Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM
Inference Pipeline
- URL: http://arxiv.org/abs/2305.13144v2
- Date: Sun, 28 May 2023 08:22:19 GMT
- Title: Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM
Inference Pipeline
- Authors: Zangwei Zheng, Xiaozhe Ren, Fuzhao Xue, Yang Luo, Xin Jiang, Yang You
- Abstract summary: Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks.
In this paper, we propose an efficient LLM inference pipeline that harnesses the power of LLMs.
- Score: 22.08897444328099
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models (LLMs) have revolutionized the field of AI,
demonstrating unprecedented capacity across various tasks. However, the
inference process for LLMs comes with significant computational costs. In this
paper, we propose an efficient LLM inference pipeline that harnesses the power
of LLMs. Our approach begins by tapping into the potential of LLMs to
accurately perceive and predict the response length with minimal overhead. By
leveraging this information, we introduce an efficient sequence scheduling
technique that groups queries with similar response lengths into micro-batches.
We evaluate our approach on real-world instruction datasets using the
LLaMA-based model, and our results demonstrate an impressive 86% improvement in
inference throughput without compromising effectiveness. Notably, our method is
orthogonal to other inference acceleration techniques, making it a valuable
addition to many existing toolkits (e.g., FlashAttention, Quantization) for LLM
inference.
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