mirror of
https://github.com/saymrwulf/onnxruntime.git
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[onnxruntime/build] Add new flag enable_generic_interface to build primary EPs by default (#23342)
### Description - Add new build flag in build.py to build onnxruntime.dll supporting interfaces for all primary EPs( QNN, TensoRT, OpenVino, VitisAI). - Modify onnxruntime.dll/onnxruntime_shared.dll build settings to remove dependency of IHV SDK Toolset to be installed on the system. - Change CMake variables to be explicit when building EP vs ORT. e.g. onnxruntime_USE_TENSORRT vs onnxruntime_USE_TENSORRT_INTERFACE, to evolve the build system to build ORT independent of EPs. ### Motivation and Context Changes in the build system required to evolve the repo to build the components independently while removing unnecessary dependencies --------- Co-authored-by: Lei Cao <jslhcl@gmail.com> Co-authored-by: Karim Vadsariya <kvadsariya@microsoft.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
This commit is contained in:
Karim Vadsariya
GitHub
parent
a770a8dec8
commit
655a23ff1d
5 changed files with 71 additions and 24 deletions
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@ -259,6 +259,12 @@ option(onnxruntime_USE_AZURE "Build with azure inferencing support" OFF)
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option(onnxruntime_USE_LOCK_FREE_QUEUE "Build with lock-free task queue for threadpool." OFF)
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option(onnxruntime_FORCE_GENERIC_ALGORITHMS "Disable optimized arch-specific algorithms. Use only for testing and debugging generic algorithms." OFF)
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option(onnxruntime_USE_TENSORRT_INTERFACE "Build ONNXRuntime shared lib which is compatible with TensorRT EP interface" OFF)
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option(onnxruntime_USE_CUDA_INTERFACE "Build ONNXRuntime shared lib which is compatible with Cuda EP interface" OFF)
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option(onnxruntime_USE_OPENVINO_INTERFACE "Build ONNXRuntime shared lib which is compatible with OpenVINO EP interface" OFF)
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option(onnxruntime_USE_VITISAI_INTERFACE "Build ONNXRuntime shared lib which is compatible with Vitis-AI EP interface" OFF)
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option(onnxruntime_USE_QNN_INTERFACE "Build ONNXRuntime shared lib which is compatible with QNN EP interface" OFF)
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# ENABLE_TRAINING includes all training functionality
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# The following 2 entry points
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# 1. ORTModule
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@ -703,7 +709,7 @@ if (WIN32)
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# structure was padded due to __declspec(align())
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list(APPEND ORT_WARNING_FLAGS "/wd4324")
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# warning C4800: Implicit conversion from 'X' to bool. Possible information loss
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if (onnxruntime_USE_OPENVINO)
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if (onnxruntime_USE_OPENVINO OR onnxruntime_USE_OPENVINO_INTERFACE)
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list(APPEND ORT_WARNING_FLAGS "/wd4800")
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endif()
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# operator 'operator-name': deprecated between enumerations of different types
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@ -864,7 +870,7 @@ else()
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set(onnxruntime_USE_MEMORY_EFFICIENT_ATTENTION OFF)
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endif()
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if (onnxruntime_USE_CUDA)
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if (onnxruntime_USE_CUDA OR onnxruntime_USE_CUDA_INTERFACE)
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list(APPEND ORT_PROVIDER_FLAGS -DUSE_CUDA=1)
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list(APPEND ORT_PROVIDER_CMAKE_FLAGS -Donnxruntime_USE_CUDA=1)
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list(APPEND ONNXRUNTIME_PROVIDER_NAMES cuda)
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@ -888,7 +894,7 @@ if (onnxruntime_USE_CUDA)
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endif()
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endif()
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if (onnxruntime_USE_VITISAI)
