Extracting bigger segments of the model into an intermediate representation makes it possible to do sophisticated whole-program optimizations and to offload computation to specialized AI accelerators which operate on graphs of computation. We have already been developing the beginnings of these optimizations, including passes that fuse GPU operations together to improve the performance of smaller RNN models.
It also allows us to use existing high-performance backends available in Caffe2 today to run the model efficiently. Additionally, @script functions (and modules!) can be fully exported to ONNX in a way that retains their dynamic nature, such that you can easily run them in a Python-free environment using the model executors from Caffe2 or by transferring the model to any other framework supporting ONNX.
Usability