Graph-LLM

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GraphLLM: Boosting Graph Reasoning Ability of Large Language Model (IEEE Transactions on Big Data)

Graph-LLM 최신버젼 다운로드

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# GraphLLM: Boosting Graph Reasoning Ability of Large Language Model

This is the implementation for the paper [GraphLLM: Boosting Graph Reasoning Ability of Large Language Model](https://arxiv.org/abs/2310.05845).

## Setup
- You may need a single 80G GPU to run the experiment. We experiment on CUDA 11.8 and torch 2.0.1.
- Setup up a new conda env and install necessary packages.
    ```bash
    conda create -n graph_llm python=3.10 -y
    pip install -r requirements.txt
    ```
- To run the code, you need the checkpoint and tokenizer of LLaMA-2-7B, which you can access at [Meta](https://ai.meta.com/resources/models-and-libraries/llama-downloads/).
After downloading LLaMA-2-7B, soft link the checkpoint folder and the tokenizer folder to the folder of this repository:
    ```bash
    ln -s /folder/of/LLaMA-2-7B/checkpoint ./LLaMA-7B-2
    ln -s /folder/of/LLaMA-2-7B/tokenizer ./Llama-2-7b-hf
    ```
- Remember to replace the directory `/folder/of/LLaMA-2-7B/checkpoint` and `/folder/of/LLaMA-2-7B/tokenizer` with actual directories!
- The four graph reasoning datasets are available on [Google Drive](https://drive.google.com/file/d/1fRXdCMHpkb1-kuzcxgZPKkILEWBSbW4M) or [Huggingface](https://huggingface.co/datasets/tianjiezhang/GraphLLM).
You may download it and place the zip file in the directory of this repository. And then run the command:
    ```bash
    unzip dataset.zip -d ./dataset
    ```
- The directory structure should be:
    ```
    .
    |- LLaMA-7B-2
    |   |- params.json
    |   |- consolidated.00.pth
    |
    |- Llama-2-7b-hf
    |   |- tokenizer.model
    |
    |- dataset
        |- sc
        |- mts
        |- sp
        |- bgm
    
    ```
## Get Start

Train and evaluate the model with default settings on graph reasoning datasets on GPU 0:

1. Substructure Counting
    ```bash
    ./scripts/sc.sh
    ```
2. Maximum Triplet Sum
    ```bash
    ./scripts/mts.sh
    ```
3. Shortest Path
    ```bash
    ./scripts/sp.sh
    ```
4. Bipartite Graph Matching
    ```bash
    ./scripts/bgm.sh
    ```

More hyperparameter settings are at `config.py`

Hyperparameter explanation:
- `--n_encoder_layers` number of transformer layers of textual encoder
- `--n_decoder_layers` number of transformer layers of textual decoder
- `--n_mp_layers` number of graph transformer layers
- `--adapter_dim` hidden dimension of textual encoder/decoder and graph transformer
- `--adapter_len` number of prefix tokens per LLM layer
- `--rrwp` graph positional encoding dimension
- `--batch_size` batch size in memory during training
- `--grad_steps` grad_step $\times$ batch_size = batch size for optimization
- `--lr` the learning rate
- `--num_epochs` number of training epochs
- `--warmup_epochs` number of linear warmup epochs
- `--wd` weight decay

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