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 *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.
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 *notice, this list of conditions and the following disclaimer in the
 *documentation and/or other materials provided with the distribution.
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 *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
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 *INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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/*! \file
    \brief Templates implementing computing the addresses of storing of tiles
   from pitch-linear rank=2 tensors.
*/

/**
 * \file
 * include/cutlass/transform/threadblock/regular_tile_access_iterator_tensor_op.h
 *
 * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
 * implied.
 *
 * This file has been modified by Megvii ("Megvii Modification").
 * All Megvii Modifications are Copyright (C) 2014-2020 Megvii Inc. All rights
 * reserved.
 */
#pragma once

#include "cutlass/array.h"
#include "cutlass/cutlass.h"
#include "cutlass/layout/pitch_linear.h"
#include "cutlass/layout/tensor_op_multiplicand_sm75.h"
#include "cutlass/matrix_coord.h"
#include "cutlass/matrix_shape.h"
#include "cutlass/tensor_ref.h"
#include "cutlass/transform/threadblock/regular_tile_access_iterator.h"

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

namespace cutlass {
namespace transform {
namespace threadblock {

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

/// Tile iterator specialized for congruous arrangements for TensorOps
///
///
/// Satisfies: ForwardTileIteratorConcept |
///            ReadableContiguousTileIteratorConcept |
///            WriteableContiguousTileIteratorConcept
///
template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int Alignment>
class RegularTileAccessIterator<
        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_;

    /// Internal details made public to facilitate introspection
    struct Detail {
        /// This iterator is specialized for an access size that is 128 bits in
        /// length.
        static int const kAccessSizeInBits = 128;

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

        ///< Number of pointers
        static int const kPointerCount =
                (ThreadMap::Iterations::kStrided > 1 ? 2 : 1);
    };

    /// Element type per access
    using AccessType = Array<Element, Layout::kElementsPerAccess>;

private:
    //
    // Data members
    //

    /// Stride value
    Index stride_;

    /// Internal pointer to first access of tile
    AccessType* pointer_[Detail::kPointerCount];

    /// Internal byte offset
    Index byte_offset_;

    /// Iteration in the contiguous dimension
    int iteration_contiguous_;

    /// Iteration in the strided dimension
    int iteration_strided_;

public:
    /// Construct a TileIterator with zero threadblock offset
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator(
            TensorRef ref,  ///< Pointer to start of tensor
            int thread_id   ///< ID of each participating thread
            )
            : stride_(ref.stride(0) / Layout::kElementsPerAccess),
              byte_offset_(0) {
        layout::PitchLinearCoord thread_offset_base =
                ThreadMap::initial_offset(thread_id);

        CUTLASS_PRAGMA_UNROLL
        for (int i = 0; i < Detail::kPointerCount; ++i) {
            // This is the offset of a thread within a threadblock tile for a
            // specific pointer (units of elements)
            layout::PitchLinearCoord thread_offset_in_threadblock_tile =
                    thread_offset_base +
                    layout::PitchLinearCoord{
                            0,
                            ThreadMap::Detail::WarpThreadArrangement::kStrided *
                                    i};

            // initialize pointer
            pointer_[i] = reinterpret_cast<AccessType*>(
                    ref.data() + ref.offset(thread_offset_in_threadblock_tile));
        }

        set_iteration_index(0);
    }

    /// Overrides the internal iteration index
    CUTLASS_HOST_DEVICE
    void set_iteration_index(int index) {
        iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
        iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
    }

    /// Adds a pointer offset in units of Element
    CUTLASS_HOST_DEVICE
    void add_pointer_offset(LongIndex pointer_offset) {
        byte_offset_ += pointer_offset * sizeof(Element);
    }

    /// Returns a pointer
    CUTLASS_HOST_DEVICE
    AccessType* get() const {
        AccessType* access_ptr = pointer_[iteration_strided_ & 1];
        int stride_idx = (iteration_strided_ & ~1);

        int access_offset = stride_idx * ThreadMap::Delta::kStrided * stride_ +
                            iteration_contiguous_ *
                                    ThreadMap::Delta::kContiguous /
                                    ThreadMap::kElementsPerAccess;

        char* access_byte_ptr =
                reinterpret_cast<char*>(access_ptr + access_offset);
        return reinterpret_cast<AccessType*>(access_byte_ptr + byte_offset_);
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator& operator++() {
        ++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) {
            return *this;
        }

