diff --git a/python/paddle/base/dygraph/math_op_patch.py b/python/paddle/base/dygraph/math_op_patch.py
index 15270ea89e19b6..c30c3067d3ff40 100644
--- a/python/paddle/base/dygraph/math_op_patch.py
+++ b/python/paddle/base/dygraph/math_op_patch.py
@@ -283,6 +283,40 @@ def _mT_(var: Tensor) -> Tensor:
         out = _C_ops.transpose(var, perm)
         return out
 
+    @property
+    def requires_grad(self: Tensor) -> bool:
+        """
+        Whether this Tensor requires gradient computation.
+
+        This is a convenience property that returns the opposite of stop_gradient.
+        Setting requires_grad=True is equivalent to setting stop_gradient=False.
+
+        Examples:
+            .. code-block:: python
+
+                >>> import paddle
+                >>> x = paddle.randn([2, 3])
+                >>> print(x.requires_grad)  # False by default
+                >>>
+                >>> x.requires_grad = False
+                >>> print(x.stop_gradient)  # True
+        """
+        return not self.stop_gradient
+
+    @requires_grad.setter
+    def requires_grad(self: Tensor, value: bool) -> None:
+        """
+        Set whether this Tensor requires gradient computation.
+
+        Args:
+            value (bool): True to enable gradient computation, False to disable.
+        """
+        if not isinstance(value, bool):
+            raise TypeError(
+                f"requires_grad must be bool, but got {type(value)}"
+            )
+        self.stop_gradient = not value
+
     eager_methods = [
         ('__neg__', _neg_),
         ('__abs__', _abs_),
@@ -301,6 +335,7 @@ def _mT_(var: Tensor) -> Tensor:
         ('size', _size_),
         ('T', _T_),
         ('mT', _mT_),
+        ("requires_grad", requires_grad),
         # for logical compare
         ('__array_ufunc__', None),
     ]
diff --git a/python/paddle/base/layers/math_op_patch.py b/python/paddle/base/layers/math_op_patch.py
index a29f85f3e1eff3..dad7bb596e1fca 100644
--- a/python/paddle/base/layers/math_op_patch.py
+++ b/python/paddle/base/layers/math_op_patch.py
@@ -561,6 +561,40 @@ def dim(self):
         """
         return len(self.shape)
 
+    @property
+    def requires_grad(self) -> bool:
+        """
+        Whether this Tensor requires gradient computation.
+
+        This is a convenience property that returns the opposite of stop_gradient.
+        Setting requires_grad=True is equivalent to setting stop_gradient=False.
+
+        Examples:
+            .. code-block:: python
+
+                >>> import paddle
+                >>> x = paddle.randn([2, 3])
+                >>> print(x.requires_grad)  # False by default
+                >>>
+                >>> x.requires_grad = False
+                >>> print(x.stop_gradient)  # True
+        """
+        return not self.stop_gradient
+
+    @requires_grad.setter
+    def requires_grad(self, value: bool) -> None:
+        """
+        Set whether this Tensor requires gradient computation.
+
+        Args:
+            value (bool): True to enable gradient computation, False to disable.
+        """
+        if not isinstance(value, bool):
+            raise TypeError(
+                f"requires_grad must be bool, but got {type(value)}"
+            )
+        self.stop_gradient = not value
+
     def _scalar_add_(var, value):
         return _scalar_op_(var, 1.0, value)
 
@@ -810,6 +844,7 @@ def to_dense(var):
         ('dim', dim),
         ('ndimension', ndimension),
         ('ndim', _ndim),
+        ("requires_grad", requires_grad),
         (
             '__add__',
             _binary_creator_('__add__', 'elementwise_add', False, _scalar_add_),
diff --git a/python/paddle/pir/math_op_patch.py b/python/paddle/pir/math_op_patch.py
index 4712433d948768..3566bba274e198 100644
--- a/python/paddle/pir/math_op_patch.py
+++ b/python/paddle/pir/math_op_patch.py
@@ -630,6 +630,40 @@ def _mT_(self):
 
         return _C_ops.transpose(self, perm)
 
+    @property
+    def requires_grad(self) -> bool:
+        """
+        Whether this Tensor requires gradient computation.
+
+        This is a convenience property that returns the opposite of stop_gradient.
+        Setting requires_grad=True is equivalent to setting stop_gradient=False.
+
+        Examples:
+            .. code-block:: python
+
+                >>> import paddle
+                >>> x = paddle.randn([2, 3])
+                >>> print(x.requires_grad)  # False by default
+                >>>
+                >>> x.requires_grad = False
+                >>> print(x.stop_gradient)  # True
+        """
+        return not self.stop_gradient
+
+    @requires_grad.setter
+    def requires_grad(self, value: bool) -> None:
+        """
+        Set whether this Tensor requires gradient computation.
+
+        Args:
+            value (bool): True to enable gradient computation, False to disable.
+        """
+        if not isinstance(value, bool):
+            raise TypeError(
+                f"requires_grad must be bool, but got {type(value)}"
+            )
+        self.stop_gradient = not value
+
     def _int_(self):
         error_msg = """\
             int(Tensor) is not supported in static graph mode. Because it's value is not available during the static mode.
@@ -1178,6 +1212,7 @@ def register_hook(self, hook):
         ('size', _size_),
         ('T', _T_),
         ('mT', _mT_),
+        ("requires_grad", requires_grad),
         ('clone', clone),
         ('clear_gradient', clear_gradient),
         ('append', append),
diff --git a/test/legacy_test/test_tensor_requires_grad.py b/test/legacy_test/test_tensor_requires_grad.py
new file mode 100644
index 00000000000000..7c8a35c04531af
--- /dev/null
+++ b/test/legacy_test/test_tensor_requires_grad.py
@@ -0,0 +1,223 @@
+#   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.
+
+import unittest
+
+import numpy as np
+
+import paddle
+
+
+class TestTensorRequiresGrad(unittest.TestCase):
+    def setUp(self):
+        """Set up test fixtures before each test method."""
+        paddle.disable_static()
+        np.random.seed(1919)
+
+    def tearDown(self):
+        """Clean up after each test method."""
+        paddle.disable_static()
+
+    def test_basic_requires_grad_property(self):
+        """Test basic requires_grad property functionality"""
+        # Test default behavior - new tensors have stop_gradient=True by default
+        x = paddle.randn([2, 3])
+        self.assertFalse(x.requires_grad)
+        self.assertTrue(x.stop_gradient)
+
+        # Test setting requires_grad to True
+        x.requires_grad = True
+        self.assertTrue(x.requires_grad)
+        self.assertFalse(x.stop_gradient)
+
+        # Test setting requires_grad to False
+        x.requires_grad = False
+        self.assertFalse(x.requires_grad)
+        self.assertTrue(x.stop_gradient)
+
+    def test_requires_grad_consistency_with_stop_gradient(self):
+        """Test that requires_grad is always the opposite of stop_gradient"""
+        x = paddle.randn([3, 4])
+
+        # Test multiple state changes
+        states = [True, False, True, False]
+        for requires_grad_state in states:
+            x.requires_grad = requires_grad_state
+            self.assertEqual(x.requires_grad, requires_grad_state)
+            self.assertEqual(x.stop_gradient, not requires_grad_state)
+
+            # Also test setting stop_gradient directly
+            x.stop_gradient = requires_grad_state
+            self.assertEqual(x.requires_grad, not requires_grad_state)
+            self.assertEqual(x.stop_gradient, requires_grad_state)
+
+    def test_requires_grad_type_checking(self):
+        """Test type checking for requires_grad setter"""
+        x = paddle.randn([2, 2])
+
+        # Valid boolean values should work
+        x.requires_grad = True
+        x.requires_grad = False
+
+        # Invalid types should raise TypeError
+        invalid_values = ["true", 1, 0, None, [], {}]
+        for invalid_value in invalid_values:
+            with self.assertRaises(TypeError) as cm:
+                x.requires_grad = invalid_value
+            self.assertIn("requires_grad must be bool", str(cm.exception))
+
+    def test_requires_grad_with_parameter(self):
+        """Test requires_grad behavior with Parameter tensors"""
+        # Create a parameter - Parameters have stop_gradient=False by default (trainable)
+        param = paddle.create_parameter([3, 4], dtype='float32')
+        self.assertTrue(
+            param.requires_grad
+        )  # Parameters require grad by default
+        self.assertFalse(
+            param.stop_gradient
+        )  # Parameters are trainable by default
+
+        # Test changing requires_grad on parameter
