diff --git a/python/paddle/fluid/tests/unittests/mlu/test_pool2d_op_mlu.py b/python/paddle/fluid/tests/unittests/mlu/test_pool2d_op_mlu.py
new file mode 100644
index 0000000000000..2d9703117671c
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/mlu/test_pool2d_op_mlu.py
@@ -0,0 +1,868 @@
+#   Copyright (c) 2022 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
+from __future__ import division
+
+import unittest
+import numpy as np
+
+import paddle
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+import sys
+sys.path.append('..')
+from op_test import OpTest
+from test_pool2d_op import pool2D_forward_naive, avg_pool2D_forward_naive, max_pool2D_forward_naive
+
+
+class TestPool2D_Op_Mixin(object):
+    def setUp(self):
+        self.place = paddle.device.MLUPlace(0)
+        self.__class__.use_mlu = True
+        self.op_type = "pool2d"
+        self.init_data_type()
+        self.init_test_case()
+        self.padding_algorithm = "EXPLICIT"
+        self.init_paddings()
+        self.init_global_pool()
+        self.init_pool_type()
+        self.init_ceil_mode()
+        self.init_exclusive()
+        self.init_adaptive()
+        self.init_data_format()
+        self.init_shape()
+
+        input = np.random.random(self.shape).astype(self.dtype)
+        output = pool2D_forward_naive(
+            input, self.ksize, self.strides, self.paddings, self.global_pool,
+            self.ceil_mode, self.exclusive, self.adaptive, self.data_format,
+            self.pool_type, self.padding_algorithm).astype(self.dtype)
+        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
+
+        self.attrs = {
+            'strides': self.strides,
+            'paddings': self.paddings,
+            'ksize': self.ksize,
+            'pooling_type': self.pool_type,
+            'global_pooling': self.global_pool,
+            'ceil_mode': self.ceil_mode,
+            'data_format': self.data_format,
+            'exclusive': self.exclusive,
+            'adaptive': self.adaptive,
+            "padding_algorithm": self.padding_algorithm,
+        }
+
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        if self.dtype == np.float16:
+            return
+
+        if self.pool_type != "max":
+            self.check_grad_with_place(
+                self.place, set(['X']), 'Out', max_relative_error=0.07)
+
+    def init_data_format(self):
+        self.data_format = "NCHW"
+
+    def init_shape(self):
+        self.shape = [2, 3, 5, 5]
+
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+
+    def init_paddings(self):
+        self.paddings = [0, 0]
+        self.padding_algorithm = "EXPLICIT"
+
+    def init_data_type(self):
+        self.dtype = np.float32
+
+    def init_pool_type(self):
+        self.pool_type = "avg"
+        self.pool2D_forward_naive = avg_pool2D_forward_naive
+
+    def init_global_pool(self):
+        self.global_pool = True
+
+    def init_ceil_mode(self):
+        self.ceil_mode = False
+
+    def init_exclusive(self):
+        self.exclusive = True
+
+    # Not support adaptive pooling currently
+    def init_adaptive(self):
+        self.adaptive = False
+
+
+class TestPool2D_Op(TestPool2D_Op_Mixin, OpTest):
+    pass
+
+
+class TestCase1(TestPool2D_Op):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+
+    def init_paddings(self):
+        self.paddings = [0, 0]
+
+    def init_pool_type(self):
+        self.pool_type = "avg"
+        self.pool2D_forward_naive = avg_pool2D_forward_naive
+
+    def init_global_pool(self):
+        self.global_pool = False
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
+class TestCase2(TestPool2D_Op):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+
+    def init_paddings(self):
+        self.paddings = [1, 1]
+
+    def init_pool_type(self):
+        self.pool_type = "avg"
+        self.pool2D_forward_naive = avg_pool2D_forward_naive
+
+    def init_global_pool(self):
+        self.global_pool = False
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
+class TestCase3(TestPool2D_Op):
+    def init_pool_type(self):
