import dgl import math import torch import torch.nn as nn import torch.nn.functional as F import dgl.function as fn from dgl.nn.functional import edge_softmax class HGTLayer(nn.Module): def __init__(self, in_dim, out_dim, node_dict, edge_dict, n_heads, dropout = 0.2, use_norm = False): super(HGTLayer, self).__init__() self.in_dim = in_dim self.out_dim = out_dim self.node_dict = node_dict self.edge_dict = edge_dict self.num_types = len(node_dict) self.num_relations = len(edge_dict) self.total_rel = self.num_types * self.num_relations * self.num_types self.n_heads = n_heads self.d_k = out_dim // n_heads self.sqrt_dk = math.sqrt(self.d_k) self.att = None self.k_linears = nn.ModuleList() self.q_linears = nn.ModuleList() self.v_linears = nn.ModuleList() self.a_linears = nn.ModuleList() self.norms = nn.ModuleList() self.use_norm = use_norm for t in range(self.num_types): self.k_linears.append(nn.Linear(in_dim, out_dim)) self.q_linears.append(nn.Linear(in_dim, out_dim)) self.v_linears.append(nn.Linear(in_dim, out_dim)) self.a_linears.append(nn.Linear(out_dim, out_dim)) if use_norm: self.norms.append(nn.LayerNorm(out_dim)) self.relation_pri = nn.Parameter(torch.ones(self.num_relations, self.n_heads)) self.relation_att = nn.Parameter(torch.Tensor(self.num_relations, n_heads, self.d_k, self.d_k)) self.relation_msg = nn.Parameter(torch.Tensor(self.num_relations, n_heads, self.d_k, self.d_k)) self.skip = nn.Parameter(torch.ones(self.num_types)) self.drop = nn.Dropout(dropout) nn.init.xavier_uniform_(self.relation_att) nn.init.xavier_uniform_(self.relation_msg) 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]] 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, relation_att) v = torch.einsum("bij,ijk->bik", v, relation_msg) 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: ''' Step 3: Target-specific Aggregation x = norm( W[node_type] * gelu( Agg(x) ) + x ) ''' n_id = node_dict[ntype] alpha = torch.sigmoid(self.skip[n_id]) 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) else: new_h[ntype] = trans_out return new_h class HGT(nn.Module): def __init__(self, G, node_dict, edge_dict, n_inp, n_hid, n_out, n_layers, n_heads, use_norm = True): super(HGT, self).__init__() self.node_dict = node_dict self.edge_dict = edge_dict self.gcs = nn.ModuleList() self.n_inp = n_inp self.n_hid = n_hid self.n_out = n_out self.n_layers = n_layers self.adapt_ws = nn.ModuleList() for t in range(len(node_dict)): self.adapt_ws.append(nn.Linear(n_inp, n_hid)) for _ in range(n_layers): self.gcs.append(HGTLayer(n_hid, n_hid, node_dict, edge_dict, n_heads, use_norm = use_norm)) self.out = nn.Linear(n_hid, n_out) def forward(self, G, out_key): h = {} for ntype in G.ntypes: n_id = self.node_dict[ntype] h[ntype] = F.gelu(self.adapt_ws[n_id](G.nodes[ntype].data['inp'])) for i in range(self.n_layers): h = self.gcs[i](G, h) return self.out(h[out_key]) class HeteroRGCNLayer(nn.Module): def __init__(self, in_size, out_size, etypes): super(HeteroRGCNLayer, self).__init__() # W_r for each relation self.weight = nn.ModuleDict({ name : nn.Linear(in_size, out_size) for name in etypes }) def forward(self, G, feat_dict): # The input is a dictionary of node features for each type funcs = {} for srctype, etype, dsttype in G.canonical_etypes: # Compute W_r * h Wh = self.weight[etype](feat_dict[srctype]) # Save it in graph for message passing G.nodes[srctype].data['Wh_%s' % etype] = Wh # Specify per-relation message passing functions: (message_func, reduce_func). # Note that the results are saved to the same destination feature 'h', which # hints the type wise reducer for aggregation. funcs[etype] = (fn.copy_u('Wh_%s' % etype, 'm'), fn.mean('m', 'h')) # Trigger message passing of multiple types. # The first argument is the message passing functions for each relation. # The second one is the type wise reducer, could be "sum", "max", # "min", "mean", "stack" G.multi_update_all(funcs, 'sum') # return the updated node feature dictionary return {ntype : G.nodes[ntype].data['h'] for ntype in G.ntypes} class HeteroRGCN(nn.Module): def __init__(self, G, in_size, hidden_size, out_size): super(HeteroRGCN, self).__init__() # create layers self.layer1 = HeteroRGCNLayer(in_size, hidden_size, G.etypes) self.layer2 = HeteroRGCNLayer(hidden_size, out_size, G.etypes) def forward(self, G, out_key): input_dict = {ntype : G.nodes[ntype].data['inp'] for ntype in G.ntypes} h_dict = self.layer1(G, input_dict) h_dict = {k : F.leaky_relu(h) for k, h in h_dict.items()} h_dict = self.layer2(G, h_dict) # get paper logits return h_dict[out_key]