Experience the power of the Muon optimizer, designed for maximum efficiency in training neural networks. Achieve record training speeds with support for diverse scenarios like CIFAR-10 and GPT-2, all while maintaining precision. With built-in backup optimizers, Muon ensures your model performs at its best, revolutionizing your approach to deep learning.
The Muon Optimizer is a cutting-edge solution designed specifically for enhancing the efficiency of training neural networks. With over 30% extra sample efficiency and under 3% wall-clock overhead, Muon stands out as the fastest optimizer for various training scenarios, including well-known datasets like CIFAR-10 and tasks such as GPT-2 scale language modeling.
Key Features
- Optimizes internal parameters of a neural network with impressive speed.
- Seamless integration with existing optimizers; Muon includes an internal AdamW backup, ensuring you don’t need to integrate a separate optimizer for other parameter types.
- Simplified usage, allowing you to focus on the model without worrying about the underlying complexity.
How to Use
Muon is specifically intended for the internal parameters of your network that have two or more dimensions. Individual layers or parameters with lower dimensions should still be handled by another optimizer like AdamW.
Here's a quick example of how to set up Muon in your training process:
from muon import Muon
# Identify ≥2D parameters in your model
muon_params = [p for p in model.body.parameters() if p.ndim >= 2]
# Identify remaining parameters for use with AdamW
adamw_params = [p for p in model.body.parameters() if p.ndim < 2]
adamw_params.extend(model.head.parameters())
adamw_params.extend(model.embed.parameters())
# Configure the Muon optimizer
optimizer = Muon(muon_params, lr=0.02, momentum=0.95,
adamw_params=adamw_params, adamw_lr=3e-4, adamw_betas=(0.90, 0.95), adamw_wd=0.01)
Remember to tailor model.body, model.head, and model.embed to match the architecture of your specific model.
Performance Metrics
Muon has demonstrated significantly improved training efficiencies:
- Reduced the CIFAR-10 training time from 3.3 A100-seconds to 2.7 A100-seconds
- Achieved GPT-2 (XL) performance for only $175 of compute
- Enhanced the training speed for GPT-2 (small) by 1.35x
For a detailed comparison between Muon and other optimizers like AdamW, Shampoo, and SOAP, you can explore the benchmarks here.
Stay ahead of the curve in neural network optimization with Muon!