diff --git a/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_cost.cc b/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_cost.cc
index 21f073e349..db1c1a025d 100644
--- a/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_cost.cc
+++ b/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_cost.cc
@@ -26,7 +26,6 @@
 
 namespace mindspore {
 namespace parallel {
-
 // Compute redistributed cost
 double CostRedis(const Graph::NodeType &node,
                  const std::vector<std::pair<std::string, StrategyRec>> &node_name_to_strategy,
diff --git a/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.cc b/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.cc
index e51bd579f3..46cb6a6e50 100644
--- a/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.cc
+++ b/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.cc
@@ -746,7 +746,6 @@ Strategys CheckBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
 
   size_t first_tensor_dim = ops[iter_ops]->inputs_tensor_info()[0].shape().size();
   size_t second_tensor_dim = ops[iter_ops]->inputs_tensor_info()[1].shape().size();
-
   // Do Broadcasting in the second tensor.
   if (second_tensor_dim < first_tensor_dim) {
     bool braoadcast_first_tensor = false;
@@ -964,7 +963,6 @@ void GenerateEliminatedOperatorStrategyBackward(const std::vector<std::shared_pt
     auto iter_ops = no_stra_op_list->at(iter_list - 1);
     Strategys stra;
     Dimensions s = CopyOutgoingOperatorInputStrategy(ops, input_tensor_names, iter_ops);
-
     if (s.size() != 0 && ops[iter_ops]->type() == SQUEEZE) {
       s = ModifyStrategyIfSqueezeOutgoing(ops, iter_ops, s);
     }
diff --git a/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_partition.cc b/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_partition.cc
index 9b6417a197..ae52047071 100644
--- a/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_partition.cc
+++ b/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_partition.cc
@@ -204,7 +204,9 @@ Status PartitionForAllDevices(const size_t num_device, const double device_memor
 
   // Comopute iter times
   int iter_times = static_cast<int>(log2(num_device));
-
+  if (iter_times > 10) {
+    MS_LOG(EXCEPTION) << "ERROR: Number of iter_times can't be larger than 10.";
+  }
   // N-cuts loop
   for (int loop = 0; loop < iter_times; loop++) {
     // Sort by weights
diff --git a/mindspore/ccsrc/frontend/parallel/step_parallel.cc b/mindspore/ccsrc/frontend/parallel/step_parallel.cc
index fcafa177ef..38fdf4e552 100644
--- a/mindspore/ccsrc/frontend/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/frontend/parallel/step_parallel.cc
@@ -341,7 +341,6 @@ void Redistribution(const std::pair<AnfNodePtr, int> &node_pair, const OperatorI
   TensorInfo tensorinfo_out = next_distribute_operator->inputs_tensor_info()[IntToSize(index - 1)];
   TensorLayout tensorlayout_out = tensorinfo_out.tensor_layout();
   TensorLayout tensorlayout_in = GetTensorInLayout(middle_node, middle_prim, distribute_operator);
-
   if (tensor_redistribution.Init(tensorlayout_in, tensorlayout_out, dev_list) == FAILED) {
     MS_LOG(ERROR) << "Redistribution: middle_prim " << middle_prim->name() << " next_prim : " << next_prim_name;
     MS_LOG(ERROR) << "Redistribution: middle_node " << middle_node->ToString() << " next_node "