Yuan WuandHongsheng Liu
cb5f508212
Signed-off-by: yuanwu <yuan.wu@intel.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
65 lines
2.3 KiB
Python
65 lines
2.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
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"""
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Online serving smoke for ``Wan-AI/Wan2.2-T2V-A14B-Diffusers`` (text-to-video via ``/v1/videos``).
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Uses a single ``default`` ``OmniServerParams`` row via ``_get_diffusion_feature_cases`` (no extra
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``server_args``), with explicit startup budgets for loading both experts from slow storage.
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Multi-variant / parallel coverage lives in ``test_wan22_expansion.py`` (L4).
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From ``tests/``::
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pytest -s -v e2e/online_serving/test_wan22_t2v.py -m "core_model and diffusion" --run-level=core_model
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pytest -s -v e2e/online_serving/test_wan22_t2v.py -m "advanced_model and diffusion" --run-level=advanced_model
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"""
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import os
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import pytest
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from tests.helpers.mark import hardware_marks
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from tests.helpers.runtime import OmniServer, OmniServerParams, OnlineOmniClient
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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MODEL = "Wan-AI/Wan2.2-T2V-A14B-Diffusers"
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PROMPT = "Two anthropomorphic cats in boxing gear on a spotlighted stage."
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NEGATIVE_PROMPT = "low quality, blurry, watermark, text"
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SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
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def _get_diffusion_feature_cases(model: str):
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"""Return a single default ``OmniServerParams`` row (no extra ``server_args``)."""
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return [
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pytest.param(
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OmniServerParams(model=model, init_timeout=1800, stage_init_timeout=1800, startup_timeout=2100),
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id="default",
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marks=SINGLE_CARD_FEATURE_MARKS,
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),
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]
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@pytest.mark.core_model
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@pytest.mark.advanced_model
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@pytest.mark.diffusion
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@pytest.mark.parametrize("omni_server", _get_diffusion_feature_cases(MODEL), indirect=True)
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def test_text_to_video_001(omni_server: OmniServer, online_client: OnlineOmniClient) -> None:
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"""Default Wan2.2 T2V smoke: async ``/v1/videos`` job completes and returns video bytes."""
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request_config = {
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"model": omni_server.model,
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"form_data": {
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"prompt": PROMPT,
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"negative_prompt": NEGATIVE_PROMPT,
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"height": 512,
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"width": 512,
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"num_frames": 8,
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"fps": 8,
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"num_inference_steps": 2,
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"guidance_scale": 4.0,
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"seed": 42,
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},
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}
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online_client.send_video_diffusion_request(request_config)
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