Options to address the "undocumented python objects":
1. Reference the functions in the .rst via the torch.nn.modules namespace. Note that this changes the generated doc filenames / locations for most of these functions!
2. [Not an option] Monkeypatch `__module__` for these objects (broke several tests in CI due to `inspect.findsource` failing after this change)
3. Update the .rst files to also document the torch.nn.modules forms of these functions, duplicating docs.
#### [this is the docs page added](https://docs-preview.pytorch.org/pytorch/pytorch/158491/nn.aliases.html)
This PR takes option 3 by adding an rst page nn.aliases that documents the aliases in nested namespaces, removing all the torch.nn.modules.* entries from the coverage skiplist except
- NLLLoss2d (deprecated)
- Container (deprecated)
- CrossMapLRN2d (what is this?)
- NonDynamicallyQuantizableLinear
This mostly required adding docstrings to `forward`, `extra_repr` and `reset_parameters`. Since forward arguments are already part of the module docstrings I just added a very basic docstring.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158491
Approved by: https://github.com/janeyx99
Fixes#106698
Also added a check for python API, because current error message
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/sehoon/pytorch-latest/torch/nn/modules/adaptive.py", line 128, in __init__
or (min(cutoffs) <= 0) \
ValueError: min() arg is an empty sequence
```
is not very comprehensible.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106777
Approved by: https://github.com/albanD
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
Summary:
Fixes https://github.com/pytorch/pytorch/issues/54211
This was a little more annoying than expected, because the `exclude = ` key in `mypy.ini` is weird. I'll file an upstream issue about that.
I ignored one file, `torch/distributed/elastic/agent/server/api.py` that had ~8 errors that were hard to figure out. This can be done in a follow-up.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55712
Reviewed By: walterddr
Differential Revision: D27694976
Pulled By: malfet
fbshipit-source-id: 228d8be6af040343ce46595dabaca212e69ccc68
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38211
Just because the annotations are inline doesn't mean the files type
check; most of the newly annotated files have type errors and I
added exclusions for them in mypy.ini. The payoff of moving
all of these modules inline is I can delete the relevant code
generation logic for the pyi files (which was added ignore
annotations that weren't actually relevant anymore.)
For the most part the translation was completely mechanical, but there
were two hairy issues. First, I needed to work around a Python 3.6 and
earlier bug where Generic has a nontrivial metaclass. This fix is in
torch/jit/__init__.py. Second, module.py, we need to apply the same
fix for avoiding contravariance checks that the pyi file used to have;
this is done by declaring forward as a variable (rather than a
function), which appears to be sufficient enough to get mypy to not
contravariantly check input arguments.
Because we aren't actually typechecking these modules in most
cases, it is inevitable that some of these type annotations are wrong.
I slavishly copied the old annotations from the pyi files unless there
was an obvious correction I could make. These annotations will probably
need fixing up later.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D21497397
Pulled By: ezyang
fbshipit-source-id: 2b08bacc152c48f074e7edc4ee5dce1b77d83702
Summary:
xref gh-32838, gh-34032
This is a major refactor of parts of the documentation to split it up using sphinx's `autosummary` feature which will build out `autofuction` and `autoclass` stub files and link to them. The end result is that the top module pages like torch.nn.rst and torch.rst are now more like table-of-contents to the actual single-class or single-function documentations pages.
Along the way, I modified many of the docstrings to eliminate sphinx warnings when building. I think the only thing I changed from a non-documentation perspective is to add names to `__all__` when adding them to `globals()` in `torch.__init__.py`
I do not know the CI system: are the documentation build artifacts available after the build, so reviewers can preview before merging?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37419
Differential Revision: D21337640
Pulled By: ezyang
fbshipit-source-id: d4ad198780c3ae7a96a9f22651e00ff2d31a0c0f
Summary:
PR to update the shape notation for all of the torch.nn modules to take a unified form. The goal is to make these definitions machine-readable and those checkable by unifying the style across all of the different modules.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15741
Differential Revision: D13709601
Pulled By: ezyang
fbshipit-source-id: fb89a03903fdf0cd0dcf76f3e469b8582b2f3634
* Implement adaptive softmax
* fix test for python 2
* add return_logprob flag
* add a test for cross-entropy path
* address review comments
* Fix docs
* pytorch 0.4 fixes
* address review comments
* don't use no_grad when computing log-probs
* add predict method
* add test for predict
* change methods order
* get rid of hardcoded int values
* Add an optional bias term to the head of AdaptiveSoftmax
