add files , build and run ok

example/65_gemm_multiply_multiply/CMakeLists.txt

@@ -5,4 +5,5 @@ add_example_executable(example_gemm_multiply_multiply_xdl_fp8_bpreshuffle gemm_m
 # target_compile_options(example_gemm_multiply_multiply_xdl_fp8_bpreshuffle PRIVATE -save-temps=$PWD -Wno-gnu-line-marker)
 add_example_executable(example_gemm_add_add_xdl_fp16 gemm_add_add_xdl_fp16.cpp)
 add_example_executable(example_gemm_multiply_multiply_xdl_int8 gemm_multiply_multiply_xdl_int8.cpp)
-add_example_executable(example_moe_gemm_fp16 moe_gemm_fp16.cpp)
+add_example_executable(example_moe_gemm1 moe_gemm1.cpp)
+add_example_executable(example_moe_gemm2 moe_gemm2.cpp)

example/65_gemm_multiply_multiply/moe_gemm2.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle_v3_b_preshuffle.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle_v3.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
+
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_moe_gemm2.hpp"
+#include "ck/library/utility/check_err.hpp"
+
+#include "ck/utility/blkgemmpipe_scheduler.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F16  = ck::half_t;
+// using BF16 = ck::bhalf_t;
+using F8  = ck::f8_t;
+using F32  = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using A0DataType       = F16;
+using B0DataType       = F16;
+using AccDataType      = F32;
+using CShuffleDataType = F32;
+using D0DataType       = F32;
+using D1DataType       = F32;
+using DsDataType       = ck::Tuple<D0DataType, D1DataType>;
+using EDataType        = F16;
+
+using A0Layout = Row;
+using B0Layout = Col;
+using D0Layout = Row;
+using D1Layout = Col;
+using DsLayout = ck::Tuple<D0Layout, D1Layout>;
+using ELayout  = Row;
+
+struct MultiplyMultiply
+{
+    template <typename E, typename C, typename D0, typename D1>
+    __host__ __device__ constexpr void
+    operator()(E& e, const C& c, const D0& d0, const D1& d1) const;
+
+    template <>
+    __host__ __device__ constexpr void operator()<EDataType, float, float, float>(EDataType& e,
+                                                                            const float& c,
+                                                                            const float& d0,
+                                                                            const float& d1) const
+    {
+        // const float x0_f = c * d0 * d1;
+        const float x0_f = c;
+        // printf("epi %f\n", c);
+        e = ck::type_convert<EDataType>(x0_f);
+    }
+
+    // template <>
+    // __host__ __device__ constexpr void operator()<BF16, float, float, float>(BF16& e,
+    //                                                                          const float& c,
+    //                                                                          const float& d0,
+    //                                                                          const float& d1) const
+    // {
+    //     const float x0_f = c;
+    //     // const float x0_f = c * d0 * d1;
+
+    //     e = ck::type_convert<BF16>(x0_f);
+    // }
+};
+
+
+void preShuffleBuffer(const B0DataType* src, B0DataType* dst, int N, int K, int NXdl)
+{
+    int KPack = 16 / sizeof(B0DataType);
+    int NLane = NXdl;
+    int KLane = 64 / NLane;
+
+    int K0 = K / (KLane * KPack);
+    // K -> K0 KLane KPack
+    // N -> N0 NLane
+    // N, K -> N0 K0 KLane NLane KPack
+    int tempk;
+    for(int n = 0; n < N; ++n)
+    {
+        for(int k = 0; k < K; ++k)
+        {
+            int n0 = n / NLane;
+            int n1 = n % NLane;
+
+            int k0 = k / (KLane * KPack);
+            tempk  = k % (KLane * KPack);
+            int k1 = tempk / KPack;
+            int k2 = tempk % KPack;
+
+            int outputIndex = n0 * KPack * NLane * KLane * K0 + k0 * KPack * NLane * KLane +
+                              k1 * KPack * NLane + n1 * KPack + k2;
+
+            dst[outputIndex] = src[n * K + k];
+        }
+    }
+}
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+using CDEElementOp = MultiplyMultiply;
+
+static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
+static constexpr ck::index_t MPerBlock = 32;
+static constexpr ck::index_t KPerBlock = 256 / sizeof(A0DataType);
+static constexpr ck::index_t MXDLPerWave = MPerBlock / 32; //todo fix this constraint
+static constexpr ck::index_t AK1 = 16 / sizeof(A0DataType);
+static constexpr ck::index_t BK1 = 16 / sizeof(B0DataType);
+static constexpr ck::index_t EVec = 16 / sizeof(EDataType);
