Package: l2hmc
ContentsOverviewPapers 📚, Slides 📊, etc.BackgroundInstallationTrainingDetailsL2HMC for LatticeQCDOrganizationDynamics / NetworkNetwork ArchitectureLatticeOverviewPapers 📚, Slides 📊, etc.📘Example Notebook(alternative linkif github won't load)Accelerated Sampling Techniques for Lattice Gauge Theory@BNL & RBRC: DWQ @ 25(12/2021)Training Topological Samplers for Lattice Gauge Theoryfrom theML for HEP, on and off the Lattice@ $\mathrm{ECT}^{*}$ Trento (09/2021) (+ 📊slides)Deep Learning Hamiltonian Monte Carlo@Deep Learning for Simulation (SimDL) WorkshopICLR 2021📚 :arXiv:2105.03418📊 :posterBackgroundThe L2HMC algorithm aims to improve uponHMCby 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 Networkwith implementation available atbrain-research/l2hmc/byDaniel
Levy,Matt D.
HoffmanandJascha
Sohl-Dickstein.Broadly, given ananalyticallydescribed target distribution, π(x), L2HMC provides astatistically exactsampler that:Quickly converges to the target distribution (fastburn-in).Quickly produces uncorrelated samples (fastmixing).Is able to efficiently mix between energy levels.Is capable of traversing low-density zones to mix between modes (often difficult for generic HMC).Installationl2hmcon PyPi:$python3-mpipinstalll2hmcTrainingThis project useshydrafor configuration management and
supports distributed training for both PyTorch and TensorFlow.Thel2hmc/conf/config.yamlcontains a brief
explanation of each of the various parameter options, and values can be
overriden either by modifying theconfig.yamlfile, or directly through the
command line, e.g.cdsrc/l2hmc
./train.shmode=debugframework=pytorch>train.log2>&1&tail-ftrain.log$(tail-1logs/latest)for more information on how this works I encourage you to readHydra's
Documentation Page.DetailsL2HMC for LatticeQCDGoal:Use L2HMC toefficientlygenerategauge configurationsfor
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 inarXiv:2105.03418.OrganizationDynamics / NetworkFor a given target distribution, π(x), theDynamicsobject
(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
configurationsxand generates a proposal configurationx' = Dynamics(x)by
evolving generalized L2HMC dynamics.Network ArchitectureAn illustration of theleapfrog layerupdating(x, v) --> (x', v')can be seen below.ContactCode author:Sam ForemanPull requests and issues should be directed to:saforem2CitationIf 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 Xiao-Yong Jin and James C. Osborn},year={2021},eprint={2105.03418},archivePrefix={arXiv},primaryClass={hep-lat}}@article{levy2017generalizing,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},year={2017}}AcknowledgementThis 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.