diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 01c21607..00000000 --- a/.gitignore +++ /dev/null @@ -1,17 +0,0 @@ -__pycache__/ -.DS_Store -vocab/.DS_Store -.idea/workspace.xml -.idea/misc.xml -.idea/GPT2-Chinese.iml -data/ -.samples.txt -.idea/modules.xml -.idea/vcs.xml -.idea -.vscode -*.pyc -model -*.ckpt -*.log -log \ No newline at end of file diff --git a/README.md b/README.md index c79d6c5c..72e9811a 100644 --- a/README.md +++ b/README.md @@ -1,146 +1,49 @@ # GPT2-Chinese -## Description +## UPDATE Jul 27 -- Chinese version of GPT2 training code, using BERT tokenizer. It is based on the extremely awesome repository from HuggingFace team [Transformers](https://github.com/huggingface/transformers). Can write poems, news, novels, or train general language models. Support char level, word level and BPE level. Support large training corpus. -- 中文的GPT2训练代码，使用BERT的Tokenizer。 +- 为了保持项目的干净整洁，我做了一次更新清理。27日之前的内容以及训练斗破苍穹的脚本可以查看old_jul_27的Branch。 -## BIG UPDATE 04.22.2021 +## 项目状态 -- 因群众普遍反映原代码对新手不够友好，且代码本身年久失修，我使用Pytorch Lightning与Transformers库重写了一版训练与预测的代码。具体使用方法请参考本README的使用方法一栏。模型本身结构与旧代码兼容。 -- 这一版经过基本测试，可以完成训练与预测任务，代码在易用性上得到了提升。但是功能上会比原版代码有所缺失一些，如加载预训练模型的功能需要自己写一段小代码来添加一下。 -- 原版代码保存在本项目的old_gpt_2 branch中，如有需要的话用户依然可以从中获取进行学习。 -- 新版代码的依赖写在了requirements.txt文件中，请记得提前安装。 -- 请注意：本项目的预测脚本直接使用的话只支持预测本项目生成的checkpoint，如果要载入huggingface官方格式的GPT2 checkpoint，请直接使用GPT2LMModel对象的from pretrained功能进行载入。之后的预测流程是一样的。 +- 目前项目处于积极开发状态，如发现任何bug或是有功能意见与改进欢迎提交Issue，PR或是联系作者。 -## UPDATE 02.06.2021 - -- 本项目新增了[通用中文GPT-2预训练模型](https://github.com/Morizeyao/GPT2-Chinese#%E6%A8%A1%E5%9E%8B%E5%88%86%E4%BA%AB)、[通用中文GPT-2预训练小模型](https://github.com/Morizeyao/GPT2-Chinese#%E6%A8%A1%E5%9E%8B%E5%88%86%E4%BA%AB)、[中文歌词GPT-2预训练模型](https://github.com/Morizeyao/GPT2-Chinese#%E6%A8%A1%E5%9E%8B%E5%88%86%E4%BA%AB)和[文言文GPT-2预训练模型](https://github.com/Morizeyao/GPT2-Chinese#%E6%A8%A1%E5%9E%8B%E5%88%86%E4%BA%AB)。模型由UER-py项目训练得到，欢迎大家使用。 -此外，模型上传到了Huggingface Model Hub中。更多模型的细节请参考[gpt2-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-chinese-cluecorpussmall)、[gpt2-distil-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-distil-chinese-cluecorpussmall)、[gpt2-chinese-lyric](https://huggingface.co/uer/gpt2-chinese-lyric)和[gpt2-chinese-ancient](https://huggingface.co/uer/gpt2-chinese-ancient)。 - - 在使用所有模型进行生成时，需要在输入的文本前加入一个起始符，如：若要输入“最美的不是下雨天，是曾与你躲过雨的屋檐”，正确的格式为“[CLS]最美的不是下雨天，是曾与你躲过雨的屋檐”。 - - -## UPDATE 11.03.2020 - -- 本项目新增了[古诗词GPT-2预训练模型](https://github.com/Morizeyao/GPT2-Chinese#%E6%A8%A1%E5%9E%8B%E5%88%86%E4%BA%AB)和[对联GPT-2预训练模型](https://github.com/Morizeyao/GPT2-Chinese#%E6%A8%A1%E5%9E%8B%E5%88%86%E4%BA%AB)。模型由UER-py项目训练得到，欢迎大家使用。 -此外，模型上传到了Huggingface Model Hub中。更多模型的细节请参考[gpt2-chinese-poem](https://huggingface.co/uer/gpt2-chinese-poem)和[gpt2-chinese-couplet](https://huggingface.co/uer/gpt2-chinese-couplet)。 - - 在使用古诗词模型进行生成时，需要在输入的文本前加入一个起始符，如：若要输入“梅山如积翠，”，正确的格式为“[CLS]梅山如积翠，”。 - - 对联模型训练时使用的语料格式为“上联-下联”，在使用对联模型进行生成时，需要在输入的文本前加入一个起始符，如：若要输入“丹枫江冷人初去-”，正确的格式为“[CLS]丹枫江冷人初去-”。 - -## NEWS 08.11.2020 - -- [CDial-GPT](https://github.com/thu-coai/CDial-GPT)(可用本代码载入)已发布。本项目包含一个经过严格清洗的大规模放开域中文对话数据集，本项目还包含在此数据集上训练的GPT对话预训练模型，以及生成样例，欢迎大家参观。 - -## NEWS 12.9.2019 - -- 新项目[GPT2-chitchat](https://github.com/yangjianxin1/GPT2-chitchat)已发布，部分基于本项目代码。包含训练GPT2对话模型的代码与与训练模型，以及生成样例，欢迎大家参观。 - -## NEWS 12.7.2019 - -- 新项目[Decoders-Chinese-TF2.0](https://github.com/Morizeyao/Decoders-Chinese-TF2.0)同样支持GPT2的中文训练，在使用上更加简单，不易产生各种问题。目前还在测试阶段，欢迎大家提出意见。 - -## NEWS 11.9 - -- [GPT2-ML](https://github.com/imcaspar/gpt2-ml)（与本项目无任何直接关联）已发布，包含1.5B中文GPT2模型。大家如有兴趣或需要可将其转换为本项目支持的Pytorch格式进行进一步训练或生成测试。 - -## UPDATE 10.25 +## 使用方法 -- 本项目第一个预训练模型已公布，为散文生成模型，具体可查看README模型分享部分。 +- 在项目根目录建立data文件夹。将训练语料以train.txt为名放入data目录中。train.txt里是一个json，json是一个列表，列表的每个元素都分别是一篇要训练的文章。 +- 运行train.py文件，会自动预处理数据。 +- 预处理完成之后，将train.py文件内的raw改成False，然后运行，即可开始训练。 -## 使用方法 +## 文件结构 -1. 准备语料，放在data/train.json文件中，该文件的结构是：每行一个json字符串。 -2. （可选）准备训练参数设置，放在config文件夹中。 -3. （可选）准备tokenizer词表，放在vocab文件夹中。 -4. 运行 bash train.sh进行训练。具体训练参数可以参考train.py文件中的argparse相关描述。 -5. 运行 python3 generate.py 进行生成，生成的前缀可以在prefix参数中进行设置。可参考源码中参数设定部分代码。 +- generate.py 与 train.py 分别是生成与训练的脚本，使用pytorch-transformers库。 +- cache/vocab_small.txt 与 config/model_config_small.json 是我目前试验性缩小Bert tokenizer词表大小从而缩小模型大小的产物，两者组合可以在四张2080ti上实现12的batch size。 ## 注意 -- 因群众普遍反映原代码对新手不够友好，且代码本身年久失修，我使用Pytorch Lightning与Transformers库重写了一版训练与预测的代码。具体使用方法请参考本README的使用方法一栏。模型本身结构与旧代码兼容。 -- 这一版经过基本测试，可以完成训练与预测任务，代码在易用性上得到了提升。但是功能上会比原版代码有所缺失一些，如加载预训练模型的功能需要自己写一行代码来添加一下。 -- 原版代码保存在本项目的old_gpt_2 branch中，如有需要的话用户依然可以从中获取进行学习。 -- 新版代码的依赖写在了requirements.txt文件中，请记得提前安装。 -- 请注意：本项目的预测脚本直接使用的话只支持预测本项目生成的checkpoint，如果要载入huggingface官方格式的GPT2 checkpoint，请直接使用GPT2LMModel对象的from pretrained功能进行载入。之后的预测流程是一样的。 +- pytorch-pretrained-bert现在已经改名为pytorch-transformers了，我已经将train.py更新，适应新的版本，旧版本的train.py更名为train_old.py，不再更新。 +- 该训练代码可以运行，近期我会进行模型运算。 +- train.txt如果太大的话内存会不足。所以我写了一个拆分若干份的代码来做预处理。 +- 如果你的内存非常大或者语料较小的话，可以改掉train.py内build files内的对应代码，不做拆分直接预处理语料。 +- 各位如果完成了预训练的话欢迎进行交流。 ## 语料 - 可以从[这里](https://github.com/brightmart/nlp_chinese_corpus)与[这里](http://thuctc.thunlp.org/#获取链接)下载。 - 斗破苍穹语料可以从[这里](https://github.com/GaoPeng97/transformer-xl-chinese/tree/master/data/doupo)下载。 -## 联系作者 - -- Mail：ned1991@gmail.com - -## Citing +## FP16与Gradient Accumulation支持 -``` -@misc{GPT2-Chinese, - author = {Zeyao Du}, - title = {GPT2-Chinese: Tools for training GPT2 model in Chinese language}, - year = {2019}, - publisher = {GitHub}, - journal = {GitHub repository}, - howpublished = {\url{https://github.com/Morizeyao/GPT2-Chinese}}, -} -``` +- 我在train.py文件中加入了fp16支持，如果你安装了apex的话并且知道fp16是什么的话，可以修改变量fp61=True来启用。 +- 我在train_with_ga.py文件中加入了gradient accumulation支持，如果你需要更大的batch size的话可以调整gradient_accumulation的值。实际batch size会是文件中batch size与gradient accumulation值的乘积。 -## 模型分享 -| 模型名称 | 模型介绍| 分享者| 链接地址1 | 链接地址2 | -| :----------- | :----------- | :----------- | :----------- | ------------ | -| 散文模型 | 使用130MB的名家散文、情感散文和散文诗歌训练所得 。 | [hughqiu](https://github.com/hughqiu "hughqiu") | [百度网盘【fpyu】](https://pan.baidu.com/s/1nbrW5iw34GRhoTin8uU2tQ) | [GDrive](https://drive.google.com/drive/folders/1rJC4niJKMVwixUQkuL9k5teLRnEYTmUf?usp=sharing "GDrive") | -| 诗词模型 | 使用180MB的约80万首古诗词训练所得。 | [hhou435](https://github.com/hhou435) | [百度网盘【7fev】](https://pan.baidu.com/s/1Hy0OQ5xZcTLer9MQZW8o3g) | [GDrive](https://drive.google.com/drive/folders/1Z6nF1nrgTkrZcRLHedQHXb4_M9I7yQPN?usp=sharing) | -| 对联模型 | 使用40MB的约70万条对联训练所得。 | [hhou435](https://github.com/hhou435) | [百度网盘【i5n0】](https://pan.baidu.com/s/1j9yVQwjlXZq58wOyXK4lcg) | [GDrive](https://drive.google.com/drive/folders/1ZnsvS7oHRVueNKj_SeEhiQt86aze3ojj?usp=sharing) | -| 通用中文模型 | 使用[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/)语料训练所得。 | [hhou435](https://github.com/hhou435) | [百度网盘【n3s8】](https://pan.baidu.com/s/16x0hfBCekWju75xPeyyRfA) | [GDrive](https://drive.google.com/drive/folders/1dLEANs5z4pWS0pzrak6Q2H2Nq4iYsMsf?usp=sharing) | -| 通用中文小模型 | 使用[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/)语料训练所得。 | [hhou435](https://github.com/hhou435) | [百度网盘【rpjk】](https://pan.baidu.com/s/1AiSm2GWhbGNxvhrcUlDXNA) | [GDrive](https://drive.google.com/drive/folders/1eerX1N8n_eFlnQ4xpxZ4iU2-Mx83pXFp?usp=sharing) | -| 中文歌词模型 | 使用140MB的约15万首中文歌词训练所得。 | [hhou435](https://github.com/hhou435) | [百度网盘【0qnn】](https://pan.baidu.com/s/19x0d0bPGCWHi9L4Pu0pSiw) | [GDrive](https://drive.google.com/drive/folders/1RFq4NoQ3phCJjrhKtu2Xbn6z0krcN9TM?usp=sharing) | -| 文言文模型 | 使用1.8GB的约300万篇文言文训练所得。 | [hhou435](https://github.com/hhou435) | [百度网盘【ek2z】](https://pan.baidu.com/s/1X3Um9HketnlGYZubY9gnew) | [GDrive](https://drive.google.com/drive/folders/1dtHTRn3fX7g8cPCCaJEXA2tmrIcImR6t?usp=sharing) | - -此处为热情大方的git友训练所得的模型文件，公开给所有朋友使用，同时也欢迎各位伙伴将自己训练完毕的模型公开于此处。 - - -## Demo - -- 由用户[JamesHujy](https://github.com/JamesHujy)根据本仓库改版代码训练得到的模型作为律诗与绝句后台，新版[九歌诗歌生成器](https://jiuge.thunlp.cn/lvshi.html)已经上线。 -- 由[leemengtaiwan](https://github.com/leemengtaiwan)贡献，提供[文章直觀介紹 GPT-2 以及如何視覺化自注意力機制](https://leemeng.tw/gpt2-language-model-generate-chinese-jing-yong-novels.html)。另提供 [Colab 筆記本與模型](https://colab.research.google.com/drive/1MaT8-HUHfZkdCra0OqZEIr0IFCq0MJBx)供任何使用者一鍵生成新樣例。 +## 联系作者 +- QQ：330501241 +- Mail：ned1991@gmail.com + ## 生成样例 --以下为文学散文的生成样例，由[hughqiu](https://github.com/hughqiu "hughqiu")贡献，模型已经分享于模型分享列表。语料130MB，Batch size 16，10层深度下训练10轮所得。 -![avatar](sample/散文1.png) -![avatar](sample/散文2.png) -![avatar](sample/散文3.png) - - 下为斗破苍穹的生成样例，使用约50M参数的GPT2以32Batch Size在16MB斗破苍穹小说内容上训练得到。此处[SEP]表示换行。 ![avatar](sample/doupo.jpeg) - -- 下为古诗词的生成样例，由用户[JamesHujy](https://github.com/JamesHujy)运算并贡献。 - -![avatar](sample/poem_1.png) -![avatar](sample/poem_2.png) - -- 下为古诗限定了生成体裁后的生成样例，由用户[JamesHujy](https://github.com/JamesHujy)运算并贡献。 - -![avatar](sample/律诗绝句.png) -![avatar](sample/浣溪沙_江城子.png) -![avatar](sample/蝶恋花_满江红.png) - -- 下为生成剧本的样例文本，由用户[chiangandy](https://github.com/chiangandy)运算并贡献 - -[starttext]爱情游戏剧情讲述了钢琴父女明致怀萌的爱情、个有着努力的热情以及现实为人生的价值观众，获得一系列爱情的故事。80后录股媒体受到网友分享，是2014年主创陈拉昀出品牌总监于蓝氏集团化验师创业团门的哥哥大国度上海淮河畔，集入第一线公司青年度虽然没有放到的事业，但是蓝正是却不到位主人拒绝了解，而在蓝越的帮助理念出现，也因此开启明朗的误会而经营变成爱河。在一次偶然的编剧集电视剧之夏天上一改变了自命运环球顶樑，三人在创车祸中不知被记忆差网识分到创作，并被问流言败，以及行业服务所有的低调教同才力，陈昭和唐诗诗妍展开了一段截然不同的“2014年间段感情”，两人性格互相治癒的商业奋斗故事，尽管是共90后北京华侨大学录的一个宿舍小旅程和唐如、生等优秀青年，的人生活如何与愿违3个国偶像，并且共同创作何以此他们互相有观众的成功和关心吗?[endtext] - -[starttext]学习爱情主要讲述了两对方小曼，经过啼笑皆非的考验，终于选择了三个孩子，携手共同创业来四个孩子，在大城市里创业的成功商。两家内事业的加入了北京城市，经过了一次元城市融风雨故、差异后得到异的他们，最终收获了梦想的真正属于自己的爱情。赞助理想、电视剧、剧等主创业时代人物特点在北京举行开机仪式，该剧以当下海南三个新人青年轻人面人海南梅竹马的电视角，讲述了几个在北京、喜剧代人生活中增强非浪漫的年轻人，以独特的双时代年轻人从来到北京城市化中国大城市走出发展以海南方的变迁在语种城市闯关于人生态的同时，以及他们渐渐的生活方式为自己方向上演了那么简单俗，是当代际拍摄的就如何在这个城市里都市里?那么平静的城市就是城市的风格特张嘉和支持工作打造，而这是一点就要打造出机场话剧组会。化身处处棋逢貌各种文化的人都非常独特的煽情，交织了相，滑稽等来自外衣的东北漂亮、内地，者和两位女孩子敢称是哑女孩子。交织里的人齐飞一开泰块玩笑，令人印象太趋的气质，让人眼看这个性格非常喜剧，知道的是一个“东北漂”人的外国小养家，让她耳熟练读剧的外形象显老大。之后齐飞、表示爱朗的齐飞、范儿、楚月子、白天杰。两代人的生活里友情似乎没有结合、精彩表态的开朗和丽丽丽。[endtext] - -- 下為金庸武俠小說的生成樣例，由[leemengtaiwan](https://github.com/leemengtaiwan)贡献。模型大小約 82M，語料 50 MB，Batch size 16。提供[文章直觀介紹 GPT-2 以及如何視覺化自注意力機制](https://leemeng.tw/gpt2-language-model-generate-chinese-jing-yong-novels.html)。另提供 [Colab 筆記本與模型](https://colab.research.google.com/drive/1MaT8-HUHfZkdCra0OqZEIr0IFCq0MJBx)供任何使用者一鍵生成新樣例。 - -![avatar](sample/金庸_天龍八部.jpg) -![avatar](sample/金庸_倚天屠龍記.jpg) -![avatar](sample/金庸_鹿鼎記.jpg) -![avatar](sample/金庸_神鵰俠侶.jpg) - - - diff --git a/vocab/vocab.txt b/cache/vocab.txt similarity index 100% rename from vocab/vocab.txt rename to cache/vocab.txt diff --git a/cache/vocab_small.txt b/cache/vocab_small.txt new file mode 100644 index 00000000..433c122d --- /dev/null +++ b/cache/vocab_small.txt @@ -0,0 +1,13317 @@ +[PAD] +“ +” +’ +‘ +[unused5] +[unused6] +[unused7] +[unused8] +[unused9] +[unused10] +[unused11] +[unused12] +[unused13] +[unused14] +[unused15] +[unused16] +[unused17] +[unused18] +[unused19] +[unused20] +[unused21] +[unused22] +[unused23] +[unused24] +[unused25] +[unused26] +[unused27] +[unused28] +[unused29] +[unused30] +[unused31] +[unused32] +[unused33] +[unused34] +[unused35] +[unused36] +[unused37] +[unused38] +[unused39] +[unused40] +[unused41] +[unused42] +[unused43] +[unused44] +[unused45] +[unused46] +[unused47] +[unused48] +[unused49] +[unused50] +[unused51] +[unused52] +[unused53] +[unused54] +[unused55] +[unused56] +[unused57] +[unused58] +[unused59] +[unused60] +[unused61] +[unused62] +[unused63] +[unused64] +[unused65] +[unused66] +[unused67] +[unused68] +[unused69] +[unused70] +[unused71] +[unused72] +[unused73] +[unused74] +[unused75] +[unused76] +[unused77] +[unused78] +[unused79] +[unused80] +[unused81] +[unused82] +[unused83] +[unused84] +[unused85] +[unused86] +[unused87] +[unused88] +[unused89] +[unused90] +[unused91] +[unused92] +[unused93] +[unused94] +[unused95] +[unused96] +[unused97] +[unused98] +[unused99] +[UNK] +[CLS] +[SEP] +[MASK] + + +! +" +# +$ +% +& +' +( +) +* ++ +, +- +. +/ +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +: +; +< += +> +? +@ +[ +\ +] +^ +_ +a +b +c +d +e +f +g +h +i +j +k +l +m +n +o +p +q +r +s +t +u +v +w +x +y +z +{ +| +} +~ +£ +¤ +¥ +§ +© +« +® +° +± +² +³ +µ +· +¹ +º +» +¼ +× +ß +æ +÷ +ø +đ +ŋ +ɔ +ə +ɡ +ʰ +ˇ +ˈ +ˊ +ˋ +ˍ +ː +˙ +˚ +ˢ +α +β +γ +δ +ε +η +θ +ι +κ +λ +μ +ν +ο +π +ρ +ς +σ +τ +υ +φ +χ +ψ +ω +а +б +в +г +д +е +ж +з +и +к +л +м +н +о +п +р +с +т +у +ф +х +ц +ч +ш +ы +ь +я +і +ا +ب +ة +ت +د +ر +س +ع +ل +م +ن +ه +و +ي +۩ +ก +ง +น +ม +ย +ร +อ +า +เ +๑ +་ +ღ +ᄀ +ᄁ +ᄂ +ᄃ +ᄅ +ᄆ +ᄇ +ᄈ +ᄉ +ᄋ +ᄌ +ᄎ +ᄏ +ᄐ +ᄑ +ᄒ +ᅡ +ᅢ +ᅣ +ᅥ +ᅦ +ᅧ +ᅨ +ᅩ +ᅪ +ᅬ +ᅭ +ᅮ +ᅯ +ᅲ +ᅳ +ᅴ +ᅵ +ᆨ +ᆫ +ᆯ +ᆷ +ᆸ +ᆺ +ᆻ +ᆼ +ᗜ +ᵃ +ᵉ +ᵍ +ᵏ +ᵐ +ᵒ +ᵘ +‖ +„ +† +• +‥ +‧ +
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diff --git a/config/model_config_test.json b/config/model_config_test.json deleted file mode 100644 index 717da161..00000000 --- a/config/model_config_test.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "initializer_range": 0.02, - "layer_norm_epsilon": 1e-05, - "n_ctx": 64, - "n_embd": 128, - "n_head": 2, - "n_layer": 1, - "n_positions": 64, - "vocab_size": 13317 -} \ No newline at end of file diff --git a/generate.py b/generate.py index f8a24f82..7f4e4072 100644 --- a/generate.py +++ b/generate.py @@ -1,231 +1,137 @@ import torch import torch.nn.functional as F +import pytorch_transformers import os -import argparse +from my_chinese_tokenizer import tokenization_bert from tqdm import trange -from transformers import GPT2LMHeadModel, GPT2Config, BertTokenizer - - -def is_word(word): - for item in list(word): - if item not in "qwertyuiopasdfghjklzxcvbnm": - return False - return True - - -def _is_chinese_char(char): - """Checks whether CP is the codepoint of a CJK character.""" - # This defines a "chinese character" as anything in the CJK Unicode block: - # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) - # - # Note that the CJK Unicode block is NOT all Japanese and Korean characters, - # despite its name. The modern Korean Hangul alphabet is a different block, - # as is Japanese Hiragana and Katakana. Those alphabets are used to write - # space-separated words, so they are not treated specially and handled - # like the all of the other languages. - cp = ord(char) - if ( - (cp >= 0x4E00 and cp <= 0x9FFF) - or (cp >= 0x3400 and cp <= 0x4DBF) # - or (cp >= 0x20000 and cp <= 0x2A6DF) # - or (cp >= 0x2A700 and cp <= 0x2B73F) # - or (cp >= 0x2B740 and cp <= 0x2B81F) # - or (cp >= 0x2B820 and cp <= 0x2CEAF) # - or (cp >= 0xF900 and cp <= 0xFAFF) - or (cp >= 0x2F800 and cp <= 0x2FA1F) # - ): # - return True - - return False - - -def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float("Inf")): - """Filter a distribution of logits using top-k and/or nucleus (top-p) filtering - Args: - logits: logits distribution shape (vocabulary size) - top_k > 0: keep only top k tokens with highest probability (top-k filtering). - top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering). - Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) - From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 +from pytorch_transformers import GPT2LMHeadModel + +os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3" # 此处设置程序使用哪些显卡 + + +def top_k_logits(logits, k): """ - assert ( - logits.dim() == 1 - ) # batch size 1 for now - could be updated for more but the code would be less clear - top_k = min(top_k, logits.size(-1)) # Safety check + Masks everything but the k top entries as -infinity (1e10). + Used to mask logits such that e^-infinity -> 0 won't contribute to the + sum of the denominator. + """ + if k == 0: + return logits + else: + values = torch.topk(logits, k)[0] + batch_mins = values[:, -1].view(-1, 1).expand_as(logits) + return torch.where(logits < batch_mins, torch.ones_like(logits) * -1e10, logits) + + +def top_filtering(logits, top_k=5, top_p=0.9, threshold=-float('Inf'), filter_value=-float('Inf')): + """ Filter a distribution of logits using top-k, top-p (nucleus) and/or threshold filtering + Args: + logits: logits distribution shape (vocabulary size) + top_k: <=0: no filtering, >0: keep only top k tokens with highest probability. + top_p: <=0.0: no filtering, >0.0: keep only a subset S of candidates, where S is the smallest subset + whose total probability mass is greater than or equal to the threshold top_p. + In practice, we select the highest probability tokens whose cumulative probability mass exceeds + the threshold top_p. + threshold: a minimal threshold to keep logits + """ + assert logits.dim() == 1 # Only work for batch size 1 for now - could update but it would obfuscate a bit the code + top_k = min(top_k, logits.size(-1)) if top_k > 0: - # Remove all tokens with a probability less than the last token of the top-k + # Remove all tokens with a probability less than the last token in the top-k tokens indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = filter_value if top_p > 0.0: + # Compute cumulative probabilities of sorted tokens sorted_logits, sorted_indices = torch.sort(logits, descending=True) - cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) + cumulative_probabilities = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold - sorted_indices_to_remove = cumulative_probs > top_p + sorted_indices_to_remove = cumulative_probabilities > top_p # Shift the indices to the right to keep also the first token above the threshold sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 + # Back to unsorted indices and set them to -infinity indices_to_remove = sorted_indices[sorted_indices_to_remove] logits[indices_to_remove] = filter_value + + indices_to_remove = logits < threshold + logits[indices_to_remove] = filter_value + return logits -def sample_sequence( - model, - context, - length, - n_ctx, - tokenizer, - temperature=1.0, - top_k=30, - top_p=0.0, - repitition_penalty=1.0, - device="cpu", -): - context = torch.tensor(context, dtype=torch.long, device=device) - context = context.unsqueeze(0) - generated = context +def sample_sequence(model, length, start_token=None, batch_size=None, context=None, temperature=1.0, top_k=0, + device='cuda', sample=True): + if start_token is None: + assert context is not None, 'Specify exactly one of start_token and context!' + context = torch.tensor(context, device=device, dtype=torch.long).unsqueeze(0).repeat(batch_size, 1) + else: + assert context is None, 'Specify exactly one of start_token and context!' + context = torch.full((batch_size, 1), start_token, device=device, dtype=torch.long) + prev = context + output = context + past = None with torch.no_grad(): for _ in trange(length): - inputs = {"input_ids": generated[0][-(n_ctx - 1) :].unsqueeze(0)} - outputs = model( - **inputs - ) # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet (cached hidden-states) - next_token_logits = outputs[0][0, -1, :] - for id in set(generated): - next_token_logits[id] /= repitition_penalty - next_token_logits = next_token_logits / temperature - next_token_logits[tokenizer.convert_tokens_to_ids("[UNK]")] = -float("Inf") - filtered_logits = top_k_top_p_filtering( - next_token_logits, top_k=top_k, top_p=top_p - ) - next_token = torch.multinomial( - F.softmax(filtered_logits, dim=-1), num_samples=1 - ) - generated = torch.cat((generated, next_token.unsqueeze(0)), dim=1) - return generated.tolist()[0] + logits, past = model(prev, past=past) + logits = logits[:, -1, :] / temperature + logits = logits.squeeze(0) + logits = top_filtering(logits) + log_probs = F.softmax(logits, dim=-1) + # log_probs[100] = 0 -def fast_sample_sequence( - model, context, length, temperature=1.0, top_k=30, top_p=0.0, device="cpu" -): - inputs = torch.LongTensor(context).view(1, -1).to(device) - if len(context) > 1: - _, past = model(inputs[:, :-1], None)[:2] - prev = inputs[:, -1].view(1, -1) - else: - past = None - prev = inputs - generate = [] + context - with torch.no_grad(): - for i in trange(length): - output = model(prev, past=past) - output, past = output[:2] - output = output[-1].squeeze(0) / temperature - filtered_logits = top_k_top_p_filtering(output, top_k=top_k, top_p=top_p) - next_token = torch.multinomial( - torch.softmax(filtered_logits, dim=-1), num_samples=1 - ) - generate.append(next_token.item()) - prev = next_token.view(1, 1) - return generate + if sample: + prev = torch.multinomial(log_probs, num_samples=1) + prev = prev.unsqueeze(dim=-1) + else: + _, prev = torch.topk(log_probs, k=1, dim=-1) + output = torch.cat((output, prev), dim=1) + return output def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--device", default="0", type=str, required=False, help="生成设备") - parser.add_argument("--length", default=512, type=int, required=False, help="生成长度") - parser.add_argument("--n_ctx", default=1024, type=int, required=False, help="生成时考虑的上下文长度") - parser.add_argument( - "--batch_size", default=1, type=int, required=False, help="生成的batch size" - ) - parser.add_argument( - "--nsamples", default=10, type=int, required=False, help="生成几个样本" - ) - parser.add_argument( - "--temperature", default=1, type=float, required=False, help="生成温度" - ) - parser.add_argument("--topk", default=8, type=int, required=False, help="最高几选一") - parser.add_argument("--topp", default=0, type=float, required=False, help="最高积累概率") - parser.add_argument( - "--model_config", - default="config/model_config.json", - type=str, - required=False, - help="模型参数", - ) - parser.add_argument( - "--tokenizer_path", - default="vocab/vocab.txt", - type=str, - required=False, - help="词表路径", - ) - parser.add_argument( - "--model_path", - default="model/epoch=0-step=99.ckpt", - type=str, - required=False, - help="模型路径", - ) - parser.add_argument( - "--prefix", default="我", type=str, required=False, help="生成文章的开头" - ) - parser.add_argument("--no_wordpiece", action="store_true", help="不做word piece切词") - parser.add_argument("--segment", action="store_true", help="中文以词为单位") - parser.add_argument("--fast_pattern", action="store_true", help="采用更加快的方式生成文本") - parser.add_argument("--save_samples", action="store_true", help="保存产生的样本") - parser.add_argument( - "--save_samples_path", default=".", type=str, required=False, help="保存样本的路径" - ) - parser.add_argument("--repetition_penalty", default=1.0, type=float, required=False) - - args = parser.parse_args() - print("args:\n" + args.__repr__()) - - os.environ["CUDA_VISIBLE_DEVICES"] = args.device # 此处设置程序使用哪些显卡 - length = args.length - n_ctx = args.n_ctx - batch_size = args.batch_size - nsamples = args.nsamples - temperature = args.temperature - topk = args.topk - topp = args.topp - repetition_penalty = args.repetition_penalty + length = -1 + batch_size = 1 + nsamples = 18 + temperature = 1 + topk = 5 device = "cuda" if torch.cuda.is_available() else "cpu" - tokenizer = BertTokenizer(vocab_file=args.tokenizer_path) - model_config = GPT2Config.from_json_file(args.model_config) - model = GPT2LMHeadModel(config=model_config) - state_dict = torch.load(args.model_path, map_location="cpu") - if 'state_dict' in state_dict: - state_dict = { - key[6:]: value for key, value in state_dict["state_dict"].items() - } - model.load_state_dict(state_dict) + tokenizer = tokenization_bert.BertTokenizer(vocab_file='cache/vocab_small.txt') + model_config = pytorch_transformers.GPT2Config.from_json_file('config/model_config_small.json') + model = GPT2LMHeadModel(config=model_config).from_pretrained('model/final_model') model.to(device) model.eval() - for i in range(nsamples): - raw_text = args.prefix - encoded = tokenizer.encode_plus(raw_text)["input_ids"][:-1] - out = sample_sequence( - model, - encoded, - length=length, - n_ctx=n_ctx, - tokenizer=tokenizer, - temperature=temperature, - top_k=topk, - top_p=topp, - repitition_penalty=repetition_penalty, - device=device, - ) - print(tokenizer.decode(out)) - - -if __name__ == "__main__": + if length == -1: + length = model.config.n_ctx // 2 + elif length > model.config.n_ctx: + raise ValueError("Can't get samples longer than window size: %s" % model.config.n_ctx) + + while True: + raw_text = '萧炎' + context_tokens = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(raw_text)) + generated = 0 + for _ in range(nsamples // batch_size): + out = sample_sequence( + model=model, length=length, + context=context_tokens, + start_token=None, + batch_size=batch_size, + temperature=temperature, top_k=topk, device=device + ) + out = out[:, len(context_tokens):].tolist() + for i in range(batch_size): + generated += 1 + text = tokenizer.convert_ids_to_tokens(out[i]) + print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40) + print(''.join(text)) + print("=" * 80) + + +if __name__ == '__main__': main() diff --git a/my_chinese_tokenizer/tokenization_bert.py b/my_chinese_tokenizer/tokenization_bert.py new file mode 100644 index 00000000..7a32c652 --- /dev/null +++ b/my_chinese_tokenizer/tokenization_bert.py @@ -0,0 +1,440 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes.""" + +from __future__ import absolute_import, division, print_function, unicode_literals + +import collections +import logging +import os +import unicodedata +from io import open + +from pytorch_transformers.tokenization_utils import PreTrainedTokenizer, clean_up_tokenization + +logger = logging.getLogger(__name__) + +VOCAB_FILES_NAMES = {'vocab_file': 'vocab.txt'} + +PRETRAINED_VOCAB_FILES_MAP = { + 'vocab_file': + { + 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt", + 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt", + 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt", + 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt", + 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt", + 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt", + 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt", + 'bert-base-german-cased': "https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-vocab.txt", + 'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-vocab.txt", + 'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-vocab.txt", + 'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-vocab.txt", + 'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-vocab.txt", + 'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-vocab.txt", + } +} + +PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { + 'bert-base-uncased': 512, + 'bert-large-uncased': 512, + 'bert-base-cased': 512, + 'bert-large-cased': 512, + 'bert-base-multilingual-uncased': 512, + 'bert-base-multilingual-cased': 512, + 'bert-base-chinese': 512, + 'bert-base-german-cased': 512, + 'bert-large-uncased-whole-word-masking': 512, + 'bert-large-cased-whole-word-masking': 512, + 'bert-large-uncased-whole-word-masking-finetuned-squad': 512, + 'bert-large-cased-whole-word-masking-finetuned-squad': 512, + 'bert-base-cased-finetuned-mrpc': 512, +} + +def load_vocab(vocab_file): + """Loads a vocabulary file into a dictionary.""" + vocab = collections.OrderedDict() + with open(vocab_file, "r", encoding="utf-8") as reader: + tokens = reader.readlines() + for index, token in enumerate(tokens): + token = token.rstrip('\n') + vocab[token] = index + return vocab + + +def whitespace_tokenize(text): + """Runs basic whitespace cleaning and splitting on a piece of text.""" + text = text.strip() + if not text: + return [] + tokens = text.split() + return tokens + + +class BertTokenizer(PreTrainedTokenizer): + r""" + Constructs a BertTokenizer. + :class:`~pytorch_pretrained_bert.BertTokenizer` runs end-to-end tokenization: punctuation splitting + wordpiece + + Args: + vocab_file: Path to a one-wordpiece-per-line vocabulary file + do_lower_case: Whether to lower case the input. Only has an effect when do_wordpiece_only=False + do_basic_tokenize: Whether to do basic tokenization before wordpiece. + max_len: An artificial maximum length to truncate tokenized_doupo sequences to; Effective maximum length is always the + minimum of this value (if specified) and the underlying BERT model's sequence length. + never_split: List of tokens which will never be split during tokenization. Only has an effect when + do_wordpiece_only=False + """ + + vocab_files_names = VOCAB_FILES_NAMES + pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP + max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES + + def __init__(self, vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None, + unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", + mask_token="[MASK]", tokenize_chinese_chars=True, **kwargs): + """Constructs a BertTokenizer. + + Args: + **vocab_file**: Path to a one-wordpiece-per-line vocabulary file + **do_lower_case**: (`optional`) boolean (default True) + Whether to lower case the input + Only has an effect when do_basic_tokenize=True + **do_basic_tokenize**: (`optional`) boolean (default True) + Whether to do basic tokenization before wordpiece. + **never_split**: (`optional`) list of string + List of tokens which will never be split during tokenization. + Only has an effect when do_basic_tokenize=True + **tokenize_chinese_chars**: (`optional`) boolean (default True) + Whether to tokenize Chinese characters. + This should likely be desactivated for Japanese: + see: https://github.com/huggingface/pytorch-pretrained-BERT/issues/328 + """ + super(BertTokenizer, self).__init__(unk_token=unk_token, sep_token=sep_token, + pad_token=pad_token, cls_token=cls_token, + mask_token=mask_token, **kwargs) + if not os.path.isfile(vocab_file): + raise ValueError( + "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained " + "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)) + self.vocab = load_vocab(vocab_file) + self.ids_to_tokens = collections.OrderedDict( + [(ids, tok) for tok, ids in self.vocab.items()]) + self.do_basic_tokenize = do_basic_tokenize + if do_basic_tokenize: + self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case, + never_split=never_split, + tokenize_chinese_chars=tokenize_chinese_chars) + self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token) + + @property + def vocab_size(self): + return len(self.vocab) + + def _tokenize(self, text): + split_tokens = [] + if self.do_basic_tokenize: + for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens): + for sub_token in self.wordpiece_tokenizer.tokenize(token): + split_tokens.append(sub_token) + else: + split_tokens = self.wordpiece_tokenizer.tokenize(text) + return split_tokens + + def _convert_token_to_id(self, token): + """ Converts a token (str/unicode) in an id using the vocab. """ + return self.vocab.get(token, self.vocab.get(self.unk_token)) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (string/unicode) using the vocab.""" + return self.ids_to_tokens.get(index, self.unk_token) + + def convert_tokens_to_string(self, tokens): + """ Converts a sequence of tokens (string) in a single string. """ + out_string = ' '.join(tokens).replace(' ##', '').strip() + return out_string + + def save_vocabulary(self, vocab_path): + """Save the tokenizer vocabulary to a directory or file.""" + index = 0 + if os.path.isdir(vocab_path): + vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES['vocab_file']) + with open(vocab_file, "w", encoding="utf-8") as writer: + for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]): + if index != token_index: + logger.warning("Saving vocabulary to {}: vocabulary indices are not consecutive." + " Please check that the vocabulary is not corrupted!".format(vocab_file)) + index = token_index + writer.write(token + u'\n') + index += 1 + return (vocab_file,) + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs): + """ Instantiate a BertTokenizer from pre-trained vocabulary files. + """ + if pretrained_model_name_or_path in PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES: + if '-cased' in pretrained_model_name_or_path and kwargs.get('do_lower_case', True): + logger.warning("The pre-trained model you are loading is a cased model but you have not set " + "`do_lower_case` to False. We are setting `do_lower_case=False` for you but " + "you may want to check this behavior.") + kwargs['do_lower_case'] = False + elif '-cased' not in pretrained_model_name_or_path and not kwargs.get('do_lower_case', True): + logger.warning("The pre-trained model you are loading is an uncased model but you have set " + "`do_lower_case` to False. We are setting `do_lower_case=True` for you " + "but you may want to check this behavior.") + kwargs['do_lower_case'] = True + + return super(BertTokenizer, cls)._from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) + + +class BasicTokenizer(object): + """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" + + def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True): + """ Constructs a BasicTokenizer. + + Args: + **do_lower_case**: Whether to lower case the input. + **never_split**: (`optional`) list of str + Kept for backward compatibility purposes. + Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`) + List of token not to split. + **tokenize_chinese_chars**: (`optional`) boolean (default True) + Whether to tokenize Chinese characters. + This should likely be desactivated for Japanese: + see: https://github.com/huggingface/pytorch-pretrained-BERT/issues/328 + """ + if never_split is None: + never_split = [] + self.do_lower_case = do_lower_case + self.never_split = never_split + self.tokenize_chinese_chars = tokenize_chinese_chars + + def tokenize(self, text, never_split=None): + """ Basic Tokenization of a piece of text. + Split on "white spaces" only, for sub-word tokenization, see WordPieceTokenizer. + + Args: + **never_split**: (`optional`) list of str + Kept for backward compatibility purposes. + Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`) + List of token not to split. + """ + never_split = self.never_split + (never_split if never_split is not None else []) + text = self._clean_text(text) + # This was added on November 1st, 2018 for the multilingual and Chinese + # models. This is also applied to the English models now, but it doesn't + # matter since the English models were not trained on any Chinese data + # and generally don't have any Chinese data in them (there are Chinese + # characters in the vocabulary because Wikipedia does have some Chinese + # words in the English Wikipedia.). + if self.tokenize_chinese_chars: + text = self._tokenize_chinese_chars(text) + orig_tokens = whitespace_tokenize(text) + split_tokens = [] + for token in orig_tokens: + if self.do_lower_case and token not in never_split: + token = token.lower() + token = self._run_strip_accents(token) + split_tokens.extend(self._run_split_on_punc(token)) + + output_tokens = whitespace_tokenize(" ".join(split_tokens)) + return output_tokens + + def _run_strip_accents(self, text): + """Strips accents from a piece of text.""" + text = unicodedata.normalize("NFD", text) + output = [] + for char in text: + cat = unicodedata.category(char) + if cat == "Mn": + continue + output.append(char) + return "".join(output) + + def _run_split_on_punc(self, text, never_split=None): + """Splits punctuation on a piece of text.""" + if never_split is not None and text in never_split: + return [text] + chars = list(text) + i = 0 + start_new_word = True + output = [] + while i < len(chars): + char = chars[i] + if _is_punctuation(char): + output.append([char]) + start_new_word = True + else: + if start_new_word: + output.append([]) + start_new_word = False + output[-1].append(char) + i += 1 + + return ["".join(x) for x in output] + + def _tokenize_chinese_chars(self, text): + """Adds whitespace around any CJK character.""" + output = [] + for char in text: + cp = ord(char) + if self._is_chinese_char(cp): + output.append(" ") + output.append(char) + output.append(" ") + elif char.isdigit(): + output.append(" ") + output.append("d") + output.append(" ") + else: + output.append(char) + return "".join(output) + + def _is_chinese_char(self, cp): + """Checks whether CP is the codepoint of a CJK character.""" + # This defines a "chinese character" as anything in the CJK Unicode block: + # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) + # + # Note that the CJK Unicode block is NOT all Japanese and Korean characters, + # despite its name. The modern Korean Hangul alphabet is a different block, + # as is Japanese Hiragana and Katakana. Those alphabets are used to write + # space-separated words, so they are not treated specially and handled + # like the all of the other languages. + if ((cp >= 0x4E00 and cp <= 0x9FFF) or # + (cp >= 0x3400 and cp <= 0x4DBF) or # + (cp >= 0x20000 and cp <= 0x2A6DF) or # + (cp >= 0x2A700 and cp <= 0x2B73F) or # + (cp >= 0x2B740 and cp <= 0x2B81F) or # + (cp >= 0x2B820 and cp <= 0x2CEAF) or + (cp >= 0xF900 and cp <= 0xFAFF) or # + (cp >= 0x2F800 and cp <= 0x2FA1F)): # + return True + + return False + + def _clean_text(self, text): + """Performs invalid character removal and whitespace cleanup on text.""" + output = [] + for char in text: + cp = ord(char) + if cp == 0 or cp == 0xfffd or _is_control(char): + continue + if _is_whitespace(char): + output.append(" ") + else: + output.append(char) + return "".join(output) + + +class WordpieceTokenizer(object): + """Runs WordPiece tokenization.""" + + def __init__(self, vocab, unk_token, max_input_chars_per_word=100): + self.vocab = vocab + self.unk_token = unk_token + self.max_input_chars_per_word = max_input_chars_per_word + + def tokenize(self, text): + """Tokenizes a piece of text into its word pieces. + + This uses a greedy longest-match-first algorithm to perform tokenization + using the given vocabulary. + + For example: + input = "unaffable" + output = ["un", "##aff", "##able"] + + Args: + text: A single token or whitespace separated tokens. This should have + already been passed through `BasicTokenizer`. + + Returns: + A list of wordpiece tokens. + """ + + output_tokens = [] + for token in whitespace_tokenize(text): + chars = list(token) + if len(chars) > self.max_input_chars_per_word: + output_tokens.append(self.unk_token) + continue + + is_bad = False + start = 0 + sub_tokens = [] + while start < len(chars): + end = len(chars) + cur_substr = None + while start < end: + substr = "".join(chars[start:end]) + if start > 0: + substr = "##" + substr + if substr in self.vocab: + cur_substr = substr + break + end -= 1 + if cur_substr is None: + is_bad = True + break + sub_tokens.append(cur_substr) + start = end + + if is_bad: + output_tokens.append(self.unk_token) + else: + output_tokens.extend(sub_tokens) + return output_tokens + + +def _is_whitespace(char): + """Checks whether `chars` is a whitespace character.""" + # \t, \n, and \r are technically contorl characters but we treat them + # as whitespace since they are generally considered as such. + if char == " " or char == "\t" or char == "\n" or char == "\r": + return True + cat = unicodedata.category(char) + if cat == "Zs": + return True + return False + + +def _is_control(char): + """Checks whether `chars` is a control character.""" + # These are technically control characters but we count them as whitespace + # characters. + if char == "\t" or char == "\n" or char == "\r": + return False + cat = unicodedata.category(char) + if cat.startswith("C"): + return True + return False + + +def _is_punctuation(char): + """Checks whether `chars` is a punctuation character.""" + cp = ord(char) + # We treat all non-letter/number ASCII as punctuation. + # Characters such as "^", "$", and "`" are not in the Unicode + # Punctuation class but we treat them as punctuation anyways, for + # consistency. + if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or + (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): + return True + cat = unicodedata.category(char) + if cat.startswith("P"): + return True + return False diff --git a/requirements.txt b/requirements.txt index 9ad1f7a3..7b25a832 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,7 @@ -pytorch-lightning==1.2.2 -transformers==4.2.1 -torch==1.8.0 -tqdm==4.56.0 \ No newline at end of file +pytorch-transformers +pytorch_pretrained_bert +torch +numpy +tqdm +sklearn +keras \ No newline at end of file diff --git a/sample/poem_1.png b/sample/poem_1.png deleted file mode 100644 index 0859dccb..00000000 Binary files a/sample/poem_1.png and /dev/null differ diff --git a/sample/poem_2.png b/sample/poem_2.png deleted file mode 100644 index 123fbac4..00000000 Binary files a/sample/poem_2.png and /dev/null differ diff --git a/sample/tiyu.jpg b/sample/tiyu.jpg deleted file mode 100644 index 4ee46c11..00000000 Binary files a/sample/tiyu.jpg and /dev/null differ diff --git "a/sample/\345\276\213\350\257\227\347\273\235\345\217\245.png" 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+++ b/train.py @@ -1,235 +1,173 @@ -from transformers import GPT2LMHeadModel, GPT2Config -from transformers import AdamW, get_linear_schedule_with_warmup, BertTokenizer -from torch.utils.data import Dataset, DataLoader -from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor -import pytorch_lightning as pl +import pytorch_transformers import torch +import numpy as np +import os import json -import argparse - -# 11846807 - - -class DS(Dataset): - def __init__(self, lines, vocab_path="vocab/vocab.txt", max_length=1024): - self.data = lines - self.tok = BertTokenizer(vocab_file=vocab_path) - self.max_length = max_length - - def __len__(self): - return len(self.data) - - def __getitem__(self, index): - line = self.data[index] - line = self.tok.encode_plus( - line, - max_length=self.max_length, - truncation=True, - padding="max_length", - return_tensors="pt", - ) - return line - - -class Net(pl.LightningModule): - def __init__( - self, - batch_size, - epochs, - t_total=100000, - config_path="config/model_config.json", - data_path="data/train.json", - valid_examples=100, - vocab_path="vocab/vocab.txt", - max_length=1024, - warm_up_steps=0, - lr=1e-4, - ): - super(Net, self).__init__() - self.batch_size = batch_size - self.epochs = epochs - self.t_total = t_total - self.warm_up_steps = warm_up_steps - self.lr = lr - self.model_name = "bert_pretrained_model" - self.config = GPT2Config.from_json_file(config_path) - self.model = GPT2LMHeadModel(config=self.config) - self.data = [json.loads(line.strip()) for line in open(data_path)] - self.dataset_train = DS( - self.data[:-valid_examples], vocab_path=vocab_path, max_length=max_length - ) - self.dataset_valid = DS( - self.data[-valid_examples:], vocab_path=vocab_path, max_length=max_length - ) - - def forward(self, input_ids, attention_mask): - input_ids = input_ids - attention_mask = attention_mask - r = self.model( - input_ids=input_ids, - attention_mask=attention_mask, - labels=input_ids, - return_dict=True, - ) - return r["loss"] - - def train_dataloader(self): - return DataLoader( - self.dataset_train, - batch_size=self.batch_size, - num_workers=8, - shuffle=True, - drop_last=True, - ) - - def val_dataloader(self): - return DataLoader( - self.dataset_valid, - batch_size=self.batch_size, - num_workers=8, - shuffle=True, - drop_last=True, - ) - - def configure_optimizers(self): - optimizer = AdamW(self.parameters(), lr=self.lr, weight_decay=0.001) - scheduler = get_linear_schedule_with_warmup( - optimizer, self.warm_up_steps, self.t_total - ) - scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1} - return [optimizer], [scheduler] - - def training_step(self, batch, batch_nb): - loss = self.forward(batch["input_ids"], batch["attention_mask"]) - - self.log( - "train_loss", - loss, - on_step=True, - on_epoch=True, - prog_bar=True, - logger=True, - ) - return loss - - def validation_step(self, batch, batch_nb): - loss = self.forward(batch["input_ids"], batch["attention_mask"]) - return loss - - def validation_epoch_end(self, outputs): - avg_loss = torch.stack(outputs).mean() - self.log( - "val_loss", - avg_loss, - on_epoch=True, - prog_bar=True, - logger=True, - ) - return {"val_loss": avg_loss} - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - parser.add_argument( - "--device", default="0", type=str, required=False, help="设置使用哪些显卡，用逗号分割" - ) - parser.add_argument( - "--config_path", - default="config/model_config.json", - type=str, - required=False, - help="选择模型参数", - ) - parser.add_argument( - "--vocab_path", - default="vocab/vocab.txt", - type=str, - required=False, - help="选择词库", - ) - parser.add_argument( - "--data_path", - default="data/train.json", - type=str, - required=False, - help="原始训练语料", - ) - parser.add_argument("--epochs", default=5, type=int, required=False, help="训练循环") - parser.add_argument( - "--batch_size", default=8, type=int, required=False, help="训练batch size" - ) - parser.add_argument("--lr", default=1.5e-4, type=float, required=False, help="学习率") - parser.add_argument( - "--warmup_steps", default=2000, type=int, required=False, help="warm up步数" - ) - parser.add_argument( - "--max_length", default=1024, type=int, required=False, help="单条文本最长长度" - ) - parser.add_argument( - "--eval_interval", default=100, type=int, required=False, help="eval 步数" - ) - parser.add_argument( - "--val_examples", default=100, type=int, required=False, help="选择多少验证集样本" - ) - parser.add_argument( - "--t_total", default=100000, type=int, required=False, help="计划训练多少步" - ) - parser.add_argument( - "--log_step", default=1, type=int, required=False, help="多少步汇报一次loss" - ) - parser.add_argument( - "--output_dir", default="model/", type=str, required=False, help="模型输出路径" - ) - args = parser.parse_args() - - val_examples = args.val_examples - vocab_path = args.vocab_path - max_length = args.max_length - batch_size = args.batch_size - epochs = args.epochs - output_path = args.output_dir - eval_interval = args.eval_interval - lr = args.lr - warmup_steps = args.warmup_steps - data_path = args.data_path - config_path = args.config_path - t_total = args.t_total - - checkpoint_callback = ModelCheckpoint( - dirpath=output_path, - verbose=True, - period=1, - save_top_k=1, - monitor="val_loss", - mode="min", - ) - learning_rate_callback = LearningRateMonitor() - trainer = pl.Trainer( - default_root_dir=output_path, - gradient_clip_val=1, - max_epochs=epochs, - gpus=args.device, - distributed_backend="dp", - val_check_interval=eval_interval, - callbacks=[learning_rate_callback, checkpoint_callback], - precision=32, - ) - net = Net( - batch_size, - epochs, - t_total=t_total, - config_path=config_path, - data_path=data_path, - valid_examples=val_examples, - vocab_path=vocab_path, - max_length=max_length, - warm_up_steps=warmup_steps, - lr=lr, - ) - # d = torch.load('output_old/best.ckpt', map_location=torch.device("cpu"))["state_dict"] - # d.pop('model.classifier.bias') - # d.pop('model.classifier.weight') - - # net.load_state_dict(d, strict=False) - trainer.fit(net) +import random +from my_chinese_tokenizer import tokenization_bert +from datetime import datetime +from tqdm import tqdm +from torch.nn import DataParallel + +os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3" # 此处设置程序使用哪些显卡 +model_config = pytorch_transformers.modeling_gpt2.GPT2Config.from_json_file('config/model_config_small.json') +n_ctx = model_config.n_ctx +full_tokenizer = tokenization_bert.BertTokenizer(vocab_file='cache/vocab_small.txt') +full_tokenizer.max_len = n_ctx +device = 'cuda' if torch.cuda.is_available() else 'cpu' +print('using device:', device) + +raw_data_path = 'data/train.txt' +tokenized_data_path = 'data/tokenized/' +raw = True # 选择是否从零开始构建数据集 +epochs = 5 +batch_size = 12 +lr = 1.5e-4 +warmup_steps = 2000 +log_step = 1 +stride = 768 +gradient_accumulation = 1 +fp16 = False # 不支持半精度的显卡请勿打开 +fp16_opt_level = 'O1' +max_grad_norm = 1.0 +num_pieces = 100 + + +def build_files(data_path=raw_data_path): + if not os.path.exists(tokenized_data_path): + os.mkdir(tokenized_data_path) + with open(data_path, 'r', encoding='utf8') as f: + print('reading lines') + lines = json.load(f) + lines = [line.replace('\n', ' [SEP] ') for line in lines] # 用[SEP]表示换行 + all_len = len(lines) + for i in tqdm(range(num_pieces)): + sublines = lines[all_len // num_pieces * i: all_len // num_pieces * (i + 1)] + sublines = [full_tokenizer.tokenize(line) for line in sublines if len(line) > 128] # 只考虑长度超过128的句子 + sublines = [full_tokenizer.convert_tokens_to_ids(line) for line in sublines] + full_line = [] + for subline in sublines: + full_line.append(103) # 103是MASK，表示文章开始 + full_line.extend(subline) + full_line.append(101) # 101是CLS，文章之间添加CLS表示文章结束, 段落之间使用SEP表示段落结束 + with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i), 'w') as f: + for id in full_line: + f.write(str(id) + ' ') + print('finish') + + +def main(): + if raw: + print('building files') + build_files(data_path=raw_data_path) + print('files built') + + model = pytorch_transformers.modeling_gpt2.GPT2LMHeadModel(config=model_config) + model.to(device) + multi_gpu = False + full_line = '' + print('calculating total steps') + for i in tqdm(range(num_pieces)): + with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i), 'r') as f: + full_line += f.read() + full_line = full_line.strip() + full_line = [int(item) for item in full_line.split()] + len_full_line = len(full_line) + samples = [] + start_point = 0 + while start_point + n_ctx < len_full_line: + samples.append(full_line[start_point:start_point+n_ctx]) + start_point += stride + total_steps = int(len(samples) * epochs / batch_size / gradient_accumulation) + print('total steps = {}'.format(total_steps)) + optimizer = pytorch_transformers.AdamW(model.parameters(), lr=lr, correct_bias=True) + scheduler = pytorch_transformers.WarmupLinearSchedule(optimizer, warmup_steps=warmup_steps, + t_total=total_steps) + if fp16: + try: + from apex import amp + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") + model, optimizer = amp.initialize(model, optimizer, opt_level=fp16_opt_level) + + if torch.cuda.device_count() > 1: + print("Let's use", torch.cuda.device_count(), "GPUs!") + model = DataParallel(model) + multi_gpu = True + print('starting training') + for epoch in range(epochs): + print('epoch {}'.format(epoch + 1)) + now = datetime.now() + print('time: {}'.format(now)) + running_loss = 0 + random.shuffle(samples) + for step in range(len(samples) // batch_size): + + # prepare data + batch = samples[step * batch_size: (step + 1) * batch_size] + batch_labels = [] + batch_inputs = [] + for ids in batch: + int_ids_for_labels = [int(x) for x in ids] + int_ids_for_inputs = [int(x) for x in ids] + batch_labels.append(int_ids_for_labels) + batch_inputs.append(int_ids_for_inputs) + batch_labels = torch.tensor(batch_labels).long().to(device) + batch_inputs = torch.tensor(batch_inputs).long().to(device) + + # forward pass + outputs = model.forward(input_ids=batch_inputs, labels=batch_labels) + loss, logits = outputs[:2] + + # get loss + if multi_gpu: + loss = loss.mean() + if gradient_accumulation > 1: + loss = loss / gradient_accumulation + + # loss backward + if fp16: + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() + torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), max_grad_norm) + else: + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm) + + # optimizer step + if (step + 1) % gradient_accumulation == 0: + running_loss += loss.item() + scheduler.step() + optimizer.step() + optimizer.zero_grad() + if (step + 1) % log_step == 0: + print('step {} of epoch {}, loss {}'.format( + (step + 1) // gradient_accumulation, + epoch + 1, + running_loss * gradient_accumulation**2 / log_step)) + running_loss = 0 + + print('saving model for epoch {}'.format(epoch + 1)) + if not os.path.exists('./model/model_epoch{}'.format(epoch + 1)): + os.mkdir('./model/model_epoch{}'.format(epoch + 1)) + model_to_save = model.module if hasattr(model, 'module') else model + model_to_save.save_pretrained('./model/model_epoch{}'.format(epoch + 1)) + torch.save(scheduler.state_dict(), './model/model_epoch{}/scheduler.pt'.format(epoch + 1)) + torch.save(optimizer.state_dict(), './model/model_epoch{}/optimizer.pt'.format(epoch + 1)) + print('epoch {} finished'.format(epoch + 1)) + + then = datetime.now() + print('time: {}'.format(then)) + print('time for one epoch: {}'.format(then - now)) + + print('training finished') + if not os.path.exists('./model/final_model'): + os.mkdir('./model/final_model') + model_to_save = model.module if hasattr(model, 'module') else model + model_to_save.save_pretrained('./model/final_model') + torch.save(scheduler.state_dict(), './model/final_model/scheduler.pt') + torch.save(optimizer.state_dict(), './model/final_model/optimizer.pt') + + +if __name__ == '__main__': + main() diff --git a/train.sh b/train.sh deleted file mode 100644 index 62ac6237..00000000 --- a/train.sh +++ /dev/null @@ -1 +0,0 @@ -python3 train.py --data_path data/train.json \ No newline at end of file diff --git a/train_doupo.py b/train_doupo.py new file mode 100644 index 00000000..77fd5dfb --- /dev/null +++ b/train_doupo.py @@ -0,0 +1,175 @@ +import pytorch_transformers +import torch +import numpy as np +import os +import json +import random +from my_chinese_tokenizer import tokenization_bert +from datetime import datetime +from tqdm import tqdm +from torch.nn import DataParallel + +os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3" # 此处设置程序使用哪些显卡 +model_config = pytorch_transformers.modeling_gpt2.GPT2Config.from_json_file('config/model_config_small.json') +n_ctx = model_config.n_ctx +full_tokenizer = tokenization_bert.BertTokenizer(vocab_file='cache/vocab_small.txt') +full_tokenizer.max_len = n_ctx +device = 'cuda' if torch.cuda.is_available() else 'cpu' +print('using device:', device) + +raw_data_path = 'data/train_doupo.txt' +tokenized_data_path = 'data/tokenized_doupo/' +raw = True # 选择是否从零开始构建数据集 +epochs = 30 +batch_size = 8 +lr = 1.5e-4 +warmup_steps = 200 +log_step = 1 +stride = 768 +gradient_accumulation = 1 +fp16 = False # 不支持半精度的显卡请勿打开 +fp16_opt_level = 'O1' +max_grad_norm = 1.0 +num_pieces = 100 + + +def build_files(data_path=raw_data_path): + if not os.path.exists(tokenized_data_path): + os.mkdir(tokenized_data_path) + with open(data_path, 'r', encoding='utf8') as f: + print('reading lines') + lines = json.load(f) + lines = [line.replace('\n', ' [SEP] ') for line in lines] # 用[SEP]表示换行 + doupo = ''.join(lines) + len_doupo = len(doupo) + doupo_ids = [] + for i in range(100): + doupo_ids.extend( + full_tokenizer.convert_tokens_to_ids( + full_tokenizer.tokenize(doupo[len_doupo // 100 * i: len_doupo // 100 * (i + 1)]))) + with open(tokenized_data_path + 'tokenized_train.txt', 'w') as f: + for id in doupo_ids[:-1]: + f.write(str(id) + ' ') + f.write(str(doupo_ids[-1])) + f.write('\n') + + print('finish') + + +def main(): + if raw: + print('building files') + build_files(data_path=raw_data_path) + print('files built') + exit(1) + + model = pytorch_transformers.modeling_gpt2.GPT2LMHeadModel(config=model_config) + model.to(device) + multi_gpu = False + print('calculating total steps') + + + with open(tokenized_data_path + 'tokenized_train.txt', 'r') as f: + all_text = f.read().split() + all_ids = [int(token) for token in all_text] + len_all_ids = len(all_ids) + num_chunks = len_all_ids // stride + samples = [] + start_point = 0 + while start_point + n_ctx < len_all_ids: + samples.append(all_ids[start_point:start_point+n_ctx]) + start_point += stride + + total_steps = int(num_chunks * epochs / batch_size / gradient_accumulation) + print('total steps = {}'.format(total_steps)) + optimizer = pytorch_transformers.AdamW(model.parameters(), lr=lr, correct_bias=True) + scheduler = pytorch_transformers.WarmupLinearSchedule(optimizer, warmup_steps=warmup_steps, + t_total=total_steps) + if fp16: + try: + from apex import amp + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") + model, optimizer = amp.initialize(model, optimizer, opt_level=fp16_opt_level) + + if torch.cuda.device_count() > 1: + print("Let's use", torch.cuda.device_count(), "GPUs!") + model = DataParallel(model) + multi_gpu = True + print('starting training') + for epoch in range(epochs): + print('epoch {}'.format(epoch + 1)) + now = datetime.now() + print('time: {}'.format(now)) + + running_loss = 0 + random.shuffle(samples) + for step in range(len(samples) // batch_size): + # prepare data + batch = samples[step * batch_size: (step + 1) * batch_size] + batch_labels = [] + batch_inputs = [] + for ids in batch: + int_ids_for_labels = [int(x) for x in ids] + int_ids_for_inputs = [int(x) for x in ids] + batch_labels.append(int_ids_for_labels) + batch_inputs.append(int_ids_for_inputs) + batch_labels = torch.tensor(batch_labels).long().to(device) + batch_inputs = torch.tensor(batch_inputs).long().to(device) + + # forward pass + outputs = model.forward(input_ids=batch_inputs, labels=batch_labels) + loss, logits = outputs[:2] + + # get loss + if multi_gpu: + loss = loss.mean() + if gradient_accumulation > 1: + loss = loss / gradient_accumulation + + # loss backward + if fp16: + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() + torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), max_grad_norm) + else: + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm) + + # optimizer step + if (step + 1) % gradient_accumulation == 0: + running_loss += loss.item() + scheduler.step() + optimizer.step() + optimizer.zero_grad() + if (step + 1) % log_step == 0: + print('step {} of epoch {}, loss {}'.format( + (step + 1) // gradient_accumulation, + epoch + 1, + running_loss * gradient_accumulation ** 2 / log_step)) + running_loss = 0 + + print('saving model for epoch {}'.format(epoch + 1)) + if not os.path.exists('./model/model_epoch{}'.format(epoch + 1)): + os.mkdir('./model/model_epoch{}'.format(epoch + 1)) + model_to_save = model.module if hasattr(model, 'module') else model + model_to_save.save_pretrained('./model/model_epoch{}'.format(epoch + 1)) + torch.save(scheduler.state_dict(), './model/model_epoch{}/scheduler.pt'.format(epoch + 1)) + torch.save(optimizer.state_dict(), './model/model_epoch{}/optimizer.pt'.format(epoch + 1)) + print('epoch {} finished'.format(epoch + 1)) + + then = datetime.now() + print('time: {}'.format(then)) + print('time for one epoch: {}'.format(then - now)) + + print('training finished') + if not os.path.exists('./model/final_model'): + os.mkdir('./model/final_model') + model_to_save = model.module if hasattr(model, 'module') else model + model_to_save.save_pretrained('./model/final_model') + torch.save(scheduler.state_dict(), './model/final_model/scheduler.pt') + torch.save(optimizer.state_dict(), './model/final_model/optimizer.pt') + + +if __name__ == '__main__': + main()