+// using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultiD_Xdl_CShuffle_V3
+using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle
+    // clang-format off
+///######|  ALayout|  BLayout| DsLayout| ELayout|      AData|      BData|     DsData|     EData|     AccData|         CShuffle|           A|           B|          CDE|           GEMM| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
+///######|         |         |         |        |       Type|       Type|       Type|      Type|        Type|         DataType| Elementwise| Elementwise|  Elementwise| Spacialization|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
+///######|         |         |         |        |           |           |           |          |            |                 |   Operation|   Operation|    Operation|               |      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
+///######|         |         |         |        |           |           |           |          |            |                 |            |            |             |               |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |    S<C, D0, D1>|
+///###### RCR
+        // kernel 1: 256->32x128x128 
+        // <      Row,      Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp, CDEElementOp,       GemmSpec,   256,   32,   128,    128,  16,  16,  32,   32,    1,    1,     S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,             16,             16,          0,     S<8, 32, 1>,    S<1, 0, 2>,     S<1, 0, 2>,             2,              16,             16,          0,          1,           1,               S<1, 32, 1, 8>,      S<8, 8, 1>,  ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v1, EDataType>;
+        // <      Row,      Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp, CDEElementOp,       GemmSpec,   256,   32,   128,    256,  16,  16,  32,   32,    1,    1,     S<16, 16, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,             16,             16,          0,     S<16, 16, 1>,    S<1, 0, 2>,     S<1, 0, 2>,             2,              16,             16,          0,          1,           1,               S<1, 32, 1, 8>,      S<8, 8, 1>,  ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v3, EDataType>;
+        <      Row,      Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,
+               AElementOp,  BElementOp, CDEElementOp,       GemmSpec,   
+               //threadnum, mblock, nblock, kblock
+               256,   MPerBlock,   128,    KPerBlock,
+               // ak1, bk1
+               AK1,   BK1,
+               // mn_perxdl
+               32,   32,
+               // mn_xdlperwave 
+               MXDLPerWave,    1,
+               // a,b: loadtranfer cluster, cluster order, srcorder,VECDIM, srcpervec, dstpervec, lds_extra
+            //    S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0,
+            //    S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0,
+               S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, AK1, AK1, 0,
+               S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, AK1, AK1, 0,
+               //    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
+               //    MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
+                //  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
+               1,    1,   S<1, 32, 1, 8>, S<EVec, EVec, 1>,
+               ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v1, A0DataType>;
+        // kernel 2: 128->32x128x128
+        //  <      Row,      Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp, CDEElementOp,       GemmSpec,   128,   32,   128,    128,  16,  16,  32,   32,    1,    2,     S<8, 16, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,             16,             16,          0,     S<8, 16, 1>,    S<1, 0, 2>,     S<1, 0, 2>,             2,              16,             16,          0,          1,           1,               S<1, 16, 1, 8>,      S<8, 8, 1>,  ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v1, EDataType>;
+
+// clang-format on
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = true;
+
+// tokens = 1
+// topk = 1
+// experts = 8
+// per expert: 
+    // GEMM shape
+    ck::index_t N = 128;
+    ck::index_t K = 1024;
+    ck::index_t experts = 1;
+    ck::index_t sorted_tile_num = 1;
+    ck::index_t sorted_tile_size = MPerBlock;
+    ck::index_t SORTED_SIZE = sorted_tile_num * sorted_tile_size;
+    ck::index_t tokens = 1;
+
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 6)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+        N = std::stoi(argv[4]);
+        K = std::stoi(argv[5]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=no, 1=yes)\n");
+        printf(
+            "arg4 to 5: N, K\n");
+        exit(0);
+    }
+
+    ck::index_t StrideA = K;
+    ck::index_t StrideB = K;
+    ck::index_t StrideD = 0;
+    ck::index_t StrideE = N;
+
+    ck::index_t KBatch = 1;
+
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            using namespace ck::literals;
+
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+    // const ck::index_t experts = 8;
+    Tensor<ck::index_t> expert_ids(HostTensorDescriptor({experts}, {1}));
+    Tensor<ck::index_t> sorted_token_ids(HostTensorDescriptor({SORTED_SIZE}, {1}));
+    for (int i = 0; i < sorted_tile_num; i++) {
+        expert_ids.mData[i] = i;
+    }
+    int token_per_tile = tokens / sorted_tile_num;
+    int tokenid = 0;
+    // sorted_token_ids.mData[0] = 0;
+    for (int i = 0; i < SORTED_SIZE; i++) {
+        int tile_off = i % sorted_tile_size;
+        if(tile_off < token_per_tile)
+            sorted_token_ids.mData[i] = tokenid++;
+        else
+            sorted_token_ids.mData[i] = tokens;
+    }
+    expert_ids.savetxt("expert_ids.txt", "int");
+    sorted_token_ids.savetxt("sorted_token_ids.txt", "int");
+    Tensor<A0DataType> a0_m_k(HostTensorDescriptor({SORTED_SIZE, K}, {K, 1}));
+    Tensor<B0DataType> b0_e_n_k(HostTensorDescriptor({experts, N, K}, {N*K, K, 1}));
+    Tensor<B0DataType> b0_preshuffled(HostTensorDescriptor({experts, N, K}, {N*K, K, 1}));
+    // Tensor<B0DataType> b0_e_n_k(f_host_tensor_descriptor(K, N * experts, StrideB, B0Layout{}));
+    // Tensor<B0DataType> b0_preshuffled(
+    //     f_host_tensor_descriptor(K, N, StrideB, B0Layout{})); // use laout only for size
+    Tensor<D0DataType> d0_t_n(f_host_tensor_descriptor(tokens, N, StrideD, D0Layout{}));
+    Tensor<D1DataType> d1_t_n(f_host_tensor_descriptor(tokens, N, StrideD, D1Layout{}));
+    Tensor<EDataType> e_t_n_host_result(HostTensorDescriptor({tokens, N}, {N, 1}));
+    Tensor<EDataType> e_t_n_device_result(HostTensorDescriptor({tokens, N}, {N, 1}));
+
+    std::cout << "a0_m_k: " << a0_m_k.mDesc << std::endl;
+    std::cout << "b0_e_n_k: " << b0_e_n_k.mDesc << std::endl;
+    std::cout << "d1_t_n: " << d1_t_n.mDesc << std::endl;
+    std::cout << "d0_t_n: " << d0_t_n.mDesc << std::endl;
+    std::cout << "e_t_n: " << e_t_n_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a0_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 2});
+        b0_e_n_k.GenerateTensorValue(GeneratorTensor_2<B0DataType>{0, 2});
+        d0_t_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-2, 2});
+        d1_t_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-2, 2});
+        break;
+    case 2:
+        a0_m_k.GenerateTensorValue(GeneratorTensor_1<A0DataType>{});
+        b0_e_n_k.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        d0_t_n.GenerateTensorValue(GeneratorTensor_1<D0DataType>{});
+        d1_t_n.GenerateTensorValue(GeneratorTensor_1<D1DataType>{});
+        break;
+    default:
+        a0_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
+        b0_e_n_k.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
+        d0_t_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
+        d1_t_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
+    }
+    DeviceMem sorted_token_ids_dev(sizeof(ck::index_t) * sorted_token_ids.mDesc.GetElementSpaceSize());
+    DeviceMem expert_ids_dev(sizeof(ck::index_t) * expert_ids.mDesc.GetElementSpaceSize());
+    DeviceMem a0_device_buf(sizeof(A0DataType) * a0_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b0_device_buf(sizeof(B0DataType) * b0_e_n_k.mDesc.GetElementSpaceSize());
+    DeviceMem d0_device_buf(sizeof(D0DataType) * d0_t_n.mDesc.GetElementSpaceSize());
+    DeviceMem d1_device_buf(sizeof(D1DataType) * d1_t_n.mDesc.GetElementSpaceSize());
+    DeviceMem e_device_buf(sizeof(EDataType) * e_t_n_device_result.mDesc.GetElementSpaceSize());
+    a0_m_k.savetxt("a.txt");
+    sorted_token_ids_dev.ToDevice(sorted_token_ids.mData.data());
+    expert_ids_dev.ToDevice(expert_ids.mData.data());
+    a0_device_buf.ToDevice(a0_m_k.mData.data());
+    d0_device_buf.ToDevice(d0_t_n.mData.data());
+    d1_device_buf.ToDevice(d1_t_n.mData.data());
+    e_device_buf.ToDevice(e_t_n_device_result.mData.data());
+
+    auto a_element_op   = AElementOp{};
+    auto b_element_op   = BElementOp{};
+    auto cde_element_op = CDEElementOp{};
+
+    constexpr ck::index_t NumDTensor = DsDataType::Size();
+
+    constexpr auto I0 = ck::Number<0>{};
+
+    // do GEMM
+    auto device_op = DeviceOpInstance{};
+
+    int NPerXdl = device_op.GetPreShuffleParameters();
+
+    preShuffleBuffer(b0_e_n_k.mData.data(), b0_preshuffled.mData.data(), N * experts, K, NPerXdl);
+
+    b0_device_buf.ToDevice(b0_preshuffled.mData.data());
+
+    auto invoker = device_op.MakeInvoker();
+    auto argument =
+        device_op.MakeArgument(sorted_token_ids_dev.GetDeviceBuffer(),
+                                expert_ids_dev.GetDeviceBuffer(),
+                                a0_device_buf.GetDeviceBuffer(),
+                               b0_device_buf.GetDeviceBuffer(),
+                               std::array<const void*, NumDTensor>{d0_device_buf.GetDeviceBuffer(),
+                                                                   d1_device_buf.GetDeviceBuffer()},
+                               e_device_buf.GetDeviceBuffer(),
+                               tokens,
+                               SORTED_SIZE,
+                               N,
+                               K,
+                               StrideA,
+                               StrideB,
+                               std::array<ck::index_t, NumDTensor>{I0, I0},
+                               StrideE,
+                               KBatch,
+                               a_element_op,
+                               b_element_op,
+                               cde_element_op);
+
+    if(!device_op.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_gemm with the specified compilation parameters does "
+            "not support this GEMM problem");
+    }
+    if (time_kernel) {
+        float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+        std::size_t flop = std::size_t(2) * SORTED_SIZE * N * K;
+        std::size_t num_btype =
+            sizeof(A0DataType) * SORTED_SIZE * K + sizeof(B0DataType) * K * N * experts + sizeof(EDataType) * SORTED_SIZE * N;
+
+        float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+        float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+        std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+                << std::endl;
+    }
+
+    if(do_verification)
+    {
+        invoker.Run(argument, StreamConfig{nullptr, false, 0 ,0,1});
+
+        e_device_buf.FromDevice(e_t_n_device_result.mData.data());
+
+        Tensor<CShuffleDataType> c_t_n({tokens, N});
+
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceMoeGemm2<A0DataType,
+                                                                                B0DataType,
+                                                                                CShuffleDataType,
+                                                                                AccDataType,
+                                                                                PassThrough,
+                                                                                PassThrough,
+                                                                                PassThrough>;
+        auto ref_moe_gemm           = ReferenceGemmInstance{};
+        auto ref_invoker            = ref_moe_gemm.MakeInvoker();
+
+        auto ref_argument = ref_moe_gemm.MakeArgument(
+           sorted_token_ids, expert_ids, sorted_tile_size, a0_m_k, b0_e_n_k, c_t_n, PassThrough{}, PassThrough{}, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+        for(int m = 0; m < SORTED_SIZE; ++m)
+        {
+            
+            const int t = sorted_token_ids(m);
+            for(int n = 0; n < N; ++n)
+            {
+                cde_element_op(e_t_n_host_result(t, n), c_t_n(t, n), d0_t_n(t, n), d1_t_n(t, n));
+            }
+        }
+
+        e_device_buf.FromDevice(e_t_n_device_result.mData.data());
+        e_t_n_device_result.savetxt("out.txt");
+        e_t_n_host_result.savetxt("ref.txt");
+        return ck::utils::check_err(
+                   e_t_n_device_result, e_t_n_host_result, "Error: Incorrect results!", 1e-3, 5e-2)
+                   ? 0
+                   : 1;
+    }
+
+    return 0;
+}

include/ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v7r3.hpp

@@ -48,6 +48,7 @@ struct ThreadGroupTensorSliceTransfer_v7r3
     static constexpr index_t nDim =
         remove_cvref_t<tuple_element_t<0, SrcDescs>>::GetNumOfDimension();
 
+    static constexpr index_t mod_num = ThreadClusterLengths{}.At( Number<3>{}); // Dirty HACK FELIX, TODO fix
     static constexpr index_t nSrc = remove_cvref_t<SrcDescs>::Size();
     static constexpr index_t nDst = remove_cvref_t<DstDescs>::Size();
 
@@ -101,7 +102,7 @@ struct ThreadGroupTensorSliceTransfer_v7r3
            ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
         {
             const auto thread_cluster_idx = thread_cluster_desc_.CalculateBottomIndex(
-                make_multi_index(ThreadGroup::GetThreadId()));
+                make_multi_index(ThreadGroup::GetThreadId() % mod_num));
 
             const auto thread_data_idx_begin = thread_cluster_idx * thread_slice_lengths;
 

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v3_multi_d_b_preshuffle.hpp

@@ -9,7 +9,7 @@
 #include "ck/tensor_description/tensor_descriptor_helper.hpp"
 #include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
 #include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_b_preshuffle_selector.hpp"
-#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1_mod8.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
 #include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
 #include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
@@ -1109,12 +1109,12 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
     {
         ignore                           = b_element_op;
         const auto a_grid_desc_ak0_m_ak1 = MakeAGridDescriptor_AK0_M_AK1(
-            problem.NumTokens, problem.MPadded, problem.K, problem.KPadded, problem.StrideA, problem.AK0);
+            problem.M, problem.MPadded, problem.K, problem.KPadded, problem.StrideA, problem.AK0);
 
         const auto b_grid_desc_bpreshuffled =
             MakeBGridDescriptor_Preshuffled(problem.BN0Shuffled, problem.BK0Shuffled);
         const auto c_grid_desc_m_n = MakeCGridDescriptor_M_N<CLayout>(
-            problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideC);
+            problem.NumTokens, problem.MPadded, problem.N, problem.NPadded, problem.StrideC);
         // printf("tido %d size %d %d MNBLOCK %d %d %d %d\n", threadIdx.x, problem.StrideC, c_grid_desc_m_n.GetElementSpaceSize(),
         // problem.MBlock, problem.NBlock, MPerBlock, NPerBlock);
         const auto c_grid_desc_mblock_mperblock_nblock_nperblock =
@@ -1125,19 +1125,6 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
         const index_t block_m_id = __builtin_amdgcn_readfirstlane(blockIdx.y);
         const index_t expert_id = __builtin_amdgcn_readfirstlane(p_sorted_expert_ids[block_m_id]);
         
-        // constexpr auto M0 = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I1);
-        constexpr auto AMThreads = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I1);
-        constexpr auto AK0Threads = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I0);
-        constexpr auto AK1Threads = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I2);
-        constexpr auto AKThreads = AK0Threads * AK1Threads;
-        constexpr auto AMRepeats = MPerBlock / AMThreads;
-        // static_assert(MLoadRepeats == 1, "only support 1 line per thread now!");
-        const index_t token_pos = block_m_id * MPerBlock + threadIdx.x / AKThreads * AMRepeats;
-        StaticallyIndexedArray<index_t, AMRepeats> gather_offsets; //= p_sorted_token_ids[token_pos];
-        static_for<0, AMRepeats, 1>{}([&](auto m0) {
-            gather_offsets(m0) = (p_sorted_token_ids[token_pos + m0] & 0xffffff) * problem.K;
-            // printf("init off tid %d m %d off %d\n", threadIdx.x, m0(), gather_offsets(m0));
-        });
         const index_t m_block_data_idx_on_grid =
             __builtin_amdgcn_readfirstlane(block_m_id * MPerBlock);
         const index_t expert_stride = __builtin_amdgcn_readfirstlane(problem.N * problem.K);
@@ -1153,10 +1140,6 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
         // if(threadIdx.x==0)
         // printf("tid %d eid %d expert_stride %d bufsize %d\n",
         // threadIdx.x, expert_id, expert_stride, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
-        
-        auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
-
 
         // A matrix in LDS memory, dst of blockwise copy
         constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
@@ -1166,7 +1149,7 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
         constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
         // A matrix blockwise copy
         auto a_blockwise_copy =
-            ThreadGroupTensorSliceTransfer_v4r1_mod8<ThisThreadBlock,
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
                                                 AElementwiseOperation,
                                                 ck::tensor_operation::element_wise::PassThrough,
                                                 InMemoryDataOperationEnum::Set,
@@ -1187,15 +1170,13 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
                                                 1,
                                                 AThreadTransferSrcResetCoordinateAfterRun,
                                                 true,
-                                                1,
                                                 BlockwiseGemmPipe::GlobalBufferNum>(
                 a_grid_desc_ak0_m_ak1,
                 make_multi_index(0, 0, 0),
                 a_element_op,
                 a_block_desc_ak0_m_ak1,
                 make_multi_index(0, 0, 0),
-                ck::tensor_operation::element_wise::PassThrough{},
-                gather_offsets);
+                ck::tensor_operation::element_wise::PassThrough{});
 
         // Thread-wise copy
         // K0 -> N0/NWave -> NWave -> KLane -> NLane -> KPack
@@ -1406,10 +1387,20 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
             const auto e_grid_desc_mblock_mperblock_nblock_nperblock =
                 c_grid_desc_mblock_mperblock_nblock_nperblock;
 
-            using CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock =
+            using CDEBlockTransferCluster =
                 CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock;
             const auto EGlobalMemoryDataOperation = CGlobalMemoryDataOperation;
 
+            constexpr auto EMThreads = CDEBlockTransferCluster{}.At(I0) * CDEBlockTransferCluster{}.At(I1);
+            constexpr auto EMRepeats = MPerBlock / EMThreads;
+            static_assert(EMRepeats == 1, "only support 1 line per thread now!");
+            const index_t token_pos = block_m_id * MPerBlock + threadIdx.x / EMThreads * EMRepeats;
+            StaticallyIndexedArray<index_t, EMRepeats> scatter_offsets; //= p_sorted_token_ids[token_pos];
+            static_for<0, EMRepeats, 1>{}([&](auto m0) {
+                scatter_offsets(m0) = (p_sorted_token_ids[token_pos + m0] & 0xffffff) * problem.N;
+                // printf("init off tid %d m %d off %d\n", threadIdx.x, m0(), gather_offsets(m0));
+            });
+
             auto cde_block_copy_lds_and_global = ThreadGroupTensorSliceTransfer_v7r3<
                 ThisThreadBlock,
                 decltype(container_concat(make_tuple(CShuffleDataType{}), DsDataType{})),
@@ -1423,7 +1414,7 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
                          CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
                          1,
                          CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
-                CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+                CDEBlockTransferCluster,
                 Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
                 Sequence<0, 1, 2, 3>, // typename SrcDimAccessOrder,
                 Sequence<0, 1, 2, 3>, // typename DstDimAccessOrder,
@@ -1439,9 +1430,12 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
                 {c_ds_desc_refs,
                  idx_c_ds_block_begin,
                  tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
-                 make_tuple(make_multi_index(block_m_id, 0, block_n_id, 0)),
+                 make_tuple(make_multi_index(0, 0, block_n_id, 0)),
                  c_element_op};
-
+            // if(threadIdx.x== 0)
+            //     printf("offset %d size %d\n", scatter_offsets(I0), c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+            auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+                p_c_grid + scatter_offsets(I0), c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize() - scatter_offsets(I0));
             // space filling curve for threadwise C in VGPR
             constexpr auto sfc_c_vgpr =
                 SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,

library/include/ck/library/reference_tensor_operation/cpu/reference_moe_gemm2.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <sstream>
+
+#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace host {
+
+template <typename ADataType,
+          typename BDataType,
+          typename CDataType,
+          typename AccDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          typename ComputeTypeA = CDataType,
+          typename ComputeTypeB = ComputeTypeA>
+struct ReferenceMoeGemm2 : public device::BaseOperator
+{
+    // Argument
+    struct Argument : public device::BaseArgument
+    {
+        Argument(const Tensor<ck::index_t>& sorted_token_ids,
+                 const Tensor<ck::index_t>& expert_ids,
+                 const index_t sorted_tile_size,
+                 const Tensor<ADataType>& a_m_k,
+                 const Tensor<BDataType>& b_e_n_k,
+                 Tensor<CDataType>& c_t_n,
+                 AElementwiseOperation a_element_op,
+                 BElementwiseOperation b_element_op,
+                 CElementwiseOperation c_element_op)
+            : sorted_token_ids_{sorted_token_ids},
+              expert_ids_{expert_ids},
+              sorted_tile_size_{sorted_tile_size},
+              a_m_k_{a_m_k},
+              b_e_n_k_{b_e_n_k},
+              c_t_n_{c_t_n},
+              a_element_op_{a_element_op},
+              b_element_op_{b_element_op},
+              c_element_op_{c_element_op}
+        {
+        }
+
+        const Tensor<ck::index_t>& expert_ids_;
+        const Tensor<ck::index_t>& sorted_token_ids_;
+        const Tensor<ADataType>& a_m_k_;
+        const Tensor<BDataType>& b_e_n_k_;
+        Tensor<CDataType>& c_t_n_;
+
+        AElementwiseOperation a_element_op_;
+        BElementwiseOperation b_element_op_;
+        CElementwiseOperation c_element_op_;
+        index_t sorted_tile_size_;
+    };
+
+    // Invoker
+    struct Invoker : public device::BaseInvoker
+    {
+        using Argument = ReferenceMoeGemm2::Argument;
+
+        float Run(const Argument& arg)
+        {
+            arg.c_t_n_.SetZero();
+            auto f_mk_kn_mn = [&](auto m, auto n) {
+                const int K = arg.a_m_k_.mDesc.GetLengths()[1];
+
+                AccDataType v_acc{0};
+                ComputeTypeA v_a{0};
+                ComputeTypeB v_b{0};
+                const int t = arg.sorted_token_ids_(m);
+                const int e = arg.expert_ids_(m / arg.sorted_tile_size_);
+                const int token_cnt = arg.a_m_k_.mDesc.GetLengths()[0];
+                if(t < token_cnt) {
+                    for(int k = 0; k < K; ++k)
+                    {
+                        // use PassThrough instead of ConvertBF16RTN for reference calculation
+                        if constexpr(is_same_v<AElementwiseOperation,
+                                            ck::tensor_operation::element_wise::ConvertBF16RTN>)
+                        {
+                            ck::tensor_operation::element_wise::PassThrough{}(v_a, arg.a_m_k_(m, k));
+                        }
+                        else
+                        {
+                            arg.a_element_op_(v_a, arg.a_m_k_(m, k));
+                        }
+                        // same for B matrix
+                        if constexpr(is_same_v<BElementwiseOperation,
+                                            ck::tensor_operation::element_wise::ConvertBF16RTN>)
+                        {
+                            ck::tensor_operation::element_wise::PassThrough{}(v_b, arg.b_e_n_k_(e, n, k));
+                        }
+                        else
+                        {
+                            arg.b_element_op_(v_b, arg.b_e_n_k_(e, n, k));
+                        }
+
+                        v_acc +=
+                            ck::type_convert<AccDataType>(v_a) * ck::type_convert<AccDataType>(v_b);
+                    }
+                }
+
+                CDataType v_c{0};
+
+                arg.c_element_op_(v_c, v_acc);
+
+                arg.c_t_n_(t, n) += v_c;
+            };
+            make_ParallelTensorFunctor(
+                f_mk_kn_mn, arg.c_t_n_.mDesc.GetLengths()[0], arg.c_t_n_.mDesc.GetLengths()[1])(
+                std::thread::hardware_concurrency());
+
+            return 0;
+        }
+
+        float Run(const device::BaseArgument* p_arg,
+                  const StreamConfig& /* stream_config */ = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg));
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
+
+    static auto MakeArgument(const Tensor<ck::index_t>& sorted_token_ids,
+                             const Tensor<ck::index_t>& expert_ids,
+                             const index_t sorted_tile_size,
+                             const Tensor<ADataType>& a_m_k,
+                             const Tensor<BDataType>& b_e_n_k,
+                             Tensor<CDataType>& c_t_n,
+                             AElementwiseOperation a_element_op,
+                             BElementwiseOperation b_element_op,
+                             CElementwiseOperation c_element_op)
+    {
+        return Argument{sorted_token_ids, expert_ids, sorted_tile_size, a_m_k, b_e_n_k, c_t_n, a_element_op, b_element_op, c_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "ReferenceMoeGemm2"
+            << std::endl;
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace host
+} // namespace tensor_operation
+} // namespace ck