-
Notifications
You must be signed in to change notification settings - Fork 2
/
train.py
168 lines (135 loc) · 6.02 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
import os
import argparse
import subprocess
import random
from tqdm import tqdm
import torch
import modeling
import data
import matplotlib.pyplot as plt
SEED = 42
torch.manual_seed(SEED)
random.seed(SEED)
import torch_xla.core.xla_model as xm
device = xm.xla_device()
# device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('device in training.py:', device) # xla:1
MODEL_MAP = {
'vanilla_bert': modeling.VanillaBertRanker,
'cedr_pacrr': modeling.CedrPacrrRanker,
'cedr_knrm': modeling.CedrKnrmRanker,
'cedr_drmm': modeling.CedrDrmmRanker
}
def main(model, dataset, train_pairs, qrels, valid_run, qrelf, model_out_dir):
model.to(device)
LR = 0.001
BERT_LR = 2e-5
# MAX_EPOCH = 100
MAX_EPOCH = 10
params = [(k, v) for k, v in model.named_parameters() if v.requires_grad]
non_bert_params = {'params': [v for k, v in params if not k.startswith('bert.')]}
bert_params = {'params': [v for k, v in params if k.startswith('bert.')], 'lr': BERT_LR}
optimizer = torch.optim.Adam([non_bert_params, bert_params], lr=LR)
epoch = 0
top_valid_score = None
valid_scores, losses = [], []
for epoch in range(MAX_EPOCH):
loss = train_iteration(model, optimizer, dataset, train_pairs, qrels)
losses.append(loss)
print(f'train epoch={epoch} loss={loss}')
valid_score = validate(model, dataset, valid_run, qrelf, epoch, model_out_dir)
valid_scores.append(valid_score)
print(f'validation epoch={epoch} score={valid_score}')
if top_valid_score is None or valid_score > top_valid_score:
top_valid_score = valid_score
print('new top validation score, saving weights')
model.save(os.path.join(model_out_dir, 'weights.p'))
plot(losses, name='training loss')
plot(valid_scores, name='validation score (p20)')
def plot(values, name):
plt.figure()
plt.plot(values)
plt.title(name)
plt.savefig(f'{name}.png')
print(f'saved {name}.png')
def train_iteration(model, optimizer, dataset, train_pairs, qrels):
BATCH_SIZE = 16
BATCHES_PER_EPOCH = 32
GRAD_ACC_SIZE = 2
total = 0
model.train()
total_loss = 0.
with tqdm('training', total=BATCH_SIZE * BATCHES_PER_EPOCH, ncols=80, desc='train', leave=False) as pbar:
for record in data.iter_train_pairs(model, dataset, train_pairs, qrels, GRAD_ACC_SIZE):
scores = model(record['query_tok'],
record['query_mask'],
record['doc_tok'],
record['doc_mask'])
count = len(record['query_id']) // 2
scores = scores.reshape(count, 2)
loss = torch.mean(1. - scores.softmax(dim=1)[:, 0]) # pariwse softmax
loss.backward()
total_loss += loss
total += count
if total % BATCH_SIZE == 0:
# optimizer.step()
xm.optimizer_step(optimizer, barrier=True)
optimizer.zero_grad()
pbar.update(count)
if total >= BATCH_SIZE * BATCHES_PER_EPOCH:
# return total_loss.item()
return total_loss.item()
def validate(model, dataset, run, qrelf, epoch, model_out_dir):
VALIDATION_METRIC = 'P.20'
runf = os.path.join(model_out_dir, f'{epoch}.run')
run_model(model, dataset, run, runf)
return trec_eval(qrelf, runf, VALIDATION_METRIC)
def run_model(model, dataset, run, runf, desc='valid'):
BATCH_SIZE = 16
rerank_run = {}
with torch.no_grad(), tqdm(total=sum(len(r) for r in run.values()), ncols=80, desc=desc, leave=False) as pbar:
model.eval()
for records in data.iter_valid_records(model, dataset, run, BATCH_SIZE):
scores = model(records['query_tok'],
records['query_mask'],
records['doc_tok'],
records['doc_mask'])
for qid, did, score in zip(records['query_id'], records['doc_id'], scores.detach().cpu().numpy()):
rerank_run.setdefault(qid, {})[did] = score.item()
pbar.update(len(records['query_id']))
with open(runf, 'wt') as runfile:
for qid in rerank_run:
scores = list(sorted(rerank_run[qid].items(), key=lambda x: (x[1], x[0]), reverse=True))
for i, (did, score) in enumerate(scores):
runfile.write(f'{qid} 0 {did} {i+1} {score} run\n')
def trec_eval(qrelf, runf, metric):
trec_eval_f = 'bin/trec_eval'
output = subprocess.check_output([trec_eval_f, '-m', metric, qrelf, runf]).decode().rstrip()
output = output.replace('\t', ' ').split('\n')
assert len(output) == 1
return float(output[0].split()[2])
def main_cli():
parser = argparse.ArgumentParser('CEDR model training and validation')
parser.add_argument('--model', choices=MODEL_MAP.keys(), default='vanilla_bert')
parser.add_argument('--datafiles', nargs='+')
# parser.add_argument('--datafiles', type=argparse.FileType('rt'), nargs='+')
parser.add_argument('--qrels', type=argparse.FileType('rt'))
parser.add_argument('--train_pairs', type=argparse.FileType('rt'))
parser.add_argument('--valid_run', type=argparse.FileType('rt'))
parser.add_argument('--initial_bert_weights', type=argparse.FileType('rb'))
parser.add_argument('--model_out_dir')
args = parser.parse_args()
print('datafiles: ', args.datafiles)
datafiles = [open(f, 'r', encoding='utf-8') for f in args.datafiles]
model = MODEL_MAP[args.model]()
# dataset = data.read_datafiles(args.datafiles)
dataset = data.read_datafiles(datafiles)
qrels = data.read_qrels_dict(args.qrels)
train_pairs = data.read_pairs_dict(args.train_pairs)
valid_run = data.read_run_dict(args.valid_run)
if args.initial_bert_weights is not None:
model.load(args.initial_bert_weights.name)
os.makedirs(args.model_out_dir, exist_ok=True)
main(model, dataset, train_pairs, qrels, valid_run, args.qrels.name, args.model_out_dir)
if __name__ == '__main__':
main_cli()