-
Notifications
You must be signed in to change notification settings - Fork 148
/
Copy pathDiffMG.py
319 lines (277 loc) · 12.8 KB
/
DiffMG.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
import torch
import torch.nn as nn
import torch.nn.functional as F
from openhgnn.models import BaseModel, register_model
import numpy as np
@register_model('DiffMG')
class DiffMG(BaseModel):
@classmethod
def build_model_from_args(cls, args,hg):
args.search_model = search_model
return cls
def __init__(self, in_dims, n_hid, n_steps, dropout=None, attn_dim=64, use_norm=True, out_nl=True):
super(DiffMG, self).__init__()
self.n_hid = n_hid
self.ws = nn.ModuleList()
assert (isinstance(in_dims, list))
for i in range(len(in_dims)):
self.ws.append(nn.Linear(in_dims[i], n_hid))
assert (isinstance(n_steps, list))
self.metas = nn.ModuleList()
for i in range(len(n_steps)):
self.metas.append(Cell(n_steps[i], n_hid, n_hid, use_norm=use_norm, use_nl=out_nl))
# * [Optional] Combine more than one meta graph?
self.attn_fc1 = nn.Linear(n_hid, attn_dim)
self.attn_fc2 = nn.Linear(attn_dim, 1)
self.feats_drop = nn.Dropout(dropout) if dropout is not None else lambda x: x
def forward(self, node_feats, node_types, adjs, idxes_seq, idxes_res, gpu):
if gpu > -1:
hid = torch.zeros((len(node_types), self.n_hid)).cuda()
else:
hid = torch.zeros((len(node_types), self.n_hid))
for i in range(len(node_feats)):
hid[node_types == i] = self.ws[i](node_feats[i])
hid = self.feats_drop(hid)
temps = [];
attns = []
for i, meta in enumerate(self.metas):
hidi = meta(hid, adjs, idxes_seq[i], idxes_res[i])
temps.append(hidi)
attni = self.attn_fc2(torch.tanh(self.attn_fc1(temps[-1])))
attns.append(attni)
hids = torch.stack(temps, dim=0).transpose(0, 1)
attns = F.softmax(torch.cat(attns, dim=-1), dim=-1)
out = (attns.unsqueeze(dim=-1) * hids).sum(dim=1)
return out
class Op(nn.Module):
def __init__(self):
super(Op, self).__init__()
# 定义图卷积的操作，其中adjs是邻接矩阵的列表，idx是当前需要使用的邻接矩阵的索引。
def forward(self, x, adjs, idx):
return torch.spmm(adjs[idx], x)
class Cell(nn.Module):
def __init__(self, n_step, n_hid_prev, n_hid, use_norm=True, use_nl=True):
super(Cell, self).__init__()
self.affine = nn.Linear(n_hid_prev, n_hid)
self.n_step = n_step
self.norm = nn.LayerNorm(n_hid) if use_norm is True else lambda x: x
self.use_nl = use_nl
self.ops_seq = nn.ModuleList()
self.ops_res = nn.ModuleList()
for i in range(self.n_step):
self.ops_seq.append(Op())
for i in range(1, self.n_step):
for j in range(i):
self.ops_res.append(Op())
def forward(self, x, adjs, idxes_seq, idxes_res):
x = self.affine(x)
states = [x]
offset = 0
for i in range(self.n_step):
seqi = self.ops_seq[i](states[i], adjs[:-1], idxes_seq[i]) # ! exclude zero Op
resi = sum(self.ops_res[offset + j](h, adjs, idxes_res[offset + j]) for j, h in enumerate(states[:i]))
offset += i
states.append(seqi + resi)
# assert(offset == len(self.ops_res))
output = self.norm(states[-1])
if self.use_nl:
output = F.gelu(output)
return output
class Op1(nn.Module):
'''
operation for one link in the DAG search space
'''
def __init__(self):
super(Op1, self).__init__()
def forward(self, x, adjs, ws, idx):
# assert(ws.size(0) == len(adjs))
return ws[idx] * torch.spmm(adjs[idx], x)
class Cell1(nn.Module):
'''
the DAG search space
'''
def __init__(self, n_step, n_hid_prev, n_hid, cstr, use_norm=True, use_nl=True):
super(Cell1, self).__init__()
self.affine = nn.Linear(n_hid_prev, n_hid)
self.n_step = n_step # * number of intermediate states (i.e., K)
self.norm = nn.LayerNorm(n_hid, elementwise_affine=False) if use_norm is True else lambda x: x
self.use_nl = use_nl
assert (isinstance(cstr, list))
self.cstr = cstr # * type constraint
self.ops_seq = nn.ModuleList() # * state (i - 1) -> state i, 1 <= i < K
for i in range(1, self.n_step):
self.ops_seq.append(Op1())
self.ops_res = nn.ModuleList() # * state j -> state i, 0 <= j < i - 1, 2 <= i < K
for i in range(2, self.n_step):
for j in range(i - 1):
self.ops_res.append(Op1())
self.last_seq = Op1() # * state (K - 1) -> state K
self.last_res = nn.ModuleList() # * state i -> state K, 0 <= i < K - 1
for i in range(self.n_step - 1):
self.last_res.append(Op1())
def forward(self, x, adjs, ws_seq, idxes_seq, ws_res, idxes_res):
# assert(isinstance(ws_seq, list))
# assert(len(ws_seq) == 2)
x = self.affine(x)
states = [x]
offset = 0
for i in range(self.n_step - 1):
seqi = self.ops_seq[i](states[i], adjs[:-1], ws_seq[0][i], idxes_seq[0][i]) # ! exclude zero Op
resi = sum(self.ops_res[offset + j](h, adjs, ws_res[0][offset + j], idxes_res[0][offset + j]) for j, h in
enumerate(states[:i]))
offset += i
states.append(seqi + resi)
# assert(offset == len(self.ops_res))
adjs_cstr = [adjs[i] for i in self.cstr]
out_seq = self.last_seq(states[-1], adjs_cstr, ws_seq[1], idxes_seq[1])
adjs_cstr.append(adjs[-1])
out_res = sum(self.last_res[i](h, adjs_cstr, ws_res[1][i], idxes_res[1][i]) for i, h in enumerate(states[:-1]))
output = self.norm(out_seq + out_res)
if self.use_nl:
output = F.gelu(output)
return output
class search_model(nn.Module):
def __init__(self, in_dims, n_hid, n_adjs, n_steps, cstr, attn_dim=64, use_norm=True, out_nl=True):
super(search_model, self).__init__()
self.cstr = cstr
self.n_adjs = n_adjs
self.n_hid = n_hid
self.ws = nn.ModuleList() # * node type-specific transformation
assert (isinstance(in_dims, list))
for i in range(len(in_dims)):
self.ws.append(nn.Linear(in_dims[i], n_hid))
assert (isinstance(n_steps, list)) # * [optional] combine more than one meta graph?
self.metas = nn.ModuleList()
for i in range(len(n_steps)):
self.metas.append(Cell1(n_steps[i], n_hid, n_hid, cstr, use_norm=use_norm, use_nl=out_nl))
self.as_seq = [] # * arch parameters for ops_seq
self.as_last_seq = [] # * arch parameters for last_seq
for i in range(len(n_steps)):
if n_steps[i] > 1:
ai = 1e-3 * torch.randn(n_steps[i] - 1, n_adjs - 1) # ! exclude zero Op
ai = ai
ai.requires_grad_(True)
self.as_seq.append(ai)
else:
self.as_seq.append(None)
ai_last = 1e-3 * torch.randn(len(cstr))
ai_last = ai_last
ai_last.requires_grad_(True)
self.as_last_seq.append(ai_last)
ks = [sum(1 for i in range(2, n_steps[k]) for j in range(i - 1)) for k in range(len(n_steps))]
self.as_res = [] # * arch parameters for ops_res
self.as_last_res = [] # * arch parameters for last_res
for i in range(len(n_steps)):
if ks[i] > 0:
ai = 1e-3 * torch.randn(ks[i], n_adjs)
ai = ai
ai.requires_grad_(True)
self.as_res.append(ai)
else:
self.as_res.append(None)
if n_steps[i] > 1:
ai_last = 1e-3 * torch.randn(n_steps[i] - 1, len(cstr) + 1)
ai_last = ai_last
ai_last.requires_grad_(True)
self.as_last_res.append(ai_last)
else:
self.as_last_res.append(None)
assert (ks[0] + n_steps[0] + (0 if self.as_last_res[0] is None else self.as_last_res[0].size(0)) == (
1 + n_steps[0]) * n_steps[0] // 2)
