[ROCm][CI] Fix ModernBERT token classification test (#31612)

Signed-off-by: Andreas Karatzas <akaratza@amd.com>

tests/models/language/pooling/test_token_classification.py

@@ -34,8 +34,8 @@ def test_bert_models(
 
     # 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()
+        hf_output = hf_output.detach().clone().cpu().float()
+        vllm_output = vllm_output.detach().clone().cpu().float()
         assert torch.allclose(hf_output, vllm_output, 1e-2)
 
 
@@ -49,11 +49,22 @@ def test_modernbert_models(
     model: str,
     dtype: str,
 ) -> None:
+    from vllm.platforms import current_platform
+
     with vllm_runner(model, max_model_len=None, dtype=dtype) as vllm_model:
         vllm_outputs = vllm_model.token_classify(example_prompts)
 
+    # Use eager attention on ROCm to avoid HF Transformers flash attention
+    # accuracy issues: https://github.com/vllm-project/vllm/issues/30167
+    hf_model_kwargs = {}
+    if current_platform.is_rocm():
+        hf_model_kwargs["attn_implementation"] = "eager"
+
     with hf_runner(
-        model, dtype=dtype, auto_cls=AutoModelForTokenClassification
+        model,
+        dtype=dtype,
+        auto_cls=AutoModelForTokenClassification,
+        model_kwargs=hf_model_kwargs,
     ) as hf_model:
         tokenizer = hf_model.tokenizer
         hf_outputs = []
@@ -65,8 +76,8 @@ def test_modernbert_models(
 
     # 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()
+        hf_output = hf_output.detach().clone().cpu().float()
+        vllm_output = vllm_output.detach().clone().cpu().float()
         assert torch.allclose(hf_output, vllm_output, atol=1e-2)
 
 
@@ -96,6 +107,6 @@ def test_auto_conversion(
 
     # 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()
+        hf_output = hf_output.detach().clone().cpu().float()
+        vllm_output = vllm_output.detach().clone().cpu().float()
         assert torch.allclose(hf_output, vllm_output, atol=1e-2)