jl9876andChendi.Xue
0d5d1115d6
Signed-off-by: jl9876 <jia.li@intel.com> Co-authored-by: Chendi.Xue <chendi.xue@intel.com>
52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
"""E2E smoke test for Wan2.1 AutoRound MXFP4 inference."""
|
|
|
|
import os
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
|
|
from tests.helpers.mark import hardware_test
|
|
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
|
|
from vllm_omni.platforms import current_omni_platform
|
|
|
|
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
|
|
|
|
QUANTIZED_MODEL = os.environ.get(
|
|
"WAN21_AUTOROUND_MXFP4_MODEL",
|
|
"INCModel/Wan2.1-T2V-1.3B-Diffusers-MXFP4-AutoRound",
|
|
)
|
|
|
|
pytestmark = [pytest.mark.full_model, pytest.mark.diffusion]
|
|
|
|
|
|
@hardware_test(res={"xpu": "B60"})
|
|
@pytest.mark.parametrize(
|
|
"omni_runner",
|
|
[(QUANTIZED_MODEL, None)],
|
|
indirect=True,
|
|
)
|
|
def test_wan21_autoround_mxfp4_generates_video(omni_runner, omni_runner_handler):
|
|
sampling_params = OmniDiffusionSamplingParams(
|
|
height=256,
|
|
width=256,
|
|
num_frames=5,
|
|
num_inference_steps=1,
|
|
guidance_scale=1.0,
|
|
generator=torch.Generator(device=current_omni_platform.device_type).manual_seed(42),
|
|
)
|
|
response = omni_runner_handler.send_diffusion_request(
|
|
{
|
|
"prompt": "A red toy car driving on a table",
|
|
"sampling_params": sampling_params,
|
|
}
|
|
)
|
|
|
|
assert response.success, response.error_message
|
|
assert response.images is not None and len(response.images) == 1
|
|
frames = np.asarray(response.images[0])
|
|
assert frames.shape[1:4] == (5, 256, 256)
|
|
assert frames.std() > 0.01
|