/
hscrf_layer.py
309 lines (251 loc) · 13.7 KB
/
hscrf_layer.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
from __future__ import print_function, division
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import utils
class HSCRF(nn.Module):
def __init__(self, tag_to_ix, word_rep_dim=300, SCRF_feature_dim=100, index_embeds_dim=10, ALLOWED_SPANLEN=6, start_id=4, stop_id=5, noBIES=False, no_index=False, no_sub=False, grconv=False):
super(HSCRF, self).__init__()
self.tag_to_ix = tag_to_ix
self.ix_to_tag = {v:k for k,v in self.tag_to_ix.items()}
self.tagset_size = len(tag_to_ix)
self.index_embeds_dim = index_embeds_dim
self.SCRF_feature_dim = SCRF_feature_dim
self.ALLOWED_SPANLEN = ALLOWED_SPANLEN
self.start_id = start_id
self.stop_id = stop_id
self.grconv = grconv
self.index_embeds = nn.Embedding(self.ALLOWED_SPANLEN, self.index_embeds_dim)
self.init_embedding(self.index_embeds.weight)
self.dense = nn.Linear(word_rep_dim, self.SCRF_feature_dim)
self.init_linear(self.dense)
# 4 for SBIE, 3 for START, STOP, O and 2 for START and O
self.CRF_tagset_size = 4*(self.tagset_size-3)+2
self.transition = nn.Parameter(
torch.zeros(self.tagset_size, self.tagset_size))
span_word_embedding_dim = 2*self.SCRF_feature_dim + self.index_embeds_dim
self.new_hidden2CRFtag = nn.Linear(span_word_embedding_dim, self.CRF_tagset_size)
self.init_linear(self.new_hidden2CRFtag)
if self.grconv:
self.Wl = nn.Linear(self.SCRF_feature_dim, self.SCRF_feature_dim)
self.Wr = nn.Linear(self.SCRF_feature_dim, self.SCRF_feature_dim)
self.Gl = nn.Linear(self.SCRF_feature_dim, 3*self.SCRF_feature_dim)
self.Gr = nn.Linear(self.SCRF_feature_dim, 3*self.SCRF_feature_dim)
self.toSCRF = nn.Linear(self.SCRF_feature_dim, self.tagset_size)
self.init_linear(self.Wl)
self.init_linear(self.Wr)
self.init_linear(self.Gl)
self.init_linear(self.Gr)
self.init_linear(self.toSCRF)
def init_embedding(self, input_embedding):
"""
Initialize embedding
"""
bias = np.sqrt(3.0 / input_embedding.size(1))
nn.init.uniform(input_embedding, -bias, bias)
def init_linear(self, input_linear):
"""
Initialize linear transformation
"""
bias = np.sqrt(6.0 / (input_linear.weight.size(0) + input_linear.weight.size(1)))
nn.init.uniform(input_linear.weight, -bias, bias)
if input_linear.bias is not None:
input_linear.bias.data.zero_()
def get_logloss_denominator(self, scores, mask):
"""
calculate all path scores of SCRF with dynamic programming
args:
scores (batch_size, sent_len, sent_len, self.tagset_size, self.tagset_size) : features for SCRF
mask (batch_size) : mask for words
"""
logalpha = Variable(torch.FloatTensor(self.batch_size, self.sent_len+1, self.tagset_size).fill_(-10000.)).cuda()
logalpha[:, 0, self.start_id] = 0.
istarts = [0] * self.ALLOWED_SPANLEN + range(self.sent_len - self.ALLOWED_SPANLEN+1)
for i in range(1, self.sent_len+1):
tmp = scores[:, istarts[i]:i, i-1] + \
logalpha[:, istarts[i]:i].unsqueeze(3).expand(self.batch_size, i - istarts[i], self.tagset_size, self.tagset_size)
tmp = tmp.transpose(1, 3).contiguous().view(self.batch_size, self.tagset_size, (i-istarts[i])*self.tagset_size)
max_tmp, _ = torch.max(tmp, dim=2)
tmp = tmp - max_tmp.view(self.batch_size, self.tagset_size, 1)
logalpha[:, i] = max_tmp + torch.log(torch.sum(torch.exp(tmp), dim=2))
mask = mask.unsqueeze(1).unsqueeze(1).expand(self.batch_size, 1, self.tagset_size)
alpha = torch.gather(logalpha, 1, mask).squeeze(1)
return alpha[:,self.stop_id].sum()
def decode(self, factexprscalars, mask):
"""
decode SCRF labels with dynamic programming
args:
factexprscalars (batch_size, sent_len, sent_len, self.tagset_size, self.tagset_size) : features for SCRF
mask (batch_size) : mask for words
"""
batch_size = factexprscalars.size(0)
sentlen = factexprscalars.size(1)
factexprscalars = factexprscalars.data
logalpha = torch.FloatTensor(batch_size, sentlen+1, self.tagset_size).fill_(-10000.).cuda()
logalpha[:, 0, self.start_id] = 0.
