/***************************************************************************************************
 * Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
 * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
 * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 **************************************************************************************************/
/*! \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/device/default_gemm_configuration.h"
#include "cutlass/epilogue/thread/linear_combination_relu.h"

#include "kernel/b2b_gemm.h"
#include "kernel/default_b2b_gemm.h"
#include "kernel/default_b2b_gemm_smem_accumulator.h"

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

namespace cutlass {
namespace gemm {
namespace device {

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

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 ThreadblockShape0_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::ThreadblockShape,
    /// Threadblock-level tile size (concept: GemmShape)
    typename ThreadblockShape1_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::ThreadblockShape,
    /// Warp-level tile size (concept: GemmShape)
    typename WarpShape0_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::WarpShape,
    /// Warp-level tile size (concept: GemmShape)
    typename WarpShape1_ = 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 EpilogueOutputOp0_ = typename DefaultGemmConfiguration<
        OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
        ElementAccumulator_>::EpilogueOutputOp,
    /// Epilogue output operator
    typename EpilogueOutputOp1_ = 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,
    /// Stage accumulator in shared memory
    bool SmemAccumulator = false,
    /// 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 B2bGemm {
 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 ThreadblockShape0 = ThreadblockShape0_;
  using ThreadblockShape1 = ThreadblockShape1_;
  using WarpShape0 = WarpShape0_;
  using WarpShape1 = WarpShape1_;
  using InstructionShape = InstructionShape_;
  using EpilogueOutputOp0 = EpilogueOutputOp0_;
  using EpilogueOutputOp1 = EpilogueOutputOp1_;
  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 = EpilogueOutputOp1::kCount;
  static bool const kSplitKSerial = SplitKSerial;
  static ComplexTransform const kTransformA = ComplexTransform::kNone;
  static ComplexTransform const kTransformB = ComplexTransform::kNone;

  /// Derived types
  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
  using LayoutScaleBias = layout::RowMajor;

  /// Define the kernel
  using B2bGemmKernel = typename kernel::DefaultB2bGemm<
    ElementA,
    LayoutA,
    kAlignmentA,
    ElementB,
    LayoutB,
    kAlignmentB,
    ElementC,
    LayoutC,
    ElementAccumulator,
    OperatorClass,
    ArchTag,
    ThreadblockShape0,
    ThreadblockShape1,
    WarpShape0,
    WarpShape1,
    InstructionShape,
    EpilogueOutputOp0,
    EpilogueOutputOp1,
    ThreadblockSwizzle,
    kStages,
    kSplitKSerial,
    Operator,
    SmemAccumulator
  >::B2bGemmKernel;

  /// Argument structure
  struct Arguments {

    //
    // Data members
    //

    GemmCoord problem_size_0;
    GemmCoord problem_size_1;
    TensorRef<ElementA const, LayoutA> ref_A0;
    TensorRef<ElementB const, LayoutB> ref_B0;
    TensorRef<ElementC const, LayoutC> ref_C0;
    TensorRef<ElementScaleBias const, LayoutScaleBias> ref_Scale0;
    TensorRef<ElementScaleBias const, LayoutScaleBias> ref_Bias0;
    TensorRef<ElementB const, LayoutB> ref_B1;
    TensorRef<ElementC const, LayoutC> ref_C1;
    TensorRef<ElementC, LayoutC> ref_D1;
    typename EpilogueOutputOp0::Params epilogue0;
    typename EpilogueOutputOp1::Params epilogue1;
    int split_k_slices;

    //
    // Methods
    //

    /// Default ctor
    CUTLASS_HOST_DEVICE
    Arguments(): problem_size_0(0, 0, 0), problem_size_1(0, 0, 0), split_k_slices(1) {

    }

    /// Constructs an Arguments structure 
    CUTLASS_HOST_DEVICE
    Arguments(
      GemmCoord problem_size_0_,
      GemmCoord problem_size_1_,
      TensorRef<ElementA const, LayoutA> ref_A0_,
      TensorRef<ElementB const, LayoutB> ref_B0_,
      TensorRef<ElementC const, LayoutC> ref_C0_,
      TensorRef<ElementScaleBias const, LayoutScaleBias> ref_Scale0_,
      TensorRef<ElementScaleBias const, LayoutScaleBias> ref_Bias0_,
      TensorRef<ElementB const, LayoutB> ref_B1_,
      TensorRef<ElementC const, LayoutC> ref_C1_,
      TensorRef<ElementC, LayoutC> ref_D1_,
      typename EpilogueOutputOp0::Params epilogue0_ = 
        typename EpilogueOutputOp0::Params(),
      typename EpilogueOutputOp1::Params epilogue1_ = 
        typename EpilogueOutputOp1::Params(),
      int split_k_slices_ = 1
    ):
      problem_size_0(problem_size_0_),
      problem_size_1(problem_size_1_),
      ref_A0(ref_A0_),
      ref_B0(ref_B0_),
      ref_C0(ref_C0_),
      ref_Scale0(ref_Scale0_),
      ref_Bias0(ref_Bias0_),
      ref_B1(ref_B1_),
      ref_C1(ref_C1_),
      ref_D1(ref_D1_),
      epilogue0(epilogue0_),
      epilogue1(epilogue1_),
      split_k_slices(split_k_slices_) {

    }
  };

private:

  /// Kernel parameters object
  typename B2bGemmKernel::Params params_;

public:

  /// Constructs the GEMM.
  B2bGemm() { }

  /// 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 = B2bGemmKernel::can_implement(
      args.problem_size_0,
      args.problem_size_1,
      args.ref_A0.non_const_ref(),
      args.ref_B0.non_const_ref(),
      args.ref_C0.non_const_ref(),
      args.ref_B1.non_const_ref(),
      args.ref_C1.non_const_ref(),
      args.ref_D1
    );

    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_0, 
      {ThreadblockShape0::kM, ThreadblockShape0::kN, ThreadblockShape0::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_0, 
      {ThreadblockShape0::kM, ThreadblockShape0::kN, ThreadblockShape0::kK},
      args.split_k_slices);
//    cutlass::gemm::GemmCoord grid_shape_1 = threadblock_swizzle.get_tiled_shape(
//      args.problem_size_1, 
//      {ThreadblockShape1::kM, ThreadblockShape1::kN, ThreadblockShape1::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 B2bGemmKernel::Params{
      args.problem_size_0,
      args.problem_size_1,
      grid_shape,
      args.ref_A0.non_const_ref(),
      args.ref_B0.non_const_ref(),
      args.ref_C0.non_const_ref(),
      args.ref_Scale0.non_const_ref(),
      args.ref_Bias0.non_const_ref(),
      args.ref_B1.non_const_ref(),
      args.ref_C1.non_const_ref(),
      args.ref_D1,
      args.epilogue0,
      args.epilogue1,
      static_cast<int *>(workspace),
    };

    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_A0.reset(args.ref_A0.non_const_ref().data());
    params_.ref_B0.reset(args.ref_B0.non_const_ref().data());
    params_.ref_C0.reset(args.ref_C0.non_const_ref().data());
    params_.ref_Scale0.reset(args.ref_Scale0.non_const_ref().data());
    params_.ref_Bias0.reset(args.ref_Bias0.non_const_ref().data());
    params_.ref_B1.reset(args.ref_B1.non_const_ref().data());
    params_.ref_C1.reset(args.ref_C1.non_const_ref().data());
    params_.ref_D1.reset(args.ref_D1.data());
    params_.output_op_0 = args.epilogue0;
    params_.output_op_1 = args.epilogue1;
    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(B2bGemmKernel::kThreadCount, 1, 1);

    cudaError_t result;

    int smem_size = int(sizeof(typename B2bGemmKernel::SharedStorage));
    if (smem_size >= (48 << 10)) {
      result = cudaFuncSetAttribute(Kernel<B2bGemmKernel>,
                                    cudaFuncAttributeMaxDynamicSharedMemorySize,
                                    smem_size);

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

    cutlass::Kernel<B2bGemmKernel><<<grid, block, smem_size, stream>>>(params_);

    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

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