Transformer is being widely used in Neural Machine Translation (NMT). Deploying Transformer models to mobile or edge devices with limited resources is challenging because of heavy computation  and memory overhead during inference  . 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 accuracy 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 with lower 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 x smaller model size than the baseline model, with less than -0.5 BLEU. We achieve 8.3 x reduction in run-time memory footprints and 3.5 x speed up (Galaxy N10+) such that our proposed compression strategy enables efficient implementation for on-device NMT.
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.
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Transformer is being widely used in Neural Machine Translation (NMT). Deploying Transformer models to mobile or edge devices with limited resources
The deployment of widely used Transformer architecture
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load
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, especially when the target device is limited in computational resources such as mobile or edge devices
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accuracy
quality
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with lower
by an extremely low number of
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