How to contribute to transformers?

Everyone is welcome to contribute, and we value everybody’s contribution. Code is thus not the only way to help the community. Answering questions, helping others, reaching out and improving the documentations are immensely valuable to the community.

It also helps us if you spread the word: reference the library from blog posts on the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply star the repo to say “thank you”.

You can contribute in so many ways!

There are 4 ways you can contribute to transformers:

  • Fixing outstanding issues with the existing code;

  • Implementing new models;

  • Contributing to the examples or to the documentation;

  • Submitting issues related to bugs or desired new features.

All are equally valuable to the community.

Submitting a new issue or feature request

Do your best to follow these guidelines when submitting an issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback.

Did you find a bug?

The transformers are robust and reliable thanks to the users who notify us of the problems they encounter. So thank you for reporting an issue.

First, we would really appreciate it if you could make sure the bug was not already reported (use the search bar on Github under Issues).

Did not find it? :( So we can act quickly on it, please follow these steps:

  • Include your OS type and version, the versions of Python, PyTorch and Tensorflow when applicable;

  • A short, self-contained, code snippet that allows us to reproduce the bug in less than 30s;

  • Provide the full traceback if an exception is raised.

To get the OS and software versions automatically, you can run the following command:

transformers-cli env

or from the root of the repository the following command:

python src/transformers/commands/transformers_cli.py env

Do you want to implement a new model?

Awesome! Please provide the following information:

  • Short description of the model and link to the paper;

  • Link to the implementation if it is open-source;

  • Link to the model weights if they are available.

If you are willing to contribute the model yourself, let us know so we can best guide you.

We have added a detailed guide and templates to guide you in the process of adding a new model. You can find them in the templates folder.

Do you want a new feature (that is not a model)?

A world-class feature request addresses the following points:

  1. Motivation first:

  • Is it related to a problem/frustration with the library? If so, please explain why. Providing a code snippet that demonstrates the problem is best.

  • Is it related to something you would need for a project? We’d love to hear about it!

  • Is it something you worked on and think could benefit the community? Awesome! Tell us what problem it solved for you.

  1. Write a full paragraph describing the feature;

  2. Provide a code snippet that demonstrates its future use;

  3. In case this is related to a paper, please attach a link;

  4. Attach any additional information (drawings, screenshots, etc.) you think may help.

If your issue is well written we’re already 80% of the way there by the time you post it.

We have added templates to guide you in the process of adding a new example script for training or testing the models in the library. You can find them in the templates folder.

Start contributing! (Pull Requests)

Before writing code, we strongly advise you to search through the exising PRs or issues to make sure that nobody is already working on the same thing. If you are unsure, it is always a good idea to open an issue to get some feedback.

You will need basic git proficiency to be able to contribute to transformers. git is not the easiest tool to use but it has the greatest manual. Type git --help in a shell and enjoy. If you prefer books, Pro Git is a very good reference.

Follow these steps to start contributing:

  1. Fork the repository by clicking on the ‘Fork’ button on the repository’s page. This creates a copy of the code under your GitHub user account.

  2. Clone your fork to your local disk, and add the base repository as a remote:

    $ git clone git@github.com:<your Github handle>/transformers.git
    $ cd transformers
    $ git remote add upstream https://github.com/huggingface/transformers.git
    
  3. Create a new branch to hold your development changes:

    $ git checkout -b a-descriptive-name-for-my-changes
    

    do not work on the master branch.

  4. Set up a development environment by running the following command in a virtual environment:

    $ pip install -e ".[dev]"
    

    (If transformers was already installed in the virtual environment, remove it with pip uninstall transformers before reinstalling it in editable mode with the -e flag.)

    To run the full test suite, you might need the additional dependency on datasets which requires a separate source install:

    $ git clone https://github.com/huggingface/datasets
    $ cd datasets
    $ pip install -e .
    

    If you have already cloned that repo, you might need to git pull to get the most recent changes in the datasets library.

