Ensure Lora Adapter Requests Return Lora Model Name Instead of Base M…

…odel

vllm/entrypoints/openai/serving_chat.py

@@ -27,6 +27,7 @@
                                                     PromptAdapterPath)
 from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
 from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.sampling_params import BeamSearchParams, SamplingParams
 from vllm.sequence import Logprob
@@ -123,6 +124,8 @@ async def create_chat_completion(
                 prompt_adapter_request,
             ) = self._maybe_get_adapters(request)
 
+            model_name = self._get_model_name(lora_request)
+
             tokenizer = await self.engine_client.get_tokenizer(lora_request)
 
             tool_parser = self.tool_parser
@@ -238,12 +241,12 @@ async def create_chat_completion(
         # Streaming response
         if request.stream:
             return self.chat_completion_stream_generator(
-                request, result_generator, request_id, conversation, tokenizer,
+                request, result_generator, request_id, model_name, conversation, tokenizer,
                 request_metadata)
 
         try:
             return await self.chat_completion_full_generator(
-                request, result_generator, request_id, conversation, tokenizer,
+                request, result_generator, request_id, model_name, conversation, tokenizer,
                 request_metadata)
         except ValueError as e:
             # TODO: Use a vllm-specific Validation Error
@@ -259,11 +262,11 @@ async def chat_completion_stream_generator(
         request: ChatCompletionRequest,
         result_generator: AsyncIterator[RequestOutput],
         request_id: str,
+        model_name: str,
         conversation: List[ConversationMessage],
         tokenizer: AnyTokenizer,
         request_metadata: RequestResponseMetadata,
     ) -> AsyncGenerator[str, None]:
-        model_name = self.base_model_paths[0].name
         created_time = int(time.time())
         chunk_object_type: Final = "chat.completion.chunk"
         first_iteration = True
@@ -592,12 +595,12 @@ async def chat_completion_full_generator(
         request: ChatCompletionRequest,
         result_generator: AsyncIterator[RequestOutput],
         request_id: str,
+        model_name: str,
         conversation: List[ConversationMessage],
         tokenizer: AnyTokenizer,
         request_metadata: RequestResponseMetadata,
     ) -> Union[ErrorResponse, ChatCompletionResponse]:
 
-        model_name = self.base_model_paths[0].name
         created_time = int(time.time())
         final_res: Optional[RequestOutput] = None
 

vllm/entrypoints/openai/serving_completion.py

@@ -85,7 +85,6 @@ async def create_completion(
             return self.create_error_response(
                 "suffix is not currently supported")
 
-        model_name = self.base_model_paths[0].name
         request_id = f"cmpl-{self._base_request_id(raw_request)}"
         created_time = int(time.time())
 
@@ -162,6 +161,7 @@ async def create_completion(
         result_generator = merge_async_iterators(
             *generators, is_cancelled=raw_request.is_disconnected)
 
+        model_name = self._get_model_name(lora_request)
         num_prompts = len(engine_prompts)
 
         # Similar to the OpenAI API, when n != best_of, we do not stream the

vllm/entrypoints/openai/serving_engine.py

@@ -661,3 +661,16 @@ async def unload_lora_adapter(
 
     def _is_model_supported(self, model_name):
         return any(model.name == model_name for model in self.base_model_paths)
+
+    def _get_model_name(self, lora: LoRARequest):
+        """
+        Returns the appropriate model name depending on the availability
+        and support of the LoRA or base model.
+        Parameters:
+        - lora: LoRARequest that contain a base_model_name.
+        Returns:
+        - str: The name of the base model or the first available model path.
+        """
+        if lora is not None:
+            return lora.lora_name
+        return self.base_model_paths[0].name