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#pragma once

#include "cutlass/cutlass.h"

#include "cute/tensor.hpp"
#include "cute/arch/simd_sm100.hpp"

#include "cutlass/arch/arch.h"
#include "cutlass/arch/memory_sm80.h"
#include "cutlass/gemm/collective/collective_builder.hpp"

#include "collective/fmha_common.hpp"

namespace cutlass::fmha::kernel {

using namespace cutlass::fmha::collective;

using namespace cute;

template<
    class Element,
    class ElementAcc,
    class TileShape,
    class Mask
>
struct Sm100FmhaBwdKernelTmaWarpSpecialized {

  using TileShapeQ = decltype(get<0>(TileShape{}));
  static_assert(std::is_same_v<TileShapeQ, _128>, "tile shape K must be 128");
  using TileShapeK = decltype(get<1>(TileShape{}));
  static_assert(std::is_same_v<TileShapeK, _128>, "tile shape K must be 128");
  using TileShapeDQK = decltype(get<2>(TileShape{}));
  using TileShapeDVO = decltype(get<2>(TileShape{}));

  using TmemAllocator = cute::TMEM::Allocator1Sm;
  struct TmemAllocation {
    static constexpr uint32_t kDK = 0;                     // TileShapeK x TileShapeDQK x acc
    static constexpr uint32_t kDV = kDK + TileShapeDQK{};  // TileShapeK x TileShapeDVO x acc
    static constexpr uint32_t kDQ = kDV + TileShapeDVO{};  // TileShapeQ x TileShapeDQK x acc
    static constexpr uint32_t kDP = kDQ;                   // TileShapeK x TileShapeQ   x inp
    static constexpr uint32_t kS = kDQ + max(TileShapeQ{}, TileShapeDQK{});
    static constexpr uint32_t kP = kS;
    static constexpr uint32_t kTotal = kS + TileShapeQ{};
  };

  static_assert(
      static_cast<int>(TmemAllocation::kTotal) <= TmemAllocator::Sm100TmemCapacityColumns,
      "using too much tmem"
  );

  enum class WarpRole {
    Empty = 0x0, Load = 0x1, Mma = 0x2, Compute = 0x3, Reduce = 0x4
  };

  static constexpr unsigned long long kWarpAssignment = 0x12'3333'3333'4444ull;
  static constexpr int kNumComputeWarps = 8;
  static constexpr int kNumReduceWarps = 4;
  CUTLASS_DEVICE WarpRole warp_idx_to_role(int warp_idx) {
    return static_cast<WarpRole>((kWarpAssignment >> (4 * warp_idx)) & 0xF);
  }

  struct RegisterAllocation {
    static constexpr int kWarpgroup0 = 160-8;
    static constexpr int kWarpgroup1 = 128;
    static constexpr int kWarpgroup2 = 96;
    static constexpr int kReduce = kWarpgroup0;
    static constexpr int kCompute = kWarpgroup1;
    static constexpr int kMma = kWarpgroup2;
    static constexpr int kEmpty = kWarpgroup2;
    static constexpr int kLoad = kWarpgroup2;

    static_assert(kWarpgroup0 + 2 * kWarpgroup1 + kWarpgroup2 <= 512);
  };

  using ArchTag = cutlass::arch::Sm100;

  using ClusterShape = Shape<_1, _1, _1>;
  using Schedule = cutlass::gemm::KernelTmaWarpSpecialized1SmSm100;

  static constexpr int MinBlocksPerMultiprocessor = 1;
  static constexpr int kNumWarps = kNumComputeWarps + kNumReduceWarps + 4;
  static constexpr int MaxThreadsPerBlock = NumThreadsPerWarp * kNumWarps;

  static constexpr int Alignment = 128 / sizeof_bits_v<Element>;
  static constexpr int kStages = 2;

  using TensorStrideContiguousK = Stride<int, _1, Stride<int, int>>;
  using TensorStrideContiguousMN = Stride<_1, int, Stride<int, int>>;

  // compute S
  using CollectiveMmaKQ = typename cutlass::gemm::collective::CollectiveBuilder<
      cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
      Element, TensorStrideContiguousK, Alignment,
      Element, TensorStrideContiguousK, Alignment,
      ElementAcc,
      Shape<TileShapeK, TileShapeQ, TileShapeDQK>,
      ClusterShape, cutlass::gemm::collective::StageCount<kStages>,
      Schedule>::CollectiveOp;
  using TileShapeKQ = typename CollectiveMmaKQ::TileShape;
  using TiledMmaKQ = typename CollectiveMmaKQ::TiledMma;

  // compute dP
  using CollectiveMmaVDO = typename cutlass::gemm::collective::CollectiveBuilder<
      cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
      Element, TensorStrideContiguousK, Alignment,
      Element, TensorStrideContiguousK, Alignment,
      ElementAcc,
      Shape<TileShapeK, TileShapeQ, TileShapeDVO>,
      ClusterShape, cutlass::gemm::collective::StageCount<kStages>,
      Schedule>::CollectiveOp;
  using TileShapeVDO = typename CollectiveMmaVDO::TileShape;
  using TiledMmaVDO = typename CollectiveMmaVDO::TiledMma;

  // compute dV
  using CollectiveMmaPDO = typename cutlass::gemm::collective::CollectiveBuilder<
      cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
      // needs to match ordering of S calculation
      Element, TensorStrideContiguousK, Alignment,
      Element, TensorStrideContiguousMN, Alignment,
      ElementAcc,
      Shape<TileShapeK, TileShapeDVO, TileShapeQ>,
      ClusterShape, cutlass::gemm::collective::StageCount<kStages>,
      Schedule>::CollectiveOp;
  using TileShapePDO = typename CollectiveMmaPDO::TileShape;
  using TiledMmaPDO = decltype(to_tiled_mma_sm100_ts(typename CollectiveMmaPDO::TiledMma{}));

  // compute dK
  using CollectiveMmaDSQ = typename cutlass::gemm::collective::CollectiveBuilder<
      cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
      // somewhat arbitrary since we dump to smem, need to agree with the next one
      Element, TensorStrideContiguousK , Alignment,
      Element, TensorStrideContiguousMN, Alignment,
      ElementAcc,
      Shape<TileShapeK, TileShapeDQK, TileShapeQ>,
      ClusterShape, cutlass::gemm::collective::StageCount<kStages>,
      Schedule>::CollectiveOp;
  using TileShapeDSQ = typename CollectiveMmaDSQ::TileShape;
  using TiledMmaDSQ = typename CollectiveMmaDSQ::TiledMma;

  // compute dQ
  using CollectiveMmaDSK = typename cutlass::gemm::collective::CollectiveBuilder<
      cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
      // somewhat arbitrary since we dump to smem, need to agree with the previous one
      Element, TensorStrideContiguousMN, Alignment,
      Element, TensorStrideContiguousMN, Alignment,
      ElementAcc,
      Shape<TileShapeQ, TileShapeDQK, TileShapeK>,
      ClusterShape, cutlass::gemm::collective::StageCount<kStages>,
      Schedule>::CollectiveOp;
  using TileShapeDSK = typename CollectiveMmaDSK::TileShape;
  using TiledMmaDSK = typename CollectiveMmaDSK::TiledMma;

  // pipelines are named Pipeline<Producer><Consumer><Resource>
  static constexpr int kStagesComputeSmem = 1;
  using PipelineLoadMmaQ = PipelineTmaUmmaAsync<2, ClusterShape>;
  using PipelineLoadMmaDO = PipelineTmaUmmaAsync<1, ClusterShape>;
  using PipelineLoadComputeLSE = PipelineAsync<1>;
  using PipelineLoadComputeSumOdO = PipelineAsync<1>;
  using PipelineMmaComputeS = PipelineUmmaAsync<1>;
  using PipelineMmaComputeDP = PipelineUmmaAsync<1>;
  using PipelineMmaReduceDQ = PipelineUmmaAsync<1>;
  using PipelineComputeMmaP = PipelineUmmaConsumerAsync<1>;
  using PipelineComputeMmaDS = PipelineUmmaConsumerAsync<kStagesComputeSmem>;
  using PipelineMmaComputeDKDV = PipelineUmmaAsync<2>;
  static constexpr int kStagesReduceTmaStore = 2;
  using PipelineReduceTmaStore = PipelineTmaStore<kStagesReduceTmaStore>;

  struct PipelineStorage {
    alignas(16) typename PipelineLoadMmaQ::SharedStorage load_mma_q;
    alignas(16) typename PipelineLoadMmaDO::SharedStorage load_mma_do;
    alignas(16) typename PipelineLoadComputeLSE::SharedStorage load_compute_lse;
    alignas(16) typename PipelineLoadComputeSumOdO::SharedStorage load_compute_sum_odo;
    alignas(16) typename PipelineMmaComputeS::SharedStorage mma_compute_s;
    alignas(16) typename PipelineMmaComputeDP::SharedStorage mma_compute_dp;
    alignas(16) typename PipelineMmaReduceDQ::SharedStorage mma_reduce_dq;
    alignas(16) typename PipelineComputeMmaP::SharedStorage compute_mma_p;
    alignas(16) typename PipelineComputeMmaDS::SharedStorage compute_mma_ds;
    alignas(16) typename PipelineMmaComputeDKDV::SharedStorage mma_compute_dkdv;
  };

  template<class Layout, class Stages = _1>
  static CUTE_DEVICE constexpr auto restage(Layout const& layout, Stages stages = {}) {
    return composition(layout, make_tuple(_, _, _, make_layout(stages)));
  }

  using SmemLayoutK = decltype(restage(typename CollectiveMmaKQ::SmemLayoutA{}));
  using SmemLayoutV = decltype(restage(typename CollectiveMmaVDO::SmemLayoutA{}));
  using SmemLayoutQ = decltype(restage(typename CollectiveMmaKQ::SmemLayoutB{}, _2{}));
  using SmemLayoutDO = decltype(restage(typename CollectiveMmaVDO::SmemLayoutB{}, _1{}));
  using SmemLayoutDS = decltype(restage(typename CollectiveMmaDSK::SmemLayoutA{}, Int<kStagesComputeSmem>{}));
  using SmemLayoutLSE = Layout<Shape<TileShapeQ, _1>>;
  using SmemLayoutSumOdO = Layout<Shape<TileShapeQ, _1>>;

  using SmemLayoutQT = decltype(restage(typename CollectiveMmaDSQ::SmemLayoutB{}, _2{}));
  using SmemLayoutKT = decltype(restage(typename CollectiveMmaDSK::SmemLayoutB{}));
  using SmemLayoutDST = decltype(restage(typename CollectiveMmaDSQ::SmemLayoutA{}, Int<kStagesComputeSmem>{}));
  using SmemLayoutDOT = decltype(restage(typename CollectiveMmaPDO::SmemLayoutB{}, _1{}));

