Dynamic Activation Pitfalls in LLaMA Models: An Empirical Study

TL;DR

This paper empirically investigates dynamic activation mechanisms in LLaMA models, revealing significant pitfalls and underperformance issues compared to static ReLU activations, especially in high sparsity scenarios.

Contribution

It systematically analyzes the limitations of current dynamic activation schemes in LLaMA models and proposes directions for improving future sparsity strategies.

Findings

Dynamic activation often underperforms ReLU in LLaMA models.

Complexity of predicting activation components hampers effectiveness.

Inadequate sparsity and information loss affect model performance.

Abstract

In this work, we systematically investigate the efficacy of dynamic activation mechanisms within the LLaMA family of language models. Despite the potential of dynamic activation methods to reduce computation and increase speed in models using the ReLU activation function, our empirical findings have uncovered several inherent pitfalls in the current dynamic activation schemes. Through extensive experiments across various dynamic activation strategies, we demonstrate that LLaMA models usually underperform when compared to their ReLU counterparts, particularly in scenarios demanding high sparsity ratio. We attribute these deficiencies to a combination of factors: 1) the inherent complexity of dynamically predicting activation heads and neurons; 2) the inadequate sparsity resulting from activation functions; 3) the insufficient preservation of information resulting from KV cache skipping. Our analysis not only sheds light on the limitations of dynamic activation in the context of large-scale LLaMA models but also proposes roadmaps for enhancing the design of future sparsity schemes.