Chapter 8. Making Transformers Efficient in Production

In the previous chapters, you’ve seen how transformers can be fine-tuned to produce great results on a wide range of tasks. However, in many situations accuracy (or whatever metric you’re optimizing for) is not enough; your state-of-the-art model is not very useful if it’s too slow or large to meet the business requirements of your application. An obvious alternative is to train a faster and more compact model, but the reduction in model capacity is often accompanied by a degradation in performance. So what can you do when you need a fast, compact, yet highly accurate model?

In this chapter we will explore four complementary techniques that can be used to speed up the predictions and reduce the memory footprint of your transformer models: knowledge distillation, quantization, pruning, and graph optimization with the Open Neural Network Exchange (ONNX) format and ONNX Runtime (ORT). We’ll also see how some of these techniques can be combined to produce significant performance gains. For example, this was the approach taken by the Roblox engineering team in their article “How We Scaled Bert to Serve 1+ Billion Daily Requests on CPUs”, who as shown in Figure 8-1 found that combining knowledge distillation and quantization enabled them to improve the latency and throughput of their BERT classifier by over a factor of 30!

Scaling BERT at Roblox
Figure ...

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