def validation(model, data_loader, ctx_l):
"""Validate the model on the dataset
Parameters
----------
model : TransformerModel
The transformer model
data_loader : DataLoader
DataLoader
ctx_l : list
List of mx.ctx.Context
Returns
-------
avg_nll_loss : float
The average negative log-likelihood loss
"""
avg_nll_loss = mx.np.array(0, dtype=np.float32, ctx=mx.cpu())
ntokens = 0
for sample_data_l in grouper(data_loader, len(ctx_l)):
loss_l = []
ntokens += sum([
ele[3].sum().asnumpy() - ele[0].shape[0] for ele in sample_data_l
if ele is not None
])
for sample_data, ctx in zip(sample_data_l, ctx_l):
if sample_data is None:
continue
src_token_ids, tgt_token_ids, src_valid_length, tgt_valid_length, sample_ids = sample_data
src_token_ids = src_token_ids.as_in_ctx(ctx)
tgt_token_ids = tgt_token_ids.as_in_ctx(ctx)
src_valid_length = src_valid_length.as_in_ctx(ctx)
tgt_valid_length = tgt_valid_length.as_in_ctx(ctx)
tgt_pred = model(src_token_ids, src_valid_length,
tgt_token_ids[:, :-1], tgt_valid_length - 1)
tgt_labels = tgt_token_ids[:, 1:]
tgt_pred_logits = mx.npx.log_softmax(tgt_pred, axis=-1)
nll_loss = -mx.npx.pick(tgt_pred_logits, tgt_labels, axis=-1)
loss = mx.npx.sequence_mask(nll_loss,
sequence_length=tgt_valid_length - 1,
use_sequence_length=True,
axis=1)
loss_l.append(loss.sum())
avg_nll_loss += sum([loss.as_in_ctx(mx.cpu()) for loss in loss_l])
mx.npx.waitall()
avg_loss = avg_nll_loss.asnumpy() / ntokens
return avg_loss
def evaluate_by_ckpt(ckpt_name, best_ckpt):
classify_net.load_parameters(ckpt_name, ctx=ctx_l, cast_dtype=True)
logging.info('Prepare dev data')
dev_data, label = get_task_data(args,
tokenizer,
segment='eval',
task=task)
dev_batchify = bf.Group(bf.Group(bf.Pad(), bf.Pad(), bf.Stack()),
bf.Stack())
dataloader = DataLoader(dev_data,
batch_size=args.batch_size,
batchify_fn=dev_batchify,
shuffle=False)
for sample_l in grouper(dataloader, len(ctx_l)):
for sample, ctx in zip(sample_l, ctx_l):
if sample is None:
continue
(token_ids, token_types, valid_length), label = sample
token_ids = mx.np.array(token_ids, ctx=ctx)
token_types = mx.np.array(token_types, ctx=ctx)
valid_length = mx.np.array(valid_length, ctx=ctx)
scores = classify_net(token_ids, token_types, valid_length)
if args.task_name == 'sts':
label = label.reshape((-1, 1))
for metric in metrics:
metric.update([label], [scores])
#pred.append(scores)
for metric in metrics:
metric_name, result = metric.get()
logging.info('checkpoint {} get result: {}:{}'.format(
ckpt_name, metric_name, result))
if best_ckpt.get(metric_name, [0, ''])[0] < result:
best_ckpt[metric_name] = [result, ckpt_name]
def train(args):
_, num_parts, rank, local_rank, _, ctx_l = init_comm(
args.comm_backend, args.gpus)
if args.comm_backend == 'horovod':
logging_config(
args.save_dir,
name=f'train_transformer_rank{rank}_local{local_rank}_{num_parts}',
console=(rank == 0))
logging.info(args)
else:
logging_config(args.save_dir, name='train_transformer', console=True)
logging.info(args)
use_amp = args.fp16
if use_amp:
from mxnet import amp
src_tokenizer = create_tokenizer(args.src_tokenizer,
args.src_subword_model_path,
args.src_vocab_path)
tgt_tokenizer = create_tokenizer(args.tgt_tokenizer,
args.tgt_subword_model_path,
args.tgt_vocab_path)
base_tgt_tokenizer = MosesTokenizer(args.tgt_lang)
src_vocab = src_tokenizer.vocab
tgt_vocab = tgt_tokenizer.vocab
train_src_data, train_tgt_data = load_dataset_with_cache(
args.train_src_corpus,
args.train_tgt_corpus,
src_tokenizer,
tgt_tokenizer,
args.overwrite_cache,
local_rank,
max_src_length=args.max_src_length,
max_tgt_length=args.max_tgt_length,
pretokenized=not args.tokenize)
dev_src_data, dev_tgt_data = load_dataset_with_cache(
args.dev_src_corpus,
args.dev_tgt_corpus,
src_tokenizer,
tgt_tokenizer,
args.overwrite_cache,
local_rank,
pretokenized=not args.tokenize)
tgt_detok_sentences = []
tgt_raw_sentences = []
with open(args.dev_tgt_corpus, 'r') as in_f:
for line in in_f:
tgt_detok_sentences.append(
base_tgt_tokenizer.decode(
tgt_tokenizer.decode(line.split()).split()))
with open(args.dev_tgt_raw_corpus, 'r') as in_f:
for line in in_f:
tgt_raw_sentences.append(line.strip())
data_train = gluon.data.SimpleDataset([
(src_tokens, tgt_tokens, len(src_tokens), len(tgt_tokens), i)
for i, (src_tokens,
tgt_tokens) in enumerate(zip(train_src_data, train_tgt_data))
])
val_samples = [
(src_tokens, tgt_tokens, len(src_tokens), len(tgt_tokens), i)
for i, (src_tokens,
tgt_tokens) in enumerate(zip(dev_src_data, dev_tgt_data))
]
if args.comm_backend == 'horovod':
slice_begin = rank * (len(val_samples) // num_parts)
slice_end = min((rank + 1) * (len(val_samples) // num_parts),
len(val_samples))
data_val = gluon.data.SimpleDataset(val_samples[slice_begin:slice_end])
else:
data_val = gluon.data.SimpleDataset(val_samples)
# Construct the model + loss function
if args.cfg.endswith('.yml'):
cfg = TransformerModel.get_cfg().clone_merge(args.cfg)
else:
cfg = TransformerModel.get_cfg(args.cfg)
cfg.defrost()
cfg.MODEL.src_vocab_size = len(src_vocab)
cfg.MODEL.tgt_vocab_size = len(tgt_vocab)
cfg.MODEL.layout = 'TN'
cfg.freeze()
model = TransformerModel.from_cfg(cfg)
model.initialize(mx.init.Xavier(magnitude=args.magnitude), ctx=ctx_l)
model.hybridize()
for v in model.collect_params().values():
