Update README.md

README.md

@@ -8,11 +8,11 @@ widget:
 ---
 
 
-# Chinese GPT2-distil Model
+# Chinese GPT2-medium 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-distil-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-distil-chinese-cluecorpussmall). The model is called GPT2-distil because the configuration of model follows [distilgpt2](https://huggingface.co/distilgpt2), which has 6 layers, 768 dimension, and 12 heads. The pre-training does not involve the supervision of larger models.
+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). 
 
 ## How to use
 
@@ -20,8 +20,8 @@ You can use the model directly with a pipeline for text generation:
 
 ```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")
+>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-medium-chinese-cluecorpussmall")
+>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-medium-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'}]
@@ -45,13 +45,13 @@ python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
 ```
 
 ```
-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
+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 \
+                      --world_size 8 --batch_size 64 \
+                      --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000
 ```
 
 Stage2:
@@ -64,22 +64,24 @@ python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
 ```
 
 ```
-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
+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 \
+                      --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
 ```
 
 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 \
+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 6
+                                                        --layers_num 24
 ```
 
 ### BibTeX entry and citation info