/* Copyright (c) 2020 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 "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/plugin/hard_swish_op_plugin.h"

namespace nvinfer1 {
class ILayer;
}  // namespace nvinfer1
namespace paddle {
namespace framework {
class Scope;
namespace proto {
class OpDesc;
}  // namespace proto
}  // namespace framework
}  // namespace paddle

namespace paddle {
namespace inference {
namespace tensorrt {

/*
 * HardSwish converter from fluid to tensorRT.
 */
class HardSwishOpConverter : public OpConverter {
 public:
  void operator()(const framework::proto::OpDesc& op,
                  const framework::Scope& scope, bool test_mode) override {
    VLOG(4) << "convert fluid HardSwish op to tensorrt HardSwish plugin";

    framework::OpDesc op_desc(op, nullptr);
    // Declare inputs
    int input_num = op_desc.Input("X").size();
    PADDLE_ENFORCE_EQ(
        input_num, 1,
        platform::errors::InvalidArgument(
            "HardSwish op has only 1 input, but got %d", input_num));
    auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
    // Get output
    size_t output_num = op_desc.Output("Out").size();
    PADDLE_ENFORCE_EQ(
        output_num, 1,
        platform::errors::InvalidArgument(
            "HardSwish op has only 1 output, but got %d", output_num));

    const float threshold =
        op_desc.HasAttr("threshold")
            ? BOOST_GET_CONST(float, op_desc.GetAttr("threshold"))
            : 6.0f;
    const float scale = op_desc.HasAttr("scale")
                            ? BOOST_GET_CONST(float, op_desc.GetAttr("scale"))
                            : 6.0f;
    const float offset = op_desc.HasAttr("offset")
                             ? BOOST_GET_CONST(float, op_desc.GetAttr("offset"))
                             : 3.0f;
    nvinfer1::ILayer* layer = nullptr;
    if (threshold == scale) {
      auto* hsig_layer = TRT_ENGINE_ADD_LAYER(
          engine_, Activation, *input, nvinfer1::ActivationType::kHARD_SIGMOID);
      hsig_layer->setAlpha(1.0 / scale);
      hsig_layer->setBeta(offset / scale);
      nvinfer1::IElementWiseLayer* eltwise_layer = TRT_ENGINE_ADD_LAYER(
          engine_, ElementWise, *input, *(hsig_layer->getOutput(0)),
          nvinfer1::ElementWiseOperation::kPROD);
      layer = eltwise_layer;
    } else {
      plugin::HardSwishPlugin* plugin =
          new plugin::HardSwishPlugin(threshold, scale, offset);
      layer = engine_->AddPlugin(&input, input_num, plugin);
    }
    auto output_name = op_desc.Output("Out")[0];
    RreplenishLayerAndOutput(layer, "hard_swish", {output_name}, test_mode);
  }
};

}  // namespace tensorrt
}  // namespace inference
}  // namespace paddle

REGISTER_TRT_OP_CONVERTER(hard_swish, HardSwishOpConverter);