Source code for lightning_fabric.strategies.single_device

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 import Tensor
from torch.nn import Module

from lightning_fabric.accelerators import Accelerator
from import CheckpointIO
from lightning_fabric.plugins.precision import Precision
from lightning_fabric.strategies.strategy import Strategy, TBroadcast
from lightning_fabric.utilities.types import _DEVICE

[docs]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) self._root_device = torch.device(device) self.global_rank = 0 self.local_rank = 0 self.world_size = 1 @property def root_device(self) -> torch.device: return self._root_device @property def is_global_zero(self) -> bool: return True
[docs] def module_to_device(self, module: Module) -> None:
[docs] def all_reduce(self, tensor: Any | Tensor, *args: Any, **kwargs: Any) -> Any | 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: Tensor, group: Any | None = None, sync_grads: bool = False) -> Tensor: """Perform a all_gather on all processes.""" return tensor
[docs] def barrier(self, *args: Any, **kwargs: Any) -> None: pass
[docs] def broadcast(self, obj: TBroadcast, src: int = 0) -> TBroadcast: return obj

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