starts = torch.zeros((batch_size, sentlen, self.tagset_size)).cuda()
ys = torch.zeros((batch_size, sentlen, self.tagset_size)).cuda()
for j in range(1, sentlen + 1):
istart = 0
if j > self.ALLOWED_SPANLEN:
istart = max(0, j - self.ALLOWED_SPANLEN)
f = factexprscalars[:, istart:j, j - 1].permute(0, 3, 1, 2).contiguous().view(batch_size, self.tagset_size, -1) + \
logalpha[:, istart:j].contiguous().view(batch_size, 1, -1).expand(batch_size, self.tagset_size, (j - istart) * self.tagset_size)
logalpha[:, j, :], argm = torch.max(f, dim=2)
starts[:, j-1, :] = (argm / self.tagset_size + istart)
ys[:, j-1, :] = (argm % self.tagset_size)
batch_scores = []
batch_spans = []
for i in range(batch_size):
spans = []
batch_scores.append(max(logalpha[i, mask[i]-1]))
end = mask[i]-1
y = self.stop_id
while end >= 0:
start = int(starts[i, end, y])
y_1 = int(ys[i, end, y])
spans.append((start, end, y_1, y))
y = y_1
end = start - 1
batch_spans.append(spans)
return batch_spans, batch_scores
def get_logloss_numerator(self, goldfactors, scores, mask):
"""
get scores of best path
args:
goldfactors (batch_size, tag_len, 4) : path labels
scores (batch_size, sent_len, sent_len, self.tagset_size, self.tagset_size) : all tag scores
mask (batch_size, tag_len) : mask for goldfactors
"""
batch_size = scores.size(0)
sent_len = scores.size(1)
tagset_size = scores.size(3)
goldfactors = goldfactors[:, :, 0]*sent_len*tagset_size*tagset_size + goldfactors[:,:,1]*tagset_size*tagset_size+goldfactors[:,:,2]*tagset_size+goldfactors[:,:,3]
factorexprs = scores.view(batch_size, -1)
val = torch.gather(factorexprs, 1, goldfactors)
numerator = val.masked_select(mask)
return numerator
def grConv_scores(self, feats):
"""
calculate SCRF scores with grConv
args:
feats (batch_size, sentence_len, featsdim) : word representations
"""
scores = Variable(torch.zeros(self.batch_size, self.sent_len, self.sent_len, self.SCRF_feature_dim)).cuda()
diag0 = torch.LongTensor(range(self.sent_len)).cuda()
ht = feats
scores[:, diag0, diag0] = ht
if self.sent_len == 1:
return self.toSCRF(scores).unsqueeze(3) + self.transition.unsqueeze(0).unsqueeze(0).unsqueeze(0)
for span_len in range(1, min(self.ALLOWED_SPANLEN, self.sent_len)):
ht_1_l = ht[:, :-1]
ht_1_r = ht[:, 1:]
h_t_hat = 4 * nn.functional.sigmoid(self.Wl(ht_1_l) + self.Wr(ht_1_r)) - 2
w = torch.exp(self.Gl(ht_1_l) + self.Gr(ht_1_r)).view(self.batch_size, self.sent_len-span_len, 3, self.SCRF_feature_dim).permute(2,0,1,3)
w = w / w.sum(0).unsqueeze(0).expand(3, self.batch_size, self.sent_len-span_len, self.SCRF_feature_dim)
ht = w[0]*h_t_hat + w[1]*ht_1_l + w[2]*ht_1_r
scores[:, diag0[:-span_len], diag0[span_len:]] = ht
return self.toSCRF(scores).unsqueeze(3) + self.transition.unsqueeze(0).unsqueeze(0).unsqueeze(0)
def HSCRF_scores(self, feats):
### TODO: need to improve
"""
calculate SCRF scores with HSCRF
args:
feats (batch_size, sentence_len, featsdim) : word representations
"""
# 3 for O, STOP, START
validtag_size = self.tagset_size-3
scores = Variable(torch.zeros(self.batch_size, self.sent_len, self.sent_len, self.tagset_size, self.tagset_size)).cuda()
diag0 = torch.LongTensor(range(self.sent_len)).cuda()
# m10000 for STOP
m10000 = Variable(torch.FloatTensor([-10000.]).expand(self.batch_size, self.sent_len, self.tagset_size, 1)).cuda()
# m30000 for STOP, START, O
m30000 = Variable(torch.FloatTensor([-10000.]).expand(self.batch_size, self.sent_len, self.tagset_size, 3)).cuda()
for span_len in range(min(self.ALLOWED_SPANLEN, self.sent_len)):
emb_x = self.concat_features(feats, span_len)
emb_x = self.new_hidden2CRFtag(emb_x)
if span_len == 0:
tmp = torch.cat((self.transition[:, :validtag_size].unsqueeze(0).unsqueeze(0) + emb_x[:, 0, :, :validtag_size].unsqueeze(2),
