Merge branch 'uif2-initial' into uif2-migraphx

profiler/src/CMakeLists.txt

@@ -16,8 +16,8 @@ set(PROFILER_SOURCES
     profile_grouped_conv_fwd.cpp
     profile_grouped_conv_bwd_weight.cpp
     profile_reduce.cpp
-    profile_groupnorm.cpp
-    profile_layernorm.cpp
+    profile_groupnorm_fwd.cpp
+    profile_layernorm_fwd.cpp
     profile_max_pool3d_fwd.cpp
     profile_avg_pool3d_bwd.cpp
     profile_max_pool3d_bwd.cpp
@@ -28,9 +28,11 @@ set(PROFILER_SOURCES
     profile_grouped_conv_bwd_data.cpp
     profile_conv_tensor_rearrange.cpp
 )
+
 if(DL_KERNELS)
   list(APPEND PROFILER_SOURCES profile_batched_gemm_multi_d.cpp)
 endif()
+
 if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
   list(APPEND PROFILER_SOURCES profile_batched_gemm_gemm.cpp)
   list(APPEND PROFILER_SOURCES profile_gemm_fastgelu.cpp)
@@ -75,7 +77,7 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_w
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_weight_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_bias_relu_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_bias_relu_add_instance)
-target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_instance)
+target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_fwd_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_softmax_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_reduce_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batchnorm_instance)
@@ -110,4 +112,5 @@ if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
   target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_instance)
   target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_fastgelu_instance)
 endif()
+
 rocm_install(TARGETS ${PROFILER_EXECUTABLE} COMPONENT profiler)

profiler/src/profile_grouped_gemm.cpp

@@ -27,6 +27,8 @@ enum struct GemmDataType
     F16_F16_F16,    // 1
     BF16_BF16_BF16, // 2
     INT8_INT8_INT8, // 3
+    F8_F16_F16,     // 4
+    F16_F8_F16,     // 5
 };
 
 #define OP_NAME "grouped_gemm"
@@ -56,7 +58,7 @@ int profile_grouped_gemm(int argc, char* argv[])
     {
         std::cout
             << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
-            << "arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n"
+            << "arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: fp8@fp6; 5: f16@f8)\n"
             << "arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n"
             << "                     1: A[m, k] * B[n, k] = C[m, n];\n"
             << "                     2: A[k, m] * B[k, n] = C[m, n];\n"
@@ -169,6 +171,46 @@ int profile_grouped_gemm(int argc, char* argv[])
                                                                                    StrideCs,
                                                                                    kbatch);
     }
+    else if(data_type == GemmDataType::F8_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
+    {
+        ck::profiler::profile_grouped_gemm_impl<ck::f8_t,
+                                                ck::half_t,
+                                                ck::half_t,
+                                                float,
+                                                ck::tensor_layout::gemm::RowMajor,
+                                                ck::tensor_layout::gemm::RowMajor,
+                                                ck::tensor_layout::gemm::RowMajor>(do_verification,
+                                                                                   init_method,
+                                                                                   do_log,
+                                                                                   time_kernel,
+                                                                                   Ms,
+                                                                                   Ns,
+                                                                                   Ks,
+                                                                                   StrideAs,
+                                                                                   StrideBs,
+                                                                                   StrideCs,
+                                                                                   kbatch);
+    }
+    else if(data_type == GemmDataType::F16_F8_F16 && layout == GemmMatrixLayout::MK_KN_MN)
+    {
+        ck::profiler::profile_grouped_gemm_impl<ck::half_t,
+                                                ck::f8_t,
+                                                ck::half_t,
+                                                float,
+                                                ck::tensor_layout::gemm::RowMajor,
+                                                ck::tensor_layout::gemm::RowMajor,
+                                                ck::tensor_layout::gemm::RowMajor>(do_verification,
+                                                                                   init_method,
+                                                                                   do_log,
+                                                                                   time_kernel,
+                                                                                   Ms,
+                                                                                   Ns,
+                                                                                   Ks,
+                                                                                   StrideAs,
+                                                                                   StrideBs,
+                                                                                   StrideCs,
+                                                                                   kbatch);
+    }
     else
     {
         throw std::runtime_error("wrong! this GEMM data_type & layout is not implemented");

profiler/src/profile_groupnorm.cpp → profiler/src/profile_groupnorm_fwd.cpp

@@ -6,7 +6,7 @@
 #include <unordered_map>
 
 #include "profiler/data_type_enum.hpp"
-#include "profiler/profile_groupnorm_impl.hpp"
+#include "profiler/profile_groupnorm_fwd_impl.hpp"
 #include "profiler_operation_registry.hpp"
 
 using ck::index_t;

profiler/src/profile_layernorm.cpp → profiler/src/profile_layernorm_fwd.cpp

@@ -6,7 +6,7 @@
 #include <unordered_map>
 
 #include "profiler/data_type_enum.hpp"
-#include "profiler/profile_layernorm_impl.hpp"
+#include "profiler/profile_layernorm_fwd_impl.hpp"
 #include "profiler_operation_registry.hpp"
 
 using ck::index_t;
@@ -78,6 +78,9 @@ int profile_layernorm(int argc, char* argv[])
 
     using F16 = ck::half_t;
     using F32 = float;
+
+    if(length.size() == 2)
+    {
         constexpr int rank = 2;
 
         if(data_type == ck::DataTypeEnum::Half)
@@ -94,6 +97,30 @@ int profile_layernorm(int argc, char* argv[])
         {
             throw std::runtime_error("not implemented yet");
         }
+    }
+    else if(length.size() == 4)
+    {
+        constexpr int rank = 4;
+
+        if(data_type == ck::DataTypeEnum::Half)
+        {
+            ck::profiler::profile_layernorm_impl<F16, F16, F16, F32, F16, F32, false, rank>(
+                do_verification, init_method, do_log, time_kernel, length);
+        }
+        else if(data_type == ck::DataTypeEnum::Float)
+        {
+            ck::profiler::profile_layernorm_impl<F32, F32, F32, F32, F32, F32, false, rank>(
+                do_verification, init_method, do_log, time_kernel, length);
+        }
+        else
+        {
+            throw std::runtime_error("not implemented yet");
+        }
+    }
+    else
+    {
+        throw std::runtime_error("not implemented yet");
+    }
 
     return 0;
 }

profiler/src/profile_transpose.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "profiler/profile_transpose_impl.hpp"
+#include "profiler_operation_registry.hpp"
+
+enum struct MatrixLayout
+{
+    NCDHW, // 0
+    NCHWD, // 1
+};
+
+enum struct DataType
+{
+    F32_F32_F32_F32_F32, // 0
+    F16_F16_F16_F16_F16, // 1
+};
+
+#define OP_NAME "transpose"
+#define OP_DESC "Transpose"
+
+int profile_transpose(int argc, char* argv[])
+{
+    if(argc != 15)
+    {
+        printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
+        printf("arg2: data type (0: fp32; 1: fp16)\n");
+        // printf("arg3: matrix layout (NCDHW -> NDCHW);\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 13: N, C, D, H, W\n");
+        exit(1);
+    }
+
+    const auto data_type = static_cast<DataType>(std::stoi(argv[2]));
+    // const auto layout          = static_cast<MatrixLayout>(std::stoi(argv[3]));
+    const bool do_verification   = std::stoi(argv[3]);
+    const int init_method        = std::stoi(argv[4]);
+    const bool do_log            = std::stoi(argv[5]);
+    const bool time_kernel       = std::stoi(argv[6]);
+    std::vector<index_t> lengths = std::stoi(argv[7]);
+
+    /**const int N = std::stoi(argv[7]);
+    const int C = std::stoi(argv[8]);
+    const int D = std::stoi(argv[9]);
+    const int H = std::stoi(argv[10]);
+    const int W = std::stoi(argv[11]);**/
+
+    using F32 = float;
+    using F16 = ck::half_t;
+
+    auto profile = [&](auto a_type, auto b_type) {
+        using ADataType = decltype(a_type);
+        using BDataType = decltype(b_type);
+
+        bool pass = ck::profiler::profile_transpose_impl<ADataType, BDataType>(
+            do_verification, init_method, do_log, time_kernel, lengths);
+
+        return pass ? 0 : 1;
+    };
+
+    if(data_type == GemmDataType::F32_F32_F32_F32_F32)
+    {
+        return profile(F32{}, F32{});
+    }
+    else if(data_type == GemmDataType::F16_F16_F16_F16_F16)
+    {
+        return profile(F16{}, F16{});
+    }
+    else
+    {
+        std::cout << "this data_type & layout is not implemented" << std::endl;
+
+        return 1;
+    }
+}
+
+REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_transpose);

script/cmake-ck-dev.sh

@@ -8,8 +8,7 @@ MY_PROJECT_SOURCE=$1
 cmake                                                                                             \
 -D CMAKE_PREFIX_PATH=/opt/rocm                                                                    \
 -D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc                                                         \
--D CMAKE_CXX_FLAGS="-std=c++17 -O3 -ftemplate-backtrace-limit=0  -fPIE  -Wno-gnu-line-marker      \
--save-temps=$PWD"                                                                                 \
+-D CMAKE_CXX_FLAGS="-std=c++17 -O3 -ftemplate-backtrace-limit=0  -fPIE  -Wno-gnu-line-marker"     \
 -D CMAKE_BUILD_TYPE=Release                                                                       \
 -D BUILD_DEV=ON                                                                                   \
 -D GPU_TARGETS="gfx908;gfx90a;gfx940"                                                             \

script/hip_fatbin_insert

+SECTIONS {
+ .hipFatBinSegment : { *(.hipFatBinSegment) }
+} INSERT AFTER .bss
+
+SECTIONS {
+  .hip_fatbin : { *(.hip_fatbin) }
+} INSERT AFTER .hipFatBinSegment

test/CMakeLists.txt

@@ -144,7 +144,7 @@ else()
     add_subdirectory(grouped_convnd_bwd_weight)
     add_subdirectory(block_to_ctile_map)
     add_subdirectory(softmax)
-    add_subdirectory(normalization)
+    add_subdirectory(normalization_fwd)
     add_subdirectory(data_type)
     add_subdirectory(elementwise_normalization)
     add_subdirectory(batchnorm)
@@ -153,6 +153,7 @@ else()
     add_subdirectory(batched_gemm_multi_d)
     add_subdirectory(grouped_convnd_bwd_data)
     add_subdirectory(conv_tensor_rearrange)
+    add_subdirectory(transpose)
     if(GPU_TARGETS MATCHES "gfx11")
         add_subdirectory(wmma_op)
     endif()

test/grouped_convnd_fwd/CMakeLists.txt

 add_gtest_executable(test_grouped_convnd_fwd test_grouped_convnd_fwd.cpp)
 target_link_libraries(test_grouped_convnd_fwd PRIVATE utility device_grouped_conv1d_fwd_instance device_grouped_conv2d_fwd_instance device_grouped_conv3d_fwd_instance)
 
+add_gtest_executable(test_grouped_convnd_fwd_multi_ab_interface test_grouped_convnd_fwd_multi_ab_interface.cpp)
+target_link_libraries(test_grouped_convnd_fwd_multi_ab_interface PRIVATE utility)

test/grouped_convnd_fwd/test_grouped_convnd_fwd_multi_ab_interface.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <cstdlib>
+#include <iostream>
+#include <initializer_list>
+#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 "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
+
+#include "ck/host_utility/device_prop.hpp"
+
+#include "ck/library/utility/convolution_parameter.hpp"
+#include "ck/library/utility/algorithm.hpp"
+#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
+
+#include <gtest/gtest.h>
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using ScaleAdd    = ck::tensor_operation::element_wise::ScaleAdd;
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+template <typename DataType,
+          typename InDataTypes,
+          typename WeiDataTypes,
+          typename InElementOp,
+          typename WeiElementOp>
+class TestGroupedConvndFwdMultiABInterfaceBase : public ::testing::Test
+{
+    protected:
+    static constexpr ck::index_t NDimSpatial = 3;
+    static constexpr ck::index_t NumAs       = 2;
+    static constexpr ck::index_t NumBs       = 2;
+    static constexpr auto ConvSpec =
+        ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
+    static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
+    using InLayout                 = ck::tensor_layout::convolution::GNDHWC;
+    using WeiLayout                = ck::tensor_layout::convolution::GKZYXC;
+    using OutLayout                = ck::tensor_layout::convolution::GNDHWK;
+    using OutElementOp             = PassThrough;
+
+    using DeviceGroupedConvNDMultiABFwdInstance =
+        ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<
+            NDimSpatial,
+            InLayout,
+            WeiLayout,
+            ck::Tuple<>,
+            OutLayout,
+            InDataTypes,
+            WeiDataTypes,
+            DataType,
+            DataType,
+            ck::Tuple<>,
+            DataType,
+            InElementOp,
+            WeiElementOp,
+            OutElementOp,
+            ConvSpec,    // ConvForwardSpecialization
+            GemmSpec,    // GemmSpecialization
+            1,           //
+            256,         // BlockSize
+            128,         // MPerBlock
+            256,         // NPerBlock
+            32,          // KPerBlock
+            8,           // AK1
+            8,           // BK1
+            32,          // MPerXdl
+            32,          // NPerXdl
+            2,           // MXdlPerWave
+            4,           // NXdlPerWave
+            S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
+            S<1, 0, 2>,  // ABlockTransferThreadClusterArrangeOrder
+            S<1, 0, 2>,  // ABlockTransferSrcAccessOrder
+            2,           // ABlockTransferSrcVectorDim
+            8,           // ABlockTransferSrcScalarPerVector
+            8,           // ABlockTransferDstScalarPerVector_AK1
+            1,           // ABlockLdsExtraM
+            S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
+            S<1, 0, 2>,  // BBlockTransferThreadClusterArrangeOrder
+            S<1, 0, 2>,  // BBlockTransferSrcAccessOrder
+            2,           // BBlockTransferSrcVectorDim
+            8,           // BBlockTransferSrcScalarPerVector
+            8,           // BBlockTransferDstScalarPerVector_BK1
+            1,           // BBlockLdsExtraN
+            1,
+            1,
+            S<1, 32, 1, 8>,
+            8>;
+
+    const ck::utils::conv::ConvParam conv_param{
+        3, 1, 16, 16, 8, {3, 3, 3}, {17, 17, 17}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}};
+
+    void SetUp() override
+    {
+        if(!ck::is_xdl_supported())
+        {
+            GTEST_SKIP();
+        }
+    }
+
+    template <typename ADataType, typename BDataType>
+    bool Run(ADataType as, BDataType bs)
+    {
+        const auto in_g_n_c_wis_desc =
+            ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
+                conv_param);
+        const auto wei_g_k_c_xs_desc =
+            ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
+                conv_param);
+        const auto out_g_n_k_wos_desc =
+            ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
+                conv_param);
+
+        std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_lengths{};
+        std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_strides{};
+        std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
+        std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
+        std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_lengths{};
+        std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_strides{};
+        std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
+        std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
+        std::array<ck::index_t, NDimSpatial> input_left_pads{};
+        std::array<ck::index_t, NDimSpatial> input_right_pads{};
+
+        auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
+
+        copy(in_g_n_c_wis_desc.GetLengths(), a_g_n_c_wis_lengths);
+        copy(in_g_n_c_wis_desc.GetStrides(), a_g_n_c_wis_strides);
+        copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
+        copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
+        copy(out_g_n_k_wos_desc.GetLengths(), e_g_n_k_wos_lengths);
+        copy(out_g_n_k_wos_desc.GetStrides(), e_g_n_k_wos_strides);
+        copy(conv_param.conv_filter_strides_, conv_filter_strides);
+        copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
+        copy(conv_param.input_left_pads_, input_left_pads);
+        copy(conv_param.input_right_pads_, input_right_pads);
+
+        std::array<const void*, 0> ds{};
+
+        // do Conv
+        auto conv     = DeviceGroupedConvNDMultiABFwdInstance{};
+        auto invoker  = conv.MakeInvoker();
+        auto argument = conv.MakeArgument(as,
+                                          bs,
+                                          ds,
+                                          nullptr,
+                                          a_g_n_c_wis_lengths,
+                                          a_g_n_c_wis_strides,
+                                          b_g_k_c_xs_lengths,
+                                          b_g_k_c_xs_strides,
+                                          {},
+                                          {},
+                                          e_g_n_k_wos_lengths,
+                                          e_g_n_k_wos_strides,
+                                          conv_filter_strides,
+                                          conv_filter_dilations,
+                                          input_left_pads,
+                                          input_right_pads,
+                                          InElementOp{},
+                                          WeiElementOp{},
+                                          OutElementOp{});
+
+        return conv.IsSupportedArgument(argument);
+    }
+};
+
+class TestGroupedConvndFwdMultiAInterface
+    : public TestGroupedConvndFwdMultiABInterfaceBase<float,
+                                                      ck::Tuple<float, float>,
+                                                      float,
+                                                      ScaleAdd,
+                                                      PassThrough>
+{
+};
+
+class TestGroupedConvndFwdMultiBInterface
+    : public TestGroupedConvndFwdMultiABInterfaceBase<float,
+                                                      float,
+                                                      ck::Tuple<float, float>,
+                                                      PassThrough,
+                                                      ScaleAdd>
+{
+};
+
+class TestGroupedConvndFwdMultiABInterface
+    : public TestGroupedConvndFwdMultiABInterfaceBase<float,
+                                                      ck::Tuple<float, float>,
+                                                      ck::Tuple<float, float>,
+                                                      ScaleAdd,
+                                                      ScaleAdd>
+{
+};
+
+class TestGroupedConvndFwdInterface
+    : public TestGroupedConvndFwdMultiABInterfaceBase<float, float, float, PassThrough, PassThrough>
+{
+};
+
+TEST_F(TestGroupedConvndFwdMultiAInterface, MultiA)
+{
+    std::array<const void*, NumAs> as{nullptr, nullptr};
+    const void* b = nullptr;
+
+    EXPECT_TRUE(this->template Run(as, b));
+}
+
+TEST_F(TestGroupedConvndFwdMultiBInterface, MultiB)
+{
+    const void* a = nullptr;
+    std::array<const void*, NumBs> bs{nullptr, nullptr};
+
+    EXPECT_TRUE(this->template Run(a, bs));
+}
+
+TEST_F(TestGroupedConvndFwdMultiABInterface, MultiAB)
+{
+    std::array<const void*, NumAs> as{nullptr, nullptr};
+    std::array<const void*, NumBs> bs{nullptr, nullptr};
+
+    EXPECT_TRUE(this->template Run(as, bs));
+}
+
+TEST_F(TestGroupedConvndFwdInterface, SingleAB)
+{
+    const void* a = nullptr;
+    const void* b = nullptr;
+
+    EXPECT_TRUE(this->template Run(a, b));
+}

test/grouped_gemm/test_grouped_gemm_interface.cpp

@@ -108,6 +108,10 @@ TEST_F(TestGGemmSplitKInterface_MKNKMN, KLoops)
 
     // kloops % 2
     Ks = std::vector<int>{256, 512, 320, 768};
+    EXPECT_FALSE(
+        DefaultGGemmInstance{}.IsSupported(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, kbatch));
+
+    Ks = std::vector<int>{256, 512, 384, 768};
     EXPECT_TRUE(
         DefaultGGemmInstance{}.IsSupported(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, kbatch));
 

test/normalization/CMakeLists.txt

-add_custom_target(test_normalization)
-add_gtest_executable(test_layernorm2d_fp32 test_layernorm2d_fp32.cpp)
-if(result EQUAL 0)
-  target_link_libraries(test_layernorm2d_fp32 PRIVATE utility device_normalization_instance)
-  add_dependencies(test_normalization test_layernorm2d_fp32)
-endif()
-add_gtest_executable(test_groupnorm_fp32 test_groupnorm_fp32.cpp)
-if(result EQUAL 0)
-  target_link_libraries(test_groupnorm_fp32 PRIVATE utility device_normalization_instance)
-  add_dependencies(test_normalization test_groupnorm_fp32)
-endif()
-add_gtest_executable(test_layernorm2d_fp16 test_layernorm2d_fp16.cpp)
-if(result EQUAL 0)
-  target_link_libraries(test_layernorm2d_fp16 PRIVATE utility device_normalization_instance)
-  add_dependencies(test_normalization test_layernorm2d_fp16)
-endif()
-add_gtest_executable(test_groupnorm_fp16 test_groupnorm_fp16.cpp)
-if(result EQUAL 0)
-  target_link_libraries(test_groupnorm_fp16 PRIVATE utility device_normalization_instance)
-  add_dependencies(test_normalization test_groupnorm_fp16)
-endif()

test/normalization_fwd/CMakeLists.txt

+add_custom_target(test_normalization_fwd)
+add_gtest_executable(test_layernorm2d_fwd_fp32 test_layernorm2d_fwd_fp32.cpp)
+if(result EQUAL 0)
+  target_link_libraries(test_layernorm2d_fwd_fp32 PRIVATE utility device_normalization_fwd_instance)
+  add_dependencies(test_normalization_fwd test_layernorm2d_fwd_fp32)
+endif()
+
+add_gtest_executable(test_groupnorm_fwd_fp32 test_groupnorm_fwd_fp32.cpp)
+if(result EQUAL 0)
+  target_link_libraries(test_groupnorm_fwd_fp32 PRIVATE utility device_normalization_fwd_instance)
+  add_dependencies(test_normalization_fwd test_groupnorm_fwd_fp32)
+endif()
+
+add_gtest_executable(test_layernorm2d_fwd_fp16 test_layernorm2d_fwd_fp16.cpp)
+if(result EQUAL 0)
+  target_link_libraries(test_layernorm2d_fwd_fp16 PRIVATE utility device_normalization_fwd_instance)
+  add_dependencies(test_normalization_fwd test_layernorm2d_fwd_fp16)
+endif()
+
+add_gtest_executable(test_layernorm4d_fwd_fp16 test_layernorm4d_fwd_fp16.cpp)
+if(result EQUAL 0)
+  target_link_libraries(test_layernorm4d_fwd_fp16 PRIVATE utility device_normalization_fwd_instance)
+  add_dependencies(test_normalization_fwd test_layernorm4d_fwd_fp16)
+endif()
+
+add_gtest_executable(test_groupnorm_fwd_fp16 test_groupnorm_fwd_fp16.cpp)
+if(result EQUAL 0)
+  target_link_libraries(test_groupnorm_fwd_fp16 PRIVATE utility device_normalization_fwd_instance)
+  add_dependencies(test_normalization_fwd test_groupnorm_fwd_fp16)
+endif()

test/normalization/test_groupnorm_fp16.cpp → test/normalization_fwd/test_groupnorm_fwd_fp16.cpp

@@ -2,7 +2,7 @@
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "gtest/gtest.h"
-#include "profiler/profile_groupnorm_impl.hpp"
+#include "profiler/profile_groupnorm_fwd_impl.hpp"
 
 using F16 = ck::half_t;
 using F32 = float;

test/normalization/test_groupnorm_fp32.cpp → test/normalization_fwd/test_groupnorm_fwd_fp32.cpp

@@ -2,7 +2,7 @@
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "gtest/gtest.h"
-#include "profiler/profile_groupnorm_impl.hpp"
+#include "profiler/profile_groupnorm_fwd_impl.hpp"
 
 using F16 = ck::half_t;
 using F32 = float;

test/normalization/test_layernorm2d_fp16.cpp → test/normalization_fwd/test_layernorm2d_fwd_fp16.cpp

@@ -2,7 +2,7 @@
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "gtest/gtest.h"
-#include "profiler/profile_layernorm_impl.hpp"
+#include "profiler/profile_layernorm_fwd_impl.hpp"
 
 using F16 = ck::half_t;
 using F32 = float;

test/normalization/test_layernorm2d_fp32.cpp → test/normalization_fwd/test_layernorm2d_fwd_fp32.cpp

@@ -2,7 +2,7 @@
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "gtest/gtest.h"
-#include "profiler/profile_layernorm_impl.hpp"
+#include "profiler/profile_layernorm_fwd_impl.hpp"
 
 using F16 = ck::half_t;
 using F32 = float;

test/normalization_fwd/test_layernorm4d_fwd_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "gtest/gtest.h"
+#include "profiler/profile_layernorm_fwd_impl.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+using ck::index_t;
+
+template <typename Tuple>
+class TestLayernorm4d : public ::testing::Test
+{
+    protected:
+    using XDataType              = std::tuple_element_t<0, Tuple>;
+    using GammaDataType          = std::tuple_element_t<1, Tuple>;
+    using BetaDataType           = std::tuple_element_t<2, Tuple>;
+    using ComputeDataType        = std::tuple_element_t<3, Tuple>;
+    using YDataType              = std::tuple_element_t<4, Tuple>;
+    using SaveMeanInvStdDataType = std::tuple_element_t<5, Tuple>;
+
+    void Run()
+    {
+        // [N, D], reduce D
+        std::vector<std::vector<ck::index_t>> lengths = {
+            {1, 1, 1, 1}, {7, 7, 7, 7}, {256, 16, 16, 8}};
+
+        for(auto length : lengths)
+        {
+            bool success = ck::profiler::profile_layernorm_impl<XDataType,
+                                                                GammaDataType,
+                                                                BetaDataType,
+                                                                ComputeDataType,
+                                                                YDataType,
+                                                                SaveMeanInvStdDataType,
+                                                                true,
+                                                                4>(true, 2, false, false, length);
+            EXPECT_TRUE(success);
+        }
+    }
+};
+
+using KernelTypes = ::testing::Types<
+    // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType>
+    std::tuple<F16, F16, F16, F32, F16, F32>>;
+
+TYPED_TEST_SUITE(TestLayernorm4d, KernelTypes);
+TYPED_TEST(TestLayernorm4d, Test_FP16) { this->Run(); }

test/transpose/CMakeLists.txt

+list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
+set(target 0)
+foreach(gpu IN LISTS GPU_TARGETS)
+ if(gpu IN_LIST gpu_list AND target EQUAL 0)
+   add_gtest_executable(test_transpose test_transpose.cpp)
+   target_link_libraries(test_transpose PRIVATE utility device_transpose_instance)
+   set(target 1)
+ endif()
+endforeach()

test/transpose/test_transpose.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <tuple>
+
+#include "gtest/gtest.h"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "test_transpose_util.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+
+template <typename Tuple>
+class TestTranspose : public ::testing::Test
+{
+};
+
+// clang-format off
+using KernelTypes = ::testing::Types<
+    std::tuple<      F16,       F16>,
+    std::tuple<      F32,       F32>
+    >;
+// clang-format on
+
+TYPED_TEST_SUITE(TestTranspose, KernelTypes);
+
+//#include "test_transpose_ut_cases.inc"