+        self.pool_type = "max"
+        self.pool2D_forward_naive = max_pool2D_forward_naive
+
+
+class TestCase4(TestCase1):
+    def init_pool_type(self):
+        self.pool_type = "max"
+        self.pool2D_forward_naive = max_pool2D_forward_naive
+
+
+class TestCase5(TestCase2):
+    def init_pool_type(self):
+        self.pool_type = "max"
+        self.pool2D_forward_naive = max_pool2D_forward_naive
+
+
+def create_test_fp16_class(parent, check_grad=True):
+    class TestFp16Case(parent):
+        def init_data_type(self):
+            self.dtype = np.float16
+
+        def test_check_output(self):
+            place = core.MLUPlace(0)
+            self.check_output_with_place(place, atol=1e-3)
+
+        def test_check_grad(self):
+            place = core.MLUPlace(0)
+            if self.pool_type != "max" and check_grad:
+                self.check_grad_with_place(
+                    place, set(['X']), 'Out', max_relative_error=0.07)
+
+    cls_name = "{0}_{1}".format(parent.__name__, "Fp16Op")
+    TestFp16Case.__name__ = cls_name
+    globals()[cls_name] = TestFp16Case
+
+
+create_test_fp16_class(TestPool2D_Op)
+create_test_fp16_class(TestCase1, check_grad=False)
+create_test_fp16_class(TestCase2)
+create_test_fp16_class(TestCase3)
+create_test_fp16_class(TestCase4)
+create_test_fp16_class(TestCase5)
+
+#--------------------test pool2d use ceil mode--------------------
+
+
+def create_test_use_ceil_class(parent):
+    class TestPool2DUseCeilCase(parent):
+        def init_ceil_mode(self):
+            self.ceil_mode = True
+
+    cls_name = "{0}_{1}".format(parent.__name__, "CeilModeCast")
+    TestPool2DUseCeilCase.__name__ = cls_name
+    globals()[cls_name] = TestPool2DUseCeilCase
+
+
+create_test_use_ceil_class(TestCase1)
+create_test_use_ceil_class(TestCase2)
+
+
+class TestAvgInclude(TestCase2):
+    def init_exclusive(self):
+        self.exclusive = False
+
+
+#-------test pool2d with asymmetric padding-----
+
+
+class TestPool2D_AsyPadding(TestPool2D_Op):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 0, 1, 2]
+
+    def init_shape(self):
+        self.shape = [2, 3, 5, 5]
+
+
+class TestCase1_AsyPadding(TestCase1):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 0, 1, 0]
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
+class TestCase2_AsyPadding(TestCase2):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 2, 1, 2]
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
+class TestCase3_AsyPadding(TestCase3):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 0, 1, 2]
+
+    def init_shape(self):
+        self.shape = [2, 3, 5, 5]
+
+
+class TestCase4_AsyPadding(TestCase4):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 0, 1, 0]
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
+class TestCase5_AsyPadding((TestCase5)):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [2, 2, 1, 2]
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
+create_test_use_ceil_class(TestCase1_AsyPadding)
+create_test_use_ceil_class(TestCase2_AsyPadding)
+
+
+class TestAvgInclude_AsyPadding(TestCase2):
+    def init_exclusive(self):
+        self.exclusive = False
+
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 2, 1, 2]
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
+#----------- test channel_last --------------
+class TestPool2D_channel_last(TestPool2D_Op):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 5, 5, 3]
+
+
+class TestCase1_channel_last(TestCase1):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 7, 7, 3]
+
+
+class TestCase2_channel_last(TestCase2):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 7, 7, 3]
+
+
+class TestCase3_channel_last(TestCase3):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 5, 5, 3]
+
+
+class TestCase4_channel_last(TestCase4):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 7, 7, 3]
+
+
+class TestCase5_channel_last(TestCase5):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 7, 7, 3]
+
+
+create_test_use_ceil_class(TestCase1_channel_last)
+create_test_use_ceil_class(TestCase2_channel_last)
