective
Public Functions
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double dis_weight(double x) const
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double get_source_dis(int i, int j) const
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virtual double compute_cost(const int *perm) const override
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virtual double cost_update(const int *perm, int iw, int jw) const override
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ReproduceDistancesObjective(int n, const double *source_dis_in, const double *target_dis_in, double dis_weight_factor)
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void set_affine_target_dis(const double *source_dis_in)
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inline ~ReproduceDistancesObjective() override
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double dis_weight(double x) const
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struct SimulatedAnnealingOptimizer : public faiss::SimulatedAnnealingParameters
- #include <PolysemousTraining.h>
Simulated annealing optimization algorithm for permutations.
Public Functions
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SimulatedAnnealingOptimizer(PermutationObjective *obj, const SimulatedAnnealingParameters &p)
logs values of the cost function
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double optimize(int *perm)
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double run_optimization(int *best_perm)
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virtual ~SimulatedAnnealingOptimizer()
Public Members
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int n
size of the permutation
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FILE *logfile
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RandomGenerator *rnd
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double init_cost
remember initial cost of optimization
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double init_temperature = 0.7
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double temperature_decay = 0.9997893011688015
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int n_iter = 500000
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int n_redo = 2
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int seed = 123
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int verbose = 0
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bool only_bit_flips = false
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bool init_random = false
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SimulatedAnnealingOptimizer(PermutationObjective *obj, const SimulatedAnnealingParameters &p)
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struct PolysemousTraining : public faiss::SimulatedAnnealingParameters
- #include <PolysemousTraining.h>
optimizes the order of indices in a ProductQuantizer
Public Types
Public Functions
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PolysemousTraining()
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void optimize_pq_for_hamming(ProductQuantizer &pq, size_t n, const float *x) const
reorder the centroids so that the Hamming distance becomes a good approximation of the SDC distance (called by train)
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void optimize_ranking(ProductQuantizer &pq, size_t n, const float *x) const
called by optimize_pq_for_hamming
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void optimize_reproduce_distances(ProductQuantizer &pq) const
called by optimize_pq_for_hamming
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size_t memory_usage_per_thread(const ProductQuantizer &pq) const
make sure we don’t blow up the memory
Public Members
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Optimization_type_t optimization_type
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int ntrain_permutation
use 1/4 of the training points for the optimization, with max. ntrain_permutation. If ntrain_permutation == 0: train on centroids
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double dis_weight_factor
decay of exp that weights distance loss
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size_t max_memory
refuse to train if it would require more than that amount of RAM
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double init_temperature = 0.7
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double temperature_decay = 0.9997893011688015
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int n_iter = 500000
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int n_redo = 2
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int seed = 123
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int verbose = 0
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bool only_bit_flips = false
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bool init_random = false
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PolysemousTraining()
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struct SimulatedAnnealingParameters