Fixes https://github.com/pytorch/pytorch/issues/136177
The motivation is that torch::deploy doesn't handle this well. The
workaround for users is to use C++ custom ops.
All torch.library APIs ultimately go through the torch.library.Library
object, so we add checks to noop for torch::deploy there.
Test Plan:
- new test
- going to test this internally and hope nothing breaks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136645
Approved by: https://github.com/ezyang
We promise the user that these custom ops (and their kernels) are black
boxes w.r.t. torch.compile. Unfortunately Dynamo can turn itself back
on in the implementation of the custom operator, so we force it off by
disabling Dynamo
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133125
Approved by: https://github.com/ezyang
This PR fixes a bug in `test_correct_module_names` introduced in #130497. It also addresses post-fix test failures in:
* `torch/ao/quantization/__init__.py` - set the correct `__module__` for several public API helpers
* `torch/library.py` - add `register_vmap` to `__all__`
* `torch/nn/attention/flex_attention.py` - make `round_up_to_multiple` private by prepending an underscore
* `torch/storage.py` - introduce `__all__` to avoid `Self` being re-exported as a public API
* `torch/distributed/pipelining/schedules.py` - add `ZeroBubbleAlgorithm` to `__all__`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131386
Approved by: https://github.com/albanD
This PR fixes a bug in `test_correct_module_names` introduced in #130497. It also addresses post-fix test failures in:
* `torch/ao/quantization/__init__.py` - set the correct `__module__` for several public API helpers
* `torch/library.py` - add `register_vmap` to `__all__`
* `torch/nn/attention/flex_attention.py` - make `round_up_to_multiple` private by prepending an underscore
* `torch/storage.py` - introduce `__all__` to avoid `Self` being re-exported as a public API
* `torch/distributed/pipelining/schedules.py` - add `ZeroBubbleAlgorithm` to `__all__`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131386
Approved by: https://github.com/albanD
Fixes#130284Fixes#130653
- Add `torch.library.register_vmap` to custom ops
- Add `register_vmap` for operators in ops in custom_op_db.
- Make `torch.autograd.Function` support kwarg-only kwargs for vmap
- test operators in op_db with `tests/test_vmap`.
- change `test_vmap` to allow custom `out_dim` and allow "None" in `out_dim` when testing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130589
Approved by: https://github.com/zou3519
Fixes#130284Fixes#130653
- Add `torch.library.register_vmap` to custom ops
- Add `register_vmap` for operators in ops in custom_op_db.
- Make `torch.autograd.Function` support kwarg-only kwargs for vmap
- test operators in op_db with `tests/test_vmap`.
- change `test_vmap` to allow custom `out_dim` and allow "None" in `out_dim` when testing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130589
Approved by: https://github.com/zou3519
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
If a user accesses an OpOverloadPacket, then creates a new OpOverload,
then uses the OpOverloadPacket, the new OpOverload never gets hit. This
is because OpOverloadPacket caches OpOverloads when it is constructed.
This PR fixes the problem by "refreshing" the OpOverloadPacket if a new
OpOverload gets constructed and the OpOverloadPacket exists.
Test Plan:
- new tests
This is the third land attempt. The first one was reverted for breaking
internal tests, the second was reverted for being erroneously suspected
of causing a perf regression.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128000
Approved by: https://github.com/albanD
Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
Resolves#126888
- #126888
This PR is split from PR #126898.
- #126898
------
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127689
Approved by: https://github.com/Skylion007
Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
UPDATE: Use `FutureWarning` instead of `DeprecationWarning`.
Resolves#126888
- #126888
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126898
Approved by: https://github.com/albanD
If a user accesses an OpOverloadPacket, then creates a new OpOverload,
then uses the OpOverloadPacket, the new OpOverload never gets hit. This
is because OpOverloadPacket caches OpOverloads when it is constructed.
This PR fixes the problem by "refreshing" the OpOverloadPacket if a new
OpOverload gets constructed and the OpOverloadPacket exists.
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126863
Approved by: https://github.com/albanD
torch.library.register_fake reports the python module the fake impl is
located in. This is used to check against
`m.set_python_module("foo.bar")` calls in C++.
The module reporting logic was wrong in most cases. This PR fixes it.
Test Plan:
- exhaustive tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125037
Approved by: https://github.com/williamwen42
