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base_experiment.py
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base_experiment.py
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import itertools
import functools
import random
from datetime import datetime
from pathlib import Path
import shutil
import warnings
import gc
import os
import pytorch_lightning.plugins.environments
from matplotlib import pyplot as plt
import numpy as np
import torch
import pytorch_lightning as pl
from pytorch_lightning.profiler import AdvancedProfiler
from pprint import pformat
from . import strictfire
from ool.data import DataSpec
from ool.utils import exp_log_dir, watermark_source
from ool.schedules import ScheduledValue
import ool.env
# Monkeypatch fixes for memory consumption
import pytorch_lightning.trainer.training_loop as loop
def fixed_update_running_loss(self):
accumulated_loss = self.accumulated_loss.mean()
if accumulated_loss is not None:
next_loss = self.accumulated_loss.mean() * self.trainer.accumulate_grad_batches
if self.trainer.move_metrics_to_cpu:
next_loss = next_loss.cpu()
# calculate running loss for display
self.running_loss.append(next_loss)
# reset for next set of accumulated grads
self.accumulated_loss.reset()
loop.TrainLoop.update_running_loss = fixed_update_running_loss
import pytorch_lightning.trainer.connectors.logger_connector as logcon
from pytorch_lightning.core.step_result import Result
def fixed_cache_training_step_metrics(self, opt_closure_result):
"""
This function is responsible to update
logger_connector internals metrics holder based for depreceated logging
"""
using_results_obj = isinstance(opt_closure_result.training_step_output, Result)
# temporary dict to collect metrics
logged_metrics_tmp = {}
pbar_metrics_tmp = {}
callback_metrics_tmp = {}
if using_results_obj:
batch_log_metrics = opt_closure_result.training_step_output_for_epoch_end.get_batch_log_metrics(
include_forked_originals=False
)
logged_metrics_tmp.update(batch_log_metrics)
batch_pbar_metrics = opt_closure_result.training_step_output_for_epoch_end.get_batch_pbar_metrics(
include_forked_originals=False
)
pbar_metrics_tmp.update(batch_pbar_metrics)
forked_metrics = opt_closure_result.training_step_output_for_epoch_end.get_forked_metrics()
callback_metrics_tmp.update(forked_metrics)
callback_metrics_tmp.update(logged_metrics_tmp)
else:
batch_log_metrics = opt_closure_result.training_step_output_for_epoch_end.log_metrics
logged_metrics_tmp.update(batch_log_metrics)
batch_pbar_metrics = opt_closure_result.training_step_output_for_epoch_end.pbar_on_batch_end
pbar_metrics_tmp.update(batch_pbar_metrics)
# track progress bar metrics
if len(pbar_metrics_tmp) > 0:
self.add_progress_bar_metrics(pbar_metrics_tmp)
self._callback_metrics.update(callback_metrics_tmp)
# save legacy log metrics
self._logged_metrics.update(logged_metrics_tmp)
self.cached_results.legacy_batch_log_metrics.update(logged_metrics_tmp)
logcon.LoggerConnector.cache_training_step_metrics = fixed_cache_training_step_metrics
class BaseExperiment(pl.LightningModule):
"""Base experiment module that handles running, training, checkpointing etc."""
