We present Sockeye 2, a modernized and streamlined version of the Sockeye
neural machine translation (NMT) toolkit. New features include a simplified
code base through the use of MXNet's Gluon API, a focus on state of the art
model architectures, distributed mixed precision training, and efficient CPU
decoding with 8-bit quantization. These improvements result in faster training
and inference, higher automatic metric scores, and a shorter path from research
to production.

Sockeye 2 是基于 MXNet 的 Gluon API 开发的神经机器翻译工具包，采用分布式混合精度训练、8 位量化的高效 CPU 解码等新特性，从而实现更快的训练、更高的自动度量分数，研究与生产部署的路径更短的优化。

2020 AMTA 平台上的 Sockeye 2 神经机器翻译工具包

The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020

The state-of-the-art (SOTA) for mixed precision training is dominated by
variants of low precision floating point operations, and in particular, FP16
accumulating into FP32 Micikevicius et al. (2017). On the other hand, while a
lot of research has also happened in the domain of low and mixed-precision
Integer training, these works either present results for non-SOTA networks (for
instance only AlexNet for ImageNet-1K), or relatively small datasets (like
CIFAR-10). In this work, we train state-of-the-art visual understanding neural
networks on the ImageNet-1K dataset, with Integer operations on General Purpose
(GP) hardware. In particular, we focus on Integer Fused-Multiply-and-Accumulate
(FMA) operations which take two pairs of INT16 operands and accumulate results
into an INT32 output.We propose a shared exponent representation of tensors and
develop a Dynamic Fixed Point (DFP) scheme suitable for common neural network
operations. The nuances of developing an efficient integer convolution kernel
is examined, including methods to handle overflow of the INT32 accumulator. We
implement CNN training for ResNet-50, GoogLeNet-v1, VGG-16 and AlexNet; and
these networks achieve or exceed SOTA accuracy within the same number of
iterations as their FP32 counterparts without any change in hyper-parameters
and with a 1.8X improvement in end-to-end training throughput. To the best of
our knowledge these results represent the first INT16 training results on GP
hardware for ImageNet-1K dataset using SOTA CNNs and achieve highest reported
accuracy using half-precision

本文使用整数计算方法，利用动态定点方案为常规神经网络操作提供合适的实现，研究在常规通用硬件上进行 ImageNet-1K 数据集上的视觉理解神经网络训练，并且实现的神经网络在相同迭代次数下，不增加超参数的情况下超过了 SOTA 的精度，并提高了 1.8 倍的训练吞吐量，这是使用 SOTA CNN 对 ImageNet-1K 数据集进行 INT16 训练的首次最高准确性结果。