def test_best_epoch():
mode = "min"
reporter = Reporter()
key1 = uuid.uuid4().hex
stats_list = [{"aa": 0.3}, {"aa": 0.5}, {"aa": 0.2}]
for e in range(len(stats_list)):
reporter.set_epoch(e + 1)
with reporter.observe(key1) as sub:
sub.register(stats_list[e])
best_epoch = reporter.get_best_epoch(key1, "aa", mode)
assert best_epoch == 3
def run(
cls,
model: AbsESPnetModel,
optimizers: Sequence[torch.optim.Optimizer],
schedulers: Sequence[Optional[AbsScheduler]],
train_iter_factory: AbsIterFactory,
valid_iter_factory: AbsIterFactory,
plot_attention_iter_factory: Optional[AbsIterFactory],
trainer_options,
distributed_option: DistributedOption,
) -> None:
"""Perform training. This method performs the main process of training."""
assert check_argument_types()
# NOTE(kamo): Don't check the type more strictly as far trainer_options
assert is_dataclass(trainer_options), type(trainer_options)
assert len(optimizers) == len(schedulers), (len(optimizers),
len(schedulers))
if isinstance(trainer_options.keep_nbest_models, int):
keep_nbest_models = [trainer_options.keep_nbest_models]
else:
if len(trainer_options.keep_nbest_models) == 0:
logging.warning("No keep_nbest_models is given. Change to [1]")
trainer_options.keep_nbest_models = [1]
keep_nbest_models = trainer_options.keep_nbest_models
output_dir = Path(trainer_options.output_dir)
reporter = Reporter()
if trainer_options.use_amp:
if LooseVersion(torch.__version__) < LooseVersion("1.6.0"):
raise RuntimeError(
"Require torch>=1.6.0 for Automatic Mixed Precision")
if trainer_options.sharded_ddp:
if fairscale is None:
raise RuntimeError(
"Requiring fairscale. Do 'pip install fairscale'")
scaler = fairscale.optim.grad_scaler.ShardedGradScaler()
else:
scaler = GradScaler()
else:
scaler = None
if trainer_options.resume and (output_dir / "checkpoint.pth").exists():
cls.resume(
checkpoint=output_dir / "checkpoint.pth",
model=model,
optimizers=optimizers,
schedulers=schedulers,
reporter=reporter,
scaler=scaler,
ngpu=trainer_options.ngpu,
)
start_epoch = reporter.get_epoch() + 1
if start_epoch == trainer_options.max_epoch + 1:
logging.warning(
f"The training has already reached at max_epoch: {start_epoch}"
)
if distributed_option.distributed:
if trainer_options.sharded_ddp:
dp_model = fairscale.nn.data_parallel.ShardedDataParallel(
module=model,
sharded_optimizer=optimizers,
)
else:
dp_model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=(
# Perform multi-Process with multi-GPUs
[torch.cuda.current_device()]
if distributed_option.ngpu == 1
# Perform single-Process with multi-GPUs
else None),
output_device=(torch.cuda.current_device()
if distributed_option.ngpu == 1 else None),
find_unused_parameters=trainer_options.unused_parameters,
)
elif distributed_option.ngpu > 1:
dp_model = torch.nn.parallel.DataParallel(
model,
device_ids=list(range(distributed_option.ngpu)),
)
else:
