  Large language models (LLMs) are omnipresent, however their practical
deployment is challenging due to their ever increasing computational and memory
demands. Quantization is one of the most effective ways to make them more
compute and memory efficient. Quantization-aware training (QAT) methods,
generally produce the best quantized performance, however it comes at the cost
of potentially long training time and excessive memory usage, making it
impractical when applying for LLMs. Inspired by parameter-efficient fine-tuning
(PEFT) and low-rank adaptation (LoRA) literature, we propose LR-QAT -- a
lightweight and memory-efficient QAT algorithm for LLMs. LR-QAT employs several
components to save memory without sacrificing predictive performance: (a)
low-rank auxiliary weights that are aware of the quantization grid; (b) a
downcasting operator using fixed-point or double-packed integers 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 pretraining framework, meaning that the resulting model
can still be utilized for any downstream task afterwards; (iii) can be applied
across a wide range of quantization settings, such as different choices
quantization granularity, activation quantization, and seamlessly combined with
many PTQ techniques. We apply LR-QAT to LLaMA-2/3 and Mistral model families
and validate its effectiveness on several downstream tasks. Our method
outperforms common post-training quantization (PTQ) 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 of
memory.
