This PR enables all PIE rules on ruff, there are already some enabled rules from this family, the new added rules are
```
PIE796 Enum contains duplicate value: {value}
PIE808 Unnecessary start argument in range
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165814
Approved by: https://github.com/ezyang
This PR enables all PIE rules on ruff, there are already some enabled rules from this family, the new added rules are
```
PIE796 Enum contains duplicate value: {value}
PIE808 Unnecessary start argument in range
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165814
Approved by: https://github.com/ezyang
This is the first PR of a series in an attempt to get the content of #134592 merged as smaller PRs (Given that the original one was closed due to a lack of reviewers).
This specific PR contains:
- Add and use a common raise_on_run_directly method for when a user runs a test file directly which should not be run this way. Print the file which the user should have run.
- Update ao tests.
There will be follow up PRs to update the other test suites but I don't have permissions to create branches directly on pytorch/pytorch so I can't create a stack and therefore will have to create them one at the time.
Cc @jerryzh168
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154612
Approved by: https://github.com/jcaip
`Sparsity` as a term doesn't reflect the tools that are developed by the AO. The `torch/ao/sparsity` also has utilities for structured pruning, which internally we always referred to as just "pruning". To avoid any confusion, we renamed `Sparsity` to `Prune`. We will not be introducing the backwards compatibility, as so far this toolset was kept under silent development.
This change will reflect the changes in the documentation as well.
**TODO:**
- [ ] Change the tutorials
- [ ] Confirm no bc-breakages
- [ ] Reflect the changes in the trackers and RFC docs
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84867
Approved by: https://github.com/supriyar
The BaseDataScheduler is the abstract scheduler class specifically for the
BaseDataSparsifier class. This class controls a specific hyperparameter of
the sparsifier class and varies it across the training process (or across time).
Args:
data_sparsifier (instance of BaseDataSparsifier)
Implemented class data sparsifier class wherein the update_mask is implemented
schedule_param (str)
A specific hyperparameter of the passed sparsifier that needs to be scheduled/varied
last_epoch (int, default=-1)
This is specifically is passed when training needs to be resumed from a particular
point.
verbose (bool, default=False)
Verbosity of the BaseDataScheduler
The *get_schedule_param()* function needs to be implemented by the user.
Test Plan:
```python test/test_ao_sparsity.py TestBaseDataScheduler```
Differential Revision: [D37358608](https://our.internmc.facebook.com/intern/diff/D37358608)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79817
Approved by: https://github.com/jerryzh168, https://github.com/z-a-f
