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

    This iterator assumes TensorNHWC or TensorCxRSKx<Interleave> 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"
#include "cutlass/conv/threadblock/conv2d_params.h"

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

namespace cutlass {
namespace conv {
namespace threadblock {

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

template <
  typename Shape_,
  typename Element_,
  typename Layout_,
  typename ThreadMap_,
  typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dFpropFilterTileAccessIteratorFixedChannels {
public:

  //
  // Types
  //

  using Shape = Shape_;
  using Element = Element_;
  using Layout = Layout_;
  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::kFixedChannels;
  static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
  static int const kConvDim = 2;
  using ConvProblemSize = typename conv::Conv2dProblemSize;

  static int const kFilterPositionsPerTile = Shape::kRow / AccessType::kElements;
  static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;

  static bool const kUseFastDivmodPrologue = true;
  static bool const kUseFastDivmodMainloop = true;

  static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
    "Vectors implied by the thread map must be divisible by the access type.");

  //
  // Simplifying assertions
  //
  static_assert(ThreadMap::Iterations::kContiguous == 1,
    "Require Iterations::kContiguous == 1");

  //
  // Parameters structure
  //

  using Params = Conv2dFewChannelsParams<Layout>;

private:

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

  int rs_index_;

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

public:

  CUTLASS_HOST_DEVICE
  Conv2dFpropFilterTileAccessIteratorFixedChannels(
    Params 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)),
    rs_index_(0) {

    layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);

    rs_index_ = (threadblock_offset.row() + thread_coord.contiguous()) / AccessType::kElements;

    CUTLASS_PRAGMA_UNROLL
    for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
      offset_k_[s] = threadblock_offset.column() + thread_coord.strided() + s * ThreadMap::Delta::kStrided;
    }

    set_iteration_index(0);
  }

  /// 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 * 8 / sizeof_bits<Element>::value;
  }

  CUTLASS_HOST_DEVICE
  void advance() {
    // moves to the next tile
    rs_index_ += kFilterPositionsPerTile * problem_size_.split_k_slices;
  }

  /// Returns the coordinate in the filter tensor W that is currently pointed to
  /// by the iterator.
  CUTLASS_HOST_DEVICE
  TensorCoord at() const {

    int rs_index = rs_index_ + iteration_vector_;

    int r = 0;
    int s = 0;

    if (kUseFastDivmodMainloop) {
      r = params_.divmod_S.divmod(s, rs_index);
    }
    else {
      s = (rs_index % problem_size_.S);
      r = (rs_index / problem_size_.S);
    }

    int k = offset_k_[iteration_strided_];

    return TensorCoord(k, r, s, 0);
  }

  /// Returns true if the current coordinate is within the activations tensor W
  CUTLASS_HOST_DEVICE
  bool valid() const {

    TensorCoord coord = at();

    return coord.n() < problem_size_.K && coord.h() >= 0 && coord.h() < problem_size_.R;
  }

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

    TensorCoord coord = at();

    int32_t offset =
      coord.n() * params_.stride_n +
      coord.h() * params_.stride_h +
      coord.w() * params_.stride_w + coord.c();

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

  /// Increments to the next memory access
  CUTLASS_HOST_DEVICE
  Conv2dFpropFilterTileAccessIteratorFixedChannels &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;
    }

    if (platform::is_same<Layout, layout::TensorCxRSKx<32>>::value) {
      if (problem_size.K % 32) {
        return Status::kErrorInvalidProblem;
      }
    }

    if (platform::is_same<Layout, layout::TensorCxRSKx<64>>::value) {
      if (problem_size.K % 64) {
        return Status::kErrorInvalidProblem;
      }
    }

    return Status::kSuccess;
  }
};

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

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

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