[MM] Pass `prefix` parameter to MMEncoderAttention (#33674)

Signed-off-by: shen-shanshan <467638484@qq.com>

vllm/model_executor/models/aimv2.py

@@ -127,7 +127,10 @@ class AIMv2Attention(nn.Module):
         self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
 
         self.attn = MMEncoderAttention(
-            self.num_heads_per_partition, self.head_dim, self.scale
+            self.num_heads_per_partition,
+            self.head_dim,
+            self.scale,
+            prefix=prefix,
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:

vllm/model_executor/models/blip.py

@@ -123,7 +123,10 @@ class BlipAttention(nn.Module):
         self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
 
         self.attn = MMEncoderAttention(
-            self.num_heads_per_partition, self.head_dim, self.scale
+            self.num_heads_per_partition,
+            self.head_dim,
+            self.scale,
+            prefix=prefix,
         )
 
     def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):

vllm/model_executor/models/glm4_1v.py

@@ -296,6 +296,7 @@ class Glm4vVisionAttention(nn.Module):
             num_heads=self.num_attention_heads_per_partition,
             head_size=self.hidden_size_per_attention_head,
             scale=self.hidden_size_per_attention_head**-0.5,
+            prefix=prefix,
         )
 
         self.apply_rotary_emb = ApplyRotaryEmb(enforce_enable=True)

vllm/model_executor/models/glm4v.py

@@ -136,7 +136,10 @@ class EVA2CLIPAttention(nn.Module):
         )
 
         self.attn = MMEncoderAttention(
-            self.num_heads_per_rank, self.head_dim, self.scale
+            self.num_heads_per_rank,
+            self.head_dim,
+            self.scale,
+            prefix=prefix,
         )
         self.output_dropout = torch.nn.Dropout(config.dropout_prob)
 

vllm/model_executor/models/idefics2_vision_model.py

@@ -163,7 +163,10 @@ class Idefics2VisionAttention(nn.Module):
         )
         # Use unified MMEncoderAttention with Flash Attention support
         self.attn = MMEncoderAttention(
-            self.num_heads_per_partition, self.head_dim, self.scale
+            self.num_heads_per_partition,
+            self.head_dim,
+            self.scale,
+            prefix=prefix,
         )
 
     def forward(

vllm/model_executor/models/intern_vit.py

@@ -212,7 +212,10 @@ class InternParallelAttention(nn.Module):
         )
 
         self.attn = MMEncoderAttention(
-            self.num_heads_per_partition, self.head_dim, self.scale
+            self.num_heads_per_partition,
+            self.head_dim,
+            self.scale,
+            prefix=prefix,
         )
 
     def _apply_qk_norm(self, q: torch.Tensor, k: torch.Tensor):

vllm/model_executor/models/interns1_vit.py

@@ -170,6 +170,7 @@ class InternSdpaAttention(nn.Module):
         config: PretrainedConfig,
         *,
         num_dummy_heads: int = 0,
+        prefix: str = "",
     ) -> None:
         super().__init__()
 
@@ -215,7 +216,12 @@ class InternSdpaAttention(nn.Module):
         self.projection_layer = nn.Linear(self.dummy_dim, self.embed_dim)
 
         # Use unified MMEncoderAttention with automatic backend selection
-        self.attn = MMEncoderAttention(self.num_heads, self.head_dim, self.scale)
+        self.attn = MMEncoderAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scale,
+            prefix=prefix,
+        )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """x shape: (B, N, C)"""
@@ -313,7 +319,11 @@ class InternS1VisionLayer(nn.Module):
         num_dummy_heads: int,
         prefix: str = "",
     ):
-        return InternSdpaAttention(config, num_dummy_heads=num_dummy_heads)
+        return InternSdpaAttention(
+            config,
+            num_dummy_heads=num_dummy_heads,
+            prefix=prefix,
+        )
 
     def forward(
         self,

vllm/model_executor/models/mllama4.py

@@ -254,7 +254,10 @@ class Llama4VisionAttention(nn.Module):
         self.scaling = self.head_dim**-0.5
 
         self.attn = MMEncoderAttention(
-            self.num_local_heads, self.head_dim, self.scaling
+            self.num_local_heads,
+            self.head_dim,
+            self.scaling,
+            prefix=prefix,
         )
 
         if use_data_parallel:

vllm/model_executor/models/molmo.py

@@ -231,7 +231,11 @@ class MultiHeadDotProductAttention(nn.Module):
 
         self.scale = self.head_dim**-0.5
         self.attn = MMEncoderAttention(
-            self.num_heads, self.head_dim, self.scale, num_kv_heads=self.num_kv_heads
+            self.num_heads,
+            self.head_dim,
+            self.scale,
+            num_kv_heads=self.num_kv_heads,
+            prefix=prefix,
         )
 
     def forward(

vllm/model_executor/models/molmo2.py

@@ -611,6 +611,7 @@ class ImagePoolingAttention(nn.Module):
                 self.head_dim,
                 self.scale,
                 num_kv_heads=self.num_kv_heads,
+                prefix=prefix,
             )
 
     def forward_sdpa(

vllm/model_executor/models/qwen2_5_vl.py

@@ -345,6 +345,7 @@ class Qwen2_5_VisionAttention(nn.Module):
             num_heads=self.num_attention_heads_per_partition,
             head_size=self.hidden_size_per_attention_head,
             scale=self.hidden_size_per_attention_head**-0.5,
+            prefix=prefix,
         )
 
         self.apply_rotary_emb = ApplyRotaryEmb(enforce_enable=True)

vllm/model_executor/models/qwen2_vl.py

@@ -319,6 +319,7 @@ class Qwen2VisionAttention(nn.Module):
             num_heads=self.num_attention_heads_per_partition,
             head_size=self.hidden_size_per_attention_head,
             scale=self.hidden_size_per_attention_head**-0.5,
+            prefix=prefix,
         )
 
         self.apply_rotary_emb = ApplyRotaryEmb(enforce_enable=True)

vllm/model_executor/models/qwen3_omni_moe_thinker.py

@@ -194,6 +194,7 @@ class Qwen3OmniMoeAudioAttention(nn.Module):
             num_heads=self.num_local_heads,
             head_size=self.head_dim,
             scale=self.scaling,
+            prefix=prefix,
         )
 
     def forward(

vllm/model_executor/models/step3_vl.py

@@ -759,7 +759,12 @@ class Step3VisionAttention(nn.Module):
         )
 
         # Use unified MMEncoderAttention with automatic backend selection
-        self.attn = MMEncoderAttention(self.num_heads, self.head_dim, self.scale)
+        self.attn = MMEncoderAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scale,
+            prefix=prefix,
+        )
 
     def forward(
         self,

vllm/model_executor/models/step_vl.py

@@ -220,7 +220,12 @@ class PerceptionEncoderVisionAttention(nn.Module):
             prefix=f"{prefix}.out_proj",
             disable_tp=use_data_parallel,
         )
-        self.attn = MMEncoderAttention(self.num_heads, self.head_dim, self.scale)
+        self.attn = MMEncoderAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scale,
+            prefix=prefix,
+        )
         self.rope = PerceptionEncoderRope2D(
             dim=self.head_dim,
             max_grid_height=max_grid_height,