/***************************************************************************************************
 * Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. 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.
 *
 * 3. Neither the name of the copyright holder 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 THE COPYRIGHT HOLDER OR CONTRIBUTORS 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 TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 **************************************************************************************************/
/*! \file
  \brief Epilogue for threadblock scoped GEMMs using Tensor Ops.

  The epilogue rearranges the result of a matrix product through shared memory to match canonical
  tensor layouts in global memory. Epilogues support conversion and reduction operations.

*/

#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/numeric_types.h"
#include "cutlass/array.h"
#include "cutlass/layout/matrix.h"
#include "cutlass/layout/tensor.h"
#include "cutlass/layout/permute.h"
#include "cutlass/matrix_shape.h"
#include "cutlass/tensor_ref.h"
#include "cutlass/transform/pitch_linear_thread_map.h"
#include "cutlass/epilogue/threadblock/output_tile_thread_map.h"
#include "cutlass/arch/arch.h"
#include "cutlass/arch/memory.h"
#include "cutlass/epilogue/threadblock/predicated_tile_iterator_params.h"
#include "cutlass/conv/conv2d_problem_size.h"
#include "cutlass/conv/conv3d_problem_size.h"

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

namespace cutlass {

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

namespace epilogue {
namespace threadblock {

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

/// Tile iterator used to load and store output tile from global memory in epilogue.
///
/// Satisfies: ReadableTileIterator | PredicatedTileIterator | ForwardTileIterator
///
template <
  typename ThreadMap_,       ///< Thread map (conept: OutputTileThreadMap)
  typename Element_,         ///< Element data type
  bool ScatterD = false,     ///< Scatter D operand or not
  typename PermuteDLayout = layout::NoPermute, ///< Permute D operand or not
  bool UseCUDAStore = false
>
class PredicatedTileIterator {
public:
  using ThreadMap = ThreadMap_;
  using Shape = typename ThreadMap::Shape;

  using Element = Element_;

  using Layout = layout::RowMajor;
  using TensorRef = TensorRef<Element, Layout>;
  using ConstTensorRef = typename TensorRef::ConstTensorRef;

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

  static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
  static int const kThreads = ThreadMap::kThreads;
  static int const kIterations = ThreadMap::Count::kTile;

  static bool constexpr PermuteD = !layout::is_trivial_permute<PermuteDLayout>;

  static_assert( ThreadMap::Iterations::kRow > 0,"ThreadMap::Iterations::kRow must be > 0");
  static_assert( ThreadMap::Iterations::kGroup > 0,"ThreadMap::Iterations::kGroup must be > 0");
  static_assert( ThreadMap::Iterations::kCluster > 0,"ThreadMap::Iterations::kCluster must be > 0");
  static_assert( ThreadMap::Iterations::kColumn > 0,"ThreadMap::Iterations::kColumn must be > 0");

  /// Fragment object
  using Fragment = Array<
    Element,
    ThreadMap::Iterations::kColumn *
    ThreadMap::Iterations::kRow *
    ThreadMap::Iterations::kGroup *
    ThreadMap::Iterations::kCluster * ThreadMap::kElementsPerAccess>;

  /// Memory access size
  using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;

  //
  // Parameters struct
  //

  /// Uses a non-template class
  struct Params : PredicatedTileIteratorParams {
    using Base = PredicatedTileIteratorParams;

    CUTLASS_HOST_DEVICE
    Params() { }

    CUTLASS_HOST_DEVICE
    Params(Layout const &layout):
      PredicatedTileIteratorParams(
        layout.stride(0) * int(sizeof(AccessType)) / kElementsPerAccess,
        make_OutputTileThreadMapDesc<ThreadMap>()
      ) 
    { }

    CUTLASS_HOST_DEVICE
    Params(Layout const &layout,
           // Not needed.  Added to be compatible with strided conv epilogue.
           cutlass::Tensor4DCoord const &tensor_extent):
      Params(layout)
    { }

    CUTLASS_HOST_DEVICE
    Params(Layout const &layout,
           // Not needed.  Added to be compatible with strided conv epilogue.
           cutlass::Tensor5DCoord const &tensor_extent):
      Params(layout)
    { }

    CUTLASS_HOST_DEVICE
    Params(Base const &base) : 
      Base(base) { }
  };

  /// Mask object
  struct Mask {

    static int const kCount = ThreadMap::Iterations::kColumn;

    /// Predicate state
    bool predicates[kCount];

    //
    // Mask
    //
    CUTLASS_HOST_DEVICE
    Mask() {
      enable();
    }

    ///< Efficiently disables all accesses guarded by mask
    CUTLASS_HOST_DEVICE void clear() {
      CUTLASS_PRAGMA_UNROLL
      for (int i = 0; i < kCount; ++i) {
        predicates[i] = false;
      }
    }

    ///< CUTLASS_HOST_DEVICE enables all accesses guarded by mask
    CUTLASS_DEVICE void enable() {
      CUTLASS_PRAGMA_UNROLL
      for (int i = 0; i < kCount; ++i) {
        predicates[i] = true;
      }
    }
  };

private:

  //
  // Data members
  //

  /// Parameters structure containing reference and precomputed state.
  PredicatedTileIteratorParams params_;

  /// Byte-level pointer. This pointer is usually for both load() and store(), unless PermuteD is performed. When having PermuteD, byte_pointer_ is only for load().
  uint8_t *byte_pointer_;

  /// Byte-level pointer for store(). Due to PermuteD Op, store_byte_pointer_ may be with different address computation compared to byte_pointer_.
  uint8_t *store_byte_pointer_;

  /// Array of boolean values to contain steady-state predicates
  Mask mask_;

  /// Extent of the matrix tile in rows
  Index extent_row_;

  /// Extent of the matrix tile in rows
  Index extent_column_;

  /// A thread's starting row position (assuming steady-state predicates have been computed)
  Index thread_start_row_;

  /// A thread's starting column
  Index thread_start_column_;

  /// Internal state counter
  int state_[3];

  /// Scatter indices
  int const *indices_;

  /// PermuteDLayout
  PermuteDLayout permute_layout_;

  //
  // Static asserts about internal strides
  //

  static_assert(sizeof(extent_row_) == 4, "Expected 32b extents");
  static_assert(sizeof(thread_start_row_) == 4, "Expected 32b extents");
  static_assert(sizeof(PredicatedTileIteratorParams::stride) == 8, "Expected 64b strides");

private:

  //
  // Methods
  //

public:

  //
  // Methods
  //

  /// Constructor
  CUTLASS_DEVICE
  PredicatedTileIterator(
    PredicatedTileIteratorParams const & params,
    Element *pointer,
    TensorCoord extent,
    int thread_idx,
    TensorCoord threadblock_offset = TensorCoord(),
    int const *indices = nullptr
  ): 
    params_(params), indices_(indices),
    permute_layout_(PitchLinearCoord(extent.column(), extent.row()), params_.stride * kElementsPerAccess / sizeof(AccessType))
  {

    TensorCoord thread_offset = ThreadMap::initial_offset(thread_idx) + threadblock_offset;

    extent_row_ = extent.row();
    extent_column_ = extent.column();

    thread_start_row_ = thread_offset.row();
    thread_start_column_ = thread_offset.column();

    // Initialize predicates
    CUTLASS_PRAGMA_UNROLL
    for (int c = 0; c < ThreadMap::Iterations::kColumn; ++c) {

      mask_.predicates[c] = ((thread_offset.column()
        + ThreadMap::Delta::kColumn * c) < extent.column());
    }

    // Null pointer performs no accesses
    if (!pointer) {
      mask_.clear();
    }

    if (ScatterD && !indices) {
      mask_.clear();
    }

    // Initialize byte_pointer_
    byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) +
      LongIndex(thread_offset.row()) * LongIndex(params_.stride) +
      LongIndex(thread_offset.column()) * sizeof(AccessType) / kElementsPerAccess;

    if (ScatterD) {
      byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) +
        LongIndex(thread_offset.column()) * sizeof(AccessType) / kElementsPerAccess;
    }

    // store_byte_pointer_ is set to be the same with byte_pointer_ unless PermuteD is used.
    store_byte_pointer_ = PermuteD ? reinterpret_cast<uint8_t *>(pointer) : byte_pointer_;

    // Initialize internal state counter
    state_[0] = state_[1] = state_[2] = 0;
  }

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

  /// Loads a fragment from memory
  CUTLASS_DEVICE
  void load_with_byte_offset(Fragment &frag, int64_t byte_offset) const {

    uint8_t *byte_pointer = byte_pointer_;
    AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);

    CUTLASS_PRAGMA_UNROLL
    for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {

      CUTLASS_PRAGMA_UNROLL
      for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {

        CUTLASS_PRAGMA_UNROLL
        for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {

          int frag_row_idx =
            (row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));

          int row_offset = row * ThreadMap::Delta::kRow 
            + group * ThreadMap::Delta::kGroup 
            + cluster * ThreadMap::Delta::kCluster;

          bool row_guard = ((row_offset + thread_start_row_) < extent_row_);

          AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset);

          if (ScatterD && row_guard) {
            assert(indices_);

            memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset +
              LongIndex(indices_[row_offset + thread_start_row_]) * LongIndex(params_.stride));
          }

          CUTLASS_PRAGMA_UNROLL
          for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {

            bool guard = row_guard && mask_.predicates[column];

            cutlass::arch::global_load<
              AccessType,
              sizeof(AccessType)
            >(
                frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn +
                         column],
                (void *)&memory_pointer[column * ThreadMap::Delta::kColumn /
                                        kElementsPerAccess],
                guard);
          }

          if (row + 1 < ThreadMap::Iterations::kRow) {
            if (!ScatterD) {
              byte_pointer += params_.increment_row;
            }
          }
        }

        if (group + 1 < ThreadMap::Iterations::kGroup) {
          byte_pointer += params_.increment_group;
        }
      }

      if (cluster + 1 < ThreadMap::Iterations::kCluster) {
        byte_pointer += params_.increment_cluster;
      }
    }
  }

  /// Loads a fragment from memory
  CUTLASS_DEVICE
  void load(Fragment &frag) const {

    load_with_byte_offset(frag, 0);
  }

  /// Stores a fragment to memory
  CUTLASS_DEVICE
  void store_with_byte_offset(Fragment const &frag, int64_t byte_offset) const {
    uint8_t *byte_pointer = store_byte_pointer_;
    AccessType const *frag_ptr = reinterpret_cast<AccessType const *>(&frag);

    CUTLASS_PRAGMA_UNROLL
    for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {

      CUTLASS_PRAGMA_UNROLL
      for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {

        CUTLASS_PRAGMA_UNROLL
        for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {

          int frag_row_idx =
            (row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));

          int row_offset = row * ThreadMap::Delta::kRow
            + group * ThreadMap::Delta::kGroup
            + cluster * ThreadMap::Delta::kCluster;

          bool row_guard = ((row_offset + thread_start_row_) < extent_row_);

          AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset);

          if (ScatterD && row_guard) {
            assert(indices_);

            memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset +
              LongIndex(indices_[row_offset + thread_start_row_]) * LongIndex(params_.stride));
          }

          CUTLASS_PRAGMA_UNROLL
          for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {

            bool guard = row_guard && mask_.predicates[column];
            
            if (PermuteD) {

              int col_offset = column * ThreadMap::Delta::kColumn;

              int col = col_offset + thread_start_column_;
              int row = row_offset + thread_start_row_;

              // Locate memory_pointer
              memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset
                 + permute_layout_(PitchLinearCoord(col, row)) * sizeof(AccessType) / kElementsPerAccess);
            }

            if (UseCUDAStore) {
              if (guard) {
                memory_pointer[0] =
                    frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column];
              }
            } else {
              cutlass::arch::global_store<AccessType, sizeof(AccessType)>(
                  frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column],
                  (void *)&memory_pointer[0],
                  guard);
            }

            if (!PermuteD) {
              memory_pointer += (ThreadMap::Delta::kColumn / kElementsPerAccess);
            }
          }

          if (row + 1 < ThreadMap::Iterations::kRow) {
            if (!ScatterD && !PermuteD) {
              byte_pointer += params_.increment_row;
            }
          }
        }

        if (group + 1 < ThreadMap::Iterations::kGroup) {
          if (!ScatterD && !PermuteD) {
            byte_pointer += params_.increment_group;
          }
        }
      }

      if (cluster + 1 < ThreadMap::Iterations::kCluster) {
        if (!ScatterD && !PermuteD) {
          byte_pointer += params_.increment_cluster;
        }
      }
    }
  }

  /// Stores a fragment to memory
  CUTLASS_DEVICE
  void store(Fragment const &frag) const {

    store_with_byte_offset(frag, 0);
  }

  /// Loads a fragment from memory
  CUTLASS_DEVICE
  void downsample_load_with_byte_offset(Fragment &frag, int64_t byte_offset, int convolution_P, int convolution_Q, int add_P, int add_Q, int problem_N) const {

    uint8_t *byte_pointer = byte_pointer_;
    AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);

    CUTLASS_PRAGMA_UNROLL
    for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {

      CUTLASS_PRAGMA_UNROLL
      for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {

        CUTLASS_PRAGMA_UNROLL
        for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {

          int frag_row_idx = 
            (row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));

          int row_offset = row * ThreadMap::Delta::kRow 
            + group * ThreadMap::Delta::kGroup 
            + cluster * ThreadMap::Delta::kCluster;

          bool row_guard = ((row_offset + thread_start_row_) < extent_row_);

          int output_row = row_offset + thread_start_row_;
          int output_N = output_row / (convolution_P * convolution_Q);
          int output_PQ = output_row % (convolution_P * convolution_Q);
          int output_P = output_PQ / convolution_Q;
          int output_Q = output_PQ % convolution_Q;

          int input_row = output_N * 2 * convolution_P * 2 * convolution_Q +
            (2 * output_P + add_P) * 2 * convolution_Q + 2 * output_Q + add_Q;

          int64_t byte_offset = (input_row-output_row)*problem_N*sizeof(float);

          AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset);

