diff --git a/mindspore/_extends/graph_kernel/expanders/layernorm.py b/mindspore/_extends/graph_kernel/expanders/layernorm.py
index 2e7ff18500..72e8b4dd54 100644
--- a/mindspore/_extends/graph_kernel/expanders/layernorm.py
+++ b/mindspore/_extends/graph_kernel/expanders/layernorm.py
@@ -115,10 +115,11 @@ class LayerNorm(Expander):
         reduce_elts = 1.0
         for i in reduce_axis:
             reduce_elts *= ori_shape_x[i]
+        # after reduced
+        ori_reduced_shape_x = self.get_reduced_ori_shape(ori_shape_x, reduce_axis)
 
         if input_x.data_format == DF.FRAC_NZ:
             reduce_axis = self.to_frac_z_axis(ori_shape_x, reduce_axis)
-            ori_shape_x = self.get_reduced_ori_shape(ori_shape_x, reduce_axis) # after reduced
 
         mean_cof = 1.0 / reduce_elts
         mean_cof_v = graph_builder.value(input_x.dtype, mean_cof)
@@ -128,7 +129,7 @@ class LayerNorm(Expander):
                                       attrs={'reduce_axis': reduce_axis, 'keep_dims': True})
         mean = graph_builder.emit('Mul', [mean_red, mean_cof_v])
         if input_x.data_format == DF.FRAC_NZ:
-            mean = graph_builder.emit('Reshape', [mean], attrs={'shape': ori_shape_x})
+            mean = graph_builder.emit('Reshape', [mean], attrs={'shape': ori_reduced_shape_x})
 
         # Calculate variance
         variance_sub = graph_builder.emit('Sub', [input_x, mean])
@@ -137,7 +138,7 @@ class LayerNorm(Expander):
                                           attrs={'reduce_axis': reduce_axis, 'keep_dims': True})
         variance = graph_builder.emit('Mul', [variance_red, mean_cof_v])
         if input_x.data_format == DF.FRAC_NZ:
-            variance = graph_builder.emit('Reshape', [variance], attrs={'shape': ori_shape_x})
+            variance = graph_builder.emit('Reshape', [variance], attrs={'shape': ori_reduced_shape_x})
 
         # Calculate normalize
         normalize_sub = graph_builder.emit('Sub', [input_x, mean])