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model_sampling.py
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import torch.nn as nn
import dgl.function as fn
import torch as th
from dgl.nn.functional import edge_softmax
class MLP(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.W = nn.Linear(in_dim, out_dim)
def apply_edges(self, edges):
h_e = edges.data['h']
h_u = edges.src['h']
h_v = edges.dst['h']
score = self.W(th.cat([h_e, h_u, h_v], -1))
return {'score': score}
def forward(self, g, e_feat, u_feat, v_feat):
with g.local_scope():
g.edges['forward'].data['h'] = e_feat
g.nodes['u'].data['h'] = u_feat
g.nodes['v'].data['h'] = v_feat
g.apply_edges(self.apply_edges, etype="forward")
return g.edges['forward'].data['score']
class GASConv(nn.Module):
"""One layer of GAS."""
def __init__(self,
e_in_dim,
u_in_dim,
v_in_dim,
e_out_dim,
u_out_dim,
v_out_dim,
activation=None,
dropout=0):
super(GASConv, self).__init__()
self.activation = activation
self.dropout = nn.Dropout(dropout)
self.e_linear = nn.Linear(e_in_dim, e_out_dim)
self.u_linear = nn.Linear(u_in_dim, e_out_dim)
self.v_linear = nn.Linear(v_in_dim, e_out_dim)
self.W_ATTN_u = nn.Linear(u_in_dim, v_in_dim + e_in_dim)
self.W_ATTN_v = nn.Linear(v_in_dim, u_in_dim + e_in_dim)
# the proportion of h_u and h_Nu are specified as 1/2 in formula 8
nu_dim = int(u_out_dim / 2)
nv_dim = int(v_out_dim / 2)
self.W_u = nn.Linear(v_in_dim + e_in_dim, nu_dim)
self.W_v = nn.Linear(u_in_dim + e_in_dim, nv_dim)
self.Vu = nn.Linear(u_in_dim, u_out_dim - nu_dim)
self.Vv = nn.Linear(v_in_dim, v_out_dim - nv_dim)
def forward(self, g, f_feat, b_feat, u_feat, v_feat):
g.srcnodes['u'].data['h'] = u_feat
g.srcnodes['v'].data['h'] = v_feat
g.dstnodes['u'].data['h'] = u_feat[:g.number_of_dst_nodes(ntype='u')]
g.dstnodes['v'].data['h'] = v_feat[:g.number_of_dst_nodes(ntype='v')]
g.edges['forward'].data['h'] = f_feat
g.edges['backward'].data['h'] = b_feat
# formula 3 and 4 (optimized implementation to save memory)
g.srcnodes["u"].data.update({'he_u': self.u_linear(g.srcnodes['u'].data['h'])})
g.srcnodes["v"].data.update({'he_v': self.v_linear(g.srcnodes['v'].data['h'])})
g.dstnodes["u"].data.update({'he_u': self.u_linear(g.dstnodes['u'].data['h'])})
g.dstnodes["v"].data.update({'he_v': self.v_linear(g.dstnodes['v'].data['h'])})
g.edges["forward"].data.update({'he_e': self.e_linear(f_feat)})
g.edges["backward"].data.update({'he_e': self.e_linear(b_feat)})
g.apply_edges(lambda edges: {'he': edges.data['he_e'] + edges.dst['he_u'] + edges.src['he_v']}, etype='backward')
g.apply_edges(lambda edges: {'he': edges.data['he_e'] + edges.src['he_u'] + edges.dst['he_v']}, etype='forward')
hf = g.edges["forward"].data['he']
hb = g.edges["backward"].data['he']
if self.activation is not None:
hf = self.activation(hf)
hb = self.activation(hb)
# formula 6
g.apply_edges(lambda edges: {'h_ve': th.cat([edges.src['h'], edges.data['h']], -1)}, etype='backward')
g.apply_edges(lambda edges: {'h_ue': th.cat([edges.src['h'], edges.data['h']], -1)}, etype='forward')
