I’m having difficulties understanding how to declare the forward pass of a function that computes steps of a dynamical system one at a time (each step depends on the previous value). At the moment I’m declaring an empty tensor and then use a loop to update the values, although autograd is not able to compute the gradients as it detects in-place operations.
I have created a snippet of code to show the function and reproduce the error using the
Any help with making this work or suggesting alternatives would be greatly appreciated.
import torch from torch import nn class Dyn(nn.Module): def __init__(self): super(Dyn, self).__init__() def forward(self, z0, params): with torch.no_grad(): out = torch.empty(10,2, requires_grad=True) out = z0 for i in range(9): out[i+1] = out[i] + 0.5*torch.mv(params.view(2,2), out[i]) return out #return (z0+0.5*torch.mv(params.view(2,2), z0)).repeat(10,1) z1 = torch.Tensor([-1., -1.]) z0 = torch.tensor([0.6, 0.3], requires_grad=True) params = torch.randn(4, requires_grad=True)/2. dyn = Dyn() sol = torch.tensor([[-0.3600, 0.5700], [-0.6270, 0.3615], [-0.7764, 0.0299], [-0.7525, -0.3598], [-0.5350, -0.7181], [-0.1493, -0.9497], [ 0.3330, -0.9768], [ 0.8048, -0.7614], [ 1.1453, -0.3210], [ 1.2485, 0.2677]]) pred = dyn(z0, params) torch.autograd.grad(pred-sol, params, grad_outputs=torch.ones(10,2))