code: # 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 os
  import re
  
  import torch
  import torch.multiprocessing as mp
  import torch.distributed as torch_distrib
  import torch.distributed as dist
  
  from pytorch_lightning import _logger as log
  from pytorch_lightning.accelerators.base_backend import Accelerator
  from pytorch_lightning.utilities import AMPType
  from pytorch_lightning.utilities.cloud_io import atomic_save, load as pl_load
  from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn
  from pytorch_lightning.utilities.seed import seed_everything
  from pytorch_lightning.distributed.dist import LightningDistributed
  from pytorch_lightning.utilities.distributed import find_free_network_port
  
  
  try:
      from hydra.core.hydra_config import HydraConfig
      from hydra.utils import get_original_cwd, to_absolute_path
  except ImportError:
      HYDRA_AVAILABLE = False
  else:
      HYDRA_AVAILABLE = True
  
  
  class DDPSpawnBackend(Accelerator):
  
      def __init__(self, trainer, nprocs, cluster_environment=None):
          super().__init__(trainer, cluster_environment)
          self.mp_queue = None
          self.nprocs = nprocs
          self.dist = LightningDistributed()
  
      def setup(self, model):
          os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', str(find_free_network_port()))
  
          # pass in a state q
          smp = mp.get_context('spawn')
          self.mp_queue = smp.SimpleQueue()
  
          self.trainer.model = model
  
      def train(self):
          model = self.trainer.model
  
          # train in children process
          mp.spawn(self.ddp_train, nprocs=self.nprocs, args=(self.mp_queue, model,))
  
          # restore main state with best weights
          best_path = self.mp_queue.get()
          results = self.mp_queue.get()
          last_path = self.mp_queue.get()
  
          # recover the weights of the processes trained in the children
          self.__recover_child_process_weights(model, best_path, last_path)
          return results
  
      def ddp_train(self, process_idx, mp_queue, model, is_master=False, proc_offset=0):
          """
          Entry point for ddp
  
          Args:
              process_idx:
              mp_queue: multiprocessing queue
              model:
  
          Returns:
  
          """
          seed = os.environ.get("PL_GLOBAL_SEED")
          if seed is not None:
              seed_everything(int(seed))
  
          # offset the process id if requested
          process_idx = process_idx + proc_offset
  
          # show progressbar only on progress_rank 0
          if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None:
              self.trainer.progress_bar_callback.disable()
  
          # determine which process we are and world size
          self.set_world_ranks(process_idx)
  
          # set warning rank
          rank_zero_only.rank = self.trainer.global_rank
  
          # set up server using proc 0's ip address
          # try to init for 20 times at max in case ports are taken
          # where to store ip_table
          model.trainer = self.trainer
          self.init_ddp_connection(
              self.trainer.global_rank,
              self.trainer.world_size,
              self.trainer.is_slurm_managing_tasks
          )
  
          # call setup after the ddp process has connected
          self.trainer.call_setup_hook(model)
  
          # on world_size=0 let everyone know training is starting
          if self.trainer.is_global_zero and not torch.distributed.is_initialized():
              log.info('-' * 100)
              log.info(f'distributed_backend={self.trainer.distributed_backend}')
              log.info(f'All DDP processes registered. Starting ddp with {self.trainer.world_size} processes')
              log.info('-' * 100)
  
          # call sync_bn before .cuda(), configure_apex and configure_ddp
          if self.trainer.sync_batchnorm:
              model = model.configure_sync_batchnorm(model)
  
          # move the model to the correct device
          self.model_to_device(model, process_idx, is_master)
  
          # CHOOSE OPTIMIZER
          # allow for lr schedulers as well
          self.setup_optimizers(model)
  
          # set model properties before going into wrapper
          self.trainer.model_connector.copy_trainer_model_properties(model)
  
          # 16-bit
          model = self.trainer.precision_connector.connect(model)
  
          # device ids change depending on the DDP setup
          device_ids = self.get_device_ids()
  
          # allow user to configure ddp
          model = model.configure_ddp(model, device_ids)
  
          # set up training routine
          self.trainer.train_loop.setup_training(model)
  
          # train or test
          results = self.train_or_test()
  
          # get original model
          model = self.trainer.get_model()
  
          # persist info in ddp_spawn
          self.transfer_distrib_spawn_state_on_fit_end(model, mp_queue, results)
  
          # clean up memory
          torch.cuda.empty_cache()
  
      def set_world_ranks(self, process_idx):
          self.trainer.local_rank = process_idx
          self.trainer.global_rank = self.trainer.node_rank * self.trainer.num_processes + process_idx
          self.trainer.world_size = self.trainer.num_nodes * self.trainer.num_processes
  
      def model_to_device(self, model, process_idx, is_master):
          gpu_idx = process_idx
          self.trainer.root_gpu = gpu_idx
          torch.cuda.set_device(self.trainer.root_gpu)
          model.cuda(self.trainer.root_gpu)
  
      def get_device_ids(self):
          device_ids = [self.trainer.root_gpu]
          return device_ids
  
      def training_step(self, args):
          if self.trainer.amp_backend == AMPType.NATIVE:
              with torch.cuda.amp.autocast():
                  output = self.trainer.model(*args)
          else:
              output = self.trainer.model(*args)
          return output
  
      def validation_step(self, args):
          output = self.training_step(args)
          return output
  
      def test_step(self, args):
          output = self.training_step(args)
          return output
  
      def barrier(self, name: str = None):
          if torch_distrib.is_initialized():
              torch_distrib.barrier()
  
      def early_stopping_should_stop(self, pl_module):
          stop = torch.tensor(int(self.trainer.should_stop), device=pl_module.device)
          dist.all_reduce(stop, op=dist.reduce_op.SUM)
          dist.barrier()
          should_stop = stop == self.trainer.world_size
          return should_stop
  
      def broadcast(self, obj, src=0):
          return self.dist.broadcast(obj)
  
      def __recover_child_process_weights(self, model, best_path, last_path):
          # transfer back the best path to the trainer
          if self.trainer.checkpoint_callback:
              self.trainer.checkpoint_callback.best_model_path = best_path
          # todo, pass also best score
  
          # load last weights
          if last_path is not None and not self.trainer.testing:
              ckpt = pl_load(last_path, map_location=lambda storage, loc: storage)
              model.load_state_dict(ckpt)
  
          self.trainer.model = model
  
      def transfer_distrib_spawn_state_on_fit_end(self, model, mp_queue, results):
          best_model_path = None
          if self.trainer.checkpoint_callback is not None:
              best_model_path = self.trainer.checkpoint_callback.best_model_path
  
          if self.trainer.global_rank == 0 and mp_queue is not None:
              rank_zero_warn('cleaning up ddp environment...')
              # todo, pass complete checkpoint as state dictionary
              mp_queue.put(best_model_path)
              mp_queue.put(results)
  
              # save the last weights
              last_path = None
              if not self.trainer.testing and best_model_path is not None and len(best_model_path) > 0:
                  last_path = re.sub('.ckpt', '.tmp_end.ckpt', best_model_path)
                  atomic_save(model.state_dict(), last_path)
              mp_queue.put(last_path)
  
apis: torch.cuda.empty_cache torch.multiprocessing.spawn torch.distributed.is_initialized torch.distributed.barrier torch.multiprocessing.get_context torch.cuda.amp.autocast torch.distributed.all_reduce torch.cuda.set_device