revert modification of opt

mindspore/nn/optim/adam.py

@@ -40,7 +40,7 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay, param, m, v, gradient, d
         beta2 (Tensor): The exponential decay rate for the 2nd moment estimates. Should be in range (0.0, 1.0).
         eps (Tensor): Term added to the denominator to improve numerical stability. Should be greater than 0.
         lr (Tensor): Learning rate.
-        weight_decay (Number): Weight decay. Should be in range [0.0, 1.0].
+        weight_decay (Number): Weight decay. Should be equal to or greater than 0.
         param (Tensor): Parameters.
         m (Tensor): m value of parameters.
         v (Tensor): v value of parameters.
@@ -200,8 +200,8 @@ class Adam(Optimizer):
         use_nesterov (bool): Whether to use Nesterov Accelerated Gradient (NAG) algorithm to update the gradients.
             If True, updates the gradients using NAG.
             If False, updates the gradients without using NAG. Default: False.
-        weight_decay (float): Weight decay (L2 penalty). It should be in range [0.0, 1.0]. Default: 0.0.
-        loss_scale (float): A floating point value for the loss scale. Should be not less than 1.0. Default: 1.0.
+        weight_decay (float): Weight decay (L2 penalty). It should be equal to or greater than 0. Default: 0.0.
+        loss_scale (float): A floating point value for the loss scale. Should be greater than 0. Default: 1.0.
 
     Inputs:
         - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
@@ -318,7 +318,7 @@ class AdamWeightDecay(Optimizer):
             Should be in range (0.0, 1.0).
         eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6.
             Should be greater than 0.
-        weight_decay (float): Weight decay (L2 penalty). It should be in range [0.0, 1.0]. Default: 0.0.
+        weight_decay (float): Weight decay (L2 penalty). It should be equal to or greater than 0. Default: 0.0.
 
     Inputs:
         - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.

mindspore/nn/optim/ftrl.py

@@ -116,7 +116,7 @@ class FTRL(Optimizer):
         l2 (float): l2 regularization strength, must be greater than or equal to zero. Default: 0.0.
         use_locking (bool): If True use locks for update operation. Default: False.
         loss_scale (float): Value for the loss scale. It should be equal to or greater than 1.0. Default: 1.0.
-        weight_decay (float): Weight decay value to multiply weight, should be in range [0.0, 1.0]. Default: 0.0.
+        weight_decay (float): Weight decay value to multiply weight, must be zero or positive value. Default: 0.0.
 
     Inputs:
         - **grads** (tuple[Tensor]) - The gradients of `params` in optimizer, the shape is as same as the `params`

mindspore/nn/optim/lamb.py

@@ -43,7 +43,7 @@ def _update_run_op(beta1, beta2, eps, global_step, lr, weight_decay, param, m, v
         beta2 (Tensor): The exponential decay rate for the 2nd moment estimates. Should be in range (0.0, 1.0).
         eps (Tensor): Term added to the denominator to improve numerical stability. Should be greater than 0.
         lr (Tensor): Learning rate.
-        weight_decay (Number): Weight decay. Should be in range [0.0, 1.0].
+        weight_decay (Number): Weight decay. Should be equal to or greater than 0.
         global_step (Tensor): Global step.
         param (Tensor): Parameters.
         m (Tensor): m value of parameters.
@@ -126,7 +126,7 @@ def _update_run_op_graph_kernel(beta1, beta2, eps, global_step, lr, weight_decay
         beta2 (Tensor): The exponential decay rate for the 2nd moment estimates. Should be in range (0.0, 1.0).
         eps (Tensor): Term added to the denominator to improve numerical stability. Should be greater than 0.
         lr (Tensor): Learning rate.
-        weight_decay (Number): Weight decay. Should be in range [0.0, 1.0].
+        weight_decay (Number): Weight decay. Should be equal to or greater than 0.
         global_step (Tensor): Global step.
         param (Tensor): Parameters.
         m (Tensor): m value of parameters.
@@ -227,7 +227,7 @@ class Lamb(Optimizer):
             Should be in range (0.0, 1.0).
         eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6.
             Should be greater than 0.
-        weight_decay (float): Weight decay (L2 penalty). Default: 0.0. Should be in range [0.0, 1.0].
+        weight_decay (float): Weight decay (L2 penalty). Default: 0.0. Should be equal to or greater than 0.
 
     Inputs:
         - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.

mindspore/nn/optim/lazyadam.py

@@ -133,7 +133,7 @@ class LazyAdam(Optimizer):
             If True, updates the gradients using NAG.
             If False, updates the gradients without using NAG. Default: False.
         weight_decay (float): Weight decay (L2 penalty). Default: 0.0.
-        loss_scale (float): A floating point value for the loss scale. It should be not less than 1.0. Default:
+        loss_scale (float): A floating point value for the loss scale. Should be equal to or greater than 1. Default:
                             1.0.
 
