chore: import upstream snapshot with attribution
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wehub-resource-sync
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"""Torch Module for Simplifying Graph Convolution layer"""
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# pylint: disable= no-member, arguments-differ, invalid-name
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import torch as th
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from torch import nn
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from .... import function as fn
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from ....base import DGLError
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from .graphconv import EdgeWeightNorm
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class SGConv(nn.Module):
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r"""SGC layer from `Simplifying Graph
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Convolutional Networks <https://arxiv.org/pdf/1902.07153.pdf>`__
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.. math::
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H^{K} = (\tilde{D}^{-1/2} \tilde{A} \tilde{D}^{-1/2})^K X \Theta
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where :math:`\tilde{A}` is :math:`A` + :math:`I`.
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Thus the graph input is expected to have self-loop edges added.
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Parameters
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----------
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in_feats : int
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Number of input features; i.e, the number of dimensions of :math:`X`.
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out_feats : int
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Number of output features; i.e, the number of dimensions of :math:`H^{K}`.
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k : int
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Number of hops :math:`K`. Defaults:``1``.
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cached : bool
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If True, the module would cache
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.. math::
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(\tilde{D}^{-\frac{1}{2}}\tilde{A}\tilde{D}^{-\frac{1}{2}})^K X\Theta
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at the first forward call. This parameter should only be set to
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``True`` in Transductive Learning setting.
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bias : bool
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If True, adds a learnable bias to the output. Default: ``True``.
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norm : callable activation function/layer or None, optional
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If not None, applies normalization to the updated node features. Default: ``False``.
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allow_zero_in_degree : bool, optional
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If there are 0-in-degree nodes in the graph, output for those nodes will be invalid
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since no message will be passed to those nodes. This is harmful for some applications
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causing silent performance regression. This module will raise a DGLError if it detects
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0-in-degree nodes in input graph. By setting ``True``, it will suppress the check
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and let the users handle it by themselves. Default: ``False``.
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Note
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----
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Zero in-degree nodes will lead to invalid output value. This is because no message
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will be passed to those nodes, the aggregation function will be appied on empty input.
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A common practice to avoid this is to add a self-loop for each node in the graph if
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it is homogeneous, which can be achieved by:
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>>> g = ... # a DGLGraph
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>>> g = dgl.add_self_loop(g)
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Calling ``add_self_loop`` will not work for some graphs, for example, heterogeneous graph
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since the edge type can not be decided for self_loop edges. Set ``allow_zero_in_degree``
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to ``True`` for those cases to unblock the code and handle zero-in-degree nodes manually.
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A common practise to handle this is to filter out the nodes with zero-in-degree when use
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after conv.
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Example
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-------
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>>> import dgl
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>>> import numpy as np
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>>> import torch as th
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>>> from dgl.nn import SGConv
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>>>
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>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
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>>> g = dgl.add_self_loop(g)
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>>> feat = th.ones(6, 10)
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>>> conv = SGConv(10, 2, k=2)
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>>> res = conv(g, feat)
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>>> res
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tensor([[-1.9441, -0.9343],
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[-1.9441, -0.9343],
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[-1.9441, -0.9343],
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[-2.7709, -1.3316],
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[-1.9297, -0.9273],
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[-1.9441, -0.9343]], grad_fn=<AddmmBackward>)
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"""
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def __init__(
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self,
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in_feats,
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out_feats,
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k=1,
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cached=False,
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bias=True,
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norm=None,
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allow_zero_in_degree=False,
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):
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super(SGConv, self).__init__()
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self.fc = nn.Linear(in_feats, out_feats, bias=bias)
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self._cached = cached
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self._cached_h = None
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self._k = k
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self.norm = norm
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self._allow_zero_in_degree = allow_zero_in_degree
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self.reset_parameters()
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def reset_parameters(self):
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r"""
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Description
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-----------
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Reinitialize learnable parameters.
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Note
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----
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The model parameters are initialized using xavier initialization
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and the bias is initialized to be zero.
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"""
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nn.init.xavier_uniform_(self.fc.weight)
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if self.fc.bias is not None:
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nn.init.zeros_(self.fc.bias)
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def set_allow_zero_in_degree(self, set_value):
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r"""
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Description
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-----------
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Set allow_zero_in_degree flag.
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Parameters
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----------
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set_value : bool
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The value to be set to the flag.
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"""
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self._allow_zero_in_degree = set_value
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def forward(self, graph, feat, edge_weight=None):
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r"""
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Description
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-----------
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Compute Simplifying Graph Convolution layer.
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Parameters
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----------
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graph : DGLGraph
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The graph.
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feat : torch.Tensor
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The input feature of shape :math:`(N, D_{in})` where :math:`D_{in}`
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is size of input feature, :math:`N` is the number of nodes.
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edge_weight: torch.Tensor, optional
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edge_weight to use in the message passing process. This is equivalent to
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using weighted adjacency matrix in the equation above, and
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:math:`\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}`
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is based on :class:`dgl.nn.pytorch.conv.graphconv.EdgeWeightNorm`.
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Returns
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-------
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torch.Tensor
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The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
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is size of output feature.
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Raises
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------
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DGLError
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If there are 0-in-degree nodes in the input graph, it will raise DGLError
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since no message will be passed to those nodes. This will cause invalid output.
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The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``.
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Note
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----
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If ``cache`` is set to True, ``feat`` and ``graph`` should not change during
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training, or you will get wrong results.
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"""
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with graph.local_scope():
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if not self._allow_zero_in_degree:
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if (graph.in_degrees() == 0).any():
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raise DGLError(
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"There are 0-in-degree nodes in the graph, "
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"output for those nodes will be invalid. "
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"This is harmful for some applications, "
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"causing silent performance regression. "
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"Adding self-loop on the input graph by "
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"calling `g = dgl.add_self_loop(g)` will resolve "
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"the issue. Setting ``allow_zero_in_degree`` "
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"to be `True` when constructing this module will "
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"suppress the check and let the code run."
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)
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msg_func = fn.copy_u("h", "m")
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if edge_weight is not None:
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graph.edata["_edge_weight"] = EdgeWeightNorm("both")(
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graph, edge_weight
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)
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msg_func = fn.u_mul_e("h", "_edge_weight", "m")
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if self._cached_h is not None:
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feat = self._cached_h
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else:
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if edge_weight is None:
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# compute normalization
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degs = graph.in_degrees().to(feat).clamp(min=1)
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norm = th.pow(degs, -0.5)
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norm = norm.to(feat.device).unsqueeze(1)
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# compute (D^-1 A^k D)^k X
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for _ in range(self._k):
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if edge_weight is None:
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feat = feat * norm
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graph.ndata["h"] = feat
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graph.update_all(msg_func, fn.sum("m", "h"))
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feat = graph.ndata.pop("h")
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if edge_weight is None:
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feat = feat * norm
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if self.norm is not None:
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feat = self.norm(feat)
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# cache feature
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if self._cached:
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self._cached_h = feat
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return self.fc(feat)
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