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[Misc] Clean up test docstrings and names (#17521)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2025-05-01 20:19:32 +08:00
committed by GitHub
parent 1903c0b8a3
commit 48e925fab5
19 changed files with 51 additions and 115 deletions
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6
tests/models/language/pooling/test_cls_models.py → tests/models/language/pooling/test_classification.py
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@@ -1,8 +1,4 @@
# SPDX-License-Identifier: Apache-2.0
"""Compare the classification outputs of HF and vLLM models.
Run `pytest tests/models/test_cls_models.py`.
"""
import pytest
import torch
from transformers import AutoModelForSequenceClassification
@@ -19,7 +15,7 @@ from vllm.platforms import current_platform
)
@pytest.mark.parametrize("dtype",
["half"] if current_platform.is_rocm() else ["float"])
def test_classification_models(
def test_models(
hf_runner,
vllm_runner,
example_prompts,

4
tests/models/language/pooling/test_embedding.py
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@@ -1,8 +1,4 @@
# SPDX-License-Identifier: Apache-2.0
"""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

11
tests/models/language/pooling/test_jina.py
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@@ -1,9 +1,4 @@
# SPDX-License-Identifier: Apache-2.0
# ruff: noqa: E501
"""Compare the scoring outputs of HF and vLLM models.
Run `pytest tests/models/embedding/language/test_jina.py`.
"""
import math
import pytest
@@ -22,9 +17,9 @@ TEXTS_2 = [
"Organic skincare for sensitive skin with aloe vera and chamomile.",
"New makeup trends focus on bold colors and innovative techniques",
"Bio-Hautpflege für empfindliche Haut mit Aloe Vera und Kamille",
"Neue Make-up-Trends setzen auf kräftige Farben und innovative Techniken",
"Cuidado de la piel orgánico para piel sensible con aloe vera y manzanilla",
"Las nuevas tendencias de maquillaje se centran en colores vivos y técnicas innovadoras",
"Neue Make-up-Trends setzen auf kräftige Farben und innovative Techniken", # noqa: E501
"Cuidado de la piel orgánico para piel sensible con aloe vera y manzanilla", # noqa: E501
"Las nuevas tendencias de maquillaje se centran en colores vivos y técnicas innovadoras", # noqa: E501
"针对敏感肌专门设计的天然有机护肤产品",
"新的化妆趋势注重鲜艳的颜色和创新的技巧",
"敏感肌のために特別に設計された天然有機スキンケア製品",

61
tests/models/language/pooling/test_scoring.py
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@@ -1,15 +1,11 @@
# SPDX-License-Identifier: Apache-2.0
"""Compare the scoring outputs of HF and vLLM models.
Run `pytest tests/models/embedding/language/test_scoring.py`.
"""
import math
import pytest
import torch
import torch.nn.functional as F
MODELS = [
CROSS_ENCODER_MODELS = [
"cross-encoder/ms-marco-MiniLM-L-6-v2", # Bert
"BAAI/bge-reranker-v2-m3", # Roberta
]
@@ -28,21 +24,21 @@ TEXTS_2 = [
"The capital of Germany is Berlin.",
]
DTYPE = "half"
@pytest.fixture(scope="module", params=MODELS)
@pytest.fixture(scope="module", params=CROSS_ENCODER_MODELS)
def model_name(request):
yield request.param
@pytest.mark.parametrize("dtype", ["half"])
def test_llm_1_to_1(vllm_runner, hf_runner, model_name, dtype: str):
def test_cross_encoder_1_to_1(vllm_runner, hf_runner, model_name):
text_pair = [TEXTS_1[0], TEXTS_2[0]]
with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model:
with hf_runner(model_name, dtype=DTYPE, is_cross_encoder=True) as hf_model:
hf_outputs = hf_model.predict([text_pair]).tolist()
with vllm_runner(model_name, task="score", dtype=dtype,
with vllm_runner(model_name, task="score", dtype=DTYPE,
max_model_len=None) as vllm_model:
vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])
@@ -52,18 +48,16 @@ def test_llm_1_to_1(vllm_runner, hf_runner, model_name, dtype: str):
assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
@pytest.mark.parametrize("dtype", ["half"])
def test_llm_1_to_N(vllm_runner, hf_runner, model_name, dtype: str):
def test_cross_encoder_1_to_N(vllm_runner, hf_runner, model_name):
text_pairs = [
[TEXTS_1[0], TEXTS_2[0]],
[TEXTS_1[0], TEXTS_2[1]],
]
with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model:
with hf_runner(model_name, dtype=DTYPE, is_cross_encoder=True) as hf_model:
hf_outputs = hf_model.predict(text_pairs).tolist()
with vllm_runner(model_name, task="score", dtype=dtype,
with vllm_runner(model_name, task="score", dtype=DTYPE,
max_model_len=None) as vllm_model:
vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)
@@ -74,18 +68,16 @@ def test_llm_1_to_N(vllm_runner, hf_runner, model_name, dtype: str):
assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)
@pytest.mark.parametrize("dtype", ["half"])
def test_llm_N_to_N(vllm_runner, hf_runner, model_name, dtype: str):
def test_cross_encoder_N_to_N(vllm_runner, hf_runner, model_name):
text_pairs = [
[TEXTS_1[0], TEXTS_2[0]],
[TEXTS_1[1], TEXTS_2[1]],
]
with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model:
with hf_runner(model_name, dtype=DTYPE, is_cross_encoder=True) as hf_model:
hf_outputs = hf_model.predict(text_pairs).tolist()
with vllm_runner(model_name, task="score", dtype=dtype,
with vllm_runner(model_name, task="score", dtype=DTYPE,
max_model_len=None) as vllm_model:
vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2)
@@ -101,13 +93,10 @@ def emb_model_name(request):
yield request.param
@pytest.mark.parametrize("dtype", ["half"])
def test_llm_1_to_1_embedding(vllm_runner, hf_runner, emb_model_name,
dtype: str):
def test_embedding_1_to_1(vllm_runner, hf_runner, emb_model_name):
text_pair = [TEXTS_1[0], TEXTS_2[0]]
with hf_runner(emb_model_name, dtype=dtype,
with hf_runner(emb_model_name, dtype=DTYPE,
is_sentence_transformer=True) as hf_model:
hf_embeddings = hf_model.encode(text_pair)
hf_outputs = [
@@ -116,7 +105,7 @@ def test_llm_1_to_1_embedding(vllm_runner, hf_runner, emb_model_name,
with vllm_runner(emb_model_name,
task="embed",
dtype=dtype,
dtype=DTYPE,
max_model_len=None) as vllm_model:
vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])
@@ -126,16 +115,13 @@ def test_llm_1_to_1_embedding(vllm_runner, hf_runner, emb_model_name,
assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
@pytest.mark.parametrize("dtype", ["half"])
def test_llm_1_to_N_embedding(vllm_runner, hf_runner, emb_model_name,
dtype: str):
def test_embedding_1_to_N(vllm_runner, hf_runner, emb_model_name):
text_pairs = [
[TEXTS_1[0], TEXTS_2[0]],
[TEXTS_1[0], TEXTS_2[1]],
]
with hf_runner(emb_model_name, dtype=dtype,
with hf_runner(emb_model_name, dtype=DTYPE,
is_sentence_transformer=True) as hf_model:
hf_embeddings = [
hf_model.encode(text_pair) for text_pair in text_pairs
@@ -147,7 +133,7 @@ def test_llm_1_to_N_embedding(vllm_runner, hf_runner, emb_model_name,
with vllm_runner(emb_model_name,
task="embed",
dtype=dtype,
dtype=DTYPE,
max_model_len=None) as vllm_model:
vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)
@@ -158,16 +144,13 @@ def test_llm_1_to_N_embedding(vllm_runner, hf_runner, emb_model_name,
assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)
@pytest.mark.parametrize("dtype", ["half"])
def test_llm_N_to_N_embedding(vllm_runner, hf_runner, emb_model_name,
dtype: str):
def test_embedding_N_to_N(vllm_runner, hf_runner, emb_model_name):
text_pairs = [
[TEXTS_1[0], TEXTS_2[0]],
[TEXTS_1[1], TEXTS_2[1]],
]
with hf_runner(emb_model_name, dtype=dtype,
with hf_runner(emb_model_name, dtype=DTYPE,
is_sentence_transformer=True) as hf_model:
hf_embeddings = [
hf_model.encode(text_pair) for text_pair in text_pairs
@@ -179,7 +162,7 @@ def test_llm_N_to_N_embedding(vllm_runner, hf_runner, emb_model_name,
with vllm_runner(emb_model_name,
task="embed",
dtype=dtype,
dtype=DTYPE,
max_model_len=None) as vllm_model:
vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2)

4
tests/models/language/pooling/test_snowflake_arctic_embed.py
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@@ -1,8 +1,4 @@
# SPDX-License-Identifier: Apache-2.0
"""Compare the embedding outputs of HF and vLLM models.
Run `pytest tests/models/embedding/language/test_snowflake_arctic_embed.py`.
"""
import pytest
from ...utils import EmbedModelInfo, check_embeddings_close

24
tests/models/language/pooling/test_truncation_control.py
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@@ -5,18 +5,18 @@ MODEL_NAME = "sentence-transformers/all-MiniLM-L12-v2"
max_model_len = 128
input_str = """Immerse yourself in the enchanting chronicle of calculus, a
mathematical domain that has radically transformed our comprehension of
change and motion. Despite its roots in ancient civilizations, the
formal birth of calculus predominantly occurred in the 17th century,
primarily under the influential guidance of Sir Isaac Newton and Gottfried
Wilhelm Leibniz. The earliest traces of calculus concepts are found in
ancient Greek mathematics,most notably in the works of Eudoxus and
Archimedes, around 300 BCE. They utilized the 'method of exhaustion'—a
technique for computing areas and volumes through the use of finite sums.
This methodology laid crucial foundational work for integral calculus.
In the 17th century, both Newton and Leibniz independently pioneered
calculus, each contributing unique perspectives that would shape this new
field."""
mathematical domain that has radically transformed our comprehension of
change and motion. Despite its roots in ancient civilizations, the
formal birth of calculus predominantly occurred in the 17th century,
primarily under the influential guidance of Sir Isaac Newton and Gottfried
Wilhelm Leibniz. The earliest traces of calculus concepts are found in
ancient Greek mathematics,most notably in the works of Eudoxus and
Archimedes, around 300 BCE. They utilized the 'method of exhaustion'—a
technique for computing areas and volumes through the use of finite sums.
This methodology laid crucial foundational work for integral calculus.
In the 17th century, both Newton and Leibniz independently pioneered
calculus, each contributing unique perspectives that would shape this new
field."""
def test_smaller_truncation_size(vllm_runner,