


"""simple docstring"""




import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


__UpperCamelCase						=      logging.getLogger(__name__)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any] ,			SCREAMING_SNAKE_CASE_		:				Dict  )   ->						int:
						# save results
						if os.path.exists(__lowerCamelCase  ):
												if os.path.exists(os.path.join(__lowerCamelCase ,			'config.json'  )  ) and os.path.isfile(
												    os.path.join(__lowerCamelCase ,			'config.json'  )  ):
																		os.remove(os.path.join(__lowerCamelCase ,			'config.json'  )  )
												if os.path.exists(os.path.join(__lowerCamelCase ,			'pytorch_model.bin'  )  ) and os.path.isfile(
												    os.path.join(__lowerCamelCase ,			'pytorch_model.bin'  )  ):
																		os.remove(os.path.join(__lowerCamelCase ,			'pytorch_model.bin'  )  )
						else:
												os.makedirs(__lowerCamelCase  )
						model.save_pretrained(__lowerCamelCase  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				int=False  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					2
						if unlogit:
												SCREAMING_SNAKE_CASE													=					torch.pow(__lowerCamelCase ,			__lowerCamelCase  )
						SCREAMING_SNAKE_CASE													=					p * torch.log(__lowerCamelCase  )
						SCREAMING_SNAKE_CASE													=					0
						return -plogp.sum(dim=-1  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str]  )   ->						List[Any]:
						logger.info('lv, h >\t' + '\t'.join(F'{x + 1}' for x in range(len(__lowerCamelCase  )  )  )  )
						for row in range(len(__lowerCamelCase  )  ):
												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     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]=True ,			SCREAMING_SNAKE_CASE_		:				int=True ,			SCREAMING_SNAKE_CASE_		:				Any=None ,			SCREAMING_SNAKE_CASE_		:				Tuple=False  )   ->						Optional[Any]:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					model.config.num_hidden_layers, model.config.num_attention_heads
						SCREAMING_SNAKE_CASE													=					torch.zeros(__lowerCamelCase ,			__lowerCamelCase  ).to(args.device  )
						SCREAMING_SNAKE_CASE													=					torch.zeros(__lowerCamelCase ,			__lowerCamelCase  ).to(args.device  )

						if head_mask is None:
												SCREAMING_SNAKE_CASE													=					torch.ones(__lowerCamelCase ,			__lowerCamelCase  ).to(args.device  )

						head_mask.requires_grad_(requires_grad=__lowerCamelCase  )
						# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
						if actually_pruned:
												SCREAMING_SNAKE_CASE													=					None

						SCREAMING_SNAKE_CASE													=					0.0
						SCREAMING_SNAKE_CASE													=					0.0
						for step, inputs in enumerate(tqdm(__lowerCamelCase ,			desc='Iteration' ,			disable=args.local_rank not in [-1, 0]  )  ):
												SCREAMING_SNAKE_CASE													=					tuple(t.to(args.device  ) for t in inputs  )
												((SCREAMING_SNAKE_CASE       )							,				       )													=					inputs

												# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
												SCREAMING_SNAKE_CASE													=					model(__lowerCamelCase ,			labels=__lowerCamelCase ,			head_mask=__lowerCamelCase  )
												#  (loss), lm_logits, presents, (all hidden_states), (attentions)
												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					(
												    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(__lowerCamelCase  ):
																								SCREAMING_SNAKE_CASE													=					entropy(attn.detach() ,			__lowerCamelCase  )
																								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(__lowerCamelCase  ).float().detach().sum().data

						# Normalize
						attn_entropy /= tot_tokens
						head_importance /= tot_tokens
						# Layerwise importance normalization
						if not args.dont_normalize_importance_by_layer:
												SCREAMING_SNAKE_CASE													=					2
												SCREAMING_SNAKE_CASE													=					torch.pow(torch.pow(__lowerCamelCase ,			__lowerCamelCase  ).sum(-1  ) ,			1 / exponent  )
												head_importance /= norm_by_layer.unsqueeze(-1  ) + 1E-20

						if not args.dont_normalize_global_importance:
												SCREAMING_SNAKE_CASE													=					(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

