WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference (2505.19427v1)

Abstract: The growing computational demands of 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.

Weight Informed Neuron Activation for Accelerating LLM Inference

The paper "WINA: Weight Informed Neuron Activation for Accelerating LLM Inference" presents an innovative approach for enhancing the computational efficiency of LLMs during inference. As the scale and complexity of LLMs continue to grow, optimizing inference processes becomes increasingly critical. The paper addresses this issue by introducing WINA, a training-free sparse activation framework that not only considers hidden state magnitudes but also incorporates the column-wise $\ell_2$-norms of weight matrices to make more informed activation decisions.

Key Contributions and Findings

1. Training-Free Sparse Activation: Traditional methods like Mixture-of-Experts (MoE) require specialized training to achieve selective activation. In contrast, WINA offers a training-free approach, applicable to off-the-shelf models. By focusing on both hidden state magnitudes and weight norms, WINA aims to reduce approximation errors that arise from simplistic sparse activation strategies that neglect the influence of weight matrices.
2. Theoretical Framework: The paper provides a comprehensive theoretical analysis, demonstrating that WINA achieves tighter bounds on approximation errors compared to existing methods, such as TEAL and CATS. By considering both hidden states and weight matrix norms, the approach offers formal guarantees for reduced output deviation, thereby enhancing inference accuracy.
3. Experimental Validation: Through extensive empirical evaluation across several LLM architectures and a diverse set of datasets, WINA shows substantial improvements over state-of-the-art methods. Results indicate up to 2.94% better performance at equivalent sparsity levels, suggesting superior retention of essential model capabilities even under aggressive pruning.
4. Computational Efficiency: WINA demonstrates significant reductions in computational overhead, with up to 60% improvement in GFLOPs at high sparsity levels. This offers potential advantages for deploying LLMs in resource-constrained or latency-sensitive environments where computational efficiency is paramount.

Implications and Future Directions

The practical implications of WINA are noteworthy, particularly for scenarios where computational resources are limited yet high-quality output from LLMs is required. The approach not only retains model capabilities but also enhances the efficiency of inference operations, making it a strong candidate for widespread adoption in industry settings where LLMs are used.

From a theoretical standpoint, WINA opens avenues for refining sparse activation methodologies, precisely by integrating more nuanced criteria such as weight importance. This shift towards more informed activation strategies may inspire further innovations, potentially leading to novel architectures or optimization techniques that can leverage this dual consideration more effectively.

Conclusion

WINA stands at the frontier of training-free sparse activation methods, offering both theoretical rigor and practical advancements in LLM inference efficiency. By uniting hidden state magnitudes with weight matrix norms, it addresses critical limitations of existing sparse activation strategies, proposing a robust framework for efficient and effective model deployment. Future research could explore the extension of these principles to different types of neural network architectures, potentially broadening the applicability and impact of the proposed methodology across various domains within artificial intelligence.