经测试 VS 2019 16.11.34 + CUDA 11.8 可成功编译 VS 2022 17.8.8编译时会报错 CUDA 11.6编译时会报错 conda create -p E:\miniconda3\envs\pytorchdebug python=3.9 conda activate pytorchdebug pip install install astunparse numpy ninja pyyaml setuptools cmake cffi typing_extensions future six requests dataclasses #git clone https://github.com/pytorch/pytorch.git git clone https://gitee.com/veenlee/pytorch.git cd D:\pytorch git checkout v2.2.1 git submodule sync git submodule update --init --recursive https://github.com/pytorch/pytorch#from-source conda install cmake ninja # Run this command from the PyTorch directory after cloning the source code using the “Get the PyTorch Source“ section below pip install -r requirements.txt conda install intel::mkl-static intel::mkl-include conda install -c conda-forge libuv=1.39 "D:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat" x64 rm -rf ./build/ https://github.com/pytorch/pytorch/blob/main/CONTRIBUTING.md#building-on-legacy-code-and-cuda 默认以release模式编译: #python setup.py build 开启debug模式: On the initial build, you can also speed things up with the environment variables DEBUG, USE_DISTRIBUTED, USE_MKLDNN, USE_CUDA, USE_FLASH_ATTENTION, USE_MEM_EFF_ATTENTION, BUILD_TEST, USE_FBGEMM, USE_NNPACK and USE_QNNPACK. DEBUG=1 will enable debug builds (-g -O0) REL_WITH_DEB_INFO=1 will enable debug symbols with optimizations (-g -O3) USE_DISTRIBUTED=0 will disable distributed (c10d, gloo, mpi, etc.) build. USE_MKLDNN=0 will disable using MKL-DNN. USE_CUDA=0 will disable compiling CUDA (in case you are developing on something not CUDA related), to save compile time. BUILD_TEST=0 will disable building C++ test binaries. USE_FBGEMM=0 will disable using FBGEMM (quantized 8-bit server operators). USE_NNPACK=0 will disable compiling with NNPACK. USE_QNNPACK=0 will disable QNNPACK build (quantized 8-bit operators). USE_XNNPACK=0 will disable compiling with XNNPACK. USE_FLASH_ATTENTION=0 and USE_MEM_EFF_ATTENTION=0 will disable compiling flash attention and memory efficient kernels respectively For example: #set DEBUG=1 #set USE_MKLDNN=0 #set USE_FBGEMM=0 #set USE_NNPACK=0 #set USE_QNNPACK=0 #set USE_XNNPACK=0 set REL_WITH_DEB_INFO=1 set USE_CUDA=0 set USE_DISTRIBUTED=0 set BUILD_TEST=0 echo %USE_CUDA% python setup.py develop 使用LibTorch的时候，C++标准要设置为C++17 Include: D:\pytorch\torch\include D:\pytorch\torch\include\torch\csrc\api\include Lib: D:\pytorch\build\lib libs: c10.lib;torch.lib;torch_cpu.lib;