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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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111
tests/entrypoints/pooling/openai/test_classification.py
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@@ -28,21 +28,16 @@ def server():
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_single_input_classification(server: RemoteOpenAIServer,
model_name: str):
def test_single_input_classification(server: RemoteOpenAIServer, model_name: str):
input_text = "This product was excellent and exceeded my expectations"
classification_response = requests.post(
server.url_for("classify"),
json={
"model": model_name,
"input": input_text
},
json={"model": model_name, "input": input_text},
)
classification_response.raise_for_status()
output = ClassificationResponse.model_validate(
classification_response.json())
output = ClassificationResponse.model_validate(classification_response.json())
assert output.object == "list"
assert output.model == MODEL_NAME
@@ -52,8 +47,7 @@ def test_single_input_classification(server: RemoteOpenAIServer,
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_multiple_inputs_classification(server: RemoteOpenAIServer,
model_name: str):
def test_multiple_inputs_classification(server: RemoteOpenAIServer, model_name: str):
input_texts = [
"The product arrived on time and works perfectly",
"I'm very satisfied with my purchase, would buy again",
@@ -65,13 +59,9 @@ def test_multiple_inputs_classification(server: RemoteOpenAIServer,
classification_response = requests.post(
server.url_for("classify"),
json={
"model": model_name,
"input": input_texts
},
json={"model": model_name, "input": input_texts},
)
output = ClassificationResponse.model_validate(
classification_response.json())
output = ClassificationResponse.model_validate(classification_response.json())
assert len(output.data) == len(input_texts)
for i, item in enumerate(output.data):
@@ -88,16 +78,11 @@ def test_truncate_prompt_tokens(server: RemoteOpenAIServer, model_name: str):
classification_response = requests.post(
server.url_for("classify"),
json={
"model": model_name,
"input": long_text,
"truncate_prompt_tokens": 5
},
json={"model": model_name, "input": long_text, "truncate_prompt_tokens": 5},
)
classification_response.raise_for_status()
output = ClassificationResponse.model_validate(
classification_response.json())
output = ClassificationResponse.model_validate(classification_response.json())
assert len(output.data) == 1
assert output.data[0].index == 0
@@ -107,15 +92,12 @@ def test_truncate_prompt_tokens(server: RemoteOpenAIServer, model_name: str):
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_invalid_truncate_prompt_tokens_error(server: RemoteOpenAIServer,
model_name: str):
def test_invalid_truncate_prompt_tokens_error(
server: RemoteOpenAIServer, model_name: str
):
classification_response = requests.post(
server.url_for("classify"),
json={
"model": model_name,
"input": "test",
"truncate_prompt_tokens": 513
},
json={"model": model_name, "input": "test", "truncate_prompt_tokens": 513},
)
error = classification_response.json()
@@ -127,10 +109,7 @@ def test_invalid_truncate_prompt_tokens_error(server: RemoteOpenAIServer,
def test_empty_input_error(server: RemoteOpenAIServer, model_name: str):
classification_response = requests.post(
server.url_for("classify"),
json={
"model": model_name,
"input": ""
},
json={"model": model_name, "input": ""},
)
error = classification_response.json()
@@ -139,18 +118,13 @@ def test_empty_input_error(server: RemoteOpenAIServer, model_name: str):
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_batch_classification_empty_list(server: RemoteOpenAIServer,
model_name: str):
def test_batch_classification_empty_list(server: RemoteOpenAIServer, model_name: str):
classification_response = requests.post(
server.url_for("classify"),
json={
"model": model_name,
"input": []
},
json={"model": model_name, "input": []},
)
classification_response.raise_for_status()
output = ClassificationResponse.model_validate(
classification_response.json())
output = ClassificationResponse.model_validate(classification_response.json())
assert output.object == "list"
assert isinstance(output.data, list)
@@ -161,15 +135,17 @@ def test_batch_classification_empty_list(server: RemoteOpenAIServer,
async def test_invocations(server: RemoteOpenAIServer):
request_args = {
"model": MODEL_NAME,
"input": "This product was excellent and exceeded my expectations"
"input": "This product was excellent and exceeded my expectations",
}
classification_response = requests.post(server.url_for("classify"),
json=request_args)
classification_response = requests.post(
server.url_for("classify"), json=request_args
)
classification_response.raise_for_status()
invocation_response = requests.post(server.url_for("invocations"),
json=request_args)
invocation_response = requests.post(
server.url_for("invocations"), json=request_args
)
invocation_response.raise_for_status()
classification_output = classification_response.json()
@@ -177,10 +153,12 @@ async def test_invocations(server: RemoteOpenAIServer):
assert classification_output.keys() == invocation_output.keys()
for classification_data, invocation_data in zip(
classification_output["data"], invocation_output["data"]):
classification_output["data"], invocation_output["data"]
):
assert classification_data.keys() == invocation_data.keys()
assert classification_data["probs"] == pytest.approx(
invocation_data["probs"], rel=0.01)
invocation_data["probs"], rel=0.01
)
@pytest.mark.asyncio
@@ -189,27 +167,26 @@ async def test_activation(server: RemoteOpenAIServer, model_name: str):
input_text = ["This product was excellent and exceeded my expectations"]
async def get_outputs(activation):
response = requests.post(server.url_for("classify"),
json={
"model": model_name,
"input": input_text,
"activation": activation
})
response = requests.post(
server.url_for("classify"),
json={"model": model_name, "input": input_text, "activation": activation},
)
outputs = response.json()
return torch.tensor([x['probs'] for x in outputs["data"]])
return torch.tensor([x["probs"] for x in outputs["data"]])
default = await get_outputs(activation=None)
w_activation = await get_outputs(activation=True)
wo_activation = await get_outputs(activation=False)
assert torch.allclose(default, w_activation,
atol=1e-2), "Default should use activation."
assert not torch.allclose(
w_activation, wo_activation,
atol=1e-2), "wo_activation should not use activation."
assert torch.allclose(
F.softmax(wo_activation, dim=-1), w_activation, atol=1e-2
), "w_activation should be close to activation(wo_activation)."
assert torch.allclose(default, w_activation, atol=1e-2), (
"Default should use activation."
)
assert not torch.allclose(w_activation, wo_activation, atol=1e-2), (
"wo_activation should not use activation."
)
assert torch.allclose(F.softmax(wo_activation, dim=-1), w_activation, atol=1e-2), (
"w_activation should be close to activation(wo_activation)."
)
@pytest.mark.asyncio
@@ -218,11 +195,7 @@ def test_pooling(server: RemoteOpenAIServer, model_name: str):
# pooling api uses ALL pooling, which does not support chunked prefill.
response = requests.post(
server.url_for("pooling"),
json={
"model": model_name,
"input": "test",
"encoding_format": "float"
},
json={"model": model_name, "input": "test", "encoding_format": "float"},
)
assert response.json()["error"]["type"] == "BadRequestError"