BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models

• URL: http://arxiv.org/abs/2401.12522v2
• Date: Thu, 25 Jan 2024 14:02:03 GMT
• Title: BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models
• Authors: Feng Lin, Hanling Yi, Hongbin Li, Yifan Yang, Xiaotian Yu, Guangming Lu, Rong Xiao
• Abstract summary: Large language models (LLMs) commonly employ autoregressive generation during inference, leading to high memory bandwidth demand and extended latency. We present Bi-directional Tuning for lossless Acceleration (BiTA), an innovative method expediting LLMs via streamlined semi-autoregressive generation and draft verification. The proposed BiTA, LLaMA-2-70B-Chat achieves a 2.7$times$ speedup on the MT-Bench benchmark.
• Score: 37.09385961422664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) commonly employ autoregressive generation during inference, leading to high memory bandwidth demand and consequently extended latency. To mitigate this inefficiency, we present Bi-directional Tuning for lossless Acceleration (BiTA), an innovative method expediting LLMs via streamlined semi-autoregressive generation and draft verification. Inspired by the concept of prompt tuning, we enhance LLMs with a parameter-efficient design called bi-directional tuning for the capability in semi-autoregressive generation. Employing efficient tree-based decoding, the models perform draft candidate generation and verification in parallel, ensuring outputs identical to their autoregressive counterparts under greedy sampling. BiTA serves as a lightweight plug-in module, seamlessly boosting the inference efficiency of existing LLMs without requiring additional assistance models or incurring significant extra memory costs. Applying the proposed BiTA, LLaMA-2-70B-Chat achieves a 2.7$\times$ speedup on the MT-Bench benchmark. Extensive experiments confirm our method surpasses state-of-the-art acceleration techniques.

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