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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
#
#     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
import os
from typing import Any, Dict, List, Optional, Union

import torch
import torch.distributed
from torch.nn import Module
from torch.nn.parallel.distributed import DistributedDataParallel

import pytorch_lightning as pl
from pytorch_lightning.overrides import LightningDistributedModule
from pytorch_lightning.overrides.distributed import prepare_for_backward
from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.launchers.spawn import _SpawnLauncher
from pytorch_lightning.strategies.parallel import ParallelStrategy
from pytorch_lightning.trainer.states import TrainerFn
from pytorch_lightning.utilities.distributed import (
    _get_process_group_backend_from_env,
    distributed_available,
    get_default_process_group_backend_for_device,
)
from pytorch_lightning.utilities.distributed import group as _group
from pytorch_lightning.utilities.distributed import (
    init_dist_connection,
    ReduceOp,
    register_ddp_comm_hook,
    sync_ddp_if_available,
)
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.seed import reset_seed
from pytorch_lightning.utilities.types import STEP_OUTPUT

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, **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 @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): return self.parallel_devices[self.local_rank] @property def num_processes(self): return len(self.parallel_devices) if self.parallel_devices is not None else 0 @property def distributed_sampler_kwargs(self): 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): return True @property def process_group_backend(self) -> Optional[str]: return self._process_group_backend def _configure_launcher(self): self._launcher = _SpawnLauncher(self)
[docs] def setup(self, trainer: "pl.Trainer") -> None: os.environ["MASTER_PORT"] = str(self.cluster_environment.main_port) 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: 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): reset_seed() self.set_world_ranks(process_idx) rank_zero_only.rank = self.global_rank self._process_group_backend = self._get_process_group_backend() init_dist_connection(self.cluster_environment, self._process_group_backend, self.global_rank, self.world_size) 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): # 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 # https://github.com/pytorch/pytorch/blob/v1.8.0/torch/nn/parallel/distributed.py#L1080-L1084 if self.root_device.type == "cuda" and self._is_single_process_single_device: 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() 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 self.setup_optimizers(self.lightning_module.trainer) optimizers_to_device(self.optimizers, self.root_device) def determine_ddp_device_ids(self): if self.root_device.type == "cpu": return None return [self.root_device.index]
[docs] def barrier(self, *args, **kwargs) -> 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: object, src: int = 0) -> object: if not distributed_available(): return obj obj = [obj] if self.global_rank != src: obj = [None] torch.distributed.broadcast_object_list(obj, src, group=_group.WORLD) return obj[0]
[docs] def model_to_device(self): if self.root_device.type == "cuda": # set the device on the spawned subprocesses torch.cuda.set_device(self.root_device) self.model.to(self.root_device)
[docs] def pre_backward(self, closure_loss: torch.Tensor) -> None: """Run before precision plugin executes backward.""" if not self.lightning_module.automatic_optimization: prepare_for_backward(self.model, closure_loss)
[docs] def reduce(self, tensor, group: Optional[Any] = None, reduce_op: Union[ReduceOp, str] = "mean") -> torch.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, torch.Tensor): tensor = sync_ddp_if_available(tensor, group, reduce_op=reduce_op) return tensor
[docs] def training_step(self, *args, **kwargs) -> STEP_OUTPUT: with self.precision_plugin.train_step_context(): return self.model(*args, **kwargs)
[docs] def validation_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]: with self.precision_plugin.val_step_context(): 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` return self.model.validation_step(*args, **kwargs)
[docs] def test_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]: with self.precision_plugin.test_step_context(): return self.model.test_step(*args, **kwargs)
[docs] def predict_step(self, *args, **kwargs) -> STEP_OUTPUT: with self.precision_plugin.predict_step_context(): return self.model.predict_step(*args, **kwargs)
def post_training_step(self): if not self.lightning_module.automatic_optimization: self.model.require_backward_grad_sync = True @classmethod def register_strategies(cls, strategy_registry: Dict) -> None: strategy_registry.register( "ddp_spawn_find_unused_parameters_false", cls, description="DDPSpawn Strategy with `find_unused_parameters` as False", find_unused_parameters=False, ) strategy_registry.register( cls.strategy_name, cls, description=f"{cls.__class__.__name__}", )
[docs] def teardown(self) -> None: log.detail(f"{self.__class__.__name__}: tearing down strategy") super().teardown() 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") ): 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 = self.lightning_module if ( self.lightning_module.trainer is not None and self.lightning_module.trainer.state.fn == TrainerFn.FITTING and self._layer_sync ): # `self.lightning_module.trainer` can be None if teardown gets called on an exception before # the trainer gets set on the LightningModule self.model = self._layer_sync.revert(self.model) if self.root_device.type == "cuda": # GPU teardown log.detail(f"{self.__class__.__name__}: moving model to CPU") self.lightning_module.cpu() # clean up memory torch.cuda.empty_cache()

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