pytorch bug?

bug.py

import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim


class WorkingModule(nn.Module):

    def __init__(self):
        super(WorkingModule, self).__init__()

        self.params = nn.Parameter(torch.randn(5,5))

    def forward(self, sequence):
        score = autograd.Variable( torch.Tensor([0]) )
        for i in xrange(len(sequence) - 1):
            score = score + self.params[sequence[i+1], sequence[i]]
        return score


class BuggyModule(nn.Module):

    def __init__(self):
        super(BuggyModule, self).__init__()

        self.params = nn.Parameter(torch.randn(5,5))

    def forward(self, sequence):
        sequence = autograd.Variable(sequence) # Wrap in a variable (or maybe it was passed in like this)...
        score = autograd.Variable( torch.Tensor([0]) )
        for i in xrange(len(sequence) - 1):
            score = score + self.params[sequence[i+1].data, sequence[i].data]
        return score

input_seq = torch.LongTensor([ 0, 1, 2, 1, 0, 3 ])


model = WorkingModule()
score = model(input_seq)
score.backward()
print model.params.grad # Good!


model = BuggyModule()
score = model(input_seq)
score.backward()
print model.params.grad # zero!