def train(data_train, data_eval, model, nsp_loss, mlm_loss, vocab_size, ctx):
"""Training function."""
hvd.broadcast_parameters(model.collect_params(), root_rank=0)
mlm_metric = nlp.metric.MaskedAccuracy()
nsp_metric = nlp.metric.MaskedAccuracy()
mlm_metric.reset()
nsp_metric.reset()
logging.debug('Creating distributed trainer...')
lr = args.lr
optim_params = {'learning_rate': lr, 'epsilon': 1e-6, 'wd': 0.01}
if args.dtype == 'float16':
optim_params['multi_precision'] = True
dynamic_loss_scale = args.dtype == 'float16'
if dynamic_loss_scale:
loss_scale_param = {'scale_window': 2000 / num_workers}
else:
loss_scale_param = None
trainer = hvd.DistributedTrainer(model.collect_params(), 'bertadam', optim_params)
fp16_trainer = FP16Trainer(trainer, dynamic_loss_scale=dynamic_loss_scale,
loss_scaler_params=loss_scale_param)
if args.start_step:
state_path = os.path.join(args.ckpt_dir, '%07d.states.%02d'%(args.start_step, local_rank))
logging.info('Loading trainer state from %s', state_path)
nlp.utils.load_states(trainer, state_path)
accumulate = args.accumulate
num_train_steps = args.num_steps
warmup_ratio = args.warmup_ratio
num_warmup_steps = int(num_train_steps * warmup_ratio)
params = [p for p in model.collect_params().values() if p.grad_req != 'null']
param_dict = model.collect_params()
# Do not apply weight decay on LayerNorm and bias terms
for _, v in model.collect_params('.*beta|.*gamma|.*bias').items():
v.wd_mult = 0.0
if accumulate > 1:
for p in params:
p.grad_req = 'add'
train_begin_time = time.time()
begin_time = time.time()
running_mlm_loss, running_nsp_loss = 0, 0
running_num_tks = 0
batch_num = 0
step_num = args.start_step
logging.debug('Training started')
while step_num < num_train_steps:
for _, dataloader in enumerate(data_train):
if step_num >= num_train_steps:
break
# create dummy data loader if needed
if args.dummy_data_len:
target_shape = (args.batch_size, args.dummy_data_len)
dataloader = get_dummy_dataloader(dataloader, target_shape)
for _, data_batch in enumerate(dataloader):
if step_num >= num_train_steps:
break
if batch_num % accumulate == 0:
step_num += 1
# if accumulate > 1, grad_req is set to 'add', and zero_grad is required
if accumulate > 1:
param_dict.zero_grad()
# update learning rate
if step_num <= num_warmup_steps:
new_lr = lr * step_num / num_warmup_steps
else:
offset = lr * step_num / num_train_steps
new_lr = lr - offset
trainer.set_learning_rate(new_lr)
if args.profile:
profile(step_num, 10, 14, profile_name=args.profile + str(rank))
# load data
if args.use_avg_len:
data_list = [[seq.as_in_context(context) for seq in shard]
for context, shard in zip([ctx], data_batch)]
else:
data_list = list(split_and_load(data_batch, [ctx]))
data = data_list[0]
# forward
with mx.autograd.record():
(ls, ns_label, classified, masked_id, decoded, \
masked_weight, ls1, ls2, valid_len) = forward(data, model, mlm_loss,
nsp_loss, vocab_size, args.dtype)
ls = ls / accumulate
# backward
if args.dtype == 'float16':
fp16_trainer.backward(ls)
else:
ls.backward()
running_mlm_loss += ls1.as_in_context(mx.cpu())
running_nsp_loss += ls2.as_in_context(mx.cpu())
running_num_tks += valid_len.sum().as_in_context(mx.cpu())
# update
if (batch_num + 1) % accumulate == 0:
# step() performs 3 things:
# 1. allreduce gradients from all workers
# 2. checking the global_norm of gradients and clip them if necessary
# 3. averaging the gradients and apply updates
fp16_trainer.step(1, max_norm=1*num_workers)
nsp_metric.update([ns_label], [classified])
mlm_metric.update([masked_id], [decoded], [masked_weight])
# logging
if (step_num + 1) % (args.log_interval) == 0 and (batch_num + 1) % accumulate == 0:
log(begin_time, running_num_tks, running_mlm_loss / accumulate,
running_nsp_loss / accumulate, step_num, mlm_metric, nsp_metric,
trainer, args.log_interval)
begin_time = time.time()
running_mlm_loss = running_nsp_loss = running_num_tks = 0
mlm_metric.reset_local()
nsp_metric.reset_local()
# saving checkpoints
if (step_num + 1) % args.ckpt_interval == 0 and (batch_num + 1) % accumulate == 0:
if is_master_node:
save_states(step_num, trainer, args.ckpt_dir, local_rank)
if local_rank == 0:
save_parameters(step_num, model, args.ckpt_dir)
if data_eval:
# eval data is always based on a fixed npz file.
dataset_eval = get_pretrain_data_npz(data_eval, args.batch_size_eval, 1,
False, False, 1)
evaluate(dataset_eval, model, nsp_loss, mlm_loss, len(vocab), [ctx],
args.log_interval, args.dtype)
batch_num += 1
if is_master_node:
save_states(step_num, trainer, args.ckpt_dir, local_rank)
if local_rank == 0:
save_parameters(step_num, model, args.ckpt_dir)
mx.nd.waitall()
train_end_time = time.time()
logging.info('Train cost={:.1f}s'.format(train_end_time - train_begin_time))
def train(data_train, model, nsp_loss, mlm_loss, vocab_size, ctx, store):
"""Training function."""
mlm_metric = nlp.metric.MaskedAccuracy()
nsp_metric = nlp.metric.MaskedAccuracy()
mlm_metric.reset()
nsp_metric.reset()
lr = args.lr
optim_params = {'learning_rate': lr, 'epsilon': 1e-6, 'wd': 0.01}
if args.dtype == 'float16':
optim_params['multi_precision'] = True
trainer = mx.gluon.Trainer(model.collect_params(),
'bertadam',
optim_params,
update_on_kvstore=False,
kvstore=store)
dynamic_loss_scale = args.dtype == 'float16'
fp16_trainer = FP16Trainer(trainer, dynamic_loss_scale=dynamic_loss_scale)
if args.start_step:
state_path = os.path.join(args.ckpt_dir,
'%07d.states.%02d' % (args.start_step, 0))
logging.info('Loading trainer state from %s', state_path)
nlp.utils.load_states(trainer, state_path)
accumulate = args.accumulate
num_train_steps = args.num_steps
warmup_ratio = args.warmup_ratio
num_warmup_steps = int(num_train_steps * warmup_ratio)
params = [
p for p in model.collect_params().values() if p.grad_req != 'null'
]
param_dict = model.collect_params()
# Do not apply weight decay on LayerNorm and bias terms
for _, v in model.collect_params('.*beta|.*gamma|.*bias').items():
v.wd_mult = 0.0
if accumulate > 1:
for p in params:
p.grad_req = 'add'
train_begin_time = time.time()
begin_time = time.time()
running_mlm_loss = running_nsp_loss = running_num_tks = 0
batch_num = 0
step_num = args.start_step
parallel_model = ParallelBERT(model,
mlm_loss,
nsp_loss,
vocab_size,
store.num_workers * accumulate,
trainer=fp16_trainer)
num_ctxes = len(ctx)
parallel = nlp.utils.Parallel(num_ctxes if num_ctxes > 1 else 0,
parallel_model)
while step_num < num_train_steps:
for _, dataloader in enumerate(data_train):
if step_num >= num_train_steps:
break
# create dummy data loader if needed
if args.dummy_data_len:
target_shape = (args.batch_size * num_ctxes,
args.dummy_data_len)
dataloader = get_dummy_dataloader(dataloader, target_shape)
for _, data_batch in enumerate(dataloader):
if step_num >= num_train_steps:
break
if batch_num % accumulate == 0:
step_num += 1
# if accumulate > 1, grad_req is set to 'add', and zero_grad is required
if accumulate > 1:
param_dict.zero_grad()
# update learning rate
if step_num <= num_warmup_steps:
new_lr = lr * step_num / num_warmup_steps
else:
offset = lr * step_num / num_train_steps
new_lr = lr - offset
trainer.set_learning_rate(new_lr)
if args.profile:
profile(step_num, 10, 12, profile_name=args.profile)
if args.use_avg_len:
data_list = [[seq.as_in_context(context) for seq in shard]
for context, shard in zip(ctx, data_batch)]
else:
if data_batch[0].shape[0] < len(ctx):
continue
data_list = split_and_load(data_batch, ctx)
ns_label_list, ns_pred_list = [], []
mask_label_list, mask_pred_list, mask_weight_list = [], [], []
# parallel forward / backward
for data in data_list:
parallel.put(data)
for _ in range(len(ctx)):
(_, next_sentence_label, classified, masked_id, decoded,
masked_weight, ls1, ls2, valid_length) = parallel.get()
ns_label_list.append(next_sentence_label)
ns_pred_list.append(classified)
mask_label_list.append(masked_id)
mask_pred_list.append(decoded)
mask_weight_list.append(masked_weight)
running_mlm_loss += ls1.as_in_context(mx.cpu()) / num_ctxes
running_nsp_loss += ls2.as_in_context(mx.cpu()) / num_ctxes
