CODEWITHSUNDEEP
pytorch
andy
andy

Reputation: 2161

what does offsets mean in pytorch nn.EmbeddingBag?

I know offsets meaning when it has two numbers, but what does it mean when more than two numbers,for example:

weight = torch.FloatTensor([[1, 2, 3], [4, 5, 6]])
embedding_sum = nn.EmbeddingBag.from_pretrained(weight, mode='sum')
print(list(embedding_sum.parameters()))
input = torch.LongTensor([0,1])
offsets = torch.LongTensor([0,1,2,1])

print(embedding_sum(input, offsets))

the result is :

[Parameter containing:
tensor([[1., 2., 3.],
        [4., 5., 6.]])]
tensor([[1., 2., 3.],
        [4., 5., 6.],
        [0., 0., 0.],
        [0., 0., 0.]])

who can help me?

Upvotes: 3

Views: 3223

Answers (2)

FelixHo
FelixHo

Reputation: 1304

import torch
import torch.nn as nn

weight = torch.FloatTensor([[1, 2, 3], [4, 5, 6]])
embedding_sum = nn.EmbeddingBag.from_pretrained(weight, mode='sum')
print(embedding_sum.weight)

""" output
Parameter containing:
tensor([[1., 2., 3.],
        [4., 5., 6.]])
"""

input = torch.LongTensor([0, 1])
offsets = torch.LongTensor([0, 1, 2, 1])

According to these offsets you will get the following samples

"""
sample_1: input[0:1] # tensor([0])
sample_2: input[1:2] # tensor([1])
sample_3: input[2:1] # tensor([])
sample_4: input[1:]  # tensor([1])
"""

Embedding the samples above

# tensor([0]) => lookup 0  => embedding_sum.weight[0] => [1., 2., 3.]
# tensor([1]) => lookup 1  => embedding_sum.weight[1] => [4., 5., 6.]
# tensor([])  => empty bag                            => [0., 0., 0.]
# tensor([1]) => lookup 1  => embedding_sum.weight[1] => [4., 5., 6.]

print(embedding_sum(input, offsets))

""" output
tensor([[1., 2., 3.],
        [4., 5., 6.],
        [0., 0., 0.],
        [4., 5., 6.]])
"""

One more example:

input = torch.LongTensor([0, 1])
offsets = torch.LongTensor([0, 1, 0])

According to these offsets you will get the following samples

"""
sample_1: input[0:1] # tensor([0])
sample_2: input[1:0] # tensor([])
sample_3: input[0:]  # tensor([0, 1])
"""

Embedding the samples above

# tensor([0])    => lookup 0 => embedding_sum.weight[0] => [1., 2., 3.]
# tensor([])     => empty bag => [0., 0., 0.]
# tensor([0, 1]) => lookup 0 and 1 then reduce by sum 
#                => embedding_sum.weight[0] + embedding_sum.weight[1] => [5., 7., 9.]

print(embedding_sum(input, offsets))

""" output
tensor([[1., 2., 3.],
        [0., 0., 0.],
        [5., 7., 9.]])
"""

Upvotes: 1

ndrwnaguib
ndrwnaguib

Reputation: 6125

As shown in the source code,

return F.embedding(
    input, self.weight, self.padding_idx, self.max_norm,
    self.norm_type, self.scale_grad_by_freq, self.sparse)

It uses the functional embedding bag, which explains the offsets parameters as

offsets (LongTensor, optional) – Only used when input is 1D. offsets determines the starting index position of each bag (sequence) in input.

In the EmbeddingBag docs:

If input is 1D of shape (N), it will be treated as a concatenation of multiple bags (sequences). offsets is required to be a 1D tensor containing the starting index positions of each bag in input. Therefore, for offsets of shape (B), input will be viewed as having B bags. Empty bags (i.e., having 0-length) will have returned vectors filled by zeros.

The last statement ("Empty bags (i.e., having 0-length) will have returned vectors filled by zeros.") explains the zero vectors in your resulting tensor.

Upvotes: 1

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