diff --git a/python/paddle/fluid/tests/unittests/mlu/test_bilinear_interp_v2_op_mlu.py b/python/paddle/fluid/tests/unittests/mlu/test_bilinear_interp_v2_op_mlu.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8c31578099e1499f10c4b374a789bebc12d6a5d
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+++ b/python/paddle/fluid/tests/unittests/mlu/test_bilinear_interp_v2_op_mlu.py
@@ -0,0 +1,663 @@
+#   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
+
+import unittest
+import numpy as np
+import sys
+
+sys.path.append('..')
+from op_test import OpTest
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.nn.functional import interpolate
+import paddle
+
+paddle.enable_static()
+
+
+def bilinear_interp_np(input,
+                       out_h,
+                       out_w,
+                       scale_w=0,
+                       scale_h=0,
+                       out_size=None,
+                       actual_shape=None,
+                       align_corners=True,
+                       align_mode=0,
+                       data_layout='NCHW'):
+    """bilinear interpolation implement in shape [N, C, H, W]"""
+    if data_layout == "NHWC":
+        input = np.transpose(input, (0, 3, 1, 2))  # NHWC => NCHW
+    if out_size is not None:
+        out_h = out_size[0]
+        out_w = out_size[1]
+    if actual_shape is not None:
+        out_h = actual_shape[0]
+        out_w = actual_shape[1]
+    batch_size, channel, in_h, in_w = input.shape
+
+    ratio_h = ratio_w = 0.0
+    if out_h > 1:
+        if (align_corners):
+            ratio_h = (in_h - 1.0) / (out_h - 1.0)
+        else:
+            if scale_h > 0:
+                ratio_h = 1.0 / scale_h
+            else:
+                ratio_h = 1.0 * in_h / out_h
+    if out_w > 1:
+        if (align_corners):
+            ratio_w = (in_w - 1.0) / (out_w - 1.0)
+        else:
+            if scale_w > 0:
+                ratio_w = 1.0 / scale_w
+            else:
+                ratio_w = 1.0 * in_w / out_w
+
+    out = np.zeros((batch_size, channel, out_h, out_w))
+
+    for i in range(out_h):
+        if (align_mode == 0 and not align_corners):
+            h = int(ratio_h * (i + 0.5) - 0.5)
+        else:
+            h = int(ratio_h * i)
+
+        h = max(0, h)
+        hid = 1 if h < in_h - 1 else 0
+        if (align_mode == 0 and not align_corners):
+            idx_src_h = max(ratio_h * (i + 0.5) - 0.5, 0)
+            h1lambda = idx_src_h - h
+        else:
+            h1lambda = ratio_h * i - h
+        h2lambda = 1.0 - h1lambda
+        for j in range(out_w):
+            if (align_mode == 0 and not align_corners):
+                w = int(ratio_w * (j + 0.5) - 0.5)
+            else:
+                w = int(ratio_w * j)
+            w = max(0, w)
+            wid = 1 if w < in_w - 1 else 0
+            if (align_mode == 0 and not align_corners):
+                idx_src_w = max(ratio_w * (j + 0.5) - 0.5, 0)
+                w1lambda = idx_src_w - w
+            else:
+                w1lambda = ratio_w * j - w
+            w2lambda = 1.0 - w1lambda
+
+            out[:, :, i, j] = h2lambda*(w2lambda*input[:, :, h, w] +
+                                        w1lambda*input[:, :, h, w+wid]) + \
+                h1lambda*(w2lambda*input[:, :, h+hid, w] +
+                          w1lambda*input[:, :, h+hid, w+wid])
+
+    if data_layout == "NHWC":
+        out = np.transpose(out, (0, 2, 3, 1))  # NCHW => NHWC
+
+    return out.astype(input.dtype)
+
+
+class TestBilinearInterpOp(OpTest):
+
+    def setUp(self):
+        self.place = paddle.device.MLUPlace(0)
+        self.__class__.use_mlu = True
+        self.out_size = None
+        self.actual_shape = None
+        self.data_layout = 'NCHW'
+        self.init_test_case()
+        self.dtype = "float32"
+        self.op_type = "bilinear_interp_v2"
+        input_np = np.random.random(self.input_shape).astype(self.dtype)
+
+        if self.data_layout == "NCHW":
+            in_h = self.input_shape[2]
+            in_w = self.input_shape[3]
+        else:
+            in_h = self.input_shape[1]
+            in_w = self.input_shape[2]
+        scale_h = 0
+        scale_w = 0
+        if self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if self.scale > 0.:
+                    scale_h = scale_w = float(self.scale)
