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Fixed left_pad_sequence - correctly flip dims based on batch_first #1523

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Sep 8, 2024
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Fixed left_pad_sequence - correctly flip dims based on batch_first #1523

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9a99d89
Fixed left_pad_sequence - correctly flip dims based on batch_first
mirceamironenco Sep 8, 2024
f30e1f9
Update left_pad test with explicit tensor.
mirceamironenco Sep 8, 2024
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3 changes: 3 additions & 0 deletions tests/torchtune/data/test_collate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,6 +57,9 @@ def test_left_pad_sequence(self):
expected = torch.tensor([[0, 0, 1, 2, 3], [0, 4, 5, 6, 7], [8, 9, 10, 11, 12]])
assert torch.equal(result, expected)

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Could I be annoying and ask if you could explicitly write out the expected tensor, please? Helps a lot to understand what's going at a glance.

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Sure! I've added the expected tensor for the batch_first=False case.

result = left_pad_sequence([a, b, c], batch_first=False, padding_value=0)
assert torch.equal(result, expected.transpose(0, 1))


class TestPaddedCollate:
def test_padded_collate_classifier_labels(self):
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2 changes: 1 addition & 1 deletion torchtune/data/_collate.py
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Expand Up @@ -49,7 +49,7 @@ def left_pad_sequence(
map(lambda x: torch.flip(x, dims=[0]), sequences),
batch_first=batch_first,
padding_value=padding_value,
).flip(dims=[1])
).flip(dims=[int(batch_first)])


def padded_collate(
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