[Bugfix][DeepseekV4] Gate expert weight load on success

[varjoranta]

Supersedes #42835. Rebased on top of the post-#43004 DeepSeek V4 restructure. Fixes #42769.

Background

#43004 ("Migrate DeepSeek V4 to vllm/models/ [1/N]") deleted vllm/model_executor/models/deepseek_v4.py, so #42835's patch no longer applies. The bug from #42769 / #42835 survived the migration and is now in vllm/models/deepseek_v4/nvidia/model.py:1434-1459, structurally identical to the pre-migration form.

The bug

In the expert-mapping loop, name_mapped is only bound inside the loop body (after the if weight_name not in name: continue guard). Two failure modes:

1. If the loop never assigns name_mapped (no mapping matched), the post-loop loaded_params.add(name_mapped) raises UnboundLocalError.
2. If the loop runs but every weight_loader(..., return_success=True) call returns False for this rank, name_mapped is bound to the last attempt and success is False, but loaded_params.add(name_mapped) still runs unconditionally and records a weight that was never actually applied.

The fix

Initialize name_mapped = None and success = False before the loop. Gate loaded_params.add(name_mapped) on if not success: continue. Successful canonical loads are unchanged; this only hardens the unsuccessful-mapping / non-local-expert path so loaded_params remains accurate.

The affected path is load_weights, which requires a fully instantiated model plus distributed init to exercise, so there is no unit-test seam beyond the CUDA-gated DSV4 tests. The change is self-contained; happy to add a CUDA integration test if a maintainer prefers.

Closes #42769

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Code Review

This pull request updates the weight loading logic in vllm/models/deepseek_v4/nvidia/model.py by initializing mapping variables and adding a check to skip weights when no expert mapping is found. This change prevents potential UnboundLocalError and ensures that weights not intended for the current rank are correctly ignored. I have no feedback to provide as there were no review comments.