[GEMM] F8 GEMM, performance optimized. (#1384)

* add ab_scale init support

* enabled interwave

* add scale type; update isSupport

* adjust example

* clean

* enable f8 pure gemm rcr ckprofiler

* Add gemm_multiply_multiply instances

* clang format

* Optimize for ScaleBlockMNK=128

* enable abscale f8 gemm ck profiler

* Add pure f8 gemm test suite

* Reverting to the state of project at f60fd77

* update copyright

* clang format

* update copyright

---------
Co-authored-by: root <jizhan@amd.com>

library/src/tensor_operation_instance/gpu/gemm_universal/device_gemm_xdl_universal_f8_f8_bf16/device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_default_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+void add_device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_default_instances(
+    std::vector<std::unique_ptr<
+        DeviceGemmV2<Row, Col, Row, F8, F8, BF16, PassThrough, PassThrough, PassThrough>>>&
+        instances)
+{
+    add_device_operation_instances(
+        instances, device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_instances<GemmDefault>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/gemm_universal/device_gemm_xdl_universal_f8_f8_bf16/device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_kpadding_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+void add_device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_kpadding_instances(
+    std::vector<std::unique_ptr<
+        DeviceGemmV2<Row, Col, Row, F8, F8, BF16, PassThrough, PassThrough, PassThrough>>>&
+        instances)
+{
+    add_device_operation_instances(
+        instances, device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_instances<GemmKPadding>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/gemm_universal/device_gemm_xdl_universal_f8_f8_bf16/device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_mnkpadding_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+void add_device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_mnkpadding_instances(
+    std::vector<std::unique_ptr<
+        DeviceGemmV2<Row, Col, Row, F8, F8, BF16, PassThrough, PassThrough, PassThrough>>>&
+        instances)
+{
+    add_device_operation_instances(
+        instances, device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_instances<GemmMNKPadding>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/gemm_universal/device_gemm_xdl_universal_f8_f8_bf16/device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_mnpadding_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+void add_device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_mnpadding_instances(
+    std::vector<std::unique_ptr<
+        DeviceGemmV2<Row, Col, Row, F8, F8, BF16, PassThrough, PassThrough, PassThrough>>>&
+        instances)
+{
+    add_device_operation_instances(
+        instances, device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_instances<GemmMNPadding>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/gemm_universal/device_gemm_xdl_universal_f8_f8_bf16/device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_v1_default_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+void add_device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_v1_default_instances(
+    std::vector<std::unique_ptr<
+        DeviceGemmV2<Row, Col, Row, F8, F8, BF16, PassThrough, PassThrough, PassThrough>>>&
+        instances)
+{
+    add_device_operation_instances(
+        instances,
+        device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_instances<Intrawave, GemmDefault>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/gemm_universal/device_gemm_xdl_universal_f8_f8_bf16/device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_v1_kpadding_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+void add_device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_v1_kpadding_instances(
+    std::vector<std::unique_ptr<
+        DeviceGemmV2<Row, Col, Row, F8, F8, BF16, PassThrough, PassThrough, PassThrough>>>&
+        instances)
+{
+    add_device_operation_instances(
+        instances,
+        device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_instances<Intrawave, GemmKPadding>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/gemm_universal/device_gemm_xdl_universal_f8_f8_bf16/device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_v1_mnkpadding_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+void add_device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_v1_mnkpadding_instances(
+    std::vector<std::unique_ptr<
+        DeviceGemmV2<Row, Col, Row, F8, F8, BF16, PassThrough, PassThrough, PassThrough>>>&
+        instances)
+{
+    add_device_operation_instances(
+        instances,
+        device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_instances<Intrawave, GemmMNKPadding>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/gemm_universal/device_gemm_xdl_universal_f8_f8_bf16/device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_v2_default_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+void add_device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_v2_default_instances(
+    std::vector<std::unique_ptr<
+        DeviceGemmV2<Row, Col, Row, F8, F8, BF16, PassThrough, PassThrough, PassThrough>>>&
+        instances)
+{
+    add_device_operation_instances(
+        instances,
+        device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_instances<Interwave, GemmDefault>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/gemm_universal/device_gemm_xdl_universal_f8_f8_bf16/device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_v2_kpadding_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+void add_device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_v2_kpadding_instances(
+    std::vector<std::unique_ptr<
+        DeviceGemmV2<Row, Col, Row, F8, F8, BF16, PassThrough, PassThrough, PassThrough>>>&
+        instances)
+{
+    add_device_operation_instances(
+        instances,
+        device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_instances<Interwave, GemmKPadding>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/gemm_universal/device_gemm_xdl_universal_f8_f8_bf16/device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_v2_mnkpadding_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+void add_device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_v2_mnkpadding_instances(
+    std::vector<std::unique_ptr<
+        DeviceGemmV2<Row, Col, Row, F8, F8, BF16, PassThrough, PassThrough, PassThrough>>>&
+        instances)
+{
+    add_device_operation_instances(
+        instances,
+        device_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_instances<Interwave, GemmMNKPadding>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

