diff --git a/mindspore/python/mindspore/nn/cell.py b/mindspore/python/mindspore/nn/cell.py
index cd705edcba5..659413f49c7 100755
--- a/mindspore/python/mindspore/nn/cell.py
+++ b/mindspore/python/mindspore/nn/cell.py
@@ -294,7 +294,7 @@ class Cell(Cell_):
 
     def get_func_graph_proto(self):
         """Return graph binary proto."""
-        exec_id = self.phase + "." + str(self.create_time) + '.' + str(id(self))
+        exec_id = ".".join([self.phase, str(self.create_time), str(id(self))])
         return _cell_graph_executor._get_func_graph_proto(self, exec_id, "anf_ir", True)
 
     def __getattr__(self, name):
@@ -730,17 +730,6 @@ class Cell(Cell_):
         else:
             object.__setattr__(self, name, value)
 
-    def _check_param_list_tuple(self, value):
-        """
-        Check the type of input in list or tuple is Parameter.
-        :param value: list or tuple.
-        :return: The types of all inputs are parameter.
-        """
-        for item in value:
-            if not isinstance(item, Parameter):
-                return False
-        return True
-
     def __setattr__(self, name, value):
         cells = self.__dict__.get('_cells')
         params = self.__dict__.get('_params')
@@ -748,7 +737,7 @@ class Cell(Cell_):
             self._set_attr_for_parameter(name, value)
         elif isinstance(value, ParameterTuple):
             self._set_attr_for_parameter_tuple(name, value)
-        elif isinstance(value, (list, tuple)) and value and self._check_param_list_tuple(value):
+        elif isinstance(value, (list, tuple)) and value and _check_param_list_tuple(value):
             self._set_attr_for_parameter_in_list_or_tuple(name, value)
         elif isinstance(value, Cell):
             self._set_attr_for_cell(name, value)
@@ -2227,3 +2216,15 @@ class GraphCell(Cell):
         self.phase = "graph_load_from_mindir"
         self._add_attr("graph_load_from_mindir", self.graph)
         return self.compile_and_run(*inputs)
+
+
+def _check_param_list_tuple(value):
+    """
+    Check the type of input in list or tuple is Parameter.
+    :param value: list or tuple.
+    :return: The types of all inputs are parameter.
+    """
+    for item in value:
+        if not isinstance(item, Parameter):
+            return False
+    return True
diff --git a/mindspore/python/mindspore/nn/wrap/loss_scale.py b/mindspore/python/mindspore/nn/wrap/loss_scale.py
index 63e3644b2a3..8cbd3be78b0 100644
--- a/mindspore/python/mindspore/nn/wrap/loss_scale.py
+++ b/mindspore/python/mindspore/nn/wrap/loss_scale.py
@@ -521,18 +521,16 @@ class _TrainPipelineWithLossScaleCell(TrainOneStepCell):
         self.opt_shard = _get_enable_parallel_optimizer()
 
     def construct(self, *inputs):
-        weights = self.weights
         loss = self.network(*inputs)
         scaling_sens = self.scale_sense
         init = self.alloc_status()
-        status_clear = self.clear_before_grad(init)
         scaling_sens_filled = C.ones_like(loss) * F.cast(scaling_sens, F.dtype(loss))
-        grads = self.grad(self.network, weights)(*inputs, scaling_sens_filled)
+        grads = self.grad(self.network, self.weights)(*inputs, scaling_sens_filled)
         init = F.depend(init, grads)
         get_status = self.get_status(init)
         init = F.depend(init, get_status)
         flag_sum = self.reduce_sum(init, (0,))
-        loss = F.depend(loss, status_clear)
+        loss = F.depend(loss, self.clear_before_grad(init))
         if self.opt_shard:
             grads = self.grad_reducer(grads)
             grads = self.hyper_map(F.partial(shard_grad_scale, scaling_sens * self.degree), grads, self.accu_grads)