/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.

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. */
#ifdef RESHAPE_OP

#include "operators/kernel/reshape_kernel.h"

namespace paddle_mobile {
namespace operators {

template <>
bool ReshapeKernel<GPU_CL, float>::Init(ReshapeParam<GPU_CL> *param) {
  this->cl_helper_.AddKernel("reshape", "reshape.cl");
  return true;
}

template <>
void ReshapeKernel<GPU_CL, float>::Compute(const ReshapeParam<GPU_CL> &param) {
  auto kernel = this->cl_helper_.KernelAt(0);
  auto default_work_size = this->cl_helper_.DefaultWorkSize(*param.Out());
  const auto *input = param.InputX();
  auto *output = param.Out();
  auto input_image = input->GetCLImage();
  auto output_image = output->GetCLImage();
  const auto &inputDim = input->dims();
  const auto &outputDim = output->dims();
  int input_dims[4] = {1, 1, 1, 1};
  int output_dims[4] = {1, 1, 1, 1};
  // 1 1000 1 1
  for (int i = 0; i < inputDim.size(); i++) {
    input_dims[4 - inputDim.size() + i] = inputDim[i];
  }

  // 1 1 1 1000
  for (int i = 0; i < outputDim.size(); i++) {
    output_dims[4 - outputDim.size() + i] = outputDim[i];
  }

  int out_C = output_dims[1];
  int out_H = output_dims[2];
  int out_W = output_dims[3];
  int in_W = input_dims[3];
  int in_H = input_dims[2];
  int in_Stride0 = in_W;
  int in_Stride1 = input_dims[2] * input_dims[3];
  int in_Stride2 = input_dims[1] * input_dims[2] * input_dims[3];
  int out_Stride0 = out_W;
  int out_Stride1 = out_H * out_W;
  int out_Stride2 = out_C * out_H * out_W;
  DLOG << "out_C=" << out_C;
  DLOG << "out_H=" << out_H;
  DLOG << "out_W=" << out_W;
  DLOG << "in_W=" << in_W;
  DLOG << "default_work_size=" << default_work_size;
  DLOG << "in_Stride0=" << in_Stride0;
  DLOG << "in_Stride1=" << in_Stride1;
  DLOG << "out_Stride0=" << out_Stride0;
  DLOG << "out_Stride1=" << out_Stride1;
  cl_int status;
  status = clSetKernelArg(kernel, 0, sizeof(cl_mem), &input_image);
  CL_CHECK_ERRORS(status);
  status = clSetKernelArg(kernel, 1, sizeof(cl_mem), &output_image);
  CL_CHECK_ERRORS(status);
  status = clSetKernelArg(kernel, 2, sizeof(int), &out_C);
  CL_CHECK_ERRORS(status);
  status = clSetKernelArg(kernel, 3, sizeof(int), &out_H);
  CL_CHECK_ERRORS(status);
  status = clSetKernelArg(kernel, 4, sizeof(int), &out_W);
  CL_CHECK_ERRORS(status);
  status = clSetKernelArg(kernel, 5, sizeof(int), &in_W);
  CL_CHECK_ERRORS(status);
  status = clSetKernelArg(kernel, 6, sizeof(int), &in_H);
  CL_CHECK_ERRORS(status);
  status = clSetKernelArg(kernel, 7, sizeof(int), &in_Stride0);
  CL_CHECK_ERRORS(status);
  status = clSetKernelArg(kernel, 8, sizeof(int), &in_Stride1);
  CL_CHECK_ERRORS(status);
  status = clSetKernelArg(kernel, 9, sizeof(int), &in_Stride2);
  CL_CHECK_ERRORS(status);
  status = clSetKernelArg(kernel, 10, sizeof(int), &out_Stride0);
  CL_CHECK_ERRORS(status);
  status = clSetKernelArg(kernel, 11, sizeof(int), &out_Stride1);
  CL_CHECK_ERRORS(status);
  status = clSetKernelArg(kernel, 12, sizeof(int), &out_Stride2);
  CL_CHECK_ERRORS(status);
  status = clEnqueueNDRangeKernel(
      this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(), NULL,
      default_work_size.data(), NULL, 0, NULL, NULL);
  CL_CHECK_ERRORS(status);
}

template class ReshapeKernel<GPU_CL, float>;

}  // namespace operators
}  // namespace paddle_mobile
#endif