modify code with buildin function (#2394)

* modify code with buildin function

* use enisum
Co-authored-by: Zihao Ye <expye@outlook.com>

examples/pytorch/hgt/model.py

@@ -4,6 +4,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import dgl.function as fn
+from dgl.ops import edge_softmax
 
 class HGTLayer(nn.Module):
     def __init__(self,
@@ -52,52 +53,42 @@ class HGTLayer(nn.Module):
         nn.init.xavier_uniform_(self.relation_att)
         nn.init.xavier_uniform_(self.relation_msg)
 
-    def edge_attention(self, edges):
-        etype = edges.data['id'][0]
-        
-        '''
-            Step 1: Heterogeneous Mutual Attention
-        '''
-        relation_att = self.relation_att[etype]
-        relation_pri = self.relation_pri[etype]
-        key   = torch.bmm(edges.src['k'].transpose(1,0), relation_att).transpose(1,0)
-        att   = (edges.dst['q'] * key).sum(dim=-1) * relation_pri / self.sqrt_dk
-        
-        '''
-            Step 2: Heterogeneous Message Passing
-        '''
-        relation_msg = self.relation_msg[etype]
-        val   = torch.bmm(edges.src['v'].transpose(1,0), relation_msg).transpose(1,0)
-        return {'a': att, 'v': val}
-    
-    def message_func(self, edges):
-        return {'v': edges.data['v'], 'a': edges.data['a']}
-    
-    def reduce_func(self, nodes):
-        '''
-            Softmax based on target node's id (edge_index_i). 
-            NOTE: Using DGL's API, there is a minor difference with this softmax with the original one.
-                  This implementation will do softmax only on edges belong to the same relation type, instead of for all of the edges.
-        '''
-        att = F.softmax(nodes.mailbox['a'], dim=1)
-        h   = torch.sum(att.unsqueeze(dim = -1) * nodes.mailbox['v'], dim=1)
-        return {'t': h.view(-1, self.out_dim)}
-        
     def forward(self, G, h):
         with G.local_scope():
             node_dict, edge_dict = self.node_dict, self.edge_dict
             for srctype, etype, dsttype in G.canonical_etypes:
+                sub_graph = G[srctype, etype, dsttype]
+
                 k_linear = self.k_linears[node_dict[srctype]]
                 v_linear = self.v_linears[node_dict[srctype]]
                 q_linear = self.q_linears[node_dict[dsttype]]
 
-                G.nodes[srctype].data['k'] = k_linear(h[srctype]).view(-1, self.n_heads, self.d_k)
-                G.nodes[srctype].data['v'] = v_linear(h[srctype]).view(-1, self.n_heads, self.d_k)
-                G.nodes[dsttype].data['q'] = q_linear(h[dsttype]).view(-1, self.n_heads, self.d_k)
+                k = k_linear(h[srctype]).view(-1, self.n_heads, self.d_k)
+                v = v_linear(h[srctype]).view(-1, self.n_heads, self.d_k)
+                q = q_linear(h[dsttype]).view(-1, self.n_heads, self.d_k)
+
+                e_id = self.edge_dict[etype]
+
+                relation_att = self.relation_att[e_id]
+                relation_pri = self.relation_pri[e_id]
+                relation_msg = self.relation_msg[e_id]
+
+                k = torch.einsum("bij,ijk->bik", k, realtion_att)
+                v = torch.einsum("bij,ijk->bik", k, relation_msg)
 
-                G.apply_edges(func=self.edge_attention, etype=etype)
-            G.multi_update_all({etype : (self.message_func, self.reduce_func) \
+                sub_graph.srcdata['k'] = k
+                sub_graph.dstdata['q'] = q
+                sub_graph.srcdata['v'] = v
+
+                sub_graph.apply_edges(fn.v_dot_u('q', 'k', 't'))
+                attn_score = sub_graph.edata.pop('t').sum(-1) * relation_pri / self.sqrt_dk
+                attn_score = edge_softmax(sub_graph, attn_score, norm_by='dst')
+
+                sub_graph.edata['t'] = attn_score.unsqueeze(-1)
+
+            G.multi_update_all({etype : (fn.u_mul_e('v', 't', 'm'), fn.sum('m', 't')) \
                                 for etype in edge_dict}, cross_reducer = 'mean')
+
             new_h = {}
             for ntype in G.ntypes:
                 '''
@@ -106,7 +97,8 @@ class HGTLayer(nn.Module):
                 '''
                 n_id = node_dict[ntype]
                 alpha = torch.sigmoid(self.skip[n_id])
-                trans_out = self.drop(self.a_linears[n_id](G.nodes[ntype].data['t']))
+                t = G.nodes[ntype].data['t'].view(-1, self.out_dim)
+                trans_out = self.drop(self.a_linears[n_id](t))
                 trans_out = trans_out * alpha + h[ntype] * (1-alpha)
                 if self.use_norm:
                     new_h[ntype] = self.norms[n_id](trans_out)

examples/pytorch/hgt/train_acm.py

@@ -13,7 +13,7 @@ from model import *
 import argparse
 
 torch.manual_seed(0)
-data_url = 'https://s3.us-east-2.amazonaws.com/dgl.ai/dataset/ACM.mat'
+data_url = 'https://data.dgl.ai/dataset/ACM.mat'
 data_file_path = '/tmp/ACM.mat'
 
 urllib.request.urlretrieve(data_url, data_file_path)