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TensorRT Command-Line Wrapper: trtexec

Table Of Contents

Description

Included in the samples directory is a command line wrapper tool, called trtexec. trtexec is a tool to quickly utilize TensorRT without having to develop your own application. The trtexec tool has two main purposes:

  • It’s useful for benchmarking networks on random or user-provided input data.
  • It’s useful for generating serialized engines from models.

Benchmarking network - If you have a model saved as an ONNX file, you can use the trtexec tool to test the performance of running inference on your network using TensorRT. The trtexec tool has many options for specifying inputs and outputs, iterations for performance timing, precision allowed, and other options.

Serialized engine generation - If you generate a saved serialized engine file, you can pull it into another application that runs inference. For example, you can use the TensorRT Laboratory to run the engine with multiple execution contexts from multiple threads in a fully pipelined asynchronous way to test parallel inference performance. Also, in INT8 mode, random weights are used, meaning trtexec does not provide calibration capability.

Using custom input data - By default trtexec will run inference with randomly generated inputs. To provide custom inputs for an inference run, trtexec expects a binary file containing the data for each input tensor. It is recommended that this binary file be generated through numpy. For example, to create custom data of all ones to an ONNX model with one input named data with shape (1,3,244,244) and type FLOAT:

import numpy as np
data = np.ones((1,3,244,244), dtype=np.float32)
data.tofile("data.bin")

This binary file can be be loaded by trtexec during inference by using the --loadInputs flag:

./trtexec --onnx=model.onnx --loadInputs="data":data.bin

Building trtexec

trtexec can be used to build engines, using different TensorRT features (see command line arguments), and run inference. trtexec also measures and reports execution time and can be used to understand performance and possibly locate bottlenecks.

Compile the sample by following build instructions in TensorRT README.

Using trtexec

trtexec can build engines from models in ONNX format.

Example 1: Profiling a custom layer

You can profile a custom layer, implemented as a TensorRT plugin, by leveraging trtexec. Plugins need to be registered in the plugin registry (instance of IPluginRegistry) to be visible to TensorRT. trtexec will load the TensorRT standard plugin library (libnvinfer_plugin.so / nvinfer_plugin.dll) that provides plugin support to TensorRT. Checkout the Non-Zero Plugins Sample for a quick sample, or the Plugins section of the TensorRT Developer Guide for a more detailed walkthrough.

Plugins can be used with trtexec in the following 2 ways:

Using TensorRT-shipped Plugins
  • If you are using TensorRT-shipped plugins (included in libnvinfer_plugin.so / nvinfer_plugin.dll), no extra steps are required from the user as these plugins are pre-registered with the plugin registry.
Using your own Plugin

  • If you want to define your own plugin and have trtexec use it as part of the network, you should define your own Plugin Shared library with specific entry-points recognized by TensorRT. Then, provide the shared plugin library path to trtexec using the --dynamicPlugins flag.

  • More information on Plugin Shared Libraries and how to define them can be seen in the Plugin Shared Libraries section of the TensorRT Developer Guide.

    In summary, there are two methods:

    1. The REGISTER_TENSORRT_PLUGIN macro can be applied to the plugin creator for each plugin that needs to be statically registered. i.e. Registered at load-time of the plugin library.

    2. For dynamic registration, the plugin shared library must expose the below symbols which will be the entry points for TensorRT:

      extern "C" void setLoggerFinder(ILoggerFinder* finder);
extern "C" IPluginCreatorInterface* const* getCreators(int32_t& nbCreators)

    In the above, setLoggerFinder() should accept a pointer to an ILoggerFinder, through which an ILogger instance can be retrieved for the purpose of logging inside the library code. getCreators() should return an array of plugin creators the library contains. Example implementations of these entry points can be found in plugin/vc/vfcCommon.cpp and plugin/vc/vfcCommon.h.

    Note: Usage of getPluginCreators instead of getCreators is also valid, but deprecated.