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if (onnxruntime_USE_VITISAI OR onnxruntime_USE_VITISAI_INTERFACE)
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list(APPEND ORT_PROVIDER_FLAGS -DUSE_VITISAI=1)
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list(APPEND ORT_PROVIDER_CMAKE_FLAGS -Donnxruntime_USE_VITISAI=1)
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list(APPEND ONNXRUNTIME_PROVIDER_NAMES vitisai)
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@ -898,12 +904,12 @@ if (onnxruntime_USE_DNNL)
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list(APPEND ONNXRUNTIME_PROVIDER_NAMES dnnl)
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list(APPEND ORT_PROVIDER_CMAKE_FLAGS -Donnxruntime_USE_DNNL=1)
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endif()
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if (onnxruntime_USE_OPENVINO)
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if (onnxruntime_USE_OPENVINO OR onnxruntime_USE_OPENVINO_INTERFACE)
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list(APPEND ORT_PROVIDER_FLAGS -DUSE_OPENVINO=1)
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list(APPEND ONNXRUNTIME_PROVIDER_NAMES openvino)
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list(APPEND ORT_PROVIDER_CMAKE_FLAGS -Donnxruntime_USE_OPENVINO=1)
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endif()
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if (onnxruntime_USE_TENSORRT)
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if (onnxruntime_USE_TENSORRT OR onnxruntime_USE_TENSORRT_INTERFACE)
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list(APPEND ORT_PROVIDER_FLAGS -DUSE_TENSORRT=1)
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#TODO: remove the following line and change the test code in onnxruntime_shared_lib_test to use the new EP API.
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list(APPEND ONNXRUNTIME_PROVIDER_NAMES tensorrt)
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@ -929,7 +935,7 @@ if (onnxruntime_USE_JSEP)
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list(APPEND ORT_PROVIDER_CMAKE_FLAGS -Donnxruntime_USE_JSEP=1)
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list(APPEND ONNXRUNTIME_PROVIDER_NAMES js)
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endif()
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if (onnxruntime_USE_QNN)
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if (onnxruntime_USE_QNN OR onnxruntime_USE_QNN_INTERFACE)
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list(APPEND ORT_PROVIDER_FLAGS -DUSE_QNN=1)
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list(APPEND ORT_PROVIDER_CMAKE_FLAGS -Donnxruntime_USE_QNN=1)
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list(APPEND ONNXRUNTIME_PROVIDER_NAMES qnn)
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@ -957,7 +963,7 @@ if (onnxruntime_USE_QNN)
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endif()
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endif()
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if (MSVC OR ${CMAKE_SYSTEM_NAME} STREQUAL "Linux")
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if ((NOT onnxruntime_USE_QNN_INTERFACE) AND (MSVC OR ${CMAKE_SYSTEM_NAME} STREQUAL "Linux"))
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file(GLOB QNN_LIB_FILES LIST_DIRECTORIES false "${onnxruntime_QNN_HOME}/lib/${QNN_ARCH_ABI}/libQnn*.so"
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"${onnxruntime_QNN_HOME}/lib/${QNN_ARCH_ABI}/Qnn*.dll"
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"${onnxruntime_QNN_HOME}/lib/${QNN_ARCH_ABI}/libHtpPrepare.so"
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@ -1416,7 +1422,7 @@ if (onnxruntime_ENABLE_TRAINING_APIS)
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)
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endif()
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if (onnxruntime_USE_OPENVINO)
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if (onnxruntime_USE_OPENVINO OR onnxruntime_USE_OPENVINO_INTERFACE)
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add_definitions(-DUSE_OPENVINO=1)
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@ -1429,7 +1435,7 @@ if (onnxruntime_USE_OPENVINO)
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add_definitions(-DOPENVINO_CONFIG_GPU=1)
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endif()
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if (onnxruntime_USE_OPENVINO_CPU)
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if (onnxruntime_USE_OPENVINO_CPU OR onnxruntime_USE_OPENVINO_INTERFACE) # OpenVino CPU interface is default built.
