forked from huawei/mindspore2022
77 lines
2.8 KiB
C++
77 lines
2.8 KiB
C++
/**
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* Copyright 2019 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_BACKEND_SESSION_GPU_SESSION_H
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#define MINDSPORE_CCSRC_BACKEND_SESSION_GPU_SESSION_H
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#include <vector>
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#include <memory>
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#include "backend/session/session_basic.h"
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#include "backend/session/kernel_graph.h"
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#include "backend/session/session_factory.h"
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using KernelGraph = mindspore::session::KernelGraph;
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namespace mindspore {
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namespace session {
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namespace gpu {
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class GPUSession : public SessionBasic {
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public:
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GPUSession() = default;
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~GPUSession() override = default;
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void Init(uint32_t device_id) override {
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SessionBasic::Init(device_id);
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context_ = std::make_shared<Context>(kGPUDevice, device_id);
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}
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GraphId CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) override;
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void RunGraph(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs) override;
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void BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
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const std::vector<tensor::TensorPtr> &input_tensors, const std::vector<int> &tensors_mask) override;
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py::tuple RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
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const std::vector<tensor::TensorPtr> &input_tensors) override;
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private:
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void SelectKernel(const std::shared_ptr<KernelGraph> &kernel_graph) const;
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void StartKernelRT() const;
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void Optimize(const std::shared_ptr<KernelGraph> &kernel_graph);
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void HardwareOptimize(const std::shared_ptr<KernelGraph> &kernel_graph);
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void AssignStream(const std::shared_ptr<KernelGraph> &kernel_graph);
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void BuildKernel(const std::shared_ptr<KernelGraph> &kernel_graph) const;
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void AllocateMemory(KernelGraph *kernel_graph) const;
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void RunOpAllocateMemory(const std::vector<tensor::TensorPtr> &input_tensors, KernelGraph *kernel_graph) const;
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void RunOpClearMemory(KernelGraph *kernel_graph) const;
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void LoadInputData(const std::shared_ptr<KernelGraph> &kernel_graph,
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const std::vector<tensor::TensorPtr> &inputs_const) const override;
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void Execute(const std::shared_ptr<KernelGraph> &kernel_graph) const;
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};
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using GPUSessionPtr = std::shared_ptr<GPUSession>;
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MS_REG_SESSION(kGPUDevice, GPUSession);
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} // namespace gpu
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} // namespace session
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_SESSION_GPU_SESSION_H
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