69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
# Copyright 2024 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pytest
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import torch
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from llamafactory.model.model_utils.packing import get_seqlens_in_batch, get_unpad_data
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@pytest.mark.parametrize(
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"attention_mask,golden_seq_lens",
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[
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(
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[
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[1, 1, 2, 2, 2, 0],
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[1, 2, 2, 3, 3, 3],
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],
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[2, 3, 1, 2, 3],
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),
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(
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[[1]],
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[1],
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),
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],
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)
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def test_get_seqlens_in_batch(attention_mask, golden_seq_lens):
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attention_mask_with_indices = torch.tensor(attention_mask)
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seqlens_in_batch = get_seqlens_in_batch(attention_mask_with_indices)
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assert torch.all(seqlens_in_batch == torch.tensor(golden_seq_lens))
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@pytest.mark.parametrize(
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"attention_mask,golden_indices,golden_cu_seqlens,golden_max_seqlen",
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[
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(
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[
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[1, 1, 2, 2, 2, 0],
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[1, 2, 2, 3, 3, 3],
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],
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[0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11],
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[0, 2, 5, 6, 8, 11],
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3,
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),
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(
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[[1]],
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[0],
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[0, 1],
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1,
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),
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],
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)
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def test_get_unpad_data(attention_mask, golden_indices, golden_cu_seqlens, golden_max_seqlen):
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attention_mask_with_indices = torch.tensor(attention_mask)
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indices, cu_seqlens, max_seqlen_in_batch = get_unpad_data(attention_mask_with_indices)
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assert torch.all(indices == torch.tensor(golden_indices))
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assert torch.all(cu_seqlens == torch.tensor(golden_cu_seqlens, dtype=torch.int32))
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assert max_seqlen_in_batch == golden_max_seqlen
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