Skip to content

Latest commit

 

History

History
88 lines (59 loc) · 4.13 KB

README.md

File metadata and controls

88 lines (59 loc) · 4.13 KB
Raw

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

Preprint pytorch

Description

This is the official repository for the paper Iterated Denoising Energy Matching for Sampling from Boltzmann Densities.

We propose iDEM, a scalable and efficient method to sample from unnormalized probability distributions. iDEM makes use of the DEM objective, inspired by the stochastic regression and simulation free principles of score and flow matching objectives while allowing one to learn off-policy, in a loop while itself generating (optionally exploratory) new states which are subsequently learned on. iDEM is also capable of incorporating symmetries, namely those represented by the product group of $SE(3) \times \mathbb{S}_n$. We experiment on a 2D GMM task as well as a number of physics-inspired problems. These include:

  • DW4 -- the 4-particle double well potential (8 dimensions total)
  • LJ13 -- the 13-particle Lennard-Jones potential (39 dimensions total)
  • LJ55 -- the 55-particle Lennard-Jones potential (165 dimensions total)

This code was taken from an internal repository and as such all commit history is lost here. Development credit for this repository goes primarily to @atong01, @jarridrb and @taraak who built out most of the code and experiments with help from @sarthmit and @msendera. Finally, the code is based off the hydra lightning template by @ashleve and makes use of the FAB torch code for the GMM task and replay buffers.

Installation

For installation, we recommend the use of Micromamba. Please refer here for an installation guide for Micromamba. First, we install dependencies

# clone project
git clone [email protected]:jarridrb/DEM.git
cd DEM

# create micromamba environment
micromamba create -f environment.yaml
micromamba activate dem

# install requirements
pip install -r requirements.txt

Note that the hydra configs interpolate using some environment variables set in the file .env. We provide an example .env.example file for convenience. Note that to use wandb we require that you set WANDB_ENTITY in your .env file.

To run an experiment, e.g., GMM with iDEM, you can run on the command line

python dem/train.py experiment=gmm_idem

We include configs for all experiments matching the settings we used in our paper for both iDEM and pDEM except LJ55 for which we only include a config for iDEM as pDEM had convergence issues on this dataset.

Current Code

The current repository contains code for experiments for iDEM and pDEM as specified in our paper.

Citations

If this codebase is useful towards other research efforts please consider citing us.

@misc{akhoundsadegh2024iterated,
      title={Iterated Denoising Energy Matching for Sampling from Boltzmann Densities},
      author={Tara Akhound-Sadegh and Jarrid Rector-Brooks and Avishek Joey Bose and Sarthak Mittal and Pablo Lemos and Cheng-Hao Liu and Marcin Sendera and Siamak Ravanbakhsh and Gauthier Gidel and Yoshua Bengio and Nikolay Malkin and Alexander Tong},
      year={2024},
      eprint={2402.06121},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Contribute

We welcome issues and pull requests (especially bug fixes) and contributions. We will try our best to improve readability and answer questions!

Licences

This repo is licensed under the MIT License.

Warning: the current code uses PyTorch 2.0.0+

The code makes heavy use of the func torch library which is included in torch 2.0.0 as well as torch vmap.