def predict(
input: Ignore[str],
output: Ignore[str],
log_prefix: Ignore[str],
model: EncoderDecoder = None,
batch_size_limit: int = 400,
batch_limit_by_tokens: bool = True,
):
logger = get_logger()
(src_vocab, _), (src_field, tgt_field) = get_vocabularies()
dataset = Corpora([src_field])
logger.info(f'{log_prefix}: Loading input file ...')
with open(input) as src_stream:
for src_sentence in src_stream:
if src_sentence.strip():
dataset.append([src_sentence])
logger.info(f'{log_prefix}: Loading done.')
if model is None:
best_model_path = find_best_model()
if best_model_path is None:
raise RuntimeError(
'Model has not been trained yet. Train the model first.')
model = build_model(src_field.vocabulary, tgt_field.vocabulary)
state_dict = torch.load(best_model_path)
model.load_state_dict(state_dict['model_state'])
model.to(get_device())
with open(output, 'w') as output_stream, torch.no_grad():
for batch in dataset.iterate(get_device(),
batch_size_limit,
batch_limit_by_tokens,
sort_by_length=False,
shuffle=False):
x_mask = batch[0] != src_vocab.pad_index
x_mask = x_mask.unsqueeze(1)
x_e = model.encode(batch[0], x_mask)
y_hat, _ = beam_search(x_e,
x_mask,
model,
get_scores=short_sent_penalty)
sentence = src_field.to_sentence_str(batch[0][-1].tolist())
generated = tgt_field.to_sentence_str(y_hat[-1].tolist())
logger.info('SENTENCE:\n ---- {}'.format(sentence))
logger.info('GENERATED:\n ---- {}'.format(generated))
for generated in (src_field.to_sentence_str(s)
for s in y_hat.tolist()):
output_stream.write(f'{generated}\n')
def __init__(self,
pad_index: Ignore[int],
teacher_config_path: str = '/DOES_NOT_EXIST'):
teacher_config_path = relative_to_config_path(teacher_config_path)
assert os.path.exists(
teacher_config_path), "Teacher model config does not exist."
nn.Module.__init__(self)
teacher_model_config = get_configuration().clone()
with open(teacher_config_path) as f:
teacher_model_config.load(json.load(f))
push_configuration(teacher_model_config)
update_and_ensure_model_output_path('test', None)
best_model_path = find_best_model()
if best_model_path is None:
raise ValueError('Could not find the teacher model.')
(src_vocab, tgt_vocab), _ = get_vocabularies()
self.teacher_model = build_model(src_vocab, tgt_vocab)
state_dict = torch.load(best_model_path)
self.teacher_model.load_state_dict(state_dict['model_state'])
self.teacher_model.to(get_device())
self.teacher_model.eval()
self.src_pad_index = src_vocab.pad_index
self.tgt_pad_index = tgt_vocab.pad_index
pop_configuration()
self.pad_index = pad_index
def init_teacher_(model_emb, teacher_emb):
assert model_emb.size(0) == teacher_emb.size(
0), "Vocabulary sizes are different."
if model_emb.size(1) == teacher_emb.size(1):
model_emb.copy_(teacher_emb.to(model_emb.device))
elif model_emb.size(1) > teacher_emb.size(1):
model_emb.narrow(1, 0, teacher_emb.size(1)).copy_(
teacher_emb.to(model_emb.device))
else:
teacher_emb = teacher_emb.to(get_device())
print(teacher_emb.size())
print(torch.matmul(teacher_emb.t(), teacher_emb).size())
_, phi = torch.symeig(torch.matmul(teacher_emb.t(), teacher_emb),
eigenvectors=True)
model_emb.copy_(
torch.matmul(teacher_emb,
phi[:, :model_emb.size(1)]).to(model_emb.device))
def train(max_steps: int = 100,
batch_size_limit: int = 400,
batch_limit_by_tokens: bool = True,
report_interval_steps: int = 10,
validation_interval_steps: int = 100,
lr_scheduler_at: str = 'every_step',
n_ckpts_to_keep: int = 3,
teacher_forcing: bool = True,
random_seed: int = 42):
set_random_seeds(random_seed)
logger = get_logger()
train_dataset = get_train_dataset()
assert len(
train_dataset.fields
) >= 2, "Train dataset must have at least two fields (source and target)."
validation_dataset = get_validation_dataset()
assert len(
validation_dataset.fields
) >= 2, "Validation dataset must have at least two fields (source and target)."
loss_function = get_loss_function(
train_dataset.fields[1].vocabulary.pad_index)
model = build_model(train_dataset.fields[0].vocabulary,
train_dataset.fields[1].vocabulary)
model.to(get_device())
loss_function.to(get_device())
optimizer = build_optimizer(model.parameters())
scheduler = build_scheduler(optimizer)
initialize(model)
def noop():
return None
def step_lr_scheduler():
return scheduler.step()
run_scheduler_at_step = noop
run_scheduler_at_validation = noop
run_scheduler_at_epoch = noop
if scheduler is not None:
if lr_scheduler_at == 'every_step':
run_scheduler_at_step = step_lr_scheduler
elif lr_scheduler_at == 'every_validation':
run_scheduler_at_validation = step_lr_scheduler
elif lr_scheduler_at == 'every_epoch':
run_scheduler_at_epoch = step_lr_scheduler
step = 0
epoch = 0
kept_checkpoint_path_score_map = {}
best_checkpoint_specs = {"score": -math.inf, "step": -1}
@configured('model')
def maybe_save_checkpoint(score: Ignore[float], output_path: str):
if len(kept_checkpoint_path_score_map) < n_ckpts_to_keep or \
any(score > s for s in kept_checkpoint_path_score_map.values()):
if len(kept_checkpoint_path_score_map) >= n_ckpts_to_keep:
worst_checkpoint_path = sorted(
kept_checkpoint_path_score_map.keys(),
key=lambda p: kept_checkpoint_path_score_map[p],
reverse=False)
worst_checkpoint_path = worst_checkpoint_path[0]
kept_checkpoint_path_score_map.pop(worst_checkpoint_path)
try:
os.unlink(worst_checkpoint_path)
except:
logger.warn(
'Could not unlink {}.'.format(worst_checkpoint_path))
if score > best_checkpoint_specs["score"]:
logger.info(
'New `best model` found with score {:.3f} at step {}.'.
format(score, step))
best_checkpoint_specs["score"] = score
best_checkpoint_specs["step"] = step
state_dict = {
"step":
step,
"best_checkpoint_specs":
best_checkpoint_specs,
"model_state":
model.state_dict(),
"optimizer_state":
optimizer.state_dict(),
"scheduler_state":
scheduler.state_dict() if scheduler is not None else None
}
checkpoint_path = '{}/step_{}_score_{:.3f}.pt'.format(
output_path, step, score)
torch.save(state_dict, checkpoint_path)
kept_checkpoint_path_score_map[checkpoint_path] = score
model.train()
validation_done_already = False
while step < max_steps:
start_time = time.time()
total_tokens_processed = 0
for batch in train_dataset.iterate(get_device(), batch_size_limit,
batch_limit_by_tokens):
step += 1
if step >= max_steps:
break
x_mask = batch[0] != model.src_vocab.pad_index
x_mask = x_mask.unsqueeze(1)
y_mask = batch[1] != model.tgt_vocab.pad_index
y_mask = y_mask.unsqueeze(1)
x_e = model.encode(batch[0], x_mask)
log_probs = model.decode(batch[1][:, :-1],
x_e,
y_mask[:, :, :-1],
x_mask,
teacher_forcing=teacher_forcing)
token_count = y_mask[:, :, 1:].sum().item()
loss = loss_function(log_probs, batch[1][:, 1:],
model.get_target_embeddings()) / token_count
loss.backward()
optimizer.step()
mark_optimization_step()
optimizer.zero_grad()
run_scheduler_at_step()
total_tokens_processed += token_count
if step > 0 and step % report_interval_steps == 0:
elapsed_time = time.time() - start_time
baseline_loss = loss_function.uniform_baseline_loss(
log_probs, batch[1][:, 1:])
logger.info(
'Epoch_{} Step_{}: loss={:.3f}(vs {:.3f} uniform), tokens/s={:.1f}, lr={}'
.format(epoch, step, loss.item(), baseline_loss,
total_tokens_processed / elapsed_time,
optimizer.param_groups[0]['lr']))
start_time = time.time()
total_tokens_processed = 0
