File size: 7,453 Bytes
74936cb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 |
#
# For licensing see accompanying LICENSE file.
# Copyright (C) 2024 Apple Inc. All Rights Reserved.
#
"""Module to generate OpenELM output given a model and an input prompt."""
import os
import logging
import time
import argparse
from typing import Optional, Union
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
def generate(
prompt: str,
model: Union[str, AutoModelForCausalLM],
hf_access_token: str = None,
tokenizer: Union[str, AutoTokenizer] = 'meta-llama/Llama-2-7b-hf',
device: Optional[str] = None,
max_length: int = 1024,
assistant_model: Optional[Union[str, AutoModelForCausalLM]] = None,
generate_kwargs: Optional[dict] = None,
) -> str:
""" Generates output given a prompt.
Args:
prompt: The string prompt.
model: The LLM Model. If a string is passed, it should be the path to
the hf converted checkpoint.
hf_access_token: Hugging face access token.
tokenizer: Tokenizer instance. If model is set as a string path,
the tokenizer will be loaded from the checkpoint.
device: String representation of device to run the model on. If None
and cuda available it would be set to cuda:0 else cpu.
max_length: Maximum length of tokens, input prompt + generated tokens.
assistant_model: If set, this model will be used for
speculative generation. If a string is passed, it should be the
path to the hf converted checkpoint.
generate_kwargs: Extra kwargs passed to the hf generate function.
Returns:
output_text: output generated as a string.
generation_time: generation time in seconds.
Raises:
ValueError: If device is set to CUDA but no CUDA device is detected.
ValueError: If tokenizer is not set.
ValueError: If hf_access_token is not specified.
"""
if not device:
if torch.cuda.is_available() and torch.cuda.device_count():
device = "cuda:0"
logging.warning(
'inference device is not set, using cuda:0, %s',
torch.cuda.get_device_name(0)
)
else:
device = 'cpu'
logging.warning(
(
'No CUDA device detected, using cpu, '
'expect slower speeds.'
)
)
if 'cuda' in device and not torch.cuda.is_available():
raise ValueError('CUDA device requested but no CUDA device detected.')
if not tokenizer:
raise ValueError('Tokenizer is not set in the generate function.')
if not hf_access_token:
raise ValueError((
'Hugging face access token needs to be specified. '
'Please refer to https://huggingface.co/docs/hub/security-tokens'
' to obtain one.'
)
)
if isinstance(model, str):
checkpoint_path = model
model = AutoModelForCausalLM.from_pretrained(
checkpoint_path,
trust_remote_code=True
)
model.to(device).eval()
if isinstance(tokenizer, str):
tokenizer = AutoTokenizer.from_pretrained(
tokenizer,
token=hf_access_token,
)
# Speculative mode
draft_model = None
if assistant_model:
draft_model = assistant_model
if isinstance(assistant_model, str):
draft_model = AutoModelForCausalLM.from_pretrained(
assistant_model,
trust_remote_code=True
)
draft_model.to(device).eval()
# Prepare the prompt
tokenized_prompt = tokenizer(prompt)
tokenized_prompt = torch.tensor(
tokenized_prompt['input_ids'],
device=device
)
tokenized_prompt = tokenized_prompt.unsqueeze(0)
# Generate
stime = time.time()
output_ids = model.generate(
tokenized_prompt,
max_length=max_length,
pad_token_id=0,
assistant_model=draft_model,
**(generate_kwargs if generate_kwargs else {}),
)
generation_time = time.time() - stime
output_text = tokenizer.decode(
output_ids[0].tolist(),
skip_special_tokens=True
)
return output_text, generation_time
def openelm_generate_parser():
"""Argument Parser"""
class KwargsParser(argparse.Action):
"""Parser action class to parse kwargs of form key=value"""
def __call__(self, parser, namespace, values, option_string=None):
setattr(namespace, self.dest, dict())
for val in values:
if '=' not in val:
raise ValueError(
(
'Argument parsing error, kwargs are expected in'
' the form of key=value.'
)
)
kwarg_k, kwarg_v = val.split('=')
try:
converted_v = int(kwarg_v)
except ValueError:
try:
converted_v = float(kwarg_v)
except ValueError:
converted_v = kwarg_v
getattr(namespace, self.dest)[kwarg_k] = converted_v
parser = argparse.ArgumentParser('OpenELM Generate Module')
parser.add_argument(
'--model',
dest='model',
help='Path to the hf converted model.',
required=True,
type=str,
)
parser.add_argument(
'--hf_access_token',
dest='hf_access_token',
help='Hugging face access token, starting with "hf_".',
type=str,
)
parser.add_argument(
'--prompt',
dest='prompt',
help='Prompt for LLM call.',
default='',
type=str,
)
parser.add_argument(
'--device',
dest='device',
help='Device used for inference.',
type=str,
)
parser.add_argument(
'--max_length',
dest='max_length',
help='Maximum length of tokens.',
default=256,
type=int,
)
parser.add_argument(
'--assistant_model',
dest='assistant_model',
help=(
(
'If set, this is used as a draft model '
'for assisted speculative generation.'
)
),
type=str,
)
parser.add_argument(
'--generate_kwargs',
dest='generate_kwargs',
help='Additional kwargs passed to the HF generate function.',
type=str,
nargs='*',
action=KwargsParser,
)
return parser.parse_args()
if __name__ == '__main__':
args = openelm_generate_parser()
prompt = args.prompt
output_text, genertaion_time = generate(
prompt=prompt,
model=args.model,
device=args.device,
max_length=args.max_length,
assistant_model=args.assistant_model,
generate_kwargs=args.generate_kwargs,
hf_access_token=args.hf_access_token,
)
print_txt = (
f'\r\n{"=" * os.get_terminal_size().columns}\r\n'
'\033[1m Prompt + Generated Output\033[0m\r\n'
f'{"-" * os.get_terminal_size().columns}\r\n'
f'{output_text}\r\n'
f'{"-" * os.get_terminal_size().columns}\r\n'
'\r\nGeneration took'
f'\033[1m\033[92m {round(genertaion_time, 2)} \033[0m'
'seconds.\r\n'
)
print(print_txt)
|