code: def _layer_memory_tracking_ddp_worker(
      gpu_id: int, sync_files: Tuple[str, str], world_size: int
  ):
      dist_init(
          world_size=world_size,
          rank=gpu_id,
          filename=sync_files[0],
          filename_rpc=sync_files[1],
      )
      torch.backends.cudnn.deterministic = True
  
      # Create different inputs on each GPU
      batch_size = 16
      torch.manual_seed(gpu_id)
      fake_inputs = torch.randn(size=(batch_size, 10)).cuda(gpu_id)
      fake_targets = torch.randn(size=(batch_size, 10)).cuda(gpu_id)
      fake_criterion = nn.MSELoss()
  
      # Create a simple model
      torch.manual_seed(0)
      torch.cuda.manual_seed(0)
      model = nn.Sequential(
          nn.Linear(10, 32),
          nn.ReLU(),
          nn.Linear(32, 32),
          nn.ReLU(),
          nn.Linear(32, 10),
      )
      model = model.cuda(gpu_id)
      ddp_model = DistributedDataParallel(model, device_ids=[gpu_id])
  
      # Track the model on a forward / backward pass
      tracker = LayerwiseMemoryTracker()
      tracker.monitor(ddp_model)
      fake_criterion(ddp_model(fake_inputs), fake_targets).backward()
      tracker.stop()
  
      # Check the overall structure of the collected traces
      forward_names = [f"module.{i}" for i in range(5)]
      backward_names = [f"module.{i}" for i in reversed(range(5))]
      trace_names = [t.module_name for t in tracker.memory_traces]
      assert trace_names == (forward_names + backward_names)
  
file_path: fairscale/tests/experimental/tooling/test_layer_memory_tracker.py
imports: from typing import Tuple
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  from torch.nn.parallel import DistributedDataParallel
  from fairscale.experimental.tooling.layer_memory_tracker import LayerwiseMemoryTracker, ProcessGroupTracker, find_best_reset_points
  from fairscale.fair_dev.testing.testing import GPT2, dist_init, skip_if_no_cuda, skip_if_single_gpu, temp_files_ctx
  from fairscale.nn import FullyShardedDataParallel
label_desc: Use image node_id 4 for calling a global function with example usage: _layer_memory_tracking_ddp_worker(gpu_id, sync_files, world_size) without return types