I am trying to re-execute a GitHub project on my computer for recommendation using embedding, the goal is to first embed the user and item present in the movieLens dataset, and then use the inner product to predict a rating, when I finished the integration of all components, I got an error in the training.

Code:

from lightfm.datasets import fetch_movielens
movielens = fetch_movielens()
ratings_train, ratings_test = movielens['train'], movielens['test']
def _binarize(dataset):

    dataset = dataset.copy()

    dataset.data = (dataset.data >= 0.0).astype(np.float32)
    dataset = dataset.tocsr()
    dataset.eliminate_zeros()

    return dataset.tocoo()
train, test = _binarize(movielens['train']), _binarize(movielens['test'])
class ScaledEmbedding(nn.Embedding):
    """ Change the scale from normal to [0,1/embedding_dim] """
    def reset_parameters(self):
        self.weight.data.normal_(0, 1.0 / self.embedding_dim)
        if self.padding_idx is not None:
            self.weight.data[self.padding_idx].fill_(0)


class ZeroEmbedding(nn.Embedding):

    def reset_parameters(self):
        self.weight.data.zero_()
        if self.padding_idx is not None:
            self.weight.data[self.padding_idx].fill_(0)
class BilinearNet(nn.Module):

    def __init__(self, num_users, num_items, embedding_dim, sparse=False):
        super().__init__()

        self.embedding_dim = embedding_dim

        self.user_embeddings = ScaledEmbedding(num_users, embedding_dim,
                                               sparse=sparse)
        self.item_embeddings = ScaledEmbedding(num_items, embedding_dim,
                                               sparse=sparse)
        self.user_biases = ZeroEmbedding(num_users, 1, sparse=sparse)
        self.item_biases = ZeroEmbedding(num_items, 1, sparse=sparse)

    def forward(self, user_ids, item_ids):

        user_embedding = self.user_embeddings(user_ids)
        item_embedding = self.item_embeddings(item_ids)

        user_embedding = user_embedding.view(-1, self.embedding_dim)
        item_embedding = item_embedding.view(-1, self.embedding_dim)

        user_bias = self.user_biases(user_ids).view(-1, 1)
        item_bias = self.item_biases(item_ids).view(-1, 1)

        dot = (user_embedding * item_embedding).sum(1)

        return dot + user_bias + item_bias

def pointwise_loss(net,users, items, ratings, num_items):

    negatives = Variable(
            torch.from_numpy(np.random.randint(0,
                                               num_items,
                                                  len(users))).cuda()
    )

    positives_loss = (1.0 - torch.sigmoid(net(users, items)))
    negatives_loss = torch.sigmoid(net(users, negatives))

    return torch.cat([positives_loss, negatives_loss]).mean()

embedding_dim = 128
minibatch_size = 1024
n_iter = 10
l2=0.0
sparse = True

num_users, num_items = train.shape
net = BilinearNet(num_users,
                            num_items,
                            embedding_dim,
                            sparse=sparse).cuda()

optimizer = optim.Adagrad(net.parameters(),
                              weight_decay=l2)
for epoch_num in range(n_iter):

    users, items, ratings = shuffle(train)

    user_ids_tensor = torch.from_numpy(users).cuda()
    item_ids_tensor = torch.from_numpy(items).cuda()
    ratings_tensor = torch.from_numpy(ratings).cuda()

    epoch_loss = 0.0

    for (batch_user,
         batch_item,
         batch_ratings) in zip(_minibatch(user_ids_tensor,
                                          minibatch_size),
                               _minibatch(item_ids_tensor,
                                          minibatch_size),
                               _minibatch(ratings_tensor,
                                          minibatch_size)):

        user_var = Variable(batch_user)
        item_var = Variable(batch_item)
        ratings_var = Variable(batch_ratings)
        optimizer.zero_grad()
        loss = pointwise_loss(net,user_var, item_var, ratings_var, num_items)
        epoch_loss += loss.data[0]
        loss.backward()
        optimizer.step()
        print('Epoch {}: loss {}'.format(epoch_num, epoch_loss))

Error:

RuntimeError Traceback (most recent call last) <ipython-input-87-dcd04440363f> in <module>()
             22         ratings_var = Variable(batch_ratings)
             23         optimizer.zero_grad()
        ---> 24         loss = pointwise_loss(net,user_var, item_var, ratings_var, num_items)
             25         epoch_loss += loss.data[0]
             26         loss.backward()

        <ipython-input-86-679e10f637a5> in pointwise_loss(net, users, items, ratings, num_items)
              8 
              9     positives_loss = (1.0 - torch.sigmoid(net(users, items)))
        ---> 10     negatives_loss = torch.sigmoid(net(users, negatives))
             11 
             12     return torch.cat([positives_loss, negatives_loss]).mean()

        ~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in
        __call__(self, *input, **kwargs)
            491             result = self._slow_forward(*input, **kwargs)
            492         else:
        --> 493             result = self.forward(*input, **kwargs)
            494         for hook in self._forward_hooks.values():
            495             hook_result = hook(self, input, result)

        <ipython-input-58-3946abf81d81> in forward(self, user_ids, item_ids)
             16 
             17         user_embedding = self.user_embeddings(user_ids)
        ---> 18         item_embedding = self.item_embeddings(item_ids)
             19 
             20         user_embedding = user_embedding.view(-1, self.embedding_dim)

        ~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in
        __call__(self, *input, **kwargs)
            491             result = self._slow_forward(*input, **kwargs)
            492         else:
        --> 493             result = self.forward(*input, **kwargs)
            494         for hook in self._forward_hooks.values():
            495             hook_result = hook(self, input, result)

        ~\Anaconda3\lib\site-packages\torch\nn\modules\sparse.py in forward(self, input)
            115         return F.embedding(
            116             input, self.weight, self.padding_idx, self.max_norm,
        --> 117             self.norm_type, self.scale_grad_by_freq, self.sparse)
            118 
            119     def extra_repr(self):

        ~\Anaconda3\lib\site-packages\torch\nn\functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)    1504         # remove once script supports set_grad_enabled    1505        
        _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
        -> 1506     return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)    1507     1508 

        RuntimeError: Expected tensor for argument #1 'indices' to have scalar type Long; but got CUDAType instead (while checking arguments for embedding)

For Error like:

Runtime Error: Expected tensor for argument #1 'indices' to have scalar type Long; but got CUDAType instead (while checking arguments for embedding)

b_input_ids = torch.tensor(b_input_ids).to(device).long()

I initiated a clean environment (using docker container), and I tried both pip and anaconda installation. After installing current dgl 0.2 and the dependencies for RGCN model, everything works fine.

What's the command you used to install all the packages like the DGL as well as the dependencies?