[Bugfix] Raise a clear error when a linear weight parameter is missing

[33modeling]

Summary

Improve the error reported by MergedColumnParallelLinear.load_weights when a checkpoint weight does not correspond to a valid parameter.

Previously, a failed parameter lookup could fall back to the layer module itself. The subsequent weight-loading operation would then fail with a confusing AttributeError, making it difficult to identify the actual checkpoint or model-definition mismatch.

This change validates the resolved object before invoking its weight loader and raises a descriptive ValueError when it is not a torch.nn.Parameter.

Changes

• Detect unresolved or invalid parameter lookups before calling weight_loader.
• Include the checkpoint weight name and resolved object type in the error message.
• Preserve the existing behavior for models with an optional, missing bias.
• Add a regression test for a missing nested parameter.

Testing

• Verified the patch with git diff --check.
• Verified both modified Python files with py_compile.
• The pytest test suite could not be executed locally because pytest is not installed in the current environment.

Fixes #53107

AI Assistance

OpenAI Codex assisted with the implementation and regression-test preparation. I reviewed the resulting changes and take responsibility for the contribution.

[claude]

Claude Code Review

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[33modeling]

Closing this as a duplicate of #53118, which was opened earlier and addresses the same issue with the same parameter-validation approach. Sorry that I missed the existing PR during the duplicate-work check.