Merge branch 'develop' into aosewski/wavelet_omniperf

test/gemm_split_k/gemm_split_k.cpp

@@ -226,9 +226,8 @@ int main(int argc, char* argv[])
     std::vector<gemmArgs> test_cases;
     if(argc == 1)
     {
-        test_cases = {{GemmMatrixLayout::MK_KN_MN, 3, 3, 3, 3, 3, 3, 1}};
-        // JD: Populate with more and meaningful
-        return 0;
+        test_cases = {{GemmMatrixLayout::MK_KN_MN, 1024, 1024, 1024, 1024, 1024, 1024, 2},
+                      {GemmMatrixLayout::MK_KN_MN, 1024, 1024, 1024, 1024, 1024, 1024, 8}};
     }
     else if(argc == 9)
     {
@@ -253,11 +252,10 @@ int main(int argc, char* argv[])
         printf("arg2 to 7: M, N, K, StrideA, StrideB, StrideC KBatch\n");
         return -1;
     }
+    bool error = false;
     for(const auto& kinder : test_cases)
     {
-        const auto res = test_gemm(kinder);
-        if(!res)
-            return -1;
+        error |= test_gemm(kinder);
     }
-    return 0;
+    return error ? 1 : 0;
 }

test/grouped_convnd_bwd_weight/grouped_convnd_bwd_weight.cpp

@@ -9,7 +9,7 @@
 
 #include <gtest/gtest.h>
 
-#include "profiler/include/profile_grouped_conv_bwd_weight_impl.hpp"
+#include "profiler/profile_grouped_conv_bwd_weight_impl.hpp"
 
 template <typename Tuple>
 class TestGroupedConvndBwdWeight : public ::testing::Test
@@ -61,7 +61,7 @@ TYPED_TEST(TestGroupedConvndBwdWeight, Test1D)
 {
     this->conv_params.clear();
     this->conv_params.push_back({1, 4, 128, 128, 256, {1}, {14}, {2}, {1}, {0}, {0}});
-    this->conv_params.push_back({1, 4, 128, 128, 256, {3}, {28}, {1}, {1}, {1}, {1}});
+    this->conv_params.push_back({1, 4, 64, 128, 256, {3}, {28}, {1}, {1}, {1}, {1}});
     this->conv_params.push_back({1, 4, 128, 128, 256, {1}, {3}, {1}, {1}, {0}, {0}});
     this->template Run<1>();
 }
@@ -72,7 +72,7 @@ TYPED_TEST(TestGroupedConvndBwdWeight, Test2D)
     this->conv_params.push_back(
         {2, 4, 128, 128, 256, {1, 1}, {7, 7}, {2, 2}, {1, 1}, {0, 0}, {0, 0}});
     this->conv_params.push_back(
-        {2, 4, 32, 128, 256, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
+        {2, 4, 8, 128, 256, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
     this->conv_params.push_back(
         {2, 4, 128, 128, 256, {1, 1}, {3, 3}, {1, 1}, {1, 1}, {0, 0}, {0, 0}});
     this->template Run<2>();
@@ -84,7 +84,7 @@ TYPED_TEST(TestGroupedConvndBwdWeight, Test3D)
     this->conv_params.push_back(
         {3, 4, 128, 128, 256, {1, 1, 1}, {7, 7, 7}, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
     this->conv_params.push_back(
-        {3, 4, 32, 128, 256, {3, 3, 3}, {14, 14, 3}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
+        {3, 4, 8, 128, 256, {3, 3, 3}, {14, 14, 3}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
     this->conv_params.push_back(
         {3, 4, 128, 128, 256, {1, 1, 1}, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
     this->template Run<3>();

test/grouped_convnd_fwd/grouped_convnd_fwd.cpp

@@ -7,7 +7,7 @@
 #include <vector>
 #include <gtest/gtest.h>
 
-#include "profiler/include/profile_grouped_conv_fwd_impl.hpp"
+#include "profiler/profile_grouped_conv_fwd_impl.hpp"
 
 class TestGroupedConvNdFwd : public ::testing::Test
 {

test/grouped_gemm/grouped_gemm_fp16.cpp

@@ -2,8 +2,9 @@
 // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
 
 #include <iostream>
+#include <random>
 
-#include "profiler/include/profile_grouped_gemm_impl.hpp"
+#include "profiler/profile_grouped_gemm_impl.hpp"
 
 namespace {
 
@@ -18,7 +19,10 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
 template <typename ALayout, typename BLayout, typename CLayout>
 bool TestGroupedGemm()
 {
-    int group_count = rand() % 10 + 1;
+
+    std::mt19937 gen(19391);
+    std::uniform_int_distribution<> distrib(1, 10);
+    int group_count = distrib(gen);
 
     // GEMM shape
     std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
@@ -29,9 +33,9 @@ bool TestGroupedGemm()
 
     for(int i = 0; i < group_count; i++)
     {
-        Ms.push_back(256 + 256 * (rand() % 10));
-        Ns.push_back(256 + 256 * (rand() % 10));
-        Ks.push_back(128 + 128 * (rand() % 10));
+        Ms.push_back(256 + 256 * distrib(gen));
+        Ns.push_back(256 + 256 * distrib(gen));
+        Ks.push_back(128 + 128 * distrib(gen));
 
         StrideAs.push_back(std::is_same<Row, ALayout>::value ? Ks[i] : Ms[i]);
         StrideBs.push_back(std::is_same<Row, BLayout>::value ? Ns[i] : Ks[i]);

test/normalization/test_groupnorm_fp16.cpp

@@ -2,7 +2,7 @@
 // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "gtest/gtest.h"
-#include "profiler/include/profile_groupnorm_impl.hpp"
+#include "profiler/profile_groupnorm_impl.hpp"
 
 using F16 = ck::half_t;
 using F32 = float;

test/normalization/test_groupnorm_fp32.cpp

@@ -2,7 +2,7 @@
 // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "gtest/gtest.h"
-#include "profiler/include/profile_groupnorm_impl.hpp"
+#include "profiler/profile_groupnorm_impl.hpp"
 
 using F16 = ck::half_t;
 using F32 = float;

test/normalization/test_layernorm2d_fp16.cpp

@@ -2,7 +2,7 @@
 // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "gtest/gtest.h"
-#include "profiler/include/profile_layernorm_impl.hpp"
+#include "profiler/profile_layernorm_impl.hpp"
 
 using F16 = ck::half_t;
 using F32 = float;

test/normalization/test_layernorm2d_fp32.cpp

@@ -2,7 +2,7 @@
 // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "gtest/gtest.h"
-#include "profiler/include/profile_layernorm_impl.hpp"
+#include "profiler/profile_layernorm_impl.hpp"
 
 using F16 = ck::half_t;
 using F32 = float;

test/reduce/reduce_no_index.cpp

@@ -4,7 +4,7 @@
 #include <getopt.h>
 
 #include "ck/library/utility/host_common_util.hpp"
-#include "profiler/include/profile_reduce_impl.hpp"
+#include "profiler/profile_reduce_impl.hpp"
 
 using namespace ck;
 

test/reduce/reduce_with_index.cpp

@@ -4,7 +4,7 @@
 #include <getopt.h>
 
 #include "ck/library/utility/host_common_util.hpp"
-#include "profiler/include/profile_reduce_impl.hpp"
+#include "profiler/profile_reduce_impl.hpp"
 
 using namespace ck;
 

test/softmax/test_softmax_util.hpp

@@ -13,7 +13,7 @@
 #include "ck/tensor_operation/gpu/device/impl/device_softmax_impl.hpp"
 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 #include "include/ck/utility/data_type.hpp"
-#include "profiler/include/profile_softmax_impl.hpp"
+#include "profiler/profile_softmax_impl.hpp"
 
 namespace ck {
 

test/wmma_op/CMakeLists.txt

+add_test_executable(test_wmma_op wmma_op.cpp)
+target_link_libraries(test_wmma_op PRIVATE utility)

test/wmma_op/wmma_op.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <algorithm>
+#include <cstdlib>
+#include <iostream>
+#include <numeric>
+#include <tuple>
+#include <vector>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "test/wmma_op/wmma_op_util.hpp"
+
+template <typename SrcType,
+          typename DstType,
+          typename GPUAccType,
+          typename CPUAccType,
+          ck::index_t AccNum>
+bool run_test()
+{
+    using Row         = ck::tensor_layout::gemm::RowMajor;
+    using Col         = ck::tensor_layout::gemm::ColumnMajor;
+    using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+    bool pass         = true;
+
+    const auto matmul_default = ck::wmma_op_util::matmul<SrcType, DstType, GPUAccType, AccNum>;
+    const auto matmul_swizzle_a =
+        ck::wmma_op_util::matmul_swizzle_a<SrcType, DstType, GPUAccType, AccNum>;
+
+    const auto wmma_kernel_container = std::make_tuple(matmul_default, matmul_swizzle_a);
+
+    ck::static_for<0, 2, 1>{}([&](auto i) {
+        pass &=
+            ck::wmma_op_util::TestWmma<decltype(std::get<ck::Number<i>{}>(wmma_kernel_container)),
+                                       SrcType,
+                                       SrcType,
+                                       DstType,
+                                       GPUAccType,
+                                       CPUAccType,
+                                       decltype(Row{}),
+                                       decltype(Col{}),
+                                       decltype(Row{}),
+                                       PassThrough,
+                                       PassThrough,
+                                       PassThrough,
+                                       AccNum>{}(std::get<ck::Number<i>{}>(wmma_kernel_container));
+    });
+
+    return pass ? 1 : 0;
+}
+int main(int, char*[])
+{
+    bool pass = true;
+    // clang-format off
+    //              |SrcType     |DstType     |GPUAccType  |CPUAccType |AccNum
+    pass &= run_test<ck::half_t,  ck::half_t,  float,       float,      8     >();
+    pass &= run_test<ck::bhalf_t, ck::bhalf_t, float,       float,      8     >();
+    pass &= run_test<ck::half_t,  ck::half_t,  ck::half_t,  ck::half_t, 16    >();
+    pass &= run_test<ck::bhalf_t, ck::bhalf_t, ck::bhalf_t, float,      16    >();
+    pass &= run_test<int8_t,      int8_t,      int32_t,     int32_t,    8     >();
+    // clang-format on
+
+    std::cout << "TestGemm ..... " << (pass ? "SUCCESS" : "FAILURE") << std::endl;
+    return pass ? 0 : 1;
+}