  5. Develop the features on your branch.

    As you work on the features, you should make sure that the test suite passes:

    $ make test
    

    Note, that this command uses -n auto pytest flag, therefore, it will start as many parallel pytest processes as the number of your computer’s CPU-cores, and if you have lots of those and a few GPUs and not a great amount of RAM, it’s likely to overload your computer. Therefore, to run the test suite, you may want to consider using this command instead:

    $ python -m pytest -n 3 --dist=loadfile -s -v ./tests/
    

    Adjust the value of -n to fit the load your hardware can support.

    transformers relies on black and isort to format its source code consistently. After you make changes, format them with:

    $ make style
    

    transformers also uses flake8 and a few custom scripts to check for coding mistakes. Quality control runs in CI, however you can also run the same checks with:

    $ make quality
    

    You can do the automatic style corrections and code verifications that can’t be automated in one go:

    $ make fixup
    

    If you’re modifying documents under docs/source, make sure to validate that they can still be built. This check also runs in CI. To run a local check make sure you have installed the documentation builder requirements, by running pip install .[tf,torch,docs] once from the root of this repository and then run:

    $ make docs
    

    Once you’re happy with your changes, add changed files using git add and make a commit with git commit to record your changes locally:

    $ git add modified_file.py
    $ git commit
    

    Please write good commit messages.

    It is a good idea to sync your copy of the code with the original repository regularly. This way you can quickly account for changes:

    $ git fetch upstream
    $ git rebase upstream/master
    

    Push the changes to your account using:

    $ git push -u origin a-descriptive-name-for-my-changes
    
  6. Once you are satisfied (and the checklist below is happy too), go to the webpage of your fork on GitHub. Click on ‘Pull request’ to send your changes to the project maintainers for review.

  7. It’s ok if maintainers ask you for changes. It happens to core contributors too! So everyone can see the changes in the Pull request, work in your local branch and push the changes to your fork. They will automatically appear in the pull request.

Checklist

  1. The title of your pull request should be a summary of its contribution;

  2. If your pull request adresses an issue, please mention the issue number in the pull request description to make sure they are linked (and people consulting the issue know you are working on it);

  3. To indicate a work in progress please prefix the title with [WIP]. These are useful to avoid duplicated work, and to differentiate it from PRs ready to be merged;

  4. Make sure existing tests pass;

  5. Add high-coverage tests. No quality testing = no merge.

    • If you are adding a new model, make sure that you use ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...), which triggers the common tests.

    • If you are adding new @slow tests, make sure they pass using RUN_SLOW=1 python -m pytest tests/test_my_new_model.py.

    • If you are adding a new tokenizer, write tests, and make sure RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py passes. CircleCI does not run the slow tests, but github actions does every night!

  6. All public methods must have informative docstrings that work nicely with sphinx. See modeling_ctrl.py for an example.

Tests

An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the tests folder and examples tests in the examples folder.

We like pytest and pytest-xdist because it’s faster. From the root of the repository, here’s how to run tests with pytest for the library:

$ python -m pytest -n auto --dist=loadfile -s -v ./tests/

and for the examples:

$ pip install -r examples/requirements.txt  # only needed the first time
$ python -m pytest -n auto --dist=loadfile -s -v ./examples/

In fact, that’s how make test and make test-examples are implemented (sans the pip install line)!

You can specify a smaller set of tests in order to test only the feature you’re working on.

By default, slow tests are skipped. Set the RUN_SLOW environment variable to yes to run them. This will download many gigabytes of models — make sure you have enough disk space and a good Internet connection, or a lot of patience!

$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/
$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/

Likewise, set the RUN_CUSTOM_TOKENIZERS environment variable to yes to run tests for custom tokenizers, which don’t run by default either.

🤗 Transformers uses pytest as a test runner only. It doesn’t use any pytest-specific features in the test suite itself.

This means unittest is fully supported. Here’s how to run tests with unittest:

$ python -m unittest discover -s tests -t . -v
$ python -m unittest discover -s examples -t examples -v

Style guide

For documentation strings, transformers follows the google style. Check our documentation writing guide for more information.

This guide was heavily inspired by the awesome scikit-learn guide to contributing