-
Notifications
You must be signed in to change notification settings - Fork 0
/
VIB_2_0.py
184 lines (159 loc) · 7.33 KB
/
VIB_2_0.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
import copy
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from utils import constant
from utils.vocab import Vocab
SMALL = 1e-08
# 0: tokens, 1: pos, 2: mask_s, 3: labels
class ToyNet(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.K = args.K
self.rnn_hidden = args.rnn_hidden
self.max_sent_len = args.max_sent_len
print("loading pretrained emb......")
self.emb_matrix = np.load(args.dset_dir+'/'+args.dataset+'/embedding.npy')
print("loading dataset vocab......")
self.vocab = Vocab(args.dset_dir+'/'+args.dataset+'/vocab.pkl')
# create embedding layers
self.emb = nn.Embedding(self.vocab.size, args.emb_dim, padding_idx=constant.PAD_ID)
self.pos_emb = nn.Embedding(len(constant.POS_TO_ID), args.pos_dim) if args.pos_dim > 0 else None
# initialize embedding with pretrained word embeddings
self.init_embeddings()
# dropout
self.input_dropout = nn.Dropout(args.input_dropout)
# define r distribution
self.r_var = self.K * self.max_sent_len
self.r_mean = nn.Parameter(torch.randn(self.max_sent_len, self.K))
self.r_std = nn.Parameter(torch.randn(self.max_sent_len, self.K))
# define encoder
self.BiLSTM = LSTMRelationModel(args)
self.layer1 = nn.Linear(self.rnn_hidden*2, 500)
self.hidden2mean = nn.Linear(500, self.K)
self.hidden2std = nn.Linear(500, self.K)
# decoder
self.rc_cla = nn.Linear(args.K*2, len(constant.LABEL_TO_ID))
self.ner_cla = nn.Linear(args.K, len(constant.BIO_TO_ID))
self.logsoft_fn = nn.LogSoftmax(dim=3)
def init_embeddings(self):
if self.emb_matrix is None:
self.emb.weight.data[1:, :].uniform_(-1.0, 1.0)
else:
self.emb_matrix = torch.from_numpy(self.emb_matrix)
self.emb.weight.data.copy_(self.emb_matrix)
def get_statistics_batch(self, embeds):
mean = self.hidden2mean(embeds) # bsz, seqlen, dim
std = self.hidden2std(embeds) # bsz, seqlen, dim
cov = std * std + SMALL
return mean, cov
def get_sample_from_param_batch(self, mean, cov, sample_size):
bsz, seqlen, tag_dim = mean.shape
z = torch.randn(bsz, sample_size, seqlen, tag_dim).cuda()
z = z * torch.sqrt(cov).unsqueeze(1).expand(-1, sample_size, -1, -1) + \
mean.unsqueeze(1).expand(-1, sample_size, -1, -1)
return z
def kl_div(self, param1, param2, real_len, mask_kl):
"""
Calculates the KL divergence between a categorical distribution and a
uniform categorical distribution.
Parameters
----------
alpha : torch.Tensor
Parameters of the categorical or gumbel-softmax distribution.
