add initial code for plugin

paddle/fluid/inference/tensorrt/CMakeLists.txt

@@ -1,4 +1,5 @@
 nv_library(tensorrt_engine SRCS engine.cc DEPS framework_proto device_context)
 nv_test(test_tensorrt SRCS test_tensorrt.cc DEPS dynload_cuda device_context dynamic_loader)
 nv_test(test_tensorrt_engine SRCS test_engine.cc DEPS dynload_cuda tensorrt_engine)
+add_subdirectory(plugin)
 add_subdirectory(convert)

paddle/fluid/inference/tensorrt/convert/CMakeLists.txt

@@ -2,7 +2,7 @@
 nv_library(tensorrt_converter
   SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc
 batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc pad_op.cc
-  DEPS tensorrt_engine operator scope framework_proto op_registry)
+  DEPS tensorrt_engine tensorrt_plugin operator scope framework_proto op_registry)
 
 nv_test(test_op_converter SRCS test_op_converter.cc DEPS
   ${FLUID_CORE_MODULES} tensorrt_engine tensorrt_converter)

paddle/fluid/inference/tensorrt/convert/concat_op.cc

@@ -19,7 +19,7 @@ namespace inference {
 namespace tensorrt {
 
 /*
- * MulOp, IMatrixMultiplyLayer in TRT. This Layer doesn't has weights.
+ * ConcatOp
  */
 class ConcatOpConverter : public OpConverter {
  public:

paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt

@@ -0,0 +1,2 @@
+nv_library(tensorrt_plugin SRCS plugin_factory.cc plugin_utils.cc
+trt_plugin.cc split_op_plugin.cu DEPS enforce)

paddle/fluid/inference/tensorrt/plugin/plugin_factory.cc

@@ -0,0 +1,64 @@
+/* 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/plugin_factory.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+PluginTensorRT* PluginFactoryTensorRT::createPlugin(const char* layer_name,
+                                                    const void* serial_data,
+                                                    size_t serial_length) {
+  size_t parsed_byte = 0;
+  std::string encoded_op_name =
+      ExtractOpName(serial_data, serial_length, &parsed_byte);
+
+  if (!IsPlugin(encoded_op_name)) {
+    return nullptr;
+  }
+
+  auto plugin_ptr =
+      plugin_registry_[encoded_op_name].first(serial_data, serial_length);
+  owned_plugins_.emplace_back(plugin_ptr);
+
+  return plugin_ptr;
+}
+
+PluginTensorRT* PluginFactoryTensorRT::CreatePlugin(
+    const std::string& op_name) {
+  if (!IsPlugin(op_name)) return nullptr;
+
+  auto plugin_ptr = plugin_registry_[op_name].second();
+  owned_plugins_.emplace_back(plugin_ptr);
+
+  return plugin_ptr;
+}
+
+bool PluginFactoryTensorRT::RegisterPlugin(
+    const std::string& op_name, PluginDeserializeFunc deserialize_func,
+    PluginConstructFunc construct_func) {
+  if (IsPlugin(op_name)) return false;
+
+  auto ret = plugin_registry_.emplace(
+      op_name, std::make_pair(deserialize_func, construct_func));
+
+  return ret.second;
+}
+
+void PluginFactoryTensorRT::DestroyPlugins() { owned_plugins_.clear(); }
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/plugin_factory.h

@@ -0,0 +1,91 @@
+/* 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. */
+
+#pragma once
+
+#include <memory>
+#include <unordered_map>
+
+#include "NvInfer.h"
+#include "paddle/fluid/inference/tensorrt/plugin/plugin_utils.h"
+#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+class PluginFactoryTensorRT : public nvinfer1::IPluginFactory {
+ public:
+  static PluginFactoryTensorRT* GetInstance() {
+    static PluginFactoryTensorRT* factory_instance =
+        new PluginFactoryTensorRT();
+    return factory_instance;
+  }
+
+  // Deserialization method
+  PluginTensorRT* createPlugin(const char* layer_name, const void* serial_data,
+                               size_t serial_length) override;
+
+  // Plugin construction, PluginFactoryTensorRT owns the plugin.
+  PluginTensorRT* CreatePlugin(const std::string& op_name);
+
+  bool RegisterPlugin(const std::string& op_name,
+                      PluginDeserializeFunc deserialize_func,
+                      PluginConstructFunc construct_func);
+
+  bool IsPlugin(const std::string& op_name) {
+    return plugin_registry_.find(op_name) != plugin_registry_.end();
+  }
+
+  size_t CountOwnedPlugins() { return owned_plugins_.size(); }
+
+  void DestroyPlugins();
+
+ protected:
+  std::unordered_map<std::string,
+                     std::pair<PluginDeserializeFunc, PluginConstructFunc>>
+      plugin_registry_;
+  std::vector<std::unique_ptr<PluginTensorRT>> owned_plugins_;
+};
+
+class TrtPluginRegistrar {
+ public:
+  TrtPluginRegistrar(const std::string& name,
+                     PluginDeserializeFunc deserialize_func,
+                     PluginConstructFunc construct_func) {
+    auto factory = PluginFactoryTensorRT::GetInstance();
+    // platform::PADDLE_ENFORCE(factory->RegisterPlugin(name, deserialize_func,
+    // construct_func),  "Falied to register plugin [%s]", name);
+    // platform::PADDLE_ENFORCE(factory->RegisterPlugin(name, deserialize_func,
+    // construct_func));
+    factory->RegisterPlugin(name, deserialize_func, construct_func);
+  }
+};
+
+#define REGISTER_TRT_PLUGIN(name, deserialize_func, construct_func)    \
+  REGISTER_TRT_PLUGIN_UNIQ_HELPER(__COUNTER__, name, deserialize_func, \
+                                  construct_func)
+#define REGISTER_TRT_PLUGIN_UNIQ_HELPER(ctr, name, deserialize_func, \
+                                        construct_func)              \
+  REGISTER_TRT_PLUGIN_UNIQ(ctr, name, deserialize_func, construct_func)
+#define REGISTER_TRT_PLUGIN_UNIQ(ctr, name, deserialize_func, construct_func) \
+  static ::paddle::inference::tensorrt::TrtPluginRegistrar                    \
+      trt_plugin_registrar##ctr __attribute__((unused)) =                     \
+          ::paddle::inference::tensorrt::TrtPluginRegistrar(                  \
+              name, deserialize_func, construct_func)
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/plugin_utils.cc

@@ -0,0 +1,37 @@
+/* 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/plugin_utils.h"
+#include <cassert>
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+std::string ExtractOpName(const void* serial_data, size_t serial_length,
+                          size_t* incremental) {
+  size_t op_name_char_count = *static_cast<const size_t*>(serial_data);
+  *incremental = sizeof(size_t) + op_name_char_count;
+
+  assert(serial_length >= *incremental);
+
+  const char* buffer = static_cast<const char*>(serial_data) + sizeof(size_t);
+  std::string op_name(buffer, op_name_char_count);
+
+  return op_name;
+}
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/plugin_utils.h

@@ -0,0 +1,34 @@
+/* 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. */
+
+#pragma once
+#include <functional>
+
+#include "NvInfer.h"
+#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+typedef std::function<PluginTensorRT*(const void*, size_t)>
+    PluginDeserializeFunc;
+typedef std::function<PluginTensorRT*(void)> PluginConstructFunc;
+
+std::string ExtractOpName(const void* serial_data, size_t serial_length,
+                          size_t* incremental);
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespze paddle

paddle/fluid/inference/tensorrt/plugin/serialize.hpp

@@ -0,0 +1,111 @@
+// 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.
+
+#pragma once
+
+#include <cassert>
+#include <cstring>
+#include <type_traits>
+#include <vector>
+
+template <typename T>
+inline void serialize_value(void** buffer, T const& value);
+
+template <typename T>
+inline void deserialize_value(void const** buffer, size_t* buffer_size,
+                              T* value);
+
+namespace {
+
+template <typename T, class Enable = void>
+struct Serializer {};
+
+template <typename T>
+struct Serializer<T, typename std::enable_if<std::is_arithmetic<T>::value ||
+                                             std::is_enum<T>::value ||
+                                             std::is_pod<T>::value>::type> {
+  static size_t serialized_size(T const& value) { return sizeof(T); }
+  static void serialize(void** buffer, T const& value) {
+    ::memcpy(*buffer, &value, sizeof(T));
+    reinterpret_cast<char*&>(*buffer) += sizeof(T);
+  }
+  static void deserialize(void const** buffer, size_t* buffer_size, T* value) {
+    assert(*buffer_size >= sizeof(T));
+    ::memcpy(value, *buffer, sizeof(T));
+    reinterpret_cast<char const*&>(*buffer) += sizeof(T);
+    *buffer_size -= sizeof(T);
+  }
+};
+
+template <>
+struct Serializer<const char*> {
+  static size_t serialized_size(const char* value) { return strlen(value) + 1; }
+  static void serialize(void** buffer, const char* value) {
+    ::strcpy(static_cast<char*>(*buffer), value);
+    reinterpret_cast<char*&>(*buffer) += strlen(value) + 1;
+  }
+  static void deserialize(void const** buffer, size_t* buffer_size,
+                          const char** value) {
+    *value = static_cast<char const*>(*buffer);
+    size_t data_size = strnlen(*value, *buffer_size) + 1;
+    assert(*buffer_size >= data_size);
+    reinterpret_cast<char const*&>(*buffer) += data_size;
+    *buffer_size -= data_size;
+  }
+};
+
+template <typename T>
+struct Serializer<std::vector<T>,
+                  typename std::enable_if<std::is_arithmetic<T>::value ||
+                                          std::is_enum<T>::value ||
+                                          std::is_pod<T>::value>::type> {
+  static size_t serialized_size(std::vector<T> const& value) {
+    return sizeof(value.size()) + value.size() * sizeof(T);
+  }
+  static void serialize(void** buffer, std::vector<T> const& value) {
+    serialize_value(buffer, value.size());
+    size_t nbyte = value.size() * sizeof(T);
+    ::memcpy(*buffer, value.data(), nbyte);
+    reinterpret_cast<char*&>(*buffer) += nbyte;
+  }
+  static void deserialize(void const** buffer, size_t* buffer_size,
+                          std::vector<T>* value) {
+    size_t size;
+    deserialize_value(buffer, buffer_size, &size);
+    value->resize(size);
+    size_t nbyte = value->size() * sizeof(T);
+    assert(*buffer_size >= nbyte);
+    ::memcpy(value->data(), *buffer, nbyte);
+    reinterpret_cast<char const*&>(*buffer) += nbyte;
+    *buffer_size -= nbyte;
+  }
+};
+
+}  // namespace
+
+template <typename T>
+inline size_t serialized_size(T const& value) {
+  return Serializer<T>::serialized_size(value);
+}
+
+template <typename T>
+inline void serialize_value(void** buffer, T const& value) {
+  return Serializer<T>::serialize(buffer, value);
+}
+
+template <typename T>
+inline void deserialize_value(void const** buffer, size_t* buffer_size,
+                              T* value) {
+  return Serializer<T>::deserialize(buffer, buffer_size, value);
+}

paddle/fluid/inference/tensorrt/plugin/split_op_plugin.cu

@@ -0,0 +1,114 @@
+// 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 <cassert>
+#include "paddle/fluid/inference/tensorrt/plugin/split_op_plugin.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+SplitPlugin* CreateSplitPlugin() { return new SplitPlugin(); };
+
+nvinfer1::Dims SplitPlugin::getOutputDimensions(int index,
+                                                const nvinfer1::Dims* inputDims,
+                                                int nbInputs) {
+  assert(nbInputs == 1);
+  assert(index < this->getNbOutputs());
+  nvinfer1::Dims const& input_dims = inputDims[0];
+  nvinfer1::Dims output_dims = input_dims;
+  output_dims.d[axis_] = output_lenght_.at(index);
+  return output_dims;
+}
+
+int SplitPlugin::initialize() {
+  std::vector<int> segment_offsets(1, 0);
+  for (int i = 0; i < this->getNbOutputs(); ++i) {
+    segment_offsets.push_back(segment_offsets.back() + output_lenght_[i]);
+  }
+  d_segment_offsets_ = segment_offsets;
+  nvinfer1::Dims dims = this->getInputDims(0);
+  nx_ = 1;
+  for (int i = dims.nbDims - 1; i > axis_; --i) {
+    nx_ *= dims.d[i];
+  }
+  ny_ = dims.d[axis_];
+  nz_ = 1;
+  for (int i = axis_ - 1; i >= 0; --i) {
+    nz_ *= dims.d[i];
+  }
+  return 0;
+}
+
+template <typename T>
+__device__ int upper_bound(T const* vals, int n, T const& key) {
+  int i = 0;
+  while (n > 0) {
+    int m = n / 2;
+    int j = i + m;
+    if (!(key < vals[j])) {
+      i = j + 1;
+      n -= m + 1;
+    } else {
+      n = m;
+    }
+  }
+  return i;
+}
+
+template <typename T>
+__global__ void split_kernel(int nsegment,
+                             int const* __restrict__ segment_offsets,
+                             T const* __restrict__ idata, T* const* odatas,
+                             int nx, int srcny_, int nz) {
+  int x0 = threadIdx.x + blockIdx.x * blockDim.x;
+  int src_y0 = threadIdx.y + blockIdx.y * blockDim.y;
+  int z0 = threadIdx.z + blockIdx.z * blockDim.z;
+  for (int z = z0; z < nz; z += blockDim.z * gridDim.z) {
+    for (int src_y = src_y0; src_y < srcny_; src_y += blockDim.y * gridDim.y) {
+      for (int x = x0; x < nx; x += blockDim.x * gridDim.x) {
+        int segment = upper_bound(segment_offsets, nsegment, src_y) - 1;
+        int dst_y = src_y - segment_offsets[segment];
+        int dstny_ = segment_offsets[segment + 1] - segment_offsets[segment];
+        odatas[segment][x + nx * (dst_y + dstny_ * z)] =
+            idata[x + nx * (src_y + srcny_ * z)];
+      }
+    }
+  }
+}
+
+int SplitPlugin::enqueue(int batchSize, const void* const* inputs,
+                         void** outputs, void* workspace, cudaStream_t stream) {
+  auto const& input_dims = this->getInputDims(0);
+  int const* d_segment_offsets_ptr =
+      thrust::raw_pointer_cast(&d_segment_offsets_[0]);
+  float const* idata = reinterpret_cast<float const*>(inputs[0]);
+  float** odatas = reinterpret_cast<float**>(outputs);
+
+  int nz = nz_ * batchSize;
+  dim3 block(32, 16);
+  dim3 grid(std::min((nx_ - 1) / block.x + 1, 65535u),
+            std::min((ny_ - 1) / block.y + 1, 65535u),
+            std::min((nz_ - 1) / block.z + 1, 65535u));
+
+  split_kernel<<<grid, block, 0, stream>>>(d_segment_offsets_.size(),
+                                           d_segment_offsets_ptr, idata, odatas,
+                                           nx_, ny_, nz);
+
+  return cudaGetLastError() != cudaSuccess;
+}
+
+}  // tensorrt
+}  // inference
+}  // paddle

paddle/fluid/inference/tensorrt/plugin/split_op_plugin.h

@@ -0,0 +1,62 @@
+
+#pragma once
+
+#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
+#include <thrust/device_vector.h>
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+class SplitPlugin : public PluginTensorRT {
+  int axis_;
+  std::vector<int> output_lenght_;
+  int nx_, ny_, nz_;
+  thrust::device_vector<int> d_segment_offsets_;
+
+ protected:
+  virtual size_t getSerializationSize() override {
+    return serialized_size(axis_) + serialized_size(output_lenght_)
+      + getBaseSerializationSize();
+  }
+
+  virtual void serialize(void *buffer) override {
+    serializeBase(buffer);
+    serialize_value(&buffer, axis_);
+    serialize_value(&buffer, output_lenght_);
+  }
+
+ public:
+  Split() {}
+  SplitPlugin(void const* serialData, size_t serialLength) {
+    deserializeBase(serialData, serialLength);
+    deserialize_value(&serialData, &serialLength, &axis_);
+    deserialize_value(&serialData, &serialLength, &output_lenght_);
+  }
+
+  SplitPlugin* clone() const override {
+    return new SplitPlugin(axis_, output_lenght_);
+  }
+
+  virtual const char* getPluginType() const override { return "split"; }
+  virtual int getNbOutputs() const override { return output_lenght_.size(); }
+  virtual nvinfer1::Dims getOutputDimensions(int index,
+                                             const nvinfer1::Dims *inputs, int nbInputDims) override;
+  virtual int initialize() override;
+  virtual int enqueue(int batchSize,
+                      const void *const *inputs, void **outputs,
+                      void *workspace, cudaStream_t stream) override;
+
+  void setAxis(int axis) {
+    axis_ = axis;
+  }
+
+  void setOutputLengths(const std::vector<int> & output_lengths) {
+    output_length_ = output_lengths;
+  }
+
+};
+
+} // tensorrt
+} // inference
+} // paddle

paddle/fluid/inference/tensorrt/plugin/trt_plugin.cc

@@ -0,0 +1,63 @@
+// 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"
+#include "paddle/fluid/inference/tensorrt/plugin/plugin_utils.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+void PluginTensorRT::serializeBase(void*& buffer) {
+  serialize_value(&buffer, input_dims_);
+  serialize_value(&buffer, max_batch_size_);
+  serialize_value(&buffer, data_type_);
+  serialize_value(&buffer, data_format_);
+}
+
+void PluginTensorRT::deserializeBase(void const*& serialData,
+                                     size_t& serialLength) {
+  deserialize_value(&serialData, &serialLength, &input_dims_);
+  deserialize_value(&serialData, &serialLength, &max_batch_size_);
+  deserialize_value(&serialData, &serialLength, &data_type_);
+  deserialize_value(&serialData, &serialLength, &data_format_);
+}
+
+size_t PluginTensorRT::getBaseSerializationSize() {
+  return (serialized_size(input_dims_) + serialized_size(max_batch_size_) +
+          serialized_size(data_type_) + serialized_size(data_format_));
+}
+
+bool PluginTensorRT::supportsFormat(nvinfer1::DataType type,
+                                    nvinfer1::PluginFormat format) const {
+  return ((type == nvinfer1::DataType::kFLOAT ||
+           type == nvinfer1::DataType::kHALF) &&
+          (format == nvinfer1::PluginFormat::kNCHW));
+}
+
+void PluginTensorRT::configureWithFormat(const nvinfer1::Dims* inputDims,
+                                         int nbInputs,
+                                         const nvinfer1::Dims* outputDims,
+                                         int nbOutputs, nvinfer1::DataType type,
+                                         nvinfer1::PluginFormat format,
+                                         int maxBatchSize) {
+  data_type_ = type;
+  data_format_ = format;
+  input_dims_.assign(inputDims, inputDims + nbInputs);
+  max_batch_size_ = maxBatchSize;
+}
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/trt_plugin.h

@@ -0,0 +1,72 @@
+// 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.
+
+#pragma once
+
+#include <NvInfer.h>
+#include <cassert>
+#include <cstring>
+#include <iostream>
+#include <unordered_map>
+#include <vector>
+
+#include "paddle/fluid/inference/tensorrt/plugin/serialize.hpp"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+class PluginTensorRT : public nvinfer1::IPluginExt {
+ public:
+  PluginTensorRT() {}
+  PluginTensorRT(const void* serialized_data, size_t length) {}
+  nvinfer1::Dims const& getInputDims(int index) const {
+    return input_dims_.at(index);
+  }
+  size_t getMaxBatchSize() const { return max_batch_size_; }
+  nvinfer1::DataType getDataType() const { return data_type_; }
+  nvinfer1::PluginFormat getDataFormat() const { return data_format_; }
+  virtual const char* getPluginVersion() const { return "1"; }
+  size_t getWorkspaceSize(int) const override { return 0; }
+  void terminate() override {}
+  virtual ~PluginTensorRT() {}
+
+  // The following functions need to be overrided in the subclass.
+  virtual nvinfer1::IPluginExt* clone() const = 0;
+  virtual const char* getPluginType() const = 0;
+  int initialize() override { return 0; }
+  bool supportsFormat(nvinfer1::DataType type,
+                      nvinfer1::PluginFormat format) const override;
+  void configureWithFormat(const nvinfer1::Dims* inputDims, int nbInputs,
+                           const nvinfer1::Dims* outputDims, int nbOutputs,
+                           nvinfer1::DataType type,
+                           nvinfer1::PluginFormat format,
+                           int maxBatchSize) override;
+  virtual void serialize(void* buffer) override;
+  virtual size_t getSerializationSize() override;
+
+ protected:
+  void deserializeBase(void const*& serialData, size_t& serialLength);
+  size_t getBaseSerializationSize();
+  void serializeBase(void*& buffer);
+
+  std::vector<nvinfer1::Dims> input_dims_;
+  size_t max_batch_size_;
+  nvinfer1::DataType data_type_;
+  nvinfer1::PluginFormat data_format_;
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle