54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
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"""Compare the outputs of HF and vLLM when using greedy sampling.
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This test only tests small models. Big models such as 7B should be tested from
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test_big_models.py because it could use a larger instance to run tests.
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Run `pytest tests/models/test_cls_models.py`.
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"""
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import pytest
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import torch
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from transformers import AutoModelForSequenceClassification
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CLASSIFICATION_MODELS = ["jason9693/Qwen2.5-1.5B-apeach"]
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@pytest.mark.parametrize("model", CLASSIFICATION_MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_classification_models(
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hf_runner,
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vllm_runner,
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example_prompts,
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model: str,
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dtype: str,
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) -> None:
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with hf_runner(model,
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dtype=dtype,
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auto_cls=AutoModelForSequenceClassification) as hf_model:
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hf_outputs = hf_model.classify(example_prompts)
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with vllm_runner(model, dtype=dtype) as vllm_model:
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vllm_outputs = vllm_model.classify(example_prompts)
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print(hf_outputs, vllm_outputs)
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# check logits difference
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for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
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hf_output = torch.tensor(hf_output)
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vllm_output = torch.tensor(vllm_output)
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assert torch.allclose(hf_output, vllm_output, 1e-3)
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@pytest.mark.parametrize("model", CLASSIFICATION_MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_classification_model_print(
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vllm_runner,
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model: str,
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dtype: str,
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) -> None:
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with vllm_runner(model, dtype=dtype) as vllm_model:
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# This test is for verifying whether the model's extra_repr
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# can be printed correctly.
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print(vllm_model.model.llm_engine.model_executor.driver_worker.
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model_runner.model)
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