Grad is None for leaf tensor

autograd
1

I am trying to compare the empirical distributions of elements of two tensors by computing a coarse histogram of the two tensors (torch.histc is not differentiable). I want to compute the gradients of a particular loss function measuring the dissimilarity between the two distributions w.r.t the input tensor which is a leaf variable. However the returned grad is always None. Below is a minimal example of what I’m trying to do. Any suggestions?

import torch

def hist_loss(X, Y, bins: int=10):
   
    X = X.view(-1)
    Y = Y.view(-1)
    xmin, xmax = X.min(), X.max()
    ymin, ymax = Y.min(), Y.max()
    xstep = (xmax - xmin)/bins
    ystep = (ymax - ymin)/bins
    hist_loss = torch.zeros(1, requires_grad=True).to(X)
    zero = torch.zeros(1, requires_grad=True).to(X)
    one = torch.ones(1, requires_grad=True).to(X)
 
    n = len(X)
    for i in range(bins):
        # x is the empirical distribution of the X
        # y is the empirical distribution of the Y
        x = torch.sum(torch.where(((X>xmin+i*xstep) & (X<xmin+(i+1)*xstep)), one, zero))
        y = torch.sum(torch.where(((Y>ymin+i*ystep) & (Y<ymin+(i+1)*ystep)), one, zero))
        hist_loss = hist_loss + ((x-y)/n)**2

    hist_loss    = torch.sqrt(hist_loss)

    return hist_loss

x = torch.randn(1000, requires_grad=True)
y = torch.randn(1000)
z = hist_loss(x, y)
z.backward()
x.grad is None