Low-Rank Quantization-Aware Training for LLMs
Keywords: transformers, LLM, quantization, quantization-aware training, QAT, low-rank adaptation, PEFT, memory efficiency, inference efficiency
TL;DR: We propose a lightweight memory- and inference-efficient quantization-aware training (QAT) algorithm for LLMs.
Abstract: In this paper we propose LR-QAT – a lightweight and memory-efficient QAT algorithm for LLMs. LR-QAT employs several components to save memory without sacrificing performance: (a) low-rank quantization-aware reparameterization; (b) downcasting operation using fixed-point or double-packing and (c) checkpointing. Unlike most related work, our method (i) is inference-efficient, leading to no additional overhead compared to traditional PTQ; (ii) can be seen as a general extended pre-training framework, meaning that the resulting model can still be utilized for any downstream task afterwards; (iii) is orthogonal to most of recent PTQ methods and thus can be seamlessly combined with them. We apply LR-QAT to the LLaMA-1/2/3 and Mistral model families and validate its effectiveness on several downstream tasks. Our method outperforms most of recent LLM quantization approaches and reaches the same model performance as full-model QAT at the fraction of its memory usage. Specifically, we can train a 7B LLM on a single consumer grade GPU with 24GB memory.
Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 10349
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