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()
