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/renderers/test_mistral.py

@@ -0,0 +1,140 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import asyncio
+import time
+from dataclasses import dataclass
+from typing import Any
+from unittest.mock import Mock
+
+import pytest
+from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy
+
+from vllm.renderers import ChatParams
+from vllm.renderers.mistral import MistralRenderer, safe_apply_chat_template
+from vllm.tokenizers.mistral import MistralTokenizer
+
+MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.3"
+
+
+@dataclass
+class MockHFConfig:
+    model_type: str = "any"
+
+
+@dataclass
+class MockModelConfig:
+    runner_type = "generate"
+    model: str = MODEL_NAME
+    tokenizer: str = MODEL_NAME
+    trust_remote_code: bool = False
+    max_model_len: int = 100
+    tokenizer_revision = None
+    tokenizer_mode = "mistral"
+    hf_config = MockHFConfig()
+    encoder_config: dict[str, Any] | None = None
+    enable_prompt_embeds: bool = True
+    skip_tokenizer_init: bool = False
+    is_encoder_decoder: bool = False
+    is_multimodal_model: bool = False
+
+
+@dataclass
+class MockParallelConfig:
+    _api_process_rank: int = 0
+
+
+@dataclass
+class MockVllmConfig:
+    model_config: MockModelConfig
+    parallel_config: MockParallelConfig
+
+
+@pytest.mark.asyncio
+async def test_async_mistral_tokenizer_does_not_block_event_loop():
+    expected_tokens = [1, 2, 3]
+
+    # Mock the blocking version to sleep
+    def mocked_apply_chat_template(*_args, **_kwargs):
+        time.sleep(2)
+        return expected_tokens
+
+    mock_model_config = MockModelConfig(skip_tokenizer_init=True)
+    mock_tokenizer = Mock(spec=MistralTokenizer)
+    mock_tokenizer.apply_chat_template = mocked_apply_chat_template
+    mock_renderer = MistralRenderer(
+        MockVllmConfig(mock_model_config, parallel_config=MockParallelConfig()),
+        tokenizer=mock_tokenizer,
+    )
+
+    task = mock_renderer.render_messages_async([], ChatParams())
+
+    # Ensure the event loop is not blocked
+    blocked_count = 0
+    for _i in range(20):  # Check over ~2 seconds
+        start = time.perf_counter()
+        await asyncio.sleep(0)
+        elapsed = time.perf_counter() - start
+
+        # an overly generous elapsed time for slow machines
+        if elapsed >= 0.5:
+            blocked_count += 1
+
+        await asyncio.sleep(0.1)
+
+    # Ensure task completes
+    _, prompt = await task
+    assert prompt["prompt_token_ids"] == expected_tokens, (
+        "Mocked blocking tokenizer was not called"
+    )
+    assert blocked_count == 0, "Event loop blocked during tokenization"
+
+
+def test_apply_mistral_chat_template_thinking_chunk():
+    messages = [
+        {
+            "role": "system",
+            "content": [
+                {"type": "text", "text": "You are a helpful assistant."},
+                {
+                    "type": "thinking",
+                    "closed": True,
+                    "thinking": "Only return the answer when you are confident.",
+                },
+            ],
+        },
+        {"role": "user", "content": "What is 2+2?"},
+        {
+            "role": "assistant",
+            "content": [
+                {"type": "text", "text": "Let me think about it."},
+                {"type": "thinking", "closed": True, "thinking": "2+2 = 4"},
+                {
+                    "type": "text",
+                    "text": "The answer is 4.",
+                },
+            ],
+        },
+        {"role": "user", "content": "Thanks, what is 3+3?"},
+    ]
+    mistral_tokenizer = MistralTokenizer.from_pretrained(
+        "mistralai/Magistral-Small-2509"
+    )
+
+    tokens_ids = safe_apply_chat_template(
+        mistral_tokenizer, messages, chat_template=None, tools=None
+    )
+
+    string_tokens = mistral_tokenizer.mistral.decode(
+        tokens_ids, special_token_policy=SpecialTokenPolicy.KEEP
+    )
+
+    expected_tokens = (
+        r"<s>[SYSTEM_PROMPT]You are a helpful assistant.[THINK]Only return the"
+        r" answer when you are confident.[/THINK][/SYSTEM_PROMPT]"
+        r"[INST]What is 2+2?[/INST]"
+        r"Let me think about it.[THINK]2+2 = 4[/THINK]The answer is 4.</s>"
+        r"[INST]Thanks, what is 3+3?[/INST]"
+    )
+
+    assert string_tokens == expected_tokens