Document Public APIs

This skill documents undocumented public APIs in PyTorch by removing entries from the coverage ignore lists in docs/source/conf.py and adding Sphinx autodoc directives (e.g., autosummary, currentmodule, autoclass, automodule) to the corresponding .md or .rst doc source files in docs/source/.

"Documenting" means adding autodoc directives to doc source files — NEVER modifying Python source code. Do not add or edit docstrings in .py files. Do not read or inspect Python source files. Sphinx will pull whatever docstring exists (or render an empty entry if none exists). Your only job is to add the correct directive to the correct doc file.

Overview

docs/source/conf.py contains two lists that suppress Sphinx coverage warnings for undocumented APIs:

• coverage_ignore_functions: undocumented functions
• coverage_ignore_classes: undocumented classes

Entries are organized by module comment groups. Each group has a module label comment followed by the function/class names that belong to that module:

coverage_ignore_functions = [
    # torch.ao.quantization.fx.convert              <-- module label comment
    "convert",                                       # <-- entries belonging to this module
    "convert_custom_module",
    "convert_standalone_module",
    "convert_weighted_module",
    # torch.ao.quantization.fx.fuse                 <-- next module group
    "fuse",
    # torch.nn.functional
    "assert_int_or_pair",  # looks unintentionally public   <-- entry with inline comment
    "constant",  # deprecated                                <-- entry with inline comment
]

There are two kinds of comments:

• Module label comments (# torch.ao.quantization.fx.convert): these label which module the entries below belong to. They appear on their own line before a group of entries.
• Inline comments (# deprecated, # documented as adaptive_max_pool1d): these appear after a string entry on the same line and explain why the entry is in the ignore list.

The module label comment directly tells you:

1. Which module the functions belong to
2. Where to add them in the docs (e.g., # torch.ao.quantization.fx.convert → the functions go under torch.ao.quantization.fx.convert in the doc file)

Instructions

Each invocation of this skill processes one batch of module groups. Pick one or more complete module groups from the ignore lists, document their functions, and verify.

Step 1: Select module groups to document

Read docs/source/conf.py and select one or more complete module groups to document. A module group is a module label comment and all entries beneath it up to the next module label comment. Process entire groups — never split a group across batches.

For example, selecting the torch.ao.quantization.fx.convert group means taking all of:

# torch.ao.quantization.fx.convert
"convert",
"convert_custom_module",
"convert_standalone_module",
"convert_weighted_module",

Work through the lists top-to-bottom. Choose enough groups to make meaningful progress (aim for 5–15 functions total, but always include complete groups even if that means going slightly over).

Check inline comments before including an entry. Some entries have inline comments that indicate they should not be documented:

• # deprecated — The function is deprecated. Leave it in the ignore list.
• # documented as <other_name> — Already documented under a different name. Leave it.
• # looks unintentionally public — Probably not meant to be public API. Leave it.
• # legacy helper for ... — Same as deprecated. Leave it.
• # utility function - Leave it.

If a module group has a mix of regular entries and entries with inline comments, still process the group — but only comment out the regular entries. Leave entries with inline comments untouched in the ignore list.

Step 2: Present the batch to the user

Before making any edits, present the selected module groups and their functions to the user. Show them organized by module:

Module: torch.ao.quantization.fx.convert
  - convert
  - convert_custom_module
  - convert_standalone_module
  - convert_weighted_module

Module: torch.ao.quantization.fx.fuse
  - fuse

Then use the AskUserQuestion tool to let the user confirm, with options like:

• "Proceed with this batch"
• "Skip some entries" (user can specify which to remove)
• "Pick a different batch"

Step 3: Comment out entries in conf.py

After the user confirms, edit docs/source/conf.py and comment out (do not delete) the selected entries. Use a # prefix on each string entry line:

# torch.ao.quantization.fx.convert
# "convert",
# "convert_custom_module",
# "convert_standalone_module",
# "convert_weighted_module",

This preserves the original entries so they can be restored if verification fails.

