[Frontend] Support min_p in the Responses API (#48084)

Signed-off-by: sungbin1015 <sbin@solbox.com>
Co-authored-by: Flora Feng <4florafeng@gmail.com>

docs/serving/online_serving/openai_compatible_server.md

@@ -204,6 +204,12 @@ To get prompt token IDs before generation, use
 a self-contained Responses request without inference and returns the prompt
 token IDs, so a separate `/tokenize` call is not needed.
 
+#### Sampling parameters
+
+In addition to the OpenAI request fields, the Responses API forwards vLLM
+[sampling parameters](../../api/README.md#inference-parameters) such as `top_k`,
+`min_p`, `repetition_penalty`, `seed`, `stop`, and `ignore_eos` to the engine.
+
 #### Extra parameters
 
 The following extra parameters in the request object are supported:

tests/benchmarks/test_responses_request_func.py

@@ -183,6 +183,7 @@ def test_request_payload_is_accepted_by_the_server_schema():
         "temperature": 0.7,
         "top_p": 0.9,
         "top_k": 20,
+        "min_p": 0.05,
         "frequency_penalty": 0.2,
         "presence_penalty": 0.1,
         "repetition_penalty": 1.05,
@@ -217,9 +218,6 @@ def test_request_payload_is_accepted_by_the_server_schema():
     assert set(seen["payload"]) <= set(ResponsesRequest.model_fields)
     ResponsesRequest.model_validate(seen["payload"])
 
-    # `vllm bench serve` rejects --min-p for this backend for the same reason.
-    assert "min_p" not in ResponsesRequest.model_fields
-
 
 def test_latency_accounting_matches_the_other_endpoints():
     """`latency - ttft` must equal `sum(itl)`.

tests/entrypoints/openai/responses/test_sampling_params.py

@@ -29,6 +29,7 @@ class TestResponsesRequestSamplingParams:
             temperature=0.8,
             top_p=0.95,
             top_k=50,
+            min_p=0.1,
             max_output_tokens=100,
         )
 
@@ -37,8 +38,20 @@ class TestResponsesRequestSamplingParams:
         assert sampling_params.temperature == 0.8
         assert sampling_params.top_p == 0.95
         assert sampling_params.top_k == 50
+        assert sampling_params.min_p == 0.1
         assert sampling_params.max_tokens == 100
 
+    def test_min_p_default(self):
+        """Test that min_p defaults to 0.0 when unset (parity with top_k)."""
+        request = ResponsesRequest(
+            model="test-model",
+            input="test input",
+        )
+
+        sampling_params = request.to_sampling_params(default_max_tokens=1000)
+
+        assert sampling_params.min_p == 0.0
+
     def test_extra_sampling_params(self):
         """Test extra sampling parameters are correctly mapped."""
         request = ResponsesRequest(

vllm/benchmarks/serve.py

@@ -2196,11 +2196,6 @@ async def main_async(args: argparse.Namespace) -> dict[str, Any]:
                 "Sampling parameters are only supported by openai-compatible backends."
             )
 
-        # The Responses API accepts every sampling parameter above except
-        # min_p, which it would silently drop as an unknown field.
-        if args.backend == "openai-responses" and "min_p" in sampling_params:
-            raise ValueError("--min-p is not supported by the Responses API.")
-
         if "temperature" not in sampling_params:
             print(
                 "WARNING: vllm bench serve no longer sets temperature==0 (greedy) "

vllm/entrypoints/openai/responses/protocol.py

@@ -259,6 +259,7 @@ class ResponsesRequest(OpenAIBaseModel):
     top_logprobs: TopLogprobsParam = 0
     top_p: float | None = None
     top_k: int | None = None
+    min_p: float | None = None
     truncation: Literal["auto", "disabled"] | None = "disabled"
     user: str | None = None
     skip_special_tokens: bool = True
@@ -444,6 +445,7 @@ class ResponsesRequest(OpenAIBaseModel):
         "temperature": 1.0,
         "top_p": 1.0,
         "top_k": 0,
+        "min_p": 0.0,
     }
 
     def extract_structured_outputs(self) -> StructuredOutputsParams | None:
@@ -494,6 +496,10 @@ class ResponsesRequest(OpenAIBaseModel):
             top_k = default_sampling_params.get(
                 "top_k", self._DEFAULT_SAMPLING_PARAMS["top_k"]
             )
+        if (min_p := self.min_p) is None:
+            min_p = default_sampling_params.get(
+                "min_p", self._DEFAULT_SAMPLING_PARAMS["min_p"]
+            )
 
         if (repetition_penalty := self.repetition_penalty) is None:
             repetition_penalty = default_sampling_params.get("repetition_penalty", 1.0)
@@ -519,6 +525,7 @@ class ResponsesRequest(OpenAIBaseModel):
             watermarking=self.watermarking,
             top_p=top_p,
             top_k=top_k,
+            min_p=min_p,
             max_tokens=max_tokens,
             logprobs=self.top_logprobs if self.is_include_output_logprobs() else None,
             stop=stop,