Apply model and optimizer state after checkpoint load

[WangXukang-cypher]

Summary

Distributed checkpoint loading currently populates the state-dict containers with torch.distributed.checkpoint.load, but does not explicitly apply the loaded state to the model and optimizer.

This change completes the checkpoint loading flow by:

• Applying model-only checkpoints with set_model_state_dict.
• Applying combined model and optimizer checkpoints with set_state_dict.
• Updating both the initial checkpoint loading path and the training resume path.
• Adding CPU regression tests for model and optimizer state restoration.

The optimizer regression test verifies that AdamW per-parameter state is installed on the resumed optimizer rather than only remaining in the temporary state-dict container.

Test plan

• Run the model-only checkpoint restoration test.
• Run the model and optimizer restoration test.
• Run Ruff static checks on the new tests.
• Run Ruff formatting checks on the new tests.
• Compile the modified Python files.
• Run git diff --check.
• Run IDE lint checks.

Results

2 passed in 0.85s

[meta-cla]

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