# NOTE(kamo): DataParallel also should work with ngpu=1,
# but for debuggability it's better to keep this block.
dp_model = model
if trainer_options.use_tensorboard and (
not distributed_option.distributed
or distributed_option.dist_rank == 0):
from torch.utils.tensorboard import SummaryWriter
train_summary_writer = SummaryWriter(
str(output_dir / "tensorboard" / "train"))
valid_summary_writer = SummaryWriter(
str(output_dir / "tensorboard" / "valid"))
else:
train_summary_writer = None
start_time = time.perf_counter()
for iepoch in range(start_epoch, trainer_options.max_epoch + 1):
if iepoch != start_epoch:
logging.info(
"{}/{}epoch started. Estimated time to finish: {}".format(
iepoch,
trainer_options.max_epoch,
humanfriendly.format_timespan(
(time.perf_counter() - start_time) /
(iepoch - start_epoch) *
(trainer_options.max_epoch - iepoch + 1)),
))
else:
logging.info(
f"{iepoch}/{trainer_options.max_epoch}epoch started")
set_all_random_seed(trainer_options.seed + iepoch)
reporter.set_epoch(iepoch)
# 1. Train and validation for one-epoch
with reporter.observe("train") as sub_reporter:
all_steps_are_invalid = cls.train_one_epoch(
model=dp_model,
optimizers=optimizers,
schedulers=schedulers,
iterator=train_iter_factory.build_iter(iepoch),
reporter=sub_reporter,
scaler=scaler,
summary_writer=train_summary_writer,
options=trainer_options,
distributed_option=distributed_option,
)
with reporter.observe("valid") as sub_reporter:
cls.validate_one_epoch(
model=dp_model,
iterator=valid_iter_factory.build_iter(iepoch),
reporter=sub_reporter,
options=trainer_options,
distributed_option=distributed_option,
)
if not distributed_option.distributed or distributed_option.dist_rank == 0:
# att_plot doesn't support distributed
if plot_attention_iter_factory is not None:
with reporter.observe("att_plot") as sub_reporter:
cls.plot_attention(
model=model,
output_dir=output_dir / "att_ws",
summary_writer=train_summary_writer,
iterator=plot_attention_iter_factory.build_iter(
iepoch),
reporter=sub_reporter,
options=trainer_options,
)
# 2. LR Scheduler step
for scheduler in schedulers:
if isinstance(scheduler, AbsValEpochStepScheduler):
scheduler.step(
reporter.get_value(
*trainer_options.val_scheduler_criterion))
elif isinstance(scheduler, AbsEpochStepScheduler):
scheduler.step()
if trainer_options.sharded_ddp:
for optimizer in optimizers:
if isinstance(optimizer, fairscale.optim.oss.OSS):
optimizer.consolidate_state_dict()
if not distributed_option.distributed or distributed_option.dist_rank == 0:
# 3. Report the results
logging.info(reporter.log_message())
if trainer_options.use_matplotlib:
reporter.matplotlib_plot(output_dir / "images")
if train_summary_writer is not None:
reporter.tensorboard_add_scalar(train_summary_writer,
key1="train")
reporter.tensorboard_add_scalar(valid_summary_writer,
key1="valid")
if trainer_options.use_wandb:
reporter.wandb_log()
# 4. Save/Update the checkpoint
torch.save(
{
"model":
model.state_dict(),
"reporter":
reporter.state_dict(),
"optimizers": [o.state_dict() for o in optimizers],
"schedulers": [
s.state_dict() if s is not None else None
for s in schedulers
],
"scaler":
scaler.state_dict() if scaler is not None else None,
},
output_dir / "checkpoint.pth",
)
# 5. Save and log the model and update the link to the best model
torch.save(model.state_dict(),
output_dir / f"{iepoch}epoch.pth")
# Creates a sym link latest.pth -> {iepoch}epoch.pth
p = output_dir / "latest.pth"
if p.is_symlink() or p.exists():
p.unlink()
p.symlink_to(f"{iepoch}epoch.pth")
_improved = []
for _phase, k, _mode in trainer_options.best_model_criterion:
# e.g. _phase, k, _mode = "train", "loss", "min"
if reporter.has(_phase, k):
best_epoch = reporter.get_best_epoch(_phase, k, _mode)
# Creates sym links if it's the best result
if best_epoch == iepoch:
p = output_dir / f"{_phase}.{k}.best.pth"
if p.is_symlink() or p.exists():
p.unlink()
p.symlink_to(f"{iepoch}epoch.pth")
_improved.append(f"{_phase}.{k}")
if len(_improved) == 0:
logging.info("There are no improvements in this epoch")
else:
logging.info("The best model has been updated: " +
", ".join(_improved))
log_model = (trainer_options.wandb_model_log_interval > 0
and iepoch %
trainer_options.wandb_model_log_interval == 0)
if log_model and trainer_options.use_wandb:
import wandb
logging.info("Logging Model on this epoch :::::")
artifact = wandb.Artifact(
name=f"model_{wandb.run.id}",
type="model",
metadata={"improved": _improved},
)
artifact.add_file(str(output_dir / f"{iepoch}epoch.pth"))
aliases = [
f"epoch-{iepoch}",
"best" if best_epoch == iepoch else "",
]
wandb.log_artifact(artifact, aliases=aliases)
# 6. Remove the model files excluding n-best epoch and latest epoch
_removed = []
# Get the union set of the n-best among multiple criterion
nbests = set().union(*[
set(
reporter.sort_epochs(ph, k, m)
[:max(keep_nbest_models)])
for ph, k, m in trainer_options.best_model_criterion
if reporter.has(ph, k)
])
# Generated n-best averaged model
if (trainer_options.nbest_averaging_interval > 0
and iepoch % trainer_options.nbest_averaging_interval
== 0):
average_nbest_models(
reporter=reporter,
output_dir=output_dir,
best_model_criterion=trainer_options.
best_model_criterion,
nbest=keep_nbest_models,
suffix=f"till{iepoch}epoch",
)
for e in range(1, iepoch):
p = output_dir / f"{e}epoch.pth"
if p.exists() and e not in nbests:
p.unlink()
_removed.append(str(p))
if len(_removed) != 0:
logging.info("The model files were removed: " +
", ".join(_removed))
# 7. If any updating haven't happened, stops the training
if all_steps_are_invalid:
logging.warning(
f"The gradients at all steps are invalid in this epoch. "
f"Something seems wrong. This training was stopped at {iepoch}epoch"
)
break
# 8. Check early stopping
if trainer_options.patience is not None:
if reporter.check_early_stopping(
trainer_options.patience,
*trainer_options.early_stopping_criterion):
break
else:
logging.info(
f"The training was finished at {trainer_options.max_epoch} epochs "
)
# Generated n-best averaged model
if not distributed_option.distributed or distributed_option.dist_rank == 0:
average_nbest_models(
reporter=reporter,
output_dir=output_dir,
best_model_criterion=trainer_options.best_model_criterion,
nbest=keep_nbest_models,
)
def run(
cls,
model: AbsESPnetModel,
optimizers: Sequence[torch.optim.Optimizer],
schedulers: Sequence[Optional[AbsScheduler]],
train_iter_factory: AbsIterFactory,
valid_iter_factory: AbsIterFactory,
plot_attention_iter_factory: Optional[AbsIterFactory],
reporter: Reporter,
scaler: Optional[GradScaler],
output_dir: Path,
max_epoch: int,
seed: int,
patience: Optional[int],
keep_nbest_models: int,
early_stopping_criterion: Sequence[str],
best_model_criterion: Sequence[Sequence[str]],
val_scheduler_criterion: Sequence[str],
trainer_options,
distributed_option: DistributedOption,
) -> None:
"""Perform training. This method performs the main process of training."""
assert check_argument_types()
# NOTE(kamo): Don't check the type more strictly as far trainer_options
assert is_dataclass(trainer_options), type(trainer_options)
start_epoch = reporter.get_epoch() + 1
if start_epoch == max_epoch + 1:
logging.warning(
f"The training has already reached at max_epoch: {start_epoch}"
)
if distributed_option.distributed:
dp_model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=(
# Perform multi-Process with multi-GPUs
[torch.cuda.current_device()]
if distributed_option.ngpu == 1
# Perform single-Process with multi-GPUs
else None),
output_device=(torch.cuda.current_device()
if distributed_option.ngpu == 1 else None),
)
elif distributed_option.ngpu > 1:
dp_model = torch.nn.parallel.DataParallel(
model,
device_ids=list(range(distributed_option.ngpu)),
)
else:
# NOTE(kamo): DataParallel also should work with ngpu=1,
# but for debuggability it's better to keep this block.
dp_model = model
if not distributed_option.distributed or distributed_option.dist_rank == 0:
summary_writer = SummaryWriter(str(output_dir / "tensorboard"))
else:
summary_writer = None
start_time = time.perf_counter()
for iepoch in range(start_epoch, max_epoch + 1):
if iepoch != start_epoch:
logging.info(
"{}/{}epoch started. Estimated time to finish: {}".format(
iepoch,
max_epoch,
humanfriendly.format_timespan(
(time.perf_counter() - start_time) /
(iepoch - start_epoch) * (max_epoch - iepoch + 1)),
))
else:
logging.info(f"{iepoch}/{max_epoch}epoch started")
set_all_random_seed(seed + iepoch)
reporter.set_epoch(iepoch)
# 1. Train and validation for one-epoch
with reporter.observe("train") as sub_reporter:
all_steps_are_invalid = cls.train_one_epoch(
model=dp_model,
optimizers=optimizers,
schedulers=schedulers,
iterator=train_iter_factory.build_iter(iepoch),
reporter=sub_reporter,
scaler=scaler,
summary_writer=summary_writer,
options=trainer_options,
)
with reporter.observe("valid") as sub_reporter:
cls.validate_one_epoch(
model=dp_model,
iterator=valid_iter_factory.build_iter(iepoch),
reporter=sub_reporter,
options=trainer_options,
)
if not distributed_option.distributed or distributed_option.dist_rank == 0:
# att_plot doesn't support distributed
if plot_attention_iter_factory is not None:
with reporter.observe("att_plot") as sub_reporter:
cls.plot_attention(
model=model,
output_dir=output_dir / "att_ws",
summary_writer=summary_writer,
iterator=plot_attention_iter_factory.build_iter(
iepoch),
reporter=sub_reporter,
options=trainer_options,
)
# 2. LR Scheduler step
for scheduler in schedulers:
if isinstance(scheduler, AbsValEpochStepScheduler):
scheduler.step(
reporter.get_value(*val_scheduler_criterion))
elif isinstance(scheduler, AbsEpochStepScheduler):
scheduler.step()
if not distributed_option.distributed or distributed_option.dist_rank == 0:
# 3. Report the results
logging.info(reporter.log_message())
reporter.matplotlib_plot(output_dir / "images")
reporter.tensorboard_add_scalar(summary_writer)
# 4. Save/Update the checkpoint
torch.save(
{
"model":
model.state_dict(),
"reporter":
reporter.state_dict(),
"optimizers": [o.state_dict() for o in optimizers],
"schedulers": [
s.state_dict() if s is not None else None
for s in schedulers
],
"scaler":
scaler.state_dict() if scaler is not None else None,
},
output_dir / "checkpoint.pth",
)
# 5. Save the model and update the link to the best model
torch.save(model.state_dict(),
output_dir / f"{iepoch}epoch.pth")
# Creates a sym link latest.pth -> {iepoch}epoch.pth
p = output_dir / "latest.pth"
if p.is_symlink() or p.exists():
p.unlink()
p.symlink_to(f"{iepoch}epoch.pth")
_improved = []
for _phase, k, _mode in best_model_criterion:
# e.g. _phase, k, _mode = "train", "loss", "min"
if reporter.has(_phase, k):
best_epoch = reporter.get_best_epoch(_phase, k, _mode)
# Creates sym links if it's the best result
if best_epoch == iepoch:
p = output_dir / f"{_phase}.{k}.best.pth"
if p.is_symlink() or p.exists():
p.unlink()
p.symlink_to(f"{iepoch}epoch.pth")
_improved.append(f"{_phase}.{k}")
if len(_improved) == 0:
logging.info("There are no improvements in this epoch")
else:
logging.info("The best model has been updated: " +
", ".join(_improved))
# 6. Remove the model files excluding n-best epoch and latest epoch
_removed = []
# Get the union set of the n-best among multiple criterion
nbests = set().union(*[
set(reporter.sort_epochs(ph, k, m)[:keep_nbest_models])
for ph, k, m in best_model_criterion
if reporter.has(ph, k)
])
for e in range(1, iepoch):
p = output_dir / f"{e}epoch.pth"
if p.exists() and e not in nbests:
p.unlink()
_removed.append(str(p))
if len(_removed) != 0:
logging.info("The model files were removed: " +
", ".join(_removed))
# 7. If any updating haven't happened, stops the training
if all_steps_are_invalid:
logging.warning(
f"The gradients at all steps are invalid in this epoch. "
f"Something seems wrong. This training was stopped at {iepoch}epoch"
)
break
# 8. Check early stopping
if patience is not None:
if reporter.check_early_stopping(patience,
*early_stopping_criterion):
break
else:
logging.info(f"The training was finished at {max_epoch} epochs ")
