def main(text):
args = Config()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Set seed
seed_everything(args.seed)
# Prepare NER task
args.task_name = args.task_name.lower()
processor = processors[args.task_name]()
label_list = processor.get_labels()
id2label = {i: label for i, label in enumerate(label_list)}
# label2id = {label: i for i, label in enumerate(label_list)}
num_labels = len(label_list)
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = BertConfig, BertCrfForNer, CNerTokenizer
config = config_class.from_pretrained(args.model_name_or_path,
num_labels=num_labels,
cache_dir=None)
if args.do_predict:
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
for checkpoint in checkpoints:
model = model_class.from_pretrained(checkpoint, config=config)
model.to(device)
result = predict(args, text, id2label, model, tokenizer)
for i in result:
print(i)
def run(args):
seed_everything(args.seed)
if args.do_train == 1:
# print('划分训练数据和验证数据')
# split_data(args=args, file_name_or_path=args.file_name_or_path)
print('加载训练数据和验证数据')
train_data, train_dataloader = load_dataset(
args=args,
model_name_or_path=args.model_name_or_path,
type='train')
valid_data, valid_dataloader = load_dataset(
args=args,
model_name_or_path=args.model_name_or_path,
type='valid')
print('训练数据和验证数据加载完成')
print('开始训练')
train(args=args,
model_name_or_path=args.model_name_or_path,
train_data=train_data,
train_dataloader=train_dataloader,
valid_data=valid_data,
valid_dataloader=valid_dataloader)
print('训练结束')
if args.do_predict == 1:
print('加载测试数据')
pre_data, predict_dataloader = load_dataset(
args=args, model_name_or_path=args.model_name_or_path, type='test')
print('测试数据加载完成')
print('开始预测')
preds, predict_label = predict(
predict_model_name_or_path=args.predict_model_name_or_path,
pre_data=pre_data,
pre_dataloader=predict_dataloader)
print('预测完成')
print('形成输出结果形式')
target_names = ['非涉网案件', '涉网案件']
outputlines = get_output(preds=preds,
predict_label=predict_label,
pre_data=pre_data,
target_names=target_names)
print('进行规则筛选')
relu = net_relus()
predict_result = relu.net_relus(
nonet_keyword_list=config['nonet_keyword_list'],
predict_result=outputlines)
print('将预测结果写入文件')
# predict_result = outputlines
write_pre_result_to_file(args=args, output_lines=predict_result)
print('预测结果写入完成')
def run(args):
seed_everything(args.seed)
if args.do_train == 1:
logger.info('划分训练数据和验证数据')
split_data(args=args, file_name_or_path=args.file_name_or_path)
logger.info('加载训练数据和验证数据')
train_data, train_dataloader = load_dataset(
args=args,
model_name_or_path=args.model_name_or_path,
type='train')
valid_data, valid_dataloader = load_dataset(
args=args,
model_name_or_path=args.model_name_or_path,
type='valid')
logger.info('训练数据和验证数据加载完成')
logger.info('开始训练')
train(args=args,
model_name_or_path=args.model_name_or_path,
train_data=train_data,
train_dataloader=train_dataloader,
valid_data=valid_data,
valid_dataloader=valid_dataloader)
logger.info('训练结束')
if args.do_predict == 1:
logger.info('加载测试数据')
pre_data, predict_dataloader = load_dataset(
args=args, model_name_or_path=args.model_name_or_path, type='test')
logger.info('测试数据加载完成')
logger.info('开始预测')
preds, predict_label = predict(
predict_model_name_or_path=args.predict_model_name_or_path,
pre_data=pre_data,
pre_dataloader=predict_dataloader)
logger.info('预测完成')
logger.info('将预测结果写入文件')
write_pre_result_to_file(args=args,
preds=preds,
predict_label=predict_label,
pre_data=pre_data)
logger.info('预测结果写入完成')
def main():
parser = ArgumentParser()
## Required parameters
parser.add_argument("--do_data", default=True, action='store_true')
parser.add_argument('--data_name', default='albert', type=str)
parser.add_argument('--max_ngram', default=3, type=int)
parser.add_argument("--do_lower_case", default=False, action='store_true')
parser.add_argument('--seed', default=42, type=int)
# parser.add_argument("--file_num", type=int, default=10, help="Number of dynamic masking to pregenerate (with different masks)")
parser.add_argument("--max_seq_len", type=int, default=128)
parser.add_argument(
"--short_seq_prob",
type=float,
default=0.1,
help="Probability of making a short sentence as a training example")
parser.add_argument(
"--masked_lm_prob",
type=float,
default=0.15,
help="Probability of masking each token for the LM task")
# 128 * 0.15
parser.add_argument(
"--max_predictions_per_seq",
type=int,
default=20,
help="Maximum number of tokens to mask in each sequence")
args = parser.parse_args()
seed_everything(args.seed)
from configs.base import config
args.vocab_path = config['albert_vocab_path']
args.data_dir = config['data_dir']
logger.info("pregenerate training data parameters:\n %s", args)
tokenizer = BertTokenizer(vocab_file=args.vocab_path,
do_lower_case=args.do_lower_case)
small_path = config['data_dir'] / "corpus/small"
files = sorted(
[f for f in small_path.iterdir() if f.exists() and '.txt' in str(f)])
file_path = files[0].absolute()
max_seq_len = args.max_seq_len
train(file_path, tokenizer, max_seq_len)
print(" dataloader ok! ")
sys.exit(0)
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task_name",
default=None,
type=str,
help="The name of the task to train selected in the list: " +
", ".join(processors.keys()))
parser.add_argument(
"--data_dir",
default=None,
type=str,
help=
"The input data dir. Should contain the training files for the CoNLL-2003 NER task.",
)
parser.add_argument(
"--model_type",
default=None,
type=str,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()),
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
help="Path to pre-trained model or shortcut name selected in the list: "
+ ", ".join(ALL_MODELS),
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
help=
"The output directory where the model predictions and checkpoints will be written.",
)
# Other parameters
parser.add_argument('--markup',
default='bios',
type=str,
choices=['bios', 'bio'])
parser.add_argument(
"--labels",
default="",
type=str,
help=
"Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.",
)
parser.add_argument(
"--config_name",
default="",
type=str,
help="Pretrained config name or path if not the same as model_name")
parser.add_argument(
"--tokenizer_name",
default="",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3",
)
parser.add_argument(
"--train_max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--eval_max_seq_length",
default=512,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument("--do_train",
default=None,
type=bool,
help="Whether to run training.")
parser.add_argument("--do_eval",
default=None,
type=bool,
help="Whether to run eval on the dev set.")
parser.add_argument("--do_predict",
default=None,
type=bool,
help="Whether to run predictions on the test set.")
parser.add_argument(
"--evaluate_during_training",
action="store_true",
help="Whether to run evaluation during training at each logging step.",
)
parser.add_argument(
"--do_lower_case",
default=None,
type=bool,
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.01,
type=float,
help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=
"If > 0: set total number of training steps to perform. Override num_train_epochs.",
)
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for,E.g., 0.1 = 10% of training."
)
parser.add_argument("--logging_steps",
type=int,
default=50,
help="Log every X updates steps.")
parser.add_argument("--save_steps",
type=int,
default=50,
help="Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action="store_true",
help=
"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
)
parser.add_argument(
'--predict_all_checkpoints',
action="store_true",
help=
"Predict all checkpoints starting with the same prefix as model_name ending and ending with step number",
)
parser.add_argument("--no_cuda",
action="store_true",
help="Avoid using CUDA when available")
parser.add_argument(
"--overwrite_output_dir",
default=None,
type=bool,
help="Overwrite the content of the output directory 将输出目录覆写")
parser.add_argument(
"--overwrite_cache",
action="store_true",
help="Overwrite the cached training and evaluation sets")
parser.add_argument("--seed",
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
"--fp16",
action="store_true",
help=
"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
parser.add_argument("--fp16_opt_level",type=str,default="O1",\
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html",)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank")
parser.add_argument("--server_ip",
type=str,
default="",
help="For distant debugging.")
parser.add_argument("--server_port",
type=str,
default="",
help="For distant debugging.")
args = parser.parse_args()
# 参数修改
# 主要必须修改labels, 位置在processors文件夹ner_seq文件中
args.task_name = 'cner'
args.model_type = 'bert'
args.model_name_or_path = '/root/models/chinese/bert/pytorch/bert-base-chinese'
args.do_train = True
args.do_eval = True
args.do_predict = True
args.do_lower_case = True
args.data_dir = '/root/A/违法主体识别/train_data'
args.train_max_seq_length = 150
args.eval_max_seq_length = 150
args.per_gpu_train_batch_size = 4
args.per_gpu_eval_batch_size = 4
args.learning_rate = 2e-5
args.num_train_epochs = 5.0
args.logging_steps = 300
args.saving_steps = 600
args.output_dir = './outputs'
args.overwrite_output_dir = True
args.seed = 42
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
args.output_dir = os.path.join(args.output_dir, args.model_type)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
init_logger(log_file=args.output_dir + '/{}-{}-{}.log'.format(
args.model_type, args.task_name,
time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime())))
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args.output_dir))
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank,
device,
args.n_gpu,
bool(args.local_rank != -1),
args.fp16,
)
# Set seed
seed_everything(args.seed)
# Prepare NER task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
label_list = processor.get_labels()
args.id2label = {i: label for i, label in enumerate(label_list)}
args.label2id = {label: i for i, label in enumerate(label_list)}
num_labels = len(label_list)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
cache_dir=args.cache_dir if args.cache_dir else None,
)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name
if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None,
)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None,
label2id=args.label2id,
device=args.device)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoint = args.output_dir
logger.info("Predict the following checkpoints: %s", checkpoint)
model = model_class.from_pretrained(checkpoint,
config=config,
label2id=args.label2id,
device=args.device)
model.to(args.device)
global _args, _model, _tokenizer
_args, _model, _tokenizer = args, model, tokenizer
def train(args, train_features, model, tokenizer, use_crf):
""" Train the model """
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ["bias", "LayerNorm.weight"]
bert_param_optimizer = list(model.bert.named_parameters())
if args.model_encdec == 'bert2crf':
crf_param_optimizer = list(model.crf.named_parameters())
linear_param_optimizer = list(model.classifier.named_parameters())
optimizer_grouped_parameters = [{
'params': [
p for n, p in bert_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay,
'lr':
args.learning_rate
}, {
'params': [
p for n, p in bert_param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0,
'lr':
args.learning_rate
}, {
'params': [
p for n, p in crf_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay,
'lr':
args.crf_learning_rate
}, {
'params': [
p for n, p in crf_param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0,
'lr':
args.crf_learning_rate
}, {
'params': [
p for n, p in linear_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay,
'lr':
args.crf_learning_rate
}, {
'params': [
p for n, p in linear_param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0,
'lr':
args.crf_learning_rate
}]
elif args.model_encdec == 'bert2gru':
gru_param_optimizer = list(model.decoder.named_parameters())
linear_param_optimizer = list(model.clsdense.named_parameters())
optimizer_grouped_parameters = [{
'params': [
p for n, p in bert_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay,
'lr':
args.learning_rate
}, {
'params': [
p for n, p in bert_param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0,
'lr':
args.learning_rate
}, {
'params': [
p for n, p in gru_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay,
'lr':
args.crf_learning_rate
}, {
'params': [
p for n, p in gru_param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0,
'lr':
args.crf_learning_rate
}, {
'params': [
p for n, p in linear_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay,
'lr':
args.crf_learning_rate
}, {
'params': [
p for n, p in linear_param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0,
'lr':
args.crf_learning_rate
}]
elif args.model_encdec == 'bert2soft':
linear_param_optimizer = list(model.classifier.named_parameters())
optimizer_grouped_parameters = [{
'params': [
p for n, p in bert_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay,
'lr':
args.learning_rate
}, {
'params': [
p for n, p in bert_param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0,
'lr':
args.learning_rate
}, {
'params': [
p for n, p in linear_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay,
'lr':
args.crf_learning_rate
}, {
'params': [
p for n, p in linear_param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0,
'lr':
args.crf_learning_rate
}]
elif args.model_encdec == 'multi2point':
# gru_param_optimizer = list(model.decoder.named_parameters())
linear_param_optimizer = list(model.pointer.named_parameters())
optimizer_grouped_parameters = [{
'params': [
p for n, p in bert_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay,
'lr':
args.learning_rate
}, {
'params': [
p for n, p in bert_param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0,
'lr':
args.learning_rate
}, {
'params': [
p for n, p in linear_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay,
'lr':
args.point_learning_rate
}, {
'params': [
p for n, p in linear_param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0,
'lr':
args.point_learning_rate
