I’m interested in subbing out pytorch’s default convolution function for one generated by tvm (https://github.com/dmlc/tvm 14) in order to look at potential speed-ups. Ideally, this would be part of a custom autograd Function that uses a new forward call and conv2d’s default backwards computation. However, I’m having a very hard time figuring out what to do for backward. It seems that ConvNdBackward cant be constructed from python. My other thought was to just implement the gradient myself using conv and convtranspose functions, but because these are part of nn they don’t seem to be compatible inside of Functions. I’m at a loss for how to proceed here, any ideas on how I can get a working conv backwards function with my own custom forward function?