Merge remote-tracking branch 'origin/develop' into aosewski/ggemm_multi_d2

client_example/16_convnd_fwd/CMakeLists.txt

 if((DTYPES MATCHES "fp16") OR NOT DEFINED DTYPES)
     add_executable(client_conv3d_fwd_fp16 conv3d_fwd_fp16.cpp)
-    target_link_libraries(client_conv3d_fwd_fp16 PRIVATE composable_kernel::device_operations)
+    target_link_libraries(client_conv3d_fwd_fp16 PRIVATE composable_kernel::device_conv_operations)
 
 endif()
 
 if((DTYPES MATCHES "fp8") OR NOT DEFINED DTYPES)
     add_executable(client_conv3d_fwd_fp16_comp_fp8 conv3d_fwd_fp16_comp_fp8.cpp)
-    target_link_libraries(client_conv3d_fwd_fp16_comp_fp8 PRIVATE composable_kernel::device_operations)
+    target_link_libraries(client_conv3d_fwd_fp16_comp_fp8 PRIVATE composable_kernel::device_conv_operations)
 endif()
 
 if((DTYPES MATCHES "fp32") OR NOT DEFINED DTYPES)
     add_executable(client_conv3d_fwd_fp32 conv3d_fwd_fp32.cpp)
-    target_link_libraries(client_conv3d_fwd_fp32 PRIVATE composable_kernel::device_operations)
+    target_link_libraries(client_conv3d_fwd_fp32 PRIVATE composable_kernel::device_conv_operations)
 endif()

client_example/17_grouped_gemm_fastgelu/CMakeLists.txt

 add_executable(client_grouped_gemm_fastgelu grouped_gemm_fastgelu.cpp)
-target_link_libraries(client_grouped_gemm_fastgelu PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_grouped_gemm_fastgelu PRIVATE composable_kernel::device_gemm_operations)
\ No newline at end of file

client_example/18_groupnorm/CMakeLists.txt

-add_executable(client_groupnorm_swish groupnorm_swish.cpp)
-target_link_libraries(client_groupnorm_swish PRIVATE composable_kernel::device_operations)
+add_executable(client_groupnorm_bwd_data groupnorm_bwd_data.cpp)
+target_link_libraries(client_groupnorm_bwd_data PRIVATE composable_kernel::device_other_operations)
+
+add_executable(client_groupnorm_swish_fwd groupnorm_swish_fwd.cpp)
+target_link_libraries(client_groupnorm_swish_fwd PRIVATE composable_kernel::device_other_operations)

