text: /**
   * Copyright 2019 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_SOFTMAX_GPU_KERNEL_H_
  #define MINDSPORE_CCSRC_KERNEL_GPU_NN_SOFTMAX_GPU_KERNEL_H_
  
  #include <vector>
  #include "kernel/gpu/gpu_kernel.h"
  #include "kernel/gpu/gpu_kernel_factory.h"
  #include "kernel/gpu/kernel_constants.h"
  #include "kernel/gpu/cuda_impl/transpose_impl.cuh"
  
  namespace mindspore {
  namespace kernel {
  template <typename T>
  class SoftmaxGpuKernel : public GpuKernel {
   public:
    SoftmaxGpuKernel()
        : cudnn_handle_(nullptr),
          input_descriptor_(nullptr),
          output_descriptor_(nullptr),
          algo_(CUDNN_SOFTMAX_ACCURATE),
          mode_(CUDNN_SOFTMAX_MODE_INSTANCE),
          cudnn_data_type_(CUDNN_DATA_FLOAT),
          is_null_input_(false),
          input_size_(0),
          output_size_(0),
          workspace_size_(0),
          axis_(0),
          shape_size_(0),
          batch_size_(0),
          channel_size_(0),
          height_(0),
          width_(0) {}
    ~SoftmaxGpuKernel() override { DestroyResource(); }
  
    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> &workspace,
                const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
      if (is_null_input_) {
        return true;
      }
      T *input_addr = GetDeviceAddress<T>(inputs, 0);
      T *output_addr = GetDeviceAddress<T>(outputs, 0);
      const float alpha = 1;
      const float beta = 0;
  
      if (axis_ == 1) {
        CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSoftmaxForward(cudnn_handle_, algo_, mode_, &alpha, input_descriptor_,
                                                        input_addr, &beta, output_descriptor_, output_addr),
                                    "cudnnSoftmaxForward failed");
      } else {
        T *transpose_input_addr = GetDeviceAddress<T>(workspace, 0);
        T *transpose_output_addr = GetDeviceAddress<T>(workspace, 1);
        int *input_shape = GetDeviceAddress<int>(workspace, 2);
        int *transpose_shape = GetDeviceAddress<int>(workspace, 3);
        int *transpose_axis = GetDeviceAddress<int>(workspace, 4);
        CHECK_CUDA_RET_WITH_EXCEPT(cudaMemcpyAsync(input_shape, &input_shape_[0], workspace_size_, cudaMemcpyHostToDevice,
                                                   reinterpret_cast<cudaStream_t>(stream_ptr)),
                                   "cudaMemcpyAsync input_shape failed");
        CHECK_CUDA_RET_WITH_EXCEPT(cudaMemcpyAsync(transpose_shape, &transpose_shape_[0], workspace_size_,
                                                   cudaMemcpyHostToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)),
                                   "cudaMemcpyAsync input_shape failed");
        CHECK_CUDA_RET_WITH_EXCEPT(cudaMemcpyAsync(transpose_axis, &transpose_axis_[0], workspace_size_,
                                                   cudaMemcpyHostToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)),
                                   "cudaMemcpyAsync input_axis failed");
        int size = SizeToInt(input_size_ / sizeof(T));
        CalTranspose(size, input_addr, input_shape, transpose_axis, shape_size_, transpose_input_addr,
                     reinterpret_cast<cudaStream_t>(stream_ptr));
        CHECK_CUDNN_RET_WITH_EXCEPT(
          cudnnSoftmaxForward(cudnn_handle_, algo_, mode_, &alpha, input_descriptor_, transpose_input_addr, &beta,
                              output_descriptor_, transpose_output_addr),
          "cudnnSoftmaxForward failed");
        CalTranspose(size, transpose_output_addr, transpose_shape, transpose_axis, shape_size_, output_addr,
                     reinterpret_cast<cudaStream_t>(stream_ptr));
      }
      return true;
    }
  
    bool Init(const CNodePtr &kernel_node) override {
      InitResource();
      cudnn_data_type_ = kCudnnDtypeMap[TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))];
      size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
      if (input_num != 1) {
        MS_LOG(ERROR) << "Input number is " << input_num << ", but softmax needs 1 input.";
        return false;
      }
      size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
      if (output_num != 1) {
        MS_LOG(ERROR) << "Output number is " << output_num << ", but softmax needs 1 output.";
        return false;
      }
      auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
      is_null_input_ = CHECK_NULL_INPUT(input_shape);
      if (is_null_input_) {
        MS_LOG(WARNING) << "SoftmaxGpuKernel input is null";
        InitSizeLists();
        return true;
      }
      shape_size_ = SizeToInt(input_shape.size());
      auto kernel_name = AnfAlgo::GetCNodeName(kernel_node);
      if (kernel_name == "LogSoftmax") {
        algo_ = CUDNN_SOFTMAX_LOG;
        auto axis = GetAttr<int>(kernel_node, "axis");
        InitSizeByAxis(input_shape, axis);
      } else {
        algo_ = CUDNN_SOFTMAX_ACCURATE;
        auto axis = GetAttr<std::vector<int>>(kernel_node, "axis");
        InitSizeByAxis(input_shape, axis[0]);
      }
      CHECK_CUDNN_RET_WITH_EXCEPT(
        cudnnSetTensor4dDescriptor(input_descriptor_, CUDNN_TENSOR_NCHW, cudnn_data_type_, SizeToInt(batch_size_),
                                   SizeToInt(channel_size_), SizeToInt(height_), SizeToInt(width_)),
        "set input_descriptor failed");
      CHECK_CUDNN_RET_WITH_EXCEPT(
        cudnnSetTensor4dDescriptor(output_descriptor_, CUDNN_TENSOR_NCHW, cudnn_data_type_, SizeToInt(batch_size_),
                                   SizeToInt(channel_size_), SizeToInt(height_), SizeToInt(width_)),
        "set output_descriptor failed");
      InitSizeLists();
      return true;
    }
  
