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

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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
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96
tests/models/language/pooling/test_pooler_config_init_behaviour.py
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@@ -10,10 +10,7 @@ from vllm.config import PoolerConfig
@pytest.mark.parametrize(
"model",
[
"jason9693/Qwen2.5-1.5B-apeach",
"papluca/xlm-roberta-base-language-detection"
],
["jason9693/Qwen2.5-1.5B-apeach", "papluca/xlm-roberta-base-language-detection"],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_classify_models_using_activation(
@@ -23,30 +20,32 @@ def test_classify_models_using_activation(
model: str,
dtype: str,
) -> None:
with vllm_runner(
model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(activation=False)) as vllm_model:
model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(activation=False),
) as vllm_model:
wo_activation_out = vllm_model.classify(example_prompts)
with vllm_runner(
model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(activation=True)) as vllm_model:
model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(activation=True),
) as vllm_model:
w_activation_out = vllm_model.classify(example_prompts)
for wo_activation, w_activation in zip(wo_activation_out,
w_activation_out):
for wo_activation, w_activation in zip(wo_activation_out, w_activation_out):
wo_activation = torch.tensor(wo_activation)
w_activation = torch.tensor(w_activation)
assert not torch.allclose(wo_activation, w_activation,
atol=1e-2), "pooler_config is not working"
assert torch.allclose(softmax(wo_activation), w_activation,
1e-3 if dtype == "float" else 1e-2)
assert not torch.allclose(wo_activation, w_activation, atol=1e-2), (
"pooler_config is not working"
)
assert torch.allclose(
softmax(wo_activation), w_activation, 1e-3 if dtype == "float" else 1e-2
)
@pytest.mark.parametrize(
@@ -63,26 +62,28 @@ def test_embed_models_using_normalize(
model: str,
dtype: str,
) -> None:
with vllm_runner(
model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(normalize=False)) as vllm_model:
model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(normalize=False),
) as vllm_model:
wo_normalize = torch.tensor(vllm_model.embed(example_prompts))
with vllm_runner(model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(normalize=True)) as vllm_model:
with vllm_runner(
model,
max_model_len=512,
dtype=dtype,
pooler_config=PoolerConfig(normalize=True),
) as vllm_model:
w_normalize = torch.tensor(vllm_model.embed(example_prompts))
assert not torch.allclose(
wo_normalize, w_normalize,
atol=1e-2), "pooler_config normalize is not working"
assert not torch.allclose(wo_normalize, w_normalize, atol=1e-2), (
"pooler_config normalize is not working"
)
assert torch.allclose(
F.normalize(wo_normalize, p=2, dim=-1), w_normalize,
atol=1e-2), "w_normal should be close to normal(wo_normal)."
F.normalize(wo_normalize, p=2, dim=-1), w_normalize, atol=1e-2
), "w_normal should be close to normal(wo_normal)."
@pytest.mark.parametrize(
@@ -99,25 +100,26 @@ def test_reward_models_using_softmax(
model: str,
dtype: str,
) -> None:
with vllm_runner(model,
max_model_len=1024,
dtype=dtype,
pooler_config=PoolerConfig(softmax=False)) as vllm_model:
with vllm_runner(
model,
max_model_len=1024,
dtype=dtype,
pooler_config=PoolerConfig(softmax=False),
) as vllm_model:
wo_softmax = vllm_model.encode(example_prompts)
with vllm_runner(model,
max_model_len=1024,
dtype=dtype,
pooler_config=PoolerConfig(softmax=True)) as vllm_model:
with vllm_runner(
model, max_model_len=1024, dtype=dtype, pooler_config=PoolerConfig(softmax=True)
) as vllm_model:
w_softmax = vllm_model.encode(example_prompts)
for wo, w in zip(wo_softmax, w_softmax):
wo = torch.tensor(wo)
w = torch.tensor(w)
assert not torch.allclose(
wo, w, atol=1e-2), "pooler_config softmax is not working"
assert torch.allclose(
softmax(wo), w,
atol=1e-2), "w_softmax should be close to softmax(wo_softmax)."
assert not torch.allclose(wo, w, atol=1e-2), (
"pooler_config softmax is not working"
)
assert torch.allclose(softmax(wo), w, atol=1e-2), (
"w_softmax should be close to softmax(wo_softmax)."
)