// Copyright (c) 2019 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.

#include "lite/kernels/opencl/elementwise_add_compute.h"
#include <memory>
#include "lite/backends/opencl/cl_include.h"
#include "lite/core/op_registry.h"
#include "lite/utils/replace_stl/stream.h"

namespace paddle {
namespace lite {
namespace kernels {
namespace opencl {

/* Buffer */
#if 0
void ElementwiseAddCompute::PrepareForRun() {
  auto& context = ctx_->As<OpenCLContext>();
  context.cl_context()->AddKernel(
      kernel_func_name_, "buffer/elementwise_add_kernel.cl", build_options_);
  ele_param_ = param_.get_mutable<param_t>();
  UpdateParams();
}

void ElementwiseAddCompute::Run() {
  auto& context = ctx_->As<OpenCLContext>();
  CHECK(context.cl_context() != nullptr);
  auto* x_buf = ele_param_->X->template data<float, cl::Buffer>();
  auto* y_buf = ele_param_->Y->template data<float, cl::Buffer>();
  auto* out_buf = ele_param_->Out->template mutable_data<float, cl::Buffer>(
      TARGET(kOpenCL));
  STL::stringstream kernel_key;
  kernel_key << kernel_func_name_ << build_options_;
  auto kernel = context.cl_context()->GetKernel(kernel_key.str());
  VLOG(4) << TargetToStr(ele_param_->X->target());
  VLOG(4) << TargetToStr(ele_param_->Y->target());
  VLOG(4) << TargetToStr(ele_param_->Out->target());
  int arg_idx = 0;
  cl_int status = kernel.setArg(arg_idx, *x_buf);
  CL_CHECK_FATAL(status);
  status = kernel.setArg(++arg_idx, *y_buf);
  CL_CHECK_FATAL(status);
  status = kernel.setArg(++arg_idx, *out_buf);
  CL_CHECK_FATAL(status);
  status = kernel.setArg(++arg_idx, (const int)batch_);
  CL_CHECK_FATAL(status);
  status = kernel.setArg(++arg_idx, (const int)channels_);
  CL_CHECK_FATAL(status);
  status = kernel.setArg(++arg_idx, (const int)num_);
  CL_CHECK_FATAL(status);

  auto global_work_size = cl::NDRange{channels_, batch_};
  status = context.cl_context()->GetCommandQueue().enqueueNDRangeKernel(
      kernel,
      cl::NullRange,
      global_work_size,
      cl::NullRange,
      nullptr,
      event_.get());
  CL_CHECK_FATAL(status);
  context.cl_wait_list()->emplace(out_buf, event_);
}

void ElementwiseAddCompute::UpdateParams() {
  auto axis = ele_param_->axis;
  const auto& x_dims = ele_param_->X->dims();
  const auto& y_dims = ele_param_->Y->dims();
  const auto& out_dims = ele_param_->Out->dims();
  if (axis < 0) {
    axis = static_cast<int>(x_dims.size() - y_dims.size());
  }
  for (int i = 0; i < axis; ++i) {
    batch_ *= x_dims[i];
  }
  for (int i = 0; i < y_dims.size(); ++i) {
    channels_ *= y_dims[i];
  }
  for (int i = static_cast<int>(y_dims.size() + axis); i < x_dims.size(); ++i) {
    num_ *= x_dims[i];
  }
  VLOG(4) << "axis: " << axis;
  VLOG(4) << "batch: " << batch_;
  VLOG(4) << "channels: " << channels_;
  VLOG(4) << "num: " << num_;
}
#endif

/* Image2D */
void ElementwiseAddImageCompute::PrepareForRun() {
  ele_param_ = param_.get_mutable<param_t>();
  auto* x = ele_param_->X;
  auto* y = ele_param_->Y;
  auto axis = ele_param_->axis;

  if (y->dims().size() == 4) {
    kernel_func_name_ = "elementwise_add";  // y: ImageDefault
  } else if (y->dims().size() == 1) {
    if (axis == x->dims().size() - 1) {
      kernel_func_name_ = "width_add";  // y: ImageDefault
    } else if (axis == x->dims().size() - 3) {
      kernel_func_name_ = "channel_add";  // y: ImageFolder
    } else {
      LOG(FATAL) << "ElementwiseAddImage doesn't support axis:" << axis
                 << ", x->dims().size():" << x->dims().size()
                 << ", y->dims.size():" << y->dims().size();
    }
  } else {
    LOG(FATAL) << "ElementwiseAddImage doesn't support axis:" << axis
               << ", x->dims().size():" << x->dims().size()
               << ", y->dims.size():" << y->dims().size();
  }
  VLOG(4) << "kernel_func_name_:" << kernel_func_name_;

  auto& context = ctx_->As<OpenCLContext>();
  context.cl_context()->AddKernel(
      kernel_func_name_, "image/elementwise_add_kernel.cl", build_options_);
}

void ElementwiseAddImageCompute::Run() {
  auto& context = ctx_->As<OpenCLContext>();
  CHECK(context.cl_context() != nullptr);

  auto* x = ele_param_->X;
  auto* y = ele_param_->Y;
  auto* out = ele_param_->Out;
  auto axis = ele_param_->axis;

