Upgrade to vllm 0.17.0 corex v4.1 overlay

vllm/v1/attention/backends/rocm_aiter_unified_attn.py

@@ -55,6 +55,16 @@ class RocmAiterUnifiedAttentionBackend(RocmAttentionBackend):
     def get_builder_cls() -> type["RocmAttentionMetadataBuilder"]:
         return RocmAttentionMetadataBuilder
 
+    @classmethod
+    def supports_attn_type(cls, attn_type: str) -> bool:
+        """RocmAiterUnifiedAttention supports all attention types."""
+        return attn_type in (
+            AttentionType.DECODER,
+            AttentionType.ENCODER,
+            AttentionType.ENCODER_ONLY,
+            AttentionType.ENCODER_DECODER,
+        )
+
 
 class RocmAiterUnifiedAttentionImpl(RocmAttentionImpl):
     def fused_output_quant_supported(self, quant_key: QuantKey):
@@ -143,6 +153,19 @@ class RocmAiterUnifiedAttentionImpl(RocmAttentionImpl):
 
         num_actual_tokens = attn_metadata.num_actual_tokens
 
+        # Handle encoder attention differently - no KV cache needed
+        if self.attn_type in (AttentionType.ENCODER_ONLY, AttentionType.ENCODER):
+            # For encoder attention,
+            # we use direct Q, K, V tensors without caching
+            return self._forward_encoder_attention(
+                query[:num_actual_tokens],
+                key[:num_actual_tokens],
+                value[:num_actual_tokens],
+                output[:num_actual_tokens],
+                attn_metadata,
+                layer,
+            )
+
         key_cache, value_cache = kv_cache.unbind(0)
 
         if self.kv_cache_dtype.startswith("fp8"):
@@ -195,6 +218,10 @@ class RocmAiterUnifiedAttentionImpl(RocmAttentionImpl):
         kv_cache: torch.Tensor,
         slot_mapping: torch.Tensor,
     ):
+        if self.attn_type in (AttentionType.ENCODER_ONLY, AttentionType.ENCODER):
+            # For encoder attention,
+            # we use direct Q, K, V tensors without caching
+            return
         key_cache, value_cache = kv_cache.unbind(0)
 
         # Reshape the input keys and values and store them in the cache.
@@ -224,6 +251,10 @@ class RocmAiterUnifiedAttentionImpl(RocmAttentionImpl):
         kv_cache: torch.Tensor,
         layer_slot_mapping: torch.Tensor,
     ):
+        if self.attn_type in (AttentionType.ENCODER_ONLY, AttentionType.ENCODER):
+            # For encoder attention,
+            # we use direct Q, K, V tensors without caching
+            return
         key_cache, value_cache = kv_cache.unbind(0)
         flash_layout = True