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l2hmc-qcd codefactor
arxiv arxiv
hydra pyTorch tensorflow
Weights & Biases monitoring



Papers 📚, Slides 📊 etc.


The L2HMC algorithm aims to improve upon HMC by optimizing a carefully chosen loss function which is designed to minimize autocorrelations within the Markov Chain, thereby improving the efficiency of the sampler.

A detailed description of the original L2HMC algorithm can be found in the paper:

Generalizing Hamiltonian Monte Carlo with Neural Network

with implementation available at brain-research/l2hmc/ by Daniel Levy, Matt D. Hoffman and Jascha Sohl-Dickstein.

Broadly, given an analytically described target distribution, π(x), L2HMC provides a statistically exact sampler that:

  • Quickly converges to the target distribution (fast burn-in).
  • Quickly produces uncorrelated samples (fast mixing).
  • Is able to efficiently mix between energy levels.
  • Is capable of traversing low-density zones to mix between modes (often difficult for generic HMC).


It is recommended to install inside an existing virtual environment
(ideally one with tensorflow, pytorch [horovod,deepspeed] already installed)

From source (RECOMMENDED)
git clone
cd l2hmc-qcd
# for development addons:
# python3 -m pip install -e ".[dev]"
python3 -m pip install -e .
From l2hmc on PyPI

python3 -m pip install l2hmc

Test install:

python3 -c 'import l2hmc ; print(l2hmc.__file__)'


Configuration Management

This project uses hydra for configuration management and supports distributed training for both PyTorch and TensorFlow.

In particular, we support the following combinations of framework + backend for distributed training:

  • TensorFlow (+ Horovod for distributed training)
  • PyTorch +
    • DDP
    • Horovod
    • DeepSpeed

The main entry point is src/l2hmc/, which contains the logic for running an end-to-end Experiment.

An Experiment consists of the following sub-tasks:

  1. Training
  2. Evaluation
  3. HMC (for comparison and to measure model improvement)

All configuration options can be dynamically overridden via the CLI at runtime, and we can specify our desired framework and backend combination via:

python3 mode=debug framework=pytorch backend=deepspeed precision=fp16

to run a (non-distributed) Experiment with pytorch + deepspeed with fp16 precision.

The l2hmc/conf/config.yaml contains a brief explanation of each of the various parameter options, and values can be overriden either by modifying the config.yaml file, or directly through the command line, e.g.

cd src/l2hmc
./ mode=debug framework=pytorch > train.log 2>&1 &
tail -f train.log $(tail -1 logs/latest)

Additional information about various configuration options can be found in:

for more information on how this works I encourage you to read Hydra's Documentation Page.

Running at ALCF

For running with distributed training on ALCF systems, we provide a complete src/l2hmc/ script which should run without issues on either Polaris or ThetaGPU @ ALCF.


Goal: Use L2HMC to efficiently generate gauge configurations for calculating observables in lattice QCD.

A detailed description of the (ongoing) work to apply this algorithm to simulations in lattice QCD (specifically, a 2D U(1) lattice gauge theory model) can be found in arXiv:2105.03418.

l2hmc-qcd poster


Dynamics / Network

For a given target distribution, π(x), the Dynamics object (src/l2hmc/dynamics/) implements methods for generating proposal configurations (x' ~ π) using the generalized leapfrog update.

This generalized leapfrog update takes as input a buffer of lattice configurations x and generates a proposal configuration x' = Dynamics(x) by evolving generalized L2HMC dynamics.

Network Architecture

An illustration of the leapfrog layer updating (x, v) --> (x', v') can be seen below.

leapfrog layer


Code author: Sam Foreman

Pull requests and issues should be directed to: saforem2


If you use this code or found this work interesting, please cite our work along with the original paper:

      title={Deep Learning Hamiltonian Monte Carlo}, 
      author={Sam Foreman and Xiao-Yong Jin and James C. Osborn},
  title={Generalizing Hamiltonian Monte Carlo with Neural Networks},
  author={Levy, Daniel and Hoffman, Matthew D. and Sohl-Dickstein, Jascha},
  journal={arXiv preprint arXiv:1711.09268},


This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under contract DE_AC02-06CH11357.
This work describes objective technical results and analysis.
Any subjective views or opinions that might be expressed in the work do not necessarily represent the views of the U.S. DOE or the United States Government.