LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
Zechun Liu Barlas Oguz Changsheng Zhao Ernie Chang Pierre Stock Yashar Mehdad Yangyang Shi Raghuraman Krishnamoorthi Vikas Chandra
Several post-training quantization methods have been applied to large
language models (LLMs), and have been shown to perform well down to 8-bits. We
find that these methods break down at lower bit precision, and investigate
quantization aware training for LLMs (LLM-QAT) to push quantization levels even
further. We propose a data-free distillation method that leverages generations
produced by the pre-trained model, which better preserves the original output
distribution and allows quantizing any generative model independent of its
training data, similar to post-training quantization methods. In addition to
quantizing weights and activations, we also quantize the KV cache, which is
critical for increasing throughput and support long sequence dependencies at
current model sizes. We experiment with LLaMA models of sizes 7B, 13B, and 30B,
at quantization levels down to 4-bits. We observe large improvements over
training-free methods, especially in the low-bit settings.