def setup_logging(args, local_rank):
"""
Setup logging configuration as well as random seed
"""
logging_config(args.output_dir,
name='finetune_squad{}'.format(args.version),# avoid race
overwrite_handler=True,
console=(local_rank == 0))
logging.info(args)
set_seed(args.seed)
logging.debug('Random seed set to {}'.format(args.seed))
def train(args):
_, num_workers, rank, local_rank, is_master_node, ctx_l = init_comm(
args.comm_backend, args.gpus)
level = logging.DEBUG if args.verbose else logging.INFO
logging_config(
args.ckpt_dir,
name='pretrain_bert_' + str(rank), # avoid race
level=level,
console=(local_rank == 0))
logging.info(args)
logging.debug('Random seed set to {}'.format(args.seed))
set_seed(args.seed)
logging.info('Training info: num_buckets: {}, '
'num_workers: {}, rank: {}'.format(args.num_buckets,
num_workers, rank))
cfg, tokenizer, model = get_pretraining_model(args.model_name, ctx_l)
if args.start_step:
logging.info('Restart training from {}'.format(args.start_step))
parameters_option(args.start_step, model, args.ckpt_dir, 'Loading',
ctx_l)
else:
model.initialize(ctx=ctx_l)
model.hybridize()
if args.raw:
get_dataset_fn = functools.partial(
get_pretrain_data_text,
max_seq_length=args.max_seq_length,
short_seq_prob=args.short_seq_prob,
masked_lm_prob=args.masked_lm_prob,
max_predictions_per_seq=args.max_predictions_per_seq,
whole_word_mask=args.whole_word_mask,
random_next_sentence=args.random_next_sentence,
tokenizer=tokenizer,
circle_length=args.circle_length,
repeat=args.repeat,
dataset_cached=args.dataset_cached,
num_max_dataset_cached=args.num_max_dataset_cached)
else:
get_dataset_fn = get_pretrain_data_npz
data_train = get_dataset_fn(args.data,
args.batch_size,
shuffle=True,
num_buckets=args.num_buckets,
vocab=tokenizer.vocab,
num_parts=num_workers,
part_idx=rank,
num_dataset_workers=args.num_dataset_workers,
num_batch_workers=args.num_batch_workers)
param_dict = model.collect_params()
# Do not apply weight decay to all the LayerNorm and bias
for _, v in model.collect_params('.*beta|.*gamma|.*bias').items():
v.wd_mult = 0.0
# Set grad_req if gradient accumulation is required
params = [p for p in param_dict.values() if p.grad_req != 'null']
num_accumulated = args.num_accumulated
if num_accumulated > 1:
logging.info(
'Using gradient accumulation. Effective global batch size = {}'.
format(num_accumulated * args.batch_size * len(ctx_l) *
num_workers))
for p in params:
p.grad_req = 'add'
num_steps = args.num_steps
warmup_steps = int(num_steps * args.warmup_ratio)
log_interval = args.log_interval
save_interval = args.ckpt_interval
logging.info(
'#Total Training Steps={}, Warmup Steps={}, Save Interval={}'.format(
num_steps, warmup_steps, save_interval))
optimizer_params = {'learning_rate': args.lr, 'wd': args.wd}
if args.optimizer == 'adamw':
optimizer_params.update({
'beta1': 0.9,
'beta2': 0.999,
'epsilon': 1e-6,
'correct_bias': False,
})
if args.comm_backend == 'horovod':
trainer = hvd.DistributedTrainer(param_dict, args.optimizer,
optimizer_params)
elif args.comm_backend == 'byteps':
trainer = bps.DistributedTrainer(param_dict, args.optimizer,
optimizer_params)
else:
trainer = mx.gluon.Trainer(param_dict,
args.optimizer,
optimizer_params,
update_on_kvstore=False)
if args.start_step:
logging.info('Restart training from {}'.format(args.start_step))
states_option(args.start_step, trainer, args.ckpt_dir, local_rank,
'Loading')
# backend specific implementation
if args.comm_backend == 'byteps':
trainer._init_params()
if args.comm_backend == 'horovod':
# Horovod: fetch and broadcast parameters
hvd.broadcast_parameters(param_dict, root_rank=0)
# prepare the loss function
nsp_loss_fn = mx.gluon.loss.SoftmaxCELoss()
mlm_loss_fn = mx.gluon.loss.SoftmaxCELoss()
nsp_loss_fn.hybridize()
mlm_loss_fn.hybridize()
mlm_metric = MaskedAccuracy()
nsp_metric = MaskedAccuracy()
mlm_metric.reset()
nsp_metric.reset()
step_num = args.start_step
if args.phase2:
step_num -= args.phase1_num_steps
running_mlm_loss, running_nsp_loss = 0., 0.
