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#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/gemm/dispatch_policy.hpp"
#include "cutlass/numeric_types.h"
#include "cutlass/pipeline/pipeline.hpp"
#include "cutlass/trace.h"

#include "cute/arch/cluster_sm90.hpp"
#include "cute/arch/copy_sm90.hpp"
#include "cute/algorithm/functional.hpp"
#include "cute/atom/mma_atom.hpp"
#include "cute/algorithm/gemm.hpp"
#include "cute/tensor_predicate.hpp"
#include "cute/numeric/arithmetic_tuple.hpp"

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

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

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

// WarpSpecialized Mainloop
template <
  int Stages,
  class ClusterShape,
  class KernelSchedule,
  class TileShape_,
  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<
    MainloopSm90ArrayTmaGmmaWarpSpecialized<Stages, ClusterShape, KernelSchedule>,
    TileShape_,
    ElementA_,
    StrideA_,
    ElementB_,
    StrideB_,
    TiledMma_,
    GmemTiledCopyA_,
    SmemLayoutAtomA_,
    SmemCopyAtomA_,
    TransformA_,
    GmemTiledCopyB_,
    SmemLayoutAtomB_,
    SmemCopyAtomB_,
    TransformB_>
{
  //
  // Type Aliases
  //
  using DispatchPolicy = MainloopSm90ArrayTmaGmmaWarpSpecialized<Stages, ClusterShape, KernelSchedule>;
  using TileShape = TileShape_;
  using ElementA = ElementA_;
  using StrideA = StrideA_;
  using UnderlyingStrideA = cute::remove_pointer_t<StrideA>;
  using ElementB = ElementB_;
  using StrideB = StrideB_;
  using UnderlyingStrideB = cute::remove_pointer_t<StrideB>;
  using TiledMma = TiledMma_;
  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 MainloopPipeline = cutlass::PipelineTmaAsync<DispatchPolicy::Stages>;
  using PipelineState = cutlass::PipelineState<DispatchPolicy::Stages>;

  using PipelineParams = typename MainloopPipeline::Params;
  using CtaShape_MNK = decltype(shape_div(TileShape{}, ClusterShape{}));
  static_assert(rank(SmemLayoutAtomA{}) == 2, "SmemLayoutAtom must be rank 2 (M/N, K)");
  static_assert((size<0>(TileShape{}) % size<0>(SmemLayoutAtomA{})) == 0, "SmemLayoutAtom must evenly divide tile shape.");
  static_assert((size<2>(TileShape{}) % size<1>(SmemLayoutAtomA{})) == 0, "SmemLayoutAtom must evenly divide tile shape.");

  static_assert(rank(SmemLayoutAtomB{}) == 2, "SmemLayoutAtom must be rank 2 (M/N, K)");
  static_assert((size<1>(TileShape{}) % size<0>(SmemLayoutAtomB{})) == 0, "SmemLayoutAtom must evenly divide tile shape.");
  static_assert((size<2>(TileShape{}) % size<1>(SmemLayoutAtomB{})) == 0, "SmemLayoutAtom must evenly divide tile shape.");

  // Tile along modes in a way that maximizes the TMA box size.
  using SmemLayoutA = decltype(tile_to_shape(
      SmemLayoutAtomA{},
      make_shape(shape<0>(TileShape{}), shape<2>(TileShape{}), Int<DispatchPolicy::Stages>{}),
      cute::conditional_t< ::cutlass::gemm::detail::is_major<0,StrideA>(), Step<_2,_1,_3>, Step<_1,_2,_3>>{}));
  using SmemLayoutB = decltype(tile_to_shape(
      SmemLayoutAtomB{},
      make_shape(shape<1>(TileShape{}), shape<2>(TileShape{}), Int<DispatchPolicy::Stages>{}),
      cute::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 2 or more.");
  static_assert(cute::is_base_of<cute::GMMA::DescriptorIterator, typename TiledMma::FrgTypeA>::value &&
                cute::is_base_of<cute::GMMA::DescriptorIterator, typename TiledMma::FrgTypeB>::value,
                "MMA atom must source both A and B operand from smem_desc for this mainloop.");
  static_assert(cute::is_same_v<GmemTiledCopyA, SM90_TMA_LOAD> || cute::is_same_v<GmemTiledCopyA, SM90_TMA_LOAD_MULTICAST>,
      "GmemTiledCopy - invalid SM90 TMA copy atom specified.");
  static_assert(cute::is_same_v<GmemTiledCopyB, SM90_TMA_LOAD> || cute::is_same_v<GmemTiledCopyB, SM90_TMA_LOAD_MULTICAST>,
      "GmemTiledCopy - invalid SM90 TMA copy atom specified.");

  // TMA converts f32 input to tf32 when copying from GMEM to SMEM
  // For all other types, cast to size equivalent uint type to avoid any rounding by TMA.
  static constexpr bool ConvertF32toTF32A = cute::is_same_v<float, ElementA>;
  static constexpr bool ConvertF32toTF32B = cute::is_same_v<float, ElementB>;
  using InternalElementA = cute::conditional_t<ConvertF32toTF32A, tfloat32_t, uint_bit_t<sizeof_bits_v<ElementA>>>;
  using InternalElementB = cute::conditional_t<ConvertF32toTF32B, tfloat32_t, uint_bit_t<sizeof_bits_v<ElementB>>>;

  // Assumption: StrideA is congruent with Problem_MK
  using TMA_A = decltype(make_tma_copy(
      GmemTiledCopyA{},
      make_tensor(static_cast<InternalElementA const*>(nullptr), repeat_like(UnderlyingStrideA{}, int32_t(0)), UnderlyingStrideA{}),
      SmemLayoutA{}(_,_,cute::Int<0>{}),
      make_shape(shape<0>(TileShape{}), shape<2>(TileShape{})),
      size<1>(ClusterShape{})));  // mcast along N mode for this M load, if any
  // Assumption: StrideB is congruent with Problem_NK
  using TMA_B = decltype(make_tma_copy(
      GmemTiledCopyB{},
      make_tensor(static_cast<InternalElementB const*>(nullptr), repeat_like(UnderlyingStrideB{}, int32_t(0)), UnderlyingStrideB{}),
      SmemLayoutB{}(_,_,cute::Int<0>{}),
      make_shape(shape<1>(TileShape{}), shape<2>(TileShape{})),
      size<0>(ClusterShape{}))); // mcast along M mode for this N load, if any

  struct SharedStorage {
    struct TensorStorage : cute::aligned_struct<128> {
      cute::array_aligned<typename TiledMma::ValTypeA, cute::cosize_v<SmemLayoutA>> smem_A;
      cute::array_aligned<typename TiledMma::ValTypeB, cute::cosize_v<SmemLayoutB>> smem_B;
    } tensors;

    struct TensorMapStorage : cute::aligned_struct<128> {
      cute::TmaDescriptor smem_tensormap_A;
      cute::TmaDescriptor smem_tensormap_B;
    } tensormaps;

    using PipelineStorage = typename MainloopPipeline::SharedStorage;
    PipelineStorage pipeline;
  };
  using TensorStorage = typename SharedStorage::TensorStorage;
  using TensorMapStorage = typename SharedStorage::TensorMapStorage;
  using PipelineStorage = typename SharedStorage::PipelineStorage;

  static constexpr bool IsGroupedGemmKernel = !cute::is_same_v<UnderlyingStrideA, StrideA>;

  // Host side kernel arguments
  struct Arguments {
    ElementA const** ptr_A;
    StrideA dA;
    ElementB const** ptr_B;
    StrideB dB;
  };

  // Device side kernel params
  struct Params {
    TMA_A tma_load_a;
    TMA_B tma_load_b;
    void* tensormaps;
    InternalElementA const** ptr_A;
    StrideA dA;
    InternalElementB const** ptr_B;
    StrideB dB;
  };

  //
  // Methods
  //

  template <class ProblemShape>
  static constexpr Params
  to_underlying_arguments(
      ProblemShape problem_shapes,
      Arguments const& args,
      void* workspace) {
    // These tensor shapes (only applicable for grouped gemm) and pointers are only used to create tensormap/tma desc.
    // These will be replaced with correct values before the initial tma load.
    auto init_shape = repeat_like(typename ProblemShape::UnderlyingProblemShape{}, int32_t(1));
    auto init_M = get<0>(init_shape);
    auto init_N = get<1>(init_shape);
    auto init_K = get<2>(init_shape);
    // Batches/Groups are managed by using appropriate pointers to input matrices
    const uint32_t mock_L = 1;
    InternalElementA const* ptr_A_first_batch = reinterpret_cast<InternalElementA const*>(args.ptr_A);
    InternalElementB const* ptr_B_first_batch = reinterpret_cast<InternalElementA const*>(args.ptr_B);

    UnderlyingStrideA stride_a;
    UnderlyingStrideB stride_b;
    if constexpr (IsGroupedGemmKernel) {
      // Strides for Grouped Gemm will be replaced prior to the first access regardless.
      stride_a = UnderlyingStrideA{};
      stride_b = UnderlyingStrideB{};
    }
    else {
      // Tensor shapes for Ptr-Array are initialized correctly only here.
      auto problem_shape_MNK = problem_shapes.get_host_problem_shape(0);
      init_M = get<0>(problem_shape_MNK);
      init_N = get<1>(problem_shape_MNK);
      init_K = get<2>(problem_shape_MNK);

      stride_a = args.dA;
      stride_b = args.dB;
    }
    Tensor tensor_a = make_tensor(ptr_A_first_batch, make_layout(make_shape(init_M,init_K,mock_L), stride_a));
    Tensor tensor_b = make_tensor(ptr_B_first_batch, make_layout(make_shape(init_N,init_K,mock_L), stride_b));
    TMA_A tma_load_a = make_tma_copy(
        GmemTiledCopyA{},
        tensor_a,
        SmemLayoutA{}(_,_,cute::Int<0>{}),
        make_shape(shape<0>(TileShape{}), shape<2>(TileShape{})),
        size<1>(ClusterShape{})); // mcast along N mode for this M load, if any
    TMA_B tma_load_b = make_tma_copy(
        GmemTiledCopyB{},
        tensor_b,
        SmemLayoutB{}(_,_,cute::Int<0>{}),
        make_shape(shape<1>(TileShape{}), shape<2>(TileShape{})),
        size<0>(ClusterShape{})); // mcast along M mode for this N load, if any

    void* tensormaps = workspace;

    return {
      tma_load_a,
      tma_load_b,
      tensormaps,
      reinterpret_cast<InternalElementA const**>(args.ptr_A),
      args.dA,
      reinterpret_cast<InternalElementB const**>(args.ptr_B),
      args.dB
    };
  }

  template <class ProblemShape>
  static size_t
  get_workspace_size(ProblemShape const& problem_shape, Arguments const& args, int sm_count) {
    constexpr uint32_t NumInputTensors = 2;
    constexpr size_t SizeOfCuTensorMap = sizeof(cute::TmaDescriptor);
    // Allocate gmem space for input tensormaps per each SM, A tensormap copies followed by B tensormap copies
    return (NumInputTensors * SizeOfCuTensorMap * sm_count);
  }

  template <class ProblemShape>
  static cutlass::Status
  initialize_workspace(ProblemShape const& problem_shape, Arguments const& args, void* workspace, cudaStream_t stream) {
    return cutlass::Status::kSuccess;
  }

  template<class ProblemShape>
  CUTLASS_HOST_DEVICE static bool
  can_implement(
      ProblemShape problem_shapes,
      Arguments const& args) {
    constexpr int tma_alignment_bits = 128;
    constexpr int min_tma_aligned_elements_A = tma_alignment_bits / cutlass::sizeof_bits<ElementA>::value;
    constexpr int min_tma_aligned_elements_B = tma_alignment_bits / cutlass::sizeof_bits<ElementB>::value;

    bool implementable = true;
    if (problem_shapes.is_host_problem_shape_available()) {
      // Check alignment for all problem sizes
      for (int i = 0; i < problem_shapes.groups(); i++) {
        auto problem_shape_MNKL = append<4>(problem_shapes.get_host_problem_shape(i), 1);
        auto [M,N,K,L] = problem_shape_MNKL;
        implementable = implementable && cutlass::detail::check_alignment<min_tma_aligned_elements_A>(cute::make_shape(M,K,L), UnderlyingStrideA{});
        implementable = implementable && cutlass::detail::check_alignment<min_tma_aligned_elements_B>(cute::make_shape(N,K,L), UnderlyingStrideB{});
      }
    }

    if (!implementable) {
      CUTLASS_TRACE_HOST("  CAN IMPLEMENT: Problem Size doesn't meet the minimum alignment requirements for TMA.\n");
    }
    return implementable;
  }

  static constexpr int K_PIPE_MAX = DispatchPolicy::Stages;
  static constexpr int K_PIPE_MMAS = 1;
  static constexpr uint32_t TmaTransactionBytes =
        cutlass::bits_to_bytes(size<0>(SmemLayoutA{}) * size<1>(SmemLayoutA{}) * static_cast<uint32_t>(sizeof_bits<ElementA>::value))+
        cutlass::bits_to_bytes(size<0>(SmemLayoutB{}) * size<1>(SmemLayoutB{}) * static_cast<uint32_t>(sizeof_bits<ElementB>::value));

  // Set up the data needed by this collective for load and mma.
  // Returns a tuple of tensors. The collective and the kernel layer have the contract that the
  // returned tuple must contain at least two elements, with the first two elements being:
  // gA_mkl - The tma tensor, A after a local tile so it has shape  (BLK_M,BLK_K,m,k,l)
  // gB_nkl - The tma tensor, B after a local tile so it has shape  (BLK_N,BLK_K,n,k,l)
  // The rest of the tensors can be specified as needed by this collective.
  template <class ProblemShape_MNKL>
  CUTLASS_DEVICE auto
  load_init(ProblemShape_MNKL const& problem_shape_MNKL, Params const& mainloop_params) const {
    using X = Underscore;
    // Separate out problem shape for convenience
    auto [M,N,K,L] = problem_shape_MNKL;
    const int32_t mock_L = 1;

    // TMA requires special handling of strides to deal with coord codomain mapping
    // Represent the full tensors -- get these from TMA
    Tensor mA_mkl = mainloop_params.tma_load_a.get_tma_tensor(make_shape(M,K,mock_L));                            // (m,k,l)
    Tensor mB_nkl = mainloop_params.tma_load_b.get_tma_tensor(make_shape(N,K,mock_L));                            // (n,k,l)

    // Make tiled views, defer the slice
    Tensor gA_mkl = local_tile(mA_mkl, TileShape{}, make_coord(_,_,_), Step<_1, X,_1>{});  // (BLK_M,BLK_K,m,k,l)
    Tensor gB_nkl = local_tile(mB_nkl, TileShape{}, make_coord(_,_,_), Step< X,_1,_1>{});  // (BLK_N,BLK_K,n,k,l)

    return cute::make_tuple(gA_mkl, gB_nkl);
  }

  // Perform a collective-scoped matrix multiply-accumulate
  // Producer Perspective
  template <
    class TensorA, class TensorB,
    class TensorMapA, class TensorMapB,
    class KTileIterator, class BlockCoord
  >
  CUTLASS_DEVICE void
  load(
      Params const& mainloop_params,
      MainloopPipeline pipeline, 
      PipelineState smem_pipe_write,
      cute::tuple<TensorA, TensorB> const& load_inputs,
      cute::tuple<TensorMapA, TensorMapB> const& input_tensormaps,
      BlockCoord const& blk_coord,
      KTileIterator k_tile_iter, int k_tile_count,
      int thread_idx,
      uint32_t block_rank_in_cluster,
      TensorStorage& shared_tensors) {
    int lane_predicate = cute::elect_one_sync();

    if (lane_predicate) {
      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()), SmemLayoutB{});        // (BLK_N,BLK_K,PIPE)

      //
      // Prepare the TMA loads for A and B
      //

      constexpr uint32_t cluster_shape_x = get<0>(DispatchPolicy::ClusterShape());
      uint2 cluster_local_block_id = {block_rank_in_cluster % cluster_shape_x, block_rank_in_cluster / cluster_shape_x};

      Tensor gA_mkl = get<0>(load_inputs);
      Tensor gB_nkl = get<1>(load_inputs);

      auto block_tma_a = mainloop_params.tma_load_a.get_slice(cluster_local_block_id.y);
      auto block_tma_b = mainloop_params.tma_load_b.get_slice(cluster_local_block_id.x);

      // Partition the inputs based on the current block coordinates.
      auto [m_coord, n_coord, k_coord, l_coord] = blk_coord;
      Tensor gA = gA_mkl(_,_,m_coord,_,l_coord);                                                     // (BLK_M,BLK_K,k)
      Tensor gB = gB_nkl(_,_,n_coord,_,l_coord);                                                     // (BLK_N,BLK_K,k)

      // Applies the mapping from block_tma_a
      Tensor tAgA = block_tma_a.partition_S(gA);                                                 // (TMA,TMA_M,TMA_K,k)
      Tensor tAsA = block_tma_a.partition_D(sA);                                              // (TMA,TMA_M,TMA_K,PIPE)

      Tensor tBgB = block_tma_b.partition_S(gB);                                                 // (TMA,TMA_N,TMA_K,k)
      Tensor tBsB = block_tma_b.partition_D(sB);                                              // (TMA,TMA_N,TMA_K,PIPE)

      uint16_t mcast_mask_a = 0;
      uint16_t mcast_mask_b = 0;

      // Issue TmaLoads
      // Maps the tile -> block, value
      if constexpr (cute::is_same_v<GmemTiledCopyA, SM90_TMA_LOAD_MULTICAST>) {
        auto block_layout = Layout<typename DispatchPolicy::ClusterShape>{}; // (m,n) -> block_id
        for (int n = 0; n < size<1>(block_layout); ++n) {
          mcast_mask_a |= (uint16_t(1) << block_layout(cluster_local_block_id.x,n,Int<0>{}));
        }
      }

      if constexpr (cute::is_same_v<GmemTiledCopyB, SM90_TMA_LOAD_MULTICAST>) {
        auto block_layout = Layout<typename DispatchPolicy::ClusterShape>{}; // (m,n) -> block_id
        for (int m = 0; m < size<0>(block_layout); ++m) {
          mcast_mask_b |= (uint16_t(1) << block_layout(m,cluster_local_block_id.y,Int<0>{}));
        }
      }

      // Mainloop
      CUTLASS_PRAGMA_NO_UNROLL
      for ( ; k_tile_count > 0; --k_tile_count)
      {
        // LOCK smem_pipe_write for _writing_
        pipeline.producer_acquire(smem_pipe_write);

        //
        // Copy gmem to smem for *k_tile_iter
        //

        using BarrierType = typename MainloopPipeline::ProducerBarrierType;
        BarrierType* tma_barrier = pipeline.producer_get_barrier(smem_pipe_write);

        int write_stage = smem_pipe_write.index();
        copy(mainloop_params.tma_load_a.with(get<0>(input_tensormaps), *tma_barrier, mcast_mask_a), tAgA(_,_,_,*k_tile_iter), tAsA(_,_,_,write_stage));
        copy(mainloop_params.tma_load_b.with(get<1>(input_tensormaps), *tma_barrier, mcast_mask_b), tBgB(_,_,_,*k_tile_iter), tBsB(_,_,_,write_stage));
        ++k_tile_iter;

        // Advance smem_pipe_write
        ++smem_pipe_write;
      }
    }
  }

  // Perform a Producer Epilogue to prevent early exit of blocks in a Cluster
  CUTLASS_DEVICE void
  load_tail(MainloopPipeline pipeline, PipelineState smem_pipe_write) {
    int lane_predicate = cute::elect_one_sync();

    // Issue the epilogue waits
    if (lane_predicate) {
      // This helps avoid early exit of blocks in Cluster.
      // Waits for all stages to either be released (all 
      // Consumer UNLOCKs), or if the stage was never used
      // then it would just be acquired since the phase was 
      // still inverted from make_producer_start_state.
      pipeline.producer_tail(smem_pipe_write);
    }
  }

  /// Perform a collective-scoped matrix multiply-accumulate
  /// Consumer Perspective
  template <
    class FrgTensorC
  >
  CUTLASS_DEVICE void
  mma(MainloopPipeline pipeline,
      PipelineState smem_pipe_read,
      FrgTensorC& accum,
      int k_tile_count,
      int thread_idx,
      TensorStorage& shared_tensors,
      Params const& mainloop_params) {
    static_assert(is_rmem<FrgTensorC>::value, "C tensor must be rmem resident.");
    static_assert(rank(SmemLayoutA{}) == 3, "Smem layout must be rank 3.");
    static_assert(rank(SmemLayoutB{}) == 3, "Smem layout must be rank 3.");
    static_assert(cute::is_void_v<SmemCopyAtomA>,
      "SM90 GMMA mainloops cannot have a non-void copy atom for smem sourced instructions.");
    static_assert(cute::is_void_v<SmemCopyAtomB>,
      "SM90 GMMA mainloops cannot have a non-void copy atom for smem sourced instructions.");

    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()), SmemLayoutB{});          // (BLK_N,BLK_K,PIPE)

    //
    // Define C accumulators and A/B partitioning
    //

    TiledMma tiled_mma;
    auto thread_mma = tiled_mma.get_thread_slice(thread_idx);

    Tensor tCsA = thread_mma.partition_A(sA);                                                 // (MMA,MMA_M,MMA_K,PIPE)
    Tensor tCsB = thread_mma.partition_B(sB);                                                 // (MMA,MMA_N,MMA_K,PIPE)

    // Allocate "fragments/descriptors"
    Tensor tCrA = thread_mma.make_fragment_A(tCsA);                                           // (MMA,MMA_M,MMA_K,PIPE)
    Tensor tCrB = thread_mma.make_fragment_B(tCsB);                                           // (MMA,MMA_N,MMA_K,PIPE)

    CUTE_STATIC_ASSERT_V(size<1>(tCsA) == size<1>(accum));                                                         // M
    CUTE_STATIC_ASSERT_V(size<1>(tCsB) == size<2>(accum));                                                         // N
    CUTE_STATIC_ASSERT_V(size<2>(tCsA) == size<2>(tCsB));                                                          // K
    CUTE_STATIC_ASSERT_V(size<3>(tCsA) == size<3>(tCsB));                                                       // PIPE
    CUTE_STATIC_ASSERT_V(Int<DispatchPolicy::Stages>{} == size<2>(sA));                                         // PIPE
    CUTE_STATIC_ASSERT_V(Int<DispatchPolicy::Stages>{} == size<2>(sB));                                         // PIPE

    //
    // PIPELINED MAIN LOOP
    //
    static_assert((0 <= K_PIPE_MMAS) && (K_PIPE_MMAS <  K_PIPE_MAX),
        "ERROR : Incorrect number of MMAs in flight");

    // We release buffers to producer warps(dma load) with some mmas in flight
    PipelineState smem_pipe_release = smem_pipe_read;

    // Prologue GMMAs
    int prologue_mma_count = min(K_PIPE_MMAS, k_tile_count);

    tiled_mma.accumulate_ = GMMA::ScaleOut::Zero;

    warpgroup_fence_operand(accum);
    CUTLASS_PRAGMA_UNROLL
    for (int k_tile_prologue = prologue_mma_count; k_tile_prologue > 0; --k_tile_prologue)
    {
      // WAIT on smem_pipe_read until its data are available (phase bit flips from rdPhaseBit value)
      auto barrier_token = pipeline.consumer_try_wait(smem_pipe_read);
      pipeline.consumer_wait(smem_pipe_read, barrier_token);

      int read_stage = smem_pipe_read.index();
      warpgroup_arrive();
      // Unroll the K mode manually to set scale D to 1
      CUTLASS_PRAGMA_UNROLL
      for (int k_block = 0; k_block < size<2>(tCrA); ++k_block) {
        // (V,M,K) x (V,N,K) => (V,M,N)
        cute::gemm(tiled_mma, tCrA(_,_,k_block,read_stage), tCrB(_,_,k_block,read_stage), accum);
        tiled_mma.accumulate_ = GMMA::ScaleOut::One;
      }

      warpgroup_commit_batch();

      ++smem_pipe_read;
    }

    warpgroup_fence_operand(accum);
    // Mainloop GMMAs
    k_tile_count -= prologue_mma_count;

    CUTLASS_PRAGMA_NO_UNROLL
    for ( ; k_tile_count > 0; --k_tile_count)
    {
      // WAIT on smem_pipe_read until its data are available (phase bit flips from rdPhaseBit value)
      auto barrier_token = pipeline.consumer_try_wait(smem_pipe_read);
      pipeline.consumer_wait(smem_pipe_read, barrier_token);

      //
      // Compute on k_tile
      //

      int read_stage = smem_pipe_read.index();
      warpgroup_fence_operand(accum);
      warpgroup_arrive();
      // Unroll the K mode manually to set scale D to 1
      CUTLASS_PRAGMA_UNROLL
      for (int k_block = 0; k_block < size<2>(tCrA); ++k_block) {
        // (V,M,K) x (V,N,K) => (V,M,N)
        cute::gemm(tiled_mma, tCrA(_,_,k_block,read_stage), tCrB(_,_,k_block,read_stage), accum);
        tiled_mma.accumulate_ = GMMA::ScaleOut::One;
      }
      warpgroup_commit_batch();

      /// Wait on the GMMA barrier for K_PIPE_MMAS (or fewer) outstanding to ensure smem_pipe_write is consumed
      warpgroup_wait<K_PIPE_MMAS>();
      warpgroup_fence_operand(accum);

      // UNLOCK smem_pipe_release, done _computing_ on it
      pipeline.consumer_release(smem_pipe_release);

      // Advance smem_pipe_read and smem_pipe_release
      ++smem_pipe_read;
      ++smem_pipe_release;
    }

    warpgroup_fence_operand(accum);
  }

  /// Perform a Consumer Epilogue to release all buffers
  CUTLASS_DEVICE void
  mma_tail(MainloopPipeline pipeline, PipelineState smem_pipe_release, int k_tile_count) {
    // Prologue GMMAs
    int prologue_mma_count = min(K_PIPE_MMAS, k_tile_count);
    k_tile_count -= prologue_mma_count;

    smem_pipe_release.advance(k_tile_count);
    
    // Wait on all GMMAs to complete
    warpgroup_wait<0>();

    for (int count = 0; count < prologue_mma_count; ++count) {
      pipeline.consumer_release(smem_pipe_release);                 // UNLOCK smem_pipe_release, done _computing_ on it
      ++smem_pipe_release;
    }
  }

  //
  // Methods to perform different parts of TMA/Tensormap modifications
  //

  CUTLASS_DEVICE auto
  tensormaps_init(Params const& mainloop_params, int32_t const sm_count, int32_t const sm_idx) const {
    cute::TmaDescriptor* gmem_tensormap = reinterpret_cast<cute::TmaDescriptor*>(mainloop_params.tensormaps);

    cute::TmaDescriptor* tma_desc_a = &gmem_tensormap[sm_idx];
    cute::TmaDescriptor* tma_desc_b = &gmem_tensormap[sm_idx + sm_count];

    if (cute::elect_one_sync()) {
      // Bringing tensormaps from params to gmem for modification later
      Tensor pA_tensormap = make_tensor(mainloop_params.tma_load_a.get_tma_descriptor(), Int<1>{}, Int<1>{});
      Tensor gA_tensormap = make_tensor(tma_desc_a, Int<1>{}, Int<1>{});
      Tensor pB_tensormap = make_tensor(mainloop_params.tma_load_b.get_tma_descriptor(), Int<1>{}, Int<1>{});
      Tensor gB_tensormap = make_tensor(tma_desc_b, Int<1>{}, Int<1>{});

      copy(recast<uint128_t>(pA_tensormap), recast<uint128_t>(gA_tensormap));
      copy(recast<uint128_t>(pB_tensormap), recast<uint128_t>(gB_tensormap));
    }

    return cute::make_tuple(tma_desc_a, tma_desc_b);
  }

  // Bringing tensormaps to smem (to be done by single thread)
  template <class TensorMapA, class TensorMapB>
  CUTLASS_DEVICE
  void
  tensormaps_fetch_to_smem(
      TensorMapStorage& shared_tensormap,
      cute::tuple<TensorMapA, TensorMapB> const& input_tensormaps) const {
    Tensor gA_tensormap = make_tensor(make_gmem_ptr(get<0>(input_tensormaps)), Int<1>{}, Int<1>{});
    Tensor sA_tensormap = make_tensor(make_smem_ptr(&shared_tensormap.smem_tensormap_A), Int<1>{}, Int<1>{});
    Tensor gB_tensormap = make_tensor(make_gmem_ptr(get<1>(input_tensormaps)), Int<1>{}, Int<1>{});
    Tensor sB_tensormap = make_tensor(make_smem_ptr(&shared_tensormap.smem_tensormap_B), Int<1>{}, Int<1>{});

    copy(recast<uint128_t>(gA_tensormap), recast<uint128_t>(sA_tensormap));
    copy(recast<uint128_t>(gB_tensormap), recast<uint128_t>(sB_tensormap));

    cp_async_fence();
    cp_async_wait<0>();
  }

  // Replace address for the global tensor (to be done by single thread)
  CUTLASS_DEVICE
  void
  tensormaps_replace_global_address(
      TensorMapStorage& shared_tensormap,
      Params const& mainloop_params,
      int32_t next_batch) {
    // Replacing global_address for the next batch
    cute::tma_descriptor_replace_addr_in_shared_mem(shared_tensormap.smem_tensormap_A,
                                                    mainloop_params.ptr_A[next_batch]);
    cute::tma_descriptor_replace_addr_in_shared_mem(shared_tensormap.smem_tensormap_B,
                                                    mainloop_params.ptr_B[next_batch]);
  }

  // Replace dim and strides for the global tensor - used only for Grouped GEMM (to be done by single thread)
  template <class ProblemShape_MNKL>
  CUTLASS_DEVICE
  void
  tensormaps_replace_global_tensor_properties(
      TensorMapStorage& shared_tensormap,
      Params const& mainloop_params,
      int32_t next_group,
      ProblemShape_MNKL problem_shape_mnkl) {
    const uint32_t M = get<0>(problem_shape_mnkl);
    const uint32_t N = get<1>(problem_shape_mnkl);
    const uint32_t K = get<2>(problem_shape_mnkl);
    // Only consider dimensions and strides that we need to recalculate and replace for each group
    constexpr int TensorRank = rank(ProblemShape_MNKL{}) - 1; // excluding either M or N
    static_assert(TensorRank == Int<3>{},
      "Descriptor modification for global dims & strides expects rank as 3.");
    cute::array<uint32_t, TensorRank> prob_shape_A  = {1,1,1};
    cute::array<uint64_t, TensorRank> prob_stride_A = {0,0,0};
    cute::array<uint32_t, TensorRank> prob_shape_B  = {1,1,1};
    cute::array<uint64_t, TensorRank> prob_stride_B = {0,0,0};

    InternalElementA const* ptr_A = nullptr;
    Tensor tensor_a = make_tensor(ptr_A, make_shape(M,K,Int<1>{}), mainloop_params.dA[next_group]);

    InternalElementB const* ptr_B = nullptr;
    Tensor tensor_b = make_tensor(ptr_B, make_shape(N,K,Int<1>{}), mainloop_params.dB[next_group]);

    cute::detail::fill_tma_gmem_shape_stride(mainloop_params.tma_load_a, tensor_a, 
                                             prob_shape_A, prob_stride_A);
    cute::detail::fill_tma_gmem_shape_stride(mainloop_params.tma_load_b, tensor_b, 
                                             prob_shape_B, prob_stride_B);

    // Convert strides to byte strides
    for (uint64_t& stride : prob_stride_A) {
      stride = (stride * sizeof_bits_v<InternalElementA>) / 8;
    }
    for (uint64_t& stride : prob_stride_B) {
      stride = (stride * sizeof_bits_v<InternalElementB>) / 8;
    }

    cute::tma_descriptor_replace_dims_strides_in_shared_mem(shared_tensormap.smem_tensormap_A,
                                                            prob_shape_A,
                                                            prob_stride_A);
    cute::tma_descriptor_replace_dims_strides_in_shared_mem(shared_tensormap.smem_tensormap_B,
                                                            prob_shape_B,
                                                            prob_stride_B);
  }

  template <class TensorMapA, class TensorMapB, class ProblemShape_MNKL>
  CUTLASS_DEVICE
  void
  tensormaps_perform_update(
      TensorMapStorage& shared_tensormap,
      Params const& mainloop_params,
      cute::tuple<TensorMapA, TensorMapB> const& input_tensormaps,
      ProblemShape_MNKL problem_shape_mnkl,
      int32_t next_batch) {
    if (cute::elect_one_sync()) {
      // Bringing tensormaps to smem
      tensormaps_fetch_to_smem(shared_tensormap, input_tensormaps);

      // Replacing global_address for the next batch
      tensormaps_replace_global_address(shared_tensormap, mainloop_params, next_batch);

      if constexpr (IsGroupedGemmKernel) {
        // Replacing global dims and strides for the next batch
        tensormaps_replace_global_tensor_properties(shared_tensormap,
          mainloop_params, next_batch, problem_shape_mnkl);
      }
    }
  }

  template <class TensorMapA, class TensorMapB>
  CUTLASS_DEVICE
  void
  tensormaps_cp_fence_release (
      TensorMapStorage& shared_tensormap,
      cute::tuple<TensorMapA, TensorMapB> const& input_tensormaps) {
    // Entire warp must do this (ie its aligned)
    tma_descriptor_cp_fence_release(get<0>(input_tensormaps), shared_tensormap.smem_tensormap_A);
    tma_descriptor_cp_fence_release(get<1>(input_tensormaps), shared_tensormap.smem_tensormap_B);
  }

  template <class TensorMapA, class TensorMapB>
  CUTLASS_DEVICE
  void
  tensormaps_fence_acquire(cute::tuple<TensorMapA, TensorMapB> const& input_tensormaps) {
    cute::tma_descriptor_fence_acquire(get<0>(input_tensormaps));
    cute::tma_descriptor_fence_acquire(get<1>(input_tensormaps));
  }


};

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

} // namespace cutlass::gemm::collective

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