mindspore.Tensor.index_add_

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Tensor.index_add_(dim, index, source, *, alpha=1)[source]

Accumulate the elements of alpha times source into the self by adding to the index in the order given in index. For example, if dim == 0, index[i] == j, and alpha = -1, then the i th row of source is subtracted from the j th row of self . The dim th dimension of source must have the same size as the length of index , and all other dimensions must match self, or an error will be raised. For a 3-D tensor the output is defined as follows:

\[\begin{split}\begin{array}{ll} self[index[i],\ :,\ :]\ +=\ alpha * src[i,\ :,\ :] \qquad \#if\ dim == 0 \\ self[:,\ \ index[i],\ :]\ +=\ alpha * src[:,\ \ i,\ :] \qquad \#if\ dim == 1 \\ self[:,\ :,\ \ index[i]]\ +=\ alpha * src[:,\ :,\ \ i] \qquad\#if\ dim == 2 \\ \end{array}\end{split}\]

Warning

This is an experimental API that is subject to change or deletion.

Parameters

  • dim (int) – The dimension along which to index.

  • index (Tensor) – Add the value of self and source along the dimension of the dim according to the specified index value, with data type int32. The index must be 1D with the same size as the size of source in the dim dimension. The values of index should be in [0, b), where the b is the size of self in the dim dimension.

  • source (Tensor) – The input tensor with the value to add. Must have same data type as self. The shape must be the same as self except the dim th dimension.

Keyword Arguments

alpha (number, optional) – The scalar multiplier for source. Default: 1.

Returns

Tensor, has the same shape and dtype as self.

Raises

  • TypeError – If neither index nor source is a Tensor.

  • ValueError – If dim is out of self rank's range.

  • ValueError – If self rank is not the same as source rank.

  • ValueError – If shape of index is not 1D or size of index is not equal to dimension of source[dim].

  • ValueError – If source's shape is not the same as self except the dim th dimension.

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32)
>>> index = Tensor(np.array([0, 2]), mindspore.int32)
>>> y = Tensor(np.array([[0.5, 1.0], [1.0, 1.5], [2.0, 2.5]]), mindspore.float32)
>>> output = x.index_add_(1, index, y, alpha=1)
>>> print(output)
[[ 1.5  2.   4. ]
 [ 5.   5.   7.5]
 [ 9.   8.  11.5]]
>>> print(x)
[[ 1.5  2.   4. ]
 [ 5.   5.   7.5]
 [ 9.   8.  11.5]]