code: def test_2d_state_dict(self, is_even_sharded_model):
      simple_model = (
          SimpleModel if is_even_sharded_model else SimpleModelUneven
      )
  
      # Create a model without wrapper
      torch.manual_seed(0)
      no_wrap_model = simple_model().cuda(self.rank)
      no_wrap_state_dict = no_wrap_model.state_dict()
  
      # Create a model and sharded it with 2D FSDP + TP
      torch.manual_seed(0)
      mesh_2d = init_device_mesh(
          self.device_type,
          (2, self.world_size // 2),
          mesh_dim_names=("dp", "tp"),
      )
      tp_mesh = mesh_2d["tp"]
      dp_mesh = mesh_2d["dp"]
      parallelize_plan = {
          "net1": ColwiseParallel(),
          "net2": RowwiseParallel(),
      }
      model_2d = parallelize_module(
          simple_model().cuda(), tp_mesh, parallelize_plan
      )
      model_2d = FSDP(
          model_2d, device_mesh=dp_mesh, use_orig_params=True
      )
  
      FSDP.set_state_dict_type(
          model_2d,
          StateDictType.SHARDED_STATE_DICT,
      )
      state_dict_2d = model_2d.state_dict()
  
      for no_wrap_items, two_d_items in zip(
          no_wrap_state_dict.items(), state_dict_2d.items()
      ):
          no_wrap_k, no_wrap_v = no_wrap_items
          two_d_k, two_d_v = two_d_items
  
          self.assertEqual(no_wrap_k, two_d_k)
  
          # check if all value in 2D state_dict are DTensor
          self.assertTrue(isinstance(two_d_v, DT))
          self.assertEqual(len(two_d_v.placements), 2)
          # the outer dimension is the FSDP dimension and the placement is always Shard(0)
          self.assertEqual(two_d_v.placements[0], Shard(0))
          self.assertEqual(two_d_v.device_mesh, mesh_2d)
  
          # check if the parameter value is the same between 2D model and the model without wrapper
          all_gather_two_d_v = two_d_v.redistribute(
              mesh_2d, (Replicate(), Replicate())
          )
          self.assertEqual(
              torch.allclose(no_wrap_v, all_gather_two_d_v.to_local()),
              True,
          )
  
imports: import io
  from copy import deepcopy
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  from torch.distributed._tensor import DTensor, init_device_mesh, Replicate, Shard
  from torch.distributed.checkpoint.state_dict import get_optimizer_state_dict, set_optimizer_state_dict
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.distributed.fsdp._common_utils import _get_module_fsdp_state, clean_tensor_name
  from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
  from torch.distributed.tensor.parallel import ColwiseParallel, parallelize_module, RowwiseParallel
  from torch.distributed.tensor.parallel.fsdp import DTensorExtensions
  from torch.distributed.tensor.parallel.input_reshard import input_reshard
  from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, with_comms
label_desc: Use image node_id 2 for calling the TestNew2dParallelStateDict obj's underlying member method code with example usage: obj.test_2d_state_dict(is_even_sharded_model) without return types