Reputation: 175
Given a 3d tenzor, say:
batch x sentence length x embedding dim
a = torch.rand((10, 1000, 96))
and an array(or tensor) of actual lengths for each sentence
lengths = torch .randint(1000,(10,))
outputs tensor([ 370., 502., 652., 859., 545., 964., 566., 576.,1000., 803.])
How to fill tensor ‘a’ with zeros after certain index along dimension 1 (sentence length) according to tensor ‘lengths’ ?
I want smth like that :
a[ : , lengths : , : ] = 0
One way of doing it (slow if batch size is big enough):
for i_batch in range(10):
a[ i_batch , lengths[i_batch ] : , : ] = 0
Upvotes: 8
Views: 9451
Reputation: 114976
You can do it using a binary mask.
Using lengths
as column-indices to mask
we indicate where each sequence ends (note that we make mask
longer than a.size(1)
to allow for sequences with full length).
Using cumsum()
we set all entries in mask
after the seq len to 1.
mask = torch.zeros(a.shape[0], a.shape[1] + 1, dtype=a.dtype, device=a.device)
mask[(torch.arange(a.shape[0]), lengths)] = 1
mask = mask.cumsum(dim=1)[:, :-1] # remove the superfluous column
a = a * (1. - mask[..., None]) # use mask to zero after each column
For a.shape = (10, 5, 96)
, and lengths = [1, 2, 1, 1, 3, 0, 4, 4, 1, 3]
.
Assigning 1 to respective lengths
at each row, mask
looks like:
mask =
tensor([[0., 1., 0., 0., 0., 0.],
[0., 0., 1., 0., 0., 0.],
[0., 1., 0., 0., 0., 0.],
[0., 1., 0., 0., 0., 0.],
[0., 0., 0., 1., 0., 0.],
[1., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 1., 0.],
[0., 0., 0., 0., 1., 0.],
[0., 1., 0., 0., 0., 0.],
[0., 0., 0., 1., 0., 0.]])
After cumsum
you get
mask =
tensor([[0., 1., 1., 1., 1.],
[0., 0., 1., 1., 1.],
[0., 1., 1., 1., 1.],
[0., 1., 1., 1., 1.],
[0., 0., 0., 1., 1.],
[1., 1., 1., 1., 1.],
[0., 0., 0., 0., 1.],
[0., 0., 0., 0., 1.],
[0., 1., 1., 1., 1.],
[0., 0., 0., 1., 1.]])
Note that it exactly has zeros where the valid sequence entries are and ones beyond the lengths of the sequences. Taking 1 - mask
gives you exactly what you want.
Enjoy ;)
Upvotes: 13