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/*! \file
    \brief Template for GEMM performing a reduction over K partitions in parallel.
*/

#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/numeric_types.h"
#include "cutlass/arch/arch.h"
#include "cutlass/device_kernel.h"

#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
#include "cutlass/gemm/kernel/gemm.h"

#include "cutlass/gemm/kernel/default_gemm_splitk_parallel.h"
#include "cutlass/gemm/device/default_gemm_configuration.h"

#include "cutlass/epilogue/thread/conversion_op.h"
#include "cutlass/reduction/kernel/reduce_split_k.h"
#include "cutlass/reduction/thread/reduction_operators.h"

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

namespace cutlass {
namespace gemm {
namespace device {

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

/*! 
  Gemm device-level operator performing parallel reduction over the K partition.

*/
template <
    /// Element type for A matrix operand
    typename ElementA_,
    /// Layout type for A matrix operand
    typename LayoutA_,
    /// Element type for B matrix operand
    typename ElementB_,
    /// Layout type for B matrix operand
    typename LayoutB_,
    /// Element type for C and D matrix operands
    typename ElementC_,
    /// Layout type for C and D matrix operands
    typename LayoutC_,
    /// Element type for internal accumulation
    typename ElementAccumulator_ = ElementC_,
    /// Operator class tag
    typename OperatorClass_ = arch::OpClassSimt,
    /// Tag indicating architecture to tune for.  This is the minimum SM that
      /// supports the intended feature. The device kernel can be built
      /// targeting any SM larger than this number.
    typename ArchTag_ = arch::Sm70,
    /// Threadblock-level tile size (concept: GemmShape)
    typename ThreadblockShape_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::ThreadblockShape,
    /// Warp-level tile size (concept: GemmShape)
    typename WarpShape_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::WarpShape,
    /// Instruction-level tile size (concept: GemmShape)
    typename InstructionShape_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::InstructionShape,
    /// Epilogue output operator
    typename EpilogueOutputOp_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::EpilogueOutputOp,
    /// Epilogue output operator
    typename ConvertScaledOp_ = cutlass::epilogue::thread::Convert<
        ElementAccumulator_,
        DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
                                 ElementAccumulator_,
                                 ElementAccumulator_>::EpilogueOutputOp::kCount,
        ElementAccumulator_>,
    /// Reduction operator
    typename ReductionOp_ = cutlass::reduction::thread::ReduceAdd<
        ElementAccumulator_, typename EpilogueOutputOp_::ElementAccumulator,
        EpilogueOutputOp_::kCount>,
    /// Threadblock-level swizzling operator
    typename ThreadblockSwizzle_ =
        threadblock::GemmSplitKHorizontalThreadblockSwizzle,
    /// Number of stages used in the pipelined mainloop
    int Stages =
        DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
                                 ElementC_, ElementAccumulator_>::kStages,
    /// Access granularity of A matrix in units of elements
    int kAlignmentA =
        DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
                                 ElementC_, ElementAccumulator_>::kAlignmentA,
    /// Access granularity of B matrix in units of elements
    int kAlignmentB =
        DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
                                 ElementC_, ElementAccumulator_>::kAlignmentB,
    /// Operation performed by GEMM
    typename Operator_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::Operator>
class GemmSplitKParallel {
 public:

  using ElementA = ElementA_;
  using LayoutA = LayoutA_;
  using ElementB = ElementB_;
  using LayoutB = LayoutB_;
  using ElementC = ElementC_;
  using LayoutC = LayoutC_;
  using ElementAccumulator = ElementAccumulator_;
  using OperatorClass = OperatorClass_;
  using ArchTag = ArchTag_;
  using ThreadblockShape = ThreadblockShape_;
  using WarpShape = WarpShape_;
  using InstructionShape = InstructionShape_;
  using ConvertScaledOp = ConvertScaledOp_;
  using EpilogueOutputOp = EpilogueOutputOp_;
  using ReductionOp = ReductionOp_;
  using ThreadblockSwizzle = ThreadblockSwizzle_;
  using Operator = Operator_;
  static int const kStages = Stages;

  /// GEMM kernel 
  using GemmKernel = typename kernel::DefaultGemmSplitKParallel<
    ElementA,
    LayoutA,
    kAlignmentA,
    ElementB,
    LayoutB,
    kAlignmentB,
    ElementAccumulator,
    LayoutC,
    ElementAccumulator,
    OperatorClass,
    ArchTag,
    ThreadblockShape,
    WarpShape,
    InstructionShape,
    ConvertScaledOp,
    ThreadblockSwizzle,
    kStages,
    Operator
  >::GemmKernel;