Step 4: Run Sphinx coverage

cd docs && make coverage

Ignore the terminal output of make coverage. It often contains unrelated tracebacks and errors from Sphinx extensions (e.g., onnx_ir, katex, sphinxcontrib) that have nothing to do with coverage. The only thing that matters is whether docs/build/coverage/python.txt was generated. Read that file to see the specific undocumented APIs.

The format of python.txt lists each undocumented API as:

torch.ao.quantization.fx.convert
   * convert
   * convert_custom_module
   * convert_standalone_module
   * convert_weighted_module

Not all commented-out functions will appear in python.txt. Some may already be documented elsewhere. This is fine — only add directives for functions that actually appear in python.txt.

If make coverage fails due to missing dependencies, first run:

cd docs && pip install -r requirements.txt

Step 5: Add documentation directives

For each function listed in python.txt, use the module label comment from conf.py to determine where it should be added. The module comment gives you the full module path, which maps to a doc source file and a section within that file.

Finding the correct doc file

The module comment maps to a doc source file in docs/source/. When unsure, search for other functions from the same module:

grep -rn "torch.module_name" docs/source/*.md docs/source/*.rst

Or list candidate files:

ls docs/source/*module_name*

If no doc file exists for a submodule, check whether a parent module's doc file has a section for it (e.g., backends.md has sections for torch.backends.cuda, torch.backends.cudnn, etc.). If not, add a new section to the parent file following existing patterns.

Adding the directives

Read the target doc file first and match the exact patterns already used there. Do not invent new patterns or use bare autofunction with fully qualified names — always use the proper hierarchical structure with automodule, currentmodule, and short names. Do not use . py:module:: since that just suppresses errors and doesn't actually document the function. Look at other files that match the target file's format (e.g., .md vs. .rst) under docs/source/ to see examples.

There are two file formats. Match the one used in the target file.

Pattern A — MyST Markdown files (.md): Used in files like accelerator.md, backends.md, cuda.md.

The hierarchical structure uses automodule to register the module, currentmodule to set context, then short names:

## torch.ao.quantization.fx.convert

```{eval-rst}
.. automodule:: torch.ao.quantization.fx.convert

.. currentmodule:: torch.ao.quantization.fx.convert

.. autofunction:: convert

.. autofunction:: convert_custom_module

For `autosummary` blocks (used in some files instead of individual directives):

```markdown
```{eval-rst}
.. autosummary::
    :toctree: generated
    :nosignatures:

    existing_function
    your_new_function
`` `

For classes:

```{eval-rst}
.. autoclass:: YourClass
    :members:
`` `

Pattern B — reStructuredText files (.rst): Used in files like torch.rst, nn.rst.

Same hierarchical structure without the markdown fences:

torch.ao.quantization.fx.convert
---------------------------------

.. automodule:: torch.ao.quantization.fx.convert

.. currentmodule:: torch.ao.quantization.fx.convert

.. autosummary::
    :toctree: generated
    :nosignatures:

    convert
    convert_custom_module
    convert_standalone_module
    convert_weighted_module

For individual directives:

.. automodule:: torch.submodule

.. currentmodule:: torch.submodule

.. autofunction:: function_name

.. autoclass:: ClassName
    :members:

Key rules:

• The module label comment from conf.py (e.g., # torch.ao.quantization.fx.convert) tells you exactly which automodule and currentmodule to use.
• Always set .. automodule:: and .. currentmodule:: before documenting functions from a module.
• Use short names (e.g., convert, not torch.ao.quantization.fx.convert.convert) after currentmodule is set.
• If the module already has an automodule/currentmodule in the file, don't add another — just add your function under the existing one.
• Match whichever style the file already uses (autosummary blocks vs. individual autofunction directives).

Placing in the right section

Read the target doc file and find the appropriate section. If the module already has a section (e.g., ## torch.backends.cuda in backends.md), add the functions there. If no section exists yet, create one following the existing section patterns in the file. Group all functions from the same module group together.

Step 6: Verify with coverage

Run coverage again:

cd docs && make coverage

Ignore the terminal output — only read docs/build/coverage/python.txt. Verification passes when python.txt contains zero undocumented functions across ALL modules. It should only have the statistics table with 100% coverage and 0 undocumented for every module. For example:

Undocumented Python objects
===========================

Statistics
----------

+---------------------------+----------+--------------+
| Module                    | Coverage | Undocumented |
+===========================+==========+==============+
| torch                     | 100.00%  | 0            |
+---------------------------+----------+--------------+
| torch.accelerator         | 100.00%  | 0            |
+---------------------------+----------+--------------+

If any module shows undocumented functions (coverage below 100% or undocumented count > 0), verification has failed.

If verification succeeds (zero undocumented across all modules): Go to Step 7.

If verification fails (any undocumented functions remain): Read docs/build/coverage/python.txt to see which functions are still listed as undocumented. Common issues include:

• Wrong doc file: the function was added to the wrong .md/.rst file. Move the directive to the correct file.
• Wrong directive type: e.g., used autofunction for a class, or autoclass for a function. Fix the directive.
• Wrong module path in the directive: e.g., torch.foo.bar should be torch.foo.baz.bar. Correct the qualified name.
• Function added to an autosummary block with the wrong currentmodule: make sure the .. currentmodule:: directive above the block matches.
• Missing automodule for a submodule that hasn't been registered yet. Add a .. automodule:: torch.submodule directive before documenting functions from that submodule.

Fix the doc directive based on the error, then re-run make coverage. Repeat until verification passes.

If a function still fails after multiple attempts, stop and show the error to the user. Present the function name and the error, then use the AskUserQuestion tool with options like:

• "Uncomment it to restore to ignore list (skip for now)"
• "Try a different approach"
• "Investigate further"

Step 7: Report progress

Present a progress summary to the user showing:

• Which module groups were processed and how many functions were documented
• Which functions were skipped or restored to the ignore list (and why)
• How many entries remain in coverage_ignore_functions and coverage_ignore_classes

Step 8: Clean up commented-out entries in conf.py

Now that verification has passed, delete the commented-out string entries from Step 3. These are lines that start with # " inside coverage_ignore_functions and coverage_ignore_classes. Commented-out string entries always contain quotes — that's how you distinguish them from module label comments:

# "disable_global_flags",       <-- commented-out string entry (has quotes) → DELETE
# torch.backends                <-- module label comment (no quotes) → KEEP if it has active entries

Also delete any module label comments that no longer have active entries beneath them (i.e., all their entries were either commented out and now deleted, or had inline comments and were left in place but the module label is otherwise empty).

Important notes

• Follow the steps exactly as written. Do not add extra investigation steps like importing Python modules to check docstrings, inspecting source code to verify function signatures, or running any commands not specified in the instructions. The make coverage step is the only verification needed — let it tell you what's wrong.
• Never modify Python source files (.py). This skill only edits docs/source/conf.py and doc source files (.md/.rst) in docs/source/. Do not add or edit docstrings, do not read Python source to check function signatures, do not inspect implementations.
• Entries are commented out in Step 3, verified in Step 6, and cleaned up in Step 8 after verification passes. Never delete uncommented entries directly.
• Read inline comments on entries before deciding to document them. Entries marked # deprecated, # documented as ..., # looks unintentionally public, or # legacy helper should stay in the ignore list.
• The coverage_ignore_functions list uses bare function names (not fully qualified), so the same name can appear multiple times for different modules. Use the module label comment above each entry to identify which module it belongs to. Be careful during Step 8 cleanup to only delete the correct commented-out lines — commented-out string entries have quotes (# "func_name",), module label comments do not.
• Always match the existing style of the target doc file — don't mix .md style directives into .rst files or vice versa.
• Use the module label comment (e.g., # torch.ao.quantization.fx.convert) as the primary guide for both the automodule/currentmodule directives and for finding the right section in the doc file.
• Always process complete module groups — never split a group across invocations.