running_num_tks += valid_length.sum().as_in_context(
mx.cpu())
# update
if (batch_num + 1) % accumulate == 0:
fp16_trainer.step(1, max_norm=1)
nsp_metric.update(ns_label_list, ns_pred_list)
mlm_metric.update(mask_label_list, mask_pred_list,
mask_weight_list)
# logging
if (step_num + 1) % (args.log_interval) == 0 and (
batch_num + 1) % accumulate == 0:
log(begin_time, running_num_tks,
running_mlm_loss / accumulate,
running_nsp_loss / accumulate, step_num, mlm_metric,
nsp_metric, trainer, args.log_interval)
begin_time = time.time()
running_mlm_loss = running_nsp_loss = running_num_tks = 0
mlm_metric.reset_local()
nsp_metric.reset_local()
# saving checkpoints
if (step_num + 1) % args.ckpt_interval == 0 \
and (batch_num + 1) % accumulate == 0 and store.rank == 0:
save_states(step_num, trainer, args.ckpt_dir)
save_parameters(step_num, model, args.ckpt_dir)
batch_num += 1
if store.rank == 0:
save_states(step_num, trainer, args.ckpt_dir)
save_parameters(step_num, model, args.ckpt_dir)
mx.nd.waitall()
train_end_time = time.time()
logging.info('Train cost={:.1f}s'.format(train_end_time -
train_begin_time))
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,
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
if args.synthetic_data:
data_train = get_dummy_dataloader(batch_size, args.max_seq_length,
args.max_predictions_per_seq)
else:
shuffle = True
logging.info(
'args.num_buckets: {}, num_workers: {}, rank: {}'.format(
args.num_buckets, num_workers, rank))
data_train = get_dataset_fn(
args.data,
batch_size,
len(ctxs),
shuffle,
args.num_buckets,
vocab,
num_parts=num_workers,
part_idx=rank,
num_dataset_workers=args.num_dataset_workers,
def train(data_train, data_eval, model):
"""Training function."""
# backend specific implementation
param_dict = model.bert.collect_params()
if backend == 'horovod':
hvd.broadcast_parameters(param_dict, root_rank=0)
mlm_metric = nlp.metric.MaskedAccuracy()
nsp_metric = nlp.metric.MaskedAccuracy()
mlm_metric.reset()
nsp_metric.reset()
logging.debug('Creating distributed trainer...')
lr = args.lr
optim_params = {'learning_rate': lr, 'epsilon': 1e-6, 'wd': 0.01}
if args.dtype == 'float16':
optim_params['multi_precision'] = True
if args.optimizer == 'lamb':
optim_params['bias_correction'] = True
dynamic_loss_scale = args.dtype == 'float16'
if dynamic_loss_scale:
loss_scale_param = {'scale_window': 2000 / num_workers, 'init_scale': 1}
else:
loss_scale_param = None
# backend specific implementation
if backend == 'horovod':
trainer = hvd.DistributedTrainer(param_dict, args.optimizer, optim_params)
elif backend == 'byteps':
trainer = bps.DistributedTrainer(param_dict, args.optimizer, optim_params)
else:
trainer = mx.gluon.Trainer(param_dict, args.optimizer, optim_params,
update_on_kvstore=False)
fp16_trainer = FP16Trainer(trainer, dynamic_loss_scale=dynamic_loss_scale,
loss_scaler_params=loss_scale_param)
if args.start_step:
state_path = os.path.join(args.ckpt_dir, '%07d.states.%02d'%(args.start_step, local_rank))
logging.info('Loading trainer state from %s', state_path)
nlp.utils.load_states(trainer, state_path)
accumulate = args.accumulate
num_train_steps = args.num_steps
warmup_ratio = args.warmup_ratio
num_warmup_steps = int(num_train_steps * warmup_ratio)
params = [p for p in param_dict.values() if p.grad_req != 'null']
# Do not apply weight decay on LayerNorm and bias terms
for _, v in model.collect_params('.*beta|.*gamma|.*bias').items():
v.wd_mult = 0.0
if accumulate > 1:
for p in params:
p.grad_req = 'add'
train_begin_time = time.time()
begin_time = time.time()
running_mlm_loss, running_nsp_loss = 0, 0
local_mlm_loss, local_num_masks = 0, mx.nd.array([0], ctx=ctxs[0])
running_num_tks = 0
batch_num = 0
step_num = args.start_step
logging.debug('Training started')
logging.info('Generating the first batch of data, which may take a few minutes ...')
