Data-Free Pruning of Self-Attention Layers in LLMs

Dhananjay Saikumar; Blesson Varghese

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arXiv · 2512.20636

Data-Free Pruning of Self-Attention Layers in LLMs

Dhananjay Saikumar·Blesson Varghese

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Abstract

Many self-attention sublayers in large language models (LLMs) can be removed with little to no loss. We attribute this to the Attention Suppression Hypothesis: during pre-training, some deep attention layers learn to mute their own contribution, leaving the residual stream and the MLP to carry the representation. We propose Gate-Norm, a one-shot, weight-only criterion that ranks attention sublayers by query--key coupling and removes the least coupled ones, requiring no calibration data, no forward passes, no fine-tuning, and no specialized kernels. On 40-layer, 13B-parameter LLaMA models, Gate-Norm prunes the model in under a second. Pruning $8$--$16$ attention sublayers yields up to $1.30\times$ higher inference throughput while keeping average zero-shot accuracy within $2\%$ of the unpruned baseline across BoolQ, RTE, HellaSwag, WinoGrande, ARC-Easy/Challenge, and OpenBookQA. Across these settings, Gate-Norm matches data-driven pruning methods in accuracy while being $\sim 1000\times$ faster to score layers, enabling practical, data-free compression of LLMs.

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Dhananjay Saikumar, Blesson Varghese. 2025-12-03. Data-Free Pruning of Self-Attention Layers in LLMs. https://arxiv.org/abs/2512.20636

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