Add fine-grained checkpoint saving controls

[DonggyuBan]

Context

What is the purpose of this PR? Is it to

• add a new feature
• fix a bug
• update tests and/or documentation
• other (please add here)

This PR introduces two new features to control checkpoint saving behavior in the LoRA recipe:

• epochs_to_save: A new argument that allows users to specify exactly which epochs to save. It can be set to 'all', 'none', or a comma-separated list of epoch numbers (e.g., '1,5,10').

• save_last_epoch_only: This existing flag is now an override. If set to True, it will save only the final epoch, regardless of the epochs_to_save setting.

These features give users more control over disk space usage by allowing them to save only the checkpoints they need.

Changelog

What are the changes made in this PR?

Feature Implementation:

• Introduced epochs_to_save to the LoRA fine-tuning recipe, allowing users to save all, none, or a specific list of epoch checkpoints (e.g., '1,5,10').
• Enhanced save_last_epoch_only to act as an override, saving only the final epoch checkpoint regardless of other settings.
• Ensured both features are compatible with regular and asynchronous checkpointing modes.

Test Coverage:

• Added test_save_last_epoch_only integration test for regular checkpointing mode
• Added test_save_last_epoch_only_with_async_checkpointing integration test for async checkpointing mode
• Tests verify correct epoch folder creation behavior when feature is enabled/disabled

Test plan

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• run pre-commit hooks and linters (make sure you've first installed via pre-commit install)
• add unit tests for any new functionality
• update docstrings for any new or updated methods or classes
• run unit tests via pytest tests
• run recipe tests via pytest tests -m integration_test
• manually run any new or modified recipes with sufficient proof of correctness
• include relevant commands and any other artifacts in this summary (pastes of loss curves, eval results, etc.)

UX

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and a tutorial example

• I did not change any public API
• I have added an example to docs or docstrings

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/torchtune/2854

• 📄 Preview Python docs built from this PR

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