def main():
print("IN NEW MAIN XD\n")
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--input_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain .hdf5 files for the task.")
parser.add_argument("--config_file",
default="bert_config.json",
type=str,
required=False,
help="The BERT model config")
parser.add_argument("--ckpt_dir",
default=None,
type=str,
required=True,
help="The ckpt directory, e.g. /results")
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('--eval', dest='do_eval', action='store_true')
group.add_argument('--prediction', dest='do_eval', action='store_false')
## Other parameters
parser.add_argument(
"--bert_model",
default="bert-large-uncased",
type=str,
required=False,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese."
)
parser.add_argument(
"--max_seq_length",
default=512,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument(
"--max_predictions_per_seq",
default=80,
type=int,
help="The maximum total of masked tokens in input sequence")
parser.add_argument("--ckpt_step",
default=-1,
type=int,
required=False,
help="The model checkpoint iteration, e.g. 1000")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for training.")
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=
"Total number of eval steps to perform, otherwise use full dataset")
parser.add_argument("--no_cuda",
default=False,
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--fp16',
default=False,
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
args = parser.parse_args()
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")
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
n_gpu = torch.cuda.device_count()
if n_gpu > 1:
assert (args.local_rank != -1
) # only use torch.distributed for multi-gpu
logger.info("device %s n_gpu %d distributed inference %r", device, n_gpu,
bool(args.local_rank != -1))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
# Prepare model
config = BertConfig.from_json_file(args.config_file)
model = BertForPreTraining(config)
if args.ckpt_step == -1:
#retrieve latest model
model_names = [
f for f in os.listdir(args.ckpt_dir) if f.endswith(".model")
]
args.ckpt_step = max([
int(x.split('.model')[0].split('_')[1].strip())
for x in model_names
])
print("load model saved at iteraton", args.ckpt_step)
model_file = os.path.join(args.ckpt_dir,
"ckpt_" + str(args.ckpt_step) + ".model")
state_dict = torch.load(model_file, map_location="cpu")
model.load_state_dict(state_dict, strict=False)
if args.fp16:
model.half(
) # all parameters and buffers are converted to half precision
model.to(device)
multi_gpu_training = args.local_rank != -1 and torch.distributed.is_initialized(
)
if multi_gpu_training:
model = DDP(model)
files = [
os.path.join(args.input_dir, f) for f in os.listdir(args.input_dir)
if os.path.isfile(os.path.join(args.input_dir, f))
]
files.sort()
logger.info("***** Running evaluation *****")
logger.info(" Batch size = %d", args.eval_batch_size)
model.eval()
print("Evaluation. . .")
nb_instances = 0
max_steps = args.max_steps if args.max_steps > 0 else np.inf
global_step = 0
with torch.no_grad():
if args.do_eval:
final_loss = 0.0 #
for data_file in files:
logger.info("file %s" % (data_file))
dataset = pretraining_dataset(
input_file=data_file,
max_pred_length=args.max_predictions_per_seq)
if not multi_gpu_training:
train_sampler = RandomSampler(dataset)
datasetloader = DataLoader(dataset,
sampler=train_sampler,
batch_size=args.eval_batch_size,
num_workers=4,
pin_memory=True)
else:
train_sampler = DistributedSampler(dataset)
datasetloader = DataLoader(dataset,
sampler=train_sampler,
batch_size=args.eval_batch_size,
num_workers=4,
pin_memory=True)
for step, batch in enumerate(
tqdm(datasetloader, desc="Iteration")):
if global_step > max_steps:
break
batch = [t.to(device) for t in batch]
input_ids, segment_ids, input_mask, masked_lm_labels, next_sentence_labels = batch #\
loss = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
next_sentence_label=next_sentence_labels)
final_loss += loss
global_step += 1
torch.cuda.empty_cache()
if global_step > max_steps:
break
final_loss /= global_step
if multi_gpu_training:
final_loss /= torch.distributed.get_world_size()
dist.all_reduce(final_loss)
if (not multi_gpu_training or
(multi_gpu_training and torch.distributed.get_rank() == 0)):
logger.info("Finished: Final Loss = {}".format(final_loss))
else: # inference
# if multi_gpu_training:
# torch.distributed.barrier()
# start_t0 = time.time()
for data_file in files:
logger.info("file %s" % (data_file))
dataset = pretraining_dataset(
input_file=data_file,
max_pred_length=args.max_predictions_per_seq)
if not multi_gpu_training:
train_sampler = RandomSampler(dataset)
datasetloader = DataLoader(dataset,
sampler=train_sampler,
batch_size=args.eval_batch_size,
num_workers=4,
pin_memory=True)
else:
train_sampler = DistributedSampler(dataset)
datasetloader = DataLoader(dataset,
sampler=train_sampler,
batch_size=args.eval_batch_size,
num_workers=4,
pin_memory=True)
for step, batch in enumerate(
tqdm(datasetloader, desc="Iteration")):
if global_step > max_steps:
break
batch = [t.to(device) for t in batch]
input_ids, segment_ids, input_mask, masked_lm_labels, next_sentence_labels = batch #\
lm_logits, nsp_logits = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
masked_lm_labels=None,
next_sentence_label=None)
nb_instances += input_ids.size(0)
global_step += 1
torch.cuda.empty_cache()
if global_step > max_steps:
break
# if multi_gpu_training:
# torch.distributed.barrier()
if (not multi_gpu_training or
(multi_gpu_training and torch.distributed.get_rank() == 0)):
logger.info("Finished")
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--input_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain .hdf5 files for the task.")
parser.add_argument("--config_file",
default="bert_config.json",
type=str,
required=False,
help="The BERT model config")
ckpt_group = parser.add_mutually_exclusive_group(required=True)
ckpt_group.add_argument("--ckpt_dir",
default=None,
type=str,
help="The ckpt directory, e.g. /results")
ckpt_group.add_argument("--ckpt_path",
default=None,
type=str,
help="Path to the specific checkpoint")
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('--eval', dest='do_eval', action='store_true')
group.add_argument('--prediction', dest='do_eval', action='store_false')
## Other parameters
parser.add_argument(
"--bert_model",
default="bert-large-uncased",
type=str,
required=False,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese."
)
parser.add_argument(
"--max_seq_length",
default=512,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument(
"--max_predictions_per_seq",
default=80,
type=int,
help="The maximum total of masked tokens in input sequence")
parser.add_argument("--ckpt_step",
default=-1,
type=int,
required=False,
help="The model checkpoint iteration, e.g. 1000")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for training.")
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=
"Total number of eval steps to perform, otherwise use full dataset")
parser.add_argument("--no_cuda",
default=False,
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--fp16',
default=False,
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument("--log_path",
help="Out file for DLLogger",
default="/workspace/dllogger_inference.out",
type=str)
args = parser.parse_args()
if 'LOCAL_RANK' in os.environ:
args.local_rank = int(os.environ['LOCAL_RANK'])
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")
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
if is_main_process():
dllogger.init(backends=[
dllogger.JSONStreamBackend(verbosity=dllogger.Verbosity.VERBOSE,
filename=args.log_path),
dllogger.StdOutBackend(verbosity=dllogger.Verbosity.VERBOSE,
step_format=format_step)
])
else:
dllogger.init(backends=[])
n_gpu = torch.cuda.device_count()
if n_gpu > 1:
assert (args.local_rank != -1
) # only use torch.distributed for multi-gpu
dllogger.log(
step=
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16),
data={})
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
# Prepare model
config = BertConfig.from_json_file(args.config_file)
# Padding for divisibility by 8
if config.vocab_size % 8 != 0:
config.vocab_size += 8 - (config.vocab_size % 8)
model = BertForPreTraining(config)
if args.ckpt_dir:
if args.ckpt_step == -1:
#retrieve latest model
model_names = [
f for f in os.listdir(args.ckpt_dir) if f.endswith(".pt")
]
args.ckpt_step = max([
int(x.split('.pt')[0].split('_')[1].strip())
for x in model_names
])
dllogger.log(step="load model saved at iteration",
data={"number": args.ckpt_step})
model_file = os.path.join(args.ckpt_dir,
"ckpt_" + str(args.ckpt_step) + ".pt")
else:
model_file = args.ckpt_path
state_dict = torch.load(model_file, map_location="cpu")["model"]
model.load_state_dict(state_dict, strict=False)
if args.fp16:
model.half(
) # all parameters and buffers are converted to half precision
model.to(device)
multi_gpu_training = args.local_rank != -1 and torch.distributed.is_initialized(
)
if multi_gpu_training:
model = DDP(model)
files = [
os.path.join(args.input_dir, f) for f in os.listdir(args.input_dir)
if os.path.isfile(os.path.join(args.input_dir, f)) and 'test' in f
]
files.sort()
dllogger.log(step="***** Running Inference *****", data={})
dllogger.log(step=" Inference batch", data={"size": args.eval_batch_size})
model.eval()
nb_instances = 0
max_steps = args.max_steps if args.max_steps > 0 else np.inf
global_step = 0
total_samples = 0
begin_infer = time.time()
with torch.no_grad():
if args.do_eval:
final_loss = 0.0 #
for data_file in files:
dllogger.log(step="Opening ", data={"file": data_file})
dataset = pretraining_dataset(
input_file=data_file,
max_pred_length=args.max_predictions_per_seq)
if not multi_gpu_training:
train_sampler = RandomSampler(dataset)
datasetloader = DataLoader(dataset,
sampler=train_sampler,
batch_size=args.eval_batch_size,
num_workers=4,
pin_memory=True)
else:
train_sampler = DistributedSampler(dataset)
datasetloader = DataLoader(dataset,
sampler=train_sampler,
batch_size=args.eval_batch_size,
num_workers=4,
pin_memory=True)
for step, batch in enumerate(
tqdm(datasetloader, desc="Iteration")):
if global_step > max_steps:
break
batch = [t.to(device) for t in batch]
input_ids, segment_ids, input_mask, masked_lm_labels, next_sentence_labels = batch #\
loss = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
next_sentence_label=next_sentence_labels)
final_loss += loss.item()
global_step += 1
total_samples += len(datasetloader)
torch.cuda.empty_cache()
if global_step > max_steps:
break
final_loss /= global_step
if multi_gpu_training:
final_loss = torch.tensor(final_loss, device=device)
dist.all_reduce(final_loss)
final_loss /= torch.distributed.get_world_size()
if (not multi_gpu_training or
(multi_gpu_training and torch.distributed.get_rank() == 0)):
dllogger.log(step="Inference Loss",
data={"final_loss": final_loss.item()})
else: # inference
# if multi_gpu_training:
# torch.distributed.barrier()
# start_t0 = time.time()
for data_file in files:
dllogger.log(step="Opening ", data={"file": data_file})
dataset = pretraining_dataset(
input_file=data_file,
max_pred_length=args.max_predictions_per_seq)
if not multi_gpu_training:
train_sampler = RandomSampler(dataset)
datasetloader = DataLoader(dataset,
sampler=train_sampler,
batch_size=args.eval_batch_size,
num_workers=4,
pin_memory=True)
else:
train_sampler = DistributedSampler(dataset)
datasetloader = DataLoader(dataset,
sampler=train_sampler,
batch_size=args.eval_batch_size,
num_workers=4,
pin_memory=True)
for step, batch in enumerate(
tqdm(datasetloader, desc="Iteration")):
if global_step > max_steps:
break
batch = [t.to(device) for t in batch]
input_ids, segment_ids, input_mask, masked_lm_labels, next_sentence_labels = batch #\
lm_logits, nsp_logits = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
masked_lm_labels=None,
next_sentence_label=None)
nb_instances += input_ids.size(0)
global_step += 1
total_samples += len(datasetloader)
torch.cuda.empty_cache()
if global_step > max_steps:
break
# if multi_gpu_training:
# torch.distributed.barrier()
if (not multi_gpu_training or
(multi_gpu_training and torch.distributed.get_rank() == 0)):
dllogger.log(step="Done Inferring on samples", data={})
end_infer = time.time()
dllogger.log(step="Inference perf",
data={
"inference_sequences_per_second":
total_samples * args.eval_batch_size /
(end_infer - begin_infer)
})
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(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--bert_model",
default='bert-base-multilingual-cased',
type=str,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument(
"--max_seq_length",
default=384,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this 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("--train_batch_size",
default=2,
type=int,
help="Total batch size for training.")
