Fix distillation checkpoint on resumption

[gagika]

Description

This PR fixes a bugs that prevented resuming distillation training from a trained student checkpoint.

Key Changes:

• Fixes Restoring from a Trained Student Checkpoint: Resolves a ValueError related to the missing 'norm' axis during checkpoint restoration by expanding the nn_partitioning.axis_rules context manager to wrap both model initialization and checkpoint loading.
• Fixes Optimizer State Saving: Explicitly passes optimizer=trainer.optimizer during the final conditional save block so the optimizer state is correctly written to disk, preventing a KeyError on resume.
• Code Refactoring: Extracts non-hardware configuration logic (Tokenizer, Strategy, TrainingConfig) into a clean build_training_components helper for better modularity and readability.

Tests

• Added test_train_save_and_resume to verify end-to-end checkpoint saving and successful loop resumption.
• Added test_checkpointing_and_resume to ensure the optimizer state and model weights are mathematically restored correctly upon initialization.
• Tested end to end with real run by killing distillation and resuming it.

Checklist

Checklist

Before submitting this PR, please make sure (put X in square brackets):

• I have performed a self-review of my code. For an optional AI review, add the gemini-review label.
• I have necessary comments in my code, particularly in hard-to-understand areas.
• I have run end-to-end tests tests and provided workload links above if applicable.
• I have made or will make corresponding changes to the doc if needed, including adding new documentation pages to the relevant Table of Contents (toctree directive) as explained in our documentation.

[github-actions]

🤖 Hi @gagika, I've received your request, and I'm working on it now! You can track my progress in the logs for more details.

[codecov]

Codecov Report

❌ Patch coverage is 80.14706% with 27 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
.../trainers/post_train/distillation/train_distill.py	82.29%	11 Missing and 6 partials ⚠️
...ners/post_train/distillation/distillation_utils.py	75.00%	4 Missing and 6 partials ⚠️

📢 Thoughts on this report? Let us know!

[github-actions]

## 📋 Review Summary

This Pull Request successfully introduces fixes for resuming distillation training from checkpoints by managing the _train_steps and _restored_custom_metadata appropriately using maybe_restore. It also reorganizes initialization steps for modularity and handles optimizer_state explicitly in the save call.

🔍 General Feedback

• The refactoring into build_training_components and the usage of nn_partitioning.axis_rules wrapper around initialization cleans up the setup significantly.
• Solid set of unit tests were added to ensure end-to-end functionality including checking whether optimizer state was loaded correctly.
• Added an inline suggestion for a type handler during array restoration to fix potential issues with primitive types.
• Pointed out an opportunity to conditionally compute grad_norm to enhance readability and ensure unnecessary XLA graphs are not added when unneeded.

[github-actions]

🤖 Hi @gagika, I've received your request, and I'm working on it now! You can track my progress in the logs for more details.

[github-actions]

## 📋 Review Summary

This PR successfully fixes the checkpoint resumption and optimizer state persistence bugs during distillation training. The implementation cleanly addresses the previously missing norm axis issue by appropriately wrapping the loading logic within the axis_rules context manager, and it effectively extracts standard configurations into build_training_components for enhanced readability.

🔍 General Feedback

• Checkpoint Resumption Logic: The new maybe_restore logic handles model bundles properly via getattr(model, "student_model", model).
• Modularity Enhancements: Extracting the build_training_components logically separates standard Tunix setups from MaxText-specific hardware contexts.
• Defensive Configurations: Added a comment inline regarding getattr over get_with_default for instances managed externally (e.g., Tunix dataclasses) to prevent potential attribute errors on property access.

[github-actions]

🟠 Standard dataclasses or configurations from external libraries (like Tunix's `TrainingConfig`) typically do not implement a `get_with_default` method. This might lead to an `AttributeError` at runtime. Using `getattr` is a safer, more pythonic approach.

Suggested change

	grad_accum_steps = self.config.get_with_default("gradient_accumulation_steps", 1)
	grad_accum_steps = getattr(self.config, "gradient_accumulation_steps", 1)

[github-actions]

🤖 Hi @gagika, I've received your request, and I'm working on it now! You can track my progress in the logs for more details.

[github-actions]

## 📋 Review Summary

This PR integrates and refactors the distillation components with Tunix and adds checkpoint management capabilities for MaxTextDistillationTrainer. The implementation handles complex orchestration between teacher and student models appropriately and isolates offline vs online distillation setups.

🔍 General Feedback

• Good use of maybe_restore and _shard_optimizer to handle sharding subtleties specific to JAX when restoring models and optimizer states from checkpoints.
• Passing trainer.model to trainer.checkpoint_manager.save() correctly mirrors the new ModelBundle abstraction.
• Be extremely cautious with source-code introspection via inspect.getsource; while functional as a fallback, it adds latent fragility that is best replaced by explicit version checks in future iterations.

[github-actions]

🟡 If `metadata` returned by `self._checkpoint_manager.metadata(step)` is a dictionary (which depends on the Orbax version), `hasattr(metadata, "custom_metadata")` will evaluate to `False`, and `custom_metadata` will silently default to an empty dictionary, losing the metadata.

Consider handling the case where metadata is a dictionary directly:

Suggested change

	metadata = self._checkpoint_manager.metadata(step)
	if metadata and hasattr(metadata, "custom_metadata") and metadata.custom_metadata is not None:
	custom_metadata = metadata.custom_metadata
	else:
	if isinstance(metadata, dict):
	custom_metadata = metadata
	elif metadata and hasattr(metadata, "custom_metadata") and metadata.custom_metadata is not None:
	custom_metadata = metadata.custom_metadata
	else:
	custom_metadata = {}

[github-actions]

🟠 Using string matching on the source code to determine an API signature is highly brittle. This could easily cause a runtime crash (`ValueError` during unpacking) if Tunix updates its code to include the word "grad_norm" in a comment, docstring, or a different context within `_train_step` without actually changing the return signature.

Consider relying on tunix.__version__ to check for this API change. If a version check isn't feasible, consider a safer inspection technique like parsing the AST or inspecting the method signature.