mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-10 00:06:06 +08:00
78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import pytest
|
|
|
|
from ...utils import EmbedModelInfo, run_embedding_correctness_test
|
|
|
|
MODELS = [
|
|
EmbedModelInfo("Snowflake/snowflake-arctic-embed-xs",
|
|
is_matryoshka=False,
|
|
architecture="BertModel",
|
|
enable_test=True),
|
|
EmbedModelInfo("Snowflake/snowflake-arctic-embed-s",
|
|
is_matryoshka=False,
|
|
architecture="BertModel",
|
|
enable_test=False),
|
|
EmbedModelInfo("Snowflake/snowflake-arctic-embed-m",
|
|
is_matryoshka=False,
|
|
architecture="BertModel",
|
|
enable_test=False),
|
|
EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-long",
|
|
is_matryoshka=False,
|
|
architecture="NomicBertModel",
|
|
enable_test=True),
|
|
EmbedModelInfo("Snowflake/snowflake-arctic-embed-l",
|
|
is_matryoshka=False,
|
|
architecture="BertModel",
|
|
enable_test=False),
|
|
EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-v1.5",
|
|
is_matryoshka=True,
|
|
architecture="BertModel",
|
|
enable_test=True),
|
|
EmbedModelInfo("Snowflake/snowflake-arctic-embed-l-v2.0",
|
|
is_matryoshka=True,
|
|
architecture="XLMRobertaModel",
|
|
enable_test=True),
|
|
EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-v2.0",
|
|
is_matryoshka=True,
|
|
architecture="GteModel",
|
|
enable_test=True),
|
|
]
|
|
|
|
|
|
@pytest.mark.parametrize("model_info", MODELS)
|
|
def test_models_mteb(
|
|
hf_runner,
|
|
vllm_runner,
|
|
model_info: EmbedModelInfo,
|
|
) -> None:
|
|
from .mteb_utils import mteb_test_embed_models
|
|
mteb_test_embed_models(hf_runner, vllm_runner, model_info)
|
|
|
|
|
|
@pytest.mark.parametrize("model_info", MODELS)
|
|
def test_models_correctness(
|
|
hf_runner,
|
|
vllm_runner,
|
|
model_info: EmbedModelInfo,
|
|
example_prompts,
|
|
) -> None:
|
|
if not model_info.enable_test:
|
|
pytest.skip("Skipping test.")
|
|
|
|
# ST will strip the input texts, see test_embedding.py
|
|
example_prompts = [str(s).strip() for s in example_prompts]
|
|
|
|
with vllm_runner(model_info.name,
|
|
task="embed",
|
|
dtype=model_info.dtype,
|
|
max_model_len=None) as vllm_model:
|
|
vllm_outputs = vllm_model.encode(example_prompts)
|
|
|
|
with hf_runner(
|
|
model_info.name,
|
|
dtype=model_info.dtype,
|
|
is_sentence_transformer=True,
|
|
) as hf_model:
|
|
run_embedding_correctness_test(hf_model, example_prompts, vllm_outputs)
|