Sync from v0.13

tests/models/language/pooling/test_classification.py

@@ -0,0 +1,49 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+from transformers import AutoModelForSequenceClassification
+
+from vllm.platforms import current_platform
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        pytest.param(
+            "jason9693/Qwen2.5-1.5B-apeach",
+            marks=[
+                pytest.mark.core_model,
+                pytest.mark.cpu_model,
+                pytest.mark.slow_test,
+            ],
+        ),
+    ],
+)
+@pytest.mark.parametrize("dtype", ["half"] if current_platform.is_rocm() else ["float"])
+def test_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+) -> None:
+    with vllm_runner(model, max_model_len=512, dtype=dtype) as vllm_model:
+        vllm_outputs = vllm_model.classify(example_prompts)
+
+    with hf_runner(
+        model, dtype=dtype, auto_cls=AutoModelForSequenceClassification
+    ) as hf_model:
+        hf_outputs = hf_model.classify(example_prompts)
+
+    # check logits difference
+    for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
+        hf_output = torch.tensor(hf_output)
+        vllm_output = torch.tensor(vllm_output)
+
+        # the tolerance value of 1e-2 is selected based on the
+        # half datatype tests in
+        # tests/models/language/pooling/test_embedding.py
+        assert torch.allclose(
+            hf_output, vllm_output, 1e-3 if dtype == "float" else 1e-2
+        )