wehub-resource-sync
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Optional real-backend checks for Diffusers quantization config conversion."""
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from __future__ import annotations
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import importlib.util
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from pathlib import Path
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import pytest
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pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
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def _load_quantization_utils():
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module_path = (
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Path(__file__).resolve().parents[3] / "vllm_omni/diffusion/models/diffusers_adapter/quantization_utils.py"
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)
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spec = importlib.util.spec_from_file_location("diffusers_quantization_utils_real_test", module_path)
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assert spec is not None
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assert spec.loader is not None
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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return module
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def test_convert_diffusers_quantization_config_from_real_torchao_dict():
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diffusers = pytest.importorskip("diffusers")
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torchao_quantization = pytest.importorskip("torchao.quantization")
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quant_type_cls = getattr(torchao_quantization, "Int8DynamicActivationInt8WeightConfig", None)
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if quant_type_cls is None:
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pytest.skip("torchao does not expose Int8DynamicActivationInt8WeightConfig")
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quantization_utils = _load_quantization_utils()
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real_torchao_config = diffusers.TorchAoConfig(quant_type=quant_type_cls())
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serialized_torchao_config = real_torchao_config.to_dict()
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assert serialized_torchao_config["quant_type"].keys() == {"default"}
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load_kwargs = {
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"quantization_config": {
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"quant_mapping": {
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"transformer": serialized_torchao_config,
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},
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},
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}
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quantization_utils.convert_diffusers_quantization_config(load_kwargs)
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quant_config = load_kwargs["quantization_config"]
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component_config = quant_config.quant_mapping["transformer"]
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assert isinstance(quant_config, diffusers.PipelineQuantizationConfig)
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assert isinstance(component_config, diffusers.TorchAoConfig)
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assert isinstance(
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component_config.quant_type,
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quant_type_cls,
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)
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