running_num_tks = 0
train_start_time = time.time()
tic = time.time()
# start training
train_loop_dataloader = grouper(repeat(data_train), len(ctx_l))
while step_num < num_steps:
step_num += 1
for _ in range(num_accumulated):
sample_l = next(train_loop_dataloader)
mlm_loss_l = []
nsp_loss_l = []
loss_l = []
ns_label_list, ns_pred_list = [], []
mask_label_list, mask_pred_list, mask_weight_list = [], [], []
for sample, ctx in zip(sample_l, ctx_l):
# prepare data
(input_id, masked_id, masked_position, masked_weight, \
next_sentence_label, segment_id, valid_length) = sample
input_id = input_id.as_in_ctx(ctx)
masked_id = masked_id.as_in_ctx(ctx)
masked_position = masked_position.as_in_ctx(ctx)
masked_weight = masked_weight.as_in_ctx(ctx)
next_sentence_label = next_sentence_label.as_in_ctx(ctx)
segment_id = segment_id.as_in_ctx(ctx)
valid_length = valid_length.as_in_ctx(ctx)
with mx.autograd.record():
_, _, nsp_score, mlm_scores = model(
input_id, segment_id, valid_length, masked_position)
denominator = (masked_weight.sum() +
1e-8) * num_accumulated * len(ctx_l)
mlm_scores_r = mx.npx.reshape(mlm_scores, (-5, -1))
masked_id_r = masked_id.reshape((-1, ))
mlm_loss = mlm_loss_fn(mlm_scores_r, masked_id_r,
masked_weight.reshape(
(-1, 1))).sum() / denominator
denominator = num_accumulated * len(ctx_l)
nsp_loss = nsp_loss_fn(
nsp_score, next_sentence_label).mean() / denominator
mlm_loss_l.append(mlm_loss)
nsp_loss_l.append(nsp_loss)
loss_l.append(mlm_loss + nsp_loss)
mask_label_list.append(masked_id_r)
mask_pred_list.append(mlm_scores_r)
mask_weight_list.append(masked_weight.reshape((-1, )))
ns_label_list.append(next_sentence_label)
ns_pred_list.append(nsp_score)
running_num_tks += valid_length.sum().as_in_ctx(mx.cpu())
for loss in loss_l:
loss.backward()
running_mlm_loss += sum([
ele.as_in_ctx(mx.cpu()) for ele in mlm_loss_l
]).asnumpy().item()
running_nsp_loss += sum([
ele.as_in_ctx(mx.cpu()) for ele in nsp_loss_l
]).asnumpy().item()
mlm_metric.update(mask_label_list, mask_pred_list,
mask_weight_list)
nsp_metric.update(ns_label_list, ns_pred_list)
# update
trainer.allreduce_grads()
total_norm, ratio, is_finite = clip_grad_global_norm(
params, args.max_grad_norm * num_workers)
total_norm = total_norm / num_workers
# update learning rate
scheduled_lr = args.lr
if step_num <= warmup_steps:
scheduled_lr *= step_num / warmup_steps
else:
offset = (num_steps - step_num) / (num_steps - warmup_steps)
scheduled_lr *= max(offset, 0)
trainer.set_learning_rate(scheduled_lr)
if args.comm_backend == 'horovod' or args.comm_backend == 'byteps':
# Note that horovod.trainer._scale is default to num_workers,
# thus trainer.update(1) will scale the gradients by 1./num_workers.
# *num_workers* of Horovod is the number of GPUs.
trainer.update(1, ignore_stale_grad=True)
else:
# gluon.trainer._scale is default to 1.
# *num_workers* of Trainer is the number of machines.
trainer.update(num_workers, ignore_stale_grad=True)
if num_accumulated > 1:
# set grad to zero for gradient accumulation
model.zero_grad()
# saving
if step_num % save_interval == 0 or step_num >= num_steps:
states_option(step_num, trainer, args.ckpt_dir, local_rank,
'Saving')
if local_rank == 0:
parameters_option(step_num, model, args.ckpt_dir, 'Saving')
# logging
if step_num % log_interval == 0:
running_mlm_loss /= log_interval
running_nsp_loss /= log_interval
toc = time.time()
logging.info(
'[step {}], Loss mlm/nsp={:.5f}/{:.3f}, Acc mlm/nsp={:.3f}/{:.3f}, '
' LR={:.7f}, grad_norm={:.4f}. Time cost={:.2f} s,'
' Throughput={:.1f}K tks/s, ETA={:.2f} h'.format(
step_num, running_mlm_loss, running_nsp_loss,
mlm_metric.get()[1],
nsp_metric.get()[1], trainer.learning_rate, total_norm,
toc - tic,
running_num_tks.asnumpy().item() / (toc - tic) / 1000,
(num_steps - step_num) /
(step_num / (toc - train_start_time)) / 3600))
mlm_metric.reset()
nsp_metric.reset()
tic = time.time()
running_mlm_loss = 0
running_nsp_loss = 0
running_num_tks = 0
logging.info('Finish training step: %d', step_num)
mx.npx.waitall()
train_end_time = time.time()
logging.info('Train cost={:.1f} s'.format(train_end_time -
train_start_time))
if local_rank == 0:
model_name = args.model_name.replace('google', 'gluon')
save_dir = os.path.join(args.ckpt_dir, model_name)
final_save(model, save_dir, tokenizer, cfg)
rtd_preds = mx.np.round((mx.np.sign(rtd_scores) + 1) / 2).astype(np.int32)
mlm_accuracy = accuracy(unmasked_tokens, mlm_preds, masked_weights)
corrupted_mlm_accuracy = accuracy(unmasked_tokens, corrupted_tokens,
masked_weights)
rtd_accuracy = accuracy(rtd_labels, rtd_preds, length_masks)
rtd_precision = accuracy(rtd_labels, rtd_preds, length_masks * rtd_preds)
rtd_recall = accuracy(rtd_labels, rtd_preds, rtd_labels * rtd_preds)
rtd_auc = auc(rtd_labels, rtd_probs, length_masks)
writer.add_scalars(
'results', {
'mlm_accuracy': mlm_accuracy.asnumpy().item(),
'corrupted_mlm_accuracy': corrupted_mlm_accuracy.asnumpy().item(),
'rtd_accuracy': rtd_accuracy.asnumpy().item(),
'rtd_precision': rtd_precision.asnumpy().item(),
'rtd_recall': rtd_recall.asnumpy().item(),
'rtd_auc': rtd_auc
}, step_num)
if __name__ == '__main__':
os.environ['MXNET_GPU_MEM_POOL_TYPE'] = 'Round'
os.environ['MXNET_USE_FUSION'] = '0' # Manually disable pointwise fusion
args = parse_args()
logging_config(args.output_dir, name='pretrain_owt')
logging.debug('Random seed set to {}'.format(args.seed))
logging.info(args)
set_seed(args.seed)
if args.do_train:
train(args)
def train(args):
store, num_workers, rank, local_rank, is_master_node, ctx_l = init_comm(
args.comm_backend, args.gpus)
logging_config(
args.output_dir,
name='pretrain_owt_' + str(rank), # avoid race
console=(local_rank == 0))
logging.info(args)
logging.debug('Random seed set to {}'.format(args.seed))
set_seed(args.seed)
logging.info('Training info: num_buckets: {}, '
'num_workers: {}, rank: {}'.format(args.num_buckets,
num_workers, rank))
cfg, tokenizer, model = get_pretraining_model(args.model_name, ctx_l,
args.max_seq_length,
args.hidden_dropout_prob,
args.attention_dropout_prob,
args.generator_units_scale,
args.generator_layers_scale)
data_masker = ElectraMasker(tokenizer,
args.max_seq_length,
mask_prob=args.mask_prob,
replace_prob=args.replace_prob)
if args.from_raw_text:
if args.cached_file_path and not os.path.exists(args.cached_file_path):
os.mkdir(args.cached_file_path)
get_dataset_fn = functools.partial(
get_pretrain_data_text,
max_seq_length=args.max_seq_length,
short_seq_prob=args.short_seq_prob,
tokenizer=tokenizer,
circle_length=args.circle_length,
repeat=args.repeat,
cached_file_path=args.cached_file_path)
logging.info(
'Processing and loading the training dataset from raw text.')
else:
logging.info('Loading the training dataset from local Numpy file.')
get_dataset_fn = get_pretrain_data_npz
data_train = get_dataset_fn(args.data,
args.batch_size,
shuffle=True,
num_buckets=args.num_buckets,
vocab=tokenizer.vocab,
num_parts=num_workers,
part_idx=rank,
num_dataset_workers=args.num_dataset_workers,
num_batch_workers=args.num_batch_workers)
logging.info('Creating distributed trainer...')
param_dict = model.collect_params()
# Do not apply weight decay to all the LayerNorm and bias
for _, v in model.collect_params('.*beta|.*gamma|.*bias').items():
v.wd_mult = 0.0
# Collect differentiable parameters
params = [p for p in param_dict.values() if p.grad_req != 'null']
# Set grad_req if gradient accumulation is required
num_accumulated = args.num_accumulated
if num_accumulated > 1:
logging.info(
'Using gradient accumulation. Effective global batch size = {}'.
format(num_accumulated * args.batch_size * len(ctx_l) *
num_workers))
for p in params:
p.grad_req = 'add'
# backend specific implementation
if args.comm_backend == 'horovod':
# Horovod: fetch and broadcast parameters
hvd.broadcast_parameters(param_dict, root_rank=0)
num_train_steps = args.num_train_steps
if args.warmup_steps is not None:
warmup_steps = args.warmup_steps
else:
warmup_steps = int(num_train_steps * args.warmup_ratio)
assert warmup_steps is not None, 'Must specify either warmup_steps or warmup_ratio'
log_interval = args.log_interval
save_interval = args.save_interval if args.save_interval is not None\
else num_train_steps // 50
logging.info(
'#Total Training Steps={}, Warmup={}, Save Interval={}'.format(
num_train_steps, warmup_steps, save_interval))
lr_scheduler = PolyScheduler(max_update=num_train_steps,
base_lr=args.lr,
warmup_begin_lr=0,
pwr=1,
final_lr=0,
warmup_steps=warmup_steps,
warmup_mode='linear')
optimizer_params = {
'learning_rate': args.lr,
'wd': args.wd,
'lr_scheduler': lr_scheduler,
}
if args.optimizer == 'adamw':
optimizer_params.update({
'beta1': 0.9,
'beta2': 0.999,
'epsilon': 1e-6,
'correct_bias': False,
})
if args.comm_backend == 'horovod':
trainer = hvd.DistributedTrainer(param_dict, args.optimizer,
optimizer_params)
else:
trainer = mx.gluon.Trainer(param_dict,
args.optimizer,
optimizer_params,
update_on_kvstore=False)
if args.start_step:
logging.info('Restart training from {}'.format(args.start_step))
# TODO(zheyuye), How about data splitting, where to start re-training
state_path = states_option(args.start_step, trainer, args.output_dir,
local_rank, 'Loading')
param_path = parameters_option(args.start_step, model, args.output_dir,
'Loading')
# prepare the loss function
mlm_loss_fn = mx.gluon.loss.SoftmaxCELoss()
rtd_loss_fn = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
mlm_loss_fn.hybridize()
rtd_loss_fn.hybridize()
# prepare the records writer
writer = None
# only one process on each worker will write the tensorboardX's records to avoid race
if args.do_eval and local_rank == 0:
from tensorboardX import SummaryWriter
record_path = os.path.join(args.output_dir, 'records')
logging.info('Evaluation records saved in {}'.format(record_path))
writer = SummaryWriter(record_path)
step_num = args.start_step
finish_flag = False
log_total_loss = 0
log_mlm_loss = 0
log_rtd_loss = 0
log_sample_num = 0
train_start_time = time.time()
# start training
train_loop_dataloader = grouper(repeat(data_train), len(ctx_l))
while step_num < num_train_steps:
tic = time.time()
for accum_idx in range(num_accumulated):
sample_l = next(train_loop_dataloader)
loss_l = []
mlm_loss_l = []
rtd_loss_l = []
for sample, ctx in zip(sample_l, ctx_l):
if sample is None:
continue
# prepare data
input_ids, segment_ids, valid_lengths = sample
input_ids = input_ids.as_in_ctx(ctx)
segment_ids = segment_ids.as_in_ctx(ctx)
valid_lengths = valid_lengths.as_in_ctx(ctx)
