KnowLA

KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation, NAACL 2024

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Preparation of KnowLA

1. Install the updated loralib:

pip install -r requirements.txt

2. Download the Llama2 checkpoints and modified modeling_llama.py from here.
3. Download the LoRA checkpoints of KnowLA. , LLama2-lora (r=16) and LLama2-lora (r=32).
4. Download the datasets from here
5. Download the KG configurations from here. And move them under the data folder.

Quickstart

1. Train Llama2-lora:

python finetune.py \
    --base_model='/llama2_7B' \
    --num_epochs=3 \
    --cutoff_len=512 \
    --group_by_length \
    --output_dir='./llama2-lora' \
    --lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
    --lora_r=16 \
    --micro_batch_size=4

2. Train KnowLA:

python finetune_kg.py \
    --base_model='/llama2_7B' \
    --num_epochs=3 \
    --cutoff_len=512 \
    --group_by_length \
    --output_dir='./llama2-lora-cn' \
    --lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
    --lora_r=16 \
    --micro_batch_size=4

We recommend you to change the hyperparameter layer_insertion in the file llama2-7B/config.json to dynamically allocate the KG adapters to the corresponding layer, like layer_insertion: [16, 32].

3. Test csqa/siqa for KnowLA:

python test_csqa.py \
    --base_model='/llama2_7B' \
    --is_KG=True \
    --lora_weights="./llama2-lora-cn" \
    --dataset="siqa"

4. Test other tasks for KnowLA:

python test.py \
    --base_model='/llama2_7B' \
    --is_KG=True \
    --lora_weights="./llama2-lora-cn" \

test.py can be replaced by any test file such as test_wqsp.py.

Notice

We finished these training and testing processes with an A800. And we offload the KG pretrained embeddings to the CPU to reduce VRAM allocation. During training, the VRAM usage and training time of KnowLA and Llama2 are similar.

We find that when we reduce the trainable parameters in KnowLA, the performance of the LLM is improved. Therefore, we recommend you to change the hyperparameter kg_intermediate_size from 1024 to 100 in the llama2-7B/config.json for training. This weight can be downloaded from here.

For loading the modified modeling_llama.py file, we use shutil.copyfile to write it in the conda environment. If you do not want to load in this way, consider using the following code to load the modified modeling_llama.py file.

model = AutoModel.from_pretrained(
        base_model,
        config = config,
        device_map="auto",
        trust_remote_code=True,
    )

Citation

If you find the work useful, please kindly cite the following paper:

@inproceedings{KnowLA,
  author    = {Xindi Luo and
               Zequn Sun and
               Jing Zhao and
               Zhe Zhao and
               Wei Hu},
  title     = {KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation},
  booktitle = {NAACL},
  year      = {2024}
}