cutlass/include/cutlass/gemm/device/gemm_universal_adapter.h

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/*! 
  \file
  \brief The universal GEMM accommodates serial reductions, parallel reductions, batched strided, and 
    batched array variants.
*/

#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/gemm/device/gemm_universal_base.h"
#include "cutlass/gemm/kernel/gemm_transpose_operands.h"

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

namespace cutlass {
namespace gemm {
namespace device {

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

template <typename GemmKernel_>
class GemmUniversalAdapter {
public:

  using GemmKernel = GemmKernel_;

  static bool const kInternalTranspose = 
    platform::is_same<typename GemmKernel::LayoutC, cutlass::layout::RowMajor>::value;

  using ThreadblockShape = typename GemmKernel::Mma::Shape;
  using WarpShape = typename GemmKernel::WarpShape;
  using InstructionShape = typename GemmKernel::InstructionShape;

  // warp-level, arch-level (instruction), math operator 
  using WarpMmaOperator = typename GemmKernel::Mma::Policy::Operator;
  using ArchMmaOperator = typename WarpMmaOperator::ArchMmaOperator;
  using MathOperator = typename WarpMmaOperator::MathOperator;
  
  // Operator class and arch tag extract bottom-up 
  // set it for top-level gemm device-level template
  using OperatorClass = typename WarpMmaOperator::OperatorClass;
  using ArchTag = typename WarpMmaOperator::ArchTag;

  // Type, layout, and complex transform deliberately exchanged with B
  using MapArguments = kernel::detail::MapArguments<
    typename GemmKernel::ElementA,
    typename GemmKernel::LayoutA,
    GemmKernel::kTransformA,
    GemmKernel::kAlignmentA,
    typename GemmKernel::ElementB,
    typename GemmKernel::LayoutB,
    GemmKernel::kTransformB,
    GemmKernel::kAlignmentB,
    typename GemmKernel::LayoutC,
    kInternalTranspose
  >;

  using ElementA = typename MapArguments::ElementA;
  using LayoutA = typename MapArguments::LayoutA;
  static ComplexTransform const kTransformA = MapArguments::kTransformA;
  static int const kAlignmentA = MapArguments::kAlignmentA;

  using ElementB = typename MapArguments::ElementB;
  using LayoutB = typename MapArguments::LayoutB;
  static ComplexTransform const kTransformB = MapArguments::kTransformB;
  static int const kAlignmentB = MapArguments::kAlignmentB;
  
  using ElementC = typename GemmKernel::ElementC;
  using LayoutC = typename MapArguments::LayoutC;
  static int const kAlignmentC = GemmKernel::kAlignmentC;
 
  using TensorRefA = TensorRef<ElementA const, LayoutA>;
  using TensorRefB = TensorRef<ElementB const, LayoutB>;
  using TensorRefC = TensorRef<ElementC const, LayoutC>;
  using TensorRefD = TensorRef<ElementC, LayoutC>;

  static int const kStages = GemmKernel::Mma::kStages;

  using EpilogueOutputOp = typename GemmKernel::EpilogueOutputOp;
  using ElementAccumulator = typename EpilogueOutputOp::ElementAccumulator;
  using ThreadblockSwizzle = typename GemmKernel::ThreadblockSwizzle;

  using UnderlyingOperator = GemmUniversalBase<GemmKernel>;
  using Arguments = typename UnderlyingOperator::Arguments;

private:

  UnderlyingOperator underlying_operator_;

public:

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

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

  /// 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

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