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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 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 ThreadMap_,
  conv::StrideSupport StrideSupport_ = conv::StrideSupport::kUnity,
  typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dDgradFilterTileAccessIteratorAnalytic;

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

// Conv2dDgradFilterTileAccessIteratorAnalytic strided dgrad needs special handling to skip MMAs
// on non-contributing w positions
template <
  typename Shape_,
  typename Element_,
  typename ThreadMap_,
  typename AccessType_
>
class Conv2dDgradFilterTileAccessIteratorAnalytic <
  Shape_,
  Element_,
  ThreadMap_,
  conv::StrideSupport::kStrided,
  AccessType_
> {
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::kAnalytic;
  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, 
    "DGRAD requires elements of size 8b or larger.");
  
  //
  // Parameters structure
  //
  
  using Params = Conv2dAnalyticParams<Layout>;

private:

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

  // For a fixed filter position (r,s) find and fill offset_k_, offset_c_ in strided and contiguous dimension 
  int filter_r_;
  int filter_s_;
  int start_r_;
  int start_s_;
  int offset_k_[ThreadMap::Iterations::kStrided]; 
  int offset_c_[ThreadMap::Iterations::kContiguous];

public:

  CUTLASS_HOST_DEVICE
  Conv2dDgradFilterTileAccessIteratorAnalytic(
    Params const &params, 
    Conv2dProblemSize const &problem_size,
    Element const *ptr,
    int thread_idx,
    int start_r, int start_s,
    MatrixCoord const &threadblock_offset = MatrixCoord()
  ):
    params_(params), 
    problem_size_(problem_size), 
    pointer_(reinterpret_cast<char const *>(ptr)), 
    filter_r_(start_r),
    filter_s_(start_s),
    start_r_(start_r),
    start_s_(start_s) {

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

    CUTLASS_PRAGMA_UNROLL
    for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
      offset_c_[c] = threadblock_offset.column() + thread_coord.contiguous() 
        + c * ThreadMap::Delta::kContiguous;
    }

    CUTLASS_PRAGMA_UNROLL
    for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
      offset_k_[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 filter_s
    filter_s_ += problem_size_.stride_w;
    if (filter_s_ < problem_size_.S) {
      return;
    }
    // Restore filter_s
    filter_s_ = start_s_;

    // Move filter_r 
    filter_r_ += problem_size_.stride_h;
    if (filter_r_ < problem_size_.R) {
      return;
    }
    // Restore filter_r
    filter_r_ = start_r_;
    
    CUTLASS_PRAGMA_UNROLL
    for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
      offset_k_[s] += Shape::kRow * 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 k = offset_k_[iteration_strided_];
    int c = offset_c_[iteration_contiguous_] + iteration_vector_ * AccessType::kElements;
    
    return TensorCoord(k, filter_r_, filter_s_, c);
  }

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

    TensorCoord coord = at();

    return coord.n() < problem_size_.K && coord.c() < problem_size_.C;
  }

  /// 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
  Conv2dDgradFilterTileAccessIteratorAnalytic &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;
  }
};
/////////////////////////////////////////////////////////////////////////////////////////////////

// Conv2dDgradFilterTileAccessIteratorAnalytic unity strided dgrad is more performant for  dgrad
// on problem sizes with stride = {1x1}
template <
  typename Shape_,
  typename Element_,
  typename ThreadMap_,
  typename AccessType_
>
class Conv2dDgradFilterTileAccessIteratorAnalytic <
  Shape_,
  Element_,
  ThreadMap_,
  conv::StrideSupport::kUnity,
  AccessType_
>{
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::kAnalytic;
  static StrideSupport const kStrideSupport = conv::StrideSupport::kUnity;
  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, 
    "DGRAD requires elements of size 8b or larger.");
  
  //
  // Parameters structure
  //
  
  using Params = Conv2dAnalyticParams<Layout>;

private:

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

  // For a fixed filter position (r,s) find and fill offset_k_, offset_c_ in strided and contiguous dimension 
  int filter_r_;
  int filter_s_;
  int offset_k_[ThreadMap::Iterations::kStrided]; 
  int offset_c_[ThreadMap::Iterations::kContiguous];

public:

  CUTLASS_HOST_DEVICE
  Conv2dDgradFilterTileAccessIteratorAnalytic(
    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)), 
    filter_r_(0),
    filter_s_(0) {

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

    CUTLASS_PRAGMA_UNROLL
    for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
      offset_c_[c] = threadblock_offset.column() + thread_coord.contiguous() 
        + c * ThreadMap::Delta::kContiguous;
    }

    CUTLASS_PRAGMA_UNROLL
    for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
      offset_k_[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 tile
    ++filter_s_;
    if (filter_s_ < problem_size_.S) {
      return;
    }
    filter_s_ = 0;
    ++filter_r_;
    if (filter_r_ < problem_size_.R) {
      return;
    }
    filter_r_ = 0;
    
    CUTLASS_PRAGMA_UNROLL
    for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
      offset_k_[s] += Shape::kRow * 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 k = offset_k_[iteration_strided_];
    int c = offset_c_[iteration_contiguous_] + iteration_vector_ * AccessType::kElements;

    return TensorCoord(k, filter_r_, filter_s_, c);
  }

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

    TensorCoord coord = at();

    return coord.n() < problem_size_.K && coord.c() < problem_size_.C;
  }

  /// 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
  Conv2dDgradFilterTileAccessIteratorAnalytic &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

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