"""
Block-sparse runtime utilities for CUTE DSL kernels.

This module contains runtime execution functions for block-sparse attention kernels.
These utilities are used by CUTE DSL kernels to produce and consume block-sparse loads.
"""

import math
from functools import partial
from typing import Callable, Optional, Tuple

import cutlass
import cutlass.cute as cute
from cutlass import Float32, Int32, const_expr
from quack import copy_utils

# Import data structures from block_sparsity
from sglang.kernels.ops.attention.flash_attn.cute.block_sparsity import (
    BlockSparseTensors,
)
from sglang.kernels.ops.attention.flash_attn.cute.named_barrier import NamedBarrierBwd
from sglang.kernels.ops.attention.flash_attn.cute.seqlen_info import SeqlenInfoQK
from sglang.kernels.ops.attention.flash_attn.cute.utils import AuxData


@cute.jit
def _get_curr_blocksparse_tensors_varlen(
    head_idx: cutlass.Int32,
    m_block: cutlass.Int32,
    blocksparse_tensors: BlockSparseTensors,
    seqlen_info: SeqlenInfoQK,
) -> Tuple[cutlass.Int32, cute.Tensor, cutlass.Int32, Optional[cute.Tensor]]:
    """Varlen path: tensors are 2D [nheads, total_m_blocks] / [nheads, total_n_blocks]."""
    mask_block_cnt, mask_block_idx, full_block_cnt, full_block_idx, *_ = (
        blocksparse_tensors
    )
    curr_m_block = seqlen_info.m_block_offset + m_block
    curr_block_idx_offset = (
        seqlen_info.block_idx_offset + m_block * seqlen_info.num_n_blocks
    )
    curr_mask_block_cnt = mask_block_cnt[head_idx, curr_m_block]
    curr_mask_block_idx = cute.domain_offset(
        curr_block_idx_offset, mask_block_idx[head_idx, None]
    )
    if const_expr(full_block_cnt is not None):
        curr_full_block_cnt = full_block_cnt[head_idx, curr_m_block]
        curr_full_block_idx = cute.domain_offset(
            curr_block_idx_offset, full_block_idx[head_idx, None]
        )
    else:
        curr_full_block_cnt = Int32(0)
        curr_full_block_idx = None
    return (
        curr_mask_block_cnt,
        curr_mask_block_idx,
        curr_full_block_cnt,
        curr_full_block_idx,
    )


@cute.jit
def _get_curr_blocksparse_tensors(
    batch_idx: cutlass.Int32,
    head_idx: cutlass.Int32,
    m_block: cutlass.Int32,
    blocksparse_tensors: BlockSparseTensors,
) -> Tuple[cutlass.Int32, cute.Tensor, cutlass.Int32, Optional[cute.Tensor]]:
    """Fixed-length path: tensors are 4D [batch, nheads, m_block, n_block]."""
    mask_block_cnt, mask_block_idx, full_block_cnt, full_block_idx, *_ = (
        blocksparse_tensors
    )
    curr_mask_block_cnt = mask_block_cnt[batch_idx, head_idx, m_block]
    curr_mask_block_idx = mask_block_idx[batch_idx, head_idx, m_block, None]
    if const_expr(full_block_cnt is not None):
        curr_full_block_cnt = full_block_cnt[batch_idx, head_idx, m_block]
        curr_full_block_idx = full_block_idx[batch_idx, head_idx, m_block, None]
    else:
        curr_full_block_cnt = Int32(0)
        curr_full_block_idx = None
    return (
        curr_mask_block_cnt,
        curr_mask_block_idx,
        curr_full_block_cnt,
        curr_full_block_idx,
    )


@cute.jit
def get_curr_blocksparse_tensors(
    batch_idx: cutlass.Int32,
    head_idx: cutlass.Int32,
    m_block: cutlass.Int32,
    blocksparse_tensors: BlockSparseTensors,
    seqlen_info: SeqlenInfoQK,
) -> Tuple[cutlass.Int32, cute.Tensor, cutlass.Int32, Optional[cute.Tensor]]:
    """Extract head, m_block, and batch-local blocksparsity data from blocksparse_tensors"""
    if const_expr(len(blocksparse_tensors.mask_block_cnt.shape) == 2):
        return _get_curr_blocksparse_tensors_varlen(
            head_idx, m_block, blocksparse_tensors, seqlen_info
        )
    return _get_curr_blocksparse_tensors(
        batch_idx, head_idx, m_block, blocksparse_tensors
    )


# NOTE [SM100 block-sparse empty tiles: mbarrier contract]
#
# For block-sparse SM100 forward, a given (m_block, stage) Q tile can have zero active
# KV blocks (total_block_cnt == 0). In that case there is no seqlen_kv iteration, so
# the softmax warp-group has no row stats to publish.
#
# The correction warp-group seeds fully-masked-row stats and runs the usual correction
# epilogue so output/LSE have well-defined values. Both warp-groups must still perform
# the softmax<->correction mbarrier handshake so phases advance correctly across
# empty->empty and empty->non-empty tile sequences.
#
# In the no-sink case, this corresponds to the usual fully-masked-row convention:
# output is zero and LSE is -inf.
#
# Barrier contract (each is `mbar_ptr + <offset> + stage`):
#
# Producer/consumer pairs:
# - `mbar_softmax_corr_full`    : softmax arrive        -> correction wait
# - `mbar_softmax_corr_empty`   : correction arrive     -> softmax wait
# - `mbar_P_full_O_rescaled`    : softmax arrive (+ correction arrive) -> MMA wait
# - `mbar_P_full_2`             : softmax arrive        -> MMA wait
# - `mbar_corr_epi_full_/empty` : correction <-> epilogue (only when epilogue is separate)
#
# Empty tile (`total_block_cnt == 0`):
# - Softmax: skips the seqlen_kv softmax path entirely (no P stores, no `mbar_P_full_*`).
#   It only arrives `mbar_softmax_corr_full` once per stage as a synthetic "no work" signal.
#   At the `softmax_loop` level, softmax unconditionally waits `mbar_softmax_corr_empty`
#   before each tile (when block-sparse) to drain a prior correction arrival and keep
#   phases aligned across non-empty -> empty transitions.
# - Correction: waits `mbar_softmax_corr_full`, seeds stats + runs `correction_epilogue(scale=0)`,
#   and arrives `mbar_softmax_corr_empty` (and `mbar_corr_epi_full_/empty` when applicable).
# - No `mbar_P_full_*` barriers are arrived (no P, no MMA O); only the softmax<->correction
#   (and correction<->epilogue) handshakes advance phases.
#
# Non-empty tile:
# - Softmax: runs `softmax_step` (produces P) and uses `mbar_softmax_corr_full/empty` to
#   publish row_max (during seqlen_kv) and final row stats (once per tile), and to advance phases;
#   arrives `mbar_P_full_*` when P is stored.
# - Correction: waits `mbar_softmax_corr_full`, may rescale/release O, arrives `mbar_softmax_corr_empty`
#   to ack/advance, and arrives `mbar_P_full_O_rescaled` when MMA can proceed.
#
# Backward (SM100):
# - Empty KV tile: for a given `n_block`, `total_m_block_cnt == 0` means no Q tiles contribute.
# - Both the load and compute loops guard all pipeline work on `process_tile`, so empty tiles
#   skip producer/consumer operations entirely (no per-tile mbarrier phase handshake like forward).
# - In the `not dKV_postprocess` path, dK/dV for empty KV tiles are explicitly written as zeros
#   even when `process_tile == False` (see `flash_bwd_sm100.py` `should_zero_dKV`).


@cute.jit
def load_block_list(
    block_indices: cute.Tensor,
    block_begin,
    block_end,
    first_block_preloaded: cutlass.Constexpr,
    kv_producer_state,
    load_K,
    load_V,
    pipeline_k,
    pipeline_v,
    intra_wg_overlap: cutlass.Constexpr,
):
    """Iterate over the sparse blocks and load K, V into the pipeline.
    For the intra_wg_overlap case, we overlap the loads of K and V. And this
    means we need to pipeline the last V load from the partial block case,
    with the loads for the full blocks. Set first_block_preloaded when the
    caller has already issued the first K load for the list.

    Q is loaded separately on its own mbarrier before this function is called.

    Note:
        we iterate along the block_n indices in reverse.

    Returns:
        Updated kv_producer_state after processing the block list.

    """
    block_count = block_end - block_begin
    if block_count > 0:
        if const_expr(not intra_wg_overlap):
            for offset in cutlass.range(block_count):
                n_block = block_indices[block_end - 1 - offset]
                pipeline_k.producer_acquire(kv_producer_state)
                load_K(src_idx=n_block, producer_state=kv_producer_state)
                pipeline_v.producer_acquire(kv_producer_state)
                load_V(src_idx=n_block, producer_state=kv_producer_state)
                kv_producer_state.advance()
        else:
            n_block_first = block_indices[block_end - 1]
            if const_expr(not first_block_preloaded):
                pipeline_k.producer_acquire(kv_producer_state)
                load_K(src_idx=n_block_first, producer_state=kv_producer_state)

            for idx in cutlass.range(block_count - 1, unroll=1):
                n_block_prev = block_indices[block_end - 1 - idx]
                n_block = block_indices[block_end - 2 - idx]
                kv_producer_state_prev = kv_producer_state.clone()
                kv_producer_state.advance()
                pipeline_k.producer_acquire(kv_producer_state)
                load_K(src_idx=n_block, producer_state=kv_producer_state)
                pipeline_v.producer_acquire(kv_producer_state_prev)
                load_V(src_idx=n_block_prev, producer_state=kv_producer_state_prev)

    return kv_producer_state


@cute.jit
def finish_overlap_v_load(
    block_indices: cute.Tensor,
    block_begin,
    block_end,
    load_V,
    pipeline_v,
    kv_producer_state,
):
    """Load the final V block after overlapped K/V loads."""
    block_count = block_end - block_begin
    if block_count > 0:
        n_block_last = block_indices[block_begin]
        pipeline_v.producer_acquire(kv_producer_state)
        load_V(src_idx=n_block_last, producer_state=kv_producer_state)
        kv_producer_state.advance()

    return kv_producer_state


@cute.jit
def sparse_tensor_m_block(
    m_block,
    qhead_per_kvhead: cutlass.Constexpr[int],
    q_subtile_factor: cutlass.Constexpr[int],
):
    """Map packed m_block indices to block-sparse tensor indices."""
    block = m_block
    if const_expr(qhead_per_kvhead != 1):
        block = block // qhead_per_kvhead
    if const_expr(q_subtile_factor != 1):
        block = block // q_subtile_factor
    return block


@cute.jit
def produce_block_sparse_loads(
    blocksparse_tensors: BlockSparseTensors,
    batch_idx,
    head_idx,
    m_block,
    seqlen_info: SeqlenInfoQK,
    kv_producer_state,
    load_K,
    load_V,
    pipeline_k,
    pipeline_v,
    intra_wg_overlap: cutlass.Constexpr,
    qhead_per_kvhead: cutlass.Constexpr[int] = 1,
    q_subtile_factor: cutlass.Constexpr[int] = 1,
    split_idx: Int32 = 0,
    num_splits: Int32 = 1,
):
    """Iterate over the mask and full block lists for a single tile.

    Q is loaded separately on its own mbarrier before this function is called.

    The masked (partial) list may leave the last V load pending when intra-warp-group
    overlap is enabled. The first full block must consume that pending V while
    issuing its own K load on the next pipeline stage.

    In the intra-wg-overlap path, the last masked block leaves its V copy in flight
    while we advance the producer state to start the next full K. Either the full list
    overlaps that pending V load, or, if no full blocks exist, we explicitly drain it.

    Args:
        qhead_per_kvhead: Pack-GQA factor. When > 1, m_block is in packed space and
            must be converted to unpacked for sparse tensor indexing.
    """
    m_block_sparse = sparse_tensor_m_block(m_block, qhead_per_kvhead, q_subtile_factor)

    (
        curr_mask_block_cnt,
        curr_mask_block_idx,
        curr_full_block_cnt,
        curr_full_block_idx,
    ) = get_curr_blocksparse_tensors(
        batch_idx,
        head_idx,
        m_block_sparse,
        blocksparse_tensors,
        seqlen_info,
    )

    mask_begin, mask_end = split_block_range(curr_mask_block_cnt, split_idx, num_splits)
    full_begin, full_end = split_block_range(curr_full_block_cnt, split_idx, num_splits)
    mask_empty = mask_begin == mask_end
    full_empty = full_begin == full_end

    if mask_empty:
        # No masked blocks: the full list owns the initial K load.
        kv_producer_state = load_block_list(
            curr_full_block_idx,
            full_begin,
            full_end,
            first_block_preloaded=False,
            kv_producer_state=kv_producer_state,
            load_K=load_K,
            load_V=load_V,
            pipeline_k=pipeline_k,
            pipeline_v=pipeline_v,
            intra_wg_overlap=intra_wg_overlap,
        )

        if const_expr(intra_wg_overlap) and not full_empty:
            kv_producer_state = finish_overlap_v_load(
                curr_full_block_idx,
                full_begin,
                full_end,
                load_V,
                pipeline_v,
                kv_producer_state,
            )
    else:
        # Masked blocks present. When overlap is disabled this fully drains the list.
        kv_producer_state = load_block_list(
            curr_mask_block_idx,
            mask_begin,
            mask_end,
            first_block_preloaded=False,
            kv_producer_state=kv_producer_state,
            load_K=load_K,
            load_V=load_V,
            pipeline_k=pipeline_k,
            pipeline_v=pipeline_v,
            intra_wg_overlap=intra_wg_overlap,
        )

        if full_empty:
            if const_expr(intra_wg_overlap):
                kv_producer_state = finish_overlap_v_load(
                    curr_mask_block_idx,
                    mask_begin,
                    mask_end,
                    load_V,
                    pipeline_v,
                    kv_producer_state,
                )
        else:
            if const_expr(intra_wg_overlap):
                # Bridge the masked list to the full list by overlapping the pending masked V
                # with the first full K load.
                n_block_mask_last = curr_mask_block_idx[mask_begin]
                n_block_full_first = curr_full_block_idx[full_end - 1]
                kv_producer_state_prev = kv_producer_state.clone()
                kv_producer_state.advance()
                pipeline_k.producer_acquire(kv_producer_state)
                load_K(src_idx=n_block_full_first, producer_state=kv_producer_state)
                pipeline_v.producer_acquire(kv_producer_state_prev)
                load_V(src_idx=n_block_mask_last, producer_state=kv_producer_state_prev)

                kv_producer_state = load_block_list(
                    curr_full_block_idx,
                    full_begin,
                    full_end,
                    first_block_preloaded=True,
                    kv_producer_state=kv_producer_state,
                    load_K=load_K,
                    load_V=load_V,
                    pipeline_k=pipeline_k,
                    pipeline_v=pipeline_v,
                    intra_wg_overlap=intra_wg_overlap,
                )

