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

"""Torch modules for graph convolutions(GCN)."""
# pylint: disable= no-member, arguments-differ, invalid-name
import torch as th
from torch import nn
from torch.nn import init

from .... import function as fn
from ....base import DGLError
from ....utils import expand_as_pair
from ....transform import reverse
from ....convert import block_to_graph
from ....heterograph import DGLBlock

[docs]class EdgeWeightNorm(nn.Module): r""" Description ----------- This module normalizes positive scalar edge weights on a graph following the form in `GCN <https://arxiv.org/abs/1609.02907>`__. Mathematically, setting ``norm='both'`` yields the following normalization term: .. math: c_{ji} = (\sqrt{\sum_{k\in\mathcal{N}(j)}e_{jk}}\sqrt{\sum_{k\in\mathcal{N}(i)}e_{ki}}) And, setting ``norm='right'`` yields the following normalization term: .. math: c_{ji} = (\sum_{k\in\mathcal{N}(i)}}e_{ki}) where :math:`e_{ji}` is the scalar weight on the edge from node :math:`j` to node :math:`i`. The module returns the normalized weight :math:`e_{ji} / c_{ji}`. Parameters ---------- norm : str, optional The normalizer as specified above. Default is `'both'`. eps : float, optional A small offset value in the denominator. Default is 0. Examples -------- >>> import dgl >>> import numpy as np >>> import torch as th >>> from dgl.nn import EdgeWeightNorm, GraphConv >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])) >>> g = dgl.add_self_loop(g) >>> feat = th.ones(6, 10) >>> edge_weight = th.tensor([0.5, 0.6, 0.4, 0.7, 0.9, 0.1, 1, 1, 1, 1, 1, 1]) >>> norm = EdgeWeightNorm(norm='both') >>> norm_edge_weight = norm(g, edge_weight) >>> conv = GraphConv(10, 2, norm='none', weight=True, bias=True) >>> res = conv(g, feat, edge_weight=norm_edge_weight) >>> print(res) tensor([[-1.1849, -0.7525], [-1.3514, -0.8582], [-1.2384, -0.7865], [-1.9949, -1.2669], [-1.3658, -0.8674], [-0.8323, -0.5286]], grad_fn=<AddBackward0>) """ def __init__(self, norm='both', eps=0.): super(EdgeWeightNorm, self).__init__() self._norm = norm self._eps = eps
[docs] def forward(self, graph, edge_weight): r""" Description ----------- Compute normalized edge weight for the GCN model. Parameters ---------- graph : DGLGraph The graph. edge_weight : torch.Tensor Unnormalized scalar weights on the edges. The shape is expected to be :math:`(|E|)`. Returns ------- torch.Tensor The normalized edge weight. Raises ------ DGLError Case 1: The edge weight is multi-dimensional. Currently this module only supports a scalar weight on each edge. Case 2: The edge weight has non-positive values with ``norm='both'``. This will trigger square root and division by a non-positive number. """ with graph.local_scope(): if isinstance(graph, DGLBlock): graph = block_to_graph(graph) if len(edge_weight.shape) > 1: raise DGLError('Currently the normalization is only defined ' 'on scalar edge weight. Please customize the ' 'normalization for your high-dimensional weights.') if self._norm == 'both' and th.any(edge_weight <= 0).item(): raise DGLError('Non-positive edge weight detected with `norm="both"`. ' 'This leads to square root of zero or negative values.') dev = graph.device graph.srcdata['_src_out_w'] = th.ones((graph.number_of_src_nodes())).float().to(dev) graph.dstdata['_dst_in_w'] = th.ones((graph.number_of_dst_nodes())).float().to(dev) graph.edata['_edge_w'] = edge_weight if self._norm == 'both': reversed_g = reverse(graph) reversed_g.edata['_edge_w'] = edge_weight reversed_g.update_all(fn.copy_edge('_edge_w', 'm'), fn.sum('m', 'out_weight')) degs = reversed_g.dstdata['out_weight'] + self._eps norm = th.pow(degs, -0.5) graph.srcdata['_src_out_w'] = norm if self._norm != 'none': graph.update_all(fn.copy_edge('_edge_w', 'm'), fn.sum('m', 'in_weight')) degs = graph.dstdata['in_weight'] + self._eps if self._norm == 'both': norm = th.pow(degs, -0.5) else: norm = 1.0 / degs graph.dstdata['_dst_in_w'] = norm graph.apply_edges(lambda e: {'_norm_edge_weights': e.src['_src_out_w'] * \ e.dst['_dst_in_w'] * \ e.data['_edge_w']}) return graph.edata['_norm_edge_weights']
