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#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/detail/cluster.hpp"
#include "cutlass/gemm/dispatch_policy.hpp"
#include "cutlass/numeric_types.h"
#include "cutlass/pipeline/pipeline.hpp"
#include "cutlass/gemm/gemm.h"
#include "cutlass/trace.h"
#include "cutlass/kernel_hardware_info.hpp"
#include "cutlass/arch/memory.h"

#include "cute/algorithm/functional.hpp"
#include "cute/arch/cluster_sm90.hpp"
#include "cute/atom/mma_atom.hpp"
#include "cute/algorithm/gemm.hpp"
#include "cute/numeric/arithmetic_tuple.hpp"

#include "cutlass/gemm/collective/collective_mma_decl.hpp"

/////////////////////////////////////////////////////////////////////////////////////////////////

namespace cutlass::gemm::collective {
using namespace cute;

/////////////////////////////////////////////////////////////////////////////////////////////////



// WarpSpecialized Mainloop
// Both DMA Load and MMA methods of this class must be run by a single thread that's picked by elect_one
template <
  int Stages,
  int SchedulerPipelineStageCount,
  int AccumulatorPipelineStageCount,
  class ArchTag_,
  class ClusterShape,   // Static cluster shape
  class TileShape_,     // (MmaAtomShapeM, MmaAtomShapeN, TileK)
  class ElementA_,
  class StrideA_,
  class ElementB_,
  class StrideB_,
  class TiledMma_,
  class GmemTiledCopyA_,
  class SmemLayoutAtomA_,
  class SmemCopyAtomA_,
  class TransformA_,
  class GmemTiledCopyB_,
  class SmemLayoutAtomB_,
  class SmemCopyAtomB_,
  class TransformB_>
struct CollectiveMma<
    MainloopSm100UmmaMixedTmaCpAsyncWarpSpecialized<
      Stages,
      SchedulerPipelineStageCount,
      AccumulatorPipelineStageCount,
      ClusterShape,
      ArchTag_>,
    TileShape_,
    ElementA_,
    StrideA_,
    ElementB_,
    StrideB_,
    TiledMma_,
    GmemTiledCopyA_,
    SmemLayoutAtomA_,
    SmemCopyAtomA_,
    TransformA_,
    GmemTiledCopyB_,
    SmemLayoutAtomB_,
    SmemCopyAtomB_,
    TransformB_>
{
  using TiledMma = TiledMma_;
  using AtomThrShapeMNK = Shape<decltype(shape<0>(typename TiledMma::ThrLayoutVMNK{})), _1, _1>;

  using DispatchPolicy = MainloopSm100UmmaMixedTmaCpAsyncWarpSpecialized<
                          Stages,
                          SchedulerPipelineStageCount,
                          AccumulatorPipelineStageCount,
                          ClusterShape,
                          ArchTag_>;
  // TileShape refers to MmaTileShape to adapt for runtime cluster
  using TileShape = TileShape_;

  CUTE_STATIC_ASSERT_V(evenly_divides(TileShape{}, tile_shape(TiledMma{})),
                       "Static cluster shape used: TileShape should be evenly divided by TiledMma");

  // Define A and B block shapes
  using MmaShapeA_MK = decltype(partition_shape_A(TiledMma{}, make_shape(size<0>(TileShape{}), size<2>(TileShape{}))));
  using MmaShapeB_NK = decltype(partition_shape_B(TiledMma{}, make_shape(size<1>(TileShape{}), size<2>(TileShape{}))));
  using LoadShapeB_NK = decltype(make_shape(
        get<1>(TileShape{}) / size(AtomThrShapeMNK{}), get<2>(TileShape{})
  ));

  // CtaShape_MNK is queried from collective in all kernel layers
  using CtaShape_MNK = decltype(shape_div(TileShape{}, AtomThrShapeMNK{}));

  using ElementA = ElementA_;
  using ElementAMma = typename TiledMma::ValTypeA;
  using StrideA = StrideA_;
  using ElementB = ElementB_;
  using ElementBMma = typename TiledMma::ValTypeB;
  using StrideB = StrideB_;

  static constexpr bool IsRuntimeDataTypeA = cute::is_same_v<ElementA, cutlass::type_erased_dynamic_float8_t>;
  static constexpr bool IsRuntimeDataTypeB = cute::is_same_v<ElementB, cutlass::type_erased_dynamic_float8_t>;

  static_assert(IsRuntimeDataTypeA == IsRuntimeDataTypeB,
                "ElementA and ElementB should be both runtime or both static.");

  static constexpr bool IsRuntimeDataType = IsRuntimeDataTypeA && IsRuntimeDataTypeB;


