File GpuIndexIVFScalarQuantizer.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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namespace gpu
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struct GpuIndexIVFScalarQuantizerConfig : public faiss::gpu::GpuIndexIVFConfig
Public Members
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bool interleavedLayout = true
Use the alternative memory layout for the IVF lists (currently the default)
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bool interleavedLayout = true
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class GpuIndexIVFScalarQuantizer : public faiss::gpu::GpuIndexIVF
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Wrapper around the GPU implementation that looks like faiss::IndexIVFScalarQuantizer
Public Functions
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GpuIndexIVFScalarQuantizer(GpuResourcesProvider *provider, const faiss::IndexIVFScalarQuantizer *index, GpuIndexIVFScalarQuantizerConfig config = GpuIndexIVFScalarQuantizerConfig())
Construct from a pre-existing faiss::IndexIVFScalarQuantizer instance, copying data over to the given GPU, if the input index is trained.
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GpuIndexIVFScalarQuantizer(GpuResourcesProvider *provider, int dims, idx_t nlist, faiss::ScalarQuantizer::QuantizerType qtype, faiss::MetricType metric = MetricType::METRIC_L2, bool encodeResidual = true, GpuIndexIVFScalarQuantizerConfig config = GpuIndexIVFScalarQuantizerConfig())
Constructs a new instance with an empty flat quantizer; the user provides the number of IVF lists desired.
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GpuIndexIVFScalarQuantizer(GpuResourcesProvider *provider, Index *coarseQuantizer, int dims, idx_t nlist, faiss::ScalarQuantizer::QuantizerType qtype, faiss::MetricType metric = MetricType::METRIC_L2, bool encodeResidual = true, GpuIndexIVFScalarQuantizerConfig config = GpuIndexIVFScalarQuantizerConfig())
Constructs a new instance with a provided CPU or GPU coarse quantizer; the user provides the number of IVF lists desired.
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~GpuIndexIVFScalarQuantizer() override
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void reserveMemory(size_t numVecs)
Reserve GPU memory in our inverted lists for this number of vectors.
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void copyFrom(const faiss::IndexIVFScalarQuantizer *index)
Initialize ourselves from the given CPU index; will overwrite all data in ourselves
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void copyTo(faiss::IndexIVFScalarQuantizer *index) const
Copy ourselves to the given CPU index; will overwrite all data in the index instance
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size_t reclaimMemory()
After adding vectors, one can call this to reclaim device memory to exactly the amount needed. Returns space reclaimed in bytes
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virtual void reset() override
Clears out all inverted lists, but retains the coarse and scalar quantizer information
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virtual void updateQuantizer() override
Should be called if the user ever changes the state of the IVF coarse quantizer manually (e.g., substitutes a new instance or changes vectors in the coarse quantizer outside the scope of training)
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virtual void train(idx_t n, const float *x) override
Trains the coarse and scalar quantizer based on the given vector data.
Public Members
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faiss::ScalarQuantizer sq
Exposed like the CPU version.
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bool by_residual
Exposed like the CPU version.
Protected Functions
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void verifySQSettings_() const
Validates index SQ parameters.
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void trainResiduals_(idx_t n, const float *x)
Called from train to handle SQ residual training.
Protected Attributes
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const GpuIndexIVFScalarQuantizerConfig ivfSQConfig_
Our configuration options.
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size_t reserveMemoryVecs_
Desired inverted list memory reservation.
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std::shared_ptr<IVFFlat> index_
Instance that we own; contains the inverted list.
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GpuIndexIVFScalarQuantizer(GpuResourcesProvider *provider, const faiss::IndexIVFScalarQuantizer *index, GpuIndexIVFScalarQuantizerConfig config = GpuIndexIVFScalarQuantizerConfig())
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struct GpuIndexIVFScalarQuantizerConfig : public faiss::gpu::GpuIndexIVFConfig
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namespace gpu