Release v0.1.6

Former-commit-id: 43c8b3c3c8bfb2e32d17fb3e8b194938e37d54bd

README.md

@@ -8,17 +8,27 @@
 
 👋 Join our [WeChat](assets/wechat.jpg).
 
+\[ English | [中文](README_zh.md) \]
+
 ## Changelog
 
-[23/07/19] Now we support training the **LLaMA-2** models in this repo. Try `--model_name_or_path meta-llama/Llama-2-7b-hf` argument to use the LLaMA-2 model. Remember to use `--prompt_template llama2` argument when you are using the LLaMA-2-chat model.
+[23/08/11] Now we support **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [this example](#dpo-training) to train your models (experimental feature).
+
+[23/08/03] Now we support training the **Qwen-7B** model in this repo. Try `--model_name_or_path Qwen/Qwen-7B-Chat` and `--lora_target c_attn` arguments to train the Qwen-7B model. Remember to use `--template chatml` argument when you are using the Qwen-7B-Chat model.
+
+[23/07/31] Now we support dataset streaming. Try `--streaming` and `--max_steps 100` arguments to stream your dataset.
+
+[23/07/29] We release two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos ([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/baichuan-13b-sft)) for details.
+
+[23/07/19] Now we support training the **LLaMA-2** models in this repo. Try `--model_name_or_path meta-llama/Llama-2-7b-hf` argument to use the LLaMA-2 model. Remember to use `--template llama2` argument when you are using the LLaMA-2-chat model.
 
 [23/07/18] Now we develop an all-in-one Web UI for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development.
 
-[23/07/11] Now we support training the **Baichuan-13B** model in this repo. Please replace the Baichuan-13B model file with `tests/modeling_baichuan.py` and try `--model_name_or_path path_to_baichuan_model` and `--lora_target W_pack` arguments to train the Baichuan-13B model. Remember to use `--prompt_template baichuan` argument when you are using the Baichuan-13B-Chat model.
+[23/07/11] Now we support training the **Baichuan-13B** model in this repo. Try `--model_name_or_path baichuan-inc/Baichuan-13B-Base` and `--lora_target W_pack` arguments to train the Baichuan-13B model. Remember to use `--template baichuan` argument when you are using the Baichuan-13B-Chat model.
 
 [23/07/09] Now we release [FastEdit](https://github.com/hiyouga/FastEdit)⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested.
 
-[23/07/07] Now we support training the **InternLM-7B** model in this repo. Try `--model_name_or_path internlm/internlm-7b` argument to use the InternLM model. Remember to use `--prompt_template intern` argument when you are using the InternLM-chat model.
+[23/07/07] Now we support training the **InternLM-7B** model in this repo. Try `--model_name_or_path internlm/internlm-7b` argument to use the InternLM model. Remember to use `--template intern` argument when you are using the InternLM-chat model.
 
 [23/07/05] Now we support training the **Falcon-7B/40B** models in this repo. Try `--model_name_or_path tiiuae/falcon-7b` and `--lora_target query_key_value` arguments to use the Falcon model.
 
@@ -28,39 +38,46 @@
 
 [23/06/15] Now we support training the **Baichuan-7B** model in this repo. Try `--model_name_or_path baichuan-inc/Baichuan-7B` and `--lora_target W_pack` arguments to use the Baichuan-7B model.
 
-[23/06/03] Now we support quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). Try `--quantization_bit 4/8` argument to work with quantized model. (experimental feature)
+[23/06/03] Now we support quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). Try `--quantization_bit 4/8` argument to work with quantized models.
 
 [23/05/31] Now we support training the **BLOOM & BLOOMZ** models in this repo. Try `--model_name_or_path bigscience/bloomz-7b1-mt` and `--lora_target query_key_value` arguments to use the BLOOMZ model.
 
 ## Supported Models
 
-- [LLaMA](https://github.com/facebookresearch/llama) (7B/13B/33B/65B)
-- [LLaMA-2](https://huggingface.co/meta-llama) (7B/13B/70B)
-- [BLOOM](https://huggingface.co/bigscience/bloom) & [BLOOMZ](https://huggingface.co/bigscience/bloomz) (560M/1.1B/1.7B/3B/7.1B/176B)
-- [Falcon](https://huggingface.co/tiiuae/falcon-7b) (7B/40B)
-- [Baichuan](https://huggingface.co/baichuan-inc/baichuan-7B) (7B/13B)
-- [InternLM](https://github.com/InternLM/InternLM) (7B)
+| Model                                                    | Model size                  | Default module    | Template |
+| -------------------------------------------------------- | --------------------------- | ----------------- |----------|
+| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B              | q_proj,v_proj     | -        |
+| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                  | q_proj,v_proj     | llama2   |
+| [BLOOM](https://huggingface.co/bigscience/bloom)         | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -        |
+| [BLOOMZ](https://huggingface.co/bigscience/bloomz)       | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -        |
+| [Falcon](https://huggingface.co/tiiuae/falcon-7b)        | 7B/40B                      | query_key_value   | -        |
+| [Baichuan](https://github.com/baichuan-inc/baichuan-13B) | 7B/13B                      | W_pack            | baichuan |
+| [InternLM](https://github.com/InternLM/InternLM)         | 7B                          | q_proj,v_proj     | intern   |
+| [Qwen](https://github.com/QwenLM/Qwen-7B)                | 7B                          | c_attn            | chatml   |
+| [XVERSE](https://github.com/xverse-ai/XVERSE-13B)        | 13B                         | q_proj,v_proj     | -        |
+| [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)         | 6B                          | query_key_value   | chatglm2 |
+
+- **Default module** is used for the `--lora_target` argument. Please use `python src/train_bash.py -h` to see all available options.
+- For the "base" models, the `--template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the corresponding template for the "chat" models.
 
 ## Supported Training Approaches
 
-- [(Continually) pre-training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
-  - Full-parameter tuning
-  - Partial-parameter tuning
-  - [LoRA](https://arxiv.org/abs/2106.09685)
-  - [QLoRA](https://arxiv.org/abs/2305.14314)
-- [Supervised fine-tuning](https://arxiv.org/abs/2109.01652)
-  - Full-parameter tuning
-  - Partial-parameter tuning
-  - [LoRA](https://arxiv.org/abs/2106.09685)
-  - [QLoRA](https://arxiv.org/abs/2305.14314)
-- [RLHF](https://arxiv.org/abs/2203.02155)
-  - [LoRA](https://arxiv.org/abs/2106.09685)
-  - [QLoRA](https://arxiv.org/abs/2305.14314)
+| Approach               | Full-parameter | Partial-parameter | LoRA | QLoRA |
+| ---------------------- | -------------- | ----------------- | ---- | ----- |
+| Pre-Training           | ✅            | ✅                | ✅   | ✅   |
+| Supervised Fine-Tuning | ✅            | ✅                | ✅   | ✅   |
+| Reward Model Training  |                |                   | ✅   | ✅   |
+| PPO Training           |                |                   | ✅   | ✅   |
+| DPO Training           | ✅            |                    | ✅   | ✅   |
 
 ## Provided Datasets
 
 - For pre-training:
   - [Wiki Demo (en)](data/wiki_demo.txt)
+  - [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
+  - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
+  - [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220)
+  - [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
 - For supervised fine-tuning:
   - [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
   - [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
@@ -68,7 +85,6 @@
   - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
   - [Self-cognition (zh)](data/self_cognition.json)
   - [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
-  - [RefGPT (zh)](https://github.com/sufengniu/RefGPT)
   - [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
   - [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
   - [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
@@ -77,12 +93,13 @@
   - [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
   - [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
   - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
+  - [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
   - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
   - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
   - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
   - [UltraChat (en)](https://github.com/thunlp/UltraChat)
   - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
-- For reward modelling:
+- For reward modelling or DPO training:
   - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
   - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
   - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
@@ -100,18 +117,13 @@ huggingface-cli login
 
 - Python 3.8+ and PyTorch 1.13.1+
 - 🤗Transformers, Datasets, Accelerate, PEFT and TRL
+- sentencepiece and tiktoken
 - jieba, rouge-chinese and nltk (used at evaluation)
 - gradio and matplotlib (used in web_demo.py)
 - uvicorn, fastapi and sse-starlette (used in api_demo.py)
 
 And **powerful GPUs**!
 
-If you want to enable quantized LoRA (QLoRA) on the Windows platform, you should install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.1.
-
-```bash
-pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
-```
-
 ## Getting Started
 
 ### Data Preparation (optional)
@@ -130,13 +142,21 @@ cd LLaMA-Efficient-Tuning
 pip install -r requirements.txt
 ```
 
+If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you will be required to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.1.
+
+```bash
+pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
+```
+
 ### All-in-one Web UI
 
 ```bash
-python src/train_web.py
+CUDA_VISIBLE_DEVICES=0 python src/train_web.py
 ```
 
-### (Continually) Pre-Training
+Currently the web UI only supports training on **a single GPU**.
+
+### Pre-Training
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
@@ -144,6 +164,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --model_name_or_path path_to_your_model \
     --do_train \
     --dataset wiki_demo \
+    --template default \
     --finetuning_type lora \
     --output_dir path_to_pt_checkpoint \
     --overwrite_cache \
@@ -166,6 +187,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --model_name_or_path path_to_your_model \
     --do_train \
     --dataset alpaca_gpt4_en \
+    --template default \
     --finetuning_type lora \
     --output_dir path_to_sft_checkpoint \
     --overwrite_cache \
@@ -180,6 +202,8 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --fp16
 ```
 
+Remember to specify `--lora_target W_pack` if you are using Baichuan models.
+
 ### Reward Model Training
 
 ```bash
@@ -188,9 +212,12 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --model_name_or_path path_to_your_model \
     --do_train \
     --dataset comparison_gpt4_en \
+    --template default \
     --finetuning_type lora \
+    --resume_lora_training False \
+    --checkpoint_dir path_to_sft_checkpoint \
     --output_dir path_to_rm_checkpoint \
-    --per_device_train_batch_size 4 \
+    --per_device_train_batch_size 2 \
     --gradient_accumulation_steps 4 \
     --lr_scheduler_type cosine \
     --logging_steps 10 \
@@ -201,7 +228,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --fp16
 ```
 
-### PPO Training (RLHF)
+### PPO Training
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
@@ -209,7 +236,9 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --model_name_or_path path_to_your_model \
     --do_train \
     --dataset alpaca_gpt4_en \
+    --template default \
     --finetuning_type lora \
+    --resume_lora_training False \
     --checkpoint_dir path_to_sft_checkpoint \
     --reward_model path_to_rm_checkpoint \
     --output_dir path_to_ppo_checkpoint \
@@ -220,10 +249,33 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --save_steps 1000 \
     --learning_rate 1e-5 \
     --num_train_epochs 1.0 \
-    --resume_lora_training False \
     --plot_loss
 ```
 
+### DPO Training
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage dpo \
+    --model_name_or_path path_to_your_model \
+    --do_train \
+    --dataset comparison_gpt4_en \
+    --template default \
+    --finetuning_type lora \
+    --resume_lora_training False \
+    --checkpoint_dir path_to_sft_checkpoint \
+    --output_dir path_to_dpo_checkpoint \
+    --per_device_train_batch_size 2 \
+    --gradient_accumulation_steps 4 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --save_steps 1000 \
+    --learning_rate 1e-5 \
+    --num_train_epochs 1.0 \
+    --plot_loss \
+    --fp16
+```
+
 ### Distributed Training
 
 ```bash
@@ -267,6 +319,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --model_name_or_path path_to_your_model \
     --do_eval \
     --dataset alpaca_gpt4_en \
+    --template default \
     --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint \
     --output_dir path_to_eval_result \
@@ -285,6 +338,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --model_name_or_path path_to_your_model \
     --do_predict \
     --dataset alpaca_gpt4_en \
+    --template default \
     --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint \
     --output_dir path_to_predict_result \
@@ -293,13 +347,12 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --predict_with_generate
 ```
 
-If you want to predict the samples with empty responses, please kindly fill the `response` column with **dummy tokens** to ensure the sample will not be discarded throughout the preprocessing phase.
-
 ### API Demo
 
 ```bash
 python src/api_demo.py \
     --model_name_or_path path_to_your_model \
+    --template default \
     --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint
 ```
@@ -311,6 +364,7 @@ Visit `http://localhost:8000/docs` for API documentation.
 ```bash
 python src/cli_demo.py \
     --model_name_or_path path_to_your_model \
+    --template default \
     --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint
 ```
@@ -320,6 +374,7 @@ python src/cli_demo.py \
 ```bash
 python src/web_demo.py \
     --model_name_or_path path_to_your_model \
+    --template default \
     --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint
 ```
@@ -329,11 +384,18 @@ python src/web_demo.py \
 ```bash
 python src/export_model.py \
     --model_name_or_path path_to_your_model \
+    --template default \
     --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint \
     --output_dir path_to_export
 ```
 
+## TODO
+
+- [ ] Supporting flash attention ([torch](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) / [xformers](https://github.com/facebookresearch/xformers) / [flashattn](https://github.com/Dao-AILab/flash-attention)).
+- [ ] Implementing multi-query attention for faster inference.
+- [ ] Supporting full-parameter RLHF training.
+
 ## License
 
 This repository is licensed under the [Apache-2.0 License](LICENSE).
@@ -344,8 +406,11 @@ Please follow the model licenses to use the corresponding model weights:
 - [LLaMA-2](https://ai.meta.com/llama/license/)
 - [BLOOM](https://huggingface.co/spaces/bigscience/license)
 - [Falcon](LICENSE)
-- [baichuan](https://huggingface.co/baichuan-inc/baichuan-7B/resolve/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)
+- [Baichuan](https://huggingface.co/baichuan-inc/baichuan-7B/resolve/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)
 - [InternLM](https://github.com/InternLM/InternLM#open-source-license)
+- [Qwen](https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/LICENSE)
+- [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf)
+- [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B/blob/main/MODEL_LICENSE)
 
 ## Citation
 

README_zh.md

@@ -0,0 +1,431 @@
+# LLaMA Efficient Tuning
+
+[![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Efficient-Tuning?style=social)](https://github.com/hiyouga/LLaMA-Efficient-Tuning/stargazers)
+[![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Efficient-Tuning)](LICENSE)
+[![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Efficient-Tuning)](https://github.com/hiyouga/LLaMA-Efficient-Tuning/commits/main)
+[![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/)
+[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Efficient-Tuning/pulls)
+
+👋 加入我们的[微信群](assets/wechat.jpg)。
+
+\[ [English](README.md) | 中文 \]
+
+## 更新日志
+
+[23/08/11] 现在我们支持了指令模型的 **[DPO 训练](https://arxiv.org/abs/2305.18290)**。详情请参阅[此示例](#dpo-训练)（实验性功能）。
+
+[23/08/03] 现在我们支持了 **Qwen-7B** 模型的训练。请尝试使用 `--model_name_or_path Qwen/Qwen-7B-Chat` 和 `--lora_target c_attn` 参数。使用 Qwen-7B-Chat 模型请添加 `--template chatml` 参数。
+
+[23/07/31] 现在我们支持了训练数据流式加载。请尝试使用 `--streaming` 和 `--max_steps 100` 参数来流式加载数据集。
+
+[23/07/29] 我们在 Hugging Face 发布了两个 13B 指令微调模型。详细内容请查阅我们的 Hugging Face 项目（[LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/baichuan-13b-sft)）。
+
+[23/07/19] 现在我们支持了 **LLaMA-2** 模型的训练。请尝试使用 `--model_name_or_path meta-llama/Llama-2-7b-hf` 参数。请注意使用 LLaMA-2-chat 模型需要添加 `--template llama2` 参数。
+
+[23/07/18] 我们开发了支持训练和测试的浏览器一键微调界面。请尝试使用 `train_web.py` 在您的浏览器中微调模型。感谢 [@KanadeSiina](https://github.com/KanadeSiina) 和 [@codemayq](https://github.com/codemayq) 在该功能开发中付出的努力。
+
+[23/07/11] 现在我们支持了 **Baichuan-13B** 模型的训练。请尝试使用 `--model_name_or_path baichuan-inc/Baichuan-13B-Base` 和 `--lora_target W_pack` 参数。请注意使用 Baichuan-13B-Chat 模型需要添加 `--template baichuan` 参数。
+
+[23/07/09] 我们开源了 [FastEdit](https://github.com/hiyouga/FastEdit)⚡🩹，一个简单易用的、能迅速编辑大模型事实记忆的工具包。如果您感兴趣请关注我们的 [FastEdit](https://github.com/hiyouga/FastEdit) 项目。
+
+[23/07/07] 现在我们支持了 **InternLM-7B** 模型的训练。请尝试使用 `--model_name_or_path internlm/internlm-7b` 参数。请注意使用 InternLM-chat 模型需要添加 `--template intern` 参数。
+
+[23/07/05] 现在我们支持了 **Falcon-7B/40B** 模型的训练。请尝试使用 `--model_name_or_path tiiuae/falcon-7b` 和 `--lora_target query_key_value` 参数。
+
+[23/06/29] 我们提供了一个**可复现的**指令模型微调示例，详细内容请查阅 [Hugging Face 项目](https://huggingface.co/hiyouga/baichuan-7b-sft)。
+
+[23/06/22] 我们对齐了[示例 API](src/api_demo.py) 与 [OpenAI API](https://platform.openai.com/docs/api-reference/chat) 的格式，您可以将微调模型接入任意基于 ChatGPT 的应用中。
+
+[23/06/15] 现在我们支持了 **Baichuan-7B** 模型的训练。请尝试使用 `--model_name_or_path baichuan-inc/Baichuan-7B` 和 `--lora_target W_pack` 参数。
+
+[23/06/03] 现在我们实现了 4 比特的 LoRA 训练（也称 [QLoRA](https://github.com/artidoro/qlora)）。请尝试使用 `--quantization_bit 4` 参数进行 4 比特量化微调。
+
+[23/05/31] 现在我们支持了 **BLOOM & BLOOMZ** 模型的训练。请尝试使用 `--model_name_or_path bigscience/bloomz-7b1-mt` 和 `--lora_target query_key_value` 参数。
+
+## 模型
+
+| 模型名                                                   | 模型大小                     | 默认模块           | Template |
+| -------------------------------------------------------- | --------------------------- | ----------------- |----------|
+| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B              | q_proj,v_proj     | -        |
+| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                  | q_proj,v_proj     | llama2   |
+| [BLOOM](https://huggingface.co/bigscience/bloom)         | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -        |
+| [BLOOMZ](https://huggingface.co/bigscience/bloomz)       | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -        |
+| [Falcon](https://huggingface.co/tiiuae/falcon-7b)        | 7B/40B                      | query_key_value   | -        |
+| [Baichuan](https://github.com/baichuan-inc/baichuan-13B) | 7B/13B                      | W_pack            | baichuan |
+| [InternLM](https://github.com/InternLM/InternLM)         | 7B                          | q_proj,v_proj     | intern   |
+| [Qwen](https://github.com/QwenLM/Qwen-7B)                | 7B                          | c_attn            | chatml   |
+| [XVERSE](https://github.com/xverse-ai/XVERSE-13B)        | 13B                         | q_proj,v_proj     | -        |
+
+- **默认模块**是 `--lora_target` 参数的部分可选项。请使用 `python src/train_bash.py -h` 查看全部可选项。
+- 对于所有“基座”（Base）模型，`--template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”（Chat）模型请务必使用对应的模板。
+
+## 训练方法
+
+| 方法        | 全参数训练 | 部分参数训练 | LoRA | QLoRA |
+| ---------- | ---------- | ----------- | ---- | ----- |
+| 预训练      | ✅        | ✅         | ✅   | ✅   |
+| 指令监督微调 | ✅        | ✅         | ✅   | ✅   |
+| 奖励模型训练 |           |             | ✅   | ✅   |
+| PPO 训练    |           |             | ✅   | ✅   |
+| DPO 训练    | ✅        |             | ✅   | ✅   |
+
+## 数据集
+
+- 用于预训练：
+  - [Wiki Demo (en)](data/wiki_demo.txt)
+  - [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
+  - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
+  - [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220)
+  - [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
+- 用于指令监督微调：
+  - [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
+  - [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
+  - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+  - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
+  - [Self-cognition (zh)](data/self_cognition.json)
+  - [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
+  - [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
+  - [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
+  - [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
+  - [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
+  - [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
+  - [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
+  - [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
+  - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
+  - [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
+  - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
+  - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
+  - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
+  - [UltraChat (en)](https://github.com/thunlp/UltraChat)
+  - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
+- 用于奖励模型或 DPO 训练：
+  - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
+  - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
+  - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+
+使用方法请参考 [data/README.md](data/README_zh.md) 文件。
+
+部分数据集的使用需要确认，我们推荐使用下述命令登录您的 Hugging Face 账户。
+
+```bash
+pip install --upgrade huggingface_hub
+huggingface-cli login
+```
+
+## 软件依赖
+
+- Python 3.8+ 和 PyTorch 1.13.1+
+- 🤗Transformers, Datasets, Accelerate, PEFT 和 TRL
+- sentencepiece 和 tiktoken
+- jieba, rouge-chinese 和 nltk (用于评估)
+- gradio 和 matplotlib (用于网页端交互)
+- uvicorn, fastapi 和 sse-starlette (用于 API)
+
+以及 **强而有力的 GPU**！
+
+## 如何使用
+
+### 数据准备（可跳过）
+
+关于数据集文件的格式，请参考 `data/example_dataset` 文件夹的内容。构建自定义数据集时，既可以使用单个 `.json` 文件，也可以使用一个[数据加载脚本](https://huggingface.co/docs/datasets/dataset_script)和多个文件。
+
+注意：使用自定义数据集时，请更新 `data/dataset_info.json` 文件，该文件的格式请参考 `data/README.md`。
+
+### 环境搭建（可跳过）
+
+```bash
+git clone https://github.com/hiyouga/LLaMA-Efficient-Tuning.git
+conda create -n llama_etuning python=3.10
+conda activate llama_etuning
+cd LLaMA-Efficient-Tuning
+pip install -r requirements.txt
+```
+
+如果要在 Windows 平台上开启量化 LoRA（QLoRA），需要安装预编译的 `bitsandbytes` 库, 支持 CUDA 11.1 到 12.1.
+
+```bash
+pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
+```
+
+### 浏览器一键微调/测试
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_web.py
+```
+
+目前网页 UI 仅支持**单卡训练**。
+
+### 预训练
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage pt \
+    --model_name_or_path path_to_your_model \
+    --do_train \
+    --dataset wiki_demo \
+    --template default \
+    --finetuning_type lora \
+    --output_dir path_to_pt_checkpoint \
+    --overwrite_cache \
+    --per_device_train_batch_size 4 \
+    --gradient_accumulation_steps 4 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --save_steps 1000 \
+    --learning_rate 5e-5 \
+    --num_train_epochs 3.0 \
+    --plot_loss \
+    --fp16
+```
+
+### 指令监督微调
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage sft \
+    --model_name_or_path path_to_your_model \
+    --do_train \
+    --dataset alpaca_gpt4_zh \
+    --template default \
+    --finetuning_type lora \
+    --output_dir path_to_sft_checkpoint \
+    --overwrite_cache \
+    --per_device_train_batch_size 4 \
+    --gradient_accumulation_steps 4 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --save_steps 1000 \
+    --learning_rate 5e-5 \
+    --num_train_epochs 3.0 \
+    --plot_loss \
+    --fp16
+```
+
+使用 Baichuan 模型时请指定 `--lora_target W_pack` 参数。
+
+### 奖励模型训练
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage rm \
+    --model_name_or_path path_to_your_model \
+    --do_train \
+    --dataset comparison_gpt4_zh \
+    --template default \
+    --finetuning_type lora \
+    --resume_lora_training False \
+    --checkpoint_dir path_to_sft_checkpoint \
+    --output_dir path_to_rm_checkpoint \
+    --per_device_train_batch_size 2 \
+    --gradient_accumulation_steps 4 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --save_steps 1000 \
+    --learning_rate 1e-5 \
+    --num_train_epochs 1.0 \
+    --plot_loss \
+    --fp16
+```
+
+### PPO 训练
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage ppo \
+    --model_name_or_path path_to_your_model \
+    --do_train \
+    --dataset alpaca_gpt4_zh \
+    --template default \
+    --finetuning_type lora \
+    --resume_lora_training False \
+    --checkpoint_dir path_to_sft_checkpoint \
+    --reward_model path_to_rm_checkpoint \
+    --output_dir path_to_ppo_checkpoint \
+    --per_device_train_batch_size 2 \
+    --gradient_accumulation_steps 4 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --save_steps 1000 \
+    --learning_rate 1e-5 \
+    --num_train_epochs 1.0 \
+    --plot_loss
+```
+
+### DPO 训练
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage dpo \
+    --model_name_or_path path_to_your_model \
+    --do_train \
+    --dataset comparison_gpt4_zh \
+    --template default \
+    --finetuning_type lora \
+    --resume_lora_training False \
+    --checkpoint_dir path_to_sft_checkpoint \
+    --output_dir path_to_dpo_checkpoint \
+    --per_device_train_batch_size 2 \
+    --gradient_accumulation_steps 4 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --save_steps 1000 \
+    --learning_rate 1e-5 \
+    --num_train_epochs 1.0 \
+    --plot_loss \
+    --fp16
+```
+
+### 多 GPU 分布式训练
+
+```bash
+accelerate config # 首先配置分布式环境
+accelerate launch src/train_bash.py # 参数同上
+```
+
+<details><summary>使用 DeepSpeed ZeRO-2 进行全参数微调的 Accelerate 配置示例</summary>
+
+```yaml
+compute_environment: LOCAL_MACHINE
+deepspeed_config:
+  gradient_accumulation_steps: 4
+  gradient_clipping: 0.5
+  offload_optimizer_device: none
+  offload_param_device: none
+  zero3_init_flag: false
+  zero_stage: 2
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+machine_rank: 0
+main_training_function: main
+mixed_precision: fp16
+num_machines: 1
+num_processes: 4
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false
+```
+
+</details>
+
+### 指标评估（BLEU分数和汉语ROUGE分数）
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage sft \
+    --model_name_or_path path_to_your_model \
+    --do_eval \
+    --dataset alpaca_gpt4_zh \
+    --template default \
+    --finetuning_type lora \
+    --checkpoint_dir path_to_checkpoint \
+    --output_dir path_to_eval_result \
+    --per_device_eval_batch_size 8 \
+    --max_samples 100 \
+    --predict_with_generate
+```
+
+我们建议在量化模型的评估中使用 `--per_device_eval_batch_size=1` 和 `--max_target_length 128` 参数。
+
+### 模型预测
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage sft \
+    --model_name_or_path path_to_your_model \
+    --do_predict \
+    --dataset alpaca_gpt4_zh \
+    --template default \
+    --finetuning_type lora \
+    --checkpoint_dir path_to_checkpoint \
+    --output_dir path_to_predict_result \
+    --per_device_eval_batch_size 8 \
+    --max_samples 100 \
+    --predict_with_generate
+```
+
+### API 服务
+
+```bash
+python src/api_demo.py \
+    --model_name_or_path path_to_your_model \
+    --template default \
+    --finetuning_type lora \
+    --checkpoint_dir path_to_checkpoint
+```
+
+关于 API 文档请见 `http://localhost:8000/docs`。
+
+### 命令行测试
+
+```bash
+python src/cli_demo.py \
+    --model_name_or_path path_to_your_model \
+    --template default \
+    --finetuning_type lora \
+    --checkpoint_dir path_to_checkpoint
+```
+
+### 浏览器测试
+
+```bash
+python src/web_demo.py \
+    --model_name_or_path path_to_your_model \
+    --template default \
+    --finetuning_type lora \
+    --checkpoint_dir path_to_checkpoint
+```
+
+### 导出微调模型
+
+```bash
+python src/export_model.py \
+    --model_name_or_path path_to_your_model \
+    --template default \
+    --finetuning_type lora \
+    --checkpoint_dir path_to_checkpoint \
+    --output_dir path_to_export
+```
+
+## TODO
+
+- [ ] 实现 flash attention ([torch](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) / [xformers](https://github.com/facebookresearch/xformers) / [flashattn](https://github.com/Dao-AILab/flash-attention))。
+- [ ] 在推理阶段使用 Multi-query attention 进行加速。
+- [ ] 支持 RLHF 的全参数微调。
+
+## 协议
+
+本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。
+
+使用模型权重时，请遵循对应的模型协议：
+
+- [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md)
+- [LLaMA-2](https://ai.meta.com/llama/license/)
+- [BLOOM](https://huggingface.co/spaces/bigscience/license)
+- [Falcon](LICENSE)
+- [Baichuan](https://huggingface.co/baichuan-inc/baichuan-7B/resolve/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)
+- [InternLM](https://github.com/InternLM/InternLM#open-source-license)
+- [Qwen](https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/LICENSE)
+
+## 引用
+
+如果您觉得此项目有帮助，请考虑以下列格式引用
+
+```bibtex
+@Misc{llama-efficient-tuning,
+  title = {LLaMA Efficient Tuning},
+  author = {hiyouga},
+  howpublished = {\url{https://github.com/hiyouga/LLaMA-Efficient-Tuning}},
+  year = {2023}
+}
+```
+
+## 致谢
+
+本项目是 [ChatGLM-Efficient-Tuning](https://github.com/hiyouga/ChatGLM-Efficient-Tuning) 的同类项目。采用了类似的代码结构和训练方法。
+
+## Star History
+
+![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Efficient-Tuning&type=Date)

data/README.md

@@ -2,16 +2,16 @@ If you are using a custom dataset, please provide your dataset definition in the
 
 ```json
 "dataset_name": {
-    "hf_hub_url": "the name of the dataset repository on the HuggingFace hub. (if specified, ignore below 3 arguments)",
-    "script_url": "the name of the directory containing a dataset loading script. (if specified, ignore below 2 arguments)",
-    "file_name": "the name of the dataset file in the this directory. (required if above are not specified)",
-    "file_sha1": "the SHA-1 hash value of the dataset file. (optional)",
-    "columns": {
-        "prompt": "the name of the column in the datasets containing the prompts. (default: instruction)",
-        "query": "the name of the column in the datasets containing the queries. (default: input)",
-        "response": "the name of the column in the datasets containing the responses. (default: output)",
-        "history": "the name of the column in the datasets containing the history of chat. (default: None)"
-    }
+  "hf_hub_url": "the name of the dataset repository on the HuggingFace hub. (if specified, ignore below 3 arguments)",
+  "script_url": "the name of the directory containing a dataset loading script. (if specified, ignore below 2 arguments)",
+  "file_name": "the name of the dataset file in the this directory. (required if above are not specified)",
+  "file_sha1": "the SHA-1 hash value of the dataset file. (optional)",
+  "columns": {
+    "prompt": "the name of the column in the datasets containing the prompts. (default: instruction)",
+    "query": "the name of the column in the datasets containing the queries. (default: input)",
+    "response": "the name of the column in the datasets containing the responses. (default: output)",
+    "history": "the name of the column in the datasets containing the history of chat. (default: None)"
+  }
 }
 ```
 

data/README_zh.md

@@ -0,0 +1,18 @@
+如果您使用自定义数据集，请务必在 `dataset_info.json` 文件中以如下格式提供您的数据集定义。
+
+```json
+"数据集名称": {
+  "hf_hub_url": "HuggingFace上的项目地址（若指定，则忽略下列三个参数）",
+  "script_url": "包含数据加载脚本的本地文件夹名称（若指定，则忽略下列两个参数）",
+  "file_name": "该目录下数据集文件的名称（若上述参数未指定，则此项必需）",
+  "file_sha1": "数据集文件的SHA-1哈希值（可选）",
+  "columns": {
+    "prompt": "数据集代表提示词的表头名称（默认：instruction）",
+    "query": "数据集代表请求的表头名称（默认：input）",
+    "response": "数据集代表回答的表头名称（默认：output）",
+    "history": "数据集代表历史对话的表头名称（默认：None）"
+  }
+}
+```
+
+其中 `prompt` 和 `response` 列应当是非空的字符串。`query` 列的内容将会和 `prompt` 列拼接作为模型输入。`history` 列应当是一个列表，其中每个元素是一个字符串二元组，分别代表用户请求和模型答复。

data/oaast_sft.json.REMOVED.git-id

@@ -1 +1 @@
-0a57fbc1d8cb08a8cd71c5eb8425cf59206ffed6
+57fd080be5bffe4153fe3ee26a175e3d56da30f3

data/refgpt_zh_50k_p1.json.REMOVED.git-id

@@ -1 +0,0 @@
-56405bb8f52727e52e99693739494b9b7b0d7ba6

data/refgpt_zh_50k_p2.json.REMOVED.git-id

@@ -1 +0,0 @@
-fa935248a5d40d2bdd5649af99a72a754d40ae7a

requirements.txt

@@ -1,10 +1,12 @@
 torch>=1.13.1
 transformers>=4.29.1
 datasets>=2.12.0
-accelerate>=0.19.0
-peft>=0.3.0
-trl>=0.4.7
+accelerate>=0.21.0
+peft>=0.4.0
+trl>=0.5.0
+scipy
 sentencepiece
+tiktoken
 jieba
 rouge-chinese
 nltk

src/api_demo.py

@@ -1,19 +1,13 @@
-# coding=utf-8
-# Implements API for fine-tuned models in OpenAI's format. (https://platform.openai.com/docs/api-reference/chat)
-# Usage: python api_demo.py --model_name_or_path path_to_model --checkpoint_dir path_to_checkpoint
-# Visit http://localhost:8000/docs for document.
-
 import uvicorn
 
-from llmtuner import ChatModel
-from llmtuner.api.app import create_app
-from llmtuner.tuner import get_infer_args
+from llmtuner import ChatModel, create_app
 
 
 def main():
-    chat_model = ChatModel(*get_infer_args())
+    chat_model = ChatModel()
     app = create_app(chat_model)
     uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
+    print("Visit http://localhost:8000/docs for API document.")
 
 
 if __name__ == "__main__":

src/cli_demo.py

@@ -1,13 +1,8 @@
-# coding=utf-8
-# Implements stream chat in command line for fine-tuned models.
-# Usage: python cli_demo.py --model_name_or_path path_to_model --checkpoint_dir path_to_checkpoint
-
 from llmtuner import ChatModel
-from llmtuner.tuner import get_infer_args
 
 
 def main():
-    chat_model = ChatModel(*get_infer_args())
+    chat_model = ChatModel()
     history = []
     print("Welcome to the CLI application, use `clear` to remove the history, use `exit` to exit the application.")
 

src/export_model.py

@@ -1,16 +1,8 @@
-# coding=utf-8
-# Exports the fine-tuned model.
-# Usage: python export_model.py --checkpoint_dir path_to_checkpoint --output_dir path_to_save_model
-
-from llmtuner.tuner import get_train_args, load_model_and_tokenizer
+from llmtuner import export_model
 
 
 def main():
-    model_args, _, training_args, finetuning_args, _ = get_train_args()
-    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
-    model.save_pretrained(training_args.output_dir, max_shard_size="10GB")
-    tokenizer.save_pretrained(training_args.output_dir)
-    print("model and tokenizer have been saved at:", training_args.output_dir)
+    export_model()
 
 
 if __name__ == "__main__":

src/llmtuner/__init__.py

@@ -1,4 +1,9 @@
+# Level: api, webui > chat > tuner > dsets > extras, hparams
+
+from llmtuner.api import create_app
 from llmtuner.chat import ChatModel
+from llmtuner.tuner import export_model, run_exp
+from llmtuner.webui import create_ui, create_web_demo
 
 
-__version__ = "0.1.2"
+__version__ = "0.1.6"

src/llmtuner/api/__init__.py

@@ -0,0 +1 @@
+from llmtuner.api.app import create_app

src/llmtuner/api/app.py

@@ -5,9 +5,8 @@ from contextlib import asynccontextmanager
 from sse_starlette import EventSourceResponse
 from typing import List, Tuple
 
-from llmtuner.tuner import get_infer_args
 from llmtuner.extras.misc import torch_gc
-from llmtuner.chat.stream_chat import ChatModel
+from llmtuner.chat import ChatModel
 from llmtuner.api.protocol import (
     Role,
     Finish,
@@ -50,8 +49,8 @@ def create_app(chat_model: ChatModel) -> FastAPI:
     async def create_chat_completion(request: ChatCompletionRequest):
         if request.messages[-1].role != Role.USER:
             raise HTTPException(status_code=400, detail="Invalid request")
-        query = request.messages[-1].content
 
+        query = request.messages[-1].content
         prev_messages = request.messages[:-1]
         if len(prev_messages) > 0 and prev_messages[0].role == Role.SYSTEM:
             prefix = prev_messages.pop(0).content
@@ -122,6 +121,6 @@ def create_app(chat_model: ChatModel) -> FastAPI:
 
 
 if __name__ == "__main__":
-    chat_model = ChatModel(*get_infer_args())
+    chat_model = ChatModel()
     app = create_app(chat_model)
     uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)

src/llmtuner/chat/stream_chat.py

@@ -1,37 +1,41 @@
 import torch
+from types import MethodType
 from typing import Any, Dict, Generator, List, Optional, Tuple
 from threading import Thread
-from transformers import TextIteratorStreamer
+from transformers import PreTrainedModel, TextIteratorStreamer
 
-from llmtuner.extras.misc import get_logits_processor
-from llmtuner.extras.template import get_template
-from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
-from llmtuner.tuner import load_model_and_tokenizer
+from llmtuner.extras.misc import dispatch_model, get_logits_processor, get_stopping_criteria
+from llmtuner.extras.template import get_template_and_fix_tokenizer
+from llmtuner.tuner.core import get_infer_args, load_model_and_tokenizer
 
 
 class ChatModel:
 
-    def __init__(
-        self,
-        model_args: ModelArguments,
-        data_args: DataArguments,
-        finetuning_args: FinetuningArguments,
-        generating_args: GeneratingArguments
-    ) -> None:
+    def __init__(self, args: Optional[Dict[str, Any]] = None) -> None:
+        model_args, data_args, finetuning_args, self.generating_args = get_infer_args(args)
         self.model, self.tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
-        self.template = get_template(data_args.prompt_template)
-        self.source_prefix = data_args.source_prefix or ""
-        self.generating_args = generating_args
+        self.model = dispatch_model(self.model)
+        self.model = self.model.eval() # change to eval mode
+        self.template = get_template_and_fix_tokenizer(data_args.template, self.tokenizer)
+        self.source_prefix = data_args.source_prefix
+        self.model.generate = MethodType(PreTrainedModel.generate, self.model) # disable custom method (for Qwen)
 
     def process_args(
-        self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = None, **input_kwargs
+        self,
+        query: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        prefix: Optional[str] = None,
+        **input_kwargs
     ) -> Tuple[Dict[str, Any], int]:
         prefix = prefix or self.source_prefix
 
-        inputs = self.tokenizer([self.template.get_prompt(query, history, prefix)], return_tensors="pt")
-        inputs = inputs.to(self.model.device)
-        prompt_length = len(inputs["input_ids"][0])
+        prompt, _ = self.template.encode_oneturn(
+            tokenizer=self.tokenizer, query=query, resp="", history=history, prefix=prefix
+        )
+        input_ids = torch.tensor([prompt], device=self.model.device)
+        prompt_length = len(input_ids[0])
 
+        do_sample = input_kwargs.pop("do_sample", None)
         temperature = input_kwargs.pop("temperature", None)
         top_p = input_kwargs.pop("top_p", None)
         top_k = input_kwargs.pop("top_k", None)
@@ -41,12 +45,14 @@ class ChatModel:
 
         gen_kwargs = self.generating_args.to_dict()
         gen_kwargs.update(dict(
-            input_ids=inputs["input_ids"],
+            input_ids=input_ids,
+            do_sample=do_sample if do_sample is not None else gen_kwargs["do_sample"],
             temperature=temperature or gen_kwargs["temperature"],
             top_p=top_p or gen_kwargs["top_p"],
             top_k=top_k or gen_kwargs["top_k"],
             repetition_penalty=repetition_penalty or gen_kwargs["repetition_penalty"],
-            logits_processor=get_logits_processor()
+            logits_processor=get_logits_processor(),
+            stopping_criteria=get_stopping_criteria(self.tokenizer.additional_special_tokens_ids)
         ))
 
         if max_length:
@@ -61,7 +67,11 @@ class ChatModel:
 
     @torch.inference_mode()
     def chat(
-        self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = None, **input_kwargs
+        self,
+        query: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        prefix: Optional[str] = None,
+        **input_kwargs
     ) -> Tuple[str, Tuple[int, int]]:
         gen_kwargs, prompt_length = self.process_args(query, history, prefix, **input_kwargs)
         generation_output = self.model.generate(**gen_kwargs)
@@ -72,7 +82,11 @@ class ChatModel:
 
     @torch.inference_mode()
     def stream_chat(
-        self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = None, **input_kwargs
+        self,
+        query: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        prefix: Optional[str] = None,
+        **input_kwargs
     ) -> Generator[str, None, None]:
         gen_kwargs, _ = self.process_args(query, history, prefix, **input_kwargs)
         streamer = TextIteratorStreamer(self.tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)

src/llmtuner/dsets/loader.py

@@ -1,40 +1,50 @@
 import os
 import hashlib
-from typing import List
+from typing import TYPE_CHECKING, List, Optional
 
-from datasets import Dataset, concatenate_datasets, load_dataset
+from datasets import Value, concatenate_datasets, interleave_datasets, load_dataset
 
 from llmtuner.extras.logging import get_logger
-from llmtuner.hparams import ModelArguments, DataArguments
+
+if TYPE_CHECKING:
+    from datasets import Dataset
+    from llmtuner.hparams import ModelArguments, DataArguments
 
 
 logger = get_logger(__name__)
 
 
+EXT2TYPE = {
+    "csv": "csv",
+    "json": "json",
+    "jsonl": "json",
+    "txt": "text"
+}
+
+
+def checksum(data_files: List[str], file_sha1: Optional[str] = None) -> None:
+    if file_sha1 is None:
+        logger.warning("Checksum failed: missing SHA-1 hash value in dataset_info.json.")
+        return
+
+    if len(data_files) != 1:
+        logger.warning("Checksum failed: too many files.")
+        return
+
+    with open(data_files[0], "rb") as f:
+        sha1 = hashlib.sha1(f.read()).hexdigest()
+        if sha1 != file_sha1:
+            logger.warning("Checksum failed: mismatched SHA-1 hash value at {}.".format(data_files[0]))
+
+
 def get_dataset(
-    model_args: ModelArguments,
-    data_args: DataArguments
-) -> Dataset:
-
-    def checksum(file_path, hash):
-        with open(file_path, "rb") as datafile:
-            binary_data = datafile.read()
-        sha1 = hashlib.sha1(binary_data).hexdigest()
-        if sha1 != hash:
-            logger.warning("Checksum failed for {}. It may vary depending on the platform.".format(file_path))
-
-    ext2type = {
-        "csv": "csv",
-        "json": "json",
-        "jsonl": "json",
-        "txt": "text"
-    }
-
+    model_args: "ModelArguments",
+    data_args: "DataArguments"
+) -> "Dataset":
     max_samples = data_args.max_samples
-    all_datasets: List[Dataset] = [] # support multiple datasets
+    all_datasets: List["Dataset"] = [] # support multiple datasets
 
     for dataset_attr in data_args.dataset_list:
-
         logger.info("Loading dataset {}...".format(dataset_attr))
 
         if dataset_attr.load_from == "hf_hub":
@@ -47,60 +57,62 @@ def get_dataset(
             data_path = None
             data_files: List[str] = []
 
-            if os.path.isdir(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)):
+            if os.path.isdir(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)): # directory
                 for file_name in os.listdir(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)):
                     data_files.append(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name, file_name))
-
                     if data_path is None:
-                        data_path = ext2type.get(data_files[0].split(".")[-1], None)
+                        data_path = EXT2TYPE.get(file_name.split(".")[-1], None)
                     else:
-                        assert data_path == ext2type.get(data_files[-1].split(".")[-1], None), "file type does not match."
-            elif os.path.isfile(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)):
+                        assert data_path == EXT2TYPE.get(file_name.split(".")[-1], None), "file type does not match."
+            elif os.path.isfile(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)): # single file
                 data_files.append(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name))
-                data_path = ext2type.get(data_files[0].split(".")[-1], None)
+                data_path = EXT2TYPE.get(dataset_attr.dataset_name.split(".")[-1], None)
             else:
                 raise ValueError("File not found.")
 
             assert data_path, "File extension must be txt, csv, json or jsonl."
-
-            if len(data_files) == 1 and dataset_attr.dataset_sha1 is not None:
-                checksum(data_files[0], dataset_attr.dataset_sha1)
-            else:
-                logger.warning("Checksum failed: missing SHA-1 hash value in dataset_info.json or too many files.")
+            checksum(data_files, dataset_attr.dataset_sha1)
         else:
             raise NotImplementedError
 
-        raw_datasets = load_dataset(
+        dataset = load_dataset(
             data_path,
             data_files=data_files,
+            split=data_args.split,
             cache_dir=model_args.cache_dir,
+            streaming=data_args.streaming,
             use_auth_token=True if model_args.use_auth_token else None
         )
-        dataset = raw_datasets[data_args.split]
 
         if max_samples is not None:
             max_samples_temp = min(len(dataset), max_samples)
             dataset = dataset.select(range(max_samples_temp))
 
-        dummy_data = [None] * len(dataset)
-        prefix_data = [dataset_attr.source_prefix] * len(dataset)
-        for column_name, target_name in [
-            ("prompt_column", "prompt"),
-            ("query_column", "query"),
-            ("response_column", "response"),
-            ("history_column", "history")
-        ]: # every dataset will have 4 columns same as each other
-            if getattr(dataset_attr, column_name) != target_name:
-                if getattr(dataset_attr, column_name):
-                    dataset = dataset.rename_column(getattr(dataset_attr, column_name), target_name)
-                else: # None or empty string
-                    dataset = dataset.add_column(target_name, dummy_data)
-        dataset = dataset.add_column("prefix", prefix_data)
+        for column_name in ["prompt", "query", "response", "history"]: # align datasets
+            if getattr(dataset_attr, column_name) and getattr(dataset_attr, column_name) != column_name:
+                dataset = dataset.rename_column(getattr(dataset_attr, column_name), column_name)
+
+        if dataset_attr.source_prefix: # add prefix
+            if data_args.streaming:
+                features = dataset.features
+                features["prefix"] = Value(dtype="string", id=None)
+                dataset = dataset.map(lambda _: {"prefix": dataset_attr.source_prefix}, features=features)
+            else:
+                prefix_data = [dataset_attr.source_prefix] * len(dataset)
+                dataset = dataset.add_column("prefix", prefix_data)
+
         all_datasets.append(dataset)
 
     if len(data_args.dataset_list) == 1:
-        all_datasets = all_datasets[0]
+        return all_datasets[0]
+    elif data_args.mix_strategy == "concat":
+        if data_args.streaming:
+            logger.warning("The samples between different datasets will not be mixed in streaming mode.")
+        return concatenate_datasets(all_datasets)
+    elif data_args.mix_strategy.startswith("interleave"):
+        if not data_args.streaming:
+            logger.warning("We recommend using `mix_strategy=concat` in non-streaming mode.")
+        stopping_strategy = "first_exhausted" if data_args.mix_strategy.endswith("under") else "all_exhausted"
+        return interleave_datasets(all_datasets, data_args.interleave_probs, stopping_strategy=stopping_strategy)
     else:
-        all_datasets = concatenate_datasets(all_datasets)
-
-    return all_datasets
+        raise ValueError("Unknown mixing strategy.")

src/llmtuner/dsets/preprocess.py

@@ -1,91 +1,88 @@
-from typing import Literal
+import tiktoken
+from typing import TYPE_CHECKING, Any, Dict, Generator, List, Literal
 from itertools import chain
-from transformers import Seq2SeqTrainingArguments
-from transformers.tokenization_utils import PreTrainedTokenizer
-
-from datasets import Dataset
 
 from llmtuner.extras.constants import IGNORE_INDEX
-from llmtuner.extras.template import get_template
-from llmtuner.hparams import DataArguments
+from llmtuner.extras.template import get_template_and_fix_tokenizer
+
+if TYPE_CHECKING:
+    from datasets import Dataset
+    from transformers import Seq2SeqTrainingArguments
+    from transformers.tokenization_utils import PreTrainedTokenizer
+    from llmtuner.hparams import DataArguments
 
 
 def preprocess_dataset(
-    dataset: Dataset,
-    tokenizer: PreTrainedTokenizer,
-    data_args: DataArguments,
-    training_args: Seq2SeqTrainingArguments,
+    dataset: "Dataset",
+    tokenizer: "PreTrainedTokenizer",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
     stage: Literal["pt", "sft", "rm", "ppo"]
-) -> Dataset:
-
+) -> "Dataset":
     column_names = list(dataset.column_names)
-    prompt_template = get_template(data_args.prompt_template)
+    template = get_template_and_fix_tokenizer(data_args.template, tokenizer)
 
-    # support question with a single answer or multiple answers
-    def get_dialog(examples):
+    def construct_example(examples: Dict[str, List[Any]]) -> Generator[Any, None, None]:
         for i in range(len(examples["prompt"])):
-            if examples["prompt"][i] and examples["response"][i]:
-                query, answer = examples["prompt"][i], examples["response"][i]
-                query = query + "\n" + examples["query"][i] if examples["query"][i] else query
-                prefix = examples["prefix"][i] if examples["prefix"][i] else ""
-                dialog = prompt_template.get_dialog(query, answer, examples["history"][i], prefix)
-                yield dialog
+            query, response = examples["prompt"][i], examples["response"][i]
+            query = query + "\n" + examples["query"][i] if "query" in examples and examples["query"][i] else query
+            history = examples["history"][i] if "history" in examples else None
+            prefix = examples["prefix"][i] if "prefix" in examples else None
+            yield query, response, history, prefix
 
-    def preprocess_pretrain_dataset(examples):
-        # build grouped texts with format `<bos> X1 X2 X3 ...` (without <eos>)
-        text_ids = tokenizer(examples["prompt"], add_special_tokens=False)["input_ids"]
-        concatenated_ids = list(chain(*text_ids))
-        total_length = len(concatenated_ids)
-        block_size = data_args.max_source_length - 1
+    def preprocess_pretrain_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
+        # build grouped texts with format `X1 X2 X3 ...` (without <eos>)
+        if isinstance(getattr(tokenizer, "tokenizer", None), tiktoken.Encoding): # for tiktoken tokenizer (Qwen)
+            kwargs = dict(allowed_special="all")
+        else:
+            kwargs = dict(add_special_tokens=False)
+
+        tokenized_examples = tokenizer(examples["prompt"], **kwargs)
+        concatenated_examples = {k: list(chain(*tokenized_examples[k])) for k in tokenized_examples.keys()}
+        total_length = len(concatenated_examples[list(concatenated_examples.keys())[0]])
+        block_size = data_args.max_source_length
         # we drop the small remainder, and if the total_length < block_size, we exclude this batch
         total_length = (total_length // block_size) * block_size
         # split by chunks of max_source_length
-        result = [[tokenizer.bos_token_id] + concatenated_ids[i: i + block_size]
-                  for i in range(0, total_length, block_size)]
-        return {
-            "input_ids": result,
-            "labels": result.copy()
+        result = {
+            k: [t[i: i + block_size] for i in range(0, total_length, block_size)]
+            for k, t in concatenated_examples.items()
         }
+        return result
 
-    def preprocess_supervised_dataset(examples):
+    def preprocess_supervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
         # build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
-        # for input with history, we build multiple input-label pairs just like:
-        # https://github.com/lm-sys/FastChat/blob/f17c092f64840fa6354ed52789dccb2daa793d0b/fastchat/train/train.py#L112
-        model_inputs = {"input_ids": [], "labels": []}
+        # for multiturn examples, we only mask the prompt part in each prompt-response pair.
+        model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
         max_length = data_args.max_source_length + data_args.max_target_length
 
-        for dialog in get_dialog(examples):
+        for query, response, history, prefix in construct_example(examples):
             input_ids, labels = [], []
 
-            for i in range(len(dialog) // 2):
-                source_ids = tokenizer.encode(text=dialog[2*i], add_special_tokens=(i == 0))
-                target_ids = tokenizer.encode(text=dialog[2*i+1], add_special_tokens=False)
-
+            for source_ids, target_ids in template.encode_multiturn(tokenizer, query, response, history, prefix):
                 if len(source_ids) > data_args.max_source_length:
                     source_ids = source_ids[:data_args.max_source_length]
-                if len(target_ids) > data_args.max_target_length - 1: # eos token
-                    target_ids = target_ids[:data_args.max_target_length - 1]
+                if len(target_ids) > data_args.max_target_length:
+                    target_ids = target_ids[:data_args.max_target_length]
 
-                if len(input_ids) + len(source_ids) + len(target_ids) + 1 > max_length:
+                if len(input_ids) + len(source_ids) + len(target_ids) > max_length:
                     break
 
-                input_ids += source_ids + target_ids + [tokenizer.eos_token_id]
-                labels += [IGNORE_INDEX] * len(source_ids) + target_ids + [tokenizer.eos_token_id]
+                input_ids += source_ids + target_ids
+                labels += [IGNORE_INDEX] * len(source_ids) + target_ids
 
             model_inputs["input_ids"].append(input_ids)
