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/*! \file
    \brief Template for a pipelined GEMM kernel. Does not compute batching or
   support split-K.
*/

#pragma once

#include "cutlass/arch/arch.h"
#include "cutlass/cutlass.h"
#include "cutlass/gemm/gemm.h"
#include "cutlass/matrix_coord.h"
#include "cutlass/semaphore.h"

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

namespace cutlass {
namespace gemm {
namespace kernel {

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

template <typename Mma_,      ///! Threadblock-scoped matrix multiply-accumulate
          typename Epilogue_, ///! Epilogue
          typename ThreadblockSwizzle_, ///! Threadblock swizzling function
          bool SplitKSerial ///! If true, code supporting split-K via serial
                            /// reduction is enabled.
          >
struct GemmUniversalBlockwise {
  using Mma = Mma_;
  using Epilogue = Epilogue_;
  using OutputOp = typename Epilogue::OutputOp;
  using ThreadblockSwizzle = ThreadblockSwizzle_;
  static bool const kSplitKSerial = SplitKSerial;

  // Added aliases for per-tile FP8 dequantisation scale tensors
  using LayoutScale = cutlass::layout::RowMajor;
  using TensorRefScale = cutlass::TensorRef<float, LayoutScale>;

  /// Warp count (concept: GemmShape)
  using WarpCount = typename Mma::WarpCount;
  static int const kThreadCount = 32 * WarpCount::kCount;

  /// Parameters structure
  struct Params {
    cutlass::gemm::GemmCoord problem_size;
    cutlass::gemm::GemmCoord grid_tiled_shape;
    int swizzle_log_tile;
    typename Mma::IteratorA::Params params_A;
    typename Mma::IteratorA::TensorRef ref_A;
    typename Mma::IteratorB::Params params_B;
    typename Mma::IteratorB::TensorRef ref_B;
    typename Epilogue::OutputTileIterator::Params params_C;
    typename Epilogue::OutputTileIterator::TensorRef ref_C;
    typename Epilogue::OutputTileIterator::Params params_D;
    typename Epilogue::OutputTileIterator::TensorRef ref_D;
    typename OutputOp::Params output_op;
    int *semaphore;
    int gemm_k_size;
    // For gather+scatter operations
    int const *gather_A_indices;
    int const *gather_B_indices;
    int const *scatter_D_indices;
    TensorRefScale scale_A;
    TensorRefScale scale_B;

    //
    // Methods
    //

    CUTLASS_HOST_DEVICE
    Params() : swizzle_log_tile(0), semaphore(0), gemm_k_size(0) {}

    CUTLASS_HOST_DEVICE
    Params(cutlass::gemm::GemmCoord const &problem_size,
           cutlass::gemm::GemmCoord const &grid_tiled_shape,
           typename Mma::IteratorA::TensorRef ref_A,
           typename Mma::IteratorB::TensorRef ref_B,
           typename Epilogue::OutputTileIterator::TensorRef ref_C,
           typename Epilogue::OutputTileIterator::TensorRef ref_D,
           TensorRefScale scale_A,
           TensorRefScale scale_B,
           typename OutputOp::Params output_op = typename OutputOp::Params(),
           int *workspace = nullptr, int const *gather_A_indices = nullptr,
           int const *gather_B_indices = nullptr,
           int const *scatter_D_indices = nullptr)
        : problem_size(problem_size), grid_tiled_shape(grid_tiled_shape),
          swizzle_log_tile(ThreadblockSwizzle().get_log_tile(grid_tiled_shape)),
          params_A(ref_A.layout()), ref_A(ref_A), params_B(ref_B.layout()),
          ref_B(ref_B), params_C(ref_C.layout()), ref_C(ref_C),
          params_D(ref_D.layout()), ref_D(ref_D), scale_A(scale_A),
          scale_B(scale_B), output_op(output_op),
          gather_A_indices(gather_A_indices),
          gather_B_indices(gather_B_indices),
          scatter_D_indices(scatter_D_indices) {
      int total_gemm_k_iterations =
          (problem_size.k() + Mma::Shape::kK - 1) / Mma::Shape::kK;
      int gemm_k_iterations =
          (total_gemm_k_iterations + grid_tiled_shape.k() - 1) /
          grid_tiled_shape.k();

      gemm_k_size = gemm_k_iterations * Mma::Shape::kK;

      semaphore = workspace;
    }
  };