  using TileShapeDQ = _32;
  using SmemAtomDQ = decltype(cutlass::gemm::collective::detail::sm100_smem_selector<
      cute::UMMA::Major::K, ElementAcc, TileShapeQ, TileShapeDQ
  >());
  using SmemShapeDQ = Shape<TileShapeQ, TileShapeDQ, Int<kStagesReduceTmaStore>>;
  using SmemLayoutDQ = decltype(tile_to_shape(SmemAtomDQ{}, SmemShapeDQ{}, Step<_2, _1, _3>{}));

  struct TensorStorage {
    union {
      alignas(2048) cute::array<Element, cute::cosize_v<SmemLayoutK>> smem_k;
      alignas(2048) cute::array<Element, cute::cosize_v<SmemLayoutKT>> smem_k_t;
    };
    alignas(2048) cute::array<Element, cute::cosize_v<SmemLayoutV>> smem_v;
    union {
      alignas(2048) cute::array<Element, cute::cosize_v<SmemLayoutQ>> smem_q;
      alignas(2048) cute::array<Element, cute::cosize_v<SmemLayoutQT>> smem_q_t;
    };
    union {
      alignas(2048) cute::array<Element, cute::cosize_v<SmemLayoutDO>> smem_do;
      alignas(2048) cute::array<Element, cute::cosize_v<SmemLayoutDOT>> smem_do_t;
    };
    union {
      alignas(2048) cute::array<Element, cute::cosize_v<SmemLayoutDS>> smem_ds;
      alignas(2048) cute::array<Element, cute::cosize_v<SmemLayoutDST>> smem_ds_t;
    };
    alignas(1024) cute::array<ElementAcc, cute::cosize_v<SmemLayoutDQ>> smem_dq;
    alignas(16) cute::array<ElementAcc, cute::cosize_v<SmemLayoutLSE>> smem_lse;
    alignas(16) cute::array<ElementAcc, cute::cosize_v<SmemLayoutSumOdO>> smem_sum_odo;
  };

  static constexpr int kTransactionsBytesLoadQ = cutlass::bits_to_bytes(cosize(take<0,3>(SmemLayoutQ{})) * cute::sizeof_bits_v<Element>);
  static constexpr int kTransactionsBytesLoadDO = cutlass::bits_to_bytes(cosize(take<0,3>(SmemLayoutDO{})) * cute::sizeof_bits_v<Element>);

  static constexpr int kTransactionsBytesLoadK = cutlass::bits_to_bytes(cosize(take<0,3>(SmemLayoutK{})) * cute::sizeof_bits_v<Element>);
  static constexpr int kTransactionsBytesLoadV = cutlass::bits_to_bytes(cosize(take<0,3>(SmemLayoutV{})) * cute::sizeof_bits_v<Element>);

  struct SharedStorage {
    TensorStorage tensors;
    PipelineStorage pipelines;
    uint32_t tmem_base_ptr;
  };

  // this is tight enough that it won't work with sizeof due to padding for alignment
  static constexpr int SharedStorageSize = offsetof(SharedStorage, tmem_base_ptr) + sizeof(uint32_t);
  static_assert(SharedStorageSize <= cutlass::arch::sm100_smem_capacity_bytes, "using too much smem");

  using ProblemShape = Shape<int, int, int, Shape<int, int>>;  // Q K D (H B), eventuall D = (D_QK, D_VO)
  using TensorStride = TensorStrideContiguousK;  // S D (H B)
  using RowTensorStride = Stride<_1, Stride<int, int>>;    // S (H B)

  struct MainloopArguments {
    const Element* ptr_q;
    TensorStride stride_q;
    const Element* ptr_k;
    TensorStride stride_k;
    const Element* ptr_v;
    TensorStride stride_v;
    const Element* ptr_do;
    TensorStride stride_do;

    const ElementAcc* ptr_lse;
    RowTensorStride stride_lse;

    const ElementAcc* ptr_sum_odo;
    RowTensorStride stride_sum_odo;

    ElementAcc* ptr_dq_acc;
    TensorStride stride_dq_acc;

    ElementAcc softmax_scale = 1.0f / sqrtf(TileShapeDQK{});
  };

  using TMA_K = typename CollectiveMmaKQ::Params::TMA_A;
  using TMA_V = typename CollectiveMmaVDO::Params::TMA_A;
  using TMA_Q = typename CollectiveMmaKQ::Params::TMA_B;
  using TMA_DO = typename CollectiveMmaVDO::Params::TMA_B;

  using TMA_DQ = decltype(make_tma_copy(SM90_TMA_REDUCE_ADD{},
      make_tensor((const ElementAcc*)nullptr, make_shape(1, 1, make_shape(1, 1)), TensorStride{}),
      SmemLayoutDQ{}(_, _, _0{})
  ));

  struct MainloopParams {
    TMA_K tma_load_k;
    TMA_V tma_load_v;
    TMA_Q tma_load_q;
    TMA_DO tma_load_do;
    TMA_DQ tma_red_dq;
  };

  struct EpilogueArguments {
    Element* ptr_dk;
    TensorStride stride_dk;
    Element* ptr_dv;
    TensorStride stride_dv;
  };

  struct Arguments {
    ProblemShape problem_shape;
    MainloopArguments mainloop;
    EpilogueArguments epilogue;
    KernelHardwareInfo hw_info;
  };

  struct Params {
    ProblemShape problem_shape;
    MainloopArguments mainloop;
    MainloopParams mainloop_params;
    EpilogueArguments epilogue;
    KernelHardwareInfo hw_info;
  };


  static bool can_implement(Arguments const& args) {
    auto [Q, K, D, HB] = args.problem_shape;
    auto [H, B] = HB;
    if (Q <= 0 || K <= 0 || D <= 0 || H <= 0 || B <= 0) {
      return false;
    }
    if (D % Alignment != 0) {
      return false;
    }
    return true;
  }


  static Status initialize_workspace(Arguments const&, void*, cudaStream_t) {
    return Status::kSuccess;
  }


  static Params to_underlying_arguments(Arguments const& args, void*) {
    auto [Q, K, D, HB] = args.problem_shape;

    auto params_kq = CollectiveMmaKQ::to_underlying_arguments(
      make_shape(K, Q, D, HB),
      typename CollectiveMmaKQ::Arguments {
        args.mainloop.ptr_k, args.mainloop.stride_k,
        args.mainloop.ptr_q, args.mainloop.stride_q,
      }, /*workspace=*/nullptr);

    auto params_vdo = CollectiveMmaVDO::to_underlying_arguments(
      make_shape(K, Q, D, HB),
      typename CollectiveMmaVDO::Arguments {
        args.mainloop.ptr_v, args.mainloop.stride_v,
        args.mainloop.ptr_do, args.mainloop.stride_do,
      }, /*workspace=*/nullptr);

    TMA_DQ tma_red_dq = make_tma_copy(
        SM90_TMA_REDUCE_ADD{},
        make_tensor(args.mainloop.ptr_dq_acc, make_shape(Q, D, HB), args.mainloop.stride_dq_acc),
        SmemLayoutDQ{}(_, _, _0{})
    );

    return Params{
      args.problem_shape,
      args.mainloop,
      MainloopParams{
        params_kq.tma_load_a,
        params_vdo.tma_load_a,
        params_kq.tma_load_b,
        params_vdo.tma_load_b,
        tma_red_dq
      },
      args.epilogue,
      args.hw_info
    };
  }


  template<class T>
  static CUTLASS_DEVICE auto quantize(T const& input) {
    constexpr int AlignmentS = 4;
    auto output = make_tensor<Element>(shape(input));
    auto input_vec = recast<Array<ElementAcc, AlignmentS>>(input);
    auto output_vec = recast<Array<Element, AlignmentS>>(output);

    cutlass::NumericArrayConverter<Element, ElementAcc, AlignmentS> epilogue_op;

    CUTLASS_PRAGMA_UNROLL
    for (int i = 0; i < size(input_vec); i++) {
      output_vec(i) = epilogue_op(input_vec(i));
    }

    return output;
  }


  template<class BlkCoord>
  CUTLASS_DEVICE void load(
      BlkCoord const& blk_coord,
      ProblemShape const& problem_shape,
      int iter_index,
      int iter_count,
      MainloopArguments const& mainloop_args,
      MainloopParams const& mainloop_params,
      TensorStorage& shared_tensors,
      PipelineLoadMmaQ& pipeline_load_mma_q,
      typename PipelineLoadMmaQ::PipelineState& pipeline_load_mma_q_producer_state,
      PipelineLoadMmaDO& pipeline_load_mma_do,
      typename PipelineLoadMmaDO::PipelineState& pipeline_load_mma_do_producer_state,
      PipelineLoadComputeLSE& pipeline_load_compute_lse,
      typename PipelineLoadComputeLSE::PipelineState& pipeline_load_compute_lse_producer_state,
      PipelineLoadComputeSumOdO& pipeline_load_compute_sum_odo,
      typename PipelineLoadComputeSumOdO::PipelineState& pipeline_load_compute_sum_odo_producer_state) {

    auto [Q, K, D, HB] = problem_shape;

    using X = Underscore;

    uint16_t mcast_mask = 0;

    auto mK = mainloop_params.tma_load_k.get_tma_tensor(make_shape(K, D, HB));
    auto mQ = mainloop_params.tma_load_q.get_tma_tensor(make_shape(Q, D, HB));
    auto mV = mainloop_params.tma_load_v.get_tma_tensor(make_shape(K, D, HB));
    auto mDO = mainloop_params.tma_load_do.get_tma_tensor(make_shape(Q, D, HB));

    auto gK = local_tile(mK, TileShapeKQ{}, make_coord(_,_,_), Step<_1, X, _1>{});
    auto gQ = local_tile(mQ, TileShapeKQ{}, make_coord(_,_,_), Step<X, _1, _1>{});
    auto gV = local_tile(mV, TileShapeVDO{}, make_coord(_,_,_), Step<_1, X, _1>{});
    auto gDO = local_tile(mDO, TileShapeVDO{}, make_coord(_,_,_), Step<X, _1, _1>{});

