Source code for pytorch_lightning.plugins.io.xla_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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# 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
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from typing import Any, Dict, Optional
from pytorch_lightning.plugins.io.torch_plugin import TorchCheckpointIO
from pytorch_lightning.utilities import _OMEGACONF_AVAILABLE, _TPU_AVAILABLE
from pytorch_lightning.utilities.apply_func import apply_to_collection
from pytorch_lightning.utilities.types import _PATH
if _TPU_AVAILABLE:
import torch_xla.core.xla_model as xm
if _OMEGACONF_AVAILABLE:
from omegaconf import DictConfig, ListConfig, OmegaConf
[docs]class XLACheckpointIO(TorchCheckpointIO):
"""CheckpointIO that utilizes :func:`xm.save` to save checkpoints for TPU 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: Optional parameters when saving the model/training states.
"""
# Todo: TypeError: 'mappingproxy' object does not support item assignment
# Ref: https://github.com/pytorch/xla/issues/2773
if _OMEGACONF_AVAILABLE:
checkpoint = apply_to_collection(checkpoint, (DictConfig, ListConfig), OmegaConf.to_container)
xm.save({k: v for k, v in checkpoint.items() if k != "callbacks"}, path)