  /// Shared memory storage structure
  union SharedStorage {
    typename Mma::SharedStorage main_loop;
    typename Epilogue::SharedStorage epilogue;
  };

  //
  // Methods
  //

  CUTLASS_HOST_DEVICE
  GemmUniversalBlockwise() {}

  /// Determines whether kernel satisfies alignment
  CUTLASS_HOST_DEVICE
  static Status
  can_implement(cutlass::gemm::GemmCoord const &problem_size,
                typename Mma::IteratorA::TensorRef ref_A,
                typename Mma::IteratorB::TensorRef ref_B,
                typename Epilogue::OutputTileIterator::TensorRef ref_C,
                typename Epilogue::OutputTileIterator::TensorRef ref_D) {
    static int const kAlignmentA =
        (platform::is_same<typename Mma::IteratorA::Layout,
                           layout::ColumnMajorInterleaved<32>>::value)
            ? 32
        : (platform::is_same<typename Mma::IteratorA::Layout,
                             layout::ColumnMajorInterleaved<64>>::value)
            ? 64
            : Mma::IteratorA::AccessType::kElements;
    static int const kAlignmentB =
        (platform::is_same<typename Mma::IteratorB::Layout,
                           layout::RowMajorInterleaved<32>>::value)
            ? 32
        : (platform::is_same<typename Mma::IteratorB::Layout,
                             layout::RowMajorInterleaved<64>>::value)
            ? 64
            : Mma::IteratorB::AccessType::kElements;
    static int const kAlignmentC =
        (platform::is_same<typename Epilogue::OutputTileIterator::Layout,
                           layout::ColumnMajorInterleaved<32>>::value)
            ? 32
        : (platform::is_same<typename Epilogue::OutputTileIterator::Layout,
                             layout::ColumnMajorInterleaved<64>>::value)
            ? 64
            : Epilogue::OutputTileIterator::kElementsPerAccess;

    if (!TensorRef_aligned(ref_A, kAlignmentA)) {
      return Status::kErrorMisalignedOperand;
    }

    if (!TensorRef_aligned(ref_B, kAlignmentB)) {
      return Status::kErrorMisalignedOperand;
    }

    if (!TensorRef_aligned(ref_C, kAlignmentC)) {
      return Status::kErrorMisalignedOperand;
    }

    if (!TensorRef_aligned(ref_D, kAlignmentC)) {
      return Status::kErrorMisalignedOperand;
    }

    return Status::kSuccess;
  }

  /// Executes one GEMM
  CUTLASS_DEVICE
  void operator()(Params const &params, SharedStorage &shared_storage) {
    // Compute threadblock location
    ThreadblockSwizzle threadblock_swizzle;

    cutlass::gemm::GemmCoord threadblock_tile_offset =
        threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);

    // Early exit if CTA is out of range
    if (params.grid_tiled_shape.m() <= threadblock_tile_offset.m() ||
        params.grid_tiled_shape.n() <= threadblock_tile_offset.n()) {
      return;
    }

    // Compute initial location in logical coordinates
    cutlass::MatrixCoord tb_offset_A{
        threadblock_tile_offset.m() * Mma::Shape::kM,
        threadblock_tile_offset.k() * params.gemm_k_size,
    };

    cutlass::MatrixCoord tb_offset_B{
        threadblock_tile_offset.k() * params.gemm_k_size,
        threadblock_tile_offset.n() * Mma::Shape::kN};

