This is follow-up of #165037. It generally recommended to use `is/is not` to compare types. Therefore this series of changes apply this suggestion in the code base, and it aims to finally enabling related linter checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165142
Approved by: https://github.com/albanD
It generally recommended to use `is/is not` to compare types. Therefore this series of changes apply this suggestion in the code base, and it aims to finally enabling related linter checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165037
Approved by: https://github.com/mlazos
# Summary
- Add a note to each `nn.LPPool*d` docstring explaining how `ceil_mode=True` interacts with right padding.
- Mirror the same clarification in the `torch.nn.functional.lp_pool*` docstrings so the rendered functional docs stay in sync.
# Motivation
The current PyTorch spec for **LPPool** does not fully match runtime behavior, which has led to downstream confusion in other specs (e.g., ONNX) and runtimes (e.g., [onnxruntime issue #25848](https://github.com/microsoft/onnxruntime/issues/25848)). A corresponding clarification was also made in the ONNX spec: [onnx/onnx#5741](https://github.com/onnx/onnx/pull/5741).
PyTorch’s **LPPool** implementation calls into **AvgPool**, which enforces the rule that windows starting entirely in the right padded region are ignored when `ceil_mode=True`. As a result, **LPPool** inherits the same behavior.
This is an edge case where the output size formula shown in the LPPool docs/spec is not sufficient on its own. Without the added caveat, the documentation is technically incorrect. This PR brings the LPPool docs in line with actual behavior.
Note that this is a trivial fix to the spec as all major implementers of the spec adhere to this caveat.
For comparison, both **MaxPool** and **AvgPool** already include this clarification in their spec. Their docstrings explicitly state:
> *When `ceil_mode=True`, sliding windows are allowed to go off-bounds if they start within the left padding or the input. Sliding windows that would start in the right padded region are ignored.*
Adding the same note to LPPool ensures consistency across all pooling operators.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163186
Approved by: https://github.com/mikaylagawarecki
Fixes#163640
This PR avoids a mask left align check in the case that we're operating under torch.compile / torch.export. Originally, I planned to make a more invasive change to auto-disable the fast path entirely underneath torch.compile / torch.export, but I realized during testing that the fast path wasn't actually causing compile issues outside of the narrow issue identified here.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163773
Approved by: https://github.com/mikaylagawarecki
Summary:
To support exporting a cuda model on a CPU-only machine under fake tensor mode.
User commonly need to move sample inputs to the cuda device with .to("cuda:0") or .to("cuda") call.
This diff supports this.
I expect the following pattern to work
```
with FakeTensorMode(allow_non_fake_inputs=True):
cuda_module = module.to("cuda:0")
cuda_sample_inputs = tuple([x.to("cuda:0") for x in sample_inputs])
with torch.no_grad():
ep = torch.export.export(cuda_module, cuda_sample_inputs)
```
Before
Moving module.to("cuda:0") under fake tensor mode would have parameter on `meta` device.
After
parameters would be on "cuda:0" .
Test Plan: buck2 run fbcode//caffe2/test:fake_tensor -- --r test_move_module
Reviewed By: mikaylagawarecki
Differential Revision: D80102876
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163433
Approved by: https://github.com/albanD
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
Followup on https://github.com/pytorch/pytorch/pull/125971
`self.register_buffer` will always be a a bound method on the instance (`self`) while `torch.nn.Module.register_buffer` is an unbound class method. `is`-ing these two things will never yield `True`. Instead, lets check the [original function object](https://docs.python.org/3/reference/datamodel.html#method.__func__). Note that the current logic doesn't break anything because the `else` branch will still do the "right thing" in the case `register_buffer` hasn't been overrridden, but it does mean we do less work!
Example demonstration:
```python
class Base:
def register_buffer(self, buffer):
pass
class InheritedOk(Base):
pass
class InheritedOverride(Base):
def register_buffer(self, buffer):
pass
b = Base()
ok = InheritedOk()
override = InheritedOverride()
print(f"b.register_buffer is Base.register_buffer: {b.register_buffer is Base.register_buffer}") # False
print(f"ok.register_buffer is Base.register_buffer: {ok.register_buffer is Base.register_buffer}") # False
print(f"override.register_buffer is Base.register_buffer: {override.register_buffer is Base.register_buffer}") # False
print(f"b.register_buffer.__func__ is Base.register_buffer: {b.register_buffer.__func__ is Base.register_buffer}") # True
print(f"ok.register_buffer.__func__ is Base.register_buffer: {ok.register_buffer.__func__ is Base.register_buffer}") # True
print(f"override.register_buffer.__func__ is Base.register_buffer: {override.register_buffer.__func__ is Base.register_buffer}") # False
```
(I can make an associated issue if needed, but didnt see it required [in the contributing guidelines](https://github.com/pytorch/pytorch/blob/main/CONTRIBUTING.md#merging-your-change))
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155963
Approved by: https://github.com/mikaylagawarecki
Fixes#134840
Added documentation to clarify padding size constraints for all padding modes in nn.modules.padding:
- Circular padding: size must be less than or equal to the corresponding input dimension
- Reflection padding: size must be less than the corresponding input dimension
- Replication padding: output dimensions must remain positive
These changes help prevent runtime errors when users attempt to use large padding values.
## PR Checklist
- [x] The PR title and message follow our [commit guidelines](https://github.com/pytorch/pytorch/blob/main/CONTRIBUTING.md#commit-message-format)
- [x] The PR is made against the correct branch
- [x] The PR is labeled with `docathon`
- [x] The PR is labeled with `module: nn`
- [x] The PR is labeled with `documentation`
- [x] The PR description includes a reference to the issue being fixed
- [x] The PR includes tests if applicable
- [x] The PR includes documentation changes
- [x] The PR has been tested locally
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155618
Approved by: https://github.com/AlannaBurke, https://github.com/malfet
