text: /**
   * Copyright 2020-2021 Huawei Technologies Co., Ltd
   *
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  #ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_RELU_GRAD_KERNEL_H_
  #define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_RELU_GRAD_KERNEL_H_
  
  #include <vector>
  #include <map>
  #include <string>
  #include "backend/kernel_compiler/gpu/gpu_kernel.h"
  #include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
  #include "backend/kernel_compiler/gpu/kernel_constants.h"
  #include "backend/kernel_compiler/gpu/cuda_impl/relu_grad_impl.cuh"
  
  namespace mindspore {
  namespace kernel {
  template <typename T>
  class ReluGradGpuFwdKernel : public GpuKernel {
   public:
    ReluGradGpuFwdKernel() { ResetResource(); }
    ~ReluGradGpuFwdKernel() override = default;
    const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
    const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
    const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
  
    bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
                const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
      if (is_null_input_) {
        return true;
      }
      T *dy = GetDeviceAddress<T>(inputs, 0);
      T *y = GetDeviceAddress<T>(inputs, 1);
      T *dx = GetDeviceAddress<T>(outputs, 0);
  
      const int size = input_size_ / sizeof(T);
      CalReLUGrad(size, dy, y, dx, reinterpret_cast<cudaStream_t>(stream_ptr));
  
      return true;
    }
    bool Init(const CNodePtr &kernel_node) override {
      InitResource();
      size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
      if (input_num != 2) {
        MS_LOG(ERROR) << "Argument number is " << input_num << ", but ReluGradGpuKernel needs 2.";
        return false;
      }
      auto input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
      is_null_input_ = CHECK_NULL_INPUT(input_shape);
      if (is_null_input_) {
        MS_LOG(WARNING) << "For 'ReLUGradGpuKernel', input is null.";
        InitSizeLists();
        return true;
      }
      size_t size = 1;
      for (size_t i = 0; i < input_shape.size(); i++) {
        size *= input_shape[i];
      }
      input_size_ = size * sizeof(T);
  
      InitSizeLists();
      return true;
    }
    void ResetResource() noexcept override {
      is_null_input_ = false;
      input_size_list_.clear();
      output_size_list_.clear();
      workspace_size_list_.clear();
      input_size_ = 0;
    }
  
   protected:
    void InitSizeLists() override {
      input_size_list_.push_back(input_size_);
      output_size_list_.push_back(input_size_);
      input_size_list_.push_back(input_size_);
    }
  
   private:
    bool is_null_input_;
    std::vector<size_t> input_size_list_;
    std::vector<size_t> output_size_list_;
    std::vector<size_t> workspace_size_list_;
    size_t input_size_;
  };
  }  // namespace kernel
  }  // namespace mindspore
  
  #endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_RELU_GRAD_KERNEL_H_
  