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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #include <iostream>
 #include <sstream>
  
 #include "ck/utility/common_header.hpp"
 #include "ck/tensor_description/tensor_descriptor.hpp"
 #include "ck/tensor_description/tensor_descriptor_helper.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 #include "ck/tensor_operation/gpu/device/device_batched_gemm.hpp"
 #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
 #include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp"
 #include "ck/host_utility/device_prop.hpp"
 #include "ck/host_utility/kernel_launch.hpp"
  
 namespace ck {
 namespace tensor_operation {
 namespace device {
  
 /*
  * \brief Wrapper function of GridwiseGemm::Run to realize BatchedGEMM.
  *
  * \tparam ComputePtrOffsetOfBatch Class that computes the base pointer offsets of A, B, C matrix
  * given the batch. For example, ComputePtrOffsetOfStridedBatch() computes the offsets of evenly
  * strided batched, but we can easily extend to other layouts. The returned offset can be either \p
  * index_t or \p long_index_t. If it returns \p long_index_t, we are not subject to the 2GB
  * limitations.
  *
  * \tparam Block2CTileMap Block2CTileMap::CalculateBottomIndex() takes in id of a workgroup and
  * returns the 2D index of the tile that it computes. \see
  * GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3::Run().
  *
  * \note Using \p ComputePtrOffsetOfBatch gives us the flexibility that 2 workgroups can compute 2
  * tiles from different matrices. Keep in mind that these 2 matrices can share the same grid
  * descriptor (like in BatchedGEMM), or use their own grid descriptors (in GroupedGemm). \link
  * device_conv3d_fwd_xdl_ndhwc_kzyxc_ndhwk.hpp kernel_gemm_xdlops_v2r3_for_conv3d \endlink for \link
  * DeviceConv3d \endlink uses the same concept, but currently does NOT encapsulate the computing of
  * pointer offset into \p ComputePtrOffsetOfStridedBatch.
  *
  * \note \p Block2CTileMap allows customized mapping between a workgroup and the C-tile it computes.
  * Together with \p ComputePtrOffsetOfBatch, we can reuse GridwiseGemm (and GridwiseGemm fusion ) to
  * realize BatchedGemm and GroupedGemm (and the corresponding GEMM fusion).
  *
  */
 template <typename DeviceOp, typename GridwiseGemm, bool HasMainKBlockLoop>
 __global__ void
 #if CK_USE_LAUNCH_BOUNDS
 __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
 #endif
     kernel_batched_gemm_xdlops_v2r3(const typename DeviceOp::Argument karg)
 {
 #if defined(__gfx9__) || defined(__gfx11__) || defined(__gfx12__)
     if constexpr(GridwiseGemm::template IsValidCompilationParameter<>())
     {
         const index_t num_blocks_per_batch =
             __builtin_amdgcn_readfirstlane(get_grid_size() / karg.Batch);
         const index_t g_idx =
             __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
  
         const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
             static_cast<long_index_t>(karg.compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
         const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
             static_cast<long_index_t>(karg.compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
         const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
             static_cast<long_index_t>(karg.compute_ptr_offset_of_batch.GetCPtrOffset(g_idx)));
  
         __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
  
         const auto a_grid_desc_k0_m_k1 =
             amd_wave_read_first_lane(GridwiseGemm::MakeAGridDescriptor_K0_M_K1(
                 karg.M, karg.MPadded, karg.K, karg.K0, karg.StrideA));
         const auto b_grid_desc_k0_n_k1 =
             amd_wave_read_first_lane(GridwiseGemm::MakeBGridDescriptor_K0_N_K1(
                 karg.K, karg.N, karg.NPadded, karg.K0, karg.StrideB));
         const auto c_grid_desc_m_n = amd_wave_read_first_lane(GridwiseGemm::MakeCGridDescriptor_M_N(
             karg.M, karg.MPadded, karg.N, karg.NPadded, karg.StrideC));
  
