File size: 19,456 Bytes
ca9a50b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 |
import ctranslate2
import functools
try:
from transformers import AutoTokenizer
autotokenizer_ok = True
except ImportError:
AutoTokenizer = object
autotokenizer_ok = False
try:
from typing import Literal
except ImportError:
from typing_extensions import Literal
from typing import Any, Union, List
import os
from hf_hub_ctranslate2.util import utils as _utils
class CTranslate2ModelfromHuggingfaceHub:
"""CTranslate2 compatibility class for Translator and Generator"""
def __init__(
self,
model_name_or_path: str,
device: Literal["cpu", "cuda"] = "cuda",
device_index=0,
compute_type: Literal["int8_float16", "int8"] = "int8_float16",
tokenizer: Union[AutoTokenizer, None] = None,
hub_kwargs: dict = {},
**kwargs: Any,
):
# adaptions from https://github.com/guillaumekln/faster-whisper
if os.path.isdir(model_name_or_path):
model_path = model_name_or_path
else:
try:
model_path = _utils._download_model(
model_name_or_path, hub_kwargs=hub_kwargs, local_files_only=True,
)
except Exception:
hub_kwargs["local_files_only"] = True
model_path = _utils._download_model(
model_name_or_path, hub_kwargs=hub_kwargs, local_files_only=True,
)
self.model = self.ctranslate_class(
model_path,
device=device,
device_index=device_index,
compute_type=compute_type,
**kwargs,
)
if tokenizer is not None:
self.tokenizer = tokenizer
else:
if "tokenizer.json" in os.listdir(model_path):
if not autotokenizer_ok:
raise ValueError(
"`pip install transformers` missing to load AutoTokenizer."
)
self.tokenizer = AutoTokenizer.from_pretrained(model_path, fast=True)
else:
raise ValueError(
"no suitable Tokenizer found. "
"Please set one via tokenizer=AutoTokenizer.from_pretrained(..) arg."
)
def _forward(self, *args: Any, **kwds: Any) -> Any:
raise NotImplementedError
def tokenize_encode(self, text, *args, **kwargs):
return [
self.tokenizer.convert_ids_to_tokens(self.tokenizer.encode(p)) for p in text
]
def tokenize_decode(self, tokens_out, *args, **kwargs):
raise NotImplementedError
def generate(
self,
text: Union[str, List[str]],
encode_kwargs={},
decode_kwargs={},
*forward_args,
**forward_kwds: Any,
):
orig_type = list
if isinstance(text, str):
orig_type = str
text = [text]
token_list = self.tokenize_encode(text, **encode_kwargs)
tokens_out = self._forward(token_list, *forward_args, **forward_kwds)
texts_out = self.tokenize_decode(tokens_out, **decode_kwargs)
if orig_type == str:
return texts_out[0]
else:
return texts_out
class TranslatorCT2fromHfHub(CTranslate2ModelfromHuggingfaceHub):
def __init__(
self,
model_name_or_path: str,
device: Literal["cpu", "cuda"] = "cuda",
device_index=0,
compute_type: Literal["int8_float16", "int8"] = "int8_float16",
tokenizer: Union[AutoTokenizer, None] = None,
hub_kwargs={},
**kwargs: Any,
):
"""for ctranslate2.Translator models, in particular m2m-100
Args:
model_name_or_path (str): _description_
device (Literal[cpu, cuda], optional): _description_. Defaults to "cuda".
device_index (int, optional): _description_. Defaults to 0.
compute_type (Literal[int8_float16, int8], optional): _description_. Defaults to "int8_float16".
tokenizer (Union[AutoTokenizer, None], optional): _description_. Defaults to None.
hub_kwargs (dict, optional): _description_. Defaults to {}.
**kwargs (Any, optional): Any additional arguments
"""
self.ctranslate_class = ctranslate2.Translator
super().__init__(
model_name_or_path,
device,
device_index,
compute_type,
tokenizer,
hub_kwargs,
**kwargs,
)
def _forward(self, *args, **kwds):
return self.model.translate_batch(*args, **kwds)
def tokenize_decode(self, tokens_out, *args, **kwargs):
return [
self.tokenizer.decode(
self.tokenizer.convert_tokens_to_ids(tokens_out[i].hypotheses[0]),
*args,
**kwargs,
)
for i in range(len(tokens_out))
]
def generate(
self,
text: Union[str, List[str]],
encode_tok_kwargs={},
decode_tok_kwargs={},
*forward_args,
**forward_kwds: Any,
):
"""_summary_
Args:
text (Union[str, List[str]]): Input texts
encode_tok_kwargs (dict, optional): additional kwargs for tokenizer
decode_tok_kwargs (dict, optional): additional kwargs for tokenizer
max_batch_size (int, optional): Batch size. Defaults to 0.
batch_type (str, optional): _. Defaults to "examples".
asynchronous (bool, optional): Only False supported. Defaults to False.
beam_size (int, optional): _. Defaults to 2.
patience (float, optional): _. Defaults to 1.
num_hypotheses (int, optional): _. Defaults to 1.
length_penalty (float, optional): _. Defaults to 1.
coverage_penalty (float, optional): _. Defaults to 0.
repetition_penalty (float, optional): _. Defaults to 1.
no_repeat_ngram_size (int, optional): _. Defaults to 0.
disable_unk (bool, optional): _. Defaults to False.
suppress_sequences (Optional[List[List[str]]], optional): _.
Defaults to None.
end_token (Optional[Union[str, List[str], List[int]]], optional): _.
Defaults to None.
return_end_token (bool, optional): _. Defaults to False.
prefix_bias_beta (float, optional): _. Defaults to 0.
max_input_length (int, optional): _. Defaults to 1024.
max_decoding_length (int, optional): _. Defaults to 256.
min_decoding_length (int, optional): _. Defaults to 1.
use_vmap (bool, optional): _. Defaults to False.
return_scores (bool, optional): _. Defaults to False.
return_attention (bool, optional): _. Defaults to False.
return_alternatives (bool, optional): _. Defaults to False.
min_alternative_expansion_prob (float, optional): _. Defaults to 0.
sampling_topk (int, optional): _. Defaults to 1.
sampling_temperature (float, optional): _. Defaults to 1.
replace_unknowns (bool, optional): _. Defaults to False.
callback (_type_, optional): _. Defaults to None.
Returns:
Union[str, List[str]]: text as output, if list, same len as input
"""
return super().generate(
text,
encode_kwargs=encode_tok_kwargs,
decode_kwargs=decode_tok_kwargs,
*forward_args,
**forward_kwds,
)
class MultiLingualTranslatorCT2fromHfHub(CTranslate2ModelfromHuggingfaceHub):
def __init__(
self,
model_name_or_path: str,
device: Literal["cpu", "cuda"] = "cuda",
device_index=0,
compute_type: Literal["int8_float16", "int8"] = "int8_float16",
tokenizer: Union[AutoTokenizer, None] = None,
hub_kwargs={},
**kwargs: Any,
):
"""for ctranslate2.Translator models
Args:
model_name_or_path (str): _description_
device (Literal[cpu, cuda], optional): _description_. Defaults to "cuda".
device_index (int, optional): _description_. Defaults to 0.
compute_type (Literal[int8_float16, int8], optional): _description_. Defaults to "int8_float16".
tokenizer (Union[AutoTokenizer, None], optional): _description_. Defaults to None.
hub_kwargs (dict, optional): _description_. Defaults to {}.
**kwargs (Any, optional): Any additional arguments
"""
self.ctranslate_class = ctranslate2.Translator
super().__init__(
model_name_or_path,
device,
device_index,
compute_type,
tokenizer,
hub_kwargs,
**kwargs,
)
def _forward(self, *args, **kwds):
target_prefix = [
[self.tokenizer.lang_code_to_token[lng]] for lng in kwds.pop("tgt_lang")
]
# target_prefix=[['__de__'], ['__fr__']]
return self.model.translate_batch(*args, **kwds, target_prefix=target_prefix)
def tokenize_encode(self, text, *args, **kwargs):
tokens = []
src_lang = kwargs.pop("src_lang")
for t, src_language in zip(text, src_lang):
self.tokenizer.src_lang = src_language
tokens.append(
self.tokenizer.convert_ids_to_tokens(self.tokenizer.encode(t))
)
return tokens
def tokenize_decode(self, tokens_out, *args, **kwargs):
return [
self.tokenizer.decode(
self.tokenizer.convert_tokens_to_ids(tokens_out[i].hypotheses[0][1:]),
*args,
**kwargs,
)
for i in range(len(tokens_out))
]
def generate(
self,
text: Union[str, List[str]],
src_lang: Union[str, List[str]],
tgt_lang: Union[str, List[str]],
*forward_args,
**forward_kwds: Any,
):
"""_summary_
Args:
text (Union[str, List[str]]): Input texts
src_lang (Union[str, List[str]]): soruce language of the Input texts
tgt_lang (Union[str, List[str]]): target language for outputs
max_batch_size (int, optional): Batch size. Defaults to 0.
