In this work, we quantize a trained Transformer machine language translation
model leveraging INT8/VNNI instructions in the latest Intel$^\circledR$
Xeon$^\circledR$ Cascade Lake processors to improve inference performance while
maintaining less than 0.5$\%$ drop in accuracy. To the best of our knowledge,
this is the first attempt in the industry to quantize the Transformer model.
This has high impact as it clearly demonstrates the various complexities of
quantizing the language translation model. We present novel quantization
techniques directly in TensorFlow to opportunistically replace 32-bit floating
point (FP32) computations with 8-bit integers (INT8) and transform the FP32
computational graph. We also present a bin-packing parallel batching technique
to maximize CPU utilization. Overall, our optimizations with INT8/VNNI deliver
1.5X improvement over the best FP32 performance. Furthermore, it reveals the
opportunities and challenges to boost performance of quantized deep learning
inference and establishes best practices to run inference with high efficiency
on Intel CPUs.

本研究尝试利用 INT8/VNNI 指令量化 Transformer 模型，提高推理性能，同时保持不到 0.5% 的准确度下降。研究者在 TensorFlow 中提出了一种新的量化技术，并采用了一种并行处理技术，优化后的结果比最佳 FP32 性能提高了 1.5 倍，讨论了量化深度学习的机会和挑战，并建立了在 Intel CPU 上高效运行推理的最佳实践。