+            model_inputs["attention_mask"].append([1] * len(input_ids))
             model_inputs["labels"].append(labels)
 
         return model_inputs
 
-    def preprocess_unsupervised_dataset(examples):
+    def preprocess_unsupervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
         # build inputs with format `<bos> X` and labels with format `<bos> Y`
-        model_inputs = {"input_ids": [], "labels": []}
+        model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
 
-        for dialog in get_dialog(examples):
-            prompt, answer = "".join(dialog[:-1]), dialog[-1]
-
-            source_ids = tokenizer.encode(text=prompt, add_special_tokens=True)
-            target_ids = tokenizer.encode(text=answer, add_special_tokens=True)
+        for query, response, history, prefix in construct_example(examples):
+            source_ids, target_ids = template.encode_oneturn(tokenizer, query, response, history, prefix)
 
             if len(source_ids) > data_args.max_source_length:
                 source_ids = source_ids[:data_args.max_source_length]
@@ -93,80 +90,82 @@ def preprocess_dataset(
                 target_ids = target_ids[:data_args.max_target_length]
 
             model_inputs["input_ids"].append(source_ids)
+            model_inputs["attention_mask"].append([1] * len(source_ids))
             model_inputs["labels"].append(target_ids)
 
         return model_inputs
 
     def preprocess_pairwise_dataset(examples):
-        # build input pairs with format `<bos> X Y1 <eos>` and `<bos> X Y2 <eos>`
-        model_inputs = {"accept_ids": [], "reject_ids": []}
-        for dialog in get_dialog(examples):
-            prompt, answer = "".join(dialog[:-1]), dialog[-1]
+        # build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
+        model_inputs = {"prompt_ids": [], "chosen_ids": [], "rejected_ids": []}
+        for query, response, history, prefix in construct_example(examples):
+            prompt_ids, chosen_ids = template.encode_oneturn(tokenizer, query, response[0], history, prefix)
+            _, rejected_ids = template.encode_oneturn(tokenizer, query, response[1], history, prefix)
 
-            source_ids = tokenizer.encode(text=prompt, add_special_tokens=True)
-            accept_ids = tokenizer.encode(text=answer[0], add_special_tokens=False)
-            reject_ids = tokenizer.encode(text=answer[1], add_special_tokens=False)
+            if len(prompt_ids) > data_args.max_source_length:
+                prompt_ids = prompt_ids[:data_args.max_source_length]
+            if len(chosen_ids) > data_args.max_target_length:
+                chosen_ids = chosen_ids[:data_args.max_target_length]
+            if len(rejected_ids) > data_args.max_target_length:
+                rejected_ids = rejected_ids[:data_args.max_target_length]
 
-            if len(source_ids) > data_args.max_source_length:
-                source_ids = source_ids[:data_args.max_source_length]
-            if len(accept_ids) > data_args.max_target_length - 1: # eos token
-                accept_ids = accept_ids[:data_args.max_target_length - 1]
-            if len(reject_ids) > data_args.max_target_length - 1: # eos token
-                reject_ids = reject_ids[:data_args.max_target_length - 1]
-
-            accept_ids = source_ids + accept_ids + [tokenizer.eos_token_id]
-            reject_ids = source_ids + reject_ids + [tokenizer.eos_token_id]
-
-            model_inputs["accept_ids"].append(accept_ids)
-            model_inputs["reject_ids"].append(reject_ids)
+            model_inputs["prompt_ids"].append(prompt_ids)
+            model_inputs["chosen_ids"].append(chosen_ids)
+            model_inputs["rejected_ids"].append(rejected_ids)
         return model_inputs
 
     def print_supervised_dataset_example(example):
         print("input_ids:\n{}".format(example["input_ids"]))
         print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
         print("label_ids:\n{}".format(example["labels"]))
-        print("labels:\n{}".format(
-            tokenizer.decode([d if d != IGNORE_INDEX else tokenizer.pad_token_id for d in example["labels"]],
-                             skip_special_tokens=False)
-        ))
+        print("labels:\n{}".format(tokenizer.decode([
+            token_id if token_id != IGNORE_INDEX else tokenizer.pad_token_id for token_id in example["labels"]
+        ], skip_special_tokens=False)))
 
     def print_pairwise_dataset_example(example):
-        print("accept_ids:\n{}".format(example["accept_ids"]))
-        print("accepts:\n{}".format(tokenizer.decode(example["accept_ids"], skip_special_tokens=False)))
-        print("reject_ids:\n{}".format(example["reject_ids"]))
-        print("rejects:\n{}".format(tokenizer.decode(example["reject_ids"], skip_special_tokens=False)))
+        print("prompt_ids:\n{}".format(example["prompt_ids"]))
+        print("prompt:\n{}".format(tokenizer.decode(example["prompt_ids"], skip_special_tokens=False)))
+        print("chosen_ids:\n{}".format(example["chosen_ids"]))
+        print("chosen:\n{}".format(tokenizer.decode(example["chosen_ids"], skip_special_tokens=False)))
+        print("rejected_ids:\n{}".format(example["rejected_ids"]))
+        print("rejected:\n{}".format(tokenizer.decode(example["rejected_ids"], skip_special_tokens=False)))
 
     def print_unsupervised_dataset_example(example):
         print("input_ids:\n{}".format(example["input_ids"]))
         print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
 
     if stage == "pt":
+        dataset = dataset.filter(lambda example: example["prompt"])
         preprocess_function = preprocess_pretrain_dataset
-    elif stage == "sft":
-        preprocess_function = preprocess_unsupervised_dataset \
-            if training_args.predict_with_generate else preprocess_supervised_dataset
+        print_function = print_unsupervised_dataset_example
+    elif stage == "sft" and not training_args.predict_with_generate:
+        dataset = dataset.filter(lambda example: example["prompt"] and example["response"])
+        preprocess_function = preprocess_supervised_dataset
+        print_function = print_supervised_dataset_example
     elif stage == "rm":
+        dataset = dataset.filter(lambda example: example["prompt"] and len(example["response"]) > 1)
         preprocess_function = preprocess_pairwise_dataset
-    elif stage == "ppo":
+        print_function = print_pairwise_dataset_example
+    else:
+        dataset = dataset.filter(lambda example: example["prompt"])
         preprocess_function = preprocess_unsupervised_dataset
+        print_function = print_unsupervised_dataset_example
 
     with training_args.main_process_first(desc="dataset map pre-processing"):
+        kwargs = {}
+        if not data_args.streaming:
+            kwargs = dict(
+                num_proc=data_args.preprocessing_num_workers,
+                load_from_cache_file=not data_args.overwrite_cache,
+                desc="Running tokenizer on dataset"
+            )
+
         dataset = dataset.map(
             preprocess_function,
-            batched=True,
-            num_proc=data_args.preprocessing_num_workers,
+            batched=True,            
             remove_columns=column_names,
-            load_from_cache_file=not data_args.overwrite_cache,
-            desc="Running tokenizer on dataset"
+            **kwargs
         )
 
-        if stage == "pt":
-            print_unsupervised_dataset_example(dataset[0])
-        elif stage == "sft":
-            print_supervised_dataset_example(dataset[0])
-        elif stage == "rm":
-            print_pairwise_dataset_example(dataset[0])
-        elif stage == "ppo":
-            print_unsupervised_dataset_example(dataset[0])
-
+        print_function(next(iter(dataset)))
         return dataset

src/llmtuner/dsets/utils.py

@@ -1,16 +1,30 @@
-from typing import Dict
-from datasets import Dataset
+from typing import TYPE_CHECKING, Dict, Union
+
+if TYPE_CHECKING:
+    from datasets import Dataset, IterableDataset
+    from transformers import TrainingArguments
+    from llmtuner.hparams import DataArguments
 
 
 def split_dataset(
-    dataset: Dataset, dev_ratio: float, do_train: bool
-) -> Dict[str, Dataset]:
-    # Split the dataset
-    if do_train:
-        if dev_ratio > 1e-6:
-            dataset = dataset.train_test_split(test_size=dev_ratio)
-            return {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
+    dataset: Union["Dataset", "IterableDataset"],
+    data_args: "DataArguments",
+    training_args: "TrainingArguments"
+) -> Dict[str, "Dataset"]:
+    if training_args.do_train:
+        if data_args.val_size > 1e-6: # Split the dataset
+            if data_args.streaming:
+                val_set = dataset.take(int(data_args.val_size))
+                train_set = dataset.skip(int(data_args.val_size))
+                dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
+                return {"train_dataset": train_set, "eval_dataset": val_set}
+            else:
+                val_size = int(data_args.val_size) if data_args.val_size > 1 else data_args.val_size
+                dataset = dataset.train_test_split(test_size=val_size, seed=training_args.seed)
+                return {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
         else:
+            if data_args.streaming:
+                dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
             return {"train_dataset": dataset}
     else: # do_eval or do_predict
         return {"eval_dataset": dataset}

src/llmtuner/extras/callbacks.py

@@ -1,74 +1,128 @@
 import os
 import json
 import time
+from typing import TYPE_CHECKING
 from datetime import timedelta
 
-from transformers import (
-    TrainerCallback,
-    TrainerControl,
-    TrainerState,
-    TrainingArguments
-)
-from transformers.trainer_callback import TrainerControl, TrainerState
-from transformers.training_args import TrainingArguments
+from transformers import TrainerCallback
+from transformers.trainer_utils import has_length
+
+from llmtuner.extras.constants import LOG_FILE_NAME
+from llmtuner.extras.logging import get_logger
+
+if TYPE_CHECKING:
+    from transformers import TrainingArguments, TrainerState, TrainerControl
+
+
+logger = get_logger(__name__)
 
 
 class LogCallback(TrainerCallback):
 
     def __init__(self, runner=None):
         self.runner = runner
+        self.in_training = False
         self.start_time = time.time()
-        self.tracker = {}
+        self.cur_steps = 0
+        self.max_steps = 0
+        self.elapsed_time = ""
+        self.remaining_time = ""
 
-    def on_train_begin(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
+    def timing(self):
+        cur_time = time.time()
+        elapsed_time = cur_time - self.start_time
+        avg_time_per_step = elapsed_time / self.cur_steps if self.cur_steps != 0 else 0
+        remaining_time = (self.max_steps - self.cur_steps) * avg_time_per_step
+        self.elapsed_time = str(timedelta(seconds=int(elapsed_time)))
+        self.remaining_time = str(timedelta(seconds=int(remaining_time)))
+
+    def on_train_begin(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
         r"""
         Event called at the beginning of training.
         """
-        self.start_time = time.time()
+        if state.is_local_process_zero:
+            self.in_training = True
+            self.start_time = time.time()
+            self.max_steps = state.max_steps
+            if os.path.exists(os.path.join(args.output_dir, LOG_FILE_NAME)):
+                logger.warning("Previous log file in this folder will be deleted.")
+                os.remove(os.path.join(args.output_dir, LOG_FILE_NAME))
 
-    def on_step_begin(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
+    def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
         r"""
-        Event called at the beginning of a training step. If using gradient accumulation, one training step
-        might take several inputs.
+        Event called at the end of training.
         """
-        if self.runner is not None and self.runner.aborted:
-            control.should_epoch_stop = True
-            control.should_training_stop = True
+        if state.is_local_process_zero:
+            self.in_training = False
+            self.cur_steps = 0
+            self.max_steps = 0
 
-    def on_substep_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
+    def on_substep_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
         r"""
         Event called at the end of an substep during gradient accumulation.
         """
-        if self.runner is not None and self.runner.aborted:
+        if state.is_local_process_zero and self.runner is not None and self.runner.aborted:
             control.should_epoch_stop = True
             control.should_training_stop = True
 
-    def on_log(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs) -> None:
+    def on_step_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called at the end of a training step.
+        """
+        if state.is_local_process_zero:
+            self.cur_steps = state.global_step
+            self.timing()
+            if self.runner is not None and self.runner.aborted:
+                control.should_epoch_stop = True
+                control.should_training_stop = True
+
+    def on_evaluate(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called after an evaluation phase.
+        """
+        if state.is_local_process_zero and not self.in_training:
+            self.cur_steps = 0
+            self.max_steps = 0
+
+    def on_predict(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", *other, **kwargs):
+        r"""
+        Event called after a successful prediction.
+        """
+        if state.is_local_process_zero and not self.in_training:
+            self.cur_steps = 0
+            self.max_steps = 0
+
+    def on_log(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs) -> None:
         r"""
         Event called after logging the last logs.
         """
-        if not state.is_world_process_zero:
+        if not state.is_local_process_zero:
             return
 
-        cur_time = time.time()
-        cur_steps = state.log_history[-1].get("step")
-        elapsed_time = cur_time - self.start_time
-        avg_time_per_step = elapsed_time / cur_steps if cur_steps != 0 else 0
-        remaining_steps = state.max_steps - cur_steps
-        remaining_time = remaining_steps * avg_time_per_step
-        self.tracker = {
-            "current_steps": cur_steps,
-            "total_steps": state.max_steps,
-            "loss": state.log_history[-1].get("loss", None),
-            "eval_loss": state.log_history[-1].get("eval_loss", None),
-            "predict_loss": state.log_history[-1].get("predict_loss", None),
-            "reward": state.log_history[-1].get("reward", None),
-            "learning_rate": state.log_history[-1].get("learning_rate", None),
-            "epoch": state.log_history[-1].get("epoch", None),
-            "percentage": round(cur_steps / state.max_steps * 100, 2) if state.max_steps != 0 else 100,
-            "elapsed_time": str(timedelta(seconds=int(elapsed_time))),
-            "remaining_time": str(timedelta(seconds=int(remaining_time)))
-        }
+        logs = dict(
+            current_steps=self.cur_steps,
+            total_steps=self.max_steps,
+            loss=state.log_history[-1].get("loss", None),
+            eval_loss=state.log_history[-1].get("eval_loss", None),
+            predict_loss=state.log_history[-1].get("predict_loss", None),
+            reward=state.log_history[-1].get("reward", None),
+            learning_rate=state.log_history[-1].get("learning_rate", None),
+            epoch=state.log_history[-1].get("epoch", None),
+            percentage=round(self.cur_steps / self.max_steps * 100, 2) if self.max_steps != 0 else 100,
+            elapsed_time=self.elapsed_time,
+            remaining_time=self.remaining_time
+        )
         os.makedirs(args.output_dir, exist_ok=True)
         with open(os.path.join(args.output_dir, "trainer_log.jsonl"), "a", encoding="utf-8") as f:
-            f.write(json.dumps(self.tracker) + "\n")
+            f.write(json.dumps(logs) + "\n")
+
+    def on_prediction_step(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called after a prediction step.
+        """
+        eval_dataloader = kwargs.pop("eval_dataloader", None)
+        if state.is_local_process_zero and has_length(eval_dataloader) and not self.in_training:
+            if self.max_steps == 0:
+                self.max_steps = len(eval_dataloader)
+            self.cur_steps += 1
+            self.timing()

src/llmtuner/extras/constants.py

@@ -1,10 +1,12 @@
 IGNORE_INDEX = -100
 
+LOG_FILE_NAME = "trainer_log.jsonl"
+
 VALUE_HEAD_FILE_NAME = "value_head.bin"
 
 FINETUNING_ARGS_NAME = "finetuning_args.json"
 
-LAYERNORM_NAMES = ["norm", "ln_f", "ln_attn", "ln_mlp"] # for LLaMA, BLOOM and Falcon settings
+LAYERNORM_NAMES = ["norm", "ln_f", "ln_attn", "ln_mlp"]
 
 METHODS = ["full", "freeze", "lora"]
 
@@ -25,15 +27,19 @@ SUPPORTED_MODELS = {
     "BLOOMZ-560M": "bigscience/bloomz-560m",
     "BLOOMZ-3B": "bigscience/bloomz-3b",
     "BLOOMZ-7B1-mt": "bigscience/bloomz-7b1-mt",
-    "Falcon-7B-Base": "tiiuae/falcon-7b",
+    "Falcon-7B": "tiiuae/falcon-7b",
     "Falcon-7B-Chat": "tiiuae/falcon-7b-instruct",
-    "Falcon-40B-Base": "tiiuae/falcon-40b",
+    "Falcon-40B": "tiiuae/falcon-40b",
     "Falcon-40B-Chat": "tiiuae/falcon-40b-instruct",
     "Baichuan-7B": "baichuan-inc/Baichuan-7B",
-    "Baichuan-13B-Base": "baichuan-inc/Baichuan-13B-Base",
+    "Baichuan-13B": "baichuan-inc/Baichuan-13B-Base",
     "Baichuan-13B-Chat": "baichuan-inc/Baichuan-13B-Chat",
-    "InternLM-7B-Base": "internlm/internlm-7b",
-    "InternLM-7B-Chat": "internlm/internlm-chat-7b"
+    "InternLM-7B": "internlm/internlm-7b",
+    "InternLM-7B-Chat": "internlm/internlm-chat-7b",
+    "Qwen-7B": "Qwen/Qwen-7B",
+    "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
+    "XVERSE-13B": "xverse/XVERSE-13B",
+    "ChatGLM2-6B": "THUDM/chatglm2-6b"
 }
 
 DEFAULT_MODULE = {
@@ -43,5 +49,8 @@ DEFAULT_MODULE = {
     "BLOOMZ": "query_key_value",
     "Falcon": "query_key_value",
     "Baichuan": "W_pack",
-    "InternLM": "q_proj,v_proj"
+    "InternLM": "q_proj,v_proj",
+    "Qwen": "c_attn",
+    "XVERSE": "q_proj,v_proj",
+    "ChatGLM2": "query_key_value"
 }

src/llmtuner/extras/logging.py

@@ -16,8 +16,16 @@ class LoggerHandler(logging.Handler):
         self.log += "\n\n"
 
 
-def get_logger(name: str) -> logging.Logger:
+def reset_logging():
+    r"""
+    Removes basic config of root logger
+    """
+    root = logging.getLogger()
+    list(map(root.removeHandler, root.handlers))
+    list(map(root.removeFilter, root.filters))
 
+
+def get_logger(name: str) -> logging.Logger:
     formatter = logging.Formatter(
         fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
         datefmt="%m/%d/%Y %H:%M:%S"

src/llmtuner/extras/misc.py

@@ -1,12 +1,17 @@
 import torch
-from typing import List, Optional
-
-from transformers.modeling_utils import PreTrainedModel
-from transformers.generation.utils import LogitsProcessorList
-from transformers.generation.logits_process import LogitsProcessor
+from typing import TYPE_CHECKING, List, Optional, Tuple
+from transformers import (
+    LogitsProcessor,
+    LogitsProcessorList,
+    StoppingCriteria,
+    StoppingCriteriaList
+)
 
 from llmtuner.extras.constants import LAYERNORM_NAMES
 
+if TYPE_CHECKING:
+    from transformers.modeling_utils import PreTrainedModel
+
 
 class AverageMeter:
     r"""
@@ -28,7 +33,6 @@ class AverageMeter:
         self.avg = self.sum / self.count
 
 
-# Avoid runtime error in model.generate(do_sample=True).
 class InvalidScoreLogitsProcessor(LogitsProcessor):
 
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
@@ -44,29 +48,53 @@ def get_logits_processor() -> LogitsProcessorList:
     return logits_processor
 
 
-def print_trainable_params(model: torch.nn.Module) -> None:
+class StopWordsCriteria(StoppingCriteria):
+
+    def __init__(self, stop_ids: List[int]) -> None:
+        super().__init__()
+        self.stop_ids = stop_ids
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        return any([stop_id in input_ids[:, -1] for stop_id in self.stop_ids])
+
+
+def get_stopping_criteria(stop_ids: List[int]) -> StoppingCriteriaList:
+    stopping_criteria = StoppingCriteriaList()
+    stopping_criteria.append(StopWordsCriteria(stop_ids))
+    return stopping_criteria
+
+
+def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
+    r"""
+    Returns the number of trainable parameters and number of all parameters in the model.
+    """
     trainable_params, all_param = 0, 0
     for param in model.parameters():
         num_params = param.numel()
         # if using DS Zero 3 and the weights are initialized empty
         if num_params == 0 and hasattr(param, "ds_numel"):
             num_params = param.ds_numel
+
+        # Due to the design of 4bit linear layers from bitsandbytes, multiply the number of parameters by 2
+        if param.__class__.__name__ == "Params4bit":
+            num_params = num_params * 2
+
         all_param += num_params
         if param.requires_grad:
             trainable_params += num_params
-    print("trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format(
-                trainable_params, all_param, 100 * trainable_params / all_param))
+
+    return trainable_params, all_param
 
 
 # Includes: (1) cast the layernorm in fp32 (2) make output embedding layer require grads (3) upcast the lm_head to fp32
 # Inspired by: https://github.com/huggingface/peft/blob/c0209c35abbf88c63aa267800d98a8e212ed0a42/src/peft/utils/other.py#L35
 def prepare_model_for_training(
-    model: PreTrainedModel,
+    model: "PreTrainedModel",
     finetuning_type: str,
-    output_embedding_layer_name: Optional[str] = "lm_head",
+    output_layer_name: Optional[str] = "lm_head",
     use_gradient_checkpointing: Optional[bool] = True,
     layer_norm_names: Optional[List[str]] = LAYERNORM_NAMES
-) -> PreTrainedModel:
+) -> "PreTrainedModel":
 
     for name, param in model.named_parameters():
         if param.ndim == 1 and any(layer_norm_name in name for layer_norm_name in layer_norm_names):
@@ -83,19 +111,23 @@ def prepare_model_for_training(
         model.gradient_checkpointing_enable()
         model.config.use_cache = False # turn off when gradient checkpointing is enabled
 
-    if finetuning_type != "full" and hasattr(model, output_embedding_layer_name):
-        output_embedding_layer: torch.nn.Linear = getattr(model, output_embedding_layer_name)
-        input_dtype = output_embedding_layer.weight.dtype
+    if finetuning_type != "full" and hasattr(model, output_layer_name):
+        if hasattr(model, "config") and hasattr(model.config, "pretraining_tp"):
+            model.config.pretraining_tp = 1 # disable TP for LoRA (https://github.com/huggingface/peft/pull/728)
+
+        output_layer: torch.nn.Linear = getattr(model, output_layer_name)
+        input_dtype = output_layer.weight.dtype
 
         class CastOutputToFloat(torch.nn.Sequential):
 
             def forward(self, x: torch.Tensor) -> torch.Tensor:
                 return super().forward(x.to(input_dtype)).to(torch.float32)
 
-        setattr(model, output_embedding_layer_name, CastOutputToFloat(output_embedding_layer))
+        setattr(model, output_layer_name, CastOutputToFloat(output_layer))
 
     return model
 
+
 def torch_gc() -> None:
     r"""
     Collects GPU memory.
@@ -103,3 +135,25 @@ def torch_gc() -> None:
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()
+
+
+def dispatch_model(model: "PreTrainedModel") -> "PreTrainedModel":
+    r"""
+    Dispatches a pre-trained model to GPUs with balanced memory.
+    Borrowed from: https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/modeling_utils.py#L2803
+    """
+    if torch.cuda.device_count() > 1:
+        from accelerate import dispatch_model
+        from accelerate.utils import infer_auto_device_map, get_balanced_memory
+
+        if model._no_split_modules is None:
+            raise ValueError("The model class needs to implement the `_no_split_modules` attribute.")
+
+        kwargs = {"dtype": model.dtype, "no_split_module_classes": model._no_split_modules}
+        max_memory = get_balanced_memory(model, **kwargs)
+        # Make sure tied weights are tied before creating the device map.
+        model.tie_weights()
+        device_map = infer_auto_device_map(model, max_memory=max_memory, **kwargs)
+        return dispatch_model(model, device_map)
+    else:
+        return model.cuda()

src/llmtuner/extras/save_and_load.py

@@ -12,8 +12,8 @@ from llmtuner.extras.logging import get_logger
 logger = get_logger(__name__)
 
 
-def get_state_dict(model: torch.nn.Module) -> Dict[str, torch.Tensor]: # get state dict containing trainable parameters
-    state_dict = model.state_dict()
+def get_state_dict(model: torch.nn.Module) -> Dict[str, torch.Tensor]:
+    state_dict: Dict[str, torch.Tensor] = model.state_dict()
     filtered_state_dict = {}
 
     for k, v in model.named_parameters():

src/llmtuner/extras/template.py

@@ -1,64 +1,213 @@
-from typing import Dict, List, Optional, Tuple
+import tiktoken
 from dataclasses import dataclass
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+
+from llmtuner.extras.logging import get_logger
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer
+
+
+logger = get_logger(__name__)
 
 
 @dataclass
 class Template:
 
-    prefix: str
-    prompt: str
-    sep: str
+    prefix: List[Union[str, Dict[str, str]]]
+    prompt: List[Union[str, Dict[str, str]]]
+    sep: List[Union[str, Dict[str, str]]]
+    stop_words: List[str]
     use_history: bool
 
-    def get_prompt(
-        self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = ""
-    ) -> str:
+    def encode_oneturn(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        query: str,
+        resp: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        prefix: Optional[str] = None
+    ) -> Tuple[List[int], List[int]]:
         r"""
-        Returns a string containing prompt without response.
+        Returns a single pair of token ids representing prompt and response respectively.
         """
-        return "".join(self._format_example(query, history, prefix))
+        prefix, history = self._format(query, resp, history, prefix)
+        encoded_pairs = self._encode(tokenizer, prefix, history)
+        prompt_ids = []
+        for query_ids, resp_ids in encoded_pairs[:-1]:
+            prompt_ids = prompt_ids + query_ids + resp_ids
+        prompt_ids = prompt_ids + encoded_pairs[-1][0]
+        return prompt_ids, encoded_pairs[-1][1]
 
-    def get_dialog(
-        self, query: str, resp: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = ""
-    ) -> List[str]:
+    def encode_multiturn(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        query: str,
+        resp: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        prefix: Optional[str] = None
+    ) -> List[Tuple[List[int], List[int]]]:
         r"""
-        Returns a list containing 2 * n elements where the 2k-th is a query and the (2k+1)-th is a response.
+        Returns multiple pairs of token ids representing prompts and responses respectively.
         """
-        return self._format_example(query, history, prefix) + [resp]
+        prefix, history = self._format(query, resp, history, prefix)
+        encoded_pairs = self._encode(tokenizer, prefix, history)
+        return encoded_pairs
 
-    def _format_example(
-        self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = ""
-    ) -> List[str]:
-        prefix = prefix or self.prefix # use prefix if provided
-        prefix = prefix + self.sep if prefix else "" # add separator for non-empty prefix
+    def _format(
+        self,
+        query: str,
+        resp: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        prefix: Optional[str] = None
+    ) -> Tuple[List[Union[str, Dict[str, str]]], List[Tuple[str, str]]]:
+        r"""
+        Aligns inputs to the standard format.
+        """
+        prefix = [prefix] if prefix else self.prefix # use prefix if provided
         history = history if (history and self.use_history) else []
-        history = history + [(query, "<dummy>")]
-        convs = []
-        for turn_idx, (user_query, bot_resp) in enumerate(history):
+        history = history + [(query, resp)]
+        return prefix, history
+
+    def _get_special_ids(
+        self,
+        tokenizer: "PreTrainedTokenizer"
+    ) -> Tuple[List[int], List[int]]:
+        if tokenizer.bos_token_id:
+            bos_ids = [tokenizer.bos_token_id]
+        else:
+            bos_ids = [] # bos token is optional
+
+        if tokenizer.eos_token_id:
+            eos_ids = [tokenizer.eos_token_id]
+        else:
+            raise ValueError("EOS token is required.")
+
+        return bos_ids, eos_ids
+
+    def _encode(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        prefix: List[Union[str, Dict[str, str]]],
+        history: List[Tuple[str, str]]
+    ) -> List[Tuple[List[int], List[int]]]:
+        r"""
+        Encodes formatted inputs to pairs of token ids.
+        Turn 0: bos + prefix + sep + query    resp + eos
+        Turn t: sep + bos + query             resp + eos
+        """
+        bos_ids, eos_ids = self._get_special_ids(tokenizer)
+        sep_ids = self._convert_inputs_to_ids(tokenizer, context=self.sep)
+        encoded_pairs = []
+        for turn_idx, (query, resp) in enumerate(history):
             if turn_idx == 0:
-                convs.append(prefix + self.prompt.format(query=user_query))
-                convs.append(bot_resp)
+                if prefix: # has prefix
+                    prefix_ids = bos_ids + self._convert_inputs_to_ids(tokenizer, context=prefix) + sep_ids
+                else:
+                    prefix_ids = bos_ids
             else:
-                convs.append(self.sep + self.prompt.format(query=user_query))
-                convs.append(bot_resp)
-        return convs[:-1] # drop last
+                prefix_ids = sep_ids + bos_ids
+
+            query_ids = self._convert_inputs_to_ids(tokenizer, context=self.prompt, query=query, idx=str(turn_idx))
+            resp_ids = self._convert_inputs_to_ids(tokenizer, context=[resp])
+            encoded_pairs.append((prefix_ids + query_ids, resp_ids + eos_ids))
+        return encoded_pairs
+
+    def _convert_inputs_to_ids(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        context: List[Union[str, Dict[str, str]]],
+        query: Optional[str] = "",
+        idx: Optional[str] = ""
+    ) -> List[int]:
+        r"""
+        Converts context to token ids.
+        """
+        if isinstance(getattr(tokenizer, "tokenizer", None), tiktoken.Encoding): # for tiktoken tokenizer (Qwen)
+            kwargs = dict(allowed_special="all")
+        else:
+            kwargs = dict(add_special_tokens=False)
+
+        token_ids = []
+        for elem in context:
+            if isinstance(elem, str):
+                elem = elem.replace("{{query}}", query, 1)
+                elem = elem.replace("{{idx}}", idx, 1)
+                token_ids = token_ids + tokenizer.encode(elem, **kwargs)
+            elif isinstance(elem, dict):
+                token_ids = token_ids + [tokenizer.convert_tokens_to_ids(elem.get("token"))]
+            else:
+                raise NotImplementedError
+        return token_ids
+
+
+@dataclass
+class Llama2Template(Template):
+
+    def _encode(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        prefix: List[Union[str, Dict[str, str]]],
+        history: List[Tuple[str, str]]
+    ) -> List[Tuple[List[int], List[int]]]:
+        r"""
+        Encodes formatted inputs to pairs of token ids.
+        Turn 0: bos + prefix + query    resp + eos
+        Turn t: bos + query             resp + eos
+        """
+        bos_ids, eos_ids = self._get_special_ids(tokenizer)
+        encoded_pairs = []
+        assert isinstance(prefix[0], str), "LLaMA-2 template only accepts list containing a single string."
+        for turn_idx, (query, resp) in enumerate(history):
+            if turn_idx == 0: # llama2 template has not sep_ids
+                query = prefix[0] + query
+            query_ids = self._convert_inputs_to_ids(tokenizer, context=self.prompt, query=query)
+            resp_ids = self._convert_inputs_to_ids(tokenizer, context=[resp])
+            encoded_pairs.append((bos_ids + query_ids, resp_ids + eos_ids))
+        return encoded_pairs
 
 
 templates: Dict[str, Template] = {}
 
 
-def register_template(name: str, prefix: str, prompt: str, sep: str, use_history: bool) -> None:
-    templates[name] = Template(
+def register_template(
+    name: str,
+    prefix: List[Union[str, Dict[str, str]]],
+    prompt: List[Union[str, Dict[str, str]]],
+    sep: List[Union[str, Dict[str, str]]],
+    stop_words: List[str],
+    use_history: bool
+) -> None:
+    template_class = Llama2Template if "llama2" in name else Template
+    templates[name] = template_class(
         prefix=prefix,
         prompt=prompt,
         sep=sep,
+        stop_words=stop_words,
         use_history=use_history
     )
 
 
-def get_template(name: str) -> Template:
+def get_template_and_fix_tokenizer(
+    name: str,
+    tokenizer: "PreTrainedTokenizer"
+) -> Template:
     template = templates.get(name, None)
     assert template is not None, "Template {} does not exist.".format(name)
+
+    if len(template.stop_words): # inplace method
+        tokenizer.eos_token = template.stop_words[0]
+        logger.info("Replace eos token: {}".format(tokenizer.eos_token))
+
+    if tokenizer.eos_token_id is None:
+        tokenizer.eos_token = "<|endoftext|>"
+        logger.info("Add eos token: {}".format(tokenizer.eos_token))
+
+    if tokenizer.pad_token_id is None:
+        tokenizer.pad_token = tokenizer.eos_token
+        logger.info("Add pad token: {}".format(tokenizer.pad_token))
+
+    tokenizer.add_special_tokens(dict(additional_special_tokens=template.stop_words))
     return template
 
 
@@ -67,9 +216,12 @@ Supports language model inference without histories.
 """
 register_template(
     name="vanilla",
-    prefix="",
-    prompt="{query}",
-    sep="",
+    prefix=[],
+    prompt=[
+        "{{query}}"
+    ],
+    sep=[],
+    stop_words=[],
     use_history=False
 )
 
@@ -79,10 +231,17 @@ Default template.
 """
 register_template(
     name="default",
-    prefix="A chat between a curious user and an artificial intelligence assistant. "
-           "The assistant gives helpful, detailed, and polite answers to the user's questions.",
-    prompt="Human: {query}\nAssistant: ",
-    sep="\n",
+    prefix=[
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ],
+    prompt=[
+        "Human: {{query}}\nAssistant: "
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[],
     use_history=True
 )
 
@@ -94,16 +253,38 @@ Supports: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
 """
 register_template(
     name="llama2",
-    prefix="<<SYS>>\nYou are a helpful, respectful and honest assistant. "
-           "Always answer as helpfully as possible, while being safe.  "
-           "Your answers should not include any harmful, unethical, "
-           "racist, sexist, toxic, dangerous, or illegal content. "
-           "Please ensure that your responses are socially unbiased and positive in nature.\n"
-           "If a question does not make any sense, or is not factually coherent, "
-           "explain why instead of answering something not correct. "
-           "If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n",
-    prompt=" [INST] {query} [/INST] ",
-    sep="</s>",
+    prefix=[
+        "<<SYS>>\nYou are a helpful, respectful and honest assistant. "
+        "Always answer as helpfully as possible, while being safe.  "
+        "Your answers should not include any harmful, unethical, "
+        "racist, sexist, toxic, dangerous, or illegal content. "
+        "Please ensure that your responses are socially unbiased and positive in nature.\n"
+        "If a question does not make any sense, or is not factually coherent, "
+        "explain why instead of answering something not correct. "
+        "If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"
+    ],
+    prompt=[
+        "[INST] {{query}} [/INST] "
+    ],
+    sep=[],
+    stop_words=[],
+    use_history=True
+)
+
+
+r"""
+Supports: https://github.com/ymcui/Chinese-LLaMA-Alpaca-2
+"""
+register_template(
+    name="llama2_zh",
+    prefix=[
+        "<<SYS>>\nYou are a helpful assistant. 你是一个乐于助人的助手。\n<</SYS>>\n\n"
+    ],
+    prompt=[
+        "[INST] {{query}} [/INST] "
+    ],
+    sep=[],
+    stop_words=[],
     use_history=True
 )
 
@@ -114,10 +295,17 @@ Supports: https://huggingface.co/tatsu-lab/alpaca-7b-wdiff
 """
 register_template(
     name="alpaca",
-    prefix="Below is an instruction that describes a task. "
-           "Write a response that appropriately completes the request.",
-    prompt="### Instruction:\n{query}\n\n### Response:\n",
-    sep="\n\n",
+    prefix=[
+        "Below is an instruction that describes a task. "
+        "Write a response that appropriately completes the request."
+    ],
+    prompt=[
+        "### Instruction:\n{{query}}\n\n### Response:\n"
+    ],
+    sep=[
+        "\n\n"
+    ],
+    stop_words=[],
     use_history=True
 )
 
@@ -128,10 +316,15 @@ Supports: https://huggingface.co/lmsys/vicuna-7b-delta-v1.1
 """
 register_template(
     name="vicuna",
-    prefix="A chat between a curious user and an artificial intelligence assistant. "
-           "The assistant gives helpful, detailed, and polite answers to the user's questions.",
-    prompt="USER: {query} ASSISTANT: ",
-    sep="</s>",
+    prefix=[
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ],
+    prompt=[
+        "USER: {{query}} ASSISTANT: "
+    ],
+    sep=[],
+    stop_words=[],
     use_history=True
 )
 
@@ -141,9 +334,14 @@ Supports: https://huggingface.co/BelleGroup/BELLE-LLaMA-EXT-13B
 """
 register_template(
     name="belle",
-    prefix="",
-    prompt="Human: {query}\n\nBelle: ",
-    sep="\n\n",
+    prefix=[],
+    prompt=[
+        "Human: {{query}}\n\nBelle: "
+    ],
+    sep=[
+        "\n\n"
+    ],
+    stop_words=[],
     use_history=True
 )
 
@@ -153,9 +351,14 @@ Supports: https://github.com/CVI-SZU/Linly
 """
 register_template(
     name="linly",
-    prefix="",
-    prompt="User: {query}\nBot: ",
-    sep="\n",
+    prefix=[],
+    prompt=[
+        "User: {{query}}\nBot: "
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[],
     use_history=True
 )
 
@@ -165,9 +368,14 @@ Supports: https://github.com/Neutralzz/BiLLa
 """
 register_template(
     name="billa",
-    prefix="",
-    prompt="Human: {query}\nAssistant: ",
-    sep="\n",
+    prefix=[],
+    prompt=[
+        "Human: {{query}}\nAssistant: "
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[],
     use_history=True
 )
 
@@ -177,9 +385,17 @@ Supports: https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1
 """
 register_template(
     name="ziya",
-    prefix="",
-    prompt="<human>:{query}\n<bot>:",
-    sep="\n",
+    prefix=[],
+    prompt=[
+        {"token": "<human>"},
+        ":{{query}}\n",
+        {"token": "<bot>"},
+        ":"
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[],
     use_history=True
 )
 
@@ -189,10 +405,17 @@ Supports: https://huggingface.co/qhduan/aquilachat-7b
 """
 register_template(
     name="aquila",
-    prefix="A chat between a curious human and an artificial intelligence assistant. "
-           "The assistant gives helpful, detailed, and polite answers to the human's questions.",
-    prompt="Human: {query}###Assistant: ",
-    sep="###",
+    prefix=[
+        "A chat between a curious human and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the human's questions."
+    ],
+    prompt=[
+        "Human: {{query}}###Assistant: "
+    ],
+    sep=[
+        "###"
+    ],
+    stop_words=[],
     use_history=True
 )
 
@@ -202,9 +425,18 @@ Supports: https://huggingface.co/internlm/internlm-chat-7b
 """
 register_template(
     name="intern",
-    prefix="",
-    prompt="<|User|>:{query}<eoh>\n<|Bot|>:",
-    sep="<eoa>\n",
+    prefix=[],
+    prompt=[
+        "<|User|>:{{query}}",
+        {"token": "<eoh>"},
+        "\n<|Bot|>:"
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[
+        "<eoa>"
+    ],
     use_history=True
 )
 
@@ -214,9 +446,17 @@ Supports: https://huggingface.co/baichuan-inc/Baichuan-13B-Chat
 """
 register_template(
     name="baichuan",
-    prefix="",
-    prompt="<reserved_102>{query}<reserved_103>",
-    sep="</s>",
+    prefix=[
+        {"token": "<reserved_102>"} # user token (a little difference in the first turn)
+    ],
+    prompt=[
+        "{{query}}",
+        {"token": "<reserved_103>"} # assistant token
+    ],
+    sep=[],
+    stop_words=[
+        "<reserved_102>" # user token
+    ],
     use_history=True
 )
 
@@ -227,8 +467,71 @@ Supports: https://huggingface.co/HuggingFaceH4/starchat-alpha
 """
 register_template(
     name="starchat",
-    prefix="<|system|>\n",
-    prompt="<|user|>\n{query}<|end|>\n<|assistant|>\n",
-    sep="<|end|>\n",
+    prefix=[
+        {"token": "<|system|>"},
+        "\n",
+        {"token": "<|end|>"}
+    ],
+    prompt=[
+        {"token": "<|user|>"},
+        "\n{{query}}",
+        {"token": "<|end|>"},
+        "\n",
+        {"token": "<|assistant|>"}
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[
+        "<|end|>"
+    ],
+    use_history=True
+)
+
+
+r"""
+Supports: https://huggingface.co/Qwen/Qwen-7B-Chat
+"""
+register_template(
+    name="chatml",
+    prefix=[
+        {"token": "<|im_start|>"},
+        "system\nYou are a helpful assistant.",
+        {"token": "<|im_end|>"}
+    ],
+    prompt=[
+        {"token": "<|im_start|>"},
+        "user\n{{query}}",
+        {"token": "<|im_end|>"},
+        "\n",
+        {"token": "<|im_start|>"},
+        "assistant\n"
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[
+        "<|im_end|>"
+    ],
+    use_history=True
+)
+
+
+r"""
+Supports: https://huggingface.co/THUDM/chatglm2-6b
+"""
+register_template(
+    name="chatglm2",
+    prefix=[
+        {"token": "[gMASK]"},
+        {"token": "sop"}
+    ],
+    prompt=[
+        "[Round {{idx}}]\n\n问：{{query}}\n\n答："
+    ],
+    sep=[
+        "\n\n"
+    ],
+    stop_words=[],
     use_history=True
 )

src/llmtuner/hparams/data_args.py

@@ -1,6 +1,6 @@
 import os
 import json
-from typing import List, Optional
+from typing import List, Literal, Optional
 from dataclasses import dataclass, field
 
 
@@ -16,19 +16,22 @@ class DatasetAttr:
         return self.dataset_name
 
     def __post_init__(self):
-        self.prompt_column = "instruction"
-        self.query_column = "input"
-        self.response_column = "output"
-        self.history_column = None
+        self.prompt = "instruction"
+        self.query = "input"
+        self.response = "output"
+        self.history = None
 
 
 @dataclass
 class DataArguments:
-    """
+    r"""
     Arguments pertaining to what data we are going to input our model for training and evaluation.
     """
+    template: str = field(
+        metadata={"help": "Which template to use for constructing prompts in training and inference."}
+    )
     dataset: Optional[str] = field(
-        default="alpaca_zh",
+        default="alpaca_en",
         metadata={"help": "The name of provided dataset(s) to use. Use commas to separate multiple datasets."}
     )
     dataset_dir: Optional[str] = field(
@@ -39,6 +42,22 @@ class DataArguments:
         default="train",
         metadata={"help": "Which dataset split to use for training and evaluation."}
     )
+    streaming: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Enable streaming mode."}
+    )
+    buffer_size: Optional[int] = field(
+        default=16384,
+        metadata={"help": "Size of the buffer to randomly sample examples from in streaming mode."}
+    )
+    mix_strategy: Optional[Literal["concat", "interleave_under", "interleave_over"]] = field(
+        default="concat",
+        metadata={"help": "Strategy to use in dataset mixing."}
+    )
+    interleave_probs: Optional[str] = field(
+        default=None,
+        metadata={"help": "Probabilities to sample data from datasets. Use commas to separate multiple datasets."}
+    )
     overwrite_cache: Optional[bool] = field(
         default=False,
         metadata={"help": "Overwrite the cached training and evaluation sets."}
@@ -71,13 +90,9 @@ class DataArguments:
         default=None,
         metadata={"help": "A prefix to add before every source text. Use `|` to separate multiple prefixes in training."}
     )
-    dev_ratio: Optional[float] = field(
+    val_size: Optional[float] = field(
         default=0,
-        metadata={"help": "Proportion of the dataset to include in the development set, should be between 0.0 and 1.0."}
-    )
-    prompt_template: Optional[str] = field(
-        default="default",
-        metadata={"help": "Which template to use for constructing prompts in training and inference."}
+        metadata={"help": "Size of the development set, should be an integer or a float in range `[0,1)`."}
     )
 
     def init_for_training(self): # support mixing multiple datasets
@@ -92,6 +107,9 @@ class DataArguments:
         else:
             prefix_list = [None] * len(dataset_names)
 
+        if self.interleave_probs is not None:
+            self.interleave_probs = [float(prob.strip()) for prob in self.interleave_probs.split(",")]
+
         self.dataset_list: List[DatasetAttr] = []
         for i, name in enumerate(dataset_names):
             if name not in dataset_info:
@@ -111,9 +129,9 @@ class DataArguments:
             dataset_attr.source_prefix = prefix_list[i]
 
             if "columns" in dataset_info[name]:
-                dataset_attr.prompt_column = dataset_info[name]["columns"].get("prompt", None)
-                dataset_attr.query_column = dataset_info[name]["columns"].get("query", None)
-                dataset_attr.response_column = dataset_info[name]["columns"].get("response", None)
-                dataset_attr.history_column = dataset_info[name]["columns"].get("history", None)
+                dataset_attr.prompt = dataset_info[name]["columns"].get("prompt", None)
+                dataset_attr.query = dataset_info[name]["columns"].get("query", None)
+                dataset_attr.response = dataset_info[name]["columns"].get("response", None)
+                dataset_attr.history = dataset_info[name]["columns"].get("history", None)
 
-            self.dataset_list.append(dataset_attr)
+            self.dataset_list.append(dataset_attr)

src/llmtuner/hparams/finetuning_args.py

@@ -5,7 +5,7 @@ from dataclasses import asdict, dataclass, field
 
 @dataclass
 class FinetuningArguments:
-    """
+    r"""
     Arguments pertaining to which techniques we are going to fine-tuning with.
     """
     finetuning_type: Optional[Literal["none", "freeze", "lora", "full"]] = field(
@@ -14,23 +14,27 @@ class FinetuningArguments:
     )
     num_hidden_layers: Optional[int] = field(
         default=32,
-        metadata={"help": "Number of decoder blocks in the model. \
+        metadata={"help": "Number of decoder blocks in the model for partial-parameter (freeze) fine-tuning. \
                   LLaMA choices: [\"32\", \"40\", \"60\", \"80\"], \
                   LLaMA-2 choices: [\"32\", \"40\", \"80\"], \
                   BLOOM choices: [\"24\", \"30\", \"70\"], \
                   Falcon choices: [\"32\", \"60\"], \
-                  Baichuan choices: [\"32\", \"40\"]"}
+                  Baichuan choices: [\"32\", \"40\"] \
+                  Qwen choices: [\"32\"], \
+                  XVERSE choices: [\"40\"]"}
     )
     num_layer_trainable: Optional[int] = field(
         default=3,
-        metadata={"help": "Number of trainable layers for Freeze fine-tuning."}
+        metadata={"help": "Number of trainable layers for partial-parameter (freeze) fine-tuning."}
     )
     name_module_trainable: Optional[Literal["mlp", "self_attn", "self_attention"]] = field(
         default="mlp",
-        metadata={"help": "Name of trainable modules for Freeze fine-tuning. \
-                  LLaMA & LLaMA-2 choices: [\"mlp\", \"self_attn\"], \
+        metadata={"help": "Name of trainable modules for partial-parameter (freeze) fine-tuning. \
+                  LLaMA choices: [\"mlp\", \"self_attn\"], \
                   BLOOM & Falcon choices: [\"mlp\", \"self_attention\"], \
-                  Baichuan choices: [\"mlp\", \"self_attn\"]"}
+                  Baichuan choices: [\"mlp\", \"self_attn\"], \
+                  Qwen choices: [\"mlp\", \"attn\"], \
+                  LLaMA-2, InternLM, XVERSE choices: the same as LLaMA."}
     )
     lora_rank: Optional[int] = field(
         default=8,
@@ -47,9 +51,19 @@ class FinetuningArguments:
     lora_target: Optional[str] = field(
         default="q_proj,v_proj",
         metadata={"help": "Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. \
-                  LLaMA & LLaMA-2 choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
+                  LLaMA choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
                   BLOOM & Falcon choices: [\"query_key_value\", \"self_attention.dense\", \"mlp.dense\"], \
-                  Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"]"}
+                  Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
