code: import torch
  import torch.distributed as dist
  import unittest
  
  from torch import nn
  from torch.nn.parallel import DistributedDataParallel
  from torch.testing._internal.dist_utils import INIT_METHOD_TEMPLATE, dist_init
  from torch.testing._internal.distributed.rpc.rpc_agent_test_fixture import (
      RpcAgentTestFixture,
  )
  from torch.testing._internal.common_distributed import (
      requires_gloo,
      requires_nccl,
      skip_if_lt_x_gpu,
      skip_if_rocm,
  )
  from torch.distributed.pipeline.sync import Pipe
  
  class PipeWithDDPTest(RpcAgentTestFixture):
      @property
      def world_size(self) -> int:
          return 2
  
      @skip_if_lt_x_gpu(4)
      @requires_nccl()
      @dist_init
      @skip_if_rocm
      def test_basic_nccl_ckpt_never(self):
          self._run_basic_test("nccl", "never")
  
      @skip_if_lt_x_gpu(4)
      @requires_nccl()
      @dist_init
      @skip_if_rocm
      @unittest.skip("DDP doesn't work with checkpointing")
      def test_basic_nccl_ckpt_always(self):
          self._run_basic_test("nccl", "always")
  
      @skip_if_lt_x_gpu(4)
      @requires_nccl()
      @dist_init
      @skip_if_rocm
      @unittest.skip("DDP doesn't work with checkpointing")
      def test_basic_nccl_ckpt_except_last(self):
          self._run_basic_test("nccl", "except_last")
  
      @skip_if_lt_x_gpu(4)
      @requires_gloo()
      @dist_init
      @skip_if_rocm
      def test_basic_gloo_ckpt_never(self):
          self._run_basic_test("gloo", "never")
  
      @skip_if_lt_x_gpu(4)
      @requires_gloo()
      @dist_init
      @skip_if_rocm
      @unittest.skip("DDP doesn't work with checkpointing")
      def test_basic_gloo_ckpt_always(self):
          self._run_basic_test("gloo", "always")
  
      @skip_if_lt_x_gpu(4)
      @requires_gloo()
      @dist_init
      @skip_if_rocm
      @unittest.skip("DDP doesn't work with checkpointing")
      def test_basic_gloo_ckpt_except_last(self):
          self._run_basic_test("gloo", "except_last")
  
      def _run_basic_test(self, backend, checkpoint):
          dist.init_process_group(
              backend="nccl",
              init_method=INIT_METHOD_TEMPLATE.format(file_name=self.file_name),
              world_size=self.world_size,
              rank=self.rank,
          )
  
          # Use 4 GPUs, two replicas of a pipe across GPU 0 and 1 and another
          # pipe between GPU 2 and 3. Both replicas are replicated via DDP.
          fc1 = nn.Linear(16, 8).cuda(2 * self.rank)
          fc2 = nn.Linear(8, 4).cuda(2 * self.rank + 1)
          model = nn.Sequential(
              fc1,
              fc2
          )
          model = Pipe(model, chunks=2, checkpoint=checkpoint)
          model = DistributedDataParallel(model)
          out = model(torch.rand(16, 16).cuda(2 * self.rank)).local_value()
          out.sum().backward()
  
          # Check grads
          output = [torch.empty_like(fc1.weight.grad), torch.empty_like(fc1.weight.grad)]
          dist.all_gather(output, fc1.weight.grad)
          self.assertEqual(output[0], output[1])
  
          output = [torch.empty_like(fc2.weight.grad), torch.empty_like(fc2.weight.grad)]
          dist.all_gather(output, fc2.weight.grad)
          self.assertEqual(output[0], output[1])
  