d4d93db2c5
Signed-off-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com> Co-authored-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com> Co-authored-by: Nicolò Lucchesi <nlucches@redhat.com> Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com> Co-authored-by: Michael Goin <michael@neuralmagic.com>
83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Compare the embedding outputs of HF and vLLM models.
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Run `pytest tests/models/embedding/language/test_embedding.py`.
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"""
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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
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@pytest.mark.parametrize(
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"model",
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[
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# [Encoder-only]
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pytest.param("BAAI/bge-base-en-v1.5",
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marks=[pytest.mark.core_model, pytest.mark.cpu_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-Qwen2-7B-instruct"),
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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("intfloat/e5-mistral-7b-instruct",
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marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
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pytest.param("Alibaba-NLP/gte-Qwen2-1.5B-instruct"),
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pytest.param("ssmits/Qwen2-7B-Instruct-embed-base"),
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# [Cross-Encoder]
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pytest.param("sentence-transformers/stsb-roberta-base-v2"),
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],
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)
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@pytest.mark.parametrize("dtype", ["half"])
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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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dtype: str,
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monkeypatch,
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) -> None:
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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")
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if model == "Alibaba-NLP/gte-Qwen2-1.5B-instruct":
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vllm_extra_kwargs["hf_overrides"] = {"is_causal": True}
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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, dtype=dtype,
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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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task="embed",
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dtype=dtype,
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max_model_len=None,
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**vllm_extra_kwargs) as vllm_model:
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vllm_outputs = vllm_model.encode(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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