client_example/18_groupnorm/groupnorm_bwd_data.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iomanip>
+#include <vector>
+#include <iostream>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_normalization_bwd_data.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/tensor_operation_instance/gpu/groupnorm_bwd_data.hpp"
+
+using DYDataType         = float;
+using XDataType          = float;
+using GammaDataType      = float;
+using MeanInvStdDataType = float;
+using DXDataType         = float;
+
+constexpr int Rank         = 5;
+constexpr int NumReduceDim = 3;
+
+struct SimpleDeviceMem
+{
+    SimpleDeviceMem() = delete;
+
+    SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
+    {
+        (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
+    }
+
+    void* GetDeviceBuffer() { return p_mem_; }
+
+    ~SimpleDeviceMem() { (void)hipFree(p_mem_); }
+
+    void* p_mem_;
+};
+
+int main(int argc, char* argv[])
+{
+    ck::index_t N = 32;
+    ck::index_t H = 16;
+    ck::index_t W = 16;
+    ck::index_t G = 64;
+    ck::index_t C = 128;
+
+    std::size_t length = N * H * W * G * C;
+
+    std::vector<ck::index_t> strideDy = {H * W * G * C, W * G * C, G * C, C, 1};
+    std::vector<ck::index_t> strideX  = strideDy;
+    std::vector<ck::index_t> strideDx = strideDy;
+
+    std::vector<ck::index_t> strideGamma      = {0, 0, 0, C, 1};
+    std::vector<ck::index_t> strideMeanInvStd = {G, 0, 0, 1, 0};
+
+    SimpleDeviceMem dy_dev(sizeof(DYDataType) * length);
+    SimpleDeviceMem x_dev(sizeof(XDataType) * length);
+    SimpleDeviceMem gamma_dev(sizeof(GammaDataType) * G * C);
+    SimpleDeviceMem mean_dev(sizeof(MeanInvStdDataType) * N * G);
+    SimpleDeviceMem inv_std_dev(sizeof(MeanInvStdDataType) * N * G);
+    SimpleDeviceMem dx_dev(sizeof(DXDataType) * length);
+
+    using DeviceOp = ck::tensor_operation::device::DeviceNormalizationBwdData<DYDataType,
+                                                                              XDataType,
+                                                                              GammaDataType,
+                                                                              MeanInvStdDataType,
+                                                                              DXDataType,
+                                                                              Rank,
+                                                                              NumReduceDim>;
+
+    // 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;
+
+    std::string best_op_name;
+    bool found            = false;
+    int best_op_id        = -1;
+    float best_ave_time   = std::numeric_limits<float>::max();
+    float best_gb_per_sec = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr      = op_ptrs[i];
+        auto argument_ptr = op_ptr->MakeArgumentPointer({N, H, W, G, C},
+                                                        strideDy,
+                                                        strideX,
+                                                        strideGamma,
+                                                        strideMeanInvStd,
+                                                        strideMeanInvStd,
+                                                        strideDx,
+                                                        {1, 2, 4}, // reduceDims
+                                                        dy_dev.GetDeviceBuffer(),
+                                                        x_dev.GetDeviceBuffer(),
+                                                        gamma_dev.GetDeviceBuffer(),
+                                                        mean_dev.GetDeviceBuffer(),
+                                                        inv_std_dev.GetDeviceBuffer(),
+                                                        dx_dev.GetDeviceBuffer());
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
+            SimpleDeviceMem workspace(workspace_sz);
+            op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
+
+            float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            std::size_t num_byte = sizeof(DYDataType) * length + sizeof(XDataType) * length +
+                                   sizeof(GammaDataType) * G * C +
+                                   sizeof(MeanInvStdDataType) * N * G * 2 +
+                                   sizeof(DXDataType) * length;
+
+            float gb_per_sec = num_byte / 1.E6 / ave_time;
+
+            std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec << " GB/s, "
+                      << op_name << std::endl;
+
+            if(ave_time < best_ave_time)
+            {
+                found           = true;
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_ave_time   = ave_time;
+                best_gb_per_sec = gb_per_sec;
+            }
+        }
+        else
+        {
+            std::cout << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    // run the best intance
+    if(found)
+    {
+        std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
+                  << best_op_name << std::endl;
+
+        auto& op_ptr = op_ptrs[best_op_id];
+        std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
+                  << std::endl;
+
+        auto argument_ptr = op_ptr->MakeArgumentPointer({N, H, W, G, C},
+                                                        strideDy,
+                                                        strideX,
+                                                        strideGamma,
+                                                        strideMeanInvStd,
+                                                        strideMeanInvStd,
+                                                        strideDx,
+                                                        {1, 2, 4}, // reduceDims
+                                                        dy_dev.GetDeviceBuffer(),
+                                                        x_dev.GetDeviceBuffer(),
+                                                        gamma_dev.GetDeviceBuffer(),
+                                                        mean_dev.GetDeviceBuffer(),
+                                                        inv_std_dev.GetDeviceBuffer(),
+                                                        dx_dev.GetDeviceBuffer());
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
+            SimpleDeviceMem workspace(workspace_sz);
+            op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
+
+            invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
+        }
+
+        std::cout << "Done" << std::endl;
+    }
+
+    return 0;
+}

client_example/19_pool/CMakeLists.txt

 add_executable(client_max_pool2d_fwd max_pool2d_fwd.cpp)
-target_link_libraries(client_max_pool2d_fwd PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_max_pool2d_fwd PRIVATE composable_kernel::device_other_operations)
 
 add_executable(client_max_pool2d_bwd max_pool2d_bwd.cpp)
-target_link_libraries(client_max_pool2d_bwd PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_max_pool2d_bwd PRIVATE composable_kernel::device_other_operations)
 
 add_executable(client_avg_pool3d_fwd avg_pool3d_fwd.cpp)
-target_link_libraries(client_avg_pool3d_fwd PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_avg_pool3d_fwd PRIVATE composable_kernel::device_other_operations)
 
 add_executable(client_avg_pool3d_bwd avg_pool3d_bwd.cpp)
-target_link_libraries(client_avg_pool3d_bwd PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_avg_pool3d_bwd PRIVATE composable_kernel::device_other_operations)

client_example/20_splitk_gemm/CMakeLists.txt

 if((DTYPES MATCHES "fp8" AND DTYPES MATCHES "fp16") OR NOT DEFINED DTYPES)
   add_executable(client_splitK_gemm splitK_gemm_fp16_f8.cpp)
-  target_link_libraries(client_splitK_gemm PRIVATE composable_kernel::device_operations)
+  target_link_libraries(client_splitK_gemm PRIVATE composable_kernel::device_gemm_operations)
 endif()

client_example/20_splitk_gemm/splitK_gemm_fp16_f8.cpp

@@ -191,6 +191,7 @@ int main(int argc, char* argv[])
               << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
 
     // run the best intance
+    if(found)
     {
         auto& op_ptr = op_ptrs[best_op_id];
 

client_example/21_grouped_gemm_bias/CMakeLists.txt

 add_executable(client_grouped_gemm_fixed_nk_bias_fp16 grouped_gemm_fixed_nk_bias_fp16.cpp)
-target_link_libraries(client_grouped_gemm_fixed_nk_bias_fp16 PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_grouped_gemm_fixed_nk_bias_fp16 PRIVATE composable_kernel::device_gemm_operations)

client_example/22_grouped_gemm/CMakeLists.txt

 add_executable(client_grouped_gemm_fixed_nk_fp16 grouped_gemm_fixed_nk_fp16.cpp)
-target_link_libraries(client_grouped_gemm_fixed_nk_fp16 PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_grouped_gemm_fixed_nk_fp16 PRIVATE composable_kernel::device_gemm_operations)
 
 add_executable(client_grouped_gemm_fixed_nk_fp8 grouped_gemm_fixed_nk_fp8.cpp)
-target_link_libraries(client_grouped_gemm_fixed_nk_fp8 PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_grouped_gemm_fixed_nk_fp8 PRIVATE composable_kernel::device_gemm_operations)
 
 add_executable(client_grouped_gemm_fixed_nk_i8 grouped_gemm_fixed_nk_i8.cpp)
-target_link_libraries(client_grouped_gemm_fixed_nk_i8 PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_grouped_gemm_fixed_nk_i8 PRIVATE composable_kernel::device_gemm_operations)

client_example/22_im2col_col2im/CMakeLists.txt

 add_executable(client_image_to_column image_to_column.cpp)
-target_link_libraries(client_image_to_column PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_image_to_column PRIVATE composable_kernel::device_other_operations)
 
 add_executable(client_column_to_image column_to_image.cpp)
-target_link_libraries(client_column_to_image PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_column_to_image PRIVATE composable_kernel::device_other_operations)

client_example/23_elementwise_transpose/CMakeLists.txt

 add_executable(client_elementwise_transpose3d elementwise_transpose_3d.cpp)
-target_link_libraries(client_elementwise_transpose3d PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_elementwise_transpose3d PRIVATE composable_kernel::device_other_operations)

client_example/23_elementwise_transpose/elementwise_transpose_3d.cpp

@@ -117,6 +117,7 @@ int main()
               << best_op_name << std::endl;
 