  using ElementAccumulator = typename TiledMma::ValTypeC;
  using GmemTiledCopyA = GmemTiledCopyA_;
  using GmemTiledCopyB = GmemTiledCopyB_;
  using SmemLayoutAtomA = SmemLayoutAtomA_;
  using SmemLayoutAtomB = SmemLayoutAtomB_;
  using SmemCopyAtomA = SmemCopyAtomA_;
  using SmemCopyAtomB = SmemCopyAtomB_;
  using TransformA = TransformA_;
  using TransformB = TransformB_;
  using ArchTag = typename DispatchPolicy::ArchTag;

  using MainloopPipelineTMA = cutlass::PipelineTmaUmmaAsync<DispatchPolicy::Stages, ClusterShape, AtomThrShapeMNK>;
  using MainloopPipelineTMAState = typename MainloopPipelineTMA::PipelineState;

  using MainloopPipelineCpAsync = cutlass::PipelineUmmaConsumerAsync<DispatchPolicy::Stages, AtomThrShapeMNK>;
  using MainloopPipelineCpAsyncState = typename MainloopPipelineCpAsync::PipelineState;

  // static_assert(size(GmemTiledCopyA{}) == size(GmemTiledCopyB{}), "A and B GmemTiledCopy should share the same thread count");
  static constexpr int NumLoadThreadsCpAsync = size(GmemTiledCopyB{});

  static_assert(rank(SmemLayoutAtomA{}) == 2, "SmemLayoutAtomA must be rank 2 (M,K)");
  static_assert(((size<0,0>(MmaShapeA_MK{}) * size<1>(MmaShapeA_MK{})) % size<0>(SmemLayoutAtomA{})) == 0,
      "SmemLayoutAtom must evenly divide tile shape.");
  static_assert(((size<0,1>(MmaShapeA_MK{}) * size<2>(MmaShapeA_MK{})) % size<1>(SmemLayoutAtomA{})) == 0,
      "SmemLayoutAtom must evenly divide tile shape.");
  static_assert(cute::is_void_v<SmemCopyAtomA>,
      "SM100 UMMA cannot have a non-void copy atom for smem sourced instructions.");

  static_assert(rank(SmemLayoutAtomB{}) == 2, "SmemLayoutAtomB must be rank 2 (N,K)");
  static_assert(((size<0,0>(MmaShapeB_NK{}) * size<1>(MmaShapeB_NK{})) % size<0>(SmemLayoutAtomB{})) == 0,
      "SmemLayoutAtom must evenly divide tile shape.");
  static_assert(((size<0,1>(MmaShapeB_NK{}) * size<2>(MmaShapeB_NK{})) % size<1>(SmemLayoutAtomB{})) == 0,
      "SmemLayoutAtom must evenly divide tile shape.");
  static_assert(cute::is_void_v<SmemCopyAtomB>,
      "SM100 UMMA cannot have a non-void copy atom for smem sourced instructions.");

  // Tile along K mode first before tiling over MN. PIPE mode last as usual.
  // (MMA_TILE_M,MMA_TILE_K),MMA_M,MMA_K,PIPE)
  using SmemLayoutA = decltype(UMMA::tile_to_mma_shape(
      SmemLayoutAtomA{},
      append(MmaShapeA_MK{}, Int<DispatchPolicy::Stages>{}),
      conditional_t< ::cutlass::gemm::detail::is_major<0,StrideA>(), Step<_2,_1,_3>, Step<_1,_2,_3>>{}));


  using MmaSmemLayoutB = decltype(UMMA::tile_to_mma_shape(
      SmemLayoutAtomB{},
      append(MmaShapeB_NK{}, Int<DispatchPolicy::Stages>{}),
      conditional_t< ::cutlass::gemm::detail::is_major<0,StrideB>(), Step<_2,_1,_3>, Step<_1,_2,_3>>{}));

  using LoadSmemLayoutB = decltype(tile_to_shape(
      SmemLayoutAtomB{},
      append(LoadShapeB_NK{}, Int<DispatchPolicy::Stages>{}),
      conditional_t< ::cutlass::gemm::detail::is_major<0,StrideB>(), Step<_2,_1,_3>, Step<_1,_2,_3>>{}));


  static_assert(DispatchPolicy::Stages >= 2, "Specialization requires Stages set to value 1 or more.");
  static_assert(cute::is_base_of<cute::UMMA::DescriptorIterator, typename TiledMma::FrgTypeA>::value &&
                cute::is_base_of<cute::UMMA::DescriptorIterator, typename TiledMma::FrgTypeB>::value,
                "MMA atom must source both A and B operand from smem_desc for this mainloop.");

  using TmaInternalElementA = cute::conditional_t<cute::is_same_v<ElementA, float>, cutlass::tfloat32_t, ElementAMma>;

