Merge pull request #88 from ROCm/merge_from_public

Merge from public

.azuredevops/rocm-ci.yml

+resources:
+  repositories:
+  - repository: pipelines_repo
+    type: github
+    endpoint: ROCm
+    name: ROCm/ROCm
+
+variables:
+- group: common
+- template: /.azuredevops/variables-global.yml@pipelines_repo
+
+trigger:
+  batch: true
+  branches:
+    include:
+    - develop
+  paths:
+    exclude:
+    - .github
+    - docs
+    - '.*.y*ml'
+    - '*.md'
+    - Jenkinsfile
+    - LICENSE
+
+pr: none
+
+jobs:
+  - template: ${{ variables.CI_COMPONENT_PATH }}/composable_kernel.yml@pipelines_repo

CMakeLists.txt

@@ -112,7 +112,7 @@ message("checking which targets are supported")
 #Setting GPU_TARGETS on command line will override this list
 if(NOT PROFILER_ONLY)
     rocm_check_target_ids(DEFAULT_GPU_TARGETS
-        TARGETS "gfx900;gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx1100;gfx1101;gfx1102")
+        TARGETS "gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx1100;gfx1101;gfx1102")
 else()
     add_definitions(-DPROFILER_ONLY)
     set(GPU_TARGETS "" CACHE STRING "" FORCE)

Dockerfile

@@ -23,11 +23,11 @@ RUN if [ "$ROCMVERSION" != "6.2" ]; then \
         wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add - && \
         sh -c "echo deb [arch=amd64 signed-by=/etc/apt/trusted.gpg.d/rocm-keyring.gpg] $DEB_ROCM_REPO focal main > /etc/apt/sources.list.d/rocm.list" && \
         sh -c 'echo deb [arch=amd64 signed-by=/etc/apt/trusted.gpg.d/rocm-keyring.gpg] https://repo.radeon.com/amdgpu/$ROCMVERSION/ubuntu focal main > /etc/apt/sources.list.d/amdgpu.list'; \
-    elif [ "$ROCMVERSION" = "6.2" ] && [ "$compiler_version" = "rc2" ]; then \
-        sh -c "wget http://artifactory-cdn.amd.com/artifactory/list/amdgpu-deb/amdgpu-install-internal_6.1-20.04-1_all.deb --no-check-certificate" && \
-        apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install dialog && DEBIAN_FRONTEND=noninteractive apt-get install ./amdgpu-install-internal_6.1-20.04-1_all.deb && \
-        sh -c 'echo deb [arch=amd64 trusted=yes] http://compute-artifactory.amd.com/artifactory/list/rocm-release-archive-20.04-deb/ 6.1 rel-48 > /etc/apt/sources.list.d/rocm-build.list' && \
-        amdgpu-repo --amdgpu-build=1736298; \
+    elif [ "$ROCMVERSION" = "6.2" ] && [ "$compiler_version" = "rc1" ]; then \
+        sh -c "wget http://artifactory-cdn.amd.com/artifactory/list/amdgpu-deb/amdgpu-install-internal_6.2-20.04-1_all.deb --no-check-certificate" && \
+        apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install dialog libpopt0 rsync && DEBIAN_FRONTEND=noninteractive apt-get install ./amdgpu-install-internal_6.2-20.04-1_all.deb && \
+        sh -c 'echo deb [arch=amd64 trusted=yes] http://compute-artifactory.amd.com/artifactory/list/rocm-release-archive-20.04-deb/ 6.2 rel-8 > /etc/apt/sources.list.d/rocm-build.list' && \
+        amdgpu-repo --amdgpu-build=1794148; \
     fi
 
 RUN sh -c "echo deb http://mirrors.kernel.org/ubuntu focal main universe | tee -a /etc/apt/sources.list"

Jenkinsfile

@@ -692,8 +692,8 @@ pipeline {
             description: "Select whether to build DL kernels (default: OFF)")
         booleanParam(
             name: "hipTensor_test",
-            defaultValue: true,
-            description: "Use the CK build to verify hipTensor build and tests (default: ON)")
+            defaultValue: false,
+            description: "Use the CK build to verify hipTensor build and tests (default: OFF)")
         string(
             name: 'hipTensor_branch',
             defaultValue: 'mainline',
@@ -879,10 +879,10 @@ pipeline {
                     }
                     agent{ label rocmnode("gfx90a") }
                     environment{
-                        setup_args = """ -DCMAKE_INSTALL_PREFIX=../install -DGPU_TARGETS="gfx908;gfx90a" -DCMAKE_CXX_FLAGS=" -O3 " """
+                        setup_args = """ -DCMAKE_INSTALL_PREFIX=../install -DGPU_TARGETS="gfx1100;gfx90a" -DCMAKE_CXX_FLAGS=" -O3 " """
                         execute_args = """ cd ../client_example && rm -rf build && mkdir build && cd build && \
                                            cmake -DCMAKE_PREFIX_PATH="${env.WORKSPACE}/install;/opt/rocm" \
-                                           -DGPU_TARGETS="gfx908;gfx90a" \
+                                           -DGPU_TARGETS="gfx1100;gfx90a" \
                                            -DCMAKE_CXX_COMPILER="${build_compiler()}" \
                                            -DCMAKE_CXX_FLAGS=" -O3 " .. && make -j """
                     }
@@ -902,9 +902,8 @@ pipeline {
                         execute_args = """ cmake -D CMAKE_PREFIX_PATH=/opt/rocm \
                                            -D CMAKE_CXX_COMPILER="${build_compiler()}" \
                                            -D CMAKE_BUILD_TYPE=Release \
-                                           -D GPU_TARGETS="gfx90a;gfx1030;gfx1101" \
                                            -D INSTANCES_ONLY=ON \
-                                           -DCMAKE_CXX_FLAGS=" -O3 " .. && make -j32 """
+                                           -DCMAKE_CXX_FLAGS=" -O3 " .. && make -j64 """
                    }
                     steps{
                         buildHipClangJobAndReboot(setup_cmd: "",  build_cmd: "", no_reboot:true, build_type: 'Release', execute_cmd: execute_args)

client_example/24_grouped_conv_activation/CMakeLists.txt

@@ -35,6 +35,22 @@ target_link_libraries(client_grouped_convnd_fwd_scaleadd_ab_int8 PRIVATE composa
 add_executable(client_grouped_convnd_fwd_bilinear_residual_fp16
                grouped_convnd_fwd_bilinear/grouped_conv_fwd_bilinear_residual_fp16.cpp)
 target_link_libraries(client_grouped_convnd_fwd_bilinear_residual_fp16 PRIVATE composable_kernel::device_conv_operations)
+# Fwd convinvscale
+add_executable(client_conv3d_fwd_convinvscale_fp8 
+               grouped_convnd_fwd_convinvscale/conv3d_fwd_convinvscale_fp8.cpp)
+target_link_libraries(client_conv3d_fwd_convinvscale_fp8 PRIVATE composable_kernel::device_conv_operations)
+# Fwd convscale
+add_executable(client_conv3d_fwd_convscale_fp8
+               grouped_convnd_fwd_convscale/conv3d_fwd_convscale_fp8.cpp)
+target_link_libraries(client_conv3d_fwd_convscale_fp8 PRIVATE composable_kernel::device_conv_operations)
+
+add_executable(client_conv3d_fwd_convscale_bf8
+               grouped_convnd_fwd_convscale/conv3d_fwd_convscale_bf8.cpp)
+target_link_libraries(client_conv3d_fwd_convscale_bf8 PRIVATE composable_kernel::device_conv_operations)
+
+add_executable(client_conv3d_fwd_convscale_fp8_bf8 
+               grouped_convnd_fwd_convscale/conv3d_fwd_convscale_fp8_bf8.cpp)
+target_link_libraries(client_conv3d_fwd_convscale_fp8_bf8 PRIVATE composable_kernel::device_conv_operations)
 # Bwd data bilinear
 add_executable(client_grouped_convnd_bwd_data_bilinear_residual_fp16
                grouped_convnd_bwd_data_bilinear/grouped_conv_bwd_data_bilinear_residual_fp16.cpp)

client_example/24_grouped_conv_activation/grouped_convnd_fwd_convinvscale/common.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <cstdlib>
+#include <iomanip>
+#include <iostream>
+#include <iterator>
+#include <numeric>
+#include <string>
+#include <vector>
+
+#include "ck/ck.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convinvscale.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_abd.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+using PassThrough  = ck::tensor_operation::element_wise::PassThrough;
+using ConvInvscale = ck::tensor_operation::element_wise::ConvInvscale;
+
+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_;
+};
+
+template <ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
+std::size_t
+GetFlops(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths,
+         const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths,
+         const std::size_t& ds_size)
+{
+    // G * N * C * <output spatial lengths product> * (2 * K * <filter spatial lengths product> +
+    // <number of scale factors>)
+    ck::index_t G = weights_lengths[0];
+    ck::index_t N = output_lengths[1];
+    ck::index_t K = weights_lengths[1];
+    ck::index_t C = weights_lengths[2];
+
+    return G * N * C *
+           std::accumulate(std::next(std::begin(output_lengths), NumNonSpatialDim),
+                           std::end(output_lengths),
+                           static_cast<std::size_t>(1),
+                           std::multiplies<>()) *
+           (static_cast<std::size_t>(2) * K *
+                std::accumulate(std::next(std::begin(weights_lengths), NumNonSpatialDim),
+                                std::end(weights_lengths),
+                                static_cast<std::size_t>(1),
+                                std::multiplies<>()) +
+            ds_size);
+}
+
+template <typename InDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
+std::size_t
+GetInputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& input_lengths)
+{
+    // sizeof(InDataType) * (G * N * C * <input spatial lengths product>) +
+    return sizeof(InDataType) * std::accumulate(std::begin(input_lengths),
+                                                std::end(input_lengths),
+                                                static_cast<std::size_t>(1),
+                                                std::multiplies<>());
+}
+
+template <typename WeiDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
+std::size_t
+GetWeightByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths)
+{
+    // sizeof(WeiDataType) * (G * K * C * <filter spatial lengths product>) +
+    return sizeof(WeiDataType) * std::accumulate(std::begin(weights_lengths),
+                                                 std::end(weights_lengths),
+                                                 static_cast<std::size_t>(1),
+                                                 std::multiplies<>());
+}
+
+template <typename OutDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
+std::size_t
+GetOutputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths)
+{
+    // sizeof(OutDataType) * (G * N * K * <output spatial lengths product>);
+    return sizeof(OutDataType) * std::accumulate(std::begin(output_lengths),
+                                                 std::end(output_lengths),
+                                                 static_cast<std::size_t>(1),
+                                                 std::multiplies<std::size_t>());
+}
+
+template <ck::index_t NumDimSpatial,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename InLayout,
+          typename WeiLayout,
+          typename OutLayout,
+          ck::index_t NumNonSpatialDim = 3,
+          typename AComputeType        = InDataType,
+          typename BComputeType        = AComputeType>
+bool run_grouped_conv_fwd_convinvscale(
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> in_lengths,
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> wei_lengths,
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> out_lengths)
+{
+    std::size_t in_mem_size  = GetInputByte<InDataType, NumDimSpatial>(in_lengths);
+    std::size_t wei_mem_size = GetWeightByte<WeiDataType, NumDimSpatial>(wei_lengths);
+    std::size_t out_mem_size = GetOutputByte<OutDataType, NumDimSpatial>(out_lengths);
+
+    SimpleDeviceMem in(in_mem_size);
+    SimpleDeviceMem wei(wei_mem_size);
+    SimpleDeviceMem out(out_mem_size);
+
+    float scale_in  = float(std::rand()) / float(RAND_MAX);
+    float scale_wei = float(std::rand()) / float(RAND_MAX);
+    float scale_out = float(std::rand()) / float(RAND_MAX);
+
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> in_strides;
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> wei_strides;
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> out_strides;
+    in_strides.fill(0);
+    wei_strides.fill(0);
+    out_strides.fill(0);
+    in_strides.back()  = 1;
+    wei_strides.back() = 1;
+    out_strides.back() = 1;
+
+    std::partial_sum(rbegin(in_lengths),
+                     std::prev(rend(in_lengths)),
+                     std::next(rbegin(in_strides)),
+                     std::multiplies<>{});
+    std::partial_sum(rbegin(wei_lengths),
+                     std::prev(rend(wei_lengths)),
+                     std::next(rbegin(wei_strides)),
+                     std::multiplies<>{});
+    std::partial_sum(rbegin(out_lengths),
+                     std::prev(rend(out_lengths)),
+                     std::next(rbegin(out_strides)),
+                     std::multiplies<>{});
+
+    // transpose NDHWGC/KZYXGC/NDHWGK to GNDHWC/GKZYXC/GNDHWK to GNCDHW/GKCZYX/GNKDHW
+    std::rotate(std::next(rbegin(in_lengths)), std::next(rbegin(in_lengths), 2), rend(in_lengths));
+    std::rotate(rbegin(in_lengths),
+                std::next(rbegin(in_lengths)),
+                std::next(rbegin(in_lengths), NumDimSpatial + 1));
+
+    std::rotate(std::next(rbegin(in_strides)), std::next(rbegin(in_strides), 2), rend(in_strides));
+    std::rotate(rbegin(in_strides),
+                std::next(rbegin(in_strides)),
+                std::next(rbegin(in_strides), NumDimSpatial + 1));
+
+    std::rotate(rbegin(wei_lengths),
+                std::next(rbegin(wei_lengths)),
+                std::next(rbegin(wei_lengths), NumDimSpatial + 1));
+
+    std::rotate(rbegin(wei_strides),
+                std::next(rbegin(wei_strides)),
+                std::next(rbegin(wei_strides), NumDimSpatial + 1));
+
+    std::rotate(
+        std::next(rbegin(out_lengths)), std::next(rbegin(out_lengths), 2), rend(out_lengths));
+    std::rotate(rbegin(out_lengths),
+                std::next(rbegin(out_lengths)),
+                std::next(rbegin(out_lengths), NumDimSpatial + 1));
+
+    std::rotate(
+        std::next(rbegin(out_strides)), std::next(rbegin(out_strides), 2), rend(out_strides));
+    std::rotate(rbegin(out_strides),
+                std::next(rbegin(out_strides)),
+                std::next(rbegin(out_strides), NumDimSpatial + 1));
+
+    std::array<ck::index_t, NumDimSpatial> conv_filter_strides;
+    std::array<ck::index_t, NumDimSpatial> conv_filter_dilations;
+    std::array<ck::index_t, NumDimSpatial> input_left_pads;
+    std::array<ck::index_t, NumDimSpatial> input_right_pads;
+    conv_filter_strides.fill(1);
+    conv_filter_dilations.fill(1);
+    input_left_pads.fill(1);
+    input_right_pads.fill(1);
+
+    std::size_t ds_size = 3; // 3 element-wise scale multipliers
+    std::size_t flop    = GetFlops<NumDimSpatial>(out_lengths, wei_lengths, ds_size);
+    std::size_t num_bytes =
+        in_mem_size + wei_mem_size + sizeof(float) + sizeof(float) + sizeof(float) + out_mem_size;
+
+    using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
+                                                                                   InLayout,
+                                                                                   WeiLayout,
+                                                                                   ck::Tuple<>,
+                                                                                   OutLayout,
+                                                                                   InDataType,
+                                                                                   WeiDataType,
+                                                                                   ck::Tuple<>,
+                                                                                   OutDataType,
+                                                                                   PassThrough,
+                                                                                   PassThrough,
+                                                                                   ConvInvscale,
+                                                                                   AComputeType,
+                                                                                   BComputeType>;
+    // 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;
+    int best_op_id        = -1;
+    float best_avg_time   = std::numeric_limits<float>::max();
+    float best_gb_per_sec = 0;
+    float best_tflops     = 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(
+            in.GetDeviceBuffer(),
+            wei.GetDeviceBuffer(),
+            std::array<const void*, 0>{},
+            out.GetDeviceBuffer(),
+            in_lengths,
+            in_strides,
+            wei_lengths,
+            wei_strides,
+            std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{},
+            std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{},
+            out_lengths,
+            out_strides,
+            conv_filter_strides,
+            conv_filter_dilations,
+            input_left_pads,
+            input_right_pads,
+            PassThrough{},
+            PassThrough{},
+            ConvInvscale{scale_in, scale_wei, scale_out});
+
+        auto invoker_ptr    = op_ptr->MakeInvokerPointer();
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
+            float gb_per_sec = num_bytes / 1.E6 / avg_time;
+
+            std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_avg_time   = avg_time;
+                best_gb_per_sec = gb_per_sec;
+                best_tflops     = tflops;
+            }
+        }
+        else
+        {
+            std::cerr << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    if(best_op_id < 0)
+    {
+        std::cerr << "no suitable instance" << std::endl;
+        return false;
+    }
+
+    std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
+              << " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+    // run the best intance
+    {
+        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(
+            in.GetDeviceBuffer(),
+            wei.GetDeviceBuffer(),
+            std::array<const void*, 0>{},
+            out.GetDeviceBuffer(),
+            in_lengths,
+            in_strides,
+            wei_lengths,
+            wei_strides,
+            std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{},
+            std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{},
+            out_lengths,
+            out_strides,
+            conv_filter_strides,
+            conv_filter_dilations,
+            input_left_pads,
+            input_right_pads,
+            PassThrough{},
+            PassThrough{},
+            ConvInvscale{scale_in, scale_wei, scale_out});
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
+        }
+
+        std::cout << "Done" << std::endl;
+    }
+    return true;
+}

client_example/24_grouped_conv_activation/grouped_convnd_fwd_convinvscale/conv3d_fwd_convinvscale_fp8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "common.hpp"
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+
+using InDataType       = ck::f8_t;
+using WeiDataType      = ck::f8_t;
+using CShuffleDataType = float;
+using OutDataType      = ck::f8_t;
+using AComputeDataType = ck::f8_t;
+using BComputeDataType = ck::f8_t;
+
+using InLayout  = ck::tensor_layout::convolution::NDHWGC;
+using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
+using OutLayout = ck::tensor_layout::convolution::NDHWGK;
+
+static constexpr ck::index_t NumDimSpatial = 3;
+static constexpr ck::index_t G             = 1;
+static constexpr ck::index_t N             = 64;
+static constexpr ck::index_t K             = 128;
+static constexpr ck::index_t C             = 64;
+static constexpr ck::index_t Z             = 3;
+static constexpr ck::index_t Y             = 3;
+static constexpr ck::index_t X             = 3;
+static constexpr ck::index_t Di            = 28;
+static constexpr ck::index_t Hi            = 28;
+static constexpr ck::index_t Wi            = 3;
+static constexpr ck::index_t Do            = 28;
+static constexpr ck::index_t Ho            = 28;
+static constexpr ck::index_t Wo            = 3;
+
+int main()
+{
+    return run_grouped_conv_fwd_convinvscale<NumDimSpatial,
+                                             InDataType,
+                                             WeiDataType,
+                                             OutDataType,
+                                             InLayout,
+                                             WeiLayout,
+                                             OutLayout,
+                                             3,
+                                             AComputeDataType,
+                                             BComputeDataType>(
+               {N, Di, Hi, Wi, G, C}, {G, K, Z, Y, X, C}, {N, Do, Ho, Wo, G, K})
+               ? EXIT_SUCCESS
+               : EXIT_FAILURE;
+}

client_example/24_grouped_conv_activation/grouped_convnd_fwd_convscale/common.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <cstdlib>
+#include <iomanip>
+#include <iostream>
+#include <iterator>
+#include <numeric>
+#include <string>
+#include <vector>
+
+#include "ck/ck.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convscale.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_abd.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using ConvScale   = ck::tensor_operation::element_wise::ConvScale;
+
+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_;
+};
+
+template <ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
+std::size_t
+GetFlops(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths,
+         const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths,
+         const std::size_t& ds_size)
+{
+    // G * N * C * <output spatial lengths product> * (2 * K * <filter spatial lengths product> +
+    // <number of scale factors>)
+    ck::index_t G = weights_lengths[0];
+    ck::index_t N = output_lengths[1];
+    ck::index_t K = weights_lengths[1];
+    ck::index_t C = weights_lengths[2];
+
+    return G * N * C *
+           std::accumulate(std::next(std::begin(output_lengths), NumNonSpatialDim),
+                           std::end(output_lengths),
+                           static_cast<std::size_t>(1),
+                           std::multiplies<>()) *
+           (static_cast<std::size_t>(2) * K *
+                std::accumulate(std::next(std::begin(weights_lengths), NumNonSpatialDim),
+                                std::end(weights_lengths),
+                                static_cast<std::size_t>(1),
+                                std::multiplies<>()) +
+            ds_size);
+}
+
+template <typename InDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
+std::size_t
+GetInputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& input_lengths)
+{
+    // sizeof(InDataType) * (G * N * C * <input spatial lengths product>) +
+    return sizeof(InDataType) * std::accumulate(std::begin(input_lengths),
+                                                std::end(input_lengths),
+                                                static_cast<std::size_t>(1),
+                                                std::multiplies<>());
+}
+
+template <typename WeiDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
+std::size_t
+GetWeightByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths)
+{
+    // sizeof(WeiDataType) * (G * K * C * <filter spatial lengths product>) +
+    return sizeof(WeiDataType) * std::accumulate(std::begin(weights_lengths),
+                                                 std::end(weights_lengths),
+                                                 static_cast<std::size_t>(1),
+                                                 std::multiplies<>());
+}
+
+template <typename OutDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
+std::size_t
+GetOutputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths)
+{
+    // sizeof(OutDataType) * (G * N * K * <output spatial lengths product>);
+    return sizeof(OutDataType) * std::accumulate(std::begin(output_lengths),
+                                                 std::end(output_lengths),
+                                                 static_cast<std::size_t>(1),
+                                                 std::multiplies<std::size_t>());
+}
+
+template <ck::index_t NumDimSpatial,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename InLayout,
+          typename WeiLayout,
+          typename OutLayout,
+          ck::index_t NumNonSpatialDim = 3,
+          typename AComputeType        = InDataType,
+          typename BComputeType        = AComputeType>
+bool run_grouped_conv_fwd_convscale(
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> in_lengths,
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> wei_lengths,
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> out_lengths)
+{
+    std::size_t in_mem_size  = GetInputByte<InDataType, NumDimSpatial>(in_lengths);
+    std::size_t wei_mem_size = GetWeightByte<WeiDataType, NumDimSpatial>(wei_lengths);
+    std::size_t out_mem_size = GetOutputByte<OutDataType, NumDimSpatial>(out_lengths);
+
+    SimpleDeviceMem in(in_mem_size);
+    SimpleDeviceMem wei(wei_mem_size);
+    SimpleDeviceMem out(out_mem_size);
+
+    float scale_in  = float(std::rand()) / float(RAND_MAX);
+    float scale_wei = float(std::rand()) / float(RAND_MAX);
+    float scale_out = float(std::rand()) / float(RAND_MAX);
+
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> in_strides;
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> wei_strides;
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> out_strides;
+    in_strides.fill(0);
+    wei_strides.fill(0);
+    out_strides.fill(0);
+    in_strides.back()  = 1;
+    wei_strides.back() = 1;
+    out_strides.back() = 1;
+
+    std::partial_sum(rbegin(in_lengths),
+                     std::prev(rend(in_lengths)),
+                     std::next(rbegin(in_strides)),
+                     std::multiplies<>{});
+    std::partial_sum(rbegin(wei_lengths),
+                     std::prev(rend(wei_lengths)),
+                     std::next(rbegin(wei_strides)),
+                     std::multiplies<>{});
+    std::partial_sum(rbegin(out_lengths),
+                     std::prev(rend(out_lengths)),
+                     std::next(rbegin(out_strides)),
+                     std::multiplies<>{});
+
+    // transpose NDHWGC/KZYXGC/NDHWGK to GNDHWC/GKZYXC/GNDHWK to GNCDHW/GKCZYX/GNKDHW
+    std::rotate(std::next(rbegin(in_lengths)), std::next(rbegin(in_lengths), 2), rend(in_lengths));
+    std::rotate(rbegin(in_lengths),
+                std::next(rbegin(in_lengths)),
+                std::next(rbegin(in_lengths), NumDimSpatial + 1));
+
+    std::rotate(std::next(rbegin(in_strides)), std::next(rbegin(in_strides), 2), rend(in_strides));
+    std::rotate(rbegin(in_strides),
+                std::next(rbegin(in_strides)),
+                std::next(rbegin(in_strides), NumDimSpatial + 1));
+
+    std::rotate(rbegin(wei_lengths),
+                std::next(rbegin(wei_lengths)),
+                std::next(rbegin(wei_lengths), NumDimSpatial + 1));
+
+    std::rotate(rbegin(wei_strides),
+                std::next(rbegin(wei_strides)),
+                std::next(rbegin(wei_strides), NumDimSpatial + 1));
+
+    std::rotate(
+        std::next(rbegin(out_lengths)), std::next(rbegin(out_lengths), 2), rend(out_lengths));
+    std::rotate(rbegin(out_lengths),
+                std::next(rbegin(out_lengths)),
+                std::next(rbegin(out_lengths), NumDimSpatial + 1));
+
+    std::rotate(
+        std::next(rbegin(out_strides)), std::next(rbegin(out_strides), 2), rend(out_strides));
+    std::rotate(rbegin(out_strides),
+                std::next(rbegin(out_strides)),
+                std::next(rbegin(out_strides), NumDimSpatial + 1));
+
+    std::array<ck::index_t, NumDimSpatial> conv_filter_strides;
+    std::array<ck::index_t, NumDimSpatial> conv_filter_dilations;
+    std::array<ck::index_t, NumDimSpatial> input_left_pads;
+    std::array<ck::index_t, NumDimSpatial> input_right_pads;
+    conv_filter_strides.fill(1);
+    conv_filter_dilations.fill(1);
+    input_left_pads.fill(1);
+    input_right_pads.fill(1);
+
+    std::size_t ds_size = 3; // 3 element-wise scale multipliers
+    std::size_t flop    = GetFlops<NumDimSpatial>(out_lengths, wei_lengths, ds_size);
+    std::size_t num_bytes =
+        in_mem_size + wei_mem_size + sizeof(float) + sizeof(float) + sizeof(float) + out_mem_size;
+
+    using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
+                                                                                   InLayout,
+                                                                                   WeiLayout,
+                                                                                   ck::Tuple<>,
+                                                                                   OutLayout,
+                                                                                   InDataType,
+                                                                                   WeiDataType,
+                                                                                   ck::Tuple<>,
+                                                                                   OutDataType,
+                                                                                   PassThrough,
+                                                                                   PassThrough,
+                                                                                   ConvScale,
+                                                                                   AComputeType,
+                                                                                   BComputeType>;
+    // 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;
+    int best_op_id        = -1;
+    float best_avg_time   = std::numeric_limits<float>::max();
+    float best_gb_per_sec = 0;
+    float best_tflops     = 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(
+            in.GetDeviceBuffer(),
+            wei.GetDeviceBuffer(),
+            std::array<const void*, 0>{},
+            out.GetDeviceBuffer(),
+            in_lengths,
+            in_strides,
+            wei_lengths,
+            wei_strides,
+            std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{},
+            std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{},
+            out_lengths,
+            out_strides,
+            conv_filter_strides,
+            conv_filter_dilations,
+            input_left_pads,
+            input_right_pads,
+            PassThrough{},
+            PassThrough{},
+            ConvScale{scale_in, scale_wei, scale_out});
+
+        auto invoker_ptr    = op_ptr->MakeInvokerPointer();
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
+            float gb_per_sec = num_bytes / 1.E6 / avg_time;
+
+            std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_avg_time   = avg_time;
+                best_gb_per_sec = gb_per_sec;
+                best_tflops     = tflops;
+            }
+        }
+        else
+        {
+            std::cerr << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    if(best_op_id < 0)
+    {
+        std::cerr << "no suitable instance" << std::endl;
+        return false;
+    }
+
+    std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
+              << " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+    // run the best intance
+    {
+        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(
+            in.GetDeviceBuffer(),
+            wei.GetDeviceBuffer(),
+            std::array<const void*, 0>{},
+            out.GetDeviceBuffer(),
+            in_lengths,
+            in_strides,
+            wei_lengths,
+            wei_strides,
+            std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{},
+            std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{},
+            out_lengths,
+            out_strides,
+            conv_filter_strides,
+            conv_filter_dilations,
+            input_left_pads,
+            input_right_pads,
+            PassThrough{},
+            PassThrough{},
+            ConvScale{scale_in, scale_wei, scale_out});
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
+        }
+
+        std::cout << "Done" << std::endl;
+    }
+    return true;
+}

client_example/24_grouped_conv_activation/grouped_convnd_fwd_convscale/conv3d_fwd_convscale_bf8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "common.hpp"
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+
+using InDataType       = ck::bf8_t;
+using WeiDataType      = ck::bf8_t;
+using CShuffleDataType = float;
+using OutDataType      = ck::f8_t;
+using AComputeDataType = InDataType;
+using BComputeDataType = AComputeDataType;
+
+using InLayout  = ck::tensor_layout::convolution::NDHWGC;
+using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
+using OutLayout = ck::tensor_layout::convolution::NDHWGK;
+
+static constexpr ck::index_t NumDimSpatial = 3;
+static constexpr ck::index_t G             = 1;
+static constexpr ck::index_t N             = 64;
+static constexpr ck::index_t K             = 128;
+static constexpr ck::index_t C             = 64;
+static constexpr ck::index_t Z             = 3;
+static constexpr ck::index_t Y             = 3;
+static constexpr ck::index_t X             = 3;
+static constexpr ck::index_t Di            = 28;
+static constexpr ck::index_t Hi            = 28;
+static constexpr ck::index_t Wi            = 3;
+static constexpr ck::index_t Do            = 28;
+static constexpr ck::index_t Ho            = 28;
+static constexpr ck::index_t Wo            = 3;
+
+int main()
+{
+    return run_grouped_conv_fwd_convscale<NumDimSpatial,
+                                          InDataType,
+                                          WeiDataType,
+                                          OutDataType,
+                                          InLayout,
+                                          WeiLayout,
+                                          OutLayout,
+                                          3,
+                                          AComputeDataType,
+                                          BComputeDataType>(
+               {N, Di, Hi, Wi, G, C}, {G, K, Z, Y, X, C}, {N, Do, Ho, Wo, G, K})
+               ? EXIT_SUCCESS
+               : EXIT_FAILURE;
+}

client_example/24_grouped_conv_activation/grouped_convnd_fwd_convscale/conv3d_fwd_convscale_fp8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "common.hpp"
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+
+using InDataType       = ck::f8_t;
+using WeiDataType      = ck::f8_t;
+using CShuffleDataType = float;
+using OutDataType      = ck::f8_t;
+using AComputeDataType = ck::f8_t;
+using BComputeDataType = ck::f8_t;
+
+using InLayout  = ck::tensor_layout::convolution::NDHWGC;
+using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
+using OutLayout = ck::tensor_layout::convolution::NDHWGK;
+
+static constexpr ck::index_t NumDimSpatial = 3;
+static constexpr ck::index_t G             = 1;
+static constexpr ck::index_t N             = 64;
+static constexpr ck::index_t K             = 128;
+static constexpr ck::index_t C             = 64;
+static constexpr ck::index_t Z             = 3;
+static constexpr ck::index_t Y             = 3;
+static constexpr ck::index_t X             = 3;
+static constexpr ck::index_t Di            = 28;
+static constexpr ck::index_t Hi            = 28;
+static constexpr ck::index_t Wi            = 3;
+static constexpr ck::index_t Do            = 28;
+static constexpr ck::index_t Ho            = 28;
+static constexpr ck::index_t Wo            = 3;
+
+int main()
+{
+    return run_grouped_conv_fwd_convscale<NumDimSpatial,
+                                          InDataType,
+                                          WeiDataType,
+                                          OutDataType,
+                                          InLayout,
+                                          WeiLayout,
+                                          OutLayout,
+                                          3,
+                                          AComputeDataType,
+                                          BComputeDataType>(
+               {N, Di, Hi, Wi, G, C}, {G, K, Z, Y, X, C}, {N, Do, Ho, Wo, G, K})
+               ? EXIT_SUCCESS
+               : EXIT_FAILURE;
+}

client_example/24_grouped_conv_activation/grouped_convnd_fwd_convscale/conv3d_fwd_convscale_fp8_bf8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "common.hpp"
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+
+using InDataType       = ck::f8_t;
+using WeiDataType      = ck::bf8_t;
+using CShuffleDataType = float;
+using OutDataType      = ck::f8_t;
+using AComputeDataType = ck::f8_t;
+using BComputeDataType = ck::bf8_t;
+
+using InLayout  = ck::tensor_layout::convolution::NDHWGC;
+using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
+using OutLayout = ck::tensor_layout::convolution::NDHWGK;
+
+static constexpr ck::index_t NumDimSpatial = 3;
+static constexpr ck::index_t G             = 1;
+static constexpr ck::index_t N             = 64;
+static constexpr ck::index_t K             = 128;
+static constexpr ck::index_t C             = 64;
+static constexpr ck::index_t Z             = 3;
+static constexpr ck::index_t Y             = 3;
+static constexpr ck::index_t X             = 3;
+static constexpr ck::index_t Di            = 28;
+static constexpr ck::index_t Hi            = 28;
+static constexpr ck::index_t Wi            = 3;
+static constexpr ck::index_t Do            = 28;
+static constexpr ck::index_t Ho            = 28;
+static constexpr ck::index_t Wo            = 3;
+
+int main()
+{
+    return run_grouped_conv_fwd_convscale<NumDimSpatial,
+                                          InDataType,
+                                          WeiDataType,
+                                          OutDataType,
+                                          InLayout,
+                                          WeiLayout,
+                                          OutLayout,
+                                          3,
+                                          AComputeDataType,
+                                          BComputeDataType>(
+               {N, Di, Hi, Wi, G, C}, {G, K, Z, Y, X, C}, {N, Do, Ho, Wo, G, K})
+               ? EXIT_SUCCESS
+               : EXIT_FAILURE;
+}

client_example/25_wrapper/wrapper_basic_gemm.cpp

@@ -7,19 +7,23 @@
 #include <initializer_list>
 #include <vector>
 
+#include "ck/utility/common_header.hpp"
+// __gfx9__ defined in the above header via ck.hpp
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
+
 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
-#include "ck/library/utility/host_tensor.hpp"
 
 #include "ck/host_utility/kernel_launch.hpp"
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/check_err.hpp"
-#include "ck/utility/common_header.hpp"
 #include "ck/library/utility/fill.hpp"
+#include "ck/library/utility/host_tensor.hpp"
 #include "ck/wrapper/layout.hpp"
 #include "ck/wrapper/tensor.hpp"
 #include "ck/wrapper/operations/copy.hpp"
 #include "ck/wrapper/operations/gemm.hpp"
 #include "ck/wrapper/utils/kernel_utils.hpp"
+#include "ck/host_utility/device_prop.hpp"
 
 struct SimpleDeviceMem
 {
@@ -204,6 +208,14 @@ void PerformGemm(const ck::index_t M,
 
 int main(int argc, char* argv[])
 {
+    bool is_supported = ck::is_xdl_supported();
+    if(!is_supported)
+    {
+        std::cout << "WARNING: xdl example not supported on the platform " << ck::get_device_name()
+                  << std::endl;
+        return 0;
+    }
+
     using DataType = ck::half_t;
     const auto thread_layout =
         ck::wrapper::make_layout(ck::make_tuple(ck::Number<64>{}, ck::Number<4>{}),
@@ -213,3 +225,4 @@ int main(int argc, char* argv[])
         3840, 4096, 4096, tile_shape, thread_layout);
     return 0;
 }
+#endif

client_example/25_wrapper/wrapper_optimized_gemm.cpp

@@ -7,18 +7,21 @@
 #include <initializer_list>
 #include <vector>
 
-#include "ck/library/utility/host_tensor.hpp"
+#include "ck/utility/common_header.hpp"
+// __gfx9__ defined in the above header via ck.hpp
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
 
 #include "ck/host_utility/kernel_launch.hpp"
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/check_err.hpp"
-#include "ck/utility/common_header.hpp"
 #include "ck/library/utility/fill.hpp"
+#include "ck/library/utility/host_tensor.hpp"
 #include "ck/wrapper/layout.hpp"
 #include "ck/wrapper/tensor.hpp"
 #include "ck/wrapper/operations/copy.hpp"
 #include "ck/wrapper/operations/gemm.hpp"
 #include "ck/wrapper/utils/kernel_utils.hpp"
+#include "ck/host_utility/device_prop.hpp"
 
 struct SimpleDeviceMem
 {
@@ -296,6 +299,14 @@ void PerformGemm(const ck::index_t M,
 
 int main(int argc, char* argv[])
 {
+    bool is_supported = ck::is_xdl_supported();
+    if(!is_supported)
+    {
+        std::cout << "WARNING: xdl example not supported on the platform " << ck::get_device_name()
+                  << std::endl;
+        return 0;
+    }
+
     using DataType = ck::half_t;
     const auto thread_layout =
         ck::wrapper::make_layout(ck::make_tuple(ck::Number<4>{}, ck::Number<64>{}, ck::Number<1>{}),
@@ -305,3 +316,4 @@ int main(int argc, char* argv[])
         3840, 4096, 4096, tile_shape, thread_layout);
     return 0;
 }
+#endif

client_example/31_grouped_gemm_bf16Aint8B/grouped_gemm_multiply_bias_fastgelu_xdl_bf16_i8.cpp

@@ -13,7 +13,7 @@
 #include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 
-#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_tile_loop_multply.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_tile_loop_multiply.hpp"
 
 #include "ck/host_utility/hip_check_error.hpp"
 

client_example/31_grouped_gemm_bf16Aint8B/grouped_gemm_multiply_xdl_bf16_i8.cpp

@@ -13,7 +13,7 @@
 #include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 
-#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_tile_loop_multply.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_tile_loop_multiply.hpp"
 
 #include "ck/host_utility/hip_check_error.hpp"
 

client_example/CMakeLists.txt

@@ -6,46 +6,36 @@ if (DTYPES)
     add_definitions(-DDTYPES)
     if (DTYPES MATCHES "int8")
         add_definitions(-DCK_ENABLE_INT8)
-        if(NOT DEFINED ${CK_ENABLE_INT8})
-            set(CK_ENABLE_INT8 "ON")
-        endif()
+        set(CK_ENABLE_INT8 "ON")
     endif()
     if (DTYPES MATCHES "fp8")
         add_definitions(-DCK_ENABLE_FP8)
-        if(NOT DEFINED ${CK_ENABLE_FP8})
-            set(CK_ENABLE_FP8 "ON")
-        endif()
+        set(CK_ENABLE_FP8 "ON")
+    endif()
+    if (DTYPES MATCHES "bf8")
+        add_definitions(-DCK_ENABLE_BF8)
+        set(CK_ENABLE_BF8 "ON")
     endif()
     if (DTYPES MATCHES "fp16")
         add_definitions(-DCK_ENABLE_FP16)
-        if(NOT DEFINED ${CK_ENABLE_FP16})
-            set(CK_ENABLE_FP16 "ON")
-        endif()
+        set(CK_ENABLE_FP16 "ON")
     endif()
     if (DTYPES MATCHES "fp32")
         add_definitions(-DCK_ENABLE_FP32)
-        if(NOT DEFINED ${CK_ENABLE_FP32})
-            set(CK_ENABLE_FP32 "ON")
-        endif()
+        set(CK_ENABLE_FP32 "ON")
     endif()
     if (DTYPES MATCHES "fp64")
         add_definitions(-DCK_ENABLE_FP64)
-        if(NOT DEFINED ${CK_ENABLE_FP64})
-            set(CK_ENABLE_FP64 "ON")
-        endif()
+        set(CK_ENABLE_FP64 "ON")
     endif()
     if (DTYPES MATCHES "bf16")
         add_definitions(-DCK_ENABLE_BF16)
-        if(NOT DEFINED ${CK_ENABLE_BF16})
-            set(CK_ENABLE_BF16 "ON")
-        endif()
+        set(CK_ENABLE_BF16 "ON")
     endif()
     message("DTYPES macro set to ${DTYPES}")
 else()
-    add_definitions(-DCK_ENABLE_INT8 -DCK_ENABLE_FP8 -DCK_ENABLE_FP16 -DCK_ENABLE_FP32 -DCK_ENABLE_FP64 -DCK_ENABLE_BF16)
-    if(NOT DEFINED ${CK_ENABLE_ALL_DTYPES})
-        set(CK_ENABLE_ALL_DTYPES "ON")
-    endif()
+    add_definitions(-DCK_ENABLE_INT8 -DCK_ENABLE_FP8 -DCK_ENABLE_BF8 -DCK_ENABLE_FP16 -DCK_ENABLE_FP32 -DCK_ENABLE_FP64 -DCK_ENABLE_BF16)
+    set(CK_ENABLE_ALL_DTYPES "ON")
 endif()
 
 if (GPU_TARGETS)
@@ -73,7 +63,8 @@ message(STATUS "Build with HIP ${hip_VERSION}")
 # add all example subdir
 file(GLOB dir_list LIST_DIRECTORIES true *)
 FOREACH(subdir ${dir_list})
-    IF(IS_DIRECTORY "${subdir}" AND (NOT "${subdir}" MATCHES "build"))
+    IF(IS_DIRECTORY "${subdir}" AND (NOT "${subdir}" MATCHES "build") 
+                                AND (NOT "${subdir}" MATCHES ".vscode"))
         add_subdirectory(${subdir})
     ENDIF()
 ENDFOREACH()

cmake/EnableCompilerWarnings.cmake

@@ -2,7 +2,7 @@
 #
 # MIT License
 #
-# Copyright (c) 2017 Advanced Micro Devices, Inc.
+# Copyright (c) 2017-2024 Advanced Micro Devices, Inc.
 #
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
@@ -96,6 +96,7 @@ else()
                 -Wno-covered-switch-default
                 -Wno-unsafe-buffer-usage
                 -Wno-unused-lambda-capture
+                -Wno-nvcc-compat
             )
         else()
             if (CMAKE_${COMPILER}_COMPILER_ID MATCHES "GNU" AND ${COMPILER} MATCHES "CXX")

codegen/CMakeLists.txt

 cmake_minimum_required(VERSION 3.16)
-project(composable_kernel_host)
+project(composable_kernel_host LANGUAGES CXX HIP)
 
