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81 lines
3.1 KiB
Python
81 lines
3.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import Any
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import pytest
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from .embed_utils import EmbedModelInfo, correctness_test_embed_models
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from .mteb_utils import mteb_test_embed_models
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MODELS = [
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########## BertModel
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EmbedModelInfo("thenlper/gte-large",
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architecture="BertModel",
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enable_test=True),
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EmbedModelInfo("thenlper/gte-base",
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architecture="BertModel",
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enable_test=False),
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EmbedModelInfo("thenlper/gte-small",
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architecture="BertModel",
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enable_test=False),
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EmbedModelInfo("thenlper/gte-large-zh",
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architecture="BertModel",
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enable_test=False),
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EmbedModelInfo("thenlper/gte-base-zh",
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architecture="BertModel",
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enable_test=False),
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EmbedModelInfo("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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EmbedModelInfo("Alibaba-NLP/gte-multilingual-base",
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architecture="GteNewModel",
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enable_test=True),
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EmbedModelInfo("Alibaba-NLP/gte-base-en-v1.5",
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architecture="GteNewModel",
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enable_test=True),
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EmbedModelInfo("Alibaba-NLP/gte-large-en-v1.5",
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architecture="GteNewModel",
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enable_test=True),
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########### Qwen2ForCausalLM
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EmbedModelInfo("Alibaba-NLP/gte-Qwen2-1.5B-instruct",
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architecture="Qwen2ForCausalLM",
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enable_test=True),
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########## ModernBertModel
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EmbedModelInfo("Alibaba-NLP/gte-modernbert-base",
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architecture="ModernBertModel",
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enable_test=True),
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########## Qwen3ForCausalLM
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EmbedModelInfo("Qwen/Qwen3-Embedding-0.6B",
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architecture="Qwen3ForCausalLM",
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dtype="float32",
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enable_test=True),
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EmbedModelInfo("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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@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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vllm_extra_kwargs: dict[str, Any] = {}
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if model_info.architecture == "GteNewModel":
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vllm_extra_kwargs["hf_overrides"] = {"architectures": ["GteNewModel"]}
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mteb_test_embed_models(hf_runner, vllm_runner, model_info,
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vllm_extra_kwargs)
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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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vllm_extra_kwargs: dict[str, Any] = {}
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if model_info.architecture == "GteNewModel":
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vllm_extra_kwargs["hf_overrides"] = {"architectures": ["GteNewModel"]}
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correctness_test_embed_models(hf_runner, vllm_runner, model_info,
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example_prompts, vllm_extra_kwargs)
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