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import torch |
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from llamafactory.v1.config.model_args import ModelArguments |
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from llamafactory.v1.core.model_engine import ModelEngine |
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def test_tiny_qwen(): |
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model_args = ModelArguments(model="llamafactory/tiny-random-qwen3") |
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model_engine = ModelEngine(model_args) |
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assert "Qwen2Tokenizer" in model_engine.processor.__class__.__name__ |
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assert "Qwen3Config" in model_engine.model_config.__class__.__name__ |
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assert "Qwen3ForCausalLM" in model_engine.model.__class__.__name__ |
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assert model_engine.model.dtype == torch.bfloat16 |
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def test_tiny_qwen_with_kernel_plugin(): |
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from llamafactory.v1.plugins.model_plugins.kernels.ops.rms_norm.npu_rms_norm import npu_rms_norm_forward |
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model_args = ModelArguments( |
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model="llamafactory/tiny-random-qwen3", kernel_config={"name": "auto", "include_kernels": "auto"} |
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) |
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model_engine = ModelEngine(model_args) |
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if hasattr(torch, "npu"): |
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assert model_engine.model.model.layers[0].input_layernorm.forward.__code__ == npu_rms_norm_forward.__code__ |
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else: |
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assert model_engine.model.model.layers[0].input_layernorm.forward.__code__ != npu_rms_norm_forward.__code__ |
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assert "Qwen3ForCausalLM" in model_engine.model.__class__.__name__ |
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if __name__ == "__main__": |
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""" |
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python -m tests_v1.core.test_model_loader |
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""" |
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test_tiny_qwen() |
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test_tiny_qwen_with_kernel_plugin() |
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