Fix dtype bug for loss_scale and weight_decay.

1.Change dtype of scale to dtype of grad in loss_scale.py;
2.Change dtype of weight_decay to dtype of weight in optimizer.py.

mindspore/nn/optim/optimizer.py

@@ -84,7 +84,7 @@ apply_decay = C.MultitypeFuncGraph("apply_decay")
 def _tensor_apply_decay(weight_decay, if_apply, weight, gradient):
     """Get grad with weight_decay."""
     if if_apply:
-        return op_add((gradient, weight * F.scalar_to_array(weight_decay)))
+        return op_add((gradient, weight * weight_decay))
     return gradient
 
 

mindspore/nn/wrap/loss_scale.py

@@ -32,7 +32,7 @@ reciprocal = P.Reciprocal()
 
 @_grad_scale.register("Tensor", "Tensor")
 def tensor_grad_scale(scale, grad):
-    return grad * reciprocal(scale)
+    return grad * F.cast(reciprocal(scale), F.dtype(grad))
 
 
 class DynamicLossScaleUpdateCell(Cell):