c4df59ad43
Signed-off-by: Reagan Lee <“reaganjlee@gmail.com”> Signed-off-by: Reagan Lee <reaganjlee@gmail.com> Signed-off-by: Reagan Lee <96998476+reaganjlee@users.noreply.github.com> Co-authored-by: Reagan Lee <“reaganjlee@gmail.com”> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
import weakref
|
|
|
|
import pytest
|
|
|
|
from vllm import LLM, SamplingParams
|
|
from vllm.assets.image import ImageAsset
|
|
from vllm.distributed import cleanup_dist_env_and_memory
|
|
|
|
MODEL = "llava-hf/llava-1.5-7b-hf"
|
|
PROMPT = "USER: <image>\nDescribe this image briefly.\nASSISTANT:"
|
|
TEXT_ONLY_PROMPT = "USER: What is 2 + 2?\nASSISTANT:"
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def llm():
|
|
"""LLM with enable_mm_embeds=True and all modality limits zeroed out."""
|
|
llm = LLM(
|
|
model=MODEL,
|
|
max_model_len=2048,
|
|
enforce_eager=True,
|
|
gpu_memory_utilization=0.8,
|
|
enable_mm_embeds=True,
|
|
limit_mm_per_prompt={"image": 0},
|
|
)
|
|
|
|
yield weakref.proxy(llm)
|
|
|
|
del llm
|
|
|
|
cleanup_dist_env_and_memory()
|
|
|
|
|
|
@pytest.mark.skip_global_cleanup
|
|
def test_generate_with_embedding(llm: LLM):
|
|
"""Pre-computed embedding produces tokens without hanging."""
|
|
embedding = ImageAsset("stop_sign").image_embeds
|
|
outputs = llm.generate(
|
|
{"prompt": PROMPT, "multi_modal_data": {"image": embedding}},
|
|
sampling_params=SamplingParams(max_tokens=32, temperature=0.0),
|
|
)
|
|
assert len(outputs) == 1
|
|
assert len(outputs[0].outputs[0].text) > 0
|
|
|
|
|
|
@pytest.mark.skip_global_cleanup
|
|
def test_raw_image_rejected(llm: LLM):
|
|
"""Raw image input is still rejected when limit=0."""
|
|
raw_image = ImageAsset("stop_sign").pil_image
|
|
with pytest.raises(ValueError, match=r"At most 0 image\(s\)"):
|
|
llm.generate(
|
|
{"prompt": PROMPT, "multi_modal_data": {"image": raw_image}},
|
|
sampling_params=SamplingParams(max_tokens=16),
|
|
)
|
|
|
|
|
|
@pytest.mark.skip_global_cleanup
|
|
def test_text_only_prompt(llm: LLM):
|
|
"""Text-only prompts still work under this config."""
|
|
outputs = llm.generate(
|
|
TEXT_ONLY_PROMPT,
|
|
sampling_params=SamplingParams(max_tokens=16, temperature=0.0),
|
|
)
|
|
assert len(outputs) == 1
|
|
assert len(outputs[0].outputs[0].text) > 0
|