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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #include "ck/utility/common_header.hpp"
 #include "ck/utility/blkgemmpipe_scheduler.hpp"
 #include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
 #include "ck/tensor_operation/gpu/warp/xdlops_gemm.hpp"
 #include "ck/tensor_description/tensor_adaptor.hpp"
  
 namespace ck {
  
 template <index_t BlockSize,
           typename ADataType,
           typename BDataType,
           typename ComputeDataType,
           typename AccDataType,
           typename ATileDesc,
           typename BTileDesc,
           typename AMmaTileDesc,
           typename BMmaTileDesc,
           index_t ABlockTransferSrcScalarPerVector,
           index_t BBlockTransferSrcScalarPerVector,
           index_t MPerBlock,
           index_t NPerBlock,
           index_t KPerBlock,
           index_t MPerXDL,
           index_t NPerXDL,
           index_t MRepeat,
           index_t NRepeat,
           index_t KPack,
           bool TransposeC = false>
 struct BlockwiseGemmXdlops_pipeline_base
 {
     static constexpr auto I0 = Number<0>{};
     static constexpr auto I1 = Number<1>{};
     static constexpr auto I2 = Number<2>{};
     static constexpr auto I3 = Number<3>{};
  
     using ThisThreadBlock = ThisThreadBlock<BlockSize>;
  
     // Hardcode to 64, as HIP-provided "WarpSize" would return 32 on RDNA GPUs.
     static constexpr index_t WaveSize = 64;
  
     static constexpr index_t A_K0 = ATileDesc{}.GetLength(I0);
     static constexpr index_t B_K0 = BTileDesc{}.GetLength(I0);
     static constexpr index_t A_K1 = ATileDesc{}.GetLength(I2);
     static constexpr index_t B_K1 =
         BTileDesc{}.GetLength(Number < BTileDesc{}.GetNumOfDimension() == 4 ? 3 : 2 > {});
  
     static constexpr auto xdlops_gemm =
         XdlopsGemm<ComputeDataType, MPerXDL, NPerXDL, KPack, ComputeDataType, TransposeC>{};
  
     static constexpr index_t AMmaKStride = KPack;
     static constexpr index_t BMmaKStride = KPack;
  
     static constexpr index_t KPerThread    = KPerBlock / xdlops_gemm.K0PerXdlops;
     static constexpr index_t KRepeat       = KPerThread / KPack;
     static constexpr index_t KPerInnerLoop = KPack;
  
     static constexpr index_t KGroup = []() {
         if constexpr(is_same_v<remove_cvref_t<ComputeDataType>, f8_t>)
             // On gfx950, we have mfma that required 32 f8 elements as input,
             // splited into 2 groups of 16 f8 elements.
             // the 2 groups is not contiguous in the B preshuffed layout.
             // and we do not want it to be contiguous in the B preshuffled layout
             // because a memory instruction can only read 16 f8 elements at a time.
             return ((MPerXDL == 16 && MPerXDL == 16 && xdlops_gemm.KPerXdlops == 128) ||
                     (MPerXDL == 32 && MPerXDL == 32 && xdlops_gemm.KPerXdlops == 64))
                        ? 2
                        : 1;
         else
             return 1;
     }();
  
     static constexpr index_t MWaves = MPerBlock / (MRepeat * MPerXDL);
     static constexpr index_t NWaves = NPerBlock / (NRepeat * NPerXDL);
  
     using HotLoopInstList =
         ck::BlockwiseGemmXdlops_pipeline_hotloop_inst<BlockSize,
                                                       MPerBlock,
                                                       NPerBlock,
                                                       KPerBlock,
                                                       ABlockTransferSrcScalarPerVector,
                                                       BBlockTransferSrcScalarPerVector,
                                                       A_K1,
                                                       B_K1,
                                                       A_K1,
                                                       B_K1,
                                                       MRepeat,
                                                       NRepeat,
                                                       MPerXDL,
                                                       NPerXDL,
                                                       xdlops_gemm.KPerXdlops>;
  
     static_assert(KPerThread % KPack == 0,
                   "Wrong KPack setting; try increasing KPerThread or decreasing KPack");
  
     StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr,
                               AccDataType,
                               MRepeat * NRepeat,
                               xdlops_gemm.GetRegSizePerXdlops(),
                               true>
         c_thread_buf_;
  
