513 lines
18 KiB
Python
513 lines
18 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 importlib
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import os
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from functools import wraps
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from typing import Callable, List, Optional, Sequence, Union
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import numpy as np
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import torch
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import _LRScheduler
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from torch.utils.data import DataLoader
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from pytorch_lightning import _logger as log
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from pytorch_lightning.callbacks import Callback
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from pytorch_lightning.core.datamodule import LightningDataModule
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.loggers.base import DummyLogger
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from pytorch_lightning.utilities import DeviceType, rank_zero_warn
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from pytorch_lightning.utilities.cloud_io import get_filesystem
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.parsing import lightning_hasattr, lightning_setattr
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# check if ipywidgets is installed before importing tqdm.auto
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# to ensure it won't fail and a progress bar is displayed
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if importlib.util.find_spec('ipywidgets') is not None:
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from tqdm.auto import tqdm
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else:
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from tqdm import tqdm
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def _run_lr_finder_internally(trainer, model: LightningModule):
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""" Call lr finder internally during Trainer.fit() """
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lr_finder = lr_find(trainer, model)
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if lr_finder is None:
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return
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lr = lr_finder.suggestion()
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# TODO: log lr.results to self.logger
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if isinstance(trainer.auto_lr_find, str):
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# Try to find requested field, may be nested
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if lightning_hasattr(model, trainer.auto_lr_find):
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lightning_setattr(model, trainer.auto_lr_find, lr)
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else:
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raise MisconfigurationException(
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f'`auto_lr_find` was set to {trainer.auto_lr_find}, however'
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' could not find this as a field in `model` or `model.hparams`.')
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else:
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if lightning_hasattr(model, 'lr'):
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lightning_setattr(model, 'lr', lr)
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elif lightning_hasattr(model, 'learning_rate'):
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lightning_setattr(model, 'learning_rate', lr)
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else:
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raise MisconfigurationException(
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'When auto_lr_find is set to True, expects that `model` or'
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' `model.hparams` either has field `lr` or `learning_rate`'
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' that can overridden')
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log.info(f'Learning rate set to {lr}')
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def lr_find(
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trainer,
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model: LightningModule,
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train_dataloader: Optional[DataLoader] = None,
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val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,
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min_lr: float = 1e-8,
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max_lr: float = 1,
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num_training: int = 100,
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mode: str = 'exponential',
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early_stop_threshold: float = 4.0,
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datamodule: Optional[LightningDataModule] = None,
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):
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r"""
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`lr_find` enables the user to do a range test of good initial learning rates,
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to reduce the amount of guesswork in picking a good starting learning rate.
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Args:
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model: Model to do range testing for
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train_dataloader: A PyTorch
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`DataLoader` with training samples. If the model has
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a predefined train_dataloader method, this will be skipped.
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min_lr: minimum learning rate to investigate
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max_lr: maximum learning rate to investigate
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num_training: number of learning rates to test
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mode: search strategy, either 'linear' or 'exponential'. If set to
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'linear' the learning rate will be searched by linearly increasing
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after each batch. If set to 'exponential', will increase learning
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rate exponentially.
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early_stop_threshold: threshold for stopping the search. If the
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loss at any point is larger than early_stop_threshold*best_loss
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then the search is stopped. To disable, set to None.
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datamodule: An optional `LightningDataModule` which holds the training
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and validation dataloader(s). Note that the `train_dataloader` and
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`val_dataloaders` parameters cannot be used at the same time as
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this parameter, or a `MisconfigurationException` will be raised.
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Example::
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# Setup model and trainer
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model = MyModelClass(hparams)
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trainer = pl.Trainer()
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# Run lr finder
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lr_finder = trainer.tuner.lr_find(model, ...)
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# Inspect results
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fig = lr_finder.plot(); fig.show()
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suggested_lr = lr_finder.suggestion()
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# Overwrite lr and create new model
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hparams.lr = suggested_lr
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model = MyModelClass(hparams)
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# Ready to train with new learning rate
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trainer.fit(model)
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"""
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if trainer.fast_dev_run:
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rank_zero_warn('Skipping learning rate finder since fast_dev_run is enabled.', UserWarning)
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return
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save_path = os.path.join(trainer.default_root_dir, 'lr_find_temp_model.ckpt')
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__lr_finder_dump_params(trainer, model)
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# Prevent going into infinite loop
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trainer.auto_lr_find = False
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# Initialize lr finder object (stores results)
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lr_finder = _LRFinder(mode, min_lr, max_lr, num_training)
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# Use special lr logger callback
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trainer.callbacks = [_LRCallback(num_training,
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early_stop_threshold,
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progress_bar_refresh_rate=1)]
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# No logging
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trainer.logger = DummyLogger()
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# Max step set to number of iterations
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trainer.max_steps = num_training
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# Disable standard progress bar for fit
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if trainer.progress_bar_callback:
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trainer.progress_bar_callback.disable()
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# Required for saving the model
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trainer.optimizers, trainer.schedulers = [], [],
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trainer.model = model
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# Dump model checkpoint
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trainer.save_checkpoint(str(save_path))
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# Configure optimizer and scheduler
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model.configure_optimizers = lr_finder._exchange_scheduler(model.configure_optimizers)
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# Fit, lr & loss logged in callback
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trainer.fit(model,
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train_dataloader=train_dataloader,
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val_dataloaders=val_dataloaders,
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datamodule=datamodule)
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# Prompt if we stopped early
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if trainer.global_step != num_training:
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log.info('LR finder stopped early due to diverging loss.')
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# Transfer results from callback to lr finder object
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lr_finder.results.update({'lr': trainer.callbacks[0].lrs,
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'loss': trainer.callbacks[0].losses})
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lr_finder._total_batch_idx = trainer.total_batch_idx # for debug purpose
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# Reset model state
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if trainer.is_global_zero:
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trainer.checkpoint_connector.restore(str(save_path), on_gpu=trainer._device_type == DeviceType.GPU)
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fs = get_filesystem(str(save_path))
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if fs.exists(save_path):
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fs.rm(save_path)
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# Finish by resetting variables so trainer is ready to fit model
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__lr_finder_restore_params(trainer, model)
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if trainer.progress_bar_callback:
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trainer.progress_bar_callback.enable()
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return lr_finder
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def __lr_finder_dump_params(trainer, model):
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# Prevent going into infinite loop
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trainer.__dumped_params = {
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'auto_lr_find': trainer.auto_lr_find,
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'callbacks': trainer.callbacks,
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'logger': trainer.logger,
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'max_steps': trainer.max_steps,
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'checkpoint_callback': trainer.checkpoint_callback,
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'configure_optimizers': model.configure_optimizers,
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}
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def __lr_finder_restore_params(trainer, model):
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trainer.auto_lr_find = trainer.__dumped_params['auto_lr_find']
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trainer.logger = trainer.__dumped_params['logger']
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trainer.callbacks = trainer.__dumped_params['callbacks']
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trainer.max_steps = trainer.__dumped_params['max_steps']
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model.configure_optimizers = trainer.__dumped_params['configure_optimizers']
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del trainer.__dumped_params
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class _LRFinder(object):
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""" LR finder object. This object stores the results of Trainer.lr_find().
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Args:
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mode: either `linear` or `exponential`, how to increase lr after each step
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lr_min: lr to start search from
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lr_max: lr to stop search
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num_training: number of steps to take between lr_min and lr_max
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Example::
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# Run lr finder
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lr_finder = trainer.lr_find(model)
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# Results stored in
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lr_finder.results
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# Plot using
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lr_finder.plot()
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# Get suggestion
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lr = lr_finder.suggestion()
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"""
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def __init__(self, mode: str, lr_min: float, lr_max: float, num_training: int):
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assert mode in ('linear', 'exponential'), \
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'mode should be either `linear` or `exponential`'
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self.mode = mode
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self.lr_min = lr_min
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self.lr_max = lr_max
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self.num_training = num_training
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self.results = {}
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self._total_batch_idx = 0 # for debug purpose
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def _exchange_scheduler(self, configure_optimizers: Callable):
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""" Decorate configure_optimizers methods such that it returns the users
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originally specified optimizer together with a new scheduler that
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that takes care of the learning rate search.
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"""
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@wraps(configure_optimizers)
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def func():
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# Decide the structure of the output from configure_optimizers
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# Same logic as method `init_optimizers` in trainer/optimizers.py
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optim_conf = configure_optimizers()
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if isinstance(optim_conf, Optimizer):
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optimizers = [optim_conf]
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elif isinstance(optim_conf, (list, tuple)) and len(optim_conf) == 2 \
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and isinstance(optim_conf[0], list):
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optimizers, _ = optim_conf
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elif isinstance(optim_conf, dict):
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optimizers = [optim_conf["optimizer"]]
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elif isinstance(optim_conf, (list, tuple)) and isinstance(optim_conf[0], dict):
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optimizers = [opt_dict["optimizer"] for opt_dict in optim_conf]
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elif isinstance(optim_conf, (list, tuple)):
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optimizers = [optim_conf]
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if len(optimizers) != 1:
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raise MisconfigurationException(
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f'`model.configure_optimizers()` returned {len(optimizers)}, but'
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' learning rate finder only works with single optimizer')
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optimizer = optimizers[0]
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new_lrs = [self.lr_min] * len(optimizer.param_groups)
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for param_group, new_lr in zip(optimizer.param_groups, new_lrs):
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param_group["lr"] = new_lr
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param_group["initial_lr"] = new_lr
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args = (optimizer, self.lr_max, self.num_training)
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scheduler = _LinearLR(*args) if self.mode == 'linear' else _ExponentialLR(*args)
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return [optimizer], [{'scheduler': scheduler,
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'interval': 'step'}]
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return func
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def plot(self, suggest: bool = False, show: bool = False):
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""" Plot results from lr_find run
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Args:
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suggest: if True, will mark suggested lr to use with a red point
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show: if True, will show figure
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"""
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import matplotlib.pyplot as plt
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lrs = self.results["lr"]
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losses = self.results["loss"]
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fig, ax = plt.subplots()
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# Plot loss as a function of the learning rate
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ax.plot(lrs, losses)
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if self.mode == 'exponential':
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ax.set_xscale("log")
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ax.set_xlabel("Learning rate")
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ax.set_ylabel("Loss")
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if suggest:
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_ = self.suggestion()
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if self._optimal_idx:
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ax.plot(lrs[self._optimal_idx], losses[self._optimal_idx],
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markersize=10, marker='o', color='red')
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if show:
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plt.show()
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return fig
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def suggestion(self, skip_begin: int = 10, skip_end: int = 1):
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""" This will propose a suggestion for choice of initial learning rate
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as the point with the steepest negative gradient.
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Returns:
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lr: suggested initial learning rate to use
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skip_begin: how many samples to skip in the beginning. Prevent too naive estimates
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skip_end: how many samples to skip in the end. Prevent too optimistic estimates
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"""
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try:
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loss = np.array(self.results["loss"][skip_begin:-skip_end])
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loss = loss[np.isfinite(loss)]
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min_grad = np.gradient(loss).argmin()
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self._optimal_idx = min_grad + skip_begin
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return self.results["lr"][self._optimal_idx]
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# todo: specify the possible exception
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except Exception:
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log.exception('Failed to compute suggesting for `lr`. There might not be enough points.')
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self._optimal_idx = None
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class _LRCallback(Callback):
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""" Special callback used by the learning rate finder. This callbacks log
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the learning rate before each batch and log the corresponding loss after
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each batch.
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Args:
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num_training: number of iterations done by the learning rate finder
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early_stop_threshold: threshold for stopping the search. If the
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loss at any point is larger than ``early_stop_threshold*best_loss``
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then the search is stopped. To disable, set to ``None``.
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progress_bar_refresh_rate: rate to refresh the progress bar for
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the learning rate finder
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beta: smoothing value, the loss being logged is a running average of
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loss values logged until now. ``beta`` controls the forget rate i.e.
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if ``beta=0`` all past information is ignored.
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"""
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def __init__(self, num_training: int,
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early_stop_threshold: float = 4.0,
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progress_bar_refresh_rate: int = 0,
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beta: float = 0.98):
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self.num_training = num_training
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self.early_stop_threshold = early_stop_threshold
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self.beta = beta
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self.losses = []
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self.lrs = []
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self.avg_loss = 0.0
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self.best_loss = 0.0
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self.progress_bar_refresh_rate = progress_bar_refresh_rate
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self.progress_bar = None
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def on_batch_start(self, trainer, pl_module):
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""" Called before each training batch, logs the lr that will be used """
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if (trainer.batch_idx + 1) % trainer.accumulate_grad_batches != 0:
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return
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if self.progress_bar_refresh_rate and self.progress_bar is None:
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self.progress_bar = tqdm(desc='Finding best initial lr', total=self.num_training)
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self.lrs.append(trainer.lr_schedulers[0]['scheduler'].lr[0])
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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""" Called when the training batch ends, logs the calculated loss """
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if (trainer.batch_idx + 1) % trainer.accumulate_grad_batches != 0:
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return
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if self.progress_bar:
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self.progress_bar.update()
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current_loss = trainer.train_loop.running_loss.last().item()
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current_step = trainer.global_step + 1 # remove the +1 in 1.0
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# Avg loss (loss with momentum) + smoothing
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self.avg_loss = self.beta * self.avg_loss + (1 - self.beta) * current_loss
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smoothed_loss = self.avg_loss / (1 - self.beta**current_step)
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# Check if we diverging
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if self.early_stop_threshold is not None:
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if current_step > 1 and smoothed_loss > self.early_stop_threshold * self.best_loss:
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trainer.max_steps = current_step # stop signal
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if self.progress_bar:
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self.progress_bar.close()
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# Save best loss for diverging checking
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if smoothed_loss < self.best_loss or current_step == 1:
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self.best_loss = smoothed_loss
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self.losses.append(smoothed_loss)
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class _LinearLR(_LRScheduler):
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"""Linearly increases the learning rate between two boundaries
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over a number of iterations.
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Arguments:
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optimizer: wrapped optimizer.
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end_lr: the final learning rate.
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num_iter: the number of iterations over which the test occurs.
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last_epoch: the index of last epoch. Default: -1.
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"""
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last_epoch: int
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base_lrs: Sequence
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def __init__(self,
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optimizer: torch.optim.Optimizer,
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end_lr: float,
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num_iter: int,
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last_epoch: int = -1):
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self.end_lr = end_lr
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self.num_iter = num_iter
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super(_LinearLR, self).__init__(optimizer, last_epoch)
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def get_lr(self):
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curr_iter = self.last_epoch + 1
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r = curr_iter / self.num_iter
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if self.last_epoch > 0:
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val = [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]
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else:
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val = [base_lr for base_lr in self.base_lrs]
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self._lr = val
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return val
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@property
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def lr(self):
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return self._lr
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class _ExponentialLR(_LRScheduler):
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"""Exponentially increases the learning rate between two boundaries
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over a number of iterations.
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Arguments:
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optimizer: wrapped optimizer.
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end_lr: the final learning rate.
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num_iter: the number of iterations over which the test occurs.
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last_epoch: the index of last epoch. Default: -1.
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"""
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last_epoch: int
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base_lrs: Sequence
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def __init__(self,
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optimizer: torch.optim.Optimizer,
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end_lr: float,
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num_iter: int,
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last_epoch: int = -1):
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self.end_lr = end_lr
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self.num_iter = num_iter
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super(_ExponentialLR, self).__init__(optimizer, last_epoch)
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def get_lr(self):
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curr_iter = self.last_epoch + 1
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r = curr_iter / self.num_iter
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if self.last_epoch > 0:
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val = [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]
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else:
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val = [base_lr for base_lr in self.base_lrs]
|
|
self._lr = val
|
|
return val
|
|
|
|
@property
|
|
def lr(self):
|
|
return self._lr
|