def log(self, name, value, on_step=None, on_epoch=None, sync_dist=None, **kwargs):
on_step = self._LightningModule__auto_choose_log_on_step(on_step) # Get around name mangling
on_epoch = self._LightningModule__auto_choose_log_on_epoch(on_epoch) # Get around name mangling
if sync_dist is None:
sync_dist = self.ddp
if self.cpu_metrics:
if isinstance(value, torch.Tensor):
value = value.mean().detach().cpu()
super(BaseExperiment, self).log(name, value, on_step=on_step, on_epoch=on_epoch, sync_dist=sync_dist, **kwargs)
if self.cpu_metrics and self._results is not None:
self._results.cpu()
@staticmethod
def handle_experiment_seed(seed):
if seed is None:
print("No seed passed setting 0; use 'random' for a random", 0)
pl.seed_everything(0)
elif seed == 'random':
seed = np.random.randint(np.iinfo(np.uint32).min, np.iinfo(np.uint32).max)
pl.seed_everything(seed)
print(f"Using random seed ", seed)
else:
print(f"Using specified seed value ", seed)
pl.seed_everything(seed)
return seed
@property
def name(self):
data_str = ''
if hasattr(self, 'data'):
data_str = str(self.data) + '/'
model_arch_str = ''
if hasattr(self.model, 'archid'):
model_arch_str = self.model.archid + '-'
return f"{data_str}{self.model.shortname}/{model_arch_str}{self.hparams.tag}"
def __init__(self, seed, monitor, mode):
super(BaseExperiment, self).__init__()
self.seed = self.handle_experiment_seed(seed)
ool.env.env_report()
self.monitor = monitor
self.mode = mode
self.workers = 0
self.drop_last_batch = False
self._data_len = None
self.__epo_scheduledvs = []
self.__itr_scheduledvs = []
self._prints = set()
self.nowatermark = False
@property
def data_len(self):
if hasattr(self, 'trainer') and isinstance(self.trainer, pl.Trainer) \
and hasattr(self.trainer, 'num_training_batches'):
try:
data_len = int(self.trainer.num_training_batches)
if data_len > 0:
return data_len
except ValueError:
pass
if self._data_len is None:
warnings.warn(
"Check for data_len requested but data has not been loaded yet -- forcing. Avoid this by delaying check logic")
self._data_len = len(self.train_dataloader())
return self._data_len
def onceprint(self, *args, **kwargs):
"""Just a useful debug function to see shapes when fisrt running"""
k = '_'.join(str(a) for a in args)
if k not in self._prints:
self.print(*args, **kwargs)
self._prints.add(k)
def add_scheduled_value(self, object, name, schedule):
v = ScheduledValue(object, name, schedule)
if v.is_itr:
self.__itr_scheduledvs.append(v)
else:
self.__epo_scheduledvs.append(v)
def update_epoch_scheduled_values(self, epoch, total_epoch, should_log=True):
for v in self.__epo_scheduledvs:
x = v.update(epoch, total_epoch)
if x is not None and should_log:
self.logger.experiment.add_scalar(v.name, x, self.current_epoch)
# self.log(v.name, x, on_step=False, on_epoch=True)
def update_itera_scheduled_values(self, step, total_steps, should_log=True):
for v in self.__itr_scheduledvs:
x = v.update(step, total_steps)
if x is not None and should_log:
self.logger.experiment.add_scalar(v.name, x, self.global_step)
# self.log(v.name, x, on_step=True, on_epoch=False)
def should_log_pictures(self):
self.onceprint(f"WARNING: will downsample the picture logging to concerve storage")
if self.current_epoch < 10: # log early stages
return True
if self.current_epoch < 50:
return self.current_epoch % 3 == 0 # log every 4
if self.current_epoch < 100:
return self.current_epoch % 5 == 0 # log every 5
return self.current_epoch % 10 == 9 # log every 10
def scheduled_values_summary(self):
summary = []
for v in self.__itr_scheduledvs:
summary.append(str(v))
for v in self.__epo_scheduledvs:
summary.append(str(v))
return '\n'.join(summary)
# def add_scalar_step(self, key, value):
# """Logging from training loop causes GPU memory to skyrocket for some reason"""
# self.logger.experiment.add_scalar(key, value, self.global_step)
def on_fit_start(self) -> None:
print(f"{ool.env.print_prefix()} - FIT START "
f"global_rank={self.trainer.global_rank} "
f"node_rank={self.trainer.node_rank} "
f"local_rank={self.trainer.local_rank}")
self.print(pformat(self.hparams))
self.print(self.scheduled_values_summary())
if self.trainer.is_global_zero:
if not self.nowatermark:
watermark_source(self.path)
# Guestimate the total length for schedule.. This is for initialisation and will be refined on epoch/step start.
with torch.no_grad():
me = self.hparams.get('epochs', None)
me = me or self.hparams.get('max_steps', None) // 1000
self.update_epoch_scheduled_values(0, me, should_log=False)
self.update_itera_scheduled_values(0, me * 1000, should_log=False)
def on_train_start(self) -> None:
self.print(f"Starting from {self.global_step} {self.current_epoch}")
def on_train_epoch_start(self) -> None:
with torch.no_grad():
step = self.current_epoch
total_steps = self.__max_epochs
self.update_epoch_scheduled_values(step, total_steps)
@functools.cached_property
def data(self):
return DataSpec(self.hparams.data)
@property
def input_shape(self):
return self.data.shape
@property
def __max_epochs(self):
if hasattr(self.hparams, 'epochs'):
return self.hparams.epochs
return int(self.hparams.max_steps / self.data_len + .5)
def get_max_epochs(self):
return self.__max_epochs
@property
def __max_steps(self):
if hasattr(self.hparams, 'max_steps'):
return self.hparams.max_steps
return self.hparams.epochs * self.data_len
def get_max_steps(self):
return self.__max_steps
def prepare_data(self):
print(f"{ool.env.print_prefix()} - PREP DATA "
f"global_rank={self.trainer.global_rank} "
f"node_rank={self.trainer.node_rank} "
f"local_rank={self.trainer.local_rank} "
f"{self.trainer.accelerator_connector.is_slurm_managing_tasks} {self.trainer.accelerator_connector.cluster_environment}")
self.data.prepare()
def train_dataloader(self) -> torch.utils.data.DataLoader:
"""This method will overwrite the parent class method"""
data = self.data.get_dataloader(self.hparams.batch_size,
workers=self.workers,
shuffle=True,
subset='train',
device=self.device,
drop_last=self.drop_last_batch)
return data
def val_dataloader(self) -> torch.utils.data.DataLoader:
"""This method will overwrite the parent class method"""
return self.data.get_dataloader(self.hparams.batch_size,
workers=self.workers,
shuffle=False,
subset='val',
device=self.device,
drop_last=self.drop_last_batch)
def test_dataloader(self) -> torch.utils.data.DataLoader:
"""This method will overwrite the parent class method"""
return self.data.get_dataloader(self.hparams.batch_size,
workers=self.workers,
shuffle=False,
subset='test',
device=self.device)
def test_step(self, *args, **kwargs):
self.onceprint(f"WARNING: Using validation loop for TESTING")
return self.validation_step(*args, **kwargs)
@torch.no_grad()
def accelated_batch_postprocessing(self, batch):
"""Incase there's some batch post-processing that needs to happen on the accelarator for speed"""
return self.data.post_processing(batch)
def get_progress_bar_dict(self):
"""Remove v_num from progbar"""
tqdm_dict = super().get_progress_bar_dict()
tqdm_dict.pop("v_num", None)
# tqdm_dict.pop("loss_step", None)
# tqdm_dict.pop("loss_epoch", None)
tqdm_dict['i'] = self.global_step
return tqdm_dict
def configure_optimizers(self):
osel, *oopts = self.hparams.optim.split('-')
opt = {
'rmsprop': torch.optim.RMSprop,
'adam': torch.optim.Adam,
'adamax': torch.optim.Adamax,
'sgd': torch.optim.SGD
}[osel]
for o in oopts:
if 'nest' in o:
opt = functools.partial(opt, nesterov=True)
if 'm' in o:
momentum = float(o[1:])
opt = functools.partial(opt, momentum=momentum)
if hasattr(self.model, 'param_groups'):
opt_params = [
{'params': list(pg['params']), 'lr': self.hparams.learning_rate * pg['lr'], **pg.get('other', {})} for pg in
self.model.param_groups()
]
elif hasattr(self.model, 'model_parameters'):
opt_params = [
{'params': list(self.model.model_parameters()), 'lr': self.hparams.learning_rate}
]
if hasattr(self.model, 'baseline_parameters'):
opt_params.append(
{'params': list(self.model.baseline_parameters()), 'lr': 10 * self.hparams.learning_rate})
else:
opt_params = [
{'params': list(self.model.parameters()), 'lr': self.hparams.learning_rate}
]
params = set(itertools.chain.from_iterable(pg['params'] for pg in opt_params))
missing_params = []
for name, param in self.model.named_parameters():
if param not in params:
print(f"{name} is missing from param_groups def")
missing_params.append(param)
if missing_params:
print(
f"{len(missing_params)} param groups missing from param_groups definition adding with lr={self.hparams.learning_rate}")
opt_params.append({'params': missing_params, 'lr': self.hparams.learning_rate})
opt = opt(opt_params)
r = {'optimizer': opt}
if self.hparams.lr is not None:
if self.hparams.lr == 'plateau':
r['lr_scheduler'] = {
'scheduler': torch.optim.lr_scheduler.ReduceLROnPlateau(opt, self.mode, 0.1, 3, True, min_lr=5e-5),
'monitor': self.monitor,
'reduce_on_plateau': True,
}
elif self.hparams.lr.startswith('decay('):
s = [n.strip() for n in self.hparams.lr[6:-1].strip().split(',')]
warmup_steps = int(s[0])
decay_steps = int(s[1])
decay_rate = 0.5
if len(s) >= 3:
decay_rate = float(s[2])
def lr_lambda(step):
if step < warmup_steps:
lr = float(step) / float(warmup_steps)
else:
lr = 1
return lr * decay_rate ** (float(step) / float(decay_steps))
r['lr_scheduler'] = {
'scheduler': torch.optim.lr_scheduler.LambdaLR(opt, lr_lambda),
'interval': 'step'
}
elif self.hparams.lr.startswith('warm('):
s = [n.strip() for n in self.hparams.lr[5:-1].strip().split(',')]
warmup_steps = int(s[0])
def lr_lambda(step):
if step < warmup_steps:
lr = float(step) / float(warmup_steps)
else:
lr = 1
return lr
r['lr_scheduler'] = {
'scheduler': torch.optim.lr_scheduler.LambdaLR(opt, lr_lambda),
'interval': 'step'
}
else:
lrs, interval = self.lr_scheduler(opt)
if lrs is not None:
r['lr_scheduler'] = {
'scheduler': lrs,
'interval': interval
}
return r
def trainer_kwargs(self):
return {}
def lr_scheduler(self, opt):
return None, None
# @property
# def ddp(self):
# print(self.trainer.accelerator.distributed_backend)
#
# return self.trainer.accelerator.distributed_backend and \
# self.trainer.accelerator.distributed_backend.startwith('ddp')
@property
def path(self):
if hasattr(self, 'trainer') and self.trainer is not None:
return self.trainer.default_root_dir
return self._path
def run(self,
dir=None,
resume=None,
with_hparams=True,
limit=None,
benchmark=True,
weights=None,
profile=None,
tune=None,
detect_anomaly=None,
no_stop=True,
gpus=None,
track_grads=False,
quiet=False,
save_top=1,
workers=-1,
dp=False,
ddp=False,
cpu_metrics=False,
amp=None,
acc=None,
drop_last_batch=False,
gc=0,
nowatermark=False,
resckpt=None,
nodes=1):
if nodes < 1:
nodes = 1
elif nodes > 1:
ddp = True
if gpus is None:
if ddp:
gpus = -1
else:
gpus = 1 if torch.cuda.is_available() else 0
resume_dict = {}
if resume:
if isinstance(resume, str):
if resume.endswith('ckpt'):
model_path = resume
p = Path(resume).parent
else:
p = Path(resume)
model_path = sorted((a for a in p.glob('*.ckpt')), key=lambda a: a.stat().st_mtime)[-1]
elif isinstance(resume, bool):
p = Path(exp_log_dir(self.name, no_unique=True, dir=dir))
experiments = itertools.chain(p.parent.glob(p.name),
p.parent.glob(p.name + '_?'),
p.parent.glob(p.name + '_??'),
p.parent.glob(p.name + '__id*'))
checkpoints = itertools.chain.from_iterable(itertools.chain(
p.glob('*.ckpt'),
p.glob('checkpoints/*.ckpt')) for p in experiments)
model_path = sorted((a for a in checkpoints), key=lambda a: a.stat().st_mtime)[-1]
p = model_path.parent
if p.name == 'checkpoints':
p = p.parent
print(f"Resuming model from {model_path} ({p})")
if with_hparams:
model = self.__class__.load_from_checkpoint(model_path, hparams_file= str(p / 'hparams.yaml'))
loaded_hparams = model.hparams
overwrites = {}
for k, v in self.hparams.items():
if k in loaded_hparams and v != loaded_hparams[k]:
overwrites[k] = v
if overwrites:
print(f"Overwriting: {pformat(overwrites)}")
model.hparams.update(overwrites)
resume_dict['resume_from_checkpoint'] = model_path
if with_hparams:
del self.model
self = model
else:
# This only prevents creation of the unique experiment directory on ddp spawn where we know the rank
p = Path(exp_log_dir(self.name, dir=dir, dist_safe=ddp or ool.env.is_slurm()))
print(f"{ool.env.print_prefix()} - Experiment path {p}")
os.environ["OOL_EXPERIMENT_PATH"] = str(p)
self.nowatermark = nowatermark
self._path = p
self.ddp = ddp
self.cpu_metrics = cpu_metrics
self.gc = gc
self.workers = workers
self.drop_last_batch = drop_last_batch
if weights:
print(f"Loading weights from model {weights}")
pl_model = self.__class__.load_from_checkpoint(weights)
self.model.load_state_dict(pl_model.model.state_dict()) # Will fail if not compat, ie do not overwrite
logger = pl.loggers.TensorBoardLogger(str(self.path), name='', version='')
checkpoint_callback = pl.callbacks.ModelCheckpoint(
monitor=self.monitor,
filename='best',
save_top_k=save_top,
mode=self.mode,
save_last=True,
)
clbs = [
checkpoint_callback,
CheckPointPNRG(),
CheckPointMetricInjector(),
pl.callbacks.LearningRateMonitor(logging_interval='epoch'),
TrainingStatusCallback(),
]
if not no_stop:
clbs.append(pl.callbacks.EarlyStopping(
monitor=self.monitor,
min_delta=1e-4,
patience=5,
verbose=True,
mode=self.mode,
strict=True
))
epochs = self.hparams.get('epochs', None)
steps = self.hparams.get('max_steps', None)
trainer_kwargs = {
'default_root_dir': str(self.path),
'gpus': gpus,
'logger': logger,
'callbacks': clbs,
'max_epochs': epochs,
'min_epochs': int(0.2 * epochs) if epochs else epochs, # run at least for 20% of the epochs
'max_steps': steps,
'min_steps': int(0.2 * steps) if steps else steps, # run at least for 20% of the epochs,
'move_metrics_to_cpu': cpu_metrics,
'num_nodes': nodes,
**resume_dict
}
if dp:
trainer_kwargs['accelerator'] = 'dp'
if ddp:
trainer_kwargs['accelerator'] = 'ddp'
trainer_kwargs['sync_batchnorm'] = True
trainer_kwargs['prepare_data_per_node'] = True
if ool.env.is_slurm():
quiet = True
trainer_kwargs['log_gpu_memory'] = 'min_max'
trainer_kwargs['precision'] = 32
if track_grads:
trainer_kwargs['track_grad_norm'] = 2
if quiet:
trainer_kwargs['progress_bar_refresh_rate'] = 0
trainer_kwargs['callbacks'].append(StdProgressLogger())
if limit:
trainer_kwargs['limit_train_batches'] = limit
trainer_kwargs['limit_val_batches'] = limit
if getattr(self.hparams, 'grad_clip', None):
trainer_kwargs['gradient_clip_val'] = self.hparams.grad_clip
if benchmark:
trainer_kwargs['benchmark'] = True
if profile:
trainer_kwargs['profiler'] = AdvancedProfiler(output_filename=str(p / 'profile.out'))
if tune:
trainer_kwargs['auto_lr_find'] = True
if amp:
trainer_kwargs['precision'] = 16
if acc:
trainer_kwargs['accumulate_grad_batches'] = int(acc)
trainer_kwargs.update(self.trainer_kwargs())
trainer = pl.Trainer(**trainer_kwargs)
print(f"{ool.env.print_prefix()} - POST TRAINER INIT "
f"Trainer properties: "
f"global_rank={trainer.global_rank} "
f"node_rank={trainer.node_rank} "
f"local_rank={trainer.local_rank} "
f"{trainer.accelerator_connector.is_slurm_managing_tasks} {trainer.accelerator_connector.cluster_environment}")
if tune:
print(f"{ool.env.print_prefix()} - Tunning {self.name}")
lr_finder = trainer.tuner.lr_find(self)
new_lr = lr_finder.suggestion()
self.print('LR resutls', lr_finder.results, '\nSuggestion', new_lr)
self.hparams.learning_rate = new_lr
else:
with torch.autograd.set_detect_anomaly(bool(detect_anomaly)):
try:
print(f"{ool.env.print_prefix()} - Fitting {self.name}")
trainer.fit(self)
except:
if self.current_epoch <= 0 and not resume:
print(f"{ool.env.print_prefix()} - Cleaning directory {self.path}")
shutil.rmtree(self.path)
raise
def just_return_the_name(self, *args, **kwargs):
return self.name
@classmethod
def parse_args_and_execute(cls):
print(cls.__name__)
strictfire.StrictFire(cls)
def on_train_batch_start(self, batch, batch_idx: int, dataloader_idx: int) -> None:
with torch.no_grad():
step = self.global_step
total_steps = self.__max_steps
self.update_itera_scheduled_values(step, total_steps)
if self.gc > 0 and batch_idx % self.gc == 0:
if not hasattr(self, '_gc_stat'):
self._gc_stat = 0
self._gc_cont = 0
if batch_idx == 0 and self._gc_cont > 0:
print(f"Extra GC collected {float(self._gc_stat) / self._gc_cont} on average")
self._gc_stat = 0
self._gc_cont = 0
self._gc_stat += gc.collect()
self._gc_cont += 1
return self.accelated_batch_postprocessing(batch)
def on_validation_batch_start(self, batch, batch_idx: int, dataloader_idx: int) -> None:
return self.accelated_batch_postprocessing(batch)
def on_test_batch_start(self, batch, batch_idx: int, dataloader_idx: int) -> None:
return self.accelated_batch_postprocessing(batch)
class TrainingStatusCallback(pl.callbacks.Callback):
def __init__(self):
self.status = None
def on_fit_start(self, trainer, pl_module):
self.status = None
def on_keyboard_interrupt(self, trainer, pl_module):
if self.status is None:
self.status = 'INTERRUPT'
def on_fit_end(self, trainer, pl_module):
if self.status is None:
self.status = 'SUCCESS'
pl_module.print(f"Stopping due to {self.status} at {trainer.global_step} step, {trainer.current_epoch} epoch")
with (Path(pl_module.path) / f"{self.status}").open('w') as outf:
pass
class StdProgressLogger(pl.callbacks.Callback):
def __init__(self):
self.start = datetime.now()
def on_train_epoch_start(self, trainer, pl_module):
self.start = datetime.now()
def on_validation_epoch_end(self, trainer, pl_module):
secs = (datetime.now() - self.start).total_seconds()
metrics = " ".join(f"{k}= {float(v):.3f}" for k, v in trainer.progress_bar_metrics.items())
pl_module.print(f"{trainer.current_epoch}/{pl_module.get_max_epochs()} [{pl_module.global_step}/{pl_module.get_max_steps()}] epoch in {secs:.2f}s: {metrics}")
class CheckPointMetricInjector(pl.callbacks.Callback):
def on_save_checkpoint(self, trainer, pl_module, checkpoint):
metrics = trainer.callback_metrics
metrics.update(trainer.logged_metrics)
key = 'metrics'
if key in checkpoint: key = str(self.__class__.__name__).lower() + '_' + key
checkpoint[key] = metrics
key = 'monitor'
if key in checkpoint: key = str(self.__class__.__name__).lower() + '_' + key
checkpoint[key] = {pl_module.monitor: metrics[pl_module.monitor]}
key = 'name'
if key in checkpoint: key = str(self.__class__.__name__).lower() + '_' + key
checkpoint[key] = pl_module.name
key = 'path'
if key in checkpoint: key = str(self.__class__.__name__).lower() + '_' + key
checkpoint[key] = pl_module.path
class CheckPointPNRG(pl.callbacks.Callback):
"""Preserve and restore PRNGs when checkpointing"""
def on_save_checkpoint(self, trainer, pl_module, checkpoint):
checkpoint['prng_states'] = {
'python.random': random.getstate(),
'numpy': np.random.get_state(),
}
if torch.cuda.is_available() and torch.cuda.is_initialized():
# TODO: how should this handle DP training?
checkpoint['prng_states']['cuda'] = torch.cuda.get_rng_state_all()
return checkpoint['prng_states']
def on_load_checkpoint(self, callback_state):
random.setstate(callback_state['python.random'])
np.random.set_state(callback_state['numpy'])
print("Restored python.random and numpy default PRNGs state")
if torch.cuda.is_available() and 'cuda' in callback_state:
torch.cuda.set_rng_state_all(callback_state['cuda'])
print(f"Restored CUDA PRNGs state to {len(callback_state['cuda'])} devices")