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Composable Kernel: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-composable-kernel/checkouts/develop/include/ck_tile/ops/fused_moe/kernel/fused_moegemm_kernel.hpp Source File
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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #include "ck_tile/core.hpp"
 #include "ck_tile/ops/common.hpp"
 #include "ck_tile/ops/elementwise.hpp"
 #include <string>
 #include <type_traits>
  
 // clang-format off
 // [indexing implementation-1]
 // using M_a as constexpr block_size to partition all tokens into different slices
 // each slice map to one expert, and one expert can have multiple slices
 // e.g. num_experts = 6, topk=3, M_a = 4, input_tokens = 5
 // before sort, topk_ids is : [[0, 3, 5], [2, 3, 5], [1, 3, 5], [1, 2, 3], [1, 3, 5]]
 //                            tok-0      tok-1      tok-2      tok-3      tok-4
 //           topk_weight is : [[a, b, c], [d, e, f], [g, h, i], [j, k, l], [m, n, o]] (some float number)
 //
 // token_id_per_expert is : [[0], [2, 3, 4], [1, 3], [0, 1, 2, 3, 4], [], [0, 1, 2, 5]]
 //  (only for reference)    exp-0  exp-1     exp-2   exp-3          exp-4  exp-5
 // weight_id_per_expert is: [[a], [g, j, m], [d, k], [b, e, h, l, n], [], [c, f, i, o]]
 //
 // max_num_tokens_padded : topk * input_tokens + num_experts * M_a - topk (updated)
 // * this could be larger than actual, since actual tokens are on GPU
 //
 // sorted_token_ids_ptr   : [0, 6, 6, 6, 2, 3, 4, 6, 1, 3, 6, 6, 0, 1, 2, 3, 4, 6, 6, 6, 6, 6, 6, 6, 0, 1, 2, 5]
 //                          |-  exp-0  -|-  exp-1  -|-  exp-2  -|-      exp-3          -|-  exp-4 -|-  exp-5  -|
 // sorted_weight_ptr      : [a, *, *, *, g, j, m, *, d, k, *, *, b, e, h, l, n, *, *, *, *, *, *, *, c, f, i, o]
 //
 // * length is max_num_tokens_padded, actual size is num_tokens_post_padded_ptr
 //
 // * Note on token_id_per_expert/sorted_token_ids_ptr data:
 // currently we do not have topk information from the data of token_id_per_expert/sorted_token_ids_ptr.
 // In some cases(like smooth-quant), we need topk information to indexing into tokens quant from 
 // different expert smooth quant. So we modify the number stored inside token_id_per_expert/sorted_token_ids_ptr
 //
 //       32bit    0........23 24.....31 bit
 //      (data) -> (token_id | topk_id)
 // low 24 bit is for token id, top 8 bit is for topk id
 //
 // the input after smooth-quant is [token, topk, hidden_dim], originally it is [token, hidden_dim]
 // the input scale for token is [topk, token, 1], the smooth-quant scale for first gemm is [expert, interm_dim]
 //
 // sorted_expert_ids_ptr  : [0, 1, 2, 3, 3, 4, 5]
 // * length is (max_num_tokens_padded + block_size - 1) / block_size
 //
 // num_tokens_post_padded_ptr : [28]
 // num_sorted_tiles_ptr : [7]
 //
 // * different from vLLM
 //   1) token_id stored in sorted_token_ids_ptr is actual token_id, not token_id*top_K expanded id
 //   2）need sorted_weight_ptr
 //   3) use num_sorted_tiles_ptr, already divided by M_a
 //
 // * below used for indexing
 //  1) sorted_token_ids_ptr [max_num_tokens_padded]
 //  2) sorted_weight_ptr
 //  3) sorted_expert_ids_ptr
 //  4）num_tokens_post_padded_ptr/num_sorted_tiles_ptr (select one)
 //
 //   max_num_tokens_padded: opk_ids.numel() + num_experts * (block_size - 1)
 //
 // [indexing implementation-2]
 // before sort, topk_ids is : [[0, 3, 5], [2, 3, 5], [1, 3, 5], [1, 2, 3], [1, 3, 5]]
 //                            tok-0      tok-1      tok-2      tok-3      tok-4
 //           topk_weight is : [[a, b, c], [d, e, f], [g, h, i], [j, k, l], [m, n, o]] (some float number)
 //
 // we generate original rol/col id as
 //              topk_rc_ids : [[0, 5, A], [1, 6, B], [2, 7, C], [3, 8, D], [4, 9, E]]
 // let x be one element of above, we can get:
 //          tpok_row_id(token_id) = x % num_tokens(5)
 //         tpok_col_id(expert_Id) = x / num_tokens
 // topk_row_id/col_id can be used to access original topk_ids/topk_weight
 //
 // token_id_per_expert is : [[0], [2, 3, 4], [1, 3], [0, 1, 2, 3, 4], [], [0, 1, 5, 5]]
 //  (only for reference)    exp-0  exp-1     exp-2   exp-3          exp-4  exp-5
 // weight_id_per_expert is: [[a], [g, j, m], [d, k], [b, e, h, l, n], [], [c, f, i, o]]
 //
 // we can get permuted_rc_ids:
 //                          [[0], [2, 3, 4], [1, 8], [5, 6, 7, D, 9], [], [A, B, C, E]]
 //
 //
 // clang-format on
 //
 namespace ck_tile {
  
