bs_inner)
if use_prog_bar:
progressbar.update(1)
progressbar.set_description(info)
if use_prog_bar:
progressbar.close()
return model.save_fast_weights()
# training start..
best = Best(max,
'corpus_bleu',
'i',
model=model,
opt=meta_opt,
path=args.model_name,
gpu=args.gpu)
train_metrics = Metrics('train', 'loss', 'real', 'fake')
dev_metrics = Metrics('dev', 'loss', 'gleu', 'real_loss', 'fake_loss',
'distance', 'alter_loss', 'distance2', 'fertility_loss',
'corpus_gleu')
# overlall progress-ba
progressbar = tqdm(total=args.eval_every, desc='start training')
while True:
# ----- saving the checkpoint ----- #
if iters % args.save_every == 0:
def train_model(args,
model,
train,
dev,
teacher_model=None,
save_path=None,
maxsteps=None):
if args.tensorboard and (not args.debug):
from tensorboardX import SummaryWriter
writer = SummaryWriter('./runs/{}'.format(args.prefix + args.hp_str))
# optimizer
if args.optimizer == 'Adam':
opt = torch.optim.Adam(
[p for p in model.parameters() if p.requires_grad],
betas=(0.9, 0.98),
eps=1e-9)
else:
raise NotImplementedError
# if resume training
if (args.load_from is not None) and (args.resume):
with torch.cuda.device(args.gpu): # very important.
offset, opt_states = torch.load(
'./models/' + args.load_from + '.pt.states',
map_location=lambda storage, loc: storage.cuda())
opt.load_state_dict(opt_states)
else:
offset = 0
# metrics
if save_path is None:
save_path = args.model_name
best = Best(max,
'corpus_bleu',
'corpus_gleu',
'gleu',
'loss',
'i',
model=model,
opt=opt,
path=save_path,
gpu=args.gpu)
train_metrics = Metrics('train', 'loss', 'real', 'fake')
dev_metrics = Metrics('dev', 'loss', 'gleu', 'real_loss', 'fake_loss',
'distance', 'alter_loss', 'distance2',
'fertility_loss', 'corpus_gleu')
progressbar = tqdm(total=args.eval_every, desc='start training.')
for iters, batch in enumerate(train):
iters += offset
if iters % args.save_every == 0:
args.logger.info(
'save (back-up) checkpoints at iter={}'.format(iters))
with torch.cuda.device(args.gpu):
torch.save(best.model.state_dict(),
'{}_iter={}.pt'.format(args.model_name, iters))
torch.save([iters, best.opt.state_dict()],
'{}_iter={}.pt.states'.format(
args.model_name, iters))
if iters % args.eval_every == 0:
progressbar.close()
dev_metrics.reset()
if args.distillation:
outputs_course = valid_model(args,
model,
dev,
dev_metrics,
distillation=True,
teacher_model=None)
outputs_data = valid_model(
args,
model,
dev,
None if args.distillation else dev_metrics,
teacher_model=None,
print_out=True)
if args.tensorboard and (not args.debug):
writer.add_scalar('dev/GLEU_sentence_', dev_metrics.gleu,
iters)
writer.add_scalar('dev/Loss', dev_metrics.loss, iters)
writer.add_scalar('dev/GLEU_corpus_',
outputs_data['corpus_gleu'], iters)
writer.add_scalar('dev/BLEU_corpus_',
outputs_data['corpus_bleu'], iters)
if args.distillation:
writer.add_scalar('dev/GLEU_corpus_dis',
outputs_course['corpus_gleu'], iters)
writer.add_scalar('dev/BLEU_corpus_dis',
outputs_course['corpus_bleu'], iters)
if not args.debug:
best.accumulate(outputs_data['corpus_bleu'],
outputs_data['corpus_gleu'], dev_metrics.gleu,
dev_metrics.loss, iters)
args.logger.info(
'the best model is achieved at {}, average greedy GLEU={}, corpus GLEU={}, corpus BLEU={}'
.format(best.i, best.gleu, best.corpus_gleu,
best.corpus_bleu))
args.logger.info('model:' + args.prefix + args.hp_str)
# ---set-up a new progressor---
progressbar = tqdm(total=args.eval_every, desc='start training.')
if maxsteps is None:
maxsteps = args.maximum_steps
if iters > maxsteps:
args.logger.info('reach the maximum updating steps.')
break
# --- training --- #
model.train()
def get_learning_rate(i, lr0=0.1, disable=False):
if not disable:
return lr0 * 10 / math.sqrt(args.d_model) * min(
1 / math.sqrt(i), i /
(args.warmup * math.sqrt(args.warmup)))
return 0.00002
opt.param_groups[0]['lr'] = get_learning_rate(
iters + 1, disable=args.disable_lr_schedule)
opt.zero_grad()
# prepare the data
inputs, input_masks, \
targets, target_masks, \
sources, source_masks,\
encoding, batch_size = model.quick_prepare(batch, args.distillation)
input_reorder, fertility_cost, decoder_inputs = None, None, inputs
batch_fer = batch.fer_dec if args.distillation else batch.fer
#print(input_masks.size(), target_masks.size(), input_masks.sum())
if type(model) is FastTransformer:
inputs, input_reorder, input_masks, fertility_cost = model.prepare_initial(
encoding, sources, source_masks, input_masks, batch_fer)
# Maximum Likelihood Training
if not args.finetuning:
loss = model.cost(targets,
target_masks,
out=model(encoding, source_masks, inputs,
input_masks))
if args.fertility:
loss += fertility_cost
else:
# finetuning:
# loss_student (MLE)
if not args.fertility:
decoding, out, probs = model(encoding,
source_masks,
inputs,
input_masks,
return_probs=True,
decoding=True)
loss_student = model.batched_cost(targets, target_masks,
probs) # student-loss (MLE)
decoder_masks = input_masks
else: # Note that MLE and decoding has different translations. We need to run the same code twice
# truth
decoding, out, probs = model(encoding,
source_masks,
inputs,
input_masks,
decoding=True,
return_probs=True)
loss_student = model.cost(targets, target_masks, out=out)
decoder_masks = input_masks
# baseline
decoder_inputs_b, _, decoder_masks_b, _, _ = model.prepare_initial(
encoding,
sources,
source_masks,
input_masks,
None,
mode='mean')
decoding_b, out_b, probs_b = model(
encoding,
source_masks,
decoder_inputs_b,
decoder_masks_b,
decoding=True,
return_probs=True) # decode again
# reinforce
decoder_inputs_r, _, decoder_masks_r, _, _ = model.prepare_initial(
encoding,
sources,
source_masks,
input_masks,
None,
mode='reinforce')
decoding_r, out_r, probs_r = model(
encoding,
source_masks,
decoder_inputs_r,
decoder_masks_r,
decoding=True,
return_probs=True) # decode again
if args.fertility:
loss_student += fertility_cost
# loss_teacher (RKL+REINFORCE)
teacher_model.eval()
if not args.fertility:
inputs_student_index, _, targets_student_soft, _, _, _, encoding_teacher, _ = model.quick_prepare(
batch, False, decoding, probs, decoder_masks,
decoder_masks, source_masks)
out_teacher, probs_teacher = teacher_model(
encoding_teacher,
source_masks,
inputs_student_index.detach(),
decoder_masks,
return_probs=True)
loss_teacher = teacher_model.batched_cost(
targets_student_soft, decoder_masks,
probs_teacher.detach())
loss = (
1 - args.beta1
) * loss_teacher + args.beta1 * loss_student # final results
else:
inputs_student_index, _, targets_student_soft, _, _, _, encoding_teacher, _ = model.quick_prepare(
batch, False, decoding, probs, decoder_masks,
decoder_masks, source_masks)
out_teacher, probs_teacher = teacher_model(
encoding_teacher,
source_masks,
inputs_student_index.detach(),
decoder_masks,
return_probs=True)
loss_teacher = teacher_model.batched_cost(
targets_student_soft, decoder_masks,
probs_teacher.detach())
inputs_student_index, _ = model.prepare_inputs(
batch, decoding_b, False, decoder_masks_b)
targets_student_soft, _ = model.prepare_targets(
batch, probs_b, False, decoder_masks_b)
out_teacher, probs_teacher = teacher_model(
encoding_teacher,
source_masks,
inputs_student_index.detach(),
decoder_masks_b,
return_probs=True)
_, loss_1 = teacher_model.batched_cost(targets_student_soft,
decoder_masks_b,
probs_teacher.detach(),
True)
inputs_student_index, _ = model.prepare_inputs(
batch, decoding_r, False, decoder_masks_r)
targets_student_soft, _ = model.prepare_targets(
batch, probs_r, False, decoder_masks_r)
out_teacher, probs_teacher = teacher_model(
encoding_teacher,
source_masks,
inputs_student_index.detach(),
decoder_masks_r,
return_probs=True)
_, loss_2 = teacher_model.batched_cost(targets_student_soft,
decoder_masks_r,
probs_teacher.detach(),
True)
rewards = -(loss_2 - loss_1).data
rewards = rewards - rewards.mean()
rewards = rewards.expand_as(source_masks)
rewards = rewards * source_masks
model.predictor.saved_fertilities.reinforce(
0.1 * rewards.contiguous().view(-1, 1))
loss = (
1 - args.beta1
) * loss_teacher + args.beta1 * loss_student # detect reinforce
# accmulate the training metrics
train_metrics.accumulate(batch_size, loss, print_iter=None)
train_metrics.reset()
# train the student
if args.finetuning and args.fertility:
torch.autograd.backward(
(loss, model.predictor.saved_fertilities),
(torch.ones(1).cuda(loss.get_device()), None))
else:
loss.backward()
opt.step()
info = 'training step={}, loss={:.3f}, lr={:.5f}'.format(
iters, export(loss), opt.param_groups[0]['lr'])
if args.finetuning:
info += '| NA:{:.3f}, AR:{:.3f}'.format(export(loss_student),
export(loss_teacher))
if args.fertility:
info += '| RL: {:.3f}'.format(export(rewards.mean()))
if args.fertility:
info += '| RE:{:.3f}'.format(export(fertility_cost))
if args.tensorboard and (not args.debug):
writer.add_scalar('train/Loss', export(loss), iters)
progressbar.update(1)
progressbar.set_description(info)
def train_model(args,
model,
train,
dev,
src=None,
trg=None,
trg_len_dic=None,
teacher_model=None,
save_path=None,
maxsteps=None):
if args.tensorboard and (not args.debug):
from tensorboardX import SummaryWriter
writer = SummaryWriter(str(args.event_path / args.id_str))
if type(model) is FastTransformer and args.denoising_prob > 0.0:
denoising_weights = [
args.denoising_weight for idx in range(args.train_repeat_dec)
]
denoising_out_weights = [
args.denoising_out_weight for idx in range(args.train_repeat_dec)
]
if type(model) is FastTransformer and args.layerwise_denoising_weight:
start, end = 0.9, 0.1
diff = (start - end) / (args.train_repeat_dec - 1)
denoising_weights = np.arange(start=end, stop=start,
step=diff).tolist()[::-1] + [0.1]
# optimizer
for k, p in zip(model.state_dict().keys(), model.parameters()):
# only finetune layers that are responsible to predicting target len
if args.finetune_trg_len:
if "pred_len" not in k:
p.requires_grad = False
else:
if "pred_len" in k:
p.requires_grad = False
params = [p for p in model.parameters() if p.requires_grad]
if args.optimizer == 'Adam':
opt = torch.optim.Adam(params, betas=(0.9, 0.98), eps=1e-9)
else:
raise NotImplementedError
# if resume training
if (args.load_from is not None) and (args.resume):
with torch.cuda.device(args.gpu): # very important.
offset, opt_states = torch.load(
str(args.model_path / args.load_from) + '.pt.states',
map_location=lambda storage, loc: storage.cuda())
opt.load_state_dict(opt_states)
else:
offset = 0
if not args.finetune_trg_len:
best = Best(max,
*[
'BLEU_dec{}'.format(ii + 1)
for ii in range(args.valid_repeat_dec)
],
'i',
model=model,
opt=opt,
path=str(args.model_path / args.id_str),
gpu=args.gpu,
which=range(args.valid_repeat_dec))
else:
best = Best(max,
*['pred_target_len_correct'],
'i',
model=model,
opt=opt,
path=str(args.model_path / args.id_str),
gpu=args.gpu,
which=[0])
train_metrics = Metrics(
'train loss',
*['loss_{}'.format(idx + 1) for idx in range(args.train_repeat_dec)],
data_type="avg")
dev_metrics = Metrics(
'dev loss',
*['loss_{}'.format(idx + 1) for idx in range(args.valid_repeat_dec)],
data_type="avg")
if "predict" in args.trg_len_option:
train_metrics_trg = Metrics('train loss target',
*[
"pred_target_len_loss",
"pred_target_len_correct",
"pred_target_len_approx"
],
data_type="avg")
train_metrics_average = Metrics(
'train loss average',
*["average_target_len_correct", "average_target_len_approx"],
data_type="avg")
dev_metrics_trg = Metrics('dev loss target',
*[
"pred_target_len_loss",
"pred_target_len_correct",
"pred_target_len_approx"
],
data_type="avg")
dev_metrics_average = Metrics(
'dev loss average',
*["average_target_len_correct", "average_target_len_approx"],
data_type="avg")
else:
train_metrics_trg = None
train_metrics_average = None
dev_metrics_trg = None
dev_metrics_average = None
if not args.no_tqdm:
progressbar = tqdm(total=args.eval_every, desc='start training.')
if maxsteps is None:
maxsteps = args.maximum_steps
#targetlength = TargetLength()
for iters, train_batch in enumerate(train):
#targetlength.accumulate( train_batch )
#continue
iters += offset
if args.save_every > 0 and iters % args.save_every == 0:
args.logger.info(
'save (back-up) checkpoints at iter={}'.format(iters))
with torch.cuda.device(args.gpu):
torch.save(
best.model.state_dict(),
'{}_iter={}.pt'.format(str(args.model_path / args.id_str),
iters))
torch.save([iters, best.opt.state_dict()],
'{}_iter={}.pt.states'.format(
str(args.model_path / args.id_str), iters))
if iters % args.eval_every == 0:
torch.cuda.empty_cache()
gc.collect()
dev_metrics.reset()
if dev_metrics_trg is not None:
dev_metrics_trg.reset()
if dev_metrics_average is not None:
dev_metrics_average.reset()
outputs_data = valid_model(args,
model,
dev,
dev_metrics,
dev_metrics_trg=dev_metrics_trg,
dev_metrics_average=dev_metrics_average,
teacher_model=None,
print_out=True,
trg_len_dic=trg_len_dic)
#outputs_data = [0, [0,0,0,0], 0, 0]
if args.tensorboard and (not args.debug):
for ii in range(args.valid_repeat_dec):
writer.add_scalar('dev/single/Loss_{}'.format(ii + 1),
getattr(dev_metrics,
"loss_{}".format(ii + 1)),
iters) # NLL averaged over dev corpus
writer.add_scalar('dev/single/BLEU_{}'.format(ii + 1),
outputs_data['real'][ii][0],
iters) # NOTE corpus bleu
if "predict" in args.trg_len_option:
writer.add_scalar("dev/single/pred_target_len_loss",
outputs_data["pred_target_len_loss"],
iters)
writer.add_scalar("dev/single/pred_target_len_correct",
outputs_data["pred_target_len_correct"],
iters)
writer.add_scalar("dev/single/pred_target_len_approx",
outputs_data["pred_target_len_approx"],
iters)
writer.add_scalar(
"dev/single/average_target_len_correct",
outputs_data["average_target_len_correct"], iters)
writer.add_scalar(
"dev/single/average_target_len_approx",
outputs_data["average_target_len_approx"], iters)
"""
writer.add_scalars('dev/total/BLEUs', {"iter_{}".format(idx+1):bleu for idx, bleu in enumerate(outputs_data['bleu']) }, iters)
writer.add_scalars('dev/total/Losses',
{ "iter_{}".format(idx+1):getattr(dev_metrics, "loss_{}".format(idx+1))
for idx in range(args.valid_repeat_dec) },
iters )
"""
if not args.debug:
if not args.finetune_trg_len:
best.accumulate(*[xx[0] for xx in outputs_data['real']],
iters)
values = list(best.metrics.values())
args.logger.info("best model : {}, {}".format( "BLEU=[{}]".format(", ".join( [ str(x) for x in values[:args.valid_repeat_dec] ] ) ), \
"i={}".format( values[args.valid_repeat_dec] ), ) )
else:
best.accumulate(*[outputs_data['pred_target_len_correct']],
iters)
values = list(best.metrics.values())
args.logger.info("best model : {}".format(
"pred_target_len_correct = {}".format(values[0])))
args.logger.info('model:' + args.prefix + args.hp_str)
# ---set-up a new progressor---
if not args.no_tqdm:
progressbar.close()
progressbar = tqdm(total=args.eval_every,
desc='start training.')
