[LoRA][1/N]Remove LoRA extra vocab (#28382)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

vllm/model_executor/models/granitemoehybrid.py

@@ -25,7 +25,6 @@ from vllm.model_executor.layers.mamba.mamba_utils import (
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
-    DEFAULT_VOCAB_PADDING_SIZE,
     ParallelLMHead,
     VocabParallelEmbedding,
 )
@@ -334,22 +333,15 @@ class GraniteMoeHybridModel(nn.Module):
         model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
-        lora_config = vllm_config.lora_config
 
         self.config = config
         self.quant_config = quant_config
-        lora_vocab = (
-            (lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
-            if lora_config
-            else 0
-        )
-        self.vocab_size = config.vocab_size + lora_vocab
-        self.org_vocab_size = config.vocab_size
+
+        self.vocab_size = config.vocab_size
 
         self.embed_tokens = VocabParallelEmbedding(
             self.vocab_size,
             config.hidden_size,
-            org_num_embeddings=config.vocab_size,
         )
         self.embedding_multiplier = config.embedding_multiplier
 
@@ -658,7 +650,7 @@ class GraniteMoeHybridForCausalLM(
         config = vllm_config.model_config.hf_config
         self.vllm_config = vllm_config
         self.model_config = vllm_config.model_config
-        lora_config = vllm_config.lora_config
+
         scheduler_config = vllm_config.scheduler_config
         self.quant_config = vllm_config.quant_config
         self.config = config
@@ -666,26 +658,17 @@ class GraniteMoeHybridForCausalLM(
         self.model = GraniteMoeHybridModel(
             vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
         )
-        self.unpadded_vocab_size = config.vocab_size
-        if lora_config:
-            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
 
         self.lm_head = ParallelLMHead(
-            self.unpadded_vocab_size,
+            config.vocab_size,
             config.hidden_size,
-            org_num_embeddings=config.vocab_size,
-            padding_size=DEFAULT_VOCAB_PADDING_SIZE
-            # We need bigger padding if using lora for kernel
-            # compatibility
-            if not lora_config
-            else lora_config.lora_vocab_padding_size,
             quant_config=self.quant_config,
             prefix=maybe_prefix(prefix, "lm_head"),
         )
         if config.tie_word_embeddings:
             self.lm_head.weight = self.model.embed_tokens.weight
         self.logits_processor = LogitsProcessor(
-            self.unpadded_vocab_size,
+            config.vocab_size,
             config.vocab_size,
             scale=1 / self.config.logits_scaling,
         )