File size: 9,531 Bytes
c790c1b a74565b c790c1b bf21b18 c790c1b 1f88281 c790c1b d611421 c790c1b d611421 c790c1b 3f0e9ed c790c1b d611421 c790c1b c485313 65d1b6b c790c1b c485313 65d1b6b c790c1b c485313 65d1b6b c790c1b c485313 44afa7b c485313 65d1b6b c790c1b c485313 bf21b18 c790c1b d611421 f905163 d611421 f905163 51f54f9 d611421 f905163 d611421 f905163 51f54f9 d611421 f905163 4dee18f d611421 c790c1b 459381f c790c1b d611421 |
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 |
---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "米饭是一种用稻米与水煮成的食物"
---
# Chinese GPT2 Models
## Model description
The set of GPT2 models, except for GPT2-xlarge model, are pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). The GPT2-xlarge model is pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework. Besides, the other models could also be pre-trained by TencentPretrain.
The model is used to generate Chinese texts. You can download the set of Chinese GPT2 models either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the links below:
| | Link |
| ----------------- | :----------------------------: |
| **GPT2-distil** | [**L=6/H=768**][distil] |
| **GPT2** | [**L=12/H=768**][base] |
| **GPT2-medium** | [**L=24/H=1024**][medium] |
| **GPT2-large** | [**L=36/H=1280**][large] |
| **GPT2-xlarge** | [**L=48/H=1600**][xlarge] |
Note that the 6-layer model is called GPT2-distil model because it follows the configuration of [distilgpt2](https://huggingface.co/distilgpt2), and the pre-training does not involve the supervision of larger models.
## How to use
You can use the model directly with a pipeline for text generation (take the case of GPT2-distil):
```python
>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-distil-chinese-cluecorpussmall")
>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-distil-chinese-cluecorpussmall")
>>> text_generator = TextGenerationPipeline(model, tokenizer)
>>> text_generator("这是很久之前的事情了", max_length=100, do_sample=True)
[{'generated_text': '这是很久之前的事情了 。 我 现 在 想 起 来 就 让 自 己 很 伤 心 ， 很 失 望 。 我 现 在 想 到 ， 我 觉 得 大 多 数 人 的 生 活 比 我 的 生 命 还 要 重 要 ， 对 一 些 事 情 的 看 法 ， 对 一 些 人 的 看 法 ， 都 是 在 发 泄 。 但 是 ， 我 们 的 生 活 是 需 要 一 个 信 用 体 系 的 。 我 不 知'}]
```
## Training data
[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data.
## Training procedure
The GPT2-xlarge model is pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain), and the others are pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 1024.
For the models pre-trained by UER-py, take the case of GPT2-distil
Stage1:
```
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
--vocab_path models/google_zh_vocab.txt \
--dataset_path cluecorpussmall_lm_seq128_dataset.pt \
--seq_length 128 --processes_num 32 --data_processor lm
```
```
python3 pretrain.py --dataset_path cluecorpussmall_lm_seq128_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--config_path models/gpt2/distil_config.json \
--output_model_path models/cluecorpussmall_gpt2_distil_seq128_model.bin \
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
--total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
--learning_rate 1e-4 --batch_size 64
```
Stage2:
```
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
--vocab_path models/google_zh_vocab.txt \
--dataset_path cluecorpussmall_lm_seq1024_dataset.pt \
--seq_length 1024 --processes_num 32 --data_processor lm
```
```
python3 pretrain.py --dataset_path cluecorpussmall_lm_seq1024_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--pretrained_model_path models/cluecorpussmall_gpt2_distil_seq128_model.bin-1000000 \
--config_path models/gpt2/distil_config.json \
--output_model_path models/cluecorpussmall_gpt2_distil_seq1024_model.bin \
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
--total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \
--learning_rate 5e-5 --batch_size 16
```
Finally, we convert the pre-trained model into Huggingface's format:
```
python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path cluecorpussmall_gpt2_distil_seq1024_model.bin-250000 \
--output_model_path pytorch_model.bin \
--layers_num 6
```
For GPT2-xlarge model, we use TencetPretrain.
Stage1:
```
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
--vocab_path models/google_zh_vocab.txt \
--dataset_path cluecorpussmall_lm_seq128_dataset.pt \
--seq_length 128 --processes_num 32 --data_processor lm
```
```
deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_config.json \
--dataset_path corpora/cluecorpussmall_lm_seq128_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--config_path models/gpt2/xlarge_config.json \
--output_model_path models/cluecorpussmall_gpt2_xlarge_seq128_model \
--world_size 8 --batch_size 64 \
--total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
--deepspeed_checkpoint_activations --deepspeed_checkpoint_layers_num 24
```
Before stage2, we extract fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints:
```
python3 models/cluecorpussmall_gpt2_xlarge_seq128_model/zero_to_fp32.py models/cluecorpussmall_gpt2_xlarge_seq128_model/ \
models/cluecorpussmall_gpt2_xlarge_seq128_model.bin
```
Stage2:
```
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
--vocab_path models/google_zh_vocab.txt \
--dataset_path cluecorpussmall_lm_seq1024_dataset.pt \
--seq_length 1024 --processes_num 32 --data_processor lm
```
```
deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_config.json \
--dataset_path corpora/cluecorpussmall_lm_seq1024_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--config_path models/gpt2/xlarge_config.json \
--pretrained_model_path models/cluecorpussmall_gpt2_xlarge_seq128_model.bin \
--output_model_path models/cluecorpussmall_gpt2_xlarge_seq1024_model \
--world_size 8 --batch_size 16 --learning_rate 5e-5 \
--total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \
--deepspeed_checkpoint_activations --deepspeed_checkpoint_layers_num 6
```
Then, we extract fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints:
```
python3 models/cluecorpussmall_gpt2_xlarge_seq1024_model/zero_to_fp32.py models/cluecorpussmall_gpt2_xlarge_seq1024_model/ \
models/cluecorpussmall_gpt2_xlarge_seq1024_model.bin
```
Finally, we convert the pre-trained model into Huggingface's format:
```
python3 scripts/convert_gpt2_from_tencentpretrain_to_huggingface.py --input_model_path models/cluecorpussmall_gpt2_xlarge_seq1024_model.bin \
--output_model_path pytorch_model.bin \
--layers_num 48
```
### BibTeX entry and citation info
```
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
@article{zhao2019uer,
title={UER: An Open-Source Toolkit for Pre-training Models},
author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
journal={EMNLP-IJCNLP 2019},
pages={241},
year={2019}
}
@article{zhao2023tencentpretrain,
title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities},
author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others},
journal={ACL 2023},
pages={217},
year={2023}
```
[distil]:https://huggingface.co/uer/gpt2-distil-chinese-cluecorpussmall
[base]:https://huggingface.co/uer/gpt2-chinese-cluecorpussmall
[medium]:https://huggingface.co/uer/gpt2-medium-chinese-cluecorpussmall
[large]:https://huggingface.co/uer/gpt2-large-chinese-cluecorpussmall
[xlarge]:https://huggingface.co/uer/gpt2-xlarge-chinese-cluecorpussmall |