# 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. import os from datetime import timedelta from typing import Dict, List, Optional, Union from pytorch_lightning.callbacks import ( Callback, GradientAccumulationScheduler, ModelCheckpoint, ModelSummary, ProgressBarBase, RichProgressBar, TQDMProgressBar, ) from pytorch_lightning.callbacks.rich_model_summary import RichModelSummary from pytorch_lightning.callbacks.timer import Timer from pytorch_lightning.utilities import ModelSummaryMode, rank_zero_info from pytorch_lightning.utilities.exceptions import MisconfigurationException from pytorch_lightning.utilities.warnings import rank_zero_deprecation class CallbackConnector: def __init__(self, trainer): self.trainer = trainer def on_trainer_init( self, callbacks: Optional[Union[List[Callback], Callback]], checkpoint_callback: Optional[bool], enable_checkpointing: bool, enable_progress_bar: bool, progress_bar_refresh_rate: Optional[int], process_position: int, default_root_dir: Optional[str], weights_save_path: Optional[str], enable_model_summary: bool, weights_summary: Optional[str], stochastic_weight_avg: bool, max_time: Optional[Union[str, timedelta, Dict[str, int]]] = None, accumulate_grad_batches: Optional[Union[int, Dict[int, int]]] = None, ): # init folder paths for checkpoint + weights save callbacks self.trainer._default_root_dir = default_root_dir or os.getcwd() self.trainer._weights_save_path = weights_save_path or self.trainer._default_root_dir if stochastic_weight_avg: rank_zero_deprecation( "Setting `Trainer(stochastic_weight_avg=True)` is deprecated in v1.5 and will be removed in v1.7." " Please pass `pytorch_lightning.callbacks.stochastic_weight_avg.StochasticWeightAveraging`" " directly to the Trainer's `callbacks` argument instead." ) self.trainer._stochastic_weight_avg = stochastic_weight_avg # init callbacks if isinstance(callbacks, Callback): callbacks = [callbacks] self.trainer.callbacks = callbacks or [] # configure checkpoint callback # pass through the required args to figure out defaults self._configure_checkpoint_callbacks(checkpoint_callback, enable_checkpointing) # configure swa callback self._configure_swa_callbacks() # configure the timer callback. # responsible to stop the training when max_time is reached. self._configure_timer_callback(max_time) # init progress bar if process_position != 0: rank_zero_deprecation( f"Setting `Trainer(process_position={process_position})` is deprecated in v1.5 and will be removed" " in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with" " `process_position` directly to the Trainer's `callbacks` argument instead." ) if progress_bar_refresh_rate is not None: rank_zero_deprecation( f"Setting `Trainer(progress_bar_refresh_rate={progress_bar_refresh_rate})` is deprecated in v1.5 and" " will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with" " `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress" " bar pass `enable_progress_bar = False` to the Trainer." ) if enable_progress_bar: self.trainer._progress_bar_callback = self.configure_progress_bar( progress_bar_refresh_rate, process_position ) else: self.trainer._progress_bar_callback = None # configure the ModelSummary callback self._configure_model_summary_callback(enable_model_summary, weights_summary) # accumulated grads self._configure_accumulated_gradients(accumulate_grad_batches) # push all checkpoint callbacks to the end # it is important that these are the last callbacks to run self.trainer.callbacks = self._reorder_callbacks(self.trainer.callbacks) def _configure_accumulated_gradients( self, accumulate_grad_batches: Optional[Union[int, Dict[int, int]]] = None ) -> None: grad_accum_callback = [cb for cb in self.trainer.callbacks if isinstance(cb, GradientAccumulationScheduler)] if grad_accum_callback: if accumulate_grad_batches is not None: raise MisconfigurationException( "You have set both `accumulate_grad_batches` and passed an instance of " "`GradientAccumulationScheduler` inside callbacks. Either remove `accumulate_grad_batches` " "from trainer or remove `GradientAccumulationScheduler` from callbacks list." ) grad_accum_callback = grad_accum_callback[0] else: if accumulate_grad_batches is None: accumulate_grad_batches = 1 if isinstance(accumulate_grad_batches, dict): grad_accum_callback = GradientAccumulationScheduler(accumulate_grad_batches) elif isinstance(accumulate_grad_batches, int): grad_accum_callback = GradientAccumulationScheduler({0: accumulate_grad_batches}) else: raise MisconfigurationException( f"`accumulate_grad_batches` should be an int or a dict. Got {accumulate_grad_batches}." ) self.trainer.callbacks.append(grad_accum_callback) self.trainer.accumulate_grad_batches = grad_accum_callback.get_accumulate_grad_batches(0) self.trainer.accumulation_scheduler = grad_accum_callback def _configure_checkpoint_callbacks(self, checkpoint_callback: Optional[bool], enable_checkpointing: bool) -> None: if checkpoint_callback is not None: rank_zero_deprecation( f"Setting `Trainer(checkpoint_callback={checkpoint_callback})` is deprecated in v1.5 and will " f"be removed in v1.7. Please consider using `Trainer(enable_checkpointing={checkpoint_callback})`." ) # if both are set then checkpoint only if both are True enable_checkpointing = checkpoint_callback and enable_checkpointing if self._trainer_has_checkpoint_callbacks() and enable_checkpointing is False: raise MisconfigurationException( "Trainer was configured with `enable_checkpointing=False`" " but found `ModelCheckpoint` in callbacks list." ) if not self._trainer_has_checkpoint_callbacks() and enable_checkpointing is True: self.trainer.callbacks.append(ModelCheckpoint()) def _configure_model_summary_callback( self, enable_model_summary: bool, weights_summary: Optional[str] = None ) -> None: if weights_summary is None: rank_zero_deprecation( "Setting `Trainer(weights_summary=None)` is deprecated in v1.5 and will be removed" " in v1.7. Please set `Trainer(enable_model_summary=False)` instead." ) return if not enable_model_summary: return model_summary_cbs = [type(cb) for cb in self.trainer.callbacks if isinstance(cb, ModelSummary)] if model_summary_cbs: rank_zero_info( f"Trainer already configured with model summary callbacks: {model_summary_cbs}." " Skipping setting a default `ModelSummary` callback." ) return if weights_summary == "top": # special case the default value for weights_summary to preserve backward compatibility max_depth = 1 else: rank_zero_deprecation( f"Setting `Trainer(weights_summary={weights_summary})` is deprecated in v1.5 and will be removed" " in v1.7. Please pass `pytorch_lightning.callbacks.model_summary.ModelSummary` with" " `max_depth` directly to the Trainer's `callbacks` argument instead." ) if weights_summary not in ModelSummaryMode.supported_types(): raise MisconfigurationException( f"`weights_summary` can be None, {', '.join(ModelSummaryMode.supported_types())}", f" but got {weights_summary}", ) max_depth = ModelSummaryMode.get_max_depth(weights_summary) is_progress_bar_rich = isinstance(self.trainer._progress_bar_callback, RichProgressBar) if self.trainer._progress_bar_callback is not None and is_progress_bar_rich: model_summary = RichModelSummary(max_depth=max_depth) else: model_summary = ModelSummary(max_depth=max_depth) self.trainer.callbacks.append(model_summary) self.trainer._weights_summary = weights_summary def _configure_swa_callbacks(self): if not self.trainer._stochastic_weight_avg: return from pytorch_lightning.callbacks.stochastic_weight_avg import StochasticWeightAveraging existing_swa = [cb for cb in self.trainer.callbacks if isinstance(cb, StochasticWeightAveraging)] if not existing_swa: self.trainer.callbacks = [StochasticWeightAveraging()] + self.trainer.callbacks def configure_progress_bar(self, refresh_rate=None, process_position=0): if os.getenv("COLAB_GPU") and refresh_rate is None: # smaller refresh rate on colab causes crashes, choose a higher value refresh_rate = 20 refresh_rate = 1 if refresh_rate is None else refresh_rate progress_bars = [c for c in self.trainer.callbacks if isinstance(c, ProgressBarBase)] if len(progress_bars) > 1: raise MisconfigurationException( "You added multiple progress bar callbacks to the Trainer, but currently only one" " progress bar is supported." ) if len(progress_bars) == 1: progress_bar_callback = progress_bars[0] elif refresh_rate > 0: progress_bar_callback = TQDMProgressBar(refresh_rate=refresh_rate, process_position=process_position) self.trainer.callbacks.append(progress_bar_callback) else: progress_bar_callback = None return progress_bar_callback def _configure_timer_callback(self, max_time: Optional[Union[str, timedelta, Dict[str, int]]] = None) -> None: if max_time is None: return if any(isinstance(cb, Timer) for cb in self.trainer.callbacks): rank_zero_info("Ignoring `Trainer(max_time=...)`, callbacks list already contains a Timer.") return timer = Timer(duration=max_time, interval="step") self.trainer.callbacks.append(timer) def _trainer_has_checkpoint_callbacks(self): return len(self.trainer.checkpoint_callbacks) > 0 def attach_model_logging_functions(self, model): for callback in self.trainer.callbacks: callback.log = model.log callback.log_dict = model.log_dict def _attach_model_callbacks(self) -> None: """Attaches the callbacks defined in the model. If a callback returned by the model's configure_callback method has the same type as one or several callbacks already present in the trainer callbacks list, it will replace them. In addition, all :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint` callbacks will be pushed to the end of the list, ensuring they run last. """ model_callbacks = self.trainer.call_hook("configure_callbacks") if not model_callbacks: return model_callback_types = {type(c) for c in model_callbacks} trainer_callback_types = {type(c) for c in self.trainer.callbacks} override_types = model_callback_types.intersection(trainer_callback_types) if override_types: rank_zero_info( "The following callbacks returned in `LightningModule.configure_callbacks` will override" " existing callbacks passed to Trainer:" f" {', '.join(sorted(t.__name__ for t in override_types))}" ) # remove all callbacks with a type that occurs in model callbacks all_callbacks = [c for c in self.trainer.callbacks if type(c) not in override_types] all_callbacks.extend(model_callbacks) all_callbacks = CallbackConnector._reorder_callbacks(all_callbacks) # TODO: connectors refactor: move callbacks list to connector and do not write Trainer state self.trainer.callbacks = all_callbacks @staticmethod def _reorder_callbacks(callbacks: List[Callback]) -> List[Callback]: """Moves all ModelCheckpoint callbacks to the end of the list. The sequential order within the group of checkpoint callbacks is preserved, as well as the order of all other callbacks. Args: callbacks: A list of callbacks. Return: A new list in which the last elements are ModelCheckpoints if there were any present in the input. """ checkpoints = [c for c in callbacks if isinstance(c, ModelCheckpoint)] not_checkpoints = [c for c in callbacks if not isinstance(c, ModelCheckpoint)] return not_checkpoints + checkpoints