266 lines
11 KiB
Python
266 lines
11 KiB
Python
# Copyright The PyTorch Lightning team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
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__)
|
|
|
|
|
|
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 = TrainingEpochLoop()
|
|
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) -> Optional[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_ttp_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._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.reset(window_length=self.trainer.accumulate_grad_batches)
|
|
|
|
self.epoch_progress.increment_ready()
|
|
|
|
def advance(self) -> None: # type: ignore[override]
|
|
"""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
|
|
|
|
def on_advance_end(self) -> None:
|
|
self.epoch_progress.increment_completed()
|
|
|
|
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_callback_hooks("on_train_end")
|
|
self.trainer._call_lightning_module_hook("on_train_end")
|
|
self.trainer._call_ttp_hook("on_train_end")
|
|
|
|
# give accelerators a chance to finish
|
|
self.trainer.training_type_plugin.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()
|