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  /*! \file
      \brief Templates implementing loading of convolution tiles mapped to GEMM A (output gradient tile) 
      matrix from memory.
  
      This iterator assumes TensorNDHWC layout of tensors in Global Memory.
  
      The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
      backward data gradient (Dgrad), and backward weight gradient (Wgrad).
  */
  
  #pragma once
  
  #include "cutlass/cutlass.h"
  #include "cutlass/array.h"
  #include "cutlass/coord.h"
  #include "cutlass/predicate_vector.h"
  #include "cutlass/tensor_ref.h"
  #include "cutlass/tensor_view.h"
  #include "cutlass/layout/pitch_linear.h"
  #include "cutlass/layout/tensor.h"
  #include "cutlass/layout/matrix.h"
  #include "cutlass/conv/convolution.h"
  #include "cutlass/conv/conv3d_problem_size.h"
  #include "cutlass/conv/threadblock/conv3d_params.h"
  
  /////////////////////////////////////////////////////////////////////////////////////////////////
  
  namespace cutlass {
  namespace conv {
  namespace threadblock {
  
  /////////////////////////////////////////////////////////////////////////////////////////////////
  
  template <
    typename Shape_,
    typename Element_,
    typename ThreadMap_
  >
  class Conv3dWgradOutputGradientTileAccessIteratorOptimized {
  public:
  
    //
    // Types
    //
    using Shape = Shape_;
    using Element = Element_;
    using Layout = layout::TensorNDHWC;
    using ThreadMap = ThreadMap_;
    using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
    using TensorRef = cutlass::TensorRef<Element, Layout>;
    using TensorCoord = typename Layout::TensorCoord;
    using Index = typename Layout::Index;
    using LongIndex = typename Layout::LongIndex;
    static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kOptimized;
    static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
    static int const kConvDim = 3;
    using ConvProblemSize = typename conv::Conv3dProblemSize;
    
    static_assert(sizeof_bits<Element>::value >= 8,
      "WGRAD requires elements of size 8b or greater.");
  
    //
    // Parameters structure
    //
  
    struct Params : Conv3dWgradOutputGradientIteratorOptimizedParams {
      //
      // Methods
      //
      CUTLASS_HOST_DEVICE
      Params() {}
  
      CUTLASS_HOST_DEVICE
      Params(Conv3dWgradOutputGradientIteratorOptimizedParams const &base)
            : Conv3dWgradOutputGradientIteratorOptimizedParams(base) {}
  
      CUTLASS_HOST_DEVICE
      Params(Conv3dProblemSize const &problem_size, Layout const &layout)
            : Conv3dWgradOutputGradientIteratorOptimizedParams(
              problem_size,
              layout,
              sizeof_bits<Element>::value,
              {Shape::kRow, Shape::kColumn},
              ThreadMap::kThreads,
              ThreadMap::kElementsPerAccess,
              {ThreadMap::Iterations::kContiguous, ThreadMap::Iterations::kStrided},
              {ThreadMap::Delta::kContiguous, ThreadMap::Delta::kStrided}) {}
      };
  
  private:
  
    Params const &params_;
    Conv3dProblemSize const &problem_size_;
    LongIndex iteration_contiguous_;
    LongIndex iteration_strided_;
    char const *pointer_;
      
    uint32_t predicates_;
    int filter_k_;
    int offset_nzpq_;
  
  public:
  
    CUTLASS_HOST_DEVICE
    Conv3dWgradOutputGradientTileAccessIteratorOptimized(
      Params const &params, 
      Conv3dProblemSize const &problem_size,
      Element const *ptr,
      int thread_idx,
      MatrixCoord const &threadblock_offset = MatrixCoord()
    ):
      params_(params), 
      problem_size_(problem_size),
      pointer_(reinterpret_cast<char const *>(ptr)),
      predicates_(0),
      filter_k_(0),
      offset_nzpq_(0) {
  
  
      layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
  
      filter_k_ = threadblock_offset.row() + thread_coord.contiguous();
      offset_nzpq_ = threadblock_offset.column() + thread_coord.strided();
  
      CUTLASS_PRAGMA_UNROLL
      for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
        CUTLASS_PRAGMA_UNROLL
        for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
  
          int filter_k = filter_k_ + c * ThreadMap::Delta::kContiguous;
          int offset_nzpq = offset_nzpq_ + s * ThreadMap::Delta::kStrided;
  
          bool predicate = valid_(at_(offset_nzpq, filter_k));
  
          uint32_t pred = (predicate ? 1u : 0);
  
          int pred_idx = c + s * ThreadMap::Iterations::kContiguous;
          
          predicates_ |= (pred << pred_idx);
        }
      }
  
      // Offset pointer to (iteration_strided_, iteration_contiguous_) = (0, 0) 
      pointer_ += (
        offset_nzpq_ * params.layout.stride()[0] + filter_k_
      ) * sizeof_bits<Element>::value / 8;
  
      set_iteration_index(0);
    }
  
    CUTLASS_HOST_DEVICE
    static Params getParams(Conv3dProblemSize const &problem_size, Layout const &layout) {
      return Params(problem_size, layout);
    }
  
    /// Overrides the internal iteration index
    CUTLASS_HOST_DEVICE
    void set_iteration_index(Index index) {
      iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
      iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
    }
  
    /// Adds a pointer offset in units of Element
    CUTLASS_HOST_DEVICE
    void add_pointer_offset(LongIndex pointer_offset) {
      pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
    }
  
    CUTLASS_HOST_DEVICE
    void advance() {
      // moves to the next GEMM-K offset (offset_npq_) in GEMM-A by a CTA-K tile
      offset_nzpq_ += Shape::kColumn * problem_size_.split_k_slices;
  
      // Clear predicates if needed
      CUTLASS_PRAGMA_UNROLL
      for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
        if (offset_nzpq_ + s * ThreadMap::Delta::kStrided >= params_.NZPQ) {
          uint32_t kClearMask = ((1u << ThreadMap::Iterations::kContiguous) - 1) << (s * ThreadMap::Iterations::kContiguous); 
          predicates_ = (predicates_ & (~kClearMask));
        }
      }
      pointer_ += params_.inc_next_nzpq; 
    }
  
  private:
    /// Returns the coordinate in the output gradient tensor Dy that is (offset_nzpq, k) pointed to
    /// by the iterator.
    CUTLASS_HOST_DEVICE
    TensorCoord at_(int offset_nzpq, int k) const {
  
      // The subseqnet fast_divmod() operations are equivalent to the following logical computation:
      //
      //
      // int nzpq = offset_nzpq_;
      // int n = nzpq / (problem_size_.Z * problem_size_.P * problem_size_.Q);
      // int residual = nzpq % (problem_size_.Z * problem_size_.P * problem_size_.Q);
      //
      // int z = residual / (problem_size_.P * problem_size_.Q);
      // residual = residual % (problem_size_.P * problem_size_.Q);
      //
      // int p = residual / problem_size_.Q;
      // int q = residual % problem_size_.Q;
  
      int residual, n, z, p, q;
      fast_divmod(n, residual, offset_nzpq, params_.ZPQ, params_.zpq_mul, params_.zpq_shr);
      fast_divmod(z, residual, residual, params_.PQ, params_.pq_mul, params_.pq_shr);
      fast_divmod(p, q, residual, problem_size_.Q, params_.q_mul, params_.q_shr);
  
      return TensorCoord(n, z, p, q, k);
    }
  
    /// Returns true if the coord is within the output gradient tensor Dy
    CUTLASS_HOST_DEVICE
    bool valid_(TensorCoord coord) const {
  
      return coord.n() < problem_size_.N &&
        coord.c() < problem_size_.K;
    }
  
  public:
  
    /// Returns true if the current coordinate is within the output gradient tensor Dy
    CUTLASS_HOST_DEVICE
    bool valid() const {
  
      LongIndex pred_idx = iteration_contiguous_ + iteration_strided_ * ThreadMap::Iterations::kContiguous;
      return (predicates_ & (1u << pred_idx));
    }
  
    /// Returns a pointer to the vector starting at the current coordinate
    CUTLASS_HOST_DEVICE
    AccessType const *get() const {
      
      return reinterpret_cast<AccessType const *>(
        pointer_ +
        iteration_strided_ * params_.offset_next_strided + 
        iteration_contiguous_ * params_.offset_next_contiguous
      );
  
    }
  
    /// Increments to the next memory access
    CUTLASS_HOST_DEVICE
    Conv3dWgradOutputGradientTileAccessIteratorOptimized &operator++() {
      ++iteration_contiguous_;
      if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
        return *this;
      }
      iteration_contiguous_ = 0;
      ++iteration_strided_;
      if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
        return *this;
      }
      iteration_strided_ = 0;
   
      return *this;
    }
  
    /// Determines whether the Implicit GEMM can execute the given problem.
    CUTLASS_HOST_DEVICE
    static Status can_implement(Conv3dProblemSize const &problem_size) {
  
      // check alignment constraint on iterator's contiguous dimension
      if (problem_size.C % (128/sizeof_bits<Element>::value)) {
        return Status::kErrorInvalidProblem;
      }
  
      return Status::kSuccess;
    }
  
  };
  /////////////////////////////////////////////////////////////////////////////////////////////////
  
  } // namespace threadblock
  } // namespace conv
  } // namespace cutlass
  
  /////////////////////////////////////////////////////////////////////////////////////////////////
  
  
  