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Implemented CUBLAS-style MXFP scale factor derivation, with test cases. #1835
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/1835
Note: Links to docs will display an error until the docs builds have been completed. This comment was automatically generated by Dr. CI and updates every 15 minutes. |
Hi @frsun-nvda! Thank you for your pull request and welcome to our community. Action RequiredIn order to merge any pull request (code, docs, etc.), we require contributors to sign our Contributor License Agreement, and we don't seem to have one on file for you. ProcessIn order for us to review and merge your suggested changes, please sign at https://code.facebook.com/cla. If you are contributing on behalf of someone else (eg your employer), the individual CLA may not be sufficient and your employer may need to sign the corporate CLA. Once the CLA is signed, our tooling will perform checks and validations. Afterwards, the pull request will be tagged with If you have received this in error or have any questions, please contact us at [email protected]. Thanks! |
@@ -79,6 +79,49 @@ class ScaleCalculationMode(Enum): | |||
FLOOR = auto() | |||
CEIL = auto() | |||
EVEN = auto() | |||
CUBLAS_CEIL = auto() |
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nice! maybe we can also update the docblock on L77 to include this one?
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Done. Thanks for catching it.
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this is awesome, thank you for adding this! As long as CI passes, lgtm. Also I did not check the test cases for numerical accuracy - trusting that they are correct :)
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sorry, would you mind accepting the CLA? Then we can run CI and get this in.
Looks great |
Forwarded Frank the CLA procedure and hopefully we can get him through ASAP |
Implemented CUBLAS-style MXFP scale factor derivation, with test cases.
ScaleCalculationMode.CUBLAS_CEIL
nan
,denorm
andnormal
cases.