-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathCailModelAlbert.py
201 lines (170 loc) · 9.73 KB
/
CailModelAlbert.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
from transformers import *
import torch
from torch.nn.functional import softmax
from torch import nn
from torch.nn import CrossEntropyLoss
import torch.nn.functional as F
VERY_NEGATIVE_NUMBER = -1e29
class CailModel(BertPreTrainedModel):
def __init__(self, config, answer_verification=True, hidden_dropout_prob=0.3):
super(CailModel, self).__init__(config)
self.albert = AlbertModel(config)
# TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version
# self.qa_dropout = nn.Dropout(config.hidden_dropout_prob)
self.qa_outputs = nn.Linear(config.hidden_size * 4, 2)
# self.apply(self.init_bert_weights)
self.answer_verification = answer_verification
if self.answer_verification:
self.retionale_outputs = nn.Linear(config.hidden_size * 4, 1)
self.unk_ouputs = nn.Linear(config.hidden_size, 1)
self.doc_att = nn.Linear(config.hidden_size * 4, 1)
self.yes_no_ouputs = nn.Linear(config.hidden_size * 4, 2)
self.ouputs_cls_3 = nn.Linear(config.hidden_size * 4, 3)
self.beta = 100
else:
# self.unk_yes_no_outputs_dropout = nn.Dropout(config.hidden_dropout_prob)
self.unk_yes_no_outputs = nn.Linear(config.hidden_size, 3)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, start_positions=None, end_positions=None,
unk_mask=None, yes_mask=None, no_mask=None):
sequence_output, pooled_output, all_hidden_states = self.albert(input_ids=input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask)
sequence_output = torch.cat((all_hidden_states[-4], all_hidden_states[-3], all_hidden_states[-2],
all_hidden_states[-1]), -1)
if self.answer_verification:
batch_size = sequence_output.size(0)
seq_length = sequence_output.size(1)
hidden_size = sequence_output.size(2)
sequence_output_matrix = sequence_output.view(batch_size * seq_length, hidden_size)
rationale_logits = self.retionale_outputs(sequence_output_matrix)
rationale_logits = F.softmax(rationale_logits)
# [batch, seq_len]
rationale_logits = rationale_logits.view(batch_size, seq_length)
# [batch, seq, hidden] [batch, seq_len, 1] = [batch, seq, hidden]
final_hidden = sequence_output * rationale_logits.unsqueeze(2)
sequence_output = final_hidden.view(batch_size * seq_length, hidden_size)
logits = self.qa_outputs(sequence_output).view(batch_size, seq_length, 2)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
# [000,11111] 1代表了文章
# [batch, seq_len] [batch, seq_len]
rationale_logits = rationale_logits * attention_mask.float()
# [batch, seq_len, 1] [batch, seq_len]
start_logits = start_logits * rationale_logits
end_logits = end_logits * rationale_logits
# unk
unk_logits = self.unk_ouputs(pooled_output)
# doc_attn
attention = self.doc_att(sequence_output)
attention = attention.view(batch_size, seq_length)
attention = attention * token_type_ids.float() + (1 - token_type_ids.float()) * VERY_NEGATIVE_NUMBER
attention = F.softmax(attention, 1)
attention = attention.unsqueeze(2)
attention_pooled_output = attention * final_hidden
attention_pooled_output = attention_pooled_output.sum(1)
yes_no_logits = self.yes_no_ouputs(attention_pooled_output)
yes_logits, no_logits = yes_no_logits.split(1, dim=-1)
# unk_yes_no_logits = self.ouputs_cls_3(attention_pooled_output)
# unk_logits, yes_logits, no_logits = unk_yes_no_logits.split(1, dim=-1)
else:
# sequence_output = self.qa_dropout(sequence_output)
logits = self.qa_outputs(sequence_output)
# self attention
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
# # unk yes_no_logits
# pooled_output = self.unk_yes_no_outputs_dropout(pooled_output)
unk_yes_no_logits = self.unk_yes_no_outputs(pooled_output)
unk_logits, yes_logits, no_logits = unk_yes_no_logits.split(1, dim=-1)
# # [batch, 1]
# unk_logits = unk_logits.squeeze(-1)
# yes_logits = yes_logits.squeeze(-1)
# no_logits = no_logits.squeeze(-1)
new_start_logits = torch.cat([start_logits, unk_logits, yes_logits, no_logits], 1)
new_end_logits = torch.cat([end_logits, unk_logits, yes_logits, no_logits], 1)
if self.answer_verification and start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = new_start_logits.size(1)
start_positions.clamp_(1, ignored_index)
end_positions.clamp_(1, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(new_start_logits, start_positions)
end_loss = loss_fct(new_end_logits, end_positions)
rationale_positions = token_type_ids.float()
alpha = 0.25
gamma = 2.