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                scale_w = scale_h = self.scale[0]
+            elif isinstance(self.scale, list) and len(self.scale) > 1:
+                scale_w = self.scale[1]
+                scale_h = self.scale[0]
+            out_h = int(in_h * scale_h)
+            out_w = int(in_w * scale_w)
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        output_np = bilinear_interp_np(input_np, out_h, out_w, 0, 0,
+                                       self.out_size, self.actual_shape,
+                                       self.align_corners, self.align_mode,
+                                       self.data_layout)
+        self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
+        if self.actual_shape is not None:
+            self.inputs['OutSize'] = self.actual_shape
+
+        self.attrs = {
+            'out_h': self.out_h,
+            'out_w': self.out_w,
+            'interp_method': self.interp_method,
+            'align_corners': self.align_corners,
+            'align_mode': self.align_mode,
+            'data_layout': self.data_layout
+        }
+
+        if self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if self.scale > 0.:
+                    self.scale = [self.scale]
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                self.scale = [self.scale[0], self.scale[0]]
+            self.attrs['scale'] = self.scale
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        self.check_grad_with_place(self.place, ['X'], 'Out', in_place=True)
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 5, 5]
+        self.out_h = 2
+        self.out_w = 2
+        self.scale = 0.
+        self.out_size = np.array([3, 3]).astype("int32")
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpCase1(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+        self.scale = 0.
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpCase2(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.scale = 0.
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpCase3(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.align_corners = True
+        self.align_mode = 1
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, atol=1e-5)
+
+
+class TestBilinearInterpCase4(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+        self.scale = 0.
+        self.out_size = np.array([2, 2]).astype("int32")
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpCase5(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.scale = 0.
+        self.out_size = np.array([11, 11]).astype("int32")
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpCase6(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.out_size = np.array([65, 33]).astype("int32")
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpCase7(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = [2.0, 0.5]
+        self.align_corners = False
+        self.align_mode = 1
+
+
+class TestBilinearInterpSame(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 32, 64]
+        self.out_h = 32
+        self.out_w = 64
+        self.scale = 0.
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpActualShape(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpDataLayout(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 5, 5, 3]
+        self.out_h = 2
+        self.out_w = 2
+        self.scale = 0.
+        self.out_size = np.array([3, 3]).astype("int32")
+        self.align_corners = True
+        self.align_mode = 1
+        self.data_layout = "NHWC"
+
+
+class TestBilinearInterpOtherMethod1(TestBilinearInterpOp):
+
+    def set_align_mode(self):
+        self.align_corners = False
+        self.align_mode = 1
+
+
+class TestBilinearInterpWithMethod2(TestBilinearInterpOp):
+
+    def set_align_mode(self):
+        self.align_corners = False
+        self.align_mode = 0
+
+
+class TestBilinearInterpWithMethod3(TestBilinearInterpOp):
+
+    def set_align_mode(self):
+        self.align_corners = True
+        self.align_mode = 0
+
+
+class TestBilinearInterpScale1(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 5, 7]
+        self.out_h = 60
+        self.out_w = 25
+        self.scale = 2.
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpScale2(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 5, 7]
+        self.out_h = 60
+        self.out_w = 25
+        self.scale = 1.
