ref: checkpoint connector methods 3/n
This commit is contained in:
William Falcon
parent
ff0064f956
commit
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# 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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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 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.trainer.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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# 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) for f in fs.ls(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.trainer.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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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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# 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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# ----------------------------------
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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'] = self.trainer.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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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) for f in fs.ls(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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@ -11,7 +11,7 @@ from pytorch_lightning.utilities.model_utils import is_overridden
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.callbacks import ProgressBarBase
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from pytorch_lightning.trainer.connectors.model_connector import ModelConnector
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from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector
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class TrainerProperties(ABC):
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@ -30,6 +30,7 @@ class TrainerProperties(ABC):
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_default_root_dir: str
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_weights_save_path: str
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model_connector: ModelConnector
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checkpoint_connector: CheckpointConnector
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@property
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def use_amp(self) -> bool:
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@ -166,3 +167,6 @@ class TrainerProperties(ABC):
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if get_filesystem(self._weights_save_path).protocol == "file":
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return os.path.normpath(self._weights_save_path)
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return self._weights_save_path
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def save_checkpoint(self, filepath, weights_only: bool = False):
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self.checkpoint_connector.save_checkpoint(filepath, weights_only)
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@ -48,6 +48,7 @@ from pytorch_lightning.trainer.connectors.training_trick_connector import Traini
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from pytorch_lightning.trainer.connectors.callback_connector import CallbackConnector
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from pytorch_lightning.trainer.connectors.model_connector import ModelConnector
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from pytorch_lightning.trainer.connectors.debugging_connector import DebuggingConnector
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from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector
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from pytorch_lightning import _logger as log
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from pytorch_lightning.tuner.tuning import Tuner
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from pytorch_lightning.trainer.connectors.precision_connector import PrecisionConnector
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@ -149,6 +150,7 @@ class Trainer(
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self.debugging_connector = DebuggingConnector(self)
|
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self.training_tricks_connector = TrainingTricksConnector(self)
|
||||
self.profile_connector = ProfilerConnector(self)
|
||||
self.checkpoint_connector = CheckpointConnector(self)
|
||||
self.tuner = Tuner(self)
|
||||
self.accelerator_backend = None
|
||||
self.evaluation_loop = EvaluationLoop(self)
|
||||
|
|
|
|||
|
|
@ -120,19 +120,6 @@ from pytorch_lightning.utilities.upgrade_checkpoint import KEYS_MAPPING as DEPRE
|
|||
from pytorch_lightning.accelerators.base_backend import Accelerator
|
||||
|
||||
|
||||
try:
|
||||
from apex import amp
|
||||
except ImportError:
|
||||
amp = None
|
||||
|
||||
try:
|
||||
from omegaconf import Container
|
||||
except ImportError:
|
||||
OMEGACONF_AVAILABLE = False
|
||||
else:
|
||||
OMEGACONF_AVAILABLE = True
|
||||
|
||||
|
||||
class TrainerIOMixin(ABC):
|
||||
|
||||
# this is just a summary on variables used in this abstract class,
|
||||
|
|
@ -164,38 +151,6 @@ class TrainerIOMixin(ABC):
|
|||
model = self.model.module if is_dp_module else self.model
|
||||
return model
|
||||
|
||||
# --------------------
|
||||
# CHECK-POINTING
|
||||
# --------------------
|
||||
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.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.on_gpu:
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
if not did_restore_hpc_weights:
|
||||
if self.resume_from_checkpoint is not None:
|
||||
self.restore(self.resume_from_checkpoint, on_gpu=self.on_gpu)
|
||||
|
||||
# wait for all to catch up
|
||||
self.accelerator_backend.barrier('TrainerIOMixin.restore_weights')
|
||||
|
||||
# clear cache after restore
|
||||
if self.on_gpu:
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
# --------------------
|
||||
# HPC SIGNAL HANDLING
|
||||
# --------------------
|
||||
|
|
@ -240,276 +195,3 @@ class TrainerIOMixin(ABC):
|
|||
def term_handler(self, signum, frame):
|
||||
# save
|
||||
log.info("bypassing sigterm")
|
||||
|
||||
# --------------------
|
||||
# MODEL SAVE CHECKPOINT
|
||||
# --------------------
|
||||
|
||||
def save_checkpoint(self, filepath, weights_only: bool = False):
|
||||
checkpoint = self.dump_checkpoint(weights_only)
|
||||
|
||||
if self.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)
|
||||
|
||||
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.get_model()
|
||||
|
||||
# load the state_dict on the model automatically
|
||||
model.load_state_dict(checkpoint['state_dict'])
|
||||
|
||||
# give model a chance to load something
|
||||
model.on_load_checkpoint(checkpoint)
|
||||
|
||||
if on_gpu:
|
||||
model.cuda(self.root_gpu)
|
||||
|
||||
# restore amp scaling
|
||||
if self.amp_backend == AMPType.NATIVE and 'native_amp_scaling_state' in checkpoint:
|
||||
self.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])
|
||||
elif self.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 dump_checkpoint(self, weights_only: bool = False) -> dict:
|
||||
"""Creating model checkpoint.
