Comment by ansk

I've seen and ignored a lot of "pytorch good, tensorflow bad" takes in my time, but this is so egregiously wrong I can't help but chime in. Facilitating graph-level optimizations has been one of the most central tenets of tensorflow's design philosophy since its inception. The XLA compiler was designed in close collaboration with the tensorflow team and was available in the tensorflow API as far back as 2017. It's not an exaggeration to say that pytorch is 5+ years behind on this front. Before anyone invokes the words "pythonic" or "ergonomic", I'd like to note that the tensorflow 2 API for compilation is nearly identical to torch.compile.