-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py
361 lines (303 loc) · 15.9 KB
/
main.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
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
import sys
import os
import json
import random
import argparse
import wandb
import numpy as np
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from src.model.model import build_model, reload_model
from src.utils import bool_flag, initialize_exp, custom_collate, gather_tensor
from src.data import CommuteEvaluationDataset, ParallelEvaluationDataset, ParallelImageEvaluationDataset, split_dataset, \
CommuteEvaluationTextOnlyDataset
from src.trainer import Trainer
# Slurm
import warnings
warnings.filterwarnings("ignore")
def get_parser():
"""
Generate a parameters parser.
"""
# parse parameters
parser = argparse.ArgumentParser(description="Language transfer")
# main parameters
parser.add_argument("--dump_path", type=str, default="./models/",
help="Experiment dump path")
parser.add_argument("--cache_dir", type=str, default="",
help="Cache directory for huggingface loading")
parser.add_argument("--exp_name", type=str, default="debug",
help="Experiment name")
parser.add_argument("--save_periodic", type=int, default=0,
help="Save the model periodically (0 to disable)")
parser.add_argument("--exp_id", type=str, default="",
help="Experiment ID")
parser.add_argument("--other_seed", type=int, default=-1,
help="Random seed for weight init/masking/misc (-1 for non-determinism).")
parser.add_argument("--iter_seed", type=int, default=12345,
help="Random seed for data iteration/shuffling (-1 for non-determinism).")
# float16 / AMP API
parser.add_argument("--fp16", type=bool_flag, default=False,
help="Run model with float16")
parser.add_argument("--amp", type=int, default=-1,
help="Use AMP wrapper for float16 / distributed / gradient accumulation. Level of optimization. -1 to disable.")
# model parameters
parser.add_argument("--dropout", type=float, default=0,
help="Dropout")
parser.add_argument("--attention_dropout", type=float, default=0,
help="Dropout in the attention layer")
parser.add_argument("--guided_self_attention", action="store_true",
help="Using guided self attention mechanism instead of standard full self-attention")
parser.add_argument("--mix_xp", type=float, default=0,
help="Mix VMLM and MMT experiments - 0 means no VMLM xp, 0.3 means training the model on 30% VMLM")
parser.add_argument("--multimodal_model", action="store_true",
help="Multimodal experiment - if false text-only MT")
parser.add_argument("--encoder_attn_mask_text_only", action="store_true",
help="Decoder embeds can only attend to position related to text embeds in the cross attn.")
parser.add_argument("--adapters", action="store_true",
help="Add adapters in the model")
parser.add_argument("--freeze_text_parameters", action="store_true",
help="Freeze text parameters - learn only (visual params +) adapters")
parser.add_argument("--prob_mask_text", type=float, default=0.0,
help="Proportion of src text to mask")
# data
parser.add_argument("--data_path", type=str, default="./data/en-de",
help="Data path")
parser.add_argument("--data_mix_path", type=str, default="",
help="Mix data path if mix xp > 0")
parser.add_argument("--features_path", type=str, default="./data/multi30k/features")
parser.add_argument("--features_mix_path", type=str, default="",
help="Mix features path if mix xp > 0")
parser.add_argument("--features_type", type=str, default="mdetr",
help="to use clip and mdetr, set to mdetr+clip")
parser.add_argument("--src_lang", type=str, default="en",
help="Source language")
parser.add_argument("--tgt_lang", type=str, default="de",
help="Target language")
# batch parameters
parser.add_argument("--max_len", type=int, default=100,
help="Maximum length of sentences (after BPE)")
parser.add_argument("--batch_size", type=int, default=32,
help="Number of sentences per batch")
parser.add_argument("--num_workers", type=int, default=4)
# training parameters
parser.add_argument("--optimizer", type=str, default="adam,lr=0.0001",
help="Optimizer (SGD / RMSprop / Adam, etc.)")
parser.add_argument("--clip_grad_norm", type=float, default=5,
help="Clip gradients norm (0 to disable)")
parser.add_argument("--grad_l2_norm", type=bool, default=False,
help="L2 normalize gradients.")