test/wmma_op/wmma_op_util.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.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/reference_tensor_operation/cpu/reference_gemm.hpp"
+#include "ck/utility/amd_wmma.hpp"
+
+namespace ck {
+namespace wmma_op_util {
+
+template <typename src_vec, typename acc_vec>
+__device__ void builtin_wmma_naive_selector(const src_vec&, const src_vec&, acc_vec&)
+{
+}
+
+template <>
+__device__ void
+builtin_wmma_naive_selector<half16_t,
+                            StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, float, 1, 8, true>>(
+    const half16_t& reg_a,
+    const half16_t& reg_b,
+    StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, float, 1, 8, true>& reg_c)
+{
+    intrin_wmma_f32_16x16x16_f16_w32<16, 16>::Run(
+        reg_a, reg_b, reg_c.GetVectorTypeReference(Number<0>{}));
+}
+
+template <>
+__device__ void
+builtin_wmma_naive_selector<bhalf16_t,
+                            StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, float, 1, 8, true>>(
+    const bhalf16_t& reg_a,
+    const bhalf16_t& reg_b,
+    StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, float, 1, 8, true>& reg_c)
+{
+    intrin_wmma_f32_16x16x16_bf16_w32<16, 16>::Run(
+        reg_a, reg_b, reg_c.GetVectorTypeReference(Number<0>{}));
+}
+
+template <>
+__device__ void
+builtin_wmma_naive_selector<half16_t,
+                            StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, half_t, 1, 16, true>>(
+    const half16_t& reg_a,
+    const half16_t& reg_b,
+    StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, half_t, 1, 16, true>& reg_c)
+{
+    intrin_wmma_f16_16x16x16_f16_w32<16, 16, 0>::Run(
+        reg_a, reg_b, reg_c.GetVectorTypeReference(Number<0>{}));
+}
+
+template <>
+__device__ void builtin_wmma_naive_selector<
+    bhalf16_t,
+    StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, bhalf_t, 1, 16, true>>(
+    const bhalf16_t& reg_a,
+    const bhalf16_t& reg_b,
+    StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, bhalf_t, 1, 16, true>& reg_c)
+{
+    intrin_wmma_bf16_16x16x16_bf16_w32<16, 16, 0>::Run(
+        reg_a, reg_b, reg_c.GetVectorTypeReference(Number<0>{}));
+}
+
+template <>
+__device__ void
+builtin_wmma_naive_selector<int8x16_t,
+                            StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, int32_t, 1, 8, true>>(
+    const int8x16_t& reg_a,
+    const int8x16_t& reg_b,
+    StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, int32_t, 1, 8, true>& reg_c)
+{
+    intrin_wmma_i32_16x16x16_iu8_w32<16, 16, true, true, false>::Run(
+        reg_a, reg_b, reg_c.GetVectorTypeReference(Number<0>{}));
+}
+
+#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
+template <>
+__device__ void
+builtin_wmma_naive_selector<int4x16_t,
+                            StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, int32_t, 1, 8, true>>(
+    const int4x16_t& reg_a,
+    const int4x16_t& reg_b,
+    StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, int32_t, 1, 8, true>& reg_c)
+{
+    intrin_wmma_i32_16x16x16_iu4_w32<16, 16, true, true, false>::Run(
+        reg_a, reg_b, reg_c.GetVectorTypeReference(Number<0>{}));
+}
+#endif
+
+template <typename src_t, typename dst_t, typename acc_t, index_t acc_num>
+__global__ void matmul(const src_t* a, const src_t* b, dst_t* c)
+{
+    __shared__ src_t p_shared[16 * 16 * 2];
+    const int lIdx = threadIdx.x;
+    // a and b fragments are stored in 8 VGPRs each, in packed format, so 16 elements each for a and
+    // b a_frag will store one column of the 16x16 matrix tile b_frag will store one row of the
+    // 16x16 matrix tile
+    using src_vec  = typename vector_type<src_t, 16>::type;
+    src_vec a_frag = {};
+    src_vec b_frag = {};
+
+    src_vec a_temp = {};
+    src_vec b_temp = {};
+    // initialize c fragment to 0
+    using acc_vec = StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, acc_t, 1, acc_num, true>;
+    acc_vec c_thread_buf_;
+
+    // lane is (0-31) mod 16 instead of 0-31 due to matrix replication in gfx11
+    // see https://atlvsp3.amd.com/sp3_gfx11_5_instructions.pdf page 482
+    // TODO: remove this dependency in gfx12 https://ontrack-internal.amd.com/browse/DEGFXSP3-101
+    const int lane    = lIdx % 16;
+    const int lane_lo = lIdx / 2;
+    const int lane_hi = lIdx % 2;
+    for(int ele = 0; ele < 8; ++ele)
+    {
+        a_temp[ele] = a[8 * lane_hi + 16 * lane_lo + ele];
+    }
+
+    for(int ele = 0; ele < 8; ++ele)
+    {
+        b_temp[ele] = b[8 * lane_hi + 16 * lane_lo + ele];
+    }
+
+    __syncthreads();
+
+    for(int ele = 0; ele < 8; ++ele)
+    {
+        p_shared[8 * 16 * lane_hi + 8 * lane_lo + ele] = a_temp[ele];
+    }
+
+    for(int ele = 0; ele < 8; ++ele)
+    {
+        p_shared[8 * 16 * lane_hi + 8 * lane_lo + ele + 16 * 16] = b_temp[ele];
+    }
+
+    asm volatile("\
+    s_waitcnt lgkmcnt(0) \n \
+    s_barrier \
+    " ::);
+
+    for(int ele = 0; ele < 16; ++ele)
+    {
+        b_frag[ele] = p_shared[(ele / 8) * 16 * 8 + 8 * lane + ele % 8 + 16 * 16];
+    }
+    // follow origin design
+    for(int ele = 0; ele < 16; ++ele)
+    {
+        a_frag[ele] = p_shared[(ele / 8) * 16 * 8 + 8 * lane + ele % 8];
+    }
+
+    asm volatile("\
+    s_waitcnt lgkmcnt(0) \n \
+    s_barrier \
+    " ::);
+
+    // sync threads, similar to mma_sync
+    // __syncthreads();
+    builtin_wmma_naive_selector<src_vec, acc_vec>(a_frag, b_frag, c_thread_buf_);
+    // since only fp16_fp32 asm wmma implemented for experiment purpose, restrict test case to fp16
+    // when enable this ck::amd_assembly_wmma_f32_16x16x16_f16_w32(a_frag, b_frag,
+    // c_thread_buf_.GetVectorTypeReference(Number<0>{}).template AsType<float8_t>()(Number<0>{}));
+    __syncthreads();
+    // wait for results, similar to mma_sync
+    static_for<0, 8, 1>{}([&](auto ele) {
+        const int r = ele * 2 + (lIdx / 16);
+        // store results from unpacked c_thread_buf_ output
+        c[16 * r + lane] = ck::type_convert<dst_t>(c_thread_buf_[Number<ele * acc_num / 8>{}]);
+    });
+}
+
+template <typename src_t, typename dst_t, typename acc_t, index_t acc_num>
+__global__ void matmul_swizzle_a(const src_t* a, const src_t* b, dst_t* c)
+{
+    const int lIdx = threadIdx.x;
+
+    using src_vec  = typename vector_type<src_t, 16>::type;
+    src_vec a_frag = {};
+    src_vec b_frag = {};
+    using acc_vec  = StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr, acc_t, 1, acc_num, true>;
+    acc_vec c_thread_buf_;
+
+    const int lane = lIdx % 16;
+
+    for(int ele = 0; ele < 16; ++ele)
+    {
+        b_frag[ele] = b[16 * lane + ele];
+    }
+
+    const int offset_m = (((lane & 1) << 3) | (lane >> 1));
+    for(int ele = 0; ele < 16; ++ele)
+    {
+        a_frag[ele] = a[16 * offset_m + ele];
+    }
+
+    __syncthreads();
+    builtin_wmma_naive_selector<src_vec, acc_vec>(a_frag, b_frag, c_thread_buf_);
+    __syncthreads();
+
+    static_for<0, 8, 1>{}([&](auto ele) {
+        const int blk = lIdx / 16;
+        const int r   = ele;
+        c[16 * 8 * blk + 16 * r + lane] =
+            ck::type_convert<dst_t>(c_thread_buf_[Number<ele * acc_num / 8>{}]);
+    });
+}
+
+struct GemmParams
+{
+    GemmParams() : M(16), N(16), K(16), StrideA(16), StrideB(16), StrideC(16), alpha(1), beta(0) {}
+
+    ck::index_t M;
+    ck::index_t N;
+    ck::index_t K;
+
+    ck::index_t StrideA;
+    ck::index_t StrideB;
+    ck::index_t StrideC;
+
+    float alpha;
+    float beta;
+};
+
+template <typename GemmInstance,
+          typename ADataType,
+          typename BDataType,
+          typename CDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation>
+void RunHostGEMM(const Tensor<ADataType>& A,
+                 const Tensor<BDataType>& B,
+                 Tensor<CDataType>& C,
+                 AElementwiseOperation a_element_op,
+                 BElementwiseOperation b_element_op,
+                 CElementwiseOperation c_element_op)
+{
+    auto ref_gemm     = GemmInstance{};
+    auto ref_invoker  = ref_gemm.MakeInvoker();
+    auto ref_argument = ref_gemm.MakeArgument(A, B, C, a_element_op, b_element_op, c_element_op);
+
+    ref_invoker.Run(ref_argument);
+}
+
+template <typename KernelType, typename ADataType, typename BDataType, typename CDataType>
+bool RunDeviceGEMM(KernelType kernel,
+                   const Tensor<ADataType>& A,
+                   const Tensor<BDataType>& B,
+                   Tensor<CDataType>& C)
+{
+    DeviceMem a_m_k_device_buf(sizeof(ADataType) * A.mDesc.GetElementSpaceSize());
+    DeviceMem b_n_k_device_buf(sizeof(BDataType) * B.mDesc.GetElementSpaceSize());
+    DeviceMem c_m_n_device_buf(sizeof(CDataType) * C.mDesc.GetElementSpaceSize());
+
+    a_m_k_device_buf.ToDevice(A.mData.data());
+    b_n_k_device_buf.ToDevice(B.mData.data());
+    kernel<<<1, 32>>>(static_cast<const ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
+                      static_cast<const BDataType*>(b_n_k_device_buf.GetDeviceBuffer()),
+                      static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()));
+    c_m_n_device_buf.FromDevice(C.mData.data());
+
+    return true;
+}
+
+template <typename DeviceWmma,
+          typename ADataType,
+          typename BDataType,
+          typename CDataType,
+          typename GPUAccDataType,
+          typename CPUAccDataType,
+          typename ALayout,
+          typename BLayout,
+          typename CLayout,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          index_t CAccNum>
+struct TestWmma
+{
+    auto PrepareGemmTensor(const ck::wmma_op_util::GemmParams& params)
+    {
+        auto f_host_tensor_descriptor =
+            [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+                if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+                {
+                    return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                                std::vector<std::size_t>({stride, 1}));
+                }
+                else
+                {
+                    return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                                std::vector<std::size_t>({1, stride}));
+                }
+            };
+
+        Tensor<ADataType> a_m_k(
+            f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{}));
+        Tensor<BDataType> b_n_k(
+            f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{}));
+        Tensor<CDataType> c_m_n_host_result(
+            f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
+        Tensor<CDataType> c_m_n_device_result(
+            f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
+
+        auto f_generate_tensor_value = [](auto& tensor, auto type) {
+            using dataType = decltype(type);
+
+            tensor.GenerateTensorValue(GeneratorTensor_2<dataType>{-5, 5});
+        };
+
+        f_generate_tensor_value(a_m_k, ADataType{});
+        f_generate_tensor_value(b_n_k, BDataType{});
+
+        return std::make_tuple(a_m_k, b_n_k, c_m_n_host_result, c_m_n_device_result);
+    }
+
+    auto operator()(const DeviceWmma& wmma_kernel)
+    {
+        std::cout << "ALayout = " << ALayout{}.name << ", BLayout = " << BLayout{}.name
+                  << ", CLayout = " << CLayout{}.name << std::endl;
+
+        // Arrange
+        ck::wmma_op_util::GemmParams params;
+        params.M       = 16;
+        params.N       = 16;
+        params.K       = 16;
+        params.StrideA = 16;
+        params.StrideB = 16;
+        params.StrideC = 16;
+
+        auto host_tensors = PrepareGemmTensor(params);
+
+        const Tensor<ADataType>& a  = std::get<0>(host_tensors);
+        const Tensor<BDataType>& b  = std::get<1>(host_tensors);
+        Tensor<CDataType>& c_host   = std::get<2>(host_tensors);
+        Tensor<CDataType>& c_device = std::get<3>(host_tensors);
+
+        auto a_element_op = AElementwiseOperation{};
+        auto b_element_op = BElementwiseOperation{};
+        auto c_element_op = CElementwiseOperation{};
+
+        using ReferenceGemmInstance =
+            ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                      BDataType,
+                                                      CDataType,
+                                                      CPUAccDataType,
+                                                      AElementwiseOperation,
+                                                      BElementwiseOperation,
+                                                      CElementwiseOperation>;
+        ck::wmma_op_util::RunHostGEMM<ReferenceGemmInstance>(
+            a, b, c_host, a_element_op, b_element_op, c_element_op);
+
+        // Act
+        bool is_supported = ck::wmma_op_util::RunDeviceGEMM(wmma_kernel, a, b, c_device);
+
+        if(is_supported)
+        {
+            // Assert
+            bool res = false;
+            if(std::is_same<CDataType, float>::value)
+            {
+                res = ck::utils::check_err(c_device.mData, c_host.mData);
+                std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
+            }
+            else if(std::is_same<CDataType, ck::half_t>::value)
+            {
+                res = ck::utils::check_err(c_device.mData, c_host.mData);
+                std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
+            }
+            else if(std::is_same<CDataType, ck::bhalf_t>::value)
+            {
+                // 0.5 Pixel Error Tolerance is introduced by Accumulator difference.
+                // BF16 WMMA Accumulator is in BF16 Type while On Host-side Accumulator is Float.
+                res = ck::utils::check_err(
+                    c_device.mData, c_host.mData, "Error: Incorrect results!", 0, 1.0);
+                std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
+            }
+            else if(std::is_same<CDataType, int8_t>::value)
+            {
+                res = ck::utils::check_err(c_device.mData, c_host.mData);
+                std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
+            }
+            else if(std::is_same<CDataType, double>::value)
+            {
+                res = ck::utils::check_err(c_device.mData, c_host.mData);
+                std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
+            }
+            else
+            {
+                std::cout << "UNSUPPORTED CDataType" << std::endl;
+            }
+
+            return res;
+        }
+        else
+        {
+            return true;
+        }
+    }
+};
+
+} // namespace wmma_op_util
+} // namespace ck