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View Code? Open in Web Editor NEW更纯粹、更高压缩率的Tokenizer
License: Apache License 2.0
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shanguanma ericxsun miracleyin hscspring ishine tsingcoo kemolo oldstree laohur nzpeng eggqq007 fandulu tlyu0419 liu-feng-deeplearning fengxingxiang jdb110 sundoge zhaicunqibytepiece's Issues
转换成 sentencepiece 的之后载入失败
通过类方法 convert_to_sentencepiece 转换为 sp model，再进行 load 的时候报错
import sentencepiece as spm
sp_model = spm.SentencePieceProcessor()
sp_model.Load("sp.model")libc++abi: terminating due to uncaught exception of type Darts::Details::Exception: /Users/runner/work/sentencepiece/sentencepiece/third_party/darts_clone/darts.h:1143: exception: failed to insert key: zero-length key
相关 issue google/sentencepiece#156
模型里面有 "\0"，是否应该在 convert 的时候去掉，以及是否有副作用？
tokenizer压缩率 与 模型最终效果 的关系
在评估tokenizer的部分给出的是tokenizer自身的评估指标，比如压缩率
但是，高压缩率的tokenizer并不意味模型的效果也更好，是否能给出最终模型层面的效果？
例如：sentencepiece实验中的BLUE
https://github.com/google/sentencepiece/blob/master/doc/experiments.md#english-to-japanese
pip 安装失败
如图所示，打包的时候没有把 readme 打包进来。
Training error by multiple works
Hey, I had an error in my training.
trainer = Trainer(order=6, max_vocab_size=100000, min_count=32)
trainer.train(w, workers=2, batch_size=1000)
but I got an error AttributeError: Can't pickle local object 'Trainer.pcount.<locals>.worker_func'
This is my env version:
python 3.8.16
multiprocess 0.70.14
苏神博客咋没页面？
加载model报错
按照以下加载模型做分词测试报错。
环境：加载模型
bytepiece.id.eu.plus.80k.model
from bytepiece import Tokenizer
tokenizer = Tokenizer('bytepiece.model')
这一步报错
是加载编码有问题吗，看代码这里的K 是二进制编码
convert_to_sentencepiece fail
bytepiece-0.6.3
line 356, in convert_to_sentencepiece
p = re.sub(' ', '▁', p.decode())
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte
建议作者增加一个词表扩充的demo或教程
目前的bytepiece算法和实现已经足够优秀，但是相较于sp而言文档较少，demo不多，所以目前好像更像是很多人研究的玩具而非生产力工具。综上，建议作者完善文档并增加一个词表扩充的demo或教程。感谢！
Redundant vocab?
There seems to be redundancy in the model you released.
Please correct me if I am wrong. 😁
For example, when I run:
grep -A 2 -B 2 -n '"，"' bytepiece_80k.model > test.txt
I got:
67637- "77yM": [
67638- 13530,
67639: "，",
67640- 52141276
67641- ],
--
114017- "gO+8jA==": [
114018- 22806,
114019: "，",
114020- 27651
114021- ],
--
114062- "ge+8jA==": [
114063- 22815,
114064: "，",
114065- 30470
114066- ],
--
114077- "gu+8jA==": [
114078- 22818,
114079: "，",
114080- 17876
114081- ],
--
114092- "g++8jA==": [
114093- 22821,
114094: "，",
114095- 15013
114096- ],
--
114112- "hO+8jA==": [
114113- 22825,
114114: "，",
114115- 28134
114116- ],
--
114127- "he+8jA==": [
114128- 22828,
114129: "，",
114130- 24900
114131- ],
--
114142- "hu+8jA==": [
114143- 22831,
114144: "，",
114145- 33913
114146- ],
--
114157- "h++8jA==": [
114158- 22834,
114159: "，",
114160- 19583
114161- ],
--
114177- "iO+8jA==": [
114178- 22838,
114179: "，",
114180- 29143
114181- ],
--
114232- "ie+8jA==": [
114233- 22849,
114234: "，",
114235- 56579
114236- ],
--
114252- "iu+8jA==": [
114253- 22853,
114254: "，",
114255- 27238
114256- ],
--
114307- "i++8jA==": [
114308- 22864,
114309: "，",
114310- 55373
114311- ],
--
114397- "jO+8jA==": [
114398- 22882,
114399: "，",
114400- 27059
114401- ],
--
114427- "je+8jA==": [
114428- 22888,
114429: "，",
114430- 26193
114431- ],
--
114437- "ju+8jA==": [
114438- 22890,
114439: "，",
114440- 24179
114441- ],
--
114462- "j++8jA==": [
114463- 22895,
114464: "，",
114465- 38457
114466- ],
--
114477- "kO+8jA==": [
114478- 22898,
114479: "，",
114480- 23832
114481- ],
--
114517- "ke+8jA==": [
114518- 22906,
114519: "，",
114520- 45492
114521- ],
114522- "ku+8jA==": [
114523- 22907,
114524: "，",
114525- 14602
114526- ],
--
114537- "k++8jA==": [
114538- 22910,
114539: "，",
114540- 16818
114541- ],
--
114557- "lO+8jA==": [
114558- 22914,
114559: "，",
114560- 25217
114561- ],
--
114572- "le+8jA==": [
114573- 22917,
114574: "，",
114575- 22115
114576- ],
--
114592- "lu+8jA==": [
114593- 22921,
114594: "，",
114595- 35881
114596- ],
--
114612- "l++8jA==": [
114613- 22925,
114614: "，",
114615- 20337
114616- ],
--
114632- "mO+8jA==": [
114633- 22929,
114634: "，",
114635- 20429
114636- ],
--
114657- "me+8jA==": [
114658- 22934,
114659: "，",
114660- 23524
114661- ],
--
114682- "mu+8jA==": [
114683- 22939,
114684: "，",
114685- 25417
114686- ],
--
114707- "m++8jA==": [
114708- 22944,
114709: "，",
114710- 39254
114711- ],
--
114722- "nO+8jA==": [
114723- 22947,
114724: "，",
114725- 21866
114726- ],
--
114737- "ne+8jA==": [
114738- 22950,
114739: "，",
114740- 30855
114741- ],
114742- "nu+8jA==": [
114743- 22951,
114744: "，",
114745- 13346
114746- ],
--
114777- "n++8jA==": [
114778- 22958,
114779: "，",
114780- 19853
114781- ],
--
114797- "oO+8jA==": [
114798- 22962,
114799: "，",
114800- 15771
114801- ],
--
114812- "oe+8jA==": [
114813- 22965,
114814: "，",
114815- 15774
114816- ],
--
114827- "ou+8jA==": [
114828- 22968,
114829: "，",
114830- 21457
114831- ],
--
114837- "o++8jA==": [
114838- 22970,
114839: "，",
114840- 19483
114841- ],
--
114847- "pO+8jA==": [
114848- 22972,
114849: "，",
114850- 21556
114851- ],
--
114877- "pe+8jA==": [
114878- 22978,
114879: "，",
114880- 31149
114881- ],
--
114892- "pu+8jA==": [
114893- 22981,
114894: "，",
114895- 24183
114896- ],
--
114907- "p++8jA==": [
114908- 22984,
114909: "，",
114910- 27728
114911- ],
--
114922- "qO+8jA==": [
114923- 22987,
114924: "，",
114925- 28618
114926- ],
--
114947- "qe+8jA==": [
114948- 22992,
114949: "，",
114950- 19995
114951- ],
--
114957- "qu+8jA==": [
114958- 22994,
114959: "，",
114960- 13975
114961- ],
--
114982- "q++8jA==": [
114983- 22999,
114984: "，",
114985- 20687
114986- ],
--
114997- "rO+8jA==": [
114998- 23002,
114999: "，",
115000- 32861
115001- ],
--
115012- "re+8jA==": [
115013- 23005,
115014: "，",
115015- 28320
115016- ],
--
115032- "ru+8jA==": [
115033- 23009,
115034: "，",
115035- 20423
115036- ],
--
115047- "r++8jA==": [
115048- 23012,
115049: "，",
115050- 35625
115051- ],
--
115072- "sO+8jA==": [
115073- 23017,
115074: "，",
115075- 25750
115076- ],
--
115082- "se+8jA==": [
115083- 23019,
115084: "，",
115085- 26685
115086- ],
--
115092- "su+8jA==": [
115093- 23021,
115094: "，",
115095- 11848
115096- ],
--
115107- "s++8jA==": [
115108- 23024,
115109: "，",
115110- 17627
115111- ],
--
115127- "tO+8jA==": [
115128- 23028,
115129: "，",
115130- 28616
115131- ],
115132- "te+8jA==": [
115133- 23029,
115134: "，",
115135- 12874
115136- ],
--
115147- "tu+8jA==": [
115148- 23032,
115149: "，",
115150- 23628
115151- ],
--
115182- "t++8jA==": [
115183- 23039,
115184: "，",
115185- 25204
115186- ],
--
115212- "ue+8jA==": [
115213- 23045,
115214: "，",
115215- 37675
115216- ],
--
115252- "uu+8jA==": [
115253- 23053,
115254: "，",
115255- 39151
115256- ],
--
115282- "u++8jA==": [
115283- 23059,
115284: "，",
115285- 26261
115286- ],
115287- "vO+8jA==": [
115288- 23060,
115289: "，",
115290- 20389
115291- ],
--
115312- "ve+8jA==": [
115313- 23065,
115314: "，",
115315- 32564
115316- ],
--
115332- "vu+8jA==": [
115333- 23069,
115334: "，",
115335- 15513
115336- ],
--
115352- "v++8jA==": [
115353- 23073,
115354: "，",
115355- 27048
115356- ],
--
118962- "77yM5A==": [
118963- 23795,
118964: "，",