masked_input = data_masker.dynamic_masking(
mx.nd, input_ids, valid_lengths)
masked_input_ids = masked_input.input_ids
length_masks = masked_input.masks
unmasked_tokens = masked_input.unmasked_tokens
masked_positions = masked_input.masked_positions
masked_weights = masked_input.masked_weights
log_sample_num += len(masked_input_ids)
with mx.autograd.record():
mlm_scores, rtd_scores, corrupted_tokens, labels = model(
masked_input_ids, segment_ids, valid_lengths,
unmasked_tokens, masked_positions)
denominator = (masked_weights.sum() +
1e-6) * num_accumulated * len(ctx_l)
mlm_loss = mlm_loss_fn(
mx.npx.reshape(mlm_scores, (-5, -1)),
unmasked_tokens.reshape(
(-1, )), masked_weights.reshape(
(-1, 1))).sum() / denominator
denominator = (length_masks.sum() +
1e-6) * num_accumulated * len(ctx_l)
rtd_loss = rtd_loss_fn(rtd_scores, labels,
length_masks).sum() / denominator
output = ElectraOutput(
mlm_scores=mlm_scores,
rtd_scores=rtd_scores,
rtd_labels=labels,
corrupted_tokens=corrupted_tokens,
)
mlm_loss_l.append(mlm_loss)
rtd_loss_l.append(rtd_loss)
loss = (args.gen_weight * mlm_loss +
args.disc_weight * rtd_loss)
loss_l.append(loss)
for loss in loss_l:
loss.backward()
# All Reduce the Step Loss
log_mlm_loss += sum(
[ele.as_in_ctx(ctx_l[0]) for ele in mlm_loss_l]).asnumpy()
log_rtd_loss += sum(
[ele.as_in_ctx(ctx_l[0]) for ele in rtd_loss_l]).asnumpy()
log_total_loss += sum([ele.as_in_ctx(ctx_l[0])
for ele in loss_l]).asnumpy()
# update
trainer.allreduce_grads()
total_norm, ratio, is_finite = clip_grad_global_norm(
params, args.max_grad_norm * num_workers)
if args.comm_backend == 'horovod':
# Note that horovod.trainer._scale is default to num_workers,
# thus trainer.update(1) will scale the gradients by 1./num_workers
trainer.update(1, ignore_stale_grad=True)
else:
# gluon.trainer._scale is default to 1
trainer.update(num_workers, ignore_stale_grad=True)
total_norm = total_norm / num_workers
step_num += 1
if num_accumulated > 1:
# set grad to zero for gradient accumulation
model.zero_grad()
# saving
if step_num % save_interval == 0 or step_num >= num_train_steps:
if is_master_node:
states_option(step_num, trainer, args.output_dir, local_rank,
'Saving')
if local_rank == 0:
param_path = parameters_option(step_num, model,
args.output_dir, 'Saving')
# logging
if step_num % log_interval == 0:
# Output the loss of per step
log_mlm_loss /= log_interval
log_rtd_loss /= log_interval
log_total_loss /= log_interval
toc = time.time()
logging.info('[step {}], Loss mlm/rtd/total={:.4f}/{:.4f}/{:.4f},'
' LR={:.6f}, grad_norm={:.4f}. Time cost={:.2f},'
' Throughput={:.2f} samples/s, ETA={:.2f}h'.format(
step_num, log_mlm_loss, log_rtd_loss,
log_total_loss, trainer.learning_rate, total_norm,
toc - tic, log_sample_num / (toc - tic),
(num_train_steps - step_num) /
(step_num / (toc - train_start_time)) / 3600))
tic = time.time()
if args.do_eval:
evaluation(writer, step_num, masked_input, output)
if writer is not None:
writer.add_scalars(
'loss', {
'total_loss': log_total_loss,
'mlm_loss': log_mlm_loss,
'rtd_loss': log_rtd_loss
}, step_num)
log_mlm_loss = 0
log_rtd_loss = 0
log_total_loss = 0
log_sample_num = 0
logging.info('Finish training step: %d', step_num)
if is_master_node:
state_path = states_option(step_num, trainer, args.output_dir,
local_rank, 'Saving')
if local_rank == 0:
param_path = parameters_option(step_num, model, args.output_dir,
'Saving')
mx.npx.waitall()
train_end_time = time.time()
logging.info('Train cost={:.1f}s'.format(train_end_time -
train_start_time))
if writer is not None:
writer.close()
if local_rank == 0:
model_name = args.model_name.replace('google', 'gluon')
save_dir = os.path.join(args.output_dir, model_name)
final_save(model, save_dir, tokenizer)
