add fallthrough kernel for AutogradMeta key (#94603)
The other `Autograd[Backend]` keys all have fallthrough kernels registered to them, but `AutogradMeta` was missing the fallthrough kernel.
This is a problem for custom ops that don't have autograd support, if you try to run them with meta tensors. If you have a custom op, and register a CPU and a Meta kernel, then:
(1) if you run the op with cpu tensors, it will dispatch straight to the CPU kernel (as expected)
(2) if you run the op with meta tensors, you will error - because we don't have a fallthrough registered to the AutogradMeta key, we will try to dispatch to the AutogradMeta key and error, since the op author hasn't provided an autograd implementation.
Here's a repro that I confirmed now works:
```
import torch
from torch._dispatch.python import enable_python_dispatcher
from torch._subclasses.fake_tensor import FakeTensorMode
lib = torch.library.Library("test", "DEF")
impl_cpu = torch.library.Library("test", "IMPL", "CPU")
impl_meta = torch.library.Library("test", "IMPL", "Meta")
def foo_impl(x):
return x + 1
lib.define("foo(Tensor a) -> Tensor")
impl_meta.impl("foo", foo_impl)
impl_cpu.impl("foo", foo_impl)
with enable_python_dispatcher():
a = torch.ones(2, device='meta')
print("@@@@@")
b = torch.ops.test.foo.default(a)
print(b)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94603
Approved by: https://github.com/ezyang, https://github.com/albanD
