diff --git a/mindspore/nn/optim/adam.py b/mindspore/nn/optim/adam.py
index 365fe8f1c5f879c56b3f10393dca62fa6078b255..40cb4b70d6bac780b7a094df1e95aca8ea9df692 100755
--- a/mindspore/nn/optim/adam.py
+++ b/mindspore/nn/optim/adam.py
@@ -51,7 +51,6 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay, param, m, v, gradient, d
     Returns:
         Tensor, the new value of v after updating.
     """
-    success = True
     if optim_filter:
         op_mul = P.Mul()
         op_square = P.Square()
@@ -81,8 +80,9 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay, param, m, v, gradient, d
         next_param = F.depend(next_param, F.assign(param, op_cast(next_param, F.dtype(param))))
         next_param = F.depend(next_param, F.assign(m, op_cast(next_m, F.dtype(m))))
         next_param = F.depend(next_param, F.assign(v, op_cast(next_v, F.dtype(v))))
-        success = F.depend(success, next_param)
-    return success
+
+        return op_cast(next_param, F.dtype(param))
+    return gradient
 
 
 @_adam_opt.register("Function", "Function", "Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
diff --git a/mindspore/nn/optim/lamb.py b/mindspore/nn/optim/lamb.py
index aa33c3eeb9cdb0b6e86a39079b91c3d86155aa5b..0197167adfcdd5892b32d8ebda40c1c6f568be3a 100755
--- a/mindspore/nn/optim/lamb.py
+++ b/mindspore/nn/optim/lamb.py
@@ -104,11 +104,11 @@ def _update_run_op(beta1, beta2, eps, global_step, lr, weight_decay, param, m, v
 
         next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
 
-        next_param = F.depend(next_param, F.assign(param, next_param))
-        next_param = F.depend(next_param, F.assign(m, next_m))
-        next_param = F.depend(next_param, F.assign(v, next_v))
+        next_param = F.depend(next_param, F.assign(param, op_cast(next_param, F.dtype(param))))
+        next_param = F.depend(next_param, F.assign(m, op_cast(next_m, F.dtype(m))))
+        next_param = F.depend(next_param, F.assign(v, op_cast(next_v, F.dtype(v))))
 
-        return next_param
+        return op_cast(next_param, F.dtype(param))
     return gradient