}]
t_total = len(train_features) // args.batch_size * args.num_train_epochs
args.warmup_steps = int(t_total * args.warmup_proportion)
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=t_total)
# Check if saved optimizer or scheduler states exist
if os.path.isfile(os.path.join(
args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
os.path.join(args.model_name_or_path, "scheduler.pt")):
# Load in optimizer and scheduler states
optimizer.load_state_dict(
torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
scheduler.load_state_dict(
torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_features))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d", args.batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.batch_size *
(torch.distributed.get_world_size() if args.local_rank != -1 else 1),
)
# logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
pre_result = {}
model.zero_grad()
seed_everything(
args.seed
) # Added here for reproductibility (even between python 2 and 3)
total_step = 0
best_spanf = -1
test_results = {}
for ep in range(int(args.num_train_epochs)):
pbar = ProgressBar(n_total=len(train_features) // args.batch_size,
desc='Training')
if ep == int(args.num_train_epochs) - 1:
eval_features = load_and_cache_examples(args,
args.data_type,
tokenizer,
data_type='dev')
train_features.extend(eval_features)
step = 0
for batch in batch_generator(features=train_features,
batch_size=args.batch_size,
use_crf=use_crf,
answer_seq_len=args.answer_seq_len):
batch_input_ids, batch_input_mask, batch_segment_ids, batch_label_ids, batch_multi_span_label, batch_context_mask, batch_start_position, batch_end_position, batch_raw_labels, _, batch_example = batch
model.train()
if args.model_encdec == 'bert2crf' or args.model_encdec == 'bert2gru' or args.model_encdec == 'bert2soft':
batch_inputs = tuple(t.to(args.device) for t in batch[0:6])
inputs = {
"input_ids": batch_inputs[0],
"attention_mask": batch_inputs[1],
"token_type_ids": batch_inputs[2],
"context_mask": batch_inputs[5],
"labels": batch_inputs[3],
"testing": False
}
elif args.model_encdec == 'multi2point':
batch_inputs = tuple(t.to(args.device) for t in batch[0:5])
inputs = {
"input_ids": batch_inputs[0],
"attention_mask": batch_inputs[1],
"token_type_ids": batch_inputs[2],
"span_label": batch_inputs[4],
"testing": False
}
outputs = model(**inputs)
loss = outputs[
0] # model outputs are always tuple in pytorch-transformers (see doc)
if args.n_gpu > 1:
loss = loss.mean(
) # mean() to average on multi-gpu parallel training
loss.backward()
if step % 15 == 0:
pbar(step, {'epoch': ep, 'loss': loss.item()})
step += 1
tr_loss += loss.item()
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
scheduler.step() # Update learning rate schedule
optimizer.step()
model.zero_grad()
global_step += 1
if args.local_rank in [
-1, 0
] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics
print("start evalue")
if args.local_rank == -1:
# Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(args=args,
model=model,
tokenizer=tokenizer,
prefix="dev",
use_crf=use_crf)
span_f = results['span_f']
if span_f > best_spanf:
output_dir = os.path.join(args.output_dir,
"checkpoint-bestf")
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
print('remove file', args.output_dir)
print('\n\n eval results:', results)
test_results = evaluate(args=args,
model=model,
tokenizer=tokenizer,
prefix="test",
use_crf=use_crf)
print('\n\n test results', test_results)
print('\n epoch = :', ep)
best_spanf = span_f
os.makedirs(output_dir)
# print('dir = ', output_dir)
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
torch.save(
args, os.path.join(output_dir,
"training_args.bin"))
logger.info("Saving model checkpoint to %s",
output_dir)
tokenizer.save_vocabulary(output_dir)
torch.save(optimizer.state_dict(),
os.path.join(output_dir, "optimizer.pt"))
torch.save(scheduler.state_dict(),
os.path.join(output_dir, "scheduler.pt"))
logger.info(
"Saving optimizer and scheduler states to %s",
output_dir)
np.random.seed()
np.random.shuffle(train_features)
logger.info("\n")
# if 'cuda' in str(args.device):
torch.cuda.empty_cache()
return global_step, tr_loss / global_step, test_results
def train_model():
metrics = {}
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = 1
if args.model_encdec == 'bert2gru' or args.model_encdec == 'bert2soft' or args.model_encdec == 'multi2point':
use_crf = False
elif args.model_encdec == 'bert2crf':
use_crf = True
args.device = device
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank,
device,
args.n_gpu,
bool(args.local_rank != -1),
)
# Set seed
seed_everything(args.seed)
# Prepare NER task
args.data_type = args.data_type.lower()
if args.data_type not in processors:
raise ValueError("Task not found: %s" % (args.data_type))
processor = processors[args.data_type]()
label_list = processor.get_labels() #这里是获取的标签列表，本任务：【B I O】
args.id2label = {i: label
for i, label in enumerate(label_list)
} #获取的是字典{0: O, 1: B, 2: I}
args.label2id = {label: i
for i, label in enumerate(label_list)
} #获取的是字典{O:0, B:1, I: 2}
num_labels = len(label_list) # 标签的个数
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[
args.model_type] #BertConfig, BertCrfForNer, EcaTokenizer
#BertConfig.from_pretrained
config = config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
cache_dir=args.cache_dir if args.cache_dir else None,
)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name
if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None,
)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None) #模型
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
train_features = load_and_cache_examples(args,
args.data_type,
tokenizer,
data_type='train')
global_step, tr_loss, metrics = train(args,
train_features,
model,
tokenizer,
use_crf=use_crf)
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
return metrics
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()))
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: "
+ ", ".join(ALL_MODELS))
parser.add_argument(
"--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train selected in the list: " +
", ".join(processors.keys()))
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
"--config_name",
default="",
type=str,
help="Pretrained config name or path if not the same as model_name")
parser.add_argument(
"--tokenizer_name",
default="",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name")
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_predict",
action='store_true',
help="Whether to run the model in inference mode on the test set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument("--learning_rate",
default=1e-6,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.01,
type=float,
help="Weight deay if we apply some.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=
"If > 0: set total number of training steps to perform. Override num_train_epochs."
)
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for,E.g., 0.1 = 10% of training."
)
parser.add_argument('--logging_steps',
type=int,
default=10,
help="Log every X updates steps.")
parser.add_argument('--save_steps',
type=int,
default=1000,
help="Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action='store_true',
help=
"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"
)
parser.add_argument("--no_cuda",
action='store_true',
default=False,
help="Avoid using CUDA when available")
parser.add_argument('--overwrite_output_dir',
action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument(
'--overwrite_cache',
action='store_true',
help="Overwrite the cached training and evaluation sets")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank")
args = parser.parse_args()
args.output_dir = args.output_dir + '{}'.format(args.model_type)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
init_logger(log_file=args.output_dir +
'/{}-{}.log'.format(args.model_type, args.task_name))
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args.output_dir))
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
print("args.n_gpu=", args.n_gpu)
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl')
args.n_gpu = 1
args.device = device
print("运算设备：", args.device)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1))
seed_everything(args.seed)
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
if args.local_rank not in [-1, 0]:
torch.distributed.barrier()
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
finetuning_task=args.task_name)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name
if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool('.ckpt' in args.model_name_or_path),
config=config)
if args.local_rank == 0:
torch.distributed.barrier()
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
data_type='train')
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step,
tr_loss)
if args.do_train and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
model = model_class.from_pretrained(args.output_dir)
model.to(args.device)
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("transformers.modeling_utils").setLevel(
logging.WARN)
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split(
'-')[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(
args,
model,
tokenizer,
prefix=prefix,
)
result = dict(
(k + '_{}'.format(global_step), v) for k, v in result.items())
results.update(result)
output_eval_file = os.path.join(args.output_dir,
"checkpoint_eval_results.txt")
with open(output_eval_file, "w") as writer:
for key in sorted(results.keys()):
writer.write("%s = %s\n" % (key, str(results[key])))
# Predict
if args.do_predict and args.local_rank in [-1, 0]:
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("transformers.modeling_utils").setLevel(
logging.WARN)
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
predict(args, model, tokenizer, prefix=prefix)
def train(args, model_name_or_path, train_data, train_dataloader, valid_data,
valid_dataloader):
pro = processer()
labellist = pro.get_labels()
trainloss = TrainLoss()
#*****加载模型*****
model = BertForSequenceClassification
config = BertConfig.from_pretrained(model_name_or_path,
num_labels=len(labellist))
model = model.from_pretrained(model_name_or_path, config=config)
# *****模型加载到设备*****
if torch.cuda.is_available():
# 单GPU计算
torch.cuda.set_device(0)
device = torch.device('cuda', 0) # 设置GPU设备号
else:
device = torch.device('cpu')
model.to(device)
#*****优化函数*****
t_total = len(train_dataloader
) // args.gradient_accumulation_steps * args.num_train_epochs
warmup_steps = int(t_total * args.warmup_proportion)
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':
args.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=args.learning_rate,
eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=warmup_steps,
t_total=t_total)
#*****训练过程相关信息*****
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_data))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
args.train_batch_size)
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
#*****开始训练*****
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
seed_everything(args.seed)
for num in range(args.num_train_epochs):
train_all_steps = 0
train_steps = []
train_losses = []
global_step = 0
logger.info(f'****************Train epoch-{num}****************')
pbar = ProgressBar(n_total=len(train_dataloader), desc='Train')
for step, batch in enumerate(train_dataloader):
#***存储step用于绘制Loss曲线***
train_all_steps += 1
train_steps.append(train_all_steps)
model.train()
#***输入模型进行计算***
batch = tuple(t.to(device) for t in batch)
inputs = {
'input_ids': batch[0],
'attention_mask': batch[1],
'token_type_ids': batch[2],
'labels': batch[3]
}
outputs = model(
**inputs) #模型原文件中已经使用损失函数对输出值和标签值进行了计算，返回的outputs中包含损失函数值
#***损失函数值反向传播***
loss = outputs[0]
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm) #梯度裁剪
#***存储loss用于绘制loss曲线***
train_losses.append(loss.detach().cpu().numpy())
#***优化器进行优化***
pbar(step, {'loss': loss.item()})
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step() #优化器优化
scheduler.step() #学习率机制更新
model.zero_grad()
global_step += 1
#训练一个epoch保存一个模型
output_dir = os.path.join(args.output_dir,
f'model_checkpoint_epoch_{num}')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
print('') #避免输出信息都在同一行
# logger.info(f'save model checkpoint-{global_step} to {output_dir} ')
model.save_pretrained(output_dir) #保存模型
#***训练一个epoch绘制一个Loss曲线***
trainloss.train_loss(steps=train_steps,
losses=train_losses,
epoch=num,
args=args,
type='train',
max_step=train_all_steps)
#*****一个epoch训练结束以后，进行验证*****
print('')
logger.info(f'****************Valid epoch-{num}****************')
logger.info(" Num examples = %d", len(valid_data))
logger.info(" Batch size = %d", args.valid_batch_size)
valid_steps, valid_losses, valid_all_steps = valid(
args=args,
model=model,
device=device,
valid_data=valid_data,
valid_dataloader=valid_dataloader)
trainloss.train_loss(steps=valid_steps,
losses=valid_losses,
epoch=num,
args=args,
type='valid',
max_steps=valid_all_steps)
#每训练一个epoch清空cuda缓存
if 'cuda' in str(device):
torch.cuda.empty_cache()
def main():
args = deal_parser()
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
args.output_dir = args.output_dir + '{}'.format(args.model_type)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
init_logger(log_file=args.output_dir +
'/{}-{}.log'.format(args.model_type, args.task_name))
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args.output_dir))
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl')
args.n_gpu = 1
args.device = device
# Setup logging
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1),
args.fp16)
# Set seed
seed_everything(args.seed)
# Prepare GLUE task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
config = BertConfig.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
finetuning_task=args.task_name)
# albert model
# tokenizer = tokenization_albert.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=args.do_lower_case,spm_model_file=args.spm_model_file)
# model = AlbertForSequenceClassification.from_pretrained(args.model_name_or_path,from_tf=bool('.ckpt' in args.model_name_or_path),config=config)
# bert model
tokenizer = tokenization_bert.BertTokenizer(
vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case,
spm_model_file=args.spm_model_file)
model = BertForSequenceClassification.from_pretrained(
args.model_name_or_path,
from_tf=bool('.ckpt' in args.model_name_or_path),
config=config)
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
data_type='train')
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step,
tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = model.module if hasattr(
model,
'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
# Evaluation
results = []
if args.do_eval and args.local_rank in [-1, 0]:
# albert model
#tokenizer = tokenization_albert.FullTokenizer(vocab_file=args.vocab_file,do_lower_case=args.do_lower_case,spm_model_file=args.spm_model_file)
# bert model
tokenizer = tokenization_bert.BertTokenizer(
vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case,
spm_model_file=args.spm_model_file)
checkpoints = [(0, args.output_dir)]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
checkpoints = [(int(checkpoint.split('-')[-1]), checkpoint)
for checkpoint in checkpoints
if checkpoint.find('checkpoint') != -1]
checkpoints = sorted(checkpoints, key=lambda x: x[0])
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for _, checkpoint in checkpoints:
global_step = checkpoint.split(
'-')[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
# albert model
# model = AlbertForSequenceClassification.from_pretrained(checkpoint)
# bert model
model = BertForSequenceClassification.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=prefix)
results.extend([(k + '_{}'.format(global_step), v)
for k, v in result.items()])
output_eval_file = os.path.join(args.output_dir,
"checkpoint_eval_results.txt")
with open(output_eval_file, "w") as writer:
for key, value in results:
writer.write("%s = %s\n" % (key, str(value)))
# Test
results = []
if args.do_predict and args.local_rank in [-1, 0]:
# albert model
# tokenizer = tokenization_albert.FullTokenizer(vocab_file=args.vocab_file,do_lower_case=args.do_lower_case,spm_model_file=args.spm_model_file)
# bert model
tokenizer = tokenization_bert.BertTokenizer(
vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case,
spm_model_file=args.spm_model_file)
checkpoints = [(0, args.output_dir)]
if args.predict_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
checkpoints = [(int(checkpoint.split('-')[-1]), checkpoint)
for checkpoint in checkpoints
if checkpoint.find('checkpoint') != -1]
checkpoints = sorted(checkpoints, key=lambda x: x[0])
logger.info("Test the following checkpoints: %s", checkpoints)
for _, checkpoint in checkpoints:
global_step = checkpoint.split(
'-')[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
# albert model
# model = AlbertForSequenceClassification.from_pretrained(checkpoint)
# bert model
model = BertForSequenceClassification.from_pretrained(checkpoint)
model.to(args.device)
result = test(args, model, tokenizer, prefix=prefix)
results.extend([(k + '_{}'.format(global_step), v)
for k, v in result.items()])
output_test_file = os.path.join(args.output_dir,
"checkpoint_test_results.txt")
with open(output_test_file, "w") as writer:
for key, value in results:
writer.write("%s = %s\n" % (key, str(value)))
def train(args, train_dataset, model, tokenizer, config):
""" Train the model """
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(
train_dataset) if args.local_rank == -1 else DistributedSampler(
train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size,
collate_fn=collate_fn)
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (
len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(
train_dataloader
) // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ["bias", "LayerNorm.weight"]
bert_param_optimizer = list(model.bert.named_parameters())
crf_param_optimizer = list(model.crf.named_parameters())
linear_param_optimizer = list(model.classifier.named_parameters())
optimizer_grouped_parameters = [{
'params': [
p for n, p in bert_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay,
'lr':
args.learning_rate
}, {
'params': [
p for n, p in bert_param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0,
'lr':
args.learning_rate
}, {
'params': [
p for n, p in crf_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay,
'lr':
args.crf_learning_rate
}, {
'params':
[p for n, p in crf_param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0,
'lr':
args.crf_learning_rate
}, {
'params': [
p for n, p in linear_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay,
'lr':
args.crf_learning_rate
}, {
'params': [
p for n, p in linear_param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0,
'lr':
args.crf_learning_rate
}]
args.warmup_steps = int(t_total * args.warmup_proportion)
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=t_total)
# Check if saved optimizer or scheduler states exist
if os.path.isfile(os.path.join(
args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
os.path.join(args.model_name_or_path, "scheduler.pt")):
# Load in optimizer and scheduler states
optimizer.load_state_dict(
torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
scheduler.load_state_dict(
torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(torch.distributed.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
steps_trained_in_current_epoch = 0
# Check if continuing training from a checkpoint
if os.path.exists(args.model_name_or_path
) and "checkpoint" in args.model_name_or_path:
# set global_step to gobal_step of last saved checkpoint from model path
global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
epochs_trained = global_step // (len(train_dataloader) //
args.gradient_accumulation_steps)
steps_trained_in_current_epoch = global_step % (
len(train_dataloader) // args.gradient_accumulation_steps)
logger.info(
" Continuing training from checkpoint, will skip to saved global_step"
)
logger.info(" Continuing training from epoch %d", epochs_trained)
logger.info(" Continuing training from global step %d", global_step)
logger.info(" Will skip the first %d steps in the first epoch",
steps_trained_in_current_epoch)
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
seed_everything(
args.seed
) # Added here for reproductibility (even between python 2 and 3)
# train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
best_eval_p = 0.0
best_eval_r = 0.0
best_eval_f1 = 0.0
for _ in range(int(args.num_train_epochs)):
# pbar = ProgressBar(n_total=len(train_dataloader), desc='Training')
epoch_iterator = tqdm(train_dataloader,
desc="Iteration",
disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"labels": batch[3],
'input_lens': batch[4]
}
if args.model_type != "distilbert":
# XLM and RoBERTa don"t use segment_ids
inputs["token_type_ids"] = (batch[2] if args.model_type
in ["bert", "xlnet"] else None)
outputs = model(**inputs)
loss = outputs[
0] # model outputs are always tuple in pytorch-transformers (see doc)
if args.n_gpu > 1:
loss = loss.mean(
) # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# pbar(step, {'loss': loss.item()})
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
scheduler.step() # Update learning rate schedule
optimizer.step()
model.zero_grad()
global_step += 1
# if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# # Log metrics
# print(" ")
# if args.local_rank == -1:
# # Only evaluate when single GPU otherwise metrics may not average well
# evaluate(args, model, tokenizer)
# if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
# # Save model checkpoint
# output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
# if not os.path.exists(output_dir):
# os.makedirs(output_dir)
# model_to_save = (
# model.module if hasattr(model, "module") else model
# ) # Take care of distributed/parallel training
# model_to_save.save_pretrained(output_dir)
# torch.save(args, os.path.join(output_dir, "training_args.bin"))
# logger.info("Saving model checkpoint to %s", output_dir)
# tokenizer.save_vocabulary(output_dir)
# torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
# torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
# logger.info("Saving optimizer and scheduler states to %s", output_dir)
# best eval
results = evaluate(args, model, tokenizer)
if results["f1"] > best_eval_f1:
best_eval_p = results['acc']
best_eval_r = results['recall']
best_eval_f1 = results["f1"]
## 保存最好模型
args.best_eval_output_dir = os.path.join(args.output_dir)
if not os.path.exists(args.best_eval_output_dir):
os.makedirs(args.best_eval_output_dir)
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(args.best_eval_output_dir)
torch.save(
args,
os.path.join(args.best_eval_output_dir, "training_args.bin"))
logger.info("eval results: p:{:.4f} r:{:.4f} f1:{:.4f}".format(
best_eval_p, best_eval_r, best_eval_f1))
logger.info("Saving step:{} as best model to {}".format(
global_step, args.best_eval_output_dir))
tokenizer.save_vocabulary(args.best_eval_output_dir)
torch.save(optimizer.state_dict(),
os.path.join(args.best_eval_output_dir, "optimizer.pt"))
torch.save(scheduler.state_dict(),
os.path.join(args.best_eval_output_dir, "scheduler.pt"))
# logger.info("Saving optimizer and scheduler states to %s", args.best_eval_output_dir)
# config_file = os.path.join(args.best_eval_output_dir, "best_config.json")
# json.dump(config, config_file)
logger.info("\n")
if 'cuda' in str(args.device):
torch.cuda.empty_cache()
if args.do_predict:
config_class, model_class, tokenizer_class = MODEL_CLASSES[
args.model_type]
model = model_class.from_pretrained(args.best_eval_output_dir,
config=config)
model.to(args.device)
predict(args, model, tokenizer)
return global_step, tr_loss / global_step
def main():
parser = ArgumentParser()
## Required parameters
parser.add_argument("--data_dir", default=None, type=str, required=True)
parser.add_argument("--vocab_path", default=None, type=str, required=True)
parser.add_argument("--output_dir", default=None, type=str, required=True)
parser.add_argument('--data_name', default='albert', type=str)
parser.add_argument('--max_ngram', default=3, type=int)
parser.add_argument("--do_data", default=False, action='store_true')
parser.add_argument("--do_split", default=False, action='store_true')
parser.add_argument("--do_lower_case", default=False, action='store_true')
parser.add_argument('--seed', default=42, type=int)
parser.add_argument("--line_per_file", default=1000000000, type=int)
parser.add_argument("--file_num", type=int, default=10,
help="Number of dynamic masking to pregenerate (with different masks)")
parser.add_argument("--max_seq_len", type=int, default=128)
parser.add_argument("--short_seq_prob", type=float, default=0.1,
help="Probability of making a short sentence as a training example")
parser.add_argument("--masked_lm_prob", type=float, default=0.15,
help="Probability of masking each token for the LM task")
parser.add_argument("--max_predictions_per_seq", type=int, default=20, # 128 * 0.15
help="Maximum number of tokens to mask in each sequence")
args = parser.parse_args()
seed_everything(args.seed)
args.data_dir = Path(args.data_dir)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
init_logger(log_file=args.output_dir +"pregenerate_training_data_ngram.log")
logger.info("pregenerate training data parameters:\n %s", args)
tokenizer = BertTokenizer(vocab_file=args.vocab_path, do_lower_case=args.do_lower_case)
# split big file
if args.do_split:
corpus_path =args.data_dir / "corpus/corpus.txt"
split_save_path = args.data_dir / "/corpus/train"
if not split_save_path.exists():
split_save_path.mkdir(exist_ok=True)
line_per_file = args.line_per_file
command = f'split -a 4 -l {line_per_file} -d {corpus_path} {split_save_path}/shard_'
os.system(f"{command}")
# generator train data
if args.do_data:
data_path = args.data_dir / "corpus/train"
files = sorted([f for f in data_path.parent.iterdir() if f.exists() and '.txt' in str(f)])
for idx in range(args.file_num):
logger.info(f"pregenetate {args.data_name}_file_{idx}.json")
save_filename = data_path / f"{args.data_name}_file_{idx}.json"
num_instances = 0
with save_filename.open('w') as fw:
for file_idx in range(len(files)):
file_path = files[file_idx]
file_examples = create_training_instances(input_file=file_path,
tokenizer=tokenizer,
max_seq_len=args.max_seq_len,
max_ngram=args.max_ngram,
short_seq_prob=args.short_seq_prob,
masked_lm_prob=args.masked_lm_prob,
max_predictions_per_seq=args.max_predictions_per_seq)
file_examples = [json.dumps(instance) for instance in file_examples]
for instance in file_examples:
fw.write(instance + '\n')
num_instances += 1
metrics_file = data_path / f"{args.data_name}_file_{idx}_metrics.json"
print(f"num_instances: {num_instances}")
with metrics_file.open('w') as metrics_file:
metrics = {
"num_training_examples": num_instances,
"max_seq_len": args.max_seq_len
}
metrics_file.write(json.dumps(metrics))
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir", default=None, type=str, required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--model_type", default=None, type=str, required=True,
help="Model type selected in the list: ")
parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
help="Path to pre-trained model or shortcut name selected in the list")
parser.add_argument("--task_name", default=None, type=str, required=True,
help="The name of the task to train selected in the list: " + ", ".join(processors.keys()))
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--vocab_file",default='', type=str)
parser.add_argument("--spm_model_file",default='',type=str)
## Other parameters
parser.add_argument("--config_name", default="", type=str,
help="Pretrained config name or path if not the same as model_name")
parser.add_argument("--tokenizer_name", default="", type=str,
help="Pretrained tokenizer name or path if not the same as model_name")
parser.add_argument("--cache_dir", default="", type=str,
help="Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument("--max_seq_length", default=512, type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--do_train", action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval", action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--output_eval", action='store_true',
help="Whether to write output result.")
parser.add_argument("--do_predict", action='store_true',
help="Whether to run the model in inference mode on the test set.")
parser.add_argument("--do_lower_case", action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument("--learning_rate", default=5e-5, type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay", default=0.0, type=float,
help="Weight deay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-6, type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs", default=3.0, type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--max_steps", default=-1, type=int,
help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
parser.add_argument("--warmup_proportion", default=0.1, type=float,
help="Proportion of training to perform linear learning rate warmup for,E.g., 0.1 = 10% of training.")
parser.add_argument('--logging_steps', type=int, default=10,
help="Log every X updates steps.")
parser.add_argument('--save_steps', type=int, default=1000,
help="Save checkpoint every X updates steps.")
parser.add_argument("--eval_all_checkpoints", action='store_true',
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
parser.add_argument("--no_cuda", action='store_true',
help="Avoid using CUDA when available")
parser.add_argument('--overwrite_output_dir', action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument('--overwrite_cache', action='store_true',
help="Overwrite the cached training and evaluation sets")
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
parser.add_argument('--fp16', action='store_true',
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
parser.add_argument('--fp16_opt_level', type=str, default='O1',
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument("--local_rank", type=int, default=-1,
help="For distributed training: local_rank")
parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.")
parser.add_argument('--server_port', type=str, default='', help="For distant debugging.")
parser.add_argument("--label_with_bi", action='store_true', help="Label with B/I")
args = parser.parse_args()
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
args.output_dir = args.output_dir + '{}'.format(args.model_type)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
init_logger(log_file=args.output_dir + '/{}-{}.log'.format(args.model_type, args.task_name))
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
args.output_dir))
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl')
args.n_gpu = 1
args.device = device
# Setup logging
logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
# Set seed
seed_everything(args.seed)
# Prepare GLUE task
args.task_name = args.task_name.lower()
if args.task_name != "ner":
raise ValueError("Task error: %s, must be ner" % (args.task_name))
processor = processors[args.task_name]()
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels_ner(args.data_dir, args.label_with_bi)
num_labels = len(label_list)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config = AlbertConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
finetuning_task=args.task_name)
tokenizer = tokenization_albert.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=args.do_lower_case,
spm_model_file=args.spm_model_file)
model =AlbertFocalLossForNer.from_pretrained(args.model_name_or_path,
from_tf=bool('.ckpt' in args.model_name_or_path),
config=config)
if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, data_type='train')
global_step, tr_loss = train(args, train_dataset, label_list, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = model.module if hasattr(model,
'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
# Evaluation
results = []
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = tokenization_albert.FullTokenizer(vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case,
spm_model_file=args.spm_model_file)
checkpoints = [(0,args.output_dir)]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
checkpoints = [(int(checkpoint.split('-')[-1]),checkpoint) for checkpoint in checkpoints if checkpoint.find('checkpoint') != -1]
checkpoints = sorted(checkpoints,key =lambda x:x[0])
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for _,checkpoint in checkpoints:
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = AlbertFocalLossForNer.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, label_list, prefix=prefix)
results.extend([(k + '_{}'.format(global_step), v) for k, v in result.items()])
output_eval_file = os.path.join(args.output_dir, "checkpoint_eval_results.txt")
with open(output_eval_file, "w") as writer:
for key,value in results:
writer.write("%s = %s\n" % (key, str(value)))
if args.do_predict and args.local_rank in [-1, 0]:
tokenizer = tokenization_albert.FullTokenizer(vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case,
spm_model_file=args.spm_model_file)
result = evaluate(args, model, tokenizer, label_list, prefix="")
output_eval_file = os.path.join(args.output_dir, "checkpoint_eval_results.txt")
with open(output_eval_file, "w") as writer:
for key,value in result.items():
writer.write("%s = %s\n" % (key, str(value)))
def train(args, train_dataset, model, tokenizer):
writer = SummaryWriter(args.summary_dir)
""" Train the model """
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(
train_dataset) if args.local_rank == -1 else DistributedSampler(
train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size,
collate_fn=collate_fn)
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (
len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(
train_dataloader
) // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ["bias", "LayerNorm.weight"]
bert_parameters = model.bert.named_parameters()
start_parameters = model.start_fc.named_parameters()
end_parameters = model.end_fc.named_parameters()
optimizer_grouped_parameters = [
{
"params": [
p for n, p in bert_parameters
if not any(nd in n for nd in no_decay)
],
"weight_decay":
args.weight_decay,
'lr':
args.learning_rate
},
{
"params":
[p for n, p in bert_parameters if any(nd in n for nd in no_decay)],
"weight_decay":
0.0,
'lr':
args.learning_rate
},
{
"params": [
p for n, p in start_parameters
if not any(nd in n for nd in no_decay)
],
"weight_decay":
args.weight_decay,
'lr':
0.001
},
{
"params": [
p for n, p in start_parameters
if any(nd in n for nd in no_decay)
],
"weight_decay":
0.0,
'lr':
0.001
},
{
"params": [
p for n, p in end_parameters
if not any(nd in n for nd in no_decay)
],
"weight_decay":
args.weight_decay,
'lr':
0.001
},
{
"params":
[p for n, p in end_parameters if any(nd in n for nd in no_decay)],
"weight_decay":
0.0,
'lr':
0.001
},
]
# 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": args.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},
# ]
if args.warmup_steps == -1:
args.warmup_steps = int(t_total * args.warmup_proportion)
# args.warmup_steps = int(t_total * args.warmup_proportion)
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=t_total)
# scheduler = get_limit_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps,
# num_training_steps=t_total, limit_lr=7e-6)
# Check if saved optimizer or scheduler states exist
if os.path.isfile(os.path.join(
args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
os.path.join(args.model_name_or_path, "scheduler.pt")):
# Load in optimizer and scheduler states
optimizer.load_state_dict(
torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
scheduler.load_state_dict(
torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(torch.distributed.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
steps_trained_in_current_epoch = 0
# Check if continuing training from a checkpoint
if os.path.exists(args.model_name_or_path
) and "checkpoint" in args.model_name_or_path:
# set global_step to gobal_step of last saved checkpoint from model path
global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
epochs_trained = global_step // (len(train_dataloader) //
args.gradient_accumulation_steps)
steps_trained_in_current_epoch = global_step % (
len(train_dataloader) // args.gradient_accumulation_steps)
logger.info(
" Continuing training from checkpoint, will skip to saved global_step"
)
logger.info(" Continuing training from epoch %d", epochs_trained)
logger.info(" Continuing training from global step %d", global_step)
logger.info(" Will skip the first %d steps in the first epoch",
steps_trained_in_current_epoch)
best_eval_f1 = 0
tr_loss, logging_loss = 0.0, 0.0
if args.do_adv:
fgm = FGM(model, emb_name=args.adv_name, epsilon=args.adv_epsilon)
model.zero_grad()
seed_everything(
args.seed
) # Added here for reproductibility (even between python 2 and 3)
for epoch in range(int(args.num_train_epochs)):
pbar = ProgressBar(n_total=len(train_dataloader), desc='Training')
for step, batch in enumerate(train_dataloader):
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"start_positions": batch[3],
"end_positions": batch[4]
}
if args.model_type != "distilbert":
# XLM and RoBERTa don"t use segment_ids
inputs["token_type_ids"] = (batch[2] if args.model_type
in ["bert", "xlnet"] else None)
outputs = model(**inputs)
loss = outputs[
0] # model outputs are always tuple in pytorch-transformers (see doc)
if args.n_gpu > 1:
loss = loss.mean(
) # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
if args.do_adv:
fgm.attack()
loss_adv = model(**inputs)[0]
if args.n_gpu > 1:
loss_adv = loss_adv.mean()
loss_adv.backward()
fgm.restore()
cur_lr = optimizer.state_dict()['param_groups'][0]['lr']
# cur_lr = scheduler.get_lr()
pbar(step, {
'epoch': int(epoch),
'loss': loss.item(),
'lr': cur_lr
})
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
writer.add_scalar('loss', loss.item(), global_step=global_step)
writer.add_scalar('lr', cur_lr, global_step=global_step)
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
scheduler.step() # Update learning rate schedule
optimizer.step()
model.zero_grad()
global_step += 1
if args.local_rank in [
-1, 0
] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics
logger.warn("\n")
if args.local_rank == -1:
# Only evaluate when single GPU otherwise metrics may not average well
prefix = f'epoch {epoch} global_step {global_step}'
results = evaluate(args,
model,
tokenizer,
prefix=prefix)
if results['f1'] > best_eval_f1:
best_eval_f1 = results['f1']
output_dir = os.path.join(args.output_dir,
"best_eval_checkpoint")
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = (
model.module
if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
torch.save(
args,
os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to %s",
output_dir)
tokenizer.save_vocabulary(output_dir)
torch.save(
optimizer.state_dict(),
os.path.join(output_dir, "optimizer.pt"))
torch.save(
scheduler.state_dict(),
os.path.join(output_dir, "scheduler.pt"))
logger.info(
"Saving optimizer and scheduler states to %s",
output_dir)
results['checkoutpoint'] = global_step
results['epoch'] = epoch
# results.update(vars(args))
json.dump(results,
open(
os.path.join(output_dir,
'eval_result.txt'),
'w'),
ensure_ascii=False,
indent=4)
# if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
# # Save model checkpoint
# output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
# if not os.path.exists(output_dir):
# os.makedirs(output_dir)
# model_to_save = (
# model.module if hasattr(model, "module") else model
# ) # Take care of distributed/parallel training
# model_to_save.save_pretrained(output_dir)
# torch.save(args, os.path.join(output_dir, "training_args.bin"))
# tokenizer.save_vocabulary(output_dir)
# logger.info("Saving model checkpoint to %s", output_dir)
# torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
# torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
# logger.info("Saving optimizer and scheduler states to %s", output_dir)
logger.warn("\n")
