add lars parameter check

mindspore/nn/optim/lars.py

@@ -21,6 +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._checkparam import Validator as validator
 from .optimizer import grad_scale, Optimizer
 
 lars_opt = C.MultitypeFuncGraph("lars_opt")
@@ -41,6 +42,11 @@ def _tensor_run_opt(lars, weight_decay, learning_rate, gradient, weight, decay_f
 
     return gradient
 
+def _check_param_value(optimizer, epsilon, hyperpara, use_clip, prim_name):
+    validator.check_value_type("optimizer", optimizer, Optimizer, prim_name)
+    validator.check_value_type("epsilon", epsilon, [float], prim_name)
+    validator.check_value_type("hyperpara", hyperpara, [float], prim_name)
+    validator.check_value_type("use_clip", use_clip, [bool], prim_name)
 
 class LARS(Optimizer):
     """
@@ -79,9 +85,10 @@ class LARS(Optimizer):
     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__(0.0, [Parameter(Tensor(0.0), name="trivial")])
+        super(LARS, self).__init__(0.0, [Parameter(Tensor(0.0), name="trivial")], weight_decay, loss_scale)
         if optimizer.is_group:
             raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
+        _check_param_value(optimizer, epsilon, hyperpara, use_clip, self.cls_name)
         self.opt = optimizer
         self.parameters = optimizer.parameters
         self.learning_rate = optimizer.learning_rate