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