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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

Environment Setup

穷人卡：2080Ti 12G
torch==2.0.0
cuda==11.8

Todo List

  • Download Vicuna Weights
  • SFT: Supervised Finetune
  • Merge Adapter into Model
  • RLHF
    • train reward model
    • tuning with RL

Run

Download Vicuna Weights

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

# #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

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

pip uninstall peft -y
pip install peft==0.2.0  # 0.3.0.dev0 has many errors
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

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

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

Tuning LM with PPO

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 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！
  3. 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)
  4. 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。
  5. 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 来自 FastChat 。

requirements 主要是按照 alpaca-lora 来配环境。

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Donation

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License

MIT © Kun