CodeSwift: Accelerating LLM Inference for Efficient Code Generation

• URL: http://arxiv.org/abs/2502.17139v1
• Date: Mon, 24 Feb 2025 13:30:30 GMT
• Title: CodeSwift: Accelerating LLM Inference for Efficient Code Generation
• Authors: Qianhui Zhao, Li Zhang, Fang Liu, Xiaoli Lian, Qiaoyuanhe Meng, Ziqian Jiao, Zetong Zhou, Borui Zhang, Runlin Guo, Jia Li,
• Abstract summary: We propose CodeSwift, a highly efficient inference acceleration approach specifically designed for code generation.<n>CodeSwift constructs a multi-source datastore, providing access to both general and project-specific knowledge.<n> Experimental results show that CodeSwift can reach up to 2.53x and 2.54x speedup compared to autoregressive decoding in repository-level and standalone code generation tasks.
• Score: 17.681848203476687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code generation is a latency-sensitive task that demands high timeliness, but the autoregressive decoding mechanism of Large Language Models (LLMs) leads to poor inference efficiency. Existing LLM inference acceleration methods mainly focus on standalone functions using only built-in components. Moreover, they treat code like natural language sequences, ignoring its unique syntax and semantic characteristics. As a result, the effectiveness of these approaches in code generation tasks remains limited and fails to align with real-world programming scenarios. To alleviate this issue, we propose CodeSwift, a simple yet highly efficient inference acceleration approach specifically designed for code generation, without comprising the quality of the output. CodeSwift constructs a multi-source datastore, providing access to both general and project-specific knowledge, facilitating the retrieval of high-quality draft sequences. Moreover, CodeSwift reduces retrieval cost by controlling retrieval timing, and enhances efficiency through parallel retrieval and a context- and LLM preference-aware cache. Experimental results show that CodeSwift can reach up to 2.53x and 2.54x speedup compared to autoregressive decoding in repository-level and standalone code generation tasks, respectively, outperforming state-of-the-art inference acceleration approaches by up to 88%.

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