add.py

from torch.autograd import Function

from .utils.gen import gen


class ScatterAdd(Function):
    @staticmethod
    def forward(ctx, out, src, index, dim):
        ctx.mark_dirty(out)
        ctx.save_for_backward(index)
        return out.scatter_add_(dim, index, src)

    @staticmethod
    def backward(ctx, grad_out):
        index, = ctx.saved_variables

        grad_src = None
        if ctx.needs_input_grad[1]:
            grad_src = grad_out[index]

        return None, grad_src, None, None


def scatter_add(src, index, dim=-1, out=None, dim_size=None, fill_value=0):
    r"""
    |

    .. image:: https://raw.githubusercontent.com/rusty1s/pytorch_scatter/
            master/docs/source/_figures/add.svg?sanitize=true
        :align: center
        :width: 400px

    |

    Sums all values from the :attr:`src` tensor into :attr:`out` at the indices
    specified in the :attr:`index` tensor along an given axis :attr:`dim`. For
    each value in :attr:`src`, its output index is specified by its index in
    :attr:`input` for dimensions outside of :attr:`dim` and by the
    corresponding value in :attr:`index` for dimension :attr:`dim`. If
    multiple indices reference the same location, their **contributions add**.

    Formally, if :attr:`src` and :attr:`index` are n-dimensional tensors with
    size :math:`(x_0, ..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})` and
    :attr:`dim` = `i`, then :attr:`out` must be an n-dimensional tensor with
    size :math:`(x_0, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})`. Moreover, the
    values of :attr:`index` must be between `0` and `out.size(dim) - 1`.

    For one-dimensional tensors, the operation computes

    .. math::
        \mathrm{out}_i = \mathrm{out}_i + \sum_j \mathrm{src}_j

    where :math:`\sum` is over :math:`j` such that
    :math:`\mathrm{index}_j = i`.

    Args:
        src (Tensor): The source tensor.
        index (LongTensor): The indices of elements to scatter.
        dim (int, optional): The axis along which to index.
            (default: :obj:`-1`)
        out (Tensor, optional): The destination tensor. (default: :obj:`None`)
        dim_size (int, optional): If :attr:`out` is not given, automatically
            create output with size :attr:`dim_size` at dimension :attr:`dim`.
            If :attr:`dim_size` is not given, a minimal sized output tensor is
            returned. (default: :obj:`None`)
        fill_value (int, optional): If :attr:`out` is not given, automatically
            fill output tensor with :attr:`fill_value`. (default: :obj:`0`)

    :rtype: :class:`Tensor`

    .. testsetup::

        import torch

    .. testcode::

        from torch_scatter import scatter_add
        src = torch.tensor([[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]])
        index = torch.tensor([[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]])
        out = src.new_zeros((2, 6))
        out = scatter_add(src, index, out=out)
        print(out)

    .. testoutput::

        0  0  4  3  3  0
        2  4  4  0  0  0
       [torch.FloatTensor of size 2x6]
    """
    src, out, index, dim = gen(src, index, dim, out, dim_size, fill_value)
    return ScatterAdd.apply(out, src, index, dim)