* 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
* Enable PERF402. Makes code more efficient and succinct by removing useless list copies that could be accomplished either via a list constructor or extend call. All test cases have noqa added since performance is not as sensitive in that folder.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115505
Approved by: https://github.com/malfet
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
When using KeyedOptimizer.init_state(), some optimizers initializes the states even if the param is empty (size() == 0) while some optimizer avoid initializing the states. There is no way FSDP can tell. Instead, FSDP should look up `optim.state`. Fortunatelly, `optim.state` does not rely on FQNs which some internal users change the FQNs.
Differential Revision: [D47285562](https://our.internmc.facebook.com/intern/diff/D47285562/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104765
Approved by: https://github.com/fduwjj
Summary:
The current `optim_state_dict()` does not require users to call `optim.state_dict()` first while `optim_state_dict_to_load()` requires users to call `optim.load_state_dict()`. This PR make both APIs provide the option for users not having to call the extra API.
This PR also changes the arguments order of `optim_state_dict_to_load` which is a breaking change. So we should do this asap before the API is adopted in production cases.
Test Plan: CI
Differential Revision: D43925068
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96534
Approved by: https://github.com/rohan-varma
1. Add param_group check logic and unit test
2. Remove unnecessary check for conditional param update
3. Return the param_group from the inner optimizer so that when param_group is None or not all params are specified, we still return the expected result.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91147
Approved by: https://github.com/fegin
In pytorch, the optim state_dict will always use number to index optimizer state_dict for parameters.
Now composability workstream need a FQN based way to index optimizer state_dict for parameters..
For example, SGD optimizer might have something in its `state_dict` like:
```
{'state':
{0:
{'momentum_buffer': tensor(...)},
{1:
{'momentum_buffer': tensor(...)},
...
}
'param_groups':
[{'lr': 0.001, 'momentum': 0.9, 'dampening': 0, 'weight_decay': 0, 'nesterov': False, 'maximize': False, 'foreach': None, 'differentiable': False, 'params': [0, 1, 2, 3, 4, 5, 6, 7]}]
}
```
And in NamedOptimizer we want the `state_dict` can be:
```
{'state':
{'net1.0.weight':
{'momentum_buffer': tensor(...)},
{'net1.0.bias':
{'momentum_buffer': tensor(...)},
...
}
'param_groups':
[{'lr': 0.001, 'momentum': 0.9, 'dampening': 0, 'weight_decay': 0, 'nesterov': False, 'maximize': False, 'foreach': None, 'differentiable': False, 'params': ['net1.0.weight', 'net1.0.bias', 'net2.0.weight', 'net2.0.bias', 'net3.weight', 'net3.bias', 'net4.1.weight', 'net4.1.bias']}]
}
```
We also want to support load_state_dict to enable optim `state_dict` override for NameOptimizer.
For the next couple PR/diffs, we also need to:
1. To make `NamedOptimizer` working with FSDP (like registering a hook for model wrapped with FSDP) and other PTD/PT components.
2. Make `NamedOptimizer` works well with apply_optim_in_backward
3. Upstream also `CombinedOptimizer`.
Differential Revision: [D41432088](https://our.internmc.facebook.com/intern/diff/D41432088/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D41432088/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89480
Approved by: https://github.com/rohan-varma
