wehub-resource-sync
71 lines
2.0 KiB
Python
71 lines
2.0 KiB
Python
"""Unit tests for Gemma3 model architecture."""
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import pytest
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from mlc_llm.model import MODEL_PRESETS, MODELS
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def test_gemma3_model_registered():
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"""Verify Gemma3 model is in the registry."""
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assert "gemma3" in MODELS, "gemma3 should be registered in MODELS"
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@pytest.mark.parametrize(
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"model_name",
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[
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"gemma3_2b",
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"gemma3_9b",
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],
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)
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def test_gemma3_creation(model_name: str):
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"""Test Gemma3 model creation and export to TVM IR.
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Verifies:
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- Config can be loaded from preset
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- Model instance can be created
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- Model exports to TVM IR successfully
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- Named parameters are extracted
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"""
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model_info = MODELS["gemma3"]
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config = model_info.config.from_dict(MODEL_PRESETS[model_name])
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model = model_info.model(config)
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mod, named_params = model.export_tvm(
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spec=model.get_default_spec(),
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)
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# Verify export succeeded
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assert mod is not None
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assert len(named_params) > 0
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# Optional: show module structure
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mod.show(black_format=False)
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# Print parameters for debugging
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for name, param in named_params:
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print(name, param.shape, param.dtype)
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def test_gemma3_config_validation():
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"""Test Gemma3 configuration has required fields."""
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model_info = MODELS["gemma3"]
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config = model_info.config.from_dict(MODEL_PRESETS["gemma3_2b"])
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# Check required config parameters
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assert hasattr(config, "hidden_size") and config.hidden_size > 0
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assert hasattr(config, "num_hidden_layers") and config.num_hidden_layers > 0
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assert hasattr(config, "num_attention_heads") and config.num_attention_heads > 0
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assert hasattr(config, "vocab_size") and config.vocab_size > 0
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print(
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f"Gemma3 Config: hidden_size={config.hidden_size}, "
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f"layers={config.num_hidden_layers}, "
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f"heads={config.num_attention_heads}, "
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f"vocab={config.vocab_size}"
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)
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if __name__ == "__main__":
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# Allow running tests directly
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test_gemma3_creation("gemma3_2b")
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test_gemma3_creation("gemma3_9b")
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