mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 00:20:18 +01:00
Some CMake and CUDA cleanup given recent update to C++17 (#90599)
The main changes are: 1. Remove outdated checks for old compiler versions because they can't support C++17. 2. Remove outdated CMake checks because it now requires 3.18. 3. Remove outdated CUDA checks because we are moving to CUDA 11. Almost all changes are in CMake files for easy audition. Pull Request resolved: https://github.com/pytorch/pytorch/pull/90599 Approved by: https://github.com/soumith
This commit is contained in:
cyy
PyTorch MergeBot
parent
d5163f5206
commit
9710ac6531
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@ -13,8 +13,6 @@ cmake_policy(SET CMP0025 NEW)
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# nice when it's possible, and it's possible on our Windows configs.
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cmake_policy(SET CMP0092 NEW)
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set(FIND_CUDA_MODULE_DEPRECATED ON)
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# ---[ Project and semantic versioning.
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project(Torch CXX C)
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@ -242,9 +240,7 @@ option(USE_OBSERVERS "Use observers module." OFF)
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option(USE_OPENCL "Use OpenCL" OFF)
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option(USE_OPENCV "Use OpenCV" OFF)
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option(USE_OPENMP "Use OpenMP for parallel code" ON)
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cmake_dependent_option(
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USE_PRECOMPILED_HEADERS "Use pre-compiled headers to accelerate build. Requires cmake >= 3.16." OFF
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"CMAKE_VERSION VERSION_GREATER_EQUAL \"3.16\"" OFF)
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option(USE_PRECOMPILED_HEADERS "Use pre-compiled headers to accelerate build." OFF)
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option(USE_PROF "Use profiling" OFF)
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option(USE_QNNPACK "Use QNNPACK (quantized 8-bit operators)" ON)
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@ -272,13 +268,7 @@ option(USE_VULKAN_FP16_INFERENCE "Vulkan - Use fp16 inference" OFF)
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option(USE_VULKAN_RELAXED_PRECISION "Vulkan - Use relaxed precision math in the kernels (mediump)" OFF)
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option(USE_VULKAN_SHADERC_RUNTIME "Vulkan - Use runtime shader compilation as opposed to build-time (needs libshaderc)" OFF)
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# option USE_XNNPACK: try to enable xnnpack by default.
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set(XNNPACK_MIN_CMAKE_VER 3.12)
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cmake_dependent_option(
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USE_XNNPACK "Use XNNPACK. Requires cmake >= ${XNNPACK_MIN_CMAKE_VER}." ON
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"CMAKE_VERSION VERSION_GREATER_EQUAL ${XNNPACK_MIN_CMAKE_VER}" OFF)
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if(NOT USE_XNNPACK AND CMAKE_VERSION VERSION_LESS ${XNNPACK_MIN_CMAKE_VER})
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message(WARNING "USE_XNNPACK is set to OFF. XNNPACK requires CMake version ${XNNPACK_MIN_CMAKE_VER} or greater.")
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endif()
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option(USE_XNNPACK "Use XNNPACK" ON)
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option(USE_ZMQ "Use ZMQ" OFF)
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option(USE_ZSTD "Use ZSTD" OFF)
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option(TORCH_DISABLE_GPU_ASSERTS "Disable GPU asserts by default" OFF)
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@ -724,16 +714,6 @@ if(USE_FLASH_ATTENTION)
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ENDIF()
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if(USE_CUDA AND (CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 10.2) AND (CMAKE_HOST_SYSTEM_NAME MATCHES "Windows"))
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# CUDA < 10.2 doesn't support compiling and extracting header dependencies in
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# one call, so instead CMake calls nvcc twice with && in between.
