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 Dict, List, Optional
  
  import torch
  import torch.distributed
  
  import pytorch_lightning as pl
  from pytorch_lightning.overrides import LightningDistributedModule
  from pytorch_lightning.overrides.torch_distributed import broadcast_object_list
  from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
  from pytorch_lightning.plugins.io.hpu_plugin import HPUCheckpointIO
  from pytorch_lightning.plugins.precision import PrecisionPlugin
  from pytorch_lightning.strategies.ddp import DDPStrategy
  from pytorch_lightning.utilities.distributed import group as _group
  from pytorch_lightning.utilities.exceptions import MisconfigurationException
  from pytorch_lightning.utilities.imports import _HPU_AVAILABLE, _TORCH_LESSER_EQUAL_1_10_2
  
  if _HPU_AVAILABLE:
      import habana_frameworks.torch.core.hccl  # noqa: F401
      from habana_frameworks.torch.utils.library_loader import load_habana_module
  
  log = logging.getLogger(__name__)
  
  
  class HPUParallelStrategy(DDPStrategy):
      """Strategy for distributed training on multiple HPU devices."""
  
      strategy_name = "hpu_parallel"
  
      def __init__(
          self,
          accelerator: Optional["pl.accelerators.accelerator.Accelerator"] = None,
          parallel_devices: Optional[List[torch.device]] = None,
          checkpoint_io: Optional[CheckpointIO] = None,
          precision_plugin: Optional[PrecisionPlugin] = None,
          process_group_backend: Optional[str] = "hccl",
      ) -> None:
  
          if not _HPU_AVAILABLE:
              raise MisconfigurationException("`HPUParallelStrategy` requires HPU devices to run")
  
          super().__init__(
              accelerator=accelerator,
              parallel_devices=parallel_devices,
              checkpoint_io=checkpoint_io or HPUCheckpointIO(),
              precision_plugin=precision_plugin,
              process_group_backend=process_group_backend,
          )
  
      def setup_environment(self) -> None:
          # This function is used to load Habana libraries required for PyTorch
          # to register HPU as one of the available devices.
          load_habana_module()
  
          os.environ["ID"] = str(self.local_rank)
          if self._process_group_backend == "hccl":
              # this env is used in overrides to check the backend initiated
              os.environ["HCCL_DISTRIBUTED_BACKEND"] = str(1)
          super().setup_environment()
  
      def determine_ddp_device_ids(self) -> None:
          return None
  
      def pre_configure_ddp(self):  # type: ignore
          # 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)
  
          self._static_graph = False
          static_graph = self._ddp_kwargs.get("static_graph")
          if static_graph:
              # when _set_static_graph() is called find_unused_parameters does not have any significance.
              # Resetting the value of find_unused_parameters to False which is the default value to DDP
              self._ddp_kwargs["find_unused_parameters"] = False
              self._static_graph = True
          if static_graph is not None:
              # DDP does not accept static_graph as a parameter, hence removing it from the list
              del self._ddp_kwargs["static_graph"]
  
      def configure_ddp(self) -> None:
          # DDP does not accept static graph as param with torch < 1.11
          if _TORCH_LESSER_EQUAL_1_10_2:
              log.detail(f"{self.__class__.__name__}: configuring DistributedDataParallel")
              self.pre_configure_ddp()
              self.model = self._setup_model(LightningDistributedModule(self.model))  # type: ignore
              if self.root_device.type == "hpu" and self._static_graph:
                  self._model._set_static_graph()  # type: ignore
              self._register_ddp_hooks()
          else:
              self.configure_ddp()
  
      def broadcast(self, obj: object, src: int = 0) -> object:  # type: ignore
          obj = [obj]
          if self.global_rank != src:
              obj = [None]
  
          broadcast_object_list(obj, src, group=_group.WORLD)
          return obj[0]
  
      def teardown(self) -> None:
          log.detail(f"{self.__class__.__name__}: tearing down strategy.")
          super().teardown()
  
          log.detail(f"{self.__class__.__name__}: moving model to CPU")
          self.lightning_module.cpu()  # type: ignore
          # Was set to local rank
          os.environ.pop("ID", None)
          os.environ.pop("HCCL_DISTRIBUTED_BACKEND", None)
  
      @classmethod
      def register_strategies(cls, strategy_registry: Dict) -> None:
          strategy_registry.register(
              cls.strategy_name,
              cls,
              description=f"{cls.__class__.__name__}",
          )
  