"""Segment aggregation operators implemented using DGL graph."""
from .. import backend as F
from ..base import DGLError
__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)