         GridwiseGemm::template Run<HasMainKBlockLoop>(karg.p_a_grid + a_batch_offset,
                                                       karg.p_b_grid + b_batch_offset,
                                                       karg.p_c_grid + c_batch_offset,
                                                       p_shared,
                                                       a_grid_desc_k0_m_k1,
                                                       b_grid_desc_k0_n_k1,
                                                       c_grid_desc_m_n);
     }
 #else
     ignore = karg;
 #endif
 }
  
 template <typename ADataType,
           typename BDataType,
           typename CDataType,
           typename AccDataType,
           typename ALayout,
           typename BLayout,
           typename CLayout,
           typename AElementwiseOperation,
           typename BElementwiseOperation,
           typename CElementwiseOperation,
           ck::index_t BlockSize,
           ck::index_t MPerBlock,
           ck::index_t NPerBlock,
           ck::index_t K0PerBlock,
           ck::index_t K1,
           ck::index_t MPerXDL,
           ck::index_t NPerXDL,
           ck::index_t MXdlPerWave,
           ck::index_t NXdlPerWave,
           typename ABlockTransferThreadClusterLengths_K0_M_K1,
           typename ABlockTransferThreadClusterArrangeOrder,
           typename ABlockTransferSrcAccessOrder,
           ck::index_t ABlockTransferSrcVectorDim,
           ck::index_t ABlockTransferSrcScalarPerVector,
           ck::index_t ABlockTransferDstScalarPerVector_K1,
           bool ABlockLdsAddExtraM,
           typename BBlockTransferThreadClusterLengths_K0_N_K1,
           typename BBlockTransferThreadClusterArrangeOrder,
           typename BBlockTransferSrcAccessOrder,
           ck::index_t BBlockTransferSrcVectorDim,
           ck::index_t BBlockTransferSrcScalarPerVector,
           ck::index_t BBlockTransferDstScalarPerVector_K1,
           bool BBlockLdsAddExtraN,
           ck::index_t CThreadTransferSrcDstVectorDim,
           ck::index_t CThreadTransferDstScalarPerVector,
           ck::index_t NumGemmKPrefetchStage = 1,
           ck::LoopScheduler LoopSched       = make_default_loop_scheduler(),
           ck::PipelineVersion PipelineVer   = ck::PipelineVersion::v1>
 struct DeviceBatchedGemmXdl : public DeviceBatchedGemm<ALayout,
                                                        BLayout,
                                                        CLayout,
                                                        ADataType,
                                                        BDataType,
                                                        CDataType,
                                                        AElementwiseOperation,
                                                        BElementwiseOperation,
                                                        CElementwiseOperation>
 {
     GET_NXDL_PER_WAVE_IMPL
     static constexpr auto NXdlPerWave64 = GetNXdlPerWave<true>();
     static constexpr auto NXdlPerWave32 = GetNXdlPerWave<false>();
  
     static constexpr auto I0 = Number<0>{};
     static constexpr auto I1 = Number<1>{};
     static constexpr auto I2 = Number<2>{};
  
     static constexpr auto K1Number = Number<K1>{};
  
     struct ComputePtrOffsetOfStridedBatch
     {
         ComputePtrOffsetOfStridedBatch(index_t BatchStrideA,
                                        index_t BatchStrideB,
                                        index_t BatchStrideC)
             : BatchStrideA_(BatchStrideA), BatchStrideB_(BatchStrideB), BatchStrideC_(BatchStrideC)
         {
         }
  
         __host__ __device__ constexpr long_index_t GetAPtrOffset(index_t g_idx) const
         {
             return g_idx * static_cast<long_index_t>(BatchStrideA_);
         }
  
         __host__ __device__ constexpr long_index_t GetBPtrOffset(index_t g_idx) const
         {
             return g_idx * static_cast<long_index_t>(BatchStrideB_);
         }
  
         __host__ __device__ constexpr long_index_t GetCPtrOffset(index_t g_idx) const
         {
             return g_idx * static_cast<long_index_t>(BatchStrideC_);
         }
  
         private:
         index_t BatchStrideA_;
         index_t BatchStrideB_;
         index_t BatchStrideC_;
     };
  
