[Frontend] Add sampling parameters to Responses API (#32609)

Signed-off-by: Daniel Mescheder <dmesch@amazon.com>
Co-authored-by: Daniel Mescheder <dmesch@amazon.com>

tests/entrypoints/openai/responses/test_sampling_params.py

@@ -0,0 +1,113 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Unit tests for ResponsesRequest.to_sampling_params() parameter mapping."""
+
+import pytest
+
+from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
+
+
+class TestResponsesRequestSamplingParams:
+    """Test that ResponsesRequest correctly maps parameters to SamplingParams."""
+
+    def test_basic_sampling_params(self):
+        """Test basic sampling parameters are correctly mapped."""
+        request = ResponsesRequest(
+            model="test-model",
+            input="test input",
+            temperature=0.8,
+            top_p=0.95,
+            top_k=50,
+            max_output_tokens=100,
+        )
+
+        sampling_params = request.to_sampling_params(default_max_tokens=1000)
+
+        assert sampling_params.temperature == 0.8
+        assert sampling_params.top_p == 0.95
+        assert sampling_params.top_k == 50
+        assert sampling_params.max_tokens == 100
+
+    def test_extra_sampling_params(self):
+        """Test extra sampling parameters are correctly mapped."""
+        request = ResponsesRequest(
+            model="test-model",
+            input="test input",
+            repetition_penalty=1.2,
+            seed=42,
+            stop=["END", "STOP"],
+            ignore_eos=True,
+            vllm_xargs={"custom": "value"},
+        )
+
+        sampling_params = request.to_sampling_params(default_max_tokens=1000)
+
+        assert sampling_params.repetition_penalty == 1.2
+        assert sampling_params.seed == 42
+        assert sampling_params.stop == ["END", "STOP"]
+        assert sampling_params.ignore_eos is True
+        assert sampling_params.extra_args == {"custom": "value"}
+
+    def test_stop_string_conversion(self):
+        """Test that single stop string is converted to list."""
+        request = ResponsesRequest(
+            model="test-model",
+            input="test input",
+            stop="STOP",
+        )
+
+        sampling_params = request.to_sampling_params(default_max_tokens=1000)
+
+        assert sampling_params.stop == ["STOP"]
+
+    def test_default_values(self):
+        """Test default values for optional parameters."""
+        request = ResponsesRequest(
+            model="test-model",
+            input="test input",
+        )
+
+        sampling_params = request.to_sampling_params(default_max_tokens=1000)
+
+        assert sampling_params.repetition_penalty == 1.0  # None → 1.0
+        assert sampling_params.stop == []  # Empty list
+        assert sampling_params.extra_args == {}  # Empty dict
+
+    def test_seed_bounds_validation(self):
+        """Test that seed values outside torch.long bounds are rejected."""
+        import torch
+        from pydantic import ValidationError
+
+        # Test seed below minimum
+        with pytest.raises(ValidationError) as exc_info:
+            ResponsesRequest(
+                model="test-model",
+                input="test input",
+                seed=torch.iinfo(torch.long).min - 1,
+            )
+        assert "greater_than_equal" in str(exc_info.value).lower()
+
+        # Test seed above maximum
+        with pytest.raises(ValidationError) as exc_info:
+            ResponsesRequest(
+                model="test-model",
+                input="test input",
+                seed=torch.iinfo(torch.long).max + 1,
+            )
+        assert "less_than_equal" in str(exc_info.value).lower()
+
+        # Test valid seed at boundaries
+        request_min = ResponsesRequest(
+            model="test-model",
+            input="test input",
+            seed=torch.iinfo(torch.long).min,
+        )
+        assert request_min.seed == torch.iinfo(torch.long).min
+
+        request_max = ResponsesRequest(
+            model="test-model",
+            input="test input",
+            seed=torch.iinfo(torch.long).max,
+        )
+        assert request_max.seed == torch.iinfo(torch.long).max

tests/entrypoints/openai/responses/test_simple.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-
 import pytest
 import pytest_asyncio
 from openai import OpenAI
@@ -147,3 +146,27 @@ async def test_max_tokens(client: OpenAI, model_name: str):
     assert response is not None
     assert response.status == "incomplete"
     assert response.incomplete_details.reason == "max_output_tokens"
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_extra_sampling_params(client: OpenAI, model_name: str):
+    """Test that extra sampling parameters are accepted and work."""
+    # Test with multiple sampling parameters - just verify they're accepted
+    response = await client.responses.create(
+        model=model_name,
+        input="Write a short sentence",
+        max_output_tokens=50,
+        temperature=0.7,
+        top_p=0.9,
+        extra_body={
+            "top_k": 40,
+            "repetition_penalty": 1.2,
+            "seed": 42,
+        },
+    )
+
+    # Verify request succeeded and parameters were accepted
+    assert response.status in ["completed", "incomplete"]
+    assert len(response.output) > 0
+    assert response.output[0].content[0].text  # Has text output

vllm/entrypoints/openai/responses/protocol.py

@@ -6,6 +6,7 @@
 import time
 from typing import Any, Literal, TypeAlias
 
+import torch
 from openai.types.responses import (
     ResponseCodeInterpreterCallCodeDeltaEvent,
     ResponseCodeInterpreterCallCodeDoneEvent,
@@ -77,6 +78,8 @@ from vllm.utils import random_uuid
 
 logger = init_logger(__name__)
 
+_LONG_INFO = torch.iinfo(torch.long)
+
 
 class InputTokensDetails(OpenAIBaseModel):
     cached_tokens: int
@@ -230,6 +233,18 @@ class ResponsesRequest(OpenAIBaseModel):
     # this cannot be used in conjunction with previous_response_id
     # TODO: consider supporting non harmony messages as well
     previous_input_messages: list[OpenAIHarmonyMessage | dict] | None = None
+
+    repetition_penalty: float | None = None
+    seed: int | None = Field(None, ge=_LONG_INFO.min, le=_LONG_INFO.max)
+    stop: str | list[str] | None = []
+    ignore_eos: bool = False
+    vllm_xargs: dict[str, str | int | float | list[str | int | float]] | None = Field(
+        default=None,
+        description=(
+            "Additional request parameters with (list of) string or "
+            "numeric values, used by custom extensions."
+        ),
+    )
     # --8<-- [end:responses-extra-params]
 
     def build_chat_params(
@@ -297,6 +312,10 @@ class ResponsesRequest(OpenAIBaseModel):
             top_k = default_sampling_params.get(
                 "top_k", self._DEFAULT_SAMPLING_PARAMS["top_k"]
             )
+
+        if (repetition_penalty := self.repetition_penalty) is None:
+            repetition_penalty = default_sampling_params.get("repetition_penalty", 1.0)
+
         stop_token_ids = default_sampling_params.get("stop_token_ids")
 
         # Structured output
@@ -313,7 +332,10 @@ class ResponsesRequest(OpenAIBaseModel):
             elif response_format.type == "json_object":
                 raise NotImplementedError("json_object is not supported")
 
-        # TODO: add more parameters
+        stop = self.stop if self.stop else []
+        if isinstance(stop, str):
+            stop = [stop]
+
         return SamplingParams.from_optional(
             temperature=temperature,
             top_p=top_p,
@@ -321,11 +343,16 @@ class ResponsesRequest(OpenAIBaseModel):
             max_tokens=max_tokens,
             logprobs=self.top_logprobs if self.is_include_output_logprobs() else None,
             stop_token_ids=stop_token_ids,
+            stop=stop,
+            repetition_penalty=repetition_penalty,
+            seed=self.seed,
+            ignore_eos=self.ignore_eos,
             output_kind=(
                 RequestOutputKind.DELTA if self.stream else RequestOutputKind.FINAL_ONLY
             ),
             structured_outputs=structured_outputs,
             logit_bias=self.logit_bias,
+            extra_args=self.vllm_xargs or {},
             skip_clone=True,  # Created fresh per request, safe to skip clone
             skip_special_tokens=self.skip_special_tokens,
             include_stop_str_in_output=self.include_stop_str_in_output,