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/*! \file
    \brief GEMM kernel to support the epilogue visitor model
    for customized softmax partial reduction epilogue fusion.

    This source file will likely be moved to `include/cutlass/gemm/kernel/` in the future once
    its usage has been stabilized. For now, it is included in this example to demonstrate
    some basic output fusion options.

    original file: 3rdparty/cutlass/examples/35_gemm_softmax/gemm_with_epilogue_visitor.h
*/

#pragma once

#include "cutlass/complex.h"
#include "cutlass/cutlass.h"
#include "cutlass/fast_math.h"
#include "cutlass/gemm/gemm.h"
#include "cutlass/matrix_coord.h"
#include "cutlass/semaphore.h"
#include "cutlass/trace.h"

#include "cutlass_extensions/epilogue/threadblock/epilogue_per_row_per_col_scale.h"

namespace tk = tensorrt_llm::common;

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

namespace cutlass
{
namespace gemm
{
namespace kernel
{

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

template <typename Mma_,         ///! Threadblock-scoped matrix multiply-accumulate
    typename Epilogue_,          ///! Epilogue
    typename ThreadblockSwizzle_ ///! Threadblock swizzling function
    >
struct GemmWithEpilogueVisitor
{
public:
    using Mma = Mma_;
    using Epilogue = Epilogue_;
    using EpilogueVisitor = typename Epilogue::Visitor;
    using ThreadblockSwizzle = ThreadblockSwizzle_;

    using ElementA = typename Mma::IteratorA::Element;
    using LayoutA = typename Mma::IteratorA::Layout;
    using TensorRefA = TensorRef<ElementA, LayoutA>;

    using ElementB = typename Mma::IteratorB::Element;
    using LayoutB = typename Mma::IteratorB::Layout;
    using TensorRefB = TensorRef<ElementB, LayoutB>;

    using ElementCompute = typename EpilogueVisitor::ElementCompute;
    using LayoutAlphaCol = cutlass::layout::RowMajor;
    using LayoutAlphaRow = cutlass::layout::ColumnMajor;
    using TensorRefAlphaCol = TensorRef<ElementCompute, LayoutAlphaCol>;
    using TensorRefAlphaRow = TensorRef<ElementCompute, LayoutAlphaRow>;

    using ElementC = typename EpilogueVisitor::ElementOutput;
    using LayoutC = typename Epilogue::Layout;
    using TensorRefC = TensorRef<ElementC, LayoutC>;

    static ComplexTransform const kTransformA = Mma::kTransformA;
    static ComplexTransform const kTransformB = Mma::kTransformB;
    using Operator = typename Mma::Operator;

    using OperatorClass = typename Mma::Operator::OperatorClass;
    using ThreadblockShape = typename Mma::Shape;
    using WarpShape = typename Mma::Operator::Shape;
    using InstructionShape = typename Mma::Policy::Operator::InstructionShape;
    using ArchTag = typename Mma::ArchTag;
    using EpilogueOutputOp =
        typename Epilogue::Visitor::ElementwiseFunctor; // Define type so GemmUniversalBase doesn't complain

    static int const kStages = Mma::kStages;
    static int const kAlignmentA = Mma::IteratorA::AccessType::kElements;
    static int const kAlignmentB = Mma::IteratorB::AccessType::kElements;
    static int const kAlignmentC = EpilogueVisitor::kElementsPerAccess;

    /// Warp count (concept: GemmShape)
    using WarpCount = typename Mma::WarpCount;
    static int const kThreadCount = 32 * WarpCount::kCount;

    /// Split-K preserves splits that are 128b aligned
    static int const kSplitKAlignment
        = const_max(128 / sizeof_bits<ElementA>::value, 128 / sizeof_bits<ElementB>::value);

    //
    // Structures
    //

    /// Argument structure
    struct Arguments
    {

        //
        // Data members
        //

        GemmUniversalMode mode;
        GemmCoord problem_size;
        int batch_count;

