Two-Stage Regularization-Based Structured Pruning for LLMs
Summary: arXiv:2505.18232v3 Announce Type: replace-cross
The rapid advancement of large language models (LLMs) has revolutionized various applications in natural language processing. However, their deployment remains a challenge due to the vast number of parameters they encompass. In response to this issue, structural pruning has emerged as a promising solution aimed at reducing the model size while maintaining performance integrity. This article introduces an innovative approach known as TRSP, or Two-Stage Regularization-Based Structured Pruning, designed to enhance the pruning process for LLMs.
Understanding TRSP
Traditional structured pruning methods typically eliminate unimportant parameters based on specific metrics. However, such approaches often result in significant knowledge loss and require extensive retraining to regain model efficacy. TRSP addresses these challenges by implementing a dual-phase regularization strategy that preserves knowledge and boosts performance.
The Two Stages of TRSP
TRSP comprises two distinct stages of regularization:
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First-Stage Regularization:
In this initial phase, each transformer layer’s output is multiplied by a learnable weight. The objective is to iteratively optimize these weights by incorporating their $\ell_1$-norm as a regularization term within the loss function. This process effectively guides the model to identify and retain the most critical parameters.
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Second-Stage Regularization:
Following the first stage, TRSP introduces additional regularization aimed at the differences between the output and input of the layers associated with smaller weights. This encourages the model to redistribute knowledge to the preserved layers, thereby enhancing overall performance and knowledge retention.
Advantages of TRSP
The implementation of TRSP presents several notable advantages:
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Knowledge Retention:
By employing a two-stage regularization process, TRSP effectively minimizes knowledge loss compared to traditional pruning methods.
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Performance Preservation:
The method ensures that model performance remains intact, avoiding the extensive retraining typically required after parameter elimination.
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Efficiency:
As a layer-wise pruning technique, TRSP achieves significant end-to-end acceleration, making it a viable option for the efficient deployment of LLMs in real-world applications.
Experimental Validation
Through comprehensive experimentation, TRSP has demonstrated superior performance when compared to established layer-wise structured pruning methods. The results indicate that TRSP not only maintains knowledge integrity but also enhances the overall efficiency of LLMs without necessitating retraining.
Conclusion
In summary, the Two-Stage Regularization-Based Structured Pruning method offers a compelling solution to the challenges posed by large language models. By effectively balancing knowledge retention and performance preservation, TRSP sets a new standard for efficient LLM deployment, paving the way for broader applications in the field of artificial intelligence.