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103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import Optional
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import pytest
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from vllm.config import PoolerConfig
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from vllm.platforms import current_platform
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from ...utils import check_embeddings_close, check_transformers_version
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@pytest.fixture(autouse=True)
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def v1(run_with_both_engines):
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# Simple autouse wrapper to run both engines for each test
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# This can be promoted up to conftest.py to run for every
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# test in a package
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pass
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@pytest.mark.parametrize(
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"model",
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[
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# Be careful of the order of models, decoder-only models should be
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# placed before encoder-only models, otherwise `Qwen2.5-0.5B-Instruct`
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# case won't pass because gte-Qwen2-1.5B-instruct will cache custom
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# model code with bidirectional attention.
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# [Decoder-only]
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pytest.param("BAAI/bge-multilingual-gemma2",
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marks=[pytest.mark.core_model]),
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pytest.param(
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"intfloat/e5-mistral-7b-instruct",
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# CPU v1 doesn't support sliding window
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marks=[pytest.mark.core_model]),
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# the qwen models interfere with each other (see PR
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# https://github.com/vllm-project/vllm/pull/18720).
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# To avoid this problem, for now we skip v0 since it will be
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# deprecated anyway.
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pytest.param("ssmits/Qwen2-7B-Instruct-embed-base",
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marks=[pytest.mark.skip_v0, pytest.mark.cpu_model]),
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# [Encoder-only]
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pytest.param("BAAI/bge-base-en-v1.5", marks=[pytest.mark.core_model]),
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pytest.param("sentence-transformers/all-MiniLM-L12-v2"),
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pytest.param("intfloat/multilingual-e5-small"),
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pytest.param("Alibaba-NLP/gte-Qwen2-1.5B-instruct",
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marks=[pytest.mark.skip_v1]),
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# [Cross-Encoder]
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pytest.param("sentence-transformers/stsb-roberta-base-v2",
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marks=[pytest.mark.skip_v1]),
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],
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)
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def test_models(
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hf_runner,
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vllm_runner,
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example_prompts,
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model,
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monkeypatch,
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) -> None:
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if model == "Alibaba-NLP/gte-Qwen2-1.5B-instruct":
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check_transformers_version(model, max_transformers_version="4.53.2")
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if model == "BAAI/bge-multilingual-gemma2" and current_platform.is_rocm():
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# ROCm Triton FA does not currently support sliding window attention
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# switch to use ROCm CK FA backend
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monkeypatch.setenv("VLLM_USE_TRITON_FLASH_ATTN", "False")
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vllm_extra_kwargs = {}
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if model == "ssmits/Qwen2-7B-Instruct-embed-base":
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vllm_extra_kwargs["override_pooler_config"] = \
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PoolerConfig(pooling_type="MEAN", normalize=False)
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max_model_len: Optional[int] = 512
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if model in [
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"sentence-transformers/all-MiniLM-L12-v2",
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"sentence-transformers/stsb-roberta-base-v2"
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]:
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max_model_len = None
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# The example_prompts has ending "\n", for example:
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# "Write a short story about a robot that dreams for the first time.\n"
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# sentence_transformers will strip the input texts, see:
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# https://github.com/UKPLab/sentence-transformers/blob/v3.1.1/sentence_transformers/models/Transformer.py#L159
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# This makes the input_ids different between hf_model and vllm_model.
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# So we need to strip the input texts to avoid test failing.
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example_prompts = [str(s).strip() for s in example_prompts]
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with hf_runner(model, is_sentence_transformer=True) as hf_model:
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hf_outputs = hf_model.encode(example_prompts)
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with vllm_runner(model,
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runner="pooling",
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max_model_len=max_model_len,
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**vllm_extra_kwargs) as vllm_model:
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vllm_outputs = vllm_model.embed(example_prompts)
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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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