47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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import torch
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from vllm.multimodal.inputs import PlaceholderRange
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@pytest.mark.parametrize(
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"is_embed,expected",
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[
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(None, 5),
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(torch.tensor([True, True, True, True, True]), 5),
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(torch.tensor([False, False, False, False, False]), 0),
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(torch.tensor([True, False, True, False, True]), 3),
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(torch.tensor([True]), 1),
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],
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)
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def test_placeholder_range_get_num_embeds(is_embed, expected):
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length = len(is_embed) if is_embed is not None else 5
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pr = PlaceholderRange(offset=0, length=length, is_embed=is_embed)
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assert pr.get_num_embeds() == expected
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@pytest.mark.parametrize(
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"is_embed,expected",
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[
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(None, None),
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(
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torch.tensor([False, True, False, True, True]),
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torch.tensor([0, 1, 1, 2, 3]),
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),
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(torch.tensor([True, True, True]), torch.tensor([1, 2, 3])),
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],
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)
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def test_placeholder_range_embeds_cumsum(is_embed, expected):
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length = len(is_embed) if is_embed is not None else 5
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pr = PlaceholderRange(offset=0, length=length, is_embed=is_embed)
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if expected is None:
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assert pr.embeds_cumsum is None
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return
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assert torch.equal(pr.embeds_cumsum, expected)
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# cached_property should return the same object on repeated access
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assert pr.embeds_cumsum is pr.embeds_cumsum
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