/**
 * 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.
 */

#include "kernel/gpu/quant/fake_quant_grad_gpu_kernel.h"
#include "kernel/gpu/cuda_impl/fake_quant_impl.cuh"

namespace mindspore {
namespace kernel {
FakeQuantGradGpuKernel::FakeQuantGradGpuKernel()
    : input_size_(0),
      min_size_(0),
      max_size_(0),
      output_size_(0),
      workspace_size_(0),
      num_bits_(0),
      quant_min_(0),
      quant_max_(0),
      quant_size_(0),
      quant_delay_(0),
      global_step_(0) {}

const std::vector<size_t> &FakeQuantGradGpuKernel::GetInputSizeList() const { return input_size_list_; }

const std::vector<size_t> &FakeQuantGradGpuKernel::GetOutputSizeList() const { return output_size_list_; }

const std::vector<size_t> &FakeQuantGradGpuKernel::GetWorkspaceSizeList() const { return workspace_size_list_; }

bool FakeQuantGradGpuKernel::Init(const CNodePtr &kernel_node) {
  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
  if (input_num != 4) {
    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but FakeQuantGrad GpuKernel OP needs 4 output.";
  }

  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
  if (output_num != 1) {
    MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but FakeQuantGrad GpuKernel OP needs 1 output.";
  }

  num_bits_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("num_bits"));
  if (num_bits_ <= 2 || num_bits_ >= 16) {
    MS_LOG(EXCEPTION) << "Attr \'num_bits\' " << num_bits_ << " is out of range, expected between 2 and 16.";
  }

  quant_delay_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"));
  if (quant_delay_ < 0) {
    MS_LOG(EXCEPTION) << "Attr \'quant_delay_\' " << quant_delay_ << " is less then 0, require larger than 0.";
  }

  quant_min_ = 0;
  quant_max_ = (1 << num_bits_) - 1;

  if (quant_size_ == 0) {
    quant_size_ = 1;
  }
  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
  for (size_t i = 0; i < input_shape.size(); ++i) {
    quant_size_ *= SizeToInt(input_shape[i]);
  }

  input_size_ = sizeof(float);
  min_size_ = sizeof(float);
  max_size_ = sizeof(float);
  for (size_t i = 0; i < input_shape.size(); i++) {
    input_size_ *= input_shape[i];
  }
  output_size_ = input_size_;

  InitSizeLists();
  return true;
}

void FakeQuantGradGpuKernel::InitSizeLists() {
  input_size_list_.push_back(input_size_);  // gradient
  input_size_list_.push_back(input_size_);  // input
  input_size_list_.push_back(min_size_);    // min
  input_size_list_.push_back(max_size_);    // max
  output_size_list_.push_back(output_size_);
  workspace_size_list_.push_back(workspace_size_);
}

bool FakeQuantGradGpuKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
                                    const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) {
  (void)workspace;
  float *output = GetDeviceAddress<float>(outputs, 0);
  float *gradient = GetDeviceAddress<float>(inputs, 0);
  float *input = GetDeviceAddress<float>(inputs, 1);
  float *input_min = GetDeviceAddress<float>(inputs, 2);
  float *input_max = GetDeviceAddress<float>(inputs, 3);

  if (gradient == nullptr) {
    MS_LOG(EXCEPTION) << "FakeQuantGradGpuKernel gradient is null";
  }
  if (input == nullptr) {
    MS_LOG(EXCEPTION) << "FakeQuantGradGpuKernel input is null.";
  }
  if (input_min == nullptr) {
    MS_LOG(EXCEPTION) << "FakeQuantGradGpuKernel input min is null.";
  }
  if (input_max == nullptr) {
    MS_LOG(EXCEPTION) << "FakeQuantGradGpuKernel input max is null.";
  }

  if (global_step_ >= quant_delay_) {
    float *d_scale = nullptr;
    float *d_nudge_min = nullptr;
    float *d_nudge_max = nullptr;
    int size = sizeof(float);
    // Allocate space for device copies
    CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_scale), size), "Malloc gpu memory failed");
    CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_min), size), "Malloc gpu memory failed");
    CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_max), size), "Malloc gpu memory failed");

    CalNudge(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale,
             reinterpret_cast<cudaStream_t>(stream_ptr));
    CalFakeQuantizeGrad(input, gradient, output, quant_size_, d_nudge_min, d_nudge_max,
                        reinterpret_cast<cudaStream_t>(stream_ptr));

    // Cleanup
    CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_scale), "Free gpu memory failed");
    CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_min), "Free gpu memory failed");
    CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_max), "Free gpu memory failed");
  } else {
    CHECK_CUDA_RET_WITH_ERROR(cudaMemcpy(output, gradient, input_size_, cudaMemcpyDeviceToDevice),
                              "Copy gpu memory failed.");
  }
  global_step_++;
  return true;
}

MS_REG_GPU_KERNEL(FakeQuantWithMinMaxGrad, FakeQuantGradGpuKernel)
}  // namespace kernel
}  // namespace mindspore