                kv_producer_state = finish_overlap_v_load(
                    curr_full_block_idx,
                    full_begin,
                    full_end,
                    load_V,
                    pipeline_v,
                    kv_producer_state,
                )
            else:
                # Non-overlap path with both lists: run the full list normally.
                kv_producer_state = load_block_list(
                    curr_full_block_idx,
                    full_begin,
                    full_end,
                    first_block_preloaded=False,
                    kv_producer_state=kv_producer_state,
                    load_K=load_K,
                    load_V=load_V,
                    pipeline_k=pipeline_k,
                    pipeline_v=pipeline_v,
                    intra_wg_overlap=intra_wg_overlap,
                )

    return kv_producer_state


@cute.jit
def consume_block_sparse_loads(
    blocksparse_tensors: BlockSparseTensors,
    batch_idx,
    head_idx,
    m_block,
    seqlen_info,
    kv_consumer_state,
    mma_pv_fn,
    mma_one_n_block,
    process_first_half_block,
    process_last_half_block,
    mask_fn,
    score_mod_fn,
    O_should_accumulate,
    mask_mod,
    fastdiv_mods,
    intra_wg_overlap: cutlass.Constexpr,
    warp_scheduler_barrier_sync: Callable,
    warp_scheduler_barrier_arrive: Callable,
    qhead_per_kvhead: cutlass.Constexpr[int] = 1,
    q_subtile_factor: cutlass.Constexpr[int] = 1,
    split_idx: Int32 = 0,
    num_splits: Int32 = 1,
):
    """Consume the mask and full block lists for a single tile on the consumer side.

    Mirrors `produce_block_sparse_loads` so that the consumer pipeline uses
    the same sparse tensor indexing.

    Args:
        qhead_per_kvhead: Pack-GQA factor. When > 1, m_block is in packed space and
            must be converted to unpacked for sparse tensor indexing.
    """
    m_block_sparse = sparse_tensor_m_block(m_block, qhead_per_kvhead, q_subtile_factor)

    (
        curr_mask_block_cnt,
        curr_mask_block_idx,
        curr_full_block_cnt,
        curr_full_block_idx,
    ) = get_curr_blocksparse_tensors(
        batch_idx,
        head_idx,
        m_block_sparse,
        blocksparse_tensors,
        seqlen_info,
    )

    mask_begin, mask_end = split_block_range(curr_mask_block_cnt, split_idx, num_splits)
    full_begin, full_end = split_block_range(curr_full_block_cnt, split_idx, num_splits)
    split_mask_block_cnt = mask_end - mask_begin
    split_full_block_cnt = full_end - full_begin
    processed_any = split_mask_block_cnt + split_full_block_cnt > 0

    if const_expr(not intra_wg_overlap):
        if split_mask_block_cnt > 0:
            mask_n_block = curr_mask_block_idx[mask_end - 1]
            warp_scheduler_barrier_sync()
            kv_consumer_state = mma_one_n_block(
                kv_consumer_state,
                n_block=mask_n_block,
                mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
                mask_fn=partial(
                    mask_fn,
                    mask_mod=mask_mod,
                    mask_seqlen=True,
                    fastdiv_mods=(
                        fastdiv_mods
                        if cutlass.const_expr(mask_mod is not None)
                        else None
                    ),
                ),
                is_first_n_block=True,
            )
            O_should_accumulate = True
            for i in cutlass.range(1, split_mask_block_cnt):
                mask_n_block = curr_mask_block_idx[mask_end - 1 - i]
                kv_consumer_state = mma_one_n_block(
                    kv_consumer_state,
                    n_block=mask_n_block,
                    mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
                    mask_fn=partial(mask_fn, mask_mod=mask_mod, mask_seqlen=False),
                    is_first_n_block=False,
                )
                O_should_accumulate = True
            if split_full_block_cnt == 0:
                warp_scheduler_barrier_arrive()

        if split_full_block_cnt > 0:
            full_n_block = curr_full_block_idx[full_end - 1]
            if split_mask_block_cnt == 0:
                warp_scheduler_barrier_sync()
                kv_consumer_state = mma_one_n_block(
                    kv_consumer_state,
                    n_block=full_n_block,
                    mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
                    mask_fn=partial(mask_fn, mask_seqlen=True),
                    is_first_n_block=True,
                )
                O_should_accumulate = True
                for i in cutlass.range(1, split_full_block_cnt):
                    full_n_block = curr_full_block_idx[full_end - 1 - i]
                    kv_consumer_state = mma_one_n_block(
                        kv_consumer_state,
                        n_block=full_n_block,
                        mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
                        mask_fn=partial(mask_fn, mask_seqlen=False),
                        is_first_n_block=False,
                    )
                    O_should_accumulate = True
            else:
                kv_consumer_state = mma_one_n_block(
                    kv_consumer_state,
                    n_block=full_n_block,
                    mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
                    mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=True),
                    is_first_n_block=False,
                )
                O_should_accumulate = True
                for i in cutlass.range(1, split_full_block_cnt):
                    full_n_block = curr_full_block_idx[full_end - 1 - i]
                    kv_consumer_state = mma_one_n_block(
                        kv_consumer_state,
                        n_block=full_n_block,
                        mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
                        mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=False),
                        is_first_n_block=False,
                    )
                    O_should_accumulate = True
            warp_scheduler_barrier_arrive()
    else:
        if split_mask_block_cnt > 0:
            mask_n_block = curr_mask_block_idx[mask_end - 1]
            kv_consumer_state = process_first_half_block(
                n_block=mask_n_block,
                seqlen=seqlen_info,
                kv_consumer_state=kv_consumer_state,
                mask_fn=partial(
                    mask_fn,
                    mask_mod=mask_mod,
                    mask_seqlen=True,
                    fastdiv_mods=(
                        fastdiv_mods
                        if cutlass.const_expr(mask_mod is not None)
                        else None
                    ),
                ),
                score_mod_fn=score_mod_fn,
                is_first_block=True,
            )
            for i in cutlass.range(1, split_mask_block_cnt):
                mask_n_block = curr_mask_block_idx[mask_end - 1 - i]
                kv_consumer_state = mma_one_n_block(
                    kv_consumer_state,
                    n_block=mask_n_block,
                    seqlen=seqlen_info,
                    mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
                    mask_fn=partial(mask_fn, mask_mod=mask_mod, mask_seqlen=False),
                )
                O_should_accumulate = True

        if split_full_block_cnt > 0:
            full_n_block = curr_full_block_idx[full_end - 1]
            if split_mask_block_cnt == 0:
                kv_consumer_state = process_first_half_block(
                    n_block=full_n_block,
                    seqlen=seqlen_info,
                    kv_consumer_state=kv_consumer_state,
                    mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=True),
                    score_mod_fn=score_mod_fn,
                    is_first_block=True,
                )
            else:
                kv_consumer_state = mma_one_n_block(
                    kv_consumer_state,
                    n_block=full_n_block,
                    seqlen=seqlen_info,
                    mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
                    mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=True),
                )
                O_should_accumulate = True
            for i in cutlass.range(1, split_full_block_cnt):
                full_n_block = curr_full_block_idx[full_end - 1 - i]
                kv_consumer_state = mma_one_n_block(
                    kv_consumer_state,
                    n_block=full_n_block,
                    seqlen=seqlen_info,
                    mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
                    mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=False),
                )
                O_should_accumulate = True

        if processed_any:
            kv_consumer_state = process_last_half_block(
                kv_consumer_state=kv_consumer_state,
                zero_init=not O_should_accumulate,
            )
            O_should_accumulate = True

    return kv_consumer_state, O_should_accumulate, processed_any


@cute.jit
def split_block_range(block_count, split_idx: Int32, num_splits: Int32):
    """Return the half-open block-list range assigned to one SplitKV partition."""
    blocks_per_split = cute.ceil_div(block_count, num_splits)
    block_begin = cutlass.min(split_idx * blocks_per_split, block_count)
    block_end = cutlass.min(block_begin + blocks_per_split, block_count)
    return block_begin, block_end