+                  Qwen choices: [\"c_attn\", \"attn.c_proj\", \"w1\", \"w2\", \"mlp.c_proj\"], \
+                  LLaMA-2, InternLM, XVERSE choices: the same as LLaMA."}
+    )
+    resume_lora_training: Optional[bool] = field(
+        default=True,
+        metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
+    )
+    dpo_beta: Optional[float] = field(
+        default=0.1,
+        metadata={"help": "The beta parameter for the DPO loss."}
     )
 
     def __post_init__(self):
@@ -66,14 +80,14 @@ class FinetuningArguments:
         assert self.finetuning_type in ["none", "freeze", "lora", "full"], "Invalid fine-tuning method."
 
     def save_to_json(self, json_path: str):
-        """Saves the content of this instance in JSON format inside `json_path`."""
+        r"""Saves the content of this instance in JSON format inside `json_path`."""
         json_string = json.dumps(asdict(self), indent=2, sort_keys=True) + "\n"
         with open(json_path, "w", encoding="utf-8") as f:
             f.write(json_string)
 
     @classmethod
     def load_from_json(cls, json_path: str):
-        """Creates an instance from the content of `json_path`."""
+        r"""Creates an instance from the content of `json_path`."""
         with open(json_path, "r", encoding="utf-8") as f:
             text = f.read()
         return cls(**json.loads(text))

src/llmtuner/hparams/general_args.py

@@ -4,10 +4,10 @@ from dataclasses import dataclass, field
 
 @dataclass
 class GeneralArguments:
+    r"""
+    Arguments pertaining to which stage we are going to perform.
     """
-    Arguments pertaining to which techniques we are going to fine-tuning with.
-    """
-    stage: Optional[Literal["pt", "sft", "rm", "ppo"]] = field(
+    stage: Optional[Literal["pt", "sft", "rm", "ppo", "dpo"]] = field(
         default="sft",
         metadata={"help": "Which stage will be performed in training."}
     )

src/llmtuner/hparams/generating_args.py

@@ -4,7 +4,7 @@ from dataclasses import asdict, dataclass, field
 
 @dataclass
 class GeneratingArguments:
-    """
+    r"""
     Arguments pertaining to specify the decoding parameters.
     """
     do_sample: Optional[bool] = field(

src/llmtuner/hparams/model_args.py

@@ -5,7 +5,7 @@ from dataclasses import dataclass, field
 
 @dataclass
 class ModelArguments:
-    """
+    r"""
     Arguments pertaining to which model/config/tokenizer we are going to fine-tune.
     """
     model_name_or_path: str = field(
@@ -55,14 +55,14 @@ class ModelArguments:
         default=None,
         metadata={"help": "Path to the directory containing the checkpoints of the reward model."}
     )
-    resume_lora_training: Optional[bool] = field(
-        default=True,
-        metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
-    )
     plot_loss: Optional[bool] = field(
         default=False,
         metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
     )
+    hf_auth_token: Optional[str] = field(
+        default=None,
+        metadata={"help": "Auth token to log in with Hugging Face Hub."}
+    )
 
     def __post_init__(self):
         if self.checkpoint_dir is not None: # support merging multiple lora weights
@@ -70,3 +70,7 @@ class ModelArguments:
 
         if self.quantization_bit is not None:
             assert self.quantization_bit in [4, 8], "We only accept 4-bit or 8-bit quantization."
+
+        if self.use_auth_token == True and self.hf_auth_token is not None:
+            from huggingface_hub.hf_api import HfFolder # lazy load
+            HfFolder.save_token(self.hf_auth_token)

src/llmtuner/tuner/__init__.py

@@ -1,5 +1 @@
-from llmtuner.tuner.core import get_train_args, get_infer_args, load_model_and_tokenizer
-from llmtuner.tuner.pt import run_pt
-from llmtuner.tuner.sft import run_sft
-from llmtuner.tuner.rm import run_rm
-from llmtuner.tuner.ppo import run_ppo
+from llmtuner.tuner.tune import export_model, run_exp

src/llmtuner/tuner/core/adapter.py

@@ -1,7 +1,7 @@
 import os
 import torch
+from typing import TYPE_CHECKING
 
-from transformers.modeling_utils import PreTrainedModel
 from peft import (
     PeftModel,
     TaskType,
@@ -12,19 +12,22 @@ from peft.utils import CONFIG_NAME, WEIGHTS_NAME
 
 from llmtuner.extras.logging import get_logger
 from llmtuner.extras.save_and_load import load_trainable_params
-from llmtuner.hparams import ModelArguments, FinetuningArguments
+
+if TYPE_CHECKING:
+    from transformers.modeling_utils import PreTrainedModel
+    from llmtuner.hparams import ModelArguments, FinetuningArguments
 
 
 logger = get_logger(__name__)
 
 
 def init_adapter(
-    model: PreTrainedModel,
-    model_args: ModelArguments,
-    finetuning_args: FinetuningArguments,
+    model: "PreTrainedModel",
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
     is_trainable: bool,
     is_mergeable: bool
-) -> PreTrainedModel:
+) -> "PreTrainedModel":
     r"""
     Initializes the adapters.
 
@@ -36,7 +39,7 @@ def init_adapter(
     if finetuning_args.finetuning_type == "none" and is_trainable:
         raise ValueError("You cannot use finetuning_type=none while training.")
 
-    if finetuning_args.finetuning_type == "full":
+    if finetuning_args.finetuning_type == "full" and is_trainable:
         logger.info("Fine-tuning method: Full")
         model = model.float()
 
@@ -62,7 +65,7 @@ def init_adapter(
             assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], CONFIG_NAME)), \
                 "The given checkpoint may be not a LoRA checkpoint, please specify `--finetuning_type full/freeze` instead."
 
-            if (is_trainable and model_args.resume_lora_training) or (not is_mergeable): # continually train on the lora weights
+            if (is_trainable and finetuning_args.resume_lora_training) or (not is_mergeable): # continually fine-tuning
                 checkpoints_to_merge, latest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
             else:
                 checkpoints_to_merge = model_args.checkpoint_dir

src/llmtuner/tuner/core/loader.py

@@ -1,43 +1,48 @@
 import os
 import torch
-from typing import Literal, Optional, Tuple
+from typing import TYPE_CHECKING, Literal, Optional, Tuple
 
 from transformers import (
     AutoConfig,
     AutoModelForCausalLM,
     AutoTokenizer,
-    BitsAndBytesConfig
+    BitsAndBytesConfig,
+    PretrainedConfig,
+    PreTrainedModel,
+    PreTrainedTokenizerBase
 )
 from transformers.utils import check_min_version
 from transformers.utils.versions import require_version
 from transformers.deepspeed import is_deepspeed_zero3_enabled
-from transformers.modeling_utils import PretrainedConfig, PreTrainedModel
-from transformers.tokenization_utils import PreTrainedTokenizerBase
 from trl import AutoModelForCausalLMWithValueHead
 
-from llmtuner.extras.logging import get_logger
-from llmtuner.extras.misc import prepare_model_for_training, print_trainable_params
+from llmtuner.extras.logging import reset_logging, get_logger
+from llmtuner.extras.misc import count_parameters, prepare_model_for_training
 from llmtuner.extras.save_and_load import load_valuehead_params
-from llmtuner.hparams import ModelArguments, FinetuningArguments
+from llmtuner.hparams import FinetuningArguments
 from llmtuner.tuner.core.adapter import init_adapter
 
+if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer
+    from llmtuner.hparams import ModelArguments
+
 
 logger = get_logger(__name__)
 
 
 check_min_version("4.29.1")
 require_version("datasets>=2.12.0", "To fix: pip install datasets>=2.12.0")
-require_version("accelerate>=0.19.0", "To fix: pip install accelerate>=0.19.0")
-require_version("peft>=0.3.0", "To fix: pip install peft>=0.3.0")
-require_version("trl>=0.4.7", "To fix: pip install trl>=0.4.7")
+require_version("accelerate>=0.21.0", "To fix: pip install accelerate>=0.21.0")
+require_version("peft>=0.4.0", "To fix: pip install peft>=0.4.0")
+require_version("trl>=0.5.0", "To fix: pip install trl>=0.5.0")
 
 
 def load_model_and_tokenizer(
-    model_args: ModelArguments,
-    finetuning_args: FinetuningArguments,
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
     is_trainable: Optional[bool] = False,
     stage: Optional[Literal["pt", "sft", "rm", "ppo"]] = "sft"
-) -> Tuple[PreTrainedModel, PreTrainedTokenizerBase]:
+) -> Tuple[PreTrainedModel, "PreTrainedTokenizer"]:
     r"""
     Loads pretrained model and tokenizer.
 
@@ -47,9 +52,6 @@ def load_model_and_tokenizer(
         logger.warning("Checkpoint is not found at evaluation, load the original model.")
         finetuning_args = FinetuningArguments(finetuning_type="none")
 
-    assert stage in ["pt", "sft"] or finetuning_args.finetuning_type == "lora", \
-        "RM and PPO training can only be performed with the LoRA method."
-
     config_kwargs = {
         "trust_remote_code": True,
         "cache_dir": model_args.cache_dir,
@@ -63,10 +65,13 @@ def load_model_and_tokenizer(
         padding_side=model_args.padding_side,
         **config_kwargs
     )
-    if tokenizer.pad_token_id is None or tokenizer.pad_token_id == 64000: # 64000 for baichuan model (older version)
-        tokenizer.pad_token_id = 0 # set as the <unk> token
 
-    config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
+    if model_args.checkpoint_dir is not None and finetuning_args.finetuning_type == "full":
+        model_to_load = model_args.checkpoint_dir[0]
+    else:
+        model_to_load = model_args.model_name_or_path
+
+    config = AutoConfig.from_pretrained(model_to_load, **config_kwargs)
     is_mergeable = True
 
     # Quantization configurations (using bitsandbytes library).
@@ -74,16 +79,10 @@ def load_model_and_tokenizer(
         if model_args.quantization_bit == 8:
             require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
             config_kwargs["load_in_8bit"] = True
-            config_kwargs["quantization_config"] = BitsAndBytesConfig(
-                load_in_8bit=True,
-                llm_int8_threshold=6.0
-            )
+            config_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
 
         elif model_args.quantization_bit == 4:
             require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
-            require_version("transformers>=4.30.1", "To fix: pip install transformers>=4.30.1")
-            require_version("accelerate>=0.20.3", "To fix: pip install accelerate>=0.20.3")
-            require_version("peft>=0.4.0.dev0", "To fix: pip install git+https://github.com/huggingface/peft.git")
             config_kwargs["load_in_4bit"] = True
             config_kwargs["quantization_config"] = BitsAndBytesConfig(
                 load_in_4bit=True,
@@ -96,14 +95,6 @@ def load_model_and_tokenizer(
         config_kwargs["device_map"] = {"": int(os.environ.get("LOCAL_RANK", "0"))}
         logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
 
-    if not is_trainable: # `device_map=auto` should be used for inference only
-        config_kwargs["device_map"] = "auto"
-
-    if model_args.checkpoint_dir is not None and finetuning_args.finetuning_type == "full":
-        model_to_load = model_args.checkpoint_dir[0]
-    else:
-        model_to_load = model_args.model_name_or_path
-
     # Load and prepare pretrained models (without valuehead).
     model = AutoModelForCausalLM.from_pretrained(
         model_to_load,
@@ -126,7 +117,8 @@ def load_model_and_tokenizer(
     model = init_adapter(model, model_args, finetuning_args, is_trainable, is_mergeable)
 
     if stage == "rm" or stage == "ppo": # add value head
-        model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
+        model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(model)
+        reset_logging()
 
         if stage == "rm" and model_args.checkpoint_dir is not None: # load valuehead weights to evaluate reward model
             logger.warning("Only the last checkpoint containing valuehead will be loaded as the valuehead.")
@@ -137,8 +129,6 @@ def load_model_and_tokenizer(
                 })
 
         if stage == "ppo": # load reward model
-            assert is_trainable, "PPO stage cannot be performed at evaluation."
-            assert model_args.reward_model is not None, "Reward model is necessary for PPO training."
             logger.info("Load reward model from {}".format(model_args.reward_model))
             model.pretrained_model.load_adapter(model_args.reward_model, "reward", is_trainable=False)
             assert load_valuehead_params(model, model_args.reward_model), "Reward model is not correctly loaded."
@@ -147,6 +137,9 @@ def load_model_and_tokenizer(
         model.requires_grad_(False) # fix all model params
         model = model.half() if model_args.quantization_bit is None else model # cast from fp32 to fp16
 
-    print_trainable_params(model)
+    trainable_params, all_param = count_parameters(model)
+    logger.info("trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format(
+        trainable_params, all_param, 100 * trainable_params / all_param
+    ))
 
     return model, tokenizer

src/llmtuner/tuner/core/parser.py

@@ -19,20 +19,66 @@ from llmtuner.hparams import (
 logger = get_logger(__name__)
 
 
+def _parse_args(parser: HfArgumentParser, args: Optional[Dict[str, Any]] = None) -> Tuple[Any]:
+    if args is not None:
+        return parser.parse_dict(args)
+    elif len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
+        return parser.parse_yaml_file(os.path.abspath(sys.argv[1]))
+    elif len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        return parser.parse_json_file(os.path.abspath(sys.argv[1]))
+    else:
+        return parser.parse_args_into_dataclasses()
+
+
+def parse_train_args(
+    args: Optional[Dict[str, Any]] = None
+) -> Tuple[
+    ModelArguments,
+    DataArguments,
+    Seq2SeqTrainingArguments,
+    FinetuningArguments,
+    GeneratingArguments,
+    GeneralArguments
+]:
+    parser = HfArgumentParser((
+        ModelArguments,
+        DataArguments,
+        Seq2SeqTrainingArguments,
+        FinetuningArguments,
+        GeneratingArguments,
+        GeneralArguments
+    ))
+    return _parse_args(parser, args)
+
+
+def parse_infer_args(
+    args: Optional[Dict[str, Any]] = None
+) -> Tuple[
+    ModelArguments,
+    DataArguments,
+    FinetuningArguments,
+    GeneratingArguments
+]:
+    parser = HfArgumentParser((
+        ModelArguments,
+        DataArguments,
+        FinetuningArguments,
+        GeneratingArguments
+    ))
+    return _parse_args(parser, args)
+
+
 def get_train_args(
     args: Optional[Dict[str, Any]] = None
-) -> Tuple[ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneralArguments]:
-
-    parser = HfArgumentParser((ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneralArguments))
-
-    if args is not None:
-        model_args, data_args, training_args, finetuning_args, general_args = parser.parse_dict(args)
-    elif len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
-        model_args, data_args, training_args, finetuning_args, general_args = parser.parse_yaml_file(os.path.abspath(sys.argv[1]))
-    elif len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        model_args, data_args, training_args, finetuning_args, general_args = parser.parse_json_file(os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, training_args, finetuning_args, general_args = parser.parse_args_into_dataclasses()
+) -> Tuple[
+    ModelArguments,
+    DataArguments,
+    Seq2SeqTrainingArguments,
+    FinetuningArguments,
+    GeneratingArguments,
+    GeneralArguments
+]:
+    model_args, data_args, training_args, finetuning_args, generating_args, general_args = parse_train_args(args)
 
     # Setup logging
     if training_args.should_log:
@@ -48,24 +94,42 @@ def get_train_args(
     # Check arguments (do not check finetuning_args since it may be loaded from checkpoints)
     data_args.init_for_training()
 
-    assert general_args.stage == "sft" or (not training_args.predict_with_generate), \
-        "`predict_with_generate` cannot be set as True at PT, RM and PPO stages."
+    if general_args.stage != "sft" and training_args.predict_with_generate:
+        raise ValueError("`predict_with_generate` cannot be set as True except SFT.")
 
-    assert not (training_args.do_train and training_args.predict_with_generate), \
-        "`predict_with_generate` cannot be set as True while training."
+    if training_args.do_train and training_args.predict_with_generate:
+        raise ValueError("`predict_with_generate` cannot be set as True while training.")
 
-    assert (not training_args.do_predict) or training_args.predict_with_generate, \
-        "Please enable `predict_with_generate` to save model predictions."
+    if general_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate:
+        raise ValueError("Please enable `predict_with_generate` to save model predictions.")
 
-    assert model_args.quantization_bit is None or finetuning_args.finetuning_type == "lora", \
-        "Quantization is only compatible with the LoRA method."
+    if general_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type != "lora":
+        raise ValueError("RM and PPO training can only be performed with the LoRA method.")
+
+    if general_args.stage in ["ppo", "dpo"] and not training_args.do_train:
+        raise ValueError("PPO and DPO stage can only be performed at training.")
+
+    if general_args.stage == "ppo" and model_args.reward_model is None:
+        raise ValueError("Reward model is necessary for PPO training.")
+
+    if training_args.max_steps == -1 and data_args.streaming:
+        raise ValueError("Please specify `max_steps` in streaming mode.")
+
+    if general_args.stage == "ppo" and data_args.streaming:
+        raise ValueError("Streaming mode does not suppport PPO training currently.")
+
+    if data_args.val_size > 1e-6 and data_args.val_size < 1 and data_args.streaming:
+        raise ValueError("Streaming mode should have an integer val size.")
+
+    if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
+        raise ValueError("Quantization is only compatible with the LoRA method.")
 
     if model_args.checkpoint_dir is not None:
         if finetuning_args.finetuning_type != "lora":
-            assert len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
-        else:
-            assert model_args.quantization_bit is None or len(model_args.checkpoint_dir) == 1, \
-                "Quantized model only accepts a single checkpoint."
+            if len(model_args.checkpoint_dir) != 1:
+                raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
+        elif model_args.quantization_bit is not None and len(model_args.checkpoint_dir) != 1:
+                raise ValueError("Quantized model only accepts a single checkpoint.")
 
     if model_args.quantization_bit is not None and (not training_args.do_train):
         logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
@@ -73,13 +137,18 @@ def get_train_args(
     if training_args.do_train and (not training_args.fp16):
         logger.warning("We recommend enable fp16 mixed precision training.")
 
-    if data_args.prompt_template == "default":
-        logger.warning("Please specify `prompt_template` if you are using other pre-trained models.")
-
-    if training_args.local_rank != -1 and training_args.ddp_find_unused_parameters is None:
-        logger.warning("`ddp_find_unused_parameters` needs to be set as False in DDP training.")
+    if (
+        training_args.local_rank != -1
+        and training_args.ddp_find_unused_parameters is None
+        and finetuning_args.finetuning_type == "lora"
+    ):
+        logger.warning("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.")
         training_args.ddp_find_unused_parameters = False
 
+    if data_args.max_samples is not None and data_args.streaming:
+        logger.warning("`max_samples` is incompatible with `streaming`. Disabling max_samples.")
+        data_args.max_samples = None
+
     training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim # suppress warning
 
     if model_args.quantization_bit is not None:
@@ -91,44 +160,36 @@ def get_train_args(
             model_args.compute_dtype = torch.float32
 
     # Log on each process the small summary:
-    logger.info(
-        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}\n"
-        + f"  distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
-    )
+    logger.info("Process rank: {}, device: {}, n_gpu: {}\n  distributed training: {}, 16-bits training: {}".format(
+        training_args.local_rank, training_args.device, training_args.n_gpu,
+        bool(training_args.local_rank != -1), training_args.fp16
+    ))
     logger.info(f"Training/evaluation parameters {training_args}")
 
     # Set seed before initializing model.
     transformers.set_seed(training_args.seed)
 
-    return model_args, data_args, training_args, finetuning_args, general_args
+    return model_args, data_args, training_args, finetuning_args, generating_args, general_args
 
 
 def get_infer_args(
     args: Optional[Dict[str, Any]] = None
-) -> Tuple[ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]:
+) -> Tuple[
+    ModelArguments,
+    DataArguments,
+    FinetuningArguments,
+    GeneratingArguments
+]:
+    model_args, data_args, finetuning_args, generating_args = parse_infer_args(args)
 
-    parser = HfArgumentParser((ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments))
-
-    if args is not None:
-        model_args, data_args, finetuning_args, generating_args = parser.parse_dict(args)
-    elif len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
-        model_args, data_args, finetuning_args, generating_args = parser.parse_yaml_file(os.path.abspath(sys.argv[1]))
-    elif len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        model_args, data_args, finetuning_args, generating_args = parser.parse_json_file(os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, finetuning_args, generating_args = parser.parse_args_into_dataclasses()
-
-    assert model_args.quantization_bit is None or finetuning_args.finetuning_type == "lora", \
-        "Quantization is only compatible with the LoRA method."
+    if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
+        raise ValueError("Quantization is only compatible with the LoRA method.")
 
     if model_args.checkpoint_dir is not None:
         if finetuning_args.finetuning_type != "lora":
-            assert len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
-        else:
-            assert model_args.quantization_bit is None or len(model_args.checkpoint_dir) == 1, \
-                "Quantized model only accepts a single checkpoint."
-
-    if data_args.prompt_template == "default":
-        logger.warning("Please specify `prompt_template` if you are using other pre-trained models.")
+            if len(model_args.checkpoint_dir) != 1:
+                raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
+        elif model_args.quantization_bit is not None and len(model_args.checkpoint_dir) != 1:
+                raise ValueError("Quantized model only accepts a single checkpoint.")
 
     return model_args, data_args, finetuning_args, generating_args

src/llmtuner/tuner/core/trainer.py

@@ -1,34 +1,37 @@
 import os
 import torch
-from typing import Dict, Optional
+from typing import TYPE_CHECKING, Dict, Optional
 
 from transformers import Seq2SeqTrainer
-from transformers.trainer import TRAINING_ARGS_NAME
-from transformers.modeling_utils import unwrap_model
+from transformers.trainer import TRAINING_ARGS_NAME, WEIGHTS_NAME
+from transformers.modeling_utils import PreTrainedModel, unwrap_model
+from peft import PeftModel
+from trl import PreTrainedModelWrapper
 
 from llmtuner.extras.constants import FINETUNING_ARGS_NAME, VALUE_HEAD_FILE_NAME
 from llmtuner.extras.logging import get_logger
-from llmtuner.extras.save_and_load import get_state_dict, load_trainable_params, load_valuehead_params
-from llmtuner.hparams import FinetuningArguments
+from llmtuner.extras.save_and_load import get_state_dict, load_trainable_params
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer, Seq2SeqTrainingArguments, TrainerState
+    from llmtuner.hparams import FinetuningArguments
 
 
 logger = get_logger(__name__)
 
 
-class PeftTrainer(Seq2SeqTrainer):
+class PeftModelMixin:
     r"""
-    Inherits Seq2SeqTrainer to support parameter-efficient checkpoints.
+    Patches the save and load methods in Hugging Face Trainer for PeftModel and ModelWithValueHead.
     """
 
-    def __init__(self, finetuning_args: FinetuningArguments, **kwargs):
-        super().__init__(**kwargs)
-        self.finetuning_args = finetuning_args
-        self._remove_log()
-
-    def _remove_log(self):
-        if self.is_world_process_zero() and os.path.exists(os.path.join(self.args.output_dir, "trainer_log.jsonl")):
