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====== Pytorch Pack目的 ======
[[https://stackoverflow.com/questions/51030782/why-do-we-pack-the-sequences-in-pytorch|why do we “pack” the sequences in pytorch?
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I have stumbled upon this problem too and below is what I figured out.

When training RNN (LSTM or GRU or vanilla-RNN), it is difficult to batch the variable length sequences. For ex: if length of sequences in a size 8 batch is [4,6,8,5,4,3,7,8], you will pad all the sequences and that will results in 8 sequences of length 8. You would end up doing 64 computation (8x8), but you needed to do only 45 computations. Moreover, if you wanted to do something fancy like using a bidirectional-RNN it would be harder to do batch computations just by padding and you might end up doing more computations than required.

Instead, pytorch allows us to pack the sequence, internally packed sequence is a tuple of two lists. One contains the elements of sequences. Elements are interleaved by time steps (see example below) and other contains the size of each sequence the batch size at each step. This is helpful in recovering the actual sequences as well as telling RNN what is the batch size at each time step. This has been pointed by @Aerin. This can be passed to RNN and it will internally optimize the computations.

I might have been unclear at some points, so let me know and I can add more explanations.

Here a code example:

  a = [torch.tensor([1,2,3]), torch.tensor([3,4])]
  b = torch.nn.utils.rnn.pad_sequence(a, batch_first=True)
  >>>>
  tensor([[ 1,  2,  3],
  [ 3,  4,  0]])
  torch.nn.utils.rnn.pack_padded_sequence(b, batch_first=True, lengths=[3,2])
  >>>>PackedSequence(data=tensor([ 1,  3,  2,  4,  3]), batch_sizes=tensor([ 2,  2,  1]))
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