if type(model) is FastTransformer and args.anneal_denoising_weight:
for ii, bb in enumerate([xx[0]
for xx in outputs_data['real']][:-1]):
denoising_weights[ii] = 0.9 - 0.1 * int(
math.floor(bb / 3.0))
if iters > maxsteps:
args.logger.info('reached the maximum updating steps.')
break
model.train()
def get_lr_transformer(i, lr0=0.1):
return lr0 * 10 / math.sqrt(args.d_model) * min(
1 / math.sqrt(i), i / (args.warmup * math.sqrt(args.warmup)))
def get_lr_anneal(iters, lr0=0.1):
lr_end = 1e-5
return max(0, (args.lr - lr_end) * (args.anneal_steps - iters) /
args.anneal_steps) + lr_end
if args.lr_schedule == "fixed":
opt.param_groups[0]['lr'] = args.lr
elif args.lr_schedule == "anneal":
opt.param_groups[0]['lr'] = get_lr_anneal(iters + 1)
elif args.lr_schedule == "transformer":
opt.param_groups[0]['lr'] = get_lr_transformer(iters + 1)
opt.zero_grad()
if args.dataset == "mscoco":
decoder_inputs, decoder_masks,\
targets, target_masks,\
_, source_masks,\
encoding, batch_size, rest = model.quick_prepare_mscoco(train_batch, all_captions=train_batch[1], fast=(type(model) is FastTransformer), inputs_dec=args.inputs_dec, trg_len_option=args.trg_len_option, max_len=args.max_offset, trg_len_dic=trg_len_dic, bp=args.bp)
else:
decoder_inputs, decoder_masks,\
targets, target_masks,\
sources, source_masks,\
encoding, batch_size, rest = model.quick_prepare(train_batch, fast=(type(model) is FastTransformer), trg_len_option=args.trg_len_option, trg_len_ratio=args.trg_len_ratio, trg_len_dic=trg_len_dic, bp=args.bp)
losses = []
if type(model) is Transformer:
loss = model.cost(targets,
target_masks,
out=model(encoding, source_masks, decoder_inputs,
decoder_masks))
losses.append(loss)
elif type(model) is FastTransformer:
all_logits = []
all_denoising_masks = []
for iter_ in range(args.train_repeat_dec):
curr_iter = min(iter_, args.num_decs - 1)
next_iter = min(curr_iter + 1, args.num_decs - 1)
out = model(encoding,
source_masks,
decoder_inputs,
decoder_masks,
iter_=curr_iter,
return_probs=False)
if args.self_distil > 0.0:
loss, logits_masked = model.cost(targets,
target_masks,
out=out,
iter_=curr_iter,
return_logits=True)
else:
loss = model.cost(targets,
target_masks,
out=out,
iter_=curr_iter)
logits = model.decoder[curr_iter].out(out)
if args.use_argmax:
_, argmax = torch.max(logits, dim=-1)
else:
probs = softmax(logits)
probs_sz = probs.size()
logits_ = Variable(probs.data, requires_grad=False)
argmax = torch.multinomial(
logits_.contiguous().view(-1, probs_sz[-1]),
1).view(*probs_sz[:-1])
if args.self_distil > 0.0:
all_logits.append(logits_masked)
losses.append(loss)
decoder_inputs_ = 0
denoising_mask = 1
if args.next_dec_input in ["both", "emb"]:
if args.denoising_prob > 0.0 and np.random.rand(
) < args.denoising_prob:
cor = corrupt_target(targets, decoder_masks,
len(trg.vocab),
denoising_weights[iter_],
args.corruption_probs)
emb = F.embedding(
cor, model.decoder[next_iter].out.weight *
math.sqrt(args.d_model))
denoising_mask = 0
else:
emb = F.embedding(
argmax, model.decoder[next_iter].out.weight *
math.sqrt(args.d_model))
if args.denoising_out_weight > 0:
if denoising_out_weights[iter_] > 0.0:
corrupted_argmax = corrupt_target(
argmax, decoder_masks,
denoising_out_weights[iter_])
else:
corrupted_argmax = argmax
emb = F.embedding(
corrupted_argmax,
model.decoder[next_iter].out.weight *
math.sqrt(args.d_model))
decoder_inputs_ += emb
all_denoising_masks.append(denoising_mask)
if args.next_dec_input in ["both", "out"]:
decoder_inputs_ += out
decoder_inputs = decoder_inputs_
# self distillation loss if requested
if args.self_distil > 0.0:
self_distil_losses = []
for logits_i in range(1, len(all_logits) - 1):
self_distill_loss_i = 0.0
for logits_j in range(logits_i + 1, len(all_logits)):
self_distill_loss_i += \
all_denoising_masks[logits_j] * \
all_denoising_masks[logits_i] * \
(1/(logits_j-logits_i)) * args.self_distil * F.mse_loss(all_logits[logits_i], all_logits[logits_j].detach())
self_distil_losses.append(self_distill_loss_i)
self_distil_loss = sum(self_distil_losses)
loss = sum(losses)
# accmulate the training metrics
train_metrics.accumulate(batch_size, *losses, print_iter=None)
if train_metrics_trg is not None:
train_metrics_trg.accumulate(batch_size,
*[rest[0], rest[1], rest[2]])
if train_metrics_average is not None:
train_metrics_average.accumulate(batch_size, *[rest[3], rest[4]])
if type(model) is FastTransformer and args.self_distil > 0.0:
(loss + self_distil_loss).backward()
else:
if "predict" in args.trg_len_option:
if args.finetune_trg_len:
rest[0].backward()
else:
loss.backward()
else:
loss.backward()
if args.grad_clip > 0:
total_norm = nn.utils.clip_grad_norm(params, args.grad_clip)
opt.step()
mid_str = ''
if type(model) is FastTransformer and args.self_distil > 0.0:
mid_str += 'distil={:.5f}, '.format(
self_distil_loss.cpu().data.numpy()[0])
if type(model) is FastTransformer and "predict" in args.trg_len_option:
mid_str += 'pred_target_len_loss={:.5f}, '.format(
rest[0].cpu().data.numpy()[0])
if type(model) is FastTransformer and args.denoising_prob > 0.0:
mid_str += "/".join(
["{:.1f}".format(ff) for ff in denoising_weights[:-1]]) + ", "
info = 'update={}, loss={}, {}lr={:.1e}'.format(
iters, "/".join(["{:.3f}".format(export(ll)) for ll in losses]),
mid_str, opt.param_groups[0]['lr'])
if args.no_tqdm:
if iters % args.eval_every == 0:
args.logger.info("update {} : {}".format(
iters, str(train_metrics)))
else:
progressbar.update(1)
progressbar.set_description(info)
if iters % args.eval_every == 0 and args.tensorboard and (
not args.debug):
for idx in range(args.train_repeat_dec):
writer.add_scalar(
'train/single/Loss_{}'.format(idx + 1),
getattr(train_metrics, "loss_{}".format(idx + 1)), iters)
if "predict" in args.trg_len_option:
writer.add_scalar(
"train/single/pred_target_len_loss",
getattr(train_metrics_trg, "pred_target_len_loss"), iters)
writer.add_scalar(
"train/single/pred_target_len_correct",
getattr(train_metrics_trg, "pred_target_len_correct"),
iters)
writer.add_scalar(
"train/single/pred_target_len_approx",
getattr(train_metrics_trg, "pred_target_len_approx"),
iters)
writer.add_scalar(
"train/single/average_target_len_correct",
getattr(train_metrics_average,
"average_target_len_correct"), iters)
writer.add_scalar(
"train/single/average_target_len_approx",
getattr(train_metrics_average,
"average_target_len_approx"), iters)
train_metrics.reset()
if train_metrics_trg is not None:
train_metrics_trg.reset()
if train_metrics_average is not None:
train_metrics_average.reset()
# ----- meta-validation ----- #
dev_iters = iters
weights = model.save_fast_weights()
self_opt = torch.optim.Adam([
p for p in model.get_parameters(type=args.finetune_params)
if p.requires_grad
],
betas=(0.9, 0.98),
eps=1e-9)
corpus_bleu = -1
# training start..
best = Best(max,
'corpus_bleu',
'i',
model=model,
opt=self_opt,
path=args.model_name,
gpu=args.gpu)
dev_metrics = Metrics('dev', 'loss', 'gleu')
outputs_data = valid_model(args,
model,
dev_real,
dev_metrics,
print_out=False)
corpus_bleu0 = outputs_data['corpus_bleu']
fast_weights = [(weights, corpus_bleu0)]
if args.tensorboard and (not args.debug):
writer.add_scalar('dev/BLEU_corpus_', outputs_data['corpus_bleu'],
def train_model(args, model, train, dev, save_path=None, maxsteps=None):
if args.tensorboard and (not args.debug):
from tensorboardX import SummaryWriter
writer = SummaryWriter('./runs/{}'.format(args.prefix + args.hp_str))
# optimizer
if args.optimizer == 'Adam':
opt = torch.optim.Adam(
[p for p in model.parameters() if p.requires_grad],
betas=(0.9, 0.98),
eps=1e-9)
else:
raise NotImplementedError
# if resume training
if (args.load_from is not None) and (args.resume):
with torch.cuda.device(args.gpu): # very important.
offset, opt_states = torch.load(
'./models/' + args.load_from + '.pt.states',
map_location=lambda storage, loc: storage.cuda())
opt.load_state_dict(opt_states)
else:
offset = 0
# metrics
if save_path is None:
save_path = args.model_name
best = Best(max,
'corpus_bleu',
'corpus_gleu',
'gleu',
'loss',
'i',
model=model,
opt=opt,
path=save_path,
gpu=args.gpu)
train_metrics = Metrics('train', 'loss', 'real', 'fake')
dev_metrics = Metrics('dev', 'loss', 'gleu', 'real_loss', 'fake_loss',
'distance', 'alter_loss', 'distance2',
'fertility_loss', 'corpus_gleu')
progressbar = tqdm(total=args.eval_every, desc='start training.')
