mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-06-07 00:13:17 +00:00
d1b1cdc5c4
Remove gsl subodule and replace with a local copy of gsl-lite Refactor for onnxruntime::make_unique gsl::span size and index are now size_t Remove lambda auto argument type detection. Remove constexpr from fail_fast in gsl due to Linux not being happy. Comment out std::stream support due to MacOS std lib broken. Move make_unique into include/core/common so it is accessible for server builds. Relax requirements for onnxruntime/test/providers/cpu/ml/write_scores_test.cc due to x86 build. Add ONNXRUNTIME_ROOT to Server Lib includes so gsl is recognized
101 lines
3.3 KiB
C++
101 lines
3.3 KiB
C++
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#pragma once
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#include <map>
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#include <string>
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#include "core/framework/allocatormgr.h"
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#include "core/framework/execution_provider.h"
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#include "core/providers/cpu/cpu_execution_provider.h"
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#include "core/framework/ml_value.h"
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#include "gsl/gsl"
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#ifdef USE_CUDA
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#include "core/providers/cuda/cuda_execution_provider.h"
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#endif
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#ifdef USE_TENSORRT
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#include "core/providers/tensorrt/tensorrt_execution_provider.h"
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#endif
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#ifdef USE_OPENVINO
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#include "core/providers/openvino/openvino_execution_provider.h"
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#endif
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#ifdef USE_NNAPI
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#include "core/providers/nnapi/nnapi_execution_provider.h"
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#endif
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namespace onnxruntime {
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class Graph;
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namespace test {
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// Doesn't work with ExecutionProviders class and KernelRegistryManager
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IExecutionProvider* TestCPUExecutionProvider();
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#ifdef USE_CUDA
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// Doesn't work with ExecutionProviders class and KernelRegistryManager
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IExecutionProvider* TestCudaExecutionProvider();
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#endif
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#ifdef USE_TENSORRT
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// Doesn't work with ExecutionProviders class and KernelRegistryManager
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IExecutionProvider* TestTensorrtExecutionProvider();
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#endif
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#ifdef USE_OPENVINO
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IExecutionProvider* TestOpenVINOExecutionProvider();
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#endif
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#ifdef USE_NNAPI
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IExecutionProvider* TestNnapiExecutionProvider();
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#endif
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template <typename T>
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inline void CopyVectorToTensor(const std::vector<T>& value, Tensor& tensor) {
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gsl::copy(gsl::make_span(value), tensor.MutableDataAsSpan<T>());
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}
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// vector<bool> is specialized so we need to handle it separately
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template <>
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inline void CopyVectorToTensor<bool>(const std::vector<bool>& value, Tensor& tensor) {
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auto output_span = tensor.MutableDataAsSpan<bool>();
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for (size_t i = 0, end = value.size(); i < end; ++i) {
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output_span[i] = value[i];
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}
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}
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template <typename T>
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void CreateMLValue(AllocatorPtr alloc, const std::vector<int64_t>& dims, const std::vector<T>& value,
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OrtValue* p_mlvalue) {
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TensorShape shape(dims);
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auto element_type = DataTypeImpl::GetType<T>();
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std::unique_ptr<Tensor> p_tensor = onnxruntime::make_unique<Tensor>(element_type,
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shape,
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alloc);
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if (value.size() > 0) {
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CopyVectorToTensor(value, *p_tensor);
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}
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p_mlvalue->Init(p_tensor.release(),
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DataTypeImpl::GetType<Tensor>(),
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DataTypeImpl::GetType<Tensor>()->GetDeleteFunc());
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}
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template <typename T>
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void AllocateMLValue(AllocatorPtr alloc, const std::vector<int64_t>& dims, OrtValue* p_mlvalue) {
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TensorShape shape(dims);
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auto element_type = DataTypeImpl::GetType<T>();
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std::unique_ptr<Tensor> p_tensor = onnxruntime::make_unique<Tensor>(element_type,
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shape,
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alloc);
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p_mlvalue->Init(p_tensor.release(),
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DataTypeImpl::GetType<Tensor>(),
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DataTypeImpl::GetType<Tensor>()->GetDeleteFunc());
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}
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// Returns a map with the number of occurrences of each operator in the graph.
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// Helper function to check that the graph transformations have been successfully applied.
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std::map<std::string, int> CountOpsInGraph(const Graph& graph);
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} // namespace test
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} // namespace onnxruntime
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