     // run the best intance
+    if(found)
     {
         auto& op_ptr = op_ptrs[best_op_id];
         std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()

client_example/23_grouped_convnd_fwd_scaleadd_scaleadd_relu/CMakeLists.txt

 add_executable(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_fp32 grouped_conv_fwd_scaleadd_scaleadd_relu_fp32.cpp)
-target_link_libraries(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_fp32 PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_fp32 PRIVATE composable_kernel::device_conv_operations)
 
 add_executable(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_fp16 grouped_conv_fwd_scaleadd_scaleadd_relu_fp16.cpp)
-target_link_libraries(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_fp16 PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_fp16 PRIVATE composable_kernel::device_conv_operations)
 
 add_executable(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_bf16 grouped_conv_fwd_scaleadd_scaleadd_relu_bf16.cpp)
-target_link_libraries(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_bf16 PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_bf16 PRIVATE composable_kernel::device_conv_operations)
 
 add_executable(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_int8 grouped_conv_fwd_scaleadd_scaleadd_relu_int8.cpp)
-target_link_libraries(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_int8 PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_int8 PRIVATE composable_kernel::device_conv_operations)

client_example/23_grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu.inc

@@ -16,6 +16,7 @@
 using InLayout             = ck::tensor_layout::convolution::NDHWGC;
 using WeiLayout            = ck::tensor_layout::convolution::GKZYXC;
 using OutLayout            = ck::tensor_layout::convolution::NDHWGK;
+using BiasLayout           = ck::tensor_layout::convolution::G_K;
 using PassThrough          = ck::tensor_operation::element_wise::PassThrough;
 using ScaleAddScaleAddRelu = ck::tensor_operation::element_wise::ScaleAddScaleAddRelu;
 
@@ -64,6 +65,9 @@ int execute_conv_fwd_scaleadd_scaleadd_relu()
     std::array<ck::index_t, 6> out_lengths{G, N, K, Do, Ho, Wo};
     std::array<ck::index_t, 6> out_strides{
         K, Do * Ho * Wo * G * K, 1, Ho * Wo * G * K, Wo * G * K, G * K};
+    // Logical broadcast bias (we have to pass bias lengths in the same format as output - GNKDHW)
+    std::array<ck::index_t, 6> bias_lengths{G, 1, K, 1, 1, 1};
+    std::array<ck::index_t, 6> bias_strides{K, 0, 1, 0, 0, 0};
 
     std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1, 1};
     std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1, 1};
@@ -74,13 +78,13 @@ int execute_conv_fwd_scaleadd_scaleadd_relu()
     SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Z * Y * X * C);
     SimpleDeviceMem out(sizeof(OutDataType) * N * Do * Ho * Wo * G * K);
     SimpleDeviceMem d0(sizeof(std::tuple_element_t<0, DDataTypes>) * N * Do * Ho * Wo * G * K);
-    SimpleDeviceMem d1(sizeof(std::tuple_element_t<1, DDataTypes>) * N * Do * Ho * Wo * G * K);
+    SimpleDeviceMem d1(sizeof(std::tuple_element_t<1, DDataTypes>) * G * K);
 
     using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<
         NumDimSpatial,
         InLayout,
         WeiLayout,
-        ck::Tuple<OutLayout, OutLayout>,
+        ck::Tuple<OutLayout, BiasLayout>,
         OutLayout,
         InDataType,
         WeiDataType,
@@ -117,8 +121,8 @@ int execute_conv_fwd_scaleadd_scaleadd_relu()
                                         in_strides,
                                         wei_lengths,
                                         wei_strides,
-                                        {out_lengths, out_lengths},
-                                        {out_strides, out_strides},
+                                        {out_lengths, bias_lengths},
+                                        {out_strides, bias_strides},
                                         out_lengths,
                                         out_strides,
                                         filter_strides,
@@ -187,8 +191,8 @@ int execute_conv_fwd_scaleadd_scaleadd_relu()
                                         in_strides,
                                         wei_lengths,
                                         wei_strides,
-                                        {out_lengths, out_lengths},
-                                        {out_strides, out_strides},
+                                        {out_lengths, bias_lengths},
+                                        {out_strides, bias_strides},
                                         out_lengths,
                                         out_strides,
                                         filter_strides,

client_example/24_grouped_convnd_fwd_scaleadd_ab/CMakeLists.txt

 add_executable(client_grouped_convnd_fwd_scaleadd_ab_fp32 grouped_conv_fwd_scaleadd_ab_fp32.cpp)
-target_link_libraries(client_grouped_convnd_fwd_scaleadd_ab_fp32 PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_grouped_convnd_fwd_scaleadd_ab_fp32 PRIVATE composable_kernel::device_conv_operations)
 
