Reapply "Add vectorized_math.h (#11204)", "Add optimized_portable_kernels test (#11205)", and "Add vectorization in elementwise_util (#9432)" #11665
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Pull Request resolved: #11603 To support passing ET_USE_PYTORCH_HEADERS only when exceptions are enabled. ghstack-source-id: 290334875 @exported-using-ghexport Differential Revision: [D76470039](https://our.internmc.facebook.com/intern/diff/D76470039/)
…nels test (#11205)", and "Add vectorization in elementwise_util (#9432)" Pull Request resolved: #11604 Stack was reverted due to internal CI failures. Reapplying as an exported internal diff so that we make sure to catch any more of those. New fixes: - straightforward op_sub build fixes - s/EXPECT_EQ/EXPECT_FLOAT_EQ/ in vectorized_math_test - define ET_USE_PYTORCH_HEADERS to detect whether exceptions are enabled, and use `#if` instead of `#ifdef` to check the macro so that we don't use PyTorch headers if exceptions are disabled. (otherwise, we might have problems with e.g. TORCH_CHECK) Original summary for #11204: Set of math functions that work on both scalars and at::vec::Vectorized, to be used in #9432. Original summary for #11205: Make sure we test the optimized versions of portable kernels even if they are shadowed by optimized implementations. Intended to support #9432. Original summary for #9432: This is a first cut at #9241 . In this PR I've vectorized a small initial set of ops: atan2, clamp, fmod_Scalar, maximum, minimum, mul, pow, and sigmoid. In addition, the following ops should have gotten vectorized automatically because they already used generic lambdas: add, div, rsub, sub. I've left covering ops that use the `unary_ufunc_*` utilities in [pattern.h](https://github.com/pytorch/executorch/blob/main/kernels/portable/cpu/pattern/pattern.h) for a follow-up push, because pattern.h and elementwise_util need some work before we can migrate pattern.h's utilities to be backed by elementwise_util. This PR adds an interesting testing problem: in theory, *all* operators might need test cases long enough to tickle vectorization, because we might accidentally vectorize ops unexpectedly and break their lambdas due to anticipated differences in semantics. I address this issue by using Vectorized for the scalar prologue/epilogue in debug mode (we run tests in both debug and release) so that we can detect broken lambdas. I additionally intentionally introduced a bug in the vectorized path in elementwise_util and manually verified that we saw test failures for each vectorized op called out above. ghstack-source-id: 290334876 Differential Revision: [D76467389](https://our.internmc.facebook.com/intern/diff/D76467389/)
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11665
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New Failure, 3 Unrelated FailuresAs of commit 4349a48 with merge base d660bde ( NEW FAILURE - The following job has failed:
FLAKY - The following job failed but was likely due to flakiness present on trunk:
BROKEN TRUNK - The following jobs failed but was present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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this one was reverted again internally, abandoning, will reapply internally and sync when it sticks internally |
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #11604 by @swolchok
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/swolchok/457/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/swolchok/457/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/swolchok/456/orig
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/swolchok/457/orig
@diff-train-skip-merge