+
+
+class TestCase5_Max(TestCase2):
+    def init_pool_type(self):
+        self.pool_type = "max"
+
+    def test_check_grad(self):
+        if self.dtype == np.float16:
+            return
+        place = core.MLUPlace(0)
+        if self.pool_type == "max":
+            self.check_grad_with_place(
+                place, set(['X']), 'Out', max_relative_error=1.00)
+
+
+class TestCase5_channel_last_Max(TestCase5_Max):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 7, 7, 3]
+
+
+class TestAvgInclude_channel_last(TestCase2_channel_last):
+    def init_exclusive(self):
+        self.exclusive = False
+
+
+class TestPool2D_AsyPadding_channel_last(TestPool2D_AsyPadding):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 5, 5, 3]
+
+
+class TestCase1_AsyPadding_channel_last(TestCase1_AsyPadding):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 7, 7, 3]
+
+
+class TestCase2_AsyPadding_channel_last(TestCase2_AsyPadding):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 7, 7, 3]
+
+
+class TestCase3_AsyPadding_channel_last(TestCase3_AsyPadding):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 5, 5, 3]
+
+
+class TestCase4_AsyPadding_channel_last(TestCase4_AsyPadding):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 7, 7, 3]
+
+
+class TestCase5_AsyPadding_channel_last(TestCase5_AsyPadding):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 7, 7, 3]
+
+
+create_test_use_ceil_class(TestCase1_AsyPadding_channel_last)
+create_test_use_ceil_class(TestCase2_AsyPadding_channel_last)
+
+
+class TestAvgInclude_AsyPadding_channel_last(TestAvgInclude_AsyPadding):
+    def init_data_format(self):
+        self.data_format = "NHWC"
+
+    def init_shape(self):
+        self.shape = [2, 7, 7, 3]
+
+
+# test paddings: SAME VALID
+
+
+def create_test_padding_SAME_class(parent):
+    class TestPaddingSMAECase(parent):
+        def init_paddings(self):
+            self.paddings = [0, 0]
+            self.padding_algorithm = "SAME"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "PaddingSAMEOp")
+    TestPaddingSMAECase.__name__ = cls_name
+    globals()[cls_name] = TestPaddingSMAECase
+
+
+create_test_padding_SAME_class(TestPool2D_Op)
+create_test_padding_SAME_class(TestCase1)
+create_test_padding_SAME_class(TestCase2)
+create_test_padding_SAME_class(TestCase3)
+create_test_padding_SAME_class(TestCase4)
+create_test_padding_SAME_class(TestCase5)
+
+create_test_padding_SAME_class(TestPool2D_channel_last)
+create_test_padding_SAME_class(TestCase1_channel_last)
+create_test_padding_SAME_class(TestCase2_channel_last)
+create_test_padding_SAME_class(TestCase3_channel_last)
+create_test_padding_SAME_class(TestCase4_channel_last)
+create_test_padding_SAME_class(TestCase5_channel_last)
+
+
+def create_test_padding_VALID_class(parent):
+    class TestPaddingVALIDCase(parent):
+        def init_paddings(self):
+            self.paddings = [1, 1]
+            self.padding_algorithm = "VALID"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "PaddingVALIDOp")
+    TestPaddingVALIDCase.__name__ = cls_name
+    globals()[cls_name] = TestPaddingVALIDCase
+
+
+create_test_padding_VALID_class(TestPool2D_Op)
+create_test_padding_VALID_class(TestCase1)
+create_test_padding_VALID_class(TestCase2)
+create_test_padding_VALID_class(TestCase3)
+create_test_padding_VALID_class(TestCase4)
+create_test_padding_VALID_class(TestCase5)
+
+create_test_padding_VALID_class(TestPool2D_channel_last)
+create_test_padding_VALID_class(TestCase1_channel_last)
+create_test_padding_VALID_class(TestCase2_channel_last)
+create_test_padding_VALID_class(TestCase3_channel_last)
+create_test_padding_VALID_class(TestCase4_channel_last)
+create_test_padding_VALID_class(TestCase5_channel_last)
+
+
+class TestCase1_strides(TestCase1):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 2]
+
+    def init_shape(self):
+        self.shape = [2, 3, 4, 5]
+
+
+create_test_padding_SAME_class(TestCase1_strides)
+
+
+# ----- test API
+class TestPool2DAPI(unittest.TestCase):
+    def test_api(self):
+        x_NHWC = np.random.random([2, 5, 5, 3]).astype("float32")
+        x_NCHW = np.random.random([2, 3, 5, 5]).astype("float32")
+
+        input_NHWC = fluid.layers.data(
+            name="input_NHWC",
+            shape=[2, 5, 5, 3],
+            append_batch_size=False,
+            dtype="float32")
+
+        input_NCHW = fluid.layers.data(
+            name="input_NCHW",
+            shape=[2, 3, 5, 5],
+            append_batch_size=False,
+            dtype="float32")
+
+        input_NHWC_negetive = fluid.layers.data(
+            name="input_NHWC_negetive",
+            shape=[2, -1, 5, 3],
+            append_batch_size=False,
+            dtype="float32")
+
+        input_NCHW_negetive = fluid.layers.data(
+            name="input_NCHW_negetive",
+            shape=[2, 3, -1, -1],
+            append_batch_size=False,
+            dtype="float32")
+
+        ksize = [3, 3]
+        out_1 = fluid.layers.pool2d(
+            input=input_NHWC,
+            pool_size=ksize,
+            pool_type="max",
+            pool_padding=[1, 1],
+            data_format="NHWC")
+
+        out_2 = fluid.layers.pool2d(
+            input=input_NHWC,
+            pool_size=ksize,
+            pool_type="avg",
+            pool_padding=[[0, 0], [1, 1], [1, 1], [0, 0]],
+            data_format="NHWC")
+
+        out_3 = fluid.layers.pool2d(
+            input=input_NCHW,
+            pool_size=ksize,
+            pool_type="avg",
+            pool_padding=[[0, 0], [0, 0], [1, 1], [1, 1]],
+            data_format="NCHW")
+
+        out_4 = fluid.layers.pool2d(
+            input=input_NCHW,
+            pool_size=ksize,
+            pool_type="avg",
+            pool_padding=[1, 2, 1, 0],
+            data_format="NCHW")
+        # test VALID
+        out_5 = fluid.layers.pool2d(
+            input=input_NCHW,
+            pool_size=ksize,
+            pool_type="avg",
+            pool_padding="VALID",
+            data_format="NCHW")
+
+        out_6 = fluid.layers.pool2d(
+            input=input_NHWC,
+            pool_size=ksize,
+            pool_type="max",
+            pool_padding="VALID",
+            data_format="NHWC")
+
+        # test SAME
+        out_7 = fluid.layers.pool2d(
+            input=input_NCHW,
+            pool_size=[4, 4],
+            pool_type="avg",
+            pool_padding="SAME",
+            data_format="NCHW")
+
+        out_8 = fluid.layers.pool2d(
+            input=input_NHWC,
+            pool_size=[4, 4],
+            pool_type="max",
+            pool_padding="SAME",
+            data_format="NHWC")
+
+        # test negetive
+        out_9 = fluid.layers.pool2d(
+            input=input_NHWC_negetive,
+            pool_size=ksize,
+            pool_type="avg",
+            pool_padding=[0, 0],
+            data_format="NHWC")
+        assert out_9.shape == (2, -1, 3, 3)
+
+        out_10 = fluid.layers.pool2d(
+            input=input_NCHW_negetive,
+            pool_size=ksize,
+            pool_type="avg",
+            pool_padding=[0, 0],
+            data_format="NCHW")
+        assert out_10.shape == (2, 3, -1, -1)
+
+        exe = fluid.Executor(place=fluid.MLUPlace(0))
+        [res_1, res_2, res_3, res_4, res_5, res_6, res_7, res_8] = exe.run(
+            fluid.default_main_program(),
+            feed={
+                "input_NHWC": x_NHWC,
+                "input_NCHW": x_NCHW,
+                "input_NHWC_negetive": x_NHWC,
+                "input_NCHW_negetive": x_NCHW
+            },
+            fetch_list=[
+                out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8
+            ])
+
+        assert np.allclose(
+            res_1,
+            pool2D_forward_naive(
+                x=x_NHWC,
+                ksize=ksize,
+                pool_type="max",
+                strides=[1, 1],
+                paddings=[1, 1],
+                data_format="NHWC"))
+
+        assert np.allclose(
+            res_2,
+            pool2D_forward_naive(
+                x=x_NHWC,
+                ksize=ksize,
+                pool_type="avg",
