code: def get_gpt2_model(args_others, mp_size=1):
      from megatron.model import GPT2Model
      from megatron.initialize import initialize_megatron
  
      args_defaults = {
          "vocab_file": get_test_path("gpt2-vocab.json"),
          "merge_file": get_test_path("gpt2-merges.txt"),
          "tokenizer_type": "GPT2BPETokenizer",
      }
  
      args_defaults.update(args_others)
  
      # setting "make-vocab-size-divisible-by" to avoid word-embedding size change in resizing testing.
      sys.argv.extend(
          [
              "--model-parallel-size",
              str(mp_size),
              "--make-vocab-size-divisible-by",
              str(1),
          ]
      )
  
      initialize_megatron(
          args_defaults=args_defaults, ignore_unknown_args=True
      )
      model = GPT2Model(num_tokentypes=0, parallel_output=False)
      model.cuda()
      from torch.nn.parallel.distributed import (
          DistributedDataParallel as torchDDP,
      )
      from megatron import mpu
  
      i = torch.cuda.current_device()
      model = torchDDP(
          model,
          device_ids=[i],
          output_device=i,
          process_group=mpu.get_data_parallel_group(),
      )
  
      return model
  
file_path: H2O/h2o_flexgen/benchmark/third_party/DeepSpeed/tests/unit/megatron_model.py
imports: import torch
  import os
  import sys
  import math
  from .common import get_test_path
  from deepspeed.pipe import PipelineModule, LayerSpec
label_desc: Use image node_id 2 for calling a global function with example usage: get_gpt2_model(args_others, mp_size) and returns: model