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Practical Solutions and Value of Unraveling Transformer Optimization
Challenges in Transformer Training
Understanding the performance gap between Adam and SGD optimizers in training Transformers is crucial for efficiency.
Research Insights
The study delves into the concept of “block heterogeneity” in Transformer models affecting optimizer performance.
Experimental Approach
Utilizing Stochastic Lanczos Quadrature (SLQ) method to analyze Hessian spectra for large-scale neural networks.
Key Findings
Transformer models exhibit block heterogeneity, impacting SGD’s performance compared to Adam.
Implications for Optimization
Insights from the study pave the way for more efficient training algorithms for Transformers and heterogeneous models.
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