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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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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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r"""Base class used to build new callbacks."""
from typing import Any, Dict, Type
from torch import Tensor
from torch.optim import Optimizer
import lightning.pytorch as pl
from lightning.pytorch.utilities.types import STEP_OUTPUT
[docs]class Callback:
r"""Abstract base class used to build new callbacks.
Subclass this class and override any of the relevant hooks
"""
@property
def state_key(self) -> str:
"""Identifier for the state of the callback.
Used to store and retrieve a callback's state from the checkpoint dictionary by
``checkpoint["callbacks"][state_key]``. Implementations of a callback need to provide a unique state key if 1)
the callback has state and 2) it is desired to maintain the state of multiple instances of that callback.
"""
return self.__class__.__qualname__
@property
def _legacy_state_key(self) -> Type["Callback"]:
"""State key for checkpoints saved prior to version 1.5.0."""
return type(self)
def _generate_state_key(self, **kwargs: Any) -> str:
"""Formats a set of key-value pairs into a state key string with the callback class name prefixed. Useful for
defining a :attr:`state_key`.
Args:
**kwargs: A set of key-value pairs. Must be serializable to :class:`str`.
"""
return f"{self.__class__.__qualname__}{repr(kwargs)}"
[docs] def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: str) -> None:
"""Called when fit, validate, test, predict, or tune begins."""
[docs] def teardown(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: str) -> None:
"""Called when fit, validate, test, predict, or tune ends."""
[docs] def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when fit begins."""
[docs] def on_fit_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when fit ends."""
[docs] def on_sanity_check_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the validation sanity check starts."""
[docs] def on_sanity_check_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the validation sanity check ends."""
[docs] def on_train_batch_start(
self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int
) -> None:
"""Called when the train batch begins."""
[docs] def on_train_batch_end(
self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: STEP_OUTPUT, batch: Any, batch_idx: int
) -> None:
"""Called when the train batch ends.
Note:
The value ``outputs["loss"]`` here will be the normalized value w.r.t ``accumulate_grad_batches`` of the
loss returned from ``training_step``.
"""
[docs] def on_train_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the train epoch begins."""
[docs] def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the train epoch ends.
To access all batch outputs at the end of the epoch, you can cache step outputs as an attribute of the
:class:`lightning.pytorch.core.LightningModule` and access them in this hook:
.. code-block:: python
class MyLightningModule(L.LightningModule):
def __init__(self):
super().__init__()
self.training_step_outputs = []
def training_step(self):
loss = ...
self.training_step_outputs.append(loss)
return loss
class MyCallback(L.Callback):
def on_train_epoch_end(self, trainer, pl_module):
# do something with all training_step outputs, for example:
epoch_mean = torch.stack(pl_module.training_step_outputs).mean()
pl_module.log("training_epoch_mean", epoch_mean)
# free up the memory
pl_module.training_step_outputs.clear()
"""
[docs] def on_validation_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the val epoch begins."""
[docs] def on_validation_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the val epoch ends."""
[docs] def on_test_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the test epoch begins."""
[docs] def on_test_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the test epoch ends."""
[docs] def on_predict_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the predict epoch begins."""
[docs] def on_predict_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the predict epoch ends."""
[docs] def on_validation_batch_start(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
"""Called when the validation batch begins."""
[docs] def on_validation_batch_end(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
outputs: STEP_OUTPUT,
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
"""Called when the validation batch ends."""
[docs] def on_test_batch_start(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
"""Called when the test batch begins."""
[docs] def on_test_batch_end(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
outputs: STEP_OUTPUT,
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
"""Called when the test batch ends."""
[docs] def on_predict_batch_start(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
"""Called when the predict batch begins."""
[docs] def on_predict_batch_end(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
outputs: Any,
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
"""Called when the predict batch ends."""
[docs] def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the train begins."""
[docs] def on_train_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the train ends."""
[docs] def on_validation_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the validation loop begins."""
[docs] def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the validation loop ends."""
[docs] def on_test_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the test begins."""
[docs] def on_test_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the test ends."""
[docs] def on_predict_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the predict begins."""
[docs] def on_predict_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when predict ends."""
[docs] def on_exception(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", exception: BaseException) -> None:
"""Called when any trainer execution is interrupted by an exception."""
[docs] def state_dict(self) -> Dict[str, Any]:
"""Called when saving a checkpoint, implement to generate callback's ``state_dict``.
Returns:
A dictionary containing callback state.
"""
return {}
[docs] def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
"""Called when loading a checkpoint, implement to reload callback state given callback's ``state_dict``.
Args:
state_dict: the callback state returned by ``state_dict``.
"""
pass
[docs] def on_save_checkpoint(
self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", checkpoint: Dict[str, Any]
) -> None:
r"""Called when saving a checkpoint to give you a chance to store anything else you might want to save.
Args:
trainer: the current :class:`~lightning.pytorch.trainer.trainer.Trainer` instance.
pl_module: the current :class:`~lightning.pytorch.core.LightningModule` instance.
checkpoint: the checkpoint dictionary that will be saved.
"""
[docs] def on_load_checkpoint(
self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", checkpoint: Dict[str, Any]
) -> None:
r"""Called when loading a model checkpoint, use to reload state.
Args:
trainer: the current :class:`~lightning.pytorch.trainer.trainer.Trainer` instance.
pl_module: the current :class:`~lightning.pytorch.core.LightningModule` instance.
checkpoint: the full checkpoint dictionary that got loaded by the Trainer.
"""
[docs] def on_before_backward(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", loss: Tensor) -> None:
"""Called before ``loss.backward()``."""
[docs] def on_after_backward(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called after ``loss.backward()`` and before optimizers are stepped."""
[docs] def on_before_optimizer_step(
self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", optimizer: Optimizer
) -> None:
"""Called before ``optimizer.step()``."""
[docs] def on_before_zero_grad(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", optimizer: Optimizer) -> None:
"""Called before ``optimizer.zero_grad()``."""
