code: #!/usr/bin/env python3
  """ This script is adapted from the Bertology pruning code (https://github.com/huggingface/transformers/blob/783d7d2629e97c5f0c5f9ef01b8c66410275c204/examples/research_projects/bertology/run_bertology.py)
  to prune GPT-like models. The author is @altsoph.
  """
  
  import argparse
  import logging
  import os
  from datetime import datetime
  
  import numpy as np
  import torch
  from torch.utils.data import DataLoader, RandomSampler, TensorDataset
  from tqdm import tqdm
  
  from transformers import GPT2LMHeadModel
  
  
  logger = logging.getLogger(__name__)
  
  
  def save_model(model, dirpath):
      # save results
      if os.path.exists(dirpath):
          if os.path.exists(os.path.join(dirpath, "config.json")) and os.path.isfile(
              os.path.join(dirpath, "config.json")
          ):
              os.remove(os.path.join(dirpath, "config.json"))
          if os.path.exists(os.path.join(dirpath, "pytorch_model.bin")) and os.path.isfile(
              os.path.join(dirpath, "pytorch_model.bin")
          ):
              os.remove(os.path.join(dirpath, "pytorch_model.bin"))
      else:
          os.makedirs(dirpath)
      model.save_pretrained(dirpath)
  
  
  def entropy(p, unlogit=False):
      """ Compute the entropy of a probability distribution """
      exponent = 2
      if unlogit:
          p = torch.pow(p, exponent)
      plogp = p * torch.log(p)
      plogp[p == 0] = 0
      return -plogp.sum(dim=-1)
  
  
  def print_2d_tensor(tensor):
      """ Print a 2D tensor """
      logger.info("lv, h >\t" + "\t".join(f"{x + 1}" for x in range(len(tensor))))
      for row in range(len(tensor)):
          if tensor.dtype != torch.long:
              logger.info(f"layer {row + 1}:\t" + "\t".join(f"{x:.5f}" for x in tensor[row].cpu().data))
          else:
              logger.info(f"layer {row + 1}:\t" + "\t".join(f"{x:d}" for x in tensor[row].cpu().data))
  
  
  def compute_heads_importance(
      args, model, eval_dataloader, compute_entropy=True, compute_importance=True, head_mask=None, actually_pruned=False
  ):
      """This method shows how to compute:
      - head attention entropy
      - head importance scores according to http://arxiv.org/abs/1905.10650
      """
      # Prepare our tensors
      n_layers, n_heads = model.config.num_hidden_layers, model.config.num_attention_heads
      head_importance = torch.zeros(n_layers, n_heads).to(args.device)
      attn_entropy = torch.zeros(n_layers, n_heads).to(args.device)
  
      if head_mask is None:
          head_mask = torch.ones(n_layers, n_heads).to(args.device)
  
      head_mask.requires_grad_(requires_grad=True)
      # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
      if actually_pruned:
          head_mask = None
  
      tot_tokens = 0.0
      total_loss = 0.0
      for step, inputs in enumerate(tqdm(eval_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
          inputs = tuple(t.to(args.device) for t in inputs)
          (input_ids,) = inputs
  
          # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
          outputs = model(input_ids, labels=input_ids, head_mask=head_mask)
          #  (loss), lm_logits, presents, (all hidden_states), (attentions)
          loss, _, all_attentions = (
              outputs[0],
              outputs[1],
              outputs[-1],
          )  # Loss and logits are the first, attention the last
          loss.backward()  # Backpropagate to populate the gradients in the head mask
          total_loss += loss.detach().cpu().numpy()
          if compute_entropy:
              for layer, attn in enumerate(all_attentions):
                  masked_entropy = entropy(attn.detach(), True)
                  attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()
  
          if compute_importance:
              head_importance += head_mask.grad.abs().detach()
          tot_tokens += torch.ones_like(input_ids).float().detach().sum().data
  
      # Normalize
      attn_entropy /= tot_tokens
      head_importance /= tot_tokens
      # Layerwise importance normalization
      if not args.dont_normalize_importance_by_layer:
          exponent = 2
          norm_by_layer = torch.pow(torch.pow(head_importance, exponent).sum(-1), 1 / exponent)
          head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20
  
      if not args.dont_normalize_global_importance:
          head_importance = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
  
