diff --git a/hapi/loss.py b/hapi/loss.py
index 7e4843b2ced3f31aee6b20cecd7f96ec3c823a05..7abddf22f1519b6bd1a649f663b22f315366ca7a 100644
--- a/hapi/loss.py
+++ b/hapi/loss.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2020 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.
@@ -20,15 +20,9 @@ from paddle import fluid
 from paddle.fluid.framework import in_dygraph_mode, Variable
 from paddle.fluid.dygraph.base import to_variable
 
-__all__ = ['Loss', 'CrossEntropy', 'SoftmaxWithCrossEntropy']
-
+from hapi.utils import to_list
 
-def to_list(value):
-    if value is None:
-        return value
-    if isinstance(value, (list, tuple)):
-        return list(value)
-    return [value]
+__all__ = ['Loss', 'CrossEntropy', 'SoftmaxWithCrossEntropy']
 
 
 class Loss(object):
diff --git a/hapi/model.py b/hapi/model.py
index 481d038f3868931eba0f87bf249015da79f76680..8c1c5216287b26645ca2d06178cb3b5176e7ab31 100644
--- a/hapi/model.py
+++ b/hapi/model.py
@@ -38,6 +38,7 @@ from hapi.loss import Loss
 from hapi.distributed import DistributedBatchSampler, _all_gather, prepare_distributed_context, _parallel_context_initialized
 from hapi.metrics import Metric
 from hapi.callbacks import config_callbacks
+from hapi.utils import to_list, to_numpy, flatten_list, restore_flatten_list
 
 __all__ = [
     'Model',
@@ -65,49 +66,6 @@ def set_device(device):
     return place
 
 
-def to_list(value):
-    if value is None:
-        return value
-    if isinstance(value, (list, tuple)):
-        return list(value)
-    return [value]
-
-
-def to_numpy(var):
-    assert isinstance(var, (Variable, fluid.core.VarBase)), "not a variable"
-    if isinstance(var, fluid.core.VarBase):
-        return var.numpy()
-    t = global_scope().find_var(var.name).get_tensor()
-    return np.array(t)
-
-
-def flatten_list(l):
-    assert isinstance(l, list), "not a list"
-    outl = []
-    splits = []
-    for sl in l:
-        assert isinstance(sl, list), "sub content not a list"
-        splits.append(len(sl))
-        outl += sl
-    return outl, splits
-
-
-def restore_flatten_list(l, splits):
-    outl = []
-    for split in splits:
-        assert len(l) >= split, "list length invalid"
-        sl, l = l[:split], l[split:]
-        outl.append(sl)
-    return outl
-
-
-def extract_args(func):
-    if hasattr(inspect, 'getfullargspec'):
-        return inspect.getfullargspec(func)[0]
-    else:
-        return inspect.getargspec(func)[0]
-
-
 class Input(fluid.dygraph.Layer):
     def __init__(self, shape=None, dtype=None, name=None):
         super(Input, self).__init__()
@@ -1180,7 +1138,6 @@ class Model(fluid.dygraph.Layer):
                              save_dir,
                              model_filename=None,
                              params_filename=None,
-                             export_for_deployment=True,
                              program_only=False):
         """
         Save inference model must in static mode.
@@ -1193,12 +1150,6 @@ class Model(fluid.dygraph.Layer):
             params_filename(str|None): The name of file to save all related parameters.
                                             If it is set None, parameters will be saved
                                             in separate files .
-            export_for_deployment(bool): If True, programs are modified to only support
-                                        direct inference deployment. Otherwise,
-                                        more information will be stored for flexible
-                                        optimization and re-training. Currently, only
-                                        True is supported.
-                                        Default: True.
             program_only(bool): If True, It will save inference program only, and do not 
                                         save params of Program.
                                         Default: False.
@@ -1226,7 +1177,6 @@ class Model(fluid.dygraph.Layer):
             main_program=infer_prog,
             model_filename=model_filename,
             params_filename=params_filename,
-            export_for_deployment=export_for_deployment,
             program_only=program_only)
 
     def _run_one_epoch(self,
diff --git a/hapi/utils.py b/hapi/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..de928945dc68a1800c3cd9b14aaa0659e50c9945
--- /dev/null
+++ b/hapi/utils.py
@@ -0,0 +1,63 @@
+# Copyright (c) 2020 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.
+
+import inspect
+import numpy as np
+
+from paddle import fluid
+from paddle.fluid.framework import Variable
+from paddle.fluid.executor import global_scope
+
+
+def to_list(value):
+    if value is None:
+        return value
+    if isinstance(value, (list, tuple)):
+        return list(value)
+    return [value]
+
+
+def to_numpy(var):
+    assert isinstance(var, (Variable, fluid.core.VarBase)), "not a variable"
+    if isinstance(var, fluid.core.VarBase):
+        return var.numpy()
+    t = global_scope().find_var(var.name).get_tensor()
+    return np.array(t)
+
+
+def flatten_list(l):
+    assert isinstance(l, list), "not a list"
+    outl = []
+    splits = []
+    for sl in l:
+        assert isinstance(sl, list), "sub content not a list"
+        splits.append(len(sl))
+        outl += sl
+    return outl, splits
+
+
+def restore_flatten_list(l, splits):
+    outl = []
+    for split in splits:
+        assert len(l) >= split, "list length invalid"
+        sl, l = l[:split], l[split:]
+        outl.append(sl)
+    return outl
+
+
+def extract_args(func):
+    if hasattr(inspect, 'getfullargspec'):
+        return inspect.getfullargspec(func)[0]
+    else:
+        return inspect.getargspec(func)[0]
\ No newline at end of file