[Platform] Add output for Attention Backend

vllm/attention/backends/abstract.py

@@ -31,6 +31,10 @@ class AttentionType:
 
 class AttentionBackend(ABC):
     """Abstract class for attention backends."""
+    # For some attention backends, we allocate an output tensor before
+    # calling the custom op. When piecewise cudagraph is enabled, this
+    # makes sure the output tensor is allocated inside the cudagraph.
+    accept_output_buffer: bool = False
 
     @staticmethod
     @abstractmethod

vllm/attention/backends/flash_attn.py

@@ -29,6 +29,8 @@
 
 class FlashAttentionBackend(AttentionBackend):
 
+    accept_output_buffer: bool = True
+
     @staticmethod
     def get_supported_head_sizes() -> List[int]:
         return [32, 64, 96, 128, 160, 192, 224, 256]

vllm/attention/layer.py

@@ -110,11 +110,7 @@ def __init__(
         self.use_direct_call = not current_platform.is_cuda_alike(
         ) and not current_platform.is_cpu()
 
-        # For some attention backends, we allocate an output tensor before
-        # calling the custom op. When piecewise cudagraph is enabled, this
-        # makes sure the output tensor is allocated inside the cudagraph.
-        self.use_output = self.backend == _Backend.FLASH_ATTN or \
-            self.backend == _Backend.FLASH_ATTN_VLLM_V1
+        self.use_output = attn_backend.accept_output_buffer
         compilation_config = get_current_vllm_config().compilation_config
         if prefix in compilation_config.static_forward_context:
             raise ValueError(f"Duplicate layer name: {prefix}")

vllm/v1/attention/backends/flash_attn.py

@@ -15,6 +15,8 @@
 
 class FlashAttentionBackend(AttentionBackend):
 
+    accept_output_buffer: bool = True
+
     @staticmethod
     def get_supported_head_sizes() -> List[int]:
         return [32, 64, 96, 128, 160, 192, 224, 256]