text: /**
   * Copyright 2020 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_KERNEL_GPU_NN_RMSPROP_KERNEL_H_
  #define MINDSPORE_CCSRC_KERNEL_GPU_NN_RMSPROP_KERNEL_H_
  
  #include <vector>
  #include "kernel/gpu/gpu_kernel.h"
  #include "kernel/gpu/gpu_kernel_factory.h"
  #include "kernel/gpu/cuda_impl/rmsprop_impl.cuh"
  
  namespace mindspore {
  namespace kernel {
  template <typename T>
  class RMSPropGpuKernel : public GpuKernel {
   public:
    RMSPropGpuKernel() : size_(1), use_center_(false) {}
    ~RMSPropGpuKernel() 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, uintptr_t stream) override {
      if (!use_center_) {
        T *variable = GetDeviceAddress<T>(inputs, 0);
        T *mean_square = GetDeviceAddress<T>(inputs, 1);
        T *moment = GetDeviceAddress<T>(inputs, 2);
        T *gradients = GetDeviceAddress<T>(inputs, 3);
        T *learning_rate = GetDeviceAddress<T>(inputs, 4);
        T *decay = GetDeviceAddress<T>(inputs, 5);
        T *momentum = GetDeviceAddress<T>(inputs, 6);
        T *epsilon = GetDeviceAddress<T>(inputs, 7);
  
        RmsProp(learning_rate, decay, momentum, epsilon, variable, mean_square, moment, gradients, size_,
                reinterpret_cast<cudaStream_t>(stream));
      } else {
        T *variable = GetDeviceAddress<T>(inputs, 0);
        T *mean_gradients = GetDeviceAddress<T>(inputs, 1);
        T *mean_square = GetDeviceAddress<T>(inputs, 2);
        T *moment = GetDeviceAddress<T>(inputs, 3);
        T *gradients = GetDeviceAddress<T>(inputs, 4);
        T *learning_rate = GetDeviceAddress<T>(inputs, 5);
        T *decay = GetDeviceAddress<T>(inputs, 6);
        T *momentum = GetDeviceAddress<T>(inputs, 7);
        T *epsilon = GetDeviceAddress<T>(inputs, 8);
  
        RmsPropCenter(learning_rate, decay, momentum, epsilon, variable, mean_gradients, mean_square, moment, gradients,
                      size_, reinterpret_cast<cudaStream_t>(stream));
      }
      return true;
    }
    bool Init(const CNodePtr &kernel_node) override {
      auto node_name = AnfAlgo::GetCNodeName(kernel_node);
      if (node_name == "ApplyCenteredRMSProp") {
        use_center_ = true;
      }
  
      auto input_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
      for (auto &dim : input_shape) {
        size_ *= dim;
      }
      InitSizeLists();
      return true;
    }
  
   protected:
    void InitSizeLists() override {
      size_t input_size = size_ * sizeof(T);
      input_size_list_.push_back(input_size);
      if (use_center_) {
        input_size_list_.push_back(input_size);
      }
  
      input_size_list_.push_back(input_size);
      input_size_list_.push_back(input_size);
      input_size_list_.push_back(input_size);
      input_size_list_.push_back(sizeof(T));
      input_size_list_.push_back(sizeof(T));
      input_size_list_.push_back(sizeof(T));
      input_size_list_.push_back(sizeof(T));
      output_size_list_.push_back(0);
    }
  
   private:
    size_t size_;
    bool use_center_;
  
    std::vector<size_t> input_size_list_;
    std::vector<size_t> output_size_list_;
    std::vector<size_t> workspace_size_list_;
  };
  }  // namespace kernel
  }  // namespace mindspore
  
  #endif
  