[API compatibility] Modify the meshgrid API and add the Tensor.split_with_sizes API.

python/paddle/tensor/__init__.py

@@ -57,6 +57,7 @@
     range,
     resize_,
     set_,
+    split_with_sizes,
     to_tensor,
     tril,
     tril_,
@@ -949,6 +950,7 @@
     'greater',
     'clamp',
     'clamp_',
+    'split_with_sizes',
 ]
 
 

python/paddle/tensor/creation.py

@@ -2556,13 +2556,15 @@ def triu_(
 
 @overload
 def meshgrid(
-    args: Sequence[paddle.Tensor], name: str | None = None
+    args: Sequence[paddle.Tensor],
+    name: str | None = None,
+    indexing: str | None = None,
 ) -> list[paddle.Tensor]: ...
 
 
 @overload
 def meshgrid(
-    *args: paddle.Tensor, name: str | None = None
+    *args: paddle.Tensor, name: str | None = None, indexing: str | None = None
 ) -> list[paddle.Tensor]: ...
 
 
@@ -2577,7 +2579,9 @@ def meshgrid(*args, **kwargs):
         **kwargs (optional): Currently, only accept name in **kwargs
             The default value is None. Normally there is no need for
             user to set this property. For more information, please refer to :ref:`api_guide_Name`.
-
+        indexing (Optional[str]) : the indexing mode, either “xy” or “ij”, defaults to “ij”.If “xy” is selected, the first dimension corresponds to the cardinality
+            of the second input and the second dimension corresponds to the cardinality of the first input. If “ij” is selected, the dimensions are in the
+            same order as the cardinality of the inputs.
     Returns:
          Tensor: k tensors. The shape of each tensor is (N1, N2, ..., Nk)
 
@@ -2597,13 +2601,26 @@ def meshgrid(*args, **kwargs):
             [100, 200]
 
     """
+    name = kwargs.get("name", None)
+    indexing = kwargs.pop("indexing", None)
+    if indexing is None:
+        indexing = "ij"
 
     if len(args) == 1 and isinstance(args[0], (list, tuple)):
         args = args[0]
+
+    if indexing not in ("ij", "xy"):
+        raise ValueError(
+            f"meshgrid: indexing must be 'ij' or 'xy', but got {indexing}"
+        )
+
+    swap_xy = indexing == "xy" and len(args) >= 2
+    if swap_xy:
+        args = (args[1], args[0], *args[2:])
+
     if in_dynamic_or_pir_mode():
-        return _C_ops.meshgrid(list(args))
+        out = _C_ops.meshgrid(list(args))
     else:
-        name = kwargs.get("name", None)
         helper = LayerHelper('meshgrid', **locals())
 
         if not isinstance(args, (list, tuple)):
@@ -2637,7 +2654,59 @@ def meshgrid(*args, **kwargs):
             type='meshgrid', inputs={'X': list(args)}, outputs={'Out': out}
         )
 
-        return out
+    if swap_xy:
+        out[0], out[1] = out[1], out[0]
+    return out
+
+
+def split_with_sizes(
+    self: paddle.Tensor, split_sizes: list[int], dim: int = 0
+) -> list[paddle.Tensor]:
+    """
+    Splits the input tensor into multiple sub tensors according to given split sizes.
+
+    Args:
+        self (Tensor): The input tensor to be split.
+        split_sizes (list[int]): A list of non negative integers specifying
+            the sizes of each split along dimension ``dim``. The sum of all
+            elements in this list must equal the size of ``self`` along ``dim``.
+        dim (int, optional): The dimension along which to split the tensor.
+            Defaults to 0.
+
+    Returns:
+        list[Tensor]: A list of sub tensors resulting from splitting ``self``
+        along the specified dimension.
+
+    Examples:
+        .. code-block:: python
+
+            >>> import paddle
+            >>> x = paddle.to_tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
+            >>> # Split into two parts along the first dimension, of sizes 1 and 2
+            >>> splits = paddle.Tensor.split_with_sizes(x, [1, 2], dim=0)
+            >>> print(splits)
+    """
+    for size in split_sizes:
+        if size < 0:
+            raise ValueError(
+                "split_with_sizes expects split_sizes have only non-negative entries"
+            )
+
+    total = sum(split_sizes)
+    if total != self.shape[dim]:
+        raise ValueError(
+            f"Split sizes add up to {total} but got the tensor's size of {self.shape[dim]}"
+        )
+
+    outs = []
+    start = 0
+    for size in split_sizes:
+        end = start + size
+        out = paddle.slice(self, axes=[dim], starts=[start], ends=[end])
+        outs.append(out)
+        start = end
+
+    return outs
 
 
 def diag_embed(

test/legacy_test/test_meshgrid_op.py

@@ -311,6 +311,74 @@ def test_api_with_dygraph(self):
             np.testing.assert_array_equal(res_4.shape, [100, 200])
 
 
+class TestMeshgridOpIndexing(unittest.TestCase):
+    def setUp(self):
+        self.input_3 = np.random.randint(0, 100, [100]).astype('int32')
+        self.input_4 = np.random.randint(0, 100, [200]).astype('int32')
+
+    def test_api_with_dygraph_indexing_xy(self):
+        np_res_3, np_res_4 = np.meshgrid(
+            self.input_3, self.input_4, indexing='xy'
+        )
+
+        with base.dygraph.guard():
+            tensor_3 = paddle.to_tensor(self.input_3)
+            tensor_4 = paddle.to_tensor(self.input_4)
+            res_3, res_4 = paddle.tensor.meshgrid(
+                tensor_3, tensor_4, indexing='xy'
+            )
+
+            np.testing.assert_array_equal(res_3.shape, np_res_3.shape)
+            np.testing.assert_array_equal(res_4.shape, np_res_4.shape)
+            np.testing.assert_array_equal(res_3.numpy(), np_res_3)
+            np.testing.assert_array_equal(res_3.numpy(), np_res_3)
+            np.testing.assert_array_equal(res_4.numpy(), np_res_4)
+
+    def test_api_with_dygraph_indexing_ij(self):
+        np_res_3, np_res_4 = np.meshgrid(
+            self.input_3, self.input_4, indexing='ij'
+        )
+
+        with base.dygraph.guard():
+            tensor_3 = paddle.to_tensor(self.input_3)
+            tensor_4 = paddle.to_tensor(self.input_4)
+            res_3, res_4 = paddle.tensor.meshgrid(
+                tensor_3, tensor_4, indexing='ij'
+            )
+
+            np.testing.assert_array_equal(res_3.shape, np_res_3.shape)
+            np.testing.assert_array_equal(res_4.shape, np_res_4.shape)
+            np.testing.assert_array_equal(res_3.numpy(), np_res_3)
+            np.testing.assert_array_equal(res_4.numpy(), np_res_4)
+
+    def test_indexing_default(self):
+        np_res_3, np_res_4 = np.meshgrid(
+            self.input_3, self.input_4, indexing='ij'
+        )
+
+        with base.dygraph.guard():
+            tensor_3 = paddle.to_tensor(self.input_3)
+            tensor_4 = paddle.to_tensor(self.input_4)
+            res_3, res_4 = paddle.tensor.meshgrid(tensor_3, tensor_4)
+            res_3_n, res_4_n = paddle.tensor.meshgrid(
+                tensor_3, tensor_4, indexing=None
+            )
+            np.testing.assert_array_equal(res_3.numpy(), np_res_3)
+            np.testing.assert_array_equal(res_4.numpy(), np_res_4)
+            np.testing.assert_array_equal(res_3_n.numpy(), np_res_3)
+            np.testing.assert_array_equal(res_4_n.numpy(), np_res_4)
+
+    def test_indexing_invalid_value(self):
+        with base.dygraph.guard():
+            tensor_3 = paddle.to_tensor(self.input_3)
+            tensor_4 = paddle.to_tensor(self.input_4)
+            invalid_indexing = "ab"
+            with self.assertRaises(ValueError) as cm:
+                res_3, res_4 = paddle.tensor.meshgrid(
+                    tensor_3, tensor_4, indexing=invalid_indexing
+                )
+
+
 class TestMeshgridOp7(unittest.TestCase):
     def test_api_with_dygraph_list_input(self):
         input_3 = np.random.randint(

test/legacy_test/test_split_with_sizes_api.py

@@ -0,0 +1,62 @@
+# Copyright (c) 2025 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 TestSplitWithSizes(unittest.TestCase):
+    def setUp(self):
+        self.x = paddle.arange(12).reshape([3, 4])
+        self.split_sizes = [1, 2]
+        self.dim = 0
+
+    def test_basic_functionality(self):
+        splits = paddle.Tensor.split_with_sizes(
+            self.x, self.split_sizes, dim=self.dim
+        )
+
+        self.assertEqual(len(splits), len(self.split_sizes))
+
+        expected_shapes = [[1, 4], [2, 4]]
+        for s, shape in zip(splits, expected_shapes):
+            self.assertListEqual(list(s.shape), shape)
+
+        np_x = self.x.numpy()
+        start = 0
+        for i, size in enumerate(self.split_sizes):
+            np_ref = np_x[start : start + size, :]
+            np.testing.assert_array_equal(splits[i].numpy(), np_ref)
+            start += size
+
+    def test_ValueError_raises(self):
+        invalid_split_sizes = [1, -2]
+        with self.assertRaises(ValueError) as cm:
+            paddle.Tensor.split_with_sizes(
+                self.x, invalid_split_sizes, dim=self.dim
+            )
+
+        invalid_split_sizes = [1, 1]
+        with self.assertRaises(ValueError) as cm:
+            paddle.Tensor.split_with_sizes(
+                self.x, invalid_split_sizes, dim=self.dim
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()