Datasets:

bird-of-paradise
/

muon-distributed

license: mit
language:
  - en
tags:
  - optimization
  - muon
  - deep-learning
  - pytorch
  - distributed-computing
  - ZeRO
  - tensor parallelism
  - data parallelism
  - tutorial
  - cpu-friendly

🧩 The "Muon is Scalable" Blueprint: A Distributed Muon Engineering Breakdown(CPU-Friendly, tutorial style)

A practical, annotated engineering breakdown for the Muon optimizer — extended into a fully distributed (DP × TP) version that actually runs on plain CPU/Gloo, so broke-but-curious builders can still get their hands dirty.

This is the expert-level systems engineering companion to my original Understanding the Muon Optimizer tutorial.

💡 “Because sometimes the best way to learn the distributed nightmare is to get your hands dirty and your eyes crossed.” 🤪

🌕 Why This Exists

Most public Muon examples (like MoonShot’s PoC) are designed for multi-GPU NCCL clusters, making them impossible to run or debug for most of us. In addition, most documentation for distributed systems is written by experts, for experts, making it a "nightmare" to learn. My goal is to change that.

This repository is not a "simplified" version that "flattens the depth" of the work.

Instead, it's a didactic refactor. I've taken the complex, real-world PoC and optimized it for readability and learning, so you can see the "blueprint" behind the "chaos":

Fully annotated to demonstrate data parallel (ZeRO-1) + tensor parallel (TP) orchestration end-to-end.
Understand every “distributed acrobatic” step (DP gather → TP gather → Newton–Schulz → TP shard → DP shard).
The code is optimized to highlight symmetrical logic and consistent naming, showing the "opposite arrow" data flow of the "virtual map" (dist_meta).
It's built to run on a single CPU machine or Colab notebook.

🧠 The “Turtle Speed” Breakthrough: The Full Story

This code is complex. It's a "distributed nightmare" 🫠.

Instead of a traditional, long-form README, the best documentation is the "making of" story. I've chronicled my entire journey of reverse-engineering and debugging this code in my "Turtle Speed Breakthrough" series on Medium.

This tutorial is the final, runnable code that resulted from that deep dive.

🚀 Quick Start

Run the CPU-safe, fully-annotated notebook right from your browser:

Or, you can clone this repo and run the Python script locally to simulate an 8-process cluster on your CPU:

git clone https://huggingface.co/datasets/bird-of-paradise/muon-distributed
cd muon-distributed

# This will spawn 8 processes and run the full test
!python distributed_muon_cpu.py

(Note: For the original, un-annotated, buggy Moonshot PoC that this work is based on, you can find it in this commit.)

🗂️ What's Inside (File Guide)

distributed_muon_cpu.ipynb: (Start Here) The Colab-friendly notebook that walks through the environment fixes and runs the code.
distributed_muon_cpu.py: The final, tested, fixed, and heavily-annotated Python script. This is the "golden" code that runs on a CPU-only environment using the "gloo" backend.
distributed_muon.py: My annotated and logically debugged version of the GPU code. This is for users who have a multi-GPU "nccl" environment. (Note: Since I don't have a multi-GPU cluster, this version is untested... unless someone wants to sponsor me with some GPUs! 😉)

🎓 What You'll Learn (The "Nightmare" Blueprint)

By exploring this code, you'll see the real implementation of the concepts I discuss in my articles:

The 2D Grid: How to set up orthogonal dist_group (DP) and tp_group (TP) handles.
The "Aisles" & "Pallets": How param_groups (buffer_idx) and communication buckets (bucket_idx) are used to organize parameters.
The "Virtual Buffer": How a "master plan" (dist_meta and global_buffer_size) is used to manage memory for sharding (ZeRO-1).
The Acrobatic Data Flow: The full (DP gather -> TP gather) -> (Run Math) -> (TP shard -> DP shard) journey.
The Nuance: You'll see why we bucket the slow DP all_gather but don't need to bucket the fast, on-node TP all_gather.

🐞 Summary of All Fixes

This repo isn't just a copy-paste. It's the result of a week-long debugging "nightmare." Here are all the bugs we had to find and fix to make it run:

Issue	Problem	Solution
Logic Bug #1	Missing `params = group["params"]`	Added the line in the Muon update loop.
Logic Bug #2	`ns_input` was 1D after unpacking, crashing `zeropower`.	Changed `.view(-1)` to `.view(dist_meta.shape)` to restore the 2D shape.
Env Bug #1	Hardcoded `"nccl"` backend.	Changed `dist.init_process_group` to use `"gloo"`.
Env Bug #2	Hardcoded `'cuda'` device.	Changed `gen_param_and_grads` to use `'cpu'`.
Env Bug #3	`mp.spawn()` crashes in Jupyter/Colab.	The `.ipynb` runs the code as a `!python` subprocess, bypassing the notebook kernel.

📖 Citation

If you use this tutorial in your work, please cite the original Muon paper and this tutorial.

@misc{wei2025muondistributed,
  author = {Wei, Jen},
  title = {A CPU-Friendly Tutorial for Distributed Muon (DPxTP)},
  year = {2025},
  howpublished = {\url{[https://huggingface.co/datasets/](https://huggingface.co/datasets/)<your-hf-handle>/muon-distributed}}
}

@misc{jordan2024muon,
  author = {Jordan, Keller, et al.},
  title = {Muon: An optimizer for hidden layers in neural networks},
  year = {2024},
  url = {[https://kellerjordan.github.io/posts/muon/](https://kellerjordan.github.io/posts/muon/)}
}

@misc{liu2025muonscalable,
  author = {Liu, Jingyuan, et al.},
  title = {Muon is Scalable for LLM Training},
  year = {2025},
  url = {[https://arxiv.org/abs/2502.16982](https://arxiv.org/abs/2502.16982)}
}