 add_executable(client_grouped_convnd_fwd_scaleadd_ab_fp16 grouped_conv_fwd_scaleadd_ab_fp16.cpp)
-target_link_libraries(client_grouped_convnd_fwd_scaleadd_ab_fp16 PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_grouped_convnd_fwd_scaleadd_ab_fp16 PRIVATE composable_kernel::device_conv_operations)
 
 add_executable(client_grouped_convnd_fwd_scaleadd_ab_bf16 grouped_conv_fwd_scaleadd_ab_bf16.cpp)
-target_link_libraries(client_grouped_convnd_fwd_scaleadd_ab_bf16 PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_grouped_convnd_fwd_scaleadd_ab_bf16 PRIVATE composable_kernel::device_conv_operations)
 
 add_executable(client_grouped_convnd_fwd_scaleadd_ab_int8 grouped_conv_fwd_scaleadd_ab_int8.cpp)
-target_link_libraries(client_grouped_convnd_fwd_scaleadd_ab_int8 PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_grouped_convnd_fwd_scaleadd_ab_int8 PRIVATE composable_kernel::device_conv_operations)

client_example/25_tensor_transforms/CMakeLists.txt

+add_executable(client_tensor_transform tensor_transform.cpp)
+target_link_libraries(client_tensor_transform PRIVATE composable_kernel::device_other_operations)
+add_executable(client_tensor_transform_using_wrapper tensor_transform_using_wrapper.cpp)
+target_link_libraries(client_tensor_transform_using_wrapper PRIVATE composable_kernel::device_other_operations)

client_example/25_tensor_transforms/tensor_transform.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+
+#include "ck/ck.hpp"
+
+#include "ck/utility/number.hpp"
+#include "ck/utility/tuple.hpp"
+#include "ck/utility/sequence.hpp"
+
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+
+static constexpr auto I0 = ck::Number<0>{};
+static constexpr auto I1 = ck::Number<1>{};
+static constexpr auto I2 = ck::Number<2>{};
+
+using DataType = int;
+
+template <typename Desc>
+void Print1d(const Desc& desc)
+{
+    std::cout << "Print1d" << std::endl;
+    for(ck::index_t w = 0; w < desc.GetLength(I0); w++)
+    {
+        std::cout << desc.CalculateOffset(ck::make_tuple(w)) << " ";
+    }
+    std::cout << std::endl;
+}
+
+template <typename Desc>
+void Print2d(const Desc& desc)
+{
+    std::cout << "Print2d" << std::endl;
+    for(ck::index_t h = 0; h < desc.GetLength(I0); h++)
+    {
+        for(ck::index_t w = 0; w < desc.GetLength(I1); w++)
+        {
+            std::cout << desc.CalculateOffset(ck::make_tuple(h, w)) << " ";
+        }
+        std::cout << std::endl;
+    }
+}
+
+template <typename Desc>
+void Print3dCustom(const Desc& desc)
+{
+    std::cout << "Print3dCustom" << std::endl;
+    for(ck::index_t d = 0; d < desc.GetLength(I0); d++)
+    {
+        for(ck::index_t h = 0; h < desc.GetLength(I1); h++)
+        {
+            for(ck::index_t w = 0; w < desc.GetLength(I2); w++)
+            {
+                std::cout << desc.CalculateOffset(ck::make_tuple(d, h, w)) << " ";
+            }
+            std::cout << std::endl;
+        }
+        std::cout << std::endl;
+    }
+}
+
+int main()
+{
+    // Tensor descriptor traverse in row-major (need to reverse dims)
+    std::cout << "Note: Tensor descriptor traverse in row-major" << std::endl;
+    // Basic descriptor 0, 1, 2, ... 30, 31
+    // (dims:4,8 strides:1,4)
+    const auto desc_4x8_s1x4 =
+        ck::make_naive_tensor_descriptor(ck::make_tuple(ck::Number<4>{}, ck::Number<8>{}),
+                                         ck::make_tuple(ck::Number<1>{}, ck::Number<4>{}));
+    std::cout << "dims:4,8 strides:1,4" << std::endl;
+    Print2d(desc_4x8_s1x4);
+
+    using Cord1x1Type                = ck::Tuple<ck::Number<1>, ck::Number<1>>;
+    constexpr ck::index_t offset_1x1 = desc_4x8_s1x4.CalculateOffset(Cord1x1Type{});
+    std::cout << "Constexpr calculated [1, 1] offset:" << offset_1x1 << std::endl;
+
+    // Basic descriptor 0, 1, 8, 9, 16, 17, ... 30, 31 (compile-time descriptor)
+    // dims:4,(2,4) strides:2,(1,8)
+    const auto desc_4x2x4_s2x1x8 =
+        ck::make_naive_tensor_descriptor(ck::make_tuple(4, 2, 4), ck::make_tuple(2, 1, 8));
+    // Transform to 2d (column-major, need to to reverse dims)
+    const auto desc_4x2x4_s2x1x8_merged = ck::transform_tensor_descriptor(
+        desc_4x2x4_s2x1x8,
+        ck::make_tuple(ck::make_pass_through_transform(4),
+                       ck::make_merge_transform(ck::make_tuple(4, 2))),
+        ck::make_tuple(ck::Sequence<0>{}, ck::Sequence<2, 1>{}),
+        ck::make_tuple(ck::Sequence<0>{}, ck::Sequence<1>{}));
+
+    std::cout << "dims:4,(2,4) strides:2,(1,8)" << std::endl;
+    Print2d(desc_4x2x4_s2x1x8_merged);
+
+    // Basic descriptor 0, 1, 8, 9, 16, 17, ... 30, 31 (compile-time descriptor)
+    // dims:(2,2),(2,4) strides:((1,4),(2,8)
+    const auto desc_2x2x2x4_s1x4x2x8 =
+        ck::make_naive_tensor_descriptor(ck::make_tuple(2, 2, 2, 4), ck::make_tuple(1, 4, 2, 8));
+    // Transform to 2d
+    const auto desc_2x2x2x4_s1x4x2x8_double_merged_2d = ck::transform_tensor_descriptor(
+        desc_2x2x2x4_s1x4x2x8,
+        ck::make_tuple(ck::make_merge_transform(ck::make_tuple(2, 2)),
+                       ck::make_merge_transform(ck::make_tuple(4, 2))),
+        ck::make_tuple(ck::Sequence<1, 0>{}, ck::Sequence<3, 2>{}),
+        ck::make_tuple(ck::Sequence<0>{}, ck::Sequence<1>{}));
+    // Transform to 3d
+    const auto desc_2x2x2x4_s1x4x2x8_double_merged_3d = ck::transform_tensor_descriptor(
+        desc_2x2x2x4_s1x4x2x8,
+        ck::make_tuple(ck::make_pass_through_transform(2),
+                       ck::make_pass_through_transform(2),
+                       ck::make_merge_transform(ck::make_tuple(4, 2))),
+        ck::make_tuple(ck::Sequence<0>{}, ck::Sequence<1>{}, ck::Sequence<3, 2>{}),
+        ck::make_tuple(ck::Sequence<0>{}, ck::Sequence<1>{}, ck::Sequence<2>{}));
+
+    std::cout << "dims:(2,2),(2,4) strides:(1,4),(2,8)" << std::endl;
+    Print2d(desc_2x2x2x4_s1x4x2x8_double_merged_2d);
+    Print3dCustom(desc_2x2x2x4_s1x4x2x8_double_merged_3d);
+
+    // Basic descriptor 0, 1, 8, 9, 16, 17, ... 30, 31 (compile-time descriptor)
+    // dims:((2,2),2),4 strides:((1,4),2),8
+    // Transform to 2d
+    const auto desc_2x2x2x4_s1x4x2x8_nested =
+        ck::make_naive_tensor_descriptor(ck::make_tuple(2, 2, 2, 4), ck::make_tuple(1, 4, 2, 8));
+    const auto desc_2x2x2x4_s1x4x2x8_nested_merged_3d = ck::transform_tensor_descriptor(
+        desc_2x2x2x4_s1x4x2x8_nested,
+        ck::make_tuple(ck::make_merge_transform(ck::make_tuple(2, 2)),
+                       ck::make_pass_through_transform(2),
+                       ck::make_pass_through_transform(4)),
+        ck::make_tuple(ck::Sequence<1, 0>{}, ck::Sequence<2>{}, ck::Sequence<3>{}),
+        ck::make_tuple(ck::Sequence<0>{}, ck::Sequence<1>{}, ck::Sequence<2>{}));
+    const auto desc_2x2x2x4_s1x4x2x8_nested_merged_1d = ck::transform_tensor_descriptor(
+        desc_2x2x2x4_s1x4x2x8_nested,
+        ck::make_tuple(ck::make_merge_transform(ck::make_tuple(4, 2, 2, 2))),
+        ck::make_tuple(ck::Sequence<3, 2, 1, 0>{}),
+        ck::make_tuple(ck::Sequence<0>{}));
+    const auto desc_2x2x2x4_s1x4x2x8_nested_merged_2d = ck::transform_tensor_descriptor(
+        desc_2x2x2x4_s1x4x2x8_nested_merged_3d,
+        ck::make_tuple(ck::make_merge_transform(ck::make_tuple(2, 4)),
+                       ck::make_pass_through_transform(4)),
+        ck::make_tuple(ck::Sequence<1, 0>{}, ck::Sequence<2>{}),
+        ck::make_tuple(ck::Sequence<0>{}, ck::Sequence<1>{}));
+
+    std::cout << "dims:((2,2),2),4 strides:((1,4),2),8" << std::endl;
+    Print1d(desc_2x2x2x4_s1x4x2x8_nested_merged_1d);
+    Print2d(desc_2x2x2x4_s1x4x2x8_nested_merged_2d);
+    Print3dCustom(desc_2x2x2x4_s1x4x2x8_nested_merged_3d);
+
+    return 0;
+}

