Caluclation error in gpytorch about lambo HOT 8 CLOSED

    def log_prob(self, X):
        print("\n\nLKJCovariancePrior.log_prob, input:\n", X)
        marginal_var = torch.diagonal(X, dim1=-2, dim2=-1)
        print("\nLKJCovariancePrior.log_prob, marginal_var:\n", marginal_var)
        if not torch.all(marginal_var >= 0):
            raise ValueError("Variance(s) cannot be negative")
        marginal_sd = marginal_var.sqrt()
        print("\nLKJCovariancePrior.log_prob, marginal_sd:\n", marginal_sd)
        sd_diag_mat = _batch_form_diag(1 / marginal_sd)
        print("\nLKJCovariancePrior.log_prob, sd_diag_mat:\n", sd_diag_mat)
        correlations = torch.matmul(torch.matmul(sd_diag_mat, X), sd_diag_mat)
        print("\nLKJCovariancePrior.log_prob, corrs:\n", correlations, "\n")
        log_prob_corr = self.correlation_prior.log_prob(correlations)
        log_prob_sd = self.sd_prior.log_prob(marginal_sd)
        return log_prob_corr + log_prob_sd

LKJCovariancePrior.log_prob, input:
 tensor([[ 2.7795,  0.3485,  0.0401],
        [ 0.3485,  2.1543, -2.6390],
        [ 0.0401, -2.6390,  5.3450]], device='cuda:0')

LKJCovariancePrior.log_prob, marginal_var:
 tensor([2.7795, 2.1543, 5.3450], device='cuda:0')

LKJCovariancePrior.log_prob, marginal_sd:
 tensor([1.6672, 1.4677, 2.3119], device='cuda:0')

LKJCovariancePrior.log_prob, sd_diag_mat:
 tensor([[0.5998, 0.0000, 0.0000],
        [0.0000, 0.6813, 0.0000],
        [0.0000, 0.0000, 0.4325]], device='cuda:0')

LKJCovariancePrior.log_prob, corrs:
 tensor([[ 0.9990,  0.1424,  0.0104],
        [ 0.1424,  0.9998, -0.7774],
        [ 0.0104, -0.7776,  1.0004]], device='cuda:0') 

[2022-05-10 21:36:44,990][root][ERROR] - Input is not a valid correlation matrix

LKJCovariancePrior.log_prob, corrs:
 tensor([[ 1.0000,  0.1424,  0.0104],
        [ 0.1424,  1.0000, -0.7777],
        [ 0.0104, -0.7777,  1.0000]])