add OpResult.clone() (PaddlePaddle#59115)

python/paddle/pir/math_op_patch.py

@@ -331,6 +331,31 @@ def _size_(self):
         """
         return paddle.numel(self)
 
+    def clone(self):
+        """
+        Returns a new static OpResult, which is the clone of the original static
+        OpResult. It remains in the current graph, that is, the cloned OpResult
+        provides gradient propagation. Calling ``out = tensor.clone()`` is same
+        as ``out = assign(tensor)`` .
+
+        Returns:
+            OpResult, The cloned OpResult.
+
+        Examples:
+            .. code-block:: python
+
+                >>> import paddle
+
+                >>> paddle.enable_static()
+
+                >>> # create a static OpResult
+                >>> x = paddle.static.data(name='x', shape=[3, 2, 1])
+                >>> # create a cloned OpResult
+                >>> y = x.clone()
+
+        """
+        return paddle.assign(self)
+
     import paddle
 
     opresult_methods = [
@@ -341,6 +366,7 @@ def _size_(self):
         ('ndim', _ndim),
         ('astype', astype),
         ('size', _size_),
+        ('clone', clone),
         (
             '__add__',
             _binary_creator_('__add__', paddle.tensor.add, False, _scalar_add_),

test/legacy_test/test_math_op_patch_pir.py

@@ -427,6 +427,23 @@ def test_size(self):
                 (output_x,) = exe.run(main_program, fetch_list=[x.size])
                 self.assertEqual(output_x, 24)
 
+    def test_clone(self):
+        x_np = np.random.random(size=[100, 10]).astype('float64')
+        with paddle.pir_utils.IrGuard():
+            main_program, exe, program_guard = new_program()
+            with program_guard:
+                x = paddle.static.data(
+                    name='x', shape=[100, 10], dtype="float64"
+                )
+                a = x.clone()
+                (a_np,) = exe.run(
+                    main_program,
+                    feed={"x": x_np},
+                    fetch_list=[a],
+                )
+                np.testing.assert_array_equal(x_np, a_np)
+                self.assertNotEqual(id(x), id(a))
+
     def test_math_exists(self):
         with paddle.pir_utils.IrGuard():
             a = paddle.static.data(name='a', shape=[1], dtype='float32')