Efficient Machine Translation with Model Pruning and Quantization
Maximiliana Behnke, Nikolay Bogoychev, Alham Fikri Aji, Kenneth Heafield, Graeme Nail, Qianqian Zhu, Svetlana Tchistiakova, Jelmer van der Linde, Pinzhen Chen, Sidharth Kashyap, Roman Grundkiewicz
Abstract
We participated in all tracks of the WMT 2021 efficient machine translation task: single-core CPU, multi-core CPU, and GPU hardware with throughput and latency conditions. Our submissions combine several efficiency strategies: knowledge distillation, a simpler simple recurrent unit (SSRU) decoder with one or two layers, lexical shortlists, smaller numerical formats, and pruning. For the CPU track, we used quantized 8-bit models. For the GPU track, we experimented with FP16 and 8-bit integers in tensorcores. Some of our submissions optimize for size via 4-bit log quantization and omitting a lexical shortlist. We have extended pruning to more parts of the network, emphasizing component- and block-level pruning that actually improves speed unlike coefficient-wise pruning.- Anthology ID:
- 2021.wmt-1.74
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Editors:
- Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 775–780
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.74
- DOI:
- Bibkey:
- Cite (ACL):
- Maximiliana Behnke, Nikolay Bogoychev, Alham Fikri Aji, Kenneth Heafield, Graeme Nail, Qianqian Zhu, Svetlana Tchistiakova, Jelmer van der Linde, Pinzhen Chen, Sidharth Kashyap, and Roman Grundkiewicz. 2021. Efficient Machine Translation with Model Pruning and Quantization. In Proceedings of the Sixth Conference on Machine Translation, pages 775–780, Online. Association for Computational Linguistics.
- Cite (Informal):
- Efficient Machine Translation with Model Pruning and Quantization (Behnke et al., WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.74.pdf
Export citation
@inproceedings{behnke-etal-2021-efficient,
title = "Efficient Machine Translation with Model Pruning and Quantization",
author = "Behnke, Maximiliana and
Bogoychev, Nikolay and
Aji, Alham Fikri and
Heafield, Kenneth and
Nail, Graeme and
Zhu, Qianqian and
Tchistiakova, Svetlana and
van der Linde, Jelmer and
Chen, Pinzhen and
Kashyap, Sidharth and
Grundkiewicz, Roman",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.74",
pages = "775--780",
abstract = "We participated in all tracks of the WMT 2021 efficient machine translation task: single-core CPU, multi-core CPU, and GPU hardware with throughput and latency conditions. Our submissions combine several efficiency strategies: knowledge distillation, a simpler simple recurrent unit (SSRU) decoder with one or two layers, lexical shortlists, smaller numerical formats, and pruning. For the CPU track, we used quantized 8-bit models. For the GPU track, we experimented with FP16 and 8-bit integers in tensorcores. Some of our submissions optimize for size via 4-bit log quantization and omitting a lexical shortlist. We have extended pruning to more parts of the network, emphasizing component- and block-level pruning that actually improves speed unlike coefficient-wise pruning.",
}
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%0 Conference Proceedings %T Efficient Machine Translation with Model Pruning and Quantization %A Behnke, Maximiliana %A Bogoychev, Nikolay %A Aji, Alham Fikri %A Heafield, Kenneth %A Nail, Graeme %A Zhu, Qianqian %A Tchistiakova, Svetlana %A van der Linde, Jelmer %A Chen, Pinzhen %A Kashyap, Sidharth %A Grundkiewicz, Roman %Y Barrault, Loic %Y Bojar, Ondrej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussa, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Kocmi, Tom %Y Martins, Andre %Y Morishita, Makoto %Y Monz, Christof %S Proceedings of the Sixth Conference on Machine Translation %D 2021 %8 November %I Association for Computational Linguistics %C Online %F behnke-etal-2021-efficient %X We participated in all tracks of the WMT 2021 efficient machine translation task: single-core CPU, multi-core CPU, and GPU hardware with throughput and latency conditions. Our submissions combine several efficiency strategies: knowledge distillation, a simpler simple recurrent unit (SSRU) decoder with one or two layers, lexical shortlists, smaller numerical formats, and pruning. For the CPU track, we used quantized 8-bit models. For the GPU track, we experimented with FP16 and 8-bit integers in tensorcores. Some of our submissions optimize for size via 4-bit log quantization and omitting a lexical shortlist. We have extended pruning to more parts of the network, emphasizing component- and block-level pruning that actually improves speed unlike coefficient-wise pruning. %U https://aclanthology.org/2021.wmt-1.74 %P 775-780
Markdown (Informal)
[Efficient Machine Translation with Model Pruning and Quantization](https://aclanthology.org/2021.wmt-1.74) (Behnke et al., WMT 2021)
- Efficient Machine Translation with Model Pruning and Quantization (Behnke et al., WMT 2021)
ACL
- Maximiliana Behnke, Nikolay Bogoychev, Alham Fikri Aji, Kenneth Heafield, Graeme Nail, Qianqian Zhu, Svetlana Tchistiakova, Jelmer van der Linde, Pinzhen Chen, Sidharth Kashyap, and Roman Grundkiewicz. 2021. Efficient Machine Translation with Model Pruning and Quantization. In Proceedings of the Sixth Conference on Machine Translation, pages 775–780, Online. Association for Computational Linguistics.