/***************************************************************************************************
 * 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 B (filter tile) 
    matrix from memory.

    This iterator assumes TensorNHWC or TensorCxRSKx<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/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>,
  bool IsDeconv_ = false
>
class Conv2dFpropFilterTileAccessIteratorOptimized{
public:
  
  //
  // Types
  //

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

  struct Params : Conv2dFpropFilterIteratorOptimizedParams<Layout> {

    CUTLASS_HOST_DEVICE
    Params() { }
    
    CUTLASS_HOST_DEVICE
    Params(Conv2dFpropFilterIteratorOptimizedParams<Layout> const &base): 
      Conv2dFpropFilterIteratorOptimizedParams<Layout>(base) { }

    CUTLASS_HOST_DEVICE
    Params(
      Conv2dProblemSize const &problem_size,
      Layout const &layout
    ):
      Conv2dFpropFilterIteratorOptimizedParams<Layout>(
        problem_size,
        layout,
        sizeof_bits<Element>::value,
        {Shape::kRow, Shape::kColumn},
        ThreadMap::kThreads,
        ThreadMap::kElementsPerAccess,
        {ThreadMap::Iterations::kContiguous, ThreadMap::Iterations::kStrided},
        {ThreadMap::Delta::kContiguous, ThreadMap::Delta::kStrided}
      ) {

    }
  };

private:

  Conv2dFpropFilterIteratorOptimizedParams<Layout> const &params_;
  Conv2dProblemSize const &problem_size_;
  LongIndex iteration_contiguous_;
  LongIndex iteration_strided_;
  LongIndex iteration_vector_;
  char const *pointer_;

  uint32_t predicates_[kAccessesPerVector];
  int filter_rs_;
  int filter_c_;
  int channels_per_group_;

  //
  // Assertions
  //

  // We map predicates into bits packed in this uint32_t container
  static_assert(ThreadMap::Iterations::kStrided < sizeof(predicates_) * 8,
    "Currently, the number of loads per iteration is limited by the size of the predicates container.");

public:

  CUTLASS_HOST_DEVICE
  Conv2dFpropFilterTileAccessIteratorOptimized(
    Conv2dFpropFilterIteratorOptimizedParams<Layout> const &params,
    Conv2dProblemSize const &problem_size,
    Element const *ptr,
    int thread_idx,
    MatrixCoord const &threadblock_offset = MatrixCoord()
  ):
    params_(params), 
    problem_size_(problem_size),
    pointer_(reinterpret_cast<char const *>(ptr)),
    predicates_{0},
    filter_rs_(0),
    filter_c_(0) {

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

    filter_c_ = threadblock_offset.row() + thread_coord.contiguous();
    Index column = threadblock_offset.column() + thread_coord.strided();
    channels_per_group_ = (IsDeconv ? problem_size_.K : problem_size_.C) / problem_size_.groups;

    CUTLASS_PRAGMA_UNROLL
    for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
      uint32_t pred = ((column + s * ThreadMap::Delta::kStrided < (IsDeconv ? problem_size_.C : problem_size_.K)) ? 1u : 0);

      CUTLASS_PRAGMA_UNROLL
      for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
        predicates_[v_idx] |= (pred << s);
      }
    }

    CUTLASS_PRAGMA_UNROLL
    for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
      clear_mask(v_idx, filter_c_ + v_idx * AccessType::kElements >= channels_per_group_);
    }

    pointer_ += (
      params_.layout({filter_c_, column}) 
    ) * sizeof_bits<Element>::value / 8;

    set_iteration_index(0);
  }

  /// 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() {

    LongIndex next = params_.inc_next_rs;

    // moves to the next tile
    ++filter_rs_;
    if (filter_rs_ == params_.RS) {

      filter_rs_ = 0;
      next = params_.inc_next_c;
      filter_c_ += params_.filter_c_delta;
    }
 
    CUTLASS_PRAGMA_UNROLL
    for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
      clear_mask(v_idx, filter_c_ + v_idx * AccessType::kElements >= channels_per_group_);
    }
      
    pointer_ += next;
  }

  /// Clears the predicates
  CUTLASS_HOST_DEVICE
  void clear_mask(int v, bool clear = true) {
    predicates_[v] = clear ? 0u : predicates_[v];
  }

  /// Returns true if the current coordinate is within the filter tensor W
  CUTLASS_HOST_DEVICE
  bool valid() {
    return (predicates_[iteration_vector_] & (1u << iteration_strided_));
  }

  /// Returns a pointer to the vector starting at the current coordinate
  CUTLASS_HOST_DEVICE
  AccessType const *get() const {
    return reinterpret_cast<AccessType const *>(pointer_) + iteration_vector_;
  }

  /// Increments to the next memory access
  CUTLASS_HOST_DEVICE
  Conv2dFpropFilterTileAccessIteratorOptimized &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) {

      // Move to the next K coordinate within the tile
      pointer_ += params_.inc_next_k;

      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) {

    auto input_channels = (IsDeconv ? problem_size.K : problem_size.C);
    auto output_channels = (IsDeconv ? problem_size.C : problem_size.K);

    // check alignment constraint on iterator's contiguous dimension
    if ((input_channels / problem_size.groups) % AccessType::kElements) {
      return Status::kErrorInvalidProblem;
    }

    if (platform::is_same<Layout, layout::TensorCxRSKx<32>>::value) {
      if (output_channels % 32) {
        return Status::kErrorInvalidProblem;
      }
    }

    if (platform::is_same<Layout, layout::TensorCxRSKx<64>>::value) {
      if (output_channels % 64) {
        return Status::kErrorInvalidProblem;
      }
    }

    return Status::kSuccess;
  }
};

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

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

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