   protected:
    void InitResource() override {
      cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
      CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&input_descriptor_), "create input_descriptor failed");
      CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&output_descriptor_), "create output_descriptor failed");
    }
  
    void InitSizeLists() override {
      input_size_list_.push_back(input_size_);
      output_size_list_.push_back(output_size_);
      workspace_size_list_.push_back(input_size_);
      workspace_size_list_.push_back(output_size_);
      workspace_size_list_.push_back(workspace_size_);
      workspace_size_list_.push_back(workspace_size_);
      workspace_size_list_.push_back(workspace_size_);
      return;
    }
  
   private:
    void DestroyResource() noexcept {
      CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(output_descriptor_), "destroy output_descriptor failed");
      CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(input_descriptor_), "destroy input_descriptor failed");
    }
  
    void InitSizeByAxis(const std::vector<size_t> &input_shape, const int &axis) {
      if (input_shape.size() == 2) {
        InitSizeByAxis2D(input_shape, axis);
      } else {
        InitSizeByAxisLastDim(input_shape, axis);
      }
    }
  
    void InitSizeByAxis2D(const std::vector<size_t> &input_shape, const int &axis) {
      axis_ = axis;
      if (axis_ < 0) {
        axis_ += shape_size_;
      }
      if (axis_ == 1) {
        batch_size_ = input_shape[0];
        channel_size_ = input_shape[1];
      } else if (axis_ == 0) {
        batch_size_ = input_shape[1];
        channel_size_ = input_shape[0];
        input_shape_.push_back(input_shape[0]);
        input_shape_.push_back(input_shape[1]);
        transpose_shape_.push_back(input_shape[1]);
        transpose_shape_.push_back(input_shape[0]);
        transpose_axis_.push_back(1);
        transpose_axis_.push_back(0);
      } else {
        MS_LOG(EXCEPTION) << "Input is " << shape_size_ << "-D, but axis(" << axis << ") is invalid.";
      }
  
      height_ = 1;
      width_ = 1;
      input_size_ = sizeof(T) * batch_size_ * channel_size_ * height_ * width_;
      output_size_ = input_size_;
      workspace_size_ = IntToSize(shape_size_) * sizeof(int);
    }
  
    void InitSizeByAxisLastDim(const std::vector<size_t> &input_shape, const int &axis) {
      int axis_pos = axis;
      if (axis_pos < 0) {
        axis_pos += input_shape.size();
      }
      // axis should be -1 with ND
      if (axis_pos != SizeToInt(input_shape.size() - 1)) {
        MS_LOG(EXCEPTION) << "Input is " << shape_size_ << "-D, but axis(" << axis << ") is invalid.";
      }
      // squeeze to 2d, then invoke cudnn
      size_t n = 1;
      for (size_t i = 0; i < input_shape.size() - 1; i++) {
        n *= input_shape[i];
      }
      axis_ = 1;
      batch_size_ = n;
      channel_size_ = input_shape[axis_pos];
      height_ = 1;
      width_ = 1;
      input_size_ = sizeof(T) * batch_size_ * channel_size_ * height_ * width_;
      output_size_ = input_size_;
      input_shape_.push_back(batch_size_);
      input_shape_.push_back(channel_size_);
    }
  
    cudnnHandle_t cudnn_handle_;
    cudnnTensorDescriptor_t input_descriptor_;
    cudnnTensorDescriptor_t output_descriptor_;
    cudnnSoftmaxAlgorithm_t algo_;
    cudnnSoftmaxMode_t mode_;
    cudnnDataType_t cudnn_data_type_;
    bool is_null_input_;
    size_t input_size_;
    size_t output_size_;
    size_t workspace_size_;
    std::vector<size_t> input_size_list_;
    std::vector<size_t> output_size_list_;
    std::vector<size_t> workspace_size_list_;
  
    std::vector<int> input_shape_;
    std::vector<int> transpose_shape_;
    std::vector<int> transpose_axis_;
    int axis_;
    int shape_size_;
  
    size_t batch_size_;
    size_t channel_size_;
    size_t height_;
    size_t width_;
  };
  }  // namespace kernel
  }  // namespace mindspore
  
  #endif  // MINDSPORE_CCSRC_KERNEL_GPU_NN_SOFTMAX_GPU_KERNEL_H_
  