279 lines
12 KiB
Python
279 lines
12 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 os
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.utilities.data import has_len
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from pytorch_lightning.utilities.parsing import lightning_hasattr, lightning_getattr, lightning_setattr
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from pytorch_lightning.utilities import rank_zero_warn
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.memory import is_oom_error, garbage_collection_cuda
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from pytorch_lightning.loggers.base import DummyLogger
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from pytorch_lightning import _logger as log
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from typing import Optional, Tuple
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def scale_batch_size(trainer,
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model: LightningModule,
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mode: str = 'power',
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steps_per_trial: int = 3,
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init_val: int = 2,
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max_trials: int = 25,
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batch_arg_name: str = 'batch_size',
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**fit_kwargs):
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r"""
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Will iteratively try to find the largest batch size for a given model
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that does not give an out of memory (OOM) error.
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Args:
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trainer: The Trainer
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model: Model to fit.
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mode: string setting the search mode. Either `power` or `binsearch`.
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If mode is `power` we keep multiplying the batch size by 2, until
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we get an OOM error. If mode is 'binsearch', we will initially
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also keep multiplying by 2 and after encountering an OOM error
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do a binary search between the last successful batch size and the
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batch size that failed.
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steps_per_trial: number of steps to run with a given batch size.
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Idealy 1 should be enough to test if a OOM error occurs,
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however in practise a few are needed
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init_val: initial batch size to start the search with
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max_trials: max number of increase in batch size done before
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algorithm is terminated
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batch_arg_name: name of the attribute that stores the batch size.
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It is expected that the user has provided a model or datamodule that has a hyperparameter
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with that name. We will look for this attribute name in the following places
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- `model`
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- `model.hparams`
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- `model.datamodule`
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- `trainer.datamodule` (the datamodule passed to the tune method)
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**fit_kwargs: remaining arguments to be passed to .fit(), e.g., dataloader
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or datamodule.
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"""
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if not lightning_hasattr(model, batch_arg_name):
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raise MisconfigurationException(
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f'Field {batch_arg_name} not found in both `model` and `model.hparams`')
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if hasattr(model, batch_arg_name) and hasattr(model, "hparams") and batch_arg_name in model.hparams:
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rank_zero_warn(
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f'Field `model.{batch_arg_name}` and `model.hparams.{batch_arg_name}` are mutually exclusive!'
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f' `model.{batch_arg_name}` will be used as the initial batch size for scaling.'
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f' If this is not the intended behavior, please remove either one.'
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)
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if hasattr(model.train_dataloader, 'patch_loader_code'):
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raise MisconfigurationException('The batch scaling feature cannot be used with dataloaders'
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' passed directly to `.fit()`. Please disable the feature or'
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' incorporate the dataloader into the model.')
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# Arguments we adjust during the batch size finder, save for restoring
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__scale_batch_dump_params(trainer)
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# Set to values that are required by the algorithm
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__scale_batch_reset_params(trainer, model, steps_per_trial)
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# Save initial model, that is loaded after batch size is found
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save_path = os.path.join(trainer.default_root_dir, 'temp_model.ckpt')
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trainer.save_checkpoint(str(save_path))
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if trainer.progress_bar_callback:
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trainer.progress_bar_callback.disable()
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# Initially we just double in size until an OOM is encountered
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new_size = _adjust_batch_size(trainer, value=init_val) # initially set to init_val
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if mode == 'power':
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new_size = _run_power_scaling(trainer, model, new_size, batch_arg_name, max_trials, **fit_kwargs)
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elif mode == 'binsearch':
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new_size = _run_binsearch_scaling(trainer, model, new_size, batch_arg_name, max_trials, **fit_kwargs)
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else:
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raise ValueError('mode in method `scale_batch_size` can only be `power` or `binsearch')
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garbage_collection_cuda()
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log.info(f'Finished batch size finder, will continue with full run using batch size {new_size}')
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# Restore initial state of model
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trainer.checkpoint_connector.restore(str(save_path), on_gpu=trainer.on_gpu)
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os.remove(save_path)
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# Finish by resetting variables so trainer is ready to fit model
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__scale_batch_restore_params(trainer)
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if trainer.progress_bar_callback:
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trainer.progress_bar_callback.enable()
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return new_size
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def __scale_batch_dump_params(trainer):
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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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'current_epoch': trainer.current_epoch,
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'max_steps': trainer.max_steps,
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'weights_summary': trainer.weights_summary,
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'logger': trainer.logger,
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'callbacks': trainer.callbacks,
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'checkpoint_callback': trainer.checkpoint_callback,
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'early_stop_callback': trainer.early_stop_callback,
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'auto_scale_batch_size': trainer.auto_scale_batch_size,
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'limit_train_batches': trainer.limit_train_batches,
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'model': trainer.model,
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}
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def __scale_batch_reset_params(trainer, model, steps_per_trial):
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trainer.auto_scale_batch_size = None # prevent recursion
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trainer.auto_lr_find = False # avoid lr find being called multiple times
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trainer.current_epoch = 0
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trainer.max_steps = steps_per_trial # take few steps
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trainer.weights_summary = None # not needed before full run
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trainer.logger = DummyLogger()
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trainer.callbacks = [] # not needed before full run
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trainer.checkpoint_callback = False # required for saving
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trainer.early_stop_callback = None
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trainer.limit_train_batches = 1.0
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trainer.optimizers, trainer.schedulers = [], [] # required for saving
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trainer.model = model # required for saving
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def __scale_batch_restore_params(trainer):
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trainer.auto_lr_find = trainer.__dumped_params['auto_lr_find']
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trainer.current_epoch = trainer.__dumped_params['current_epoch']
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trainer.max_steps = trainer.__dumped_params['max_steps']
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trainer.weights_summary = trainer.__dumped_params['weights_summary']
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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.checkpoint_callback = trainer.__dumped_params['checkpoint_callback']
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trainer.auto_scale_batch_size = trainer.__dumped_params['auto_scale_batch_size']
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trainer.early_stop_callback = trainer.__dumped_params['early_stop_callback']
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trainer.limit_train_batches = trainer.__dumped_params['limit_train_batches']
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trainer.model = trainer.__dumped_params['model']
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del trainer.__dumped_params
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def _run_power_scaling(trainer, model, new_size, batch_arg_name, max_trials, **fit_kwargs):
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""" Batch scaling mode where the size is doubled at each iteration until an
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OOM error is encountered. """
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for _ in range(max_trials):
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garbage_collection_cuda()
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trainer.global_step = 0 # reset after each try
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try:
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# Try fit
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trainer.fit(model, **fit_kwargs)
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# Double in size
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new_size, changed = _adjust_batch_size(trainer, batch_arg_name, factor=2.0, desc='succeeded')
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except RuntimeError as exception:
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# Only these errors should trigger an adjustment
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if is_oom_error(exception):
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# If we fail in power mode, half the size and return
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garbage_collection_cuda()
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new_size, _ = _adjust_batch_size(trainer, batch_arg_name, factor=0.5, desc='failed')
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break
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else:
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raise # some other error not memory related
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if not changed:
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break
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return new_size
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def _run_binsearch_scaling(trainer, model, new_size, batch_arg_name, max_trials, **fit_kwargs):
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""" Batch scaling mode where the size is initially is doubled at each iteration
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until an OOM error is encountered. Hereafter, the batch size is further
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refined using a binary search """
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high = None
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count = 0
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while True:
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garbage_collection_cuda()
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trainer.global_step = 0 # reset after each try
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try:
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# Try fit
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trainer.fit(model, **fit_kwargs)
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count += 1
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if count > max_trials:
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break
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# Double in size
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low = new_size
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if high:
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if high - low <= 1:
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break
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midval = (high + low) // 2
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new_size, changed = _adjust_batch_size(trainer, batch_arg_name, value=midval, desc='succeeded')
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else:
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new_size, changed = _adjust_batch_size(trainer, batch_arg_name, factor=2.0, desc='succeeded')
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if not changed:
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break
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except RuntimeError as exception:
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# Only these errors should trigger an adjustment
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if is_oom_error(exception):
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# If we fail in power mode, half the size and return
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garbage_collection_cuda()
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high = new_size
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midval = (high + low) // 2
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new_size, _ = _adjust_batch_size(trainer, value=midval, desc='failed')
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if high - low <= 1:
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break
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else:
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raise # some other error not memory related
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return new_size
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def _adjust_batch_size(trainer,
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batch_arg_name: str = 'batch_size',
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factor: float = 1.0,
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value: Optional[int] = None,
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desc: str = None) -> Tuple[int, bool]:
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""" Helper function for adjusting the batch size.
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Args:
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trainer: instance of pytorch_lightning.Trainer
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batch_arg_name: name of the field where batch_size is stored.
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factor: value which the old batch size is multiplied by to get the
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new batch size
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value: if a value is given, will override the batch size with this value.
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Note that the value of `factor` will not have an effect in this case
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desc: either `succeeded` or `failed`. Used purely for logging
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Returns:
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The new batch size for the next trial and a bool that signals whether the
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new value is different than the previous batch size.
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"""
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model = trainer.get_model()
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batch_size = lightning_getattr(model, batch_arg_name)
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new_size = value if value is not None else int(batch_size * factor)
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if desc:
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log.info(f'Batch size {batch_size} {desc}, trying batch size {new_size}')
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if not _is_valid_batch_size(new_size, trainer.train_dataloader):
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new_size = min(new_size, len(trainer.train_dataloader.dataset))
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changed = new_size != batch_size
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lightning_setattr(model, batch_arg_name, new_size)
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return new_size, changed
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def _is_valid_batch_size(current_size, dataloader):
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return not has_len(dataloader) or current_size <= len(dataloader)
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