[Kernel] Flash Attention 3 Support (#12093)

Signed-off-by: Lucas Wilkinson <lwilkinson@neuralmagic.com>

CMakeLists.txt

@@ -24,9 +24,6 @@ include(${CMAKE_CURRENT_LIST_DIR}/cmake/utils.cmake)
 # Suppress potential warnings about unused manually-specified variables
 set(ignoreMe "${VLLM_PYTHON_PATH}")
 
-# Prevent installation of dependencies (cutlass) by default.
-install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
-
 #
 # Supported python versions.  These versions will be searched in order, the
 # first match will be selected.  These should be kept in sync with setup.py.
@@ -535,7 +532,7 @@ if(VLLM_GPU_LANG STREQUAL "HIP")
 endif()
 
 # vllm-flash-attn currently only supported on CUDA
-if (NOT VLLM_TARGET_DEVICE STREQUAL "cuda")
+if (NOT VLLM_GPU_LANG STREQUAL "CUDA")
   return()
 endif ()
 
@@ -558,7 +555,7 @@ endif()
 # They should be identical but if they aren't, this is a massive footgun.
 #
 # The vllm-flash-attn install rules are nested under vllm to make sure the library gets installed in the correct place.
-# To only install vllm-flash-attn, use --component vllm_flash_attn_c.
+# To only install vllm-flash-attn, use --component _vllm_fa2_C (for FA2) or --component _vllm_fa3_C (for FA3).
 # If no component is specified, vllm-flash-attn is still installed.
 
 # If VLLM_FLASH_ATTN_SRC_DIR is set, vllm-flash-attn is installed from that directory instead of downloading.
@@ -570,43 +567,41 @@ if (DEFINED ENV{VLLM_FLASH_ATTN_SRC_DIR})
 endif()
 
 if(VLLM_FLASH_ATTN_SRC_DIR)
-  FetchContent_Declare(vllm-flash-attn SOURCE_DIR ${VLLM_FLASH_ATTN_SRC_DIR})
+  FetchContent_Declare(
+          vllm-flash-attn SOURCE_DIR 
+          ${VLLM_FLASH_ATTN_SRC_DIR}
+          BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn
+  )
 else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 96266b1111111f3d11aabefaf3bacbab6a89d03c
+          GIT_TAG 90eacc1af2a7c3de62ea249e929ed5faccf38954
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn
   )
 endif()
 
-# Set the parent build flag so that the vllm-flash-attn library does not redo compile flag and arch initialization.
-set(VLLM_PARENT_BUILD ON)
-
-# Ensure the vllm/vllm_flash_attn directory exists before installation
-install(CODE "file(MAKE_DIRECTORY \"\${CMAKE_INSTALL_PREFIX}/vllm/vllm_flash_attn\")" COMPONENT vllm_flash_attn_c)
-
-# Make sure vllm-flash-attn install rules are nested under vllm/
-install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY FALSE)" COMPONENT vllm_flash_attn_c)
-install(CODE "set(OLD_CMAKE_INSTALL_PREFIX \"\${CMAKE_INSTALL_PREFIX}\")" COMPONENT vllm_flash_attn_c)
-install(CODE "set(CMAKE_INSTALL_PREFIX \"\${CMAKE_INSTALL_PREFIX}/vllm/\")" COMPONENT vllm_flash_attn_c)
 
 # Fetch the vllm-flash-attn library
 FetchContent_MakeAvailable(vllm-flash-attn)
 message(STATUS "vllm-flash-attn is available at ${vllm-flash-attn_SOURCE_DIR}")
 
-# Restore the install prefix
-install(CODE "set(CMAKE_INSTALL_PREFIX \"\${OLD_CMAKE_INSTALL_PREFIX}\")" COMPONENT vllm_flash_attn_c)
-install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" COMPONENT vllm_flash_attn_c)
+# Copy over the vllm-flash-attn python files (duplicated for fa2 and fa3, in
+# case only one is built, in the case both are built redundant work is done)
+install(
+  DIRECTORY ${vllm-flash-attn_SOURCE_DIR}/vllm_flash_attn/
+  DESTINATION vllm_flash_attn
+  COMPONENT _vllm_fa2_C
+  FILES_MATCHING PATTERN "*.py"
+)
 
