[QST] How to define a new custom kernel

What is your question?
Hi, I want to define a new convolution2d kernel Fprop based on the bare convolution kernel.

My idea is to modify the weights or filters before the convolution operation.

I would need a little help to know or understand how Cutlass allows the definition of a new custom kernel and how it is executed later in the GPU (my GPU is a Tesla v100).

I am a little confused by looking at examples like the number 9 on how I must perform the modification of the weights or filters because it seems only to execute templates to other templates like default convolution but no operation is performed.

Any help or link to a clarifier example on how to program my kernel and later use it or a base explanation would be appreciated.

Thank you.

Start with examples/09_turing_tensorop_conv2dfprop and include/cutlass/conv/kernel/default_conv2d.h, then trace how the template composition leads to execution on the GPU. Done would be a clear explanation or example showing where custom weight or filter modification belongs and how the resulting kernel is used.