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https://github.com/saymrwulf/onnxruntime.git
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dd72192cf4
### Description This PR is to refactor ExecutionProvider API for memory management, which is to move allocators from EP level to SessionState level and indexed by OrtDevice ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> This PR is to refactor ExecutionProvider API for memory management, which is to move allocators from EP level to SessionState level and indexed by OrtDevice. By this change, EP level will shift the burden of maintaining allocators, which will be user friendly for EP developers --------- Co-authored-by: Lei Cao <leca@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
104 lines
3.2 KiB
C++
104 lines
3.2 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 <variant>
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#include "core/common/common.h"
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#include "core/framework/tensor_shape.h"
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#include "core/framework/float16.h"
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#include "core/framework/framework_common.h"
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#include "core/framework/ort_value.h"
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#include "test/framework/test_utils.h"
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namespace onnxruntime {
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namespace test {
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/**
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* @brief Class represent a input data (dimensions, data type and value).
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*/
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struct TestInputData {
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template <typename T>
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TestInputData(const std::string& name, const TensorShapeVector& dims, const std::vector<T>& values)
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: name_(name), dims_(dims), values_(values) {}
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OrtValue ToOrtValue() {
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OrtValue ortvalue;
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std::vector<int64_t> dims;
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dims.reserve(dims_.size());
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dims.insert(dims.end(), dims_.begin(), dims_.end());
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std::visit([&ortvalue, &dims](auto&& arg) {
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using T = std::decay_t<decltype(arg)>;
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if constexpr (std::is_same_v<T, std::vector<int64_t>> ||
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std::is_same_v<T, std::vector<float>> ||
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std::is_same_v<T, std::vector<MLFloat16>>)
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CreateMLValue<typename T::value_type>(
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TestCPUExecutionProvider()->CreatePreferredAllocators()[0], dims, arg, &ortvalue);
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else
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static_assert("Unspported types!");
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},
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values_);
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return ortvalue;
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}
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std::string GetName() const {
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return name_;
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}
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private:
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std::string name_;
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TensorShapeVector dims_;
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std::variant<std::vector<float>, std::vector<MLFloat16>, std::vector<int64_t>> values_;
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};
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/**
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* @brief A container for all input data.
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*
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*/
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struct InputContainer {
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InputContainer() = default;
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template <typename T>
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TestInputData& AddInput(const std::string& name, const TensorShapeVector dims, const std::vector<T>& values) {
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inputs_.emplace_back(TestInputData(name, dims, values));
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return inputs_.back();
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}
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template <typename T>
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TestInputData& AddInput(const std::string& name, TensorShapeVector dims,
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std::function<
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void(const TensorShapeVector& shape, std::vector<T>& data)>
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func = nullptr) {
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std::vector<T> values(TensorShape(dims).Size());
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if (func) {
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func(dims, values);
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}
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inputs_.emplace_back(TestInputData(name, dims, values));
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return inputs_.back();
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}
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void ToInputMap(NameMLValMap& feeds) const {
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for (auto input : inputs_) {
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feeds.insert({input.GetName(), input.ToOrtValue()});
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}
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}
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private:
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std::vector<TestInputData> inputs_;
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};
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void RandomFillFloatVector(const TensorShapeVector& shape, std::vector<float>& data);
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void RandomFillHalfVector(const TensorShapeVector& shape, std::vector<MLFloat16>& data);
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void RandomMasks(int64_t batch, int64_t sequence_length, std::vector<int64_t>& data);
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void RunModelWithData(const PathString& model_uri, const std::string session_log_id,
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const std::string& provider_type, const InputContainer& input_container,
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const std::vector<std::string>& output_names,
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std::vector<OrtValue>& run_results);
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} // namespace test
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} // namespace onnxruntime
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