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/** |
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* Copyright 2020 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_KERNEL_COMPILER_GPU_DYNAMIC_SHAPE_GPU_KERNEL_H_ |
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_DYNAMIC_SHAPE_GPU_KERNEL_H_ |
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#include <cuda_runtime.h> |
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#include <vector> |
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#include "backend/kernel_compiler/gpu/gpu_kernel.h" |
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#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h" |
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namespace mindspore { |
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namespace kernel { |
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template <typename T> |
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class DynamicShapeGpuKernel : public GpuKernel { |
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public: |
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DynamicShapeGpuKernel() { ResetResource(); } |
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~DynamicShapeGpuKernel() = default; |
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const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; } |
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const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; } |
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const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; } |
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bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace, |
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const std::vector<AddressPtr> &outputs, void *stream_ptr) override { |
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int *output_device_address = GetDeviceAddress<int>(outputs, 0); |
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size_t prev_node_output_shape_size = prev_node_output_shape_.size() * sizeof(int); |
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CHECK_CUDA_RET_WITH_EXCEPT( |
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cudaMemcpyAsync(output_device_address, prev_node_output_shape_.data(), prev_node_output_shape_size, |
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cudaMemcpyHostToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)), |
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"cudaMemcpyAsync prev_node_output_shape failed"); |
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return true; |
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} |
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bool Init(const CNodePtr &kernel_node) override { |
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size_t input_count = AnfAlgo::GetInputTensorNum(kernel_node); |
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if (input_count != 1) { |
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MS_LOG(EXCEPTION) << input_count << " arguments were provided, but DynamicShapeGpuKernel expects 1."; |
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} |
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std::vector<size_t> prev_node_output_shape_tmp = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); |
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input_size_ = 1; |
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for (const size_t &e : prev_node_output_shape_tmp) { |
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input_size_ *= e; |
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// shapes are Tensors with elements of type int32, but GetPrevNodeOutputInferShape returns vector of size_t, |
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// so we use an int* for allocated output memory and cast to an int here, otherwise the memcpy will fail with a |
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// silently. |
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prev_node_output_shape_.push_back(e); |
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} |
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output_size_ = prev_node_output_shape_.size(); |
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InitSizeLists(); |
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return true; |
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} |
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void ResetResource() noexcept override { |
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input_size_ = -1; |
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output_size_ = -1; |
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prev_node_output_shape_.clear(); |
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input_size_list_.clear(); |
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output_size_list_.clear(); |
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workspace_size_list_.clear(); |
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} |
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protected: |
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void InitSizeLists() override { |
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input_size_list_.push_back(input_size_ * sizeof(T)); |
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output_size_list_.push_back(output_size_ * sizeof(int)); |
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} |
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private: |
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size_t input_size_; |
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size_t output_size_; |
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std::vector<int> prev_node_output_shape_; |
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std::vector<size_t> input_size_list_; |
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std::vector<size_t> output_size_list_; |
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std::vector<size_t> workspace_size_list_; |
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}; |
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} // namespace kernel |
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} // namespace mindspore |
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_DYNAMIC_SHAPE_GPU_KERNEL_H_ |