@cute.jit
def load_block_list_sm100(
    block_indices: cute.Tensor,
    block_begin,
    block_end,
    load_q_with_first: cutlass.Constexpr,
    q_stage: cutlass.Constexpr,
    kv_producer_state,
    load_Q,
    load_K,
    load_V,
    pipeline_kv,
):
    """SM100 version of load_block_list (no intra_wg_overlap, no extra_tx_count)."""
    block_count = block_end - block_begin
    if block_count > 0:
        # First iteration: load Q alongside K if requested
        n_block_first = block_indices[block_end - 1]

        if const_expr(load_q_with_first):
            # SM100 loads Q0 and optionally Q1
            load_Q(block=0, stage=0)
            if const_expr(q_stage == 2):
                load_Q(block=1, stage=1)

        # SM100 doesn't use producer_acquire for pipeline_kv in load path
        # The pipeline barriers are handled inside load_KV
        load_K(block=n_block_first, producer_state=kv_producer_state, page_idx=None)
        kv_producer_state.advance()
        load_V(block=n_block_first, producer_state=kv_producer_state, page_idx=None)
        kv_producer_state.advance()

        # Remaining blocks
        for offset in cutlass.range(1, block_count):
            n_block = block_indices[block_end - 1 - offset]
            load_K(block=n_block, producer_state=kv_producer_state, page_idx=None)
            kv_producer_state.advance()
            load_V(block=n_block, producer_state=kv_producer_state, page_idx=None)
            kv_producer_state.advance()

    return kv_producer_state


# SM100-specific tile processor using SM100 helpers
@cute.jit
def produce_block_sparse_loads_sm100(
    blocksparse_tensors: BlockSparseTensors,
    batch_idx,
    head_idx,
    m_block,
    seqlen_info: SeqlenInfoQK,
    split_idx: Int32,
    num_splits: Int32,
    kv_producer_state,
    load_Q,
    load_K,
    load_V,
    pipeline_kv,
    q_stage: cutlass.Constexpr,
    q_producer_phase: Int32,
    qhead_per_kvhead: cutlass.Constexpr,
    q_subtile_factor: cutlass.Constexpr,
):
    """SM100 entry point for sparse block iteration.

    SM100 uses PipelineTmaUmma which doesn't support extra_tx_count, so we use
    simplified block processing that just calls producer_acquire without extras.

    Args:
        m_block: which tile of m we are processing
        qhead_per_kvhead: Constexpr pack factor
    """
    m_block_sparse = sparse_tensor_m_block(m_block, qhead_per_kvhead, q_subtile_factor)

    (
        curr_mask_block_cnt,
        curr_mask_block_idx,
        curr_full_block_cnt,
        curr_full_block_idx,
    ) = get_curr_blocksparse_tensors(
        batch_idx,
        head_idx,
        m_block_sparse,
        blocksparse_tensors,
        seqlen_info,
    )

    mask_begin, mask_end = split_block_range(curr_mask_block_cnt, split_idx, num_splits)
    full_begin, full_end = split_block_range(curr_full_block_cnt, split_idx, num_splits)
    mask_empty = mask_begin == mask_end
    full_empty = full_begin == full_end

    q_phase_flipped = False

    if mask_empty:
        # No masked blocks: process full list with Q loading
        kv_producer_state = load_block_list_sm100(
            curr_full_block_idx,
            full_begin,
            full_end,
            load_q_with_first=True,
            q_stage=q_stage,
            kv_producer_state=kv_producer_state,
            load_Q=load_Q,
            load_K=load_K,
            load_V=load_V,
            pipeline_kv=pipeline_kv,
        )
        q_phase_flipped = not full_empty
    else:
        # Process masked blocks with Q loading
        kv_producer_state = load_block_list_sm100(
            curr_mask_block_idx,
            mask_begin,
            mask_end,
            load_q_with_first=True,
            q_stage=q_stage,
            kv_producer_state=kv_producer_state,
            load_Q=load_Q,
            load_K=load_K,
            load_V=load_V,
            pipeline_kv=pipeline_kv,
        )
        q_phase_flipped = True

        if not full_empty:
            # Process full blocks without Q loading
            kv_producer_state = load_block_list_sm100(
                curr_full_block_idx,
                full_begin,
                full_end,
                load_q_with_first=False,
                q_stage=q_stage,
                kv_producer_state=kv_producer_state,
                load_Q=load_Q,
                load_K=load_K,
                load_V=load_V,
                pipeline_kv=pipeline_kv,
            )

    if q_phase_flipped:
        q_producer_phase ^= 1

    return kv_producer_state, q_producer_phase


@cute.jit
def get_total_block_count(
    blocksparse_tensors: BlockSparseTensors,
    batch_idx,
    head_idx,
    m_block,
    split_idx: Int32,
    num_splits: Int32,
    qhead_per_kvhead: cutlass.Constexpr,
    q_subtile_factor: cutlass.Constexpr,
    seqlen_info: SeqlenInfoQK,
):
    m_block_sparse = sparse_tensor_m_block(m_block, qhead_per_kvhead, q_subtile_factor)
    (
        curr_mask_block_cnt,
        _,
        curr_full_block_cnt,
        _,
    ) = get_curr_blocksparse_tensors(
        batch_idx,
        head_idx,
        m_block_sparse,
        blocksparse_tensors,
        seqlen_info,
    )

    mask_begin, mask_end = split_block_range(curr_mask_block_cnt, split_idx, num_splits)
    full_begin, full_end = split_block_range(curr_full_block_cnt, split_idx, num_splits)
    return mask_end - mask_begin + full_end - full_begin


@cute.jit
def handle_block_sparse_empty_tile_correction_sm100(
    tidx: Int32,
    q_stage: cutlass.Constexpr,
    m_block_size: cutlass.Constexpr,
    qhead_per_kvhead,
    pack_gqa: cutlass.Constexpr,
    is_split_kv: cutlass.Constexpr,
    learnable_sink,
    mLSE,
    seqlen_info,
    m_block: Int32,
    head_idx: Int32,
    batch_idx: Int32,
    split_idx: Int32,
    sScale: cute.Tensor,
    stats: list,
    correction_epilogue: Callable,
    thr_mma_pv: cute.ThrMma,
    tOtO: cute.Tensor,
    sO: cute.Tensor,
    pipeline_sm_stats: cutlass.pipeline.PipelineAsync,
    sm_stats_barrier: cutlass.pipeline.NamedBarrier,
    pipeline_o_epi: cutlass.pipeline.PipelineAsync,
    sm_stats_consumer_phase: Int32,
    o_corr_consumer_phase: Int32,
    corr_epi_producer_phase: Int32,
    softmax_scale_log2: Float32,
    max_offset: Float32,
    max_offset_scale: Float32,
    mO_cur: Optional[cute.Tensor] = None,
    gO: Optional[cute.Tensor] = None,
    gmem_tiled_copy_O: Optional[cute.TiledCopy] = None,
):
    """Handle SM100 forward block-sparse tiles with no active KV blocks.

