Source code for pytorch_lightning.plugins.training_type.single_device
# 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.
from typing import Any, Optional, Union
import torch
from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin
from pytorch_lightning.utilities import _XLA_AVAILABLE
[docs]class SingleDevicePlugin(TrainingTypePlugin):
"""Plugin that handles communication on a single device."""
def __init__(
self,
device: torch.device,
checkpoint_io: Optional[CheckpointIO] = None,
):
super().__init__(checkpoint_io)
self.device: torch.device = device
self.global_rank = 0
self.local_rank = 0
self.world_size = 1
@property
def on_tpu(self) -> bool:
return self.root_device.type == "xla" and _XLA_AVAILABLE
@property
def on_gpu(self) -> bool:
return self.root_device.type == "cuda" and torch.cuda.is_available()
[docs] def reduce(self, tensor: Union[Any, torch.Tensor], *args: Any, **kwargs: Any) -> Union[Any, torch.Tensor]:
"""Reduces a tensor from several distributed processes to one aggregated tensor. As this plugin only
operates with a single device, the reduction is simply the identity.
Args:
tensor: the tensor to sync and reduce
*args: ignored
**kwargs: ignored
Return:
the unmodified input as reduction is not needed for single process operation
"""
return tensor
[docs] def all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> torch.Tensor:
"""Perform a all_gather on all processes."""
return tensor
@property
def root_device(self) -> torch.device:
return self.device
@property
def is_global_zero(self) -> bool:
return True
[docs] def teardown(self) -> None:
if self.on_gpu:
# GPU teardown
self.lightning_module.cpu()
# clean up memory
torch.cuda.empty_cache()