The user provides a `setup_context` and a `backward_function`. These
get put into a torch.autograd.Function that gets registered as the
custom op's autograd implementation.
Test Plan:
- we update custom ops in the custom_op_db to use the new
register_autograd API.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123110
Approved by: https://github.com/albanD
ghstack dependencies: #123108, #123109
This is the entrypoint for defining an opaque/blackbox (e.g. PyTorch will
never peek into it) custom op. In this PR, you can specify backend impls
and the abstract impl for this op.
NB: most of this PR is docstrings, please don't be intimidated by the
line count.
There are a number of interesting features:
- we infer the schema from type hints. In a followup I add the ability
to manually specify a schema.
- name inference. The user needs to manually specify an op name for now.
In a followup we add the ability to automatically infer a name (this
is a little tricky).
- custom_op registrations can override each other. This makes them
more pleasant to work with in environments like colab.
- we require that the outputs of the custom_op do not alias any inputs
or each other. We enforce this via a runtime check, but can relax this
into an opcheck test if it really matters in the future.
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122344
Approved by: https://github.com/ezyang, https://github.com/albanD
