Source code for pytorch_lightning.loops.fit_loop
# Copyright The PyTorch Lightning team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# See the License for the specific language governing permissions and
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import logging
from typing import Optional
from pytorch_lightning.loops import Loop
from pytorch_lightning.loops.epoch import TrainingEpochLoop
from pytorch_lightning.loops.utilities import _is_max_limit_reached
from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
from pytorch_lightning.trainer.progress import Progress
from pytorch_lightning.trainer.supporters import TensorRunningAccum
from pytorch_lightning.utilities import rank_zero_deprecation
from pytorch_lightning.utilities.exceptions import MisconfigurationException
log = logging.getLogger(__name__)
[docs]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, 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: Optional[TrainingEpochLoop] = None
self.epoch_progress = Progress()
self._is_fresh_start_epoch: bool = True
@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) -> 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: 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."""
assert self.epoch_loop.batch_loop is not None
assert self.epoch_loop.batch_loop.optimizer_loop is not None
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."""
assert self.epoch_loop.batch_loop is not None
assert self.epoch_loop.batch_loop.optimizer_loop is not None
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
[docs] def connect(self, epoch_loop: TrainingEpochLoop):
"""Connects a training epoch loop to this fit loop."""
self.epoch_loop = epoch_loop
[docs] def reset(self) -> None:
"""Resets the internal state of this loop."""
if self.restarting:
self.epoch_progress.reset_on_restart()
[docs] def on_run_start(self) -> None:
"""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_hook("on_train_start")
[docs] 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 not self._is_fresh_start_epoch and self.trainer._should_reload_dl_epoch:
self.trainer.reset_train_dataloader(model)
self._is_fresh_start_epoch = False
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 = TensorRunningAccum(
window_length=self.trainer.accumulate_grad_batches
)
self.epoch_progress.increment_ready()
[docs] def advance(self) -> None:
"""Runs one whole epoch."""
dataloader = self.trainer.training_type_plugin.process_dataloader(self.trainer.train_dataloader)
data_fetcher = self.trainer._data_connector.get_profiled_dataloader(dataloader)
with self.trainer.profiler.profile("run_training_epoch"):
self.epoch_loop.run(data_fetcher)
# 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
[docs] def on_run_end(self) -> None:
"""Calls the ``on_train_end`` hook."""
# 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_hook("on_train_end")
# give accelerators a chance to finish
self.trainer.training_type_plugin.on_train_end()
def _should_accumulate(self) -> bool:
"""Whether the gradients should be accumulated."""
return self.epoch_loop._should_accumulate()