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

        return *this;
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator operator++(int) {
        RegularTileAccessIterator prev(*this);
        this->operator++();

        return prev;
    }

    /// Adds a tile offset
    CUTLASS_DEVICE
    void add_tile_offset(TensorCoord const& coord) {
        add_pointer_offset(coord.contiguous() * Shape::kContiguous +
                           coord.strided() * Shape::kStrided * stride_ *
                                   Layout::kElementsPerAccess);
    }
};

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

/// Tile Iterator specialized for column-major congruous TensorOp formats.
///
///
/// Satisfies: ForwardTileIteratorConcept |
///            ReadableContiguousTileIteratorConcept |
///            WriteableContiguousTileIteratorConcept
///
template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int Alignment>
class RegularTileAccessIterator<
        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_;

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

    using AccessType = typename UnderlyingIterator::AccessType;

private:
    /// Underlying iterator
    UnderlyingIterator iterator_;

public:
    /// Construct a TileIterator with zero threadblock offset
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator(
            TensorRef ref,  ///< Pointer to start of tensor
            int thread_id   ///< ID of each participating thread
            )
            : iterator_({ref.data(), ref.stride()}, thread_id) {}

    /// Overrides the internal iteration index
    CUTLASS_HOST_DEVICE
    void set_iteration_index(int index) {
        iterator_.set_iteration_index(index);
    }

    /// Adds a pointer offset in units of Element
    CUTLASS_HOST_DEVICE
    void add_pointer_offset(LongIndex pointer_offset) {
        iterator_.add_pointer_offset(pointer_offset);
    }

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

    /// Adds a tile offset
    CUTLASS_DEVICE
    void add_tile_offset(TensorCoord const& coord) {
        iterator_.add_tile_offset({coord.row(), coord.column()});
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator& operator++() {
        ++iterator_;
        return *this;
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator operator++(int) {
        RegularTileAccessIterator prev(*this);
        ++iterator_;

        return prev;
    }
};

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

/// Tile Iterator specialized for row-major congruous TensorOp formats.
///
///
/// Satisfies: ForwardTileIteratorConcept |
///            ReadableContiguousTileIteratorConcept |
///            WriteableContiguousTileIteratorConcept
///
template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int Alignment>
class RegularTileAccessIterator<
        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_;

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

    using AccessType = typename UnderlyingIterator::AccessType;

private:
    /// Underlying iterator
    UnderlyingIterator iterator_;

public:
    /// Construct a TileIterator with zero threadblock offset
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator(
            TensorRef ref,  ///< Pointer to start of tensor
            int thread_id   ///< ID of each participating thread
            )
            : iterator_({ref.data(), ref.stride()}, thread_id) {}

    /// Overrides the internal iteration index
    CUTLASS_HOST_DEVICE
    void set_iteration_index(int index) {
        iterator_.set_iteration_index(index);
    }

    /// Adds a pointer offset in units of Element
    CUTLASS_HOST_DEVICE
    void add_pointer_offset(LongIndex pointer_offset) {
        iterator_.add_pointer_offset(pointer_offset);
    }

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

    /// Adds a tile offset
    CUTLASS_DEVICE
    void add_tile_offset(TensorCoord const& coord) {
        iterator_.add_tile_offset({coord.column(), coord.row()});
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator& operator++() {
        ++iterator_;
        return *this;
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator operator++(int) {
        RegularTileAccessIterator prev(*this);
        ++iterator_;

        return prev;
    }
};

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

/// Tile iterator specialized for crosswise arrangements for TensorOps
///
///
/// Satisfies: ForwardTileIteratorConcept |
///            ReadableContiguousTileIteratorConcept |
///            WriteableContiguousTileIteratorConcept
///
template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int Alignment, int Crosswise>
class RegularTileAccessIterator<
        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;
    static int const kCrosswise = Crosswise;

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

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

    using ThreadMap = ThreadMap_;

    static_assert(!(ThreadMap::Delta::kContiguous % kCrosswise),
                  "kCrosswise is the smallest unit in the contiguous dimension "
                  "for shared memory swizzling.");

    /// Internal details made public to facilitate introspection
    struct Detail {
        /// This iterator is specialized for an access size that is 128 bits in
        /// length.
        static int const kAccessSizeInBits = 128;

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

        /// Number of pointers
        ///
        /// Note:TN kblock32 layouts only needs 1 pointer, but strangely
        /// reducing pointer count hurts perfomrnace
        static int const kPointerCount =
                (ThreadMap::Iterations::kStrided > 1 ? 2 : 1);
    };

    /// Element type per access
    using AccessType = Array<Element, Layout::kElementsPerAccess>;

private:
    //
    // Data members
    //

    /// Total number of sections.  The memory is divided into stages.  One stage
    /// can store one tile.  Stage is divided into sections.  Interleaved layout
    /// can have multiple sections in a stage.  The rest layout only has one
    /// section in a stage.
    int sections_;

    /// Sections that a stage has
    int sections_per_stage_;

    /// Stride value
    Index stride_;

    /// Internal pointer to first access of tile
    AccessType* pointer_[Detail::kPointerCount];

    /// Internal byte offset
    Index byte_offset_;

    /// Iteration in the contiguous dimension
    int iteration_contiguous_;

    /// Iteration in the strided dimension
    int iteration_strided_;

public:
    /// Construct a TileIterator with zero threadblock offset
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator(
            TensorRef ref,  ///< Pointer to start of tensor
            int thread_id   ///< ID of each participating thread
            )
            : sections_(ref.stride(0) / kCrosswise),
              sections_per_stage_(Shape::kContiguous / kCrosswise),
              // stride_ = kCrosswise x sections_ x kFactor
              stride_(ref.stride(0) * Layout::kFactor /
                      Layout::kElementsPerAccess),
              byte_offset_(0) {
        layout::PitchLinearCoord thread_offset_base =
                ThreadMap::initial_offset(thread_id);

        CUTLASS_PRAGMA_UNROLL
        for (int i = 0; i < Detail::kPointerCount; ++i) {
            // This is the offset of a thread within a threadblock tile for a
            // specific pointer (units of elements)
            layout::PitchLinearCoord thread_offset_in_threadblock_tile =
                    thread_offset_base +
                    layout::PitchLinearCoord{
                            0,
                            ThreadMap::Detail::WarpThreadArrangement::kStrided *
                                    i};
            // initialize pointer
            pointer_[i] = reinterpret_cast<AccessType*>(ref.data()) +
                          ref.offset(thread_offset_in_threadblock_tile) /
                                  Layout::kElementsPerAccess;
        }

        set_iteration_index(0);
    }

    /// Overrides the internal iteration index
    CUTLASS_HOST_DEVICE
    void set_iteration_index(int index) {
        iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
        iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
    }

    /// Adds a pointer offset in units of Element
    CUTLASS_HOST_DEVICE
    void add_pointer_offset(LongIndex pointer_offset) {
        byte_offset_ += pointer_offset * sizeof_bits<Element>::value / 8;
    }

    /// Returns a pointer
    CUTLASS_HOST_DEVICE
    AccessType* get() const {
        AccessType* access_ptr = pointer_[iteration_strided_ & 1];
        int stride_idx = (iteration_strided_ & ~1);

        int access_offset =
                stride_idx * ThreadMap::Delta::kStrided * stride_ /
                        Layout::kFactor +
                // kCrosswise elements in the contiguous dimension would span to
                // a shared memory cache line.
                iteration_contiguous_ *
                        (ThreadMap::Delta::kContiguous / kCrosswise) *
                        Layout::TileShape::kContiguous;
        char* access_byte_ptr =
                reinterpret_cast<char*>(access_ptr + access_offset);
        return reinterpret_cast<AccessType*>(access_byte_ptr + byte_offset_);
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator& operator++() {
        ++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) {
            return *this;
        }

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

        return *this;
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator operator++(int) {
        RegularTileAccessIterator prev(*this);
        this->operator++();

        return prev;
    }

    /// Adds a tile offset
    CUTLASS_DEVICE
    void add_tile_offset(TensorCoord const& coord) {
        add_pointer_offset(coord.contiguous() * sections_per_stage_ * stride_ *
                                   ThreadMap::kElementsPerAccess / sections_ +
                           coord.strided() * Shape::kStrided * stride_ *
                                   Layout::kElementsPerAccess);
    }
};

