56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
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# 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 torch
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from llamafactory.data.collator import prepare_4d_attention_mask
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def test_4d_attention_mask():
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o = 0.0
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x = torch.finfo(torch.float16).min
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attention_mask_with_indices = torch.tensor(
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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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)
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attention_mask_computed = prepare_4d_attention_mask(attention_mask_with_indices, torch.float16)
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attention_mask_expected = torch.tensor(
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[
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[
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[
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[o, x, x, x, x, x],
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[o, o, x, x, x, x],
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[x, x, o, x, x, x],
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[x, x, o, o, x, x],
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[x, x, o, o, o, x],
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[x, x, x, x, x, x],
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]
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],
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[
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[
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[o, x, x, x, x, x],
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[x, o, x, x, x, x],
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[x, o, o, x, x, x],
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[x, x, x, o, x, x],
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[x, x, x, o, o, x],
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[x, x, x, o, o, o],
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]
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],
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],
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dtype=torch.float16,
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)
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assert torch.all(attention_mask_computed == attention_mask_expected)
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