491 lines
15 KiB
Python
491 lines
15 KiB
Python
"""
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Lightning can automate saving and loading checkpoints
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=====================================================
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Checkpointing is enabled by default to the current working directory.
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To change the checkpoint path pass in::
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Trainer(default_save_path='/your/path/to/save/checkpoints')
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To modify the behavior of checkpointing pass in your own callback.
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.. code-block:: python
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from pytorch_lightning.callbacks import ModelCheckpoint
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# DEFAULTS used by the Trainer
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checkpoint_callback = ModelCheckpoint(
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filepath=os.getcwd(),
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save_best_only=True,
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verbose=True,
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monitor='val_loss',
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mode='min',
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prefix=''
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)
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trainer = Trainer(checkpoint_callback=checkpoint_callback)
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Restoring training session
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--------------------------
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You might want to not only load a model but also continue training it. Use this method to
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restore the trainer state as well. This will continue from the epoch and global step you last left off.
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However, the dataloaders will start from the first batch again (if you shuffled it shouldn't matter).
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Lightning will restore the session if you pass a logger with the same version and there's a saved checkpoint.
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.. code-block:: python
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from pytorch_lightning import Trainer
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from pytorch_lightning.logging import TestTubeLogger
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logger = TestTubeLogger(
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save_dir='./savepath',
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version=1 # An existing version with a saved checkpoint
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)
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trainer = Trainer(
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logger=logger,
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default_save_path='./savepath'
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)
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# this fit call loads model weights and trainer state
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# the trainer continues seamlessly from where you left off
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# without having to do anything else.
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trainer.fit(model)
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The trainer restores:
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- global_step
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- current_epoch
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- All optimizers
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- All lr_schedulers
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- Model weights
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You can even change the logic of your model as long as the weights and "architecture" of
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the system isn't different. If you add a layer, for instance, it might not work.
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At a rough level, here's what happens inside Trainer :py:mod:`pytorch_lightning.base_module.model_saving.py`:
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.. code-block:: python
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self.global_step = checkpoint['global_step']
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self.current_epoch = checkpoint['epoch']
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# restore the optimizers
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optimizer_states = checkpoint['optimizer_states']
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for optimizer, opt_state in zip(self.optimizers, optimizer_states):
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optimizer.load_state_dict(opt_state)
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# restore the lr schedulers
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lr_schedulers = checkpoint['lr_schedulers']
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for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers):
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scheduler.load_state_dict(lrs_state)
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# uses the model you passed into trainer
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model.load_state_dict(checkpoint['state_dict'])
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"""
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import os
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import re
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import signal
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import warnings
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from subprocess import call
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import logging
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from abc import ABC
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from argparse import Namespace
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import pandas as pd
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import torch
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import torch.distributed as dist
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from pytorch_lightning.overrides.data_parallel import (
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LightningDistributedDataParallel,
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LightningDataParallel,
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)
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class TrainerIOMixin(ABC):
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def __init__(self):
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# this is just a summary on variables used in this abstract class,
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# the proper values/initialisation should be done in child class
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self.model = None
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self.on_gpu = None
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self.root_gpu = None
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self.resume_from_checkpoint = None
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self.use_ddp = None
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self.use_ddp2 = None
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self.checkpoint_callback = None
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self.proc_rank = None
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self.weights_save_path = None
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self.logger = None
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self.early_stop_callback = None
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self.lr_schedulers = None
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self.optimizers = None
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def get_model(self):
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is_dp_module = isinstance(self.model, (LightningDistributedDataParallel,
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LightningDataParallel))
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model = self.model.module if is_dp_module else self.model
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return model
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# --------------------
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# CHECK-POINTING
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# --------------------
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def restore_weights(self, model):
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"""
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To restore weights we have two cases.
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First, attempt to restore hpc weights. If successful, don't restore
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other weights.
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Otherwise, try to restore actual weights
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:param model:
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:return:
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"""
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# clear cache before restore
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if self.on_gpu:
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torch.cuda.empty_cache()
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# if script called from hpc resubmit, load weights
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did_restore_hpc_weights = self.restore_hpc_weights_if_needed(model)
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# clear cache after restore
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if self.on_gpu:
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torch.cuda.empty_cache()
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if not did_restore_hpc_weights:
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if self.resume_from_checkpoint is not None:
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self.restore(self.resume_from_checkpoint, on_gpu=self.on_gpu)
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else:
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# restore weights if same exp version
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self.restore_state_if_checkpoint_exists(model)
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# wait for all models to restore weights
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if self.use_ddp or self.use_ddp2:
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# wait for all processes to catch up
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dist.barrier()
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# clear cache after restore
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if self.on_gpu:
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torch.cuda.empty_cache()
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def restore_state_if_checkpoint_exists(self, model):
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did_restore = False
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# do nothing if there's not dir or callback
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no_ckpt_callback = (self.checkpoint_callback is None) or (not self.checkpoint_callback)
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if no_ckpt_callback or not os.path.exists(self.checkpoint_callback.filepath):
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return did_restore
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# restore trainer state and model if there is a weight for this experiment
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last_epoch = -1
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last_ckpt_name = None
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# find last epoch
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checkpoints = os.listdir(self.checkpoint_callback.filepath)
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for name in checkpoints:
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# ignore hpc ckpts
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if 'hpc_' in name:
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continue
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if '.ckpt' in name:
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epoch = name.split('epoch_')[1]
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epoch = int(re.sub('[^0-9]', '', epoch))
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if epoch > last_epoch:
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last_epoch = epoch
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last_ckpt_name = name
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# restore last checkpoint
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if last_ckpt_name is not None:
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last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name)
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self.restore(last_ckpt_path, self.on_gpu)
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logging.info(f'model and trainer restored from checkpoint: {last_ckpt_path}')
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did_restore = True
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return did_restore
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# --------------------
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# HPC SIGNAL HANDLING
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# --------------------
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def register_slurm_signal_handlers(self):
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# see if we're using slurm (not interactive)
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on_slurm = False
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try:
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job_name = os.environ['SLURM_JOB_NAME']
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if job_name != 'bash':
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on_slurm = True
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except Exception as e:
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pass
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if on_slurm:
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logging.info('set slurm handle signals')
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signal.signal(signal.SIGUSR1, self.sig_handler)
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signal.signal(signal.SIGTERM, self.term_handler)
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def sig_handler(self, signum, frame):
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if self.proc_rank == 0:
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# save weights
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logging.info('handling SIGUSR1')
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self.hpc_save(self.weights_save_path, self.logger)
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# find job id
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job_id = os.environ['SLURM_JOB_ID']
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cmd = 'scontrol requeue {}'.format(job_id)
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# requeue job
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logging.info('\nrequeing job {job_id}...')
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result = call(cmd, shell=True)
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# print result text
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if result == 0:
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logging.info('requeued exp {job_id}')
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else:
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logging.info('requeue failed...')
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# close experiment to avoid issues
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self.logger.close()
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def term_handler(self, signum, frame):
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# save
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logging.info("bypassing sigterm")
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# --------------------
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# MODEL SAVE CHECKPOINT
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# --------------------
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def save_checkpoint(self, filepath):
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checkpoint = self.dump_checkpoint()
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# do the actual save
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try:
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torch.save(checkpoint, filepath)
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except AttributeError:
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if 'hparams' in checkpoint:
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del checkpoint['hparams']
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torch.save(checkpoint, filepath)
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def restore(self, checkpoint_path, on_gpu):
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# if on_gpu:
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# checkpoint = torch.load(checkpoint_path)
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# else:
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# load on CPU first
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checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
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# load model state
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model = self.get_model()
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# load the state_dict on the model automatically
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model.load_state_dict(checkpoint['state_dict'])
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if on_gpu:
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model.cuda(self.root_gpu)
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# load training state (affects trainer only)
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self.restore_training_state(checkpoint)
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def dump_checkpoint(self):
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checkpoint = {
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'epoch': self.current_epoch,
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'global_step': self.global_step
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}
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if self.checkpoint_callback is not None and self.checkpoint_callback is not False:
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checkpoint['checkpoint_callback_best'] = self.checkpoint_callback.best
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if self.early_stop_callback is not None and self.checkpoint_callback is not False:
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checkpoint['early_stop_callback_wait'] = self.early_stop_callback.wait
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checkpoint['early_stop_callback_patience'] = self.early_stop_callback.patience
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# save optimizers
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optimizer_states = []
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for i, optimizer in enumerate(self.optimizers):
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optimizer_states.append(optimizer.state_dict())
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checkpoint['optimizer_states'] = optimizer_states
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# save lr schedulers
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lr_schedulers = []
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for i, scheduler in enumerate(self.lr_schedulers):
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lr_schedulers.append(scheduler.state_dict())
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checkpoint['lr_schedulers'] = lr_schedulers
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# add the hparams and state_dict from the model
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model = self.get_model()
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checkpoint['state_dict'] = model.state_dict()
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if hasattr(model, "hparams"):
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checkpoint['hparams'] = vars(model.hparams)
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else:
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warnings.warn(
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"Did not find hyperparameters at model.hparams. Saving checkpoint without"
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" hyperparameters"
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)
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# give the model a chance to add a few things
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model.on_save_checkpoint(checkpoint)
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return checkpoint
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# --------------------
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# HPC IO
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# --------------------
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def restore_hpc_weights_if_needed(self, model):
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"""
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If there is a set of hpc weights, use as signal to restore model
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:param model:
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:return:
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"""
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did_restore = False
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# look for hpc weights
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folderpath = self.weights_save_path
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if os.path.exists(folderpath):
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files = os.listdir(folderpath)
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hpc_weight_paths = [x for x in files if 'hpc_ckpt' in x]
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# if hpc weights exist restore model
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if len(hpc_weight_paths) > 0:
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self.hpc_load(folderpath, self.on_gpu)
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did_restore = True
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return did_restore
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def restore_training_state(self, checkpoint):
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"""
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Restore trainer state.
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Model will get its change to update
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:param checkpoint:
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:return:
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"""
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if self.checkpoint_callback is not None and self.checkpoint_callback is not False:
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self.checkpoint_callback.best = checkpoint['checkpoint_callback_best']
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if self.early_stop_callback is not None and self.early_stop_callback is not False:
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self.early_stop_callback.wait = checkpoint['early_stop_callback_wait']
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self.early_stop_callback.patience = checkpoint['early_stop_callback_patience']
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self.global_step = checkpoint['global_step']
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self.current_epoch = checkpoint['epoch']
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# restore the optimizers
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optimizer_states = checkpoint['optimizer_states']
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for optimizer, opt_state in zip(self.optimizers, optimizer_states):
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optimizer.load_state_dict(opt_state)
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# move optimizer to GPU 1 weight at a time
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# avoids OOM
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if self.root_gpu is not None:
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for state in optimizer.state.values():
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for k, v in state.items():
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if isinstance(v, torch.Tensor):
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state[k] = v.cuda(self.root_gpu)
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# restore the lr schedulers
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lr_schedulers = checkpoint['lr_schedulers']
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for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers):
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scheduler.load_state_dict(lrs_state)
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# ----------------------------------
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# PRIVATE OPS
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# ----------------------------------
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def hpc_save(self, folderpath, logger):
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# make sure the checkpoint folder exists
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os.makedirs(folderpath, exist_ok=True)
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# save logger to make sure we get all the metrics
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logger.save()
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ckpt_number = self.max_ckpt_in_folder(folderpath) + 1
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if not os.path.exists(folderpath):
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os.makedirs(folderpath, exist_ok=True)
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filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, ckpt_number)
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# give model a chance to do something on hpc_save
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model = self.get_model()
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checkpoint = self.dump_checkpoint()
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model.on_hpc_save(checkpoint)
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# do the actual save
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# TODO: fix for anything with multiprocess DP, DDP, DDP2
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try:
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torch.save(checkpoint, filepath)
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except AttributeError:
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if 'hparams' in checkpoint:
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del checkpoint['hparams']
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torch.save(checkpoint, filepath)
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return filepath
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def hpc_load(self, folderpath, on_gpu):
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filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, self.max_ckpt_in_folder(folderpath))
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# load on CPU first
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checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)
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# load model state
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model = self.get_model()
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# load the state_dict on the model automatically
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model.load_state_dict(checkpoint['state_dict'])
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if self.root_gpu is not None:
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model.cuda(self.root_gpu)
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# load training state (affects trainer only)
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self.restore_training_state(checkpoint)
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# call model hook
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model.on_hpc_load(checkpoint)
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logging.info(f'restored hpc model from: {filepath}')
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def max_ckpt_in_folder(self, path, name_key='ckpt_'):
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files = os.listdir(path)
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files = [x for x in files if name_key in x]
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if len(files) == 0:
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return 0
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ckpt_vs = []
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for name in files:
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name = name.split(name_key)[-1]
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name = re.sub('[^0-9]', '', name)
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ckpt_vs.append(int(name))
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return max(ckpt_vs)
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def load_hparams_from_tags_csv(tags_csv):
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if not os.path.isfile(tags_csv):
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logging.warning(f'Missing Tags: {tags_csv}.')
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return Namespace()
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tags_df = pd.read_csv(tags_csv)
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dic = tags_df.to_dict(orient='records')
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ns_dict = {row['key']: convert(row['value']) for row in dic}
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ns = Namespace(**ns_dict)
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return ns
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def convert(val):
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constructors = [int, float, str]
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if type(val) is str:
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if val.lower() == 'true':
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return True
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if val.lower() == 'false':
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return False
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for c in constructors:
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try:
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return c(val)
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except ValueError:
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pass
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return val
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