  Sparse computation offers a compelling solution for the inference of Large
Language Models (LLMs) in low-resource scenarios by dynamically skipping the
computation of inactive neurons. While traditional approaches focus on
ReLU-based LLMs, leveraging zeros in activation values, we broaden the scope of
sparse LLMs beyond zero activation values. We introduce a general method that
defines neuron activation through neuron output magnitudes and a tailored
magnitude threshold, demonstrating that non-ReLU LLMs also exhibit sparse
activation. To find the most efficient activation function for sparse
computation, we propose a systematic framework to examine the sparsity of LLMs
from three aspects: the trade-off between sparsity and performance, the
predictivity of sparsity, and the hardware affinity. We conduct thorough
experiments on LLMs utilizing different activation functions, including ReLU,
SwiGLU, ReGLU, and ReLU$^2$. The results indicate that models employing
ReLU$^2$ excel across all three evaluation aspects, highlighting its potential
as an efficient activation function for sparse LLMs. We will release the code
to facilitate future research.
