def train(
self,
train_data_set: DataLoader,
dev_data_set: DataLoader = None,
test_data_set: DataLoader = None,
epochs: int = 3,
batch_size: int = 8,
optimizer=None,
max_grad_norm: float = 5.0,
logging_steps: int = 50,
save_steps: int = 100,
save_path: str = None,
distiller: TeacherStudentDistill = None,
):
"""
Train a tagging model
Args:
train_data_set (DataLoader): train examples dataloader. If distiller object is
provided train examples should contain a tuple of student/teacher data examples.
dev_data_set (DataLoader, optional): dev examples dataloader. Defaults to None.
test_data_set (DataLoader, optional): test examples dataloader. Defaults to None.
epochs (int, optional): num of epochs to train. Defaults to 3.
batch_size (int, optional): batch size. Defaults to 8.
optimizer (fn, optional): optimizer function. Defaults to default model optimizer.
max_grad_norm (float, optional): max gradient norm. Defaults to 5.0.
logging_steps (int, optional): number of steps between logging. Defaults to 50.
save_steps (int, optional): number of steps between model saves. Defaults to 100.
save_path (str, optional): model output path. Defaults to None.
distiller (TeacherStudentDistill, optional): KD model for training the model using
a teacher model. Defaults to None.
"""
if optimizer is None:
optimizer = self.get_optimizer()
train_batch_size = batch_size * max(1, self.n_gpus)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_data_set.dataset))
logger.info(" Num Epochs = %d", epochs)
logger.info(" Instantaneous batch size per GPU/CPU = %d", batch_size)
logger.info(" Total batch size = %d", train_batch_size)
global_step = 0
self.model.zero_grad()
epoch_it = trange(epochs, desc="Epoch")
for _ in epoch_it:
step_it = tqdm(train_data_set, desc="Train iteration")
avg_loss = 0
for step, batch in enumerate(step_it):
self.model.train()
if distiller:
batch, t_batch = batch[:2]
t_batch = tuple(t.to(self.device) for t in t_batch)
t_logits = distiller.get_teacher_logits(t_batch)
batch = tuple(t.to(self.device) for t in batch)
inputs = self.batch_mapper(batch)
logits = self.model(**inputs)
if self.use_crf:
loss = -1.0 * self.crf(
logits, inputs["labels"], mask=inputs["mask"] != 0.0)
else:
loss_fn = CrossEntropyLoss(ignore_index=0)
loss = loss_fn(logits.view(-1, self.num_labels),
inputs["labels"].view(-1))
if self.n_gpus > 1:
loss = loss.mean()
# add distillation loss if activated
if distiller:
loss = distiller.distill_loss(loss, logits, t_logits)
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
max_grad_norm)
optimizer.step()
# self.model.zero_grad()
optimizer.zero_grad()
global_step += 1
avg_loss += loss.item()
if global_step % logging_steps == 0:
if step != 0:
logger.info(" global_step = %s, average loss = %s",
global_step, avg_loss / step)
self._get_eval(dev_data_set, "dev")
self._get_eval(test_data_set, "test")
if save_path is not None and global_step % save_steps == 0:
self.save_model(save_path)
def train_pseudo(
self,
labeled_data_set: DataLoader,
unlabeled_data_set: DataLoader,
distiller: TeacherStudentDistill,
dev_data_set: DataLoader = None,
test_data_set: DataLoader = None,
batch_size_l: int = 8,
batch_size_ul: int = 8,
epochs: int = 100,
optimizer=None,
max_grad_norm: float = 5.0,
logging_steps: int = 50,
save_steps: int = 100,
save_path: str = None,
save_best: bool = False,
):
"""
Train a tagging model
Args:
train_data_set (DataLoader): train examples dataloader. If distiller object is
provided train examples should contain a tuple of student/teacher data examples.
dev_data_set (DataLoader, optional): dev examples dataloader. Defaults to None.
test_data_set (DataLoader, optional): test examples dataloader. Defaults to None.
batch_size_l (int, optional): batch size for the labeled dataset. Defaults to 8.
batch_size_ul (int, optional): batch size for the unlabeled dataset. Defaults to 8.
epochs (int, optional): num of epochs to train. Defaults to 100.
optimizer (fn, optional): optimizer function. Defaults to default model optimizer.
max_grad_norm (float, optional): max gradient norm. Defaults to 5.0.
logging_steps (int, optional): number of steps between logging. Defaults to 50.
save_steps (int, optional): number of steps between model saves. Defaults to 100.
save_path (str, optional): model output path. Defaults to None.
save_best (str, optional): wether to save model when result is best on dev set
distiller (TeacherStudentDistill, optional): KD model for training the model using
a teacher model. Defaults to None.
"""
if optimizer is None:
optimizer = self.get_optimizer()
train_batch_size_l = batch_size_l * max(1, self.n_gpus)
train_batch_size_ul = batch_size_ul * max(1, self.n_gpus)
logger.info("***** Running training *****")
logger.info(" Num labeled examples = %d",
len(labeled_data_set.dataset))
logger.info(" Num unlabeled examples = %d",
len(unlabeled_data_set.dataset))
logger.info(" Instantaneous labeled batch size per GPU/CPU = %d",
batch_size_l)
logger.info(" Instantaneous unlabeled batch size per GPU/CPU = %d",
batch_size_ul)
logger.info(" Total batch size labeled= %d", train_batch_size_l)
logger.info(" Total batch size unlabeled= %d", train_batch_size_ul)
global_step = 0
self.model.zero_grad()
avg_loss = 0
iter_l = iter(labeled_data_set)
iter_ul = iter(unlabeled_data_set)
epoch_l = 0
epoch_ul = 0
s_idx = -1
best_dev = 0
best_test = 0
while True:
logger.info("labeled epoch=%d, unlabeled epoch=%d", epoch_l,
epoch_ul)
loss_labeled = 0
loss_unlabeled = 0
try:
batch_l = next(iter_l)
s_idx += 1
except StopIteration:
iter_l = iter(labeled_data_set)
epoch_l += 1
batch_l = next(iter_l)
s_idx = 0
avg_loss = 0
try:
batch_ul = next(iter_ul)
except StopIteration:
iter_ul = iter(unlabeled_data_set)
epoch_ul += 1
batch_ul = next(iter_ul)
if epoch_ul > epochs:
logger.info("Done")
return
self.model.train()
batch_l, t_batch_l = batch_l[:2]
batch_ul, t_batch_ul = batch_ul[:2]
t_batch_l = tuple(t.to(self.device) for t in t_batch_l)
t_batch_ul = tuple(t.to(self.device) for t in t_batch_ul)
t_logits = distiller.get_teacher_logits(t_batch_l)
t_logits_ul = distiller.get_teacher_logits(t_batch_ul)
batch_l = tuple(t.to(self.device) for t in batch_l)
batch_ul = tuple(t.to(self.device) for t in batch_ul)
inputs = self.batch_mapper(batch_l)
inputs_ul = self.batch_mapper(batch_ul)
logits = self.model(**inputs)
logits_ul = self.model(**inputs_ul)
t_labels = torch.argmax(F.log_softmax(t_logits_ul, dim=2), dim=2)
if self.use_crf:
loss_labeled = -1.0 * self.crf(
logits, inputs["labels"], mask=inputs["mask"] != 0.0)
loss_unlabeled = -1.0 * self.crf(
logits_ul, t_labels, mask=inputs_ul["mask"] != 0.0)
else:
loss_fn = CrossEntropyLoss(ignore_index=0)
loss_labeled = loss_fn(logits.view(-1, self.num_labels),
inputs["labels"].view(-1))
loss_unlabeled = loss_fn(logits_ul.view(-1, self.num_labels),
t_labels.view(-1))
if self.n_gpus > 1:
loss_labeled = loss_labeled.mean()
loss_unlabeled = loss_unlabeled.mean()
# add distillation loss
loss_labeled = distiller.distill_loss(loss_labeled, logits,
t_logits)
loss_unlabeled = distiller.distill_loss(loss_unlabeled, logits_ul,
t_logits_ul)
# sum labeled and unlabeled losses
loss = loss_labeled + loss_unlabeled
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
max_grad_norm)
optimizer.step()
# self.model.zero_grad()
optimizer.zero_grad()
global_step += 1
avg_loss += loss.item()
if global_step % logging_steps == 0:
if s_idx != 0:
logger.info(" global_step = %s, average loss = %s",
global_step, avg_loss / s_idx)
dev = self._get_eval(dev_data_set, "dev")
test = self._get_eval(test_data_set, "test")
if dev > best_dev:
best_dev = dev
best_test = test
if save_path is not None and save_best:
self.save_model(save_path)
logger.info("Best result: dev= %s, test= %s", str(best_dev),
str(best_test))
if save_path is not None and global_step % save_steps == 0:
self.save_model(save_path)
def train(
self,
train_data_set: DataLoader,
dev_data_set: DataLoader = None,
test_data_set: DataLoader = None,
epochs: int = 3,
batch_size: int = 8,
optimizer=None,
max_grad_norm: float = 5.0,
logging_steps: int = 50,
save_steps: int = 100,
save_path: str = None,
distiller: TeacherStudentDistill = None,
best_result_file: str = None,
word_dropout: float = 0,
):
"""
Train a tagging model
Args:
train_data_set (DataLoader): train examples dataloader.
- If distiller object is provided train examples should contain a tuple of
student/teacher data examples.
dev_data_set (DataLoader, optional): dev examples dataloader. Defaults to None.
test_data_set (DataLoader, optional): test examples dataloader. Defaults to None.
epochs (int, optional): num of epochs to train. Defaults to 3.
batch_size (int, optional): batch size. Defaults to 8.
optimizer (fn, optional): optimizer function. Defaults to default model optimizer.
max_grad_norm (float, optional): max gradient norm. Defaults to 5.0.
logging_steps (int, optional): number of steps between logging. Defaults to 50.
save_steps (int, optional): number of steps between model saves. Defaults to 100.
save_path (str, optional): model output path. Defaults to None.
distiller (TeacherStudentDistill, optional): KD model for training the model using
a teacher model. Defaults to None.
best_result_file (str, optional): path to save best dev results when it's updated.
word_dropout (float, optional): whole-word (-> oov) dropout rate. Defaults to 0.
"""
if optimizer is None:
optimizer = self.get_optimizer()
train_batch_size = batch_size * max(1, self.n_gpus)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_data_set.dataset))
logger.info(" Num Epochs = %d", epochs)
logger.info(" Instantaneous batch size per GPU/CPU = %d", batch_size)
logger.info(" Total batch size = %d", train_batch_size)
global_step = 0
best_dev = 0
dev_test = 0
self.model.zero_grad()
epoch_it = trange(epochs, desc="Epoch")
for epoch in epoch_it:
step_it = tqdm(train_data_set, desc="Train iteration")
avg_loss = 0
for step, batches in enumerate(step_it):
self.model.train()
batch, t_batch = (batches, []) if not distiller else (batches[:2])
batch = tuple(t.to(self.device) for t in batch)
inputs = self.batch_mapper(batch)
logits = self.model(**inputs)
if distiller:
t_batch = tuple(t.to(self.device) for t in t_batch)
t_logits = distiller.get_teacher_logits(t_batch)
valid_positions = (
t_batch[3] != 0.0
) # TODO: implement method to get only valid logits from the model itself
valid_t_logits = {}
max_seq_len = logits.shape[1]
for i in range(len(logits)): # each example in batch
valid_logit_i = t_logits[i][valid_positions[i]]
valid_t_logits[i] = (
valid_logit_i
if valid_logit_i.shape[0] <= max_seq_len
else valid_logit_i[:][:max_seq_len]
) # cut to max len
# prepare teacher labels for non-labeled examples
t_labels_dict = {}
for i in range(len(valid_t_logits.keys())):
t_labels_dict[i] = torch.argmax(
F.log_softmax(valid_t_logits[i], dim=-1), dim=-1
)
# pseudo labeling
for i, is_labeled in enumerate(inputs["is_labeled"]):
if not is_labeled:
t_labels_i = t_labels_dict[i]
# add the padded teacher label:
inputs["labels"][i] = torch.cat(
(
t_labels_i,
torch.zeros([max_seq_len - len(t_labels_i)], dtype=torch.long).to(
self.device
),
),
0,
)
# apply word dropout to the input
if word_dropout != 0:
tokens = inputs["words"]
tokens = np.array(tokens.detach().cpu())
word_probs = np.random.random(tokens.shape)
drop_indices = np.where(
(word_probs > word_dropout) & (tokens != 0)
) # ignore padding indices
inputs["words"][drop_indices[0], drop_indices[1]] = self.word_vocab.oov_id
# loss
if self.use_crf:
loss = -1.0 * self.crf(logits, inputs["labels"], mask=inputs["mask"] != 0.0)
else:
loss_fn = CrossEntropyLoss(ignore_index=0)
loss = loss_fn(logits.view(-1, self.num_labels), inputs["labels"].view(-1))
# for idcnn training - add dropout penalty loss
module = self.model.module if self.n_gpus > 1 else self.model
if isinstance(module, IDCNN) and module.drop_penalty != 0:
logits_no_drop = self.model(**inputs, no_dropout=True)
sub = logits.sub(logits_no_drop)
drop_loss = torch.div(torch.sum(torch.pow(sub, 2)), 2)
loss += module.drop_penalty * drop_loss
if self.n_gpus > 1:
loss = loss.mean()
# add distillation loss if activated
if distiller:
# filter masked student logits (no padding)
valid_s_logits = {}
valid_s_positions = inputs["mask"] != 0.0
for i in range(len(logits)):
valid_s_logit_i = logits[i][valid_s_positions[i]]
valid_s_logits[i] = valid_s_logit_i
loss = distiller.distill_loss_dict(loss, valid_s_logits, valid_t_logits)
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_grad_norm)
optimizer.step()
optimizer.zero_grad()
global_step += 1
avg_loss += loss.item()
if global_step % logging_steps == 0:
if step != 0:
logger.info(
" global_step = %s, average loss = %s", global_step, avg_loss / step
)
best_dev, dev_test = self.update_best_model(
dev_data_set,
test_data_set,
best_dev,
dev_test,
best_result_file,
avg_loss / step,
epoch,
save_path=None,
)
if save_steps != 0 and save_path is not None and global_step % save_steps == 0:
self.save_model(save_path)
self.update_best_model(
dev_data_set,
test_data_set,
best_dev,
dev_test,
best_result_file,
"end_training",
"end_training",
save_path=save_path + "/best_dev",
)