East: Efficient and Accurate Secure Transformer Framework for Inference
Yuanchao Ding Hua Guo Yewei Guan Weixin Liu Jiarong Huo Zhenyu Guan Xiyong Zhang
Transformer has been successfully used in practical applications, such as
ChatGPT, due to its powerful advantages. However, users' input is leaked to the
model provider during the service. With people's attention to privacy,
privacy-preserving Transformer inference is on the demand of such services.
Secure protocols for non-linear functions are crucial in privacy-preserving
Transformer inference, which are not well studied. Thus, designing practical
secure protocols for non-linear functions is hard but significant to model
performance. In this work, we propose a framework \emph{East} to enable
efficient and accurate secure Transformer inference. Firstly, we propose a new
oblivious piecewise polynomial evaluation algorithm and apply it to the
activation functions, which reduces the runtime and communication of GELU by
over 1.5$\times$ and 2.5$\times$, compared to prior arts. Secondly, the secure
protocols for softmax and layer normalization are carefully designed to
faithfully maintain the desired functionality. Thirdly, several optimizations
are conducted in detail to enhance the overall efficiency. We applied
\emph{East} to BERT and the results show that the inference accuracy remains
consistent with the plaintext inference without fine-tuning. Compared to Iron,
we achieve about 1.8$\times$ lower communication within 1.2$\times$ lower
runtime.