[Attention] FlashAttention ViT support, make default backend (#28763)

Signed-off-by: Matthew Bonanni <mbonanni@redhat.com>

cmake/external_projects/vllm_flash_attn.cmake

@@ -38,7 +38,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 58e0626a692f09241182582659e3bf8f16472659
+          GIT_TAG 71bb26f6295449be880344b93b51791cc009237d
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

tests/kernels/attention/test_flash_attn.py

@@ -13,14 +13,14 @@ from vllm.vllm_flash_attn import (
 )
 
 NUM_HEADS = [(4, 4), (8, 2)]
-HEAD_SIZES = [128, 256]
+HEAD_SIZES = [40, 72, 80, 128, 256]
 BLOCK_SIZES = [16]
 DTYPES = [torch.bfloat16]
 QDTYPES = [None, torch.float8_e4m3fn]
 # one value large enough to test overflow in index calculation.
 # one value small enough to test the schema op check
 NUM_BLOCKS = [32768, 2048]
-SOFT_CAPS = [None, 50.0]
+SOFT_CAPS = [None]
 SLIDING_WINDOWS = [None, 256]
 
 

tests/kernels/attention/test_mha_attn.py

@@ -62,38 +62,10 @@ def test_mha_attn_platform(device: str):
             assert attn.attn_backend == AttentionBackendEnum.FLASH_ATTN
 
         # Test CUDA with head_size=72 (not divisible by 32)
-        # - with upstream FA not available
-        # - should use xformers
+        # - should use vLLM's FlashAttention
         with (
             patch("vllm.attention.layer.current_platform", CudaPlatform()),
             patch("vllm.model_executor.models.vision.current_platform", CudaPlatform()),
-            patch(
-                "vllm.attention.layer.check_upstream_fa_availability",
-                return_value=False,
-            ),
-        ):
-            attn = MultiHeadAttention(16, 72, scale=1)
-            assert attn.attn_backend == AttentionBackendEnum.XFORMERS
-
-        # Test CUDA with head_size=72 (not divisible by 32)
-        # - with upstream FA available
-        # - should use upstream FA
-        with (
-            patch("vllm.attention.layer.current_platform", CudaPlatform()),
-            patch("vllm.model_executor.models.vision.current_platform", CudaPlatform()),
-            patch(
-                "vllm.attention.layer.check_upstream_fa_availability", return_value=True
-            ),
-            patch.dict(
-                "sys.modules",
-                {
-                    "flash_attn": type(
-                        "MockFlashAttn",
-                        (),
-                        {"flash_attn_varlen_func": lambda *args, **kwargs: None},
-                    )()
-                },
-            ),
         ):
             attn = MultiHeadAttention(16, 72, scale=1)
             assert attn.attn_backend == AttentionBackendEnum.FLASH_ATTN

vllm/platforms/cuda.py

@@ -267,24 +267,21 @@ class CudaPlatformBase(Platform):
     ) -> "AttentionBackendEnum":
         from vllm.attention.backends.registry import AttentionBackendEnum
 
-        # For Blackwell GPUs, force TORCH_SDPA for now.
-        # See https://github.com/facebookresearch/xformers/issues/1317#issuecomment-3199392579 # noqa: E501
-        if cls.has_device_capability(100):
-            return AttentionBackendEnum.TORCH_SDPA
-
-        if dtype not in (torch.float16, torch.bfloat16):
-            return AttentionBackendEnum.XFORMERS
-
-        if cls.has_device_capability(80):
+        # Try FlashAttention first
+        try:
             backend_class = AttentionBackendEnum.FLASH_ATTN.get_class()
             if backend_class.supports_head_size(
                 head_size
             ) and backend_class.supports_dtype(dtype):
                 return AttentionBackendEnum.FLASH_ATTN
-            else:
-                return AttentionBackendEnum.XFORMERS
+        except ImportError:
+            pass
+
+        if cls.has_device_capability(100):
+            # xFormers doesn't support Blackwell, fall back to SDPA
+            # See https://github.com/facebookresearch/xformers/issues/1317#issuecomment-3199392579 # noqa: E501
+            return AttentionBackendEnum.TORCH_SDPA
         else:
-            # Fallback for Volta/Turing GPUs or FA not supported
             return AttentionBackendEnum.XFORMERS
 
     @classmethod

vllm/v1/attention/backends/flash_attn.py

@@ -119,8 +119,8 @@ class FlashAttentionBackend(AttentionBackend):
             raise ValueError(f"Unrecognized FP8 dtype: {kv_cache_dtype}")
 
     @classmethod
-    def get_supported_head_sizes(cls) -> list[int]:
-        return [32, 64, 96, 128, 160, 192, 224, 256]
+    def supports_head_size(cls, head_size: int) -> bool:
+        return head_size % 8 == 0 and head_size <= 256
 
     @classmethod
     def supports_kv_cache_dtype(cls, kv_cache_dtype: CacheDType | None) -> bool: