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DOC: Convert to markdown: torch.overrides.rst, type_info.rst, utils.rst, xpu.rst (#155088)

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Fixes #155041

Pull Request resolved: https://github.com/pytorch/pytorch/pull/155088
Approved by: https://github.com/svekars

Co-authored-by: Svetlana Karslioglu <svekars@meta.com>
This commit is contained in:
loganthomas
2025-06-06 20:16:13 +00:00
committed by PyTorch MergeBot
parent 067fd0b3ab
commit 4f5b34427b
5 changed files with 123 additions and 84 deletions
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31
docs/source/torch.overrides.rst → docs/source/torch.overrides.md
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@ -1,30 +1,49 @@
```{eval-rst}
.. currentmodule:: torch.overrides
```
torch.overrides
---------------
# torch.overrides
```{eval-rst}
.. py:module:: torch.overrides
```
This module exposes various helper functions for the ``__torch_function__``
protocol. See :ref:`extending-torch-python` for more details on the
protocol. See {ref}`extending-torch-python` for more details on the
``__torch_function__`` protocol.
Functions
~~~~~~~~~
## Functions
```{eval-rst}
.. autofunction:: get_ignored_functions
```
```{eval-rst}
.. autofunction:: get_overridable_functions
```
```{eval-rst}
.. autofunction:: resolve_name
```
```{eval-rst}
.. autofunction:: get_testing_overrides
```
```{eval-rst}
.. autofunction:: handle_torch_function
```
```{eval-rst}
.. autofunction:: has_torch_function
```
```{eval-rst}
.. autofunction:: is_tensor_like
```
```{eval-rst}
.. autofunction:: is_tensor_method_or_property
```
```{eval-rst}
.. autofunction:: wrap_torch_function
```

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docs/source/type_info.md Normal file
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@ -0,0 +1,61 @@
```{eval-rst}
.. currentmodule:: torch
```
(type-info-doc)=
# Type Info
The numerical properties of a {class}`torch.dtype` can be accessed through either the {class}`torch.finfo` or the {class}`torch.iinfo`.
(finfo-doc)=
## torch.finfo
```{eval-rst}
.. class:: torch.finfo
```
A {class}`torch.finfo` is an object that represents the numerical properties of a floating point
{class}`torch.dtype`, (i.e. ``torch.float32``, ``torch.float64``, ``torch.float16``, and ``torch.bfloat16``).
This is similar to [numpy.finfo](https://numpy.org/doc/stable/reference/generated/numpy.finfo.html).
A {class}`torch.finfo` provides the following attributes:
| Name | Type | Description |
| :-------------- | :---- | :------------------------------------------------------------------------- |
| bits | int | The number of bits occupied by the type. |
| eps | float | The smallest representable number such that ``1.0 + eps != 1.0``. |
| max | float | The largest representable number. |
| min | float | The smallest representable number (typically ``-max``). |
| tiny | float | The smallest positive normal number. Equivalent to ``smallest_normal``. |
| smallest_normal | float | The smallest positive normal number. See notes. |
| resolution | float | The approximate decimal resolution of this type, i.e., ``10**-precision``. |
```{note}
The constructor of {class}`torch.finfo` can be called without argument,
in which case the class is created for the pytorch default dtype (as returned by {func}`torch.get_default_dtype`).
```
```{note}
`smallest_normal` returns the smallest *normal* number, but there are smaller
subnormal numbers. See https://en.wikipedia.org/wiki/Denormal_number
for more information.
```
(iinfo-doc)=
## torch.iinfo
```{eval-rst}
.. class:: torch.iinfo
```
A {class}`torch.iinfo` is an object that represents the numerical properties of a integer
{class}`torch.dtype` (i.e. ``torch.uint8``, ``torch.int8``, ``torch.int16``, ``torch.int32``, and ``torch.int64``).
This is similar to [numpy.iinfo](https://numpy.org/doc/stable/reference/generated/numpy.iinfo.html).
A {class}`torch.iinfo` provides the following attributes:
| Name | Type | Description |
| :--- | :--- | :--------------------------------------- |
| bits | int | The number of bits occupied by the type. |
| max | int | The largest representable number. |
| min | int | The smallest representable number. |

62
docs/source/type_info.rst
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@ -1,62 +0,0 @@
.. currentmodule:: torch
.. _type-info-doc:
Type Info
=========
The numerical properties of a :class:`torch.dtype` can be accessed through either the :class:`torch.finfo` or the :class:`torch.iinfo`.
.. _finfo-doc:
torch.finfo
-----------
.. class:: torch.finfo
A :class:`torch.finfo` is an object that represents the numerical properties of a floating point
:class:`torch.dtype`, (i.e. ``torch.float32``, ``torch.float64``, ``torch.float16``, and ``torch.bfloat16``). This is similar to `numpy.finfo <https://numpy.org/doc/stable/reference/generated/numpy.finfo.html>`_.
A :class:`torch.finfo` provides the following attributes:
=============== ===== ==========================================================================
Name Type Description
=============== ===== ==========================================================================
bits int The number of bits occupied by the type.
eps float The smallest representable number such that ``1.0 + eps != 1.0``.
max float The largest representable number.
min float The smallest representable number (typically ``-max``).
tiny float The smallest positive normal number. Equivalent to ``smallest_normal``.
smallest_normal float The smallest positive normal number. See notes.
resolution float The approximate decimal resolution of this type, i.e., ``10**-precision``.
=============== ===== ==========================================================================
.. note::
The constructor of :class:`torch.finfo` can be called without argument, in which case the class is created for the pytorch default dtype (as returned by :func:`torch.get_default_dtype`).
.. note::
`smallest_normal` returns the smallest *normal* number, but there are smaller
subnormal numbers. See https://en.wikipedia.org/wiki/Denormal_number
for more information.
.. _iinfo-doc:
torch.iinfo
------------
.. class:: torch.iinfo
A :class:`torch.iinfo` is an object that represents the numerical properties of a integer
:class:`torch.dtype` (i.e. ``torch.uint8``, ``torch.int8``, ``torch.int16``, ``torch.int32``, and ``torch.int64``). This is similar to `numpy.iinfo <https://numpy.org/doc/stable/reference/generated/numpy.iinfo.html>`_.
A :class:`torch.iinfo` provides the following attributes:
========= ===== ========================================
Name Type Description
========= ===== ========================================
bits int The number of bits occupied by the type.
max int The largest representable number.
min int The smallest representable number.
========= ===== ========================================

18
docs/source/utils.rst → docs/source/utils.md
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@ -1,8 +1,13 @@
torch.utils
===================================
# torch.utils
```{eval-rst}
.. automodule:: torch.utils
.. currentmodule:: torch.utils
```
```{eval-rst}
.. currentmodule:: torch.utils
```
```{eval-rst}
.. autosummary::
:toctree: generated
:nosignatures:
@ -12,9 +17,11 @@ torch.utils
get_cpp_backtrace
set_module
swap_tensors
```
.. This module needs to be documented. Adding here in the meantime
.. for tracking purposes
<!-- This module needs to be documented. Adding here in the meantime
for tracking purposes -->
```{eval-rst}
.. py:module:: torch.utils.backend_registration
.. py:module:: torch.utils.benchmark.examples.compare
.. py:module:: torch.utils.benchmark.examples.fuzzer
@ -87,3 +94,4 @@ torch.utils
.. py:module:: torch.utils.tensorboard.writer
.. py:module:: torch.utils.throughput_benchmark
.. py:module:: torch.utils.weak
```

35
docs/source/xpu.rst → docs/source/xpu.md
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@ -1,8 +1,12 @@
torch.xpu
===================================
# torch.xpu
```{eval-rst}
.. automodule:: torch.xpu
```
```{eval-rst}
.. currentmodule:: torch.xpu
```
```{eval-rst}
.. autosummary::
:toctree: generated
:nosignatures:
@ -26,9 +30,10 @@ torch.xpu
set_stream
stream
synchronize
```
Random Number Generator
-------------------------
## Random Number Generator
```{eval-rst}
.. autosummary::
:toctree: generated
:nosignatures:
@ -42,21 +47,27 @@ Random Number Generator
seed_all
set_rng_state
set_rng_state_all
```
Streams and events
------------------
## Streams and events
```{eval-rst}
.. autosummary::
:toctree: generated
:nosignatures:
Event
Stream
```
```{eval-rst}
.. automodule:: torch.xpu.memory
```
```{eval-rst}
.. currentmodule:: torch.xpu.memory
```
Memory management
-----------------
## Memory management
```{eval-rst}
.. autosummary::
:toctree: generated
:nosignatures:
@ -71,9 +82,11 @@ Memory management
memory_stats_as_nested_dict
reset_accumulated_memory_stats
reset_peak_memory_stats
```
.. This module needs to be documented. Adding here in the meantime
.. for tracking purposes
<!-- This module needs to be documented. Adding here in the meantime
for tracking purposes -->
```{eval-rst}
.. py:module:: torch.xpu.random
.. py:module:: torch.xpu.streams
```