Gemm+layernorm instance, ckProfiler, client example (#568)

* Add gemm + layernorm instance

* Add ckProfiler

* Add test

* Add client example

* Detect if user forger to set the workrspace

* Use literal in the example

* [What] use builtin function for sqrt
[Why] compiler will not use v_sqrt_f64_e64 if we use ::sqrt()

* check gemm vaildity in IsSupportedArgument

* Add more testcases

* Merge duplicated folder in client example

* Print more infomation

* Use better kernel parameter for MS problem size

* clang format

* Add constexpr for if condition and remove redundant include

* Remove cstdlib and add constexpr

profiler/include/profiler/profile_gemm_add_relu_add_layernorm_impl.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iomanip>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d_layernorm.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/tensor_operation_instance/gpu/gemm_add_relu_add_layernorm.hpp"
+
+#include "ck/library/utility/check_err.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_gemm.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_layernorm.hpp"
+
+namespace ck {
+namespace profiler {
+
+template <typename ADataType,
+          typename BDataType,
+          typename AccDataType,
+          typename D0DataType,
+          typename D1DataType,
+          typename EMeanVarDataType,
+          typename GammaDataType,
+          typename BetaDataType,
+          typename HDataType,
+          typename AElementOp,
+          typename BElementOp,
+          typename CDEElementOp,
+          typename HElementOp>
+void host_gemm_layernorm(Tensor<HDataType>& h_m_n,
+                         const Tensor<ADataType>& a_m_k,
+                         const Tensor<BDataType>& b_k_n,
+                         const Tensor<D0DataType>& d0_m_n,
+                         const Tensor<D1DataType>& d1_m_n,
+                         const Tensor<GammaDataType>& gamma_n,
+                         const Tensor<BetaDataType>& beta_n,
+                         AElementOp a_element_op,
+                         BElementOp b_element_op,
+                         CDEElementOp cde_element_op,
+                         HElementOp h_element_op,
+                         int M,
+                         int N,
+                         AccDataType epsilon = 1e-5)
+{
+    using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+    using ReferenceGemm = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                    BDataType,
+                                                                    AccDataType,
+                                                                    AccDataType,
+                                                                    AElementOp,
+                                                                    BElementOp,
+                                                                    PassThrough>;
+
+    using ReferenceLayernorm = ck::tensor_operation::host::ReferenceLayernorm<EMeanVarDataType,
+                                                                              GammaDataType,
+                                                                              BetaDataType,
+                                                                              HDataType,
+                                                                              AccDataType,
+                                                                              HElementOp,
+                                                                              2,
+                                                                              1>;
+
+    Tensor<EMeanVarDataType> e_m_n(HostTensorDescriptor{M, N});
+    Tensor<AccDataType> c_m_n(HostTensorDescriptor{M, N});
+
+    auto ref_gemm         = ReferenceGemm{};
+    auto ref_gemm_invoker = ref_gemm.MakeInvoker();
+
+    auto ref_gemm_argument =
+        ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
+
+    ref_gemm_invoker.Run(ref_gemm_argument);
+
+    for(int n = 0; n < N; ++n)
+    {
+        for(int m = 0; m < M; ++m)
+        {
+            AccDataType e  = static_cast<AccDataType>(e_m_n(m, n));
+            AccDataType d0 = static_cast<AccDataType>(d0_m_n(m, n));
+            AccDataType d1 = static_cast<AccDataType>(d1_m_n(m, n));
+            cde_element_op(e, c_m_n(m, n), d0, d1);
+            e_m_n(m, n) = static_cast<EMeanVarDataType>(e);
+        }
+    }
+
+    ReferenceLayernorm ref_layernorm;
+    auto ref_layernorm_invoker = ref_layernorm.MakeInvoker();
+
+    auto ref_layernorm_argument = ref_layernorm.MakeArgument(
+        e_m_n, gamma_n, beta_n, h_m_n, h_element_op, {M, N}, {1}, epsilon);
+    ref_layernorm_invoker.Run(ref_layernorm_argument);
+}
+
+template <typename ADataType,
+          typename BDataType,
+          typename AccDataType,
+          typename D0DataType,
+          typename D1DataType,
+          typename EMeanVarDataType,
+          typename GammaDataType,
+          typename BetaDataType,
+          typename HDataType,
+          typename ALayout,
+          typename BLayout,
+          typename D0Layout,
+          typename D1Layout,
+          typename HLayout>
+bool profile_gemm_add_relu_add_layernorm_impl(int do_verification,
+                                              int init_method,
+                                              bool /*do_log*/,
+                                              bool time_kernel,
+                                              int M,
+                                              int N,
+                                              int K,
+                                              int StrideA,
+                                              int StrideB,
+                                              int StrideD0,
+                                              int StrideD1,
+                                              int StrideH,
+                                              AccDataType epsilon = 1e-5)
+{
+    auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
+        return HostTensorDescriptor({len}, {stride});
+    };
+
+    auto f_host_tensor_descriptor2d =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            using namespace ck::literals;
+
+            if constexpr(std::is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+    Tensor<ADataType> a_m_k(f_host_tensor_descriptor2d(M, K, StrideA, ALayout{}));
+    Tensor<BDataType> b_k_n(f_host_tensor_descriptor2d(K, N, StrideB, BLayout{}));
+    Tensor<D1DataType> d0_m_n(f_host_tensor_descriptor2d(M, N, StrideD0, D0Layout{}));
+    Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor2d(M, N, StrideD1, D1Layout{}));
+    Tensor<GammaDataType> gamma_n(f_host_tensor_descriptor1d(N, 1));
