-
Notifications
You must be signed in to change notification settings - Fork 1
/
trainer_base.py
193 lines (153 loc) · 6.92 KB
/
trainer_base.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
from pathlib import Path
import torch
import math
from transformers import BartConfig
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim import AdamW
proj_dir = Path(__file__).resolve().parent.parent
class TrainerBase(object):
def __init__(self, args, train_loader=None, valid_loader=None, test_loader=None, tokenizer=None):
self.args = args
self.train_loader = train_loader
self.valid_loader = valid_loader
self.test_loader = test_loader
self.tokenizer = tokenizer
self.verbose = True
def create_config(self):
config_class = BartConfig
config = config_class.from_pretrained("cogint/in-boxbart")
args = self.args
config.dropout_rate = args.dropout
config.dropout = 0.1# args.dropout
config.attention_dropout = args.dropout
config.activation_dropout = args.dropout
return config
def create_model(self, model_class, config=None, **kwargs):
print(f'Building Model at GPU {self.args.gpu}')
model = model_class.from_pretrained(
"cogint/in-boxbart",
config=config,
**kwargs
)
return model
def create_optimizer_and_scheduler(self):
if self.verbose:
print('Building Optimizer')
lr_scheduler = None
batch_per_epoch = len(self.train_loader)
t_total = batch_per_epoch * self.args.epochs
no_decay = ["bias",
"LayerNorm.bias",
"LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay) and p.requires_grad],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay) and p.requires_grad],
"weight_decay": 0.0,
},
]
optim = AdamW(optimizer_grouped_parameters,
lr=self.args.lr, eps=self.args.adam_eps, betas=(0.9, 0.98))
lr_scheduler = CosineAnnealingWarmupRestarts(
optim,
first_cycle_steps=t_total,
cycle_mult=self.args.lr_mul,
max_lr=self.args.lr,
min_lr=self.args.min_lr,
warmup_steps=self.args.warmup_steps,
)
return optim, lr_scheduler
def load_checkpoint(self, ckpt_path):
print("Load model from %s" % ckpt_path)
pretrained_dict = torch.load("%s.pth" % ckpt_path)
model_dict = self.model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
self.model.load_state_dict(model_dict, strict=False)
def predict(self):
pass
def evaluate(self):
pass
def save(self):
pass
def load(self):
pass
# Source: https://github.com/katsura-jp/pytorch-cosine-annealing-with-warmup'
class CosineAnnealingWarmupRestarts(_LRScheduler):
"""
optimizer (Optimizer): Wrapped optimizer.
first_cycle_steps (int): First cycle step size.
cycle_mult(float): Cycle steps magnification. Default: -1.
max_lr(float): First cycle's max learning rate. Default: 0.1.
min_lr(float): Min learning rate. Default: 0.001.
warmup_steps(int): Linear warmup step size. Default: 0.
gamma(float): Decrease rate of max learning rate by cycle. Default: 1.
last_epoch (int): The index of last epoch. Default: -1.
"""
def __init__(self,
optimizer : torch.optim.Optimizer,
first_cycle_steps : int,
cycle_mult : float = 1.,
max_lr : float = 0.1,
min_lr : float = 0.001,
warmup_steps : int = 0,
gamma : float = 1.,
last_epoch : int = -1
):
assert warmup_steps < first_cycle_steps
self.first_cycle_steps = first_cycle_steps # first cycle step size
self.cycle_mult = cycle_mult # cycle steps magnification
self.base_max_lr = max_lr # first max learning rate
self.max_lr = max_lr # max learning rate in the current cycle
self.min_lr = min_lr # min learning rate
self.warmup_steps = warmup_steps # warmup step size
self.gamma = gamma # decrease rate of max learning rate by cycle
self.cur_cycle_steps = first_cycle_steps # first cycle step size
self.cycle = 0 # cycle count
self.step_in_cycle = last_epoch # step size of the current cycle
super(CosineAnnealingWarmupRestarts, self).__init__(optimizer, last_epoch)
# set learning rate min_lr
self.init_lr()
def init_lr(self):
self.base_lrs = []
for param_group in self.optimizer.param_groups:
param_group['lr'] = self.min_lr
self.base_lrs.append(self.min_lr)
def get_lr(self):
if self.step_in_cycle == -1:
return self.base_lrs
elif self.step_in_cycle < self.warmup_steps:
return [(self.max_lr - base_lr)*self.step_in_cycle / self.warmup_steps + base_lr for base_lr in self.base_lrs]
else:
return [base_lr + (self.max_lr - base_lr) \
* (1 + math.cos(math.pi * (self.step_in_cycle-self.warmup_steps) \
/ (self.cur_cycle_steps - self.warmup_steps))) / 2
for base_lr in self.base_lrs]
def step(self, epoch=None):
if epoch is None:
epoch = self.last_epoch + 1
self.step_in_cycle = self.step_in_cycle + 1
if self.step_in_cycle >= self.cur_cycle_steps:
self.cycle += 1
self.step_in_cycle = self.step_in_cycle - self.cur_cycle_steps
self.cur_cycle_steps = int((self.cur_cycle_steps - self.warmup_steps) * self.cycle_mult) + self.warmup_steps
else:
if epoch >= self.first_cycle_steps:
if self.cycle_mult == 1.:
self.step_in_cycle = epoch % self.first_cycle_steps
self.cycle = epoch // self.first_cycle_steps
else:
n = int(math.log((epoch / self.first_cycle_steps * (self.cycle_mult - 1) + 1), self.cycle_mult))
self.cycle = n
self.step_in_cycle = epoch - int(self.first_cycle_steps * (self.cycle_mult ** n - 1) / (self.cycle_mult - 1))
self.cur_cycle_steps = self.first_cycle_steps * self.cycle_mult ** (n)
else:
self.cur_cycle_steps = self.first_cycle_steps
self.step_in_cycle = epoch
self.max_lr = self.base_max_lr * (self.gamma**self.cycle)
self.last_epoch = math.floor(epoch)
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
param_group['lr'] = lr