     // GridwiseGemm
     template <index_t NXdlPerWave_>
     using GridwiseGemmBase = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3_ext<
         BlockSize,
         ADataType, // TODO: distinguish A/B datatype
         AccDataType,
         CDataType,
         InMemoryDataOperationEnum::Set,
         ALayout,
         BLayout,
         CLayout,
         AElementwiseOperation,
         BElementwiseOperation,
         CElementwiseOperation,
         GemmSpecialization::MNKPadding,
         MPerBlock,
         NPerBlock,
         K0PerBlock,
         MPerXDL,
         NPerXDL,
         K1,
         MXdlPerWave,
         NXdlPerWave_,
         ABlockTransferThreadClusterLengths_K0_M_K1,
         ABlockTransferThreadClusterArrangeOrder,
         ABlockTransferSrcAccessOrder,
         ABlockTransferSrcVectorDim,
         ABlockTransferSrcScalarPerVector,
         ABlockTransferDstScalarPerVector_K1,
         false, // AThreadTransferSrcResetCoordinateAfterRun,
         ABlockLdsAddExtraM,
         BBlockTransferThreadClusterLengths_K0_N_K1,
         BBlockTransferThreadClusterArrangeOrder,
         BBlockTransferSrcAccessOrder,
         BBlockTransferSrcVectorDim,
         BBlockTransferSrcScalarPerVector,
         BBlockTransferDstScalarPerVector_K1,
         false, // BThreadTransferSrcResetCoordinateAfterRun,
         BBlockLdsAddExtraN,
         Sequence<2, 3, 0, 1, 7, 5, 4, 6>,
         CThreadTransferSrcDstVectorDim,
         CThreadTransferDstScalarPerVector,
         NumGemmKPrefetchStage,
         LoopSched,
         PipelineVer>;
     using GridwiseGemm64 = GridwiseGemmBase<math::max(NXdlPerWave64, 1)>;
     using GridwiseGemm32 = GridwiseGemmBase<NXdlPerWave32>;
  
     using Problem = typename GridwiseGemm64::Problem;
  
     // Argument
     struct Argument : public Problem, public BaseArgument
     {
         Argument(const ADataType* p_a_grid_,
                  const BDataType* p_b_grid_,
                  CDataType* p_c_grid_,
                  index_t M_,
                  index_t N_,
                  index_t K_,
                  index_t StrideA_,
                  index_t StrideB_,
                  index_t StrideC_,
                  index_t BatchStrideA,
                  index_t BatchStrideB,
                  index_t BatchStrideC,
                  index_t Batch_)
             : Problem{M_, N_, K_, StrideA_, StrideB_, StrideC_},
               p_a_grid{p_a_grid_},
               p_b_grid{p_b_grid_},
               p_c_grid{p_c_grid_},
               Batch(Batch_),
               compute_ptr_offset_of_batch{BatchStrideA, BatchStrideB, BatchStrideC}
         {
         }
  
         const ADataType* p_a_grid;
         const BDataType* p_b_grid;
         CDataType* p_c_grid;
         index_t Batch;
         ComputePtrOffsetOfStridedBatch compute_ptr_offset_of_batch;
     };
  
     // Invoker
     struct Invoker : public BaseInvoker
     {
         using Argument = DeviceBatchedGemmXdl::Argument;
  
         template <typename GridwiseGemm>
         float RunImp(const Argument& karg, const StreamConfig& stream_config = StreamConfig{})
         {
             if(stream_config.log_level_ > 0)
             {
                 karg.Print();
             }
  
             typename GridwiseGemm::Problem arg(
                 karg.M, karg.N, karg.K, karg.StrideA, karg.StrideB, karg.StrideC);
             if(!GridwiseGemm::CheckValidity(arg))
             {
                 throw std::runtime_error(
                     "wrong! GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3_ext has invalid setting");
             }
  
             auto [gdx, gdy, gdz] = GridwiseGemm::CalculateGridSize(karg.M, karg.N);
             gdx *= karg.Batch;
  
             float ave_time = 0;
  
             if(GridwiseGemm::CalculateHasMainKBlockLoop(karg.K))
             {
                 const auto kernel =
                     kernel_batched_gemm_xdlops_v2r3<DeviceBatchedGemmXdl, GridwiseGemm, true>;
  
                 ave_time = launch_and_time_kernel(
                     stream_config, kernel, dim3(gdx, gdy, gdz), dim3(BlockSize), 0, karg);
             }
             else
             {
                 const auto kernel =
                     kernel_batched_gemm_xdlops_v2r3<DeviceBatchedGemmXdl, GridwiseGemm, false>;
  
                 ave_time = launch_and_time_kernel(
                     stream_config, kernel, dim3(gdx, gdy, gdz), dim3(BlockSize), 0, karg);
             }
  
             return ave_time;
         }
  
         INVOKER_RUN_IMPL
  
         // polymorphic
         float Run(const BaseArgument* p_arg,
                   const StreamConfig& stream_config = StreamConfig{}) override
         {
             return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
         }
     };
  
     static constexpr bool IsValidCompilationParameter()
     {
         // TODO: properly implement this check
         return true;
     }
  
     static bool IsSupportedArgument(const Problem& problem)
     {
         if(!ck::is_xdl_wmma_supported<ADataType, BDataType, MPerXDL, NPerXDL>())
         {
             return false;
         }
         // temp disable on gfx11
         if(ck::is_gfx11_supported())
         {
             return false;
         }
         if(get_warp_size() == 64)
         {
             if constexpr(NXdlPerWave64 > 0)
             {
                 return GridwiseGemm64::CheckValidity(problem);
             }
         }
         else
         {
             if constexpr(NXdlPerWave32 > 0)
             {
                 return GridwiseGemm32::CheckValidity(
                     reinterpret_cast<const typename GridwiseGemm32::Problem&>(problem));
             }
         }
         return false;
     }
  
     // polymorphic
     bool IsSupportedArgument(const BaseArgument* p_arg) override
     {
         return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
     }
  
     static auto MakeArgument(const ADataType* p_a,
                              const BDataType* p_b,
                              CDataType* p_c,
                              index_t M,
                              index_t N,
                              index_t K,
                              index_t StrideA,
                              index_t StrideB,
                              index_t StrideC,
                              index_t BatchStrideA,
                              index_t BatchStrideB,
                              index_t BatchStrideC,
                              index_t Batch)
     {
         return Argument{p_a,
                         p_b,
                         p_c,
                         M,
                         N,
                         K,
                         StrideA,
                         StrideB,
                         StrideC,
                         BatchStrideA,
                         BatchStrideB,
                         BatchStrideC,
                         Batch};
     }
  
     static auto MakeInvoker() { return Invoker{}; }
  
     // polymorphic
     std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
                                                       const void* p_b,
                                                       void* p_c,
                                                       index_t M,
                                                       index_t N,
                                                       index_t K,
                                                       index_t StrideA,
                                                       index_t StrideB,
                                                       index_t StrideC,
                                                       index_t BatchStrideA,
                                                       index_t BatchStrideB,
                                                       index_t BatchStrideC,
                                                       index_t Batch,
                                                       AElementwiseOperation,
                                                       BElementwiseOperation,
                                                       CElementwiseOperation) override
     {
         return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
                                           static_cast<const BDataType*>(p_b),
                                           static_cast<CDataType*>(p_c),
                                           M,
                                           N,
                                           K,
                                           StrideA,
                                           StrideB,
                                           StrideC,
                                           BatchStrideA,
                                           BatchStrideB,
                                           BatchStrideC,
                                           Batch);
     }
  
     // polymorphic
     std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
     {
         return std::make_unique<Invoker>(Invoker{});
     }
  
     // polymorphic
     std::string GetTypeString() const override
     {
         auto str = std::stringstream();
  
         std::map<LoopScheduler, std::string> LoopSchedToString{
             {LoopScheduler::Default, "Default"}, {LoopScheduler::Interwave, "Interwave"}};
  
         std::map<PipelineVersion, std::string> PipelineVersionToString{{PipelineVersion::v1, "v1"},
                                                                        {PipelineVersion::v2, "v2"}};
  
         // clang-format off
         str << "DeviceBatchedGemmXdl"
             << "<"
             << BlockSize << ", "
             << MPerBlock << ", "
             << NPerBlock << ", "
             << K0PerBlock << ", "
             << K1 << ", "
             << MPerXDL << ", "
             << NPerXDL << ", "
             << MXdlPerWave << ", "
             << NXdlPerWave << ", "
             << ">"
             << " NumGemmKPrefetchStage: "
             << NumGemmKPrefetchStage << ", "
             << "LoopScheduler: "
             << LoopSchedToString[LoopSched] << ", "
             << "PipelineVersion: "
             << PipelineVersionToString[PipelineVer];
         // clang-format on
  
         return str.str();
     }
 };
  
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck