Sync from v0.13

tests/models/multimodal/pooling/test_prithvi_mae.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+from ....conftest import VllmRunner
+
+
+def generate_test_mm_data():
+    mm_data = {
+        "pixel_values": torch.full((6, 512, 512), 1.0, dtype=torch.float16),
+        "location_coords": torch.full((1, 2), 1.0, dtype=torch.float16),
+    }
+    return mm_data
+
+
+def _run_test(
+    vllm_runner: type[VllmRunner],
+    model: str,
+) -> None:
+    prompt = [
+        {
+            # This model deals with no text input
+            "prompt_token_ids": [1],
+            "multi_modal_data": generate_test_mm_data(),
+        }
+        for _ in range(10)
+    ]
+
+    with vllm_runner(
+        model,
+        runner="pooling",
+        dtype="half",
+        enforce_eager=True,
+        skip_tokenizer_init=True,
+        enable_mm_embeds=True,
+        # Limit the maximum number of sequences to avoid the
+        # test going OOM during the warmup run
+        max_num_seqs=32,
+        default_torch_num_threads=1,
+    ) as vllm_model:
+        vllm_model.llm.encode(prompt, pooling_task="plugin")
+
+
+MODELS = ["mgazz/Prithvi-EO-2.0-300M-TL-Sen1Floods11"]
+
+
+@pytest.mark.core_model
+@pytest.mark.parametrize("model", MODELS)
+def test_models_image(
+    hf_runner,
+    vllm_runner,
+    image_assets,
+    model: str,
+) -> None:
+    _run_test(
+        vllm_runner,
+        model,
+    )