if 'cuda' in str(args.device):
torch.cuda.empty_cache()
return global_step, tr_loss / global_step
def main():
args = get_argparse().parse_args()
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
args.output_dir = args.output_dir + '{}'.format(args.model_type)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
time_ = time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime())
init_logger(log_file=args.output_dir +
f'/{args.model_type}-{args.task_name}-{time_}.log')
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args.output_dir))
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank,
device,
args.n_gpu,
bool(args.local_rank != -1),
args.fp16,
)
# Set seed
seed_everything(args.seed)
# Prepare NER task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
label_list = processor.get_labels()
args.id2label = {i: label for i, label in enumerate(label_list)}
args.label2id = {label: i for i, label in enumerate(label_list)}
num_labels = len(label_list)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(args.model_name_or_path,
num_labels=num_labels)
config.soft_label = True
config.loss_type = args.loss_type
tokenizer = tokenizer_class.from_pretrained(
args.model_name_or_path, do_lower_case=args.do_lower_case)
model = model_class.from_pretrained(args.model_name_or_path, config=config)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
data_type='train')
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step,
tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = (model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_vocabulary(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(
logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split(
"-")[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=prefix)
if global_step:
result = {
"{}_{}".format(global_step, k): v
for k, v in result.items()
}
results.update(result)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
for key in sorted(results.keys()):
writer.write("{} = {}\n".format(key, str(results[key])))
if args.do_predict and args.local_rank in [-1, 0]:
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.predict_checkpoints > 0:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("transformers.modeling_utils").setLevel(
logging.WARN) # Reduce logging
checkpoints = [
x for x in checkpoints
if x.split('-')[-1] == str(args.predict_checkpoints)
]
logger.info("Predict the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
predict(args, model, tokenizer, prefix=prefix)
def train(args, train_dataset, model, tokenizer):
""" Train the model """
""" Train the model """
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter()
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(
train_dataset) if args.local_rank == -1 else DistributedSampler(
train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size,
collate_fn=xlnet_collate_fn if
args.model_type in ['xlnet'] else collate_fn)
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (
len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(
train_dataloader
) // args.gradient_accumulation_steps * args.num_train_epochs
args.warmup_steps = int(t_total * args.warmup_proportion)
# Prepare optimizer and schedule (linear warmup and decay)
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":
args.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=args.learning_rate,
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=t_total)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(torch.distributed.get_world_size() if args.local_rank != -1 else 1))
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
epochs_trained = 0
steps_trained_in_current_epoch = 0
# Check if continuing training from a checkpoint
if os.path.exists(args.model_name_or_path):
# set global_step to global_step of last saved checkpoint from model path
try:
global_step = int(
args.model_name_or_path.split("-")[-1].split("/")[0])
except ValueError:
global_step = 0
epochs_trained = global_step // (len(train_dataloader) //
args.gradient_accumulation_steps)
steps_trained_in_current_epoch = global_step % (
len(train_dataloader) // args.gradient_accumulation_steps)
logger.info(
" Continuing training from checkpoint, will skip to saved global_step"
)
logger.info(" Continuing training from epoch %d", epochs_trained)
logger.info(" Continuing training from global step %d", global_step)
logger.info(" Will skip the first %d steps in the first epoch",
steps_trained_in_current_epoch)
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
seed_everything(
args.seed
) # Added here for reproductibility (even between python 2 and 3)
for _ in range(int(args.num_train_epochs)):
pbar = ProgressBar(n_total=len(train_dataloader), desc='Training')
for step, batch in enumerate(train_dataloader):
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
'input_ids': batch[0],
'attention_mask': batch[1],
'labels': batch[3]
}
if args.model_type != 'distilbert':
inputs['token_type_ids'] = batch[2] if args.model_type in [
'bert', 'xlnet', 'albert', 'roberta'
] else None # XLM, DistilBERT don't use segment_ids
outputs = model(**inputs)
loss = outputs[
0] # model outputs are always tuple in transformers (see doc)
if args.n_gpu > 1:
loss = loss.mean(
) # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
pbar(step, {'loss': loss.item()})
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
if args.local_rank in [
-1, 0
] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
print(" ")
logs = {}
# Log metrics
if (
args.local_rank == -1
and args.evaluate_during_training
): # Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(args, model, tokenizer)
for key, value in results.items():
eval_key = "eval_{}".format(key)
logs[eval_key] = value
loss_scalar = (tr_loss - logging_loss) / args.logging_steps
learning_rate_scalar = scheduler.get_lr()[0]
logs["learning_rate"] = learning_rate_scalar
logs["loss"] = loss_scalar
logging_loss = tr_loss
for key, value in logs.items():
tb_writer.add_scalar(key, value, global_step)
print(json.dumps({**logs, **{"step": global_step}}))
if args.local_rank in [
-1, 0
] and args.save_steps > 0 and global_step % args.save_steps == 0:
# Save model checkpoint
output_dir = os.path.join(
args.output_dir, "checkpoint-{}".format(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
torch.save(args,
os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to %s", output_dir)
torch.save(optimizer.state_dict(),
os.path.join(output_dir, "optimizer.pt"))
torch.save(scheduler.state_dict(),
os.path.join(output_dir, "scheduler.pt"))
logger.info("Saving optimizer and scheduler states to %s",
output_dir)
print(" ")
if 'cuda' in str(args.device):
torch.cuda.empty_cache()
if args.local_rank in [-1, 0]:
tb_writer.close()
return global_step, tr_loss / global_step
def main():
args = get_argparse()
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
args.output_dir = args.output_dir + '{}'.format(args.model_type)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
time_ = time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime())
init_logger(log_file=args.output_dir + f'/{args.model_type}-{args.task_name}-{time_}.log')
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
args.output_dir))
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl')
args.n_gpu = 1
args.device = device
# Setup logging
logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
# Set seed
seed_everything(args.seed)
# Prepare GLUE task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
finetuning_task=args.task_name,
cache_dir=args.cache_dir if args.cache_dir else None,
)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None,
)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None,
)
if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, data_type='train')
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = model.module if hasattr(model,
'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
with torch.no_grad():
for batch_idx,(data, target) in enumerate(valid_loader):
data, target = data.to(device), target.to(device)
output = model(data)
loss = loss_fn(output, target).item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct = pred.eq(target.view_as(pred)).sum().item()
valid_loss.update(loss,n = data.size(0))
valid_acc.update(correct, n=1)
count += data.size(0)
pbar(step=batch_idx)
return {'valid_loss':valid_loss.avg,
'valid_acc':valid_acc.sum /count}
if __name__ =="__main__":
parser = argparse.ArgumentParser(description='CIFAR10')
parser.add_argument("--model", type=str, default='ResNet50')
parser.add_argument("--task", type=str, default='image')
parser.add_argument("--epochs", default=50,type=int)
parser.add_argument('--batch_size',default=256,type=int)
parser.add_argument("--seed",default=42,type=int)
parser.add_argument('--norm_type',default='bn',choices = ['bn','enb0','ens0'])
args = parser.parse_args()
seed_everything(args.seed)
loaders = data_loader(args)
model = resnet18(args.norm_type)
device = torch.device("cuda")
model.to(device)
train(args,loaders,model)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir", default='dataset/car_data', type=str, required=False,
help="输入数据文件地址")
parser.add_argument("--model_type", default='albert', type=str, required=False,
help="模型种类")
parser.add_argument("--model_name_or_path", default='prev_trained_model/albert_chinese_small', type=str,
required=False,
help="模型参数文件地址")
parser.add_argument("--task_name", default='car', type=str, required=False,
help="那个种类数据" + ", ".join(processors.keys()))
parser.add_argument("--output_dir", default='outputs', type=str, required=False,
help="输出文件地址")
parser.add_argument("--vocab_file", default='prev_trained_model/albert_chinese_small/vocab.txt', type=str)
## Other parameters
parser.add_argument("--config_name", default="", type=str,
help="配置文件地址")
parser.add_argument("--tokenizer_name", default="", type=str,
help="Pretrained tokenizer name or path if not the same as model_name")
parser.add_argument("--cache_dir", default="", type=str,
help="Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument("--max_seq_length", default=512, type=int,
help="句子最大长度")
parser.add_argument("--do_train", action='store_true',
help="训练")
parser.add_argument("--do_eval", action='store_true',
help="验证")
parser.add_argument("--do_predict", action='store_true',
help="预测")
parser.add_argument("--do_lower_case", action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
help="批量大小")
parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
help="验证批量大小")
parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument("--learning_rate", default=5e-5, type=float,
help="Adam学习率")
parser.add_argument("--weight_decay", default=0.0, type=float,
help="Weight deay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-6, type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs", default=3.0, type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--max_steps", default=-1, type=int,
help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
parser.add_argument("--warmup_proportion", default=0.1, type=float,
help="Proportion of training to perform linear learning rate warmup for,E.g., 0.1 = 10% of training.")
parser.add_argument('--logging_steps', type=int, default=10,
help="Log every X updates steps.")
