code: def main():
      parser = argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--data_dir",
          default=None,
          type=str,
          required=True,
          help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
      )
      parser.add_argument(
          "--model_name_or_path",
          default=None,
          type=str,
          required=True,
          help="Path to pretrained model or model identifier from huggingface.co/models",
      )
      parser.add_argument(
          "--output_dir",
          default=None,
          type=str,
          required=True,
          help="The output directory where the model predictions and checkpoints will be written.",
      )
  
      # Other parameters
      parser.add_argument(
          "--config_name",
          default="",
          type=str,
          help="Pretrained config name or path if not the same as model_name_or_path",
      )
      parser.add_argument(
          "--tokenizer_name",
          default="",
          type=str,
          help="Pretrained tokenizer name or path if not the same as model_name_or_path",
      )
      parser.add_argument(
          "--cache_dir",
          default=None,
          type=str,
          help="Where do you want to store the pre-trained models downloaded from s3",
      )
      parser.add_argument(
          "--data_subset",
          type=int,
          default=-1,
          help="If > 0: limit the data to a subset of data_subset instances.",
      )
      parser.add_argument(
          "--overwrite_output_dir",
          action="store_true",
          help="Whether to overwrite data in output directory",
      )
      parser.add_argument(
          "--overwrite_cache",
          action="store_true",
          help="Overwrite the cached training and evaluation sets",
      )
  
      parser.add_argument(
          "--dont_normalize_importance_by_layer",
          action="store_true",
          help="Don't normalize importance score by layers",
      )
      parser.add_argument(
          "--dont_normalize_global_importance",
          action="store_true",
          help="Don't normalize all importance scores between 0 and 1",
      )
  
      parser.add_argument(
          "--try_masking",
          action="store_true",
          help="Whether to try to mask head until a threshold of accuracy.",
      )
      parser.add_argument(
          "--masking_threshold",
          default=0.9,
          type=float,
          help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value).",
      )
      parser.add_argument(
          "--masking_amount",
          default=0.1,
          type=float,
          help="Amount to heads to masking at each masking step.",
      )
      parser.add_argument(
          "--metric_name",
          default="acc",
          type=str,
          help="Metric to use for head masking.",
      )
  
      parser.add_argument(
          "--max_seq_length",
          default=128,
          type=int,
          help=(
              "The maximum total input sequence length after WordPiece tokenization. \n"
              "Sequences longer than this will be truncated, sequences shorter padded."
          ),
      )
      parser.add_argument(
          "--batch_size", default=1, type=int, help="Batch size."
      )
  
      parser.add_argument("--seed", type=int, default=42)
      parser.add_argument(
          "--local_rank",
          type=int,
          default=-1,
          help="local_rank for distributed training on gpus",
      )
      parser.add_argument(
          "--no_cuda",
          action="store_true",
          help="Whether not to use CUDA when available",
      )
      parser.add_argument(
          "--server_ip",
          type=str,
          default="",
          help="Can be used for distant debugging.",
      )
      parser.add_argument(
          "--server_port",
          type=str,
          default="",
          help="Can be used for distant debugging.",
      )
      args = parser.parse_args()
  
      if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd
  
          print("Waiting for debugger attach")
          ptvsd.enable_attach(
              address=(args.server_ip, args.server_port),
              redirect_output=True,
          )
          ptvsd.wait_for_attach()
  
      # Setup devices and distributed training
      if args.local_rank == -1 or args.no_cuda:
          args.device = torch.device(
              "cuda"
              if torch.cuda.is_available() and not args.no_cuda
              else "cpu"
          )
          args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
      else:
          torch.cuda.set_device(args.local_rank)
          args.device = torch.device("cuda", args.local_rank)
          args.n_gpu = 1
          torch.distributed.init_process_group(
              backend="nccl"
          )  # Initializes the distributed backend
  
      # Setup logging
      logging.basicConfig(
          level=logging.INFO
          if args.local_rank in [-1, 0]
          else logging.WARN
      )
      logger.info(
          "device: {} n_gpu: {}, distributed: {}".format(
              args.device, args.n_gpu, bool(args.local_rank != -1)
          )
      )
  
      model = GPT2LMHeadModel.from_pretrained(args.model_name_or_path)
  
      # Distributed and parallel training
      model.to(args.device)
      if args.local_rank != -1:
          model = nn.parallel.DistributedDataParallel(
              model,
              device_ids=[args.local_rank],
              output_device=args.local_rank,
              find_unused_parameters=True,
          )
      elif args.n_gpu > 1:
          model = nn.DataParallel(model)
  
      # Print/save training arguments
      os.makedirs(args.output_dir, exist_ok=True)
      torch.save(args, os.path.join(args.output_dir, "run_args.bin"))
      logger.info("Training/evaluation parameters %s", args)
  
      # Prepare dataset
      numpy_data = np.concatenate(
          [
              np.loadtxt(args.data_dir, dtype=np.int64),
          ]
      )
      train_tensor_dataset = (torch.from_numpy(numpy_data),)
      train_data = TensorDataset(*train_tensor_dataset)
      train_sampler = RandomSampler(train_data)
      eval_dataloader = DataLoader(
          train_data, sampler=train_sampler, batch_size=args.batch_size
      )
  
      # Compute head entropy and importance score
      compute_heads_importance(args, model, eval_dataloader)
  
      # Try head masking (set heads to zero until the score goes under a threshole)
      # and head pruning (remove masked heads and see the effect on the network)
      if (
          args.try_masking
          and args.masking_threshold > 0.0
          and args.masking_threshold < 1.0
      ):
          head_mask = mask_heads(args, model, eval_dataloader)
          prune_heads(args, model, eval_dataloader, head_mask)
  
file_path: transformers/examples/research_projects/bertology/run_prune_gpt.py
imports: import argparse
  import logging
  import os
  from datetime import datetime
  import numpy
  import torch
  from torch import nn
  from torch.utils.data import DataLoader, RandomSampler, TensorDataset
  from tqdm import tqdm
  from transformers import GPT2LMHeadModel
label_desc: Use image node_id 7 for calling a global function with example usage: main() without return types