      # Print matrices
      if compute_entropy:
          logger.info("Attention entropies")
          print_2d_tensor(attn_entropy)
      if compute_importance:
          logger.info("Head importance scores")
          print_2d_tensor(head_importance)
      logger.info("Head ranked by importance scores")
      head_ranks = torch.zeros(head_importance.numel(), dtype=torch.long, device=args.device)
      head_ranks[head_importance.view(-1).sort(descending=True)[1]] = torch.arange(
          head_importance.numel(), device=args.device
      )
      head_ranks = head_ranks.view_as(head_importance)
      print_2d_tensor(head_ranks)
      return attn_entropy, head_importance, total_loss
  
  
  def mask_heads(args, model, eval_dataloader):
      """This method shows how to mask head (set some heads to zero), to test the effect on the network,
      based on the head importance scores, as described in Michel et al. (http://arxiv.org/abs/1905.10650)
      """
      _, head_importance, loss = compute_heads_importance(args, model, eval_dataloader, compute_entropy=False)
      original_score = 1 / loss  # instead of downsteam score use the LM loss
      logger.info("Pruning: original score: %f, threshold: %f", original_score, original_score * args.masking_threshold)
  
      new_head_mask = torch.ones_like(head_importance)
      num_to_mask = max(1, int(new_head_mask.numel() * args.masking_amount))
  
      current_score = original_score
      while current_score >= original_score * args.masking_threshold:
          head_mask = new_head_mask.clone().detach()  # save current head mask
          # heads from least important to most - keep only not-masked heads
          head_importance[head_mask == 0.0] = float("Inf")
          current_heads_to_mask = head_importance.view(-1).sort()[1]
  
          if len(current_heads_to_mask) <= num_to_mask:
              print("BREAK BY num_to_mask")
              break
  
          # mask heads
          current_heads_to_mask = current_heads_to_mask[:num_to_mask]
          logger.info("Heads to mask: %s", str(current_heads_to_mask.tolist()))
          new_head_mask = new_head_mask.view(-1)
          new_head_mask[current_heads_to_mask] = 0.0
          new_head_mask = new_head_mask.view_as(head_mask)
          new_head_mask = new_head_mask.clone().detach()
          print_2d_tensor(new_head_mask)
  
          # Compute metric and head importance again
          _, head_importance, loss = compute_heads_importance(
              args, model, eval_dataloader, compute_entropy=False, head_mask=new_head_mask
          )
          current_score = 1 / loss
          logger.info(
              "Masking: current score: %f, remaining heads %d (%.1f percents)",
              current_score,
              new_head_mask.sum(),
              new_head_mask.sum() / new_head_mask.numel() * 100,
          )
  
      logger.info("Final head mask")
      print_2d_tensor(head_mask)
      np.save(os.path.join(args.output_dir, "head_mask.npy"), head_mask.detach().cpu().numpy())
  
      return head_mask
  
  
  def prune_heads(args, model, eval_dataloader, head_mask):
      """This method shows how to prune head (remove heads weights) based on
      the head importance scores as described in Michel et al. (http://arxiv.org/abs/1905.10650)
      """
      # Try pruning and test time speedup
      # Pruning is like masking but we actually remove the masked weights
      before_time = datetime.now()
      _, _, loss = compute_heads_importance(
          args, model, eval_dataloader, compute_entropy=False, compute_importance=False, head_mask=head_mask
      )
      score_masking = 1 / loss
      original_time = datetime.now() - before_time
  
      original_num_params = sum(p.numel() for p in model.parameters())
      heads_to_prune = dict(
          (layer, (1 - head_mask[layer].long()).nonzero().squeeze().tolist()) for layer in range(len(head_mask))
      )
  
      for k, v in heads_to_prune.items():
          if isinstance(v, int):
              heads_to_prune[k] = [
                  v,
              ]
  
      assert sum(len(h) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
      model.prune_heads(heads_to_prune)
      pruned_num_params = sum(p.numel() for p in model.parameters())
  
      before_time = datetime.now()
      _, _, loss = compute_heads_importance(
          args,
          model,
          eval_dataloader,
          compute_entropy=False,
          compute_importance=False,
          head_mask=None,
          actually_pruned=True,
      )
  
      score_pruning = 1 / loss
      new_time = datetime.now() - before_time
  
      logger.info(
          "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)",
          original_num_params,
          pruned_num_params,
          pruned_num_params / original_num_params * 100,
      )
      logger.info("Pruning: score with masking: %f score with pruning: %f", score_masking, score_pruning)
      logger.info("Pruning: speed ratio (original timing / new timing): %f percents", original_time / new_time * 100)
      save_model(model, args.output_dir)
  
  
  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 = torch.nn.parallel.DistributedDataParallel(
              model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
          )
      elif args.n_gpu > 1:
          model = torch.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)
  
  
  if __name__ == "__main__":
      main()
  