for iters, batch in enumerate(train):
iters += offset
# --- saving --- #
if iters % args.save_every == 0:
args.logger.info(
'save (back-up) checkpoints at iter={}'.format(iters))
with torch.cuda.device(args.gpu):
torch.save(best.model.state_dict(),
'{}_iter={}.pt'.format(args.model_name, iters))
torch.save([iters, best.opt.state_dict()],
'{}_iter={}.pt.states'.format(
args.model_name, iters))
# --- validation --- #
if iters % args.eval_every == 0:
progressbar.close()
dev_metrics.reset()
if args.distillation:
outputs_course = valid_model(args,
model,
dev,
dev_metrics,
distillation=True)
outputs_data = valid_model(
args,
model,
dev,
None if args.distillation else dev_metrics,
print_out=True)
if args.tensorboard and (not args.debug):
writer.add_scalar('dev/GLEU_sentence_', dev_metrics.gleu,
iters)
writer.add_scalar('dev/Loss', dev_metrics.loss, iters)
writer.add_scalar('dev/GLEU_corpus_',
outputs_data['corpus_gleu'], iters)
writer.add_scalar('dev/BLEU_corpus_',
outputs_data['corpus_bleu'], iters)
if args.distillation:
writer.add_scalar('dev/GLEU_corpus_dis',
outputs_course['corpus_gleu'], iters)
writer.add_scalar('dev/BLEU_corpus_dis',
outputs_course['corpus_bleu'], iters)
if not args.debug:
best.accumulate(outputs_data['corpus_bleu'],
outputs_data['corpus_gleu'], dev_metrics.gleu,
dev_metrics.loss, iters)
args.logger.info(
'the best model is achieved at {}, average greedy GLEU={}, corpus GLEU={}, corpus BLEU={}'
.format(best.i, best.gleu, best.corpus_gleu,
best.corpus_bleu))
args.logger.info('model:' + args.prefix + args.hp_str)
# ---set-up a new progressor---
progressbar = tqdm(total=args.eval_every, desc='start training.')
if maxsteps is None:
maxsteps = args.maximum_steps
if iters > maxsteps:
args.logger.info('reach the maximum updating steps.')
break
# --- training --- #
model.train()
def get_learning_rate(i, lr0=0.1, disable=False):
if not disable:
return lr0 * 10 / math.sqrt(args.d_model) * min(
1 / math.sqrt(i), i /
(args.warmup * math.sqrt(args.warmup)))
return 0.00002
opt.param_groups[0]['lr'] = get_learning_rate(
iters + 1, disable=args.disable_lr_schedule)
opt.zero_grad()
# prepare the data
inputs, input_masks, \
targets, target_masks, \
sources, source_masks,\
encoding, batch_size = model.quick_prepare(batch, args.distillation)
input_reorder, fertility_cost, decoder_inputs = None, None, inputs
#print(input_masks.size(), target_masks.size(), input_masks.sum())
if type(model) is FastTransformer:
batch_fer = batch.fer_dec if args.distillation else batch.fer
inputs, input_reorder, input_masks, fertility_cost = model.prepare_initial(
encoding, sources, source_masks, input_masks, batch_fer)
# Maximum Likelihood Training
loss = model.cost(targets,
target_masks,
out=model(encoding, source_masks, inputs,
input_masks))
if hasattr(args, 'fertility') and args.fertility:
loss += fertility_cost
# accmulate the training metrics
train_metrics.accumulate(batch_size, loss, print_iter=None)
train_metrics.reset()
loss.backward()
opt.step()
info = 'training step={}, loss={:.3f}, lr={:.5f}'.format(
iters, export(loss), opt.param_groups[0]['lr'])
if hasattr(args, 'fertility') and args.fertility:
info += '| RE:{:.3f}'.format(export(fertility_cost))
if args.tensorboard and (not args.debug):
writer.add_scalar('train/Loss', export(loss), iters)
progressbar.update(1)
progressbar.set_description(info)
def train_model(args, model, train, dev, save_path=None, maxsteps=None, writer=None):
# optimizer
if args.optimizer == 'Adam':
opt = torch.optim.Adam([p for p in model.parameters() if p.requires_grad], betas=(0.9, 0.98), eps=1e-9)
else:
raise NotImplementedError
# if resume training
if (args.load_from is not None) and (args.resume):
with torch.cuda.device(args.gpu): # very important.
offset, opt_states = torch.load(args.models_dir + '/' + args.load_from + '.pt.states',
map_location=lambda storage, loc: storage.cuda())
if not args.finetune: # if finetune, do not have history
opt.load_state_dict(opt_states)
else:
offset = 0
# metrics
if save_path is None:
save_path = args.model_name
args.eval_every *= args.inter_size
best = Best(max, 'corpus_bleu', 'corpus_gleu', 'gleu', 'loss', 'i', model=model, opt=opt, path=save_path, gpu=args.gpu)
train_metrics = Metrics('train', 'loss', 'real', 'fake')
dev_metrics = Metrics('dev', 'loss', 'gleu', 'real_loss', 'fake_loss', 'distance', 'alter_loss', 'distance2', 'fertility_loss', 'corpus_gleu')
progressbar = tqdm(total=args.eval_every, desc='start training.')
