def _reduce_and_log_stats(self, logging_outputs, sample_size, grad_norm=None):
if grad_norm is not None:
metrics.log_speed("ups", 1., priority=100, round=2)
metrics.log_scalar("gnorm", grad_norm, priority=400, round=3)
if self.args['optimization']['clip_norm'] > 0:
metrics.log_scalar(
"clip",
torch.where(
grad_norm > self.args['optimization']['clip_norm'],
grad_norm.new_tensor(100),
grad_norm.new_tensor(0),
),
priority=500,
round=1,
)
with metrics.aggregate() as agg:
if logging_outputs is not None:
self.task.reduce_metrics(logging_outputs, self.get_criterion())
# support legacy interface
logging_output = agg.get_smoothed_values()
logging_output["sample_size"] = sample_size
for key_to_delete in ["ppl", "wps", "wpb", "bsz"]:
if key_to_delete in logging_output:
del logging_output[key_to_delete]
return logging_output
def reduce_metrics(self, logging_outputs, criterion):
super().reduce_metrics(logging_outputs, criterion)
if self.args['task']['eval_bleu']:
def sum_logs(key):
return sum(log.get(key, 0) for log in logging_outputs)
metrics.log_scalar('bleu', sum_logs('bleu'))
def reduce_metrics(logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training."""
loss_sum = sum(log.get('loss', 0) for log in logging_outputs)
sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)
metrics.log_scalar('loss',
loss_sum / sample_size,
sample_size,
round=3)
def set_num_updates(self, num_updates):
"""Set the number of parameters updates."""
self.num_updates = num_updates
self.lr_step_update()
metrics.log_scalar("num_updates",
self.num_updates,
weight=0,
priority=200)
def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None:
"""Aggregate logging outputs from data parallel training."""
utils.deprecation_warning(
'Criterions should implement the reduce_metrics API. '
'Falling back to deprecated aggregate_logging_outputs API.'
)
agg_logging_outputs = cls.aggregate_logging_outputs(logging_outputs)
for k, v in agg_logging_outputs.items():
if k in {'nsentences', 'ntokens', 'sample_size'}:
continue
metrics.log_scalar(k, v)
def reduce_metrics(self, logging_outputs, criterion):
super().reduce_metrics(logging_outputs, criterion)
with torch.no_grad():
if self.args['task']['eval_mrr']:
def mean_logs(key):
return sum(
log.get(key, 0)
for log in logging_outputs) / len(logging_outputs)
metrics.log_scalar('mrr', mean_logs('mrr'))
def reduce_metrics(logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training."""
loss_sum = sum(log.get('loss', 0) for log in logging_outputs)
ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)
sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)
metrics.log_scalar('loss',
loss_sum / sample_size / math.log(2),
sample_size,
round=3)
if sample_size != ntokens:
metrics.log_scalar('nll_loss',
loss_sum / ntokens / math.log(2),
ntokens,
round=3)
metrics.log_derived(
'ppl',
lambda meters: utils.get_perplexity(meters['nll_loss'].avg))
else:
metrics.log_derived(
'ppl', lambda meters: utils.get_perplexity(meters['loss'].avg))
def reduce_metrics(self, logging_outputs, criterion):
"""Aggregate logging outputs from data parallel training."""
# backward compatibility for tasks that override aggregate_logging_outputs
base_func = NccTask.aggregate_logging_outputs
self_func = getattr(self, "aggregate_logging_outputs").__func__
if self_func is not base_func:
utils.deprecation_warning(
"Tasks should implement the reduce_metrics API. "
"Falling back to deprecated aggregate_logging_outputs API.")
agg_logging_outputs = self.aggregate_logging_outputs(
logging_outputs, criterion)
for k, v in agg_logging_outputs.items():
metrics.log_scalar(k, v)
return
if not any("ntokens" in log for log in logging_outputs):
warnings.warn(
"ntokens not found in Criterion logging outputs, cannot log wpb or wps"
)
else:
ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
metrics.log_scalar("wpb", ntokens, priority=180, round=1)
metrics.log_speed("wps", ntokens, priority=90, round=1)
if not any("nsentences" in log for log in logging_outputs):
warnings.warn(
"nsentences not found in Criterion logging outputs, cannot log bsz"
)
else:
nsentences = sum(
log.get("nsentences", 0) for log in logging_outputs)
metrics.log_scalar("bsz", nsentences, priority=190, round=1)
criterion.__class__.reduce_metrics(logging_outputs)
def reduce_metrics(self, logging_outputs, criterion):
super().reduce_metrics(logging_outputs, criterion)
def sum_logs(key):
return sum(log.get(key, 0) for log in logging_outputs)
if self.args['task']['eval_accuracy']:
if sum_logs(
'accuracy'
) > 0: # ==0: no accuracy items in the logging outputs, it means the training stage
metrics.log_scalar('accuracy', sum_logs('accuracy'))
if self.args['task']['eval_last_accuracy']:
if sum_logs(
'last_accuracy'
) > 0: # ==0: no accuracy items in the logging outputs, it means the training stage
metrics.log_scalar('last_accuracy', sum_logs('last_accuracy'))
if self.args['task']['eval_mrr']:
if sum_logs('mrr') > 0:
metrics.log_scalar('mrr', sum_logs('mrr'))
def lr_step_update(self):
"""Update the learning rate after each update."""
new_lr = self.lr_scheduler.step_update(self.get_num_updates())
metrics.log_scalar("lr", new_lr, weight=0, priority=300)
return new_lr
def train_step(self, samples, raise_oom=False):
"""Do forward, backward and parameter update."""
if self._dummy_batch == "DUMMY":
self._dummy_batch = samples[0]
self._set_seed()
self.model.train()
self.criterion.train()
self.zero_grad()
metrics.log_start_time("train_wall", priority=800, round=0)
# forward and backward pass
logging_outputs, sample_size, ooms = [], 0, 0
for i, sample in enumerate(samples):
sample = self._prepare_sample(sample)
if sample is None:
# when sample is None, run forward/backward on a dummy batch
# and ignore the resulting gradients
sample = self._prepare_sample(self._dummy_batch)
is_dummy_batch = True
else:
is_dummy_batch = False
def maybe_no_sync():
"""
Whenever *samples* contains more than one mini-batch, we
want to accumulate gradients locally and only call
all-reduce in the last backwards pass.
"""
if (
self.args['distributed_training']['distributed_world_size'] > 1
and hasattr(self.model, "no_sync")
and i < len(samples) - 1
):
return self.model.no_sync()
else:
return contextlib.ExitStack() # dummy contextmanager
try:
with maybe_no_sync():
# forward and backward
loss, sample_size_i, logging_output = self.task.train_step(
sample=sample,
model=self.model,
criterion=self.criterion,
optimizer=self.optimizer,
update_num=self.get_num_updates(),
ignore_grad=is_dummy_batch,
)
del loss
# self.model.train()
# self.set_num_updates(self.task.get_num_updates())
# loss, sample_size, logging_output = self.criterion(self.model, sample)
# # if ignore_grad:
# # loss *= 0
# print('loss: ', loss.item())
# # optimizer.backward(loss)
# loss.backward()
# # self.optimizer.step()
logging_outputs.append(logging_output)
sample_size += sample_size_i
# emptying the CUDA cache after the first step can
# reduce the chance of OOM
if self.cuda and self.get_num_updates() == 0:
torch.cuda.empty_cache()
except RuntimeError as e:
if "out of memory" in str(e):
self._log_oom(e)
if raise_oom:
raise e
logger.warning(
"attempting to recover from OOM in forward/backward pass"
)
ooms += 1
self.zero_grad()
if self.cuda:
torch.cuda.empty_cache()
if self.args['distributed_training']['distributed_world_size'] == 1:
return None
else:
raise e
if torch.is_tensor(sample_size):
sample_size = sample_size.float()
else:
sample_size = float(sample_size)
if is_dummy_batch:
sample_size *= 0. # multiply by 0 to preserve device
# gather logging outputs from all replicas
if self._sync_stats():
logging_outputs, (sample_size, ooms) = self._aggregate_logging_outputs(
logging_outputs, sample_size, ooms, ignore=is_dummy_batch,
)
try:
# multiply gradients by (# GPUs / sample_size) since DDP
# already normalizes by the number of GPUs. Thus we get
# (sum_of_gradients / sample_size).
if not self.args['optimization']['use_bmuf']:
self.optimizer.multiply_grads(
self.args['distributed_training']['distributed_world_size'] / sample_size
)
elif sample_size > 0: # BMUF needs to check sample size
num = self.args['distributed_training']['distributed_world_size'] if self._sync_stats() else 1
self.optimizer.multiply_grads(num / sample_size)
with torch.autograd.profiler.record_function("clip-grads"):
# clip grads
grad_norm = self.clip_grad_norm(self.args['optimization']['clip_norm'])
# check that grad norms are consistent across workers
if not self.args['optimization']['use_bmuf']:
self._check_grad_norms(grad_norm)
# take an optimization step
self.optimizer.step()
# TODO: Warning, the learning rate will be updated by its scheduler here, commented currently
# must set. plz, update learning rate by other methods
self.set_num_updates(self.get_num_updates() + 1)
# log stats
logging_output = self._reduce_and_log_stats(
logging_outputs, sample_size, grad_norm,
)
# clear CUDA cache to reduce memory fragmentation
if (
self.args['common']['empty_cache_freq'] > 0
and (
(self.get_num_updates() + self.args['common']['empty_cache_freq'] - 1)
% self.args['common']['empty_cache_freq']
) == 0
and torch.cuda.is_available()
and not self.args['common']['cpu']
):
torch.cuda.empty_cache()
except FloatingPointError:
# re-run the forward and backward pass with hooks attached to print out where it fails
with NanDetector(self.model):
self.task.train_step(
sample, self.model, self.criterion, self.optimizer, self.get_num_updates(),
ignore_grad=False
)
raise
except OverflowError as e:
logger.info("NOTE: overflow detected, " + str(e))
self.zero_grad()
logging_output = None
except RuntimeError as e:
if "out of memory" in str(e):
self._log_oom(e)
logger.error("OOM during optimization, irrecoverable")
raise e
if self.args['common']['fp16']:
metrics.log_scalar("loss_scale", self.optimizer.scaler.loss_scale, priority=700, round=4)
metrics.log_stop_time("train_wall")
return logging_output
