Spaces:
No application file
No application file
File size: 15,038 Bytes
d08dd00 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 |
import argparse
import logging
import os
import glob
import random
import copy
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss
from torch.utils.data import DataLoader, TensorDataset
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoModelForMultipleChoice,
AutoTokenizer,
get_linear_schedule_with_warmup,
)
from ..data import load_dataset
from ..data.examples import *
logger = logging.getLogger(__name__)
MODEL_MODES = {
'base': AutoModel,
'sequence-classification': AutoModelForSequenceClassification,
'question-answering': AutoModelForQuestionAnswering,
'pretraining': AutoModelForPreTraining,
'token-classification': AutoModelForTokenClassification,
'language-modeling': AutoModelWithLMHead,
'multiple-choice': AutoModelForMultipleChoice,
}
def get_model_class(model_type, mode):
return MODEL_MODES[mode]
def set_seed(hparams):
random.seed(hparams['seed'])
np.random.seed(hparams['seed'])
torch.manual_seed(hparams['seed'])
if hparams['n_gpu'] > 0:
torch.cuda.manual_seed_all(hparams['seed'])
class BaseModule(pl.LightningModule):
"""
The base module has 4 components: config, tokenizer, transformer model,
and dataset
Loading of a dataset:
1. Load instances of a dataset in the form of `Examples`
2. Convert all examples into features - may require tokenizer
3. Create a tensor dataset and loader given all the converted features
"""
def __init__(self, hparams):
super().__init__()
hparams['mode'] = self.mode
hparams['output_mode'] = self.output_mode
hparams['example_type'] = self.example_type
hparams['dev_lang'] = hparams['train_lang']
self.hparams = hparams # must come after super
self.dataset = load_dataset(hparams['dataset'], hparams['data_dir'])
if self.output_mode == 'classification':
self.labels = self.dataset.get_labels(hparams['train_lang'])
# setup config object
config_name = hparams['config_name'] or hparams['model_name_or_path']
args = {}
if self.output_mode == 'classification':
hparams['num_labels'] = len(self.dataset.get_labels(hparams['train_lang']))
args = {'num_labels': hparams['num_labels']}
self.config = AutoConfig.from_pretrained(
config_name,
**args,
cache_dir=hparams['cache_dir']
)
# setup tokenizer object
tok_name = hparams['tokenizer_name'] or hparams['model_name_or_path']
self.tokenizer = AutoTokenizer.from_pretrained(
tok_name,
config=self.config,
cache_dir=hparams['cache_dir'],
)
# setup transformer model
model_class = get_model_class(self.config.model_type, hparams['mode'])
self.model = model_class.from_pretrained(
hparams['model_name_or_path'],
config=self.config,
cache_dir=hparams['cache_dir'],
)
def forward(self, **inputs):
return self.model(**inputs)
def prepare_data(self):
"""Cache feature files on disk for every mode at the onset"""
modes = self.dataset.modes()
for mode in modes:
cached_features_file = self._feature_file(mode)
if not os.path.exists(cached_features_file)\
or self.hparams['overwrite_cache']:
self.load_features(mode)
def load_features(self, mode):
"""Load examples and convert them into features"""
if mode in ('train', 'dev', 'test'):
lang = self.hparams['{}_lang'.format(mode)]
else:
lang = self.hparams['test_lang']
examples = self.dataset.get_examples(lang, mode)
cached_features_file = self._feature_file(mode)
if os.path.exists(cached_features_file)\
and not self.hparams['overwrite_cache']:
features = torch.load(cached_features_file)
else:
features = self.convert_examples_to_features(examples)
torch.save(features, cached_features_file)
return features
def convert_examples_to_features(self, examples):
if self.hparams['example_type'] == 'multiple-choice':
features = convert_multiple_choice_examples_to_features(
examples,
self.tokenizer,
max_length=self.hparams['max_seq_length'],
label_list=self.labels
)
