[WIP] Support qwen3-omni

lmdeploy/archs.py

@@ -110,9 +110,9 @@ def check_vl_llm(backend: str, config: dict) -> bool:
         'InternVLChatModel', 'MiniCPMV', 'LlavaForConditionalGeneration', 'LlavaNextForConditionalGeneration',
         'Phi3VForCausalLM', 'Qwen2VLForConditionalGeneration', 'Qwen2_5_VLForConditionalGeneration',
         'Qwen3VLForConditionalGeneration', 'Qwen3VLMoeForConditionalGeneration', 'Qwen3_5ForConditionalGeneration',
-        'Qwen3_5MoeForConditionalGeneration', 'MllamaForConditionalGeneration', 'MolmoForCausalLM',
-        'Gemma3ForConditionalGeneration', 'Llama4ForConditionalGeneration', 'InternVLForConditionalGeneration',
-        'InternS1ForConditionalGeneration', 'InternS1ProForConditionalGeneration',
+        'Qwen3_5MoeForConditionalGeneration', 'Qwen3OmniMoeForConditionalGeneration', 'MllamaForConditionalGeneration',
+        'MolmoForCausalLM', 'Gemma3ForConditionalGeneration', 'Llama4ForConditionalGeneration',
+        'InternVLForConditionalGeneration', 'InternS1ForConditionalGeneration', 'InternS1ProForConditionalGeneration',
         'InternS1_1_ForConditionalGeneration', 'Glm4vForConditionalGeneration'
     ])
     if arch == 'QWenLMHeadModel' and 'visual' in config:

lmdeploy/model.py

@@ -688,8 +688,19 @@ class HFChatTemplate(BaseChatTemplate):
     def __init__(self, model_path: str = '', **kwargs):
         self.model_path = model_path
         try:
-            from transformers import AutoTokenizer
+            from transformers import AutoProcessor, AutoTokenizer
             self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+            # Some tokenizers do not have chat_template, in this case try to get chat_template from processor
+            # If this still does not work, fallback to BaseChatTemplate.
+            if getattr(self.tokenizer, 'chat_template', None) is None:
+                try:
+                    processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
+                    self.tokenizer.chat_template = getattr(processor, 'chat_template', None)
+                except Exception as e:
+                    logger.warning(f'Failed to load processor from {model_path} for chat template. '
+                                   f'Fallback to tokenizer only. Error: {e}')
+
             # Verify if the model can perform apply_chat_template with different roles.
             self.user_start, self.user_end, _, _ = self._user_instruction()
             self.assistant_start, self.assistant_end, _, _ = self._assistant_instruction()

lmdeploy/pytorch/configurations/qwen3_omni.py

@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .builder import AutoModelConfigBuilder
+from .default import DefaultModelConfigBuilder
+
+
+class Qwen3OmniModelConfigBuilder(AutoModelConfigBuilder):
+
+    @classmethod
+    def condition(cls, hf_config):
+        """config."""
+        return hf_config.model_type == 'qwen3_omni_moe'
+
+    @classmethod
+    def build(cls, hf_config, model_path: str = None, **kwargs):
+        """build."""
+        cfg = DefaultModelConfigBuilder.build(hf_config.thinker_config.text_config, model_path, **kwargs)
+        cfg.hf_config = hf_config
+        cfg.use_mrope = True
+        return cfg

lmdeploy/pytorch/models/module_map.py

@@ -190,6 +190,13 @@
     'Qwen3_5MTPModel': f'{LMDEPLOY_PYTORCH_MODEL_PATH}.qwen3_5_mtp.Qwen3_5MTPModel',
 })
 
+# qwen3 omni moe thinker
+# only support thinker module, so map to Qwen3OmniMoeThinkerForConditionalGeneration
+MODULE_MAP.update({
+    'Qwen3OmniMoeForConditionalGeneration':
+    f'{LMDEPLOY_PYTORCH_MODEL_PATH}.qwen3_omni_moe_thinker.Qwen3OmniMoeThinkerForConditionalGeneration',
+})
+
 # starcoder2
 MODULE_MAP.update({
     'Starcoder2ForCausalLM': f'{LMDEPLOY_PYTORCH_MODEL_PATH}.starcoder2.Starcoder2ForCausalLM',