https://github.com/MachineLearningSystem/swarm

Official code for "SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient"
https://github.com/MachineLearningSystem/swarm

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Official code for "SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient"

• Host: GitHub
• URL: https://github.com/MachineLearningSystem/swarm
• Owner: MachineLearningSystem
• Fork: true (yandex-research/swarm)
• Created: 2023-02-02T00:59:47.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-02-01T12:28:53.000Z (almost 2 years ago)
• Last Synced: 2024-08-02T19:33:29.427Z (5 months ago)
• Homepage:
• Size: 4.12 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient

![Illustration of SWARM parallelism](swarm.png)

This repository contains the code to replicate experiments of

["SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient"](https://arxiv.org/abs/2301.11913).

**Note:** this codebase, as well as the project itself, is a work in progress: 

certain features (e.g., rebalancing between pipeline stages) are not yet added to the repository, expect the paper to get updated as well.

In the meantime, you can watch this repository or visit the [repository](https://github.com/bigscience-workshop/petals)

of [Petals](https://petals.ml/) — a similar project for *inference* of large language models that was inspired by SWARM

and shares portions of codebase with it.

# Large-scale experiments and throughput estimation

Instructions to replicate the experiments on large-scale language model pretraining and throughput estimation on

multiple preemptible nodes, as well as the prototype implementation of SWARM, are located in

the [swarm](./swarm) subfolder.

# Bottleneck experiments

Instructions to replicate the compression-aware architecture experiments can be found

in [bottleneck/README.md](bottleneck/README.md).

# Contacts

Feel free to ask any questions about this work [by email](mailto:[email protected]).

# References

```

@misc{ryabinin2023swarm,

    title={SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient},

    author={Max Ryabinin and Tim Dettmers and Michael Diskin and Alexander Borzunov},

    year={2023},

    eprint={2301.11913},

    archivePrefix={arXiv},

    primaryClass={cs.DC}

}

```