Fix (scaling/standalone): better switch from runtime stats to param

[Giuseppe5]

Reason for this PR

Currently, if we switch from training to eval before stats_collection_steps is done, we never update the value parameter to store the buffer value. This has a few side effects:

• When applying learned round, we might keep the model in eval model but still accumulate gradients. If the value parameter is not being used, no gradients are accumulated
• When exporting state_dict, value is not exported
• When doing PTQ calibration, current setup is such that the buffer is never converted to its corresponding parameter value, causing some of the issues mentioned above.

Changes Made in this PR

At eval time, during the first iteration the buffer is always converted to param.
The side effect of this happens in the case the user would want to switch multiple times between training/evaluation mode very early on in the training process. Although it is common to switch between training/eval to check loss on the validation set, it is usually done after enough iteration that the buffer has already been converted to parameter anyway.
I'd admit that it could be marked as breaking change for this edge cases.

This has been removed in a more recent commit. I believe there are no more breaking changes at this point.

All fixed, no more breaking changes.
After calibration, we forcefully convert the buffer to parameters.

Testing Summary

Risk Highlight

• This PR includes code from another work (please detail).
• This PR contains API-breaking changes.
• This PR depends on work in another PR (please provide links/details).
• This PR introduces new dependencies (please detail).
• There are coverage gaps not covered by tests.
• Documentation updates required in subsequent PR.

Checklist

• Code comments added to any hard-to-understand areas, if applicable.
• Changes generate no new warnings.
• Updated any relevant tests, if applicable.
• No conflicts with destination dev branch.
• I reviewed my own code changes.
• Initial CI/CD passing.
• 1+ reviews given, and any review issues addressed and approved.
• Post-review full CI/CD passing.

[nickfraser]

The side effect of this happens in the case the user would want to switch multiple times between training/evaluation mode very early on in the training process.

Could the accuracy difference in the LLM tests be caused by this? I think the tests run at some very small seqlen (2?)

Otherwise, LGTM!

[nickfraser]

Check comment about self.init_done, otherwise LGTM!

[Giuseppe5]

This avoids double re-init, since we're modifying in place the value of value.