[Feature] Add process_weights_after_loading to AttentionImpl (#26870)

Signed-off-by: rongfu.leng <rongfu.leng@daocloud.io>

vllm/attention/backends/abstract.py

@@ -207,6 +207,9 @@ class AttentionImpl(ABC, Generic[T]):
         """
         return False
 
+    def process_weights_after_loading(self, act_dtype: torch.dtype):
+        pass
+
 
 class MLAAttentionImpl(AttentionImpl[T], Generic[T]):
     @abstractmethod

vllm/attention/layer.py

@@ -404,17 +404,8 @@ class Attention(nn.Module, AttentionLayerBase):
         return s
 
     def process_weights_after_loading(self, act_dtype: torch.dtype):
-        if hasattr(self.impl, "process_weights_after_loading"):
         self.impl.process_weights_after_loading(act_dtype)
 
-        # FlashInfer requires attention sinks to be float32
-        if self.backend == _Backend.FLASHINFER and hasattr(self.impl, "sinks"):
-            from vllm.v1.attention.backends.flashinfer import FlashInferImpl
-
-            assert isinstance(self.impl, FlashInferImpl)
-            if self.impl.sinks is not None and self.impl.sinks.dtype != torch.float32:
-                self.impl.sinks = self.impl.sinks.to(torch.float32)
-
     def get_attn_backend(self) -> type[AttentionBackend]:
         return self.attn_backend
 

vllm/v1/attention/backends/flashinfer.py

@@ -833,6 +833,11 @@ class FlashInferImpl(AttentionImpl):
 
         return self.support_trtllm_attn
 
+    # FlashInfer requires attention sinks to be float32
+    def process_weights_after_loading(self, act_dtype: torch.dtype):
+        if self.sinks is not None and self.sinks.dtype != torch.float32:
+            self.sinks = self.sinks.to(torch.float32)
+
     def forward(
         self,
         layer: torch.nn.Module,