[Bugfix] Revert custom attention mask for gemma3-mm (#28995)

Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>

vllm/model_executor/models/gemma3_mm.py

@@ -596,7 +596,7 @@ class Gemma3ForConditionalGeneration(
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings:
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
             return []
@@ -644,142 +644,6 @@ class Gemma3ForConditionalGeneration(
 
         return hidden_states
 
-    def generate_attention_masks(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        mask_dtype: torch.dtype,
-    ) -> dict[str, Any]:
-        """Generate custom attention masks for Gemma3 multimodal inputs.
-
-        This is called by V1 engine's gpu_model_runner during preprocessing
-        to generate attention masks that allow bidirectional attention between
-        image tokens while maintaining causal attention for text.
-        """
-        # NOTE(woosuk): Here, we distinguish the sequences by the position id 0.
-        # This is a HACK. Fix this.
-        start_indices = (positions == 0).cpu().nonzero()
-        num_seqs = len(start_indices)
-        seq_lens = []
-        for i in range(num_seqs):
-            start_idx = start_indices[i]
-            end_idx = start_indices[i + 1] if i < num_seqs - 1 else len(input_ids)
-            seq_lens.append(end_idx - start_idx)
-
-        global_attn_masks = []
-        local_attn_masks = []
-        start_idx = 0
-        for seq_idx, seq_len in enumerate(seq_lens):
-            end_idx = start_idx + seq_len
-            input_token_ids = input_ids[start_idx:end_idx]
-
-            # Find image token positions
-            img_pos = input_token_ids == self.config.image_token_index
-
-            start_idx = end_idx
-
-            # Create a global causal mask
-            global_attn_mask = torch.empty(
-                1,
-                1,
-                seq_len,
-                seq_len,
-                dtype=mask_dtype,
-                device=input_ids.device,
-            )
-            global_attn_mask.fill_(float("-inf"))
-            # Fill the lower triangle with 0 (causal attention)
-            global_attn_mask = global_attn_mask.triu(diagonal=1)
-
-            # Enable bidirectional attention between image tokens
-            img_mask = torch.zeros_like(global_attn_mask)
-            img_mask[:, :, :, img_pos] += 1
-            img_mask[:, :, img_pos, :] += 1
-            global_attn_mask = torch.where(img_mask == 2, 0, global_attn_mask)
-            global_attn_masks.append(global_attn_mask)
-
-            # GGUF compatibility: config might be Gemma3TextConfig directly
-            text_config = getattr(self.config, "text_config", self.config)
-            sliding_window = text_config.sliding_window
-            if sliding_window is not None:
-                # Create a local causal mask with sliding window (1024)
-                local_attn_mask = torch.ones_like(global_attn_mask)
-                local_attn_mask = torch.tril(local_attn_mask, diagonal=-sliding_window)
-                local_attn_mask = torch.where(
-                    local_attn_mask == 0, global_attn_mask, float("-inf")
-                )
-                local_attn_masks.append(local_attn_mask)
-
-        return {
-            "has_images": True,
-            "seq_lens": seq_lens,
-            "global_attn_masks": global_attn_masks,
-            "local_attn_masks": local_attn_masks,
-        }
-
-    def prepare_attn_masks(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        mask_dtype: torch.dtype,
-        **kwargs,
-    ):
-        kwargs["has_images"] = True
-        # NOTE(woosuk): Here, we distinguish the sequences by the position id 0.
-        # This is a HACK. Fix this.
-        start_indices = (positions == 0).cpu().nonzero()
-        num_seqs = len(start_indices)
-        seq_lens = []
-        for i in range(num_seqs):
-            start_idx = start_indices[i].item()
-            if i < num_seqs - 1:
-                end_idx = start_indices[i + 1].item()
-            else:
-                end_idx = len(input_ids)
-            seq_lens.append(end_idx - start_idx)
-        kwargs["seq_lens"] = seq_lens
-
-        global_attn_masks = []
-        local_attn_masks = []
-        start_idx = 0
-        for seq_len in seq_lens:
-            end_idx = start_idx + seq_len
-            input_token_ids = input_ids[start_idx:end_idx]
-            start_idx = end_idx
-            # Create a global causal mask.
-            global_attn_mask = torch.empty(
-                1,
-                1,
-                seq_len,
-                seq_len,
-                dtype=mask_dtype,
-                device=input_ids.device,
-            )
-            global_attn_mask.fill_(float("-inf"))
-            # Fill the lower triangle with 0.
-            global_attn_mask = global_attn_mask.triu(diagonal=1)
-
-            # Consider the bidirectional attention between image tokens.
-            img_mask = torch.zeros_like(global_attn_mask)
-            img_pos = input_token_ids == self.config.image_token_index
-            img_mask[:, :, :, img_pos] += 1
-            img_mask[:, :, img_pos, :] += 1
-            global_attn_mask = torch.where(img_mask == 2, 0, global_attn_mask)
-            global_attn_masks.append(global_attn_mask)
-
-            sliding_window = self.config.text_config.sliding_window
-            if sliding_window is not None:
-                # Create a local causal mask with sliding window (1024).
-                local_attn_mask = torch.ones_like(global_attn_mask)
-                local_attn_mask = torch.tril(local_attn_mask, diagonal=-sliding_window)
-                local_attn_mask = torch.where(
-                    local_attn_mask == 0, global_attn_mask, float("-inf")
-                )
-                local_attn_masks.append(local_attn_mask)
-        kwargs["global_attn_masks"] = global_attn_masks
-        kwargs["local_attn_masks"] = local_attn_masks
-        return kwargs
-
     def compute_logits(
         self,
         hidden_states: torch.Tensor,