



"""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


snake_case_								=			{
    """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"""},
}

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

						UpperCAmelCase			 =					parser.parse_args()

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

						return args

def 					_lowerCAmelCase (  ):
						transformers.utils.logging.set_verbosity_error()
						UpperCAmelCase			 =					parse_args()

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

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

						UpperCAmelCase			 =					compute_module_sizes(lowercase_		)
						UpperCAmelCase			 =					{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]
						UpperCAmelCase			 =					'\n'.join([F"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()]		)
						print(F"""\nTheoretical use:\n{message}"""		)

						UpperCAmelCase			 =					AutoTokenizer.from_pretrained(args.tokenizer_name		)

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

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

						UpperCAmelCase			 =					end_measure(lowercase_		)
						log_measures(lowercase_						,      'Model generation'		)

						UpperCAmelCase			 =					[gen / tok for gen, tok in zip(lowercase_						,      lowercase_		)]
						UpperCAmelCase			 =					sum(lowercase_		) / len(lowercase_		)
						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"""		)
						UpperCAmelCase			 =					sum(generation_times_per_token[1:]		) / (len(lowercase_		) - 1)
						print(F"""Average time of generation per token (excluding the first): {avg_gen:.2f}s"""		)


if __name__ == "__main__":
				main()








from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class 					__UpperCamelCase					:







	def __init__( self:			Optional[Any]	,			__UpperCamelCase:			Any   ):

						'''simple docstring'''





						__magic_name__				=     data
						__magic_name__				=     None







	def __str__( self:			Optional[int]   ):

						'''simple docstring'''





						return F'{self.data}'




class 					__UpperCamelCase					:







	def __init__( self:			List[Any]   ):

						'''simple docstring'''





						__magic_name__				=     None
						__magic_name__				=     None







	def __iter__( self:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.front
						while node:
											yield node.data
											__magic_name__				=     node.next







	def __len__( self:			Any   ):

						'''simple docstring'''





						return len(tuple(iter(self   )   )   )







	def __str__( self:			List[str]   ):

						'''simple docstring'''





						return " <- ".join(str(__UpperCamelCase   ) for item in self   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





						return len(self   ) == 0







	def 						_SCREAMING_SNAKE_CASE				( self:			Any	,			__UpperCamelCase:			Any   ):

						'''simple docstring'''





						__magic_name__				=     Node(__UpperCamelCase   )
						if self.is_empty():
											__magic_name__				=     __magic_name__				=     node
						else:
											assert isinstance(self.rear	,			__UpperCamelCase   )
											__magic_name__				=     node
											__magic_name__				=     node







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





						if self.is_empty():
											raise IndexError('dequeue from empty queue'   )
						assert isinstance(self.front	,			__UpperCamelCase   )
						__magic_name__				=     self.front
						__magic_name__				=     self.front.next
						if self.front is None:
											__magic_name__				=     None
						return node.data







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						__magic_name__				=     __magic_name__				=     None


if __name__ == "__main__":
						from doctest import testmod

						testmod()




