hf-doc-builder 0.4.0

Doc building utility

doc-builder

This is the package we use to build the documentation of our Hugging Face repos.

Installation

You can install from PyPi with

pip install hf-doc-builder

To install from source, clone this repository then

cd doc-builder
pip install -e .

Previewing

To preview the docs, use the following command:

doc-builder preview {package_name} {path_to_docs}

For example:

doc-builder preview datasets ~/Desktop/datasets/docs/source/

**preview command only works with existing doc files. When you add a completely new file, you need to update _toctree.yml & restart preview command (ctrl-c to stop it & call doc-builder preview ... again).

Doc building

To build the documentation of a given package, use the following command:

doc-builder build {package_name} {path_to_docs} --build_dir {build_dir}

For instance, here is how you can build the Datasets documentation (requires pip install datasets[dev]) if you have cloned the repo in ~/git/datasets:

doc-builder datasets ~/git/datasets/docs/source --build_dir ~/tmp/test-build

This will generate MDX files that you can preview like any Markdown file in your favorite editor. To have a look at the documentation in HTML, you need to install node version 14 or higher. Then you can run (still with the example on Datasets)

doc-builder datasets ~/git/datasets/docs/source --build_dir ~/tmp/test-build --html

which will build HTML files in ~/tmp/test-build. You can then inspect those files in your browser.

doc-builder can also automatically convert some of the documentation guides or tutorials into notebooks. This requires two steps:

• add [[open-in-colab]] in the tutorial for which you want to build a notebook
• add --notebook_dir {path_to_notebook_folder} to the build command.

Templates for GitHub Actions

doc-builder provides templates for GitHub Actions, so you can build your documentation with every pull request, push to some branch etc. To use them in your project, simply create the following three files in the .github/workflows/ directory:

• build_main_documentation.yml: responsible for building the docs for the main branch, releases etc.
• build_pr_documentation.yml: responsible for building the docs on each PR
• delete_doc_comment.yml: responsible for removing the comments from the HuggingFaceDocBuilderDev bot that provides a URL to the PR docs.

Within each workflow, the main thing to include is a pointer from the uses field to the corresponding workflow in doc-builder. For example, this is what the PR workflow looks like in the datasets library:

name: Build PR Documentation

on:
  pull_request:

concurrency:
  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
  cancel-in-progress: true

jobs:
  build:
    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main # Runs this doc-builder workflow
    with:
      commit_sha: ${{ github.event.pull_request.head.sha }}
      pr_number: ${{ github.event.number }}
      package: datasets # Replace this with your package name

Note the use of special arguments like pr_number and package under the with field. You can find the various options by inspecting each of the doc-builder workflow files.

Enabling multilingual documentation

doc-builder can also convert documentation that's been translated from the English source into one or more languages. To enable the conversion, the documentation directories should be structured as follows:

doc_folder
├── en
│   ├── _toctree.yml
│   ...
└── es
    ├── _toctree.yml
    ...

Note that each language directory has it's own table of contents file _toctree.yml and that all languages are arranged under a single doc_folder directory - see the course repo for an example. You can then build the individual language subsets as follows:

doc-builder {package_name} {path_to_docs} --build_dir {build_dir} --language {lang_id}

To automatically build the documentation for all languages via the GitHub Actions templates, simply provide the languages argument to your workflow, with a space-separated list of the languages you wish to build, e.g. languages: en es.

Writing documentation for Hugging Face libraries

doc-builder expects Markdown so you should write any new documentation in ".mdx" files for tutorials, guides, API documentations. For docstrings, we follow the Google format with the main difference that you should use Markdown instead of restructured text (hopefully, that will be easier!)

Values that should be put in code should either be surrounded by backticks: `like so`. Note that argument names and objects like True, None or any strings should usually be put in code.

When mentioning a class, function or method, it is recommended to use the following syntax for internal links so that our tool automarically adds a link to its documentation: [`XXXClass`] or [`function`]. This requires the class or function to be in the main package.

If you want to create a link to some internal class or function, you need to provide its path. For instance, in the Transformers documentation [`file_utils.ModelOutput`] will create a link to the documnetation of ModelOutput. This link will have file_utils.ModelOutput in the description. To get rid of the path and only keep the name of the object you are linking to in the description, add a ~: [`~file_utils.ModelOutput`] will generate a link with ModelOutput in the description.

The same works for methods so you can either use [`XXXClass.method`] or [~`XXXClass.method`].

Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:

```
# first line of code
# second line
# etc
```

We follow the doctest syntax for the examples to automatically test the results stay consistent with the library.