    // Problem size is a function of threadblock index in the K dimension
    int problem_size_k =
        min(params.problem_size.k(),
            (threadblock_tile_offset.k() + 1) * params.gemm_k_size);

    // Compute threadblock-scoped matrix multiply-add
    int gemm_k_iterations =
        (problem_size_k - tb_offset_A.column() + Mma::Shape::kK - 1) /
        Mma::Shape::kK;

    // Compute position within threadblock
    int thread_idx = threadIdx.x;

    // Construct iterators to A and B operands
    typename Mma::IteratorA iterator_A(
        params.params_A, params.ref_A.data(),
        {params.problem_size.m(), problem_size_k}, thread_idx, tb_offset_A,
        params.gather_A_indices);

    typename Mma::IteratorB iterator_B(
        params.params_B, params.ref_B.data(),
        {problem_size_k, params.problem_size.n()}, thread_idx, tb_offset_B,
        params.gather_B_indices);

    // Broadcast the warp_id computed by lane 0 to ensure dependent code
    // is compiled as warp-uniform.
    int warp_idx = canonical_warp_idx_sync();
    int lane_idx = threadIdx.x % 32;

    //
    // Main loop
    //

    // Construct thread-scoped matrix multiply
    Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);

    typename Mma::FragmentC accumulators;

    accumulators.clear();

    if (!kSplitKSerial || gemm_k_iterations > 0) {
      // Compute threadblock-scoped matrix multiply-add
      mma(gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators,
          params.scale_A, params.scale_B);
    }

    //
    // Epilogue
    //

    OutputOp output_op(params.output_op);

    //
    // Masked tile iterators constructed from members
    //

    threadblock_tile_offset =
        threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);

    // assume identity swizzle
    MatrixCoord threadblock_offset(threadblock_tile_offset.m() * Mma::Shape::kM,
                                   threadblock_tile_offset.n() *
                                       Mma::Shape::kN);

    int block_idx = threadblock_tile_offset.m() +
                    threadblock_tile_offset.n() * params.grid_tiled_shape.m();

    // Construct the semaphore.
    Semaphore semaphore(params.semaphore + block_idx, thread_idx);

    // If performing a reduction via split-K, fetch the initial synchronization
    if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
      // Fetch the synchronization lock initially but do not block.
      semaphore.fetch();

      // Indicate which position in a serial reduction the output operator is
      // currently updating
      output_op.set_k_partition(threadblock_tile_offset.k(),
                                params.grid_tiled_shape.k());
    }

    // Tile iterator loading from source tensor.
    typename Epilogue::OutputTileIterator iterator_C(
        params.params_C, params.ref_C.data(), params.problem_size.mn(),
        thread_idx, threadblock_offset, params.scatter_D_indices);

    // Tile iterator writing to destination tensor.
    typename Epilogue::OutputTileIterator iterator_D(
        params.params_D, params.ref_D.data(), params.problem_size.mn(),
        thread_idx, threadblock_offset, params.scatter_D_indices);

    Epilogue epilogue(shared_storage.epilogue, thread_idx, warp_idx, lane_idx);

    // Wait on the semaphore - this latency may have been covered by iterator
    // construction
    if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
      // For subsequent threadblocks, the source matrix is held in the 'D'
      // tensor.
      if (threadblock_tile_offset.k()) {
        iterator_C = iterator_D;
      }

      semaphore.wait(threadblock_tile_offset.k());
    }

    // Execute the epilogue operator to update the destination tensor.
    epilogue(output_op, iterator_D, accumulators, iterator_C);

    //
    // Release the semaphore
    //

    if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
      int lock = 0;
      if (params.grid_tiled_shape.k() == threadblock_tile_offset.k() + 1) {
        // The final threadblock resets the semaphore for subsequent grids.
        lock = 0;
      } else {
        // Otherwise, the semaphore is incremented
        lock = threadblock_tile_offset.k() + 1;
      }

      semaphore.release(lock);
    }
  }
};

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

} // namespace kernel
} // namespace gemm
} // namespace cutlass