+    Tensor<BetaDataType> beta_n(f_host_tensor_descriptor1d(N, 1));
+    Tensor<HDataType> h_m_n(f_host_tensor_descriptor2d(M, N, StrideH, HLayout{}));
+    Tensor<HDataType> h_m_n_host(f_host_tensor_descriptor2d(M, N, StrideH, HLayout{}));
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-1, 1});
+        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-1, 1});
+        d0_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{-1, 1});
+        d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{-1, 1});
+        gamma_n.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{-1, 1});
+        beta_n.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{-1, 1});
+        break;
+    }
+
+    using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+    using AddReluAdd  = ck::tensor_operation::element_wise::AddReluAdd;
+
+    using AElementOp   = PassThrough;
+    using BElementOp   = PassThrough;
+    using CDEElementOp = AddReluAdd;
+    using HElementOp   = PassThrough;
+
+    const auto a_element_op   = AElementOp{};
+    const auto b_element_op   = BElementOp{};
+    const auto cde_element_op = CDEElementOp{};
+    const auto h_element_op   = HElementOp{};
+
+    using DeviceOp = ck::tensor_operation::device::DeviceGemmMultipleDLayernorm<
+        ALayout,
+        BLayout,
+        ck::Tuple<D0Layout, D1Layout>,
+        HLayout,
+        ADataType,
+        BDataType,
+        ck::Tuple<D0DataType, D1DataType>,
+        GammaDataType,
+        BetaDataType,
+        HDataType,
+        ck::tensor_operation::element_wise::PassThrough,
+        ck::tensor_operation::element_wise::PassThrough,
+        ck::tensor_operation::element_wise::AddReluAdd,
+        ck::tensor_operation::element_wise::PassThrough>;
+
+    // get device op instances
+    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
+        DeviceOp>::GetInstances();
+
+    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
+
+    // run reference
+    if(do_verification)
+    {
+        host_gemm_layernorm<ADataType,
+                            BDataType,
+                            AccDataType,
+                            D0DataType,
+                            D1DataType,
+                            EMeanVarDataType,
+                            GammaDataType,
+                            BetaDataType,
+                            HDataType>(h_m_n_host,
+                                       a_m_k,
+                                       b_k_n,
+                                       d0_m_n,
+                                       d1_m_n,
+                                       gamma_n,
+                                       beta_n,
+                                       a_element_op,
+                                       b_element_op,
+                                       cde_element_op,
+                                       h_element_op,
+                                       M,
+                                       N,
+                                       epsilon);
+    }
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem d0_m_n_device_buf(sizeof(D0DataType) * d0_m_n.mDesc.GetElementSpaceSize());
+    DeviceMem d1_m_n_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpaceSize());
+    DeviceMem gamma_device_buf(sizeof(GammaDataType) * gamma_n.mDesc.GetElementSpaceSize());
+    DeviceMem beta_device_buf(sizeof(BetaDataType) * beta_n.mDesc.GetElementSpaceSize());
+    DeviceMem h_device_buf(sizeof(HDataType) * h_m_n.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a_m_k.mData.data());
+    b_device_buf.ToDevice(b_k_n.mData.data());
+    d0_m_n_device_buf.ToDevice(d0_m_n.mData.data());
+    d1_m_n_device_buf.ToDevice(d1_m_n.mData.data());
+    gamma_device_buf.ToDevice(gamma_n.mData.data());
+    beta_device_buf.ToDevice(beta_n.mData.data());
+
+    std::string best_op_name;
+    float best_ave_time   = std::numeric_limits<float>::max();
+    float best_gb_per_sec = 0;
+
+    bool pass      = true;
+    int num_kernel = 0;
+
+    // profile device operation instances
+    for(auto& op_ptr : op_ptrs)
+    {
+        auto argument_ptr = op_ptr->MakeArgumentPointer(
+            a_device_buf.GetDeviceBuffer(),
+            b_device_buf.GetDeviceBuffer(),
+            {d0_m_n_device_buf.GetDeviceBuffer(), d1_m_n_device_buf.GetDeviceBuffer()},
+            gamma_device_buf.GetDeviceBuffer(),
+            beta_device_buf.GetDeviceBuffer(),
+            h_device_buf.GetDeviceBuffer(),
+            M,
+            N,
+            K,
+            StrideA,
+            StrideB,
+            {StrideD0, StrideD1},
+            StrideH,
+            epsilon,
+            a_element_op,
+            b_element_op,
+            cde_element_op,
+            h_element_op);
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            ++num_kernel;
+
+            size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
+            DeviceMem workspace_dev(workspace_sz);
+            op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
+
+            // re-init E to zero before profiling a kernel
+            h_device_buf.SetZero();
+
+            float ave_time =
+                invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
+
+            std::size_t num_byte =
+                sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
+                (sizeof(D0DataType) + sizeof(D1DataType) + sizeof(HDataType)) * M * N +
+                (sizeof(GammaDataType) + sizeof(BetaDataType)) * N;
+
+            float gb_per_sec = num_byte / 1.E6 / ave_time;
+
+            if(time_kernel)
+                std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec
+                          << " GB/s, " << op_name << std::endl;
+
+            if(ave_time < best_ave_time)
+            {
+                best_op_name    = op_name;
+                best_ave_time   = ave_time;
+                best_gb_per_sec = gb_per_sec;
+            }
+
+            if(do_verification)
+            {
+                h_device_buf.FromDevice(h_m_n.mData.data());
+
+                pass = pass && ck::utils::check_err(
+                                   h_m_n, h_m_n_host, "Error: Incorrect results h_m_n", 1e-2, 1e-2);
+            }
+        }
+        else
+        {
+            if(time_kernel)
+                std::cout << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    if(num_kernel == 0)
+    {
+        std::cout << "Error: No kernel is applicable" << std::endl;
+        pass = false;
+    }
+    else
+    {
+        if(time_kernel)
+            std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
+                      << best_op_name << std::endl;
+    }
+
+    return pass;
+}
+
+} // namespace profiler
+} // namespace ck

profiler/src/CMakeLists.txt

@@ -8,6 +8,7 @@ set(PROFILER_SOURCES
     profile_gemm_add_add_fastgelu.cpp
     profile_gemm_add_multiply.cpp
     profile_gemm_add_fastgelu.cpp
+    profile_gemm_add_relu_add_layernorm.cpp
     profile_gemm_fastgelu.cpp
     profile_gemm_reduce.cpp
     profile_batched_gemm.cpp
@@ -43,6 +44,7 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_add_fastgel
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_multiply_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_fastgelu_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_fastgelu_instance)
+target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_relu_add_layernorm_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_reduce_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_bias_add_reduce_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_instance)
@@ -66,5 +68,4 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_instan
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_softmax_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_reduce_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batchnorm_instance)
-
 rocm_install(TARGETS ${PROFILER_EXECUTABLE} COMPONENT profiler)

profiler/src/profile_gemm_add_relu_add_layernorm.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "profiler/profile_gemm_add_relu_add_layernorm_impl.hpp"
+#include "profiler_operation_registry.hpp"
+
+#define OP_NAME "gemm_add_relu_add_layernorm"
+#define OP_DESC "GEMM+Add+Relu+Add+Layernorm"
+
+int profile_gemm_add_relu_add_layernorm(int argc, char* argv[])
+{
+    enum struct MatrixLayout
+    {
+        MK_KN_MN_MN_MN, // 0
+        MK_NK_MN_MN_MN, // 1
+        KM_KN_MN_MN_MN, // 2
+        KM_NK_MN_MN_MN, // 3
+    };
+
+    enum struct MatrixDataType
+    {
+        F32,  // 0
+        F16,  // 1
+        BF16, // 2
+    };
+
+    if(argc != 16)
+    {
+        // clang-format off
+        printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
+        printf("arg2: data type (0: fp32; 1: fp16; 2: bf16)\n");
+        printf("arg3: matrix layout (0: H[m, n] = Layernorm(Relu(A[m, k] * B[k, n] + D0[m, n]) + D1[m, n]);\n");
+        printf("                     1: H[m, n] = Layernorm(Relu(A[m, k] * B[n, k] + D0[m, n]) + D1[m, n]);\n");
+        printf("                     2: H[m, n] = Layernorm(Relu(A[k, m] * B[k, n] + D0[m, n]) + D1[m, n]);\n");
+        printf("                     3: H[m, n] = Layernorm(Relu(A[k, m] * B[n, k] + D0[m, n]) + D1[m, n]))\n");
+        printf("arg4: verification (0: no; 1: yes)\n");
+        printf("arg5: initialization (0: no init; 1: decimal value)\n");
+        printf("arg6: print tensor value (0: no; 1: yes)\n");
+        printf("arg7: time kernel (0=no, 1=yes)\n");
+        printf("arg8 to 15: M, N, K, StrideA, StrideB, StrideD0, StrideD1, StrideH\n");
+        // clang-format on
+        exit(1);
+    }
+
+    const auto data_type       = static_cast<MatrixDataType>(std::stoi(argv[2]));
+    const auto layout          = static_cast<MatrixLayout>(std::stoi(argv[3]));
+    const bool do_verification = std::stoi(argv[4]);
+    const int init_method      = std::stoi(argv[5]);
+    const bool do_log          = std::stoi(argv[6]);
+    const bool time_kernel     = std::stoi(argv[7]);
+
+    const int M = std::stoi(argv[8]);
+    const int N = std::stoi(argv[9]);
+    const int K = std::stoi(argv[10]);
+
+    const int StrideA  = std::stoi(argv[11]);
+    const int StrideB  = std::stoi(argv[12]);
+    const int StrideD0 = std::stoi(argv[13]);
+    const int StrideD1 = std::stoi(argv[14]);
+    const int StrideH  = std::stoi(argv[15]);
+
+    using F16 = ck::half_t;
+    using F32 = float;
+
+    using Row = ck::tensor_layout::gemm::RowMajor;
+    using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+    auto profile = [&](auto a_type,
+                       auto b_type,
+                       auto acc_type,
+                       auto d0_type,
+                       auto d1_type,
+                       auto e_mean_var_type,
+                       auto gamma_type,
+                       auto beta_type,
+                       auto h_type,
+                       auto a_layout,
+                       auto b_layout,
+                       auto d0_layout,
+                       auto d1_layout,
+                       auto h_layout) {
+        using ADataType        = decltype(a_type);
+        using BDataType        = decltype(b_type);
+        using AccDataType      = decltype(acc_type);
+        using D0DataType       = decltype(d0_type);
+        using D1DataType       = decltype(d1_type);
+        using EMeanVarDataType = decltype(e_mean_var_type);
+        using GammaDataType    = decltype(gamma_type);
+        using BetaDataType     = decltype(beta_type);
+        using HDataType        = decltype(h_type);
+
+        using ALayout  = decltype(a_layout);
+        using BLayout  = decltype(b_layout);
+        using D0Layout = decltype(d0_layout);
+        using D1Layout = decltype(d1_layout);
+        using HLayout  = decltype(h_layout);
+
+        const int DefaultStrideA  = ck::is_same_v<ALayout, Row> ? K : M;
+        const int DefaultStrideB  = ck::is_same_v<BLayout, Row> ? N : K;
+        const int DefaultStrideD0 = ck::is_same_v<D0Layout, Row> ? N : M;
+        const int DefaultStrideD1 = ck::is_same_v<D1Layout, Row> ? N : M;
+        const int DefaultStrideH  = ck::is_same_v<HLayout, Row> ? N : M;
+
+        bool pass = ck::profiler::profile_gemm_add_relu_add_layernorm_impl<ADataType,
+                                                                           BDataType,
+                                                                           AccDataType,
+                                                                           D0DataType,
+                                                                           D1DataType,
+                                                                           EMeanVarDataType,
+                                                                           GammaDataType,
+                                                                           BetaDataType,
+                                                                           HDataType,
+                                                                           ALayout,
+                                                                           BLayout,
+                                                                           D0Layout,
+                                                                           D1Layout,
+                                                                           HLayout>(
+            do_verification,
+            init_method,
+            do_log,
+            time_kernel,
+            M,
+            N,
+            K,
+            (StrideA < 0) ? DefaultStrideA : StrideA,
+            (StrideB < 0) ? DefaultStrideB : StrideB,
+            (StrideD0 < 0) ? DefaultStrideD0 : StrideD0,
+            (StrideD1 < 0) ? DefaultStrideD1 : StrideD1,
+            (StrideH < 0) ? DefaultStrideH : StrideH);
+
+        return pass ? 0 : 1;
+    };
+
+    if(data_type == MatrixDataType::F16 && layout == MatrixLayout::MK_KN_MN_MN_MN)
+    {
+        return profile(F16{},
+                       F16{},
+                       F32{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       Row{},
+                       Row{},
+                       Row{},
+                       Row{},
+                       Row{});
+    }
+    else if(data_type == MatrixDataType::F16 && layout == MatrixLayout::MK_NK_MN_MN_MN)
+    {
+        return profile(F16{},
+                       F16{},
+                       F32{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       Row{},
+                       Col{},
+                       Row{},
+                       Row{},
+                       Row{});
+    }
+    else if(data_type == MatrixDataType::F16 && layout == MatrixLayout::KM_KN_MN_MN_MN)
+    {
+        return profile(F16{},
+                       F16{},
+                       F32{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       Col{},
+                       Row{},
+                       Row{},
+                       Row{},
+                       Row{});
+    }
+    else if(data_type == MatrixDataType::F16 && layout == MatrixLayout::KM_NK_MN_MN_MN)
+    {
+        return profile(F16{},
+                       F16{},
+                       F32{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       F16{},
+                       Col{},
+                       Col{},
+                       Row{},
+                       Row{},
+                       Row{});
+    }
+    else
+    {
+        std::cout << "this data_type & layout is not implemented" << std::endl;
+
+        return 1;
+    }
+}
+
+REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_add_relu_add_layernorm);

test/CMakeLists.txt

@@ -27,7 +27,7 @@ function(add_gtest_executable TEST_NAME)
     # suppress gtest warnings
     target_compile_options(${TEST_NAME} PRIVATE -Wno-global-constructors -Wno-undef)
     target_link_libraries(${TEST_NAME} PRIVATE gtest_main)
-    add_test(NAME ${TEST_NAME} COMMAND $<TARGET_FILE:${TEST_NAME}> )
+    add_test(NAME ${TEST_NAME} COMMAND $<TARGET_FILE:${TEST_NAME}>)
     rocm_install(TARGETS ${TEST_NAME} COMPONENT tests)
 endfunction(add_gtest_executable TEST_NAME)
 
@@ -36,6 +36,7 @@ add_subdirectory(space_filling_curve)
 add_subdirectory(conv_util)
 add_subdirectory(reference_conv_fwd)
 add_subdirectory(gemm)
+add_subdirectory(gemm_layernorm)
 add_subdirectory(gemm_split_k)
 add_subdirectory(gemm_reduce)
 add_subdirectory(batched_gemm)

test/gemm_layernorm/CMakeLists.txt

+add_custom_target(test_gemm_layernorm)
+
+add_gtest_executable(test_gemm_add_relu_add_layernorm_fp16 test_gemm_add_relu_add_layernorm_fp16.cpp)
+
+target_link_libraries(test_gemm_add_relu_add_layernorm_fp16 PRIVATE utility device_gemm_add_relu_add_layernorm_instance)
+
+add_dependencies(test_gemm_layernorm test_gemm_add_relu_add_layernorm_fp16)

test/gemm_layernorm/test_gemm_add_relu_add_layernorm_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "gtest/gtest.h"
+#include "profiler/profile_gemm_add_relu_add_layernorm_impl.hpp"
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using F16 = ck::half_t;
+using F32 = float;
+using ck::index_t;
+
+template <typename Tuple>
+class TestGemmAddReluAddLayernorm : public ::testing::Test
+{
+    protected:
+    using ADataType        = std::tuple_element_t<0, Tuple>;
+    using BDataType        = std::tuple_element_t<1, Tuple>;
+    using AccDataType      = std::tuple_element_t<2, Tuple>;
+    using D0DataType       = std::tuple_element_t<3, Tuple>;
+    using D1DataType       = std::tuple_element_t<4, Tuple>;
+    using EMeanVarDataType = std::tuple_element_t<5, Tuple>;
+    using GammaDataType    = std::tuple_element_t<6, Tuple>;
+    using BetaDataType     = std::tuple_element_t<7, Tuple>;
+    using HDataType        = std::tuple_element_t<8, Tuple>;
+    using ALayout          = std::tuple_element_t<9, Tuple>;
+    using BLayout          = std::tuple_element_t<10, Tuple>;
+    using D0Layout         = std::tuple_element_t<11, Tuple>;
+    using D1Layout         = std::tuple_element_t<12, Tuple>;
+    using HLayout          = std::tuple_element_t<13, Tuple>;
+
+    void Run()
+    {
+        std::vector<std::vector<ck::index_t>> lengths = {
+            {1024, 1024, 1024}, {2048, 640, 640}, {1, 1, 1}};
+
+        for(auto length : lengths)
+        {
+            int M        = length[0];
+            int N        = length[1];
+            int K        = length[2];
+            int StrideA  = ck::is_same_v<ALayout, Row> ? K : M;
+            int StrideB  = ck::is_same_v<BLayout, Row> ? N : K;
+            int StrideD0 = 0;
+            int StrideD1 = ck::is_same_v<D1Layout, Row> ? N : M;
+            int StrideH  = ck::is_same_v<HLayout, Row> ? N : M;
+
+            bool success = ck::profiler::profile_gemm_add_relu_add_layernorm_impl<ADataType,
+                                                                                  BDataType,
+                                                                                  AccDataType,
+                                                                                  D0DataType,
+                                                                                  D1DataType,
+                                                                                  EMeanVarDataType,
+                                                                                  GammaDataType,
+                                                                                  BetaDataType,
+                                                                                  HDataType,
+                                                                                  ALayout,
+                                                                                  BLayout,
+                                                                                  D0Layout,
+                                                                                  D1Layout,
+                                                                                  HLayout>(
+                true, 1, false, false, M, N, K, StrideA, StrideB, StrideD0, StrideD1, StrideH);
+
+            EXPECT_TRUE(success);
+        }
+    }
+};
+
+using KernelTypes = ::testing::Types<
+    std::tuple<F16, F16, F32, F16, F16, F16, F16, F16, F16, Row, Row, Row, Row, Row>,
+    std::tuple<F16, F16, F32, F16, F16, F16, F16, F16, F16, Row, Col, Row, Row, Row>,
+    std::tuple<F16, F16, F32, F16, F16, F16, F16, F16, F16, Col, Row, Row, Row, Row>,
+    std::tuple<F16, F16, F32, F16, F16, F16, F16, F16, F16, Col, Col, Row, Row, Row>>;
+
+TYPED_TEST_SUITE(TestGemmAddReluAddLayernorm, KernelTypes);
+TYPED_TEST(TestGemmAddReluAddLayernorm, Test_FP16) { this->Run(); }

test/normalization/CMakeLists.txt

-add_custom_target(test_layernorm)
+add_custom_target(test_normalization)
 
 add_gtest_executable(test_layernorm2d_fp32 test_layernorm2d_fp32.cpp)
 add_gtest_executable(test_layernorm2d_fp16 test_layernorm2d_fp16.cpp)
 add_gtest_executable(test_groupnorm_fp16 test_groupnorm_fp16.cpp)
 add_gtest_executable(test_groupnorm_fp32 test_groupnorm_fp32.cpp)
 
-
 target_link_libraries(test_layernorm2d_fp32 PRIVATE utility device_normalization_instance)
 target_link_libraries(test_layernorm2d_fp16 PRIVATE utility device_normalization_instance)
 target_link_libraries(test_groupnorm_fp16 PRIVATE utility device_normalization_instance)
 target_link_libraries(test_groupnorm_fp32 PRIVATE utility device_normalization_instance)
 
-add_dependencies(test_layernorm test_layernorm2d_fp32)
-add_dependencies(test_layernorm test_layernorm2d_fp16)
-add_dependencies(test_layernorm test_groupnorm_fp16)
-add_dependencies(test_layernorm test_groupnorm_fp32)
+add_dependencies(test_normalization test_layernorm2d_fp32)
+add_dependencies(test_normalization test_layernorm2d_fp16)
+add_dependencies(test_normalization test_groupnorm_fp16)
+add_dependencies(test_normalization test_groupnorm_fp32)