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166 lines
6.2 KiB
Markdown
166 lines
6.2 KiB
Markdown
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# Contributing to JAX
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Everyone can contribute to JAX, and we value everyone's contributions. There are several
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ways to contribute, including:
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- Answering questions on JAX's [discussions page](https://github.com/google/jax/discussions)
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- Improving or expanding JAX's [documentation](http://jax.readthedocs.io/)
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- Contributing to JAX's [code-base](http://github.com/google/jax/)
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- Contributing in any of the above ways to the broader ecosystem of [libraries built on JAX](https://github.com/google/jax#neural-network-libraries)
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The JAX project follows [Google's Open Source Community Guidelines](https://opensource.google/conduct/).
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## Ways to contribute
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We welcome pull requests, in particular for those issues marked with
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[contributions welcome](https://github.com/google/jax/issues?q=is%3Aopen+is%3Aissue+label%3A%22contributions+welcome%22) or
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[good first issue](https://github.com/google/jax/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22).
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For other proposals, we ask that you first open a GitHub
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[Issue](https://github.com/google/jax/issues/new/choose) or
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[Discussion](https://github.com/google/jax/discussions)
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to seek feedback on your planned contribution.
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## Contributing code using pull requests
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We do all of our development using git, so basic knowledge is assumed.
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Follow these steps to contribute code:
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1. Fork the JAX repository by clicking the **Fork** button on the
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[repository page](http://www.github.com/google/jax). This creates
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a copy of the JAX repository in your own account.
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2. Install Python >=3.6 locally in order to run tests.
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3. `pip` installing your fork from source. This allows you to modify the code
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and immediately test it out:
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```bash
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git clone https://github.com/YOUR_USERNAME/jax
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cd jax
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pip install -r requirements.txt # Installs all testing requirements.
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pip install -e . # Installs JAX from the current directory in editable mode.
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```
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4. Add the JAX repo as an upstream remote, so you can use it to sync your
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changes.
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```bash
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git remote add upstream http://www.github.com/google/jax
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```
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5. Create a branch where you will develop from:
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```bash
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git checkout -b name-of-change
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```
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And implement your changes using your favorite editor (we recommend
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[Visual Studio Code](https://code.visualstudio.com/)).
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6. Make sure the tests pass by running the following command from the top of
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the repository:
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```bash
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pytest -n auto tests/
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```
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JAX's test suite is quite large, so if you know the specific test file that covers your
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changes, you can limit the tests to that; for example:
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```bash
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pytest -n auto tests/lax_scipy_test.py
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```
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JAX also offers more fine-grained control over which particular tests are run;
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see {ref}`running-tests` for more information.
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7. Once you are satisfied with your change, create a commit as follows (
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[how to write a commit message](https://chris.beams.io/posts/git-commit/)):
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```bash
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git add file1.py file2.py ...
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git commit -m "Your commit message"
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```
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Then sync your code with the main repo:
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```bash
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git fetch upstream
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git rebase upstream/master
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```
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Finally, push your commit on your development branch and create a remote
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branch in your fork that you can use to create a pull request from:
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```bash
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git push --set-upstream origin name-of-change
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```
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8. Create a pull request from the JAX repository and send it for review.
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Check the {ref}`pr-checklist` for considerations when preparing your PR, and
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consult [GitHub Help](https://help.github.com/articles/about-pull-requests/)
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if you need more information on using pull requests.
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(pr-checklist)=
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## JAX pull request checklist
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As you prepare a JAX pull request, here are a few things to keep in mind:
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### Google contributor license agreement
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Contributions to this project must be accompanied by a Google Contributor License
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Agreement (CLA). You (or your employer) retain the copyright to your contribution;
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this simply gives us permission to use and redistribute your contributions as
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part of the project. Head over to <https://cla.developers.google.com/> to see
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your current agreements on file or to sign a new one.
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You generally only need to submit a CLA once, so if you've already submitted one
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(even if it was for a different project), you probably don't need to do it
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again. If you're not certain whether you've signed a CLA, you can open your PR
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and our friendly CI bot will check for you.
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### Single-change commits and pull requests
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A git commit ought to be a self-contained, single change with a descriptive
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message. This helps with review and with identifying or reverting changes if
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issues are uncovered later on.
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**Pull requests typically comprise a single git commit.** (In some cases, for
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instance for large refactors or internal rewrites, they may contain several.)
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In preparing a pull request for review, you may need to squash together
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multiple commits. We ask that you do this prior to sending the PR for review if
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possible. The `git rebase -i` command might be useful to this end.
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### Linting and Type-checking
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JAX uses [mypy](https://mypy.readthedocs.io/) and [flake8](https://flake8.pycqa.org/)
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to statically test code quality; the easiest way to run these checks locally is via
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the [pre-commit](https://pre-commit.com/) framework:
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```bash
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pip install pre-commit
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pre-commit run --all
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```
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If your pull request touches documentation notebooks, this will also run some checks
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on those (See {ref}`update-notebooks` for more details).
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### Full GitHub test suite
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Your PR will automatically be run through a full test suite on GitHub CI, which
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covers a range of Python versions, dependency versions, and configuration options.
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It's normal for these tests to turn up failures that you didn't catch locally; to
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fix the issues you can push new commits to your branch.
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### Restricted test suite
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Once your PR has been reviewed, a JAX maintainer will mark it as `Pull Ready`. This
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will trigger a larger set of tests, including tests on GPU and TPU backends that are
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not available via standard GitHub CI. Detailed results of these tests are not publicly
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viweable, but the JAX mantainer assigned to your PR will communicate with you regarding
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any failures these might uncover; it's not uncommon, for example, that numerical tests
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need different tolerances on TPU than on CPU.
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