biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

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

Browse Source 
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
Download Patch File Download Diff File Expand all files Collapse all files

61
tests/models/language/pooling/test_scoring.py
View File

@@ -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)