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/*
 * Copyright (c) 2023-2024, NVIDIA CORPORATION.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
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 *
 *     http://www.apache.org/licenses/LICENSE-2.0
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/*! \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.

This file contains a customized version of EpilogueWithBroadcast from CUTLASS 2.9.1
(https://github.com/NVIDIA/cutlass/blob/v2.9.1/include/cutlass/epilogue/threadblock/epilogue_with_broadcast.h)

Changes:
- customized the compute_source_needed_() and apply_output_operator_() to suit the needs of per row
reduction
*/

#pragma once

#if defined(__CUDACC_RTC__)
#include <cuda/std/cassert>
#include <cuda/std/utility>
#else
#include <assert.h>

#include <utility>
#endif

#include <cutlass/aligned_buffer.h>
#include <cutlass/array.h>
#include <cutlass/cutlass.h>
#include <cutlass/epilogue/threadblock/epilogue_base.h>
#include <cutlass/epilogue/threadblock/predicated_tile_iterator.h>
#include <cutlass/fast_math.h>
#include <cutlass/functional.h>
#include <cutlass/gemm/gemm.h>
#include <cutlass/layout/tensor.h>
#include <cutlass/layout/vector.h>
#include <cutlass/numeric_conversion.h>
#include <cutlass/numeric_types.h>
#include <cutlass/tensor_coord.h>
#include <cutlass/transform/pitch_linear_thread_map.h>
#include <cutlass/transform/threadblock/regular_tile_iterator.h>

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

namespace cutlass {
namespace epilogue {
namespace threadblock {

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

/// This base class is meant to define the concept required of the
/// EpilogueWithBroadcast::OutputOp
template <typename ElementC_,
          typename ElementAccumulator_,
          typename ElementCompute_,
          typename ElementZ_,
          typename ElementT_,
          int ElementsPerAccess,
          bool StoreZ = true,
          bool StoreT = true>
struct EpilogueWithBroadcastOpBaseCustom {
  using ElementOutput                 = ElementC_;
  using ElementAccumulator            = ElementAccumulator_;
  using ElementCompute                = ElementCompute_;
  using ElementZ                      = ElementZ_;
  using ElementT                      = ElementT_;
  static int const kElementsPerAccess = ElementsPerAccess;

  using FragmentAccumulator = Array<ElementAccumulator, kElementsPerAccess>;
  using FragmentCompute     = Array<ElementCompute, kElementsPerAccess>;
  using FragmentC           = Array<ElementOutput, kElementsPerAccess>;
  using FragmentZ           = Array<ElementZ, kElementsPerAccess>;
  using FragmentT           = Array<ElementT, kElementsPerAccess>;

  /// If true, the 'Z' tensor is stored
  static bool const kStoreZ = StoreZ;

  /// If true, the 'T' tensor is stored
  static bool const kStoreT = StoreT;

  /// Parameters structure - required
  struct Params {};

  //
  // Methods
  //

  /// Constructor from Params
  EpilogueWithBroadcastOpBaseCustom(Params const& params_) {}

  /// Determine if the source is needed. May return false if
  bool is_source_needed() const { return true; }

  CUTLASS_HOST_DEVICE
  void set_k_partition(int k_partition, int k_partition_count) {}

  /// Applies the operation when is_source_needed() is true
  CUTLASS_HOST_DEVICE
  void operator()(FragmentZ& frag_Z,
                  FragmentT& frag_T,
                  FragmentAccumulator const& AB,
                  FragmentC const& frag_C,
                  FragmentCompute const& V) const
  {
  }

  /// Applies the operation when is_source_needed() is false
  CUTLASS_HOST_DEVICE
  void operator()(FragmentZ& frag_Z,
                  FragmentT& frag_T,
                  FragmentAccumulator const& AB,
                  FragmentCompute const& V) const
  {
  }
};

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

/// Epilogue operator with bias vector broadcast over columns.
///
/// Computes the following:
///
///
///  Z, T = OutputOp(AB, C, Broadcast)
///
///  if (ElementwiseOp::kStoreZ) {
///    store(converted_u);
///  }
///
///  if (ElementwiseOp::kStoreT) {
///    store(v);
///  }
///
template <
  typename Shape_,               ///< Shape of threadblock tile (concept: GemmShape)
  typename WarpMmaOperator_,     ///< Warp-level MMA operator (concept: gemm::warp::MmaTensorOp)
  int PartitionsK,               ///< Number of partitions of the K dimension
  typename OutputTileIterator_,  ///< Tile iterator reading and writing output tensors (z)
  typename TensorTileIterator_,  ///< Additional tile iterator for tensor-valued operands (t)
  typename ElementVector_,       ///< Pointer to broadcast vector
  typename AccumulatorFragmentIterator_,  ///< Fragment iterator selecting accumulators
  typename WarpTileIterator_,    ///< Warp-scoped tile iterator writing accumulators to SMEM
  typename SharedLoadIterator_,  ///< Threadblock-scoped tile iterator loading from SMEM
  typename OutputOp_,            ///< Output operator - concept is EpilogueWithBroadcastOp
  typename Padding_,  ///< Padding added to SMEM allocation to avoid bank conflicts (concept:
                      ///< MatrixShape)
  int FragmentsPerPartition = 1,  ///< Used to coarsten the epilogue granularity
  int IterationsUnroll      =     ///< Used to reduce binary size when epilogue op is large
  (!IsEpilogueFunctorHeavy<OutputOp_>::value)>
class EpilogueWithBroadcastCustom : public EpilogueBase<Shape_,
                                                        typename WarpMmaOperator_::Shape,
                                                        PartitionsK,
                                                        AccumulatorFragmentIterator_,
                                                        WarpTileIterator_,
                                                        Padding_,
                                                        FragmentsPerPartition> {
 public:
  using Base = EpilogueBase<Shape_,
                            typename WarpMmaOperator_::Shape,
                            PartitionsK,
                            AccumulatorFragmentIterator_,
                            WarpTileIterator_,
                            Padding_,
                            FragmentsPerPartition>;

  using Shape                       = Shape_;
  using WarpMmaOperator             = WarpMmaOperator_;
  static int const kPartitionsK     = PartitionsK;
  using OutputTileIterator          = OutputTileIterator_;
  using TensorTileIterator          = TensorTileIterator_;
  using ElementVector               = ElementVector_;
  using AccumulatorFragmentIterator = AccumulatorFragmentIterator_;
  using WarpTileIterator            = WarpTileIterator_;
  using SharedLoadIterator          = SharedLoadIterator_;
  using OutputOp                    = OutputOp_;
  using Padding                     = Padding_;

  using Layout    = layout::RowMajor;
  using LongIndex = typename Layout::LongIndex;

  /// The complete warp-level accumulator tile
  using AccumulatorTile = typename Base::AccumulatorTile;

  /// Accumulator element
  using ElementAccumulator = typename WarpTileIterator::Element;

  /// Compute data type produced by the output op
  using ElementCompute = typename OutputOp::ElementCompute;

  /// Compute fragment
  using FragmentCompute = Array<ElementCompute, OutputTileIterator::Fragment::kElements>;

  /// Thread map used by output tile iterators
  using ThreadMap = typename OutputTileIterator::ThreadMap;

  /// Fragment object used to store the broadcast values
  using BroadcastFragment =
    Array<ElementCompute, ThreadMap::Iterations::kColumn * ThreadMap::kElementsPerAccess>;

  /// Output element
  using ElementOutput = typename OutputTileIterator::Element;

  /// Data type of additional tensor
  using ElementTensor = typename TensorTileIterator::Element;

  /// Output access size
  static int const kElementsPerAccess = OutputTileIterator::kElementsPerAccess;

  /// Tensor reference to destination tensor
  using TensorRef = typename OutputTileIterator::TensorRef;

  /// Tensor reference to sync tensor
  using SyncTensorRef = typename cutlass::TensorRef<int, cutlass::layout::PackedVectorLayout>;

  /// Const tensor reference to source tensor
  using ConstTensorRef = typename OutputTileIterator::ConstTensorRef;

  /// Array type used to output
  using OutputAccessType =
    Array<typename OutputTileIterator::Element, OutputTileIterator::kElementsPerAccess>;

  /// Array type used by output functor
  using AccumulatorAccessType =
    Array<typename WarpTileIterator::Element, OutputTileIterator::kElementsPerAccess>;

  /// Array type used by output functor
  using ComputeAccessType = Array<ElementCompute, OutputTileIterator::kElementsPerAccess>;

  /// Tensor access type
  using TensorAccessType = Array<ElementTensor, OutputTileIterator::kElementsPerAccess>;

  /// Number of warps
  using WarpCount = typename Base::WarpCount;

  /// Shared memory allocation from epilogue base class
  using BaseSharedStorage = typename Base::SharedStorage;

  static int constexpr kSmemTiles =
    Base::kFragmentsPerIteration > 1 ? Base::kFragmentsPerIteration : kPartitionsK;
  static int constexpr kSmemPointerOffset = Base::SharedStorage::StorageShape::kCount / kSmemTiles;

  /// Used for the broadcast
  struct BroadcastDetail {
    /// Number of threads per warp
    static int const kWarpSize = 32;

    static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;

    /// Number of distinct scalar column indices handled by each thread
    static int const kColumnsPerThread =
      ThreadMap::Iterations::kColumn * ThreadMap::kElementsPerAccess;

    /// Number of distinct scalar row indices handled by each thread
    static int const kRowsPerThread =
      ThreadMap::Iterations::kCount / ThreadMap::Iterations::kColumn;

    /// Number of threads per threadblock
    static int const kThreadCount = kWarpSize * WarpCount::kCount;

    /// Number of distinct threads per row of output tile
    static int const kThreadsPerRow = (Shape::kN / kColumnsPerThread);

    /// Number of distinct threads which must be reduced during the final reduction phase within the
    /// threadblock.
    static int const kThreadRows = kThreadCount / kThreadsPerRow;

    /// I'm not sure what I meant here.
    static int const kThreadAccessesPerRow =
      const_max(1, (Shape::kN + kThreadCount - 1) / kThreadCount);

    /// Shape of the shared memory allocation for the epilogue
    using StorageShape = MatrixShape<kThreadRows, Shape::kN>;

    /// Debug printing
    CUTLASS_DEVICE
    static void print()
    {
#if 0
      printf("BroadcastDetail {\n");
      printf(
        "  kColumnsPerThread: %d\nkRowsPerThread: %d\n,kThreadCount: %d\nkThreadsPerRow: %d\n"
        "kThreadRows: %d\nThreadAccessesPerRow: %d\nStorageShape: %d x %d (count: %d)\n",
        kColumnsPerThread,
        kRowsPerThread,
        kThreadCount,
        kThreadsPerRow,
        kThreadRows,
        kThreadAccessesPerRow,
        StorageShape::kRow,
        StorageShape::kColumn,
        StorageShape::kCount
      );
      printf("};\n");
#endif
    }
  };

  /// Shared storage structure (shadows base) with additional SMEM buffer for reduction
  struct SharedStorage {
    union {
      BaseSharedStorage base;
    };

    CUTLASS_HOST_DEVICE
    SharedStorage() {}
  };

 public:
  static_assert(SharedLoadIterator::Fragment::kElements == TensorTileIterator::Fragment::kElements,
                "Mismatch between shared load iterator and output tile iterator.");

  static_assert(OutputTileIterator::kElementsPerAccess,
                "OutputTileIterator::kElementsPerAccess must not be zero.");

  static_assert(!(OutputTileIterator::Fragment::kElements % OutputTileIterator::kElementsPerAccess),
                "Divisibility");

 private:
  /// Loads fragment from shared memory aligned with output tensor
  SharedLoadIterator shared_load_iterator_;

  /// Thread index within the threadblock
  int thread_idx_;

 public:
  /// Constructor
  CUTLASS_DEVICE
  EpilogueWithBroadcastCustom(SharedStorage& shared_storage,  ///< Shared storage object
                              int thread_idx,  ///< ID of a thread within the threadblock
                              int warp_idx,    ///< ID of warp within threadblock
                              int lane_idx     ///< Id of thread within warp
                              )
    : Base(shared_storage.base, thread_idx, warp_idx, lane_idx),
      shared_load_iterator_(shared_storage.base.reference(), thread_idx),
      thread_idx_(thread_idx)
  {
  }

  /// Streams the result to global memory
  CUTLASS_DEVICE
  void operator()(
    OutputOp const& output_op,            ///< Output operator
    ElementVector const* broadcast_ptr,   ///< Broadcast vector
    AccumulatorTile const& accumulators,  ///< Complete warp-level accumulator tile
    OutputTileIterator source_iterator,   ///< Tile iterator for source accumulator matrix
    TensorTileIterator
      tensor_iterator,  ///< Threadblock tile iterator for additional tensor operand
    MatrixCoord const&
      problem_size =  ///< Problem size needed to guard against out-of-bounds accesses
    MatrixCoord(Shape::kM, Shape::kN),
    MatrixCoord const&
      threadblock_offset =  ///< Threadblock's initial offset within the problem size space
    MatrixCoord())
  {
    BroadcastFragment broadcast_fragment;

    load_broadcast_fragment_(broadcast_fragment, broadcast_ptr, problem_size, threadblock_offset);

    compute_source_needed_(
      output_op, broadcast_fragment, accumulators, source_iterator, tensor_iterator);
  }

 private:
  CUTLASS_DEVICE
  void load_broadcast_fragment_(
    BroadcastFragment&
      broadcast_fragment,  ///< Fragment containing the accumulated partial reduction over columns
    ElementVector const* broadcast_ptr,  ///< Broadcast vector
    MatrixCoord const&
      problem_size,  ///< Problem size needed to guard against out-of-bounds accesses
    MatrixCoord const&
      threadblock_offset  ///< Threadblock's initial offset within the problem size space
  )
  {
    broadcast_fragment.clear();

    // If no pointer is supplied, set with all zeros and avoid memory accesses
    if (!broadcast_ptr) { return; }

    int thread_initial_column = ThreadMap::initial_offset(thread_idx_).column();

    int thread_column_idx = threadblock_offset.column() + thread_initial_column;
    broadcast_ptr += thread_initial_column;

    NumericArrayConverter<ElementCompute, ElementVector, BroadcastDetail::kElementsPerAccess>
      converter;
    using AccessType          = AlignedArray<ElementVector, BroadcastDetail::kElementsPerAccess>;
    using ComputeFragmentType = Array<ElementCompute, BroadcastDetail::kElementsPerAccess>;

    ComputeFragmentType* frag_ptr = reinterpret_cast<ComputeFragmentType*>(&broadcast_fragment);

    CUTLASS_PRAGMA_UNROLL
    for (int j = 0; j < ThreadMap::Iterations::kColumn; ++j) {
      AccessType loaded;

      loaded.clear();

      if (thread_column_idx < problem_size.column()) {
        loaded = *reinterpret_cast<AccessType const*>(broadcast_ptr);
      }

      ComputeFragmentType cvt = converter(loaded);
      frag_ptr[j]             = cvt;

      thread_column_idx += ThreadMap::Delta::kColumn;
      broadcast_ptr += ThreadMap::Delta::kColumn;
    }
  }

  template <class Seq>
  struct acc2smem_source_not_needed;

  template <size_t... Seq>
  struct acc2smem_source_not_needed<cutlass::index_sequence<Seq...>> {
    template <int Advance>
    CUTLASS_DEVICE static void helper(AccumulatorFragmentIterator accum_fragment_iterator,
                                      WarpTileIterator& warp_tile_iterator)
    {
      CUTLASS_PRAGMA_UNROLL
      for (int i = 0; i < Advance; i++) {
        ++accum_fragment_iterator;
      }

      CUTLASS_PRAGMA_UNROLL
      for (int p = 0; p < Base::kFragmentsPerIteration; ++p) {
        typename AccumulatorFragmentIterator::Fragment accum_fragment;

        accum_fragment_iterator.load(accum_fragment);
        ++accum_fragment_iterator;

        warp_tile_iterator.store(accum_fragment);
        if (p < Base::kFragmentsPerIteration - 1) {
          warp_tile_iterator.add_pointer_offset(kSmemPointerOffset);
        }
      }

      if (Base::kFragmentsPerIteration > 1) {
        warp_tile_iterator.add_pointer_offset(kSmemPointerOffset *
                                              (1 - Base::kFragmentsPerIteration));
      }
    }

    CUTLASS_DEVICE
    static void push(size_t pos,
                     AccumulatorFragmentIterator const& iterator_begin,
                     WarpTileIterator& warp_tile_iterator)
    {
      int dummy[] = {
        (pos == (Seq * Base::kFragmentsPerIteration)) &&
        (helper<Seq * Base::kFragmentsPerIteration>(iterator_begin, warp_tile_iterator), 0)...};

      CUTLASS_UNUSED(dummy[0]);
    }
  };

  /// Streams the result to global memory
  CUTLASS_DEVICE
  void compute_source_not_needed_(
    OutputOp const& output_op,  ///< Output operator
    BroadcastFragment const&
      broadcast_fragment,  ///< Fragment containing the accumulated partial reduction over columns
    OutputTileIterator destination_iterator,  ///< Tile iterator for destination
    AccumulatorTile const& accumulators,      ///< Complete warp-level accumulator tile
    TensorTileIterator tensor_iterator  ///< Threadblock tile iterator for additioanl tensor operand
  )
  {
  }

  template <class Seq>
  struct acc2smem_source_needed;

  template <size_t... Seq>
  struct acc2smem_source_needed<cutlass::index_sequence<Seq...>> {
    template <int Advance>
    CUTLASS_DEVICE static void helper(AccumulatorFragmentIterator accum_fragment_iterator,
                                      WarpTileIterator& warp_tile_iterator)
    {
      CUTLASS_PRAGMA_UNROLL
      for (int i = 0; i < Advance; i++) {
        ++accum_fragment_iterator;
      }

      typename AccumulatorFragmentIterator::Fragment accum_fragment;
      accum_fragment_iterator.load(accum_fragment);
      warp_tile_iterator.store(accum_fragment);
    }

    CUTLASS_DEVICE
    static void push(size_t pos,
                     AccumulatorFragmentIterator const& iterator_begin,
                     WarpTileIterator& warp_tile_iterator)
    {
      int dummy[] = {(pos == Seq) && (helper<Seq>(iterator_begin, warp_tile_iterator), 0)...};
    }
  };

  /// Streams the result to global memory
  CUTLASS_DEVICE
  void compute_source_needed_(
    OutputOp const& output_op,  ///< Output operator
    BroadcastFragment const&
      broadcast_fragment,  ///< Fragment containing the accumulated partial reduction over columns
    AccumulatorTile const& accumulators,  ///< Complete warp-level accumulator tile
    OutputTileIterator
      source_iterator,  ///< Threadblock tile coordinate in GEMM (in units of threadblock tiles)
    TensorTileIterator tensor_iterator  ///< Threadblock tile iterator for additioanl tensor operand
  )
  {
    typename OutputTileIterator::Fragment source_fragment;
    source_fragment.clear();

    //
    // Iterator over warp-level accumulator fragment
    //

    AccumulatorFragmentIterator accum_fragment_iterator(accumulators);

    //
    // Iterate over accumulator tile
    //

#pragma unroll(IterationsUnroll ? OutputTileIterator::kIterations : 1)
    for (int iter = 0; iter < OutputTileIterator::kIterations; ++iter) {
      //
      // Convert and store fragment
      //

      //__syncthreads();

      acc2smem_source_needed<cutlass::make_index_sequence<OutputTileIterator::kIterations>>::push(
        iter, accum_fragment_iterator, this->warp_tile_iterator_);

      __syncthreads();

      //
      // Load fragments from shared memory
      //

      typename SharedLoadIterator::Fragment aligned_accum_fragment[kPartitionsK];

      shared_load_iterator_.load(aligned_accum_fragment[0]);

      //
      // Apply output operation
      //

      typename TensorTileIterator::Fragment frag_T;

      //
      // Load the source
      //

      source_iterator.load(source_fragment);
      ++source_iterator;

      apply_output_operator_(
        frag_T, output_op, aligned_accum_fragment[0], source_fragment, broadcast_fragment);

      //
      // Conditionally store fragments
      //
      if (OutputOp::kStoreT) {
        tensor_iterator.store(frag_T);
        ++tensor_iterator;
      }
    }
    tensor_iterator.dumpToGmem();
  }

  /// Helper to invoke the output functor over each vector of output
  CUTLASS_DEVICE
  void apply_output_operator_(typename TensorTileIterator::Fragment& frag_T,
                              OutputOp const& output_op,
                              typename SharedLoadIterator::Fragment const& frag_AB,
                              typename OutputTileIterator::Fragment const& frag_C,
                              BroadcastFragment const& frag_Broadcast)
  {
    using AccessTypeT         = Array<typename TensorTileIterator::OutValT, kElementsPerAccess>;
    using AccessTypeBroadcast = Array<ElementCompute, kElementsPerAccess>;

    AccessTypeT* frag_T_ptr = reinterpret_cast<AccessTypeT*>(&frag_T);

    AccumulatorAccessType const* frag_AB_ptr =
      reinterpret_cast<AccumulatorAccessType const*>(&frag_AB);

    OutputAccessType const* frag_C_ptr = reinterpret_cast<OutputAccessType const*>(&frag_C);

    AccessTypeBroadcast const* frag_Broadcast_ptr =
      reinterpret_cast<AccessTypeBroadcast const*>(&frag_Broadcast);

    int const kOutputOpIterations =
      TensorTileIterator::Fragment::kElements / TensorTileIterator::kElementsPerAccess;

    CUTLASS_PRAGMA_UNROLL
    for (int i = 0; i < kOutputOpIterations; ++i) {
      output_op(frag_T_ptr[i],
                frag_AB_ptr[i],
                frag_C_ptr[(i / ThreadMap::Iterations::kColumn)],
                frag_Broadcast_ptr[i % ThreadMap::Iterations::kColumn]);
    }
  }

  /// Helper to invoke the output functor over each vector of output
  CUTLASS_DEVICE
  void apply_output_operator_source_not_needed_(
    typename OutputTileIterator::Fragment& frag_Z,
    typename TensorTileIterator::Fragment& frag_T,
    OutputOp const& output_op,
    typename SharedLoadIterator::Fragment const& frag_AB,
    BroadcastFragment const& frag_Broadcast)
  {
  }
};

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

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

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