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115 lines
6.9 KiB
115 lines
6.9 KiB
# ChatGLM-6B-PT
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This repository implements tuning of the ChatGLM-6B model based on [P-Tuning v2](https://github.com/THUDM/P-tuning-v2). P-Tuning v2 reduces the amount of parameters that need to be optimized to 0.1% of the full fine-tuning, and then through model quantization, Gradient Checkpoint and other methods, it only needs a minimum of 7GB of video memory to run.
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The following uses the [ADGEN](https://aclanthology.org/D19-1321.pdf) (advertising generation) dataset as an example to introduce how to use the code.
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## Software dependencies
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Running p-tuning requires version 4.27.1 of `transformers`. In addition to the dependencies of ChatGLM-6B, the following dependencies are required
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```
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pip install rouge_chinese nltk jieba datasets
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```
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## Instructions
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### Download the dataset
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The task of the ADGEN dataset is to generate an advertisement word (summary) based on the input (content).
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```json
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{
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"content": "类型#上衣*版型#宽松*版型#显瘦*图案#线条*衣样式#衬衫*衣袖型#泡泡袖*衣款式#抽绳",
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"summary": "这件衬衫的款式非常的宽松，利落的线条可以很好的隐藏身材上的小缺点，穿在身上有着很好的显瘦效果。领口装饰了一个可爱的抽绳，漂亮的绳结展现出了十足的个性，配合时尚的泡泡袖型，尽显女性甜美可爱的气息。"
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}
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```
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From [Google Drive](https://drive.google.com/file/d/13_vf0xRTQsyneRKdD1bZIr93vBGOczrk/view?usp=sharing) or [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/b3f119a008264b1cabd1/?dl=1) Download the processed ADGEN dataset, and put the decompressed `AdvertiseGen` directory into this directory.
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### Training
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Run the following commands for training:
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```shell
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bash train.sh
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```
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`PRE_SEQ_LEN` and `LR` in `train.sh` are soft prompt length and training learning rate respectively, which can be adjusted to achieve the best results. The P-Tuning-v2 method will freeze all model parameters, and the quantization level of the original model can be adjusted by adjusting `quantization_bit`. If this option is not added, it will be loaded with FP16 precision.
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Under the default configuration of `per_device_train_batch_size=1`, `gradient_accumulation_steps=16`, the model parameters of INT4 are frozen, and a training iteration will perform 16 cumulative forward and backward propagations with a batch size of 1, which is equivalent to the total batch size of 16, and only 6.7G GPU memory is required at this time with `quantization_bit=4`. If you want to improve the training efficiency under the same batch size, you can increase the value of `per_device_train_batch_size` while keeping the product of the two unchanged, but it will also bring more GPU memory consumption, please adjust it according to the actual situation.
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### Inference
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Change `CHECKPOINT` in `evaluate.sh` to the checkpoint name saved during training, and run the following commands for model inference and evaluation:
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```shell
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bash evaluate.sh
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```
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The evaluation indicators are Chinese Rouge score and BLEU-4. The generated results are saved in
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`./output/adgen-chatglm-6b-pt-8-1e-2/generated_predictions.txt`.
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### Example
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#### Example 1
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* Input: 类型#上衣\*材质#牛仔布\*颜色#白色\*风格#简约\*图案#刺绣\*衣样式#外套\*衣款式#破洞
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* Label: 简约而不简单的牛仔外套,白色的衣身十分百搭。衣身多处有做旧破洞设计,打破单调乏味,增加一丝造型看点。衣身后背处有趣味刺绣装饰,丰富层次感,彰显别样时尚。
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* Output[微调前]: 这件上衣的材质是牛仔布,颜色是白色,风格是简约,图案是刺绣,衣样式是外套,衣款式是破洞。
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* Output[微调后]: 这是一款简约的牛仔外套,破洞设计,将牛仔布破洞,带来一种随意与个性。破洞的牛仔外套,展现出时尚气息,带来一种休闲感。同时,刺绣图案,让整件外套更加立体。
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#### Example 2
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* Input: 类型#裙\*版型#显瘦\*风格#文艺\*风格#简约\*图案#印花\*图案#撞色\*裙下摆#压褶\*裙长#连衣裙\*裙领型#圆领
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* Label: 文艺个性的印花连衣裙,藏青色底蕴,低调又大气,撞色太阳花分布整个裙身,绚丽而美好,带来时尚减龄的气质。基础款的舒适圆领,简约不失大方,勾勒精致脸庞。领后是一粒包布扣固定,穿脱十分方便。前片立体的打褶设计,搭配后片压褶的做工,增添层次和空间感,显瘦又有型。
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* Output[微调前]: 类型#裙*版型#显瘦*风格#文艺*风格#简约*图案#印花*图案#撞色*裙下摆#压褶*裙长#连衣裙*裙领型#圆领 1\. 连衣裙:简约风格,裙长为膝盖以上,裙领型为圆领。2\. 裙下摆:压褶设计,使裙摆呈现出流畅的褶皱效果。3\. 裙领型:裙领型为圆领,使穿上连衣裙后更加有型。4\. 版型:采用显瘦设计,让连衣裙看起来更加苗条。5\. 风格:文艺风格,让连衣裙更加有内涵和品味。6\. 图案:印花设计,在连衣裙上印有独特的图案。7\. 撞色:采用撞色设计,让连衣裙在色彩上更加鲜明、富有层次感。
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* Output[微调后]: 这是一款文艺范的连衣裙,以印花为元素,采用简约的印花,既能够突出文艺气质,又能够展现简约风。在印花的同时又有领子和裙摆的压褶设计,更加凸显文艺气质。简约而不会过于单调,搭配出街,穿着十分舒适。
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### evaluation result
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| | P-tuning v2 | LoRA |
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| ------- | ----------- | ----- |
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| BLEU-4 | 7.71 | 6.13 |
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| Rouge-1 | 31.35 | 28.36 |
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| Rouge-2 | 7.19 | 4.38 |
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| Rouge-l | 25.17 | 17.54 |
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#### Experiment Settings
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```
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max_source_length=64
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max_target_length=64
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per_device_train_batch_size=1
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gradient_accumulation_steps=16
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max_steps=3000
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```
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##### P-tuning v2
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```
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pre_seq_len=128
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learning_rate=2e-2
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quantization_bit=4
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```
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##### LoRA
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```
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learning_rate=5e-4
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```
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The implementation uses [simple_thu_chatglm6b](https://github.com/yuanzhoulvpi2017/zero_nlp/tree/main/simple_thu_chatglm6b)
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## Model Deployment
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Replace `THUDM/chatglm-6b` in the corresponding demo or code with the path of the checkpoint after P-Tuning(in the example, `./output/adgen-chatglm-6b-pt-8-1e-2/ checkpoint-3000`). Note that the current fine-tuning does not support multiple rounds of data, so only the responses from the first round of the conversation are fine-tuned.
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## Use your own dataset
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Modify `train_file`, `validation_file` and `test_file` in `train.sh` and `evaluate.sh` to your own JSON format dataset paths, and change `prompt_column` and `response_column` to the keys in the JSON file corresponding to input text and output text.
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## TODO
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* [ ] Support for chat data
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* [ ] Support for full finetuning
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## quoting
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```
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@inproceedings{liu2022p,
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title={P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks},
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author={Liu, Xiao and Ji, Kaixuan and Fu, Yicheng and Tam, Weng and Du, Zhengxiao and Yang, Zhilin and Tang, Jie},
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booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
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pages={61--68},
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year={2022}
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}
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``` |