Contents

📝 Papers:
 LeapfrogLayers: A Trainable Framework for Effective Topological Sampling, 2022
 Accelerated Sampling Techniques for Lattice Gauge Theory @ BNL & RBRC: DWQ @ 25 (12/2021)

Training Topological Samplers for Lattice Gauge Theory from the ML for HEP, on and off the Lattice @
$\mathrm{ECT}^{*}$ Trento (09/2021) (+ 📊 slides) 
Deep Learning Hamiltonian Monte Carlo @ Deep Learning for Simulation (SimDL) Workshop ICLR 2021
 📚 : arXiv:2105.03418
 📊 : poster
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 brainresearch/l2hmc/ by Daniel Levy, Matt D. Hoffman and Jascha SohlDickstein.
Broadly, given an analytically described target distribution, π(x), L2HMC provides a statistically exact sampler that:
 Quickly converges to the target distribution (fast burnin).
 Quickly produces uncorrelated samples (fast mixing).
 Is able to efficiently mix between energy levels.
 Is capable of traversing lowdensity zones to mix between modes (often difficult for generic HMC).
Warning
It is recommended to install inside an existing virtual environment
(ideally one withtensorflow, pytorch [horovod,deepspeed]
already installed)
From source (RECOMMENDED)
git clone https://github.com/saforem2/l2hmcqcd
cd l2hmcqcd
# for development addons:
# python3 m pip install e ".[dev]"
python3 m pip install e .
Test install:
python3 c 'import l2hmc ; print(l2hmc.__file__)'
/path/to/l2hmcqcd/src/l2hmc/__init__.py
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/main.py
,
which contains the logic for running an endtoend Experiment
.
An Experiment
consists of the following subtasks:
 Training
 Evaluation
 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 main.py mode=debug framework=pytorch backend=deepspeed precision=fp16
to run a (nondistributed) 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
./train.sh 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:

src/l2hmc/configs.py
: Contains implementations of the (concrete python objects) that are adjustable for our experiment. 
src/l2hmc/conf/config.yaml
: Starting point with default configuration options for a genericExperiment
.
for more information on how this works I encourage you to read Hydra's Documentation Page.
For running with distributed training on ALCF systems, we provide a complete
src/l2hmc/train.sh
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.
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.
An illustration of the leapfrog layer
updating (x, v) > (x', v')
can be seen below.
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:
@misc{foreman2021deep,
title={Deep Learning Hamiltonian Monte Carlo},
author={Sam Foreman and XiaoYong Jin and James C. Osborn},
year={2021},
eprint={2105.03418},
archivePrefix={arXiv},
primaryClass={heplat}
}
@article{levy2017generalizing,
title={Generalizing Hamiltonian Monte Carlo with Neural Networks},
author={Levy, Daniel and Hoffman, Matthew D. and SohlDickstein, Jascha},
journal={arXiv preprint arXiv:1711.09268},
year={2017}
}
Note
This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under contract DE_AC0206CH11357.
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.