[Bugfix] Guard `_may_reorder_batch` for encoder-only models on CPU (#24319) (#24348)

Signed-off-by: Remy <eunhwan.shin@dtonic.io>
Co-authored-by: Li, Jiang <jiang1.li@intel.com>

tests/models/language/pooling/test_embedding.py

@@ -27,11 +27,17 @@ from ...utils import check_embeddings_close
         pytest.param("ssmits/Qwen2-7B-Instruct-embed-base",
                      marks=[pytest.mark.cpu_model]),
         # [Encoder-only]
-        pytest.param("BAAI/bge-base-en-v1.5", marks=[pytest.mark.core_model]),
+        pytest.param(
+            "BAAI/bge-base-en-v1.5",
+            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
+        ),
         pytest.param("sentence-transformers/all-MiniLM-L12-v2"),
         pytest.param("intfloat/multilingual-e5-small"),
         # [Cross-Encoder]
-        pytest.param("sentence-transformers/stsb-roberta-base-v2"),
+        pytest.param(
+            "sentence-transformers/stsb-roberta-base-v2",
+            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
+        ),
     ],
 )
 def test_models(

vllm/config/__init__.py

@@ -3665,7 +3665,8 @@ class VllmConfig:
             # logger should only print warning message for hybrid models. As we
             # can't know whether the model is hybrid or not now, so we don't log
             # warning message here and will log it later.
-            if not (current_platform.is_cuda() or current_platform.is_rocm()):
+            if not (current_platform.is_cuda() or current_platform.is_rocm()
+                    or current_platform.is_cpu()):
                 # Hybrid KV cache manager is not supported on non-GPU platforms.
                 self.scheduler_config.disable_hybrid_kv_cache_manager = True
             if self.kv_transfer_config is not None:

vllm/v1/worker/cpu_model_runner.py

@@ -55,11 +55,23 @@ class CPUModelRunner(GPUModelRunner):
             raise ValueError("Multiple KVCacheGroups is not"
                              "currently supported with CPU model runner.")
 
-        assert type(self.attn_groups[0]
-                    [0].metadata_builder) is TorchSDPAMetadataBuilderV1
+        # Guard against encoder-only / pooling models where `attn_groups`
+        # may be empty or lack the expected metadata_builder.
+        # Without this check, accessing `attn_groups[0][0]` would trigger
+        # an AssertionError on CPU backend.
+        if not hasattr(self, "attn_groups") or not self.attn_groups:
+            return
+        if not self.attn_groups[0]:
+            return
+
+        mb = getattr(self.attn_groups[0][0], "metadata_builder", None)
+        if not isinstance(mb, TorchSDPAMetadataBuilderV1):
+            # Encoder-only / rerank models do not benefit from reordering,
+            # so we safely skip here.
+            return
 
-        self.attn_groups[0][0].metadata_builder.reorder_batch(
-            self.input_batch, scheduler_output)
+        # Safe path for decoder/attention-heavy models
+        mb.reorder_batch(self.input_batch, scheduler_output)
 
     def _postprocess_tensors(self) -> None:
         # Note: replace device tensors with cpu tensors