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
Fixes#91648
As explained in the tracking issue, the incomplete type stubs in `torch/nn/parallel` mask `DataParallel` methods relevant for subclassing and also mask type issues present in the code as well.
One notable change here is the addition of [`allow_redefinition = True`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-allow_redefinition) in `mypy.ini`, which allows for a common pattern:
> Allows variables to be redefined with an arbitrary type, as long as the redefinition is in the same block and nesting level as the original definition.
This is added specifically to allow for the type narrowing of `device_ids` in `torch.nn.parallel.data_parallel.data_parallel` from `Sequence[Union[int, torch.device]]` to `Sequence[int]`.
Other than this, there are various renamings and `type: ignore` comments added to bypass errors that arose from the merging.
@ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101528
Approved by: https://github.com/ezyang
#71033 moved test_data_parallel_module et al under `instantiate_device_type_tests`. This had the side effect of now running the tests on a non-default stream. The parallel_apply creates new threads, one per device, but does not forward the thread local current streams from the parent thread. This defaults the new per-device threads to use the null stream. The null stream will not sync with the non-default non-blocking streams, resulting in errors when these tests assert tensors are equal.
CC @janeyx99
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78824
Approved by: https://github.com/pruthvistony, https://github.com/janeyx99
Summary:
Take care of the state of autocast in `parallel_apply`, so there is no need to decorate model implementations.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43102
Reviewed By: ngimel
Differential Revision: D23294610
Pulled By: mrshenli
fbshipit-source-id: 0fbe0c79de976c88cadf2ceb3f2de99d9342d762
Summary:
When an exception occurs in one of the modules passed to `parallel_apply()`, it is caught and re-raised in the main thread. This preserves the original exception type and message, but has the traceback point at the position where it's re-raised, rather than the original point of failure.
This PR saves the exception information required to generate the traceback, and includes the original traceback in the message of the exception raised in the main thread.
Before:
```
...
File ".../torch/nn/parallel/data_parallel.py", line 153, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File ".../torch/nn/parallel/parallel_apply.py", line 84, in parallel_apply
raise output
RuntimeError: expected type torch.FloatTensor but got torch.cuda.FloatTensor
```
After:
```
...
File ".../torch/nn/parallel/data_parallel.py", line 153, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File ".../torch/nn/parallel/parallel_apply.py", line 88, in parallel_apply
''.join(traceback.format_exception(*exc_info)))
RuntimeError: Caught exception in replica 0. Original traceback and message:
Traceback (most recent call last):
...
File "../models/foo.py", line 319, in bar
baz = asdf / ghij[:, np.newaxis]
RuntimeError: expected type torch.FloatTensor but got torch.cuda.FloatTensor
```
I took care to raise an exception of the original type (in case the main code checks for that), but replaced the message. It helped me find a bug that did not occur outside `data_parallel()`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18055
Differential Revision: D16444972
Pulled By: zhangguanheng66
fbshipit-source-id: ec436c9d4677fad18106a8046cfa835a20a101ce
* Codemod to update our codebase to 0.4 standard
* Update some of the test scri[ts
* remove Variable in test_clip_grad_value
* fix _symbolic_override_wrapper_maker
This removes volatile from Variable. The functionality is mostly
replaced by a global (thread-local) flag, which is controlled by
torch.set_grad_enabled() and the context manager torch.no_grad().
In C++, the flag is exposed through GradMode::is_enabled() and GradMode::set_enabled()
Fixes#3627
Here's the command I used to invoke autopep8 (in parallel!):
git ls-files | grep '\.py$' | xargs -n1 -P`nproc` autopep8 -i
Several rules are ignored in setup.cfg. The goal is to let autopep8
handle everything which it can handle safely, and to disable any rules
which are tricky or controversial to address. We may want to come back
and re-enable some of these rules later, but I'm trying to make this
patch as safe as possible.
Also configures flake8 to match pep8's behavior.
Also configures TravisCI to check the whole project for lint.