if v.grad_req != 'null':
v.grad_req = 'add'
# 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
param_dict = deduplicate_param_dict(model.collect_params())
inference_model = TransformerInference(model=model)
inference_model.hybridize()
if local_rank == 0:
logging.info(model)
with open(os.path.join(args.save_dir, 'config.yml'), 'w') as cfg_f:
cfg_f.write(cfg.dump())
label_smooth_loss = LabelSmoothCrossEntropyLoss(
num_labels=len(tgt_vocab),
alpha=args.label_smooth_alpha,
from_logits=False)
label_smooth_loss.hybridize()
# Construct the beam search sampler
scorer = BeamSearchScorer(alpha=args.lp_alpha,
K=args.lp_k,
from_logits=False)
beam_search_sampler = BeamSearchSampler(beam_size=args.beam_size,
decoder=inference_model,
vocab_size=len(tgt_vocab),
eos_id=tgt_vocab.eos_id,
scorer=scorer,
stochastic=False,
max_length_a=args.max_length_a,
max_length_b=args.max_length_b)
logging.info(beam_search_sampler)
if args.comm_backend == 'horovod':
hvd.broadcast_parameters(param_dict, root_rank=0)
# Construct the trainer
if args.lr is None:
base_lr = 2.0 / math.sqrt(args.num_units) / math.sqrt(
args.warmup_steps)
else:
base_lr = args.lr
lr_scheduler = InverseSquareRootScheduler(
warmup_steps=args.warmup_steps,
base_lr=base_lr,
warmup_init_lr=args.warmup_init_lr)
optimizer_params = {
'learning_rate': args.lr,
'beta1': 0.9,
'beta2': 0.997,
'epsilon': 1e-9,
'lr_scheduler': lr_scheduler,
'wd': args.wd
}
user_provided_ptimizer_params = json.loads(args.optimizer_params)
optimizer_params.update(user_provided_ptimizer_params)
if args.fp16:
optimizer_params.update({'multi_precision': True})
if args.comm_backend == 'horovod':
trainer = hvd.DistributedTrainer(param_dict, args.optimizer,
optimizer_params)
else:
trainer = gluon.Trainer(param_dict,
args.optimizer,
optimizer_params,
update_on_kvstore=False)
# Load Data
if args.sampler == 'BoundedBudgetSampler':
train_batch_sampler = BoundedBudgetSampler(
lengths=[(ele[2], ele[3]) for ele in data_train],
max_num_tokens=args.max_num_tokens,
max_num_sentences=args.max_num_sentences,
shuffle=True,
seed=args.seed)
elif args.sampler == 'FixedBucketSampler':
if args.comm_backend == 'horovod':
raise NotImplementedError(
'FixedBucketSampler does not support horovod at present')
if args.bucket_scheme == 'constant':
bucket_scheme = ConstWidthBucket()
elif args.bucket_scheme == 'linear':
bucket_scheme = LinearWidthBucket()
elif args.bucket_scheme == 'exp':
bucket_scheme = ExpWidthBucket(bucket_len_step=1.2)
else:
raise NotImplementedError
# TODO(sxjscience) Support auto-bucket-size tuning
train_batch_sampler = FixedBucketSampler(lengths=[
(ele[2], ele[3]) for ele in data_train
],
batch_size=args.batch_size,
num_buckets=args.num_buckets,
ratio=args.bucket_ratio,
shuffle=True,
use_average_length=True,
bucket_scheme=bucket_scheme,
seed=args.seed)
else:
raise NotImplementedError
num_updates_per_epoch = int(
math.ceil(
len(train_batch_sampler) /
(num_parts * len(ctx_l) * args.num_accumulated)))
# Convert the batch sampler to multiple shards
if num_parts > 1:
train_batch_sampler = ShardedIterator(train_batch_sampler,
num_parts=num_parts,
part_index=rank,
even_size=True,
seed=args.seed + 1000 * rank)
logging.info(train_batch_sampler)
batchify_fn = bf.Tuple(bf.Pad(), bf.Pad(), bf.Stack(), bf.Stack(),
bf.Stack())
train_data_loader = gluon.data.DataLoader(
data_train,
batch_sampler=train_batch_sampler,
batchify_fn=batchify_fn,
num_workers=0)
val_data_loader = gluon.data.DataLoader(data_val,
batch_size=args.val_batch_size,
batchify_fn=batchify_fn,
num_workers=0,
shuffle=False)
params = [p for p in param_dict.values() if p.grad_req != 'null']
model_averager = AverageSGDTracker(param_dict)
log_start_time = time.time()
num_params, num_fixed_params = None, None
# TODO(sxjscience) Add a log metric class
log_avg_loss_l = [mx.np.array(0.0, ctx=ctx) for ctx in ctx_l]
# Maintain the denominator of the loss.
log_avg_loss_denom_l = [mx.np.array(0.0, ctx=ctx) for ctx in ctx_l]
log_wc_l = [mx.np.array(0, dtype=np.int64, ctx=ctx) for ctx in ctx_l]
log_tgt_wc_l = [mx.np.array(0, dtype=np.int64, ctx=ctx) for ctx in ctx_l]
log_avg_grad_norm = 0
log_iter_num = 0
if local_rank == 0:
writer = SummaryWriter(
logdir=os.path.join(args.save_dir, 'tensorboard'))
if use_amp:
amp.init_trainer(trainer)
train_multi_data_loader = grouper(repeat(train_data_loader), len(ctx_l))
# when args.epochs < 0, the model will keep training
if args.epochs < 0:
if args.max_update > 0:
total_train_iters = args.max_update
if args.num_averages > 0:
assert args.num_averages <= total_train_iters // args.save_iterval_update
avg_start_iter = (
total_train_iters // args.save_iterval_update -
args.num_averages) * args.save_iterval_update
else:
avg_start_iter = -1
else:
total_train_iters = np.inf
avg_start_iter = -1
else:
total_train_iters = args.epochs * num_updates_per_epoch
if args.num_averages > 0:
assert args.num_averages <= args.epochs
avg_start_iter = (args.epochs -
args.num_average) * num_updates_per_epoch
else:
avg_start_iter = -1
# Here, we are manually setting up the scale to 1.0 because
# in horovod, the scale can be the number of workers:
# See the code here: https://github.com/horovod/horovod/blob/125115583b7029196e2ec530decd4209459d5479/horovod/mxnet/__init__.py#L141
# Since we will need to use the dynamic scaling in amp, we will manually call amp.unscale().
# A scale that is larger than 1.0 can be problematic in this case.
trainer._scale = 1.0
if args.max_num_tokens > 0:
const_scale = args.max_num_tokens
else:
const_scale = 100
train_start_time = time.time()
for train_iter in range(total_train_iters):
model.zero_grad()
loss_denom_l = [mx.np.array(0.0, ctx=ctx) for ctx in ctx_l]
for i in range(args.num_accumulated):
loss_l = []
sample_data_l = next(train_multi_data_loader)
for j, (sample_data, ctx) in enumerate(zip(sample_data_l, ctx_l)):
src_token_ids, tgt_token_ids, src_valid_length,\
tgt_valid_length, sample_ids = sample_data
src_token_ids = src_token_ids.as_in_ctx(ctx)
tgt_token_ids = tgt_token_ids.as_in_ctx(ctx)
src_valid_length = src_valid_length.as_in_ctx(ctx)
tgt_valid_length = tgt_valid_length.as_in_ctx(ctx)
src_wc, tgt_wc, bs = src_valid_length.sum(), \
tgt_valid_length.sum(), src_token_ids.shape[0]
log_wc_l[j] += src_wc + tgt_wc
log_tgt_wc_l[j] += tgt_wc
token_count = (tgt_valid_length - 1).sum()
loss_denom_l[j] += token_count / const_scale
log_avg_loss_denom_l[j] += token_count / const_scale
with mx.autograd.record():
if model.layout == 'NT':
tgt_pred = model(src_token_ids, src_valid_length,
tgt_token_ids[:, :-1],
tgt_valid_length - 1)
tgt_labels = tgt_token_ids[:, 1:]
loss = label_smooth_loss(tgt_pred, tgt_labels)
loss = mx.npx.sequence_mask(
loss,
sequence_length=tgt_valid_length - 1,
use_sequence_length=True,
axis=1)
loss = loss.sum() / const_scale
loss_l.append(loss)
elif model.layout == 'TN':
tgt_pred = model(src_token_ids.T, src_valid_length,
tgt_token_ids.T[:-1, :],
tgt_valid_length - 1)
tgt_labels = tgt_token_ids.T[1:, :]
loss = label_smooth_loss(tgt_pred, tgt_labels)
loss = mx.npx.sequence_mask(
loss,
sequence_length=tgt_valid_length - 1,
use_sequence_length=True,
axis=0)
loss = loss.sum() / const_scale
loss_l.append(loss)
log_avg_loss_l[j] += loss
if use_amp:
with mx.autograd.record():
with amp.scale_loss(loss_l, trainer) as amp_loss_l:
for loss in amp_loss_l:
loss.backward()
else:
with mx.autograd.record():
for loss in loss_l:
loss.backward()
# Print the total number of parameters
if local_rank == 0 and num_params is None:
num_params, num_fixed_params = count_parameters(param_dict)
logging.info(
'Total Number of Parameters (not-fixed/fixed): {}/{}'.format(
num_params, num_fixed_params))
# All-Reduce the gradient
trainer.allreduce_grads()
if args.comm_backend == 'horovod':
# All-Reduce the loss denominator
assert len(loss_denom_l) == 1
loss_denom = hvd.allreduce(loss_denom_l[0],
average=False).asnumpy()
else:
loss_denom = sum([ele.asnumpy() for ele in loss_denom_l])
if use_amp:
# We need to first unscale the gradient and then perform allreduce.
grad_scale = trainer.amp_loss_scale * loss_denom
else:
grad_scale = loss_denom
if args.max_grad_norm is not None:
total_norm, ratio, is_finite\
= clip_grad_global_norm(params, args.max_grad_norm * grad_scale)
total_norm = total_norm / grad_scale
else:
total_norm = grad_global_norm(params)
total_norm = total_norm / grad_scale
log_avg_grad_norm += total_norm
log_iter_num += 1
trainer.update(loss_denom, ignore_stale_grad=True)
if avg_start_iter > 0 and train_iter >= avg_start_iter:
model_averager.step()
if ((train_iter + 1) % args.log_interval == 0
or train_iter + 1 == total_train_iters):
if args.comm_backend == 'horovod':
# Use allreduce to get the total number of tokens and loss
log_wc = hvd.allreduce(log_wc_l[0], average=False).asnumpy()
log_tgt_wc = hvd.allreduce(log_tgt_wc_l[0],
average=False).asnumpy()
log_avg_loss = hvd.allreduce(log_avg_loss_l[0] /
log_avg_loss_denom_l[0],
average=True)
log_avg_loss = log_avg_loss.asnumpy()
else:
log_wc = sum([ele.asnumpy() for ele in log_wc_l])
log_tgt_wc = sum([ele.asnumpy() for ele in log_tgt_wc_l])
log_avg_loss =\
sum([log_avg_loss_l[i].asnumpy() / log_avg_loss_denom_l[i].asnumpy()
for i in range(len(log_avg_loss_l))]) / len(log_avg_loss_l)
log_avg_grad_norm = log_avg_grad_norm / log_iter_num
log_end_time = time.time()
wps = log_wc / (log_end_time - log_start_time)
epoch_id = train_iter // num_updates_per_epoch
logging.info(
'[Epoch {} Iter {}/{}, Overall {}/{}] loss={:.4f}, ppl={:.4f}, '
'throughput={:.2f}K wps, total wc={:.2f}K, wpb={:.2f}K,'
' LR={}, gnorm={:.4f}, ETA={:.2f}h'.format(
epoch_id, train_iter % num_updates_per_epoch + 1,
num_updates_per_epoch,
train_iter + 1, total_train_iters, log_avg_loss,
np.exp(log_avg_loss), wps / 1000, log_wc / 1000,
log_tgt_wc / 1000 / log_iter_num, trainer.learning_rate,
log_avg_grad_norm,
(log_end_time - train_start_time) / (train_iter + 1) *
(total_train_iters - train_iter - 1) / 3600))
if local_rank == 0:
writer.add_scalar('throughput_wps', wps, train_iter)
writer.add_scalar('train_loss', log_avg_loss, train_iter)
writer.add_scalar('lr', trainer.learning_rate, train_iter)
writer.add_scalar('grad_norm', log_avg_grad_norm, train_iter)
# Reinitialize the log variables
log_start_time = time.time()
log_avg_loss_l = [mx.np.array(0.0, ctx=ctx) for ctx in ctx_l]
log_avg_loss_denom_l = [mx.np.array(0.0, ctx=ctx) for ctx in ctx_l]
log_avg_grad_norm = 0
log_iter_num = 0
log_wc_l = [
mx.np.array(0, dtype=np.int64, ctx=ctx) for ctx in ctx_l
]
log_tgt_wc_l = [
mx.np.array(0, dtype=np.int64, ctx=ctx) for ctx in ctx_l
]
if (args.max_update > 0 and (train_iter + 1) % args.save_interval_update == 0) \
or ((train_iter + 1) % num_updates_per_epoch == 0) \
or train_iter + 1 == total_train_iters:
epoch_id = (train_iter + 1) // num_updates_per_epoch
if local_rank == 0:
if args.max_update <= 0:
model.save_parameters(os.path.join(
args.save_dir, 'epoch{}.params'.format(epoch_id)),
deduplicate=True)
else:
model.save_parameters(os.path.join(
args.save_dir, 'iter{}.params'.format(train_iter + 1)),
deduplicate=True)
avg_val_loss, ntokens, pred_sentences, pred_lengths, sentence_ids\
= validation(model, val_data_loader, inference_model, beam_search_sampler,
tgt_tokenizer, ctx_l)
if args.comm_backend == 'horovod':
flatten_pred_sentences = np.concatenate(pred_sentences, axis=0)
all_val_loss = hvd.allgather(
mx.np.array([avg_val_loss * ntokens],
dtype=np.float32,
ctx=ctx_l[0]))
all_ntokens = hvd.allgather(
mx.np.array([ntokens], dtype=np.int64, ctx=ctx_l[0]))
flatten_pred_sentences = hvd.allgather(
mx.np.array(flatten_pred_sentences,
dtype=np.int32,
ctx=ctx_l[0]))
pred_lengths = hvd.allgather(
mx.np.array(pred_lengths, dtype=np.int64, ctx=ctx_l[0]))
sentence_ids = hvd.allgather(
mx.np.array(sentence_ids, dtype=np.int64, ctx=ctx_l[0]))
avg_val_loss = all_val_loss.asnumpy().sum(
) / all_ntokens.asnumpy().sum()
flatten_pred_sentences = flatten_pred_sentences.asnumpy()
pred_lengths = pred_lengths.asnumpy()
sentence_ids = sentence_ids.asnumpy()
pred_sentences = [None for _ in range(len(sentence_ids))]
ptr = 0
assert sentence_ids.min() == 0 and sentence_ids.max(
) == len(sentence_ids) - 1
for sentence_id, length in zip(sentence_ids, pred_lengths):
pred_sentences[sentence_id] = flatten_pred_sentences[ptr:(
ptr + length)]
ptr += length
if local_rank == 0:
# Perform detokenization
pred_sentences_bpe_decode = []
pred_sentences_raw = []
for sentence in pred_sentences:
bpe_decode_sentence = tgt_tokenizer.decode(
sentence.tolist())
raw_sentence = base_tgt_tokenizer.decode(
bpe_decode_sentence.split())
pred_sentences_bpe_decode.append(bpe_decode_sentence)
pred_sentences_raw.append(raw_sentence)
detok_sacrebleu_out = sacrebleu.corpus_bleu(
sys_stream=pred_sentences_bpe_decode,
ref_streams=[tgt_detok_sentences])
raw_sacrebleu_out = sacrebleu.corpus_bleu(
sys_stream=pred_sentences_raw,
ref_streams=[tgt_raw_sentences])
with open(
os.path.join(args.save_dir,
f'epoch{epoch_id}_dev_prediction.txt'),
'w') as of:
for line in pred_sentences_raw:
of.write(line + '\n')
logging.info(
'[Epoch {}][Iter {}/{}] validation loss/ppl={:.4f}/{:.4f}, '
'SacreBlEU={}, Detok SacreBLUE={}'.format(
epoch_id, train_iter, total_train_iters, avg_val_loss,
np.exp(avg_val_loss), raw_sacrebleu_out.score,
detok_sacrebleu_out.score))
writer.add_scalar('valid_loss', avg_val_loss, train_iter)
writer.add_scalar('valid_bleu', raw_sacrebleu_out.score,
train_iter)
if args.num_averages > 0:
model_averager.copy_back(
param_dict) # TODO(sxjscience) Rewrite using update
model.save_parameters(os.path.join(args.save_dir, 'average.params'),
deduplicate=True)
def validation(model, data_loader, inference_model, sequence_sampler,
tgt_tokenizer, ctx_l):
"""Validate the model on the dataset
Parameters
----------
model : TransformerModel
The transformer model
data_loader : DataLoader
DataLoader
inference_model
The model for inference
sequence_sampler:
The sequence sampler for doing beam search
tgt_tokenizer
The target tokenizer
ctx_l : list
List of mx.ctx.Context
Returns
-------
avg_nll_loss : float
The average negative log-likelihood loss
ntokens : int
The total number of tokens
pred_sentences
The predicted sentences. Each element will be a numpy array.
pred_lengths
The length of the predicted sentences.
sentence_ids
IDs of the predicted sentences.
"""
avg_nll_loss = mx.np.array(0, dtype=np.float32, ctx=mx.cpu())
ntokens = 0
pred_sentences = []
sentence_ids = []
pred_lengths = []
for sample_data_l in grouper(data_loader, len(ctx_l)):
loss_l = []
ntokens += sum([
ele[3].sum().asnumpy() - ele[0].shape[0] for ele in sample_data_l
if ele is not None
])
for sample_data, ctx in zip(sample_data_l, ctx_l):
if sample_data is None:
continue
src_token_ids, tgt_token_ids, src_valid_length, tgt_valid_length, sample_ids = sample_data
src_token_ids = src_token_ids.as_in_ctx(ctx)
tgt_token_ids = tgt_token_ids.as_in_ctx(ctx)
src_valid_length = src_valid_length.as_in_ctx(ctx)
tgt_valid_length = tgt_valid_length.as_in_ctx(ctx)
if model.layout == 'NT':
tgt_pred = model(src_token_ids, src_valid_length,
tgt_token_ids[:, :-1], tgt_valid_length - 1)
tgt_labels = tgt_token_ids[:, 1:]
tgt_pred_logits = mx.npx.log_softmax(tgt_pred, axis=-1)
nll_loss = -mx.npx.pick(tgt_pred_logits, tgt_labels, axis=-1)
loss = mx.npx.sequence_mask(nll_loss,
sequence_length=tgt_valid_length -
1,
use_sequence_length=True,
axis=1)
loss_l.append(loss.sum())
elif model.layout == 'TN':
tgt_pred = model(src_token_ids.T, src_valid_length,
tgt_token_ids.T[:-1, :], tgt_valid_length - 1)
tgt_labels = tgt_token_ids.T[1:, :]
tgt_pred_logits = mx.npx.log_softmax(tgt_pred, axis=-1)
nll_loss = -mx.npx.pick(tgt_pred_logits, tgt_labels, axis=-1)
loss = mx.npx.sequence_mask(nll_loss,
sequence_length=tgt_valid_length -
1,
use_sequence_length=True,
axis=0)
loss_l.append(loss.sum())
init_input = mx.np.array([
tgt_tokenizer.vocab.bos_id
for _ in range(src_token_ids.shape[0])
],
ctx=ctx)
# Perform beam search
if model.layout == 'NT':
states = inference_model.init_states(src_token_ids,
src_valid_length)
elif model.layout == 'TN':
states = inference_model.init_states(src_token_ids.T,
src_valid_length)
samples, scores, sample_valid_length = sequence_sampler(
init_input, states, src_valid_length)
samples = samples.asnumpy()
sample_valid_length = sample_valid_length.asnumpy()
for j in range(samples.shape[0]):
valid_length = sample_valid_length[j, 0]
# Ignore the BOS + EOS tokens
pred_sentences.append(samples[j, 0, 1:(valid_length - 1)])
pred_lengths.append(valid_length - 2)
sentence_ids.append(sample_ids.asnumpy())
avg_nll_loss += sum([loss.as_in_ctx(mx.cpu()) for loss in loss_l])
mx.npx.waitall()
avg_loss = avg_nll_loss.asnumpy() / ntokens
pred_lengths = np.array(pred_lengths)
sentence_ids = np.concatenate(sentence_ids, axis=0)
return avg_loss, ntokens, pred_sentences, pred_lengths, sentence_ids
def train(args):
store, num_parts, rank, local_rank, is_master_node, ctx_l = init_comm(
args.comm_backend, args.gpus)
src_tokenizer = create_tokenizer(args.src_tokenizer,
args.src_subword_model_path,
args.src_vocab_path)
tgt_tokenizer = create_tokenizer(args.tgt_tokenizer,
args.tgt_subword_model_path,
args.tgt_vocab_path)
src_vocab = src_tokenizer.vocab
tgt_vocab = tgt_tokenizer.vocab
train_src_data, train_tgt_data = load_dataset_with_cache(
args.train_src_corpus, args.train_tgt_corpus, src_tokenizer,
tgt_tokenizer, args.overwrite_cache)
dev_src_data, dev_tgt_data = load_dataset_with_cache(
args.dev_src_corpus, args.dev_tgt_corpus, src_tokenizer, tgt_tokenizer,
args.overwrite_cache)
data_train = gluon.data.SimpleDataset([
(src_tokens, tgt_tokens, len(src_tokens), len(tgt_tokens), i)
for i, (src_tokens,
tgt_tokens) in enumerate(zip(train_src_data, train_tgt_data))
])
data_val = gluon.data.SimpleDataset([
(src_tokens, tgt_tokens, len(src_tokens), len(tgt_tokens), i)
for i, (src_tokens,
tgt_tokens) in enumerate(zip(dev_src_data, dev_tgt_data))
])
# Construct the model + loss function
if args.cfg.endswith('.yml'):
cfg = TransformerModel.get_cfg().clone_merge(args.cfg)
else:
cfg = TransformerModel.get_cfg(args.cfg)
cfg.defrost()
cfg.MODEL.src_vocab_size = len(src_vocab)
cfg.MODEL.tgt_vocab_size = len(tgt_vocab)
if args.fp16:
raise NotImplementedError
# cfg.MODEL.dtype = 'float16'
cfg.freeze()
model = TransformerModel.from_cfg(cfg)
model.initialize(mx.init.Xavier(magnitude=args.magnitude), ctx=ctx_l)
model.hybridize()
if local_rank == 0:
logging.info(model)
with open(os.path.join(args.save_dir, 'config.yml'), 'w') as cfg_f:
cfg_f.write(cfg.dump())
label_smooth_loss = LabelSmoothCrossEntropyLoss(
num_labels=len(tgt_vocab),
alpha=args.label_smooth_alpha,
from_logits=False)
label_smooth_loss.hybridize()
rescale_loss = 100.0
if args.comm_backend == 'horovod':
hvd.broadcast_parameters(model.collect_params(), root_rank=0)
# Construct the trainer
# TODO(sxjscience) Support AMP
if args.lr is None:
base_lr = 2.0 / math.sqrt(args.num_units) / math.sqrt(
args.warmup_steps)
else:
base_lr = args.lr
lr_scheduler = InverseSquareRootScheduler(
warmup_steps=args.warmup_steps,
base_lr=base_lr,
warmup_init_lr=args.warmup_init_lr)
trainer_settings = (model.collect_params(), 'adam', {
'learning_rate': args.lr,
'beta1': 0.9,
'beta2': 0.98,
'epsilon': 1e-9,
'lr_scheduler': lr_scheduler
})
if args.comm_backend == 'horovod':
trainer = hvd.DistributedTrainer(*trainer_settings)
else:
trainer = gluon.Trainer(*trainer_settings)
# Load Data
if args.sampler == 'BoundedBudgetSampler':
train_batch_sampler = BoundedBudgetSampler(
lengths=[(ele[2], ele[3]) for ele in data_train],
max_num_tokens=args.max_num_tokens,
max_num_sentences=args.max_num_sentences,
seed=args.seed,
num_parts=num_parts,
part_index=rank)
elif args.sampler == 'FixedBucketSampler':
if args.comm_backend == 'horovod':
raise NotImplementedError(
'FixedBucketSampler does not support horovod at present')
if args.bucket_scheme == 'constant':
bucket_scheme = ConstWidthBucket()
elif args.bucket_scheme == 'linear':
bucket_scheme = LinearWidthBucket()
elif args.bucket_scheme == 'exp':
bucket_scheme = ExpWidthBucket(bucket_len_step=1.2)
else:
raise NotImplementedError
# TODO(sxjscience) Support auto-bucket-size tuning
train_batch_sampler = FixedBucketSampler(lengths=[
(ele[2], ele[3]) for ele in data_train
],
batch_size=args.batch_size,
num_buckets=args.num_buckets,
ratio=args.bucket_ratio,
shuffle=True,
use_average_length=True,
bucket_scheme=bucket_scheme,
seed=args.seed)
else:
raise NotImplementedError
if local_rank == 0:
logging.info(train_batch_sampler)
batchify_fn = bf.Tuple(bf.Pad(), bf.Pad(), bf.Stack(), bf.Stack(),
bf.Stack())
train_data_loader = gluon.data.DataLoader(
data_train,
batch_sampler=train_batch_sampler,
batchify_fn=batchify_fn,
num_workers=0)
val_data_loader = gluon.data.DataLoader(data_val,
batch_size=args.val_batch_size,
batchify_fn=batchify_fn,
num_workers=0,
shuffle=False)
for v in model.collect_params().values():
if v.grad_req != 'null':
v.grad_req = 'add'
model.zero_grad()
model_averager = AverageSGDTracker(model.collect_params())
log_start_time = time.time()
num_params, num_fixed_params = None, None
# TODO(sxjscience) Add a log metric class
accum_count = 0
loss_denom = 0
n_train_iters = 0
log_wc = 0
log_avg_loss = 0.0
log_loss_denom = 0
epoch_id = 0
while (args.epochs < 0 or epoch_id < args.epochs
): # when args.epochs < 0, the model will keep training
n_epoch_train_iters = 0
processed_batch_num = 0
train_multi_data_loader = grouper(train_data_loader, len(ctx_l))
is_last_batch = False
sample_data_l = next(train_multi_data_loader)
while not is_last_batch:
processed_batch_num += len(sample_data_l)
loss_l = []
for sample_data, ctx in zip(sample_data_l, ctx_l):
if sample_data is None:
continue
src_token_ids, tgt_token_ids, src_valid_length, tgt_valid_length, sample_ids = sample_data
src_wc, tgt_wc, bs = src_valid_length.sum(
), tgt_valid_length.sum(), src_token_ids.shape[0]
loss_denom += tgt_wc - bs
log_loss_denom += tgt_wc - bs
log_wc += src_wc + tgt_wc
src_token_ids = src_token_ids.as_in_ctx(ctx)
tgt_token_ids = tgt_token_ids.as_in_ctx(ctx)
src_valid_length = src_valid_length.as_in_ctx(ctx)
tgt_valid_length = tgt_valid_length.as_in_ctx(ctx)
with mx.autograd.record():
tgt_pred = model(src_token_ids, src_valid_length,
tgt_token_ids[:, :-1],
tgt_valid_length - 1)
tgt_labels = tgt_token_ids[:, 1:]
loss = label_smooth_loss(tgt_pred, tgt_labels)
loss = mx.npx.sequence_mask(
loss,
sequence_length=tgt_valid_length - 1,
use_sequence_length=True,
axis=1)
loss_l.append(loss.sum() / rescale_loss)
for l in loss_l:
l.backward()
accum_count += 1
try:
sample_data_l = next(train_multi_data_loader)
except StopIteration:
is_last_batch = True
if local_rank == 0 and num_params is None:
num_params, num_fixed_params = count_parameters(
model.collect_params())
logging.info(
'Total Number of Parameters (not-fixed/fixed): {}/{}'.
format(num_params, num_fixed_params))
sum_loss = sum([l.as_in_ctx(mx.cpu())
for l in loss_l]) * rescale_loss
log_avg_loss += sum_loss
mx.npx.waitall()
if accum_count == args.num_accumulated or is_last_batch:
# Update the parameters
n_train_iters += 1
n_epoch_train_iters += 1
trainer.step(loss_denom.asnumpy() / rescale_loss)
accum_count = 0
loss_denom = 0
model.zero_grad()
if (args.epochs > 0 and epoch_id >= args.epochs - args.num_averages) or \
(args.max_update > 0 and n_train_iters >= args.max_update - args.num_averages * args.save_interval_update):
model_averager.step()
if local_rank == 0 and \
(n_epoch_train_iters % args.log_interval == 0 or is_last_batch):
log_end_time = time.time()
log_wc = log_wc.asnumpy()
wps = log_wc / (log_end_time - log_start_time)
log_avg_loss = (log_avg_loss / log_loss_denom).asnumpy()
logging.info(
'[Epoch {} Batch {}/{}] loss={:.4f}, ppl={:.4f}, '
'throughput={:.2f}K wps, wc={:.2f}K, LR={}'.format(
epoch_id, processed_batch_num * num_parts,
len(train_data_loader), log_avg_loss,
np.exp(log_avg_loss), wps / 1000, log_wc / 1000,
trainer.learning_rate))
log_start_time = time.time()
log_avg_loss = 0
log_loss_denom = 0
log_wc = 0
if local_rank == 0 and \
(args.max_update > 0 and n_train_iters % args.save_interval_update == 0):
model.save_parameters(os.path.join(
args.save_dir, 'update{:d}.params'.format(
n_train_iters // args.save_interval_update)),
deduplicate=True)
if args.max_update > 0 and n_train_iters >= args.max_update:
break
if local_rank == 0 and args.epochs > 0:
model.save_parameters(os.path.join(
args.save_dir, 'epoch{:d}.params'.format(epoch_id)),
deduplicate=True)
avg_valid_loss = validation(model, val_data_loader, ctx_l)
logging.info('[Epoch {}] validation loss/ppl={:.4f}/{:.4f}'.format(
epoch_id, avg_valid_loss, np.exp(avg_valid_loss)))
if args.max_update > 0 and n_train_iters >= args.max_update:
break
epoch_id += 1
if args.num_averages > 0:
model_averager.copy_back(
model.collect_params()) # TODO(sxjscience) Rewrite using update
model.save_parameters(os.path.join(args.save_dir, 'average.params'),
deduplicate=True)
def eval_validation(ckpt_name, best_eval):
"""
Model inference during validation or final evaluation.
"""
dev_dataloader = mx.gluon.data.DataLoader(
dev_all_chunk_features,
batchify_fn=dataset_processor.BatchifyFunction,
batch_size=args.eval_batch_size,
num_workers=0,
shuffle=False)
log_interval = args.eval_log_interval
all_results = []
epoch_tic = time.time()
tic = time.time()
epoch_size = len(dev_features)
total_num = 0
log_num = 0
for batch_idx, dev_batch in enumerate(
grouper(dev_dataloader, len(ctx_l))):
# Predict for each chunk
for sample, ctx in zip(dev_batch, ctx_l):
if sample is None:
continue
# Copy the data to device
tokens = sample.data.as_in_ctx(ctx)
total_num += len(tokens)
log_num += len(tokens)
segment_ids = sample.segment_ids.as_in_ctx(
ctx) if use_segmentation else None
valid_length = sample.valid_length.as_in_ctx(ctx)
p_mask = sample.masks.as_in_ctx(ctx)
p_mask = 1 - p_mask # In the network, we use 1 --> no_mask, 0 --> mask
start_top_logits, start_top_index, end_top_logits, end_top_index, answerable_logits \
= qa_net.inference(tokens, segment_ids, valid_length, p_mask,
args.start_top_n, args.end_top_n)
for i, qas_id in enumerate(sample.qas_id):
result = RawResultExtended(
qas_id=qas_id,
start_top_logits=start_top_logits[i].asnumpy(),
start_top_index=start_top_index[i].asnumpy(),
end_top_logits=end_top_logits[i].asnumpy(),
end_top_index=end_top_index[i].asnumpy(),
answerable_logits=answerable_logits[i].asnumpy())
all_results.append(result)
# logging
if (batch_idx + 1) % log_interval == 0:
# Output the loss of per step
toc = time.time()
logging.info(
'[batch {}], Time cost={:.2f},'
' Throughput={:.2f} samples/s, ETA={:.2f}h'.format(
batch_idx + 1, toc - tic, log_num / (toc - tic),
(epoch_size - total_num) / (total_num /
(toc - epoch_tic)) / 3600))
tic = time.time()
log_num = 0
epoch_toc = time.time()
logging.info('Time cost=%2f s, Thoughput=%.2f samples/s',
epoch_toc - epoch_tic,
total_num / (epoch_toc - epoch_tic))
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
no_answer_score_json = collections.OrderedDict()
for index, (left_index, right_index) in enumerate(
zip(dev_chunk_feature_ptr[:-1], dev_chunk_feature_ptr[1:])):
chunked_features = dev_all_chunk_features[left_index:right_index]
results = all_results[left_index:right_index]
original_feature = dev_features[index]
qas_ids = set([result.qas_id for result in results] +
[feature.qas_id for feature in chunked_features])
assert len(
qas_ids) == 1, 'Mismatch Occured between features and results'
example_qas_id = list(qas_ids)[0]
assert example_qas_id == original_feature.qas_id, \
'Mismatch Occured between original feature and chunked features'
not_answerable_score, best_pred, nbest_json = predict_extended(
original_feature=original_feature,
chunked_features=chunked_features,
results=results,
n_best_size=args.n_best_size,
max_answer_length=args.max_answer_length,
start_top_n=args.start_top_n,
end_top_n=args.end_top_n)
no_answer_score_json[example_qas_id] = not_answerable_score
all_predictions[example_qas_id] = best_pred
all_nbest_json[example_qas_id] = nbest_json
if args.version == '2.0':
exact = 'best_exact'
f1 = 'best_f1'
na_prob = no_answer_score_json
else:
exact = 'exact'
f1 = 'f1'
na_prob = None
cur_eval, revised_predictions = squad_eval(dev_data_path,
all_predictions,
na_prob,
revise=na_prob is not None)
logging.info('The evaluated results are {}'.format(
json.dumps(cur_eval)))
cur_metrics = 0.5 * (cur_eval[exact] + cur_eval[f1])
if best_eval:
best_metrics = 0.5 * (best_eval[exact] + best_eval[f1])
else:
best_metrics = 0.
if cur_metrics > best_metrics:
logging.info('The evaluated files are saved in {}'.format(
args.output_dir))
output_prediction_file = os.path.join(args.output_dir,
'predictions.json')
output_nbest_file = os.path.join(args.output_dir,
'nbest_predictions.json')
na_prob_file = os.path.join(args.output_dir, 'na_prob.json')
revised_prediction_file = os.path.join(args.output_dir,
'revised_predictions.json')
with open(output_prediction_file, 'w') as of:
of.write(json.dumps(all_predictions, indent=4) + '\n')
with open(output_nbest_file, 'w') as of:
of.write(json.dumps(all_nbest_json, indent=4) + '\n')
with open(na_prob_file, 'w') as of:
of.write(json.dumps(no_answer_score_json, indent=4) + '\n')
with open(revised_prediction_file, 'w') as of:
of.write(json.dumps(revised_predictions, indent=4) + '\n')
best_eval = cur_eval
best_eval.update({'best_ckpt': ckpt_name})
return best_eval
def train(args):
store, num_workers, rank, local_rank, is_master_node, ctx_l = init_comm(
args.comm_backend, args.gpus)
setup_logging(args, local_rank)
cfg, tokenizer, qa_net, use_segmentation = \
get_network(args.model_name, ctx_l,
args.classifier_dropout,
args.param_checkpoint,
args.backbone_path)
logging.info('Prepare training data')
train_features = get_squad_features(args, tokenizer, segment='train')
dataset_processor = SquadDatasetProcessor(
tokenizer=tokenizer,
doc_stride=args.doc_stride,
max_seq_length=args.max_seq_length,
max_query_length=args.max_query_length)
logging.info('Processing the Training data:')
train_dataset, num_answer_mismatch, num_unreliable \
= dataset_processor.get_train(train_features, skip_unreliable=True)
logging.info(
'Done! #Unreliable Span={} / #Mismatched Answer={} / #Total={}'.format(
num_unreliable, num_answer_mismatch, len(train_features)))
# Get dataset statistics
num_impossible = 0
for sample in train_dataset:
num_impossible += sample.is_impossible
logging.info('Before Chunking, #Train/Is Impossible = {}/{}'.format(
len(train_features),
sum([ele.is_impossible for ele in train_features])))
logging.info('After Chunking, #Train Sample/Is Impossible = {}/{}'.format(
len(train_dataset), num_impossible))
# Shuffle the dataset using a fixed seed across all workers
rs = np.random.RandomState(args.pre_shuffle_seed)
rs.shuffle(train_dataset)
sampler = SplitSampler(len(train_dataset),
num_parts=num_workers,
part_index=rank,
even_size=True)
train_dataloader = mx.gluon.data.DataLoader(
train_dataset,
batchify_fn=dataset_processor.BatchifyFunction,
batch_size=args.batch_size,
num_workers=0,
sampler=sampler)
if 'electra' in args.model_name:
# Froze parameters, does not work for albert model since parameters in all layers are shared
if args.untunable_depth > 0:
qa_net.backbone.frozen_params(args.untunable_depth)
if args.layerwise_decay > 0:
qa_net.backbone.apply_layerwise_decay(args.layerwise_decay)
logging.info('Creating distributed trainer...')
# Collect differentiable parameters
param_dict = qa_net.collect_params()
# Do not apply weight decay to all the LayerNorm and bias
for _, v in qa_net.collect_params('.*beta|.*gamma|.*bias').items():
v.wd_mult = 0.0
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)
epoch_size = (len(train_dataloader) + len(ctx_l) - 1) // len(ctx_l)
if args.num_train_steps is not None:
num_train_steps = args.num_train_steps
else:
num_train_steps = int(args.epochs * epoch_size / args.num_accumulated)
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 epoch_size // args.num_accumulated
logging.info(
'#Total Training Steps={}, Warmup={}, Save Interval={}'.format(
num_train_steps, warmup_steps, save_interval))
# set up optimization
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,
}
adam_betas = eval(args.adam_betas)
if args.optimizer == 'adamw':
optimizer_params.update({
'beta1': adam_betas[0],
'beta2': adam_betas[1],
'epsilon': args.adam_epsilon,
'correct_bias': False,
})
elif args.optimizer == 'adam':
optimizer_params.update({
'beta1': adam_betas[0],
'beta2': adam_betas[1],
'epsilon': args.adam_epsilon,
})
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)
log_span_loss = 0
log_answerable_loss = 0
log_total_loss = 0
log_sample_num = 0
global_tic = time.time()
tic = time.time()
for step_num, batch_data in enumerate(
grouper(repeat(train_dataloader),
len(ctx_l) * num_accumulated)):
for sample_l in grouper(batch_data, len(ctx_l)):
loss_l = []
span_loss_l = []
answerable_loss_l = []
for sample, ctx in zip(sample_l, ctx_l):
if sample is None:
continue
# Copy the data to device
tokens = sample.data.as_in_ctx(ctx)
log_sample_num += len(tokens)
segment_ids = sample.segment_ids.as_in_ctx(
ctx) if use_segmentation else None
valid_length = sample.valid_length.as_in_ctx(ctx)
p_mask = sample.masks.as_in_ctx(ctx)
gt_start = sample.gt_start.as_in_ctx(ctx).astype(np.int32)
gt_end = sample.gt_end.as_in_ctx(ctx).astype(np.int32)
is_impossible = sample.is_impossible.as_in_ctx(ctx).astype(
np.int32)
batch_idx = mx.np.arange(tokens.shape[0],
dtype=np.int32,
ctx=ctx)
p_mask = 1 - p_mask # In the network, we use 1 --> no_mask, 0 --> mask
with mx.autograd.record():
start_logits, end_logits, answerable_logits \
= qa_net(tokens, segment_ids, valid_length, p_mask, gt_start)
sel_start_logits = start_logits[batch_idx, gt_start]
sel_end_logits = end_logits[batch_idx, gt_end]
sel_answerable_logits = answerable_logits[batch_idx,
is_impossible]
span_loss = -0.5 * (sel_start_logits +
sel_end_logits).mean()
answerable_loss = -0.5 * sel_answerable_logits.mean()
loss = span_loss + answerable_loss
loss_l.append(loss)
span_loss_l.append(span_loss)
answerable_loss_l.append(answerable_loss)
for loss in loss_l:
loss.backward()
# All Reduce the Step Loss
log_span_loss += sum(
[ele.as_in_ctx(ctx_l[0]) for ele in span_loss_l]).asnumpy()
log_total_loss += sum([ele.as_in_ctx(ctx_l[0])
for ele in loss_l]).asnumpy()
log_answerable_loss += sum([
ele.as_in_ctx(ctx_l[0]) for ele in answerable_loss_l
]).asnumpy()
# update
trainer.allreduce_grads()
if args.max_grad_norm > 0:
total_norm, ratio, is_finite = clip_grad_global_norm(
params, args.max_grad_norm * num_workers)
else:
total_norm = grad_global_norm(params)
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
if args.num_accumulated > 1:
# set grad to zero for gradient accumulation
qa_net.zero_grad()
# saving
if local_rank == 0 and (step_num + 1) % save_interval == 0 or (
step_num + 1) >= num_train_steps:
version_prefix = 'squad' + args.version
ckpt_name = '{}_{}_{}.params'.format(args.model_name,
version_prefix,
(step_num + 1))
params_saved = os.path.join(args.output_dir, ckpt_name)
qa_net.save_parameters(params_saved)
ckpt_candidates = [
f for f in os.listdir(args.output_dir) if f.endswith('.params')
]
# keep last `max_saved_ckpt` checkpoints
if len(ckpt_candidates) > args.max_saved_ckpt:
ckpt_candidates.sort(key=lambda ele: (len(ele), ele))
os.remove(os.path.join(args.output_dir, ckpt_candidates[0]))
logging.info('Params saved in: {}'.format(params_saved))
# logging
if (step_num + 1) % log_interval == 0:
log_span_loss /= log_sample_num
log_answerable_loss /= log_sample_num
log_total_loss /= log_sample_num
toc = time.time()
logging.info(
'Step: {}/{}, Loss span/answer/total={:.4f}/{:.4f}/{:.4f},'
' LR={:.8f}, grad_norm={:.4f}. Time cost={:.2f}, Throughput={:.2f} samples/s'
' ETA={:.2f}h'.format(
(step_num + 1), num_train_steps, log_span_loss,
log_answerable_loss, log_total_loss, trainer.learning_rate,
total_norm, toc - tic, log_sample_num / (toc - tic),
(num_train_steps - (step_num + 1)) /
((step_num + 1) / (toc - global_tic)) / 3600))
tic = time.time()
log_span_loss = 0
log_answerable_loss = 0
log_total_loss = 0
log_sample_num = 0
num_samples_per_update = 0
if (step_num + 1) >= num_train_steps:
toc = time.time()
logging.info('Finish training step: {} within {} hours'.format(
step_num + 1, (toc - global_tic) / 3600))
break
return params_saved
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)
def train(args):
store, num_workers, rank, local_rank, is_master_node, ctx_l = init_comm(
args.comm_backend, args.gpus)
task = get_task(args.task_name)
#setup_logging(args, local_rank)
level = logging.INFO
detail_dir = os.path.join(args.output_dir, args.task_name)
if not os.path.exists(detail_dir):
os.mkdir(detail_dir)
logging_config(
detail_dir,
name='train_{}_{}_'.format(args.task_name, args.model_name) +
str(rank), # avoid race
level=level,
console=(local_rank == 0))
logging.info(args)
cfg, tokenizer, classify_net, use_segmentation = \
get_network(args.model_name, ctx_l,
args.param_checkpoint,
args.backbone_path,
task)
logging.info('Prepare training data')
train_data, _ = get_task_data(args, tokenizer, segment='train', task=task)
train_batchify = bf.Group(bf.Group(bf.Pad(), bf.Pad(), bf.Stack()),
bf.Stack())
epoch_num_updates = len(train_data) // args.batch_size
max_update = epoch_num_updates * args.epochs
warmup_steps = int(np.ceil(max_update * args.warmup_ratio))
dataloader = DataLoader(train_data,
batch_size=args.batch_size,
batchify_fn=train_batchify,
num_workers=4,
shuffle=True)
dataloader = grouper(repeat(dataloader), len(ctx_l))
param_dict = classify_net.collect_params()
# Do not apply weight decay to all the LayerNorm and bias
for _, v in classify_net.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'
if args.comm_backend == 'horovod':
# Horovod: fetch and broadcast parameters
hvd.broadcast_parameters(param_dict, root_rank=0)
lr_scheduler = PolyScheduler(max_update=max_update,
base_lr=args.lr,
warmup_begin_lr=0.0,
pwr=1,
final_lr=0.0,
warmup_steps=warmup_steps,
warmup_mode='linear')
optimizer_params = {
'learning_rate': args.lr,
'wd': args.wd,
'lr_scheduler': lr_scheduler
}
if args.comm_backend == 'horovod':
trainer = hvd.DistributedTrainer(param_dict, args.optimizer,
optimizer_params)
else:
trainer = mx.gluon.Trainer(classify_net.collect_params(), 'adamw',
optimizer_params)
if args.task_name == 'sts':
loss_function = gluon.loss.L2Loss()
else:
loss_function = gluon.loss.SoftmaxCELoss()
#prepare loss function
log_loss = 0
log_gnorm = 0
log_step = 0
if args.log_interval > 0:
log_interval = args.log_interval
else:
log_interval = int(epoch_num_updates * 0.5)
for i in range(max_update):
sample_l = next(dataloader)
loss_l = []
for sample, ctx in zip(sample_l, ctx_l):
(token_ids, token_types, valid_length), label = sample
# Move to the corresponding context
token_ids = mx.np.array(token_ids, ctx=ctx)
token_types = mx.np.array(token_types, ctx=ctx)
valid_length = mx.np.array(valid_length, ctx=ctx)
label = mx.np.array(label, ctx=ctx)
with mx.autograd.record():
scores = classify_net(token_ids, token_types, valid_length)
loss = loss_function(scores, label).mean() / len(ctx_l)
loss_l.append(loss)
for loss in loss_l:
loss.backward()
trainer.allreduce_grads()
# Begin Norm Clipping
total_norm, ratio, is_finite = clip_grad_global_norm(
params, args.max_grad_norm)
trainer.update(1.0)
step_loss = sum([loss.asnumpy() for loss in loss_l])
log_loss += step_loss
log_gnorm += total_norm
log_step += 1
if log_step >= log_interval or i == max_update - 1:
logging.info(
'[Iter {} / {}] avg {} = {:.2f}, avg gradient norm = {:.2f}'.
format(i + 1, max_update, 'nll', log_loss / log_step,
log_gnorm / log_step))
log_loss = 0
log_gnorm = 0
log_step = 0
if local_rank == 0 and (i == max_update - 1 or i %
(max_update // args.epochs) == 0 and i > 0):
ckpt_name = '{}_{}_{}.params'.format(args.model_name,
args.task_name, (i + 1))
params_saved = os.path.join(detail_dir, ckpt_name)
classify_net.save_parameters(params_saved)
logging.info('Params saved in: {}'.format(params_saved))
def train(args):
store, num_workers, rank, local_rank, is_master_node, ctx_l = init_comm(
args.comm_backend, args.gpus)
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, args.mask_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
if args.num_accumulated > 1:
logging.info(
'Using gradient accumulation. Effective global batch size = {}'.
format(args.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
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
num_samples_per_update = 0
loss_denom = float(len(ctx_l) * args.num_accumulated * num_workers)
log_total_loss = 0
log_mlm_loss = 0
log_rtd_loss = 0
log_sample_num = 0
train_start_time = time.time()
if args.num_accumulated != 1:
# set grad to zero for gradient accumulation
model.zero_grad()
# 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(args.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)
num_samples_per_update += 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)
# the official implementation takes the sum of each batch inside the loss function
# while SigmoidBinaryCrossEntropyLoss and SoftmaxCELoss takes the mean value
mlm_loss = mlm_loss_fn(
mlm_scores, unmasked_tokens, masked_weights.reshape(
-1)).mean() / (masked_weights.mean() + 1e-6)
rtd_loss = rtd_loss_fn(
rtd_scores, labels,
length_masks).mean() / (length_masks.mean() + 1e-6)
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_denom
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() * loss_denom
# update
trainer.allreduce_grads()
# Here, the accumulated gradients are
# \sum_{n=1}^N g_n / loss_denom
# Thus, in order to clip the average gradient
# \frac{1}{N} \sum_{n=1}^N --> clip to args.max_grad_norm
# We need to change the ratio to be
# \sum_{n=1}^N g_n / loss_denom --> clip to args.max_grad_norm * N / loss_denom
total_norm, ratio, is_finite = clip_grad_global_norm(
params, args.max_grad_norm * num_samples_per_update / loss_denom)
total_norm = total_norm / (num_samples_per_update / loss_denom)
trainer.update(num_samples_per_update / loss_denom,
ignore_stale_grad=True)
step_num += 1
if args.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 and local_rank == 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)
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
num_samples_per_update = 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)
