wehub-resource-sync
79 lines
3.7 KiB
Python
79 lines
3.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Unit tests for OmniDiffusionConfig.is_moe (fix is_moe type and threshold, 6663c0b)."""
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import pytest
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from vllm_omni.diffusion.data import OmniDiffusionConfig, TransformerConfig
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pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
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class TestOmniDiffusionConfigIsMoE:
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"""Tests for OmniDiffusionConfig.is_moe property.
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Covers commit 6663c0b: fix is_moe type and threshold
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- num_experts must be (list, tuple, int); otherwise return False.
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- Threshold: is_moe is True when num_experts > 0 (not > 1).
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"""
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def test_is_moe_missing_num_experts_returns_false(self):
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"""When num_experts is absent, is_moe should be False."""
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tf_config = TransformerConfig.from_dict({})
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config = OmniDiffusionConfig(model="test", tf_model_config=tf_config)
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assert config.is_moe is False
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def test_is_moe_none_num_experts_returns_false(self):
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"""When num_experts is explicitly None (e.g. in params), is_moe should be False."""
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tf_config = TransformerConfig.from_dict({"num_experts": None})
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config = OmniDiffusionConfig(model="test", tf_model_config=tf_config)
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assert config.is_moe is False
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def test_is_moe_non_allowed_type_returns_false(self):
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"""When num_experts is not int/list/tuple (e.g. str), is_moe should be False."""
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tf_config = TransformerConfig.from_dict({"num_experts": "2"})
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config = OmniDiffusionConfig(model="test", tf_model_config=tf_config)
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assert config.is_moe is False
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def test_is_moe_int_zero_returns_false(self):
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"""num_experts int 0 should yield is_moe False (threshold > 0)."""
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tf_config = TransformerConfig.from_dict({"num_experts": 0})
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config = OmniDiffusionConfig(model="test", tf_model_config=tf_config)
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assert config.is_moe is False
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def test_is_moe_int_one_returns_true(self):
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"""num_experts int 1 should yield is_moe True (threshold > 0, not > 1)."""
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tf_config = TransformerConfig.from_dict({"num_experts": 1})
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config = OmniDiffusionConfig(model="test", tf_model_config=tf_config)
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assert config.is_moe is True
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def test_is_moe_int_gt_one_returns_true(self):
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"""num_experts int > 1 should yield is_moe True."""
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tf_config = TransformerConfig.from_dict({"num_experts": 2})
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config = OmniDiffusionConfig(model="test", tf_model_config=tf_config)
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assert config.is_moe is True
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def test_is_moe_list_all_zero_returns_false(self):
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"""num_experts list with all <= 0 should yield is_moe False."""
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tf_config = TransformerConfig.from_dict({"num_experts": [0]})
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config = OmniDiffusionConfig(model="test", tf_model_config=tf_config)
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assert config.is_moe is False
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def test_is_moe_list_has_positive_returns_true(self):
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"""num_experts list with any int > 0 should yield is_moe True."""
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tf_config = TransformerConfig.from_dict({"num_experts": [0, 1]})
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config = OmniDiffusionConfig(model="test", tf_model_config=tf_config)
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assert config.is_moe is True
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def test_is_moe_tuple_has_positive_returns_true(self):
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"""num_experts tuple with any int > 0 should yield is_moe True."""
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tf_config = TransformerConfig.from_dict({"num_experts": (0, 2)})
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config = OmniDiffusionConfig(model="test", tf_model_config=tf_config)
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assert config.is_moe is True
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def test_is_moe_list_non_int_ignored(self):
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"""num_experts list with only non-int entries should yield is_moe False."""
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tf_config = TransformerConfig.from_dict({"num_experts": ["a", 0.0]})
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config = OmniDiffusionConfig(model="test", tf_model_config=tf_config)
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assert config.is_moe is False
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