def train_loop(FLAGS, model, trainer, training_data_iter, eval_iterators,
logger):
# Accumulate useful statistics.
A = Accumulator(maxlen=FLAGS.deque_length)
# Train.
logger.Log("Training.")
# New Training Loop
progress_bar = SimpleProgressBar(msg="Training",
bar_length=60,
enabled=FLAGS.show_progress_bar)
progress_bar.step(i=0, total=FLAGS.statistics_interval_steps)
log_entry = pb.SpinnEntry()
for _ in range(trainer.step, FLAGS.training_steps):
if (trainer.step -
trainer.best_dev_step) > FLAGS.early_stopping_steps_to_wait:
logger.Log('No improvement after ' +
str(FLAGS.early_stopping_steps_to_wait) +
' steps. Stopping training.')
break
model.train()
log_entry.Clear()
log_entry.step = trainer.step
should_log = False
start = time.time()
batch = get_batch(next(training_data_iter))
X_batch, transitions_batch, y_batch, num_transitions_batch, train_ids = batch
total_tokens = sum([(nt + 1) / 2
for nt in num_transitions_batch.reshape(-1)])
# Reset cached gradients.
trainer.optimizer_zero_grad()
temperature = math.sin(
math.pi / 2 +
trainer.step / float(FLAGS.rl_confidence_interval) * 2 * math.pi)
temperature = (temperature + 1) / 2
# Confidence Penalty for Transition Predictions.
if FLAGS.rl_confidence_penalty:
epsilon = FLAGS.rl_epsilon * \
math.exp(-trainer.step / float(FLAGS.rl_epsilon_decay))
temp = 1 + \
(temperature - .5) * FLAGS.rl_confidence_penalty * epsilon
model.spinn.temperature = max(1e-3, temp)
# Soft Wake/Sleep based on temperature.
if FLAGS.rl_wake_sleep:
model.rl_weight = temperature * FLAGS.rl_weight
# Run model.
output = model(X_batch,
transitions_batch,
y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions)
# Calculate class accuracy.
target = torch.from_numpy(y_batch).long()
# get the index of the max log-probability
pred = output.data.max(1, keepdim=False)[1].cpu()
class_acc = pred.eq(target).sum() / float(target.size(0))
# Calculate class loss.
xent_loss = nn.CrossEntropyLoss()(output,
to_gpu(
Variable(target,
volatile=False)))
# Optionally calculate transition loss.
transition_loss = model.transition_loss if hasattr(
model, 'transition_loss') else None
# Accumulate Total Loss Variable
total_loss = 0.0
total_loss += xent_loss
if transition_loss is not None and model.optimize_transition_loss:
total_loss += transition_loss
aux_loss = auxiliary_loss(model)
total_loss += aux_loss
# Backward pass.
total_loss.backward()
# Hard Gradient Clipping
nn.utils.clip_grad_norm([
param for name, param in model.named_parameters()
if name not in ["embed.embed.weight"]
], FLAGS.clipping_max_value)
# Gradient descent step.
trainer.optimizer_step()
end = time.time()
total_time = end - start
train_accumulate(model, A, batch)
A.add('class_acc', class_acc)
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
train_rl_accumulate(model, A, batch)
if trainer.step % FLAGS.statistics_interval_steps == 0:
progress_bar.step(i=FLAGS.statistics_interval_steps,
total=FLAGS.statistics_interval_steps)
progress_bar.finish()
A.add('xent_cost', xent_loss.data[0])
stats(model, trainer, A, log_entry)
should_log = True
if trainer.step % FLAGS.sample_interval_steps == 0 and FLAGS.num_samples > 0:
should_log = True
model.train()
model(X_batch,
transitions_batch,
y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions)
tr_transitions_per_example, tr_strength = model.spinn.get_transitions_per_example(
)
model.eval()
model(X_batch,
transitions_batch,
y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions)
ev_transitions_per_example, ev_strength = model.spinn.get_transitions_per_example(
)
if model.use_sentence_pair and len(transitions_batch.shape) == 3:
transitions_batch = np.concatenate(
[transitions_batch[:, :, 0], transitions_batch[:, :, 1]],
axis=0)
# This could be done prior to running the batch for a tiny speed
# boost.
t_idxs = list(range(FLAGS.num_samples))
random.shuffle(t_idxs)
t_idxs = sorted(t_idxs[:FLAGS.num_samples])
for t_idx in t_idxs:
log = log_entry.rl_sampling.add()
gold = transitions_batch[t_idx]
pred_tr = tr_transitions_per_example[t_idx]
pred_ev = ev_transitions_per_example[t_idx]
strength_tr = sparks([1] + tr_strength[t_idx].tolist(),
dec_str)
strength_ev = sparks([1] + ev_strength[t_idx].tolist(),
dec_str)
_, crossing = evalb.crossing(gold, pred)
log.t_idx = t_idx
log.crossing = crossing
log.gold_lb = "".join(map(str, gold))
log.pred_tr = "".join(map(str, pred_tr))
log.pred_ev = "".join(map(str, pred_ev))
log.strg_tr = strength_tr[1:]
log.strg_ev = strength_ev[1:]
if trainer.step > 0 and trainer.step % FLAGS.eval_interval_steps == 0:
should_log = True
for index, eval_set in enumerate(eval_iterators):
acc, _ = evaluate(FLAGS,
model,
eval_set,
log_entry,
logger,
trainer,
eval_index=index)
if index == 0:
trainer.new_dev_accuracy(acc)
progress_bar.reset()
if trainer.step > FLAGS.ckpt_step and trainer.step % FLAGS.ckpt_interval_steps == 0:
should_log = True
trainer.checkpoint()
if should_log:
logger.LogEntry(log_entry)
progress_bar.step(i=(trainer.step % FLAGS.statistics_interval_steps) +
1,
total=FLAGS.statistics_interval_steps)
def train_loop(FLAGS, data_manager, model, optimizer, trainer, training_data_iter, eval_iterators, logger, step, best_dev_error):
# Accumulate useful statistics.
A = Accumulator(maxlen=FLAGS.deque_length)
M = MetricsWriter(os.path.join(FLAGS.metrics_path, FLAGS.experiment_name))
# Checkpoint paths.
standard_checkpoint_path = get_checkpoint_path(FLAGS.ckpt_path, FLAGS.experiment_name)
best_checkpoint_path = get_checkpoint_path(FLAGS.ckpt_path, FLAGS.experiment_name, best=True)
# Build log format strings.
model.train()
X_batch, transitions_batch, y_batch, num_transitions_batch, train_ids = get_batch(training_data_iter.next())
model(X_batch, transitions_batch, y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions
)
logger.Log("")
logger.Log("# ----- BEGIN: Log Configuration ----- #")
# Preview train string template.
train_str = train_format(model)
logger.Log("Train-Format: {}".format(train_str))
train_extra_str = train_extra_format(model)
logger.Log("Train-Extra-Format: {}".format(train_extra_str))
# Preview eval string template.
eval_str = eval_format(model)
logger.Log("Eval-Format: {}".format(eval_str))
eval_extra_str = eval_extra_format(model)
logger.Log("Eval-Extra-Format: {}".format(eval_extra_str))
logger.Log("# ----- END: Log Configuration ----- #")
logger.Log("")
# Train.
logger.Log("Training.")
# New Training Loop
progress_bar = SimpleProgressBar(msg="Training", bar_length=60, enabled=FLAGS.show_progress_bar)
progress_bar.step(i=0, total=FLAGS.statistics_interval_steps)
for step in range(step, FLAGS.training_steps):
model.train()
start = time.time()
batch = get_batch(training_data_iter.next())
X_batch, transitions_batch, y_batch, num_transitions_batch, train_ids = batch
total_tokens = sum([(nt+1)/2 for nt in num_transitions_batch.reshape(-1)])
# Reset cached gradients.
optimizer.zero_grad()
# Run model.
output = model(X_batch, transitions_batch, y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions
)
# Normalize output.
logits = F.log_softmax(output)
# Calculate class accuracy.
target = torch.from_numpy(y_batch).long()
pred = logits.data.max(1)[1].cpu() # get the index of the max log-probability
class_acc = pred.eq(target).sum() / float(target.size(0))
# Calculate class loss.
xent_loss = nn.NLLLoss()(logits, to_gpu(Variable(target, volatile=False)))
# Optionally calculate transition loss.
transition_loss = model.transition_loss if hasattr(model, 'transition_loss') else None
# Extract L2 Cost
l2_loss = l2_cost(model, FLAGS.l2_lambda) if FLAGS.use_l2_cost else None
# Accumulate Total Loss Variable
total_loss = 0.0
total_loss += xent_loss
if l2_loss is not None:
total_loss += l2_loss
if transition_loss is not None and model.optimize_transition_loss:
total_loss += transition_loss
total_loss += auxiliary_loss(model)
# Backward pass.
total_loss.backward()
# Hard Gradient Clipping
clip = FLAGS.clipping_max_value
for p in model.parameters():
if p.requires_grad:
p.grad.data.clamp_(min=-clip, max=clip)
# Learning Rate Decay
if FLAGS.actively_decay_learning_rate:
optimizer.lr = FLAGS.learning_rate * (FLAGS.learning_rate_decay_per_10k_steps ** (step / 10000.0))
# Gradient descent step.
optimizer.step()
end = time.time()
total_time = end - start
train_accumulate(model, data_manager, A, batch)
A.add('class_acc', class_acc)
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
if step % FLAGS.statistics_interval_steps == 0:
progress_bar.step(i=FLAGS.statistics_interval_steps, total=FLAGS.statistics_interval_steps)
progress_bar.finish()
A.add('xent_cost', xent_loss.data[0])
A.add('l2_cost', l2_loss.data[0])
stats_args = train_stats(model, optimizer, A, step)
train_metrics(M, stats_args, step)
logger.Log(train_str.format(**stats_args))
logger.Log(train_extra_str.format(**stats_args))
if step % FLAGS.sample_interval_steps == 0 and FLAGS.num_samples > 0:
model.train()
model(X_batch, transitions_batch, y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions
)
tr_transitions_per_example, tr_strength = model.spinn.get_transitions_per_example()
model.eval()
model(X_batch, transitions_batch, y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions
)
ev_transitions_per_example, ev_strength = model.spinn.get_transitions_per_example()
transition_str = "Samples:"
if model.use_sentence_pair and len(transitions_batch.shape) == 3:
transitions_batch = np.concatenate([
transitions_batch[:,:,0], transitions_batch[:,:,1]], axis=0)
# This could be done prior to running the batch for a tiny speed boost.
t_idxs = range(FLAGS.num_samples)
random.shuffle(t_idxs)
t_idxs = sorted(t_idxs[:FLAGS.num_samples])
for t_idx in t_idxs:
gold = transitions_batch[t_idx]
pred_tr = tr_transitions_per_example[t_idx]
pred_ev = ev_transitions_per_example[t_idx]
stength_tr = sparks([1] + tr_strength[t_idx].tolist())
stength_ev = sparks([1] + ev_strength[t_idx].tolist())
_, crossing = evalb.crossing(gold, pred)
transition_str += "\n{}. crossing={}".format(t_idx, crossing)
transition_str += "\n g{}".format("".join(map(str, gold)))
transition_str += "\n {}".format(stength_tr[1:].encode('utf-8'))
transition_str += "\n pt{}".format("".join(map(str, pred_tr)))
transition_str += "\n {}".format(stength_ev[1:].encode('utf-8'))
transition_str += "\n pe{}".format("".join(map(str, pred_ev)))
logger.Log(transition_str)
if step > 0 and step % FLAGS.eval_interval_steps == 0:
for index, eval_set in enumerate(eval_iterators):
acc, tacc = evaluate(FLAGS, model, data_manager, eval_set, index, logger, step)
if FLAGS.ckpt_on_best_dev_error and index == 0 and (1 - acc) < 0.99 * best_dev_error and step > FLAGS.ckpt_step:
best_dev_error = 1 - acc
logger.Log("Checkpointing with new best dev accuracy of %f" % acc)
trainer.save(best_checkpoint_path, step, best_dev_error)
progress_bar.reset()
if step > FLAGS.ckpt_step and step % FLAGS.ckpt_interval_steps == 0:
logger.Log("Checkpointing.")
trainer.save(standard_checkpoint_path, step, best_dev_error)
progress_bar.step(i=step % FLAGS.statistics_interval_steps, total=FLAGS.statistics_interval_steps)
def train_loop(FLAGS, data_manager, model, optimizer, trainer,
training_data_iter, eval_iterators, logger, step, best_dev_error, vocabulary):
# Accumulate useful statistics.
A = Accumulator(maxlen=FLAGS.deque_length)
# Checkpoint paths.
standard_checkpoint_path = get_checkpoint_path(FLAGS.ckpt_path, FLAGS.experiment_name)
best_checkpoint_path = get_checkpoint_path(FLAGS.ckpt_path, FLAGS.experiment_name, best=True)
# Build log format strings.
model.train()
X_batch, transitions_batch, y_batch, num_transitions_batch, train_ids = get_batch(
training_data_iter.next())
model(X_batch, transitions_batch, y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
pyramid_temperature_multiplier=1.0,
example_lengths=num_transitions_batch
)
# Train.
logger.Log("Training.")
# New Training Loop
progress_bar = SimpleProgressBar(msg="Training", bar_length=60, enabled=FLAGS.show_progress_bar)
progress_bar.step(i=0, total=FLAGS.statistics_interval_steps)
log_entry = pb.SpinnEntry()
for step in range(step, FLAGS.training_steps):
model.train()
log_entry.Clear()
log_entry.step = step
should_log = False
start = time.time()
batch = get_batch(training_data_iter.next())
X_batch, transitions_batch, y_batch, num_transitions_batch, train_ids = batch
total_tokens = sum([(nt + 1) / 2 for nt in num_transitions_batch.reshape(-1)])
# Reset cached gradients.
optimizer.zero_grad()
if FLAGS.model_type in ["Pyramid", "ChoiPyramid"]:
pyramid_temperature_multiplier = FLAGS.pyramid_temperature_decay_per_10k_steps ** (
step / 10000.0)
if FLAGS.pyramid_temperature_cycle_length > 0.0:
min_temp = 1e-5
pyramid_temperature_multiplier *= (math.cos((step) /
FLAGS.pyramid_temperature_cycle_length) + 1 + min_temp) / 2
else:
pyramid_temperature_multiplier = None
# Run model.
output = model(X_batch, transitions_batch, y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
pyramid_temperature_multiplier=pyramid_temperature_multiplier,
example_lengths=num_transitions_batch
)
# Normalize output.
logits = F.log_softmax(output)
# Calculate class accuracy.
target = torch.from_numpy(y_batch).long()
# get the index of the max log-probability
pred = logits.data.max(1, keepdim=False)[1].cpu()
class_acc = pred.eq(target).sum() / float(target.size(0))
# Calculate class loss.
xent_loss = nn.NLLLoss()(logits, to_gpu(Variable(target, volatile=False)))
# Optionally calculate transition loss.
transition_loss = model.transition_loss if hasattr(model, 'transition_loss') else None
# Extract L2 Cost
l2_loss = get_l2_loss(model, FLAGS.l2_lambda) if FLAGS.use_l2_loss else None
# Accumulate Total Loss Variable
total_loss = 0.0
total_loss += xent_loss
if l2_loss is not None:
total_loss += l2_loss
if transition_loss is not None and model.optimize_transition_loss:
total_loss += transition_loss
aux_loss = auxiliary_loss(model)
total_loss += aux_loss
# Backward pass.
total_loss.backward()
# Hard Gradient Clipping
clip = FLAGS.clipping_max_value
for p in model.parameters():
if p.requires_grad:
p.grad.data.clamp_(min=-clip, max=clip)
# Learning Rate Decay
if FLAGS.actively_decay_learning_rate:
optimizer.lr = FLAGS.learning_rate * \
(FLAGS.learning_rate_decay_per_10k_steps ** (step / 10000.0))
# Gradient descent step.
optimizer.step()
end = time.time()
total_time = end - start
train_accumulate(model, data_manager, A, batch)
A.add('class_acc', class_acc)
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
if step % FLAGS.statistics_interval_steps == 0:
A.add('xent_cost', xent_loss.data[0])
A.add('l2_cost', l2_loss.data[0])
stats(model, optimizer, A, step, log_entry)
should_log = True
progress_bar.finish()
if step % FLAGS.sample_interval_steps == 0 and FLAGS.num_samples > 0:
should_log = True
model.train()
model(X_batch, transitions_batch, y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
pyramid_temperature_multiplier=pyramid_temperature_multiplier,
example_lengths=num_transitions_batch
)
tr_transitions_per_example, tr_strength = model.spinn.get_transitions_per_example()
model.eval()
model(X_batch, transitions_batch, y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
pyramid_temperature_multiplier=pyramid_temperature_multiplier,
example_lengths=num_transitions_batch
)
ev_transitions_per_example, ev_strength = model.spinn.get_transitions_per_example()
if model.use_sentence_pair and len(transitions_batch.shape) == 3:
transitions_batch = np.concatenate([
transitions_batch[:, :, 0], transitions_batch[:, :, 1]], axis=0)
# This could be done prior to running the batch for a tiny speed boost.
t_idxs = range(FLAGS.num_samples)
random.shuffle(t_idxs)
t_idxs = sorted(t_idxs[:FLAGS.num_samples])
for t_idx in t_idxs:
log = log_entry.rl_sampling.add()
gold = transitions_batch[t_idx]
pred_tr = tr_transitions_per_example[t_idx]
pred_ev = ev_transitions_per_example[t_idx]
strength_tr = sparks([1] + tr_strength[t_idx].tolist(), dec_str)
strength_ev = sparks([1] + ev_strength[t_idx].tolist(), dec_str)
_, crossing = evalb.crossing(gold, pred_ev)
log.t_idx = t_idx
log.crossing = crossing
log.gold_lb = "".join(map(str, gold))
log.pred_tr = "".join(map(str, pred_tr))
log.pred_ev = "".join(map(str, pred_ev))
log.strg_tr = strength_tr[1:].encode('utf-8')
log.strg_ev = strength_ev[1:].encode('utf-8')
if step > 0 and step % FLAGS.eval_interval_steps == 0:
should_log = True
for index, eval_set in enumerate(eval_iterators):
acc, tacc = evaluate(FLAGS, model, data_manager, eval_set, log_entry, logger, step,
show_sample=(
step %
FLAGS.sample_interval_steps == 0), vocabulary=vocabulary, eval_index=index)
if FLAGS.ckpt_on_best_dev_error and index == 0 and (
1 - acc) < 0.99 * best_dev_error and step > FLAGS.ckpt_step:
best_dev_error = 1 - acc
logger.Log("Checkpointing with new best dev accuracy of %f" % acc) # TODO: This mixes information across dev sets. Fix.
trainer.save(best_checkpoint_path, step, best_dev_error)
progress_bar.reset()
if step > FLAGS.ckpt_step and step % FLAGS.ckpt_interval_steps == 0:
should_log = True
logger.Log("Checkpointing.")
trainer.save(standard_checkpoint_path, step, best_dev_error)
if should_log:
logger.LogEntry(log_entry)
progress_bar.step(i=(step % FLAGS.statistics_interval_steps) + 1,
total=FLAGS.statistics_interval_steps)
def train_loop(FLAGS, data_manager, model, optimizer, trainer,
training_data_iter, eval_iterators, logger, step, best_dev_error):
# Accumulate useful statistics.
A = Accumulator(maxlen=FLAGS.deque_length)
# Checkpoint paths.
standard_checkpoint_path = get_checkpoint_path(
FLAGS.ckpt_path, FLAGS.experiment_name)
best_checkpoint_path = get_checkpoint_path(
FLAGS.ckpt_path, FLAGS.experiment_name, best=True)
# Build log format strings.
model.train()
X_batch, transitions_batch, y_batch, num_transitions_batch, train_ids = get_batch(
training_data_iter.next())
model(X_batch, transitions_batch, y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions
)
# Train.
logger.Log("Training.")
# New Training Loop
progress_bar = SimpleProgressBar(
msg="Training", bar_length=60, enabled=FLAGS.show_progress_bar)
progress_bar.step(i=0, total=FLAGS.statistics_interval_steps)
log_entry = pb.SpinnEntry()
for step in range(step, FLAGS.training_steps):
model.train()
log_entry.Clear()
log_entry.step = step
should_log = False
start = time.time()
batch = get_batch(training_data_iter.next())
X_batch, transitions_batch, y_batch, num_transitions_batch, train_ids = batch
total_tokens = sum(
[(nt + 1) / 2 for nt in num_transitions_batch.reshape(-1)])
# Reset cached gradients.
optimizer.zero_grad()
epsilon = FLAGS.rl_epsilon * math.exp(-step / FLAGS.rl_epsilon_decay)
# Epsilon Greedy w. Decay.
model.spinn.epsilon = epsilon
# Confidence Penalty for Transition Predictions.
temperature = math.sin(math.pi / 2 + step /
float(FLAGS.rl_confidence_interval) * 2 * math.pi)
temperature = (temperature + 1) / 2
if FLAGS.rl_confidence_penalty:
temp = 1 + \
(temperature - .5) * FLAGS.rl_confidence_penalty * epsilon
model.spinn.temperature = max(1e-3, temp)
# Soft Wake/Sleep based on temperature.
if FLAGS.rl_wake_sleep:
model.rl_weight = temperature * FLAGS.rl_weight
# Run model.
output = model(X_batch, transitions_batch, y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions
)
# Normalize output.
logits = F.log_softmax(output)
# Calculate class accuracy.
target = torch.from_numpy(y_batch).long()
pred = logits.data.max(1)[
1].cpu() # get the index of the max log-probability
class_acc = pred.eq(target).sum() / float(target.size(0))
# Calculate class loss.
xent_loss = nn.NLLLoss()(
logits, to_gpu(Variable(target, volatile=False)))
# Optionally calculate transition loss.
transition_loss = model.transition_loss if hasattr(
model, 'transition_loss') else None
# Extract L2 Cost
l2_loss = get_l2_loss(
model, FLAGS.l2_lambda) if FLAGS.use_l2_loss else None
# Accumulate Total Loss Variable
total_loss = 0.0
total_loss += xent_loss
if l2_loss is not None:
total_loss += l2_loss
if transition_loss is not None and model.optimize_transition_loss:
total_loss += transition_loss
aux_loss = auxiliary_loss(model)
total_loss += aux_loss
# Backward pass.
total_loss.backward()
# Hard Gradient Clipping
clip = FLAGS.clipping_max_value
for p in model.parameters():
if p.requires_grad:
p.grad.data.clamp_(min=-clip, max=clip)
# Learning Rate Decay
if FLAGS.actively_decay_learning_rate:
optimizer.lr = FLAGS.learning_rate * \
(FLAGS.learning_rate_decay_per_10k_steps ** (step / 10000.0))
# Gradient descent step.
optimizer.step()
end = time.time()
total_time = end - start
train_accumulate(model, data_manager, A, batch)
A.add('class_acc', class_acc)
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
train_rl_accumulate(model, data_manager, A, batch)
if step % FLAGS.statistics_interval_steps == 0 \
or step % FLAGS.metrics_interval_steps == 0:
if step % FLAGS.statistics_interval_steps == 0:
progress_bar.step(i=FLAGS.statistics_interval_steps,
total=FLAGS.statistics_interval_steps)
progress_bar.finish()
A.add('xent_cost', xent_loss.data[0])
A.add('l2_cost', l2_loss.data[0])
stats(model, optimizer, A, step, log_entry)
if step % FLAGS.sample_interval_steps == 0 and FLAGS.num_samples > 0:
should_log = True
model.train()
model(X_batch, transitions_batch, y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions
)
tr_transitions_per_example, tr_strength = model.spinn.get_transitions_per_example(
)
model.eval()
model(X_batch, transitions_batch, y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions
)
ev_transitions_per_example, ev_strength = model.spinn.get_transitions_per_example(
)
if model.use_sentence_pair and len(transitions_batch.shape) == 3:
transitions_batch = np.concatenate([
transitions_batch[:, :, 0], transitions_batch[:, :, 1]], axis=0)
# This could be done prior to running the batch for a tiny speed
# boost.
t_idxs = range(FLAGS.num_samples)
random.shuffle(t_idxs)
t_idxs = sorted(t_idxs[:FLAGS.num_samples])
for t_idx in t_idxs:
log = log_entry.rl_sampling.add()
gold = transitions_batch[t_idx]
pred_tr = tr_transitions_per_example[t_idx]
pred_ev = ev_transitions_per_example[t_idx]
strength_tr = sparks(
[1] + tr_strength[t_idx].tolist(), dec_str)
strength_ev = sparks(
[1] + ev_strength[t_idx].tolist(), dec_str)
_, crossing = evalb.crossing(gold, pred)
log.t_idx = t_idx
log.crossing = crossing
log.gold_lb = "".join(map(str, gold))
log.pred_tr = "".join(map(str, pred_tr))
log.pred_ev = "".join(map(str, pred_ev))
log.strg_tr = strength_tr[1:].encode('utf-8')
log.strg_ev = strength_ev[1:].encode('utf-8')
if step > 0 and step % FLAGS.eval_interval_steps == 0:
should_log = True
for index, eval_set in enumerate(eval_iterators):
acc, tacc = evaluate(
FLAGS, model, data_manager, eval_set, log_entry, step)
if FLAGS.ckpt_on_best_dev_error and index == 0 and (
1 - acc) < 0.99 * best_dev_error and step > FLAGS.ckpt_step:
best_dev_error = 1 - acc
logger.Log(
"Checkpointing with new best dev accuracy of %f" % acc)
trainer.save(best_checkpoint_path, step, best_dev_error)
progress_bar.reset()
if step > FLAGS.ckpt_step and step % FLAGS.ckpt_interval_steps == 0:
should_log = True
logger.Log("Checkpointing.")
trainer.save(standard_checkpoint_path, step, best_dev_error)
log_level = afs_safe_logger.ProtoLogger.INFO
if not should_log and step % FLAGS.metrics_interval_steps == 0:
# Log to file, but not to stderr.
should_log = True
log_level = afs_safe_logger.ProtoLogger.DEBUG
if should_log:
logger.LogEntry(log_entry, level=log_level)
progress_bar.step(i=step % FLAGS.statistics_interval_steps,
total=FLAGS.statistics_interval_steps)
def train_loop(FLAGS, model, trainer, training_data_iter, eval_iterators,
logger, vocabulary, target_vocabulary):
# Accumulate useful statistics.
A = Accumulator(maxlen=FLAGS.deque_length)
# Train.
logger.Log("Training.")
# New Training Loop
progress_bar = SimpleProgressBar(msg="Training",
bar_length=60,
enabled=FLAGS.show_progress_bar)
progress_bar.step(i=0, total=FLAGS.statistics_interval_steps)
rl_only = False
log_entry = pb.SpinnEntry()
for _ in range(trainer.step, FLAGS.training_steps):
if FLAGS.rl_alternate and trainer.step % 1000 == 0 and trainer.step > 0:
rl_only = not rl_only
if rl_only:
logger.Log('Switching training mode: RL only.')
else:
logger.Log('Switching training mode: MT only.')
if (trainer.step -
trainer.best_dev_step) > FLAGS.early_stopping_steps_to_wait:
logger.Log('No improvement after ' +
str(FLAGS.early_stopping_steps_to_wait) +
' steps. Stopping training.')
break
model.train()
log_entry.Clear()
log_entry.step = trainer.step
should_log = False
start = time.time()
batch = get_batch(next(training_data_iter))
X_batch, transitions_batch, y_batch, num_transitions_batch, train_ids = batch
total_tokens = sum([(nt + 1) / 2
for nt in num_transitions_batch.reshape(-1)])
# Reset cached gradients.
trainer.optimizer_zero_grad()
temperature = math.sin(
math.pi / 2 +
trainer.step / float(FLAGS.rl_confidence_interval) * 2 * math.pi)
temperature = (temperature + 1) / 2
# Confidence Penalty for Transition Predictions.
if FLAGS.rl_confidence_penalty:
epsilon = FLAGS.rl_epsilon * \
math.exp(-trainer.step / float(FLAGS.rl_epsilon_decay))
temp = 1 + \
(temperature - .5) * FLAGS.rl_confidence_penalty * epsilon
model.spinn.temperature = max(1e-3, temp)
# Soft Wake/Sleep based on temperature.
if FLAGS.rl_wake_sleep:
model.rl_weight = temperature * FLAGS.rl_weight
# Run model.
output, trg, attention, mask = model(
X_batch,
transitions_batch,
y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
example_lengths=num_transitions_batch)
criterion = nn.NLLLoss()
batch_size = len(y_batch)
trg_seq_len = trg.shape[0]
mt_loss = 0.0
if rl_only == False:
num_classes = output.shape[-1]
mask = to_gpu(mask)
for i in range(trg_seq_len):
mt_loss += criterion(
output[i, :].index_select(0, mask[i].nonzero().squeeze(1)),
trg[i].index_select(0,
mask[i].nonzero().squeeze(1)).view(-1))
elif FLAGS.rl_alternate:
model.policy_loss = 0.0
model.value_loss = 0.0
# Optionally calculate transition loss.
mt_loss = mt_loss / trg_seq_len
model.transition_loss = model.encoder.transition_loss if hasattr(
model.encoder, 'transition_loss') else None
transition_loss = model.transition_loss if hasattr(
model, 'transition_loss') else None
model.mt_loss = mt_loss
# Accumulate Total Loss Variable
total_loss = 0.0
total_loss += mt_loss
if transition_loss is not None and model.encoder.optimize_transition_loss:
model.optimize_transition_loss = model.encoder.optimize_transition_loss
total_loss += transition_loss
aux_loss = auxiliary_loss(model)
total_loss += aux_loss[0]
# Backward pass.
total_loss.backward()
# Hard Gradient Clipping
nn.utils.clip_grad_norm_([
param for name, param in model.named_parameters()
if name not in ["embed.embed.weight"]
], FLAGS.clipping_max_value)
# Gradient descent step.
trainer.optimizer_step()
bb = list(model.parameters())[-1].clone()
end = time.time()
total_time = end - start
train_accumulate(model, A, batch)
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
A.add('mt_loss', float(mt_loss))
train_rl_accumulate(model, A, batch)
if trainer.step % FLAGS.statistics_interval_steps == 0:
stats(model, trainer, A, log_entry)
should_log = True
progress_bar.finish()
if trainer.step % FLAGS.sample_interval_steps == 0 and FLAGS.num_samples > 0:
should_log = True
model.train()
model(X_batch,
transitions_batch,
y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
example_lengths=num_transitions_batch)
tr_transitions_per_example, tr_strength = model.spinn.get_transitions_per_example(
)
model.eval()
model(X_batch,
transitions_batch,
y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
example_lengths=num_transitions_batch)
ev_transitions_per_example, ev_strength = model.spinn.get_transitions_per_example(
)
if model.use_sentence_pair and len(transitions_batch.shape) == 3:
transitions_batch = np.concatenate(
[transitions_batch[:, :, 0], transitions_batch[:, :, 1]],
axis=0)
# This could be done prior to running the batch for a tiny speed
# boost.
t_idxs = list(range(FLAGS.num_samples))
random.shuffle(t_idxs)
t_idxs = sorted(t_idxs[:FLAGS.num_samples])
for t_idx in t_idxs:
log = log_entry.rl_sampling.add()
gold = transitions_batch[t_idx]
pred_tr = tr_transitions_per_example[t_idx]
pred_ev = ev_transitions_per_example[t_idx]
strength_tr = sparks([1] + tr_strength[t_idx].tolist(),
dec_str)
strength_ev = sparks([1] + ev_strength[t_idx].tolist(),
dec_str)
_, crossing = evalb.crossing(gold, pred_ev)
log.t_idx = t_idx
log.crossing = crossing
log.gold_lb = "".join(map(str, gold))
log.pred_tr = "".join(map(str, pred_tr))
log.pred_ev = "".join(map(str, pred_ev))
log.strg_tr = strength_tr[1:]
log.strg_ev = strength_ev[1:]
if trainer.step > 0 and trainer.step % FLAGS.eval_interval_steps == 0:
should_log = True
for index, eval_set in enumerate(eval_iterators):
acc, _ = evaluate(
FLAGS,
model,
eval_set,
log_entry,
logger,
trainer,
show_sample=(trainer.step %
FLAGS.sample_interval_steps == 0),
vocabulary=vocabulary,
eval_index=index,
target_vocabulary=target_vocabulary)
if index == 0:
trainer.new_dev_accuracy(acc)
progress_bar.reset()
if trainer.step > FLAGS.ckpt_step and trainer.step % FLAGS.ckpt_interval_steps == 0:
should_log = True
trainer.checkpoint()
if should_log:
logger.LogEntry(log_entry)
progress_bar.step(i=(trainer.step % FLAGS.statistics_interval_steps) +
1,
total=FLAGS.statistics_interval_steps)
def run_pyramid(self, x, show_sample=False):
batch_size, seq_len, model_dim = x.data.size()
all_state_pairs = []
all_state_pairs.append(torch.chunk(x, seq_len, 1))
# Temp fix:
show_sample = False
if show_sample:
print
for layer in range(seq_len - 1, 0, -1):
composition_results = []
selection_logits_list = []
for position in range(layer):
left = torch.squeeze(all_state_pairs[-1][position])
right = torch.squeeze(all_state_pairs[-1][position + 1])
composition_results.append(self.composition_fn(left, right))
if self.gated:
for position in range(layer):
selection_logits_list.append(
self.selection_fn(composition_results[position]))
selection_logits = torch.cat(selection_logits_list, 1)
if show_sample:
selection_probs = F.softmax(selection_logits)
print sparks(
np.transpose(
selection_probs[0, :].data.cpu().numpy()).tolist())
if self.training and self.selection_keep_rate is not None:
noise = torch.bernoulli(
(to_gpu(torch.ones(1, 1)) * self.selection_keep_rate
).expand_as(selection_logits)) * -1000.
selection_logits += Variable(noise)
selection_probs = F.softmax(selection_logits)
layer_state_pairs = []
for position in range(layer):
if position < (layer - 1):
copy_left = torch.sum(
selection_probs[:, position + 1:], 1)
else:
copy_left = to_gpu(Variable(torch.zeros(1, 1)))
if position > 0:
copy_right = torch.sum(selection_probs[:, :position],
1)
else:
copy_right = to_gpu(Variable(torch.zeros(1, 1)))
select = selection_probs[:, position]
left = torch.squeeze(all_state_pairs[-1][position])
right = torch.squeeze(all_state_pairs[-1][position + 1])
composition_result = composition_results[position]
new_state_pair = copy_left.expand_as(left) * left \
+ copy_right.expand_as(right) * right \
+ select.unsqueeze(1).expand_as(composition_result) * composition_result
layer_state_pairs.append(new_state_pair)
else:
layer_state_pairs = composition_results
all_state_pairs.append(layer_state_pairs)
return all_state_pairs[-1][-1]
