- https://pytorch.org/docs/master/fx.html
X is a toolkit for developers to use to transform nn.Module instances. FX consists of three main components: a symbolic tracer, an intermediate representation, and Python code generation.I understand that people building PyTorch usually favour flexible toolkits (and they expose a lot to an end user) and most likely they just realized that static quantization was too complex for an average user to handle and they wrote this as an engine for automated quantization transformations, which is cool. Designing a proper API is always a balancing act.
Over the years, I became quite good in monkey patching PyTorch code just using python's and pytorch tools (e.g.
module.named_modules()). So I wonder what the killer use case of this feature would be?One thing comes to mind immediately - when you have the same models with static control flows and you need to create a quantized / torch script version of it. Now it is a pain in the ass - because it requires manually switching them back and forth (switch on, create a quantized TorchScript version one, switch back, create another one, etc).
Will I use it? I guess I need to sleep on it. We ended up not using static quantization very much. Looks very cool and flexible, serves a real purpose, but usually stupid one line hacks can do the same without learning a new tool.
So idk, what do you think? Do you like any of the examples? I like the invert one.
#deep_learning