torch.Tensor.scatter_add_

Tensor.scatter_add_(dim, index, src) → Tensor

Adds all values from the tensor src into self at the indices specified in the index tensor in a similar fashion as scatter_(). For each value in src, it is added to an index in self which is specified by its index in src for dimension != dim and by the corresponding value in index for dimension = dim.

For a 3-D tensor, self is updated as:

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

self, index and src should have same number of dimensions. It is also required that index.size(d) <= src.size(d) for all dimensions d, and that index.size(d) <= self.size(d) for all dimensions d != dim. Note that index and src do not broadcast.

Note

This operation may behave nondeterministically when given tensors on a CUDA device. See Reproducibility for more information.

Note

The backward pass is implemented only for src.shape == index.shape.

Parameters

• dim (int) – the axis along which to index

• index (LongTensor) – the indices of elements to scatter and add, can be either empty or of the same dimensionality as src. When empty, the operation returns self unchanged.

• src (Tensor) – the source elements to scatter and add

Example:

>>> src = torch.ones((2, 5))
>>> index = torch.tensor([[0, 1, 2, 0, 0]])
>>> torch.zeros(3, 5, dtype=src.dtype).scatter_add_(0, index, src)
tensor([[1., 0., 0., 1., 1.],
        [0., 1., 0., 0., 0.],
        [0., 0., 1., 0., 0.]])
>>> index = torch.tensor([[0, 1, 2, 0, 0], [0, 1, 2, 2, 2]])
>>> torch.zeros(3, 5, dtype=src.dtype).scatter_add_(0, index, src)
tensor([[2., 0., 0., 1., 1.],
        [0., 2., 0., 0., 0.],
        [0., 0., 2., 1., 1.]])