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpScale3(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 5, 7]
+        self.out_h = 60
+        self.out_w = 25
+        self.scale = 1.5
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpScale4(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 5, 7]
+        self.out_h = 60
+        self.out_w = 25
+        self.scale = [1.5, 0.5]
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpZero(TestBilinearInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 5, 7]
+        self.out_h = 60
+        self.out_w = 25
+        self.scale = 0.2
+        self.align_corners = False
+        self.align_mode = 0
+
+
+class TestBilinearInterpOp_attr_tensor(OpTest):
+
+    def setUp(self):
+        self.place = paddle.device.MLUPlace(0)
+        self.__class__.use_mlu = True
+        self.out_size = None
+        self.actual_shape = None
+        self.init_test_case()
+        self.op_type = "bilinear_interp_v2"
+        self.shape_by_1Dtensor = False
+        self.scale_by_1Dtensor = False
+        self.attrs = {
+            'interp_method': self.interp_method,
+            'align_corners': self.align_corners,
+        }
+
+        input_np = np.random.random(self.input_shape).astype("float32")
+        self.inputs = {'X': input_np}
+
+        if self.scale_by_1Dtensor:
+            self.inputs['Scale'] = np.array([self.scale]).astype("float32")
+        elif self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if self.scale > 0:
+                    scale_h = scale_w = float(self.scale)
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                scale_w = scale_h = self.scale[0]
+            elif isinstance(self.scale, list) and len(self.scale) > 1:
+                scale_w = self.scale[1]
+                scale_h = self.scale[0]
+            out_h = int(self.input_shape[2] * scale_h)
+            out_w = int(self.input_shape[3] * scale_w)
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        if self.shape_by_1Dtensor:
+            self.inputs['OutSize'] = self.out_size
+        elif self.out_size is not None:
+            size_tensor = []
+            for index, ele in enumerate(self.out_size):
+                size_tensor.append(("x" + str(index), np.ones(
+                    (1)).astype('int32') * ele))
+            self.inputs['SizeTensor'] = size_tensor
+
+        self.attrs['out_h'] = self.out_h
+        self.attrs['out_w'] = self.out_w
+        if self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if self.scale > 0:
+                    self.scale = [self.scale]
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                self.scale = [self.scale[0], self.scale[0]]
+            self.attrs['scale'] = self.scale
+        output_np = bilinear_interp_np(input_np, out_h, out_w, 0, 0,
+                                       self.out_size, self.actual_shape,
+                                       self.align_corners)
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        self.check_grad_with_place(self.place, ['X'], 'Out', in_place=True)
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 5, 5]
+        self.out_h = 3
+        self.out_w = 3
+        self.scale = 0.
+        self.out_size = [3, 3]
+        self.align_corners = True
+
+
+# out_size is a 1-D tensor
+class TestBilinearInterp_attr_tensor_Case1(TestBilinearInterpOp_attr_tensor):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.scale = 0.
+        self.out_size = [8, 12]
+        self.align_corners = True
+
+
+# scale is a 1-D tensor
+class TestBilinearInterp_attr_tensor_Case2(TestBilinearInterpOp_attr_tensor):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = True
+        self.shape_by_1Dtensor = True
+
+
+# scale is a 1-D tensor
+class TestBilinearInterp_attr_tensor_Case3(TestBilinearInterpOp_attr_tensor):
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 2.0
+        self.out_size = None
+        self.align_corners = True
+        self.scale_by_1Dtensor = True
+
+
+#TODO: comment this test for now until bilinear_interp_op added.
+# class TestBilinearInterpOpAPI(unittest.TestCase):
+#     def test_case(self):
+#         x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
+
+#         dim = fluid.data(name="dim", shape=[1], dtype="int32")
+#         shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
+#         actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
+#         scale_tensor = fluid.data(
+#             name="scale_tensor", shape=[1], dtype="float32")
+
+#         out1 = fluid.layers.resize_bilinear(x, out_shape=[12, 12])
+#         out2 = fluid.layers.resize_bilinear(x, out_shape=[12, dim])
+#         out3 = fluid.layers.resize_bilinear(x, out_shape=shape_tensor)
+#         out4 = fluid.layers.resize_bilinear(
+#             x, out_shape=[4, 4], actual_shape=actual_size)
+#         out5 = fluid.layers.resize_bilinear(x, scale=scale_tensor)
+
+#         x_data = np.random.random((2, 3, 6, 6)).astype("float32")
+#         dim_data = np.array([12]).astype("int32")
+#         shape_data = np.array([12, 12]).astype("int32")
+#         actual_size_data = np.array([12, 12]).astype("int32")
+#         scale_data = np.array([2.0]).astype("float32")
+
+#         if core.is_compiled_with_mlu():
+#             place = paddle.device.MLUPlace(0)
+#         else:
+#             place = core.CPUPlace()
+#         exe = fluid.Executor(place)
+#         exe.run(fluid.default_startup_program())
+#         results = exe.run(fluid.default_main_program(),
+#                           feed={
+#                               "x": x_data,
+#                               "dim": dim_data,
+#                               "shape_tensor": shape_data,
+#                               "actual_size": actual_size_data,
+#                               "scale_tensor": scale_data
+#                           },
+#                           fetch_list=[out1, out2, out3, out4, out5],
+#                           return_numpy=True)
+
+#         expect_res = bilinear_interp_np(
+#             x_data, out_h=12, out_w=12, align_corners=True)
+#         for res in results:
+#             self.assertTrue(np.allclose(res, expect_res))
+
+
+class TestBilinearInterpOpAPI_dy(unittest.TestCase):
+
+    def test_case(self):
+        import paddle
+        if core.is_compiled_with_mlu():
+            place = paddle.device.MLUPlace(0)
+        else:
+            place = core.CPUPlace()
+        with fluid.dygraph.guard(place):
+            input_data = np.random.random((2, 3, 6, 6)).astype("float32")
+            input_data = np.load('input.npy').astype("float32")
+            # print(input_data)
+            input_x = paddle.to_tensor(input_data)
+            expect_res = bilinear_interp_np(input_data,
+                                            out_h=12,
+                                            out_w=12,
+                                            align_corners=False)
+            out = interpolate(x=input_x,
+                              size=[12, 12],
+                              mode="bilinear",
+                              align_corners=False)
+            self.assertTrue(np.allclose(out.numpy(), expect_res))
+
+
+class TestBilinearInterpOpAPI_dy2(unittest.TestCase):
+
+    def test_case(self):
+        import paddle
+        if core.is_compiled_with_mlu():
+            place = paddle.device.MLUPlace(0)
+        else:
+            place = core.CPUPlace()
+        with fluid.dygraph.guard(place):
+            input_data = np.random.random((2, 3, 6, 6)).astype("float32")
+            size_np = np.array([12, 12]).astype("int64")
+            input_x = paddle.to_tensor(input_data)
+            size = paddle.to_tensor(size_np)
+            expect_res = bilinear_interp_np(input_data,
+                                            out_h=12,
+                                            out_w=12,
+                                            align_corners=False)
+            out = interpolate(x=input_x,
+                              size=size,
+                              mode="bilinear",
+                              align_corners=False)
+            self.assertTrue(np.allclose(out.numpy(), expect_res))
+
+
+class TestBilinearInterpOpAPI_dy3(unittest.TestCase):
+
+    def test_case(self):
+        import paddle
+        if core.is_compiled_with_mlu():
+            place = paddle.device.MLUPlace(0)
+        else:
+            place = core.CPUPlace()
+        with fluid.dygraph.guard(place):
+            input_data = np.random.random((2, 3, 6, 6)).astype("float32")
+            size_1 = np.array([12]).astype("int64")
+            input_x = paddle.to_tensor(input_data)
+            size = paddle.to_tensor(size_1)
+            expect_res = bilinear_interp_np(input_data,
+                                            out_h=12,
+                                            out_w=12,
+                                            align_corners=False)
+            out = interpolate(x=input_x,
+                              size=[size, size],
+                              mode="bilinear",
+                              align_corners=False)
+            self.assertTrue(np.allclose(out.numpy(), expect_res))
+
+
+class TestBilinearInterpOpAPI_dy4(unittest.TestCase):
+
+    def test_case(self):
+        import paddle
+        if core.is_compiled_with_mlu():
+            place = paddle.device.MLUPlace(0)
+        else:
+            place = core.CPUPlace()
+        with fluid.dygraph.guard(place):
+            input_data = np.random.random((2, 3, 6, 6)).astype("float32")
+            scale_np = np.array([2, 2]).astype("int64")
+            input_x = paddle.to_tensor(input_data)
+            scale = paddle.to_tensor(scale_np)
+            expect_res = bilinear_interp_np(input_data,
+                                            out_h=12,
+                                            out_w=12,
+                                            align_corners=False)
+            out = interpolate(x=input_x,
+                              scale_factor=scale,
+                              mode="bilinear",
+                              align_corners=False)
+
+            self.assertTrue(np.allclose(out.numpy(), expect_res))
+
+
+if __name__ == "__main__":
+    unittest.main()