Source code for pytorch_lightning.callbacks.base

# 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
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""
Abstract base class used to build new callbacks.

"""

import abc
from typing import Any, Dict, List, Optional

import torch
from torch.optim import Optimizer

import pytorch_lightning as pl
from pytorch_lightning.utilities.types import STEP_OUTPUT


class Callback(abc.ABC):
    r"""
    Abstract base class used to build new callbacks.

    Subclass this class and override any of the relevant hooks
    """

    def on_configure_sharded_model(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called before configure sharded model"""

    def on_before_accelerator_backend_setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called before accelerator is being setup"""
        pass

    def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
        """Called when fit, validate, test, predict, or tune begins"""
        pass

    def teardown(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
        """Called when fit, validate, test, predict, or tune ends"""
        pass

    def on_init_start(self, trainer: "pl.Trainer") -> None:
        """Called when the trainer initialization begins, model has not yet been set."""
        pass

    def on_init_end(self, trainer: "pl.Trainer") -> None:
        """Called when the trainer initialization ends, model has not yet been set."""
        pass

    def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when fit begins"""
        pass

    def on_fit_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when fit ends"""
        pass

    def on_sanity_check_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the validation sanity check starts."""
        pass

    def on_sanity_check_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the validation sanity check ends."""
        pass

    def on_train_batch_start(
        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
    ) -> None:
        """Called when the train batch begins."""
        pass

    def on_train_batch_end(
        self,
        trainer: "pl.Trainer",
        pl_module: "pl.LightningModule",
        outputs: STEP_OUTPUT,
        batch: Any,
        batch_idx: int,
        dataloader_idx: int,
    ) -> None:
        """Called when the train batch ends."""
        pass

    def on_train_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the train epoch begins."""
        pass

    def on_train_epoch_end(
        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", unused: Optional = None
    ) -> None:
        """Called when the train epoch ends.

        To access all batch outputs at the end of the epoch, either:

        1. Implement `training_epoch_end` in the `LightningModule` and access outputs via the module OR
        2. Cache data across train batch hooks inside the callback implementation to post-process in this hook.
        """
        pass

    def on_validation_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the val epoch begins."""
        pass

    def on_validation_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the val epoch ends."""
        pass

    def on_test_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the test epoch begins."""
        pass

    def on_test_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the test epoch ends."""
        pass

    def on_predict_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the predict epoch begins."""
        pass

    def on_predict_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: List[Any]) -> None:
        """Called when the predict epoch ends."""
        pass

    def on_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when either of train/val/test epoch begins."""
        pass

    def on_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when either of train/val/test epoch ends."""
        pass

    def on_batch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the training batch begins."""
        pass

    def on_validation_batch_start(
        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
    ) -> None:
        """Called when the validation batch begins."""
        pass

    def on_validation_batch_end(
        self,
        trainer: "pl.Trainer",
        pl_module: "pl.LightningModule",
        outputs: Optional[STEP_OUTPUT],
        batch: Any,
        batch_idx: int,
        dataloader_idx: int,
    ) -> None:
        """Called when the validation batch ends."""
        pass

    def on_test_batch_start(
        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
    ) -> None:
        """Called when the test batch begins."""
        pass

    def on_test_batch_end(
        self,
        trainer: "pl.Trainer",
        pl_module: "pl.LightningModule",
        outputs: Optional[STEP_OUTPUT],
        batch: Any,
        batch_idx: int,
        dataloader_idx: int,
    ) -> None:
        """Called when the test batch ends."""
        pass

    def on_predict_batch_start(
        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
    ) -> None:
        """Called when the predict batch begins."""
        pass

    def on_predict_batch_end(
        self,
        trainer: "pl.Trainer",
        pl_module: "pl.LightningModule",
        outputs: Any,
        batch: Any,
        batch_idx: int,
        dataloader_idx: int,
    ) -> None:
        """Called when the predict batch ends."""
        pass

    def on_batch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the training batch ends."""
        pass

    def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the train begins."""
        pass

    def on_train_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the train ends."""
        pass

    def on_pretrain_routine_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the pretrain routine begins."""
        pass

    def on_pretrain_routine_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the pretrain routine ends."""
        pass

    def on_validation_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the validation loop begins."""
        pass

    def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the validation loop ends."""
        pass

    def on_test_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the test begins."""
        pass

    def on_test_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the test ends."""
        pass

    def on_predict_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the predict begins."""
        pass

    def on_predict_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when predict ends."""
        pass

    def on_keyboard_interrupt(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called when the training is interrupted by ``KeyboardInterrupt``."""
        pass

    def on_save_checkpoint(
        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", checkpoint: Dict[str, Any]
    ) -> dict:
        """
        Called when saving a model checkpoint, use to persist state.

        Args:
            trainer: the current :class:`~pytorch_lightning.trainer.Trainer` instance.
            pl_module: the current :class:`~pytorch_lightning.core.lightning.LightningModule` instance.
            checkpoint: the checkpoint dictionary that will be saved.

        Returns:
            The callback state.
        """
        pass

    def on_load_checkpoint(
        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", callback_state: Dict[str, Any]
    ) -> None:
        """Called when loading a model checkpoint, use to reload state.

        Args:
            trainer: the current :class:`~pytorch_lightning.trainer.Trainer` instance.
            pl_module: the current :class:`~pytorch_lightning.core.lightning.LightningModule` instance.
            callback_state: the callback state returned by ``on_save_checkpoint``.

        Note:
            The ``on_load_checkpoint`` won't be called with an undefined state.
            If your ``on_load_checkpoint`` hook behavior doesn't rely on a state,
            you will still need to override ``on_save_checkpoint`` to return a ``dummy state``.
        """
        pass

    def on_before_backward(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", loss: torch.Tensor) -> None:
        """Called before ``loss.backward()``."""
        pass

    def on_after_backward(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        """Called after ``loss.backward()`` and before optimizers are stepped."""
        pass

    def on_before_optimizer_step(
        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", optimizer: Optimizer, opt_idx: int
    ) -> None:
        """Called before ``optimizer.step()``."""
        pass

    def on_before_zero_grad(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", optimizer: Optimizer) -> None:
        """Called after ``optimizer.step()`` and before ``optimizer.zero_grad()``."""
        pass