[Feat] QWen-1M context support[1/2]: Update block sparse attention backend utils kernel (#5847)

Co-authored-by: sighingnow <sighingnow@gmail.com>

sgl-kernel/python/sgl_kernel/sparse_flash_attn.py

@@ -8,6 +8,124 @@ def maybe_contiguous(x):
     return x.contiguous() if x is not None and x.stride(-1) != 1 else x
 
 
+# Sparse attention utils
+def convert_vertical_slash_indexes(
+    q_seqlens: torch.Tensor,  # [BATCH, ]
+    kv_seqlens: torch.Tensor,  # [BATCH, ]
+    vertical_indexes: torch.Tensor,  # [BATCH, N_HEADS, NNZ_V]
+    slash_indexes: torch.Tensor,  # [BATCH, N_HEADS, NNZ_S]
+    context_size: int,
+    block_size_M: int,
+    block_size_N: int,
+    causal: bool = True,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    batch_size = slash_indexes.size(0)
+    num_heads = slash_indexes.size(1)
+    nnz_slash = slash_indexes.size(2)
+    nnz_vertical = vertical_indexes.size(2)
+    num_rows = (context_size + block_size_M - 1) // block_size_M
+
+    block_count = torch.zeros(
+        batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
+    )
+    block_offset = torch.zeros(
+        batch_size,
+        num_heads,
+        num_rows,
+        nnz_slash,
+        dtype=q_seqlens.dtype,
+        device=q_seqlens.device,
+    )
+    column_count = torch.zeros(
+        batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
+    )
+    column_index = torch.zeros(
+        batch_size,
+        num_heads,
+        num_rows,
+        nnz_vertical,
+        dtype=q_seqlens.dtype,
+        device=q_seqlens.device,
+    )
+
+    torch.ops.sgl_kernel.convert_vertical_slash_indexes.default(
+        block_count,
+        block_offset,
+        column_count,
+        column_index,
+        q_seqlens,
+        kv_seqlens,
+        vertical_indexes,
+        slash_indexes,
+        context_size,
+        block_size_M,
+        block_size_N,
+        causal,
+    )
+    return block_count, block_offset, column_count, column_index
+
+
+def convert_vertical_slash_indexes_mergehead(
+    q_seqlens: torch.Tensor,  # [BATCH, ]
+    kv_seqlens: torch.Tensor,  # [BATCH, ]
+    vertical_indexes: torch.Tensor,  # [BATCH, N_HEADS, NNZ_V]
+    slash_indexes: torch.Tensor,  # [BATCH, N_HEADS, NNZ_S]
+    # [N_HEADS] : different head use different number of indices
+    vertical_indices_count: torch.Tensor,
+    slash_indices_count: torch.Tensor,
+    context_size: int,
+    block_size_M: int,
+    block_size_N: int,
+    causal: bool = True,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    batch_size = slash_indexes.size(0)
+    num_heads = slash_indexes.size(1)
+    nnz_slash = slash_indexes.size(2)
+    nnz_vertical = vertical_indexes.size(2)
+    num_rows = (context_size + block_size_M - 1) // block_size_M
+
+    block_count = torch.empty(
+        batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
+    )
+    block_offset = torch.empty(
+        batch_size,
+        num_heads,
+        num_rows,
+        nnz_slash,
+        dtype=q_seqlens.dtype,
+        device=q_seqlens.device,
+    )
+    column_count = torch.empty(
+        batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
+    )
+    column_index = torch.empty(
+        batch_size,
+        num_heads,
+        num_rows,
+        nnz_vertical,
+        dtype=q_seqlens.dtype,
+        device=q_seqlens.device,
+    )
+
+    torch.ops.sgl_kernel.convert_vertical_slash_indexes_mergehead.default(
+        block_count,
+        block_offset,
+        column_count,
+        column_index,
+        q_seqlens,
+        kv_seqlens,
+        vertical_indexes,
+        slash_indexes,
+        vertical_indices_count,
+        slash_indices_count,
+        context_size,
+        block_size_M,
+        block_size_N,
+        causal,
+    )
+    return block_count, block_offset, column_count, column_index
+
+
 def sparse_attn_func(
     q,
     k,