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/v1/sample/test_logprobs_e2e.py

@@ -0,0 +1,57 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import lm_eval
+
+from ...utils import RemoteOpenAIServer
+
+# arc-easy uses prompt_logprobs=1, logprobs=1
+TASK = "arc_easy"
+FILTER = "acc_norm,none"
+RTOL = 0.03
+EXPECTED_VALUE = 0.62
+
+# FIXME(rob): enable prefix caching once supported.
+MODEL = "meta-llama/Llama-3.2-1B-Instruct"
+MODEL_ARGS = f"pretrained={MODEL},enforce_eager=True,enable_prefix_caching=False,gpu_memory_utilization=0.8"  # noqa: E501
+SERVER_ARGS = [
+    "--enforce_eager",
+    "--no_enable_prefix_caching",
+    "--gpu-memory-utilization=0.8",
+]
+NUM_CONCURRENT = 100
+
+
+def test_prompt_logprobs_e2e():
+    results = lm_eval.simple_evaluate(
+        model="vllm", model_args=MODEL_ARGS, tasks=TASK, batch_size="auto"
+    )
+
+    measured_value = results["results"][TASK][FILTER]
+    assert (
+        measured_value - RTOL < EXPECTED_VALUE
+        and measured_value + RTOL > EXPECTED_VALUE
+    ), f"Expected: {EXPECTED_VALUE} |  Measured: {measured_value}"
+
+
+def test_prompt_logprobs_e2e_server():
+    with RemoteOpenAIServer(MODEL, SERVER_ARGS) as remote_server:
+        url = f"{remote_server.url_for('v1')}/completions"
+
+        model_args = (
+            f"model={MODEL},"
+            f"base_url={url},"
+            f"num_concurrent={NUM_CONCURRENT},tokenized_requests=False"
+        )
+
+        results = lm_eval.simple_evaluate(
+            model="local-completions",
+            model_args=model_args,
+            tasks=TASK,
+        )
+
+        measured_value = results["results"][TASK][FILTER]
+        assert (
+            measured_value - RTOL < EXPECTED_VALUE
+            and measured_value + RTOL > EXPECTED_VALUE
+        ), f"Expected: {EXPECTED_VALUE} |  Measured: {measured_value}"