batch_type (str, optional): _. Defaults to "examples".
asynchronous (bool, optional): Only False supported. Defaults to False.
beam_size (int, optional): _. Defaults to 2.
patience (float, optional): _. Defaults to 1.
num_hypotheses (int, optional): _. Defaults to 1.
length_penalty (float, optional): _. Defaults to 1.
coverage_penalty (float, optional): _. Defaults to 0.
repetition_penalty (float, optional): _. Defaults to 1.
no_repeat_ngram_size (int, optional): _. Defaults to 0.
disable_unk (bool, optional): _. Defaults to False.
suppress_sequences (Optional[List[List[str]]], optional): _.
Defaults to None.
end_token (Optional[Union[str, List[str], List[int]]], optional): _.
Defaults to None.
return_end_token (bool, optional): _. Defaults to False.
prefix_bias_beta (float, optional): _. Defaults to 0.
max_input_length (int, optional): _. Defaults to 1024.
max_decoding_length (int, optional): _. Defaults to 256.
min_decoding_length (int, optional): _. Defaults to 1.
use_vmap (bool, optional): _. Defaults to False.
return_scores (bool, optional): _. Defaults to False.
return_attention (bool, optional): _. Defaults to False.
return_alternatives (bool, optional): _. Defaults to False.
min_alternative_expansion_prob (float, optional): _. Defaults to 0.
sampling_topk (int, optional): _. Defaults to 1.
sampling_temperature (float, optional): _. Defaults to 1.
replace_unknowns (bool, optional): _. Defaults to False.
callback (_type_, optional): _. Defaults to None.
Returns:
Union[str, List[str]]: text as output, if list, same len as input
"""
if not len(text) == len(src_lang) == len(tgt_lang):
raise ValueError(
f"unequal len: text={len(text)} src_lang={len(src_lang)} tgt_lang={len(tgt_lang)}"
)
forward_kwds["tgt_lang"] = tgt_lang
return super().generate(
text, *forward_args, **forward_kwds, encode_kwargs={"src_lang": src_lang}
)
class EncoderCT2fromHfHub(CTranslate2ModelfromHuggingfaceHub):
def __init__(
self,
model_name_or_path: str,
device: Literal["cpu", "cuda"] = "cuda",
device_index=0,
compute_type: Literal["int8_float16", "int8"] = "int8_float16",
tokenizer: Union[AutoTokenizer, None] = None,
hub_kwargs={},
**kwargs: Any,
):
"""for ctranslate2.Translator models, in particular m2m-100
Args:
model_name_or_path (str): _description_
device (Literal[cpu, cuda], optional): _description_. Defaults to "cuda".
device_index (int, optional): _description_. Defaults to 0.
compute_type (Literal[int8_float16, int8], optional): _description_. Defaults to "int8_float16".
tokenizer (Union[AutoTokenizer, None], optional): _description_. Defaults to None.
hub_kwargs (dict, optional): _description_. Defaults to {}.
