fix lars base class type

mindspore/nn/optim/lars.py

@@ -21,8 +21,7 @@ from mindspore.common.parameter import Parameter
 from mindspore.ops import operations as P
 from mindspore.ops import composite as C
 from mindspore.ops import functional as F
-from mindspore.nn.cell import Cell
-from .optimizer import grad_scale
+from .optimizer import grad_scale, Optimizer
 
 lars_opt = C.MultitypeFuncGraph("lars_opt")
 
@@ -61,7 +60,7 @@ def _tensor_run_opt_v2(lars, weight_decay, learning_rate, gradient, weight, deca
     return gradient
 
 
-class LARS(Cell):
+class LARS(Optimizer):
     """
     Implements the LARS algorithm with LARSUpdate Operator.
 
@@ -98,7 +97,7 @@ class LARS(Cell):
     def __init__(self, optimizer, epsilon=1e-05, hyperpara=0.001, weight_decay=0.0, use_clip=False,
                  decay_filter=lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name,
                  lars_filter=lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name, loss_scale=1.0):
-        super(LARS, self).__init__(auto_prefix=False)
+        super(LARS, self).__init__(0.0, [Parameter(Tensor(0.0), name="trivial")])
         self.opt = optimizer
         self.parameters = optimizer.parameters
         self.learning_rate = optimizer.learning_rate

mindspore/nn/optim/optimizer.py

@@ -57,7 +57,7 @@ class Optimizer(Cell):
 
     def __init__(self, learning_rate, parameters, weight_decay=0.0, loss_scale=1.0,
                  decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
-        super(Optimizer, self).__init__()
+        super(Optimizer, self).__init__(auto_prefix=False)
         if isinstance(learning_rate, float):
             self.dynamic_lr = False
             self.gather = None