# pylint: disable=W0235
[docs]class GraphConv(nn.Module): r""" Description ----------- Graph convolution was introduced in `GCN <https://arxiv.org/abs/1609.02907>`__ and mathematically is defined as follows: .. math:: h_i^{(l+1)} = \sigma(b^{(l)} + \sum_{j\in\mathcal{N}(i)}\frac{1}{c_{ji}}h_j^{(l)}W^{(l)}) where :math:`\mathcal{N}(i)` is the set of neighbors of node :math:`i`, :math:`c_{ji}` is the product of the square root of node degrees (i.e., :math:`c_{ji} = \sqrt{|\mathcal{N}(j)|}\sqrt{|\mathcal{N}(i)|}`), and :math:`\sigma` is an activation function. If a weight tensor on each edge is provided, the weighted graph convolution is defined as: .. math:: h_i^{(l+1)} = \sigma(b^{(l)} + \sum_{j\in\mathcal{N}(i)}\frac{e_{ji}}{c_{ji}}h_j^{(l)}W^{(l)}) where :math:`e_{ji}` is the scalar weight on the edge from node :math:`j` to node :math:`i`. This is NOT equivalent to the weighted graph convolutional network formulation in the paper. To customize the normalization term :math:`c_{ji}`, one can first set ``norm='none'`` for the model, and send the pre-normalized :math:`e_{ji}` to the forward computation. We provide :class:`~dgl.nn.pytorch.EdgeWeightNorm` to normalize scalar edge weight following the GCN paper. Parameters ---------- in_feats : int Input feature size; i.e, the number of dimensions of :math:`h_j^{(l)}`. out_feats : int Output feature size; i.e., the number of dimensions of :math:`h_i^{(l+1)}`. norm : str, optional How to apply the normalizer. If is `'right'`, divide the aggregated messages by each node's in-degrees, which is equivalent to averaging the received messages. If is `'none'`, no normalization is applied. Default is `'both'`, where the :math:`c_{ji}` in the paper is applied. weight : bool, optional If True, apply a linear layer. Otherwise, aggregating the messages without a weight matrix. bias : bool, optional If True, adds a learnable bias to the output. Default: ``True``. activation : callable activation function/layer or None, optional If not None, applies an activation function to the updated node features. Default: ``None``. 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``. Attributes ---------- weight : torch.Tensor The learnable weight tensor. bias : torch.Tensor The learnable bias tensor. 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 >>> g = dgl.add_self_loop(g) 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. Examples -------- >>> import dgl >>> import numpy as np >>> import torch as th >>> from dgl.nn import GraphConv >>> # Case 1: Homogeneous graph >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])) >>> g = dgl.add_self_loop(g) >>> feat = th.ones(6, 10) >>> conv = GraphConv(10, 2, norm='both', weight=True, bias=True) >>> res = conv(g, feat) >>> print(res) tensor([[ 1.3326, -0.2797], [ 1.4673, -0.3080], [ 1.3326, -0.2797], [ 1.6871, -0.3541], [ 1.7711, -0.3717], [ 1.0375, -0.2178]], grad_fn=<AddBackward0>) >>> # allow_zero_in_degree example >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])) >>> conv = GraphConv(10, 2, norm='both', weight=True, bias=True, allow_zero_in_degree=True) >>> res = conv(g, feat) >>> print(res) tensor([[-0.2473, -0.4631], [-0.3497, -0.6549], [-0.3497, -0.6549], [-0.4221, -0.7905], [-0.3497, -0.6549], [ 0.0000, 0.0000]], grad_fn=<AddBackward0>) >>> # Case 2: Unidirectional bipartite graph >>> u = [0, 1, 0, 0, 1] >>> v = [0, 1, 2, 3, 2] >>> g = dgl.heterograph({('_U', '_E', '_V') : (u, v)}) >>> u_fea = th.rand(2, 5) >>> v_fea = th.rand(4, 5) >>> conv = GraphConv(5, 2, norm='both', weight=True, bias=True) >>> res = conv(g, (u_fea, v_fea)) >>> res tensor([[-0.2994, 0.6106], [-0.4482, 0.5540], [-0.5287, 0.8235], [-0.2994, 0.6106]], grad_fn=<AddBackward0>) """ def __init__(self, in_feats, out_feats, norm='both', weight=True, bias=True, activation=None, allow_zero_in_degree=False): super(GraphConv, self).__init__() if norm not in ('none', 'both', 'right'): raise DGLError('Invalid norm value. Must be either "none", "both" or "right".' ' But got "{}".'.format(norm)) self._in_feats = in_feats self._out_feats = out_feats self._norm = norm self._allow_zero_in_degree = allow_zero_in_degree if weight: self.weight = nn.Parameter(th.Tensor(in_feats, out_feats)) else: self.register_parameter('weight', None) if bias: self.bias = nn.Parameter(th.Tensor(out_feats)) else: self.register_parameter('bias', None) self.reset_parameters() self._activation = activation
