/**
 * 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_BACKEND_KERNEL_COMPILER_GPU_RANDOMOP_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_RANDOMOP_GPU_KERNEL_H_

#include <curand_kernel.h>
#include <cuda_runtime_api.h>
#include <vector>
#include <string>
#include <map>
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
#include "backend/kernel_compiler/gpu/cuda_impl/random_op_impl.cuh"

namespace mindspore {
namespace kernel {
enum RandomOptype { RANDOM_OP_NORMAL = 0, RANDOM_OP_UNIFORM_REAL, RANDOM_OP_INVALID_TYPE = 255 };

const std::map<std::string, RandomOptype> kRandomOpTypeMap = {{"StandardNormal", RANDOM_OP_NORMAL},
                                                              {"UniformReal", RANDOM_OP_UNIFORM_REAL}};
template <typename T>
class RandomOpGpuKernel : public GpuKernel {
 public:
  RandomOpGpuKernel()
      : random_op_type_(RANDOM_OP_INVALID_TYPE),
        input_size_0_(sizeof(int)),
        input_size_1_(sizeof(T)),
        input_size_2_(sizeof(T)),
        output_size_(sizeof(T)),
        workspace_size_(sizeof(curandState)),
        seed_(0),
        seed2_(0) {}
  ~RandomOpGpuKernel() 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> &workspace,
              const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
    void *workspace_addr = GetDeviceAddress<void *>(workspace, 0);
    curandState *devStates = reinterpret_cast<curandState *>(workspace_addr);
    T *output_addr = GetDeviceAddress<T>(outputs, 0);

    switch (random_op_type_) {
      case RANDOM_OP_NORMAL: {
        StandardNormal(seed_, seed2_, devStates, output_addr, outputs[0]->size / sizeof(T),
                       reinterpret_cast<cudaStream_t>(stream_ptr));
        break;
      }
      case RANDOM_OP_UNIFORM_REAL: {
        T *input_addr_1 = GetDeviceAddress<T>(inputs, 1);
        T *input_addr_2 = GetDeviceAddress<T>(inputs, 2);
        UniformReal(seed_, devStates, input_addr_1, inputs[1]->size / sizeof(T), input_addr_2,
                    inputs[2]->size / sizeof(T), output_addr, outputs[0]->size / sizeof(T),
                    reinterpret_cast<cudaStream_t>(stream_ptr));
        break;
      }
      default: {
        MS_LOG(EXCEPTION) << "Random operation " << random_op_type_ << " is not supported.";
      }
    }
    return true;
  }

  bool Init(const CNodePtr &kernel_node) override {
    std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
    auto iter = kRandomOpTypeMap.find(kernel_name);
    if (iter == kRandomOpTypeMap.end()) {
      MS_LOG(EXCEPTION) << "Random operation " << kernel_name << " is not supported.";
    } else {
      random_op_type_ = iter->second;
    }
    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
    if (random_op_type_ == RANDOM_OP_NORMAL && input_num != 1) {
      MS_LOG(ERROR) << "Input number is " << input_num << ", but random op needs 1 input.";
      return false;
    }
    if (random_op_type_ == RANDOM_OP_UNIFORM_REAL && input_num != 3) {
      MS_LOG(ERROR) << "Input number is " << input_num << ", but random op needs 3 inputs.";
      return false;
    }
    size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
    if (output_num != 1) {
      MS_LOG(ERROR) << "Output number is " << output_num << ", but random op needs 1 output.";
      return false;
    }
    auto input_shape_0 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
    for (size_t i = 0; i < input_shape_0.size(); i++) {
      input_size_0_ += input_shape_0[i];
    }
    input_size_0_ *= sizeof(int);
    if (random_op_type_ == RANDOM_OP_UNIFORM_REAL) {
      auto input_shape_1 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
      for (size_t i = 0; i < input_shape_1.size(); i++) {
        input_size_1_ *= input_shape_1[i];
      }
      auto input_shape_2 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2);
      for (size_t i = 0; i < input_shape_2.size(); i++) {
        input_size_2_ *= input_shape_2[i];
      }
    }
    auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
    for (size_t i = 0; i < output_shape.size(); i++) {
      output_size_ *= output_shape[i];
      workspace_size_ *= output_shape[i];
    }
    seed_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("seed"));
    if (random_op_type_ == RANDOM_OP_NORMAL) {
      seed2_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("seed2"));
    }
    InitSizeLists();
    return true;
  }

 protected:
  void InitSizeLists() override {
    input_size_list_.push_back(input_size_0_);
    if (random_op_type_ == RANDOM_OP_UNIFORM_REAL) {
      input_size_list_.push_back(input_size_1_);
      input_size_list_.push_back(input_size_2_);
    }
    output_size_list_.push_back(output_size_);
    workspace_size_list_.push_back(workspace_size_);
  }

 private:
  RandomOptype random_op_type_;
  size_t input_size_0_;
  size_t input_size_1_;
  size_t input_size_2_;
  size_t output_size_;
  size_t workspace_size_;
  int seed_;
  int seed2_;
  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  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_RANDOMOP_GPU_KERNEL_H_
