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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 abc import ABC
from typing import Any, Optional
import torch
from torch import Tensor
from typing_extensions import override
from lightning_fabric.accelerators.accelerator import Accelerator
from lightning_fabric.plugins.environments.cluster_environment import ClusterEnvironment
from lightning_fabric.plugins.io.checkpoint_io 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
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
@override
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}
@override
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)
@override
def reduce_boolean_decision(self, decision: bool, all: bool = True) -> bool:
"""Reduces a boolean decision over distributed processes. By default is analogous 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
@override
def teardown(self) -> None:
assert self.cluster_environment is not None
self.cluster_environment.teardown()
return super().teardown()
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