text: # 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 import _TORCH_GREATER_EQUAL_1_8
  from pytorch_lightning.utilities.distributed import distributed_available
  from pytorch_lightning.utilities.distributed import group as _group
  from pytorch_lightning.utilities.distributed import init_dist_connection, ReduceOp, sync_ddp_if_available
  from pytorch_lightning.utilities.rank_zero import rank_zero_only, rank_zero_warn
  from pytorch_lightning.utilities.seed import reset_seed
  from pytorch_lightning.utilities.types import STEP_OUTPUT
  
  if _TORCH_GREATER_EQUAL_1_8:
      from pytorch_lightning.utilities.distributed import register_ddp_comm_hook
  
  log = logging.getLogger(__name__)
  
  
  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,
          **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
  
      @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
  
      def _configure_launcher(self):
          self._launcher = _SpawnLauncher(self)
  
      def setup(self, trainer: "pl.Trainer") -> None:
          os.environ["MASTER_PORT"] = str(self.cluster_environment.main_port)
          super().setup(trainer)
  
          # move the model to the correct device
          self.model_to_device()
  
          trainer_fn = self.lightning_module.trainer.state.fn
          if trainer_fn != TrainerFn.FITTING:
              return
  
          if self._layer_sync:
              self.model = self._layer_sync.apply(self.model)
  
          # skip wrapping the model if we are not fitting as no gradients need to be exchanged
          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
          init_dist_connection(
              self.cluster_environment, self.torch_distributed_backend, self.global_rank, self.world_size
          )
  
      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)
          if not self.lightning_module.automatic_optimization and not self._ddp_kwargs.get(
              "find_unused_parameters", False
          ):
              # TODO: PyTorch 1.7.0 DDP introduces `self.reducer._rebuild_buckets()` breaking manual_optimization
              rank_zero_warn(
                  "From PyTorch 1.7.0, Lightning `manual_optimization` needs to set `find_unused_parameters=True` to"
                  " properly work with DDP. Using `find_unused_parameters=True`."
              )
              self._ddp_kwargs["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 _TORCH_GREATER_EQUAL_1_8 and 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()
  
      def determine_ddp_device_ids(self):
          if self.root_device.type == "cpu":
              return None
          return [self.root_device.index]
  
      def barrier(self, *args, **kwargs) -> None:
          if not distributed_available():
              return
          if _TORCH_GREATER_EQUAL_1_8 and torch.distributed.get_backend() == "nccl":
              torch.distributed.barrier(device_ids=self.determine_ddp_device_ids())
          else:
              torch.distributed.barrier()
  
      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]
  
      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)
  
      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)
  
      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
  
      def training_step(self, *args, **kwargs) -> STEP_OUTPUT:
          with self.precision_plugin.train_step_context():
              return self.model(*args, **kwargs)
  
      def validation_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
          with self.precision_plugin.val_step_context():
              if isinstance(self.model, DistributedDataParallel):
                  # used when calling `trainer.fit`
                  return self.model(*args, **kwargs)
              else:
                  # used when calling `trainer.validate`
                  return self.lightning_module.validation_step(*args, **kwargs)
  
      def test_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
          with self.precision_plugin.test_step_context():
              return self.lightning_module.test_step(*args, **kwargs)
  
      def predict_step(self, *args, **kwargs) -> STEP_OUTPUT:
          with self.precision_plugin.predict_step_context():
              return self.lightning_module.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__}",
          )
  
      def teardown(self) -> None:
          super().teardown()
          if isinstance(self.model, DistributedDataParallel):
              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.model = self._layer_sync.revert(self.model)
  
          if self.root_device.type == "cuda":
              # GPU teardown
              self.lightning_module.cpu()
              # clean up memory
              torch.cuda.empty_cache()
  