release v0.3.0

Former-commit-id: de7f5b622340ab09ebbe57ad2703e63d06dfdeea

.gitignore

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+# Byte-compiled / optimized / DLL files
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+*$py.class
+
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CODE_OF_CONDUCT.md

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+# Contributor Covenant Code of Conduct
+
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+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
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README.md

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-# LLaMA Efficient Tuning
+# LLaMA Factory: Training and Evaluating Large Language Models with Minimal Effort
 
-[![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)
+[![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers)
+[![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE)
+[![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/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)
+[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
+[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
+[![Discord](https://dcbadge.vercel.app/api/server/c2EPEt5NU?compact=true&style=flat)](https://discord.gg/c2EPEt5NU)
+[![Spaces](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
 
 👋 Join our [WeChat](assets/wechat.jpg).
 
+\[ English | [中文](README_zh.md) \]
+
+## LLaMA Board: A One-stop Web UI for Getting Started with LLaMA Factory
+
+Preview LLaMA Board at **[🤗 Spaces](https://huggingface.co/spaces/hiyouga/LLaMA-Board)**.
+
+Launch LLaMA Board via `CUDA_VISIBLE_DEVICES=0 python src/train_web.py`. (multiple GPUs are not supported yet)
+
+Here is an example of altering the self-cognition of an instruction-tuned language model within 10 minutes on a single GPU.
+
+https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846-2d88920d5ba1
+
 ## Changelog
 
-[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.
+[23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `--neft_alpha` argument to activate NEFTune, e.g., `--neft_alpha 5`.
 
-[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.
+[23/09/27] We supported **$S^2$-Attn** proposed by [LongLoRA](https://github.com/dvlab-research/LongLoRA) for the LLaMA models. Try `--shift_attn` argument to enable shift short attention.
 
-[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.
+[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models.
 
-[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.
+[23/09/10] We supported using **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)** for the LLaMA models. Try `--flash_attn` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs.
 
-[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.
+[23/08/12] We supported **RoPE scaling** to extend the context length of the LLaMA models. Try `--rope_scaling linear` argument in training and `--rope_scaling dynamic` argument at inference to extrapolate the position embeddings.
 
-[23/06/29] We provide a **reproducible example** of training a chat model using instruction-following datasets, see this [HuggingFace Repo](https://huggingface.co/hiyouga/baichuan-7b-sft) for details.
+[23/08/11] We supported **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [this example](#dpo-training) to train your models.
 
-[23/06/22] Now we align the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in **arbitrary ChatGPT-based applications**.
+[23/07/31] We supported **dataset streaming**. Try `--streaming` and `--max_steps 10000` arguments to load your dataset in streaming mode.
 
-[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. If you want to train with RTX3090, use `git checkout baichuan-7b-rtx3090` to switch to the `baichuan-7b-rtx3090` branch and try the `--baichuan_rtx_gpu true` argument. (Other RTX series GPUs can also be tried)
+[23/07/29] We released 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.
 
-[23/06/03] 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)
+[23/07/18] We developed 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.
 
-[23/05/31] 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.
+[23/07/09] We released **[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.
+
+[23/06/29] We provided a **reproducible example** of training a chat model using instruction-following datasets, see [Baichuan-7B-sft](https://huggingface.co/hiyouga/Baichuan-7B-sft) for details.
+
+[23/06/22] We aligned the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in **arbitrary ChatGPT-based applications**.
+
+[23/06/03] We supported quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). Try `--quantization_bit 4/8` argument to work with quantized models.
 
 ## Supported Models
 
-- [LLaMA](https://github.com/facebookresearch/llama) (7B/13B/33B/65B)
-- [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  |
+| -------------------------------------------------------- | --------------------------- | ----------------- | --------- |
+| [Baichuan](https://github.com/baichuan-inc/Baichuan-13B) | 7B/13B                      | W_pack            | baichuan  |
+| [Baichuan2](https://github.com/baichuan-inc/Baichuan2)   | 7B/13B                      | W_pack            | baichuan2 |
+| [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   | -         |
+| [ChatGLM3](https://github.com/THUDM/ChatGLM3)            | 6B                          | query_key_value   | chatglm3  |
+| [Falcon](https://huggingface.co/tiiuae/falcon-7b)        | 7B/40B/180B                 | query_key_value   | falcon    |
+| [InternLM](https://github.com/InternLM/InternLM)         | 7B/20B                      | q_proj,v_proj     | intern    |
+| [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    |
+| [Mistral](https://huggingface.co/mistralai)              | 7B                          | q_proj,v_proj     | mistral   |
+| [Phi-1.5](https://huggingface.co/microsoft/phi-1_5)      | 1.3B                        | Wqkv              | -         |
+| [Qwen](https://github.com/QwenLM/Qwen)                   | 7B/14B                      | c_attn            | qwen      |
+| [XVERSE](https://github.com/xverse-ai)                   | 7B/13B/65B                  | q_proj,v_proj     | xverse    |
+
+> [!NOTE]
+> **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules.
+>
+> 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.
+
+Please refer to [constants.py](src/llmtuner/extras/constants.py) for a full list of models we supported.
 
 ## 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           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| Reward Modeling        | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| PPO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| DPO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+
+> [!NOTE]
+> Use `--quantization_bit 4/8` argument to enable QLoRA.
 
 ## Provided Datasets
 
-- For pre-training:
-  - [Wiki Demo](data/wiki_demo.txt)
-- For supervised fine-tuning:
-  - [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
-  - [Stanford Alpaca (Chinese)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
-  - [GPT-4 Generated Data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
-  - [BELLE 2M](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
-  - [BELLE 1M](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
-  - [BELLE 0.5M](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
-  - [BELLE Dialogue 0.4M](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
-  - [BELLE School Math 0.25M](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
-  - [BELLE Multiturn Chat 0.8M](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
-  - [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
-  - [Firefly 1.1M](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
-  - [CodeAlpaca 20k](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
-  - [Alpaca CoT](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
-  - [Web QA (Chinese)](https://huggingface.co/datasets/suolyer/webqa)
-  - [UltraChat](https://github.com/thunlp/UltraChat)
-  - [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1)
-  - [Open Assistant (Chinese)](https://huggingface.co/datasets/OpenAssistant/oasst1)
-  - [WebNovel (Chinese)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
-- For reward model training:
-  - [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf)
-  - [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1)
-  - [Open Assistant (Chinese)](https://huggingface.co/datasets/OpenAssistant/oasst1)
-  - [GPT-4 Generated Data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
-  - [GPT-4 Generated Data (Chinese)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+<details><summary>Pre-training datasets</summary>
+
+- [Wiki Demo (en)](data/wiki_demo.txt)
+- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
+- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2)
+- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220)
+- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
+- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile)
+- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
+- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
+- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
+
+</details>
+
+<details><summary>Supervised fine-tuning datasets</summary>
+
+- [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)
+- [Self-cognition (zh)](data/self_cognition.json)
+- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
+- [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)
+- [UltraChat (en)](https://github.com/thunlp/UltraChat)
+- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
+- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
+- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
+- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
+- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
+- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
+- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
+- [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
+- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
+- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
+- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
+- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
+- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
+- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
+- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
+- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
+
+</details>
+
+<details><summary>Preference datasets</summary>
+
+- [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)
+
+</details>
 
 Please refer to [data/README.md](data/README.md) for details.
 
-Some datasets require confirmation before using them, so we recommend logging in with your HuggingFace account using these commands.
+Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.
 
 ```bash
 pip install --upgrade huggingface_hub
@@ -97,51 +161,53 @@ huggingface-cli login
 
 - Python 3.8+ and PyTorch 1.13.1+
 - 🤗Transformers, Datasets, Accelerate, PEFT and TRL
-- 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)
+- sentencepiece, protobuf and tiktoken
+- jieba, rouge-chinese and nltk (used at evaluation and predict)
+- gradio and matplotlib (used in web UI)
+- uvicorn, fastapi and sse-starlette (used in API)
 
 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)
 
-Please refer to `data/example_dataset` for checking the details about the format of dataset files. You can either use a single `.json` file or a [dataset loading script](https://huggingface.co/docs/datasets/dataset_script) with multiple files to create a custom dataset.
+Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use a single `.json` file or a [dataset loading script](https://huggingface.co/docs/datasets/dataset_script) with multiple files to create a custom dataset.
 
-Note: please update `data/dataset_info.json` to use your custom dataset. About the format of this file, please refer to `data/README.md`.
+> [!NOTE]
+> Please update `data/dataset_info.json` to use your custom dataset. About the format of this file, please refer to `data/README.md`.
 
 ### Dependence Installation (optional)
 
 ```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
+git clone https://github.com/hiyouga/LLaMA-Factory.git
+conda create -n llama_factory python=3.10
+conda activate llama_factory
+cd LLaMA-Factory
 pip install -r requirements.txt
 ```
 
-### All-in-one Web UI
+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
-python src/train_web.py
+pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
 ```
 
-### (Continually) Pre-Training
+### Train on a single GPU
+
+> [!IMPORTANT]
+> If you want to train models on multiple GPUs, please refer to [Distributed Training](#distributed-training).
+
+#### Pre-Training
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --stage pt \
-    --model_name_or_path path_to_your_model \
+    --model_name_or_path path_to_llama_model \
     --do_train \
     --dataset wiki_demo \
     --finetuning_type lora \
+    --lora_target q_proj,v_proj \
     --output_dir path_to_pt_checkpoint \
     --overwrite_cache \
     --per_device_train_batch_size 4 \
@@ -155,15 +221,17 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --fp16
 ```
 
-### Supervised Fine-Tuning
+#### Supervised Fine-Tuning
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --stage sft \
-    --model_name_or_path path_to_your_model \
+    --model_name_or_path path_to_llama_model \
     --do_train \
     --dataset alpaca_gpt4_en \
+    --template default \
     --finetuning_type lora \
+    --lora_target q_proj,v_proj \
     --output_dir path_to_sft_checkpoint \
     --overwrite_cache \
     --per_device_train_batch_size 4 \
@@ -177,36 +245,43 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --fp16
 ```
 
-### Reward Model Training
+#### Reward Modeling
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --stage rm \
-    --model_name_or_path path_to_your_model \
+    --model_name_or_path path_to_llama_model \
     --do_train \
     --dataset comparison_gpt4_en \
+    --template default \
     --finetuning_type lora \
+    --lora_target q_proj,v_proj \
+    --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 \
     --save_steps 1000 \
-    --learning_rate 1e-5 \
+    --learning_rate 1e-6 \
     --num_train_epochs 1.0 \
     --plot_loss \
     --fp16
 ```
 
-### PPO Training (RLHF)
+#### PPO Training
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --stage ppo \
-    --model_name_or_path path_to_your_model \
+    --model_name_or_path path_to_llama_model \
     --do_train \
     --dataset alpaca_gpt4_en \
+    --template default \
     --finetuning_type lora \
+    --lora_target q_proj,v_proj \
+    --resume_lora_training False \
     --checkpoint_dir path_to_sft_checkpoint \
     --reward_model path_to_rm_checkpoint \
     --output_dir path_to_ppo_checkpoint \
@@ -217,30 +292,51 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --save_steps 1000 \
     --learning_rate 1e-5 \
     --num_train_epochs 1.0 \
+    --plot_loss \
+    --fp16
+```
+
+#### DPO Training
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage dpo \
+    --model_name_or_path path_to_llama_model \
+    --do_train \
+    --dataset comparison_gpt4_en \
+    --template default \
+    --finetuning_type lora \
+    --lora_target q_proj,v_proj \
     --resume_lora_training False \
-    --plot_loss
+    --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
 
+#### Use Huggingface Accelerate
+
 ```bash
 accelerate config # configure the environment
 accelerate launch src/train_bash.py # arguments (same as above)
 ```
 
-<details><summary>Example configuration for full-tuning with DeepSpeed ZeRO-2</summary>
+<details><summary>Example config for LoRA training</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
+distributed_type: MULTI_GPU
 downcast_bf16: 'no'
+gpu_ids: all
 machine_rank: 0
 main_training_function: main
 mixed_precision: fp16
@@ -256,38 +352,76 @@ use_cpu: false
 
 </details>
 
-### Evaluation (BLEU and ROUGE_CHINESE)
+#### Use DeepSpeed
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage pt \
-    --model_name_or_path path_to_your_model \
-    --do_eval \
-    --dataset alpaca_gpt4_en \
-    --checkpoint_dir path_to_checkpoint \
-    --output_dir path_to_eval_result \
-    --per_device_eval_batch_size 8 \
-    --max_samples 50 \
-    --predict_with_generate
+deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \
+    --deepspeed ds_config.json \
+    ... # arguments (same as above)
 ```
 
-We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit evaluation.
+<details><summary>Example config for full-parameter training with DeepSpeed ZeRO-2</summary>
+
+```json
+{
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "zero_allow_untested_optimizer": true,
+  "fp16": {
+    "enabled": "auto",
+    "loss_scale": 0,
+    "initial_scale_power": 16,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },  
+  "zero_optimization": {
+    "stage": 2,
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 5e8,
+    "overlap_comm": false,
+    "contiguous_gradients": true
+  }
+}
+```
+
+</details>
+
+### Export model
+
+```bash
+python src/export_model.py \
+    --model_name_or_path path_to_llama_model \
+    --template default \
+    --finetuning_type lora \
+    --checkpoint_dir path_to_checkpoint \
+    --export_dir path_to_export
+```
 
 ### API Demo
 
 ```bash
 python src/api_demo.py \
-    --model_name_or_path path_to_your_model \
+    --model_name_or_path path_to_llama_model \
+    --template default \
+    --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint
 ```
 
-See `http://localhost:8000/docs` for API documentation.
+> [!NOTE]
+> Visit `http://localhost:8000/docs` for API documentation.
 
 ### CLI Demo
 
 ```bash
 python src/cli_demo.py \
-    --model_name_or_path path_to_your_model \
+    --model_name_or_path path_to_llama_model \
+    --template default \
+    --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint
 ```
 
@@ -295,48 +429,77 @@ python src/cli_demo.py \
 
 ```bash
 python src/web_demo.py \
-    --model_name_or_path path_to_your_model \
+    --model_name_or_path path_to_llama_model \
+    --template default \
+    --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint
 ```
 
-### Export model
+### Evaluation
 
 ```bash
-python src/export_model.py \
-    --model_name_or_path path_to_your_model \
+CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
+    --model_name_or_path path_to_llama_model \
+    --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint \
-    --output_dir path_to_export
+    --template vanilla \
+    --task mmlu \
+    --split test \
+    --lang en \
+    --n_shot 5 \
+    --batch_size 4
 ```
 
+### Predict
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage sft \
+    --model_name_or_path path_to_llama_model \
+    --do_predict \
+    --dataset alpaca_gpt4_en \
+    --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
+```
+
+> [!NOTE]
+> We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit predict.
+
+## Projects using LLaMA Factory
+
+- **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
+- **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge.
+- **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
+- **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
+
 ## License
 
 This repository is licensed under the [Apache-2.0 License](LICENSE).
 
-Please follow the model licenses to use the corresponding model weights:
-
-- [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md)
-- [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)
+Please follow the model licenses to use the corresponding model weights: [Baichuan](https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/Community%20License%20for%20Baichuan-13B%20Model.pdf) / [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/resolve/main/Community%20License%20for%20Baichuan2%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [InternLM](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [Phi-1.5](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/LICENSE) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf)
 
 ## Citation
 
 If this work is helpful, please kindly cite as:
 
 ```bibtex
-@Misc{llama-efficient-tuning,
-  title = {LLaMA Efficient Tuning},
+@Misc{llama-factory,
+  title = {LLaMA Factory},
   author = {hiyouga},
-  howpublished = {\url{https://github.com/hiyouga/LLaMA-Efficient-Tuning}},
+  howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}},
   year = {2023}
 }
 ```
 
 ## Acknowledgement
 
-This repo is a sibling of [ChatGLM-Efficient-Tuning](https://github.com/hiyouga/ChatGLM-Efficient-Tuning). They share a similar code structure of efficient tuning on large language models.
+This repo benefits from [PEFT](https://github.com/huggingface/peft), [QLoRA](https://github.com/artidoro/qlora) and [FastChat](https://github.com/lm-sys/FastChat). Thanks for their wonderful works.
 
 ## Star History
 
-![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Efficient-Tuning&type=Date)
+![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date)

README_zh.md

@@ -0,0 +1,504 @@
+# LLaMA Factory: 轻松的大模型训练与评估
+
+[![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers)
+[![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE)
+[![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main)
+[![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/)
+[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
+[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
+[![Discord](https://dcbadge.vercel.app/api/server/c2EPEt5NU?compact=true&style=flat)](https://discord.gg/c2EPEt5NU)
+[![Spaces](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
+
+👋 加入我们的[微信群](assets/wechat.jpg)。
+
+\[ [English](README.md) | 中文 \]
+
+## LLaMA Board: 通过一站式网页界面快速上手 LLaMA Factory
+
+通过 **[🤗 Spaces](https://huggingface.co/spaces/hiyouga/LLaMA-Board)** 预览 LLaMA Board。
+
+使用 `CUDA_VISIBLE_DEVICES=0 python src/train_web.py` 启动 LLaMA Board。（该模式目前仅支持单卡训练）
+
+下面是使用单张 GPU 在 10 分钟内更改对话式大型语言模型自我认知的示例。
+
+https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846-2d88920d5ba1
+
+## 更新日志
+
+[23/10/21] 我们支持了 **[NEFTune](https://arxiv.org/abs/2310.05914)** 训练技巧。请使用 `--neft_alpha` 参数启用 NEFTune，例如 `--neft_alpha 5`。
+
+[23/09/27] 我们针对 LLaMA 模型支持了 [LongLoRA](https://github.com/dvlab-research/LongLoRA) 提出的 **$S^2$-Attn**。请使用 `--shift_attn` 参数以启用该功能。
+
+[23/09/23] 我们在项目中集成了 MMLU、C-Eval 和 CMMLU 评估集。使用方法请参阅[此示例](#模型评估)。
+
+[23/09/10] 我们针对 LLaMA 模型支持了 **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**。如果您使用的是 RTX4090、A100 或 H100 GPU，请使用 `--flash_attn` 参数以启用 FlashAttention-2。
+
+[23/08/12] 我们支持了 **RoPE 插值**来扩展 LLaMA 模型的上下文长度。请使用 `--rope_scaling linear` 参数训练模型或使用 `--rope_scaling dynamic` 参数评估模型。
+
+[23/08/11] 我们支持了指令模型的 **[DPO 训练](https://arxiv.org/abs/2305.18290)**。使用方法请参阅[此示例](#dpo-训练)。
+
+[23/07/31] 我们支持了**数据流式加载**。请尝试使用 `--streaming` 和 `--max_steps 10000` 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)）。
+
+[23/07/18] 我们开发了支持训练和测试的**浏览器一体化界面**。请使用 `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。
+
+[23/07/09] 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。
+
+[23/06/29] 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 [Baichuan-7B-sft](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。
+
+[23/06/03] 我们实现了 4 比特的 LoRA 训练（也称 **[QLoRA](https://github.com/artidoro/qlora)**）。请使用 `--quantization_bit 4` 参数进行 4 比特量化微调。
+
+## 模型
+
+| 模型名                                                   | 模型大小                     | 默认模块           | Template  |
+| -------------------------------------------------------- | --------------------------- | ----------------- | --------- |
+| [Baichuan](https://github.com/baichuan-inc/Baichuan-13B) | 7B/13B                      | W_pack            | baichuan  |
+| [Baichuan2](https://github.com/baichuan-inc/Baichuan2)   | 7B/13B                      | W_pack            | baichuan2 |
+| [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   | -         |
+| [ChatGLM3](https://github.com/THUDM/ChatGLM3)            | 6B                          | query_key_value   | chatglm3  |
+| [Falcon](https://huggingface.co/tiiuae/falcon-7b)        | 7B/40B/180B                 | query_key_value   | falcon    |
+| [InternLM](https://github.com/InternLM/InternLM)         | 7B/20B                      | q_proj,v_proj     | intern    |
+| [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    |
+| [Mistral](https://huggingface.co/mistralai)              | 7B                          | q_proj,v_proj     | mistral   |
+| [Phi-1.5](https://huggingface.co/microsoft/phi-1_5)      | 1.3B                        | Wqkv              | -         |
+| [Qwen](https://github.com/QwenLM/Qwen)                   | 7B/14B                      | c_attn            | qwen      |
+| [XVERSE](https://github.com/xverse-ai)                   | 7B/13B/65B                  | q_proj,v_proj     | xverse    |
+
+> [!NOTE]
+> **默认模块**应作为 `--lora_target` 参数的默认值，可使用 `--lora_target all` 参数指定全部模块。
+>
+> 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**。
+
+项目所支持模型的完整列表请参阅 [constants.py](src/llmtuner/extras/constants.py)。
+
+## 训练方法
+
+| 方法                   |     全参数训练      |    部分参数训练     |       LoRA         |       QLoRA        |
+| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
+| 预训练                 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| 指令监督微调            | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| 奖励模型训练            | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| PPO 训练               | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| DPO 训练               | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+
+> [!NOTE]
+> 请使用 `--quantization_bit 4/8` 参数来启用 QLoRA 训练。
+
+## 数据集
+
+<details><summary>预训练数据集</summary>
+
+- [Wiki Demo (en)](data/wiki_demo.txt)
+- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
+- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2)
+- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220)
+- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
+- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile)
+- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
+- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
+- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
+
+</details>
+
+<details><summary>指令微调数据集</summary>
+
+- [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)
+- [Self-cognition (zh)](data/self_cognition.json)
+- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
+- [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)
+- [UltraChat (en)](https://github.com/thunlp/UltraChat)
+- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
+- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
+- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
+- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
+- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
+- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
+- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
+- [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
+- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
+- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
+- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
+- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
+- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
+- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
+- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
+- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
+
+</details>
+
+<details><summary>偏好数据集</summary>
+
+- [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)
+
+</details>
+
+使用方法请参考 [data/README_zh.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, protobuf 和 tiktoken
+- jieba, rouge-chinese 和 nltk (用于评估及预测)
+- 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/README_zh.md](data/README_zh.md) 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。
+
+> [!NOTE]
+> 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_zh.md`。
+
+### 环境搭建（可跳过）
+
+```bash
+git clone https://github.com/hiyouga/LLaMA-Factory.git
+conda create -n llama_factory python=3.10
+conda activate llama_factory
+cd LLaMA-Factory
+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
+```
+
+### 单 GPU 训练
+
+> [!IMPORTANT]
+> 如果您使用多张 GPU 训练模型，请移步[多 GPU 分布式训练](#多-gpu-分布式训练)部分。
+
+#### 预训练
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage pt \
+    --model_name_or_path path_to_llama_model \
+    --do_train \
+    --dataset wiki_demo \
+    --finetuning_type lora \
+    --lora_target q_proj,v_proj \
+    --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_llama_model \
+    --do_train \
+    --dataset alpaca_gpt4_zh \
+    --template default \
+    --finetuning_type lora \
+    --lora_target q_proj,v_proj \
+    --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
+```
+
+#### 奖励模型训练
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage rm \
+    --model_name_or_path path_to_llama_model \
+    --do_train \
+    --dataset comparison_gpt4_zh \
+    --template default \
+    --finetuning_type lora \
+    --lora_target q_proj,v_proj \
+    --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-6 \
+    --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_llama_model \
+    --do_train \
+    --dataset alpaca_gpt4_zh \
+    --template default \
+    --finetuning_type lora \
+    --lora_target q_proj,v_proj \
+    --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_llama_model \
+    --do_train \
+    --dataset comparison_gpt4_zh \
+    --template default \
+    --finetuning_type lora \
+    --lora_target q_proj,v_proj \
+    --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
+
+#### 使用 Huggingface Accelerate
+
+```bash
+accelerate config # 首先配置分布式环境
+accelerate launch src/train_bash.py # 参数同上
+```
+
+<details><summary>LoRA 训练的 Accelerate 配置示例</summary>
+
+```yaml
+compute_environment: LOCAL_MACHINE
+distributed_type: MULTI_GPU
+downcast_bf16: 'no'
+gpu_ids: all
+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>
+
+#### 使用 DeepSpeed
+
+```bash
+deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \
+    --deepspeed ds_config.json \
+    ... # 参数同上
+```
+
+<details><summary>使用 DeepSpeed ZeRO-2 进行全参数训练的 DeepSpeed 配置示例</summary>
+
+```json
+{
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "zero_allow_untested_optimizer": true,
+  "fp16": {
+    "enabled": "auto",
+    "loss_scale": 0,
+    "initial_scale_power": 16,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },  
+  "zero_optimization": {
+    "stage": 2,
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 5e8,
+    "overlap_comm": false,
+    "contiguous_gradients": true
+  }
+}
+```
+
+</details>
+
+### 导出微调后的完整模型
+
+```bash
+python src/export_model.py \
+    --model_name_or_path path_to_llama_model \
+    --template default \
+    --finetuning_type lora \
+    --checkpoint_dir path_to_checkpoint \
+    --export_dir path_to_export
+```
+
+### API 服务
+
+```bash
+python src/api_demo.py \
+    --model_name_or_path path_to_llama_model \
+    --template default \
+    --finetuning_type lora \
+    --checkpoint_dir path_to_checkpoint
+```
+
+> [!NOTE]
+> 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_llama_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_llama_model \
+    --template default \
+    --finetuning_type lora \
+    --checkpoint_dir path_to_checkpoint
+```
+
+### 模型评估
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
+    --model_name_or_path path_to_llama_model \
+    --finetuning_type lora \
+    --checkpoint_dir path_to_checkpoint \
+    --template vanilla \
+    --task ceval \
+    --split validation \
+    --lang zh \
+    --n_shot 5 \
+    --batch_size 4
+```
+
+### 模型预测
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage sft \
+    --model_name_or_path path_to_llama_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
+```
+
+> [!NOTE]
+> 我们建议在量化模型的预测中使用 `--per_device_eval_batch_size=1` 和 `--max_target_length 128`。
+
+## 使用了 LLaMA Factory 的项目
+
+- **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: 天文大模型 StarWhisper，基于 ChatGLM2-6B 和 Qwen-14B 在天文数据上微调而得。
+- **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: 中文法律领域大模型 DISC-LawLLM，基于 Baichuan-13B 微调而得，具有法律推理和知识检索能力。
+- **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: 孙思邈中文医疗大模型 Sumsimiao，基于 Baichuan-7B 和 ChatGLM-6B 在中文医疗数据上微调而得。
+- **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: 医疗大模型项目 CareGPT，基于 LLaMA2-7B 和 Baichuan-13B 在中文医疗数据上微调而得。
+
+## 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：[Baichuan](https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/Community%20License%20for%20Baichuan-13B%20Model.pdf) / [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/resolve/main/Community%20License%20for%20Baichuan2%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [InternLM](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [Phi-1.5](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/LICENSE) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf)
+
+## 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-factory,
+  title = {LLaMA Factory},
+  author = {hiyouga},
+  howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}},
+  year = {2023}
+}
+```
+
+## 致谢
+
+本项目受益于 [PEFT](https://github.com/huggingface/peft)、[QLoRA](https://github.com/artidoro/qlora) 和 [FastChat](https://github.com/lm-sys/FastChat)，感谢以上诸位作者的付出。
+
+## Star History
+
+![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date)

data/README.md

@@ -1,53 +1,107 @@
-Data format in `dataset_info.json`:
+If you are using a custom dataset, please provide your dataset definition in the following format in `dataset_info.json`.
+
 ```json
 "dataset_name": {
-    "hf_hub_url": "the name of the dataset repository on the HuggingFace hub. (if specified, ignore below 3 arguments)",
+  "hf_hub_url": "the name of the dataset repository on the Hugging Face 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)",
+  "file_sha1": "the SHA-1 hash value of the dataset file. (optional, does not affect training)",
+  "subset": "the name of the subset. (optional, default: None)",
+  "ranking": "whether the dataset is a preference dataset or not. (default: false)",
+  "formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})",
   "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)"
+    "prompt": "the column name in the dataset containing the prompts. (default: instruction, for alpaca)",
+    "query": "the column name in the dataset containing the queries. (default: input, for alpaca)",
+    "response": "the column name in the dataset containing the responses. (default: output, for alpaca)",
+    "history": "the column name in the dataset containing the histories. (default: None, for alpaca)",
+    "messages": "the column name in the dataset containing the messages. (default: conversations, for sharegpt)",
+    "role": "the key in the message represents the identity. (default: from, for sharegpt)",
+    "content": "the key in the message represents the content. (default: value, for sharegpt)"
   }
 }
 ```
 
-`dataset_info.json` 中的数据集定义格式：
+Given above, you can use the custom dataset via specifying `--dataset dataset_name`.
+
+Currently we support dataset in **alpaca** or **sharegpt** format, the dataset in alpaca format should follow the below format:
+
 ```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）",
+[
+  {
+    "instruction": "user instruction (required)",
+    "input": "user input (optional)",
+    "output": "model response (required)",
+    "history": [
+      ["user instruction in the first round (optional)", "model response in the first round (optional)"],
+      ["user instruction in the second round (optional)", "model response in the second round (optional)"]
+    ]
+  }
+]
+```
+
+Regarding the above dataset, the `columns` in `dataset_info.json` should be:
+
+```json
+"dataset_name": {
   "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）"
+    "prompt": "instruction",
+    "query": "input",
+    "response": "output",
+    "history": "history"
   }
 }
 ```
 
-部分预置数据集简介：
+where the `prompt` and `response` columns should contain non-empty values, represent instruction and response respectively. The `query` column will be concatenated with the `prompt` column and used as input for the model.
 
-| 数据集名称 | 规模 | 描述 |
-| --- | --- | --- |
-| [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 52k | 斯坦福大学开源的 Alpaca 数据集，训练了 Alpaca 这类早期基于 LLaMA 的模型 |
-| [Stanford Alpaca (Chinese)](https://github.com/ymcui/Chinese-LLaMA-Alpaca) | 51k | 使用 ChatGPT 翻译的 Alpaca 数据集 |
-| [GPT-4 Generated Data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) | 100k+ | 基于 GPT-4 的 self-instruction 数据集 |
-| [BELLE 2M](https://huggingface.co/datasets/BelleGroup/train_2M_CN) | 2m | 包含约 200 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的中文指令数据 |
-| [BELLE 1M](https://huggingface.co/datasets/BelleGroup/train_1M_CN) | 1m | 包含约 100 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的中文指令数据 |
-| [BELLE 0.5M](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN) | 500k  | 包含约 50 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的中文指令数据 |
-| [BELLE Dialogue 0.4M](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M) | 400k | 包含约 40 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的个性化角色对话数据，包含角色介绍 |
-| [BELLE School Math 0.25M](https://huggingface.co/datasets/BelleGroup/school_math_0.25M) | 250k  | 包含约 25 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的中文数学题数据，包含解题过程 |
-| [BELLE Multiturn Chat 0.8M](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M) | 800k | 包含约 80 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的用户与助手的多轮对话 |
-| [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) | 100k+ | 包含日文、简繁体中文、英文等多类数据，数据集原用于 Guanaco 模型训练 |
-| [Firefly 1.1M](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) | 1.1M  | 中文对话大模型 firefly（流萤）的中文数据集，包含多个 NLP 任务 |
-| [CodeAlpaca 20k](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) | 20k | 英文代码生成任务数据集 |
-| [Alpaca CoT](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT) | 6M | 用于微调的指令数据集集合 |
-| [Web QA](https://huggingface.co/datasets/suolyer/webqa) | 36k | 百度知道汇集的中文问答数据集 |
-| [UltraChat](https://github.com/thunlp/UltraChat) | 1.57M | 清华 NLP 发布的大规模多轮对话数据集 |
+The `history` column is a list consisting string tuples representing query-response pairs in history. Note that the responses **in each round will be used for training**.
 
