[New Model] Support BertForTokenClassification / Named Entity Recognition (NER) task (#24872)

Signed-off-by: wang.yuqi <noooop@126.com>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>

tests/models/language/pooling/test_token_classification.py

@@ -0,0 +1,39 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+from transformers import AutoModelForTokenClassification
+
+from tests.models.utils import softmax
+
+
+@pytest.mark.parametrize("model", ["boltuix/NeuroBERT-NER"])
+# The float32 is required for this tiny model to pass the test.
+@pytest.mark.parametrize("dtype", ["float"])
+@torch.inference_mode
+def test_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+) -> None:
+    with vllm_runner(model, max_model_len=None, dtype=dtype) as vllm_model:
+        vllm_outputs = vllm_model.encode(example_prompts)
+
+    with hf_runner(model,
+                   dtype=dtype,
+                   auto_cls=AutoModelForTokenClassification) as hf_model:
+        tokenizer = hf_model.tokenizer
+        hf_outputs = []
+        for prompt in example_prompts:
+            inputs = tokenizer([prompt], return_tensors="pt")
+            inputs = hf_model.wrap_device(inputs)
+            output = hf_model.model(**inputs)
+            hf_outputs.append(softmax(output.logits[0]))
+
+    # check logits difference
+    for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
+        hf_output = torch.tensor(hf_output).cpu().float()
+        vllm_output = torch.tensor(vllm_output).cpu().float()
+        assert torch.allclose(hf_output, vllm_output, 1e-2)