/***************************************************************************************************
 * Copyright (c) 2017 - 2024 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 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_,
  typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dFpropActivationTileAccessIteratorFixedChannels {
public:

  //
  // Types
  //

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

  static int const kFilterPositionsPerTile = Shape::kColumn / AccessType::kElements;

  static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;

  static bool const kUseFastDivmodPrologue = true;
  static bool const kUseFastDivmodMainloop = true;

  static int const kStrideH = 0;
  static int const kStrideW = 0;
  static int const kDilationH = 0;
  static int const kDilationW = 0;

  static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
    "Vectors implied by the thread map must be divisible by the access type.");

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

  //
  // Parameters structure
  //

  using Params = Conv2dFewChannelsParams<Layout>;

private:

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

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

public:

  CUTLASS_HOST_DEVICE
  Conv2dFpropActivationTileAccessIteratorFixedChannels(
    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)),
    rs_index_(0) {

    //
    // This requires problem_size.C == AccessType::kElements
    //

    layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);

    rs_index_ = (threadblock_offset.column() + thread_coord.contiguous()) / AccessType::kElements;

    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;

      if (kUseFastDivmodPrologue) {
        int residual = params_.divmod_Q.divmod(offset_q_[s], offset_npq);
        offset_n_[s] = params_.divmod_P.divmod(offset_p_[s], residual);
      }
      else {
        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);
  }

  CUTLASS_HOST_DEVICE
  static Params getParams(Conv2dProblemSize const &problem_size, Layout const &layout) {
    return Params(problem_size, layout);
  }

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

    rs_index_ += kFilterPositionsPerTile * 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 rs_index = rs_index_ + iteration_vector_;

    int r = 0;
    int s = 0;

    if (kUseFastDivmodMainloop) {
      r = params_.divmod_S.divmod(s, rs_index);
    }
    else {
      s = (rs_index % problem_size_.S);
      r = (rs_index / problem_size_.S);
    }

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

    int stride_h = kStrideH;
    if (!kStrideH) {
      stride_h = problem_size_.stride_h;
    }

    int stride_w = kStrideW;
    if (!kStrideW) {
      stride_w = problem_size_.stride_w;
    }

    int dilation_h = kDilationH;
    if (!kDilationH) {
      dilation_h = problem_size_.dilation_h;
    }

    int dilation_w = kDilationW;
    if (!kDilationW) {
      dilation_w = problem_size_.dilation_w;
    }

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

    return TensorCoord(n, h, w, 0);
  }

  /// 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;
  }

  /// Returns a pointer to the vector starting at the current coordinate
  CUTLASS_HOST_DEVICE
  AccessType const *get() const {

    TensorCoord coord = at();

    int32_t offset =
      coord.n() * params_.stride_n +
      coord.h() * params_.stride_h +
      coord.w() * params_.stride_w + coord.c();

    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
  Conv2dFpropActivationTileAccessIteratorFixedChannels &operator++() {
    ++iteration_vector_;
    if (iteration_vector_ < kAccessesPerVector) {
      return *this;
    }
    iteration_vector_ = 0;

    ++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 != AccessType::kElements) {
      return Status::kErrorInvalidProblem;
    }

    if (kDilationH && problem_size.dilation_h != kDilationH) {
      return Status::kErrorInvalidProblem;
    }

    if (kDilationW && problem_size.dilation_w != kDilationW) {
      return Status::kErrorInvalidProblem;
    }

    if (kStrideH && problem_size.stride_h != kStrideH) {
      return Status::kErrorInvalidProblem;
    }

    if (kStrideW && problem_size.stride_w != kStrideW) {
      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

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