Use Paddle-TensorRT Library for inference¶

Paddle-TRT interface usage¶

When using AnalysisPredictor, we enable Paddle-TRT by setting

config->EnableTensorRtEngine(1 << 20      /* workspace_size*/,
                        batch_size        /* max_batch_size*/,
                        3                 /* min_subgraph_size*/,
                        AnalysisConfig::Precision::kFloat32 /* precision*/,
                        false             /* use_static*/,
                        false             /* use_calib_mode*/);

The details of this interface is as following:

• workspace_size: type:int, default is 1 << 20. Sets the max workspace size of TRT. TensorRT will choose kernels under this constraint.

• max_batch_size: type:int, default is 1. Sets the max batch size. Batch sizes during runtime cannot exceed this value.

• min_subgraph_size: type:int, default is 3. Subgraph is used to integrate TensorRT in PaddlePaddle. To avoid low performance, Paddle-TRT is only enabled when th number of nodes in th subgraph is more than min_subgraph_size.

• precision: type:enum class Precision {kFloat32 = 0, kHalf, kInt8,};, default is AnalysisConfig::Precision::kFloat32. Sets the precision of TRT, supporting FP32(kFloat32), FP16(kHalf), Int8(kInt8). Using Paddle-TRT int8 calibration requires setting precision to AnalysisConfig::Precision::kInt8, and use_calib_mode to true.

• use_static: type:bool, default is false. If set to true, Paddle-TRT will serialize optimization information to disk, to deserialize next time without optimizing again.

• use_calib_mode: type:bool, default is false. Using Paddle-TRT int8 calibration requires setting this option to true.

Note： Paddle-TRT currently only supports fixed input shape.

Paddle-TRT example compiling test¶

1. Download or compile Paddle Inference with TensorRT support, refer to Install and Compile C++ Inference Library.

2. Download NVIDIA TensorRT(with consistent version of cuda and cudnn in local environment) from NVIDIA TensorRT with an NVIDIA developer account.

3. Download Paddle Inference sample and uncompress, and enter sample/paddle-TRT directory.

   paddle-TRT directory structure is as following:

   paddle-TRT
   ├── CMakeLists.txt
   ├── mobilenet_test.cc
   ├── fluid_generate_calib_test.cc
   ├── fluid_int8_test.cc
   ├── mobilenetv1
   │   ├── model
   │   └── params
   ├── run.sh
   └── run_impl.sh
   

   • mobilenet_test.cc is the c++ source code of inference using Paddle-TRT

   • fluid_generate_calib_test.cc is the c++ source code of inference using Paddle-TRT int8 calibration to generate calibration table

   • fluid_int8_test.cc is the c++ source code of inference using Paddle-TRT int8

   • mobilenetv1 is the model dir

   • run.sh is the script for running inference

   Here we assume that the current directory is SAMPLE_BASE_DIR/sample/paddle-TRT.

   # set whether to enable MKL, GPU or TensorRT. Enabling TensorRT requires WITH_GPU being ON
   WITH_MKL=ON
   WITH_GPU=OFF
   USE_TENSORRT=OFF
   
   # set path to CUDA lib dir, CUDNN lib dir, TensorRT root dir and model dir
   LIB_DIR=YOUR_LIB_DIR
   CUDA_LIB_DIR=YOUR_CUDA_LIB_DIR
   CUDNN_LIB_DIR=YOUR_CUDNN_LIB_DIR
   TENSORRT_ROOT_DIR=YOUR_TENSORRT_ROOT_DIR
   MODEL_DIR=YOUR_MODEL_DIR
   

   Please configure run.sh depending on your environment.

4. Build and run the sample.

   sh run.sh
   

Paddle-TRT INT8 usage¶

1. Paddle-TRT INT8 introduction The parameters of the neural network are redundant to some extent. In many tasks, we can turn the Float32 model into Int8 model on the premise of precision. At present, Paddle-TRT supports to turn the trained Float32 model into Int8 model off line. The specific processes are as follows:

   1）Create the calibration table. We prepare about 500 real input data, and input the data to the model. Paddle-TRT will count the range information of each op input and output value in the model, and record in the calibration table. The information can reduce the information loss during model transformation.

   2）After creating the calibration table, run the model again, Paddle-TRT will load the calibration table automatically, and conduct the inference in the INT8 mode.

2. compile and test the INT8 example

   change the mobilenet_test in run.sh to fluid_generate_calib_test and run

   sh run.sh
   

   We generate 500 input data to simulate the process, and it’s suggested that you use real example for experiment. After the running period, there will be a new file named trt_calib_* under the SAMPLE_BASE_DIR/sample/paddle-TRT/build/mobilenetv1/_opt_cache model directory, which is the calibration table.

   Then copy the model dir with calibration infomation to path

   cp -rf SAMPLE_BASE_DIR/sample/paddle-TRT/build/mobilenetv1/ SAMPLE_BASE_DIR/sample/paddle-TRT/mobilenetv1_calib
   

   change fluid_generate_calib_test in run.sh to fluid_int8_test, and change model dir path to SAMPLE_BASE_DIR/sample/paddle-TRT/mobilenetv1_calib and run

   sh run.sh
   

Paddle-TRT subgraph operation principle¶

Subgraph is used to integrate TensorRT in PaddlePaddle. After model is loaded, neural network can be represented as a computing graph composed of variables and computing nodes. Functions Paddle TensorRT implements are to scan the whole picture, discover subgraphs that can be optimized with TensorRT and replace them with TensorRT nodes. During the inference of model, Paddle will call TensorRT library to optimize TensorRT nodes and call native library of Paddle to optimize other nodes. During the inference, TensorRT can integrate Op horizonally and vertically to filter redundant Ops and is able to choose appropriate kernel for specific Op in specific platform to speed up the inference of model.

A simple model expresses the process :

Original Network

Transformed Network

We can see in the Original Network that the green nodes represent nodes supported by TensorRT, the red nodes represent variables in network and yellow nodes represent nodes which can only be operated by native functions in Paddle. Green nodes in original network are extracted to compose subgraph which is replaced by a single TensorRT node to be transformed into block-25 node in network. When such nodes are encountered during the runtime, TensorRT library will be called to execute them.

Paddle-TRT benchmark¶