Initial int4 moe, compile pass, function not check.

example/01_gemm/gemm_xdl_fp8_pk_i4_bpreshuffle_v3.cpp

@@ -5,7 +5,7 @@
 
 #include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3_b_preshuffle.hpp"
 
-using F8 = ck::f8_t;
+using F8  = ck::f8_t;
 using I4  = ck::pk_i4_t;
 using F16 = ck::half_t;
 using F32 = float;

example/65_gemm_multiply_multiply/CMakeLists.txt

@@ -5,3 +5,4 @@ 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_gemm1 moe_gemm1.cpp)
 add_example_executable(example_moe_gemm2 moe_gemm2.cpp)
+add_example_executable(example_moe_pk_i4_gemm1 moe_pk_i4_gemm1.cpp)

example/65_gemm_multiply_multiply/moe_pk_i4_gemm1.cpp

@@ -0,0 +1,431 @@
+// 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_moe_gemm.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_gemm.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 I4  = ck::pk_i4_t;
+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       = F8;
+using B0DataType       = I4;
+using EDataType        = F32;
+using AccDataType      = F32;
+using CShuffleDataType = F32;
+using D0DataType       = F32;
+using D1DataType       = F32;
+using DsDataType       = ck::Tuple<D0DataType, D1DataType>;
+
+using A0Layout = Row;
+using B0Layout = Col;
+using ELayout  = Row;
+using D0Layout = Row;
+using D1Layout = Col;
+using DsLayout = ck::Tuple<D0Layout, D1Layout>;
+
+// for gate, a_scale, b_scale
+struct MulABScale
+{
+    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
+    {
+        e = ck::type_convert<EDataType>(c * d1 * d0);
+    }
+};
+
+// for gate, a_scale, b_scale, fuse silu, 
+struct MulABScaleSilu
+{
+    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>
+                                                                           (EDataType& e,
+                                                                            const float& c,
+                                                                            const float& d0,
+                                                                            const float& d1) const
+    {
+        // act
+        float x0 = 0;
+        ck::tensor_operation::element_wise::Silu{}(x0, c * d1 * d0);
+        e = ck::type_convert<EDataType>(x0);
+    }
+};
+
+// using DsLayout = DsLayoutGate;
+// using DsDataType       = DsDataTypeGate;
+using CDEElementOp = MulABScale;
+// using CDEElementOp = MulABScaleSiluMulGate;
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+
+static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
+static constexpr ck::index_t MPerBlock = 128;
+static constexpr ck::index_t MNPerXDL = 32;
+static constexpr ck::index_t CShuffleMXDLPerWave = MPerBlock / 32;
+static constexpr ck::index_t KPerBlock = 128 / 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 = 32 / sizeof(B0DataType);
+static constexpr ck::index_t EVec = 16 / sizeof(EDataType);
+static constexpr ck::index_t D0Vec = 1;
+static constexpr ck::index_t D1Vec = 1;
+
+// clang-format off
+using DeviceOpInstance = ck::tensor_operation::device::DeviceMoeGemm<
+            Row, Col, DsLayout, ELayout, 
+            A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,
+            AElementOp,  BElementOp, CDEElementOp,       GemmSpec,   
+            256,   MPerBlock,   128,    KPerBlock,
+            AK1,   BK1,
+            MNPerXDL,   MNPerXDL,
+            MXDLPerWave,    1,
+            S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, AK1, AK1, 0,
+            S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, BK1, BK1, 0,
+            CShuffleMXDLPerWave,    1,   S<1, 32, 1, 8>, S<EVec, D0Vec, D1Vec>,
+            ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v1, true, A0DataType>;
+// 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 = 6144;
+    ck::index_t K = 8192;
+    ck::index_t experts = 8;
+    ck::index_t sorted_tile_num = 8;
+    ck::index_t sorted_tile_size = MPerBlock;
+    ck::index_t SORTED_SIZE = sorted_tile_num * sorted_tile_size;
+    ck::index_t tokens = 128;
+
+    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 StrideE              = N;
+    ck::index_t batch_stride_B       = K * N;
+    constexpr ck::index_t NumDTensor = DsDataType::Size();
+    constexpr auto StrideDs = std::array<ck::index_t, NumDTensor>{0, 0};
+
+    ck::index_t KBatch = 1;
+
+    // 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_t_k(HostTensorDescriptor({tokens, 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<D0DataType> d0_t_n(HostTensorDescriptor({tokens, N}, {StrideDs[0], 0}));
+    Tensor<D1DataType> d1_e_n(HostTensorDescriptor({experts, N}, {1, StrideDs[1]}));
+    Tensor<EDataType> e_m_n_host_result(HostTensorDescriptor({SORTED_SIZE, N}, {N, 1}));
+    Tensor<EDataType> e_m_n_device_result(HostTensorDescriptor({SORTED_SIZE, N}, {N, 1}));
+
+    std::cout << "a0_t_k: " << a0_t_k.mDesc << std::endl;
+    std::cout << "b0_e_n_k: " << b0_e_n_k.mDesc << std::endl;
+    std::cout << "d1_e_n: " << d1_e_n.mDesc << std::endl;
+    std::cout << "d0_t_n: " << d0_t_n.mDesc << std::endl;
+    std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a0_t_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 2});
+        b0_e_n_k.GenerateTensorValue(GeneratorTensor_2<B0DataType>{0, 2});
+        d0_t_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{1, 3});
+        d1_e_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{1, 3});
+        break;
+    case 2:
+        a0_t_k.GenerateTensorValue(GeneratorTensor_1<A0DataType>{});
+        b0_e_n_k.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        d0_t_n.GenerateTensorValue(GeneratorTensor_1<D0DataType>{});
+        d1_e_n.GenerateTensorValue(GeneratorTensor_1<D1DataType>{});
+        break;
+    default:
+        a0_t_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_e_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
+    }
+    d0_t_n.savetxt("d0_t_n.txt", "int");
+    d1_e_n.savetxt("d1_e_n.txt", "int");
+    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_t_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_e_n.mDesc.GetElementSpaceSize());
+    DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
+    a0_t_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_t_k.mData.data());
+    d0_device_buf.ToDevice(d0_t_n.mData.data());
+    d1_device_buf.ToDevice(d1_e_n.mData.data());
+    e_device_buf.ToDevice(e_m_n_device_result.mData.data());
+
+    auto a_element_op   = AElementOp{};
+    auto b_element_op   = BElementOp{};
+    auto cde_element_op = CDEElementOp{};
+
+    // do GEMM
+    auto device_op = DeviceOpInstance{};
+
+    // preShuffleBuffer(b0_e_n_k.mData.data(), b0_preshuffled.mData.data(), N * experts, K, NPerXdl);
+    printf("Start PreShuffle\n");
+    // weight pre-shuffle
+    int KPack = 32; // int4 -> 32, fp8 -> 16, fp16 -> 8
+    int NLane = device_op.GetPreShuffleParameters();
+    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 e = 0; e < experts; ++e)
+    {
+        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;
+
+                b0_preshuffled(e * batch_stride_B + outputIndex) =
+                    b0_e_n_k(e * batch_stride_B + n * K + k);
+            }
+        }
+    }
+    printf("End PreShuffle, and Start vector permute\n");
+    // vector pk_i4x4 permute
+    for(int e = 0; e < experts; e++)
+    {
+        for(int i = 0; i < N; i++)
+        {
+            for(int j = 0; j < K; j++)
+            {
+                int input[8];
+
+                for(int k = 0; k < 4; k++)
+                {
+                    int i4x2         = b0_preshuffled(e, j + k * 2, i).data;
+                    input[k * 2 + 0] = (i4x2 >> 4) & 0xf;
+                    input[k * 2 + 1] = (i4x2 >> 0) & 0xf;
+                }
+
+                // permute 01234567->20643175
+                {
+                    int hi   = input[2];
+                    int lo   = input[0];
+                    int i4x2 = (hi << 4) | lo;
+
+                    b0_preshuffled(e, j + 0, i) = i4x2;
+                }
+
+                {
+                    int hi   = input[6];
+                    int lo   = input[4];
+                    int i4x2 = (hi << 4) | lo;
+
+                    b0_preshuffled(e, j + 2, i) = i4x2;
+                }
+
+                {
+                    int hi   = input[3];
+                    int lo   = input[1];
+                    int i4x2 = (hi << 4) | lo;
+
+                    b0_preshuffled(e, j + 4, i) = i4x2;
+                }
+
+                {
+                    int hi   = input[7];
+                    int lo   = input[5];
+                    int i4x2 = (hi << 4) | lo;
+
+                    b0_preshuffled(e, j + 6, i) = i4x2;
+                }
+            }
+        }
+    }
+
+    b0_device_buf.ToDevice(b0_preshuffled.mData.data());
+
+    printf("End Permute and Start GEMM\n");
+    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,
+                               StrideDs,
+                               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_m_n_device_result.mData.data());
+
+        Tensor<CShuffleDataType> c_m_n({SORTED_SIZE, N});
+
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceMoeGemm<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_t_k, b0_e_n_k, c_m_n, PassThrough{}, PassThrough{}, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+        for(int m = 0; m < SORTED_SIZE; ++m)
+        {
+            
+            const int t = sorted_token_ids(m);
+            const int e = expert_ids(m / sorted_tile_size);
+            for(int n = 0; n < N; ++n)
+            {
+                cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d0_t_n(t, n), d1_e_n(e, n));
+            }
+        }
+
+        e_device_buf.FromDevice(e_m_n_device_result.mData.data());
+        e_m_n_device_result.savetxt("out.txt");
+        e_m_n_host_result.savetxt("ref.txt");
+        return ck::utils::check_err(
+                   e_m_n_device_result, e_m_n_host_result, "Error: Incorrect results!", 1e-3, 5e-2)
+                   ? 0
+                   : 1;
+    }
+
+    return 0;
+}

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

@@ -83,21 +83,30 @@ struct ReferenceMoeGemm : public device::BaseOperator
                 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>)
+                        if constexpr(is_same_v<ADataType, pk_i4_t>)
                         {
-                            ck::tensor_operation::element_wise::PassThrough{}(v_a, arg.a_t_k_(t, k));
+                            uint8_t i4x2 = arg.a_t_k_(t, k).data;
+                            uint8_t i4    = 0;
+                            if(k % 2 == 1)
+                                i4 = (i4x2 >> 0) & 0xf;
+                            else
+                                i4 = (i4x2 >> 4) & 0xf;
+                            v_a = i4_to_f32_gfx9(i4);
                         }
                         else
                         {
                             arg.a_element_op_(v_a, arg.a_t_k_(t, k));
                         }
                         // same for B matrix
-                        if constexpr(is_same_v<BElementwiseOperation,
-                                            ck::tensor_operation::element_wise::ConvertBF16RTN>)
+                        if constexpr(is_same_v<BDataType, pk_i4_t>)
                         {
-                            ck::tensor_operation::element_wise::PassThrough{}(v_b, arg.b_e_n_k_(e, n, k));
+                            uint8_t i4x2 = arg.b_e_n_k_(e, k, n).data;
+                            uint8_t i4    = 0;
+                            if(k % 2 == 1)
+                                i4 = (i4x2 >> 0) & 0xf;
+                            else
+                                i4 = (i4x2 >> 4) & 0xf;
+                            v_b = i4_to_f32_gfx9(i4);
                         }
                         else
                         {
@@ -170,6 +179,28 @@ struct ReferenceMoeGemm : public device::BaseOperator
 
         return str.str();
     }
+
+    static float i4_to_f32_gfx9(uint8_t i4)
+    {
+        static std::unordered_map<uint8_t, float> u = {{0b1000, -0.5000f},
+                                                       {0b1001, -0.4375f},
+                                                       {0b1010, -0.3750f},
+                                                       {0b1011, -0.3125f},
+                                                       {0b1100, -0.2500f},
+                                                       {0b1101, -0.1875f},
+                                                       {0b1110, -0.1250f},
+                                                       {0b1111, -0.0625f},
+                                                       {0b0, +0.0000f},
+                                                       {0b1, +0.0625f},
+                                                       {0b10, +0.1250f},
+                                                       {0b11, +0.1875f},
+                                                       {0b100, +0.2500f},
+                                                       {0b101, +0.3125f},
+                                                       {0b110, +0.3750f},
+                                                       {0b111, +0.4375f}};
+
+        return u[i4];
+    }
 };
 
 } // namespace host