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add_definitions(-DOPENVINO_CONFIG_CPU=1)
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endif()
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@ -178,7 +178,6 @@ struct ProviderHost {
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virtual std::string demangle(const char* name) = 0;
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virtual std::string demangle(const std::string& name) = 0;
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#ifdef USE_CUDA
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virtual std::unique_ptr<IAllocator> CreateCUDAAllocator(int16_t device_id, const char* name) = 0;
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virtual std::unique_ptr<IAllocator> CreateCUDAPinnedAllocator(const char* name) = 0;
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virtual std::unique_ptr<IDataTransfer> CreateGPUDataTransfer() = 0;
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@ -190,7 +189,6 @@ struct ProviderHost {
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virtual Status CudaCall_false(int retCode, const char* exprString, const char* libName, int successCode, const char* msg, const char* file, const int line) = 0;
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virtual void CudaCall_true(int retCode, const char* exprString, const char* libName, int successCode, const char* msg, const char* file, const int line) = 0;
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#endif
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#ifdef USE_MIGRAPHX
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virtual std::unique_ptr<IAllocator> CreateMIGraphXAllocator(int16_t device_id, const char* name) = 0;
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@ -200,7 +198,6 @@ struct ProviderHost {
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#ifdef USE_ROCM
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virtual std::unique_ptr<IAllocator> CreateROCMAllocator(int16_t device_id, const char* name) = 0;
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virtual std::unique_ptr<IAllocator> CreateROCMPinnedAllocator(const char* name) = 0;
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virtual std::unique_ptr<IDataTransfer> CreateGPUDataTransfer() = 0;
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virtual void rocm__Impl_Cast(void* stream, const int64_t* input_data, int32_t* output_data, size_t count) = 0;
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virtual void rocm__Impl_Cast(void* stream, const int32_t* input_data, int64_t* output_data, size_t count) = 0;
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@ -1256,9 +1253,7 @@ struct ProviderHost {
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virtual training::DistributedRunContext& GetDistributedRunContextInstance() = 0;
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#endif
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#if defined(USE_CUDA) || defined(USE_ROCM)
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virtual PhiloxGenerator& PhiloxGenerator__Default() = 0;
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#endif
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#ifdef ENABLE_TRAINING_TORCH_INTEROP
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virtual void contrib__PythonOpBase__Init(contrib::PythonOpBase* p, const OpKernelInfo& info) = 0;
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@ -258,10 +258,8 @@ struct ProviderHostImpl : ProviderHost {
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void* CPUAllocator__Alloc(CPUAllocator* p, size_t size) override { return p->CPUAllocator::Alloc(size); }
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void CPUAllocator__Free(CPUAllocator* p, void* allocation) override { return p->CPUAllocator::Free(allocation); }
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#ifdef USE_CUDA
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std::unique_ptr<IAllocator> CreateCUDAAllocator(int16_t device_id, const char* name) override { return GetProviderInfo_CUDA().CreateCUDAAllocator(device_id, name); }
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std::unique_ptr<IAllocator> CreateCUDAPinnedAllocator(const char* name) override { return GetProviderInfo_CUDA().CreateCUDAPinnedAllocator(name); }
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std::unique_ptr<IDataTransfer> CreateGPUDataTransfer() override { return GetProviderInfo_CUDA().CreateGPUDataTransfer(); }
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void cuda__Impl_Cast(void* stream, const int64_t* input_data, int32_t* output_data, size_t count) override { return GetProviderInfo_CUDA().cuda__Impl_Cast(stream, input_data, output_data, count); }
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void cuda__Impl_Cast(void* stream, const int32_t* input_data, int64_t* output_data, size_t count) override { return GetProviderInfo_CUDA().cuda__Impl_Cast(stream, input_data, output_data, count); }
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@ -271,7 +269,6 @@ struct ProviderHostImpl : ProviderHost {
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Status CudaCall_false(int retCode, const char* exprString, const char* libName, int successCode, const char* msg, const char* file, const int line) override { return GetProviderInfo_CUDA().CudaCall_false(retCode, exprString, libName, successCode, msg, file, line); }
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void CudaCall_true(int retCode, const char* exprString, const char* libName, int successCode, const char* msg, const char* file, const int line) override { GetProviderInfo_CUDA().CudaCall_true(retCode, exprString, libName, successCode, msg, file, line); }
