lightning/pytorch_lightning/utilities/cloud_io.py

# 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
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import io
from pathlib import Path
from typing import Any, Callable, Dict, IO, Optional, Union

import fsspec
import torch
from fsspec.implementations.local import AbstractFileSystem, LocalFileSystem
from packaging.version import Version


def load(
    path_or_url: Union[str, IO, Path],
    map_location: Optional[
        Union[str, Callable, torch.device, Dict[Union[str, torch.device], Union[str, torch.device]]]
    ] = None,
) -> Any:
    if not isinstance(path_or_url, (str, Path)):
        # any sort of BytesIO or similiar
        return torch.load(path_or_url, map_location=map_location)
    if str(path_or_url).startswith("http"):
        return torch.hub.load_state_dict_from_url(str(path_or_url), map_location=map_location)
    fs = get_filesystem(path_or_url)
    with fs.open(path_or_url, "rb") as f:
        return torch.load(f, map_location=map_location)


def get_filesystem(path: Union[str, Path]) -> AbstractFileSystem:
    path = str(path)
    if "://" in path:
        # use the fileystem from the protocol specified
        return fsspec.filesystem(path.split(":", 1)[0])
    # use local filesystem
    return LocalFileSystem()


def atomic_save(checkpoint: Dict[str, Any], filepath: Union[str, Path]) -> None:
    """Saves a checkpoint atomically, avoiding the creation of incomplete checkpoints.

    Args:
        checkpoint: The object to save.
            Built to be used with the ``dump_checkpoint`` method, but can deal with anything which ``torch.save``
            accepts.
        filepath: The path to which the checkpoint will be saved.
            This points to the file that the checkpoint will be stored in.
    """

    bytesbuffer = io.BytesIO()
    # Can't use the new zipfile serialization for 1.6.0 because there's a bug in
    # torch.hub.load_state_dict_from_url() that prevents it from loading the new files.
    # More details can be found here: https://github.com/pytorch/pytorch/issues/42239
    if Version(torch.__version__).release[:3] == (1, 6, 0):
        torch.save(checkpoint, bytesbuffer, _use_new_zipfile_serialization=False)
    else:
        torch.save(checkpoint, bytesbuffer)
    with fsspec.open(filepath, "wb") as f:
        f.write(bytesbuffer.getvalue())