[API compatibility] add msort api

python/paddle/__init__.py

@@ -579,6 +579,7 @@
     kthvalue,
     masked_select,
     mode,
+    msort,
     nonzero,
     searchsorted,
     sort,
@@ -879,6 +880,7 @@
     'summary',
     'flops',
     'sort',
+    'msort',
     'searchsorted',
     'bucketize',
     'split',

python/paddle/tensor/__init__.py

@@ -459,6 +459,7 @@
     kthvalue,
     masked_select,
     mode,
+    msort,
     nonzero,
     searchsorted,
     sort,
@@ -726,6 +727,7 @@
     'index_select',
     'nonzero',
     'sort',
+    'msort',
     'index_sample',
     'mean',
     'std',

python/paddle/tensor/search.py

@@ -676,6 +676,44 @@ def sort(
         return out
 
 
+def msort(input: Tensor) -> Tensor:
+    """
+
+    Sorts the input along the given axis = 0, and returns the sorted output tensor. The sort algorithm is ascending.
+
+    Args:
+        input (Tensor): An input N-D Tensor with type float32, float64, int16,
+            int32, int64, uint8.
+
+    Returns:
+        Tensor, sorted tensor(with the same shape and data type as ``input``).
+
+    Examples:
+
+        .. code-block:: python
+
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([[[5,8,9,5],
+            ...                        [0,0,1,7],
+            ...                        [6,9,2,4]],
+            ...                       [[5,2,4,2],
+            ...                        [4,7,7,9],
+            ...                        [1,7,0,6]]],
+            ...                      dtype='float32')
+            >>> out1 = paddle.msort(input=x)
+            >>> print(out1.numpy())
+            [[[5. 2. 4. 2.]
+              [0. 0. 1. 7.]
+              [1. 7. 0. 4.]]
+             [[5. 8. 9. 5.]
+              [4. 7. 7. 9.]
+              [6. 9. 2. 6.]]]
+    """
+
+    return sort(input, axis=0)
+
+
 def mode(
     x: Tensor, axis: int = -1, keepdim: bool = False, name: str | None = None
 ) -> tuple[Tensor, Tensor]:

test/legacy_test/test_msort_op.py

@@ -0,0 +1,74 @@
+# Copyright (c) 2020 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
+from paddle import base
+from paddle.base import core
+
+
+class TestMsortOnCPU(unittest.TestCase):
+    def setUp(self):
+        self.place = core.CPUPlace()
+
+    def test_api_0(self):
+        with base.program_guard(base.Program()):
+            input = paddle.static.data(
+                name="input", shape=[2, 3, 4], dtype="float32"
+            )
+            output = paddle.msort(input=input)
+            exe = base.Executor(self.place)
+            data = np.array(
+                [
+                    [[5, 8, 9, 5], [0, 0, 1, 7], [6, 9, 2, 4]],
+                    [[5, 2, 4, 2], [4, 7, 7, 9], [1, 7, 0, 6]],
+                ],
+                dtype='float32',
+            )
+            (result,) = exe.run(feed={'input': data}, fetch_list=[output])
+            np_result = np.sort(result, axis=0)
+            self.assertEqual((result == np_result).all(), True)
+
+
+class TestMsortOnGPU(TestMsortOnCPU):
+    def init_place(self):
+        if core.is_compiled_with_cuda():
+            self.place = core.CUDAPlace(0)
+        else:
+            self.place = core.CPUPlace()
+
+
+class TestMsortDygraph(unittest.TestCase):
+    def setUp(self):
+        self.input_data = np.random.rand(10, 10)
+        if core.is_compiled_with_cuda():
+            self.place = core.CUDAPlace(0)
+        else:
+            self.place = core.CPUPlace()
+
+    def test_api_0(self):
+        paddle.disable_static(self.place)
+        var_x = paddle.to_tensor(self.input_data)
+        out = paddle.msort(input=var_x)
+        self.assertEqual(
+            (np.sort(self.input_data, axis=0) == out.numpy()).all(), True
+        )
+        paddle.enable_static()
+
+
+if __name__ == '__main__':
+    unittest.main()