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110 lines
4.9 KiB
Python
110 lines
4.9 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 (
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correctness_test_embed_models)
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from tests.models.utils import (CLSPoolingEmbedModelInfo,
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CLSPoolingRerankModelInfo, EmbedModelInfo,
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LASTPoolingEmbedModelInfo, RerankModelInfo)
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from .mteb_utils import mteb_test_embed_models, mteb_test_rerank_models
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MODELS = [
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########## BertModel
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CLSPoolingEmbedModelInfo("thenlper/gte-large",
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mteb_score=0.76807651,
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architecture="BertModel",
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enable_test=True),
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CLSPoolingEmbedModelInfo("thenlper/gte-base",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("thenlper/gte-small",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("thenlper/gte-large-zh",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("thenlper/gte-base-zh",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("thenlper/gte-small-zh",
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architecture="BertModel",
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enable_test=False),
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########### NewModel
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# These three architectures are almost the same, but not exactly the same.
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# For example,
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# - whether to use token_type_embeddings
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# - whether to use context expansion
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# So only test one (the most widely used) model
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CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-multilingual-base",
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architecture="GteNewModel",
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mteb_score=0.775074696,
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hf_overrides={"architectures": ["GteNewModel"]},
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enable_test=True),
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CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-base-en-v1.5",
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architecture="GteNewModel",
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hf_overrides={"architectures": ["GteNewModel"]},
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enable_test=False),
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CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-large-en-v1.5",
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architecture="GteNewModel",
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hf_overrides={"architectures": ["GteNewModel"]},
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enable_test=False),
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########### Qwen2ForCausalLM
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LASTPoolingEmbedModelInfo("Alibaba-NLP/gte-Qwen2-1.5B-instruct",
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mteb_score=0.758473459018872,
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architecture="Qwen2ForCausalLM",
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enable_test=True),
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########## ModernBertModel
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CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-modernbert-base",
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mteb_score=0.748193353,
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architecture="ModernBertModel",
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enable_test=True),
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########## Qwen3ForCausalLM
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LASTPoolingEmbedModelInfo("Qwen/Qwen3-Embedding-0.6B",
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mteb_score=0.771163695,
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architecture="Qwen3ForCausalLM",
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dtype="float32",
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enable_test=True),
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LASTPoolingEmbedModelInfo("Qwen/Qwen3-Embedding-4B",
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architecture="Qwen3ForCausalLM",
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dtype="float32",
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enable_test=False),
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]
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RERANK_MODELS = [
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CLSPoolingRerankModelInfo(
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# classifier_pooling: mean
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"Alibaba-NLP/gte-reranker-modernbert-base",
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mteb_score=0.33386,
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architecture="ModernBertForSequenceClassification",
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enable_test=True),
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CLSPoolingRerankModelInfo(
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"Alibaba-NLP/gte-multilingual-reranker-base",
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mteb_score=0.33062,
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architecture="GteNewForSequenceClassification",
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hf_overrides={"architectures": ["GteNewForSequenceClassification"]},
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enable_test=True),
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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,
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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(hf_runner, vllm_runner,
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model_info: EmbedModelInfo,
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example_prompts) -> None:
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correctness_test_embed_models(hf_runner, vllm_runner, model_info,
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example_prompts)
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@pytest.mark.parametrize("model_info", RERANK_MODELS)
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def test_rerank_models_mteb(hf_runner, vllm_runner,
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model_info: RerankModelInfo) -> None:
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mteb_test_rerank_models(hf_runner, vllm_runner, model_info)
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