depthwise_conv2d_buffer_compute.cc

// 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"
#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 DepthwiseConv2dCompute
    : public KernelLite<TARGET(kOpenCL), PRECISION(kFloat), DATALAYOUT(kNCHW)> {
 public:
  using param_t = operators::ConvParam;

  std::string doc() const override {
    return "DepthwiseConv2d using cl::Buffer, kFloat";
  }

  void PrepareForRun() override {
    const auto& param = *param_.get_mutable<param_t>();
    if (param.fuse_relu) {
      build_options_ += " -DRELU";
    } else if (param.activation_param.active_type ==
               lite_api::ActivationType::kRelu6) {
      build_options_ += " -DRELU6";
    }
    auto& context = ctx_->As<OpenCLContext>();
    context.cl_context()->AddKernel(kernel_func_name_,
                                    "buffer/depthwise_conv2d_kernel.cl",
                                    build_options_,
                                    time_stamp_);
  }

  void Run() override {
    const auto& param = *param_.get_mutable<param_t>();
    auto x_dims = param.x->dims();
    auto filter_dims = param.filter->dims();
    auto output_dims = param.output->dims();
    auto paddings = *param.paddings;
    auto strides = param.strides;

    auto& context = ctx_->As<OpenCLContext>();
    CHECK(context.cl_context() != nullptr);
    auto* input_buf = param.x->data<float, cl::Buffer>();
    auto* filter_buf = param.filter->data<float, cl::Buffer>();
    auto* bias_buf = param.bias == nullptr
                         ? static_cast<cl::Buffer*>(nullptr)
                         : param.bias->data<float, cl::Buffer>();
    auto* output_buf =
        param.output->mutable_data<float, cl::Buffer>(TARGET(kOpenCL));

    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 = output_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>(x_dims[2]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(x_dims[3]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(output_dims[1]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(output_dims[2]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(output_dims[3]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(filter_dims[2]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, static_cast<const int>(filter_dims[3]));
    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[1]));
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, *output_buf);
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, *filter_buf);
    CL_CHECK_FATAL(status);
    status = kernel.setArg(++arg_idx, *bias_buf);
    CL_CHECK_FATAL(status);
    auto global_work_size = cl::NDRange(static_cast<size_t>(numel));

    status = context.cl_context()->GetCommandQueue().enqueueNDRangeKernel(
        kernel,
        cl::NullRange,
        global_work_size,
        cl::NullRange,
        nullptr,
        nullptr);
    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_{"depthwise_conv2d"};
  std::string build_options_{"-DCL_DTYPE_float"};
  std::string time_stamp_{GetTimeStamp()};
};

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

REGISTER_LITE_KERNEL(depthwise_conv2d,
                     kOpenCL,
                     kFloat,
                     kNCHW,
                     paddle::lite::kernels::opencl::DepthwiseConv2dCompute,
                     def)
    .BindInput("Input", {LiteType::GetTensorTy(TARGET(kOpenCL))})
    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kOpenCL))})
    .BindInput("Filter", {LiteType::GetTensorTy(TARGET(kOpenCL))})
    .BindOutput("Output", {LiteType::GetTensorTy(TARGET(kOpenCL))})
    .Finalize();