[Model] Pooling model activation supports per request control by PoolingParams (#20538)

Signed-off-by: wang.yuqi <noooop@126.com>

tests/entrypoints/openai/test_classification.py

@@ -3,6 +3,8 @@
 
 import pytest
 import requests
+import torch
+import torch.nn.functional as F
 
 from vllm.entrypoints.openai.protocol import ClassificationResponse
 
@@ -181,3 +183,32 @@ async def test_invocations(server: RemoteOpenAIServer):
         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)."