vllm/tests/engine/test_short_mm_context.py
Cyrus Leung 4ebc0b9640
[Bugfix] Proper input validation for multi-modal encoder-decoder models (#16156)
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
2025-04-08 09:45:21 -07:00

33 lines
972 B
Python

# SPDX-License-Identifier: Apache-2.0
import pytest
from ..conftest import IMAGE_ASSETS
HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
"stop_sign":
"USER: <image>\nWhat's the content of the image?\nASSISTANT:",
"cherry_blossom":
"USER: <image>\nWhat is the season?\nASSISTANT:",
})
models = ["llava-hf/llava-1.5-7b-hf"]
@pytest.mark.parametrize("model", models)
def test_context_length_too_short(vllm_runner, image_assets, model):
images = [asset.pil_image for asset in image_assets]
with pytest.raises(ValueError,
match="longer than the maximum model length"):
vllm_model = vllm_runner(
model,
max_model_len=128, # LLaVA has a feature size of 576
enforce_eager=True,
)
with vllm_model:
vllm_model.generate_greedy([HF_IMAGE_PROMPTS[0]],
max_tokens=1,
images=[images[0]])