Update README.md

README.md

@@ -8,20 +8,32 @@ widget:
 ---
 
 
-# Chinese GPT2-medium Model
+# Chinese GPT2-distil Model
 
 ## Model description
 
-The model is used to generate Chinese texts. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gpt2-medium-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-medium-chinese-cluecorpussmall). 
+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:
+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-medium-chinese-cluecorpussmall")
->>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-medium-chinese-cluecorpussmall")
+>>> 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'}]
@@ -33,7 +45,9 @@ You can use the model directly with a pipeline for text generation:
 
 ## Training procedure
 
-The model is 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. 
+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:
 
@@ -44,14 +58,71 @@ python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
                       --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/medium_config.json \
-                      --output_model_path models/cluecorpussmall_gpt2_medium_seq128.bin \
+                      --config_path models/gpt2/xlarge_config.json \
+                      --output_model_path models/cluecorpussmall_gpt2_xlarge_seq128 \
                       --world_size 8 --batch_size 64 \
-                      --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000
+                      --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/zero_to_fp32.py models/cluecorpussmall_gpt2_xlarge_seq128/ \
+                                                                  models/cluecorpussmall_gpt2_xlarge_seq128.bin
 ```
 
 Stage2:
@@ -60,28 +131,34 @@ 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 
+                      --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/medium_config.json \
-                      --pretrained_model_path models/cluecorpussmall_gpt2_medium_seq128_pt.bin \
-                      --output_model_path models/cluecorpussmall_gpt2_medium_seq1024_stage2 \
+                      --config_path models/gpt2/xlarge_config.json \
+                      --pretrained_model_path models/cluecorpussmall_gpt2_xlarge_seq128.bin \
+                      --output_model_path models/cluecorpussmall_gpt2_xlarge_seq1024_stage2 \
                       --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 12
+                      --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_stage2/zero_to_fp32.py models/cluecorpussmall_gpt2_xlarge_seq1024_stage2/ \
+                                                                          models/cluecorpussmall_gpt2_xlarge_seq1024_stage2.bin
 ```
 
 Finally, we convert the pre-trained model into Huggingface's format:
 
 ```
-python3 models/cluecorpussmall_gpt2_medium_seq1024_stage2/zero_to_fp32.py models/cluecorpussmall_gpt2_medium_seq1024_stage2 cluecorpussmall_gpt2_medium_seq1024_model.bin
-python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path cluecorpussmall_gpt2_medium_seq1024_model.bin \
-                                                        --output_model_path pytorch_model.bin \
-                                                        --layers_num 24
+python3 scripts/convert_gpt2_from_tencentpretrain_to_huggingface.py --input_model_path models/cluecorpussmall_gpt2_xlarge_seq1024_stage2.bin \
+                                                                    --output_model_path pytorch_model.bin \
+                                                                    --layers_num 48
 ```
 
 ### BibTeX entry and citation info
@@ -100,4 +177,17 @@ python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path cluec
   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