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/offline_inference/encoder_decoder_multimodal.py

@@ -0,0 +1,133 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+This example shows how to use vLLM for running offline inference with
+the explicit/implicit prompt format on enc-dec LMMs for text generation.
+"""
+
+import os
+import time
+from collections.abc import Sequence
+from dataclasses import asdict
+from typing import NamedTuple
+
+from vllm import LLM, EngineArgs, PromptType, SamplingParams
+from vllm.assets.audio import AudioAsset
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+
+
+class ModelRequestData(NamedTuple):
+    engine_args: EngineArgs
+    prompts: Sequence[PromptType]
+
+
+def run_whisper():
+    os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
+    engine_args = EngineArgs(
+        model="openai/whisper-large-v3-turbo",
+        max_model_len=448,
+        max_num_seqs=16,
+        limit_mm_per_prompt={"audio": 1},
+        dtype="half",
+    )
+
+    prompts = [
+        {  # Test implicit prompt
+            "prompt": "<|startoftranscript|>",
+            "multi_modal_data": {
+                "audio": AudioAsset("mary_had_lamb").audio_and_sample_rate,
+            },
+        },
+        {  # Test explicit encoder/decoder prompt
+            "encoder_prompt": {
+                "prompt": "",
+                "multi_modal_data": {
+                    "audio": AudioAsset("winning_call").audio_and_sample_rate,
+                },
+            },
+            "decoder_prompt": "<|startoftranscript|>",
+        },
+    ]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
+model_example_map = {
+    "whisper": run_whisper,
+}
+
+
+def parse_args():
+    parser = FlexibleArgumentParser(
+        description="Demo on using vLLM for offline inference with "
+        "vision language models for text generation"
+    )
+    parser.add_argument(
+        "--model-type",
+        "-m",
+        type=str,
+        default="whisper",
+        choices=model_example_map.keys(),
+        help='Huggingface "model_type".',
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=0,
+        help="Set the seed when initializing `vllm.LLM`.",
+    )
+    return parser.parse_args()
+
+
+def main(args):
+    model = args.model_type
+    if model not in model_example_map:
+        raise ValueError(f"Model type {model} is not supported.")
+
+    req_data = model_example_map[model]()
+
+    # Disable other modalities to save memory
+    default_limits = {"image": 0, "video": 0, "audio": 0}
+    req_data.engine_args.limit_mm_per_prompt = default_limits | dict(
+        req_data.engine_args.limit_mm_per_prompt or {}
+    )
+
+    engine_args = asdict(req_data.engine_args) | {"seed": args.seed}
+    llm = LLM(**engine_args)
+
+    prompts = req_data.prompts
+
+    # Create a sampling params object.
+    sampling_params = SamplingParams(
+        temperature=0,
+        top_p=1.0,
+        max_tokens=64,
+        skip_special_tokens=False,
+    )
+
+    start = time.time()
+
+    # Generate output tokens from the prompts. The output is a list of
+    # RequestOutput objects that contain the prompt, generated
+    # text, and other information.
+    outputs = llm.generate(prompts, sampling_params)
+
+    # Print the outputs.
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Decoder prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+    duration = time.time() - start
+
+    print("Duration:", duration)
+    print("RPS:", len(prompts) / duration)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)