# parser.add_argument("--eval_batch_size",
# default=2,
# type=int,
# help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=3e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
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("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on GPUs")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumualte before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
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('--visdom',
action='store_true',
help='Use visdom for loss visualization')
parser.add_argument('--check_saved_model',
action='store_true',
help='Use visdom for loss visualization')
parser.add_argument('--last_final_epoch',
type=int,
default=-1,
help="저번에 이미 최종 학습을 했고, 이에 이어서 트레이닝을 원할때 사용,\n"
"기존에 train_epoch를 3으로 세팅했다면, 2가 아닌 3을 입력하세요.")
args = parser.parse_args()
print(args)
if args.visdom:
import visdom
viz = visdom.Visdom()
# visdom을 통해서 loss를 시각화
os.makedirs(args.output_dir, exist_ok=True)
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")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train:
raise ValueError(
"Training is currently the only implemented execution option. Please set `do_train`."
)
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=False)
processor = DataProcessor()
label_list = processor.get_labels()
num_train_optimization_steps = None
if args.do_train:
print("Loading Train Dataset", args.data_dir)
train_examples = processor.get_train_examples(args.data_dir)
train_dataset = LazyDataset(train_examples, args.max_seq_length,
tokenizer)
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset)
else:
train_sampler = DistributedSampler(train_dataset)
num_train_optimization_steps = int(
len(train_dataset) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
# Prepare model
loaded_epoch = -1
saved_model_path = -1
if args.last_final_epoch != -1:
last_model = os.path.join(args.output_dir, WEIGHTS_NAME)
if os.path.exists(last_model):
saved_model_path = last_model
loaded_epoch = args.last_final_epoch - 1
elif args.check_saved_model:
for epoch in range(int(args.num_train_epochs)):
tmp = os.path.join(args.output_dir,
(f"weight_on_ep{epoch}_" + WEIGHTS_NAME))
if os.path.exists(tmp):
saved_model_path = tmp
loaded_epoch = epoch
if saved_model_path != -1:
logger.info(f"Loading on saved model {saved_model_path}")
config_file = os.path.join(args.output_dir, CONFIG_NAME)
config = BertConfig(config_file)
logger.info("Model config {}".format(config))
model = BertForPreTraining(config)
model.load_state_dict(torch.load(saved_model_path))
else:
loaded_epoch = -1
model = BertForPreTraining.from_pretrained(args.bert_model)
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
# 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':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
if args.visdom:
# 일단 visdom 기본 figure를 정의
vis_title = f'Baseline on {len(train_dataset)} dataset'
vis_legend = ['LM Loss', 'Click Loss', 'Total Loss']
iter_plot = create_vis_plot(viz, 'Iteration', 'Loss', vis_title,
vis_legend)
epoch_plot = create_vis_plot(viz, 'Epoch', 'Loss', vis_title,
vis_legend)
