def __init__(self, args):
self.cuda = 'True'
self.model_name = 'bert'
self.train_path = args.train_path
self.dev_path = args.dev_path
self.test_path = args.test_path
self.save_path = args.save_path
self.use_cuda = args.use_cuda
self.num_class = args.num_class
self.num_epoch = args.num_epoch
self.batch_size = args.batch_size
self.pad_size = args.pad_size
self.learning_rate = args.lr
self.bert_pretrain_model = args.pretrain_model_path
self.tokenizer = BertTokenizer.from_pretrained(
self.bert_pretrain_model + "/bert-base-chinese-vocab.txt")
self.hidden_size = 768
self.log_path = 'logs/' + self.model_name
from configs.base import config
from model.modeling_albert import BertConfig, BertModel
from model.tokenization_bert import BertTokenizer
from keras.preprocessing.sequence import pad_sequences
from torch.utils.data import TensorDataset, DataLoader, RandomSampler
import os
device = torch.device('cuda' if torch.cuda.is_available() else "cpu")
MAX_LEN = 10
if __name__ == '__main__':
bert_config = BertConfig.from_pretrained(str(config['albert_config_path']),
share_type='all')
base_path = os.getcwd()
VOCAB = base_path + '/configs/vocab.txt' # your path for model and vocab
tokenizer = BertTokenizer.from_pretrained(VOCAB)
# encoder text
tag2idx = {
'[SOS]': 101,
'[EOS]': 102,
'[PAD]': 0,
'B_LOC': 1,
'I_LOC': 2,
'O': 3
}
sentences = ['我是中华人民共和国国民', '我爱祖国']
tags = ['O O B_LOC I_LOC I_LOC I_LOC I_LOC I_LOC O O', 'O O O O']
tokenized_text = [tokenizer.tokenize(sent) for sent in sentences]
# 利用pad_sequence对序列长度进行截断和padding
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)
)
features.append(
InputFeatures(
data_id=data_id,
example_id=example.example_id,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
label=label,
)
)
for f in features[:2]:
logger.info("*** Example ***")
logger.info("feature: %s" % f)
return features
if __name__ == '__main__':
pretrained_token = './albert_model_pretrain/'
tokenizer = BertTokenizer.from_pretrained(pretrained_token)
dataset = MultipleChoiceDataset(data_dir='data/',
tokenizer=tokenizer,
task='justice_race',
max_seq_length=512,
overwrite_cache=False,
mode=Split.train, )
def main():
config = {
"model_type": "albert",
"model_name_or_path": "outputs/lm-checkpoint",
"task_name": "lcqmc",
"do_train": True,
"do_eval": True,
"do_lower_case": True,
"data_dir": "dataset/lcqmc",
"vocab_file": "outputs/lm-checkpoint/vocab.txt",
"config_path": "outputs/lm-checkpoint/config.json",
"max_seq_length": 128,
"output_dir": "outputs/lcqmc_output",
"overwrite_output_dir": True,
"learning_rate": 1e-5,
"num_train_epochs": 3.0,
"logging_steps": 14923,
"save_steps": 14923,
# "per_gpu_train_batch_size": 16,
# "per_gpu_eval_batch_size": 16,
}
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default=config["data_dir"],
type=str,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--model_type",
default=config["model_type"],
type=str,
help="Model type selected in the list: ")
parser.add_argument(
"--model_name_or_path",
default=config["model_name_or_path"],
type=str,
help="Path to pre-trained model or shortcut name selected in the list")
parser.add_argument(
"--task_name",
default=config["task_name"],
type=str,
help="The name of the task to train selected in the list: " +
", ".join(processors.keys()))
parser.add_argument(
"--output_dir",
default=config["output_dir"],
type=str,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument("--vocab_file", default=config["vocab_file"], type=str)
parser.add_argument("--spm_model_file", default='', type=str)
parser.add_argument("--config_path",
default=config["config_path"],
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=config["max_seq_length"],
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=config["do_train"],
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
default=config["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",
default=config["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',
default=config["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()
print(f" fun_classifier args {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 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 = 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)
config = AlbertConfig.from_pretrained(args.config_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)
tokenizer = BertTokenizer.from_pretrained(args.vocab_file,
do_lower_case=args.do_lower_case)
model = AlbertForSequenceClassification.from_pretrained(
args.model_name_or_path,
from_tf=bool('.ckpt' in args.model_name_or_path),