m10000,
self.transition[:, -2:].unsqueeze(0).unsqueeze(0) + emb_x[:, 0, :, -2:].unsqueeze(2)), 3)
scores[:, diag0, diag0] = tmp
elif span_len == 1:
tmp = torch.cat((self.transition[:, :validtag_size].unsqueeze(0).unsqueeze(0).expand(self.batch_size, self.sent_len-1, self.tagset_size, validtag_size) + \
(emb_x[:, 0, :, validtag_size:2*validtag_size] +
emb_x[:, 1, :, 3*validtag_size:4*validtag_size]).unsqueeze(2), m30000[:, 1:]), 3)
scores[:, diag0[:-1], diag0[1:]] = tmp
elif span_len == 2:
tmp = torch.cat((self.transition[:, :validtag_size].unsqueeze(0).unsqueeze(0).expand(self.batch_size, self.sent_len-2, self.tagset_size, validtag_size) + \
(emb_x[:, 0, :, validtag_size:2*validtag_size] +
emb_x[:, 1, :, 2*validtag_size:3*validtag_size] +
emb_x[:, 2, :, 3*validtag_size:4*validtag_size]).unsqueeze(2), m30000[:, 2:]), 3)
scores[:, diag0[:-2], diag0[2:]] = tmp
elif span_len >= 3:
tmp0 = self.transition[:, :validtag_size].unsqueeze(0).unsqueeze(0).expand(self.batch_size, self.sent_len-span_len, self.tagset_size, validtag_size) + \
(emb_x[:, 0, :, validtag_size:2*validtag_size] +
emb_x[:, 1:span_len, :, 2*validtag_size:3*validtag_size].sum(1) +
emb_x[:, span_len,:, 3*validtag_size:4*validtag_size]).unsqueeze(2)
tmp = torch.cat((tmp0, m30000[:, span_len:]), 3)
scores[:, diag0[:-span_len], diag0[span_len:]] = tmp
return scores
def concat_features(self, emb_z, span_len):
"""
concatenate two features
args:
emb_z (batch_size, sentence_len, featsdim) : word representations
span_len: a number (from 0)
"""
batch_size = emb_z.size(0)
sent_len = emb_z.size(1)
hidden_dim = emb_z.size(2)
emb_z = emb_z.unsqueeze(1).expand(batch_size, sent_len, sent_len, hidden_dim)
new_emb_z1 = [emb_z[:, i:i + 1, i:i + span_len + 1] for i in range(sent_len - span_len)]
new_emb_z1 = torch.cat(new_emb_z1, 1)
new_emb_z2 = (new_emb_z1[:, :, 0]-new_emb_z1[:, :, span_len]).unsqueeze(2).expand(batch_size, sent_len-span_len, span_len+1, hidden_dim)
index = Variable(torch.LongTensor(range(span_len+1))).cuda()
index = self.index_embeds(index).unsqueeze(0).unsqueeze(0).expand(batch_size, sent_len-span_len, span_len+1, self.index_embeds_dim)
new_emb = torch.cat((new_emb_z1, new_emb_z2, index), 3).transpose(1,2).contiguous()
return new_emb
def forward(self, feats, mask_word, tags, mask_tag):
"""
calculate loss
args:
feats (batch_size, sent_len, featsdim) : word representations
mask_word (batch_size) : sentence lengths
tags (batch_size, tag_len, 4) : target
mask_tag (batch_size, tag_len) : tag_len <= sentence_len
"""
self.batch_size = feats.size(0)
self.sent_len = feats.size(1)
feats = self.dense(feats)
if self.grconv:
self.SCRF_scores = self.grConv_scores(feats)
else:
self.SCRF_scores = self.HSCRF_scores(feats)
forward_score = self.get_logloss_denominator(self.SCRF_scores, mask_word)
numerator = self.get_logloss_numerator(tags, self.SCRF_scores, mask_tag)
return (forward_score - numerator.sum()) / self.batch_size
def get_scrf_decode(self, feats, mask):
"""
decode with SCRF
args:
feats (batch_size, sent_len, featsdim) : word representations
mask (batch_size) : mask for words
"""
self.batch_size = feats.size(0)
self.sent_len = feats.size(1)
feats = self.dense(feats)
if self.grconv:
self.SCRF_scores = self.grConv_scores(feats)
else:
self.SCRF_scores = self.HSCRF_scores(feats)
batch_spans, batch_scores = self.decode(self.SCRF_scores, mask)
batch_answer = self.tuple_to_seq(batch_spans)
return batch_answer, np.array(batch_scores)
def tuple_to_seq(self, batch_spans):
batch_answer = []
for spans in batch_spans:
answer = utils.tuple_to_seq_BIOES(spans, self.ix_to_tag)
batch_answer.append(answer[:-1])
return batch_answer