test_jina.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from functools import partial

import pytest

from vllm import PoolingParams

from ...utils import EmbedModelInfo, RerankModelInfo
from .embed_utils import (check_embeddings_close,
                          correctness_test_embed_models, matryoshka_fy)
from .mteb_utils import mteb_test_embed_models, mteb_test_rerank_models

EMBEDDING_MODELS = [
    EmbedModelInfo("jinaai/jina-embeddings-v3",
                   architecture="XLMRobertaModel",
                   is_matryoshka=True)
]

RERANK_MODELS = [
    RerankModelInfo("jinaai/jina-reranker-v2-base-multilingual",
                    architecture="XLMRobertaForSequenceClassification")
]


@pytest.fixture(autouse=True)
def v1(run_with_both_engines):
    # Simple autouse wrapper to run both engines for each test
    # This can be promoted up to conftest.py to run for every
    # test in a package
    pass


@pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
def test_embed_models_mteb(hf_runner, vllm_runner,
                           model_info: EmbedModelInfo) -> None:

    def hf_model_callback(model):
        model.encode = partial(model.encode, task="text-matching")

    mteb_test_embed_models(hf_runner,
                           vllm_runner,
                           model_info,
                           hf_model_callback=hf_model_callback)


@pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
def test_embed_models_correctness(hf_runner, vllm_runner,
                                  model_info: EmbedModelInfo,
                                  example_prompts) -> None:

    def hf_model_callback(model):
        model.encode = partial(model.encode, task="text-matching")

    correctness_test_embed_models(hf_runner,
                                  vllm_runner,
                                  model_info,
                                  example_prompts,
                                  hf_model_callback=hf_model_callback)


@pytest.mark.parametrize("model_info", RERANK_MODELS)
def test_rerank_models_mteb(hf_runner, vllm_runner,
                            model_info: RerankModelInfo) -> None:
    mteb_test_rerank_models(hf_runner, vllm_runner, model_info)


@pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("dimensions", [16, 32])
def test_matryoshka(
    hf_runner,
    vllm_runner,
    model_info,
    dtype: str,
    dimensions: int,
    example_prompts,
    monkeypatch,
) -> None:
    if not model_info.is_matryoshka:
        pytest.skip("Model is not matryoshka")

    # ST will strip the input texts, see test_embedding.py
    example_prompts = [str(s).strip() for s in example_prompts]

    with hf_runner(
            model_info.name,
            dtype=dtype,
            is_sentence_transformer=True,
    ) as hf_model:
        hf_outputs = hf_model.encode(example_prompts, task="text-matching")
        hf_outputs = matryoshka_fy(hf_outputs, dimensions)

    with vllm_runner(model_info.name,
                     task="embed",
                     dtype=dtype,
                     max_model_len=None) as vllm_model:
        assert vllm_model.llm.llm_engine.model_config.is_matryoshka

        matryoshka_dimensions = (
            vllm_model.llm.llm_engine.model_config.matryoshka_dimensions)
        assert matryoshka_dimensions is not None

        if dimensions not in matryoshka_dimensions:
            with pytest.raises(ValueError):
                vllm_model.embed(
                    example_prompts,
                    pooling_params=PoolingParams(dimensions=dimensions))
        else:
            vllm_outputs = vllm_model.embed(
                example_prompts,
                pooling_params=PoolingParams(dimensions=dimensions))

            check_embeddings_close(
                embeddings_0_lst=hf_outputs,
                embeddings_1_lst=vllm_outputs,
                name_0="hf",
                name_1="vllm",
                tol=1e-2,
            )