Revised partial checkpoint support for Sagemaker Model Parallel
Author: cavdard
Pull request: yes

# What does this PR do?

- Uses `smp.rdp_rank()` instead of `smp.rank()` for partial checkpoint saving in `should_save`.
- Uses `local_state_dict()` with partial checkpoint saving.
- Uses `smp.save ` for SMP.
- Uses` smp.load ` for SMP. Reorders partial checkpoint loading to happen after wrapping of model, since `smp.load` can only load to a smp model.
- Updated checks for the existence of checkpoint files since smp partial checkpoints contain postfixes in addition to filename(example: `filename_0_0` or` filename_0_0_0`).
- adds `load_best_model_at_end` support for SMP
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Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_16950). All of your documentation changes will be reflected on that endpoint.

> Thanks for your PR. Two comments on it:
> 
> 1. This breaks the current behavior of the `Trainer` where each checkpoint can be loaded as a model. In particular, this will push to the Hub the partial checkpoints with no config during training when `push_to_hub=True` (whereas a regular training pushes models that can be used).
> 2. The feature is always on. Maybe we should let the user decide if they want it or not?

Thanks for reviewing. 

In order user to decide to save/load partial checkpoints or not, we need new training args. I[n my previous PR](https://github.com/huggingface/transformers/pull/16734/files#diff-bfceaff300c851b8e24fc50dc6638482abaec8f7d2a718e877c3828c166bcf79R426-R431), I got feedback not to introduce new HF training args. So we decided to support partial checkpointing as default. 


There are plenty of other ways to control whether a feature is on or off. For instance, you could use the environment variable `"SM_HP_MP_PARAMETERS"`. 

Since this partial checkpointing is completely incompatible with `from_pretrained`, thus won't work with the Hugging Face Hub and its inference widget, it should be turned off by default.

@sgugger Thanks for you feedback. Based on your comments, we decided to enable partial checkpointing for optimizer state only where model weights will be saved in full. With this approach, model weights will be saved using `save_pretrained`. 

Here is the link for the new PR: https://github.com/huggingface/transformers/pull/17219

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.