predicated_tile_access_iterator_2dthreadtile.h

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#pragma once

#include "cutlass/array.h"
#include "cutlass/coord.h"
#include "cutlass/cutlass.h"
#include "cutlass/layout/matrix.h"
#include "cutlass/layout/pitch_linear.h"
#include "cutlass/matrix_shape.h"
#include "cutlass/predicate_vector.h"
#include "cutlass/tensor_ref.h"
#include "cutlass/tensor_view.h"



namespace cutlass {
namespace transform {
namespace threadblock {


template <typename Shape, typename Element, typename Layout, int AdvanceRank,
          typename ThreadMap, typename AccessType>
class PredicatedTileAccessIterator2dThreadTile;


template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, typename AccessType_>
class PredicatedTileAccessIterator2dThreadTile<Shape_, Element_, layout::PitchLinear,
                                   AdvanceRank, ThreadMap_, AccessType_> {
 public:
  static_assert(
      AdvanceRank == 0 || AdvanceRank == 1,
      "Specialization for pitch-linear iterator may along advance along the "
      "contiguous(rank=0) or strided(rank=1) dimension.");

  using Shape = Shape_;
  using Element = Element_;
  using Layout = layout::PitchLinear;
  static int const kAdvanceRank = AdvanceRank;
  using ThreadMap = ThreadMap_;
  using AccessType = AccessType_;

  using Index = typename Layout::Index;
  using LongIndex = typename Layout::LongIndex;

  using TensorRef = TensorRef<Element, Layout>;
  using TensorView = TensorView<Element, Layout>;
  using TensorCoord = typename Layout::TensorCoord;

  using Pointer = Element *;
  using NonConstPointer = typename platform::remove_const<Element>::type *;

  static int const kPredicatesPerByte = 4;
  static int const kPredicatesPerWord = 4 * kPredicatesPerByte;

  static int const kPredicateByteCount = (ThreadMap::Iterations::kCount * ThreadMap::ThreadAccessShape::kStrided + kPredicatesPerByte - 1) / kPredicatesPerByte;
  static int const kPredicateWordCount = (kPredicateByteCount + 3) / 4;

  static unsigned const kPredicateMask = (1u << kPredicatesPerByte) - 1u;

  static_assert(kPredicateWordCount <= 4, "Too many predicates.");

  using Mask = Array<uint32_t, kPredicateWordCount>;

  class Params {
   public:
    friend PredicatedTileAccessIterator2dThreadTile;

   private:
    int stride_;
    int inc_strided_;
    int inc_next_;
    int inc_advance_;

   public:

    // Default ctor
    CUTLASS_HOST_DEVICE
    Params(): stride_(0), inc_strided_(0), inc_next_(0), inc_advance_(0) { }

    CUTLASS_HOST_DEVICE
    Params(Layout const &layout) : stride_(layout.stride(0)) {

      inc_strided_ =
          (stride_ * ThreadMap::Delta::kStrided) * int(sizeof(Element));

      if (kAdvanceRank) {
        // advance along strided dimension
        inc_advance_ = Shape::kStrided * stride_ * int(sizeof(Element));
      } else {
        // advance along contiguous dimension
        inc_advance_ = Shape::kContiguous * int(sizeof(Element));
      }

      inc_next_ = inc_advance_ - (ThreadMap::Iterations::kStrided - 1) *
                                     ThreadMap::Delta::kStrided * stride_ *
                                     int(sizeof(Element));
    };
  };

 private:
  using BytePointer = char *;

 private:
  //
  // Data members
  //

  Params const &params_;

  BytePointer pointer_;

  uint32_t predicates_[kPredicateWordCount];

  TensorCoord extent_;

  TensorCoord thread_offset_;

  int residue_tile_idx_;

  bool is_residue_tile_;

  int iteration_contiguous_;

  int iteration_strided_;

  int iteration_thread_;

 private:
  CUTLASS_HOST_DEVICE
  void compute_predicates_(
      bool is_steady_state = false) {

    CUTLASS_PRAGMA_UNROLL
    for (int i = 0; i < kPredicateWordCount; ++i) {
      predicates_[i] = 0u;
    }

