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/examples/online_serving/openai_transcription_client.py

@@ -0,0 +1,178 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+This script demonstrates how to use the vLLM API server to perform audio
+transcription with the `openai/whisper-large-v3` model.
+
+Before running this script, you must start the vLLM server with the following command:
+
+    vllm serve openai/whisper-large-v3
+
+Requirements:
+- vLLM with audio support
+- openai Python SDK
+- httpx for streaming support
+
+The script performs:
+1. Synchronous transcription using OpenAI-compatible API.
+2. Streaming transcription using raw HTTP request to the vLLM server.
+"""
+
+import argparse
+import asyncio
+
+from openai import AsyncOpenAI, OpenAI
+
+from vllm.assets.audio import AudioAsset
+
+
+def sync_openai(
+    audio_path: str, client: OpenAI, model: str, *, repetition_penalty: float = 1.3
+):
+    """
+    Perform synchronous transcription using OpenAI-compatible API.
+    """
+    with open(audio_path, "rb") as f:
+        transcription = client.audio.transcriptions.create(
+            file=f,
+            model=model,
+            language="en",
+            response_format="json",
+            temperature=0.0,
+            # Additional sampling params not provided by OpenAI API.
+            extra_body=dict(
+                seed=4419,
+                repetition_penalty=repetition_penalty,
+            ),
+        )
+        print("transcription result [sync]:", transcription.text)
+
+
+async def stream_openai_response(audio_path: str, client: AsyncOpenAI, model: str):
+    """
+    Perform asynchronous transcription using OpenAI-compatible API.
+    """
+    print("\ntranscription result [stream]:", end=" ")
+    with open(audio_path, "rb") as f:
+        transcription = await client.audio.transcriptions.create(
+            file=f,
+            model=model,
+            language="en",
+            response_format="json",
+            temperature=0.0,
+            # Additional sampling params not provided by OpenAI API.
+            extra_body=dict(
+                seed=420,
+                top_p=0.6,
+            ),
+            stream=True,
+        )
+        async for chunk in transcription:
+            if chunk.choices:
+                content = chunk.choices[0].get("delta", {}).get("content")
+                print(content, end="", flush=True)
+
+    print()  # Final newline after stream ends
+
+
+def stream_api_response(audio_path: str, model: str, openai_api_base: str):
+    """
+    Perform streaming transcription using raw HTTP requests to the vLLM API server.
+    """
+    import json
+    import os
+
+    import requests
+
+    api_url = f"{openai_api_base}/audio/transcriptions"
+    headers = {"User-Agent": "Transcription-Client"}
+    with open(audio_path, "rb") as f:
+        files = {"file": (os.path.basename(audio_path), f)}
+        data = {
+            "stream": "true",
+            "model": model,
+            "language": "en",
+            "response_format": "json",
+        }
+
+        print("\ntranscription result [stream]:", end=" ")
+        response = requests.post(
+            api_url, headers=headers, files=files, data=data, stream=True
+        )
+        for chunk in response.iter_lines(
+            chunk_size=8192, decode_unicode=False, delimiter=b"\n"
+        ):
+            if chunk:
+                data = chunk[len("data: ") :]
+                data = json.loads(data.decode("utf-8"))
+                data = data["choices"][0]
+                delta = data["delta"]["content"]
+                print(delta, end="", flush=True)
+
+                finish_reason = data.get("finish_reason")
+                if finish_reason is not None:
+                    print(f"\n[Stream finished reason: {finish_reason}]")
+                    break
+
+
+def main(args):
+    mary_had_lamb = str(AudioAsset("mary_had_lamb").get_local_path())
+    winning_call = str(AudioAsset("winning_call").get_local_path())
+
+    # Modify OpenAI's API key and API base to use vLLM's API server.
+    openai_api_key = "EMPTY"
+    openai_api_base = "http://localhost:8000/v1"
+    client = OpenAI(
+        api_key=openai_api_key,
+        base_url=openai_api_base,
+    )
+
+    model = client.models.list().data[0].id
+    print(f"Using model: {model}")
+
+    # Run the synchronous function
+    sync_openai(
+        audio_path=args.audio_path if args.audio_path else mary_had_lamb,
+        client=client,
+        model=model,
+        repetition_penalty=args.repetition_penalty,
+    )
+
+    # Run the asynchronous function
+    if "openai" in model:
+        client = AsyncOpenAI(
+            api_key=openai_api_key,
+            base_url=openai_api_base,
+        )
+        asyncio.run(
+            stream_openai_response(
+                args.audio_path if args.audio_path else winning_call, client, model
+            )
+        )
+    else:
+        stream_api_response(
+            args.audio_path if args.audio_path else winning_call,
+            model,
+            openai_api_base,
+        )
+
+
+if __name__ == "__main__":
+    # setup argparser
+    parser = argparse.ArgumentParser(
+        description="OpenAI Transcription Client using vLLM API Server"
+    )
+    parser.add_argument(
+        "--audio_path",
+        type=str,
+        default=None,
+        help="The path to the audio file to transcribe.",
+    )
+    parser.add_argument(
+        "--repetition_penalty",
+        type=float,
+        default=1.3,
+        help="repetition penalty",
+    )
+    args = parser.parse_args()
+    main(args)