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Michael Norrisfacebook-github-bot
Michael Norris
authored andcommitted
Correct capitalization of FAISS to Faiss
Summary: For the release, we want to standardize on Faiss instead of FAISS. Changed everything except the CHANGELOG.md which I assume should not be changed after it lands. This doesn't aim to fix any existing lints / errors. Those can be handled at another time. Reviewed By: junjieqi Differential Revision: D68842649 fbshipit-source-id: c0b60d5baa0e1f710db3638ffcc6f223fb3408ad
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.github/actions/build_conda/action.yml

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name: Conda build
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description: Builds FAISS inside a Conda environment and uploads to repository when label is provided.
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description: Builds Faiss inside a Conda environment and uploads to repository when label is provided.
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inputs:
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label:
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description: "The label to be used for uploads to Conda."

INSTALL.md

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### Building with NVIDIA cuVS
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[cuVS](https://docs.rapids.ai/api/cuvs/nightly/) contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It is built on top of the [RAPIDS RAFT](https://github.com/rapidsai/raft) library of high performance machine learning primitives. Building FAISS with cuVS enabled allows a user to choose between regular GPU implementations in FAISS and cuVS implementations for specific algorithms.
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[cuVS](https://docs.rapids.ai/api/cuvs/nightly/) contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It is built on top of the [RAPIDS RAFT](https://github.com/rapidsai/raft) library of high performance machine learning primitives. Building Faiss with cuVS enabled allows a user to choose between regular GPU implementations in Faiss and cuVS implementations for specific algorithms.
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The libcuvs dependency should be installed via conda:
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1. With CUDA 12.0 - 12.5:

benchs/README.md

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# Additional benchmarks
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This directory also contains certain additional benchmarks (and serve as an additional source of examples of how to use the FAISS code).
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This directory also contains certain additional benchmarks (and serve as an additional source of examples of how to use the Faiss code).
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Certain tests / benchmarks might be outdated.
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* bench_6bit_codec.cpp - tests vector codecs for SQ6 quantization on a synthetic dataset

demos/demo_auto_tune.py

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if use_gpu:
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# if this fails, it means that the GPU version was not comp
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assert faiss.StandardGpuResources, \
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"FAISS was not compiled with GPU support, or loading _swigfaiss_gpu.so failed"
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"Faiss was not compiled with GPU support, or loading _swigfaiss_gpu.so failed"
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res = faiss.StandardGpuResources()
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dev_no = 0
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faiss/gpu/StandardGpuResources.h

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/**
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* FIXME: Integrating these in a separate code path for now. Ultimately,
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* it would be nice if we use a simple memory resource abstraction
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* in FAISS so we could plug in whether to use RMM's memory resources
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* in Faiss so we could plug in whether to use RMM's memory resources
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* or the default.
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*
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* There's enough duplicated logic that it doesn't *seem* to make sense

faiss/utils/hamming_distance/avx512-inl.h

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// AVX512 version
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// The _mm512_popcnt_epi64 intrinsic is used to accelerate Hamming distance
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// calculations in HammingComputerDefault and HammingComputer64. This intrinsic
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// is not available in the default FAISS avx512 build mode but is only
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// is not available in the default Faiss avx512 build mode but is only
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// available in the avx512_spr build mode, which targets Intel(R) Sapphire
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// Rapids.
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faiss/utils/utils.h

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* Get some stats about the system
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**************************************************/
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// Expose FAISS version as a string
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// Expose Faiss version as a string
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std::string get_version();
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/// ms elapsed since some arbitrary epoch

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