




'''simple docstring'''



import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


UpperCAmelCase_       :  int      =		{
    '''gpt-j-6b''': {'''is_causal''': True, '''model''': '''sgugger/sharded-gpt-j-6B''', '''tokenizer''': '''EleutherAI/gpt-j-6B'''},
    '''gpt-neox''': {'''is_causal''': True, '''model''': '''EleutherAI/gpt-neox-20b'''},
    '''opt''': {'''is_causal''': True, '''model''': '''facebook/opt-30b'''},
    '''T0pp''': {'''is_causal''': False, '''model''': '''bigscience/T0pp''', '''model_revision''': '''sharded'''},
}

UpperCAmelCase_       :  Optional[int]      =		[
    '''Hello, my name is''',
    '''Are unicorns real? Unicorns are''',
    '''For the first time in several years,''',
    '''My name is Julien and I am''',
    '''The goal of life is''',
    '''Whenever I\'m sad, I like to''',
]







def 			_UpperCamelCase ()->   List[str]:




     '''simple docstring'''





     __snake_case    					=  argparse.ArgumentParser(description='''Run and time generations on a big model using Accelerate.'''	)
     parser.add_argument('''model_name'''   ,	type=_lowerCamelCase   ,	default=_lowerCamelCase   ,	help='''The name of the model to try.'''	)
     parser.add_argument(
         '''--tokenizer_name'''   ,	type=_lowerCamelCase   ,	default=_lowerCamelCase   ,	help='''The name of the tokenizer (if different from the model.'''	)
     parser.add_argument('''--is_causal'''   ,	type=_lowerCamelCase   ,	default=_lowerCamelCase   ,	help='''Whether or not the model is causal.'''	)
     parser.add_argument(
         '''--model_revision'''   ,	type=_lowerCamelCase   ,	default=_lowerCamelCase   ,	help='''The revision to use for the model checkpoint.'''	)
     parser.add_argument('''--torch_dtype'''   ,	type=_lowerCamelCase   ,	default=_lowerCamelCase   ,	help='''The dtype for the model.'''	)
     parser.add_argument('''--disk_offload'''   ,	action='''store_true'''	)

     __snake_case    					=  parser.parse_args()

     # Sanitize args
     if args.model_name in DEFAULT_MODELS:
          __snake_case    					=  DEFAULT_MODELS[args.model_name]
          __snake_case    					=  defaults['''model''']
          if args.tokenizer_name is None:
               __snake_case    					=  defaults.get('''tokenizer'''   ,	args.model_name	)
          if args.is_causal is None:
               __snake_case    					=  defaults['''is_causal''']
          if args.model_revision is None:
               __snake_case    					=  defaults.get('''model_revision'''   ,	'''main'''	)

     if args.is_causal is None:
          raise ValueError('''Could not infer the default for `--is_causal`, pass either True or False for it.'''	)
     if args.tokenizer_name is None:
          __snake_case    					=  args.model_name
     if args.model_revision is None:
          __snake_case    					=  '''main'''

     return args







def 			_UpperCamelCase ()->   Tuple:




     '''simple docstring'''





     transformers.utils.logging.set_verbosity_error()
     __snake_case    					=  parse_args()

     if args.torch_dtype is None:
          __snake_case    					=  AutoConfig.from_pretrained(args.model_name	)
          __snake_case    					=  getattr(_lowerCamelCase   ,	'''torch_dtype'''   ,	torch.floataa	)
     else:
          __snake_case    					=  getattr(_lowerCamelCase   ,	args.torch_dtype	)
     __snake_case    					=  AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
     __snake_case    					=  {
         '''torch_dtype''': torch_dtype,
         '''revision''': args.model_revision,
     }
     if args.disk_offload:
          __snake_case    					=  '''tmp_offload'''
          __snake_case    					=  True

     __snake_case    					=  start_measure()
     __snake_case    					=  model_cls.from_pretrained(args.model_name   ,	device_map='''auto'''   ,	**_lowerCamelCase	)
     __snake_case    					=  end_measure(_lowerCamelCase	)
     log_measures(_lowerCamelCase   ,	'''Model loading'''	)

     __snake_case    					=  compute_module_sizes(_lowerCamelCase	)
     __snake_case    					=  {v: 0 for v in model.hf_device_map.values()}
     for module, device in model.hf_device_map.items():
          device_size[device] += module_sizes[module]
     __snake_case    					=  '''\n'''.join([f'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]	)
     print(f'''\nTheoretical use:\n{message}'''	)

     __snake_case    					=  AutoTokenizer.from_pretrained(args.tokenizer_name	)

     __snake_case    					=  start_measure()
     __snake_case    					=  []
     __snake_case    					=  []
     __snake_case    					=  []
     for prompt in PROMPTS:
          __snake_case    					=  tokenizer(_lowerCamelCase   ,	return_tensors='''pt'''	).to(0	)
          __snake_case    					=  inputs['''input_ids'''][0].tolist()
          __snake_case    					=  time.time()
          __snake_case    					=  model.generate(inputs['''input_ids''']	)
          __snake_case    					=  time.time()
          __snake_case    					=  outputs[0].tolist()
          __snake_case    					=  len(_lowerCamelCase	) if outputs[: len(_lowerCamelCase	)] != tokens else len(_lowerCamelCase	) - len(_lowerCamelCase	)
          __snake_case    					=  after_generate - before_generate

