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# Copyright The Lightning AI 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 __future__ import annotations
from typing import Any
import torch
from torch.nn import Module
from typing_extensions import override
from lightning_fabric.accelerators import Accelerator
from lightning_fabric.plugins.io.checkpoint_io import CheckpointIO
from lightning_fabric.plugins.precision import Precision
from lightning_fabric.strategies.strategy import Strategy, TBroadcast
from lightning_fabric.utilities.types import _DEVICE
class SingleDeviceStrategy(Strategy):
"""Strategy that handles communication on a single device."""
def __init__(
self,
device: _DEVICE = "cpu",
accelerator: Accelerator | None = None,
checkpoint_io: CheckpointIO | None = None,
precision: Precision | None = None,
):
super().__init__(accelerator=accelerator, checkpoint_io=checkpoint_io, precision=precision)
if not isinstance(device, torch.device):
device = torch.device(device)
self._root_device = device
self.global_rank = 0
self.local_rank = 0
self.world_size = 1
@property
@override
def root_device(self) -> torch.device:
return self._root_device
@property
@override
def is_global_zero(self) -> bool:
return True
@override
def module_to_device(self, module: Module) -> None:
module.to(self.root_device)
@override
def all_reduce(self, tensor: Any | torch.Tensor, *args: Any, **kwargs: Any) -> 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
@override
def all_gather(self, tensor: torch.Tensor, group: Any | None = None, sync_grads: bool = False) -> torch.Tensor:
"""Perform a ``all_gather`` on all processes."""
return tensor
@override
def barrier(self, *args: Any, **kwargs: Any) -> None:
pass
@override
def broadcast(self, obj: TBroadcast, src: int = 0) -> TBroadcast:
return obj
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