Vicuna-LoRA-RLHF-PyTorch

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Vicuna-LoRA-RLHF-PyTorch

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A full pipeline to finetune Vicuna LLM with LoRA and RLHF on consumer hardware. Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the Vicuna architecture. Basically ChatGPT but with Vicuna

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# **Vicuna-LoRA-RLHF-PyTorch**
a full pipeline to finetune Vicuna LLM with LoRA and RLHF on consumer hardware

---
## **Table of Contents**
- [**Vicuna-LoRA-RLHF-PyTorch**](#vicuna-lora-rlhf-pytorch)
  - [**Table of Contents**](#table-of-contents)
  - [**Environment Setup**](#environment-setup)
  - [**Todo List**](#todo-list)
  - [**Run**](#run)
    - [**Download Vicuna Weights**](#download-vicuna-weights)
    - [**Supervised Finetune**](#supervised-finetune)
    - [**Merge PEFT adapter into Model**](#merge-peft-adapter-into-model)
    - [**Train Reward Model**](#train-reward-model)
    - [**Merge Reward adapter into Model**](#merge-reward-adapter-into-model)
    - [**Tuning LM with PPO**](#tuning-lm-with-ppo)
  - [**Topics**](#topics)
  - [**Reference**](#reference)
  - [**Star-History**](#star-history)
  - [Donation](#donation)
  - [**License**](#license)
---

## **Environment Setup**
```
穷人卡：2080Ti 12G
torch==2.0.0
cuda==11.8
```

---
## **Todo List**
- [x] Download Vicuna Weights
- [x] SFT: Supervised Finetune
- [x] Merge Adapter into Model
- [x] RLHF
  - [x] train reward model
  - [x] tuning with RL

## **Run**
---

### **Download Vicuna Weights**

```bash
python apply_delta.py --base 'decapoda-research/llama-7b-hf' --target './weights/vicuna-7b' --delta lmsys/vicuna-7b-delta-v1.1
```

### **Supervised Finetune**

check **src/peft/utils/save_and_load.py** first, Only comment the line 52 to
 ```python
 # #to_return = {k: v for k, v in to_return.items() if (("lora_" in k and adapter_name in k) or ("bias" in k))}
 ```
then run
```bash
python supervised_finetune.py --data_path './data/merge_sample.json' --output_path 'lora-Vicuna' --model_path './weights/vicuna-7b' --eval_steps 200 --save_steps 200 --test_size 1
```

### **Merge PEFT adapter into Model**

check peft version first, if peft not 0.2.0, should install peft==0.2.0
```bash
pip uninstall peft -y
pip install peft==0.2.0  # 0.3.0.dev0 has many errors
```

```bash
python merge_peft_adapter.py --model_name 'lora-Vicuna'

pip uninstall peft -y
pip install git+https://github.com/huggingface/peft.git # then comments peft/utis/save_and_load.py line 52.
```

### **Train Reward Model**

```bash
python train_reward_model.py --model_name './weights/vicuna-7b' --gradient_accumulation_steps 32 --per_device_train_batch_size 1 --train_subset 100 --eval_subset 10 --local_rank 0 --bf16 False
```

### **Merge Reward adapter into Model**

```bash
python merge_peft_adapter.py --model_name ./reward_model_vicuna-7b
```

### **Tuning LM with PPO**

```bash
python tuning_lm_with_rl.py --model_name './lora-Vicuna-adapter-merged' --reward_model_name './reward_model_vicuna-7b-adapter-merged' --adafactor False --tokenizer_name 'decapoda-research/llama-7b-hf' --save_freq 100 --output_max_length 128 --batch_size 1 --gradient_accumulation_steps 1 --batched_gen True --ppo_epochs 1 --seed 0 --learning_rate 1.4e-5 --early_stopping True --output_dir './tuning_llama_rl_checkpoints'
```

---

## **Topics**
1. Vicuna model weight not on HuggingFace hub, so you need download first by runing [apply_delta.py](./apply_delta.py) scripts.
2. SFT之前，切记有个注意事项，需要检查下 安装的peft代码， src/peft/utils/save_and_load.py , 如果 line 52 有这行代码  #to_return = {k: v for k, v in to_return.items() if (("lora_" in k and adapter_name in k) or ("bias" in k))}，需要将其注释掉，否则在finetune完之后，保存不了 adapter model 的参数。切记！
2. PEFT的版本，目前从git上安装的是 0.3.0.dev0 版本，在merge_peft_adapter的时候有问题，需要切换到peft==0.2.0 (0.3.0.dev0 没有 _get_submodules()这个函数)
3. train reward model的时候 会发生另一个问题： ValueError: weight is on the meta device, we need a `value` to put in on 0. 需要参看 transformer 在github上的最新代码，我在发现这个问题的时候，隔天发现在transformer的github上 8小时前才刚刚修复了这个问题。
4. 最后一步，代码上基本是ok的，但是本人只有2080Ti的卡，加载完finetune model之后，再加载Reward model的时候 直接CUDA out of memory了，所以并未执行。

## **Reference**
[apply_delta.py](apply_delta.py) 来自 [FastChat](https://github.com/lm-sys/FastChat) 。

requirements 主要是按照 [alpaca-lora](https://github.com/tloen/alpaca-lora) 来配环境。
* [https://github.com/lm-sys/FastChat](https://github.com/lm-sys/FastChat)
* [https://github.com/tloen/alpaca-lora](https://github.com/tloen/alpaca-lora)
* [https://github.com/lvwerra/trl](https://github.com/lvwerra/trl)
* [https://github.com/jasonvanf/llama-trl](https://github.com/jasonvanf/llama-trl)

------
## **Star-History**

![star-history](https://api.star-history.com/svg?repos=jackaduma/Vicuna-LoRA-RLHF-PyTorch&type=Date "star-history")

------

## Donation
If this project help you reduce time to develop, you can give me a cup of coffee :)

AliPay(支付宝)
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WechatPay(微信)
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------

## **License**

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