parser.add_argument("--epoch_size", type=int, default=10e7,
help="Epoch size / evaluation frequency (-1 for parallel data size)")
parser.add_argument("--max_epoch", type=int, default=100000,
help="Maximum epoch size")
parser.add_argument("--min_epoch", type=int, default=-1)
parser.add_argument("--init_epoch", type=int, default=-1)
parser.add_argument("--stopping_criterion", type=str, default="",
help="Stopping criterion, and number of non-increase before stopping the experiment")
parser.add_argument("--validation_metrics", type=str, default="",
help="Validation metrics")
parser.add_argument("--accumulate_gradients", type=int, default=1,
help="Accumulate model gradients over N iterations (N times larger batch sizes)")
parser.add_argument("--smoothing", type=float, default=0.0,
help="Label smoothing")
# beam search (for MT only)
parser.add_argument("--beam_size", type=int, default=3,
help="Beam size, default = 1 (greedy decoding)")
parser.add_argument("--length_penalty", type=float, default=1,
help="Length penalty, values < 1.0 favor shorter sentences, while values > 1.0 favor longer ones.")
parser.add_argument("--early_stopping", type=bool_flag, default=False,
help="Early stopping, stop as soon as we have `beam_size` hypotheses, although longer ones may have better scores.")
# evaluation
parser.add_argument("--eval_bleu", type=bool_flag, default=False,
help="Evaluate BLEU score during MT training")
parser.add_argument("--eval_only", type=bool_flag, default=False,
help="Only run evaluations")
parser.add_argument("--test_data_set", type=str, default="",
help="Name of the evaluation dataset")
parser.add_argument("--commute_generation", action="store_true",
help="Generation mode en CoMMuTE")
# debug
parser.add_argument("--debug", help="Enable all debug flags",
action="store_true")
# multi-gpu / multi-node
parser.add_argument("--local_rank", type=int, default=-1,
help="Multi-GPU - Local rank")
parser.add_argument("--global_rank", type=int, default=-1,
help="Multi-GPU - Global rank")
parser.add_argument("--master_port", type=int, default=-1,
help="Master port (for multi-node SLURM jobs)")
# reload pretrained embeddings / pretrained model / checkpoint
parser.add_argument("--reload_model", type=str, default="",
help="Reload a pretrained model")
parser.add_argument("--reload_checkpoint", type=str, default="",
help="Reload a checkpoint")
parser.add_argument("--reload_optim", action="store_true")
parser.add_argument("--init_dec_from_enc", action='store_true',
help="Initialize missing decoder params from encoder layers.")
parser.add_argument("--start_new_xp_from_ckpt", action="store_true",
help="New experiment from existing checkpoint. Create a new folder instead of the old one.")
return parser
def main(params):
# Debug
if params.debug:
params.exp_name = "debug"
# Init distributed training
if params.local_rank != -1:
params.global_rank = int(os.environ["RANK"])
torch.cuda.set_device(params.local_rank)
params.device = torch.device("cuda", params.local_rank)
dist.init_process_group(backend='nccl',
init_method='env://')
params.world_size = torch.distributed.get_world_size()
else:
params.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
params.world_size = 1
# initialize the experiment & wandb
if params.global_rank <= 0:
logger = initialize_exp(params)
wandb.init(project="VGAMT", name=params.exp_name)
if params.other_seed > -1:
# deterministic
torch.manual_seed(params.other_seed)
torch.cuda.manual_seed(params.other_seed)
np.random.seed(params.other_seed)
random.seed(params.other_seed)
if params.iter_seed == -1:
# non-deterministic
params.iter_seed = None
# Load data
if not params.eval_only:
train_set, valid_set, test_set = split_dataset(params)
train_mix_set = None
if params.mix_xp:
train_mix_set = train_set[1]
train_set = train_set[0]
else:
if params.multimodal_model and not params.test_data_set.startswith("commute"):
test_set = ParallelImageEvaluationDataset(params, split="test")
elif params.multimodal_model and params.test_data_set.startswith("commute"):
test_set = CommuteEvaluationDataset(params)
elif not params.multimodal_model and params.test_data_set.startswith("commute"):
test_set = CommuteEvaluationTextOnlyDataset(params)
