File size: 21,524 Bytes
7ceb6fd |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 |
# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Tokenization classes for QWen."""
import base64
import logging
import os
import requests
import unicodedata
from typing import Collection, Dict, List, Set, Tuple, Union, Any, Callable, Optional
import tiktoken
import numpy as np
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
from transformers import PreTrainedTokenizer, AddedToken
from transformers.utils import try_to_load_from_cache
import matplotlib.colors as mcolors
from matplotlib.font_manager import FontProperties
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken", "ttf": "SimSun.ttf"}
FONT_PATH = try_to_load_from_cache("Qwen/Qwen-VL-Chat", "SimSun.ttf")
if FONT_PATH is None:
if not os.path.exists("SimSun.ttf"):
ttf = requests.get("https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/SimSun.ttf")
open("SimSun.ttf", "wb").write(ttf.content)
FONT_PATH = "SimSun.ttf"
PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
ENDOFTEXT = "<|endoftext|>"
IMSTART = "<|im_start|>"
IMEND = "<|im_end|>"
# as the default behavior is changed to allow special tokens in
# regular texts, the surface forms of special tokens need to be
# as different as possible to minimize the impact
EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
SPECIAL_TOKENS = (
ENDOFTEXT,
IMSTART,
IMEND,
) + EXTRAS
IMG_TOKEN_SPAN = 256
def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
with open(tiktoken_bpe_file, "rb") as f:
contents = f.read()
return {
base64.b64decode(token): int(rank)
for token, rank in (line.split() for line in contents.splitlines() if line)
}
def _list_find(
input_list: List[Any],
candidates: Tuple[Any],
start: int = 0,
):
for i in range(start, len(input_list)):
if input_list[i] in candidates:
return i
return -1
def _replace_closed_tag(
input_tokens: List[Any],
start_tags: Union[Any, Tuple[Any]],
end_tags: Union[Any, Tuple[Any]],
inclusive_replace_func: Callable,
exclusive_replace_func: Callable = lambda x: x,
):
if isinstance(start_tags, (str, int)):
start_tags = (start_tags,)
if isinstance(end_tags, (str, int)):
end_tags = (end_tags,)
assert len(start_tags) == len(end_tags)
output_tokens = []
end = 0
while True:
start = _list_find(input_tokens, start_tags, end)
if start == -1:
break
output_tokens.extend(exclusive_replace_func(input_tokens[end : start]))
tag_idx = start_tags.index(input_tokens[start])
end = _list_find(input_tokens, (end_tags[tag_idx],), start)
if end == -1:
raise ValueError("Unclosed image token")
output_tokens.extend(inclusive_replace_func(input_tokens[start : end + 1]))
end += 1
output_tokens.extend(exclusive_replace_func(input_tokens[end : ]))
return output_tokens
class QWenTokenizer(PreTrainedTokenizer):
"""QWen tokenizer."""
vocab_files_names = VOCAB_FILES_NAMES
def __init__(
self,
vocab_file,
errors="replace",
image_start_tag='<img>',
image_end_tag='</img>',
image_pad_tag='<imgpad>',
ref_start_tag='<ref>',
ref_end_tag='</ref>',
box_start_tag='<box>',
box_end_tag='</box>',
quad_start_tag='<quad>',
quad_end_tag='</quad>',
**kwargs,
):
super().__init__(**kwargs)
self.image_start_tag = image_start_tag
self.image_end_tag = image_end_tag
self.image_pad_tag = image_pad_tag
self.ref_start_tag = ref_start_tag
self.ref_end_tag = ref_end_tag
self.box_start_tag = box_start_tag
self.box_end_tag = box_end_tag
self.quad_start_tag = quad_start_tag
self.quad_end_tag = quad_end_tag
self.IMAGE_ST = (
ref_start_tag, ref_end_tag,
box_start_tag, box_end_tag,
quad_start_tag, quad_end_tag,
image_start_tag, image_end_tag,
image_pad_tag
)
self.errors = errors # how to handle errors in decoding
self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: dict[bytes, int]
self.special_tokens = {
token: index
for index, token in enumerate(
SPECIAL_TOKENS + self.IMAGE_ST, start=len(self.mergeable_ranks)
)
}
self.img_start_id = self.special_tokens[self.image_start_tag]
self.img_end_id = self.special_tokens[self.image_end_tag]
self.img_pad_id = self.special_tokens[self.image_pad_tag]
self.ref_start_id = self.special_tokens[self.ref_start_tag]
self.ref_end_id = self.special_tokens[self.ref_end_tag]
self.box_start_id = self.special_tokens[self.box_start_tag]
self.box_end_id = self.special_tokens[self.box_end_tag]
self.quad_start_id = self.special_tokens[self.quad_start_tag]
self.quad_end_id = self.special_tokens[self.quad_end_tag]
enc = tiktoken.Encoding(
"Qwen",
pat_str=PAT_STR,
mergeable_ranks=self.mergeable_ranks,
special_tokens=self.special_tokens,
)
assert (
len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
self.decoder = {
v: k for k, v in self.mergeable_ranks.items()
} # type: dict[int, bytes|str]
self.decoder.update({v: k for k, v in self.special_tokens.items()})
self.tokenizer = enc # type: tiktoken.Encoding
self.eod_id = self.tokenizer.eot_token
self.im_start_id = self.special_tokens[IMSTART]
self.im_end_id = self.special_tokens[IMEND]
def __getstate__(self):
# for pickle lovers
state = self.__dict__.copy()
del state['tokenizer']
return state
def __setstate__(self, state):
# tokenizer is not python native; don't pass it; rebuild it
self.__dict__.update(state)
enc = tiktoken.Encoding(
"Qwen",
pat_str=PAT_STR,
mergeable_ranks=self.mergeable_ranks,
special_tokens=self.special_tokens,
)
self.tokenizer = enc
def __len__(self) -> int:
return self.tokenizer.n_vocab
def get_vocab(self) -> Dict[bytes, int]:
return self.mergeable_ranks
def convert_tokens_to_ids(
self, tokens: Union[bytes, str, List[Union[bytes, str]]]
) -> List[int]:
ids = []
if isinstance(tokens, (str, bytes)):
if tokens in self.special_tokens:
return self.special_tokens[tokens]
else:
return self.mergeable_ranks.get(tokens)
for token in tokens:
if token in self.special_tokens:
ids.append(self.special_tokens[token])
else:
ids.append(self.mergeable_ranks.get(token))
return ids
def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
if not special_tokens and new_tokens:
raise ValueError('Adding regular tokens is not supported')
for token in new_tokens:
surface_form = token.content if isinstance(token, AddedToken) else token
if surface_form not in SPECIAL_TOKENS + self.IMAGE_ST:
raise ValueError('Adding unknown special tokens is not supported')
return 0
def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
"""
Save only the vocabulary of the tokenizer (vocabulary).
Returns:
`Tuple(str)`: Paths to the files saved.
"""
file_path = os.path.join(save_directory, "qwen.tiktoken")
with open(file_path, "w", encoding="utf8") as w:
for k, v in self.mergeable_ranks.items():
line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
w.write(line)
return (file_path,)
def tokenize(
self,
text: str,
allowed_special: Union[Set, str] = "all",
disallowed_special: Union[Collection, str] = (),
**kwargs,
) -> List[Union[bytes, str]]:
"""
Converts a string in a sequence of tokens.
Args:
text (`str`):
The sequence to be encoded.
allowed_special (`Literal["all"]` or `set`):
The surface forms of the tokens to be encoded as special tokens in regular texts.
Default to "all".
disallowed_special (`Literal["all"]` or `Collection`):
The surface forms of the tokens that should not be in regular texts and trigger errors.
Default to an empty tuple.
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific encode method.
Returns:
`List[bytes|str]`: The list of tokens.
"""
tokens = []
text = unicodedata.normalize("NFC", text)
# this implementation takes a detour: text -> token id -> token surface forms
for t in self.tokenizer.encode(
text, allowed_special=allowed_special, disallowed_special=disallowed_special
):
tokens.append(self.decoder[t])
def _encode_imgurl(img_tokens):
assert img_tokens[0] == self.image_start_tag and img_tokens[-1] == self.image_end_tag
img_tokens = img_tokens[1:-1]
img_url = b''.join(img_tokens)
out_img_tokens = list(map(self.decoder.get, img_url))
if len(out_img_tokens) > IMG_TOKEN_SPAN:
raise ValueError("The content in {}..{} is too long".format(
self.image_start_tag, self.image_end_tag))
out_img_tokens.extend([self.image_pad_tag] * (IMG_TOKEN_SPAN - len(out_img_tokens)))
out_img_tokens = [self.image_start_tag] + out_img_tokens + [self.image_end_tag]
return out_img_tokens
return _replace_closed_tag(tokens, self.image_start_tag, self.image_end_tag, _encode_imgurl)
def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
"""
Converts a sequence of tokens in a single string.
