SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
This addresses efficiency challenges for deploying LLMs in real-world applications, but it is incremental as it builds on existing pruning techniques.
The paper tackles the problem of large language models (LLMs) being too slow for practical deployment by introducing SLEB, a pruning method that removes redundant transformer blocks, resulting in faster inference while maintaining better perplexity and accuracy than previous methods.
Large language models (LLMs) have proven to be highly effective across various natural language processing tasks. However, their large number of parameters poses significant challenges for practical deployment. Pruning, a technique aimed at reducing the size and complexity of LLMs, offers a potential solution by removing redundant components from the network. Despite the promise of pruning, existing methods often struggle to achieve substantial end-to-end LLM inference speedup. In this paper, we introduce SLEB, a novel approach designed to streamline LLMs by eliminating redundant transformer blocks. We choose the transformer block as the fundamental unit for pruning, because LLMs exhibit block-level redundancy with high similarity between the outputs of neighboring blocks. This choice allows us to effectively enhance the processing speed of LLMs. Our experimental results demonstrate that SLEB outperforms previous LLM pruning methods in accelerating LLM inference while also maintaining superior perplexity and accuracy, making SLEB as a promising technique for enhancing the efficiency of LLMs. The code is available at: https://github.com/jiwonsong-dev/SLEB.