This repository is the open-source codebase of the PPoPP'22 paper, FasterMoE:
Modeling and Optimizing Training of Large-Scale Dynamic Pre-Trained Models. It
is a prototype to verify the ideas in the paper. Based on
FastMoE, the hard-coding and ad-hoc
modifications when we were working on the paper are preserved as they were in
this repository. We have already released a clean and elegant version that is
merged into FastMoE's v1.0.0 release.
If you want to try this prototype, refer to FastMoE's README for installation guide.
Dynamic shadowing is enabled by environment variable FMOE_ENABLE_DYNREP=1,
and the related code can be found in fmoe/transformer.py.
The smart schedule is enabled by environment variable FMOE_ENABLE_FUSE=1.
The topology-aware gate is in fmoe/gates/hir_gate.py. You may use it as a
customized gate in FastMoE.
Additionally, the Artifical Evaluation package is located in https://zenodo.org/record/5728493#.YaBlGyURVBU, which contains a copy of this repo, as well as scripts to reproduce the experiments in the paper.