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/*! \file
    \brief Template for a double-buffered threadblock-scoped GEMM kernel.
*/

#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/array.h"
#include "cutlass/aligned_buffer.h"

#include "cutlass/numeric_types.h"
#include "cutlass/matrix_shape.h"

#include "cutlass/gemm/gemm.h"
#include "cutlass/gemm/threadblock/mma_base.h"



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

namespace cutlass {
namespace gemm {
namespace threadblock {

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

/// Structure to compute the matrix product targeting CUDA cores and SIMT math instructions.
template <
  /// Size of the Gemm problem - concept: gemm::GemmShape<>
  typename Shape_,
  /// Iterates over tiles of A operand in global memory 
  //  (concept: ReadableTileIterator | ForwardTileIterator | MaskedTileIterator)
  typename IteratorA_,
  /// Iterates over tiles of A operand in shared memory
  /// (concept: WriteableTileIterator | RandomAccessTileIterator)
  typename SmemIteratorA_,
  /// Iterates over tiles of B operand in global memory
  //  (concept: ReadableTileIterator | ForwardTileIterator | MaskedTileIterator)
  typename IteratorB_,
  /// Iterates over tiles of B operand in shared memory
  /// (concept: WriteableTileIterator | RandomAccessTileIterator)
  typename SmemIteratorB_,
  /// Data type of accumulator matrix
  typename ElementC_,
  /// Data type of accumulator matrix
  typename LayoutC_,
  /// Policy describing tuning details (concept: MmaPolicy)
  typename Policy_,
  /// Used for partial specialization
  typename Enable = bool
>
class MmaSingleStage : public MmaBase<Shape_, Policy_, 1> {
public:

  ///< Base class
  using Base = MmaBase<Shape_, Policy_, 1>;

  using Shape = Shape_;             ///< Size of the Gemm problem - concept: gemm::GemmShape<>
  using IteratorA = IteratorA_;     ///< Iterates over tiles of A operand in global memory
  using IteratorB = IteratorB_;     ///< Iterates over tiles of B operand in global memory
  using ElementC = ElementC_;       ///< Data type of accumulator matrix
  using LayoutC = LayoutC_;         ///< Layout of accumulator matrix
  using Policy = Policy_;           ///< Policy describing tuning details

  using SmemIteratorA = SmemIteratorA_;
  using SmemIteratorB = SmemIteratorB_;

  //
  // Dependent types
  //

  /// Fragment of operand A loaded from global memory
  using FragmentA = typename IteratorA::Fragment;

  /// Fragment of operand B loaded from global memory
  using FragmentB = typename IteratorB::Fragment;

  /// Fragment of accumulator tile
  using FragmentC = typename Policy::Operator::FragmentC;

  /// Warp-level Mma
  using Operator = typename Policy::Operator;

  using ArchTag = arch::Sm70;

  /// Complex transform on A operand
  static ComplexTransform const kTransformA = Operator::kTransformA;

  /// Complex transform on B operand
  static ComplexTransform const kTransformB = Operator::kTransformB;

  // staticaly assert kStages for MmaSingleStage is 1 (single stage mma pipeline)
  static_assert((Base::kStages==1), "MmaSingleStage requires kStages set to value 1");
private:

  using WarpFragmentA = typename Operator::FragmentA;
  using WarpFragmentB = typename Operator::FragmentB;

protected:

  /// Iterator to write threadblock-scoped tile of A operand to shared memory
  SmemIteratorA smem_iterator_A_;

  /// Iterator to write threadblock-scoped tile of B operand to shared memory
  SmemIteratorB smem_iterator_B_;

public:

  /// Construct from tensor references
  CUTLASS_DEVICE
  MmaSingleStage(
    typename Base::SharedStorage &shared_storage,       ///< Shared storage needed for internal use by threadblock-scoped GEMM
    int thread_idx,                                     ///< ID within the threadblock
    int warp_idx,                                       ///< ID of warp
    int lane_idx                                        ///< ID of each thread within a warp
  ):
    Base(shared_storage, thread_idx, warp_idx, lane_idx),
    smem_iterator_A_(shared_storage.operand_A_ref(), thread_idx),
    smem_iterator_B_(shared_storage.operand_B_ref(), thread_idx) {