-注：BELLE 数据集是由 ChatGPT 产生的数据集，不保证数据准确性，所有类 GPT 模型产生的 self-instruction 数据集均不能保证其准确性。
+For the pre-training datasets, only the `prompt` column will be used for training.
+
+For the preference datasets, the `response` column should be a string list whose length is 2, with the preferred answers appearing first, for example:
+
+```json
+{
+  "instruction": "user instruction",
+  "input": "user input",
+  "output": [
+    "chosen answer",
+    "rejected answer"
+  ]
+}
+```
+
+The dataset in sharegpt format should follow the below format:
+
+```json
+[
+  {
+    "conversations": [
+      {
+        "from": "human",
+        "value": "user instruction"
+      },
+      {
+        "from": "gpt",
+        "value": "model response"
+      }
+    ]
+  }
+]
+```
+
+Regarding the above dataset, the `columns` in `dataset_info.json` should be:
+
+```json
+"dataset_name": {
+  "columns": {
+    "messages": "conversations",
+    "role": "from",
+    "content": "value"
+  }
+}
+```
+
+where the `messages` column should be a list whose length is even, and follow the `u/a/u/a/u/a` order.
+
+Pre-training datasets and preference datasets are incompatible with the sharegpt format yet.

data/README_zh.md

@@ -0,0 +1,107 @@
+如果您使用自定义数据集，请务必在 `dataset_info.json` 文件中按照以下格式提供数据集定义。
+
+```json
+"数据集名称": {
+  "hf_hub_url": "Hugging Face 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，留空不影响训练）",
+  "subset": "数据集子集的名称（可选，默认：None）",
+  "ranking": "是否为偏好数据集（可选，默认：False）",
+  "formatting": "数据集格式（可选，默认：alpaca，可以为 alpaca 或 sharegpt）",
+  "columns": {
+    "prompt": "数据集代表提示词的表头名称（默认：instruction，用于 alpaca 格式）",
+    "query": "数据集代表请求的表头名称（默认：input，用于 alpaca 格式）",
+    "response": "数据集代表回答的表头名称（默认：output，用于 alpaca 格式）",
+    "history": "数据集代表历史对话的表头名称（默认：None，用于 alpaca 格式）",
+    "messages": "数据集代表消息列表的表头名称（默认：conversations，用于 sharegpt 格式）",
+    "role": "消息中代表发送者身份的键名（默认：from，用于 sharegpt 格式）",
+    "content": "消息中代表文本内容的键名（默认：value，用于 sharegpt 格式）"
+  }
+}
+```
+
+添加后可通过指定 `--dataset 数据集名称` 参数使用自定义数据集。
+
+该项目目前支持两种格式的数据集：**alpaca** 和 **sharegpt**，其中 alpaca 格式的数据集按照以下方式组织：
+
+```json
+[
+  {
+    "instruction": "用户指令（必填）",
+    "input": "用户输入（选填）",
+    "output": "模型回答（必填）",
+    "history": [
+      ["第一轮指令（选填）", "第一轮回答（选填）"],
+      ["第二轮指令（选填）", "第二轮回答（选填）"]
+    ]
+  }
+]
+```
+
+对于上述格式的数据，`dataset_info.json` 中的 `columns` 应为：
+
+```json
+"数据集名称": {
+  "columns": {
+    "prompt": "instruction",
+    "query": "input",
+    "response": "output",
+    "history": "history"
+  }
+}
+```
+
+其中 `prompt` 和 `response` 列应当是非空的字符串，分别代表用户指令和模型回答。`query` 列的内容将会和 `prompt` 列拼接作为模型输入。
+
+`history` 列是由多个字符串二元组构成的列表，分别代表历史消息中每轮的指令和回答。注意每轮的模型回答**均会被用于训练**。
+
+对于预训练数据集，仅 `prompt` 列中的内容会用于模型训练。
+
+对于偏好数据集，`response` 列应当是一个长度为 2 的字符串列表，排在前面的代表更优的回答，例如：
+
+```json
+{
+  "instruction": "用户指令",
+  "input": "用户输入",
+  "output": [
+    "优质回答",
+    "劣质回答"
+  ]
+}
+```
+
+而 sharegpt 格式的数据集按照以下方式组织：
+
+```json
+[
+  {
+    "conversations": [
+      {
+        "from": "human",
+        "value": "用户指令"
+      },
+      {
+        "from": "gpt",
+        "value": "模型回答"
+      }
+    ]
+  }
+]
+```
+
+对于上述格式的数据，`dataset_info.json` 中的 `columns` 应为：
+
+```json
+"数据集名称": {
+  "columns": {
+    "messages": "conversations",
+    "role": "from",
+    "content": "value"
+  }
+}
+```
+
+其中 `messages` 列必须为偶数长度的列表，且符合 `用户/模型/用户/模型/用户/模型` 的顺序。
+
+预训练数据集和偏好数据集尚不支持 sharegpt 格式。

data/belle_multiturn/belle_multiturn.py

@@ -1,6 +1,5 @@
 import json
 import datasets
-from typing import Any, Dict, List
 
 
 _DESCRIPTION = "BELLE multiturn chat dataset."
@@ -23,11 +22,9 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
 
     VERSION = datasets.Version("0.0.0")
 
-    def _info(self) -> datasets.DatasetInfo:
+    def _info(self):
         features = datasets.Features({
-            "instruction": datasets.Value("string"),
-            "output": datasets.Value("string"),
-            "history": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
+            "conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]
         })
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -37,7 +34,7 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
             citation=_CITATION
         )
 
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
         file_path = dl_manager.download(_URL)
         return [
             datasets.SplitGenerator(
@@ -48,10 +45,11 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
             )
         ]
 
-    def _generate_examples(self, filepath: str) -> Dict[int, Dict[str, Any]]: # generate multi-turn chat with history
+    def _generate_examples(self, filepath: str):
         with open(filepath, "r", encoding="utf-8") as f:
             for key, row in enumerate(f):
                 data = json.loads(row)
+                conversations = []
                 prompt = data["instruction"].strip()
                 response = data["output"].strip()
 
@@ -59,7 +57,8 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
                 human_idx = prompt.rfind("Human:")
                 query = prompt[human_idx+6:assist_idx].strip()
                 prompt = prompt[:human_idx].strip()
-                history = []
+                conversations.insert(0, {"from": "gpt", "value": response})
+                conversations.insert(0, {"from": "human", "value": query})
 
                 while prompt.rfind("Assistant:") != -1:
                     assist_idx = prompt.rfind("Assistant:")
@@ -67,13 +66,10 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
                     if human_idx != -1:
                         old_query = prompt[human_idx+6:assist_idx].strip()
                         old_resp = prompt[assist_idx+10:].strip()
-                        history.insert(0, (old_query, old_resp))
+                        conversations.insert(0, {"from": "gpt", "value": old_resp})
+                        conversations.insert(0, {"from": "human", "value": old_query})
                     else:
                         break
                     prompt = prompt[:human_idx].strip()
 
-                yield key, {
-                    "instruction": query,
-                    "output": response,
-                    "history": history
-                }
+                yield key, {"conversations": conversations}

data/example_dataset/example_dataset.py

@@ -3,7 +3,7 @@ import datasets
 from typing import Any, Dict, List
 
 
-_DESCRIPTION = "An example of dataset for LLaMA."
+_DESCRIPTION = "An example of dataset."
 _CITATION = ""
 _HOMEPAGE = ""
 _LICENSE = ""

data/hh_rlhf_en/hh_rlhf_en.py

@@ -1,9 +1,9 @@
 import json
 import datasets
-from typing import Any, Dict, List
+from typing import List
 
 
-_DESCRIPTION = "Human preference data about helpfulness and harmlessness for ChatGLM."
+_DESCRIPTION = "Human preference data about helpfulness and harmlessness."
 _CITATION = ""
 _HOMEPAGE = "https://huggingface.co/datasets/Anthropic/hh-rlhf"
 _LICENSE = "mit"
@@ -42,7 +42,7 @@ class HhRlhfEn(datasets.GeneratorBasedBuilder):
             citation=_CITATION
         )
 
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
         file_path = dl_manager.download_and_extract(_URLS)
         return [
             datasets.SplitGenerator(
@@ -59,7 +59,7 @@ class HhRlhfEn(datasets.GeneratorBasedBuilder):
             )
         ]
 
-    def _generate_examples(self, filepaths: List[str]) -> Dict[int, Dict[str, Any]]: # generate multi-turn chat for ChatGLM
+    def _generate_examples(self, filepaths: List[str]):
         key = 0
         for filepath in filepaths:
             with open(filepath, "r", encoding="utf-8") as f:

data/oaast_sft.json.REMOVED.git-id

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

data/pretrain_data/wiki_long.jsonl

@@ -1,2 +0,0 @@
-{"id": 0,"title": "大卫·亨利","content": "大卫·亨利\n\n大卫·克莱顿·亨利（David Clayton Henrie，），美国演员。近来在迪士尼频道原创电视影集《少年魔法师》（Wizards of Waverly Place）当中演出贾斯汀·鲁索（Justin Russo）一角。\n\n大卫·亨利出生在加州Mission Viejo，在凤凰城长大。他的胞弟劳伦斯·亨利（Lorenzo Henrie）也是演员。大卫·亨利就读夏安传统学校。家中是信奉罗马天主教。 \n\n大卫在2007年拍摄少年魔法师期间认识女演员露西·海尔（Lucy Hale），之后与其交往，于2009年分手。\n\n10岁时，大卫·亨利和SAG在凤凰城签订了合约，并开始走出去试镜。 9岁的时候，在沙加缅度进行商业拍摄，SAG董事建议大卫·亨利搬到洛杉矶。在10岁那年夏天，他和他的家人搬到了好莱坞。他预定他的前2支商业试镜，扮演主要角色为汉堡王和桂格燕麦。他初演电视节目为Providence。 \n\n到了13岁，大卫有了他的第一次重大突破，在福克斯公司的喜剧The Pitts饰演 Petey Pitt一角。大卫下出作品为的Hallmark movie为Monster Maker，和琳达布莱儿、乔治甘迺迪共同演出，并要求回来Hallmark movie公司。 \n\n在18岁时，大卫得到了迪士尼频道原创系列演出机会，该节目2007年10月12日首播。大卫2008年参加了迪士尼频道的游戏节目。他是绿色团队的队长，隔年，为旋风队队长。他在迪士尼原创电影《少年魔法师》之后在《酷爸的疯狂假期》中有饰演一角。\n"}
-{"id": 1,"title": "大卫·亨利","content": "大卫·亨利\n\n大卫·克莱顿·亨利（David Clayton Henrie，），美国演员。近来在迪士尼频道原创电视影集《少年魔法师》（Wizards of Waverly Place）当中演出贾斯汀·鲁索（Justin Russo）一角。\n\n大卫·亨利出生在加州Mission Viejo，在凤凰城长大。他的胞弟劳伦斯·亨利（Lorenzo Henrie）也是演员。大卫·亨利就读夏安传统学校。家中是信奉罗马天主教。 \n\n大卫在2007年拍摄少年魔法师期间认识女演员露西·海尔（Lucy Hale），之后与其交往，于2009年分手。\n\n10岁时，大卫·亨利和SAG在凤凰城签订了合约，并开始走出去试镜。 9岁的时候，在沙加缅度进行商业拍摄，SAG董事建议大卫·亨利搬到洛杉矶。在10岁那年夏天，他和他的家人搬到了好莱坞。他预定他的前2支商业试镜，扮演主要角色为汉堡王和桂格燕麦。他初演电视节目为Providence。 \n\n到了13岁，大卫有了他的第一次重大突破，在福克斯公司的喜剧The Pitts饰演 Petey Pitt一角。大卫下出作品为的Hallmark movie为Monster Maker，和琳达布莱儿、乔治甘迺迪共同演出，并要求回来Hallmark movie公司。 \n\n在18岁时，大卫得到了迪士尼频道原创系列演出机会，该节目2007年10月12日首播。大卫2008年参加了迪士尼频道的游戏节目。他是绿色团队的队长，隔年，为旋风队队长。他在迪士尼原创电影《少年魔法师》之后在《酷爸的疯狂假期》中有饰演一角。\n"}

data/sharegpt_zh_27k.json.REMOVED.git-id

@@ -0,0 +1 @@
+38c89869c6aeca2a3af9ea1e09afe460f9b46810

data/ultra_chat/ultra_chat.py

@@ -1,6 +1,6 @@
 import json
 import datasets
-from typing import Any, Dict, List
+from typing import List
 
 
 _DESCRIPTION = "UltraChat: Large-scale, Informative, and Diverse Multi-round Dialogue Data."
@@ -21,15 +21,13 @@ _LICENSE = "cc-by-nc-4.0"
 _BASE_DATA_URL = "https://huggingface.co/datasets/stingning/ultrachat/resolve/main/train_{idx}.jsonl"
 
 
-class BelleMultiturn(datasets.GeneratorBasedBuilder):
+class UltraChat(datasets.GeneratorBasedBuilder):
 
     VERSION = datasets.Version("0.0.0")
 
-    def _info(self) -> datasets.DatasetInfo:
+    def _info(self):
         features = datasets.Features({
-            "instruction": datasets.Value("string"),
-            "output": datasets.Value("string"),
-            "history": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
+            "conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]
         })
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -39,8 +37,8 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
             citation=_CITATION
         )
 
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        file_paths = [dl_manager.download(_BASE_DATA_URL.format(idx=idx)) for idx in range(9)] # multiple shards
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        file_paths = [dl_manager.download(_BASE_DATA_URL.format(idx=idx)) for idx in range(10)] # multiple shards
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -50,7 +48,7 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
             )
         ]
 
-    def _generate_examples(self, filepaths: List[str]) -> Dict[int, Dict[str, Any]]: # generate multi-turn chat for ChatGLM
+    def _generate_examples(self, filepaths: List[str]):
         for filepath in filepaths:
             with open(filepath, "r", encoding="utf-8") as f:
                 for row in f:
@@ -58,19 +56,14 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
                         data = json.loads(row)
                     except:
                         continue
-                    key = data["id"]
-                    content = data["data"]
+                    key: int = data["id"]
+                    content: List[str] = data["data"]
                     if len(content) % 2 == 1:
                         content.pop(-1)
                     if len(content) < 2:
                         continue
-
-                    query = content[-2]
-                    response = content[-1]
-                    history = [[content[2*i], content[2*i+1]] for i in range(len(content) // 2 - 1)]
-
-                    yield key, {
-                        "instruction": query,
-                        "output": response,
-                        "history": history
-                    }
+                    conversations = [{
+                        "from": "human" if i % 2 == 0 else "gpt",
+                        "value": content[i]
+                    } for i in range(len(content))]
+                    yield key, {"conversations": conversations}

evaluation/ceval/ceval.py

@@ -0,0 +1,166 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+
+import datasets
+import pandas as pd
+
+
+_CITATION = """\
+@article{huang2023ceval,
+  title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, 
+  author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian},
+  journal={arXiv preprint arXiv:2305.08322},
+  year={2023}
+}
+"""
+
+_DESCRIPTION = """\
+C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels.
+"""
+
+_HOMEPAGE = "https://cevalbenchmark.com"
+
+_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License"
+
+_URL = "ceval.zip"
+
+task_list = [
+    "computer_network",
+    "operating_system",
+    "computer_architecture",
+    "college_programming",
+    "college_physics",
+    "college_chemistry",
+    "advanced_mathematics",
+    "probability_and_statistics",
+    "discrete_mathematics",
+    "electrical_engineer",
+    "metrology_engineer",
+    "high_school_mathematics",
+    "high_school_physics",
+    "high_school_chemistry",
+    "high_school_biology",
+    "middle_school_mathematics",
+    "middle_school_biology",
+    "middle_school_physics",
+    "middle_school_chemistry",
+    "veterinary_medicine",
+    "college_economics",
+    "business_administration",
+    "marxism",
+    "mao_zedong_thought",
+    "education_science",
+    "teacher_qualification",
+    "high_school_politics",
+    "high_school_geography",
+    "middle_school_politics",
+    "middle_school_geography",
+    "modern_chinese_history",
+    "ideological_and_moral_cultivation",
+    "logic",
+    "law",
+    "chinese_language_and_literature",
+    "art_studies",
+    "professional_tour_guide",
+    "legal_professional",
+    "high_school_chinese",
+    "high_school_history",
+    "middle_school_history",
+    "civil_servant",
+    "sports_science",
+    "plant_protection",
+    "basic_medicine",
+    "clinical_medicine",
+    "urban_and_rural_planner",
+    "accountant",
+    "fire_engineer",
+    "environmental_impact_assessment_engineer",
+    "tax_accountant",
+    "physician",
+]
+
+
+class CevalConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
+
+
+class Ceval(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        CevalConfig(
+            name=task_name,
+        )
+        for task_name in task_list
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("int32"),
+                "question": datasets.Value("string"),
+                "A": datasets.Value("string"),
+                "B": datasets.Value("string"),
+                "C": datasets.Value("string"),
+                "D": datasets.Value("string"),
+                "answer": datasets.Value("string"),
+                "explanation": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URL)
+        task_name = self.config.name
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "test", f"{task_name}_test.csv"
+                    ),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "val", f"{task_name}_val.csv"
+                    ),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "dev", f"{task_name}_dev.csv"
+                    ),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        df = pd.read_csv(filepath, encoding="utf-8")
+        for i, instance in enumerate(df.to_dict(orient="records")):
+            if "answer" not in instance.keys():
+                instance["answer"] = ""
+            if "explanation" not in instance.keys():
+                instance["explanation"] = ""
+            yield i, instance

evaluation/cmmlu/cmmlu.py

@@ -0,0 +1,167 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+
+import datasets
+import pandas as pd
+
+
+_CITATION = """\
+@article{li2023cmmlu,
+  title={CMMLU: Measuring massive multitask language understanding in Chinese},
+  author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and Hai Zhao and Yeyun Gong and Nan Duan and Timothy Baldwin},
+  journal={arXiv preprint arXiv:2306.09212},
+  year={2023}
+}
+"""
+
+_DESCRIPTION = """\
+CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge and reasoning abilities of LLMs within the Chinese language and cultural context.
+"""
+
+_HOMEPAGE = "https://github.com/haonan-li/CMMLU"
+
+_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License"
+
+_URL = "cmmlu.zip"
+
+task_list = [
+     'agronomy',
+     'anatomy',
+     'ancient_chinese',
+     'arts',
+     'astronomy',
+     'business_ethics',
+     'chinese_civil_service_exam',
+     'chinese_driving_rule',
+     'chinese_food_culture',
+     'chinese_foreign_policy',
+     'chinese_history',
+     'chinese_literature',
+     'chinese_teacher_qualification',
+     'clinical_knowledge',
+     'college_actuarial_science',
+     'college_education',
+     'college_engineering_hydrology',
+     'college_law',
+     'college_mathematics',
+     'college_medical_statistics',
+     'college_medicine',
+     'computer_science',
+     'computer_security',
+     'conceptual_physics',
+     'construction_project_management',
+     'economics',
+     'education',
+     'electrical_engineering',
+     'elementary_chinese',
+     'elementary_commonsense',
+     'elementary_information_and_technology',
+     'elementary_mathematics',
+     'ethnology',
+     'food_science',
+     'genetics',
+     'global_facts',
+     'high_school_biology',
+     'high_school_chemistry',
+     'high_school_geography',
+     'high_school_mathematics',
+     'high_school_physics',
+     'high_school_politics',
+     'human_sexuality',
+     'international_law',
+     'journalism',
+     'jurisprudence',
+     'legal_and_moral_basis',
+     'logical',
+     'machine_learning',
+     'management',
+     'marketing',
+     'marxist_theory',
+     'modern_chinese',
+     'nutrition',
+     'philosophy',
+     'professional_accounting',
+     'professional_law',
+     'professional_medicine',
+     'professional_psychology',
+     'public_relations',
+     'security_study',
+     'sociology',
+     'sports_science',
+     'traditional_chinese_medicine',
+     'virology',
+     'world_history',
+     'world_religions',
+]
+
+
+class CMMLUConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super().__init__(version=datasets.Version("1.0.1"), **kwargs)
+
+
+class CMMLU(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        CMMLUConfig(
+            name=task_name,
+        )
+        for task_name in task_list
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "question": datasets.Value("string"),
+                "A": datasets.Value("string"),
+                "B": datasets.Value("string"),
+                "C": datasets.Value("string"),
+                "D": datasets.Value("string"),
+                "answer": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URL)
+        task_name = self.config.name
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, f"test/{task_name}.csv"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, f"dev/{task_name}.csv"),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        df = pd.read_csv(filepath, header=0, index_col=0, encoding="utf-8")
+        for i, instance in enumerate(df.to_dict(orient="records")):
+            question = instance.pop("Question", "")
+            answer = instance.pop("Answer", "")
+            instance["question"] = question
+            instance["answer"] = answer
+            yield i, instance

evaluation/mmlu/mmlu.py

@@ -0,0 +1,167 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+
+import datasets
+import pandas as pd
+
+
+_CITATION = """\
+@article{hendryckstest2021,
+  title={Measuring Massive Multitask Language Understanding},
+  author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
+  journal={Proceedings of the International Conference on Learning Representations (ICLR)},
+  year={2021}
+}
+"""
+
+_DESCRIPTION = """\
+Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).
+"""
+
+_HOMEPAGE = "https://github.com/hendrycks/test"
+
+_LICENSE = "MIT"
+
+_URL = "mmlu.zip"
+
+task_list = [
+    "high_school_european_history",
+    "business_ethics",
+    "clinical_knowledge",
+    "medical_genetics",
+    "high_school_us_history",
+    "high_school_physics",
+    "high_school_world_history",
+    "virology",
+    "high_school_microeconomics",
+    "econometrics",
+    "college_computer_science",
+    "high_school_biology",
+    "abstract_algebra",
+    "professional_accounting",
+    "philosophy",
+    "professional_medicine",
+    "nutrition",
+    "global_facts",
+    "machine_learning",
+    "security_studies",
+    "public_relations",
+    "professional_psychology",
+    "prehistory",
+    "anatomy",
+    "human_sexuality",
+    "college_medicine",
+    "high_school_government_and_politics",
+    "college_chemistry",
+    "logical_fallacies",
+    "high_school_geography",
+    "elementary_mathematics",
+    "human_aging",
+    "college_mathematics",
+    "high_school_psychology",
+    "formal_logic",
+    "high_school_statistics",
+    "international_law",
+    "high_school_mathematics",
+    "high_school_computer_science",
+    "conceptual_physics",
+    "miscellaneous",
+    "high_school_chemistry",
+    "marketing",
+    "professional_law",
+    "management",
+    "college_physics",
+    "jurisprudence",
+    "world_religions",
+    "sociology",
+    "us_foreign_policy",
+    "high_school_macroeconomics",
+    "computer_security",
+    "moral_scenarios",
+    "moral_disputes",
+    "electrical_engineering",
+    "astronomy",
+    "college_biology",
+]
+
+
+class MMLUConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
+
+
+class MMLU(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        MMLUConfig(
+            name=task_name,
+        )
+        for task_name in task_list
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "question": datasets.Value("string"),
+                "A": datasets.Value("string"),
+                "B": datasets.Value("string"),
+                "C": datasets.Value("string"),
+                "D": datasets.Value("string"),
+                "answer": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URL)
+        task_name = self.config.name
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "data", "test", f"{task_name}_test.csv"
+                    ),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "data", "val", f"{task_name}_val.csv"
+                    ),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "data", "dev", f"{task_name}_dev.csv"
+                    ),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        df = pd.read_csv(filepath)
+        df.columns = ["question", "A", "B", "C", "D", "answer"]
+
+        for i, instance in enumerate(df.to_dict(orient="records")):
+            yield i, instance

requirements.txt

@@ -1,14 +1,17 @@
 torch>=1.13.1
-transformers>=4.29.1
-datasets>=2.12.0
-accelerate>=0.19.0
-peft>=0.3.0
-trl>=0.4.7
+transformers>=4.31.0,<4.35.0
+datasets>=2.14.0
+accelerate>=0.21.0
+peft>=0.6.0
+trl>=0.7.4
+gradio>=3.38.0,<4.0.0
+scipy
 sentencepiece
+protobuf
+tiktoken
 jieba
 rouge-chinese
 nltk
-gradio>=3.36.0
 uvicorn
 pydantic
 fastapi

setup.py

@@ -25,12 +25,12 @@ def main():
         version=get_version(),
         author="hiyouga",
         author_email="hiyouga" "@" "buaa.edu.cn",
-        description="Easy-to-use fine-tuning framework using PEFT",
+        description="Easy-to-use LLM fine-tuning framework",
         long_description=open("README.md", "r", encoding="utf-8").read(),
         long_description_content_type="text/markdown",
         keywords=["LLaMA", "BLOOM", "Falcon", "LLM", "ChatGPT", "transformer", "pytorch", "deep learning"],
         license="Apache 2.0 License",
-        url="https://github.com/hiyouga/LLaMA-Efficient-Tuning",
+        url="https://github.com/hiyouga/LLaMA-Factory",
         package_dir={"": "src"},
         packages=find_packages("src"),
         python_requires=">=3.8.0",

src/api_demo.py

@@ -1,13 +1,14 @@
-# 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 create_app
+from llmtuner import ChatModel, create_app
+
+
+def main():
+    chat_model = ChatModel()
+    app = create_app(chat_model)
+    print("Visit http://localhost:8000/docs for API document.")
+    uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
 
 
 if __name__ == "__main__":
-    app = create_app()
-    uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
+    main()

src/cli_demo.py

@@ -1,12 +1,16 @@
-# 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.extras.misc import torch_gc
 
-from llmtuner import ChatModel, get_infer_args
+try:
+    import platform
+    if platform.system() != "Windows":
+        import readline
+except ImportError:
+    print("Install `readline` for a better experience.")
 
 
 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.")
 
@@ -24,6 +28,7 @@ def main():
 
         if query.strip() == "clear":
             history = []
+            torch_gc()
             print("History has been removed.")
             continue
 

src/evaluate.py

@@ -0,0 +1,10 @@
+from llmtuner import Evaluator
+
+
+def main():
+    evaluator = Evaluator()
+    evaluator.eval()
+
+
+if __name__ == "__main__":
+    main()

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 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

@@ -1,7 +1,10 @@
+# Level: api, webui > chat, eval, train > data, model > extras, hparams
+
 from llmtuner.api import create_app
 from llmtuner.chat import ChatModel
-from llmtuner.tuner import get_train_args, get_infer_args, load_model_and_tokenizer, run_pt, run_sft, run_rm, run_ppo
-from llmtuner.webui import create_ui
+from llmtuner.eval import Evaluator
+from llmtuner.train import export_model, run_exp
+from llmtuner.webui import create_ui, create_web_demo
 
 
-__version__ = "0.1.0"
+__version__ = "0.3.0"

src/llmtuner/api/app.py

@@ -1,14 +1,8 @@
 import json
-import uvicorn
-from fastapi import FastAPI, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-from contextlib import asynccontextmanager
-from sse_starlette import EventSourceResponse
 from typing import List, Tuple
+from pydantic import BaseModel
+from contextlib import asynccontextmanager
 
-from llmtuner.tuner import get_infer_args
-from llmtuner.extras.misc import torch_gc
-from llmtuner.chat.stream_chat import ChatModel
 from llmtuner.api.protocol import (
     Role,
     Finish,
@@ -23,17 +17,40 @@ from llmtuner.api.protocol import (
     ChatCompletionResponseStreamChoice,
     ChatCompletionResponseUsage
 )
+from llmtuner.chat import ChatModel
+from llmtuner.extras.misc import torch_gc
+from llmtuner.extras.packages import (
+    is_fastapi_availble, is_starlette_available, is_uvicorn_available
+)
+
+
+if is_fastapi_availble():
+    from fastapi import FastAPI, HTTPException, status
+    from fastapi.middleware.cors import CORSMiddleware
+
+
+if is_starlette_available():
+    from sse_starlette import EventSourceResponse
+
+
+if is_uvicorn_available():
+    import uvicorn
 
 
 @asynccontextmanager
-async def lifespan(app: FastAPI): # collects GPU memory
+async def lifespan(app: "FastAPI"): # collects GPU memory
     yield
     torch_gc()
 
 
-def create_app():
-    chat_model = ChatModel(*get_infer_args())
+def to_json(data: BaseModel) -> str:
+    try: # pydantic v2
+        return json.dumps(data.model_dump(exclude_unset=True), ensure_ascii=False)
+    except: # pydantic v1
+        return data.json(exclude_unset=True, ensure_ascii=False)
 
+
+def create_app(chat_model: "ChatModel") -> "FastAPI":
     app = FastAPI(lifespan=lifespan)
 
     app.add_middleware(
@@ -49,57 +66,75 @@ def create_app():
         model_card = ModelCard(id="gpt-3.5-turbo")
         return ModelList(data=[model_card])
 
-    @app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
+    @app.post("/v1/chat/completions", response_model=ChatCompletionResponse, status_code=status.HTTP_200_OK)
     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
+        if len(request.messages) == 0 or request.messages[-1].role != Role.USER:
+            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")
 
+        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
+        if len(prev_messages) and prev_messages[0].role == Role.SYSTEM:
+            system = prev_messages.pop(0).content
         else:
-            prefix = None
+            system = None
 
         history = []
         if len(prev_messages) % 2 == 0:
             for i in range(0, len(prev_messages), 2):
                 if prev_messages[i].role == Role.USER and prev_messages[i+1].role == Role.ASSISTANT:
                     history.append([prev_messages[i].content, prev_messages[i+1].content])
+                else:
+                    raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
+        else:
+            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
 
         if request.stream:
-            generate = predict(query, history, prefix, request)
+            generate = predict(query, history, system, request)
             return EventSourceResponse(generate, media_type="text/event-stream")
 
-        response, (prompt_length, response_length) = chat_model.chat(
-            query, history, prefix, temperature=request.temperature, top_p=request.top_p, max_new_tokens=request.max_tokens
+        responses = chat_model.chat(
+            query, history, system,
+            do_sample=request.do_sample,
+            temperature=request.temperature,
+            top_p=request.top_p,
+            max_new_tokens=request.max_tokens,
+            num_return_sequences=request.n
         )
 
+        prompt_length, response_length = 0, 0
+        choices = []
+        for i, response in enumerate(responses):
+            choices.append(ChatCompletionResponseChoice(
+                index=i,
+                message=ChatMessage(role=Role.ASSISTANT, content=response.response_text),
+                finish_reason=Finish.STOP if response.finish_reason == "stop" else Finish.LENGTH
+            ))
+            prompt_length = response.prompt_length
+            response_length += response.response_length
+
         usage = ChatCompletionResponseUsage(
             prompt_tokens=prompt_length,
             completion_tokens=response_length,
             total_tokens=prompt_length+response_length
         )
 
