  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.
