Dovetail: A CPU/GPU Heterogeneous Speculative Decoding for LLM inference

• URL: http://arxiv.org/abs/2412.18934v1
• Date: Wed, 25 Dec 2024 15:45:18 GMT
• Title: Dovetail: A CPU/GPU Heterogeneous Speculative Decoding for LLM inference
• Authors: Libo Zhang, Zhaoning Zhang, Baizhou Xu, Songzhu Mei, Dongsheng Li,
• Abstract summary: Dovetail is an approach that deploys the draft model on the GPU to generate draft tokens while allowing the target model to perform parallel verification on the CPU. Dovetail achieves an inference speed of 5.86 tokens per second for LLaMA2-Chat-7B using 3GB of VRAM, representing an approximately 2.77x improvement over CPU-only inference.
• Score: 20.68731158617374
• License:
• Abstract: Due to the high resource demands of Large Language Models (LLMs), achieving widespread deployment on consumer-grade devices presents significant challenges. Typically, personal or consumer-grade devices, including servers configured prior to the era of large-scale models, generally have relatively weak GPUs and relatively strong CPUs. However, most current methods primarily depend on GPUs for computation. Therefore, we propose Dovetail, an approach that deploys the draft model on the GPU to generate draft tokens while allowing the target model to perform parallel verification on the CPU, thereby improving the utilization of all available hardware resources and occupying less inter-device communication bandwidth. Accordingly, we have redesigned the draft model to better align with heterogeneous hardware characteristics. To this end, we implemented several optimizations: reducing the number of draft tokens to mitigate latency in parallel verification, increasing the depth of the draft model to enhance its predictive capacity, and introducing DGF (Dynamic Gating Fusion) to improve the integration of features and token embeddings. In the HumanEval benchmark, Dovetail achieved an inference speed of 5.86 tokens per second for LLaMA2-Chat-7B using 3GB of VRAM, representing an approximately 2.77x improvement over CPU-only inference. Furthermore, the inference speed was increased to 8 tokens per second when utilizing 7GB of VRAM.

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