[qwen-omni] fix training

docs/source/en/quantization/torchao.md

@@ -112,8 +112,6 @@ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
 output = quantized_model.generate(**input_ids, max_new_tokens=10, cache_implementation="static")
 print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
-</hfoption>
-
 </hfoption>
 <hfoption id="int4-weight-only">
 
@@ -332,6 +330,7 @@ quantized_model.push_to_hub(f"{USER_ID}/llama3-8b-int4wo-128", safe_serializatio
 tokenizer.push_to_hub(f"{USER_ID}/llama3-8b-int4wo-128")
 ```
 </hfoption>
+</hfoptions>
 
 
 ## Loading quantized models

src/transformers/models/qwen2_5_omni/configuration_qwen2_5_omni.py

@@ -1045,5 +1045,20 @@ def __init__(
 
         super().__init__(**kwargs)
 
+    @classmethod
+    def get_text_config(self, decoder=False) -> "PretrainedConfig":
+        """
+        Returns the config that is meant to be used with text IO. On most models, it is the original config instance
+        itself. On specific composite models, it is under a set of valid names.
+
+        Args:
+            decoder (`Optional[bool]`, *optional*, defaults to `False`):
+                If set to `True`, then only search for decoder config names.
+        """
+        # Overriden for deeply nested config like Qwen2-Omni. We don't have any omni model
+        # except for Qwen yet. This has to be generalized if more deeply nested configs are
+        # added. NOTE: currently method used only by vLLM
+        return self.thinker_config.get_text_config()
+
 
 __all__ = ["Qwen2_5OmniConfig", "Qwen2_5OmniThinkerConfig", "Qwen2_5OmniTalkerConfig", "Qwen2_5OmniToken2WavConfig"]

src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py

@@ -2503,7 +2503,9 @@ def forward(
 
         loss = None
         if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size)
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.get_text_config().vocab_size
+            )
 
         if not return_dict:
             output = (logits,) + outputs
@@ -4384,6 +4386,7 @@ def __init__(self, config):
         self.speaker_map = {}
         if config.enable_audio_output:
             self.enable_talker()
+        self.post_init()
 
     def enable_talker(self):
         self.talker = Qwen2_5OmniTalkerForConditionalGeneration(self.config.talker_config)

src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py

@@ -1030,6 +1030,21 @@ def __init__(
 
         super().__init__(**kwargs)
 
+    @classmethod
+    def get_text_config(self, decoder=False) -> "PretrainedConfig":
+        """
+        Returns the config that is meant to be used with text IO. On most models, it is the original config instance
+        itself. On specific composite models, it is under a set of valid names.
+
+        Args:
+            decoder (`Optional[bool]`, *optional*, defaults to `False`):
+                If set to `True`, then only search for decoder config names.
+        """
+        # Overriden for deeply nested config like Qwen2-Omni. We don't have any omni model
+        # except for Qwen yet. This has to be generalized if more deeply nested configs are
+        # added. NOTE: currently method used only by vLLM
+        return self.thinker_config.get_text_config()
+
 
 class Qwen2_5OmniPreTrainedModel(Qwen2_5_VLPreTrainedModel):
     config_class = Qwen2_5OmniConfig
@@ -2463,7 +2478,9 @@ def forward(
 
         loss = None
         if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size)
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.get_text_config().vocab_size
+            )
 
         if not return_dict:
             output = (logits,) + outputs
@@ -4053,6 +4070,7 @@ def __init__(self, config):
         self.speaker_map = {}
         if config.enable_audio_output:
             self.enable_talker()
+        self.post_init()
 
     def enable_talker(self):
         self.talker = Qwen2_5OmniTalkerForConditionalGeneration(self.config.talker_config)