388 lines
16 KiB
Python
388 lines
16 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Any, Dict, Iterator, List, Optional, Union
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import torch
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from pytorch_lightning import loops # import as loops to avoid circular imports
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from pytorch_lightning.loops.batch import TrainingBatchLoop
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from pytorch_lightning.loops.utilities import _prepare_dataloader_iter
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from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
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from pytorch_lightning.trainer.progress import Progress, SchedulerProgress
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.model_helpers import is_overridden
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from pytorch_lightning.utilities.types import STEP_OUTPUT
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class TrainingEpochLoop(loops.Loop):
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"""
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Runs over all batches in a dataloader (one epoch).
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Args:
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min_steps: The minimum number of steps (batches) to process
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max_steps: The maximum number of steps (batches) to process
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"""
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def __init__(self, min_steps: int, max_steps: int):
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super().__init__()
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self.min_steps: int = min_steps
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self.max_steps: int = max_steps
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self.global_step: int = 0
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# manually tracking which is the last batch is necessary for iterable dataset support
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self.is_last_batch: Optional[bool] = None
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self.batch_progress = Progress()
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self.scheduler_progress = SchedulerProgress()
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self.batch_loop: Optional[TrainingBatchLoop] = None
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self.val_loop: Optional["loops.EvaluationLoop"] = None
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self._results = ResultCollection(training=True)
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self._epoch_output: Optional[List[List[STEP_OUTPUT]]] = None
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@property
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def total_batch_idx(self) -> int:
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"""Returns the current batch index (across epochs)"""
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# use `ready` instead of `completed` in case this is accessed after `completed` has been increased
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# but before the next `ready` increase
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return self.batch_progress.total.ready - 1
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@property
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def batch_idx(self) -> int:
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"""Returns the current batch index (within this epoch)"""
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# use `ready` instead of `completed` in case this is accessed after `completed` has been increased
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# but before the next `ready` increase
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return self.batch_progress.current.ready - 1
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@property
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def done(self) -> bool:
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"""Returns whether the training should be stopped.
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The criteria are that the number of steps reached the max steps,
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the last batch is reached or the trainer signals to stop (e.g. by early stopping).
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"""
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max_steps_reached = self.max_steps is not None and self.global_step >= self.max_steps
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return max_steps_reached or self.trainer.should_stop or self._num_training_batches_reached(self.is_last_batch)
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def connect(
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self,
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batch_loop: TrainingBatchLoop = None,
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val_loop: Optional["loops.EvaluationLoop"] = None,
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) -> None:
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"""Optionally connect a custom batch or validation loop to this training epoch loop."""
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if batch_loop is not None:
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self.batch_loop = batch_loop
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if val_loop is not None:
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self.val_loop = val_loop
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def reset(self) -> None:
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"""Resets the internal state of the loop for a new run"""
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self.is_last_batch = False
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# track epoch output
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self._epoch_output = [[] for _ in range(self.batch_loop.num_active_optimizers(self.total_batch_idx))]
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if not self.restarting:
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self.batch_progress.current.reset()
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self.scheduler_progress.current.reset()
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self.batch_loop.optim_progress.reset_on_epoch()
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def on_run_start(self, dataloader_iter: Iterator, **kwargs: Any) -> None:
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# hook
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self.trainer.logger_connector.on_epoch_start()
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self.trainer.call_hook("on_epoch_start")
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self.trainer.call_hook("on_train_epoch_start")
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self.trainer.fit_loop.epoch_progress.increment_started()
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self.dataloader_iter = _prepare_dataloader_iter(dataloader_iter, self.batch_idx + 1)
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def advance(self, *args: Any, **kwargs: Any) -> None:
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"""Runs a single training batch.
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Args:
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dataloader_iter: the iterator over the dataloader producing the new batch
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Raises:
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StopIteration: When the epoch is canceled by the user returning -1
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"""
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batch_idx, (batch, is_last) = next(self.dataloader_iter)
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if not self.trainer.data_connector.train_data_fetcher.store_on_device:
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with self.trainer.profiler.profile("training_batch_to_device"):
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batch = self.trainer.accelerator.batch_to_device(batch)
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self.batch_progress.increment_ready()
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with self.trainer.profiler.profile("run_training_batch"):
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batch_output = self.batch_loop.run(batch, batch_idx)
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self.batch_progress.increment_processed()
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self.is_last_batch = is_last
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# when returning -1 from train_step, we end epoch early
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if batch_output.signal == -1:
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raise StopIteration
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# update non-plateau LR schedulers
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# update epoch-interval ones only when we are at the end of training epoch
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self.update_lr_schedulers("step", update_plateau_schedulers=False)
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if self._num_training_batches_reached(is_last):
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self.update_lr_schedulers("epoch", update_plateau_schedulers=False)
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batch_end_outputs = [opt_idx_out for opt_idx_out in batch_output.training_step_output if len(opt_idx_out)]
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processed_batch_end_outputs = self._prepare_outputs(batch_end_outputs, batch_mode=True)
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# hook
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self.trainer.call_hook("on_train_batch_end", processed_batch_end_outputs, batch, self.batch_idx, 0)
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self.trainer.call_hook("on_batch_end")
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self.trainer.logger_connector.on_batch_end()
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self.batch_progress.increment_completed()
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# figure out what to track for epoch end
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self._track_epoch_end_reduce_metrics(self._epoch_output, batch_end_outputs)
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# -----------------------------------------
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# SAVE METRICS TO LOGGERS AND PROGRESS_BAR
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# -----------------------------------------
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self.trainer.logger_connector.update_train_step_metrics()
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def on_advance_end(self):
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"""Runs validation and Checkpointing if necessary.
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Raises:
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StopIteration: if :attr:`done` evaluates to ``True`` to finish this epoch
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"""
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# -----------------------------------------
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# VALIDATE IF NEEDED + CHECKPOINT CALLBACK
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# -----------------------------------------
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should_check_val = self._should_check_val_fx(self.batch_idx, self.is_last_batch)
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if should_check_val:
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self.trainer.validating = True
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self._run_validation()
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self.trainer.training = True
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# -----------------------------------------
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# SAVE LOGGERS (ie: Tensorboard, etc...)
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# -----------------------------------------
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self._save_loggers_on_train_batch_end()
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# update plateau LR scheduler after metrics are logged
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self.update_lr_schedulers("step", update_plateau_schedulers=True)
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# progress global step according to grads progress
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self._increment_accumulated_grad_global_step()
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def on_run_end(self) -> List[List[STEP_OUTPUT]]:
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"""Calls the on_epoch_end hook.
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Returns:
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The output of each training step for each optimizer
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Raises:
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MisconfigurationException: ``train_epoch_end`` does not return ``None``
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"""
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if self.batch_progress.current.ready == 0:
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# dataloader/iterator did not produce a batch
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return
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# inform logger the batch loop has finished
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self.trainer.logger_connector.epoch_end_reached()
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# prepare epoch output
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processed_outputs = self._prepare_outputs(self._epoch_output, batch_mode=False)
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# get the model and call model.training_epoch_end
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model = self.trainer.lightning_module
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if is_overridden("training_epoch_end", model):
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# run training_epoch_end
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# refresh the result for custom logging at the epoch level
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model._current_fx_name = "training_epoch_end"
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# lightningmodule hook
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training_epoch_end_output = model.training_epoch_end(processed_outputs)
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if training_epoch_end_output is not None:
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raise MisconfigurationException(
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"training_epoch_end expects a return of None. "
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"HINT: remove the return statement in training_epoch_end"
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)
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self.trainer.fit_loop.epoch_progress.increment_processed()
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# call train epoch end hooks
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self.trainer.call_hook("on_train_epoch_end")
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self.trainer.call_hook("on_epoch_end")
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self.trainer.logger_connector.on_epoch_end()
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if self._num_training_batches_reached(self.is_last_batch):
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self.update_lr_schedulers("epoch", update_plateau_schedulers=True)
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epoch_output = self._epoch_output
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# free memory
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self._epoch_output = None
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return epoch_output
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def teardown(self) -> None:
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self._results.cpu()
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self.batch_loop.teardown()
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self.val_loop.teardown()
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def _run_validation(self):
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# reload dataloaders
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self.val_loop.reload_evaluation_dataloaders()
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with torch.no_grad():
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self.val_loop.run()
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def _accumulated_batches_reached(self) -> bool:
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"""Determine if accumulation will be finished by the end of the current batch."""
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return self.batch_progress.current.ready % self.trainer.accumulate_grad_batches == 0
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def _num_training_batches_reached(self, is_last_batch: bool = False) -> bool:
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"""Checks if we are in the last batch or if there are more batches to follow.
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Args:
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is_last_batch: Whether the current batch is the last one
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"""
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return self.batch_progress.current.ready == self.trainer.num_training_batches or is_last_batch
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def _should_accumulate(self) -> bool:
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"""Checks if the optimizer step should be performed or gradients should be accumulated for the current step."""
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accumulation_done = self._accumulated_batches_reached()
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is_final_batch = self._num_training_batches_reached()
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return not (accumulation_done or is_final_batch)
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def _track_epoch_end_reduce_metrics(
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self, epoch_output: List[List[STEP_OUTPUT]], batch_end_outputs: STEP_OUTPUT
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) -> None:
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"""Adds the batch outputs to the epoch outputs and prepares reduction"""
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hook_overridden = is_overridden("training_epoch_end", self.trainer.lightning_module)
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if not hook_overridden:
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return
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# track the outputs to reduce at the end of the epoch
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for opt_idx, opt_outputs in enumerate(batch_end_outputs):
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# with 1 step (no tbptt) don't use a sequence at epoch end
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if isinstance(opt_outputs, list) and len(opt_outputs) == 1:
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opt_outputs = opt_outputs[0]
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epoch_output[opt_idx].append(opt_outputs)
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@staticmethod
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def _prepare_outputs(
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outputs: List[List[List["ResultCollection"]]], batch_mode: bool
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) -> Union[List[List[List[Dict]]], List[List[Dict]], List[Dict], Dict]:
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"""
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Extract required information from batch or epoch end results.
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Args:
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outputs: A 3-dimensional list of ``ResultCollection`` objects with dimensions:
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``[optimizer outs][batch outs][tbptt steps]``.
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batch_mode: If True, ignore the batch output dimension.
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Returns:
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The cleaned outputs with ``ResultCollection`` objects converted to dictionaries.
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All list dimensions of size one will be collapsed.
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"""
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processed_outputs = []
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for opt_outputs in outputs:
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# handle an edge case where an optimizer output is the empty list
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if len(opt_outputs) == 0:
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continue
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processed_batch_outputs = []
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if batch_mode:
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opt_outputs = [opt_outputs]
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for batch_outputs in opt_outputs:
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processed_tbptt_outputs = []
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if isinstance(batch_outputs, ResultCollection):
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batch_outputs = [batch_outputs]
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for tbptt_output in batch_outputs:
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out = {}
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if tbptt_output.minimize is not None:
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out["loss"] = tbptt_output.minimize.detach()
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out.update(tbptt_output.extra)
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processed_tbptt_outputs.append(out)
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# if there was only one tbptt step then we can collapse that dimension
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if len(processed_tbptt_outputs) == 1:
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processed_tbptt_outputs = processed_tbptt_outputs[0]
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processed_batch_outputs.append(processed_tbptt_outputs)
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# batch_outputs should be just one dict (or a list of dicts if using tbptt) per optimizer
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if batch_mode:
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processed_batch_outputs = processed_batch_outputs[0]
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processed_outputs.append(processed_batch_outputs)
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# if there is only one optimiser then we collapse that dimension
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if len(processed_outputs) == 1:
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processed_outputs = processed_outputs[0]
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return processed_outputs
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def update_lr_schedulers(self, interval: str, update_plateau_schedulers: bool) -> None:
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"""updates the lr schedulers based on the given interval"""
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if interval == "step" and self._should_accumulate():
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return
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self.trainer.optimizer_connector.update_learning_rates(
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interval=interval,
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update_plateau_schedulers=update_plateau_schedulers,
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opt_indices=[opt_idx for opt_idx, _ in self.batch_loop.get_active_optimizers(self.total_batch_idx)],
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)
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def _increment_accumulated_grad_global_step(self) -> None:
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"""Increments global step according to grads progress"""
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if not self._should_accumulate():
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self.global_step = self.trainer.accelerator.update_global_step(
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self.batch_progress.current.ready, self.trainer.global_step
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)
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def _should_check_val_fx(self, batch_idx: int, is_last_batch: bool) -> bool:
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"""Decide if we should run validation."""
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if not self.trainer.enable_validation:
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return False
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is_val_check_epoch = (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch == 0
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if not is_val_check_epoch:
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return False
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# val_check_batch is inf for iterable datasets with no length defined
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is_infinite_dataset = self.trainer.val_check_batch == float("inf")
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if is_last_batch and is_infinite_dataset:
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return True
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if self.trainer.should_stop:
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return True
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# TODO(@awaelchli): let training/eval loop handle logic around limit_*_batches and val_check_batch
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is_val_check_batch = is_last_batch
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if isinstance(self.trainer.limit_train_batches, int) and is_infinite_dataset:
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is_val_check_batch = (batch_idx + 1) % self.trainer.limit_train_batches == 0
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elif self.trainer.val_check_batch != float("inf"):
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is_val_check_batch = (batch_idx + 1) % self.trainer.val_check_batch == 0
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return is_val_check_batch
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def _save_loggers_on_train_batch_end(self) -> None:
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"""Flushes loggers to disk"""
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# when loggers should save to disk
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should_flush_logs = self.trainer.logger_connector.should_flush_logs
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if should_flush_logs and self.trainer.is_global_zero and self.trainer.logger is not None:
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self.trainer.logger.save()
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