model: support qwen3-vl series

python/sglang/srt/configs/model_config.py

@@ -749,6 +749,8 @@ def is_generation_model(model_architectures: List[str], is_embedding: bool = Fal
     "Qwen2AudioForConditionalGeneration",
     "Qwen2VLForConditionalGeneration",
     "Qwen2_5_VLForConditionalGeneration",
+    "Qwen3VLForConditionalGeneration",
+    "Qwen3VLMoeForConditionalGeneration",
     "KimiVLForConditionalGeneration",
     "InternVLChatModel",
     "InternS1ForConditionalGeneration",

python/sglang/srt/layers/rotary_embedding.py

@@ -1177,7 +1177,7 @@ def get_rope_index(
 
                         time_tensor_long = time_tensor.long()
                         t_index = time_tensor_long.flatten()
-                    elif model_type == "qwen2_vl":
+                    elif model_type in ("qwen2_vl", "qwen3_vl", "qwen3_vl_moe"):
                         t_index = (
                             torch.arange(llm_grid_t)
                             .view(-1, 1)

python/sglang/srt/managers/mm_utils.py

@@ -507,6 +507,7 @@ def embed_mm_inputs(
         Modality, Callable[[List[MultimodalDataItem]], torch.Tensor]
     ] = None,
     placeholder_tokens: dict[Modality, List[int]] = None,
+    use_deepstack: bool = False,
 ) -> Optional[torch.Tensor]:
     """
     Embed multimodal inputs and integrate them with text token embeddings.
@@ -522,7 +523,7 @@ def embed_mm_inputs(
     Returns:
         Combined embedding tensor with multimodal content integrated
     """
-
+    other_info = {}
     if mm_inputs_list is None:
         return None
 
@@ -532,7 +533,7 @@ def embed_mm_inputs(
     for mm_inputs in mm_inputs_list:
         item_flatten_list += [item for item in mm_inputs.mm_items if item is not None]
 
-    embeddings, masks = [], []
+    embeddings, masks, deepstack_embeddings = [], [], []
     # 2. Get multimodal embedding separately
     # Try get mm embedding if any
     for modality in Modality.all():
@@ -578,6 +579,12 @@ def embed_mm_inputs(
                 extend_length=extend_seq_lens,
                 items_offset_list=items_offsets,
             )
+
+            if use_deepstack and embedding is not None:
+                embedding, deepstack_embedding = (
+                    multimodal_model.separate_deepstack_embeds(embedding)
+                )
+                deepstack_embeddings += [deepstack_embedding]
             embeddings += [embedding]
             masks += [mask]
 
@@ -591,13 +598,37 @@ def embed_mm_inputs(
     inputs_embeds = input_embedding(input_ids)
 
     # 4. scatter embeddings into input embedding
-    for embedding, mask in zip(embeddings, masks):
+
+    # deepstack embedding
+    if use_deepstack:
+        num_deepstack_embeddings = (
+            len(multimodal_model.deepstack_visual_indexes) if use_deepstack else 0
+        )
+        deepstack_embedding_shape = inputs_embeds.shape[:-1] + (
+            inputs_embeds.shape[-1] * num_deepstack_embeddings,
+        )
+
+        input_deepstack_embeds = torch.zeros(
+            deepstack_embedding_shape,
+            device=inputs_embeds.device,
+            dtype=inputs_embeds.dtype,
+        )
+
+        other_info["input_deepstack_embeds"] = input_deepstack_embeds
+
+    for i, embedding, mask in zip(range(len(embeddings)), embeddings, masks):
         if embedding is None or mask is None:
             continue
         # in-place update
         indices = torch.where(mask.squeeze(dim=-1))[0]
         inputs_embeds[indices] = embedding.to(inputs_embeds.device, inputs_embeds.dtype)
-    return inputs_embeds
+
+        if use_deepstack:
+            input_deepstack_embeds[indices] = deepstack_embeddings[i].to(
+                inputs_embeds.device, inputs_embeds.dtype
+            )
+
+    return inputs_embeds, other_info
 
 
 def general_mm_embed_routine(
@@ -609,6 +640,7 @@ def general_mm_embed_routine(
         Modality, Callable[[List[MultimodalDataItem]], torch.Tensor]
     ] = None,
     placeholder_tokens: Optional[dict[Modality, List[int]]] = None,
+    use_deepstack: bool = False,
     **kwargs,
 ) -> torch.Tensor:
     """
@@ -620,6 +652,7 @@ def general_mm_embed_routine(
         language_model: Base language model to use
         data_embedding_funcs: A dictionary mapping from modality type to the corresponding embedding function.
         placeholder_tokens: Token IDs for multimodal placeholders
+        use_deepstack: Whether to use deepstack embeddings
         **kwargs: Additional arguments passed to language model
 
     Returns:
@@ -645,16 +678,20 @@ def general_mm_embed_routine(
             for i, seq_len in enumerate(forward_batch.extend_seq_lens_cpu)
             if forward_batch.mm_inputs[i] is not None
         ]
-        inputs_embeds = embed_mm_inputs(
+        inputs_embeds, other_info = embed_mm_inputs(
             mm_inputs_list=mm_inputs_list,
             extend_prefix_lens=extend_prefix_lens,
             extend_seq_lens=extend_seq_lens,
             input_ids=input_ids,
-            input_embedding=embed_tokens,
             multimodal_model=multimodal_model,
+            input_embedding=embed_tokens,
             data_embedding_func_mapping=data_embedding_funcs,
             placeholder_tokens=placeholder_tokens,
+            use_deepstack=use_deepstack,
         )
+        # add for qwen3_vl deepstack
+        if use_deepstack:
+            kwargs["input_deepstack_embeds"] = other_info["input_deepstack_embeds"]
         # once used, mm_inputs is useless, considering chunked-prefill is disabled for multimodal models
         # just being defensive here
         forward_batch.mm_inputs = None