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58fab50d82
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk> Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
45 lines
1.2 KiB
Python
45 lines
1.2 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 pytest
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import torch
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from tests.conftest import VllmRunner
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@pytest.mark.parametrize(
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"model",
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[
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"ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11",
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"mgazz/Prithvi_v2_eo_300_tl_unet_agb",
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],
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)
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def test_inference(
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vllm_runner: type[VllmRunner],
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model: str,
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) -> None:
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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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prompt = dict(
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prompt_token_ids=[1],
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multi_modal_data=dict(
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pixel_values=pixel_values, location_coords=location_coords
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),
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)
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with vllm_runner(
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model,
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runner="pooling",
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dtype="half",
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enforce_eager=True,
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skip_tokenizer_init=True,
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enable_mm_embeds=True,
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# Limit the maximum number of sequences to avoid the
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# test going OOM during the warmup run
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max_num_seqs=32,
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default_torch_num_threads=1,
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) as vllm_model:
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vllm_output = vllm_model.llm.encode(prompt)
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assert torch.equal(
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torch.isnan(vllm_output[0].outputs.data).any(), torch.tensor(False)
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)
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