    ThrMMA cta_mma_kq = TiledMmaKQ{}.get_slice(_0{});
    ThrMMA cta_mma_vdo = TiledMmaVDO{}.get_slice(_0{});

    auto tSTgK = cta_mma_kq.partition_A(gK);
    auto tSTgQ = cta_mma_kq.partition_B(gQ);
    auto tDPTgV = cta_mma_vdo.partition_A(gV);
    auto tDPTgDO = cta_mma_vdo.partition_B(gDO);

    auto sQ = make_tensor(make_smem_ptr(shared_tensors.smem_q.begin()), SmemLayoutQ{});
    auto sK = make_tensor(make_smem_ptr(shared_tensors.smem_k.begin()), SmemLayoutK{});
    auto sV = make_tensor(make_smem_ptr(shared_tensors.smem_v.begin()), SmemLayoutV{});
    auto sDO = make_tensor(make_smem_ptr(shared_tensors.smem_do.begin()), SmemLayoutDO{});

    auto [tKgK_mkl, tKsK] = tma_partition(
        mainloop_params.tma_load_k, _0{}, make_layout(_1{}),
        group_modes<0,3>(sK), group_modes<0,3>(tSTgK));
    auto [tQgQ_mkl, tQsQ] = tma_partition(
        mainloop_params.tma_load_q, _0{}, make_layout(_1{}),
        group_modes<0,3>(sQ), group_modes<0,3>(tSTgQ));
    auto [tVgV_mkl, tVsV] = tma_partition(
        mainloop_params.tma_load_v, _0{}, make_layout(_1{}),
        group_modes<0,3>(sV), group_modes<0,3>(tDPTgV));
    auto [tDOgDO_mkl, tDOsDO] = tma_partition(
        mainloop_params.tma_load_do, _0{}, make_layout(_1{}),
        group_modes<0,3>(sDO), group_modes<0,3>(tDPTgDO));

    // set up lse and sum_odo

    auto [blk_coord_q, blk_coord_k, blk_coord_batch] = blk_coord;

    pipeline_load_mma_q.producer_acquire(pipeline_load_mma_q_producer_state);
    auto tma_barrier = pipeline_load_mma_q.producer_get_barrier(pipeline_load_mma_q_producer_state);

    pipeline_load_mma_q.producer_expect_transaction(pipeline_load_mma_q_producer_state, kTransactionsBytesLoadK);

    // load K
    if (cute::elect_one_sync()) {
      cute::copy(
          mainloop_params.tma_load_k.with(*tma_barrier, mcast_mask),
          tKgK_mkl(_, blk_coord_k, _0{}, blk_coord_batch),
          tKsK(_, _0{})
      );
    }

    // load Q
    if (cute::elect_one_sync()) {
      cute::copy(
          mainloop_params.tma_load_q.with(*tma_barrier, mcast_mask),
          tQgQ_mkl(_, iter_index, _0{}, blk_coord_batch),
          tQsQ(_, pipeline_load_mma_q_producer_state.index())
      );
    }

    ++pipeline_load_mma_q_producer_state;

    pipeline_load_compute_lse.producer_acquire(pipeline_load_compute_lse_producer_state);

    // load LSE
    // 32 threads loading 128 values of 32b each
    // so 4*32b=128b

    int thread_idx = threadIdx.x % NumThreadsPerWarp;
    int smem_idx = TileShapeQ{} * pipeline_load_compute_lse_producer_state.index() + thread_idx * 4;
    int gmem_idx = TileShapeQ{} * iter_index + thread_idx * 4;
    auto mLSE = make_tensor(mainloop_args.ptr_lse, make_shape(Q, HB), mainloop_args.stride_lse);
    cutlass::arch::cp_async_zfill<16>(
        shared_tensors.smem_lse.begin() + smem_idx,
        &mLSE(gmem_idx, blk_coord_batch),
         gmem_idx < Q
    );

    pipeline_load_compute_lse.producer_commit(pipeline_load_compute_lse_producer_state, cutlass::arch::cpasync_barrier_arrive);
    ++pipeline_load_compute_lse_producer_state;


    pipeline_load_mma_do.producer_acquire(pipeline_load_mma_do_producer_state);
    tma_barrier = pipeline_load_mma_do.producer_get_barrier(pipeline_load_mma_do_producer_state);

    pipeline_load_mma_do.producer_expect_transaction(pipeline_load_mma_do_producer_state, kTransactionsBytesLoadV);

    // load V
    if (cute::elect_one_sync()) {
      cute::copy(
          mainloop_params.tma_load_v.with(*tma_barrier, mcast_mask),
          tVgV_mkl(_, blk_coord_k, _0{}, blk_coord_batch),
          tVsV(_, _0{})
      );
    }

    // load dO
    if (cute::elect_one_sync()) {
      cute::copy(
          mainloop_params.tma_load_do.with(*tma_barrier, mcast_mask),
          tDOgDO_mkl(_, iter_index, _0{}, blk_coord_batch),
          tDOsDO(_, pipeline_load_mma_do_producer_state.index())
      );
    }

    ++pipeline_load_mma_do_producer_state;

    pipeline_load_compute_sum_odo.producer_acquire(pipeline_load_compute_sum_odo_producer_state);

    // load sum_OdO
    smem_idx = TileShapeQ{} * pipeline_load_compute_sum_odo_producer_state.index() + thread_idx * 4;
    gmem_idx = TileShapeQ{} * iter_index + thread_idx * 4;
    auto mSumOdO = make_tensor(mainloop_args.ptr_sum_odo, make_shape(Q, HB), mainloop_args.stride_sum_odo);
    cutlass::arch::cp_async<16>(
        shared_tensors.smem_sum_odo.begin() + smem_idx,
        &mSumOdO(gmem_idx, blk_coord_batch),
        gmem_idx < Q
    );

    pipeline_load_compute_sum_odo.producer_commit(pipeline_load_compute_sum_odo_producer_state, cutlass::arch::cpasync_barrier_arrive);
    ++pipeline_load_compute_sum_odo_producer_state;

    iter_count -= 1;
    iter_index += 1;

    while (iter_count > 0) {
      pipeline_load_mma_q.producer_acquire(pipeline_load_mma_q_producer_state);
      tma_barrier = pipeline_load_mma_q.producer_get_barrier(pipeline_load_mma_q_producer_state);

      // load Q
      if (cute::elect_one_sync()) {
        cute::copy(
            mainloop_params.tma_load_q.with(*tma_barrier, mcast_mask),
            tQgQ_mkl(_, iter_index, _0{}, blk_coord_batch),
            tQsQ(_, pipeline_load_mma_q_producer_state.index())
        );
      }

      ++pipeline_load_mma_q_producer_state;

      pipeline_load_compute_lse.producer_acquire(pipeline_load_compute_lse_producer_state);

      // load LSE
      smem_idx = TileShapeQ{} * pipeline_load_compute_lse_producer_state.index() + thread_idx * 4;
      gmem_idx = TileShapeQ{} * iter_index + thread_idx * 4;
      cutlass::arch::cp_async<16>(
          shared_tensors.smem_lse.begin() + smem_idx,
          &mLSE(gmem_idx, blk_coord_batch),
          gmem_idx < Q
      );

      pipeline_load_compute_lse.producer_commit(pipeline_load_compute_lse_producer_state, cutlass::arch::cpasync_barrier_arrive);
      ++pipeline_load_compute_lse_producer_state;

      pipeline_load_mma_do.producer_acquire(pipeline_load_mma_do_producer_state);
      tma_barrier = pipeline_load_mma_do.producer_get_barrier(pipeline_load_mma_do_producer_state);

      // load dO
      if (cute::elect_one_sync()) {
        cute::copy(
            mainloop_params.tma_load_do.with(*tma_barrier, mcast_mask),
            tDOgDO_mkl(_, iter_index, _0{}, blk_coord_batch),
            tDOsDO(_, pipeline_load_mma_do_producer_state.index())
        );
      }

      ++pipeline_load_mma_do_producer_state;

      pipeline_load_compute_sum_odo.producer_acquire(pipeline_load_compute_sum_odo_producer_state);

      // load sum_OdO
      smem_idx = TileShapeQ{} * pipeline_load_compute_sum_odo_producer_state.index() + thread_idx * 4;
      gmem_idx = TileShapeQ{} * iter_index + thread_idx * 4;
      cutlass::arch::cp_async_zfill<16>(
          shared_tensors.smem_sum_odo.begin() + smem_idx,
          &mSumOdO(gmem_idx, blk_coord_batch),
          gmem_idx < Q
      );

      pipeline_load_compute_sum_odo.producer_commit(pipeline_load_compute_sum_odo_producer_state, cutlass::arch::cpasync_barrier_arrive);
      ++pipeline_load_compute_sum_odo_producer_state;

      iter_count -= 1;
      iter_index += 1;
    }
  }


  template<class BlkCoord>
  CUTLASS_DEVICE void mma(
      BlkCoord const& blk_coord,
      ProblemShape const& problem_shape,
      int iter_index,
      int iter_count,
      MainloopArguments const& mainloop_args,
      TensorStorage& shared_tensors,
      PipelineLoadMmaQ& pipeline_load_mma_q,
      typename PipelineLoadMmaQ::PipelineState& pipeline_load_mma_q_consumer_state,
      PipelineLoadMmaDO& pipeline_load_mma_do,
      typename PipelineLoadMmaDO::PipelineState& pipeline_load_mma_do_consumer_state,
      PipelineMmaComputeS& pipeline_mma_compute_s,
      typename PipelineMmaComputeS::PipelineState& pipeline_mma_compute_s_producer_state,
      PipelineMmaComputeDP& pipeline_mma_compute_dp,
      typename PipelineMmaComputeDP::PipelineState& pipeline_mma_compute_dp_producer_state,
      PipelineMmaReduceDQ& pipeline_mma_reduce_dq,
      typename PipelineMmaReduceDQ::PipelineState& pipeline_mma_reduce_dq_producer_state,
      PipelineComputeMmaP& pipeline_compute_mma_p,
      typename PipelineComputeMmaP::PipelineState& pipeline_compute_mma_p_consumer_state,
      PipelineComputeMmaDS& pipeline_compute_mma_ds,
      typename PipelineComputeMmaDS::PipelineState& pipeline_compute_mma_ds_consumer_state,
      PipelineMmaComputeDKDV& pipeline_mma_compute_dkdv,
      typename PipelineMmaComputeDKDV::PipelineState& pipeline_mma_compute_dkdv_producer_state) {

    auto [Q, K, D, HB] = problem_shape;

    auto sQ = make_tensor(make_smem_ptr(shared_tensors.smem_q.begin()), SmemLayoutQ{});
    auto sK = make_tensor(make_smem_ptr(shared_tensors.smem_k.begin()), SmemLayoutK{});
    auto sV = make_tensor(make_smem_ptr(shared_tensors.smem_v.begin()), SmemLayoutV{});
    auto sDO = make_tensor(make_smem_ptr(shared_tensors.smem_do.begin()), SmemLayoutDO{});

    auto sQT = make_tensor(make_smem_ptr(shared_tensors.smem_q_t.begin()), SmemLayoutQT{});
    auto sKT = make_tensor(make_smem_ptr(shared_tensors.smem_k_t.begin()), SmemLayoutKT{});
    auto sDS = make_tensor(make_smem_ptr(shared_tensors.smem_ds.begin()), SmemLayoutDS{});
    auto sDST = make_tensor(make_smem_ptr(shared_tensors.smem_ds_t.begin()), SmemLayoutDST{});
    auto sP = make_tensor(make_smem_ptr((Element*) nullptr), typename CollectiveMmaPDO::SmemLayoutA{});
    auto sDOT = make_tensor(make_smem_ptr(shared_tensors.smem_do_t.begin()), SmemLayoutDOT{});

    Tensor tSTrK = TiledMmaKQ::make_fragment_A(sK);
    Tensor tSTrQ = TiledMmaKQ::make_fragment_B(sQ);

    Tensor tDPTrV = TiledMmaVDO::make_fragment_A(sV);
    Tensor tDPTrDO = TiledMmaVDO::make_fragment_B(sDO);

    Tensor tDQrDS = TiledMmaDSK::make_fragment_A(sDS);
    Tensor tDQrKT = TiledMmaDSK::make_fragment_B(sKT);

    Tensor tDKrDST = TiledMmaDSQ::make_fragment_A(sDST);
    Tensor tDKrQT = TiledMmaDSQ::make_fragment_B(sQT);

    Tensor tDVrP = TiledMmaPDO::make_fragment_A(sP)(_, _, _, _0{});
    tDVrP.data() = TmemAllocation::kP;
    Tensor tDVrDOT = TiledMmaPDO::make_fragment_B(sDOT);

    TiledMmaKQ tiled_mma_kq;
    TiledMmaVDO tiled_mma_vdo;
    TiledMmaDSK tiled_mma_dsk;
    TiledMmaDSQ tiled_mma_dsq;
    TiledMmaPDO tiled_mma_pdo;

    tiled_mma_dsq.accumulate_ = UMMA::ScaleOut::Zero;
    tiled_mma_pdo.accumulate_ = UMMA::ScaleOut::Zero;

    Tensor tSTtST =  partition_fragment_C(tiled_mma_kq, select<0,1>(TileShapeKQ{}));
    tSTtST.data() = TmemAllocation::kS;

    Tensor tDPTtDPT = partition_fragment_C(tiled_mma_vdo, select<0,1>(TileShapeVDO{}));
    tDPTtDPT.data() = TmemAllocation::kDP;

    Tensor tDQtDQ = partition_fragment_C(tiled_mma_dsk, select<0,1>(TileShapeDSK{}));
    tDQtDQ.data() = TmemAllocation::kDQ;

    Tensor tDKtDK = partition_fragment_C(tiled_mma_dsq, select<0,1>(TileShapeDSQ{}));
    tDKtDK.data() = TmemAllocation::kDK;

    Tensor tDVtDV = partition_fragment_C(tiled_mma_pdo, select<0,1>(TileShapePDO{}));
    tDVtDV.data() = TmemAllocation::kDV;

    auto pipeline_load_mma_q_release_state = pipeline_load_mma_q_consumer_state;

    pipeline_load_mma_q.consumer_wait(pipeline_load_mma_q_consumer_state);
    pipeline_mma_compute_s.producer_acquire(pipeline_mma_compute_s_producer_state);

    // S = Q*K
    tiled_mma_kq.accumulate_ = UMMA::ScaleOut::Zero;
    CUTLASS_PRAGMA_UNROLL
    for (int k_block = 0; k_block < size<2>(tSTrQ); ++k_block) {
      cute::gemm(tiled_mma_kq,
                 tSTrK(_,_,k_block,_0{}),
                 tSTrQ(_,_,k_block,pipeline_load_mma_q_consumer_state.index()),
                 tSTtST);
      tiled_mma_kq.accumulate_ = UMMA::ScaleOut::One;
    }

    ++pipeline_load_mma_q_consumer_state;

    pipeline_mma_compute_s.producer_commit(pipeline_mma_compute_s_producer_state);
    ++pipeline_mma_compute_s_producer_state;

    pipeline_load_mma_do.consumer_wait(pipeline_load_mma_do_consumer_state);

    pipeline_mma_compute_dp.producer_acquire(pipeline_mma_compute_dp_producer_state);
    pipeline_mma_reduce_dq.producer_acquire(pipeline_mma_reduce_dq_producer_state);

    // dP = dO*V
    tiled_mma_vdo.accumulate_ = UMMA::ScaleOut::Zero;
    CUTLASS_PRAGMA_UNROLL
    for (int k_block = 0; k_block < size<2>(tDPTrV); ++k_block) {
      cute::gemm(tiled_mma_vdo,
                 tDPTrV(_,_,k_block,_0{}),
                 tDPTrDO(_,_,k_block,pipeline_load_mma_do_consumer_state.index()),
                 tDPTtDPT);
      tiled_mma_vdo.accumulate_ = UMMA::ScaleOut::One;
    }

    pipeline_mma_compute_dp.producer_commit(pipeline_mma_compute_dp_producer_state);
    ++pipeline_mma_compute_dp_producer_state;

    pipeline_compute_mma_p.consumer_wait(pipeline_compute_mma_p_consumer_state);

    // dV = P*dO
    CUTLASS_PRAGMA_UNROLL
    for (int k_block = 0; k_block < size<2>(tDVrP); ++k_block) {
      cute::gemm(tiled_mma_pdo,
                 tDVrP(_,_,k_block),
                 tDVrDOT(_,_,k_block,pipeline_load_mma_do_consumer_state.index()),
                 tDVtDV);
      tiled_mma_pdo.accumulate_ = UMMA::ScaleOut::One;
    }

    pipeline_compute_mma_p.consumer_release(pipeline_compute_mma_p_consumer_state);
    ++pipeline_compute_mma_p_consumer_state;

    pipeline_load_mma_do.consumer_release(pipeline_load_mma_do_consumer_state);
    ++pipeline_load_mma_do_consumer_state;

    iter_count -= 1;

    // in tmem, S & P overlap
    // and dP and dQ overlap
    // so we need to acquire dQ and dP at the same time
    while (iter_count > 0) {
      pipeline_load_mma_q.consumer_wait(pipeline_load_mma_q_consumer_state);
      pipeline_mma_compute_s.producer_acquire(pipeline_mma_compute_s_producer_state);

      // S = Q*K
      tiled_mma_kq.accumulate_ = UMMA::ScaleOut::Zero;
      CUTLASS_PRAGMA_UNROLL
      for (int k_block = 0; k_block < size<2>(tSTrQ); ++k_block) {
        cute::gemm(tiled_mma_kq,
                   tSTrK(_,_,k_block,_0{}),
                   tSTrQ(_,_,k_block,pipeline_load_mma_q_consumer_state.index()),
                   tSTtST);
        tiled_mma_kq.accumulate_ = UMMA::ScaleOut::One;
      }

      ++pipeline_load_mma_q_consumer_state;

      pipeline_mma_compute_s.producer_commit(pipeline_mma_compute_s_producer_state);
      ++pipeline_mma_compute_s_producer_state;

      pipeline_compute_mma_ds.consumer_wait(pipeline_compute_mma_ds_consumer_state);

      // we need to acquire dP here, because tmem dQ == tmem dP
      pipeline_mma_compute_dp.producer_acquire(pipeline_mma_compute_dp_producer_state);

      // dQ = dS*K
      tiled_mma_dsk.accumulate_ = UMMA::ScaleOut::Zero;
      CUTLASS_PRAGMA_UNROLL
      for (int k_block = 0; k_block < size<2>(tDQrDS); ++k_block) {
        cute::gemm(tiled_mma_dsk,
                   tDQrDS(_,_,k_block,pipeline_compute_mma_ds_consumer_state.index()),
                   tDQrKT(_,_,k_block,_0{}),
                   tDQtDQ);
        tiled_mma_dsk.accumulate_ = UMMA::ScaleOut::One;
      }

      pipeline_mma_reduce_dq.producer_commit(pipeline_mma_reduce_dq_producer_state);
      ++pipeline_mma_reduce_dq_producer_state;

      // dK = dS*Q
      CUTLASS_PRAGMA_UNROLL
      for (int k_block = 0; k_block < size<2>(tDKrDST); ++k_block) {
        cute::gemm(tiled_mma_dsq,
                   tDKrDST(_,_,k_block,pipeline_compute_mma_ds_consumer_state.index()),
                   tDKrQT(_,_,k_block,pipeline_load_mma_q_release_state.index()),
                   tDKtDK);
        tiled_mma_dsq.accumulate_ = UMMA::ScaleOut::One;
      }

      pipeline_load_mma_q.consumer_release(pipeline_load_mma_q_release_state);
      ++pipeline_load_mma_q_release_state;

      pipeline_compute_mma_ds.consumer_release(pipeline_compute_mma_ds_consumer_state);
      ++pipeline_compute_mma_ds_consumer_state;

      // we grab dq here, because in tmem dq == dp
      pipeline_mma_reduce_dq.producer_acquire(pipeline_mma_reduce_dq_producer_state);

      pipeline_load_mma_do.consumer_wait(pipeline_load_mma_do_consumer_state);

      // dP = dO*V
      tiled_mma_vdo.accumulate_ = UMMA::ScaleOut::Zero;
      CUTLASS_PRAGMA_UNROLL
      for (int k_block = 0; k_block < size<2>(tDPTrV); ++k_block) {
        cute::gemm(tiled_mma_vdo,
                   tDPTrV(_,_,k_block,_0{}),
                   tDPTrDO(_,_,k_block,pipeline_load_mma_do_consumer_state.index()),
                   tDPTtDPT);
        tiled_mma_vdo.accumulate_ = UMMA::ScaleOut::One;
      }

      pipeline_mma_compute_dp.producer_commit(pipeline_mma_compute_dp_producer_state);
      ++pipeline_mma_compute_dp_producer_state;

      pipeline_compute_mma_p.consumer_wait(pipeline_compute_mma_p_consumer_state);

      // dV = P*dO
      CUTLASS_PRAGMA_UNROLL
      for (int k_block = 0; k_block < size<2>(tDVrP); ++k_block) {
        cute::gemm(tiled_mma_pdo,
                   tDVrP(_,_,k_block),
                   tDVrDOT(_,_,k_block,pipeline_load_mma_do_consumer_state.index()),
                   tDVtDV);
        tiled_mma_pdo.accumulate_ = UMMA::ScaleOut::One;
      }

      pipeline_compute_mma_p.consumer_release(pipeline_compute_mma_p_consumer_state);
      ++pipeline_compute_mma_p_consumer_state;

      pipeline_load_mma_do.consumer_release(pipeline_load_mma_do_consumer_state);
      ++pipeline_load_mma_do_consumer_state;

      iter_count -= 1;
    }

    // signal to the epilogue that dV is ready
    pipeline_mma_compute_dkdv.producer_acquire(pipeline_mma_compute_dkdv_producer_state);
    pipeline_mma_compute_dkdv.producer_commit(pipeline_mma_compute_dkdv_producer_state);
    ++pipeline_mma_compute_dkdv_producer_state;

    pipeline_mma_compute_dkdv.producer_acquire(pipeline_mma_compute_dkdv_producer_state);

    pipeline_compute_mma_ds.consumer_wait(pipeline_compute_mma_ds_consumer_state);

