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With the rapid increase in the size of neural networks, model compression hasbecome an important area of research. Quantization is an effective technique atdecreasing the model size, memory access, and compute load of large models.Despite recent advances in quantization aware training (QAT) technique, mostpapers present evaluations that are focused on computer vision tasks, whichhave different training dynamics compared to sequence tasks. In this paper, wefirst benchmark the impact of popular techniques such as straight throughestimator, pseudo-quantization noise, learnable scale parameter, clipping, etc.on 4-bit seq2seq models across a suite of speech recognition datasets rangingfrom 1,000 hours to 1 million hours, as well as one machine translation datasetto illustrate its applicability outside of speech.  Through the experiments, we report that noise based QAT suffers when there isinsufficient regularization signal flowing back to the quantization scale. Wepropose low complexity changes to the QAT process to improve model accuracy(outperforming popular learnable scale and clipping methods). With the improvedaccuracy, it opens up the possibility to exploit some of the other benefits ofnoise based QAT: 1) training a single model that performs well in mixedprecision mode and 2) improved generalization on long form speech recognition.

RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models