[MM] Add text-only mode for Qwen3-VL (vllm-project#26000)

Signed-off-by: Tomer Asida <57313761+tomeras91@users.noreply.github.com>

Author: ywang96

vllm/model_executor/models/qwen3_vl.py

@@ -1125,14 +1125,17 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
         self.config = config
         self.multimodal_config = multimodal_config
         self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
-
-        self.visual = Qwen3_VisionTransformer(
-            config.vision_config,
-            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
-            quant_config=quant_config,
-            prefix=maybe_prefix(prefix, "visual"),
-            use_data_parallel=self.use_data_parallel,
-        )
+        if not multimodal_config.get_limit_per_prompt("image") and \
+            not multimodal_config.get_limit_per_prompt("video"):
+            self.visual = None
+        else:
+            self.visual = Qwen3_VisionTransformer(
+                config.vision_config,
+                norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "visual"),
+                use_data_parallel=self.use_data_parallel,
+            )
 
         self.language_model = Qwen3LLMForCausalLM(vllm_config=vllm_config,
                                                   prefix=maybe_prefix(
@@ -1148,11 +1151,15 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
             config.vision_config.deepstack_visual_indexes
         ) if self.use_deepstack else 0
         # register buffer for deepstack
-        self.deepstack_input_embeds = [
-            torch.zeros(vllm_config.scheduler_config.max_num_batched_tokens,
-                        config.text_config.hidden_size)
-            for _ in range(self.deepstack_num_level)
-        ] if self.use_deepstack else None
+        if self.use_deepstack and self.visual is not None:
+            self.deepstack_input_embeds = [
+                torch.zeros(
+                    vllm_config.scheduler_config.max_num_batched_tokens,
+                    config.text_config.hidden_size)
+                for _ in range(self.deepstack_num_level)
+            ]
+        else:
+            self.deepstack_input_embeds = None
         self.visual_dim = config.vision_config.out_hidden_size
         self.multiscale_dim = self.visual_dim * self.deepstack_num_level
 
@@ -1526,7 +1533,11 @@ def compute_logits(
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
-        loader = AutoWeightsLoader(self)
+
+        skip_prefixes = []
+        if self.visual is None:
+            skip_prefixes.extend(["visual."])
+        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
         return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
 
     def get_mm_mapping(self) -> MultiModelKeys:

vllm/model_executor/models/qwen3_vl_moe.py

@@ -319,13 +319,17 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.multimodal_config = multimodal_config
         self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
 
-        self.visual = Qwen3_VisionTransformer(
-            config.vision_config,
-            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
-            quant_config=quant_config,
-            prefix=maybe_prefix(prefix, "visual"),
-            use_data_parallel=self.use_data_parallel,
-        )
+        if not multimodal_config.get_limit_per_prompt("image") and \
+            not multimodal_config.get_limit_per_prompt("video"):
+            self.visual = None
+        else:
+            self.visual = Qwen3_VisionTransformer(
+                config.vision_config,
+                norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "visual"),
+                use_data_parallel=self.use_data_parallel,
+            )
 
         self.language_model = Qwen3MoeLLMForCausalLM(vllm_config=vllm_config,
                                                      prefix=maybe_prefix(
@@ -341,10 +345,14 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             config.vision_config.deepstack_visual_indexes
         ) if self.use_deepstack else 0
         # register buffer for deepstack
-        self.deepstack_input_embeds = [
-            torch.zeros(vllm_config.scheduler_config.max_num_batched_tokens,
-                        config.text_config.hidden_size)
-            for _ in range(self.deepstack_num_level)
-        ] if self.use_deepstack else None
+        if self.use_deepstack and self.visual is not None:
+            self.deepstack_input_embeds = [
+                torch.zeros(
+                    vllm_config.scheduler_config.max_num_batched_tokens,
+                    config.text_config.hidden_size)
+                for _ in range(self.deepstack_num_level)
+            ]
+        else:
+            self.deepstack_input_embeds = None
         self.visual_dim = config.vision_config.out_hidden_size
         self.multiscale_dim = self.visual_dim * self.deepstack_num_level