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Gate division bitwise numerics under a flag #165566
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[ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/165566
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 3821222 with merge base f58f301 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov coconutruben [ghstack-poisoned]
#164144 ensures that division for compile is bitwise equivalent with eager. However, in #164301, the kernel performance is regressed. On B200: With standard triton `/`: 6511 GB/s With triton `div_rn`: 4692 GB/s Further investigation is required for the generated PTX to see why there is such a large slowdown. For now, enable bitwise equivalent results under `TORCHINDUCTOR_EMULATE_DIVISION_ROUNDING` similar to emulate_precision_cast cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov coconutruben [ghstack-poisoned]
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too bad this is slow. is it crazy to change pytorch eager to use approx ?
@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
Stack from ghstack (oldest at bottom):
#164144 ensures that division for compile is bitwise equivalent with eager. However, in #164301, the kernel performance is regressed.
On B200:
With standard triton
/
:6511 GB/s
With triton
div_rn
:4692 GB/s
Further investigation is required for the generated PTX to see why there is such a large slowdown. For now, enable bitwise equivalent results under
TORCHINDUCTOR_EMULATE_DIVISION_ROUNDING
similar to emulate_precision_castcc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben