class FitLoop(Loop):
"""This Loop iterates over the epochs to run the training
Args:
min_epochs: The minimum number of epochs
max_epochs: The maximum number of epochs
min_steps: The minimum number of steps
max_steps: The maximum number of epoch
.. note::
If neither the minimum epochs nor steps are specified the minimum number of epochs is set to 1
and if neither the maximum steps nor epochs are specified, the maximum epochs are set to 1000.
"""
def __init__(
self,
min_epochs: Optional[int] = None,
max_epochs: Optional[int] = None,
min_steps: Optional[int] = None,
max_steps: Optional[int] = None
):
super().__init__()
self.max_epochs = 1000 if (max_epochs is None and max_steps is None) else max_epochs
self.min_epochs = 1 if (min_epochs is None and min_steps is None) else min_epochs
self.epoch_loop = TrainingEpochLoop(min_steps, max_steps)
@property
def results(self) -> ResultCollection:
return self.epoch_loop.results
@property
def current_epoch(self) -> int:
"""Return the current epoch"""
return self.iteration_count
@current_epoch.setter
def current_epoch(self, value: int) -> None:
"""Setter for the current epoch"""
self.iteration_count = value
@property
def global_step(self) -> int:
"""Returns the global step"""
return self.epoch_loop.global_step
@global_step.setter
def global_step(self, value: int) -> None:
"""Sets the global step (forwards to epoch_loop)"""
self.epoch_loop.global_step = value
@property
def total_batch_idx(self) -> int:
"""Returns the total number of batches already run (across all epochs)"""
return self.epoch_loop.total_batch_idx
@property
def batch_idx(self) -> int:
"""Returns the number of batches already run within this epoch"""
return self.epoch_loop.iteration_count
@property
def split_idx(self) -> int:
"""Returns the index of the current batch split (within the current batch) for bptt"""
return self.epoch_loop.split_idx
@property
def min_steps(self) -> int:
# TODO(@justusschock): Why aren't we using the attribute in this class?
"""Returns the minimum numnber of steps to run"""
return self.epoch_loop.min_steps
@property
def max_steps(self) -> int:
"""Returns the maximum number of steps to run"""
return self.epoch_loop.max_steps
@max_steps.setter
def max_steps(self, value: int) -> None:
"""Sets the maximum number of steps (forwards to epoch_loop)"""
# TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided
self.epoch_loop.max_steps = value
@property
def running_loss(self) -> TensorRunningAccum:
"""Returns the running loss"""
return self.epoch_loop.batch_loop.running_loss
@property
def _skip_backward(self) -> bool:
""" Determines whether the loop will skip backward during automatic optimization. """
return self.epoch_loop.batch_loop._skip_backward
@_skip_backward.setter
def _skip_backward(self, value: bool) -> None:
""" Determines whether the loop will skip backward during automatic optimization. """
self.epoch_loop.batch_loop._skip_backward = value
@property
def done(self) -> bool:
"""Evaluates when to leave the loop.
Returns True if trainer.should_stop was set (e.g. by early stopping)
or if the maximum number of steps or epochs is reached.
"""
# TODO(@awaelchli): Move track steps inside training loop and move part of these condition inside training loop
stop_steps = self.max_steps is not None and self.global_step >= self.max_steps
stop_epochs = self.max_epochs is not None and self.current_epoch >= self.max_epochs
should_stop = False
if self.trainer.should_stop:
# early stopping
met_min_epochs = self.current_epoch >= self.min_epochs if self.min_epochs else True
met_min_steps = self.global_step >= self.min_steps if self.min_steps else True
if met_min_epochs and met_min_steps:
should_stop = True
else:
log.info(
'Trainer was signaled to stop but required minimum epochs'
f' ({self.min_epochs}) or minimum steps ({self.min_steps}) has'
' not been met. Training will continue...'
)
self.trainer.should_stop = should_stop
return stop_steps or should_stop or stop_epochs
@property
def skip(self) -> bool:
"""Whether we should skip the training and immediately return from the call to :meth:`run`."""
return self.done or self.trainer.num_training_batches == 0
def connect(self, trainer: 'pl.Trainer', *args: Any, **kwargs: Any) -> None:
"""Connects the loop with necessary arguments like the trainer"""
super().connect(trainer, *args, **kwargs)
self.epoch_loop.connect(trainer)
def reset(self) -> None:
"""Resets the internal state of this loop"""
def on_run_start(self) -> None:
"""Calls the ``on_train_start`` hook."""
self.results.to(device=self.trainer.lightning_module.device)
self.trainer.call_hook("on_train_start")
def on_advance_start(self) -> None:
"""Prepares the dataloader for training and calls the hooks ``on_epoch_start`` and ``on_train_epoch_start``"""
model = self.trainer.lightning_module
# reset train dataloader
if self.current_epoch != 0 and self.trainer.reload_dataloaders_every_epoch:
self.trainer.reset_train_dataloader(model)
# TODO: specify the possible exception
with suppress(Exception):
# set seed for distributed sampler (enables shuffling for each epoch)
self.trainer.train_dataloader.sampler.set_epoch(self.current_epoch)
# changing gradient according accumulation_scheduler
self.trainer.accumulation_scheduler.on_train_epoch_start(self.trainer, self.trainer.lightning_module)
# stores accumulated grad fractions per batch
self.epoch_loop.batch_loop.accumulated_loss = TensorRunningAccum(
window_length=self.trainer.accumulate_grad_batches
)
def advance(self) -> None:
"""Runs one whole epoch."""
train_dataloader = self.trainer.accelerator.process_dataloader(self.trainer.train_dataloader)
train_dataloader = self.trainer.data_connector.get_profiled_train_dataloader(train_dataloader)
with self.trainer.profiler.profile("run_training_epoch"):
# run train epoch
epoch_output = self.epoch_loop.run(train_dataloader)
if epoch_output is None:
return
# the global step is manually decreased here due to backwards compatibility with existing loggers
# as they expect that the same step is used when logging epoch end metrics even when the batch loop has
# finished. this means the attribute does not exactly track the number of optimizer steps applied.
# TODO(@carmocca): deprecate and rename so users don't get confused
self.global_step -= 1
# log epoch metrics
self.trainer.logger_connector.update_train_epoch_metrics()
self.global_step += 1
def on_advance_end(self) -> None:
"""Updates the LR schedulers and does some internal bookkeeping"""
if self.epoch_loop.batches_seen == 0:
return
self.epoch_loop.update_lr_schedulers('epoch', update_plateau_schedulers=True)
did_train_only = self.trainer.disable_validation or self.trainer.evaluation_loop.skip
if did_train_only:
self.global_step -= 1
self._check_checkpoint_callback(True)
self.global_step += 1
def on_run_end(self) -> None:
"""Runs teardown logic and calls the ``on_train_end`` hook"""
# NOTE: the iteration_count/current_epoch is already incremented
# Lightning today does not increment the current epoch at the last epoch run in Trainer.fit
# To simulate that current behavior, we decrement here.
# TODO: must be fixed by https://github.com/PyTorchLightning/pytorch-lightning/issues/5007
self.current_epoch -= 1
# trigger checkpoint check. need to temporarily decrease the global step to avoid saving duplicates
# when a checkpoint was saved at the last step
self.epoch_loop.global_step -= 1
# TODO: see discussion/rework https://github.com/PyTorchLightning/pytorch-lightning/issues/7406
self._check_checkpoint_callback(should_update=True, is_last=True)
self.epoch_loop.global_step += 1
# hook
self.trainer.call_hook("on_train_end")
# todo: TPU 8 cores hangs in flush with TensorBoard. Might do for all loggers.
# It might be related to xla tensors blocked when moving the cpu
# kill loggers
if self.trainer.logger is not None:
self.trainer.logger.finalize("success")
# summarize profile results
self.trainer.profiler.describe()
# give accelerators a chance to finish
self.trainer.accelerator.on_train_end()
# reset bookkeeping
self.trainer._running_stage = None
def should_accumulate(self) -> bool:
"""Whether the gradients should be accumulated"""
return self.epoch_loop.batch_loop.should_accumulate()
def _check_checkpoint_callback(self, should_update: bool, is_last: bool = False):
"""Checks if checkpointing needs to be done"""
# TODO: bake this logic into the ModelCheckpoint callback
if should_update and self.trainer.checkpoint_connector.has_trained:
callbacks = self.trainer.checkpoint_callbacks
if is_last and any(cb.save_last and cb.verbose for cb in callbacks):
rank_zero_info("Saving latest checkpoint...")
model = self.trainer.lightning_module
for cb in callbacks:
cb.on_validation_end(self.trainer, model)
class FitLoop(Loop[None]):
"""This Loop iterates over the epochs to run the training.
Args:
min_epochs: The minimum number of epochs
max_epochs: The maximum number of epochs, can be set -1 to turn this limit off
"""
def __init__(
self,
min_epochs: int = 0,
max_epochs: int = 1000,
) -> None:
super().__init__()
if max_epochs < -1:
# Allow max_epochs to be zero, since this will be handled by fit_loop.done
raise MisconfigurationException(
f"`max_epochs` must be a non-negative integer or -1. You passed in {max_epochs}."
)
self.max_epochs = max_epochs
self.min_epochs = min_epochs
self.epoch_loop = TrainingEpochLoop()
self.epoch_progress = Progress()
self._is_fresh_start_epoch: bool = True
self._outputs: _EPOCH_OUTPUTS_TYPE = []
self._data_fetcher: Optional[AbstractDataFetcher] = None
@property
def total_batch_idx(self) -> int:
"""Returns the current batch index (across epochs)"""
return self.epoch_loop.total_batch_idx
@property
def batch_idx(self) -> int:
"""Returns the current batch index (within this epoch)"""
return self.epoch_loop.batch_idx
@property
def split_idx(self) -> int:
"""Returns the index of the current batch split (within the current batch) for bptt."""
return self.epoch_loop.batch_loop.split_idx
@property
def min_steps(self) -> Optional[int]:
# TODO(@justusschock): Why aren't we using the attribute in this class?
"""Returns the minimum number of steps to run."""
return self.epoch_loop.min_steps
@min_steps.setter
def min_steps(self, value: Optional[int]) -> None:
"""Sets the minimum number of steps (forwards to epoch_loop)"""
# TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided
self.epoch_loop.min_steps = value
@property
def max_steps(self) -> int:
"""Returns the maximum number of steps to run."""
return self.epoch_loop.max_steps
@max_steps.setter
def max_steps(self, value: int) -> None:
"""Sets the maximum number of steps (forwards to epoch_loop)"""
# TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided
if value is None:
rank_zero_deprecation(
"Setting `max_steps = None` is deprecated in v1.5 and will no longer be supported in v1.7."
" Use `max_steps = -1` instead.")
value = -1
elif value < -1:
raise MisconfigurationException(
f"`max_steps` must be a non-negative integer or -1 (infinite steps). You passed in {value}."
)
self.epoch_loop.max_steps = value
@property
def running_loss(self) -> TensorRunningAccum:
"""Returns the running loss."""
return self.epoch_loop.batch_loop.running_loss
@Loop.restarting.setter
def restarting(self, restarting: bool) -> None:
# if the last epoch completely finished, we are not actually restarting, we can check this to see if all
# current values are equal
values = (
self.epoch_progress.current.ready,
self.epoch_progress.current.started,
self.epoch_progress.current.processed,
)
finished_before_on_train_end = any(
v != self.epoch_progress.current.completed for v in values)
if finished_before_on_train_end:
self.epoch_progress.current.completed = self.epoch_progress.current.processed
restarting &= finished_before_on_train_end
Loop.restarting.fset(self, restarting) # call the parent setter
@property
def prefetch_batches(self) -> int:
is_unsized = self.trainer.num_training_batches == float("inf")
inter_batch_parallelism = os.getenv("PL_INTER_BATCH_PARALLELISM",
"0") == "1"
return 1 if is_unsized or inter_batch_parallelism else 0
@property
def _skip_backward(self) -> bool:
"""Determines whether the loop will skip backward during automatic optimization."""
return self.epoch_loop.batch_loop.optimizer_loop._skip_backward
@_skip_backward.setter
def _skip_backward(self, value: bool) -> None:
"""Determines whether the loop will skip backward during automatic optimization."""
self.epoch_loop.batch_loop.optimizer_loop._skip_backward = value
@property
def _results(self) -> _ResultCollection:
if self.trainer.training:
return self.epoch_loop._results
if self.trainer.validating:
return self.epoch_loop.val_loop._results
raise RuntimeError(
"`FitLoop._results` property isn't defined. Accessed outside of scope"
)
@property
def done(self) -> bool:
"""Evaluates when to leave the loop."""