-# Copy over the vllm-flash-attn python files
 install(
-        DIRECTORY ${vllm-flash-attn_SOURCE_DIR}/vllm_flash_attn/
-        DESTINATION vllm/vllm_flash_attn
-        COMPONENT vllm_flash_attn_c
-        FILES_MATCHING PATTERN "*.py"
+  DIRECTORY ${vllm-flash-attn_SOURCE_DIR}/vllm_flash_attn/
+  DESTINATION vllm_flash_attn
+  COMPONENT _vllm_fa3_C
+  FILES_MATCHING PATTERN "*.py"
 )
 
 # Nothing after vllm-flash-attn, see comment about macros above

setup.py

@@ -228,8 +228,11 @@ class cmake_build_ext(build_ext):
 
             # CMake appends the extension prefix to the install path,
             # and outdir already contains that prefix, so we need to remove it.
+            # We assume only the final component of extension prefix is added by
+            # CMake, this is currently true for current extensions but may not
+            # always be the case.
             prefix = outdir
-            for i in range(ext.name.count('.')):
+            if '.' in ext.name:
                 prefix = prefix.parent
 
             # prefix here should actually be the same for all components
@@ -298,7 +301,8 @@ class repackage_wheel(build_ext):
             files_to_copy = [
                 "vllm/_C.abi3.so",
                 "vllm/_moe_C.abi3.so",
-                "vllm/vllm_flash_attn/vllm_flash_attn_c.abi3.so",
+                "vllm/vllm_flash_attn/_vllm_fa2_C.abi3.so",
+                "vllm/vllm_flash_attn/_vllm_fa3_C.abi3.so",
                 "vllm/vllm_flash_attn/flash_attn_interface.py",
                 "vllm/vllm_flash_attn/__init__.py",
                 "vllm/cumem_allocator.abi3.so",
@@ -593,8 +597,8 @@ if _is_hip():
     ext_modules.append(CMakeExtension(name="vllm._rocm_C"))
 
 if _is_cuda():
-    ext_modules.append(
-        CMakeExtension(name="vllm.vllm_flash_attn.vllm_flash_attn_c"))
+    ext_modules.append(CMakeExtension(name="vllm.vllm_flash_attn._vllm_fa2_C"))
+    ext_modules.append(CMakeExtension(name="vllm.vllm_flash_attn._vllm_fa3_C"))
     ext_modules.append(CMakeExtension(name="vllm.cumem_allocator"))
 
 if _build_custom_ops():

tests/kernels/test_cascade_flash_attn.py

@@ -78,6 +78,7 @@ CASES = [
 @pytest.mark.parametrize("block_size", BLOCK_SIZES)
 @pytest.mark.parametrize("soft_cap", [None, 50])
 @pytest.mark.parametrize("num_blocks", [2048])
+@pytest.mark.parametrize("fa_version", [2, 3])
 @torch.inference_mode()
 def test_cascade(
     seq_lens_and_common_prefix: Tuple[List[Tuple[int, int]], int],
@@ -87,8 +88,14 @@ def test_cascade(
     block_size: int,
     soft_cap: Optional[float],
     num_blocks: int,
+    fa_version: int,
 ) -> None:
     torch.set_default_device("cuda")
+    if fa_version == 3 and (torch.cuda.get_device_capability() == (8, 6)
+                            or torch.cuda.get_device_capability() == (8, 9)):
+        pytest.skip("Flash attention version 3 fails on 8.6 and 8.9 due to "
+                    "insufficient shared memory for some shapes")
+
     current_platform.seed_everything(0)
 
     window_size = (-1, -1)
@@ -118,9 +125,7 @@ def test_cascade(
     cu_query_lens = torch.tensor([0] + query_lens,
                                  dtype=torch.int32).cumsum(dim=0,
                                                            dtype=torch.int32)
-    cu_kv_lens = torch.tensor([0] + kv_lens,
-                              dtype=torch.int32).cumsum(dim=0,
-                                                        dtype=torch.int32)
+    kv_lens_tensor = torch.tensor(kv_lens, dtype=torch.int32)
     max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
     block_tables = torch.randint(0,
                                  num_blocks,
@@ -140,7 +145,7 @@ def test_cascade(
         k=key_cache,
         v=value_cache,
         cu_seqlens_q=cu_query_lens,
-        cu_seqlens_k=cu_kv_lens,
+        seqused_k=kv_lens_tensor,
         max_seqlen_q=max_query_len,
         max_seqlen_k=max_kv_len,
         softmax_scale=scale,
@@ -154,10 +159,8 @@ def test_cascade(
     assert all(common_prefix_len < kv_len for kv_len in kv_lens)
     cu_prefix_query_lens = torch.tensor([0, total_num_query_tokens],
                                         dtype=torch.int32)
-    cu_prefix_kv_lens = torch.tensor([0, common_prefix_len], dtype=torch.int32)
-    cu_suffix_kv_lens = (
-        cu_kv_lens -
-        torch.arange(num_seqs + 1, dtype=torch.int32) * common_prefix_len)
+    prefix_kv_lens = torch.tensor([common_prefix_len], dtype=torch.int32)
+    suffix_kv_lens = kv_lens_tensor - common_prefix_len
     output = torch.empty_like(query)
     cascade_attention(
         output=output,
@@ -167,8 +170,8 @@ def test_cascade(
         cu_query_lens=cu_query_lens,
         max_query_len=max_query_len,
         cu_prefix_query_lens=cu_prefix_query_lens,
-        cu_prefix_kv_lens=cu_prefix_kv_lens,
-        cu_suffix_kv_lens=cu_suffix_kv_lens,
+        prefix_kv_lens=prefix_kv_lens,
+        suffix_kv_lens=suffix_kv_lens,
         max_kv_len=max_kv_len,
         softmax_scale=scale,
         alibi_slopes=None,
@@ -176,6 +179,7 @@ def test_cascade(
         logits_soft_cap=soft_cap if soft_cap is not None else 0,
         block_table=block_tables,
         common_prefix_len=common_prefix_len,
+        fa_version=fa_version,
     )
 
     # Compare the results.

tests/kernels/test_flash_attn.py

@@ -80,6 +80,7 @@ def ref_paged_attn(
 @pytest.mark.parametrize("soft_cap", [None, 10.0, 50.0])
 @pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
 @pytest.mark.parametrize("sliding_window", [None, 256])
+@pytest.mark.parametrize("fa_version", [2, 3])
 @torch.inference_mode()
 def test_flash_attn_with_paged_kv(
     use_out: bool,
@@ -91,8 +92,14 @@ def test_flash_attn_with_paged_kv(
     soft_cap: Optional[float],
     num_blocks: int,
     sliding_window: Optional[int],
+    fa_version: int,
 ) -> None:
     torch.set_default_device("cuda")
+    if fa_version == 3 and (torch.cuda.get_device_capability() == (8, 6)
+                            or torch.cuda.get_device_capability() == (8, 9)):
+        pytest.skip("Flash attention version 3 fails on 8.6 and 8.9 due to "
+                    "insufficient shared memory for some shapes")
+
     current_platform.seed_everything(0)
     num_seqs = len(kv_lens)
     num_query_heads = num_heads[0]
@@ -131,6 +138,7 @@ def test_flash_attn_with_paged_kv(
         cache_seqlens=kv_lens_tensor,
         softcap=soft_cap if soft_cap is not None else 0,
         window_size=window_size,
+        fa_version=fa_version,
     )
     output = output if not use_out else out
     output = output.squeeze(1)
@@ -159,6 +167,7 @@ def test_flash_attn_with_paged_kv(
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("soft_cap", [None, 10.0, 50.0])
 @pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
+@pytest.mark.parametrize("fa_version", [2, 3])
 @torch.inference_mode()
 def test_varlen_with_paged_kv(
     use_out: bool,
@@ -170,8 +179,14 @@ def test_varlen_with_paged_kv(
     block_size: int,
     soft_cap: Optional[float],
     num_blocks: int,
+    fa_version: int,
 ) -> None:
     torch.set_default_device("cuda")
+    if fa_version == 3 and (torch.cuda.get_device_capability() == (8, 6)
+                            or torch.cuda.get_device_capability() == (8, 9)):
+        pytest.skip("Flash attention version 3 fails on 8.6 and 8.9 due to "
+                    "insufficient shared memory for some shapes")
+
     current_platform.seed_everything(0)
     num_seqs = len(seq_lens)
     query_lens = [x[0] for x in seq_lens]
@@ -198,9 +213,7 @@ def test_varlen_with_paged_kv(
     cu_query_lens = torch.tensor([0] + query_lens,
                                  dtype=torch.int32).cumsum(dim=0,
                                                            dtype=torch.int32)
-    cu_kv_lens = torch.tensor([0] + kv_lens,
-                              dtype=torch.int32).cumsum(dim=0,
-                                                        dtype=torch.int32)
+    kv_lens = torch.tensor(kv_lens, dtype=torch.int32)
 
