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/worker/test_utils.py

@@ -0,0 +1,92 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+from vllm.v1.worker.utils import bind_kv_cache
+
+
+def test_bind_kv_cache(default_vllm_config):
+    from vllm.model_executor.layers.attention import Attention
+
+    ctx = {
+        "layers.0.self_attn": Attention(32, 128, 0.1, prefix="layers.0.self_attn"),
+        "layers.1.self_attn": Attention(32, 128, 0.1, prefix="layers.1.self_attn"),
+        "layers.2.self_attn": Attention(32, 128, 0.1, prefix="layers.2.self_attn"),
+        "layers.3.self_attn": Attention(32, 128, 0.1, prefix="layers.3.self_attn"),
+    }
+    kv_cache = {
+        "layers.0.self_attn": torch.zeros((1,)),
+        "layers.1.self_attn": torch.zeros((1,)),
+        "layers.2.self_attn": torch.zeros((1,)),
+        "layers.3.self_attn": torch.zeros((1,)),
+    }
+    runner_kv_caches: list[torch.Tensor] = []
+    bind_kv_cache(kv_cache, ctx, runner_kv_caches)
+    assert ctx["layers.0.self_attn"].kv_cache[0] is kv_cache["layers.0.self_attn"]
+    assert ctx["layers.1.self_attn"].kv_cache[0] is kv_cache["layers.1.self_attn"]
+    assert ctx["layers.2.self_attn"].kv_cache[0] is kv_cache["layers.2.self_attn"]
+    assert ctx["layers.3.self_attn"].kv_cache[0] is kv_cache["layers.3.self_attn"]
+
+    assert runner_kv_caches[0] is kv_cache["layers.0.self_attn"]
+    assert runner_kv_caches[1] is kv_cache["layers.1.self_attn"]
+    assert runner_kv_caches[2] is kv_cache["layers.2.self_attn"]
+    assert runner_kv_caches[3] is kv_cache["layers.3.self_attn"]
+
+
+def test_bind_kv_cache_non_attention(default_vllm_config):
+    from vllm.model_executor.layers.attention import Attention
+
+    # example from Jamba PP=2
+    ctx = {
+        "model.layers.20.attn": Attention(32, 128, 0.1, prefix="model.layers.20.attn"),
+        "model.layers.28.attn": Attention(32, 128, 0.1, prefix="model.layers.28.attn"),
+    }
+    kv_cache = {
+        "model.layers.20.attn": torch.zeros((1,)),
+        "model.layers.28.attn": torch.zeros((1,)),
+    }
+
+    runner_kv_caches: list[torch.Tensor] = []
+    bind_kv_cache(kv_cache, ctx, runner_kv_caches)
+
+    assert ctx["model.layers.20.attn"].kv_cache[0] is kv_cache["model.layers.20.attn"]
+    assert ctx["model.layers.28.attn"].kv_cache[0] is kv_cache["model.layers.28.attn"]
+
+    assert runner_kv_caches[0] is kv_cache["model.layers.20.attn"]
+    assert runner_kv_caches[1] is kv_cache["model.layers.28.attn"]
+
+
+def test_bind_kv_cache_draft_model(default_vllm_config):
+    from vllm.model_executor.layers.attention import Attention
+
+    layer_names = [
+        "model.layers.0.attn",
+        "model.layers.1.attn",
+        "draft_model.layers.0.attn",
+        "draft_model.layers.1.attn",
+    ]
+    ctx = {
+        layer_name: Attention(32, 128, 0.1, prefix=layer_name)
+        for layer_name in layer_names
+    }
+    kv_cache = {layer_name: torch.zeros((1,)) for layer_name in layer_names}
+    runner_kv_caches: list[torch.Tensor] = []
+    bind_kv_cache(kv_cache, ctx, runner_kv_caches)
+
+    assert ctx["model.layers.0.attn"].kv_cache[0] is kv_cache["model.layers.0.attn"]
+    assert ctx["model.layers.1.attn"].kv_cache[0] is kv_cache["model.layers.1.attn"]
+    assert (
+        ctx["draft_model.layers.0.attn"].kv_cache[0]
+        is kv_cache["draft_model.layers.0.attn"]
+    )
+    assert (
+        ctx["draft_model.layers.1.attn"].kv_cache[0]
+        is kv_cache["draft_model.layers.1.attn"]
+    )
+
+    # caches are ordered by layer_index, interleaving target and draft model
+    assert runner_kv_caches[0] is kv_cache["model.layers.0.attn"]
+    assert runner_kv_caches[1] is kv_cache["draft_model.layers.0.attn"]
+    assert runner_kv_caches[2] is kv_cache["model.layers.1.attn"]
+    assert runner_kv_caches[3] is kv_cache["draft_model.layers.1.attn"]