# * [optional] combine more than one meta graph?
self.attn_fc1 = nn.Linear(n_hid, attn_dim)
self.attn_fc2 = nn.Linear(attn_dim, 1)
def alphas(self):
alphas = []
for each in self.as_seq:
if each is not None:
alphas.append(each)
for each in self.as_last_seq:
alphas.append(each)
for each in self.as_res:
if each is not None:
alphas.append(each)
for each in self.as_last_res:
if each is not None:
alphas.append(each)
return alphas
def sample(self, eps):
'''
to sample one candidate edge type per link
'''
idxes_seq = []
idxes_res = []
if np.random.uniform() < eps:
for i in range(len(self.metas)):
temp = []
temp.append(None if self.as_seq[i] is None else torch.randint(low=0, high=self.as_seq[i].size(-1),
size=self.as_seq[i].size()[:-1]))
temp.append(torch.randint(low=0, high=self.as_last_seq[i].size(-1), size=(1,)))
idxes_seq.append(temp)
for i in range(len(self.metas)):
temp = []
temp.append(None if self.as_res[i] is None else torch.randint(low=0, high=self.as_res[i].size(-1),
size=self.as_res[i].size()[:-1]))
temp.append(
None if self.as_last_res[i] is None else torch.randint(low=0, high=self.as_last_res[i].size(-1),
size=self.as_last_res[i].size()[:-1]))
idxes_res.append(temp)
else:
for i in range(len(self.metas)):
temp = []
temp.append(None if self.as_seq[i] is None else torch.argmax(F.softmax(self.as_seq[i], dim=-1), dim=-1))
temp.append(torch.argmax(F.softmax(self.as_last_seq[i], dim=-1), dim=-1))
idxes_seq.append(temp)
for i in range(len(self.metas)):
temp = []
temp.append(None if self.as_res[i] is None else torch.argmax(F.softmax(self.as_res[i], dim=-1), dim=-1))
temp.append(
None if self.as_last_res[i] is None else torch.argmax(F.softmax(self.as_last_res[i], dim=-1),
dim=-1))
idxes_res.append(temp)
return idxes_seq, idxes_res
def forward(self, node_feats, node_types, adjs, idxes_seq, idxes_res):
hid = torch.zeros((len(node_types), self.n_hid))
for i in range(len(node_feats)):
hid[node_types == i] = self.ws[i](node_feats[i])
temps = [];
attns = []
for i, meta in enumerate(self.metas):
ws_seq = []
ws_seq.append(None if self.as_seq[i] is None else F.softmax(self.as_seq[i], dim=-1))
ws_seq.append(F.softmax(self.as_last_seq[i], dim=-1))
ws_res = []
ws_res.append(None if self.as_res[i] is None else F.softmax(self.as_res[i], dim=-1))
ws_res.append(None if self.as_last_res[i] is None else F.softmax(self.as_last_res[i], dim=-1))
hidi = meta(hid, adjs, ws_seq, idxes_seq[i], ws_res, idxes_res[i])
temps.append(hidi)
attni = self.attn_fc2(torch.tanh(self.attn_fc1(temps[-1])))
attns.append(attni)
hids = torch.stack(temps, dim=0).transpose(0, 1)
attns = F.softmax(torch.cat(attns, dim=-1), dim=-1)
out = (attns.unsqueeze(dim=-1) * hids).sum(dim=1)
return out
def parse(self):
'''
to derive a meta graph indicated by arch parameters
'''
idxes_seq, idxes_res = self.sample(0.)
msg_seq = [];
msg_res = []
for i in range(len(idxes_seq)):
map_seq = [self.cstr[idxes_seq[i][1].item()]]
msg_seq.append(map_seq if idxes_seq[i][0] is None else idxes_seq[i][0].tolist() + map_seq)
assert (len(msg_seq[i]) == self.metas[i].n_step)
temp_res = []
if idxes_res[i][1] is not None:
for item in idxes_res[i][1].tolist():
if item < len(self.cstr):
temp_res.append(self.cstr[item])
else:
assert (item == len(self.cstr))
temp_res.append(self.n_adjs - 1)
if idxes_res[i][0] is not None:
temp_res = idxes_res[i][0].tolist() + temp_res
assert (len(temp_res) == self.metas[i].n_step * (self.metas[i].n_step - 1) // 2)
msg_res.append(temp_res)
return msg_seq, msg_res