  using SmemAllocTypeA = cute::conditional_t<cute::sizeof_bits_v<ElementAMma> < 8, uint8_t, ElementAMma>;
  using SmemAllocTypeB = cute::conditional_t<cute::sizeof_bits_v<ElementBMma> < 8, uint8_t, ElementBMma>;

  using BitTypeElementA = cute::uint_bit_t<cute::sizeof_bits_v<ElementA>>;
  using BitTypeElementB = cute::uint_bit_t<cute::sizeof_bits_v<ElementB>>;

  using ArrayElementA = cute::conditional_t<IsRuntimeDataTypeA, BitTypeElementA, ElementA>;
  using ArrayElementB = cute::conditional_t<IsRuntimeDataTypeB, BitTypeElementB, ElementB>;

  using RuntimeDataTypeA = cute::conditional_t<IsRuntimeDataTypeA, cute::UMMA::MXF8F6F4Format, void*>;
  using RuntimeDataTypeB = cute::conditional_t<IsRuntimeDataTypeB, cute::UMMA::MXF8F6F4Format, void*>;

  struct SharedStorage {
    struct TensorStorage : cute::aligned_struct<128, _0> {
      cute::array_aligned<SmemAllocTypeA, cute::cosize_v<SmemLayoutA>> smem_A;
      cute::array_aligned<SmemAllocTypeB, cute::cosize_v<LoadSmemLayoutB>> smem_B;
    } tensors;

    using PipelineStorageTMA = typename MainloopPipelineTMA::SharedStorage;
    using PipelineStorageCpAsync = typename MainloopPipelineCpAsync::SharedStorage;

    struct PipelineStorage : cute::aligned_struct<16, _0> {
      alignas(16) PipelineStorageTMA tma;
      alignas(16) PipelineStorageCpAsync cpasync;
    } pipelines;
  };

  // Expose shared storage for tensors/pipelines separately to allow kernel layer to reorder them.
  using TensorStorage = typename SharedStorage::TensorStorage;
  using PipelineStorage = typename SharedStorage::PipelineStorage;

  static constexpr uint32_t TmaTransactionBytes =
    cutlass::bits_to_bytes(size(AtomThrShapeMNK{}) * cosize(take<0,3>(SmemLayoutA{})) * cute::sizeof_bits_v<ElementA>);

  template <class AccTensor>
  struct TmemStorage {
    AccTensor accumulators;
  };

  // Host side kernel arguments
  struct Arguments {
    ArrayElementA const* ptr_A{nullptr};
    ArrayElementB const* ptr_B{nullptr};
    RuntimeDataTypeA runtime_data_type_a{};
    RuntimeDataTypeB runtime_data_type_b{};
  };

  // Device side kernel params
  struct Params {
    static_assert(cute::is_static_v<ClusterShape>, "`ClusterShape` must be static in mixed TMA cpasync kernel.");
    using ClusterLayout_VMNK = decltype(tiled_divide(make_layout(ClusterShape{}),
                                                     make_tile(typename TiledMma::AtomThrID{})));

    using TMA_A = decltype(make_tma_atom_A_sm100<TmaInternalElementA>(
        GmemTiledCopyA{},
        make_tensor(recast_ptr<TmaInternalElementA>(nullptr), repeat_like(StrideA{}, int32_t(0)), StrideA{}),
        SmemLayoutA{}(_,_,_,cute::Int<0>{}),
        TileShape{},
        TiledMma{},
        ClusterLayout_VMNK{})
      );

    TMA_A tma_load_a;

    ArrayElementB const* ptr_B{nullptr};

    RuntimeDataTypeA runtime_data_type_a;
    RuntimeDataTypeB runtime_data_type_b;
  };

  CUTLASS_DEVICE
  CollectiveMma(Params const& params)
    : runtime_data_type_a_(params.runtime_data_type_a)
    , runtime_data_type_b_(params.runtime_data_type_b) {


    observed_tma_load_a_ = &params.tma_load_a;

  }

  template <class ProblemShape>
  static constexpr Params
  to_underlying_arguments(
    ProblemShape const& problem_shape,
    Arguments const& args,
    [[maybe_unused]] void* workspace,
    cutlass::KernelHardwareInfo const& hw_info = cutlass::KernelHardwareInfo{}) {
    // Optionally append 1s until problem shape is rank-4 (MNKL), in case it is only rank-3 (MNK)
    auto problem_shape_MNKL = append<4>(problem_shape, 1);
    auto [M,N,K,L] = problem_shape_MNKL;

    auto ptr_A = recast_ptr<TmaInternalElementA>(args.ptr_A);
    auto ptr_B = recast_ptr<ElementBMma>(args.ptr_B);

    auto shape_a = make_shape(M, K, L);
    StrideA stride_a = cutlass::make_internal_packed_stride(StrideA{}, shape_a);
    Tensor tensor_a = make_tensor(ptr_A, make_layout(shape_a, stride_a));

    auto cluster_layout_vmnk = tiled_divide(make_layout(ClusterShape{}), make_tile(typename TiledMma::AtomThrID{}));

    typename Params::TMA_A tma_load_a = make_tma_atom_A_sm100<TmaInternalElementA>(
        GmemTiledCopyA{},
        tensor_a,
        SmemLayoutA{}(_,_,_,cute::Int<0>{}),
        TileShape{},
        TiledMma{},
        cluster_layout_vmnk);

    return {
      tma_load_a,
      args.ptr_B,
      args.runtime_data_type_a,
      args.runtime_data_type_b
    };
  }

  template <class ProblemShape>
  static bool
  can_implement(
      ProblemShape const& problem_shape,
      [[maybe_unused]] Arguments const& args) {
    auto problem_shape_MNKL = append<4>(problem_shape, 1);
    auto [M,N,K,L] = problem_shape_MNKL;

    static constexpr bool IsF8F6F4 = detail::is_sm100_mma_f8f6f4<TiledMma, ElementA, ElementB>();
    constexpr int tma_alignment_bits_A = cutlass::detail::get_input_alignment_bits<ElementA, IsF8F6F4>();
    constexpr int min_tma_aligned_elements_A = tma_alignment_bits_A / cute::sizeof_bits<ElementA>::value;

    bool implementable = true;

    implementable = implementable && cutlass::detail::check_alignment<min_tma_aligned_elements_A>(cute::make_shape(M,K,L), StrideA{});
    if (!implementable) {
      CUTLASS_TRACE_HOST("  CAN IMPLEMENT: Problem Size doesn't meet the minimum alignment requirements for TMA.\n");
    }

    implementable = implementable && cutlass::detail::check_alignment<GmemTiledCopyB::NumValSrc>(cute::make_shape(N,K,L), StrideB{});
    if (!implementable) {
      CUTLASS_TRACE_HOST("  CAN IMPLEMENT: Problem Size doesn't meet the minimum alignment requirements for CpAsync.\n");
    }
    
    return implementable;
  }

  /// Issue Tma Descriptor Prefetch -- ideally from a single thread for best performance
  CUTLASS_DEVICE void
  prefetch_tma_descriptors() {
    cute::prefetch_tma_descriptor(observed_tma_load_a_->get_tma_descriptor());
  }

  /// Construct A Single Stage's Accumulator Shape
  CUTLASS_DEVICE static 
  auto
  partition_accumulator_shape() {
    auto acc_shape = partition_shape_C(TiledMma{}, take<0,2>(TileShape{}));  // ((MMA_TILE_M,MMA_TILE_N),MMA_M,MMA_N)

    return acc_shape;
  }

  template <class TmemStorage>
  CUTLASS_DEVICE static
  auto
  slice_accumulator(TmemStorage tmem_storage, int stage) {
    return cute::make_tuple(tmem_storage.accumulators(_,_,_,stage));
  }

  template <class EpilogueTile, bool IsOverlappingAccum = false>
  CUTLASS_DEVICE static
  auto
  init_tmem_tensors(EpilogueTile epi_tile) {
    TiledMma tiled_mma;
    auto acc_shape = partition_accumulator_shape();
    // ((MMA_TILE_M,MMA_TILE_N),MMA_M,MMA_N,ACC_PIPE) where ACC_PIPE=2 so we can double buffer our accumulators for mainloop and epilogue.
    Tensor accumulators = cutlass::detail::make_sm100_accumulator<AccumulatorPipelineStageCount, IsOverlappingAccum>(
        tiled_mma, acc_shape, EpilogueTile{});
    TmemStorage<decltype(accumulators)> tmem_storage;
    tmem_storage.accumulators = accumulators;
    return tmem_storage;
  }

  template <class TmemStorage>
  CUTLASS_DEVICE static
  void
  set_tmem_offsets(TmemStorage& tmem_storage, uint32_t tmem_base_addr) {
    tmem_storage.accumulators.data() = tmem_base_addr;
  }

  /// Set up the data needed by this collective for load.
  /// Return tuple element contain
  /// gA_mkl - The tiled tensor for input A
  /// tAsA - partitioned smem tensor for A
  /// mcast_mask_a - tma multicast mask for A
  template <class ProblemShape_MNKL>
  CUTLASS_DEVICE auto
  load_init_tma(
      ProblemShape_MNKL const& problem_shape_MNKL,
      TensorStorage& shared_tensors) const {
    using X = Underscore;
    // Separate out problem shape for convenience
    auto [M,N,K,L] = problem_shape_MNKL;

    // TMA
    Tensor mA_mkl = observed_tma_load_a_->get_tma_tensor(make_shape(M,K,L));
    Tensor gA_mkl = local_tile(mA_mkl, TileShape{}, make_coord(_,_,_), Step<_1, X,_1>{});    // (BLK_M, BLK_K, m, k, l)

    ThrMMA cta_mma = TiledMma{}.get_slice(blockIdx.x % size(typename TiledMma::AtomThrID{}));
    Tensor tCgA_mkl = cta_mma.partition_A(gA_mkl);          // (MMA, MMA_M, MMA_K, m, k, l)

    Tensor sA = make_tensor(make_smem_ptr(shared_tensors.smem_A.begin()), SmemLayoutA{});  // (MMA,MMA_M,MMA_K,PIPE)

    // Define the CTA-in-cluster Layout and Coord
    Layout cta_layout_mnk  = make_layout(ClusterShape{});
    Layout cta_layout_vmnk = tiled_divide(cta_layout_mnk, make_tile(typename TiledMma::AtomThrID{}));
    uint32_t cta_rank_in_cluster = static_cast<uint32_t>(cute::block_rank_in_cluster());
    auto cta_coord_vmnk  = cta_layout_vmnk.get_flat_coord(cta_rank_in_cluster);

    // Project the cta_layout for tma_a along the n-modes
    auto [tAgA_mkl, tAsA] = tma_partition(*observed_tma_load_a_,
                                      get<2>(cta_coord_vmnk), make_layout(size<2>(cta_layout_vmnk)),
                                      group_modes<0,3>(sA), group_modes<0,3>(tCgA_mkl));

    uint16_t mcast_mask_a = create_tma_multicast_mask<2>(cta_layout_vmnk, cta_coord_vmnk);

    return cute::make_tuple(
      shape<3>(gA_mkl),      // for scheduler
      tAgA_mkl, tAsA,        // for input tensor values
      mcast_mask_a           // for TMA multicast
    );
  }

  template <class ProblemShape_MNKL, class TileScheduler>
  CUTLASS_DEVICE auto
  load_init_cpasync(
      ProblemShape_MNKL const& problem_shape_MNKL,
      Params const& params,
      TensorStorage& shared_tensors,
      TileScheduler const& scheduler,
      typename TileScheduler::WorkTileInfo const& work_tile_info) const {
    using X = Underscore;
    // Separate out problem shape for convenience
    auto [M,N,K,L] = problem_shape_MNKL;

    // Represent the full tensors

    auto shape_b = make_shape(N, K, L);
    StrideB stride_b = cutlass::make_internal_packed_stride(StrideB{}, shape_b);

    Tensor mB_nkl = make_tensor(make_gmem_ptr(params.ptr_B), shape_b, stride_b); //(n,k,l)
    // Partition for cpasync
    Tensor gB_nkl = local_tile(mB_nkl, TileShape{}, make_coord(_,_,_), Step< X,_1,_1>{}); // (BLK_N,BLK_K,n,k,l)
    Tensor tBgB_nkl = flatten(flat_divide(gB_nkl, make_shape(safe_div(size(get<1>(TileShape{})), size(AtomThrShapeMNK{}))))); 

    // Build the coordinate tensors with the same shape as input matrices
    Tensor cB_nk  = make_identity_tensor(make_shape(N,K));
    // Slice the coordinate tensors in the same way as A/B tensor partitioning
    Tensor cgB_nk = local_tile(cB_nk, TileShape{}, make_coord(_,_,_), Step< X,_1,_1>{}); // (BLK_N,BLK_K,n,k)
    Tensor ctBgB_nk = flatten(flat_divide(cgB_nk, make_shape(safe_div(size(get<1>(TileShape{})), size(AtomThrShapeMNK{})))));

    Tensor sB = make_tensor(make_smem_ptr(shared_tensors.smem_B.data()), LoadSmemLayoutB{});

    GmemTiledCopyB gmem_to_smem_b_tiled_copy;

    int thread_idx = threadIdx.x % NumLoadThreadsCpAsync;
    auto thr_copy_b = gmem_to_smem_b_tiled_copy.get_slice(thread_idx);

    return cute::make_tuple(
      tBgB_nkl, ctBgB_nk, sB, 
      gmem_to_smem_b_tiled_copy, thr_copy_b);
  }

  /// Set up the data needed by this collective for mma compute.
  template <class TmemStorage>
  CUTLASS_DEVICE auto
  mma_init(
      Params const& params,
      [[maybe_unused]] TmemStorage tmem_storage,
      // [[maybe_unused]] cute::tuple<cute::Tensor<FrgEngine, FrgLayout>, cute::Tensor<FrgEngine, FrgLayout>> const& accumulators_pair,
      TensorStorage& shared_tensors) const {
    Tensor sA = make_tensor(make_smem_ptr(shared_tensors.smem_A.data()), SmemLayoutA{});          // (BLK_M,BLK_K,PIPE)
    Tensor sB = make_tensor(make_smem_ptr(shared_tensors.smem_B.data()), MmaSmemLayoutB{});          // (BLK_N,BLK_K,PIPE)

    // Allocate "fragments/descriptors" for A and B matrices
    Tensor tCrA = TiledMma::make_fragment_A(sA);                                           // (MMA,MMA_M,MMA_K,PIPE)
    Tensor tCrB = TiledMma::make_fragment_B(sB);                                           // (MMA,MMA_N,MMA_K,PIPE)

    CUTE_STATIC_ASSERT_V(Int<DispatchPolicy::Stages>{} == size<3>(sA));                                     // PIPE
    CUTE_STATIC_ASSERT_V(Int<DispatchPolicy::Stages>{} == size<3>(sB));

    TiledMma tiled_mma;

    if constexpr (IsRuntimeDataType) {
      // Update instruction descriptor according to runtime argument.
      // Applying bitmask (0b111) to help compiler deduce that the conversion and assignment are safe.
      tiled_mma.idesc_.a_format_ = uint8_t(params.runtime_data_type_a) & 0b111;
      tiled_mma.idesc_.b_format_ = uint8_t(params.runtime_data_type_b) & 0b111;
    }

    return cute::make_tuple(tiled_mma, tCrA, tCrB);
  }

  /// Perform a collective-scoped matrix multiply-accumulate
  /// Producer Perspective
  template <
    class KTileCount,
    class GTensorPartitionedA,
    class STensorA,
    class TileCoordMNKL,
    class KTileIterator
  >
  CUTLASS_DEVICE auto
  load_tma(
    MainloopPipelineTMA mainloop_pipeline,
    MainloopPipelineTMAState mainloop_pipe_producer_state,
    cute::tuple<KTileCount, 
                GTensorPartitionedA,
                STensorA,
                uint16_t> const& load_inputs,
    TileCoordMNKL const& cta_coord_mnkl,
    KTileIterator k_tile_iter, int k_tile_count) {
    
    // Unpack from load_inputs
    KTileCount k_tiles = get<0>(load_inputs);
    GTensorPartitionedA tAgA_mkl = get<1>(load_inputs);
    STensorA tAsA = get<2>(load_inputs);
    uint16_t mcast_mask_a = get<3>(load_inputs);

    // slice out the work coord from partitioned tensors
    Tensor tAgA = tAgA_mkl(_, get<0>(cta_coord_mnkl) / size(typename TiledMma::AtomThrID{}), _, get<3>(cta_coord_mnkl));

    auto barrier_token = mainloop_pipeline.producer_try_acquire(mainloop_pipe_producer_state);

    // Issue the Mainloop loads
    CUTLASS_PRAGMA_NO_UNROLL
    while (k_tile_count > 0) {
      // LOCK mainloop_pipe_producer_state for _writing_
      mainloop_pipeline.producer_acquire(mainloop_pipe_producer_state, barrier_token);

      using BarrierType = typename MainloopPipelineTMA::ProducerBarrierType;
      BarrierType* tma_barrier = mainloop_pipeline.producer_get_barrier(mainloop_pipe_producer_state);

      int write_stage = mainloop_pipe_producer_state.index();
      ++mainloop_pipe_producer_state;
      barrier_token = mainloop_pipeline.producer_try_acquire(mainloop_pipe_producer_state);

      if (cute::elect_one_sync()) {
        copy(observed_tma_load_a_->with(*tma_barrier, mcast_mask_a), tAgA(_,*k_tile_iter), tAsA(_,write_stage));
      }

      --k_tile_count;
      ++k_tile_iter;
    }

    return cute::make_tuple(mainloop_pipe_producer_state, k_tile_iter);
  }


  template <
    // class GTensorB,
    // class CTensorB,
    // class STensorB,
    // class ProblemShape_MNKL,
    // class TiledCopyB,
    // class ThreadCopyB,
    class TileCoordMNKL,
    class KTileIterator,
    class ProblemShape_MNKL,
    class... TParams
  >
  CUTLASS_DEVICE auto
  load_cpasync(
    Params const& params,
    MainloopPipelineCpAsync mainloop_pipeline,
    MainloopPipelineCpAsyncState mainloop_pipe_producer_state,
    cute::tuple<TParams...> const& load_inputs,
    TileCoordMNKL const& cta_coord_mnkl,
    KTileIterator k_tile_iter, int k_tile_count,
    ProblemShape_MNKL effective_shape
  ) {

    auto [
      tBgB_nkl, cgB_nk, sB, 
      // problem_shape_MNKL, 
      gmem_to_smem_b_tiled_copy, thr_copy_b] = load_inputs;

    auto [M,N,K,L] = effective_shape;

    auto peer_cta_idx = get<0>(cta_coord_mnkl) % size(AtomThrShapeMNK{});

    // Slice out the work coord from partitioned tensors
    Tensor gB_in = tBgB_nkl(_, peer_cta_idx, _, get<1>(cta_coord_mnkl), _, get<3>(cta_coord_mnkl));
    Tensor cgB_nk_in = cgB_nk(_, peer_cta_idx, _, get<1>(cta_coord_mnkl), _);

    auto k_residue    = K - size<1>(gB_in) * size<2>(gB_in);  // K - BLK_K * k is negative

    // Repeat slicing out coordinate tensor exactly the same as input tensor does
    Tensor gB = gB_in;
    Tensor cB = cgB_nk_in;

    auto tBgB = thr_copy_b.partition_S(gB);
    auto tBsB = thr_copy_b.partition_D(sB);

    // Allocate predicate tensors for n
    Tensor tBpB = make_tensor<bool>(make_shape(size<1>(tBsB), size<2>(tBsB)), Stride<_1,_0>{});
    Tensor tBcB_nk = thr_copy_b.partition_S(cgB_nk_in);
    Tensor tBcB = thr_copy_b.partition_S(cB);

    // Copy gmem to smem for *k_tile_iter, predicating for k residue
    Tensor tBgBk = tBgB(_,_,_,*k_tile_iter);

    // Repeating on predicators with the same operations on tBgB
    Tensor tBcBk = tBcB(_,_,_,*k_tile_iter);

    // Set predicates for n bounds
    CUTLASS_PRAGMA_UNROLL
    for (int n = 0; n < size<0>(tBpB); ++n) {
      tBpB(n,0) = elem_less(get<0>(tBcBk(0,n,0)), N);  // blk_n coord < N
    }

    // we will process the last tile after the mainloop
    if (k_residue != 0) {
      --k_tile_count;
    }

    // Issue the Mainloop loads
    CUTLASS_PRAGMA_NO_UNROLL
    while (k_tile_count > 0) {

      mainloop_pipeline.producer_acquire(mainloop_pipe_producer_state);
      int write_stage = mainloop_pipe_producer_state.index();

      copy_if(gmem_to_smem_b_tiled_copy, tBpB, tBgB(_,_,_,*k_tile_iter), tBsB(_,_,_,write_stage));

      mainloop_pipeline.producer_commit_local(mainloop_pipe_producer_state, cutlass::arch::cpasync_barrier_arrive);
      --k_tile_count;
      ++k_tile_iter;
      ++mainloop_pipe_producer_state;
    }
    // last tile with predication on k to account for residue
    // For performance consideration,
    // this predicated block for K-tail is only activated when there is k-residue
    if (k_residue != 0)  {
      // LOCK mainloop_pipe_producer_state for _writing_
      mainloop_pipeline.producer_acquire(mainloop_pipe_producer_state);
      int write_stage = mainloop_pipe_producer_state.index();

      CUTLASS_PRAGMA_UNROLL
      for (int k = 0; k < size<2>(tBsB); ++k) {
        if (int(get<1>(tBcBk(0,0,k))) >= 0) {      // blk_k coord < K
          copy_if(gmem_to_smem_b_tiled_copy, tBpB(_,k), tBgB(_,_,k,*k_tile_iter), tBsB(_,_,k,write_stage));
        }
        else {
          clear(tBsB(_,_,k,write_stage));
        }
      }
      ++k_tile_iter;
      --k_tile_count;

      // UNLOCK mainloop_pipe_producer_state
      mainloop_pipeline.producer_commit_local(mainloop_pipe_producer_state, cutlass::arch::cpasync_barrier_arrive);

      // Advance mainloop_pipe_producer_state
      ++mainloop_pipe_producer_state;
    }

    return cute::make_tuple(mainloop_pipe_producer_state, k_tile_iter);
  }

  /// Perform a Producer Epilogue to prevent early exit of ctas in a Cluster
  CUTLASS_DEVICE void
  load_tail_tma(MainloopPipelineTMA mainloop_pipeline, MainloopPipelineTMAState mainloop_pipe_producer_state) {
    mainloop_pipeline.producer_tail(mainloop_pipe_producer_state);
  }
  CUTLASS_DEVICE void
  load_tail_cpasync(MainloopPipelineCpAsync mainloop_pipeline, MainloopPipelineCpAsyncState mainloop_pipe_producer_state) {
    mainloop_pipeline.producer_tail(mainloop_pipe_producer_state);
  }

  /// Perform a collective-scoped matrix multiply-accumulate
  /// Consumer Perspective
  template <
    class AccumulatorPipeline,
    class FrgEngine, class FrgLayout,
    class FragmentA, class FragmentB,
    class CtaTileCoord
  >
  CUTLASS_DEVICE auto
  mma(cute::tuple<MainloopPipelineTMA,
                  MainloopPipelineCpAsync,
                  AccumulatorPipeline> pipelines,
      cute::tuple<MainloopPipelineTMAState,
                  MainloopPipelineCpAsyncState,
                  typename AccumulatorPipeline::PipelineState> pipeline_states,
      cute::tuple<cute::Tensor<FrgEngine, FrgLayout>> const& accumulators_pair,
      cute::tuple<TiledMma, FragmentA, FragmentB> const& mma_inputs,
      CtaTileCoord cta_tile_coord,
      int k_tile_count,
      bool is_mma_leader_cta,
      uint32_t mma_peer_cta_rank,
      arch::ClusterBarrier& mma_trampoline_barrier,
      uint32_t mma_trampoline_barrier_phase
  ) {
    static_assert(is_tmem<FrgEngine>::value, "Accumulator must be tmem resident.");
    static_assert(rank(FrgLayout{}) == 3, "Accumulator must be MMA-partitioned: (MMA, MMA_M, MMA_N)");
    auto accumulators = get<0>(accumulators_pair);
    auto [tiled_mma, tCrA, tCrB] = mma_inputs;

    auto [mainloop_pipeline_tma, mainloop_pipeline_cpasync, accumulator_pipeline] = pipelines;
    auto [mainloop_pipe_tma_consumer_state, mainloop_pipe_cpasync_consumer_state, accumulator_pipe_producer_state] = pipeline_states;

    constexpr bool is_2sm = size(AtomThrShapeMNK{}) > 1;

    //
    // PIPELINED MAIN LOOP
    //
    tiled_mma.accumulate_ = UMMA::ScaleOut::Zero;
    // Wait for tmem accumulator buffer to become empty with a flipped phase
    if (is_mma_leader_cta) {
      accumulator_pipeline.producer_acquire(accumulator_pipe_producer_state);
    }

    CUTLASS_PRAGMA_NO_UNROLL
    while (k_tile_count > 0) {
      if (is_mma_leader_cta) {
        mainloop_pipeline_tma.consumer_wait(mainloop_pipe_tma_consumer_state);
      }
      mainloop_pipeline_cpasync.consumer_wait(mainloop_pipe_cpasync_consumer_state);

      int read_stage_tma = mainloop_pipe_tma_consumer_state.index();
      int read_stage_cpasync = mainloop_pipe_cpasync_consumer_state.index();

      if (is_mma_leader_cta) {
        if constexpr (is_2sm) {
          mma_trampoline_barrier.wait(mma_trampoline_barrier_phase);
        }
        CUTLASS_PRAGMA_UNROLL
        for (int k_block = 0; k_block < size<2>(tCrA); ++k_block) {
          // (V,M) x (V,N) => (V,M,N)
          cute::gemm(tiled_mma, tCrA(_,_,k_block,read_stage_tma), tCrB(_,_,k_block,read_stage_cpasync), accumulators);
          tiled_mma.accumulate_ = UMMA::ScaleOut::One;
        }
      } else {
        if constexpr (is_2sm) {
          mma_trampoline_barrier.arrive(mma_peer_cta_rank);
        }
      }

      if constexpr (is_2sm) {
        if (is_mma_leader_cta) {
          mma_trampoline_barrier.arrive(mma_peer_cta_rank);
        } else {
          mma_trampoline_barrier.wait(mma_trampoline_barrier_phase);
        }
      }

      if (is_mma_leader_cta) {
        mainloop_pipeline_tma.consumer_release(mainloop_pipe_tma_consumer_state);
        mainloop_pipeline_cpasync.consumer_release(mainloop_pipe_cpasync_consumer_state);
      }
      --k_tile_count;
      ++mainloop_pipe_tma_consumer_state;
      ++mainloop_pipe_cpasync_consumer_state;

      mma_trampoline_barrier_phase ^= 1;
    }

    return cute::make_tuple(mainloop_pipe_tma_consumer_state, mainloop_pipe_cpasync_consumer_state, mma_trampoline_barrier_phase);
  }

protected:

  typename Params::TMA_A const* observed_tma_load_a_{nullptr};
  RuntimeDataTypeA runtime_data_type_a_{};
  RuntimeDataTypeB runtime_data_type_b_{};
};

/////////////////////////////////////////////////////////////////////////////////////////////////

} // namespace cutlass::gemm::collective

/////////////////////////////////////////////////////////////////////////////////////////////////