# if args.do_eval:
# eval_examples = processor.get_dev_examples(args.data_dir)
#
# logger.info("***** Running evaluation *****")
# logger.info(" Num examples = %d", len(eval_examples))
# logger.info(" Batch size = %d", args.eval_batch_size)
#
# eval_data = LazyDatasetClassifier(eval_examples, label_list, args.max_seq_length, tokenizer)
# # Run prediction for full data
# """
# cur_tensors = (torch.tensor(f.input_ids),
# torch.tensor(f.input_mask),
# torch.tensor(f.segment_ids),
# torch.tensor(f.lm_label_ids),
# torch.tensor(f.label))
# """
# eval_sampler = SequentialSampler(eval_data)
# eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
# save_eval_loss = []
global_step = 0
if args.do_train:
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size)
"""
cur_tensors = (torch.tensor(f.input_ids),
torch.tensor(f.input_mask),
torch.tensor(f.segment_ids),
torch.tensor(f.lm_label_ids),
torch.tensor(f.label))
"""
save_loss = []
save_epoch_loss = []
save_step = int(len(train_dataloader) // 5)
for epoch in trange((loaded_epoch + 1),
int(args.num_train_epochs),
desc="Epoch"):
# if args.do_eval and loaded_epoch != -1:
# model.eval()
# eval_loss, eval_accuracy = 0, 0
# nb_eval_steps, nb_eval_examples = 0, 0
#
# for batch in tqdm(eval_dataloader, desc="Evaluating"):
# batch = tuple(t.to(device) for t in batch)
# input_ids, input_mask, segment_ids, label_ids = batch
#
# with torch.no_grad():
# tmp_eval_loss = model(input_ids, segment_ids, input_mask, None, label_ids)
# prediction_scores, logits = model(input_ids, segment_ids, input_mask)
#
# if n_gpu > 1:
# tmp_eval_loss = tmp_eval_loss.mean() # mean() to average on multi-gpu.
#
# logits = logits.detach().cpu().numpy()
# label_ids = label_ids.to('cpu').numpy()
# tmp_eval_accuracy = accuracy(logits, label_ids)
#
# eval_loss += tmp_eval_loss.mean().item()
# eval_accuracy += tmp_eval_accuracy
#
# nb_eval_examples += input_ids.size(0)
# nb_eval_steps += 1
#
# eval_loss = eval_loss / nb_eval_steps
# eval_accuracy = eval_accuracy / nb_eval_examples
# result = {'eval_loss': eval_loss,
# 'eval_accuracy': eval_accuracy,
# 'global_step': global_step}
#
# save_eval_loss.append(eval_loss)
#
# output_eval_file = os.path.join(args.output_dir, f"Epoch_{epoch}_eval_results.txt")
# with open(output_eval_file, "w") as writer:
# logger.info(f"***** Eval results on Epoch {epoch} *****")
# for key in sorted(result.keys()):
# logger.info(" %s = %s", key, str(result[key]))
# writer.write("%s = %s\n" % (key, str(result[key])))
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
tr_loss_ml = 0
tr_loss_click = 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, lm_label_ids, label = batch
# if global_step == 0:
# print(input_ids.shape, input_mask.shape, segment_ids.shape, lm_label_ids.shape, label.shape)
loss, loss_ml, loss_click = model(input_ids, segment_ids,
input_mask, lm_label_ids,
label)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
loss_ml = loss_ml.mean()
loss_click = loss_click.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss_ml = loss_ml / args.gradient_accumulation_steps
loss_click = loss_click / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
tr_loss_ml += loss_ml.item()
tr_loss_click += loss_click.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used that handles this automatically
lr_this_step = args.learning_rate * warmup_linear(
global_step / num_train_optimization_steps,
args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
if global_step != 0 and global_step % save_step == 0:
# 한 에포치당 5번 저장
logger.info(f'Saving state, iter: {global_step}')
model_to_save = model.module if hasattr(
model, 'module') else model
# Only save the model it-self
model_name = f"weight_on_{global_step}_" + WEIGHTS_NAME
