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86 lines
2.4 KiB
Python
86 lines
2.4 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 io
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import numpy as np
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import pytest
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import requests
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import torch
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from ...utils import RemoteOpenAIServer
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MODEL_NAME = "ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11"
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DTYPE = "float16"
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@pytest.fixture(scope="module")
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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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# use half precision for speed and memory savings in CI environment
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"--dtype",
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DTYPE,
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"--enforce-eager",
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"--trust-remote-code",
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"--skip-tokenizer-init",
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"--max-num-seqs",
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"32",
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"--model-impl",
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"terratorch",
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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_single_request(server: RemoteOpenAIServer, model_name: str):
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pixel_values = torch.full((6, 512, 512), 1.0, dtype=torch.float16)
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location_coords = torch.full((1, 2), 1.0, dtype=torch.float16)
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buffer_tiff = io.BytesIO()
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torch.save(pixel_values, buffer_tiff)
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buffer_tiff.seek(0)
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binary_data = buffer_tiff.read()
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base64_tensor_embedding = base64.b64encode(binary_data).decode("utf-8")
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buffer_coord = io.BytesIO()
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torch.save(location_coords, buffer_coord)
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buffer_coord.seek(0)
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binary_data = buffer_coord.read()
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base64_coord_embedding = base64.b64encode(binary_data).decode("utf-8")
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prompt = {
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"model": model_name,
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"additional_data": {"prompt_token_ids": [1]},
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"encoding_format": "base64",
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "image_embeds",
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"image_embeds": {
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"pixel_values": base64_tensor_embedding,
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"location_coords": base64_coord_embedding,
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},
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}
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],
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}
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],
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}
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# test single pooling
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response = requests.post(server.url_for("pooling"), json=prompt)
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response.raise_for_status()
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output = response.json()["data"][0]["data"]
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np_response = np.frombuffer(base64.b64decode(output), dtype=np.float32)
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assert len(np_response) == 524288
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