Megatron 推進新優化器加速 LLM 訓練

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🟩閱讀原文： NVIDIA Developer Blog

#optimizers#llm-training#higher-ordernvidia-megatronnvidiamegatronshampoomuonkimi-k2glm-5

💡使用 Megatron 中的 Shampoo/Muon 提升 LLM 訓練—驅動 Kimi K2、GLM-5（30字元）

⚡ 30 秒速覽

有什麼變化

整合 Shampoo 優化器，在神經網路中有效超過十年

為什麼重要

這些優化器實現更快、更高效的 LLM 訓練，有助研究者和開發者建構競爭性開源模型。透過 NVIDIA 框架民主化頂尖訓練技術存取。

下一步行動

將 Muon 優化器整合至您的 Megatron 流程中，以加速多節點叢集上的 LLM 訓練。

誰應關注：Researchers & Academics

關鍵要點

•整合 Shampoo 優化器，在神經網路中有效超過十年
•支援 Muon（MomentUm Orthogonalized by Newton-Schulz）用於頂尖 LLM
•驅動如 Kimi K2 和 GLM-5 等開源模型訓練
•透過 Megatron 中的高階優化提升 LLM 訓練速度

🧠 深度解析

本篇為 AI 生成分析，非原文內容。

🔑 增強重點摘要

•Shampoo utilizes preconditioning matrices to approximate second-order optimization, significantly reducing the number of training steps required compared to standard Adam-based approaches.
•Muon specifically targets the weight matrices of linear layers by applying Newton-Schulz iterations to enforce orthogonality, which has been shown to improve convergence stability in high-parameter models.
•The integration within Megatron-LM leverages custom CUDA kernels to mitigate the high computational overhead typically associated with calculating and inverting large preconditioning matrices.

📊 競品分析▸ Show

Feature	NVIDIA Megatron (Shampoo/Muon)	DeepSpeed (Microsoft)	MosaicML Composer
Primary Optimization	Higher-order (Shampoo/Muon)	Adam/AdamW variants	Standard/Custom optimizers
Hardware Focus	NVIDIA GPU-specific (CUDA)	Agnostic (GPU/TPU/NPU)	Agnostic
Key Advantage	Extreme throughput for massive models	Ease of use/Memory efficiency	Training stability/Efficiency

🛠️ 技術深入

•Shampoo implementation: Uses Kronecker-factored preconditioning to approximate the inverse Hessian, reducing memory complexity from O(N^2) to O(N).
•Muon implementation: Performs a Newton-Schulz iteration to compute the matrix inverse square root, ensuring the update direction remains orthogonal.
•Memory overhead: Requires additional memory for storing preconditioning statistics; Megatron manages this via periodic updates rather than per-step updates to balance speed and accuracy.
•Integration: Implemented as a drop-in optimizer class within the Megatron-LM training loop, compatible with existing Tensor Parallelism and Pipeline Parallelism configurations.

🔮 前景展望基於引用來源的 AI 分析

Standard AdamW will be deprecated as the default optimizer for foundation model pre-training.

The superior convergence rates of higher-order methods like Shampoo and Muon provide a clear efficiency advantage that outweighs the added implementation complexity.

Training costs for trillion-parameter models will decrease by at least 20% within 18 months.

The reduction in total training steps enabled by these optimizers directly translates to lower GPU-hour consumption for large-scale model development.