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/*! \file
  \brief Functor for performing tensor-tensor broadacasts atop existing epilogues.

  Concretely, the opeartion performed is the following:
    UnaryOp(
        BinaryOp1(
            BinaryOp0(
                Activation((alpha * A @ B) + bias),
                beta * C0
            ),
            beta * C1
        )
    )

    where:
        - C0 and C1 have the same extents as the output
        - BinaryOp0 and BinaryOp1 perform elementwise binary operations
        - UnaryOp is an elementwise operation
*/

#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/epilogue/collective/detail.hpp"

#include "cute/tensor.hpp"
#include "cutlass/cuda_host_adapter.hpp"

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

namespace cutlass {
namespace epilogue {
namespace collective {
/////////////////////////////////////////////////////////////////////////////////////////////////

/// Collective epilogue that applies elementwise tensor-tensor operations atop other epilogues
///
template <
  class StrideC_,
  class StrideD_,
  class ThreadEpilogueOp_,
  class EpilogueSchedule_,
  bool PerColumnBias_ = false
>
class EpilogueTensorBroadcast {
public:
  //
  // Type Aliases
  //
  using EpilogueSchedule = EpilogueSchedule_;

  // derived types of output thread level operator
  using ThreadEpilogueOp = ThreadEpilogueOp_;
  using ElementOutput = typename ThreadEpilogueOp::ElementOutput;
  using ElementAccumulator = typename ThreadEpilogueOp::ElementAccumulator;
  using ElementCompute = typename ThreadEpilogueOp::ElementCompute;
  using ElementScalar = ElementCompute;
  using ElementBias = typename ThreadEpilogueOp::ElementBias;
  using ElementC = typename ThreadEpilogueOp::ElementC;
  using StrideC = StrideC_;
  using ElementD = typename ThreadEpilogueOp::ElementD;
  using StrideD = StrideD_;
  using ActivationFunctor = typename ThreadEpilogueOp::ActivationFunctor;

  static_assert(cute::rank(StrideC{}) == 3, "StrideCD must be rank-3: [M, N, L]");
  static_assert(cute::rank(StrideD{}) == 3, "StrideCD must be rank-3: [M, N, L]");

  static constexpr int kOutputAlignment = ThreadEpilogueOp::kCount;
  using AlignmentType = typename cute::uint_bit<sizeof_bits<ElementOutput>::value * kOutputAlignment>::type;

  static constexpr bool IsBinaryOp0Enabled = ThreadEpilogueOp::IsBinaryOp0Enabled;
  static constexpr bool IsBinaryOp1Enabled = ThreadEpilogueOp::IsBinaryOp1Enabled;
  static constexpr bool IsUnaryOpEnabled = ThreadEpilogueOp::IsUnaryOpEnabled;

  static constexpr bool PerColumnBias = PerColumnBias_;
  using BiasStride = typename cute::conditional_t<PerColumnBias, Stride<_0, _1, _0>, Stride<_1, _0, _0>>;

  struct SharedStorage { };

  // Host side epilogue arguments
  struct Arguments {
    typename ThreadEpilogueOp::Params thread{};
    StrideC dC{};
    ElementD* ptr_D = nullptr;
    StrideD dD{};
    ElementBias* ptr_Bias = nullptr;
    ElementC* ptr_C0 = nullptr;
    ElementC* ptr_C1 = nullptr;
  };

  // Device side epilogue params
  using Params = Arguments;

  //
  // Methods
  //

  template <class ProblemShape>
  static constexpr Params
  to_underlying_arguments(
      [[maybe_unused]] ProblemShape const& _,
      Arguments const& args,
      [[maybe_unused]] void* workspace) {
    return args;
  }

  template <class ProblemShape>
  static size_t
  get_workspace_size(ProblemShape const& problem_shape, Arguments const& args) {
    return 0;
  }

  template <class ProblemShape>
  static cutlass::Status
  initialize_workspace(ProblemShape const& problem_shape, Arguments const& args, void* workspace, cudaStream_t stream,
    CudaHostAdapter* cuda_adapter = nullptr) {
    return cutlass::Status::kSuccess;
  }

  template <class ProblemShape>
  static bool
  can_implement(
      [[maybe_unused]] ProblemShape const& problem_shape,
      [[maybe_unused]] Arguments const& args) {
    return true;
  }

  CUTLASS_HOST_DEVICE
  EpilogueTensorBroadcast(Params const& params_)
      : params(params_), epilogue_op(params_.thread) { }

  CUTLASS_DEVICE
  bool
  is_source_needed() {
    return epilogue_op.is_source0_needed() || epilogue_op.is_source1_needed();
  }

  template<
    class ProblemShapeMNKL,
    class BlockShapeMNK,
    class BlockCoordMNKL,
    class FrgEngine, class FrgLayout,
    class TiledMma,
    class ResidueMNK
  >
  CUTLASS_HOST_DEVICE void
  operator()(
      ProblemShapeMNKL problem_shape_mnkl,
      BlockShapeMNK blk_shape_MNK,
      BlockCoordMNKL blk_coord_mnkl,
      cute::Tensor<FrgEngine, FrgLayout> const& accumulators,
      TiledMma tiled_mma,
      ResidueMNK residue_mnk,
      int thread_idx,
      [[maybe_unused]] char* smem_buf)
  {
    using namespace cute;
    using X = Underscore;

    static_assert(cute::rank(ProblemShapeMNKL{}) == 4, "ProblemShapeMNKL must be rank 4");
    static_assert(is_static<BlockShapeMNK>::value, "ThreadBlock tile shape must be static");
    static_assert(cute::rank(BlockShapeMNK{}) == 3, "BlockShapeMNK must be rank 3");
    static_assert(cute::rank(BlockCoordMNKL{}) == 4, "BlockCoordMNKL must be rank 4");

    // Separate out problem shape for convenience
    auto M = get<0>(problem_shape_mnkl);
    auto N = get<1>(problem_shape_mnkl);
    auto L = get<3>(problem_shape_mnkl);

    auto stride_c    = detail::get_epilogue_stride<EpilogueSchedule>(params.dC);
    auto stride_d    = detail::get_epilogue_stride<EpilogueSchedule>(params.dD);
    auto stride_bias = detail::get_epilogue_stride<EpilogueSchedule>(BiasStride{});

    // Represent the full output tensor
    Tensor mC0_mnl = make_tensor(make_gmem_ptr(params.ptr_C0), make_shape(M,N,L), stride_c);                   // (m,n,l)
    Tensor mC1_mnl = make_tensor(make_gmem_ptr(params.ptr_C1), make_shape(M,N,L), stride_c);                   // (m,n,l)
    Tensor mD_mnl = make_tensor(make_gmem_ptr(params.ptr_D), make_shape(M,N,L), stride_d);                     // (m,n,l)
    Tensor mBias_mnl = make_tensor(make_gmem_ptr(params.ptr_Bias), make_shape(M,N,L), stride_bias);            // (m,n,l)

    Tensor gC0_mnl = local_tile(mC0_mnl, blk_shape_MNK, make_coord(_,_,_), Step<_1,_1, X>{});      // (BLK_M,BLK_N,m,n,l)
    Tensor gC1_mnl = local_tile(mC1_mnl, blk_shape_MNK, make_coord(_,_,_), Step<_1,_1, X>{});      // (BLK_M,BLK_N,m,n,l)

    Tensor gD_mnl = local_tile(mD_mnl, blk_shape_MNK, make_coord(_,_,_), Step<_1,_1, X>{});        // (BLK_M,BLK_N,m,n,l)
    Tensor gBias_mnl = local_tile(mBias_mnl, blk_shape_MNK, make_coord(_,_,_), Step<_1,_1, X>{});  // (BLK_M,BLK_N,m,n,l)

    // Slice to get the tile this thread block is responsible for
    auto [m_coord, n_coord, k_coord, l_coord] = blk_coord_mnkl;
    Tensor gC0 = gC0_mnl(_,_,m_coord,n_coord,l_coord);                                                   // (BLK_M,BLK_N)
    Tensor gC1 = gC1_mnl(_,_,m_coord,n_coord,l_coord);                                                   // (BLK_M,BLK_N)
    Tensor gD = gD_mnl(_,_,m_coord,n_coord,l_coord);                                                     // (BLK_M,BLK_N)
    Tensor gBias = gBias_mnl(_,_,m_coord,n_coord,l_coord);                                               // (BLK_M,BLK_N)

    // Partition source and destination tiles to match the accumulator partitioning
    auto thr_mma = tiled_mma.get_thread_slice(thread_idx);
    Tensor tCgD = thr_mma.partition_C(gD);                                                           // (VEC,THR_M,THR_N)
    Tensor tCgC0 = thr_mma.partition_C(gC0);                                                         // (VEC,THR_M,THR_N)
    Tensor tCgC1 = thr_mma.partition_C(gC1);                                                         // (VEC,THR_M,THR_N)
    Tensor tCgBias = thr_mma.partition_C(gBias);                                                     // (VEC,THR_M,THR_N)

    static_assert(is_static<FrgLayout>::value,
        "Accumulator layout must be static");
    CUTE_STATIC_ASSERT_V(size(tCgC0) == size(tCgD),
        "Source and destination must have the same number of elements.");
    CUTE_STATIC_ASSERT_V(size(tCgC1) == size(tCgD),
        "Source and destination must have the same number of elements.");
    CUTE_STATIC_ASSERT_V(size(tCgD) == size(accumulators),
        "Accumulator count must have the same destination element count.");
    CUTE_STATIC_ASSERT_V(size(tCgBias) == size(accumulators),
        "Accumulator count must have the same destination element count.");

    auto cD = make_identity_tensor(make_shape(unwrap(shape<0>(gD)), unwrap(shape<1>(gD))));
    Tensor tCcD = thr_mma.partition_C(cD);

    bool bias_needed = params.ptr_Bias != nullptr;
    bool c0_needed = (params.ptr_C0 != nullptr) && epilogue_op.is_source0_needed();
    bool c1_needed = (params.ptr_C1 != nullptr) && epilogue_op.is_source1_needed();
    CUTLASS_PRAGMA_UNROLL
    for (int i = 0; i < size(accumulators); ++i) {
      if (elem_less(tCcD(i), make_coord(get<0>(residue_mnk), get<1>(residue_mnk)))) {
        ElementBias bias = bias_needed ? tCgBias(i) : ElementBias(0);
        ElementC c0 = c0_needed ? tCgC0(i) : ElementC(0);
        ElementC c1 = c1_needed ? tCgC1(i) : ElementC(0);

        tCgD(i) = epilogue_op(accumulators(i), c0, c1, bias);
      }
    }
  }

private:
  Params params;
  ThreadEpilogueOp epilogue_op;
};

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

} // namespace collective
} // namespace epilogue
} // namespace cutlass

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