    // Compute warp location within threadblock tile by mapping the warp_id to
    // three coordinates:
    //   _m: the warp's position within the threadblock along the M dimension
    //   _n: the warp's position within the threadblock along the N dimension
    //   _k: the warp's position within the threadblock along the K dimension

    int warp_idx_mn = warp_idx % (Base::WarpCount::kM * Base::WarpCount::kN);
    int warp_idx_k = warp_idx / (Base::WarpCount::kM * Base::WarpCount::kN);

    int warp_idx_m = warp_idx_mn % Base::WarpCount::kM;
    int warp_idx_n = warp_idx_mn / Base::WarpCount::kM;

    // Add per-warp offsets in units of warp-level tiles
    this->warp_tile_iterator_A_.add_tile_offset({warp_idx_m, Base::kWarpGemmIterations * warp_idx_k});
    this->warp_tile_iterator_B_.add_tile_offset({Base::kWarpGemmIterations * warp_idx_k, warp_idx_n});

  }

  /// Perform a threadblock-scoped matrix multiply-accumulate
  CUTLASS_DEVICE
  void operator()(
    int gemm_k_iterations,            ///< number of iterations of the mainloop
    FragmentC &accum,                 ///< destination accumulator tile
    IteratorA iterator_A,             ///< iterator over A operand in global memory
    IteratorB iterator_B,             ///< iterator over B operand in global memory
    FragmentC const &src_accum) {     ///< source accumualtor tile

    //
    // Prologue
    //

    // Perform accumulation in the 'd' output operand
    accum = src_accum;

    FragmentA tb_frag_A;
    FragmentB tb_frag_B;

    tb_frag_A.clear();
    tb_frag_B.clear();

    // The last kblock is loaded in the prolog
    iterator_A.load(tb_frag_A);
    iterator_B.load(tb_frag_B);

    ++iterator_A;
    ++iterator_B;

    // Pair of fragments used to overlap shared memory loads and math instructions
    WarpFragmentA warp_frag_A;
    WarpFragmentB warp_frag_B;

    Operator warp_mma;

    // Avoid reading out of bounds
    iterator_A.clear_mask(gemm_k_iterations <= 1);
    iterator_B.clear_mask(gemm_k_iterations <= 1);

    //
    // Mainloop
    //

    CUTLASS_GEMM_LOOP
    for (; gemm_k_iterations > 0; --gemm_k_iterations) {
      this->smem_iterator_A_.store(tb_frag_A);
      this->smem_iterator_B_.store(tb_frag_B);

      __syncthreads();

      //
      // Loop over GEMM K dimension
      //

      CUTLASS_PRAGMA_UNROLL
      for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations; ++warp_mma_k) {

        // Load warp-level tiles from shared memory, wrapping to k offset if this is the last group
        // as the case may be.
        
        this->warp_tile_iterator_A_.set_kgroup_index(warp_mma_k % Base::kWarpGemmIterations);
        this->warp_tile_iterator_B_.set_kgroup_index(warp_mma_k % Base::kWarpGemmIterations);

        this->warp_tile_iterator_A_.load(warp_frag_A);
        this->warp_tile_iterator_B_.load(warp_frag_B);

        ++this->warp_tile_iterator_A_;
        ++this->warp_tile_iterator_B_;

        warp_mma(accum, warp_frag_A, warp_frag_B, accum);
      }

      // Add negative offsets to return smem load iterators to the 'start' of the shared memory
      this->warp_tile_iterator_A_.add_tile_offset({0, -Policy::kPartitionsK * Base::kWarpGemmIterations});
      this->warp_tile_iterator_B_.add_tile_offset({-Policy::kPartitionsK * Base::kWarpGemmIterations, 0});

      __syncthreads();

      iterator_A.load(tb_frag_A);
      iterator_B.load(tb_frag_B);

      ++iterator_A;
      ++iterator_B;

      // Avoid reading out of bounds if this was the last loop iteration
      iterator_A.clear_mask(gemm_k_iterations <= 2);
      iterator_B.clear_mask(gemm_k_iterations <= 2);
    }

  }
};

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

} // namespace threadblock
} // namespace gemm
} // namespace cutlass
