LLaGA: Large Language and Graph Assistant

Runjin Chen, Tong Zhao, Ajay Jaiswal, Neil Shah and Zhangyang Wang*. (*Correspondence )

**VITA Lab@University of Texas at Austin, Snap Inc.

Step 1: Environment Preparation

# create a new environment
conda create -n llaga python=3.10
conda activate llaga

# install pytorch. Modify the command to align with your own CUDA version.
pip3 install torch  --index-url https://download.pytorch.org/whl/cu118

# install related libraries
pip install -r requirements.txt

# install flash-attn
pip install flash-attn --no-build-isolation

# install pyg
pip install torch_geometric
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.1.0+cu118.html

Step 2: Data Preparation

Download our datasets from Box (updated link prediction data on 4/11/2024). And move the processed data to ./dataset

.
├── README.md
├── __init__.py
├── doc
├── dataset
│   ├── ogbn-arxiv
│   │   ├── sampled_2_10_test.jsonl
│   │   ├── sampled_2_10_train.jsonl
│   │   ├── edge_sampled_2_10_only_test.jsonl
│   │   ├── edge_sampled_2_10_only_train.jsonl
│   │   ├── processed_data.pt
│   │   ├── roberta_x.pt
│   │   ├── sbert_x.pt
│   │   ├── simteg_*_x.pt
│   ├── ogbn-products
│   │   ├── sampled_2_10_test.jsonl
│   │   ├── sampled_2_10_train.jsonl
│   │   ├── edge_sampled_2_10_only_test.jsonl
│   │   ├── edge_sampled_2_10_only_train.jsonl
│   │   ├── processed_data.pt
│   │   ├── roberta_x.pt
│   │   ├── sbert_x.pt
│   │   ├── simteg_*_x.pt
│   └── pubmed
│   │   ├── sampled_2_10_test.jsonl
│   │   ├── sampled_2_10_train.jsonl
│   │   ├── edge_sampled_2_10_only_test.jsonl
│   │   ├── edge_sampled_2_10_only_train.jsonl
│   │   ├── processed_data.pt
│   │   ├── roberta_x.pt
│   │   ├── sbert_x.pt
│   │   ├── simteg_*_x.pt
│   ├── cora
│   │   ├── sampled_2_10_test.jsonl
│   │   ├── sampled_2_10_train.jsonl
│   │   ├── edge_sampled_2_10_only_test.jsonl
│   │   ├── edge_sampled_2_10_only_train.jsonl
│   │   ├── processed_data.pt
│   │   ├── roberta_x.pt
│   │   ├── sbert_x.pt
│   │   ├── simteg_*_x.pt
│   ├── laplacian_2_10.pt
│   ├── laplacian_2_20.pt
│   ├── laplacian_2_5.pt
├── eval
├── model
├── requirements.txt
├── scripts
├── train
├── utils

Step 3: Training

To execute the training process, you can run either ./scripts/train.sh or ./scripts/train_deepspeed.sh. The usage instructions are as follows:

#Auguments
# $1 = model type, e.g. vicuna, vicuna_4hop
# $2 = training task Use nc/lp/nd for single task. For multiple tasks combined, connect these abbreviations with '-', e.g. nc-lp
# $3 = dataset arxiv/products/pubmed/cora  For multiple datasets combined, connect these abbreviations with '-', and use '.n' to repeat multiple times e.g. arxiv-products-pubmed-cora.3 means using arxiv+products+pubmed+cora, and repeat cora for 3 times
# $4 = batch size  default: 16
# $5 = embedding e.g. simteg, sbert, roberta


#  training on single GPU
CUDA_VISIBLE_DEVICES=0 ./scripts/train.sh vicuna nc-lp  arxiv-products-pubmed-cora.3 16 simteg

#  training on multiple GPU
./scripts/train_deepspeed.sh vicuna nc-lp  arxiv-products-pubmed-cora.3 4 simteg

We also uploaded four general projectors to huggingface.

Setting	Template	Repo
General Model	HO	Runjin/llaga-vicuna-7b-simteg-HO-general_model-2-layer-mlp-projector
General Model	ND	Runjin/llaga-vicuna-7b-simteg-ND-general_model-2-layer-mlp-projector
Classification Expert	HO	Runjin/llaga-vicuna-7b-simteg-HO-classification_expert-linear-projector
Classification Expert	ND	Runjin/llaga-vicuna-7b-simteg-ND-classification_expert-linear-projector

Step 4: Evaluation

You can evaluate LLaGA with the command:

model_path="/path/to/projector" # local path or huggingface repo
model_base="lmsys/vicuna-7b-v1.5-16k" #meta-llama/Llama-2-7b-hf
mode="v1" # use 'llaga_llama_2' for llama and "v1" for others
dataset="arxiv" #test dataset
task="nc" #test task
emb="simteg"
use_hop=2 # 2 for ND and 4 for HO
sample_size=10
template="ND"
output_path="/path/to/output"

python eval/eval_pretrain.py \
--model_path ${model_path} \
--model_base ${model_base} \
--conv_mode  ${mode} \
--dataset ${dataset} \
--pretrained_embedding_type ${emb} \
--use_hop ${use_hop} \
--sample_neighbor_size ${sample_size} \
--answers_file ${output_path} \
--task ${task} \
--cache_dir ../../checkpoint \
--template ${template}

To evaluate your predicted results, please run:

python eval/eval_res.py --dataset ${dataset} --task ${task}  --res_path ${output_path}