diff --git a/mmengine/optim/optimizer/amp_optimizer_wrapper.py b/mmengine/optim/optimizer/amp_optimizer_wrapper.py
index 4f3323f2cc..d2c8cf1f19 100644
--- a/mmengine/optim/optimizer/amp_optimizer_wrapper.py
+++ b/mmengine/optim/optimizer/amp_optimizer_wrapper.py
@@ -91,16 +91,21 @@ def __init__(self,
         else:
             scaler_type = GradScaler
 
+        enable_loss_scaler = dtype != torch.bfloat16
+
         if loss_scale == 'dynamic':
             #  If loss_scale is a string, it must be 'dynamic', then dynamic
             #  loss scaling will be used.
-            self.loss_scaler = scaler_type()
+            self.loss_scaler = scaler_type(enabled=enable_loss_scaler)
         elif isinstance(loss_scale, float):
             # Static loss scaling
             self._scale_update_param = loss_scale
-            self.loss_scaler = scaler_type(init_scale=loss_scale)
+            self.loss_scaler = scaler_type(
+                init_scale=loss_scale, enabled=enable_loss_scaler)
         elif isinstance(loss_scale, dict):
             # More specific configuration.
+            loss_scale['enabled'] = loss_scale.pop('enabled',
+                                                   True) and enable_loss_scaler
             self.loss_scaler = scaler_type(**loss_scale)
         else:
             raise TypeError('loss_scale must be of type float, dict, or '