Contributing¶

Thanks for considering contributing! We want AllenNLP to be the way to do cutting-edge NLP research, but we cannot get there without community support.

How Can I Contribute?¶

Bug fixes and new features¶

Did you find a bug?

First, do a quick search to see whether your issue has already been reported. If your issue has already been reported, please comment on the existing issue.

Otherwise, open a new GitHub issue. Be sure to include a clear title and description. The description should include as much relevant information as possible. The description should explain how to reproduce the erroneous behavior as well as the behavior you expect to see. Ideally you would include a code sample or an executable test case demonstrating the expected behavior.

Do you have a suggestion for an enhancement?

We use GitHub issues to track enhancement requests. Before you create an enhancement request:

Make sure you have a clear idea of the enhancement you would like. If you have a vague idea, consider discussing it first on a GitHub issue.
Check the documentation to make sure your feature does not already exist.
Do a quick search to see whether your enhancement has already been suggested.

When creating your enhancement request, please:

Provide a clear title and description.
Explain why the enhancement would be useful. It may be helpful to highlight the feature in other libraries.
Include code examples to demonstrate how the enhancement would be used.

Making a pull request¶

When you're ready to contribute code to address an open issue, please follow these guidelines to help us be able to review your pull request (PR) quickly.

Initial setup (only do this once)
Expand details 👇

If you haven't already done so, please fork this repository on GitHub.

Then clone your fork locally with
```
git clone https://github.com/USERNAME/allennlp.git
```
or
```
git clone git@github.com:USERNAME/allennlp.git
```
At this point the local clone of your fork only knows that it came from your repo, github.com/USERNAME/allennlp.git, but doesn't know anything the main repo, https://github.com/allenai/allennlp.git. You can see this by running
```
git remote -v
```
which will output something like this:
```
origin https://github.com/USERNAME/allennlp.git (fetch)
origin https://github.com/USERNAME/allennlp.git (push)
```
This means that your local clone can only track changes from your fork, but not from the main repo, and so you won't be able to keep your fork up-to-date with the main repo over time. Therefor you'll need to add another "remote" to your clone that points to https://github.com/allenai/allennlp.git. To do this, run the following:
```
git remote add upstream https://github.com/allenai/allennlp.git
```
Now if you do git remote -v again, you'll see
```
origin https://github.com/USERNAME/allennlp.git (fetch)
origin https://github.com/USERNAME/allennlp.git (push)
upstream https://github.com/allenai/allennlp.git (fetch)
upstream https://github.com/allenai/allennlp.git (push)
```
Finally, you'll need to create a Python 3 virtual environment suitable for working on AllenNLP. There a number of tools out there that making working with virtual environments easier, but the most direct way is with the venv module in the standard library.

Once your virtual environment is activated, you can install your local clone in "editable mode" with
```
pip install -U pip setuptools wheel
pip install -e .[dev,all]
```
The "editable mode" comes from the -e argument to pip, and essential just creates a symbolic link from the site-packages directory of your virtual environment to the source code in your local clone. That way any changes you make will be immediately reflected in your virtual environment.
Ensure your fork is up-to-date
Expand details 👇

Once you've added an "upstream" remote pointing to https://github.com/allenai/allennlp.git, keeping your fork up-to-date is easy:
```
git checkout main  # if not already on main
git pull --rebase upstream main
git push
```
Create a new branch to work on your fix or enhancement
Expand details 👇

Commiting directly to the main branch of your fork is not recommended. It will be easier to keep your fork clean if you work on a seperate branch for each contribution you intend to make.

You can create a new branch with
```
# replace BRANCH with whatever name you want to give it
git checkout -b BRANCH
git push -u origin BRANCH
```
Test your changes
Expand details 👇

Our continuous integration (CI) testing runs a number of checks for each pull request on GitHub Actions. You can run most of these tests locally, which is something you should do before opening a PR to help speed up the review process and make it easier for us.

First, you should run black to make sure you code is formatted consistently. Many IDEs support code formatters as plugins, so you may be able to setup black to run automatically everytime you save. black.vim will give you this functionality in Vim, for example. But black is also easy to run directly from the command line. Just run this from the root of your clone:
```
black .
```
Our CI also uses flake8 to lint the code base and mypy for type-checking. You should run both of these next with
```
flake8 .
```
and
```
make typecheck
```
We also strive to maintain high test coverage, so most contributions should include additions to the unit tests. These tests are run with pytest, which you can use to locally run any test modules that you've added or changed.

