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/*! \file
    \brief Templates implementing loading of convolution tiles mapped to GEMM B (activation tile) 
    matrix from memory.

    This iterator assumes TensorNHWC 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/conv2d_problem_size.h"

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

namespace cutlass {
namespace conv {
namespace threadblock {

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

template <
  typename Shape_,
  typename Element_,
  typename ThreadMap_,
  typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dWgradActivationTileAccessIteratorOptimized {
public:

  //
  // Types
  //
  using Shape = Shape_;
  using Element = Element_;
  using Layout = layout::TensorNHWC;
  using ThreadMap = ThreadMap_;
  using AccessType = AccessType_;
  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 = 2;
  using ConvProblemSize = typename conv::Conv2dProblemSize;
 
  static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
  
  static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements), 
    "Vectors implied by the thread map must be divisible by the access type.");
 
  static_assert(sizeof_bits<Element>::value >= 8,
    "WGRAD requires elements of size 8b or greater.");

  //
  // Parameters structure
  //

  using Params = Conv2dWgradActivationIteratorOptimizedParams;

private:

  Conv2dWgradActivationIteratorOptimizedParams const &params_;
  Conv2dProblemSize const &problem_size_;
  LongIndex iteration_contiguous_;
  LongIndex iteration_strided_;
  LongIndex iteration_vector_;
  char const *pointer_;

  // Precomputed effective filter postion (r,s) in contiguous dimension stays constant for each gemm_iteration_k
  // required for npq -> nhw translation
  int precomputed_filter_r_[ThreadMap::Iterations::kContiguous];
  int precomputed_filter_s_[ThreadMap::Iterations::kContiguous];

  // Channel dimension in contiguous dimension stays constant for each gemm_iteration_k
  int filter_c_[ThreadMap::Iterations::kContiguous];

  int offset_npq_[ThreadMap::Iterations::kStrided];

public:

  CUTLASS_HOST_DEVICE
  Conv2dWgradActivationTileAccessIteratorOptimized(
    Conv2dWgradActivationIteratorOptimizedParams const &params, 
    Conv2dProblemSize 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))
  {

    layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
    
    // initialize r,s,c filter position for every contiguous iteration
    CUTLASS_PRAGMA_UNROLL
    for(int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {

      int rsc_offset = threadblock_offset.column() + thread_coord.contiguous()
                        + c * ThreadMap::Delta::kContiguous;

      // The subseqnet fast_divmod() operations are equivalent to the following logical computation:
      //
      //
      // filter_r_[c] = rsc_offset / (problem_size_.S * problem_size_.C);
      // int residual = rsc_offset % (problem_size_.S * problem_size_.C);
      //
      // filter_s_[c] = residual / problem_size_.C;
      // filter_c_[c] = residual % problem_size_.C;

      int residual;
      params_.sc_divmod(precomputed_filter_r_[c], residual, rsc_offset);
      params_.c_divmod(precomputed_filter_s_[c], filter_c_[c], residual);

      int r = precomputed_filter_r_[c];
      int s = precomputed_filter_s_[c];

      if (problem_size_.mode == Mode::kConvolution) {
        r = (problem_size_.R - 1 - r);
        s = (problem_size_.S - 1 - s);
      }

      precomputed_filter_r_[c] =  -problem_size_.pad_h + r * problem_size_.dilation_h;
      precomputed_filter_s_[c] =  -problem_size_.pad_w + s * problem_size_.dilation_w;
    }

    // initialize n, p, q offset for every strided iteration
    CUTLASS_PRAGMA_UNROLL
    for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
    
      offset_npq_[s] = threadblock_offset.row() + thread_coord.strided() 
                      + s * ThreadMap::Delta::kStrided;   
    }
  }

  /// Overrides the internal iteration index
  CUTLASS_HOST_DEVICE
  void set_iteration_index(Index index) {
    iteration_vector_ = index % kAccessesPerVector;
    int residual_access = index / kAccessesPerVector;
    iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
    iteration_strided_ = residual_access / 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-B by a CTA-K tile
    CUTLASS_PRAGMA_UNROLL
    for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
      offset_npq_[s] += Shape::kRow * problem_size_.split_k_slices;
    }
  }

  /// Returns the coordinate in the activation tensor x that is currently pointed to
  /// by the iterator.
  CUTLASS_HOST_DEVICE
  TensorCoord at() const {
    int r = precomputed_filter_r_[iteration_contiguous_];
    int s = precomputed_filter_s_[iteration_contiguous_];
    int c = filter_c_[iteration_contiguous_];

    if (kAccessesPerVector > 1) {
      // This code section is only to support non-128b alignment
      // Multiple access to support non-128b alignment in contiguous dimension
      int wrap_c;
      params_.c_divmod(wrap_c, c, c + iteration_vector_ * AccessType::kElements);

      if (problem_size_.mode == Mode::kConvolution) {
        s -= (problem_size_.dilation_w * wrap_c);
        
        int wrap_s;
        params_.s_divmod(wrap_s, s, params_.small_channel_conv_s_offset - s);
        s = params_.small_channel_conv_s_offset - s;

        r -= (problem_size_.dilation_h * wrap_s);

      } else {
        s += (problem_size_.dilation_w * wrap_c);

        int wrap_s;
        params_.s_divmod(wrap_s, s, s + problem_size_.pad_w);
        s -= problem_size_.pad_w;

        r += (problem_size_.dilation_h * wrap_s);
      }
    }

    // The subseqnet fast_divmod() operations are equivalent to the following logical computation:
    //
    //
    // int n = offset_npq_[iteration_strided_] / (problem_size_.P * problem_size_.Q);
    // int residual = offset_npq_[iteration_strided_] % (problem_size_.P * problem_size_.Q);
    //
    // int p = residual / problem_size_.Q;
    // int q = residual % problem_size_.Q;

    int residual, n, p, q;
    
    params_.pq_divmod(n, residual, offset_npq_[iteration_strided_]);
    params_.q_divmod(p, q, residual);

    int h = p * problem_size_.stride_h + r;
    int w = q * problem_size_.stride_w + s;

    return TensorCoord(n, h, w, c);
  }

  /// Returns true if the current coordinate is within the activation tensor x
  CUTLASS_HOST_DEVICE
  bool valid() const {
    TensorCoord coord = at();

    return coord.n() < problem_size_.N &&
      coord.h() >= 0 && coord.h() < problem_size_.H &&
      coord.w() >= 0 && coord.w() < problem_size_.W;
  }

  /// Returns a pointer to the vector starting at the current coordinate
  CUTLASS_HOST_DEVICE
  AccessType const *get() const {

    TensorCoord coord = at();
    LongIndex offset = params_.layout(coord);

    return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
  }

  /// Increments to the next memory access
  CUTLASS_HOST_DEVICE
  Conv2dWgradActivationTileAccessIteratorOptimized &operator++() {
    ++iteration_vector_;
    if (iteration_vector_ < kAccessesPerVector) {
      return *this;
    }
    iteration_vector_ = 0;

    ++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(Conv2dProblemSize const &problem_size) {

    // check alignment constraint on iterator's contiguous dimension
    if (problem_size.C % AccessType::kElements) {
      return Status::kErrorInvalidProblem;
    }

    return Status::kSuccess;
  }
};

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

} // namespace threadblock
} // namespace conv
} // namespace cutlass

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