Getting a similar issue here,
the inputs provided for pack_padded_sequence: sent, sent_len.
Where sent is the input (batch_size, seq_length, features/embedding_dim), with dimension [torch.FloatTensor of size 100x16x200]
and sent_len is a list containing the actual (unpadded) lengths of all sequences in the batch.
print sent.size() # (100L, 16L, 200L), i.e., (batch size, padded sequence length,embedding dimension / feature)
print len(sent_len) # 100, i.e., the length of the list containing sequence lengths of each element in the batch

sent_packed = nn.utils.rnn.pack_padded_sequence(sent, sent_len,batch_first=True)
print sent_packed

Now the output of sent_packed is expected to have a dimension same as sent, right?
I am getting the following:
PackedSequence(data=Variable containing:
 0.1084  0.3546  0.4458  ...  -1.1613  1.1618  0.4275
 0.0564 -1.0614  0.1452  ...  -0.7359 -0.2980 -1.9538
 0.8342  0.2849  0.5471  ...  -0.9297 -0.3760  0.4382
          ...             â±             ...          
 0.5107  1.6905  0.3308  ...  -0.8220  0.7505 -0.9616
 1.5038  0.3528 -1.4010  ...  -0.9663 -0.7744 -0.5839
-0.7513  1.6879 -0.1883  ...   1.1898  0.5734  0.1458
[torch.FloatTensor of size 818x200]
, batch_sizes=[100, 100, 100, 100, 100, 99, 77, 58, 35, 25, 11, 5, 3, 2, 2, 1])

Any idea what’s the bug?