To write a block that you'd like to see highlighted as a note or warning, place your content between the following markers:

<Tip>

Write your note here

</Tip>

For warnings, change the introduction to <Tip warning={true}>.

If your documentation has a block that is framework-dependent (PyTorch vs TensorFlow vs Flax), you can use the following syntax:

<frameworkcontent>
<pt>
PyTorch content goes here
</pt>
<tf>
TensorFlow content goes here
</tf>
<flax>
Flax content goes here
</flax>
</frameworkcontent>

Note that all frameworks are optional (you can write a PyTorch-only block for instance) and the order does not matter.

Anchor links for markdown headings are generated automatically (with the following rule: 1. lowercase, 2. replace space with dash -, 3. strip [^a-z0-9-]):

## My awesome section
// the anchor link is: `my-awesome-section`

Moreover, there is a way to customize the anchor link. Example:

## My awesome section[[some-section]]
// the anchor link is: `some-section`

Writing API documentation

To show the full documentation of any object of the library you are documenting, use the [[autodoc]] marker:

[[autodoc]] SomeObject

If the object is a class, this will include every public method of it that is documented. If for some reason you wish for a method not to be displayed in the documentation, you can do so by specifying which methods should be in the docs, here is an example:

[[autodoc]] XXXTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

If you just want to add a method that is not documented (for instance magic method like __call__ are not documented by default) you can put the list of methods to add in a list that contains all:

## XXXTokenizer

[[autodoc]] XXXTokenizer
    - all
    - __call__

You can create a code-block by referencing a file excerpt with <literalinclude> (sphinx-inspired) syntax. There should be json between <literalinclude> open & close tags.

<literalinclude>
{"path": "./data/convert_literalinclude_dummy.txt", # relative path
"language": "python", # defaults to " (empty str)
"start-after": "START python_import",  # defaults to start of file
"end-before": "END python_import",  # defaults to end of file
"dedent": 7 # defaults to 0
}
</literalinclude>

Writing source documentation

Arguments of a function/class/method should be defined with the Args: (or Arguments: or Parameters:) prefix, followed by a line return and an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon and its description:

    Args:
        n_layers (`int`): The number of layers of the model.

If the description is too long to fit in one line, another indentation is necessary before writing the description after th argument.

Here's an example showcasing everything so far:

    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AlbertTokenizer`]. See [`~PreTrainedTokenizer.encode`] and
            [`~PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)

You can check the full example it comes from here

If a class is similar to that of a dataclass but the parameters do not align to the available attributes of the class, such as in the below example, Attributes instance should be rewritten as **Attributes** in order to have the documentation properly render these. Otherwise it will assume that Attributes is synonymous to Parameters.

  class SomeClass:
      """
      Docstring
-     Attributes:
+     **Attributes**:
          - **attr_a** (`type_a`) -- Doc a
          - **attr_b** (`type_b`) -- Doc b
      """
      def __init__(self, param_a, param_b):
          ...

For optional arguments or arguments with defaults we follow the following syntax. Imagine we have a function with the following signature:

def my_function(x: str = None, a: float = 1):

then its documentation should look like this:

    Args:
        x (`str`, *optional*):
            This argument controls ...
        a (`float`, *optional*, defaults to 1):
            This argument is used to ...

Note that we always omit the "defaults to `None`" when None is the default for any argument. Also note that even if the first line describing your argument type and its default gets long, you can't break it on several lines. You can however write as many lines as you want in the indented description (see the example above with input_ids).

If your argument has for type a class defined in the package, you can use the syntax we saw earlier to link to its documentation:

    Args:
         config ([`BertConfig`]):
            Model configuration class with all the parameters of the model.

            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.

The return block should be introduced with the Returns: prefix, followed by a line return and an indentation. The first line should be the type of the return, followed by a line return. No need to indent further for the elements building the return.

Here's an example for a single value return:

    Returns:
        `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.

Here's an example for tuple return, comprising several objects:

    Returns:
        `tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
        - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` --
          Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
        - **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
          Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

There are directives for Added, Changed, & Deprecated. Here's an example:

    Args:
        cache_dir (`str`, *optional*): Directory to cache data.
        config_name (`str`, *optional*): Name of the dataset configuration.
            It affects the data generated on disk: different configurations will have their own subdirectories and
            versions.
            If not provided, the default configuration is used (if it exists).

            <Added version="2.3.0">

            `name` was renamed to `config_name`.

            </Added>
        name (`str`): Configuration name for the dataset.

            <Deprecated version="2.3.0">

            Use `config_name` instead.

            </Deprecated>