* Automatically applies ruff rule 401. Turns loops into equivalent list comprehensions which are faster and do not leak the scope of the loop variables.
* list comprehensions not only often have better typing, but are 50+% faster than for loops on overhead. They also preserve length information etc and are better for the interpreter to optimize.
* Manually went back and made mypy happy after the change.
* Also fixed style lints in files covered by flake8 but not by pyfmt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140980
Approved by: https://github.com/justinchuby, https://github.com/malfet
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
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Unrelated, to bypass CI failures due to the gcc9 dependency update in Ubuntu-18.04:
- Add hack to squash older libstdc++ from conda environment in favor one from OS to `.ci/docker/install_conda.sh`
- Update bazel cuda builds to focal, as with libstdc++-6.0.32 bazel builds loose the ability to catch exceptions (probably because they link with cupti statically, but I could not found where it is done)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
Not sure, how it worked before, but if arguments must be annotated is optional if they are defaulted to None
Towards enabling mypy-1.4.1 in lintrunner
<!--
copilot:poem
-->
### <samp>🤖 Generated by Copilot at 5e1b9f4</samp>
> _We annotate the arguments of doom_
> _To show the `None` values of gloom_
> _We improve the type checking and readability_
> _With `Optional` annotations of metal-ity_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105022
Approved by: https://github.com/izaitsevfb, https://github.com/huydhn, https://github.com/Skylion007
Fixes#64601 and #98906
Adds an `assign` argument to `load_state_dict` that loads params/buffers by assignment instead of doing `param.copy_(param_from_state_dict)`.
Primarily intended to remove the need for the `.to_empty()` in
```
with torch.device('meta'):
m = SomeModule()
m.to_empty()
state_dict = torch.load('...pth')
m.load_state_dict(state_dict)
```
so we can instead do
```
with torch.device('meta'):
m = SomeModule()
state_dict = torch.load('...pth')
m.load_state_dict(state_dict, assign=True)
```
**A problem with this PR for the case where the model is initialized on meta is what happens to nonpersistent buffers/params corresponding to keys missing from the state dict?**
What happens in the case where `load_state_dict(state_dict, strict=False, assign=True)` and the state_dict is missing some keys? The corresponding params missing from the `state_dict` and nonpersistent buffers would still be on `meta` and need to be manually initialized. However, I don't think we offer an API that would initialize these.
One solution would be to make these empty tensors but it might not be semantically correct...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102212
Approved by: https://github.com/albanD
Attempts to fix#92656
BC-breaking! This changes the default of zero_grad in optim and in nn to default set grads to None instead of zero tensors. We are changing the default because there are proven perf wins and existing code has typically not regressed due to this change. (will probably have to flesh out this note more).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92731
Approved by: https://github.com/ngimel
Fixes#91654.
Currently, the `hook` parameters of `nn.Module.register_forward_pre_hook` and `nn.Module.register_forward_hook` are typed as `Callable[..., None]`, which 1) does not enable the validation of the signature of `hook` and 2) incorrectly restricts the return type of `hook`, which the docstrings of these methods themselves state can be non-`None`.
The typing of the first parameter of `hook` as `TypeVar("T", bound="Module")` allows the binding of `Callable` whose first parameter is a subclass of `Module`.
---
Here are some examples of:
1. forward hooks and pre-hook hooks being accepted by mypy according to the new type hints
2. mypy throwing errors d.t. incorrect `hook` signatures
3. false negatives of pre-hooks being accepted as forward hooks
4. false negatives of hooks with kwargs being accepted irrespective of the value provided for `with_kwargs`
```python
from typing import Any, Dict, Tuple
import torch
from torch import nn
def forward_pre_hook(
module: nn.Linear,
args: Tuple[torch.Tensor, ...],
) -> None:
...
def forward_pre_hook_return_input(
module: nn.Linear,
args: Tuple[torch.Tensor, ...],
) -> Tuple[torch.Tensor, ...]:
...
def forward_pre_hook_with_kwargs(
module: nn.Linear,
args: Tuple[torch.Tensor, ...],
kwargs: Dict[str, Any],
) -> None:
...
def forward_pre_hook_with_kwargs_return_input(
module: nn.Linear,
args: Tuple[torch.Tensor, ...],
kwargs: Dict[str, Any],
) -> Tuple[Tuple[torch.Tensor, ...], Dict[str, Any]]:
...
def forward_hook(
module: nn.Linear,
args: Tuple[torch.Tensor, ...],
output: torch.Tensor,
) -> None:
...
def forward_hook_return_output(
module: nn.Linear,
args: Tuple[torch.Tensor, ...],
output: torch.Tensor,
) -> torch.Tensor:
...
def forward_hook_with_kwargs(
module: nn.Linear,
args: Tuple[torch.Tensor, ...],
kwargs: Dict[str, Any],
output: torch.Tensor,
) -> None:
...
def forward_hook_with_kwargs_return_output(
module: nn.Linear,
args: Tuple[torch.Tensor, ...],
kwargs: Dict[str, Any],
output: torch.Tensor,
) -> torch.Tensor:
...
model = nn.Module()
# OK
model.register_forward_pre_hook(forward_pre_hook)
model.register_forward_pre_hook(forward_pre_hook_return_input)
model.register_forward_pre_hook(forward_pre_hook_with_kwargs, with_kwargs=True)
model.register_forward_pre_hook(forward_pre_hook_with_kwargs_return_input, with_kwargs=True)
model.register_forward_hook(forward_hook)
model.register_forward_hook(forward_hook_return_output)
model.register_forward_hook(forward_hook_with_kwargs, with_kwargs=True)
model.register_forward_hook(forward_hook_with_kwargs_return_output, with_kwargs=True)
# mypy(error): [arg-type]
model.register_forward_pre_hook(forward_hook)
model.register_forward_pre_hook(forward_hook_return_output)
model.register_forward_pre_hook(forward_hook_with_kwargs)
model.register_forward_pre_hook(forward_hook_with_kwargs_return_output)
model.register_forward_hook(forward_pre_hook)
model.register_forward_hook(forward_pre_hook_return_input)
# false negatives
model.register_forward_hook(forward_pre_hook_with_kwargs)
model.register_forward_hook(forward_pre_hook_with_kwargs_return_input)
model.register_forward_pre_hook(forward_pre_hook_with_kwargs, with_kwargs=False)
model.register_forward_pre_hook(forward_pre_hook_with_kwargs_return_input, with_kwargs=False)
...
