include/cutlass/gemm/device/gemm.h

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


namespace cutlass {
namespace gemm {
namespace device {

















template <
    typename ElementA_,
    typename LayoutA_,
    typename ElementB_,
    typename LayoutB_,
    typename ElementC_,
    typename LayoutC_,
    typename ElementAccumulator_ = ElementC_,
    typename OperatorClass_ = arch::OpClassSimt,
    typename ArchTag_ = arch::Sm70,
    typename ThreadblockShape_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::ThreadblockShape,
    typename WarpShape_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::WarpShape,
    typename InstructionShape_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::InstructionShape,
    typename EpilogueOutputOp_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::EpilogueOutputOp,
    typename ThreadblockSwizzle_ = threadblock::GemmIdentityThreadblockSwizzle,
    int Stages =
        DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
                                 ElementC_, ElementAccumulator_>::kStages,
    int AlignmentA =
        DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
                                 ElementC_, ElementAccumulator_>::kAlignmentA,
    int AlignmentB =
        DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
                                 ElementC_, ElementAccumulator_>::kAlignmentB,
    bool SplitKSerial = false,
    typename Operator_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::Operator,
    bool IsBetaZero = false>
class Gemm {
 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_;
  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 bool const kIsBetaZero = IsBetaZero;

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

  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;

    //
    // Methods
    //

    CUTLASS_HOST_DEVICE
    Arguments(): problem_size(0, 0, 0), split_k_slices(1) {

    }

    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
    ):
      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) {

    }
  };

private:

  typename GemmKernel::Params params_;

public:

  Gemm() { }

  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
    );

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

    return Status::kSuccess;
  }

  static size_t get_workspace_size(Arguments const &args) {

    if (kSplitKSerial && args.split_k_slices > 1) {

      // 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);

      return sizeof(int) * size_t(tiled_shape.m()) * size_t(tiled_shape.n());
    }

    return 0;
  }

  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.epilogue,
      static_cast<int *>(workspace)
    };

    return Status::kSuccess;
  }

  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_.semaphore = static_cast<int *>(workspace);

    return Status::kSuccess;
  }

  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);

    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;
      }

      result = cudaFuncSetAttribute(
          Kernel<GemmKernel>,
          cudaFuncAttributePreferredSharedMemoryCarveout, 100);

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

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

    result = cudaGetLastError();

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

  Status operator()(cudaStream_t stream = nullptr) {
    return run(stream);
  }

  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;
  }
};


template <
    typename ElementA_,
    typename LayoutA_,
    typename ElementB_,
    typename LayoutB_,
    typename ElementC_,
    typename ElementAccumulator_,
    typename OperatorClass_,
    typename ArchTag_,
    typename ThreadblockShape_,
    typename WarpShape_,
    typename InstructionShape_,
    typename EpilogueOutputOp_,
    typename ThreadblockSwizzle_,
    int Stages,
    int AlignmentA,
    int AlignmentB,
    bool SplitKSerial,
    typename Operator_,
    bool IsBetaZero>
class Gemm<ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_,
           layout::ColumnMajor,  // partially specialized on LayoutC
           ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_,
           WarpShape_, InstructionShape_, EpilogueOutputOp_,
           ThreadblockSwizzle_, Stages, AlignmentA, AlignmentB, SplitKSerial,
           Operator_, IsBetaZero> {
 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 = 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;
  static bool const kSplitKSerial = SplitKSerial;
  static bool const kIsBetaZero = IsBetaZero;

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

  using UnderlyingArguments = typename UnderlyingOperator::Arguments;
  using GemmKernel = typename UnderlyingOperator::GemmKernel;
  static int const kAlignmentC = UnderlyingOperator::kAlignmentC;

  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;

    //
    // Methods
    //

    CUTLASS_HOST_DEVICE
    Arguments() { }

    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
    ):
      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) { }
  };

private:

  UnderlyingOperator underlying_operator_;

public:

  Gemm() { }

  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
    );
  }

  static Status can_implement(Arguments const &args) {

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

  static size_t get_workspace_size(Arguments const &args) {
    
    return UnderlyingOperator::get_workspace_size(to_underlying_arguments(args));
  }

  Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) {

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

  Status update(Arguments const &args, void *workspace = nullptr) {

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

  Status run(cudaStream_t stream = nullptr) {

    return underlying_operator_.run(stream);
  }

  Status operator()(cudaStream_t stream = nullptr) {
    return run(stream);
  }

  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;
  }
};


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