This AI Paper from Microsoft Introduces WINA: A Training-Free Sparse Activation Framework for Efficient Large Language Model Inference

Large language models (LLMs) with billions of parameters are integral to many AI-driven services across various industries. However, their substantial size and intricate architectures present significant computational challenges during inference. As these models continue to evolve, optimizing the balance between computational efficiency and output quality remains a critical area of research.

The Challenge of Inference in LLMs

When processing input, LLMs typically activate the entire model, which consumes extensive computational resources. This full activation is often unnecessary, as only a small subset of neurons significantly contributes to the final output. Existing sparse activation methods aim to selectively deactivate less important neurons but usually focus solely on the magnitude of hidden states, neglecting the crucial role of weight matrices in error propagation. This oversight can lead to high approximation errors and reduced model performance, particularly at elevated levels of sparsity.

Current Sparse Activation Techniques

Sparse activation techniques, such as Mixture-of-Experts (MoE) used in models like GPT-4 and Mistral, require additional training to learn which experts to activate for each input. Other methods, including TEAL and CATS, seek to minimize computation by using the size of hidden activations to prune neurons. However, these approaches often struggle to balance sparsity and accuracy, sometimes mistakenly deactivating important neurons or retaining those with minimal influence. Additionally, they require model-specific threshold tuning, limiting their flexibility across different architectures.

Introducing WINA

Researchers from Microsoft, Renmin University of China, New York University, and the South China University of Technology have proposed a new method called WINA (Weight Informed Neuron Activation) to tackle these challenges. WINA introduces a training-free sparse activation technique that utilizes both hidden state magnitudes and column-wise ℓ2 norms of weight matrices to determine which neurons to activate during inference. By considering the combined impact of input magnitudes and weight importance, WINA develops a more effective sparsification strategy adaptable to different layers of the model without the need for retraining or fine-tuning.

How WINA Works

The WINA method is built on a straightforward yet powerful principle: neurons with strong activations and large weight magnitudes are more likely to influence downstream computations. To implement this, WINA calculates the element-wise product of hidden states and weight norms, selecting the top-K components based on this combined metric. This strategy enables WINA to construct a sparse sub-network that retains the most important signals while disregarding redundant activations. The method also includes a tensor transformation step that enforces column-wise orthogonality in weight matrices, ensuring that theoretical error bounds translate effectively to real-world performance. By integrating these steps, WINA maintains tight approximation errors while delivering substantial computational savings.

Performance Evaluation

The research team evaluated WINA on several large language models, including Qwen-2.5-7B, LLaMA-2-7B, LLaMA-3-8B, and Phi-4-14B, across various tasks and levels of sparsity. WINA consistently outperformed TEAL and CATS across all tested models and sparsity settings. For instance, on Qwen-2.5-7B at 65% sparsity, WINA achieved an average performance improvement of up to 2.94% over TEAL and 1.41% better than TEAL-Transform. On LLaMA-3-8B, WINA delivered gains of 1.06% at 50% sparsity and 2.41% at 65% sparsity. Notably, even at high sparsity levels, WINA maintained superior performance on reasoning-intensive tasks such as GSM8K and ARC Challenge. WINA also provided consistent computational savings, reducing floating-point operations by up to 63.7% on LLaMA-2-7B and 62.7% on Phi-4-14B.

Conclusion

WINA presents a robust, training-free solution for sparse activation in large language models by integrating hidden state magnitudes with weight matrix norms. This approach addresses the limitations of prior methods, such as TEAL, resulting in lower approximation errors, enhanced accuracy, and significant computational savings. The research team’s work marks a significant advancement in developing more efficient LLM inference methods that can adapt to diverse models without requiring additional training.

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