Automatic mixed precision#2359
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I'm still working on the changes model.cpp and the amp.{cpp,cu} files. I suppose I should also read the .py file too. But just a few small things.
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Still looking good after the rebase/recent commits. I touched the memory_profile callback and added an example of how to use the SwitchDispatcher. I found it a bit clunkier than I remembered to use, but I think I have some ideas to improve it in future H2 versions (in particular, I want to make it possible to just use a lambda as the functor).
Also, please clang-format the whole PR.
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This allows specifying float as a datatype even when it would not be the default.
- Permit using tensor cores by default, if no data conversion is needed. - Change "no tensor ops" mode to prevent TF32 conversion. - Add mode to allow conversion.
This is a hack that we should revisit.
This only matters on cuDNN, where we want "pseudo half".
Co-authored-by: Tom Benson <benson31@llnl.gov>
Co-authored-by: Tom Benson <benson31@llnl.gov>
This includes a fix from @tbennun.
DistData also includes the datatype, and so spuriously indicated things were sharded when mixing datatypes.
Co-authored-by: Tom Benson <benson31@llnl.gov>
Co-authored-by: Tom Benson <benson31@llnl.gov>
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This is an initial pass at AMP. It appears correct in the cases I've tested.
It has some particularly pointy bits in batchnorm and in gradient unscaling.