One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models
This work addresses the scalability problem for pruning large language models in real-world multi-user scenarios, representing an incremental improvement over single-request methods.
The paper tackles the inefficiency of existing pruning methods for large language models when handling multiple simultaneous compression requests by proposing UniCuCo, a universal model that uses a StratNet to map requests to optimal pruning strategies, achieving a 28x speedup over baselines for 64 requests while maintaining comparable accuracy.
Existing pruning methods for large language models (LLMs) focus on achieving high compression rates while maintaining model performance. Although these methods have demonstrated satisfactory performance in handling a single user's compression request, their processing time increases linearly with the number of requests, making them inefficient for real-world scenarios with multiple simultaneous requests. To address this limitation, we propose a Univeral Model for Customized Compression (UniCuCo) for LLMs, which introduces a StratNet that learns to map arbitrary requests to their optimal pruning strategy. The challenge in training StratNet lies in the high computational cost of evaluating pruning strategies and the non-differentiable nature of the pruning process, which hinders gradient backpropagation for StratNet updates. To overcome these challenges, we leverage a Gaussian process to approximate the evaluation process. Since the gradient of the Gaussian process is computable, we can use it to approximate the gradient of the non-differentiable pruning process, thereby enabling StratNet updates. Experimental results show that UniCuCo is 28 times faster than baselines in processing 64 requests, while maintaining comparable accuracy to baselines.