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[Frontend][1/n] Make pooling entrypoints request schema consensus | CompletionRequest (#32395)

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Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>
This commit is contained in:
wang.yuqi
2026-01-16 14:17:04 +08:00
committed by GitHub
parent 73f635a75f
commit 4ae77dfd42
22 changed files with 635 additions and 600 deletions
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147
tests/entrypoints/pooling/classify/test_online.py
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@@ -12,6 +12,8 @@ from vllm.entrypoints.pooling.pooling.protocol import PoolingResponse
MODEL_NAME = "jason9693/Qwen2.5-1.5B-apeach"
DTYPE = "float32" # Use float32 to avoid NaN issue
input_text = "This product was excellent and exceeded my expectations"
input_tokens = [1986, 1985, 572, 9073, 323, 33808, 847, 16665]
@pytest.fixture(scope="module")
@@ -29,9 +31,23 @@ def server():
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_single_input_classification(server: RemoteOpenAIServer, model_name: str):
input_text = "This product was excellent and exceeded my expectations"
def test_basic(server: RemoteOpenAIServer, model_name: str):
# test /v1/models
response = requests.get(server.url_for("/v1/models"))
served_model = response.json()["data"][0]["id"]
assert served_model == MODEL_NAME
# test /tokenize
response = requests.post(
server.url_for("/tokenize"),
json={"model": model_name, "prompt": input_text},
)
assert response.json()["tokens"] == input_tokens
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_completion_request(server: RemoteOpenAIServer, model_name: str):
# test input: str
classification_response = requests.post(
server.url_for("classify"),
json={"model": model_name, "input": input_text},
@@ -46,35 +62,34 @@ def test_single_input_classification(server: RemoteOpenAIServer, model_name: str
assert hasattr(output.data[0], "label")
assert hasattr(output.data[0], "probs")
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_add_special_tokens_false(server: RemoteOpenAIServer, model_name: str):
response = requests.post(
server.url_for("classify"),
json={"model": model_name, "input": "hello", "add_special_tokens": False},
)
response.raise_for_status()
ClassificationResponse.model_validate(response.json())
@pytest.mark.parametrize("model_name", [MODEL_NAME])
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",
"The customer service was helpful and resolved my issue quickly",
"This product broke after one week, terrible quality",
"I'm very disappointed with this purchase, complete waste of money",
"The customer service was rude and unhelpful",
]
# test input: list[int]
classification_response = requests.post(
server.url_for("classify"),
json={"model": model_name, "input": input_texts},
json={"model": model_name, "input": input_tokens},
)
classification_response.raise_for_status()
output = ClassificationResponse.model_validate(classification_response.json())
assert output.object == "list"
assert output.model == MODEL_NAME
assert len(output.data) == 1
assert hasattr(output.data[0], "label")
assert hasattr(output.data[0], "probs")
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_completion_request_batched(server: RemoteOpenAIServer, model_name: str):
N = 10
# test input: list[str]
classification_response = requests.post(
server.url_for("classify"),
json={"model": model_name, "input": [input_text] * N},
)
output = ClassificationResponse.model_validate(classification_response.json())
assert len(output.data) == len(input_texts)
assert len(output.data) == N
for i, item in enumerate(output.data):
assert item.index == i
assert hasattr(item, "label")
@@ -82,6 +97,44 @@ def test_multiple_inputs_classification(server: RemoteOpenAIServer, model_name:
assert len(item.probs) == item.num_classes
assert item.label in ["Default", "Spoiled"]
# test input: list[list[int]]
classification_response = requests.post(
server.url_for("classify"),
json={"model": model_name, "input": [input_tokens] * N},
)
output = ClassificationResponse.model_validate(classification_response.json())
assert len(output.data) == N
for i, item in enumerate(output.data):
assert item.index == i
assert hasattr(item, "label")
assert hasattr(item, "probs")
assert len(item.probs) == item.num_classes
assert item.label in ["Default", "Spoiled"]
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_empty_input_error(server: RemoteOpenAIServer, model_name: str):
classification_response = requests.post(
server.url_for("classify"),
json={"model": model_name, "input": ""},
)
error = classification_response.json()
assert classification_response.status_code == 400
assert "error" in error
classification_response = requests.post(
server.url_for("classify"),
json={"model": model_name, "input": []},
)
classification_response.raise_for_status()
output = ClassificationResponse.model_validate(classification_response.json())
assert output.object == "list"
assert isinstance(output.data, list)
assert len(output.data) == 0
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_truncate_prompt_tokens(server: RemoteOpenAIServer, model_name: str):
@@ -101,11 +154,7 @@ def test_truncate_prompt_tokens(server: RemoteOpenAIServer, model_name: str):
assert output.usage.prompt_tokens == 5
assert output.usage.total_tokens == 5
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_invalid_truncate_prompt_tokens_error(
server: RemoteOpenAIServer, model_name: str
):
# invalid_truncate_prompt_tokens
classification_response = requests.post(
server.url_for("classify"),
json={"model": model_name, "input": "test", "truncate_prompt_tokens": 513},
@@ -117,36 +166,28 @@ def test_invalid_truncate_prompt_tokens_error(
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_empty_input_error(server: RemoteOpenAIServer, model_name: str):
classification_response = requests.post(
def test_add_special_tokens(server: RemoteOpenAIServer, model_name: str):
# FIXME: The add_special_tokens parameter doesn't seem to be working.
response = requests.post(
server.url_for("classify"),
json={"model": model_name, "input": ""},
json={"model": model_name, "input": input_text, "add_special_tokens": False},
)
response.raise_for_status()
ClassificationResponse.model_validate(response.json())
error = classification_response.json()
assert classification_response.status_code == 400
assert "error" in error
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_batch_classification_empty_list(server: RemoteOpenAIServer, model_name: str):
classification_response = requests.post(
response = requests.post(
server.url_for("classify"),
json={"model": model_name, "input": []},
json={"model": model_name, "input": input_text, "add_special_tokens": True},
)
classification_response.raise_for_status()
output = ClassificationResponse.model_validate(classification_response.json())
assert output.object == "list"
assert isinstance(output.data, list)
assert len(output.data) == 0
response.raise_for_status()
ClassificationResponse.model_validate(response.json())
@pytest.mark.asyncio
async def test_invocations(server: RemoteOpenAIServer):
request_args = {
"model": MODEL_NAME,
"input": "This product was excellent and exceeded my expectations",
"input": input_text,
}
classification_response = requests.post(
@@ -175,8 +216,6 @@ async def test_invocations(server: RemoteOpenAIServer):
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_use_activation(server: RemoteOpenAIServer, model_name: str):
input_text = ["This product was excellent and exceeded my expectations"]
async def get_outputs(use_activation):
response = requests.post(
server.url_for("classify"),
@@ -237,7 +276,6 @@ async def test_rerank(server: RemoteOpenAIServer, model_name: str):
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_pooling_classify(server: RemoteOpenAIServer, model_name: str):
input_text = "This product was excellent and exceeded my expectations"
response = requests.post(
server.url_for("pooling"),
json={
@@ -256,7 +294,6 @@ async def test_pooling_classify(server: RemoteOpenAIServer, model_name: str):
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_pooling_token_classify(server: RemoteOpenAIServer, model_name: str):
task = "token_classify"
input_text = ["This product was excellent and exceeded my expectations"]
response = requests.post(
server.url_for("pooling"),
json={
@@ -282,7 +319,7 @@ async def test_pooling_not_supported(
server.url_for("pooling"),
json={
"model": model_name,
"input": "test",
"input": input_text,
"encoding_format": "float",
"task": task,
},