Problems while using pretrained vectors for seq2seq

I am following a seq2seq tutorial here.

I want to use pretrained vectors. I have edited the code to get the vector of the word rather than index. Following is the code:

#This piece of code loads the vectors from a json file {'word':[vector]..}
class Lang:
    def __init__(self, name, savedVectorsFile):
        def getSavedVectors(filename):
            import json
            word2vec = {}
            with open(filename) as json_data:
                word2vec = json.load(json_data)
            return word2vec = name
        self.word2vector = {}
        self.word2count = {}
        self.index2word = {0: "SOS", 1: "EOS"}
        self.n_words = 2  # Count SOS and EOS

        self.get_saved_vector = getSavedVectors(savedVectorsFile)
        self.word2vector['unknown'] = self.get_saved_vector['unknown']

    def addSentence(self, sentence):
        for word in sentence.split(' '):

    def addWord(self, word):
        if word not in self.word2vector:
            self.word2vector[word] = self.get_saved_vector[word]
            self.word2count[word] = 1
            self.index2word[self.n_words] = word
            self.n_words += 1
            self.word2count[word] += 1
# this piece deals with returning a vector of given word, all vectors are just concatenated into one giant vector
def vectorFromSentence(lang, sentence):
    vectors = []
    for word in sentence.split(' '):
        if word not in lang.word2vector:
            vectors += (lang.word2vector["unknown"])
            vectors += (lang.word2vector[word])
    return vectors
# in the train method, I am passing  a vector instead of index to the encoder
    for ei in range(0, input_length, VEC_SIZE):
        encoder_output, encoder_hidden = encoder(input_variable[ei*VEC_SIZE:(ei+1)*VEC_SIZE], encoder_hidden)
        encoder_outputs[ei] = encoder_output[0][0]

Now, I cannot figure out what should be the input size of my encoder. I tried with using VEC_SIZE instead of input_size, but to no avail.

I get this error that

TypeError: torch.index_select received an invalid combination of arguments - got (torch.cuda.FloatTensor, int, torch.cuda.FloatTensor), but expected (torch.cuda.FloatTensor source, int dim, torch.cuda.LongTensor index)

Following is the trace:

Traceback (most recent call last):
  File "", line 710, in <module>
    trainIters(encoder1, attn_decoder1, 50000, print_every=1000)
  File "", line 566, in trainIters
    decoder, encoder_optimizer, decoder_optimizer, criterion)
  File "", line 472, in train
    encoder_output, encoder_hidden = encoder(input_variable[ei*VEC_SIZE:(ei+1)*VEC_SIZE], encoder_hidden)
  File "/home/sagar/anaconda3/lib/python3.6/site-packages/torch/nn/modules/", line 224, in __call__
    result = self.forward(*input, **kwargs)
  File "", line 214, in forward
    embedded = self.embedding(input).view(1, 1, -1)
  File "/home/sagar/anaconda3/lib/python3.6/site-packages/torch/nn/modules/", line 224, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/sagar/anaconda3/lib/python3.6/site-packages/torch/nn/modules/", line 94, in forward
    self.scale_grad_by_freq, self.sparse
  File "/home/sagar/anaconda3/lib/python3.6/site-packages/torch/nn/_functions/thnn/", line 53, in forward
    output = torch.index_select(weight, 0, indices.view(-1))

How to write encoder and decoder to occupy the word2vec embeddings? Several things are involved, like does my output of encoder gru has to be a vector of size VEC_SIZE or not, in the decoder - my loss has to be calculated using some similarity metric. I think I will go for cosine similarity, but before that i will have to make sure that decoder takes the output of encoder and generates a vector of size VEC_SIZE.

I would appreciate if someone has already done this homework in the tutorial and has a code readily available?