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--- |
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license: mit |
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tags: |
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- benchmark |
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- systems-ml |
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- distributed-training |
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- muon |
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- optimizer |
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- performance-analysis |
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--- |
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# 🔬 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) |
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🛠️ **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. |
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* `comparison/`: Side-by-side traces of AdamW vs. Muon (Hybrid DP=2/TP=2). |
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* `distributed_muon/`: Scaling traces for DP=4, TP=4, and Hybrid configurations. |
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* **`analysis_scripts/`**: The exact Python scripts used to generate the traces and parse the performance metrics. |
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* **`figures/`**: High-resolution charts and trace visualizations used in the report. |
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* **`report/`**: A PDF archive of the full technical investigation. |
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--- |
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## 🔍 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). |
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* *Evidence:* Compare `traces/comparison/adamw_fullstep_rank0.json` vs `muon_fullstep_dp2_tp2_rank0.json`. |
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2. **Optimizer Latency:** The Muon step accounts for **~1.1%** of total training time, validating the paper's "negligible overhead" claim. |
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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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--- |
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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. |
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2. Install dependencies: `torch`. |
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3. Run the benchmark script: |
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```bash |
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# This will generate new JSON traces in your local directory |
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python analysis_scripts/muon_vs_adam.py |
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``` |
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4. Run the performance analysis on included trace files |
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```bash |
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python analysis_scripts/performance_comparison.py |
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``` |
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--- |
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## 🙏 Acknowledgments |
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- [Mahdi Chaker](https://github.com/mchaker) for generously providing GPU cluster access |
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- MoonShot AI team for open-sourcing their [PoC implementation](https://github.com/NVIDIA/Megatron-LM/pull/1428/commits/f432fbe45c169aeb5a0805ff6f41e13f989c6730#diff-61c8e9370cb7fd634a4019472368c487898093f5d330375524c76eac15c7390c) |
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--- |
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📖 Citation |
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If you use these traces or analysis in your work, please cite: |
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@misc{wei2025muoneproducibility, |
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author = {Wei, Jen}, |
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title = {Distributed Muon: Performance Artifacts and Benchmarks}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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journal = {Hugging Face Datasets}, |
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howpublished = {\url{[https://huggingface.co/datasets/bird-of-paradise/muon-distributed-reproducibility](https://huggingface.co/datasets/bird-of-paradise/muon-distributed-reproducibility)}} |
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} |
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