        TensorRefA ref_A;
        TensorRefB ref_B;
        tk::QuantMode quant_option;
        TensorRefAlphaCol ref_alpha_col;
        TensorRefAlphaRow ref_alpha_row;
        TensorRefC ref_C;
        TensorRefC ref_D;

        int64_t batch_stride_A;
        int64_t batch_stride_B;
        int64_t batch_stride_D;

        typename EpilogueVisitor::Arguments epilogue_visitor;

        //
        // Methods
        //

        Arguments()
            : mode(GemmUniversalMode::kGemm)
            , batch_count(1)
        {
        }

        /// constructs an arguments structure
        Arguments(GemmUniversalMode mode_, GemmCoord problem_size_, int batch_count_, TensorRefA ref_A_,
            TensorRefB ref_B_, tk::QuantMode quant_option_, TensorRefAlphaCol ref_alpha_col_,
            TensorRefAlphaRow ref_alpha_row_, TensorRefC ref_C_, TensorRefC ref_D_, int64_t batch_stride_A_,
            int64_t batch_stride_B_, typename EpilogueVisitor::Arguments epilogue_visitor_)
            : mode(mode_)
            , problem_size(problem_size_)
            , batch_count(batch_count_)
            , ref_A(ref_A_)
            , ref_B(ref_B_)
            , quant_option(quant_option_)
            , ref_alpha_col(ref_alpha_col_)
            , ref_alpha_row(ref_alpha_row_)
            , ref_C(ref_C_)
            , ref_D(ref_D_)
            , batch_stride_A(batch_stride_A_)
            , batch_stride_B(batch_stride_B_)
            , batch_stride_D(0)
            , epilogue_visitor(epilogue_visitor_)
        {
        }
    };

    //
    // Structure for precomputing values in host memory and passing to kernels
    //

    /// Parameters structure
    struct Params
    {

        cutlass::gemm::GemmCoord problem_size;
        cutlass::gemm::GemmCoord grid_tiled_shape;
        int swizzle_log_tile;

        typename Mma::IteratorA::Params params_A;
        typename Mma::IteratorB::Params params_B;
        typename EpilogueVisitor::ScaleTileIterator::Params params_alpha_col;
        typename EpilogueVisitor::ScaleTileIterator::Params params_alpha_row;
        typename EpilogueVisitor::OutputTileIterator::Params params_C;
        typename EpilogueVisitor::OutputTileIterator::Params params_D;

        GemmUniversalMode mode;
        int batch_count;
        int gemm_k_size;

        void* ptr_A;
        void* ptr_B;
        tk::QuantMode quant_option;
        typename EpilogueVisitor::ScaleTileIterator::Element* ptr_alpha_col;
        typename EpilogueVisitor::ScaleTileIterator::Element* ptr_alpha_row;
        ElementC* ptr_C;
        ElementC* ptr_D;

        int64_t batch_stride_A;
        int64_t batch_stride_B;

        typename EpilogueVisitor::Params epilogue_visitor;

        //
        // Methods
        //

        CUTLASS_HOST_DEVICE
        Params()
            : swizzle_log_tile(0)
            , params_A(0)
            , params_B(0)
            , params_alpha_col(0)
            , params_C(0)
            , params_D(0)
            , batch_count(0)
            , gemm_k_size(0)
            , mode(cutlass::gemm::GemmUniversalMode::kGemm)
            , ptr_A(nullptr)
            , ptr_B(nullptr)
            , ptr_alpha_col(nullptr)
            , ptr_alpha_row(nullptr)
            , ptr_C(nullptr)
            , ptr_D(nullptr)
            , batch_stride_A(0)
            , batch_stride_B(0)
        {
        }

