57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
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# 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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EmbedModelInfo(
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"voyageai/voyage-4-nano",
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architecture="VoyageQwen3BidirectionalEmbedModel",
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enable_test=True,
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seq_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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hf_overrides={
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"architectures": ["VoyageQwen3BidirectionalEmbedModel"],
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"num_labels": 2048,
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},
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mteb_score=0.7054,
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# === MTEB Results ===
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# STS12: 0.6613
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# STS13: 0.6906
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# STS14: 0.6556
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# STS15: 0.7843
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# STS16: 0.7340
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# STSBenchmark: 0.7063
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# Average score: 0.7054
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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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# Encoder-only attention models need enforce_eager=True to avoid
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# CUDA graph capture issues with piecewise compilation
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mteb_test_embed_models(
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hf_runner, vllm_runner, model_info, vllm_extra_kwargs={"enforce_eager": True}
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)
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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(
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hf_runner,
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vllm_runner,
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model_info,
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example_prompts,
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vllm_extra_kwargs={"enforce_eager": True},
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)
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