    // dK = dS*Q
    CUTLASS_PRAGMA_UNROLL
    for (int k_block = 0; k_block < size<2>(tDKrDST); ++k_block) {
      cute::gemm(tiled_mma_dsq,
                 tDKrDST(_,_,k_block,pipeline_compute_mma_ds_consumer_state.index()),
                 tDKrQT(_,_,k_block,pipeline_load_mma_q_release_state.index()),
                 tDKtDK);
      tiled_mma_dsq.accumulate_ = UMMA::ScaleOut::One;
    }

    // signal to epilgue that dK is ready
    pipeline_mma_compute_dkdv.producer_commit(pipeline_mma_compute_dkdv_producer_state);
    ++pipeline_mma_compute_dkdv_producer_state;

    // we've already acquired mma_reduce_dq in the loop

    // dQ = dS*K
    tiled_mma_dsk.accumulate_ = UMMA::ScaleOut::Zero;
    CUTLASS_PRAGMA_UNROLL
    for (int k_block = 0; k_block < size<2>(tDQrDS); ++k_block) {
      cute::gemm(tiled_mma_dsk,
                 tDQrDS(_,_,k_block,pipeline_compute_mma_ds_consumer_state.index()),
                 tDQrKT(_,_,k_block,_0{}),
                 tDQtDQ);
      tiled_mma_dsk.accumulate_ = UMMA::ScaleOut::One;
    }

    pipeline_mma_reduce_dq.producer_commit(pipeline_mma_reduce_dq_producer_state);
    ++pipeline_mma_reduce_dq_producer_state;

    pipeline_load_mma_q.consumer_release(pipeline_load_mma_q_release_state);
    ++pipeline_load_mma_q_release_state;

    pipeline_compute_mma_ds.consumer_release(pipeline_compute_mma_ds_consumer_state);
    ++pipeline_compute_mma_ds_consumer_state;
  }



  template<class TensorG, class TensorR, class TensorC, class TensorShape>
  CUTLASS_DEVICE void store(
      TensorG gmem,
      TensorR const& regs,
      TensorC const& coord,
      TensorShape const& tensor_shape) {

    Tensor preds = cute::lazy::transform(coord, [&](auto const& c) { return elem_less(c, tensor_shape); });

    auto copy_op = make_cotiled_copy(
        Copy_Atom<UniversalCopy<uint128_t>, Element>{},
        make_layout(make_shape(_1{}, Int<sizeof(uint128_t) / sizeof(Element)>{})),
        regs.layout()
    );
    auto thr_copy = copy_op.get_slice(_0{});

    Tensor tCg = thr_copy.partition_D(gmem);
    Tensor tCr = thr_copy.partition_S(quantize(regs));
    Tensor tPc = thr_copy.partition_D(preds);

    copy_if(copy_op, tPc, tCr, tCg);
  }


  template<class BlkCoord>
  CUTLASS_DEVICE void epilogue(
      BlkCoord const& blk_coord,
      ProblemShape const& problem_shape,
      MainloopArguments const& mainloop_args,
      EpilogueArguments const& epilogue_args,
      PipelineMmaComputeDKDV& pipeline_mma_compute_dkdv,
      typename PipelineMmaComputeDKDV::PipelineState& pipeline_mma_compute_dkdv_consumer_state) {

    auto [Q, K, D, HB] = problem_shape;
    auto [blk_coord_q, blk_coord_k, blk_coord_batch] = blk_coord;

    auto load_op = SM100_TMEM_LOAD_32dp32b16x{};

    auto tDKtDK = partition_fragment_C(TiledMmaDSQ{}, select<0,1>(TileShapeDSQ{}))(make_coord(_,_),_0{},_0{});
    tDKtDK.data() = TmemAllocation::kDK;

    auto mDK = make_tensor(make_gmem_ptr(epilogue_args.ptr_dk), make_shape(K, TileShapeDQK{}, HB), epilogue_args.stride_dk);
    auto gDK = local_tile(mDK, TileShapeDSQ{}, make_coord(_,_,_), Step<_1, _1, X>{})
        (_, _, blk_coord_k, _0{}, blk_coord_batch);

    Tensor cDK = domain_offset(
        make_coord(get<1>(blk_coord) * TileShapeK{}, _0{}),
        make_identity_tensor(take<0,2>(TileShapeDSQ{}))
    );

    constexpr int kNumWarpgroups = kNumComputeWarps / 4;
    int dp_idx = threadIdx.x % 128;
    int wg_idx = (threadIdx.x % (kNumComputeWarps * NumThreadsPerWarp)) / 128;

    auto split_wg = [&](auto const& t) {
      if constexpr (decltype(rank(t))::value == 3) {
        auto p = t.compose(make_layout(make_shape(size<0>(t), size<1>(t), make_shape(Int<kNumWarpgroups>{}, size<2>(t) / Int<kNumWarpgroups>{}))));
        return p(_, _, make_coord(wg_idx, _));
      }
      else {
        auto p = t.compose(make_layout(make_shape(size<0>(t), size<1>(t), size<2>(t), make_shape(Int<kNumWarpgroups>{}, size<3>(t) / Int<kNumWarpgroups>{}))));
        return p(_, _, _, make_coord(wg_idx, _));
      }
    };

    auto tiled_t2r_dk = make_tmem_copy(load_op, tDKtDK);
    auto thread_t2r_dk = tiled_t2r_dk.get_slice(dp_idx);

    Tensor tTR_cDK   = split_wg(thread_t2r_dk.partition_D(cDK));
    Tensor tTR_gDK   = split_wg(thread_t2r_dk.partition_D(gDK));
    Tensor tTR_rDK = make_tensor<ElementAcc>(shape(tTR_cDK));
    Tensor tTR_tDK = split_wg(thread_t2r_dk.partition_S(tDKtDK));

    auto tDVtDV = partition_fragment_C(TiledMmaDSQ{}, select<0,1>(TileShapeDSQ{}))(make_coord(_,_),_0{},_0{});
    tDVtDV.data() = TmemAllocation::kDV;

    auto mDV = make_tensor(make_gmem_ptr(epilogue_args.ptr_dv), make_shape(K, TileShapeDVO{}, HB), epilogue_args.stride_dv);
    auto gDV = local_tile(mDV, TileShapePDO{}, make_coord(_,_,_), Step<_1, _1, X>{})
        (_, _, blk_coord_k, _0{}, blk_coord_batch);

    Tensor cDV = domain_offset(
        make_coord(get<1>(blk_coord) * TileShapeK{}, _0{}),
        make_identity_tensor(take<0,2>(TileShapePDO{}))
    );

    auto tiled_t2r_dv = make_tmem_copy(load_op, tDVtDV);
    auto thread_t2r_dv = tiled_t2r_dv.get_slice(dp_idx);

    Tensor tTR_cDV   = split_wg(thread_t2r_dv.partition_D(cDV));
    Tensor tTR_gDV   = split_wg(thread_t2r_dv.partition_D(gDV));
    Tensor tTR_rDV = make_tensor<ElementAcc>(shape(tTR_cDV));
    Tensor tTR_tDV = split_wg(thread_t2r_dv.partition_S(tDVtDV));

    pipeline_mma_compute_dkdv.consumer_wait(pipeline_mma_compute_dkdv_consumer_state);

    // load tDVtDV
    cute::copy(tiled_t2r_dv, tTR_tDV, tTR_rDV);

    // store tDVgDV
    store(tTR_gDV, tTR_rDV, tTR_cDV, select<1,2>(problem_shape));

    cutlass::arch::fence_view_async_tmem_load();
    pipeline_mma_compute_dkdv.consumer_release(pipeline_mma_compute_dkdv_consumer_state);
    ++pipeline_mma_compute_dkdv_consumer_state;

    pipeline_mma_compute_dkdv.consumer_wait(pipeline_mma_compute_dkdv_consumer_state);

    // load tDKtDK
    cute::copy(tiled_t2r_dk, tTR_tDK, tTR_rDK);

    CUTLASS_PRAGMA_UNROLL
    for (int i = 0; i < size(tTR_rDK); i++) {
      tTR_rDK(i) = mainloop_args.softmax_scale * tTR_rDK(i);
    }

    // store tDKgDK
    store(tTR_gDK, tTR_rDK, tTR_cDK, select<1,2>(problem_shape));

    cutlass::arch::fence_view_async_tmem_load();
    pipeline_mma_compute_dkdv.consumer_release(pipeline_mma_compute_dkdv_consumer_state);
    ++pipeline_mma_compute_dkdv_consumer_state;

  }


  template<class BlkCoord>
  CUTLASS_DEVICE void compute(
      BlkCoord const& blk_coord,
      ProblemShape const& problem_shape,
      int iter_index,
      int iter_count,
      MainloopArguments const& mainloop_args,
      EpilogueArguments const& epilogue_args,
      TensorStorage& shared_tensors,
      PipelineLoadComputeLSE& pipeline_load_compute_lse,
      typename PipelineLoadComputeLSE::PipelineState& pipeline_load_compute_lse_consumer_state,
      PipelineLoadComputeSumOdO& pipeline_load_compute_sum_odo,
      typename PipelineLoadComputeSumOdO::PipelineState& pipeline_load_compute_sum_odo_consumer_state,
      PipelineMmaComputeS& pipeline_mma_compute_s,
      typename PipelineMmaComputeS::PipelineState& pipeline_mma_compute_s_consumer_state,
      PipelineMmaComputeDP& pipeline_mma_compute_dp,
      typename PipelineMmaComputeDP::PipelineState& pipeline_mma_compute_dp_consumer_state,
      PipelineComputeMmaP& pipeline_compute_mma_p,
      typename PipelineComputeMmaP::PipelineState& pipeline_compute_mma_p_producer_state,
      PipelineComputeMmaDS& pipeline_compute_mma_ds,
      typename PipelineComputeMmaDS::PipelineState& pipeline_compute_mma_ds_producer_state,
      PipelineMmaComputeDKDV& pipeline_mma_compute_dkdv,
      typename PipelineMmaComputeDKDV::PipelineState& pipeline_mma_compute_dkdv_consumer_state) {


    auto [Q, K, D, HB] = problem_shape;

    // in tmem, S & P overlap
    // and dP and dQ overlap

    // there are two compute wg's that cooperatively compute softmax
    // they are striped by this tmem atom, i.e. wg0 has 16 elems, then wg1 etc

    auto load_op = SM100_TMEM_LOAD_32dp32b16x{};
    auto store_op = SM100_TMEM_STORE_32dp32b8x{};

    Tensor tSTtST =  partition_fragment_C(TiledMmaKQ{}, select<0,1>(TileShapeKQ{}))(make_coord(_,_),_0{},_0{});
    tSTtST.data() = TmemAllocation::kS;

    Tensor tDPTtDPT =  partition_fragment_C(TiledMmaVDO{}, select<0,1>(TileShapeVDO{}))(make_coord(_,_),_0{},_0{});
    tDPTtDPT.data() = TmemAllocation::kDP;

