[Bugfix] Skip unregistered checkpoint tensors in RoutedExperts.load_weights

[afierka-intel]

Summary

Same root cause as afierka-intel/vllm#9, for the MoE-experts weight-loading path instead of linear layers. RoutedExperts.load_weights() (vllm/model_executor/layers/fused_moe/routed_experts.py) did param = getattr(self, param_name) with no fallback for a checkpoint tensor that matches an expert_mapping entry by name but was never registered as a param by the quant method (e.g. per-expert bias when the quant method doesn't support bias). This crashes with AttributeError: 'RoutedExperts' object has no attribute '<name>' instead of being skipped.

Why this isn't a duplicate

Searched gh issue list/gh pr list for RoutedExperts AttributeError bias g_idx, RoutedExperts load_weights bias — no open issue/PR addresses this.

Root cause

weight_name = qual_name.replace(weight_name, param_name)
param_name = weight_name.removeprefix(f"{self.layer_name}.")
param = getattr(self, param_name)  # <-- crashes if param_name was never registered

Fix: check hasattr(self, param_name) first; if missing, skip this checkpoint entry (mirroring the existing if matched and is_fused: break early-exit already used elsewhere in the same loop for fused-layer boundary detection).

How I hit this

Same investigation as #9: testing the tensor-descriptor MoE kernel work in afierka-intel/vllm#6 (VLLMZ-1811) required --quantization moe_wna16 to route Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4 through the Triton-kernel path. That model's checkpoint exports per-expert bias tensors for every MoE layer despite the model architecture never setting has_bias=True on the MoE config, exposing this same class of bug on the experts-loading path (separate from the linear-layer path fixed in #9).

Test plan

• New regression tests in tests/model_executor/layers/test_routed_experts_load_weights.py: construct a bare RoutedExperts (no full model, no distributed init — FusedMoEParallelConfig.make_no_parallel() + quant_config=None so it uses UnquantizedFusedMoEMethod, which registers only w13_weight/w2_weight, no bias), call .load_weights() directly with an unmatched bias tensor and confirm it's skipped, plus a control test confirming a real matched weight still loads correctly.
• Verified the new test fails against the pre-fix code with the exact reported AttributeError, and passes with the fix.
• pre-commit run (ruff check/format, mypy, SPDX headers, etc.) all green locally.

AI assistance was used (Claude Code) for investigation and implementation; I reviewed every changed line and ran the tests above personally.

[github-actions]

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