KerasNLP is an actively growing project and community! We would love for you to get involved. Below are instructions for how to plug into KerasNLP development.

Before contributing code, please review our Style Guide and API Design Guide.

Our Roadmap contains an overview of the project goals and our current focus areas.

We follow Google's Open Source Community Guidelines.

The fastest way to contribute it to find open issues that need an assignee. We maintain two lists of github tags for contributors:

• good first issue: a list of small, well defined issues for newcomers to the project.
• contributions welcome: a larger list of issues that may range in complexity.

If you would like propose a new symbol or feature, please first review our design guide and roadmap linked above, and open an issue to discuss. If you have a specific design in mind, please include a Colab notebook showing the proposed design in a end-to-end example. Keep in mind that design for a new feature or use case may take longer than contributing to an open issue with a vetted-design.

Follow these steps to submit your code contribution.

Before making any changes, we recommend opening an issue (if one doesn't already exist) and discussing your proposed changes. This way, we can give you feedback and validate the proposed changes.

If your code change involves the fixing of a bug, please include a Colab notebook that shows how to reproduce the broken behavior.

If the changes are minor (simple bug fix or documentation fix), then feel free to open a PR without discussion.

To make code changes, you need to fork the repository. You will need to setup a development environment and run the unit tests. This is covered in section "Setup environment".

Once the change is ready, open a pull request from your branch in your fork to the master branch in keras-team/keras-nlp.

After creating the pull request, you will need to sign the Google CLA agreement. The agreement can be found at https://cla.developers.google.com/clas.

CI tests will automatically be run directly on your pull request. Their status will be reported back via GitHub actions.

There may be several rounds of comments and code changes before the pull request gets approved by the reviewer.

Once the pull request is approved, a team member will take care of merging.

Python 3.9 or later is required.

Setting up your KerasNLP development environment requires you to fork the KerasNLP repository and clone it locally. With the GitHub CLI installed, you can do this as follows:

gh repo fork keras-team/keras-nlp --clone --remote
cd keras-nlp

Next we must setup a python environment with the correct dependencies. We recommend using conda to set up a base environment, and pip to install python packages from PyPI. The exact method will depend on your OS.

Note: Be careful not to use mix pre-packaged tensorflow and jax libraries in conda with PyPI packages from pip. We recommend pulling all KerasNLP dependencies via pip as described below.

For developing and unit testing the library, a CPU-only environment is often sufficient. For any training or inference with the library, you will quickly want accelerator support. The easiest way to get GPU support across all of our backends is to set up a few different python environements and pull in all cuda dependencies via pip.

The shell snippet below will install four conda environments: keras-nlp-cpu, keras-nlp-jax, keras-nlp-torch, and keras-nlp-tensorflow. The cpu environement supports all backends without cuda, and each backend environement has cuda support.

conda create -y -n keras-nlp-cpu python=3.10
conda activate keras-nlp-cpu
pip install -r requirements.txt  # install deps
pip install -e .  # install keras-nlp

for backend in "jax" "torch" "tensorflow"; do
    conda create -y -n keras-nlp-${backend} python=3.10
    conda activate keras-nlp-${backend}
    pip install -r requirements-${backend}-cuda.txt  # install deps
    pip install -e .  # install keras-nlp
done

To activate the jax environment and set keras to use jax, run:

conda activate keras-nlp-jax && export KERAS_BACKEND=jax

tensorflow-text does not release precompiled binaries for MacOS M-series chips, though the library does support building from source on MacOS.

We strongly recommend a Linux development environment for an easy contribution experience. To build a dev environement from scratch on MacOS, see the following guides:

• https://developer.apple.com/metal/tensorflow-plugin/
• https://github.com/tensorflow/text

For the best experience developing on windows, please install WSL, and proceed with the linux installation instruction above.

To run the format and lint scripts, make sure you clone the repo with Linux style line endings and change any line separator settings in your editor. This is automatically done if you clone using git inside WSL.

Note that will not support Windows Shell/PowerShell for any scripts in this repository.

Run API generation script when creating PRs that update keras_nlp_export public APIs. Add the files changed in keras_nlp/api to the same PR.

./shell/api_gen.sh

KerasNLP is tested using PyTest.

To run a test file, run pytest path/to/file from the root directory of the repository.

To run a single test, you can use -k=<your_regex> to use regular expression to match the test you want to run. For example, you can use the following command to run all the tests in import_test.py whose names contain import:

pytest keras_nlp/keras_nlp/integration_tests/import_test.py -k="import"

You can run the default testing suite by simply invoking pytest:

pytest

We annotate tests that are slower or require a network connection as "large", and by default pytest will skip these tests. We run large tests continuously on GCP. You can specify these by running:

pytest --run_large

Finally, for tests that are very slow and resource intensive (e.g. downloading a 5GB checkpoint), we use an "extra_large" annotation and do not run them continuously at all. You can specify these by running:

pytest --run_extra_large

When running "extra_large" tests, we recommend also specify a specific test file so you aren't waiting around forever!

We use flake8, isort and black for code formatting. You can run the following commands manually every time you want to format your code:

• Run shell/format.sh to format your code
• Run shell/lint.sh to check the result.

If after running these the CI flow is still failing, try updating flake8, isort and black. This can be done by running pip install --upgrade black, pip install --upgrade flake8, and pip install --upgrade isort.