-
Notifications
You must be signed in to change notification settings - Fork 126
/
train.py
233 lines (173 loc) · 6.86 KB
/
train.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
import torch
import torch.nn as nn
import torch.nn.functional as F
import src.data.config as cfg
import src.data.data as data
import src.train.utils as train_utils
import src.train.batch as batch
import src.evaluate.evaluate as evaluate
import src.evaluate.generate as gen
import src.evaluate.sampler as sampling
import utils.utils as utils
from tensorboardX import SummaryWriter
class Trainer(object):
def __init__(self, opt, meta, data_loader, model, optimizer):
self.optimizer = optimizer
self.model = model
if opt.trainer == "epoch":
self.epochs = meta.epochs
self.data_loader = data_loader
self.opt = opt
self.losses = {"dev": {}, "test": {}, "train": {}}
self.top_score = None
self.lrs = {}
self.batch_variables = {
"data": self.data_loader,
"model": self.model,
"split": "train"
}
self.do_gen = cfg.do_gen
self.samplers = {}
def decide_to_save(self):
to_save = cfg.save and not cfg.toy
to_save = to_save or cfg.test_save
print(cfg.save_strategy)
if cfg.save_strategy == "best":
if self.top_score[0] != self.opt.train.dynamic.epoch:
print("DOING IT RIGHT")
to_save = False
return to_save
def save_model(self, tracked_score):
lrs = {}
for i, param_group in enumerate(self.optimizer.param_groups):
lrs[i] = param_group['lr']
self.lrs[self.opt.train.dynamic.epoch] = lrs
to_save = self.decide_to_save()
if to_save:
data.save_step(
self.model, self.data_loader.vocab_encoder,
self.optimizer, self.opt,
self.opt.train.dynamic.epoch, self.lrs)
def log_losses(self, opt, losses):
if (not cfg.toy and cfg.save) or cfg.test_save:
data.save_eval_file(opt, losses["train"], "losses", split="train")
data.save_eval_file(opt, losses['dev'], "losses", split="dev")
data.save_eval_file(opt, losses['test'], "losses", split="test")
def set_logger(self):
if cfg.toy:
self.logger = SummaryWriter(utils.make_name(
self.opt, prefix="garbage/logs/", eval_=True, do_epoch=False))
else:
self.logger = SummaryWriter(utils.make_name(
self.opt, prefix="logs/", eval_=True, do_epoch=False))
print("Logging Tensorboard Files at: {}".format(self.logger.logdir))
def stop_logger(self):
self.logger.close()
def run(self):
self.set_logger()
self.count = 0
for epoch in range(self.epochs):
self.model.train()
self.opt.train.dynamic.epoch += 1
self.epoch()
self.stop_logger()
def epoch(self):
nums = self.reset_losses()
# Initialize progress bar
bar = utils.initialize_progress_bar(
self.data_loader.sequences["train"])
reset = False
while not reset:
loss, nums, reset = self.do_forward_pass(nums)
self.do_backward_pass(loss)
self.update_parameters()
bar.update(self.opt.train.dynamic.bs)
self.count += 1
for loss_name in self.losses["train"]:
self.logger.add_scalar(
"train/{}".format(loss_name),
loss.item() / self.opt.train.dynamic.bs,
self.count)
if cfg.toy and self.counter(nums) > 300:
break
with torch.no_grad():
self.run_evaluation_cycle()
self.log_losses(self.opt, self.losses)
self.update_top_score(self.opt)
self.save_model(self.get_tracked_score())
self.data_loader.reset_offsets("train")
def run_evaluation_cycle(self):
for split in ["dev", "test"]:
self.evaluator.validate(
self.opt.train.dynamic.epoch, split,
self.losses[split])
if self.do_gen:
gen.do_gen_run(
self.opt, self.generator, self.opt.train.dynamic.epoch,
split, self.losses[split])
iter_num = self.opt.train.dynamic.epoch
for loss_name in self.losses[split]:
self.logger.add_scalar(
"{}/{}".format(split, loss_name),
self.losses[split][loss_name][iter_num],
iter_num)
def clip_gradients(self):
if self.opt.train.static.clip:
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), self.opt.train.static.clip)
def do_forward_pass(self, nums):
token_loss, nums, reset = self.batch(
self.opt, nums, self.losses["train"],
self.batch_variables)
return token_loss, nums, reset
def do_backward_pass(self, loss):
loss.backward()
def update_parameters(self):
if self.opt.model == "lstm":
self.clip_gradients()
self.optimizer.step()
self.optimizer.zero_grad()
def reset_losses(self):
loss_names = set([i.rstrip("maicro").rstrip("_") for
i in self.losses["train"].keys()])
return self.initialize_losses(list(loss_names))
class IteratorTrainer(Trainer):
def __init__(self, opt, meta, data_loader, model, optimizer):
super(IteratorTrainer, self).__init__(
opt, meta, data_loader, model, optimizer)
self.iters = meta.cycle
self.total_iters = meta.iterations
def run(self):
self.set_logger()
# Initialize progress bar
bar = utils.set_progress_bar(self.total_iters)
for cycle_num in range(int(self.total_iters / self.iters)):
self.model.train()
self.cycle(bar, cycle_num)
with torch.no_grad():
self.run_evaluation_cycle()
self.log_losses(self.opt, self.losses)
self.update_top_score(self.opt)
self.save_model(self.get_tracked_score())
self.stop_logger()
def cycle(self, bar, cycle_num):
nums = self.reset_losses()
print(self.losses["train"])
for i in range(1, self.iters + 1):
# self.model.zero_grad()
loss, nums, reset = self.do_forward_pass(nums)
self.do_backward_pass(loss)
self.update_parameters()
# print(loss)
# print(loss.item())
self.opt.train.dynamic.epoch += 1
for loss_name in self.losses["train"]:
self.logger.add_scalar(
"train/{}".format(loss_name),
loss.item() / self.opt.train.dynamic.bs,
self.opt.train.dynamic.epoch)
bar.update(1)
if cfg.toy and i > 10:
break
if reset:
self.data_loader.reset_offsets("train")