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# However, on windows cmd.exe has a 8191 character limit for commands which we
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# start hitting. This moves most argments into a file to avoid going over the limit
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set(CMAKE_CUDA_USE_RESPONSE_FILE_FOR_OBJECTS ON)
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set(CMAKE_NINJA_FORCE_RESPONSE_FILE ON CACHE INTERNAL "")
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endif()
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if(USE_FBGEMM)
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string(APPEND CMAKE_CXX_FLAGS " -DUSE_FBGEMM")
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endif()
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@ -837,10 +817,6 @@ if(NOT MSVC)
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string(APPEND CMAKE_CXX_FLAGS " -Wno-range-loop-analysis")
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string(APPEND CMAKE_CXX_FLAGS " -Wno-pass-failed")
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endif()
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if(CMAKE_COMPILER_IS_GNUCXX AND (CMAKE_CXX_COMPILER_VERSION VERSION_LESS 6.0.0))
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# Suppress issue: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=43407
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string(APPEND CMAKE_CXX_FLAGS " -Wno-attributes")
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endif()
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if(CMAKE_COMPILER_IS_GNUCXX AND NOT (CMAKE_CXX_COMPILER_VERSION VERSION_LESS 7.0.0))
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string(APPEND CMAKE_CXX_FLAGS " -Wno-stringop-overflow")
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endif()
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@ -156,12 +156,12 @@ They require JetPack 4.2 and above, and [@dusty-nv](https://github.com/dusty-nv)
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#### Prerequisites
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If you are installing from source, you will need:
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- Python 3.7 or later (for Linux, Python 3.7.6+ or 3.8.1+ is needed)
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- A C++14 compatible compiler, such as clang
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- A C++17 compatible compiler, such as clang
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We highly recommend installing an [Anaconda](https://www.anaconda.com/distribution/#download-section) environment. You will get a high-quality BLAS library (MKL) and you get controlled dependency versions regardless of your Linux distro.
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If you want to compile with CUDA support, install the following (note that CUDA is not supported on macOS)
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- [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads) 10.2 or above
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- [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads) 11.0 or above
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- [NVIDIA cuDNN](https://developer.nvidia.com/cudnn) v7 or above
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- [Compiler](https://gist.github.com/ax3l/9489132) compatible with CUDA
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@ -1,4 +1,4 @@
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cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
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cmake_minimum_required(VERSION 3.18 FATAL_ERROR)
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set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake ${CMAKE_MODULE_PATH})
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if(NOT MSVC)
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@ -1,4 +1,4 @@
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cmake_minimum_required(VERSION 3.10 FATAL_ERROR)
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cmake_minimum_required(VERSION 3.18 FATAL_ERROR)
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project(c10 CXX)
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set(CMAKE_CXX_STANDARD 17 CACHE STRING "The C++ standard whose features are requested to build this target.")
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@ -836,13 +836,9 @@ endif()
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# Pass path to PocketFFT
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if(AT_POCKETFFT_ENABLED)
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if(CMAKE_VERSION VERSION_LESS "3.11")
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target_include_directories(torch_cpu PRIVATE "${POCKETFFT_INCLUDE_DIR}")
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else()
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set_source_files_properties(
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"${PROJECT_SOURCE_DIR}/aten/src/ATen/native/mkl/SpectralOps.cpp"
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PROPERTIES INCLUDE_DIRECTORIES "${POCKETFFT_INCLUDE_DIR}")
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endif()
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set_source_files_properties(
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"${PROJECT_SOURCE_DIR}/aten/src/ATen/native/mkl/SpectralOps.cpp"
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PROPERTIES INCLUDE_DIRECTORIES "${POCKETFFT_INCLUDE_DIR}")
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endif()
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if(CMAKE_COMPILER_IS_GNUCXX AND BUILD_LIBTORCH_CPU_WITH_DEBUG)
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@ -1634,10 +1634,8 @@ function(add_onnx_tensorrt_subdir)
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set(CUDNN_INCLUDE_DIR "${CUDNN_INCLUDE_PATH}")
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set(CUDNN_LIBRARY "${CUDNN_LIBRARY_PATH}")
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set(CMAKE_VERSION_ORIG "{CMAKE_VERSION}")
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if(FIND_CUDA_MODULE_DEPRECATED)
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# TODO: this WAR is for https://github.com/pytorch/pytorch/issues/18524
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set(CMAKE_VERSION "3.9.0")
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endif()
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# TODO: this WAR is for https://github.com/pytorch/pytorch/issues/18524
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set(CMAKE_VERSION "3.9.0")
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add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/../third_party/onnx-tensorrt EXCLUDE_FROM_ALL)
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set(CMAKE_VERSION "{CMAKE_VERSION_ORIG}")
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endfunction()
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@ -1683,32 +1681,20 @@ if(NOT INTERN_BUILD_MOBILE)
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string(APPEND CMAKE_CUDA_FLAGS " -Xcompiler=/wd4819,/wd4503,/wd4190,/wd4244,/wd4251,/wd4275,/wd4522")
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endif()
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if(NOT MSVC)
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set(CMAKE_C_STANDARD 11 CACHE STRING "The C standard whose features are requested to build this target.")
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endif()
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string(APPEND CMAKE_CUDA_FLAGS " -Wno-deprecated-gpu-targets --expt-extended-lambda")
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if(NOT CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
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set(CMAKE_CXX_STANDARD 17 CACHE STRING "The C++ standard whose features are requested to build this target.")
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endif()
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# use cub in a safe manner, see:
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# https://github.com/pytorch/pytorch/pull/55292
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if(NOT ${CUDA_VERSION} LESS 11.5)
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string(APPEND CMAKE_CUDA_FLAGS " -DCUB_WRAPPED_NAMESPACE=at_cuda_detail")
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endif()
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if(CUDA_HAS_FP16 OR NOT ${CUDA_VERSION} LESS 7.5)
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message(STATUS "Found CUDA with FP16 support, compiling with torch.cuda.HalfTensor")
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string(APPEND CMAKE_CUDA_FLAGS " -DCUDA_HAS_FP16=1"
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" -D__CUDA_NO_HALF_OPERATORS__"
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" -D__CUDA_NO_HALF_CONVERSIONS__"
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" -D__CUDA_NO_HALF2_OPERATORS__"
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" -D__CUDA_NO_BFLOAT16_CONVERSIONS__")
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else()
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message(STATUS "Could not find CUDA with FP16 support, compiling without torch.CudaHalfTensor")
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endif()
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message(STATUS "Found CUDA with FP16 support, compiling with torch.cuda.HalfTensor")
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string(APPEND CMAKE_CUDA_FLAGS " -DCUDA_HAS_FP16=1"
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" -D__CUDA_NO_HALF_OPERATORS__"
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" -D__CUDA_NO_HALF_CONVERSIONS__"
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" -D__CUDA_NO_HALF2_OPERATORS__"
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" -D__CUDA_NO_BFLOAT16_CONVERSIONS__")
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string(APPEND CMAKE_C_FLAGS_RELEASE " -DNDEBUG")
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string(APPEND CMAKE_CXX_FLAGS_RELEASE " -DNDEBUG")
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@ -41,57 +41,7 @@ if(NOT INTERN_BUILD_MOBILE)
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cmake_pop_check_state()
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endif()
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if(NOT INTERN_BUILD_MOBILE)
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# ---[ Check if certain std functions are supported. Sometimes
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# _GLIBCXX_USE_C99 macro is not defined and some functions are missing.
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cmake_push_check_state(RESET)
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set(CMAKE_REQUIRED_FLAGS "-std=c++14")
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CHECK_CXX_SOURCE_COMPILES("
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#include <cmath>
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#include <string>
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int main() {
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int a = std::isinf(3.0);
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int b = std::isnan(0.0);
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std::string s = std::to_string(1);
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return 0;
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}" SUPPORT_GLIBCXX_USE_C99)
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if(NOT SUPPORT_GLIBCXX_USE_C99)
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# Force cmake to retest next time around
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unset(SUPPORT_GLIBCXX_USE_C99 CACHE)
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message(FATAL_ERROR
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"The C++ compiler does not support required functions. "
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"This is very likely due to a known bug in GCC 5 "
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"(and maybe other versions) on Ubuntu 17.10 and newer. "
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"For more information, see: "
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"https://github.com/pytorch/pytorch/issues/5229")
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endif()
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cmake_pop_check_state()
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endif()
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# ---[ Check if std::exception_ptr is supported.
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cmake_push_check_state(RESET)
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set(CMAKE_REQUIRED_FLAGS "-std=c++14")
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CHECK_CXX_SOURCE_COMPILES(
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"#include <string>
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#include <exception>
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int main(int argc, char** argv) {
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std::exception_ptr eptr;
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try {
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std::string().at(1);
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} catch(...) {
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eptr = std::current_exception();
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}
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}" CAFFE2_EXCEPTION_PTR_SUPPORTED)
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if(CAFFE2_EXCEPTION_PTR_SUPPORTED)
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message(STATUS "std::exception_ptr is supported.")
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set(CAFFE2_USE_EXCEPTION_PTR 1)
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else()
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message(STATUS "std::exception_ptr is NOT supported.")
