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update tests

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hiyouga 2024-07-04 04:00:12 +08:00
parent 0c699de39d
commit 636bb9c1e6
1 changed files with 45 additions and 30 deletions
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tests/model/model_utils/test_packing.py
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@ -12,42 +12,57 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import pytest
import torch import torch
from llamafactory.model.model_utils.packing import get_seqlens_in_batch, get_unpad_data from llamafactory.model.model_utils.packing import get_seqlens_in_batch, get_unpad_data
def test_get_seqlens_in_batch(): @pytest.mark.parametrize(
attention_mask_with_indices = torch.tensor( "attention_mask,golden_seq_lens",
[ [
[1, 1, 2, 2, 2, 0], (
[1, 2, 2, 3, 3, 3], [
] [1, 1, 2, 2, 2, 0],
) [1, 2, 2, 3, 3, 3],
],
[2, 3, 1, 2, 3],
),
(
[[1]],
[1],
),
],
)
def test_get_seqlens_in_batch(attention_mask, golden_seq_lens):
attention_mask_with_indices = torch.tensor(attention_mask)
seqlens_in_batch = get_seqlens_in_batch(attention_mask_with_indices) seqlens_in_batch = get_seqlens_in_batch(attention_mask_with_indices)
assert list(seqlens_in_batch.size()) == [5] assert torch.all(seqlens_in_batch == torch.tensor(golden_seq_lens))
assert torch.all(seqlens_in_batch == torch.tensor([2, 3, 1, 2, 3]))
attention_mask_with_indices = torch.tensor([[1, 1, 1]])
seqlens_in_batch = get_seqlens_in_batch(attention_mask_with_indices)
assert list(seqlens_in_batch.size()) == [1]
assert torch.all(seqlens_in_batch == torch.tensor([3]))
def test_get_unpad_data(): @pytest.mark.parametrize(
attention_mask_with_indices = torch.tensor( "attention_mask,golden_indices,golden_cu_seqlens,golden_max_seqlen",
[ [
[1, 1, 2, 2, 2, 0], (
[1, 2, 2, 3, 3, 3], [
] [1, 1, 2, 2, 2, 0],
) [1, 2, 2, 3, 3, 3],
],
[0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11],
[0, 2, 5, 6, 8, 11],
3,
),
(
[[1]],
[0],
[0, 1],
1,
),
],
)
def test_get_unpad_data(attention_mask, golden_indices, golden_cu_seqlens, golden_max_seqlen):
attention_mask_with_indices = torch.tensor(attention_mask)
indices, cu_seqlens, max_seqlen_in_batch = get_unpad_data(attention_mask_with_indices) indices, cu_seqlens, max_seqlen_in_batch = get_unpad_data(attention_mask_with_indices)
assert torch.all(indices == torch.tensor([0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11])) assert torch.all(indices == torch.tensor(golden_indices))
assert torch.all(cu_seqlens == torch.tensor([0, 2, 5, 6, 8, 11], dtype=torch.int32)) assert torch.all(cu_seqlens == torch.tensor(golden_cu_seqlens, dtype=torch.int32))
assert max_seqlen_in_batch == 3 assert max_seqlen_in_batch == golden_max_seqlen
attention_mask_with_indices = torch.tensor([[1, 1, 1]])
indices, cu_seqlens, max_seqlen_in_batch = get_unpad_data(attention_mask_with_indices)
assert torch.all(indices == torch.tensor([0, 1, 2]))
assert torch.all(cu_seqlens == torch.tensor([0, 3], dtype=torch.int32))
assert max_seqlen_in_batch == 3