Two-Stage Regularization-Based Structured Pruning for LLMs
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908974612217856 |
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| author | Feng, Mingkuan Wu, Jinyang Liu, Siyuan Zhang, Shuai Fang, Hongjian Jin, Ruihan Che, Feihu Shao, Pengpeng Wen, Zhengqi Tao, Jianhua |
| author_facet | Feng, Mingkuan Wu, Jinyang Liu, Siyuan Zhang, Shuai Fang, Hongjian Jin, Ruihan Che, Feihu Shao, Pengpeng Wen, Zhengqi Tao, Jianhua |
| contents | The deployment of large language models (LLMs) is largely hindered by their large number of parameters. Structural pruning has emerged as a promising solution. Prior structured pruning methods directly remove unimportant parameters based on certain metrics, which often causes knowledge loss and necessitates extensive retraining. To overcome this, we introduce a novel pruning method TRSP: Two-Stage Regularization-Based Structured Pruning for LLMs. Specifically, we multiply the output of each transformer layer by an initial learnable weight and iteratively learn these weights by adding their $\ell_1$-norm as a regularization term to the loss function, serving as the first-stage regularization. Subsequently, we apply additional regularization to the difference between the output and input of layers with smaller weights, encouraging the shift of knowledge to the preserved layers. This serves as the second-stage regularization. TRSP retains more knowledge and better preserves model performance than direct parameter elimination. Through extensive experimentation we show that TRSP outperforms strong layer-wise structured pruning methods without requiring retraining. As a layer-wise pruning method, it delivers notable end-to-end acceleration, making it a promising solution for efficient LLM deployment.Code is available at https://github.com/fmk345/TRSP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Two-Stage Regularization-Based Structured Pruning for LLMs Feng, Mingkuan Wu, Jinyang Liu, Siyuan Zhang, Shuai Fang, Hongjian Jin, Ruihan Che, Feihu Shao, Pengpeng Wen, Zhengqi Tao, Jianhua Machine Learning Artificial Intelligence Computation and Language The deployment of large language models (LLMs) is largely hindered by their large number of parameters. Structural pruning has emerged as a promising solution. Prior structured pruning methods directly remove unimportant parameters based on certain metrics, which often causes knowledge loss and necessitates extensive retraining. To overcome this, we introduce a novel pruning method TRSP: Two-Stage Regularization-Based Structured Pruning for LLMs. Specifically, we multiply the output of each transformer layer by an initial learnable weight and iteratively learn these weights by adding their $\ell_1$-norm as a regularization term to the loss function, serving as the first-stage regularization. Subsequently, we apply additional regularization to the difference between the output and input of layers with smaller weights, encouraging the shift of knowledge to the preserved layers. This serves as the second-stage regularization. TRSP retains more knowledge and better preserves model performance than direct parameter elimination. Through extensive experimentation we show that TRSP outperforms strong layer-wise structured pruning methods without requiring retraining. As a layer-wise pruning method, it delivers notable end-to-end acceleration, making it a promising solution for efficient LLM deployment.Code is available at https://github.com/fmk345/TRSP. |
| title | Two-Stage Regularization-Based Structured Pruning for LLMs |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2505.18232 |