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[Bug Fix] Fix naive_block_assignment always defaulting to False due to arg misalignment #33848

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Merged
DarkLight1337 merged 2 commits into from
Feb 12, 2026
Merged

[Bug Fix] Fix naive_block_assignment always defaulting to False due to arg misalignment #33848

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ccfb4b7
unpermute bug fixing
RunkaiTao Feb 5, 2026
99d77c7
Merge remote-tracking branch 'upstream/main' into unpermute/fix
RunkaiTao Feb 10, 2026
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2 changes: 1 addition & 1 deletion vllm/lora/layers/fused_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -219,7 +219,7 @@ def wrapper(*args, **kwargs):
self.max_loras,
self.adapter_enabled,
expert_map,
naive_block_assignment,
naive_block_assignment=naive_block_assignment,
)

moe_state_dict["sorted_token_ids_lora"] = sorted_token_ids_lora
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1 change: 1 addition & 0 deletions vllm/lora/punica_wrapper/punica_base.py
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Expand Up @@ -458,6 +458,7 @@ def moe_lora_align_block_size(
adapter_enabled: torch.Tensor,
expert_map: torch.Tensor | None = None,
pad_sorted_ids: bool = False,
naive_block_assignment: bool = False,
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@gemini-code-assist gemini-code-assist bot Feb 5, 2026

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high

This bug was caused by passing a positional argument that was misinterpreted. To prevent this class of bugs, consider making optional boolean flags keyword-only by adding a * in the function signature before them (e.g., before pad_sorted_ids). This would enforce keyword arguments for all subsequent parameters and would have raised a TypeError for the original buggy call.

) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Aligns tokens and experts into block-sized chunks for LoRA-based
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