code: def test_gpt(self):
      self.MANUAL_SEED = 42
      self.inds = None
      self.data_idx = 0
      self.N_VOCAB = 128
      init = True
  
      tensor_model_parallel_size = (
          2 if self.world_size % 2 == 0 and self.world_size >= 4 else 1
      )
      pipeline_model_parallel_size = (
          self.world_size // tensor_model_parallel_size
      )
  
      override_args = {
          "micro_batch_size": 2,
          "num_layers": 16,
          "hidden_size": 256,
          "num_attention_heads": 8,
          "max_position_embeddings": 512,
          "seq_length": 512,
          "global_batch_size": 128,
          "pipeline_model_parallel_size": pipeline_model_parallel_size,
          "tensor_model_parallel_size": tensor_model_parallel_size,
          "world_size": self.world_size,
          "rank": self.rank,
      }
  
      global_vars.set_global_variables(
          override_args=override_args, ignore_unknown_args=True
      )
      args = global_vars.get_args()
  
      for async_comm in (
          (False,) if args.sequence_parallel else (False, True)
      ):
          global fancy_data
          global effective_length
  
          if init:
              init = False
  
              fancy_data = self._download_fancy_data()
              args = global_vars.get_args()
              args.model_type = ModelType.encoder_or_decoder
              effective_length = fancy_data.size(0) // args.seq_length
              effective_length = fancy_data.size(0) - args.seq_length
  
              args.padded_vocab_size = 128
              setup_microbatch_calculator(
                  args.rank,
                  args.rampup_batch_size,
                  args.global_batch_size,
                  args.micro_batch_size,
                  args.data_parallel_size,
              )
  
          print(args.tensor_model_parallel_size, "MODEL PARALLEL SIZE")
  
          parallel_state.initialize_model_parallel(
              tensor_model_parallel_size_=args.tensor_model_parallel_size,
              pipeline_model_parallel_size_=args.pipeline_model_parallel_size,
              default_backend="nccl",
              p2p_backend=self.DISTRIBUTED_BACKEND,
          )
  
          model_parallel_cuda_manual_seed(0)
          model = build_model(
              gpt_model_provider,
              wrap_with_ddp=parallel_state.get_data_parallel_world_size()
              > 1,
              virtual_pipeline_model_parallel_size=None,
              cpu_offload=args.cpu_offload,
          )
          assert isinstance(model, list), model
          _param_groups = _get_params_for_weight_decay_optimization(
              model
          )
          optim = torch.optim.Adam(_param_groups)
          runtime = self._train(
              model,
              optim,
              args.pipeline_model_parallel_size,
              async_comm,
          )
  
          parallel_state.destroy_model_parallel()
      torch.cuda.synchronize()
  
imports: from functools import partial
  from typing import List
  import time
  import torch
  import unittest
  from apex.transformer._ucc_util import HAS_UCC
  from apex.transformer import parallel_state
  from apex.transformer.enums import ModelType
  from apex.transformer.tensor_parallel import model_parallel_cuda_manual_seed
  from apex.transformer.pipeline_parallel.utils import average_losses_across_data_parallel_group, unwrap_model, setup_microbatch_calculator, get_ltor_masks_and_position_ids
  from apex.transformer.pipeline_parallel.schedules.common import _get_params_for_weight_decay_optimization, build_model
  from apex.transformer.pipeline_parallel.schedules.fwd_bwd_pipelining_without_interleaving import forward_backward_pipelining_without_interleaving
  from apex.transformer.testing.standalone_gpt import gpt_model_provider
  from apex.transformer.testing import global_vars
  from apex.transformer.testing.distributed_test_base import UccDistributedTestBase, NcclDistributedTestBase
  from torch.testing._internal import common_utils
  from torch.testing._internal.common_device_type import instantiate_device_type_tests
label_desc: Use image node_id 7 for calling the GptTestBase obj's underlying member method code with example usage: obj.test_gpt() without return types