Applies sparse addition to individual values or slices in a tensor.

Using given values to update tensor value through the add operation, along with the input indices. This operation outputs the input_x after the update is done, which makes it convenient to use the updated value.

input_x has rank P and indices has rank Q where Q >= 2.

indices has shape $$(i_0, i_1, ..., i_{Q-2}, N)$$ where N <= P.

The last dimension of indices (with length N ) indicates slices along the N th dimension of input_x.

updates is a tensor of rank Q-1+P-N. Its shape is: $$(i_0, i_1, ..., i_{Q-2}, x\_shape_N, ..., x\_shape_{P-1})$$.

Inputs of input_x and updates comply with the implicit type conversion rules to make the data types consistent. If they have different data types, the lower priority data type will be converted to the relatively highest priority data type.

Parameters

use_locking (bool) – Whether to protect the assignment by a lock. Default: False.

Inputs:
• input_x (Parameter) - The target tensor, with data type of Parameter. The shape is $$(N,*)$$ where $$*$$ means,any number of additional dimensions.

• indices (Tensor) - The index to do min operation whose data type must be mindspore.int32. The rank of indices must be at least 2 and indices_shape[-1] <= len(shape).

• updates (Tensor) - The tensor doing the min operation with input_x, the data type is same as input_x, the shape is indices_shape[:-1] + x_shape[indices_shape[-1]:].

Outputs:

Tensor, the updated input_x, has the same shape and type as input_x.

Raises
• TypeError – If use_locking is not a bool.

• TypeError – If indices is not an int32.

• ValueError – If the shape of updates is not equal to indices_shape[:-1] + x_shape[indices_shape[-1]:].

• RuntimeError – If the data type of input_x and updates conversion of Parameter is required when data type conversion of Parameter is not supported.

Supported Platforms:

Ascend GPU

Examples

>>> input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mindspore.float32), name="x")
>>> indices = Tensor(np.array([[2], [4], [1], [7]]), mindspore.int32)
>>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32)
>>> print(output)
[ 1. 10.  9.  4. 12.  6.  7. 17.]
>>> input_x = Parameter(Tensor(np.zeros((4, 4, 4)), mindspore.int32))
>>> indices = Tensor(np.array([[0], [2]]), mindspore.int32)
>>> updates = Tensor(np.array([[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
...                            [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]]), mindspore.int32)
>>> print(output)
[[[1 1 1 1]
[2 2 2 2]
[3 3 3 3]
[4 4 4 4]]
[[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]]
[[5 5 5 5]
[6 6 6 6]
[7 7 7 7]
[8 8 8 8]]
[[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]]]