[Bugfix] OpenAI entrypoint limits logprobs while ignoring server defined --max-logprobs (vllm-project#5312)

Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>

Author: 2 people

tests/entrypoints/test_openai_server.py

@@ -264,7 +264,9 @@ async def test_too_many_completion_logprobs(server, client: openai.AsyncOpenAI,
             prompt=[0, 0, 0, 0, 0],
             max_tokens=5,
             temperature=0.0,
-            logprobs=6,
+            # vLLM has higher default max_logprobs (20 instead of 5) to support
+            # both Completion API and Chat Completion API
+            logprobs=21,
         )
         ...
     with pytest.raises(
@@ -274,7 +276,9 @@ async def test_too_many_completion_logprobs(server, client: openai.AsyncOpenAI,
             prompt=[0, 0, 0, 0, 0],
             max_tokens=5,
             temperature=0.0,
-            logprobs=6,
+            # vLLM has higher default max_logprobs (20 instead of 5) to support
+            # both Completion API and Chat Completion API
+            logprobs=30,
             stream=True,
         )
         async for chunk in stream:

vllm/config.py

@@ -100,7 +100,7 @@ def __init__(
         enforce_eager: bool = False,
         max_context_len_to_capture: Optional[int] = None,
         max_seq_len_to_capture: Optional[int] = None,
-        max_logprobs: int = 5,
+        max_logprobs: int = 20,
         disable_sliding_window: bool = False,
         skip_tokenizer_init: bool = False,
         served_model_name: Optional[Union[str, List[str]]] = None,

vllm/engine/arg_utils.py

@@ -48,7 +48,7 @@ class EngineArgs:
     gpu_memory_utilization: float = 0.90
     max_num_batched_tokens: Optional[int] = None
     max_num_seqs: int = 256
-    max_logprobs: int = 5  # OpenAI default value
+    max_logprobs: int = 20  # Default value for OpenAI Chat Completions API
     disable_log_stats: bool = False
     revision: Optional[str] = None
     code_revision: Optional[str] = None

vllm/entrypoints/openai/protocol.py

@@ -322,9 +322,9 @@ def check_logprobs(cls, data):
                 raise ValueError(
                     "when using `top_logprobs`, `logprobs` must be set to true."
                 )
-            elif not 0 <= data["top_logprobs"] <= 20:
+            elif data["top_logprobs"] < 0:
                 raise ValueError(
-                    "`top_logprobs` must be a value in the interval [0, 20].")
+                    "`top_logprobs` must be a value a positive value.")
         return data
 
 
@@ -478,9 +478,8 @@ def check_guided_decoding_count(cls, data):
     @classmethod
     def check_logprobs(cls, data):
         if "logprobs" in data and data[
-                "logprobs"] is not None and not 0 <= data["logprobs"] <= 5:
-            raise ValueError(("if passed, `logprobs` must be a value",
-                              " in the interval [0, 5]."))
+                "logprobs"] is not None and not data["logprobs"] >= 0:
+            raise ValueError("if passed, `logprobs` must be a positive value.")
         return data
 
     @model_validator(mode="before")