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#endif
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#ifdef USE_MIGRAPHX
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std::unique_ptr<IAllocator> CreateMIGraphXAllocator(int16_t device_id, const char* name) override { return GetProviderInfo_MIGraphX().CreateMIGraphXAllocator(device_id, name); }
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@ -291,6 +288,8 @@ struct ProviderHostImpl : ProviderHost {
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Status RocmCall_false(int retCode, const char* exprString, const char* libName, int successCode, const char* msg, const char* file, const int line) override { return GetProviderInfo_ROCM().RocmCall_false(retCode, exprString, libName, successCode, msg, file, line); }
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void RocmCall_true(int retCode, const char* exprString, const char* libName, int successCode, const char* msg, const char* file, const int line) override { GetProviderInfo_ROCM().RocmCall_true(retCode, exprString, libName, successCode, msg, file, line); }
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#else
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std::unique_ptr<IDataTransfer> CreateGPUDataTransfer() override { return GetProviderInfo_CUDA().CreateGPUDataTransfer(); }
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#endif
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std::string GetEnvironmentVar(const std::string& var_name) override { return Env::Default().GetEnvironmentVar(var_name); }
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@ -1560,9 +1559,7 @@ struct ProviderHostImpl : ProviderHost {
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training::DistributedRunContext& GetDistributedRunContextInstance() override { return training::DistributedRunContext::GetInstance(); }
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#endif
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#if defined(USE_CUDA) || defined(USE_ROCM)
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PhiloxGenerator& PhiloxGenerator__Default() override { return PhiloxGenerator::Default(); }
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#endif
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#ifdef ENABLE_TRAINING_TORCH_INTEROP
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void contrib__PythonOpBase__Init(contrib::PythonOpBase* p, const OpKernelInfo& info) override { p->PythonOpBase::Init(info); }
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@ -782,6 +782,12 @@ def parse_arguments():
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parser.add_argument("--use_triton_kernel", action="store_true", help="Use triton compiled kernels")
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parser.add_argument("--use_lock_free_queue", action="store_true", help="Use lock-free task queue for threadpool.")
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parser.add_argument(
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"--enable_generic_interface",
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action="store_true",
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help="build ORT shared library and compatible bridge with primary EPs(tensorRT, OpenVino, Qnn, vitisai) but not tests",
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)
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if not is_windows():
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parser.add_argument(
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"--allow_running_as_root",
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@ -1042,6 +1048,12 @@ def generate_build_tree(
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"-Donnxruntime_USE_TENSORRT=" + ("ON" if args.use_tensorrt else "OFF"),
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"-Donnxruntime_USE_TENSORRT_BUILTIN_PARSER="
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+ ("ON" if args.use_tensorrt_builtin_parser and not args.use_tensorrt_oss_parser else "OFF"),
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# interface variables are used only for building onnxruntime/onnxruntime_shared.dll but not EPs
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"-Donnxruntime_USE_TENSORRT_INTERFACE=" + ("ON" if args.enable_generic_interface else "OFF"),
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"-Donnxruntime_USE_CUDA_INTERFACE=" + ("ON" if args.enable_generic_interface else "OFF"),
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"-Donnxruntime_USE_OPENVINO_INTERFACE=" + ("ON" if args.enable_generic_interface else "OFF"),
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"-Donnxruntime_USE_VITISAI_INTERFACE=" + ("ON" if args.enable_generic_interface else "OFF"),
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"-Donnxruntime_USE_QNN_INTERFACE=" + ("ON" if args.enable_generic_interface else "OFF"),
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# set vars for migraphx
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"-Donnxruntime_USE_MIGRAPHX=" + ("ON" if args.use_migraphx else "OFF"),
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"-Donnxruntime_DISABLE_CONTRIB_OPS=" + ("ON" if args.disable_contrib_ops else "OFF"),
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@ -1372,6 +1384,8 @@ def generate_build_tree(
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cmake_args += ["-Donnxruntime_BUILD_QNN_EP_STATIC_LIB=ON"]
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if args.android and args.use_qnn != "static_lib":
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raise BuildError("Only support Android + QNN builds with QNN EP built as a static library.")
