File GpuIndexIVFFlat.h

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.

namespace gpu
struct GpuIndexIVFFlatConfig : public faiss::gpu::GpuIndexIVFConfig

Public Members

bool interleavedLayout = true

Use the alternative memory layout for the IVF lists (currently the default)

class GpuIndexIVFFlat : public faiss::gpu::GpuIndexIVF
#include <GpuIndexIVFFlat.h>

Wrapper around the GPU implementation that looks like faiss::IndexIVFFlat

Public Functions

GpuIndexIVFFlat(GpuResourcesProvider *provider, const faiss::IndexIVFFlat *index, GpuIndexIVFFlatConfig config = GpuIndexIVFFlatConfig())

Construct from a pre-existing faiss::IndexIVFFlat instance, copying data over to the given GPU, if the input index is trained.

GpuIndexIVFFlat(GpuResourcesProvider *provider, int dims, idx_t nlist, faiss::MetricType metric = faiss::METRIC_L2, GpuIndexIVFFlatConfig config = GpuIndexIVFFlatConfig())

Constructs a new instance with an empty flat quantizer; the user provides the number of IVF lists desired.

GpuIndexIVFFlat(GpuResourcesProvider *provider, Index *coarseQuantizer, int dims, idx_t nlist, faiss::MetricType metric = faiss::METRIC_L2, GpuIndexIVFFlatConfig config = GpuIndexIVFFlatConfig())

Constructs a new instance with a provided CPU or GPU coarse quantizer; the user provides the number of IVF lists desired.

~GpuIndexIVFFlat() override
void reserveMemory(size_t numVecs)

Reserve GPU memory in our inverted lists for this number of vectors.

void copyFrom(const faiss::IndexIVFFlat *index)

Initialize ourselves from the given CPU index; will overwrite all data in ourselves

void copyTo(faiss::IndexIVFFlat *index) const

Copy ourselves to the given CPU index; will overwrite all data in the index instance

size_t reclaimMemory()

After adding vectors, one can call this to reclaim device memory to exactly the amount needed. Returns space reclaimed in bytes

virtual void reset() override

Clears out all inverted lists, but retains the coarse centroid information

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)

virtual void train(idx_t n, const float *x) override

Trains the coarse quantizer based on the given vector data.

virtual void reconstruct_n(idx_t i0, idx_t n, float *out) const override

Reconstruct vectors i0 to i0 + ni - 1

this function may not be defined for some indexes

Parameters:

  • i0 – index of the first vector in the sequence

  • ni – number of vectors in the sequence

  • recons – reconstucted vector (size ni * d)

Protected Functions

void setIndex_(GpuResources *resources, int dim, int nlist, faiss::MetricType metric, float metricArg, bool useResidual, faiss::ScalarQuantizer *scalarQ, bool interleavedLayout, IndicesOptions indicesOptions, MemorySpace space)

Initialize appropriate index.

Parameters:

scalarQ – Optional ScalarQuantizer

Protected Attributes

const GpuIndexIVFFlatConfig ivfFlatConfig_

Our configuration options.

size_t reserveMemoryVecs_

Desired inverted list memory reservation.

std::shared_ptr<IVFFlat> index_

Instance that we own; contains the inverted lists.