split out varlen batch search into utils (Dao-AILab#2556)

* split out varlen batch search into utils

* more descriptive name

Author: reubenconducts

flash_attn/cute/compute_block_sparsity.py

@@ -18,7 +18,12 @@
     get_aux_tensor_metadata,
     to_cute_aux_tensor,
 )
-from flash_attn.cute.utils import hash_callable, scalar_to_ssa, ssa_to_scalar
+from flash_attn.cute.utils import (
+    hash_callable,
+    scalar_to_ssa,
+    ssa_to_scalar,
+    get_batch_from_cu_tensor,
+)
 from flash_attn.cute.seqlen_info import SeqlenInfoQK
 
 
@@ -161,16 +166,7 @@ class SharedStorage:
             m_block, head_idx, batch_idx = cute.arch.block_idx()
         else:
             global_m_block, head_idx, _ = cute.arch.block_idx()
-            # Binary search over cu_total_m_blocks to find batch_idx
-            lo = Int32(0)
-            hi = batch_size
-            while lo < hi:
-                mid = (lo + hi) // 2
-                if mCuTotalMBlocks[mid + 1] <= global_m_block:
-                    lo = mid + 1
-                else:
-                    hi = mid
-            batch_idx = lo
+            batch_idx = get_batch_from_cu_tensor(global_m_block, mCuTotalMBlocks)
             m_block = global_m_block - mCuTotalMBlocks[batch_idx]
 
         seqlen = SeqlenInfoCls(batch_idx)

flash_attn/cute/utils.py

@@ -949,3 +949,20 @@ def scalar_to_ssa(a: cute.Numeric, dtype) -> cute.TensorSSA:
 def ssa_to_scalar(val):
     """Could inline but nice for reflecting the above api"""
     return val[0]
+
+
+@cute.jit
+def get_batch_from_cu_tensor(idx: Int32, cu_tensor: cute.Tensor) -> Int32:
+    """Binary search to determine batch from packed index in a cumulative tensor"""
+    batch_size = cute.size(cu_tensor) - 1
+    lo = Int32(0)
+    hi = batch_size
+
+    while lo < hi:
+        mid = (lo + hi) // 2
+        if cu_tensor[mid + 1] <= idx:
+            lo = mid + 1
+        else:
+            hi = mid
+
+    return lo