We add torch.library.Library._register_torch_dispatch_rule. Here, a user
can provide us a specific rule to run for a specific
(torch_dispatch_class, operator) pair. The motivation is that a user
might want to extend a subclass/mode but may not have access to the
source code of the subclass/mode.
I'll make this public in a follow-up PR if we think the approach and API
is good.
Keep in mind that many subclasses will likely deliver their own open
registration solution (DTensor has register_sharding_prop_rule and NJT
has register_jagged_op); _register_torch_dispatch_rule is meant as a
catch-all open registration mechanism for when the subclass hasn't
provided anything more specific.
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130064
Approved by: https://github.com/albanD
We add torch.library.Library._register_torch_dispatch_rule. Here, a user
can provide us a specific rule to run for a specific
(torch_dispatch_class, operator) pair. The motivation is that a user
might want to extend a subclass/mode but may not have access to the
source code of the subclass/mode.
I'll make this public in a follow-up PR if we think the approach and API
is good.
Keep in mind that many subclasses will likely deliver their own open
registration solution (DTensor has register_sharding_prop_rule and NJT
has register_jagged_op); _register_torch_dispatch_rule is meant as a
catch-all open registration mechanism for when the subclass hasn't
provided anything more specific.
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130064
Approved by: https://github.com/albanD
We add torch.library.Library._register_torch_dispatch_rule. Here, a user
can provide us a specific rule to run for a specific
(torch_dispatch_class, operator) pair. The motivation is that a user
might want to extend a subclass/mode but may not have access to the
source code of the subclass/mode.
I'll make this public in a follow-up PR if we think the approach and API
is good.
Keep in mind that many subclasses will likely deliver their own open
registration solution (DTensor has register_sharding_prop_rule and NJT
has register_jagged_op); _register_torch_dispatch_rule is meant as a
catch-all open registration mechanism for when the subclass hasn't
provided anything more specific.
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130064
Approved by: https://github.com/albanD
This PR renames the implementation details of register_fake to align
more with the new name. It is in its own PR because this is risky
(torch.package sometimes depends on private library functions and
implementation details).
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123938
Approved by: https://github.com/williamwen42
This PR:
- adds a new torch.library.register_fake and deprecates
torch.library.impl_abstract. The motivation is that we have a lot of
confusion around the naming so we are going to align the naming with
the actual subsystem (FakeTensor).
- renames `m.impl_abstract_pystub("fbgemm_gpu.sparse_ops")` to
`m.has_python_registration("fbgemm_gpu.sparse_ops")`. No deprecation
here yet; I need to test how this works with static initialization.
- Renames a bunch of internals to match (e.g. abstractimplpystub ->
pystub)
I'm scared to rename the Python-side internal APIs (e.g.
torch._library.abstract_impl) because of torch.package concerns. I'll do
that in its own isolated PR next just in case it causes problems.
DEPRECATION NOTE: torch.library.impl_abstract was renamed to to
torch.library.register_fake. Please use register_fake. We'll delete
impl_abstract in a future version of PyTorch.
Test Plan:
- existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123937
Approved by: https://github.com/albanD
Changelog:
- torch.library.impl_abstract optionally accepts a torch.library.Library
object. If passed in, then the lifetime of the registration is tied to
the Library object.
- we've also changed torch.library.impl_abstract to work on all
operators, including overloads.
- we refactored the `torch._custom_ops.*` and `torch._custom_op.*`
impl_abstract APIs and put them under torch._library. This is the
final resting place for them. I will follow-up with deleting
all the `torch._custom_ops.*` stuff later.
- There is a new "SimpleOperatorRegistry" where we actually collect the
abstract_impl. We will expand this to also hold the other
torch._custom_ops.* APIs when we move those to torch.library
NB: Previously we had designed
`impl_abstract` assuming a very high-level Python-only custom op API.
We've revisited that since; now, impl_abstract works for all custom ops,
no matter python or C++, no matter the schema. The new refactored design
reflects this better.
Test Plan:
- existing and new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109912
Approved by: https://github.com/ezyang