"""
text = ""
temp = b""
for t in tokens:
if isinstance(t, str):
if temp:
text += temp.decode("utf-8", errors=self.errors)
temp = b""
text += t
elif isinstance(t, bytes):
temp += t
else:
raise TypeError("token should only be of type types or str")
if temp:
text += temp.decode("utf-8", errors=self.errors)
return text
@property
def vocab_size(self):
return self.tokenizer.n_vocab
def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
"""Converts an id to a token, special tokens included"""
if index in self.decoder:
return self.decoder[index]
raise ValueError("unknown ids")
def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
"""Converts a token to an id using the vocab, special tokens included"""
if token in self.special_tokens:
return self.special_tokens[token]
if token in self.mergeable_ranks:
return self.mergeable_ranks[token]
raise ValueError("unknown token")
def _tokenize(self, text: str, **kwargs):
"""
Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
Do NOT take care of added tokens.
"""
raise NotImplementedError
def _decode(
self,
token_ids: Union[int, List[int]],
skip_special_tokens: bool = False,
errors: str = None,
**kwargs,
) -> str:
if isinstance(token_ids, int):
token_ids = [token_ids]
def _decode_imgurl(img_token_ids):
assert img_token_ids[0] == self.img_start_id and img_token_ids[-1] == self.img_end_id
img_token_ids = img_token_ids[1:-1]
img_token_ids = img_token_ids[ : img_token_ids.index(self.img_pad_id)]
img_url = bytes(img_token_ids).decode('utf-8')
return [self.img_start_id] + self.tokenizer.encode(img_url) + [self.img_end_id]
token_ids = _replace_closed_tag(token_ids, self.img_start_id, self.img_end_id, _decode_imgurl)
if skip_special_tokens:
token_ids = [i for i in token_ids if i < self.eod_id]
return self.tokenizer.decode(token_ids, errors=errors or self.errors)
def to_list_format(self, text: str):
text = unicodedata.normalize("NFC", text)
token_ids = self.tokenizer.encode(
text, allowed_special=set(self.IMAGE_ST + (ENDOFTEXT,)))
def _encode_vl_info(tokens):
if len(tokens) == 0:
return []
if tokens[0] == self.img_start_id and tokens[-1] == self.img_end_id:
key = 'image'
elif tokens[0] == self.ref_start_id and tokens[-1] == self.ref_end_id:
key = 'ref'
elif tokens[0] == self.box_start_id and tokens[-1] == self.box_end_id:
key = 'box'
elif tokens[0] == self.quad_start_id and tokens[-1] == self.quad_end_id:
key = 'quad'
else:
_tobytes = lambda x: x.encode('utf-8') if isinstance(x, str) else x
return [{'text': b''.join(map(_tobytes, map(self.decoder.get, tokens))).decode('utf-8')}]
_tobytes = lambda x: x.encode('utf-8') if isinstance(x, str) else x
val = b''.join(map(_tobytes, map(self.decoder.get, tokens[1:-1]))).decode('utf-8')
return [{key: val}]
return _replace_closed_tag(
token_ids,
(self.img_start_id, self.ref_start_id, self.box_start_id, self.quad_start_id),
(self.img_end_id, self.ref_end_id, self.box_end_id, self.quad_end_id),
_encode_vl_info,
_encode_vl_info,
)
def from_list_format(self, list_format: List[Dict]):
text = ''
num_images = 0
for ele in list_format:
if 'image' in ele:
num_images += 1
text += f'Picture {num_images}:'
text += self.image_start_tag + ele['image'] + self.image_end_tag
text += '\n'
elif 'text' in ele:
text += ele['text']
elif 'box' in ele:
if 'ref' in ele:
text += self.ref_start_tag + ele['ref'] + self.ref_end_tag
for box in ele['box']:
text += self.box_start_tag + '(%d,%d),(%d,%d)' % (box[0], box[1], box[2], box[3]) + self.box_end_tag
else:
raise ValueError("Unsupport element: " + str(ele))
return text
def _fetch_latest_picture(self, response, history):
if history is None:
history = []
_history = history + [(response, None)]
for q, r in _history[::-1]:
for ele in self.to_list_format(q)[::-1]:
if 'image' in ele:
return ele['image']
return None
def _fetch_all_box_with_ref(self, text):
list_format = self.to_list_format(text)
output = []
for i, ele in enumerate(list_format):
if 'box' in ele:
bbox = tuple(map(int, ele['box'].replace('(', '').replace(')', '').split(',')))
assert len(bbox) == 4
output.append({'box': bbox})
if i > 0 and 'ref' in list_format[i-1]:
output[-1]['ref'] = list_format[i-1]['ref'].strip()
return output
def draw_bbox_on_latest_picture(
self,
response,
history=None,
) -> Optional[Image.Image]:
image = self._fetch_latest_picture(response, history)
if image is None:
return None
if image.startswith("http://") or image.startswith("https://"):
image = Image.open(requests.get(image, stream=True).raw).convert("RGB")
h, w = image.height, image.width
else:
image = np.asarray(Image.open(image).convert("RGB"))
h, w = image.shape[0], image.shape[1]
visualizer = Visualizer(image)
boxes = self._fetch_all_box_with_ref(response)
if not boxes:
return None
color = random.choice([_ for _ in mcolors.TABLEAU_COLORS.keys()]) # init color
for box in boxes:
if 'ref' in box: # random new color for new refexps
color = random.choice([_ for _ in mcolors.TABLEAU_COLORS.keys()])
x1, y1, x2, y2 = box['box']
x1, y1, x2, y2 = (int(x1 / 1000 * w), int(y1 / 1000 * h), int(x2 / 1000 * w), int(y2 / 1000 * h))
visualizer.draw_box((x1, y1, x2, y2), alpha=1, edge_color=color)
if 'ref' in box:
visualizer.draw_text(box['ref'], (x1, y1), color=color, horizontal_alignment="left")
return visualizer.output
import colorsys
import logging
import math
import numpy as np
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg
from PIL import Image
import random
logger = logging.getLogger(__name__)
class VisImage:
def __init__(self, img, scale=1.0):
self.img = img
self.scale = scale
self.width, self.height = img.shape[1], img.shape[0]
self._setup_figure(img)
def _setup_figure(self, img):
fig = mplfigure.Figure(frameon=False)
self.dpi = fig.get_dpi()
# add a small 1e-2 to avoid precision lost due to matplotlib's truncation
# (https://github.com/matplotlib/matplotlib/issues/15363)
fig.set_size_inches(
(self.width * self.scale + 1e-2) / self.dpi,
(self.height * self.scale + 1e-2) / self.dpi,
)
self.canvas = FigureCanvasAgg(fig)
# self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
ax.axis("off")
self.fig = fig
self.ax = ax
self.reset_image(img)
def reset_image(self, img):
img = img.astype("uint8")
self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest")
def save(self, filepath):
self.fig.savefig(filepath)
def get_image(self):
canvas = self.canvas
s, (width, height) = canvas.print_to_buffer()
buffer = np.frombuffer(s, dtype="uint8")
img_rgba = buffer.reshape(height, width, 4)
rgb, alpha = np.split(img_rgba, [3], axis=2)
return rgb.astype("uint8")
class Visualizer:
def __init__(self, img_rgb, metadata=None, scale=1.0):
self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
self.font_path = FONT_PATH
self.output = VisImage(self.img, scale=scale)
self.cpu_device = torch.device("cpu")
# too small texts are useless, therefore clamp to 14
self._default_font_size = max(
np.sqrt(self.output.height * self.output.width) // 30, 15 // scale
)
def draw_text(
self,
text,
position,
*,
font_size=None,
color="g",
horizontal_alignment="center",
rotation=0,
):
if not font_size:
font_size = self._default_font_size
# since the text background is dark, we don't want the text to be dark
color = np.maximum(list(mplc.to_rgb(color)), 0.2)
color[np.argmax(color)] = max(0.8, np.max(color))
x, y = position
self.output.ax.text(
x,
y,
text,
size=font_size * self.output.scale,
fontproperties=FontProperties(fname=self.font_path),
bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
verticalalignment="top",
horizontalalignment=horizontal_alignment,
color=color,
zorder=10,
rotation=rotation,
)
return self.output
def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"):
x0, y0, x1, y1 = box_coord
width = x1 - x0
height = y1 - y0
linewidth = max(self._default_font_size / 4, 1)
self.output.ax.add_patch(
mpl.patches.Rectangle(
(x0, y0),
width,
height,
fill=False,
edgecolor=edge_color,
linewidth=linewidth * self.output.scale,
alpha=alpha,
linestyle=line_style,
)
)
return self.output
def get_output(self):
return self.output
|