118965- 9190
118966- ],
118967- "77yM5Q==": [
118968- 23796,
118969: "，",
118970- 205451
118971- ],
118972- "77yM5g==": [
118973- 23797,
118974: "，",
118975- 172391
118976- ],
118977- "77yM5w==": [
118978- 23798,
118979: "，",
118980- 67781
118981- ],
118982- "77yM6A==": [
118983- 23799,
118984: "，",
118985- 128861
118986- ],
118987- "77yM6Q==": [
118988- 23800,
118989: "，",
118990- 55129
118991- ],
--
158192- "uK3vvIw=": [
158193- 31641,
158194: "，",
158195- 9172
158196- ],
--
160082- "77yM5Lg=": [
160083- 32019,
160084: "，",
160085- 113663
160086- ],
160087- "77yM5Lk=": [
160088- 32020,
160089: "，",
160090- 47189
160091- ],
160092- "77yM5Lo=": [
160093- 32021,
160094: "，",
160095- 27242
160096- ],
160097- "77yM5Ls=": [
160098- 32022,
160099: "，",
160100- 61788
160101- ],
160102- "77yM5Lw=": [
160103- 32023,
160104: "，",
160105- 14111
160106- ],
160107- "77yM5L0=": [
160108- 32024,
160109: "，",
160110- 36471
160111- ],
160112- "77yM5YU=": [
160113- 32025,
160114: "，",
160115- 45865
160116- ],
160117- "77yM5YY=": [
160118- 32026,
160119: "，",
160120- 20102
160121- ],
160122- "77yM5Yc=": [
160123- 32027,
160124: "，",
160125- 18068
160126- ],
160127- "77yM5Yg=": [
160128- 32028,
160129: "，",
160130- 22664
160131- ],
160132- "77yM5Y0=": [
160133- 32029,
160134: "，",
160135- 45655
160136- ],
160137- "77yM5Y8=": [
160138- 32030,
160139: "，",
160140- 78634
160141- ],
160142- "77yM5ZA=": [
160143- 32031,
160144: "，",
160145- 40057
160146- ],
160147- "77yM5ZI=": [
160148- 32032,
160149: "，",
160150- 10479
160151- ],
160152- "77yM5Zs=": [
160153- 32033,
160154: "，",
160155- 10167
160156- ],
160157- "77yM5aQ=": [
160158- 32034,
160159: "，",
160160- 35717
160161- ],
160162- "77yM5aU=": [
160163- 32035,
160164: "，",
160165- 24338
160166- ],
160167- "77yM5aY=": [
160168- 32036,
160169: "，",
160170- 13069
160171- ],
160172- "77yM5a4=": [
160173- 32037,
160174: "，",
160175- 31083
160176- ],
160177- "77yM5a8=": [
160178- 32038,
160179: "，",
160180- 22719
160181- ],
160182- "77yM5bA=": [
160183- 32039,
160184: "，",
160185- 60831
160186- ],
160187- "77yM5bw=": [
160188- 32040,
160189: "，",
160190- 12414
160191- ],
160192- "77yM5b4=": [
160193- 32041,
160194: "，",
160195- 15229
160196- ],
160197- "77yM5b8=": [
160198- 32042,
160199: "，",
160200- 11786
160201- ],
160202- "77yM5oM=": [
160203- 32043,
160204: "，",
160205- 9275
160206- ],
160207- "77yM5og=": [
160208- 32044,
160209: "，",
160210- 25038
160211- ],
160212- "77yM5ok=": [
160213- 32045,
160214: "，",
160215- 30394
160216- ],
160217- "77yM5oo=": [
160218- 32046,
160219: "，",
160220- 19145
160221- ],
160222- "77yM5os=": [
160223- 32047,
160224: "，",
160225- 18043
160226- ],
160227- "77yM5o4=": [
160228- 32048,
160229: "，",
160230- 10899
160231- ],
160232- "77yM5pU=": [
160233- 32049,
160234: "，",
160235- 12918
160236- ],
160237- "77yM5pc=": [
160238- 32050,
160239: "，",
160240- 9034
160241- ],
160242- "77yM5pg=": [
160243- 32051,
160244: "，",
160245- 16571
160246- ],
160247- "77yM5pw=": [
160248- 32052,
160249: "，",
160250- 21640
160251- ],
160252- "77yM5p0=": [
160253- 32053,
160254: "，",
160255- 13906
160256- ],
160257- "77yM5q0=": [
160258- 32054,
160259: "，",
160260- 7468
160261- ],
160262- "77yM5q8=": [
160263- 32055,
160264: "，",
160265- 12216
160266- ],
160267- "77yM5rI=": [
160268- 32056,
160269: "，",
160270- 11066
160271- ],
160272- "77yM55Q=": [
160273- 32057,
160274: "，",
160275- 11502
160276- ],
160277- "77yM55s=": [
160278- 32058,
160279: "，",
160280- 11973
160281- ],
160282- "77yM57s=": [
160283- 32059,
160284: "，",
160285- 16988
160286- ],
160287- "77yM6IA=": [
