def train():
"""Training function."""
trainer = gluon.Trainer(model.collect_params(), args.optimizer, {
'learning_rate': args.lr,
'beta2': 0.98,
'epsilon': 1e-9
})
train_batchify_fn = btf.Tuple(btf.Pad(), btf.Pad(),
btf.Stack(dtype='float32'),
btf.Stack(dtype='float32'))
test_batchify_fn = btf.Tuple(btf.Pad(), btf.Pad(),
btf.Stack(dtype='float32'),
btf.Stack(dtype='float32'), btf.Stack())
target_val_lengths = list(map(lambda x: x[-1], data_val_lengths))
target_test_lengths = list(map(lambda x: x[-1], data_test_lengths))
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
train_batch_sampler = FixedBucketSampler(lengths=data_train_lengths,
batch_size=args.batch_size,
num_buckets=args.num_buckets,
ratio=args.bucket_ratio,
shuffle=True,
use_average_length=True,
num_shards=len(ctx),
bucket_scheme=bucket_scheme)
logging.info('Train Batch Sampler:\n{}'.format(
train_batch_sampler.stats()))
train_data_loader = ShardedDataLoader(data_train,
batch_sampler=train_batch_sampler,
batchify_fn=train_batchify_fn,
num_workers=8)
val_batch_sampler = FixedBucketSampler(lengths=target_val_lengths,
batch_size=args.test_batch_size,
num_buckets=args.num_buckets,
ratio=args.bucket_ratio,
shuffle=False,
use_average_length=True,
bucket_scheme=bucket_scheme)
logging.info('Valid Batch Sampler:\n{}'.format(val_batch_sampler.stats()))
val_data_loader = DataLoader(data_val,
batch_sampler=val_batch_sampler,
batchify_fn=test_batchify_fn,
num_workers=8)
test_batch_sampler = FixedBucketSampler(lengths=target_test_lengths,
batch_size=args.test_batch_size,
num_buckets=args.num_buckets,
ratio=args.bucket_ratio,
shuffle=False,
use_average_length=True,
bucket_scheme=bucket_scheme)
logging.info('Test Batch Sampler:\n{}'.format(test_batch_sampler.stats()))
test_data_loader = DataLoader(data_test,
batch_sampler=test_batch_sampler,
batchify_fn=test_batchify_fn,
num_workers=8)
if args.bleu == 'tweaked':
bpe = bool(args.dataset != 'IWSLT2015' and args.dataset != 'TOY')
split_compound_word = bpe
tokenized = True
elif args.bleu == '13a' or args.bleu == 'intl':
bpe = False
split_compound_word = False
tokenized = False
else:
raise NotImplementedError
best_valid_bleu = 0.0
step_num = 0
warmup_steps = args.warmup_steps
grad_interval = args.num_accumulated
model.collect_params().setattr('grad_req', 'add')
average_start = (len(train_data_loader) //
grad_interval) * (args.epochs - args.average_start)
average_param_dict = None
model.collect_params().zero_grad()
for epoch_id in range(args.epochs):
log_avg_loss = 0
log_wc = 0
loss_denom = 0
step_loss = 0
log_start_time = time.time()
for batch_id, seqs \
in enumerate(train_data_loader):
if batch_id % grad_interval == 0:
step_num += 1
new_lr = args.lr / math.sqrt(args.num_units) \
* min(1. / math.sqrt(step_num), step_num * warmup_steps ** (-1.5))
trainer.set_learning_rate(new_lr)
src_wc, tgt_wc, bs = np.sum(
[(shard[2].sum(), shard[3].sum(), shard[0].shape[0])
for shard in seqs],
axis=0)
src_wc = src_wc.asscalar()
tgt_wc = tgt_wc.asscalar()
loss_denom += tgt_wc - bs
seqs = [[seq.as_in_context(context) for seq in shard]
for context, shard in zip(ctx, seqs)]
Ls = []
with mx.autograd.record():
for src_seq, tgt_seq, src_valid_length, tgt_valid_length in seqs:
out, _ = model(src_seq, tgt_seq[:, :-1], src_valid_length,
tgt_valid_length - 1)
smoothed_label = label_smoothing(tgt_seq[:, 1:])
ls = loss_function(out, smoothed_label,
tgt_valid_length - 1).sum()
Ls.append((ls * (tgt_seq.shape[1] - 1)) / args.batch_size /
100.0)
for L in Ls:
L.backward()
if batch_id % grad_interval == grad_interval - 1 or\
batch_id == len(train_data_loader) - 1:
if average_param_dict is None:
average_param_dict = {
k: v.data(ctx[0]).copy()
for k, v in model.collect_params().items()
}
trainer.step(float(loss_denom) / args.batch_size / 100.0)
param_dict = model.collect_params()
param_dict.zero_grad()
if step_num > average_start:
alpha = 1. / max(1, step_num - average_start)
for name, average_param in average_param_dict.items():
average_param[:] += alpha * (
param_dict[name].data(ctx[0]) - average_param)
step_loss += sum([L.asscalar() for L in Ls])
if batch_id % grad_interval == grad_interval - 1 or\
batch_id == len(train_data_loader) - 1:
log_avg_loss += step_loss / loss_denom * args.batch_size * 100.0
loss_denom = 0
step_loss = 0
log_wc += src_wc + tgt_wc
if (batch_id + 1) % (args.log_interval * grad_interval) == 0:
wps = log_wc / (time.time() - log_start_time)
logging.info('[Epoch {} Batch {}/{}] loss={:.4f}, ppl={:.4f}, '
'throughput={:.2f}K wps, wc={:.2f}K'.format(
epoch_id, batch_id + 1,
len(train_data_loader),
log_avg_loss / args.log_interval,
np.exp(log_avg_loss / args.log_interval),
wps / 1000, log_wc / 1000))
log_start_time = time.time()
log_avg_loss = 0
log_wc = 0
mx.nd.waitall()
valid_loss, valid_translation_out = evaluate(val_data_loader, ctx[0])
valid_bleu_score, _, _, _, _ = compute_bleu(
[val_tgt_sentences],
valid_translation_out,
tokenized=tokenized,
tokenizer=args.bleu,
split_compound_word=split_compound_word,
bpe=bpe)
logging.info(
'[Epoch {}] valid Loss={:.4f}, valid ppl={:.4f}, valid bleu={:.2f}'
.format(epoch_id, valid_loss, np.exp(valid_loss),
valid_bleu_score * 100))
test_loss, test_translation_out = evaluate(test_data_loader, ctx[0])
test_bleu_score, _, _, _, _ = compute_bleu(
[test_tgt_sentences],
test_translation_out,
tokenized=tokenized,
tokenizer=args.bleu,
split_compound_word=split_compound_word,
bpe=bpe)
logging.info(
'[Epoch {}] test Loss={:.4f}, test ppl={:.4f}, test bleu={:.2f}'.
format(epoch_id, test_loss, np.exp(test_loss),
test_bleu_score * 100))
write_sentences(
valid_translation_out,
os.path.join(args.save_dir,
'epoch{:d}_valid_out.txt').format(epoch_id))
write_sentences(
test_translation_out,
os.path.join(args.save_dir,
'epoch{:d}_test_out.txt').format(epoch_id))
if valid_bleu_score > best_valid_bleu:
best_valid_bleu = valid_bleu_score
save_path = os.path.join(args.save_dir, 'valid_best.params')
logging.info('Save best parameters to {}'.format(save_path))
model.save_parameters(save_path)
save_path = os.path.join(args.save_dir,
'epoch{:d}.params'.format(epoch_id))
model.save_parameters(save_path)
save_path = os.path.join(args.save_dir, 'average.params')
mx.nd.save(save_path, average_param_dict)
if args.average_checkpoint:
for j in range(args.num_averages):
params = mx.nd.load(
os.path.join(args.save_dir,
'epoch{:d}.params'.format(args.epochs - j - 1)))
alpha = 1. / (j + 1)
for k, v in model._collect_params_with_prefix().items():
for c in ctx:
v.data(c)[:] += alpha * (params[k].as_in_context(c) -
v.data(c))
save_path = os.path.join(
args.save_dir,
'average_checkpoint_{}.params'.format(args.num_averages))
model.save_parameters(save_path)
elif args.average_start > 0:
for k, v in model.collect_params().items():
v.set_data(average_param_dict[k])
save_path = os.path.join(args.save_dir, 'average.params')
model.save_parameters(save_path)
else:
model.load_parameters(os.path.join(args.save_dir, 'valid_best.params'),
ctx)
valid_loss, valid_translation_out = evaluate(val_data_loader, ctx[0])
valid_bleu_score, _, _, _, _ = compute_bleu(
[val_tgt_sentences],
valid_translation_out,
tokenized=tokenized,
tokenizer=args.bleu,
bpe=bpe,
split_compound_word=split_compound_word)
logging.info(
'Best model valid Loss={:.4f}, valid ppl={:.4f}, valid bleu={:.2f}'.
format(valid_loss, np.exp(valid_loss), valid_bleu_score * 100))
test_loss, test_translation_out = evaluate(test_data_loader, ctx[0])
test_bleu_score, _, _, _, _ = compute_bleu(
[test_tgt_sentences],
test_translation_out,
tokenized=tokenized,
tokenizer=args.bleu,
bpe=bpe,
split_compound_word=split_compound_word)
logging.info(
'Best model test Loss={:.4f}, test ppl={:.4f}, test bleu={:.2f}'.
format(test_loss, np.exp(test_loss), test_bleu_score * 100))
write_sentences(valid_translation_out,
os.path.join(args.save_dir, 'best_valid_out.txt'))
write_sentences(test_translation_out,
os.path.join(args.save_dir, 'best_test_out.txt'))
def train():
"""Training function."""
trainer = gluon.Trainer(model.collect_params(), args.optimizer,
{'learning_rate': args.lr})
train_batchify_fn = btf.Tuple(btf.Pad(), btf.Pad(),
btf.Stack(dtype='float32'),
btf.Stack(dtype='float32'))
test_batchify_fn = btf.Tuple(btf.Pad(), btf.Pad(),
btf.Stack(dtype='float32'),
btf.Stack(dtype='float32'), btf.Stack())
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
train_batch_sampler = FixedBucketSampler(lengths=data_train_lengths,
batch_size=args.batch_size,
num_buckets=args.num_buckets,
ratio=args.bucket_ratio,
shuffle=True,
bucket_scheme=bucket_scheme)
logging.info('Train Batch Sampler:\n{}'.format(
train_batch_sampler.stats()))
train_data_loader = DataLoader(data_train,
batch_sampler=train_batch_sampler,
batchify_fn=train_batchify_fn,
num_workers=8)
val_batch_sampler = FixedBucketSampler(lengths=data_val_lengths,
batch_size=args.test_batch_size,
num_buckets=args.num_buckets,
ratio=args.bucket_ratio,
shuffle=False)
logging.info('Valid Batch Sampler:\n{}'.format(val_batch_sampler.stats()))
val_data_loader = DataLoader(data_val,
batch_sampler=val_batch_sampler,
batchify_fn=test_batchify_fn,
num_workers=8)
test_batch_sampler = FixedBucketSampler(lengths=data_test_lengths,
batch_size=args.test_batch_size,
num_buckets=args.num_buckets,
ratio=args.bucket_ratio,
shuffle=False)
logging.info('Test Batch Sampler:\n{}'.format(test_batch_sampler.stats()))
test_data_loader = DataLoader(data_test,
batch_sampler=test_batch_sampler,
batchify_fn=test_batchify_fn,
num_workers=8)
best_valid_bleu = 0.0
for epoch_id in range(args.epochs):
log_avg_loss = 0
log_avg_gnorm = 0
log_wc = 0
log_start_time = time.time()
for batch_id, (src_seq, tgt_seq, src_valid_length, tgt_valid_length)\
in enumerate(train_data_loader):
# logging.info(src_seq.context) Context suddenly becomes GPU.
src_seq = src_seq.as_in_context(ctx)
tgt_seq = tgt_seq.as_in_context(ctx)
src_valid_length = src_valid_length.as_in_context(ctx)
tgt_valid_length = tgt_valid_length.as_in_context(ctx)
with mx.autograd.record():
out, _ = model(src_seq, tgt_seq[:, :-1], src_valid_length,
tgt_valid_length - 1)
loss = loss_function(out, tgt_seq[:, 1:],
tgt_valid_length - 1).mean()
loss = loss * (tgt_seq.shape[1] - 1) / (tgt_valid_length -
1).mean()
loss.backward()
grads = [p.grad(ctx) for p in model.collect_params().values()]
gnorm = gluon.utils.clip_global_norm(grads, args.clip)
trainer.step(1)
src_wc = src_valid_length.sum().asscalar()
tgt_wc = (tgt_valid_length - 1).sum().asscalar()
step_loss = loss.asscalar()
log_avg_loss += step_loss
log_avg_gnorm += gnorm
log_wc += src_wc + tgt_wc
if (batch_id + 1) % args.log_interval == 0:
wps = log_wc / (time.time() - log_start_time)
logging.info(
'[Epoch {} Batch {}/{}] loss={:.4f}, ppl={:.4f}, gnorm={:.4f}, '
'throughput={:.2f}K wps, wc={:.2f}K'.format(
epoch_id, batch_id + 1, len(train_data_loader),
log_avg_loss / args.log_interval,
np.exp(log_avg_loss / args.log_interval),
log_avg_gnorm / args.log_interval, wps / 1000,
log_wc / 1000))
log_start_time = time.time()
log_avg_loss = 0
log_avg_gnorm = 0
log_wc = 0
valid_loss, valid_translation_out = evaluate(val_data_loader)
valid_bleu_score, _, _, _, _ = compute_bleu([val_tgt_sentences],
valid_translation_out)
logging.info(
'[Epoch {}] valid Loss={:.4f}, valid ppl={:.4f}, valid bleu={:.2f}'
.format(epoch_id, valid_loss, np.exp(valid_loss),
valid_bleu_score * 100))
test_loss, test_translation_out = evaluate(test_data_loader)
test_bleu_score, _, _, _, _ = compute_bleu([test_tgt_sentences],
test_translation_out)
logging.info(
'[Epoch {}] test Loss={:.4f}, test ppl={:.4f}, test bleu={:.2f}'.
format(epoch_id, test_loss, np.exp(test_loss),
test_bleu_score * 100))
write_sentences(
valid_translation_out,
os.path.join(args.save_dir,
'epoch{:d}_valid_out.txt').format(epoch_id))
write_sentences(
test_translation_out,
os.path.join(args.save_dir,
'epoch{:d}_test_out.txt').format(epoch_id))
if valid_bleu_score > best_valid_bleu:
best_valid_bleu = valid_bleu_score
save_path = os.path.join(args.save_dir, 'valid_best.params')
logging.info('Save best parameters to {}'.format(save_path))
model.save_params(save_path)
if epoch_id + 1 >= (args.epochs * 2) // 3:
new_lr = trainer.learning_rate * args.lr_update_factor
logging.info('Learning rate change to {}'.format(new_lr))
trainer.set_learning_rate(new_lr)
model.load_params(os.path.join(args.save_dir, 'valid_best.params'))
valid_loss, valid_translation_out = evaluate(val_data_loader)
valid_bleu_score, _, _, _, _ = compute_bleu([val_tgt_sentences],
valid_translation_out)
logging.info(
'Best model valid Loss={:.4f}, valid ppl={:.4f}, valid bleu={:.2f}'.
format(valid_loss, np.exp(valid_loss), valid_bleu_score * 100))
test_loss, test_translation_out = evaluate(test_data_loader)
test_bleu_score, _, _, _, _ = compute_bleu([test_tgt_sentences],
test_translation_out)
logging.info(
'Best model test Loss={:.4f}, test ppl={:.4f}, test bleu={:.2f}'.
format(test_loss, np.exp(test_loss), test_bleu_score * 100))
write_sentences(valid_translation_out,
os.path.join(args.save_dir, 'best_valid_out.txt'))
write_sentences(test_translation_out,
os.path.join(args.save_dir, 'best_test_out.txt'))