-        choice_data = ChatCompletionResponseChoice(
-            index=0,
-            message=ChatMessage(role=Role.ASSISTANT, content=response),
-            finish_reason=Finish.STOP
-        )
+        return ChatCompletionResponse(model=request.model, choices=choices, usage=usage)
 
-        return ChatCompletionResponse(model=request.model, choices=[choice_data], usage=usage)
-
-    async def predict(query: str, history: List[Tuple[str, str]], prefix: str, request: ChatCompletionRequest):
+    async def predict(query: str, history: List[Tuple[str, str]], system: str, request: ChatCompletionRequest):
         choice_data = ChatCompletionResponseStreamChoice(
             index=0,
             delta=DeltaMessage(role=Role.ASSISTANT),
             finish_reason=None
         )
         chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
-        yield json.dumps(chunk, ensure_ascii=False)
+        yield to_json(chunk)
 
         for new_text in chat_model.stream_chat(
-            query, history, prefix, temperature=request.temperature, top_p=request.top_p, max_new_tokens=request.max_tokens
+            query, history, system,
+            do_sample=request.do_sample,
+            temperature=request.temperature,
+            top_p=request.top_p,
+            max_new_tokens=request.max_tokens
         ):
             if len(new_text) == 0:
                 continue
@@ -110,7 +145,7 @@ def create_app():
                 finish_reason=None
             )
             chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
-            yield json.dumps(chunk, ensure_ascii=False)
+            yield to_json(chunk)
 
         choice_data = ChatCompletionResponseStreamChoice(
             index=0,
@@ -118,12 +153,13 @@ def create_app():
             finish_reason=Finish.STOP
         )
         chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
-        yield json.dumps(chunk, ensure_ascii=False)
+        yield to_json(chunk)
         yield "[DONE]"
 
     return app
 
 
 if __name__ == "__main__":
-    app = create_app()
+    chat_model = ChatModel()
+    app = create_app(chat_model)
     uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)

src/llmtuner/api/protocol.py

@@ -20,9 +20,6 @@ class ModelCard(BaseModel):
     object: Optional[str] = "model"
     created: Optional[int] = Field(default_factory=lambda: int(time.time()))
     owned_by: Optional[str] = "owner"
-    root: Optional[str] = None
-    parent: Optional[str] = None
-    permission: Optional[list] = []
 
 
 class ModelList(BaseModel):
@@ -43,6 +40,7 @@ class DeltaMessage(BaseModel):
 class ChatCompletionRequest(BaseModel):
     model: str
     messages: List[ChatMessage]
+    do_sample: Optional[bool] = True
     temperature: Optional[float] = None
     top_p: Optional[float] = None
     n: Optional[int] = 1

src/llmtuner/chat/__init__.py

@@ -1 +1 @@
-from llmtuner.chat.stream_chat import ChatModel
+from llmtuner.chat.chat_model import ChatModel

src/llmtuner/chat/chat_model.py

@@ -0,0 +1,132 @@
+import torch
+from dataclasses import dataclass
+from typing import Any, Dict, Generator, List, Literal, Optional, Tuple
+from threading import Thread
+from transformers import GenerationConfig, TextIteratorStreamer
+
+from llmtuner.data.template import get_template_and_fix_tokenizer
+from llmtuner.extras.misc import get_logits_processor
+from llmtuner.model import dispatch_model, get_infer_args, load_model_and_tokenizer
+
+
+@dataclass
+class Response:
+
+    response_text: str
+    response_length: int
+    prompt_length: int
+    finish_reason: Literal["stop", "length"]
+
+
+class ChatModel:
+
+    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.tokenizer.padding_side = "left"
+        self.model = dispatch_model(self.model)
+        self.template = get_template_and_fix_tokenizer(data_args.template, self.tokenizer)
+        self.system_prompt = data_args.system_prompt
+
+    def _process_args(
+        self,
+        query: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        system: Optional[str] = None,
+        **input_kwargs
+    ) -> Tuple[Dict[str, Any], int]:
+        system = system or self.system_prompt
+        prompt, _ = self.template.encode_oneturn(
+            tokenizer=self.tokenizer, query=query, resp="", history=history, system=system
+        )
+        prompt_length = len(prompt)
+        input_ids = torch.tensor([prompt], device=self.model.device)
+
+        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)
+        num_return_sequences = input_kwargs.pop("num_return_sequences", None)
+        repetition_penalty = input_kwargs.pop("repetition_penalty", None)
+        max_length = input_kwargs.pop("max_length", None)
+        max_new_tokens = input_kwargs.pop("max_new_tokens", None)
+
+        generating_args = self.generating_args.to_dict()
+        generating_args.update(dict(
+            do_sample=do_sample if do_sample is not None else generating_args["do_sample"],
+            temperature=temperature or generating_args["temperature"],
+            top_p=top_p or generating_args["top_p"],
+            top_k=top_k or generating_args["top_k"],
+            num_return_sequences=num_return_sequences or 1,
+            repetition_penalty=repetition_penalty or generating_args["repetition_penalty"],
+            eos_token_id=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
+            pad_token_id=self.tokenizer.pad_token_id
+        ))
+
+        if isinstance(num_return_sequences, int) and num_return_sequences > 1:
+            generating_args["do_sample"] = True
+
+        if max_length:
+            generating_args.pop("max_new_tokens", None)
+            generating_args["max_length"] = max_length
+
+        if max_new_tokens:
+            generating_args.pop("max_length", None)
+            generating_args["max_new_tokens"] = max_new_tokens
+
+        gen_kwargs = dict(
+            inputs=input_ids,
+            generation_config=GenerationConfig(**generating_args),
+            logits_processor=get_logits_processor()
+        )
+
+        return gen_kwargs, prompt_length
+
+    @torch.inference_mode()
+    def chat(
+        self,
+        query: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        system: Optional[str] = None,
+        **input_kwargs
+    ) -> List[Response]:
+        r"""
+        Args: query, history, system, **input_kwargs
+
+        Returns: [(response_text, prompt_length, response_length)] * n (default n=1)
+        """
+        gen_kwargs, prompt_length = self._process_args(query, history, system, **input_kwargs)
+        generate_output = self.model.generate(**gen_kwargs)
+        response_ids = generate_output[:, prompt_length:]
+        response = self.tokenizer.batch_decode(
+            response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
+        )
+        results = []
+        for i in range(len(response)):
+            eos_index = (response_ids[i] == self.tokenizer.eos_token_id).nonzero()
+            response_length = (eos_index[0].item() + 1) if len(eos_index) else len(response_ids[i])
+            results.append(Response(
+                response_text=response[i],
+                response_length=response_length,
+                prompt_length=prompt_length,
+                finish_reason="stop" if len(eos_index) else "length"
+            ))
+
+        return results
+
+    @torch.inference_mode()
+    def stream_chat(
+        self,
+        query: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        system: Optional[str] = None,
+        **input_kwargs
+    ) -> Generator[str, None, None]:
+        gen_kwargs, _ = self._process_args(query, history, system, **input_kwargs)
+        streamer = TextIteratorStreamer(self.tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
+        gen_kwargs["streamer"] = streamer
+
+        thread = Thread(target=self.model.generate, kwargs=gen_kwargs)
+        thread.start()
+
+        yield from streamer

src/llmtuner/chat/stream_chat.py

@@ -1,85 +0,0 @@
-import torch
-from typing import Any, Dict, Generator, List, Optional, Tuple
-from threading import Thread
-from transformers import TextIteratorStreamer
-
-from llmtuner.extras.misc import get_logits_processor
-from llmtuner.extras.template import Template
-from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
-from llmtuner.tuner import load_model_and_tokenizer
-
-
-class ChatModel:
-
-    def __init__(
-        self,
-        model_args: ModelArguments,
-        data_args: DataArguments,
-        finetuning_args: FinetuningArguments,
-        generating_args: GeneratingArguments
-    ) -> None:
-        self.model, self.tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
-        self.template = Template(data_args.prompt_template)
-        self.source_prefix = data_args.source_prefix if data_args.source_prefix else ""
-        self.generating_args = generating_args
-
-    def process_args(
-        self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = None, **input_kwargs
-    ) -> Tuple[Dict[str, Any], int]:
-        prefix = prefix if prefix else 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])
-
-        temperature = input_kwargs.pop("temperature", None)
-        top_p = input_kwargs.pop("top_p", None)
-        top_k = input_kwargs.pop("top_k", None)
-        repetition_penalty = input_kwargs.pop("repetition_penalty", None)
-        max_length = input_kwargs.pop("max_length", None)
-        max_new_tokens = input_kwargs.pop("max_new_tokens", None)
-
-        gen_kwargs = self.generating_args.to_dict()
-        gen_kwargs.update(dict(
-            input_ids=inputs["input_ids"],
-            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()
-        ))
-
-        if max_length:
-            gen_kwargs.pop("max_new_tokens", None)
-            gen_kwargs["max_length"] = max_length
-
-        if max_new_tokens:
-            gen_kwargs.pop("max_length", None)
-            gen_kwargs["max_new_tokens"] = max_new_tokens
-
-        return gen_kwargs, prompt_length
-
-    @torch.inference_mode()
-    def chat(
-        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)
-        outputs = generation_output.tolist()[0][prompt_length:]
-        response = self.tokenizer.decode(outputs, skip_special_tokens=True)
-        response_length = len(outputs)
-        return response, (prompt_length, response_length)
-
-    @torch.inference_mode()
-    def stream_chat(
-        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)
-        gen_kwargs["streamer"] = streamer
-
-        thread = Thread(target=self.model.generate, kwargs=gen_kwargs)
-        thread.start()
-
-        for new_text in streamer:
-            yield new_text

src/llmtuner/data/__init__.py

@@ -0,0 +1,4 @@
+from llmtuner.data.loader import get_dataset
+from llmtuner.data.preprocess import preprocess_dataset
+from llmtuner.data.template import get_template_and_fix_tokenizer
+from llmtuner.data.utils import split_dataset

src/llmtuner/data/loader.py

@@ -0,0 +1,145 @@
+import os
+from typing import TYPE_CHECKING, Any, Dict, List, Union
+
+from datasets import concatenate_datasets, interleave_datasets, load_dataset
+
+from llmtuner.data.utils import checksum, EXT2TYPE
+from llmtuner.extras.logging import get_logger
+
+if TYPE_CHECKING:
+    from datasets import Dataset, IterableDataset
+    from llmtuner.hparams import ModelArguments, DataArguments
+
+
+logger = get_logger(__name__)
+
+
+def get_dataset(
+    model_args: "ModelArguments",
+    data_args: "DataArguments"
+) -> Union["Dataset", "IterableDataset"]:
+    max_samples = data_args.max_samples
+    all_datasets: List[Union["Dataset", "IterableDataset"]] = [] # 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":
+            data_path = dataset_attr.dataset_name
+            data_name = dataset_attr.subset
+            data_files = None
+        elif dataset_attr.load_from == "script":
+            data_path = os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)
+            data_name = dataset_attr.subset
+            data_files = None
+        elif dataset_attr.load_from == "file":
+            data_path, data_name = None, None
+            data_files: List[str] = []
+            if os.path.isdir(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)): # is 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(file_name.split(".")[-1], None)
+                    else:
+                        assert data_path == EXT2TYPE.get(file_name.split(".")[-1], None), "file types are not identical."
+            elif os.path.isfile(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)): # is file
+                data_files.append(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name))
+                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."
+            checksum(data_files, dataset_attr.dataset_sha1)
+        else:
+            raise NotImplementedError
+
+        dataset = load_dataset(
+            path=data_path,
+            name=data_name,
+            data_files=data_files,
+            split=data_args.split,
+            cache_dir=model_args.cache_dir,
+            token=model_args.hf_hub_token,
+            streaming=data_args.streaming
+        )
+
+        if max_samples is not None: # truncate dataset
+            dataset = dataset.select(range(min(len(dataset), max_samples)))
+
+        def convert_format(examples: Dict[str, List[Any]]) -> Dict[str, List[Any]]:
+            # convert dataset from sharegpt format to alpaca format
+            outputs = {"prompt": [], "query": [], "response": [], "history": []}
+            for msg_list in examples[dataset_attr.messages]:
+                msg_list = msg_list[:len(msg_list) // 2 * 2] # should be multiples of 2
+                if len(msg_list) == 0:
+                    continue
+
+                msg_pairs = []
+                user_role, assistant_role = None, None
+                for idx in range(0, len(msg_list), 2):
+                    if user_role is None and assistant_role is None:
+                        user_role = msg_list[idx][dataset_attr.role]
+                        assistant_role = msg_list[idx + 1][dataset_attr.role]
+                    else:
+                        if (
+                            msg_list[idx][dataset_attr.role] != user_role
+                            or msg_list[idx+1][dataset_attr.role] != assistant_role
+                        ):
+                            raise ValueError("Only accepts conversation in u/a/u/a/u/a order.")
+                    msg_pairs.append((msg_list[idx][dataset_attr.content], msg_list[idx + 1][dataset_attr.content]))
+
+                if len(msg_pairs) != 0:
+                    outputs["prompt"].append(msg_pairs[-1][0])
+                    outputs["query"].append("")
+                    outputs["response"].append(msg_pairs[-1][1])
+                    outputs["history"].append(msg_pairs[:-1])
+
+            return outputs
+
+        if dataset_attr.formatting == "sharegpt": # convert format
+            column_names = list(next(iter(dataset)).keys())
+            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="Converting format of dataset"
+                )
+
+            dataset = dataset.map(
+                convert_format,
+                batched=True,
+                remove_columns=column_names,
+                **kwargs
+            )
+        else:
+            for column_name in ["prompt", "query", "response", "history"]: # align dataset
+                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.system_prompt: # add system prompt
+            system_prompt = dataset_attr.system_prompt
+            if data_args.streaming:
+                dataset = dataset.map(lambda _: {"system": system_prompt})
+            else:
+                dataset = dataset.add_column("system", [system_prompt] * len(dataset))
+
+        all_datasets.append(dataset)
+
+    if len(data_args.dataset_list) == 1:
+        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.")
+        return interleave_datasets(
+            datasets=all_datasets,
+            probabilities=data_args.interleave_probs,
+            seed=data_args.seed,
+            stopping_strategy="first_exhausted" if data_args.mix_strategy.endswith("under") else "all_exhausted"
+        )
+    else:
+        raise ValueError("Unknown mixing strategy.")

src/llmtuner/data/preprocess.py

@@ -0,0 +1,275 @@
+import os
+import tiktoken
+from itertools import chain
+from typing import TYPE_CHECKING, Any, Dict, Generator, List, Literal, Tuple, Union
+
+from datasets import load_from_disk
+
+from llmtuner.data.template import get_template_and_fix_tokenizer
+from llmtuner.extras.constants import IGNORE_INDEX
+from llmtuner.extras.logging import get_logger
+
+if TYPE_CHECKING:
+    from datasets import Dataset, IterableDataset
+    from transformers import Seq2SeqTrainingArguments
+    from transformers.tokenization_utils import PreTrainedTokenizer
+    from llmtuner.hparams import DataArguments
+
+
+logger = get_logger(__name__)
+
+
+def construct_example(examples: Dict[str, List[Any]]) -> Generator[Any, None, None]:
+    for i in range(len(examples["prompt"])):
+        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
+        system = examples["system"][i] if "system" in examples else None
+        yield query, response, history, system
+
+
+def infer_max_len(source_len: int, target_len: int, data_args: "DataArguments") -> Tuple[int, int]:
+    max_target_len = int(data_args.cutoff_len * (target_len / (source_len + target_len)))
+    max_target_len = max(max_target_len, data_args.reserved_label_len)
+    max_source_len = data_args.cutoff_len - max_target_len
+    return max_source_len, max_target_len
+
+
+def preprocess_dataset(
+    dataset: Union["Dataset", "IterableDataset"],
+    tokenizer: "PreTrainedTokenizer",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    stage: Literal["pt", "sft", "rm", "ppo"]
+) -> Union["Dataset", "IterableDataset"]:
+    template = get_template_and_fix_tokenizer(data_args.template, tokenizer)
+
+    if data_args.train_on_prompt and template.efficient_eos:
+        raise ValueError("Current template does not support `train_on_prompt`.")
+
+    def preprocess_pretrain_dataset(examples: Dict[str, List[Any]]) -> Dict[str, List[List[int]]]:
+        # build grouped texts with format `X1 X2 X3 ...`
+        if isinstance(getattr(tokenizer, "tokenizer", None), tiktoken.Encoding): # for tiktoken tokenizer (Qwen)
+            kwargs = dict(allowed_special="all")
+        else:
+            kwargs = dict(add_special_tokens=True)
+
+        if hasattr(tokenizer, "add_eos_token"): # for LLaMA tokenizer
+            add_eos_token_flag = getattr(tokenizer, "add_eos_token")
+            setattr(tokenizer, "add_eos_token", True)
+
+        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.cutoff_len
+        # 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 cutoff_len
+        result = {
+            k: [t[i: i + block_size] for i in range(0, total_length, block_size)]
+            for k, t in concatenated_examples.items()
+        }
+        # make sure the saved tokenizer is the same as the original one
+        if hasattr(tokenizer, "add_eos_token"):
+            setattr(tokenizer, "add_eos_token", add_eos_token_flag)
+        return result
+
+    def preprocess_supervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, List[List[int]]]:
+        # build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
+        # for multiturn examples, we only mask the prompt part in each prompt-response pair.
+        model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
+
+        for query, response, history, system in construct_example(examples):
+            if not (isinstance(query, str) and isinstance(response, str) and query != "" and response != ""):
+                continue
+
+            input_ids, labels = [], []
+            for turn_idx, (source_ids, target_ids) in enumerate(template.encode_multiturn(
+                tokenizer, query, response, history, system
+            )):
+                source_len, target_len = len(source_ids), len(target_ids)
+                max_source_len, max_target_len = infer_max_len(source_len, target_len, data_args)
+                if source_len > max_source_len:
+                    source_ids = source_ids[:max_source_len]
+                if target_len > max_target_len:
+                    target_ids = target_ids[:max_target_len]
+
+                if data_args.train_on_prompt:
+                    source_mask = source_ids
+                elif turn_idx != 0 and template.efficient_eos:
+                    source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
+                else:
+                    source_mask = [IGNORE_INDEX] * len(source_ids)
+
+                input_ids += source_ids + target_ids
+                labels += source_mask + target_ids
+
+            if template.efficient_eos:
+                input_ids += [tokenizer.eos_token_id]
+                labels += [tokenizer.eos_token_id]
+
+            if len(input_ids) > data_args.cutoff_len:
+                input_ids = input_ids[:data_args.cutoff_len]
+                labels = labels[:data_args.cutoff_len]
+
+            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_packed_supervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, List[List[int]]]:
+        # build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
+        # and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>`
+        model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
+        input_ids, labels = [], []
+        for query, response, history, system in construct_example(examples):
+            if not (isinstance(query, str) and isinstance(response, str) and query != "" and response != ""):
+                continue
+
+            for turn_idx, (source_ids, target_ids) in enumerate(template.encode_multiturn(
+                tokenizer, query, response, history, system
+            )):
+                if data_args.train_on_prompt:
+                    source_mask = source_ids
+                elif turn_idx != 0 and template.efficient_eos:
+                    source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
+                else:
+                    source_mask = [IGNORE_INDEX] * len(source_ids)
+                input_ids += source_ids + target_ids
+                labels += source_mask + target_ids
+
+        if template.efficient_eos:
+            input_ids += [tokenizer.eos_token_id]
+            labels += [tokenizer.eos_token_id]
+
+        total_length = len(input_ids)
+        block_size = data_args.cutoff_len
+        # 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 cutoff_len
+        for i in range(0, total_length, block_size):
+            model_inputs["input_ids"].append(input_ids[i: i + block_size])
+            model_inputs["attention_mask"].append([1] * block_size)
+            model_inputs["labels"].append(labels[i: i + block_size])
+
+        return model_inputs
+
+    def preprocess_unsupervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, List[List[int]]]:
+        # build inputs with format `<bos> X` and labels with format `Y <eos>`
+        model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
+
+        for query, response, history, system in construct_example(examples):
+            if not (isinstance(query, str) and query != ""):
+                continue
+
+            input_ids, labels = template.encode_oneturn(tokenizer, query, response, history, system)
+
+            if template.efficient_eos:
+                labels += [tokenizer.eos_token_id]
+
+            if len(input_ids) > data_args.cutoff_len:
+                input_ids = input_ids[:data_args.cutoff_len]
+            if len(labels) > data_args.cutoff_len:
+                labels = labels[:data_args.cutoff_len]
+
+            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_pairwise_dataset(examples: Dict[str, List[Any]]) -> Dict[str, List[List[int]]]:
+        # build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
+        model_inputs = {"prompt_ids": [], "chosen_ids": [], "rejected_ids": []}
+        for query, response, history, system in construct_example(examples):
+            if not (isinstance(query, str) and isinstance(response, list) and query != "" and len(response) > 1):
+                continue
+
+            prompt_ids, chosen_ids = template.encode_oneturn(tokenizer, query, response[0], history, system)
+            _, rejected_ids = template.encode_oneturn(tokenizer, query, response[1], history, system)
+
+            if template.efficient_eos:
+                chosen_ids += [tokenizer.eos_token_id]
+                rejected_ids += [tokenizer.eos_token_id]
+
+            source_len, target_len = len(prompt_ids), max(len(chosen_ids), len(rejected_ids))
+            max_source_len, max_target_len = infer_max_len(source_len, target_len, data_args)
+            if source_len > max_source_len:
+                prompt_ids = prompt_ids[:max_source_len]
+            if target_len > max_target_len:
+                chosen_ids = chosen_ids[:max_target_len]
+                rejected_ids = rejected_ids[:max_target_len]
+
+            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: Dict[str, List[int]]) -> None:
+        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(list(filter(lambda x: x != IGNORE_INDEX, example["labels"])), skip_special_tokens=False)
+        ))
+
+    def print_pairwise_dataset_example(example: Dict[str, List[int]]) -> None:
+        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: Dict[str, List[int]]) -> None:
+        print("input_ids:\n{}".format(example["input_ids"]))
+        print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
+
+    if stage == "pt":
+        preprocess_func = preprocess_pretrain_dataset
+        print_function = print_unsupervised_dataset_example
+    elif stage == "sft" and not training_args.predict_with_generate:
+        preprocess_func = preprocess_packed_supervised_dataset if data_args.sft_packing else preprocess_supervised_dataset
+        print_function = print_supervised_dataset_example
+    elif stage == "rm":
+        preprocess_func = preprocess_pairwise_dataset
+        print_function = print_pairwise_dataset_example
+    else:
+        preprocess_func = preprocess_unsupervised_dataset
+        print_function = print_unsupervised_dataset_example
+
+    if data_args.cache_path is not None and os.path.exists(data_args.cache_path):
+        logger.warning("Loading dataset from disk will ignore other data arguments.")
+        return load_from_disk(data_args.cache_path)
+
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        column_names = list(next(iter(dataset)).keys())
+        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_func,
+            batched=True,
+            remove_columns=column_names,
+            **kwargs
+        )
+
+        if data_args.cache_path is not None and not os.path.exists(data_args.cache_path):
+            if training_args.should_save:
+                dataset.save_to_disk(data_args.cache_path)
+            raise SystemExit("Dataset saved, rerun this script with the same `--cache_path`.")
+
+        if training_args.should_log:
+            try:
+                print_function(next(iter(dataset)))
+            except StopIteration:
+                raise RuntimeError("Empty dataset!")
+
+        return dataset

src/llmtuner/data/template.py

@@ -0,0 +1,682 @@
+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: List[Union[str, Dict[str, str]]]
+    prompt: List[Union[str, Dict[str, str]]]
+    system: str
+    sep: List[Union[str, Dict[str, str]]]
+    stop_words: List[str]
+    use_history: bool
+    efficient_eos: bool
+
+    def encode_oneturn(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        query: str,
+        resp: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        system: Optional[str] = None
+    ) -> Tuple[List[int], List[int]]:
+        r"""
+        Returns a single pair of token ids representing prompt and response respectively.
+        """
+        system, history = self._format(query, resp, history, system)
+        encoded_pairs = self._encode(tokenizer, system, history)
+        prompt_ids = []
+        for query_ids, resp_ids in encoded_pairs[:-1]:
+            prompt_ids = prompt_ids + query_ids + resp_ids
+        prompt_ids, answer_ids = prompt_ids + encoded_pairs[-1][0], encoded_pairs[-1][1]
+        return prompt_ids, answer_ids
+
+    def encode_multiturn(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        query: str,
+        resp: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        system: Optional[str] = None
+    ) -> List[Tuple[List[int], List[int]]]:
+        r"""
+        Returns multiple pairs of token ids representing prompts and responses respectively.
+        """
+        system, history = self._format(query, resp, history, system)
+        encoded_pairs = self._encode(tokenizer, system, history)
+        return encoded_pairs
+
+    def _format(
+        self,
+        query: str,
+        resp: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        system: Optional[str] = None
+    ) -> Tuple[str, List[Tuple[str, str]]]:
+        r"""
+        Aligns inputs to the standard format.
+        """
+        system = system or self.system # use system if provided
+        history = history if (history and self.use_history) else []
+        history = history + [(query, resp)]
+        return system, history
+
+    def _get_special_ids(
+        self,
+        tokenizer: "PreTrainedTokenizer"
+    ) -> Tuple[List[int], List[int]]:
+        if tokenizer.bos_token_id is not None and getattr(tokenizer, "add_bos_token", True):
+            bos_ids = [tokenizer.bos_token_id]
+        else: # baichuan, qwen and gpt2 models have no bos token
+            bos_ids = []
+
+        if tokenizer.eos_token_id is None:
+            raise ValueError("EOS token is required.")
+
+        if self.efficient_eos: # used in baichuan, qwen, chatglm, etc.
+            eos_ids = []
+        else:
+            eos_ids = [tokenizer.eos_token_id]
+
+        return bos_ids, eos_ids
+
+    def _encode(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        system: 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:
+                prefix_ids = self._convert_inputs_to_ids(tokenizer, context=self.prefix, system=system)
+                if len(prefix_ids) != 0: # has prefix
+                    prefix_ids = bos_ids + prefix_ids + sep_ids
+                else:
+                    prefix_ids = bos_ids
+            else:
+                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]]],
+        system: Optional[str] = None,
+        query: Optional[str] = None,
+        idx: Optional[str] = None
+    ) -> 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("{{system}}", system, 1) if system is not None else elem
+                elem = elem.replace("{{query}}", query, 1) if query is not None else elem
+                elem = elem.replace("{{idx}}", idx, 1) if idx is not None else elem
+                if len(elem) != 0:
+                    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 ValueError("Input must be string or dict[str, str], got {}".format(type(elem)))
+
+        return token_ids
+
+
+@dataclass
+class Llama2Template(Template):
+
+    def _encode(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        system: 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 = []
+        for turn_idx, (query, resp) in enumerate(history):
+            if turn_idx == 0: # llama2 template has no sep_ids
+                query = self.prefix[0].replace("{{system}}", system) + 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: List[Union[str, Dict[str, str]]],
+    prompt: List[Union[str, Dict[str, str]]],
+    system: str,
+    sep: List[Union[str, Dict[str, str]]],
+    stop_words: Optional[List[str]] = [],
+    use_history: Optional[bool] = True,
+    efficient_eos: Optional[bool] = False
+) -> None:
+    template_class = Llama2Template if "llama2" in name else Template
+    templates[name] = template_class(
+        prefix=prefix,
+        prompt=prompt,
+        system=system,
+        sep=sep,
+        stop_words=stop_words,
+        use_history=use_history,
+        efficient_eos=efficient_eos
+    )
+
+
+def get_template_and_fix_tokenizer(
+    name: str,
+    tokenizer: "PreTrainedTokenizer"
+) -> Template:
+    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))
+
+    if name is None:
+        return None
+
+    template = templates.get(name, None)
+    assert template is not None, "Template {} does not exist.".format(name)
+    tokenizer.add_special_tokens(
+        dict(additional_special_tokens=template.stop_words),
+        replace_additional_special_tokens=False
+    )
+    return template
+
+
+register_template(
+    name="alpaca",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        "### Instruction:\n{{query}}\n\n### Response:\n"
+    ],
+    system=(
+        "Below is an instruction that describes a task. "
+        "Write a response that appropriately completes the request."
+    ),
+    sep=[
+        "\n\n"
+    ]
+)
+
+
+register_template(
+    name="aquila",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        "Human: {{query}}###Assistant:"
+    ],
+    system=(
+        "A chat between a curious human and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the human's questions."
+    ),
+    sep=[
+        "###"
+    ],
+    stop_words=[
+        "</s>"
+    ],
+    efficient_eos=True
+)
+
+
+register_template(
+    name="baichuan",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        {"token": "<reserved_102>"}, # user token
+        "{{query}}",
+        {"token": "<reserved_103>"}  # assistant token
+    ],
+    system="",
+    sep=[],
+    efficient_eos=True
+)
+
+
+register_template(
+    name="baichuan2",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        {"token": "<reserved_106>"}, # user token
+        "{{query}}",
+        {"token": "<reserved_107>"}  # assistant token
+    ],
+    system="",
+    sep=[],
+    efficient_eos=True
+)
+
+
+register_template(
+    name="belle",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        "Human: {{query}}\n\nBelle: "
+    ],
+    system="",
+    sep=[
+        "\n\n"
+    ]
+)
+
+
+register_template(
+    name="bluelm",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        {"token": "[|Human|]:"},
+        "{{query}}",
+        {"token": "[|AI|]:"}
+    ],
+    system="",
+    sep=[]
+)
+
+
+register_template(
+    name="chatglm2",
+    prefix=[
+        {"token": "[gMASK]"},
+        {"token": "sop"},
+        "{{system}}"
+    ],
+    prompt=[
+        "[Round {{idx}}]\n\n问：{{query}}\n\n答："
+    ],
+    system="",
+    sep=[
+        "\n\n"
+    ],
+    efficient_eos=True
+)
+
+
+register_template(
+    name="chatglm3",
+    prefix=[
+        {"token": "[gMASK]"},
+        {"token": "sop"},
+        "{{system}}"
+    ],
+    prompt=[
+        {"token": "<|user|>"},
+        "\n",
+        "{{query}}",
+        {"token": "<|assistant|>"}
+    ],
+    system="",
+    sep=[],
+    stop_words=[
+        "<|user|>",
+        "<|observation|>"
+    ],
+    efficient_eos=True
+)
+
+
+register_template(
+    name="deepseek",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        "### Instruction:\n{{query}}\n\n### Response:\n"
+    ],
+    system=(
+        "You are an AI programming assistant, utilizing the Deepseek Coder model, "
+        "developed by Deepseek Company, and you only answer questions related to computer science. "
+        "For politically sensitive questions, security and privacy issues, "
+        "and other non-computer science questions, you will refuse to answer."
+    ),
+    sep=[
+        "\n",
+        {"token": "<|EOT|>"},
+        "\n\n"
+    ],
+    stop_words=[
+        "<|EOT|>"
+    ],
+    efficient_eos=True
+)
+
+
+register_template(
+    name="default",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        "Human: {{query}}\nAssistant:"
+    ],
+    system=(
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ),
+    sep=[
+        "\n"
+    ]
+)
+
+
+register_template(
+    name="falcon",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        "User: {{query}}\nFalcon:"
+    ],
+    system="",
+    sep=[
+        "\n"
+    ],
+    efficient_eos=True
+)
+
+
+register_template(
+    name="intern",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        "<|User|>:{{query}}",
+        {"token": "<eoh>"},
+        "\n<|Bot|>:"
+    ],
+    system="",
+    sep=[
+        {"token": "<eoa>"},
+        "\n"
+    ],
+    stop_words=[
+        "<eoa>"
+    ],
+    efficient_eos=True
+)
+
+
+register_template(
+    name="llama2",
+    prefix=[
+        "<<SYS>>\n{{system}}\n<</SYS>>\n\n"
+    ],
+    prompt=[
+        "[INST] {{query}} [/INST]"
+    ],
+    system=(
+        "You 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\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."
+    ),
+    sep=[]
+)
+
+
+register_template(
+    name="llama2_zh",
+    prefix=[
+        "<<SYS>>\n{{system}}\n<</SYS>>\n\n"
+    ],
+    prompt=[
+        "[INST] {{query}} [/INST]"
+    ],
+    system="You are a helpful assistant. 你是一个乐于助人的助手。",
+    sep=[]
+)
+
+
+register_template(
+    name="mistral",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        "[INST] {{query}} [/INST]"
+    ],
+    system="",
+    sep=[]
+)
+
+
+register_template(
+    name="openchat",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        "GPT4 Correct User: {{query}}",
+        {"token": "<|end_of_turn|>"},
+        "GPT4 Correct Assistant:"
+    ],
+    system="",
+    sep=[
+        {"token": "<|end_of_turn|>"}
+    ],
+    stop_words=[
+        "<|end_of_turn|>"
+    ],
+    efficient_eos=True
+)
+
+
+register_template(
+    name="qwen",
+    prefix=[
+        {"token": "<|im_start|>"},
+        "system\n{{system}}"
+    ],
+    prompt=[
+        {"token": "<|im_start|>"},
+        "user\n{{query}}",
+        {"token": "<|im_end|>"},
+        "\n",
+        {"token": "<|im_start|>"},
+        "assistant\n"
+    ],
+    system="You are a helpful assistant.",
+    sep=[
+        {"token": "<|im_end|>"},
+        "\n"
+    ],
+    stop_words=[
+        "<|im_end|>"
+    ],
+    efficient_eos=True
+)
+
+
+register_template(
+    name="starchat",
+    prefix=[
+        {"token": "<|system|>"},
+        "\n{{system}}",
+    ],
+    prompt=[
+        {"token": "<|user|>"},
+        "\n{{query}}",
+        {"token": "<|end|>"},
+        "\n",
+        {"token": "<|assistant|>"}
+    ],
+    system="",
+    sep=[
+        {"token": "<|end|>"},
+        "\n"
+    ],
+    stop_words=[
+        "<|end|>"
+    ],
+    efficient_eos=True
+)
+
+
+r"""
+Supports language model inference without histories.
+"""
+register_template(
+    name="vanilla",
+    prefix=[],
+    prompt=[
+        "{{query}}"
+    ],
+    system="",
+    sep=[],
+    use_history=False
+)
+
+
+register_template(
+    name="vicuna",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        "USER: {{query}} ASSISTANT:"
+    ],
+    system=(
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ),
+    sep=[]
+)
+
+
+register_template(
+    name="xverse",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        "Human: {{query}}\n\nAssistant: "
+    ],
+    system="",
+    sep=[]
+)
+
+
+register_template(
+    name="yayi",
+    prefix=[
+        {"token": "<|System|>"},
+        ":\n{{system}}"
+    ],
+    prompt=[
+        {"token": "<|Human|>"},
+        ":\n{{query}}\n\n",
+        {"token": "<|YaYi|>"},
+        ":"
+    ],
+    system=(
+        "You are a helpful, respectful and honest assistant named YaYi "
+        "developed by Beijing Wenge Technology Co.,Ltd. "
+        "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\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."
+    ),
+    sep=[
+        "\n\n"
+    ],
+    stop_words=[
+        "<|End|>"
+    ]
+)
+
+
+register_template(
+    name="zephyr",
+    prefix=[
+        {"token": "<|system|>"},
+        "\n{{system}}",
+        {"token": "</s>"}
+    ],
+    prompt=[
+        {"token": "<|user|>"},
+        "\n{{query}}",
+        {"token": "</s>"},
+        {"token": "<|assistant|>"}
+    ],
+    system="You are a friendly chatbot who always responds in the style of a pirate",
+    sep=[]
+)
+
+
+register_template(
+    name="ziya",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        {"token": "<human>"},
+        ":{{query}}\n",
+        {"token": "<bot>"},
+        ":"
+    ],
+    system="",
+    sep=[
+        "\n"
+    ]
+)