parser.add_argument('--save_steps', type=int, default=1000,
help="每多少部保存一次")
parser.add_argument("--eval_all_checkpoints",type=str,default='do',# action='store_true',
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
parser.add_argument("--no_cuda", type=int, default=0, # action='store_true',
help="GPU")
parser.add_argument('--overwrite_output_dir', action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument('--overwrite_cache', action='store_true',
help="Overwrite the cached training and evaluation sets")
parser.add_argument('--seed', type=int, default=42,
help="随机种子")
parser.add_argument('--fp16', action='store_true',
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
parser.add_argument('--fp16_opt_level', type=str, default='O1',
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument("--local_rank", type=int, default=0,
help="For distributed training: local_rank")
args = parser.parse_args()
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
type_task = args.model_type + '_' + '{}'.format(args.task_name)
if not os.path.exists(os.path.join(args.output_dir, type_task)):
os.mkdir(os.path.join(args.output_dir, type_task))
init_logger(log_file=args.output_dir + '/{}-{}.log'.format(args.model_type, args.task_name))
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
# torch.distributed.init_process_group(backend='nccl')
args.n_gpu = 1
args.device = device
# Setup logging
logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
# Set seed
seed_everything(args.seed)
# Prepare GLUE task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
args.model_type = args.model_type.lower()
config = AlbertConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
finetuning_task=args.task_name)
tokenizer = tokenization_albert.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=args.do_lower_case,
)
model =AlbertForSequenceClassification.from_pretrained(args.model_name_or_path, config=config)
#if args.local_rank == 0:
# torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
# args.do_train = True
if args.do_train:
train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, data_type='train')
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train:# and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = model.module if hasattr(model,
'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
# Evaluation
# args.do_eval = True
results = []
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = tokenization_albert.FullTokenizer(vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case,
)
checkpoints = [(0,args.output_dir)]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
checkpoints = [(int(checkpoint.split('-')[-1]),checkpoint) for checkpoint in checkpoints if checkpoint.find('checkpoint') != -1]
checkpoints = sorted(checkpoints,key =lambda x:x[0])
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for _,checkpoint in checkpoints:
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model =AlbertForSequenceClassification.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=prefix)
results.extend([(k + '_{}'.format(global_step), v) for k, v in result.items()])
output_eval_file = os.path.join(args.output_dir, "checkpoint_eval_results.txt")
with open(output_eval_file, "w") as writer:
for key,value in results:
writer.write("%s = %s\n" % (key, str(value)))
# args.do_predict = True
predict_results = []
if args.do_predict and args.local_rank in [-1, 0]:
tokenizer = tokenization_albert.FullTokenizer(vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case,
)
# checkpoints_path = os.path.join(args.output_dir, 'checkpoint-4000')
checkpoints = [(0, args.output_dir)]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
checkpoints = [(int(checkpoint.split('-')[-1]), checkpoint) for checkpoint in checkpoints if
checkpoint.find('checkpoint') != -1]
checkpoints = sorted(checkpoints, key=lambda x: x[0])
logger.info("Evaluate the following checkpoints: %s", checkpoints)
checkpoints = [checkpoints[-1]]
for _, checkpoint in checkpoints:
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = AlbertForSequenceClassification.from_pretrained(checkpoint)
model.to(args.device)
result = predict(args, model, tokenizer, prefix=prefix)
predict_results.extend([(k + '_{}'.format(global_step), v) for k, v in result.items()])
output_eval_file = os.path.join(args.output_dir, "checkpoint_eval_results.txt")
with open(output_eval_file, "w") as writer:
for key, value in predict_results:
writer.write("%s = %s\n" % (key, str(value)))
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
args.n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
f"device: {device} , distributed training: {bool(args.local_rank != -1)}, 16-bits training: {args.fp16}"
)
if args.gradient_accumulation_steps < 1:
raise ValueError(
f"Invalid gradient_accumulation_steps parameter: {args.gradient_accumulation_steps}, should be >= 1"
)
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
seed_everything(args.seed)
tokenizer = BertTokenizer.from_pretrained(args.vocab_path,
do_lower_case=args.do_lower_case)
total_train_examples = samples_per_epoch * args.epochs
num_train_optimization_steps = int(total_train_examples /
args.train_batch_size /
args.gradient_accumulation_steps)
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
args.warmup_steps = int(num_train_optimization_steps *
args.warmup_proportion)
bert_config = AlbertConfig.from_pretrained(args.config_path)
model = AlbertForPreTraining(config=bert_config)
def main():
args = get_argparse().parse_args()
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
args.output_dir = args.output_dir + '{}'.format(args.model_type)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
time_ = time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime())
init_logger(log_file=args.output_dir +
f'/{args.model_type}-{args.task_name}-{time_}.log')
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args.output_dir))
seed_everything(args.seed)
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
# Prepare NER task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
label_list = processor.get_labels()
args.id2label = {i: label for i, label in enumerate(label_list)}
args.label2id = {label: i for i, label in enumerate(label_list)}
args.num_labels = len(label_list)
tokenizer = CNerTokenizer.from_pretrained(
args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None,
)
args.vocab_size = tokenizer.vocab_size
model = BiLSTMForNer(args)
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
data_type='train')
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step,
tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = (model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
# model_to_save.save_pretrained(args.output_dir)
save_model(model_to_save, args.output_dir)
tokenizer.save_vocabulary(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = CNerTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(
logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split(
"-")[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = load_model(model, checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=prefix)
if global_step:
result = {
"{}_{}".format(global_step, k): v
for k, v in result.items()
}
results.update(result)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "a") as writer:
writer.write("task_name: {}, model_type: {}\n".format(
args.task_name, args.model_type))
for key in sorted(results.keys()):
writer.write("{} = {}\n".format(key, str(results[key])))
writer.write("\n")
if args.do_predict and args.local_rank in [-1, 0]:
tokenizer = CNerTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.predict_checkpoints > 0:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("transformers.modeling_utils").setLevel(
logging.WARN) # Reduce logging
checkpoints = [
x for x in checkpoints
if x.split('-')[-1] == str(args.predict_checkpoints)
]
logger.info("Predict the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = load_model(model, checkpoint)
model.to(args.device)
predict(args, model, tokenizer, prefix=prefix)
def main():
args = get_argparse().parse_args()
# 模型保存目录
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
args.output_dir = args.output_dir + '{}'.format(args.model_type)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
# CUDA, GPU
if torch.cuda.is_available() and not args.no_cuda:
args.device = torch.device("cuda:0")
else:
args.device = torch.device("cpu")
# 打印参数
time_ = time.strftime("%Y-%m-%d", time.localtime())
init_logger(log_file=args.output_dir + f'/{args.model_type}-{args.task_name}-{time_}.log')
logger.info("="*20+" args "+"="*20)
for para in args.__dict__:
msg = para + " = " + str(args.__dict__[para])
logger.info(msg)
# Set seed
seed_everything(args.seed)
# Prepare NER task
processor = NerProcessor()
label_list = processor.get_labels()
args.id2label = {i: label for i, label in enumerate(label_list)}
args.label2id = {label: i for i, label in enumerate(label_list)}
num_labels = len(label_list)
num_labels = int((num_labels+1)/2) # 部分B I
pad_token_label_id = CrossEntropyLoss().ignore_index # -100
# Load pretrained model and tokenizer
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels, cache_dir=args.cache_dir if args.cache_dir else None, )
tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None, )
setattr(config, 'soft_label', args.soft_label)
model = model_class(config=config)
# 训练
if args.do_train:
if args.continue_train:
model = model_class.from_pretrained(args.continue_train_checkpoint, config=config)
print(f"Continue training from {args.continue_train_checkpoint}")
# 基础预训练模型
elif args.model_type.lower() == "electra":
model.BaseModel = ElectraModel.from_pretrained(args.model_name_or_path)
logger.info(f"Loading Electra from {args.model_name_or_path}...")
elif args.model_type.lower() == "bert" :
model.BaseModel = BertModel.from_pretrained(args.model_name_or_path)
logger.info(f"Loading Bert from {args.model_name_or_path}...")
elif args.model_type.lower() == "albert":
model.BaseModel = AlbertModel.from_pretrained(args.model_name_or_path)
logger.info(f"Loading AlBert from {args.model_name_or_path}...")
print(model)
model.to(args.device)
train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, label_list, pad_token_label_id, data_type='train')
global_step, lr_loss = train(args, train_dataset, model, tokenizer, label_list, pad_token_label_id)
logger.info(" global_step = %s, average loss = %s", global_step, lr_loss)
# 保存
logger.info("Saving model checkpoint to %s", args.output_dir)
model.save_pretrained(args.output_dir)
tokenizer.save_vocabulary(args.output_dir)
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
# 测试集
if args.do_predict:
tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
logger.info("Predict the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = model_class.from_pretrained(checkpoint, config=config)
model.to(args.device)
predict(args, model, tokenizer, label_list, pad_token_label_id, prefix=prefix)
def main():
parser = ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default="dataset",
type=str,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--config_path",
default="prev_trained_model/electra_small/config.json",
type=str)
parser.add_argument("--vocab_path",
default="prev_trained_model/electra_small/vocab.txt",
type=str)
parser.add_argument(
"--output_dir",
default="outputs",
type=str,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument("--model_path",
default='prev_trained_model/electra_small',
type=str)
parser.add_argument('--data_name', default='electra', type=str)
parser.add_argument(
"--file_num",
type=int,
default=10,
help="Number of dynamic masking to pregenerate (with different masks)")
parser.add_argument(
"--reduce_memory",
action="store_true",
help=
"Store training data as on-disc memmaps to massively reduce memory usage"
)
parser.add_argument("--epochs",
type=int,
default=4,
help="Number of epochs to train for")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument('--num_eval_steps', default=100)
parser.add_argument('--num_save_steps', default=2000)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument("--weight_decay",
default=0.01,
type=float,
help="Weight deay if we apply some.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument("--train_batch_size",
default=128,
type=int,
help="Total batch size for training.")
parser.add_argument("--gen_weight",
default=1.0,
type=float,
help='masked language modeling / generator loss')
parser.add_argument("--disc_weight",
default=50,
type=float,
help='discriminator loss')
parser.add_argument('--untied_generator',
action='store_true',
help='tie all generator/discriminator weights?')
parser.add_argument('--temperature',
default=0,
type=float,
help='temperature for sampling from generator')
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Linear warmup over warmup_steps.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument('--max_grad_norm', default=1.0, type=float)
parser.add_argument("--learning_rate",
default=0.000176,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--fp16_opt_level',
type=str,
default='O2',
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--continue_train',
default='',
help="continue train path")
args = parser.parse_args()
args.data_dir = Path(args.data_dir)
args.output_dir = Path(args.output_dir)
pregenerated_data = args.data_dir / "corpus/train"
init_logger(log_file=str(args.output_dir / "train_albert_model.log"))
assert pregenerated_data.is_dir(), \
"--pregenerated_data should point to the folder of files made by prepare_lm_data_mask.py!"
samples_per_epoch = 0
for i in range(args.file_num):
data_file = pregenerated_data / f"{args.data_name}_file_{i}.json"
metrics_file = pregenerated_data / f"{args.data_name}_file_{i}_metrics.json"
if data_file.is_file() and metrics_file.is_file():
metrics = json.loads(metrics_file.read_text())
samples_per_epoch += metrics['num_training_examples']
else:
if i == 0:
exit("No training data was found!")
print(
f"Warning! There are fewer epochs of pregenerated data ({i}) than training epochs ({args.epochs})."
)
print(
"This script will loop over the available data, but training diversity may be negatively impacted."
