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118 lines
4.0 KiB
Python
118 lines
4.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from functools import partial
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import pytest
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from tests.models.language.pooling.embed_utils import (
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check_embeddings_close, correctness_test_embed_models, matryoshka_fy)
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from tests.models.utils import (CLSPoolingEmbedModelInfo,
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CLSPoolingRerankModelInfo, EmbedModelInfo,
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RerankModelInfo)
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from vllm import PoolingParams
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from .mteb_utils import mteb_test_embed_models, mteb_test_rerank_models
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EMBEDDING_MODELS = [
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CLSPoolingEmbedModelInfo("jinaai/jina-embeddings-v3",
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mteb_score=0.824413164,
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architecture="XLMRobertaModel",
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is_matryoshka=True)
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]
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RERANK_MODELS = [
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CLSPoolingRerankModelInfo(
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"jinaai/jina-reranker-v2-base-multilingual",
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mteb_score=0.33643,
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architecture="XLMRobertaForSequenceClassification")
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]
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@pytest.mark.parametrize("model_info", EMBEDDING_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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def hf_model_callback(model):
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model.encode = partial(model.encode, task="text-matching")
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mteb_test_embed_models(hf_runner,
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vllm_runner,
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model_info,
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hf_model_callback=hf_model_callback)
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@pytest.mark.parametrize("model_info", EMBEDDING_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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def hf_model_callback(model):
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model.encode = partial(model.encode, task="text-matching")
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correctness_test_embed_models(hf_runner,
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vllm_runner,
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model_info,
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example_prompts,
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hf_model_callback=hf_model_callback)
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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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@pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
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@pytest.mark.parametrize("dtype", ["half"])
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@pytest.mark.parametrize("dimensions", [16, 32])
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def test_matryoshka(
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hf_runner,
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vllm_runner,
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model_info,
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dtype: str,
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dimensions: int,
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example_prompts,
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monkeypatch,
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) -> None:
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if not model_info.is_matryoshka:
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pytest.skip("Model is not matryoshka")
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# ST will strip the input texts, see test_embedding.py
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example_prompts = [str(s).strip() for s in example_prompts]
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with hf_runner(
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model_info.name,
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dtype=dtype,
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is_sentence_transformer=True,
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) as hf_model:
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hf_outputs = hf_model.encode(example_prompts, task="text-matching")
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hf_outputs = matryoshka_fy(hf_outputs, dimensions)
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with vllm_runner(model_info.name,
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runner="pooling",
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dtype=dtype,
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max_model_len=None) as vllm_model:
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assert vllm_model.llm.llm_engine.model_config.is_matryoshka
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matryoshka_dimensions = (
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vllm_model.llm.llm_engine.model_config.matryoshka_dimensions)
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assert matryoshka_dimensions is not None
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if dimensions not in matryoshka_dimensions:
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with pytest.raises(ValueError):
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vllm_model.embed(
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example_prompts,
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pooling_params=PoolingParams(dimensions=dimensions))
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else:
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vllm_outputs = vllm_model.embed(
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example_prompts,
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pooling_params=PoolingParams(dimensions=dimensions))
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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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