File AutoTune.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 AutoTuneCriterion
- #include <AutoTune.h>
Evaluation criterion. Returns a performance measure in [0,1], higher is better.
Subclassed by faiss::IntersectionCriterion, faiss::OneRecallAtRCriterion
Public Functions
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void set_groundtruth(int gt_nnn, const float *gt_D_in, const idx_t *gt_I_in)
Intitializes the gt_D and gt_I vectors. Must be called before evaluating
- Parameters:
gt_D_in – size nq * gt_nnn
gt_I_in – size nq * gt_nnn
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virtual double evaluate(const float *D, const idx_t *I) const = 0
Evaluate the criterion.
- Parameters:
D – size nq * nnn
I – size nq * nnn
- Returns:
the criterion, between 0 and 1. Larger is better.
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inline virtual ~AutoTuneCriterion()
Public Members
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void set_groundtruth(int gt_nnn, const float *gt_D_in, const idx_t *gt_I_in)
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struct OneRecallAtRCriterion : public faiss::AutoTuneCriterion
Public Functions
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virtual double evaluate(const float *D, const idx_t *I) const override
Evaluate the criterion.
- Parameters:
D – size nq * nnn
I – size nq * nnn
- Returns:
the criterion, between 0 and 1. Larger is better.
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inline ~OneRecallAtRCriterion() override
Public Members
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virtual double evaluate(const float *D, const idx_t *I) const override
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struct IntersectionCriterion : public faiss::AutoTuneCriterion
Public Functions
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virtual double evaluate(const float *D, const idx_t *I) const override
Evaluate the criterion.
- Parameters:
D – size nq * nnn
I – size nq * nnn
- Returns:
the criterion, between 0 and 1. Larger is better.
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inline ~IntersectionCriterion() override
Public Members
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virtual double evaluate(const float *D, const idx_t *I) const override
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struct OperatingPoint
- #include <AutoTune.h>
Maintains a list of experimental results. Each operating point is a (perf, t, key) triplet, where higher perf and lower t is better. The key field is an arbitrary identifier for the operating point.
Includes primitives to extract the Pareto-optimal operating points in the (perf, t) space.
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struct OperatingPoints
Public Functions
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OperatingPoints()
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int merge_with(const OperatingPoints &other, const std::string &prefix = "")
add operating points from other to this, with a prefix to the keys
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void clear()
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bool add(double perf, double t, const std::string &key, size_t cno = 0)
add a performance measure. Return whether it is an optimal point
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double t_for_perf(double perf) const
get time required to obtain a given performance measure
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void display(bool only_optimal = true) const
easy-to-read output
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void all_to_gnuplot(const char *fname) const
output to a format easy to digest by gnuplot
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void optimal_to_gnuplot(const char *fname) const
Public Members
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std::vector<OperatingPoint> all_pts
all operating points
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std::vector<OperatingPoint> optimal_pts
optimal operating points, sorted by perf
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OperatingPoints()
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struct ParameterRange
- #include <AutoTune.h>
possible values of a parameter, sorted from least to most expensive/accurate
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struct ParameterSpace
- #include <AutoTune.h>
Uses a-priori knowledge on the Faiss indexes to extract tunable parameters.
Subclassed by faiss::gpu::GpuParameterSpace
Public Functions
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ParameterSpace()
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size_t n_combinations() const
nb of combinations, = product of values sizes
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bool combination_ge(size_t c1, size_t c2) const
returns whether combinations c1 >= c2 in the tuple sense
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void display() const
print a description on stdout
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ParameterRange &add_range(const std::string &name)
add a new parameter (or return it if it exists)
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void set_index_parameters(Index *index, size_t cno) const
set a combination of parameters on an index
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void set_index_parameters(Index *index, const char *param_string) const
set a combination of parameters described by a string
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virtual void set_index_parameter(Index *index, const std::string &name, double val) const
set one of the parameters, returns whether setting was successful
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void update_bounds(size_t cno, const OperatingPoint &op, double *upper_bound_perf, double *lower_bound_t) const
find an upper bound on the performance and a lower bound on t for configuration cno given another operating point op
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void explore(Index *index, size_t nq, const float *xq, const AutoTuneCriterion &crit, OperatingPoints *ops) const
explore operating points
- Parameters:
index – index to run on
xq – query vectors (size nq * index.d)
crit – selection criterion
ops – resulting operating points
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inline virtual ~ParameterSpace()
Public Members
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std::vector<ParameterRange> parameter_ranges
all tunable parameters
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int verbose
verbosity during exploration
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int n_experiments
nb of experiments during optimization (0 = try all combinations)
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size_t batchsize
maximum number of queries to submit at a time.
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bool thread_over_batches
use multithreading over batches (useful to benchmark independent single-searches)
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double min_test_duration
run tests several times until they reach at least this duration (to avoid jittering in MT mode)
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ParameterSpace()
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struct AutoTuneCriterion