Summary of Batchllm: Optimizing Large Batched Llm Inference with Global Prefix Sharing and Throughput-oriented Token Batching, by Zhen Zheng et al.
BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batching
by Zhen Zheng, Xin Ji, Taosong Fang, Fanghao Zhou, Chuanjie Liu, Gang Peng
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract discusses the limitations of existing large language model (LLM) inference engines in supporting batched tasks with prefix sharing characteristics. These engines typically optimize for streaming requests, which can lead to poor performance when dealing with large batches. The proposed solution, BatchLLM, addresses these issues by explicitly identifying common prefixes globally and scheduling requests accordingly. Additionally, it reorders requests to mix decoding tokens with prefill chunks more efficiently, applies memory-centric token batching to increase GPU utilization, and optimizes the prefix-shared Attention kernel for better performance. BatchLLM outperforms existing models (vLLM and SGLang) by 1.3x to 10.8x on microbenchmarks and industry workloads under different hardware environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making big language models work better when processing lots of information at once. Right now, these models are good at handling small tasks one by one, but they struggle when asked to do many things simultaneously. The researchers propose a new solution called BatchLLM that can handle this type of task more efficiently. It works by identifying common patterns in the data and scheduling requests accordingly. This allows it to reuse information and process tasks more quickly. The new model outperforms existing models on various tests, making it useful for industries that rely heavily on language processing. |
Keywords
» Artificial intelligence » Attention » Inference » Large language model » Token