For example, if you've fixed a bug in allennlp/nn/util.py, you can run the tests specific to that module with
```
pytest -v tests/nn/util_test.py
```
Our CI will automatically check that test coverage stays above a certain threshold (around 90%). To check the coverage locally in this example, you could run
```
pytest -v --cov allennlp.nn.util tests/nn/util_test.py
```
If your contribution involves additions to any public part of the API, we require that you write docstrings for each function, method, class, or module that you add. See the Writing docstrings section below for details on the syntax. You should test to make sure the API documentation can build without errors by running
```
make build-docs
```
If the build fails, it's most likely due to small formatting issues. If the error message isn't clear, feel free to comment on this in your pull request.

You can also serve and view the docs locally with
```
make serve-docs
```
And finally, please update the CHANGELOG with notes on your contribution in the "Unreleased" section at the top.

After all of the above checks have passed, you can now open a new GitHub pull request. Make sure you have a clear description of the problem and the solution, and include a link to relevant issues.

We look forward to reviewing your PR!

Writing docstrings¶

Our docstrings are written in a syntax that is essentially just Markdown with additional special syntax for writing parameter descriptions.

Class docstrings should start with a description of the class, followed by a # Parameters section that lists the names, types, and purpose of all parameters to the class's __init__() method. Parameter descriptions should look like:

name : `type`
    Description of the parameter, indented by four spaces.

Optional parameters can also be written like this:

name : `type`, optional (default = `default_value`)
    Description of the parameter, indented by four spaces.

Sometimes you can omit the description if the parameter is self-explanatory.

Method and function docstrings are similar, but should also include a # Returns section when the return value is not obvious. Other valid sections are

# Attributes, for listing class attributes. These should be formatted in the same way as parameters.
# Raises, for listing any errors that the function or method might intentionally raise.
# Examples, where you can include code snippets.

Here is an example of what the docstrings should look like in a class:

class SentenceClassifier(Model):
    """
    A model for classifying sentences.

    This is based on [this paper](link-to-paper). The input is a sentence and
    the output is a score for each target label.

    # Parameters

    vocab : `Vocabulary`

    text_field_embedder : `TextFieldEmbedder`
        The text field embedder that will be used to create a representation of the
        source tokens.

    seq2vec_encoder : `Seq2VeqEncoder`
        This encoder will take the embeddings from the `text_field_embedder` and
        encode them into a vector which represents the un-normalized scores
        for the target labels.

    dropout : `Optional[float]`, optional (default = `None`)
        Optional dropout to apply to the text field embeddings before passing through
        the `seq2vec_encoder`.
    """

    def __init__(
        self,
        vocab: Vocabulary,
        text_field_embedder: TextFieldEmbedder,
        seq2vec_encoder: Seq2SeqEncoder,
        dropout: Optional[float] = None,
    ) -> None:
        pass

    def forward(
        self,
        tokens: TextFieldTensors,
        labels: Optional[Tensor] = None,
    ) -> Dict[str, Tensor]:
        """
        Runs a forward pass of the model, computing the predicted logits and also the loss
        when `labels` is provided.

        # Parameters

        tokens : `TextFieldTensors`
            The tokens corresponding to the source sequence.

        labels : `Optional[Tensor]`, optional (default = `None`)
            The target labels.

        # Returns

        `Dict[str, Tensor]`
            An output dictionary with keys for the `loss` and `logits`.
        """
        pass

New models¶

Do you have a new state-of-the-art model?

We are always looking for new models to add to our collection. The most popular models are usually added to the official AllenNLP Models repository, and in some cases to the AllenNLP Demo.

If you think your model should be part of AllenNLP Models, please create a pull request in the models repo that includes:

Any code changes needed to support your new model.
A link to the model itself. Please do not check your model into the GitHub repository, but instead upload it in the PR conversation or provide a link to it at an external location.

In the description of your PR, please clearly explain the task your model performs along with the relevant metrics on an established dataset.