141 lines
4.3 KiB
Python
141 lines
4.3 KiB
Python
"""
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E2E Online tests for Qwen2.5-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 tests.conftest import (
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OmniServerParams,
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dummy_messages_from_mix_data,
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generate_synthetic_audio,
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generate_synthetic_image,
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generate_synthetic_video,
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modify_stage_config,
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)
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from tests.utils import hardware_test
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from vllm_omni.platforms import current_omni_platform
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "0"
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models = ["Qwen/Qwen2.5-Omni-7B"]
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def get_config():
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path = modify_stage_config(
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str(Path(__file__).parent.parent / "stage_configs" / "qwen2_5_omni_ci.yaml"),
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)
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return path
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# CI stage config for 2xH100-80G GPUs or AMD GPU MI325
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if current_omni_platform.is_rocm():
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# ROCm stage config optimized for MI325 GPU
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stage_configs = [str(Path(__file__).parent.parent / "stage_configs" / "rocm" / "qwen2_5_omni_ci.yaml")]
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else:
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stage_configs = [get_config()]
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# Create parameter combinations for model and stage config
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test_params = [
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OmniServerParams(model=model, stage_config_path=stage_config) for model in models for stage_config in stage_configs
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]
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def get_system_prompt():
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return {
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"role": "system",
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"content": [
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{
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"type": "text",
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"text": (
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"You are Qwen, a virtual human developed by the Qwen Team, "
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"Alibaba Group, capable of perceiving auditory and visual inputs, "
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"as well as generating text and speech."
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),
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}
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],
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}
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def get_prompt(prompt_type="text_only"):
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prompts = {
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"text_only": "What is the capital of China? Answer in 20 words.",
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"mix": "What is recited in the audio? What is in this image? Describe the video briefly.",
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}
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return prompts.get(prompt_type, prompts["text_only"])
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def get_max_batch_size(size_type="few"):
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batch_sizes = {"few": 5, "medium": 100, "large": 256}
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return batch_sizes.get(size_type, 5)
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@pytest.mark.skip(
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reason="qwen2.5-omni model cannot recognize the synthetic video and audio data, need to update the test data to enable this test case"
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)
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@pytest.mark.advanced_model
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@pytest.mark.core_model
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@pytest.mark.omni
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@hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards=2)
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@pytest.mark.parametrize("omni_server", test_params, indirect=True)
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def test_mix_to_text_audio_001(omni_server, openai_client) -> None:
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"""
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Test multi-modal input processing and text/audio output generation via OpenAI API.
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Deploy Setting: default yaml
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Input Modal: text + audio + video + image
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Output Modal: text + audio
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Input Setting: stream=True
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Datasets: single request
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"""
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video_data_url = f"data:video/mp4;base64,{generate_synthetic_video(224, 224, 300)['base64']}"
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image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(224, 224)['base64']}"
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audio_data_url = f"data:audio/wav;base64,{generate_synthetic_audio(5, 1, sample_rate=16000)['base64']}"
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messages = dummy_messages_from_mix_data(
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system_prompt=get_system_prompt(),
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video_data_url=video_data_url,
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image_data_url=image_data_url,
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audio_data_url=audio_data_url,
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content_text=get_prompt("mix"),
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)
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request_config = {
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"model": omni_server.model,
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"messages": messages,
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"stream": True,
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"key_words": {
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"video": ["sphere", "globe", "circle", "round", "ball"],
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},
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}
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# Test single completion
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openai_client.send_omni_request(request_config)
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@pytest.mark.advanced_model
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@pytest.mark.omni
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@pytest.mark.parametrize("omni_server", test_params, indirect=True)
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def test_text_to_text_001(omni_server, openai_client) -> None:
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"""
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Test text input processing and text/audio output generation via OpenAI API.
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Deploy Setting: default yaml
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Input Modal: text
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Output Modal: text
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Datasets: few requests
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"""
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messages = dummy_messages_from_mix_data(system_prompt=get_system_prompt(), content_text=get_prompt())
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request_config = {
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"model": omni_server.model,
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"messages": messages,
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"stream": False,
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"modalities": ["text"],
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"key_words": {"text": ["beijing"]},
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}
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openai_client.send_omni_request(request_config, request_num=get_max_batch_size())
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