73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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E2E offline tests for Omni model with video input and audio output.
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"""
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import os
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from pathlib import Path
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import pytest
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from vllm.assets.video import VideoAsset
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from .conftest import OmniRunner
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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models = ["Qwen/Qwen3-Omni-30B-A3B-Instruct"]
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# CI stage config for 2xH100-80G GPUs
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stage_configs = [str(Path(__file__).parent / "stage_configs" / "qwen3_omni_ci.yaml")]
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# Create parameter combinations for model and stage config
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test_params = [(model, stage_config) for model in models for stage_config in stage_configs]
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@pytest.mark.parametrize("test_config", test_params)
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def test_video_to_audio(omni_runner: type[OmniRunner], test_config) -> None:
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"""Test processing video, generating audio output."""
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model, stage_config_path = test_config
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with omni_runner(model, seed=42, stage_configs_path=stage_config_path, stage_init_timeout=300) as runner:
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# Prepare inputs
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question = "Describe the video briefly."
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video = VideoAsset(name="baby_reading", num_frames=4).np_ndarrays
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outputs = runner.generate_multimodal(
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prompts=question,
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videos=video,
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)
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# Find and verify text output (thinker stage)
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text_output = None
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output_count = 0
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for stage_output in outputs:
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if stage_output.final_output_type == "text":
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text_output = stage_output
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output_count += 1
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break
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assert output_count > 0
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assert text_output is not None
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assert len(text_output.request_output) > 0
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text_content = text_output.request_output[0].outputs[0].text
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assert text_content is not None
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assert len(text_content.strip()) > 0
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# Find and verify audio output (code2wav stage)
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audio_output = None
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output_count = 0
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for stage_output in outputs:
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if stage_output.final_output_type == "audio":
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audio_output = stage_output
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output_count += 1
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break
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assert output_count > 0
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assert audio_output is not None
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assert len(audio_output.request_output) > 0
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# Verify audio tensor exists and has content
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audio_tensor = audio_output.request_output[0].multimodal_output["audio"]
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assert audio_tensor is not None
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assert audio_tensor.numel() > 0
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