wehub-resource-sync
243 lines
9.2 KiB
Python
243 lines
9.2 KiB
Python
"""Torch modules for graph attention networks(GAT)."""
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# pylint: disable= no-member, arguments-differ, invalid-name
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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 ....utils import expand_as_pair
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from ...functional import edge_softmax
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class DotGatConv(nn.Module):
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r"""Apply dot product version of self attention in `Graph Attention Network
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<https://arxiv.org/pdf/1710.10903.pdf>`__
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.. math::
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h_i^{(l+1)} = \sum_{j\in \mathcal{N}(i)} \alpha_{i, j} h_j^{(l)}
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where :math:`\alpha_{ij}` is the attention score bewteen node :math:`i` and node :math:`j`:
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.. math::
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\alpha_{i, j} &= \mathrm{softmax_i}(e_{ij}^{l})
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e_{ij}^{l} &= ({W_i^{(l)} h_i^{(l)}})^T \cdot {W_j^{(l)} h_j^{(l)}}
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where :math:`W_i` and :math:`W_j` transform node :math:`i`'s and node :math:`j`'s
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features into the same dimension, so that when compute note features' similarity,
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it can use dot-product.
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Parameters
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----------
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in_feats : int, or pair of ints
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Input feature size; i.e, the number of dimensions of :math:`h_i^{(l)}`.
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DotGatConv can be applied on homogeneous graph and unidirectional
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`bipartite graph <https://docs.dgl.ai/generated/dgl.bipartite.html?highlight=bipartite>`__.
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If the layer is to be applied to a unidirectional bipartite graph, ``in_feats``
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specifies the input feature size on both the source and destination nodes. If
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a scalar is given, the source and destination node feature size would take the
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same value.
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out_feats : int
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Output feature size; i.e, the number of dimensions of :math:`h_i^{(l+1)}`.
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num_heads : int
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Number of head in Multi-Head Attention
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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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Examples
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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 DotGatConv
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>>> # Case 1: Homogeneous graph
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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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>>> dotgatconv = DotGatConv(10, 2, num_heads=3)
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>>> res = dotgatconv(g, feat)
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>>> res
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tensor([[[ 3.4570, 1.8634],
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[ 1.3805, -0.0762],
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[ 1.0390, -1.1479]],
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[[ 3.4570, 1.8634],
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[ 1.3805, -0.0762],
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[ 1.0390, -1.1479]],
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[[ 3.4570, 1.8634],
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[ 1.3805, -0.0762],
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[ 1.0390, -1.1479]],
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[[ 3.4570, 1.8634],
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[ 1.3805, -0.0762],
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[ 1.0390, -1.1479]],
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[[ 3.4570, 1.8634],
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[ 1.3805, -0.0762],
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[ 1.0390, -1.1479]],
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[[ 3.4570, 1.8634],
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[ 1.3805, -0.0762],
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[ 1.0390, -1.1479]]], grad_fn=<BinaryReduceBackward>)
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>>> # Case 2: Unidirectional bipartite graph
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>>> u = [0, 1, 0, 0, 1]
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>>> v = [0, 1, 2, 3, 2]
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>>> g = dgl.heterograph({('_N', '_E', '_N'):(u, v)})
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>>> u_feat = th.tensor(np.random.rand(2, 5).astype(np.float32))
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>>> v_feat = th.tensor(np.random.rand(4, 10).astype(np.float32))
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>>> dotgatconv = DotGatConv((5,10), 2, 3)
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>>> res = dotgatconv(g, (u_feat, v_feat))
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>>> res
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tensor([[[-0.6066, 1.0268],
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[-0.5945, -0.4801],
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[ 0.1594, 0.3825]],
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[[ 0.0268, 1.0783],
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[ 0.5041, -1.3025],
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[ 0.6568, 0.7048]],
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[[-0.2688, 1.0543],
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[-0.0315, -0.9016],
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[ 0.3943, 0.5347]],
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[[-0.6066, 1.0268],
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[-0.5945, -0.4801],
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[ 0.1594, 0.3825]]], grad_fn=<BinaryReduceBackward>)
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"""
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def __init__(
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self, in_feats, out_feats, num_heads, allow_zero_in_degree=False
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):
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super(DotGatConv, self).__init__()
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self._in_src_feats, self._in_dst_feats = expand_as_pair(in_feats)
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self._out_feats = out_feats
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self._allow_zero_in_degree = allow_zero_in_degree
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self._num_heads = num_heads
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if isinstance(in_feats, tuple):
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self.fc_src = nn.Linear(
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self._in_src_feats,
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self._out_feats * self._num_heads,
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bias=False,
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)
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self.fc_dst = nn.Linear(
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self._in_dst_feats,
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self._out_feats * self._num_heads,
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bias=False,
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)
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else:
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self.fc = nn.Linear(
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self._in_src_feats,
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self._out_feats * self._num_heads,
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bias=False,
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)
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def forward(self, graph, feat, get_attention=False):
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r"""
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Description
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-----------
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Apply dot product version of self attention in GCN.
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Parameters
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----------
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graph: DGLGraph or bi_partities graph
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The graph
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feat: torch.Tensor or pair of torch.Tensor
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If a torch.Tensor is given, the input feature of shape :math:`(N, D_{in})` where
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:math:`D_{in}` is size of input feature, :math:`N` is the number of nodes.
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If a pair of torch.Tensor is given, the pair must contain two tensors of shape
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:math:`(N_{in}, D_{in_{src}})` and :math:`(N_{out}, D_{in_{dst}})`.
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get_attention : bool, optional
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Whether to return the attention values. Default to False.
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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}` is size
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of output feature.
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torch.Tensor, optional
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The attention values of shape :math:`(E, 1)`, where :math:`E` is the number of
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edges. This is returned only when :attr:`get_attention` is ``True``.
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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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"""
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graph = graph.local_var()
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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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# check if feat is a tuple
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if isinstance(feat, tuple):
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h_src = feat[0]
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h_dst = feat[1]
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feat_src = self.fc_src(h_src).view(
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-1, self._num_heads, self._out_feats
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)
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feat_dst = self.fc_dst(h_dst).view(
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-1, self._num_heads, self._out_feats
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)
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else:
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h_src = feat
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feat_src = feat_dst = self.fc(h_src).view(
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-1, self._num_heads, self._out_feats
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)
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if graph.is_block:
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feat_dst = feat_src[: graph.number_of_dst_nodes()]
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# Assign features to nodes
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graph.srcdata.update({"ft": feat_src})
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graph.dstdata.update({"ft": feat_dst})
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# Step 1. dot product
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graph.apply_edges(fn.u_dot_v("ft", "ft", "a"))
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# Step 2. edge softmax to compute attention scores
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graph.edata["sa"] = edge_softmax(
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graph, graph.edata["a"] / self._out_feats**0.5
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)
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# Step 3. Broadcast softmax value to each edge, and aggregate dst node
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graph.update_all(
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fn.u_mul_e("ft", "sa", "attn"), fn.sum("attn", "agg_u")
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)
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# output results to the destination nodes
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rst = graph.dstdata["agg_u"]
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if get_attention:
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return rst, graph.edata["sa"]
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else:
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return rst
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