test_embedding.py

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

import pytest

from vllm.config import PoolerConfig
from vllm.platforms import current_platform

from ...utils import check_embeddings_close


@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",
    [
        # Be careful of the order of models, decoder-only models should be
        # placed before encoder-only models, otherwise `Qwen2.5-0.5B-Instruct`
        # case won't pass because gte-Qwen2-1.5B-instruct will cache custom
        # model code with bidirectional attention.
        # [Decoder-only]
        pytest.param("BAAI/bge-multilingual-gemma2",
                     marks=[pytest.mark.core_model]),
        pytest.param(
            "intfloat/e5-mistral-7b-instruct",
            # CPU v1 doesn't support sliding window
            marks=[pytest.mark.core_model]),
        # the qwen models interfere with each other (see PR
        # https://github.com/vllm-project/vllm/pull/18720).
        # To avoid this problem, for now we skip v0 since it will be
        # deprecated anyway.
        pytest.param("ssmits/Qwen2-7B-Instruct-embed-base",
                     marks=[pytest.mark.skip_v0, pytest.mark.cpu_model]),
        # [Encoder-only]
        pytest.param("BAAI/bge-base-en-v1.5", marks=[pytest.mark.core_model]),
        pytest.param("sentence-transformers/all-MiniLM-L12-v2"),
        pytest.param("intfloat/multilingual-e5-small"),
        pytest.param("Alibaba-NLP/gte-Qwen2-1.5B-instruct",
                     marks=[pytest.mark.skip_v1]),
        # [Cross-Encoder]
        pytest.param("sentence-transformers/stsb-roberta-base-v2",
                     marks=[pytest.mark.skip_v1]),
    ],
)
def test_models(
    hf_runner,
    vllm_runner,
    example_prompts,
    model,
    monkeypatch,
) -> None:
    if model == "BAAI/bge-multilingual-gemma2" and current_platform.is_rocm():
        # ROCm Triton FA does not currently support sliding window attention
        # switch to use ROCm CK FA backend
        monkeypatch.setenv("VLLM_USE_TRITON_FLASH_ATTN", "False")

    vllm_extra_kwargs = {}
    if model == "ssmits/Qwen2-7B-Instruct-embed-base":
        vllm_extra_kwargs["override_pooler_config"] = \
            PoolerConfig(pooling_type="MEAN", normalize=False)

    max_model_len: Optional[int] = 512
    if model in [
            "sentence-transformers/all-MiniLM-L12-v2",
            "sentence-transformers/stsb-roberta-base-v2"
    ]:
        max_model_len = None

    # The example_prompts has ending "\n", for example:
    # "Write a short story about a robot that dreams for the first time.\n"
    # sentence_transformers will strip the input texts, see:
    # https://github.com/UKPLab/sentence-transformers/blob/v3.1.1/sentence_transformers/models/Transformer.py#L159
    # This makes the input_ids different between hf_model and vllm_model.
    # So we need to strip the input texts to avoid test failing.
    example_prompts = [str(s).strip() for s in example_prompts]

    with hf_runner(model, is_sentence_transformer=True) as hf_model:
        hf_outputs = hf_model.encode(example_prompts)

    with vllm_runner(model,
                     runner="pooling",
                     max_model_len=max_model_len,
                     **vllm_extra_kwargs) as vllm_model:
        vllm_outputs = vllm_model.embed(example_prompts)

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