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#pragma once

#include "cutlass/cutlass.h"
#include "cute/arch/cluster_sm90.hpp"
#include "cute/arch/copy_sm90.hpp"
#include "cutlass/gemm/dispatch_policy.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"
#include "cutlass/pipeline/pipeline.hpp"
#include "cutlass/trace.h"

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

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

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

// WarpSpecialized Mainloop
template <
  int Stages,
  class ClusterShape_,
  class TileShape_,
  class KernelSchedule,
  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<
    MainloopSm90CpAsyncGmmaWarpSpecialized<Stages,ClusterShape_,KernelSchedule>,
    TileShape_,
    ElementA_,
    StrideA_,
    ElementB_,
    StrideB_,
    TiledMma_,
    GmemTiledCopyA_,
    SmemLayoutAtomA_,
    SmemCopyAtomA_,
    TransformA_,
    GmemTiledCopyB_,
    SmemLayoutAtomB_,
    SmemCopyAtomB_,
    TransformB_>
{
  //
  // Type Aliases
  //
  using DispatchPolicy = MainloopSm90CpAsyncGmmaWarpSpecialized<Stages,ClusterShape_,KernelSchedule>;
  using TileShape = TileShape_;
  using ClusterShape = ClusterShape_;
  using ElementA = ElementA_;
  using StrideA = StrideA_;
  using ElementB = ElementB_;
  using StrideB = 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 CtaShape_MNK = decltype(shape_div(TileShape{}, ClusterShape{}));
  using MainloopPipeline = cutlass::PipelineAsync<DispatchPolicy::Stages>;
  using PipelineState    = typename MainloopPipeline::PipelineState;
  using PipelineParams   = typename MainloopPipeline::Params;

  static_assert(cute::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(cute::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.");

  using SmemLayoutA = decltype(tile_to_shape(
      SmemLayoutAtomA{},
      make_shape(shape<0>(TileShape{}), shape<2>(TileShape{}), Int<DispatchPolicy::Stages>{})));
  using SmemLayoutB = decltype(tile_to_shape(
      SmemLayoutAtomB{},
      make_shape(shape<1>(TileShape{}), shape<2>(TileShape{}), Int<DispatchPolicy::Stages>{})));

  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.");

  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;

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

  // Host side kernel arguments
  struct Arguments {
    ElementA const* ptr_A = nullptr;
    StrideA dA{};
    ElementB const* ptr_B = nullptr;
    StrideB dB{};
    uint32_t mma_promotion_interval = 4;
  };

  // Device side kernel params
  using Params = Arguments;

  //
  // Methods
  //

  template <class ProblemShape>
  static constexpr Params
  to_underlying_arguments(
    [[maybe_unused]] ProblemShape const& problem_shape,
    Arguments const& args,
    [[maybe_unused]] void* workspace) {
    return args;
  }

  template<class ProblemShape>
  CUTLASS_HOST_DEVICE 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;

    bool implementable = true;
    implementable = implementable && cutlass::detail::check_alignment<GmemTiledCopyA::NumValSrc>(cute::make_shape(M,K,L), StrideA{});
    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 TMA.\n");
    }
    return implementable;
  }

  static constexpr int K_PIPE_MAX = DispatchPolicy::Stages;
  static constexpr int K_PIPE_MMAS = 1;
  
  /// Perform a collective-scoped matrix multiply-accumulate
  /// Producer Perspective
  template <
    class TensorA,
    class TensorB,
    class KTileIterator,
    class ResidueMNK
  >
  CUTLASS_DEVICE void
  load(
      MainloopPipeline pipeline, 
      PipelineState smem_pipe_write,
      TensorA const& gA_in,
      TensorB const& gB_in,
      KTileIterator k_tile_iter, int k_tile_count,
      ResidueMNK residue_mnk,
      int thread_idx,
      TensorStorage& shared_tensors)
  {
    using namespace cute;

    static_assert(is_gmem<TensorA>::value, "A tensor must be gmem resident.");
    static_assert(is_gmem<TensorB>::value, "B tensor must be gmem resident.");

    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)

    // Shift tensor so residue_k is at origin (Can't read any k_coord < residue_k)
    // This aligns the tensor with BLK_K for all but the 0th k_tile
    Tensor gA = domain_offset(make_coord(0, get<2>(residue_mnk), 0), gA_in);
    Tensor gB = domain_offset(make_coord(0, get<2>(residue_mnk), 0), gB_in);

    // Partition the copying of A and B tiles across the threads
    GmemTiledCopyA gmem_tiled_copy_a;
    GmemTiledCopyB gmem_tiled_copy_b;
    auto gmem_thr_copy_a = gmem_tiled_copy_a.get_slice(thread_idx);
    auto gmem_thr_copy_b = gmem_tiled_copy_b.get_slice(thread_idx);

    Tensor tAgA = gmem_thr_copy_a.partition_S(gA);                        // (ACPY,ACPY_M,ACPY_K,k)
    Tensor tAsA = gmem_thr_copy_a.partition_D(sA);                        // (ACPY,ACPY_M,ACPY_K,PIPE)
    Tensor tBgB = gmem_thr_copy_b.partition_S(gB);                        // (BCPY,BCPY_N,BCPY_K,k)
    Tensor tBsB = gmem_thr_copy_b.partition_D(sB);                        // (BCPY,BCPY_N,BCPY_K,PIPE)

    // Allocate predicate tensors for m and n
    Tensor tApA = make_tensor<bool>(make_shape(size<1>(tAsA), size<2>(tAsA)), Stride<_1,_0>{});
    Tensor tBpB = make_tensor<bool>(make_shape(size<1>(tBsB), size<2>(tBsB)), Stride<_1,_0>{});

    // Construct identity layout for sA and sB
    Tensor cA = make_identity_tensor(make_shape(size<0>(sA), size<1>(sA)));    // (BLK_M,BLK_K) -> (blk_m,blk_k)
    Tensor cB = make_identity_tensor(make_shape(size<0>(sB), size<1>(sB)));    // (BLK_N,BLK_K) -> (blk_n,blk_k)

    // Repeat the partitioning with identity layouts
    Tensor tAcA = gmem_thr_copy_a.partition_S(cA);                             // (ACPY,ACPY_M,ACPY_K) -> (blk_m,blk_k)
    Tensor tBcB = gmem_thr_copy_b.partition_S(cB);                             // (BCPY,BCPY_N,BCPY_K) -> (blk_n,blk_k)

    // Set predicates for m bounds
    CUTLASS_PRAGMA_UNROLL
    for (int m = 0; m < size<0>(tApA); ++m) {
      tApA(m,0) = get<0>(tAcA(0,m,0)) < get<0>(residue_mnk);  // blk_m coord < residue_m
    }
    // Set predicates for n bounds
    CUTLASS_PRAGMA_UNROLL
    for (int n = 0; n < size<0>(tBpB); ++n) {
      tBpB(n,0) = get<0>(tBcB(0,n,0)) < get<1>(residue_mnk);  // blk_n coord < residue_n
    }

    // 0-th stage with predication on k to account for residue
    {
      // LOCK smem_pipe_write for _writing_
      pipeline.producer_acquire(smem_pipe_write);
      int write_stage = smem_pipe_write.index();

      // Copy gmem to smem for *k_tile_iter, predicating for k residue
      Tensor tAgAk = tAgA(_,_,_,*k_tile_iter);
      CUTLASS_PRAGMA_UNROLL
      for (int k = 0; k < size<2>(tAsA); ++k) {
        if (get<1>(tAcA(0,0,k)) >= -get<2>(residue_mnk)) {      // blk_k coord < residue_k (gA shifted)
          copy_if(gmem_tiled_copy_a, tApA(_,k), tAgAk(_,_,k), tAsA(_,_,k,write_stage));
        }
        else {
          clear(tAsA(_,_,k,write_stage));
        }
      }
      Tensor tBgBk = tBgB(_,_,_,*k_tile_iter);
      CUTLASS_PRAGMA_UNROLL
      for (int k = 0; k < size<2>(tBsB); ++k) {
        if (get<1>(tBcB(0,0,k)) >= -get<2>(residue_mnk)) {      // blk_k coord < residue_k (gB shifted)
          copy_if(gmem_tiled_copy_b, tBpB(_,k), tBgBk(_,_,k), tBsB(_,_,k,write_stage));
        }
        else {
          clear(tBsB(_,_,k,write_stage));
        }
      }
      ++k_tile_iter;
      --k_tile_count;

      // UNLOCK smem_pipe_write
      pipeline.producer_commit(smem_pipe_write, cutlass::arch::cpasync_barrier_arrive);

      // Advance smem_pipe_write
      ++smem_pipe_write;
    }

    // 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);
      int write_stage = smem_pipe_write.index();

      // Copy gmem to smem for *k_tile_iter
      copy_if(gmem_tiled_copy_a, tApA, tAgA(_,_,_,*k_tile_iter), tAsA(_,_,_,write_stage));
      copy_if(gmem_tiled_copy_b, tBpB, tBgB(_,_,_,*k_tile_iter), tBsB(_,_,_,write_stage));
      ++k_tile_iter;

      // UNLOCK smem_pipe_write
      pipeline.producer_commit(smem_pipe_write, cutlass::arch::cpasync_barrier_arrive);

      // 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) {
    // Issue the epilogue waits
    /* 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 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)
  {
    using namespace cute;

    static_assert(is_rmem<FrgTensorC>::value, "C tensor must be rmem resident.");
    static_assert(cute::rank(SmemLayoutA{}) == 3, "Smem layout must be rank 3.");
    static_assert(cute::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);

      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;
    }
  }
};

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

} // namespace cutlass::gemm::collective

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