File GpuDistance.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.
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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namespace gpu
Enums
Functions
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void bfKnn(GpuResourcesProvider *resources, const GpuDistanceParams &args)
A wrapper for gpu/impl/Distance.cuh to expose direct brute-force k-nearest neighbor searches on an externally-provided region of memory (e.g., from a pytorch tensor). The data (vectors, queries, outDistances, outIndices) can be resident on the GPU or the CPU, but all calculations are performed on the GPU. If the result buffers are on the CPU, results will be copied back when done.
All GPU computation is performed on the current CUDA device, and ordered with respect to resources->getDefaultStreamCurrentDevice().
For each vector in
queries, searches all ofvectorsto find its k nearest neighbors with respect to the given metric
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void bruteForceKnn(GpuResourcesProvider *resources, faiss::MetricType metric, const float *vectors, bool vectorsRowMajor, idx_t numVectors, const float *queries, bool queriesRowMajor, idx_t numQueries, int dims, int k, float *outDistances, idx_t *outIndices)
Deprecated legacy implementation.
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struct GpuDistanceParams
- #include <GpuDistance.h>
Arguments to brute-force GPU k-nearest neighbor searching.
Public Functions
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inline GpuDistanceParams()
Public Members
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faiss::MetricType metric
Search parameter: distance metric.
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float metricArg
Search parameter: distance metric argument (if applicable) For metric == METRIC_Lp, this is the p-value
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int k
Search parameter: return k nearest neighbors If the value provided is -1, then we report all pairwise distances without top-k filtering
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int dims
Vector dimensionality.
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const void *vectors
If vectorsRowMajor is true, this is numVectors x dims, with dims innermost; otherwise, dims x numVectors, with numVectors innermost
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DistanceDataType vectorType
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bool vectorsRowMajor
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const float *vectorNorms
Precomputed L2 norms for each vector in
vectors, which can be optionally provided in advance to speed computation for METRIC_L2
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const void *queries
If queriesRowMajor is true, this is numQueries x dims, with dims innermost; otherwise, dims x numQueries, with numQueries innermost
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DistanceDataType queryType
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bool queriesRowMajor
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float *outDistances
A region of memory size numQueries x k, with k innermost (row major) if k > 0, or if k == -1, a region of memory of size numQueries x numVectors
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bool ignoreOutDistances
Do we only care about the indices reported, rather than the output distances? Not used if k == -1 (all pairwise distances)
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IndicesDataType outIndicesType
A region of memory size numQueries x k, with k innermost (row major). Not used if k == -1 (all pairwise distances)
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void *outIndices
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int device
On which GPU device should the search run? -1 indicates that the current CUDA thread-local device (via cudaGetDevice/cudaSetDevice) is used Otherwise, an integer 0 <= device < numDevices indicates the device for execution
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inline GpuDistanceParams()
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void bfKnn(GpuResourcesProvider *resources, const GpuDistanceParams &args)
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namespace gpu