src/llmtuner/data/utils.py

@@ -0,0 +1,61 @@
+import hashlib
+from typing import TYPE_CHECKING, Dict, List, Optional, Union
+
+from llmtuner.extras.logging import get_logger
+
+if TYPE_CHECKING:
+    from datasets import Dataset, IterableDataset
+    from transformers import TrainingArguments
+    from llmtuner.hparams import DataArguments
+
+
+logger = get_logger(__name__)
+
+
+EXT2TYPE = {
+    "arrow": "arrow",
+    "csv": "csv",
+    "json": "json",
+    "jsonl": "json",
+    "parquet": "parquet",
+    "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 split_dataset(
+    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/dsets/__init__.py

@@ -1,2 +0,0 @@
-from llmtuner.dsets.loader import get_dataset
-from llmtuner.dsets.preprocess import preprocess_dataset

src/llmtuner/dsets/callbacks.py

@@ -1,63 +0,0 @@
-import os
-import json
-import time
-from datetime import timedelta
-
-from transformers import (
-    TrainerCallback,
-    TrainerControl,
-    TrainerState,
-    TrainingArguments
-)
-
-
-class LogCallback(TrainerCallback):
-
-    def __init__(self, runner=None):
-        self.runner = runner
-        self.start_time = time.time()
-        self.tracker = {}
-
-    def on_step_begin(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.
-        """
-        if self.runner is not None and self.runner.aborted:
-            control.should_epoch_stop = True
-            control.should_training_stop = True
-
-    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:
-            control.should_epoch_stop = True
-            control.should_training_stop = True
-
-    def on_log(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs) -> None:
-        r"""
-        Event called after logging the last logs.
-        """
-        if "loss" not in state.log_history[-1]:
-            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),
-            "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)))
-        }
-        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")

src/llmtuner/dsets/loader.py

@@ -1,106 +0,0 @@
-import os
-import hashlib
-from typing import List
-
-from datasets import Dataset, concatenate_datasets, load_dataset
-
-from llmtuner.extras.logging import get_logger
-from llmtuner.hparams import ModelArguments, DataArguments
-
-
-logger = get_logger(__name__)
-
-
-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"
-    }
-
-    max_samples = data_args.max_samples
-    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":
-            data_path = dataset_attr.dataset_name
-            data_files = None
-        elif dataset_attr.load_from == "script":
-            data_path = os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)
-            data_files = None
-        elif dataset_attr.load_from == "file":
-            data_path = None
-            data_files: List[str] = []
-
-            if os.path.isdir(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)):
-                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)
-                    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)):
-                data_files.append(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name))
-                data_path = ext2type.get(data_files[0].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.")
-        else:
-            raise NotImplementedError
-
-        raw_datasets = load_dataset(
-            data_path,
-            data_files=data_files,
-            cache_dir=model_args.cache_dir,
-            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)
-        all_datasets.append(dataset)
-
-    if len(data_args.dataset_list) == 1:
-        all_datasets = all_datasets[0]
-    else:
-        all_datasets = concatenate_datasets(all_datasets)
-
-    return all_datasets

src/llmtuner/dsets/preprocess.py

@@ -1,172 +0,0 @@
-from typing import 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 Template
-from llmtuner.hparams import DataArguments
-
-
-def preprocess_dataset(
-    dataset: Dataset,
-    tokenizer: PreTrainedTokenizer,
-    data_args: DataArguments,
-    training_args: Seq2SeqTrainingArguments,
-    stage: Literal["pt", "sft", "rm", "ppo"]
-) -> Dataset:
-
-    column_names = list(dataset.column_names)
-    prompt_template = Template(data_args.prompt_template)
-
-    # support question with a single answer or multiple answers
-    def get_dialog(examples):
-        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
-
-    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
-        # 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()
-        }
-
-    def preprocess_supervised_dataset(examples):
-        # 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": []}
-        max_length = data_args.max_source_length + data_args.max_target_length
-
-        for dialog in get_dialog(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)
-
-                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(input_ids) + len(source_ids) + len(target_ids) + 1 > 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]
-
-            model_inputs["input_ids"].append(input_ids)
-            model_inputs["labels"].append(labels)
-
-        return model_inputs
-
-    def preprocess_unsupervised_dataset(examples):
-        # build inputs with format `<bos> X` and labels with format `<bos> Y`
-        model_inputs = {"input_ids": [], "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)
-
-            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:
-                target_ids = target_ids[:data_args.max_target_length]
-
-            model_inputs["input_ids"].append(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]
-
-            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(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)
-        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)
-        ))
-
-    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)))
-
-    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":
-        preprocess_function = preprocess_pretrain_dataset
-    elif stage == "sft":
-        preprocess_function = preprocess_unsupervised_dataset \
-            if training_args.predict_with_generate else preprocess_supervised_dataset
-    elif stage == "rm":
-        preprocess_function = preprocess_pairwise_dataset
-    elif stage == "ppo":
-        preprocess_function = preprocess_unsupervised_dataset
-
-    with training_args.main_process_first(desc="dataset map pre-processing"):
-        dataset = dataset.map(
-            preprocess_function,
-            batched=True,
-            num_proc=data_args.preprocessing_num_workers,
-            remove_columns=column_names,
-            load_from_cache_file=not data_args.overwrite_cache,
-            desc="Running tokenizer on dataset"
-        )
-
-        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])
-
-        return dataset

src/llmtuner/eval/__init__.py

@@ -0,0 +1 @@
+from llmtuner.eval.evaluator import Evaluator

src/llmtuner/eval/evaluator.py

@@ -0,0 +1,116 @@
+# Inspired by: https://github.com/hendrycks/test/blob/master/evaluate_flan.py
+
+import os
+import json
+import torch
+import tiktoken
+import numpy as np
+from tqdm import tqdm, trange
+from typing import Any, Dict, List, Optional
+
+from datasets import load_dataset
+from transformers.utils import cached_file
+
+from llmtuner.data.template import get_template_and_fix_tokenizer
+from llmtuner.eval.template import get_eval_template
+from llmtuner.extras.constants import CHOICES, SUBJECTS
+from llmtuner.model import dispatch_model, get_eval_args, load_model_and_tokenizer
+
+
+class Evaluator:
+
+    def __init__(self, args: Optional[Dict[str, Any]] = None) -> None:
+        self.model_args, self.data_args, self.eval_args, finetuning_args = get_eval_args(args)
+        self.model, self.tokenizer = load_model_and_tokenizer(self.model_args, finetuning_args)
+        self.tokenizer.padding_side = "right" # avoid overflow issue in batched inference for llama2
+        self.model = dispatch_model(self.model)
+        self.template = get_template_and_fix_tokenizer(self.data_args.template, self.tokenizer)
+        self.eval_template = get_eval_template(self.eval_args.lang)
+        self.choice_inputs = self._encode_choices()
+
+    def _encode_choices(self) -> List[int]:
+        if isinstance(getattr(self.tokenizer, "tokenizer", None), tiktoken.Encoding): # for tiktoken tokenizer (Qwen)
+            kwargs = dict(allowed_special="all")
+        else:
+            kwargs = dict(add_special_tokens=False)
+
+        return [self.tokenizer.encode(self.eval_template.prefix + ch, **kwargs)[-1] for ch in CHOICES]
+
+    @torch.inference_mode()
+    def batch_inference(self, batch_input: Dict[str, torch.Tensor]) -> List[str]:
+        logits = self.model(**batch_input).logits
+        lengths = torch.sum(batch_input["attention_mask"], dim=-1)
+        word_probs = torch.stack([logits[i, lengths[i] - 1] for i in range(len(lengths))], dim=0)
+        choice_probs = torch.nn.functional.softmax(word_probs[:, self.choice_inputs], dim=-1).detach()
+        return [chr(ord("A") + offset.item()) for offset in torch.argmax(choice_probs, dim=-1)]
+
+    def eval(self) -> None:
+        mapping = cached_file(
+            path_or_repo_id = os.path.join(self.eval_args.task_dir, self.eval_args.task),
+            filename="mapping.json",
+            cache_dir=self.model_args.cache_dir,
+            token=self.model_args.hf_hub_token,
+            revision=self.model_args.model_revision
+        )
+        with open(mapping, "r", encoding="utf-8") as f:
+            categorys: Dict[str, Dict[str, str]] = json.load(f)
+
+        category_corrects = {subj: np.array([], dtype="bool") for subj in SUBJECTS}
+        pbar = tqdm(categorys.keys(), desc="Processing subjects", position=0)
+        results = {}
+        for subject in pbar:
+            dataset = load_dataset(
+                path=os.path.join(self.eval_args.task_dir, self.eval_args.task),
+                name=subject,
+                download_mode="force_redownload"
+            )
+            pbar.set_postfix_str(categorys[subject]["name"])
+            inputs, outputs, labels = [], [], []
+            for i in trange(len(dataset[self.data_args.split]), desc="Formatting batches", position=1, leave=False):
+                support_set = dataset["train"].shuffle().select(range(min(self.eval_args.n_shot, len(dataset["train"]))))
+                query, resp, history = self.eval_template.format_example(
+                    target_data=dataset[self.data_args.split][i],
+                    support_set=support_set,
+                    subject_name=categorys[subject]["name"],
+                    use_history=self.template.use_history
+                )
+                input_ids, _ = self.template.encode_oneturn(
+                    tokenizer=self.tokenizer, query=query, resp=resp, history=history
+                )
+                inputs.append({"input_ids": input_ids, "attention_mask": [1] * len(input_ids)})
+                labels.append(resp)
+
+            for i in trange(0, len(inputs), self.eval_args.batch_size, desc="Predicting batches", position=1, leave=False):
+                batch_input = self.tokenizer.pad(
+                    inputs[i : i + self.eval_args.batch_size], return_attention_mask=True, return_tensors="pt"
+                ).to(self.model.device)
+                preds = self.batch_inference(batch_input)
+                outputs += preds
+
+            corrects = (np.array(outputs) == np.array(labels))
+            category_name = categorys[subject]["category"]
+            category_corrects[category_name] = np.concatenate([category_corrects[category_name], corrects], axis=0)
+            category_corrects["Average"] = np.concatenate([category_corrects["Average"], corrects], axis=0)
+            results[subject] = {str(i): outputs[i] for i in range(len(outputs))}
+
+        pbar.close()
+        self._save_results(category_corrects, results)
+
+    def _save_results(self, category_corrects: Dict[str, np.ndarray], results: Dict[str, Dict[int, str]]) -> None:
+        score_info = "\n".join([
+            "{:>15}: {:.2f}".format(category_name, 100 * np.mean(category_correct))
+            for category_name, category_correct in category_corrects.items() if len(category_correct)
+        ])
+        print(score_info)
+        if self.eval_args.save_dir is not None:
+            os.makedirs(self.eval_args.save_dir, exist_ok=False)
+            with open(os.path.join(self.eval_args.save_dir, "results.json"), "w", encoding="utf-8", newline="\n") as f:
+                json.dump(results, f, indent=2)
+
+            with open(os.path.join(self.eval_args.save_dir, "results.log"), "w", encoding="utf-8", newline="\n") as f:
+                f.write(score_info)
+
+
+if __name__ == "__main__":
+    evaluator = Evaluator()
+    evaluator.eval()

src/llmtuner/eval/template.py

@@ -0,0 +1,86 @@
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Dict, List, Tuple
+
+from llmtuner.extras.constants import CHOICES
+
+if TYPE_CHECKING:
+    from datasets import Dataset
+
+
+@dataclass
+class EvalTemplate:
+
+    system: str
+    choice: str
+    answer: str
+    prefix: str
+
+    def parse_example(
+        self,
+        example: Dict[str, str]
+    ) -> Tuple[str, str]:
+        candidates = [self.choice.format(choice=ch, content=example[ch]) for ch in CHOICES if ch in example]
+        return "".join([example["question"]] + candidates + [self.answer]), example["answer"]
+
+    def format_example(
+        self,
+        target_data: Dict[str, str],
+        support_set: "Dataset",
+        subject_name: str,
+        use_history: bool
+    ) -> Tuple[str, str, List[Tuple[str, str]]]:
+        query, resp = self.parse_example(target_data)
+        history = [self.parse_example(support_set[k]) for k in range(len(support_set))]
+
+        if len(history):
+            temp = history.pop(0)
+            history.insert(0, (self.system.format(subject=subject_name) + temp[0], temp[1]))
+        else:
+            query = self.system.format(subject=subject_name) + query
+
+        if not use_history:
+            query = "\n\n".join(["".join(item) for item in history] + [query])
+            history = []
+        return query.strip(), resp, history
+
+
+eval_templates: Dict[str, EvalTemplate] = {}
+
+
+def register_eval_template(
+    name: str,
+    system: str,
+    choice: str,
+    answer: str,
+    prefix: str
+) -> None:
+    eval_templates[name] = EvalTemplate(
+        system=system,
+        choice=choice,
+        answer=answer,
+        prefix=prefix
+    )
+
+
+def get_eval_template(name: str) -> EvalTemplate:
+    eval_template = eval_templates.get(name, None)
+    assert eval_template is not None, "Template {} does not exist.".format(name)
+    return eval_template
+
+
+register_eval_template(
+    name="en",
+    system="The following are multiple choice questions (with answers) about {subject}.\n\n",
+    choice="\n{choice}. {content}",
+    answer="\nAnswer: ",
+    prefix=" "
+)
+
+
+register_eval_template(
+    name="zh",
+    system="以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n",
+    choice="\n{choice}. {content}",
+    answer="\n答案：",
+    prefix="\n"
+)

src/llmtuner/extras/callbacks.py

@@ -1,74 +1,162 @@
 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, PREFIX_CHECKPOINT_DIR
+
+from llmtuner.extras.constants import LOG_FILE_NAME
+from llmtuner.extras.logging import get_logger
+
+if TYPE_CHECKING:
+    from transformers import TrainingArguments, TrainerState, TrainerControl
+    from trl import AutoModelForCausalLMWithValueHead
+
+
+logger = get_logger(__name__)
+
+
+class SavePeftModelCallback(TrainerCallback):
+
+    def on_save(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called after a checkpoint save.
+        """
+        if args.should_save:
+            output_dir = os.path.join(args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, state.global_step))
+            model: "AutoModelForCausalLMWithValueHead" = kwargs.pop("model")
+            model.pretrained_model.config.save_pretrained(output_dir)
+            if model.pretrained_model.can_generate():
+                model.pretrained_model.generation_config.save_pretrained(output_dir)
+            if getattr(model, "is_peft_model", False):
+                model.pretrained_model.save_pretrained(output_dir)
+
+    def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called at the end of training.
+        """
+        if args.should_save:
+            model: "AutoModelForCausalLMWithValueHead" = kwargs.pop("model")
+            model.pretrained_model.config.save_pretrained(args.output_dir)
+            if model.pretrained_model.can_generate():
+                model.pretrained_model.generation_config.save_pretrained(args.output_dir)
+            if getattr(model, "is_peft_model", False):
+                model.pretrained_model.save_pretrained(args.output_dir)
 
 
 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.
         """
+        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)) and args.overwrite_output_dir:
+                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 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_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_log(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs) -> None:
+    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
+        )
+        if self.runner is not None:
+            logger.info("{{'loss': {:.4f}, 'learning_rate': {:2.4e}, 'epoch': {:.2f}}}".format(
+                logs["loss"] or 0, logs["learning_rate"] or 0, logs["epoch"] or 0
+            ))
+
         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

@@ -1,40 +1,278 @@
+from collections import defaultdict, OrderedDict
+from typing import Dict, Optional
+
+
+CHOICES = ["A", "B", "C", "D"]
+
+DEFAULT_MODULE = defaultdict(str)
+
+DEFAULT_TEMPLATE = defaultdict(str)
+
 IGNORE_INDEX = -100
 
-VALUE_HEAD_FILE_NAME = "value_head.bin"
+LAYERNORM_NAMES = {"norm", "ln"}
 
-FINETUNING_ARGS_NAME = "finetuning_args.json"
-
-LAYERNORM_NAMES = ["norm", "ln_f", "ln_attn", "ln_mlp"] # for LLaMA, BLOOM and Falcon settings
+LOG_FILE_NAME = "trainer_log.jsonl"
 
 METHODS = ["full", "freeze", "lora"]
 
-SUPPORTED_MODELS = {
+SUBJECTS = ["Average", "STEM", "Social Sciences", "Humanities", "Other"]
+
+SUPPORTED_MODELS = OrderedDict()
+
+TRAINING_STAGES = {
+    "Supervised Fine-Tuning": "sft",
+    "Reward Modeling": "rm",
+    "PPO": "ppo",
+    "DPO": "dpo",
+    "Pre-Training": "pt"
+}
+
+
+def register_model_group(
+    models: Dict[str, str],
+    module: Optional[str] = None,
+    template: Optional[str] = None
+) -> None:
+    prefix = None
+    for name, path in models.items():
+        if prefix is None:
+            prefix = name.split("-")[0]
+        else:
+            assert prefix == name.split("-")[0], "prefix should be identical."
+        SUPPORTED_MODELS[name] = path
+    if module is not None:
+        DEFAULT_MODULE[prefix] = module
+    if template is not None:
+        DEFAULT_TEMPLATE[prefix] = template
+
+
+register_model_group(
+    models={
+        "Baichuan-7B-Base": "baichuan-inc/Baichuan-7B",
+        "Baichuan-13B-Base": "baichuan-inc/Baichuan-13B-Base",
+        "Baichuan-13B-Chat": "baichuan-inc/Baichuan-13B-Chat"
+    },
+    module="W_pack",
+    template="baichuan"
+)
+
+
+register_model_group(
+    models={
+        "Baichuan2-7B-Base": "baichuan-inc/Baichuan2-7B-Base",
+        "Baichuan2-13B-Base": "baichuan-inc/Baichuan2-13B-Base",
+        "Baichuan2-7B-Chat": "baichuan-inc/Baichuan2-7B-Chat",
+        "Baichuan2-13B-Chat": "baichuan-inc/Baichuan2-13B-Chat"
+    },
+    module="W_pack",
+    template="baichuan2"
+)
+
+
+register_model_group(
+    models={
+        "BLOOM-560M": "bigscience/bloom-560m",
+        "BLOOM-3B": "bigscience/bloom-3b",
+        "BLOOM-7B1": "bigscience/bloom-7b1"
+    },
+    module="query_key_value"
+)
+
+
+register_model_group(
+    models={
+        "BLOOMZ-560M": "bigscience/bloomz-560m",
+        "BLOOMZ-3B": "bigscience/bloomz-3b",
+        "BLOOMZ-7B1-mt": "bigscience/bloomz-7b1-mt"
+    },
+    module="query_key_value"
+)
+
+
+register_model_group(
+    models={
+        "BlueLM-7B-Base": "vivo-ai/BlueLM-7B-Base",
+        "BlueLM-7B-Chat": "vivo-ai/BlueLM-7B-Chat"
+    },
+    template="bluelm"
+)
+
+
+register_model_group(
+    models={
+        "ChatGLM2-6B-Chat": "THUDM/chatglm2-6b"
+    },
+    module="query_key_value",
+    template="chatglm2"
+)
+
+
+register_model_group(
+    models={
+        "ChatGLM3-6B-Base": "THUDM/chatglm3-6b-base",
+        "ChatGLM3-6B-Chat": "THUDM/chatglm3-6b"
+    },
+    module="query_key_value",
+    template="chatglm3"
+)
+
+
+register_model_group(
+    models={
+        "ChineseLLaMA2-1.3B": "hfl/chinese-llama-2-1.3b",
+        "ChineseLLaMA2-7B": "hfl/chinese-llama-2-7b",
+        "ChineseLLaMA2-13B": "hfl/chinese-llama-2-13b",
+        "ChineseLLaMA2-1.3B-Chat": "hfl/chinese-alpaca-2-1.3b",
+        "ChineseLLaMA2-7B-Chat": "hfl/chinese-alpaca-2-7b",
+        "ChineseLLaMA2-13B-Chat": "hfl/chinese-alpaca-2-13b"
+    },
+    template="llama2_zh"
+)
+
+
+register_model_group(
+    models={
+        "Falcon-7B": "tiiuae/falcon-7b",
+        "Falcon-40B": "tiiuae/falcon-40b",
+        "Falcon-180B": "tiiuae/falcon-180B",
+        "Falcon-7B-Chat": "tiiuae/falcon-7b-instruct",
+        "Falcon-40B-Chat": "tiiuae/falcon-40b-instruct",
+        "Falcon-180B-Chat": "tiiuae/falcon-180B-chat"
+    },
+    module="query_key_value",
+    template="falcon"
+)
+
+
+register_model_group(
+    models={
+        "InternLM-7B": "internlm/internlm-7b",
+        "InternLM-20B": "internlm/internlm-20b",
+        "InternLM-7B-Chat": "internlm/internlm-chat-7b",
+        "InternLM-20B-Chat": "internlm/internlm-chat-20b"
+    },
+    template="intern"
+)
+
+
+register_model_group(
+    models={
+        "LingoWhale-8B": "deeplang-ai/LingoWhale-8B"
+    },
+    module="qkv_proj"
+)
+
+
+register_model_group(
+    models={
         "LLaMA-7B": "huggyllama/llama-7b",
         "LLaMA-13B": "huggyllama/llama-13b",
         "LLaMA-30B": "huggyllama/llama-30b",
-    "LLaMA-65B": "huggyllama/llama-65b",
-    "BLOOM-560M": "bigscience/bloom-560m",
-    "BLOOM-3B": "bigscience/bloom-3b",
-    "BLOOM-7B1": "bigscience/bloom-7b1",
-    "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-Chat": "tiiuae/falcon-7b-instruct",
-    "Falcon-40B-Base": "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-Chat": "baichuan-inc/Baichuan-13B-Chat",
-    "InternLM-7B-Base": "internlm/internlm-7b",
-    "InternLM-7B-Chat": "internlm/internlm-chat-7b"
-}
+        "LLaMA-65B": "huggyllama/llama-65b"
+    }
+)
 
-DEFAULT_MODULE = { # will be deprecated
-    "LLaMA": "q_proj,v_proj",
-    "BLOOM": "query_key_value",
-    "BLOOMZ": "query_key_value",
-    "Falcon": "query_key_value",
-    "Baichuan": "W_pack",
-    "InternLM": "q_proj,v_proj"
-}
+
+register_model_group(
+    models={
+        "LLaMA2-7B": "meta-llama/Llama-2-7b-hf",
+        "LLaMA2-13B": "meta-llama/Llama-2-13b-hf",
+        "LLaMA2-70B": "meta-llama/Llama-2-70b-hf",
+        "LLaMA2-7B-Chat": "meta-llama/Llama-2-7b-chat-hf",
+        "LLaMA2-13B-Chat": "meta-llama/Llama-2-13b-chat-hf",
+        "LLaMA2-70B-Chat": "meta-llama/Llama-2-70b-chat-hf"
+    },
+    template="llama2"
+)
+
+
+register_model_group(
+    models={
+        "Mistral-7B": "mistralai/Mistral-7B-v0.1",
+        "Mistral-7B-Chat": "mistralai/Mistral-7B-Instruct-v0.1"
+    },
+    template="mistral"
+)
+
+
+register_model_group(
+    models={
+        "OpenChat3.5-7B-Chat": "openchat/openchat_3.5"
+    },
+    template="openchat"
+)
+
+
+register_model_group(
+    models={
+        "Phi1.5-1.3B": "microsoft/phi-1_5"
+    },
+    module="Wqkv"
+)
+
+
+register_model_group(
+    models={
+        "Qwen-7B": "Qwen/Qwen-7B",
+        "Qwen-14B": "Qwen/Qwen-14B",
+        "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
+        "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat"
+    },
+    module="c_attn",
+    template="qwen"
+)
+
+
+register_model_group(
+    models={
+        "Skywork-13B-Base": "Skywork/Skywork-13B-base"
+    }
+)
+
+
+register_model_group(
+    models={
+        "Vicuna1.5-7B-Chat": "lmsys/vicuna-7b-v1.5",
+        "Vicuna1.5-13B-Chat": "lmsys/vicuna-13b-v1.5"
+    },
+    template="vicuna"
+)
+
+
+register_model_group(
+    models={
+        "XVERSE-7B": "xverse/XVERSE-7B",
+        "XVERSE-13B": "xverse/XVERSE-13B",
+        "XVERSE-65B": "xverse/XVERSE-65B",
+        "XVERSE-7B-Chat": "xverse/XVERSE-7B-Chat",
+        "XVERSE-13B-Chat": "xverse/XVERSE-13B-Chat"
+    },
+    template="xverse"
+)
+
+
+register_model_group(
+    models={
+        "Yayi-7B": "wenge-research/yayi-7b-llama2",
+        "Yayi-13B": "wenge-research/yayi-13b-llama2"
+    },
+    template="yayi"
+)
+
+
+register_model_group(
+    models={
+        "Yi-6B": "01-ai/Yi-6B",
+        "Yi-34B": "01-ai/Yi-34B"
+    }
+)
+
+
+register_model_group(
+    models={
+        "Zephyr-7B-Alpha-Chat": "HuggingFaceH4/zephyr-7b-alpha",
+        "Zephyr-7B-Beta-Chat": "HuggingFaceH4/zephyr-7b-beta"
+    },
+    template="zephyr"
+)

src/llmtuner/extras/logging.py

@@ -3,11 +3,17 @@ import logging
 
 
 class LoggerHandler(logging.Handler):
+    r"""
+    Logger handler used in Web UI.
+    """
 
     def __init__(self):
         super().__init__()
         self.log = ""
 
+    def reset(self):
+        self.log = ""
+
     def emit(self, record):
         if record.name == "httpx":
             return
@@ -17,7 +23,9 @@ class LoggerHandler(logging.Handler):
 
 
 def get_logger(name: str) -> logging.Logger:
-
+    r"""
+    Gets a standard logger with a stream hander to stdout.
+    """
     formatter = logging.Formatter(
         fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
         datefmt="%m/%d/%Y %H:%M:%S"
@@ -30,3 +38,12 @@ def get_logger(name: str) -> logging.Logger:
     logger.addHandler(handler)
 
     return logger
+
+
+def reset_logging() -> None:
+    r"""
+    Removes basic config of root logger. (unused in script)
+    """
+    root = logging.getLogger()
+    list(map(root.removeHandler, root.handlers))
+    list(map(root.removeFilter, root.filters))

src/llmtuner/extras/misc.py

@@ -1,11 +1,25 @@
+import gc
+import os
+import sys
 import torch
-from typing import List, Optional
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple
+from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList
 
