363 lines
14 KiB
Python
363 lines
14 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 io
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import os
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import re
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import signal
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from abc import ABC
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from subprocess import call
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import torch
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import torch.distributed as torch_distrib
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import pytorch_lightning
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from pytorch_lightning import _logger as log
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from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.loggers import LightningLoggerBase
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from pytorch_lightning.overrides.data_parallel import LightningDataParallel, LightningDistributedDataParallel
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from pytorch_lightning.utilities import AMPType, rank_zero_warn
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from pytorch_lightning.utilities.cloud_io import atomic_save, get_filesystem
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from pytorch_lightning.utilities.cloud_io import load as pl_load
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from pytorch_lightning.utilities.upgrade_checkpoint import KEYS_MAPPING as DEPRECATED_CHECKPOINT_KEYS
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from pytorch_lightning.accelerators.base_backend import Accelerator
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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try:
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from apex import amp
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except ImportError:
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amp = None
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try:
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from omegaconf import Container
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except ImportError:
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OMEGACONF_AVAILABLE = False
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else:
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OMEGACONF_AVAILABLE = True
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class CheckpointConnector:
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def __init__(self, trainer):
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self.trainer = trainer
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def restore_weights(self, model: LightningModule):
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"""
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We attempt to restore weights in this order:
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1. HPC weights.
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2. if no HPC weights restore checkpoint_path weights
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3. otherwise don't restore weights
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"""
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# clear cache before restore
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if self.trainer.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.trainer.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.trainer.resume_from_checkpoint is not None:
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self.restore(self.trainer.resume_from_checkpoint, on_gpu=self.trainer.on_gpu)
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# wait for all to catch up
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self.trainer.accelerator_backend.barrier('TrainerIOMixin.restore_weights')
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# clear cache after restore
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if self.trainer.on_gpu:
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torch.cuda.empty_cache()
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def restore(self, checkpoint_path: str, on_gpu: bool):
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"""
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Restore training state from checkpoint.
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Also restores all training state like:
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- epoch
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- callbacks
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- schedulers
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- optimizer
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"""
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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 = pl_load(checkpoint_path, map_location=lambda storage, loc: storage)
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# load model state
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model = self.trainer.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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# give the datamodule a chance to load something
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if self.trainer.datamodule is not None:
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self.trainer.datamodule.on_load_checkpoint(checkpoint)
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# give model a chance to load something
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model.on_load_checkpoint(checkpoint)
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if on_gpu:
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model.cuda(self.trainer.root_gpu)
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# restore amp scaling
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if self.trainer.amp_backend == AMPType.NATIVE and 'native_amp_scaling_state' in checkpoint:
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self.trainer.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])
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elif self.trainer.amp_backend == AMPType.APEX and 'amp_scaling_state' in checkpoint:
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amp.load_state_dict(checkpoint['amp_scaling_state'])
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# load training state (affects trainer only)
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self.restore_training_state(checkpoint)
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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 'optimizer_states' not in checkpoint or 'lr_schedulers' not in checkpoint:
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raise KeyError(
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'Trying to restore training state but checkpoint contains only the model.'
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' This is probably due to `ModelCheckpoint.save_weights_only` being set to `True`.'
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)
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if any([key in checkpoint for key in DEPRECATED_CHECKPOINT_KEYS]):
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raise ValueError(
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"The checkpoint you're attempting to load follows an"
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" outdated schema. You can upgrade to the current schema by running"
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" `python -m pytorch_lightning.utilities.upgrade_checkpoint --file model.ckpt`"
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" where `model.ckpt` is your checkpoint file."
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)
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# load callback states
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self.trainer.on_load_checkpoint(checkpoint)
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self.trainer.global_step = checkpoint['global_step']
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self.trainer.current_epoch = checkpoint['epoch']
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# crash if max_epochs is lower than the current epoch from the checkpoint
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if self.trainer.current_epoch > self.trainer.max_epochs:
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m = f"""
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you restored a checkpoint with current_epoch={self.trainer.current_epoch}
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but the Trainer(max_epochs={self.trainer.max_epochs})
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"""
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raise MisconfigurationException(m)
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# Division deals with global step stepping once per accumulated batch
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# Inequality deals with different global step for odd vs even num_training_batches
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n_accum = 1 if self.trainer.accumulate_grad_batches is None else self.trainer.accumulate_grad_batches
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expected_steps = self.trainer.num_training_batches / n_accum
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if self.trainer.num_training_batches != 0 and self.trainer.global_step % expected_steps > 1:
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rank_zero_warn(
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"You're resuming from a checkpoint that ended mid-epoch. "
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"This can cause unreliable results if further training is done, "
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"consider using an end of epoch checkpoint. "
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)
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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.trainer.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.trainer.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.trainer.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.trainer.lr_schedulers, lr_schedulers):
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scheduler['scheduler'].load_state_dict(lrs_state)
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def restore_hpc_weights_if_needed(self, model: LightningModule):
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"""If there is a set of hpc weights, use as signal to restore model."""
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did_restore = False
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# look for hpc weights
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folderpath = str(self.trainer.weights_save_path)
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fs = get_filesystem(folderpath)
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if fs.exists(folderpath):
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files = [os.path.basename(f['name']) for f in fs.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.trainer.on_gpu)
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did_restore = True
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return did_restore
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# ----------------------------------
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# PRIVATE OPS
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# ----------------------------------
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def hpc_save(self, folderpath: str, logger):
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# make sure the checkpoint folder exists
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folderpath = str(folderpath) # because the tests pass a path object
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fs = get_filesystem(folderpath)
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fs.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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fs.makedirs(folderpath, exist_ok=True)
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filepath = os.path.join(folderpath, f'hpc_ckpt_{ckpt_number}.ckpt')
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# give model a chance to do something on hpc_save
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model = self.trainer.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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atomic_save(checkpoint, filepath)
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except AttributeError as err:
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if LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in checkpoint:
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del checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]
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rank_zero_warn(
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'warning, `module_arguments` dropped from checkpoint.' f' An attribute is not picklable {err}'
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)
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atomic_save(checkpoint, filepath)
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return filepath
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def dump_checkpoint(self, weights_only: bool = False) -> dict:
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"""Creating model checkpoint.
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Args:
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weights_only: saving model weights only
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Return:
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structured dictionary
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"""
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checkpoint = {
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'epoch': self.trainer.current_epoch + 1,
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'global_step': self.trainer.global_step + 1,
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'pytorch-lightning_version': pytorch_lightning.__version__,
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}
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if not weights_only:
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# save callbacks
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callback_states = self.trainer.on_save_checkpoint()
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checkpoint['callbacks'] = callback_states
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# save optimizers
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optimizer_states = []
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for i, optimizer in enumerate(self.trainer.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 scheduler in self.trainer.lr_schedulers:
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lr_schedulers.append(scheduler['scheduler'].state_dict())
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checkpoint['lr_schedulers'] = lr_schedulers
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# save native amp scaling
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if self.trainer.amp_backend == AMPType.NATIVE and not self.trainer.use_tpu and self.trainer.scaler is not None:
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checkpoint['native_amp_scaling_state'] = self.trainer.scaler.state_dict()
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elif self.trainer.amp_backend == AMPType.APEX:
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checkpoint['amp_scaling_state'] = amp.state_dict()
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# add the module_arguments and state_dict from the model
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model = self.trainer.get_model()
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checkpoint['state_dict'] = model.state_dict()
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if model.hparams:
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if hasattr(model, '_hparams_name'):
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checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_NAME] = model._hparams_name
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# add arguments to the checkpoint
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if OMEGACONF_AVAILABLE:
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checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY] = model.hparams
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if isinstance(model.hparams, Container):
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checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_TYPE] = type(model.hparams)
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else:
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checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY] = dict(model.hparams)
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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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if self.trainer.datamodule is not None:
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self.trainer.datamodule.on_save_checkpoint(checkpoint)
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return checkpoint
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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.trainer.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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# restore amp scaling
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if self.trainer.amp_backend == AMPType.NATIVE and 'native_amp_scaling_state' in checkpoint:
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self.trainer.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])
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elif self.trainer.amp_backend == AMPType.APEX and 'amp_scaling_state' in checkpoint:
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amp.load_state_dict(checkpoint['amp_scaling_state'])
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if self.trainer.root_gpu is not None:
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model.cuda(self.trainer.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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log.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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fs = get_filesystem(path)
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files = [os.path.basename(f["name"]) for f in fs.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 save_checkpoint(self, filepath, weights_only: bool = False):
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checkpoint = self.dump_checkpoint(weights_only)
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if self.trainer.is_global_zero:
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# do the actual save
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try:
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atomic_save(checkpoint, filepath)
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except AttributeError as err:
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if LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in checkpoint:
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del checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]
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rank_zero_warn(
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'Warning, `module_arguments` dropped from checkpoint.' f' An attribute is not picklable {err}'
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)
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atomic_save(checkpoint, filepath)
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