pytorch

010700b4 - Update on "Delete ProxyTensor wrapper subclass"

Update on "Delete ProxyTensor wrapper subclass" I was working on https://github.com/pytorch/torchdynamo/issues/80 and my working hypothesis for what was causing the error was that proxy tensor was not advertising correct dispatch keys, causing AMP to operate differently when you traced. I could have fixed this directly by replicating fake tensor's fix for setting dispatch keys to also apply to proxy tensor, but I was like, "Why must I repeat myself." This PR is the result. It completely deletes the ProxyTensor wrapper subclass, so that when we are tracing, the tensors flowing through the program are the *original* real or fake tensors, depending on what the user requested in the top-level API. There is no more wrapping. To store the Proxy objects necessary for actually doing tracing, I store the property directly on the tensors. (Note: I never clean up old entries from the map at the moment, this is easily fixed by using a weak map) Benefits of doing this: * No more tip-toeing around no_dispatch() creation of new ProxyTensors; we never create new tensors (except when we call the underlying func), so you don't have to worry about accidentally tracing them. * No more syncing up metadata from in place operators. In particular https://github.com/pytorch/pytorch/issues/81526 is mooted * This fixes https://github.com/pytorch/torchdynamo/issues/519 as we no longer need to teach proxy tensor to support sparse tensor. * No more schlepping symbolic integers from the inner fake tensor to the outer proxy tensor. If you can make a fake tensor with symbolic ints, you're done, nothing else to do. To avoid having to rewrite all of the guts, when I get to the actual proxy tensor handler, I first "fetch" the stored ProxyTensor data from the weakmap via a tree_map, and then operate on the consequent data as before. A more optimized implementation is possible. Signed-off-by: Edward Z. Yang <ezyangfb.com> [ghstack-poisoned]