CONTRIBUTING.md

Contribute to optimum-quanto

Everyone is welcome to contribute, and we value everybody's contribution. Code contributions are not the only way to help the community. Answering questions, helping others, and improving the documentation are also immensely valuable.

It also helps us if you spread the word! Reference the library in blog posts about the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply ⭐️ the repository to say thank you.

However you choose to contribute, please be mindful and respect our code of conduct.

This guide is directly inspired by transformers guide to contributing.

Ways to contribute

There are several ways you can contribute:

• Fix outstanding issues with the existing code.
• Submit issues related to bugs or desired new features.
• Implement new kernels.

All contributions are equally valuable to the community. 🥰

Fixing outstanding issues

If you notice an issue with the existing code and have a fix in mind, feel free to start contributing and open a Pull Request!

Submitting a bug-related issue or feature request

Do your best to follow these guidelines when submitting a bug-related 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 optimum-quanto backend will become more robust and reliable thanks to users who will report the problems they encounter.

Before you report an issue, we would really appreciate it if you could make sure the bug was not already reported (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code. If you're unsure whether the bug is in your code or the library, please ask in the forum first. This helps us respond quicker to fixing issues related to the library versus general questions.

Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it:

• Your OS type and version and Python and PyTorch versions.
• A short, self-contained, code snippet that allows us to reproduce the bug in less than 30s.
• The full traceback if an exception is raised.
• Attach any other additional information, like screenshots, you think may help.

Do you want a new feature?

If there is a new feature you'd like to see, please open an issue and describe:

1. What is the motivation behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community?

   Whatever it is, we'd love to hear about it!

2. Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you.

3. Provide a code snippet that demonstrates the features usage.

4. If the feature is related to a paper, please include a link.

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

Do you want to implement a new kernel?

With the constant evolution of hardware backends, there is always a need for updating the kernels for better performance.

• The hardware configuration(s) it will apply to.
• If any, a short description of the novel techniques that should be used to implement the kernel.

If you are willing to contribute the kernel yourself, let us know so we can help you add it to optimum-quanto!

Create a Pull Request

Before writing any code, we strongly advise you to search through the existing PRs or issues to make sure 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 contribute. While git is not the easiest tool to use, it has the greatest manual. Type git --help in a shell and enjoy! If you prefer books, Pro Git is a very good reference.

You'll need Python 3.8 or above to contribute. Follow the steps below 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 [email protected]:<your Github handle>/optimum-quanto.git
   cd optimum-quanto
   git remote add upstream https://github.com/huggingface/optimum-quanto.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 main branch!

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

   pip install -e ".[dev]"

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

5. Develop the features in your branch.

   As you work on your code, you should make sure the test suite passes. Run the tests impacted by your changes like this:

   pytest test/<TEST_TO_RUN>.py

   optimum-quanto relies on black and ruff to format its source code consistently. After you make changes, apply automatic style corrections and code verifications that can't be automated in one go with:

   make style

   Once you're happy with your changes, add the changed files with git add and record your changes locally with git commit:

   git add modified_file.py
   git commit

   This repository uses a rebase strategy when merging pull-requests, meaning that your commits will not be squashed automatically.

   We therefore request you to keep a tidy queue of commits in your pull-request, clearly communicating the changes you made in each commit.

   This is enforced by the continuous integration, so your pull-request will not be reviewed if your commit queue is not clean.

   Although this is not mandatory, we kindly ask you to consider using conventional commits (here the full specification)!

   This article gives a brief rationale of why this will make our life and yours easier.

   To keep your copy of the code up to date with the original repository, rebase your branch on upstream/branch before you open a pull request or if requested by a maintainer:

   git fetch upstream
   git rebase upstream/main

   Before submitting, cleanup your commit history to make it more readable for the reviewer (like squashing temporary commits and editing commit messages to clearly explain what you changed).

   Push your changes to your branch:

   git push -u origin a-descriptive-name-for-my-changes

   If you've already opened a pull request, you'll need to force push with the --force flag. Otherwise, if the pull request hasn't been opened yet, you can just push your changes normally.

6. Now you can go to your fork of the repository on GitHub and click on Pull Request to open a pull request. Make sure you tick off all the boxes on our checklist below. When you're ready, you can send your changes to the project maintainers for review.

7. It's ok if maintainers request changes, it happens to our 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.

Pull request checklist

☐ The pull request title should summarize your contribution.
☐ If your pull request addresses an issue, please mention the issue number in the pull request description to make sure they are linked (and people viewing the issue know you are working on it).
☐ 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.
☐ Make sure existing tests pass.
☐ If adding a new feature, also add tests for it.
☐ All public methods must have informative docstrings.

Tests

An extensive test suite is included to test the library behavior in the test folder.

From the root of the repository, specify a path to a subfolder or a test file to run the test.

python -m pytest -sv ./test/<subfolder>/<test>.py

You can run all tests by typing:

make test

Style guide

For documentation strings, optimum-quanto follows the Google Python Style Guide. Check transformers documentation writing guide for more information.