scatter.py

import warnings
import os.path as osp
from typing import Optional, Tuple

import torch

try:
    torch.ops.load_library(
        osp.join(osp.dirname(osp.abspath(__file__)), '_scatter.so'))
except OSError:
    warnings.warn('Failed to load `scatter` binaries.')

    def scatter_placeholder(src: torch.Tensor, index: torch.Tensor, dim: int,
                            out: Optional[torch.Tensor],
                            dim_size: Optional[int]) -> torch.Tensor:
        raise ImportError
        return src

    def scatter_with_arg_placeholder(src: torch.Tensor, index: torch.Tensor,
                                     dim: int, out: Optional[torch.Tensor],
                                     dim_size: Optional[int]
                                     ) -> Tuple[torch.Tensor, torch.Tensor]:
        raise ImportError
        return src, index

    torch.ops.torch_scatter.scatter_sum = scatter_placeholder
    torch.ops.torch_scatter.scatter_mean = scatter_placeholder
    torch.ops.torch_scatter.scatter_min = scatter_with_arg_placeholder
    torch.ops.torch_scatter.scatter_max = scatter_with_arg_placeholder


@torch.jit.script
def scatter_sum(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
                out: Optional[torch.Tensor] = None,
                dim_size: Optional[int] = None) -> torch.Tensor:
    return torch.ops.torch_scatter.scatter_sum(src, index, dim, out, dim_size)


@torch.jit.script
def scatter_add(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
                out: Optional[torch.Tensor] = None,
                dim_size: Optional[int] = None) -> torch.Tensor:
    return torch.ops.torch_scatter.scatter_sum(src, index, dim, out, dim_size)


@torch.jit.script
def scatter_mean(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
                 out: Optional[torch.Tensor] = None,
                 dim_size: Optional[int] = None) -> torch.Tensor:
    return torch.ops.torch_scatter.scatter_mean(src, index, dim, out, dim_size)


@torch.jit.script
def scatter_min(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
                out: Optional[torch.Tensor] = None,
                dim_size: Optional[int] = None
                ) -> Tuple[torch.Tensor, torch.Tensor]:
    return torch.ops.torch_scatter.scatter_min(src, index, dim, out, dim_size)


@torch.jit.script
def scatter_max(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
                out: Optional[torch.Tensor] = None,
                dim_size: Optional[int] = None
                ) -> Tuple[torch.Tensor, torch.Tensor]:
    return torch.ops.torch_scatter.scatter_max(src, index, dim, out, dim_size)


def scatter(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
            out: Optional[torch.Tensor] = None, dim_size: Optional[int] = None,
            reduce: str = "sum") -> torch.Tensor:
    r"""
    |

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

    |

    Reduces all values from the :attr:`src` tensor into :attr:`out` at the
    indices specified in the :attr:`index` tensor along a given axis
    :attr:`dim`.
    For each value in :attr:`src`, its output index is specified by its index
    in :attr:`src` for dimensions outside of :attr:`dim` and by the
    corresponding value in :attr:`index` for dimension :attr:`dim`.
    The applied reduction is defined via the :attr:`reduce` argument.

    Formally, if :attr:`src` and :attr:`index` are :math:`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 :math:`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 :math:`0` and
    :math:`y - 1` in ascending order.
    The :attr:`index` tensor supports broadcasting in case its dimensions do
    not match with :attr:`src`.

    For one-dimensional tensors with :obj:`reduce="sum"`, the operation
    computes

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

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

    .. note::

        This operation is implemented via atomic operations on the GPU and is
        therefore **non-deterministic** since the order of parallel operations
        to the same value is undetermined.
        For floating-point variables, this results in a source of variance in
        the result.

    :param src: The source tensor.
    :param index: The indices of elements to scatter.
    :param dim: The axis along which to index. (default: :obj:`-1`)
    :param out: The destination tensor.
    :param dim_size: 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
        according to :obj:`index.max() + 1` is returned.
    :param reduce: The reduce operation (:obj:`"sum"`, :obj:`"mean"`,
        :obj:`"min"` or :obj:`"max"`). (default: :obj:`"sum"`)

    :rtype: :class:`Tensor`

    .. code-block:: python

        from torch_scatter import scatter

        src = torch.randn(10, 6, 64)
        index = torch.tensor([0, 1, 0, 1, 2, 1])

        # Broadcasting in the first and last dim.
        out = scatter(src, index, dim=1, reduce="sum")

        print(out.size())

    .. code-block::

        torch.Size([10, 3, 64])
    """
    if reduce == 'sum' or reduce == 'add':
        return scatter_sum(src, index, dim, out, dim_size)
    elif reduce == 'mean':
        return scatter_mean(src, index, dim, out, dim_size)
    elif reduce == 'min':
        return scatter_min(src, index, dim, out, dim_size)[0]
    elif reduce == 'max':
        return scatter_max(src, index, dim, out, dim_size)[0]
    else:
        raise ValueError