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Update contribution guide (#19700) 

* update the contribution guide

* apply review feedback

* fix checkboxes

* checkbox fix #2

* clarify force push
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CONTRIBUTING.md
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......@@ -14,124 +14,126 @@ See the License for the specific language governing permissions and
limitations under the License.
-->
# How to contribute to transformers?
# 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.
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 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".
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.
Whichever way you choose to contribute, please be mindful to respect our
However you choose to contribute, please be mindful and respect our
[code of conduct](https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md).
## You can contribute in so many ways!
**This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).**
## Ways to contribute
There are several ways you can contribute to 🤗 Transformers:
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.
* Fix outstanding issues with the existing code.
* Submit issues related to bugs or desired new features.
* Implement new models.
* Contribute to the examples or to the documentation.
In particular, there is a special [Good First
If you don't know where to start, there is a special [Good First
Issue](https://github.com/huggingface/transformers/contribute) listing. It will give you a list of
open Issues that are open to anybody to work on. Just comment in the issue that you'd like to work
on it. In that same listing you will also find some Issues with `Good Second Issue` label. These are
typically slightly more complicated than the Issues with just `Good First Issue` label. But if you
feel you know what you're doing, go for it.
open issues that are beginner-friendly and help you start contributing to open-source. Just comment in the issue that you'd like to work
on it.
For something slightly more challenging, you can also take a look at the [Good Second Issue](https://github.com/huggingface/transformers/labels/Good%20Second%20Issue) list. In general though, if you feel like you know what you're doing, go for it and we'll help you get there! 🚀
> All contributions are equally valuable to the community. 🥰
## Fixing outstanding issues
*All are equally valuable to the community.*
If you notice an issue with the existing code and have a fix in mind, feel free to [start contributing](contributing#start-contributing-pull-requests) and open a Pull Request!
## Submitting a new issue or feature request
## Submitting a bug-related issue or feature request
Do your best to follow these guidelines when submitting an issue or a feature
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 🤗 Transformers library is robust and reliable thanks to the users who notify us of
the problems they encounter. So thank you for reporting an issue.
The 🤗 Transformers library is robust and reliable thanks to users who report the problems they encounter.
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).
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 on the [forum](https://discuss.huggingface.co/) first. This helps us respond quicker to fixing issues related to the library versus general questions.
Did not find it? :( So we can act quickly on it, please follow these steps:
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:
* Include your **OS type and version**, the versions of **Python**, **PyTorch** and
**Tensorflow** when applicable;
* Your **OS type and version** and **Python**, **PyTorch** and
**TensorFlow** versions 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.
less than 30s.
* The *full* traceback if an exception is raised.
* Attach any other additional information, like screenshots, you think may help.
To get the OS and software versions automatically, you can run the following command:
To get the OS and software versions automatically, run the following command:
```bash
transformers-cli env
```
or from the root of the repository the following command:
You can also run the same command from the root of the repository:
```bash
python src/transformers/commands/transformers_cli.py env
```
### Do you want a new feature?
### Do you want to implement a new model?
If there is a new feature you'd like to see in 🤗 Transformers, please open an issue and describe:
Awesome! Please provide the following information:
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?
* 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.
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.
If you are willing to contribute the model yourself, let us know so we can best
guide you.
We have added [templates](https://github.com/huggingface/transformers/tree/main/templates) to help you get started with your issue.
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`](https://github.com/huggingface/transformers/tree/main/templates) folder.
## Do you want to implement a new model?
### Do you want a new feature (that is not a model)?
New models are constantly released and if you want to implement a new model, please provide the following information
* A short description of the model and link to the paper.
* Link to the implementation if it is open-sourced.
* Link to the model weights if they are available.
A world-class feature request addresses the following points:
If you are willing to contribute the model yourself, let us know so we can help you add it to 🤗 Transformers!
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.
