1. DeFT: Decoding with Flash Tree-attention for Efficient Tree-structured LLM Inference
- Author
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Yao, Jinwei, Chen, Kaiqi, Zhang, Kexun, You, Jiaxuan, Yuan, Binhang, Wang, Zeke, and Lin, Tao
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Large language models (LLMs) are increasingly employed for complex tasks that process multiple generation calls in a tree structure with shared prefixes of tokens, including few-shot prompting, multi-step reasoning, speculative decoding, etc. However, existing inference systems for tree-based applications are inefficient due to improper partitioning of queries and KV cache during attention calculation. This leads to two main issues: (1) a lack of memory access (IO) reuse for KV cache of shared prefixes, and (2) poor load balancing.As a result, there is redundant KV cache IO between GPU global memory and shared memory, along with low GPU utilization. To address these challenges, we propose DeFT(Decoding with Flash Tree-Attention), a hardware-efficient attention algorithm with prefix-aware and load-balanced KV cache partitions. DeFT reduces the number of read/write operations of KV cache during attention calculation through KV-Guided Grouping, a method that avoids repeatedly loading KV cache of shared prefixes in attention computation. Additionally, we propose Flattened Tree KV Splitting, a mechanism that ensures even distribution of the KV cache across partitions with little computation redundancy, enhancing GPU utilization during attention computations. By reducing 73-99 KV cache IO and nearly 100 IO for partial results during attention calculation, DeFT achieves up to 2.52/3.82x speedup in the end-to-end/attention latency across three practical tree-based workloads compared to state-of-the-art attention algorithms., Comment: Update DeFT-v3 with more ablation studies. DeFT-v1 was accepted by ICLR'24 AGI Workshop ( https://openreview.net/forum?id=HqfLHoX8bR ). Code will be released soon
- Published
- 2024