We are  working on interactive visualization of multi-dimensional data
Usually O(2-7) dimensions (still dense) Currently we are using numpy implementation  for multidimensional histogram filling , slicing,  summing:

creating NDimensional histogram  once:

H, axis = np.histogramdd(inputArray.values, bins=bins, range=hRange)


querying interactively - many times

y = np.sum(H[hSliceLocal], axis=axis)

I would like to use PyTorch (or Numba) to get faster GPU implementation
My questions are following (my current Python experience is limitted):

Before investing time, which speed-up factor do  you expect in histogramm filling resp. slicing ?
Did PyTorch team consider providing implementation such as  in numpy? We would be happy to contribute, but our experience is limited

I’m aware of the discussion (from 2017) in

  
    
    
    Histogram function in pytorch 
  
  
    Is there any function in pytorch like numpy.histogram?
  


related to histogramming in 1D  resp. hack for 3D.
I’m looking for equivalent of the numpy.histogramdd.html (CPU/GPU)
https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogramdd.html 12