[Bugfix] Fix humming kernel crash when layer.has_bias is None

[kylesayrs]

Summary

• LinearBase.__init__ sets self.has_bias = bias, and some custom model layers (e.g. Hy3-NVFP4-FP8) pass bias=None. This None propagates as has_bias=None into HummingLayerMeta.
• BaseHummingConfig.to_cpp_str() skips any field whose value fails isinstance(value, (bool, int, Enum)) — None fails this check, so kHasBias is omitted from the generated C++ struct.
• NVRTC then fails with: class "LayerConfig" has no member "kHasBias".
• Fix: convert None → False at the two call sites that forward layer.has_bias to prepare_layer_meta().

Why this is not a duplicate

Searched open PRs for humming has_bias, kHasBias humming, humming NoneType bias — no results.

Test commands and results

Sanity check (basic generation with Hy3-NVFP4-FP8, 4×A100, TP=4):

SANITY CHECK PASSED

GPQA Diamond eval (inspect_evals/gpqa_diamond, 1 epoch, 198 samples):

accuracy  0.778

Notes

• AI assistance (Claude Sonnet 4.6) was used to identify the root cause and write the fix. All changed lines have been reviewed.
• The fix is conservative: None → False matches the existing default (has_bias: bool = False in LayerConfig) and is safe for all callers where the layer genuinely has no bias.

🤖 Generated with Claude Code

[claude]

Claude Code Review

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[yewentao256]

LGTM, thanks for the work!

[mgoin]

@kylesayrs can we fix the model definition instead? LinearBase's bias arg is typed as bool, so we should try to follow the type (even if it isn't currently being enforced)

vllm/vllm/model_executor/layers/linear.py

Lines 245 to 263 in de100ff

	def __init__(
	self,
	input_size: int,
	output_size: int,
	bias: bool = False,
	skip_bias_add: bool = False,
	params_dtype: torch.dtype | None = None,
	quant_config: QuantizationConfig | None = None,
	prefix: str = "",
	*,
	return_bias: bool = True,
	disable_tp: bool = False,
	):
	super().__init__()
	# Keep input parameters
	self.input_size = input_size
	self.output_size = output_size
	self.has_bias = bias

[jinzhen-lin]

I feel that the model definition might have been passed incorrectly because the parameter name bias is misleading, it looks like it accepts a bias tensor. Maybe we can change it to something like has_bias?

[kylesayrs]

@jinzhen-lin I definitely agree, but there's also precedent from torch.nn.Linear