The deployment of widely used Transformer architecture is challenging because
of heavy computation load and memory overhead during inference, especially when
the target device is limited in computational resources such as mobile or edge
devices. Quantization is an effective technique to address such challenges. Our
analysis shows that for a given number of quantization bits, each block of
Transformer contributes to translation quality and inference computations in
different manners. Moreover, even inside an embedding block, each word presents
vastly different contributions. Correspondingly, we propose a mixed precision
quantization strategy to represent Transformer weights by an extremely low
number of bits (e.g., under 3 bits). For example, for each word in an embedding
block, we assign different quantization bits based on statistical property. Our
quantized Transformer model achieves 11.8$\times$ smaller model size than the
baseline model, with less than -0.5 BLEU. We achieve 8.3$\times$ reduction in
run-time memory footprints and 3.5$\times$ speed up (Galaxy N10+) such that our
proposed compression strategy enables efficient implementation for on-device
NMT.

本研究提出一种混合精度量化策略，将 Transformer 权重表示为极低位数（例如小于 3 位），该压缩策略使得在设备上实现高效的神经机器翻译变得可行，实现了模型大小 11.8 倍的减小，内存占用减少 8.3 倍，速度提高 3.5 倍，同时 BLEU 低于 - 0.5。