wehub-resource-sync
138 lines
5.6 KiB
Python
138 lines
5.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Tests that parallel_config survives the create_default_diffusion roundtrip.
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Regression tests for https://github.com/vllm-project/vllm-omni/issues/1862
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"""
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from collections.abc import Mapping
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import pytest
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import torch
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from vllm_omni.config.config_factory import StageConfigFactory
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from vllm_omni.diffusion.data import (
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DiffusionParallelConfig,
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OmniDiffusionConfig,
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)
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from vllm_omni.diffusion.model_metadata import QWEN_IMAGE_EDIT_PLUS_MAX_INPUT_IMAGES
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pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
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def _roundtrip_diffusion_config(**kwargs) -> OmniDiffusionConfig:
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"""Simulate the real path: create_default_diffusion → OmniDiffusionConfig.
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Does NOT manually reconstruct parallel_config — relies on
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OmniDiffusionConfig.__post_init__ to handle the dict, just like
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the production code path does.
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"""
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stages = StageConfigFactory.create_default_diffusion(kwargs)
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engine_args = dict(stages[0]["engine_args"])
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return OmniDiffusionConfig.from_kwargs(**engine_args)
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class TestParallelConfigPropagation:
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"""Core regression tests: parallel_config must survive serialization."""
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def test_tp2_roundtrip(self):
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pc = DiffusionParallelConfig(tensor_parallel_size=2)
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od = _roundtrip_diffusion_config(model="test-model", parallel_config=pc)
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assert od.parallel_config.tensor_parallel_size == 2
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assert od.parallel_config.world_size == 2
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def test_tp4_devices_and_config(self):
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pc = DiffusionParallelConfig(tensor_parallel_size=4)
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stages = StageConfigFactory.create_default_diffusion({"parallel_config": pc, "model": "x"})
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assert stages[0]["runtime"]["devices"] == "0,1,2,3"
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# Let __post_init__ reconstruct from dict (real code path)
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ea = dict(stages[0]["engine_args"])
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od = OmniDiffusionConfig.from_kwargs(**ea)
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assert od.parallel_config.tensor_parallel_size == 4
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assert od.parallel_config.world_size == 4
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def test_sp_config_roundtrip(self):
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pc = DiffusionParallelConfig(
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tensor_parallel_size=2,
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ulysses_degree=2,
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ring_degree=1,
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)
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od = _roundtrip_diffusion_config(model="x", parallel_config=pc)
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assert od.parallel_config.ulysses_degree == 2
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assert od.parallel_config.ring_degree == 1
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def test_mask_sp_padding_roundtrip(self):
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pc = DiffusionParallelConfig(ulysses_degree=2, mask_sp_padding=True)
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od = _roundtrip_diffusion_config(model="x", parallel_config=pc)
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assert od.parallel_config.mask_sp_padding is True
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def test_mask_sp_padding_defaults_false(self):
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pc = DiffusionParallelConfig(ulysses_degree=2)
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od = _roundtrip_diffusion_config(model="x", parallel_config=pc)
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assert od.parallel_config.mask_sp_padding is False
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def test_cfg_parallel_roundtrip(self):
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pc = DiffusionParallelConfig(cfg_parallel_size=2)
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od = _roundtrip_diffusion_config(model="x", parallel_config=pc)
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assert od.parallel_config.cfg_parallel_size == 2
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assert od.parallel_config.world_size == 2
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def test_no_parallel_config_defaults_to_tp1(self):
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od = _roundtrip_diffusion_config(model="x")
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assert od.parallel_config.tensor_parallel_size == 1
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assert od.parallel_config.world_size == 1
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def test_num_gpus_derived_from_world_size(self):
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pc = DiffusionParallelConfig(tensor_parallel_size=2)
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od = _roundtrip_diffusion_config(model="x", parallel_config=pc)
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assert od.num_gpus == 2
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class TestCreateDefaultDiffusion:
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"""Verify engine_args structure from create_default_diffusion."""
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def test_parallel_config_serialized_as_dict(self):
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"""The key fix: parallel_config must appear in engine_args as a dict."""
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pc = DiffusionParallelConfig(tensor_parallel_size=2)
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stages = StageConfigFactory.create_default_diffusion({"model": "x", "parallel_config": pc})
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ea = stages[0]["engine_args"]
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assert "parallel_config" in ea
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assert isinstance(ea["parallel_config"], Mapping)
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assert ea["parallel_config"]["tensor_parallel_size"] == 2
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def test_dtype_serialized_as_string(self):
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stages = StageConfigFactory.create_default_diffusion({"dtype": torch.float16, "model": "x"})
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assert stages[0]["engine_args"]["dtype"] == "torch.float16"
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def test_cache_backend_defaults_to_none(self):
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stages = StageConfigFactory.create_default_diffusion({"model": "x"})
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assert stages[0]["engine_args"]["cache_backend"] == "none"
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def test_single_gpu_default_devices(self):
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stages = StageConfigFactory.create_default_diffusion({"model": "x"})
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assert stages[0]["runtime"]["devices"] == "0"
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def test_extra_kwargs_forwarded(self):
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stages = StageConfigFactory.create_default_diffusion(
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{"model": "x", "enforce_eager": True, "lora_path": "/tmp/lora"}
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)
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ea = stages[0]["engine_args"]
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assert ea["enforce_eager"] is True
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assert ea["lora_path"] == "/tmp/lora"
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def test_qwen_image_edit_plus_sets_generic_multimodal_limit():
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od_config = OmniDiffusionConfig(model="Qwen/Qwen-Image-Edit-2511", model_class_name="QwenImageEditPlusPipeline")
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od_config.update_multimodal_support()
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assert od_config.supports_multimodal_inputs is True
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assert od_config.max_multimodal_image_inputs == QWEN_IMAGE_EDIT_PLUS_MAX_INPUT_IMAGES
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def test_additional_config_roundtrip():
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additional_config = {"torchair_graph_config": {"enabled": True}}
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od = _roundtrip_diffusion_config(model="x", additional_config=additional_config)
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assert od.additional_config == additional_config
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