else:
test_set = ParallelEvaluationDataset(params, split="test")
train_sampler, train_mix_sampler, valid_sampler, test_sampler = None, None, None, None
if params.world_size > 1:
if not params.eval_only:
valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_set,
num_replicas=params.world_size,
rank=params.global_rank)
test_sampler = torch.utils.data.distributed.DistributedSampler(test_set,
num_replicas=params.world_size,
rank=params.global_rank)
collate_fn = custom_collate if params.multimodal_model else None
train_loader, train_mix_loader, valid_loader, test_loader = None, None, None, None
if not params.eval_only:
train_loader = DataLoader(train_set, batch_size=params.batch_size, num_workers=params.num_workers, pin_memory=False,
collate_fn=collate_fn)
valid_loader = DataLoader(valid_set, batch_size=params.batch_size, num_workers=params.num_workers + 4,
shuffle=False, pin_memory=False, sampler=valid_sampler, collate_fn=collate_fn)
if params.mix_xp:
train_mix_loader = DataLoader(train_mix_set, batch_size=params.batch_size, num_workers=params.num_workers,
pin_memory=False,
collate_fn=collate_fn)
test_loader = DataLoader(test_set, batch_size=params.batch_size, num_workers=params.num_workers + 4,
shuffle=False, pin_memory=False, sampler=test_sampler, collate_fn=collate_fn)
data = {"train": train_loader, "train_mix": train_mix_loader, "valid": valid_loader, "test": test_loader}
# Build model
model, params.tokenizer = build_model(params)
params.model = model
if params.reload_model or params.reload_checkpoint:
reload_model(params)
params.model = model.to(params.device)
if params.local_rank != -1:
params.model = DDP(params.model, device_ids=[params.local_rank], output_device=params.local_rank,
find_unused_parameters=True)
params.lang2id = params.tokenizer.lang_code_to_id
if params.global_rank <= 0:
# Save config
wandb.config.update(params.config.to_dict())
wandb.config.update({"batch_size": params.batch_size,
"dropout": params.dropout,
"optimizer": params.optimizer,
"src_lang": params.src_lang,
"tgt_lang": params.tgt_lang
}, allow_val_change=True)
# Build Trainer
trainer = Trainer(data, params)
# evaluation
if params.eval_only:
if params.test_data_set.startswith("commute"):
if params.multimodal_model:
if not params.commute_generation:
scores = trainer.evaluate_commute()
else:
scores = trainer.evaluate_commute_generation()
else:
scores = trainer.evaluate_commute_mt_generation()
else:
if params.multimodal_model:
scores = trainer.evaluate_mmt(mode="test")
else:
scores = trainer.evaluate_mt(mode="test")
for k, v in scores.items():
logger.info("%s -> %.6f" % (k, v))
logger.info("__log__:%s" % json.dumps(scores))
sys.exit()
_iter = 0
for _ in range(params.max_epoch):
if params.global_rank <= 0:
logger.info("============ Starting epoch %i ... ============" % trainer.epoch)
trainer.n_sentences = 0
while trainer.n_sentences < trainer.epoch_size:
if params.mix_xp:
# Handle asynchronous random generators
prob_xp = torch.rand(1, device=params.device)
prob_xp = gather_tensor(prob_xp)[0].item()
if params.multimodal_model:
if params.mix_xp:
if prob_xp <= params.mix_xp:
trainer.vmlm_step(mode="train_mix")
else:
trainer.mmt_step(mode="train")
else:
trainer.mmt_step(mode="train")
else:
if params.mix_xp:
if prob_xp <= params.mix_xp:
trainer.mlm_step(mode="train_mix")
else:
trainer.mt_step(mode="train")
else:
trainer.mt_step(mode="train")
trainer.iter()
_iter += 1
torch.cuda.empty_cache()
if params.global_rank <= 0:
logger.info("============ End of epoch %i ============" % trainer.epoch)
# evaluate perplexity
if params.multimodal_model:
scores = trainer.evaluate_mmt(mode="valid")
scores_test = trainer.evaluate_mmt(mode="test")
else:
scores = trainer.evaluate_mt(mode="valid")
scores_test = trainer.evaluate_mt(mode="test")
scores.update(scores_test)
if params.global_rank <= 0:
# print / JSON log
for k, v in scores.items():
logger.info("%s -> %.6f" % (k, v))
logger.info("__log__:%s" % json.dumps(scores))
# end of epoch
trainer.save_best_model(scores)
trainer.save_periodic()
trainer.end_epoch(scores)
torch.cuda.synchronize()
if __name__ == "__main__":
parser = get_parser()
params = parser.parse_args()
main(params)