mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-17 21:10:43 +00:00
c716b56f26
Switched the code to C++17. To build ONNX Runtime on old distros like CentOS 7, you need to install a newer GCC from additionary repos. If you build onnxruntime with the newer GCC, typically the result binary can't be distributed to other places because it depends on the new GCC's runtime libraries, something that the stock OS doesn't have. But on RHEL/CentOS, it can be better. We use Red Hat devtoolset 8/9/10 with CentOS7 building our code. The new library features(like std::filesystem) that not exists in the old C++ runtime will be statically linked into the applications with some restrictions: 1. GCC has dual ABI, but we can only use the old one. It means std::string is still copy-on-write and std::list::size() is still O(n). Also, if you build onnxruntime on CentOS 7 and link it with some binaries that were built on CentOS 8 or Ubuntu with the new ABI and export C++ symbols directly(instead of using a C API), the it won't work. 2. We still can't use std::optional. It is a limitation coming from macOS. We will solve it when we got macOS 11 build machines. It won't be too long. 3. Please avoid to use C++17 in CUDA files(*.cu). Also, the *.h files that they include(like core/framework/float16.h). This is Because CUDA 10.2 doesn't support C++17. You are welcome to use the new features in any *.cc files.
86 lines
4.5 KiB
CMake
86 lines
4.5 KiB
CMake
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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file(GLOB_RECURSE onnxruntime_framework_srcs CONFIGURE_DEPENDS
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"${ONNXRUNTIME_INCLUDE_DIR}/core/framework/*.h"
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"${ONNXRUNTIME_ROOT}/core/framework/*.h"
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"${ONNXRUNTIME_ROOT}/core/framework/*.cc"
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)
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if (onnxruntime_ENABLE_TRAINING_TORCH_INTEROP)
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# todo: move those training related files into orttraining/core/framework/torch folder.
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list(APPEND onnxruntime_framework_srcs
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"${ORTTRAINING_SOURCE_DIR}/core/framework/torch/dlpack_python.cc"
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"${ORTTRAINING_SOURCE_DIR}/core/framework/torch/dlpack_python.h"
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"${ORTTRAINING_SOURCE_DIR}/core/framework/torch/python_common.h"
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"${ONNXRUNTIME_ROOT}/core/language_interop_ops/torch/custom_function_register.cc"
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"${ONNXRUNTIME_ROOT}/core/language_interop_ops/torch/custom_function_register.h"
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"${ONNXRUNTIME_ROOT}/core/language_interop_ops/torch/gil.h"
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"${ONNXRUNTIME_ROOT}/core/language_interop_ops/torch/refcount_tracker.cc"
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"${ONNXRUNTIME_ROOT}/core/language_interop_ops/torch/refcount_tracker.h"
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"${ONNXRUNTIME_ROOT}/core/language_interop_ops/torch/torch_proxy.cc"
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"${ONNXRUNTIME_ROOT}/core/language_interop_ops/torch/torch_proxy.h"
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)
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endif()
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if (onnxruntime_MINIMAL_BUILD)
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set(onnxruntime_framework_src_exclude
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"${ONNXRUNTIME_ROOT}/core/framework/provider_bridge_ort.cc"
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"${ONNXRUNTIME_ROOT}/core/framework/fallback_cpu_capability.h"
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"${ONNXRUNTIME_ROOT}/core/framework/fallback_cpu_capability.cc"
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)
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# custom ops support must be explicitly enabled in a minimal build. exclude if not.
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if (NOT onnxruntime_MINIMAL_BUILD_CUSTOM_OPS)
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list(APPEND onnxruntime_framework_src_exclude
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"${ONNXRUNTIME_INCLUDE_DIR}/core/framework/customregistry.h"
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"${ONNXRUNTIME_ROOT}/core/framework/customregistry.cc"
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)
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endif()
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list(REMOVE_ITEM onnxruntime_framework_srcs ${onnxruntime_framework_src_exclude})
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endif()
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source_group(TREE ${REPO_ROOT} FILES ${onnxruntime_framework_srcs})
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onnxruntime_add_static_library(onnxruntime_framework ${onnxruntime_framework_srcs})
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if(onnxruntime_ENABLE_INSTRUMENT)
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target_compile_definitions(onnxruntime_framework PRIVATE ONNXRUNTIME_ENABLE_INSTRUMENT)
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endif()
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if(onnxruntime_USE_TENSORRT OR onnxruntime_USE_NCCL)
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# TODO: for now, core framework depends on CUDA. It should be moved to TensorRT EP
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# TODO: provider_bridge_ort.cc should not include nccl.h
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target_include_directories(onnxruntime_framework PRIVATE ${ONNXRUNTIME_ROOT} ${eigen_INCLUDE_DIRS} ${onnxruntime_CUDNN_HOME}/include PUBLIC ${CMAKE_CURRENT_BINARY_DIR} ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
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else()
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target_include_directories(onnxruntime_framework PRIVATE ${ONNXRUNTIME_ROOT} ${eigen_INCLUDE_DIRS} PUBLIC ${CMAKE_CURRENT_BINARY_DIR})
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endif()
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# Needed for the provider interface, as it includes training headers when training is enabled
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if (onnxruntime_ENABLE_TRAINING OR onnxruntime_ENABLE_TRAINING_OPS)
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target_include_directories(onnxruntime_framework PRIVATE ${ORTTRAINING_ROOT})
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if (onnxruntime_ENABLE_TRAINING_TORCH_INTEROP)
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onnxruntime_add_include_to_target(onnxruntime_framework Python::Module)
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target_include_directories(onnxruntime_framework PRIVATE ${PROJECT_SOURCE_DIR}/external/dlpack/include)
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endif()
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if (onnxruntime_USE_NCCL OR onnxruntime_USE_MPI)
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target_include_directories(onnxruntime_framework PUBLIC ${MPI_CXX_INCLUDE_DIRS})
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endif()
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endif()
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onnxruntime_add_include_to_target(onnxruntime_framework onnxruntime_common onnx onnx_proto ${PROTOBUF_LIB} flatbuffers)
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set_target_properties(onnxruntime_framework PROPERTIES FOLDER "ONNXRuntime")
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# need onnx to build to create headers that this project includes
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add_dependencies(onnxruntime_framework ${onnxruntime_EXTERNAL_DEPENDENCIES})
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# In order to find the shared provider libraries we need to add the origin to the rpath for all executables we build
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# For the shared onnxruntime library, this is set in onnxruntime.cmake through CMAKE_SHARED_LINKER_FLAGS
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# But our test files don't use the shared library so this must be set for them.
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# For Win32 it generates an absolute path for shared providers based on the location of the executable/onnxruntime.dll
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if (UNIX AND NOT APPLE AND NOT onnxruntime_MINIMAL_BUILD AND NOT onnxruntime_BUILD_WEBASSEMBLY)
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set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -Wl,-rpath='$ORIGIN'")
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endif()
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if (onnxruntime_DEBUG_NODE_INPUTS_OUTPUTS)
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target_compile_definitions(onnxruntime_framework PRIVATE DEBUG_NODE_INPUTS_OUTPUTS)
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endif()
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install(DIRECTORY ${PROJECT_SOURCE_DIR}/../include/onnxruntime/core/framework DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/onnxruntime/core)
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