Summary of Progressive Mixed-precision Decoding For Efficient Llm Inference, by Hao Mark Chen et al.
Progressive Mixed-Precision Decoding for Efficient LLM Inference
by Hao Mark Chen, Fuwen Tan, Alexandros Kouris, Royson Lee, Hongxiang Fan, Stylianos I. Venieris
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 This paper proposes novel methods for efficiently deploying large language models (LLMs) on resource-constrained devices. The authors argue that existing quantization techniques fail to account for the diversity in computational patterns, redundancy, and sensitivity to approximations during different phases of LLM inference. To address this limitation, they introduce a phase-aware method that selectively allocates precision during different phases of LLM inference, achieving strong context extraction during prefill and efficient memory bandwidth utilization during decoding. Additionally, they propose Progressive Mixed-Precision Decoding (PMPD), a technique that enables the gradual lowering of precision deeper in the generated sequence, driven by task-adaptive or prompt-adaptive schedulers. Experimental results demonstrate significant speedup gains on Nvidia GPUs and an LLM-optimized NPU while preserving output quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make big language models work better on devices with limited resources. Right now, these models are too big to fit on smaller devices like smartphones. The authors found that existing ways of making the models smaller don’t take into account how the model is used. They came up with a new way to make the model smaller and faster by being smarter about when and where to reduce precision. This allowed them to make the model 1.4-12.2 times faster on certain devices while keeping the same quality. |
Keywords
» Artificial intelligence » Inference » Precision » Prompt » Quantization
