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/*! \file
    \brief
*/

#pragma once

#include "cutlass/array.h"
#include "cutlass/tensor_ref.h"
#include "cutlass/layout/matrix.h"
#include "cutlass/layout/pitch_linear.h"

#include "cutlass/epilogue/warp/tensor_op_policy.h"

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

namespace cutlass {
namespace epilogue {
namespace warp {

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

/// Template for reading and writing tiles of accumulators to shared memory
template <typename WarpShape,      ///< shape of warp-level GEMM (concept:
                                   ///< MatrixShape)
          typename OperatorShape,  ///< matrix multiply operation shape
                                   ///< (concept: gemm::GemmShape)
          typename Element,        ///< data type of element to be written
          typename Layout          ///< target shared memory layout
          >
class TileIteratorTensorOp;

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

/// Template for reading and writing tiles of accumulators to shared memory
template <typename WarpShape_,      ///< shape of warp-level GEMM (concept:
                                    ///< GemmShape)
          typename OperatorShape_,  ///< matrix multiply operation shape
                                    ///< (concept: gemm::GemmShape)
          typename Element_         ///< data type of element to be written
          >
class TileIteratorTensorOp<WarpShape_, OperatorShape_, Element_,
                           layout::RowMajor> {
public:
    using WarpShape = WarpShape_;
    using OperatorShape = OperatorShape_;
    using Element = Element_;
    using Layout = layout::RowMajor;

    using TensorRef = TensorRef<Element, Layout>;  ///< Tensor Reference object
    using TensorCoord =
            MatrixCoord;  ///< Logical coordinate in referenced tensor
    using Index = typename TensorRef::Index;
    using LongIndex = typename TensorRef::LongIndex;

    using Policy = TensorOpPolicy<WarpShape, OperatorShape, Layout>;

    /// Shape of the tile in memory
    using Shape = MatrixShape<Policy::kRowsPerIteration, WarpShape::kN>;

    /// This is the fragment size produced by one access of the iterator.
    using Fragment = Array<Element, Policy::OperatorCount::kColumn *
                                            Policy::kElementsPerAccess>;

    /// This is the complete warp-level accumulator tile.
    // using AccumulatorTile = typename Operator::FragmentC;

    /// Number of times this iterator can be incremented
    static int const kIterations = Policy::kIterations;

    // Internal constants
    struct Detail {
        static int const kLanesInQuad = 4;
    };

    /// Padding quantity
    using Padding =
            MatrixShape<0, Detail::kLanesInQuad * Policy::kElementsPerAccess>;

private:
    /// Storage type for accessing memory
    using AccessType = AlignedArray<Element, Policy::kElementsPerAccess>;

    //
    // Data members
    //

    /// Internal pointer to memory
    AccessType* pointer_;

    /// Internal layout object
    Layout layout_;

    /// Thread offset
    MatrixCoord thread_offset_;

public:
    /// Default constructor
    CUTLASS_HOST_DEVICE
    TileIteratorTensorOp() : pointer_(nullptr) {}

    /// Constructor from TensorRef
    CUTLASS_HOST_DEVICE
    TileIteratorTensorOp(TensorRef const& ref, unsigned lane_id)
            : pointer_(reinterpret_cast<AccessType*>(ref.data())),
              layout_(ref.stride()[0] / Policy::kElementsPerAccess) {
        int quad_id = (lane_id / Detail::kLanesInQuad);
        int lane_in_quad = (lane_id % Detail::kLanesInQuad);

        thread_offset_ = {quad_id, lane_in_quad * Policy::kElementsPerAccess};

        pointer_ +=
                layout_({thread_offset_.row(),
                         thread_offset_.column() / Policy::kElementsPerAccess});
    }

    /// Adds a pointer offset
    CUTLASS_HOST_DEVICE
    TileIteratorTensorOp& add_pointer_offset(Index pointer_offset) {
        pointer_ += pointer_offset / Policy::kElementsPerAccess;
        return *this;
    }

    ///< advances in units of whole tiles along the logical coordinate space of
    ///< the tensor
    CUTLASS_HOST_DEVICE
    TileIteratorTensorOp& add_tile_offset(TensorCoord const& tile_offset) {
        MatrixCoord coord_offset(tile_offset.row() * Shape::kRow,
                                 tile_offset.column() * Shape::kColumn);

        thread_offset_ += coord_offset;

        pointer_ +=
                layout_({coord_offset.row(),
                         coord_offset.column() / Policy::kElementsPerAccess});

        return *this;
    }

    ///< advances in units of whole tiles along the logical coordinate space of
    ///< the tensor
    CUTLASS_HOST_DEVICE
    TileIteratorTensorOp& operator+=(TensorCoord const& tile_offset) {
        add_tile_offset(tile_offset);
        return *this;
    }

    /// Store
    CUTLASS_HOST_DEVICE
    void store_with_pointer_offset(Fragment const& frag, Index pointer_offset) {
        AccessType const* frag_ptr = reinterpret_cast<AccessType const*>(&frag);

        CUTLASS_PRAGMA_UNROLL
        for (int n = 0; n < Policy::OperatorCount::kColumn; ++n) {
            pointer_[n * Detail::kLanesInQuad +
                     pointer_offset / Policy::kElementsPerAccess] = frag_ptr[n];
        }
    }

    /// Store
    CUTLASS_HOST_DEVICE
    void store(Fragment const& frag) { store_with_pointer_offset(frag, 0); }

    /// Load
    CUTLASS_HOST_DEVICE
    void load_with_pointer_offset(Fragment& frag, Index pointer_offset) const {
        AccessType* frag_ptr = reinterpret_cast<AccessType*>(&frag);

        CUTLASS_PRAGMA_UNROLL
        for (int n = 0; n < Policy::OperatorCount::kColumn; ++n) {
            frag_ptr[n] = pointer_[n * Detail::kLanesInQuad +
                                   pointer_offset / Policy::kElementsPerAccess];
        }
    }

    /// Load
    CUTLASS_HOST_DEVICE
    void load(Fragment& frag) const { load_with_pointer_offset(frag, 0); }

    CUTLASS_HOST_DEVICE
    TileIteratorTensorOp& operator++() { return add_tile_offset({1, 0}); }
};

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

/// Template for reading and writing tiles of accumulators to shared memory
template <typename WarpShape_,      ///< shape of warp-level GEMM (concept:
                                    ///< GemmShape)
          typename OperatorShape_,  ///< matrix multiply operation shape
                                    ///< (concept: gemm::GemmShape)
          typename Element_,        ///< data type of element to be written
          typename Layout_>
class TileIteratorTensorOpCanonical {
public:
    using WarpShape = WarpShape_;
    using OperatorShape = OperatorShape_;
    using Element = Element_;
    using Layout = Layout_;

    using TensorRef = TensorRef<Element, Layout>;  ///< Tensor Reference object
    using TensorCoord =
            MatrixCoord;  ///< Logical coordinate in referenced tensor
    using Index = typename TensorRef::Index;
    using LongIndex = typename TensorRef::LongIndex;

    using Policy = TensorOpPolicy<WarpShape, OperatorShape, Layout>;

    static int const kAccessSize = 1;
    static int const kAccessCount = Policy::kElementsPerAccess / kAccessSize;

    /// Shape of the tile in memory
    using Shape = MatrixShape<Policy::kRowsPerIteration, WarpShape::kN>;

    /// This is the fragment size produced by one access of the iterator.
    using Fragment = Array<Element, Policy::OperatorCount::kColumn *
                                            Policy::kElementsPerAccess>;

    /// This is the complete warp-level accumulator tile.
    // using AccumulatorTile = typename Operator::FragmentC;

    /// Number of times this iterator can be incremented
    static int const kIterations = Policy::kIterations;

    // Internal constants
    struct Detail {
        static int const kLanesInQuad = 4;
    };

    /// Padding quantity
    using Padding =
            MatrixShape<0, Detail::kLanesInQuad * Policy::kElementsPerAccess>;

private:
    /// Storage type for accessing memory
    using AccessType = AlignedArray<Element, kAccessSize>;

    //
    // Data members
    //

    /// Internal pointer to memory
    AccessType* pointer_;

    /// Internal layout object
    Layout layout_;

    /// Guard to indicate whether the shape is divisible
    bool divisible_;

    /// Extent of the output tensor
    MatrixCoord extent_;

    /// Thread offset
    MatrixCoord thread_offset_;

public:
    /// Default constructor
    CUTLASS_HOST_DEVICE
    TileIteratorTensorOpCanonical() : pointer_(nullptr) {}

    /// Constructor from TensorRef
    CUTLASS_HOST_DEVICE
    TileIteratorTensorOpCanonical(TensorRef const& ref, unsigned lane_id)
            : pointer_(reinterpret_cast<AccessType*>(ref.data())),
              layout_(ref.stride()[0]),
              divisible_(true),
              extent_(WarpShape::kM, WarpShape::kN) {
        int quad_id = (lane_id / Detail::kLanesInQuad);
        int lane_in_quad = (lane_id % Detail::kLanesInQuad);

        thread_offset_ = {quad_id, lane_in_quad * Policy::kElementsPerAccess};

        pointer_ += layout_({thread_offset_.row(), thread_offset_.column()});
    }

    /// Constructor from TensorRef
    CUTLASS_HOST_DEVICE
    TileIteratorTensorOpCanonical(TensorRef const& ref,
                                  TensorCoord const& extent, unsigned lane_id)
            : pointer_(reinterpret_cast<AccessType*>(ref.data())),
              layout_(ref.stride()[0]),
              divisible_(false),
              extent_(extent) {
        int quad_id = (lane_id / Detail::kLanesInQuad);
        int lane_in_quad = (lane_id % Detail::kLanesInQuad);

        thread_offset_ = {quad_id, lane_in_quad * Policy::kElementsPerAccess};

        pointer_ += layout_({thread_offset_.row(), thread_offset_.column()});
    }

    /// Adds a pointer offset
    CUTLASS_HOST_DEVICE
    TileIteratorTensorOpCanonical& add_pointer_offset(Index pointer_offset) {
        pointer_ += pointer_offset;
        return *this;
    }

    ///< advances in units of whole tiles along the logical coordinate space of
    ///< the tensor
    CUTLASS_HOST_DEVICE
    TileIteratorTensorOpCanonical& add_tile_offset(
            TensorCoord const& tile_offset) {
        MatrixCoord coord_offset(tile_offset.row() * Shape::kRow,
                                 tile_offset.column() * Shape::kColumn);

        thread_offset_ += coord_offset;

        pointer_ += layout_({coord_offset.row(), coord_offset.column()});

        return *this;
    }

    ///< advances in units of whole tiles along the logical coordinate space of
    ///< the tensor
    CUTLASS_HOST_DEVICE
    TileIteratorTensorOpCanonical& operator+=(TensorCoord const& tile_offset) {
        add_tile_offset(tile_offset);
        return *this;
    }

    /// Store
    CUTLASS_HOST_DEVICE
    void store_with_pointer_offset(Fragment const& frag, Index pointer_offset) {
        AccessType const* frag_ptr = reinterpret_cast<AccessType const*>(&frag);

        CUTLASS_PRAGMA_UNROLL
        for (int n = 0; n < Policy::OperatorCount::kColumn; ++n) {
            CUTLASS_PRAGMA_UNROLL
            for (int a = 0; a < kAccessCount; ++a) {
                int ptr_idx = n * Detail::kLanesInQuad * kAccessCount +
                              pointer_offset + a;
                int frag_idx = n * kAccessCount + a;

                int col =
                        thread_offset_.column() +
                        n * Detail::kLanesInQuad * Policy::kElementsPerAccess +
                        a;

                if (divisible_ || (thread_offset_.row() < extent_.row() &&
                                   col < extent_.column())) {
                    pointer_[ptr_idx] = frag_ptr[frag_idx];
                }
            }
        }
    }

    /// Store
    CUTLASS_HOST_DEVICE
    void store(Fragment const& frag) { store_with_pointer_offset(frag, 0); }

    /// Load
    CUTLASS_HOST_DEVICE
    void load_with_pointer_offset(Fragment& frag, Index pointer_offset) const {
        AccessType* frag_ptr = reinterpret_cast<AccessType*>(&frag);

        CUTLASS_PRAGMA_UNROLL
        for (int n = 0; n < Policy::OperatorCount::kColumn; ++n) {
            CUTLASS_PRAGMA_UNROLL
            for (int a = 0; a < kAccessCount; ++a) {
                int ptr_idx = n * Detail::kLanesInQuad * kAccessCount +
                              pointer_offset + a;
                int frag_idx = n * kAccessCount + a;

                int col =
                        thread_offset_.column() +
                        n * Detail::kLanesInQuad * Policy::kElementsPerAccess +
                        a;

                if (divisible_ || (thread_offset_.row() < extent_.row() &&
                                   col < extent_.column())) {
                    frag_ptr[frag_idx] = pointer_[ptr_idx];
                }
            }
        }
    }

    /// Load
    CUTLASS_HOST_DEVICE
    void load(Fragment& frag) const { load_with_pointer_offset(frag, 0); }

    CUTLASS_HOST_DEVICE
    TileIteratorTensorOpCanonical& operator++() {
        return add_tile_offset({1, 0});
    }
};

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

}  // namespace warp
}  // namespace epilogue
}  // namespace cutlass

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