rationale_loss = -alpha * ((1 - rationale_logits) ** gamma) * rationale_positions * torch.log(
rationale_logits + 1e-8) - (1 - alpha) * (rationale_logits ** gamma) * (
1 - rationale_positions) * torch.log(1 - rationale_logits + 1e-8)
rationale_loss = (rationale_loss * token_type_ids.float()).sum() / token_type_ids.float().sum()
total_loss = (start_loss + end_loss) / 2 + rationale_loss * self.beta
# total_loss = (start_loss + end_loss) / 2
return total_loss
elif start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = new_start_logits.size(1)
start_positions.clamp_(1, ignored_index)
end_positions.clamp_(1, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(new_start_logits, start_positions)
end_loss = loss_fct(new_end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
return total_loss
else:
return start_logits, end_logits, unk_logits, yes_logits, no_logits
class MultiLinearLayer(nn.Module):
def __init__(self, layers, hidden_size, output_size, activation=None):
super(MultiLinearLayer, self).__init__()
self.net = nn.Sequential()
for i in range(layers - 1):
self.net.add_module(str(i) + 'linear', nn.Linear(hidden_size, hidden_size))
self.net.add_module(str(i) + 'relu', nn.ReLU(inplace=True))
self.net.add_module('linear', nn.Linear(hidden_size, output_size))
def forward(self, x):
return self.net(x)
if __name__ == '__main__':
pretrained_token = 'albert_model/'
tokenizer = BertTokenizer.from_pretrained(pretrained_token)
config = AlbertConfig.from_pretrained('albert_model/')
config.output_hidden_states = True
model = CailModel.from_pretrained('albert_model/')
inputtext = "今天心情很好"
inputtext2 = "今天心情很好"
maskpos = tokenizer.encode(inputtext, inputtext2, add_special_tokens=True)
input_ids = torch.tensor(tokenizer.encode(inputtext,inputtext2, add_special_tokens=True)).unsqueeze(0)
outputs = model(input_ids=input_ids,attention_mask=input_ids)
print(outputs)
# ______________________________________________
# pretrained_token = 'albert_model/'
# pretrained_config = 'albert_model/'
# pretrained_model = 'albert_model/'
# tokenizer = BertTokenizer.from_pretrained(pretrained_token)
# # config = AlbertConfig.from_pretrained(pretrained_config, output_hidden_states=True)
# # print(config.output_hidden_states)
# # model = AlbertForMaskedLM(config)
# model = AlbertForMaskedLM.from_pretrained(pretrained_model)
#
# inputtext = "今天[MASK]情很好"
# inputtext2 = "今天[MASK]情很好"
#
# maskpos = tokenizer.encode(inputtext, inputtext2,add_special_tokens=True).index(103)
#
# input_ids = torch.tensor(tokenizer.encode(inputtext, add_special_tokens=True)).unsqueeze(0) # Batch size 1
# outputs = model(input_ids, masked_lm_labels=input_ids)
# loss, prediction_scores = outputs[:2]
# logit_prob = softmax(prediction_scores[0, maskpos], -1).data.tolist()
# predicted_index = torch.argmax(prediction_scores[0, maskpos]).item()
# predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
# print(predicted_token, logit_prob[predicted_index])