I have my own quantization operator written in cuda (according to Custom C++ and CUDA Extensions — PyTorch Tutorials 1.11.0+cu102 documentation) and it works fine. But I need to use ASP (automatic sparsity package) which uses torch.fx, which throws error at my quantization operator
TypeError: fpquantizer_(): incompatible function arguments. The following argument types are supported:
1. (arg0: at::Tensor) → None
Invoked with: Proxy(getattr_1)
So far as I understood from torch.fx manual(hopefully correctly), I need to decorate my operator with torch.fx.wrap.
py::object fx_wrap = py::module::import(“torch.fx”).attr(“wrap”);
m.def(“fpquantizer_”, &fp_conversion_, “inplace quantization and storing in a float array”);
m.attr(“fpquantizer_”) = fx_wrap(m.attr(“fpquantizer_”));
which seems to do job of decorating operator, but torch.fx.wrap have problem with c++ function itself, it doesn’t have
attribute and also
is False, so torch.fx.wrap throws an error
ImportError: AssertionError: fn_or_name must be a global function or string name
Is there any way how to handle it? Is my approach correct?
Thanks a lot