Text Generation
Transformers
Safetensors
openelm
custom_code
File size: 7,348 Bytes
200856b
 
 
 
 
 
 
 
 
 
 
 
 
 
771d259
200856b
 
 
771d259
200856b
 
 
 
 
 
 
 
771d259
200856b
 
 
 
 
771d259
200856b
 
771d259
200856b
 
 
 
 
 
 
771d259
 
200856b
 
 
 
 
 
 
 
 
 
771d259
 
 
 
 
 
200856b
 
 
 
771d259
200856b
 
771d259
200856b
771d259
200856b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
771d259
200856b
 
 
 
771d259
 
 
200856b
771d259
200856b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
771d259
 
 
200856b
 
 
 
771d259
 
 
200856b
 
 
 
 
771d259
200856b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
771d259
 
200856b
771d259
 
 
 
200856b
 
 
 
 
 
771d259
200856b
 
 
 
 
 
 
 
 
 
 
 
 
771d259
200856b
 
771d259
200856b
771d259
200856b
 
 
 
 
 
 
 
 
 
 
 
 
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
"""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)