[Bugfix] Fix encoder-only model support for transformers backend

tests/models/registry.py

@@ -886,27 +886,27 @@ def check_available_online(
 
 _TRANSFORMERS_BACKEND_MODELS = {
     "TransformersEmbeddingModel": _HfExamplesInfo(
-        "BAAI/bge-base-en-v1.5", min_transformers_version="5.0.0"
+        "BAAI/bge-base-en-v1.5", min_transformers_version="5.0.0.dev"
     ),
     "TransformersForSequenceClassification": _HfExamplesInfo(
         "papluca/xlm-roberta-base-language-detection",
-        min_transformers_version="5.0.0",
+        min_transformers_version="5.0.0.dev",
     ),
     "TransformersForCausalLM": _HfExamplesInfo(
         "hmellor/Ilama-3.2-1B", trust_remote_code=True
     ),
     "TransformersMultiModalForCausalLM": _HfExamplesInfo("BAAI/Emu3-Chat-hf"),
     "TransformersMoEForCausalLM": _HfExamplesInfo(
-        "allenai/OLMoE-1B-7B-0924", min_transformers_version="5.0.0"
+        "allenai/OLMoE-1B-7B-0924", min_transformers_version="5.0.0.dev"
     ),
     "TransformersMultiModalMoEForCausalLM": _HfExamplesInfo(
-        "Qwen/Qwen3-VL-30B-A3B-Instruct", min_transformers_version="5.0.0"
+        "Qwen/Qwen3-VL-30B-A3B-Instruct", min_transformers_version="5.0.0.dev"
     ),
     "TransformersMoEEmbeddingModel": _HfExamplesInfo(
-        "Qwen/Qwen3-30B-A3B", min_transformers_version="5.0.0"
+        "Qwen/Qwen3-30B-A3B", min_transformers_version="5.0.0.dev"
     ),
     "TransformersMoEForSequenceClassification": _HfExamplesInfo(
-        "Qwen/Qwen3-30B-A3B", min_transformers_version="5.0.0"
+        "Qwen/Qwen3-30B-A3B", min_transformers_version="5.0.0.dev"
     ),
     "TransformersMultiModalEmbeddingModel": _HfExamplesInfo("google/gemma-3-4b-it"),
     "TransformersMultiModalForSequenceClassification": _HfExamplesInfo(

tests/models/test_transformers.py

@@ -82,7 +82,7 @@ def test_models(
     from packaging.version import Version
 
     installed = Version(transformers.__version__)
-    required = Version("5.0.0")
+    required = Version("5.0.0.dev")
     if model == "allenai/OLMoE-1B-7B-0924" and installed < required:
         pytest.skip(
             "MoE models with the Transformers backend require "

vllm/model_executor/models/transformers/base.py

@@ -28,6 +28,7 @@
 from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
 
 from vllm.attention import Attention, AttentionType
+from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
 from vllm.config.utils import getattr_iter
 from vllm.distributed import get_pp_group, get_tp_group
 from vllm.distributed.utils import get_pp_indices
@@ -317,7 +318,7 @@ def create_attention_instances(self) -> dict[int, Attention]:
         # vLLM does not support encoder-decoder models, so if any encoder layer is
         # found in a text only model, we assume the whole model is an encoder model
         if has_encoder(self.model) and not is_multimodal(self.config):
-            self.check_version("4.57.0.dev0", "encoder models support")
+            self.check_version("5.0.0.dev0", "encoder models support")
             attn_type = AttentionType.ENCODER_ONLY
         else:
             attn_type = AttentionType.DECODER
@@ -336,7 +337,12 @@ def create_attention_instances(self) -> dict[int, Attention]:
             ):
                 per_layer_sliding_window = self.config.sliding_window
 
-            attention_instances[i] = Attention(
+            attn_cls = (
+                EncoderOnlyAttention
+                if attn_type == AttentionType.ENCODER_ONLY
+                else Attention
+            )
+            attention_instances[i] = attn_cls(
                 num_heads=num_heads,
                 head_size=head_size,
                 # NOTE: We use Llama scale as default, if it's set by

vllm/model_executor/models/transformers/moe.py

@@ -115,7 +115,7 @@ def transformers_moe_forward_fake(
 
 class MoEMixin(MixtureOfExperts):
     def __init__(self, *, vllm_config: "VllmConfig", prefix: str = ""):
-        self.check_version("4.57.0.dev0", "MoE models support")
+        self.check_version("5.0.0.dev0", "MoE models support")
         # Skip MixtureOfExperts.__init__ and call the next class in MRO
         super(MixtureOfExperts, self).__init__(vllm_config=vllm_config, prefix=prefix)