MobileNMT: Enabling Translation in 15MB and 30ms
Deploying NMT models on mobile devices is essential for privacy, low latency,
and offline scenarios. For high model capacity, NMT models are rather large.
Running these models on devices is challenging with limited storage, memory,
computation, and power consumption. Existing work either only focuses on a
single metric such as FLOPs or general engine which is not good at
auto-regressive decoding. In this paper, we present MobileNMT, a system that
can translate in 15MB and 30ms on devices. We propose a series of principles
for model compression when combined with quantization. Further, we implement an
engine that is friendly to INT8 and decoding. With the co-design of model and
engine, compared with the existing system, we speed up 47.0x and save 99.5% of
memory with only 11.6% loss of BLEU. The code is publicly available at
https://github.com/zjersey/Lightseq-ARM.
64814905856901b0edbda48c
Ye Lin
64814905856901b0edbda48d
Xiaohui Wang
64814905856901b0edbda48e
Zhexi Zhang
64814905856901b0edbda48f
Mingxuan Wang
admin_assigned
2023-06-12T13:49:23.570Z
641f1cc26d51620635e1dd7b
/avatars/58512d4fdaab900ea0bd5aace7d69e7c.svg
wangmingxuan
user
wmx6866
64814905856901b0edbda490
Tong Xiao
64814905856901b0edbda491
Jingbo Zhu
MobileNMT: Enabling Translation in 15MB and 30ms
Deploying NMT models on mobile devices is essential for privacy, low latency,
and offline scenarios. For high model capacity, NMT models are rather large.
Running these models on devices is challenging with limited storage, memory,
computation, and power consumption. Existing work either only focuses on a
single metric such as FLOPs or general engine which is not good at
auto-regressive decoding. In this paper, we present MobileNMT, a system that
can translate in 15MB and 30ms on devices. We propose a series of principles
for model compression when combined with quantization. Further, we implement an
engine that is friendly to INT8 and decoding. With the co-design of model and
engine, compared with the existing system, we speed up 47.0x and save 99.5% of
memory with only 11.6% loss of BLEU. The code is publicly available at
https://github.com/zjersey/Lightseq-ARM.
MobileNMT system translates with low memory and fast decoding on mobile devices through model compression and INT8-friendly engine.
NMT models model compression quantization auto-regressive decoding INT8 BLEU