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/pooling/test_radio.py

@@ -0,0 +1,102 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+import torch.nn as nn
+from huggingface_hub import snapshot_download
+from transformers import AutoConfig, AutoModel, CLIPImageProcessor
+
+from vllm.distributed import cleanup_dist_env_and_memory
+from vllm.model_executor.models.radio import RadioModel
+from vllm.transformers_utils.configs.radio import RadioConfig
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+
+from ....conftest import ImageTestAssets
+
+# we use snapshot_download to prevent conflicts between
+# dynamic_module and trust_remote_code for hf_runner
+DOWNLOAD_PATTERN = ["*.json", "*.py", "*.safetensors", "*.txt", "*.model"]
+
+
+@torch.inference_mode()
+def run_radio_test(
+    image_assets: ImageTestAssets,
+    model_id: str,
+    *,
+    dtype: str,
+):
+    model = snapshot_download(model_id, allow_patterns=DOWNLOAD_PATTERN)
+    torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
+
+    img_processor = CLIPImageProcessor.from_pretrained(model)
+    images = [asset.pil_image for asset in image_assets]
+    # Input resolution must be a multiple of `self.min_resolution_step`.
+    # Using `self.get_nearest_supported_resolution`, for assets 432x642 the
+    # nearest supported resolution is 432x640.
+    pixel_values = [
+        img_processor(image, return_tensors="pt").pixel_values.to(torch_dtype)[
+            :, :, :, :640
+        ]
+        for image in images
+    ]
+
+    hf_config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
+
+    # RADIO model on HF does not properly handle torch_dtype argument
+    # And relies on args["dtype"] which we have to patch manually:
+    hf_config.args["dtype"] = torch_dtype
+
+    hf_model = AutoModel.from_pretrained(
+        model_id,
+        config=hf_config,
+        dtype=torch_dtype,
+        trust_remote_code=True,
+    ).to("cuda")
+    hf_model.eval()
+
+    # A HF model has image normalization as a part of model's forward
+    # However in vLLM we don't make normalization a part of the model
+    # forward step since mean/std stored as model's parameters and
+    # subject to precision loss (when using fp16/bf16) which negatively
+    # affects evaluation benchmarks.
+    hf_model.make_preprocessor_external()
+
+    hf_outputs_per_image = [
+        hf_model(pixel_value.to("cuda")) for pixel_value in pixel_values
+    ]
+
+    vllm_config = RadioConfig(
+        model_name=hf_config.args["model"],
+        **hf_config.args,
+    )
+    vllm_model = RadioModel(vllm_config)
+    vllm_model.load_weights(hf_model.state_dict())
+    vllm_model = vllm_model.to("cuda", torch_dtype)
+
+    vllm_outputs_per_image = [
+        vllm_model(pixel_values=pixel_value.to("cuda")) for pixel_value in pixel_values
+    ]
+    del vllm_model, hf_model
+    cleanup_dist_env_and_memory()
+
+    cos_similar = nn.CosineSimilarity(dim=-1)
+    for vllm_output, hf_output in zip(vllm_outputs_per_image, hf_outputs_per_image):
+        assert cos_similar(vllm_output[0], hf_output[0]).mean() > 0.99
+        assert cos_similar(vllm_output[1], hf_output[1]).mean() > 0.99
+
+
+@pytest.mark.parametrize(
+    "model_id",
+    [
+        "nvidia/C-RADIOv2-H",
+    ],
+)
+@pytest.mark.parametrize("dtype", ["half", "bfloat16"])
+def test_radio(
+    default_vllm_config, dist_init, image_assets, model_id, dtype: str
+) -> None:
+    run_radio_test(
+        image_assets,
+        model_id,
+        dtype=dtype,
+    )