Source code for dgl.ops.segment

"""Segment aggregation operators implemented using DGL graph."""

from ..base import DGLError
from .. import backend as F

__all__ = ['segment_reduce', 'segment_softmax', 'segment_mm']

[docs]def segment_reduce(seglen, value, reducer='sum'): """Segment reduction operator. It aggregates the value tensor along the first dimension by segments. The first argument ``seglen`` stores the length of each segment. Its summation must be equal to the first dimension of the ``value`` tensor. Zero-length segments are allowed. Parameters ---------- seglen : Tensor Segment lengths. value : Tensor Value to aggregate. reducer : str, optional Aggregation method. Can be 'sum', 'max', 'min', 'mean'. Returns ------- Tensor Aggregated tensor of shape ``(len(seglen), value.shape[1:])``. Examples -------- >>> import dgl >>> import torch as th >>> val = th.ones(10, 3) >>> seg = th.tensor([1, 0, 5, 4]) # 4 segments >>> dgl.segment_reduce(seg, val) tensor([[1., 1., 1.], [0., 0., 0.], [5., 5., 5.], [4., 4., 4.]]) """ offsets = F.cumsum( F.cat([F.zeros((1,), F.dtype(seglen), F.context(seglen)), seglen], 0), 0) if reducer == 'mean': rst = F.segment_reduce('sum', value, offsets) rst_shape = F.shape(rst) z = F.astype(F.clamp(seglen, 1, len(value)), F.dtype(rst)) z_shape = (rst_shape[0],) + (1,) * (len(rst_shape) - 1) return rst / F.reshape(z, z_shape) elif reducer in ['min', 'sum', 'max']: rst = F.segment_reduce(reducer, value, offsets) if reducer in ['min', 'max']: rst = F.replace_inf_with_zero(rst) return rst else: raise DGLError("reducer {} not recognized.".format(reducer))
def segment_softmax(seglen, value): """Performa softmax on each segment. The first argument ``seglen`` stores the length of each segment. Its summation must be equal to the first dimension of the ``value`` tensor. Zero-length segments are allowed. Parameters ---------- seglen : Tensor Segment lengths. value : Tensor Value to aggregate. Returns ------- Tensor Result tensor of the same shape as the ``value`` tensor. Examples -------- >>> import dgl >>> import torch as th >>> val = th.ones(10, 3) >>> seg = th.tensor([1, 0, 5, 4]) # 4 segments >>> dgl.segment_softmax(seg, val) tensor([[1.0000, 1.0000, 1.0000], [0.2000, 0.2000, 0.2000], [0.2000, 0.2000, 0.2000], [0.2000, 0.2000, 0.2000], [0.2000, 0.2000, 0.2000], [0.2000, 0.2000, 0.2000], [0.2500, 0.2500, 0.2500], [0.2500, 0.2500, 0.2500], [0.2500, 0.2500, 0.2500], [0.2500, 0.2500, 0.2500]]) """ value_max = segment_reduce(seglen, value, reducer='max') value = F.exp(value - F.repeat(value_max, seglen, dim=0)) value_sum = segment_reduce(seglen, value, reducer='sum') return value / F.repeat(value_sum, seglen, dim=0)
[docs]def segment_mm(a, b, seglen_a): r""" Performs matrix multiplication according to segments. Suppose ``seglen_a == [10, 5, 0, 3]``, the operator will perform four matrix multiplications:: a[0:10] @ b[0], a[10:15] @ b[1], a[15:15] @ b[2], a[15:18] @ b[3] Parameters ---------- a : Tensor The left operand, 2-D tensor of shape ``(N, D1)`` b : Tensor The right operand, 3-D tensor of shape ``(R, D1, D2)`` seglen_a : Tensor An integer tensor of shape ``(R,)``. Each element is the length of segments of input ``a``. The summation of all elements must be equal to ``N``. Returns ------- Tensor The output dense matrix of shape ``(N, D2)`` """ return F.segment_mm(a, b, seglen_a)