diff --git a/python/paddle/fluid/tests/unittests/test_sparse_norm_op.py b/python/paddle/fluid/tests/unittests/test_sparse_norm_op.py
index b86a915f239a4c61a6936a823c084fa90120308b..c17a252ee75a6d7713f6ee0085c6267d0d032fb6 100644
--- a/python/paddle/fluid/tests/unittests/test_sparse_norm_op.py
+++ b/python/paddle/fluid/tests/unittests/test_sparse_norm_op.py
@@ -88,6 +88,39 @@ class TestSparseBatchNorm(unittest.TestCase):
         assert np.allclose(dense_out.numpy(), batch_norm_out.values().numpy())
         # [1, 6, 6, 6, 3]
 
+    def check(self, shape):
+        np.random.seed(0)
+        data = np.random.uniform(-0.01, 0.01, shape).astype("float32")
+        x = paddle.to_tensor(data)
+        x.stop_gradient = False
+        dim = len(shape)
+        data_format = "NHWC" if dim == 4 else "NDHWC"
+        if dim == 4:
+            bn = paddle.nn.BatchNorm2D(shape[-1], data_format=data_format)
+        else:
+            bn = paddle.nn.BatchNorm3D(shape[-1], data_format=data_format)
+        y = bn(x)
+        y.backward()
+
+        sp_x = paddle.to_tensor(data).to_sparse_coo(dim - 1)
+        sp_x.stop_gradient = False
+        sp_bn = paddle.sparse.nn.BatchNorm(shape[-1], data_format=data_format)
+        sp_y = sp_bn(sp_x)
+        sp_y.backward()
+
+        np.testing.assert_allclose(
+            y.numpy(), sp_y.to_dense().numpy(), rtol=1e-5
+        )
+        np.testing.assert_allclose(
+            x.grad.numpy(), sp_x.grad.to_dense().numpy(), rtol=1e-5
+        )
+
+    def test_nd(self):
+        # 2D
+        self.check([2, 8, 8, 3])
+        # 3D
+        self.check([2, 8, 8, 3, 4])
+
 
 class TestSyncBatchNorm(unittest.TestCase):
     def test_sync_batch_norm(self):
diff --git a/python/paddle/sparse/nn/layer/norm.py b/python/paddle/sparse/nn/layer/norm.py
index 30e69560428306e68ca1c91a7987ed23516b0452..dc8c2713f45a4ca6d9a1b99d09d252d87ed6437b 100644
--- a/python/paddle/sparse/nn/layer/norm.py
+++ b/python/paddle/sparse/nn/layer/norm.py
@@ -73,7 +73,7 @@ class BatchNorm(paddle.nn.BatchNorm1D):
         name(str, optional): Name for the BatchNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
 
     Shape:
-        - x: A SparseCooTensor with layout = 'NDHWC'.
+        - x: A SparseCooTensor with layout = 'NDHWC' or 'NHWC'.
         - output: SparseCooTensor with same shape as input x.
 
     Returns:
@@ -119,8 +119,10 @@ class BatchNorm(paddle.nn.BatchNorm1D):
         )
 
     def _check_data_format(self, input):
-        if input != "NDHWC":
-            raise ValueError('sparse BatchNorm only support layout of "NDHWC"')
+        if input not in ["NDHWC", "NHWC"]:
+            raise ValueError(
+                'sparse BatchNorm only support layout of "NDHWC" and "NHWC"'
+            )
 
     def forward(self, input):
         self._check_data_format(self._data_format)