conv_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 "lite/kernels/fpga/conv_compute.h"
#include <vector>
#include "lite/core/op_registry.h"
#include "lite/core/type_system.h"

#include "lite/backends/fpga/KD/debugger.hpp"

namespace paddle {
namespace lite {
namespace kernels {
namespace fpga {

using float16 = zynqmp::float16;

void ConvCompute::PrepareForRun() {
  auto& param = this->Param<param_t>();
  param.output->mutable_data<float16>();
  int pad_h = (*param.paddings)[0];
  int pad_w = (*param.paddings)[2];
  // ====================================================
  if (param.x->ZynqTensor()->shape().channel() != 1 &&
      param.groups == param.x->ZynqTensor()->shape().channel()) {
    zynqmp::DepthwiseConvParam& conv_param = dw_conv_pe_.param();

    conv_param.input = param.x->ZynqTensor();
    conv_param.output = param.output->ZynqTensor();
    conv_param.filter = param.filter->ZynqTensor();
    conv_param.filter->setDataType(zynqmp::FP32);
    conv_param.groups = param.groups;
    conv_param.strides = param.strides;
    conv_param.paddings = std::vector<int>({pad_h, pad_w});
    conv_param.dilations = *param.dilations;
    fill_scale_bias_const(&conv_param);
    conv_param.bias()->copyFrom(param.bias->ZynqTensor());

    if (param.fuse_relu) {
      conv_param.activeParam.type = zynqmp::TYPE_RELU;
    }

    if (param.activation_param.Leaky_relu_alpha > 0.001) {
      conv_param.activeParam.type = zynqmp::TYPE_LEAKY_RELU;
      conv_param.activeParam.leaky_relu_factor =
          param.activation_param.Leaky_relu_alpha;
    }

    dw_conv_pe_.init();
    dw_conv_pe_.apply();
  } else {
    zynqmp::ConvParam& conv_param = conv_pe_.param();
    conv_param.input = param.x->ZynqTensor();
    conv_param.output = param.output->ZynqTensor();
    conv_param.filter = param.filter->ZynqTensor();
    conv_param.filter->setDataType(zynqmp::FP32);
    conv_param.groups = param.groups;
    conv_param.strides = param.strides;
    conv_param.paddings = std::vector<int>({pad_h, pad_w});
    conv_param.dilations = *param.dilations;
    fill_scale_bias_const(&conv_param);
    if (param.bias != nullptr) {
      conv_param.bias()->copyFrom(param.bias->ZynqTensor());
    }

    if (param.fuse_relu) {
      conv_param.activeParam.type = zynqmp::TYPE_RELU;
    }

    if (param.activation_param.Leaky_relu_alpha > 0.001) {
      conv_param.activeParam.type = zynqmp::TYPE_LEAKY_RELU;
      conv_param.activeParam.leaky_relu_factor =
          param.activation_param.Leaky_relu_alpha;
    }

    conv_pe_.init();
    conv_pe_.apply();
  }
  // std::cout << "Leaky_relu_alpha:" << param.activation_param.Leaky_relu_alpha
  // << std::endl;
}

void ConvCompute::Run() {
  auto& param = this->Param<param_t>();
  if (param.x->ZynqTensor()->shape().channel() != 1 &&
      param.groups == param.x->ZynqTensor()->shape().channel()) {
    dw_conv_pe_.dispatch();
#ifdef FPGA_PRINT_TENSOR
    zynqmp::DepthwiseConvParam& dwconv_param = dw_conv_pe_.param();
    Debugger::get_instance().registerOutput("dwconv", dwconv_param.output);
#endif
  } else {
    // zynqmp::ConvParam& conv_param = conv_pe_.param();
    conv_pe_.dispatch();

#ifdef FPGA_PRINT_TENSOR
    zynqmp::ConvParam& conv_param = conv_pe_.param();
    Debugger::get_instance().registerOutput("conv", conv_param.output);
#endif
  }
}

}  // namespace fpga
}  // namespace kernels
}  // namespace lite
}  // namespace paddle

REGISTER_LITE_KERNEL(
    conv2d, kFPGA, kFP16, kNHWC, paddle::lite::kernels::fpga::ConvCompute, def)
    .BindInput("Input",
               {LiteType::GetTensorTy(TARGET(kFPGA),
                                      PRECISION(kFP16),
                                      DATALAYOUT(kNHWC))})
    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM))})
    .BindInput("Filter", {LiteType::GetTensorTy(TARGET(kARM))})
    .BindOutput("Output",
                {LiteType::GetTensorTy(TARGET(kFPGA),
                                       PRECISION(kFP16),
                                       DATALAYOUT(kNHWC))})
    .Finalize();

REGISTER_LITE_KERNEL(depthwise_conv2d,
                     kFPGA,
                     kFP16,
                     kNHWC,
                     paddle::lite::kernels::fpga::ConvCompute,
                     def)
    .BindInput("Input",
               {LiteType::GetTensorTy(TARGET(kFPGA),
                                      PRECISION(kFP16),
                                      DATALAYOUT(kNHWC))})
    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM))})
    .BindInput("Filter", {LiteType::GetTensorTy(TARGET(kARM))})
    .BindOutput("Output",
                {LiteType::GetTensorTy(TARGET(kFPGA),
                                       PRECISION(kFP16),
                                       DATALAYOUT(kNHWC))})
    .Finalize();