conv_func.cpp

/* 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. */

#include "operators/kernel/cl/cl-kernel-func/conv_func.h"
#include "framework/cl/cl_image_converter.h"
#include "framework/cl/cl_tensor.h"

namespace paddle_mobile {
namespace operators {

template <>
void winograd_transform_weight<4, 3>(framework::CLHelper &cl_helper,
                                     framework::CLImage &weight){};

template <>
void WinogradConv3x3<4, 3>(framework::CLHelper &cl_helper,
                           const ConvParam<GPU_CL> &param) {}

void ConvAddBnRelu(framework::CLHelper &cl_helper,
                   const ConvParam<GPU_CL> &param, bool ifRelu,
                   const CLImage *biase, const CLImage *new_scale,
                   const CLImage *new_bias) {
  auto kernel = cl_helper.KernelAt(0);
  auto default_work_size = cl_helper.DefaultWorkSize(*param.Output());
  int c_block = default_work_size[0];
  int w = default_work_size[1];
  int nh = default_work_size[2];
  auto input = param.Input()->GetCLImage();
  auto filter = param.Filter()->GetCLImage();

  auto output = param.Output()->GetCLImage();
  int stride = param.Strides()[0];
  int offset = param.Offset();
  int input_c = reinterpret_cast<framework::CLImageConverterFolder *>(
                    param.Input()->Converter())
                    ->GetCBlock();
  int dilation = param.Dilations()[0];
  int input_width = param.Input()->dims()[3];
  int input_height = param.Input()->dims()[2];
  int output_width = param.Output()->dims()[3];
  int output_height = param.Output()->dims()[2];

  //  DLOG << " c block " << c_block;
  //  DLOG << " w " << w;
  //  DLOG << " nh " << nh;
  //  DLOG << " stride " << stride;
  //  DLOG << " offset " << offset;
  //  DLOG << " input_c " << input_c;
  //  DLOG << " dilation " << dilation;
  //  DLOG << " input width " << input_width;
  //  DLOG << " input height " << input_height;
  //  DLOG << " output width " << output_width;
  //  DLOG << " output height " << output_height;
  //  DLOG << " input dim " << param.Input()->dims();
  //  DLOG << " output dim " << param.Output()->dims();
  //  DLOG << " filter dim " << param.Filter()->dims();

  cl_int status;
  int index = 0;

  if (param.Filter()->dims()[2] == 1 && param.Filter()->dims()[3] == 1) {
    status = clSetKernelArg(kernel, index++, sizeof(int), &c_block);
    CL_CHECK_ERRORS(status);

    int maped_w = maptofactor(w, 4);
    status = clSetKernelArg(kernel, index++, sizeof(int), &maped_w);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &nh);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(cl_mem), &input);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(cl_mem), &filter);
    CL_CHECK_ERRORS(status);

    if (biase) {
      auto bias_mem = biase->GetCLImage();
      status = clSetKernelArg(kernel, index++, sizeof(cl_mem), &bias_mem);
      CL_CHECK_ERRORS(status);
    }

    if (new_scale && new_bias) {
      auto new_scale_mem = new_scale->GetCLImage();
      status = clSetKernelArg(kernel, index++, sizeof(cl_mem), &new_scale_mem);
      CL_CHECK_ERRORS(status);

      auto new_bias_mem = new_bias->GetCLImage();
      status = clSetKernelArg(kernel, index++, sizeof(cl_mem), &new_bias_mem);
      CL_CHECK_ERRORS(status);
    }

    status = clSetKernelArg(kernel, index++, sizeof(cl_mem), &output);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &stride);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &offset);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &input_c);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &dilation);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &input_width);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &input_height);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &output_width);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &output_height);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &w);
    CL_CHECK_ERRORS(status);

    const size_t work_size[3] = {
        static_cast<const uint32_t>(default_work_size.data()[0]),
        static_cast<const uint32_t>(maped_w),
        static_cast<const uint32_t>(default_work_size.data()[2])};

    status = clEnqueueNDRangeKernel(cl_helper.CLCommandQueue(), kernel,
                                    default_work_size.size(), NULL, work_size,
                                    NULL, 0, NULL, NULL);
    CL_CHECK_ERRORS(status);
  } else {
    status = clSetKernelArg(kernel, index++, sizeof(int), &c_block);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &w);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &nh);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(cl_mem), &input);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(cl_mem), &filter);
    CL_CHECK_ERRORS(status);

    if (biase) {
      auto bias_mem = biase->GetCLImage();
      status = clSetKernelArg(kernel, index++, sizeof(cl_mem), &bias_mem);
      CL_CHECK_ERRORS(status);
    }

    if (new_scale && new_bias) {
      auto new_scale_mem = new_scale->GetCLImage();
      status = clSetKernelArg(kernel, index++, sizeof(cl_mem), &new_scale_mem);
      CL_CHECK_ERRORS(status);

      auto new_bias_mem = new_bias->GetCLImage();
      status = clSetKernelArg(kernel, index++, sizeof(cl_mem), &new_bias_mem);
      CL_CHECK_ERRORS(status);
    }

    status = clSetKernelArg(kernel, index++, sizeof(cl_mem), &output);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &stride);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &offset);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &input_c);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &dilation);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &input_width);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &input_height);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &output_width);
    CL_CHECK_ERRORS(status);

    status = clSetKernelArg(kernel, index++, sizeof(int), &output_height);
    CL_CHECK_ERRORS(status);

    status = clEnqueueNDRangeKernel(
        cl_helper.CLCommandQueue(), kernel, default_work_size.size(), NULL,
        default_work_size.data(), NULL, 0, NULL, NULL);
    CL_CHECK_ERRORS(status);
  }
}

}  // namespace operators
}  // namespace paddle_mobile