class_docstr: Unit and integration tests to check that the behavior of DINO is correct
  under FSDP.
class_docstr_tok: ["Unit","and","integration","tests","to","check","that","the","behavior","of","DINO","is","correct","under","FSDP","."]
code: def _dino_head_worker(
      cls, gpu_id: int, sync_file: str, world_size: int
  ):
      init_distributed_on_file(
          world_size=world_size, gpu_id=gpu_id, sync_file=sync_file
      )
      ddp_config = cls._create_dino_pretraining_config(
          with_fsdp=False,
          with_mixed_precision=True,
          gpu_count=world_size,
      )
      fsdp_config = cls._create_dino_pretraining_config(
          with_fsdp=True,
          with_mixed_precision=True,
          gpu_count=world_size,
      )
  
      # Create fake inputs
      torch.random.manual_seed(gpu_id)
      batch_size = 1
      x = torch.randn(size=(batch_size, 384)).cuda()
  
      model_init_seed = 0
      args = {
          "in_dim": 384,
          "num_clusters": [65536],
          "normalize_last_layer": True,
      }
      with set_torch_seed(model_init_seed):
          ddp_model = DINOHead(ddp_config["MODEL"], **args).cuda()
          ddp_model = DistributedDataParallel(
              ddp_model, device_ids=[gpu_id]
          )
      with set_torch_seed(model_init_seed):
          fsdp_model = DINOHeadFSDP(fsdp_config["MODEL"], **args).cuda()
          print(fsdp_model)
  
      # Check that local_state_dict is valid
      fsdp_model_2 = DINOHeadFSDP(fsdp_config["MODEL"], **args).cuda()
      fsdp_model_2.load_local_state_dict(fsdp_model.local_state_dict())
      fsdp_model_2.load_state_dict(fsdp_model.state_dict())
  
      # DDP vs FSDP matching
      ddp_optimizer = optim.AdamW(ddp_model.parameters())
      fsdp_optimizer = optim.AdamW(fsdp_model.parameters())
      for _ in range(2):
          with torch.cuda.amp.autocast():
              ddp_loss = ddp_model(x)[0].sum()
              fsdp_loss = fsdp_model(x)[0].sum()
          ddp_optimizer.zero_grad()
          fsdp_optimizer.zero_grad()
          ddp_loss.backward()
          fsdp_loss.backward()
          ddp_optimizer.step()
          fsdp_optimizer.step()
          assert torch.allclose(ddp_loss, fsdp_loss)
  
imports: import os
  import unittest
  import torch
  import torch.multiprocessing
  import torch.optim
  from torch.nn.parallel import DistributedDataParallel
  from vissl.models import build_model
  from vissl.models.heads.dino_head import DINOHead, DINOHeadFSDP
  from vissl.utils.checkpoint import CheckpointFormatConverter, DINOCheckpointUtils
  from vissl.utils.fsdp_utils import fsdp_wrapper
  from vissl.utils.hydra_config import compose_hydra_configuration, convert_to_attrdict
  from vissl.utils.misc import set_torch_seed
  from vissl.utils.test_utils import gpu_test, in_temporary_directory, init_distributed_on_file, run_integration_test, with_temp_files
label_desc: Use image node_id 7 for calling the TestDINO obj's underlying member method code with example usage: obj._dino_head_worker(cls, gpu_id, sync_file, world_size) without return types