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/entrypoints/pooling/embed/test_offline.py

@@ -0,0 +1,72 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import weakref
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from vllm import LLM, PoolingParams
+from vllm.distributed import cleanup_dist_env_and_memory
+from vllm.platforms import current_platform
+
+MODEL_NAME = "intfloat/multilingual-e5-small"
+
+prompts = ["The chef prepared a delicious meal."]
+
+
+@pytest.fixture(scope="module")
+def llm():
+    # ROCm: Use FLEX_ATTENTION backend as it's the only attention backend
+    # that supports encoder-only models on ROCm.
+    attention_config = None
+    if current_platform.is_rocm():
+        attention_config = {"backend": "FLEX_ATTENTION"}
+
+    # pytest caches the fixture so we use weakref.proxy to
+    # enable garbage collection
+    llm = LLM(
+        model=MODEL_NAME,
+        max_num_batched_tokens=32768,
+        tensor_parallel_size=1,
+        gpu_memory_utilization=0.75,
+        enforce_eager=True,
+        seed=0,
+        attention_config=attention_config,
+    )
+
+    yield weakref.proxy(llm)
+
+    del llm
+
+    cleanup_dist_env_and_memory()
+
+
+@pytest.mark.skip_global_cleanup
+def test_token_embed(llm: LLM):
+    outputs = llm.encode(prompts, pooling_task="token_embed", use_tqdm=False)
+    multi_vector = outputs[0].outputs.data
+    assert multi_vector.shape == (11, 384)
+
+
+def test_pooling_params(llm: LLM):
+    def get_outputs(normalize):
+        outputs = llm.embed(
+            prompts,
+            pooling_params=PoolingParams(use_activation=normalize),
+            use_tqdm=False,
+        )
+        return torch.tensor([x.outputs.embedding for x in outputs])
+
+    default = get_outputs(normalize=None)
+    w_normal = get_outputs(normalize=True)
+    wo_normal = get_outputs(normalize=False)
+
+    assert torch.allclose(default, w_normal, atol=1e-2), "Default should use normal."
+    assert not torch.allclose(w_normal, wo_normal, atol=1e-2), (
+        "wo_normal should not use normal."
+    )
+    assert torch.allclose(w_normal, F.normalize(wo_normal, p=2, dim=-1), atol=1e-2), (
+        "w_normal should be close to normal(wo_normal)."
+    )