fix codedex warning

mindspore/ccsrc/backend/optimizer/ascend/ir_fission/bn_split.cc

@@ -216,8 +216,9 @@ AnfNodePtr CreateAllReduceAndMul(const FuncGraphPtr &graph, const AnfNodePtr &al
   // use SyncBatchNorm's opid as AllReduce's fusion attr
   auto sync_bn_opname = sync_bn_cnode->fullname_with_scope();
   auto opid_pos = sync_bn_opname.rfind("-op");
-  if (opid_pos == std::string::npos) {
+  if (opid_pos == std::string::npos || opid_pos + 3 >= sync_bn_opname.size()) {
     MS_LOG(EXCEPTION) << "op[" << sync_bn_cnode->DebugString() << "] has no opid.";
+    return nullptr;
   }
   int64_t opid = std::stol(sync_bn_opname.substr(opid_pos + 3));
   // user defined fusion should be greater than 1

mindspore/ccsrc/backend/optimizer/ascend/mindir/avg_pool_grad_unify_mindir.cc

@@ -67,19 +67,8 @@ int64_t windowed_output_size(int64_t input_size, int64_t ksize, int64_t stride,
   return output;
 }
 
-ValueNodePtr CreateMeanMatrixValueNode(const FuncGraphPtr &func_graph, const std::vector<int64_t> &x_shape,
-                                       const std::vector<int64_t> &k_size, const std::vector<int64_t> &stride,
-                                       const PadMode pad_mode, const TypeId x_dtype) {
-  MS_EXCEPTION_IF_NULL(func_graph);
-  auto kernel_graph = func_graph->cast<KernelGraphPtr>();
-  MS_EXCEPTION_IF_NULL(kernel_graph);
-  if (x_shape.size() != kShapeDimNum || k_size.size() != kShapeDimNum || stride.size() != kShapeDimNum) {
-    MS_LOG(EXCEPTION) << "The dim of x_shape or kernel_size or strides of AvgPoolGrad should be 4.";
-  }
-  int64_t pad_top, pad_bottom, pad_left, pad_right;
-  int64_t h_output = windowed_output_size(x_shape[2], k_size[2], stride[2], pad_mode, &pad_top, &pad_bottom);
-  int64_t w_output = windowed_output_size(x_shape[3], k_size[3], stride[3], pad_mode, &pad_left, &pad_right);
-
+std::vector<std::vector<float>> GetAssistInputMatrix(const std::vector<int64_t> &x_shape, int64_t pad_top,
+                                                     int64_t pad_bottom, int64_t pad_left, int64_t pad_right) {
   // `assist_input_matrix` is a 2d matrix with input_shape after padding,
   // the value of element which is padded is 0, else are 1.
   // For each element of output, it is mapped for slide window: `[h*h_stride : h*h_stride + h_ksize,
@@ -102,6 +91,22 @@ ValueNodePtr CreateMeanMatrixValueNode(const FuncGraphPtr &func_graph, const std
       assist_input_matrix.emplace_back(tmp_one_vector);
     }
   }
+  return assist_input_matrix;
+}
+
+ValueNodePtr CreateMeanMatrixValueNode(const FuncGraphPtr &func_graph, const std::vector<int64_t> &x_shape,
+                                       const std::vector<int64_t> &k_size, const std::vector<int64_t> &stride,
+                                       const PadMode pad_mode, const TypeId x_dtype) {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  auto kernel_graph = func_graph->cast<KernelGraphPtr>();
+  MS_EXCEPTION_IF_NULL(kernel_graph);
+  if (x_shape.size() != kShapeDimNum || k_size.size() != kShapeDimNum || stride.size() != kShapeDimNum) {
+    MS_LOG(EXCEPTION) << "The dim of x_shape or kernel_size or strides of AvgPoolGrad should be 4.";
+  }
+  int64_t pad_top, pad_bottom, pad_left, pad_right;
+  int64_t h_output = windowed_output_size(x_shape[2], k_size[2], stride[2], pad_mode, &pad_top, &pad_bottom);
+  int64_t w_output = windowed_output_size(x_shape[3], k_size[3], stride[3], pad_mode, &pad_left, &pad_right);
+  auto assist_input_matrix = GetAssistInputMatrix(x_shape, pad_top, pad_bottom, pad_left, pad_right);
 
   // calculate output
   std::vector<float> hw_output(h_output * w_output, 0.0);