pytorch中的变长bi-lstm

在Tensorflow 1.12中如果LSTM输入的序列是变长的话，有dynamic_rnn()或bidirection_dynamic_rnn()方法来处理，但是在pytorch中怎么处理呢？

在pytorch中也有相对应的方法在torch.nn.utils包中的pack_padded_sequence()和pad_packed_sequence()用来处理变长序列的问题。pack是压缩的意思，pad是填充的意思。pytorch的处理是先根据seq_len来压缩输入，经过LSTM后再填充。所以这两个方法都要知道padding的index。

# -*- coding: utf-8 -*-
import torch
from torch import nn
from torch.nn import LSTM
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

X = torch.tensor([[1, 2, 3],
                  [1, 2, 0],
                  [3, 0, 0],
                  [2, 1, 0]])
seq_len = torch.tensor([3, 2, 1, 2])
print('X shape', X.shape)
vocab_size = 4
embedding_dim = 2
hidden_size = 6
batch_size = 4

# tell word embedding the padding_idx，
# whenever encounter the padding idx, get the vector with padding idx
word_embedding = nn.Embedding(num_embeddings=vocab_size, embedding_dim=embedding_dim, padding_idx=0)
word_vectors = word_embedding(X)
print(word_vectors)

lstm = LSTM(input_size=embedding_dim,
            hidden_size=hidden_size // 2,
            num_layers=1,
            bidirectional=True,
            batch_first=True)

packed_X = pack_padded_sequence(word_vectors, seq_len, batch_first=True, enforce_sorted=False)

print('--->pack padded sequence:{}'.format(packed_X))

# num_layers* num_directions=1*2=2
h0 = (torch.randn(2, batch_size, hidden_size // 2),
      torch.randn(2, batch_size, hidden_size // 2))

outputs, h = lstm(packed_X, h0)
print('after lstm outputs: {}'.format(outputs))
pad_packed_X = pad_packed_sequence(outputs, batch_first=True, padding_value=0.0)
# pad_packed_X是一个tuple,
print('---->pad_packed:{}'.format(pad_packed_X))
# [batch_size, max_seq_len, hidden_size]
print('---->pad_outputs:{}'.format(pad_packed_X[0].shape))
print('---->seq_len:{}'.format(pad_packed_X[1].shape))

X shape torch.Size([4, 3])
tensor([[[ 0.7049, -0.6178],
         [-2.0429,  0.7651],
         [-0.4018,  0.5503]],

        [[ 0.7049, -0.6178],
         [-2.0429,  0.7651],
         [ 0.0000,  0.0000]],

        [[-0.4018,  0.5503],
         [ 0.0000,  0.0000],
         [ 0.0000,  0.0000]],

        [[-2.0429,  0.7651],
         [ 0.7049, -0.6178],
         [ 0.0000,  0.0000]]], grad_fn=<EmbeddingBackward>)
--->pack padded sequence:PackedSequence(data=tensor([[ 0.7049, -0.6178],
        [ 0.7049, -0.6178],
        [-2.0429,  0.7651],
        [-0.4018,  0.5503],
        [-2.0429,  0.7651],
        [-2.0429,  0.7651],
        [ 0.7049, -0.6178],
        [-0.4018,  0.5503]], grad_fn=<PackPaddedSequenceBackward>), batch_sizes=tensor([4, 3, 1]), sorted_indices=tensor([0, 1, 3, 2]), unsorted_indices=tensor([0, 1, 3, 2]))
after lstm outputs: PackedSequence(data=tensor([[ 0.0594, -0.1153,  0.2028,  0.0240, -0.3177,  0.1364],
        [ 0.1639, -0.0723,  0.2713,  0.0129, -0.3103, -0.0151],
        [ 0.0144,  0.3130,  0.0122, -0.1323, -0.0634,  0.0550],
        [ 0.2420, -0.3547,  0.1539,  0.0024, -0.4087,  0.3998],
        [ 0.0393,  0.2744,  0.0270, -0.0392, -0.0846,  0.2874],
        [ 0.0752,  0.3018,  0.0351, -0.0354, -0.2119, -0.5068],
        [ 0.2033,  0.1408,  0.0661, -0.4325, -0.0149,  0.0146],
        [ 0.0668,  0.3305,  0.0304,  0.2233, -0.0265,  0.1531]],
       grad_fn=<CatBackward>), batch_sizes=tensor([4, 3, 1]), sorted_indices=tensor([0, 1, 3, 2]), unsorted_indices=tensor([0, 1, 3, 2]))
---->pad_packed:(tensor([[[ 0.0594, -0.1153,  0.2028,  0.0240, -0.3177,  0.1364],
         [ 0.0393,  0.2744,  0.0270, -0.0392, -0.0846,  0.2874],
         [ 0.0668,  0.3305,  0.0304,  0.2233, -0.0265,  0.1531]],

        [[ 0.1639, -0.0723,  0.2713,  0.0129, -0.3103, -0.0151],
         [ 0.0752,  0.3018,  0.0351, -0.0354, -0.2119, -0.5068],
         [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000]],

        [[ 0.2420, -0.3547,  0.1539,  0.0024, -0.4087,  0.3998],
         [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000],
         [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000]],

        [[ 0.0144,  0.3130,  0.0122, -0.1323, -0.0634,  0.0550],
         [ 0.2033,  0.1408,  0.0661, -0.4325, -0.0149,  0.0146],
         [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000]]],
       grad_fn=<IndexSelectBackward>), tensor([3, 2, 1, 2]))
---->pad_outputs:torch.Size([4, 3, 6])
---->seq_len:torch.Size([4])

Process finished with exit code 0

参考 ：
https://blog.csdn.net/So_that/article/details/94731614