343 lines
15 KiB
Python
343 lines
15 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 abc import ABC
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from copy import deepcopy
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from inspect import signature
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from typing import Any, Callable, Dict, List, Optional, Type
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import torch
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import pytorch_lightning as pl
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from pytorch_lightning.callbacks import Callback
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from pytorch_lightning.utilities import rank_zero_deprecation, rank_zero_warn
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from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
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from pytorch_lightning.utilities.types import EPOCH_OUTPUT, STEP_OUTPUT
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from pytorch_lightning.utilities.warnings import WarningCache
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warning_cache = WarningCache()
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class TrainerCallbackHookMixin(ABC):
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# this is just a summary on variables used in this abstract class,
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# the proper values/initialisation should be done in child class
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callbacks: List[Callback] = []
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lightning_module: 'pl.LightningModule'
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def on_before_accelerator_backend_setup(self, model: 'pl.LightningModule') -> None:
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"""Called at the beginning of fit (train + validate), validate, test, or predict, or tune."""
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for callback in self.callbacks:
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callback.on_before_accelerator_backend_setup(self, model)
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def configure_sharded_model(self, model: 'pl.LightningModule') -> None:
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"""Called at the beginning of fit (train + validate), validate, test, or predict, or tune."""
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for callback in self.callbacks:
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callback.on_configure_sharded_model(self, model)
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def setup(self, model: 'pl.LightningModule', stage: Optional[str]) -> None:
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"""Called at the beginning of fit (train + validate), validate, test, or predict, or tune."""
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for callback in self.callbacks:
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callback.setup(self, model, stage=stage)
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def teardown(self, stage: Optional[str] = None) -> None:
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"""Called at the end of fit (train + validate), validate, test, or predict, or tune."""
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for callback in self.callbacks:
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callback.teardown(self, self.lightning_module, stage=stage)
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def on_init_start(self):
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"""Called when the trainer initialization begins, model has not yet been set."""
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for callback in self.callbacks:
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callback.on_init_start(self)
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def on_init_end(self):
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"""Called when the trainer initialization ends, model has not yet been set."""
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for callback in self.callbacks:
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callback.on_init_end(self)
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def on_fit_start(self):
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"""Called when the trainer initialization begins, model has not yet been set."""
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for callback in self.callbacks:
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callback.on_fit_start(self, self.lightning_module)
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def on_fit_end(self):
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"""Called when the trainer initialization begins, model has not yet been set."""
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for callback in self.callbacks:
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callback.on_fit_end(self, self.lightning_module)
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def on_sanity_check_start(self):
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"""Called when the validation sanity check starts."""
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for callback in self.callbacks:
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callback.on_sanity_check_start(self, self.lightning_module)
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def on_sanity_check_end(self):
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"""Called when the validation sanity check ends."""
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for callback in self.callbacks:
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callback.on_sanity_check_end(self, self.lightning_module)
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def on_train_epoch_start(self):
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"""Called when the epoch begins."""
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for callback in self.callbacks:
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callback.on_train_epoch_start(self, self.lightning_module)
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def on_train_epoch_end(self, outputs: EPOCH_OUTPUT):
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"""Called when the epoch ends.
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Args:
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outputs: List of outputs on each ``train`` epoch
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"""
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for callback in self.callbacks:
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if is_param_in_hook_signature(callback.on_train_epoch_end, "outputs"):
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warning_cache.deprecation(
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"The signature of `Callback.on_train_epoch_end` has changed in v1.3."
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" `outputs` parameter has been removed."
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" Support for the old signature will be removed in v1.5"
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)
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callback.on_train_epoch_end(self, self.lightning_module, outputs)
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else:
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callback.on_train_epoch_end(self, self.lightning_module)
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def on_validation_epoch_start(self):
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"""Called when the epoch begins."""
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for callback in self.callbacks:
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callback.on_validation_epoch_start(self, self.lightning_module)
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def on_validation_epoch_end(self):
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"""Called when the validation epoch ends."""
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for callback in self.callbacks:
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callback.on_validation_epoch_end(self, self.lightning_module)
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def on_test_epoch_start(self):
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"""Called when the epoch begins."""
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for callback in self.callbacks:
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callback.on_test_epoch_start(self, self.lightning_module)
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def on_test_epoch_end(self):
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"""Called when the test epoch ends."""
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for callback in self.callbacks:
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callback.on_test_epoch_end(self, self.lightning_module)
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def on_predict_epoch_start(self) -> None:
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"""Called when the epoch begins."""
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for callback in self.callbacks:
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callback.on_predict_epoch_start(self, self.lightning_module)
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def on_predict_epoch_end(self, outputs: List[Any]) -> None:
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"""Called when the epoch ends."""
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for callback in self.callbacks:
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callback.on_predict_epoch_end(self, self.lightning_module, outputs)
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def on_epoch_start(self):
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"""Called when either of train/val/test epoch begins."""
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for callback in self.callbacks:
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callback.on_epoch_start(self, self.lightning_module)
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def on_epoch_end(self):
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"""Called when either of train/val/test epoch ends."""
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for callback in self.callbacks:
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callback.on_epoch_end(self, self.lightning_module)
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def on_train_start(self):
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"""Called when the train begins."""
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for callback in self.callbacks:
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callback.on_train_start(self, self.lightning_module)
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def on_train_end(self):
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"""Called when the train ends."""
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for callback in self.callbacks:
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callback.on_train_end(self, self.lightning_module)
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def on_pretrain_routine_start(self) -> None:
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"""Called when the pre-train routine begins."""
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for callback in self.callbacks:
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callback.on_pretrain_routine_start(self, self.lightning_module)
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def on_pretrain_routine_end(self) -> None:
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"""Called when the pre-train routine ends."""
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for callback in self.callbacks:
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callback.on_pretrain_routine_end(self, self.lightning_module)
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def on_batch_start(self):
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"""Called when the training batch begins."""
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for callback in self.callbacks:
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callback.on_batch_start(self, self.lightning_module)
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def on_batch_end(self):
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"""Called when the training batch ends."""
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for callback in self.callbacks:
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callback.on_batch_end(self, self.lightning_module)
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def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
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"""Called when the training batch begins."""
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for callback in self.callbacks:
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callback.on_train_batch_start(self, self.lightning_module, batch, batch_idx, dataloader_idx)
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def on_train_batch_end(self, outputs: STEP_OUTPUT, batch, batch_idx, dataloader_idx):
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"""Called when the training batch ends."""
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for callback in self.callbacks:
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callback.on_train_batch_end(self, self.lightning_module, outputs, batch, batch_idx, dataloader_idx)
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def on_validation_batch_start(self, batch, batch_idx, dataloader_idx):
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"""Called when the validation batch begins."""
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for callback in self.callbacks:
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callback.on_validation_batch_start(self, self.lightning_module, batch, batch_idx, dataloader_idx)
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def on_validation_batch_end(self, outputs: STEP_OUTPUT, batch, batch_idx, dataloader_idx):
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"""Called when the validation batch ends."""
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for callback in self.callbacks:
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callback.on_validation_batch_end(self, self.lightning_module, outputs, batch, batch_idx, dataloader_idx)
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def on_test_batch_start(self, batch, batch_idx, dataloader_idx):
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"""Called when the test batch begins."""
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for callback in self.callbacks:
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callback.on_test_batch_start(self, self.lightning_module, batch, batch_idx, dataloader_idx)
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def on_test_batch_end(self, outputs: STEP_OUTPUT, batch, batch_idx, dataloader_idx):
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"""Called when the test batch ends."""
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for callback in self.callbacks:
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callback.on_test_batch_end(self, self.lightning_module, outputs, batch, batch_idx, dataloader_idx)
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def on_predict_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
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"""Called when the predict batch begins."""
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for callback in self.callbacks:
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callback.on_predict_batch_start(self, self.lightning_module, batch, batch_idx, dataloader_idx)
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def on_predict_batch_end(self, outputs: STEP_OUTPUT, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
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"""Called when the predict batch ends."""
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for callback in self.callbacks:
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callback.on_predict_batch_end(self, self.lightning_module, outputs, batch, batch_idx, dataloader_idx)
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def on_validation_start(self):
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"""Called when the validation loop begins."""
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for callback in self.callbacks:
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callback.on_validation_start(self, self.lightning_module)
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def on_validation_end(self):
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"""Called when the validation loop ends."""
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for callback in self.callbacks:
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callback.on_validation_end(self, self.lightning_module)
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def on_test_start(self):
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"""Called when the test begins."""
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for callback in self.callbacks:
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callback.on_test_start(self, self.lightning_module)
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def on_test_end(self):
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"""Called when the test ends."""
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for callback in self.callbacks:
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callback.on_test_end(self, self.lightning_module)
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def on_predict_start(self) -> None:
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"""Called when predict begins."""
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for callback in self.callbacks:
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callback.on_predict_start(self, self.lightning_module)
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def on_predict_end(self) -> None:
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"""Called when predict ends."""
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for callback in self.callbacks:
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callback.on_predict_end(self, self.lightning_module)
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def on_keyboard_interrupt(self):
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"""Called when the training is interrupted by KeyboardInterrupt."""
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for callback in self.callbacks:
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callback.on_keyboard_interrupt(self, self.lightning_module)
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@staticmethod
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def __is_old_signature_on_save_checkpoint(fn: Callable) -> bool:
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parameters = list(signature(fn).parameters)
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return len(parameters) == 2 and parameters[0] != "args"
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@staticmethod
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def __is_old_signature_on_load_checkpoint(fn: Callable) -> bool:
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parameters = list(signature(fn).parameters)
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return len(parameters) == 1 and parameters[0] != "args"
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def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> Dict[Type, dict]:
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"""Called when saving a model checkpoint."""
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callback_states = {}
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for callback in self.callbacks:
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if self.__is_old_signature_on_save_checkpoint(callback.on_save_checkpoint):
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rank_zero_deprecation(
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"`Callback.on_save_checkpoint` signature has changed in v1.3."
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" A `checkpoint` parameter has been added."
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" Support for the old signature will be removed in v1.5"
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)
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state = callback.on_save_checkpoint(self, self.lightning_module) # noqa: parameter-unfilled
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else:
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state = callback.on_save_checkpoint(self, self.lightning_module, checkpoint)
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if state:
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callback_states[type(callback)] = state
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return callback_states
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def on_load_checkpoint(self, checkpoint):
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"""Called when loading a model checkpoint."""
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# Todo: the `callback_states` are dropped with TPUSpawn as they
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# can't be saved using `xm.save`
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# https://github.com/pytorch/xla/issues/2773
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callback_states = checkpoint.get('callbacks')
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if callback_states is None:
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return
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current_callbacks_type = {type(cb) for cb in self.callbacks}
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saved_callbacks_type = set(callback_states.keys())
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difference = saved_callbacks_type.difference(current_callbacks_type)
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if difference:
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rank_zero_warn(
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"Be aware that when using ``resume_from_checkpoint``, "
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"callbacks used to create the checkpoint need to be provided. "
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f"Please, add the following callbacks: {list(difference)}. ", UserWarning
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)
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for callback in self.callbacks:
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state = callback_states.get(type(callback))
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if state:
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state = deepcopy(state)
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if self.__is_old_signature_on_load_checkpoint(callback.on_load_checkpoint):
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rank_zero_deprecation(
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"`Callback.on_load_checkpoint` signature has changed in v1.3."
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" `trainer` and `pl_module` parameters have been added."
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" Support for the old signature will be removed in v1.5"
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)
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callback.on_load_checkpoint(state) # noqa: parameter-unfilled
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else:
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callback.on_load_checkpoint(self, self.lightning_module, state)
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def on_before_backward(self, loss: torch.Tensor) -> None:
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"""Called before ``loss.backward()``."""
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for callback in self.callbacks:
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callback.on_before_backward(self, self.lightning_module, loss)
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def on_after_backward(self):
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"""
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Called after loss.backward() and before optimizers do anything.
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"""
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for callback in self.callbacks:
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callback.on_after_backward(self, self.lightning_module)
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def on_before_optimizer_step(self, optimizer, optimizer_idx):
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"""
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Called after on_after_backward() once the gradient is accumulated and before optimizer.step().
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"""
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for callback in self.callbacks:
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callback.on_before_optimizer_step(self, self.lightning_module, optimizer, optimizer_idx)
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def on_before_zero_grad(self, optimizer):
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"""
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Called after optimizer.step() and before optimizer.zero_grad().
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"""
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for callback in self.callbacks:
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callback.on_before_zero_grad(self, self.lightning_module, optimizer)
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