test_rerank.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import pytest
import requests
import torch
import torch.nn.functional as F

from tests.utils import RemoteOpenAIServer
from vllm.entrypoints.openai.protocol import RerankResponse

MODEL_NAME = "BAAI/bge-reranker-base"
DTYPE = "bfloat16"


@pytest.fixture(scope="module")
def server():
    args = ["--enforce-eager", "--max-model-len", "100", "--dtype", DTYPE]

    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
        yield remote_server


@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_rerank_texts(server: RemoteOpenAIServer, model_name: str):
    query = "What is the capital of France?"
    documents = [
        "The capital of Brazil is Brasilia.",
        "The capital of France is Paris.",
    ]

    rerank_response = requests.post(
        server.url_for("rerank"),
        json={
            "model": model_name,
            "query": query,
            "documents": documents,
        },
    )
    rerank_response.raise_for_status()
    rerank = RerankResponse.model_validate(rerank_response.json())

    assert rerank.id is not None
    assert rerank.results is not None
    assert len(rerank.results) == 2
    assert rerank.results[0].relevance_score >= 0.9
    assert rerank.results[1].relevance_score <= 0.01


@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_top_n(server: RemoteOpenAIServer, model_name: str):
    query = "What is the capital of France?"
    documents = [
        "The capital of Brazil is Brasilia.",
        "The capital of France is Paris.",
        "Cross-encoder models are neat",
    ]

    rerank_response = requests.post(
        server.url_for("rerank"),
        json={"model": model_name, "query": query, "documents": documents, "top_n": 2},
    )
    rerank_response.raise_for_status()
    rerank = RerankResponse.model_validate(rerank_response.json())

    assert rerank.id is not None
    assert rerank.results is not None
    assert len(rerank.results) == 2
    assert rerank.results[0].relevance_score >= 0.9
    assert rerank.results[1].relevance_score <= 0.01


@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_rerank_max_model_len(server: RemoteOpenAIServer, model_name: str):
    query = "What is the capital of France?" * 100
    documents = [
        "The capital of Brazil is Brasilia.",
        "The capital of France is Paris.",
    ]

    rerank_response = requests.post(
        server.url_for("rerank"),
        json={"model": model_name, "query": query, "documents": documents},
    )
    assert rerank_response.status_code == 400
    # Assert just a small fragments of the response
    assert "Please reduce the length of the input." in rerank_response.text


def test_invocations(server: RemoteOpenAIServer):
    query = "What is the capital of France?"
    documents = [
        "The capital of Brazil is Brasilia.",
        "The capital of France is Paris.",
    ]

    request_args = {
        "model": MODEL_NAME,
        "query": query,
        "documents": documents,
    }

    rerank_response = requests.post(server.url_for("rerank"), json=request_args)
    rerank_response.raise_for_status()

    invocation_response = requests.post(
        server.url_for("invocations"), json=request_args
    )
    invocation_response.raise_for_status()

    rerank_output = rerank_response.json()
    invocation_output = invocation_response.json()

    assert rerank_output.keys() == invocation_output.keys()
    for rerank_result, invocations_result in zip(
        rerank_output["results"], invocation_output["results"]
    ):
        assert rerank_result.keys() == invocations_result.keys()
        assert rerank_result["relevance_score"] == pytest.approx(
            invocations_result["relevance_score"], rel=0.05
        )
        # TODO: reset this tolerance to 0.01 once we find
        # an alternative to flash_attn with bfloat16


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_activation(server: RemoteOpenAIServer, model_name: str):
    async def get_outputs(activation):
        query = "What is the capital of France?"
        documents = [
            "The capital of Brazil is Brasilia.",
            "The capital of France is Paris.",
        ]

        response = requests.post(
            server.url_for("rerank"),
            json={
                "model": model_name,
                "query": query,
                "documents": documents,
                "activation": activation,
            },
        )
        outputs = response.json()

        return torch.tensor([x["relevance_score"] for x in outputs["results"]])

    default = await get_outputs(activation=None)
    w_activation = await get_outputs(activation=True)
    wo_activation = await get_outputs(activation=False)

    assert torch.allclose(default, w_activation, atol=1e-2), (
        "Default should use activation."
    )
    assert not torch.allclose(w_activation, wo_activation, atol=1e-2), (
        "wo_activation should not use activation."
    )
    assert torch.allclose(F.sigmoid(wo_activation), w_activation, atol=1e-2), (
        "w_activation should be close to activation(wo_activation)."
    )