Source code for pytorch_lightning.plugins.io.hpu_plugin
# 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.
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#
# 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,
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import os
from typing import Any, Dict, Optional
import torch
from lightning_lite.plugins import TorchCheckpointIO
from lightning_lite.utilities import move_data_to_device
from lightning_lite.utilities.cloud_io import _atomic_save, get_filesystem
from lightning_lite.utilities.types import _PATH
[docs]class HPUCheckpointIO(TorchCheckpointIO):
"""CheckpointIO to save checkpoints for HPU training strategies."""
[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 ``XLACheckpointIO.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)
checkpoint = move_data_to_device(checkpoint, torch.device("cpu"))
# write the checkpoint dictionary to the provided path
_atomic_save(checkpoint, path)