2. Write a *full paragraph* describing the feature;
3. Provide a **code snippet** that demonstrates its future use;
4. In case this is related to a paper, please attach a link;
5. Attach any additional information (drawings, screenshots, etc.) you think may help.
We have added a [detailed guide and templates](https://github.com/huggingface/transformers/tree/main/templates) to help you get started with adding a new model, and we also have a more technical guide for [how to add a model to 🤗 Transformers](https://huggingface.co/docs/transformers/add_new_model).
If your issue is well written we're already 80% of the way there by the time you
post it.
## Do you want to add documentation?
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`](https://github.com/huggingface/transformers/tree/main/templates)
folder.
We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be happy to make the changes or help you make a contribution if you're interested!
## Start contributing! (Pull Requests)
For more details about how to generate, build, and write the documentation, take a look at the documentation [README](https://github.com/huggingface/transformers/tree/main/docs).
Before writing code, we strongly advise you to search through the existing PRs or
issues to make sure that nobody is already working on the same thing. If you are
## 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 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
You will need basic `git` proficiency to contribute to
🤗 Transformers. 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](https://git-scm.com/book/en/v2) is a very good reference.
Follow these steps to start contributing ([supported Python versions](https://github.com/huggingface/transformers/blob/main/setup.py#L426)):
You'll need **[Python 3.7]((https://github.com/huggingface/transformers/blob/main/setup.py#L426))** or above to contribute to 🤗 Transformers. Follow the steps below to start contributing:
1. Fork the [repository](https://github.com/huggingface/transformers) by
clicking on the 'Fork' button on the repository's page. This creates a copy of the code
clicking on the **[Fork](https://github.com/huggingface/transformers/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:
......@@ -148,7 +150,7 @@ Follow these steps to start contributing ([supported Python versions](https://gi
$ git checkout -b a-descriptive-name-for-my-changes
```
**Do not** work on the `main` branch.
🚨 **Do not** work on the `main` branch!
4. Set up a development environment by running the following command in a virtual environment:
......@@ -156,25 +158,13 @@ Follow these steps to start contributing ([supported Python versions](https://gi
$ pip install -e ".[dev]"
```
(If transformers was already installed in the virtual environment, remove
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:
```bash
$ git clone https://github.com/huggingface/datasets
$ cd datasets
$ pip install -e .
```
mode with the `-e` flag.
If you have already cloned that repo, you might need to `git pull` to get the most recent changes in the `datasets`
library.
Depending on your OS, you may need to install some external libraries as well if the `pip` installation fails.
Depending on your OS, you might need to install some external libraries, as well, if the `pip` installation fails.
For macOS, you will likely need [MeCab](https://taku910.github.io/mecab/), which can be installed from Homebrew:
For macOS, you will likely need [MeCab](https://taku910.github.io/mecab/) which can be installed from Homebrew:
```bash
brew install mecab
......@@ -182,23 +172,15 @@ Follow these steps to start contributing ([supported Python versions](https://gi
5. Develop the features on your branch.
As you work on the features, you should make sure that the test suite
passes. You should run the tests impacted by your changes like this:
As you work on your code, you should make sure the test suite
passes. Run the tests impacted by your changes like this:
```bash
$ pytest tests/<TEST_TO_RUN>.py
```
You can also run the full suite with the following command, but it takes
a beefy machine to produce a result in a decent amount of time now that
Transformers has grown a lot. Here is the command for it:
```bash
$ make test
```
For more information about tests, check out the
[dedicated documentation](https://huggingface.co/docs/transformers/testing)
[Testing](https://huggingface.co/docs/transformers/testing) guide.
🤗 Transformers relies on `black` and `isort` to format its source code
consistently. After you make changes, apply automatic style corrections and code verifications
......@@ -210,7 +192,7 @@ Follow these steps to start contributing ([supported Python versions](https://gi
This target is also optimized to only work with files modified by the PR you're working on.
