Replace VL model implementations with updated version

vllm/model_executor/models/glm4_1v.py

@@ -280,10 +280,9 @@ class Glm4vVisionAttention(nn.Module):
                 f"GLM-4V does not support {self.attn_backend} backend now.")
 
     def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
-        if qkv.dim() == 2:
-            qkv = qkv.unsqueeze(1) # dim加上batch维度
-        
         # [s, b, 3 * head * head_dim]
+        if qkv.dim() == 2:
+            qkv = qkv.unsqueeze(1)   # 在 dim=1 加 batch 维度
         seq_len, bs, _ = qkv.shape
 
         # [s, b, 3 * head * head_dim] -> 3 * [s, b, head * head_dim]
@@ -427,9 +426,6 @@ class Glm4vVisionBlock(nn.Module):
             max_seqlen: Optional[int] = None,  # Only used for Flash Attention
             seqlens: Optional[list[int]] = None,  # Only used for xFormers
     ) -> torch.Tensor:
-        # -------------------------
-        # 1) Attention
-        # -------------------------
         normed_x = self.norm1(x)
         x_attn = self.attn(
             normed_x,
@@ -438,22 +434,12 @@ class Glm4vVisionBlock(nn.Module):
             max_seqlen=max_seqlen,
             seqlens=seqlens,
         )
-        # 保证 attn 输出为 3D tensor
         if x_attn.dim() == 2:
             x_attn = x_attn.unsqueeze(1)
         elif x_attn.dim() == 1:
             x_attn = x_attn.unsqueeze(1).unsqueeze(2)
-
         assert x_attn.dim() == 3, f"x_attn must be 3D, got {x_attn.shape}"
-
-        # -------------------------
-        # 2) norm2 + residual
-        # -------------------------
         x_fused_norm, residual = self.norm2(x, residual=x_attn)
-        
-        # -------------------------
-        # 3) MLP 前形状检查（核心）
-        # ------------------------
         if x_fused_norm.dim() == 3 and x_fused_norm.shape[1] == 1:
             mlp_in = x_fused_norm.squeeze(1)
             restore_3d = True
@@ -462,18 +448,9 @@ class Glm4vVisionBlock(nn.Module):
             restore_3d = False
         else:
             raise RuntimeError(f"Unexpected x_fused_norm shape {x_fused_norm.shape}, expect (N,D) or (N,1,D)")
-
-        # -------------------------
-        # 4) MLP
-        # ------------------------
         out = self.mlp(mlp_in)
-        # MLP 可能返回 (N,D)，恢复回三维
         if restore_3d:
             out = out.unsqueeze(1)
-
-        # -------------------------
-        # 5) residual + mlp_out
-        # -------------------------
         assert out.shape == residual.shape, \
             f"residual {residual.shape} vs mlp_out {out.shape} mismatch"
         x = residual + out

vllm/model_executor/models/qwen2_5_vl.py

@@ -923,6 +923,7 @@ class Qwen2_5_VisionTransformer(nn.Module):
                 weight_loader(param, loaded_weight)
             loaded_params.add(name)
         return loaded_params
+                
 
 
 class Qwen2_5_VLProcessingInfo(Qwen2VLProcessingInfo):