     max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
     block_tables = torch.randint(0,
@@ -215,7 +228,7 @@ def test_varlen_with_paged_kv(
         v=value_cache,
         out=out,
         cu_seqlens_q=cu_query_lens,
-        cu_seqlens_k=cu_kv_lens,
+        seqused_k=kv_lens,
         max_seqlen_q=max_query_len,
         max_seqlen_k=max_kv_len,
         softmax_scale=scale,
@@ -223,6 +236,7 @@ def test_varlen_with_paged_kv(
         window_size=window_size,
         block_table=block_tables,
         softcap=soft_cap if soft_cap is not None else 0,
+        fa_version=fa_version,
     )
     output = output if not use_out else out
 

vllm/attention/backends/flash_attn.py

@@ -17,7 +17,9 @@ from vllm.attention.backends.utils import (
     compute_slot_mapping_start_idx, get_num_prefill_decode_query_kv_tokens,
     get_seq_len_block_table_args, is_all_cross_attn_metadata_set,
     is_all_encoder_attn_metadata_set, is_block_tables_empty)
+from vllm.envs import VLLM_FLASH_ATTN_VERSION
 from vllm.multimodal import MultiModalPlaceholderMap
+from vllm.platforms import current_platform
 from vllm.utils import async_tensor_h2d, make_tensor_with_pad
 
 if TYPE_CHECKING:
@@ -25,7 +27,8 @@ if TYPE_CHECKING:
                                           ModelInputForGPUWithSamplingMetadata)
 
 from vllm.vllm_flash_attn import (flash_attn_varlen_func,
-                                  flash_attn_with_kvcache)
+                                  flash_attn_with_kvcache,
+                                  is_fa_version_supported)
 
 
 class FlashAttentionBackend(AttentionBackend):
@@ -634,6 +637,20 @@ class FlashAttentionImpl(AttentionImpl):
                 f"Supported head sizes are: {support_head_sizes}.")
         self.attn_type = attn_type
 
+        # if hopper default to FA3, otherwise stick to FA2 for now
+        # TODO(lucas): profile FA3 on ampere to see if it makes sense to
+        #  use FA3 as default for both
+        if current_platform.get_device_capability()[0] >= 9:
+            self.fa_version = 3 if is_fa_version_supported(3) else 2
+        else:
+            self.fa_version = 2
+
+        if VLLM_FLASH_ATTN_VERSION is not None:
+            assert VLLM_FLASH_ATTN_VERSION in [2, 3]
+            self.fa_version = VLLM_FLASH_ATTN_VERSION
+
+        assert is_fa_version_supported(self.fa_version)
+
     def forward(
         self,
         layer: AttentionLayer,
@@ -752,6 +769,7 @@ class FlashAttentionImpl(AttentionImpl):
                     alibi_slopes=alibi_slopes,
                     softcap=logits_soft_cap,
                     out=prefill_output,
+                    fa_version=self.fa_version,
                 )
             else:
                 # prefix-enabled attention
@@ -765,7 +783,7 @@ class FlashAttentionImpl(AttentionImpl):
                     v=value_cache,
                     cu_seqlens_q=prefill_meta.query_start_loc,
                     max_seqlen_q=prefill_meta.max_query_len,
-                    cu_seqlens_k=prefill_meta.seq_start_loc,
+                    seqused_k=prefill_meta.seq_lens_tensor,
                     max_seqlen_k=max_seq_len,
                     softmax_scale=softmax_scale,
                     causal=True,
@@ -774,6 +792,7 @@ class FlashAttentionImpl(AttentionImpl):
                     block_table=prefill_meta.block_tables,
                     softcap=logits_soft_cap,
                     out=prefill_output,
+                    fa_version=self.fa_version,
                 )
 
         if decode_meta := attn_metadata.decode_metadata:
@@ -793,7 +812,7 @@ class FlashAttentionImpl(AttentionImpl):
                     v=value_cache,
                     cu_seqlens_q=decode_meta.query_start_loc,
                     max_seqlen_q=decode_meta.max_decode_query_len,
-                    cu_seqlens_k=decode_meta.seq_start_loc,
+                    seqused_k=decode_meta.seq_lens_tensor,
                     max_seqlen_k=decode_meta.max_decode_seq_len,
                     softmax_scale=softmax_scale,
                     causal=True,
@@ -802,6 +821,7 @@ class FlashAttentionImpl(AttentionImpl):
                     softcap=logits_soft_cap,
                     block_table=decode_meta.block_tables,
                     out=decode_output,
+                    fa_version=self.fa_version,
                 )
             else:
                 # Use flash_attn_with_kvcache for normal decoding.
@@ -822,6 +842,7 @@ class FlashAttentionImpl(AttentionImpl):
                     alibi_slopes=alibi_slopes,
                     softcap=logits_soft_cap,
                     out=decode_output.unsqueeze(1),
+                    fa_version=self.fa_version,
                 )
         return output
 

vllm/envs.py

@@ -11,6 +11,7 @@ if TYPE_CHECKING:
     VLLM_NCCL_SO_PATH: Optional[str] = None
     LD_LIBRARY_PATH: Optional[str] = None
     VLLM_USE_TRITON_FLASH_ATTN: bool = False
+    VLLM_FLASH_ATTN_VERSION: Optional[int] = None
     LOCAL_RANK: int = 0
     CUDA_VISIBLE_DEVICES: Optional[str] = None
     VLLM_ENGINE_ITERATION_TIMEOUT_S: int = 60
@@ -90,6 +91,12 @@ def get_default_config_root():
     )
 
 
+def maybe_convert_int(value: Optional[str]) -> Optional[int]:
+    if value is None:
+        return None
+    return int(value)
+
+
 # The begin-* and end* here are used by the documentation generator
 # to extract the used env vars.
 
@@ -203,6 +210,11 @@ environment_variables: Dict[str, Callable[[], Any]] = {
     lambda: (os.environ.get("VLLM_USE_TRITON_FLASH_ATTN", "True").lower() in
              ("true", "1")),
 
+    # Force vllm to use a specific flash-attention version (2 or 3), only valid
+    # when using the flash-attention backend.
+    "VLLM_FLASH_ATTN_VERSION":
+    lambda: maybe_convert_int(os.environ.get("VLLM_FLASH_ATTN_VERSION", None)),
+
     # Internal flag to enable Dynamo fullgraph capture
     "VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE":
     lambda: bool(

vllm/v1/attention/backends/flash_attn.py

@@ -9,8 +9,11 @@ import triton.language as tl
 
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                               AttentionMetadata, AttentionType)
+from vllm.envs import VLLM_FLASH_ATTN_VERSION
+from vllm.platforms import current_platform
 from vllm.utils import cdiv
-from vllm.vllm_flash_attn import flash_attn_varlen_func
+from vllm.vllm_flash_attn import (flash_attn_varlen_func,
+                                  is_fa_version_supported)
 
 
 class FlashAttentionBackend(AttentionBackend):
@@ -63,7 +66,7 @@ class FlashAttentionMetadata:
     max_query_len: int
     query_start_loc: torch.Tensor
     max_seq_len: int
-    seq_start_loc: torch.Tensor
+    seq_lens: torch.Tensor
     block_table: torch.Tensor
     slot_mapping: torch.Tensor
 
@@ -71,8 +74,8 @@ class FlashAttentionMetadata:
     use_cascade: bool
     common_prefix_len: int
     cu_prefix_query_lens: Optional[torch.Tensor]
-    cu_prefix_kv_lens: Optional[torch.Tensor]
-    cu_suffix_kv_lens: Optional[torch.Tensor]
+    prefix_kv_lens: Optional[torch.Tensor]
+    suffix_kv_lens: Optional[torch.Tensor]
 
     # For logging.
     num_input_tokens: int = 0  # Number of tokens including padding.
@@ -128,6 +131,20 @@ class FlashAttentionImpl(AttentionImpl):
                                       "are not implemented for "
                                       "FlashAttentionImpl")
 