```
---
Though it is not functional as of mypy 0.991, the ideal typing of these methods would use [`typing.Literal`](https://mypy.readthedocs.io/en/stable/literal_types.html#literal-types):
```python
T = TypeVar("T", bound="Module")
class Module:
@overload
def register_forward_hook(
self,
hook: Callable[[T, Tuple[Any, ...], Any], Optional[Any]],
*,
prepend: bool = ...,
with_kwargs: Literal[False] = ...,
) -> RemovableHandle:
...
@overload
def register_forward_hook(
self,
hook: Callable[[T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]],
*,
prepend: bool = ...,
with_kwargs: Literal[True] = ...,
) -> RemovableHandle:
...
def register_forward_hook(...):
...
```
which would:
1. validate the signature of `hook` according to the corresponding literal value provided for `with_kwargs` (and fix the false negative examples above)
2. implicitly define the [fallback `bool` signature](https://github.com/python/mypy/issues/6113#issuecomment-1266186192) e.g. to handle if a non-literal is provided for `with_kwargs`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92061
Approved by: https://github.com/albanD
closes#35643
This PR is mostly borrowed from #82042. Thanks @Padarn for implementing
the first version and debugging into the errors.
Based on the discussion in #82042 this PR adds a with_kwargs
argument to register_forward_pre_hook and register_forward_hook
methods. When the arg is set to true, the provided hook must accept
kwargs args. Under the hook, this PR adds a
`_forward_pre_hooks_with_kwargs` and a `_forward_hook_with_kwargs`
set to keep track of which hooks accept kwargs.
Differential Revision: [D41431111](https://our.internmc.facebook.com/intern/diff/D41431111)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89389
Approved by: https://github.com/soulitzer
This is a new version of #15648 based on the latest master branch.
Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR.
In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.)
Fixes https://github.com/pytorch/pytorch/issues/71105
@ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/torchrec/pull/39
Pull Request resolved: https://github.com/facebookresearch/torchrec/pull/6
This makes it so that shared parameters get their own entry in `named_parameters`.
More broadly, this makes it so that
```
params_and_buffers = {**mod.named_named_parameters(remove_duplicate=False), **mod.named_buffers(remove_duplicate=False)}
_stateless.functional_call(mod, params_and_buffers, args, kwargs)
```
is identical to calling the original module's forwards pass.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71542
Reviewed By: jbschlosser, albanD
Differential Revision: D33716716
Pulled By: Chillee
fbshipit-source-id: ff1ed9980bd1a3f7ebaf695ee5e401202b543213
(cherry picked from commit d6e3ad3cd0)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68035
RemoteModule is sometimes created using object.__new__ (ex:
init_from_module_rref), in this case the logging in the __init__ method would
not pick this up.
As a result, adding a `__new__` method to RemoteModule to log all usages
appropriately.
ghstack-source-id: 142762019
Test Plan: waitforbuildbot
Reviewed By: vipannalla
Differential Revision: D32263978
fbshipit-source-id: a95ab0bb5d0836da8fe6333c41593af164b008d9
Summary:
RecursiveScriptModule has its customized `__copy__` and `__deepcopy__` defined. The warning/error that says it is not copiable is outdated
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64085
Reviewed By: rohan-varma
Differential Revision: D30598623
Pulled By: gmagogsfm
fbshipit-source-id: 0701d8617f42d818bc7b88244caee4cd47fbe976
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62927
As part of the ShardedTensor work, we realized we do need some sort of
_RemoteDevice structure that deals with our format of "workername/device" so
that users don't have to worry about parsing this string directly.
Right now this structure is just the bare minimum and is mostly a container for
describing a remote device. It is currently only used in ShardedTensor,
ShardingSpec and RemoteModule.
Once we actually have a consolidated remote device proposal, this class can be
extended appropriately if needed.
ghstack-source-id: 135534086
Test Plan:
1) unit tests
2) waitforbuildbot
Reviewed By: SciPioneer
Differential Revision: D30170689
fbshipit-source-id: 1ac2e81c7a597dc40bf3fbf2c1168c382c66649f
Summary:
During development it is common practice to put `type: ignore` comments on lines that are correct, but `mypy` doesn't recognize this. This often stems from the fact, that the used `mypy` version wasn't able to handle the used pattern.
With every new release `mypy` gets better at handling complex code. In addition to fix all the previously accepted but now failing patterns, we should also revisit all `type: ignore` comments to see if they are still needed or not. Fortunately, we don't need to do it manually: by adding `warn_unused_ignores = True` to the configuration, `mypy` will error out in case it encounters an `type: ignore` that is no longer needed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60006
Reviewed By: jbschlosser, malfet
Differential Revision: D29133237
Pulled By: albanD
fbshipit-source-id: 41e82edc5cd5affa7ccedad044b59b94dad4425a
