Source code for lightning_fabric.strategies.parallel

# 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 abc import ABC
from typing import Any, Dict, List, Optional

import torch
from torch import Tensor

from lightning_fabric.accelerators.accelerator import Accelerator
from lightning_fabric.plugins.environments.cluster_environment import ClusterEnvironment
from import CheckpointIO
from lightning_fabric.plugins.precision import Precision
from lightning_fabric.strategies.strategy import Strategy
from lightning_fabric.utilities.distributed import _all_gather_ddp_if_available
from lightning_fabric.utilities.types import ReduceOp

[docs]class ParallelStrategy(Strategy, ABC): """Strategy for training with multiple processes in parallel.""" def __init__( self, accelerator: Optional[Accelerator] = None, parallel_devices: Optional[List[torch.device]] = None, cluster_environment: Optional[ClusterEnvironment] = None, checkpoint_io: Optional[CheckpointIO] = None, precision: Optional[Precision] = None, ): super().__init__(accelerator=accelerator, checkpoint_io=checkpoint_io, precision=precision) self.parallel_devices = parallel_devices self.cluster_environment: Optional[ClusterEnvironment] = cluster_environment @property def global_rank(self) -> int: return self.cluster_environment.global_rank() if self.cluster_environment is not None else 0 @property def local_rank(self) -> int: return self.cluster_environment.local_rank() if self.cluster_environment is not None else 0 @property def node_rank(self) -> int: return self.cluster_environment.node_rank() if self.cluster_environment is not None else 0 @property def world_size(self) -> int: return self.cluster_environment.world_size() if self.cluster_environment is not None else 1 @property def is_global_zero(self) -> bool: return self.global_rank == 0 @property def parallel_devices(self) -> Optional[List[torch.device]]: return self._parallel_devices @parallel_devices.setter def parallel_devices(self, parallel_devices: Optional[List[torch.device]]) -> None: self._parallel_devices = parallel_devices @property def distributed_sampler_kwargs(self) -> Optional[Dict[str, Any]]: """Arguments for the ``DistributedSampler``. If this method is not defined, or it returns ``None``, then the ``DistributedSampler`` will not be used. """ return {"num_replicas": self.world_size, "rank": self.global_rank}
[docs] def all_gather(self, tensor: Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> Tensor: """Perform a all_gather on all processes.""" return _all_gather_ddp_if_available(tensor, group=group, sync_grads=sync_grads)
[docs] def reduce_boolean_decision(self, decision: bool, all: bool = True) -> bool: """Reduces a boolean decision over distributed processes. By default is analagous to ``all`` from the standard library, returning ``True`` only if all input decisions evaluate to ``True``. If ``all`` is set to ``False``, it behaves like ``any`` instead. Args: decision: A single input decision. all: Whether to logically emulate ``all`` or ``any``. Defaults to True. Returns: bool: The reduced boolean decision. """ decision = torch.tensor(int(decision), device=self.root_device) decision = self.all_reduce( decision, reduce_op=ReduceOp.SUM, # type: ignore[arg-type] ) decision = bool(decision == self.world_size) if all else bool(decision) return decision
[docs] def teardown(self) -> None: assert self.cluster_environment is not None self.cluster_environment.teardown() return super().teardown()

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