Source code for lightning.fabric.plugins.io.torch_io
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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import logging
import os
from typing import Any, Callable, Dict, Optional
from lightning.fabric.plugins.io.checkpoint_io import CheckpointIO
from lightning.fabric.utilities.cloud_io import _atomic_save
from lightning.fabric.utilities.cloud_io import _load as pl_load
from lightning.fabric.utilities.cloud_io import get_filesystem
from lightning.fabric.utilities.rank_zero import rank_zero_warn
from lightning.fabric.utilities.types import _PATH
log = logging.getLogger(__name__)
[docs]class TorchCheckpointIO(CheckpointIO):
"""CheckpointIO that utilizes :func:`torch.save` and :func:`torch.load` to save and load checkpoints
respectively, common for most use cases.
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
"""
[docs] def save_checkpoint(self, checkpoint: Dict[str, Any], path: _PATH, storage_options: Optional[Any] = None) -> None:
"""Save model/training states as a checkpoint file through state-dump and file-write.
Args:
checkpoint: dict containing model and trainer state
path: write-target path
storage_options: not used in ``TorchCheckpointIO.save_checkpoint``
Raises:
TypeError:
If ``storage_options`` arg is passed in
"""
if storage_options is not None:
raise TypeError(
"`Trainer.save_checkpoint(..., storage_options=...)` with `storage_options` arg"
f" is not supported for `{self.__class__.__name__}`. Please implement your custom `CheckpointIO`"
" to define how you'd like to use `storage_options`."
)
fs = get_filesystem(path)
fs.makedirs(os.path.dirname(path), exist_ok=True)
try:
# write the checkpoint dictionary on the file
_atomic_save(checkpoint, path)
except AttributeError as err:
# todo: is this try catch necessary still?
# https://github.com/Lightning-AI/lightning/pull/431
# TODO(fabric): Fabric doesn't support hyperparameters in the checkpoint, so this should be refactored
key = "hyper_parameters"
checkpoint.pop(key, None)
rank_zero_warn(f"Warning, `{key}` dropped from checkpoint. An attribute is not picklable: {err}")
_atomic_save(checkpoint, path)
[docs] def load_checkpoint(
self, path: _PATH, map_location: Optional[Callable] = lambda storage, loc: storage
) -> Dict[str, Any]:
"""Loads checkpoint using :func:`torch.load`, with additional handling for ``fsspec`` remote loading of
files.
Args:
path: Path to checkpoint
map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage
locations.
Returns: The loaded checkpoint.
Raises:
FileNotFoundError: If ``path`` is not found by the ``fsspec`` filesystem
"""
# Try to read the checkpoint at `path`. If not exist, do not restore checkpoint.
fs = get_filesystem(path)
if not fs.exists(path):
raise FileNotFoundError(f"Checkpoint at {path} not found. Aborting training.")
return pl_load(path, map_location=map_location)
[docs] def remove_checkpoint(self, path: _PATH) -> None:
"""Remove checkpoint file from the filesystem.
Args:
path: Path to checkpoint
"""
fs = get_filesystem(path)
if fs.exists(path):
fs.rm(path, recursive=True)
log.debug(f"Removed checkpoint: {path}")