[docs] def reset_parameters(self): r""" Description ----------- Reinitialize learnable parameters. Note ---- The model parameters are initialized as in the `original implementation <https://github.com/tkipf/gcn/blob/master/gcn/layers.py>`__ where the weight :math:`W^{(l)}` is initialized using Glorot uniform initialization and the bias is initialized to be zero. """ if self.weight is not None: init.xavier_uniform_(self.weight) if self.bias is not None: init.zeros_(self.bias)
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, weight=None, edge_weight=None): r""" Description ----------- Compute graph convolution. Parameters ---------- graph : DGLGraph The graph. feat : torch.Tensor or pair of torch.Tensor If a torch.Tensor is given, it represents the input feature of shape :math:`(N, D_{in})` where :math:`D_{in}` is size of input feature, :math:`N` is the number of nodes. If a pair of torch.Tensor is given, which is the case for bipartite graph, the pair must contain two tensors of shape :math:`(N_{in}, D_{in_{src}})` and :math:`(N_{out}, D_{in_{dst}})`. weight : torch.Tensor, optional Optional external weight tensor. edge_weight : torch.Tensor, optional Optional tensor on the edge. If given, the convolution will weight with regard to the message. Returns ------- torch.Tensor The output feature Raises ------ DGLError Case 1: 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``. Case 2: External weight is provided while at the same time the module has defined its own weight parameter. Note ---- * Input shape: :math:`(N, *, \text{in_feats})` where * means any number of additional dimensions, :math:`N` is the number of nodes. * Output shape: :math:`(N, *, \text{out_feats})` where all but the last dimension are the same shape as the input. * Weight shape: :math:`(\text{in_feats}, \text{out_feats})`. """ 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.') aggregate_fn = fn.copy_src('h', 'm') if edge_weight is not None: assert edge_weight.shape[0] == graph.number_of_edges() graph.edata['_edge_weight'] = edge_weight aggregate_fn = fn.u_mul_e('h', '_edge_weight', 'm') # (BarclayII) For RGCN on heterogeneous graphs we need to support GCN on bipartite. feat_src, feat_dst = expand_as_pair(feat, graph) if self._norm == 'both': degs = graph.out_degrees().float().clamp(min=1) norm = th.pow(degs, -0.5) shp = norm.shape + (1,) * (feat_src.dim() - 1) norm = th.reshape(norm, shp) feat_src = feat_src * norm if weight is not None: if self.weight is not None: raise DGLError('External weight is provided while at the same time the' ' module has defined its own weight parameter. Please' ' create the module with flag weight=False.') else: weight = self.weight if self._in_feats > self._out_feats: # mult W first to reduce the feature size for aggregation. if weight is not None: feat_src = th.matmul(feat_src, weight) graph.srcdata['h'] = feat_src graph.update_all(aggregate_fn, fn.sum(msg='m', out='h')) rst = graph.dstdata['h'] else: # aggregate first then mult W graph.srcdata['h'] = feat_src graph.update_all(aggregate_fn, fn.sum(msg='m', out='h')) rst = graph.dstdata['h'] if weight is not None: rst = th.matmul(rst, weight) if self._norm != 'none': degs = graph.in_degrees().float().clamp(min=1) if self._norm == 'both': norm = th.pow(degs, -0.5) else: norm = 1.0 / degs shp = norm.shape + (1,) * (feat_dst.dim() - 1) norm = th.reshape(norm, shp) rst = rst * norm if self.bias is not None: rst = rst + self.bias if self._activation is not None: rst = self._activation(rst) return rst
def extra_repr(self): """Set the extra representation of the module, which will come into effect when printing the model. """ summary = 'in={_in_feats}, out={_out_feats}' summary += ', normalization={_norm}' if '_activation' in self.__dict__: summary += ', activation={_activation}' return summary.format(**self.__dict__)