For example, I have a 2D tensor Y of size MxN and another 2D tensor X of size Mx3, while N>3.
I want to compute the gradient of Y with respect to X.
I can compute as follows:
for i in range(Y.shape): dY_dX[i,:,:] = grad(Y[:,i],X)
I have the following gradient function that I am using:
def grad(outputs, inputs): return torch.autograd.grad(outputs, inputs, grad_outputs=torch.ones_like(outputs), create_graph=True)
The shape of dY_dX should be NxMx3. With the above-mentioned operation, it is possible to compute the gradients, however, it is computationally expensive. Can anyone recommend a possible faster way to compute the same without the loop?