diff --git a/mindspore/ccsrc/backend/optimizer/ascend/ir_fission/bn_split.cc b/mindspore/ccsrc/backend/optimizer/ascend/ir_fission/bn_split.cc
index 15d6101d15..5762e18268 100644
--- a/mindspore/ccsrc/backend/optimizer/ascend/ir_fission/bn_split.cc
+++ b/mindspore/ccsrc/backend/optimizer/ascend/ir_fission/bn_split.cc
@@ -163,7 +163,7 @@ AnfNodePtr CreateValueNodeOfDeviceNumReciprocal(const FuncGraphPtr &graph, const
   }
   auto device_num = AnfAlgo::GetNodeAttr<int64_t>(sync_bn_cnode, kDeviceNum);
   MS_LOG(INFO) << "device_num value: " << device_num;
-  float device_num_reciprocal = 1.0 / device_num;
+  const float device_num_reciprocal = 1.0 / device_num;
 
   std::vector<int64_t> device_num_shape = {};
   auto device_num_reciprocal_tensor = std::make_shared<tensor::Tensor>(kNumberTypeFloat32, device_num_shape);
diff --git a/mindspore/ccsrc/backend/optimizer/ascend/mindir/avg_pool_grad_unify_mindir.cc b/mindspore/ccsrc/backend/optimizer/ascend/mindir/avg_pool_grad_unify_mindir.cc
index bb08950612..3e099715e4 100644
--- a/mindspore/ccsrc/backend/optimizer/ascend/mindir/avg_pool_grad_unify_mindir.cc
+++ b/mindspore/ccsrc/backend/optimizer/ascend/mindir/avg_pool_grad_unify_mindir.cc
@@ -50,6 +50,10 @@ int64_t windowed_output_size(int64_t input_size, int64_t ksize, int64_t stride,
   int64_t output = 0;
   *pad_before = 0;
   *pad_after = 0;
+  if (stride == 0) {
+    MS_LOG(EXCEPTION) << "The stride of AvgPoolGrad should not be 0.";
+    return 0;
+  }
   if (pad_mode == PadMode::VALID) {
     output = (input_size - ksize + stride) / stride;
   } else if (pad_mode == PadMode::SAME) {
@@ -120,8 +124,13 @@ ValueNodePtr CreateMeanMatrixValueNode(const FuncGraphPtr &func_graph, const std
   auto output_size = std::accumulate(output_shape.begin(), output_shape.end(), int64_t(1), std::multiplies<int64_t>());
   std::vector<float> output(output_size, 0.0);
   for (int64_t i = 0; i < output_shape[0] * output_shape[1]; ++i) {
-    size_t copy_size = hw_output.size() * kFloat32Len;
-    (void)memcpy_s(&output[i * hw_output.size()], copy_size, &hw_output[0], copy_size);
+    size_t src_size = hw_output.size() * kFloat32Len;
+    size_t dst_size = output_shape[2] * output_shape[3] * kFloat32Len;
+    auto ret = memcpy_s(&output[i * hw_output.size()], dst_size, &hw_output[0], src_size);
+    if (ret != 0) {
+      MS_LOG(EXCEPTION) << "memcpy_s error, errorno(" << ret << ")";
+      return nullptr;
+    }
   }
   auto output_tensor = std::make_shared<tensor::Tensor>(x_dtype, output_shape, &output[0], kNumberTypeFloat32);
   MS_EXCEPTION_IF_NULL(output_tensor);
diff --git a/mindspore/ccsrc/backend/optimizer/common/helper.cc b/mindspore/ccsrc/backend/optimizer/common/helper.cc
index 87183e7ff0..36e5ae14a9 100644
--- a/mindspore/ccsrc/backend/optimizer/common/helper.cc
+++ b/mindspore/ccsrc/backend/optimizer/common/helper.cc
@@ -517,6 +517,7 @@ ValueNodePtr CreateShapeValueNode(const FuncGraphPtr &func_graph, const std::vec
     auto ret_code = memcpy_s(data_ptr, static_cast<size_t>(shape_tensor->data().nbytes()), &shape[0], elem_num);
     if (ret_code != 0) {
       MS_LOG(EXCEPTION) << "Failed to copy data into Tensor.";
+      return nullptr;
     }
     shape_value = shape_tensor;
     abstract = std::make_shared<abstract::AbstractTensor>(kInt64, shape_vec_shape);
diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index 340521e792..ff00102b11 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -714,7 +714,7 @@ class SyncBatchNorm(_BatchNorm):
         TypeError: If `process_groups` is not a list.
         ValueError: If `num_features` is less than 1.
         ValueError: If `momentum` is not in range [0, 1].
-        ValueError: If `device_num_each_group` is less than 2.
+        ValueError: If rank_id in `process_groups` is not in range [0, rank_size).
 
     Supported Platforms:
         ``Ascend``