 // m: num_tokens (or token*input-batch)
 // k: intermediate_size
 // n: intermediate_size used between 2 FC (TP slice this)
 // e: num expert
 // if doing pre-shuffle
 // nr : n / Block_Nr
 // kr : k / Block_Kr
 // w  : fattened 1d wave buffer
 struct FusedMoeGemmHostArgs
 {
     const void* a_ptr;              // [m, k], input token
     const void* a_scale_ptr;        // [m, 1], token scale
     const void* g_ptr;              // [e, n, k]/[e, 2*n, k], pre-shuffle([e, nr, kr, w])
     const void* d_ptr;              // [e, n, k], pre-shuffle([e, nr, kr, w])
     const void* g_scale_ptr;        // [e, 1, n], gate(up) scale
     const void* d_scale_ptr;        // [e, 1, k], down scale
     const void* y_smooth_scale_ptr; // [e, 1, n], smooth-quant-scale for 2nd gemm input
     void* o_ptr;                    // [m, k], output token
  
     const void* sorted_token_ids_ptr;  // [max_num_tokens_padded]
     const void* sorted_weight_ptr;     // [max_num_tokens_padded]
     const void* sorted_expert_ids_ptr; // [(max_num_tokens_padded + block_size - 1) / block_size]
     const void* num_sorted_tiles_ptr;  // [1]
  
     index_t hidden_size;       // k
     index_t intermediate_size; // n / TP, for Gate/UP/Down
     index_t num_tokens;        // input number of tokens for current iteration
     index_t num_experts;       // number of groups
     index_t topk;              // need this?
  
     index_t stride_token; // for input/output, stride for each row, should >= hidden_size
 };
  
 // This is scatter/gather b2b group-gemm
 template <typename Partitioner_, typename Pipeline_, typename Epilogue_>
 struct FusedMoeGemmKernel
 {
     using Partitioner = remove_cvref_t<Partitioner_>;
     using Pipeline    = remove_cvref_t<Pipeline_>;
     using Epilogue    = remove_cvref_t<Epilogue_>; // TODO: not used
     // static constexpr index_t kBlockPerCu = Pipeline::kBlockPerCu;
     // static_assert(kBlockPerCu > 0);
  
     using BlockShape = typename Pipeline::BlockShape; // this is FusedMoeGemmShape
     static constexpr index_t kBlockSize = BlockShape::BlockSize;
  
     using ADataType            = typename Pipeline::Problem::ADataType;
     using GDataType            = typename Pipeline::Problem::GDataType;
     using DDataType            = typename Pipeline::Problem::DDataType;
     using AccDataType          = typename Pipeline::Problem::AccDataType;
     using ODataType            = typename Pipeline::Problem::ODataType;
     using AScaleDataType       = typename Pipeline::Problem::AScaleDataType;
     using GScaleDataType       = typename Pipeline::Problem::GScaleDataType;
     using DScaleDataType       = typename Pipeline::Problem::DScaleDataType;
     using YSmoothScaleDataType = typename Pipeline::Problem::YSmoothScaleDataType;
     using TopkWeightDataType   = typename Pipeline::Problem::TopkWeightDataType;
     using IndexDataType        = typename Pipeline::Problem::IndexDataType;
     using YDataType            = typename Pipeline::Problem::YDataType;
  
     using Traits                = typename Pipeline::Problem::Traits;
     static constexpr bool UseUK = true;
  
     static constexpr bool IsGateOnly          = Traits::IsGateOnly;
     static constexpr bool UseSmoothQuant      = Traits::UseSmoothQuant;
     static constexpr bool PadHiddenSize       = Traits::PadHiddenSize;
     static constexpr bool PadIntermediateSize = Traits::PadIntermediateSize;
  
