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

#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/sparse_gemm.h"

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

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

namespace cutlass {
namespace gemm {
namespace device {

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

/*! Gemm device-level operator. This is an interface to efficient CUTLASS GEMM kernels that may
  be invoked from host code.

  The contributions of this class are:
    
    1. At compile time, it maps data types and high-level structural parameters onto 
       specific CUTLASS components.

    2. At runtime, it maps logical arguments to GEMM problems to kernel parameters.

    3. At runtime, it launches kernels on the device.

  The intent is to provide a convenient mechanism for interacting with most plausible GEMM
  configurations for each supported architecture. Consequently, not all parameters are exposed
  to the top-level interface. Rather, sensible defaults at each level of the CUTLASS hierarchy
  are selected to tradeoff simplicity of the interface with flexibility. We expect 
  most configurations to be specified at this level. Applications with more exotic requirements 
  may construct their kernels of interest using CUTLASS components at the threadblock, warp, 
  and thread levels of abstraction.

  CUTLASS exposes computations using the functor design pattern in which objects compose some
  internal state with an overloaded function call operator. This enables decoupling of
  initialization from execution, possibly reducing overhead during steady state phases of
  application execution.

  CUTLASS device-level operators expose an Arguments structure encompassing each logical
  input to the computation. This is distinct from the kernel-level Params structure pattern
  which contains application-specific precomputed state needed by the device code.

  Example of a CUTLASS GEMM operator implementing the functionality of cuBLAS's SGEMM NN
  is as follows:

    //
    // Instantiate the CUTLASS GEMM operator.
    //

    cutlass::gemm::device::Gemm<
      float,
      cutlass::layout::ColumnMajor,
      float,
      cutlass::layout::ColumnMajor,
      float,
      cutlass::layout::ColumnMajor
    > gemm_op;

    //
    // Launch the GEMM operation on the device
    //

    cutlass::Status status = gemm_op({
      {m, n, k},                          // GemmCoord problem_size,
      {A, lda},                           // TensorRef<float, layout::ColumnMajor> ref_A,
      {B, ldb},                           // TensorRef<float, layout::ColumnMajor> ref_B,
      {C, ldc},                           // TensorRef<float, layout::ColumnMajor> ref_C,
      {D, ldd},                           // TensorRef<float, layout::ColumnMajor> ref_D,
      {alpha, beta}                       // EpilogueOutputOp::Params epilogue_op_params
    });


  A simplified view of the template is listed below.

    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,

      /// 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,
      
      /// Warp-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
    >
    class Gemm;
*/
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
    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_ =
        typename 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,
    /// If true, kernel supports split-K with serial reduction
    bool SplitKSerial = false,
    /// Operation performed by GEMM
    typename Operator_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::Operator>
class SparseGemm {
 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 ElementC = ElementC_;
  using LayoutC = LayoutC_;
  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_;
  using MathOperator = Operator;
  static int const kStages = Stages;
  static int const kAlignmentA = AlignmentA;
  static int const kAlignmentB = AlignmentB;
  static int const kAlignmentC = EpilogueOutputOp::kCount;
  static bool const kSplitKSerial = SplitKSerial;
  static ComplexTransform const kTransformA = ComplexTransform::kNone;
  static ComplexTransform const kTransformB = ComplexTransform::kNone;

  /// Define the kernel
  using GemmKernel = typename kernel::DefaultSparseGemm<
    ElementA,
    LayoutA,
    kAlignmentA,
    ElementB,
    LayoutB,
    kAlignmentB,
    ElementC,
    LayoutC,
    ElementAccumulator,
    OperatorClass,
    ArchTag,
    ThreadblockShape,
    WarpShape,
    InstructionShape,
    EpilogueOutputOp,
    ThreadblockSwizzle,
    kStages,
    kSplitKSerial,
    Operator
  >::GemmKernel;

  using ElementE = typename GemmKernel::ElementE;

  using LayoutE = typename GemmKernel::LayoutE;

  static int const kAlignmentE = 128 / sizeof_bits<ElementE>::value;

  static int const kSparse = GemmKernel::kSparse;
  static int const kMetaSizeInBits = GemmKernel::kMetaSizeInBits;
  static int const kElementsPerElementE = GemmKernel::kElementsPerElementE;

  /// 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;
    TensorRef<ElementE const, LayoutE> ref_E;
    typename EpilogueOutputOp::Params epilogue;
    int split_k_slices;

    //
    // Methods
    //

    /// Default ctor
    CUTLASS_HOST_DEVICE
    Arguments(): problem_size(0, 0, 0), split_k_slices(1) {

    }

    /// 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_,
      TensorRef<ElementE, LayoutE> ref_E_,
      typename EpilogueOutputOp::Params epilogue_ = 
        typename EpilogueOutputOp::Params(),
      int split_k_slices = 1
    ):
      problem_size(problem_size_),
      ref_A(ref_A_),
      ref_B(ref_B_),
      ref_C(ref_C_),
      ref_D(ref_D_),
      ref_E(ref_E_),
      epilogue(epilogue_),
      split_k_slices(split_k_slices) {

    }
  };

private:

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

public:

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

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

    if (!kSplitKSerial && args.split_k_slices > 1) {
      return Status::kErrorInvalidProblem;
    }

    Status status = GemmKernel::can_implement(
      args.problem_size,
      args.ref_A.non_const_ref(),
      args.ref_B.non_const_ref(),
      args.ref_C.non_const_ref(),
      args.ref_D,
      args.ref_E.non_const_ref()
    );

    if (status != Status::kSuccess) {
      return status;
    }

    return Status::kSuccess;
  }

  /// Gets the workspace size
  static size_t get_workspace_size(Arguments const &args) {
    
    size_t bytes = 0;

    // Determine grid shape
    ThreadblockSwizzle threadblock_swizzle;

    cutlass::gemm::GemmCoord tiled_shape = threadblock_swizzle.get_tiled_shape(
      args.problem_size, 
      {ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
      args.split_k_slices);
    
    if (kSplitKSerial && args.split_k_slices > 1) {

      bytes += sizeof(int) * size_t(tiled_shape.m()) * size_t(tiled_shape.n());
    }

    return bytes;
  }

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

    // 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);

    if (kSplitKSerial) {
      if (args.split_k_slices > 1) {
        if (!workspace) {
          return Status::kErrorWorkspaceNull;
        }

        size_t bytes = get_workspace_size(args);
      
        cudaError_t result = cudaMemsetAsync(workspace, 0, bytes, stream);

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

      if (args.split_k_slices > 1) {
        return Status::kErrorInvalidProblem;
      }
    }

    // Initialize the Params structure
    params_ = typename GemmKernel::Params{
      args.problem_size,
      grid_shape,
      args.ref_A.non_const_ref(),
      args.ref_B.non_const_ref(),
      args.ref_C.non_const_ref(),
      args.ref_D,
      args.ref_E.non_const_ref(),
      args.epilogue,
      static_cast<int *>(workspace)
    };
    
    int smem_size = int(sizeof(typename GemmKernel::SharedStorage));
    if (smem_size >= (48 << 10)) {
      cudaError_t result = cudaFuncSetAttribute(Kernel<GemmKernel>,
                                    cudaFuncAttributeMaxDynamicSharedMemorySize,
                                    smem_size);

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

    return Status::kSuccess;
  }

  /// Lightweight update given a subset of arguments
  Status update(Arguments const &args, void *workspace = nullptr) {
    
    if (kSplitKSerial && args.split_k_slices > 1) {  
      if (!workspace) {
        return Status::kErrorWorkspaceNull;
      }
    }

    params_.ref_A.reset(args.ref_A.non_const_ref().data());
    params_.ref_B.reset(args.ref_B.non_const_ref().data());
    params_.ref_C.reset(args.ref_C.non_const_ref().data());
    params_.ref_D.reset(args.ref_D.data());
    params_.ref_E.reset(args.ref_E.non_const_ref().data());
    params_.output_op = args.epilogue;
    params_.semaphore = static_cast<int *>(workspace);

    return Status::kSuccess;
  }

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

    ThreadblockSwizzle threadblock_swizzle;

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

    int smem_size = int(sizeof(typename GemmKernel::SharedStorage));

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

    cudaError_t result = cudaGetLastError();

    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, stream);
    
    if (status == Status::kSuccess) {
      status = run(stream);
    }

    return status;
  }
};

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

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