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 io
  import os
  import time
  from multiprocessing.queues import SimpleQueue
  from typing import Any, Callable, Dict, List, Optional, Union
  
  import torch
  import torch.multiprocessing as mp
  from torch.nn import Module
  from torch.utils.data import DataLoader
  
  import pytorch_lightning as pl
  from pytorch_lightning.overrides import LightningDistributedModule
  from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
  from pytorch_lightning.plugins.io.xla_plugin import XLACheckpointIO
  from pytorch_lightning.plugins.precision import PrecisionPlugin
  from pytorch_lightning.plugins.training_type.ddp_spawn import _FakeQueue, _SpawnOutput, DDPSpawnPlugin
  from pytorch_lightning.trainer.connectors.data_connector import DataConnector
  from pytorch_lightning.trainer.states import TrainerFn
  from pytorch_lightning.utilities import _TPU_AVAILABLE, find_shared_parameters, rank_zero_warn, set_shared_parameters
  from pytorch_lightning.utilities.apply_func import move_data_to_device
  from pytorch_lightning.utilities.data import has_len
  from pytorch_lightning.utilities.distributed import rank_zero_only, ReduceOp
  from pytorch_lightning.utilities.exceptions import MisconfigurationException
  from pytorch_lightning.utilities.model_helpers import is_overridden
  from pytorch_lightning.utilities.seed import reset_seed
  from pytorch_lightning.utilities.types import _PATH, STEP_OUTPUT
  
  if _TPU_AVAILABLE:
      import torch_xla.core.xla_env_vars as xenv
      import torch_xla.core.xla_model as xm
      import torch_xla.distributed.xla_multiprocessing as xmp
      from torch_xla.core.xla_model import rendezvous
      from torch_xla.distributed.parallel_loader import MpDeviceLoader
  else:
      xm, xmp, MpDeviceLoader, rendezvous = [None] * 4
  
  
  class TPUSpawnPlugin(DDPSpawnPlugin):
      """Plugin for training multiple TPU devices using the :func:`torch.multiprocessing.spawn` method."""
  
      def __init__(
          self,
          parallel_devices: Optional[List[int]] = None,
          checkpoint_io: Optional[CheckpointIO] = None,
          precision_plugin: Optional[PrecisionPlugin] = None,
          debug: bool = False,
          **_: Any,
      ) -> None:
          checkpoint_io = checkpoint_io or XLACheckpointIO()
          super().__init__(
              parallel_devices=parallel_devices, checkpoint_io=checkpoint_io, precision_plugin=precision_plugin
          )
          self.debug = debug
          self.tpu_local_core_rank = 0
          self.tpu_global_core_rank = 0
          self.start_method = None
  
      @property
      def global_rank(self) -> int:
          return self.tpu_global_core_rank
  
      @property
      def local_rank(self) -> int:
          return self.tpu_local_core_rank
  
      @property
      def world_size(self) -> int:
          return xm.xrt_world_size()
  
      @property
      def root_device(self) -> torch.device:
          return xm.xla_device()
  
      @staticmethod
      def _validate_dataloader(dataloaders: Union[List[DataLoader], DataLoader]) -> None:
          if not isinstance(dataloaders, list):
              dataloaders = [dataloaders]
  
          for dataloader in dataloaders:
              if not has_len(dataloader):
                  raise MisconfigurationException(
                      "TPUs do not currently support IterableDataset objects, the dataset must implement `__len__`."
                      " HINT: You can mock the length on your dataset to bypass this MisconfigurationException."
                  )
  
      @staticmethod
      def _validate_patched_dataloaders(model: "pl.LightningModule") -> None:
          """Validate and fail fast if the dataloaders were passed directly to fit."""
          connector: DataConnector = model.trainer._data_connector
          sources = (
              connector._train_dataloader_source,
              connector._val_dataloader_source,
              connector._test_dataloader_source,
              connector._predict_dataloader_source,
          )
          for source in sources:
              if not source.is_module():
                  TPUSpawnPlugin._validate_dataloader(source.instance)
  
      def connect(self, model: "pl.LightningModule") -> None:
          TPUSpawnPlugin._validate_patched_dataloaders(model)
          self.wrapped_model = xmp.MpModelWrapper(LightningDistributedModule(model))
          return super().connect(model)
  
      def pre_dispatch(self, trainer: "pl.Trainer") -> None:
          self._move_optimizer_state()
          if self.debug:
              os.environ["PT_XLA_DEBUG"] = str(1)
  
          if self.tpu_global_core_rank != 0 and trainer.progress_bar_callback is not None:
              trainer.progress_bar_callback.disable()
  
          shared_params = find_shared_parameters(self.model)
          self.model_to_device()
          if is_overridden("on_post_move_to_device", self.lightning_module):
              self.model.module.on_post_move_to_device()
          else:
              set_shared_parameters(self.model.module, shared_params)
  
          self.setup_optimizers(trainer)
          self.precision_plugin.connect(self._model, None, None)
  
      def setup(self, trainer: "pl.Trainer") -> None:
          self.start_method = "fork"
          super().setup(trainer)
  
      def _setup_model(self, model: Module) -> Module:
          return model
  
      @property
      def distributed_sampler_kwargs(self) -> Dict[str, int]:
          return dict(num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())
  
      @property
      def is_distributed(self) -> bool:
          # HOST_WORLD_SIZE is None outside the xmp.spawn process
          return os.getenv(xenv.HOST_WORLD_SIZE, None) and self.world_size != 1
  
      def process_dataloader(self, dataloader: DataLoader) -> MpDeviceLoader:
          TPUSpawnPlugin._validate_dataloader(dataloader)
          dataloader = MpDeviceLoader(dataloader, self.root_device)
          # Mimic interface to torch.utils.data.DataLoader
          dataloader.dataset = dataloader._loader.dataset
          return dataloader
  
      def configure_ddp(self) -> None:
          pass
  
      def init_dist_connection(self, global_rank: int, world_size: int) -> None:
          pass
  
      def set_world_ranks(self, process_idx: int = 0) -> None:
          pass
  
      def model_to_device(self) -> None:
          self.model = self.wrapped_model.to(self.root_device)
  
      def barrier(self, name: Optional[str] = None) -> None:
          if self.is_distributed:
              rendezvous(name)
  
      def _collect_rank_zero_results(self, trainer: "pl.Trainer", results: Any) -> Optional["_SpawnOutput"]:
          rank_zero_warn("cleaning up tpu spawn environment...")
          checkpoint_callback = trainer.checkpoint_callback
          best_model_path = checkpoint_callback.best_model_path if checkpoint_callback else None
  
          # requires to compute the state_dict on all processes in case Metrics are present
          state_dict = self.lightning_module.state_dict()
  
          # save the last weights
          weights_path = None
          if trainer.state.fn == TrainerFn.FITTING:
              weights_path = os.path.join(trainer.default_root_dir, ".temp.ckpt")
              self.checkpoint_io.save_checkpoint(state_dict, weights_path)
  
          # We use `local_rank` here as separate filesystems are used for each VM for TPU Pod Training
          if self.local_rank != 0:
              return
  
          # adds the `callback_metrics` to the queue
          extra = _FakeQueue()
          if is_overridden("add_to_queue", self.lightning_module):
              # TODO: Remove the if in v1.7
              self.lightning_module.add_to_queue(extra)
          self.add_to_queue(trainer, extra)
  
          return _SpawnOutput(best_model_path, weights_path, trainer.state, results, extra)
  
      def broadcast(self, obj: object, src: int = 0) -> object:
          if not self.is_distributed:
              return obj
          buffer = io.BytesIO()
          torch.save(obj, buffer)
          data = bytearray(buffer.getbuffer())
          data_tensor = torch.tensor(data, device=self.root_device, dtype=torch.float)
          data = xm.all_gather(data_tensor)
          buffer = io.BytesIO(data.cpu().byte().numpy())
          obj = torch.load(buffer)
          return obj
  
      def reduce_boolean_decision(self, decision: bool) -> bool:
          decision = torch.tensor(int(decision), device=self.lightning_module.device)
          decision = self.reduce(decision, reduce_op="sum")
          decision = bool(decision == self.world_size)
          return decision
  
      def reduce(self, output, group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = None):
          if not isinstance(output, torch.Tensor):
              output = torch.tensor(output, device=self.lightning_module.device)
  
