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checkpoint_connector.py
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checkpoint_connector.py
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# 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 io
import os
import re
import signal
from abc import ABC
from subprocess import call
import torch
import torch.distributed as torch_distrib
import pytorch_lightning
from pytorch_lightning import _logger as log
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.utilities import AMPType, rank_zero_warn
from pytorch_lightning.utilities.cloud_io import atomic_save, get_filesystem
from pytorch_lightning.utilities.cloud_io import load as pl_load
from pytorch_lightning.utilities.upgrade_checkpoint import KEYS_MAPPING as DEPRECATED_CHECKPOINT_KEYS
from pytorch_lightning.utilities.exceptions import MisconfigurationException
try:
from apex import amp
except ImportError:
amp = None
try:
from omegaconf import Container
except ImportError:
OMEGACONF_AVAILABLE = False
else:
OMEGACONF_AVAILABLE = True
class CheckpointConnector:
def __init__(self, trainer):
self.trainer = trainer
# used to validate checkpointing logic
self.has_trained = False
def restore_weights(self, model: LightningModule):
"""
We attempt to restore weights in this order:
1. HPC weights.
2. if no HPC weights restore checkpoint_path weights
3. otherwise don't restore weights
"""
# clear cache before restore
if self.trainer.on_gpu:
torch.cuda.empty_cache()
# if script called from hpc resubmit, load weights
did_restore_hpc_weights = self.restore_hpc_weights_if_needed(model)
# clear cache after restore
if self.trainer.on_gpu:
torch.cuda.empty_cache()
if not did_restore_hpc_weights:
if self.trainer.resume_from_checkpoint is not None:
self.restore(self.trainer.resume_from_checkpoint, on_gpu=self.trainer.on_gpu)
# wait for all to catch up
self.trainer.accelerator_backend.barrier('TrainerIOMixin.restore_weights')
# clear cache after restore
if self.trainer.on_gpu:
torch.cuda.empty_cache()
def restore(self, checkpoint_path: str, on_gpu: bool):
"""
Restore training state from checkpoint.
Also restores all training state like:
- epoch
- callbacks
- schedulers
- optimizer
"""
# if on_gpu:
# checkpoint = torch.load(checkpoint_path)
# else:
# load on CPU first
checkpoint = pl_load(checkpoint_path, map_location=lambda storage, loc: storage)
# load model state
model = self.trainer.get_model()
# give the datamodule a chance to load something
if self.trainer.datamodule is not None:
self.trainer.datamodule.on_load_checkpoint(checkpoint)
# give model a chance to load something
model.on_load_checkpoint(checkpoint)
# load the state_dict on the model automatically
model.load_state_dict(checkpoint['state_dict'])
if on_gpu:
model.cuda(self.trainer.root_gpu)
# restore amp scaling
if self.trainer.amp_backend == AMPType.NATIVE and 'native_amp_scaling_state' in checkpoint:
self.trainer.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])
elif self.trainer.amp_backend == AMPType.APEX and 'amp_scaling_state' in checkpoint:
amp.load_state_dict(checkpoint['amp_scaling_state'])
# load training state (affects trainer only)
self.restore_training_state(checkpoint)
def restore_training_state(self, checkpoint):
"""
Restore trainer state.
Model will get its change to update
:param checkpoint:
:return:
"""
if 'optimizer_states' not in checkpoint or 'lr_schedulers' not in checkpoint:
raise KeyError(
'Trying to restore training state but checkpoint contains only the model.'
' This is probably due to `ModelCheckpoint.save_weights_only` being set to `True`.'
)
if any([key in checkpoint for key in DEPRECATED_CHECKPOINT_KEYS]):
raise ValueError(
"The checkpoint you're attempting to load follows an"
" outdated schema. You can upgrade to the current schema by running"
" `python -m pytorch_lightning.utilities.upgrade_checkpoint --file model.ckpt`"
" where `model.ckpt` is your checkpoint file."
)
# load callback states
self.trainer.on_load_checkpoint(checkpoint)
self.trainer.global_step = checkpoint['global_step']
self.trainer.current_epoch = checkpoint['epoch']
# crash if max_epochs is lower then the current epoch from the checkpoint
if self.trainer.current_epoch > self.trainer.max_epochs:
m = f"""
you restored a checkpoint with current_epoch={self.trainer.current_epoch}
but the Trainer(max_epochs={self.trainer.max_epochs})
"""
raise MisconfigurationException(m)
# Division deals with global step stepping once per accumulated batch
# Inequality deals with different global step for odd vs even num_training_batches
n_accum = 1 if self.trainer.accumulate_grad_batches is None else self.trainer.accumulate_grad_batches
expected_steps = self.trainer.num_training_batches / n_accum
if self.trainer.num_training_batches != 0 and self.trainer.global_step % expected_steps > 1:
rank_zero_warn(
"You're resuming from a checkpoint that ended mid-epoch. "
"This can cause unreliable results if further training is done, "
"consider using an end of epoch checkpoint. "
)
# restore the optimizers
optimizer_states = checkpoint['optimizer_states']
for optimizer, opt_state in zip(self.trainer.optimizers, optimizer_states):
optimizer.load_state_dict(opt_state)
# move optimizer to GPU 1 weight at a time
# avoids OOM
if self.trainer.root_gpu is not None:
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda(self.trainer.root_gpu)
# restore the lr schedulers
lr_schedulers = checkpoint['lr_schedulers']
for scheduler, lrs_state in zip(self.trainer.lr_schedulers, lr_schedulers):
scheduler['scheduler'].load_state_dict(lrs_state)
def restore_hpc_weights_if_needed(self, model: LightningModule):
"""If there is a set of hpc weights, use as signal to restore model."""
did_restore = False
# look for hpc weights
folderpath = str(self.trainer.weights_save_path)
fs = get_filesystem(folderpath)
if fs.exists(folderpath):
files = [os.path.basename(f['name']) for f in fs.listdir(folderpath)]
hpc_weight_paths = [x for x in files if 'hpc_ckpt' in x]
# if hpc weights exist restore model
if len(hpc_weight_paths) > 0:
self.hpc_load(folderpath, self.trainer.on_gpu)
did_restore = True
return did_restore
# ----------------------------------
# PRIVATE OPS
# ----------------------------------
def hpc_save(self, folderpath: str, logger):
# make sure the checkpoint folder exists
folderpath = str(folderpath) # because the tests pass a path object
fs = get_filesystem(folderpath)
fs.makedirs(folderpath, exist_ok=True)
# save logger to make sure we get all the metrics
logger.save()
ckpt_number = self.max_ckpt_in_folder(folderpath) + 1
fs.makedirs(folderpath, exist_ok=True)
filepath = os.path.join(folderpath, f'hpc_ckpt_{ckpt_number}.ckpt')
# give model a chance to do something on hpc_save
model = self.trainer.get_model()
checkpoint = self.dump_checkpoint()
model.on_hpc_save(checkpoint)
# do the actual save
# TODO: fix for anything with multiprocess DP, DDP, DDP2
try:
atomic_save(checkpoint, filepath)
except AttributeError as err:
if LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in checkpoint:
del checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]
rank_zero_warn(
'warning, `module_arguments` dropped from checkpoint.' f' An attribute is not picklable {err}'
)
atomic_save(checkpoint, filepath)
return filepath
def dump_checkpoint(self, weights_only: bool = False) -> dict:
"""Creating model checkpoint.
Args:
weights_only: saving model weights only
Return:
structured dictionary
"""
current_epoch = self.trainer.current_epoch
global_step = self.trainer.global_step
has_reached_max_steps = self.trainer.max_steps and self.trainer.max_steps <= global_step
global_step += 1
if self.has_trained:
if not has_reached_max_steps:
current_epoch += 1
checkpoint = {
'epoch': current_epoch,
'global_step': global_step,
'pytorch-lightning_version': pytorch_lightning.__version__,
}
if not weights_only:
# save callbacks
callback_states = self.trainer.on_save_checkpoint()
checkpoint['callbacks'] = callback_states
# save optimizers
optimizer_states = []
for i, optimizer in enumerate(self.trainer.optimizers):
optimizer_states.append(optimizer.state_dict())
checkpoint['optimizer_states'] = optimizer_states
# save lr schedulers
lr_schedulers = []
for scheduler in self.trainer.lr_schedulers:
lr_schedulers.append(scheduler['scheduler'].state_dict())
checkpoint['lr_schedulers'] = lr_schedulers
# save native amp scaling
if self.trainer.amp_backend == AMPType.NATIVE and not self.trainer.use_tpu and self.trainer.scaler is not None:
checkpoint['native_amp_scaling_state'] = self.trainer.scaler.state_dict()
elif self.trainer.amp_backend == AMPType.APEX:
checkpoint['amp_scaling_state'] = amp.state_dict()
# add the module_arguments and state_dict from the model
model = self.trainer.get_model()
checkpoint['state_dict'] = model.state_dict()
if model.hparams:
if hasattr(model, '_hparams_name'):
checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_NAME] = model._hparams_name
# add arguments to the checkpoint
if OMEGACONF_AVAILABLE:
checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY] = model.hparams
if isinstance(model.hparams, Container):
checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_TYPE] = type(model.hparams)
else:
checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY] = dict(model.hparams)
# give the model a chance to add a few things
model.on_save_checkpoint(checkpoint)
if self.trainer.datamodule is not None:
self.trainer.datamodule.on_save_checkpoint(checkpoint)
return checkpoint
def hpc_load(self, folderpath, on_gpu):
filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, self.max_ckpt_in_folder(folderpath))
# load on CPU first
checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)
# load model state
model = self.trainer.get_model()
# load the state_dict on the model automatically
model.load_state_dict(checkpoint['state_dict'])
# restore amp scaling
if self.trainer.amp_backend == AMPType.NATIVE and 'native_amp_scaling_state' in checkpoint:
self.trainer.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])
elif self.trainer.amp_backend == AMPType.APEX and 'amp_scaling_state' in checkpoint:
amp.load_state_dict(checkpoint['amp_scaling_state'])
if self.trainer.root_gpu is not None:
model.cuda(self.trainer.root_gpu)
# load training state (affects trainer only)
self.restore_training_state(checkpoint)
# call model hook
model.on_hpc_load(checkpoint)
log.info(f'restored hpc model from: {filepath}')
def max_ckpt_in_folder(self, path, name_key='ckpt_'):
fs = get_filesystem(path)
files = [os.path.basename(f["name"]) for f in fs.listdir(path)]
files = [x for x in files if name_key in x]
if len(files) == 0:
return 0
ckpt_vs = []
for name in files:
name = name.split(name_key)[-1]
name = re.sub('[^0-9]', '', name)
ckpt_vs.append(int(name))
return max(ckpt_vs)
def save_checkpoint(self, filepath, weights_only: bool = False):
checkpoint = self.dump_checkpoint(weights_only)
if self.trainer.is_global_zero:
# do the actual save
try:
atomic_save(checkpoint, filepath)
except AttributeError as err:
if LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in checkpoint:
del checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]
rank_zero_warn(
'Warning, `module_arguments` dropped from checkpoint.' f' An attribute is not picklable {err}'
)
atomic_save(checkpoint, filepath)