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/*! \file
    \brief Device-level GEMM with layernorm elementwise operations fused in mainloop
*/

#pragma once

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

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

#include "cutlass/gemm/kernel/default_gemm_layernorm_mainloop_fusion.h"
#include "cutlass/gemm/device/default_gemm_configuration.h"
#include "cutlass/gemm/device/gemm_universal_base.h"

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

namespace cutlass {
namespace gemm {
namespace device {

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

/*! 
  The universal GEMM accommodates serial reductions, parallel reductions, batched strided, and 
  batched array variants.
*/
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 Scale/Bias vectors
    typename ElementScaleBias_,
    /// Layout type for Scale/Bias vectors
    typename LayoutScaleBias_,
    /// 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,
    /// Threadblock-level swizzling operator
    typename ThreadblockSwizzle_ = threadblock::GemmIdentityThreadblockSwizzle<>,
    /// 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 AlignmentA =
        DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
                                 ElementC_, ElementAccumulator_>::kAlignmentA,
    /// Access granularity of B matrix in units of elements
    int AlignmentB =
        DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
                                 ElementC_, ElementAccumulator_>::kAlignmentB,
    /// Operation performed by GEMM
    typename Operator_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::Operator
>
class GemmLayernormMainloopFusion : 
  public GemmUniversalBase<
    typename kernel::DefaultGemmLayernormMainloopFusion<
      ElementA_,
      LayoutA_,
      AlignmentA,
      ElementB_,
      LayoutB_,
      AlignmentB,
      ElementScaleBias_,
      LayoutScaleBias_,
      ElementC_,
      LayoutC_,
      ElementAccumulator_,
      OperatorClass_,
      ArchTag_,
      ThreadblockShape_,
      WarpShape_,
      InstructionShape_,
      EpilogueOutputOp_,
      ThreadblockSwizzle_,
      Stages,
      Operator_,
      SharedMemoryClearOption::kNone
    >::GemmKernel
  > {

 public:

  using ElementAccumulator = ElementAccumulator_;
  using OperatorClass = OperatorClass_;
  using ArchTag = ArchTag_;
  using ThreadblockShape = ThreadblockShape_;
  using WarpShape = WarpShape_;
  using InstructionShape = InstructionShape_;
  using EpilogueOutputOp = EpilogueOutputOp_;
  using ThreadblockSwizzle = ThreadblockSwizzle_;
  using Operator = Operator_;
  static int const kStages = Stages;
  static int const kAlignmentA = AlignmentA;
  static int const kAlignmentB = AlignmentB;
  static int const kAlignmentC = EpilogueOutputOp::kCount;

  using Base = GemmUniversalBase<
    typename kernel::DefaultGemmLayernormMainloopFusion<
      ElementA_,
      LayoutA_,
      AlignmentA,
      ElementB_,
      LayoutB_,
      AlignmentB,
      ElementScaleBias_,
      LayoutScaleBias_,
      ElementC_,
      LayoutC_,
      ElementAccumulator_,
      OperatorClass_,
      ArchTag_,
      ThreadblockShape_,
      WarpShape_,
      InstructionShape_,
      EpilogueOutputOp_,
      ThreadblockSwizzle_,
      Stages,
      Operator_,
      SharedMemoryClearOption::kNone
    >::GemmKernel
  >;

  using Arguments = typename Base::Arguments;
  using GemmKernel = typename Base::GemmKernel;
};

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

/// Parital specialization for column-major output exchanges problem size and operand.
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 Scale/Bias vectors
    typename ElementScaleBias_,
    /// Layout type for Scale/Bias vectors
    typename LayoutScaleBias_,
    /// 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_,
    /// Threadblock-level swizzling operator
    typename ThreadblockSwizzle_,
    /// Number of stages used in the pipelined mainloop
    int Stages,
    /// Access granularity of A matrix in units of elements
    int AlignmentA,
    /// Access granularity of B matrix in units of elements
    int AlignmentB,
    /// Operation performed by GEMM
    typename Operator_
>
class GemmLayernormMainloopFusion<ElementA_, LayoutA_, ElementB_, LayoutB_, 
           ElementScaleBias_, LayoutScaleBias_,
           ElementC_,
           layout::ColumnMajor,  // partially specialized on LayoutC
           ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_,
           WarpShape_, InstructionShape_, EpilogueOutputOp_,
           ThreadblockSwizzle_, Stages, AlignmentA, AlignmentB,
           Operator_> {
 public:

  using ElementA = ElementA_;
  using LayoutA = LayoutA_;
  using TensorRefA = TensorRef<ElementA const, LayoutA>;
  using ElementB = ElementB_;
  using LayoutB = LayoutB_;
  using TensorRefB = TensorRef<ElementB const, LayoutB>;
  using ElementScaleBias = ElementScaleBias_;
  using LayoutScaleBias = LayoutScaleBias_;
  using ElementC = ElementC_;
  using LayoutC = layout::ColumnMajor;
  using TensorRefC = TensorRef<ElementC const, LayoutC>;
  using TensorRefD = TensorRef<ElementC, LayoutC>;
  using ElementAccumulator = ElementAccumulator_;
  using OperatorClass = OperatorClass_;
  using ArchTag = ArchTag_;
  using ThreadblockShape = ThreadblockShape_;
  using WarpShape = WarpShape_;
  using InstructionShape = InstructionShape_;
  using EpilogueOutputOp = EpilogueOutputOp_;
  using ThreadblockSwizzle = ThreadblockSwizzle_;
  using Operator = Operator_;
  static int const kStages = Stages;
  static int const kAlignmentA = AlignmentA;
  static int const kAlignmentB = AlignmentB;

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

  using GemmKernel = typename UnderlyingOperator::GemmKernel;
  static int const kAlignmentC = EpilogueOutputOp::kCount;

  /// Argument structure
  using Arguments = typename UnderlyingOperator::Arguments;

private:

  UnderlyingOperator underlying_operator_;

public:

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

  /// Helper to construct a transposed equivalent for the underlying GEMM operator
  static Arguments to_underlying_arguments(Arguments const &args) {
    return args.transposed_problem();
  }

  /// 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));
  }

  /// Computes the grid shape
  static dim3 get_grid_shape(Arguments const &args) { 
    return UnderlyingOperator::get_grid_shape(to_underlying_arguments(args));
  }

  /// Computes the maximum number of active blocks per multiprocessor
  static int maximum_active_blocks(int smem_capacity = -1) {
    return UnderlyingOperator::maximum_active_blocks(smem_capacity);
  }

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

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

  /// 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

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