Sync from v0.13

2026-01-19 10:38:50 +08:00
parent b2ef04d792
commit 5aef6c175a
3714 changed files with 854317 additions and 89342 deletions
--- a/tests/plugins_tests/test_io_processor_plugins.py
+++ b/tests/plugins_tests/test_io_processor_plugins.py
@@ -0,0 +1,119 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import base64
+
+import pytest
+import requests
+
+from tests.utils import RemoteOpenAIServer
+from vllm.config import VllmConfig
+from vllm.entrypoints.pooling.pooling.protocol import IOProcessorResponse
+from vllm.plugins.io_processors import get_io_processor
+
+MODEL_NAME = "ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11"
+
+image_url = "https://huggingface.co/christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM/resolve/main/valencia_example_2024-10-26.tiff"  # noqa: E501
+
+
+def test_loading_missing_plugin():
+    vllm_config = VllmConfig()
+    with pytest.raises(ValueError):
+        get_io_processor(vllm_config, "wrong_plugin")
+
+
+@pytest.fixture(scope="function")
+def server():
+    args = [
+        "--runner",
+        "pooling",
+        "--enforce-eager",
+        "--trust-remote-code",
+        "--skip-tokenizer-init",
+        # Limit the maximum number of parallel requests
+        # to avoid the model going OOM in CI.
+        "--max-num-seqs",
+        "32",
+        "--io-processor-plugin",
+        "prithvi_to_tiff",
+        "--model-impl",
+        "terratorch",
+        "--enable-mm-embeds",
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_prithvi_mae_plugin_online(
+    server: RemoteOpenAIServer,
+    model_name: str,
+):
+    request_payload_url = {
+        "data": {
+            "data": image_url,
+            "data_format": "url",
+            "image_format": "tiff",
+            "out_data_format": "b64_json",
+        },
+        "priority": 0,
+        "model": model_name,
+        "softmax": False,
+    }
+
+    ret = requests.post(
+        server.url_for("pooling"),
+        json=request_payload_url,
+    )
+
+    response = ret.json()
+
+    # verify the request response is in the correct format
+    assert (parsed_response := IOProcessorResponse(**response))
+
+    # verify the output is formatted as expected for this plugin
+    plugin_data = parsed_response.data
+
+    assert all(
+        plugin_data.get(attr) for attr in ["type", "format", "data", "request_id"]
+    )
+
+    # We just check that the output is a valid base64 string.
+    # Raises an exception and fails the test if the string is corrupted.
+    base64.b64decode(plugin_data["data"])
+
+
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+def test_prithvi_mae_plugin_offline(vllm_runner, model_name: str):
+    img_prompt = dict(
+        data=image_url,
+        data_format="url",
+        image_format="tiff",
+        out_data_format="b64_json",
+    )
+
+    with vllm_runner(
+        model_name,
+        runner="pooling",
+        skip_tokenizer_init=True,
+        enable_mm_embeds=True,
+        trust_remote_code=True,
+        enforce_eager=True,
+        # Limit the maximum number of parallel requests
+        # to avoid the model going OOM in CI.
+        max_num_seqs=1,
+        model_impl="terratorch",
+        io_processor_plugin="prithvi_to_tiff",
+    ) as llm_runner:
+        pooler_output = llm_runner.get_llm().encode(img_prompt, pooling_task="plugin")
+    output = pooler_output[0].outputs
+
+    # verify the output is formatted as expected for this plugin
+    assert all(
+        hasattr(output, attr) for attr in ["type", "format", "data", "request_id"]
+    )
+
+    # We just check that the output is a valid base64 string.
+    # Raises an exception and fails the test if the string is corrupted.
+    base64.b64decode(output.data)