# formula 7, self-attention
g.srcnodes['u'].data['h_att_u'] = self.W_ATTN_u(g.srcnodes['u'].data['h'])
g.srcnodes['v'].data['h_att_v'] = self.W_ATTN_v(g.srcnodes['v'].data['h'])
g.dstnodes['u'].data['h_att_u'] = self.W_ATTN_u(g.dstnodes['u'].data['h'])
g.dstnodes['v'].data['h_att_v'] = self.W_ATTN_v(g.dstnodes['v'].data['h'])
# Step 1: dot product
g.apply_edges(fn.e_dot_v('h_ve', 'h_att_u', 'edotv'), etype='backward')
g.apply_edges(fn.e_dot_v('h_ue', 'h_att_v', 'edotv'), etype='forward')
# Step 2. softmax
g.edges['backward'].data['sfm'] = edge_softmax(g['backward'], g.edges['backward'].data['edotv'])
g.edges['forward'].data['sfm'] = edge_softmax(g['forward'], g.edges['forward'].data['edotv'])
# Step 3. Broadcast softmax value to each edge, and then attention is done
g.apply_edges(lambda edges: {'attn': edges.data['h_ve'] * edges.data['sfm']}, etype='backward')
g.apply_edges(lambda edges: {'attn': edges.data['h_ue'] * edges.data['sfm']}, etype='forward')
# Step 4. Aggregate attention to dst,user nodes, so formula 7 is done
g.update_all(fn.copy_e('attn', 'm'), fn.sum('m', 'agg_u'), etype='backward')
g.update_all(fn.copy_e('attn', 'm'), fn.sum('m', 'agg_v'), etype='forward')
# formula 5
h_nu = self.W_u(g.dstnodes['u'].data['agg_u'])
h_nv = self.W_v(g.dstnodes['v'].data['agg_v'])
if self.activation is not None:
h_nu = self.activation(h_nu)
h_nv = self.activation(h_nv)
# Dropout
hf = self.dropout(hf)
hb = self.dropout(hb)
h_nu = self.dropout(h_nu)
h_nv = self.dropout(h_nv)
# formula 8
hu = th.cat([self.Vu(g.dstnodes['u'].data['h']), h_nu], -1)
hv = th.cat([self.Vv(g.dstnodes['v'].data['h']), h_nv], -1)
return hf, hb, hu, hv
class GAS(nn.Module):
def __init__(self,
e_in_dim,
u_in_dim,
v_in_dim,
e_hid_dim,
u_hid_dim,
v_hid_dim,
out_dim,
num_layers=2,
dropout=0.0,
activation=None):
super(GAS, self).__init__()
self.e_in_dim = e_in_dim
self.u_in_dim = u_in_dim
self.v_in_dim = v_in_dim
self.e_hid_dim = e_hid_dim
self.u_hid_dim = u_hid_dim
self.v_hid_dim = v_hid_dim
self.out_dim = out_dim
self.num_layer = num_layers
self.dropout = dropout
self.activation = activation
self.predictor = MLP(e_hid_dim + u_hid_dim + v_hid_dim, out_dim)
self.layers = nn.ModuleList()
# Input layer
self.layers.append(GASConv(self.e_in_dim,
self.u_in_dim,
self.v_in_dim,
self.e_hid_dim,
self.u_hid_dim,
self.v_hid_dim,
activation=self.activation,
dropout=self.dropout))
# Hidden layers with n - 1 CompGraphConv layers
for i in range(self.num_layer - 1):
self.layers.append(GASConv(self.e_hid_dim,
self.u_hid_dim,
self.v_hid_dim,
self.e_hid_dim,
self.u_hid_dim,
self.v_hid_dim,
activation=self.activation,
dropout=self.dropout))
def forward(self, subgraph, blocks, f_feat, b_feat, u_feat, v_feat):
# Forward of n layers of GAS
for layer, block in zip(self.layers, blocks):
f_feat, b_feat, u_feat, v_feat = layer(block,
f_feat[:block.num_edges(etype='forward')],
b_feat[:block.num_edges(etype='backward')],
u_feat,
v_feat)
# return the result of final prediction layer
return self.predictor(subgraph, f_feat[:subgraph.num_edges(etype='forward')], u_feat, v_feat)