diff --git a/python/paddle/fluid/contrib/tests/test_model_cast_to_bf16.py b/python/paddle/fluid/contrib/tests/test_model_cast_to_bf16.py
index e04bf0b2b7058f70d63c8290692fe66a3c3384be..7254dd9df31821c2c8653676f22c98030943aa2b 100644
--- a/python/paddle/fluid/contrib/tests/test_model_cast_to_bf16.py
+++ b/python/paddle/fluid/contrib/tests/test_model_cast_to_bf16.py
@@ -97,26 +97,26 @@ class TestModelCastBF16(unittest.TestCase):
 
         with self.static_graph():
             t_bf16 = layers.data(
-                name='t_bf16', shape=[size, size], dtype=np.uint16
+                name='t_bf16', shape=[size, size], dtype=np.int32
             )
             tt_bf16 = layers.data(
-                name='tt_bf16', shape=[size, size], dtype=np.uint16
+                name='tt_bf16', shape=[size, size], dtype=np.int32
             )
             t = layers.data(name='t', shape=[size, size], dtype='float32')
             tt = layers.data(name='tt', shape=[size, size], dtype='float32')
 
-            ret = layers.elementwise_add(t, tt)
-            ret = layers.elementwise_mul(ret, t)
+            ret = paddle.add(t, tt)
+            ret = paddle.multiply(ret, t)
             ret = paddle.reshape(ret, [0, 0])
 
             with amp.bf16.bf16_guard():
-                ret_bf16 = layers.elementwise_add(t_bf16, tt_bf16)
-                ret_bf16 = layers.elementwise_mul(ret_bf16, t_bf16)
+                ret_bf16 = paddle.add(t_bf16, tt_bf16)
+                ret_bf16 = paddle.multiply(ret_bf16, t_bf16)
                 ret_bf16 = paddle.reshape(ret_bf16, [0, 0])
 
             with amp.bf16.bf16_guard():
-                ret_fp32bf16 = layers.elementwise_add(t, tt)
-                ret_fp32bf16 = layers.elementwise_mul(ret_fp32bf16, t)
+                ret_fp32bf16 = paddle.add(t, tt)
+                ret_fp32bf16 = paddle.multiply(ret_fp32bf16, t)
                 ret_fp32bf16 = paddle.reshape(ret_fp32bf16, [0, 0])
 
             (
@@ -147,11 +147,11 @@ class TestModelCastBF16(unittest.TestCase):
             tt = layers.data(name='tt', shape=[size, size], dtype='float32')
 
             with amp.bf16.bf16_guard():
-                ret = layers.elementwise_add(t, tt)
+                ret = paddle.add(t, tt)
                 ret = paddle.reshape(ret, [0, 0])
                 ret = paddle.nn.functional.elu(ret)
-                ret = layers.elementwise_mul(ret, t)
-            ret = layers.elementwise_add(ret, tt)
+                ret = paddle.multiply(ret, t)
+            ret = paddle.add(ret, tt)
 
             static_ret_bf16 = self.get_static_graph_result(
                 feed={'t': n, 'tt': nn},