diff --git a/python/paddle/incubate/autograd/composite_rules.py b/python/paddle/incubate/autograd/composite_rules.py
index ddcf0a9b004e790b616c9a9bd8dfe6fb0e3c9cbf..d7eb54ba571974b7a127a674546bd347264ef2b0 100644
--- a/python/paddle/incubate/autograd/composite_rules.py
+++ b/python/paddle/incubate/autograd/composite_rules.py
@@ -80,8 +80,12 @@ def composite_batchnorm(
             reshape(batch_var, stats_shape) + epsilon
         )
 
-        run_mean = momentum * run_mean + (1 - momentum) * batch_mean
-        run_var = momentum * run_var + (1 - momentum) * batch_var
+        run_mean = momentum * run_mean + (1 - momentum) * reshape(
+            batch_mean, run_mean.shape
+        )
+        run_var = momentum * run_var + (1 - momentum) * reshape(
+            batch_var, run_var.shape
+        )
     else:
         x_hat = (x - reshape(run_mean, stats_shape)) / sqrt(
             reshape(run_var, stats_shape) + epsilon
@@ -89,8 +93,8 @@ def composite_batchnorm(
     y = reshape(scale, stats_shape) * x_hat + reshape(bias, stats_shape)
 
     # add op assign to detach tensor in void unsafe change outside the rule.
-    batch_mean_ = assign(batch_mean)
-    batch_var_ = assign(batch_var)
+    batch_mean_ = assign(reshape(batch_mean, run_mean.shape))
+    batch_var_ = assign(reshape(batch_var, run_var.shape))
     run_mean_ = assign(run_mean)
     run_var_ = assign(run_var)
     if trainable_statistics or not is_test: