trt_plugin.cc

// 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/plugin/trt_plugin.h"

namespace paddle {
namespace inference {
namespace tensorrt {
namespace plugin {

inline void Seria(void*& buffer,  // NOLINT
                  const std::vector<nvinfer1::Dims>& input_dims,
                  size_t max_batch_size, nvinfer1::DataType data_type,
                  nvinfer1::PluginFormat data_format, bool with_fp16) {
  SerializeValue(&buffer, input_dims);
  SerializeValue(&buffer, max_batch_size);
  SerializeValue(&buffer, data_type);
  SerializeValue(&buffer, data_format);
  SerializeValue(&buffer, with_fp16);
}

inline void Deseria(void const*& serial_data, size_t& serial_length,  // NOLINT
                    std::vector<nvinfer1::Dims>* input_dims,
                    size_t* max_batch_size, nvinfer1::DataType* data_type,
                    nvinfer1::PluginFormat* data_format, bool* with_fp16) {
  DeserializeValue(&serial_data, &serial_length, input_dims);
  DeserializeValue(&serial_data, &serial_length, max_batch_size);
  DeserializeValue(&serial_data, &serial_length, data_type);
  DeserializeValue(&serial_data, &serial_length, data_format);
  DeserializeValue(&serial_data, &serial_length, with_fp16);
}

inline size_t SeriaSize(const std::vector<nvinfer1::Dims>& input_dims,
                        size_t max_batch_size, nvinfer1::DataType data_type,
                        nvinfer1::PluginFormat data_format, bool with_fp16) {
  return (SerializedSize(input_dims) + SerializedSize(max_batch_size) +
          SerializedSize(data_type) + SerializedSize(data_format) +
          SerializedSize(with_fp16));
}

void PluginTensorRT::serializeBase(void*& buffer) {
  Seria(buffer, input_dims_, max_batch_size_, data_type_, data_format_,
        with_fp16_);
}

void PluginTensorRT::deserializeBase(void const*& serial_data,
                                     size_t& serial_length) {
  Deseria(serial_data, serial_length, &input_dims_, &max_batch_size_,
          &data_type_, &data_format_, &with_fp16_);
}

size_t PluginTensorRT::getBaseSerializationSize() {
  return SeriaSize(input_dims_, max_batch_size_, data_type_, data_format_,
                   with_fp16_);
}

bool PluginTensorRT::supportsFormat(nvinfer1::DataType type,
                                    nvinfer1::PluginFormat format) const {
  return ((type == nvinfer1::DataType::kFLOAT) &&
          (format == nvinfer1::PluginFormat::kLINEAR));
}

void PluginTensorRT::configureWithFormat(
    const nvinfer1::Dims* input_dims, int num_inputs,
    const nvinfer1::Dims* output_dims, int num_outputs, nvinfer1::DataType type,
    nvinfer1::PluginFormat format, int max_batch_size) {
  data_type_ = type;
  data_format_ = format;
  input_dims_.assign(input_dims, input_dims + num_inputs);
  max_batch_size_ = max_batch_size;
}

void PluginTensorRTV2Ext::serializeBase(void*& buffer) const {
  Seria(buffer, input_dims_, max_batch_size_, data_type_, data_format_,
        with_fp16_);
}

void PluginTensorRTV2Ext::deserializeBase(void const*& serial_data,
                                          size_t& serial_length) {
  Deseria(serial_data, serial_length, &input_dims_, &max_batch_size_,
          &data_type_, &data_format_, &with_fp16_);
}

size_t PluginTensorRTV2Ext::getBaseSerializationSize() const {
  return SeriaSize(input_dims_, max_batch_size_, data_type_, data_format_,
                   with_fp16_);
}

void PluginTensorRTV2Ext::configurePlugin(
    const nvinfer1::Dims* input_dims, int32_t nb_inputs,
    const nvinfer1::Dims* output_dims, int32_t nb_outputs,
    const nvinfer1::DataType* input_types,
    const nvinfer1::DataType* output_types, const bool* input_is_broadcast,
    const bool* output_is_broadcast, nvinfer1::PluginFormat float_format,
    int32_t max_batch_size) {
  input_dims_.assign(input_dims, input_dims + nb_inputs);
  max_batch_size_ = max_batch_size;
  data_format_ = float_format;
  data_type_ = input_types[0];
}

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