  /// Reduction kernel
  using ReductionKernel = cutlass::reduction::kernel::ReduceSplitK<
    cutlass::MatrixShape<4, 32 * EpilogueOutputOp::kCount>,
    EpilogueOutputOp,
    ReductionOp
  >;

  //
  //
  //

  /// Argument structure
  struct Arguments {

    //
    // Data members
    //

    GemmCoord problem_size;
    TensorRef<ElementA const, LayoutA> ref_A;
    TensorRef<ElementB const, LayoutB> ref_B;
    TensorRef<ElementC const, LayoutC> ref_C;
    TensorRef<ElementC, LayoutC> ref_D;
    typename EpilogueOutputOp::Params epilogue;
    int split_k_slices;
    typename ConvertScaledOp::Params convert;
    typename ReductionOp::Params reduction;

    //
    // Methods
    //

    /// Default ctor
    CUTLASS_HOST_DEVICE
    Arguments() { }

    /// Constructs an Arguments structure 
    CUTLASS_HOST_DEVICE
    Arguments(
      GemmCoord problem_size_,
      TensorRef<ElementA const, LayoutA> ref_A_,
      TensorRef<ElementB const, LayoutB> ref_B_,
      TensorRef<ElementC const, LayoutC> ref_C_,
      TensorRef<ElementC, LayoutC> ref_D_,
      typename EpilogueOutputOp::Params epilogue_ = 
        typename EpilogueOutputOp::Params(),
      int split_k_slices = 1,
      typename ConvertScaledOp::Params convert_ = 
        typename ConvertScaledOp::Params(),
      typename ReductionOp::Params reduction_ =
        typename ReductionOp::Params()
    ):
      problem_size(problem_size_),
      ref_A(ref_A_),
      ref_B(ref_B_),
      ref_C(ref_C_),
      ref_D(ref_D_),
      epilogue(epilogue_),
      split_k_slices(split_k_slices),
      convert(convert_),
      reduction(reduction_) { }
  };

private:

  /// Kernel parameters object
  typename GemmKernel::Params gemm_params_;

  /// Reduction kernel parameters object
  typename ReductionKernel::Params reduction_params_;

public:

  /// Constructs the GEMM.
  GemmSplitKParallel() { }

  /// Determines whether the GEMM can execute the given problem.
  static Status can_implement(Arguments const &args) {
    return Status::kSuccess;
  }

  /// Gets the workspace size
  static size_t get_workspace_size(Arguments const &args) {
    
    // Determine grid shape
    ThreadblockSwizzle threadblock_swizzle;

    cutlass::gemm::GemmCoord grid_shape = threadblock_swizzle.get_tiled_shape(
      args.problem_size, 
      {ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
      args.split_k_slices);

    return sizeof(ElementAccumulator_) * size_t(args.problem_size.m()) * size_t(args.problem_size.n()) * grid_shape.k();
  }

  /// Initializes GEMM state from arguments.
  Status initialize(Arguments const &args, void *workspace) {

    // Determine grid shape
    ThreadblockSwizzle threadblock_swizzle;

    cutlass::gemm::GemmCoord grid_shape = threadblock_swizzle.get_tiled_shape(
      args.problem_size, 
      {ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
      args.split_k_slices);

    // Define a reference to the workspace - this is an aligned region in device memory.
    if (!workspace) {
      return Status::kErrorWorkspaceNull;
    }
    
    TensorRef<ElementAccumulator_, layout::RowMajor> ref_workspace(
      static_cast<ElementAccumulator_ *>(workspace), 
      args.problem_size.n());

    int64_t partition_stride = int64_t(args.problem_size.m()) * int64_t(args.problem_size.n());

    // Initialize the Params structure
    gemm_params_ = typename GemmKernel::Params{
      args.problem_size,
      grid_shape,
      args.ref_A.non_const_ref(),
      args.ref_B.non_const_ref(),
      ref_workspace,
      args.convert,
      partition_stride
    };

    reduction_params_ = typename ReductionKernel::Params(
      args.problem_size.mn(),
      grid_shape.k(),
      partition_stride,
      ref_workspace,
      args.ref_D,
      args.ref_C.non_const_ref(),
      args.epilogue
    );

    return Status::kSuccess;
  }

  /// Lightweight update given a subset of arguments
  Status update(Arguments const &args, void *workspace = nullptr) {

    if (!workspace) {
      return Status::kErrorWorkspaceNull;
    }

    gemm_params_.ref_A.reset(args.ref_A.data());
    gemm_params_.ref_B.reset(args.ref_B.data());
    gemm_params_.ref_D.reset(workspace);     

    reduction_params_.ref_D.reset(args.ref_D.data());
    reduction_params_.ref_C.reset(args.ref_C.data());

    return Status::kSuccess;
  }

  /// Runs the kernel using initialized state.
  Status run(cudaStream_t stream = nullptr) {

    //
    // Launch GEMM kernel
    //

    ThreadblockSwizzle threadblock_swizzle;

    dim3 grid = threadblock_swizzle.get_grid_shape(gemm_params_.grid_tiled_shape);
    dim3 block(GemmKernel::kThreadCount, 1, 1);

    cudaError_t result;

    int smem_size = int(sizeof(typename GemmKernel::SharedStorage));
    if (smem_size >= (48 << 10)) {

      result = cudaFuncSetAttribute(
        Kernel<GemmKernel>,
        cudaFuncAttributeMaxDynamicSharedMemorySize,
        smem_size);

      if (result != cudaSuccess) {
        return Status::kErrorInternal;
      }
    }

    cutlass::arch::synclog_setup();
    Kernel<GemmKernel><<<grid, block, smem_size, stream>>>(gemm_params_);

    result = cudaGetLastError();

    if (result != cudaSuccess) {
      return Status::kErrorInternal;
    }

    //
    // Launch reduction kernel
    //

    block = ReductionKernel::block_shape();
    grid = ReductionKernel::grid_shape(gemm_params_.problem_size.mn());

    Kernel<ReductionKernel><<< grid, block, 0, stream >>>(reduction_params_);

    result = cudaGetLastError();

    if (result != cudaSuccess) {
      return Status::kErrorInternal;
    }

    return result == cudaSuccess ? Status::kSuccess : Status::kErrorInternal;
  }

  /// Runs the kernel using initialized state.
  Status operator()(cudaStream_t stream = nullptr) {
    return run(stream);
  }

  /// Runs the kernel using initialized state.
  Status operator()(
    Arguments const &args, 
    void *workspace = nullptr, 
    cudaStream_t stream = nullptr) {
    
    Status status = initialize(args, workspace);
    
    if (status == Status::kSuccess) {
      status = run(stream);
    }

    return status;
  }
};

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

/// Partial specialization for column-major output
template <
    /// Element type for A matrix operand
    typename ElementA_,
    /// Layout type for A matrix operand
    typename LayoutA_,
    /// Element type for B matrix operand
    typename ElementB_,
    /// Layout type for B matrix operand
    typename LayoutB_,
    /// Element type for C and D matrix operands
    typename ElementC_,
    /// Element type for internal accumulation
    typename ElementAccumulator_,
    /// Operator class tag
    typename OperatorClass_,
    /// Tag indicating architecture to tune for.  This is the minimum SM that
      /// supports the intended feature. The device kernel can be built
      /// targeting any SM larger than this number.
    typename ArchTag_,
    /// Threadblock-level tile size (concept: GemmShape)
    typename ThreadblockShape_,
    /// Warp-level tile size (concept: GemmShape)
    typename WarpShape_,
    /// Instruction-level tile size (concept: GemmShape)
    typename InstructionShape_,
    /// Epilogue output operator
    typename EpilogueOutputOp_,
    /// Epilogue output operator
    typename ConvertScaledOp_,
    /// Reduction operator
    typename ReductionOp_,
    /// Threadblock-level swizzling operator
    typename ThreadblockSwizzle_,
    /// Number of stages used in the pipelined mainloop
    int Stages, int kAlignmentA, int kAlignmentB,
    /// Operation performed by GEMM
    typename Operator_>
class GemmSplitKParallel<ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_,
                         layout::ColumnMajor, ElementAccumulator_,
                         OperatorClass_, ArchTag_, ThreadblockShape_,
                         WarpShape_, InstructionShape_, EpilogueOutputOp_,
                         ConvertScaledOp_, ReductionOp_, ThreadblockSwizzle_,
                         Stages, kAlignmentA, kAlignmentB, Operator_> {
 public:

  using ElementA = ElementA_;
  using LayoutA = LayoutA_;
  using ElementB = ElementB_;
  using LayoutB = LayoutB_;
  using ElementC = ElementC_;
  using LayoutC = layout::ColumnMajor;
  using ElementAccumulator = ElementAccumulator_;
  using OperatorClass = OperatorClass_;
  using ArchTag = ArchTag_;
  using ThreadblockShape = ThreadblockShape_;
  using WarpShape = WarpShape_;
  using InstructionShape = InstructionShape_;
  using ConvertScaledOp = ConvertScaledOp_;
  using EpilogueOutputOp = EpilogueOutputOp_;
  using ReductionOp = ReductionOp_;
  using ThreadblockSwizzle = ThreadblockSwizzle_;
  using Operator = Operator_;
  static int const kStages = Stages;

  using UnderlyingOperator = GemmSplitKParallel< 
    ElementB,
    typename layout::LayoutTranspose<LayoutB>::type,
    ElementA,
    typename layout::LayoutTranspose<LayoutA>::type,
    ElementC,
    layout::RowMajor,    
    ElementAccumulator,
    OperatorClass,
    ArchTag,
    ThreadblockShape,
    WarpShape,
    InstructionShape,
    EpilogueOutputOp,
    ConvertScaledOp,
    ReductionOp,
    ThreadblockSwizzle,
    Stages,
    kAlignmentA,
    kAlignmentB,
    Operator
  >;

  using UnderlyingArguments = typename UnderlyingOperator::Arguments;
  using GemmKernel = typename UnderlyingOperator::GemmKernel;
  using ReductionKernel = typename UnderlyingOperator::ReductionKernel;

  /// Argument structure
  struct Arguments {

    //
    // Data members
    //

    GemmCoord problem_size;
    TensorRef<ElementA const, LayoutA> ref_A;
    TensorRef<ElementB const, LayoutB> ref_B;
    TensorRef<ElementC const, LayoutC> ref_C;
    TensorRef<ElementC, LayoutC> ref_D;
    typename EpilogueOutputOp::Params epilogue;
    int split_k_slices;
    typename ConvertScaledOp::Params convert;
    typename ReductionOp::Params reduction;

    //
    // Methods
    //

    /// Default ctor
    CUTLASS_HOST_DEVICE
    Arguments() { }

    /// Constructs an Arguments structure 
    CUTLASS_HOST_DEVICE
    Arguments(
      GemmCoord problem_size_,
      TensorRef<ElementA const, LayoutA> ref_A_,
      TensorRef<ElementB const, LayoutB> ref_B_,
      TensorRef<ElementC const, LayoutC> ref_C_,
      TensorRef<ElementC, LayoutC> ref_D_,
      typename EpilogueOutputOp::Params epilogue_ = 
        typename EpilogueOutputOp::Params(),
      int split_k_slices = 1,
      typename ConvertScaledOp::Params convert_ = 
        typename ConvertScaledOp::Params(),
      typename ReductionOp::Params reduction_ =
        typename ReductionOp::Params()
    ):
      problem_size(problem_size_),
      ref_A(ref_A_),
      ref_B(ref_B_),
      ref_C(ref_C_),
      ref_D(ref_D_),
      epilogue(epilogue_),
      split_k_slices(split_k_slices),
      convert(convert_),
      reduction(reduction_) { }
  };

private:

  /// Kernel parameters object
  UnderlyingOperator underlying_operator_;

public:

  /// Constructs the GEMM.
  GemmSplitKParallel() { }

  /// Helper to construct a transposed equivalent for the underlying GEMM operator
  static UnderlyingArguments to_underlying_arguments(Arguments const &args) {
    return UnderlyingArguments(
      {args.problem_size.n(), args.problem_size.m(), args.problem_size.k()},
      {args.ref_B.data(), args.ref_B.stride(0)},
      {args.ref_A.data(), args.ref_A.stride(0)},
      {args.ref_C.data(), args.ref_C.stride(0)},
      {args.ref_D.data(), args.ref_D.stride(0)},
      args.epilogue,
      args.split_k_slices,
      args.convert,
      args.reduction
    );
  }

  /// Determines whether the GEMM can execute the given problem.
  static Status can_implement(Arguments const &args) {

    return UnderlyingOperator::can_implement(to_underlying_arguments(args));
  }

  /// Gets the workspace size
  static size_t get_workspace_size(Arguments const &args) {
    
    return UnderlyingOperator::get_workspace_size(to_underlying_arguments(args));
  }

  /// Initializes GEMM state from arguments.
  Status initialize(Arguments const &args, void *workspace) {

    return underlying_operator_.initialize(to_underlying_arguments(args), workspace);
  }

  /// Lightweight update given a subset of arguments
  Status update(Arguments const &args, void *workspace = nullptr) {

    return underlying_operator_.update(to_underlying_arguments(args), workspace);
  }

  /// Runs the kernel using initialized state.
  Status run(cudaStream_t stream = nullptr) {

    return underlying_operator_.run(stream);
  }

  /// Runs the kernel using initialized state.
  Status operator()(cudaStream_t stream = nullptr) {
    return run(stream);
  }

  /// Runs the kernel using initialized state.
  Status operator()(
    Arguments const &args, 
    void *workspace = nullptr, 
    cudaStream_t stream = nullptr) {
    
    Status status = initialize(args, workspace, stream);
    
    if (status == Status::kSuccess) {
      status = run(stream);
    }

    return status;
  }
};

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

} // namespace device
} // namespace gemm
} // namespace cutlass

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