)
break
logger.info(f"samples_per_epoch: {samples_per_epoch}")
if args.local_rank == -1 or args.no_cuda:
device = torch.device(f"cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
args.n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
f"device: {device} , distributed training: {bool(args.local_rank != -1)}, 16-bits training: {args.fp16}"
)
if args.gradient_accumulation_steps < 1:
raise ValueError(
f"Invalid gradient_accumulation_steps parameter: {args.gradient_accumulation_steps}, should be >= 1"
)
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
seed_everything(args.seed)
tokenizer = BertTokenizer.from_pretrained(args.vocab_path,
do_lower_case=args.do_lower_case)
total_train_examples = samples_per_epoch * args.epochs
num_train_optimization_steps = int(total_train_examples /
args.train_batch_size /
args.gradient_accumulation_steps)
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
args.warmup_steps = int(num_train_optimization_steps *
args.warmup_proportion)
bert_config = ElectraConfig.from_pretrained(args.config_path,
gen_weight=args.gen_weight,
temperature=args.temperature,
disc_weight=args.disc_weight)
model = ElectraForPreTraining(config=bert_config)
if args.continue_train:
print(f"Continue train from {args.continue_train}")
model = model.from_pretrained(args.continue_train)
elif args.model_path:
print("载入预训练模型")
model.generator = AutoModel.from_pretrained(args.model_path + "/G")
model.electra = AutoModel.from_pretrained(args.model_path + "/D")
# print(model)
model.to(device)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
args.weight_decay
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
optimizer = AdamW(params=optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=num_train_optimization_steps)
# optimizer = Lamb(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
# if args.model_path:
# optimizer.load_state_dict(torch.load(args.model_path + "/optimizer.bin"))
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
if args.n_gpu > 1:
# model = BalancedDataParallel(gpu0_bsz=32,dim=0,model).to(device)
model = torch.nn.DataParallel(model)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank], output_device=args.local_rank)
global_step = 0
g_metric = LMAccuracy()
d_metric = AccuracyThresh()
tr_g_acc = AverageMeter()
tr_d_acc = AverageMeter()
tr_loss = AverageMeter()
tr_g_loss = AverageMeter()
tr_d_loss = AverageMeter()
train_logs = {}
logger.info("***** Running training *****")
logger.info(f" Num examples = {total_train_examples}")
logger.info(f" Batch size = {args.train_batch_size}")
logger.info(f" Num steps = {num_train_optimization_steps}")
logger.info(f" warmup_steps = {args.warmup_steps}")
logger.info(f" Num workable gpus = {args.n_gpu}")
start_time = time.time()
seed_everything(args.seed) # Added here for reproducibility
for epoch in range(args.epochs):
for idx in range(args.file_num):
epoch_dataset = PregeneratedDataset(
file_id=idx,
training_path=pregenerated_data,
tokenizer=tokenizer,
reduce_memory=args.reduce_memory,
data_name=args.data_name)
if args.local_rank == -1:
train_sampler = RandomSampler(epoch_dataset)
else:
train_sampler = DistributedSampler(epoch_dataset)
train_dataloader = DataLoader(epoch_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size)
model.train()
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(train_dataloader):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, lm_label_ids = batch
outputs = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
masked_lm_labels=lm_label_ids)
loss, g_loss, d_loss, d_logits, g_logits, is_replaced_label = outputs
active_indices = input_mask.view(-1) == 1
active_logits = d_logits.view(-1)[active_indices]
active_labels = is_replaced_label.view(-1)[active_indices]
g_metric(logits=g_logits.view(-1, bert_config.vocab_size),
target=lm_label_ids.view(-1))
d_metric(logits=active_logits.view(-1, 1),
target=active_labels)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
g_loss = g_loss.mean()
d_loss = d_loss.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
nb_tr_steps += 1
tr_g_acc.update(g_metric.value(), n=input_ids.size(0))
tr_d_acc.update(d_metric.value(), n=input_ids.size(0))
tr_loss.update(loss.item(), n=1)
tr_g_loss.update(g_loss.item(), n=1)
tr_d_loss.update(d_loss.item(), n=1)
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
scheduler.step()
optimizer.step()
optimizer.zero_grad()
global_step += 1
if global_step % args.num_eval_steps == 0:
now = time.time()
eta = now - start_time
if eta > 3600:
eta_format = ('%d:%02d:%02d' %
(eta // 3600,
(eta % 3600) // 60, eta % 60))
elif eta > 60:
eta_format = '%d:%02d' % (eta // 60, eta % 60)
else:
eta_format = '%ds' % eta
train_logs['loss'] = tr_loss.avg
train_logs['g_acc'] = tr_g_acc.avg
train_logs['d_acc'] = tr_d_acc.avg
train_logs['g_loss'] = tr_g_loss.avg
train_logs['d_loss'] = tr_d_loss.avg
show_info = f'[Training]:[{epoch}/{args.epochs}]{global_step}/{num_train_optimization_steps} ' \
f'- ETA: {eta_format}' + "-".join(
[f' {key}: {value:.4f} ' for key, value in train_logs.items()])
logger.info(show_info)
tr_g_acc.reset()
tr_d_acc.reset()
tr_loss.reset()
tr_g_loss.reset()
tr_d_loss.reset()
start_time = now
if global_step % args.num_save_steps == 0:
if args.local_rank in [-1, 0] and args.num_save_steps > 0:
# Save model checkpoint
output_dir = args.output_dir / f'lm-checkpoint-{global_step}'
if not output_dir.exists():
output_dir.mkdir()
# save model
model_to_save = model.module if hasattr(
model, 'module'
) else model # Take care of distributed/parallel training
model_to_save.save_pretrained(str(output_dir))
torch.save(args, str(output_dir / 'training_args.bin'))
logger.info("Saving model checkpoint to %s",
output_dir)
model.module.generator.save_pretrained(
str(output_dir / "G"))
logger.info("Saving generator model checkpoint to %s",
output_dir / "G")
model.module.electra.save_pretrained(
str(output_dir / "D"))
logger.info("Saving electra model checkpoint to %s",
output_dir / "D")
torch.save(optimizer.state_dict(),
str(output_dir / "optimizer.bin"))
# save config
output_config_file = output_dir / CONFIG_NAME
output_config_file_D = output_dir / "D" / CONFIG_NAME
output_config_file_G = output_dir / "G" / CONFIG_NAME
with open(str(output_config_file), 'w') as f:
f.write(model_to_save.config.to_json_string())
with open(str(output_config_file_D), 'w') as f:
f.write(
model.module.electra.config.to_json_string())
with open(str(output_config_file_G), 'w') as f:
f.write(
model.module.generator.config.to_json_string())
# save vocab
tokenizer.save_vocabulary(output_dir)
def train(args, train_dataset, model, tokenizer, label_list, pad_token_label_id):
""" Train the model """
# 载入数据
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size,
collate_fn=collate_fn)
# 总训练步数
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
# 优化器
if args.optimizer.lower() == "adamw":
no_decay = ["bias", "LayerNorm.weight"]
# bert_parameters = eval('model.{}'.format(args.model_type)).named_parameters()
base_model_param_optimizer = list(model.BaseModel.named_parameters())
start_parameters = model.start_fc.named_parameters()
end_parameters = model.end_fc.named_parameters()
args.bert_lr = args.bert_lr if args.bert_lr else args.learning_rate
args.start_lr = args.start_lr if args.start_lr else args.learning_rate
args.end_lr = args.end_lr if args.end_lr else args.learning_rate
optimizer_grouped_parameters = [
{"params": [p for n, p in base_model_param_optimizer if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
"lr": args.bert_lr},
{"params": [p for n, p in base_model_param_optimizer if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
"lr": args.bert_lr},
{"params": [p for n, p in start_parameters if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
"lr": args.start_lr},
{"params": [p for n, p in start_parameters if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
"lr": args.start_lr},
{"params": [p for n, p in end_parameters if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
"lr": args.end_lr},
{"params": [p for n, p in end_parameters if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
"lr": args.end_lr}
]
if "lstm" in args.model_type.lower():
lstm_param_optimizer = list(model.Bilstm.named_parameters())
optimizer_grouped_parameters.extend([{'params': [p for n, p in lstm_param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay': args.weight_decay, 'lr': args.crf_learning_rate},
{'params': [p for n, p in lstm_param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay': 0.0,'lr': args.crf_learning_rate}])
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
else:
optimizer = Lamb(model.parameters())
# 学习率
args.warmup_steps = int(t_total * args.warmup_proportion) # 学习率预热
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps,
num_training_steps=t_total)
# 继续训练
if args.continue_train and \
os.path.isfile(os.path.join(args.continue_train_checkpoint, "optimizer.pt")) and \
os.path.isfile(os.path.join(args.continue_train_checkpoint, "scheduler.pt")):
# Load in optimizer and scheduler states
optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
if args.fp16:
try:
from apex import amp
logger.info("using fp16 !!!")
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
# 多GPU训练 (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# adversarial_training
if args.adv_training == 'fgm':
adv = FGM(model=model, param_name='word_embeddings')
elif args.adv_training == 'pgd':
adv = PGD(model=model, param_name='word_embeddings')
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size
* args.gradient_accumulation_steps,
)
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
seed_everything(args.seed) # 复现
for _ in range(int(args.num_train_epochs)):
logger.info(f"############### Epoch_{_} ###############")
pbar = ProgressBar(n_total=len(train_dataloader), desc='Training')
for step, batch in enumerate(train_dataloader):
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {"input_ids": batch[0], "attention_mask": batch[1],
"start_positions": batch[5], "end_positions": batch[6]}
if args.model_type != "distilbert": # XLM and RoBERTa don"t use segment_ids
inputs["token_type_ids"] = (batch[2] if args.model_type in ["bert", "xlnet"] else None)
outputs = model(**inputs)
loss = outputs[0]
if args.n_gpu > 1:
loss = loss.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
if args.adv_training:
adv.adversarial_training(args, inputs, optimizer)
pbar(step, {'loss': loss.item()})
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
scheduler.step()
optimizer.step()
model.zero_grad()
global_step += 1
if args.logging_steps > 0 and global_step % args.logging_steps == 0: # 验证集
logger.info("\n global_step： %s", global_step)
logger.info("average tr_loss: %s", tr_loss/global_step)
evaluate(args, model, tokenizer, label_list, pad_token_label_id)
if args.save_steps > 0 and global_step % args.save_steps == 0: # 保存模型
logger.info("global_step： %s 模型已保存！", global_step)
output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
logger.info("Saving model checkpoint to %s", output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, "training_args.bin"))
tokenizer.save_vocabulary(output_dir)
torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
logger.info("\n")
if 'cuda' in str(args.device):
torch.cuda.empty_cache()
return global_step, tr_loss / global_step
def train(args, train_dataset, model, tokenizer):
""" Train the model """
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(
train_dataset) if args.local_rank == -1 else DistributedSampler(
train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size,
collate_fn=collate_fn)
if args.max_steps > 0:
num_training_steps = args.max_steps
args.num_train_epochs = args.max_steps // (
len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
num_training_steps = len(
train_dataloader
) // args.gradient_accumulation_steps * args.num_train_epochs
args.warmup_steps = int(num_training_steps * args.warmup_proportion)
# Prepare optimizer and schedule (linear warmup and decay)
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':
args.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 = Lamb(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
optimizer = AdamW(params=optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=num_training_steps)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(torch.distributed.get_world_size() if args.local_rank != -1 else 1))
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", num_training_steps)
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
seed_everything(
args.seed
) # Added here for reproductibility (even between python 2 and 3)
for _ in range(int(args.num_train_epochs)):
pbar = ProgressBar(n_total=len(train_dataloader), desc='Training')
for step, batch in enumerate(train_dataloader):
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
'input_ids': batch[0],
'attention_mask': batch[1],
'labels': batch[3],
'token_type_ids': batch[2]
}
outputs = model(**inputs)
loss = outputs[
0] # model outputs are always tuple in transformers (see doc)
if args.n_gpu > 1:
loss = loss.mean(
) # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer),
args.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
if args.local_rank in [
-1, 0
] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics
if args.local_rank == -1: # Only evaluate when single GPU otherwise metrics may not average well
evaluate(args, model, tokenizer)
if args.local_rank in [
-1, 0
] and args.save_steps > 0 and global_step % args.save_steps == 0:
# Save model checkpoint
output_dir = os.path.join(args.output_dir,
'checkpoint-{}'.format(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = model.module if hasattr(
model, 'module'
) else model # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, 'training_args.bin'))
logger.info("Saving model checkpoint to %s", output_dir)
pbar(step, {'loss': loss.item()})
print(" ")
if 'cuda' in str(args.device):
torch.cuda.empty_cache()
return global_step, tr_loss / global_step
def main():
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
# type_task = args.model_type + '_' + '{}'.format(args.task_name)
# if not os.path.exists(os.path.join(args.output_dir, type_task)):
# os.mkdir(os.path.join(args.output_dir, type_task))
init_logger(log_file=args.output_dir +
'/{}-{}.log'.format(args.model_type, args.task_name))
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
args.n_gpu = 1
args.device = device
# logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
# args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
seed_everything(args.seed)
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
# if args.local_rank not in [-1, 0]:
# torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config = AlbertConfig.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
finetuning_task=args.task_name)
tokenizer = tokenization_albert.FullTokenizer(
vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case,
)
model = AlbertForSequenceClassification.from_pretrained(
args.model_name_or_path, config=config)
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
args.do_train = True
if args.do_train:
train_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
data_type='train')
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step,
tr_loss)
if args.do_train:
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
model_to_save = model.module if hasattr(
model,
'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
# Evaluation
args.do_eval = True
results = []
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = tokenization_albert.FullTokenizer(
vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case,
)
checkpoints = [(0, args.output_dir)]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
checkpoints = [(int(checkpoint.split('-')[-1]), checkpoint)
for checkpoint in checkpoints
if checkpoint.find('checkpoint') != -1]
checkpoints = sorted(checkpoints, key=lambda x: x[0])
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for _, checkpoint in checkpoints:
global_step = checkpoint.split(
'-')[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = AlbertForSequenceClassification.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=prefix)
results.extend([(k + '_{}'.format(global_step), v)
for k, v in result.items()])
output_eval_file = os.path.join(args.output_dir,
"checkpoint_eval_results.txt")
with open(output_eval_file, "w") as writer:
for key, value in results:
writer.write("%s = %s\n" % (key, str(value)))
args.do_predict = True
predict_results = []
if args.do_predict and args.local_rank in [-1, 0]:
tokenizer = tokenization_albert.FullTokenizer(
vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case,
)
checkpoints = [(0, args.output_dir)]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
checkpoints = [(int(checkpoint.split('-')[-1]), checkpoint)
for checkpoint in checkpoints
if checkpoint.find('checkpoint') != -1]
checkpoints = sorted(checkpoints, key=lambda x: x[0])
logger.info("Evaluate the following checkpoints: %s", checkpoints)
checkpoints = [checkpoints[-1]]
for _, checkpoint in checkpoints:
global_step = checkpoint.split(
'-')[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = AlbertForSequenceClassification.from_pretrained(checkpoint)
model.to(args.device)
result = predict(args, model, tokenizer, prefix=prefix)
predict_results.extend([(k + '_{}'.format(global_step), v)
for k, v in result.items()])
output_eval_file = os.path.join(args.output_dir,
"checkpoint_eval_results.txt")
with open(output_eval_file, "w") as writer:
for key, value in predict_results:
writer.write("%s = %s\n" % (key, str(value)))
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train selected in the list: " +
", ".join(processors.keys()))
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the training files for the CoNLL-2003 NER task.",
)
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()),
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: "
+ ", ".join(ALL_MODELS),
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written.",
)
# Other parameters
parser.add_argument('--markup',
default='bios',
type=str,
choices=['bios', 'bio'])
parser.add_argument('--loss_type',
default='ce',
type=str,
choices=['lsr', 'focal', 'ce'])
parser.add_argument(
"--config_name",
default="",
type=str,
help="Pretrained config name or path if not the same as model_name")
parser.add_argument(
"--tokenizer_name",
default="",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3",
)
parser.add_argument(
"--train_max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--eval_max_seq_length",
default=512,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument("--do_train",
action="store_true",
help="Whether to run training.")
