wehub-resource-sync
edf74f4e18
Build and Deploy Sphinx Docs / deploy (push) Has been skipped
tests / tests (ubuntu-latest, 3.11, 4.57.1) (push) Failing after 1s
tests / tests (ubuntu-latest, 3.13, ) (push) Failing after 1s
docker / build (cuda) (push) Failing after 1s
docker / build (npu-a3) (push) Failing after 1s
tests / tests (ubuntu-latest, 3.11, ) (push) Failing after 1s
docker / build (npu-a2) (push) Failing after 1s
Build and Deploy Sphinx Docs / build (push) Failing after 1s
tests / tests (ubuntu-latest, 3.11, 4.55.0) (push) Failing after 0s
tests / tests (ubuntu-latest, 3.12, ) (push) Failing after 1s
tests / tests (windows-latest, 3.11, ) (push) Has been cancelled
tests / tests (windows-latest, 3.12, ) (push) Has been cancelled
tests / tests (macos-latest, 3.11, ) (push) Has been cancelled
tests / tests (macos-latest, 3.12, ) (push) Has been cancelled
tests / tests (macos-latest, 3.13, ) (push) Has been cancelled
tests / tests (windows-latest, 3.13, ) (push) Has been cancelled
tests_cuda / tests (linux-x86_64-gpu-2, 3.11) (push) Has been cancelled
tests_npu / tests (linux-aarch64-a2-4, 3.11, 2.7.1) (push) Has been cancelled
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
# Copyright 2025 the LlamaFactory team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from llamafactory.model.model_utils.packing import get_seqlens_in_batch, get_unpad_data
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"attention_mask,golden_seq_lens",
|
|
[
|
|
(
|
|
[
|
|
[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)
|
|
assert torch.all(seqlens_in_batch == torch.tensor(golden_seq_lens))
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"attention_mask,golden_indices,golden_cu_seqlens,golden_max_seqlen",
|
|
[
|
|
(
|
|
[
|
|
[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)
|
|
assert torch.all(indices == torch.tensor(golden_indices))
|
|
assert torch.all(cu_seqlens == torch.tensor(golden_cu_seqlens, dtype=torch.int32))
|
|
assert max_seqlen_in_batch == golden_max_seqlen
|