release v0.3.0

Former-commit-id: de7f5b622340ab09ebbe57ad2703e63d06dfdeea

CODE_OF_CONDUCT.md

@@ -0,0 +1,128 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
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+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+`hoshihiyouga AT gmail DOT com`.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.

README.md

@@ -1,59 +1,80 @@
-# 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
 
-[23/09/10] Now we support using **[FlashAttention](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 (experimental feature).
+[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`.
 
-[23/08/18] Now we support **resuming training**, upgrade `transformers` to `4.31.0` to enjoy this feature.
+[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.
 
-[23/08/12] Now we support **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.
+[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models.
 
-[23/08/11] Now we support **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [this example](#dpo-training) to train your models.
+[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.
 
-[23/07/31] Now we support **dataset streaming**. Try `--streaming` and `--max_steps 10000` arguments to load your dataset in streaming mode.
+[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.
 
-[23/07/29] We release two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos ([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)) for details.
+[23/08/11] We supported **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [this example](#dpo-training) to train your models.
 
-[23/07/18] Now we develop an **all-in-one Web UI** for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development.
+[23/07/31] We supported **dataset streaming**. Try `--streaming` and `--max_steps 10000` arguments to load your dataset in streaming mode.
 
-[23/07/09] Now we release **[FastEdit](https://github.com/hiyouga/FastEdit)** ⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested.
+[23/07/29] 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.
 
-[23/06/29] We provide 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.
+[23/07/18] 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.
 
-[23/06/22] 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**.
+[23/07/09] 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.
 
-[23/06/03] Now we support quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). Try `--quantization_bit 4/8` argument to work with quantized models.
+[23/06/29] 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
 
 | Model                                                    | Model size                  | Default module    | Template  |
 | -------------------------------------------------------- | --------------------------- | ----------------- | --------- |
-| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B              | q_proj,v_proj     | -         |
-| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                  | q_proj,v_proj     | llama2    |
-| [BLOOM](https://huggingface.co/bigscience/bloom)         | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -         |
-| [BLOOMZ](https://huggingface.co/bigscience/bloomz)       | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -         |
-| [Falcon](https://huggingface.co/tiiuae/falcon-7b)        | 7B/40B                      | query_key_value   | -         |
 | [Baichuan](https://github.com/baichuan-inc/Baichuan-13B) | 7B/13B                      | W_pack            | baichuan  |
 | [Baichuan2](https://github.com/baichuan-inc/Baichuan2)   | 7B/13B                      | W_pack            | baichuan2 |
-| [InternLM](https://github.com/InternLM/InternLM)         | 7B                          | q_proj,v_proj     | intern    |
-| [Qwen](https://github.com/QwenLM/Qwen-7B)                | 7B                          | c_attn            | chatml    |
-| [XVERSE](https://github.com/xverse-ai/XVERSE-13B)        | 13B                         | q_proj,v_proj     | xverse    |
-| [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)         | 6B                          | query_key_value   | chatglm2  |
+| [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.
+> 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
 
@@ -61,27 +82,36 @@
 | ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
 | 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: |
-| PPO Training           |                    |                    | :white_check_mark: | :white_check_mark: |
-| DPO Training           | :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:
+<details><summary>Pre-training datasets</summary>
+
 - [Wiki Demo (en)](data/wiki_demo.txt)
 - [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
-  - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
+- [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)
-- For supervised fine-tuning:
+- [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)
-  - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
 - [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)
@@ -90,19 +120,34 @@
 - [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
 - [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
 - [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
-  - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
+- [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)
-  - [UltraChat (en)](https://github.com/thunlp/UltraChat)
 - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
 - [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
-- For reward modeling or DPO training:
+- [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 Hugging Face account using these commands.
@@ -117,9 +162,9 @@ huggingface-cli login
 - Python 3.8+ and PyTorch 1.13.1+
 - 🤗Transformers, Datasets, Accelerate, PEFT and TRL
 - sentencepiece, protobuf and tiktoken
-- jieba, rouge-chinese and nltk (used at evaluation)
-- gradio and matplotlib (used in web_demo.py)
-- uvicorn, fastapi and sse-starlette (used in api_demo.py)
+- 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**!
 
@@ -127,7 +172,7 @@ And **powerful GPUs**!
 
 ### 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`.
@@ -135,10 +180,10 @@ Please refer to `data/example_dataset` for checking the details about the format
 ### 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
 ```
 
@@ -148,17 +193,6 @@ If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you wi
 pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
 ```
 
-### All-in-one Web UI
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_web.py
-```
-
-We strongly recommend using the all-in-one Web UI for newcomers since it can also generate training scripts **automatically**.
-
-> [!WARNING]
-> Currently the web UI only supports training on **a single GPU**.
-
 ### Train on a single GPU
 
 > [!IMPORTANT]
@@ -365,7 +399,7 @@ python src/export_model.py \
     --template default \
     --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint \
-    --output_dir path_to_export
+    --export_dir path_to_export
 ```
 
 ### API Demo
@@ -401,26 +435,21 @@ python src/web_demo.py \
     --checkpoint_dir path_to_checkpoint
 ```
 
-### Evaluation (BLEU and ROUGE_CHINESE)
+### Evaluation
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage sft \
+CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
     --model_name_or_path path_to_llama_model \
-    --do_eval \
-    --dataset alpaca_gpt4_en \
-    --template default \
     --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint \
-    --output_dir path_to_eval_result \
-    --per_device_eval_batch_size 8 \
-    --max_samples 100 \
-    --predict_with_generate
+    --template vanilla \
+    --task mmlu \
+    --split test \
+    --lang en \
+    --n_shot 5 \
+    --batch_size 4
 ```
 
-> [!NOTE]
-> We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit evaluation.
-
 ### Predict
 
 ```bash
@@ -438,40 +467,39 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --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)
-- [LLaMA-2](https://ai.meta.com/llama/license/)
-- [BLOOM](https://huggingface.co/spaces/bigscience/license)
-- [Falcon](LICENSE)
-- [Baichuan](https://huggingface.co/baichuan-inc/baichuan-7B/resolve/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)
-- [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)
-- [InternLM](https://github.com/InternLM/InternLM#open-source-license)
-- [Qwen](https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/LICENSE)
-- [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf)
-- [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B/blob/main/MODEL_LICENSE)
+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 benefits from [PEFT](https://github.com/huggingface/peft), [QLoRA](https://github.com/artidoro/qlora) and [OpenChatKit](https://github.com/togethercomputer/OpenChatKit). Thanks for their wonderful works.
+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

@@ -1,30 +1,47 @@
-# LLaMA Efficient Tuning
+# LLaMA Factory: 轻松的大模型训练与评估
 
-[![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)
 
 👋 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
 
-[23/09/10] 现在我们支持了 LLaMA 模型的 **[FlashAttention](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（实验性功能）。
+[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`。
 
-[23/08/18] 现在我们支持了**训练状态恢复**，请将 `transformers` 升级至 `4.31.0` 以启用此功能。
+[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。
 
-[23/08/12] 现在我们支持了 **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。
+[23/09/23] 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)。
 
-[23/08/11] 现在我们支持了指令模型的 **[DPO 训练](https://arxiv.org/abs/2305.18290)**。详情请参阅[此示例](#dpo-训练)。
+[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。
 
-[23/07/31] 现在我们支持了**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。
+[23/08/12] 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**。请尝试使用 `train_web.py` 在您的浏览器中微调模型。感谢 [@KanadeSiina](https://github.com/KanadeSiina) 和 [@codemayq](https://github.com/codemayq) 在该功能开发中付出的努力。
+[23/07/18] 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。
 
@@ -32,28 +49,32 @@
 
 [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] 现在我们实现了 4 比特的 LoRA 训练（也称 **[QLoRA](https://github.com/artidoro/qlora)**）。请尝试使用 `--quantization_bit 4` 参数进行 4 比特量化微调。
+[23/06/03] 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  |
 | -------------------------------------------------------- | --------------------------- | ----------------- | --------- |
-| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B              | q_proj,v_proj     | -         |
-| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                  | q_proj,v_proj     | llama2    |
-| [BLOOM](https://huggingface.co/bigscience/bloom)         | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -         |
-| [BLOOMZ](https://huggingface.co/bigscience/bloomz)       | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -         |
-| [Falcon](https://huggingface.co/tiiuae/falcon-7b)        | 7B/40B                      | query_key_value   | -         |
 | [Baichuan](https://github.com/baichuan-inc/Baichuan-13B) | 7B/13B                      | W_pack            | baichuan  |
 | [Baichuan2](https://github.com/baichuan-inc/Baichuan2)   | 7B/13B                      | W_pack            | baichuan2 |
-| [InternLM](https://github.com/InternLM/InternLM)         | 7B                          | q_proj,v_proj     | intern    |
-| [Qwen](https://github.com/QwenLM/Qwen-7B)                | 7B                          | c_attn            | chatml    |
-| [XVERSE](https://github.com/xverse-ai/XVERSE-13B)        | 13B                         | q_proj,v_proj     | xverse    |
-| [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)         | 6B                          | query_key_value   | chatglm2  |
+| [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。
+> 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
 
@@ -61,27 +82,36 @@
 | ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
 | 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: |
-| PPO 训练               |                    |                    | :white_check_mark: | :white_check_mark: |
-| DPO 训练               | :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
 
-- 用于预训练：
+<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)
-  - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
+- [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)
-- 用于指令监督微调：
+- [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)
-  - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
 - [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)
@@ -90,20 +120,35 @@
 - [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
 - [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
 - [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
-  - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
+- [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)
-  - [UltraChat (en)](https://github.com/thunlp/UltraChat)
 - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
 - [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
-- 用于训练奖励模型或 DPO 训练：
+- [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)
 
-使用方法请参考 [data/README.md](data/README_zh.md) 文件。
+</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。
 
@@ -117,7 +162,7 @@ huggingface-cli login
 - 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)
+- 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)
 
@@ -127,18 +172,18 @@ huggingface-cli login
 
 ### 数据准备（可跳过）
 
-关于数据集文件的格式，请参考 `data/example_dataset` 文件夹的内容。构建自定义数据集时，既可以使用单个 `.json` 文件，也可以使用一个[数据加载脚本](https://huggingface.co/docs/datasets/dataset_script)和多个文件。
+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.md`。
+> 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-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
 ```
 
@@ -148,17 +193,6 @@ pip install -r requirements.txt
 pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
 ```
 
-### 浏览器一体化界面
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_web.py
-```
-
-我们极力推荐新手使用浏览器一体化界面，因为它还可以**自动**生成运行所需的命令行脚本。
-
-> [!WARNING]
-> 目前网页 UI 仅支持**单卡训练**。
-
 ### 单 GPU 训练
 
 > [!IMPORTANT]
@@ -356,7 +390,7 @@ deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \
 
 </details>
 
-### 导出微调后的模型
+### 导出微调后的完整模型
 
 ```bash
 python src/export_model.py \
@@ -364,7 +398,7 @@ python src/export_model.py \
     --template default \
     --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint \
-    --output_dir path_to_export
+    --export_dir path_to_export
 ```
 
 ### API 服务
@@ -400,26 +434,21 @@ python src/web_demo.py \
     --checkpoint_dir path_to_checkpoint
 ```
 
-### 指标评估（BLEU 分数和汉语 ROUGE 分数）
+### 模型评估
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage sft \
+CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
     --model_name_or_path path_to_llama_model \
-    --do_eval \
-    --dataset alpaca_gpt4_zh \
-    --template default \
     --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint \
-    --output_dir path_to_eval_result \
-    --per_device_eval_batch_size 8 \
-    --max_samples 100 \
-    --predict_with_generate
+    --template vanilla \
+    --task ceval \
+    --split validation \
+    --lang zh \
+    --n_shot 5 \
+    --batch_size 4
 ```
 
-> [!NOTE]
-> 我们建议在量化模型的评估中使用 `--per_device_eval_batch_size=1` 和 `--max_target_length 128`。
-
 ### 模型预测
 
 ```bash
@@ -437,40 +466,39 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --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：
-
-- [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md)
-- [LLaMA-2](https://ai.meta.com/llama/license/)
-- [BLOOM](https://huggingface.co/spaces/bigscience/license)
-- [Falcon](LICENSE)
-- [Baichuan](https://huggingface.co/baichuan-inc/baichuan-7B/resolve/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)
-- [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)
-- [InternLM](https://github.com/InternLM/InternLM#open-source-license)
-- [Qwen](https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/LICENSE)
-- [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf)
-- [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B/blob/main/MODEL_LICENSE)
+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-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}
 }
 ```
 
 ## 致谢
 
-本项目受益于 [PEFT](https://github.com/huggingface/peft)、[QLoRA](https://github.com/artidoro/qlora) 和 [OpenChatKit](https://github.com/togethercomputer/OpenChatKit)，感谢以上诸位作者的付出。
+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-Efficient-Tuning&type=Date)
+![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date)

data/README.md

@@ -5,28 +5,103 @@ If you are using a custom dataset, please provide your dataset definition in the
   "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)",
-  "ranking": "whether the examples contains ranked responses or not. (default: false)",
+  "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)"
   }
 }
 ```
 
-where the `prompt` and `response` columns should contain non-empty values. The `query` column will be concatenated with the `prompt` column and used as input for the model. The `history` column should contain a list where each element is a string tuple representing a query-response pair.
+Given above, you can use the custom dataset via specifying `--dataset dataset_name`.
 
-For datasets used in reward modeling or DPO training, the `response` column should be a string list, with the preferred answers appearing first, for example:
+Currently we support dataset in **alpaca** or **sharegpt** format, the dataset in alpaca format should follow the below format:
+
+```json
+[
+  {
+    "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": "instruction",
+    "query": "input",
+    "response": "output",
+    "history": "history"
+  }
+}
+```
+
+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.
+
+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**.
+
+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": "Question",
-  "input": "",
+  "instruction": "user instruction",
+  "input": "user input",
   "output": [
-    "Chosen answer",
-    "Rejected answer"
+    "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

@@ -1,32 +1,107 @@
-如果您使用自定义数据集，请务必在 `dataset_info.json` 文件中以如下格式提供您的数据集定义。
+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）",
-  "ranking": "数据集是否包含排序后的回答（默认：false）",
+  "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）",
-    "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": "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）"
   }
 }
 ```
 
-其中 `prompt` 和 `response` 列应当是非空的字符串。`query` 列的内容将会和 `prompt` 列拼接作为模型输入。`history` 列应当是一个列表，其中每个元素是一个字符串二元组，分别代表用户请求和模型答复。
+添加后可通过指定 `--dataset 数据集名称` 参数使用自定义数据集。
 
-对于训练奖励模型或 DPO 训练的数据集，`response` 列应当是一个字符串列表，排在前面的代表更优的答案，例如：
+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": "Question",
-  "input": "",
+  "instruction": "用户指令",
+  "input": "用户输入",
   "output": [
-    "Chosen answer",
-    "Rejected answer"
+    "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/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

@@ -1,9 +1,10 @@
 torch>=1.13.1
-transformers>=4.30.0
-datasets>=2.12.0
+transformers>=4.31.0,<4.35.0
+datasets>=2.14.0
 accelerate>=0.21.0
-peft==0.4.0
-trl>=0.7.1
+peft>=0.6.0
+trl>=0.7.4
+gradio>=3.38.0,<4.0.0
 scipy
 sentencepiece
 protobuf
@@ -11,9 +12,8 @@ tiktoken
 jieba
 rouge-chinese
 nltk
-gradio>=3.36.0
 uvicorn
-pydantic==1.10.11
-fastapi==0.95.1
+pydantic
+fastapi
 sse-starlette
 matplotlib

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

@@ -6,8 +6,8 @@ from llmtuner import ChatModel, create_app
 def main():
     chat_model = ChatModel()
     app = create_app(chat_model)
-    uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
     print("Visit http://localhost:8000/docs for API document.")
+    uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
 
 
 if __name__ == "__main__":

src/cli_demo.py

@@ -1,4 +1,12 @@
 from llmtuner import ChatModel
+from llmtuner.extras.misc import torch_gc
+
+try:
+    import platform
+    if platform.system() != "Windows":
+        import readline
+except ImportError:
+    print("Install `readline` for a better experience.")
 
 
 def main():
@@ -20,6 +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/llmtuner/__init__.py

@@ -1,9 +1,10 @@
-# Level: api, webui > chat > tuner > dsets > extras, hparams
+# 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 export_model, run_exp
+from llmtuner.eval import Evaluator
+from llmtuner.train import export_model, run_exp
 from llmtuner.webui import create_ui, create_web_demo
 
 
-__version__ = "0.1.8"
+__version__ = "0.3.0"

src/llmtuner/api/app.py

@@ -1,12 +1,8 @@
-import uvicorn
-from fastapi import FastAPI, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-from contextlib import asynccontextmanager
-from sse_starlette import EventSourceResponse
+import json
 from typing import List, Tuple
+from pydantic import BaseModel
+from contextlib import asynccontextmanager
 
-from llmtuner.extras.misc import torch_gc
-from llmtuner.chat import ChatModel
 from llmtuner.api.protocol import (
     Role,
     Finish,
@@ -21,15 +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) -> FastAPI:
+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(
@@ -45,14 +66,14 @@ def create_app(chat_model: ChatModel) -> FastAPI:
         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 len(request.messages) < 1 or request.messages[-1].role != Role.USER:
-            raise HTTPException(status_code=400, detail="Invalid request")
+        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:
+        if len(prev_messages) and prev_messages[0].role == Role.SYSTEM:
             system = prev_messages.pop(0).content
         else:
             system = None
@@ -62,28 +83,42 @@ def create_app(chat_model: ChatModel) -> FastAPI:
             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, system, request)
             return EventSourceResponse(generate, media_type="text/event-stream")
 
-        response, (prompt_length, response_length) = chat_model.chat(
-            query, history, system, 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=[choice_data], usage=usage)
+        return ChatCompletionResponse(model=request.model, choices=choices, usage=usage)
 
     async def predict(query: str, history: List[Tuple[str, str]], system: str, request: ChatCompletionRequest):
         choice_data = ChatCompletionResponseStreamChoice(
@@ -92,10 +127,14 @@ def create_app(chat_model: ChatModel) -> FastAPI:
             finish_reason=None
         )
         chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
-        yield chunk.json(exclude_unset=True, ensure_ascii=False)
+        yield to_json(chunk)
 
         for new_text in chat_model.stream_chat(
-            query, history, system, 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
@@ -106,7 +145,7 @@ def create_app(chat_model: ChatModel) -> FastAPI:
                 finish_reason=None
             )
             chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
-            yield chunk.json(exclude_unset=True, ensure_ascii=False)
+            yield to_json(chunk)
 
         choice_data = ChatCompletionResponseStreamChoice(
             index=0,
@@ -114,7 +153,7 @@ def create_app(chat_model: ChatModel) -> FastAPI:
             finish_reason=Finish.STOP
         )
         chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
-        yield chunk.json(exclude_unset=True, ensure_ascii=False)
+        yield to_json(chunk)
         yield "[DONE]"
 
     return app

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

@@ -1,11 +1,21 @@
 import torch
-from typing import Any, Dict, Generator, List, Optional, Tuple
+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.extras.misc import dispatch_model, get_logits_processor
-from llmtuner.extras.template import get_template_and_fix_tokenizer
-from llmtuner.tuner.core import get_infer_args, load_model_and_tokenizer
+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:
@@ -13,11 +23,12 @@ 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(
+    def _process_args(
         self,
         query: str,
         history: Optional[List[Tuple[str, str]]] = None,
@@ -25,17 +36,17 @@ class ChatModel:
         **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)
-        prompt_length = len(input_ids[0])
 
         do_sample = input_kwargs.pop("do_sample", None)
         temperature = input_kwargs.pop("temperature", None)
         top_p = input_kwargs.pop("top_p", None)
         top_k = input_kwargs.pop("top_k", None)
+        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)
@@ -46,11 +57,15 @@ class ChatModel:
             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
@@ -74,13 +89,30 @@ class ChatModel:
         history: Optional[List[Tuple[str, str]]] = None,
         system: Optional[str] = None,
         **input_kwargs
-    ) -> Tuple[str, Tuple[int, int]]:
-        gen_kwargs, prompt_length = self.process_args(query, history, system, **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)
+    ) -> 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(
@@ -90,7 +122,7 @@ class ChatModel:
         system: Optional[str] = None,
         **input_kwargs
     ) -> Generator[str, None, None]:
-        gen_kwargs, _ = self.process_args(query, history, system, **input_kwargs)
+        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
 

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

@@ -138,16 +138,15 @@ class Template:
         token_ids = []
         for elem in context:
             if isinstance(elem, str):
-                if len(elem) == 0:
-                    continue
                 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 NotImplementedError
+                raise ValueError("Input must be string or dict[str, str], got {}".format(type(elem)))
 
         return token_ids
 
@@ -226,90 +225,6 @@ def get_template_and_fix_tokenizer(
     return template
 
 
-r"""
-Supports language model inference without histories.
-"""
-register_template(
-    name="vanilla",
-    prefix=[],
-    prompt=[
-        "{{query}}"
-    ],
-    system="",
-    sep=[],
-    use_history=False
-)
-
-
-r"""
-Default template.
-"""
-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"
-    ]
-)
-
-
-r"""
-Supports: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
-          https://huggingface.co/meta-llama/Llama-2-13b-chat-hf
-          https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
-"""
-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=[]
-)
-
-
-r"""
-Supports: https://github.com/ymcui/Chinese-LLaMA-Alpaca-2
-          https://huggingface.co/ziqingyang/chinese-alpaca-2-7b
-"""
-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=[]
-)
-
-
-r"""
-Supports: https://huggingface.co/tatsu-lab/alpaca-7b-wdiff
-          https://github.com/ymcui/Chinese-LLaMA-Alpaca
-"""
 register_template(
     name="alpaca",
     prefix=[
@@ -328,29 +243,60 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/lmsys/vicuna-7b-delta-v1.1
-          https://huggingface.co/lmsys/vicuna-13b-delta-v1.1
-"""
 register_template(
-    name="vicuna",
+    name="aquila",
     prefix=[
         "{{system}}"
     ],
     prompt=[
-        "USER: {{query}} ASSISTANT: "
+        "Human: {{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."
+        "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=[]
+    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
 )
 
 
-r"""
-Supports: https://huggingface.co/BelleGroup/BELLE-LLaMA-EXT-13B
-"""
 register_template(
     name="belle",
     prefix=[
@@ -366,87 +312,122 @@ register_template(
 )
 
 
-r"""
-Supports: https://github.com/CVI-SZU/Linly
-"""
 register_template(
-    name="linly",
+    name="bluelm",
     prefix=[
         "{{system}}"
     ],
     prompt=[
-        "User: {{query}}\nBot: "
+        {"token": "[|Human|]:"},
+        "{{query}}",
+        {"token": "[|AI|]:"}
     ],
     system="",
-    sep=[
-        "\n"
-    ]
+    sep=[]
 )
 
 
-r"""
-Supports: https://github.com/Neutralzz/BiLLa
-"""
 register_template(
-    name="billa",
+    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="",
-    sep=[
-        "\n"
-    ]
-)
-
-
-r"""
-Supports: https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1
-"""
-register_template(
-    name="ziya",
-    prefix=[
-        "{{system}}"
-    ],
-    prompt=[
-        {"token": "<human>"},
-        ":{{query}}\n",
-        {"token": "<bot>"},
-        ":"
-    ],
-    system="",
-    sep=[
-        "\n"
-    ]
-)
-
-
-r"""
-Supports: https://huggingface.co/qhduan/aquilachat-7b
-"""
-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."
+        "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"
     ]
 )
 
 
-r"""
-Supports: https://huggingface.co/internlm/internlm-chat-7b
-"""
+register_template(
+    name="falcon",
+    prefix=[
+        "{{system}}"
+    ],
+    prompt=[
+        "User: {{query}}\nFalcon:"
+    ],
+    system="",
+    sep=[
+        "\n"
+    ],
+    efficient_eos=True
+)
+
+
 register_template(
     name="intern",
     prefix=[
@@ -469,49 +450,101 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/baichuan-inc/Baichuan-13B-Chat
-"""
 register_template(
-    name="baichuan",
+    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=[
-        {"token": "<reserved_102>"}, # user token
-        "{{query}}",
-        {"token": "<reserved_103>"}  # assistant token
+        "[INST] {{query}} [/INST]"
     ],
     system="",
-    sep=[],
-    efficient_eos=True
+    sep=[]
 )
 
 
-r"""
-Supports: https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat
-          https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat
-"""
 register_template(
-    name="baichuan2",
+    name="openchat",
     prefix=[
         "{{system}}"
     ],
     prompt=[
-        {"token": "<reserved_106>"}, # user token
-        "{{query}}",
-        {"token": "<reserved_107>"}  # assistant token
+        "GPT4 Correct User: {{query}}",
+        {"token": "<|end_of_turn|>"},
+        "GPT4 Correct Assistant:"
     ],
     system="",
-    sep=[],
+    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
 )
 
 
-r"""
-Supports: https://huggingface.co/HuggingFaceH4/starchat-alpha
-          https://huggingface.co/HuggingFaceH4/starchat-beta
-"""
 register_template(
     name="starchat",
     prefix=[
@@ -538,58 +571,36 @@ register_template(
 
 
 r"""
-Supports: https://huggingface.co/Qwen/Qwen-7B-Chat
+Supports language model inference without histories.
 """
 register_template(
-    name="chatml",
-    prefix=[
-        {"token": "<|im_start|>"},
-        "system\n{{system}}"
-    ],
+    name="vanilla",
+    prefix=[],
     prompt=[
-        {"token": "<|im_start|>"},
-        "user\n{{query}}",
-        {"token": "<|im_end|>"},
-        "\n",
-        {"token": "<|im_start|>"},
-        "assistant\n"
+        "{{query}}"
     ],
-    system="You are a helpful assistant.",
-    sep=[
-        {"token": "<|im_end|>"},
-        "\n"
-    ],
-    stop_words=[
-        "<|im_end|>"
-    ],
-    efficient_eos=True
+    system="",
+    sep=[],
+    use_history=False
 )
 
 
-r"""
-Supports: https://huggingface.co/THUDM/chatglm2-6b
-"""
 register_template(
-    name="chatglm2",
+    name="vicuna",
     prefix=[
-        {"token": "[gMASK]"},
-        {"token": "sop"},
         "{{system}}"
     ],
     prompt=[
-        "[Round {{idx}}]\n\n问：{{query}}\n\n答："
+        "USER: {{query}} ASSISTANT:"
     ],
-    system="",
-    sep=[
-        "\n\n"
-    ],
-    efficient_eos=True
+    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=[]
 )
 
 
-r"""
-Supports: https://huggingface.co/xverse/XVERSE-13B-Chat
-"""
 register_template(
     name="xverse",
     prefix=[
@@ -601,3 +612,71 @@ register_template(
     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

@@ -13,9 +13,11 @@ logger = get_logger(__name__)
 
 
 EXT2TYPE = {
+    "arrow": "arrow",
     "csv": "csv",
     "json": "json",
     "jsonl": "json",
+    "parquet": "parquet",
     "txt": "text"
 }
 

src/llmtuner/dsets/__init__.py

@@ -1,3 +0,0 @@
-from llmtuner.dsets.loader import get_dataset
-from llmtuner.dsets.preprocess import preprocess_dataset
-from llmtuner.dsets.utils import split_dataset

src/llmtuner/dsets/loader.py

@@ -1,92 +0,0 @@
-import os
-from typing import TYPE_CHECKING, List, Union
-
-from datasets import concatenate_datasets, interleave_datasets, load_dataset
-
-from llmtuner.dsets.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_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)): # 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 type does not match."
-            elif os.path.isfile(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)): # single file
-                data_files.append(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name))
-                data_path = EXT2TYPE.get(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(
-            data_path,
-            data_files=data_files,
-            split=data_args.split,
-            cache_dir=model_args.cache_dir,
-            streaming=data_args.streaming,
-            use_auth_token=True if model_args.use_auth_token else None
-        )
-
-        if max_samples is not None:
-            max_samples_temp = min(len(dataset), max_samples)
-            dataset = dataset.select(range(max_samples_temp))
-
-        for column_name in ["prompt", "query", "response", "history"]: # align datasets
-            if getattr(dataset_attr, column_name) and getattr(dataset_attr, column_name) != column_name:
-                dataset = dataset.rename_column(getattr(dataset_attr, column_name), column_name)
-
-        if dataset_attr.system_prompt: # add system prompt
-            if data_args.streaming:
-                dataset = dataset.map(lambda _: {"system": dataset_attr.system_prompt})
-            else:
-                dataset = dataset.add_column("system", [dataset_attr.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.")
-        stopping_strategy = "first_exhausted" if data_args.mix_strategy.endswith("under") else "all_exhausted"
-        return interleave_datasets(all_datasets, data_args.interleave_probs, stopping_strategy=stopping_strategy)
-    else:
-        raise ValueError("Unknown mixing strategy.")

src/llmtuner/dsets/preprocess.py

@@ -1,193 +0,0 @@
-import tiktoken
-from typing import TYPE_CHECKING, Any, Dict, Generator, List, Literal, Union
-from itertools import chain
-
-from llmtuner.extras.constants import IGNORE_INDEX
-from llmtuner.extras.template import get_template_and_fix_tokenizer
-
-if TYPE_CHECKING:
-    from datasets import Dataset, IterableDataset
-    from transformers import Seq2SeqTrainingArguments
-    from transformers.tokenization_utils import PreTrainedTokenizer
-    from llmtuner.hparams import DataArguments
-
-
-def preprocess_dataset(
-    dataset: Union["Dataset", "IterableDataset"],
-    tokenizer: "PreTrainedTokenizer",
-    data_args: "DataArguments",
-    training_args: "Seq2SeqTrainingArguments",
-    stage: Literal["pt", "sft", "rm", "ppo"]
-) -> Union["Dataset", "IterableDataset"]:
-    column_names = list(next(iter(dataset)).keys())
-    template = get_template_and_fix_tokenizer(data_args.template, tokenizer)
-
-    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 preprocess_pretrain_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
-        # build grouped texts with format `X1 X2 X3 ...`
-        if isinstance(getattr(tokenizer, "tokenizer", None), tiktoken.Encoding):
-            kwargs = dict(allowed_special="all") # for tiktoken tokenizer (Qwen)
-        else:
-            kwargs = dict(add_special_tokens=True)
-
-        if hasattr(tokenizer, "add_bos_token") and hasattr(tokenizer, "add_eos_token"):
-            setattr(tokenizer, "add_bos_token", True) # for LLaMA tokenizer
-            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.max_source_length
-        # we drop the small remainder, and if the total_length < block_size, we exclude this batch
-        total_length = (total_length // block_size) * block_size
-        # split by chunks of max_source_length
-        result = {
-            k: [t[i: i + block_size] for i in range(0, total_length, block_size)]
-            for k, t in concatenated_examples.items()
-        }
-        return result
-
-    def preprocess_supervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
-        # 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": []}
-        max_length = data_args.max_source_length + data_args.max_target_length
-
-        for query, response, history, system in construct_example(examples):
-            input_ids, labels = [], []
-
-            for turn_idx, (source_ids, target_ids) in enumerate(template.encode_multiturn(
-                tokenizer, query, response, history, system
-            )):
-                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]
-
-                if len(input_ids) + len(source_ids) + len(target_ids) > max_length:
-                    break
-
-                if 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]
-
-            model_inputs["input_ids"].append(input_ids)
-            model_inputs["attention_mask"].append([1] * len(input_ids))
-            model_inputs["labels"].append(labels)
-
-        return model_inputs
-
-    def preprocess_unsupervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
-        # 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):
-            source_ids, target_ids = template.encode_oneturn(tokenizer, query, response, history, system)
-
-            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]
-
-            if template.efficient_eos:
-                target_ids += [tokenizer.eos_token_id]
-
-            model_inputs["input_ids"].append(source_ids)
-            model_inputs["attention_mask"].append([1] * len(source_ids))
-            model_inputs["labels"].append(target_ids)
-
-        return model_inputs
-
-    def preprocess_pairwise_dataset(examples):
-        # build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
-        model_inputs = {"prompt_ids": [], "chosen_ids": [], "rejected_ids": []}
-        for query, response, history, system in construct_example(examples):
-            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 len(prompt_ids) > data_args.max_source_length:
-                prompt_ids = prompt_ids[:data_args.max_source_length]
-            if len(chosen_ids) > data_args.max_target_length:
-                chosen_ids = chosen_ids[:data_args.max_target_length]
-            if len(rejected_ids) > data_args.max_target_length:
-                rejected_ids = rejected_ids[:data_args.max_target_length]
-
-            if template.efficient_eos:
-                chosen_ids += [tokenizer.eos_token_id]
-                rejected_ids += [tokenizer.eos_token_id]
-
-            model_inputs["prompt_ids"].append(prompt_ids)
-            model_inputs["chosen_ids"].append(chosen_ids)
-            model_inputs["rejected_ids"].append(rejected_ids)
-        return model_inputs
-
-    def print_supervised_dataset_example(example):
-        print("input_ids:\n{}".format(example["input_ids"]))
-        print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
-        print("label_ids:\n{}".format(example["labels"]))
-        print("labels:\n{}".format(tokenizer.decode([
-            token_id if token_id != IGNORE_INDEX else tokenizer.pad_token_id for token_id in example["labels"]
-        ], skip_special_tokens=False)))
-
-    def print_pairwise_dataset_example(example):
-        print("prompt_ids:\n{}".format(example["prompt_ids"]))
-        print("prompt:\n{}".format(tokenizer.decode(example["prompt_ids"], skip_special_tokens=False)))
-        print("chosen_ids:\n{}".format(example["chosen_ids"]))
-        print("chosen:\n{}".format(tokenizer.decode(example["chosen_ids"], skip_special_tokens=False)))
-        print("rejected_ids:\n{}".format(example["rejected_ids"]))
-        print("rejected:\n{}".format(tokenizer.decode(example["rejected_ids"], skip_special_tokens=False)))
-
-    def print_unsupervised_dataset_example(example):
-        print("input_ids:\n{}".format(example["input_ids"]))
-        print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
-
-    if stage == "pt":
-        dataset = dataset.filter(lambda example: example["prompt"])
-        preprocess_function = preprocess_pretrain_dataset
-        print_function = print_unsupervised_dataset_example
-    elif stage == "sft" and not training_args.predict_with_generate:
-        dataset = dataset.filter(lambda example: example["prompt"] and example["response"])
-        preprocess_function = preprocess_supervised_dataset
-        print_function = print_supervised_dataset_example
-    elif stage == "rm":
-        dataset = dataset.filter(lambda example: example["prompt"] and len(example["response"]) > 1)
-        preprocess_function = preprocess_pairwise_dataset
-        print_function = print_pairwise_dataset_example
-    else:
-        dataset = dataset.filter(lambda example: example["prompt"])
-        preprocess_function = preprocess_unsupervised_dataset
-        print_function = print_unsupervised_dataset_example
-
-    with training_args.main_process_first(desc="dataset map pre-processing"):
-        kwargs = {}
-        if not data_args.streaming:
-            kwargs = dict(
-                num_proc=data_args.preprocessing_num_workers,
-                load_from_cache_file=not data_args.overwrite_cache,
-                desc="Running tokenizer on dataset"
-            )
-
-        dataset = dataset.map(
-            preprocess_function,
-            batched=True,            
-            remove_columns=column_names,
-            **kwargs
-        )
-
-        print_function(next(iter(dataset)))
-        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

@@ -5,15 +5,14 @@ from typing import TYPE_CHECKING
 from datetime import timedelta
 
 from transformers import TrainerCallback
-from transformers.trainer_callback import TrainerControl, TrainerState
 from transformers.trainer_utils import has_length, PREFIX_CHECKPOINT_DIR
-from transformers.training_args import TrainingArguments
 
 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__)
@@ -27,14 +26,24 @@ class SavePeftModelCallback(TrainerCallback):
         """
         if args.should_save:
             output_dir = os.path.join(args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, state.global_step))
-            getattr(kwargs.get("model"), "pretrained_model").save_pretrained(output_dir)
+            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:
-            getattr(kwargs.get("model"), "pretrained_model").save_pretrained(args.output_dir)
+            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):
@@ -64,7 +73,7 @@ class LogCallback(TrainerCallback):
             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)):
+            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))
 
@@ -132,6 +141,11 @@ class LogCallback(TrainerCallback):
             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(logs) + "\n")

src/llmtuner/extras/constants.py

@@ -1,11 +1,25 @@
+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
 
+LAYERNORM_NAMES = {"norm", "ln"}
+
 LOG_FILE_NAME = "trainer_log.jsonl"
 
-LAYERNORM_NAMES = ["norm", "ln_f", "ln_attn", "ln_mlp"]
-
 METHODS = ["full", "freeze", "lora"]
 
+SUBJECTS = ["Average", "STEM", "Social Sciences", "Humanities", "Other"]
+
+SUPPORTED_MODELS = OrderedDict()
+
 TRAINING_STAGES = {
     "Supervised Fine-Tuning": "sft",
     "Reward Modeling": "rm",
@@ -14,69 +28,251 @@ TRAINING_STAGES = {
     "Pre-Training": "pt"
 }
 
-SUPPORTED_MODELS = {
+
+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",
+        "LLaMA-65B": "huggyllama/llama-65b"
+    }
+)
+
+
+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",
-    "ChineseLLaMA2-7B": "ziqingyang/chinese-llama-2-7b",
-    "ChineseLLaMA2-13B": "ziqingyang/chinese-llama-2-13b",
-    "ChineseLLaMA2-7B-Chat": "ziqingyang/chinese-alpaca-2-7b",
-    "ChineseLLaMA2-13B-Chat": "ziqingyang/chinese-alpaca-2-13b",
-    "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": "tiiuae/falcon-7b",
-    "Falcon-7B-Chat": "tiiuae/falcon-7b-instruct",
-    "Falcon-40B": "tiiuae/falcon-40b",
-    "Falcon-40B-Chat": "tiiuae/falcon-40b-instruct",
-    "Baichuan-7B": "baichuan-inc/Baichuan-7B",
-    "Baichuan-13B": "baichuan-inc/Baichuan-13B-Base",
-    "Baichuan-13B-Chat": "baichuan-inc/Baichuan-13B-Chat",
-    "Baichuan2-7B": "baichuan-inc/Baichuan2-7B-Base",
-    "Baichuan2-13B": "baichuan-inc/Baichuan2-13B-Base",
-    "Baichuan2-7B-Chat": "baichuan-inc/Baichuan2-7B-Chat",
-    "Baichuan2-13B-Chat": "baichuan-inc/Baichuan2-13B-Chat",
-    "InternLM-7B": "internlm/internlm-7b",
-    "InternLM-7B-Chat": "internlm/internlm-chat-7b",
+        "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-13B-Chat": "xverse/XVERSE-13B-Chat",
-    "ChatGLM2-6B-Chat": "THUDM/chatglm2-6b"
-}
+        "XVERSE-65B": "xverse/XVERSE-65B",
+        "XVERSE-7B-Chat": "xverse/XVERSE-7B-Chat",
+        "XVERSE-13B-Chat": "xverse/XVERSE-13B-Chat"
+    },
+    template="xverse"
+)
 
-DEFAULT_MODULE = {
-    "LLaMA": "q_proj,v_proj",
-    "LLaMA2": "q_proj,v_proj",
-    "ChineseLLaMA2": "q_proj,v_proj",
-    "BLOOM": "query_key_value",
-    "BLOOMZ": "query_key_value",
-    "Falcon": "query_key_value",
-    "Baichuan": "W_pack",
-    "Baichuan2": "W_pack",
-    "InternLM": "q_proj,v_proj",
-    "Qwen": "c_attn",
-    "XVERSE": "q_proj,v_proj",
-    "ChatGLM2": "query_key_value"
-}
 
-DEFAULT_TEMPLATE = {
-    "LLaMA2": "llama2",
-    "ChineseLLaMA2": "llama2_zh",
-    "Baichuan": "baichuan",
-    "Baichuan2": "baichuan2",
-    "InternLM": "intern",
-    "Qwen": "chatml",
-    "XVERSE": "xverse",
-    "ChatGLM2": "chatglm2"
+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

@@ -3,6 +3,9 @@ import logging
 
 
 class LoggerHandler(logging.Handler):
+    r"""
+    Logger handler used in Web UI.
+    """
 
     def __init__(self):
         super().__init__()
@@ -19,16 +22,10 @@ class LoggerHandler(logging.Handler):
         self.log += "\n\n"
 
 
-def reset_logging():
-    r"""
-    Removes basic config of root logger
-    """
-    root = logging.getLogger()
-    list(map(root.removeHandler, root.handlers))
-    list(map(root.removeFilter, root.filters))
-
-
 def get_logger(name: str) -> logging.Logger:
+    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"
@@ -41,3 +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

@@ -1,10 +1,25 @@
 import gc
+import os
+import sys
 import torch
-from typing import TYPE_CHECKING, Tuple
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple
 from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList
 
+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()
+
 if TYPE_CHECKING:
-    from transformers.modeling_utils import PreTrainedModel
+    from transformers import HfArgumentParser
 
 
 class AverageMeter:
@@ -49,12 +64,48 @@ def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
     return trainable_params, all_param
 
 
-def get_logits_processor() -> LogitsProcessorList:
+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:
+        return dummy_accelerator.local_process_index if torch.cuda.is_available() else "cpu"
+
+
+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
 
 
+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
+
+
+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.
@@ -63,28 +114,3 @@ def torch_gc() -> None:
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()
-
-
-def dispatch_model(model: "PreTrainedModel") -> "PreTrainedModel":
-    r"""
-    Dispatches a pre-trained model to GPUs with balanced memory.
-    Borrowed from: https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/modeling_utils.py#L2803
-    """
-    if 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()

src/llmtuner/extras/models/flash_llama.py

@@ -1,305 +0,0 @@
-# coding=utf-8
-# Modified from:
-# [1] https://huggingface.co/Birchlabs/flash_llama/blob/main/modeling_flash_llama.py
-# [2] https://github.com/lm-sys/FastChat/blob/main/fastchat/train/llama2_flash_attn_monkey_patch.py
-# [3] https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/blob/main/modeling_flash_llama.py
-# [4] https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
-# With fix from Alex Birch: https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/discussions/17
-
-import torch
-from typing import Optional, Tuple
-from transformers.utils import logging
-from transformers.models.llama.configuration_llama import LlamaConfig
-
-
-try:
-    from flash_attn.flash_attn_interface import (
-        flash_attn_kvpacked_func,
-        flash_attn_varlen_kvpacked_func,
-    )
-    from flash_attn.bert_padding import unpad_input, pad_input
-    flash_attn_v2_installed = True
-    print('>>>> Flash Attention installed')
-except ImportError:
-    flash_attn_v2_installed = False
-    raise ImportError('Please install Flash Attention: `pip install flash-attn --no-build-isolation`')
-
-try:
-    from flash_attn.layers.rotary import apply_rotary_emb_func
-    flash_rope_installed = True
-    print('>>>> Flash RoPE installed')
-except ImportError:
-    flash_rope_installed = False
-    raise ImportError('Please install RoPE kernels: `pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary`')
-
-
-logger = logging.get_logger(__name__)
-
-
-class LlamaRMSNorm(torch.nn.Module):
-
-    def __init__(self, hidden_size, eps=1e-6):
-        super().__init__()
-        self.weight = torch.nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return (self.weight * hidden_states).to(input_dtype) # for fp32 weight
-
-
-class FlashRotaryEmbedding(torch.nn.Module):
-
-    def __init__(
-        self,
-        dim: int,
-        base=10000.0,
-        interleaved=False,
-        scale_base=None,
-        scaling_factor=1.0,
-        pos_idx_in_fp32=True,
-        device=None
-    ):
-        super().__init__()
-        self.dim = dim
-        self.base = float(base)
-        self.pos_idx_in_fp32 = pos_idx_in_fp32
-        # Generate and save the inverse frequency buffer (non trainable)
-        inv_freq = self._compute_inv_freq(device)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.interleaved = interleaved
-        self.scale_base = scale_base
-        self.scaling_factor = scaling_factor
-        scale = (
-            (torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim)
-            if scale_base is not None else None
-        )
-        self.register_buffer("scale", scale)
-
-        self._seq_len_cached = 0
-        self._cos_cached = None
-        self._sin_cached = None
-        self._cos_k_cached = None
-        self._sin_k_cached = None
-
-    def _compute_inv_freq(self, device=None):
-        return 1 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
-
-    def _update_cos_sin_cache(self, seqlen, device=None, dtype=None):
-        if (
-            seqlen > self._seq_len_cached or self._cos_cached.device != device
-            or self._cos_cached.dtype != dtype
-            or (self.training and self._cos_cached.is_inference())
-        ):
-            self._seq_len_cached = seqlen
-            if self.pos_idx_in_fp32:
-                t = torch.arange(seqlen, device=device, dtype=torch.float32)
-                t /= self.scaling_factor
-                if self.inv_freq.dtype != torch.float32:
-                    inv_freq = self.inv_freq.to(torch.float32)
-                else:
-                    inv_freq = self.inv_freq
-            else:
-                t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
-                t /= self.scaling_factor
-                inv_freq = self.inv_freq
-            freqs = torch.outer(t, inv_freq)
-            if self.scale is None:
-                self._cos_cached = torch.cos(freqs).to(dtype)
-                self._sin_cached = torch.sin(freqs).to(dtype)
-            else:
-                power = (
-                    (torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2) / self.scale_base
-                )
-                scale = self.scale.to(device=power.device) ** power.unsqueeze(-1)
-                # We want the multiplication by scale to happen in fp32
-                self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
-                self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
-                self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
-                self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
-
-    def forward(self, q: torch.Tensor, k: torch.Tensor, seqlen_offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
-        r"""
-        q: (batch, seqlen, nheads, headdim)
-        k: (batch, seqlen, nheads, headdim)
-        seqlen_offset: can be used in generation where the qkv being passed in is only the last
-        token in the batch.
-        """
-        self._update_cos_sin_cache(q.shape[1] + seqlen_offset, device=q.device, dtype=q.dtype)
-        if self.scale is None:
-            return apply_rotary_emb_func(
-                q, self._cos_cached[seqlen_offset:], self._sin_cached[seqlen_offset:],
-                self.interleaved, True # inplace=True
-            ), apply_rotary_emb_func(
-                k, self._cos_cached[seqlen_offset:], self._sin_cached[seqlen_offset:],
-                self.interleaved, True # inplace=True
-            )
-        else:
-            assert False
-
-
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    r"""
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, slen, _, num_key_value_heads, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, :, :, None, :].expand(batch, slen, 2, num_key_value_heads, n_rep, head_dim)
-    return hidden_states.reshape(batch, slen, 2, num_key_value_heads * n_rep, head_dim)
-
-
-class LlamaAttention(torch.nn.Module):
-
-    def __init__(self, config: "LlamaConfig"):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.num_key_value_heads = config.num_key_value_heads
-        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
-        self.max_position_embeddings = config.max_position_embeddings
-
-        if (self.head_dim * self.num_heads) != self.hidden_size:
-            raise ValueError(
-                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
-                f" and `num_heads`: {self.num_heads})."
-            )
-
-        self.q_proj = torch.nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.k_proj = torch.nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
-        self.v_proj = torch.nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
-        self.o_proj = torch.nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
-
-        self.register_buffer(
-            "norm_factor",
-            torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype()),
-            persistent=False,
-        )
-
-        if self.config.rope_scaling is None:
-            scaling_factor = 1
-        else:
-            scaling_type = self.config.rope_scaling["type"]
-            scaling_factor = self.config.rope_scaling["factor"]
-            assert scaling_type == "linear"
-
-        self.rotary_emb = FlashRotaryEmbedding(
-            self.head_dim, base=10000, interleaved=False, scaling_factor=scaling_factor
-        )
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    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
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        bsz, q_len, h_size = hidden_states.size()
-
-        has_layer_past = past_key_value is not None
-
-        if has_layer_past:
-            past_kv = past_key_value[0]
-            past_len = past_key_value[1]
-        else:
-            past_len = 0
-
-        q = self.q_proj(hidden_states)
-        k = self.k_proj(hidden_states)
-        v = self.v_proj(hidden_states)
-
-        q = q.view(bsz, q_len, self.num_heads, self.head_dim)
-        k = k.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
-        v = v.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
-
-        q, k = self.rotary_emb(q, k, past_len)
-
-        kv = torch.stack([k, v], 2)
-        kv = repeat_kv(kv, self.num_key_value_groups)
-
-        # Cache QKV values
-        if has_layer_past:
-            new_len = past_len+q.size(1)
-            if new_len > past_kv.size(1):
-                past_kv = torch.cat(
-                    [past_kv, torch.empty(bsz, 256, 2, kv.size(3), kv.size(4), dtype=kv.dtype, device=kv.device)], 1
-                )
-            past_kv[:, past_len:new_len] = kv
-            kv = past_kv[:, :new_len]
-        else:
-            past_kv = kv
-
-        past_key_value = (past_kv, past_len + q.size(1)) if use_cache else None
-
-        if attention_mask is not None:
-            # varlen, ignore padding tokens, efficient for large batch with many paddings
-            logger.warning_once("padded sequences is less efficient")
-
-            unpadded_kv, indices_k, cu_seqlens_k, max_seqlen_k = unpad_input(kv, attention_mask)
-            unpadded_q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, attention_mask[:, -q.size(1):])
-            attn_outputs = flash_attn_varlen_kvpacked_func(
-                unpadded_q, unpadded_kv, cu_seqlens_q, cu_seqlens_k,
-                max_seqlen_q, max_seqlen_k,
-                dropout_p=0.0, softmax_scale=1.0/self.norm_factor,
-                causal=(not has_layer_past), return_attn_probs=output_attentions
-            )
-
-            attn_output = attn_outputs[0] if output_attentions else attn_outputs
-            attn_output = pad_input(
-                attn_output, indices_q, bsz, q_len
-            ).reshape(bsz, q_len, h_size)
-            attn_weights = attn_outputs[2] if output_attentions else None
-
-        else:
-            # no padding tokens, more efficient
-            attn_outputs = flash_attn_kvpacked_func(
-                q, kv, dropout_p=0.0, softmax_scale=1.0/self.norm_factor,
-                causal=(not has_layer_past), return_attn_probs=output_attentions
-            )
-            attn_output = attn_outputs[0] if output_attentions else attn_outputs
-            attn_output = attn_output.reshape(bsz, q_len, h_size)
-            attn_weights = attn_outputs[2] if output_attentions else None
-
-        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 key_padding_mask. Fills in the past kv length for use in forward.
-def _prepare_decoder_attention_mask(
-    self, attention_mask, input_shape, inputs_embeds, past_key_values_length
-):
-    # [bsz, seq_len]
-    if past_key_values_length > 0 and attention_mask is not None:
-        attention_mask = torch.cat(
-            (
-                torch.full(
-                    (input_shape[0], past_key_values_length),
-                    True,
-                    dtype=attention_mask.dtype,
-                    device=attention_mask.device
-                ),
-                attention_mask
-            ),
-            dim=-1
-        )
-
-    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/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