     // clang-format off
     template <typename T> struct t2s;
     template <> struct t2s<float> { static constexpr const char * name = "fp32"; };
     template <> struct t2s<fp16_t> { static constexpr const char * name = "fp16"; };
     template <> struct t2s<bf16_t> { static constexpr const char * name = "bf16"; };
     template <> struct t2s<fp8_t> { static constexpr const char * name = "fp8"; };
     template <> struct t2s<bf8_t> { static constexpr const char * name = "bf8"; };
     template <> struct t2s<int8_t> { static constexpr const char * name = "int8"; };
     // clang-format on
  
     CK_TILE_HOST static std::string GetName()
     {
 #define _SS_ std::string
 #define _TS_ std::to_string
         // clang-format off
         using S_ = BlockShape;
  
         auto prec_str = [&] () {
             std::string base_str = _SS_(t2s<ADataType>::name);
             if (!std::is_same_v<ADataType, GDataType>) {
                 base_str += _SS_("_") + _SS_(t2s<GDataType>::name);
             }
             return base_str;
         }();
  
         return _SS_("fused_moe_") + _SS_(prec_str) + "_" + (IsGateOnly ? "g1u0_":"g1u1_") +
              _TS_(S_::Block_M0) + "x" + _TS_(S_::Block_N0) + "x" + _TS_(S_::Block_K0) + "x" + _TS_(S_::Block_N1) + "_" +
              _TS_(S_::WarpPerBlock_M0) + "x" + _TS_(S_::WarpPerBlock_N0) + "x" + _TS_(S_::WarpPerBlock_K0) + "_" +
              _TS_(S_::Warp_M0) + "x" + _TS_(S_::Warp_N0) + "x" + _TS_(S_::Warp_K0) + "_" + _SS_(Pipeline::name);
 #undef _SS_
 #undef _TS_
         // clang-format on
     }
  
     struct FusedMoeGemmKargs
     {
         const void* a_ptr;              // [m, k], input token
         const void* a_scale_ptr;        // [m, 1], token scale
         const void* g_ptr;              // [e, n, k]/[e, 2*n, k], pre-shuffle([e, nr, kr, w])
         const void* d_ptr;              // [e, n, k], pre-shuffle([e, nr, kr, w])
         const void* g_scale_ptr;        // [e, 1, n], gate(up) scale
         const void* d_scale_ptr;        // [e, 1, k], down scale
         const void* y_smooth_scale_ptr; // [e, 1, n], smooth-quant-scale for 2nd gemm input
         void* o_ptr;                    // [m, k], output token
  
         const void* sorted_token_ids_ptr;
         const void* sorted_weight_ptr;
         const void* sorted_expert_ids_ptr;
         const void* num_sorted_tiles_ptr;
  
         index_t hidden_size;       // k
         index_t intermediate_size; // n / TP, for Gate/Up/Down
         index_t num_tokens;        // input number of tokens for current iteration
         index_t num_experts;       // number of groups
         index_t topk;              // need this?
  
         index_t stride_token; // for input/output, stride for each row, should >= hidden_size
     };
  
     // TODO: switch karg based on
     using Kargs = FusedMoeGemmKargs;
     using Hargs = FusedMoeGemmHostArgs;
  
     CK_TILE_HOST static constexpr Kargs MakeKargs(const Hargs& hargs)
     {
         // TODO: hargs/kargs not guranteed to be the same
         return bit_cast<Kargs>(hargs);
     }
  
     CK_TILE_HOST static constexpr auto GridSize(const Hargs& hargs)
     {
         constexpr index_t block_m = BlockShape::Block_M0;
         int max_num_tokens_padded =
             hargs.topk * hargs.num_tokens + hargs.num_experts * block_m - hargs.topk;
         // printf("xxx max_num_tokens_padded:%d\n", max_num_tokens_padded);
         return Partitioner::GridSize(max_num_tokens_padded, hargs.intermediate_size);
     }
  
     CK_TILE_HOST static constexpr auto BlockSize() { return dim3(kBlockSize); }
  
     CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize() { return Pipeline::GetSmemSize(); }
  
     CK_TILE_DEVICE void operator()(Kargs kargs) const
     {
         if constexpr(UseUK)
         {
             __shared__ CK_TILE_LDS_ADDR char smem[GetSmemSize()];
             IndexDataType num_sorted_tiles = __builtin_amdgcn_readfirstlane(
                 *reinterpret_cast<const IndexDataType*>(kargs.num_sorted_tiles_ptr));
  
             num_sorted_tiles = num_sorted_tiles / BlockShape::Block_M0;
  
             const auto [sorted_tile_id, intermediate_tile_id] =
                 Partitioner{}(num_sorted_tiles, kargs.intermediate_size);
             // if(threadIdx.x == 0)
             // printf("bid:%d,%d, num_sorted_tiles:%d, sorted_tile_id:%d(%d),
             // intermediate_tile_id:%d\n", static_cast<int>(blockIdx.x),
             //     static_cast<int>(blockIdx.y), num_sorted_tiles, sorted_tile_id, sorted_tile_id >=
             //     num_sorted_tiles? 1 : 0, intermediate_tile_id);
             if(sorted_tile_id >= num_sorted_tiles)
                 return;
  
             Pipeline{}(kargs, smem, sorted_tile_id, intermediate_tile_id);
         }
         else
         {
             // allocate LDS
             // __shared__ char smem_ptr[GetSmemSize()];
             IndexDataType num_sorted_tiles = __builtin_amdgcn_readfirstlane(
                 *reinterpret_cast<const IndexDataType*>(kargs.num_sorted_tiles_ptr));
             constexpr index_t hidden_radio_0 = IsGateOnly ? 1 : 2;
  
             index_t nr_0 = kargs.intermediate_size / BlockShape::Block_Nr0;
             index_t kr_0 = kargs.hidden_size / BlockShape::Block_Kr0;
             index_t nr_1 = kargs.hidden_size / BlockShape::Block_Nr1; // should be same as kr_0
             index_t kr_1 =
                 kargs.intermediate_size / BlockShape::Block_Kr1; // should be same as nr_0
  
             index_t expert_stride_0 = kargs.intermediate_size * hidden_radio_0 * kargs.hidden_size;
             index_t expert_stride_1 = kargs.intermediate_size * kargs.hidden_size;
  
             __shared__ CK_TILE_LDS_ADDR ADataType smem[GetSmemSize()];
  
             // note this is in unit of tile, need multiple tile size to get the index
             const auto [sorted_tile_id, intermediate_tile_id] =
                 Partitioner{}(num_sorted_tiles, kargs.intermediate_size);
             if(sorted_tile_id >= num_sorted_tiles)
                 return;
  
             const IndexDataType expert_id =
                 __builtin_amdgcn_readfirstlane(reinterpret_cast<const IndexDataType*>(
                     kargs.sorted_expert_ids_ptr)[sorted_tile_id]);
  
             // index along intermediate_size
             // index_t hidden_idx = __builtin_amdgcn_readfirstlane(intermediate_tile_id *
             // BlockShape::Block_N0);
             index_t interm_idx_nr =
                 __builtin_amdgcn_readfirstlane(intermediate_tile_id * BlockShape::Block_Nr0);
  
             const auto a_coord = Pipeline::GetACoord(); // 2d thread offset, [i_row, i_col]
             const auto sorted_token_id =
                 a_coord[number<0>{}] + sorted_tile_id * BlockShape::Block_M0;
  
             index_t token_id =
                 reinterpret_cast<const index_t*>(kargs.sorted_token_ids_ptr)[sorted_token_id];
 #if CK_TILE_REFERENCE_MOE_SORTING_MOCK_ID
             token_id &= 0xffffff;
 #endif
             auto topk_weight = reinterpret_cast<const TopkWeightDataType*>(
                 kargs.sorted_weight_ptr)[sorted_token_id];
  
             const auto a_window = [&]() {
                 // A is already pre-padded in previous kernel
                 const ADataType* a_ptr = reinterpret_cast<const ADataType*>(kargs.a_ptr);
                 const auto a_view_     = make_naive_tensor_view<address_space_enum::global>(
                     a_ptr,
                     make_tuple(kargs.num_tokens, kargs.hidden_size),
                     make_tuple(kargs.stride_token, 1),
                     number<Pipeline::kAlignmentA>{},
                     number<1>{});
  
                 // gather is here use indexing transform
                 const auto a_gather_view_ = transform_tensor_view(
                     a_view_,
                     make_tuple(make_indexing_transform(kargs.num_tokens, token_id),
                                make_pass_through_transform(kargs.hidden_size)),
                     make_tuple(sequence<0>{}, sequence<1>{}),
                     make_tuple(sequence<0>{}, sequence<1>{}));
  
                 const auto a_window_ = make_tile_window(
                     a_gather_view_,
                     make_tuple(number<BlockShape::Block_M0>{}, number<BlockShape::Block_K0>{}),
                     {0, 0});
                 return a_window_;
             }();
  
             // TODO: gtile using NSub to have less register pressure
             const auto g_window = [&]() {
                 const GDataType* g_ptr = reinterpret_cast<const GDataType*>(kargs.g_ptr) +
                                          static_cast<long_index_t>(expert_id) * expert_stride_0 +
                                          interm_idx_nr * kr_0 * BlockShape::Block_W0;
                 const auto g_view_ = make_naive_tensor_view<address_space_enum::global>(
                     g_ptr,
                     make_tuple(nr_0, kr_0, number<BlockShape::Block_W0>{}),
                     make_tuple(kr_0 * BlockShape::Block_W0, number<BlockShape::Block_W0>{}, 1),
                     number<Pipeline::kAlignmentG>{},
                     number<1>{});
                 const auto g_view_1_ =
                     pad_tensor_view(g_view_,
                                     make_tuple(number<BlockShape::Block_Nr0>{},
                                                number<BlockShape::Block_Kr0>{},
                                                number<BlockShape::Block_W0>{}),
                                     sequence<PadIntermediateSize, PadHiddenSize, 0>{});
  
                 const auto g_window_ = make_tile_window(g_view_1_,
                                                         make_tuple(number<BlockShape::Block_Nr0>{},
                                                                    number<BlockShape::Block_Kr0>{},
                                                                    number<BlockShape::Block_W0>{}),
                                                         {0, 0, 0});
                 return g_window_;
             }();
  
             const auto d_window = [&]() {
                 const DDataType* d_ptr = reinterpret_cast<const DDataType*>(kargs.d_ptr) +
                                          static_cast<long_index_t>(expert_id) * expert_stride_1 +
                                          interm_idx_nr * BlockShape::Block_W1;
                 // note interm_idx_nr is along the gemm-k dim of 2nd gemm
  
                 const auto d_view_ = make_naive_tensor_view<address_space_enum::global>(
                     d_ptr,
                     make_tuple(nr_1, kr_1, BlockShape::Block_W1),
                     make_tuple(kr_1 * BlockShape::Block_W1, BlockShape::Block_W1, 1),
                     number<Pipeline::kAlignmentD>{},
                     number<1>{});
                 const auto d_view_1_ =
                     pad_tensor_view(d_view_,
                                     make_tuple(number<BlockShape::Block_Nr1>{},
                                                number<BlockShape::Block_Kr1>{},
                                                number<BlockShape::Block_W1>{}),
                                     sequence<PadHiddenSize, PadIntermediateSize, 0>{});
  
                 const auto d_window_ = make_tile_window(d_view_1_,
                                                         make_tuple(number<BlockShape::Block_Nr1>{},
                                                                    number<BlockShape::Block_Kr1>{},
                                                                    number<BlockShape::Block_W1>{}),
                                                         {0, 0, 0});
                 return d_window_;
             }();
  
             auto o_window = [&]() {
                 ODataType* o_ptr = reinterpret_cast<ODataType*>(kargs.o_ptr);
                 auto o_view_     = make_naive_tensor_view<address_space_enum::global,
                                                           memory_operation_enum::atomic_add>(
                     o_ptr,
                     make_tuple(kargs.num_tokens, kargs.hidden_size),
                     make_tuple(kargs.stride_token, 1),
                     number<Pipeline::kAlignmentO>{},
                     number<1>{});
  
                 // gather is here
                 auto o_scatter_view_ = transform_tensor_view(
                     o_view_,
                     make_tuple(make_indexing_transform(kargs.num_tokens, token_id),
                                make_pass_through_transform(kargs.hidden_size)),
                     make_tuple(sequence<0>{}, sequence<1>{}),
                     make_tuple(sequence<0>{}, sequence<1>{}));
  
                 auto o_window_ = make_tile_window(
                     o_scatter_view_,
                     make_tuple(number<BlockShape::Block_M1>{}, number<BlockShape::Block_N1>{}),
                     {0, 0});
                 return o_window_;
             }();
  
             // do compute yeah
             Pipeline{}(a_window,
                        g_window,
                        d_window,
                        o_window,
                        topk_weight,
                        smem,
                        kargs.hidden_size,
                        kargs.intermediate_size,
                        kargs.stride_token);
         }
     }
 };
  
 } // namespace ck_tile