- name
- tripy-documentation
- description
- Write API documentation for nvtripy following project conventions. Use when: writing docstrings for ops or modules, adding code examples, using @export.public_api document_under paths, creating Sphinx RST cross-references, understanding the docs build pipeline.
# API Documentation for nvtripy
## When to Use
- Writing or updating docstrings for public API functions, classes, or modules
- Adding working code examples to documentation
- Choosing the correct `document_under` path for `@export.public_api`
- Understanding how documentation is generated and built
## Documentation Pipeline
1. **`@export.public_api(document_under="...")`** registers APIs in `PUBLIC_APIS` list
2. **`docs/generate_rsts.py`** reads `PUBLIC_APIS` and generates `.rst` files in the docs hierarchy
3. **Sphinx** builds the final HTML docs from those `.rst` files
4. Docstring code examples are extracted and validated during testing
## `@export.public_api` Parameters
```python
@export.public_api(
document_under="operations/functions", # Doc hierarchy path
autodoc_options=[":special-members:"], # Sphinx autodoc options
bypass_dispatch=True, # Skip function registry overhead
)
```
### `document_under` Path Conventions
| Path | Use For |
|------|---------|
| `"operations/functions"` | General tensor operations (softmax, reshape, etc.) |
| `"operations/initializers"` | Tensor creation (ones, zeros, full, arange) |
| `"operations/modules"` | Neural network modules (Linear, LayerNorm, etc.) |
| `"compiling_code/compile.rst"` | Compilation-related APIs |
| `"compiling_code/input_info/index.rst"` | InputInfo and related classes |
| `"config.rst"` | Configuration variables |
The path creates a directory structure: `"operations/functions"` → `operations/functions/<name>.rst`.
APIs targeting the same `.rst` file render on the same page.
### `autodoc_options`
- `[":special-members:"]` — Include `__init__`, `__call__`, etc.
- `[":no-members:", ":no-special-members:"]` — Show only the class/module itself
- `[":no-value:"]` — Hide the default value of a variable
### `bypass_dispatch`
- `True` on a function: Disables the function registry's overload dispatch and type-checking (performance optimization)
- `True` on a class: Bypass dispatch for ALL methods
- `["__init__", "__call__"]`: Bypass only for listed methods
## Docstring Format
### Functions
```python
def my_op(input: "nvtripy.Tensor", dim: int = 0) -> "nvtripy.Tensor":
r"""
Brief one-line description of what the function does.
Longer description with math if applicable:
:math:`\text{my_op}(x) = f(x)`
Args:
input: The input tensor.
dim: The dimension to operate along.
Returns:
A tensor of the same shape as the input.
.. code-block:: python
:linenos:
input = tp.iota([2, 3], dtype=tp.float32)
output = tp.my_op(input, dim=0)
assert tp.allclose(output, expected)
.. seealso:: :func:`related_func`, :class:`RelatedClass`
"""
```
### Classes (Modules)
```python
class MyModule(Module):
r"""
Brief description with math notation.
:math:`\text{MyModule}(x) = xW^T + b`
"""
dtype: datatype.dtype
r"""The data type used to perform the operation."""
weight: Tensor
r"""The :math:`W` parameter of shape :math:`[\text{out}, \text{in}]`."""
def __init__(self, features: int, dtype: datatype.dtype = datatype.float32) -> None:
r"""
Args:
features: Size of the feature dimension.
dtype: The data type for parameters.
.. code-block:: python
:linenos:
module = tp.MyModule(3)
module.weight = tp.iota(module.weight.shape)
input = tp.iota((2, 3))
output = module(input)
assert cp.from_dlpack(output).get().shape == (2, 3)
torch_out = torch.nn.functional.my_op(torch.from_dlpack(input)) # doc: omit
assert np.allclose(cp.from_dlpack(output).get(), cp.from_dlpack(torch_out).get())
"""
```
## Code Example Conventions
### Required elements
1. **Setup**: Create inputs using `tp.iota()`, `tp.ones()`, `tp.zeros()`, or `tp.Tensor()`
2. **Operation**: Call the function/module under test
3. **Assertion**: Verify the result with `assert` (using `tp.allclose`, `np.array_equal`, or shape checks)
### Available imports in code blocks
Code examples automatically have access to:
- `tp` (nvtripy)
- `np` (numpy)
- `cp` (cupy)
- `torch`
### Special directives
- `# doc: omit` — Line is excluded from rendered documentation but still executes
- `# doc: no-print-locals <var>` — Suppresses automatic printing of the variable
- `:linenos:` — Always include for numbered lines
### Cross-references
- `:func:`function_name`` — Link to a function
- `:class:`ClassName`` — Link to a class
- `:math:`expression`` — Inline LaTeX math
- `.. seealso:: :func:`related`" — "See also" section at the end
### Math notation
Use `r"""` raw strings for docstrings containing `:math:` to avoid backslash issues.
LaTeX examples:
- Inline: `:math:`\text{softmax}(x_{i})``
- Block: Use `\Large` / `\normalsize` for fraction sizing
- Common: `:math:`\bar{x}`` (mean), `:math:`\sigma^2`` (variance), `:math:`\epsilon`` (epsilon)
## Checklist
- [ ] `@export.public_api(document_under="...")` with correct hierarchy path
- [ ] Docstring uses `r"""` if it contains `:math:` directives
- [ ] Args section documents all parameters with types
- [ ] Returns section describes output shape/type
- [ ] `.. code-block:: python` with `:linenos:` and working assertions
- [ ] `.. seealso::` links to related functions/classes
- [ ] Field docstrings for all dataclass fields on modules
- [ ] `# doc: omit` for verification-only lines that shouldn't appear in docs
Auf GitHub ansehen