tzhouam
efda9369a8
Reuse upstream weight mapping and multimodal processing, consolidate Omni processor bridges, and trim unused Hunyuan internals. Route no-media kwargs through the active renderer and preserve GLM generation prompt formatting. Validation: 453 CPU tests passed, 7 skipped; processor imports, Ruff, and diff checks passed. GPU and end-to-end CI not rerun. Signed-off-by: tzhouam <tzhouam@connect.ust.hk>
39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
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import pytest
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import torch
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from torch import nn
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from vllm.model_executor.models.utils import AutoWeightsLoader, WeightsMapper
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pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
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class _TinyModel(nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.kept = nn.Linear(1, 1, bias=False)
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self.skipped = nn.Linear(1, 1, bias=False)
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self.rotary_embed = nn.Module()
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self.rotary_embed.register_parameter("inv_freq", nn.Parameter(torch.zeros(1)))
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def test_upstream_mapper_preserves_stage_exclusions_after_renaming() -> None:
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model = _TinyModel()
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model.skipped.weight.data.zero_()
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loaded = AutoWeightsLoader(model).load_weights(
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[
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("checkpoint.kept.weight", torch.ones(1, 1)),
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("checkpoint.skipped.weight", torch.full((1, 1), 2.0)),
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("rotary_embed.inv_freq", torch.ones(1)),
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],
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mapper=WeightsMapper(orig_to_new_prefix={"checkpoint.": ""})
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| WeightsMapper(orig_to_new_prefix={"skipped.": None}, orig_to_new_substr={"rotary_embed.inv_freq": None}),
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)
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assert loaded == {"kept.weight"}
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torch.testing.assert_close(model.kept.weight, torch.ones(1, 1))
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torch.testing.assert_close(model.skipped.weight, torch.zeros(1, 1))
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torch.testing.assert_close(model.rotary_embed.inv_freq, torch.zeros(1))
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