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61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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from tests.models.language.pooling.embed_utils import correctness_test_embed_models
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from tests.models.utils import EmbedModelInfo
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from .mteb_embed_utils import mteb_test_embed_models
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MODELS = [
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########## BertModel
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EmbedModelInfo(
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"intfloat/e5-small",
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architecture="BertModel",
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mteb_score=0.742285423,
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pooling_type="MEAN",
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attn_type="encoder_only",
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is_prefix_caching_supported=False,
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is_chunked_prefill_supported=False,
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enable_test=True,
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),
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EmbedModelInfo("intfloat/e5-base", architecture="BertModel", enable_test=False),
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EmbedModelInfo("intfloat/e5-large", architecture="BertModel", enable_test=False),
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EmbedModelInfo(
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"intfloat/multilingual-e5-small", architecture="BertModel", enable_test=False
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),
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########## XLMRobertaModel
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EmbedModelInfo(
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"intfloat/multilingual-e5-base",
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architecture="XLMRobertaModel",
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mteb_score=0.779325955,
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pooling_type="MEAN",
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attn_type="encoder_only",
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is_prefix_caching_supported=False,
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is_chunked_prefill_supported=False,
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enable_test=True,
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),
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EmbedModelInfo(
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"intfloat/multilingual-e5-large",
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architecture="XLMRobertaModel",
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enable_test=False,
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),
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EmbedModelInfo(
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"intfloat/multilingual-e5-large-instruct",
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architecture="XLMRobertaModel",
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enable_test=False,
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),
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]
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@pytest.mark.parametrize("model_info", MODELS)
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def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
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mteb_test_embed_models(hf_runner, vllm_runner, model_info)
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@pytest.mark.parametrize("model_info", MODELS)
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def test_embed_models_correctness(
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hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
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) -> None:
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correctness_test_embed_models(hf_runner, vllm_runner, model_info, example_prompts)
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