Learning Accurate Integer Transformer Machine-Translation Models

We describe a method for training accurate Transformer machine-translation models to run inference using 8-bit integer (INT8) hardware matrix multipliers, as opposed to the more costly single-precision floating-point (FP32) hardware. Unlike previous work, which converted only 85 Transformer matrix multiplications to INT8, leaving 48 out of 133 of them in FP32 because of unacceptable accuracy loss, we convert them all to INT8 without compromising accuracy. Tested on the new-stest2014 English-to-German translation task, our INT8 Transformer Base and Transformer Big models yield BLEU scores that are 99.3% to 100% relative to those of the corresponding FP32 models. Our approach converts all matrix-multiplication tensors from an existing FP32 model into INT8 tensors by automatically making range-precision tradeoffs during training. To demonstrate the robustness of this approach, we also include results from INT6 Transformer models. 1 Introduction We report a method for training accurate yet compact Transformer machine-translation models [ V aswaniet al., 2017 ] . Specifically, we aim these models at hardware with 8-bit integer (INT8) matrix multipliers. Compared to single-precision floating-point (FP32) matrix multiplications, INT8 matrix multiplications not only reduce both storage and bandwidth four times, but they also consume 15 times less energy [ Horowitz, 2014 ] .