How to get the output at the last timestep for batched sequences?

I use max_seq_len * batch_size * embed_size for batch input, also with a list of actual lengthes for each sequence to GRU/LSTM,
then I get the outputs and last hidden vector with size of max_seq_len * batch_size * hidden_size and layer_num * batch_size * hidden_size.
How can I get the actual output vector at the last (actual) time step for each sequence? whose size should be batch_size * hidden_size.

The test code for my question:

import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
import numpy as np

max_seq_len = 3
batch_size = 4
layer_num = 2

input_size = 10 # 0 - 9
emb_size = 8
hidden_size = 10

# test data:
a = [[1,2,3], [4,5,0], [6,0,0], [7,8,9]]
lens = [3,2,1,3]

# sort the input batch data by reversed actual length for pad_pack operation
pairs = sorted( zip(a, lens), key=lambda p: p[1], reverse=True)
(a, lens) = zip(*pairs)

# actual length
lens = np.array(lens)
# lens = torch.LongTensor(lens)

va = Variable(torch.LongTensor(a))
vlens = Variable(torch.LongTensor(lens))

print(va, vlens)

embedding = nn.Embedding(input_size, emb_size)
gru = nn.GRU(emb_size, hidden_size, layer_num)

inputs = va.transpose(0, 1); print("inputs size: ", inputs.size()) # max_seq_len * batch_size
inputs = embedding(inputs); print("embedded size: ", embedded.size()) # max_seq_len * batch_size * emb_size

inputs = torch.nn.utils.rnn.pack_padded_sequence(inputs, lens)

h0 = Variable(torch.randn(layer_num, 4, 10))
outputs, hn = gru(inputs, h0)

# print("after packed:")
# print("outputs.size: ", outputs.size())
# print("hn size: ", hn.size())

tmp = torch.nn.utils.rnn.pad_packed_sequence(outputs)

outputs, output_lengths = tmp
# outputs: (max_seq_len * batch_size, output_size)

print("after padded:")
print("outputs.size: ", outputs.size())
print("hn size: ", hn.size())

# outputs.index_select(torch.LongTensor(lens-1))
# print("lens: ", lens-1)

# idxs = Variable(torch.LongTensor(lens-1))
# outputs = outputs.index_select(0, idxs)

# print("selected outputs size: ", outputs.size())
# outputs.gather(0, idxs)
1 Like

My direct question is

How to implement [ outputs[lens[i]][i] for i in range(len(lens)) ] ?
which is to get the last output vector at the last timestep for each sequence.

Thank you.

My own solution is

masks = (vlens-1).unsqueeze(0).unsqueeze(2).expand(max_seq_len, outputs.size(1), outputs.size(2))
output = outputs.gather(0, masks)[0]


Thanks for posting this. I think I’ve found an easier way. For GRUs, the last hidden state is equivalent to the last output state I believe.

So you should be able to do:

outputs, hn = gru(inputs, h0)

For the LSTM the equivalent code would be:

outputs, (hn, cn) = lstm(inputs, h0)

I used your code to verify this.

And you can more compactly express your code by using .view() to add the unit axes:

masks = (vlens-1).view(1, -1, 1).expand(max_seq_len, outputs.size(1), outputs.size(2))
output = outputs.gather(0, masks)[0]

Why are we expanding in the 0th dimension also? Wouldn’t the following return the same thing? If they are same, is the following faster? (because of less number of gather calls)

masks = (vlens-1).unsqueeze(0).unsqueeze(2).expand(1, outputs.size(1), outputs.size(2))
output = outputs.gather(0, masks)