Make meta a device (getting rid of empty_meta) (#53143)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53143
Meta is now an honest to goodness device type, like cpu, so you can use
device='meta' to trigger allocation of meta tensors. This way better
than empty_meta since we now have working API for most factory functions
(they don't necessarily work yet, though, because need to register Meta
versions of those functions.)
Some subtleties:
- I decided to drop the concept of CPU versus CUDA meta tensors; meta
tensors are device agnostic. It's hard to say exactly what the
correct level of abstraction here is, but in this particular case
implementation considerations trump semantic considerations: it
is way easier to have just a meta device, than to have a meta device
AND a cpu device AND a cuda device. This may limit the applicability
of meta tensors for tracing models that do explicit cpu()/cuda()
conversions (unless, perhaps, we make those operations no-ops on meta
tensors).
- I noticed that the DeviceType uppercase strings are kind of weird.
Are they really supposed to be all caps? That's weird.
- I moved the Meta dispatch key to live with the rest of the "device"
dispatch keys.
- I intentionally did NOT add a Backend for Meta. For now, I'm going to
hope meta tensors never exercise any of the Backend conversion code;
even if it does, better to fix the code to just stop converting to and
from Backend.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: samestep
Differential Revision: D26763552
Pulled By: ezyang
fbshipit-source-id: 14633b6ca738e60b921db66a763155d01795480d
