Hi, I’m facing a bit of an issue in terms of running an algorithm on top of a computational graph. Some context, m_alpha_beta and m_beta_alpha are parameters that are a result from some neural network computation. I’m trying to run an algorithm on top of these parameters, followed by taking the loss of the output and backpropagating it to learn the network. At this stage, training is a problem because I’m getting an error of “RuntimeError: leaf variable has been moved into the graph interior”, which I suspect is due to in-place operations? My model will return m_alpha_beta_k and m_beta_alpha_k and I will calculate some cross-entropy loss based on it.
Is there any way to prevent this issue or to code it better?
def simp_min_sum_batch(self, m_alpha_beta, m_beta_alpha):
        n = m_alpha_beta.size(1)
        # Define message vectors
        # Each row belongs to one of the nodes, we have n alpha nodes and n beta nodes
        # For row i and column j, each entry will be the message vector of alpha_i to beta_j, M_{alpha_i -> beta_j} of length n,
        # where each entry of this vector is m_{alpha_i -> beta_j} (q)
        m_alpha_beta_k = torch.zeros((m_alpha_beta.size(0), m_alpha_beta.size(1), m_alpha_beta.size(2)),
                                     device=m_alpha_beta.device, requires_grad=True)
        m_beta_alpha_k = torch.zeros((m_beta_alpha.size(0), m_beta_alpha.size(1), m_beta_alpha.size(2)),
                                     device=m_beta_alpha.device, requires_grad=True)

        # Message passing
        for i in range(n):
            m_beta_alpha_k[:, :, i] = m_alpha_beta[:, i, :] - torch.max(
                torch.cat((m_alpha_beta[:, :i, :], m_alpha_beta[:, (i + 1):, :]), dim=1), dim=1)[0]
            m_alpha_beta_k[:, :, i] = m_alpha_beta[:, :, i] - torch.max(
                torch.cat((m_beta_alpha[:, :i, :], m_beta_alpha[:, (i + 1):, :]), dim=1), dim=1)[0]

        return m_alpha_beta_k, m_beta_alpha_k