WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference
This addresses the computational bottleneck in LLM inference for users needing efficient deployment, offering a plug-and-play solution that is incremental but sets a new performance benchmark.
The paper tackles the problem of inefficient inference in large language models by proposing WINA, a training-free sparse activation framework that uses both hidden state magnitudes and weight matrix norms to reduce computational demands, achieving up to 2.94% better average performance than state-of-the-art methods at the same sparsity levels.
The growing computational demands of large language models (LLMs) make efficient inference and activation strategies increasingly critical. While recent approaches, such as Mixture-of-Experts (MoE), leverage selective activation but require specialized training, training-free sparse activation methods offer broader applicability and superior resource efficiency through their plug-and-play design. However, many existing methods rely solely on hidden state magnitudes to determine activation, resulting in high approximation errors and suboptimal inference accuracy. To address these limitations, we propose WINA (Weight Informed Neuron Activation), a novel, simple, and training-free sparse activation framework that jointly considers hidden state magnitudes and the column-wise $\ell_2$-norms of weight matrices. We show that this leads to a sparsification strategy that obtains optimal approximation error bounds with theoretical guarantees tighter than existing techniques. Empirically, WINA also outperforms state-of-the-art methods (e.g., TEAL) by up to $2.94\%$ in average performance at the same sparsity levels, across a diverse set of LLM architectures and datasets. These results position WINA as a new performance frontier for training-free sparse activation in LLM inference, advancing training-free sparse activation methods and setting a robust baseline for efficient inference. The source code is available at https://github.com/microsoft/wina.