     __host__ __device__ constexpr auto& GetCThreadBuffer() { return c_thread_buf_; }
  
     __device__ static auto GetWaveIdx()
     {
         const index_t thread_id = ThisThreadBlock::GetThreadId();
  
         constexpr auto threadid_to_wave_idx_adaptor = make_single_stage_tensor_adaptor(
             make_tuple(make_merge_transform(make_tuple(MWaves, NWaves, WaveSize))),
             make_tuple(Sequence<0, 1, 2>{}),
             make_tuple(Sequence<0>{}));
  
         return threadid_to_wave_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
     }
  
     __device__ static auto CalculateAThreadOriginDataIndex()
     {
         const auto wave_idx = GetWaveIdx();
  
         const auto waveId_m = wave_idx[I0];
  
         const auto xdlops_a_idx = xdlops_gemm.CalculateAThreadOriginDataIndex();
  
         return make_tuple(0, waveId_m, xdlops_a_idx[I1], KPerThread * xdlops_a_idx[I0]);
     }
  
     __device__ static auto CalculateAThreadOriginDataIndex6D()
     {
         const auto wave_idx = GetWaveIdx();
  
         const auto waveId_m = wave_idx[I0];
  
         const auto xdlops_a_idx = xdlops_gemm.CalculateAThreadOriginDataIndex();
  
         return make_tuple(0, waveId_m, xdlops_a_idx[I1], 0, xdlops_a_idx[I0], 0);
     }
  
     __device__ static auto CalculateBThreadOriginDataIndex()
     {
         const auto wave_idx = GetWaveIdx();
  
         const auto waveId_n = wave_idx[I1];
  
         const auto xdlops_b_idx = xdlops_gemm.CalculateBThreadOriginDataIndex();
  
         return make_tuple(0, waveId_n, xdlops_b_idx[I1], KPerThread * xdlops_b_idx[I0]);
     }
  
     template <index_t m0, index_t n0, index_t xdlops_i, index_t blk_i>
     __device__ static auto
         CalculateCThreadOriginDataIndex(Number<m0>, Number<n0>, Number<xdlops_i>, Number<blk_i>)
     {
         const auto wave_idx = GetWaveIdx();
  
         const auto waveId_m = wave_idx[I0];
         const auto waveId_n = wave_idx[I1];
  
         const auto blk_idx = xdlops_gemm.GetBeginOfThreadBlk(xdlops_i, blk_i);
  
         constexpr auto mrepeat_mwave_mperxdl_to_m_adaptor = make_single_stage_tensor_adaptor(
             make_tuple(make_unmerge_transform(make_tuple(MRepeat, MWaves, MPerXDL))),
             make_tuple(Sequence<0>{}),
             make_tuple(Sequence<0, 1, 2>{}));
  
         constexpr auto nrepeat_nwave_nperxdl_to_n_adaptor = make_single_stage_tensor_adaptor(
             make_tuple(make_unmerge_transform(make_tuple(NRepeat, NWaves, NPerXDL))),
             make_tuple(Sequence<0>{}),
             make_tuple(Sequence<0, 1, 2>{}));
  
         const index_t c_thread_m = mrepeat_mwave_mperxdl_to_m_adaptor.CalculateBottomIndex(
             make_tuple(m0, waveId_m, blk_idx[I0]))[I0];
         const index_t c_thread_n = nrepeat_nwave_nperxdl_to_n_adaptor.CalculateBottomIndex(
             make_tuple(n0, waveId_n, blk_idx[I1]))[I0];
  
         return make_tuple(c_thread_m, c_thread_n);
     }
  
     template <index_t m0, index_t n0, index_t xdlops_i, index_t blk_i>
     __device__ static auto
         CalculateCThreadOriginDataIndex8D(Number<m0>, Number<n0>, Number<xdlops_i>, Number<blk_i>)
     {
         const auto wave_idx = GetWaveIdx();
  
         const auto waveId_m = wave_idx[I0];
         const auto waveId_n = wave_idx[I1];
  
         const auto blk_idx = xdlops_gemm.GetBeginOfThreadBlk4D(xdlops_i, blk_i);
  
         return make_tuple(
             m0, n0, waveId_m, waveId_n, blk_idx[I0], blk_idx[I1], blk_idx[I2], blk_idx[I3]);
     }
  
     using Tuple4 = decltype(CalculateAThreadOriginDataIndex());
  
