modify the limit of loss scale

mindspore/nn/optim/optimizer.py

@@ -79,9 +79,10 @@ class Optimizer(Cell):
               the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
               in the value of 'order_params' should be in one of group parameters.
 
-        weight_decay (float): A floating point value for the weight decay. It should be equal to or greater than 0.
+        weight_decay (float): A floating point value for the weight decay. It should be not less than 0 and not
+                              greater than 1.
             If the type of `weight_decay` input is int, it will be converted to float. Default: 0.0.
-        loss_scale (float): A floating point value for the loss scale. It should be greater than 0. If the
+        loss_scale (float): A floating point value for the loss scale. It should be not less than 1. If the
             type of `loss_scale` input is int, it will be converted to float. Default: 1.0.
 
     Raises:
@@ -103,12 +104,12 @@ class Optimizer(Cell):
         if isinstance(loss_scale, int):
             loss_scale = float(loss_scale)
         validator.check_value_type("loss_scale", loss_scale, [float], self.cls_name)
-        validator.check_number_range("loss_scale", loss_scale, 0.0, float("inf"), Rel.INC_NEITHER, self.cls_name)
+        validator.check_number_range("loss_scale", loss_scale, 1.0, float("inf"), Rel.INC_LEFT, self.cls_name)
 
         if isinstance(weight_decay, int):
             weight_decay = float(weight_decay)
         validator.check_value_type("weight_decay", weight_decay, [float], self.cls_name)
-        validator.check_number_range("weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name)
+        validator.check_number_range("weight_decay", weight_decay, 0.0, 1.0, Rel.INC_BOTH, self.cls_name)
 
         self.is_group = False
         self.is_group_lr = False

tests/ut/python/parallel/test_loss_and_optimizer.py

@@ -98,7 +98,7 @@ def test_momentum_with_loss_scale():
 
     net = Net(strategy1, strategy2, weight)
 
-    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9, loss_scale=0.5)
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9, loss_scale=1.0)
 
     net_with_loss = NetWithLoss(net, strategy3)
 
@@ -169,7 +169,7 @@ def test_momentum_with_loss_scale_and_dynamic_lr():
     net = Net(strategy1, strategy2, weight)
 
     lr = Tensor(np.ones([6]), dtype=ms.float32)
-    optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=0.9, loss_scale=0.5)
+    optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=0.9, loss_scale=1.0)
 
     net_with_loss = NetWithLoss(net, strategy3)