If you prefer to run the checks one after the other, the following command apply the
If you prefer to run the checks one after the other, the following command applies the
style corrections:
```bash
......@@ -218,145 +200,144 @@ Follow these steps to start contributing ([supported Python versions](https://gi
```
🤗 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:
controls are run by the CI, but you can run the same checks with:
```bash
$ make quality
```
Finally we have a lot of scripts that check we didn't forget to update
some files when adding a new model, that you can run with
Finally, we have a lot of scripts to make sure we didn't forget to update
some files when adding a new model. You can run these scripts with:
```bash
$ make repo-consistency
```
To learn more about those checks and how to fix any issue with them, check out the
[documentation](https://huggingface.co/docs/transformers/pr_checks)
To learn more about those checks and how to fix any issues with them, check out the
[Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide.
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. First you will need to clone the
repository containing our tools to build the documentation:
```bash
$ pip install git+https://github.com/huggingface/doc-builder
```
Then, make sure you have all the dependencies to be able to build the doc with:
If you're modifying documents under `docs/source` directory, make sure the documentation can still be built. This check will also run in the CI when you open a pull request. To run a local check
make sure you install the documentation builder:
```bash
$ pip install ".[docs]"
```
Finally run the following command from the root of the repository:
Run the following command from the root of the repository:
```bash
$ doc-builder build transformers docs/source/ --build_dir ~/tmp/test-build
$ doc-builder build transformers docs/source/en --build_dir ~/tmp/test-build
```
This will build the documentation in the `~/tmp/test-build` folder where you can inspect the generated
Markdown files with your favorite editor. You won't be able to see the final rendering on the website
before your PR is merged, we are actively working on adding a tool for this.
Markdown files with your favorite editor. You can also preview the docs on GitHub when you open a pull request.
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:
Once you're happy with your changes, add changed files with `git add` and
record your changes locally with `git commit`:
```bash
$ git add modified_file.py
$ git commit
```
Please write [good commit
messages](https://chris.beams.io/posts/git-commit/).
Please remember to write [good commit
messages](https://chris.beams.io/posts/git-commit/) to clearly communicate the changes you made!
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:
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:
```bash
$ git fetch upstream
$ git rebase upstream/main
```
Push the changes to your account using:
Push your changes to your branch:
```bash
$ 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.
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 in our [checklist](contributing#checklist) below. When you're ready, you can 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
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.
### Checklist
1. The title of your pull request should be a summary of its contribution;
2. 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
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.
### Pull request checklist
☐ The pull request title should summarize your contribution.<br>
☐ 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).<br>
☐ 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.<br>
☐ If adding a new feature, also add tests for it.<br>
- If you are adding a new model, make sure you use
`ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)` to trigger 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_bert.py` for an
example.
7. Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference
them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
to this dataset.
See more about the checks run on a pull request in our [PR guide](pr_checks)
`RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py`.
- If you are adding a new tokenizer, write tests and make sure
`RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py` passes.
CircleCI does not run the slow tests, but GitHub Actions does every night!<br>
☐ All public methods must have informative docstrings (see
[`modeling_bert.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py)
for an example).<br>
☐ Due to the rapidly growing repository, don't add any images, videos and other
non-text files that'll significantly weigh down the repository. Instead, use a Hub
repository such as [`hf-internal-testing`](https://huggingface.co/hf-internal-testing)
to host these files and reference them by URL. We recommend placing documentation
related images in the following repository:
[huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
You can open a PR on this dataset repostitory and ask a Hugging Face member to merge it.
For more information about the checks run on a pull request, take a look at our [Checks on a Pull Request](pr_checks) guide.
### Tests
An extensive test suite is included to test the library behavior and several examples. Library tests can be found in
the [tests folder](https://github.com/huggingface/transformers/tree/main/tests) and examples tests in the
[examples folder](https://github.com/huggingface/transformers/tree/main/examples).
the [tests](https://github.com/huggingface/transformers/tree/main/tests) folder and examples tests in the
[examples](https://github.com/huggingface/transformers/tree/main/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:
repository, specify a *path to a subfolder or a test file* to run the test.
