pytorch/cmake/Modules
chunyuan 9ad05f2c3a Upgrade oneDNN to v2.3.3 and package oneDNN Graph API together (#63748)
Summary:
This PR upgrades oneDNN to [v2.3.3](https://github.com/oneapi-src/oneDNN/releases/tag/v2.3.3) and includes [Graph API preview release](https://github.com/oneapi-src/oneDNN/releases/tag/graph-v0.2) in one package.

- oneDNN will be located at `pytorch/third_party/ideep/mkl-dnn/third_party/oneDNN`
- The version of oneDNN will be [v2.3.3](https://github.com/oneapi-src/oneDNN/releases/tag/v2.3.3)
  The main changes on CPU:

  - v2.3
    - Extended primitive cache to improve primitive descriptor creation performance.
    - Improved primitive cache performance in multithreaded configurations.
    - Introduced initial optimizations for bfloat16 compute functionality for future Intel Xeon Scalable processor (code name Sapphire Rapids).
    - Improved performance of binary primitive and binary post-op for cases with broadcast and mixed source and destination formats.
    - Improved performance of reduction primitive
    - Improved performance of depthwise convolution primitive with NHWC activations for training cases
  - v2.3.1
    -  Improved int8 GEMM performance for processors with Intel AVX2 and Intel DL Boost support
    - Fixed integer overflow for inner product implementation on CPUs
    - Fixed out of bounds access in GEMM implementation for Intel SSE 4.1
  - v2.3.2
    - Fixed performance regression in fp32 inner product primitive for processors with Intel AVX512 support
  - v2.3.3
    - Reverted check for memory descriptor stride validity for unit dimensions
    - Fixed memory leak in CPU GEMM implementation

  More changes can be found in https://github.com/oneapi-src/oneDNN/releases.
- The Graph API provides flexible API for aggressive fusion, and the preview2 supports fusion for FP32 inference.  See the [Graph API release branch](https://github.com/oneapi-src/oneDNN/tree/dev-graph-preview2) and [spec](https://spec.oneapi.io/onednn-graph/latest/introduction.html) for more details. A separate PR will be submitted to integrate the oneDNN Graph API to Torchscript graph.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/63748

Reviewed By: albanD

Differential Revision: D32153889

Pulled By: malfet

fbshipit-source-id: 536071168ffe312d452f75d54f34c336ca3778c1
2021-12-09 13:42:40 -08:00
..
FindARM.cmake
FindAtlas.cmake
FindAVX.cmake
FindBenchmark.cmake
FindBLAS.cmake Modify "gemm" code to enable access to "sbgemm_" routine in OpenBLAS (#58831) 2021-11-03 08:53:27 -07:00
FindBLIS.cmake
FindCUB.cmake Update CMake and use native CUDA language support (#62445) 2021-10-11 09:05:48 -07:00
FindFFmpeg.cmake
FindFlexiBLAS.cmake Add FlexiBLAS build support per #64752 (#64815) 2021-10-28 11:28:00 -07:00
FindGloo.cmake
FindHiredis.cmake
FindLAPACK.cmake Add FlexiBLAS build support per #64752 (#64815) 2021-10-28 11:28:00 -07:00
FindLevelDB.cmake
FindLMDB.cmake
FindMAGMA.cmake
FindMatlabMex.cmake
FindMKL.cmake
FindMKLDNN.cmake Upgrade oneDNN to v2.3.3 and package oneDNN Graph API together (#63748) 2021-12-09 13:42:40 -08:00
FindNCCL.cmake
FindNuma.cmake
FindNumPy.cmake
FindOpenBLAS.cmake
FindOpenMP.cmake
Findpybind11.cmake
FindRocksDB.cmake
FindSnappy.cmake
FindvecLib.cmake
FindVSX.cmake
FindZMQ.cmake
README.md

This folder contains various custom cmake modules for finding libraries and packages. Details about some of them are listed below.

FindOpenMP.cmake

This is modified from the file included in CMake 3.13 release, with the following changes:

  • Replace VERSION_GREATER_EQUAL with NOT ... VERSION_LESS as VERSION_GREATER_EQUAL is not supported in CMake 3.5 (our min supported version).

  • Update the separate_arguments commands to not use NATIVE_COMMAND which is not supported in CMake 3.5 (our min supported version).

  • Make it respect the QUIET flag so that, when it is set, try_compile failures are not reported.

  • For AppleClang compilers, use -Xpreprocessor instead of -Xclang as the later is not documented.

  • For AppleClang compilers, an extra flag option is tried, which is -Xpreprocessor -openmp -I${DIR_OF_omp_h}, where ${DIR_OF_omp_h} is a obtained using find_path on omp.h with brew's default include directory as a hint. Without this, the compiler will complain about missing headers as they are not natively included in Apple's LLVM.

  • For non-GNU compilers, whenever we try a candidate OpenMP flag, first try it with directly linking MKL's libomp if it has one. Otherwise, we may end up linking two libomps and end up with this nasty error:

    OMP: Error #15: Initializing libomp.dylib, but found libiomp5.dylib already
    initialized.
    
    OMP: Hint This means that multiple copies of the OpenMP runtime have been
    linked into the program. That is dangerous, since it can degrade performance
    or cause incorrect results. The best thing to do is to ensure that only a
    single OpenMP runtime is linked into the process, e.g. by avoiding static
    linking of the OpenMP runtime in any library. As an unsafe, unsupported,
    undocumented workaround you can set the environment variable
    KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but
    that may cause crashes or silently produce incorrect results. For more
    information, please see http://openmp.llvm.org/
    

    See NOTE [ Linking both MKL and OpenMP ] for details.