Shape (N, D)
"""
mean1, cov1 = param1
mean2, cov2 = param2
bsz, seqlen, tag_dim = mean1.shape
var_len = tag_dim * real_len
cov2_inv = 1 / cov2
mean_diff = mean1 - mean2
mean_diff = mean_diff.view(bsz, -1)
cov1 = cov1.view(bsz, -1)
cov2 = cov2.view(bsz, -1)
cov2_inv = cov2_inv.view(bsz, -1)
mask_kl = mask_kl.view(bsz, -1)
temp = (mean_diff * cov2_inv*mask_kl).view(bsz, 1, -1)
KL = 0.5 * (torch.sum(torch.log(cov2)*mask_kl ,dim=1) - torch.sum(torch.log(cov1)*mask_kl, dim=1) - var_len
+ torch.sum(cov2_inv * cov1*mask_kl, dim=1) + torch.bmm(temp, mean_diff.view(bsz, -1, 1)).view(bsz))
return KL
# 0: tokens, 1: pos, 2: mask_s
def forward(self, inputs, num_sample=1):
# construct input feature X
tokens, pos, mask_s = inputs
tokens_emb = self.emb(tokens)
tokens_emb = [tokens_emb]
if self.args.pos_dim > 0:
tokens_emb += [self.pos_emb(pos)]
tokens_emb = torch.cat(tokens_emb, dim=2)
lens = mask_s.sum(dim=1)
tokens_emb = self.input_dropout(tokens_emb)
# forward into BiLSTM
temp = self.BiLSTM((tokens_emb, lens)) # bsz, len, K
temp = F.relu(self.layer1(temp))
# encode t
mean, cov = self.get_statistics_batch(temp)
encoding = self.get_sample_from_param_batch(mean, cov, num_sample)
# mask for output
s_len = encoding.size(2)
ner_mask = mask_s.unsqueeze(-1).expand(-1, -1, len(constant.BIO_TO_ID))
tmp_mask = mask_s.unsqueeze(-1).expand(-1, -1, len(constant.LABEL_TO_ID))
tmp_mask = tmp_mask.unsqueeze(1).expand(-1, s_len, -1, -1)
rc_mask = torch.zeros_like(tmp_mask)
real_len = mask_s.sum(dim=1).int()
for i in range(tmp_mask.size(0)):
rc_mask[i, :real_len[i], :real_len[i], :] = tmp_mask[i, :real_len[i], :real_len[i], :]
ner_mask = ner_mask.unsqueeze(1).expand(-1, num_sample, -1, -1)
rc_mask = rc_mask.unsqueeze(1).expand(-1, num_sample, -1, -1, -1)
# ner prediction
ner_logit = self.ner_cla(encoding)
ner_logit = self.logsoft_fn(ner_logit)
ner_logit = ner_logit * ner_mask
# rc prediction
encoding_e1 = encoding.unsqueeze(3).expand(-1, -1, -1, s_len, -1) # bsz, sample_num, len, len, K
encoding_e2 = encoding.unsqueeze(2).expand(-1, -1, s_len, -1, -1)
encoding_e = torch.cat([encoding_e1, encoding_e2], dim=4)
rc_logit = torch.sigmoid(self.rc_cla(encoding_e))
rc_logit = rc_logit * rc_mask
# caculate KL divergence
seqlen, bsz = s_len, mask_s.size(0)
mask_kl = mask_s.unsqueeze(-1).repeat(1, 1, self.K)
mean_r = self.r_mean[:seqlen].unsqueeze(0).expand(bsz, -1, -1)
std_r = self.r_std[:seqlen].unsqueeze(0).expand(bsz, -1, -1)
cov_r = std_r * std_r + SMALL
mean, cov = mean, cov
mean_r, cov_r = mean_r, cov_r
kl_div = self.kl_div((mean, cov), (mean_r, cov_r), real_len, mask_kl)
return kl_div.mean(), ner_logit.view(-1, len(constant.BIO_TO_ID)), rc_logit.view(-1, len(constant.LABEL_TO_ID))
# BiLSTM model
class LSTMRelationModel(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.in_dim = args.emb_dim + args.pos_dim
self.rnn = nn.LSTM(self.in_dim, self.args.rnn_hidden, 1, batch_first=True, \
dropout=0, bidirectional=True)
def encode_with_rnn(self, rnn_inputs, seq_lens, batch_size):
h0, c0 = rnn_zero_state(batch_size, self.args.rnn_hidden, 1, True)
rnn_inputs = nn.utils.rnn.pack_padded_sequence(rnn_inputs, seq_lens, batch_first=True)
rnn_outputs, (ht, ct) = self.rnn(rnn_inputs, (h0, c0))
rnn_outputs, _ = nn.utils.rnn.pad_packed_sequence(rnn_outputs, batch_first=True)
return rnn_outputs
def forward(self, inputs):
# unpack inputs
inputs, lens = inputs[0], inputs[1]
return self.encode_with_rnn(inputs, lens, inputs.size()[0])
# Initialize zero state
def rnn_zero_state(batch_size, hidden_dim, num_layers, bidirectional=True, use_cuda=True):
total_layers = num_layers * 2 if bidirectional else num_layers
state_shape = (total_layers, batch_size, hidden_dim)
h0 = c0 = Variable(torch.zeros(*state_shape), requires_grad=False)
if use_cuda:
return h0.cuda(), c0.cuda()
else:
return h0, c0