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/models/multimodal/processing/test_mllama4.py

@@ -0,0 +1,55 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for mllama's multimodal preprocessing and profiling."""
+
+import pytest
+from torch import prod
+from transformers import Llama4Config
+
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+from ...utils import build_model_context
+
+
+@pytest.mark.parametrize("model_id", ["meta-llama/Llama-Guard-4-12B"])
+@pytest.mark.parametrize("max_model_len", [4096, 8192, 25600, 131072])
+def test_profiling(model_id: str, max_model_len: int):
+    model_config_kwargs = {
+        "max_model_len": max_model_len,
+    }
+    mm_counts = {"image": 1}
+    ctx = build_model_context(
+        model_id,
+        model_config_kwargs=model_config_kwargs,
+        limit_mm_per_prompt=mm_counts,
+    )
+
+    mm_inputs = MULTIMODAL_REGISTRY.get_dummy_mm_inputs(
+        ctx.model_config,
+        mm_counts=mm_counts,
+    )
+
+    hf_config = ctx.get_hf_config(Llama4Config)
+    image_size = hf_config.vision_config.image_size
+    patch_size = hf_config.vision_config.patch_size
+    downsample_ratio = int(
+        round(1.0 / (hf_config.vision_config.pixel_shuffle_ratio**2))
+    )
+    tokens_per_patch = ((image_size // patch_size) ** 2) // downsample_ratio
+
+    mm_data = mm_inputs["mm_kwargs"].get_data()
+    chunks_per_image = prod(mm_data["patches_per_image"])
+    total_num_patches = chunks_per_image * tokens_per_patch
+    num_tiles = (
+        mm_data["aspect_ratios"][0][0] * mm_data["aspect_ratios"][0][1]
+    )  # x-y separator tokens
+    total_tokens = (
+        total_num_patches.item() + num_tiles.item() + 3
+    )  # image start, image, image end
+
+    assert total_num_patches == sum(
+        item.get_num_embeds() for item in mm_inputs["mm_placeholders"]["image"]
+    )
+    assert total_tokens == sum(
+        placeholder.length for placeholder in mm_inputs["mm_placeholders"]["image"]
+    )