Source code for pytorch_lightning.loops.fit_loop

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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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import logging
import os
from typing import Any, Optional, Type

import pytorch_lightning as pl
from pytorch_lightning.accelerators import CUDAAccelerator
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, _set_sampler_epoch
from pytorch_lightning.trainer.connectors.logger_connector.result import _ResultCollection
from pytorch_lightning.trainer.progress import Progress
from pytorch_lightning.trainer.supporters import CombinedLoader, 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_debug, rank_zero_info, 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: Optional[int] = 0, max_epochs: Optional[int] = None, ) -> None: super().__init__() if isinstance(max_epochs, int) and 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: 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: This setter is required by debugging connector (fast dev run), should be avoided if 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 _can_stop_early(self) -> bool: 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 return met_min_epochs and met_min_steps @property def done(self) -> bool: """Evaluates when to leave the loop.""" if self.trainer.num_training_batches == 0: rank_zero_info("`` stopped: No training batches.") return True # TODO: 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) if stop_steps: rank_zero_info(f"`` stopped: `max_steps={self.max_steps!r}` reached.") return True # `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 assert isinstance(self.max_epochs, int) 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 rank_zero_info(f"`` stopped: `max_epochs={self.max_epochs!r}` reached.") return True if self.trainer.should_stop and self._can_stop_early: rank_zero_debug("`` stopped: `trainer.should_stop` was set.") return True return False @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: """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 self.trainer.reset_train_dataloader(self.trainer.lightning_module) # reload the evaluation dataloaders too for proper display in the progress bar if self.epoch_loop._should_check_val_epoch(): self.epoch_loop.val_loop._reload_evaluation_dataloaders() 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: """Prepares the dataloader for training and calls the hook ``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: assert isinstance(self.trainer.train_dataloader, CombinedLoader) _set_sampler_epoch(self.trainer.train_dataloader, 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_train_epoch_start") self.trainer._call_lightning_module_hook("on_train_epoch_start") self.epoch_progress.increment_started()
[docs] def advance(self) -> None: """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 def batch_to_device(batch: Any) -> Any: batch = self.trainer.lightning_module._on_before_batch_transfer(batch, dataloader_idx=0) batch = self.trainer._call_strategy_hook("batch_to_device", batch, dataloader_idx=0) return batch assert self._data_fetcher is not None self._data_fetcher.setup(dataloader, batch_to_device=batch_to_device) 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._logger_connector.on_epoch_end() if self.epoch_loop._num_ready_batches_reached(): # if we are restarting and the above condition holds, it's because we are reloading an epoch-end checkpoint. # since metric-based schedulers require access to metrics and those are not currently saved in the # checkpoint, the plateau schedulers shouldn't be updated self.epoch_loop.update_lr_schedulers("epoch", update_plateau_schedulers=not self.restarting) # 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 self.epoch_progress.increment_completed() # 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, CUDAAccelerator): raise MisconfigurationException("Inter batch parallelism is available only when using Nvidia GPUs.") return InterBatchParallelDataFetcher return DataFetcher

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