e32014ac1d
### What this PR does / why we need it? Support pooling models (like `bge-reranker-v2-m3`) in vllm-ascend, this pr covered the three model types of embed (cls_token, mean_token, lasttoken). After this [commit](17373dcd93), vllm has provided support for adapting pooling models on the v1 engine. This PR includes corresponding adaptations on the vllm-ascend side. Fixes #1960 - vLLM version: v0.12.0 - vLLM main:ad32e3e19c--------- Signed-off-by: lianyibo <lianyibo1@kunlunit.com> Signed-off-by: MengqingCao <cmq0113@163.com> Co-authored-by: MengqingCao <cmq0113@163.com>
60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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#
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import pytest
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from modelscope import snapshot_download # type: ignore[import-untyped]
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from tests.e2e.conftest import HfRunner, VllmRunner
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from tests.e2e.utils import check_embeddings_close
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MODELS = [
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"Qwen/Qwen3-Embedding-0.6B", # lasttoken
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"BAAI/bge-small-en-v1.5", # cls_token
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"intfloat/multilingual-e5-small" # mean_tokens
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]
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@pytest.mark.parametrize("model", MODELS)
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def test_embed_models_correctness(model: str):
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queries = ['What is the capital of China?', 'Explain gravity']
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model_name = snapshot_download(model)
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with VllmRunner(
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model_name,
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runner="pooling",
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enforce_eager=False,
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max_model_len=None,
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cudagraph_capture_sizes=[4],
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) as vllm_runner:
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vllm_outputs = vllm_runner.embed(queries)
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with HfRunner(
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model_name,
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dtype="float32",
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is_sentence_transformer=True,
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) as hf_runner:
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hf_outputs = hf_runner.encode(queries)
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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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