Add foreach option for faster EMA

timm/utils/model_ema.py

@@ -102,30 +102,34 @@ class ModelEmaV2(nn.Module):
     This class is sensitive where it is initialized in the sequence of model init,
     GPU assignment and distributed training wrappers.
     """
-    def __init__(self, model, decay=0.9999, device=None):
+    def __init__(self, model, decay=0.9999, device=None, foreach=False):
         super(ModelEmaV2, self).__init__()
         # make a copy of the model for accumulating moving average of weights
         self.module = deepcopy(model)
         self.module.eval()
         self.decay = decay
+        self.foreach = foreach
         self.device = device  # perform ema on different device from model if set
-        if self.device is not None:
+        if self.device is not None and device != next(model.parameters()).device:
+            self.foreach = False  # cannot use foreach methods with different devices
             self.module.to(device=device)
 
-    def _update(self, model, update_fn):
-        with torch.no_grad():
-            for ema_v, model_v in zip(self.module.state_dict().values(), model.state_dict().values()):
-                if self.device is not None:
-                    model_v = model_v.to(device=self.device)
-                update_fn(ema_v, model_v)
-
+    @torch.no_grad()
     def update(self, model):
+        ema_params = tuple(self.module.parameters())
+        model_params = tuple(model.parameters())
+        if self.foreach:
+            torch._foreach_mul_(ema_params, scalar=self.decay)
+            torch._foreach_add_(ema_params, model_params, alpha=1 - self.decay)
+        else:
+            for ema_p, model_p in zip(ema_params, model_params):
+                ema_p.mul_(self.decay).add_(model_p.to(device=self.device), alpha=1 - self.decay)
 
-        def ema_update(e, m):
-            if m.is_floating_point():
-                e.mul_(self.decay).add_(m, alpha=1 - self.decay)
-
-        self._update(model, update_fn=ema_update)
+        # copy buffers instead of EMA
+        for ema_b, model_b in zip(self.module.buffers(), model.buffers()):
+            ema_b.copy_(model_b.to(device=self.device))
 
+    @torch.no_grad()
     def set(self, model):
-        self._update(model, update_fn=lambda e, m: e.copy_(m))
+        for ema_v, model_v in zip(self.module.state_dict().values(), model.state_dict().values()):
+            ema_v.copy_(model_v.to(device=self.device))