-from transformers.modeling_utils import PreTrainedModel
-from transformers.generation.utils import LogitsProcessorList
-from transformers.generation.logits_process import LogitsProcessor
+try:
+    from transformers.utils import (
+        is_torch_bf16_cpu_available,
+        is_torch_bf16_gpu_available,
+        is_torch_cuda_available,
+        is_torch_npu_available
+    )
+    _is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
+    _is_bf16_available = is_torch_bf16_gpu_available() or is_torch_bf16_cpu_available()
+except ImportError:
+    _is_fp16_available = torch.cuda.is_available()
+    _is_bf16_available = torch.cuda.is_bf16_supported()
 
-from llmtuner.extras.constants import LAYERNORM_NAMES
+if TYPE_CHECKING:
+    from transformers import HfArgumentParser
 
 
 class AverageMeter:
@@ -28,78 +42,75 @@ 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:
-        if torch.isnan(scores).any() or torch.isinf(scores).any():
-            scores.zero_()
-            scores[..., 0] = 1.0
-        return scores
-
-
-def get_logits_processor() -> LogitsProcessorList:
-    logits_processor = LogitsProcessorList()
-    logits_processor.append(InvalidScoreLogitsProcessor())
-    return logits_processor
-
-
-def print_trainable_params(model: torch.nn.Module) -> None:
+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,
-    finetuning_type: str,
-    output_embedding_layer_name: Optional[str] = "lm_head",
-    use_gradient_checkpointing: Optional[bool] = True,
-    layer_norm_names: Optional[List[str]] = LAYERNORM_NAMES
-) -> 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):
-            param.data = param.data.to(torch.float32)
-
-    if use_gradient_checkpointing:
-        if hasattr(model, "enable_input_require_grads"):
-            model.enable_input_require_grads()
+def get_current_device() -> str:
+    import accelerate
+    from accelerate import Accelerator
+    dummy_accelerator = Accelerator()
+    if accelerate.utils.is_xpu_available():
+        return "xpu:{}".format(dummy_accelerator.local_process_index)
     else:
-            def make_inputs_require_grad(module, input, output):
-                output.requires_grad_(True)
-            model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+        return dummy_accelerator.local_process_index if torch.cuda.is_available() else "cpu"
 
-        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
+def get_logits_processor() -> "LogitsProcessorList":
+    r"""
+    Gets logits processor that removes NaN and Inf logits.
+    """
+    logits_processor = LogitsProcessorList()
+    logits_processor.append(InfNanRemoveLogitsProcessor())
+    return logits_processor
 
-        class CastOutputToFloat(torch.nn.Sequential):
 
-            def forward(self, x: torch.Tensor) -> torch.Tensor:
-                return super().forward(x.to(input_dtype)).to(torch.float32)
+def infer_optim_dtype(model_dtype: torch.dtype) -> torch.dtype:
+    r"""
+    Infers the optimal dtype according to the model_dtype and device compatibility.
+    """
+    if _is_bf16_available and model_dtype == torch.bfloat16:
+        return torch.bfloat16
+    elif _is_fp16_available:
+        return torch.float16
+    else:
+        return torch.float32
 
-        setattr(model, output_embedding_layer_name, CastOutputToFloat(output_embedding_layer))
 
-    return model
+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 torch_gc() -> None:
     r"""
     Collects GPU memory.
     """
+    gc.collect()
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()

src/llmtuner/extras/packages.py

@@ -0,0 +1,55 @@
+import importlib.metadata
+import importlib.util
+
+
+def is_package_available(name: str) -> bool:
+    return importlib.util.find_spec(name) is not None
+
+
+def get_package_version(name: str) -> str:
+    try:
+        return importlib.metadata.version(name)
+    except:
+        return "0.0.0"
+
+
+_fastapi_available = is_package_available("fastapi")
+_flash_attn2_available = is_package_available("flash_attn") and get_package_version("flash_attn").startswith("2")
+_jieba_available = is_package_available("jieba")
+_matplotlib_available = is_package_available("matplotlib")
+_nltk_available = is_package_available("nltk")
+_rouge_available = is_package_available("rouge-chinese")
+_starlette_available = is_package_available("sse-starlette")
+_uvicorn_available = is_package_available("uvicorn")
+
+
+def is_fastapi_availble():
+    return _fastapi_available
+
+
+def is_flash_attn2_available():
+    return _flash_attn2_available
+
+
+def is_jieba_available():
+    return _jieba_available
+
+
+def is_matplotlib_available():
+    return _matplotlib_available
+
+
+def is_nltk_available():
+    return _nltk_available
+
+
+def is_rouge_available():
+    return _rouge_available
+
+
+def is_starlette_available():
+    return _starlette_available
+
+
+def is_uvicorn_available():
+    return _uvicorn_available

src/llmtuner/extras/patches/llama_patch.py

@@ -0,0 +1,224 @@
+import math
+import torch
+import torch.nn as nn
+from typing import Optional, Tuple
+from transformers.utils import logging
+from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb
+
+try:
+    from transformers.models.llama.modeling_llama import repeat_kv
+except ImportError:
+    print("Please upgrade `transformers`.")
+
+from llmtuner.extras.packages import is_flash_attn2_available
+
+
+if is_flash_attn2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func # type: ignore
+    from flash_attn.bert_padding import pad_input, unpad_input # type: ignore
+
+
+logger = logging.get_logger(__name__)
+
+
+# Modified from: https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
+class LlamaShiftShortAttention(LlamaAttention):
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None: # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        if getattr(self, "num_key_value_groups"):
+            key_states = repeat_kv(key_states, self.num_key_value_groups)
+            value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if getattr(self.config, "group_size_ratio", None) and self.training: # shift
+            groupsz = int(q_len * getattr(self.config, "group_size_ratio"))
+            assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz)
+            num_groups = q_len // groupsz
+            def shift(state: torch.Tensor) -> torch.Tensor:
+                state = state.transpose(1, 2) # output: (bsz, seq_len, n_heads, head_dim)
+                state = torch.cat((
+                    state[:, :, :self.num_heads//2], state[:, :, self.num_heads//2:].roll(-groupsz//2, dims=1)
+                ), dim=2)
+                return state.reshape(bsz * num_groups, groupsz, self.num_heads, self.head_dim).transpose(1, 2)
+
+            query_states, key_states, value_states = shift(query_states), shift(key_states), shift(value_states)
+            if attention_mask is not None:
+                attention_mask = attention_mask[:, :, :groupsz, :groupsz].repeat(num_groups, 1, 1, 1)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states) # (bsz, :, seq_len, :) or (bsz*n_group, :, groupsz, :)
+        attn_output = attn_output.transpose(1, 2).contiguous()
+
+        if getattr(self.config, "group_size_ratio", None) and self.training: # shift back
+            attn_output.reshape(bsz, q_len, self.num_heads, self.head_dim)
+            attn_output = torch.cat((
+                attn_output[:, :, :self.num_heads//2], attn_output[:, :, self.num_heads//2:].roll(groupsz//2, dims=1)
+            ))
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class LlamaFlashAttention2(LlamaAttention):
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # LlamaFlashAttention2 attention does not support output_attentions
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # FlashAttention requires the input to have the shape (bsz, seq_len, n_heads, head_dim)
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None: # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        # cast to half precision
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            logger.warning_once("The input hidden states seems to be silently casted in float32.")
+            query_states = query_states.to(self.config.torch_dtype)
+            key_states = key_states.to(self.config.torch_dtype)
+            value_states = value_states.to(self.config.torch_dtype)
+
+        if getattr(self, "num_key_value_groups", None):
+            key_states = repeat_kv(key_states, self.num_key_value_groups)
+            value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        query_states = query_states.transpose(1, 2) # (bsz, seq_len, n_heads, head_dim)
+        key_states = key_states.transpose(1, 2) # (bsz, seq_len, n_heads, head_dim)
+        value_states = value_states.transpose(1, 2) # (bsz, seq_len, n_heads, head_dim)
+
+        if getattr(self.config, "group_size_ratio", None) and self.training: # shift
+            groupsz = int(q_len * getattr(self.config, "group_size_ratio"))
+            assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz)
+            num_groups = q_len // groupsz
+            def shift(state: torch.Tensor) -> torch.Tensor:
+                state = torch.cat((
+                    state[:, :, :self.num_heads//2], state[:, :, self.num_heads//2:].roll(-groupsz//2, dims=1)
+                ), dim=2)
+                return state.reshape(bsz * num_groups, groupsz, self.num_heads, self.head_dim)
+
+            query_states, key_states, value_states = shift(query_states), shift(key_states), shift(value_states)
+            if attention_mask is not None:
+                attention_mask = attention_mask.reshape(bsz * num_groups, groupsz)
+
+        if attention_mask is not None:
+            logger.warning_once("Padded sequences are less efficient in FlashAttention.")
+            # -q_len: assumes left padding when q_len != kv_len
+            unpadded_q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(query_states, attention_mask[:, -q_len:])
+            unpadded_k, _, cu_seqlens_k, max_seqlen_k = unpad_input(key_states, attention_mask)
+            unpadded_v, _, _, _ = unpad_input(value_states, attention_mask)
+            attn_output_unpad = flash_attn_varlen_func(
+                unpadded_q,
+                unpadded_k,
+                unpadded_v,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_q,
+                max_seqlen_k=max_seqlen_k,
+                dropout_p=0.0,
+                softmax_scale=None,
+                causal=True,
+            )
+            attn_output = pad_input(attn_output_unpad, indices_q, bsz, q_len)
+        else:
+            attn_output = flash_attn_func(
+                query_states, key_states, value_states, 0.0, softmax_scale=None, causal=True
+            )
+
+        if getattr(self.config, "group_size_ratio", None) and self.training: # shift back
+            attn_output.reshape(bsz, q_len, self.num_heads, self.head_dim)
+            attn_output = torch.cat((
+                attn_output[:, :, :self.num_heads//2], attn_output[:, :, self.num_heads//2:].roll(groupsz//2, dims=1)
+            ))
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Disable the transformation of the attention mask in LlamaModel as flash attention
+# takes a boolean padding_mask. Fills in the past kv length for use in forward.
+def _prepare_decoder_attention_mask(
+    self,
+    attention_mask: torch.Tensor,
+    input_shape: torch.Tensor,
+    inputs_embeds: torch.Tensor,
+    past_key_values_length: int
+) -> torch.Tensor:
+    if attention_mask is not None and torch.all(attention_mask):
+        return None  # This uses the faster call when training with full samples
+
+    return attention_mask

src/llmtuner/extras/ploting.py

@@ -1,11 +1,14 @@
 import os
 import math
 import json
-import matplotlib.pyplot as plt
 from typing import List, Optional
 from transformers.trainer import TRAINER_STATE_NAME
 
 from llmtuner.extras.logging import get_logger
+from llmtuner.extras.packages import is_matplotlib_available
+
+if is_matplotlib_available():
+    import matplotlib.pyplot as plt
 
 
 logger = get_logger(__name__)

src/llmtuner/extras/save_and_load.py

@@ -1,49 +0,0 @@
-import os
-import torch
-from typing import Dict
-
-from transformers.trainer import WEIGHTS_NAME, WEIGHTS_INDEX_NAME
-from transformers.modeling_utils import load_sharded_checkpoint
-
-from llmtuner.extras.constants import VALUE_HEAD_FILE_NAME
-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()
-    filtered_state_dict = {}
-
-    for k, v in model.named_parameters():
-        if v.requires_grad:
-            filtered_state_dict[k] = state_dict[k].cpu().clone().detach()
-
-    return filtered_state_dict
-
-
-def load_trainable_params(model: torch.nn.Module, checkpoint_dir: os.PathLike) -> bool:
-    weights_file = os.path.join(checkpoint_dir, WEIGHTS_NAME)
-    if os.path.exists(weights_file):
-        model_state_dict = torch.load(weights_file, map_location="cpu")
-        model.load_state_dict(model_state_dict, strict=False) # skip missing keys
-    elif os.path.exists(os.path.join(checkpoint_dir, WEIGHTS_INDEX_NAME)):
-        load_sharded_checkpoint(model, checkpoint_dir, strict=False)
-    else:
-        logger.warning("Provided path ({}) does not contain pre-trained weights.".format(checkpoint_dir))
-        return False
-    return True
-
-
-def load_valuehead_params(model: torch.nn.Module, checkpoint_dir: os.PathLike) -> bool:
-    valuehead_file = os.path.join(checkpoint_dir, VALUE_HEAD_FILE_NAME)
-    if not os.path.exists(valuehead_file):
-        logger.warning("Provided path ({}) does not contain valuehead weights.".format(checkpoint_dir))
-        return False
-    valuehead_state_dict = torch.load(valuehead_file, map_location="cpu")
-    model.register_buffer("reward_head_weight", valuehead_state_dict["summary.weight"])
-    model.register_buffer("reward_head_bias", valuehead_state_dict["summary.bias"])
-    model.register_buffer("default_head_weight", torch.zeros_like(valuehead_state_dict["summary.weight"]))
-    model.register_buffer("default_head_bias", torch.zeros_like(valuehead_state_dict["summary.bias"]))
-    return True

src/llmtuner/extras/template.py

@@ -1,221 +0,0 @@
-from typing import Dict, List, Optional, Tuple
-from dataclasses import dataclass
-
-
-@dataclass
-class Format:
-    prefix: str
-    prompt: str
-    sep: str
-    use_history: bool
-
-
-templates: Dict[str, Format] = {}
-
-
-@dataclass
-class Template:
-
-    name: str
-
-    def __post_init__(self):
-        if self.name in templates:
-            self.prefix = templates[self.name].prefix
-            self.prompt = templates[self.name].prompt
-            self.sep = templates[self.name].sep
-            self.use_history = templates[self.name].use_history
-        else:
-            raise ValueError("Template {} does not exist.".format(self.name))
-
-    def get_prompt(
-        self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = ""
-    ) -> str:
-        r"""
-        Returns a string containing prompt without response.
-        """
-        return "".join(self._format_example(query, history, prefix))
-
-    def get_dialog(
-        self, query: str, resp: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = ""
-    ) -> List[str]:
-        r"""
-        Returns a list containing 2 * n elements where the 2k-th is a query and the (2k+1)-th is a response.
-        """
-        return self._format_example(query, history, prefix) + [resp]
-
-    def _format_example(
-        self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = ""
-    ) -> List[str]:
-        prefix = prefix if prefix else self.prefix # use prefix if provided
-        prefix = prefix + self.sep if prefix else "" # add separator for non-empty prefix
-        history = history if (history and self.use_history) else []
-        history = history + [(query, "<dummy>")]
-        convs = []
-        for turn_idx, (user_query, bot_resp) in enumerate(history):
-            if turn_idx == 0:
-                convs.append(prefix + self.prompt.format(query=user_query))
-                convs.append(bot_resp)
-            else:
-                convs.append(self.sep + self.prompt.format(query=user_query))
-                convs.append(bot_resp)
-        return convs[:-1] # drop last
-
-
-def register_template(name: str, prefix: str, prompt: str, sep: str, use_history: bool) -> None:
-    templates[name] = Format(
-        prefix=prefix,
-        prompt=prompt,
-        sep=sep,
-        use_history=use_history
-    )
-
-
-r"""
-Supports language model inference without histories.
-"""
-register_template(
-    name="vanilla",
-    prefix="",
-    prompt="{query}",
-    sep="",
-    use_history=False
-)
-
-
-r"""
-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",
-    use_history=True
-)
-
-
-r"""
-Supports: https://huggingface.co/tatsu-lab/alpaca-7b-wdiff
-          https://github.com/ymcui/Chinese-LLaMA-Alpaca
-"""
-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",
-    use_history=True
-)
-
-
-r"""
-Supports: https://huggingface.co/lmsys/vicuna-7b-delta-v1.1
-          https://huggingface.co/lmsys/vicuna-13b-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>",
-    use_history=True
-)
-
-
-r"""
-Supports: https://huggingface.co/BelleGroup/BELLE-LLaMA-EXT-13B
-"""
-register_template(
-    name="belle",
-    prefix="",
-    prompt="Human: {query}\n\nBelle: ",
-    sep="\n\n",
-    use_history=True
-)
-
-
-r"""
-Supports: https://github.com/CVI-SZU/Linly
-"""
-register_template(
-    name="linly",
-    prefix="",
-    prompt="User: {query}\nBot: ",
-    sep="\n",
-    use_history=True
-)
-
-
-r"""
-Supports: https://github.com/Neutralzz/BiLLa
-"""
-register_template(
-    name="billa",
-    prefix="",
-    prompt="Human: {query}\nAssistant: ",
-    sep="\n",
-    use_history=True
-)
-
-
-r"""
-Supports: https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1
-"""
-register_template(
-    name="ziya",
-    prefix="",
-    prompt="<human>:{query}\n<bot>:",
-    sep="\n",
-    use_history=True
-)
-
-
-r"""
-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="###",
-    use_history=True
-)
-
-
-r"""
-Supports: https://huggingface.co/internlm/internlm-chat-7b
-"""
-register_template(
-    name="intern",
-    prefix="",
-    prompt="<|User|>:{query}<eoh>\n<|Bot|>:",
-    sep="<eoa>\n",
-    use_history=True
-)
-
-
-r"""
-Supports: https://huggingface.co/baichuan-inc/Baichuan-13B-Chat
-"""
-register_template(
-    name="baichuan",
-    prefix="",
-    prompt=" <reserved_102> {query} <reserved_103> ",
-    sep="</s>",
-    use_history=True
-)
-
-
-r"""
-Supports: https://huggingface.co/HuggingFaceH4/starchat-alpha
-          https://huggingface.co/HuggingFaceH4/starchat-beta
-"""
-register_template(
-    name="starchat",
-    prefix="<|system|>\n",
-    prompt="<|user|>\n{query}<|end|>\n<|assistant|>\n",
-    sep="<|end|>\n",
-    use_history=True
-)

src/llmtuner/hparams/__init__.py

@@ -1,5 +1,5 @@
 from .data_args import DataArguments
+from .evaluation_args import EvaluationArguments
 from .finetuning_args import FinetuningArguments
-from .general_args import GeneralArguments
 from .generating_args import GeneratingArguments
 from .model_args import ModelArguments

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
 
 
@@ -10,35 +10,72 @@ class DatasetAttr:
     load_from: str
     dataset_name: Optional[str] = None
     dataset_sha1: Optional[str] = None
-    source_prefix: Optional[str] = None
+    system_prompt: Optional[str] = None
+    subset: Optional[str] = None
+    ranking: Optional[bool] = False
+    formatting: Optional[Literal["alpaca", "sharegpt"]] = "alpaca"
+
+    prompt: Optional[str] = "instruction"
+    query: Optional[str] = "input"
+    response: Optional[str] = "output"
+    history: Optional[str] = None
+    messages: Optional[str] = "conversations"
+    role: Optional[str] = "from"
+    content: Optional[str] = "value"
 
     def __repr__(self) -> str:
         return self.dataset_name
 
-    def __post_init__(self):
-        self.prompt_column = "instruction"
-        self.query_column = "input"
-        self.response_column = "output"
-        self.history_column = None
-
 
 @dataclass
 class DataArguments:
-    """
+    r"""
     Arguments pertaining to what data we are going to input our model for training and evaluation.
     """
+    template: Optional[str] = field(
+        default=None,
+        metadata={"help": "Which template to use for constructing prompts in training and inference."}
+    )
     dataset: Optional[str] = field(
-        default="alpaca_zh",
+        default=None,
         metadata={"help": "The name of provided dataset(s) to use. Use commas to separate multiple datasets."}
     )
     dataset_dir: Optional[str] = field(
         default="data",
-        metadata={"help": "The name of the folder containing datasets."}
+        metadata={"help": "Path to the folder containing the datasets."}
     )
     split: Optional[str] = field(
         default="train",
         metadata={"help": "Which dataset split to use for training and evaluation."}
     )
+    cutoff_len: Optional[int] = field(
+        default=1024,
+        metadata={"help": "The maximum length of the model inputs after tokenization."}
+    )
+    reserved_label_len: Optional[int] = field(
+        default=1,
+        metadata={"help": "The maximum length reserved for label after tokenization."}
+    )
+    train_on_prompt: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether to disable the mask on the prompt or not."}
+    )
+    streaming: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Enable dataset streaming."}
+    )
+    buffer_size: Optional[int] = field(
+        default=16384,
+        metadata={"help": "Size of the buffer to randomly sample examples from in dataset streaming."}
+    )
+    mix_strategy: Optional[Literal["concat", "interleave_under", "interleave_over"]] = field(
+        default="concat",
+        metadata={"help": "Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling)."}
+    )
+    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."}
@@ -47,14 +84,6 @@ class DataArguments:
         default=None,
         metadata={"help": "The number of processes to use for the preprocessing."}
     )
-    max_source_length: Optional[int] = field(
-        default=512,
-        metadata={"help": "The maximum total input sequence length after tokenization."}
-    )
-    max_target_length: Optional[int] = field(
-        default=512,
-        metadata={"help": "The maximum total output sequence length after tokenization."}
-    )
     max_samples: Optional[int] = field(
         default=None,
         metadata={"help": "For debugging purposes, truncate the number of examples for each dataset."}
@@ -67,30 +96,53 @@ class DataArguments:
         default=True,
         metadata={"help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."}
     )
-    source_prefix: Optional[str] = field(
+    system_prompt: Optional[str] = field(
         default=None,
-        metadata={"help": "A prefix to add before every source text. Use `|` to separate multiple prefixes in training."}
+        metadata={"help": "System prompt to add before the user query. Use `|` to separate multiple prompts 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."}
+        metadata={"help": "Size of the development set, should be an integer or a float in range `[0,1)`."}
     )
-    prompt_template: Optional[str] = field(
-        default="default",
-        metadata={"help": "Which template to use for constructing prompts in training and inference."}
+    sft_packing: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Packing the questions and answers in the supervised fine-tuning stage."}
+    )
+    cache_path: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to save or load the preprocessed datasets."}
     )
 
-    def init_for_training(self): # support mixing multiple datasets
-        dataset_names = [ds.strip() for ds in self.dataset.split(",")]
+    def __post_init__(self):
+        if self.reserved_label_len >= self.cutoff_len:
+            raise ValueError("`reserved_label_len` must be smaller than `cutoff_len`.")
+
+        if self.streaming and self.val_size > 1e-6 and self.val_size < 1:
+            raise ValueError("Streaming mode should have an integer val size.")
+
+        if self.streaming and self.max_samples is not None:
+            raise ValueError("`max_samples` is incompatible with `streaming`.")
+
+        if self.streaming and self.cache_path:
+            raise ValueError("`cache_path` is incompatible with `streaming`.")
+
+    def init_for_training(self, seed: int): # support mixing multiple datasets
+        self.seed = seed
+        dataset_names = [ds.strip() for ds in self.dataset.split(",")] if self.dataset is not None else []
+        try:
             with open(os.path.join(self.dataset_dir, "dataset_info.json"), "r") as f:
                 dataset_info = json.load(f)
+        except Exception:
+            if self.dataset is not None:
+                raise ValueError("Cannot find dataset_info.json in `dataset_dir`.")
+            dataset_info = None
 
-        if self.source_prefix is not None:
-            prefix_list = self.source_prefix.split("|")
-            prefix_list = prefix_list * len(dataset_names) if len(prefix_list) == 1 else prefix_list
-            assert len(prefix_list) == len(dataset_names), "The number of prefixes should be either identical with datasets or 1."
-        else:
-            prefix_list = [None] * len(dataset_names)
+        prompt_list = self.system_prompt.split("|") if self.system_prompt else [None]
+        prompt_list = prompt_list * (len(dataset_names) // len(prompt_list))
+        assert len(prompt_list) == len(dataset_names), "Number of system prompts should be equal to datasets or 1."
+
+        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):
@@ -108,12 +160,17 @@ class DataArguments:
                     dataset_sha1=dataset_info[name].get("file_sha1", None)
                 )
 
-            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)
+                dataset_attr.messages = dataset_info[name]["columns"].get("messages", None)
+                dataset_attr.role = dataset_info[name]["columns"].get("role", None)
+                dataset_attr.content = dataset_info[name]["columns"].get("content", None)
 
+            dataset_attr.subset = dataset_info[name].get("subset", None)
+            dataset_attr.ranking = dataset_info[name].get("ranking", False)
+            dataset_attr.formatting = dataset_info[name].get("formatting", "alpaca")
+            dataset_attr.system_prompt = prompt_list[i]
             self.dataset_list.append(dataset_attr)

src/llmtuner/hparams/evaluation_args.py

@@ -0,0 +1,55 @@
+import os
+from typing import Literal, Optional
+from dataclasses import dataclass, field
+
+from datasets import DownloadMode
+
+
+@dataclass
+class EvaluationArguments:
+    r"""
+    Arguments pertaining to specify the evaluation parameters.
+    """
+    task: str = field(
+        metadata={"help": "Name of the evaluation task."}
+    )
+    task_dir: Optional[str] = field(
+        default="evaluation",
+        metadata={"help": "Path to the folder containing the evaluation datasets."}
+    )
+    batch_size: Optional[int] = field(
+        default=4,
+        metadata={"help": "The batch size per GPU for evaluation."}
+    )
+    seed: Optional[int] = field(
+        default=42,
+        metadata={"help": "Random seed to be used with data loaders."}
+    )
+    lang: Optional[Literal["en", "zh"]] = field(
+        default="en",
+        metadata={"help": "Language used at evaluation."}
+    )
+    n_shot: Optional[int] = field(
+        default=5,
+        metadata={"help": "Number of examplars for few-shot learning."}
+    )
+    save_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to save the evaluation results."}
+    )
+    download_mode: Optional[DownloadMode] = field(
+        default=DownloadMode.REUSE_DATASET_IF_EXISTS,
+        metadata={"help": "Download mode used for the evaluation datasets."}
+    )
+
+    def __post_init__(self):
+        task_available = []
+        for folder in os.listdir(self.task_dir):
+            if os.path.isdir(os.path.join(self.task_dir, folder)):
+                task_available.append(folder)
+
+        if self.task not in task_available:
+            raise ValueError("Task {} not found in {}.".format(self.task, self.task_dir))
+
+        if self.save_dir is not None and os.path.exists(self.save_dir):
+            raise ValueError("`save_dir` already exists, use another one.")

src/llmtuner/hparams/finetuning_args.py

@@ -4,75 +4,181 @@ from dataclasses import asdict, dataclass, field
 
 
 @dataclass
-class FinetuningArguments:
+class FreezeArguments:
+    r"""
+    Arguments pertaining to the freeze (partial-parameter) training.
     """
-    Arguments pertaining to which techniques we are going to fine-tuning with.
-    """
-    finetuning_type: Optional[Literal["none", "freeze", "lora", "full"]] = field(
-        default="lora",
-        metadata={"help": "Which fine-tuning method to use."}
-    )
-    num_hidden_layers: Optional[int] = field(
-        default=32,
-        metadata={"help": "Number of decoder blocks in the model. \
-                  LLaMA choices: [\"32\", \"40\", \"60\", \"80\"], \
-                  BLOOM choices: [\"24\", \"30\", \"70\"], \
-                  Falcon choices: [\"32\", \"60\"], \
-                  Baichuan choices: [\"32\"]"}
-    )
     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(
+    name_module_trainable: Optional[str] = field(
         default="mlp",
-        metadata={"help": "Name of trainable modules for Freeze fine-tuning. \
+        metadata={"help": "Name of trainable modules for partial-parameter (freeze) fine-tuning. \
+                  Use commas to separate multiple modules. \
                   LLaMA choices: [\"mlp\", \"self_attn\"], \
-                  BLOOM & Falcon choices: [\"mlp\", \"self_attention\"], \
-                  Baichuan choices: [\"mlp\", \"self_attn\"]"}
+                  BLOOM & Falcon & ChatGLM choices: [\"mlp\", \"self_attention\"], \
+                  Qwen choices: [\"mlp\", \"attn\"], \
+                  Phi-1.5 choices: [\"mlp\", \"mixer\"], \
+                  Others choices: the same as LLaMA."}
     )
+
+
+@dataclass
+class LoraArguments:
+    r"""
+    Arguments pertaining to the LoRA training.
+    """
     lora_rank: Optional[int] = field(
         default=8,
         metadata={"help": "The intrinsic dimension for LoRA fine-tuning."}
     )
     lora_alpha: Optional[float] = field(
-        default=32.0,
-        metadata={"help": "The scale factor for LoRA fine-tuning (similar with the learning rate)."}
+        default=None,
+        metadata={"help": "The scale factor for LoRA fine-tuning (default: lora_rank * 2.0)."}
     )
     lora_dropout: Optional[float] = field(
         default=0.1,
         metadata={"help": "Dropout rate for the LoRA fine-tuning."}
     )
     lora_target: Optional[str] = field(
-        default="q_proj,v_proj",
+        default=None,
         metadata={"help": "Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. \
                   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\"]"}
+                  BLOOM & Falcon & ChatGLM choices: [\"query_key_value\", \"dense\", \"dense_h_to_4h\", \"dense_4h_to_h\"], \
+                  Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
+                  Qwen choices: [\"c_attn\", \"attn.c_proj\", \"w1\", \"w2\", \"mlp.c_proj\"], \
+                  Phi-1.5 choices: [\"Wqkv\", \"out_proj\", \"fc1\", \"fc2\"], \
+                  Others choices: the same as LLaMA."}
+    )
+    additional_target: Optional[str] = field(
+        default=None,
+        metadata={"help": "Name(s) of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint."}
+    )
+    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."}
+    )
+
+
+@dataclass
+class RLHFArguments:
+    r"""
+    Arguments pertaining to the PPO and DPO training.
+    """
+    dpo_beta: Optional[float] = field(
+        default=0.1,
+        metadata={"help": "The beta parameter for the DPO loss."}
+    )
+    ppo_logger: Optional[str] = field(
+        default=None,
+        metadata={"help": "Log with either 'wandb' or 'tensorboard' in PPO training."}
+    )
+    ppo_score_norm: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Use score normalization in PPO training."}
+    )
+    ppo_target: Optional[float] = field(
+        default=6.0,
+        metadata={"help": "Target KL value for adaptive KL control in PPO training."}
+    )
+    ppo_whiten_rewards: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whiten the rewards before compute advantages in PPO training."}
+    )
+    ref_model: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the reference model used for the PPO or DPO training."}
+    )
+    ref_model_checkpoint: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the directory(s) containing the model checkpoints of the reference model."}
+    )
+    ref_model_quantization_bit: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of bits to quantize the reference model."}
+    )
+    reward_model: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the directory containing the checkpoints of the reward model."}
+    )
+    reward_model_checkpoint: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the directory(s) containing the model checkpoints of the reward model."}
+    )
+    reward_model_quantization_bit: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of bits to quantize the reward model."}
+    )
+    reward_model_type: Optional[Literal["lora", "full"]] = field(
+        default="lora",
+        metadata={"help": "The checkpoint type of the reward model. The lora type only supports lora training."}
+    )
+
+
+@dataclass
+class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments):
+    r"""
+    Arguments pertaining to which techniques we are going to fine-tuning with.
+    """
+    stage: Optional[Literal["pt", "sft", "rm", "ppo", "dpo"]] = field(
+        default="sft",
+        metadata={"help": "Which stage will be performed in training."}
+    )
+    finetuning_type: Optional[Literal["lora", "freeze", "full"]] = field(
+        default="lora",
+        metadata={"help": "Which fine-tuning method to use."}
+    )
+    upcast_layernorm: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether to upcast the layernorm weights in fp32."}
+    )
+    neft_alpha: Optional[float] = field(
+        default=0,
+        metadata={"help": "The alpha parameter to control the noise magnitude in NEFTune."}
+    )
+    export_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the directory to save the exported model."}
+    )
+    plot_loss: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
     )
 
     def __post_init__(self):
-        if isinstance(self.lora_target, str): # support custom target modules/layers of LoRA
-            self.lora_target = [target.strip() for target in self.lora_target.split(",")]
+        def split_arg(arg):
+            if isinstance(arg, str):
+                return [item.strip() for item in arg.split(",")]
+            return arg
 
-        if self.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
-            trainable_layer_ids = [self.num_hidden_layers - k - 1 for k in range(self.num_layer_trainable)]
-        else: # fine-tuning the first n layers if num_layer_trainable < 0
-            trainable_layer_ids = [k for k in range(-self.num_layer_trainable)]
+        self.name_module_trainable = split_arg(self.name_module_trainable)
+        self.lora_alpha = self.lora_alpha or float(self.lora_rank * 2.0)
+        self.lora_target = split_arg(self.lora_target)
+        self.additional_target = split_arg(self.additional_target)
+        self.ref_model_checkpoint = split_arg(self.ref_model_checkpoint)
+        self.reward_model_checkpoint = split_arg(self.reward_model_checkpoint)
 
-        self.trainable_layers = ["{:d}.{}".format(idx, self.name_module_trainable) for idx in trainable_layer_ids]
+        assert self.finetuning_type in ["lora", "freeze", "full"], "Invalid fine-tuning method."
+        assert self.ref_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
+        assert self.reward_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
 
-        assert self.finetuning_type in ["none", "freeze", "lora", "full"], "Invalid fine-tuning method."
+        if self.stage == "ppo" and self.reward_model is None:
+            raise ValueError("Reward model is necessary for PPO training.")
+
+        if self.stage == "ppo" and self.reward_model_type == "lora" and self.finetuning_type != "lora":
+            raise ValueError("Lora reward model only supports lora training.")
 
