FusionBench: A Comprehensive Benchmark/ToolKit of Deep Model Fusion

Tip

Documentation is available at tanganke.github.io/fusion_bench/.

Overview

FusionBench is a benchmark suite designed to evaluate the performance of various deep model fusion techniques. It aims to provide a comprehensive comparison of different methods on a variety of datasets and tasks.

Projects based on FusionBench:

Jinluan Yang et al. Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace. Oct, 2024. http://arxiv.org/abs/2410.13910

Anke Tang et al. SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models. Aug, 2024. http://arxiv.org/abs/2408.10174

Example notebooks can be found at examples/smile_upscaling.

Installation

install from PyPI:

pip install fusion-bench

or install the latest version in development from github repository

git clone https://github.com/tanganke/fusion_bench.git
cd fusion_bench

pip install -e . # install the package in editable mode

Introduction to Deep Model Fusion

Deep model fusion is a technique that merges, ensemble, or fuse multiple deep neural networks to obtain a unified model. It can be used to improve the performance and robustness of model or to combine the strengths of different models, such as fuse multiple task-specific models to create a multi-task model. For a more detailed introduction to deep model fusion, you can refer to W. Li, 2023, 'Deep Model Fusion: A Survey'. We also provide a brief overview of deep model fusion in our documentation. In this benchmark, we evaluate the performance of different fusion methods on a variety of datasets and tasks.

Project Structure

The project is structured as follows:

• fusion_bench/: the main package of the benchmark.
• config/: configuration files for the benchmark. We use Hydra to manage the configurations.
• docs/: documentation for the benchmark. We use mkdocs to generate the documentation. Start the documentation server locally with mkdocs serve. The required packages can be installed with pip install -r mkdocs-requirements.txt.
• examples/: example scripts for running some of the experiments.
• tests/: unit tests for the benchmark.

A Unified Command Line Interface

The fusion_bench command-line interface is a powerful tool for researchers and practitioners in the field of model fusion. It provides a streamlined way to experiment with various fusion algorithms, model combinations, and evaluation tasks. By leveraging Hydra's configuration management, fusion_bench offers flexibility in setting up experiments and reproducibility in results. The CLI's design allows for easy extension to new fusion methods, model types, and tasks, making it a versatile platform for advancing research in model fusion techniques.

Read the CLI documentation for more information.

Implement your own model fusion algorithm

from fusion_bench import BaseModelFusionAlgorithm, BaseModelPool

class DerivedModelFusionAlgorithm(BaseModelFusionAlgorithm):
    """
    An example of a derived model fusion algorithm.
    """

    # _config_mapping maps the attribution to the corresponding key in the configuration file.
    # this is optional and can be used to serialize the object to a configuration file.
    # `self.config.hyperparam_1` will be mapped to the attribute `hyperparam_attr_1`.
    _config_mapping = BaseModelFusionAlgorithm._config_mapping | {
        "hyperparam_attr_1": "hyperparam_1",
        "hyperparam_attr_2": "hyperparam_2",
    }

    def __init__(self, hyperparam_1, hyperparam_2, **kwargs):
        self.hyperparam_attr_1 = hyperparam_1
        self.hyperparam_attr_2 = hyperparam_2
        super().__init__(**kwargs)

    def run(self, modelpool: BaseModelPool):
        # modelpool is an object that responsible for managing the models and dataset to be loaded.
        # implement the fusion algorithm here.
        raise NotImplementedError(
            "DerivedModelFusionAlgorithm.run() is not implemented."
        )

A corresponding configuration file should be created to specify the class and hyperparameters of the algorithm. Here we assume the configuration file is placed at config/method/your_algorithm_config.yaml.

_target_: path_to_the_module.DerivedModelFusionAlgorithm

hyperparam_1: some_value
hyperparam_2: another_value

Use the algorithm in the FusionBench:

fusion_bench \
  method=your_algorithm_config \
  method.hyperparam_1=you_can_override_this \
  method.hyperparam_2=and_this \
  ... # other configurations

FusionBench Command Generator WebUI (for v0.1.x)

FusionBench Command Generator is a user-friendly web interface for generating FusionBench commands based on configuration files. It provides an interactive way to select and customize FusionBench configurations, making it easier to run experiments with different settings. Read more here.

Citation

If you find this benchmark useful, please consider citing our work:

@misc{tangFusionBenchComprehensiveBenchmark2024,
  title = {{{FusionBench}}: {{A Comprehensive Benchmark}} of {{Deep Model Fusion}}},
  shorttitle = {{{FusionBench}}},
  author = {Tang, Anke and Shen, Li and Luo, Yong and Hu, Han and Du, Bo and Tao, Dacheng},
  year = {2024},
  month = jun,
  number = {arXiv:2406.03280},
  eprint = {2406.03280},
  publisher = {arXiv},
  url = {http://arxiv.org/abs/2406.03280},
  archiveprefix = {arxiv},
  langid = {english},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Machine Learning}
}