@@ -1,21 +0,0 @@
-import os
-import torch
-from transformers.trainer import WEIGHTS_NAME
-
-from llmtuner.extras.logging import get_logger
-
-
-logger = get_logger(__name__)
-
-
-def load_valuehead_params(model: torch.nn.Module, checkpoint_dir: os.PathLike) -> bool:
-    vhead_file = os.path.join(checkpoint_dir, WEIGHTS_NAME)
-    if not os.path.exists(vhead_file):
-        logger.warning("Provided path ({}) does not contain valuehead weights.".format(checkpoint_dir))
-        return False
-    vhead_params = torch.load(vhead_file, map_location="cpu")
-    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)
-    return 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

@@ -11,11 +11,17 @@ class DatasetAttr:
     dataset_name: Optional[str] = None
     dataset_sha1: 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
@@ -31,28 +37,40 @@ class DataArguments:
         metadata={"help": "Which template to use for constructing prompts in training and inference."}
     )
     dataset: Optional[str] = field(
-        default="alpaca_en",
+        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 streaming mode."}
+        metadata={"help": "Enable dataset streaming."}
     )
     buffer_size: Optional[int] = field(
-        default=1024,
-        metadata={"help": "Size of the buffer to randomly sample examples from in streaming mode."}
+        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."}
+        metadata={"help": "Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling)."}
     )
     interleave_probs: Optional[str] = field(
         default=None,
@@ -66,14 +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."}
@@ -94,11 +104,38 @@ class DataArguments:
         default=0,
         metadata={"help": "Size of the development set, should be an integer or a float in range `[0,1)`."}
     )
+    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
 
         prompt_list = self.system_prompt.split("|") if self.system_prompt else [None]
         prompt_list = prompt_list * (len(dataset_names) // len(prompt_list))
@@ -128,7 +165,12 @@ class DataArguments:
                 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

@@ -4,46 +4,38 @@ from dataclasses import asdict, dataclass, field
 
 
 @dataclass
-class FinetuningArguments:
+class FreezeArguments:
     r"""
-    Arguments pertaining to which techniques we are going to fine-tuning with.
+    Arguments pertaining to the freeze (partial-parameter) training.
     """
-    finetuning_type: Optional[Literal["lora", "freeze", "full", "none"]] = 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 for partial-parameter (freeze) fine-tuning. \
-                  LLaMA choices: [\"32\", \"40\", \"60\", \"80\"], \
-                  LLaMA-2 choices: [\"32\", \"40\", \"80\"], \
-                  BLOOM choices: [\"24\", \"30\", \"70\"], \
-                  Falcon choices: [\"32\", \"60\"], \
-                  Baichuan choices: [\"32\", \"40\"] \
-                  Qwen choices: [\"32\"], \
-                  XVERSE choices: [\"40\"], \
-                  ChatGLM2 choices: [\"28\"]"}
-    )
     num_layer_trainable: Optional[int] = field(
         default=3,
         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 partial-parameter (freeze) fine-tuning. \
+                  Use commas to separate multiple modules. \
                   LLaMA choices: [\"mlp\", \"self_attn\"], \
-                  BLOOM & Falcon & ChatGLM2 choices: [\"mlp\", \"self_attention\"], \
-                  Baichuan choices: [\"mlp\", \"self_attn\"], \
+                  BLOOM & Falcon & ChatGLM choices: [\"mlp\", \"self_attention\"], \
                   Qwen choices: [\"mlp\", \"attn\"], \
-                  LLaMA-2, InternLM, XVERSE choices: the same as LLaMA."}
+                  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,
@@ -53,36 +45,129 @@ class FinetuningArguments:
         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 & ChatGLM2 choices: [\"query_key_value\", \"self_attention.dense\", \"mlp.dense\"], \
+                  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\"], \
-                  LLaMA-2, InternLM, XVERSE choices: the same as LLaMA."}
+                  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."}
     )
-    ppo_score_norm: Optional[bool] = field(
-        default=False,
-        metadata={"help": "Use score normalization in PPO Training."}
-    )
+
+
+@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 ["lora", "freeze", "full", "none"], "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):
         r"""Saves the content of this instance in JSON format inside `json_path`."""
@@ -95,4 +180,5 @@ class FinetuningArguments:
         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:
-    r"""
-    Arguments pertaining to which stage we are going to perform.
-    """
-    stage: Optional[Literal["pt", "sft", "rm", "ppo", "dpo"]] = field(
-        default="sft",
-        metadata={"help": "Which stage will be performed in training."}
-    )

src/llmtuner/hparams/generating_args.py

@@ -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

@@ -1,6 +1,5 @@
-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
@@ -19,9 +18,9 @@ class ModelArguments:
         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",
@@ -43,45 +42,34 @@ class ModelArguments:
         default=None,
         metadata={"help": "Adopt scaled rotary positional embeddings."}
     )
-    flash_attn: Optional[bool] = field(
-        default=False,
-        metadata={"help": "Enable flash attention for faster training."}
-    )
     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."}
-    )
-    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."}
     )
-    hf_auth_token: Optional[str] = field(
+    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."}
     )
-    compute_dtype: Optional[torch.dtype] = field(
-        default=None,
-        metadata={"help": "Used in quantization configs. Do not specify this argument manually."}
-    )
-    model_max_length: Optional[int] = field(
-        default=None,
-        metadata={"help": "Used in rope scaling. Do not specify this argument manually."}
-    )
 
     def __post_init__(self):
-        if self.compute_dtype is not None or self.model_max_length is not None:
-            raise ValueError("These arguments cannot be specified.")
+        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."
 
-        if self.use_auth_token == True and self.hf_auth_token is not None:
-            from huggingface_hub.hf_api import HfFolder # lazy load
-            HfFolder.save_token(self.hf_auth_token)
+    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

@@ -1,17 +1,9 @@
-import os
 import torch
 from typing import TYPE_CHECKING
-
-from peft import (
-    PeftModel,
-    TaskType,
-    LoraConfig,
-    get_peft_model
-)
-from peft.utils import CONFIG_NAME, WEIGHTS_NAME
+from peft import PeftModel, TaskType, LoraConfig, get_peft_model
 
 from llmtuner.extras.logging import get_logger
-from llmtuner.tuner.core.utils import find_all_linear_modules
+from llmtuner.model.utils import find_all_linear_modules
 
 if TYPE_CHECKING:
     from transformers.modeling_utils import PreTrainedModel
@@ -25,8 +17,7 @@ def init_adapter(
     model: "PreTrainedModel",
     model_args: "ModelArguments",
     finetuning_args: "FinetuningArguments",
-    is_trainable: bool,
-    is_mergeable: bool
+    is_trainable: bool
 ) -> "PreTrainedModel":
     r"""
     Initializes the adapters.
@@ -36,34 +27,46 @@ def init_adapter(
     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 (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":
+    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 finetuning_args.trainable_layers):
+            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")
-        latest_checkpoint = None
+        checkpoint_to_resume = 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 finetuning_args.resume_lora_training) or (not is_mergeable): # continually fine-tuning
-                checkpoints_to_merge, latest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
+            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
 
@@ -74,10 +77,10 @@ def init_adapter(
             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 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 latest_checkpoint is None: # create new lora weights while training
+        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:
@@ -89,7 +92,8 @@ def init_adapter(
                 r=finetuning_args.lora_rank,
                 lora_alpha=finetuning_args.lora_alpha,
                 lora_dropout=finetuning_args.lora_dropout,
-                target_modules=target_modules
+                target_modules=target_modules,
+                modules_to_save=finetuning_args.additional_target
             )
             model = get_peft_model(model, lora_config)
 

src/llmtuner/model/loader.py

@@ -4,7 +4,6 @@ import torch
 from types import MethodType
 from typing import TYPE_CHECKING, Literal, Optional, Tuple
 
-import transformers
 from transformers import (
     AutoConfig,
     AutoModelForCausalLM,
@@ -14,21 +13,22 @@ from transformers import (
     PreTrainedModel,
     PreTrainedTokenizerBase
 )
-from transformers.utils import check_min_version
+from transformers.models.llama import modeling_llama as LlamaModule
 from transformers.utils.versions import require_version
 from trl import AutoModelForCausalLMWithValueHead
 
 try:
-    from transformers.deepspeed import is_deepspeed_zero3_enabled
-except ImportError:
     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
-from llmtuner.extras.save_and_load import load_valuehead_params
+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.tuner.core.adapter import init_adapter
-from llmtuner.tuner.core.utils import prepare_model_for_training
+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
@@ -38,11 +38,11 @@ if TYPE_CHECKING:
 logger = get_logger(__name__)
 
 
-check_min_version("4.30.0")
-require_version("datasets>=2.12.0", "To fix: pip install datasets>=2.12.0")
+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==0.4.0", "To fix: pip install peft==0.4.0")
-require_version("trl>=0.7.1", "To fix: pip install trl>=0.7.1")
+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(
@@ -56,58 +56,51 @@ def load_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")
 
     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,
+        "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
     )
 
-    # Fix tokenizer (for ChatGLM2)
-    if "PreTrainedTokenizerBase" not in str(tokenizer._pad.__func__):
-        tokenizer._pad = MethodType(PreTrainedTokenizerBase._pad, tokenizer)
-
     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 is_trainable and hasattr(config, "fp16") and hasattr(config, "bf16"):
-        if model_args.compute_dtype == torch.bfloat16:
-            setattr(config, "bf16", True)
-        else:
-            setattr(config, "fp16", True)
+    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 hasattr(config, "use_dynamic_ntk"): # for Qwen models
-            if is_trainable:
-                logger.warning("Qwen model does not support RoPE scaling in training.")
+        if not hasattr(config, "rope_scaling"):
+            logger.warning("Current model does not support RoPE scaling.")
         else:
-                setattr(config, "use_dynamic_ntk", True)
-                setattr(config, "use_logn_attn", True)
-                logger.info("Using dynamic NTK scaling.")
-
-        elif hasattr(config, "rope_scaling"): # for LLaMA and Falcon models
-            require_version("transformers>=4.31.0", "RoPE scaling requires transformers>=4.31.0")
-
             if is_trainable:
                 if model_args.rope_scaling == "dynamic":
-                    assert not model_args.flash_attn, "Flash attention does not support dynamic rope scaling."
                     logger.warning(
                         "Dynamic NTK may not work well with fine-tuning. "
                         "See: https://github.com/huggingface/transformers/pull/24653"
@@ -127,23 +120,32 @@ def load_model_and_tokenizer(
                 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("Current model does not support RoPE scaling.")
+                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 flash attention
-    if model_args.flash_attn and getattr(config, "model_type", None) == "llama":
-        import transformers.models.llama.modeling_llama as LlamaModule
-        from llmtuner.extras.models.flash_llama import LlamaRMSNorm, LlamaAttention, _prepare_decoder_attention_mask
-        LlamaModule.LlamaRMSNorm = LlamaRMSNorm
-        LlamaModule.LlamaAttention = LlamaAttention
-        LlamaModule.LlamaModel._prepare_decoder_attention_mask = _prepare_decoder_attention_mask
-        if not hasattr(config, "num_key_value_heads"):
-            setattr(config, "num_key_value_heads", getattr(config, "num_attention_heads"))
-        if getattr(config, "pretraining_tp", 1) != 1:
-            setattr(config, "pretraining_tp", 1)
+    # 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).
-    is_mergeable = True
+    # 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.")
@@ -153,7 +155,7 @@ def load_model_and_tokenizer(
             config_kwargs["load_in_8bit"] = True
             config_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
 
-        elif model_args.quantization_bit == 4:
+        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(
@@ -163,11 +165,10 @@ def load_model_and_tokenizer(
                 bnb_4bit_quant_type=model_args.quantization_type
             )
 
-        is_mergeable = False
-        config_kwargs["device_map"] = {"": int(os.environ.get("LOCAL_RANK", "0"))} if is_trainable else "auto"
+        config_kwargs["device_map"] = {"": get_current_device()}
         logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
 
-    # Load and prepare pre-trained models (without valuehead).
+    # Load pre-trained models (without valuehead)
     model = AutoModelForCausalLM.from_pretrained(
         model_to_load,
         config=config,
@@ -176,15 +177,16 @@ def load_model_and_tokenizer(
         **config_kwargs
     )
 
-    # Disable custom generate method (for Qwen)
-    if "GenerationMixin" not in str(model.generate.__func__):
+    # 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)
-    if not hasattr(model, "lm_head") and hasattr(model, "transformer"):
+    # 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.
+    # 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", {}):
@@ -193,37 +195,32 @@ def load_model_and_tokenizer(
         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)
+    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 == "rm" or stage == "ppo":
-        model: AutoModelForCausalLMWithValueHead = AutoModelForCausalLMWithValueHead.from_pretrained(model)
-        model._keys_to_ignore_on_save = None
-        reset_logging()
-        if stage == "rm" and model_args.checkpoint_dir is not None: # load valuehead weights to evaluate reward model
-            logger.warning("Only the last checkpoint containing valuehead will be loaded as the valuehead.")
-            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
-            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 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
-        infer_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16 # detect cuda capability
-        model = model.to(infer_dtype) if model_args.quantization_bit is None else model
+        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/trainer.py

@@ -1,6 +1,6 @@
 import torch
 from collections import defaultdict
-from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
+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
@@ -19,6 +19,7 @@ class CustomDPOTrainer(DPOTrainer):
         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:
@@ -29,9 +30,11 @@ class CustomDPOTrainer(DPOTrainer):
         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)
@@ -40,8 +43,11 @@ class CustomDPOTrainer(DPOTrainer):
 
         if ref_model is not None:
             if self.is_deepspeed_enabled:
-                self.ref_model, = self.accelerator._prepare_deepspeed(self.ref_model)
-                self.ref_model.eval()
+                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)
 

src/llmtuner/train/dpo/workflow.py

@@ -1,20 +1,21 @@
 # Inspired by: https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py
 
-from copy import deepcopy
 from peft import PeftModel
 from typing import TYPE_CHECKING, Optional, List
 from transformers import Seq2SeqTrainingArguments
 
-from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
+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.tuner.core import load_model_and_tokenizer
-from llmtuner.tuner.dpo.collator import DPODataCollatorWithPadding
-from llmtuner.tuner.dpo.trainer import CustomDPOTrainer
+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 ModelArguments, DataArguments, FinetuningArguments
+    from llmtuner.hparams import DataArguments, FinetuningArguments
 
 
 def run_dpo(
@@ -29,9 +30,17 @@ def run_dpo(
     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)
@@ -40,7 +49,7 @@ def run_dpo(
     trainer = CustomDPOTrainer(
         beta=finetuning_args.dpo_beta,
         model=model,
-        ref_model=deepcopy(model) if not isinstance(model, PeftModel) else None,
+        ref_model=ref_model,
         args=training_args,
         tokenizer=tokenizer,
         data_collator=data_collator,
@@ -51,9 +60,26 @@ def run_dpo(
     # 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()
-        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
+    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

@@ -1,23 +1,25 @@
 import os
+import sys
 import math
 import torch
 from tqdm import tqdm
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple
+from typing import TYPE_CHECKING, List, Optional, Tuple
 
-from transformers import GenerationConfig, Trainer, TrainerState, TrainerControl
+from transformers import BatchEncoding, GenerationConfig, Trainer, TrainerState, TrainerControl
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
 
 from trl import PPOTrainer
-from trl.core import LengthSampler, PPODecorators, logprobs_from_logits
+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.tuner.ppo.utils import cast_layernorm_dtype, replace_model
+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 GeneratingArguments
+    from llmtuner.hparams import ModelArguments, FinetuningArguments, GeneratingArguments
 
 
 logger = get_logger(__name__)
@@ -30,34 +32,66 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
 
     def __init__(
         self,
+        model_args: "ModelArguments",
         training_args: "Seq2SeqTrainingArguments",
+        finetuning_args: "FinetuningArguments",
         generating_args: "GeneratingArguments",
         callbacks: List["TrainerCallback"],
-        compute_dtype: torch.dtype,
+        reward_model: "AutoModelForCausalLMWithValueHead",
         **kwargs
     ):
         PPOTrainer.__init__(self, **kwargs)
-        if getattr(self.accelerator.state, "deepspeed_plugin", None) is not None:
-            raise ValueError("PPOTrainer is incompatible with DeepSpeed.")
 
         self.args = training_args
-        self.generating_args = generating_args
-        self.log_callback, self.save_callback = callbacks[0], callbacks[1]
-        self.compute_dtype = compute_dtype
+        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)
 
-    def ppo_train(self, max_target_length: int) -> None:
+        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
@@ -74,25 +108,18 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
             logger.info(f"  Total optimization steps = {max_steps}")
             logger.info(f"  Number of trainable parameters = {count_parameters(self.model)[0]}")
 
