4c4b6f7a97
Signed-off-by: Daniel Mescheder <dmesch@amazon.com> Co-authored-by: Daniel Mescheder <dmesch@amazon.com>
114 lines
3.8 KiB
Python
114 lines
3.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Unit tests for ResponsesRequest.to_sampling_params() parameter mapping."""
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import pytest
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from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
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class TestResponsesRequestSamplingParams:
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"""Test that ResponsesRequest correctly maps parameters to SamplingParams."""
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def test_basic_sampling_params(self):
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"""Test basic sampling parameters are correctly mapped."""
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request = ResponsesRequest(
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model="test-model",
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input="test input",
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temperature=0.8,
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top_p=0.95,
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top_k=50,
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max_output_tokens=100,
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)
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sampling_params = request.to_sampling_params(default_max_tokens=1000)
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assert sampling_params.temperature == 0.8
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assert sampling_params.top_p == 0.95
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assert sampling_params.top_k == 50
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assert sampling_params.max_tokens == 100
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def test_extra_sampling_params(self):
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"""Test extra sampling parameters are correctly mapped."""
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request = ResponsesRequest(
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model="test-model",
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input="test input",
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repetition_penalty=1.2,
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seed=42,
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stop=["END", "STOP"],
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ignore_eos=True,
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vllm_xargs={"custom": "value"},
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)
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sampling_params = request.to_sampling_params(default_max_tokens=1000)
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assert sampling_params.repetition_penalty == 1.2
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assert sampling_params.seed == 42
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assert sampling_params.stop == ["END", "STOP"]
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assert sampling_params.ignore_eos is True
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assert sampling_params.extra_args == {"custom": "value"}
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def test_stop_string_conversion(self):
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"""Test that single stop string is converted to list."""
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request = ResponsesRequest(
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model="test-model",
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input="test input",
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stop="STOP",
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)
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sampling_params = request.to_sampling_params(default_max_tokens=1000)
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assert sampling_params.stop == ["STOP"]
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def test_default_values(self):
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"""Test default values for optional parameters."""
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request = ResponsesRequest(
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model="test-model",
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input="test input",
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)
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sampling_params = request.to_sampling_params(default_max_tokens=1000)
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assert sampling_params.repetition_penalty == 1.0 # None → 1.0
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assert sampling_params.stop == [] # Empty list
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assert sampling_params.extra_args == {} # Empty dict
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def test_seed_bounds_validation(self):
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"""Test that seed values outside torch.long bounds are rejected."""
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import torch
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from pydantic import ValidationError
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# Test seed below minimum
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with pytest.raises(ValidationError) as exc_info:
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ResponsesRequest(
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model="test-model",
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input="test input",
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seed=torch.iinfo(torch.long).min - 1,
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)
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assert "greater_than_equal" in str(exc_info.value).lower()
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# Test seed above maximum
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with pytest.raises(ValidationError) as exc_info:
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ResponsesRequest(
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model="test-model",
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input="test input",
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seed=torch.iinfo(torch.long).max + 1,
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)
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assert "less_than_equal" in str(exc_info.value).lower()
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# Test valid seed at boundaries
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request_min = ResponsesRequest(
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model="test-model",
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input="test input",
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seed=torch.iinfo(torch.long).min,
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)
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assert request_min.seed == torch.iinfo(torch.long).min
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request_max = ResponsesRequest(
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model="test-model",
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input="test input",
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seed=torch.iinfo(torch.long).max,
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)
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assert request_max.seed == torch.iinfo(torch.long).max
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