Old behavior would have adadelta foreach sending tensors to the slow path if they were not all the same dtype nor on the same device.
This PR adds grouping for adadelta optimizer so that it would run foreach in batches, allowing more users to benefit from foreach perf.
Of course, we should ensure that the new implementation works, so there are new tests to ensure this behavior is not broken.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92048
Approved by: https://github.com/albanD
### Description
Across PyTorch's docstrings, both `callable` and `Callable` for variable types. The Callable should be capitalized as we are referring to the `Callable` type, and not the Python `callable()` function.
### Testing
There shouldn't be any testing required.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82487
Approved by: https://github.com/albanD
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69980
- Merged `torch/optim/adadelta.py` and `torch/optim/_multitensor/adadelta.py` into `torch/optim/adadelta.py`
- Moved adadelta functional forms from `torch/optim/_functional.py` and `torch/optim/_multi_tensor/_functional.py` to `torch/optim/adadelta.py`
- `torch/optim/_functional.py` just imports from `torch/optim/adadelta.py`
- Added a test `test_optimizers_foreach_flag` which replicates `test_multi_tensor_optimizers` in `test/test_optim.py`
- Add a test `test_adadelta_new` that replicates the behavior of `test_adadelta` but with `foreach` flag instead of using the multitensor adadleta class. If we delete `_multitensor/` we could replace `test_adadelta` with this
Remaining TODO:
- [ ] single_tensor adadelta supports complex but multitensor does not, need to integrate the singletensor logic in multitensor and switch the `test_adadelta_complex` to test for foreach in [True, False]
Test Plan: Imported from OSS
Reviewed By: VitalyFedyunin, albanD
Differential Revision: D33413059
Pulled By: mikaylagawarecki
fbshipit-source-id: 92a9fa98705762bb6bd464261671e49aef40070e
(cherry picked from commit a008227d22)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52944
This fix the bug introduced during refactoring optimizers https://github.com/pytorch/pytorch/pull/50411. When all parameters have no grads, we should still allows `beta` like hyper params to be defined.
Reviewed By: ngimel
Differential Revision: D26699827
fbshipit-source-id: 8a7074127704c7a4a1fbc17d48a81e23a649f280
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51316
Make optim functional API be private until we release with beta
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D26213469
fbshipit-source-id: b0fd001a8362ec1c152250bcd57c7205ed893107
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.
Scales `delta` before it is applied to the parameters in order to control the learning rate of the optimizer (inspired from climin optim lib for theano).
Also changed the link to the Adadelta paper to point to the right location.
