/* 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 "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/plugin/gelu_op_plugin.h"

namespace paddle {
namespace inference {
namespace tensorrt {

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

    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(
                          "gelu 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(
                          "gelu op has only 1 output, but got %d", output_num));

    nvinfer1::ILayer* layer = nullptr;
    if (engine_->with_dynamic_shape()) {
#if IS_TRT_VERSION_GE(6000)

#ifdef USE_NVINFER_PLUGIN
      auto creator =
          GetPluginRegistry()->getPluginCreator("CustomGeluPluginDynamic", "1");
      assert(creator != nullptr);
      int type = static_cast<int>((engine_->WithFp16() == 1)
                                      ? nvinfer1::DataType::kHALF
                                      : nvinfer1::DataType::kFLOAT);
      const std::vector<nvinfer1::PluginField> fields{
          { "type_id",
            &type,
            nvinfer1::PluginFieldType::kINT32,
            1 }};
      nvinfer1::PluginFieldCollection* pluginPtr =
          static_cast<nvinfer1::PluginFieldCollection*>(
              malloc(sizeof(*pluginPtr) +
                     fields.size() * sizeof(nvinfer1::PluginField)));
      pluginPtr->nbFields = static_cast<int>(fields.size());
      pluginPtr->fields = fields.data();
      auto pluginObj =
          creator->createPlugin("CustomGeluPluginDynamic", pluginPtr);
      layer = engine_->network()->addPluginV2(&input, input_num, *pluginObj);
      assert(layer != nullptr);
#else
      plugin::GeluPluginDynamic* plugin = new plugin::GeluPluginDynamic();
      layer = engine_->AddPluginV2(&input, input_num, plugin);
#endif

#else
      PADDLE_THROW(platform::errors::Fatal(
          "You are running the TRT Dynamic Shape mode, need to confirm that "
          "your TRT version is no less than 6.0"));
#endif
    } else {
      plugin::GeluPlugin* plugin = new plugin::GeluPlugin();
      layer = engine_->AddPlugin(&input, input_num, plugin);
    }
    auto output_name = op_desc.Output("Out")[0];
    RreplenishLayerAndOutput(layer, "gelu", {output_name}, test_mode);
  }
};

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

REGISTER_TRT_OP_CONVERTER(gelu, GeluOpConverter);