        Params(
            Arguments const& args, cutlass::gemm::GemmCoord const& grid_tiled_shape_, int gemm_k_size_, int* workspace_)
            : problem_size(args.problem_size)
            , swizzle_log_tile(0)
            , params_A(args.ref_A.layout())
            , params_B(args.ref_B.layout())
            , params_alpha_col(args.ref_alpha_col.layout())
            , params_alpha_row(args.ref_alpha_col.layout())
            , params_C(args.ref_C.layout())
            , params_D(args.ref_D.layout())
            , mode(args.mode)
            , batch_count(args.batch_count)
            , gemm_k_size(args.problem_size.k())
            , ptr_A(args.ref_A.data())
            , ptr_B(args.ref_B.data())
            , quant_option(args.quant_option)
            , ptr_alpha_col(args.ref_alpha_col.data())
            , ptr_alpha_row(args.ref_alpha_row.data())
            , ptr_C(args.ref_C.data())
            , ptr_D(args.ref_D.data())
            , batch_stride_A(args.batch_stride_A)
            , batch_stride_B(args.batch_stride_B)
            , epilogue_visitor(args.epilogue_visitor)
        {

            ThreadblockSwizzle threadblock_swizzle;

            grid_tiled_shape = threadblock_swizzle.get_tiled_shape(args.problem_size,
                {ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK}, args.batch_count);

            if (args.mode == GemmUniversalMode::kGemm || args.mode == GemmUniversalMode::kGemmSplitKParallel)
            {

                int const kAlignK
                    = const_max(const_max(128 / sizeof_bits<ElementA>::value, 128 / sizeof_bits<ElementB>::value), 1);

                gemm_k_size = round_up(ceil_div(args.problem_size.k(), args.batch_count), kAlignK);

                if (gemm_k_size)
                {
                    grid_tiled_shape.k() = ceil_div(args.problem_size.k(), gemm_k_size);
                }
            }

            swizzle_log_tile = threadblock_swizzle.get_log_tile(grid_tiled_shape);
        }
    };

    /// Shared memory storage structure
    union SharedStorage
    {

        typename Mma::SharedStorage main_loop;

        struct
        {
            typename Epilogue::SharedStorage epilogue;
            typename EpilogueVisitor::SharedStorage visitor;
        } epilogue;
    };

public:
    //
    // Methods
    //

    CUTLASS_DEVICE
    GemmWithEpilogueVisitor() {}

    /// Determines whether kernel satisfies alignment
    static Status can_implement(cutlass::gemm::GemmCoord const& problem_size)
    {

        CUTLASS_TRACE_HOST("GemmWithEpilogueVisitor::can_implement()");

        static int const kAlignmentA = Mma::IteratorA::AccessType::kElements;
        static int const kAlignmentB = Mma::IteratorB::AccessType::kElements;
        static int const kAlignmentC = EpilogueVisitor::OutputTileIterator::kElementsPerAccess;

        bool isAMisaligned = false;
        bool isBMisaligned = false;
        bool isCMisaligned = false;

        if (platform::is_same<LayoutA, layout::RowMajor>::value)
        {
            isAMisaligned = problem_size.k() % kAlignmentA;
        }
        else if (platform::is_same<LayoutA, layout::ColumnMajor>::value)
        {
            isAMisaligned = problem_size.m() % kAlignmentA;
        }
        else if (platform::is_same<LayoutA, layout::ColumnMajorInterleaved<32>>::value
            || platform::is_same<LayoutA, layout::ColumnMajorInterleaved<64>>::value)
        {
            isAMisaligned = problem_size.k() % kAlignmentA;
        }

        if (platform::is_same<LayoutB, layout::RowMajor>::value)
        {
            isBMisaligned = problem_size.n() % kAlignmentB;
        }
        else if (platform::is_same<LayoutB, layout::ColumnMajor>::value)
        {
            isBMisaligned = problem_size.k() % kAlignmentB;
        }
        else if (platform::is_same<LayoutB, layout::RowMajorInterleaved<32>>::value
            || platform::is_same<LayoutB, layout::RowMajorInterleaved<64>>::value)
        {
            isBMisaligned = problem_size.k() % kAlignmentB;
        }

        if (platform::is_same<LayoutC, layout::RowMajor>::value)
        {
            isCMisaligned = problem_size.n() % kAlignmentC;
        }
        else if (platform::is_same<LayoutC, layout::ColumnMajor>::value)
        {
            isCMisaligned = problem_size.m() % kAlignmentC;
        }
        else if (platform::is_same<LayoutC, layout::ColumnMajorInterleaved<32>>::value
            || platform::is_same<LayoutC, layout::ColumnMajorInterleaved<64>>::value)
        {
            isCMisaligned = problem_size.n() % kAlignmentC;
        }

        if (isAMisaligned)
        {
            CUTLASS_TRACE_HOST("  returning kErrorMisalignedOperand for A operand");
            return Status::kErrorMisalignedOperand;
        }

        if (isBMisaligned)
        {
            CUTLASS_TRACE_HOST("  returning kErrorMisalignedOperand for B operand");
            return Status::kErrorMisalignedOperand;
        }

        if (isCMisaligned)
        {
            CUTLASS_TRACE_HOST("  returning kErrorMisalignedOperand for C operand");
            return Status::kErrorMisalignedOperand;
        }

        CUTLASS_TRACE_HOST("  returning kSuccess");

        return Status::kSuccess;
    }

    static Status can_implement(Arguments const& args)
    {
        return can_implement(args.problem_size);
    }

    static size_t get_extra_workspace_size(Arguments const& args, cutlass::gemm::GemmCoord const& grid_tiled_shape)
    {

        return 0;
    }

#define SPLIT_K_ENABLED 1

    /// Executes one GEMM
    CUTLASS_DEVICE
    void run_kernel_(Params const& params, SharedStorage& shared_storage)
    {

        // Compute threadblock location
        ThreadblockSwizzle threadblock_swizzle;

        cutlass::gemm::GemmCoord threadblock_tile_offset = threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);

        // Early exit if CTA is out of range
        if (params.grid_tiled_shape.m() <= threadblock_tile_offset.m()
            || params.grid_tiled_shape.n() <= threadblock_tile_offset.n())
        {

            return;
        }

        int offset_k = 0;
        int problem_size_k = params.problem_size.k();

        ElementA* ptr_A = static_cast<ElementA*>(params.ptr_A);
        ElementB* ptr_B = static_cast<ElementB*>(params.ptr_B);

#if SPLIT_K_ENABLED
        //
        // Fetch pointers based on mode.
        //
        if (params.mode == GemmUniversalMode::kGemm || params.mode == GemmUniversalMode::kGemmSplitKParallel)
        {

            if (threadblock_tile_offset.k() + 1 < params.grid_tiled_shape.k())
            {

                problem_size_k = (threadblock_tile_offset.k() + 1) * params.gemm_k_size;
            }

            offset_k = threadblock_tile_offset.k() * params.gemm_k_size;
        }
        else if (params.mode == GemmUniversalMode::kBatched)
        {
            ptr_A += threadblock_tile_offset.k() * params.batch_stride_A;
            ptr_B += threadblock_tile_offset.k() * params.batch_stride_B;
        }
        else if (params.mode == GemmUniversalMode::kArray)
        {
            ptr_A = static_cast<ElementA* const*>(params.ptr_A)[threadblock_tile_offset.k()];
            ptr_B = static_cast<ElementB* const*>(params.ptr_B)[threadblock_tile_offset.k()];
        }
#endif

        // Compute initial location in logical coordinates
        cutlass::MatrixCoord tb_offset_A{
            threadblock_tile_offset.m() * Mma::Shape::kM,
            offset_k,
        };

        cutlass::MatrixCoord tb_offset_B{offset_k, threadblock_tile_offset.n() * Mma::Shape::kN};

        // Compute position within threadblock
        int thread_idx = threadIdx.x;

        // Construct iterators to A and B operands
        typename Mma::IteratorA iterator_A(
            params.params_A, ptr_A, {params.problem_size.m(), problem_size_k}, thread_idx, tb_offset_A);

        typename Mma::IteratorB iterator_B(
            params.params_B, ptr_B, {problem_size_k, params.problem_size.n()}, thread_idx, tb_offset_B);

        // Broadcast the warp_id computed by lane 0 to ensure dependent code
        // is compiled as warp-uniform.
        int warp_idx = __shfl_sync(0xffffffff, threadIdx.x / 32, 0);

        int lane_idx = threadIdx.x % 32;

        //
        // Main loop
        //

        // Construct thread-scoped matrix multiply
        Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);

        typename Mma::FragmentC accumulators;

        accumulators.clear();

        // Compute threadblock-scoped matrix multiply-add
        int gemm_k_iterations = (problem_size_k - offset_k + Mma::Shape::kK - 1) / Mma::Shape::kK;

        // Compute threadblock-scoped matrix multiply-add
        mma(gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators);

        //
        // Masked tile iterators constructed from members
        //

        threadblock_tile_offset = threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);

        // assume identity swizzle
        MatrixCoord threadblock_offset(
            threadblock_tile_offset.m() * Mma::Shape::kM, threadblock_tile_offset.n() * Mma::Shape::kN);

        int block_idx = threadblock_tile_offset.m() + threadblock_tile_offset.n() * params.grid_tiled_shape.m();

        //
        // Construct the epilogue visitor
        //

        EpilogueVisitor epilogue_visitor(params.epilogue_visitor, shared_storage.epilogue.visitor,
            params.problem_size.mn(), thread_idx, warp_idx, lane_idx, params.params_alpha_col, params.params_C,
            params.params_D, params.quant_option, params.ptr_alpha_row, params.ptr_alpha_col, params.ptr_C,
            params.ptr_D, threadblock_offset, blockIdx.y * params.problem_size.m());

        if (params.mode == GemmUniversalMode::kGemm)
        {
            // Indicate which position in a serial reduction the output operator is currently updating
            epilogue_visitor.set_k_partition(threadblock_tile_offset.k(), params.grid_tiled_shape.k());
        }
        else if (params.mode == GemmUniversalMode::kBatched || params.mode == GemmUniversalMode::kArray)
        {
            epilogue_visitor.set_batch_index(threadblock_tile_offset.k());
        }

        // Construct the epilogue
        Epilogue epilogue(shared_storage.epilogue.epilogue, thread_idx, warp_idx, lane_idx);

        // Execute the epilogue operator to update the destination tensor.
        epilogue(epilogue_visitor, accumulators);
    }

    template <typename CompilationArch>
    CUTLASS_DEVICE void run_kernel(Params const& params, SharedStorage& shared_storage)
    {
        if constexpr (platform::is_same<ArchTag, CompilationArch>::value)
        {
            run_kernel_(params, shared_storage);
        }
        else
        {
            CUTLASS_NOT_IMPLEMENTED();
        }
    }

    /*
        To improve compilation speed, we do not compile the device operator if the CUDA_ARCH does not correspond
        to the ArchTag of the cutlass kernel operator.
      */
    /// Executes one GEMM
    CUTLASS_DEVICE
    void operator()(Params const& params, SharedStorage& shared_storage)
    {
#if defined(__CUDA_ARCH__)
#if (__CUDA_ARCH__ >= 720) && (__CUDA_ARCH__ < 750)
        run_kernel<arch::Sm72>(params, shared_storage);
#elif (__CUDA_ARCH__ >= 750) && (__CUDA_ARCH__ < 800)
        run_kernel<arch::Sm75>(params, shared_storage);
#elif (__CUDA_ARCH__ >= 800) && (__CUDA_ARCH__ < 900)
        run_kernel<arch::Sm80>(params, shared_storage);
#elif (__CUDA_ARCH__ >= 900)
        // TODO - replace with CUTLASS_NOT_IMPLEMENTED() and upgrade to 3.x kernels.
        run_kernel<arch::Sm80>(params, shared_storage);
#else
        static_assert(
            false, "Invalid architecture being compiled. Only Volta+ supported in weight-only quantization kernels.");
#endif
#else
        CUTLASS_NOT_IMPLEMENTED();
#endif
    }
};

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

} // namespace kernel
} // namespace gemm
} // namespace cutlass

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