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Padding_idx in Embedding supports negative indexing #4496

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Merged
apaszke merged 3 commits into from
Jan 9, 2018
Merged

Padding_idx in Embedding supports negative indexing #4496

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781de59
Padding_idx in Embedding supports negative indexing
dasguptar Jan 5, 2018
eadb453
Flake8 fixes
dasguptar Jan 5, 2018
c983380
Adding unit tests
dasguptar Jan 7, 2018
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18 changes: 18 additions & 0 deletions test/test_nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -985,6 +985,24 @@ def test_embedding_padding_idx(self):
self.assertEqual(output[0][0].sum().data[0], 0)
self.assertEqual(output[1][2].sum().data[0], 0)

# negative indexing check for padding_idx
# padding_idx=-2, num_embeddings=10 ==> index 8 padded
embedding = nn.Embedding(10, 20, padding_idx=-2)
input = Variable(torch.LongTensor([[0, 2, 8, 5], [4, 8, 0, 9]]))
output = embedding(input)
self.assertEqual(output[0][2].sum().data[0], 0)
self.assertEqual(output[1][1].sum().data[0], 0)

embedding = nn.Embedding(10, 20, padding_idx=-2, sparse=True)
input = Variable(torch.LongTensor([[0, 2, 8, 5], [4, 8, 0, 9]]))
output = embedding(input)
self.assertEqual(output[0][2].sum().data[0], 0)
self.assertEqual(output[1][1].sum().data[0], 0)

# out of bounds check for padding_idx
self.assertRaises(AssertionError, nn.Embedding, num_embeddings=10, embedding_dim=20, padding_idx=25)
self.assertRaises(AssertionError, nn.Embedding, num_embeddings=10, embedding_dim=20, padding_idx=-25)

def test_embedding_max_norm(self):
embedding = nn.Embedding(22, 5, max_norm=1.0)
input = Variable(torch.LongTensor([2, 8, 8, 6]))
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10 changes: 8 additions & 2 deletions torch/nn/functional.py
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Expand Up @@ -1048,8 +1048,14 @@ def embedding(input, weight, padding_idx=None, max_norm=None, norm_type=2,

"""
input = input.contiguous()
if padding_idx is None:
padding_idx = -1
if padding_idx is not None:
if padding_idx > 0:
assert padding_idx < weight.size(0), 'Padding_idx must be within num_embeddings'
elif padding_idx < 0:
assert padding_idx >= -weight.size(0), 'Padding_idx must be within num_embeddings'
padding_idx = weight.size(0) + padding_idx
elif padding_idx is None:
padding_idx = -1
if max_norm is not None:
with torch.no_grad():
torch._C._VariableBase.embedding_renorm_(weight, input, max_norm, norm_type)
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6 changes: 6 additions & 0 deletions torch/nn/modules/sparse.py
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Expand Up @@ -79,6 +79,12 @@ def __init__(self, num_embeddings, embedding_dim, padding_idx=None,
super(Embedding, self).__init__()
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
if padding_idx is not None:
if padding_idx > 0:
assert padding_idx < self.num_embeddings, 'Padding_idx must be within num_embeddings'
elif padding_idx < 0:
assert padding_idx >= -self.num_embeddings, 'Padding_idx must be within num_embeddings'
padding_idx = self.num_embeddings + padding_idx
self.padding_idx = padding_idx
self.max_norm = max_norm
self.norm_type = norm_type
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