mindspore.ops.sort

mindspore.ops.sort(input_x, axis=- 1, descending=False)[source]

Sorts the elements of the input tensor along the given dimension in the specified order.

Parameters

  • input_x (Tensor) – The input tensor to sort. The shape is \((N,*)\) where \(*\) means, any number of additional dimensions.

  • axis (int, optional) – The dimension to sort along. Default: -1, means the last dimension. The Ascend backend only supports sorting the last dimension.

  • descending (bool, optional) – Controls the sort order. If descending is True, the elements are sorted in descending order, or else sorted in ascending order. Default: False .

Warning

Currently, the data types of float16, uint8, int8, int16, int32, int64 are well supported. If use float32, it may cause loss of accuracy.

Returns

  • y1, a tensor whose values are the sorted values, with the same shape and data type as input.

  • y2, a tensor that consists of the indices of the elements in the original input tensor. Data type is int32.

Raises

  • TypeError – If axis is not an int.

  • TypeError – If descending is not a bool.

  • TypeError – If dtype of input_x is neither float16, float32, uint8, int8, int16, int32, int64.

  • ValueError – If axis is not in range of [-len(input_x.shape), len(input_x.shape)).

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[8, 2, 1], [5, 9, 3], [4, 6, 7]]), mindspore.float16)
>>> output = ops.sort(x)
>>> # The output below is based on the Ascend platform.
>>> print(output)
(Tensor(shape=[3, 3], dtype=Float16, value=
[[ 1.0000e+00,  2.0000e+00,  8.0000e+00],
[ 3.0000e+00,  5.0000e+00,  9.0000e+00],
[ 4.0000e+00,  6.0000e+00,  7.0000e+00]]), Tensor(shape=[3, 3], dtype=Int32, value=
[[2, 1, 0],
[2, 0, 1],
[0, 1, 2]]))