Skip to content

Re-reapply "Add vectorized_math.h (#11204)", "Add optimized_portable_kernels test (#11205)", and "Add vectorization in elementwise_util (#9432)" #11802

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: gh/swolchok/465/base
Choose a base branch
from

Conversation

swolchok
Copy link
Contributor

@swolchok swolchok commented Jun 18, 2025

Stack from ghstack (oldest at bottom):

Stack was reverted (again! I bypassed some broken jobs and it turns
out this re-broke them) due to internal CI failures. Reapplying as an
exported internal diff so that we make sure to catch any more of
those.

New fixes in first reapply:

  • 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)

New fixes in second reapply:

  • So far, none; D76843086 and D76857541 fix things up in preparation for this diff. (some rebase conflict fixes though)

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
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.

Differential Revision: D76754826

…kernels test (#11205)", and "Add vectorization in elementwise_util (#9432)"

Stack was reverted (again! I bypassed some broken jobs and it turns
out this re-broke them) due to internal CI failures. Reapplying as an
exported internal diff so that we make sure to catch any more of
those.

New fixes in first reapply:
- 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)

New fixes in second reapply:
- So far, none; D76843086 and D76857541 fix things up in preparation for this diff. (some rebase conflict fixes though)

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.

Differential Revision: [D76754826](https://our.internmc.facebook.com/intern/diff/D76754826/)

[ghstack-poisoned]
Copy link

pytorch-bot bot commented Jun 18, 2025

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11802

Note: Links to docs will display an error until the docs builds have been completed.

✅ You can merge normally! (1 Unrelated Failure)

As of commit 1c7d063 with merge base 44d2643 (image):

BROKEN TRUNK - The following job failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

This comment was automatically generated by Dr. CI and updates every 15 minutes.

swolchok added a commit that referenced this pull request Jun 18, 2025
…kernels test (#11205)", and "Add vectorization in elementwise_util (#9432)"

Stack was reverted (again! I bypassed some broken jobs and it turns
out this re-broke them) due to internal CI failures. Reapplying as an
exported internal diff so that we make sure to catch any more of
those.

New fixes in first reapply:
- 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)

New fixes in second reapply:
- So far, none; D76843086 and D76857541 fix things up in preparation for this diff. (some rebase conflict fixes though)

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.

Differential Revision: [D76754826](https://our.internmc.facebook.com/intern/diff/D76754826/)

ghstack-source-id: 291370586
Pull Request resolved: #11802
@facebook-github-bot facebook-github-bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Jun 18, 2025
@facebook-github-bot
Copy link
Contributor

This pull request was exported from Phabricator. Differential Revision: D76754826

@swolchok swolchok added the release notes: ops & kernels Changes to the opset and any new / changed kernel implementations label Jun 20, 2025
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. fb-exported release notes: ops & kernels Changes to the opset and any new / changed kernel implementations
Projects
None yet
Development

Successfully merging this pull request may close these issues.

2 participants