profiler/include/profiler/profile_gemm_ab_scale_impl.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iomanip>
+#include <iostream>
+#include <typeinfo>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle_v3_ab_scale.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/tensor_operation_instance/gpu/gemm_ab_scale.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"
+
+namespace ck {
+namespace profiler {
+
+template <typename A0DataType,
+          typename A1DataType,
+          typename B0DataType,
+          typename B1DataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename EDataType,
+          index_t ScaleBlockM,
+          index_t ScaleBlockN,
+          index_t ScaleBlockK,
+          typename ALayout,
+          typename BLayout,
+          typename ELayout>
+bool profile_gemm_ab_scale_impl(int do_verification,
+                                int init_method,
+                                bool do_log,
+                                bool time_kernel,
+                                int M,
+                                int N,
+                                int K,
+                                int StrideA,
+                                int StrideB,
+                                int StrideE,
+                                int n_warmup,
+                                int n_iter,
+                                uint64_t rotating = 0)
+{
+    bool pass = true;
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            using namespace ck::literals;
+
+            if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+    ck::index_t Scale_Stride_AM = ck::is_same_v<ALayout, tensor_layout::gemm::RowMajor>
+                                      ? ((K + ScaleBlockK - 1) / ScaleBlockK)
+                                      : ((M + ScaleBlockM - 1) / ScaleBlockM);
+    ck::index_t Scale_Stride_BN = ck::is_same_v<BLayout, ck::tensor_layout::gemm::ColumnMajor>
+                                      ? ((K + ScaleBlockK - 1) / ScaleBlockK)
+                                      : ((N + ScaleBlockN - 1) / ScaleBlockN);
+
+    Tensor<A0DataType> a0_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+    Tensor<A1DataType> a1_m_k(f_host_tensor_descriptor((M + ScaleBlockM - 1) / ScaleBlockM,
+                                                       (K + ScaleBlockK - 1) / ScaleBlockK,
+                                                       Scale_Stride_AM,
+                                                       ALayout{}));
+    Tensor<B0DataType> b0_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+    Tensor<B1DataType> b1_k_n(f_host_tensor_descriptor((K + ScaleBlockK - 1) / ScaleBlockK,
+                                                       (N + ScaleBlockN - 1) / ScaleBlockN,
+                                                       Scale_Stride_BN,
+                                                       BLayout{}));
+    Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
+    Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
+
+    int total_gemm_needed =
+        a0_m_k.GetElementSpaceSizeInBytes() + b0_k_n.GetElementSpaceSizeInBytes() +
+        a1_m_k.GetElementSpaceSizeInBytes() + b1_k_n.GetElementSpaceSizeInBytes();
+    int rotating_count = std::max(
+        1,
+        std::min(n_iter,
+                 static_cast<int>(std::ceil(static_cast<double>(rotating) / total_gemm_needed))));
+
+    std::cout << "a0_m_k: " << a0_m_k.mDesc << std::endl;
+    std::cout << "a1_m_k: " << a1_m_k.mDesc << std::endl;
+    std::cout << "b0_k_n: " << b0_k_n.mDesc << std::endl;
+    std::cout << "b1_k_n: " << b1_k_n.mDesc << std::endl;
+    std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl;
+    std::cout << "rotating count: " << rotating_count << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a0_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 2});
+        b0_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        a1_m_k.GenerateTensorValue(GeneratorTensor_3<A1DataType>{0, 1.0});
+        b1_k_n.GenerateTensorValue(GeneratorTensor_3<B1DataType>{0, 1.0});
+        break;
+    default:
+        a0_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{-0.5, 0.5});
+        b0_k_n.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
+        a1_m_k.GenerateTensorValue(GeneratorTensor_3<A1DataType>{0, 1.0});
+        b1_k_n.GenerateTensorValue(GeneratorTensor_3<B1DataType>{0, 1.0});
+    }
+
+    using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+    using AElementOp = PassThrough;
+    using BElementOp = PassThrough;
+    using CElementOp = PassThrough;
+
+    const auto a_element_op = AElementOp{};
+    const auto b_element_op = BElementOp{};
+    const auto c_element_op = CElementOp{};
+
+    DeviceMem a0_device_buf(sizeof(A0DataType) * a0_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b0_device_buf(sizeof(B0DataType) * b0_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem a1_device_buf(sizeof(A1DataType) * a1_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b1_device_buf(sizeof(B1DataType) * b1_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem c_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
+
+    a0_device_buf.ToDevice(a0_m_k.mData.data());
+    b0_device_buf.ToDevice(b0_k_n.mData.data());
+    a1_device_buf.ToDevice(a1_m_k.mData.data());
+    b1_device_buf.ToDevice(b1_k_n.mData.data());
+
+    using DeviceOp = ck::tensor_operation::device::DeviceGemmMultipleD_ABScale<ALayout,
+                                                                               BLayout,
+                                                                               ck::Tuple<>,
+                                                                               ELayout,
+                                                                               A0DataType,
+                                                                               A1DataType,
+                                                                               B0DataType,
+                                                                               B1DataType,
+                                                                               ck::Tuple<>,
+                                                                               EDataType,
+                                                                               ScaleBlockM,
+                                                                               ScaleBlockN,
+                                                                               ScaleBlockK,
+                                                                               AElementOp,
+                                                                               BElementOp,
+                                                                               CElementOp>;
+
+    // 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 GEMM
+    if(do_verification)
+    {
+        Tensor<AccDataType> c_m_n({M, N});
+        Tensor<float> a_m_k({M, K});
+        Tensor<float> b_k_n({K, N});
+
+        for(int m = 0; m < M; m++)