examples = 0
first_step = True
loss_outer = 0
for iters, batch in enumerate(train):
iters += offset
# --- saving --- #
if iters % args.save_every == 0:
args.logger.info('save (back-up) checkpoints at iter={}'.format(iters))
with torch.cuda.device(args.gpu):
torch.save(best.model.state_dict(), '{}_iter={}.pt'.format(args.model_name, iters))
torch.save([iters, best.opt.state_dict()], '{}_iter={}.pt.states'.format(args.model_name, iters))
# --- validation --- #
if ((args.eval_every_examples == -1) and (iters % args.eval_every == 0)) \
or ((args.eval_every_examples > 0) and (examples > args.eval_every_examples)) \
or first_step:
first_step = False
if args.eval_every_examples > 0:
examples = examples % args.eval_every_examples
for dev_iters, dev_batch in enumerate(dev):
progressbar.close()
dev_metrics.reset()
if args.distillation:
outputs_course = valid_model(args, model, dev, dev_metrics, distillation=True)
outputs_data = valid_model(args, model, dev, None if args.distillation else dev_metrics, print_out=True)
if args.tensorboard and (not args.debug):
writer.add_scalar('dev/GLEU_sentence_', dev_metrics.gleu, iters / args.inter_size)
writer.add_scalar('dev/Loss', dev_metrics.loss, iters / args.inter_size)
writer.add_scalar('dev/GLEU_corpus_', outputs_data['corpus_gleu'], iters / args.inter_size)
writer.add_scalar('dev/BLEU_corpus_', outputs_data['corpus_bleu'], iters / args.inter_size)
if not args.debug:
best.accumulate(outputs_data['corpus_bleu'], outputs_data['corpus_gleu'], dev_metrics.gleu, dev_metrics.loss, iters / args.inter_size)
args.logger.info('the best model is achieved at {}, average greedy GLEU={}, corpus GLEU={}, corpus BLEU={}'.format(
best.i, best.gleu, best.corpus_gleu, best.corpus_bleu))
args.logger.info('model:' + args.prefix + args.hp_str)
# ---set-up a new progressor---
progressbar = tqdm(total=args.eval_every, desc='start training.')
if maxsteps is None:
maxsteps = args.maximum_steps
if iters > maxsteps:
args.logger.info('reach the maximum updating steps.')
break
# --- training --- #
model.train()
def get_learning_rate(i, lr0=0.1, disable=False):
if not disable:
return lr0 * 10 / math.sqrt(args.d_model) * min(1 / math.sqrt(i), i / (args.warmup * math.sqrt(args.warmup)))
return 0.00002
if iters % args.inter_size == 0:
opt.param_groups[0]['lr'] = get_learning_rate(iters / args.inter_size + 1, disable=args.disable_lr_schedule)
opt.zero_grad()
loss_outer = 0
# prepare the data
inputs, input_masks, \
targets, target_masks, \
sources, source_masks,\
encoding, batch_size = model.quick_prepare(batch, args.distillation)
input_reorder, fertility_cost, decoder_inputs = None, None, inputs
examples += batch_size
# Maximum Likelihood Training
loss = model.cost(targets, target_masks, out=model(encoding, source_masks, inputs, input_masks)) / args.inter_size
loss_outer = loss_outer + loss
# accmulate the training metrics
train_metrics.accumulate(batch_size, loss, print_iter=None)
train_metrics.reset()
loss.backward()
if iters % args.inter_size == (args.inter_size - 1):
if args.universal_options == 'no_update_encdec':
for p in model.parameters():
if p is not model.encoder.uni_out.weight:
if p.grad is not None:
p.grad.detach_()
p.grad.zero_()
opt.step()
info = 'training step={}, loss={:.3f}, lr={:.8f}'.format(iters / args.inter_size, export(loss_outer), opt.param_groups[0]['lr'])
if args.tensorboard and (not args.debug):
writer.add_scalar('train/Loss', export(loss_outer), iters / args.inter_size)
progressbar.update(1)
progressbar.set_description(info)
def train_model(args, model, train, dev, src, trg, teacher_model=None, save_path=None, maxsteps=None):
if args.tensorboard and (not args.debug):
from tensorboardX import SummaryWriter
writer = SummaryWriter('{}{}'.format(args.event_path, args.prefix+args.hp_str))
# optimizer
params = [p for p in model.parameters() if p.requires_grad]
if args.optimizer == 'Adam':
opt = torch.optim.Adam(params, betas=(0.9, 0.98), eps=1e-9)
else:
raise NotImplementedError
# if resume training
if (args.load_from is not None) and (args.resume):
with torch.cuda.device(args.gpu): # very important.
offset, opt_states = torch.load(os.path.join(args.model_path, args.load_from + '.pt.states'),
map_location=lambda storage, loc: storage.cuda())
opt.load_state_dict(opt_states)
else:
offset = 0
# metrics
if save_path is None:
save_path = args.model_name
best = Best(max, *['BLEU_dec{}'.format(ii+1) for ii in range(args.valid_repeat_dec)], \
'i', model=model, opt=opt, path=save_path, gpu=args.gpu, \
which=range(args.valid_repeat_dec))
train_metrics = Metrics('train loss', *['loss_{}'.format(idx+1) for idx in range(args.train_repeat_dec)], data_type = "avg")
dev_metrics = Metrics('dev loss', *['loss_{}'.format(idx+1) for idx in range(args.valid_repeat_dec)], data_type = "avg")
if not args.no_tqdm:
progressbar = tqdm(total=args.eval_every, desc='start training.')
for iters, batch in enumerate(train):
iters += offset
if iters % args.save_every == 0:
args.logger.info('save (back-up) checkpoints at iter={}'.format(iters))
with torch.cuda.device(args.gpu):
torch.save(best.model.state_dict(), '{}.pt'.format(args.model_name))
torch.save([iters, best.opt.state_dict()], '{}.pt.states'.format(args.model_name))
if iters % args.eval_every == 0:
dev_metrics.reset()
outputs_data = valid_model(args, model, dev, dev_metrics, teacher_model=None, print_out=True)
if args.tensorboard and (not args.debug):
for ii in range(args.valid_repeat_dec):
writer.add_scalar('dev/single/Loss_{}'.format(ii + 1), getattr(dev_metrics, "loss_{}".format(ii+1)), iters)
writer.add_scalar('dev/single/BLEU_{}'.format(ii + 1), outputs_data['bleu'][ii], iters)
writer.add_scalars('dev/multi/BLEUs', {"iter_{}".format(idx+1):bleu for idx, bleu in enumerate(outputs_data['bleu']) }, iters)
writer.add_scalars('dev/multi/Losses', \
{ "iter_{}".format(idx+1):getattr(dev_metrics, "loss_{}".format(idx+1)) \
for idx in range(args.valid_repeat_dec) }, \
iters)
if not args.debug:
best.accumulate(*outputs_data['bleu'], iters)
values = list( best.metrics.values() )
args.logger.info("best model : {}, {}".format( "BLEU=[{}]".format(", ".join( [ str(x) for x in values[:args.valid_repeat_dec] ] ) ), \
"i={}".format( values[args.valid_repeat_dec] ), ) )
args.logger.info('model:' + args.prefix + args.hp_str)
# ---set-up a new progressor---
if not args.no_tqdm:
progressbar.close()
progressbar = tqdm(total=args.eval_every, desc='start training.')
if maxsteps is None:
maxsteps = args.maximum_steps
if iters > maxsteps:
args.logger.info('reach the maximum updating steps.')
break
# --- training --- #
model.train()
def get_learning_rate(i, lr0=0.1, disable=False):
if not disable:
return max(0.00003, args.lr / math.pow(5, math.floor(i/50000)))
'''
return lr0 * 10 / math.sqrt(args.d_model) * min(
1 / math.sqrt(i), i / (args.warmup * math.sqrt(args.warmup)))
'''
return args.lr
opt.param_groups[0]['lr'] = get_learning_rate(iters + 1, disable=args.disable_lr_schedule)
opt.zero_grad()
# prepare the data
inputs, input_masks, \
targets, target_masks, \
sources, source_masks,\
encoding, batch_size = model.quick_prepare(batch)
#print(input_masks.size(), target_masks.size(), input_masks.sum())
if type(model) is Transformer:
decoder_inputs, decoder_masks = inputs, input_masks
elif type(model) is FastTransformer:
decoder_inputs, _, decoder_masks = \
model.prepare_initial(encoding, sources, source_masks, input_masks)
initial_inputs = decoder_inputs
if type(model) is Transformer:
out = model(encoding, source_masks, decoder_inputs, decoder_masks)
loss = model.cost(targets, target_masks, out)
elif type(model) is FastTransformer:
losses = []
for iter_ in range(args.train_repeat_dec):
curr_iter = min(iter_, args.num_shared_dec-1)
next_iter = min(curr_iter + 1, args.num_shared_dec-1)
out = model(encoding, source_masks, decoder_inputs, decoder_masks, iter_=curr_iter)
losses.append( model.cost(targets, target_masks, out=out, iter_=curr_iter) )
logits = model.decoder[curr_iter].out(out)
if args.use_argmax:
_, argmax = torch.max(logits, dim=-1)
else:
logits = softmax(logits)
logits_sz = logits.size()
logits_ = Variable(logits.data, requires_grad=False)
argmax = torch.multinomial(logits_.contiguous().view(-1, logits_sz[-1]), 1)\
.view(*logits_sz[:-1])
decoder_inputs = F.embedding(argmax, model.decoder[next_iter].out.weight *
math.sqrt(args.d_model))
if args.sum_out_and_emb:
decoder_inputs += out
if args.diff_loss_w > 0 and ((args.diff_loss_dec1 == False) or (args.diff_loss_dec1 == True and iter_ == 0)):
num_words = out.size(1)
# first L2 normalize
out_norm = out.div(out.norm(p=2, dim=-1, keepdim=True))
# calculate loss
diff_loss = torch.mean((out_norm[:,1:,:] * out_norm[:,:-1,:]).sum(-1).clamp(min=0)) * args.diff_loss_w
# add this losses to all losses
losses.append(diff_loss)
loss = sum(losses)
# accmulate the training metrics
train_metrics.accumulate(batch_size, *losses, print_iter=None)
# train the student
loss.backward()
if args.grad_clip > 0:
total_norm = nn.utils.clip_grad_norm(params, args.grad_clip)
opt.step()
info = 'training step={}, loss={}, lr={:.5f}'.format(
iters,
"/".join(["{:.3f}".format(export(ll)) for ll in losses]),
opt.param_groups[0]['lr'])
if iters % args.eval_every == 0 and args.tensorboard and (not args.debug):
for idx in range(args.train_repeat_dec):
writer.add_scalar('train/single/Loss_{}'.format(idx+1), export(losses[idx]), iters)
if args.no_tqdm:
if iters % args.eval_every == 0:
args.logger.info(train_metrics)
else:
progressbar.update(1)
progressbar.set_description(info)
train_metrics.reset()