						# Print matrices
						if compute_entropy:
												logger.info('Attention entropies'  )
												print_ad_tensor(__lowerCamelCase  )
						if compute_importance:
												logger.info('Head importance scores'  )
												print_ad_tensor(__lowerCamelCase  )
						logger.info('Head ranked by importance scores'  )
						SCREAMING_SNAKE_CASE													=					torch.zeros(head_importance.numel() ,			dtype=torch.long ,			device=args.device  )
						SCREAMING_SNAKE_CASE													=					torch.arange(
						    head_importance.numel() ,			device=args.device  )
						SCREAMING_SNAKE_CASE													=					head_ranks.view_as(__lowerCamelCase  )
						print_ad_tensor(__lowerCamelCase  )
						return attn_entropy, head_importance, total_loss


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					compute_heads_importance(__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase ,			compute_entropy=__lowerCamelCase  )
						SCREAMING_SNAKE_CASE													=					1 / loss  # instead of downsteam score use the LM loss
						logger.info('Pruning: original score: %f, threshold: %f' ,			__lowerCamelCase ,			original_score * args.masking_threshold  )

						SCREAMING_SNAKE_CASE													=					torch.ones_like(__lowerCamelCase  )
						SCREAMING_SNAKE_CASE													=					max(1 ,			int(new_head_mask.numel() * args.masking_amount  )  )

						SCREAMING_SNAKE_CASE													=					original_score
						while current_score >= original_score * args.masking_threshold:
												SCREAMING_SNAKE_CASE													=					new_head_mask.clone().detach()  # save current head mask
												# heads from least important to most - keep only not-masked heads
												SCREAMING_SNAKE_CASE													=					float('Inf'  )
												SCREAMING_SNAKE_CASE													=					head_importance.view(-1  ).sort()[1]

												if len(__lowerCamelCase  ) <= num_to_mask:
																		print('BREAK BY num_to_mask'  )
																		break

												# mask heads
												SCREAMING_SNAKE_CASE													=					current_heads_to_mask[:num_to_mask]
												logger.info('Heads to mask: %s' ,			str(current_heads_to_mask.tolist()  )  )
												SCREAMING_SNAKE_CASE													=					new_head_mask.view(-1  )
												SCREAMING_SNAKE_CASE													=					0.0
												SCREAMING_SNAKE_CASE													=					new_head_mask.view_as(__lowerCamelCase  )
												SCREAMING_SNAKE_CASE													=					new_head_mask.clone().detach()
												print_ad_tensor(__lowerCamelCase  )

												# Compute metric and head importance again
												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					compute_heads_importance(
												    __lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase ,			compute_entropy=__lowerCamelCase ,			head_mask=__lowerCamelCase  )
												SCREAMING_SNAKE_CASE													=					1 / loss
												logger.info(
												    'Masking: current score: %f, remaining heads %d (%.1f percents)' ,			__lowerCamelCase ,			new_head_mask.sum() ,			new_head_mask.sum() / new_head_mask.numel() * 1_00 ,			)

						logger.info('Final head mask'  )
						print_ad_tensor(__lowerCamelCase  )
						np.save(os.path.join(args.output_dir ,			'head_mask.npy'  ) ,			head_mask.detach().cpu().numpy()  )

						return head_mask


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Dict ,			SCREAMING_SNAKE_CASE_		:				Dict ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						str:
						SCREAMING_SNAKE_CASE													=					datetime.now()
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					compute_heads_importance(
						    __lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase ,			compute_entropy=__lowerCamelCase ,			compute_importance=__lowerCamelCase ,			head_mask=__lowerCamelCase  )
						SCREAMING_SNAKE_CASE													=					1 / loss
						SCREAMING_SNAKE_CASE													=					datetime.now() - before_time

						SCREAMING_SNAKE_CASE													=					sum(p.numel() for p in model.parameters()  )
						SCREAMING_SNAKE_CASE													=					{
						    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__lowerCamelCase  )  )
						}

						for k, v in heads_to_prune.items():
												if isinstance(__lowerCamelCase ,			__lowerCamelCase  ):
																		SCREAMING_SNAKE_CASE													=					[
																		    v,
																		]

						assert sum(len(__lowerCamelCase  ) for h in heads_to_prune.values()  ) == (1 - head_mask.long()).sum().item()
						model.prune_heads(__lowerCamelCase  )
						SCREAMING_SNAKE_CASE													=					sum(p.numel() for p in model.parameters()  )

						SCREAMING_SNAKE_CASE													=					datetime.now()
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					compute_heads_importance(
						    __lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase ,			compute_entropy=__lowerCamelCase ,			compute_importance=__lowerCamelCase ,			head_mask=__lowerCamelCase ,			actually_pruned=__lowerCamelCase ,			)

						SCREAMING_SNAKE_CASE													=					1 / loss
						SCREAMING_SNAKE_CASE													=					datetime.now() - before_time

						logger.info(
						    'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)' ,			__lowerCamelCase ,			__lowerCamelCase ,			pruned_num_params / original_num_params * 1_00 ,			)
						logger.info('Pruning: score with masking: %f score with pruning: %f' ,			__lowerCamelCase ,			__lowerCamelCase  )
						logger.info('Pruning: speed ratio (original timing / new timing): %f percents' ,			original_time / new_time * 1_00  )
						save_model(__lowerCamelCase ,			args.output_dir  )


def     lowercase			()   ->						Tuple:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--data_dir' ,			default=__lowerCamelCase ,			type=__lowerCamelCase ,			required=__lowerCamelCase ,			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=__lowerCamelCase ,			type=__lowerCamelCase ,			required=__lowerCamelCase ,			help='Path to pretrained model or model identifier from huggingface.co/models' ,			)
						parser.add_argument(
						    '--output_dir' ,			default=__lowerCamelCase ,			type=__lowerCamelCase ,			required=__lowerCamelCase ,			help='The output directory where the model predictions and checkpoints will be written.' ,			)

						# Other parameters
						parser.add_argument(
						    '--config_name' ,			default='' ,			type=__lowerCamelCase ,			help='Pretrained config name or path if not the same as model_name_or_path' ,			)
						parser.add_argument(
						    '--tokenizer_name' ,			default='' ,			type=__lowerCamelCase ,			help='Pretrained tokenizer name or path if not the same as model_name_or_path' ,			)
						parser.add_argument(
						    '--cache_dir' ,			default=__lowerCamelCase ,			type=__lowerCamelCase ,			help='Where do you want to store the pre-trained models downloaded from s3' ,			)
						parser.add_argument(
						    '--data_subset' ,			type=__lowerCamelCase ,			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=__lowerCamelCase ,			help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).' ,			)
						parser.add_argument(
						    '--masking_amount' ,			default=0.1 ,			type=__lowerCamelCase ,			help='Amount to heads to masking at each masking step.'  )
						parser.add_argument('--metric_name' ,			default='acc' ,			type=__lowerCamelCase ,			help='Metric to use for head masking.'  )

						parser.add_argument(
						    '--max_seq_length' ,			default=1_28 ,			type=__lowerCamelCase ,			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=__lowerCamelCase ,			help='Batch size.'  )

						parser.add_argument('--seed' ,			type=__lowerCamelCase ,			default=42  )
						parser.add_argument('--local_rank' ,			type=__lowerCamelCase ,			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=__lowerCamelCase ,			default='' ,			help='Can be used for distant debugging.'  )
						parser.add_argument('--server_port' ,			type=__lowerCamelCase ,			default='' ,			help='Can be used for distant debugging.'  )
						SCREAMING_SNAKE_CASE													=					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=__lowerCamelCase  )
												ptvsd.wait_for_attach()

						# Setup devices and distributed training
						if args.local_rank == -1 or args.no_cuda:
												SCREAMING_SNAKE_CASE													=					torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'  )
												SCREAMING_SNAKE_CASE													=					0 if args.no_cuda else torch.cuda.device_count()
						else:
												torch.cuda.set_device(args.local_rank  )
												SCREAMING_SNAKE_CASE													=					torch.device('cuda' ,			args.local_rank  )
												SCREAMING_SNAKE_CASE													=					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  )  )  )