**kwargs (Any, optional): Any additional arguments
"""
self.ctranslate_class = ctranslate2.Encoder
super().__init__(
model_name_or_path,
device,
device_index,
compute_type,
tokenizer,
hub_kwargs,
**kwargs,
)
self.device = device
if device == "cuda":
try:
import torch
except ImportError:
raise ValueError(
"decoding storageview on CUDA of encoder requires torch"
)
self.tensor_decode_method = functools.partial(
torch.as_tensor, device=device
)
self.input_dtype=torch.int32
else:
try:
import numpy as np
except ImportError:
raise ValueError(
"decoding storageview on CPU of encoder requires numpy"
)
self.tensor_decode_method = np.asarray
def _forward(self, features, *args, **kwds):
input_ids = features["input_ids"]
tokens_out = self.model.forward_batch(input_ids, *args, **kwds)
outputs = dict(
pooler_output = self.tensor_decode_method(tokens_out.pooler_output),
last_hidden_state = self.tensor_decode_method(tokens_out.last_hidden_state),
attention_mask=features["attention_mask"]
)
return outputs
def tokenize_encode(self, text, *args, **kwargs):
return self.tokenizer(text)
def tokenize_decode(self, tokens_out, *args, **kwargs):
return tokens_out
def generate(
self,
text: Union[str, List[str]],
encode_tok_kwargs={},
decode_tok_kwargs={},
*forward_args,
**forward_kwds: Any,
):
return super().generate(
text,
encode_kwargs=encode_tok_kwargs,
decode_kwargs=decode_tok_kwargs,
*forward_args,
**forward_kwds,
)
class GeneratorCT2fromHfHub(CTranslate2ModelfromHuggingfaceHub):
def __init__(
self,
model_name_or_path: str,
device: Literal["cpu", "cuda"] = "cuda",
device_index=0,
compute_type: Literal["int8_float16", "int8"] = "int8_float16",
tokenizer: Union[AutoTokenizer, None] = None,
hub_kwargs={},
**kwargs: Any,
):
"""for ctranslate2.Generator models
Args:
model_name_or_path (str): _description_
device (Literal[cpu, cuda], optional): _description_. Defaults to "cuda".
device_index (int, optional): _description_. Defaults to 0.
compute_type (Literal[int8_float16, int8], optional): _description_. Defaults to "int8_float16".
tokenizer (Union[AutoTokenizer, None], optional): _description_. Defaults to None.
hub_kwargs (dict, optional): _description_. Defaults to {}.
**kwargs (Any, optional): Any additional arguments
"""
self.ctranslate_class = ctranslate2.Generator
super().__init__(
model_name_or_path,
device,
device_index,
compute_type,
tokenizer,
hub_kwargs,
**kwargs,
)
def _forward(self, *args, **kwds):
return self.model.generate_batch(*args, **kwds)
def tokenize_decode(self, tokens_out, *args, **kwargs):
return [
self.tokenizer.decode(tokens_out[i].sequences_ids[0], *args, **kwargs)
for i in range(len(tokens_out))
]
def generate(
self,
text: Union[str, List[str]],
encode_tok_kwargs={},
decode_tok_kwargs={},
*forward_args,
**forward_kwds: Any,
):
"""_summary_
Args:
text (str | List[str]): Input texts
encode_tok_kwargs (dict, optional): additional kwargs for tokenizer
decode_tok_kwargs (dict, optional): additional kwargs for tokenizer
max_batch_size (int, optional): _. Defaults to 0.
batch_type (str, optional): _. Defaults to 'examples'.
asynchronous (bool, optional): _. Defaults to False.
beam_size (int, optional): _. Defaults to 1.
patience (float, optional): _. Defaults to 1.
num_hypotheses (int, optional): _. Defaults to 1.
length_penalty (float, optional): _. Defaults to 1.
repetition_penalty (float, optional): _. Defaults to 1.
no_repeat_ngram_size (int, optional): _. Defaults to 0.
disable_unk (bool, optional): _. Defaults to False.
suppress_sequences (Optional[List[List[str]]], optional): _.
Defaults to None.
end_token (Optional[Union[str, List[str], List[int]]], optional): _.
Defaults to None.
return_end_token (bool, optional): _. Defaults to False.
max_length (int, optional): _. Defaults to 512.
min_length (int, optional): _. Defaults to 0.
include_prompt_in_result (bool, optional): _. Defaults to True.
return_scores (bool, optional): _. Defaults to False.
return_alternatives (bool, optional): _. Defaults to False.
min_alternative_expansion_prob (float, optional): _. Defaults to 0.
sampling_topk (int, optional): _. Defaults to 1.
sampling_temperature (float, optional): _. Defaults to 1.
Returns:
str | List[str]: text as output, if list, same len as input
"""
return super().generate(
text,
encode_kwargs=encode_tok_kwargs,
decode_kwargs=decode_tok_kwargs,
*forward_args,
**forward_kwds,
) |