regular_tile_iterator_tensor_op.h

Go to the documentation of this file.

/***************************************************************************************************
 * Copyright (c) 2017-2019, NVIDIA CORPORATION.  All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification, are permitted
 * provided that the following conditions are met:
 *     * Redistributions of source code must retain the above copyright notice, this list of
 *       conditions and the following disclaimer.
 *     * Redistributions in binary form must reproduce the above copyright notice, this list of
 *       conditions and the following disclaimer in the documentation and/or other materials
 *       provided with the distribution.
 *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
 *       to endorse or promote products derived from this software without specific prior written
 *       permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
 * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
 * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
 * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
 * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 **************************************************************************************************/
#pragma once

#include "cutlass/transform/threadblock/regular_tile_iterator.h"
#include "cutlass/transform/threadblock/regular_tile_access_iterator_tensor_op.h"


namespace cutlass {
namespace transform {
namespace threadblock {


template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int Alignment>
class RegularTileIterator<
    Shape_, Element_,
    layout::TensorOpMultiplicandCongruous<sizeof_bits<Element_>::value,
                                          int(128 / sizeof(Element_))>,
    AdvanceRank, ThreadMap_, Alignment> {
 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::TensorOpMultiplicandCongruous<sizeof_bits<Element_>::value,
                                            int(128 / sizeof(Element))>;
  static int const kAdvanceRank = AdvanceRank;
  static int const kAlignment = Alignment;

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

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

  using ThreadMap = ThreadMap_;

  struct Detail {

    static int const kAccessSizeInBits = 128;

    static_assert(
      sizeof_bits<Element_>::value * ThreadMap::kElementsPerAccess == kAccessSizeInBits,
      "This iterator requires a policy whose access size is 128bs");
  };

private:

  using AccessType = Array<Element, Layout::kElementsPerAccess>;

public:

  using Fragment = Array<Element, ThreadMap::Iterations::kCount * Layout::kElementsPerAccess>;

  using TileAccessIterator = RegularTileAccessIterator<Shape, Element, Layout,
                                                       kAdvanceRank, ThreadMap>;

private:

  //
  // Data members
  //

  TileAccessIterator address_iterator_;

public:

  CUTLASS_HOST_DEVICE
  RegularTileIterator(TensorRef ref,  
                      int thread_id   
                      )
      : address_iterator_(ref, thread_id) {}

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

  CUTLASS_HOST_DEVICE
  RegularTileIterator &operator++() {
    address_iterator_.add_tile_offset({0, 1});
    return *this;
  }

  CUTLASS_HOST_DEVICE
  RegularTileIterator operator++(int) {
    RegularTileIterator prev(*this);
    this->operator++();

    return prev;
  }

  CUTLASS_DEVICE
  void add_tile_offset(TensorCoord const &coord) {
    address_iterator_.add_tile_offset(coord);
  }

  CUTLASS_DEVICE
  void load_with_pointer_offset(Fragment &frag, Index pointer_offset) {
    address_iterator_.set_iteration_index(0);
    AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);

    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 access_idx = c + s * ThreadMap::Iterations::kContiguous;
        frag_ptr[access_idx] = *(address_iterator_.get() + pointer_offset);
        ++address_iterator_;
      }
    }
  }

  CUTLASS_DEVICE
  void load(Fragment &frag) {
    load_with_pointer_offset(frag, 0);
  }

  CUTLASS_DEVICE
  void store_with_pointer_offset(Fragment const &frag, Index pointer_offset) {
    address_iterator_.set_iteration_index(0);
    AccessType const *frag_ptr = reinterpret_cast<AccessType const *>(&frag);

    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 access_idx = c + s * ThreadMap::Iterations::kContiguous;
        *(address_iterator_.get() + pointer_offset) = frag_ptr[access_idx];
        ++address_iterator_;
      }
    }
  }

  CUTLASS_DEVICE
  void store(Fragment const &frag) {
    store_with_pointer_offset(frag, 0);
  }
};


template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int Alignment>
class RegularTileIterator<
    Shape_, Element_,
    layout::ColumnMajorTensorOpMultiplicandCongruous<
        sizeof_bits<Element_>::value, int(128 / sizeof(Element_))>,
    AdvanceRank, ThreadMap_, Alignment> {
 public:

  static_assert(AdvanceRank == 0 || AdvanceRank == 1, 
    "Specialization for column-major iterator may along advance along the "
    "columns(rank=0) or rows(rank=1) dimension.");

  using Shape = Shape_;
  using Element = Element_;
  using Layout = layout::ColumnMajorTensorOpMultiplicandCongruous<
      sizeof_bits<Element_>::value, int(128 / sizeof(Element))>;
  static int const kAdvanceRank = AdvanceRank;
  static int const kAlignment = Alignment;