if step > 0 and step % validation_interval_steps == 0:
log_prefix = 'Epoch_{} Step_{}'.format(epoch, step)
score = evaluate(validation_dataset, log_prefix, model,
loss_function)
maybe_save_checkpoint(score)
model.train()
run_scheduler_at_validation()
start_time = time.time()
total_tokens_processed = 0
validation_done_already = True
else:
validation_done_already = False
epoch += 1
logger.info('Epoch {} finished.'.format(epoch))
run_scheduler_at_epoch()
if not validation_done_already:
log_prefix = 'Final (epoch={} ~ step={})'.format(epoch, step)
score = evaluate(validation_dataset, log_prefix, model, loss_function)
maybe_save_checkpoint(score)
logger.info('Best validation loss was {:.3f} at step {}.'.format(
best_checkpoint_specs["score"], best_checkpoint_specs["step"]))
def evaluate(validation_dataset: Corpora,
log_prefix: Ignore[str],
model: EncoderDecoder = None,
loss_function: Callable = None,
batch_size_limit: int = 400,
batch_limit_by_tokens: bool = True,
teacher_forcing: bool = True,
metrics: Tuple[Metric] = None):
assert len(
validation_dataset.fields
) >= 2, "Validation dataset must have at least two fields (source and target)."
logger = get_logger()
if loss_function is None:
loss_function = get_loss_function(
validation_dataset.fields[1].vocabulary.pad_index)
loss_function.to(get_device())
if model is None:
best_model_path = find_best_model()
if best_model_path is None:
raise RuntimeError(
'Model has not been trained yet. Train the model first.')
model = build_model(validation_dataset.fields[0].vocabulary,
validation_dataset.fields[1].vocabulary)
state_dict = torch.load(best_model_path)
model.load_state_dict(state_dict['model_state'])
model.to(get_device())
pad_index = model.tgt_vocab.pad_index
total_item_count = 0
total_validation_loss = 0
model.eval()
printed_samples = 0
if metrics is None:
metrics = (BleuMetric(), )
else:
metrics = (BleuMetric(), ) + tuple(
m for m in metrics if not isinstance(m, BleuMetric))
with torch.no_grad():
start_time = time.time()
for validation_batch in validation_dataset.iterate(
get_device(),
batch_size_limit,
batch_limit_by_tokens,
sort_by_length=False,
shuffle=False):
x_mask = validation_batch[0] != model.src_vocab.pad_index
x_mask = x_mask.unsqueeze(1)
y_mask = validation_batch[1] != model.tgt_vocab.pad_index
y_mask = y_mask.unsqueeze(1)
x_e = model.encode(validation_batch[0], x_mask)
log_probs = model.decode(validation_batch[1][:, :-1],
x_e,
y_mask[:, :, :-1],
x_mask,
teacher_forcing=teacher_forcing)
loss = loss_function(log_probs, validation_batch[1][:, 1:],
model.get_target_embeddings())
total_item_count += y_mask[:, :, 1:].sum().item()
total_validation_loss += loss.item()
y_hat, _ = beam_search(x_e,
x_mask,
model,
get_scores=short_sent_penalty)
if printed_samples < 4:
sentence = validation_dataset.fields[0].to_sentence_str(
validation_batch[0][-1].tolist())
reference = validation_dataset.fields[1].to_sentence_str(
validation_batch[1][-1].tolist())
generated = validation_dataset.fields[1].to_sentence_str(
y_hat[-1].tolist())
logger.info('SENTENCE:\n ---- {}'.format(sentence))
logger.info('REFERENCE:\n ---- {}'.format(reference))
logger.info('GENERATED:\n ---- {}'.format(generated))
printed_samples += 1
update_metric_params(y_hat, validation_batch[1], pad_index,
metrics)
elapsed_time = time.time() - start_time
logger.info(
f'{log_prefix}: '
f'evaluation_loss={total_validation_loss / total_item_count:.3f}, '
f'elapsed_time={int(elapsed_time + 0.5)}s')
for metric_repr in (str(m) for m in metrics):
logger.info(f'{log_prefix}: evaluation {metric_repr}')
return metrics[0].get_score()