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endif()
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cmake_pop_check_state()
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set(CAFFE2_USE_EXCEPTION_PTR 1)
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# ---[ Check if we want to turn off deprecated warning due to glog.
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# Note(jiayq): on ubuntu 14.04, the default glog install uses ext/hash_set that
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@ -181,7 +181,7 @@ if(NOT @BUILD_SHARED_LIBS@)
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endif()
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set_target_properties(torch PROPERTIES
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INTERFACE_INCLUDE_DIRECTORIES "${TORCH_INCLUDE_DIRS}"
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CXX_STANDARD 14
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CXX_STANDARD 17
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)
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if(TORCH_CXX_FLAGS)
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set_property(TARGET torch PROPERTY INTERFACE_COMPILE_OPTIONS "${TORCH_CXX_FLAGS}")
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@ -51,8 +51,8 @@ set(CMAKE_CUDA_STANDARD_REQUIRED ON)
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message(STATUS "Caffe2: CUDA detected: " ${CUDA_VERSION})
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message(STATUS "Caffe2: CUDA nvcc is: " ${CUDA_NVCC_EXECUTABLE})
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message(STATUS "Caffe2: CUDA toolkit directory: " ${CUDA_TOOLKIT_ROOT_DIR})
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if(CUDA_VERSION VERSION_LESS 10.2)
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message(FATAL_ERROR "PyTorch requires CUDA 10.2 or above.")
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if(CUDA_VERSION VERSION_LESS 11.0)
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message(FATAL_ERROR "PyTorch requires CUDA 11.0 or above.")
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endif()
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if(CUDA_FOUND)
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@ -1,4 +1,4 @@
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cmake_minimum_required(VERSION 3.12)
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cmake_minimum_required(VERSION 3.18)
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project(functorch)
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set(CMAKE_CXX_STANDARD 17)
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@ -2,7 +2,7 @@
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# Now it only builds the Torch python bindings.
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if(NOT CAFFE2_CMAKE_BUILDING_WITH_MAIN_REPO)
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cmake_minimum_required(VERSION 3.10 FATAL_ERROR)
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cmake_minimum_required(VERSION 3.18 FATAL_ERROR)
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project(torch CXX C)
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find_package(torch REQUIRED)
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option(USE_CUDA "Use CUDA" ON)
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@ -1,5 +1,5 @@
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project(libshm C CXX)
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cmake_minimum_required(VERSION 3.13 FATAL_ERROR)
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cmake_minimum_required(VERSION 3.18 FATAL_ERROR)
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set(TORCH_ROOT ${CMAKE_CURRENT_LIST_DIR}/../../../)
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include(${TORCH_ROOT}/cmake/public/threads.cmake)
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@ -50,7 +50,6 @@ VersionMap = Dict[str, VersionRange]
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# Or from include/crt/host_config.h in the CUDA SDK
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# The second value is the exclusive(!) upper bound, i.e. min <= version < max
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CUDA_GCC_VERSIONS: VersionMap = {
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'10.2': (MINIMUM_GCC_VERSION, (9, 0)),
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'11.0': (MINIMUM_GCC_VERSION, (10, 0)),
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'11.1': (MINIMUM_GCC_VERSION, (11, 0)),
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'11.2': (MINIMUM_GCC_VERSION, (11, 0)),
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@ -63,7 +62,6 @@ CUDA_GCC_VERSIONS: VersionMap = {
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MINIMUM_CLANG_VERSION = (3, 3, 0)
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CUDA_CLANG_VERSIONS: VersionMap = {
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'10.2': (MINIMUM_CLANG_VERSION, (9, 0)),
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'11.1': (MINIMUM_CLANG_VERSION, (11, 0)),
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'11.2': (MINIMUM_CLANG_VERSION, (12, 0)),
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'11.3': (MINIMUM_CLANG_VERSION, (12, 0)),
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@ -2147,7 +2145,7 @@ def _write_ninja_file(path,
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# --generate-dependencies-with-compile was added in CUDA 10.2.
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# Compilation will work on earlier CUDA versions but header file
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# dependencies are not correctly computed.
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required_cuda_version = packaging.version.parse('10.2')
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required_cuda_version = packaging.version.parse('11.0')
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has_cuda_version = torch.version.cuda is not None
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if has_cuda_version and packaging.version.parse(torch.version.cuda) >= required_cuda_version:
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cuda_compile_rule.append(' depfile = $out.d')
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