Source code for pytorch_lightning.strategies.ddp_spawn

# Copyright The PyTorch Lightning 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.
import logging
import os
from datetime import timedelta
from typing import Any, Callable, Dict, List, Optional, Union

import torch
import torch.distributed
from torch import Tensor
from torch.distributed.constants import default_pg_timeout
from torch.nn import Module
from torch.nn.parallel.distributed import DistributedDataParallel
from typing_extensions import Literal

import pytorch_lightning as pl
from pytorch_lightning.overrides import LightningDistributedModule
from pytorch_lightning.overrides.base import _LightningPrecisionModuleWrapperBase
from pytorch_lightning.overrides.distributed import prepare_for_backward
from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
from import CheckpointIO
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.launchers.multiprocessing import _MultiProcessingLauncher
from pytorch_lightning.strategies.parallel import ParallelStrategy
from pytorch_lightning.strategies.strategy import TBroadcast
from pytorch_lightning.trainer.states import TrainerFn
from pytorch_lightning.utilities.distributed import (
from pytorch_lightning.utilities.distributed import group as _group
from pytorch_lightning.utilities.distributed import (
from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_1_11
from pytorch_lightning.utilities.optimizer import optimizers_to_device
from pytorch_lightning.utilities.rank_zero import rank_zero_info, rank_zero_only
from pytorch_lightning.utilities.types import PredictStep, STEP_OUTPUT, TestStep, ValidationStep

log = logging.getLogger(__name__)


[docs]class DDPSpawnStrategy(ParallelStrategy): """Spawns processes using the :func:`torch.multiprocessing.spawn` method and joins processes after training finishes.""" strategy_name = "ddp_spawn" def __init__( self, accelerator: Optional["pl.accelerators.accelerator.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, process_group_backend: Optional[str] = None, timeout: Optional[timedelta] = default_pg_timeout, start_method: Literal["spawn", "fork", "forkserver"] = "spawn", **kwargs: Any, ): super().__init__( accelerator=accelerator, parallel_devices=parallel_devices, cluster_environment=cluster_environment, checkpoint_io=checkpoint_io, precision_plugin=precision_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._local_rank = 0 self._process_group_backend: Optional[str] = process_group_backend self._timeout: Optional[timedelta] = timeout self._start_method = start_method @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 local_rank(self) -> int: return self._local_rank @property def root_device(self) -> torch.device: assert self.parallel_devices is not None return self.parallel_devices[self.local_rank] @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, int]: distributed_sampler_kwargs = dict(num_replicas=(self.num_nodes * self.num_processes), rank=self.global_rank) return distributed_sampler_kwargs @property def _is_single_process_single_device(self) -> bool: return True @property def process_group_backend(self) -> Optional[str]: return self._process_group_backend def _configure_launcher(self) -> None: self._launcher = _MultiProcessingLauncher(self, start_method=self._start_method)
[docs] def setup(self, trainer: "pl.Trainer") -> None: assert self.cluster_environment is not None os.environ["MASTER_PORT"] = str(self.cluster_environment.main_port) 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()
def _setup_model(self, model: Module) -> DistributedDataParallel: """Wraps the model into a :class:`~torch.nn.parallel.distributed.DistributedDataParallel` module.""" return DistributedDataParallel(module=model, device_ids=self.determine_ddp_device_ids(), **self._ddp_kwargs) def set_world_ranks(self, process_idx: int = 0) -> None: self._local_rank = process_idx 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 _worker_setup(self, process_idx: int) -> None: self.set_world_ranks(process_idx) 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, self.global_rank, self.world_size, timeout=self._timeout, ) def _get_process_group_backend(self) -> str: return ( self._process_group_backend or _get_process_group_backend_from_env() or get_default_process_group_backend_for_device(self.root_device) ) def pre_configure_ddp(self) -> None: # if unset, default `find_unused_parameters` `True` # Many models require setting this parameter to True, as there are corner cases # when not all parameter backward hooks are fired by the autograd engine even if require_grad is set to True. # This flag does come with a performance hit, so it is suggested to disable in cases where it is possible. self._ddp_kwargs["find_unused_parameters"] = self._ddp_kwargs.get("find_unused_parameters", True) def _register_ddp_hooks(self) -> None: # currently, DDP communication hooks only work with NCCL backend and SPSD (single process single device) mode # if self.root_device.type == "cuda" and self._is_single_process_single_device: 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 configure_ddp(self) -> None: self.pre_configure_ddp() assert isinstance(self.model, (pl.LightningModule, _LightningPrecisionModuleWrapperBase)) self.model = self._setup_model(LightningDistributedModule(self.model)) self._register_ddp_hooks() # set up optimizers after the wrapped module has been moved to the device assert self.lightning_module is not None self.setup_optimizers(self.lightning_module.trainer) optimizers_to_device(self.optimizers, self.root_device) 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 model_to_device(self) -> None: if self.root_device.type == "cuda": # set the device on the spawned subprocesses torch.cuda.set_device(self.root_device) assert self.model is not None
[docs] def pre_backward(self, closure_loss: Tensor) -> None: """Run before precision plugin executes backward.""" assert self.lightning_module is not None if not self.lightning_module.automatic_optimization: assert isinstance(self.model, DistributedDataParallel) prepare_for_backward(self.model, closure_loss)
[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 `` 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_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_spawn_find_unused_parameters_false", "spawn"), ("ddp_fork_find_unused_parameters_false", "fork"), ("ddp_notebook_find_unused_parameters_false", "fork"), ) for name, start_method in entries: strategy_registry.register( name, cls, description=f"DDP strategy with `find_unused_parameters` as False and `start_method` '{start_method}'", find_unused_parameters=False, start_method=start_method, )
[docs] def teardown(self) -> None: log.detail(f"{self.__class__.__name__}: tearing down strategy") pl_module = self.lightning_module if isinstance(self.model, DistributedDataParallel): if ( _TORCH_GREATER_EQUAL_1_11 and not self.model.static_graph and self.model._get_ddp_logging_data().get("can_set_static_graph") # type: ignore[operator] ): 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()

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