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Contributing

Forms of contribution

Submit an Issue

torch2trt is use case driven. We originally created it to solve use cases related to NVIDIA Jetson, but the layer support has grown largely since it's release and we've found that it has helped many other developers as well.

The growth of torch2trt has been largely driven by issues submitted on GitHub. We learn a lot from the reported issues. Submitting an issue it is one of the best ways to begin contributing to torch2trt.

The reported issues typically are one of the following,

  • A bug or unexpected result
  • A model with unsupported layers

If you report an issue, we typically find the following information helpful

  • PyTorch version
  • TensorRT version
  • Platform (ie: Jetson Nano)
  • The PyTorch Module you're attempting to convert
  • The steps taken to convert the PyTorch module

If you're not sure how to provide any of these pieces of information, don't worry. Just open the issue and we're happy to discuss and help work out the details.

Ask a Question

Another great way to contribute is to ask a question on GitHub. There are often other developers who share your question, and they may find the discussion helpful. This also helps us gauge feature interest and identify gaps in documentation.

Submit a Pull Request

torch2trt is use case driven and has limited maintainence, for this reason we value community contributions greatly. Another great way to contribute is by submitting a pull request. Pull requests which are most likely to be accepted are

  • A new converter
  • A test case
  • A bug fix

If you add a new converter, it is best to include a few test cases that cross validate the converter against the original PyTorch. We provide a utility function to do this, as described in the Custom Converter usage guide.

Ideally pull requests solve one thing at a time. This makes it easy to evaluate the impact that the changes have on the project step-by-step. The more confident we are that the changes will not adversely impact the experience of other developers, the more likely we are to accept them.

Running module test cases

Before any change is accepted, we run the test cases on at least one platform. This performs a large number of cross validation checks against PyTorch. To do this

python3 -m torch2trt.test --name=converters --tolerance=1e-2

This will not hard-fail, but will highlight any build errors or max error checks. It is helpful if you include the status of this command in any pull-request, as well as system information like

  • PyTorch version
  • TensorRT version
  • Platform (ie: Jetson Nano)

Testing documentation

If you have a change that modifies the documentation, it is relatively straightforward to test. We use mkdocs-material for documentation, which parses markdown files in the docs folder.

To view the docs, simply call

./scripts/test_docs.sh

And then navigate to https://<ip_address>:8000.

Please note, this will not include dynamically generated documentation pages like the converters page. These contain cross reference links to the GitHub source code. If you want to test these you can call 

./scripts/build_docs.sh <github url> <tag>

Pointing to the public reflection of your local repository. For example, if we're working off the upstream master branch, we would call 

./scripts/build_docs.sh https://github.com/NVIDIA-AI-IOT/torch2trt master

If your changes are pushed to your fork, you would do 

./scripts/build_docs.sh https://github.com/<user>/torch2trt my_branch