  • If the user wants to build a TensorRT engine first and run later, the user has the option to serialize the shared plugin library as part of the engine itself by specifying --setPluginsToSerialize. By doing so, the user does not have to specify --dynamicPlugins to trtexec when running the built engine.

  • For more information on these flags, run ./trtexec --help.

Example 2: Running a network on DLA

To run the MNIST network on NVIDIA DLA (Deep Learning Accelerator) using trtexec in FP16 mode, issue:

./trtexec --onnx=data/mnist/mnist.onnx --useDLACore=1 --fp16 --allowGPUFallback

To run the MNIST network on DLA using trtexec in INT8 mode, issue:

./trtexec --onnx=data/mnist/mnist.onnx --useDLACore=1 --int8 --allowGPUFallback

To run the MNIST network on DLA using trtexec, issue:

./trtexec --onnx=data/mnist/mnist.onnx --useDLACore=0 --fp16 --allowGPUFallback

For more information about DLA, see Working With DLA.

Example 3: Running an ONNX model with full dimensions and dynamic shapes

To run an ONNX model in full-dimensions mode with static input shapes:

./trtexec --onnx=model.onnx

The following examples assumes an ONNX model with one dynamic input with name input and dimensions [-1, 3, 244, 244]

To run an ONNX model in full-dimensions mode with an given input shape:

./trtexec --onnx=model.onnx --shapes=input:32x3x244x244

To benchmark your ONNX model with a range of possible input shapes:

./trtexec --onnx=model.onnx --minShapes=input:1x3x244x244 --optShapes=input:16x3x244x244 --maxShapes=input:32x3x244x244 --shapes=input:5x3x244x244

Example 4: Collecting and printing a timing trace

When running, trtexec prints the measured performance, but can also export the measurement trace to a json file:

./trtexec --onnx=data/mnist/mnist.onnx --exportTimes=trace.json

Once the trace is stored in a file, it can be printed using the tracer.py utility. This tool prints timestamps and duration of input, compute, and output, in different forms:

./tracer.py trace.json

Similarly, profiles can also be printed and stored in a json file. The utility profiler.py can be used to read and print the profile from a json file.

Example 5: Tune throughput with multi-streaming

Tuning throughput may require running multiple concurrent streams of execution. This is the case for example when the latency achieved is well within the desired threshold, and we can increase the throughput, even at the expense of some latency. For example, saving engines with different precisions and assume that both execute within 2ms, the latency threshold:

trtexec --onnx=resnet50.onnx --saveEngine=g1.trt --int8 --skipInference
trtexec --onnx=resnet50.onnx --saveEngine=g2.trt --best --skipInference

Now, the saved engines can be tried to find the combination precision/streams below 2 ms that maximizes the throughput:

trtexec --loadEngine=g1.trt --streams=2
trtexec --loadEngine=g1.trt --streams=3
trtexec --loadEngine=g1.trt --streams=4
trtexec --loadEngine=g2.trt --streams=2

Example 6: Create a strongly typed plan file

This flag will create a network with the NetworkDefinitionCreationFlag::kSTRONGLY_TYPED flag where tensor data types are inferred from network input types and operator type specification. Use of specific builder precision flags such as --int8 or --best with this option is not allowed.

./trtexec --onnx=model.onnx --stronglyTyped

Tool command line arguments

To see the full list of available options and their descriptions, issue the ./trtexec --help command.

Note: Specifying the --safe parameter turns the safety mode switch ON. By default, the --safe parameter is not specified; the safety mode switch is OFF. The layers and parameters that are contained within the --safe subset are restricted if the switch is set to ON. The switch is used for prototyping the safety restricted flows until the TensorRT safety runtime is made available. This parameter is required when loading or saving safe engines with the standard TensorRT package. For more information, see the Working With Automotive Safety section in the TensorRT Developer Guide.

Additional resources

The following resources provide more details about trtexec:

Documentation

License

For terms and conditions for use, reproduction, and distribution, see the TensorRT Software License Agreement documentation.

Changelog

April 2019 This is the first release of this README.md file.

Known issues

There are no known issues in this sample.