|
||||
|
||||
Args:
|
||||
weights_only: saving model weights only
|
||||
|
||||
Return:
|
||||
structured dictionary
|
||||
"""
|
||||
checkpoint = {
|
||||
'epoch': self.current_epoch + 1,
|
||||
'global_step': self.global_step + 1,
|
||||
'pytorch-lightning_version': pytorch_lightning.__version__,
|
||||
}
|
||||
|
||||
if not weights_only:
|
||||
|
||||
# save callbacks
|
||||
callback_states = self.on_save_checkpoint()
|
||||
checkpoint['callbacks'] = callback_states
|
||||
|
||||
# save optimizers
|
||||
optimizer_states = []
|
||||
for i, optimizer in enumerate(self.optimizers):
|
||||
optimizer_states.append(optimizer.state_dict())
|
||||
checkpoint['optimizer_states'] = optimizer_states
|
||||
|
||||
# save lr schedulers
|
||||
lr_schedulers = []
|
||||
for scheduler in self.lr_schedulers:
|
||||
lr_schedulers.append(scheduler['scheduler'].state_dict())
|
||||
checkpoint['lr_schedulers'] = lr_schedulers
|
||||
|
||||
# save native amp scaling
|
||||
if self.amp_backend == AMPType.NATIVE and not self.use_tpu and self.scaler is not None:
|
||||
checkpoint['native_amp_scaling_state'] = self.scaler.state_dict()
|
||||
elif self.amp_backend == AMPType.APEX:
|
||||
checkpoint['amp_scaling_state'] = amp.state_dict()
|
||||
|
||||
# add the module_arguments and state_dict from the model
|
||||
model = self.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)
|
||||
|
||||
return checkpoint
|
||||
|
||||
# --------------------
|
||||
# HPC IO
|
||||
# --------------------
|
||||
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.weights_save_path)
|
||||
fs = get_filesystem(folderpath)
|
||||
if fs.exists(folderpath):
|
||||
files = [os.path.basename(f) for f in fs.ls(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.on_gpu)
|
||||
did_restore = True
|
||||
return did_restore
|
||||
|
||||
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.on_load_checkpoint(checkpoint)
|
||||
|
||||
self.global_step = checkpoint['global_step']
|
||||
self.current_epoch = checkpoint['epoch']
|
||||
|
||||
# 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.accumulate_grad_batches is None else self.accumulate_grad_batches
|
||||
expected_steps = self.num_training_batches / n_accum
|
||||
if self.num_training_batches != 0 and self.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.optimizers, optimizer_states):
|
||||
optimizer.load_state_dict(opt_state)
|
||||
|
||||
# move optimizer to GPU 1 weight at a time
|
||||
# avoids OOM
|
||||
if self.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.root_gpu)
|
||||
|
||||
# restore the lr schedulers
|
||||
lr_schedulers = checkpoint['lr_schedulers']
|
||||
for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers):
|
||||
scheduler['scheduler'].load_state_dict(lrs_state)
|
||||
|
||||
# ----------------------------------
|
||||
# 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.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 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.get_model()
|
||||
|
||||
# load the state_dict on the model automatically
|
||||
model.load_state_dict(checkpoint['state_dict'])
|
||||
|
||||
# restore amp scaling
|
||||
if self.amp_backend == AMPType.NATIVE and 'native_amp_scaling_state' in checkpoint:
|
||||
self.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])
|
||||
elif self.amp_backend == AMPType.APEX and 'amp_scaling_state' in checkpoint:
|
||||
amp.load_state_dict(checkpoint['amp_scaling_state'])
|
||||
|
||||
if self.root_gpu is not None:
|
||||
model.cuda(self.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) for f in fs.ls(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)
|
||||
|
|
|
|||
|
|
@ -150,7 +150,7 @@ class TrainLoop:
|
|||
self.trainer.model = model
|
||||
|
||||
# restore training and model before hpc is called
|
||||
self.trainer.restore_weights(model)
|
||||
self.trainer.checkpoint_connector.restore_weights(model)
|
||||
|
||||
# on pretrain routine end
|
||||
self.trainer.on_pretrain_routine_end(ref_model)
|
||||
|
|
|
|||
|
|
@ -42,14 +42,6 @@ class TrainerTrainingTricksMixin(ABC):
|
|||
def get_model(self) -> LightningModule:
|
||||
"""Warning: this is just empty shell for code implemented in other class."""
|
||||
|
||||
@abstractmethod
|
||||
def save_checkpoint(self, *args):
|
||||
"""Warning: this is just empty shell for code implemented in other class."""
|
||||
|
||||
@abstractmethod
|
||||
def restore(self, *args):
|
||||
"""Warning: this is just empty shell for code implemented in other class."""
|
||||
|
||||
@abstractmethod
|
||||
def fit(self, *args):
|
||||
"""Warning: this is just empty shell for code implemented in other class."""
|
||||
|
|
|
|||
Loading…
Reference in New Issue