    This path is taken when `total_block_cnt == 0`. The softmax warp-group still
    arrives `mbar_softmax_corr_full` (synthetic "no work") so the correction
    warp-group can:

    - seed fully-masked-row stats (row_sum=1; row_max=-inf when tracked) for LSE
    - run `correction_epilogue` with `scale=0` so the output tile is written as zeros
      (independent of any prior tmem contents)
    - wait on `mbar_softmax_corr_full` and arrive `mbar_softmax_corr_empty`
      (and `mbar_corr_epi_*` when applicable) so phases stay aligned across tiles

    This helper intentionally does not touch `mbar_P_full_*` since no P is produced.
    See NOTE [SM100 block-sparse empty tiles: mbarrier contract].
    """
    LOG2_E = Float32(math.log2(math.e))
    warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) % 4

    for stage in cutlass.range_constexpr(q_stage):
        row_sum_value = Float32(1.0)
        row_max_value = (
            -Float32.inf
            if const_expr(mLSE is not None or learnable_sink is not None)
            else None
        )
        if const_expr(learnable_sink is not None):
            sink_val = -Float32.inf
            if const_expr(not pack_gqa):
                sink_val = Float32(learnable_sink[head_idx])
            elif tidx < m_block_size:
                q_head_idx = (
                    (q_stage * m_block + stage) * m_block_size + tidx
                ) % qhead_per_kvhead + head_idx * qhead_per_kvhead
                sink_val = Float32(learnable_sink[q_head_idx])
            if sink_val != -Float32.inf and (
                const_expr(not is_split_kv) or split_idx == 0
            ):
                if row_max_value == -Float32.inf:
                    row_max_value = sink_val * (LOG2_E / softmax_scale_log2)
                    row_sum_value = max_offset_scale
                else:
                    row_sum_value = row_sum_value + cute.math.exp2(
                        sink_val * LOG2_E
                        - row_max_value * softmax_scale_log2
                        + max_offset,
                        fastmath=True,
                    )
        if tidx < m_block_size:
            scale_row_idx = tidx + stage * m_block_size
            sScale[scale_row_idx] = row_sum_value
            if const_expr(mLSE is not None or learnable_sink is not None):
                sScale[scale_row_idx + q_stage * m_block_size] = row_max_value
        acc_flag = row_sum_value == Float32(0.0) or row_sum_value != row_sum_value
        stats[stage] = (row_sum_value, row_max_value, acc_flag)

        # See NOTE [SM100 block-sparse empty tiles: mbarrier contract].
        # pipeline_sm_stats.consumer_wait_w_index_phase(stage, sm_stats_consumer_phase)
        sm_stats_barrier.arrive_and_wait_w_index(index=stage * 4 + warp_idx)
        pipeline_sm_stats.consumer_release_w_index(stage)

        if const_expr(gmem_tiled_copy_O is None):
            pipeline_o_epi.producer_acquire_w_index_phase(
                stage, corr_epi_producer_phase
            )

        gO_stage = gO[None, None, stage] if const_expr(gO is not None) else None
        correction_epilogue(
            thr_mma_pv,
            tOtO[None, None, None, stage],
            tidx,
            stage,
            m_block,
            seqlen_info.seqlen_q,
            Float32(
                0.0
            ),  # zero scale ensures empty tile writes zeros into staged outputs
            sO[None, None, stage],
            mO_cur,
            gO_stage,
            gmem_tiled_copy_O,
        )
        if const_expr(gmem_tiled_copy_O is None):
            pipeline_o_epi.producer_commit_w_index(stage)

    sm_stats_consumer_phase ^= 1
    corr_epi_producer_phase ^= 1

    return (
        sm_stats_consumer_phase,
        o_corr_consumer_phase,
        corr_epi_producer_phase,
    )


@cute.jit
def softmax_block_sparse_sm100(
    blocksparse_tensors: BlockSparseTensors,
    batch_idx,
    head_idx,
    m_block,
    seqlen_info: SeqlenInfoQK,
    split_idx: Int32,
    num_splits: Int32,
    softmax_step: Callable,
    mask_fn: Callable,
    mask_fn_none: Callable,
    mma_si_consumer_phase: Int32,
    si_corr_producer_phase: Int32,
    s0_s1_sequence_phase: Int32,
    pipeline_sm_stats: cutlass.pipeline.PipelineAsync,
    sm_stats_barrier: cutlass.pipeline.NamedBarrier,
    q_stage: cutlass.Constexpr,
    stage_idx: Int32,
    check_m_boundary: bool,
    qhead_per_kvhead: cutlass.Constexpr,
    q_subtile_factor: cutlass.Constexpr[int] = 1,
):
    warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) % 4
    m_block_sparse = sparse_tensor_m_block(m_block, qhead_per_kvhead, q_subtile_factor)

    (
        curr_mask_block_cnt,
        curr_mask_block_idx,
        curr_full_block_cnt,
        curr_full_block_idx,
    ) = get_curr_blocksparse_tensors(
        batch_idx,
        head_idx,
        m_block_sparse,
        blocksparse_tensors,
        seqlen_info,
    )

    mask_begin, mask_end = split_block_range(curr_mask_block_cnt, split_idx, num_splits)
    full_begin, full_end = split_block_range(curr_full_block_cnt, split_idx, num_splits)
    split_mask_block_cnt = mask_end - mask_begin
    split_full_block_cnt = full_end - full_begin
    total_block_cnt = split_mask_block_cnt + split_full_block_cnt

    if total_block_cnt == 0:
        sm_stats_barrier.arrive_w_index(index=stage_idx * 4 + warp_idx)
    else:
        if split_mask_block_cnt > 0:
            mask_n_block = curr_mask_block_idx[mask_end - 1]
            (
                mma_si_consumer_phase,
                si_corr_producer_phase,
                s0_s1_sequence_phase,
            ) = softmax_step(
                mma_si_consumer_phase,
                si_corr_producer_phase,
                s0_s1_sequence_phase,
                mask_n_block,
                is_first=True,
                mask_fn=partial(
                    mask_fn, mask_seqlen=True, check_q_boundary=check_m_boundary
                ),
            )
            for i in cutlass.range(1, split_mask_block_cnt):
                mask_n_block = curr_mask_block_idx[mask_end - 1 - i]
                (
                    mma_si_consumer_phase,
                    si_corr_producer_phase,
                    s0_s1_sequence_phase,
                ) = softmax_step(
                    mma_si_consumer_phase,
                    si_corr_producer_phase,
                    s0_s1_sequence_phase,
                    mask_n_block,
                    mask_fn=partial(
                        mask_fn, mask_seqlen=False, check_q_boundary=check_m_boundary
                    ),
                )

        if split_full_block_cnt > 0:
            full_n_block = curr_full_block_idx[full_end - 1]
            if split_mask_block_cnt == 0:
                (
                    mma_si_consumer_phase,
                    si_corr_producer_phase,
                    s0_s1_sequence_phase,
                ) = softmax_step(
                    mma_si_consumer_phase,
                    si_corr_producer_phase,
                    s0_s1_sequence_phase,
                    full_n_block,
                    is_first=True,
                    mask_fn=partial(
                        mask_fn_none,
                        mask_seqlen=True,
                        check_q_boundary=check_m_boundary,
                    ),
                )
            else:
                (
                    mma_si_consumer_phase,
                    si_corr_producer_phase,
                    s0_s1_sequence_phase,
                ) = softmax_step(
                    mma_si_consumer_phase,
                    si_corr_producer_phase,
                    s0_s1_sequence_phase,
                    full_n_block,
                    is_first=False,
                    mask_fn=partial(
                        mask_fn_none,
                        mask_seqlen=True,
                        check_q_boundary=check_m_boundary,
                    ),
                )
            for i in cutlass.range(1, split_full_block_cnt):
                full_n_block = curr_full_block_idx[full_end - 1 - i]
                (
                    mma_si_consumer_phase,
                    si_corr_producer_phase,
                    s0_s1_sequence_phase,
                ) = softmax_step(
                    mma_si_consumer_phase,
                    si_corr_producer_phase,
                    s0_s1_sequence_phase,
                    full_n_block,
                    mask_fn=partial(
                        mask_fn_none,
                        mask_seqlen=False,
                        check_q_boundary=check_m_boundary,
                    ),
                )

    return (
        mma_si_consumer_phase,
        si_corr_producer_phase,
        s0_s1_sequence_phase,
        total_block_cnt == 0,
    )


# =============================================================================
# Backward-specific block-sparse helpers (SM100)
# =============================================================================
#
# In backward, iteration is transposed compared to forward:
# - Forward: outer loop over m_blocks (Q tiles), inner loop over n_blocks (KV tiles)
# - Backward: outer loop over n_blocks (KV tiles), inner loop over m_blocks (Q tiles)
#
# The backward block-sparse tensors use "Q direction" indexing:
# - q_block_cnt[batch, head, n_block] → count of m_blocks to process for this KV tile
# - q_block_idx[batch, head, n_block, :] → indices of m_blocks to process
#


@cute.jit
def get_total_q_block_count_bwd(
    blocksparse_tensors: BlockSparseTensors,
    batch_idx,
    head_idx,
    n_block,
    subtile_factor: cutlass.Constexpr = 1,
    m_block_max: int = 0,
):
    """Count total tile iterations for given n_block (KV tile) in backward."""
    q_block_cnt, _, full_block_cnt, _, *_ = blocksparse_tensors
    total = q_block_cnt[batch_idx, head_idx, n_block]
    if const_expr(full_block_cnt is not None):
        total = total + full_block_cnt[batch_idx, head_idx, n_block]
    return total * subtile_factor


@cute.jit
def produce_block_sparse_q_loads_bwd_sm100(
    blocksparse_tensors: BlockSparseTensors,
    batch_idx,
    head_idx,
    n_block,
    # Pipeline states (will be returned after advancing)
    producer_state_Q_LSE,
    producer_state_dO_dPsum,
    # Pipelines
    pipeline_Q,
    pipeline_LSE,
    pipeline_dO,
    pipeline_dPsum,
    # Load functions
    load_K,
    load_V,
    load_Q,
    load_dO,
    copy_stats,
    # Global tensors for LSE/dPsum
    gLSE,
    sLSE,
    gdPsum,
    sdPsum,
    # TMA copy bytes for extra_tx_count
    tma_copy_bytes_K,
    tma_copy_bytes_V,
    # Flags for which loads to perform
    should_load_Q: cutlass.Constexpr,
    should_load_dO: cutlass.Constexpr,
    # Subtiling factor and bounds
    subtile_factor: cutlass.Constexpr = 1,
    m_block_max: int = 0,
):
    """SM100 backward block sparse loading with subtiling.

    Returns updated (producer_state_Q_LSE, producer_state_dO_dPsum).
    First iteration loads K/V alongside Q/dO; subsequent iterations load only Q/dO.
    """
    (
        curr_q_cnt,
        curr_q_idx,
        curr_full_cnt,
        curr_full_idx,
        loop_count,
    ) = get_block_sparse_iteration_info_bwd(
        blocksparse_tensors, batch_idx, head_idx, n_block, subtile_factor, m_block_max
    )

    for iter_idx in cutlass.range(loop_count, unroll=1):
        m_block, _ = get_m_block_from_iter_bwd(
            iter_idx,
            curr_q_cnt,
            curr_q_idx,
            curr_full_cnt,
            curr_full_idx,
            subtile_factor,
            m_block_max,
        )
        m_block_safe = m_block
        if m_block_max > 0:
            m_block_safe = cutlass.min(m_block, m_block_max - 1)

        if iter_idx == 0:
            # First block: load K/V alongside Q/dO
            if const_expr(should_load_Q):
                pipeline_Q.producer_acquire(
                    producer_state_Q_LSE, extra_tx_count=tma_copy_bytes_K
                )
                load_K(
                    tma_bar_ptr=pipeline_Q.producer_get_barrier(producer_state_Q_LSE)
                )
                load_Q(m_block_safe, producer_state=producer_state_Q_LSE)
                pipeline_Q.producer_commit(producer_state_Q_LSE)
                pipeline_LSE.producer_acquire(producer_state_Q_LSE)
                with cute.arch.elect_one():
                    copy_stats(
                        gLSE[None, m_block_safe],
                        sLSE[None, producer_state_Q_LSE.index],
                        mbar_ptr=pipeline_LSE.producer_get_barrier(
                            producer_state_Q_LSE
                        ),
                    )
                producer_state_Q_LSE.advance()
            if const_expr(should_load_dO):
                pipeline_dO.producer_acquire(
                    producer_state_dO_dPsum, extra_tx_count=tma_copy_bytes_V
                )
                load_V(
                    tma_bar_ptr=pipeline_dO.producer_get_barrier(
                        producer_state_dO_dPsum
                    )
                )
                load_dO(m_block_safe, producer_state=producer_state_dO_dPsum)
                pipeline_dO.producer_commit(producer_state_dO_dPsum)
                pipeline_dPsum.producer_acquire(producer_state_dO_dPsum)
                with cute.arch.elect_one():
                    copy_stats(
                        gdPsum[None, m_block_safe],
                        sdPsum[None, producer_state_dO_dPsum.index],
                        mbar_ptr=pipeline_dPsum.producer_get_barrier(
                            producer_state_dO_dPsum
                        ),
                    )
                producer_state_dO_dPsum.advance()
        else:
            # Subsequent blocks: just load Q/dO (K/V already loaded)
            if const_expr(should_load_Q):
                pipeline_Q.producer_acquire(producer_state_Q_LSE)
                load_Q(m_block_safe, producer_state=producer_state_Q_LSE)
                pipeline_Q.producer_commit(producer_state_Q_LSE)
                pipeline_LSE.producer_acquire(producer_state_Q_LSE)
                with cute.arch.elect_one():
                    copy_stats(
                        gLSE[None, m_block_safe],
                        sLSE[None, producer_state_Q_LSE.index],
                        mbar_ptr=pipeline_LSE.producer_get_barrier(
                            producer_state_Q_LSE
                        ),
                    )
                producer_state_Q_LSE.advance()
            if const_expr(should_load_dO):
                pipeline_dO.producer_acquire(producer_state_dO_dPsum)
                load_dO(m_block_safe, producer_state=producer_state_dO_dPsum)
                pipeline_dO.producer_commit(producer_state_dO_dPsum)
                pipeline_dPsum.producer_acquire(producer_state_dO_dPsum)
                with cute.arch.elect_one():
                    copy_stats(
                        gdPsum[None, m_block_safe],
                        sdPsum[None, producer_state_dO_dPsum.index],
                        mbar_ptr=pipeline_dPsum.producer_get_barrier(
                            producer_state_dO_dPsum
                        ),
                    )
                producer_state_dO_dPsum.advance()

    return producer_state_Q_LSE, producer_state_dO_dPsum


@cute.jit
def get_block_sparse_iteration_info_bwd(
    blocksparse_tensors: BlockSparseTensors,
    batch_idx,
    head_idx,
    n_block,
    subtile_factor: cutlass.Constexpr = 1,
    m_block_max: int = 0,
):
    """Extract block-sparse iteration info for backward pass.

    Returns (curr_q_cnt, curr_q_idx, curr_full_cnt, curr_full_idx, total_count).
    """
    q_cnt, q_idx, full_cnt, full_idx, *_ = blocksparse_tensors
    curr_q_cnt = q_cnt[batch_idx, head_idx, n_block]
    curr_q_idx = q_idx[batch_idx, head_idx, n_block, None]

    if const_expr(full_cnt is not None):
        curr_full_cnt = full_cnt[batch_idx, head_idx, n_block]
        curr_full_idx = full_idx[batch_idx, head_idx, n_block, None]
    else:
        curr_full_cnt = Int32(0)
        curr_full_idx = None

    sparse_block_count = curr_q_cnt
    if const_expr(full_cnt is not None):
        sparse_block_count = sparse_block_count + curr_full_cnt
    total_count = sparse_block_count * subtile_factor

    return curr_q_cnt, curr_q_idx, curr_full_cnt, curr_full_idx, total_count


@cute.jit
def get_m_block_from_iter_bwd(
    iter_idx,
    curr_q_cnt,
    curr_q_idx: cute.Tensor,
    curr_full_cnt,
    curr_full_idx: Optional[cute.Tensor],
    subtile_factor: cutlass.Constexpr = 1,
    m_block_max: int = 0,
):
    """Derive m_block index and is_full_block flag from iteration index.

    Returns (m_block, is_full_block):
        - m_block: The actual Q-tile block index
        - is_full_block: True if this is a full block (no mask_mod needed)
    """
    sparse_iter_idx = iter_idx // subtile_factor
    subtile_offset = iter_idx % subtile_factor

    sparse_m_block = Int32(0)
    is_full_block = False
    if const_expr(curr_full_idx is not None):
        if sparse_iter_idx < curr_q_cnt:
            sparse_m_block = curr_q_idx[sparse_iter_idx]
        else:
            sparse_m_block = curr_full_idx[sparse_iter_idx - curr_q_cnt]
            is_full_block = True
    else:
        sparse_m_block = curr_q_idx[sparse_iter_idx]

    return sparse_m_block * subtile_factor + subtile_offset, is_full_block


@cute.jit
def _load_q_do_block_sm90(
    m_block,
    producer_state_Q,
    producer_state_dO,
    pipeline_Q,
    pipeline_dO,
    load_K,
    load_V,
    load_Q,
    load_dO,
    load_LSE,
    load_dPsum,
    tma_copy_bytes_K,
    tma_copy_bytes_V,
    Q_stage_eq_dO_stage: cutlass.Constexpr,
    load_kv: bool,
):
    """Load one Q/dO block, optionally loading K/V on first iteration."""
    if load_kv:
        pipeline_Q.producer_acquire(producer_state_Q, extra_tx_count=tma_copy_bytes_K)
        load_K(tma_bar_ptr=pipeline_Q.producer_get_barrier(producer_state_Q))
    else:
        pipeline_Q.producer_acquire(producer_state_Q)
    load_Q(m_block, producer_state=producer_state_Q)
    load_LSE(m_block, producer_state=producer_state_Q)

    producer_state_dO_cur = (
        producer_state_dO if const_expr(not Q_stage_eq_dO_stage) else producer_state_Q
    )
    if load_kv:
        pipeline_dO.producer_acquire(
            producer_state_dO_cur, extra_tx_count=tma_copy_bytes_V
        )
        load_V(tma_bar_ptr=pipeline_dO.producer_get_barrier(producer_state_dO_cur))
    else:
        pipeline_dO.producer_acquire(producer_state_dO_cur)
    load_dO(m_block, producer_state=producer_state_dO_cur)
    load_dPsum(m_block, producer_state=producer_state_dO_cur)

    producer_state_Q.advance()
    producer_state_dO.advance()
    return producer_state_Q, producer_state_dO


@cute.jit
def produce_block_sparse_q_loads_bwd_sm90(
    blocksparse_tensors: BlockSparseTensors,
    batch_idx,
    head_idx,
    n_block,
    producer_state_Q,
    producer_state_dO,
    pipeline_Q,
    pipeline_dO,
    load_K,
    load_V,
    load_Q,
    load_dO,
    load_LSE,
    load_dPsum,
    tma_copy_bytes_K,
    tma_copy_bytes_V,
    Q_stage_eq_dO_stage: cutlass.Constexpr,
    subtile_factor: cutlass.Constexpr,
    m_block_max: int,
):
    """SM90 backward block sparse loading with separate partial/full loops.

    K/V are loaded with the first valid block. Iterates partial blocks first,
    then full blocks, matching consumer order.

    Returns updated (producer_state_Q, producer_state_dO).
    """
    q_cnt, q_idx, full_cnt, full_idx, *_ = blocksparse_tensors
    curr_q_cnt = q_cnt[batch_idx, head_idx, n_block]
    curr_q_idx = q_idx[batch_idx, head_idx, n_block, None]

    if const_expr(full_cnt is not None):
        curr_full_cnt = full_cnt[batch_idx, head_idx, n_block]
        curr_full_idx = full_idx[batch_idx, head_idx, n_block, None]
    else:
        curr_full_cnt = Int32(0)
        curr_full_idx = None

    kv_loaded = False

    for iter_idx in cutlass.range(curr_q_cnt * subtile_factor, unroll=1):
        sparse_idx = iter_idx // subtile_factor
        subtile_offset = iter_idx % subtile_factor
        m_block = curr_q_idx[sparse_idx] * subtile_factor + subtile_offset

        if m_block < m_block_max:
            producer_state_Q, producer_state_dO = _load_q_do_block_sm90(
                m_block,
                producer_state_Q,
                producer_state_dO,
                pipeline_Q,
                pipeline_dO,
                load_K,
                load_V,
                load_Q,
                load_dO,
                load_LSE,
                load_dPsum,
                tma_copy_bytes_K,
                tma_copy_bytes_V,
                Q_stage_eq_dO_stage,
                load_kv=not kv_loaded,
            )
            kv_loaded = True

    if const_expr(full_cnt is not None):
        for iter_idx in cutlass.range(curr_full_cnt * subtile_factor, unroll=1):
            sparse_idx = iter_idx // subtile_factor
            subtile_offset = iter_idx % subtile_factor
            m_block = curr_full_idx[sparse_idx] * subtile_factor + subtile_offset

            if m_block < m_block_max:
                producer_state_Q, producer_state_dO = _load_q_do_block_sm90(
                    m_block,
                    producer_state_Q,
                    producer_state_dO,
                    pipeline_Q,
                    pipeline_dO,
                    load_K,
                    load_V,
                    load_Q,
                    load_dO,
                    load_LSE,
                    load_dPsum,
                    tma_copy_bytes_K,
                    tma_copy_bytes_V,
                    Q_stage_eq_dO_stage,
                    load_kv=not kv_loaded,
                )
                kv_loaded = True

    return producer_state_Q, producer_state_dO


@cute.jit
def consume_block_sparse_mma_bwd_sm90(
    blocksparse_tensors: BlockSparseTensors,
    batch_idx,
    head_idx,
    n_block,
    consumer_state_Q,
    consumer_state_dO,
    mma_one_m_block_fn,
    mask,
    mask_mod,
    is_causal: cutlass.Constexpr,
    is_local: cutlass.Constexpr,
    thr_mma_SdP,
    score_mod_fn=None,
    score_mod_bwd_fn=None,
    subtile_factor: cutlass.Constexpr = 1,
    m_block_max: int = 0,
    aux_data: AuxData = AuxData(),
    fastdiv_mods=(None, None),
):
    """SM90 backward block sparse MMA consumption with separate partial/full loops.

    Partial blocks are processed first (with mask_mod applied), then full blocks
    (without mask_mod). This ensures mask_mod is only applied where needed.

    Returns updated (consumer_state_Q, consumer_state_dO).
    """
    q_cnt, q_idx, full_cnt, full_idx, *_ = blocksparse_tensors
    curr_q_cnt = q_cnt[batch_idx, head_idx, n_block]
    curr_q_idx = q_idx[batch_idx, head_idx, n_block, None]

    if const_expr(full_cnt is not None):
        curr_full_cnt = full_cnt[batch_idx, head_idx, n_block]
        curr_full_idx = full_idx[batch_idx, head_idx, n_block, None]
    else:
        curr_full_cnt = Int32(0)
        curr_full_idx = None

    dKV_accumulate = False

    mask_fn_partial = partial(
        mask.apply_mask,
        batch_idx=batch_idx,
        head_idx=head_idx,
        n_block=n_block,
        thr_mma=thr_mma_SdP,
        mask_seqlen=True,
        mask_causal=is_causal,
        mask_local=is_local,
        mask_mod=mask_mod,
        aux_data=aux_data,
        fastdiv_mods=fastdiv_mods,
    )

    mask_fn_full = partial(
        mask.apply_mask,
        batch_idx=batch_idx,
        head_idx=head_idx,
        n_block=n_block,
        thr_mma=thr_mma_SdP,
        mask_seqlen=True,
        mask_causal=is_causal,
        mask_local=is_local,
        aux_data=aux_data,
        fastdiv_mods=fastdiv_mods,
    )

    for iter_idx in cutlass.range(curr_q_cnt * subtile_factor, unroll=1):
        sparse_idx = iter_idx // subtile_factor
        subtile_offset = iter_idx % subtile_factor
        m_block = curr_q_idx[sparse_idx] * subtile_factor + subtile_offset

        if m_block < m_block_max:
            consumer_state_Q, consumer_state_dO = mma_one_m_block_fn(
                m_block,
                consumer_state_Q,
                consumer_state_dO,
                mask_fn=mask_fn_partial,
                score_mod_fn=score_mod_fn,
                score_mod_bwd_fn=score_mod_bwd_fn,
                dKV_accumulate=dKV_accumulate,
            )
            dKV_accumulate = True

    if const_expr(full_cnt is not None):
        for iter_idx in cutlass.range(curr_full_cnt * subtile_factor, unroll=1):
            sparse_idx = iter_idx // subtile_factor
            subtile_offset = iter_idx % subtile_factor
            m_block = curr_full_idx[sparse_idx] * subtile_factor + subtile_offset

            if m_block < m_block_max:
                consumer_state_Q, consumer_state_dO = mma_one_m_block_fn(
                    m_block,
                    consumer_state_Q,
                    consumer_state_dO,
                    mask_fn=mask_fn_full,
                    score_mod_fn=score_mod_fn,
                    score_mod_bwd_fn=score_mod_bwd_fn,
                    dKV_accumulate=dKV_accumulate,
                )
                dKV_accumulate = True

    return consumer_state_Q, consumer_state_dO


@cute.jit
def _store_one_dQaccum_sm90(
    m_block,
    sdQaccum: cute.Tensor,
    gdQaccum: cute.Tensor,
    num_dQ_warp_groups: cutlass.Constexpr,
    num_threads_per_warp_group: cutlass.Constexpr,
    tma_copy_bytes_dQ,
):
    """Store dQaccum for a single m_block."""
    for warp_group_idx in cutlass.range_constexpr(num_dQ_warp_groups):
        cute.arch.cp_async_bulk_wait_group(
            num_dQ_warp_groups - 1 - warp_group_idx, read=True
        )
        cute.arch.barrier_arrive(
            barrier_id=int(NamedBarrierBwd.dQEmptyWG0) + warp_group_idx,
            number_of_threads=num_threads_per_warp_group + cute.arch.WARP_SIZE,
        )
    for warp_group_idx in cutlass.range_constexpr(num_dQ_warp_groups):
        cute.arch.barrier(
            barrier_id=int(NamedBarrierBwd.dQFullWG0) + warp_group_idx,
            number_of_threads=num_threads_per_warp_group + cute.arch.WARP_SIZE,
        )
        with cute.arch.elect_one():
            copy_utils.cpasync_reduce_bulk_add_f32(
                sdQaccum[None, warp_group_idx].iterator,
                gdQaccum[(None, warp_group_idx), m_block].iterator,
                tma_copy_bytes_dQ,
            )
        cute.arch.cp_async_bulk_commit_group()


@cute.jit
def dQaccum_store_block_sparse_bwd_sm90(
    blocksparse_tensors: BlockSparseTensors,
    batch_idx,
    head_idx,
    n_block,
    sdQaccum: cute.Tensor,
    gdQaccum: cute.Tensor,
    subtile_factor: cutlass.Constexpr,
    m_block_max: int,
    num_dQ_warp_groups: cutlass.Constexpr,
    num_threads_per_warp_group: cutlass.Constexpr,
    tma_copy_bytes_dQ,
):
    """SM90 backward block sparse dQaccum store with separate partial/full loops.

    Iterates partial blocks first, then full blocks, matching producer/consumer order.
    """
    q_cnt, q_idx, full_cnt, full_idx, *_ = blocksparse_tensors
    curr_q_cnt = q_cnt[batch_idx, head_idx, n_block]
    curr_q_idx = q_idx[batch_idx, head_idx, n_block, None]

    if const_expr(full_cnt is not None):
        curr_full_cnt = full_cnt[batch_idx, head_idx, n_block]
        curr_full_idx = full_idx[batch_idx, head_idx, n_block, None]
    else:
        curr_full_cnt = Int32(0)
        curr_full_idx = None

    for iter_idx in cutlass.range(curr_q_cnt * subtile_factor, unroll=1):
        sparse_idx = iter_idx // subtile_factor
        subtile_offset = iter_idx % subtile_factor
        m_block = curr_q_idx[sparse_idx] * subtile_factor + subtile_offset

        if m_block < m_block_max:
            _store_one_dQaccum_sm90(
                m_block,
                sdQaccum,
                gdQaccum,
                num_dQ_warp_groups,
                num_threads_per_warp_group,
                tma_copy_bytes_dQ,
            )

    if const_expr(full_cnt is not None):
        for iter_idx in cutlass.range(curr_full_cnt * subtile_factor, unroll=1):
            sparse_idx = iter_idx // subtile_factor
            subtile_offset = iter_idx % subtile_factor
            m_block = curr_full_idx[sparse_idx] * subtile_factor + subtile_offset

            if m_block < m_block_max:
                _store_one_dQaccum_sm90(
                    m_block,
                    sdQaccum,
                    gdQaccum,
                    num_dQ_warp_groups,
                    num_threads_per_warp_group,
                    tma_copy_bytes_dQ,
                )