output_model_file = os.path.join(args.output_dir,
model_name)
torch.save(model_to_save.state_dict(), output_model_file)
output_config_file = os.path.join(args.output_dir,
CONFIG_NAME)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
print("Loss at ", global_step, loss_ml.item(),
loss_click.item(), loss.item())
save_loss.append(
[loss_ml.item(),
loss_click.item(),
loss.item()])
if args.visdom:
update_vis_plot(viz, global_step, loss_ml.item(),
loss_click.item(), iter_plot, epoch_plot,
'append')
if epoch != (int(args.num_train_epochs) - 1):
# 각 에포치가 끝날때 마다 저장
logger.info(f'Saving state, epoch: {epoch}')
model_to_save = model.module if hasattr(model,
'module') else model
# Only save the model it-self
model_name = f"weight_on_ep{epoch}_" + WEIGHTS_NAME
output_model_file = os.path.join(args.output_dir, model_name)
torch.save(model_to_save.state_dict(), output_model_file)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
print("Loss at epoch", epoch, tr_loss_ml, tr_loss_click,
tr_loss)
save_epoch_loss.append([tr_loss_ml, tr_loss_click, tr_loss])
if args.visdom:
update_vis_plot(viz, epoch, tr_loss_ml, tr_loss_click,
epoch_plot, None, 'append',
len(train_dataset) // args.train_batch_size)
# if args.do_eval and loaded_epoch == -1:
#
# model.eval()
# eval_loss, eval_accuracy = 0, 0
# nb_eval_steps, nb_eval_examples = 0, 0
#
# for batch in tqdm(eval_dataloader, desc="Evaluating"):
# batch = tuple(t.to(device) for t in batch)
# input_ids, input_mask, segment_ids, label_ids = batch
#
# with torch.no_grad():
# tmp_eval_loss = model(input_ids, segment_ids, input_mask, None, label_ids)
# prediction_scores, logits = model(input_ids, segment_ids, input_mask)
#
# if n_gpu > 1:
# tmp_eval_loss = tmp_eval_loss.mean() # mean() to average on multi-gpu.
#
# logits = logits.detach().cpu().numpy()
# label_ids = label_ids.to('cpu').numpy()
# tmp_eval_accuracy = accuracy(logits, label_ids)
#
# eval_loss += tmp_eval_loss.mean().item()
# eval_accuracy += tmp_eval_accuracy
#
# nb_eval_examples += input_ids.size(0)
# nb_eval_steps += 1
#
# eval_loss = eval_loss / nb_eval_steps
# eval_accuracy = eval_accuracy / nb_eval_examples
# result = {'eval_loss': eval_loss,
# 'eval_accuracy': eval_accuracy,
# 'global_step': global_step}
#
# save_eval_loss.append(eval_loss)
#
# output_eval_file = os.path.join(args.output_dir, f"Epoch_{epoch}_eval_results.txt")
# with open(output_eval_file, "w") as writer:
# logger.info(f"***** Eval results on Epoch {epoch} *****")
# for key in sorted(result.keys()):
# logger.info(" %s = %s", key, str(result[key]))
# writer.write("%s = %s\n" % (key, str(result[key])))
# Save a trained model
logger.info("** ** * Saving fine - tuned model ** ** * ")
# model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
# output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
# if args.do_train:
# torch.save(model_to_save.state_dict(), output_model_file)
save_loss = np.array(save_loss)
save_epoch_loss = np.array(save_epoch_loss)
np.save(os.path.join(args.output_dir, "save_loss.npy"), save_loss)
np.save(os.path.join(args.output_dir, "save_epoch_loss.npy"),
save_epoch_loss)
# if args.do_eval:
# save_eval_loss = np.array(save_eval_loss)
# np.save(os.path.join(args.output_dir, "save_eval_loss.npy"), save_eval_loss)
model_to_save = model.module if hasattr(model, 'module') else model
# Only save the model it-self
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())