[Test] Add full test for Qwen3-Omni-30B-A3B-Instruct (#720)
Signed-off-by: wangyu31577 <wangyu31577@hundsun.com> Co-authored-by: wangyu31577 <wangyu31577@hundsun.com>
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wangyuandwangyu31577 authored and GitHub committed
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@@ -1,8 +1,19 @@
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import base64
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import os
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import socket
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import subprocess
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import sys
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import time
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from pathlib import Path
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from typing import Any
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import psutil
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import pytest
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import torch
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import whisper
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import yaml
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from vllm.logger import init_logger
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from vllm.utils import get_open_port
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logger = init_logger(__name__)
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@@ -34,3 +45,286 @@ def clean_gpu_memory_between_tests():
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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gc.collect()
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def dummy_messages_from_mix_data(
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system_prompt: dict[str, Any] = None,
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video_data_url: Any = None,
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audio_data_url: Any = None,
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image_data_url: Any = None,
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content_text: str = None,
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):
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"""Create messages with video、image、audio data URL for OpenAI API."""
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if content_text is not None:
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content = [{"type": "text", "text": content_text}]
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else:
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content = []
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media_items = []
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if isinstance(video_data_url, list):
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for video_url in video_data_url:
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media_items.append((video_url, "video"))
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else:
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media_items.append((video_data_url, "video"))
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if isinstance(image_data_url, list):
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for url in image_data_url:
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media_items.append((url, "image"))
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else:
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media_items.append((image_data_url, "image"))
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if isinstance(audio_data_url, list):
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for url in audio_data_url:
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media_items.append((url, "audio"))
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else:
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media_items.append((audio_data_url, "audio"))
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content.extend(
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{"type": f"{media_type}_url", f"{media_type}_url": {"url": url}}
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for url, media_type in media_items
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if url is not None
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)
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messages = [{"role": "user", "content": content}]
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if system_prompt is not None:
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messages = [system_prompt] + messages
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return messages
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def cosine_similarity_text(s1, s2):
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"""
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Calculate cosine similarity between two text strings.
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Notes:
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------
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- Higher score means more similar texts
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- Score of 1.0 means identical word composition (bag-of-words)
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- Score of 0.0 means completely different vocabulary
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"""
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.metrics.pairwise import cosine_similarity
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vectorizer = CountVectorizer().fit_transform([s1, s2])
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vectors = vectorizer.toarray()
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return cosine_similarity([vectors[0]], [vectors[1]])[0][0]
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def convert_audio_to_text(audio_data):
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"""
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Convert base64 encoded audio data to text using speech recognition.
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"""
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audio_data = base64.b64decode(audio_data)
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output_path = f"./test_{int(time.time())}"
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with open(output_path, "wb") as audio_file:
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audio_file.write(audio_data)
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print(f"audio data is saved: {output_path}")
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model = whisper.load_model("base")
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text = model.transcribe(output_path)["text"]
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if text:
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return text
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else:
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return ""
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def modify_stage_config(
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yaml_path: str,
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stage_updates: dict[int, dict[str, Any]],
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) -> str:
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"""
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Batch modify configurations for multiple stages in a YAML file.
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Args:
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yaml_path: Path to the YAML configuration file.
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stage_updates: Dictionary where keys are stage IDs and values are dictionaries of
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modifications for that stage. Each modification dictionary uses
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dot-separated paths as keys and new configuration values as values.
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Example: {
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0: {'engine_args.max_model_len': 5800},
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1: {'runtime.max_batch_size': 2}
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}
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Returns:
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str: Path to the newly created modified YAML file with timestamp suffix.
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Example:
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>>> output_file = modify_stage_config(
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... 'config.yaml',
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... {
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... 0: {'engine_args.max_model_len': 5800},
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... 1: {'runtime.max_batch_size': 2}
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... }
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... )
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>>> print(f"Modified configuration saved to: {output_file}")
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Modified configuration saved to: config_1698765432.yaml
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"""
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path = Path(yaml_path)
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if not path.exists():
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raise FileNotFoundError(f"yaml does not exist: {path}")
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try:
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with open(yaml_path, encoding="utf-8") as f:
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config = yaml.safe_load(f) or {}
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except Exception as e:
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raise ValueError(f"Cannot parse YAML file: {e}")
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stage_args = config.get("stage_args", [])
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if not stage_args:
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raise ValueError("the stage_args does not exist")
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for stage_id, config_dict in stage_updates.items():
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target_stage = None
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for stage in stage_args:
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if stage.get("stage_id") == stage_id:
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target_stage = stage
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break
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if target_stage is None:
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available_ids = [s.get("stage_id") for s in stage_args if "stage_id" in s]
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raise KeyError(f"Stage ID {stage_id} is not exist, available IDs: {available_ids}")
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for key_path, value in config_dict.items():
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current = target_stage
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keys = key_path.split(".")
