PipeDec: Low-Latency Pipeline-based Inference with Dynamic Speculative Decoding towards Large-scale Models

Autoregressive large language model inference primarily consists of two stages: pre-filling and decoding. Decoding involves sequential computation for each token, which leads to significant latency. Speculative decoding is a technique that leverages the draft model combined with large model verification to enhance parallelism without sacrificing accuracy. However, existing external prediction methods face challenges in adapting to multi-node serial deployments. While they can maintain speedup under such conditions, the high latency of multi-node deployments ultimately results in low overall efficiency. We propose a speculative decoding framework named PipeDec to address the low global resource utilization of single tasks in pipeline deployments thereby reducing decoding latency. We integrate a draft model into the pipeline of the large model and immediately forward each prediction from the draft model to subsequent pipeline stages. A dynamic prediction tree manages prediction sequences across nodes, enabling efficient updating and pruning. This approach leverages the draft model's predictions to utilize all pipeline nodes for parallel decoding of a single task. Experiments were conducted using LLama3.2 1B as the draft model in conjunction with a 14-stage parallel pipeline to accelerate LLama3.1 70B by six different types of datasets. During the decoding phase of a single task, PipeDec achieved a 4.46x-7.79x speedup compared to traditional pipeline parallelism and a 2.2x-2.69x speedup compared to baseline tree-based speculative decoding methods. The code will be released after the review process.