def test_accumulator(self):
A = Accumulator()
A.add('key', 0)
A.add('key', 0)
assert len(A.get('key')) == 2
assert len(A.get('key')) == 0
def evaluate(FLAGS,
model,
eval_set,
log_entry,
logger,
trainer,
vocabulary=None,
show_sample=False,
eval_index=0):
filename, dataset = eval_set
A = Accumulator()
eval_log = log_entry.evaluation.add()
reporter = EvalReporter()
tree_strs = None
# Evaluate
total_batches = len(dataset)
progress_bar = SimpleProgressBar(msg="Run Eval",
bar_length=60,
enabled=FLAGS.show_progress_bar)
progress_bar.step(0, total=total_batches)
total_tokens = 0
start = time.time()
model.eval()
for i, dataset_batch in enumerate(dataset):
batch = get_batch(dataset_batch)
eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch, eval_ids = batch
# Run model.
output = model(eval_X_batch,
eval_transitions_batch,
eval_y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
store_parse_masks=show_sample,
example_lengths=eval_num_transitions_batch)
can_sample = (FLAGS.model_type == "RLSPINN"
and FLAGS.use_internal_parser)
if show_sample and can_sample:
tmp_samples = model.get_samples(
eval_X_batch, vocabulary, only_one=not FLAGS.write_eval_report)
tree_strs = prettyprint_trees(tmp_samples)
if not FLAGS.write_eval_report:
# Only show one sample, regardless of the number of batches.
show_sample = False
# Calculate class accuracy.
target = torch.from_numpy(eval_y_batch).long()
# get the index of the max log-probability
pred = output.data.max(1, keepdim=False)[1].cpu()
eval_accumulate(model, A, batch)
A.add('class_correct', pred.eq(target).sum())
A.add('class_total', target.size(0))
# Update Aggregate Accuracies
total_tokens += sum([(nt + 1) / 2
for nt in eval_num_transitions_batch.reshape(-1)])
if FLAGS.write_eval_report:
transitions_per_example, _ = model.spinn.get_transitions_per_example(
style="preds" if FLAGS.eval_report_use_preds else "given") if (
FLAGS.model_type == "SPINN"
and FLAGS.use_internal_parser) else (None, None)
if model.use_sentence_pair:
batch_size = pred.size(0)
sent1_transitions = transitions_per_example[:
batch_size] if transitions_per_example is not None else None
sent2_transitions = transitions_per_example[
batch_size:] if transitions_per_example is not None else None
sent1_trees = tree_strs[:
batch_size] if tree_strs is not None else None
sent2_trees = tree_strs[
batch_size:] if tree_strs is not None else None
else:
sent1_transitions = transitions_per_example if transitions_per_example is not None else None
sent2_transitions = None
sent1_trees = tree_strs if tree_strs is not None else None
sent2_trees = None
reporter.save_batch(pred, target, eval_ids,
output.data.cpu().numpy(), sent1_transitions,
sent2_transitions, sent1_trees, sent2_trees)
# Print Progress
progress_bar.step(i + 1, total=total_batches)
progress_bar.finish()
if tree_strs is not None:
logger.Log('Sample: ' + tree_strs[0])
end = time.time()
total_time = end - start
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
eval_stats(model, A, eval_log)
eval_log.filename = filename
if FLAGS.write_eval_report:
eval_report_path = os.path.join(
FLAGS.log_path,
FLAGS.experiment_name + ".eval_set_" + str(eval_index) + ".report")
reporter.write_report(eval_report_path)
eval_class_acc = eval_log.eval_class_accuracy
eval_trans_acc = eval_log.eval_transition_accuracy
return eval_class_acc, eval_trans_acc
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 evaluate(FLAGS, model, data_manager, eval_set, index, logger, step, vocabulary=None):
filename, dataset = eval_set
A = Accumulator()
M = MetricsWriter(os.path.join(FLAGS.metrics_path, FLAGS.experiment_name))
reporter = EvalReporter()
eval_str = eval_format(model)
eval_extra_str = eval_extra_format(model)
# Evaluate
total_batches = len(dataset)
progress_bar = SimpleProgressBar(msg="Run Eval", bar_length=60, enabled=FLAGS.show_progress_bar)
progress_bar.step(0, total=total_batches)
total_tokens = 0
invalid = 0
start = time.time()
model.eval()
for i, dataset_batch in enumerate(dataset):
batch = get_batch(dataset_batch)
eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch, eval_ids = batch
# Run model.
output = model(eval_X_batch, eval_transitions_batch, eval_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(eval_y_batch).long()
pred = logits.data.max(1)[1].cpu() # get the index of the max log-probability
eval_accumulate(model, data_manager, A, batch)
A.add('class_correct', pred.eq(target).sum())
A.add('class_total', target.size(0))
# Optionally calculate transition loss/acc.
transition_loss = model.transition_loss if hasattr(model, 'transition_loss') else None
# Update Aggregate Accuracies
total_tokens += sum([(nt+1)/2 for nt in eval_num_transitions_batch.reshape(-1)])
if FLAGS.write_eval_report:
reporter_args = [pred, target, eval_ids, output.data.cpu().numpy()]
if hasattr(model, 'transition_loss'):
transitions_per_example, _ = model.spinn.get_transitions_per_example(
style="preds" if FLAGS.eval_report_use_preds else "given")
if model.use_sentence_pair:
batch_size = pred.size(0)
sent1_transitions = transitions_per_example[:batch_size]
sent2_transitions = transitions_per_example[batch_size:]
reporter_args.append(sent1_transitions)
reporter_args.append(sent2_transitions)
else:
reporter_args.append(transitions_per_example)
reporter.save_batch(*reporter_args)
# Print Progress
progress_bar.step(i+1, total=total_batches)
progress_bar.finish()
end = time.time()
total_time = end - start
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
stats_args = eval_stats(model, A, step)
stats_args['filename'] = filename
logger.Log(eval_str.format(**stats_args))
logger.Log(eval_extra_str.format(**stats_args))
if FLAGS.write_eval_report:
eval_report_path = os.path.join(FLAGS.log_path, FLAGS.experiment_name + ".report")
reporter.write_report(eval_report_path)
eval_class_acc = stats_args['class_acc']
eval_trans_acc = stats_args['transition_acc']
if index == 0:
eval_metrics(M, stats_args, step)
return eval_class_acc, eval_trans_acc
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 evaluate(FLAGS, model, data_manager, eval_set, log_entry,
logger, step, vocabulary=None, show_sample=False, eval_index=0):
filename, dataset = eval_set
A = Accumulator()
index = len(log_entry.evaluation)
eval_log = log_entry.evaluation.add()
reporter = EvalReporter()
tree_strs = None
# Evaluate
total_batches = len(dataset)
progress_bar = SimpleProgressBar(msg="Run Eval", bar_length=60, enabled=FLAGS.show_progress_bar)
progress_bar.step(0, total=total_batches)
total_tokens = 0
start = time.time()
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
model.eval()
for i, dataset_batch in enumerate(dataset):
batch = get_batch(dataset_batch)
eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch, eval_ids = batch
# Run model.
output = model(eval_X_batch, eval_transitions_batch, eval_y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
pyramid_temperature_multiplier=pyramid_temperature_multiplier,
store_parse_masks=show_sample,
example_lengths=eval_num_transitions_batch)
can_sample = FLAGS.model_type in ["ChoiPyramid"] or (FLAGS.model_type == "SPINN" and FLAGS.use_internal_parser) # TODO: Restore support in Pyramid if using.
if show_sample and can_sample:
tmp_samples = model.get_samples(eval_X_batch, vocabulary, only_one=not FLAGS.write_eval_report)
tree_strs = prettyprint_trees(tmp_samples)
if not FLAGS.write_eval_report:
show_sample = False # Only show one sample, regardless of the number of batches.
# Normalize output.
logits = F.log_softmax(output)
# Calculate class accuracy.
target = torch.from_numpy(eval_y_batch).long()
# get the index of the max log-probability
pred = logits.data.max(1, keepdim=False)[1].cpu()
eval_accumulate(model, data_manager, A, batch)
A.add('class_correct', pred.eq(target).sum())
A.add('class_total', target.size(0))
# Optionally calculate transition loss/acc.
model.transition_loss if hasattr(model, 'transition_loss') else None
# Update Aggregate Accuracies
total_tokens += sum([(nt + 1) / 2 for nt in eval_num_transitions_batch.reshape(-1)])
if FLAGS.write_eval_report:
transitions_per_example, _ = model.spinn.get_transitions_per_example(
style="preds" if FLAGS.eval_report_use_preds else "given") if (FLAGS.model_type == "SPINN" and FLAGS.use_internal_parser) else (None, None)
if model.use_sentence_pair:
batch_size = pred.size(0)
sent1_transitions = transitions_per_example[:batch_size] if transitions_per_example is not None else None
sent2_transitions = transitions_per_example[batch_size:] if transitions_per_example is not None else None
sent1_trees = tree_strs[:batch_size] if tree_strs is not None else None
sent2_trees = tree_strs[batch_size:] if tree_strs is not None else None
else:
sent1_transitions = transitions_per_example if transitions_per_example is not None else None
sent2_transitions = None
sent1_trees = tree_strs if tree_strs is not None else None
sent2_trees = None
reporter.save_batch(pred, target, eval_ids, output.data.cpu().numpy(), sent1_transitions, sent2_transitions, sent1_trees, sent2_trees)
# Print Progress
progress_bar.step(i + 1, total=total_batches)
progress_bar.finish()
if tree_strs is not None:
logger.Log('Sample: ' + tree_strs[0])
end = time.time()
total_time = end - start
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
eval_stats(model, A, eval_log)
eval_log.filename = filename
if FLAGS.write_eval_report:
eval_report_path = os.path.join(FLAGS.log_path, FLAGS.experiment_name + ".eval_set_" + str(eval_index) + ".report")
reporter.write_report(eval_report_path)
eval_class_acc = eval_log.eval_class_accuracy
eval_trans_acc = eval_log.eval_transition_accuracy
return eval_class_acc, eval_trans_acc
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 evaluate(FLAGS,
model,
data_manager,
eval_set,
log_entry,
logger,
step,
vocabulary=None,
show_sample=False,
eval_index=0):
filename, dataset = eval_set
A = Accumulator()
index = len(log_entry.evaluation)
eval_log = log_entry.evaluation.add()
reporter = EvalReporter()
tree_strs = None
# Evaluate
total_batches = len(dataset)
progress_bar = SimpleProgressBar(msg="Run Eval",
bar_length=60,
enabled=FLAGS.show_progress_bar)
progress_bar.step(0, total=total_batches)
total_tokens = 0
start = time.time()
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
model.eval()
for i, dataset_batch in enumerate(dataset):
batch = get_batch(dataset_batch)
eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch, eval_ids, _, silver_tree = batch
# eval_X_batch: <batch x maxlen x 2>
# eval_y_batch: <batch >
# silver_tree:
# the dist is invalid for val
# Run model.
output = model(
eval_X_batch,
eval_transitions_batch,
eval_y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
pyramid_temperature_multiplier=pyramid_temperature_multiplier,
store_parse_masks=True,
example_lengths=eval_num_transitions_batch)
# TODO: Restore support in Pyramid if using.
can_sample = FLAGS.model_type in [
"ChoiPyramid"
] or (FLAGS.model_type == "SPINN" and FLAGS.use_internal_parser)
if show_sample and can_sample:
tmp_samples = model.get_samples(
eval_X_batch, vocabulary, only_one=not FLAGS.write_eval_report)
# tree_strs = prettyprint_trees(tmp_samples)
tree_strs = [tree for tree in tmp_samples]
tmp_samples = model.get_samples(eval_X_batch,
vocabulary,
only_one=False)
# def get_max(s):
# # test f1
# max = 0
# for x in s:
# _, idx = x.split(',')
# if int(idx) > max:
# max = int(idx)
# return max
for s in (range(int(model.use_sentence_pair) + 1)):
for b in (range(silver_tree.shape[0])):
model_out = tmp_samples[s * silver_tree.shape[0] + b]
std_out = silver_tree[b, :, s]
std_out = set([x for x in std_out if x != '-1,-1'])
model_out_brackets, model_out_max_l = get_brackets(model_out)
model_out = set(convert_brackets_to_string(model_out_brackets))
outmost_bracket = '{:d},{:d}'.format(0, model_out_max_l)
std_out.add(outmost_bracket)
model_out.add(outmost_bracket)
# print get_max(model_out), get_max(std_out)
# print model_out
# print std_out
# print '=' * 30
# assert get_max(model_out) == get_max(std_out)
overlap = model_out & std_out
prec = float(len(overlap)) / (len(model_out) + 1e-8)
reca = float(len(overlap)) / (len(std_out) + 1e-8)
if len(std_out) == 0:
reca = 1.
if len(model_out) == 0:
prec = 1.
f1 = 2 * prec * reca / (prec + reca + 1e-8)
A.add('f1', f1)
if not FLAGS.write_eval_report:
# Only show one sample, regardless of the number of batches.
show_sample = False
# Normalize output.
logits = F.log_softmax(output)
# Calculate class accuracy.
target = torch.from_numpy(eval_y_batch).long()
# get the index of the max log-probability
pred = logits.data.max(1, keepdim=False)[1].cpu()
eval_accumulate(model, data_manager, A, batch)
A.add('class_correct', pred.eq(target).sum())
A.add('class_total', target.size(0))
# Optionally calculate transition loss/acc.
#model.transition_loss if hasattr(model, 'transition_loss') else None
# TODO: review this. the original line seems to have no effect
# Update Aggregate Accuracies
total_tokens += sum([(nt + 1) / 2
for nt in eval_num_transitions_batch.reshape(-1)])
if FLAGS.write_eval_report:
transitions_per_example, _ = model.spinn.get_transitions_per_example(
style="preds" if FLAGS.eval_report_use_preds else "given") if (
FLAGS.model_type == "SPINN"
and FLAGS.use_internal_parser) else (None, None)
if model.use_sentence_pair:
batch_size = pred.size(0)
sent1_transitions = transitions_per_example[:
batch_size] if transitions_per_example is not None else None
sent2_transitions = transitions_per_example[
batch_size:] if transitions_per_example is not None else None
sent1_trees = tree_strs[:
batch_size] if tree_strs is not None else None
sent2_trees = tree_strs[
batch_size:] if tree_strs is not None else None
else:
sent1_transitions = transitions_per_example if transitions_per_example is not None else None
sent2_transitions = None
sent1_trees = tree_strs if tree_strs is not None else None
sent2_trees = None
reporter.save_batch(pred, target, eval_ids,
output.data.cpu().numpy(), sent1_transitions,
sent2_transitions, sent1_trees, sent2_trees)
# Print Progress
progress_bar.step(i + 1, total=total_batches)
progress_bar.finish()
if tree_strs is not None:
logger.Log('Sample: ' + str(tree_strs[0]))
end = time.time()
total_time = end - start
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
eval_stats(model, A, eval_log) # get the eval statistics (e.g. average F1)
eval_log.filename = filename
if FLAGS.write_eval_report:
eval_report_path = os.path.join(
FLAGS.log_path,
FLAGS.experiment_name + ".eval_set_" + str(eval_index) + ".report")
reporter.write_report(eval_report_path)
eval_class_acc = eval_log.eval_class_accuracy
eval_trans_acc = eval_log.eval_transition_accuracy
eval_f1 = eval_log.f1
return eval_class_acc, eval_trans_acc, eval_f1
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 evaluate(FLAGS,
model,
eval_set,
log_entry,
logger,
trainer,
vocabulary=None,
show_sample=False,
eval_index=0,
target_vocabulary=None):
filename, dataset = eval_set
A = Accumulator()
len(log_entry.evaluation)
eval_log = log_entry.evaluation.add()
reporter = EvalReporter()
tree_strs = None
# Evaluate
total_batches = len(dataset)
progress_bar = SimpleProgressBar(msg="Run Eval",
bar_length=60,
enabled=FLAGS.show_progress_bar)
progress_bar.step(0, total=total_batches)
total_tokens = 0
start = time.time()
model.eval()
ref_file_name = FLAGS.log_path + "/ref_file"
pred_file_name = FLAGS.log_path + "/pred_file"
reference_file = open(ref_file_name, "w")
predict_file = open(pred_file_name, "w")
full_ref = []
full_pred = []
for i, dataset_batch in enumerate(dataset):
batch = get_batch(dataset_batch)
eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch, eval_ids = batch
# Run model.
output = model(eval_X_batch,
eval_transitions_batch,
eval_y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
example_lengths=eval_num_transitions_batch)
can_sample = (FLAGS.model_type == "RLSPINN"
and FLAGS.use_internal_parser)
if show_sample and can_sample:
tmp_samples = model.encoder.get_samples(
eval_X_batch, vocabulary, only_one=not FLAGS.write_eval_report)
tree_strs = prettyprint_trees(tmp_samples)
if not FLAGS.write_eval_report:
# Only show one sample, regardless of the number of batches.
show_sample = False
# Get reference translation
ref_out = [" ".join(map(str, k[:-1])) + " ." for k in eval_y_batch]
full_ref += ref_out
# Get predicted translation
predicted = [[] for i in range(len(eval_y_batch))]
done = []
for x in output:
index = -1
for x_0 in x:
index += 1
val = int(x_0)
if val == 1:
if index in done:
continue
done.append(index)
elif index not in done:
predicted[index].append(val)
pred_out = [" ".join(map(str, k)) + " ." for k in predicted]
full_pred += pred_out
eval_accumulate(model, A, batch)
# Optionally calculate transition loss/acc.
model.encoder.transition_loss if hasattr(model.encoder,
'transition_loss') else None
# Update Aggregate Accuracies
total_tokens += sum([(nt + 1) / \
2 for nt in eval_num_transitions_batch.reshape(-1)])
if FLAGS.write_eval_report:
transitions_per_example, _ = model.encoder.spinn.get_transitions_per_example(
style="preds" if FLAGS.eval_report_use_preds else "given") if (
FLAGS.model_type == "SPINN"
and FLAGS.use_internal_parser) else (None, None)
sent1_transitions = transitions_per_example if transitions_per_example is not None else None
sent2_transitions = None
sent1_trees = tree_strs if tree_strs is not None else None
sent2_trees = None
reporter.save_batch(full_pred,
full_ref,
eval_ids, [None],
sent1_transitions,
sent2_transitions,
sent1_trees,
sent2_trees,
mt=True)
# Print Progress
progress_bar.step(i + 1, total=total_batches)
progress_bar.finish()
if tree_strs is not None:
logger.Log('Sample: ' + tree_strs[0])
reference_file.write("\n".join(full_ref))
reference_file.close()
predict_file.write("\n".join(full_pred))
predict_file.close()
bleu_score = os.popen("perl spinn/util/multi-bleu.perl " + ref_file_name +
" < " + pred_file_name).read()
try:
bleu_score = float(bleu_score)
except:
bleu_score = 0.0
end = time.time()
total_time = end - start
A.add('class_correct', bleu_score)
A.add('class_total', 1)
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
eval_stats(model, A, eval_log)
eval_log.filename = filename
if FLAGS.write_eval_report:
eval_report_path = os.path.join(
FLAGS.log_path,
FLAGS.experiment_name + ".eval_set_" + str(eval_index) + ".report")
reporter.write_report(eval_report_path)
stats = parse_comparison.run_main(
data_type="mt",
main_report_path_template=FLAGS.log_path + "/" +
FLAGS.experiment_name + ".eval_set_0.report",
main_data_path=FLAGS.source_eval_path)
# To-do: include the following into lgog-formatter so it's reported in standard format.
if tree_strs is not None:
logger.Log(
'F1 w/ GT: ' + str(stats['gt']) + '\n' +\
'F1 w/ LB: ' + str(stats['lb']) + '\n' +\
'F1 w/ RB: ' + str(stats['rb']) + '\n' +\
'Avg. tree depth: ' + str(stats['depth'])
)
eval_class_acc = eval_log.eval_class_accuracy
eval_trans_acc = eval_log.eval_transition_accuracy
return eval_class_acc, eval_trans_acc
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(only_forward=False):
logger = afs_safe_logger.Logger(os.path.join(FLAGS.log_path, FLAGS.experiment_name) + ".log")
# Select data format.
if FLAGS.data_type == "bl":
data_manager = load_boolean_data
elif FLAGS.data_type == "sst":
data_manager = load_sst_data
elif FLAGS.data_type == "snli":
data_manager = load_snli_data
elif FLAGS.data_type == "arithmetic":
data_manager = load_simple_data
else:
logger.Log("Bad data type.")
return
pp = pprint.PrettyPrinter(indent=4)
logger.Log("Flag values:\n" + pp.pformat(FLAGS.FlagValuesDict()))
# Make Metrics Logger.
metrics_path = "{}/{}".format(FLAGS.metrics_path, FLAGS.experiment_name)
if not os.path.exists(metrics_path):
os.makedirs(metrics_path)
metrics_logger = MetricsLogger(metrics_path)
M = Accumulator(maxlen=FLAGS.deque_length)
# Load the data.
raw_training_data, vocabulary = data_manager.load_data(
FLAGS.training_data_path, FLAGS.lowercase)
# Load the eval data.
raw_eval_sets = []
if FLAGS.eval_data_path:
for eval_filename in FLAGS.eval_data_path.split(":"):
raw_eval_data, _ = data_manager.load_data(eval_filename, FLAGS.lowercase)
raw_eval_sets.append((eval_filename, raw_eval_data))
# Prepare the vocabulary.
if not vocabulary:
logger.Log("In open vocabulary mode. Using loaded embeddings without fine-tuning.")
train_embeddings = False
vocabulary = util.BuildVocabulary(
raw_training_data, raw_eval_sets, FLAGS.embedding_data_path, logger=logger,
sentence_pair_data=data_manager.SENTENCE_PAIR_DATA)
else:
logger.Log("In fixed vocabulary mode. Training embeddings.")
train_embeddings = True
# Load pretrained embeddings.
if FLAGS.embedding_data_path:
logger.Log("Loading vocabulary with " + str(len(vocabulary))
+ " words from " + FLAGS.embedding_data_path)
initial_embeddings = util.LoadEmbeddingsFromText(
vocabulary, FLAGS.word_embedding_dim, FLAGS.embedding_data_path)
else:
initial_embeddings = None
# Trim dataset, convert token sequences to integer sequences, crop, and
# pad.
logger.Log("Preprocessing training data.")
training_data = util.PreprocessDataset(
raw_training_data, vocabulary, FLAGS.seq_length, data_manager, eval_mode=False, logger=logger,
sentence_pair_data=data_manager.SENTENCE_PAIR_DATA,
for_rnn=sequential_only(),
use_left_padding=FLAGS.use_left_padding)
training_data_iter = util.MakeTrainingIterator(
training_data, FLAGS.batch_size, FLAGS.smart_batching, FLAGS.use_peano,
sentence_pair_data=data_manager.SENTENCE_PAIR_DATA)
# Preprocess eval sets.
eval_iterators = []
for filename, raw_eval_set in raw_eval_sets:
logger.Log("Preprocessing eval data: " + filename)
eval_data = util.PreprocessDataset(
raw_eval_set, vocabulary,
FLAGS.eval_seq_length if FLAGS.eval_seq_length is not None else FLAGS.seq_length,
data_manager, eval_mode=True, logger=logger,
sentence_pair_data=data_manager.SENTENCE_PAIR_DATA,
for_rnn=sequential_only(),
use_left_padding=FLAGS.use_left_padding)
eval_it = util.MakeEvalIterator(eval_data,
FLAGS.batch_size, FLAGS.eval_data_limit, bucket_eval=FLAGS.bucket_eval,
shuffle=FLAGS.shuffle_eval, rseed=FLAGS.shuffle_eval_seed)
eval_iterators.append((filename, eval_it))
# Choose model.
model_specific_params = {}
logger.Log("Building model.")
if FLAGS.model_type == "CBOW":
model_module = spinn.cbow
elif FLAGS.model_type == "RNN":
model_module = spinn.plain_rnn
elif FLAGS.model_type == "SPINN":
model_module = spinn.fat_stack
elif FLAGS.model_type == "RLSPINN":
model_module = spinn.rl_spinn
elif FLAGS.model_type == "RAESPINN":
model_module = spinn.rae_spinn
elif FLAGS.model_type == "GENSPINN":
model_module = spinn.gen_spinn
elif FLAGS.model_type == "ATTSPINN":
model_module = spinn.att_spinn
model_specific_params['using_diff_in_mlstm'] = FLAGS.using_diff_in_mlstm
model_specific_params['using_prod_in_mlstm'] = FLAGS.using_prod_in_mlstm
model_specific_params['using_null_in_attention'] = FLAGS.using_null_in_attention
attlogger = logging.getLogger('spinn.attention')
attlogger.setLevel(logging.INFO)
fh = logging.FileHandler(os.path.join(FLAGS.log_path, '{}.att.log'.format(FLAGS.experiment_name)))
fh.setLevel(logging.INFO)
fh.setFormatter(logging.Formatter('%(asctime)s %(levelname)s: %(message)s'))
attlogger.addHandler(fh)
else:
raise Exception("Requested unimplemented model type %s" % FLAGS.model_type)
# Build model.
vocab_size = len(vocabulary)
num_classes = len(data_manager.LABEL_MAP)
if data_manager.SENTENCE_PAIR_DATA:
trainer_cls = model_module.SentencePairTrainer
model_cls = model_module.SentencePairModel
use_sentence_pair = True
else:
trainer_cls = model_module.SentenceTrainer
model_cls = model_module.SentenceModel
num_classes = len(data_manager.LABEL_MAP)
use_sentence_pair = False
model = model_cls(model_dim=FLAGS.model_dim,
word_embedding_dim=FLAGS.word_embedding_dim,
vocab_size=vocab_size,
initial_embeddings=initial_embeddings,
num_classes=num_classes,
mlp_dim=FLAGS.mlp_dim,
embedding_keep_rate=FLAGS.embedding_keep_rate,
classifier_keep_rate=FLAGS.semantic_classifier_keep_rate,
tracking_lstm_hidden_dim=FLAGS.tracking_lstm_hidden_dim,
transition_weight=FLAGS.transition_weight,
encode_style=FLAGS.encode_style,
encode_reverse=FLAGS.encode_reverse,
encode_bidirectional=FLAGS.encode_bidirectional,
encode_num_layers=FLAGS.encode_num_layers,
use_sentence_pair=use_sentence_pair,
use_skips=FLAGS.use_skips,
lateral_tracking=FLAGS.lateral_tracking,
use_tracking_in_composition=FLAGS.use_tracking_in_composition,
use_difference_feature=FLAGS.use_difference_feature,
use_product_feature=FLAGS.use_product_feature,
num_mlp_layers=FLAGS.num_mlp_layers,
mlp_bn=FLAGS.mlp_bn,
rl_mu=FLAGS.rl_mu,
rl_baseline=FLAGS.rl_baseline,
rl_reward=FLAGS.rl_reward,
rl_weight=FLAGS.rl_weight,
predict_leaf=FLAGS.predict_leaf,
gen_h=FLAGS.gen_h,
model_specific_params=model_specific_params,
)
# Build optimizer.
if FLAGS.optimizer_type == "Adam":
optimizer = optim.Adam(model.parameters(), lr=FLAGS.learning_rate, betas=(0.9, 0.999), eps=1e-08)
elif FLAGS.optimizer_type == "RMSprop":
optimizer = optim.RMSprop(model.parameters(), lr=FLAGS.learning_rate, eps=1e-08)
else:
raise NotImplementedError
# Build trainer.
classifier_trainer = trainer_cls(model, optimizer)
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)
# Load checkpoint if available.
if FLAGS.load_best and os.path.isfile(best_checkpoint_path):
logger.Log("Found best checkpoint, restoring.")
step, best_dev_error = classifier_trainer.load(best_checkpoint_path)
logger.Log("Resuming at step: {} with best dev accuracy: {}".format(step, 1. - best_dev_error))
elif os.path.isfile(standard_checkpoint_path):
logger.Log("Found checkpoint, restoring.")
step, best_dev_error = classifier_trainer.load(standard_checkpoint_path)
logger.Log("Resuming at step: {} with best dev accuracy: {}".format(step, 1. - best_dev_error))
else:
assert not only_forward, "Can't run an eval-only run without a checkpoint. Supply a checkpoint."
step = 0
best_dev_error = 1.0
# Print model size.
logger.Log("Architecture: {}".format(model))
total_params = sum([reduce(lambda x, y: x * y, w.size(), 1.0) for w in model.parameters()])
logger.Log("Total params: {}".format(total_params))
# GPU support.
the_gpu.gpu = FLAGS.gpu
if FLAGS.gpu >= 0:
model.cuda()
else:
model.cpu()
# Debug
def set_debug(self):
self.debug = FLAGS.debug
model.apply(set_debug)
# Accumulate useful statistics.
A = Accumulator(maxlen=FLAGS.deque_length)
# Do an evaluation-only run.
if only_forward:
for index, eval_set in enumerate(eval_iterators):
acc = evaluate(model, eval_set, logger, metrics_logger, step, vocabulary)
else:
# 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()
X_batch, transitions_batch, y_batch, num_transitions_batch, train_ids = training_data_iter.next()
if FLAGS.truncate_train_batch:
X_batch, transitions_batch = truncate(
X_batch, transitions_batch, num_transitions_batch)
total_tokens = num_transitions_batch.ravel().sum()
# 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))
A.add('class_acc', class_acc)
M.add('class_acc', class_acc)
# Calculate class loss.
xent_loss = nn.NLLLoss()(logits, to_gpu(Variable(target, volatile=False)))
# Optionally calculate transition loss/accuracy.
transition_acc = model.transition_acc if hasattr(model, 'transition_acc') else 0.0
transition_loss = model.transition_loss if hasattr(model, 'transition_loss') else None
rl_loss = model.rl_loss if hasattr(model, 'rl_loss') else None
policy_loss = model.policy_loss if hasattr(model, 'policy_loss') else None
rae_loss = model.spinn.rae_loss if hasattr(model.spinn, 'rae_loss') else None
leaf_loss = model.spinn.leaf_loss if hasattr(model.spinn, 'leaf_loss') else None
gen_loss = model.spinn.gen_loss if hasattr(model.spinn, 'gen_loss') else None
# Force Transition Loss Optimization
if FLAGS.force_transition_loss:
model.optimize_transition_loss = True
# Accumulate stats for transition accuracy.
if transition_loss is not None:
preds = [m["t_preds"] for m in model.spinn.memories]
truth = [m["t_given"] for m in model.spinn.memories]
A.add('preds', preds)
A.add('truth', truth)
# Accumulate stats for leaf prediction accuracy.
if leaf_loss is not None:
A.add('leaf_acc', model.spinn.leaf_acc)
# Accumulate stats for word prediction accuracy.
if gen_loss is not None:
A.add('gen_acc', model.spinn.gen_acc)
# Note: Keep track of transition_acc, although this is a naive average.
# Should be weighted by length of sequences in batch.
M.add('transition_acc', transition_acc)
# Extract L2 Cost
l2_loss = l2_cost(model, FLAGS.l2_lambda) if FLAGS.use_l2_cost else None
# Boilerplate for calculating loss values.
xent_cost_val = xent_loss.data[0]
transition_cost_val = transition_loss.data[0] if transition_loss is not None else 0.0
l2_cost_val = l2_loss.data[0] if l2_loss is not None else 0.0
rl_cost_val = rl_loss.data[0] if rl_loss is not None else 0.0
policy_cost_val = policy_loss.data[0] if policy_loss is not None else 0.0
rae_cost_val = rae_loss.data[0] if rae_loss is not None else 0.0
leaf_cost_val = leaf_loss.data[0] if leaf_loss is not None else 0.0
gen_cost_val = gen_loss.data[0] if gen_loss is not None else 0.0
# Accumulate Total Loss Data
total_cost_val = 0.0
total_cost_val += xent_cost_val
if transition_loss is not None and model.optimize_transition_loss:
total_cost_val += transition_cost_val
total_cost_val += l2_cost_val
total_cost_val += rl_cost_val
total_cost_val += policy_cost_val
total_cost_val += rae_cost_val
total_cost_val += leaf_cost_val
total_cost_val += gen_cost_val
M.add('total_cost', total_cost_val)
M.add('xent_cost', xent_cost_val)
M.add('transition_cost', transition_cost_val)
M.add('l2_cost', l2_cost_val)
# Logging for RL
rl_keys = ['rl_loss', 'policy_loss', 'norm_rewards', 'norm_baseline', 'norm_advantage']
for k in rl_keys:
if hasattr(model, k):
val = getattr(model, k)
val = val.data[0] if isinstance(val, Variable) else val
M.add(k, val)
# 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
if rl_loss is not None:
total_loss += rl_loss
if policy_loss is not None:
total_loss += policy_loss
if rae_loss is not None:
total_loss += rae_loss
if leaf_loss is not None:
total_loss += leaf_loss
if gen_loss is not None:
total_loss += gen_loss
# Useful for debugging gradient flow.
if FLAGS.debug:
losses = [('total_loss', total_loss), ('xent_loss', xent_loss)]
if l2_loss is not None:
losses.append(('l2_loss', l2_loss))
if transition_loss is not None and model.optimize_transition_loss:
losses.append(('transition_loss', transition_loss))
if rl_loss is not None:
losses.append(('rl_loss', rl_loss))
if policy_loss is not None:
losses.append(('policy_loss', policy_loss))
debug_gradient(model, losses)
import ipdb; ipdb.set_trace()
# 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
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()
avg_class_acc = A.get_avg('class_acc')
if transition_loss is not None:
all_preds = np.array(flatten(A.get('preds')))
all_truth = np.array(flatten(A.get('truth')))
avg_trans_acc = (all_preds == all_truth).sum() / float(all_truth.shape[0])
else:
avg_trans_acc = 0.0
if leaf_loss is not None:
avg_leaf_acc = A.get_avg('leaf_acc')
else:
avg_leaf_acc = 0.0
if gen_loss is not None:
avg_gen_acc = A.get_avg('gen_acc')
else:
avg_gen_acc = 0.0
time_metric = time_per_token(A.get('total_tokens'), A.get('total_time'))
stats_args = {
"step": step,
"class_acc": avg_class_acc,
"transition_acc": avg_trans_acc,
"total_cost": total_cost_val,
"xent_cost": xent_cost_val,
"transition_cost": transition_cost_val,
"l2_cost": l2_cost_val,
"rl_cost": rl_cost_val,
"policy_cost": policy_cost_val,
"rae_cost": rae_cost_val,
"leaf_acc": avg_leaf_acc,
"leaf_cost": leaf_cost_val,
"gen_acc": avg_gen_acc,
"gen_cost": gen_cost_val,
"time": time_metric,
}
stats_str = "Step: {step}"
# Accuracy Component.
stats_str += " Acc: {class_acc:.5f} {transition_acc:.5f}"
if leaf_loss is not None:
stats_str += " leaf{leaf_acc:.5f}"
if gen_loss is not None:
stats_str += " gen{gen_acc:.5f}"
# Cost Component.
stats_str += " Cost: {total_cost:.5f} {xent_cost:.5f} {transition_cost:.5f} {l2_cost:.5f}"
if rl_loss is not None:
stats_str += " r{rl_cost:.5f}"
if policy_loss is not None:
stats_str += " p{policy_cost:.5f}"
if rae_loss is not None:
stats_str += " rae{rae_cost:.5f}"
if leaf_loss is not None:
stats_str += " leaf{leaf_cost:.5f}"
if gen_loss is not None:
stats_str += " gen{gen_cost:.5f}"
# Time Component.
stats_str += " Time: {time:.5f}"
logger.Log(stats_str.format(**stats_args))
if step > 0 and step % FLAGS.eval_interval_steps == 0:
for index, eval_set in enumerate(eval_iterators):
acc = evaluate(model, eval_set, logger, metrics_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)
classifier_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.")
classifier_trainer.save(standard_checkpoint_path, step, best_dev_error)
if step % FLAGS.metrics_interval_steps == 0:
m_keys = M.cache.keys()
for k in m_keys:
metrics_logger.Log(k, M.get_avg(k), step)
progress_bar.step(i=step % FLAGS.statistics_interval_steps, total=FLAGS.statistics_interval_steps)
def run(only_forward=False):
logger = afs_safe_logger.Logger(os.path.join(FLAGS.log_path, FLAGS.experiment_name) + ".log")
# Select data format.
data_manager = get_data_manager(FLAGS.data_type)
logger.Log("Flag Values:\n" + json.dumps(FLAGS.FlagValuesDict(), indent=4, sort_keys=True))
# Load the data.
raw_training_data, vocabulary = data_manager.load_data(
FLAGS.training_data_path, FLAGS.lowercase)
# Load the eval data.
raw_eval_sets = []
if FLAGS.eval_data_path:
for eval_filename in FLAGS.eval_data_path.split(":"):
raw_eval_data, _ = data_manager.load_data(eval_filename, FLAGS.lowercase)
raw_eval_sets.append((eval_filename, raw_eval_data))
# Prepare the vocabulary.
if not vocabulary:
logger.Log("In open vocabulary mode. Using loaded embeddings without fine-tuning.")
train_embeddings = False
vocabulary = util.BuildVocabulary(
raw_training_data, raw_eval_sets, FLAGS.embedding_data_path, logger=logger,
sentence_pair_data=data_manager.SENTENCE_PAIR_DATA)
else:
logger.Log("In fixed vocabulary mode. Training embeddings.")
train_embeddings = True
# Load pretrained embeddings.
if FLAGS.embedding_data_path:
logger.Log("Loading vocabulary with " + str(len(vocabulary))
+ " words from " + FLAGS.embedding_data_path)
initial_embeddings = util.LoadEmbeddingsFromText(
vocabulary, FLAGS.word_embedding_dim, FLAGS.embedding_data_path)
else:
initial_embeddings = None
# Trim dataset, convert token sequences to integer sequences, crop, and
# pad.
logger.Log("Preprocessing training data.")
training_data = util.PreprocessDataset(
raw_training_data, vocabulary, FLAGS.seq_length, data_manager, eval_mode=False, logger=logger,
sentence_pair_data=data_manager.SENTENCE_PAIR_DATA,
for_rnn=sequential_only())
training_data_iter = util.MakeTrainingIterator(
training_data, FLAGS.batch_size, FLAGS.smart_batching, FLAGS.use_peano,
sentence_pair_data=data_manager.SENTENCE_PAIR_DATA)
# Preprocess eval sets.
eval_iterators = []
for filename, raw_eval_set in raw_eval_sets:
logger.Log("Preprocessing eval data: " + filename)
eval_data = util.PreprocessDataset(
raw_eval_set, vocabulary,
FLAGS.eval_seq_length if FLAGS.eval_seq_length is not None else FLAGS.seq_length,
data_manager, eval_mode=True, logger=logger,
sentence_pair_data=data_manager.SENTENCE_PAIR_DATA,
for_rnn=sequential_only())
eval_it = util.MakeEvalIterator(eval_data,
FLAGS.batch_size, FLAGS.eval_data_limit, bucket_eval=FLAGS.bucket_eval,
shuffle=FLAGS.shuffle_eval, rseed=FLAGS.shuffle_eval_seed)
eval_iterators.append((filename, eval_it))
# Build model.
vocab_size = len(vocabulary)
num_classes = len(data_manager.LABEL_MAP)
model, optimizer, trainer = init_model(FLAGS, logger, initial_embeddings, vocab_size, num_classes, data_manager)
# Build trainer.
trainer = ModelTrainer(model, optimizer)
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)
# Load checkpoint if available.
if FLAGS.load_best and os.path.isfile(best_checkpoint_path):
logger.Log("Found best checkpoint, restoring.")
step, best_dev_error = trainer.load(best_checkpoint_path)
logger.Log("Resuming at step: {} with best dev accuracy: {}".format(step, 1. - best_dev_error))
elif os.path.isfile(standard_checkpoint_path):
logger.Log("Found checkpoint, restoring.")
step, best_dev_error = trainer.load(standard_checkpoint_path)
logger.Log("Resuming at step: {} with best dev accuracy: {}".format(step, 1. - best_dev_error))
else:
assert not only_forward, "Can't run an eval-only run without a checkpoint. Supply a checkpoint."
step = 0
best_dev_error = 1.0
# GPU support.
the_gpu.gpu = FLAGS.gpu
if FLAGS.gpu >= 0:
model.cuda()
else:
model.cpu()
recursively_set_device(optimizer.state_dict(), the_gpu.gpu)
# Debug
def set_debug(self):
self.debug = FLAGS.debug
model.apply(set_debug)
# Accumulate useful statistics.
A = Accumulator(maxlen=FLAGS.deque_length)
# Do an evaluation-only run.
if only_forward:
for index, eval_set in enumerate(eval_iterators):
acc = evaluate(model, eval_set, logger, step, vocabulary)
else:
# Build log format strings.
model.train()
X_batch, transitions_batch, y_batch, num_transitions_batch = get_batch(training_data_iter.next())[:4]
model(X_batch, transitions_batch, y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions
)
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))
# 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 = batch[:4]
total_tokens = sum([(nt+1)/2 for nt in num_transitions_batch.reshape(-1)])
# Reset cached gradients.
optimizer.zero_grad()
if FLAGS.model_type == "RLSPINN":
model.spinn.epsilon = FLAGS.rl_epsilon * math.exp(-step/FLAGS.rl_epsilon_decay)
# 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)
logger.Log(train_str.format(**stats_args))
logger.Log(train_extra_str.format(**stats_args))
if step > 0 and step % FLAGS.eval_interval_steps == 0:
for index, eval_set in enumerate(eval_iterators):
acc = evaluate(model, eval_set, 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)