  VLOG(4) << "x->target():" << TargetToStr(x->target());
  VLOG(4) << "y->target():" << TargetToStr(y->target());
  VLOG(4) << "out->target():" << TargetToStr(out->target());
  VLOG(4) << "x->dims():" << x->dims();
  VLOG(4) << "y->dims():" << y->dims();
  VLOG(4) << "out->dims():" << out->dims();
  VLOG(4) << "axis:" << axis;

  paddle::lite::CLImageConverterDefault default_convertor;
  auto x_img_shape = default_convertor.InitImageDimInfoWith(x->dims());  // w, h
  auto x_img_width = x_img_shape[0];
  auto x_img_height = x_img_shape[1];
  auto out_img_shape =
      default_convertor.InitImageDimInfoWith(out->dims());  // w, h
  auto y_img_shape = default_convertor.InitImageDimInfoWith(y->dims());

  auto* x_img = x->data<float, cl::Image2D>();
  auto* y_img = y->data<float, cl::Image2D>();
  auto* out_img =
      out->mutable_data<float, cl::Image2D>(out_img_shape[0], out_img_shape[1]);

  VLOG(4) << "x_img_shape[w,h]:" << x_img_width << " " << x_img_height;
  VLOG(4) << "y_img_shape[w,h]:" << y_img_shape[0] << " " << y_img_shape[1];
  VLOG(4) << "out_img_shape[w,h]:" << out_img_shape[0] << " "
          << out_img_shape[1];

  STL::stringstream kernel_key;
  kernel_key << kernel_func_name_ << build_options_;
  auto kernel = context.cl_context()->GetKernel(kernel_key.str());

  int arg_idx = 0;
  auto y_dims = y->dims();
  if (y_dims.size() == 4) {
    cl_int status = kernel.setArg(arg_idx, *x_img);
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, *y_img);
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, *out_img);
    CL_CHECK_FATAL(status);
  } else if (y_dims.size() == 1) {
    if (axis == x->dims().size() - 1 || axis == x->dims().size() - 3) {
      int tensor_w = x->dims()[x->dims().size() - 1];
      VLOG(4) << "tensor_w:" << tensor_w;

      cl_int status = kernel.setArg(arg_idx, *x_img);
      CL_CHECK_FATAL(status);
      status = kernel.setArg(++arg_idx, *y_img);
      CL_CHECK_FATAL(status);
      status = kernel.setArg(++arg_idx, *out_img);
      CL_CHECK_FATAL(status);
      status = kernel.setArg(++arg_idx, static_cast<const int>(tensor_w));
      CL_CHECK_FATAL(status);
    } else {
      LOG(FATAL) << "ElementwiseAddImage doesn't support axis:" << axis
                 << ", x->dims().size():" << x->dims().size()
                 << ", y->dims.size():" << y->dims().size();
    }
  } else {
    LOG(FATAL) << "ElementwiseAddImage doesn't support axis:" << axis
               << ", x->dims().size():" << x->dims().size()
               << ", y->dims.size():" << y->dims().size();
  }

  auto global_work_size = cl::NDRange{static_cast<cl::size_type>(x_img_width),
                                      static_cast<cl::size_type>(x_img_height)};
  VLOG(4) << "global_work_size:[2D]:" << x_img_width << " " << x_img_height;
  auto status = context.cl_context()->GetCommandQueue().enqueueNDRangeKernel(
      kernel,
      cl::NullRange,
      global_work_size,
      cl::NullRange,
      nullptr,
      event_.get());
  CL_CHECK_FATAL(status);
  context.cl_wait_list()->emplace(out_img, event_);
}

}  // namespace opencl
}  // namespace kernels
}  // namespace lite
}  // namespace paddle

namespace ocl = paddle::lite::kernels::opencl;

// REGISTER_LITE_KERNEL(
//    elementwise_add, kOpenCL, kFloat, kNCHW, ocl::ElementwiseAddCompute, def)
//    .BindInput("X", {LiteType::GetTensorTy(TARGET(kOpenCL))})
//    .BindInput("Y", {LiteType::GetTensorTy(TARGET(kOpenCL))})
//    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kOpenCL))})
//    .Finalize();

// TODO(ysh329): Not fix.
// "Y" may from constant value like conv bias (kARM, need do cl_image_converter
// on CPU);
//     may from anther branch like "X" (kOpenCL, nothing to do).
// Consider 2 situations have different actions when pass running(pick kernel),
//     set target of "Y" as kOpenCL temporarily.
REGISTER_LITE_KERNEL(elementwise_add,
                     kOpenCL,
                     kFloat,
                     kImageDefault,
                     ocl::ElementwiseAddImageCompute,
                     def)
    .BindInput("X",
               {LiteType::GetTensorTy(TARGET(kOpenCL),
                                      PRECISION(kFloat),
                                      DATALAYOUT(kImageDefault))})
    .BindInput("Y",
               {LiteType::GetTensorTy(TARGET(kOpenCL),
                                      PRECISION(kFloat),
                                      DATALAYOUT(kImageDefault))})
    .BindOutput("Out",
                {LiteType::GetTensorTy(TARGET(kOpenCL),
                                       PRECISION(kFloat),
                                       DATALAYOUT(kImageDefault))})
    .Finalize();