Add vLLM v0.18.1 source tree with KV transfer abort fix

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>

third_party/vllm/tests/reasoning/test_deepseekv3_reasoning_parser.py

@@ -0,0 +1,76 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+from transformers import AutoTokenizer
+
+from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
+from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.reasoning.deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser
+from vllm.reasoning.deepseek_v3_reasoning_parser import DeepSeekV3ReasoningParser
+from vllm.reasoning.identity_reasoning_parser import IdentityReasoningParser
+
+REASONING_MODEL_NAME = "deepseek-ai/DeepSeek-V3.1"
+
+
+@pytest.fixture(scope="module")
+def tokenizer():
+    return AutoTokenizer.from_pretrained(REASONING_MODEL_NAME)
+
+
+@pytest.mark.parametrize(
+    "thinking,expected_parser_type",
+    [
+        (True, DeepSeekR1ReasoningParser),
+        (False, IdentityReasoningParser),
+    ],
+)
+def test_parser_selection(tokenizer, thinking, expected_parser_type):
+    parser = DeepSeekV3ReasoningParser(
+        tokenizer, chat_template_kwargs={"thinking": thinking}
+    )
+
+    assert isinstance(parser._parser, expected_parser_type)
+
+
+def test_identity_reasoning_parser_basic(tokenizer):
+    parser = IdentityReasoningParser(tokenizer)
+
+    # Test is_reasoning_end always returns True
+    input_text = "This is some output"
+    input_tokens = tokenizer.tokenize(input_text)
+    input_ids = tokenizer.convert_tokens_to_ids(input_tokens)
+    assert parser.is_reasoning_end(input_ids) is True
+    assert parser.is_reasoning_end_streaming(input_ids, input_ids) is True
+
+    # Test extract_content_ids returns all input_ids
+    assert parser.extract_content_ids(input_ids) == input_ids
+
+    # Test extract_reasoning returns (None, model_output)
+    request = ChatCompletionRequest(model="test-model", messages=[], temperature=1.0)
+    reasoning, content = parser.extract_reasoning(input_text, request)
+    assert reasoning is None
+    assert content == input_text
+
+    # Test extract_reasoning_streaming returns DeltaMessage or None
+    result = parser.extract_reasoning_streaming(
+        previous_text="",
+        current_text="Hello world",
+        delta_text="Hello world",
+        previous_token_ids=[],
+        current_token_ids=input_ids,
+        delta_token_ids=input_ids,
+    )
+    assert isinstance(result, DeltaMessage)
+    assert result.content == "Hello world"
+
+    # If delta_text is empty, should return None
+    result_none = parser.extract_reasoning_streaming(
+        previous_text="Hello world",
+        current_text="Hello world",
+        delta_text="",
+        previous_token_ids=input_ids,
+        current_token_ids=input_ids,
+        delta_token_ids=[],
+    )
+    assert result_none is None