+        # if hopper default to FA3, otherwise stick to FA2 for now
+        # TODO(lucas): profile FA3 on ampere to see if it makes sense to
+        #  use FA3 as default for both
+        if current_platform.get_device_capability()[0] >= 9:
+            self.fa_version = 3 if is_fa_version_supported(3) else 2
+        else:
+            self.fa_version = 2
+
+        if VLLM_FLASH_ATTN_VERSION is not None:
+            assert VLLM_FLASH_ATTN_VERSION in [2, 3]
+            self.fa_version = VLLM_FLASH_ATTN_VERSION
+
+        assert is_fa_version_supported(self.fa_version)
+
     def forward(
         self,
         layer: torch.nn.Module,
@@ -196,7 +213,7 @@ class FlashAttentionImpl(AttentionImpl):
                 out=output[:num_actual_tokens],
                 cu_seqlens_q=attn_metadata.query_start_loc,
                 max_seqlen_q=attn_metadata.max_query_len,
-                cu_seqlens_k=attn_metadata.seq_start_loc,
+                seqused_k=attn_metadata.seq_lens,
                 max_seqlen_k=attn_metadata.max_seq_len,
                 softmax_scale=self.scale,
                 causal=True,
@@ -204,6 +221,7 @@ class FlashAttentionImpl(AttentionImpl):
                 window_size=self.sliding_window,
                 block_table=attn_metadata.block_table,
                 softcap=self.logits_soft_cap,
+                fa_version=self.fa_version,
             )
             return output
 
@@ -216,8 +234,8 @@ class FlashAttentionImpl(AttentionImpl):
             cu_query_lens=attn_metadata.query_start_loc,
             max_query_len=attn_metadata.max_query_len,
             cu_prefix_query_lens=attn_metadata.cu_prefix_query_lens,
-            cu_prefix_kv_lens=attn_metadata.cu_prefix_kv_lens,
-            cu_suffix_kv_lens=attn_metadata.cu_suffix_kv_lens,
+            prefix_kv_lens=attn_metadata.prefix_kv_lens,
+            suffix_kv_lens=attn_metadata.suffix_kv_lens,
             max_kv_len=attn_metadata.max_seq_len,
             softmax_scale=self.scale,
             alibi_slopes=self.alibi_slopes,
@@ -225,6 +243,7 @@ class FlashAttentionImpl(AttentionImpl):
             logits_soft_cap=self.logits_soft_cap,
             block_table=attn_metadata.block_table,
             common_prefix_len=attn_metadata.common_prefix_len,
+            fa_version=self.fa_version,
         )
         return output
 
@@ -305,8 +324,8 @@ def cascade_attention(
     cu_query_lens: torch.Tensor,
     max_query_len: int,
     cu_prefix_query_lens: torch.Tensor,
-    cu_prefix_kv_lens: torch.Tensor,
-    cu_suffix_kv_lens: torch.Tensor,
+    prefix_kv_lens: torch.Tensor,
+    suffix_kv_lens: torch.Tensor,
     max_kv_len: int,
     softmax_scale: float,
     alibi_slopes: Optional[torch.Tensor],
@@ -314,6 +333,7 @@ def cascade_attention(
     logits_soft_cap: float,
     block_table: torch.Tensor,
     common_prefix_len: int,
+    fa_version: int,
 ) -> torch.Tensor:
     assert alibi_slopes is None, ("Cascade attention does not support ALiBi.")
     # TODO: Support sliding window.
@@ -332,7 +352,7 @@ def cascade_attention(
         k=key_cache,
         v=value_cache,
         cu_seqlens_q=cu_prefix_query_lens,
-        cu_seqlens_k=cu_prefix_kv_lens,
+        seqused_k=prefix_kv_lens,
         max_seqlen_q=num_tokens,
         max_seqlen_k=common_prefix_len,
         softmax_scale=softmax_scale,
@@ -341,6 +361,7 @@ def cascade_attention(
         block_table=block_table[:1],
         softcap=logits_soft_cap,
         return_softmax_lse=True,
+        fa_version=fa_version,
     )
 
     # Process suffix per query.
@@ -349,7 +370,7 @@ def cascade_attention(
         k=key_cache,
         v=value_cache,
         cu_seqlens_q=cu_query_lens,
-        cu_seqlens_k=cu_suffix_kv_lens,
+        seqused_k=suffix_kv_lens,
         max_seqlen_q=max_query_len,
         max_seqlen_k=max_kv_len - common_prefix_len,
         softmax_scale=softmax_scale,
@@ -358,6 +379,7 @@ def cascade_attention(
         block_table=block_table[:, num_common_kv_blocks:],
         softcap=logits_soft_cap,
         return_softmax_lse=True,
+        fa_version=fa_version,
     )
 
