mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-10 02:55:40 +08:00
109 lines
4.1 KiB
Python
109 lines
4.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
import os
|
|
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",
|
|
marks=[pytest.mark.core_model, pytest.mark.cpu_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.mark.cpu_model,
|
|
pytest.mark.skip_v1
|
|
]),
|
|
pytest.param("sentence-transformers/all-MiniLM-L12-v2",
|
|
marks=[pytest.mark.skip_v1]),
|
|
pytest.param("intfloat/multilingual-e5-small",
|
|
marks=[pytest.mark.skip_v1]),
|
|
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 == "intfloat/e5-mistral-7b-instruct" and current_platform.is_cpu(
|
|
) and os.environ.get("VLLM_USE_V1", "0") == "1":
|
|
pytest.skip("CPU V1 doesn't support sliding window")
|
|
|
|
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,
|
|
task="embed",
|
|
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,
|
|
)
|