     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

@@ -1,13 +0,0 @@
-from typing import Literal, Optional
-from dataclasses import dataclass, field
-
-
-@dataclass
-class GeneralArguments:
-    """
-    Arguments pertaining to which techniques we are going to fine-tuning with.
-    """
-    stage: Optional[Literal["pt", "sft", "rm", "ppo"]] = 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(
@@ -28,7 +28,7 @@ class GeneratingArguments:
         metadata={"help": "Number of beams for beam search. 1 means no beam search."}
     )
     max_length: Optional[int] = field(
-        default=None,
+        default=512,
         metadata={"help": "The maximum length the generated tokens can have. It can be overridden by max_new_tokens."}
     )
     max_new_tokens: Optional[int] = field(
@@ -46,6 +46,8 @@ class GeneratingArguments:
 
     def to_dict(self) -> Dict[str, Any]:
         args = asdict(self)
-        if args.get("max_new_tokens", None):
+        if args.get("max_new_tokens", -1) > 0:
             args.pop("max_length", None)
+        else:
+            args.pop("max_new_tokens", None)
         return args

src/llmtuner/hparams/model_args.py

@@ -1,11 +1,10 @@
-import torch
-from typing import Literal, Optional
-from dataclasses import dataclass, field
+from typing import Any, Dict, Literal, Optional
+from dataclasses import asdict, 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(
@@ -16,21 +15,17 @@ class ModelArguments:
         metadata={"help": "Where to store the pretrained models downloaded from huggingface.co."}
     )
     use_fast_tokenizer: Optional[bool] = field(
-        default=False,
+        default=True,
         metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}
     )
-    use_auth_token: Optional[bool] = field(
+    split_special_tokens: Optional[bool] = field(
         default=False,
-        metadata={"help": "Will use the token generated when running `huggingface-cli login`."}
+        metadata={"help": "Whether or not the special tokens should be split during the tokenization process."}
     )
     model_revision: Optional[str] = field(
         default="main",
         metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}
     )
-    padding_side: Optional[Literal["left", "right"]] = field(
-        default="left",
-        metadata={"help": "The side on which the model should have padding applied."}
-    )
     quantization_bit: Optional[int] = field(
         default=None,
         metadata={"help": "The number of bits to quantize the model."}
@@ -43,30 +38,38 @@ class ModelArguments:
         default=True,
         metadata={"help": "Whether to use double quantization in int4 training or not."}
     )
-    compute_dtype: Optional[torch.dtype] = field(
+    rope_scaling: Optional[Literal["linear", "dynamic"]] = field(
         default=None,
-        metadata={"help": "Used in quantization configs. Do not specify this argument manually."}
+        metadata={"help": "Adopt scaled rotary positional embeddings."}
     )
     checkpoint_dir: Optional[str] = field(
         default=None,
-        metadata={"help": "Path to the directory(s) containing the delta model checkpoints as well as the configurations."}
+        metadata={"help": "Path to the directory(s) containing the model checkpoints as well as the configurations."}
     )
-    reward_model: Optional[str] = field(
-        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(
+    flash_attn: Optional[bool] = field(
         default=False,
-        metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
+        metadata={"help": "Enable FlashAttention-2 for faster training."}
+    )
+    shift_attn: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."}
+    )
+    hf_hub_token: Optional[str] = field(
+        default=None,
+        metadata={"help": "Auth token to log in with Hugging Face Hub."}
     )
 
     def __post_init__(self):
+        self.compute_dtype = None
+        self.model_max_length = None
+
+        if self.split_special_tokens and self.use_fast_tokenizer:
+            raise ValueError("`split_special_tokens` is only supported for slow tokenizers.")
+
         if self.checkpoint_dir is not None: # support merging multiple lora weights
             self.checkpoint_dir = [cd.strip() for cd in self.checkpoint_dir.split(",")]
 
-        if self.quantization_bit is not None:
-            assert self.quantization_bit in [4, 8], "We only accept 4-bit or 8-bit quantization."
+        assert self.quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
+
+    def to_dict(self) -> Dict[str, Any]:
+        return asdict(self)

src/llmtuner/model/__init__.py

@@ -0,0 +1,5 @@
+# Level: loader > adapter > parser, utils
+
+from llmtuner.model.loader import load_model_and_tokenizer
+from llmtuner.model.parser import get_train_args, get_infer_args, get_eval_args
+from llmtuner.model.utils import dispatch_model, generate_model_card, load_valuehead_params

src/llmtuner/model/adapter.py

@@ -0,0 +1,103 @@
+import torch
+from typing import TYPE_CHECKING
+from peft import PeftModel, TaskType, LoraConfig, get_peft_model
+
+from llmtuner.extras.logging import get_logger
+from llmtuner.model.utils import find_all_linear_modules
+
+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",
+    is_trainable: bool
+) -> "PreTrainedModel":
+    r"""
+    Initializes the adapters.
+
+    Support full-parameter, freeze and LoRA training.
+
+    Note that the trainable parameters must be cast to float32.
+    """
+
+    if (not is_trainable) and model_args.checkpoint_dir is None:
+        logger.info("Checkpoint is not found at evaluation, load the original model.")
+        return model
+
+    if finetuning_args.finetuning_type == "full" and is_trainable:
+        logger.info("Fine-tuning method: Full")
+        model = model.float()
+
+    if finetuning_args.finetuning_type == "freeze" and is_trainable:
+        logger.info("Fine-tuning method: Freeze")
+        num_layers = (
+            getattr(model.config, "num_hidden_layers", None)
+            or getattr(model.config, "num_layers", None)
+            or getattr(model.config, "n_layer", None)
+        )
+        if not num_layers:
+            raise ValueError("Current model does not support freeze tuning.")
+        if finetuning_args.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
+            trainable_layer_ids = [num_layers - k - 1 for k in range(finetuning_args.num_layer_trainable)]
+        else: # fine-tuning the first n layers if num_layer_trainable < 0
+            trainable_layer_ids = [k for k in range(-finetuning_args.num_layer_trainable)]
+
+        trainable_layers = []
+        for module_name in finetuning_args.name_module_trainable:
+            for idx in trainable_layer_ids:
+                trainable_layers.append("{:d}.{}".format(idx, module_name))
+
+        for name, param in model.named_parameters():
+            if not any(trainable_layer in name for trainable_layer in trainable_layers):
+                param.requires_grad_(False)
+            else:
+                param.data = param.data.to(torch.float32)
+
+    if finetuning_args.finetuning_type == "lora":
+        logger.info("Fine-tuning method: LoRA")
+        checkpoint_to_resume = None
+
+        if model_args.checkpoint_dir is not None:
+            if is_trainable and finetuning_args.resume_lora_training:
+                checkpoints_to_merge, checkpoint_to_resume = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
+            else:
+                checkpoints_to_merge = model_args.checkpoint_dir
+
+            for checkpoint in checkpoints_to_merge:
+                model = PeftModel.from_pretrained(model, checkpoint)
+                model = model.merge_and_unload()
+
+            if len(checkpoints_to_merge) > 0:
+                logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge)))
+
+            if checkpoint_to_resume is not None: # resume lora training
+                model = PeftModel.from_pretrained(model, checkpoint_to_resume, is_trainable=is_trainable)
+
+        if is_trainable and checkpoint_to_resume is None: # create new lora weights while training
+            if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all":
+                target_modules = find_all_linear_modules(model, model_args.quantization_bit)
+            else:
+                target_modules = finetuning_args.lora_target
+
+            lora_config = LoraConfig(
+                task_type=TaskType.CAUSAL_LM,
+                inference_mode=False,
+                r=finetuning_args.lora_rank,
+                lora_alpha=finetuning_args.lora_alpha,
+                lora_dropout=finetuning_args.lora_dropout,
+                target_modules=target_modules,
+                modules_to_save=finetuning_args.additional_target
+            )
+            model = get_peft_model(model, lora_config)
+
+    if model_args.checkpoint_dir is not None:
+        logger.info("Loaded fine-tuned model from checkpoint(s): {}".format(",".join(model_args.checkpoint_dir)))
+
+    return model

src/llmtuner/model/loader.py

@@ -0,0 +1,226 @@
+import os
+import math
+import torch
+from types import MethodType
+from typing import TYPE_CHECKING, Literal, Optional, Tuple
+
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+    PretrainedConfig,
+    PreTrainedModel,
+    PreTrainedTokenizerBase
+)
+from transformers.models.llama import modeling_llama as LlamaModule
+from transformers.utils.versions import require_version
+from trl import AutoModelForCausalLMWithValueHead
+
+try:
+    from transformers.integrations import is_deepspeed_zero3_enabled
+except ImportError: # https://github.com/huggingface/transformers/releases/tag/v4.33.1
+    from transformers.deepspeed import is_deepspeed_zero3_enabled
+
+from llmtuner.extras.logging import reset_logging, get_logger
+from llmtuner.extras.misc import count_parameters, get_current_device, infer_optim_dtype
+from llmtuner.extras.packages import is_flash_attn2_available
+from llmtuner.extras.patches import llama_patch as LlamaPatches
+from llmtuner.hparams import FinetuningArguments
+from llmtuner.model.adapter import init_adapter
+from llmtuner.model.utils import load_valuehead_params, prepare_model_for_training
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer
+    from llmtuner.hparams import ModelArguments
+
+
+logger = get_logger(__name__)
+
+
+require_version("transformers>=4.31.0,<4.35.0", "To fix: pip install \"transformers>=4.31.0,<4.35.0\"")
+require_version("datasets>=2.14.0", "To fix: pip install datasets>=2.14.0")
+require_version("accelerate>=0.21.0", "To fix: pip install accelerate>=0.21.0")
+require_version("peft>=0.6.0", "To fix: pip install peft>=0.6.0")
+require_version("trl>=0.7.4", "To fix: pip install trl>=0.7.4")
+
+
+def load_model_and_tokenizer(
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
+    is_trainable: Optional[bool] = False,
+    stage: Optional[Literal["pt", "sft", "rm", "ppo"]] = "sft"
+) -> Tuple[PreTrainedModel, "PreTrainedTokenizer"]:
+    r"""
+    Loads pretrained model and tokenizer.
+
+    Support both training and inference.
+    """
+
+    config_kwargs = {
+        "trust_remote_code": True,
+        "cache_dir": model_args.cache_dir,
+        "revision": model_args.model_revision,
+        "token": model_args.hf_hub_token
+    }
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.model_name_or_path,
+        use_fast=model_args.use_fast_tokenizer,
+        split_special_tokens=model_args.split_special_tokens,
+        padding_side="right", # training with left-padded tensors in fp16 precision may cause overflow
+        **config_kwargs
+    )
+
+    if finetuning_args.finetuning_type != "lora" and model_args.checkpoint_dir is not None:
+        logger.info("Use `model_name_or_path` to specify the model trained with full/freeze method.")
+        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)
+
+    # Fix tokenizer (for ChatGLM2 and ChatGLM3)
+    if getattr(config, "model_type", None) == "chatglm":
+        tokenizer._pad = MethodType(PreTrainedTokenizerBase._pad, tokenizer)
+
+    # Set model dtype
+    if model_args.compute_dtype is None: # priority: bf16 > fp16 > fp32
+        model_args.compute_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))
+    setattr(config, "torch_dtype", model_args.compute_dtype)
+
+    # Fix config (for Qwen)
+    if getattr(config, "model_type", None) == "qwen":
+        for dtype_name, dtype in [("fp16", torch.float16), ("bf16", torch.bfloat16), ("fp32", torch.float32)]:
+            setattr(config, dtype_name, getattr(config, "torch_dtype", None) == dtype)
+
+    # Set RoPE scaling
+    if model_args.rope_scaling is not None:
+        if not hasattr(config, "rope_scaling"):
+            logger.warning("Current model does not support RoPE scaling.")
+        else:
+            if is_trainable:
+                if model_args.rope_scaling == "dynamic":
+                    logger.warning(
+                        "Dynamic NTK may not work well with fine-tuning. "
+                        "See: https://github.com/huggingface/transformers/pull/24653"
+                    )
+
+                current_max_length = getattr(config, "max_position_embeddings", None)
+                if current_max_length and model_args.model_max_length > current_max_length:
+                    scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length))
+                else:
+                    logger.warning("Input length is smaller than max length. Consider increase input length.")
+                    scaling_factor = 1.0
+            else:
+                scaling_factor = 2.0
+
+            setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor})
+            logger.info("Using {} scaling strategy and setting scaling factor to {}".format(
+                model_args.rope_scaling, scaling_factor
+            ))
+
+    # Set FlashAttention-2
+    if model_args.flash_attn:
+        if getattr(config, "model_type", None) == "llama":
+            if is_flash_attn2_available():
+                LlamaModule.LlamaAttention = LlamaPatches.LlamaFlashAttention2
+                LlamaModule.LlamaModel._prepare_decoder_attention_mask = LlamaPatches._prepare_decoder_attention_mask
+                logger.info("Using FlashAttention-2 for faster training and inference.")
+            else:
+                logger.warning("FlashAttention-2 is not installed.")
+        elif getattr(config, "model_type", None) in ["qwen", "Yi"]:
+            logger.info("Current model automatically enables FlashAttention if installed.")
+        else:
+            logger.warning("Current model does not support FlashAttention.")
+    elif is_trainable and model_args.shift_attn and getattr(config, "model_type", None) == "llama":
+        LlamaModule.LlamaAttention = LlamaPatches.LlamaShiftShortAttention
+        logger.warning("Using `--flash_attn` for faster training in large context length.")
+
+    # Set shift short attention (S^2-Attn)
+    if is_trainable and model_args.shift_attn:
+        if getattr(config, "model_type", None) == "llama":
+            setattr(config, "group_size_ratio", 0.25)
+            logger.info("Using shift short attention with group_size_ratio=1/4.")
+        else:
+            logger.warning("Current model does not support shift short attention.")
+
+    # Quantization configurations (using bitsandbytes library)
+    if model_args.quantization_bit is not None:
+        if is_deepspeed_zero3_enabled():
+            raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.")
+
+        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)
+
+        if model_args.quantization_bit == 4:
+            require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
+            config_kwargs["load_in_4bit"] = True
+            config_kwargs["quantization_config"] = BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_compute_dtype=model_args.compute_dtype,
+                bnb_4bit_use_double_quant=model_args.double_quantization,
+                bnb_4bit_quant_type=model_args.quantization_type
+            )
+
+        config_kwargs["device_map"] = {"": get_current_device()}
+        logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
+
+    # Load pre-trained models (without valuehead)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_to_load,
+        config=config,
+        torch_dtype=model_args.compute_dtype,
+        low_cpu_mem_usage=(not is_deepspeed_zero3_enabled()),
+        **config_kwargs
+    )
+
+    # Disable custom generate method (for Qwen and Baichuan2)
+    if isinstance(model, PreTrainedModel) and "GenerationMixin" not in str(model.generate.__func__):
+        model.generate = MethodType(PreTrainedModel.generate, model)
+
+    # Fix LM head (for ChatGLM2 and ChatGLM3)
+    if getattr(config, "model_type", None) == "chatglm":
+        setattr(model, "lm_head", model.transformer.output_layer)
+        setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"])
+
+    # Register auto class to save the custom code files
+    if isinstance(config, PretrainedConfig) and "AutoConfig" in getattr(config, "auto_map", {}):
+        config.__class__.register_for_auto_class()
+    if isinstance(model, PreTrainedModel) and "AutoModelForCausalLM" in getattr(config, "auto_map", {}):
+        model.__class__.register_for_auto_class()
+    if isinstance(tokenizer, PreTrainedTokenizerBase) and "AutoTokenizer" in tokenizer.init_kwargs.get("auto_map", {}):
+        tokenizer.__class__.register_for_auto_class()
+
+    # Initialize adapters
+    model = prepare_model_for_training(model=model, finetuning_args=finetuning_args) if is_trainable else model
+    model = init_adapter(model, model_args, finetuning_args, is_trainable)
+    model = model.train() if is_trainable else model.eval()
+
+    # Prepare model with valuehead for RLHF
+    if stage in ["rm", "ppo"]:
+        model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(model)
+        vhead_path = (
+            model_args.checkpoint_dir[-1] if model_args.checkpoint_dir is not None else model_args.model_name_or_path
+        )
+        vhead_params = load_valuehead_params(vhead_path, model_args)
+        if vhead_params is not None:
+            model.load_state_dict(vhead_params, strict=False)
+            logger.info("Loaded valuehead from checkpoint: {}".format(vhead_path))
+
+    # Prepare model for inference
+    if not is_trainable:
+        model.requires_grad_(False) # fix all model params
+        model = model.to(model_args.compute_dtype) if model_args.quantization_bit is None else 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
+    ))
+
+    if not is_trainable:
+        logger.info("This IS expected that the trainable params is 0 if you are using model for inference only.")
+
+    return model, tokenizer

src/llmtuner/model/parser.py

@@ -0,0 +1,210 @@
+import os
+import torch
+import datasets
+import transformers
+from typing import Any, Dict, Optional, Tuple
+from transformers import HfArgumentParser, Seq2SeqTrainingArguments
+from transformers.trainer_utils import get_last_checkpoint
+
+from llmtuner.extras.logging import get_logger
+from llmtuner.extras.misc import parse_args
+from llmtuner.hparams import (
+    ModelArguments,
+    DataArguments,
+    EvaluationArguments,
+    FinetuningArguments,
+    GeneratingArguments
+)
+
+
+logger = get_logger(__name__)
+
+
+_TRAIN_ARGS = [
+    ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments
+]
+_TRAIN_CLS = Tuple[
+    ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments
+]
+_INFER_ARGS = [
+    ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
+]
+_INFER_CLS = Tuple[
+    ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
+]
+_EVAL_ARGS = [
+    ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments
+]
+_EVAL_CLS = Tuple[
+    ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments
+]
+
+
+def parse_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
+    parser = HfArgumentParser(_TRAIN_ARGS)
+    return parse_args(parser, args)
+
+
+def parse_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
+    parser = HfArgumentParser(_INFER_ARGS)
+    return parse_args(parser, args)
+
+
+def parse_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
+    parser = HfArgumentParser(_EVAL_ARGS)
+    return parse_args(parser, args)
+
+
+def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
+    model_args, data_args, training_args, finetuning_args, generating_args = parse_train_args(args)
+
+    # Setup logging
+    if training_args.should_log:
+        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
+        transformers.utils.logging.set_verbosity_info()
+
+    log_level = training_args.get_process_log_level()
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # Check arguments
+    data_args.init_for_training(training_args.seed)
+
+    if finetuning_args.stage != "pt" and data_args.template is None:
+        raise ValueError("Please specify which `template` to use.")
+
+    if finetuning_args.stage != "sft" and training_args.predict_with_generate:
+        raise ValueError("`predict_with_generate` cannot be set as True except SFT.")
+
+    if finetuning_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.")
+
+    if finetuning_args.stage in ["rm", "ppo"]:
+        if training_args.resume_from_checkpoint is not None:
+            raise ValueError("RM and PPO stages do not support `resume_from_checkpoint`.")
+        if training_args.load_best_model_at_end:
+            raise ValueError("RM and PPO stages do not support `load_best_model_at_end`.")
+
+    if finetuning_args.stage == "ppo" and not training_args.do_train:
+        raise ValueError("PPO training does not support evaluation, use the SFT stage to evaluate models.")
+
+    if finetuning_args.stage in ["rm", "dpo"]:
+        for dataset_attr in data_args.dataset_list:
+            if not dataset_attr.ranking:
+                raise ValueError("Please use ranked datasets for reward modeling or DPO training.")
+
+    if finetuning_args.stage == "ppo" and model_args.shift_attn:
+        raise ValueError("PPO training is incompatible with S^2-Attn.")
+
+    if training_args.max_steps == -1 and data_args.streaming:
+        raise ValueError("Please specify `max_steps` in streaming mode.")
+
+    if training_args.do_train and training_args.predict_with_generate:
+        raise ValueError("`predict_with_generate` cannot be set as True while training.")
+
+    if training_args.do_train and finetuning_args.finetuning_type == "lora" and finetuning_args.lora_target is None:
+        raise ValueError("Please specify `lora_target` in LoRA training.")
+
+    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
+        and len(model_args.checkpoint_dir) != 1
+        and finetuning_args.finetuning_type != "lora"
+    ):
+        raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
+
+    if training_args.do_train and model_args.quantization_bit is not None and (not finetuning_args.upcast_layernorm):
+        logger.warning("We recommend enable `upcast_layernorm` in quantized training.")
+
+    if training_args.do_train and (not training_args.fp16) and (not training_args.bf16):
+        logger.warning("We recommend enable mixed precision training.")
+
+    if (not training_args.do_train) and model_args.quantization_bit is not None:
+        logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
+
+    if (not training_args.do_train) and finetuning_args.stage == "dpo" and finetuning_args.ref_model is None:
+        logger.warning("Specify `ref_model` for computing rewards at evaluation.")
+
+    # postprocess training_args
+    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_dict = training_args.to_dict()
+        training_args_dict.update(dict(ddp_find_unused_parameters=False))
+        training_args = Seq2SeqTrainingArguments(**training_args_dict)
+
+    if (
+        training_args.resume_from_checkpoint is None
+        and training_args.do_train
+        and os.path.isdir(training_args.output_dir)
+        and not training_args.overwrite_output_dir
+    ):
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError("Output directory already exists and is not empty. Please set `overwrite_output_dir`.")
+
+        if last_checkpoint is not None:
+            training_args_dict = training_args.to_dict()
+            training_args_dict.update(dict(resume_from_checkpoint=last_checkpoint))
+            training_args = Seq2SeqTrainingArguments(**training_args_dict)
+            logger.info(
+                "Resuming from checkpoint. Change `output_dir` or use `overwrite_output_dir` to avoid."
+            )
+
+    # postprocess model_args
+    model_args.compute_dtype = (
+        torch.bfloat16 if training_args.bf16 else (torch.float16 if training_args.fp16 else None)
+    )
+    model_args.model_max_length = data_args.cutoff_len
+
+    # Log on each process the small summary:
+    logger.info("Process rank: {}, device: {}, n_gpu: {}\n  distributed training: {}, compute dtype: {}".format(
+        training_args.local_rank, training_args.device, training_args.n_gpu,
+        bool(training_args.local_rank != -1), str(model_args.compute_dtype)
+    ))
+    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, generating_args
+
+
+def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
+    model_args, data_args, finetuning_args, generating_args = parse_infer_args(args)
+
+    if data_args.template is None:
+        raise ValueError("Please specify which `template` to use.")
+
+    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
+        and len(model_args.checkpoint_dir) != 1
+        and finetuning_args.finetuning_type != "lora"
+    ):
+        raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
+
+    return model_args, data_args, finetuning_args, generating_args
+
+
+def get_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
+    model_args, data_args, eval_args, finetuning_args = parse_eval_args(args)
+
+    if data_args.template is None:
+        raise ValueError("Please specify which `template` to use.")
+
+    if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
+        raise ValueError("Quantization is only compatible with the LoRA method.")
+
+    transformers.set_seed(eval_args.seed)
+
+    return model_args, data_args, eval_args, finetuning_args

src/llmtuner/model/utils.py

@@ -0,0 +1,165 @@
+import torch
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple
+
+from transformers.utils import cached_file
+from transformers.trainer import WEIGHTS_NAME, SAFE_WEIGHTS_NAME
+
+from llmtuner.extras.constants import LAYERNORM_NAMES
+from llmtuner.extras.logging import get_logger
+from llmtuner.hparams import ModelArguments, FinetuningArguments
+
+if TYPE_CHECKING:
+    from transformers.modeling_utils import PreTrainedModel
+    from llmtuner.hparams import DataArguments
+
+
+logger = get_logger(__name__)
+
+
+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 getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False): # do nothing
+        return model
+
+    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()
+
+
+def find_all_linear_modules(
+    model: "PreTrainedModel",
+    quantization_bit: Optional[int] = None
+) -> List[str]:
+    r"""
+    Finds all available modules to apply lora.
+    """
+    if quantization_bit is not None:
+        import bitsandbytes as bnb
+        linear_cls = bnb.nn.Linear4bit if quantization_bit == 4 else bnb.nn.Linear8bitLt
+    else:
+        linear_cls = torch.nn.Linear
+
+    output_layer_names = ["lm_head"]
+    if model.config.model_type == "chatglm":
+        output_layer_names.append("output_layer")
+
+    module_names = set()
+    for name, module in model.named_modules():
+        if (
+            isinstance(module, linear_cls)
+            and not any([output_layer in name for output_layer in output_layer_names])
+        ):
+            module_names.add(name.split(".")[-1])
+
+    logger.info("Found linear modules: {}".format(",".join(module_names)))
+    return list(module_names)
+
+
+def generate_model_card(
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    finetuning_args: "FinetuningArguments"
+) -> Dict[str, Any]:
+    return {
+        "tasks": "text-generation",
+        "finetuned_from": model_args.model_name_or_path,
+        "dataset": [dataset.strip() for dataset in data_args.dataset.split(",")],
+        "tags": ["llama-factory"] + (["lora"] if finetuning_args.finetuning_type == "lora" else [])
+    }
+
+
+def load_valuehead_params(
+    path_or_repo_id: str,
+    model_args: "ModelArguments"
+) -> Dict[str, torch.Tensor]:
+    r"""
+    Loads value head parameters from Hugging Face Hub or local disk.
+
+    Returns: dict with keys `v_head.summary.weight` and `v_head.summary.bias`.
+    """
+    kwargs = {
+        "path_or_repo_id": path_or_repo_id,
+        "cache_dir": model_args.cache_dir,
+        "token": model_args.hf_hub_token
+    }
+    try:
+        vhead_file = cached_file(filename=WEIGHTS_NAME, **kwargs)
+    except:
+        try:
+            vhead_file = cached_file(filename=SAFE_WEIGHTS_NAME, **kwargs)
+        except:
+            logger.warning("Provided path ({}) does not contain valuehead weights.".format(path_or_repo_id))
+            return None
+
+    return torch.load(vhead_file, map_location="cpu")
+
+
+def prepare_model_for_training(
+    model: "PreTrainedModel",
+    finetuning_args: "FinetuningArguments",
+    output_layer_name: Optional[str] = "lm_head",
+    use_gradient_checkpointing: Optional[bool] = True,
+    layernorm_names: Optional[Set[str]] = LAYERNORM_NAMES
+) -> "PreTrainedModel":
+    r"""
+    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/v0.2.0/src/peft/utils/other.py#L33
+    """
+    if finetuning_args.upcast_layernorm:
+        for name, param in model.named_parameters():
+            if param.ndim == 1 and any(ln_name in name for ln_name in layernorm_names):
+                param.data = param.data.to(torch.float32)
+        logger.info("Upcasting weights in layernorm in float32.")
+
+    if finetuning_args.neft_alpha > 1e-6:
+        def neftune_forward_hook(module: torch.nn.Module, args: Tuple[torch.Tensor], output: torch.Tensor):
+            if module.training:
+                dims = torch.tensor(output.size(1) * output.size(2))
+                mag_norm = finetuning_args.neft_alpha / torch.sqrt(dims)
+                output = output + torch.zeros_like(output).uniform_(-mag_norm, mag_norm)
+            return output
+
+        model.get_input_embeddings().register_forward_hook(neftune_forward_hook)
+        logger.info("Using noisy embedding with alpha={:.2f}".format(finetuning_args.neft_alpha))
+
+    if use_gradient_checkpointing:
+        if hasattr(model, "enable_input_require_grads"):
+            model.enable_input_require_grads()
+        else:
+            def make_inputs_require_grad(module: torch.nn.Module, args: Tuple[torch.Tensor], output: torch.Tensor):
+                output.requires_grad_(True)
+            model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+        model.gradient_checkpointing_enable()
+        model.config.use_cache = False # turn off when gradient checkpointing is enabled
+        logger.info("Gradient checkpointing enabled.")
+
+    if finetuning_args.finetuning_type != "full" and hasattr(model, output_layer_name):
+        output_layer = getattr(model, output_layer_name)
+        if isinstance(output_layer, torch.nn.Linear):
+            def fp32_forward_pre_hook(module: torch.nn.Module, args: Tuple[torch.Tensor]):
+                return args[0].to(output_layer.weight.dtype)
+            def fp32_forward_post_hook(module: torch.nn.Module, args: Tuple[torch.Tensor], output: torch.Tensor):
+                return output.to(torch.float32)
+            output_layer.register_forward_pre_hook(fp32_forward_pre_hook)
+            output_layer.register_forward_hook(fp32_forward_post_hook)
+
+    return model

src/llmtuner/train/__init__.py

@@ -0,0 +1 @@
+from llmtuner.train.tuner import export_model, run_exp

src/llmtuner/train/dpo/__init__.py

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

src/llmtuner/train/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, 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/train/dpo/trainer.py

@@ -0,0 +1,75 @@
+import torch
+from collections import defaultdict
+from typing import TYPE_CHECKING, Dict, Literal, Optional, Tuple, Union
+from transformers import BatchEncoding, Trainer
+from trl import DPOTrainer
+from trl.trainer.utils import disable_dropout_in_model
+
+from llmtuner.extras.constants import IGNORE_INDEX
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel
+
+
+class CustomDPOTrainer(DPOTrainer):
+
+    def __init__(
+        self,
+        beta: float,
+        model: Union["PreTrainedModel", torch.nn.Module],
+        ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]] = None,
+        disable_dropout: Optional[bool] = True,
+        loss_type: Optional[Literal["sigmoid", "hinge"]] = "sigmoid",
+        **kwargs
+    ):
+        if disable_dropout:
+            disable_dropout_in_model(model)
+            if ref_model is not None:
+                disable_dropout_in_model(ref_model)
+
+        self.is_encoder_decoder = model.config.is_encoder_decoder
+        self.ref_model = ref_model
+        self.use_dpo_data_collator = True # hack to avoid warning
+        self.generate_during_eval = False # disable at evaluation
+        self.label_pad_token_id = IGNORE_INDEX
+        self.padding_value = 0
+        self.beta = beta
+        self.loss_type = loss_type
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+
+        Trainer.__init__(self, model=model, **kwargs)
+        if not hasattr(self, "accelerator"):
+            raise AttributeError("Please update `transformers`.")
+
+        if ref_model is not None:
+            if self.is_deepspeed_enabled:
+                if not (
+                    getattr(ref_model, "is_loaded_in_8bit", False)
+                    or getattr(ref_model, "is_loaded_in_4bit", False)
+                ): # quantized models are already set on the correct device
+                    self.ref_model = self._prepare_deepspeed(self.ref_model)
+            else:
+                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
+
+    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]:
+        batch_copied = BatchEncoding({k: v.detach().clone() for k, v in batch.items()}) # avoid error
+
+        all_logits = model(
+            input_ids=batch_copied["input_ids"],
+            attention_mask=batch_copied["attention_mask"],
+            return_dict=True
+        ).logits.to(torch.float32)
+
+        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/train/dpo/workflow.py

@@ -0,0 +1,85 @@
+# Inspired by: https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py
+
+from peft import PeftModel
+from typing import TYPE_CHECKING, Optional, List
+from transformers import Seq2SeqTrainingArguments
+
+from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
+from llmtuner.extras.constants import IGNORE_INDEX
+from llmtuner.extras.ploting import plot_loss
+from llmtuner.hparams import ModelArguments
+from llmtuner.model import generate_model_card, load_model_and_tokenizer
+from llmtuner.train.utils import create_ref_model
+from llmtuner.train.dpo.collator import DPODataCollatorWithPadding
+from llmtuner.train.dpo.trainer import CustomDPOTrainer
+
+if TYPE_CHECKING:
+    from transformers import TrainerCallback
+    from llmtuner.hparams import DataArguments, FinetuningArguments
+
+
+def run_dpo(
+    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="sft")
+    dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="rm")
+    data_collator = DPODataCollatorWithPadding(
+        tokenizer=tokenizer,
+        pad_to_multiple_of=4,
+        label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
+    )
+
+    # Create reference model
+    if finetuning_args.ref_model is None and (not training_args.do_train): # use the model itself
+        ref_model = model
+    else:
+        ref_model = create_ref_model(model_args, finetuning_args, stage="dpo")
+
+    # Update arguments
+    training_args_dict = training_args.to_dict()
+    training_args_dict.update(dict(remove_unused_columns=False)) # important for pairwise dataset
+    training_args = Seq2SeqTrainingArguments(**training_args_dict)
+
+    # Initialize our Trainer
+    trainer = CustomDPOTrainer(
+        beta=finetuning_args.dpo_beta,
+        model=model,
+        ref_model=ref_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(resume_from_checkpoint=training_args.resume_from_checkpoint)
+        trainer.save_model()
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
+            plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
+
+    # Evaluation
+    if training_args.do_eval:
+        metrics = trainer.evaluate(metric_key_prefix="eval")
+        if id(model) == id(ref_model): # unable to compute rewards without a reference model
+            remove_keys = [key for key in metrics.keys() if "rewards" in key]
+            for key in remove_keys:
+                metrics.pop(key)
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Create model card
+    if training_args.do_train:
+        if training_args.push_to_hub:
+            trainer.push_to_hub(**generate_model_card(model_args, data_args, finetuning_args))
+        else:
+            trainer.create_model_card(**generate_model_card(model_args, data_args, finetuning_args))

src/llmtuner/train/ppo/__init__.py

@@ -0,0 +1 @@
+from llmtuner.train.ppo.workflow import run_ppo

src/llmtuner/train/ppo/trainer.py

@@ -0,0 +1,338 @@
+import os
+import sys
+import math
+import torch
+from tqdm import tqdm
+from typing import TYPE_CHECKING, List, Optional, Tuple
+
+from transformers import BatchEncoding, GenerationConfig, Trainer, TrainerState, TrainerControl
+from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
+
+from trl import PPOTrainer
+from trl.core import PPODecorators, logprobs_from_logits
+
+from llmtuner.extras.callbacks import LogCallback, SavePeftModelCallback
+from llmtuner.extras.logging import get_logger
+from llmtuner.extras.misc import AverageMeter, count_parameters, get_logits_processor
+from llmtuner.train.ppo.utils import dump_layernorm, restore_layernorm, replace_model
+
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+    from trl import AutoModelForCausalLMWithValueHead
+    from llmtuner.hparams import ModelArguments, FinetuningArguments, GeneratingArguments
+
+
+logger = get_logger(__name__)
+
+
+class CustomPPOTrainer(PPOTrainer, Trainer):
+    r"""
+    Inherits PPOTrainer.
+    """
+
+    def __init__(
+        self,
+        model_args: "ModelArguments",
+        training_args: "Seq2SeqTrainingArguments",
+        finetuning_args: "FinetuningArguments",
+        generating_args: "GeneratingArguments",
+        callbacks: List["TrainerCallback"],
+        reward_model: "AutoModelForCausalLMWithValueHead",
+        **kwargs
+    ):
+        PPOTrainer.__init__(self, **kwargs)
+
+        self.args = training_args
+        self.model_args = model_args
+        self.finetuning_args = finetuning_args
+        self.reward_model = reward_model
+
+        self.generation_config = GenerationConfig(
+            pad_token_id=self.tokenizer.pad_token_id,
+            eos_token_id=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
+            **generating_args.to_dict()
+        )
+
+        self.state = TrainerState()
+        self.control = TrainerControl()
+        self.log_callback, self.save_callback = callbacks[0], callbacks[1]
+        assert isinstance(self.log_callback, LogCallback) and isinstance(self.save_callback, SavePeftModelCallback)
+
+        if self.args.max_steps > 0:
+            logger.info("max_steps is given, it will override any value given in num_train_epochs")
+
+        if reward_model is not None:
+            is_deepspeed_enabled = self.accelerator.distributed_type == "DEEPSPEED" and hasattr(
+                self.accelerator.state, "deepspeed_plugin"
+            )
+            if is_deepspeed_enabled:
+                if not (
+                    getattr(reward_model.pretrained_model, "is_loaded_in_8bit", False)
+                    or getattr(reward_model.pretrained_model, "is_loaded_in_4bit", False)
+                ): # quantized models are already set on the correct device
+                    self.reward_model = self._prepare_deepspeed(self.reward_model)
+            else:
+                self.reward_model = self.accelerator.prepare_model(self.reward_model, evaluation_mode=True)
+
+    def ppo_train(self) -> None:
+        r"""
+        Implements training loop for the PPO stage, like _inner_training_loop() in Huggingface's Trainer.
+        """
+        total_train_batch_size = (
+            self.args.per_device_train_batch_size * self.args.gradient_accumulation_steps * self.args.world_size
+        )
+        if self.args.max_steps > 0:
+            num_examples = total_train_batch_size * self.args.max_steps
+            num_train_epochs = sys.maxsize
+            max_steps = self.args.max_steps
+            steps_in_epoch = self.args.max_steps * self.args.gradient_accumulation_steps
+        else:
+            len_dataloader = len(self.dataloader)
+            num_examples = len(self.dataset)
+            num_train_epochs = self.args.num_train_epochs
+            max_steps = math.ceil(num_train_epochs * len_dataloader)
+            steps_in_epoch = len_dataloader
+
+        self.state.max_steps = max_steps
+        self.state.num_train_epochs = num_train_epochs
+        self.state.is_local_process_zero = self.is_local_process_zero()
+        self.state.is_world_process_zero = self.is_world_process_zero()
+
+        if self.is_world_process_zero():
+            logger.info("***** Running training *****")
+            logger.info(f"  Num examples = {num_examples}")
+            logger.info(f"  Num Epochs = {num_train_epochs}")
+            logger.info(f"  Instantaneous batch size per device = {self.args.per_device_train_batch_size}")
+            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 = {count_parameters(self.model)[0]}")
+
+        unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model)
+        dataiter = iter(self.dataloader)
+        loss_meter = AverageMeter()
+        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_local_process_zero()):
+            try:
+                batch = next(dataiter)
+            except StopIteration:
+                dataiter = iter(self.dataloader)
+                batch = next(dataiter)
+
+            # Cast to inference mode
+            unwrapped_model.gradient_checkpointing_disable()
+            unwrapped_model.config.use_cache = True
+            self.model.eval()
+
+            # Get inputs
+            self.tokenizer.padding_side = "right" # change padding side
+            queries, responses, rewards = [], [], []
+            for idx in range(0, self.config.batch_size, self.config.mini_batch_size):
+                mini_batch_queries, mini_batch_responses = self.get_inputs(batch[idx:idx+self.config.mini_batch_size])
+                mini_batch_rewards = self.get_rewards(mini_batch_queries, mini_batch_responses, unwrapped_model)
+                queries.extend(mini_batch_queries)
+                responses.extend(mini_batch_responses)
+                rewards.extend(mini_batch_rewards)
+
+            # Cast to training mode
+            unwrapped_model.gradient_checkpointing_enable()
+            unwrapped_model.config.use_cache = False
+            self.model.train()
+
+            # Run PPO step
+            stats = self.step(queries, responses, rewards)
+            self.tokenizer.padding_side = "left" # restore padding side
+            loss_meter.update(float(stats["ppo/loss/total"]), n=len(rewards))
+            reward_meter.update(torch.stack(rewards).mean().item(), n=len(rewards))
+
+            if self.config.log_with is not None:
+                try:
+                    batch["query"] = self.tokenizer.batch_decode(queries, skip_special_tokens=True)
+                    batch["response"] = self.tokenizer.batch_decode(responses, skip_special_tokens=True)
+                    self.log_stats(stats, batch, rewards)
+                except:
+                    logger.warning("Failed to save stats due to unknown errors.")
+
+            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 / steps_in_epoch, 2)
+                )
+                tqdm.write(str(logs))
+                logs["step"] = step
+                self.state.log_history.append(logs)
+                self.log_callback.on_log(self.args, self.state, self.control)
+                loss_meter.reset()
+                reward_meter.reset()
+
+            if (step+1) % self.args.save_steps == 0: # save checkpoint
+                self.save_model(os.path.join(
+                    self.args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, self.state.global_step)
+                ))
+                self.save_callback.on_save(
+                    self.args, self.state, self.control, model=self.accelerator.unwrap_model(self.model)
+                )
+
+            if self.control.should_epoch_stop or self.control.should_training_stop:
+                break
+
+        self.log_callback.on_train_end(self.args, self.state, self.control)
+        self.save_callback.on_train_end(
+            self.args, self.state, self.control, model=self.accelerator.unwrap_model(self.model)
+        )
+
+    @torch.no_grad()
+    def get_inputs(self, batch: BatchEncoding) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
+        r"""
+        Generates model's responses given queries.
+        """
+        if self.finetuning_args.upcast_layernorm:
+            layernorm_params = dump_layernorm(self.model)
+
+        unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model)
+        response: torch.Tensor = unwrapped_model.generate(
+            generation_config=self.generation_config,
+            logits_processor=get_logits_processor(),
+            **batch
+        )
+
+        if self.finetuning_args.upcast_layernorm:
+            restore_layernorm(self.model, layernorm_params)
+
+        query, response = batch["input_ids"].detach().cpu(), response[:, batch["input_ids"].size(-1):].detach().cpu()
+        queries, responses = [], []
+        for i in range(len(query)):
+            query_length = (query[i] != self.tokenizer.pad_token_id).nonzero()[0].item()
+            response_index = (response[i] != self.tokenizer.pad_token_id).nonzero()
+
+            if len(response_index) == 0:
+                response_length = 1 # allow empty response
+            else:
+                response_length = response_index[-1].item() + 1
+
+            queries.append(query[i, query_length:]) # remove padding from left
+            responses.append(response[i, :response_length]) # remove padding from right
+
+        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.
+        """
+        if self.reward_model is None:
+            replace_model(unwrapped_model, target="reward")
+
+        batch = self.prepare_model_inputs(queries, responses)
+
+        with torch.cuda.amp.autocast(dtype=self.model_args.compute_dtype): # support bf16
+            reward_model = self.reward_model if self.reward_model is not None else self.model
+            _, _, values = reward_model(**batch, output_hidden_states=True, return_dict=True)
+
+        if values.size(0) != batch["input_ids"].size(0): # adapt to chatglm2
+            values = torch.transpose(values, 0, 1)
+
+        rewards = []
+        for i in range(values.size(0)):
+            end_indexes = (batch["input_ids"][i] != self.tokenizer.pad_token_id).nonzero()
+            end_index = end_indexes[-1].item() if len(end_indexes) else 0
+            rewards.append(values[i, end_index].float().detach().cpu()) # use fp32 type
+
+        if self.reward_model is None:
+            replace_model(unwrapped_model, target="default")
+
+        return rewards
+
+    @PPODecorators.empty_device_cache()
+    def batched_forward_pass(
+        self,
+        model: "AutoModelForCausalLMWithValueHead",
+        queries: torch.Tensor,
+        responses: torch.Tensor,
+        model_inputs: dict,
+        return_logits: Optional[bool] = False,
+        response_masks: Optional[torch.Tensor] = None
+    ):
+        r"""
+        Calculates model outputs in multiple batches.
+
+        Subclass and override to inject custom behavior.
+        """
+        bs = len(queries)
+        fbs = self.config.mini_batch_size
+        all_logprobs = []
+        all_logits = []
+        all_masks = []
+        all_values = []
+
+        for i in range(math.ceil(bs / fbs)):
+            input_kwargs = {key: value[i * fbs : (i + 1) * fbs] for key, value in model_inputs.items()}
+            query_batch = queries[i * fbs : (i + 1) * fbs]
+            response_batch = responses[i * fbs : (i + 1) * fbs]
+            if response_masks is not None:
+                response_masks_batch = response_masks[i * fbs : (i + 1) * fbs]
+            input_ids = input_kwargs["input_ids"]
+            attention_mask = input_kwargs["attention_mask"]
+
+            with torch.cuda.amp.autocast(dtype=self.model_args.compute_dtype): # support bf16
+                logits, _, values = model(**input_kwargs)
+
+            if values.size(0) != input_ids.size(0): # adapt to chatglm2
+                values = torch.transpose(values, 0, 1)
+
+            logprobs = logprobs_from_logits(logits[:, :-1, :], input_ids[:, 1:])
+            masks = torch.zeros_like(attention_mask)
+            masks[:, :-1] = attention_mask[:, 1:]
+
+            for j in range(len(query_batch)):
+                start = len(query_batch[j]) - 1
+                if attention_mask[j, 0] == 0: # offset left padding
+                    start += attention_mask[j, :].nonzero()[0].item()
+                end = start + len(response_batch[j])
+
+                if response_masks is not None:
+                    response_masks_batch = torch.cat(
+                        (torch.zeros_like(query_batch[j]), response_masks_batch[j])
+                    )[1:]
+
+                masks[j, :start] = 0
+                masks[j, end:] = 0
+                if response_masks is not None:
+                    masks[j, start:end] = masks[j, start:end] * response_masks_batch[j][start:end]
+
+            if return_logits:
+                all_logits.append(logits)
+            else:
+                del logits
+
+            all_values.append(values)
+            all_logprobs.append(logprobs)
+            all_masks.append(masks)
+
+        return (
+            torch.cat(all_logprobs),
+            torch.cat(all_logits)[:, :-1] if return_logits else None,
+            torch.cat(all_values)[:, :-1],
+            torch.cat(all_masks)[:, :-1],
+        )
+
+    def save_model(self, output_dir: Optional[str] = None) -> None:
+        r"""
+        Saves model checkpoint.
+
+        Subclass and override to inject custom behavior.
+        """
+        if self.args.should_save:
+            self._save(output_dir)

src/llmtuner/train/ppo/utils.py

@@ -0,0 +1,35 @@
+import torch
+from typing import TYPE_CHECKING, Dict, Literal, Optional
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel
+    from trl import AutoModelForCausalLMWithValueHead
+
+
+def replace_model(model: "AutoModelForCausalLMWithValueHead", target: Literal["default", "reward"]) -> None:
+    if target == "reward": # save default head temporarily
+        valuehead_state_dict: Dict[str, torch.Tensor] = model.v_head.state_dict()
+        setattr(model, "default_head_weight", valuehead_state_dict["summary.weight"].detach().clone())
+        setattr(model, "default_head_bias", valuehead_state_dict["summary.bias"].detach().clone())
+
+    model.pretrained_model.set_adapter(target) # set the LoRA adapter to be active
+    model.v_head.load_state_dict({
+        "summary.weight": model.get_buffer("{}_head_weight".format(target)).detach().clone(),
+        "summary.bias": model.get_buffer("{}_head_bias".format(target)).detach().clone()
+    })
+
+
+def dump_layernorm(model: "PreTrainedModel") -> Dict[str, torch.Tensor]:
+    layer_norm_params = {}
+    for name, param in model.named_parameters():
+        if param.data.dtype == torch.float32:
+            layer_norm_params[name] = param.data.detach().clone()
+            param.data = param.data.to(model.config.torch_dtype)
+
+    return layer_norm_params
+
+
+def restore_layernorm(model: "PreTrainedModel", layernorm_params: Optional[Dict[str, torch.Tensor]] = None) -> None:
+    for name, param in model.named_parameters():
+        if name in layernorm_params:
+            param.data = layernorm_params[name]

src/llmtuner/train/ppo/workflow.py

@@ -0,0 +1,101 @@
+# 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 TYPE_CHECKING, Optional, List
+from transformers import DataCollatorWithPadding
+from transformers.optimization import get_scheduler
+
+from llmtuner.data import get_dataset, preprocess_dataset
+from llmtuner.extras.callbacks import SavePeftModelCallback
+from llmtuner.extras.ploting import plot_loss
+from llmtuner.model import load_model_and_tokenizer
+from llmtuner.train.utils import create_ref_model, create_reward_model
+from llmtuner.train.ppo.trainer import CustomPPOTrainer
+
+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",
+    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")
+    dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="ppo")
+
+    tokenizer.padding_side = "left" # use left-padding in generation while using right-padding in training
+    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+    # Create reference model and reward model
+    ref_model = create_ref_model(model_args, finetuning_args, stage="ppo")
+    reward_model = create_reward_model(model, model_args, finetuning_args)
+
+    # Create ppo config
+    ppo_config = PPOConfig(
+        model_name=model_args.model_name_or_path,
+        learning_rate=training_args.learning_rate,
+        mini_batch_size=training_args.per_device_train_batch_size,
+        batch_size=training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps,
+        gradient_accumulation_steps=training_args.gradient_accumulation_steps,
+        ppo_epochs=1,
+        max_grad_norm=training_args.max_grad_norm,
+        seed=training_args.seed,
+        optimize_device_cache=True,
+        target=finetuning_args.ppo_target,
+        log_with=finetuning_args.ppo_logger,
+        use_score_scaling=finetuning_args.ppo_score_norm,
+        use_score_norm=finetuning_args.ppo_score_norm,
+        whiten_rewards=finetuning_args.ppo_whiten_rewards,
+        accelerator_kwargs={"step_scheduler_with_optimizer": False}
+    )
+
+    # Create optimizer and scheduler
+    optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=training_args.learning_rate)
+    if training_args.max_steps > 0:
+        num_training_steps = training_args.max_steps
+    else:
+        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.get_warmup_steps(num_training_steps),
+        num_training_steps=num_training_steps
+    )
+
+    # Initialize our Trainer
+    ppo_trainer = CustomPPOTrainer(
+        model_args=model_args,
+        training_args=training_args,
+        finetuning_args=finetuning_args,
+        generating_args=generating_args,
+        callbacks=callbacks + [SavePeftModelCallback()],
+        reward_model=reward_model,
+        config=ppo_config,
+        model=model,
+        ref_model=ref_model,
+        tokenizer=tokenizer,
+        dataset=dataset,
+        data_collator=data_collator,
+        optimizer=optimizer,
+        lr_scheduler=lr_scheduler
+    )
+
+    # Training
+    if training_args.do_train:
+        ppo_trainer.ppo_train()
+        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 finetuning_args.plot_loss:
+            plot_loss(training_args.output_dir, keys=["loss", "reward"])

src/llmtuner/train/pt/__init__.py

@@ -0,0 +1 @@
+from llmtuner.train.pt.workflow import run_pt

src/llmtuner/train/pt/workflow.py

@@ -0,0 +1,65 @@
+# Inspired by: https://github.com/huggingface/transformers/blob/v4.34.1/examples/pytorch/language-modeling/run_clm.py
+
+import math
+from typing import TYPE_CHECKING, Optional, List
+from transformers import DataCollatorForLanguageModeling, Trainer
+
+from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
+from llmtuner.extras.ploting import plot_loss
+from llmtuner.model import generate_model_card, load_model_and_tokenizer
+
+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"]] = 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 = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+    # Initialize our Trainer
+    trainer = Trainer(
+        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(resume_from_checkpoint=training_args.resume_from_checkpoint)
+        trainer.save_model()
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
+            plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
+
+    # Evaluation
+    if training_args.do_eval:
+        metrics = trainer.evaluate(metric_key_prefix="eval")
+        try:
+            perplexity = math.exp(metrics["eval_loss"])
+        except OverflowError:
+            perplexity = float("inf")
+
+        metrics["perplexity"] = perplexity
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Create model card
+    if training_args.do_train:
+        if training_args.push_to_hub:
+            trainer.push_to_hub(**generate_model_card(model_args, data_args, finetuning_args))
+        else:
+            trainer.create_model_card(**generate_model_card(model_args, data_args, finetuning_args))

src/llmtuner/train/rm/__init__.py

@@ -0,0 +1 @@
+from llmtuner.train.rm.workflow import run_rm

src/llmtuner/train/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/train/rm/trainer.py

@@ -0,0 +1,101 @@
+import os
+import json
+import torch
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+from transformers import Trainer
+
+from llmtuner.extras.logging import get_logger
+
+if TYPE_CHECKING:
+    from transformers.trainer import PredictionOutput
+    from transformers.modeling_utils import PreTrainedModel
+
+
+logger = get_logger(__name__)
+
+
+class PairwiseTrainer(Trainer):
+    r"""
+    Inherits PeftTrainer to compute pairwise loss.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.can_return_loss = True # override property to return eval_loss
+
+    def compute_loss(
+        self,
+        model: "PreTrainedModel",
+        inputs: Dict[str, torch.Tensor],
+        return_outputs: Optional[bool] = False
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]]]:
+        r"""
+        Computes pairwise loss. The first n examples are chosen and the last n examples are rejected.
+
+        Subclass and override to inject custom behavior.
+
+        Note that the first element will be removed from the output tuple.
+        See: https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/trainer.py#L3509
+        """
+        # Compute rewards
+        _, _, values = model(**inputs, output_hidden_states=True, return_dict=True)
+        if values.size(0) != inputs["input_ids"].size(0): # adapt to chatglm2
+            values = torch.transpose(values, 0, 1)
+
+        # Split the inputs and rewards into two parts, chosen and rejected
+        batch_size = inputs["input_ids"].size(0) // 2
+        chosen_input_ids, rejected_input_ids = inputs["input_ids"][:batch_size], inputs["input_ids"][batch_size:]
+        chosen_rewards, rejected_rewards = values[:batch_size], values[batch_size:]
+        chosen_scores, rejected_scores = [], []
+
+        # Compute pairwise loss. Only backprop on the different tokens before padding
+        # Inspired by: https://github.com/CarperAI/trlx/blob/main/examples/summarize_rlhf/reward_model/reward_model.py
+        loss = 0
+        for i in range(batch_size):
+            chosen_length = (chosen_input_ids[i] != self.tokenizer.pad_token_id).nonzero()[-1] + 1
+            rejected_length = (rejected_input_ids[i] != self.tokenizer.pad_token_id).nonzero()[-1] + 1
+            check_divergence = (chosen_input_ids[i] != rejected_input_ids[i]).nonzero()
+
+            if len(check_divergence) == 0:
+                end_index = chosen_length
+                div_index = end_index - 1
+            else:
+                end_index = max(chosen_length, rejected_length)
+                div_index = check_divergence[0]
+
+            assert div_index > 0
+            chosen_trunc_rewards = chosen_rewards[i, div_index:end_index]
+            rejected_trunc_rewards = rejected_rewards[i, div_index:end_index]
+            if return_outputs: # use the score on the last token except pad token for inference
+                chosen_scores.append(chosen_rewards[i, chosen_length-1])
+                rejected_scores.append(rejected_rewards[i, rejected_length-1])
+            loss += -torch.nn.functional.logsigmoid(chosen_trunc_rewards - rejected_trunc_rewards).mean()
+
+        loss = loss / batch_size
+        if return_outputs:
+            chosen_scores, rejected_scores = torch.stack(chosen_scores), torch.stack(rejected_scores)
+            return loss, [loss, chosen_scores, rejected_scores]
+
+        return 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}")
+        chosen_scores, rejected_scores = predict_results.predictions
+
+        with open(output_prediction_file, "w", encoding="utf-8") as writer:
+            res: List[str] = []
+            for c_score, r_score in zip(chosen_scores, rejected_scores):
+                res.append(json.dumps({"chosen": round(float(c_score), 2), "rejected": round(float(r_score), 2)}))
+            writer.write("\n".join(res))

src/llmtuner/train/rm/workflow.py

@@ -0,0 +1,75 @@
+# Inspired by: https://github.com/CarperAI/trlx/blob/main/examples/summarize_rlhf/reward_model/train_reward_model_gptj.py
+
+from typing import TYPE_CHECKING, Optional, List
+from transformers import Seq2SeqTrainingArguments
+
+from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
+from llmtuner.extras.callbacks import SavePeftModelCallback
+from llmtuner.extras.ploting import plot_loss
+from llmtuner.model import generate_model_card, load_model_and_tokenizer
+from llmtuner.train.rm.collator import PairwiseDataCollatorWithPadding
+from llmtuner.train.rm.metric import compute_accuracy
+from llmtuner.train.rm.trainer import PairwiseTrainer
+
+if TYPE_CHECKING:
+    from transformers import 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"]] = None
+):
+    dataset = get_dataset(model_args, data_args)
+    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="rm")
+    dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="rm")
+    data_collator = PairwiseDataCollatorWithPadding(tokenizer, pad_to_multiple_of=4)
+
+    # Update arguments
+    training_args_dict = training_args.to_dict()
+    training_args_dict.update(dict(remove_unused_columns=False)) # important for pairwise dataset
+    training_args = Seq2SeqTrainingArguments(**training_args_dict)
+
+    # Initialize our Trainer
+    trainer = PairwiseTrainer(
+        model=model,
+        args=training_args,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        callbacks=callbacks + [SavePeftModelCallback()],
+        compute_metrics=compute_accuracy,
+        **split_dataset(dataset, data_args, training_args)
+    )
+
+    # Training
+    if training_args.do_train:
+        train_result = trainer.train()
+        trainer.save_model()
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
+            plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
+
+    # Evaluation
+    if training_args.do_eval:
+        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)
+
+    # Create model card
+    if training_args.do_train:
+        if training_args.push_to_hub:
+            trainer.push_to_hub(**generate_model_card(model_args, data_args, finetuning_args))
+        else:
+            trainer.create_model_card(**generate_model_card(model_args, data_args, finetuning_args))

src/llmtuner/train/sft/__init__.py

@@ -0,0 +1 @@
+from llmtuner.train.sft.workflow import run_sft

src/llmtuner/train/sft/metric.py

@@ -1,13 +1,23 @@
 import numpy as np
 from dataclasses import dataclass
-from typing import Dict, Sequence, Tuple, Union
-from transformers.tokenization_utils import PreTrainedTokenizer
-
-import jieba
-from rouge_chinese import Rouge
-from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
+from typing import TYPE_CHECKING, Dict, Sequence, Tuple, Union
 
 from llmtuner.extras.constants import IGNORE_INDEX
+from llmtuner.extras.packages import (
+    is_jieba_available, is_nltk_available, is_rouge_available
+)
+
+if TYPE_CHECKING:
+    from transformers.tokenization_utils import PreTrainedTokenizer
+
+if is_jieba_available():
+    import jieba
+
+if is_nltk_available():
+    from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
+
+if is_rouge_available():
+    from rouge_chinese import Rouge
 
 
 @dataclass
@@ -16,14 +26,14 @@ 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"""
         Uses the model predictions to compute metrics.
         """
         preds, labels = eval_preds
-        score_dict = {"accuracy": [], "rouge-1": [], "rouge-2": [], "rouge-l": [], "bleu-4": []}
+        score_dict = {"rouge-1": [], "rouge-2": [], "rouge-l": [], "bleu-4": []}
 
         preds = np.where(preds != IGNORE_INDEX, preds, self.tokenizer.pad_token_id)
         labels = np.where(labels != IGNORE_INDEX, labels, self.tokenizer.pad_token_id)
@@ -47,6 +57,5 @@ class ComputeMetrics:
 
             bleu_score = sentence_bleu([list(label)], list(pred), smoothing_function=SmoothingFunction().method3)
             score_dict["bleu-4"].append(round(bleu_score * 100, 4))
-            score_dict["accuracy"].append(float(len(label) != 0 and pred[:len(label)] == label))
 
         return {k: float(np.mean(v)) for k, v in score_dict.items()}

src/llmtuner/train/sft/trainer.py

@@ -3,18 +3,20 @@ 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 transformers import Seq2SeqTrainer
 
 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__)
 
 
-class Seq2SeqPeftTrainer(PeftTrainer):
+class CustomSeq2SeqTrainer(Seq2SeqTrainer):
     r"""
     Inherits PeftTrainer to compute generative metrics such as BLEU and ROUGE.
     """
@@ -31,44 +33,40 @@ class Seq2SeqPeftTrainer(PeftTrainer):
 
         Subclass and override to inject custom behavior.
         """
+        labels = inputs["labels"].detach().clone() if "labels" in inputs else None # backup labels
+        if self.args.predict_with_generate:
+            assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor."
             prompt_len, label_len = inputs["input_ids"].size(-1), inputs["labels"].size(-1)
             if prompt_len > label_len:
                 inputs["labels"] = self._pad_tensors_to_target_len(inputs["labels"], inputs["input_ids"])
             if label_len > prompt_len:
-            inputs["input_ids"] = self._pad_tensors_to_target_len(inputs["input_ids"], inputs["labels"])
+                inputs["labels"] = inputs["labels"][:, :prompt_len] # truncate the labels instead of padding the inputs
 
