I’ve noticed that the example for a custom linear function 54 fails, while this tutorial 65 works fine. The difference is in the @staticmethod, and using variables for grad computation where tensors should be used.
The first example changed to make it work:
class Linear(torch.autograd.Function):
    def forward(ctx, input, weight, bias=None):
        ctx.save_for_backward(input, weight, bias)
        output = input.mm(weight.t())
        if bias is not None:
            output += bias.unsqueeze(0).expand_as(output)
        return output

    def backward(ctx, grad_output):
        input, weight, bias = ctx.saved_tensors
        grad_input = grad_weight = grad_bias = None

        if ctx.needs_input_grad[0]:
            grad_input = grad_output.mm(weight)
        if ctx.needs_input_grad[1]:
            grad_weight = grad_output.t().mm(input)
        if bias is not None and ctx.needs_input_grad[2]:
            grad_bias = grad_output.sum(0).squeeze(0)

        return grad_input, grad_weight, grad_bias

Also, custom functions don’t seem to take keyword arguments (in forward). Maybe the docs could mention that.
Still loving PyTorch,
Max