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3fa2c12185
Signed-off-by: wang.yuqi <noooop@126.com> Signed-off-by: Christian Pinto <christian.pinto@ibm.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: Christian Pinto <christian.pinto@ibm.com>
120 lines
3.5 KiB
Python
120 lines
3.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import base64
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import pytest
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import requests
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from tests.utils import RemoteOpenAIServer
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from vllm.config import VllmConfig
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from vllm.entrypoints.openai.protocol import IOProcessorResponse
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from vllm.plugins.io_processors import get_io_processor
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MODEL_NAME = "ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11"
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image_url = "https://huggingface.co/christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM/resolve/main/valencia_example_2024-10-26.tiff" # noqa: E501
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def test_loading_missing_plugin():
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vllm_config = VllmConfig()
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with pytest.raises(ValueError):
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get_io_processor(vllm_config, "wrong_plugin")
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@pytest.fixture(scope="function")
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def server():
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args = [
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"--runner",
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"pooling",
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"--enforce-eager",
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"--trust-remote-code",
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"--skip-tokenizer-init",
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# Limit the maximum number of parallel requests
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# to avoid the model going OOM in CI.
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"--max-num-seqs",
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"32",
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"--io-processor-plugin",
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"prithvi_to_tiff",
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"--model-impl",
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"terratorch",
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"--enable-mm-embeds",
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]
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with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
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yield remote_server
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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async def test_prithvi_mae_plugin_online(
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server: RemoteOpenAIServer,
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model_name: str,
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):
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request_payload_url = {
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"data": {
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"data": image_url,
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"data_format": "url",
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"image_format": "tiff",
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"out_data_format": "b64_json",
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},
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"priority": 0,
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"model": model_name,
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"softmax": False,
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}
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ret = requests.post(
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server.url_for("pooling"),
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json=request_payload_url,
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)
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response = ret.json()
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# verify the request response is in the correct format
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assert (parsed_response := IOProcessorResponse(**response))
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# verify the output is formatted as expected for this plugin
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plugin_data = parsed_response.data
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assert all(
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plugin_data.get(attr) for attr in ["type", "format", "data", "request_id"]
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)
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# We just check that the output is a valid base64 string.
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# Raises an exception and fails the test if the string is corrupted.
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base64.b64decode(plugin_data["data"])
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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def test_prithvi_mae_plugin_offline(vllm_runner, model_name: str):
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img_prompt = dict(
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data=image_url,
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data_format="url",
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image_format="tiff",
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out_data_format="b64_json",
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)
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with vllm_runner(
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model_name,
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runner="pooling",
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skip_tokenizer_init=True,
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enable_mm_embeds=True,
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trust_remote_code=True,
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enforce_eager=True,
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# Limit the maximum number of parallel requests
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# to avoid the model going OOM in CI.
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max_num_seqs=1,
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model_impl="terratorch",
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io_processor_plugin="prithvi_to_tiff",
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) as llm_runner:
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pooler_output = llm_runner.get_llm().encode(img_prompt, pooling_task="plugin")
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output = pooler_output[0].outputs
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# verify the output is formatted as expected for this plugin
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assert all(
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hasattr(output, attr) for attr in ["type", "format", "data", "request_id"]
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)
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# We just check that the output is a valid base64 string.
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# Raises an exception and fails the test if the string is corrupted.
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base64.b64decode(output.data)
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