# create dummy data loader if needed
parallel_model = DataParallelBERT(model, trainer=fp16_trainer)
num_ctxes = len(ctxs)
parallel = nlp.utils.Parallel(num_ctxes if num_ctxes > 1 else 0, parallel_model)
if backend == 'byteps':
bps.byteps_declare_tensor("local_num_masks")
bps.byteps_push_pull(local_num_masks, is_average=False, name="local_num_masks", priority=0)
logging.debug('Broadcast local_num_masks tensor')
next_batch = next(iter(get_dummy_dataloader(batch_size, args.max_seq_length, args.max_predictions_per_seq)))
data_list = list(split_and_load(next_batch, ctxs))
parallel.put(data_list[0])
parallel.get()
trainer._init_params()
while step_num < num_train_steps:
data_train_iter = iter(data_train)
end_of_batch = False
next_data_batch = next(data_train_iter)
while not end_of_batch:
data_batch = next_data_batch
if step_num >= num_train_steps:
break
if batch_num % accumulate == 0:
step_num += 1
# if accumulate > 1, grad_req is set to 'add', and zero_grad is required
if accumulate > 1:
param_dict.zero_grad()
# update learning rate
if step_num <= num_warmup_steps:
new_lr = lr * step_num / num_warmup_steps
else:
offset = lr * step_num / num_train_steps
new_lr = lr - offset
trainer.set_learning_rate(new_lr)
if args.profile:
profile(step_num, 10, 14, profile_name=args.profile + str(rank))
if early_stop and step_num == 10:
mx.nd.waitall()
exit()
# load data
data_list = list(split_and_load(data_batch, ctxs))
ns_label_list, ns_pred_list = [], []
mask_label_list, mask_pred_list, mask_weight_list = [], [], []
with mx.autograd.record():
num_data = len(data_list)
for i in range(num_data):
parallel.put(data_list[i])
for _ in range(num_data):
(next_sentence_label, classified, masked_id,
decoded, masked_weight, ls1, ls2, valid_length, num_masks) = parallel.get()
ns_label_list.append(next_sentence_label)
ns_pred_list.append(classified)
mask_label_list.append(masked_id)
mask_pred_list.append(decoded)
mask_weight_list.append(masked_weight)
local_num_masks += num_masks
local_mlm_loss += ls1
running_num_tks += valid_length.sum()
# pre fetch next batch
try:
next_data_batch = next(data_train_iter)
except StopIteration:
end_of_batch = True
# update
if (batch_num + 1) % accumulate == 0:
running_mlm_loss += local_mlm_loss / local_num_masks
if backend == 'horovod':
hvd.allreduce_(local_num_masks, average=False, name='local_num_masks')
elif backend == 'byteps':
bps.byteps_push_pull(local_num_masks, is_average=False,
name="local_num_masks", priority=0)
# because byteps implicitly set scale /= num_workers
fp16_trainer.step(local_num_masks * num_workers, max_norm=local_num_masks,
num_ctxs=len(ctxs) * num_workers)
local_num_masks, local_mlm_loss = 0, 0
# update metrics
if args.no_compute_acc:
for mask_pred_i in mask_pred_list:
mask_pred_i.wait_to_read()
else:
nsp_metric.update(ns_label_list, ns_pred_list)
mlm_metric.update(mask_label_list, mask_pred_list, mask_weight_list)
# logging
if (step_num + 1) % (args.log_interval) == 0 and (batch_num + 1) % accumulate == 0:
if args.no_compute_acc:
log_noacc(begin_time, running_num_tks, running_mlm_loss,
0, step_num, trainer, args.log_interval)
else:
log(begin_time, running_num_tks, running_mlm_loss / accumulate,
running_nsp_loss / accumulate, step_num, mlm_metric, nsp_metric,
trainer, args.log_interval)
mlm_metric.reset_local()
nsp_metric.reset_local()
begin_time = time.time()
running_mlm_loss = running_nsp_loss = running_num_tks = 0
# saving checkpoints
if (step_num + 1) % args.ckpt_interval == 0 and (batch_num + 1) % accumulate == 0:
# if is_master_node:
# save_states(step_num, trainer, args.ckpt_dir, local_rank)
# if local_rank == 0:
# save_parameters(step_num, model.bert, args.ckpt_dir)
if (step_num + 1) % args.eval_interval == 0 and data_eval:
# eval data is always based on a fixed npz file.
dataset_eval = get_pretrain_data_npz(data_eval, batch_size_eval,
1, False, 1, vocab)
evaluate(dataset_eval, model, ctxs, args.log_interval, args.dtype, rank, num_workers)
batch_num += 1
# if is_master_node:
# save_states(step_num, trainer, args.ckpt_dir, local_rank)
# if local_rank == 0:
# save_parameters(step_num, model, args.ckpt_dir)
mx.nd.waitall()
train_end_time = time.time()
logging.info('Train cost={:.1f}s'.format(train_end_time - train_begin_time))
