diff --git a/python/paddle/fluid/optimizer.py b/python/paddle/fluid/optimizer.py
index ec8bed45dc4833f47e67895a5e316f13bcae5261..e0ee9955b8cadbd329758f7f21e216859ddb8176 100644
--- a/python/paddle/fluid/optimizer.py
+++ b/python/paddle/fluid/optimizer.py
@@ -18,7 +18,7 @@ from collections import defaultdict
 from contextlib import contextmanager
 
 from paddle.fluid.framework import Program, Variable, name_scope, default_main_program
-import paddle.fluid.transpiler.details.distribute_lookuptable_utils as distribute_lookuptable_utils
+from paddle.fluid.transpiler.details.distribute_lookuptable_utils import find_distributed_lookup_table
 
 from . import framework
 from . import layers
@@ -40,6 +40,30 @@ __all__ = [
 ]
 
 
+def _process_distribute_lookuptable(program, param_grads, learning_rate):
+    table_name = find_distributed_lookup_table(program)
+    table_param = None
+    table_grad = None
+    new_param_grads = []
+    for p, g in param_grads:
+        if p.name == table_name:
+            if table_param is not None:
+                raise RuntimeError(
+                    "multi dist table var found, only support one now!")
+            table_param = p
+            table_grad = g
+        else:
+            new_param_grads.append((p, g))
+    sgd_op = None
+    if table_param is not None:
+        with table_param.block.program._optimized_guard(
+            [table_param, table_grad]), framework.name_scope("optimizer"):
+            sgd_optimizer = SGD(learning_rate)
+            sgd_op = sgd_optimizer._append_optimize_op(table_param.block, (
+                table_param, table_grad))
+    return new_param_grads, (table_param, table_grad), sgd_op
+
+
 class Optimizer(object):
     """Optimizer Base class.
 
@@ -263,7 +287,7 @@ class Optimizer(object):
         params_grads = sorted(params_grads, key=lambda x: x[0].name)
 
         params_grads, table_param_and_grad, table_optimize_op = \
-            distribute_lookuptable_utils.process_distribute_lookuptable(loss.block.program, params_grads, self._learning_rate)
+            _process_distribute_lookuptable(loss.block.program, params_grads, self._learning_rate)
 
         params_grads = append_gradient_clip_ops(params_grads)
 
@@ -273,8 +297,9 @@ class Optimizer(object):
 
         optimize_ops = self._create_optimization_pass(params_grads, loss,
                                                       startup_program)
-        optimize_ops.append(table_optimize_op)
-        params_grads.append(table_param_and_grad)
+        if table_optimize_op is not None:
+            optimize_ops.append(table_optimize_op)
+            params_grads.append(table_param_and_grad)
         return optimize_ops, params_grads
 
 
diff --git a/python/paddle/fluid/transpiler/details/distribute_lookuptable_utils.py b/python/paddle/fluid/transpiler/details/distribute_lookuptable_utils.py
index ab1b551a2eeb4af6a09748816476e679b55052a9..bc4a9e7a4e9df9c778e560d58aa5c6ff70165710 100644
--- a/python/paddle/fluid/transpiler/details/distribute_lookuptable_utils.py
+++ b/python/paddle/fluid/transpiler/details/distribute_lookuptable_utils.py
@@ -40,27 +40,3 @@ def find_distributed_lookup_table(program):
                     assert op.input("W")[0] != table_name
 
     return table_name
-
-
-def process_distribute_lookuptable(program, param_grads, learning_rate):
-    table_name = find_distributed_lookup_table(program)
-    table_param = None
-    table_grad = None
-    new_param_grads = []
-    for p, g in param_grads:
-        if p.name == table_name:
-            if table_param is not None:
-                raise RuntimeError(
-                    "multi dist table var found, only support one now!")
-            table_param = p
-            table_grad = g
-        else:
-            new_param_grads.append((p, g))
-    sgd_op = None
-    if table_param is not None:
-        with table_param.block.program._optimized_guard(
-            [table_param, table_grad]), framework.name_scope("optimizer"):
-            sgd_optimizer = optimizer.SGD(learning_rate)
-            sgd_op = sgd_optimizer._append_optimize_op(table_param.block, (
-                table_param, table_grad))
-    return new_param_grads, (table_param, table_grad), sgd_op