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The Need for Speed: Pruning Transformers with One Recipe
March 27, 2024, 4:42 a.m. | Samir Khaki, Konstantinos N. Plataniotis
cs.LG updates on arXiv.org arxiv.org
Abstract: We introduce the $\textbf{O}$ne-shot $\textbf{P}$runing $\textbf{T}$echnique for $\textbf{I}$nterchangeable $\textbf{N}$etworks ($\textbf{OPTIN}$) framework as a tool to increase the efficiency of pre-trained transformer architectures $\textit{without requiring re-training}$. Recent works have explored improving transformer efficiency, however often incur computationally expensive re-training procedures or depend on architecture-specific characteristics, thus impeding practical wide-scale adoption. To address these shortcomings, the OPTIN framework leverages intermediate feature distillation, capturing the long-range dependencies of model parameters (coined $\textit{trajectory}$), to produce state-of-the-art results on natural …
abstract architecture architectures arxiv cs.lg efficiency framework however improving pruning recipe speed tool training transformer transformers type
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