Hi,
My usecase is to take a FP32 pre-trained PyTorch model, convert it to FP16 (both weights and computation that is amenable to Fp16 computation) and then trace the model. Later, I will read this model in TVM (a deep learning compiler) and use it to generate code for ARM servers. ARM servers have instructions to speed up FP16 computation. Please let me know if this is possible today.
Note that I need to use Torchscript (as TVM input is traced model). Also, the goal here is get speedup so my hope is that the resulting traced model has some operators (like conv2d, dense) whose inputs dtypes are in FP16.
@ptrblck