+                strides=[1, 1],
+                paddings=[1, 1, 1, 1],
+                data_format="NHWC"))
+        assert np.allclose(
+            res_3,
+            pool2D_forward_naive(
+                x=x_NCHW,
+                ksize=ksize,
+                pool_type="avg",
+                strides=[1, 1],
+                paddings=[1, 1, 1, 1],
+                data_format="NCHW"),
+            rtol=0.07,
+            atol=1e-05)
+
+        assert np.allclose(
+            res_4,
+            pool2D_forward_naive(
+                x=x_NCHW,
+                ksize=ksize,
+                pool_type="avg",
+                strides=[1, 1],
+                paddings=[1, 2, 1, 0],
+                data_format="NCHW"),
+            rtol=0.07,
+            atol=1e-05)
+
+        # VALID
+        assert np.allclose(
+            res_5,
+            pool2D_forward_naive(
+                x=x_NCHW,
+                ksize=ksize,
+                pool_type="avg",
+                strides=[1, 1],
+                paddings=[10, 20],  # any ele is ok
+                padding_algorithm="VALID",
+                data_format="NCHW"),
+            rtol=0.07,
+            atol=1e-05)
+        assert np.allclose(
+            res_6,
+            pool2D_forward_naive(
+                x=x_NHWC,
+                ksize=ksize,
+                pool_type="max",
+                strides=[1, 1],
+                paddings=[10, 20],
+                padding_algorithm="VALID",
+                data_format="NHWC"))
+        # SAME
+        assert np.allclose(
+            res_7,
+            pool2D_forward_naive(
+                x=x_NCHW,
+                ksize=[4, 4],
+                pool_type="avg",
+                strides=[1, 1],
+                paddings=[10, 20],
+                padding_algorithm="SAME",
+                data_format="NCHW"),
+            rtol=0.07,
+            atol=1e-05)
+
+        assert np.allclose(
+            res_8,
+            pool2D_forward_naive(
+                x=x_NHWC,
+                ksize=[4, 4],
+                pool_type="max",
+                strides=[1, 1],
+                paddings=[10, 20],
+                padding_algorithm="SAME",
+                data_format="NHWC"))
+
+
+class TestPool2DAPI_Error(unittest.TestCase):
+    def test_api(self):
+        input_NHWC = fluid.layers.data(
+            name="input_NHWC",
+            shape=[2, 5, 5, 3],
+            append_batch_size=False,
+            dtype="float32")
+        ksize = [3, 3]
+
+        # data_format value error
+        def run_2():
+            out_2 = fluid.layers.pool2d(
+                input=input_NHWC,
+                pool_size=ksize,
+                pool_type="max",
+                pool_padding=[1, 1],
+                data_format="NHWCC")
+
+        self.assertRaises(ValueError, run_2)
+
+        # padding str value error
+        def run_3():
+            out_3 = fluid.layers.pool2d(
+                input=input_NHWC,
+                pool_size=ksize,
+                pool_type="max",
+                pool_padding="VALIDSAME",
+                data_format="NHWC")
+
+        self.assertRaises(ValueError, run_3)
+
+        # padding str valid and ceil_mode value error
+        def run_4():
+            out_4 = fluid.layers.pool2d(
+                input=input_NHWC,
+                pool_size=ksize,
+                pool_type="max",
+                pool_padding="VALID",
+                ceil_mode=True,
+                data_format="NHWC")
+
+        self.assertRaises(ValueError, run_4)
+
+        # padding with 8 ele. value error
+        def run_5():
+            out_5 = fluid.layers.pool2d(
+                input=input_NHWC,
+                pool_size=ksize,
+                pool_type="max",
+                pool_padding=[[1, 1], [0, 0], [0, 0], [1, 1]],
+                data_format="NHWC")
+
+        self.assertRaises(ValueError, run_5)
+
+
+class TestDygraphPool2DAPIError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            # the input of Pool2D must be Variable.
