vllm/tests/models/embedding/language/test_embedding.py
Maximilien de Bayser 1fe554bac3
treat do_lower_case in the same way as the sentence-transformers library (#11815)
Signed-off-by: Max de Bayser <mbayser@br.ibm.com>
2025-01-09 11:05:43 +08:00

75 lines
2.7 KiB
Python

"""Compare the embedding outputs of HF and vLLM models.
Run `pytest tests/models/embedding/language/test_embedding.py`.
"""
import pytest
from vllm.config import PoolerConfig
from ..utils import check_embeddings_close
@pytest.mark.parametrize(
"model",
[
# [Encoder-only]
pytest.param("BAAI/bge-base-en-v1.5",
marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
pytest.param("sentence-transformers/all-MiniLM-L12-v2"),
pytest.param("intfloat/multilingual-e5-large"),
# [Encoder-decoder]
pytest.param("intfloat/e5-mistral-7b-instruct",
marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
pytest.param("BAAI/bge-multilingual-gemma2",
marks=[pytest.mark.core_model]),
pytest.param("ssmits/Qwen2-7B-Instruct-embed-base"),
pytest.param("Alibaba-NLP/gte-Qwen2-1.5B-instruct"),
pytest.param("Alibaba-NLP/gte-Qwen2-7B-instruct"),
],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_models(
hf_runner,
vllm_runner,
example_prompts,
model,
dtype: str,
) -> None:
vllm_extra_kwargs = {}
if model == "ssmits/Qwen2-7B-Instruct-embed-base":
vllm_extra_kwargs["override_pooler_config"] = \
PoolerConfig(pooling_type="MEAN")
if model == "Alibaba-NLP/gte-Qwen2-7B-instruct":
vllm_extra_kwargs["hf_overrides"] = {"is_causal": False}
# 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, dtype=dtype,
is_sentence_transformer=True) as hf_model:
hf_outputs = hf_model.encode(example_prompts)
with vllm_runner(model,
task="embed",
dtype=dtype,
max_model_len=None,
**vllm_extra_kwargs) as vllm_model:
vllm_outputs = vllm_model.encode(example_prompts)
# This test is for verifying whether the model's extra_repr
# can be printed correctly.
print(vllm_model.model.llm_engine.model_executor.driver_worker.
model_runner.model)
check_embeddings_close(
embeddings_0_lst=hf_outputs,
embeddings_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
tol=1e-2,
)