```bash
$ python -m pytest -n auto --dist=loadfile -s -v ./tests/
$ python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model
```
and for the examples:
Similarly, for the `examples` directory, specify a *path to a subfolder or test file* to run the test. For example, the following command tests the text classification subfolder in the PyTorch `examples` directory:
```bash
$ pip install -r examples/xxx/requirements.txt # only needed the first time
$ python -m pytest -n auto --dist=loadfile -s -v ./examples/
$ python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification
```
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
In fact, this is actually how our `make test` and `make test-examples` commands are implemented (not including the `pip install`)!
You can also 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!
By default, slow tests are skipped but you can set the `RUN_SLOW` environment variable to
`yes` to run them. This will download many gigabytes of models so make sure you
have enough disk space, a good internet connection or a lot of patience!
<Tip warning={true}>
Remember to specify a *path to a subfolder or a test file* to run the test. Otherwise, you'll run all the tests in the `tests` or `examples` folder, which will take a very long time!
</Tip>
```bash
$ 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/
$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model
$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification
```
Likewise, set the `RUN_CUSTOM_TOKENIZERS` environment variable to `yes` to run
tests for custom tokenizers, which don't run by default either.
Like the slow tests, custom tokenizer tests are skipped but you can set the `RUN_CUSTOM_TOKENIZERS` environment variable to `yes` to run them.
🤗 Transformers uses `pytest` as a test runner only. It doesn't use any
`pytest`-specific features in the test suite itself.
......@@ -369,37 +350,37 @@ $ 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](https://google.github.io/styleguide/pyguide.html).
For documentation strings, 🤗 Transformers follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html).
Check our [documentation writing guide](https://github.com/huggingface/transformers/tree/main/docs#writing-documentation---specification)
for more information.
**This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).**
### Develop on Windows
On windows, you need to configure git to transform Windows `CRLF` line endings to Linux `LF` line endings:
On Windows (unless you're working in [Windows Subsytem for Linux](https://learn.microsoft.com/en-us/windows/wsl/) or WSL), you need to configure git to transform Windows `CRLF` line endings to Linux `LF` line endings:
`git config core.autocrlf input`
```bash
git config core.autocrlf input
```
One way one can run the make command on Window is to pass by MSYS2:
One way to run the `make` command on Windows is with MSYS2:
1. [Download MSYS2](https://www.msys2.org/), we assume to have it installed in C:\msys64
2. Open the command line C:\msys64\msys2.exe (it should be available from the start menu)
3. Run in the shell: `pacman -Syu` and install make with `pacman -S make`
1. [Download MSYS2](https://www.msys2.org/), and we assume it's installed in `C:\msys64`.
2. Open the command line `C:\msys64\msys2.exe` (it should be available from the **Start** menu).
3. Run in the shell: `pacman -Syu` and install `make` with `pacman -S make`.
4. Add `C:\msys64\usr\bin` to your PATH environment variable.
You can now use `make` from any terminal (Powershell, cmd.exe, etc) 🎉
You can now use `make` from any terminal (Powershell, cmd.exe, etc.)! 🎉
### Sync a forked repository with upstream main (the Hugging Face repository)
### Syncing forked main with upstream (HuggingFace) main
When updating the main branch of a forked repository, please follow these steps to avoid pinging the upstream repository which adds reference notes to each upstream PR, and sends unnecessary notifications to the developers involved in these PRs.
To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs,
when syncing the main branch of a forked repository, please, follow these steps:
1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead merge directly into the forked main.
1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main.
2. If a PR is absolutely necessary, use the following steps after checking out your branch:
```
```bash
$ git checkout -b your-branch-for-syncing
$ git pull --squash --no-commit upstream main
$ git commit -m '<your message without GitHub references>'
......
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