parser.add_argument("--do_eval",
action="store_true",
help="Whether to run eval on the dev set.")
parser.add_argument("--do_predict",
action="store_true",
help="Whether to run predictions on the test set.")
parser.add_argument(
"--evaluate_during_training",
action="store_true",
help="Whether to run evaluation during training at each logging step.",
)
parser.add_argument(
"--do_lower_case",
action="store_true",
help="Set this flag if you are using an uncased model.")
parser.add_argument("--do_adv",
action="store_true",
help="Whether to adversarial training.")
parser.add_argument('--adv_epsilon',
default=1.0,
type=float,
help="Epsilon for adversarial.")
parser.add_argument('--adv_name',
default='word_embeddings',
type=str,
help="name for adversarial layer.")
parser.add_argument("--per_gpu_train_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.01,
type=float,
help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=
"If > 0: set total number of training steps to perform. Override num_train_epochs.",
)
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for,E.g., 0.1 = 10% of training."
)
parser.add_argument("--logging_steps",
type=int,
default=50,
help="Log every X updates steps.")
parser.add_argument("--save_steps",
type=int,
default=50,
help="Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action="store_true",
help=
"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
)
parser.add_argument(
'--predict_checkpoints',
action="store_true",
help=
"Predict checkpoints starting with the same prefix as model_name ending and ending with step number",
)
parser.add_argument("--no_cuda",
action="store_true",
help="Avoid using CUDA when available")
parser.add_argument("--overwrite_output_dir",
action="store_true",
help="Overwrite the content of the output directory")
parser.add_argument(
"--overwrite_cache",
action="store_true",
help="Overwrite the cached training and evaluation sets")
parser.add_argument("--seed",
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
"--fp16",
action="store_true",
help=
"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
parser.add_argument(
"--fp16_opt_level",
type=str,
default="O1",
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html",
)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank")
parser.add_argument("--server_ip",
type=str,
default="",
help="For distant debugging.")
parser.add_argument("--server_port",
type=str,
default="",
help="For distant debugging.")
args = parser.parse_args()
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
args.output_dir = args.output_dir + '{}'.format(args.model_type)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
time_ = time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime())
init_logger(log_file=args.output_dir +
f'/{args.model_type}-{args.task_name}-{time_}.log')
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args.output_dir))
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank,
device,
args.n_gpu,
bool(args.local_rank != -1),
args.fp16,
)
# Set seed
seed_everything(args.seed)
# Prepare NER task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
label_list = processor.get_labels()
args.id2label = {i: label for i, label in enumerate(label_list)}
args.label2id = {label: i for i, label in enumerate(label_list)}
num_labels = len(label_list)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
loss_type=args.loss_type,
cache_dir=args.cache_dir if args.cache_dir else None,
)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name
if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None,
)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None,
)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
data_type='train')
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step,
tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = (model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_vocabulary(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(
logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split(
"-")[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=prefix)
if global_step:
result = {
"{}_{}".format(global_step, k): v
for k, v in result.items()
}
results.update(result)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
for key in sorted(results.keys()):
writer.write("{} = {}\n".format(key, str(results[key])))
if args.do_predict and args.local_rank in [-1, 0]:
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.predict_checkpoints > 0:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("transformers.modeling_utils").setLevel(
logging.WARN) # Reduce logging
checkpoints = [
x for x in checkpoints
if x.split('-')[-1] == str(args.predict_checkpoints)
]
logger.info("Predict the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
predict(args, model, tokenizer, prefix=prefix)
def train(args, train_dataset, model, tokenizer):
""" Train the model """
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size,
collate_fn=collate_fn)
if args.max_steps > 0:
num_training_steps = args.max_steps
args.num_train_epochs = args.max_steps // (
len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
num_training_steps = len(
train_dataloader
) // args.gradient_accumulation_steps * args.num_train_epochs
args.warmup_steps = int(num_training_steps * args.warmup_proportion)
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':
args.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(params=optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=num_training_steps)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
args.per_gpu_train_batch_size)
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", num_training_steps)
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
seed_everything(
args.seed
) # Added here for reproductibility (even between python 2 and 3)
for _ in range(int(args.num_train_epochs)):
pbar = ProgressBar(n_total=len(train_dataloader), desc='Training')
for step, batch in enumerate(train_dataloader):
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
'input_ids': batch[0],
'attention_mask': batch[1],
'labels': batch[3]
}
inputs['token_type_ids'] = batch[2]
outputs = model(**inputs)
loss = outputs[0]
if args.n_gpu > 1:
loss = loss.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
scheduler.step()
model.zero_grad()
global_step += 1
if args.local_rank in [
-1, 0
] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics
if args.local_rank == -1:
evaluate(args, model, tokenizer)
# if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
# output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
# if not os.path.exists(output_dir):
# os.makedirs(output_dir)
# model_to_save = model.module if hasattr(model,
# 'module') else model
# model_to_save.save_pretrained(output_dir)
# torch.save(args, os.path.join(output_dir, 'training_args.bin'))
# logger.info("Saving model checkpoint to %s", output_dir)
pbar(step, {'loss': loss.item()})
print(" ")
if 'cuda' in str(args.device):
torch.cuda.empty_cache()
# if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
output_dir = os.path.join(args.output_dir,
'checkpoint-{}'.format(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, 'training_args.bin'))
logger.info("Saving model checkpoint to %s", output_dir)
return global_step, tr_loss / global_step
def main():
args = get_argparse().parse_args()
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
args.output_dir = args.output_dir + '{}'.format(args.model_type)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
time_ = time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime())
init_logger(log_file=args.output_dir +
f'/{args.model_type}-{args.task_name}-{time_}.log')
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args.output_dir))
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank,
device,
args.n_gpu,
bool(args.local_rank != -1),
args.fp16,
)
# Set seed
seed_everything(args.seed)
# Prepare NER task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
label_list = processor.get_labels()
args.id2label = {i: label for i, label in enumerate(label_list)}
args.label2id = {label: i for i, label in enumerate(label_list)}
num_labels = len(label_list)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
cache_dir=args.cache_dir if args.cache_dir else None)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name
if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
data_type='train')
global_step, tr_loss = train(args, train_dataset, model, tokenizer,
config)
logger.info(" global_step = %s, average loss = %s", global_step,
tr_loss)
def train(args, train_dataset, model, tokenizer):
""" Train the model """
###################################################################
# 初始化一个pandas DataFrame进行训练日志的存储
df_path = args.output_dir + "/bert" + "df_log.pickle"
if not os.path.isfile(df_path):
df = pd.DataFrame(columns=[
"epoch", "train_loss", "train_auc", "test_loss", "test_auc"
])
df.to_pickle(df_path)
print("log DataFrame created!")
#################################################################
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(
train_dataset) if args.local_rank == -1 else DistributedSampler(
train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size,
collate_fn=collate_fn)
t_total = len(train_dataloader
) // args.gradient_accumulation_steps * args.num_train_epochs
args.warmup_steps = int(t_total * args.warmup_proportion)
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':
args.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=args.learning_rate,
eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=args.warmup_steps,
t_total=t_total)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
seed_everything(args.seed)
for _ in range(int(args.num_train_epochs)):
all_predictions, all_labels = [], []
pbar = ProgressBar(n_total=len(train_dataloader), desc='Train')
for step, batch in enumerate(train_dataloader):
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
'input_ids': batch[0],
'attention_mask': batch[1],
'labels': batch[3]
}
if args.model_type != 'distilbert':
inputs['token_type_ids'] = batch[2] if args.model_type in [
'bert', 'xlnet', 'albert', 'roberta'
] else None # XLM, DistilBERT don't use segment_ids
outputs = model(**inputs)
loss = outputs[0]
#############################################################################################################################
# 提取预测的结果和标记, 并存到all_predictions, all_labels里
# 用来计算auc
# 存储所有预测的结果和标记, 用来计算auc
predictions = fl.softmax(outputs[1], dim=-1)[:, 1]
predictions = predictions.detach().cpu().numpy().reshape(
-1).tolist()
labels = batch[3].cpu().numpy().reshape(-1).tolist()
all_predictions.extend(predictions)
all_labels.extend(labels)
# 计算auc
fpr, tpr, thresholds = metrics.roc_curve(y_true=all_labels,
y_score=all_predictions)
auc = metrics.auc(fpr, tpr)
#############################################################################################################################
if args.n_gpu > 1:
loss = loss.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
tr_loss += loss.item()
###############################################################################
log_dic = {
"epoch": _,
"train_loss": tr_loss / (step + 1),
"train_auc": auc,
"test_loss": 0,
"test_auc": 0
}
if step % 10 == 0:
print((str({k: v for k, v in log_dic.items() if v != 0})))
df = pd.read_pickle(df_path)
df = df.append([log_dic])
df.reset_index(inplace=True, drop=True)
df.to_pickle(df_path)
##############################################################################
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
scheduler.step()
model.zero_grad()
global_step += 1
# flag
if args.local_rank in [
-1, 0
] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
if args.local_rank == -1:
results = evaluate(args, model, tokenizer)
# 每args.save_steps步保存一次模型
if args.local_rank in [
-1, 0
] and args.save_steps > 0 and global_step % args.save_steps == 0:
output_dir = os.path.join(
args.output_dir, 'checkpoint-{}'.format(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = model.module if hasattr(
model, 'module'
) else model # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
torch.save(args,
os.path.join(output_dir, 'training_args.bin'))
logger.info("Saving model checkpoint to %s", output_dir)
tokenizer.save_vocabulary(vocab_path=output_dir)
pbar(step, {'loss': loss.item()})
####################################################################################################################
global all_auc
global threshold
all_auc.append(auc)
best_auc = max(all_auc)
if all_auc[-1] < best_auc:
threshold += 1
args.learning_rate *= 0.8
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
predict(args, model, tokenizer, prefix="")
else:
# 如果
threshold = 0
if threshold >= 5:
print("epoch {} has the lowest loss".format(
0 + np.argmax(np.array(all_auc))))
print("early stop!")
break
#####################################################################################################################
print(" ")
if 'cuda' in str(args.device):
torch.cuda.empty_cache()
return global_step, tr_loss / global_step