+            data1 = np.random.random((3, 32, 32, 5)).astype('float32')
+            pool2d = fluid.dygraph.Pool2D(
+                pool_size=2,
+                pool_type='max',
+                pool_stride=1,
+                global_pooling=False)
+            self.assertRaises(TypeError, pool2d, data1)
+
+            # the input dtype of mlu Pool2D must be float16 or float32 
+            data2 = fluid.layers.data(
+                name='x1', shape=[3, 32, 32, 5], dtype="int32")
+            self.assertRaises(TypeError, pool2d, data2)
+
+    def test_data_format_error(self):
+        with program_guard(Program(), Program()):
+            # the data_format must be 'NCHW' or 'NHWC'
+            data1 = np.random.random((3, 32, 32, 5)).astype('float32')
+            self.assertRaises(
+                ValueError,
+                fluid.dygraph.Pool2D,
+                pool_size=2,
+                pool_type='max',
+                pool_stride=1,
+                global_pooling=False,
+                data_format='NWHC')
+
+
+class TestDygraphPool2DAPI(unittest.TestCase):
+    def test_nhwc(self):
+        with fluid.dygraph.guard():
+            data = np.random.random((3, 32, 32, 5)).astype('float32')
+            x = fluid.dygraph.to_variable(data)
+            pool2d = fluid.dygraph.Pool2D(
+                pool_size=2,
+                pool_type='max',
+                pool_stride=1,
+                pool_padding=[0, 0],
+                global_pooling=False,
+                data_format='NHWC')
+            out1 = pool2d(x)
+            out2 = pool2D_forward_naive(
+                data, [2, 2], [1, 1],
+                paddings=[0, 0],
+                pool_type='max',
+                data_format='NHWC')
+            self.assertTrue(np.allclose(out1.numpy(), out2))
+
+    def test_lower_case(self):
+        with fluid.dygraph.guard():
+            data = np.random.random((3, 32, 32, 5)).astype('float32')
+            x = fluid.dygraph.to_variable(data)
+            pool2d = fluid.dygraph.Pool2D(
+                pool_size=2,
+                pool_type='max',
+                pool_stride=1,
+                pool_padding=[0, 0],
+                global_pooling=False,
+                data_format='nhwc')
+            out1 = pool2d(x)
+            out2 = pool2D_forward_naive(
+                data, [2, 2], [1, 1],
+                paddings=[0, 0],
+                pool_type='max',
+                data_format='NHWC')
+            self.assertTrue(np.allclose(out1.numpy(), out2))
+
+    def test_upper_case(self):
+        with fluid.dygraph.guard():
+            data = np.random.random((3, 32, 32, 5)).astype('float32')
+            x = fluid.dygraph.to_variable(data)
+            pool2d = fluid.dygraph.Pool2D(
+                pool_size=2,
+                pool_type='MAX',
+                pool_stride=1,
+                pool_padding=[0, 0],
+                global_pooling=False,
+                data_format='nhwc')
+            out1 = pool2d(x)
+            out2 = pool2D_forward_naive(
+                data, [2, 2], [1, 1],
+                paddings=[0, 0],
+                pool_type='max',
+                data_format='NHWC')
+            self.assertTrue(np.allclose(out1.numpy(), out2))
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()