diff --git a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py
index 11d56e84913a6caec6f48bb2d1141a9b7ad78275..d7437f9c47ea5650a27fbbdb1abffabeba64e1a3 100644
--- a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py
+++ b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py
@@ -470,10 +470,10 @@ class DistributedAdam(DistributedOptimizerImplBase):
                                     strategy.get("scale_datanorm", -1)
                                 })
 
-                    program_configs[program_id]["pull_dense"].extend(
-                        [dense_table_index])
-                    program_configs[program_id]["push_dense"].extend(
-                        [dense_table_index])
+                        program_configs[program_id]["pull_dense"].extend(
+                            [dense_table_index])
+                        program_configs[program_id]["push_dense"].extend(
+                            [dense_table_index])
                     dense_table_index += 1
 
             # Todo(guru4elephant): figure out how to support more sparse parameters
diff --git a/python/paddle/fluid/tests/unittests/test_fleet.py b/python/paddle/fluid/tests/unittests/test_fleet.py
index 6657f5a1202de222e8a0b572f2d566b6765fbefe..ca232dd2ff09b9435ce9554199306dd3e31489c9 100644
--- a/python/paddle/fluid/tests/unittests/test_fleet.py
+++ b/python/paddle/fluid/tests/unittests/test_fleet.py
@@ -62,7 +62,9 @@ class TestFleet1(unittest.TestCase):
             emb = fluid.layers.embedding(input=show, size=[1, 1], \
                 is_sparse=True, is_distributed=True, \
                 param_attr=fluid.ParamAttr(name="embedding"))
-            fc = fluid.layers.fc(input=emb, size=1, act=None)
+            bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
+            bow = fluid.layers.data_norm(input=bow, epsilon=1e-4, name="norm")
+            fc = fluid.layers.fc(input=bow, size=1, act=None)
             label = fluid.layers.data(name="click", shape=[-1, 1], \
                 dtype="int64", lod_level=1, append_batch_size=False)
             label_cast = fluid.layers.cast(label, dtype='float32')