Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,148 Bytes
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from functools import wraps
import torch
from huggingface_hub import HfApi
import os
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class DeviceManager:
_instance = None
def __new__(cls):
if cls._instance is None:
cls._instance = super(DeviceManager, cls).__new__(cls)
cls._instance._initialized = False
return cls._instance
def __init__(self):
if self._initialized:
return
self._initialized = True
self._current_device = None
self._zero_gpu_available = None
def check_zero_gpu_availability(self):
try:
# 檢查是否在 Spaces 環境中
if 'SPACE_ID' not in os.environ:
return False
# 檢查是否為 Pro 用戶（ZeroGPU 可用）
api = HfApi()
space_info = api.get_space_runtime(os.environ['SPACE_ID'])
# 檢查是否有 ZeroGPU 資源
if (hasattr(space_info, 'hardware') and
space_info.hardware.get('zerogpu', False)):
self._zero_gpu_available = True
return True
except Exception as e:
logger.warning(f"Error checking ZeroGPU availability: {e}")
self._zero_gpu_available = False
return False
def get_optimal_device(self):
if self._current_device is None:
if self.check_zero_gpu_availability():
try:
# 特別標記這是 ZeroGPU 環境
os.environ['ZERO_GPU'] = '1'
self._current_device = torch.device('cuda')
logger.info("Using ZeroGPU")
except Exception as e:
logger.warning(f"Failed to initialize ZeroGPU: {e}")
self._current_device = torch.device('cpu')
logger.info("Fallback to CPU due to ZeroGPU initialization failure")
else:
self._current_device = torch.device('cpu')
logger.info("Using CPU (ZeroGPU not available)")
return self._current_device
def move_to_device(self, tensor_or_model):
device = self.get_optimal_device()
if hasattr(tensor_or_model, 'to'):
try:
return tensor_or_model.to(device)
except Exception as e:
logger.warning(f"Failed to move tensor/model to {device}: {e}")
self._current_device = torch.device('cpu')
return tensor_or_model.to('cpu')
return tensor_or_model
def device_handler(func):
"""Decorator for handling device placement with ZeroGPU support"""
@wraps(func)
async def wrapper(*args, **kwargs):
device_mgr = DeviceManager()
def process_arg(arg):
if torch.is_tensor(arg) or hasattr(arg, 'to'):
return device_mgr.move_to_device(arg)
return arg
processed_args = [process_arg(arg) for arg in args]
processed_kwargs = {k: process_arg(v) for k, v in kwargs.items()}
try:
# 如果函數是異步的，使用 await
if asyncio.iscoroutinefunction(func):
result = await func(*processed_args, **processed_kwargs)
else:
result = func(*processed_args, **processed_kwargs)
# 處理輸出
if torch.is_tensor(result):
return device_mgr.move_to_device(result)
elif isinstance(result, tuple):
return tuple(device_mgr.move_to_device(r) if torch.is_tensor(r) else r for r in result)
return result
except RuntimeError as e:
if "out of memory" in str(e) or "CUDA" in str(e):
logger.warning("ZeroGPU resources unavailable, falling back to CPU")
device_mgr._current_device = torch.device('cpu')
return await wrapper(*args, **kwargs)
raise e
return wrapper |