          _invalid_reduce_op = isinstance(reduce_op, ReduceOp) and reduce_op != ReduceOp.SUM
          _invalid_reduce_op_str = isinstance(reduce_op, str) and reduce_op.lower() not in ("sum", "mean", "avg")
          if _invalid_reduce_op or _invalid_reduce_op_str:
              raise MisconfigurationException(
                  "Currently, TPUSpawn TrainingTypePlugin only support `sum`, `mean`, `avg` reduce operation."
              )
  
          output = xm.mesh_reduce("reduce", output, sum)
  
          if isinstance(reduce_op, str) and reduce_op.lower() in ("avg", "mean"):
              output = output / self.world_size
  
          return output
  
      def get_mp_spawn_kwargs(self, trainer: Optional["pl.Trainer"] = None) -> Dict[str, Any]:
          return {
              "nprocs": len(self.parallel_devices),
              "start_method": self.start_method,
          }
  
      def spawn(self, function: Callable, *args: Any, **kwargs: Any) -> Optional[Union[Any, "_SpawnOutput"]]:
          # todo: precision pluging is call in accelerator setup and should be moved
          if "XLA_USE_BF16" in os.environ:
              del os.environ["XLA_USE_BF16"]
          context = mp.get_context(self.start_method or "fork")
          return_queue = context.SimpleQueue()
          xmp.spawn(self._wrapped_function, args=(function, args, kwargs, return_queue), **self.get_mp_spawn_kwargs())
          return return_queue.get()
  
      def _wrapped_function(
          self, process_idx: int, function: Callable, args: Any, kwargs: Any, return_queue: SimpleQueue
      ) -> None:
          self._worker_setup(process_idx)
          result = function(*args, **kwargs)
          if self.local_rank == 0:
              return_queue.put(move_data_to_device(result, "cpu"))
  
          # https://github.com/pytorch/xla/issues/1801#issuecomment-602799542
          self.barrier("end-process")
  
          # Ensure that the rank 0 process is the one exiting last
          # https://github.com/pytorch/xla/issues/2190#issuecomment-641665358
          if self.local_rank == 0:
              time.sleep(2)
  
      def _worker_setup(self, process_idx: int):
          reset_seed()
          self.tpu_local_core_rank = xm.get_local_ordinal()
          self.tpu_global_core_rank = xm.get_ordinal()
          rank_zero_only.rank = self.global_rank
  
      def validation_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
          with self.precision_plugin.val_step_context():
              return self.model(*args, **kwargs)
  
      def test_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
          with self.precision_plugin.test_step_context():
              return self.model(*args, **kwargs)
  
      def predict_step(self, *args, **kwargs) -> STEP_OUTPUT:
          with self.precision_plugin.predict_step_context():
              return self.model(*args, **kwargs)
  
      def training_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:
          self._pod_progress_bar_force_stdout()
          return output
  
      def validation_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:
          self._pod_progress_bar_force_stdout()
          return output
  
      def test_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:
          self._pod_progress_bar_force_stdout()
          return output
  
      def _pod_progress_bar_force_stdout(self) -> None:
          # Why is it required? The way `pytorch_xla.distributed` streams logs
          # from different vms to the main worker doesn't work well with tqdm
          # Ref: https://github.com/pytorch/xla/blob/master/torch_xla/distributed/xla_dist.py#L140
          # The print statement seems to force tqdm to flush stdout.
          if self.tpu_global_core_rank == 0 and int(os.getenv(xenv.TPUVM_MODE, 0)) == 1:
              print()
  
      def save_checkpoint(self, checkpoint: Dict[str, Any], filepath: _PATH) -> None:
          """Save model/training states as a checkpoint file through state-dump and file-write.
  
          Args:
              checkpoint: dict containing model and trainer state
              filepath: write-target file's path
          """
          return self.checkpoint_io.save_checkpoint(checkpoint, filepath)
  
      def all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> torch.Tensor:
          """
          Function to gather a tensor from several distributed processes
          Args:
              tensor: tensor of shape (batch, ...)
              group: not available with TPUs
              sync_grads: not available with TPUs
          Return:
              A tensor of shape (world_size, batch, ...)
          """
          if isinstance(tensor, torch.Tensor) and tensor.dim() == 0:
              tensor = tensor.unsqueeze(0)
          return xm.all_gather(tensor)
  
      def teardown(self) -> None:
          os.environ.pop("PT_XLA_DEBUG", None)
  
      @property
      def should_rank_save_checkpoint(self) -> bool:
          return self.local_rank == 0
  
      @classmethod
      def register_plugins(cls, plugin_registry: Dict) -> None:
          plugin_registry.register("tpu_spawn_debug", cls, description="TPUSpawn Plugin with `debug` as True", debug=True)
  
      @property
      def checkpoint_io(self) -> CheckpointIO:
          return self._checkpoint_io
  
      @checkpoint_io.setter
      def checkpoint_io(self, plugin: CheckpointIO) -> None:
          raise MisconfigurationException("TPU Spawn Plugin currently does not support custom checkpoint plugins.")
  