4a18fd14ba
Signed-off-by: Max de Bayser <mbayser@br.ibm.com> Signed-off-by: Flavia Beo <flavia.beo@ibm.com> Co-authored-by: Flavia Beo <flavia.beo@ibm.com>
59 lines
1.7 KiB
Python
59 lines
1.7 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 vllm.utils import current_platform
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from ..utils import check_embeddings_close
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# Model, Guard
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MODELS = [
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"intfloat/e5-mistral-7b-instruct",
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"BAAI/bge-base-en-v1.5",
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"BAAI/bge-multilingual-gemma2",
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"intfloat/multilingual-e5-large",
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]
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ENCODER_ONLY = [
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"BAAI/bge-base-en-v1.5",
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"intfloat/multilingual-e5-large",
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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,
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dtype: str,
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) -> None:
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if model not in ENCODER_ONLY and current_platform.is_cpu():
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pytest.skip("Skip large embedding models test on CPU.")
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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, max_model_len=None) 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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