


"""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


__SCREAMING_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"""},
}

__SCREAMING_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 						__a      (  ):





       """simple docstring"""







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

       _a          =				parser.parse_args()

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

       return args



def 						__a      (  ):





       """simple docstring"""







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

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

       _a          =				start_measure()
       _a          =				model_cls.from_pretrained(args.model_name,		device_map="auto",		**a					)
       _a          =				end_measure(a					)
       log_measures(a,		"Model loading"					)

       _a          =				compute_module_sizes(a					)
       _a          =				{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]
       _a          =				"\n".join([F'- {device}: {size // 2**2_0}MiB' for device, size in device_size.items()]					)
       print(F'\nTheoretical use:\n{message}'					)

       _a          =				AutoTokenizer.from_pretrained(args.tokenizer_name					)

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

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

       _a          =				end_measure(a					)
       log_measures(a,		"Model generation"					)

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


if __name__ == "__main__":
   main()




"""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


__SCREAMING_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"""},
}

__SCREAMING_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 						__a      (  ):





       """simple docstring"""







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

       _a          =				parser.parse_args()

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

       return args



def 						__a      (  ):





       """simple docstring"""







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

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

       _a          =				start_measure()
       _a          =				model_cls.from_pretrained(args.model_name,		device_map="auto",		**a					)
       _a          =				end_measure(a					)
       log_measures(a,		"Model loading"					)

       _a          =				compute_module_sizes(a					)
       _a          =				{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]
       _a          =				"\n".join([F'- {device}: {size // 2**2_0}MiB' for device, size in device_size.items()]					)
       print(F'\nTheoretical use:\n{message}'					)

       _a          =				AutoTokenizer.from_pretrained(args.tokenizer_name					)

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

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

       _a          =				end_measure(a					)
       log_measures(a,		"Model generation"					)

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


if __name__ == "__main__":
   main()
