[CI] Split pooling from entrypoints Test (#24632)
Signed-off-by: wang.yuqi <noooop@126.com>
This commit is contained in:
wang.yuqi
257
tests/entrypoints/pooling/openai/test_classification.py
Normal file
257
tests/entrypoints/pooling/openai/test_classification.py
Normal file
@@ -0,0 +1,257 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
import pytest
|
||||
import requests
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
from tests.utils import RemoteOpenAIServer
|
||||
from vllm.entrypoints.openai.protocol import ClassificationResponse
|
||||
|
||||
MODEL_NAME = "jason9693/Qwen2.5-1.5B-apeach"
|
||||
DTYPE = "float32" # Use float32 to avoid NaN issue
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def server():
|
||||
args = [
|
||||
"--enforce-eager",
|
||||
"--max-model-len",
|
||||
"512",
|
||||
"--dtype",
|
||||
DTYPE,
|
||||
]
|
||||
|
||||
with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
|
||||
yield remote_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"
|
||||
|
||||
classification_response = requests.post(
|
||||
server.url_for("classify"),
|
||||
json={
|
||||
"model": model_name,
|
||||
"input": input_text
|
||||
},
|
||||
)
|
||||
|
||||
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_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",
|
||||
]
|
||||
|
||||
classification_response = requests.post(
|
||||
server.url_for("classify"),
|
||||
json={
|
||||
"model": model_name,
|
||||
"input": input_texts
|
||||
},
|
||||
)
|
||||
output = ClassificationResponse.model_validate(
|
||||
classification_response.json())
|
||||
|
||||
assert len(output.data) == len(input_texts)
|
||||
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_truncate_prompt_tokens(server: RemoteOpenAIServer, model_name: str):
|
||||
long_text = "hello " * 600
|
||||
|
||||
classification_response = requests.post(
|
||||
server.url_for("classify"),
|
||||
json={
|
||||
"model": model_name,
|
||||
"input": long_text,
|
||||
"truncate_prompt_tokens": 5
|
||||
},
|
||||
)
|
||||
|
||||
classification_response.raise_for_status()
|
||||
output = ClassificationResponse.model_validate(
|
||||
classification_response.json())
|
||||
|
||||
assert len(output.data) == 1
|
||||
assert output.data[0].index == 0
|
||||
assert hasattr(output.data[0], "probs")
|
||||
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):
|
||||
classification_response = requests.post(
|
||||
server.url_for("classify"),
|
||||
json={
|
||||
"model": model_name,
|
||||
"input": "test",
|
||||
"truncate_prompt_tokens": 513
|
||||
},
|
||||
)
|
||||
|
||||
error = classification_response.json()
|
||||
assert classification_response.status_code == 400
|
||||
assert "truncate_prompt_tokens" in error["error"]["message"]
|
||||
|
||||
|
||||
@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
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model_name", [MODEL_NAME])
|
||||
def test_batch_classification_empty_list(server: RemoteOpenAIServer,
|
||||
model_name: str):
|
||||
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.asyncio
|
||||
async def test_invocations(server: RemoteOpenAIServer):
|
||||
request_args = {
|
||||
"model": MODEL_NAME,
|
||||
"input": "This product was excellent and exceeded my expectations"
|
||||
}
|
||||
|
||||
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.raise_for_status()
|
||||
|
||||
classification_output = classification_response.json()
|
||||
invocation_output = invocation_response.json()
|
||||
|
||||
assert classification_output.keys() == invocation_output.keys()
|
||||
for classification_data, invocation_data in zip(
|
||||
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)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("model_name", [MODEL_NAME])
|
||||
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
|
||||
})
|
||||
outputs = response.json()
|
||||
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)."
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("model_name", [MODEL_NAME])
|
||||
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"
|
||||
},
|
||||
)
|
||||
assert response.json()["error"]["type"] == "BadRequestError"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("model_name", [MODEL_NAME])
|
||||
def test_score(server: RemoteOpenAIServer, model_name: str):
|
||||
# score api is only enabled for num_labels == 1.
|
||||
response = requests.post(
|
||||
server.url_for("score"),
|
||||
json={
|
||||
"model": model_name,
|
||||
"text_1": "ping",
|
||||
"text_2": "pong",
|
||||
},
|
||||
)
|
||||
assert response.json()["error"]["type"] == "BadRequestError"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("model_name", [MODEL_NAME])
|
||||
def test_rerank(server: RemoteOpenAIServer, model_name: str):
|
||||
# rerank api is only enabled for num_labels == 1.
|
||||
response = requests.post(
|
||||
server.url_for("rerank"),
|
||||
json={
|
||||
"model": model_name,
|
||||
"query": "ping",
|
||||
"documents": ["pong"],
|
||||
},
|
||||
)
|
||||
assert response.json()["error"]["type"] == "BadRequestError"
|
||||
Reference in New Issue
Block a user