[VLM] Fix paligemma, fuyu and persimmon with transformers 4.45 : use config.text_config.vocab_size (#8707)

Author: janimo

vllm/model_executor/models/fuyu.py

@@ -229,7 +229,7 @@ def __init__(self,
         self.multimodal_config = multimodal_config
 
         self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
+        self.vocab_size = config.text_config.vocab_size
         self.image_token_id = _IMAGE_TOKEN_ID
         self.image_feature_size = config.patch_size**2 * config.num_channels
 

vllm/model_executor/models/paligemma.py

@@ -152,7 +152,8 @@ def __init__(self,
         self.unpadded_vocab_size = config.text_config.vocab_size
         logit_scale = getattr(config, "logit_scale", 1.0)
         self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
-                                                config.vocab_size, logit_scale)
+                                                config.text_config.vocab_size,
+                                                logit_scale)
         self.sampler = Sampler()
 
     def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:

vllm/model_executor/models/persimmon.py

@@ -213,10 +213,10 @@ def __init__(self,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
-        self.vocab_size = config.vocab_size
+        self.vocab_size = config.text_config.vocab_size
 
-        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
-                                                   config.hidden_size)
+        self.embed_tokens = VocabParallelEmbedding(
+            config.text_config.vocab_size, config.hidden_size)
         self.layers = nn.ModuleList([
             PersimmonDecoderLayer(config,
                                   cache_config=cache_config,
@@ -257,14 +257,14 @@ def __init__(self,
                  quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.config = config
-        self.vocab_size = config.vocab_size
+        self.vocab_size = config.text_config.vocab_size
         self.model = PersimmonModel(config,
                                     cache_config=cache_config,
                                     quant_config=quant_config)
-        self.lm_head = ParallelLMHead(config.vocab_size,
+        self.lm_head = ParallelLMHead(config.text_config.vocab_size,
                                       config.hidden_size,
                                       bias=False)
-        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.logits_processor = LogitsProcessor(config.text_config.vocab_size)
         self.sampler = Sampler()
 
     def forward(