client_example/25_tensor_transforms/tensor_transform_using_wrapper.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+
+#include "ck/ck.hpp"
+
+#include "ck/utility/number.hpp"
+#include "ck/utility/tuple.hpp"
+#include "ck/utility/sequence.hpp"
+
+#include "ck/wrapper/layout.hpp"
+
+using DataType = int;
+
+template <typename Layout>
+void Print1d(const Layout& layout)
+{
+    std::cout << "Print1d" << std::endl;
+    for(ck::index_t w = 0; w < ck::wrapper::size(layout); w++)
+    {
+        std::cout << layout(ck::make_tuple(w)) << " ";
+    }
+    std::cout << std::endl;
+}
+
+template <typename Layout>
+void Print2d(const Layout& layout)
+{
+    std::cout << "Print2d" << std::endl;
+    for(ck::index_t h = 0; h < ck::wrapper::size<0>(layout); h++)
+    {
+        for(ck::index_t w = 0; w < ck::wrapper::size<1>(layout); w++)
+        {
+            std::cout << layout(ck::make_tuple(h, w)) << " ";
+        }
+        std::cout << std::endl;
+    }
+}
+
+// Print in (x,y),z pattern
+template <typename Layout>
+void Print3dCustom(const Layout& layout)
+{
+    std::cout << "Print3dCustom" << std::endl;
+    for(ck::index_t d = 0; d < ck::wrapper::size<0>(ck::wrapper::get<0>(layout)); d++)
+    {
+        for(ck::index_t h = 0; h < ck::wrapper::size<1>(ck::wrapper::get<0>(layout)); h++)
+        {
+            for(ck::index_t w = 0; w < ck::wrapper::size<1>(layout); w++)
+            {
+                std::cout << layout(ck::make_tuple(ck::make_tuple(d, h), w)) << " ";
+            }
+            std::cout << std::endl;
+        }
+        std::cout << std::endl;
+    }
+}
+
+int main()
+{
+    // Layout traverse in row-major
+    std::cout << "Note: Layout traverse in column-major" << std::endl;
+    // Basic descriptor 0, 1, 2, ... 30, 31 (compile-time descriptor)
+    // (dims:4,8 strides:1,4)
+    const auto shape_4x8       = ck::make_tuple(ck::Number<4>{}, ck::Number<8>{});
+    const auto layout_4x8_s1x4 = ck::wrapper::make_layout(shape_4x8);
+    std::cout << "dims:4,8 strides:1,4" << std::endl;
+    Print2d(layout_4x8_s1x4);
+    using Cord1x1Type                = ck::Tuple<ck::Number<1>, ck::Number<1>>;
+    constexpr ck::index_t offset_1x1 = layout_4x8_s1x4.template operator()<Cord1x1Type>();
+    std::cout << "Constexpr calculated [1, 1] offset:" << offset_1x1 << std::endl;
+
+    // Basic descriptor 0, 1, 8, 9, 16, 17, ... 30, 31 (runtime descriptor)
+    // dims:4,(2,4) strides:2,(1,8)
+    const auto shape_4x2x4         = ck::make_tuple(4, ck::make_tuple(2, 4));
+    const auto strides_s2x1x8      = ck::make_tuple(2, ck::make_tuple(1, 8));
+    const auto layout_4x2x4_s2x1x8 = ck::wrapper::make_layout(shape_4x2x4, strides_s2x1x8);
+
+    std::cout << "dims:4,(2,4) strides:2,(1,8)" << std::endl;
+    Print2d(layout_4x2x4_s2x1x8);
+
+    // Basic descriptor 0, 1, 8, 9, 16, 17, ... 30, 31 (compile-time descriptor)
+    // dims:(2,2),(2,4) strides:((1,4),(2,8)
+    const auto shape_2x2x2x4    = ck::make_tuple(ck::make_tuple(ck::Number<2>{}, ck::Number<2>{}),
+                                              ck::make_tuple(ck::Number<2>{}, ck::Number<4>{}));
+    const auto strides_s1x4x2x8 = ck::make_tuple(ck::make_tuple(ck::Number<1>{}, ck::Number<4>{}),
+                                                 ck::make_tuple(ck::Number<2>{}, ck::Number<8>{}));
+    static const auto layout_2x2x2x4_s1x4x2x8 =
+        ck::wrapper::make_layout(shape_2x2x2x4, strides_s1x4x2x8);
+
+    std::cout << "dims:(2,2),(2,4) strides:(1,4),(2,8)" << std::endl;
+    Print2d(layout_2x2x2x4_s1x4x2x8);
+    Print3dCustom(layout_2x2x2x4_s1x4x2x8);
+
+    // Basic descriptor 0, 1, 8, 9, 16, 17, ... 30, 31 (compile-time descriptor)
+    // dims:((2,2),2),4 strides:((1,4),2),8
+    // Transform to 2d
+    const auto shape_2x2x2x4_nested = ck::make_tuple(
+        ck::make_tuple(ck::make_tuple(ck::Number<2>{}, ck::Number<2>{}), ck::Number<2>{}),
+        ck::Number<4>{});
+    const auto strides_s1x4x2x8_nested = ck::make_tuple(
+        ck::make_tuple(ck::make_tuple(ck::Number<1>{}, ck::Number<4>{}), ck::Number<2>{}),
+        ck::Number<8>{});
+    static const auto layout_2x2x2x4_s1x4x2x8_nested =
+        ck::wrapper::make_layout(shape_2x2x2x4_nested, strides_s1x4x2x8_nested);
+
+    std::cout << "dims:((2,2),2),4 strides:((1,4),2),8" << std::endl;
+    Print1d(layout_2x2x2x4_s1x4x2x8_nested);
+    Print2d(layout_2x2x2x4_s1x4x2x8_nested);
+    Print3dCustom(layout_2x2x2x4_s1x4x2x8_nested);
+
+    return 0;
+}

client_example/CMakeLists.txt

@@ -48,7 +48,7 @@ else()
     endif()
 endif()
 
-find_package(composable_kernel COMPONENTS device_operations)
+find_package(composable_kernel COMPONENTS device_other_operations device_gemm_operations device_conv_operations device_contraction_operations device_reduction_operations)
 find_package(hip REQUIRED PATHS /opt/rocm)
 message(STATUS "Build with HIP ${hip_VERSION}")