Not only is this change usually shorter and more readable, it also can yield better performance. size() is not always a constant time operation (such as on LinkedLists), but empty() always is.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93236
Approved by: https://github.com/malfet
This reverts commit bb4e96c9644a034e593085026b781ee78a4d6a77.
Reverted https://github.com/pytorch/pytorch/pull/84675 on behalf of https://github.com/osalpekar due to causing asan xplat link-time errors like ld.lld: error: undefined symbol: torch::jit::has_jit_decomposition(c10::FunctionSchema const&)
This PR:
- updates forward AD codegen in core to generate code that tries calling into decompositions registered to jit when
- (1) the function is not in-place or out variant
- AND (2) the function is differentiable (requires_derivative=True)
- AND (3) there are no forward AD formulas registered
- To simplify things we always generating the if/else (as long as (1) is true), but generate 'false' when either (2) or (3) are false.
- removes the mechanism from functorch
- (follow up) some functorch tests should be updated here so they no longer have to compute the Jacobian with vjp
- factors out some logic to generate the any_has_forward_grad condition
- (bc-breaking) when TensorList inputs unexpectedly have forward grad, the error will no longer contain the name
See https://github.com/pytorch/pytorch/pull/84151#issuecomment-1238519247 for codegen output and more discussion.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84151
Approved by: https://github.com/samdow, https://github.com/albanD, https://github.com/zou3519
- Allow registering custom decompositions
- Add easier API for invoking decompositions
- Shorten API names (no users yet)
I am doing these as one pr because they are fairly short/simple and because github first does not support ghstack yet.
cc @Chillee @zou3519
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76252
Approved by: https://github.com/davidberard98
Summary:
[Comment](https://github.com/pytorch/pytorch/pull/62445/files#r680132022) claims, it got added for consistency with top level CMakeLists.txt, but `-Wno-unused-variable` is not mentioned there.
Modify violations in 50+ files that were added in the interim by either removing unused variables, or decorating the code with `C10_UNUSED` if local variable is likely used to extend object lifetime until the end of the block.
Caused preventable revert in https://github.com/pytorch/pytorch/pull/72633#issuecomment-1092300787
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75538
Reviewed By: anjali411
Differential Revision: D35747333
Pulled By: malfet
fbshipit-source-id: 3fc5828e44a4c05ba0e89e92613e6ebbdb260626
(cherry picked from commit c179fba21cfa2a0093fad50ccad5a22dd7cff52c)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74186
Make the execution settings mutable on function_impl so that we can set it for running op decompositions. Add mapping to function objects and show example in test of executing op decompositions.
Test Plan: Imported from OSS
Reviewed By: gchanan
Differential Revision: D34938125
Pulled By: eellison
fbshipit-source-id: adf108b2f6c1bd166910c6d7b94245661d67ce0d
(cherry picked from commit 9957e33803002d9e71abe4ff802769270b6960d3)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73938
This is a first step in porting and making usable all of the decompositions defined in [functorch](https://github.com/pytorch/functorch/blob/main/functorch/_src/decompositions.py#L349) in core and in JIT as well as C++.
The decompositions are defined in python, scripted and inlined, and then serialized as C++ code which TorchScript can parse. The workflow is edit python decomposition file then run [tools/codegen/decompositions/gen_jit_decompositions.py](https://github.com/pytorch/pytorch/pull/73938/files#diff-6adef2116be233c3524e3b583e373ab0ffc9169beb6c1f6d96b5d0385e75afa1).
Decompositions are mapped to their corresponding aten schemas via the schema in their python def. This allows multiple decompositions for an overloaded op like `aten.var` (shown here in the example).
This is just a first PR, i'm sure there will be many follows ups such as:
- making these runnable in C++ with simple executor
- porting over more decompositions from AOT Autograd
- Using opinfos / more robust testing
- Categorizing decompositions
- Hooking in decompositions at various points of JIT execution
Test Plan: Imported from OSS
Reviewed By: gchanan
Differential Revision: D34938126
Pulled By: eellison
fbshipit-source-id: 9559a7cb731982e3a726f2f95af498b84fb09c13
(cherry picked from commit a4e0e748791e378e7e12a9dd0b63fb3c62dc1890)
