/
TinyClinicalBERT-Distillation.py
191 lines (142 loc) · 6.12 KB
/
TinyClinicalBERT-Distillation.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
""" The code used for distillation of the TinyClinicalBERT.
It it partially taken from the implementation of the TinyBERT model at https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/TinyBERT
"""
import transformers as ts
from datasets import Dataset
from datasets import load_dataset, load_from_disk
import numpy as np
import numpy.core.defchararray as nchar
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.optim as optim
from transformers.modeling_outputs import MaskedLMOutput
import math
savePath = "clincalModels/models/TinyClinicalBERT"
teacherPath = "emilyalsentzer/Bio_ClinicalBERT"
ds = load_from_disk("tokenizedDatasets/mimic-large/") #Use the pre-processing code availabe in https://github.com/EmilyAlsentzer/clinicalBERT
tokenizer = ts.AutoTokenizer.from_pretrained(teacherPath)
teacher = ts.AutoModelForMaskedLM.from_pretrained(teacherPath)
#Use for random initialisation
studentConfig = ts.AutoModel.from_pretrained("huawei-noah/TinyBERT_General_4L_312D").config.to_dict()
studentConfig["vocab_size"] = teacher.config.vocab_size
student = ts.BertForMaskedLM(config=ts.BertConfig.from_dict(studentConfig))
#Use for initialising this model from biomedical checkpoint
#student = ts.AutoModelForMaskedLM.from_pretrained("nlpie/tiny-biobert")
for param in teacher.parameters():
param.requires_grad = False
print(tokenizer)
class DistillationWrapper(nn.Module):
def __init__(self, student, teacher):
super().__init__()
self.student = student
self.teacher = teacher
self.mse_loss = nn.MSELoss()
self.output_loss = nn.CrossEntropyLoss()
self.teacherDim = self.teacher.config.hidden_size
self.studentDim = self.student.config.hidden_size
self.fit_dense = nn.Linear(self.studentDim, self.teacherDim)
self.temperature = 1.0
def forward(self,
input_ids,
attention_mask=None,
labels=None,
**kargs):
student_outputs = self.student(input_ids=input_ids,
attention_mask=attention_mask,
labels=labels,
output_hidden_states=True,
output_attentions=True,
**kargs)
with torch.no_grad():
teacher_outputs = self.teacher(input_ids=input_ids,
attention_mask=attention_mask,
output_hidden_states=True,
output_attentions=True,
**kargs)
s_attentions = student_outputs["attentions"]
t_attentions = [att.detach() for att in teacher_outputs["attentions"]]
s_hiddens = student_outputs["hidden_states"]
t_hiddens = [hidden.detach() for hidden in teacher_outputs["hidden_states"]]
s_logits = student_outputs["logits"]
t_logits = teacher_outputs["logits"].detach()
att_loss = 0
rep_loss = 0
teacher_layer_num = len(t_attentions)
student_layer_num = len(s_attentions)
assert teacher_layer_num % student_layer_num == 0
layers_per_block = int(teacher_layer_num / student_layer_num)
new_teacher_atts = [t_attentions[i * layers_per_block + layers_per_block - 1]
for i in range(student_layer_num)]
for student_att, teacher_att in zip(s_attentions, new_teacher_atts):
att_loss += self.mse_loss(student_att, teacher_att)
new_teacher_reps = [t_hiddens[i * layers_per_block] for i in range(student_layer_num + 1)]
new_student_reps = s_hiddens
for student_rep, teacher_rep in zip(new_student_reps, new_teacher_reps):
rep_loss += self.mse_loss(self.fit_dense(student_rep), teacher_rep)
mask = (labels > -1).unsqueeze(-1).expand_as(s_logits).bool()
s_logits_slct = torch.masked_select(s_logits, mask)
s_logits_slct = s_logits_slct.view(-1, s_logits.size(-1))
t_logits_slct = torch.masked_select(t_logits, mask)
t_logits_slct = t_logits_slct.view(-1, s_logits.size(-1))
assert t_logits_slct.size() == s_logits_slct.size()
output_loss = self.output_loss(
(s_logits_slct / self.temperature),
nn.functional.softmax(t_logits_slct / self.temperature, dim=-1),
)
loss = (att_loss + rep_loss) + output_loss
return MaskedLMOutput(
loss=loss,
logits=student_outputs.logits,
hidden_states=student_outputs.hidden_states,
attentions=student_outputs.attentions,
)
model = DistillationWrapper(student=student, teacher=teacher)
count = 0
for name , param in model.named_parameters():
if param.requires_grad == True:
print(name)
count += param.numel()
print(count / 1e6)
data_collator = ts.DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True, mlm_probability=0.15, return_tensors="pt")
try:
with open(savePath + "logs.txt", "w+") as f:
f.write("")
except:
pass
class CustomCallback(ts.TrainerCallback):
def on_log(self, args, state, control, logs=None, **kwargs):
_ = logs.pop("total_flos", None)
if state.is_local_process_zero:
print(logs)
with open(savePath + "logs.txt", "a+") as f:
f.write(str(logs) + "\n")
trainingArguments = ts.TrainingArguments(
savePath + "checkpoints",
logging_steps=250,
overwrite_output_dir=True,
save_steps=2500,
num_train_epochs=3,
learning_rate=5e-4,
lr_scheduler_type="linear",
warmup_steps=5000,
per_gpu_train_batch_size=48,
weight_decay=1e-4,
save_total_limit=5,
remove_unused_columns=True,
)
trainer = ts.Trainer(
model=model,
args=trainingArguments,
train_dataset=ds,
data_collator=data_collator,
callbacks=[ts.ProgressCallback(), CustomCallback()],
)
trainer.train()
def load_and_save_pretrained(model, checkpoint_path, save_path):
print(model.load_state_dict(torch.load(checkpoint_path)))
model.student.save_pretrained(save_path)
return model
trainer.save_model(savePath + "final/rawModel/")
load_and_save_pretrained(model, savePath + "final/rawModel/pytorch_model.bin", savePath + "final/model/")