193 lines
9.3 KiB
Markdown
193 lines
9.3 KiB
Markdown
|
---
|
|||
|
|
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 models/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
|