fe4a2a7477
Signed-off-by: Acerak01-fy <wfy2003324@163.com> Signed-off-by: zwhzzz0821 <zwhzzz0821@users.noreply.github.com> Signed-off-by: zwhzzz0821 <2831474076@qq.com> Co-authored-by: zwhzzz0821 <zwhzzz0821@users.noreply.github.com> Co-authored-by: zwhzzz0821 <2831474076@qq.com>
83 lines
3.3 KiB
Python
83 lines
3.3 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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from vllm_omni.config.omni_config import _DiffusionConfigProjection
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from vllm_omni.diffusion.data import OmniDiffusionConfig
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from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode
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pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
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@pytest.fixture(autouse=True)
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def _fixed_master_port(monkeypatch) -> None:
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monkeypatch.setattr(OmniDiffusionConfig, "_resolve_master_port", lambda _self: 29500)
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def test_dense_legacy_is_default() -> None:
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config = OmniDiffusionConfig.from_kwargs()
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assert config.diffusion_kv_mode is DiffusionKVCacheMode.DENSE_LEGACY
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def test_paged_worker_local_is_rejected_until_implemented() -> None:
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with pytest.raises(ValueError, match="reserved but not implemented"):
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OmniDiffusionConfig.from_kwargs(diffusion_kv_mode="paged_worker_local")
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def test_unknown_cache_mode_is_rejected() -> None:
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with pytest.raises(ValueError, match="Unsupported Diffusion KV diffusion_kv_mode"):
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OmniDiffusionConfig.from_kwargs(diffusion_kv_mode="unknown")
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def test_non_mapping_omni_kv_config_is_rejected() -> None:
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with pytest.raises(TypeError, match="omni_kv_config must be a mapping"):
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OmniDiffusionConfig.from_kwargs(omni_kv_config="paged_scheduler")
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def test_paged_scheduler_rejects_dense_connector_kv_receive() -> None:
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with pytest.raises(ValueError, match="does not support imported AR KV"):
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OmniDiffusionConfig.from_kwargs(
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diffusion_kv_mode="paged_scheduler",
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diffusion_kv_max_rows_per_request=1,
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max_num_batched_tokens=1,
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omni_kv_config={"need_recv_cache": True},
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)
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def test_paged_scheduler_does_not_depend_on_model_registry() -> None:
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config = OmniDiffusionConfig.from_kwargs(
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model_class_name="FutureDiffusionModel",
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diffusion_kv_mode="paged_scheduler",
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diffusion_kv_max_rows_per_request=1,
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max_num_batched_tokens=1,
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)
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assert config.diffusion_kv_mode is DiffusionKVCacheMode.PAGED_SCHEDULER
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@pytest.mark.parametrize("config_cls", [OmniDiffusionConfig, _DiffusionConfigProjection])
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def test_paged_scheduler_mode_is_platform_agnostic(config_cls) -> None:
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config = config_cls.from_kwargs(
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diffusion_kv_mode="paged_scheduler",
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diffusion_kv_max_rows_per_request=1,
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)
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assert config.diffusion_kv_mode is DiffusionKVCacheMode.PAGED_SCHEDULER
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def test_paged_scheduler_requires_a_worker_row_limit() -> None:
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with pytest.raises(ValueError, match="requires diffusion_kv_max_rows_per_request"):
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OmniDiffusionConfig.from_kwargs(diffusion_kv_mode="paged_scheduler")
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with pytest.raises(ValueError, match="requires diffusion_kv_max_rows_per_request"):
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_DiffusionConfigProjection.from_kwargs(diffusion_kv_mode="paged_scheduler")
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@pytest.mark.parametrize("invalid_limit", [0, -1, True, 1.5])
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def test_worker_row_limit_must_be_a_positive_integer(invalid_limit: object) -> None:
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with pytest.raises((TypeError, ValueError)):
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OmniDiffusionConfig.from_kwargs(diffusion_kv_max_rows_per_request=invalid_limit)
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with pytest.raises((TypeError, ValueError)):
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_DiffusionConfigProjection.from_kwargs(diffusion_kv_max_rows_per_request=invalid_limit)
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