Hi,

I’m using Gpytorch to implement a multi output regression, but i have an error when i try to use a Periodic kernel.

**RuntimeError: NaNs encountered when trying to perform matrix-vector multiplication**

It seems to work with the RBF kernel but as soon as i use the Periodic kernel, it doesn’t work anymore.

The definition of the class is the following :

```
class MultitaskGPModel(gpytorch.models.ExactGP):
def __init__(self, train_x, train_y, likelihood):
super(MultitaskGPModel, self).__init__(train_x, train_y, likelihood)
self.mean_module = gpytorch.means.MultitaskMean(
gpytorch.means.ConstantMean(),num_tasks=5)
self.covar_module = gpytorch.kernels.MultitaskKernel(
gpytorch.kernels.PeriodicKernel(),num_tasks=5, rank=1)
def forward(self, x):
mean_x = self.mean_module(x)
covar_x = self.covar_module(x)
return gpytorch.distributions.MultitaskMultivariateNormal(mean_x, covar_x)
likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(num_tasks=5)
model = MultitaskGPModel(x_train, y_train, likelihood)
```

and my for loop is :

```
for i in range(n_iter):
optimizer.zero_grad()
output = model(x_train)
loss = -mll(output, y_train)
loss.backward()
print('Iter %d/%d - Loss: %.3f' % (i + 1, n_iter, loss.item()))
optimizer.step()
```

**The error occurs when i call optimizer.step()**, and i think that it is related to the differentiation of the variables and to the require_grat attribute, so i have tried to enable the grad in the data like that :

```
x_train = torch.tensor(x_train, requires_grad=True).float()
y_train = torch.tensor(y_train, requires_grad=True).float()
x_test = torch.tensor(x_test, requires_grad=True).float()
y_test = torch.tensor(y_test, requires_grad=True).float()
```

But it hasn’t changed anything…

It would be really nice to have some help ! Thank you so much !

PS : Pytorch version 1.0