+        {
+            for(int k = 0; k < K; k++)
+            {
+                a_m_k(m, k) = ck::type_convert<float>(a0_m_k(m, k)) *
+                              a1_m_k(m / ScaleBlockM, k / ScaleBlockK);
+            }
+        }
+
+        for(int n = 0; n < N; n++)
+        {
+            for(int k = 0; k < K; k++)
+            {
+                b_k_n(k, n) = ck::type_convert<float>(b0_k_n(k, n)) *
+                              b1_k_n(k / ScaleBlockK, n / ScaleBlockN);
+            }
+        }
+
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<float,
+                                                                                float,
+                                                                                AccDataType,
+                                                                                AccDataType,
+                                                                                AElementOp,
+                                                                                BElementOp,
+                                                                                PassThrough,
+                                                                                float>;
+
+        auto ref_gemm    = ReferenceGemmInstance{};
+        auto ref_invoker = ref_gemm.MakeInvoker();
+
+        auto ref_argument =
+            ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, PassThrough{}, PassThrough{}, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+
+        for(int m = 0; m < M; ++m)
+        {
+            for(int n = 0; n < N; ++n)
+            {
+                e_m_n_host_result(m, n) = ck::type_convert<EDataType>(c_m_n(m, n));
+            }
+        }
+    }
+
+    std::string best_op_name;
+    float best_ave_time   = 0;
+    float best_tflops     = 0;
+    float best_gb_per_sec = 0;
+
+    // profile device GEMM instances
+    for(auto& op_ptr : op_ptrs)
+    {
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(static_cast<A0DataType*>(a0_device_buf.GetDeviceBuffer()),
+                                        static_cast<B0DataType*>(b0_device_buf.GetDeviceBuffer()),
+                                        std::array<const void*, 0>{},
+                                        static_cast<EDataType*>(c_device_buf.GetDeviceBuffer()),
+                                        M,
+                                        N,
+                                        K,
+                                        StrideA,
+                                        StrideB,
+                                        std::array<ck::index_t, 0>{},
+                                        StrideE,
+                                        a1_device_buf.GetDeviceBuffer(),
+                                        b1_device_buf.GetDeviceBuffer(),
+                                        a_element_op,
+                                        b_element_op,
+                                        c_element_op);
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+
+            // re-init C to zero before profiling next kernel
+            c_device_buf.SetZero();
+
+            invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false, 0, n_warmup, n_iter});
+
+            if(do_verification)
+            {
+                c_device_buf.FromDevice(e_m_n_device_result.mData.data());
+
+#if defined CK_ENABLE_FP8
+                // set softer tolerances for fp8
+                if constexpr(is_same_v<A0DataType, f8_t> || is_same_v<B0DataType, f8_t> ||
+                             is_same_v<EDataType, f8_t>)
+                {
+                    std::string msg = "Error: Incorrect results!";
+                    double rtol     = 5e-2;
+                    double atol     = 5e-2;
+                    pass            = pass & ck::utils::check_err(
+                                      e_m_n_device_result, e_m_n_host_result, msg, rtol, atol);
+                }
+                else
+                {
+#endif
+                    pass = pass & ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
+#if defined CK_ENABLE_FP8
+                }
+#endif
+
+                if(do_log)
+                {
+                    LogRangeAsType<float>(std::cout << "a : ", a0_m_k.mData, ",") << std::endl;
+                    LogRangeAsType<float>(std::cout << "b: ", b0_k_n.mData, ",") << std::endl;
+                    LogRangeAsType<float>(std::cout << "c_host  : ", e_m_n_host_result.mData, ",")
+                        << std::endl;
+                    LogRangeAsType<float>(std::cout << "c_device: ", e_m_n_device_result.mData, ",")
+                        << std::endl;
+                }
+            }
+
+            std::string op_name = op_ptr->GetTypeString();
+
+            float ave_time = invoker_ptr->Run(
+                argument_ptr.get(),
+                StreamConfig{
+                    nullptr, time_kernel, 0, n_warmup, n_iter, rotating_count > 1, rotating_count});
+
+            std::size_t flop = std::size_t(2) * M * N * K;
+
+            std::size_t num_btype =
+                sizeof(A0DataType) * M * K + sizeof(B0DataType) * K * N + sizeof(EDataType) * M * N;
+
+            float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+            float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+            std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                best_op_name    = op_name;
+                best_tflops     = tflops;
+                best_ave_time   = ave_time;
+                best_gb_per_sec = gb_per_sec;
+            }
+        }
+        else
+        {
+            std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
+        }
+    }
+
+    if constexpr(is_same<EDataType, float>::value)
+    {
+        std::cout << "Best Perf for datatype = f32";
+    }
+    else if constexpr(is_same<EDataType, half_t>::value)
+    {
+        std::cout << "Best Perf for datatype = f16";
+    }
+    else if constexpr(is_same<EDataType, bhalf_t>::value)
+    {
+        std::cout << "Best Perf for datatype = bf16";
+    }
+    else if constexpr(is_same<EDataType, int8_t>::value)
+    {
+        std::cout << "Best Perf for datatype = int8";
+    }
+
+    if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value)
+    {
+        std::cout << " ALayout =  RowMajor";
+    }
+    else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value)
+    {
+        std::cout << " ALayout =  ColumnMajor";
+    }
+
+    if constexpr(is_same<BLayout, tensor_layout::gemm::RowMajor>::value)
+    {
+        std::cout << " BLayout =  RowMajor";
+    }
+    else if constexpr(is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value)
+    {
+        std::cout << " BLayout =  ColumnMajor";
+    }
+
+    std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
+              << " StrideB = " << StrideB << " StrideE = " << StrideE << " : " << best_ave_time
+              << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec << " GB/s, "
+              << best_op_name << std::endl;
+
+    return pass;
+}
+
+} // namespace profiler
+} // namespace ck

profiler/include/profiler/profile_gemm_multiply_multiply_impl.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iomanip>
+#include <iostream>
+#include <typeinfo>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.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/library/tensor_operation_instance/gpu/gemm_multiply_multiply.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"
+
+namespace ck {
+namespace profiler {
+
+template <typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename D0DataType,
+          typename D1DataType,
+          typename EDataType,
+          typename ALayout,
+          typename BLayout,
+          typename D0Layout,
+          typename D1Layout,
+          typename ELayout>
+bool profile_gemm_multiply_multiply_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 StrideE,
+                                         int n_warmup,
+                                         int n_iter,
+                                         uint64_t rotating = 0)
+{
+    bool pass = true;
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            using namespace ck::literals;
+
+            if(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_descriptor(M, K, StrideA, ALayout{}));
+    Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+    Tensor<D0DataType> d0_m_n(f_host_tensor_descriptor(M, N, StrideD0, D0Layout{}));
+    Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor(M, N, StrideD1, D1Layout{}));
+    Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
+    Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
+
+    int total_gemm_needed =
+        a_m_k.GetElementSpaceSizeInBytes() + b_k_n.GetElementSpaceSizeInBytes() +
+        d0_m_n.GetElementSpaceSizeInBytes() + d1_m_n.GetElementSpaceSizeInBytes();
+    int rotating_count = std::max(
+        1,
+        std::min(n_iter,
+                 static_cast<int>(std::ceil(static_cast<double>(rotating) / total_gemm_needed))));
+
+    std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
+    std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
+    std::cout << "d0_m_n: " << d0_m_n.mDesc << std::endl;
+    std::cout << "d1_m_n: " << d1_m_n.mDesc << std::endl;
+    std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl;
+    std::cout << "rotating count: " << rotating_count << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-1, 2});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-1, 2});
+        d0_m_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-5, 5});
+        d1_m_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-1, 1});
+        break;
+    default:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+        d0_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
+        d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
+    }
+
+    using PassThrough      = ck::tensor_operation::element_wise::PassThrough;
+    using MultiplyMultiply = ck::tensor_operation::element_wise::MultiplyMultiply;
+
+    using AElementOp = PassThrough;
+    using BElementOp = PassThrough;
+    using CElementOp = MultiplyMultiply;
+
+    const auto a_element_op = AElementOp{};
+    const auto b_element_op = BElementOp{};
+    const auto c_element_op = CElementOp{};
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem d0_device_buf(sizeof(D0DataType) * d0_m_n.mDesc.GetElementSpaceSize());
+    DeviceMem d1_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpaceSize());
+    DeviceMem c_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a_m_k.mData.data());
+    b_device_buf.ToDevice(b_k_n.mData.data());
+    d0_device_buf.ToDevice(d0_m_n.mData.data());
+    d1_device_buf.ToDevice(d1_m_n.mData.data());
+
+    using DeviceOp =
+        ck::tensor_operation::device::DeviceGemmMultipleD<ALayout,
+                                                          BLayout,
+                                                          ck::Tuple<D0Layout, D1Layout>,
+                                                          ELayout,
+                                                          ADataType,
+                                                          BDataType,
+                                                          ck::Tuple<D0DataType, D1DataType>,
+                                                          EDataType,
+                                                          AElementOp,
+                                                          BElementOp,
+                                                          CElementOp>;
+
+    // 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 GEMM
+    if(do_verification)
+    {
+        Tensor<AccDataType> c_m_n({M, N});
+
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                                BDataType,
+                                                                                AccDataType,
+                                                                                AccDataType,
+                                                                                AElementOp,
+                                                                                BElementOp,
+                                                                                PassThrough,
+                                                                                ComputeDataType>;
+
+        auto ref_gemm    = ReferenceGemmInstance{};
+        auto ref_invoker = ref_gemm.MakeInvoker();
+
+        auto ref_argument =
+            ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, PassThrough{}, PassThrough{}, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+
+        for(int m = 0; m < M; ++m)
+        {
+            for(int n = 0; n < N; ++n)
+            {
+                c_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d0_m_n(m, n), d1_m_n(m, n));
+            }
+        }
+    }
+
+    std::string best_op_name;
+    float best_ave_time   = 0;
+    float best_tflops     = 0;
+    float best_gb_per_sec = 0;
+
+    // profile device GEMM instances
+    for(auto& op_ptr : op_ptrs)
+    {
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
+                                        static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
+                                        std::array<const void*, 2>{d0_device_buf.GetDeviceBuffer(),
+                                                                   d1_device_buf.GetDeviceBuffer()},
+                                        static_cast<EDataType*>(c_device_buf.GetDeviceBuffer()),
+                                        M,
+                                        N,
+                                        K,
+                                        StrideA,
+                                        StrideB,
+                                        std::array<ck::index_t, 2>{StrideD0, StrideD1},
+                                        StrideE,
+                                        a_element_op,
+                                        b_element_op,
+                                        c_element_op);
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+
+            // re-init C to zero before profiling next kernel
+            c_device_buf.SetZero();
+
+            invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false, 0, n_warmup, n_iter});
+
+            if(do_verification)
+            {
+                c_device_buf.FromDevice(e_m_n_device_result.mData.data());
+
+                pass = pass & ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
+
+                if(do_log)
+                {
+                    LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
+                    LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
+                    LogRangeAsType<float>(std::cout << "c_host  : ", e_m_n_host_result.mData, ",")
+                        << std::endl;
+                    LogRangeAsType<float>(std::cout << "c_device: ", e_m_n_device_result.mData, ",")
+                        << std::endl;
+                }
+            }
+
+            std::string op_name = op_ptr->GetTypeString();
+
+            float ave_time = invoker_ptr->Run(
+                argument_ptr.get(),
+                StreamConfig{
+                    nullptr, time_kernel, 0, n_warmup, n_iter, rotating_count > 1, rotating_count});
+
+            std::size_t flop = std::size_t(2) * M * N * K;
+
+            std::size_t num_btype =
+                sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
+
+            float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+            float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+            std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+#if defined CK_ENABLE_FP8
+            // set softer tolerances for fp8
+            if constexpr(is_same_v<ADataType, f8_t> || is_same_v<BDataType, f8_t> ||
+                         is_same_v<EDataType, f8_t>)
+            {
+                std::string msg = "Error: Incorrect results!";
+                double rtol     = 1e-1;
+                double atol     = 1e-1;
+                pass            = pass & ck::utils::check_err(
+                                  e_m_n_device_result, e_m_n_host_result, msg, rtol, atol);
+            }
+            else
+            {
+#endif
+                pass = pass & ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
+#if defined CK_ENABLE_FP8
+            }
+#endif
+
+            if(tflops > best_tflops)
+            {
+                best_op_name    = op_name;
+                best_tflops     = tflops;
+                best_ave_time   = ave_time;
+                best_gb_per_sec = gb_per_sec;
+            }
+        }
+        else
+        {
+            std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
+        }
+    }
+
+    if constexpr(is_same<EDataType, float>::value)
+    {
+        std::cout << "Best Perf for datatype = f32";
+    }
+    else if constexpr(is_same<EDataType, half_t>::value)
+    {
+        std::cout << "Best Perf for datatype = f16";
+    }
+    else if constexpr(is_same<EDataType, bhalf_t>::value)
+    {
+        std::cout << "Best Perf for datatype = bf16";
+    }
+    else if constexpr(is_same<EDataType, int8_t>::value)
+    {
+        std::cout << "Best Perf for datatype = int8";
+    }
+
+    if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value)
+    {
+        std::cout << " ALayout =  RowMajor";
+    }
+    else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value)
+    {
+        std::cout << " ALayout =  ColumnMajor";
+    }
+
+    if constexpr(is_same<BLayout, tensor_layout::gemm::RowMajor>::value)
+    {
+        std::cout << " BLayout =  RowMajor";
+    }
+    else if constexpr(is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value)
+    {
+        std::cout << " BLayout =  ColumnMajor";
+    }
+
+    std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
+              << " StrideB = " << StrideB << " StrideE = " << StrideE << " : " << best_ave_time
+              << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec << " GB/s, "
+              << best_op_name << std::endl;
+
+    return pass;
+}
+
+} // namespace profiler
+} // namespace ck

profiler/include/profiler/profile_gemm_universal_impl.hpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -26,6 +26,7 @@ namespace profiler {
 
 template <typename ADataType,
           typename BDataType,
+          typename ComputeDataType,
           typename AccDataType,
           typename CDataType,
           typename ALayout,
@@ -130,7 +131,8 @@ bool profile_gemm_universal_impl(int do_verification,
                                                                                 AccDataType,
                                                                                 AElementOp,
                                                                                 BElementOp,
-                                                                                CElementOp>;
+                                                                                CElementOp,
+                                                                                ComputeDataType>;
 
         auto ref_gemm    = ReferenceGemmInstance{};
         auto ref_invoker = ref_gemm.MakeInvoker();

profiler/src/CMakeLists.txt

@@ -46,6 +46,8 @@ if(GPU_TARGETS MATCHES "gfx9")
     list(APPEND PROFILER_SOURCES profile_grouped_gemm_multiply_tile_loop.cpp)
   endif()
   list(APPEND PROFILER_SOURCES profile_gemm_multiply_add.cpp)
+  list(APPEND PROFILER_SOURCES profile_gemm_multiply_multiply.cpp)
+  list(APPEND PROFILER_SOURCES profile_gemm_ab_scale.cpp)
   list(APPEND PROFILER_SOURCES profile_batched_gemm.cpp)
   list(APPEND PROFILER_SOURCES profile_batched_gemm_reduce.cpp)
   list(APPEND PROFILER_SOURCES profile_gemm_add_multiply.cpp)
@@ -121,6 +123,8 @@ if(GPU_TARGETS MATCHES "gfx9")
   target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_instance)
   target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_reduce_instance)
   target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_multiply_add_instance)
+  target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_multiply_multiply_instance)
+  target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_ab_scale_instance)
   target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_splitk_instance)
   target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_universal_instance)
   target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_universal_reduce_instance)

