46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
"""Compare the embedding outputs of HF and vLLM models.
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Run `pytest tests/models/embedding/language/test_embedding.py`.
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"""
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import pytest
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from ..utils import check_embeddings_close
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MODELS = [
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"intfloat/e5-mistral-7b-instruct",
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"BAAI/bge-multilingual-gemma2",
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]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["half"])
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def test_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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# The example_prompts has ending "\n", for example:
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# "Write a short story about a robot that dreams for the first time.\n"
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# sentence_transformers will strip the input texts, see:
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# https://github.com/UKPLab/sentence-transformers/blob/v3.1.1/sentence_transformers/models/Transformer.py#L159
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# This makes the input_ids different between hf_model and vllm_model.
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# So we need to strip the input texts to avoid test failing.
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example_prompts = [str(s).strip() for s in example_prompts]
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with hf_runner(model, dtype=dtype,
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is_sentence_transformer=True) as hf_model:
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hf_outputs = hf_model.encode(example_prompts)
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with vllm_runner(model, dtype=dtype) as vllm_model:
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vllm_outputs = vllm_model.encode(example_prompts)
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check_embeddings_close(
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embeddings_0_lst=hf_outputs,
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embeddings_1_lst=vllm_outputs,
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name_0="hf",
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name_1="vllm",
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tol=1e-2,
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)
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