mindspore.Tensor.scatter_add_

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Tensor.scatter_add_(dim, index, src)[source]

Add all elements in src to the index specified by index to self along dimension specified by dim, scatter_add is an in-place operation. The ranks of self, index and src must be greater or equal to 1.

For a 3-D tensor, the operation updates self as follows:

self[index[i][j][k]][j][k] += src[i][j][k]  # if dim == 0

self[i][index[i][j][k]][k] += src[i][j][k]  # if dim == 1

self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2
Parameters

  • dim (int) – Which dim to scatter. Accepted range is [-r, r) where r = rank(self).

  • index (Tensor) – The index of self to do scatter operation whose data type must be int32 or int64. Same rank as self. Except for the dimension specified by dim, size of each dimension of index must be less than or equal to the size of the corresponding dimension of self.

  • src (Tensor) – The tensor doing the scatter operation with self, has the same type as self and the size of each dimension must be greater than or equal to that of index.

Returns

Tensor, has the same shape and type as self.

Raises

  • TypeError – If index is neither int32 nor int64.

  • ValueError – If anyone of the rank among self, index and src is less than 1.

  • ValueError – If the ranks of self, index and src are not the same.

  • ValueError – The size of any dimension of index except the dimension specified by dim is greater than the size of the corresponding dimension of self.

  • ValueError – If the size of any dimension of src is less than that of index.

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import Tensor
>>> input = Tensor(np.array([[1, 2, 3, 4, 5]]), dtype=ms.float32)
>>> src = Tensor(np.array([[8, 8]]), dtype=ms.float32)
>>> index = Tensor(np.array([[2, 4]]), dtype=ms.int64)
>>> out = input.scatter_add_(1, index, src)
>>> print(out)
[[1. 2. 11. 4. 13.]]
>>> input = Tensor(np.zeros((5, 5)), dtype=ms.float32)
>>> src = Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), dtype=ms.float32)
>>> index = Tensor(np.array([[0, 0, 0], [2, 2, 2], [4, 4, 4]]), dtype=ms.int64)
>>> out = input.scatter_add_(0, index, src)
>>> print(out)
[[1. 2. 3. 0. 0.]
[0. 0. 0. 0. 0.]
[4. 5. 6. 0. 0.]
[0. 0. 0. 0. 0.]
[7. 8. 9. 0. 0.]]
>>> input = Tensor(np.zeros((5, 5)), dtype=ms.float32)
>>> src = Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), dtype=ms.float32)
>>> index = Tensor(np.array([[0, 2, 4], [0, 2, 4], [0, 2, 4]]), dtype=ms.int64)
>>> out = input.scatter_add_(1, index, src)
>>> print(out)
[[1. 0. 2. 0. 3.]
[4. 0. 5. 0. 6.]
[7. 0. 8. 0. 9.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]]