Test install:

Shell python3 -c 'import l2hmc ; print(l2hmc.__file__)' /path/to/l2hmc-qcd/src/l2hmc/__init__.py

Training

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/main.py, 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:

Shell python3 main.py 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.

Shell 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 generic Experiment.

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/train.sh script which should run without issues on either Polaris or ThetaGPU @ ALCF.

Details

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.

Organization

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.

Contact

Code author: Sam Foreman

Pull requests and issues should be directed to: saforem2

Citation

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

bibtex @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} }

bibtex @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} }

Acknowledgement

Note
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.