     # Merge prefix and suffix outputs, and store the result in output.

vllm/v1/worker/gpu_model_runner.py

@@ -199,11 +199,11 @@ class GPUModelRunner:
                                                device="cpu",
                                                pin_memory=self.pin_memory)
         self.query_start_loc_np = self.query_start_loc_cpu.numpy()
-        self.seq_start_loc_cpu = torch.zeros(self.max_num_reqs + 1,
-                                             dtype=torch.int32,
-                                             device="cpu",
-                                             pin_memory=self.pin_memory)
-        self.seq_start_loc_np = self.seq_start_loc_cpu.numpy()
+        self.seq_lens_cpu = torch.zeros(self.max_num_reqs,
+                                        dtype=torch.int32,
+                                        device="cpu",
+                                        pin_memory=self.pin_memory)
+        self.seq_lens_np = self.seq_lens_cpu.numpy()
 
     def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
         # Remove stopped requests from the cached states.
@@ -412,11 +412,10 @@ class GPUModelRunner:
         np.cumsum(num_scheduled_tokens,
                   out=self.query_start_loc_np[1:num_reqs + 1])
 
-        seq_lens = (self.input_batch.num_computed_tokens_cpu[:num_reqs] +
-                    num_scheduled_tokens)
-        max_seq_len = seq_lens.max()
-        self.seq_start_loc_np[0] = 0
-        np.cumsum(seq_lens, out=self.seq_start_loc_np[1:num_reqs + 1])
+        self.seq_lens_np[:num_reqs] = (
+            self.input_batch.num_computed_tokens_cpu[:num_reqs] +
+            num_scheduled_tokens)
+        max_seq_len = self.seq_lens_np[:num_reqs].max()
 
         # Copy the tensors to the GPU.
         self.input_ids[:total_num_scheduled_tokens].copy_(
@@ -433,8 +432,8 @@ class GPUModelRunner:
                 non_blocking=True)
         query_start_loc = self.query_start_loc_cpu[:num_reqs + 1].to(
             self.device, non_blocking=True)
-        seq_start_loc = self.seq_start_loc_cpu[:num_reqs + 1].to(
-            self.device, non_blocking=True)
+        seq_lens = self.seq_lens_cpu[:num_reqs].to(self.device,
+                                                   non_blocking=True)
         slot_mapping = self.slot_mapping_cpu[:total_num_scheduled_tokens].to(
             self.device, non_blocking=True).long()
 
@@ -506,33 +505,30 @@ class GPUModelRunner:
                 [0, total_num_scheduled_tokens],
                 dtype=torch.int32,
                 device=self.device)
-            cu_prefix_kv_lens = torch.tensor([0, common_prefix_len],
-                                             dtype=torch.int32,
-                                             device=self.device)
-            cu_suffix_kv_lens = (
-                self.seq_start_loc_np[:num_reqs + 1] -
-                self.arange_np[:num_reqs + 1] * common_prefix_len)
-            cu_suffix_kv_lens = torch.from_numpy(cu_suffix_kv_lens).to(
-                self.device)
+            prefix_kv_lens = torch.tensor([common_prefix_len],
+                                          dtype=torch.int32,
+                                          device=self.device)
+            suffix_kv_lens = (self.seq_lens_np[:num_reqs] - common_prefix_len)
+            suffix_kv_lens = torch.from_numpy(suffix_kv_lens).to(self.device)
         else:
             cu_prefix_query_lens = None
-            cu_prefix_kv_lens = None
-            cu_suffix_kv_lens = None
+            prefix_kv_lens = None
+            suffix_kv_lens = None
 
         attn_metadata = FlashAttentionMetadata(
             num_actual_tokens=total_num_scheduled_tokens,
             max_query_len=max_num_scheduled_tokens,
             query_start_loc=query_start_loc,
             max_seq_len=max_seq_len,
-            seq_start_loc=seq_start_loc,
+            seq_lens=seq_lens,
             block_table=(
                 self.input_batch.block_table.get_device_tensor()[:num_reqs]),
             slot_mapping=slot_mapping,
             use_cascade=use_cascade,
             common_prefix_len=common_prefix_len,
             cu_prefix_query_lens=cu_prefix_query_lens,
-            cu_prefix_kv_lens=cu_prefix_kv_lens,
-            cu_suffix_kv_lens=cu_suffix_kv_lens,
+            prefix_kv_lens=prefix_kv_lens,
+            suffix_kv_lens=suffix_kv_lens,
         )
         # NOTE(woosuk): Due to chunked prefills, there can be at most 1 partial
         # request in the batch. While we should not sample any token from this