    Tensor cST = make_identity_tensor(take<0,2>(TileShapeKQ{}));
    Tensor cDPT = make_identity_tensor(take<0,2>(TileShapeVDO{}));

    constexpr int kNumWarpgroups = kNumComputeWarps / 4;
    int dp_idx = threadIdx.x % 128;
    int wg_idx = (threadIdx.x % (kNumComputeWarps * NumThreadsPerWarp)) / 128;
    auto tiled_t2r = make_tmem_copy(load_op, tSTtST);
    auto thread_t2r = tiled_t2r.get_slice(dp_idx);

    auto split_wg = [&](auto const& t) {
      if constexpr (decltype(rank(t))::value == 3) {
        auto p = t.compose(make_layout(make_shape(size<0>(t), size<1>(t), make_shape(Int<kNumWarpgroups>{}, size<2>(t) / Int<kNumWarpgroups>{}))));
        return p(_, _, make_coord(wg_idx, _));
      }
      else {
        auto p = t.compose(make_layout(make_shape(size<0>(t), size<1>(t), size<2>(t), make_shape(Int<kNumWarpgroups>{}, size<3>(t) / Int<kNumWarpgroups>{}))));
        return p(_, _, _, make_coord(wg_idx, _));
      }
    };

    Tensor tTR_cST   = split_wg(thread_t2r.partition_D(cST));
    Tensor tTR_rST = make_tensor<ElementAcc>(shape(tTR_cST));
    Tensor tTR_tST = split_wg(thread_t2r.partition_S(tSTtST));

    Tensor tTR_cDPT_p = thread_t2r.partition_D(cDPT);
    Tensor tTR_cDPT = split_wg(tTR_cDPT_p);
    Tensor tTR_rDPT = make_tensor<ElementAcc>(shape(tTR_cDPT));
    Tensor tTR_tDPT = split_wg(thread_t2r.partition_S(tDPTtDPT));

    Tensor sLSE = make_tensor(make_smem_ptr(shared_tensors.smem_lse.begin()), SmemLayoutLSE{});
    Tensor sSumOdO = make_tensor(make_smem_ptr(shared_tensors.smem_sum_odo.begin()), SmemLayoutSumOdO{});

    auto sP = make_tensor(make_smem_ptr((Element*) nullptr), typename CollectiveMmaPDO::SmemLayoutA{});


    auto tDVrP = TiledMmaPDO::make_fragment_A(sP)(_, _, _, _0{});
    auto tDVcST = TiledMmaPDO{}.get_slice(_0{}).partition_A(cST);
    tDVrP.data() = TmemAllocation::kP;

    auto tiled_r2t = make_tmem_copy(store_op, tDVrP);
    auto thread_r2t = tiled_r2t.get_slice(dp_idx);

    auto tRT_tP = split_wg(thread_r2t.partition_D(tDVrP));
    auto tRT_cST = split_wg(thread_r2t.partition_S(tDVcST));

    CUTLASS_PRAGMA_NO_UNROLL
    while (iter_count > 0) {
      // wait for S and P
      pipeline_mma_compute_s.consumer_wait(pipeline_mma_compute_s_consumer_state);
      pipeline_compute_mma_p.producer_acquire(pipeline_compute_mma_p_producer_state);
      // wait for LSE
      pipeline_load_compute_lse.consumer_wait(pipeline_load_compute_lse_consumer_state);

      auto dispatch_bool = [](bool b, auto fn) {
        if (b) {
          fn(cute::true_type{});
        }
        else {
          fn(cute::false_type{});
        }
      };

      dispatch_bool(std::is_base_of_v<cutlass::fmha::collective::CausalMask, Mask> &&
          warp_uniform(iter_index == get<1>(blk_coord)), [&](auto is_causal_masked_tile) {

        // compute P = softmax(S, LSE)
        cute::copy(tiled_t2r, tTR_tST, tTR_rST);

        if constexpr (std::is_base_of_v<cutlass::fmha::collective::CausalMask, Mask> && decltype(is_causal_masked_tile)::value) {
          Mask{}.apply_mask(tTR_rST, [&](int i) {
            auto c_transpose = tTR_cST(i);
            return make_coord(get<1>(c_transpose) + iter_index * TileShapeQ{}, get<0>(c_transpose) + get<1>(blk_coord) * TileShapeK{});
          }, problem_shape);
        }

        ElementAcc log2_e = static_cast<ElementAcc>(M_LOG2E);
        float2 softmax_scale_log2_e;
        softmax_scale_log2_e.x = mainloop_args.softmax_scale * log2_e;
        softmax_scale_log2_e.y = mainloop_args.softmax_scale * log2_e;

        CUTLASS_PRAGMA_UNROLL
        for (int i = 0; i < size(tTR_rST); i += 2) {
          float2 acc;
          float2 lse;
          float2 out;
          acc.x = tTR_rST(i);
          acc.y = tTR_rST(i + 1);
          lse.x = sLSE(get<1>(tTR_cST(i)), pipeline_load_compute_lse_consumer_state.index());
          lse.y = sLSE(get<1>(tTR_cST(i+1)), pipeline_load_compute_lse_consumer_state.index());
          cute::fma(out, softmax_scale_log2_e, acc, lse);
          tTR_rST(i) = ::exp2f(out.x);
          tTR_rST(i+1) = ::exp2f(out.y);
        }

        auto tRT_rST = quantize(tTR_rST);
        auto tRT_rST_reshaped = make_tensor(tRT_rST.data(), shape(tRT_cST));

        cutlass::arch::fence_view_async_tmem_load();
        cutlass::arch::NamedBarrier(
          kNumComputeWarps * NumThreadsPerWarp,
          cutlass::arch::ReservedNamedBarriers::TransformBarrier
        ).arrive_and_wait();

        cute::copy(tiled_r2t, tRT_rST_reshaped, tRT_tP);
      });

      // notify for P
      cutlass::arch::fence_view_async_tmem_store();
      pipeline_compute_mma_p.producer_commit(pipeline_compute_mma_p_producer_state);
      ++pipeline_compute_mma_p_producer_state;
      // release S
      pipeline_mma_compute_s.consumer_release(pipeline_mma_compute_s_consumer_state);
      ++pipeline_mma_compute_s_consumer_state;
      // release LSE
      pipeline_load_compute_lse.consumer_release(pipeline_load_compute_lse_consumer_state);
      ++pipeline_load_compute_lse_consumer_state;

      // wait for OdO
      pipeline_load_compute_sum_odo.consumer_wait(pipeline_load_compute_sum_odo_consumer_state);
      // wait for dP
      pipeline_mma_compute_dp.consumer_wait(pipeline_mma_compute_dp_consumer_state);

      // wait for dS
      // in principle, we could defer waiting for dS, and move in the freeing of dP
      // however, that would force us to keep dS in registers longer
      pipeline_compute_mma_ds.producer_acquire(pipeline_compute_mma_ds_producer_state);

      // compute dS = dsoftmax(P, dP, sum_OdO)
      cute::copy(tiled_t2r, tTR_tDPT, tTR_rDPT);

      CUTLASS_PRAGMA_UNROLL
      for (int i = 0; i < size(tTR_rDPT); i += 2) {
        float2 st;
        st.x = tTR_rST(i);
        st.y = tTR_rST(i+1);
        float2 dpt;
        dpt.x = tTR_rDPT(i);
        dpt.y = tTR_rDPT(i+1);
        float2 odo;
        odo.x = sSumOdO(get<1>(tTR_cDPT(i)), pipeline_load_compute_sum_odo_consumer_state.index());
        odo.y = sSumOdO(get<1>(tTR_cDPT(i+1)), pipeline_load_compute_sum_odo_consumer_state.index());
        float2 dif;
        // sum odo is negated during preprocess
        cute::add(dif, dpt, odo);
        float2 out;
        cute::mul(out, dif, st);
        tTR_rDPT(i) = out.x;
        tTR_rDPT(i+1) = out.y;
      }

      auto tTR_rDST = quantize(tTR_rDPT);

      // release dP
      cutlass::arch::fence_view_async_tmem_load();
      pipeline_mma_compute_dp.consumer_release(pipeline_mma_compute_dp_consumer_state);
      ++pipeline_mma_compute_dp_consumer_state;

      Tensor sDS = make_tensor(make_smem_ptr((Element*) shared_tensors.smem_ds.begin()), SmemLayoutDS{})
          (_, _, _, pipeline_compute_mma_ds_producer_state.index());

      auto thread_layout = make_ordered_layout(
          make_shape(_128{}, _128{}),
          make_stride(_1{}, _0{})
      );

      auto sDS_pi = as_position_independent_swizzle_tensor(sDS);
      auto sDS_pi_slice_p = sDS_pi.compose(thread_layout)(dp_idx, _).compose(make_layout(shape(tTR_cDPT_p)));
      auto sDS_pi_slice = split_wg(sDS_pi_slice_p);

      copy_aligned(tTR_rDST, sDS_pi_slice);

      // notify for dS
      cutlass::arch::fence_view_async_shared();
      pipeline_compute_mma_ds.producer_commit(pipeline_compute_mma_ds_producer_state);
      ++pipeline_compute_mma_ds_producer_state;
      // release OdO
      pipeline_load_compute_sum_odo.consumer_release(pipeline_load_compute_sum_odo_consumer_state);
      ++pipeline_load_compute_sum_odo_consumer_state;

      iter_count -= 1;
      iter_index += 1;
    }

    epilogue(
        blk_coord, problem_shape, mainloop_args, epilogue_args,
        pipeline_mma_compute_dkdv, pipeline_mma_compute_dkdv_consumer_state
    );
  }

  template<class BlkCoord>
  CUTLASS_DEVICE void reduce(
      BlkCoord const& blk_coord,
      ProblemShape const& problem_shape,
      int iter_index,
      int iter_count,
      MainloopArguments const& mainloop_args,
      MainloopParams const& mainloop_params,
      TensorStorage& shared_tensors,
      PipelineMmaReduceDQ& pipeline_mma_reduce_dq,
      typename PipelineMmaReduceDQ::PipelineState& pipeline_mma_reduce_dq_consumer_state,
      PipelineReduceTmaStore& pipeline_reduce_tma_store,
      typename PipelineReduceTmaStore::PipelineState& pipeline_reduce_tma_store_producer_state) {

    using X = Underscore;

    auto [Q, K, D, HB] = problem_shape;

    auto [blk_coord_q, blk_coord_k, blk_coord_batch] = blk_coord;

    // must match TileShapeDQ
    auto load_op = SM100_TMEM_LOAD_32dp32b32x{};

    auto tDQtDQ = partition_fragment_C(TiledMmaDSK{}, select<0,1>(TileShapeDSK{}))(make_coord(_,_),_0{},_0{});
    tDQtDQ.data() = TmemAllocation::kDQ;

    Tensor mDQ = mainloop_params.tma_red_dq.get_tma_tensor(make_shape(Q, D, HB));
    auto gDQ = local_tile(mDQ, TileShapeKQ{}, make_coord(_,_,_), Step<X, _1, _1>{})
        (_, _, _, _0{}, blk_coord_batch);

