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https://github.com/pytorch/pytorch.git
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e6779d4357
Summary:
I've written custom parsers and emitters for everything from docstrings to classes and functions. However, I recently came across an issue when I was parsing/generating from the TensorFlow codebase: inconsistent use of `Args:` and `Arguments:` in its docstrings.
```sh
(pytorch#c348fae)$ for name in 'Args:' 'Arguments:'; do
printf '%-10s %04d\n' "$name" "$(rg -IFtpy --count-matches "$name" | paste -s -d+ -- | bc)"; done
Args: 1095
Arguments: 0336
```
It is easy enough to extend my parsers to support both variants, however it looks like `Arguments:` is wrong anyway, as per:
- https://google.github.io/styleguide/pyguide.html#doc-function-args @ [`ddccc0f`](https://github.com/google/styleguide/blob/ddccc0f/pyguide.md)
- https://chromium.googlesource.com/chromiumos/docs/+/master/styleguide/python.md#describing-arguments-in-docstrings @ [`9fc0fc0`](https://chromium.googlesource.com/chromiumos/docs/+/9fc0fc0/styleguide/python.md)
- https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html @ [`c0ae8e3`](https://github.com/sphinx-contrib/napoleon/blob/c0ae8e3/docs/source/example_google.rst)
Therefore, only `Args:` is valid. This PR replaces them throughout the codebase.
PS: For related PRs, see tensorflow/tensorflow/pull/45420
PPS: The trackbacks automatically appearing below are sending the same changes to other repositories in the [PyTorch](https://github.com/pytorch) organisation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49736
Reviewed By: albanD
Differential Revision: D25710534
Pulled By: soumith
fbshipit-source-id: 61e8ff01abb433e9f78185c2d1d0cbd7c22c1619
50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
import types
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import torch._C
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class _ClassNamespace(types.ModuleType):
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def __init__(self, name):
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super(_ClassNamespace, self).__init__('torch.classes' + name)
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self.name = name
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def __getattr__(self, attr):
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proxy = torch._C._get_custom_class_python_wrapper(self.name, attr)
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if proxy is None:
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raise RuntimeError(f'Class {self.name}.{attr} not registered!')
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return proxy
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class _Classes(types.ModuleType):
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def __init__(self):
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super(_Classes, self).__init__('torch.classes')
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def __getattr__(self, name):
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namespace = _ClassNamespace(name)
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setattr(self, name, namespace)
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return namespace
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@property
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def loaded_libraries(self):
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return torch.ops.loaded_libraries
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def load_library(self, path):
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"""
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Loads a shared library from the given path into the current process.
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The library being loaded may run global initialization code to register
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custom classes with the PyTorch JIT runtime. This allows dynamically
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loading custom classes. For this, you should compile your class
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and the static registration code into a shared library object, and then
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call ``torch.classes.load_library('path/to/libcustom.so')`` to load the
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shared object.
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After the library is loaded, it is added to the
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``torch.classes.loaded_libraries`` attribute, a set that may be inspected
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for the paths of all libraries loaded using this function.
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Args:
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path (str): A path to a shared library to load.
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"""
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torch.ops.load_library(path)
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# The classes "namespace"
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classes = _Classes()
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