Thank you for addressing this: #300
However, there are still stalls in the compute stream after the decompressions (in blue):
This seems to be some type of status synchronisation, that - if you know that the data is fine because you have compressed it yourself - shouldn't be necessary. Does the python API expose that currently?
I haven't found it.
Claude has come up with a way to circumvent it by using the C API directly, but it's not nice code: https://github.com/dxqb/OneTrainer/blob/5a4dc6a44e7a7b4887c8ec6cea6ac9a6a14b21f0/modules/util/nvcomp_util_lowlevel.py
opening this separately, because it might get lost here #300 (comment) and this is now merged into production code: Nerogar/OneTrainer#1630
Thank you for addressing this: #300
However, there are still stalls in the compute stream after the decompressions (in blue):
This seems to be some type of status synchronisation, that - if you know that the data is fine because you have compressed it yourself - shouldn't be necessary. Does the python API expose that currently?
I haven't found it.
Claude has come up with a way to circumvent it by using the C API directly, but it's not nice code: https://github.com/dxqb/OneTrainer/blob/5a4dc6a44e7a7b4887c8ec6cea6ac9a6a14b21f0/modules/util/nvcomp_util_lowlevel.py
opening this separately, because it might get lost here #300 (comment) and this is now merged into production code: Nerogar/OneTrainer#1630