253 lines
10 KiB
Python
253 lines
10 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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import logging
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from contextlib import suppress
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from typing import Any, Dict, Optional
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from pytorch_lightning.loops import Loop
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from pytorch_lightning.loops.epoch import TrainingEpochLoop
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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
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from pytorch_lightning.trainer.supporters import TensorRunningAccum
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log = logging.getLogger(__name__)
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class FitLoop(Loop):
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"""
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This Loop iterates over the epochs to run the training.
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Args:
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min_epochs: The minimum number of epochs
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max_epochs: The maximum number of epochs
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"""
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def __init__(self, min_epochs: Optional[int] = None, max_epochs: Optional[int] = None):
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super().__init__()
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self.max_epochs = max_epochs
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self.min_epochs = min_epochs
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self.epoch_loop: Optional[TrainingEpochLoop] = None
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self.epoch_progress = Progress()
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# caches the loaded dataloader state until dataloader objects are available
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self._dataloader_state_dict: Dict[str, Any] = {}
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@property
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def current_epoch(self) -> int:
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"""Return the current epoch"""
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return self.epoch_progress.current.completed
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@current_epoch.setter
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def current_epoch(self, value: int) -> None:
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"""Setter for the current epoch"""
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self.epoch_progress.current.completed = value
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@property
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def global_step(self) -> int:
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"""Returns the global step"""
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return self.epoch_loop.global_step
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@global_step.setter
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def global_step(self, value: int) -> None:
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"""Sets the global step (forwards to epoch_loop)"""
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self.epoch_loop.global_step = value
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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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return self.epoch_loop.total_batch_idx
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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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return self.epoch_loop.batch_idx
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@property
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def split_idx(self) -> int:
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"""Returns the index of the current batch split (within the current batch) for bptt"""
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return self.epoch_loop.batch_loop.split_idx
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@property
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def min_steps(self) -> int:
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# TODO(@justusschock): Why aren't we using the attribute in this class?
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"""Returns the minimum numnber of steps to run"""
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return self.epoch_loop.min_steps
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@min_steps.setter
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def min_steps(self, value: int) -> None:
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"""Sets the minimum number of steps (forwards to epoch_loop)"""
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# TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided
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self.epoch_loop.min_steps = value
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@property
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def max_steps(self) -> int:
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"""Returns the maximum number of steps to run"""
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return self.epoch_loop.max_steps
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@max_steps.setter
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def max_steps(self, value: int) -> None:
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"""Sets the maximum number of steps (forwards to epoch_loop)"""
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# TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided
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self.epoch_loop.max_steps = value
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@property
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def running_loss(self) -> TensorRunningAccum:
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"""Returns the running loss"""
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return self.epoch_loop.batch_loop.running_loss
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@property
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def _skip_backward(self) -> bool:
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"""Determines whether the loop will skip backward during automatic optimization."""
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return self.epoch_loop.batch_loop._skip_backward
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@_skip_backward.setter
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def _skip_backward(self, value: bool) -> None:
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"""Determines whether the loop will skip backward during automatic optimization."""
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self.epoch_loop.batch_loop._skip_backward = value
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@property
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def _results(self) -> ResultCollection:
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if self.trainer.training:
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return self.epoch_loop._results
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if self.trainer.validating:
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return self.epoch_loop.val_loop._results
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raise RuntimeError("`FitLoop._results` property isn't defined. Accessed outside of scope")
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@property
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def done(self) -> bool:
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"""Evaluates when to leave the loop.
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Returns True if trainer.should_stop was set (e.g. by early stopping)
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or if the maximum number of steps or epochs is reached.
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"""
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# TODO(@awaelchli): Move track steps inside training loop and move part of these condition inside training loop
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stop_steps = self.max_steps is not None and self.global_step >= self.max_steps
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stop_epochs = self.max_epochs is not None and self.current_epoch >= self.max_epochs
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should_stop = False
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if self.trainer.should_stop:
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# early stopping
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met_min_epochs = self.current_epoch >= self.min_epochs if self.min_epochs else True
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met_min_steps = self.global_step >= self.min_steps if self.min_steps else True
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if met_min_epochs and met_min_steps:
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should_stop = True
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else:
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log.info(
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"Trainer was signaled to stop but required minimum epochs"
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f" ({self.min_epochs}) or minimum steps ({self.min_steps}) has"
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" not been met. Training will continue..."
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)
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self.trainer.should_stop = should_stop
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return stop_steps or should_stop or stop_epochs
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@property
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def skip(self) -> bool:
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"""Whether we should skip the training and immediately return from the call to :meth:`run`."""
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return self.done or self.trainer.num_training_batches == 0
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def connect(self, epoch_loop: TrainingEpochLoop):
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"""Connects a training epoch loop to this fit loop."""
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self.epoch_loop = epoch_loop
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def reset(self) -> None:
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"""Resets the internal state of this loop"""
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def on_run_start(self) -> None:
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"""Calls the ``on_train_start`` hook."""
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self._results.to(device=self.trainer.lightning_module.device)
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self.trainer.call_hook("on_train_start")
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def on_advance_start(self) -> None:
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"""Prepares the dataloader for training and calls the hooks ``on_epoch_start`` and ``on_train_epoch_start``"""
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model = self.trainer.lightning_module
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# reset train dataloader
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if self.current_epoch != 0 and self.trainer._should_reload_dl_epoch:
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self.trainer.reset_train_dataloader(model)
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if self._dataloader_state_dict:
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self.trainer.train_dataloader.load_state_dict(self._dataloader_state_dict)
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self._dataloader_state_dict = {}
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# TODO: specify the possible exception
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with suppress(Exception):
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# set seed for distributed sampler (enables shuffling for each epoch)
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self.trainer.train_dataloader.sampler.set_epoch(self.current_epoch)
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# changing gradient according accumulation_scheduler
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self.trainer.accumulation_scheduler.on_train_epoch_start(self.trainer, self.trainer.lightning_module)
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# stores accumulated grad fractions per batch
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self.epoch_loop.batch_loop.accumulated_loss = TensorRunningAccum(
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window_length=self.trainer.accumulate_grad_batches
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)
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self.epoch_progress.increment_ready()
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def advance(self) -> None:
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"""Runs one whole epoch."""
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dataloader = self.trainer.accelerator.process_dataloader(self.trainer.train_dataloader)
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data_fetcher = self.trainer.data_connector.get_profiled_dataloader(dataloader)
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with self.trainer.profiler.profile("run_training_epoch"):
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# run train epoch
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epoch_output = self.epoch_loop.run(data_fetcher)
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if epoch_output is None:
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return
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# the global step is manually decreased here due to backwards compatibility with existing loggers
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# as they expect that the same step is used when logging epoch end metrics even when the batch loop has
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# finished. this means the attribute does not exactly track the number of optimizer steps applied.
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# TODO(@carmocca): deprecate and rename so users don't get confused
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self.global_step -= 1
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# log epoch metrics
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self.trainer.logger_connector.update_train_epoch_metrics()
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self.global_step += 1
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def on_advance_end(self) -> None:
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self.epoch_progress.increment_completed()
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def on_run_end(self) -> None:
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"""Calls the ``on_train_end`` hook"""
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# NOTE: the current_epoch is already incremented
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# Lightning today does not increment the current epoch at the last epoch run in Trainer.fit
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# To simulate that current behavior, we decrement here.
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# TODO: must be fixed by https://github.com/PyTorchLightning/pytorch-lightning/issues/5007
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self.current_epoch -= 1
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# hook
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self.trainer.call_hook("on_train_end")
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# give accelerators a chance to finish
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self.trainer.accelerator.on_train_end()
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def should_accumulate(self) -> bool:
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"""Whether the gradients should be accumulated"""
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return self.epoch_loop._should_accumulate()
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def teardown(self) -> None:
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self.epoch_loop.teardown()
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def on_save_checkpoint(self) -> Dict:
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state_dict = super().on_save_checkpoint()
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# FIXME(@tchaton) Should pass has_completed=True when iterator is exhausted ?
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state_dict["dataloader_state_dict"] = self.trainer.train_dataloader.state_dict(has_completed=False)
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return state_dict
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def on_load_checkpoint(self, state_dict: Dict) -> None:
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# cache the dataloader state dict until the dataloader objects are available
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self._dataloader_state_dict = state_dict.get("dataloader_state_dict", {})
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