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)
warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
logging.info("***** Running training *****")
logging.info(" Num examples = %d", total_train_examples)
logging.info(" Batch size = %d", args.train_batch_size)
logging.info(" Num steps = %d", num_train_optimization_steps)
for epoch in range(args.epochs):
# get dataloader
epoch_dataset = PregeneratedDataset(epoch=epoch,
training_path=params.pretrain_data_dir,
tokenizer=tokenizer,
num_data_epochs=num_data_epochs,
do_ngram=True,
fp16=args.fp16)
if args.local_rank == -1:
def main():
parser = ArgumentParser()
parser.add_argument('--pregenerated_data', type=Path, required=True)
parser.add_argument('--output_dir', type=Path, required=True)
parser.add_argument(
"--bert_model",
type=str,
required=True,
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("--do_lower_case", action="store_true")
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=3,
help="Number of epochs to train for")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
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=32,
type=int,
help="Total batch size for training.")
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--scratch',
action='store_true',
help="Whether to train from scratch")
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=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--learning_rate",
default=3e-5,
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('--save_name',
type=str,
default="zen",
help="The prefix used for saving the remote model")
parser.add_argument("--already_trained_epoch", default=0, type=int)
args = parser.parse_args()
assert args.pregenerated_data.is_dir(), \
"--pregenerated_data should point to the folder of files made by pregenerate_training_data.py!"
samples_per_epoch = []
for i in range(args.epochs):
epoch_file = args.pregenerated_data / f"epoch_{i}.json"
metrics_file = args.pregenerated_data / f"epoch_{i}_metrics.json"
if epoch_file.is_file() and metrics_file.is_file():
metrics = json.loads(metrics_file.read_text())
samples_per_epoch.append(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."
)
num_data_epochs = i
break
else:
num_data_epochs = args.epochs
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')
logging.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)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
total_train_examples = 0
for i in range(args.epochs):
# The modulo takes into account the fact that we may loop over limited epochs of data
total_train_examples += samples_per_epoch[i % len(samples_per_epoch)]
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(
)
if args.scratch:
config = ZenConfig(21128, 104089)
model = ZenForPreTraining(config)
else:
model = ZenForPreTraining.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)
warmup_linear = WarmupLinearSchedule(
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
logging.info("***** Running training *****")
logging.info(" Num examples = %d", total_train_examples)
logging.info(" Batch size = %d", args.train_batch_size)
logging.info(" Num steps = %d", num_train_optimization_steps)
model.train()
for epoch in range(args.epochs):
epoch_dataset = PregeneratedDataset(
epoch=epoch,
training_path=args.pregenerated_data,
tokenizer=tokenizer,
num_data_epochs=num_data_epochs,
reduce_memory=args.reduce_memory,
fp16=args.fp16)
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)
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
with tqdm(total=len(train_dataloader), desc=f"Epoch {epoch}") as pbar:
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, is_next, ngram_ids, ngram_masks, ngram_positions, \
ngram_starts, \
ngram_lengths, ngram_segment_ids = batch
loss = model(input_ids, ngram_ids, ngram_positions,
segment_ids, ngram_segment_ids, input_mask,
ngram_masks, lm_label_ids, is_next)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
pbar.update(1)
mean_loss = tr_loss * args.gradient_accumulation_steps / nb_tr_steps
pbar.set_postfix_str(f"Loss: {mean_loss:.5f}")
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.get_lr(
global_step, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
# Save a trained model
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime('%m%d%H%M%S')
saving_path = args.output_dir
saving_path = Path(
os.path.join(
saving_path, args.save_name + st + "_epoch_" +
str(epoch + args.already_trained_epoch)))
if saving_path.is_dir() and list(saving_path.iterdir()):
logging.warning(
f"Output directory ({ saving_path }) already exists and is not empty!"
)
saving_path.mkdir(parents=True, exist_ok=True)
logging.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(saving_path, WEIGHTS_NAME)
output_config_file = os.path.join(saving_path, CONFIG_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(saving_path)
def train(args, model, tokenizer, ngram_dict, processor, label_list):
train_dataset = load_examples(args,
tokenizer,
ngram_dict,
processor,
label_list,
mode="train")
if args.fp16:
model.half()
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 args.n_gpu > 1:
model = torch.nn.DataParallel(model)
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(
)
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)
warmup_linear = WarmupLinearSchedule(
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
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)
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset)
else:
train_sampler = DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size)
a = train_dataloader.dataset[0]
best_f1 = -1
best_epoch = -1
epoch_after_best_one = 3
for epoch_num in trange(int(args.num_train_epochs), desc="Epoch"):
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(args.device) for t in batch)
input_ids, input_mask, segment_ids, label_ids, ngram_ids, ngram_positions, ngram_lengths, ngram_seg_ids, ngram_masks, valid_ids, l_mask = batch
loss = model(input_ids,
token_type_ids=None,
attention_mask=None,
labels=label_ids,
valid_ids=valid_ids,
attention_mask_label=None,
ngram_ids=ngram_ids,
ngram_positions=ngram_positions)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.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 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)
save_zen_model(output_dir, model, tokenizer, ngram_dict,
args)
evaluate(args, model, tokenizer, ngram_dict, processor, label_list)
def train(args, model, tokenizer, ngram_dict, processor, label_list):
# train_dataset = load_examples(args, tokenizer, ngram_dict, processor, label_list, mode="train")
examples = processor.get_train_examples(args.data_dir)
train_dataset = BERTDataset(args, tokenizer, ngram_dict, processor, label_list, examples)
model = model.cuda()
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()
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}
]
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
if args.fp16:
try:
from apex.parallel import DistributedDataParallel as DDP
# from apex.fp16_utils import *
from apex import amp, optimizers
from apex.multi_tensor_apply import multi_tensor_applier
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this example.")
warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
# if args.fp16:
# model.half()
# Distributed training
if args.local_rank != -1:
# By default, apex.parallel.DistributedDataParallel overlaps communication with
# computation in the backward pass.
# model = DDP(model)
# delay_allreduce delays all communication to the end of the backward pass.
model = DDP(model, delay_allreduce=True)
elif args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# if args.fp16:
# try:
# from apex.fp16_utils 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,
# )
# 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:
global_step = 0
tb_log_dir = os.path.join(args.output_dir, 'tb-log')
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter(tb_log_dir)
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)
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset)
else:
train_sampler = DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, num_workers=8)
best_f1 = -1
best_epoch = -1
epoch_after_best_one = 3
for epoch_num in trange(int(args.num_train_epochs), desc="Epoch"):
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(args.device) for t in batch.values())
input_ids, input_mask, segment_ids, label_ids, ngram_ids, ngram_positions, ngram_lengths, ngram_seg_ids, ngram_masks, valid_ids, l_mask = batch
loss = model(input_ids, token_type_ids=None, attention_mask=None, labels=label_ids, valid_ids=valid_ids,
attention_mask_label=None, ngram_ids=ngram_ids, ngram_positions=ngram_positions)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
# optimizer.backward(loss)
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.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.get_lr_(global_step / num_train_optimization_steps)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
if args.local_rank in [-1, 0]:
tb_writer.add_scalar('lr', optimizer.param_groups[0]['lr'], global_step)
tb_writer.add_scalar('loss', loss.item(), 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)
# save_zen_model(output_dir, model, tokenizer, ngram_dict, args)
if epoch_num % 3 == 0:
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
result = evaluate(args, model, tokenizer, ngram_dict, processor, label_list)
tb_writer.add_scalar('f1', result['f1'], global_step)
logger.info("\nf1=%s\n" % (str(result["f1"])))
if result['f1'] > best_f1:
best_f1 = result['f1']
# output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
output_dir = os.path.join(args.output_dir, "checkpoint-best")
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
os.makedirs(output_dir)
save_zen_model(output_dir, model, tokenizer, ngram_dict, args)
logging.info(f'Saving best model, f1 is {best_f1}')