xc-llm-ascend/tests/e2e/singlecard/test_embedding.py

#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
#
from collections.abc import Sequence
from typing import Optional

from modelscope import snapshot_download  # type: ignore[import-untyped]

from tests.e2e.conftest import HfRunner
from tests.e2e.utils import check_embeddings_close, matryoshka_fy


def run_embedding_correctness_test(
    hf_model: "HfRunner",
    inputs: list[str],
    vllm_outputs: Sequence[list[float]],
    dimensions: Optional[int] = None,
):
    hf_outputs = hf_model.encode(inputs)
    if dimensions:
        hf_outputs = matryoshka_fy(hf_outputs, dimensions)

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


# dummy to avoid pytest collect nothing and exit code 5
def test_dummy():
    assert True


def test_embed_models_correctness(hf_runner, vllm_runner):
    queries = ['What is the capital of China?', 'Explain gravity']

    model_name = snapshot_download("Qwen/Qwen3-Embedding-0.6B")
    with vllm_runner(
            model_name,
            task="embed",
            enforce_eager=True,
    ) as vllm_model:
        vllm_outputs = vllm_model.encode(queries)

    with hf_runner(
            model_name,
            dtype="float32",
            is_sentence_transformer=True,
    ) as hf_model:
        run_embedding_correctness_test(hf_model, queries, vllm_outputs)