Create device_manager.py

device_manager.py

@@ -0,0 +1,89 @@
+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:
+            api = HfApi()
+            # 檢查環境變數或其他方式確認是否在 Spaces 環境
+            if 'SPACE_ID' in os.environ:
+                # 這裡可以添加更多具體的 ZeroGPU 可用性檢查
+                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():
+                self._current_device = torch.device('cuda')
+                logger.info("Using ZeroGPU")
+            else:
+                self._current_device = torch.device('cpu')
+                logger.info("Using CPU")
+        return self._current_device
+    
+    def move_to_device(self, tensor_or_model):
+        device = self.get_optimal_device()
+        if hasattr(tensor_or_model, 'to'):
+            return tensor_or_model.to(device)
+        return tensor_or_model
+
+def device_handler(func):
+    """Decorator for handling device placement"""
+    @wraps(func)
+    async def wrapper(*args, **kwargs):
+        device_mgr = DeviceManager()
+        
+        # 處理輸入參數的設備轉換
+        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:
+            result = await func(*processed_args, **processed_kwargs)
+            
+            # 處理輸出結果的設備轉換
+            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):
+                logger.warning("GPU memory exceeded, falling back to CPU")
+                device_mgr._current_device = torch.device('cpu')
+                return await wrapper(*args, **kwargs)
+            raise e
+            
+    return wrapper