Sync from v0.13

tests/entrypoints/openai/test_vision_embeds.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import base64
+
+import numpy as np
+import pytest
+import requests
+import torch
+
+from vllm.utils.serial_utils import tensor2base64
+
+from ...utils import RemoteOpenAIServer
+
+
+def _terratorch_dummy_messages():
+    pixel_values = torch.full((6, 512, 512), 1.0, dtype=torch.float16)
+    location_coords = torch.full((1, 2), 1.0, dtype=torch.float16)
+
+    return [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_embeds",
+                    "image_embeds": {
+                        "pixel_values": tensor2base64(pixel_values),
+                        "location_coords": tensor2base64(location_coords),
+                    },
+                }
+            ],
+        }
+    ]
+
+
+@pytest.mark.parametrize(
+    "model_name", ["ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11"]
+)
+def test_single_request(model_name: str):
+    args = [
+        "--runner",
+        "pooling",
+        # use half precision for speed and memory savings in CI environment
+        "--dtype",
+        "float16",
+        "--enforce-eager",
+        "--trust-remote-code",
+        "--max-num-seqs",
+        "32",
+        "--model-impl",
+        "terratorch",
+        "--skip-tokenizer-init",
+        "--enable-mm-embeds",
+    ]
+
+    with RemoteOpenAIServer(model_name, args) as server:
+        response = requests.post(
+            server.url_for("pooling"),
+            json={
+                "model": model_name,
+                "messages": _terratorch_dummy_messages(),
+                "encoding_format": "base64",
+            },
+        )
+        response.raise_for_status()
+
+        output = response.json()["data"][0]["data"]
+
+        np_response = np.frombuffer(base64.b64decode(output), dtype=np.float32)
+        assert len(np_response) == 524288