          __snake_case    					=  tokenizer.decode(_lowerCamelCase   ,	skip_special_tokens=_lowerCamelCase	)
          texts_outs.append(_lowerCamelCase	)
          generation_times.append(_lowerCamelCase	)
          gen_tokens.append(_lowerCamelCase	)
          print(f'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'''	)

     __snake_case    					=  end_measure(_lowerCamelCase	)
     log_measures(_lowerCamelCase   ,	'''Model generation'''	)

     __snake_case    					=  [gen / tok for gen, tok in zip(_lowerCamelCase   ,	_lowerCamelCase	)]
     __snake_case    					=  sum(_lowerCamelCase	) / len(_lowerCamelCase	)
     print(f'''Average time of generation per token: {avg_gen:.2f}s'''	)
     print(f'''First generation (avg time per token): {generation_times_per_token[0]:.2f}s'''	)
     __snake_case    					=  sum(generation_times_per_token[1:]	) / (len(_lowerCamelCase	) - 1)
     print(f'''Average time of generation per token (excluding the first): {avg_gen:.2f}s'''	)


if __name__ == "__main__":
     main()












'''simple docstring'''



from typing import Any

import numpy as np







def 			_UpperCamelCase (_lowerCamelCase							:     np.ndarray	)->   bool:




     '''simple docstring'''





     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[1]

     # If matrix not symmetric, exit right away.
     if np.allclose(_lowerCamelCase   ,	matrix.T	) is False:
          return False

     # Get eigenvalues and eignevectors for a symmetric matrix.
     __snake_case					,    __snake_case    					=  np.linalg.eigh(_lowerCamelCase	)

     # Check sign of all eigenvalues.
     # np.all returns a value of type np.bool_
     return bool(np.all(eigen_values > 0	)	)







def 			_UpperCamelCase (_lowerCamelCase							:     int	)->   Any:




     '''simple docstring'''





     __snake_case    					=  np.random.randn(_lowerCamelCase   ,	_lowerCamelCase	)
     __snake_case    					=  np.dot(_lowerCamelCase   ,	random_matrix.T	)
     assert _is_matrix_spd(_lowerCamelCase	)
     return spd_matrix







def 			_UpperCamelCase (_lowerCamelCase							:     np.ndarray   ,	_lowerCamelCase							:     np.ndarray   ,	_lowerCamelCase							:     int = 10_00   ,	_lowerCamelCase							:     float = 1E-8   ,	)->   Any:




     '''simple docstring'''





     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[1]
     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[0]
     assert _is_matrix_spd(_lowerCamelCase	)

     # Initialize solution guess, residual, search direction.
     __snake_case    					=  np.zeros((np.shape(_lowerCamelCase	)[0], 1)	)
     __snake_case    					=  np.copy(_lowerCamelCase	)
     __snake_case    					=  np.copy(_lowerCamelCase	)

     # Set initial errors in solution guess and residual.
     __snake_case    					=  1E9
     __snake_case    					=  1E9
     __snake_case    					=  1E9

     # Set iteration counter to threshold number of iterations.
     __snake_case    					=  0

     while error > tol:
          # Save this value so we only calculate the matrix-vector product once.
          __snake_case    					=  np.dot(_lowerCamelCase   ,	_lowerCamelCase	)

          # The main algorithm.

          # Update search direction magnitude.
          __snake_case    					=  np.dot(ra.T   ,	_lowerCamelCase	) / np.dot(pa.T   ,	_lowerCamelCase	)
          # Update solution guess.
          __snake_case    					=  xa + alpha * pa
          # Calculate new residual.
          __snake_case    					=  ra - alpha * w
          # Calculate new Krylov subspace scale.
          __snake_case    					=  np.dot(r.T   ,	_lowerCamelCase	) / np.dot(ra.T   ,	_lowerCamelCase	)
          # Calculate new A conjuage search direction.
          __snake_case    					=  r + beta * pa

          # Calculate errors.
          __snake_case    					=  np.linalg.norm(r - ra	)
          __snake_case    					=  np.linalg.norm(x - xa	)
          __snake_case    					=  np.maximum(_lowerCamelCase   ,	_lowerCamelCase	)

          # Update variables.
          __snake_case    					=  np.copy(_lowerCamelCase	)
          __snake_case    					=  np.copy(_lowerCamelCase	)
          __snake_case    					=  np.copy(_lowerCamelCase	)

          # Update number of iterations.
          iterations += 1
          if iterations > max_iterations:
               break

     return x







def 			_UpperCamelCase ()->   None:




     '''simple docstring'''





     __snake_case    					=  3
     __snake_case    					=  _create_spd_matrix(_lowerCamelCase	)
     __snake_case    					=  np.random.randn(_lowerCamelCase   ,	1	)
     __snake_case    					=  np.dot(_lowerCamelCase   ,	_lowerCamelCase	)

     # Numpy solution.
     __snake_case    					=  np.linalg.solve(_lowerCamelCase   ,	_lowerCamelCase	)

     # Our implementation.
     __snake_case    					=  conjugate_gradient(_lowerCamelCase   ,	_lowerCamelCase	)

     # Ensure both solutions are close to x_true (and therefore one another).
     assert np.linalg.norm(x_numpy - x_true	) <= 1E-6
     assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1E-6


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     test_conjugate_gradient()






