[Bugfix] Skip unregistered checkpoint tensors in Merged/QKVParallelLinear.load_weights

[afierka-intel]

Summary

MergedColumnParallelLinear.load_weights() and QKVParallelLinear.load_weights() used getattr(self, name, self) — the layer module itself as a "not found" sentinel. When a checkpoint provides a tensor with no matching registered param, the if param is None and name == "bias": continue guard only catches the literal name "bias"; any other unmatched name (e.g. a GPTQ exporter's g_idx when desc_act=False) falls through and routes the whole module into param.weight_loader(param, loaded_weight, shard_id), crashing with AttributeError: '<LayerClass>' object has no attribute 'data' deep inside weight_loader instead of being skipped.

Why this isn't a duplicate

Searched gh issue list/gh pr list for AttributeError has no attribute data weight_loader, MergedColumnParallelLinear has no attribute, QKVParallelLinear has no attribute — no open issue/PR addresses this specific sentinel bug in linear.py. (Separately, while investigating this I found vllm-project/vllm#35865 already covers a related-but-distinct problem — modules_in_block_to_quantize list-of-lists flattening and packed_modules_mapping propagation into per-layer delegate quant configs for the moe_wna16 override path — so I did not duplicate that work here.)

Root cause

if "." in name:
    submodule, _, attr = name.rpartition(".")
    param = getattr(self.get_submodule(submodule), attr, self)  # <-- sentinel = self
else:
    param = getattr(self, name, self)  # <-- sentinel = self
if param is None and name == "bias":  # <-- only catches literal "bias"
    continue
param.weight_loader(param, loaded_weight, shard_id)  # <-- crashes if param is `self`

Fix: use None as the sentinel (so a real miss is detectable), and extend the skip condition to any unmatched checkpoint key ending in bias or g_idx — both are legitimate cases where a real quantized checkpoint exports a tensor the layer never registered as a param (bias when the layer's bias=False; g_idx when desc_act=False, since some GPTQ exporters still emit a trivial per-layer g_idx regardless).

How I hit this

While testing the tensor-descriptor MoE kernel work in afierka-intel/vllm#6 (VLLMZ-1811), I needed --quantization moe_wna16 to route Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4 through the Triton-kernel code path instead of the native Marlin/XPU path. That model's real checkpoint exports g_idx and per-expert bias tensors for every quantized linear layer, despite desc_act=false and has_bias=False — exposing this sentinel bug during weight loading. Not specific to XPU or to that PR's kernel change; reproducible on any platform with any checkpoint that has this shape.

Test plan

• New regression tests in tests/model_executor/layers/test_linear_load_weights.py: construct a bare MergedColumnParallelLinear/QKVParallelLinear (no full model), call .load_weights() directly with an unmatched bias/g_idx tensor and confirm it's skipped (empty loaded list, no exception), plus a control test confirming a real matched weight still loads and is reported.
• Verified the new tests fail against the pre-fix code with the exact reported AttributeError, and pass with the fix — confirms the tests exercise the real bug, not a tautology.
• pre-commit run (ruff check/format, mypy, SPDX headers, etc.) all green locally.
• Ran the existing kernel-level MoE test suite (tests/kernels/moe/test_fused_moe_kernel_gptq_awq.py, 33 tests) and the new E2E model-level test for VLLMZ-1811 (tests/models/quantization/test_gptq_awq_moe_td.py) on real B70 (Intel Arc Pro) hardware with this fix applied (plus the two other independent fixes for [Bugfix] fixes wna16 quantization for dense layers. vllm-project/vllm#35865-adjacent issues, applied locally only, not part of this PR) — model loads and generates correctly end-to-end.

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

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