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

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

  using ThreadMap = ThreadMap_;

  using UnderlyingIterator = RegularTileIterator<
      layout::PitchLinearShape<Shape::kRow, Shape::kColumn>, Element,
      layout::TensorOpMultiplicandCongruous<sizeof_bits<Element_>::value,
                                            int(128 / sizeof(Element))>,
      (kAdvanceRank == 0 ? 0 : 1), ThreadMap_>;

 public:

  using Fragment = Array<Element, UnderlyingIterator::Fragment::kElements>;

private:

  UnderlyingIterator iterator_;

public:

  CUTLASS_HOST_DEVICE
  RegularTileIterator(
    TensorRef ref,                              
    int thread_id                               
  ): iterator_({ref.data(), ref.stride()}, thread_id) {

  }

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

  CUTLASS_DEVICE
  void add_tile_offset(TensorCoord const &coord) {
    iterator_.add_tile_offset({coord.row(), coord.column()});
  }

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

  CUTLASS_HOST_DEVICE
  RegularTileIterator operator++(int) {
    RegularTileIterator prev(*this);
    ++iterator_;

    return prev;
  }

  CUTLASS_DEVICE
  void load_with_pointer_offset(Fragment &frag, Index pointer_offset) {
    iterator_.load_with_pointer_offset(frag, pointer_offset);
  }

  CUTLASS_DEVICE
  void load(Fragment &frag) {
    load_with_pointer_offset(frag, 0);
  }

  CUTLASS_DEVICE
  void store_with_pointer_offset(
    Fragment const &frag, 
    Index pointer_offset) {
    
    iterator_.store_with_pointer_offset(frag, pointer_offset);
  }

  CUTLASS_DEVICE
  void store(Fragment const &frag) {
    store_with_pointer_offset(frag, 0);
  }
};


template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int Alignment>
class RegularTileIterator<
    Shape_, Element_,
    layout::RowMajorTensorOpMultiplicandCongruous<sizeof_bits<Element_>::value,
                                                  int(128 / sizeof(Element_))>,
    AdvanceRank, ThreadMap_, Alignment> {
 public:

  static_assert(AdvanceRank == 0 || AdvanceRank == 1, 
    "Specialization for row-major iterator may along advance along the "
    "columns(rank=0) or rows(rank=1) dimension.");

  using Shape = Shape_;
  using Element = Element_;
  using Layout = layout::RowMajorTensorOpMultiplicandCongruous<
      sizeof_bits<Element_>::value, int(128 / sizeof(Element))>;
  static int const kAdvanceRank = AdvanceRank;
  static int const kAlignment = Alignment;

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

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

  using ThreadMap = ThreadMap_;

  using UnderlyingIterator = RegularTileIterator<
      layout::PitchLinearShape<Shape::kColumn, Shape::kRow>, Element,
      layout::TensorOpMultiplicandCongruous<sizeof_bits<Element_>::value,
                                            int(128 / sizeof(Element))>,
      (kAdvanceRank == 0 ? 1 : 0), ThreadMap_>;

 public:

  using Fragment = Array<Element, UnderlyingIterator::Fragment::kElements>;

private:

  UnderlyingIterator iterator_;

public:

  CUTLASS_HOST_DEVICE
  RegularTileIterator(
    TensorRef ref,                              
    int thread_id                               
  ): iterator_({ref.data(), ref.stride()}, thread_id) {

  }

  CUTLASS_HOST_DEVICE
  void add_pointer_offset(LongIndex pointer_offset) {
    iterator_.add_pointer_offset(pointer_offset);
  }
  
  CUTLASS_DEVICE
  void add_tile_offset(TensorCoord const &coord) {
    iterator_.add_tile_offset({coord.column(), coord.row()});
  }

  CUTLASS_HOST_DEVICE
  RegularTileIterator &operator++() {

    ++iterator_;
    return *this;
  }

  CUTLASS_HOST_DEVICE
  RegularTileIterator operator++(int) {

    RegularTileIterator prev(*this);
    ++iterator_;

    return prev;
  }

  CUTLASS_DEVICE
  void load_with_pointer_offset(Fragment &frag, Index pointer_offset) {
    iterator_.load_with_pointer_offset(frag, pointer_offset);
  }

  CUTLASS_DEVICE
  void load(Fragment &frag) {
    load_with_pointer_offset(frag, 0);
  }

  CUTLASS_DEVICE
  void store_with_pointer_offset(
    Fragment const &frag, 
    Index pointer_offset) {
    
    iterator_.store_with_pointer_offset(frag, pointer_offset);
  }

  CUTLASS_DEVICE
  void store(Fragment const &frag) {
    store_with_pointer_offset(frag, 0);
  }
};


template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int Alignment, int Crosswise>
class RegularTileIterator<Shape_, Element_,
                          layout::TensorOpMultiplicandCrosswise<
                              sizeof_bits<Element_>::value, Crosswise>,
                          AdvanceRank, ThreadMap_, Alignment> {
 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::TensorOpMultiplicandCrosswise<sizeof_bits<Element_>::value,
                                            Crosswise>;

  static int const kAdvanceRank = AdvanceRank;
  static int const kAlignment = Alignment;

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

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

  using ThreadMap = ThreadMap_;

  struct Detail {
    static int const kAccessSizeInBits = 128;

    static_assert(sizeof_bits<Element_>::value * ThreadMap::kElementsPerAccess ==
                      kAccessSizeInBits,
                  "This iterator requires a policy whose access size is 128bs");
  };

 private:
  using AccessType = Array<Element, Layout::kElementsPerAccess>;

 public:
  using Fragment =
      Array<Element, ThreadMap::Iterations::kCount * Layout::kElementsPerAccess>;

  using TileAccessIterator = RegularTileAccessIterator<Shape, Element, Layout,
                                                       kAdvanceRank, ThreadMap>;

 private:
  //
  // Data members
  //

  TileAccessIterator address_iterator_;

 public:
  CUTLASS_HOST_DEVICE
  RegularTileIterator(TensorRef ref,  
                      int thread_id   
                      )
      : address_iterator_(ref, thread_id) {}

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

  CUTLASS_HOST_DEVICE
  RegularTileIterator &operator++() {
    address_iterator_.add_tile_offset({1, 0});
    return *this;
  }

  CUTLASS_HOST_DEVICE
  RegularTileIterator operator++(int) {
    RegularTileIterator prev(*this);
    this->operator++();

    return prev;
  }

  CUTLASS_DEVICE
  void add_tile_offset(TensorCoord const &coord) {
    address_iterator_.add_tile_offset(coord);
  }

  CUTLASS_DEVICE
  void load_with_pointer_offset(Fragment &frag, Index pointer_offset) {
    address_iterator_.set_iteration_index(0);
    AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);

    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 access_idx = c + s * ThreadMap::Iterations::kContiguous;
        frag_ptr[access_idx] = *(address_iterator_.get() + pointer_offset);
        ++address_iterator_;
      }
    }
  }

  CUTLASS_DEVICE
  void load(Fragment &frag) { load_with_pointer_offset(frag, 0); }

  CUTLASS_DEVICE
  void store_with_pointer_offset(Fragment const &frag, Index pointer_offset) {
    address_iterator_.set_iteration_index(0);
    AccessType const *frag_ptr = reinterpret_cast<AccessType const *>(&frag);

    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 access_idx = c + s * ThreadMap::Iterations::kContiguous;
        *(address_iterator_.get() + pointer_offset) = frag_ptr[access_idx];
        ++address_iterator_;
      }
    }
  }

  CUTLASS_DEVICE
  void store(Fragment const &frag) { store_with_pointer_offset(frag, 0); }
};


template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int Alignment, int Crosswise>
class RegularTileIterator<Shape_, Element_,
                          layout::ColumnMajorTensorOpMultiplicandCrosswise<
                              sizeof_bits<Element_>::value, Crosswise>,
                          AdvanceRank, ThreadMap_, Alignment> {
 public:
  static_assert(
      AdvanceRank == 0 || AdvanceRank == 1,
      "Specialization for column-major iterator may along advance along the "
      "columns(rank=0) or rows(rank=1) dimension.");

  using Shape = Shape_;
  using Element = Element_;
  using Layout = layout::ColumnMajorTensorOpMultiplicandCrosswise<
      sizeof_bits<Element_>::value, Crosswise>;
  static int const kAdvanceRank = AdvanceRank;
  static int const kAlignment = Alignment;

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

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

  using ThreadMap = ThreadMap_;

  using UnderlyingIterator = RegularTileIterator<
      layout::PitchLinearShape<Shape::kRow, Shape::kColumn>, Element,
      layout::TensorOpMultiplicandCrosswise<sizeof_bits<Element_>::value,
                                            Crosswise>,
      (kAdvanceRank == 0 ? 0 : 1), ThreadMap_>;

 public:
  using Fragment = Array<Element, UnderlyingIterator::Fragment::kElements>;

 private:
  UnderlyingIterator iterator_;

 public:
  CUTLASS_HOST_DEVICE
  RegularTileIterator(TensorRef ref,  
                      int thread_id   
                      )
      : iterator_({ref.data(), ref.stride()}, thread_id) {}

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

  CUTLASS_DEVICE
  void add_tile_offset(TensorCoord const &coord) {
    iterator_.add_tile_offset({coord.row(), coord.column()});
  }

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

  CUTLASS_HOST_DEVICE
  RegularTileIterator operator++(int) {
    RegularTileIterator prev(*this);
    ++iterator_;

    return prev;
  }

  CUTLASS_DEVICE
  void load_with_pointer_offset(Fragment &frag, Index pointer_offset) {
    iterator_.load_with_pointer_offset(frag, pointer_offset);
  }

  CUTLASS_DEVICE
  void load(Fragment &frag) { load_with_pointer_offset(frag, 0); }

  CUTLASS_DEVICE
  void store_with_pointer_offset(Fragment const &frag, Index pointer_offset) {
    iterator_.store_with_pointer_offset(frag, pointer_offset);
  }

  CUTLASS_DEVICE
  void store(Fragment const &frag) { store_with_pointer_offset(frag, 0); }
};


template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int Alignment, int Crosswise>
class RegularTileIterator<Shape_, Element_,
                          layout::RowMajorTensorOpMultiplicandCrosswise<
                              sizeof_bits<Element_>::value, Crosswise>,
                          AdvanceRank, ThreadMap_, Alignment> {
 public:
  static_assert(
      AdvanceRank == 0 || AdvanceRank == 1,
      "Specialization for row-major iterator may along advance along the "
      "columns(rank=0) or rows(rank=1) dimension.");

  using Shape = Shape_;
  using Element = Element_;
  using Layout = layout::RowMajorTensorOpMultiplicandCrosswise<
      sizeof_bits<Element_>::value, Crosswise>;
  static int const kAdvanceRank = AdvanceRank;
  static int const kAlignment = Alignment;

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

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

  using ThreadMap = ThreadMap_;

  using UnderlyingIterator = RegularTileIterator<
      layout::PitchLinearShape<Shape::kColumn, Shape::kRow>, Element,
      layout::TensorOpMultiplicandCrosswise<sizeof_bits<Element_>::value,
                                            Crosswise>,
      (kAdvanceRank == 0 ? 1 : 0), ThreadMap_>;

 public:
  using Fragment = Array<Element, UnderlyingIterator::Fragment::kElements>;

 private:
  UnderlyingIterator iterator_;

 public:
  CUTLASS_HOST_DEVICE
  RegularTileIterator(TensorRef ref,  
                      int thread_id   
                      )
      : iterator_({ref.data(), ref.stride()}, thread_id) {}

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

  CUTLASS_DEVICE
  void add_tile_offset(TensorCoord const &coord) {
    iterator_.add_tile_offset({coord.column(), coord.row()});
  }

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

  CUTLASS_HOST_DEVICE
  RegularTileIterator operator++(int) {
    RegularTileIterator prev(*this);
    ++iterator_;

    return prev;
  }

  CUTLASS_DEVICE
  void load_with_pointer_offset(Fragment &frag, Index pointer_offset) {
    iterator_.load_with_pointer_offset(frag, pointer_offset);
  }

  CUTLASS_DEVICE
  void load(Fragment &frag) { load_with_pointer_offset(frag, 0); }

  CUTLASS_DEVICE
  void store_with_pointer_offset(Fragment const &frag, Index pointer_offset) {
    iterator_.store_with_pointer_offset(frag, pointer_offset);
  }

  CUTLASS_DEVICE
  void store(Fragment const &frag) { store_with_pointer_offset(frag, 0); }
};

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