Change const tensor dtype to fp16

mindspore/nn/optim/optimizer.py

@@ -138,14 +138,14 @@ class Optimizer(Cell):
         if self.is_group:
             self.parameters = ParameterTuple(self.group_params)
             self.weight_decay = tuple(self.group_weight_decay)
-            self.weight_decay_tensor_tuple = tuple(Tensor(x, mstype.float32) for x in self.group_weight_decay)
+            self.weight_decay_tensor_tuple = tuple(Tensor(x, mstype.float16) for x in self.group_weight_decay)
             decay_filter = lambda x: x > 0
             self.decay_flags = tuple(decay_filter(x) for x in self.weight_decay)
             self.exec_weight_decay = any(self.decay_flags)
         else:
             self.parameters = ParameterTuple(parameters)
             self.weight_decay = weight_decay * loss_scale
-            self.weight_decay_tensor = Tensor(self.weight_decay, mstype.float32)
+            self.weight_decay_tensor = Tensor(self.weight_decay, mstype.float16)
             decay_filter = lambda x: 'beta' not in x.name and 'gamma' not in x.name
             self.decay_flags = tuple(decay_filter(x) for x in self.parameters)
             self.exec_weight_decay = self.weight_decay > 0
@@ -156,7 +156,7 @@ class Optimizer(Cell):
                 break
         ps_filter = lambda x: x.is_param_ps
         self.ps_parameters = tuple(ps_filter(x) for x in self.parameters)
-        self.reciprocal_scale = Tensor(1.0 / loss_scale, mstype.float32)
+        self.reciprocal_scale = Tensor(1.0 / loss_scale, mstype.float16)
         self.need_scale = loss_scale != 1.0
         self.param_length = len(self.parameters)
         self.map_ = C.Map()