Transformer-based models are the state-of-the-art for Natural Language
Understanding (NLU) applications. Models are getting bigger and better on
various tasks. However, Transformer models remain computationally challenging
since they are not efficient at inference-time compared to traditional
approaches. In this paper, we present FastFormers, a set of recipes to achieve
efficient inference-time performance for Transformer-based models on various
NLU tasks. We show how carefully utilizing knowledge distillation, structured
pruning and numerical optimization can lead to drastic improvements on
inference efficiency. We provide effective recipes that can guide practitioners
to choose the best settings for various NLU tasks and pretrained models.
Applying the proposed recipes to the SuperGLUE benchmark, we achieve from 9.8x
up to 233.9x speed-up compared to out-of-the-box models on CPU. On GPU, we also
achieve up to 12.4x speed-up with the presented methods. We show that
FastFormers can drastically reduce cost of serving 100 million requests from
4,223 USD to just 18 USD on an Azure F16s_v2 instance. This translates to a
sustainable runtime by reducing energy consumption 6.9x - 125.8x according to
the metrics used in the SustaiNLP 2020 shared task.

本研究提出了 FastFormers 来加快基于 Transformer 模型的 NLU 任务的推理效率，可以通过运用知识蒸馏、结构剪枝和数值优化等方法，将预处理模型在 CPU 和 GPU 上的速度提高至 9.8-233.9 倍，节省成本的同时降低能耗。