[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