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/lora/test_deepseekv2_tp.py

@@ -0,0 +1,101 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# NOTE To avoid overloading the CI pipeline, this test script will
+# not be triggered on CI and is primarily intended for local testing
+# and verification.
+
+import vllm
+from vllm.lora.request import LoRARequest
+
+from ..utils import multi_gpu_test
+
+MODEL_PATH = "deepseek-ai/DeepSeek-V2-Lite-Chat"
+
+PROMPT_TEMPLATE = "<｜begin▁of▁sentence｜>You are a helpful assistant.\n\nUser: {context}\n\nAssistant:"  # noqa: E501
+
+
+def generate_and_test(llm: vllm.LLM, lora_path: str, lora_id: int):
+    prompts = [
+        PROMPT_TEMPLATE.format(context="Who are you?"),
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=64)
+    outputs = llm.generate(
+        prompts,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path) if lora_id else None,
+    )
+    # Print the outputs.
+    generated_texts: list[str] = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    # return generated_texts
+    expected_lora_output = [
+        "I am \u5f20\u5b50\u8c6a, an AI assistant developed by \u9648\u58eb\u680b.",  # noqa: E501
+    ]
+    for i in range(len(expected_lora_output)):
+        assert generated_texts[i].startswith(expected_lora_output[i])
+
+
+def test_deepseekv2_lora(deepseekv2_lora_files):
+    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
+    # Otherwise, the lora-test will fail due to CUDA OOM.
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+    )
+    generate_and_test(llm, deepseekv2_lora_files, 1)
+
+
+def test_deepseekv2(deepseekv2_lora_files):
+    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
+    # Otherwise, the lora-test will fail due to CUDA OOM.
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+    )
+    generate_and_test(llm, deepseekv2_lora_files, 1)
+
+
+@multi_gpu_test(num_gpus=2)
+def test_deepseekv2_tp2(deepseekv2_lora_files):
+    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
+    # Otherwise, the lora-test will fail due to CUDA OOM.
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        tensor_parallel_size=2,
+    )
+    generate_and_test(llm, deepseekv2_lora_files, 2)
+
+
+@multi_gpu_test(num_gpus=4)
+def test_deepseekv2_tp4(deepseekv2_lora_files):
+    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
+    # Otherwise, the lora-test will fail due to CUDA OOM.
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        tensor_parallel_size=4,
+    )
+    generate_and_test(llm, deepseekv2_lora_files, 2)