     Inputs:

mindspore/nn/optim/momentum.py

@@ -92,8 +92,8 @@ class Momentum(Optimizer):
             equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float.
         momentum (float): Hyperparameter of type float, means momentum for the moving average.
             It should be at least 0.0.
-        weight_decay (int, float): Weight decay (L2 penalty). It should be in range [0.0, 1.0]. Default: 0.0.
-        loss_scale (int, float): A floating point value for the loss scale. Should be not less than 1.0. Default: 1.0.
+        weight_decay (int, float): Weight decay (L2 penalty). It should be equal to or greater than 0.0. Default: 0.0.
+        loss_scale (int, float): A floating point value for the loss scale. It should be greater than 0.0. Default: 1.0.
         use_nesterov (bool): Enable Nesterov momentum. Default: False.
 
     Inputs:

mindspore/nn/optim/optimizer.py

@@ -78,9 +78,9 @@ 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 in range [0.0, 1.0].
+        weight_decay (float): A floating point value for the weight decay. It should be equal to or greater than 0.
             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 not less than 1.0. If the
+        loss_scale (float): A floating point value for the loss scale. It should be greater than 0. If the
             type of `loss_scale` input is int, it will be converted to float. Default: 1.0.
 
     Raises:
@@ -102,7 +102,7 @@ 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, 1.0, float("inf"), Rel.INC_LEFT, self.cls_name)
+        validator.check_number_range("loss_scale", loss_scale, 0.0, float("inf"), Rel.INC_NEITHER, self.cls_name)
         self.loss_scale = loss_scale
 
         weight_decay = self._preprocess_weight_decay(weight_decay)

mindspore/nn/optim/proximal_ada_grad.py

@@ -98,8 +98,8 @@ class ProximalAdagrad(Optimizer):
         l1 (float): l1 regularization strength, must be greater than or equal to zero. Default: 0.0.
         l2 (float): l2 regularization strength, must be greater than or equal to zero. Default: 0.0.
         use_locking (bool): If True use locks for update operation. Default: False.
-        loss_scale (float): Value for the loss scale. It should be not less than 1.0. Default: 1.0.
-        weight_decay (float): Weight decay value to multiply weight, should be in range [0.0, 1.0]. Default: 0.0.
+        loss_scale (float): Value for the loss scale. It should be greater than 0.0. Default: 1.0.
+        weight_decay (float): Weight decay value to multiply weight, must be zero or positive value. Default: 0.0.
 
     Inputs:
         - **grads** (tuple[Tensor]) - The gradients of `params` in optimizer, the shape is as same as the `params`

mindspore/nn/optim/rmsprop.py

@@ -121,8 +121,8 @@ class RMSProp(Optimizer):
                          0. Default: 1e-10.
         use_locking (bool): Enable a lock to protect the update of variable and accumlation tensors. Default: False.
         centered (bool): If True, gradients are normalized by the estimated variance of the gradient. Default: False.
-        loss_scale (float): A floating point value for the loss scale. Should be not less than 1.0. Default: 1.0.
-        weight_decay (float): Weight decay (L2 penalty). Should be in range [0.0, 1.0]. Default: 0.0.
+        loss_scale (float): A floating point value for the loss scale. Should be greater than 0. Default: 1.0.
+        weight_decay (float): Weight decay (L2 penalty). Should be equal to or greater than 0. Default: 0.0.
 
     Inputs:
         - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.

mindspore/nn/optim/sgd.py

@@ -88,10 +88,11 @@ class SGD(Optimizer):
             Default: 0.1.
         momentum (float): A floating point value the momentum. should be at least 0.0. Default: 0.0.
         dampening (float): A floating point value of dampening for momentum. should be at least 0.0. Default: 0.0.
-        weight_decay (float): Weight decay (L2 penalty). It should be in range [0.0, 1.0]. Default: 0.0.
+        weight_decay (float): Weight decay (L2 penalty). It should be equal to or greater than 0. Default: 0.0.
         nesterov (bool): Enables the Nesterov momentum. If use nesterov, momentum must be positive,
                          and dampening must equal to 0.0. Default: False.
-        loss_scale (float): A floating point value for the loss scale. Should be not less than 1.0. Default: 1.0.
+        loss_scale (float): A floating point value for the loss scale, which should be larger
+                            than 0.0. Default: 1.0.
 
     Inputs:
         - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.

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=1.0)
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9, loss_scale=0.5)
 
     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=1.0)
+    optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=0.9, loss_scale=0.5)
 
     net_with_loss = NetWithLoss(net, strategy3)