Gahow Wang
445e491123
third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:
vllm/v1/core/sched/scheduler.py:
Replace fatal assert with graceful skip when KV transfer callback
arrives for an already-aborted request during PD disaggregated serving.
Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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from transformers import AutoTokenizer
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from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
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from vllm.entrypoints.openai.engine.protocol import DeltaMessage
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from vllm.reasoning.deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser
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from vllm.reasoning.deepseek_v3_reasoning_parser import DeepSeekV3ReasoningParser
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from vllm.reasoning.identity_reasoning_parser import IdentityReasoningParser
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REASONING_MODEL_NAME = "deepseek-ai/DeepSeek-V3.1"
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@pytest.fixture(scope="module")
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def tokenizer():
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return AutoTokenizer.from_pretrained(REASONING_MODEL_NAME)
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@pytest.mark.parametrize(
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"thinking,expected_parser_type",
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[
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(True, DeepSeekR1ReasoningParser),
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(False, IdentityReasoningParser),
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],
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)
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def test_parser_selection(tokenizer, thinking, expected_parser_type):
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parser = DeepSeekV3ReasoningParser(
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tokenizer, chat_template_kwargs={"thinking": thinking}
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)
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assert isinstance(parser._parser, expected_parser_type)
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def test_identity_reasoning_parser_basic(tokenizer):
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parser = IdentityReasoningParser(tokenizer)
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# Test is_reasoning_end always returns True
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input_text = "This is some output"
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input_tokens = tokenizer.tokenize(input_text)
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input_ids = tokenizer.convert_tokens_to_ids(input_tokens)
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assert parser.is_reasoning_end(input_ids) is True
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assert parser.is_reasoning_end_streaming(input_ids, input_ids) is True
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# Test extract_content_ids returns all input_ids
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assert parser.extract_content_ids(input_ids) == input_ids
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# Test extract_reasoning returns (None, model_output)
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request = ChatCompletionRequest(model="test-model", messages=[], temperature=1.0)
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reasoning, content = parser.extract_reasoning(input_text, request)
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assert reasoning is None
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assert content == input_text
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# Test extract_reasoning_streaming returns DeltaMessage or None
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result = parser.extract_reasoning_streaming(
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previous_text="",
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current_text="Hello world",
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delta_text="Hello world",
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previous_token_ids=[],
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current_token_ids=input_ids,
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delta_token_ids=input_ids,
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)
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assert isinstance(result, DeltaMessage)
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assert result.content == "Hello world"
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# If delta_text is empty, should return None
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result_none = parser.extract_reasoning_streaming(
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previous_text="Hello world",
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current_text="Hello world",
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delta_text="",
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previous_token_ids=input_ids,
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current_token_ids=input_ids,
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delta_token_ids=[],
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)
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assert result_none is None
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