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if args.use_qnn == "static_lib" and args.enable_generic_interface:
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raise BuildError("Generic ORT interface only supported with QNN EP built as a shared library.")
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if args.use_coreml:
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cmake_args += ["-Donnxruntime_USE_COREML=ON"]
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@ -1529,6 +1543,12 @@ def generate_build_tree(
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"-Donnxruntime_USE_FULL_PROTOBUF=ON",
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]
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# When this flag is enabled, that means we only build ONNXRuntime shared library, expecting some compatible EP
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# shared lib being build in a seperate process. So we skip the test for now as ONNXRuntime shared lib built under
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# this flag is not expected to work alone
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if args.enable_generic_interface:
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cmake_args += ["-Donnxruntime_BUILD_UNIT_TESTS=OFF"]
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if args.enable_lazy_tensor:
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import torch
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@ -2649,6 +2669,9 @@ def main():
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# Disable ONNX Runtime's builtin memory checker
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args.disable_memleak_checker = True
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if args.enable_generic_interface:
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args.test = False
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# If there was no explicit argument saying what to do, default
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# to update, build and test (for native builds).
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if not (args.update or args.clean or args.build or args.test or args.gen_doc):
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@ -2752,7 +2775,10 @@ def main():
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source_dir = os.path.normpath(os.path.join(script_dir, "..", ".."))
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# if using cuda, setup cuda paths and env vars
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cuda_home, cudnn_home = setup_cuda_vars(args)
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cuda_home = ""
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cudnn_home = ""
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if args.use_cuda:
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cuda_home, cudnn_home = setup_cuda_vars(args)
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mpi_home = args.mpi_home
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nccl_home = args.nccl_home
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@ -2765,10 +2791,14 @@ def main():
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armnn_home = args.armnn_home
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armnn_libs = args.armnn_libs
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qnn_home = args.qnn_home
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qnn_home = ""
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if args.use_qnn:
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qnn_home = args.qnn_home
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# if using tensorrt, setup tensorrt paths
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tensorrt_home = setup_tensorrt_vars(args)
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tensorrt_home = ""
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if args.use_tensorrt:
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tensorrt_home = setup_tensorrt_vars(args)
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# if using migraphx, setup migraphx paths
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migraphx_home = setup_migraphx_vars(args)
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@ -2853,9 +2883,9 @@ def main():
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toolset = "host=" + host_arch + ",version=" + args.msvc_toolset
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else:
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toolset = "host=" + host_arch
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if args.cuda_version:
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if args.use_cuda and args.cuda_version:
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toolset += ",cuda=" + args.cuda_version
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elif args.cuda_home:
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elif args.use_cuda and args.cuda_home:
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toolset += ",cuda=" + args.cuda_home
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if args.windows_sdk_version:
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target_arch += ",version=" + args.windows_sdk_version
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@ -177,6 +177,25 @@ stages:
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WITH_CACHE: false
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MachinePool: 'onnxruntime-Win-CPU-2022'
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- stage: x64_release_ep_generic_interface
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dependsOn: []
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jobs:
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- template: templates/jobs/win-ci-vs-2022-job.yml
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parameters:
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BuildConfig: 'RelWithDebInfo'
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buildArch: x64
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additionalBuildFlags: --enable_generic_interface
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msbuildPlatform: x64
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isX86: false
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job_name_suffix: x64_release_ep_generic_interface
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RunOnnxRuntimeTests: false # --enable_generic_interface does not build tests
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EnablePython: false
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isTraining: false
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ORT_EP_NAME: CPU
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GenerateDocumentation: false
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WITH_CACHE: false
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MachinePool: 'onnxruntime-Win-CPU-2022'
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- stage: x86_release
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dependsOn: []
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jobs:
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