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| baseline_values = python.lltm_baseline.LLTMFunction.apply(*variables) | ||
| cpp_values = cpp.lltm.LLTMFunction.apply(*variables) | ||
| cpp_variables = [v.cpu() for v in variables] | ||
| cpp_values = torch.ops.myops.lltm(*cpp_variables) |
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It looks like it's something you already call from LLTMFunction in cpp/lltm.py, so why not keep the LLTMFunction object here as it was before and place the backend dependent code in evry module (cpp and cuda) ?
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Since this has been migrated into an operator that is registered via dispatcher API, python implementation of LLTMFunction no longer exists (see cpp/lltm.cpp:115).
If I understand the intent of the code correctly, this use of LLTMFunction was a way to directly call the C++ forward implementation via python, but now we are using the dispatcher API, and can call the forward op directly. (I think ideally the baseline solution would be updated to match this pattern too.)
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C++ LLTMFunction impl is primarily used for re-dispatching from autograd op, as per: https://pytorch.org/tutorials/advanced/dispatcher.html#adding-autograd-support
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| options = parser.parse_args() | ||
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| LIB_EXT = torch.utils.cpp_extension.LIB_EXT |
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Looks like all the changes you made in benchmark.py are not used.
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The op is called from LLTM module class' forward() See {cpp|cuda}/lltm.py
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Hey, thanks for putting me as reviewer. Waiting for the tutorial part to better understand the dispatcher API and I'll finish my review ASAP. Overall, looks good to me, even though the part where you load the cpp module with |
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Custom C++ and CUDA Extensions tutorial is updated to use dispatcher API.
Updating tutorial to support tutorial changes as part of:
pytorch/tutorials#1495