  With the rapid increase in the size of neural networks, model compression has
become an important area of research. Quantization is an effective technique at
decreasing the model size, memory access, and compute load of large models.
Despite recent advances in quantization aware training (QAT) technique, most
papers present evaluations that are focused on computer vision tasks, which
have different training dynamics compared to sequence tasks. In this paper, we
first benchmark the impact of popular techniques such as straight through
estimator, pseudo-quantization noise, learnable scale parameter, clipping, etc.
on 4-bit seq2seq models across a suite of speech recognition datasets ranging
from 1,000 hours to 1 million hours, as well as one machine translation dataset
to illustrate its applicability outside of speech.
  Through the experiments, we report that noise based QAT suffers when there is
insufficient regularization signal flowing back to the quantization scale. We
propose low complexity changes to the QAT process to improve model accuracy
(outperforming popular learnable scale and clipping methods). With the improved
accuracy, it opens up the possibility to exploit some of the other benefits of
noise based QAT: 1) training a single model that performs well in mixed
precision mode and 2) improved generalization on long form speech recognition.
