Sure! Initially, the hidden states are probably initialized to zero, but then after you run a forward pass multiple times, like the hidden states accumulate. So, if you also print out some of the hidden states of your model, that may show that each time they have different values.
So, in that case, you can reinitialize the hidden states to zero and hopefully that will fix the problem.  In the tutorial 33 there is an example for resetting the hidden states:
    def init_hidden(self):
        # Before we've done anything, we dont have any hidden state.
        # Refer to the Pytorch documentation to see exactly
        # why they have this dimensionality.
        # The axes semantics are (num_layers, minibatch_size, hidden_dim)
        return (torch.zeros(1, 1, self.hidden_dim),
                torch.zeros(1, 1, self.hidden_dim))

then, you can call init_hidden() function in order to initialize the hidden states:
# Also, we need to clear out the hidden state of the LSTM,
# detaching it from its history on the last instance.
model.hidden = model.init_hidden()
## (copied from PyTorch tutorial)