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

Convert formatting to use ruff instead of yapf + isort (#26247)

Browse Source 
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
Download Patch File Download Diff File Expand all files Collapse all files

69
tests/models/language/pooling/test_scoring.py
View File

@@ -37,10 +37,9 @@ def test_cross_encoder_1_to_1(vllm_runner, hf_runner, model_name):
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,
runner="pooling",
dtype=DTYPE,
max_model_len=None) as vllm_model:
with vllm_runner(
model_name, runner="pooling", dtype=DTYPE, max_model_len=None
) as vllm_model:
vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])
assert len(vllm_outputs) == 1
@@ -58,10 +57,9 @@ def test_cross_encoder_1_to_N(vllm_runner, hf_runner, model_name):
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,
runner="pooling",
dtype=DTYPE,
max_model_len=None) as vllm_model:
with vllm_runner(
model_name, runner="pooling", dtype=DTYPE, max_model_len=None
) as vllm_model:
vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)
assert len(vllm_outputs) == 2
@@ -80,10 +78,9 @@ def test_cross_encoder_N_to_N(vllm_runner, hf_runner, model_name):
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,
runner="pooling",
dtype=DTYPE,
max_model_len=None) as vllm_model:
with vllm_runner(
model_name, runner="pooling", dtype=DTYPE, max_model_len=None
) as vllm_model:
vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2)
assert len(vllm_outputs) == 2
@@ -101,17 +98,15 @@ def emb_model_name(request):
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,
is_sentence_transformer=True) as hf_model:
with hf_runner(
emb_model_name, dtype=DTYPE, is_sentence_transformer=True
) as hf_model:
hf_embeddings = hf_model.encode(text_pair)
hf_outputs = [
F.cosine_similarity(*map(torch.tensor, hf_embeddings), dim=0)
]
hf_outputs = [F.cosine_similarity(*map(torch.tensor, hf_embeddings), dim=0)]
with vllm_runner(emb_model_name,
runner="pooling",
dtype=DTYPE,
max_model_len=None) as vllm_model:
with vllm_runner(
emb_model_name, runner="pooling", dtype=DTYPE, max_model_len=None
) as vllm_model:
vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])
assert len(vllm_outputs) == 1
@@ -126,20 +121,18 @@ def test_embedding_1_to_N(vllm_runner, hf_runner, emb_model_name):
[TEXTS_1[0], TEXTS_2[1]],
]
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
]
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]
hf_outputs = [
F.cosine_similarity(*map(torch.tensor, pair), dim=0)
for pair in hf_embeddings
]
with vllm_runner(emb_model_name,
runner="pooling",
dtype=DTYPE,
max_model_len=None) as vllm_model:
with vllm_runner(
emb_model_name, runner="pooling", dtype=DTYPE, max_model_len=None
) as vllm_model:
vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)
assert len(vllm_outputs) == 2
@@ -155,20 +148,18 @@ def test_embedding_N_to_N(vllm_runner, hf_runner, emb_model_name):
[TEXTS_1[1], TEXTS_2[1]],
]
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
]
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]
hf_outputs = [
F.cosine_similarity(*map(torch.tensor, pair), dim=0)
for pair in hf_embeddings
]
with vllm_runner(emb_model_name,
runner="pooling",
dtype=DTYPE,
max_model_len=None) as vllm_model:
with vllm_runner(
emb_model_name, runner="pooling", dtype=DTYPE, max_model_len=None
) as vllm_model:
vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2)
assert len(vllm_outputs) == 2