    Tensor cDQ = make_identity_tensor(take<0,2>(TileShapeDSK{}));

    Tensor sDQ = make_tensor(make_smem_ptr(shared_tensors.smem_dq.begin()), SmemLayoutDQ{});

    int thread_idx = threadIdx.x % (kNumComputeWarps * NumThreadsPerWarp);
    auto tiled_t2r = make_tmem_copy(load_op, tDQtDQ);
    auto thread_t2r = tiled_t2r.get_slice(thread_idx);

    Tensor tTR_cDQ   = thread_t2r.partition_D(cDQ);
    Tensor tTR_gDQ   = thread_t2r.partition_D(gDQ);
    Tensor tTR_sDQ   = thread_t2r.partition_D(sDQ);
    Tensor tTR_tDQ = thread_t2r.partition_S(tDQtDQ);

    auto block_tma = mainloop_params.tma_red_dq.get_slice(_0{});

    Tensor tDQsDQ = block_tma.partition_S(sDQ);
    Tensor tDQcDQ = block_tma.partition_S(cDQ);
    Tensor tDQgDQ = block_tma.partition_D(gDQ);

    int lane_predicate = (threadIdx.x % (kNumReduceWarps * NumThreadsPerWarp)) == 0;

    while (iter_count > 0) {
      pipeline_mma_reduce_dq.consumer_wait(pipeline_mma_reduce_dq_consumer_state);

      Tensor tTR_rDQ = make_tensor<ElementAcc>(shape(tTR_cDQ));

      // load dQ from tmem to rmem
      cute::copy(tiled_t2r, tTR_tDQ, tTR_rDQ);

      cutlass::arch::fence_view_async_tmem_load();
      pipeline_mma_reduce_dq.consumer_release(pipeline_mma_reduce_dq_consumer_state);
      ++pipeline_mma_reduce_dq_consumer_state;

      // we don't have enough smem to dump it all to smem, so we do it in stages
      CUTLASS_PRAGMA_UNROLL
      for (int i = 0; i < size<2>(tTR_cDQ); i++) {
        if (lane_predicate) {
          pipeline_reduce_tma_store.producer_acquire(pipeline_reduce_tma_store_producer_state);
        }
        // wait in all threads for the acquire to complete
        cutlass::arch::NamedBarrier(
            kNumReduceWarps * NumThreadsPerWarp,
            cutlass::arch::ReservedNamedBarriers::TransposeBarrier
        ).arrive_and_wait();

        cute::copy(tTR_rDQ(_, _, i), tTR_sDQ(_, _, _0{}, pipeline_reduce_tma_store_producer_state.index()));

        // wait for the stores to all be visible to the TMA
        cutlass::arch::fence_view_async_shared();
        cutlass::arch::NamedBarrier(
            kNumReduceWarps * NumThreadsPerWarp,
            cutlass::arch::ReservedNamedBarriers::TransposeBarrier
        ).arrive_and_wait();
        if (lane_predicate) {
          // launch tma store
          copy(mainloop_params.tma_red_dq, tDQsDQ(_,_,_0{}, pipeline_reduce_tma_store_producer_state.index()), tDQgDQ(_,_,i,iter_index));
          pipeline_reduce_tma_store.producer_commit(pipeline_reduce_tma_store_producer_state);
        }

        ++pipeline_reduce_tma_store_producer_state;
      }

      iter_count -= 1;
      iter_index += 1;
    }
  }


  CUTLASS_DEVICE void operator()(Params const& params, char* smem) {
    int warp_idx = cutlass::canonical_warp_idx_sync();
    auto role = warp_idx_to_role(warp_idx);
    uint32_t lane_predicate = cute::elect_one_sync();

    if (role == WarpRole::Load && lane_predicate) {
      prefetch_tma_descriptor(params.mainloop_params.tma_load_q.get_tma_descriptor());
      prefetch_tma_descriptor(params.mainloop_params.tma_load_k.get_tma_descriptor());
      prefetch_tma_descriptor(params.mainloop_params.tma_load_v.get_tma_descriptor());
      prefetch_tma_descriptor(params.mainloop_params.tma_load_do.get_tma_descriptor());
    }

    SharedStorage& shared_storage = *reinterpret_cast<SharedStorage*>(smem);

    int initializing_warp = 0;
    typename PipelineLoadMmaQ::Params pipeline_load_mma_q_params;
    if (role == WarpRole::Load) {
      pipeline_load_mma_q_params.role = PipelineLoadMmaQ::ThreadCategory::Producer;
    }
    if (role == WarpRole::Mma) {
      pipeline_load_mma_q_params.role = PipelineLoadMmaQ::ThreadCategory::Consumer;
    }
    pipeline_load_mma_q_params.is_leader = lane_predicate && (role == WarpRole::Load);
    // Also loads K in the first iteration
    pipeline_load_mma_q_params.transaction_bytes = kTransactionsBytesLoadQ;
    pipeline_load_mma_q_params.initializing_warp = initializing_warp++;
    PipelineLoadMmaQ pipeline_load_mma_q(shared_storage.pipelines.load_mma_q, pipeline_load_mma_q_params,
      ClusterShape{}, /*barrier init*/ cute::true_type{}, /*mask calc*/cute::false_type{});

    typename PipelineLoadMmaDO::Params pipeline_load_mma_do_params;
    if (role == WarpRole::Load) {
      pipeline_load_mma_do_params.role = PipelineLoadMmaDO::ThreadCategory::Producer;
    }
    if (role == WarpRole::Mma) {
      pipeline_load_mma_do_params.role = PipelineLoadMmaDO::ThreadCategory::Consumer;
    }
    pipeline_load_mma_do_params.is_leader = lane_predicate && (role == WarpRole::Load);
    // Also loads V in the first iteration
    pipeline_load_mma_do_params.transaction_bytes = kTransactionsBytesLoadDO;
    pipeline_load_mma_do_params.initializing_warp = initializing_warp++;
    PipelineLoadMmaDO pipeline_load_mma_do(shared_storage.pipelines.load_mma_do, pipeline_load_mma_do_params,
      ClusterShape{}, /*barrier init*/ cute::true_type{}, /*mask calc*/cute::false_type{});

    typename PipelineLoadComputeLSE::Params pipeline_load_compute_lse_params;
    if (role == WarpRole::Load) {
      pipeline_load_compute_lse_params.role = PipelineLoadComputeLSE::ThreadCategory::Producer;
    }
    if (role == WarpRole::Compute) {
      pipeline_load_compute_lse_params.role = PipelineLoadComputeLSE::ThreadCategory::Consumer;
    }
    pipeline_load_compute_lse_params.producer_arv_count = NumThreadsPerWarp;
    pipeline_load_compute_lse_params.consumer_arv_count = kNumComputeWarps * NumThreadsPerWarp;
    pipeline_load_compute_lse_params.initializing_warp = initializing_warp++;
    PipelineLoadComputeLSE pipeline_load_compute_lse(
      shared_storage.pipelines.load_compute_lse,
      pipeline_load_compute_lse_params,
      /*barrier init*/ cute::true_type{});

    typename PipelineLoadComputeSumOdO::Params pipeline_load_compute_sum_odo_params;
    if (role == WarpRole::Load) {
      pipeline_load_compute_sum_odo_params.role = PipelineLoadComputeSumOdO::ThreadCategory::Producer;
    }
    if (role == WarpRole::Compute) {
      pipeline_load_compute_sum_odo_params.role = PipelineLoadComputeSumOdO::ThreadCategory::Consumer;
    }
    pipeline_load_compute_sum_odo_params.producer_arv_count = NumThreadsPerWarp;
    pipeline_load_compute_sum_odo_params.consumer_arv_count = kNumComputeWarps * NumThreadsPerWarp;
    pipeline_load_compute_sum_odo_params.initializing_warp = initializing_warp++;
    PipelineLoadComputeSumOdO pipeline_load_compute_sum_odo(
      shared_storage.pipelines.load_compute_sum_odo,
      pipeline_load_compute_sum_odo_params,
      /*barrier init*/ cute::true_type{});

    typename PipelineMmaComputeS::Params pipeline_mma_compute_s_params;
    if (role == WarpRole::Mma) {
      pipeline_mma_compute_s_params.role = PipelineMmaComputeS::ThreadCategory::Producer;
    }
    if (role == WarpRole::Compute) {
      pipeline_mma_compute_s_params.role = PipelineMmaComputeS::ThreadCategory::Consumer;
    }
    pipeline_mma_compute_s_params.consumer_arv_count = kNumComputeWarps * cutlass::NumThreadsPerWarp;
    pipeline_mma_compute_s_params.initializing_warp = initializing_warp++;
    PipelineMmaComputeS pipeline_mma_compute_s(
      shared_storage.pipelines.mma_compute_s,
      pipeline_mma_compute_s_params,
      ClusterShape{}, /*barrier init*/ cute::true_type{}, /*mask calc*/cute::false_type{});

    typename PipelineMmaComputeDP::Params pipeline_mma_compute_dp_params;
    if (role == WarpRole::Mma) {
      pipeline_mma_compute_dp_params.role = PipelineMmaComputeDP::ThreadCategory::Producer;
    }
    if (role == WarpRole::Compute) {
      pipeline_mma_compute_dp_params.role = PipelineMmaComputeDP::ThreadCategory::Consumer;
    }
    pipeline_mma_compute_dp_params.consumer_arv_count = kNumComputeWarps * cutlass::NumThreadsPerWarp;
    pipeline_mma_compute_dp_params.initializing_warp = initializing_warp++;
    PipelineMmaComputeDP pipeline_mma_compute_dp(
      shared_storage.pipelines.mma_compute_dp,
      pipeline_mma_compute_dp_params,
      ClusterShape{}, /*barrier init*/ cute::true_type{}, /*mask calc*/cute::false_type{});

    typename PipelineMmaReduceDQ::Params pipeline_mma_reduce_dq_params;
    if (role == WarpRole::Mma) {
      pipeline_mma_reduce_dq_params.role = PipelineMmaReduceDQ::ThreadCategory::Producer;
    }
    if (role == WarpRole::Reduce) {
      pipeline_mma_reduce_dq_params.role = PipelineMmaReduceDQ::ThreadCategory::Consumer;
    }
    pipeline_mma_reduce_dq_params.consumer_arv_count = kNumReduceWarps * cutlass::NumThreadsPerWarp;
    pipeline_mma_reduce_dq_params.initializing_warp = initializing_warp++;
    PipelineMmaReduceDQ pipeline_mma_reduce_dq(
      shared_storage.pipelines.mma_reduce_dq,
      pipeline_mma_reduce_dq_params,
      ClusterShape{}, /*barrier init*/ cute::true_type{}, /*mask calc*/cute::false_type{});

    typename PipelineComputeMmaP::Params pipeline_compute_mma_p_params;
    if (role == WarpRole::Mma) {
      pipeline_compute_mma_p_params.role = PipelineComputeMmaP::ThreadCategory::Consumer;
    }
    if (role == WarpRole::Compute) {
      pipeline_compute_mma_p_params.role = PipelineComputeMmaP::ThreadCategory::Producer;
    }
    pipeline_compute_mma_p_params.producer_arv_count = kNumComputeWarps * cutlass::NumThreadsPerWarp;
    pipeline_compute_mma_p_params.consumer_arv_count = 1;
    pipeline_compute_mma_p_params.initializing_warp = initializing_warp++;
    PipelineComputeMmaP pipeline_compute_mma_p(
      shared_storage.pipelines.compute_mma_p,
      pipeline_compute_mma_p_params,
      ClusterShape{}, /*barrier init*/ cute::true_type{}, /*mask calc*/cute::false_type{});

    typename PipelineComputeMmaDS::Params pipeline_compute_mma_ds_params;
    if (role == WarpRole::Mma) {
      pipeline_compute_mma_ds_params.role = PipelineComputeMmaDS::ThreadCategory::Consumer;
    }
    if (role == WarpRole::Compute) {
      pipeline_compute_mma_ds_params.role = PipelineComputeMmaDS::ThreadCategory::Producer;
    }
    pipeline_compute_mma_ds_params.producer_arv_count = kNumComputeWarps * cutlass::NumThreadsPerWarp;
    pipeline_compute_mma_ds_params.consumer_arv_count = 1;
    pipeline_compute_mma_ds_params.initializing_warp = initializing_warp++;
    PipelineComputeMmaDS pipeline_compute_mma_ds(
      shared_storage.pipelines.compute_mma_ds,
      pipeline_compute_mma_ds_params,
      ClusterShape{}, /*barrier init*/ cute::true_type{}, /*mask calc*/cute::false_type{});

    typename PipelineMmaComputeDKDV::Params pipeline_mma_compute_dkdv_params;
    if (role == WarpRole::Mma) {
      pipeline_mma_compute_dkdv_params.role = PipelineMmaComputeDKDV::ThreadCategory::Producer;
    }
    if (role == WarpRole::Compute) {
      pipeline_mma_compute_dkdv_params.role = PipelineMmaComputeDKDV::ThreadCategory::Consumer;
    }
    pipeline_mma_compute_dkdv_params.consumer_arv_count = kNumComputeWarps * cutlass::NumThreadsPerWarp;
    pipeline_mma_compute_dkdv_params.initializing_warp = initializing_warp++;
    PipelineMmaComputeDKDV pipeline_mma_compute_dkdv(
      shared_storage.pipelines.mma_compute_dkdv,
      pipeline_mma_compute_dkdv_params,
      ClusterShape{}, /*barrier init*/ cute::true_type{}, /*mask calc*/cute::false_type{});
    PipelineReduceTmaStore pipeline_reduce_tma_store;

    TmemAllocator tmem_allocator;

    pipeline_init_arrive_relaxed(size(ClusterShape{}));

    pipeline_load_mma_q.init_masks(ClusterShape{});
    pipeline_load_mma_do.init_masks(ClusterShape{});
    pipeline_mma_compute_s.init_masks(ClusterShape{});
    pipeline_mma_compute_dp.init_masks(ClusterShape{});
    pipeline_mma_reduce_dq.init_masks(ClusterShape{});
    pipeline_compute_mma_p.init_masks(ClusterShape{});
    pipeline_compute_mma_ds.init_masks(ClusterShape{});
    pipeline_mma_compute_dkdv.init_masks(ClusterShape{});

    typename decltype(pipeline_load_mma_q)::PipelineState pipeline_load_mma_q_consumer_state;
    typename decltype(pipeline_load_mma_do)::PipelineState pipeline_load_mma_do_consumer_state;
    typename decltype(pipeline_load_compute_lse)::PipelineState pipeline_load_compute_lse_consumer_state;
    typename decltype(pipeline_load_compute_sum_odo)::PipelineState pipeline_load_compute_sum_odo_consumer_state;
    typename decltype(pipeline_mma_compute_s)::PipelineState pipeline_mma_compute_s_consumer_state;
    typename decltype(pipeline_mma_compute_dp)::PipelineState pipeline_mma_compute_dp_consumer_state;
    typename decltype(pipeline_mma_reduce_dq)::PipelineState pipeline_mma_reduce_dq_consumer_state;
    typename decltype(pipeline_compute_mma_p)::PipelineState pipeline_compute_mma_p_consumer_state;
    typename decltype(pipeline_compute_mma_ds)::PipelineState pipeline_compute_mma_ds_consumer_state;
    typename decltype(pipeline_mma_compute_dkdv)::PipelineState pipeline_mma_compute_dkdv_consumer_state;

    auto pipeline_load_mma_q_producer_state = make_producer_start_state<decltype(pipeline_load_mma_q)>();
    auto pipeline_load_mma_do_producer_state = make_producer_start_state<decltype(pipeline_load_mma_do)>();
    auto pipeline_load_compute_lse_producer_state = make_producer_start_state<decltype(pipeline_load_compute_lse)>();
    auto pipeline_load_compute_sum_odo_producer_state = make_producer_start_state<decltype(pipeline_load_compute_sum_odo)>();
    auto pipeline_mma_compute_s_producer_state = make_producer_start_state<decltype(pipeline_mma_compute_s)>();
    auto pipeline_mma_compute_dp_producer_state = make_producer_start_state<decltype(pipeline_mma_compute_dp)>();
    auto pipeline_mma_reduce_dq_producer_state = make_producer_start_state<decltype(pipeline_mma_reduce_dq)>();
    auto pipeline_compute_mma_p_producer_state = make_producer_start_state<decltype(pipeline_compute_mma_p)>();
    auto pipeline_compute_mma_ds_producer_state = make_producer_start_state<decltype(pipeline_compute_mma_ds)>();
    auto pipeline_mma_compute_dkdv_producer_state = make_producer_start_state<decltype(pipeline_mma_compute_dkdv)>();
    auto pipeline_reduce_tma_store_producer_state = make_producer_start_state<decltype(pipeline_reduce_tma_store)>();

    pipeline_init_wait(size(ClusterShape{}));

    auto blk_coord = make_coord(_0{}, blockIdx.x, make_coord(blockIdx.y, blockIdx.z));
    auto problem_shape = params.problem_shape;
    int iter_count = ceil_div(get<0>(problem_shape), TileShapeQ{});
    int iter_start = 0;
    if constexpr (std::is_base_of_v<cutlass::fmha::collective::CausalMask, Mask>) {
      iter_start = (get<1>(blk_coord) * TileShapeK{}) / TileShapeQ{};
    }
    iter_count -= iter_start;

    if (role == WarpRole::Load) {
      warpgroup_reg_set<RegisterAllocation::kLoad>();

      load(
          blk_coord,
          problem_shape,
          iter_start,
          iter_count,
          params.mainloop,
          params.mainloop_params,
          shared_storage.tensors,
          pipeline_load_mma_q, pipeline_load_mma_q_producer_state,
          pipeline_load_mma_do, pipeline_load_mma_do_producer_state,
          pipeline_load_compute_lse, pipeline_load_compute_lse_producer_state,
          pipeline_load_compute_sum_odo, pipeline_load_compute_sum_odo_producer_state
      );

    }
    else if (role == WarpRole::Mma) {
      warpgroup_reg_set<RegisterAllocation::kMma>();

      tmem_allocator.allocate(TmemAllocator::Sm100TmemCapacityColumns, &shared_storage.tmem_base_ptr);
      __syncwarp();

      mma(
          blk_coord,
          problem_shape,
          iter_start,
          iter_count,
          params.mainloop,
          shared_storage.tensors,
          pipeline_load_mma_q, pipeline_load_mma_q_consumer_state,
          pipeline_load_mma_do, pipeline_load_mma_do_consumer_state,
          pipeline_mma_compute_s, pipeline_mma_compute_s_producer_state,
          pipeline_mma_compute_dp, pipeline_mma_compute_dp_producer_state,
          pipeline_mma_reduce_dq, pipeline_mma_reduce_dq_producer_state,
          pipeline_compute_mma_p, pipeline_compute_mma_p_consumer_state,
          pipeline_compute_mma_ds, pipeline_compute_mma_ds_consumer_state,
          pipeline_mma_compute_dkdv, pipeline_mma_compute_dkdv_producer_state
      );

    }
    else if (role == WarpRole::Compute) {
      warpgroup_reg_set<RegisterAllocation::kCompute>();

      compute(
          blk_coord,
          problem_shape,
          iter_start,
          iter_count,
          params.mainloop,
          params.epilogue,
          shared_storage.tensors,
          pipeline_load_compute_lse, pipeline_load_compute_lse_consumer_state,
          pipeline_load_compute_sum_odo, pipeline_load_compute_sum_odo_consumer_state,
          pipeline_mma_compute_s, pipeline_mma_compute_s_consumer_state,
          pipeline_mma_compute_dp, pipeline_mma_compute_dp_consumer_state,
          pipeline_compute_mma_p, pipeline_compute_mma_p_producer_state,
          pipeline_compute_mma_ds, pipeline_compute_mma_ds_producer_state,
          pipeline_mma_compute_dkdv, pipeline_mma_compute_dkdv_consumer_state
      );

      cutlass::arch::NamedBarrier(
          kNumComputeWarps * NumThreadsPerWarp,
          cutlass::arch::ReservedNamedBarriers::EpilogueBarrier
      ).arrive_and_wait();

      if (warp_idx % kNumComputeWarps == 0) {
        uint32_t free_stage_ptr = shared_storage.tmem_base_ptr;
        tmem_allocator.free(free_stage_ptr, TmemAllocator::Sm100TmemCapacityColumns);
      }

    }
    else if (role == WarpRole::Reduce) {
      warpgroup_reg_set<RegisterAllocation::kReduce>();

      reduce(
          blk_coord,
          problem_shape,
          iter_start,
          iter_count,
          params.mainloop,
          params.mainloop_params,
          shared_storage.tensors,
          pipeline_mma_reduce_dq, pipeline_mma_reduce_dq_consumer_state,
          pipeline_reduce_tma_store, pipeline_reduce_tma_store_producer_state
      );

      pipeline_reduce_tma_store.producer_tail(pipeline_reduce_tma_store_producer_state);
    }
    else {
      warpgroup_reg_set<RegisterAllocation::kEmpty>();

      /* no-op */

    }
  }

  static dim3 get_block_shape() {
    dim3 block(MaxThreadsPerBlock, 1, 1);
    return block;
  }

  static dim3 get_grid_shape(Params const& params) {
    auto [Q, K, D, HB] = params.problem_shape;
    auto [H, B] = HB;
    dim3 grid(ceil_div(K, TileShapeK{}), H, B);
    return grid;
  }
};

}  // namespace cutlass::fmha::kernel
