diff --git a/examples/singa_peft/examples/autograd/xceptionnet.py b/examples/singa_peft/examples/autograd/xceptionnet.py
new file mode 100644
index 000000000..088fe66cb
--- /dev/null
+++ b/examples/singa_peft/examples/autograd/xceptionnet.py
@@ -0,0 +1,119 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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 singa import autograd
+from singa import tensor
+from singa import device
+from singa import layer
+from singa import opt
+
+import numpy as np
+from tqdm import trange
+
+# the code is modified from
+# https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/xception.py
+
+
+class Block(layer.Layer):
+
+    def __init__(self,
+                 in_filters,
+                 out_filters,
+                 reps,
+                 strides=1,
+                 padding=0,
+                 start_with_relu=True,
+                 grow_first=True):
+        super(Block, self).__init__()
+
+        if out_filters != in_filters or strides != 1:
+            self.skip = layer.Conv2d(in_filters,
+                                     out_filters,
+                                     1,
+                                     stride=strides,
+                                     padding=padding,
+                                     bias=False)
+            self.skipbn = layer.BatchNorm2d(out_filters)
+        else:
+            self.skip = None
+
+        self.layers = []
+
+        filters = in_filters
+        if grow_first:
+            self.layers.append(layer.ReLU())
+            self.layers.append(
+                layer.SeparableConv2d(in_filters,
+                                      out_filters,
+                                      3,
+                                      stride=1,
+                                      padding=1,
+                                      bias=False))
+            self.layers.append(layer.BatchNorm2d(out_filters))
+            filters = out_filters
+
+        for i in range(reps - 1):
+            self.layers.append(layer.ReLU())
+            self.layers.append(
+                layer.SeparableConv2d(filters,
+                                      filters,
+                                      3,
+                                      stride=1,
+                                      padding=1,
+                                      bias=False))
+            self.layers.append(layer.BatchNorm2d(filters))
+
+        if not grow_first:
+            self.layers.append(layer.ReLU())
+            self.layers.append(
+                layer.SeparableConv2d(in_filters,
+                                      out_filters,
+                                      3,
+                                      stride=1,
+                                      padding=1,
+                                      bias=False))
+            self.layers.append(layer.BatchNorm2d(out_filters))
+
+        if not start_with_relu:
+            self.layers = self.layers[1:]
+        else:
+            self.layers[0] = layer.ReLU()
+
+        if strides != 1:
+            self.layers.append(layer.MaxPool2d(3, strides, padding + 1))
+
+        self.register_layers(*self.layers)
+
+        self.add = layer.Add()
+
+    def forward(self, x):
+        y = self.layers[0](x)
+        for layer in self.layers[1:]:
+            if isinstance(y, tuple):
+                y = y[0]
+            y = layer(y)
+
+        if self.skip is not None:
+            skip = self.skip(x)
+            skip = self.skipbn(skip)
+        else:
+            skip = x
+        y = self.add(y, skip)
+        return y
+
+
+__all__ = ['Xception']