Page History

This document provides a detailed description of the MXNet-TensorRT runtime integration feature.  This document covers advanced techniques, contains a roadmap reflecting the current state of the feature and future directions, and also contains up-to-date benchmarks.  If you'd like a quick overview of the feature with a tutorial describing a simple use-case please refer to this MXNet hosted tutorial.  For more information you may also visit the original design proposal page.

Why is TensorRT integration useful?

...

Initial integration has been completed and launched as of MXNet 1.3.  We've tested this integration against a variety of models, including all the gluonCV models, Wavenet and some custom computer vision models.  Performance is roughly in line with expectations, but we're seeing a few regressions over earlier measurements that require investigation.

Continuous Integration support is enabled and running continually for all active PRs opened with MXNet.

...

https://jira.apache.org/jira/browse/MXNET-1085

Conditional Checkout and Compilation of Dependencies

TensorRT integration required us to add a number of third party code sub-repositories to the project.  This is not ideal for users who would like to checkout and build MXNet without using the TensorRT feature.  In the future we should migrate the feature to be CMake only, and checkout the project at pre-compilation time to avoid forcing all users to checkout these subrepos.  We can also model these dependencies using CMake such that they're automatically built and linked against when required, which would make building from scratch easier for those that do want to use TensorRT integration.

Make use of Cached TRT Engines

Similar to the cudnn auto-tuning feature we've received requests from users that we cache TensorRT engine compilations so that we avoid the delay of building the engine each time we start the process.

Increased Operator (/Layer) Coverage

...

Decouple  NNVM to ONNX from NNVM to TensorRT in MXNet

The current nnvm_to_onnx classes are tightly coupled to TensorRT.  We could extract all of the TensorRT specific functionality and have a proper separation between nnvm_to_onnx and onnx_to_tensorrt.  When structuring nnvm_to_onnx we should make use of object hierarchy to convert to specific opsets of onnx to help us maintain compatibility with different toolsets.  We should create a base class that performs generic onnx conversions.  We should then specialized objects that inherit from the base onnx class and take care of the  differences between opsets.  We should also create unit tests on a per-op basis to make sure we're introducing regressions.

Currently supported operators:

Operators to be added:

Benchmarks

TensorRT is still an experimental feature, so benchmarks are likely to improve over time.  As of Oct 11, 2018 we've measured the following improvements which have all been run with FP32 weighted networks.

Related articles

https://mxnet.incubator.apache.org/tutorials/tensorrt/inference_with_trt.html

...