# TODO(@awaelchli): Move track steps inside training loop and move part of these condition inside training loop
stop_steps = _is_max_limit_reached(self.epoch_loop.global_step,
self.max_steps)
# `processed` is increased before `on_train_epoch_end`, the hook where checkpoints are typically saved.
# we use it here because the checkpoint data won't have `completed` increased yet
stop_epochs = _is_max_limit_reached(
self.epoch_progress.current.processed, self.max_epochs)
should_stop = False
if self.trainer.should_stop:
# early stopping
met_min_epochs = self.epoch_progress.current.processed >= self.min_epochs if self.min_epochs else True
met_min_steps = self.epoch_loop.global_step >= self.min_steps if self.min_steps else True
if met_min_epochs and met_min_steps:
should_stop = True
else:
log.info(
"Trainer was signaled to stop but required minimum epochs"
f" ({self.min_epochs}) or minimum steps ({self.min_steps}) has"
" not been met. Training will continue...")
self.trainer.should_stop = should_stop
return stop_steps or should_stop or stop_epochs or self.trainer.num_training_batches == 0
@property
def skip(self) -> bool:
"""Whether we should skip the training and immediately return from the call to :meth:`run`."""
# since `trainer.num_training_batches` depends on the `train_dataloader` but that won't be called
# until `on_run_start`, we use `limit_train_batches` instead
return self.done or self.trainer.limit_train_batches == 0
def connect(
self,
epoch_loop: TrainingEpochLoop) -> None: # type: ignore[override]
"""Connects a training epoch loop to this fit loop."""
self.epoch_loop = epoch_loop
def reset(self) -> None:
"""Resets the internal state of this loop."""
if self.restarting:
self.epoch_progress.reset_on_restart()
def on_run_start(self) -> None: # type: ignore[override]
"""Calls the ``on_train_start`` hook."""
# reset train dataloader and val dataloader
self.trainer.reset_train_val_dataloaders(self.trainer.lightning_module)
data_fetcher_cls = _select_data_fetcher(self.trainer)
self._data_fetcher = data_fetcher_cls(
prefetch_batches=self.prefetch_batches)
self._is_fresh_start_epoch = True
self._results.to(device=self.trainer.lightning_module.device)
self.trainer._call_callback_hooks("on_train_start")
self.trainer._call_lightning_module_hook("on_train_start")
self.trainer._call_strategy_hook("on_train_start")
def on_advance_start(self) -> None: # type: ignore[override]
"""Prepares the dataloader for training and calls the hooks ``on_epoch_start`` and
``on_train_epoch_start``"""
model = self.trainer.lightning_module
# reset train dataloader
if not self._is_fresh_start_epoch and self.trainer._data_connector._should_reload_train_dl:
log.detail(
f"{self.__class__.__name__}: resetting train dataloader")
self.trainer.reset_train_dataloader(model)
self._is_fresh_start_epoch = False
# reset outputs here instead of in `reset` as they are not accumulated between epochs
self._outputs = []
if self.trainer.train_dataloader is not None and callable(
getattr(self.trainer.train_dataloader.sampler, "set_epoch",
None)):
# set seed for distributed sampler (enables shuffling for each epoch)
self.trainer.train_dataloader.sampler.set_epoch(
self.epoch_progress.current.processed)
# changing gradient according accumulation_scheduler
self.trainer.accumulation_scheduler.on_train_epoch_start(
self.trainer, self.trainer.lightning_module)
# stores accumulated grad fractions per batch
self.epoch_loop.batch_loop.accumulated_loss.reset(
window_length=self.trainer.accumulate_grad_batches)
self.epoch_progress.increment_ready()
self.trainer._logger_connector.on_epoch_start()
self.trainer._call_callback_hooks("on_epoch_start")
self.trainer._call_lightning_module_hook("on_epoch_start")
self.trainer._call_callback_hooks("on_train_epoch_start")
self.trainer._call_lightning_module_hook("on_train_epoch_start")
self.epoch_progress.increment_started()
def advance(self) -> None: # type: ignore[override]
"""Runs one whole epoch."""