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
@@ -12,24 +12,38 @@ find_package(ROCM)
 include(ROCMInstallTargets)
 include(ROCMTest)
 
+add_compile_options(-std=c++17)
+find_package(hip)
+## HIP
+set(CMAKE_HIP_PLATFORM amd)
+set(CMAKE_HIP_COMPILER ${CMAKE_CXX_COMPILER})
+set(CMAKE_HIP_EXTENSIONS ON)
+message("CMAKE_HIP_COMPILER: ${CMAKE_HIP_COMPILER}")
+
+# add include directories
+include_directories(BEFORE
+            ${PROJECT_BINARY_DIR}/include
+            ${PROJECT_SOURCE_DIR}/include
+            ${PROJECT_SOURCE_DIR}/library/include
+            ${HIP_INCLUDE_DIRS}
+            )
+
 list(APPEND CMAKE_MODULE_PATH ${CK_ROOT}/cmake)
 include(Embed)
 file(GLOB_RECURSE KERNEL_FILES CONFIGURE_DEPENDS
-    ${CK_ROOT}/include/ck/*.hpp)
+	${CK_ROOT}/include/ck/*.hpp)
 message(STATUS "KERNEL_FILES: ${KERNEL_FILES}")
 message(STATUS "RELATIVE: ${CK_ROOT}/include")
 add_embed_library(ck_headers ${KERNEL_FILES} RELATIVE ${CK_ROOT}/include)
 
-add_definitions(-std=c++17)
-
 file(GLOB SOURCES CONFIGURE_DEPENDS src/*.cpp)
 # TODO: Use object library
 add_library(ck_host STATIC ${SOURCES})
 target_link_libraries(ck_host PRIVATE ck_headers)
 
-set_target_properties(ck_host PROPERTIES 
-    LINKER_LANGUAGE CXX
-    POSITION_INDEPENDENT_CODE ON)
+set_target_properties(ck_host PROPERTIES
+	LINKER_LANGUAGE CXX
+        POSITION_INDEPENDENT_CODE ON)
 
 target_include_directories(ck_host PUBLIC
     $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>

codegen/driver/main.cpp

@@ -5,24 +5,27 @@
 #include <unordered_map>
 #include <vector>
 #include "ck/host/device_gemm_multiple_d/operation.hpp"
+#include "ck/host/device_grouped_conv_fwd_multiple_d/conv_fwd_op.hpp"
 #include "ck/host/stringutils.hpp"
 
 using ck::host::Transform;
 
 struct Emitters
 {
+    // retrieve the hard-coded instances provided, template them, and then store them in a map
     std::unordered_map<std::string, std::function<std::vector<std::string>()>> m;
 
     template <class T>
-    void Register(const std::string& name)
+    void Register(const std::string& name, const std::string& prologue, const std::string& epilogue)
     {
-        m[name] = [] {
-            auto configs = T::CreateOperations();
+        m[name] = [&] {
+            auto configs = T::CreateOperations(prologue, epilogue);
 
             return Transform(configs, [](const auto& ops) { return ToTuple(ops); });
         };
     }
 
+    // takes in an operation instance and uses it to substitute the correct values into the template
     template <class T>
     static std::string ToTuple(const T& ops)
     {
@@ -31,6 +34,7 @@ struct Emitters
         return "std::tuple<\n" + ck::host::JoinStrings(templates, ",\n") + ">";
     }
 
+    // Join together all the strings in the map
     std::string Emit(const std::string& name) { return ck::host::JoinStrings(m.at(name)(), "\n"); }
 
     std::vector<std::string> List() const
@@ -43,9 +47,38 @@ int main(int argc, const char* argv[])
 {
     std::string prog = argv[0];
     std::vector<std::string> args(argv + 1, argv + argc);
+
+    // Specify problem type and problem size
+    ck::host::device_gemm_multiple_d::Problem prob;
+    prob.M = 1024;
+    prob.N = 1024;
+    prob.K = 1024;
+
+    // user provided fusion
+    std::string prologue = "";
+    std::string epilogue = R"(
+struct Epilogue
+{
+    __host__ __device__ Epilogue(float alpha, float beta) : alpha_(alpha), beta_(beta){};
+
+    template <typename E, typename D>
+    __host__ __device__ constexpr void operator()(E& e, const D& d) const;
+
+    template <>
+    __host__ __device__ constexpr void operator()<ck::half_t, ck::half_t>(ck::half_t& e,
+                                                                          const ck::half_t& d) const
+    {
+        e = ck::type_convert<ck::half_t>(alpha_ * e + beta_ * ck::type_convert<float>(d));
+    }
+
+    float alpha_;
+    float beta_;
+};)";
+
+    // Load in operations into the Register
     Emitters e;
     e.Register<ck::host::device_gemm_multiple_d::Operation_Xdl_CShuffle>(
-        "DeviceGemmMultipleD_Xdl_CShuffle");
+        "DeviceGemmMultipleD_Xdl_CShuffle", prologue, epilogue);
 
     if(args.empty() or std::any_of(args.begin(), args.end(), [](auto arg) {
            return arg == "-h" or arg == "--help";
@@ -64,6 +97,7 @@ int main(int argc, const char* argv[])
         return 0;
     }
 
+    // print out all the instances for the operation that was chosen at the command line
     for(auto name : args)
         std::cout << e.Emit(name) << std::endl;