[Core] Remove tokenizer group in vLLM (#24078)

Signed-off-by: Zhuohan Li <zhuohan123@gmail.com>

vllm/entrypoints/llm.py

@@ -301,23 +301,17 @@ class LLM:
         self.io_processor = get_io_processor(self.llm_engine.vllm_config,
                                              io_processor_plugin)
 
-    def get_tokenizer(
-        self,
-        lora_request: Optional[LoRARequest] = None,
-    ) -> AnyTokenizer:
-        return self.llm_engine.get_tokenizer_group().get_lora_tokenizer(
-            lora_request)
+    def get_tokenizer(self) -> AnyTokenizer:
+        return self.llm_engine.get_tokenizer()
 
     def set_tokenizer(self, tokenizer: AnyTokenizer) -> None:
-        tokenizer_group = self.llm_engine.get_tokenizer_group()
-
         # While CachedTokenizer is dynamic, have no choice but
         # compare class name. Misjudgment will arise from
         # user-defined tokenizer started with 'Cached'
         if tokenizer.__class__.__name__.startswith("Cached"):
-            tokenizer_group.tokenizer = tokenizer
+            self.llm_engine.tokenizer = tokenizer
         else:
-            tokenizer_group.tokenizer = get_cached_tokenizer(tokenizer)
+            self.llm_engine.tokenizer = get_cached_tokenizer(tokenizer)
 
     def get_default_sampling_params(self) -> SamplingParams:
         if self.default_sampling_params is None:
@@ -707,7 +701,6 @@ class LLM:
         self,
         messages: Union[list[ChatCompletionMessageParam],
                         list[list[ChatCompletionMessageParam]]],
-        lora_request: Optional[LoRARequest] = None,
         chat_template: Optional[str] = None,
         chat_template_content_format: ChatTemplateContentFormatOption = "auto",
         add_generation_prompt: bool = True,
@@ -739,7 +732,7 @@ class LLM:
                 cast(list[ChatCompletionMessageParam], messages)
             ]
 
-        tokenizer = self.get_tokenizer(lora_request)
+        tokenizer = self.get_tokenizer()
         model_config = self.llm_engine.get_model_config()
         resolved_content_format = resolve_chat_template_content_format(
             chat_template,
@@ -872,7 +865,6 @@ class LLM:
 
         prompts = self.preprocess_chat(
             messages=messages,
-            lora_request=lora_request,
             chat_template=chat_template,
             chat_template_content_format=chat_template_content_format,
             add_generation_prompt=add_generation_prompt,
@@ -1519,7 +1511,7 @@ class LLM:
     ):
         """
         Validate that if any multi-modal data is skipped (i.e. None),
-        then its corresponding UUID must be set. 
+        then its corresponding UUID must be set.
         """
         if multi_modal_data is None:
             return