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[Model] Switch to Fused RMSNorm in GLM-4.1V model #24733

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Merged
merged 2 commits into from
Sep 12, 2025
+3 −2
Merged

[Model] Switch to Fused RMSNorm in GLM-4.1V model #24733

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490b1ce
use fused rms norm
SamitHuang Sep 9, 2025
745db81
Merge branch 'main' into glmv_x
SamitHuang Sep 12, 2025
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5 changes: 3 additions & 2 deletions vllm/model_executor/models/glm4_1v.py
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Original file line number Diff line number Diff line change
Expand Up @@ -419,15 +419,16 @@ def forward(
max_seqlen: Optional[int] = None, # Only used for Flash Attention
seqlens: Optional[list[int]] = None, # Only used for xFormers
) -> torch.Tensor:
x = x + self.attn(
x_attn = self.attn(
self.norm1(x),
cu_seqlens=cu_seqlens,
rotary_pos_emb=rotary_pos_emb,
max_seqlen=max_seqlen,
seqlens=seqlens,
)
x_fused_norm, residual = self.norm2(x, residual=x_attn)
x = residual + self.mlp(x_fused_norm)
Comment on lines +429 to +430
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high

The original code x = residual + self.mlp(x_fused_norm) might introduce a correctness issue. It seems that the residual x from the input of norm2 is not considered in the calculation. It is crucial to add the initial x before applying the MLP to maintain the residual connection properly. This could lead to incorrect results in the model's output.

Consider changing the calculation to x = x + residual + self.mlp(x_fused_norm) to address this issue.

x = x + residual + self.mlp(x_fused_norm)


x = x + self.mlp(self.norm2(x))
return x


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