Shortcuts

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 contextlib import nullcontext
from datetime import timedelta
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Union

import torch
import torch.distributed
from lightning_utilities.core.rank_zero import rank_zero_only as utils_rank_zero_only
from torch import Tensor
from torch.nn import Module
from torch.nn.parallel.distributed import DistributedDataParallel
from torch.optim.optimizer import Optimizer
from typing_extensions import override

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.strategies import _StrategyRegistry
from lightning.fabric.utilities.distributed import (
    _distributed_is_initialized,
    _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.distributed import _register_ddp_comm_hook, _sync_module_states, prepare_for_backward
from lightning.pytorch.plugins.precision import Precision
from lightning.pytorch.strategies.launchers import _MultiProcessingLauncher, _SubprocessScriptLauncher
from lightning.pytorch.strategies.parallel import ParallelStrategy
from lightning.pytorch.strategies.strategy import TBroadcast, _ForwardRedirection
from lightning.pytorch.trainer.states import TrainerFn
from lightning.pytorch.utilities.exceptions import _augment_message
from lightning.pytorch.utilities.rank_zero import rank_zero_deprecation, rank_zero_info, rank_zero_only

if TYPE_CHECKING:
    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[Precision] = 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 strategy") self._forward_redirection = _DDPForwardRedirection() 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: # pragma: no-cover """Legacy property kept for backwards compatibility.""" rank_zero_deprecation( f"`{type(self).__name__}.is_distributed` is deprecated. Use is discouraged.", stacklevel=6 ) return True @property @override 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 @override def distributed_sampler_kwargs(self) -> Dict[str, Any]: return {"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 @override 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] @override def setup_environment(self) -> None: super().setup_environment() self.setup_distributed()
[docs] @override def setup(self, trainer: "pl.Trainer") -> None: assert self.accelerator is not None self.accelerator.setup(trainer) trainer_fn = trainer.state.fn assert self.model is not None if trainer_fn == TrainerFn.FITTING and self._layer_sync: self.model = self._layer_sync.apply(self.model) self.precision_plugin.convert_module(self.model) self.model_to_device() if trainer_fn == TrainerFn.FITTING: # do not wrap with DDP if not fitting as there's no gradients to reduce self.configure_ddp() # set up optimizers after the wrapped module has been moved to the device self.setup_optimizers(trainer) else: # we need to manually synchronize the module's states since we aren't using the DDP wrapper _sync_module_states(self.model) self.setup_precision_plugin() if trainer_fn == TrainerFn.FITTING: _optimizers_to_device(self.optimizers, self.root_device) 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()
@override 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}") # https://pytorch.org/docs/stable/notes/cuda.html#id5 ctx = torch.cuda.stream(torch.cuda.Stream()) if device_ids is not None else nullcontext() with ctx: 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() 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 not None: 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) # `LightningEnvironment.set_global_rank` will do this too, but we cannot rely on that implementation detail # additionally, for some implementations, the setter is a no-op, so it's safer to access the getter rank_zero_only.rank = utils_rank_zero_only.rank = self.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 isinstance(optimizer, (ZeroRedundancyOptimizer, PostLocalSGDOptimizer)) or is_distributed_optimizer: 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] @override 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) self.model = self._setup_model(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] @override def barrier(self, *args: Any, **kwargs: Any) -> None: if not _distributed_is_initialized(): return if torch.distributed.get_backend() == "nccl": torch.distributed.barrier(device_ids=self.determine_ddp_device_ids()) else: torch.distributed.barrier()
[docs] @override def broadcast(self, obj: TBroadcast, src: int = 0) -> TBroadcast: if not _distributed_is_initialized(): return obj obj = [obj] torch.distributed.broadcast_object_list(obj, src, group=_group.WORLD) return obj[0]
[docs] @override 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] @override 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] @override 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): return _sync_ddp_if_available(tensor, group, reduce_op=reduce_op) return tensor
@classmethod @override def register_strategies(cls, strategy_registry: _StrategyRegistry) -> 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] @override 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] @override 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()
class _DDPForwardRedirection(_ForwardRedirection): @override def on_after_inner_forward(self, wrapper_module: Module, original_module: "pl.LightningModule") -> None: # In manual_optimization, we need to prevent DDP reducer as # it is done manually in `LightningModule.manual_backward` if isinstance(wrapper_module, DistributedDataParallel) and not original_module.automatic_optimization: wrapper_module.require_backward_grad_sync = False @override def on_after_outer_forward(self, wrapper_module: Module, original_module: "pl.LightningModule") -> None: if isinstance(wrapper_module, DistributedDataParallel) and not original_module.automatic_optimization: wrapper_module.require_backward_grad_sync = True