Summary of Microadam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence, by Ionut-vlad Modoranu et al.
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence
by Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtarik, Dan Alistarh
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary MicroAdam is a novel optimizer variant that minimizes memory overheads while maintaining theoretical convergence guarantees. By compressing gradient information before feeding it into the optimizer state, MicroAdam reduces its memory footprint significantly. The resulting approach maintains competitive convergence guarantees to AMSGrad and provides good practical performance on GPUs. On million-scale (BERT) and billion-scale (LLaMA) models, MicroAdam achieves practical convergence comparable to the uncompressed Adam baseline, with lower memory usage and similar running time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MicroAdam is a new way to optimize machine learning models using less memory. It does this by squishing down the information needed for the optimizer, which helps make it more efficient on devices like GPUs. The results show that MicroAdam can be used on big models like BERT and LLaMA without sacrificing performance. In fact, it’s almost as good as the regular way of doing things, but uses less memory. |
Keywords
» Artificial intelligence » Bert » Llama » Machine learning
