(中文版) <guide_cn-nn-forward>
In NN module, forward()
function does the actual message passing and computation. Compared with PyTorch’s NN module which usually takes tensors as the parameters, DGL NN module takes an additional parameter dgl.DGLGraph
. The workload for forward()
function can be split into three parts:
- Graph checking and graph type specification.
- Message passing.
- Feature update.
The rest of the section takes a deep dive into the forward()
function in SAGEConv example.
def forward(self, graph, feat):
with graph.local_scope():
# Specify graph type then expand input feature according to graph type
feat_src, feat_dst = expand_as_pair(feat, graph)
forward()
needs to handle many corner cases on the input that can lead to invalid values in computing and message passing. One typical check in conv modules like ~dgl.nn.pytorch.conv.GraphConv
is to verify that the input graph has no 0-in-degree nodes. When a node has 0 in-degree, the mailbox
will be empty and the reduce function will produce all-zero values. This may cause silent regression in model performance. However, in ~dgl.nn.pytorch.conv.SAGEConv
module, the aggregated representation will be concatenated with the original node feature, the output of forward()
will not be all-zero. No such check is needed in this case.
DGL NN module should be reusable across different types of graph input including: homogeneous graph, heterogeneous graph (guide-graph-heterogeneous
), subgraph block (guide-minibatch
).
The math formulas for SAGEConv are:
h𝒩(dst)(l + 1) = aggregate({hsrcl,∀src∈𝒩(dst)})
hdst(l + 1) = σ(W⋅concat(hdstl,h𝒩(dst)l + 1)+b)
hdst(l + 1) = norm(hdstl + 1)
One needs to specify the source node feature feat_src
and destination node feature feat_dst
according to the graph type. ~dgl.utils.expand_as_pair
is a function that specifies the graph type and expand feat
into feat_src
and feat_dst
. The detail of this function is shown below.
def expand_as_pair(input_, g=None):
if isinstance(input_, tuple):
# Bipartite graph case
return input_
elif g is not None and g.is_block:
# Subgraph block case
if isinstance(input_, Mapping):
input_dst = {
k: F.narrow_row(v, 0, g.number_of_dst_nodes(k))
for k, v in input_.items()}
else:
input_dst = F.narrow_row(input_, 0, g.number_of_dst_nodes())
return input_, input_dst
else:
# Homogeneous graph case
return input_, input_
For homogeneous whole graph training, source nodes and destination nodes are the same. They are all the nodes in the graph.
For heterogeneous case, the graph can be split into several bipartite graphs, one for each relation. The relations are represented as (src_type, edge_type, dst_dtype)
. When it identifies that the input feature feat
is a tuple, it will treat the graph as bipartite. The first element in the tuple will be the source node feature and the second element will be the destination node feature.
In mini-batch training, the computing is applied on a subgraph sampled based on a bunch of destination nodes. The subgraph is called as block
in DGL. In the block creation phase, dst nodes
are in the front of the node list. One can find the feat_dst
by the index [0:g.number_of_dst_nodes()]
.
After determining feat_src
and feat_dst
, the computing for the above three graph types are the same.
import dgl.function as fn
import torch.nn.functional as F
from dgl.utils import check_eq_shape
if self._aggre_type == 'mean':
graph.srcdata['h'] = feat_src
graph.update_all(fn.copy_u('h', 'm'), fn.mean('m', 'neigh'))
h_neigh = graph.dstdata['neigh']
elif self._aggre_type == 'gcn':
check_eq_shape(feat)
graph.srcdata['h'] = feat_src
graph.dstdata['h'] = feat_dst
graph.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'neigh'))
# divide in_degrees
degs = graph.in_degrees().to(feat_dst)
h_neigh = (graph.dstdata['neigh'] + graph.dstdata['h']) / (degs.unsqueeze(-1) + 1)
elif self._aggre_type == 'pool':
graph.srcdata['h'] = F.relu(self.fc_pool(feat_src))
graph.update_all(fn.copy_u('h', 'm'), fn.max('m', 'neigh'))
h_neigh = graph.dstdata['neigh']
else:
raise KeyError('Aggregator type {} not recognized.'.format(self._aggre_type))
# GraphSAGE GCN does not require fc_self.
if self._aggre_type == 'gcn':
rst = self.fc_neigh(h_neigh)
else:
rst = self.fc_self(h_self) + self.fc_neigh(h_neigh)
The code actually does message passing and reducing computing. This part of code varies module by module. Note that all the message passing in the above code are implemented using ~dgl.DGLGraph.update_all
API and built-in
message/reduce functions to fully utilize DGL’s performance optimization as described in guide-message-passing-efficient
.
# activation
if self.activation is not None:
rst = self.activation(rst)
# normalization
if self.norm is not None:
rst = self.norm(rst)
return rst
The last part of forward()
function is to update the feature after the reduce function
. Common update operations are applying activation function and normalization according to the option set in the object construction phase.