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for i in range(len(keys) - 1):
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key = keys[i]
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if key not in current:
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raise KeyError(f"the {'.'.join(keys[: i + 1])} does not exist")
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elif not isinstance(current[key], dict) and i < len(keys) - 2:
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raise ValueError(f"{'.'.join(keys[: i + 1])}' cannot continue deeper because it's not a dict")
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current = current[key]
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current[keys[-1]] = value
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output_path = f"{yaml_path.split('.')[0]}_{int(time.time())}.yaml"
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with open(output_path, "w", encoding="utf-8") as f:
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yaml.dump(config, f, default_flow_style=False, sort_keys=False, allow_unicode=True, indent=2)
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return output_path
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class OmniServer:
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"""Omniserver for vLLM-Omni tests."""
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def __init__(
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self,
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model: str,
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serve_args: list[str],
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*,
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env_dict: dict[str, str] | None = None,
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) -> None:
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self.model = model
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self.serve_args = serve_args
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self.env_dict = env_dict
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self.proc: subprocess.Popen | None = None
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self.host = "127.0.0.1"
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self.port = get_open_port()
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def _start_server(self) -> None:
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"""Start the vLLM-Omni server subprocess."""
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env = os.environ.copy()
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env["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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if self.env_dict is not None:
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env.update(self.env_dict)
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cmd = [
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sys.executable,
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"-m",
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"vllm_omni.entrypoints.cli.main",
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"serve",
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self.model,
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"--omni",
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"--host",
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self.host,
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"--port",
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str(self.port),
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] + self.serve_args
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print(f"Launching OmniServer with: {' '.join(cmd)}")
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self.proc = subprocess.Popen(
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cmd,
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env=env,
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cwd=os.path.dirname(os.path.dirname(os.path.abspath(__file__))), # Set working directory to vllm-omni root
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)
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# Wait for server to be ready
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max_wait = 600 # 10 minutes
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start_time = time.time()
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while time.time() - start_time < max_wait:
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try:
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
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sock.settimeout(1)
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result = sock.connect_ex((self.host, self.port))
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if result == 0:
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print(f"Server ready on {self.host}:{self.port}")
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return
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except Exception:
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pass
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time.sleep(2)
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raise RuntimeError(f"Server failed to start within {max_wait} seconds")
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def _kill_process_tree(self, pid):
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"""kill process and its children"""
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try:
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parent = psutil.Process(pid)
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children = parent.children(recursive=True)
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for child in children:
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try:
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child.terminate()
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except psutil.NoSuchProcess:
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pass
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gone, still_alive = psutil.wait_procs(children, timeout=10)
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for child in still_alive:
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try:
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child.kill()
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except psutil.NoSuchProcess:
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pass
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try:
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parent.terminate()
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parent.wait(timeout=10)
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except (psutil.NoSuchProcess, psutil.TimeoutExpired):
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try:
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parent.kill()
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except psutil.NoSuchProcess:
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pass
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except psutil.NoSuchProcess:
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pass
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def __enter__(self):
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self._start_server()
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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if self.proc:
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try:
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parent = psutil.Process(self.proc.pid)
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children = parent.children(recursive=True)
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for child in children:
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try:
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child.terminate()
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except psutil.NoSuchProcess:
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pass
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gone, still_alive = psutil.wait_procs(children, timeout=10)
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for child in still_alive:
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try:
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child.kill()
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except psutil.NoSuchProcess:
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pass
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try:
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parent.terminate()
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parent.wait(timeout=10)
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except (psutil.NoSuchProcess, psutil.TimeoutExpired):
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try:
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parent.kill()
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except psutil.NoSuchProcess:
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pass
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except psutil.NoSuchProcess:
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pass
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