[Misc] Add get_name method to attention backends

vllm/attention/backends/abstract.py

@@ -9,6 +9,11 @@
 class AttentionBackend(ABC):
     """Abstract class for attention backends."""
 
+    @staticmethod
+    @abstractmethod
+    def get_name() -> str:
+        raise NotImplementedError
+
     @staticmethod
     @abstractmethod
     def get_impl_cls() -> Type["AttentionImpl"]:

vllm/attention/backends/flash_attn.py

@@ -19,6 +19,10 @@
 
 class FlashAttentionBackend(AttentionBackend):
 
+    @staticmethod
+    def get_name() -> str:
+        return "flash-attn"
+
     @staticmethod
     def get_impl_cls() -> Type["FlashAttentionImpl"]:
         return FlashAttentionImpl

vllm/attention/backends/flashinfer.py

@@ -1,16 +1,10 @@
 from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Set, Tuple, Type
 
-try:
-    import flashinfer
-    from flash_attn import flash_attn_varlen_func
-    from flashinfer import BatchDecodeWithPagedKVCacheWrapper
-except ImportError:
-    flashinfer = None
-    flash_attn_varlen_func = None
-    BatchDecodeWithPagedKVCacheWrapper = None
-
+import flashinfer
 import torch
+from flash_attn import flash_attn_varlen_func
+from flashinfer import BatchDecodeWithPagedKVCacheWrapper
 
 from vllm import _custom_ops as ops
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
@@ -20,6 +14,10 @@
 
 class FlashInferBackend(AttentionBackend):
 
+    @staticmethod
+    def get_name() -> str:
+        return "flashinfer"
+
     @staticmethod
     def get_impl_cls() -> Type["FlashInferImpl"]:
         return FlashInferImpl

vllm/attention/backends/rocm_flash_attn.py

@@ -17,6 +17,10 @@
 
 class ROCmFlashAttentionBackend(AttentionBackend):
 
+    @staticmethod
+    def get_name() -> str:
+        return "rocm-flash-attn"
+
     @staticmethod
     def get_impl_cls() -> Type["ROCmFlashAttentionImpl"]:
         return ROCmFlashAttentionImpl

vllm/attention/backends/torch_sdpa.py

@@ -15,6 +15,10 @@
 
 class TorchSDPABackend(AttentionBackend):
 
+    @staticmethod
+    def get_name() -> str:
+        return "torch-sdpa"
+
     @staticmethod
     def get_impl_cls() -> Type["TorchSDPABackendImpl"]:
         return TorchSDPABackendImpl

vllm/attention/backends/xformers.py

@@ -20,6 +20,10 @@
 
 class XFormersBackend(AttentionBackend):
 
+    @staticmethod
+    def get_name() -> str:
+        return "xformers"
+
     @staticmethod
     def get_impl_cls() -> Type["XFormersImpl"]:
         return XFormersImpl

vllm/worker/model_runner.py

@@ -9,7 +9,6 @@
 
 from vllm.attention import (AttentionMetadata, AttentionMetadataPerStage,
                             get_attn_backend)
-from vllm.attention.backends.flashinfer import FlashInferBackend
 from vllm.config import (DeviceConfig, LoadConfig, LoRAConfig, ModelConfig,
                          ParallelConfig, SchedulerConfig, VisionLanguageConfig)
 from vllm.distributed import broadcast_tensor_dict, with_pynccl_for_all_reduce
@@ -395,7 +394,7 @@ def _prepare_prompt(
                      dtype=seq_start_loc.dtype,
                      out=seq_start_loc[1:])
 
-        if self.attn_backend is FlashInferBackend:
+        if self.attn_backend.get_name() == "flashinfer":
             attn_metadata = self.attn_backend.make_metadata(
                 is_prompt=True,
                 use_cuda_graph=False,
@@ -556,7 +555,7 @@ def _prepare_decode(
                 device=self.device,
             )
 
-        if self.attn_backend is FlashInferBackend:
+        if self.attn_backend.get_name() == "flashinfer":
             if not hasattr(self, "flashinfer_workspace_buffer"):
                 # Allocate 16MB workspace buffer
                 # Follow the example of flashinfer: https://docs.flashinfer.ai/api/python/decode.html