-        loss, generated_tokens, labels = super().prediction_step(
+        loss, generated_tokens, _ = super().prediction_step(
             model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
         )
-        generated_tokens = generated_tokens[:, max(prompt_len, label_len):] if generated_tokens is not None else None
+        if generated_tokens is not None and self.args.predict_with_generate:
+            generated_tokens[:, :prompt_len] = self.tokenizer.pad_token_id
+            generated_tokens = generated_tokens.contiguous()
 
-        return (loss, generated_tokens, labels)
+        return loss, generated_tokens, labels
 
-    def _pad_tensors_to_target_len(self, src_tensor: torch.Tensor, tgt_tensor: torch.Tensor) -> torch.Tensor:
+    def _pad_tensors_to_target_len(
+        self,
+        src_tensor: torch.Tensor,
+        tgt_tensor: torch.Tensor
+    ) -> torch.Tensor:
         r"""
         Pads the tensor to the same length as the target tensor.
-
-        Should only be called when predict_with_generate=True.
         """
-        if self.tokenizer is not None and hasattr(self.tokenizer, "pad_token_id"):
-            assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor."
-            # If PAD token is not defined at least EOS token has to be defined
-            pad_token_id = (
-                self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
-            )
-        else:
-            if self.model.config.pad_token_id is not None:
-                pad_token_id = self.model.config.pad_token_id
-            else:
-                raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors")
-
-        padded_tensor = pad_token_id * torch.ones_like(tgt_tensor)
+        assert self.tokenizer.pad_token_id is not None, "Pad token is required."
+        padded_tensor = self.tokenizer.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/train/sft/workflow.py

@@ -1,79 +1,75 @@
-# Inspired by: https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/summarization/run_summarization.py
+# Inspired by: https://github.com/huggingface/transformers/blob/v4.34.1/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, Seq2SeqTrainingArguments
 
-from llmtuner.dsets import get_dataset, preprocess_dataset
-from llmtuner.extras.callbacks import LogCallback
+from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.constants import IGNORE_INDEX
 from llmtuner.extras.misc import get_logits_processor
 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
+from llmtuner.model import generate_model_card, load_model_and_tokenizer
+from llmtuner.train.sft.metric import ComputeMetrics
+from llmtuner.train.sft.trainer import CustomSeq2SeqTrainer
+
+if TYPE_CHECKING:
+    from transformers import 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")
     dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="sft")
+
+    if training_args.predict_with_generate:
+        tokenizer.padding_side = "left" # use left-padding in generation
+
     data_collator = DataCollatorForSeq2Seq(
         tokenizer=tokenizer,
+        pad_to_multiple_of=4 if tokenizer.padding_side == "right" else None, # for shift short attention
         label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
     )
 
     # Override the decoding parameters of Seq2SeqTrainer
-    training_args.generation_max_length = training_args.generation_max_length if \
-                training_args.generation_max_length is not None else data_args.max_target_length
-    training_args.generation_num_beams = data_args.eval_num_beams if \
-                data_args.eval_num_beams is not None else training_args.generation_num_beams
-
-    # Split the dataset
-    if training_args.do_train:
-        if data_args.dev_ratio > 1e-6:
-            dataset = dataset.train_test_split(test_size=data_args.dev_ratio)
-            trainer_kwargs = {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
-        else:
-            trainer_kwargs = {"train_dataset": dataset}
-    else: # do_eval or do_predict
-        trainer_kwargs = {"eval_dataset": dataset}
+    training_args_dict = training_args.to_dict()
+    training_args_dict.update(dict(
+        generation_max_length=training_args.generation_max_length or data_args.cutoff_len,
+        generation_num_beams=data_args.eval_num_beams or training_args.generation_num_beams
+    ))
+    training_args = Seq2SeqTrainingArguments(**training_args_dict)
 
     # Initialize our Trainer
-    trainer = Seq2SeqPeftTrainer(
-        finetuning_args=finetuning_args,
+    trainer = CustomSeq2SeqTrainer(
         model=model,
         args=training_args,
         tokenizer=tokenizer,
         data_collator=data_collator,
         callbacks=callbacks,
         compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None,
-        **trainer_kwargs
+        **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["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
+    gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
+    gen_kwargs["logits_processor"] = get_logits_processor()
 
     # Training
     if training_args.do_train:
-        train_result = trainer.train()
+        train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
+        trainer.save_model()
         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:
+        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
             plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
 
     # Evaluation
@@ -92,3 +88,10 @@ def run_sft(
         trainer.log_metrics("predict", predict_results.metrics)
         trainer.save_metrics("predict", predict_results.metrics)
         trainer.save_predictions(predict_results)
+
+    # Create model card
+    if training_args.do_train:
+        if training_args.push_to_hub:
+            trainer.push_to_hub(**generate_model_card(model_args, data_args, finetuning_args))
+        else:
+            trainer.create_model_card(**generate_model_card(model_args, data_args, finetuning_args))

src/llmtuner/train/tuner.py

@@ -0,0 +1,51 @@
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+from llmtuner.extras.callbacks import LogCallback
+from llmtuner.extras.logging import get_logger
+from llmtuner.model import get_train_args, get_infer_args, load_model_and_tokenizer
+from llmtuner.train.pt import run_pt
+from llmtuner.train.sft import run_sft
+from llmtuner.train.rm import run_rm
+from llmtuner.train.ppo import run_ppo
+from llmtuner.train.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 = get_train_args(args)
+    callbacks = [LogCallback()] if callbacks is None else callbacks
+
+    if finetuning_args.stage == "pt":
+        run_pt(model_args, data_args, training_args, finetuning_args, callbacks)
+    elif finetuning_args.stage == "sft":
+        run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+    elif finetuning_args.stage == "rm":
+        run_rm(model_args, data_args, training_args, finetuning_args, callbacks)
+    elif finetuning_args.stage == "ppo":
+        run_ppo(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+    elif finetuning_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, _, finetuning_args, _ = get_infer_args(args)
+    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
+    model.config.use_cache = True
+    model.save_pretrained(finetuning_args.export_dir, max_shard_size=max_shard_size)
+    try:
+        tokenizer.padding_side = "left" # restore padding side
+        tokenizer.init_kwargs["padding_side"] = "left"
+        tokenizer.save_pretrained(finetuning_args.export_dir)
+    except:
+        logger.warning("Cannot save tokenizer, please copy the files manually.")
+
+
+if __name__ == "__main__":
+    run_exp()

src/llmtuner/train/utils.py

@@ -0,0 +1,80 @@
+import torch
+from typing import TYPE_CHECKING, Literal, Union
+
+from llmtuner.extras.logging import get_logger
+from llmtuner.hparams import ModelArguments, FinetuningArguments
+from llmtuner.model import load_model_and_tokenizer, load_valuehead_params
+
+if TYPE_CHECKING:
+    from transformers.modeling_utils import PreTrainedModel
+    from trl import AutoModelForCausalLMWithValueHead
+
+
+logger = get_logger(__name__)
+
+
+def create_ref_model(
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
+    stage: Literal["ppo", "dpo"]
+) -> Union["PreTrainedModel", "AutoModelForCausalLMWithValueHead"]:
+    r"""
+    Creates reference model for PPO/DPO training. Evaluation mode is not supported.
+
+    The valuehead parameter is randomly initialized since it is useless for PPO training.
+    """
+    if finetuning_args.ref_model is not None:
+        ref_model_args_dict = model_args.to_dict()
+        ref_model_args_dict.update(dict(
+            model_name_or_path=finetuning_args.ref_model,
+            checkpoint_dir=finetuning_args.ref_model_checkpoint,
+            quantization_bit=finetuning_args.ref_model_quantization_bit
+        ))
+        ref_model_args = ModelArguments(**ref_model_args_dict)
+        ref_finetuning_args = FinetuningArguments(finetuning_type="lora")
+        ref_model, _ = load_model_and_tokenizer(ref_model_args, ref_finetuning_args, is_trainable=False, stage=stage)
+        logger.info("Created reference model from {}".format(finetuning_args.ref_model))
+    else:
+        if finetuning_args.finetuning_type == "lora":
+            ref_model = None
+        else:
+            ref_model, _ = load_model_and_tokenizer(model_args, finetuning_args, is_trainable=False, stage=stage)
+            logger.info("Created reference model from the model itself.")
+
+    return ref_model
+
+
+def create_reward_model(
+    model: "AutoModelForCausalLMWithValueHead",
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments"
+) -> "AutoModelForCausalLMWithValueHead":
+    r"""
+    Creates reward model for PPO training.
+    """
+    if finetuning_args.reward_model_type == "lora":
+        model.pretrained_model.load_adapter(finetuning_args.reward_model, "reward")
+        for name, param in model.named_parameters(): # https://github.com/huggingface/peft/issues/1090
+            if "default" in name:
+                param.data = param.data.to(torch.float32) # trainable params should in fp32
+        vhead_params = load_valuehead_params(finetuning_args.reward_model, model_args)
+        assert vhead_params is not None, "Reward model is not correctly loaded."
+        model.register_buffer("reward_head_weight", vhead_params["v_head.summary.weight"], persistent=False)
+        model.register_buffer("reward_head_bias", vhead_params["v_head.summary.bias"], persistent=False)
+        model.register_buffer("default_head_weight", torch.zeros_like(vhead_params["v_head.summary.weight"]), persistent=False)
+        model.register_buffer("default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False)
+        logger.info("Loaded adapter weights of reward model from {}".format(finetuning_args.reward_model))
+        return None
+    else:
+        reward_model_args_dict = model_args.to_dict()
+        reward_model_args_dict.update(dict(
+            model_name_or_path=finetuning_args.reward_model,
+            checkpoint_dir=finetuning_args.reward_model_checkpoint,
+            quantization_bit=finetuning_args.reward_model_quantization_bit
+        ))
+        reward_model_args = ModelArguments(**reward_model_args_dict)
+        reward_finetuning_args = FinetuningArguments(finetuning_type="lora")
+        reward_model, _ = load_model_and_tokenizer(reward_model_args, reward_finetuning_args, is_trainable=False, stage="ppo")
+        logger.info("Load full weights of reward model from {}".format(finetuning_args.reward_model))
+        logger.warning("Please ensure the ppo model and reward model share SAME tokenizer and vocabulary.")
+        return reward_model

src/llmtuner/tuner/__init__.py

@@ -1,5 +0,0 @@
-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

src/llmtuner/tuner/core/__init__.py

@@ -1,2 +0,0 @@
-from llmtuner.tuner.core.parser import get_train_args, get_infer_args
-from llmtuner.tuner.core.loader import load_model_and_tokenizer

src/llmtuner/tuner/core/adapter.py

@@ -1,94 +0,0 @@
-import os
-import torch
-
-from transformers.modeling_utils import PreTrainedModel
-from peft import (
-    PeftModel,
-    TaskType,
-    LoraConfig,
-    get_peft_model
-)
-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
-
-
-logger = get_logger(__name__)
-
-
-def init_adapter(
-    model: PreTrainedModel,
-    model_args: ModelArguments,
-    finetuning_args: FinetuningArguments,
-    is_trainable: bool,
-    is_mergeable: bool
-) -> PreTrainedModel:
-    r"""
-    Initializes the adapters.
-
-    Support full-parameter, freeze and LoRA training.
-
-    Note that the trainable parameters must be cast to float32.
-    """
-
-    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":
-        logger.info("Fine-tuning method: Full")
-        model = model.float()
-
-    if finetuning_args.finetuning_type == "freeze":
-        logger.info("Fine-tuning method: Freeze")
-
-        for name, param in model.named_parameters():
-            if not any(trainable_layer in name for trainable_layer in finetuning_args.trainable_layers):
-                param.requires_grad_(False)
-            else:
-                param.data = param.data.to(torch.float32)
-
-        if model_args.checkpoint_dir is not None:
-            assert load_trainable_params(model, model_args.checkpoint_dir[0]), "Model checkpoint is not correctly loaded."
-
-    if finetuning_args.finetuning_type == "lora":
-        logger.info("Fine-tuning method: LoRA")
-        latest_checkpoint = None
-
-        if model_args.checkpoint_dir is not None:
-            assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], WEIGHTS_NAME)), \
-                "Provided path ({}) does not contain a LoRA weight.".format(model_args.checkpoint_dir[0])
-            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
-                checkpoints_to_merge, latest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
-            else:
-                checkpoints_to_merge = model_args.checkpoint_dir
-
-            for checkpoint in checkpoints_to_merge:
-                model = PeftModel.from_pretrained(model, checkpoint)
-                model = model.merge_and_unload()
-
-            if len(checkpoints_to_merge) > 0:
-                logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge)))
-
-            if latest_checkpoint is not None: # resume lora training or quantized inference
-                model = PeftModel.from_pretrained(model, latest_checkpoint, is_trainable=is_trainable)
-
-        if is_trainable and latest_checkpoint is None: # create new lora weights while training
-            lora_config = LoraConfig(
-                task_type=TaskType.CAUSAL_LM,
-                inference_mode=False,
-                r=finetuning_args.lora_rank,
-                lora_alpha=finetuning_args.lora_alpha,
-                lora_dropout=finetuning_args.lora_dropout,
-                target_modules=finetuning_args.lora_target
-            )
-            model = get_peft_model(model, lora_config)
-
-    if model_args.checkpoint_dir is not None:
-        logger.info("Loaded fine-tuned model from checkpoint(s): {}".format(",".join(model_args.checkpoint_dir)))
-
-    return model

src/llmtuner/tuner/core/loader.py

@@ -1,151 +0,0 @@
-import os
-import torch
-from typing import Literal, Optional, Tuple
-
-from transformers import (
-    AutoConfig,
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    BitsAndBytesConfig
-)
-from transformers.utils import check_min_version
-from transformers.utils.versions import require_version
-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.save_and_load import load_valuehead_params
-from llmtuner.hparams import ModelArguments, FinetuningArguments
-from llmtuner.tuner.core.adapter import init_adapter
-
-
-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")
-
-
-def load_model_and_tokenizer(
-    model_args: ModelArguments,
-    finetuning_args: FinetuningArguments,
-    is_trainable: Optional[bool] = False,
-    stage: Optional[Literal["pt", "sft", "rm", "ppo"]] = "sft"
-) -> Tuple[PreTrainedModel, PreTrainedTokenizerBase]:
-    r"""
-    Loads pretrained model and tokenizer.
-
-    Support both training and inference.
-    """
-    if (not is_trainable) and model_args.checkpoint_dir is None:
-        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,
-        "revision": model_args.model_revision,
-        "use_auth_token": True if model_args.use_auth_token else None,
-    }
-
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_args.model_name_or_path,
-        use_fast=model_args.use_fast_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)
-    is_mergeable = True
-
-    # Quantization configurations (using bitsandbytes library).
-    if model_args.quantization_bit is not None:
-        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
-            )
-
-        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,
-                bnb_4bit_compute_dtype=model_args.compute_dtype,
-                bnb_4bit_use_double_quant=model_args.double_quantization,
-                bnb_4bit_quant_type=model_args.quantization_type
-            )
-
-        is_mergeable = False
-        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,
-        config=config,
-        torch_dtype=torch.bfloat16 if model_args.compute_dtype == torch.bfloat16 else torch.float16,
-        low_cpu_mem_usage=True,
-        **config_kwargs
-    )
-
-    # Register auto class to save the custom code files.
-    if isinstance(config, PretrainedConfig) and "AutoConfig" in getattr(config, "auto_map", {}):
-        config.__class__.register_for_auto_class()
-    if isinstance(model, PreTrainedModel) and "AutoModelForCausalLM" in getattr(config, "auto_map", {}):
-        model.__class__.register_for_auto_class()
-    if isinstance(tokenizer, PreTrainedTokenizerBase) and "AutoTokenizer" in tokenizer.init_kwargs.get("auto_map", {}):
-        tokenizer.__class__.register_for_auto_class()
-
-    # Initialize adapters
-    model = prepare_model_for_training(model, finetuning_args.finetuning_type) if is_trainable else model
-    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)
-
-        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.")
-            if load_valuehead_params(model, model_args.checkpoint_dir[-1]):
-                model.v_head.load_state_dict({
-                    "summary.weight": getattr(model, "reward_head_weight"),
-                    "summary.bias": getattr(model, "reward_head_bias")
-                })
-
-        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."
-
-    if not is_trainable:
-        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)
-
-    return model, tokenizer

src/llmtuner/tuner/core/parser.py

@@ -1,134 +0,0 @@
-import os
-import sys
-import torch
-import datasets
-import transformers
-from typing import Any, Dict, Optional, Tuple
-from transformers import HfArgumentParser, Seq2SeqTrainingArguments
-
-from llmtuner.extras.logging import get_logger
-from llmtuner.hparams import (
-    ModelArguments,
-    DataArguments,
-    FinetuningArguments,
-    GeneratingArguments,
-    GeneralArguments
-)
-
-
-logger = get_logger(__name__)
-
-
-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()
-
-    # Setup logging
-    if training_args.should_log:
-        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
-        transformers.utils.logging.set_verbosity_info()
-
-    log_level = training_args.get_process_log_level()
-    datasets.utils.logging.set_verbosity(log_level)
-    transformers.utils.logging.set_verbosity(log_level)
-    transformers.utils.logging.enable_default_handler()
-    transformers.utils.logging.enable_explicit_format()
-
-    # 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."
-
-    assert not (training_args.do_train and training_args.predict_with_generate), \
-        "`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."
-
-    assert model_args.quantization_bit is None or finetuning_args.finetuning_type == "lora", \
-        "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 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.")
-
-    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.")
-        training_args.ddp_find_unused_parameters = False
-
-    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:
-        if training_args.fp16:
-            model_args.compute_dtype = torch.float16
-        elif training_args.bf16:
-            model_args.compute_dtype = torch.bfloat16
-        else:
-            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(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
-
-
-def get_infer_args(
-    args: Optional[Dict[str, Any]] = None
-) -> Tuple[ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]:
-
-    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.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.")
-
-    return model_args, data_args, finetuning_args, generating_args

src/llmtuner/tuner/core/trainer.py

@@ -1,88 +0,0 @@
-import os
-import torch
-from typing import Dict, Optional
-
-from transformers import Seq2SeqTrainer
-from transformers.trainer import TRAINING_ARGS_NAME
-from transformers.modeling_utils import unwrap_model
-
-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
-
-
-logger = get_logger(__name__)
-
-
-class PeftTrainer(Seq2SeqTrainer):
-    r"""
-    Inherits Seq2SeqTrainer to support parameter-efficient checkpoints.
-    """
-
-    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 _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, torch.Tensor]] = None) -> None:
-        r"""
-        Saves trainable parameters as model checkpoint.
-
-        This function will only be executed at the process zero.
-
-        Subclass and override to inject custom behavior. It should not be directly used by external scripts.
-        """
-        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 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))
-        else:
-            backbone_model = model
-
-        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:
-                self.tokenizer.save_pretrained(output_dir)
-
-        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):
-        r"""
-        Loads trainable parameters from model checkpoint.
-
-        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")
-                })
-        else: # freeze/full-tuning
-            load_trainable_params(backbone_model, self.state.best_model_checkpoint)

src/llmtuner/tuner/ppo/__init__.py

@@ -1 +0,0 @@
-from llmtuner.tuner.ppo.workflow import run_ppo

src/llmtuner/tuner/ppo/trainer.py

@@ -1,186 +0,0 @@
-import os
-import math
-import torch
-from tqdm import tqdm
-from typing import Callable, Dict, List, Optional
-
-from transformers import Seq2SeqTrainingArguments, TrainerState, TrainerControl
-from transformers.modeling_utils import PreTrainedModel
-
-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.tuner.core.trainer import PeftTrainer
-from llmtuner.tuner.ppo.utils import cast_layernorm_dtype, replace_model
-
-
-logger = get_logger(__name__)
-
-
-class PPOPeftTrainer(PPOTrainer, PeftTrainer):
-    r"""
-    Inherits PPOTrainer.
-    """
-    def __init__(
-        self,
-        training_args: Seq2SeqTrainingArguments,
-        finetuning_args: FinetuningArguments,
-        callbacks: List[LogCallback],
-        **kwargs
-    ):
-        PPOTrainer.__init__(self, **kwargs)
-        self.args = training_args
-        self.finetuning_args = finetuning_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"""
-        Implements training loop for the PPO stage, like _inner_training_loop() in Huggingface's Trainer.
-        """
-        total_train_batch_size = (
-            self.args.per_device_train_batch_size * self.args.gradient_accumulation_steps * self.args.world_size
-        )
-        len_dataloader = len(self.dataloader)
-        num_examples = len(self.dataset)
-        num_train_epochs = self.args.num_train_epochs
-        max_steps = math.ceil(num_train_epochs * len_dataloader)
-
-        self.state.max_steps = max_steps
-        self.state.num_train_epochs = num_train_epochs
-        self.state.is_local_process_zero = self.is_local_process_zero()
-        self.state.is_world_process_zero = self.is_world_process_zero()
-
-        if self.is_world_process_zero():
-            logger.info("***** Running training *****")
-            logger.info(f"  Num examples = {num_examples}")
-            logger.info(f"  Num Epochs = {num_train_epochs}")
-            logger.info(f"  Instantaneous batch size per device = {self.args.per_device_train_batch_size}")
-            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)}")
-
-        # 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()
-        }
-        length_sampler = LengthSampler(max_target_length // 2, max_target_length)
-        unwrapped_model: PreTrainedModel = self.accelerator.unwrap_model(self.model)
-
-        dataiter = iter(self.dataloader)
-        steps_trained = 0
-        loss_meter = AverageMeter()
-        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):
-            batch = next(dataiter)
-            steps_trained += 1
-
-            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)
-
-            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))
-            rewards = [reward for reward in values[:, -1].to(torch.float32)] # use float32 type
-            replace_model(unwrapped_model, target="default")
-
-            # Run PPO step
-            unwrapped_model.gradient_checkpointing_enable()
-            unwrapped_model.config.use_cache = False
-            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:
-                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)
-                logs["step"] = step
-                self.state.log_history.append(logs)
-                self.log_callback.on_log(self.args, self.state, self.control)
-                loss_meter.reset()
-                reward_meter.reset()
-
-            if (step+1) % self.args.save_steps == 0: # save checkpoint
-                self.save_model(os.path.join(self.args.output_dir, f"checkpoint-{step+1}"))
-
-            if self.control.should_epoch_stop or self.control.should_training_stop:
-                break
-
-            if steps_trained == len_dataloader:
-                dataiter = iter(self.dataloader)
-                steps_trained = 0
-
-    @torch.no_grad()
-    def generate(
-        self,
-        inputs: Dict[str, torch.Tensor],
-        length_sampler: Optional[Callable] = None,
-        return_prompt: Optional[bool] = True,
-        **generation_kwargs
-    ) -> 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)
-
-        # 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)
-
-        if not return_prompt and not self.is_encoder_decoder:
-            return response[:, inputs["input_ids"].size(1):]
-        return response
-
-    def save_model(self, output_dir: Optional[str] = None) -> None:
-        r"""
-        Saves model checkpoint.
-
-        Subclass and override to inject custom behavior.
-        """
-        if self.args.should_save:
-            self._save(output_dir)

src/llmtuner/tuner/ppo/utils.py

@@ -1,37 +0,0 @@
-import torch
-from typing import Dict, List, Literal, Optional, Tuple
-from trl import AutoModelForCausalLMWithValueHead
-
-from llmtuner.extras.constants import LAYERNORM_NAMES
-
-
-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"])
-        setattr(model, "default_head_bias", valuehead_state_dict["summary.bias"])
-
-    model.pretrained_model.set_adapter(target) # set the LoRA adapter to be active
-    model.v_head.load_state_dict({
-        "summary.weight": getattr(model, "{}_head_weight".format(target)),
-        "summary.bias": getattr(model, "{}_head_bias".format(target))
-    })
-
-
-def cast_layernorm_dtype(
-    model: AutoModelForCausalLMWithValueHead,
-    layer_norm_names: List[str] = LAYERNORM_NAMES,
-    layer_norm_params: Optional[Dict[str, torch.Tensor]] = None
-) -> Tuple[AutoModelForCausalLMWithValueHead, Dict[str, torch.Tensor]]:
-
-    layer_norm_state_dict = {}
-
-    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):
-            if layer_norm_params is not None:
-                param.data = layer_norm_params[name] # restore float32 weights
-            else:
-                layer_norm_state_dict[name] = param.data.detach().clone() # store float32 weights for stability
-                param.data = param.data.to(torch.float16)
-
-    return model, layer_norm_state_dict

src/llmtuner/tuner/ppo/workflow.py

@@ -1,70 +0,0 @@
-# Inspired by:
-# https://github.com/lvwerra/trl/blob/main/examples/sentiment/scripts/gpt-neox-20b_peft/gpt-neo-20b_sentiment_peft.py
-
-import math
-from trl import PPOConfig
-from torch.optim import AdamW
-from typing import Optional, List
-from transformers import DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, TrainerCallback
-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
-
-
-def run_ppo(
-    model_args: ModelArguments,
-    data_args: DataArguments,
-    training_args: Seq2SeqTrainingArguments,
-    finetuning_args: FinetuningArguments,
-    callbacks: Optional[List[TrainerCallback]] = [LogCallback()]
-):
-    dataset = get_dataset(model_args, data_args)
-    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="ppo")
-    dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="ppo")
-    data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, label_pad_token_id=tokenizer.pad_token_id)
-
-    ppo_config = PPOConfig(
-        model_name=model_args.model_name_or_path,
-        learning_rate=training_args.learning_rate,
-        mini_batch_size=training_args.per_device_train_batch_size,
-        batch_size=training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps,
-        gradient_accumulation_steps=training_args.gradient_accumulation_steps,
-        ppo_epochs=1,
-        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 = \
-        training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps * training_args.world_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))
-    )
-
-    # Initialize our Trainer
-    ppo_trainer = PPOPeftTrainer(
-        training_args=training_args,
-        finetuning_args=finetuning_args,
-        callbacks=callbacks,
-        config=ppo_config,
-        model=model,
-        ref_model=None,
-        tokenizer=tokenizer,
-        dataset=dataset,
-        data_collator=data_collator,
-        optimizer=optimizer,
-        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"])

src/llmtuner/tuner/pt/__init__.py

@@ -1 +0,0 @@
-from llmtuner.tuner.pt.workflow import run_pt

src/llmtuner/tuner/pt/workflow.py

@@ -1,73 +0,0 @@
-# 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 llmtuner.dsets import get_dataset, preprocess_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
-
-
-def run_pt(
-    model_args: ModelArguments,
-    data_args: DataArguments,
-    training_args: Seq2SeqTrainingArguments,
-    finetuning_args: FinetuningArguments,
-    callbacks: Optional[List[TrainerCallback]] = [LogCallback()]
-):
-    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
-    )
-
-    # Split the dataset
-    if training_args.do_train:
-        if data_args.dev_ratio > 1e-6:
-            dataset = dataset.train_test_split(test_size=data_args.dev_ratio)
-            trainer_kwargs = {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
-        else:
-            trainer_kwargs = {"train_dataset": dataset}
-    else: # do_eval or do_predict
-        trainer_kwargs = {"eval_dataset": dataset}
-
-    # Initialize our Trainer
-    trainer = PeftTrainer(
-        finetuning_args=finetuning_args,
-        model=model,
-        args=training_args,
-        tokenizer=tokenizer,
-        data_collator=data_collator,
-        callbacks=callbacks,
-        **trainer_kwargs
-    )
-
-    # 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"])
-
-    # Evaluation
-    if training_args.do_eval:
-        metrics = trainer.evaluate(metric_key_prefix="eval")
-        try:
-            perplexity = math.exp(metrics["eval_loss"])
-        except OverflowError:
-            perplexity = float("inf")
-
-        metrics["perplexity"] = perplexity
-
-        trainer.log_metrics("eval", metrics)
-        trainer.save_metrics("eval", metrics)

src/llmtuner/tuner/rm/__init__.py

@@ -1 +0,0 @@
-from llmtuner.tuner.rm.workflow import run_rm

src/llmtuner/tuner/rm/trainer.py

@@ -1,38 +0,0 @@
-import torch
-from typing import Dict, List, Optional, Tuple, Union
-from transformers.modeling_utils import PreTrainedModel
-
-from llmtuner.tuner.core.trainer import PeftTrainer
-
-
-class PairwisePeftTrainer(PeftTrainer):
-    r"""
-    Inherits PeftTrainer to compute pairwise loss.
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.can_return_loss = True # override property to return eval_loss
-
-    def compute_loss(
-        self,
-        model: PreTrainedModel,
-        inputs: Dict[str, torch.Tensor],
-        return_outputs: Optional[bool] = False
-    ) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]]]:
-        r"""
-        Computes pairwise loss. The first n examples are chosen and the last n examples are rejected.
-
-        We use score on the EOS token to represent reward of the whole sentence.
-
-        Subclass and override to inject custom behavior. It should not be directly used by external scripts.
-
-        Note that the first element will be removed from the output tuple.
-
-        See: https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/trainer.py#L3509
-        """
-        batch_size = inputs["input_ids"].size(0) // 2
-        _, _, values = model(**inputs)
-        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

src/llmtuner/tuner/rm/workflow.py

@@ -1,68 +0,0 @@
-# Inspired by:
-# 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 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.rm.metric import compute_accuracy
-from llmtuner.tuner.rm.collator import PairwiseDataCollatorWithPadding
-from llmtuner.tuner.rm.trainer import PairwisePeftTrainer
-
-
-def run_rm(
-    model_args: ModelArguments,
-    data_args: DataArguments,
-    training_args: Seq2SeqTrainingArguments,
-    finetuning_args: FinetuningArguments,
-    callbacks: Optional[List[TrainerCallback]] = [LogCallback()]
-):
-    dataset = get_dataset(model_args, data_args)
-    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="rm")
-    dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="rm")
-    data_collator = PairwiseDataCollatorWithPadding(tokenizer)
-
-    training_args.remove_unused_columns = False # important for pairwise dataset
-
-    # Split the dataset
-    if training_args.do_train:
-        if data_args.dev_ratio > 1e-6:
-            dataset = dataset.train_test_split(test_size=data_args.dev_ratio)
-            trainer_kwargs = {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
-        else:
-            trainer_kwargs = {"train_dataset": dataset}
-    else: # do_eval or do_predict
-        trainer_kwargs = {"eval_dataset": dataset}
-
-    # Initialize our Trainer
-    trainer = PairwisePeftTrainer(
-        finetuning_args=finetuning_args,
-        model=model,
-        args=training_args,
-        tokenizer=tokenizer,
-        data_collator=data_collator,
-        callbacks=callbacks,
-        compute_metrics=compute_accuracy,
-        **trainer_kwargs
-    )
-
-    # 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"])
-
-    # Evaluation
-    if training_args.do_eval:
-        metrics = trainer.evaluate(metric_key_prefix="eval")
-        trainer.log_metrics("eval", metrics)
-        trainer.save_metrics("eval", metrics)

src/llmtuner/tuner/sft/__init__.py

@@ -1 +0,0 @@
-from llmtuner.tuner.sft.workflow import run_sft