Data-Free Pruning of Self-Attention Layers in LLMs
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909974906535936 |
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| author | Saikumar, Dhananjay Varghese, Blesson |
| author_facet | Saikumar, Dhananjay Varghese, Blesson |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20636 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Free Pruning of Self-Attention Layers in LLMs Saikumar, Dhananjay Varghese, Blesson Machine Learning Artificial Intelligence 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. |
| title | Data-Free Pruning of Self-Attention Layers in LLMs |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.20636 |