raise value error on init `ParametrizationList`, if `original.device != new.device`.
currently `_maybe_set` will throw below error in such situations, which I think it's not convenient to debug.
```
[rank1]: RuntimeError: Attempted to set the storage of a tensor on device "cuda:1" to a storage on different device "cpu". This is no longer allowed; the devices must match.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162717
Approved by: https://github.com/lezcano
This pull request fixes a minor typo in the doc comments of `test/nn/test_parametrization.py`.
- Replaced `'Intializing'` with `'Initializing'` in two docstring comments to improve clarity and maintain consistency across the codebase.
This is a non-functional change and does not impact behavior or test outcomes.
Thank you for maintaining such a high-quality codebase. Please let me know if any adjustments are needed. I'd be happy to help!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157362
Approved by: https://github.com/ezyang
Add similar semantics for creating a buffer object similar to creating a parameter. This is done by introducing a new Buffer class that can be used for type disambiguation. The underlying functionality of registering a buffer remains the same as the register_buffer method has not been changed. The persistent parameter in the Buffer type is to indicate whether a buffer object should be persistent or not. Other non-test changes have to do with getting the new Buffer type recognized by inductor and dynamo. Remaining changes are test changes to make sure that the Buffer type can be used as a drop in replacement for register_buffer as it just leads to register_buffer being called. The addition of this new functionality still allows for normal tensors to be used as buffers so these changes are intended to be backwards compatible.
Fixes#35735
Co-authored-by: Mikayla Gawarecki <mikaylagawarecki@gmail.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125971
Approved by: https://github.com/albanD, https://github.com/anijain2305, https://github.com/mlazos
Summary:
Previously the `requires_grad` is not propagated from original Tensor to decomposed tensors
Test Plan:
python test/test_parametrization.py -k test_register_parametrization_no_grad
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124888
Approved by: https://github.com/lezcano
Spectral norm implementation has extensive tests, but there doesn't appear to be any checking that indeed the spectral norm (= top singular value) is correctly calculated. There should at least be one such testcase.
This adds one such testcase for the parameterizations.py implementation of spectral norm.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121068
Approved by: https://github.com/soulitzer
Add similar semantics for creating a buffer object similar to creating a parameter. This is done by introducing a new `Buffer` class that can be used for type disambiguation. The underlying functionality of registering a buffer remains the same as the `register_buffer` method has not been changed. The `persistent` parameter in the `Buffer` type is to indicate whether a buffer object should be persistent or not. Other non-test changes have to do with getting the new `Buffer` type recognized by inductor and dynamo. Remaining changes are test changes to make sure that the `Buffer` type can be used as a drop in replacement for `register_buffer` as it just leads to `register_buffer` being called. The addition of this new functionality still allows for normal tensors to be used as buffers so these changes are intended to be backwards compatible.
Fixes#35735
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104069
Approved by: https://github.com/mikaylagawarecki
This done in the ordinary way, but also:
* Deprecation warning for the old API, and a migration guide
* Backwards compatibility for state_dict loading the old weight_norm
* Test for pickling and deepcopy, which was the motivating reason
weight_norm is still used by HuggingFace Wav2Vec2.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103001
Approved by: https://github.com/albanD
