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/kernels/quantization/test_awq.py

@@ -0,0 +1,49 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+from tests.kernels.utils import opcheck
+from vllm import _custom_ops as ops  # noqa: F401
+
+
+@pytest.mark.skipif(
+    not hasattr(torch.ops._C, "awq_dequantize"),
+    reason="AWQ is not supported on this GPU type.",
+)
+def test_awq_dequantize_opcheck(monkeypatch: pytest.MonkeyPatch):
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_TRITON_AWQ", "0")
+        qweight = torch.randint(
+            -2000000000, 2000000000, (8192, 256), device="cuda", dtype=torch.int32
+        )
+        scales = torch.rand((64, 2048), device="cuda", dtype=torch.float16)
+        zeros = torch.empty((64, 256), device="cuda", dtype=torch.int32)
+        split_k_iters = 0
+        thx = 0
+        thy = 0
+        opcheck(
+            torch.ops._C.awq_dequantize,
+            (qweight, scales, zeros, split_k_iters, thx, thy),
+        )
+
+
+@pytest.mark.skip(reason="Not working; needs investigation.")
+@pytest.mark.skipif(
+    not hasattr(torch.ops._C, "awq_gemm"),
+    reason="AWQ is not supported on this GPU type.",
+)
+def test_awq_gemm_opcheck(monkeypatch: pytest.MonkeyPatch):
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_TRITON_AWQ", "0")
+        input = torch.rand((2, 8192), device="cuda", dtype=torch.float16)
+        qweight = torch.randint(
+            -2000000000, 2000000000, (8192, 256), device="cuda", dtype=torch.int32
+        )
+        scales = torch.empty((64, 2048), device="cuda", dtype=torch.float16)
+        qzeros = torch.randint(
+            -2000000000, 2000000000, (64, 256), device="cuda", dtype=torch.int32
+        )
+        split_k_iters = 8
+        opcheck(torch.ops._C.awq_gemm, (input, qweight, scales, qzeros, split_k_iters))