kongds/MoRA

MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

Setup

We implement MoRA in peft-mora based on HF peft in the apply_mora and get_delta_weight.

pip install -e ./peft-mora

After installation, it can be used like

from peft import LoraConfig, get_peft_model
config = LoraConfig(
    # enable MoRA
    use_mora=True,
    # type 1 (Sharing) for large lora ranks, Eq. 6 in paper
    # type 6 (RoPE based) for small lora ranks, Eq. 9 in paper
    mora_type=6,
    # lora rank here, we will calculate corresponding $\hat{r}$ in MoRA
    r=lora_r,
    # MoRA does not use lora_alpha
    # lora_alpha=lora_alpha,
    target_modules=lora_target_modules,
    lora_dropout=lora_dropout,
    task_type="CAUSAL_LM",
    **kwargs,
)
model = get_peft_model(model, config)

# training here...

# can be merged into model via `merge_and_unload` like LoRA
model = model.merge_and_unload() 

Examples

fine-tuning MetaMath with MoRA

RANK=8
deepspeed --num_gpus=8 --num_nodes=2 train.py \
           --base_model <LLAMA-2> --micro_batch_size 4\
            --wandb_run_name mora_math_r8 --lora_target_modules q_proj,k_proj,v_proj,o_proj,gate_proj,down_proj,up_proj \
            --num_epochs 3 --deepspeed ds.config --wandb_project lora-math --lora_r $RANK --batch_size 128 \
            --data_path meta-math/MetaMath \
            --save_steps 3000 \
            --learning_rate 3e-4 --mora_type 6 \
            --logging_steps 5  --use_bf16  --use_16bit --use_mora 

pretraining

deepspeed --num_gpus=8 --num_nodes=4 train.py \
        --micro_batch_size 16 --wandb_run_name mora-pretrain250m-r128 \
        --num_epochs 1 --wandb_project lora-pretrain --batch_size 1024 \
        --data_path <processed C4> --logging_steps 1 \
        --lora_target_modules q_proj,k_proj,v_proj,o_proj,gate_proj,down_proj,up_proj \
        --lora_r 128 --lora_alpha 64 --warmup_steps 1000  \
        --force_tqdm_update --lr_scheduler_type cosine \
        --max_steps 10000 --pretrain 250m \
        --train_embhead --learning_rate 5e-4 \
        --use_mora --use_relora --use_relora_step 2000  # ReMoRA merge per 2000 steps 

Acknowledgement

Our Code is based on peft, alpaca-lora and ReLoRA