Source code for

# Copyright The PyTorch Lightning team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
from typing import Any, Callable, Dict, Optional

from 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:`` and :func:`torch.load` to save and load checkpoints respectively, common for most use cases."""
[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? # # 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}")

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