log.detail(f"{self.__class__.__name__}: advancing loop")
assert self.trainer.train_dataloader is not None
dataloader = self.trainer.train_dataloader
assert self._data_fetcher is not None
self._data_fetcher.setup(dataloader,
batch_to_device=partial(
self.trainer._call_strategy_hook,
"batch_to_device",
dataloader_idx=0))
with self.trainer.profiler.profile("run_training_epoch"):
self._outputs = self.epoch_loop.run(self._data_fetcher)
def on_advance_end(self) -> None:
# inform logger the batch loop has finished
self.trainer._logger_connector.epoch_end_reached()
# get the model and call model.training_epoch_end
model = self.trainer.lightning_module
if is_overridden("training_epoch_end", model) and self._outputs:
epoch_end_outputs = self.epoch_loop._prepare_outputs_training_epoch_end(
self._outputs,
lightning_module=model,
num_optimizers=len(self.trainer.optimizers),
)
# run lightning module hook training_epoch_end
# refresh the result for custom logging at the epoch level
epoch_end_outputs = self.trainer._call_lightning_module_hook(
"training_epoch_end", epoch_end_outputs)
if epoch_end_outputs is not None:
raise MisconfigurationException(
"`training_epoch_end` expects a return of None. "
"HINT: remove the return statement in `training_epoch_end`."
)
# free memory
self._outputs = []
self.epoch_progress.increment_processed()
# call train epoch end hooks
self.trainer._call_callback_hooks("on_train_epoch_end")
self.trainer._call_lightning_module_hook("on_train_epoch_end")
self.trainer._call_callback_hooks("on_epoch_end")
self.trainer._call_lightning_module_hook("on_epoch_end")
self.trainer._logger_connector.on_epoch_end()
if self.epoch_loop._num_ready_batches_reached():
self.epoch_loop.update_lr_schedulers(
"epoch", update_plateau_schedulers=True)
self.epoch_progress.increment_completed()
# we manually decrease here because loggers expect that the same step is used when logging epoch-end metrics
# even when the batch loop has finished
self.epoch_loop._batches_that_stepped -= 1
# log epoch metrics
self.trainer._logger_connector.update_train_epoch_metrics()
self.epoch_loop._batches_that_stepped += 1
# if fault tolerant is enabled and process has been notified, exit.
self.trainer._exit_gracefully_on_signal()
def on_run_end(self) -> None:
"""Calls the ``on_train_end`` hook."""
log.detail(f"{self.__class__.__name__}: train run ended")
# hook
self.trainer._call_callback_hooks("on_train_end")
self.trainer._call_lightning_module_hook("on_train_end")
self.trainer._call_strategy_hook("on_train_end")
def teardown(self) -> None:
if self._data_fetcher is not None:
self._data_fetcher.teardown()
self._data_fetcher = None
self.epoch_loop.teardown()
def _should_accumulate(self) -> bool:
"""Whether the gradients should be accumulated."""
return self.epoch_loop._should_accumulate()
class FitLoop(Loop[None]):
"""This Loop iterates over the epochs to run the training.
Args:
min_epochs: The minimum number of epochs
max_epochs: The maximum number of epochs, can be set -1 to turn this limit off
"""
def __init__(
self,
min_epochs: Optional[int] = 1,
max_epochs: int = 1000,
) -> None:
super().__init__()
if max_epochs < -1:
# Allow max_epochs to be zero, since this will be handled by fit_loop.done
raise MisconfigurationException(
f"`max_epochs` must be a non-negative integer or -1. You passed in {max_epochs}."
)
self.max_epochs = max_epochs
self.min_epochs = min_epochs
self.epoch_loop = TrainingEpochLoop()
self.epoch_progress = Progress()
self._is_fresh_start_epoch: bool = True
self._outputs: _EPOCH_OUTPUTS_TYPE = []
@property
def current_epoch(self) -> int:
"""Return the current epoch."""
return self.epoch_progress.current.completed
@current_epoch.setter
def current_epoch(self, value: int) -> None:
"""Setter for the current epoch."""
self.epoch_progress.current.completed = value
@property
def global_step(self) -> int:
"""Returns the global step."""
return self.epoch_loop.global_step
@global_step.setter
def global_step(self, value: int) -> None:
"""Sets the global step (forwards to epoch_loop)"""
self.epoch_loop.global_step = value
@property
def total_batch_idx(self) -> int:
"""Returns the current batch index (across epochs)"""
return self.epoch_loop.total_batch_idx
@property
def batch_idx(self) -> int:
"""Returns the current batch index (within this epoch)"""
return self.epoch_loop.batch_idx
@property
def split_idx(self) -> int:
"""Returns the index of the current batch split (within the current batch) for bptt."""
return self.epoch_loop.batch_loop.split_idx
@property
def min_steps(self) -> Optional[int]:
# TODO(@justusschock): Why aren't we using the attribute in this class?
"""Returns the minimum numnber of steps to run."""
return self.epoch_loop.min_steps
@min_steps.setter
def min_steps(self, value: Optional[int]) -> None:
"""Sets the minimum number of steps (forwards to epoch_loop)"""
# TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided
self.epoch_loop.min_steps = value
@property
def max_steps(self) -> int:
"""Returns the maximum number of steps to run."""
return self.epoch_loop.max_steps
@max_steps.setter
def max_steps(self, value: int) -> None:
"""Sets the maximum number of steps (forwards to epoch_loop)"""
# TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided
if value is None:
rank_zero_deprecation(
"Setting `max_steps = None` is deprecated in v1.5 and will no longer be supported in v1.7."