    CUTLASS_PRAGMA_UNROLL
    for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
      CUTLASS_PRAGMA_UNROLL
      for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
        CUTLASS_PRAGMA_UNROLL
        for (int ts = 0; ts < ThreadMap::ThreadAccessShape::kStrided; ts++) {

          TensorCoord iteration_coord(c * ThreadMap::Delta::kContiguous,
                                      ts + s * ThreadMap::Delta::kStrided);

          TensorCoord coord = thread_offset_ + iteration_coord;

          bool guard;

          if (is_steady_state) {
            if (kAdvanceRank == 0) {
              guard = (coord.strided() < extent_.strided());
            } else {
              guard = (coord.contiguous() < extent_.contiguous());
            }
          } else {
            guard = (coord.strided() < extent_.strided() &&
                     coord.contiguous() < extent_.contiguous());
          }

          int pred_idx = ts + c *  ThreadMap::ThreadAccessShape::kStrided + s * ThreadMap::Iterations::kContiguous *  ThreadMap::ThreadAccessShape::kStrided;
          int word_idx = pred_idx / kPredicatesPerWord;
          int residual = pred_idx % kPredicatesPerWord;
          int byte_idx = residual / kPredicatesPerByte;
          int bit_idx = residual % kPredicatesPerByte;
          
          predicates_[word_idx] |= (unsigned(guard) << (byte_idx * 8 + bit_idx));

        }
      }
    }

  }

 public:
  CUTLASS_HOST_DEVICE
  PredicatedTileAccessIterator2dThreadTile(
      Params const &params,
      Pointer pointer,
      TensorCoord extent,
      int thread_id,
      TensorCoord const &threadblock_offset)
      : params_(params),
        pointer_(reinterpret_cast<BytePointer>(
            const_cast<NonConstPointer>(pointer))),
        extent_(extent),
        is_residue_tile_(true) {
          

    TensorCoord residue_offset;
    if (kAdvanceRank) {
      residue_tile_idx_ =
          (extent_[kAdvanceRank] - threadblock_offset[kAdvanceRank] - 1) /
          Shape::kStrided;
      residue_offset = make_Coord(0, residue_tile_idx_ * Shape::kStrided);
    } else {
      residue_tile_idx_ =
          (extent_[kAdvanceRank] - threadblock_offset[kAdvanceRank] - 1) /
          Shape::kContiguous;
      residue_offset = make_Coord(residue_tile_idx_ * Shape::kContiguous, 0);
    }

    // Per-thread offset in logical coordinates of tensor
    thread_offset_ = threadblock_offset + residue_offset +
                     ThreadMap::initial_offset(thread_id);

    // update internal pointers
    Layout layout(params_.stride_);
    add_pointer_offset(layout(thread_offset_));

    compute_predicates_(false);

    set_iteration_index(0);
  }

  CUTLASS_HOST_DEVICE
  PredicatedTileAccessIterator2dThreadTile(
      Params const &params,
      Pointer pointer,
      TensorCoord extent,
      int thread_id)
      : PredicatedTileAccessIterator2dThreadTile(params, pointer, extent, thread_id,
                                     make_Coord(0, 0)) {}

  CUTLASS_HOST_DEVICE
  void set_iteration_index(int index) {

    int residual = index % (ThreadMap::Iterations::kContiguous * ThreadMap::ThreadAccessShape::kStrided);
    iteration_strided_ = index / (ThreadMap::Iterations::kContiguous * ThreadMap::ThreadAccessShape::kStrided);
    
    iteration_contiguous_ = residual / ThreadMap::ThreadAccessShape::kStrided;
    iteration_thread_ = residual % ThreadMap::ThreadAccessShape::kStrided;

  }

  CUTLASS_HOST_DEVICE
  void add_pointer_offset(LongIndex pointer_offset) {
    pointer_ += int(sizeof(Element)) * pointer_offset;
  }

  CUTLASS_DEVICE
  void add_tile_offset(
      TensorCoord const &tile_offset) {
    if (is_residue_tile_) {
      TensorCoord residue_offset;
      if (kAdvanceRank) {
        residue_offset = TensorCoord(0, residue_tile_idx_ * Shape::kStrided);
      } else {
        residue_offset = TensorCoord(residue_tile_idx_ * Shape::kContiguous, 0);
      }

      thread_offset_ -= residue_offset;

      Layout layout(params_.stride_);
      add_pointer_offset(-layout(residue_offset));

      compute_predicates_(true);

      if (kAdvanceRank) {
        pointer_ += params_.inc_advance_ * (tile_offset.strided() - 1);
        pointer_ += Shape::kContiguous * tile_offset.contiguous();
      } else {
        pointer_ += params_.inc_advance_ * (tile_offset.contiguous() - 1);
        pointer_ += Shape::kStrided * tile_offset.strided();
      }
    } else {
      if (kAdvanceRank) {
        pointer_ += params_.inc_advance_ * tile_offset.strided();
        pointer_ += Shape::kContiguous * tile_offset.contiguous();
      } else {
        pointer_ += params_.inc_advance_ * tile_offset.contiguous();
        pointer_ += Shape::kStrided * tile_offset.strided();
      }
    }
    is_residue_tile_ = false;
  }

  CUTLASS_HOST_DEVICE
  AccessType *get() const {

    AccessType *ret_val = reinterpret_cast<AccessType *>(
                pointer_ + (iteration_thread_ * params_.stride_  + iteration_contiguous_ * ThreadMap::Delta::kContiguous) * int(sizeof(Element)));

    return ret_val;
  }

  CUTLASS_HOST_DEVICE
  PredicatedTileAccessIterator2dThreadTile &operator++() {

    iteration_thread_++;

    if (iteration_thread_ < ThreadMap::ThreadAccessShape::kStrided)
      return *this;

    iteration_thread_ = 0;

    ++iteration_contiguous_;

    if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous)
      return *this;

    // Enter here only if (iteration_contiguous_ ==
    // ThreadMap::Iteration::kContiguous)
    iteration_contiguous_ = 0;
    ++iteration_strided_;

    if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
      pointer_ += params_.inc_strided_;
      return *this;
    }

    // Enter here only if (iteration_stride_ == ThreadMap::Iteration::kStrided)
    // which means we enter the next tile.
    iteration_strided_ = 0;

    // advance to next tile
    pointer_ += params_.inc_next_;

    // now return to start tile - if the iterator is subsequently advanced, this
    // subtraction as well as the subsequent integer addition are both elided by
    // the compiler.
    pointer_ -= params_.inc_advance_;

    return *this;
  }

  CUTLASS_HOST_DEVICE
  PredicatedTileAccessIterator2dThreadTile operator++(int) {
    PredicatedTileAccessIterator2dThreadTile self(*this);
    operator++();
    return self;
  }

  CUTLASS_HOST_DEVICE
  void clear_mask() {
    CUTLASS_PRAGMA_UNROLL
    for (int i = 0; i < kPredicateWordCount; ++i) {
      predicates_[i] = 0u;
    }

  }

  CUTLASS_HOST_DEVICE
  void enable_mask() {
    CUTLASS_PRAGMA_UNROLL
    for (int i = 0; i < kPredicateWordCount; ++i) {
      predicates_[i] = 0xffffffff;
    }
  }

  CUTLASS_HOST_DEVICE
  void set_mask(Mask const &mask) { 
    CUTLASS_PRAGMA_UNROLL
    for (int i = 0; i < kPredicateWordCount; ++i) {
      predicates_[i] = mask[i];
    }

  }

  CUTLASS_HOST_DEVICE
  void get_mask(Mask &mask) {
     CUTLASS_PRAGMA_UNROLL
    for (int i = 0; i < kPredicateWordCount; ++i) {
      mask[i] = predicates_[i];
    }
  }

  CUTLASS_HOST_DEVICE
  bool valid() {

    int pred_idx = 
      iteration_thread_ + 
      iteration_contiguous_ * ThreadMap::ThreadAccessShape::kStrided + 
      iteration_strided_ * ThreadMap::Iterations::kContiguous * ThreadMap::ThreadAccessShape::kStrided;

    int word_idx = pred_idx / kPredicatesPerWord;
    int residual = pred_idx % kPredicatesPerWord;
    int byte_idx = residual / kPredicatesPerByte;
    int bit_idx = residual % kPredicatesPerByte;
    
    bool pred = (predicates_[word_idx] & (1u << (byte_idx * 8 + bit_idx))) != 0;
    
    return pred;
  }
};


template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, typename AccessType_>
class PredicatedTileAccessIterator2dThreadTile<Shape_, Element_, layout::ColumnMajor,
                                   AdvanceRank, ThreadMap_, AccessType_> {
 public:
  static_assert(
      AdvanceRank == 0 || AdvanceRank == 1,
      "Specialization for pitch-linear iterator may along advance along the "
      "contiguous(rank=0) or strided(rank=1) dimension.");

  using Shape = Shape_;
  using Element = Element_;
  using Layout = layout::ColumnMajor;
  static int const kAdvanceRank = AdvanceRank;
  using ThreadMap = ThreadMap_;
  using AccessType = AccessType_;

  using Index = typename Layout::Index;
  using LongIndex = typename Layout::LongIndex;

  using TensorRef = TensorRef<Element, Layout>;
  using TensorView = TensorView<Element, Layout>;
  using TensorCoord = typename Layout::TensorCoord;

  using Pointer = Element *;
  using NonConstPointer = typename platform::remove_const<Element>::type *;

  using UnderlyingIterator = PredicatedTileAccessIterator2dThreadTile<
      layout::PitchLinearShape<Shape::kRow, Shape::kColumn>, Element,
      layout::PitchLinear, (kAdvanceRank == 0 ? 0 : 1), ThreadMap, AccessType>;

  using Mask = typename UnderlyingIterator::Mask;

  class Params {
   private:
    friend PredicatedTileAccessIterator2dThreadTile;

    typename UnderlyingIterator::Params params_;

   public:

    CUTLASS_HOST_DEVICE
    Params() { }

    CUTLASS_HOST_DEVICE
    Params(Layout const &layout)
        : params_(layout::PitchLinear(layout.stride(0))){};
  };

 private:
  //
  // Data members
  //

  UnderlyingIterator iterator_;

 public:
  CUTLASS_HOST_DEVICE
  PredicatedTileAccessIterator2dThreadTile(
      Params const &params,
      Pointer pointer,
      TensorCoord extent,
      int thread_id,
      TensorCoord const &threadblock_offset)
      : iterator_(params.params_, pointer,
                  layout::PitchLinearCoord(extent.row(), extent.column()),
                  thread_id,
                  layout::PitchLinearCoord(threadblock_offset.row(),
                                           threadblock_offset.column())) {}

  CUTLASS_HOST_DEVICE
  PredicatedTileAccessIterator2dThreadTile(
      Params const &params,  
      Pointer pointer,       
      TensorCoord extent,    
      int thread_id          
      )
      : PredicatedTileAccessIterator2dThreadTile(params, pointer, extent, thread_id,
                                     make_Coord(0, 0)) {}

  CUTLASS_HOST_DEVICE
  void set_iteration_index(int index) { iterator_.set_iteration_index(index); }

  CUTLASS_HOST_DEVICE
  void add_pointer_offset(LongIndex pointer_offset) {
    iterator_.add_pointer_offset(pointer_offset);
  }

  CUTLASS_HOST_DEVICE
  void add_tile_offset(TensorCoord const &tile_offset) {
    iterator_.add_tile_offset({tile_offset.row(), tile_offset.column()});
  }

  CUTLASS_HOST_DEVICE
  AccessType *get() const {
    return reinterpret_cast<AccessType *>(iterator_.get());
  }

  CUTLASS_HOST_DEVICE
  PredicatedTileAccessIterator2dThreadTile &operator++() {
    ++iterator_;
    return *this;
  }

  CUTLASS_HOST_DEVICE
  PredicatedTileAccessIterator2dThreadTile operator++(int) {
    PredicatedTileAccessIterator2dThreadTile self(*this);
    operator++();
    return self;
  }

  CUTLASS_HOST_DEVICE
  void clear_mask() { iterator_.clear_mask(); }

  CUTLASS_HOST_DEVICE
  void enable_mask() { iterator_.enable_mask(); }

  CUTLASS_HOST_DEVICE
  void set_mask(Mask const &mask) { iterator_.set_mask(mask); }

  CUTLASS_HOST_DEVICE
  void get_mask(Mask &mask) { iterator_.get_mask(mask); }

  CUTLASS_HOST_DEVICE
  bool valid() {
    return iterator_.valid();
  }
};


template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, typename AccessType_>
class PredicatedTileAccessIterator2dThreadTile<Shape_, Element_, layout::RowMajor,
                                   AdvanceRank, ThreadMap_, AccessType_> {
 public:
  static_assert(
      AdvanceRank == 0 || AdvanceRank == 1,
      "Specialization for pitch-linear iterator may along advance along the "
      "contiguous(rank=0) or strided(rank=1) dimension.");

  using Shape = Shape_;
  using Element = Element_;
  using Layout = layout::RowMajor;
  static int const kAdvanceRank = AdvanceRank;
  using ThreadMap = ThreadMap_;
  using AccessType = AccessType_;

  using Index = typename Layout::Index;
  using LongIndex = typename Layout::LongIndex;

  using TensorRef = TensorRef<Element, Layout>;
  using TensorView = TensorView<Element, Layout>;
  using TensorCoord = typename Layout::TensorCoord;

  using Pointer = Element *;
  using NonConstPointer = typename platform::remove_const<Element>::type *;

  using UnderlyingIterator = PredicatedTileAccessIterator2dThreadTile<
      layout::PitchLinearShape<Shape::kColumn, Shape::kRow>, Element,
      layout::PitchLinear, (kAdvanceRank == 0 ? 1 : 0), ThreadMap, AccessType>;

  using Mask = typename UnderlyingIterator::Mask;

  class Params {
   private:
    friend PredicatedTileAccessIterator2dThreadTile;

    typename UnderlyingIterator::Params params_;

   public:

    CUTLASS_HOST_DEVICE
    Params() { }

    CUTLASS_HOST_DEVICE
    Params(Layout const &layout)
        : params_(layout::PitchLinear(layout.stride(0))){};
  };

 private:
  //
  // Data members
  //

  UnderlyingIterator iterator_;

 public:
  CUTLASS_HOST_DEVICE
  PredicatedTileAccessIterator2dThreadTile(
      Params const &params,
      Pointer pointer,
      TensorCoord extent,
      int thread_id,
      TensorCoord const &threadblock_offset)
      : iterator_(params.params_, pointer,
                  layout::PitchLinearCoord(extent.column(), extent.row()),
                  thread_id,
                  layout::PitchLinearCoord(threadblock_offset.column(),
                                           threadblock_offset.row())) {}

  CUTLASS_HOST_DEVICE
  PredicatedTileAccessIterator2dThreadTile(
      Params const &params,  
      Pointer pointer,       
      TensorCoord extent,    
      int thread_id          
      )
      : PredicatedTileAccessIterator2dThreadTile(params, pointer, extent, thread_id,
                                     make_Coord(0, 0)) {}

  CUTLASS_HOST_DEVICE
  void set_iteration_index(int index) { iterator_.set_iteration_index(index); }

  CUTLASS_HOST_DEVICE
  void add_pointer_offset(LongIndex pointer_offset) {
    iterator_.add_pointer_offset(pointer_offset);
  }

  CUTLASS_HOST_DEVICE
  void add_tile_offset(TensorCoord const &tile_offset) {
    iterator_.add_tile_offset({tile_offset.column(), tile_offset.row()});
  }

  CUTLASS_HOST_DEVICE
  AccessType *get() const {
    return reinterpret_cast<AccessType *>(iterator_.get());
  }

  CUTLASS_HOST_DEVICE
  PredicatedTileAccessIterator2dThreadTile &operator++() {
    ++iterator_;
    return *this;
  }

  CUTLASS_HOST_DEVICE
  PredicatedTileAccessIterator2dThreadTile operator++(int) {
    PredicatedTileAccessIterator2dThreadTile self(*this);
    operator++();
    return self;
  }

  CUTLASS_HOST_DEVICE
  void clear_mask() { iterator_.clear_mask(); }

  CUTLASS_HOST_DEVICE
  void enable_mask() { iterator_.enable_mask(); }

  CUTLASS_HOST_DEVICE
  void set_mask(Mask const &mask) { iterator_.set_mask(mask); }

  CUTLASS_HOST_DEVICE
  void get_mask(Mask &mask) { iterator_.get_mask(mask); }

  CUTLASS_HOST_DEVICE
  bool valid() {
    return iterator_.valid();
  }
};



}  // namespace threadblock
}  // namespace transform
}  // namespace cutlass