Source code for pytorch_lightning.loops.fit_loop

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
from functools import partial
from typing import Optional, Type

import pytorch_lightning as pl
from pytorch_lightning.accelerators import GPUAccelerator
from pytorch_lightning.loops import Loop
from pytorch_lightning.loops.epoch import TrainingEpochLoop
from pytorch_lightning.loops.epoch.training_epoch_loop import _OUTPUTS_TYPE as _EPOCH_OUTPUTS_TYPE
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.exceptions import MisconfigurationException
from pytorch_lightning.utilities.fetching import (
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_warn
from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature

log = logging.getLogger(__name__)

[docs]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 values = self.epoch_progress.current.ready, self.epoch_progress.current.started epoch_unfinished = any(v != self.epoch_progress.current.processed for v in values) restarting = restarting and epoch_unfinished or self._iteration_based_training() 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 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) if stop_epochs: # in case they are not equal, override so `trainer.current_epoch` has the expected value self.epoch_progress.current.completed = self.epoch_progress.current.processed 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: "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) -> None: # type: ignore[override] """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: # type: ignore[override] """Calls the ``on_train_start`` hook.""" # update the current_epoch in-case of checkpoint reload if not self._iteration_based_training(): self.epoch_progress.current.completed = self.epoch_progress.current.processed # 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.trainer._call_callback_hooks("on_train_start") self.trainer._call_lightning_module_hook("on_train_start") self.trainer._call_strategy_hook("on_train_start")
[docs] 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()
[docs] 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 =
[docs] 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()
[docs] 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")
[docs] 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() def _iteration_based_training(self) -> bool: return self.trainer.max_steps != -1
def _select_data_fetcher(trainer: "pl.Trainer") -> Type[AbstractDataFetcher]: training_step_fx = getattr(trainer.lightning_module, "training_step") if is_param_in_hook_signature(training_step_fx, "dataloader_iter", explicit=True): rank_zero_warn( "Found `dataloader_iter` argument in the `training_step`. Note that the support for " "this signature is experimental and the behavior is subject to change." ) return DataLoaderIterDataFetcher elif os.getenv("PL_INTER_BATCH_PARALLELISM", "0") == "1": if not isinstance(trainer.accelerator, GPUAccelerator): raise MisconfigurationException("Inter batch parallelism is available only when using Nvidia GPUs.") return InterBatchParallelDataFetcher return DataFetcher

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