-        # Keyword arguments for `model.generate`
-        generating_args = self.generating_args.to_dict()
-        generating_args.update(dict(
-            eos_token_id=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
-            pad_token_id=self.tokenizer.pad_token_id
-        ))
-
-        length_sampler = LengthSampler(max_target_length // 2, max_target_length)
         unwrapped_model: "AutoModelForCausalLMWithValueHead" = 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_local_process_zero()):
+            try:
+                batch = next(dataiter)
+            except StopIteration:
+                dataiter = iter(self.dataloader)
                 batch = next(dataiter)
-            steps_trained += 1
 
             # Cast to inference mode
             unwrapped_model.gradient_checkpointing_disable()
@@ -100,9 +127,14 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
             self.model.eval()
 
             # Get inputs
-            queries, responses = self.get_inputs(batch, length_sampler, generating_args)
             self.tokenizer.padding_side = "right" # change padding side
-            rewards = self.get_rewards(queries, responses, unwrapped_model)
+            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()
@@ -112,9 +144,17 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
             # Run PPO step
             stats = self.step(queries, responses, rewards)
             self.tokenizer.padding_side = "left" # restore padding side
-            loss_meter.update(stats["ppo/loss/total"], n=len(rewards))
+            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)
 
@@ -123,7 +163,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
                     loss=round(loss_meter.avg, 4),
                     reward=round(reward_meter.avg, 4),
                     learning_rate=stats["ppo/learning_rate"],
-                    epoch=round(step / len_dataloader, 2)
+                    epoch=round(step / steps_in_epoch, 2)
                 )
                 tqdm.write(str(logs))
                 logs["step"] = step
@@ -143,49 +183,39 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
             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
-
-        self.log_callback.on_train_end(
+        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: Dict[str, torch.Tensor],
-        length_sampler: Callable,
-        generating_args: Dict[str, Any]
-    ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
+    def get_inputs(self, batch: BatchEncoding) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
         r"""
         Generates model's responses given queries.
         """
-        generating_args["max_new_tokens"] = length_sampler()
-        gen_kwargs = dict(
-            generation_config=GenerationConfig(**generating_args),
+        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
         )
 
-        input_ids = batch["input_ids"]
-        self.model, layer_norm_params = cast_layernorm_dtype(self.model, self.compute_dtype)
-        unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model)
-        response: torch.Tensor = unwrapped_model.generate(**gen_kwargs)
-        self.model, _ = cast_layernorm_dtype(self.model, self.compute_dtype, layer_norm_params)
-        query, response = input_ids.detach().cpu(), response[:, input_ids.size(-1):].detach().cpu()
+        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]
+            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
-            elif self.tokenizer.pad_token_id == self.tokenizer.eos_token_id:
-                response_length = response_index[-1] + 2 # save the EOS token
             else:
-                response_length = response_index[-1] + 1
+                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
@@ -202,24 +232,30 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
         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.compute_dtype): # support bf16
-            _, _, values = self.model(**batch, output_hidden_states=True, return_dict=True)
+        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_index = batch["attention_mask"][i].nonzero()[-1] # use the score on the EOS token
+            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_cuda_cache()
+    @PPODecorators.empty_device_cache()
     def batched_forward_pass(
         self,
         model: "AutoModelForCausalLMWithValueHead",
@@ -250,7 +286,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
             input_ids = input_kwargs["input_ids"]
             attention_mask = input_kwargs["attention_mask"]
 
-            with torch.cuda.amp.autocast(dtype=self.compute_dtype): # support bf16
+            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
@@ -263,7 +299,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
             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]
+                    start += attention_mask[j, :].nonzero()[0].item()
                 end = start + len(response_batch[j])
 
                 if response_masks is not None:

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

@@ -7,11 +7,12 @@ from typing import TYPE_CHECKING, Optional, List
 from transformers import DataCollatorWithPadding
 from transformers.optimization import get_scheduler
 
-from llmtuner.dsets import get_dataset, preprocess_dataset
+from llmtuner.data import get_dataset, preprocess_dataset
 from llmtuner.extras.callbacks import SavePeftModelCallback
 from llmtuner.extras.ploting import plot_loss
-from llmtuner.tuner.core import load_model_and_tokenizer
-from llmtuner.tuner.ppo.trainer import CustomPPOTrainer
+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
@@ -33,6 +34,11 @@ def run_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,
@@ -42,18 +48,25 @@ def run_ppo(
         ppo_epochs=1,
         max_grad_norm=training_args.max_grad_norm,
         seed=training_args.seed,
-        optimize_cuda_cache=True
+        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}
     )
 
-    if finetuning_args.ppo_score_norm:
-        ppo_config.use_score_scaling = True
-        ppo_config.use_score_norm = True
-
+    # 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,
@@ -63,13 +76,15 @@ def run_ppo(
 
     # 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()],
-        compute_dtype=model_args.compute_dtype,
+        reward_model=reward_model,
         config=ppo_config,
         model=model,
-        ref_model=None,
+        ref_model=ref_model,
         tokenizer=tokenizer,
         dataset=dataset,
         data_collator=data_collator,
@@ -79,8 +94,8 @@ def run_ppo(
 
     # Training
     if training_args.do_train:
-        ppo_trainer.ppo_train(max_target_length=data_args.max_target_length)
+        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 model_args.plot_loss:
+        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

@@ -1,12 +1,12 @@
-# Inspired by: https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/language-modeling/run_clm.py
+# 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.dsets import get_dataset, preprocess_dataset, split_dataset
+from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.ploting import plot_loss
-from llmtuner.tuner.core import load_model_and_tokenizer
+from llmtuner.model import generate_model_card, load_model_and_tokenizer
 
 if TYPE_CHECKING:
     from transformers import Seq2SeqTrainingArguments, TrainerCallback
@@ -38,11 +38,11 @@ def run_pt(
     # 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()
-        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
@@ -56,3 +56,10 @@ def run_pt(
         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/trainer.py

@@ -45,9 +45,6 @@ class PairwiseTrainer(Trainer):
         # 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_attn_mask, rejected_attn_mask = (
-            inputs["attention_mask"][:batch_size], inputs["attention_mask"][batch_size:]
-        )
         chosen_rewards, rejected_rewards = values[:batch_size], values[batch_size:]
         chosen_scores, rejected_scores = [], []
 
@@ -55,8 +52,8 @@ class PairwiseTrainer(Trainer):
         # 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_attn_mask[i].nonzero()[-1] + 1
-            rejected_length = rejected_attn_mask[i].nonzero()[-1] + 1
+            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:
@@ -69,7 +66,7 @@ class PairwiseTrainer(Trainer):
             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 EOS token for inference
+            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()
@@ -95,7 +92,6 @@ class PairwiseTrainer(Trainer):
 
         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:

src/llmtuner/train/rm/workflow.py

@@ -1,16 +1,15 @@
-# Inspired by:
-# https://github.com/CarperAI/trlx/blob/main/examples/summarize_rlhf/reward_model/train_reward_model_gptj.py
+# 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.dsets import get_dataset, preprocess_dataset, split_dataset
+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.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 PairwiseTrainer
+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
@@ -27,8 +26,9 @@ def run_rm(
     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)
+    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)
@@ -47,11 +47,11 @@ def run_rm(
     # 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()
-        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
@@ -66,3 +66,10 @@ def run_rm(
         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

@@ -2,15 +2,23 @@ import numpy as np
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Dict, Sequence, Tuple, Union
 
-import jieba
-from rouge_chinese import Rouge
-from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
-
 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
 class ComputeMetrics:

src/llmtuner/train/sft/trainer.py

@@ -33,28 +33,20 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
 
         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."
-            assert self.tokenizer.pad_token_id is not None, "Pad token is required."
             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"])
-                if "attention_mask" in inputs:
-                    inputs["attention_mask"] = self._pad_tensors_to_target_len(
-                        inputs["attention_mask"], inputs["labels"], pad_token_id=0
-                    )
-                if "position_ids" in inputs:
-                    inputs["position_ids"] = self._pad_tensors_to_target_len(
-                        inputs["position_ids"], inputs["labels"], pad_token_id=0
-                    )
+                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
         )
         if generated_tokens is not None and self.args.predict_with_generate:
-            generated_tokens[:, :max(prompt_len, label_len)] = self.tokenizer.pad_token_id
+            generated_tokens[:, :prompt_len] = self.tokenizer.pad_token_id
             generated_tokens = generated_tokens.contiguous()
 
         return loss, generated_tokens, labels
@@ -62,14 +54,13 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
     def _pad_tensors_to_target_len(
         self,
         src_tensor: torch.Tensor,
-        tgt_tensor: torch.Tensor,
-        pad_token_id: Optional[int] = None
+        tgt_tensor: torch.Tensor
     ) -> torch.Tensor:
         r"""
         Pads the tensor to the same length as the target tensor.
         """
-        pad_token_id = pad_token_id if pad_token_id is not None else self.tokenizer.pad_token_id
-        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.contiguous() # in contiguous memory
 

src/llmtuner/train/sft/workflow.py

@@ -1,15 +1,15 @@
-# 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 TYPE_CHECKING, Optional, List
 from transformers import DataCollatorForSeq2Seq, Seq2SeqTrainingArguments
 
-from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
+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.tuner.core import load_model_and_tokenizer
-from llmtuner.tuner.sft.metric import ComputeMetrics
-from llmtuner.tuner.sft.trainer import CustomSeq2SeqTrainer
+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
@@ -33,13 +33,14 @@ def run_sft(
 
     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_dict = training_args.to_dict()
     training_args_dict.update(dict(
-        generation_max_length=training_args.generation_max_length or data_args.max_target_length,
+        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)
@@ -64,11 +65,11 @@ def run_sft(
     # 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()
-        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
@@ -87,3 +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

@@ -2,12 +2,12 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional
 
 from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.logging import get_logger
-from llmtuner.tuner.core import get_train_args, load_model_and_tokenizer
-from llmtuner.tuner.pt import run_pt
-from llmtuner.tuner.sft import run_sft
-from llmtuner.tuner.rm import run_rm
-from llmtuner.tuner.ppo import run_ppo
-from llmtuner.tuner.dpo import run_dpo
+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
@@ -17,29 +17,32 @@ logger = get_logger(__name__)
 
 
 def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: Optional[List["TrainerCallback"]] = None):
-    model_args, data_args, training_args, finetuning_args, generating_args, general_args = get_train_args(args)
+    model_args, data_args, training_args, finetuning_args, generating_args = get_train_args(args)
     callbacks = [LogCallback()] if callbacks is None else callbacks
 
-    if general_args.stage == "pt":
+    if finetuning_args.stage == "pt":
         run_pt(model_args, data_args, training_args, finetuning_args, callbacks)
-    elif general_args.stage == "sft":
+    elif finetuning_args.stage == "sft":
         run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
-    elif general_args.stage == "rm":
+    elif finetuning_args.stage == "rm":
         run_rm(model_args, data_args, training_args, finetuning_args, callbacks)
-    elif general_args.stage == "ppo":
+    elif finetuning_args.stage == "ppo":
         run_ppo(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
-    elif general_args.stage == "dpo":
+    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, _, training_args, finetuning_args, _, _ = get_train_args(args)
+    model_args, _, finetuning_args, _ = get_infer_args(args)
     model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
-    model.save_pretrained(training_args.output_dir, max_shard_size=max_shard_size)
+    model.config.use_cache = True
+    model.save_pretrained(finetuning_args.export_dir, max_shard_size=max_shard_size)
     try:
-        tokenizer.save_pretrained(training_args.output_dir)
+        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.")
 

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

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/parser.py

@@ -1,240 +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 transformers.utils.versions import require_version
-from transformers.trainer_utils import get_last_checkpoint
-
-from llmtuner.extras.logging import get_logger
-from llmtuner.hparams import (
-    ModelArguments,
-    DataArguments,
-    FinetuningArguments,
-    GeneratingArguments,
-    GeneralArguments
-)
-
-
-logger = get_logger(__name__)
-
-
-def _parse_args(parser: HfArgumentParser, args: Optional[Dict[str, Any]] = None) -> Tuple[Any]:
-    if args is not None:
-        return parser.parse_dict(args)
-    elif len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
-        return parser.parse_yaml_file(os.path.abspath(sys.argv[1]))
-    elif len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        return parser.parse_json_file(os.path.abspath(sys.argv[1]))
-    else:
-        return parser.parse_args_into_dataclasses()
-
-
-def parse_train_args(
-    args: Optional[Dict[str, Any]] = None
-) -> Tuple[
-    ModelArguments,
-    DataArguments,
-    Seq2SeqTrainingArguments,
-    FinetuningArguments,
-    GeneratingArguments,
-    GeneralArguments
-]:
-    parser = HfArgumentParser((
-        ModelArguments,
-        DataArguments,
-        Seq2SeqTrainingArguments,
-        FinetuningArguments,
-        GeneratingArguments,
-        GeneralArguments
-    ))
-    return _parse_args(parser, args)
-
-
-def parse_infer_args(
-    args: Optional[Dict[str, Any]] = None
-) -> Tuple[
-    ModelArguments,
-    DataArguments,
-    FinetuningArguments,
-    GeneratingArguments
-]:
-    parser = HfArgumentParser((
-        ModelArguments,
-        DataArguments,
-        FinetuningArguments,
-        GeneratingArguments
-    ))
-    return _parse_args(parser, args)
-
-
-def get_train_args(
-    args: Optional[Dict[str, Any]] = None
-) -> Tuple[
-    ModelArguments,
-    DataArguments,
-    Seq2SeqTrainingArguments,
-    FinetuningArguments,
-    GeneratingArguments,
-    GeneralArguments
-]:
-    model_args, data_args, training_args, finetuning_args, generating_args, general_args = parse_train_args(args)
-
-    # Setup logging
-    if training_args.should_log:
-        # 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()
-
-    if general_args.stage != "pt" and data_args.template is None:
-        raise ValueError("Please specify which `template` to use.")
-
-    if general_args.stage != "sft" and training_args.predict_with_generate:
-        raise ValueError("`predict_with_generate` cannot be set as True except SFT.")
-
-    if general_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate:
-        raise ValueError("Please enable `predict_with_generate` to save model predictions.")
-
-    if general_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type != "lora":
-        raise ValueError("RM and PPO stages can only be performed with the LoRA method.")
-
-    if general_args.stage in ["rm", "ppo"] and training_args.resume_from_checkpoint is not None:
-        raise ValueError("RM and PPO stages do not support `resume_from_checkpoint`.")
-
-    if general_args.stage in ["ppo", "dpo"] and not training_args.do_train:
-        raise ValueError("PPO and DPO stages can only be performed at training.")
-
-    if general_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 general_args.stage == "ppo" and model_args.reward_model is None:
-        raise ValueError("Reward model is necessary for PPO training.")
-
-    if general_args.stage == "ppo" and training_args.deepspeed is not None:
-        raise ValueError("PPO training is incompatible with DeepSpeed, use Accelerate instead.")
-
-    if general_args.stage == "ppo" and data_args.streaming:
-        raise ValueError("Streaming mode does not suppport PPO training currently.")
-
-    if training_args.max_steps == -1 and data_args.streaming:
-        raise ValueError("Please specify `max_steps` in streaming mode.")
-
-    if data_args.val_size > 1e-6 and data_args.val_size < 1 and data_args.streaming:
-        raise ValueError("Streaming mode should have an integer val size.")
-
-    if 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:
-        if finetuning_args.finetuning_type != "lora":
-            if len(model_args.checkpoint_dir) != 1:
-                raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
-        elif model_args.quantization_bit is not None and len(model_args.checkpoint_dir) != 1:
-                raise ValueError("Quantized model only accepts a single checkpoint.")
-
-    if model_args.quantization_bit is not None and (not training_args.do_train):
-        logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
-
-    if training_args.do_train and (not training_args.fp16) and (not training_args.bf16):
-        logger.warning("We recommend enable mixed precision training.")
-
-    # postprocess data_args
-    if data_args.max_samples is not None and data_args.streaming:
-        logger.warning("`max_samples` is incompatible with `streaming`. Disabling max_samples.")
-        data_args.max_samples = None
-
-    # 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
-    ):
-        require_version("transformers>=4.31.0", "Resuming training requires transformers>=4.31.0.")
-        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. Use `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
-    if training_args.bf16:
-        if not torch.cuda.is_bf16_supported():
-            raise ValueError("Current device does not support bf16 training.")
-        model_args.compute_dtype = torch.bfloat16
-    else:
-        model_args.compute_dtype = torch.float16
-
-    model_args.model_max_length = data_args.max_source_length + data_args.max_target_length
-
-    # 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, general_args
-
-
-def get_infer_args(
-    args: Optional[Dict[str, Any]] = None
-) -> Tuple[
-    ModelArguments,
-    DataArguments,
-    FinetuningArguments,
-    GeneratingArguments
-]:
-    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:
-        if finetuning_args.finetuning_type != "lora":
-            if len(model_args.checkpoint_dir) != 1:
-                raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
-        elif model_args.quantization_bit is not None and len(model_args.checkpoint_dir) != 1:
-                raise ValueError("Quantized model only accepts a single checkpoint.")
-
-    return model_args, data_args, finetuning_args, generating_args