profiler/src/profile_gemm_ab_scale.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 "profiler/profile_gemm_ab_scale_impl.hpp"
+#include "profiler_operation_registry.hpp"
+
+enum struct GemmMatrixLayout
+{
+    MK_KN_MN, // 0
+    MK_NK_MN, // 1
+    KM_KN_MN, // 2
+    KM_NK_MN, // 3
+};
+
+enum struct GemmDataType
+{
+    F32_F32_F32,    // 0
+    F16_F16_F16,    // 1
+    BF16_BF16_BF16, // 2
+    INT8_INT8_INT8, // 3
+    F8_F16_F16,     // 4
+    F16_F8_F16,     // 5
+    F16_F16_F16_F8, // 6
+    F8_F8_BF16,     // 7
+};
+
+enum struct ScaleBlockTile
+{
+    Tile_128_128_128, // 0
+};
+
+#define OP_NAME "gemm_ab_scale"
+#define OP_DESC "GEMM_AB_Scale"
+
+int profile_gemm_ab_scale(int argc, char* argv[])
+{
+    if(argc != 15 && argc != 18)
+    {
+        printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
+        printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: f8@f16; 5: f16@f8; 6: "
+               "f16->f8; 7: f8->bf16, "
+               "comp f8)\n");
+        printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
+        printf("                     1: A[m, k] * B[n, k] = C[m, n];\n");
+        printf("                     2: A[k, m] * B[k, n] = C[m, n];\n");
+        printf("                     3: A[k, m] * B[n, k] = C[m, n])\n");
+        printf("arg4: scale block tile (0: ScaleBlockM/N/K = [128, 128, 128];\n");
+        printf("arg5: verification (0: no; 1: yes)\n");
+        printf("arg6: initialization (0: no init; 1: integer value; 2: decimal value)\n");
+        printf("arg7: print tensor value (0: no; 1: yes)\n");
+        printf("arg8: time kernel (0=no, 1=yes)\n");
+        printf("arg9 to 14: M, N, K, StrideA, StrideB, StrideE\n");
+        printf("optional:\n");
+        printf("arg15: number of warm-up cycles (default 1)\n");
+        printf("arg16: number of iterations (default 10)\n");
+        printf("arg17: memory for rotating buffer (default 0, size in MB)\n");
+        exit(1);
+    }
+
+    const auto data_type        = static_cast<GemmDataType>(std::stoi(argv[2]));
+    const auto layout           = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
+    const auto scale_block_tile = static_cast<ScaleBlockTile>(std::stoi(argv[4]));
+    const bool do_verification  = std::stoi(argv[5]);
+    const int init_method       = std::stoi(argv[6]);
+    const bool do_log           = std::stoi(argv[7]);
+    const bool time_kernel      = std::stoi(argv[8]);
+
+    const int M = std::stoi(argv[9]);
+    const int N = std::stoi(argv[10]);
+    const int K = std::stoi(argv[11]);
+
+    const int StrideA = std::stoi(argv[12]);
+    const int StrideB = std::stoi(argv[13]);
+    const int StrideE = std::stoi(argv[14]);
+
+    int n_warmup      = 1;
+    int n_iter        = 10;
+    uint64_t rotating = 0;
+    if(argc == 18)
+    {
+        n_warmup = std::stoi(argv[15]);
+        n_iter   = std::stoi(argv[16]);
+        rotating = std::stoull(argv[17]) * 1024 * 1024;
+    }
+
+    using F32  = float;
+    using BF16 = ck::bhalf_t;
+    using F8   = ck::f8_t;
+
+    using Row = ck::tensor_layout::gemm::RowMajor;
+    using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+    auto profile = [&](auto a0_type,
+                       auto a1_type,
+                       auto b0_type,
+                       auto b1_type,
+                       auto comp_type,
+                       auto acc_type,
+                       auto c_type,
+                       auto scale_block_m,
+                       auto scale_block_n,
+                       auto scale_block_k,
+                       auto a_layout,
+                       auto b_layout,
+                       auto e_layout) {
+        using A0DataType      = decltype(a0_type);
+        using A1DataType      = decltype(a1_type);
+        using B0DataType      = decltype(b0_type);
+        using B1DataType      = decltype(b1_type);
+        using ComputeDataType = decltype(comp_type);
+        using AccDataType     = decltype(acc_type);
+        using EDataType       = decltype(c_type);
+
+        using ALayout = decltype(a_layout);
+        using BLayout = decltype(b_layout);
+        using ELayout = decltype(e_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 DefaultStrideE = ck::is_same_v<ELayout, Row> ? N : M;
+
+        bool pass = ck::profiler::profile_gemm_ab_scale_impl<A0DataType,
+                                                             A1DataType,
+                                                             B0DataType,
+                                                             B1DataType,
+                                                             ComputeDataType,
+                                                             AccDataType,
+                                                             EDataType,
+                                                             scale_block_m,
+                                                             scale_block_n,
+                                                             scale_block_k,
+                                                             ALayout,
+                                                             BLayout,
+                                                             ELayout>(
+            do_verification,
+            init_method,
+            do_log,
+            time_kernel,
+            M,
+            N,
+            K,
+            (StrideA < 0) ? DefaultStrideA : StrideA,
+            (StrideB < 0) ? DefaultStrideB : StrideB,
+            (StrideE < 0) ? DefaultStrideE : StrideE,
+            n_warmup,
+            n_iter,
+            rotating);
+
+        return pass ? 0 : 1;
+    };
+
+    if(data_type == GemmDataType::F8_F8_BF16 && layout == GemmMatrixLayout::MK_NK_MN &&
+       scale_block_tile == ScaleBlockTile::Tile_128_128_128)
+    {
+        return profile(F8{},
+                       F32{},
+                       F8{},
+                       F32{},
+                       F8{},
+                       F32{},
+                       BF16{},
+                       ck::Number<128>{},
+                       ck::Number<128>{},
+                       ck::Number<128>{},
+                       Row{},
+                       Col{},
+                       Row{});
+    }
+    else
+    {
+        std::cout << "this data_type & layout is not implemented" << std::endl;
+
+        return 1;
+    }
+}
+
+REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_ab_scale);

profiler/src/profile_gemm_multiply_multiply.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 "profiler/profile_gemm_multiply_multiply_impl.hpp"
+#include "profiler_operation_registry.hpp"
+
+enum struct GemmMatrixLayout
+{
+    MK_KN_MN, // 0
+    MK_NK_MN, // 1
+    KM_KN_MN, // 2
+    KM_NK_MN, // 3
+};
+
+enum struct GemmDataType
+{
+    F32_F32_F32,    // 0
+    F16_F16_F16,    // 1
+    BF16_BF16_BF16, // 2
+    INT8_INT8_INT8, // 3
+    F8_F16_F16,     // 4
+    F16_F8_F16,     // 5
+    F16_F16_F16_F8, // 6
+    F8_F8_BF16,     // 7
+};
+
+#define OP_NAME "gemm_multiply_multiply"
+#define OP_DESC "GEMM_Multiply_Multiply"
+
+int profile_gemm_multiply_multiply(int argc, char* argv[])
+{
+    if(argc != 16 && argc != 19)
+    {
+        printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
+        printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: f8@f16; 5: f16@f8; 6: "
+               "f16->f8; 7: f8->bf16, "
+               "comp f8)\n");
+        printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
+        printf("                     1: A[m, k] * B[n, k] = C[m, n];\n");
+        printf("                     2: A[k, m] * B[k, n] = C[m, n];\n");
+        printf("                     3: A[k, m] * B[n, k] = C[m, n])\n");
+        printf("arg4: verification (0: no; 1: yes)\n");
+        printf("arg5: initialization (0: no init; 1: integer value; 2: 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, StrideE\n");
+        printf("optional:\n");
+        printf("arg16: number of warm-up cycles (default 1)\n");
+        printf("arg17: number of iterations (default 10)\n");
+        printf("arg18: memory for rotating buffer (default 0, size in MB)\n");
+        exit(1);
+    }
+
+    const auto data_type       = static_cast<GemmDataType>(std::stoi(argv[2]));
+    const auto layout          = static_cast<GemmMatrixLayout>(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 StrideE  = std::stoi(argv[15]);
+
+    int n_warmup      = 1;
+    int n_iter        = 10;
+    uint64_t rotating = 0;
+    if(argc == 18)
+    {
+        n_warmup = std::stoi(argv[16]);
+        n_iter   = std::stoi(argv[17]);
+        rotating = std::stoull(argv[18]) * 1024 * 1024;
+    }
+
+    using F32  = float;
+    using BF16 = ck::bhalf_t;
+    using F8   = ck::f8_t;
+
+    using Row = ck::tensor_layout::gemm::RowMajor;
+    using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+    auto profile = [&](auto a_type,
+                       auto b_type,
+                       auto comp_type,
+                       auto acc_type,
+                       auto d0_type,
+                       auto d1_type,
+                       auto c_type,
+                       auto a_layout,
+                       auto b_layout,
+                       auto d0_layout,
+                       auto d1_layout,
+                       auto e_layout) {
+        using ADataType       = decltype(a_type);
+        using BDataType       = decltype(b_type);
+        using ComputeDataType = decltype(comp_type);
+        using D0DataType      = decltype(d0_type);
+        using D1DataType      = decltype(d1_type);
+        using AccDataType     = decltype(acc_type);
+        using EDataType       = decltype(c_type);
+
+        using ALayout  = decltype(a_layout);
+        using BLayout  = decltype(b_layout);
+        using D0Layout = decltype(d0_layout);
+        using D1Layout = decltype(d1_layout);
+        using ELayout  = decltype(e_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 DefaultStrideE  = ck::is_same_v<ELayout, Row> ? N : M;
+
+        bool pass = ck::profiler::profile_gemm_multiply_multiply_impl<ADataType,
+                                                                      BDataType,
+                                                                      ComputeDataType,
+                                                                      AccDataType,
+                                                                      D0DataType,
+                                                                      D1DataType,
+                                                                      EDataType,
+                                                                      ALayout,
+                                                                      BLayout,
+                                                                      D0Layout,
+                                                                      D1Layout,
+                                                                      ELayout>(
+            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,
+            (StrideE < 0) ? DefaultStrideE : StrideE,
+            n_warmup,
+            n_iter,
+            rotating);
+
+        return pass ? 0 : 1;
+    };
+
+    if(data_type == GemmDataType::F8_F8_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
+    {
+        return profile(
+            F8{}, F8{}, F8{}, F32{}, F32{}, F32{}, BF16{}, Row{}, Col{}, Row{}, Col{}, Row{});
+    }
+    else
+    {
+        std::cout << "this data_type & layout is not implemented" << std::endl;
+
+        return 1;
+    }
+}
+
+REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_multiply_multiply);

profiler/src/profile_gemm_universal.cpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #include <iostream>
 #include <numeric>
@@ -26,6 +26,7 @@ enum struct GemmDataType
     F8_F16_F16,     // 4
     F16_F8_F16,     // 5
     F16_F16_F16_F8, // 6
+    F8_F8_BF16,     // 7
 };
 
 #define OP_NAME "gemm_universal"
@@ -36,7 +37,8 @@ int profile_gemm_universal(int argc, char* argv[])
     if(argc != 15 && argc != 18)
     {
         printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
-        printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: f8@f16; 5: f16@f8; 6: f16, "
+        printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: f8@f16; 5: f16@f8; 6: "
+               "f16->f8; 7: f8->bf16, "
                "comp f8)\n");
         printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
         printf("                     1: A[m, k] * B[n, k] = C[m, n];\n");
@@ -91,15 +93,17 @@ int profile_gemm_universal(int argc, char* argv[])
 
     auto profile = [&](auto a_type,
                        auto b_type,
+                       auto comp_type,
                        auto acc_type,
                        auto c_type,
                        auto a_layout,
                        auto b_layout,
                        auto c_layout) {
-        using ADataType   = decltype(a_type);
-        using BDataType   = decltype(b_type);
-        using AccDataType = decltype(acc_type);
-        using CDataType   = decltype(c_type);
+        using ADataType       = decltype(a_type);
+        using BDataType       = decltype(b_type);
+        using ComputeDataType = decltype(comp_type);
+        using AccDataType     = decltype(acc_type);
+        using CDataType       = decltype(c_type);
 
         using ALayout = decltype(a_layout);
         using BLayout = decltype(b_layout);
@@ -111,6 +115,7 @@ int profile_gemm_universal(int argc, char* argv[])
 
         bool pass = ck::profiler::profile_gemm_universal_impl<ADataType,
                                                               BDataType,
+                                                              ComputeDataType,
                                                               AccDataType,
                                                               CDataType,
                                                               ALayout,
@@ -136,35 +141,39 @@ int profile_gemm_universal(int argc, char* argv[])
 
     if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
     {
-        return profile(F16{}, F16{}, F32{}, F16{}, Row{}, Row{}, Row{});
+        return profile(F16{}, F16{}, F16{}, F32{}, F16{}, Row{}, Row{}, Row{});
     }
     else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_NK_MN)
     {
-        return profile(F16{}, F16{}, F32{}, F16{}, Row{}, Col{}, Row{});
+        return profile(F16{}, F16{}, F16{}, F32{}, F16{}, Row{}, Col{}, Row{});
     }
     else if(data_type == GemmDataType::F16_F8_F16 && layout == GemmMatrixLayout::MK_KN_MN)
     {
-        return profile(F16{}, F8{}, F32{}, F16{}, Row{}, Row{}, Row{});
+        return profile(F16{}, F8{}, F16{}, F32{}, F16{}, Row{}, Row{}, Row{});
     }
     else if(data_type == GemmDataType::F16_F8_F16 && layout == GemmMatrixLayout::MK_NK_MN)
     {
-        return profile(F16{}, F8{}, F32{}, F16{}, Row{}, Col{}, Row{});
+        return profile(F16{}, F8{}, F16{}, F32{}, F16{}, Row{}, Col{}, Row{});
     }
     else if(data_type == GemmDataType::F8_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
     {
-        return profile(F8{}, F16{}, F32{}, F16{}, Row{}, Row{}, Row{});
+        return profile(F8{}, F16{}, F16{}, F32{}, F16{}, Row{}, Row{}, Row{});
     }
     else if(data_type == GemmDataType::F8_F16_F16 && layout == GemmMatrixLayout::MK_NK_MN)
     {
-        return profile(F8{}, F16{}, F32{}, F16{}, Row{}, Col{}, Row{});
+        return profile(F8{}, F16{}, F16{}, F32{}, F16{}, Row{}, Col{}, Row{});
     }
     else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_KN_MN)
     {
-        return profile(BF16{}, BF16{}, F32{}, BF16{}, Row{}, Row{}, Row{});
+        return profile(BF16{}, BF16{}, BF16{}, F32{}, BF16{}, Row{}, Row{}, Row{});
     }
     else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
     {
-        return profile(BF16{}, BF16{}, F32{}, BF16{}, Row{}, Col{}, Row{});
+        return profile(BF16{}, BF16{}, BF16{}, F32{}, BF16{}, Row{}, Col{}, Row{});
+    }
+    else if(data_type == GemmDataType::F8_F8_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
+    {
+        return profile(F8{}, F8{}, F8{}, F32{}, BF16{}, Row{}, Col{}, Row{});
     }
     else
     {

test/gemm_universal/test_gemm_universal_util.hpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -25,12 +25,13 @@ class TestGemmUniversal : public testing::Test
     using F32 = float;
 
     protected:
-    using ALayout   = std::tuple_element_t<0, Tuple>;
-    using BLayout   = std::tuple_element_t<1, Tuple>;
-    using CLayout   = Row;
-    using ADataType = std::tuple_element_t<2, Tuple>;
-    using BDataType = std::tuple_element_t<3, Tuple>;
-    using CDataType = std::tuple_element_t<4, Tuple>;
+    using ALayout         = std::tuple_element_t<0, Tuple>;
+    using BLayout         = std::tuple_element_t<1, Tuple>;
+    using CLayout         = Row;
+    using ADataType       = std::tuple_element_t<2, Tuple>;
+    using BDataType       = std::tuple_element_t<3, Tuple>;
+    using ComputeDataType = std::tuple_element_t<4, Tuple>;
+    using CDataType       = std::tuple_element_t<5, Tuple>;
 
     public:
     static constexpr bool verify_     = true;
@@ -66,6 +67,7 @@ class TestGemmUniversal : public testing::Test
     {
         bool pass = ck::profiler::profile_gemm_universal_impl<ADataType,
                                                               BDataType,
+                                                              ComputeDataType,
                                                               F32,
                                                               CDataType,
                                                               ALayout,

test/gemm_universal/test_gemm_universal_xdl.cpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #include <tuple>
 
@@ -41,16 +41,24 @@ class TestGemmUniversal_MK_NK
 };
 
 // clang-format off
-using KernelTypes = ::testing::Types<
-    //         ADataType, BDataType, CDataType
-    std::tuple<      F16,       F16,       F16>,
-    std::tuple<      F16,        F8,       F16>,
-    std::tuple<      F8,        F16,       F16>,
-    std::tuple<     BF16,      BF16,      BF16>
+using KernelTypes_MK_KN = ::testing::Types<
+    //         ADataType, BDataType, ComputeDataType, CDataType
+    std::tuple<      F16,       F16,             F16,     F16>,
+    std::tuple<      F16,        F8,             F16,     F16>,
+    std::tuple<       F8,       F16,             F16,     F16>,
+    std::tuple<     BF16,      BF16,            BF16,    BF16>
+    >;
+using KernelTypes_MK_NK = ::testing::Types<
+    //         ADataType, BDataType, ComputeDataType, CDataType
+    std::tuple<      F16,       F16,             F16,     F16>,
+    std::tuple<      F16,        F8,             F16,     F16>,
+    std::tuple<       F8,       F16,             F16,     F16>,
+    std::tuple<     BF16,      BF16,            BF16,    BF16>,
+    std::tuple<       F8,        F8,              F8,    BF16>
     >;
 // clang-format on
 
-TYPED_TEST_SUITE(TestGemmUniversal_MK_KN, KernelTypes);
-TYPED_TEST_SUITE(TestGemmUniversal_MK_NK, KernelTypes);
+TYPED_TEST_SUITE(TestGemmUniversal_MK_KN, KernelTypes_MK_KN);
+TYPED_TEST_SUITE(TestGemmUniversal_MK_NK, KernelTypes_MK_NK);
 
 #include "test_gemm_universal_ut_cases.inc"