[HTTP Server] Make model param optional in request

tests/entrypoints/openai/test_chat.py

@@ -9,6 +9,7 @@
 import openai  # use the official client for correctness check
 import pytest
 import pytest_asyncio
+import requests
 import torch
 from openai import BadRequestError
 
@@ -996,3 +997,34 @@ async def test_long_seed(client: openai.AsyncOpenAI):
 
         assert ("greater_than_equal" in exc_info.value.message
                 or "less_than_equal" in exc_info.value.message)
+
+
+@pytest.mark.asyncio
+async def test_http_chat_wo_model_name(server: RemoteOpenAIServer):
+    url = f"http://localhost:{server.port}/v1/chat/completions"
+    headers = {
+        "Content-Type": "application/json",
+    }
+    data = {
+        # model_name is avoided here.
+        "messages": [{
+            "role": "system",
+            "content": "You are a helpful assistant."
+        }, {
+            "role": "user",
+            "content": "what is 1+1?"
+        }],
+        "max_tokens":
+        5
+    }
+
+    response = requests.post(url, headers=headers, json=data)
+    response_data = response.json()
+    print(response_data)
+
+    choice = response_data.get("choices")[0]
+    message = choice.get("message")
+    assert message is not None
+    content = message.get("content")
+    assert content is not None
+    assert len(content) > 0

vllm/entrypoints/openai/protocol.py

@@ -213,7 +213,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
     # Ordered by official OpenAI API documentation
     # https://platform.openai.com/docs/api-reference/chat/create
     messages: List[ChatCompletionMessageParam]
-    model: str
+    model: Optional[str] = None
     frequency_penalty: Optional[float] = 0.0
     logit_bias: Optional[Dict[str, float]] = None
     logprobs: Optional[bool] = False
@@ -642,7 +642,7 @@ def check_generation_prompt(cls, data):
 class CompletionRequest(OpenAIBaseModel):
     # Ordered by official OpenAI API documentation
     # https://platform.openai.com/docs/api-reference/completions/create
-    model: str
+    model: Optional[str] = None
     prompt: Union[List[int], List[List[int]], str, List[str]]
     best_of: Optional[int] = None
     echo: Optional[bool] = False
@@ -907,7 +907,7 @@ def validate_stream_options(cls, data):
 class EmbeddingCompletionRequest(OpenAIBaseModel):
     # Ordered by official OpenAI API documentation
     # https://platform.openai.com/docs/api-reference/embeddings
-    model: str
+    model: Optional[str] = None
     input: Union[List[int], List[List[int]], str, List[str]]
     encoding_format: Literal["float", "base64"] = "float"
     dimensions: Optional[int] = None
@@ -939,7 +939,7 @@ def to_pooling_params(self):
 
 
 class EmbeddingChatRequest(OpenAIBaseModel):
-    model: str
+    model: Optional[str] = None
     messages: List[ChatCompletionMessageParam]
 
     encoding_format: Literal["float", "base64"] = "float"
@@ -1007,7 +1007,7 @@ def to_pooling_params(self):
 
 
 class ScoreRequest(OpenAIBaseModel):
-    model: str
+    model: Optional[str] = None
     text_1: Union[List[str], str]
     text_2: Union[List[str], str]
     truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
@@ -1031,7 +1031,7 @@ def to_pooling_params(self):
 
 
 class RerankRequest(OpenAIBaseModel):
-    model: str
+    model: Optional[str] = None
     query: str
     documents: List[str]
     top_n: int = Field(default_factory=lambda: 0)
@@ -1345,7 +1345,7 @@ class BatchRequestOutput(OpenAIBaseModel):
 
 
 class TokenizeCompletionRequest(OpenAIBaseModel):
-    model: str
+    model: Optional[str] = None
     prompt: str
 
     add_special_tokens: bool = Field(
@@ -1357,7 +1357,7 @@ class TokenizeCompletionRequest(OpenAIBaseModel):
 
 
 class TokenizeChatRequest(OpenAIBaseModel):
-    model: str
+    model: Optional[str] = None
     messages: List[ChatCompletionMessageParam]
 
     add_generation_prompt: bool = Field(
@@ -1423,7 +1423,7 @@ class TokenizeResponse(OpenAIBaseModel):
 
 
 class DetokenizeRequest(OpenAIBaseModel):
-    model: str
+    model: Optional[str] = None
     tokens: List[int]
 
