diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 584829fe493bbc6a9638aff43b46d984cbe190a5..32ba0ecf86b66a62bbb9d2cca347ead41cdbb6ba 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -4686,6 +4686,15 @@ def data_norm(input,
         "BatchSum": batch_sum,
         "BatchSquareSum": batch_square_sum
     }
+    attrs = {
+        "epsilon": epsilon,
+        "sync_stats": sync_stats,
+        "summary_decay_rate": summary_decay_rate,
+    }
+    if slot_dim > 0:
+        attrs["slot_dim"] = slot_dim
+    if enable_scale_and_shift:
+        attrs["enable_scale_and_shift"] = enable_scale_and_shift
     if enable_scale_and_shift:
         inputs["scale_w"] = scale_w
         inputs["bias"] = bias
@@ -4700,13 +4709,7 @@ def data_norm(input,
             "BatchSum": batch_sum,
             "BatchSquareSum": batch_square_sum
         },
-        attrs={
-            "epsilon": epsilon,
-            "slot_dim": slot_dim,
-            "sync_stats": sync_stats,
-            "summary_decay_rate": summary_decay_rate,
-            "enable_scale_and_shift": enable_scale_and_shift
-        })
+        attrs=attrs)
 
     return helper.append_activation(data_norm_out)
 
diff --git a/python/paddle/fluid/tests/unittests/test_data_norm_op.py b/python/paddle/fluid/tests/unittests/test_data_norm_op.py
index 0b7ed20f4b1c8f30f75613a2023eab3e1cea7855..2758a2e09ed8d9b7f89d4358428b31f2fde16f81 100644
--- a/python/paddle/fluid/tests/unittests/test_data_norm_op.py
+++ b/python/paddle/fluid/tests/unittests/test_data_norm_op.py
@@ -271,7 +271,7 @@ class TestDataNormOpWithEnableScaleAndShift(OpTest):
         self.use_mkldnn = False
         epsilon = 0.00001
         slot_dim = -1
-        enable_scale_and_shitf = True
+        enable_scale_and_shift = True
         x_shape = [2, 50]
         scale_shape = [50]
         tp = np.float32
@@ -319,6 +319,63 @@ class TestDataNormOpWithEnableScaleAndShift(OpTest):
         self.check_grad(['X'], 'Y', no_grad_set=set([]))
 
 
+class TestDataNormOpWithoutEnableScaleAndShift(OpTest):
+    """
+    test class for data norm op
+    test forward and backward
+    """
+
+    def setUp(self):
+        """
+        init data norm op test env
+        """
+        self.op_type = 'data_norm'
+        self.use_mkldnn = False
+        epsilon = 0.00001
+        slot_dim = -1
+        enable_scale_and_shift = True
+        x_shape = [2, 50]
+        scale_shape = [50]
+        tp = np.float32
+
+        x_val = np.random.uniform(-1, 1, x_shape).astype(tp)
+        batch_size = np.ones(scale_shape).astype(tp)
+        batch_size *= 1e4
+        batch_sum = np.zeros(scale_shape).astype(tp)
+        batch_square_sum = np.ones(scale_shape).astype(tp)
+        batch_square_sum *= 1e4
+        scale_w = np.ones(scale_shape).astype(tp)
+        bias = np.zeros(scale_shape).astype(tp)
+
+        y = np.array(x_val)
+
+        mean = np.zeros(x_shape).astype(tp)
+        scale = np.ones(x_shape).astype(tp)
+
+        self.inputs = {
+            "X": x_val,
+            "BatchSize": batch_size,
+            "BatchSum": batch_sum,
+            "BatchSquareSum": batch_square_sum,
+            "scale_w": scale_w,
+            "bias": bias
+        }
+        self.outputs = {"Y": y, "Means": mean, "Scales": scale}
+        self.attrs = {"epsilon": epsilon, "use_mkldnn": self.use_mkldnn}
+
+    def test_check_output(self):
+        """
+        test check forward, check output
+        """
+        self.check_output()
+
+    def test_check_grad(self):
+        """
+        test check backward, check grad
+        """
+        self.check_grad(['X'], 'Y', no_grad_set=set([]))
+
+
 class TestDataNormOpWithEnableScaleAndShift_1(OpTest):
     """
     test class for data norm op
@@ -333,7 +390,7 @@ class TestDataNormOpWithEnableScaleAndShift_1(OpTest):
         self.use_mkldnn = False
         epsilon = 0.00001
         slot_dim = 1
-        enable_scale_and_shitf = True
+        enable_scale_and_shift = True
         x_shape = [2, 50]
         scale_shape = [50]
         tp = np.float32