text: import os
  import sys
  
  import numpy as np
  import torch
  
  from pytorch_lightning import seed_everything, Trainer
  from pytorch_lightning.utilities import AllGatherGrad
  from tests.helpers.boring_model import BoringModel
  from tests.helpers.runif import RunIf
  
  
  def setup_ddp(rank, world_size):
      """Setup ddp enviroment."""
      os.environ["MASTER_ADDR"] = "localhost"
      os.environ["MASTER_PORT"] = "8088"
  
      if torch.distributed.is_available() and sys.platform not in ("win32", "cygwin"):
          torch.distributed.init_process_group("gloo", rank=rank, world_size=world_size)
  
  
  def _test_all_gather_ddp(rank, world_size):
      setup_ddp(rank, world_size)
  
      tensor1 = torch.ones(8, requires_grad=True)
      tensor2 = torch.ones((8, 16, 32), requires_grad=True)
  
      tensor1_gathered = AllGatherGrad.apply(tensor1)
      tensor2_gathered = AllGatherGrad.apply(tensor2)
  
      tensor1_gathered = tensor1_gathered * rank
      tensor2_gathered = tensor2_gathered * rank
  
      tensor1_gathered.sum().backward()
      tensor2_gathered.sum().backward()
  
      grad1 = torch.zeros_like(tensor1.grad).fill_(torch.arange(world_size).sum().float())
      grad2 = torch.zeros_like(tensor2.grad).fill_(torch.arange(world_size).sum().float())
  
      assert torch.allclose(grad1, tensor1.grad)
      assert torch.allclose(grad2, tensor2.grad)
  
  
  @RunIf(skip_windows=True, skip_49370=True)
  def test_all_gather_ddp_spawn():
      world_size = 3
      torch.multiprocessing.spawn(_test_all_gather_ddp, args=(world_size,), nprocs=world_size)
  
  
  @RunIf(min_gpus=2, skip_windows=True, special=True)
  def test_all_gather_collection(tmpdir):
      class TestModel(BoringModel):
  
          training_epoch_end_called = False
  
          def training_epoch_end(self, outputs) -> None:
              losses = torch.stack([x["loss"] for x in outputs])
              gathered_loss = self.all_gather(
                  {
                      "losses_tensor_int": torch.rand(2, 2).int().t(),
                      "losses_tensor_float": torch.rand(2, 2).t(),
                      "losses_np_ndarray": np.array([1, 2, 3]),
                      "losses_bool": [True, False],
                      "losses_float": [0.0, 1.0, 2.0],
                      "losses_int": [0, 1, 2],
                      "losses": losses,
                      "losses_list": [losses, losses],
                  }
              )
              assert gathered_loss["losses_tensor_int"][0].dtype == torch.int32
              assert gathered_loss["losses_tensor_float"][0].dtype == torch.float
              assert gathered_loss["losses_np_ndarray"][0].dtype == torch.int64
              # torch.bool can't be all_gathered
              assert gathered_loss["losses_bool"][0].dtype == torch.uint8
              assert gathered_loss["losses_float"][0].dtype == torch.float
              assert gathered_loss["losses_int"][0].dtype == torch.int
              assert gathered_loss["losses_list"][0].numel() == 2 * len(losses)
              assert gathered_loss["losses"].numel() == 2 * len(losses)
              self.training_epoch_end_called = True
  
      seed_everything(42)
  
      model = TestModel()
  
      limit_train_batches = 8
      trainer = Trainer(
          default_root_dir=tmpdir,
          limit_train_batches=limit_train_batches,
          limit_val_batches=2,
          max_epochs=1,
          log_every_n_steps=1,
          accumulate_grad_batches=2,
          gpus=2,
          strategy="ddp",
      )
  
      trainer.fit(model)
      assert model.training_epoch_end_called
  
  
  @RunIf(min_gpus=2, skip_windows=True, special=True)
  def test_all_gather_sync_grads(tmpdir):
      class TestModel(BoringModel):
  
          training_step_called = False
  
          def training_step(self, batch, batch_idx):
              self.training_step_called = True
              tensor = torch.rand(2, 2, requires_grad=True, device=self.device)
              gathered_tensor = self.all_gather(tensor, sync_grads=True)
              assert gathered_tensor.shape == torch.Size([2, 2, 2])
  
              loss = gathered_tensor.sum()
  
              return loss
  
      model = TestModel()
      trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, gpus=2, strategy="ddp")
      trainer.fit(model)
      assert model.training_step_called
  