GitLab

* Fix gradient accumulation

Add ``is_scaled_loss`` flag to support both scaled / unscaled loss
Fix ``test_grad_accum`` and``test_set_num_gradients_to_accumulate``

* Add a method to scale grad for grad_accum using unscaled loss

- Revert the changes in `step` method
- Add a method `scale_grad_by_num_grads_to_accum`to handle gradient accumulation using unscaled loss more explicitly
- Add gradient tests

* Implement _compute_corr_mean_between_grads

* Improve tests and comments

* Use ubuntu-20.04 instead of latest

Use ubuntu-20.04 to fix the `arch x64 not found` issue
[Version 3.10 with arch x64 not found actions/setup-python#401](https://github.com/actions/setup-python/issues/401)

* Switch flake8 from gitlab to github

Flake8 was moved to Github
See discussions https://www.reddit.com/r/Python/comments/yvfww8/flake8_took_down_the_gitlab_repository_in_favor/

* Fix scikit-learn package

* Update PyTorch versions

* Resolve comments from Min

* Minor fix

* Disable broken tests for new versions of PyTorch

* Fix gradient accumulation

- Add ``is_scaled_loss`` flag to support both scaled / unscaled loss
- Add a method `scale_grad_by_num_grads_to_accum`to handle gradient accumulation using unscaled loss more explicitly
- Fix ``test_grad_accum`` and``test_set_num_gradients_to_accumulate``
- Add tests for gradient

Co-authored-by: Min Xu <min.xu.public@gmail.com>

- cover them in terms of code path only
- numerically, AdaScale is different on SDP/FSDP than DDP, mainly
  due to partial view of the gradients.
- this doesn't mean it is definitely not useful but it is yet to
  be validated.
- not going to spend too much time until we have a real use case.

* adding an assert + corresponding unit test
* updated changelog
* adjusting the adascale tests

* [test]: test adascale with oss

* minor fix

* add a small comment

* refactor: moved find_tensor_by_shape

* refactor: move test golden data into its own module

* refactor: simplied the train function

* refactor: added comments as suggested

* adding the pytest timeout plugin to properly root out hanging tests
* removing redundant code, slightly more reasonable timeout, works on single cuda
* finding the root bug for some of the cpu hangs, rpc init
* propagating all the rpc init test changes to the pipe and model parallel tests

* better ddp adascale tests

* make sure the single node test use the same test cases and expected gains

* added unit test that covers smoothing factor

- tested by re-introducing the bug and see the test fail as expected.

* added AdaScale to README

* [adascale] added gradient accumulation

- added gradient accumulation
- tested with cifar full trainings with different value of accumulation
and verified the full accuracy is obtained
- also removed the patch optimize flag until we need it

* [adascale] adding pytest

- added basic and ddp tests and grad_accum
- closes #195

* added changelog

* added ddp grad_accum test

* moved ddp and non-ddp tests into separate files

* added checkpoint test

* more doc

* addressed Mike's comments