// 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 <vector>
#include "lite/backends/opencl/cl_include.h"
#include "lite/core/kernel.h"
#include "lite/core/op_registry.h"
#include "lite/kernels/opencl/image_helper.h"
#include "lite/operators/op_params.h"
#include "lite/utils/replace_stl/stream.h"
#include "lite/utils/string.h"
#ifdef LITE_WITH_PROFILE
#include "lite/core/profile/profiler.h"
#endif
#include "lite/backends/opencl/cl_utility.h"

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

class PoolCompute
    : public KernelLite<TARGET(kOpenCL), PRECISION(kFP16), DATALAYOUT(kNCHW)> {
 public:
  using param_t = operators::PoolParam;

  std::string doc() const override { return "Pool using cl::Buffer, kFP16"; }

  void PrepareForRun() override {
    const auto& param = *param_.get_mutable<param_t>();
    kernel_func_name_ += param.pooling_type;
    auto& context = ctx_->As<OpenCLContext>();
    context.cl_context()->AddKernel(kernel_func_name_,
                                    "buffer/pool_kernel.cl",
                                    build_options_,
                                    time_stamp_);
  }

  void Run() override {
    const auto& param = *param_.get_mutable<param_t>();
    const auto& in_dims = param.x->dims();
    const auto& out_dims = param.output->dims();
    const std::string pooling_type = param.pooling_type;
    const bool global_pooling = param.global_pooling;
    std::vector<int> paddings = *param.paddings;
    std::vector<int> strides = param.strides;
    std::vector<int> ksize = param.ksize;
    if (global_pooling) {
      for (size_t i = 0; i < ksize.size(); ++i) {
        paddings[2 * i] = 0;
        paddings[2 * i + 1] = 0;
        ksize[i] = static_cast<int>(in_dims[i + 2]);
      }
    }
    bool pads_equal =
        (paddings[0] == paddings[1]) && (paddings[2] == paddings[3]);
    if (!pads_equal) {
      LOG(FATAL)
          << "padding requires pad_left == pad_right, pad_top == pad_bottom";
    }
    auto& context = ctx_->As<OpenCLContext>();
    CHECK(context.cl_context() != nullptr);
    auto* input_buf = GET_BUFFER_GPU(param.x);
    auto* output_buf = MUTABLE_BUFFER_GPU(param.output);
    STL::stringstream kernel_key;
    kernel_key << kernel_func_name_ << build_options_ << time_stamp_;
    auto kernel = context.cl_context()->GetKernel(kernel_key.str());
    cl_int status;
    auto numel = out_dims.production();
    int arg_idx = 0;
    status = kernel.setArg(arg_idx, static_cast<const int>(numel));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, *input_buf);
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(in_dims[1]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(in_dims[2]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(in_dims[3]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(out_dims[2]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(out_dims[3]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(ksize[0]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(ksize[1]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(strides[0]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(strides[1]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(paddings[0]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(paddings[2]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, *output_buf);
    CL_CHECK_FATAL(status);
    auto global_work_size = cl::NDRange(static_cast<size_t>(numel));

    status = EnqueueNDRangeKernel(context,
                                  kernel,
                                  cl::NullRange,
                                  global_work_size,
                                  cl::NullRange,
                                  nullptr,
                                  event_);
    CL_CHECK_FATAL(status);
  }

#ifdef LITE_WITH_PROFILE
  void SetProfileRuntimeKernelInfo(paddle::lite::profile::OpCharacter* ch) {
    ch->kernel_func_name = kernel_func_name_;
    ch->cl_event =
        event_;  // `event_` defined in `kernel.h`, valid after kernel::Run
  }
#endif

 private:
  std::string kernel_func_name_{"pool_"};
  std::string build_options_{""};
  std::string time_stamp_{GetTimeStamp()};
};

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

REGISTER_LITE_KERNEL(pool2d,
                     kOpenCL,
                     kFP16,
                     kNCHW,
                     paddle::lite::kernels::opencl::PoolCompute,
                     def)
    .BindInput("X", {LiteType::GetTensorTy(TARGET(kOpenCL))})
    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kOpenCL))})
    .Finalize();
