Pytorch 源码编译 Windows GPU
编译工具和第三方库1.1 Visual studio 20191.2 Cuda toolkit1.3 cudnn1.4 mkl1.5 magma1.6 sccache1.7 安装Ananconda 或者 miniconda1.8 安装python 包
设置环境变量开始编译编译Pytorch只编译libtorch
最新的pytorch的源码编译（包括1.6以上）
编译工具和第三方库
1.1 Visual studio 2019
不过最新的VS 2019有点问题，所以需要装https://docs.microsoft.com/en-us/visualstudio/releases/2019/history 里面的16.6.5版本。 注意需要把VS 的升级给禁了，不然升上去编译又过不去
1.2 Cuda toolkit
去Nvidia官网下 [cudatoolkit] (https://developer.nvidia.com/cuda-toolkit)
1.3 cudnn
这个的安装可以参考 windows_cudnn_install.
1.4 mkl
这个的安装可以参考 install_mkl
1.5 magma
参考 install_magma 需要注意的是，要注意区别release和debug版本，如果你想下载cuda110的release版本，就xxx_cuda110_release,否则就是xxx_cuda110_debug。编译的时候release就用release版本，debug就用debug版本
1.6 sccache
参考 install_sccache
mkl，magma和sccache下载解压后最好放在同一个目录下。
1.7 安装Ananconda 或者 miniconda
1.8 安装python 包
conda install numpy ninja pyyaml mkl mkl-include setuptools cmake cffi typing_extensions future six requests dataclasses
设置环境变量
set -x
set USE_CUDA=1
set DEBUG=
rem set DEBUG=1 for debug version
set USE_DISTRIBUTED=0
set CMAKE_VERBOSE_MAKEFILE=1
set TMP_DIR_WIN=C:\git\
set CMAKE_INCLUDE_PATH=%TMP_DIR_WIN%\mkl\include
set LIB=%TMP_DIR_WIN%\mkl\lib;%LIB
set MAGMA_HOME=%TMP_DIR_WIN%\magma
set CUDA_SUFFIX=cuda110
set CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0
set CUDNN_LIB_DIR=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\lib\x64
set CUDA_TOOLKIT_ROOT_DIR=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0
set CUDNN_ROOT_DIR=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0
set NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt
set CUDNN_INCLUDE_DIR=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\include
set PATH=%CUDA_PATH%\bin;%CUDA_PATH%\libnvvp;%PATH%
set DISTUTILS_USE_SDK=1
set TORCH_CUDA_ARCH_LIST=3.7+PTX;5.0;6.0;6.1;7.0;7.5;8.0
set TORCH_NVCC_FLAGS=-Xfatbin -compress-all
set PATH=%TMP_DIR_WIN%\bin;%PATH%
set SCCACHE_IDLE_TIMEOUT=0
sccache --stop-server
sccache --start-server
sccache --zero-stats
set CC=sccache-cl
set CXX=sccache-cl
set CMAKE_GENERATOR=Ninja
if "%USE_CUDA%"=="1" (
copy %TMP_DIR_WIN%\bin\sccache.exe %TMP_DIR_WIN%\bin\nvcc.exe
:: randomtemp is used to resolve the intermittent build error related to CUDA.
:: code: https://github.com/peterjc123/randomtemp
:: issue: https://github.com/pytorch/pytorch/issues/25393
::
:: Previously, CMake uses CUDA_NVCC_EXECUTABLE for finding nvcc and then
:: the calls are redirected to sccache. sccache looks for the actual nvcc
:: in PATH, and then pass the arguments to it.
:: Currently, randomtemp is placed before sccache (%TMP_DIR_WIN%\bin\nvcc)
:: so we are actually pretending sccache instead of nvcc itself.
curl -kL https://github.com/peterjc123/randomtemp/releases/download/v0.3/randomtemp.exe --output %TMP_DIR_WIN%\bin\randomtemp.exe
set RANDOMTEMP_EXECUTABLE=%TMP_DIR_WIN%\bin\nvcc.exe
set CUDA_NVCC_EXECUTABLE=%TMP_DIR_WIN%\bin\randomtemp.exe
set RANDOMTEMP_BASEDIR=%TMP_DIR_WIN%\bin
)
"C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\VC\Auxiliary\Build\vcvarsall.bat" x64 -vcvars_ver=14.26
注意几点 1.如果编译的是Cuda10的话，TORCH_CUDA_ARCH_LIST要去掉 8.0 2. 编译debug/release版本是，下载对应的Magma debug/release版本包 3. 如果只编译CPU版本的话，设置USE_CUDA=0
开始编译
编译Pytorch
python setup.py install --cmake
只编译libtorch
python tools\build_libtorch.py