+        param.requires_grad = False
+        self.assertFalse(param.requires_grad)
+        self.assertTrue(param.stop_gradient)
+
+    def test_requires_grad_in_gradient_computation(self):
+        """Test requires_grad behavior in actual gradient computation"""
+        x = paddle.randn([2, 3])
+        y = paddle.randn([2, 3])
+
+        # Set both tensors to require grad
+        x.requires_grad = True
+        y.requires_grad = True
+
+        z = x * y + x.sum()
+        z.backward()
+
+        self.assertIsNotNone(x.grad)
+        self.assertIsNotNone(y.grad)
+
+        # Clear gradients and test with requires_grad=False
+        x.grad._clear_data()
+        y.grad._clear_data()
+
+        x.requires_grad = False
+        y.requires_grad = True
+
+        z = x * y + x.sum()
+        z.backward()
+
+        self.assertIsNone(x.grad)  # x doesn't require grad
+        self.assertIsNotNone(y.grad)  # y requires grad
+
+    def test_requires_grad_with_different_tensor_types(self):
+        """Test requires_grad with different tensor creation methods"""
+        # Test with different tensor creation functions
+        tensor_creators = [
+            lambda: paddle.randn([2, 3]),
+            lambda: paddle.zeros([2, 3]),
+            lambda: paddle.ones([2, 3]),
+            lambda: paddle.to_tensor([[1, 2, 3], [4, 5, 6]], dtype='float32'),
+            lambda: paddle.arange(6, dtype='float32').reshape([2, 3]),
+        ]
+
+        for creator in tensor_creators:
+            x = creator()
+            # All newly created tensors should have requires_grad=False by default
+            self.assertFalse(x.requires_grad)
+            self.assertTrue(x.stop_gradient)
+
+            # Test modification
+            x.requires_grad = True
+            self.assertTrue(x.requires_grad)
+            self.assertFalse(x.stop_gradient)
+
+    def test_requires_grad_with_tensor_operations(self):
+        """Test requires_grad preservation through tensor operations"""
+        x = paddle.randn([3, 3])
+        y = paddle.randn([3, 3])
+
+        x.requires_grad = True
+        y.requires_grad = False
+
+        # Operations should preserve requires_grad appropriately
+        z1 = x + y  # Should require grad (x requires grad)
+        z2 = x * 2.0  # Should require grad (x requires grad)
+        z3 = y.sin()  # Should not require grad (y doesn't require grad)
+
+        self.assertTrue(z1.requires_grad)
+        self.assertTrue(z2.requires_grad)
+        self.assertFalse(z3.requires_grad)
+
+    def test_requires_grad_with_detach(self):
+        """Test requires_grad behavior with detach operation"""
+        x = paddle.randn([2, 3])
+        x.requires_grad = True
+
+        y = x.detach()
+
+        # Detached tensor should not require grad
+        self.assertTrue(x.requires_grad)
+        self.assertFalse(y.requires_grad)
+        self.assertTrue(y.stop_gradient)
+
+    def test_requires_grad_static_mode(self):
+        """Test requires_grad behavior in static mode"""
+        paddle.enable_static()
+
+        try:
+            with paddle.static.program_guard(paddle.static.Program()):
+                x = paddle.static.data(name='x', shape=[2, 3], dtype='float32')
+
+                # In static mode, variables also have stop_gradient=True by default
+                self.assertFalse(x.requires_grad)
+                self.assertTrue(x.stop_gradient)
+
+                # Test setting requires_grad in static mode
+                x.requires_grad = True
+                self.assertTrue(x.requires_grad)
+                self.assertFalse(x.stop_gradient)
+
+        finally:
+            paddle.disable_static()
+
+    def test_requires_grad_edge_cases(self):
+        """Test edge cases for requires_grad"""
+        # Test with scalar tensor
+        scalar = paddle.to_tensor(3.14)
+        self.assertFalse(scalar.requires_grad)  # False
+        scalar.requires_grad = True
+        self.assertTrue(scalar.requires_grad)
+
+        # Test with empty tensor
+        empty = paddle.empty([0, 3])
+        self.assertFalse(empty.requires_grad)  # False
+        empty.requires_grad = True
+        self.assertTrue(empty.requires_grad)
+
+        # Test with different dtypes
+        dtypes = [paddle.float32, paddle.float64, paddle.int32, paddle.int64]
+        for dtype in dtypes:
+            x = paddle.ones([2, 2], dtype=dtype)
+            # All tensors should have requires_grad=False by default
+            self.assertFalse(x.requires_grad)
+
+            # Float tensors should support requires_grad
+            if dtype in [paddle.float32, paddle.float64]:
+                x.requires_grad = True
+                self.assertTrue(x.requires_grad)
+
+
+if __name__ == '__main__':
+    unittest.main()