config=config)
# bert_config = AlbertConfig.from_pretrained(args.config_path)
# model = AlbertForPreTraining(config=bert_config)
# if args.model_path:
# model = AlbertForPreTraining.from_pretrained(args.model_path)
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'))
# 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 = 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)))
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser(
(ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if (os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir) and training_args.do_train
and not training_args.overwrite_output_dir):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO
if training_args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
)
logger.info("Training/evaluation parameters %s", training_args)
# Set seed
set_seed(training_args.seed)
try:
processor = processors[data_args.task_name]()
label_list = processor.get_labels()
num_labels = len(label_list)
except KeyError:
raise ValueError("Task not found: %s" % (data_args.task_name))
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
tokenizer = BertTokenizer.from_pretrained(
model_args.tokenizer_name
if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
)
model = AlbertForJustice.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
cache_dir=model_args.cache_dir,
)
# Get datasets
train_dataset = (MultipleChoiceDataset(
data_dir=data_args.data_dir,
tokenizer=tokenizer,
task=data_args.task_name,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
mode=Split.train,
) if training_args.do_train else None)
eval_dataset = (MultipleChoiceDataset(
data_dir=data_args.data_dir,
tokenizer=tokenizer,
task=data_args.task_name,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
mode=Split.dev,
) if training_args.do_eval else None)
def compute_metrics(p: EvalPrediction) -> Dict:
preds = p.predictions
ac = (np.argmax(preds, axis=1) == p.label_ids).mean()
# preds = np.argmax(p.predictions, axis=1)
return {"acc": np.array(ac).mean()}
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
)
# Training
if training_args.do_train:
trainer.train(model_path=model_args.model_name_or_path if os.path.
isdir(model_args.model_name_or_path) else None)
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation
results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
result = trainer.evaluate()
output_eval_file = os.path.join(training_args.output_dir,
"eval_results.txt")
if trainer.is_world_master():
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key, value in result.items():
logger.info(" %s = %s", key, value)
writer.write("%s = %s\n" % (key, value))
results.update(result)
return results
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
)
logger.info("Training/evaluation parameters %s", training_args)
# Set seed
set_seed(training_args.seed)
try:
processor = processors[data_args.task_name]()
label_list = processor.get_labels()
except KeyError:
raise ValueError("Task not found: %s" % (data_args.task_name))
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
tokenizer = BertTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
)
model = AlbertForJustice.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
cache_dir=model_args.cache_dir,
)
eval_dataset = (
MultipleChoiceDataset(
data_dir=data_args.data_dir,
tokenizer=tokenizer,
task=data_args.task_name,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
mode=Split.dev,
)
if training_args.do_eval
else None
)
def compute_metrics(p: EvalPrediction) -> Dict:
preds = p.predictions
ac = (preds == p.label_ids).mean()
# preds = np.argmax(p.predictions, axis=1)
return {"acc": np.array(ac).mean()}
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
)
# Evaluation
output_map = {
0: [],
1: ['A'],
2: ['B'],
4: ['C'],
8: ['D'],
5: ['A', 'B'],
6: ['B', 'C'],
7: ['A', 'B', 'C'],
9: ['A', 'D'],
10: ['B', 'D'],
11: ['A', 'B', 'D'],
12: ['C', 'D'],
13: ['A', 'C', 'D'],
14: ['B', 'C', 'D'],
15: ['A', 'B', 'C', 'D']
}
if training_args.do_eval:
logger.info("*** Evaluate ***")
# result = trainer.evaluate()
result = trainer.predict(eval_dataset)
result_out = {}
for i, j in zip(result.predictions, result.id):
result_out[j] = output_map[i]
output_eval_file = os.path.join(training_args.output_dir, "result.txt")
json.dump(result_out, open(output_eval_file, "w", encoding="utf8"), indent=2, ensure_ascii=False,
sort_keys=True)
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)
if args.model_path:
model = AlbertForPreTraining.from_pretrained(args.model_path)
