diff --git a/python/paddle/fluid/tests/unittests/test_dist_lookup_sparse_table_fuse_ops.py b/python/paddle/fluid/tests/unittests/test_dist_lookup_sparse_table_fuse_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2bf6e546e724fbe61e78ce792c951a4f8aeb40a
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_dist_lookup_sparse_table_fuse_ops.py
@@ -0,0 +1,169 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+
+
+class TestLookupTableFuseOp(unittest.TestCase):
+    def test_fuse(self):
+        places = [core.CPUPlace()]
+        # currently only support CPU
+        for place in places:
+            self.check_with_place(place)
+
+    def check_with_place(self, place):
+        scope = fluid.global_scope()
+        scope.var("LearningRate").get_tensor().set([0.01], place)
+        scope.var("Ids").get_tensor().set([i for i in range(100)], place)
+
+        init_program = fluid.Program()
+
+        lr = init_program.global_block().create_var(
+            name="LearningRate",
+            persistable=True,
+            type=fluid.core.VarDesc.VarType.LOD_TENSOR,
+            shape=[1],
+            dtype="float32")
+
+        ids = init_program.global_block().create_var(
+            name="Ids",
+            persistable=True,
+            type=fluid.core.VarDesc.VarType.LOD_TENSOR,
+            shape=[100],
+            dtype="int64")
+
+        output = init_program.global_block().create_var(
+            name="output",
+            type=fluid.core.VarDesc.VarType.LOD_TENSOR,
+            shape=[100, 8],
+            dtype="float32")
+
+        metas = []
+        metas.append(
+            "embedding_1.block0:Param,Moment1,Moment2:8,8,8:0:embedding_1@GRAD.block0:embedding_1.block0,embedding_1_moment1_0,embedding_1_moment2_0,kSparseIDs@embedding_1.block0:uniform_random&0&-0.5&0.5,fill_constant&0.0,fill_constant&0.0:none"
+        )
+        metas.append(
+            "embedding_2.block0:Param:8:0:embedding_2@GRAD.block0:embedding_2.block0,kSparseIDs@embedding_2.block0:uniform_random&0&-0.5&0.5:none"
+        )
+
+        init_program.global_block().append_op(
+            type="lookup_sparse_table_init",
+            inputs=None,
+            outputs=None,
+            attrs={"large_scale_metas": metas})
+
+        init_program.global_block().append_op(
+            type="lookup_sparse_table_read",
+            inputs={"Ids": ids},
+            outputs={"Out": output},
+            attrs={
+                "tablename": "embedding_1.block0",
+                "init": True,
+                "value_names": ["Param"],
+            })
+
+        init_program.global_block().append_op(
+            type="lookup_sparse_table_read",
+            inputs={"Ids": ids},
+            outputs={"Out": output},
+            attrs={
+                "tablename": "embedding_2.block0",
+                "init": True,
+                "value_names": ["Param"],
+            })
+
+        executor = fluid.Executor(place)
+        executor.run(init_program)
+
+        training_program = fluid.Program()
+
+        scope.var('Beta1Pow').get_tensor().set([0], place)
+        scope.var('Beta2Pow').get_tensor().set([0], place)
+
+        rows = [0, 1, 2, 3, 4, 5, 6]
+        row_numel = 8
+        w_selected_rows = scope.var('Grad').get_selected_rows()
+        w_selected_rows.set_height(len(rows))
+        w_selected_rows.set_rows(rows)
+        w_array = np.ones((len(rows), row_numel)).astype("float32")
+        for i in range(len(rows)):
+            w_array[i] *= i
+        w_tensor = w_selected_rows.get_tensor()
+        w_tensor.set(w_array, place)
+
+        lr = training_program.global_block().create_var(
+            name="LearningRate",
+            persistable=True,
+            type=fluid.core.VarDesc.VarType.LOD_TENSOR,
+            shape=[1],
+            dtype="float32")
+
+        grads = training_program.global_block().create_var(
+            name="Grad",
+            persistable=True,
+            type=fluid.core.VarDesc.VarType.SELECTED_ROWS,
+            shape=[100, 8],
+            dtype="float32")
+
+        beta1 = training_program.global_block().create_var(
+            name="Beta1Pow",
+            persistable=True,
+            type=fluid.core.VarDesc.VarType.LOD_TENSOR,
+            shape=[1],
+            dtype="float32")
+
+        beta2 = training_program.global_block().create_var(
+            name="Beta2Pow",
+            persistable=True,
+            type=fluid.core.VarDesc.VarType.LOD_TENSOR,
+            shape=[1],
+            dtype="float32")
+
+        training_program.global_block().append_op(
+            type="lookup_sparse_table_fuse_adam",
+            inputs={
+                "Grad": grads,
+                "LearningRate": lr,
+                "Beta1Pow": beta1,
+                "Beta2Pow": beta2,
+            },
+            outputs={"Beta1PowOut": beta1,
+                     "Beta2PowOut": beta2},
+            attrs={
+                "is_entry": False,
+                "tablename": "embedding_1.block0",
+                "value_names": ["Param", "Moment1", "Moment2"],
+            })
+
+        training_program.global_block().append_op(
+            type="lookup_sparse_table_fuse_sgd",
+            inputs={"Grad": grads,
+                    "LearningRate": lr},
+            attrs={
+                "is_entry": False,
+                "tablename": "embedding_2.block0",
+                "value_names": ["Param"],
+            })
+
+        executor.run(training_program)
+
+
+if __name__ == "__main__":
+    unittest.main()