Support AcceleratorConfig.use_stateful_dataloader in Trainer 
Author: byi8220
Pull request: yes

# What does this PR do?

This PR does the following:

1. Add a new field to `TrainerArguments.AcceleratorConfig`, `use_stateful_dataloader` which when set to true passes through `use_stateful_dataloader` to the DataLoaderConfiguration used to initialize the Trainer's backing Accelerator.
2. Add a new field `train_dataloader_state_dict` to TrainerState, which is used to persist the StatefulDataLoader's state_dict at the time of checkpointing.
3. When resuming from checkpoint for a StatefulDataLoader backed Trainer, instead of the training dataloader skipping batches to resume within an epoch, have the train dataloader load the state_dict derived from the loaded train_dataloader_state_dict.

This PR was tested through the following:

1. Unit test `TrainerIntegrationTest.test_train_and_eval_dataloaders_with_use_stateful_dataloader`, which is a sanity test on the dataloaders used.
2. Unit test `TrainerIntegrationTest.test_resume_training_with_stateful_dataloaders`, which mirrors the other `test_resume_training.*` tests, asserting that resuming from checkpoint behaves sensibly, and that the saved checkpoints do contain saved state_dicts.

Minor changes: this PR adds dependencies on `accelerate>=1.0.0` and `torchdata>=0.8.0` to `transformers` and a minor issue with Trainer test cases

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes #31441


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [X] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [X] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [X] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [X] Did you write any new necessary tests?


## Who can review?
@muellerzr and @SunMarc

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @ylacombe, @eustlb
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @SunMarc
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @muellerzr
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_34205). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

gentle ping @muellerzr and @SunMarc

I can get rid of the requirement, but may I ask why?

The reasons I bumped up the version req was just so 1. The tests would be guaranteed to be included in CI and 2. I wanted to avoid weird, unforeseen regressions due to versioning.

I think I can remove it without a problem but I still find it weird to use features ahead of a minimum version.

Because many users rely on minimal versions, we don't want to jump the gun. 

@muellerzr 

Reverted the version requirements. Note, the tests in TrainerHyperParameterRayIntegrationTest are broken for StatefulDataLoader due to a pickling error.

I got around this by resetting the GradientState at the end of my test (to clear out the dataloader references that were attempting to be pickled). Is it expected that StatefulDataLoader can't be pickled?

Unless I'm mistaken, I believe this unfortunately weakens the CI.

Since we can't just trust the CI pipeline to run the tests for us, could you possibly run the `make test` suite manually for me to verify it passes and there are no regressions in the relevant cases? I.e. for both single and multigpu (and other relevant hardware)

1. accelerate at minimum version (0.26.0), no torchdata installed. (Expect tests to skip, but check for regressions)
2. accelerate>=1.0.0, torchdata>=0.8.0 (Only case where my tests would run)
3. accelerate>=1.0.0, no torchdata (Expect tests to skip, but check for regression)
4. Repeat the above 3 for multi-gpu (and other hardware)

Normally I would do it myself, but my dev workstation kinda broke, and I'm probably gonna need to setup literally everything again (and possibly buy new hardware), so I'm kinda unavailable to run the tests manually...

FWIW I got my (1xGPU) workstation up and running again and have tested the first 3 test cases above (and have had to slightly refactor the tests).

Excited to the see the progress, as this is useful to me! What is the status on this?

@byi8220 Hi, any progress on this PR? Really looking forward to this feature. 

Hi, still waiting for a review from @muellerzr as this PR should be ready for review.

Any updates on this? Really excited for this feature!

gentle ping @sunmarc!

Hey @byi8220, I'll review this PR, sorry for the wait ! Are you still up to tweak/finish this PR with my guidance ? If so, if you can first fix the conflicts that would be super nice. 

> Hey @byi8220, I'll review this PR, sorry for the wait ! Are you still up to tweak/finish this PR with my guidance ? If so, if you can first fix the conflicts that would be super nice.

This PR is very important for large-scale pre-training. Do you have any plans to merge this PR? @SunMarc @byi8220