						SCREAMING_SNAKE_CASE													=					GPTaLMHeadModel.from_pretrained(args.model_name_or_path  )

						# Distributed and parallel training
						model.to(args.device  )
						if args.local_rank != -1:
												SCREAMING_SNAKE_CASE													=					nn.parallel.DistributedDataParallel(
												    __lowerCamelCase ,			device_ids=[args.local_rank] ,			output_device=args.local_rank ,			find_unused_parameters=__lowerCamelCase  )
						elif args.n_gpu > 1:
												SCREAMING_SNAKE_CASE													=					nn.DataParallel(__lowerCamelCase  )

						# Print/save training arguments
						os.makedirs(args.output_dir ,			exist_ok=__lowerCamelCase  )
						torch.save(__lowerCamelCase ,			os.path.join(args.output_dir ,			'run_args.bin'  )  )
						logger.info('Training/evaluation parameters %s' ,			__lowerCamelCase  )

						# Prepare dataset
						SCREAMING_SNAKE_CASE													=					np.concatenate(
						    [
						        np.loadtxt(args.data_dir ,			dtype=np.intaa  ),
						    ]  )
						SCREAMING_SNAKE_CASE													=					(torch.from_numpy(__lowerCamelCase  ),)
						SCREAMING_SNAKE_CASE													=					TensorDataset(*__lowerCamelCase  )
						SCREAMING_SNAKE_CASE													=					RandomSampler(__lowerCamelCase  )
						SCREAMING_SNAKE_CASE													=					DataLoader(__lowerCamelCase ,			sampler=__lowerCamelCase ,			batch_size=args.batch_size  )

						# Compute head entropy and importance score
						compute_heads_importance(__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase  )

						# 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:
												SCREAMING_SNAKE_CASE													=					mask_heads(__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase  )
												prune_heads(__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase  )


if __name__ == "__main__":
	main()









'''simple docstring'''

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
      # setup instructions, if using on-demand hardware

      # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
      # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
      # Throw an error if user passes both BYO and on-demand cluster args
      # Otherwise, use default values
      lowercase_					= argparse.ArgumentParser()
      parser.add_argument("""--user""", type=str, default="""ubuntu""")
      parser.add_argument("""--host""", type=str, default="""localhost""")
      parser.add_argument("""--key_path""", type=str, default=None)
      parser.add_argument("""--instance""", type=str, default="""V100:1""")
      parser.add_argument("""--provider""", type=str, default="""cheapest""")
      parser.add_argument("""--use_spot""", type=bool, default=False)
      parser.add_argument("""--example""", type=str, default="""pytorch/text-generation/run_generation.py""")
      lowercase_				,			lowercase_					= parser.parse_known_args()
      if args.host != "localhost":
            if args.instance != "V100:1" or args.provider != "cheapest":
                  raise ValueError("""Cannot specify both BYO and on-demand cluster args""")
            lowercase_					= rh.cluster(
                name="""rh-cluster""", ips=[args.host], ssh_creds={"""ssh_user""": args.user, """ssh_private_key""": args.key_path}
            )
      else:
            lowercase_					= rh.cluster(
                name="""rh-cluster""", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
            )
      lowercase_					= args.example.rsplit("""/""", 1)[0]

      # Set up remote environment
      cluster.install_packages(["""pip:./"""])  # Installs transformers from local source
      # Note transformers is copied into the home directory on the remote machine, so we can install from there
      cluster.run([f"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
      cluster.run(["""pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"""])

      # Run example. You can bypass the CLI wrapper and paste your own code here.
      cluster.run([f"""python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}"""])

      # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
      # from my_script... import train
      # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
      # launch_train_gpu = rh.function(fn=train,
      #                                system=gpu,
      #                                reqs=reqs,
      #                                name='train_bert_glue')
      #
      # We can pass in arguments just like we would to a function:
      # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
      #                  stream_logs=True)



