This kind of functionality is applicable in many scenarios. For example, the FX-based Graph Mode Quantization product is releasing as a prototype contemporaneously with FX. Graph Mode Quantization automates the process of quantizing a neural net and does so by leveraging FX’s program capture, analysis and transformation facilities. We are also developing many other transformation products with FX and we are excited to share this powerful toolkit with the community.
Because FX transforms consume and produce nn.Module instances, they can be used within many existing PyTorch workflows. This includes workflows that, for example, train in Python then deploy via TorchScript.