" Use `max_steps = -1` instead.")
value = -1
elif value < -1:
raise MisconfigurationException(
f"`max_steps` must be a non-negative integer or -1 (infinite steps). You passed in {value}."
)
self.epoch_loop.max_steps = value
@property
def running_loss(self) -> TensorRunningAccum:
"""Returns the running loss."""
return self.epoch_loop.batch_loop.running_loss
@property
def _skip_backward(self) -> bool:
"""Determines whether the loop will skip backward during automatic optimization."""
return self.epoch_loop.batch_loop.optimizer_loop._skip_backward
@_skip_backward.setter
def _skip_backward(self, value: bool) -> None:
"""Determines whether the loop will skip backward during automatic optimization."""
self.epoch_loop.batch_loop.optimizer_loop._skip_backward = value
@property
def _results(self) -> _ResultCollection:
if self.trainer.training:
return self.epoch_loop._results
if self.trainer.validating:
return self.epoch_loop.val_loop._results
raise RuntimeError(
"`FitLoop._results` property isn't defined. Accessed outside of scope"
)
@property
def done(self) -> bool:
"""Evaluates when to leave the loop.
Returns True if trainer.should_stop was set (e.g. by early stopping) or if the maximum number of steps or epochs
is reached.
"""
# TODO(@awaelchli): Move track steps inside training loop and move part of these condition inside training loop
stop_steps = _is_max_limit_reached(self.global_step, self.max_steps)
stop_epochs = _is_max_limit_reached(self.current_epoch,
self.max_epochs)
should_stop = False
if self.trainer.should_stop:
# early stopping
met_min_epochs = self.current_epoch >= self.min_epochs if self.min_epochs else True
met_min_steps = self.global_step >= self.min_steps if self.min_steps else True
if met_min_epochs and met_min_steps:
should_stop = True
else:
log.info(
"Trainer was signaled to stop but required minimum epochs"
f" ({self.min_epochs}) or minimum steps ({self.min_steps}) has"
" not been met. Training will continue...")
self.trainer.should_stop = should_stop
return stop_steps or should_stop or stop_epochs or self.trainer.num_training_batches == 0
@property
def skip(self) -> bool:
"""Whether we should skip the training and immediately return from the call to :meth:`run`."""
# since `trainer.num_training_batches` depends on the `train_dataloader` but that won't be called
# until `on_run_start`, we use `limit_train_batches` instead
return self.done or self.trainer.limit_train_batches == 0
def connect(
self,
epoch_loop: TrainingEpochLoop) -> None: # type: ignore[override]
"""Connects a training epoch loop to this fit loop."""
self.epoch_loop = epoch_loop
def reset(self) -> None:
"""Resets the internal state of this loop."""
if self.restarting:
self.epoch_progress.reset_on_restart()
def on_run_start(self) -> None: # type: ignore[override]
"""Calls the ``on_train_start`` hook."""
# reset train dataloader and val dataloader
self.trainer.reset_train_val_dataloaders(self.trainer.lightning_module)
self._is_fresh_start_epoch = True
self._results.to(device=self.trainer.lightning_module.device)
self.trainer._call_callback_hooks("on_train_start")
self.trainer._call_lightning_module_hook("on_train_start")
self.trainer._call_strategy_hook("on_train_start")
def on_advance_start(self) -> None: # type: ignore[override]
"""Prepares the dataloader for training and calls the hooks ``on_epoch_start`` and
``on_train_epoch_start``"""
model = self.trainer.lightning_module
# reset train dataloader
if not self._is_fresh_start_epoch and self.trainer._data_connector._should_reload_train_dl:
log.detail(
f"{self.__class__.__name__}: resetting train dataloader")
self.trainer.reset_train_dataloader(model)
self._is_fresh_start_epoch = False
# reset outputs here instead of in `reset` as they are not accumulated between epochs
self._outputs = []
if self.trainer.train_dataloader is not None and callable(
getattr(self.trainer.train_dataloader.sampler, "set_epoch",
None)):
# set seed for distributed sampler (enables shuffling for each epoch)
self.trainer.train_dataloader.sampler.set_epoch(self.current_epoch)
# changing gradient according accumulation_scheduler
self.trainer.accumulation_scheduler.on_train_epoch_start(
self.trainer, self.trainer.lightning_module)
# stores accumulated grad fractions per batch
self.epoch_loop.batch_loop.accumulated_loss.reset(
window_length=self.trainer.accumulate_grad_batches)
self.epoch_progress.increment_ready()
self.trainer.logger_connector.on_epoch_start()
self.trainer._call_callback_hooks("on_epoch_start")
self.trainer._call_lightning_module_hook("on_epoch_start")
self.trainer._call_callback_hooks("on_train_epoch_start")
self.trainer._call_lightning_module_hook("on_train_epoch_start")
self.epoch_progress.increment_started()
def advance(self) -> None: # type: ignore[override]
"""Runs one whole epoch."""
log.detail(f"{self.__class__.__name__}: advancing loop")
assert self.trainer.train_dataloader is not None
dataloader = self.trainer.strategy.process_dataloader(
self.trainer.train_dataloader)
data_fetcher = self.trainer._data_connector.get_profiled_dataloader(
dataloader, 0)
with self.trainer.profiler.profile("run_training_epoch"):
self._outputs = self.epoch_loop.run(data_fetcher)
def on_advance_end(self) -> None:
# inform logger the batch loop has finished
self.trainer.logger_connector.epoch_end_reached()
# get the model and call model.training_epoch_end
model = self.trainer.lightning_module
if is_overridden("training_epoch_end", model) and self._outputs:
epoch_end_outputs = self.epoch_loop._prepare_outputs_training_epoch_end(
self._outputs,
automatic=model.automatic_optimization,
num_optimizers=len(self.trainer.optimizers),
)
# run lightning module hook training_epoch_end
# refresh the result for custom logging at the epoch level
epoch_end_outputs = self.trainer._call_lightning_module_hook(
"training_epoch_end", epoch_end_outputs)
if epoch_end_outputs is not None:
raise MisconfigurationException(
"`training_epoch_end` expects a return of None. "
"HINT: remove the return statement in `training_epoch_end`."
)
# free memory
self._outputs = []
self.epoch_progress.increment_processed()
# call train epoch end hooks
self.trainer._call_callback_hooks("on_train_epoch_end")
self.trainer._call_lightning_module_hook("on_train_epoch_end")
self.trainer._call_callback_hooks("on_epoch_end")
self.trainer._call_lightning_module_hook("on_epoch_end")
self.trainer.logger_connector.on_epoch_end()
if self.epoch_loop._num_ready_batches_reached():
self.epoch_loop.update_lr_schedulers(
"epoch", update_plateau_schedulers=True)
self.epoch_progress.increment_completed()
# the global step is manually decreased here due to backwards compatibility with existing loggers
# as they expect that the same step is used when logging epoch end metrics even when the batch loop has
# finished. this means the attribute does not exactly track the number of optimizer steps applied.
# TODO(@carmocca): deprecate and rename so users don't get confused
self.global_step -= 1
# log epoch metrics
self.trainer.logger_connector.update_train_epoch_metrics()
self.global_step += 1
# if fault tolerant is enabled and process has been notified, exit.
self.trainer._exit_gracefully_on_signal()
def on_run_end(self) -> None:
"""Calls the ``on_train_end`` hook."""
log.detail(f"{self.__class__.__name__}: train run ended")
# NOTE: the current_epoch is already incremented
# Lightning today does not increment the current epoch at the last epoch run in Trainer.fit
# To simulate that current behavior, we decrement here.
# TODO: must be fixed by https://github.com/PyTorchLightning/pytorch-lightning/issues/5007
self.current_epoch = max(self.current_epoch - 1, 0)
# hook
self.trainer._call_callback_hooks("on_train_end")
self.trainer._call_lightning_module_hook("on_train_end")
self.trainer._call_strategy_hook("on_train_end")
# give accelerators a chance to finish
self.trainer.strategy.on_train_end()
def teardown(self) -> None:
self.epoch_loop.teardown()
def _should_accumulate(self) -> bool:
"""Whether the gradients should be accumulated."""
return self.epoch_loop._should_accumulate()