pygfm

A Unified Python Toolkit for Graph Foundation Model Research

Installation · Quick Start · Supported Baselines · Documentation

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pygfm is a unified Python toolkit for Graph Foundation Model (GFM) research. It integrates 19 state-of-the-art baseline methods under a single, pip-installable package with shared utilities, standardized interfaces, and fully reproducible experiment pipelines.

Developed by Beihang University · School of Computer Science and Engineering · ACT Lab · MAGIC GROUP.

Framework Overview

PyGFM is organized into four stacked layers — Graph Data Abstraction → Alignment & Fusion Bridge → Representation Backbones → Task Heads & Orchestration — with a unified CLI, model recipes, and an auto-experiment tracker sitting on top.

Highlights

• One package, 19 baselines — prompt-based GFMs, structure-aware models, LLM-integrated approaches, and retrieval-augmented methods all available via a single pip install.
• Reproducible pipelines — every baseline ships with YAML-driven experiment configs, training scripts, and evaluation helpers.
• Shared backbone library — common GNN encoders, loss functions, and data utilities are factored out and reused across all baselines, reducing code duplication.
• CLI-first design — launch pre-training, fine-tuning, and evaluation jobs directly from the command line without writing any boilerplate.
• LLM-ready — first-class support for LLM-integrated GFMs (GraphGPT, GraphText, LLaGA, OneForAll) with HuggingFace-compatible YAML configs.

Installation

Minimal install (utilities only)

pip install python-gfm

With PyTorch + PyG (recommended for running experiments)

# 1. Install PyTorch with CUDA 12.8 support
pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cu128

# 2. Install pygfm with the full ML stack (PyG extensions are resolved automatically)
pip install "python-gfm[torch]" -f https://data.pyg.org/whl/torch-2.8.0+cu128.html

CPU-only machines: replace the CUDA index URLs with https://download.pytorch.org/whl/cpu and https://data.pyg.org/whl/torch-2.8.0+cpu.html respectively.

Development install (full checkout with experiment scripts)

git clone <repo-url> && cd pygfm
pip install -e ".[torch,dev]"

The dev extra adds pytest and ruff for testing and linting.

Quick Start

import pygfm

print(pygfm.__version__)

Run a pre-training job from the CLI:

# SA2GFM contrastive pre-training
gfm-sa2gfm-pretrain -c scripts/sa2gfm/configs/pretrain.yaml

# SA2GFM downstream fine-tuning
gfm-sa2gfm-downstream -c scripts/sa2gfm/configs/downstream.yaml

Package Structure

pygfm/
├── src/pygfm/
│   ├── baseline_models/   # 19 GFM baseline implementations
│   ├── public/            # Shared utilities, losses, and backbone encoders
│   │   ├── backbone_models/
│   │   ├── utils/
│   │   └── cli/
│   ├── private/           # Core encoders and internal data generation
│   └── cli/               # Console entry points
└── scripts/               # Per-baseline experiment scripts and configs
    ├── <baseline>/
    │   ├── README.md
    │   ├── configs/
    │   ├── pretrain.py / downstream.py / ...
    │   └── eval_script/

Supported Baselines

Category	Methods
Prompt-based GFM	MDGPT, SAMGPT, MDGFM, GraphPrompt, HGPrompt, MultiGPrompt, GCoT
Structure-aware GFM	SA2GFM, Bridge, GraphKeeper, GraphMore, Graver, BIM-GFM
LLM-integrated GFM	GraphGPT, GraphText, LLaGA, OneForAll
Retrieval-augmented GFM	RAG-GFM
Classic Baseline	Classic GNN

Running Experiments

All scripts are under scripts/<baseline>/ and should be run from the repository root.

# Prompt-based: MDGPT pre-training
python scripts/mdgpt/pretrain.py

# Structure-aware: SA2GFM downstream fine-tuning
python scripts/sa2gfm/downstream.py

# LLM-integrated: GCoT full pipeline
python scripts/gcot/pretrain.py
python scripts/gcot/finetune.py
python scripts/gcot/finetune_graph.py

# LLM-integrated: GraphGPT (YAML-driven HuggingFace-style training)
python scripts/graphgpt/run_with_config.py -c scripts/graphgpt/configs/train_mem_template.yaml

Console Commands

After installation the following CLI entry points are registered:

Command	Description
pygfm / gfm	Generic YAML-driven runner (-c <config.yaml>)
gfm-sa2gfm-pretrain	SA2GFM contrastive pre-training
gfm-sa2gfm-downstream	SA2GFM MoE downstream fine-tuning

Configuration

All experiment hyperparameters are stored as YAML files under scripts/<baseline>/configs/. Pass configs via the -c flag:

python scripts/<baseline>/pretrain.py -c scripts/<baseline>/configs/default.yaml

API keys: baselines that call external LLM APIs (e.g., GraphText) read credentials from a local env file. Never commit API keys to the repository. Copy the example template and fill in your keys:

cp scripts/graphtext/config/user/env.yaml.example scripts/graphtext/config/user/env.yaml
# Then edit env.yaml and add your API key

Baseline Documentation

Each baseline ships a dedicated README with setup instructions, data preparation steps, and evaluation notes:

Baseline	Docs
MDGPT	scripts/mdgpt/README.md
SA2GFM	scripts/sa2gfm/README.md
SAMGPT	scripts/samgpt/README.md
MDGFM	scripts/mdgfm/README.md
GraphPrompt	scripts/graphprompt/README.md
HGPrompt	scripts/hgprompt/README.md
MultiGPrompt	scripts/multigprompt/README.md
GCoT	scripts/gcot/README.md
Graver	scripts/graver/README.md
GraphMore	scripts/graphmore/README.md
Bridge	scripts/bridge/README.md
GraphKeeper	scripts/graphkeeper/README.md
GraphGPT	scripts/graphgpt/README.md
GraphText	scripts/graphtext/README.md
LLaGA	scripts/llaga/README.md
OneForAll	scripts/oneforall/README.md
RAG-GFM	scripts/rag_gfm/README.md

Requirements

Dependency	Version
Python	≥ 3.12
PyTorch	2.8.0 (CUDA 12.8 recommended)
PyTorch Geometric	≥ 2.3.0
Transformers	≥ 4.36.0
Accelerate	≥ 0.26.0

See pyproject.toml for the full dependency specification.

License

This project is licensed under the Apache License 2.0.

Team

MAGIC GROUP — Beihang University, School of Computer Science and Engineering, ACT Lab.

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_{If you find this toolkit useful in your research, please consider starring the repository ⭐}