elif self.hparams['example_type'] == 'text':
features = convert_text_examples_to_features(
examples,
self.tokenizer,
max_length=self.hparams['max_seq_length'],
label_list=self.labels,
output_mode=self.output_mode,
)
elif self.hparams['example_type'] == 'tokens':
features = convert_tokens_examples_to_features(
examples,
self.labels,
self.hparams['max_seq_length'],
self.tokenizer,
cls_token_at_end=bool(self.config.model_type in ["xlnet"]),
cls_token=self.tokenizer.cls_token,
cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0,
sep_token=self.tokenizer.sep_token,
sep_token_extra=bool(self.config.model_type in ["roberta"]),
pad_on_left=bool(self.config.model_type in ["xlnet"]),
pad_token=self.tokenizer.pad_token_id,
pad_token_segment_id=self.tokenizer.pad_token_type_id,
pad_token_label_id=self.pad_token_label_id,
)
return features
def make_loader(self, features, batch_size):
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
all_token_type_ids = torch.tensor([f.token_type_ids or 0 for f in features], dtype=torch.long)
# all_candidates = torch.tensor([f.candidates for f in features], dtype=torch.long)
if self.hparams['output_mode'] == 'classification':
all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
elif self.hparams['output_mode'] == 'regression':
all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
return DataLoader(
TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels),
# TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels, all_candidates),
batch_size=batch_size,
)
def train_dataloader(self):
train_batch_size = self.hparams['train_batch_size']
train_features = self.load_features('train')
dataloader = self.make_loader(train_features, train_batch_size)
t_total = (
(len(dataloader.dataset) // (train_batch_size * max(1, self.hparams['n_gpu'])))
// self.hparams['gradient_accumulation_steps']
* float(self.hparams['num_train_epochs'])
)
scheduler = get_linear_schedule_with_warmup(
self.opt, num_warmup_steps=self.hparams['warmup_steps'], num_training_steps=t_total
)
self.lr_scheduler = scheduler
return dataloader
def val_dataloader(self):
dev_features = self.load_features('dev')
dataloader = self.make_loader(dev_features, self.hparams['eval_batch_size'])
return dataloader
def test_dataloader(self):
test_features = self.load_features('test')
dataloader = self.make_loader(test_features, self.hparams['eval_batch_size'])
return dataloader
def training_step(self, batch, batch_idx):
inputs = {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type != 'distilbert':
inputs['token_type_ids'] = (
batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
) # XLM and RoBERTa don't use token_type_ids
outputs = self(**inputs)
loss = outputs[0]
tensorboard_logs = {'loss': loss, 'rate': self.lr_scheduler.get_last_lr()[-1]}
return {'loss': loss, 'log': tensorboard_logs}
def validation_step(self, batch, batch_nb):
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'labels': batch[3]}
# XLM and RoBERTa don't use token_type_ids
inputs['token_type_ids'] = None
if self.config.model_type in ['bert', 'xlnet', 'albert']:
inputs['token_type_ids'] = batch[2]
outputs = self(**inputs)
tmp_eval_loss, logits = outputs[:2]
preds = logits.detach().cpu().numpy()
out_label_ids = inputs['labels'].detach().cpu().numpy()
return {'val_loss': tmp_eval_loss.detach().cpu(),
'pred': preds,
'target': out_label_ids}
def test_step(self, batch, batch_nb):
return self.validation_step(batch, batch_nb)
def _feature_file(self, mode):
if mode in ('train', 'dev', 'test'):
lang = self.hparams['{}_lang'.format(mode)]
else:
lang = self.hparams['test_lang']
return os.path.join(
self.hparams['data_dir'],
'cached_{}_{}_{}_{}'.format(
lang,
mode,
list(filter(None, self.hparams['model_name_or_path'].split('/'))).pop(),
str(self.hparams['max_seq_length']),
),
)
def is_logger(self):
return self.trainer.global_rank <= 0
def configure_optimizers(self):
