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/tracing/test_tracing.py

@@ -0,0 +1,87 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# ruff: noqa
+# type: ignore
+import pytest
+import time
+from opentelemetry.sdk.environment_variables import OTEL_EXPORTER_OTLP_TRACES_INSECURE
+
+from vllm import LLM, SamplingParams
+from vllm.tracing import SpanAttributes
+
+# Import shared fixtures from the tracing conftest
+from tests.tracing.conftest import (  # noqa: F401
+    FAKE_TRACE_SERVER_ADDRESS,
+    FakeTraceService,
+    trace_service,
+)
+
+
+def test_traces(
+    monkeypatch: pytest.MonkeyPatch,
+    trace_service: FakeTraceService,
+):
+    with monkeypatch.context() as m:
+        m.setenv(OTEL_EXPORTER_OTLP_TRACES_INSECURE, "true")
+
+        sampling_params = SamplingParams(
+            temperature=0.01,
+            top_p=0.1,
+            max_tokens=256,
+        )
+        model = "facebook/opt-125m"
+        llm = LLM(
+            model=model,
+            otlp_traces_endpoint=FAKE_TRACE_SERVER_ADDRESS,
+            gpu_memory_utilization=0.3,
+            disable_log_stats=False,
+        )
+        prompts = ["This is a short prompt"]
+        outputs = llm.generate(prompts, sampling_params=sampling_params)
+        print(f"test_traces outputs is : {outputs}")
+
+        # Wait for the "llm_request" span to be exported.
+        # The BatchSpanProcessor batches spans and exports them periodically,
+        # so we need to wait specifically for the llm_request span to appear.
+        timeout = 15
+        deadline = time.time() + timeout
+        llm_request_spans = []
+        while time.time() < deadline:
+            all_spans = trace_service.get_all_spans()
+            llm_request_spans = [s for s in all_spans if s["name"] == "llm_request"]
+            if llm_request_spans:
+                break
+            time.sleep(0.5)
+
+        assert len(llm_request_spans) == 1, (
+            f"Expected exactly 1 'llm_request' span, but got {len(llm_request_spans)}. "
+            f"All span names: {[s['name'] for s in all_spans]}"
+        )
+
+        attributes = llm_request_spans[0]["attributes"]
+        # assert attributes.get(SpanAttributes.GEN_AI_RESPONSE_MODEL) == model
+        assert attributes.get(SpanAttributes.GEN_AI_REQUEST_ID) == outputs[0].request_id
+        assert (
+            attributes.get(SpanAttributes.GEN_AI_REQUEST_TEMPERATURE)
+            == sampling_params.temperature
+        )
+        assert (
+            attributes.get(SpanAttributes.GEN_AI_REQUEST_TOP_P) == sampling_params.top_p
+        )
+        assert (
+            attributes.get(SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS)
+            == sampling_params.max_tokens
+        )
+        assert attributes.get(SpanAttributes.GEN_AI_REQUEST_N) == sampling_params.n
+        assert attributes.get(SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS) == len(
+            outputs[0].prompt_token_ids
+        )
+        completion_tokens = sum(len(o.token_ids) for o in outputs[0].outputs)
+        assert (
+            attributes.get(SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS)
+            == completion_tokens
+        )
+
+        assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE) > 0
+        assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN) > 0
+        assert attributes.get(SpanAttributes.GEN_AI_LATENCY_E2E) > 0