diff --git a/intel_extension_for_transformers/transformers/config.py b/intel_extension_for_transformers/transformers/config.py
index a0009e7d3ed..f5918267491 100644
--- a/intel_extension_for_transformers/transformers/config.py
+++ b/intel_extension_for_transformers/transformers/config.py
@@ -19,7 +19,7 @@
 import yaml
 from enum import Enum
 
-from neural_compressor.conf.dotdict import DotDict
+from neural_compressor.utils.utility import DotDict
 from .utils.metrics import Metric
 from .utils.objectives import Objective, performance
 
diff --git a/intel_extension_for_transformers/transformers/utils/utility_tf.py b/intel_extension_for_transformers/transformers/utils/utility_tf.py
deleted file mode 100644
index f19785740af..00000000000
--- a/intel_extension_for_transformers/transformers/utils/utility_tf.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2022 Intel Corporation
-#
-# 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.
-"""Utils for tensorflow framework."""
-
-import os
-import json
-from collections import OrderedDict, UserDict
-from neural_compressor.experimental import common
-
-TMPPATH = os.path.join('tmp', 'model')
-TEACHERPATH = os.path.join('tmp', 'teacher_model')
-class TFDataloader(object):
-    """Tensorflow dataloader.
-
-    Args:
-        dataset (string): Dataset
-    """
-
-    def __init__(self, dataset, batch_size=None):
-        """Init an instance."""
-        self.dataset = dataset
-        self.batch_size = batch_size
-
-    def __iter__(self):
-        """Get the iteration of dataset."""
-        for inputs, labels in self.dataset:
-            if isinstance(inputs, dict) or isinstance(inputs, OrderedDict) \
-                  or isinstance(inputs, UserDict):
-                for name in inputs.keys():
-                    inputs[name] = inputs[name].numpy()
-            elif isinstance(inputs, list) or isinstance(inputs, tuple):
-                inputs = [input.numpy() for input in inputs]
-            else:
-                inputs = inputs.numpy()
-
-            if isinstance(labels, dict) or isinstance(labels, OrderedDict) \
-                  or isinstance(labels, UserDict):   # pragma: no cover
-                for name in labels.keys():
-                    labels[name] = labels[name].numpy()
-            elif isinstance(labels, list) or isinstance(labels, tuple):
-                labels = [label.numpy() for label in labels]
-            else:
-                labels = labels.numpy()
-            yield inputs, labels
-
-    def __len__(self):
-        """Return the length of dataset."""
-        return len(self.dataset)
-
-
-def distributed_init(worker_addresses, type='worker', index=0):
-    """Init distribute environment.
-
-    Args:
-        worker_addresses: Addresses of all nodes.
-        type: The type of node, such as worker.
-        index: When index is 0, the node treat as a chief.
-    """
-    tf_config = {
-        'cluster': {
-            'worker': worker_addresses
-        },
-        'task': {'type': type, 'index': index}
-    }
-    os.environ['TF_CONFIG'] = json.dumps(tf_config)
-
-def _is_chief(task_type, task_id):
-    # here only consider the case in which TF_CONFIG task_type is set as worker
-    # and task_id=0 represents the chief
-    return (task_type == 'worker' and task_id == 0)
-
-# get model folder path for the distributed environment
-def get_filepath(base_dirpath, task_type, task_id):
-    """Get model folder path for the distributed environment.
-
-    Args:
-        base_dirpath: The basic folder path.
-        task_type: Task_type is set as worker.
-        task_id: Task id. When task_id=0, the node treat as a chief.
-    """
-    if task_type is None:    # single node
-        return base_dirpath
-    elif _is_chief(task_type, task_id):
-        return os.path.join(base_dirpath, 'chief')
-    else:
-        return os.path.join(base_dirpath, 'worker_' + str(task_id))
-
-
-# convert a Keras model to SavedModel
-def keras2SavedModel(model):   # pragma: no cover
-    """Transfer keras model into save_model."""
-    model = common.Model(model)
-    return model.model