-            logger.warning("Previous log file in this folder will be deleted.")
-            os.remove(os.path.join(self.args.output_dir, "trainer_log.jsonl"))
+    def __init__(self) -> None: # for type checking
+        self.model: PreTrainedModel = None
+        self.tokenizer: "PreTrainedTokenizer" = None
+        self.args: "Seq2SeqTrainingArguments" = None
+        self.finetuning_args: "FinetuningArguments" = None
+        self.state: "TrainerState" = None
+        raise AssertionError("Mixin should not be initialized.")
 
     def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, torch.Tensor]] = None) -> None:
         r"""
@@ -41,29 +44,36 @@ class PeftTrainer(Seq2SeqTrainer):
         output_dir = output_dir if output_dir is not None else self.args.output_dir
         os.makedirs(output_dir, exist_ok=True)
         logger.info(f"Saving model checkpoint to {output_dir}")
+
         model = unwrap_model(self.model)
+        if isinstance(model, PreTrainedModelWrapper):
+            # Custom state dict: https://github.com/lvwerra/trl/blob/v0.4.7/trl/models/modeling_value_head.py#L200
+            model_state_dict = state_dict or model.state_dict()
+            v_head_state_dict = {
+                name.replace("v_head.", ""): model_state_dict[name].cpu().clone().detach()
+                for name in model_state_dict.keys() if name.startswith("v_head.")
+            }
 
-        if hasattr(model, "pretrained_model"): # for models with valuehead (currently using LoRA only)
-            backbone_model = getattr(model, "pretrained_model")
-            torch.save(get_state_dict(getattr(model, "v_head")), os.path.join(output_dir, VALUE_HEAD_FILE_NAME))
+            torch.save(v_head_state_dict, os.path.join(output_dir, VALUE_HEAD_FILE_NAME))
+            model = model.pretrained_model
+
+        state_dict = state_dict or get_state_dict(model)
+        if isinstance(model, (PeftModel, PreTrainedModel)):
+            model.config.use_cache = True
+            model.save_pretrained(output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors)
+            model.config.use_cache = False
         else:
-            backbone_model = model
+            torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
 
-        if self.finetuning_args.finetuning_type == "lora":
-            backbone_model.save_pretrained(output_dir, state_dict=get_state_dict(backbone_model))
-        else: # freeze/full tuning
-            backbone_model.config.use_cache = True
-            backbone_model.save_pretrained(
-                output_dir,
-                state_dict=get_state_dict(backbone_model),
-                safe_serialization=self.args.save_safetensors
-            )
-            backbone_model.config.use_cache = False
-            if self.tokenizer is not None:
+        if self.finetuning_args.finetuning_type == "full" and self.tokenizer is not None:
+            try:
                 self.tokenizer.save_pretrained(output_dir)
+            except:
+                logger.warning("Cannot save tokenizer, copy the files manually.")
 
         with open(os.path.join(output_dir, TRAINING_ARGS_NAME), "w", encoding="utf-8") as f:
             f.write(self.args.to_json_string() + "\n")
+
         self.finetuning_args.save_to_json(os.path.join(output_dir, FINETUNING_ARGS_NAME))
 
     def _load_best_model(self):
@@ -73,16 +83,25 @@ class PeftTrainer(Seq2SeqTrainer):
         Subclass and override to inject custom behavior. It should not be directly used by external scripts.
         """
         logger.info(f"Loading best model from {self.state.best_model_checkpoint} (score: {self.state.best_metric}).")
-
         model = unwrap_model(self.model)
-        backbone_model = getattr(model, "pretrained_model") if hasattr(model, "pretrained_model") else model
 
-        if self.finetuning_args.finetuning_type == "lora":
-            backbone_model.load_adapter(self.state.best_model_checkpoint, getattr(backbone_model, "active_adapter"))
-            if hasattr(model, "v_head") and load_valuehead_params(model, self.state.best_model_checkpoint):
-                model.v_head.load_state_dict({
-                    "summary.weight": getattr(model, "reward_head_weight"),
-                    "summary.bias": getattr(model, "reward_head_bias")
-                })
+        if isinstance(model, PreTrainedModelWrapper):
+            model.v_head.load_state_dict(torch.load(
+                os.path.join(self.state.best_model_checkpoint, VALUE_HEAD_FILE_NAME), map_location="cpu"
+            ))
+            model = model.pretrained_model
+
+        if isinstance(model, PeftModel):
+            model.load_adapter(self.state.best_model_checkpoint, model.active_adapter)
         else: # freeze/full-tuning
-            load_trainable_params(backbone_model, self.state.best_model_checkpoint)
+            load_trainable_params(model, self.state.best_model_checkpoint)
+
+
+class PeftTrainer(PeftModelMixin, Seq2SeqTrainer):
+    r"""
+    Inherits Seq2SeqTrainer to support parameter-efficient checkpoints.
+    """
+
+    def __init__(self, finetuning_args: "FinetuningArguments", **kwargs):
+        Seq2SeqTrainer.__init__(self, **kwargs)
+        self.finetuning_args = finetuning_args

src/llmtuner/tuner/dpo/__init__.py

@@ -0,0 +1 @@
+from llmtuner.tuner.dpo.workflow import run_dpo

src/llmtuner/tuner/dpo/collator.py

@@ -0,0 +1,51 @@
+import torch
+from dataclasses import dataclass
+from typing import Any, Dict, List, Sequence, Tuple
+from transformers import DataCollatorForSeq2Seq
+
+
+@dataclass
+class DPODataCollatorWithPadding(DataCollatorForSeq2Seq):
+    r"""
+    Data collator for pairwise data.
+    """
+
+    def _pad_labels(self, batch: torch.Tensor, positions: List[Tuple[int, int]]) -> torch.Tensor:
+        padded_labels = []
+        for feature, (prompt_len, answer_len) in zip(batch, positions):
+            if self.tokenizer.padding_side == "left":
+                start, end = feature.size(0) - answer_len, feature.size(0)
+            else:
+                start, end = prompt_len, answer_len
+            padded_tensor = self.label_pad_token_id * torch.ones_like(feature)
+            padded_tensor[start:end] = feature[start:end]
+            padded_labels.append(padded_tensor)
+        return torch.stack(padded_labels, dim=0).contiguous() # in contiguous memory
+
+    def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
+        r"""
+        Pads batched data to the longest sequence in the batch.
+
+        We generate 2 * n examples where the first n examples represent chosen examples and
+        the last n examples represent rejected examples.
+        """
+        concatenated_features = []
+        label_positions = []
+        for key in ("chosen_ids", "rejected_ids"):
+            for feature in features:
+                prompt_len, answer_len = len(feature["prompt_ids"]), len(feature[key])
+                concatenated_features.append({
+                    "input_ids": feature["prompt_ids"] + feature[key],
+                    "attention_mask": [1] * (prompt_len + answer_len)
+                })
+                label_positions.append((prompt_len, answer_len))
+
+        batch = self.tokenizer.pad(
+            concatenated_features,
+            padding=self.padding,
+            max_length=self.max_length,
+            pad_to_multiple_of=self.pad_to_multiple_of,
+            return_tensors=self.return_tensors,
+        )
+        batch["labels"] = self._pad_labels(batch["input_ids"], label_positions)
+        return batch

src/llmtuner/tuner/dpo/trainer.py

@@ -0,0 +1,75 @@
+import torch
+from collections import defaultdict
+from peft import PeftModel
+from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
+from transformers import Trainer
+from trl import DPOTrainer
+
+from llmtuner.extras.constants import IGNORE_INDEX
+from llmtuner.tuner.core.trainer import PeftModelMixin
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel
+    from llmtuner.hparams import FinetuningArguments, GeneratingArguments
+
+
+class DPOPeftTrainer(PeftModelMixin, DPOTrainer):
+
+    def __init__(
+        self,
+        finetuning_args: "FinetuningArguments",
+        generating_args: "GeneratingArguments",
+        ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]] = None,
+        **kwargs
+    ):
+        self.finetuning_args = finetuning_args
+        self.generating_args = generating_args
+        self.ref_model = ref_model
+        self.use_dpo_data_collator = True # hack to avoid warning
+        self.label_pad_token_id = IGNORE_INDEX
+        self.padding_value = 0
+        self.beta = finetuning_args.dpo_beta
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+
+        Trainer.__init__(self, **kwargs)
+        if ref_model is not None:
+            if hasattr(self, "accelerator"):
+                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
+            else:
+                raise AttributeError("Please update `transformers`.")
+
+    def concatenated_forward(
+        self,
+        model: Optional[torch.nn.Module] = None,
+        batch: Optional[Dict[str, torch.Tensor]] = None
+    ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+        unwrapped_model: "PreTrainedModel" = self.accelerator.unwrap_model(self.model)
+        if not torch.is_grad_enabled():
+            unwrapped_model.gradient_checkpointing_disable()
+
+        if model is None and isinstance(unwrapped_model, PeftModel): # peft model has no ref_model
+            with unwrapped_model.disable_adapter():
+                all_logits: torch.Tensor = self.model(
+                    batch["input_ids"],
+                    attention_mask=batch["attention_mask"],
+                    return_dict=True
+                ).logits.to(torch.float32)
+        else:
+            all_logits: torch.Tensor = model(
+                batch["input_ids"],
+                attention_mask=batch["attention_mask"],
+                return_dict=True
+            ).logits.to(torch.float32)
+
+        if not torch.is_grad_enabled():
+            unwrapped_model.gradient_checkpointing_enable()
+
+        all_logps = self._get_batch_logps(
+            all_logits,
+            batch["labels"],
+            average_log_prob=False
+        )
+        batch_size = batch["input_ids"].size(0) // 2
+        chosen_logps, rejected_logps = all_logps.split(batch_size, dim=0)
+        chosen_logits, rejected_logits = all_logits.split(batch_size, dim=0)
+        return chosen_logps, rejected_logps, chosen_logits, rejected_logits

src/llmtuner/tuner/dpo/workflow.py

@@ -0,0 +1,59 @@
+# Inspired by: https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py
+
+from copy import deepcopy
+from peft import PeftModel
+from typing import TYPE_CHECKING, Optional, List
+
+from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
+from llmtuner.extras.constants import IGNORE_INDEX
+from llmtuner.extras.ploting import plot_loss
+from llmtuner.tuner.core import load_model_and_tokenizer
+from llmtuner.tuner.dpo.collator import DPODataCollatorWithPadding
+from llmtuner.tuner.dpo.trainer import DPOPeftTrainer
+
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
+
+
+def run_dpo(
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    generating_args: "GeneratingArguments",
+    callbacks: Optional[List["TrainerCallback"]] = None
+):
+    dataset = get_dataset(model_args, data_args)
+    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="sft")
+    dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="rm")
+    data_collator = DPODataCollatorWithPadding(
+        tokenizer=tokenizer,
+        label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
+    )
+
+    training_args.remove_unused_columns = False # important for pairwise dataset
+    ref_model = deepcopy(model) if not isinstance(model, PeftModel) else None
+
+    # Initialize our Trainer
+    trainer = DPOPeftTrainer(
+        finetuning_args=finetuning_args,
+        generating_args=generating_args,
+        ref_model=ref_model,
+        model=model,
+        args=training_args,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        callbacks=callbacks,
+        **split_dataset(dataset, data_args, training_args)
+    )
+
+    # Training
+    if training_args.do_train:
+        train_result = trainer.train()
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+        trainer.save_model()
+        if trainer.is_world_process_zero() and model_args.plot_loss:
+            plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])

src/llmtuner/tuner/ppo/trainer.py

@@ -2,21 +2,24 @@ import os
 import math
 import torch
 from tqdm import tqdm
-from typing import Callable, Dict, List, Optional
+from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple
 
-from transformers import Seq2SeqTrainingArguments, TrainerState, TrainerControl
-from transformers.modeling_utils import PreTrainedModel
+from transformers import TrainerState, TrainerControl
 
 from trl import PPOTrainer
 from trl.core import LengthSampler
 
-from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.logging import get_logger
-from llmtuner.extras.misc import AverageMeter, get_logits_processor
-from llmtuner.hparams import FinetuningArguments
+from llmtuner.extras.misc import AverageMeter, count_parameters, get_logits_processor, get_stopping_criteria
 from llmtuner.tuner.core.trainer import PeftTrainer
 from llmtuner.tuner.ppo.utils import cast_layernorm_dtype, replace_model
 
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments
+    from trl import AutoModelForCausalLMWithValueHead
+    from llmtuner.extras.callbacks import LogCallback
+    from llmtuner.hparams import FinetuningArguments, GeneratingArguments
+
 
 logger = get_logger(__name__)
 
@@ -25,21 +28,22 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
     r"""
     Inherits PPOTrainer.
     """
+
     def __init__(
         self,
-        training_args: Seq2SeqTrainingArguments,
-        finetuning_args: FinetuningArguments,
-        callbacks: List[LogCallback],
+        training_args: "Seq2SeqTrainingArguments",
+        finetuning_args: "FinetuningArguments",
+        generating_args: "GeneratingArguments",
+        callbacks: List["LogCallback"],
         **kwargs
     ):
         PPOTrainer.__init__(self, **kwargs)
         self.args = training_args
         self.finetuning_args = finetuning_args
+        self.generating_args = generating_args
         self.log_callback = callbacks[0]
         self.state = TrainerState()
         self.control = TrainerControl()
-        self.data_collator = self.accelerator.prepare(kwargs["data_collator"]) # override the data collator of PPOTrainer
-        self._remove_log()
 
     def ppo_train(self, max_target_length: int) -> None:
         r"""
@@ -66,19 +70,15 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
             logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}")
             logger.info(f"  Gradient Accumulation steps = {self.args.gradient_accumulation_steps}")
             logger.info(f"  Total optimization steps = {max_steps}")
-            logger.info(f"  Number of trainable parameters = {sum(p.numel() for p in self.model.parameters() if p.requires_grad)}")
+            logger.info(f"  Number of trainable parameters = {count_parameters(self.model)[0]}")
 
         # Keyword arguments for `model.generate`
-        gen_kwargs = {
-            "top_k": 0.0,
-            "top_p": 1.0,
-            "do_sample": True,
-            "pad_token_id": self.tokenizer.pad_token_id,
-            "eos_token_id": self.tokenizer.eos_token_id,
-            "logits_processor": get_logits_processor()
-        }
+        gen_kwargs = self.generating_args.to_dict()
+        gen_kwargs["logits_processor"] = get_logits_processor()
+        gen_kwargs["stopping_criteria"] = get_stopping_criteria(self.tokenizer.additional_special_tokens_ids)
+
         length_sampler = LengthSampler(max_target_length // 2, max_target_length)
-        unwrapped_model: PreTrainedModel = self.accelerator.unwrap_model(self.model)
+        unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model)
 
         dataiter = iter(self.dataloader)
         steps_trained = 0
@@ -86,51 +86,38 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
         reward_meter = AverageMeter()
         self.log_callback.on_train_begin(self.args, self.state, self.control)
 
-        for step in tqdm(range(max_steps), disable=not self.is_world_process_zero(), leave=False):
+        for step in tqdm(range(max_steps), disable=not self.is_local_process_zero()):
             batch = next(dataiter)
             steps_trained += 1
 
+            # Cast to inference mode
             unwrapped_model.gradient_checkpointing_disable()
             unwrapped_model.config.use_cache = True
 
-            # Get responses
-            query_tensors = batch["input_ids"]
-            response_tensors = self.generate(batch, length_sampler, return_prompt=False, **gen_kwargs)
+            # Get inputs
+            queries, responses = self.get_inputs(batch, length_sampler, **gen_kwargs)
+            rewards = self.get_rewards(queries, responses, unwrapped_model)
 
-            queries, responses = [], []
-            for i in range(len(query_tensors)):
-                query_length = (query_tensors[i] != self.tokenizer.pad_token_id).nonzero()[0]
-                response_length = (response_tensors[i] != self.tokenizer.pad_token_id).nonzero()[-1] + 1
-                queries.append(query_tensors[i, query_length:]) # remove padding from left
-                responses.append(response_tensors[i, :response_length]) # remove padding from right
-
-            # Compute rewards
-            replace_model(unwrapped_model, target="reward")
-            with torch.no_grad():
-                _, _, values = self.model(
-                    **self.prepare_model_inputs(queries, responses),
-                    output_hidden_states=True,
-                    return_dict=True
-                )
-            rewards = [reward for reward in values[:, -1].to(torch.float32)] # use float32 type
-            replace_model(unwrapped_model, target="default")
-
-            # Run PPO step
+            # Cast to training mode
             unwrapped_model.gradient_checkpointing_enable()
             unwrapped_model.config.use_cache = False
-            stats = self.step(queries, responses, rewards)
 
+            # Run PPO step
+            stats = self.step(queries, responses, rewards)
             loss_meter.update(stats["ppo/loss/total"], n=len(rewards))
             reward_meter.update(torch.stack(rewards).mean().item(), n=len(rewards))
 
-            if self.is_world_process_zero() and (step+1) % self.args.logging_steps == 0:
+            self.state.global_step += 1
+            self.log_callback.on_step_end(self.args, self.state, self.control)
+
+            if self.is_local_process_zero() and (step+1) % self.args.logging_steps == 0:
                 logs = dict(
                     loss=round(loss_meter.avg, 4),
                     reward=round(reward_meter.avg, 4),
                     learning_rate=stats["ppo/learning_rate"],
                     epoch=round(step / len_dataloader, 2)
                 )
-                print(logs)
+                tqdm.write(str(logs))
                 logs["step"] = step
                 self.state.log_history.append(logs)
                 self.log_callback.on_log(self.args, self.state, self.control)
@@ -147,38 +134,57 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
                 dataiter = iter(self.dataloader)
                 steps_trained = 0
 
+        self.log_callback.on_train_end(self.args, self.state, self.control)
+
     @torch.no_grad()
-    def generate(
+    def get_inputs(
         self,
-        inputs: Dict[str, torch.Tensor],
+        batch: Dict[str, torch.Tensor],
         length_sampler: Optional[Callable] = None,
-        return_prompt: Optional[bool] = True,
         **generation_kwargs
-    ) -> torch.Tensor:
+    ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
         r"""
         Generates model's responses given queries.
-
-        Subclass and override to inject custom behavior.
         """
-        self.model, layer_norm_params = cast_layernorm_dtype(self.model)
-
         if length_sampler is not None:
             generation_kwargs["max_new_tokens"] = length_sampler()
 
-        unwrapped_model = self.accelerator.unwrap_model(self.model)
-
-        response = unwrapped_model.generate(**inputs, **generation_kwargs)
+        self.model, layer_norm_params = cast_layernorm_dtype(self.model)
+        unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model)
+        response: torch.Tensor = unwrapped_model.generate(**batch, **generation_kwargs)
+        self.model, _ = cast_layernorm_dtype(self.model, layer_norm_params)
 
         # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
         # Inspired by: https://github.com/huggingface/transformers/blob/v4.28.1/src/transformers/trainer_seq2seq.py#L273
         if unwrapped_model.pretrained_model.generation_config._from_model_config:
             unwrapped_model.pretrained_model.generation_config._from_model_config = False
 
-        self.model, _ = cast_layernorm_dtype(self.model, layer_norm_params)
+        queries, responses = [], []
+        query, response = batch["input_ids"].detach().cpu(), response[:, batch["input_ids"].size(-1):].detach().cpu()
+        for i in range(len(query)):
+            query_length = (query[i] != self.tokenizer.pad_token_id).nonzero()[0]
+            response_length = (response[i] != self.tokenizer.pad_token_id).nonzero()[-1] + 1
+            queries.append(query[i, query_length:]) # remove padding from left
+            responses.append(response[i, :response_length]) # remove padding from right
 
-        if not return_prompt and not self.is_encoder_decoder:
-            return response[:, inputs["input_ids"].size(1):]
-        return response
+        return queries, responses
+
+    @torch.no_grad()
+    def get_rewards(
+        self,
+        queries: List[torch.Tensor],
+        responses: List[torch.Tensor],
+        unwrapped_model: "AutoModelForCausalLMWithValueHead"
+    ) -> List[torch.Tensor]:
+        r"""
+        Computes scores using given reward model.
+        """
+        replace_model(unwrapped_model, target="reward")
+        batch = self.prepare_model_inputs(queries, responses)
+        _, _, values = self.model(**batch, output_hidden_states=True, return_dict=True)
+        rewards = [reward for reward in values[:, -1].float().detach().cpu()] # use fp32 type
+        replace_model(unwrapped_model, target="default")
+        return rewards
 
     def save_model(self, output_dir: Optional[str] = None) -> None:
         r"""

src/llmtuner/tuner/ppo/utils.py

@@ -1,11 +1,13 @@
 import torch
-from typing import Dict, List, Literal, Optional, Tuple
-from trl import AutoModelForCausalLMWithValueHead
+from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Tuple
 
 from llmtuner.extras.constants import LAYERNORM_NAMES
 
+if TYPE_CHECKING:
+    from trl import AutoModelForCausalLMWithValueHead
 
-def replace_model(model: AutoModelForCausalLMWithValueHead, target: Literal["default", "reward"]) -> None:
+
+def replace_model(model: "AutoModelForCausalLMWithValueHead", target: Literal["default", "reward"]) -> None:
     if target == "reward": # save default head temporarily
         valuehead_state_dict = model.v_head.state_dict()
         setattr(model, "default_head_weight", valuehead_state_dict["summary.weight"])
@@ -19,10 +21,10 @@ def replace_model(model: AutoModelForCausalLMWithValueHead, target: Literal["def
 
 
 def cast_layernorm_dtype(
-    model: AutoModelForCausalLMWithValueHead,
+    model: "AutoModelForCausalLMWithValueHead",
     layer_norm_names: List[str] = LAYERNORM_NAMES,
     layer_norm_params: Optional[Dict[str, torch.Tensor]] = None
-) -> Tuple[AutoModelForCausalLMWithValueHead, Dict[str, torch.Tensor]]:
+) -> Tuple["AutoModelForCausalLMWithValueHead", Dict[str, torch.Tensor]]:
 
     layer_norm_state_dict = {}
 

src/llmtuner/tuner/ppo/workflow.py

@@ -1,27 +1,29 @@
-# Inspired by:
-# https://github.com/lvwerra/trl/blob/main/examples/sentiment/scripts/gpt-neox-20b_peft/gpt-neo-20b_sentiment_peft.py
+# Inspired by: https://github.com/lvwerra/trl/blob/main/examples/research_projects/stack_llama/scripts/rl_training.py
 
 import math
 from trl import PPOConfig
 from torch.optim import AdamW
-from typing import Optional, List
-from transformers import DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, TrainerCallback
+from typing import TYPE_CHECKING, Optional, List
+from transformers import DataCollatorForSeq2Seq
 from transformers.optimization import get_scheduler
 
 from llmtuner.dsets import get_dataset, preprocess_dataset
-from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.ploting import plot_loss
-from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
 from llmtuner.tuner.core import load_model_and_tokenizer
 from llmtuner.tuner.ppo.trainer import PPOPeftTrainer
 
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
+
 
 def run_ppo(
-    model_args: ModelArguments,
-    data_args: DataArguments,
-    training_args: Seq2SeqTrainingArguments,
-    finetuning_args: FinetuningArguments,
-    callbacks: Optional[List[TrainerCallback]] = [LogCallback()]
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    generating_args: "GeneratingArguments",
+    callbacks: Optional[List["TrainerCallback"]] = None
 ):
     dataset = get_dataset(model_args, data_args)
     model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="ppo")
@@ -38,20 +40,23 @@ def run_ppo(
         max_grad_norm=training_args.max_grad_norm
     )
 
-    optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=ppo_config.learning_rate)
-    total_train_batch_size = \
+    optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=training_args.learning_rate)
+    total_train_batch_size = (
         training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
+    )
+    num_training_steps = training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size)
     lr_scheduler = get_scheduler(
         training_args.lr_scheduler_type,
         optimizer=optimizer,
-        num_warmup_steps=training_args.warmup_steps,
-        num_training_steps=(training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size))
+        num_warmup_steps=training_args.get_warmup_steps(num_training_steps),
+        num_training_steps=num_training_steps
     )
 
     # Initialize our Trainer
     ppo_trainer = PPOPeftTrainer(
         training_args=training_args,
         finetuning_args=finetuning_args,
+        generating_args=generating_args,
         callbacks=callbacks,
         config=ppo_config,
         model=model,
@@ -63,8 +68,10 @@ def run_ppo(
         lr_scheduler=lr_scheduler
     )
 
-    ppo_trainer.ppo_train(max_target_length=data_args.max_target_length)
-    ppo_trainer.save_model()
-    ppo_trainer.save_state() # must be after save_model
-    if ppo_trainer.is_world_process_zero() and model_args.plot_loss:
-        plot_loss(training_args.output_dir, keys=["loss", "reward"])
+    # Training
+    if training_args.do_train:
+        ppo_trainer.ppo_train(max_target_length=data_args.max_target_length)
+        ppo_trainer.save_model()
+        ppo_trainer.save_state() # must be called after save_model to have a folder
+        if ppo_trainer.is_world_process_zero() and model_args.plot_loss:
+            plot_loss(training_args.output_dir, keys=["loss", "reward"])

src/llmtuner/tuner/pt/workflow.py

@@ -1,32 +1,30 @@
 # Inspired by: https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/language-modeling/run_clm.py
 
 import math
-from typing import Optional, List
-from transformers import Seq2SeqTrainingArguments, DataCollatorForSeq2Seq, TrainerCallback
+from typing import TYPE_CHECKING, Optional, List
+from transformers import DataCollatorForLanguageModeling
 
 from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
-from llmtuner.extras.callbacks import LogCallback
-from llmtuner.extras.constants import IGNORE_INDEX
 from llmtuner.extras.ploting import plot_loss
-from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
 from llmtuner.tuner.core import load_model_and_tokenizer
 from llmtuner.tuner.core.trainer import PeftTrainer
 
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
+
 
 def run_pt(
-    model_args: ModelArguments,
-    data_args: DataArguments,
-    training_args: Seq2SeqTrainingArguments,
-    finetuning_args: FinetuningArguments,
-    callbacks: Optional[List[TrainerCallback]] = [LogCallback()]
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    callbacks: Optional[List["TrainerCallback"]] = None
 ):
     dataset = get_dataset(model_args, data_args)
     model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="pt")
     dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="pt")
-    data_collator = DataCollatorForSeq2Seq(
-        tokenizer=tokenizer,
-        label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
-    )
+    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
 
     # Initialize our Trainer
     trainer = PeftTrainer(
@@ -36,7 +34,7 @@ def run_pt(
         tokenizer=tokenizer,
         data_collator=data_collator,
         callbacks=callbacks,
-        **split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
+        **split_dataset(dataset, data_args, training_args)
     )
 
     # Training

src/llmtuner/tuner/rm/collator.py

@@ -1,8 +1,10 @@
 import torch
+from dataclasses import dataclass
 from typing import Any, Dict, Sequence
 from transformers import DataCollatorWithPadding
 
 
+@dataclass
 class PairwiseDataCollatorWithPadding(DataCollatorWithPadding):
     r"""
     Data collator for pairwise data.
@@ -15,5 +17,11 @@ class PairwiseDataCollatorWithPadding(DataCollatorWithPadding):
         We generate 2 * n examples where the first n examples represent chosen examples and
         the last n examples represent rejected examples.
         """
-        features = [{"input_ids": feature[key]} for key in ("accept_ids", "reject_ids") for feature in features]
+        features = [
+            {
+                "input_ids": feature["prompt_ids"] + feature[key],
+                "attention_mask": [1] * (len(feature["prompt_ids"]) + len(feature[key]))
+            }
+            for key in ("chosen_ids", "rejected_ids") for feature in features
+        ]
         return super().__call__(features)

src/llmtuner/tuner/rm/trainer.py

@@ -1,9 +1,18 @@
+import os
+import json
 import torch
-from typing import Dict, List, Optional, Tuple, Union
-from transformers.modeling_utils import PreTrainedModel
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
 
+from llmtuner.extras.logging import get_logger
 from llmtuner.tuner.core.trainer import PeftTrainer
 
+if TYPE_CHECKING:
+    from transformers.trainer import PredictionOutput
+    from transformers.modeling_utils import PreTrainedModel
+
+
+logger = get_logger(__name__)
+
 
 class PairwisePeftTrainer(PeftTrainer):
     r"""
@@ -16,7 +25,7 @@ class PairwisePeftTrainer(PeftTrainer):
 
     def compute_loss(
         self,
-        model: PreTrainedModel,
+        model: "PreTrainedModel",
         inputs: Dict[str, torch.Tensor],
         return_outputs: Optional[bool] = False
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]]]:
@@ -36,3 +45,26 @@ class PairwisePeftTrainer(PeftTrainer):
         r_accept, r_reject = values[:, -1].split(batch_size, dim=0)
         loss = -torch.log(torch.sigmoid(r_accept - r_reject)).mean()
         return (loss, [loss, r_accept, r_reject]) if return_outputs else loss
+
+    def save_predictions(
+        self,
+        predict_results: "PredictionOutput"
+    ) -> None:
+        r"""
+        Saves model predictions to `output_dir`.
+
+        A custom behavior that not contained in Seq2SeqTrainer.
+        """
+        if not self.is_world_process_zero():
+            return
+
+        output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
+        logger.info(f"Saving prediction results to {output_prediction_file}")
+
+        acc_scores, rej_scores = predict_results.predictions
+
+        with open(output_prediction_file, "w", encoding="utf-8") as writer:
+            res: List[str] = []
+            for acc_score, rej_score in zip(acc_scores, rej_scores):
+                res.append(json.dumps({"accept": round(float(acc_score), 2), "reject": round(float(rej_score), 2)}))
+            writer.write("\n".join(res))

src/llmtuner/tuner/rm/workflow.py

@@ -2,25 +2,26 @@
 # https://github.com/lvwerra/trl/blob/main/examples/summarization/scripts/reward_summarization.py
 # https://github.com/CarperAI/trlx/blob/main/examples/summarize_rlhf/reward_model/train_reward_model_gptj.py
 
-from typing import Optional, List
-from transformers import Seq2SeqTrainingArguments, TrainerCallback
+from typing import TYPE_CHECKING, Optional, List
 
 from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
-from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.ploting import plot_loss
-from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
 from llmtuner.tuner.core import load_model_and_tokenizer
 from llmtuner.tuner.rm.metric import compute_accuracy
 from llmtuner.tuner.rm.collator import PairwiseDataCollatorWithPadding
 from llmtuner.tuner.rm.trainer import PairwisePeftTrainer
 
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
+
 
 def run_rm(
-    model_args: ModelArguments,
-    data_args: DataArguments,
-    training_args: Seq2SeqTrainingArguments,
-    finetuning_args: FinetuningArguments,
-    callbacks: Optional[List[TrainerCallback]] = [LogCallback()]
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    callbacks: Optional[List["TrainerCallback"]] = None
 ):
     dataset = get_dataset(model_args, data_args)
     model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="rm")
@@ -38,7 +39,7 @@ def run_rm(
         data_collator=data_collator,
         callbacks=callbacks,
         compute_metrics=compute_accuracy,
-        **split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
+        **split_dataset(dataset, data_args, training_args)
     )
 
     # Training
@@ -56,3 +57,10 @@ def run_rm(
         metrics = trainer.evaluate(metric_key_prefix="eval")
         trainer.log_metrics("eval", metrics)
         trainer.save_metrics("eval", metrics)
+
+    # Predict
+    if training_args.do_predict:
+        predict_results = trainer.predict(dataset, metric_key_prefix="predict")
+        trainer.log_metrics("predict", predict_results.metrics)
+        trainer.save_metrics("predict", predict_results.metrics)
+        trainer.save_predictions(predict_results)

src/llmtuner/tuner/sft/metric.py

@@ -1,7 +1,6 @@
 import numpy as np
 from dataclasses import dataclass
-from typing import Dict, Sequence, Tuple, Union
-from transformers.tokenization_utils import PreTrainedTokenizer
+from typing import TYPE_CHECKING, Dict, Sequence, Tuple, Union
 
 import jieba
 from rouge_chinese import Rouge
@@ -9,6 +8,9 @@ from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
 
 from llmtuner.extras.constants import IGNORE_INDEX
 
+if TYPE_CHECKING:
+    from transformers.tokenization_utils import PreTrainedTokenizer
+
 
 @dataclass
 class ComputeMetrics:
@@ -16,7 +18,7 @@ class ComputeMetrics:
     Wraps the tokenizer into metric functions, used in Seq2SeqPeftTrainer.
     """
 
-    tokenizer: PreTrainedTokenizer
+    tokenizer: "PreTrainedTokenizer"
 
     def __call__(self, eval_preds: Sequence[Union[np.ndarray, Tuple[np.ndarray]]]) -> Dict[str, float]:
         r"""

src/llmtuner/tuner/sft/trainer.py

@@ -3,13 +3,15 @@ import json
 import torch
 import numpy as np
 import torch.nn as nn
-from typing import Any, Dict, List, Optional, Tuple, Union
-from transformers.trainer import PredictionOutput
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
 from llmtuner.extras.constants import IGNORE_INDEX
 from llmtuner.extras.logging import get_logger
 from llmtuner.tuner.core.trainer import PeftTrainer
 
+if TYPE_CHECKING:
+    from transformers.trainer import PredictionOutput
+
 
 logger = get_logger(__name__)
 
@@ -77,11 +79,11 @@ class Seq2SeqPeftTrainer(PeftTrainer):
 
         padded_tensor = pad_token_id * torch.ones_like(tgt_tensor)
         padded_tensor[:, -src_tensor.shape[-1]:] = src_tensor # adopt left-padding
-        return padded_tensor
+        return padded_tensor.contiguous() # in contiguous memory
 
     def save_predictions(
         self,
-        predict_results: PredictionOutput
+        predict_results: "PredictionOutput"
     ) -> None:
         r"""
         Saves model predictions to `output_dir`.

src/llmtuner/tuner/sft/workflow.py

@@ -1,25 +1,28 @@
 # Inspired by: https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/summarization/run_summarization.py
 
-from typing import Optional, List
-from transformers import Seq2SeqTrainingArguments, DataCollatorForSeq2Seq, TrainerCallback
+from typing import TYPE_CHECKING, Optional, List
+from transformers import DataCollatorForSeq2Seq
 
 from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
-from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.constants import IGNORE_INDEX
-from llmtuner.extras.misc import get_logits_processor
+from llmtuner.extras.misc import get_logits_processor, get_stopping_criteria
 from llmtuner.extras.ploting import plot_loss
-from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
 from llmtuner.tuner.core import load_model_and_tokenizer
 from llmtuner.tuner.sft.metric import ComputeMetrics
 from llmtuner.tuner.sft.trainer import Seq2SeqPeftTrainer
 
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
+
 
 def run_sft(
-    model_args: ModelArguments,
-    data_args: DataArguments,
-    training_args: Seq2SeqTrainingArguments,
-    finetuning_args: FinetuningArguments,
-    callbacks: Optional[List[TrainerCallback]] = [LogCallback()]
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    generating_args: "GeneratingArguments",
+    callbacks: Optional[List["TrainerCallback"]] = None
 ):
     dataset = get_dataset(model_args, data_args)
     model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="sft")
@@ -44,17 +47,13 @@ def run_sft(
         data_collator=data_collator,
         callbacks=callbacks,
         compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None,
-        **split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
+        **split_dataset(dataset, data_args, training_args)
     )
 
     # Keyword arguments for `model.generate`
-    gen_kwargs = {
-        "do_sample": True,
-        "top_p": 0.7,
-        "max_new_tokens": data_args.max_target_length + 1,
-        "temperature": 0.95,
-        "logits_processor": get_logits_processor()
-    }
+    gen_kwargs = generating_args.to_dict()
+    gen_kwargs["logits_processor"] = get_logits_processor()
+    gen_kwargs["stopping_criteria"] = get_stopping_criteria(tokenizer.additional_special_tokens_ids)
 
     # Training
     if training_args.do_train:

src/llmtuner/tuner/tune.py

@@ -0,0 +1,48 @@
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+from llmtuner.extras.callbacks import LogCallback
+from llmtuner.extras.logging import get_logger
+from llmtuner.tuner.core import get_train_args, load_model_and_tokenizer
+from llmtuner.tuner.pt import run_pt
+from llmtuner.tuner.sft import run_sft
+from llmtuner.tuner.rm import run_rm
+from llmtuner.tuner.ppo import run_ppo
+from llmtuner.tuner.dpo import run_dpo
+
+if TYPE_CHECKING:
+    from transformers import TrainerCallback
+
+
+logger = get_logger(__name__)
+
+
+def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: Optional[List["TrainerCallback"]] = None):
+    model_args, data_args, training_args, finetuning_args, generating_args, general_args = get_train_args(args)
+    callbacks = [LogCallback()] if callbacks is None else callbacks
+
+    if general_args.stage == "pt":
+        run_pt(model_args, data_args, training_args, finetuning_args, callbacks)
+    elif general_args.stage == "sft":
+        run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+    elif general_args.stage == "rm":
+        run_rm(model_args, data_args, training_args, finetuning_args, callbacks)
+    elif general_args.stage == "ppo":
+        run_ppo(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+    elif general_args.stage == "dpo":
+        run_dpo(model_args, data_args, training_args, finetuning_args, callbacks)
+    else:
+        raise ValueError("Unknown task.")
+
+
+def export_model(args: Optional[Dict[str, Any]] = None, max_shard_size: Optional[str] = "10GB"):
+    model_args, _, training_args, finetuning_args, _ = get_train_args(args)
+    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
+    model.save_pretrained(training_args.output_dir, max_shard_size=max_shard_size)
+    try:
+        tokenizer.save_pretrained(training_args.output_dir)
+    except:
+        logger.warning("Cannot save tokenizer, please copy the files manually.")
+
+
+if __name__ == "__main__":
+    run_exp()

src/llmtuner/webui/__init__.py

@@ -0,0 +1 @@
+from llmtuner.webui.interface import create_ui, create_web_demo

src/llmtuner/webui/chat.py

@@ -1,22 +1,22 @@
 import os
-from typing import List, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 from llmtuner.chat.stream_chat import ChatModel
 from llmtuner.extras.misc import torch_gc
 from llmtuner.hparams import GeneratingArguments
-from llmtuner.tuner import get_infer_args
 from llmtuner.webui.common import get_model_path, get_save_dir
 from llmtuner.webui.locales import ALERTS
 
 
 class WebChatModel(ChatModel):
 
-    def __init__(self, *args):
-        self.model = None
-        self.tokenizer = None
-        self.generating_args = GeneratingArguments()
-        if len(args) != 0:
-            super().__init__(*args)
+    def __init__(self, args: Optional[Dict[str, Any]] = None, lazy_init: Optional[bool] = True) -> None:
+        if lazy_init:
+            self.model = None
+            self.tokenizer = None
+            self.generating_args = GeneratingArguments()
+        else:
+            super().__init__(args)
 
     def load_model(
         self,
@@ -54,10 +54,10 @@ class WebChatModel(ChatModel):
             checkpoint_dir=checkpoint_dir,
             finetuning_type=finetuning_type,
             quantization_bit=int(quantization_bit) if quantization_bit else None,
-            prompt_template=template,
+            template=template,
             source_prefix=source_prefix
         )
-        super().__init__(*get_infer_args(args))
+        super().__init__(args)
 
         yield ALERTS["info_loaded"][lang]
 
@@ -84,6 +84,14 @@ class WebChatModel(ChatModel):
             query, history, prefix, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
         ):
             response += new_text
+            response = self.postprocess(response)
             new_history = history + [(query, response)]
             chatbot[-1] = [query, response]
             yield chatbot, new_history
+
+    def postprocess(self, response: str) -> str:
+        blocks = response.split("```")
+        for i, block in enumerate(blocks):
+            if i % 2 == 0:
+                blocks[i] = block.replace("<", "&lt;").replace(">", "&gt;")
+        return "```".join(blocks)

src/llmtuner/webui/components/__init__.py

@@ -1,4 +1,6 @@
-from llmtuner.webui.components.eval import create_eval_tab
-from llmtuner.webui.components.infer import create_infer_tab
 from llmtuner.webui.components.top import create_top
 from llmtuner.webui.components.sft import create_sft_tab
+from llmtuner.webui.components.eval import create_eval_tab
+from llmtuner.webui.components.infer import create_infer_tab
+from llmtuner.webui.components.export import create_export_tab
+from llmtuner.webui.components.chatbot import create_chat_box

src/llmtuner/webui/components/chatbot.py

@@ -1,16 +1,17 @@
-from typing import Dict, Optional, Tuple
+from typing import TYPE_CHECKING, Dict, Optional, Tuple
 
 import gradio as gr
-from gradio.blocks import Block
-from gradio.components import Component
 
-from llmtuner.webui.chat import WebChatModel
+if TYPE_CHECKING:
+    from gradio.blocks import Block
+    from gradio.components import Component
+    from llmtuner.webui.chat import WebChatModel
 
 
 def create_chat_box(
-    chat_model: WebChatModel,
+    chat_model: "WebChatModel",
     visible: Optional[bool] = False
-) -> Tuple[Block, Component, Component, Dict[str, Component]]:
+) -> Tuple["Block", "Component", "Component", Dict[str, "Component"]]:
     with gr.Box(visible=visible) as chat_box:
         chatbot = gr.Chatbot()
 
@@ -22,13 +23,9 @@ def create_chat_box(
 
             with gr.Column(scale=1):
                 clear_btn = gr.Button()
-                max_new_tokens = gr.Slider(
-                    10, 2048, value=chat_model.generating_args.max_new_tokens, step=1, interactive=True
-                )
-                top_p = gr.Slider(0.01, 1, value=chat_model.generating_args.top_p, step=0.01, interactive=True)
-                temperature = gr.Slider(
-                    0.01, 1.5, value=chat_model.generating_args.temperature, step=0.01, interactive=True
-                )
+                max_new_tokens = gr.Slider(10, 2048, value=chat_model.generating_args.max_new_tokens, step=1)
+                top_p = gr.Slider(0.01, 1, value=chat_model.generating_args.top_p, step=0.01)
+                temperature = gr.Slider(0.01, 1.5, value=chat_model.generating_args.temperature, step=0.01)
 
     history = gr.State([])
 

src/llmtuner/webui/components/data.py

@@ -1,10 +1,12 @@
 import gradio as gr
-from gradio.blocks import Block
-from gradio.components import Component
-from typing import Tuple
+from typing import TYPE_CHECKING, Tuple
+
+if TYPE_CHECKING:
+    from gradio.blocks import Block
+    from gradio.components import Component
 
 
-def create_preview_box() -> Tuple[Block, Component, Component, Component]:
+def create_preview_box() -> Tuple["Block", "Component", "Component", "Component"]:
     with gr.Box(visible=False, elem_classes="modal-box") as preview_box:
         with gr.Row():
             preview_count = gr.Number(interactive=False)
@@ -14,6 +16,6 @@ def create_preview_box() -> Tuple[Block, Component, Component, Component]:
 
         close_btn = gr.Button()
 
-    close_btn.click(lambda: gr.update(visible=False), outputs=[preview_box])
+    close_btn.click(lambda: gr.update(visible=False), outputs=[preview_box], queue=False)
 
     return preview_box, preview_count, preview_samples, close_btn

src/llmtuner/webui/components/eval.py

@@ -1,14 +1,16 @@
-from typing import Dict
+from typing import TYPE_CHECKING, Dict
 import gradio as gr
-from gradio.components import Component
 
 from llmtuner.webui.common import list_dataset, DEFAULT_DATA_DIR
 from llmtuner.webui.components.data import create_preview_box
-from llmtuner.webui.runner import Runner
 from llmtuner.webui.utils import can_preview, get_preview
 
+if TYPE_CHECKING:
+    from gradio.components import Component
+    from llmtuner.webui.runner import Runner
 
-def create_eval_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str, Component]:
+
+def create_eval_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[str, "Component"]:
     with gr.Row():
         dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
         dataset = gr.Dropdown(multiselect=True, scale=4)
@@ -18,7 +20,12 @@ def create_eval_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str
 
     dataset_dir.change(list_dataset, [dataset_dir], [dataset])
     dataset.change(can_preview, [dataset_dir, dataset], [preview_btn])
-    preview_btn.click(get_preview, [dataset_dir, dataset], [preview_count, preview_samples, preview_box])
+    preview_btn.click(
+        get_preview,
+        [dataset_dir, dataset],
+        [preview_count, preview_samples, preview_box],
+        queue=False
+    )
 
     with gr.Row():
         max_source_length = gr.Slider(value=512, minimum=4, maximum=4096, step=1)
@@ -31,6 +38,9 @@ def create_eval_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str
         start_btn = gr.Button()
         stop_btn = gr.Button()
 
+    with gr.Row():
+        process_bar = gr.Slider(visible=False, interactive=False)
+
     with gr.Box():
         output_box = gr.Markdown()
 
@@ -52,7 +62,10 @@ def create_eval_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str
             batch_size,
             predict
         ],
-        [output_box]
+        [
+            output_box,
+            process_bar
+        ]
     )
     stop_btn.click(runner.set_abort, queue=False)
 

src/llmtuner/webui/components/export.py

@@ -0,0 +1,37 @@
+from typing import TYPE_CHECKING, Dict
+import gradio as gr
+
+from llmtuner.webui.utils import save_model
+
+if TYPE_CHECKING:
+    from gradio.components import Component
+
+
+def create_export_tab(top_elems: Dict[str, "Component"]) -> Dict[str, "Component"]:
+    with gr.Row():
+        save_dir = gr.Textbox()
+        max_shard_size = gr.Slider(value=10, minimum=1, maximum=100)
+
+    export_btn = gr.Button()
+    info_box = gr.Textbox(show_label=False, interactive=False)
+
+    export_btn.click(
+        save_model,
+        [
+            top_elems["lang"],
+            top_elems["model_name"],
+            top_elems["checkpoints"],
+            top_elems["finetuning_type"],
+            top_elems["template"],
+            max_shard_size,
+            save_dir
+        ],
+        [info_box]
+    )
+
+    return dict(
+        save_dir=save_dir,
+        max_shard_size=max_shard_size,
+        export_btn=export_btn,
+        info_box=info_box
+    )

src/llmtuner/webui/components/infer.py

@@ -1,13 +1,15 @@
-from typing import Dict
+from typing import TYPE_CHECKING, Dict
 
 import gradio as gr
-from gradio.components import Component
 
 from llmtuner.webui.chat import WebChatModel
 from llmtuner.webui.components.chatbot import create_chat_box
 
+if TYPE_CHECKING:
+    from gradio.components import Component
 
-def create_infer_tab(top_elems: Dict[str, Component]) -> Dict[str, Component]:
+
+def create_infer_tab(top_elems: Dict[str, "Component"]) -> Dict[str, "Component"]:
     with gr.Row():
         load_btn = gr.Button()
         unload_btn = gr.Button()

src/llmtuner/webui/components/sft.py

@@ -1,16 +1,18 @@
-from typing import Dict
+from typing import TYPE_CHECKING, Dict
 from transformers.trainer_utils import SchedulerType
 
 import gradio as gr
-from gradio.components import Component
 
 from llmtuner.webui.common import list_dataset, DEFAULT_DATA_DIR
 from llmtuner.webui.components.data import create_preview_box
-from llmtuner.webui.runner import Runner
 from llmtuner.webui.utils import can_preview, get_preview, gen_plot
 
+if TYPE_CHECKING:
+    from gradio.components import Component
+    from llmtuner.webui.runner import Runner
 
-def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str, Component]:
+
+def create_sft_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[str, "Component"]:
     with gr.Row():
         dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
         dataset = gr.Dropdown(multiselect=True, scale=4)
@@ -20,7 +22,12 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
 
     dataset_dir.change(list_dataset, [dataset_dir], [dataset])
     dataset.change(can_preview, [dataset_dir, dataset], [preview_btn])
-    preview_btn.click(get_preview, [dataset_dir, dataset], [preview_count, preview_samples, preview_box])
+    preview_btn.click(
+        get_preview,
+        [dataset_dir, dataset],
+        [preview_count, preview_samples, preview_box],
+        queue=False
+    )
 
     with gr.Row():
         max_source_length = gr.Slider(value=512, minimum=4, maximum=4096, step=1)
@@ -36,7 +43,7 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
             value="cosine", choices=[scheduler.value for scheduler in SchedulerType]
         )
         max_grad_norm = gr.Textbox(value="1.0")
-        dev_ratio = gr.Slider(value=0, minimum=0, maximum=1, step=0.001)
+        val_size = gr.Slider(value=0, minimum=0, maximum=1, step=0.001)
 
     with gr.Accordion(label="Advanced config", open=False) as advanced_tab:
         with gr.Row():
@@ -44,20 +51,26 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
             save_steps = gr.Slider(value=100, minimum=10, maximum=5000, step=10)
             warmup_steps = gr.Slider(value=0, minimum=0, maximum=5000, step=1)
             compute_type = gr.Radio(choices=["fp16", "bf16"], value="fp16")
+            padding_side = gr.Radio(choices=["left", "right"], value="left")
 
     with gr.Accordion(label="LoRA config", open=False) as lora_tab:
         with gr.Row():
             lora_rank = gr.Slider(value=8, minimum=1, maximum=1024, step=1, scale=1)
-            lora_dropout = gr.Slider(value=0, minimum=0, maximum=1, step=0.01, scale=1)
+            lora_dropout = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=1)
             lora_target = gr.Textbox(scale=2)
+            resume_lora_training = gr.Checkbox(value=True, scale=1)
 
     with gr.Row():
         start_btn = gr.Button()
         stop_btn = gr.Button()
 
     with gr.Row():
-        with gr.Column(scale=4):
-            output_dir = gr.Textbox(interactive=True)
+        with gr.Column(scale=3):
+            with gr.Row():
+                output_dir = gr.Textbox()
+
+            with gr.Row():
+                process_bar = gr.Slider(visible=False, interactive=False)
 
             with gr.Box():
                 output_box = gr.Markdown()
@@ -86,21 +99,26 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
             gradient_accumulation_steps,
             lr_scheduler_type,
             max_grad_norm,
-            dev_ratio,
+            val_size,
             logging_steps,
             save_steps,
             warmup_steps,
             compute_type,
+            padding_side,
             lora_rank,
             lora_dropout,
             lora_target,
+            resume_lora_training,
             output_dir
         ],
-        [output_box]
+        [
+            output_box,
+            process_bar
+        ]
     )
     stop_btn.click(runner.set_abort, queue=False)
 
-    output_box.change(
+    process_bar.change(
         gen_plot, [top_elems["model_name"], top_elems["finetuning_type"], output_dir], loss_viewer, queue=False
     )
 
@@ -120,16 +138,18 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
         gradient_accumulation_steps=gradient_accumulation_steps,
         lr_scheduler_type=lr_scheduler_type,
         max_grad_norm=max_grad_norm,
-        dev_ratio=dev_ratio,
+        val_size=val_size,
         advanced_tab=advanced_tab,
         logging_steps=logging_steps,
         save_steps=save_steps,
         warmup_steps=warmup_steps,
         compute_type=compute_type,
+        padding_side=padding_side,
         lora_tab=lora_tab,
         lora_rank=lora_rank,
         lora_dropout=lora_dropout,
         lora_target=lora_target,
+        resume_lora_training=resume_lora_training,
         start_btn=start_btn,
         stop_btn=stop_btn,
         output_dir=output_dir,

src/llmtuner/webui/components/top.py

@@ -1,15 +1,17 @@
-from typing import Dict
+from typing import TYPE_CHECKING, Dict
 
 import gradio as gr
-from gradio.components import Component
 
 from llmtuner.extras.constants import METHODS, SUPPORTED_MODELS
 from llmtuner.extras.template import templates
 from llmtuner.webui.common import list_checkpoint, get_model_path, save_config
 from llmtuner.webui.utils import can_quantize
 
+if TYPE_CHECKING:
+    from gradio.components import Component
 
-def create_top() -> Dict[str, Component]:
+
+def create_top() -> Dict[str, "Component"]:
     available_models = list(SUPPORTED_MODELS.keys()) + ["Custom"]
 
     with gr.Row():
@@ -41,7 +43,7 @@ def create_top() -> Dict[str, Component]:
         can_quantize, [finetuning_type], [quantization_bit]
     )
 
-    refresh_btn.click(list_checkpoint, [model_name, finetuning_type], [checkpoints])
+    refresh_btn.click(list_checkpoint, [model_name, finetuning_type], [checkpoints], queue=False)
 
     return dict(
         lang=lang,

src/llmtuner/webui/interface.py

@@ -5,8 +5,11 @@ from llmtuner.webui.components import (
     create_top,
     create_sft_tab,
     create_eval_tab,
-    create_infer_tab
+    create_infer_tab,
+    create_export_tab,
+    create_chat_box
 )
+from llmtuner.webui.chat import WebChatModel
 from llmtuner.webui.css import CSS
 from llmtuner.webui.manager import Manager
 from llmtuner.webui.runner import Runner
@@ -30,7 +33,10 @@ def create_ui() -> gr.Blocks:
         with gr.Tab("Chat"):
             infer_elems = create_infer_tab(top_elems)
 
-        elem_list = [top_elems, sft_elems, eval_elems, infer_elems]
+        with gr.Tab("Export"):
+            export_elems = create_export_tab(top_elems)
+
+        elem_list = [top_elems, sft_elems, eval_elems, infer_elems, export_elems]
         manager = Manager(elem_list)
 
         demo.load(
@@ -43,12 +49,30 @@ def create_ui() -> gr.Blocks:
             manager.gen_label,
             [top_elems["lang"]],
             [elem for elems in elem_list for elem in elems.values()],
+            queue=False
         )
 
     return demo
 
 
+def create_web_demo() -> gr.Blocks:
+    chat_model = WebChatModel(lazy_init=False)
+
+    with gr.Blocks(title="Web Demo", css=CSS) as demo:
+        lang = gr.Dropdown(choices=["en", "zh"], value="en")
+
+        _, _, _, chat_elems = create_chat_box(chat_model, visible=True)
+
+        manager = Manager([{"lang": lang}, chat_elems])
+
+        demo.load(manager.gen_label, [lang], [lang] + list(chat_elems.values()))
+
+        lang.change(manager.gen_label, [lang], [lang] + list(chat_elems.values()), queue=False)
+
+    return demo
+
+
 if __name__ == "__main__":
     demo = create_ui()
     demo.queue()
-    demo.launch(server_name="0.0.0.0", share=False, inbrowser=True)
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=False, inbrowser=True)

src/llmtuner/webui/locales.py

@@ -227,9 +227,9 @@ LOCALES = {
             "info": "用于梯度裁剪的范数。"
         }
     },
-    "dev_ratio": {
+    "val_size": {
         "en": {
-            "label": "Dev ratio",
+            "label": "Val size",
             "info": "Proportion of data in the dev set."
         },
         "zh": {
@@ -277,6 +277,16 @@ LOCALES = {
             "info": "是否启用 FP16 或 BF16 混合精度训练。"
         }
     },
+    "padding_side": {
+        "en": {
+            "label": "Padding side",
+            "info": "The side on which the model should have padding applied."
+        },
+        "zh": {
+            "label": "填充位置",
+            "info": "使用左填充或右填充。"
+        }
+    },
     "lora_tab": {
         "en": {
             "label": "LoRA configurations"
@@ -315,6 +325,16 @@ LOCALES = {
             "info": "应用 LoRA 的线性层名称。使用英文逗号分隔多个名称。"
         }
     },
+    "resume_lora_training": {
+        "en": {
+            "label": "Resume LoRA training",
+            "info": "Whether to resume training from the last LoRA weights or create new lora weights."
+        },
+        "zh": {
+            "label": "继续上次的训练",
+            "info": "接着上次的 LoRA 权重训练或创建一个新的 LoRA 权重。"
+        }
+    },
     "start_btn": {
         "en": {
             "value": "Start"
@@ -452,6 +472,34 @@ LOCALES = {
         "zh": {
             "label": "温度系数"
         }
+    },
+    "save_dir": {
+        "en": {
+            "label": "Export dir",
+            "info": "Directory to save exported model."
+        },
+        "zh": {
+            "label": "导出目录",
+            "info": "保存导出模型的文件夹路径。"
+        }
+    },
+    "max_shard_size": {
+        "en": {
+            "label": "Max shard size (GB)",
+            "info": "The maximum size for a model file."
+        },
+        "zh": {
+            "label": "最大分块大小（GB）",
+            "info": "模型文件的最大大小。"
+        }
+    },
+    "export_btn": {
+        "en": {
+            "value": "Export"
+        },
+        "zh": {
+            "value": "开始导出"
+        }
     }
 }
 
@@ -477,6 +525,18 @@ ALERTS = {
         "en": "Please choose a dataset.",
         "zh": "请选择数据集。"
     },
+    "err_no_checkpoint": {
+        "en": "Please select a checkpoint.",
+        "zh": "请选择断点。"
+    },
+    "err_no_save_dir": {
+        "en": "Please provide export dir.",
+        "zh": "请填写导出目录"
+    },
+    "err_failed": {
+        "en": "Failed.",
+        "zh": "训练出错。"
+    },
     "info_aborting": {
         "en": "Aborted, wait for terminating...",
         "zh": "训练中断，正在等待线程结束……"
@@ -504,5 +564,13 @@ ALERTS = {
     "info_unloaded": {
         "en": "Model unloaded.",
         "zh": "模型已卸载。"
+    },
+    "info_exporting": {
+        "en": "Exporting model...",
+        "zh": "正在导出模型……"
+    },
+    "info_exported": {
+        "en": "Model exported.",
+        "zh": "模型导出完成。"
     }
 }

src/llmtuner/webui/manager.py

@@ -1,6 +1,6 @@
 import gradio as gr
-from typing import Any, Dict, List
 from gradio.components import Component
+from typing import Any, Dict, List
 
 from llmtuner.webui.common import get_model_path, list_dataset, load_config
 from llmtuner.webui.locales import LOCALES
@@ -24,7 +24,7 @@ class Manager:
 
         return refresh_dict
 
-    def gen_label(self, lang: str) -> Dict[Component, dict]:
+    def gen_label(self, lang: str) -> Dict[Component, Dict[str, Any]]: # cannot use TYPE_CHECKING
         update_dict = {}
         refresh_dict = self.gen_refresh()
 

src/llmtuner/webui/runner.py

@@ -1,18 +1,20 @@
+import gradio as gr
 import logging
 import os
 import threading
 import time
 import transformers
-from typing import List, Optional, Tuple
+from transformers.trainer import TRAINING_ARGS_NAME
+from typing import Generator, List, Tuple
 
 from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.constants import DEFAULT_MODULE
 from llmtuner.extras.logging import LoggerHandler
 from llmtuner.extras.misc import torch_gc
-from llmtuner.tuner import get_train_args, run_sft
+from llmtuner.tuner import run_exp
 from llmtuner.webui.common import get_model_path, get_save_dir
 from llmtuner.webui.locales import ALERTS
-from llmtuner.webui.utils import format_info, get_eval_results
+from llmtuner.webui.utils import get_eval_results, update_process_bar
 
 
 class Runner:
@@ -25,7 +27,9 @@ class Runner:
         self.aborted = True
         self.running = False
 
-    def initialize(self, lang: str, model_name: str, dataset: list) -> Tuple[str, str, LoggerHandler, LogCallback]:
+    def initialize(
+        self, lang: str, model_name: str, dataset: List[str]
+    ) -> Tuple[str, str, LoggerHandler, LogCallback]:
         if self.running:
             return None, ALERTS["err_conflict"][lang], None, None
 
@@ -50,13 +54,15 @@ class Runner:
 
         return model_name_or_path, "", logger_handler, trainer_callback
 
-    def finalize(self, lang: str, finish_info: Optional[str] = None) -> str:
+    def finalize(
+        self, lang: str, finish_info: str
+    ) -> str:
         self.running = False
         torch_gc()
         if self.aborted:
             return ALERTS["info_aborted"][lang]
         else:
-            return finish_info if finish_info is not None else ALERTS["info_finished"][lang]
+            return finish_info
 
     def run_train(
         self,
@@ -78,19 +84,21 @@ class Runner:
         gradient_accumulation_steps: int,
         lr_scheduler_type: str,
         max_grad_norm: str,
-        dev_ratio: float,
+        val_size: float,
         logging_steps: int,
         save_steps: int,
         warmup_steps: int,
         compute_type: str,
+        padding_side: str,
         lora_rank: int,
         lora_dropout: float,
         lora_target: str,
+        resume_lora_training: bool,
         output_dir: str
-    ):
+    ) -> Generator[str, None, None]:
         model_name_or_path, error, logger_handler, trainer_callback = self.initialize(lang, model_name, dataset)
         if error:
-            yield error
+            yield error, gr.update(visible=False)
             return
 
         if checkpoints:
@@ -100,14 +108,17 @@ class Runner:
         else:
             checkpoint_dir = None
 
+        output_dir = os.path.join(get_save_dir(model_name), finetuning_type, output_dir)
+
         args = dict(
+            stage="sft",
             model_name_or_path=model_name_or_path,
             do_train=True,
             overwrite_cache=True,
             checkpoint_dir=checkpoint_dir,
             finetuning_type=finetuning_type,
             quantization_bit=int(quantization_bit) if quantization_bit else None,
-            prompt_template=template,
+            template=template,
             source_prefix=source_prefix,
             dataset_dir=dataset_dir,
             dataset=",".join(dataset),
@@ -125,38 +136,37 @@ class Runner:
             warmup_steps=warmup_steps,
             fp16=(compute_type == "fp16"),
             bf16=(compute_type == "bf16"),
+            padding_side=padding_side,
             lora_rank=lora_rank,
             lora_dropout=lora_dropout,
             lora_target=lora_target or DEFAULT_MODULE.get(model_name.split("-")[0], "q_proj,v_proj"),
-            output_dir=os.path.join(get_save_dir(model_name), finetuning_type, output_dir)
+            resume_lora_training=resume_lora_training,
+            output_dir=output_dir
         )
 
-        if dev_ratio > 1e-6:
-            args["dev_ratio"] = dev_ratio
+        if val_size > 1e-6:
+            args["val_size"] = val_size
             args["evaluation_strategy"] = "steps"
             args["eval_steps"] = save_steps
             args["load_best_model_at_end"] = True
 
-        model_args, data_args, training_args, finetuning_args, _ = get_train_args(args)
-
-        run_args = dict(
-            model_args=model_args,
-            data_args=data_args,
-            training_args=training_args,
-            finetuning_args=finetuning_args,
-            callbacks=[trainer_callback]
-        )
-        thread = threading.Thread(target=run_sft, kwargs=run_args)
+        run_kwargs = dict(args=args, callbacks=[trainer_callback])
+        thread = threading.Thread(target=run_exp, kwargs=run_kwargs)
         thread.start()
 
         while thread.is_alive():
-            time.sleep(1)
+            time.sleep(2)
             if self.aborted:
-                yield ALERTS["info_aborting"][lang]
+                yield ALERTS["info_aborting"][lang], gr.update(visible=False)
             else:
-                yield format_info(logger_handler.log, trainer_callback.tracker)
+                yield logger_handler.log, update_process_bar(trainer_callback)
 
-        yield self.finalize(lang)
+        if os.path.exists(os.path.join(output_dir, TRAINING_ARGS_NAME)):
+            finish_info = ALERTS["info_finished"][lang]
+        else:
+            finish_info = ALERTS["err_failed"][lang]
+
+        yield self.finalize(lang, finish_info), gr.update(visible=False)
 
     def run_eval(
         self,
@@ -174,10 +184,10 @@ class Runner:
         max_samples: str,
         batch_size: int,
         predict: bool
-    ):
+    ) -> Generator[str, None, None]:
         model_name_or_path, error, logger_handler, trainer_callback = self.initialize(lang, model_name, dataset)
         if error:
-            yield error
+            yield error, gr.update(visible=False)
             return
 
         if checkpoints:
@@ -190,6 +200,7 @@ class Runner:
             output_dir = os.path.join(get_save_dir(model_name), finetuning_type, "eval_base")
 
         args = dict(
+            stage="sft",
             model_name_or_path=model_name_or_path,
             do_eval=True,
             overwrite_cache=True,
@@ -197,7 +208,7 @@ class Runner:
             checkpoint_dir=checkpoint_dir,
             finetuning_type=finetuning_type,
             quantization_bit=int(quantization_bit) if quantization_bit else None,
-            prompt_template=template,
+            template=template,
             source_prefix=source_prefix,
             dataset_dir=dataset_dir,
             dataset=",".join(dataset),
@@ -212,23 +223,20 @@ class Runner:
             args.pop("do_eval", None)
             args["do_predict"] = True
 
-        model_args, data_args, training_args, finetuning_args, _ = get_train_args(args)
-
-        run_args = dict(
-            model_args=model_args,
-            data_args=data_args,
-            training_args=training_args,
-            finetuning_args=finetuning_args,
-            callbacks=[trainer_callback]
-        )
-        thread = threading.Thread(target=run_sft, kwargs=run_args)
+        run_kwargs = dict(args=args, callbacks=[trainer_callback])
+        thread = threading.Thread(target=run_exp, kwargs=run_kwargs)
         thread.start()
 
         while thread.is_alive():
-            time.sleep(1)
+            time.sleep(2)
             if self.aborted:
-                yield ALERTS["info_aborting"][lang]
+                yield ALERTS["info_aborting"][lang], gr.update(visible=False)
             else:
-                yield format_info(logger_handler.log, trainer_callback.tracker)
+                yield logger_handler.log, update_process_bar(trainer_callback)
 
-        yield self.finalize(lang, get_eval_results(os.path.join(output_dir, "all_results.json")))
+        if os.path.exists(os.path.join(output_dir, "all_results.json")):
+            finish_info = get_eval_results(os.path.join(output_dir, "all_results.json"))
+        else:
+            finish_info = ALERTS["err_failed"][lang]
+
+        yield self.finalize(lang, finish_info), gr.update(visible=False)

src/llmtuner/webui/utils.py

@@ -3,20 +3,30 @@ import json
 import gradio as gr
 import matplotlib.figure
 import matplotlib.pyplot as plt
-from typing import Any, Dict, Tuple
+from typing import TYPE_CHECKING, Any, Dict, Generator, List, Tuple
 from datetime import datetime
 
 from llmtuner.extras.ploting import smooth
-from llmtuner.webui.common import get_save_dir, DATA_CONFIG
+from llmtuner.tuner import export_model
+from llmtuner.webui.common import get_model_path, get_save_dir, DATA_CONFIG
+from llmtuner.webui.locales import ALERTS
+
+if TYPE_CHECKING:
+    from llmtuner.extras.callbacks import LogCallback
 
 
-def format_info(log: str, tracker: dict) -> str:
-    info = log
-    if "current_steps" in tracker:
-        info += "Running **{:d}/{:d}**: {} < {}\n".format(
-            tracker["current_steps"], tracker["total_steps"], tracker["elapsed_time"], tracker["remaining_time"]
-        )
-    return info
+def update_process_bar(callback: "LogCallback") -> Dict[str, Any]:
+    if not callback.max_steps:
+        return gr.update(visible=False)
+
+    percentage = round(100 * callback.cur_steps / callback.max_steps, 0) if callback.max_steps != 0 else 100.0
+    label = "Running {:d}/{:d}: {} < {}".format(
+        callback.cur_steps,
+        callback.max_steps,
+        callback.elapsed_time,
+        callback.remaining_time
+    )
+    return gr.update(label=label, value=percentage, visible=True)
 
 
 def get_time() -> str:
@@ -83,3 +93,46 @@ def gen_plot(base_model: str, finetuning_type: str, output_dir: str) -> matplotl
     ax.set_xlabel("step")
     ax.set_ylabel("loss")
     return fig
+
+
+def save_model(
+    lang: str,
+    model_name: str,
+    checkpoints: List[str],
+    finetuning_type: str,
+    template: str,
+    max_shard_size: int,
+    save_dir: str
+) -> Generator[str, None, None]:
+    if not model_name:
+        yield ALERTS["err_no_model"][lang]
+        return
+
+    model_name_or_path = get_model_path(model_name)
+    if not model_name_or_path:
+        yield ALERTS["err_no_path"][lang]
+        return
+
+    if not checkpoints:
+        yield ALERTS["err_no_checkpoint"][lang]
+        return
+
+    checkpoint_dir = ",".join(
+            [os.path.join(get_save_dir(model_name), finetuning_type, checkpoint) for checkpoint in checkpoints]
+        )
+
+    if not save_dir:
+        yield ALERTS["err_no_save_dir"][lang]
+        return
+
+    args = dict(
+        model_name_or_path=model_name_or_path,
+        checkpoint_dir=checkpoint_dir,
+        finetuning_type=finetuning_type,
+        template=template,
+        output_dir=save_dir
+    )
+
+    yield ALERTS["info_exporting"][lang]
+    export_model(args, max_shard_size="{}GB".format(max_shard_size))
+    yield ALERTS["info_exported"][lang]

src/train_bash.py

@@ -1,17 +1,8 @@
-from llmtuner.tuner import get_train_args, run_pt, run_sft, run_rm, run_ppo
+from llmtuner import run_exp
 
 
 def main():
-    model_args, data_args, training_args, finetuning_args, general_args = get_train_args()
-
-    if general_args.stage == "pt":
-        run_pt(model_args, data_args, training_args, finetuning_args)
-    elif general_args.stage == "sft":
-        run_sft(model_args, data_args, training_args, finetuning_args)
-    elif general_args.stage == "rm":
-        run_rm(model_args, data_args, training_args, finetuning_args)
-    elif general_args.stage == "ppo":
-        run_ppo(model_args, data_args, training_args, finetuning_args)
+    run_exp()
 
 
 def _mp_fn(index):

src/train_web.py

@@ -1,10 +1,10 @@
-from llmtuner.webui.interface import create_ui
+from llmtuner import create_ui
 
 
 def main():
     demo = create_ui()
     demo.queue()
-    demo.launch(server_name="0.0.0.0", share=False, inbrowser=True)
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=False, inbrowser=True)
 
 
 if __name__ == "__main__":

src/web_demo.py

@@ -1,35 +1,10 @@
-# coding=utf-8
-# Implements user interface in browser for fine-tuned models.
-# Usage: python web_demo.py --model_name_or_path path_to_model --checkpoint_dir path_to_checkpoint
-
-import gradio as gr
-from transformers.utils.versions import require_version
-
-from llmtuner.tuner import get_infer_args
-from llmtuner.webui.chat import WebChatModel
-from llmtuner.webui.components.chatbot import create_chat_box
-from llmtuner.webui.manager import Manager
-
-
-require_version("gradio>=3.36.0", "To fix: pip install gradio>=3.36.0")
+from llmtuner import create_web_demo
 
 
 def main():
-    chat_model = WebChatModel(*get_infer_args())
-
-    with gr.Blocks(title="Web Demo") as demo:
-        lang = gr.Dropdown(choices=["en", "zh"], value="en")
-
-        _, _, _, chat_elems = create_chat_box(chat_model, visible=True)
-
-        manager = Manager([{"lang": lang}, chat_elems])
-
-        demo.load(manager.gen_label, [lang], [lang] + list(chat_elems.values()))
-
-        lang.change(manager.gen_label, [lang], [lang] + list(chat_elems.values()))
-
+    demo = create_web_demo()
     demo.queue()
-    demo.launch(server_name="0.0.0.0", share=False, inbrowser=True)
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=False, inbrowser=True)
 
 
 if __name__ == "__main__":