Gahow Wang
445e491123
third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:
vllm/v1/core/sched/scheduler.py:
Replace fatal assert with graceful skip when KV transfer callback
arrives for an already-aborted request during PD disaggregated serving.
Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
84 lines
2.2 KiB
Python
84 lines
2.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from dataclasses import asdict
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from typing import NamedTuple
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import pytest
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from PIL.Image import Image
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from transformers import AutoProcessor
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from vllm import LLM, EngineArgs, SamplingParams
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from vllm.multimodal.utils import encode_image_url
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MODEL_NAME = "Kwai-Keye/Keye-VL-8B-Preview"
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QUESTION = "What is the content of each image?"
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class ModelRequestData(NamedTuple):
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engine_args: EngineArgs
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prompt: str
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image_data: list[Image]
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stop_token_ids: list[int] | None = None
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chat_template: str | None = None
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sampling_params: SamplingParams | None = None
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@pytest.mark.core_model
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@pytest.mark.parametrize("question", [QUESTION])
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def test_keye_vl(
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image_assets,
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question: str,
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):
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images = [asset.pil_image for asset in image_assets]
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image_urls = [encode_image_url(image) for image in images]
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engine_args = EngineArgs(
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model=MODEL_NAME,
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trust_remote_code=True,
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max_model_len=8192,
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max_num_seqs=5,
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limit_mm_per_prompt={"image": len(image_urls)},
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)
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placeholders = [{"type": "image", "image": url} for url in image_urls]
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messages = [
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{
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"role": "user",
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"content": [
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*placeholders,
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{"type": "text", "text": question},
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],
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},
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]
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processor = AutoProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True)
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prompt = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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engine_args = asdict(engine_args) | {"seed": 42}
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llm = LLM(**engine_args)
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sampling_params = SamplingParams(
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temperature=0.0, max_tokens=256, stop_token_ids=None
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)
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outputs = llm.generate(
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{
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"prompt": prompt,
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"multi_modal_data": {"image": images},
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},
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sampling_params=sampling_params,
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)
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print("-" * 50)
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for o in outputs:
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generated_text = o.outputs[0].text
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print(generated_text)
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assert len(generated_text) > 10, (
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f"Generated text is too short: {generated_text}"
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)
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print("-" * 50)
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