160288- 32060,
160289: "，",
160290- 15794
160291- ],
160292- "77yM6Jk=": [
160293- 32061,
160294: "，",
160295- 8488
160296- ],
160297- "77yM6K4=": [
160298- 32062,
160299: "，",
160300- 18199
160301- ],
160302- "77yM6K8=": [
160303- 32063,
160304: "，",
160305- 33792
160306- ],
160307- "77yM6LA=": [
160308- 32064,
160309: "，",
160310- 13559
160311- ],
160312- "77yM6LU=": [
160313- 32065,
160314: "，",
160315- 14349
160316- ],
160317- "77yM6L8=": [
160318- 32066,
160319: "，",
160320- 51748
160321- ],
160322- "77yM6YA=": [
160323- 32067,
160324: "，",
160325- 23202
160326- ],
160327- "77yM6YE=": [
160328- 32068,
160329: "，",
160330- 10723
160331- ],
160332- "77yM6YI=": [
160333- 32069,
160334: "，",
160335- 25998
160336- ],
160337- "77yM6Zk=": [
160338- 32070,
160339: "，",
160340- 8209
160341- ],
大数据量训练的时候卡住
20GB 数据量可以正常训练，100GB 在跑到某一步的时候会卡住。bytepiece==0.6.3 。
某个 thread 的堆栈信息，看不出来，直接问 GPT 似乎是多进程的问题：
#0 0x00007f168f6207a4 in do_futex_wait.constprop () from /lib64/libpthread.so.0
#1 0x00007f168f620898 in __new_sem_wait_slow.constprop.0 () from /lib64/libpthread.so.0
#2 0x00007f1683848699 in semlock_acquire ()
from /opt/rh/rh-python38/root/usr/lib64/python3.8/lib-dynload/_multiprocessing.cpython-38-x86_64-linux-gnu.so
#3 0x00007f168f7ed4e6 in PyCFunction_Call () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#4 0x00007f168f7ac932 in _PyObject_MakeTpCall () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#5 0x00007f168f862c5c in _PyEval_EvalFrameDefault () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#6 0x00007f168f84fe05 in _PyFunction_Vectorcall () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#7 0x00007f168f7ab7bd in PyObject_Call () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#8 0x00007f168f860081 in _PyEval_EvalFrameDefault () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#9 0x00007f168f84fe05 in _PyFunction_Vectorcall () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#10 0x00007f168f85e323 in _PyEval_EvalFrameDefault () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#11 0x00007f168f84fe05 in _PyFunction_Vectorcall () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#12 0x00007f168f85e323 in _PyEval_EvalFrameDefault () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#13 0x00007f168f84fe05 in _PyFunction_Vectorcall () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#14 0x00007f168f8507cb in method_vectorcall () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#15 0x00007f168f7ab7bd in PyObject_Call () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#16 0x00007f168f8ad6d1 in t_bootstrap () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#17 0x00007f168f86bbc4 in pythread_wrapper () from /opt/rh/rh-python38/root/usr/lib64/libpython3.8.so.rh-python38-1.0
#18 0x00007f168f6174e2 in start_thread () from /lib64/libpthread.so.0
#19 0x00007f168f3f25b3 in clone () from /lib64/libc.so.6
使用 huggingface tokenizers 加载 bytepiece 的模型文件
通过以下代码转换模型文件后，可以 huggingface tokenizers 的接口直接加载 bytepiece 的模型。转换后，同样支持 byte_fallback 等特性。
以下代码为抛转引玉，未经过完全的测试，希望苏神可以测试一下。
# convert.py
from itertools import product
from pathlib import Path
from dataclasses import dataclass
import math
import json
@dataclass(slots=True)
class WordItem:
word: str
log_probability: float
def aslist(self):
return [self.word, self.log_probability]
def convert_bytepiece_model_to_hugging_face(bytepiece_model_file: str | Path, output_file: str | Path):
bytepiece_model_file = Path(bytepiece_model_file)
if not bytepiece_model_file.exists():
raise FileNotFoundError(f'{bytepiece_model_file} not found.')