     __host__ __device__
     BlockwiseGemmXdlops_pipeline_base(Tuple4 a_origin = CalculateAThreadOriginDataIndex(),
                                       Tuple4 b_origin = CalculateBThreadOriginDataIndex())
         : a_thread_copy_(a_origin), b_thread_copy_(b_origin)
     {
         static_assert(AMmaTileDesc::IsKnownAtCompileTime() && BMmaTileDesc::IsKnownAtCompileTime(),
                       "wrong! Desc should be known at compile-time");
  
         static_assert(ThisThreadBlock::GetNumOfThread() == MWaves * NWaves * WaveSize,
                       "ThisThreadBlock::GetNumOfThread() != MWaves * NWaves * WaveSize\n");
  
         static_assert(MPerBlock % (MPerXDL * MRepeat) == 0 && NPerBlock % (NPerXDL * NRepeat) == 0,
                       "wrong!");
     }
  
     // transposed XDL output supporting C_xdl' = B_xdl' * A_xdl'
     __host__ __device__ static constexpr auto GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4()
     {
         constexpr auto c_m0_m1_m2_n_tblk_lens = xdlops_gemm.GetCM0M1M2NThreadBlkLengths();
  
         constexpr auto M0 = c_m0_m1_m2_n_tblk_lens[I0];
         constexpr auto M1 = c_m0_m1_m2_n_tblk_lens[I1];
         constexpr auto M2 = c_m0_m1_m2_n_tblk_lens[I2];
         constexpr auto N  = c_m0_m1_m2_n_tblk_lens[I3];
  
         return make_naive_tensor_descriptor_packed(
             make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, I1, I1, N, M0, M1, M2));
     }
  
     // XDL output supporting C_xdl = A_xdl * B_xdl
     __host__ __device__ static constexpr auto GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2()
     {
         constexpr auto c_m0_m1_m2_n_tblk_lens = xdlops_gemm.GetCM0M1M2NThreadBlkLengths();
  
         constexpr auto M0 = c_m0_m1_m2_n_tblk_lens[I0];
         constexpr auto M1 = c_m0_m1_m2_n_tblk_lens[I1];
         constexpr auto M2 = c_m0_m1_m2_n_tblk_lens[I2];
         constexpr auto N  = c_m0_m1_m2_n_tblk_lens[I3];
  
         return make_naive_tensor_descriptor_packed(
             make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, I1, I1, M0, M1, M2, N));
     }
  
     __host__ __device__ static constexpr auto GetCThreadDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2()
     {
         constexpr auto c_m0_m1_m2_n_tblk_lens = xdlops_gemm.GetCM0M1M2NThreadBlkLengths();
  
         constexpr auto M0 = c_m0_m1_m2_n_tblk_lens[I0];
         constexpr auto M1 = c_m0_m1_m2_n_tblk_lens[I1];
         constexpr auto M2 = c_m0_m1_m2_n_tblk_lens[I2];
         constexpr auto N  = c_m0_m1_m2_n_tblk_lens[I3];
  
         return make_naive_tensor_descriptor_packed(
             make_tuple(I1, Number<MRepeat>{}, Number<NRepeat>{}, I1, I1, M0, M1, M2, N));
     }
  
     // transposed XDL output supporting C_xdl' = B_xdl' * A_xdl'
     __host__ __device__ static constexpr auto GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4()
     {
         constexpr auto c_block_desc_m0_n0_m1_n1_m2_n2 =
             make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
                                                            Number<NRepeat>{},
                                                            Number<MWaves>{},
                                                            Number<NWaves>{},
                                                            Number<MPerXDL>{},
                                                            Number<NPerXDL>{}));
  
         return xdlops_gemm.MakeCDescriptor_M0_N0_M1_N1_M2_N2_N3_N4(c_block_desc_m0_n0_m1_n1_m2_n2);
     }
  
     // XDL output supporting C_xdl = A_xdl * B_xdl
     __host__ __device__ static constexpr auto GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2()
     {
         constexpr auto c_block_desc_m0_n0_m1_n1_m2_n2 =
             make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
                                                            Number<NRepeat>{},
                                                            Number<MWaves>{},
                                                            Number<NWaves>{},
                                                            Number<MPerXDL>{},
                                                            Number<NPerXDL>{}));
  
         return xdlops_gemm.MakeCDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_block_desc_m0_n0_m1_n1_m2_n2);
     }
  
     __host__ __device__ static constexpr auto GetCBlockDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2()
     {
         constexpr auto c_block_desc_g_m0_n0_m1_n1_m2_n2 =
             make_naive_tensor_descriptor_packed(make_tuple(I1,
                                                            Number<MRepeat>{},
                                                            Number<NRepeat>{},
                                                            Number<MWaves>{},
                                                            Number<NWaves>{},
                                                            Number<MPerXDL>{},
                                                            Number<NPerXDL>{}));
  
         return xdlops_gemm.MakeCDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(
             c_block_desc_g_m0_n0_m1_n1_m2_n2);
     }
  
     template <typename CGridDesc_M_N>
     __host__ __device__ static constexpr auto
     MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(const CGridDesc_M_N& c_grid_desc_m_n)
     {
         const auto M = c_grid_desc_m_n.GetLength(I0);
         const auto N = c_grid_desc_m_n.GetLength(I1);
  
         const auto c_grid_desc_m0_n0_m1_n1_m2_n2 = transform_tensor_descriptor(
             c_grid_desc_m_n,
             make_tuple(make_unmerge_transform(make_tuple(M / (MWaves * MPerXDL), MWaves, MPerXDL)),
                        make_unmerge_transform(make_tuple(N / (NWaves * NPerXDL), NWaves, NPerXDL))),
             make_tuple(Sequence<0>{}, Sequence<1>{}),
             make_tuple(Sequence<0, 2, 4>{}, Sequence<1, 3, 5>{}));
  
         return xdlops_gemm.MakeCDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m0_n0_m1_n1_m2_n2);
     }
  
     template <typename CGridDesc_G_M_N>
     __host__ __device__ static constexpr auto
     MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(const CGridDesc_G_M_N& c_grid_desc_g_m_n)
     {
         const auto G = c_grid_desc_g_m_n.GetLength(I0);
         const auto M = c_grid_desc_g_m_n.GetLength(I1);
         const auto N = c_grid_desc_g_m_n.GetLength(I2);
  
         const auto c_grid_desc_g_m0_n0_m1_n1_m2_n2 = transform_tensor_descriptor(
             c_grid_desc_g_m_n,
             make_tuple(make_pass_through_transform(G),
                        make_unmerge_transform(make_tuple(M / (MWaves * MPerXDL), MWaves, MPerXDL)),
                        make_unmerge_transform(make_tuple(N / (NWaves * NPerXDL), NWaves, NPerXDL))),
             make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
             make_tuple(Sequence<0>{}, Sequence<1, 3, 5>{}, Sequence<2, 4, 6>{}));
  
         return xdlops_gemm.MakeCDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(
             c_grid_desc_g_m0_n0_m1_n1_m2_n2);
     }
     __host__ __device__ static constexpr auto GetCThreadDesc() { return c_thread_desc_; }
     static constexpr AMmaTileDesc a_block_desc_m0_m1_m2_k;
     static constexpr BMmaTileDesc b_block_desc_n0_n1_n2_k;
  
     protected:
     // M1, N1 as double buffer index
     // Read buffer + Compute buffer
     // A[M0, M1, M2, KPack]
     static constexpr auto a_thread_desc_ = make_naive_tensor_descriptor(
         make_tuple(Number<MRepeat>{}, I1, Number<KRepeat>{}, Number<KPack>{}),
         make_tuple(
             Number<KPack>{}, Number<KRepeat * MRepeat * KPack>{}, Number<MRepeat * KPack>{}, I1));
  
     // B[N0, N1, N2, KPack]
     static constexpr auto b_thread_desc_ = make_naive_tensor_descriptor(
         make_tuple(Number<NRepeat>{}, I1, Number<KRepeat>{}, Number<KPack>{}),
         make_tuple(
             Number<KPack>{}, Number<KRepeat * NRepeat * KPack>{}, Number<NRepeat * KPack>{}, I1));
  
     // C[M, N, NumRegXdlops]
     static constexpr auto c_thread_desc_ = make_naive_tensor_descriptor_packed(
         make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, xdlops_gemm.GetRegSizePerXdlops()));
  
     using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<ADataType,
                                                          ComputeDataType,
                                                          decltype(a_block_desc_m0_m1_m2_k),
                                                          decltype(a_thread_desc_),
                                                          Sequence<1, 1, 1, KPack>,
                                                          Sequence<0, 1, 2, 3>,
                                                          3,
                                                          A_K1,
                                                          A_K1>;
  
     using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<BDataType,
                                                          ComputeDataType,
                                                          decltype(b_block_desc_n0_n1_n2_k),
                                                          decltype(b_thread_desc_),
                                                          Sequence<1, 1, 1, KPack>,
                                                          Sequence<0, 1, 2, 3>,
                                                          3,
                                                          B_K1,
                                                          B_K1>;
  
     AThreadCopy a_thread_copy_;
     BThreadCopy b_thread_copy_;
 };
  
 } // namespace ck