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

/// Tile Iterator specialized for column-major crosswise TensorOp formats.
///
///
/// Satisfies: ForwardTileIteratorConcept |
///            ReadableContiguousTileIteratorConcept |
///            WriteableContiguousTileIteratorConcept
///
template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int Alignment, int Crosswise>
class RegularTileAccessIterator<
        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_;

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

    using AccessType = typename UnderlyingIterator::AccessType;

private:
    /// Underlying iterator
    UnderlyingIterator iterator_;

public:
    /// Construct a TileIterator with zero threadblock offset
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator(
            TensorRef ref,  ///< Pointer to start of tensor
            int thread_id   ///< ID of each participating thread
            )
            : iterator_({ref.data(), ref.stride()}, thread_id) {}

    /// Overrides the internal iteration index
    CUTLASS_HOST_DEVICE
    void set_iteration_index(int index) {
        iterator_.set_iteration_index(index);
    }

    /// Adds a pointer offset in units of Element
    CUTLASS_HOST_DEVICE
    void add_pointer_offset(LongIndex pointer_offset) {
        iterator_.add_pointer_offset(pointer_offset);
    }

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

    /// Adds a tile offset
    CUTLASS_DEVICE
    void add_tile_offset(TensorCoord const& coord) {
        iterator_.add_tile_offset({coord.row(), coord.column()});
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator& operator++() {
        ++iterator_;
        return *this;
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator operator++(int) {
        RegularTileAccessIterator prev(*this);
        ++iterator_;

        return prev;
    }
};

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

/// Tile Iterator specialized for row-major crosswise TensorOp formats.
///
///
/// Satisfies: ForwardTileIteratorConcept |
///            ReadableContiguousTileIteratorConcept |
///            WriteableContiguousTileIteratorConcept
///
template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int Alignment, int Crosswise>
class RegularTileAccessIterator<
        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_;

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

    using AccessType = typename UnderlyingIterator::AccessType;

private:
    /// Underlying iterator
    UnderlyingIterator iterator_;

public:
    /// Construct a TileIterator with zero threadblock offset
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator(
            TensorRef ref,  ///< Pointer to start of tensor
            int thread_id   ///< ID of each participating thread
            )
            : iterator_({ref.data(), ref.stride()}, thread_id) {}

    /// Overrides the internal iteration index
    CUTLASS_HOST_DEVICE
    void set_iteration_index(int index) {
        iterator_.set_iteration_index(index);
    }

    /// Adds a pointer offset in units of Element
    CUTLASS_HOST_DEVICE
    void add_pointer_offset(LongIndex pointer_offset) {
        iterator_.add_pointer_offset(pointer_offset);
    }

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

    /// Adds a tile offset
    CUTLASS_DEVICE
    void add_tile_offset(TensorCoord const& coord) {
        iterator_.add_tile_offset({coord.column(), coord.row()});
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator& operator++() {
        ++iterator_;
        return *this;
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator operator++(int) {
        RegularTileAccessIterator prev(*this);
        ++iterator_;

        return prev;
    }
};

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

/// Tile iterator specialized for k interleaved arrangements for TensorOps
///
///
/// Satisfies: ForwardTileIteratorConcept |
///            ReadableContiguousTileIteratorConcept |
///            WriteableContiguousTileIteratorConcept
///
template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int InterleavedK, int Alignment>
class RegularTileAccessIterator<
        Shape_, Element_,
        layout::TensorOpMultiplicandRowMajorInterleaved<
                sizeof_bits<Element_>::value, InterleavedK>,
        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::TensorOpMultiplicandRowMajorInterleaved<
            sizeof_bits<Element_>::value, InterleavedK>;
    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_;

    /// Internal details made public to facilitate introspection
    struct Detail {
        /// This iterator is specialized for an access size that is 128 bits in
        /// length.
        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:
    /// Element type per access
    using AccessType = Array<Element, Layout::kElementsPerAccess>;

private:
    //
    // Data members
    //

    /// Internal pointer to first access of tile
    AccessType* pointer_;

    /// Internal byte offset
    Index byte_offset_;

    /// Iteration in the contiguous dimension
    int iteration_contiguous_;

    /// Iteration in the strided dimension
    int iteration_strided_;

public:
    /// Construct a TileIterator with zero threadblock offset
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator(
            TensorRef ref,  ///< Pointer to start of tensor
            int thread_id   ///< ID of each participating thread
            )
            : byte_offset_(0) {
        layout::PitchLinearCoord thread_offset_base =
                ThreadMap::initial_offset(thread_id);

        // initialize pointer
        pointer_ = reinterpret_cast<AccessType*>(
                ref.data() + ref.offset(thread_offset_base));

        set_iteration_index(0);
    }

    /// Overrides the internal iteration index
    CUTLASS_HOST_DEVICE
    void set_iteration_index(int index) {
        iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
        iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
    }

    /// Adds a pointer offset in units of Element
    CUTLASS_HOST_DEVICE
    void add_pointer_offset(LongIndex pointer_offset) {
        byte_offset_ += pointer_offset * sizeof(Element);
    }

    /// Returns a pointer
    CUTLASS_HOST_DEVICE
    AccessType* get() const {
        AccessType* access_ptr = pointer_;

        int access_offset =
                (iteration_strided_ * ThreadMap::Delta::kStrided *
                         Layout::kInterleavedK +
                 iteration_contiguous_ * ThreadMap::Delta::kContiguous) /
                ThreadMap::kElementsPerAccess;

        char* access_byte_ptr =
                reinterpret_cast<char*>(access_ptr + access_offset);

        return reinterpret_cast<AccessType*>(access_byte_ptr + byte_offset_);
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator& operator++() {
        ++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) {
            return *this;
        }

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

        return *this;
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator operator++(int) {
        RegularTileAccessIterator prev(*this);
        this->operator++();

        return prev;
    }

    /// Adds a tile offset
    CUTLASS_DEVICE
    void add_tile_offset(TensorCoord const& coord) {
        add_pointer_offset(coord.contiguous() * Shape::kCount);
    }
};

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

/// Tile iterator specialized for k interleaved arrangements for TensorOps
///
///
/// Satisfies: ForwardTileIteratorConcept |
///            ReadableContiguousTileIteratorConcept |
///            WriteableContiguousTileIteratorConcept
///

template <typename Shape_, typename Element_, int AdvanceRank,
          typename ThreadMap_, int InterleavedK, int Alignment>
class RegularTileAccessIterator<
        Shape_, Element_,
        layout::TensorOpMultiplicandColumnMajorInterleaved<
                sizeof_bits<Element_>::value, InterleavedK>,
        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::TensorOpMultiplicandColumnMajorInterleaved<
            sizeof_bits<Element_>::value, InterleavedK>;
    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_;

    /// Underlying iterator type
    using UnderlyingIterator = RegularTileAccessIterator<
            cutlass::MatrixShape<Shape::kColumn, Shape::kRow>, Element,
            layout::TensorOpMultiplicandRowMajorInterleaved<
                    sizeof_bits<Element_>::value, InterleavedK>,
            (kAdvanceRank == 1 ? 0 : 1), ThreadMap>;

private:
    /// Element type per access
    using AccessType = Array<Element, Layout::kElementsPerAccess>;

private:
    /// Underlying iterator
    UnderlyingIterator iterator_;

public:
    /// Construct a TileIterator with zero threadblock offset
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator(
            TensorRef ref,  ///< Pointer to start of tensor
            int thread_id   ///< ID of each participating thread
            )
            : iterator_({ref.data(), ref.stride()}, thread_id) {}

    /// Overrides the internal iteration index
    CUTLASS_HOST_DEVICE
    void set_iteration_index(int index) {
        iterator_.set_iteration_index(index);
    }

    /// Adds a pointer offset in units of Element
    CUTLASS_HOST_DEVICE
    void add_pointer_offset(LongIndex pointer_offset) {
        iterator_.add_pointer_offset(pointer_offset);
    }

    /// Returns a pointer
    CUTLASS_HOST_DEVICE
    AccessType* get() const { return iterator_.get(); }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator& operator++() {
        ++iterator_;
        return *this;
    }

    /// Advances to the next tile in memory.
    CUTLASS_HOST_DEVICE
    RegularTileAccessIterator operator++(int) {
        RegularTileAccessIterator prev(*this);
        ++iterator_;

        return prev;
    }

    /// Adds a tile offset
    CUTLASS_DEVICE
    void add_tile_offset(TensorCoord const& coord) {
        iterator_.add_tile_offset({coord.strided(), coord.contiguous()});
    }
};

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

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

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