Source code for dgl.nn.pytorch.conv.agnnconv

"""Torch Module for Attention-based Graph Neural Network layer"""
# pylint: disable= no-member, arguments-differ, invalid-name
import torch as th
from torch import nn
from torch.nn import functional as F

from .... import function as fn
from ...functional import edge_softmax
from ....base import DGLError
from ....utils import expand_as_pair

[docs]class AGNNConv(nn.Module):
r"""Attention-based Graph Neural Network layer from Attention-based Graph Neural Network for
Semi-Supervised Learning <https://arxiv.org/abs/1803.03735>__

.. math::
H^{l+1} = P H^{l}

where :math:P is computed as:

.. math::
P_{ij} = \mathrm{softmax}_i ( \beta \cdot \cos(h_i^l, h_j^l))

where :math:\beta is a single scalar parameter.

Parameters
----------
init_beta : float, optional
The :math:\beta in the formula, a single scalar parameter.
learn_beta : bool, optional
If True, :math:\beta will be learnable parameter.
allow_zero_in_degree : bool, optional
If there are 0-in-degree nodes in the graph, output for those nodes will be invalid
since no message will be passed to those nodes. This is harmful for some applications
causing silent performance regression. This module will raise a DGLError if it detects
0-in-degree nodes in input graph. By setting True, it will suppress the check
and let the users handle it by themselves. Default: False.

Note
----
Zero in-degree nodes will lead to invalid output value. This is because no message
will be passed to those nodes, the aggregation function will be appied on empty input.
A common practice to avoid this is to add a self-loop for each node in the graph if
it is homogeneous, which can be achieved by:

>>> g = ... # a DGLGraph

Calling add_self_loop will not work for some graphs, for example, heterogeneous graph
since the edge type can not be decided for self_loop edges. Set allow_zero_in_degree
to True for those cases to unblock the code and handle zero-in-degree nodes manually.
A common practise to handle this is to filter out the nodes with zero-in-degree when use
after conv.

Example
-------
>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import AGNNConv
>>>
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> feat = th.ones(6, 10)
>>> conv = AGNNConv()
>>> res = conv(g, feat)
>>> res
tensor([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]],
"""
def __init__(self,
init_beta=1.,
learn_beta=True,
allow_zero_in_degree=False):
super(AGNNConv, self).__init__()
self._allow_zero_in_degree = allow_zero_in_degree
if learn_beta:
self.beta = nn.Parameter(th.Tensor([init_beta]))
else:
self.register_buffer('beta', th.Tensor([init_beta]))

def set_allow_zero_in_degree(self, set_value):
r"""

Description
-----------
Set allow_zero_in_degree flag.

Parameters
----------
set_value : bool
The value to be set to the flag.
"""
self._allow_zero_in_degree = set_value

[docs]    def forward(self, graph, feat):
r"""

Description
-----------
Compute AGNN layer.

Parameters
----------
graph : DGLGraph
The graph.
feat : torch.Tensor
The input feature of shape :math:(N, *) :math:N is the
number of nodes, and :math:* could be of any shape.
If a pair of torch.Tensor is given, the pair must contain two tensors of shape
:math:(N_{in}, *) and :math:(N_{out}, *), the :math:* in the later
tensor must equal the previous one.

Returns
-------
torch.Tensor
The output feature of shape :math:(N, *) where :math:*
should be the same as input shape.

Raises
------
DGLError
If there are 0-in-degree nodes in the input graph, it will raise DGLError
since no message will be passed to those nodes. This will cause invalid output.
The error can be ignored by setting allow_zero_in_degree parameter to True.
"""
with graph.local_scope():
if not self._allow_zero_in_degree:
if (graph.in_degrees() == 0).any():
raise DGLError('There are 0-in-degree nodes in the graph, '
'output for those nodes will be invalid. '
'This is harmful for some applications, '
'causing silent performance regression. '
'Adding self-loop on the input graph by '
'calling g = dgl.add_self_loop(g) will resolve '
'the issue. Setting allow_zero_in_degree '
'to be True when constructing this module will '
'suppress the check and let the code run.')

feat_src, feat_dst = expand_as_pair(feat, graph)

graph.srcdata['h'] = feat_src
graph.srcdata['norm_h'] = F.normalize(feat_src, p=2, dim=-1)
if isinstance(feat, tuple) or graph.is_block:
graph.dstdata['norm_h'] = F.normalize(feat_dst, p=2, dim=-1)
# compute cosine distance
graph.apply_edges(fn.u_dot_v('norm_h', 'norm_h', 'cos'))
cos = graph.edata.pop('cos')
e = self.beta * cos
graph.edata['p'] = edge_softmax(graph, e)
graph.update_all(fn.u_mul_e('h', 'p', 'm'), fn.sum('m', 'h'))
return graph.dstdata.pop('h')