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/*! \file
    \brief Templates implementing loading of convolution tiles mapped to GEMM A
   (activation tile) matrix from memory.

    This iterator assumes TensorNHWC or TensorNCxHWx<Interleave> layout of
   tensors in Global Memory.

    The iterator is specialized for each of the three convolution operators:
   forward propagation (Fprop), backward data gradient (Dgrad), and backward
   weight gradient (Wgrad).
*/

#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/array.h"
#include "cutlass/coord.h"
#include "cutlass/matrix_shape.h"
#include "cutlass/predicate_vector.h"
#include "cutlass/tensor_ref.h"
#include "cutlass/tensor_view.h"
#include "cutlass/layout/pitch_linear.h"
#include "cutlass/layout/tensor.h"
#include "cutlass/layout/matrix.h"
#include "cutlass/conv/convolution.h"
#include "cutlass/conv/conv2d_problem_size.h"
#include "cutlass/conv/threadblock/conv2d_params.h"

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

namespace cutlass {
namespace conv {
namespace threadblock {

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

template <typename Shape_, typename Element_, typename Layout_,
          typename ThreadMap_>
class Conv2dFpropActivationTileAccessIteratorAnalytic {
public:
    //
    // Types
    //

    using Shape = Shape_;
    using Element = Element_;
    using Layout = Layout_;
    using TensorCoord = typename Layout::TensorCoord;
    using ThreadMap = ThreadMap_;
    using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
    using TensorRef = cutlass::TensorRef<Element, Layout>;
    using Index = typename Layout::Index;
    using LongIndex = typename Layout::LongIndex;
    static IteratorAlgorithm const kIteratorAlgorithm =
            conv::IteratorAlgorithm::kAnalytic;
    static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
    static int const kConvDim = 2;
    using ConvProblemSize = typename conv::Conv2dProblemSize;

    //
    // Simplifying assertions
    //
    static_assert(ThreadMap::Iterations::kContiguous == 1,
                  "Require Iterations::kContiguous == 1");

    //
    // Parameters structure
    //

    using Params = Conv2dAnalyticParams<Layout>;

private:
    Params const& params_;
    Conv2dProblemSize const& problem_size_;
    LongIndex iteration_contiguous_;
    LongIndex iteration_strided_;
    char const* pointer_;

    int filter_c_;
    int filter_r_;
    int filter_s_;

    int offset_n_[ThreadMap::Iterations::kStrided];
    int offset_p_[ThreadMap::Iterations::kStrided];
    int offset_q_[ThreadMap::Iterations::kStrided];

public:
    CUTLASS_HOST_DEVICE
    Conv2dFpropActivationTileAccessIteratorAnalytic(
            Params const& params, Conv2dProblemSize const& problem_size,
            Element const* ptr, int thread_idx,
            MatrixCoord const& threadblock_offset =
                    MatrixCoord()  // tile index - units are threadblock-scoped
                                   // tiles
            )
            : params_(params),
              problem_size_(problem_size),
              pointer_(reinterpret_cast<char const*>(ptr)),
              filter_c_(0),
              filter_r_(0),
              filter_s_(0) {
        layout::PitchLinearCoord thread_coord =
                ThreadMap::initial_offset(thread_idx);

        filter_c_ = threadblock_offset.column() + thread_coord.contiguous();

        CUTLASS_PRAGMA_UNROLL
        for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
            int offset_npq = threadblock_offset.row() + thread_coord.strided() +
                             s * ThreadMap::Delta::kStrided;

            offset_n_[s] = offset_npq / (problem_size_.P * problem_size_.Q);
            int residual = offset_npq % (problem_size_.P * problem_size_.Q);

            offset_p_[s] = residual / problem_size_.Q;
            offset_q_[s] = residual % problem_size_.Q;
        }

        set_iteration_index(0);
    }

    /// Overrides the internal iteration index
    CUTLASS_HOST_DEVICE
    void set_iteration_index(Index 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) {
        pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
    }

    CUTLASS_HOST_DEVICE
    void advance() {
        // moves to the next tile
        ++filter_s_;
        if (filter_s_ < problem_size_.S) {
            return;
        }
        filter_s_ = 0;
        ++filter_r_;
        if (filter_r_ < problem_size_.R) {
            return;
        }
        filter_r_ = 0;

        filter_c_ += Shape::kColumn * problem_size_.split_k_slices;
    }

    /// Returns the coordinate in the activations tensor X that is currently
    /// pointed to by the iterator.
    CUTLASS_HOST_DEVICE
    TensorCoord at() const {
        int n = offset_n_[iteration_strided_];
        int p = offset_p_[iteration_strided_];
        int q = offset_q_[iteration_strided_];

        int r = filter_r_;
        int s = filter_s_;

        if (problem_size_.mode == Mode::kConvolution) {
            r = (problem_size_.R - 1 - filter_r_);
            s = (problem_size_.S - 1 - filter_s_);
        }

        int h = p * problem_size_.stride_h - problem_size_.pad_h +
                r * problem_size_.dilation_h;
        int w = q * problem_size_.stride_w - problem_size_.pad_w +
                s * problem_size_.dilation_w;

        return TensorCoord(n, h, w, filter_c_);
    }

    /// Returns true if the current coordinate is within the activations tensor
    /// X
    CUTLASS_HOST_DEVICE
    bool valid() const {
        TensorCoord coord = at();

        return coord.n() < problem_size_.N && coord.h() >= 0 &&
               coord.h() < problem_size_.H && coord.w() >= 0 &&
               coord.w() < problem_size_.W && coord.c() < problem_size_.C;
    }

    /// Returns a pointer to the vector starting at the current coordinate
    CUTLASS_HOST_DEVICE
    AccessType const* get() const {
        TensorCoord coord = at();
        LongIndex offset = params_.layout(coord);

        AccessType const* ptr = reinterpret_cast<AccessType const*>(
                pointer_ + offset * sizeof_bits<Element>::value / 8);

        return ptr;
    }

    /// Increments to the next memory access
    CUTLASS_HOST_DEVICE
    Conv2dFpropActivationTileAccessIteratorAnalytic& operator++() {
        ++iteration_contiguous_;
        if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
            return *this;
        }
        iteration_contiguous_ = 0;

        ++iteration_strided_;
        if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
            return *this;
        }
        iteration_strided_ = 0;

        return *this;
    }

    /// Determines whether the Implicit GEMM can execute the given problem.
    CUTLASS_HOST_DEVICE
    static Status can_implement(Conv2dProblemSize const& problem_size) {
        // check alignment constraint on iterator's contiguous dimension
        if (problem_size.C % (128 / sizeof_bits<Element>::value)) {
            return Status::kErrorInvalidProblem;
        }

        if (platform::is_same<Layout, layout::TensorNCxHWx<32>>::value) {
            if (problem_size.C % 32) {
                return Status::kErrorInvalidProblem;
            }
        }

        if (platform::is_same<Layout, layout::TensorNCxHWx<64>>::value) {
            if (problem_size.C % 64) {
                return Status::kErrorInvalidProblem;
            }
        }

        return Status::kSuccess;
    }
};

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

}  // namespace threadblock
}  // namespace conv
}  // namespace cutlass

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