Embedding 3D data in Pytorch - nlp

Question

I want to implement character-level embedding.
This is usual word embedding.
Word Embedding
Input: [ [‘who’, ‘is’, ‘this’] ]
-> [ [3, 8, 2] ] # (batch_size, sentence_len)
-> // Embedding(Input)
# (batch_size, seq_len, embedding_dim)
This is what i want to do.
Character Embedding
Input: [ [ [‘w’, ‘h’, ‘o’, 0], [‘i’, ‘s’, 0, 0], [‘t’, ‘h’, ‘i’, ‘s’] ] ]
-> [ [ [2, 3, 9, 0], [ 11, 4, 0, 0], [21, 10, 8, 9] ] ] # (batch_size, sentence_len, word_len)
-> // Embedding(Input) # (batch_size, sentence_len, word_len, embedding_dim)
-> // sum each character embeddings # (batch_size, sentence_len, embedding_dim)
The final output shape is same as Word embedding. Because I want to concat them later.
Although I tried it, I am not sure how to implement 3-D embedding. Do you know how to implement such a data?
def forward(self, x):
print('x', x.size()) # (N, seq_len, word_len)
bs = x.size(0)
seq_len = x.size(1)
word_len = x.size(2)
embd_list = []
for i, elm in enumerate(x):
tmp = torch.zeros(1, word_len, self.embd_size)
for chars in elm:
tmp = torch.add(tmp, 1.0, self.embedding(chars.unsqueeze(0)))
Above code got an error because output of self.embedding is Variable.
TypeError: torch.add received an invalid combination of arguments - got (torch.FloatTensor, float, Variable), but expected one of:
* (torch.FloatTensor source, float value)
* (torch.FloatTensor source, torch.FloatTensor other)
* (torch.FloatTensor source, torch.SparseFloatTensor other)
* (torch.FloatTensor source, float value, torch.FloatTensor other)
didn't match because some of the arguments have invalid types: (torch.FloatTensor, float, Variable)
* (torch.FloatTensor source, float value, torch.SparseFloatTensor other)
didn't match because some of the arguments have invalid types: (torch.FloatTensor, float, Variable)
Update
I could do this. But for is not effective for batch. Do you guys know more efficient way?
def forward(self, x):
print('x', x.size()) # (N, seq_len, word_len)
bs = x.size(0)
seq_len = x.size(1)
word_len = x.size(2)
embd = Variable(torch.zeros(bs, seq_len, self.embd_size))
for i, elm in enumerate(x): # every sample
for j, chars in enumerate(elm): # every sentence. [ [‘w’, ‘h’, ‘o’, 0], [‘i’, ‘s’, 0, 0], [‘t’, ‘h’, ‘i’, ‘s’] ]
chars_embd = self.embedding(chars.unsqueeze(0)) # (N, word_len, embd_size) [‘w’,‘h’,‘o’,0]
chars_embd = torch.sum(chars_embd, 1) # (N, embd_size). sum each char's embedding
embd[i,j] = chars_embd[0] # set char_embd as word-like embedding
x = embd # (N, seq_len, embd_dim)
Update2
This is my final code. Thank you, Wasi Ahmad!
def forward(self, x):
# x: (N, seq_len, word_len)
input_shape = x.size()
bs = x.size(0)
seq_len = x.size(1)
word_len = x.size(2)
x = x.view(-1, word_len) # (N*seq_len, word_len)
x = self.embedding(x) # (N*seq_len, word_len, embd_size)
x = x.view(*input_shape, -1) # (N, seq_len, word_len, embd_size)
x = x.sum(2) # (N, seq_len, embd_size)
return x

Answer 1

I am assuming you have a 3d tensor of shape BxSxW where:
B = Batch size
S = Sentence length
W = Word length
And you have declared embedding layer as follows.
self.embedding = nn.Embedding(dict_size, emsize)
Where:
dict_size = No. of unique characters in the training corpus
emsize = Expected size of embeddings
So, now you need to convert the 3d tensor of shape BxSxW to a 2d tensor of shape BSxW and give it to the embedding layer.
emb = self.embedding(input_rep.view(-1, input_rep.size(2)))
The shape of emb will be BSxWxE where E is the embedding size. You can convert the resulting 3d tensor to a 4d tensor as follows.
emb = emb.view(*input_rep.size(), -1)
The final shape of emb will be BxSxWxE which is what you are expecting.

Answer 2

What you are looking for is implemented in allennlp TimeDistributed layer
Here is a demonstration:
from allennlp.modules.time_distributed import TimeDistributed
batch_size = 16
sent_len = 30
word_len = 5
Consider a sentence in input:
sentence = torch.randn(batch_size, sent_len, word_len) # suppose is your data
Define a char embedding layer (suppose you have also the input padded):
char_embedding = torch.nn.Embedding(char_vocab_size, char_emd_dim, padding_idx=char_pad_idx)
Wrap it!
embedding_sentence = TimeDistributed(char_embedding)(sentence) # shape: batch_size, sent_len, word_len, char_emb_dim
embedding_sentence has shape batch_size, sent_len, word_len, char_emb_dim
Actually, you can easily redefine a module in PyTorch to do this.