mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-26 22:35:43 +00:00
100 lines
No EOL
3 KiB
C++
100 lines
No EOL
3 KiB
C++
// Copyright (c) Microsoft Corporation.
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// Licensed under the MIT License.
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#include "pch.h"
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// Needed to work around the fact that OnnxRuntime defines ERROR
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#ifdef ERROR
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#undef ERROR
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#endif
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#include "core/session/inference_session.h"
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// Restore ERROR define
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#define ERROR 0
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#include "CpuOrtSessionBuilder.h"
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#include "WinMLAdapter.h"
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// winml includes
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#include "core/providers/dml/GraphTransformers/GraphTransformerHelpers.h"
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// ort includes
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#include "core/providers/cpu/cpu_execution_provider.h"
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#include "core/optimizer/conv_activation_fusion.h"
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#include "core/optimizer/gemm_activation_fusion.h"
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#include "core/session/abi_session_options_impl.h"
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using namespace Windows::AI::MachineLearning;
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namespace Windows::AI::MachineLearning::Adapter {
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CpuOrtSessionBuilder::CpuOrtSessionBuilder() {
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}
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HRESULT
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CpuOrtSessionBuilder::CreateSessionOptions(
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OrtSessionOptions** options) try {
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RETURN_HR_IF_NULL(E_POINTER, options);
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Ort::ThrowOnError(Ort::GetApi().CreateSessionOptions(options));
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std::unique_ptr<Ort::SessionOptions> session_options = std::make_unique<Ort::SessionOptions>(*options);
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// set the graph optimization level to all (used to be called level 3)
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session_options->SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
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// Onnxruntime will use half the number of concurrent threads supported on the system
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// by default. This causes MLAS to not exercise every logical core.
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// We force the thread pool size to be maxxed out to ensure that WinML always
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// runs the fastest.
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session_options->SetIntraOpNumThreads(std::thread::hardware_concurrency());
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// all done with the smart ptr
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session_options.release();
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return S_OK;
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}
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WINML_CATCH_ALL_COM
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HRESULT
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CpuOrtSessionBuilder::CreateSession(
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OrtSessionOptions* options,
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winmla::IInferenceSession** p_session,
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onnxruntime::IExecutionProvider** pp_provider) try {
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RETURN_HR_IF_NULL(E_POINTER, p_session);
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RETURN_HR_IF_NULL(E_POINTER, pp_provider);
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RETURN_HR_IF(E_POINTER, *pp_provider != nullptr);
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// Create the inference session
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auto session = std::make_unique<onnxruntime::InferenceSession>(options->value);
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// Create the cpu execution provider
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onnxruntime::CPUExecutionProviderInfo xpInfo;
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#ifndef _WIN64
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xpInfo.create_arena = false;
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#endif
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auto cpu_provider = std::make_unique<onnxruntime::CPUExecutionProvider>(xpInfo);
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// Cache the provider's raw pointer
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*pp_provider = cpu_provider.get();
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// Register the cpu xp
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ORT_THROW_IF_ERROR(session->RegisterExecutionProvider(std::move(cpu_provider)));
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// assign the session to the out parameter
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auto sessionptr = wil::MakeOrThrow<winmla::InferenceSession>(session.release());
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RETURN_IF_FAILED(sessionptr.CopyTo(_uuidof(winmla::IInferenceSession), (void**)p_session));
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return S_OK;
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}
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WINML_CATCH_ALL_COM
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HRESULT
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CpuOrtSessionBuilder::Initialize(
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winmla::IInferenceSession* p_session,
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onnxruntime::IExecutionProvider* /*p_provider*/
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) try {
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ORT_THROW_IF_ERROR(p_session->get()->Initialize());
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return S_OK;
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}
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WINML_CATCH_ALL_COM
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} // Windows::AI::MachineLearning::Adapter
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