 
@@ -1456,7 +1456,7 @@ class TranscriptionRequest(OpenAIBaseModel):
     formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.
     """
 
-    model: str
+    model: Optional[str] = None
     """ID of the model to use.
     """
 

vllm/entrypoints/openai/serving_chat.py

@@ -141,7 +141,7 @@ async def create_chat_completion(
                 prompt_adapter_request,
             ) = self._maybe_get_adapters(request)
 
-            model_name = self.models.model_name(lora_request)
+            model_name = self._get_model_name(request.model, lora_request)
 
             tokenizer = await self.engine_client.get_tokenizer(lora_request)
 

vllm/entrypoints/openai/serving_completion.py

@@ -166,7 +166,7 @@ async def create_completion(
 
         result_generator = merge_async_iterators(*generators)
 
-        model_name = self.models.model_name(lora_request)
+        model_name = self._get_model_name(request.model, 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_embedding.py

@@ -83,7 +83,7 @@ async def create_embedding(
             return self.create_error_response(
                 "dimensions is currently not supported")
 
-        model_name = request.model
+        model_name = self._get_model_name(request.model)
         request_id = f"embd-{self._base_request_id(raw_request)}"
         created_time = int(time.time())
 

vllm/entrypoints/openai/serving_engine.py

@@ -523,5 +523,16 @@ def _get_decoded_token(logprob: Logprob,
             return logprob.decoded_token
         return tokenizer.decode(token_id)
 
-    def _is_model_supported(self, model_name):
+    def _is_model_supported(self, model_name) -> bool:
+        if not model_name:
+            return True
         return self.models.is_base_model(model_name)
+
+    def _get_model_name(self,
+                        model_name: Optional[str] = None,
+                        lora_request: Optional[LoRARequest] = None) -> str:
+        if lora_request:
+            return lora_request.lora_name
+        if model_name is None:
+            return self.models.base_model_paths[0].name
+        return model_name

vllm/entrypoints/openai/serving_models.py

@@ -95,7 +95,7 @@ async def init_static_loras(self):
             if isinstance(load_result, ErrorResponse):
                 raise ValueError(load_result.message)
 
-    def is_base_model(self, model_name):
+    def is_base_model(self, model_name) -> bool:
         return any(model.name == model_name for model in self.base_model_paths)
 
     def model_name(self, lora_request: Optional[LoRARequest] = None) -> str:

vllm/entrypoints/openai/serving_pooling.py

@@ -79,7 +79,7 @@ async def create_pooling(
             return self.create_error_response(
                 "dimensions is currently not supported")
 
-        model_name = request.model
+        model_name = self._get_model_name(request.model)
         request_id = f"pool-{self._base_request_id(raw_request)}"
         created_time = int(time.time())
 

vllm/entrypoints/openai/serving_score.py

@@ -318,7 +318,7 @@ async def create_score(
                 final_res_batch,
                 request_id,
                 created_time,
-                request.model,
+                self._get_model_name(request.model),
             )
         except asyncio.CancelledError:
             return self.create_error_response("Client disconnected")
@@ -358,7 +358,7 @@ async def do_rerank(
                 request.truncate_prompt_tokens,
             )
             return self.request_output_to_rerank_response(
-                final_res_batch, request_id, request.model, documents, top_n)
+                final_res_batch, request_id, self._get_model_name(request.model), documents, top_n)
         except asyncio.CancelledError:
             return self.create_error_response("Client disconnected")
         except ValueError as e: