| --- |
| license: mit |
| tags: |
| - benchmark |
| - systems-ml |
| - distributed-training |
| - muon |
| - optimizer |
| - performance-analysis |
| --- |
| |
| # 🔬 Distributed Muon: Field Notes & Reproducibility Artifacts |
|
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| **Code, Performance Traces, and Analysis Logs** |
|
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| This repository contains the raw engineering artifacts for the deep-dive investigation: **"Reproducing and Validating Distributed Muon"**. |
|
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| It serves as the **proof of work** for the performance claims regarding the Muon optimizer's communication efficiency and computational overhead in a distributed setting (Data Parallel + Tensor Parallel). |
|
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| 📄 **Read the Full Report:** [Reproducing and Validating Distributed Muon 🐢✨: A Practical Verification of Communication Efficiency Claims](https://medium.com/@jenwei0312/reproducing-and-validating-distributed-muon-a-practical-verification-of-communication-0be4d1d9b893) |
| 🛠️ **Get the Tutorial Code:** [bird-of-paradise/muon-distributed](https://huggingface.co/datasets/bird-of-paradise/muon-distributed) |
|
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| --- |
|
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| ## 📂 Repository Structure |
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| * **`traces/`**: Raw Chrome Trace (`.json`) files generated by PyTorch Profiler. You can load these into `chrome://tracing` or [ui.perfetto.dev](https://ui.perfetto.dev) to visualize the exact CPU/GPU execution timeline. |
| * `comparison/`: Side-by-side traces of AdamW vs. Muon (Hybrid DP=2/TP=2). |
| * `distributed_muon/`: Scaling traces for DP=4, TP=4, and Hybrid configurations. |
| * **`analysis_scripts/`**: The exact Python scripts used to generate the traces and parse the performance metrics. |
| * **`figures/`**: High-resolution charts and trace visualizations used in the report. |
| * **`report/`**: A PDF archive of the full technical investigation. |
| |
| --- |
| |
| ## 🔍 Key Findings (Verified in Traces) |
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| The traces in this repository provide empirical evidence for the following: |
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| 1. **Communication Efficiency:** Muon (Hybrid DP2/TP2) demonstrates **0.57x** the communication overhead of AdamW on a bandwidth-constrained cluster (PCIe Gen4 x4). |
| * *Evidence:* Compare `traces/comparison/adamw_fullstep_rank0.json` vs `muon_fullstep_dp2_tp2_rank0.json`. |
| 2. **Optimizer Latency:** The Muon step accounts for **~1.1%** of total training time, validating the paper's "negligible overhead" claim. |
| 3. **Hybrid Scaling:** The `DP=2, TP=2` configuration outperforms pure DP or pure TP on 4 GPUs, balancing memory bandwidth with communication overhead. |
| |
| --- |
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| ## 🛠️ How to Reproduce |
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| To run these benchmarks yourself on a 4-GPU cluster: |
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| 1. Clone this repository. |
| 2. Install dependencies: `torch`. |
| 3. Run the benchmark script: |
| |
| ```bash |
| # This will generate new JSON traces in your local directory |
| python analysis_scripts/muon_vs_adam.py |
| ``` |
| |
| 4. Run the performance analysis on included trace files |
| ```bash |
| python analysis_scripts/performance_comparison.py |
| ``` |
| |
| --- |
| |
| ## 🙏 Acknowledgments |
| - [Mahdi Chaker](https://github.com/mchaker) for generously providing GPU cluster access |
| - MoonShot AI team for open-sourcing their [PoC implementation](https://github.com/NVIDIA/Megatron-LM/pull/1428/commits/f432fbe45c169aeb5a0805ff6f41e13f989c6730#diff-61c8e9370cb7fd634a4019472368c487898093f5d330375524c76eac15c7390c) |
| |
| --- |
| |
| 📖 Citation |
| If you use these traces or analysis in your work, please cite: |
| |
| |
| @misc{wei2025muoneproducibility, |
| author = {Wei, Jen}, |
| title = {Distributed Muon: Performance Artifacts and Benchmarks}, |
| year = {2025}, |
| publisher = {Hugging Face}, |
| journal = {Hugging Face Datasets}, |
| howpublished = {\url{[https://huggingface.co/datasets/bird-of-paradise/muon-distributed-reproducibility](https://huggingface.co/datasets/bird-of-paradise/muon-distributed-reproducibility)}} |
| } |
| |
