diff --git a/python/paddle/__init__.py b/python/paddle/__init__.py
index 7bac330376c44fb9632258b81ccb00255ab33a7c..72d6f9562f102bf0c55eb41f76d2bff1d55f25ec 100755
--- a/python/paddle/__init__.py
+++ b/python/paddle/__init__.py
@@ -72,6 +72,7 @@ from .tensor.attribute import real  # noqa: F401
 from .tensor.attribute import imag  # noqa: F401
 from .tensor.creation import to_tensor  # noqa: F401
 from .tensor.creation import diag  # noqa: F401
+from .tensor.creation import diagflat  # noqa: F401
 from .tensor.creation import eye  # noqa: F401
 from .tensor.creation import linspace  # noqa: F401
 from .tensor.creation import ones  # noqa: F401
@@ -301,6 +302,7 @@ __all__ = [     #noqa
            'add',
            'subtract',
            'diag',
+           'diagflat',
            'isnan',
            'scatter_nd_add',
            'unstack',
diff --git a/python/paddle/fluid/tests/unittests/test_diagflat.py b/python/paddle/fluid/tests/unittests/test_diagflat.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec74855ba25232e6f96f4fb062bb80aaf0c6b44a
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_diagflat.py
@@ -0,0 +1,109 @@
+#   Copyright (c) 2021 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 paddle
+from paddle.static import Program, program_guard
+
+
+class TestDiagFlatError(unittest.TestCase):
+    def test_errors(self):
+        paddle.enable_static()
+        with program_guard(Program(), Program()):
+
+            def test_diagflat_type():
+                x = [1, 2, 3]
+                output = paddle.diagflat(x)
+
+            self.assertRaises(TypeError, test_diagflat_type)
+
+            x = paddle.static.data('data', [3, 3])
+            self.assertRaises(TypeError, paddle.diagflat, x, offset=2.5)
+
+
+class TestDiagFlatAPI(unittest.TestCase):
+    def setUp(self):
+        self.input_np = np.random.random(size=(10, 10)).astype(np.float64)
+        self.expected0 = np.diagflat(self.input_np)
+        self.expected1 = np.diagflat(self.input_np, k=1)
+        self.expected2 = np.diagflat(self.input_np, k=-1)
+
+        self.input_np2 = np.random.random(size=(20)).astype(np.float64)
+        self.expected3 = np.diagflat(self.input_np2)
+        self.expected4 = np.diagflat(self.input_np2, k=1)
+        self.expected5 = np.diagflat(self.input_np2, k=-1)
+
+    def run_imperative(self):
+        x = paddle.to_tensor(self.input_np)
+        y = paddle.diagflat(x)
+        self.assertTrue(np.allclose(y.numpy(), self.expected0))
+
+        y = paddle.diagflat(x, offset=1)
+        self.assertTrue(np.allclose(y.numpy(), self.expected1))
+
+        y = paddle.diagflat(x, offset=-1)
+        self.assertTrue(np.allclose(y.numpy(), self.expected2))
+
+        x = paddle.to_tensor(self.input_np2)
+        y = paddle.diagflat(x)
+        self.assertTrue(np.allclose(y.numpy(), self.expected3))
+
+        y = paddle.diagflat(x, offset=1)
+        self.assertTrue(np.allclose(y.numpy(), self.expected4))
+
+        y = paddle.diagflat(x, offset=-1)
+        self.assertTrue(np.allclose(y.numpy(), self.expected5))
+
+    def run_static(self, use_gpu=False):
+        x = paddle.static.data(name='input', shape=[10, 10], dtype='float64')
+        x2 = paddle.static.data(name='input2', shape=[20], dtype='float64')
+        result0 = paddle.diagflat(x)
+        result3 = paddle.diagflat(x2)
+
+        place = paddle.CUDAPlace(0) if use_gpu else paddle.CPUPlace()
+        exe = paddle.static.Executor(place)
+        exe.run(paddle.static.default_startup_program())
+        res0, res3 = exe.run(
+            feed={"input": self.input_np,
+                  'input2': self.input_np2},
+            fetch_list=[result0, result3])
+
+        self.assertTrue(np.allclose(res0, self.expected0))
+        self.assertTrue(np.allclose(res3, self.expected3))
+
+    def test_cpu(self):
+        paddle.disable_static(place=paddle.CPUPlace())
+        self.run_imperative()
+        paddle.enable_static()
+
+        with paddle.static.program_guard(Program()):
+            self.run_static()
+
+    def test_gpu(self):
+        if not paddle.is_compiled_with_cuda():
+            return
+
+        paddle.disable_static(place=paddle.CUDAPlace(0))
+        self.run_imperative()
+        paddle.enable_static()
+
+        with paddle.static.program_guard(Program()):
+            self.run_static(use_gpu=True)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py
index c8d80fc9bc68cbbff4e270bbab4d8203e663bb2e..6a75c8e78bcda764073057902952a9a87c1c52ce 100755
--- a/python/paddle/tensor/__init__.py
+++ b/python/paddle/tensor/__init__.py
@@ -18,6 +18,7 @@ from .attribute import real  # noqa: F401
 from .attribute import imag  # noqa: F401
 from .creation import to_tensor  # noqa: F401
 from .creation import diag  # noqa: F401
+from .creation import diagflat  # noqa: F401
 from .creation import eye  # noqa: F401
 from .creation import linspace  # noqa: F401
 from .creation import ones  # noqa: F401
diff --git a/python/paddle/tensor/creation.py b/python/paddle/tensor/creation.py
index 51a5e8df0fc2f2f6f0853148d3ff9cc3a347e1d6..b446a5921b06739ebbcb860d0813cc70ac62bb4e 100644
--- a/python/paddle/tensor/creation.py
+++ b/python/paddle/tensor/creation.py
@@ -772,6 +772,131 @@ def meshgrid(*args, **kwargs):
     return out
 
 
+def diagflat(x, offset=0, name=None):
+    """
+    If ``x`` is a vector (1-D tensor), a 2-D square tensor whth the elements of ``x`` as the diagonal is returned.
+
+    If ``x`` is a tensor (more than 1-D), a 2-D square tensor with the elements of flattened ``x`` as the diagonal is returned.
+
+    The argument ``offset`` controls the diagonal offset.
+
+
+    If ``offset`` = 0, it is the main diagonal.
+
+    If ``offset`` > 0, it is superdiagonal.
+
+    If ``offset`` < 0, it is subdiagonal.
+
+    Args:
+        x (Tensor): The input tensor. It can be any shape. Its data type should be float32, float64, int32, int64.
+        offset (int, optional): The diagonal offset. A positive value represents superdiagonal, 0 represents the main diagonal, and a negative value represents subdiagonal. Default: 0 (main diagonal).
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        Tensor, a square matrix. The output data type is the same as input data type.
+
+    Examples:
+        .. code-block:: python
+
+          import paddle
+
+          x = paddle.to_tensor([1, 2, 3])
+          y = paddle.diagflat(x)
+          print(y.numpy())
+          # [[1 0 0]
+          #  [0 2 0]
+          #  [0 0 3]]
+
+          y = paddle.diagflat(x, offset=1)
+          print(y.numpy())
+          # [[0 1 0 0]
+          #  [0 0 2 0]
+          #  [0 0 0 3]
+          #  [0 0 0 0]]
+
+          y = paddle.diagflat(x, offset=-1)
+          print(y.numpy())
+          # [[0 0 0 0]
+          #  [1 0 0 0]
+          #  [0 2 0 0]
+          #  [0 0 3 0]]
+        
+        .. code-block:: python
+
+          import paddle
+
+          x = paddle.to_tensor([[1, 2], [3, 4]])
+          y = paddle.diagflat(x)
+          print(y.numpy())
+          # [[1 0 0 0]
+          #  [0 2 0 0]
+          #  [0 0 3 0]
+          #  [0 0 0 4]]
+
+          y = paddle.diagflat(x, offset=1)
+          print(y.numpy())
+          # [[0 1 0 0 0]
+          #  [0 0 2 0 0]
+          #  [0 0 0 3 0]
+          #  [0 0 0 0 4]
+          #  [0 0 0 0 0]]
+
+          y = paddle.diagflat(x, offset=-1)
+          print(y.numpy())
+          # [[0 0 0 0 0]
+          #  [1 0 0 0 0]
+          #  [0 2 0 0 0]
+          #  [0 0 3 0 0]
+          #  [0 0 0 4 0]]
+    """
+    padding_value = 0
+    if in_dygraph_mode():
+        if len(x.shape) == 1:
+            return core.ops.diag_v2(x, "offset", offset, "padding_value",
+                                    padding_value)
+        else:
+            y, _ = core.ops.flatten_contiguous_range(x, "start_axis", 0,
+                                                     "stop_axis", -1)
+            return core.ops.diag_v2(y, "offset", offset, "padding_value",
+                                    padding_value)
+
+    check_type(x, 'x', (Variable), 'diagflat')
+    check_dtype(x.dtype, 'x', ['float32', 'float64', 'int32', 'int64'],
+                'diagflat')
+    check_type(offset, 'offset', (int), 'diagflat')
+
+    helper = LayerHelper("diagflat", **locals())
+    out1 = helper.create_variable_for_type_inference(dtype=x.dtype)
+    out1_shape = helper.create_variable_for_type_inference(x.dtype)
+    out2 = helper.create_variable_for_type_inference(dtype=x.dtype)
+
+    if len(x.shape) == 1:
+        helper.append_op(
+            type='diag_v2',
+            inputs={'X': x},
+            outputs={'Out': out2},
+            attrs={'offset': offset,
+                   'padding_value': padding_value})
+    else:
+        helper.append_op(
+            type='flatten_contiguous_range',
+            inputs={'X': x},
+            outputs={'Out': out1,
+                     'XShape': out1_shape},
+            attrs={'start_axis': 0,
+                   'stop_axis': -1})
+        out1.stop_gradient = True
+
+        helper.append_op(
+            type='diag_v2',
+            inputs={'X': out1},
+            outputs={'Out': out2},
+            attrs={'offset': offset,
+                   'padding_value': padding_value})
+    out2.stop_gradient = True
+    return out2
+
+
 def diag(x, offset=0, padding_value=0, name=None):
     """
     If ``x`` is a vector (1-D tensor), a 2-D square tensor whth the elements of ``x`` as the diagonal is returned.