src/llmtuner/tuner/core/utils.py

@@ -1,72 +0,0 @@
-import torch
-from typing import TYPE_CHECKING, List, Optional
-
-from llmtuner.extras.constants import LAYERNORM_NAMES
-
-if TYPE_CHECKING:
-    from transformers.modeling_utils import PreTrainedModel
-
-
-def find_all_linear_modules(
-    model: "PreTrainedModel",
-    quantization_bit: Optional[int] = None,
-    output_layer_name: Optional[str] = "lm_head"
-) -> List[str]:
-    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
-
-    module_names = set()
-    for name, module in model.named_modules():
-        if output_layer_name not in name and isinstance(module, linear_cls):
-            module_names.add(name.split(".")[-1])
-
-    if output_layer_name in module_names:
-        module_names.pop(output_layer_name)
-
-    return list(module_names)
-
-
-def prepare_model_for_training(
-    model: "PreTrainedModel",
-    finetuning_type: str,
-    output_layer_name: Optional[str] = "lm_head",
-    use_gradient_checkpointing: Optional[bool] = True,
-    layer_norm_names: Optional[List[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
-    """
-    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()
-        else:
-            def make_inputs_require_grad(module, input, output):
-                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
-
-    if finetuning_type != "full" and hasattr(model, output_layer_name):
-        output_layer: torch.nn.Linear = getattr(model, output_layer_name)
-        input_dtype = output_layer.weight.dtype
-
-        class CastOutputToFloat(torch.nn.Sequential):
-
-            def forward(self, x: torch.Tensor) -> torch.Tensor:
-                return super().forward(x.to(input_dtype)).to(torch.float32)
-
-        setattr(model, output_layer_name, CastOutputToFloat(output_layer))
-
-    return model

src/llmtuner/tuner/dpo/__init__.py

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

src/llmtuner/tuner/ppo/__init__.py

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

src/llmtuner/tuner/ppo/utils.py

@@ -1,40 +0,0 @@
-import torch
-from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Tuple
-
-from llmtuner.extras.constants import LAYERNORM_NAMES
-
-if TYPE_CHECKING:
-    from trl import AutoModelForCausalLMWithValueHead
-
-
-def replace_model(model: "AutoModelForCausalLMWithValueHead", target: Literal["default", "reward"]) -> None:
-    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",
-    compute_dtype: torch.dtype,
-    layer_norm_params: Optional[Dict[str, torch.Tensor]] = None,
-    layer_norm_names: Optional[List[str]] = LAYERNORM_NAMES
-) -> 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 None:
-                layer_norm_state_dict[name] = param.data.detach().clone() # store float32 weights for stability
-                param.data = param.data.to(compute_dtype)
-            else:
-                param.data = layer_norm_params[name] # restore float32 weights
-
-    return model, layer_norm_state_dict

src/llmtuner/tuner/pt/__init__.py

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

src/llmtuner/tuner/rm/__init__.py

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

src/llmtuner/tuner/sft/__init__.py

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

src/llmtuner/webui/chat.py

@@ -1,97 +0,0 @@
-import os
-from typing import Any, Dict, List, Optional, Tuple
-
-from llmtuner.chat.stream_chat import ChatModel
-from llmtuner.extras.misc import torch_gc
-from llmtuner.hparams import GeneratingArguments
-from llmtuner.webui.common import get_model_path, get_save_dir
-from llmtuner.webui.locales import ALERTS
-
-
-class WebChatModel(ChatModel):
-
-    def __init__(self, args: Optional[Dict[str, Any]] = None, lazy_init: Optional[bool] = True) -> None:
-        if lazy_init:
-            self.model = None
-            self.tokenizer = None
-            self.generating_args = GeneratingArguments()
-        else:
-            super().__init__(args)
-
-    def load_model(
-        self,
-        lang: str,
-        model_name: str,
-        checkpoints: List[str],
-        finetuning_type: str,
-        quantization_bit: str,
-        template: str,
-        system_prompt: str
-    ):
-        if self.model is not None:
-            yield ALERTS["err_exists"][lang]
-            return
-
-        if not model_name:
-            yield ALERTS["err_no_model"][lang]
-            return
-
-        model_name_or_path = get_model_path(model_name)
-        if not model_name_or_path:
-            yield ALERTS["err_no_path"][lang]
-            return
-
-        if checkpoints:
-            checkpoint_dir = ",".join(
-                [os.path.join(get_save_dir(model_name), finetuning_type, checkpoint) for checkpoint in checkpoints]
-            )
-        else:
-            checkpoint_dir = None
-
-        yield ALERTS["info_loading"][lang]
-        args = dict(
-            model_name_or_path=model_name_or_path,
-            checkpoint_dir=checkpoint_dir,
-            finetuning_type=finetuning_type,
-            quantization_bit=int(quantization_bit) if quantization_bit and quantization_bit != "None" else None,
-            template=template,
-            system_prompt=system_prompt
-        )
-        super().__init__(args)
-
-        yield ALERTS["info_loaded"][lang]
-
-    def unload_model(self, lang: str):
-        yield ALERTS["info_unloading"][lang]
-        self.model = None
-        self.tokenizer = None
-        torch_gc()
-        yield ALERTS["info_unloaded"][lang]
-
-    def predict(
-        self,
-        chatbot: List[Tuple[str, str]],
-        query: str,
-        history: List[Tuple[str, str]],
-        system: str,
-        max_new_tokens: int,
-        top_p: float,
-        temperature: float
-    ):
-        chatbot.append([query, ""])
-        response = ""
-        for new_text in self.stream_chat(
-            query, history, system, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
-        ):
-            response += new_text
-            response = self.postprocess(response)
-            new_history = history + [(query, response)]
-            chatbot[-1] = [query, response]
-            yield chatbot, new_history
-
-    def postprocess(self, response: str) -> str:
-        blocks = response.split("```")
-        for i, block in enumerate(blocks):
-            if i % 2 == 0:
-                blocks[i] = block.replace("<", "&lt;").replace(">", "&gt;")
-        return "```".join(blocks)

src/llmtuner/webui/chatter.py

@@ -0,0 +1,118 @@
+import gradio as gr
+from gradio.components import Component # cannot use TYPE_CHECKING here
+from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Tuple
+
+from llmtuner.chat import ChatModel
+from llmtuner.extras.misc import torch_gc
+from llmtuner.hparams import GeneratingArguments
+from llmtuner.webui.common import get_save_dir
+from llmtuner.webui.locales import ALERTS
+
+if TYPE_CHECKING:
+    from llmtuner.webui.manager import Manager
+
+
+class WebChatModel(ChatModel):
+
+    def __init__(
+        self,
+        manager: "Manager",
+        demo_mode: Optional[bool] = False,
+        lazy_init: Optional[bool] = True
+    ) -> None:
+        self.manager = manager
+        self.demo_mode = demo_mode
+        self.model = None
+        self.tokenizer = None
+        self.generating_args = GeneratingArguments()
+
+        if not lazy_init: # read arguments from command line
+            super().__init__()
+
+        if demo_mode: # load openchat 3.5 by default
+            super().__init__(dict(model_name_or_path="openchat/openchat_3.5", template="openchat"))
+
+    @property
+    def loaded(self) -> bool:
+        return self.model is not None
+
+    def load_model(self, data: Dict[Component, Any]) -> Generator[str, None, None]:
+        get = lambda name: data[self.manager.get_elem_by_name(name)]
+        lang = get("top.lang")
+        error = ""
+        if self.loaded:
+            error = ALERTS["err_exists"][lang]
+        elif not get("top.model_name"):
+            error = ALERTS["err_no_model"][lang]
+        elif not get("top.model_path"):
+            error = ALERTS["err_no_path"][lang]
+        elif self.demo_mode:
+            error = ALERTS["err_demo"][lang]
+
+        if error:
+            gr.Warning(error)
+            yield error
+            return
+
+        if get("top.checkpoints"):
+            checkpoint_dir = ",".join([
+                get_save_dir(get("top.model_name"), get("top.finetuning_type"), ckpt) for ckpt in get("top.checkpoints")
+            ])
+        else:
+            checkpoint_dir = None
+
+        yield ALERTS["info_loading"][lang]
+        args = dict(
+            model_name_or_path=get("top.model_path"),
+            checkpoint_dir=checkpoint_dir,
+            finetuning_type=get("top.finetuning_type"),
+            quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None,
+            template=get("top.template"),
+            system_prompt=get("top.system_prompt"),
+            flash_attn=get("top.flash_attn"),
+            shift_attn=get("top.shift_attn"),
+            rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None
+        )
+        super().__init__(args)
+
+        yield ALERTS["info_loaded"][lang]
+
+    def unload_model(self, data: Dict[Component, Any]) -> Generator[str, None, None]:
+        lang = data[self.manager.get_elem_by_name("top.lang")]
+
+        if self.demo_mode:
+            yield ALERTS["err_demo"][lang]
+            return
+
+        yield ALERTS["info_unloading"][lang]
+        self.model = None
+        self.tokenizer = None
+        torch_gc()
+        yield ALERTS["info_unloaded"][lang]
+
+    def predict(
+        self,
+        chatbot: List[Tuple[str, str]],
+        query: str,
+        history: List[Tuple[str, str]],
+        system: str,
+        max_new_tokens: int,
+        top_p: float,
+        temperature: float
+    ) -> Generator[Tuple[List[Tuple[str, str]], List[Tuple[str, str]]], None, None]:
+        chatbot.append([query, ""])
+        response = ""
+        for new_text in self.stream_chat(
+            query, history, system, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
+        ):
+            response += new_text
+            new_history = history + [(query, response)]
+            chatbot[-1] = [query, self.postprocess(response)]
+            yield chatbot, new_history
+
+    def postprocess(self, response: str) -> str:
+        blocks = response.split("```")
+        for i, block in enumerate(blocks):
+            if i % 2 == 0:
+                blocks[i] = block.replace("<", "&lt;").replace(">", "&gt;")
+        return "```".join(blocks)

src/llmtuner/webui/common.py

@@ -1,12 +1,17 @@
-import json
 import os
-from typing import Any, Dict, Optional
-
+import json
 import gradio as gr
-from peft.utils import WEIGHTS_NAME as PEFT_WEIGHTS_NAME
-from transformers.trainer import WEIGHTS_NAME, WEIGHTS_INDEX_NAME
+from typing import Any, Dict, Optional
+from transformers.utils import (
+    WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
+    SAFE_WEIGHTS_NAME,
+    SAFE_WEIGHTS_INDEX_NAME,
+    ADAPTER_WEIGHTS_NAME,
+    ADAPTER_SAFE_WEIGHTS_NAME
+)
 
-from llmtuner.extras.constants import DEFAULT_TEMPLATE, SUPPORTED_MODELS, TRAINING_STAGES
+from llmtuner.extras.constants import DEFAULT_MODULE, DEFAULT_TEMPLATE, SUPPORTED_MODELS, TRAINING_STAGES
 
 
 DEFAULT_CACHE_DIR = "cache"
@@ -14,6 +19,14 @@ DEFAULT_DATA_DIR = "data"
 DEFAULT_SAVE_DIR = "saves"
 USER_CONFIG = "user.config"
 DATA_CONFIG = "dataset_info.json"
+CKPT_NAMES = [
+    WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
+    SAFE_WEIGHTS_NAME,
+    SAFE_WEIGHTS_INDEX_NAME,
+    ADAPTER_WEIGHTS_NAME,
+    ADAPTER_SAFE_WEIGHTS_NAME
+]
 
 
 def get_save_dir(*args) -> os.PathLike:
@@ -32,7 +45,7 @@ def load_config() -> Dict[str, Any]:
         return {"lang": None, "last_model": None, "path_dict": {}, "cache_dir": None}
 
 
-def save_config(lang: str, model_name: str, model_path: str) -> None:
+def save_config(lang: str, model_name: Optional[str] = None, model_path: Optional[str] = None) -> None:
     os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
     user_config = load_config()
     user_config["lang"] = lang or user_config["lang"]
@@ -45,26 +58,32 @@ def save_config(lang: str, model_name: str, model_path: str) -> None:
 
 def get_model_path(model_name: str) -> str:
     user_config = load_config()
-    return user_config["path_dict"].get(model_name, SUPPORTED_MODELS.get(model_name, ""))
+    return user_config["path_dict"].get(model_name, None) or SUPPORTED_MODELS.get(model_name, "")
+
+
+def get_prefix(model_name: str) -> str:
+    return model_name.split("-")[0]
+
+
+def get_module(model_name: str) -> str:
+    return DEFAULT_MODULE.get(get_prefix(model_name), "q_proj,v_proj")
 
 
 def get_template(model_name: str) -> str:
-    if model_name.endswith("Chat") and model_name.split("-")[0] in DEFAULT_TEMPLATE:
-        return DEFAULT_TEMPLATE[model_name.split("-")[0]]
+    if model_name and model_name.endswith("Chat") and get_prefix(model_name) in DEFAULT_TEMPLATE:
+        return DEFAULT_TEMPLATE[get_prefix(model_name)]
     return "default"
 
 
 def list_checkpoint(model_name: str, finetuning_type: str) -> Dict[str, Any]:
     checkpoints = []
+    if model_name:
         save_dir = get_save_dir(model_name, finetuning_type)
         if save_dir and os.path.isdir(save_dir):
             for checkpoint in os.listdir(save_dir):
                 if (
                     os.path.isdir(os.path.join(save_dir, checkpoint))
-                and any([
-                    os.path.isfile(os.path.join(save_dir, checkpoint, name))
-                    for name in (WEIGHTS_NAME, WEIGHTS_INDEX_NAME, PEFT_WEIGHTS_NAME)
-                ])
+                    and any([os.path.isfile(os.path.join(save_dir, checkpoint, name)) for name in CKPT_NAMES])
                 ):
                     checkpoints.append(checkpoint)
     return gr.update(value=[], choices=checkpoints)
@@ -75,6 +94,7 @@ def load_dataset_info(dataset_dir: str) -> Dict[str, Any]:
         with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
             return json.load(f)
     except:
+        print("Cannot find {} in {}.".format(DATA_CONFIG, dataset_dir))
         return {}
 
 

src/llmtuner/webui/components/chatbot.py

@@ -1,20 +1,19 @@
-from typing import TYPE_CHECKING, Dict, Optional, Tuple
-
 import gradio as gr
+from typing import TYPE_CHECKING, Dict, Optional, Tuple
 
 if TYPE_CHECKING:
     from gradio.blocks import Block
     from gradio.components import Component
-    from llmtuner.webui.chat import WebChatModel
+    from llmtuner.webui.engine import Engine
 
 
 def create_chat_box(
-    chat_model: "WebChatModel",
+    engine: "Engine",
     visible: Optional[bool] = False
 ) -> Tuple["Block", "Component", "Component", Dict[str, "Component"]]:
     with gr.Box(visible=visible) as chat_box:
         chatbot = gr.Chatbot()
-
+        history = gr.State([])
         with gr.Row():
             with gr.Column(scale=4):
                 system = gr.Textbox(show_label=False)
@@ -23,14 +22,13 @@ def create_chat_box(
 
             with gr.Column(scale=1):
                 clear_btn = gr.Button()
-                max_new_tokens = gr.Slider(10, 2048, value=chat_model.generating_args.max_new_tokens, step=1)
-                top_p = gr.Slider(0.01, 1, value=chat_model.generating_args.top_p, step=0.01)
-                temperature = gr.Slider(0.01, 1.5, value=chat_model.generating_args.temperature, step=0.01)
-
-    history = gr.State([])
+                gen_kwargs = engine.chatter.generating_args
+                max_new_tokens = gr.Slider(10, 2048, value=gen_kwargs.max_new_tokens, step=1)
+                top_p = gr.Slider(0.01, 1, value=gen_kwargs.top_p, step=0.01)
+                temperature = gr.Slider(0.01, 1.5, value=gen_kwargs.temperature, step=0.01)
 
     submit_btn.click(
-        chat_model.predict,
+        engine.chatter.predict,
         [chatbot, query, history, system, max_new_tokens, top_p, temperature],
         [chatbot, history],
         show_progress=True

src/llmtuner/webui/components/data.py

@@ -1,21 +1,103 @@
+import os
+import json
 import gradio as gr
-from typing import TYPE_CHECKING, Tuple
+from typing import TYPE_CHECKING, Any, Dict, Tuple
+
+from llmtuner.webui.common import DATA_CONFIG
 
 if TYPE_CHECKING:
-    from gradio.blocks import Block
     from gradio.components import Component
 
 
-def create_preview_box() -> Tuple["Block", "Component", "Component", "Component"]:
-    with gr.Box(visible=False, elem_classes="modal-box") as preview_box:
+PAGE_SIZE = 2
+
+
+def prev_page(page_index: int) -> int:
+    return page_index - 1 if page_index > 0 else page_index
+
+
+def next_page(page_index: int, total_num: int) -> int:
+    return page_index + 1 if (page_index + 1) * PAGE_SIZE < total_num else page_index
+
+
+def can_preview(dataset_dir: str, dataset: list) -> Dict[str, Any]:
+    with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
+        dataset_info = json.load(f)
+
+    if (
+        len(dataset) > 0
+        and "file_name" in dataset_info[dataset[0]]
+        and os.path.isfile(os.path.join(dataset_dir, dataset_info[dataset[0]]["file_name"]))
+    ):
+        return gr.update(interactive=True)
+    else:
+        return gr.update(interactive=False)
+
+
+def get_preview(dataset_dir: str, dataset: list, page_index: int) -> Tuple[int, list, Dict[str, Any]]:
+    with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
+        dataset_info = json.load(f)
+
+    data_file: str = dataset_info[dataset[0]]["file_name"]
+    with open(os.path.join(dataset_dir, data_file), "r", encoding="utf-8") as f:
+        if data_file.endswith(".json"):
+            data = json.load(f)
+        elif data_file.endswith(".jsonl"):
+            data = [json.loads(line) for line in f]
+        else:
+            data = [line for line in f]
+    return len(data), data[PAGE_SIZE * page_index : PAGE_SIZE * (page_index + 1)], gr.update(visible=True)
+
+
+def create_preview_box(dataset_dir: "gr.Textbox", dataset: "gr.Dropdown") -> Dict[str, "Component"]:
+    data_preview_btn = gr.Button(interactive=False, scale=1)
+    with gr.Column(visible=False, elem_classes="modal-box") as preview_box:
         with gr.Row():
-            preview_count = gr.Number(interactive=False)
+            preview_count = gr.Number(value=0, interactive=False, precision=0)
+            page_index = gr.Number(value=0, interactive=False, precision=0)
+
+        with gr.Row():
+            prev_btn = gr.Button()
+            next_btn = gr.Button()
+            close_btn = gr.Button()
 
         with gr.Row():
             preview_samples = gr.JSON(interactive=False)
 
-        close_btn = gr.Button()
-
+    dataset.change(
+        can_preview, [dataset_dir, dataset], [data_preview_btn], queue=False
+    ).then(
+        lambda: 0, outputs=[page_index], queue=False
+    )
+    data_preview_btn.click(
+        get_preview,
+        [dataset_dir, dataset, page_index],
+        [preview_count, preview_samples, preview_box],
+        queue=False
+    )
+    prev_btn.click(
+        prev_page, [page_index], [page_index], queue=False
+    ).then(
+        get_preview,
+        [dataset_dir, dataset, page_index],
+        [preview_count, preview_samples, preview_box],
+        queue=False
+    )
+    next_btn.click(
+        next_page, [page_index, preview_count], [page_index], queue=False
+    ).then(
+        get_preview,
+        [dataset_dir, dataset, page_index],
+        [preview_count, preview_samples, preview_box],
+        queue=False
+    )
     close_btn.click(lambda: gr.update(visible=False), outputs=[preview_box], queue=False)
-
-    return preview_box, preview_count, preview_samples, close_btn
+    return dict(
+        data_preview_btn=data_preview_btn,
+        preview_count=preview_count,
+        page_index=page_index,
+        prev_btn=prev_btn,
+        next_btn=next_btn,
+        close_btn=close_btn,
+        preview_samples=preview_samples
+    )

src/llmtuner/webui/components/eval.py

@@ -1,90 +1,70 @@
-from typing import TYPE_CHECKING, Dict
 import gradio as gr
+from typing import TYPE_CHECKING, Dict
 
 from llmtuner.webui.common import list_dataset, DEFAULT_DATA_DIR
 from llmtuner.webui.components.data import create_preview_box
-from llmtuner.webui.utils import can_preview, get_preview
 
 if TYPE_CHECKING:
     from gradio.components import Component
-    from llmtuner.webui.runner import Runner
+    from llmtuner.webui.engine import Engine
 
 
-def create_eval_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[str, "Component"]:
+def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:
+    input_elems = engine.manager.get_base_elems()
+    elem_dict = dict()
+
     with gr.Row():
         dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
         dataset = gr.Dropdown(multiselect=True, scale=4)
-        data_preview_btn = gr.Button(interactive=False, scale=1)
+        preview_elems = create_preview_box(dataset_dir, dataset)
 
-    preview_box, preview_count, preview_samples, close_btn = create_preview_box()
+    dataset_dir.change(list_dataset, [dataset_dir], [dataset], queue=False)
 
-    dataset_dir.change(list_dataset, [dataset_dir], [dataset])
-    dataset.change(can_preview, [dataset_dir, dataset], [data_preview_btn])
-    data_preview_btn.click(
-        get_preview,
-        [dataset_dir, dataset],
-        [preview_count, preview_samples, preview_box],
-        queue=False
-    )
+    input_elems.update({dataset_dir, dataset})
+    elem_dict.update(dict(dataset_dir=dataset_dir, dataset=dataset, **preview_elems))
 
     with gr.Row():
-        max_source_length = gr.Slider(value=512, minimum=4, maximum=4096, step=1)
-        max_target_length = gr.Slider(value=512, minimum=4, maximum=4096, step=1)
+        cutoff_len = gr.Slider(value=1024, minimum=4, maximum=8192, step=1)
         max_samples = gr.Textbox(value="100000")
         batch_size = gr.Slider(value=8, minimum=1, maximum=512, step=1)
         predict = gr.Checkbox(value=True)
 
+    input_elems.update({cutoff_len, max_samples, batch_size, predict})
+    elem_dict.update(dict(
+        cutoff_len=cutoff_len, max_samples=max_samples, batch_size=batch_size, predict=predict
+    ))
+
+    with gr.Row():
+        max_new_tokens = gr.Slider(10, 2048, value=128, step=1)
+        top_p = gr.Slider(0.01, 1, value=0.7, step=0.01)
+        temperature = gr.Slider(0.01, 1.5, value=0.95, step=0.01)
+
+    input_elems.update({max_new_tokens, top_p, temperature})
+    elem_dict.update(dict(
+        max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
+    ))
+
     with gr.Row():
         cmd_preview_btn = gr.Button()
         start_btn = gr.Button()
         stop_btn = gr.Button()
 
     with gr.Row():
+        resume_btn = gr.Checkbox(visible=False, interactive=False, value=False)
         process_bar = gr.Slider(visible=False, interactive=False)
 
     with gr.Box():
         output_box = gr.Markdown()
 
-    input_components = [
-        top_elems["lang"],
-        top_elems["model_name"],
-        top_elems["checkpoints"],
-        top_elems["finetuning_type"],
-        top_elems["quantization_bit"],
-        top_elems["template"],
-        top_elems["system_prompt"],
-        dataset_dir,
-        dataset,
-        max_source_length,
-        max_target_length,
-        max_samples,
-        batch_size,
-        predict
-    ]
+    output_elems = [output_box, process_bar]
+    elem_dict.update(dict(
+        cmd_preview_btn=cmd_preview_btn, start_btn=start_btn, stop_btn=stop_btn,
+        resume_btn=resume_btn, process_bar=process_bar, output_box=output_box
+    ))
 
-    output_components = [
-        output_box,
-        process_bar
-    ]
+    cmd_preview_btn.click(engine.runner.preview_eval, input_elems, output_elems)
+    start_btn.click(engine.runner.run_eval, input_elems, output_elems)
+    stop_btn.click(engine.runner.set_abort, queue=False)
+    resume_btn.change(engine.runner.monitor, outputs=output_elems)
 
-    cmd_preview_btn.click(runner.preview_eval, input_components, output_components)
-    start_btn.click(runner.run_eval, input_components, output_components)
-    stop_btn.click(runner.set_abort, queue=False)
-
-    return dict(
-        dataset_dir=dataset_dir,
-        dataset=dataset,
-        data_preview_btn=data_preview_btn,
-        preview_count=preview_count,
-        preview_samples=preview_samples,
-        close_btn=close_btn,
-        max_source_length=max_source_length,
-        max_target_length=max_target_length,
-        max_samples=max_samples,
-        batch_size=batch_size,
-        predict=predict,
-        cmd_preview_btn=cmd_preview_btn,
-        start_btn=start_btn,
-        stop_btn=stop_btn,
-        output_box=output_box
-    )
+    return elem_dict

src/llmtuner/webui/components/export.py

@@ -1,15 +1,56 @@
-from typing import TYPE_CHECKING, Dict
 import gradio as gr
+from typing import TYPE_CHECKING, Dict, Generator, List
 
-from llmtuner.webui.utils import save_model
+from llmtuner.train import export_model
+from llmtuner.webui.common import get_save_dir
+from llmtuner.webui.locales import ALERTS
 
 if TYPE_CHECKING:
     from gradio.components import Component
+    from llmtuner.webui.engine import Engine
 
 
-def create_export_tab(top_elems: Dict[str, "Component"]) -> Dict[str, "Component"]:
+def save_model(
+    lang: str,
+    model_name: str,
+    model_path: str,
+    checkpoints: List[str],
+    finetuning_type: str,
+    template: str,
+    max_shard_size: int,
+    export_dir: str
+) -> Generator[str, None, None]:
+    error = ""
+    if not model_name:
+        error = ALERTS["err_no_model"][lang]
+    elif not model_path:
+        error = ALERTS["err_no_path"][lang]
+    elif not checkpoints:
+        error = ALERTS["err_no_checkpoint"][lang]
+    elif not export_dir:
+        error = ALERTS["err_no_export_dir"][lang]
+
+    if error:
+        gr.Warning(error)
+        yield error
+        return
+
+    args = dict(
+        model_name_or_path=model_path,
+        checkpoint_dir=",".join([get_save_dir(model_name, finetuning_type, ckpt) for ckpt in checkpoints]),
+        finetuning_type=finetuning_type,
+        template=template,
+        export_dir=export_dir
+    )
+
+    yield ALERTS["info_exporting"][lang]
+    export_model(args, max_shard_size="{}GB".format(max_shard_size))
+    yield ALERTS["info_exported"][lang]
+
+
+def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
     with gr.Row():
-        save_dir = gr.Textbox()
+        export_dir = gr.Textbox()
         max_shard_size = gr.Slider(value=10, minimum=1, maximum=100)
 
     export_btn = gr.Button()
@@ -18,19 +59,20 @@ def create_export_tab(top_elems: Dict[str, "Component"]) -> Dict[str, "Component
     export_btn.click(
         save_model,
         [
-            top_elems["lang"],
-            top_elems["model_name"],
-            top_elems["checkpoints"],
-            top_elems["finetuning_type"],
-            top_elems["template"],
+            engine.manager.get_elem_by_name("top.lang"),
+            engine.manager.get_elem_by_name("top.model_name"),
+            engine.manager.get_elem_by_name("top.model_path"),
+            engine.manager.get_elem_by_name("top.checkpoints"),
+            engine.manager.get_elem_by_name("top.finetuning_type"),
+            engine.manager.get_elem_by_name("top.template"),
             max_shard_size,
-            save_dir
+            export_dir
         ],
         [info_box]
     )
 
     return dict(
-        save_dir=save_dir,
+        export_dir=export_dir,
         max_shard_size=max_shard_size,
         export_btn=export_btn,
         info_box=info_box

src/llmtuner/webui/components/infer.py

@@ -1,51 +1,39 @@
+import gradio as gr
 from typing import TYPE_CHECKING, Dict
 
-import gradio as gr
-
-from llmtuner.webui.chat import WebChatModel
 from llmtuner.webui.components.chatbot import create_chat_box
 
 if TYPE_CHECKING:
     from gradio.components import Component
+    from llmtuner.webui.engine import Engine
 
 
-def create_infer_tab(top_elems: Dict[str, "Component"]) -> Dict[str, "Component"]:
+def create_infer_tab(engine: "Engine") -> Dict[str, "Component"]:
+    input_elems = engine.manager.get_base_elems()
+    elem_dict = dict()
+
     with gr.Row():
         load_btn = gr.Button()
         unload_btn = gr.Button()
 
     info_box = gr.Textbox(show_label=False, interactive=False)
+    elem_dict.update(dict(load_btn=load_btn, unload_btn=unload_btn, info_box=info_box))
 
-    chat_model = WebChatModel(lazy_init=True)
-    chat_box, chatbot, history, chat_elems = create_chat_box(chat_model)
+    chat_box, chatbot, history, chat_elems = create_chat_box(engine, visible=False)
+    elem_dict.update(dict(chat_box=chat_box, **chat_elems))
 
     load_btn.click(
-        chat_model.load_model,
-        [
-            top_elems["lang"],
-            top_elems["model_name"],
-            top_elems["checkpoints"],
-            top_elems["finetuning_type"],
-            top_elems["quantization_bit"],
-            top_elems["template"],
-            top_elems["system_prompt"]
-        ],
-        [info_box]
+        engine.chatter.load_model, input_elems, [info_box]
     ).then(
-        lambda: gr.update(visible=(chat_model.model is not None)), outputs=[chat_box]
+        lambda: gr.update(visible=engine.chatter.loaded), outputs=[chat_box]
     )
 
     unload_btn.click(
-        chat_model.unload_model, [top_elems["lang"]], [info_box]
+        engine.chatter.unload_model, input_elems, [info_box]
     ).then(
         lambda: ([], []), outputs=[chatbot, history]
     ).then(
-        lambda: gr.update(visible=(chat_model.model is not None)), outputs=[chat_box]
+        lambda: gr.update(visible=engine.chatter.loaded), outputs=[chat_box]
     )
 
-    return dict(
-        info_box=info_box,
-        load_btn=load_btn,
-        unload_btn=unload_btn,
-        **chat_elems
-    )
+    return elem_dict

src/llmtuner/webui/components/top.py

@@ -1,10 +1,9 @@
+import gradio as gr
 from typing import TYPE_CHECKING, Dict
 
-import gradio as gr
-
+from llmtuner.data.template import templates
 from llmtuner.extras.constants import METHODS, SUPPORTED_MODELS
-from llmtuner.extras.template import templates
-from llmtuner.webui.common import list_checkpoint, get_model_path, get_template, save_config
+from llmtuner.webui.common import get_model_path, get_template, list_checkpoint, save_config
 from llmtuner.webui.utils import can_quantize
 
 if TYPE_CHECKING:
@@ -26,26 +25,31 @@ def create_top() -> Dict[str, "Component"]:
 
     with gr.Accordion(label="Advanced config", open=False) as advanced_tab:
         with gr.Row():
-            quantization_bit = gr.Dropdown(choices=["None", "8", "4"], value="None", scale=1)
+            quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none", scale=1)
             template = gr.Dropdown(choices=list(templates.keys()), value="default", scale=1)
             system_prompt = gr.Textbox(scale=2)
 
-    lang.change(save_config, [lang, model_name, model_path])
+    with gr.Accordion(label="Model config (LLaMA only)", open=False) as llama_tab:
+        with gr.Row():
+            with gr.Column():
+                flash_attn = gr.Checkbox(value=False)
+                shift_attn = gr.Checkbox(value=False)
+            rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none")
 
     model_name.change(
-        list_checkpoint, [model_name, finetuning_type], [checkpoints]
+        list_checkpoint, [model_name, finetuning_type], [checkpoints], queue=False
     ).then(
-        get_model_path, [model_name], [model_path]
+        get_model_path, [model_name], [model_path], queue=False
     ).then(
-        get_template, [model_name], [template]
+        get_template, [model_name], [template], queue=False
     ) # do not save config since the below line will save
 
-    model_path.change(save_config, [lang, model_name, model_path])
+    model_path.change(save_config, inputs=[lang, model_name, model_path], queue=False)
 
     finetuning_type.change(
-        list_checkpoint, [model_name, finetuning_type], [checkpoints]
+        list_checkpoint, [model_name, finetuning_type], [checkpoints], queue=False
     ).then(
-        can_quantize, [finetuning_type], [quantization_bit]
+        can_quantize, [finetuning_type], [quantization_bit], queue=False
     )
 
     refresh_btn.click(
@@ -62,5 +66,9 @@ def create_top() -> Dict[str, "Component"]:
         advanced_tab=advanced_tab,
         quantization_bit=quantization_bit,
         template=template,
-        system_prompt=system_prompt
+        system_prompt=system_prompt,
+        llama_tab=llama_tab,
+        flash_attn=flash_attn,
+        shift_attn=shift_attn,
+        rope_scaling=rope_scaling
     )

src/llmtuner/webui/components/train.py

@@ -1,45 +1,49 @@
+import gradio as gr
 from typing import TYPE_CHECKING, Dict
 from transformers.trainer_utils import SchedulerType
 
-import gradio as gr
-
 from llmtuner.extras.constants import TRAINING_STAGES
 from llmtuner.webui.common import list_checkpoint, list_dataset, DEFAULT_DATA_DIR
 from llmtuner.webui.components.data import create_preview_box
-from llmtuner.webui.utils import can_preview, get_preview, gen_plot
+from llmtuner.webui.utils import gen_plot
 
 if TYPE_CHECKING:
     from gradio.components import Component
-    from llmtuner.webui.runner import Runner
+    from llmtuner.webui.engine import Engine
 
 
-def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[str, "Component"]:
+def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
+    input_elems = engine.manager.get_base_elems()
+    elem_dict = dict()
+
     with gr.Row():
         training_stage = gr.Dropdown(
             choices=list(TRAINING_STAGES.keys()), value=list(TRAINING_STAGES.keys())[0], scale=2
         )
         dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
         dataset = gr.Dropdown(multiselect=True, scale=4)
-        data_preview_btn = gr.Button(interactive=False, scale=1)
+        preview_elems = create_preview_box(dataset_dir, dataset)
 
-    preview_box, preview_count, preview_samples, close_btn = create_preview_box()
+    training_stage.change(list_dataset, [dataset_dir, training_stage], [dataset], queue=False)
+    dataset_dir.change(list_dataset, [dataset_dir, training_stage], [dataset], queue=False)
 
-    training_stage.change(list_dataset, [dataset_dir, training_stage], [dataset])
-    dataset_dir.change(list_dataset, [dataset_dir, training_stage], [dataset])
-    dataset.change(can_preview, [dataset_dir, dataset], [data_preview_btn])
-    data_preview_btn.click(
-        get_preview,
-        [dataset_dir, dataset],
-        [preview_count, preview_samples, preview_box],
-        queue=False
-    )
+    input_elems.update({training_stage, dataset_dir, dataset})
+    elem_dict.update(dict(
+        training_stage=training_stage, dataset_dir=dataset_dir, dataset=dataset, **preview_elems
+    ))
 
     with gr.Row():
-        max_source_length = gr.Slider(value=512, minimum=4, maximum=4096, step=1)
-        max_target_length = gr.Slider(value=512, minimum=4, maximum=4096, step=1)
+        cutoff_len = gr.Slider(value=1024, minimum=4, maximum=8192, step=1)
         learning_rate = gr.Textbox(value="5e-5")
         num_train_epochs = gr.Textbox(value="3.0")
         max_samples = gr.Textbox(value="100000")
+        compute_type = gr.Radio(choices=["fp16", "bf16"], value="fp16")
+
+    input_elems.update({cutoff_len, learning_rate, num_train_epochs, max_samples, compute_type})
+    elem_dict.update(dict(
+        cutoff_len=cutoff_len, learning_rate=learning_rate, num_train_epochs=num_train_epochs,
+        max_samples=max_samples, compute_type=compute_type
+    ))
 
     with gr.Row():
         batch_size = gr.Slider(value=4, minimum=1, maximum=512, step=1)
@@ -50,33 +54,59 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
         max_grad_norm = gr.Textbox(value="1.0")
         val_size = gr.Slider(value=0, minimum=0, maximum=1, step=0.001)
 
+    input_elems.update({batch_size, gradient_accumulation_steps, lr_scheduler_type, max_grad_norm, val_size})
+    elem_dict.update(dict(
+        batch_size=batch_size, gradient_accumulation_steps=gradient_accumulation_steps,
+        lr_scheduler_type=lr_scheduler_type, max_grad_norm=max_grad_norm, val_size=val_size
+    ))
+
     with gr.Accordion(label="Advanced config", open=False) as advanced_tab:
         with gr.Row():
             logging_steps = gr.Slider(value=5, minimum=5, maximum=1000, step=5)
             save_steps = gr.Slider(value=100, minimum=10, maximum=5000, step=10)
             warmup_steps = gr.Slider(value=0, minimum=0, maximum=5000, step=1)
-            compute_type = gr.Radio(choices=["fp16", "bf16"], value="fp16")
+            neft_alpha = gr.Slider(value=0, minimum=0, maximum=10, step=0.1)
+
+            with gr.Column():
+                train_on_prompt = gr.Checkbox(value=False)
+                upcast_layernorm = gr.Checkbox(value=False)
+
+    input_elems.update({logging_steps, save_steps, warmup_steps, neft_alpha, train_on_prompt, upcast_layernorm})
+    elem_dict.update(dict(
+        advanced_tab=advanced_tab, logging_steps=logging_steps, save_steps=save_steps, warmup_steps=warmup_steps,
+        neft_alpha=neft_alpha, train_on_prompt=train_on_prompt, upcast_layernorm=upcast_layernorm
+    ))
 
     with gr.Accordion(label="LoRA config", open=False) as lora_tab:
         with gr.Row():
             lora_rank = gr.Slider(value=8, minimum=1, maximum=1024, step=1, scale=1)
             lora_dropout = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=1)
-            lora_target = gr.Textbox(scale=2)
+            lora_target = gr.Textbox(scale=1)
+            additional_target = gr.Textbox(scale=1)
             resume_lora_training = gr.Checkbox(value=True, scale=1)
 
+    input_elems.update({lora_rank, lora_dropout, lora_target, additional_target, resume_lora_training})
+    elem_dict.update(dict(
+        lora_tab=lora_tab, lora_rank=lora_rank, lora_dropout=lora_dropout, lora_target=lora_target,
+        additional_target=additional_target, resume_lora_training=resume_lora_training,
+    ))
+
     with gr.Accordion(label="RLHF config", open=False) as rlhf_tab:
         with gr.Row():
-            dpo_beta = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=2)
-            reward_model = gr.Dropdown(scale=2)
+            dpo_beta = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=1)
+            reward_model = gr.Dropdown(scale=3)
             refresh_btn = gr.Button(scale=1)
 
     refresh_btn.click(
         list_checkpoint,
-        [top_elems["model_name"], top_elems["finetuning_type"]],
+        [engine.manager.get_elem_by_name("top.model_name"), engine.manager.get_elem_by_name("top.finetuning_type")],
         [reward_model],
         queue=False
     )
 
+    input_elems.update({dpo_beta, reward_model})
+    elem_dict.update(dict(rlhf_tab=rlhf_tab, dpo_beta=dpo_beta, reward_model=reward_model, refresh_btn=refresh_btn))
+
     with gr.Row():
         cmd_preview_btn = gr.Button()
         start_btn = gr.Button()
@@ -88,6 +118,7 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
                 output_dir = gr.Textbox()
 
             with gr.Row():
+                resume_btn = gr.Checkbox(visible=False, interactive=False, value=False)
                 process_bar = gr.Slider(visible=False, interactive=False)
 
             with gr.Box():
@@ -96,89 +127,28 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
         with gr.Column(scale=1):
             loss_viewer = gr.Plot()
 
-    input_components = [
-        top_elems["lang"],
-        top_elems["model_name"],
-        top_elems["checkpoints"],
-        top_elems["finetuning_type"],
-        top_elems["quantization_bit"],
-        top_elems["template"],
-        top_elems["system_prompt"],
-        training_stage,
-        dataset_dir,
-        dataset,
-        max_source_length,
-        max_target_length,
-        learning_rate,
-        num_train_epochs,
-        max_samples,
-        batch_size,
-        gradient_accumulation_steps,
-        lr_scheduler_type,
-        max_grad_norm,
-        val_size,
-        logging_steps,
-        save_steps,
-        warmup_steps,
-        compute_type,
-        lora_rank,
-        lora_dropout,
-        lora_target,
-        resume_lora_training,
-        dpo_beta,
-        reward_model,
+    input_elems.add(output_dir)
+    output_elems = [output_box, process_bar]
+
+    cmd_preview_btn.click(engine.runner.preview_train, input_elems, output_elems)
+    start_btn.click(engine.runner.run_train, input_elems, output_elems)
+    stop_btn.click(engine.runner.set_abort, queue=False)
+    resume_btn.change(engine.runner.monitor, outputs=output_elems)
+
+    elem_dict.update(dict(
+        cmd_preview_btn=cmd_preview_btn, start_btn=start_btn, stop_btn=stop_btn, output_dir=output_dir,
+        resume_btn=resume_btn, process_bar=process_bar, output_box=output_box, loss_viewer=loss_viewer
+    ))
+
+    output_box.change(
+        gen_plot,
+        [
+            engine.manager.get_elem_by_name("top.model_name"),
+            engine.manager.get_elem_by_name("top.finetuning_type"),
             output_dir
-    ]
-
-    output_components = [
-        output_box,
-        process_bar
-    ]
-
-    cmd_preview_btn.click(runner.preview_train, input_components, output_components)
-    start_btn.click(runner.run_train, input_components, output_components)
-    stop_btn.click(runner.set_abort, queue=False)
-
-    process_bar.change(
-        gen_plot, [top_elems["model_name"], top_elems["finetuning_type"], output_dir], loss_viewer, queue=False
+        ],
+        loss_viewer,
+        queue=False
     )
 
-    return dict(
-        training_stage=training_stage,
-        dataset_dir=dataset_dir,
-        dataset=dataset,
-        data_preview_btn=data_preview_btn,
-        preview_count=preview_count,
-        preview_samples=preview_samples,
-        close_btn=close_btn,
-        max_source_length=max_source_length,
-        max_target_length=max_target_length,
-        learning_rate=learning_rate,
-        num_train_epochs=num_train_epochs,
-        max_samples=max_samples,
-        batch_size=batch_size,
-        gradient_accumulation_steps=gradient_accumulation_steps,
-        lr_scheduler_type=lr_scheduler_type,
-        max_grad_norm=max_grad_norm,
-        val_size=val_size,
-        advanced_tab=advanced_tab,
-        logging_steps=logging_steps,
-        save_steps=save_steps,
-        warmup_steps=warmup_steps,
-        compute_type=compute_type,
-        lora_tab=lora_tab,
-        lora_rank=lora_rank,
-        lora_dropout=lora_dropout,
-        lora_target=lora_target,
-        resume_lora_training=resume_lora_training,
-        rlhf_tab=rlhf_tab,
-        dpo_beta=dpo_beta,
-        reward_model=reward_model,
-        refresh_btn=refresh_btn,
-        cmd_preview_btn=cmd_preview_btn,
-        start_btn=start_btn,
-        stop_btn=stop_btn,
-        output_dir=output_dir,
-        output_box=output_box,
-        loss_viewer=loss_viewer
-    )
+    return elem_dict

src/llmtuner/webui/css.py

@@ -1,4 +1,11 @@
 CSS = r"""
+.duplicate-button {
+  margin: auto !important;
+  color: white !important;
+  background: black !important;
+  border-radius: 100vh !important;
+}
+
 .modal-box {
   position: fixed !important;
   top: 50%;
@@ -6,10 +13,12 @@ CSS = r"""
   transform: translate(-50%, -50%); /* center horizontally */
   max-width: 1000px;
   max-height: 750px;
-  overflow-y: scroll !important;
+  overflow-y: auto;
   background-color: var(--input-background-fill);
+  flex-wrap: nowrap !important;
   border: 2px solid black !important;
   z-index: 1000;
+  padding: 10px;
 }
 
 .dark .modal-box {

src/llmtuner/webui/engine.py

@@ -0,0 +1,57 @@
+import gradio as gr
+from gradio.components import Component # cannot use TYPE_CHECKING here
+from typing import Any, Dict, Generator, Optional
+
+from llmtuner.webui.chatter import WebChatModel
+from llmtuner.webui.common import get_model_path, list_dataset, load_config
+from llmtuner.webui.locales import LOCALES
+from llmtuner.webui.manager import Manager
+from llmtuner.webui.runner import Runner
+from llmtuner.webui.utils import get_time
+
+
+class Engine:
+
+    def __init__(self, demo_mode: Optional[bool] = False, pure_chat: Optional[bool] = False) -> None:
+        self.pure_chat = pure_chat
+        self.manager = Manager()
+        self.runner = Runner(self.manager, demo_mode=demo_mode)
+        self.chatter = WebChatModel(manager=self.manager, demo_mode=demo_mode, lazy_init=(not pure_chat))
+
+    def _form_dict(self, resume_dict: Dict[str, Dict[str, Any]]):
+        return {self.manager.get_elem_by_name(k): gr.update(**v) for k, v in resume_dict.items()}
+
+    def resume(self) -> Generator[Dict[Component, Dict[str, Any]], None, None]:
+        user_config = load_config()
+        lang = user_config.get("lang", None) or "en"
+
+        init_dict = {
+            "top.lang": {"value": lang},
+            "infer.chat_box": {"visible": self.chatter.loaded}
+        }
+
+        if not self.pure_chat:
+            init_dict["train.dataset"] = {"choices": list_dataset()["choices"]}
+            init_dict["eval.dataset"] = {"choices": list_dataset()["choices"]}
+
+            if user_config.get("last_model", None):
+                init_dict["top.model_name"] = {"value": user_config["last_model"]}
+                init_dict["top.model_path"] = {"value": get_model_path(user_config["last_model"])}
+
+        yield self._form_dict(init_dict)
+
+        if not self.pure_chat:
+            if self.runner.alive:
+                yield {elem: gr.update(value=value) for elem, value in self.runner.running_data.items()}
+                if self.runner.do_train:
+                    yield self._form_dict({"train.resume_btn": {"value": True}})
+                else:
+                    yield self._form_dict({"eval.resume_btn": {"value": True}})
+            else:
+                yield self._form_dict({"train.output_dir": {"value": get_time()}})
+
+    def change_lang(self, lang: str) -> Dict[Component, Dict[str, Any]]:
+        return {
+            component: gr.update(**LOCALES[name][lang])
+            for elems in self.manager.all_elems.values() for name, component in elems.items() if name in LOCALES
+        }

src/llmtuner/webui/interface.py

@@ -1,4 +1,5 @@
 import gradio as gr
+from typing import Optional
 from transformers.utils.versions import require_version
 
 from llmtuner.webui.components import (
@@ -9,65 +10,64 @@ from llmtuner.webui.components import (
     create_export_tab,
     create_chat_box
 )
-from llmtuner.webui.chat import WebChatModel
+from llmtuner.webui.common import save_config
 from llmtuner.webui.css import CSS
-from llmtuner.webui.manager import Manager
-from llmtuner.webui.runner import Runner
+from llmtuner.webui.engine import Engine
 
 
-require_version("gradio>=3.36.0", "To fix: pip install gradio>=3.36.0")
+require_version("gradio>=3.38.0,<4.0.0", "To fix: pip install \"gradio>=3.38.0,<4.0.0\"")
 
 
-def create_ui() -> gr.Blocks:
-    runner = Runner()
+def create_ui(demo_mode: Optional[bool] = False) -> gr.Blocks:
+    engine = Engine(demo_mode=demo_mode, pure_chat=False)
 
-    with gr.Blocks(title="Web Tuner", css=CSS) as demo:
-        top_elems = create_top()
+    with gr.Blocks(title="LLaMA Board", css=CSS) as demo:
+        if demo_mode:
+            gr.HTML(
+                "<h1><center>LLaMA Board: A One-stop Web UI for Getting Started with LLaMA Factory</center></h1>"
+            )
+            gr.HTML(
+                "<h3><center>Visit <a href=\"https://github.com/hiyouga/LLaMA-Factory\" target=\"_blank\">"
+                "LLaMA Factory</a> for details.</center></h3>"
+            )
+            gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
+
+        engine.manager.all_elems["top"] = create_top()
+        lang: "gr.Dropdown" = engine.manager.get_elem_by_name("top.lang")
 
         with gr.Tab("Train"):
-            train_elems = create_train_tab(top_elems, runner)
+            engine.manager.all_elems["train"] = create_train_tab(engine)
 
-        with gr.Tab("Evaluate"):
-            eval_elems = create_eval_tab(top_elems, runner)
+        with gr.Tab("Evaluate & Predict"):
+            engine.manager.all_elems["eval"] = create_eval_tab(engine)
 
         with gr.Tab("Chat"):
-            infer_elems = create_infer_tab(top_elems)
+            engine.manager.all_elems["infer"] = create_infer_tab(engine)
 
+        if not demo_mode:
             with gr.Tab("Export"):
-            export_elems = create_export_tab(top_elems)
+                engine.manager.all_elems["export"] = create_export_tab(engine)
 
-        elem_list = [top_elems, train_elems, eval_elems, infer_elems, export_elems]
-        manager = Manager(elem_list)
-
-        demo.load(
-            manager.gen_label,
-            [top_elems["lang"]],
-            [elem for elems in elem_list for elem in elems.values()],
-        )
-
-        top_elems["lang"].change(
-            manager.gen_label,
-            [top_elems["lang"]],
-            [elem for elems in elem_list for elem in elems.values()],
-            queue=False
-        )
+        demo.load(engine.resume, outputs=engine.manager.list_elems())
+        lang.change(engine.change_lang, [lang], engine.manager.list_elems(), queue=False)
+        lang.input(save_config, inputs=[lang], queue=False)
 
     return demo
 
 
 def create_web_demo() -> gr.Blocks:
-    chat_model = WebChatModel(lazy_init=False)
+    engine = Engine(pure_chat=True)
 
     with gr.Blocks(title="Web Demo", css=CSS) as demo:
-        lang = gr.Dropdown(choices=["en", "zh"], value="en")
+        lang = gr.Dropdown(choices=["en", "zh"])
+        engine.manager.all_elems["top"] = dict(lang=lang)
 
-        _, _, _, chat_elems = create_chat_box(chat_model, visible=True)
+        chat_box, _, _, chat_elems = create_chat_box(engine, visible=True)
+        engine.manager.all_elems["infer"] = dict(chat_box=chat_box, **chat_elems)
 
-        manager = Manager([{"lang": lang}, chat_elems])
-
-        demo.load(manager.gen_label, [lang], [lang] + list(chat_elems.values()))
-
-        lang.select(manager.gen_label, [lang], [lang] + list(chat_elems.values()), queue=False)
+        demo.load(engine.resume, outputs=engine.manager.list_elems())
+        lang.change(engine.change_lang, [lang], engine.manager.list_elems(), queue=False)
+        lang.input(save_config, inputs=[lang], queue=False)
 
     return demo