          CUTLASS_PRAGMA_UNROLL
          for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {

            bool guard = row_guard && mask_.predicates[column];

            cutlass::arch::global_load<
              AccessType, 
              sizeof(AccessType)
            >(
                frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn +
                         column],
                (void *)&memory_pointer[column * ThreadMap::Delta::kColumn /
                                        kElementsPerAccess],
                guard);
          }

          if (row + 1 < ThreadMap::Iterations::kRow) {
            byte_pointer += params_.increment_row;
          }
        }

        if (group + 1 < ThreadMap::Iterations::kGroup) {
          byte_pointer += params_.increment_group;
        }
      }

      if (cluster + 1 < ThreadMap::Iterations::kCluster) {
        byte_pointer += params_.increment_cluster;
      }
    }
  }

  /// Loads a fragment from memory
  CUTLASS_DEVICE
  void upsample_load_with_byte_offset(Fragment &frag, int64_t byte_offset, int convolution_P, int convolution_Q, int add_P, int add_Q, int problem_N) const {

    uint8_t *byte_pointer = byte_pointer_;
    AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);

    CUTLASS_PRAGMA_UNROLL
    for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {

      CUTLASS_PRAGMA_UNROLL
      for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {

        CUTLASS_PRAGMA_UNROLL
        for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {

          int frag_row_idx = 
            (row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));

          int row_offset = row * ThreadMap::Delta::kRow 
            + group * ThreadMap::Delta::kGroup 
            + cluster * ThreadMap::Delta::kCluster;

          bool row_guard = ((row_offset + thread_start_row_) < extent_row_);

          int output_row = row_offset + thread_start_row_;
          int output_N = output_row / (convolution_P * convolution_Q);
          int output_PQ = output_row % (convolution_P * convolution_Q);
          int output_P = output_PQ / convolution_Q;
          int output_Q = output_PQ % convolution_Q;
          int row_add_P = add_P;
          int row_add_Q = add_Q;
	  if (output_P > convolution_P - 2) row_add_P = 0;
	  if (output_Q > convolution_Q - 2) row_add_Q = 0;

          int input_row = output_N * (convolution_P/2) * (convolution_Q/2) +
            ((output_P + row_add_P)/2) * (convolution_Q/2) + (output_Q + row_add_Q)/2;

          int64_t byte_offset = (input_row-output_row)*problem_N*sizeof(float);

          AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset);

          CUTLASS_PRAGMA_UNROLL
          for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {

            bool guard = row_guard && mask_.predicates[column];

            cutlass::arch::global_load<
              AccessType, 
              sizeof(AccessType)
            >(
                frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn +
                         column],
                (void *)&memory_pointer[column * ThreadMap::Delta::kColumn /
                                        kElementsPerAccess],
                guard);
          }

          if (row + 1 < ThreadMap::Iterations::kRow) {
            byte_pointer += params_.increment_row;
          }
        }

        if (group + 1 < ThreadMap::Iterations::kGroup) {
          byte_pointer += params_.increment_group;
        }
      }

      if (cluster + 1 < ThreadMap::Iterations::kCluster) {
        byte_pointer += params_.increment_cluster;
      }
    }
  }

  CUTLASS_DEVICE
  MatrixCoord thread_start() const {
    return MatrixCoord(thread_start_row_, thread_start_column_);
  }

  /// Need to get the thread start row from the tile iterator
  CUTLASS_DEVICE
  int32_t thread_start_row() const {
    return thread_start_row_;
  }

  /// Need to get the thread start row from the tile iterator
  CUTLASS_DEVICE
  int32_t thread_start_column() const {
    return thread_start_column_;
  }

  /// Extent of the matrix in rows
  CUTLASS_DEVICE
  Index extent_row() const {
    return extent_row_;
  }

  /// Extent of the matrix in columns
  CUTLASS_DEVICE
  Index extent_column() const {
    return extent_column_;
  }

  /// Advances to the next position to load or store
  CUTLASS_HOST_DEVICE
  PredicatedTileIterator &operator++() {

    ++state_[0];

    if (!ScatterD) {
      byte_pointer_ += params_.advance_row;
    }

    if (!ScatterD && !PermuteD) {
      store_byte_pointer_ += params_.advance_row;
    }

    thread_start_row_ += ThreadMap::Shape::kRow;

    if (state_[0] == ThreadMap::Count::kRow) {

      state_[0] = 0;
      ++state_[1];

      if (!ScatterD) {
        byte_pointer_ += params_.advance_group;
      }

      if (!ScatterD && !PermuteD) {
        store_byte_pointer_ += params_.advance_group;
      }

      thread_start_row_ += (ThreadMap::Shape::kGroup - 1) *
        ThreadMap::Shape::kRow * ThreadMap::Count::kRow;

      if (state_[1] == ThreadMap::Count::kGroup) {

        state_[1] = 0;
        ++state_[2];

        if (!ScatterD) {
          byte_pointer_ += params_.advance_cluster;
        }

        if (!ScatterD && !PermuteD) {
          store_byte_pointer_ += params_.advance_cluster;
        }

        thread_start_row_ += ThreadMap::Count::kGroup *
          ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * ThreadMap::Shape::kRow;

        if (state_[2] == ThreadMap::Count::kCluster) {
          state_[2] = 0;

          if (!ScatterD) {
            byte_pointer_ += params_.advance_tile;
          }

          if (!ScatterD && !PermuteD) {
            store_byte_pointer_ += params_.advance_tile;
          }

          thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow
            * ThreadMap::Shape::kCluster * ThreadMap::Shape::kTile;
        }
      }
    }

    return *this;
  }

  /// Advances a number of positions to load or store
  CUTLASS_HOST_DEVICE
  PredicatedTileIterator &operator+=(int increment)
  {
    // Row
    state_[0] += increment;
    int increment_row = state_[0] / ThreadMap::Count::kRow;
    state_[0] = state_[0] % ThreadMap::Count::kRow;

    byte_pointer_ += (params_.advance_row * increment);
    store_byte_pointer_ += (params_.advance_row * increment);
    thread_start_row_ += (ThreadMap::Shape::kRow * increment);

    // Group
    state_[1] += increment_row;
    int increment_group = state_[1] / ThreadMap::Count::kGroup;
    state_[1] = state_[1] % ThreadMap::Count::kGroup;

    byte_pointer_ += (params_.advance_group * increment_row);
    store_byte_pointer_ += (params_.advance_group * increment_row);
    thread_start_row_ +=
        (ThreadMap::Shape::kGroup - 1) *
        ThreadMap::Shape::kRow *
        ThreadMap::Count::kRow *
        increment_row;


    // Cluster
    state_[2] += increment_group;
    int increment_cluster = state_[2] / ThreadMap::Count::kCluster;
    state_[2] = state_[2] % ThreadMap::Count::kCluster;

    byte_pointer_ += (params_.advance_cluster * increment_group);
    store_byte_pointer_ += (params_.advance_cluster * increment_group);
    thread_start_row_ +=
        ThreadMap::Count::kGroup *
        ThreadMap::Shape::kGroup *
        ThreadMap::Count::kRow *
        ThreadMap::Shape::kRow *
        increment_group;

    // Tile
    byte_pointer_ += (params_.advance_tile * increment_cluster);
    store_byte_pointer_ += (params_.advance_tile * increment_cluster);
    thread_start_row_ +=
        ThreadMap::Shape::kGroup *
        ThreadMap::Shape::kRow *
        ThreadMap::Shape::kCluster *
        ThreadMap::Shape::kTile *
        increment_cluster;

    return *this;
  }

  ///< Efficiently disables all accesses guarded by mask
  CUTLASS_DEVICE void clear_mask() {
    mask_.clear();
  }

  ///< Efficiently enables all accesses guarded by mask
  CUTLASS_DEVICE void enable_mask() {
    mask_.enable();
  }

  ///< Sets the mask
  CUTLASS_DEVICE void get_mask(Mask &mask) const {
    mask = mask_;
  }

  ///< Sets the mask
  CUTLASS_DEVICE void set_mask(Mask const &mask) {
    mask_ = mask;
  }
};

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

/// Tile iterator used to load output tile from global memory in epilogue.
///
/// Satisfies: ReadableTileIterator | InterleavedPredicatedTileIterator | ForwardTileIterator
///
template <
  typename ThreadMap_,       ///< Thread map (conept: OutputTileThreadMap)
  typename Element_,         ///< Element data type
  int InterleavedN           ///< Number of Interleaved N 
>
class InterleavedPredicatedTileIterator {
public:
  using ThreadMap = ThreadMap_;

  using Element = Element_;

  using Layout = layout::ColumnMajorInterleaved<InterleavedN>;
  using TensorRef = TensorRef<Element, Layout>;
  using ConstTensorRef = typename TensorRef::ConstTensorRef;

  using Index = typename Layout::Index;
  using LongIndex = typename Layout::LongIndex;
  using TensorCoord = layout::PitchLinearCoord;

  static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
  static int const kThreads = ThreadMap::kThreads;
  static int const kIterations = ThreadMap::Iterations::kCount;

  /// Fragment object
  using Fragment = Array<Element, ThreadMap::kElementsPerAccess>;

  /// Memory access size
  using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;

  /// Uses a non-template class
  struct Params : InterleavedPredicatedTileIteratorParams {
    using Base = InterleavedPredicatedTileIteratorParams;

    CUTLASS_HOST_DEVICE
    Params() { }

    CUTLASS_HOST_DEVICE
    Params(Layout const &layout): 
      Base(
        layout.stride(0) * int(sizeof(AccessType)) / kElementsPerAccess,
        make_InterleavedPredicatedTileIteratorDesc<Element, ThreadMap>()
      ) { }

    CUTLASS_HOST_DEVICE
    Params(Base const &base) : 
      Base(base) { }
  };

  /// Mask object
  struct Mask {
    static int const kCount = (ThreadMap::Iterations::kContiguous < 8)
                                  ? 8
                                  : ThreadMap::Iterations::kContiguous;

    /// Predicate state
    bool predicates[kCount];

    //
    // Mask
    //
    CUTLASS_HOST_DEVICE
    Mask() {
      enable();
    }

    ///< Efficiently disables all accesses guarded by mask
    CUTLASS_HOST_DEVICE void clear() {
      CUTLASS_PRAGMA_UNROLL
      for (int i = 0; i < kCount; ++i) {
        predicates[i] = false;
      }
    }

    ///< CUTLASS_HOST_DEVICE enables all accesses guarded by mask
    CUTLASS_DEVICE void enable() {
      CUTLASS_PRAGMA_UNROLL
      for (int i = 0; i < kCount; ++i) {
        predicates[i] = true;
      }
    }
  };

private:

  //
  // Data members
  //

  /// Parameters structure containing reference and precomputed state.
  Params params_;

  /// Byte-level pointer
  uint8_t *byte_pointer_;

  /// Array of boolean values to contain steady-state predicates
  Mask mask_;

  /// Extent of the matrix tile in columns
  Index extent_col_;

  /// A thread's starting column position (assuming steady-state predicates have
  /// been computed)
  Index thread_start_col_;

  /// Internal iteration counter
  int iteration_contiguous_;

  int iteration_strided_;

private:

  //
  // Methods
  //

public:

  //
  // Methods
  //

  /// Constructor
  CUTLASS_DEVICE
  InterleavedPredicatedTileIterator(
    Params const & params,
    Element *pointer,
    TensorCoord extent,
    int thread_idx,
    TensorCoord threadblock_offset,
    int const *indices = nullptr     ///< gather/scatter indices, note no support for gather/scatter at this specialization
  ):
    params_(params) {
    TensorCoord thread_offset = ThreadMap::initial_offset(thread_idx) +
                                TensorCoord(threadblock_offset.contiguous() * InterleavedN,
                                 threadblock_offset.strided() / InterleavedN);

    extent_col_ = extent.strided() / InterleavedN;
    thread_start_col_ = thread_offset.strided();

    // Initialize predicates
    CUTLASS_PRAGMA_UNROLL
    for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
      mask_.predicates[c] =
          ((thread_offset.contiguous() + ThreadMap::Delta::kContiguous * c) <
           (extent.contiguous() * InterleavedN));
    }

    // Initialize pointer
    byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) + 
      LongIndex(thread_offset.strided()) * LongIndex(params_.stride) + 
      LongIndex(thread_offset.contiguous()) * sizeof(AccessType) / kElementsPerAccess;

    // Initialize internal state counter
    iteration_contiguous_ = iteration_strided_ = 0;
  }

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

  /// Loads a fragment from memory
  CUTLASS_DEVICE
  void load(Fragment &frag) {

    uint8_t *byte_pointer = byte_pointer_;
    AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);
    AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer);

    int col_offset = iteration_strided_ * ThreadMap::Delta::kStrided;

    bool col_guard = ((thread_start_col_ + col_offset) < extent_col_);

    bool guard = col_guard && mask_.predicates[iteration_contiguous_];

    cutlass::arch::global_load<
      AccessType, 
      sizeof(AccessType)
    >(
        *frag_ptr,
        (void *)memory_pointer,
        guard);
  }

  /// Stores a fragment to memory
  CUTLASS_DEVICE
  void store(Fragment const &frag) {
    uint8_t *byte_pointer = byte_pointer_;
    AccessType const *frag_ptr = reinterpret_cast<AccessType const *>(&frag);
    AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer);

    int col_offset = iteration_strided_ * ThreadMap::Delta::kStrided;

    bool col_guard = ((thread_start_col_ + col_offset) < extent_col_);

    bool guard = col_guard && mask_.predicates[iteration_contiguous_];

    cutlass::arch::global_store<AccessType, sizeof(AccessType)>(
        *frag_ptr, (void *)memory_pointer, guard);
  }

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

  /// Advances to the next position to load or store
  CUTLASS_HOST_DEVICE
  InterleavedPredicatedTileIterator &operator++() {

    ++iteration_contiguous_;
    byte_pointer_ += params_.advance_row;

    if (iteration_contiguous_ == ThreadMap::Iterations::kContiguous) {

      iteration_contiguous_ = 0;
      ++iteration_strided_;
      byte_pointer_ += params_.advance_column;

      if (iteration_strided_ == ThreadMap::Iterations::kStrided) {
        iteration_strided_ = 0;
      }
    }

    return *this;
  }

  /// Advances a number of positions to load or store
  CUTLASS_HOST_DEVICE
  InterleavedPredicatedTileIterator &operator+=(int increment)
  {
    // Contiguous
    iteration_contiguous_ += increment;
    int increment_strided = iteration_contiguous_ / ThreadMap::Iterations::kContiguous;
    iteration_contiguous_ = iteration_contiguous_ % ThreadMap::Iterations::kContiguous;
    byte_pointer_ += (params_.advance_row * increment);

    // Strided
    iteration_strided_ += increment_strided;
    byte_pointer_ += (params_.advance_column * increment_strided);

    return *this;
  }

  ///< Efficiently disables all accesses guarded by mask
  CUTLASS_DEVICE void clear_mask() {
    mask_.clear();
  }

  ///< Efficiently enables all accesses guarded by mask
  CUTLASS_DEVICE void enable_mask() {
    mask_.enable();
  }

  ///< Sets the mask
  CUTLASS_DEVICE void get_mask(Mask &mask) {
    mask = mask_;
  }

  ///< Sets the mask
  CUTLASS_DEVICE void set_mask(Mask const &mask) {
    mask_ = mask;
  }
};

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

/// Tile iterator used to load output tile from global memory in epilogue.
///
/// Satisfies: ReadableTileIterator | InterleavedMaskedTileIterator | ForwardTileIterator
///
template <
  typename ThreadMap_,       ///< Thread map (conept: OutputTileThreadMap)
  typename Element_,         ///< Element data type
  int InterleavedN           ///< Number of Interleaved N
>
class InterleavedConvPredicatedTileIterator {
public:
  using ThreadMap = ThreadMap_;

  using Element = Element_;

  using Layout = layout::TensorNCxHWx<InterleavedN>;
  using TensorRef = TensorRef<Element, Layout>;
  using ConstTensorRef = typename TensorRef::ConstTensorRef;

  using Index = typename Layout::Index;
  using LongIndex = typename Layout::LongIndex;
  using TensorCoord = Tensor4DCoord;

  static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
  static int const kThreads = ThreadMap::kThreads;
  static int const kIterations = ThreadMap::Iterations::kCount;

  /// Fragment object
  using Fragment = Array<Element, ThreadMap::kElementsPerAccess>;

  /// Memory access size
  using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;

  //
  // Parameters struct
  //

  struct Params {

    //
    // Data members
    //

    LongIndex stride_col;           ///< stride in bytes between columns
    LongIndex stride_row;           ///< stride in bytes between rows

    //
    // Methods
    //

    CUTLASS_HOST_DEVICE
    Status initialize(typename Layout::Stride stride_) {
      stride_col = stride_[1];
      stride_row = stride_[2];

      return Status::kSuccess;
    }

    CUTLASS_HOST_DEVICE
    Params() {
      initialize(cutlass::make_Coord(0, 0, 0));
    }

    CUTLASS_HOST_DEVICE
    Params(Layout const &layout) {

      initialize(layout.stride());
    }

    CUTLASS_HOST_DEVICE
    Params(Layout const &layout,
           // Not needed.  Added to be compatible with strided conv epilogue.
           cutlass::Tensor4DCoord const &tensor_extent):
      Params(layout)
    { }

  };

  /// Mask object
  struct Mask {
    static int const kCount =
        (ThreadMap::Iterations::kRow < 8) ? 8 : ThreadMap::Iterations::kRow;

    /// Predicate state
    bool predicates[kCount];

    //
    // Mask
    //
    CUTLASS_HOST_DEVICE
    Mask() {
      enable();
    }

    ///< Efficiently disables all accesses guarded by mask
    CUTLASS_HOST_DEVICE void clear() {
      CUTLASS_PRAGMA_UNROLL
      for (int i = 0; i < kCount; ++i) {
        predicates[i] = false;
      }
    }

    ///< CUTLASS_HOST_DEVICE enables all accesses guarded by mask
    CUTLASS_DEVICE void enable() {
      CUTLASS_PRAGMA_UNROLL
      for (int i = 0; i < kCount; ++i) {
        predicates[i] = true;
      }
    }
  };

private:

  //
  // Data members
  //

  /// Parameters structure containing reference and precomputed state.
  Params params_;

  /// Byte-level pointer
  uint8_t *byte_pointer_;

  /// Array of boolean values to contain steady-state predicates
  Mask mask_;

  /// Extent of the matrix tile in columns
  Index extent_col_;

  /// Extent of the matrix tile in rows
  Index extent_row_;

  /// Extent of the matrix tile in pq 
  Index extent_pq_;

  /// A thread's starting row position (assuming steady-state predicates have
  /// been computed)
  Index thread_start_row_;

  /// A thread's starting column position (assuming steady-state predicates have
  /// been computed)
  Index thread_start_col_;

  /// Internal iteration counter
  LongIndex iteration_row_;
  LongIndex iteration_col_;

  uint32_t pq_mul_;

  uint32_t pq_shr_;

private:

  //
  // Methods
  //

public:

  //
  // Methods
  //

  /// Constructor
  CUTLASS_DEVICE
  InterleavedConvPredicatedTileIterator(
    Params const & params,
    Element *pointer,
    TensorCoord extent,
    int thread_idx,
    MatrixCoord threadblock_offset
  ):
    params_(params) {
    MatrixCoord thread_offset = ThreadMap::initial_offset(thread_idx) + threadblock_offset;
                                
    extent_col_ = extent.c();
    extent_pq_ = extent.h() * extent.w();
    extent_row_ = extent.n() * extent_pq_;

    find_divisor(pq_mul_, pq_shr_, extent_pq_);

    thread_start_row_ = thread_offset.row();
    thread_start_col_ = thread_offset.column();

    // Initialize predicates
    CUTLASS_PRAGMA_UNROLL
    for (int r = 0; r < ThreadMap::Iterations::kRow; ++r) {
      mask_.predicates[r] =
          ((thread_offset.row() + ThreadMap::Delta::kRow * r) < extent_row_);
    }

    // Initialize pointer
    byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) +
                    ((thread_start_col_ / InterleavedN) * params_.stride_col +
                     (thread_start_col_ % InterleavedN)) *
                        sizeof_bits<Element>::value / 8;

    // Initialize internal state counter
    iteration_row_ = iteration_col_ = 0;
  }

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

  /// Loads a fragment from memory
  CUTLASS_DEVICE
  void load(Fragment &frag) {

    int col_offset = iteration_col_ * ThreadMap::Delta::kColumn;
    bool col_guard = ((thread_start_col_ + col_offset) < extent_col_);
    bool guard = col_guard && mask_.predicates[iteration_row_];

    int n, pq_rem;

    fast_divmod(n, pq_rem,
                thread_start_row_ + iteration_row_ * ThreadMap::Delta::kRow,
                extent_pq_, pq_mul_, pq_shr_);

    uint8_t *byte_pointer =
        byte_pointer_ + (n * params_.stride_row + pq_rem * InterleavedN) *
                            sizeof_bits<Element>::value / 8;
    AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);
    AccessType const *memory_pointer =
        reinterpret_cast<AccessType const *>(byte_pointer);

    cutlass::arch::global_load<
      AccessType, 
      sizeof(AccessType)
    >(
        *frag_ptr,
        (void *)memory_pointer,
        guard);
  }

  /// Stores a fragment to memory
  CUTLASS_DEVICE
  void store(Fragment const &frag) {

    int col_offset = iteration_col_ * ThreadMap::Delta::kColumn;
    bool col_guard = ((thread_start_col_ + col_offset) < extent_col_);
    bool guard = col_guard && mask_.predicates[iteration_row_];

    int n, pq_rem;

    fast_divmod(n, pq_rem,
                thread_start_row_ + iteration_row_ * ThreadMap::Delta::kRow,
                extent_pq_, pq_mul_, pq_shr_);

    uint8_t *byte_pointer =
        byte_pointer_ + (n * params_.stride_row + pq_rem * InterleavedN) *
                            sizeof_bits<Element>::value / 8;
    AccessType const *frag_ptr = reinterpret_cast<AccessType const *>(&frag);
    AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer);

    cutlass::arch::global_store<AccessType, sizeof(AccessType)>(
        *frag_ptr, (void *)memory_pointer, guard);
  }

  /// Overrides the internal iteration index
  CUTLASS_HOST_DEVICE
  void set_iteration_index(int iteration) {
    iteration_row_ = iteration % ThreadMap::Iterations::kRow;
    iteration_col_ = iteration / ThreadMap::Iterations::kRow;
  }

  /// Advances to the next position to load or store
  CUTLASS_HOST_DEVICE
  InterleavedConvPredicatedTileIterator &operator++() {

    ++iteration_row_;

    if (iteration_row_ == ThreadMap::Iterations::kRow) {

      iteration_row_ = 0;
      ++iteration_col_;
      byte_pointer_ += params_.stride_col;

      if (iteration_col_ == ThreadMap::Iterations::kColumn) {
        iteration_col_ = 0;
      }
    }

    return *this;
  }

  ///< Efficiently disables all accesses guarded by mask
  CUTLASS_DEVICE void clear_mask() {
    mask_.clear();
  }

  ///< Efficiently enables all accesses guarded by mask
  CUTLASS_DEVICE void enable_mask() {
    mask_.enable();
  }

  ///< Sets the mask
  CUTLASS_DEVICE void get_mask(Mask &mask) {
    mask = mask_;
  }

  ///< Sets the mask
  CUTLASS_DEVICE void set_mask(Mask const &mask) {
    mask_ = mask;
  }
};

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

} // namespace threadblock
} // namespace epilogue
} // namespace cutlass

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