"""Prepare optimizer and schedule (linear warmup and decay)"""
model = self.model
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{
'params': [p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)],
'weight_decay': self.hparams['weight_decay'],
},
{
'params': [p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)],
'weight_decay': 0.0,
},
]
optimizer = AdamW(optimizer_grouped_parameters,
lr=self.hparams['learning_rate'],
eps=self.hparams['adam_epsilon'])
self.opt = optimizer
return [optimizer]
def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
second_order_closure=None):
if self.trainer.use_tpu:
import torch_xla.core.xla_model as xm
xm.optimizer_step(optimizer)
else:
optimizer.step()
optimizer.zero_grad()
self.lr_scheduler.step()
def get_tqdm_dict(self):
avg_loss = getattr(self.trainer, 'avg_loss', 0.0)
tqdm_dict = {'loss': '{:.3f}'.format(avg_loss), 'lr': self.lr_scheduler.get_last_lr()[-1]}
return tqdm_dict
def run_module(self):
trainer = create_trainer(self, self.hparams)
hparams_copy = copy.deepcopy(self.hparams)
if self.hparams['do_train']:
checkpoints = list(sorted(glob.glob(os.path.join(self.hparams['output_dir'], 'checkpointepoch=*.ckpt'), recursive=True)))
if len(checkpoints) == 0:
trainer.fit(self)
checkpoints = list(sorted(glob.glob(os.path.join(self.hparams['output_dir'], 'checkpointepoch=*.ckpt'), recursive=True)))
self.trained_model = self.load_from_checkpoint(checkpoints[-1])
self.trained_model.hparams = hparams_copy
# Optionally, predict on dev set and write to output_dir
if self.hparams['do_predict']:
trainer.test(self.trained_model)
# Fixes __temp_weight_ddp_end.ckpt bug
# See https://github.com/PyTorchLightning/pytorch-lightning/issues/1142
class MonkeyPatchedTrainer(pl.Trainer):
def load_spawn_weights(self, original_model):
pass
pl.Trainer = MonkeyPatchedTrainer
class LoggingCallback(pl.Callback):
def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
logger.info("***** Validation results *****")
if pl_module.is_logger():
metrics = trainer.callback_metrics
# Log results
for key in sorted(metrics):
if key not in ["log", "progress_bar"]:
logger.info("{} = {}\n".format(key, str(metrics[key])))
def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
logger.info("***** Test results *****")
print(trainer.callback_metrics)
if pl_module.is_logger():
metrics = trainer.callback_metrics
# Log and save results to file
output_dir = pl_module.hparams['output_dir']
test_lang = pl_module.hparams['test_lang']
output_test_results_file = os.path.join(output_dir, 'test_results_{}.txt'.format(test_lang))
with open(output_test_results_file, "w") as writer:
for key in sorted(metrics):
if key not in ["log", "progress_bar"]:
logger.info("{} = {}\n".format(key, str(metrics[key])))
writer.write("{} = {}\n".format(key, str(metrics[key])))
def create_trainer(model, hparams):
# init model
set_seed(hparams)
# if os.path.exists(hparams['output_dir']) and os.listdir(hparams['output_dir']) and hparams['do_train']:
# raise ValueError('Output directory ({}) already exists and is not empty.'.format(hparams['output_dir']))
checkpoint_callback = pl.callbacks.ModelCheckpoint(
filepath=hparams['output_dir'], prefix='checkpoint', monitor='val_loss', mode='min', save_top_k=5
)
train_params = dict(
accumulate_grad_batches=hparams['gradient_accumulation_steps'],
gpus=hparams['n_gpu'],
max_epochs=hparams['num_train_epochs'],
early_stop_callback=False,
gradient_clip_val=hparams['max_grad_norm'],
checkpoint_callback=checkpoint_callback,
callbacks=[LoggingCallback()],
)
if hparams['fp16']:
train_params['use_amp'] = hparams['fp16']
train_params['amp_level'] = hparams['fp16_opt_level']
if hparams['n_tpu_cores'] > 0:
train_params['tpu_cores'] = hparams['n_tpu_cores']
train_params['gpus'] = 0
if hparams['n_gpu'] > 1:
train_params['distributed_backend'] = 'ddp'
trainer = pl.Trainer(**train_params)
return trainer
|