output_file = Path(output_file)
if output_file.exists():
raise FileExistsError(f'{output_file} already exists.')
initial_word_items = [WordItem(f'<0x{a}{b}>', 0.0) for a, b in product('0123456789ABCDEF', '0123456789ABCDEF')]
bytepiece_model_data = json.load(bytepiece_model_file.open())
log_sum = math.log(sum(item[2] for item in bytepiece_model_data.values()))
vocab_word_items: list[WordItem] = []
for item in bytepiece_model_data.values():
_, word, frequence = item
if not word.strip():
continue
word_item = WordItem(word, math.log(frequence) - log_sum)
vocab_word_items.append(word_item)
word_items = initial_word_items + vocab_word_items
hugging_face_dict = {
'version': "1.0",
"pre_tokenizer": {
"type": "CharDelimiterSplit",
"delimiter": "\x00",
},
"model": {
"type": "Unigram",
"unk_id": 0,
"vocab": [item.aslist() for item in word_items],
"byte_fallback": True,
}
}
json.dump(hugging_face_dict, output_file.open('w'), indent=2, ensure_ascii=False)
if __name__ == '__main__':
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('bytepiece_model_file', type=str)
parser.add_argument('output_file', type=str)
args = parser.parse_args()
convert_bytepiece_model_to_hugging_face(args.bytepiece_model_file, args.output_file)python convert.py bytepiece.model bytepiece.jsonfrom tokenizers import Tokenizer
bytepiece_tokenizer = Tokenizer.from_file('bytepiece_plus_240k.json')
sentences = [
'中外科学名著',
'提高产品质量',
'鞭炮声响彻夜空',
'这事的确定不下来',
'邓颖超生前使用过的物品',
'쯈',
]
for sentence in sentences:
print(bytepiece_tokenizer.encode(sentence).tokens)
for sentence in sentences:
print(bytepiece_tokenizer.encode(sentence).offsets)
# OUTPUT:
# ['中外', '科学', '名著']
# ['提高', '产品', '质量']
# ['鞭炮', '声', '响彻', '夜空']
# ['这事', '的确', '定', '不', '下来']
# ['邓', '颖', '超', '生前', '使用', '过的', '物品']
# ['<0xEC>', '<0xAF>', '<0x88>']
# [(0, 2), (2, 4), (4, 6)]
# [(0, 2), (2, 4), (4, 6)]
# [(0, 2), (2, 3), (3, 5), (5, 7)]
# [(0, 2), (2, 4), (4, 5), (5, 6), (6, 8)]
# [(0, 1), (1, 2), (2, 3), (3, 5), (5, 7), (7, 9), (9, 11)]
# [(0, 1), (0, 1), (0, 1)]安装遇到的问题并解决办法
今天安装了一天，遇到了几个问题，分享一下，方便后面类似遇到的同学。
1. python的版本问题：
- 测试了多个python版本，最终完美的版本是3.11
- 3.12版本imp模块已被移除。虽然官方有替代方案，我捣鼓半天失败了：（
2. pyahocorasick的安装问题：
AHOCORASICK_BYTES=1 pip install git+https://github.com/WojciechMula/pyahocorasick.git，我在虚拟环境下没有安装成功。- 我的摸索的方法，先
git clone https://github.com/WojciechMula/pyahocorasick.git - 然后进入目录，打开setup.py。
build_as_bytes这个变量直接设置为True。保证AHOCORASICK_BYTES安装 - 直接
python setup.py install，当然，还需要一个C++的库，这个直接按照错误提示下载安装就好了，这个简单，就是有点大。
3. 会提示GBK编码问题（这个应该就是在windows中存在）：
pieces = json.load(open(pieces))直接指定编码：pieces = json.load(open(pieces, encoding="utf-8"))
不支持指定special_token吗？
比如将\n处理为<n>
弱问下训练一个tokenizer的训练目标是什么？
多谢！
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