Source code for lightning.pytorch.strategies.ddp
# 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.
import logging
from datetime import timedelta
from typing import Any, Callable, Dict, List, Literal, Optional, Union
import torch
import torch.distributed
from torch import Tensor
from torch.nn import Module
from torch.nn.parallel.distributed import DistributedDataParallel
from torch.optim.optimizer import Optimizer
import lightning.pytorch as pl
from lightning.fabric.plugins import CheckpointIO, ClusterEnvironment
from lightning.fabric.plugins.collectives.torch_collective import default_pg_timeout
from lightning.fabric.utilities.distributed import (
_distributed_available,
_get_default_process_group_backend_for_device,
_init_dist_connection,
_sync_ddp_if_available,
)
from lightning.fabric.utilities.distributed import group as _group
from lightning.fabric.utilities.imports import _IS_WINDOWS
from lightning.fabric.utilities.optimizer import _optimizers_to_device
from lightning.fabric.utilities.seed import reset_seed
from lightning.fabric.utilities.types import ReduceOp
from lightning.pytorch.core.optimizer import LightningOptimizer
from lightning.pytorch.overrides.base import _LightningModuleWrapperBase, _LightningPrecisionModuleWrapperBase
from lightning.pytorch.overrides.distributed import prepare_for_backward
from lightning.pytorch.plugins.precision import PrecisionPlugin
from lightning.pytorch.strategies.launchers import _MultiProcessingLauncher, _SubprocessScriptLauncher
from lightning.pytorch.strategies.parallel import ParallelStrategy
from lightning.pytorch.strategies.strategy import TBroadcast
from lightning.pytorch.trainer.states import TrainerFn
from lightning.pytorch.utilities.distributed import _register_ddp_comm_hook
from lightning.pytorch.utilities.exceptions import _augment_message
from lightning.pytorch.utilities.rank_zero import rank_zero_info, rank_zero_only
from lightning.pytorch.utilities.types import PredictStep, STEP_OUTPUT, TestStep, ValidationStep
if torch.distributed.is_available():
from torch.distributed.algorithms.model_averaging.averagers import ModelAverager
log = logging.getLogger(__name__)
_DDP_FORK_ALIASES = (
"ddp_fork",
"ddp_fork_find_unused_parameters_false",
"ddp_fork_find_unused_parameters_true",
"ddp_notebook",
"ddp_notebook_find_unused_parameters_false",
"ddp_notebook_find_unused_parameters_true",
)
[docs]class DDPStrategy(ParallelStrategy):
"""Strategy for multi-process single-device training on one or multiple nodes."""
def __init__(
self,
accelerator: Optional["pl.accelerators.Accelerator"] = None,
parallel_devices: Optional[List[torch.device]] = None,
cluster_environment: Optional[ClusterEnvironment] = None,
checkpoint_io: Optional[CheckpointIO] = None,
precision_plugin: Optional[PrecisionPlugin] = None,
ddp_comm_state: Optional[object] = None,
ddp_comm_hook: Optional[Callable] = None,
ddp_comm_wrapper: Optional[Callable] = None,
model_averaging_period: Optional[int] = None,
process_group_backend: Optional[str] = None,
timeout: Optional[timedelta] = default_pg_timeout,
start_method: Literal["popen", "spawn", "fork", "forkserver"] = "popen",
**kwargs: Any,
) -> None:
super().__init__(
accelerator=accelerator,
parallel_devices=parallel_devices,
cluster_environment=cluster_environment,
checkpoint_io=checkpoint_io,
precision_plugin=precision_plugin,
)
log.debug(f"{self.__class__.__name__}: initializing DDP plugin")
self._num_nodes = 1
self._ddp_kwargs = kwargs
self._ddp_comm_state = ddp_comm_state
self._ddp_comm_hook = ddp_comm_hook
self._ddp_comm_wrapper = ddp_comm_wrapper
self._model_averaging_period = model_averaging_period
self._model_averager: Optional[ModelAverager] = None
self._process_group_backend: Optional[str] = process_group_backend
self._timeout: Optional[timedelta] = timeout
self._start_method = start_method
@property
def is_distributed(self) -> bool:
return True
@property
def root_device(self) -> torch.device:
assert self.parallel_devices is not None
return self.parallel_devices[self.local_rank]
@property
def num_nodes(self) -> int:
return self._num_nodes
@num_nodes.setter
def num_nodes(self, num_nodes: int) -> None:
# note that world ranks is related to num_nodes, when resetting it, need to reset world ranks
self._num_nodes = num_nodes
@property
def num_processes(self) -> int:
return len(self.parallel_devices) if self.parallel_devices is not None else 0
@property
def distributed_sampler_kwargs(self) -> Dict[str, Any]:
return dict(num_replicas=(self.num_nodes * self.num_processes), rank=self.global_rank)
@property
def process_group_backend(self) -> Optional[str]:
return self._process_group_backend
def _configure_launcher(self) -> None:
assert self.cluster_environment is not None
if self._start_method == "popen":
self._launcher = _SubprocessScriptLauncher(self.cluster_environment, self.num_processes, self.num_nodes)
else:
self._launcher = _MultiProcessingLauncher(self, start_method=self._start_method)
[docs] def setup(self, trainer: "pl.Trainer") -> None:
assert self.accelerator is not None
self.accelerator.setup(trainer)
# move the model to the correct device
self.model_to_device()
# skip wrapping the model if we are not fitting as no gradients need to be exchanged
trainer_fn = trainer.state.fn
if trainer_fn == TrainerFn.FITTING:
if self._layer_sync:
assert self.model is not None
self.model = self._layer_sync.apply(self.model)
self.setup_precision_plugin()
if trainer_fn == TrainerFn.FITTING:
self.configure_ddp()
# set up optimizers after the wrapped module has been moved to the device
self.setup_optimizers(trainer)
_optimizers_to_device(self.optimizers, self.root_device)
if trainer_fn == TrainerFn.FITTING:
import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD
if isinstance(self._ddp_comm_state, post_localSGD.PostLocalSGDState):
self._enable_model_averaging()
def _setup_model(self, model: Module) -> DistributedDataParallel:
"""Wraps the model into a :class:`~torch.nn.parallel.distributed.DistributedDataParallel` module."""
device_ids = self.determine_ddp_device_ids()
log.debug(f"setting up DDP model with device ids: {device_ids}, kwargs: {self._ddp_kwargs}")
return DistributedDataParallel(module=model, device_ids=device_ids, **self._ddp_kwargs)
def setup_distributed(self) -> None:
log.debug(f"{self.__class__.__name__}: setting up distributed...")
reset_seed()
self.set_world_ranks()
rank_zero_only.rank = self.global_rank
self._process_group_backend = self._get_process_group_backend()
assert self.cluster_environment is not None
_init_dist_connection(self.cluster_environment, self._process_group_backend, timeout=self._timeout)
def _get_process_group_backend(self) -> str:
return self._process_group_backend or _get_default_process_group_backend_for_device(self.root_device)
def set_world_ranks(self) -> None:
if self.cluster_environment is None:
return
self.cluster_environment.set_global_rank(self.node_rank * self.num_processes + self.local_rank)
self.cluster_environment.set_world_size(self.num_nodes * self.num_processes)
rank_zero_only.rank = self.cluster_environment.global_rank()
def _register_ddp_hooks(self) -> None:
log.debug(f"{self.__class__.__name__}: registering ddp hooks")
# currently, DDP communication hooks only work with NCCL backend and SPSD (single process single device) mode
# https://github.com/pytorch/pytorch/blob/v1.8.0/torch/nn/parallel/distributed.py#L1080-L1084
if self.root_device.type == "cuda":
assert isinstance(self.model, DistributedDataParallel)
_register_ddp_comm_hook(
model=self.model,
ddp_comm_state=self._ddp_comm_state,
ddp_comm_hook=self._ddp_comm_hook,
ddp_comm_wrapper=self._ddp_comm_wrapper,
)
def _enable_model_averaging(self) -> None:
log.debug(f"{self.__class__.__name__}: reinitializing optimizers with post localSGD")
if self._model_averaging_period is None:
raise ValueError(
"Post-localSGD algorithm is used, but model averaging period is not provided to DDP strategy."
)
from torch.distributed.optim import DistributedOptimizer, PostLocalSGDOptimizer, ZeroRedundancyOptimizer
for optimizer in self.optimizers:
if isinstance(optimizer, LightningOptimizer):
optimizer = optimizer._optimizer
is_distributed_optimizer = isinstance(optimizer, DistributedOptimizer) if not _IS_WINDOWS else False
if (
is_distributed_optimizer
or isinstance(optimizer, ZeroRedundancyOptimizer)
or isinstance(optimizer, PostLocalSGDOptimizer)
):
raise ValueError(
f"Currently model averaging cannot work with a distributed optimizer of type "
f"{optimizer.__class__.__name__}."
)
assert self._ddp_comm_state is not None
self._model_averager = torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager(
period=self._model_averaging_period, warmup_steps=self._ddp_comm_state.start_localSGD_iter
)
[docs] def optimizer_step(
self,
optimizer: Optimizer,
closure: Callable[[], Any],
model: Optional[Union["pl.LightningModule", Module]] = None,
**kwargs: Any,
) -> Any:
"""Performs the actual optimizer step.
Args:
optimizer: the optimizer performing the step
closure: closure calculating the loss value
model: reference to the model, optionally defining optimizer step related hooks
**kwargs: Any extra arguments to ``optimizer.step``
"""
optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
if self._model_averager is None:
return optimizer_output
params = [param for group in optimizer.param_groups for param in group["params"] if param.grad is not None]
self._model_averager.average_parameters(iter(params))
return optimizer_output
def configure_ddp(self) -> None:
log.debug(f"{self.__class__.__name__}: configuring DistributedDataParallel")
assert isinstance(self.model, (pl.LightningModule, _LightningPrecisionModuleWrapperBase))
self.model = self._setup_model(_LightningModuleWrapperBase(self.model))
self._register_ddp_hooks()
def determine_ddp_device_ids(self) -> Optional[List[int]]:
if self.root_device.type == "cpu":
return None
return [self.root_device.index]
[docs] def barrier(self, *args: Any, **kwargs: Any) -> None:
if not _distributed_available():
return
if torch.distributed.get_backend() == "nccl":
torch.distributed.barrier(device_ids=self.determine_ddp_device_ids())
else:
torch.distributed.barrier()
[docs] def broadcast(self, obj: TBroadcast, src: int = 0) -> TBroadcast:
if not _distributed_available():
return obj
obj = [obj]
if self.global_rank != src:
obj = [None] # type: ignore[list-item]
torch.distributed.broadcast_object_list(obj, src, group=_group.WORLD)
return obj[0]
[docs] def pre_backward(self, closure_loss: Tensor) -> None:
"""Run before precision plugin executes backward."""
if not isinstance(self.model, DistributedDataParallel):
return
assert self.lightning_module is not None
if not self.lightning_module.automatic_optimization:
prepare_for_backward(self.model, closure_loss)
[docs] def model_to_device(self) -> None:
log.debug(f"{self.__class__.__name__}: moving model to device [{self.root_device}]...")
assert self.model is not None
self.model.to(self.root_device)
[docs] def reduce(
self, tensor: Tensor, group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = "mean"
) -> Tensor:
"""Reduces a tensor from several distributed processes to one aggregated tensor.
Args:
tensor: the tensor to sync and reduce
group: the process group to gather results from. Defaults to all processes (world)
reduce_op: the reduction operation. Defaults to 'mean'/'avg'.
Can also be a string 'sum' to calculate the sum during reduction.
Return:
reduced value, except when the input was not a tensor the output remains is unchanged
"""
if isinstance(tensor, Tensor):
tensor = _sync_ddp_if_available(tensor, group, reduce_op=reduce_op)
return tensor
[docs] def training_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
assert self.model is not None
with self.precision_plugin.train_step_context():
return self.model(*args, **kwargs)
[docs] def validation_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]:
with self.precision_plugin.val_step_context():
assert self.lightning_module is not None
assert self.model is not None
if self.lightning_module.trainer.state.fn == TrainerFn.FITTING:
# used when calling `trainer.fit`
return self.model(*args, **kwargs)
else:
# used when calling `trainer.validate`
assert isinstance(self.model, ValidationStep)
return self.model.validation_step(*args, **kwargs)
[docs] def test_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]:
with self.precision_plugin.test_step_context():
assert isinstance(self.model, TestStep)
return self.model.test_step(*args, **kwargs)
[docs] def predict_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
with self.precision_plugin.predict_step_context():
assert isinstance(self.model, PredictStep)
return self.model.predict_step(*args, **kwargs)
def post_training_step(self) -> None:
assert self.lightning_module is not None
if not self.lightning_module.automatic_optimization:
assert self.model is not None
self.model.require_backward_grad_sync = True # type: ignore[assignment]
@classmethod
def register_strategies(cls, strategy_registry: Dict) -> None:
entries = (
("ddp", "popen"),
("ddp_spawn", "spawn"),
("ddp_fork", "fork"),
("ddp_notebook", "fork"),
)
for name, start_method in entries:
strategy_registry.register(
name,
cls,
description=f"DDP strategy with `start_method` '{start_method}'",
start_method=start_method,
)
entries = (
("ddp_find_unused_parameters_false", False, "popen"),
("ddp_find_unused_parameters_true", True, "popen"),
("ddp_spawn_find_unused_parameters_false", False, "spawn"),
("ddp_spawn_find_unused_parameters_true", True, "spawn"),
("ddp_fork_find_unused_parameters_false", False, "fork"),
("ddp_fork_find_unused_parameters_true", True, "fork"),
("ddp_notebook_find_unused_parameters_false", False, "fork"),
("ddp_notebook_find_unused_parameters_true", True, "fork"),
)
for name, fup, start_method in entries:
strategy_registry.register(
name,
cls,
description=f"DDP strategy with `find_unused_parameters` as {fup} and `start_method` '{start_method}'",
find_unused_parameters=fup,
start_method=start_method,
)
[docs] def on_exception(self, exception: BaseException) -> None:
_augment_message(
exception,
pattern=".*Expected to have finished reduction in the prior iteration.*",
new_message=(
"It looks like your LightningModule has parameters that were not used in producing the loss returned"
" by training_step. If this is intentional, you must enable the detection of unused parameters in DDP,"
" either by setting the string value `strategy='ddp_find_unused_parameters_true'`"
" or by setting the flag in the strategy with `strategy=DDPStrategy(find_unused_parameters=True)`."
),
)
[docs] def teardown(self) -> None:
log.debug(f"{self.__class__.__name__}: tearing down strategy")
pl_module = self.lightning_module
if isinstance(self.model, DistributedDataParallel):
if not self.model.static_graph and self.model._get_ddp_logging_data().get("can_set_static_graph"):
rank_zero_info(
"Your model can run with static graph optimizations. For future training runs, we suggest you"
f" pass `Trainer(..., strategy={self.__class__.__name__}(static_graph=True))` to enable them."
)
# unwrap model
self.model = pl_module
if (
pl_module is not None
# `self.lightning_module._trainer` can be None if teardown gets called on an exception before
# the trainer gets set on the LightningModule
and pl_module._trainer is not None
and pl_module._trainer.state.fn == TrainerFn.FITTING
and self._layer_sync
):
assert self.model is not None
self.model = self._layer_sync.revert(self.model)
super().teardown()
