Merge remote-tracking branch 'upstream/develop' into merge_upstream_1129

also fix regression

client_example/23_grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu.inc

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <cstdlib>
+#include <iomanip>
+#include <iostream>
+#include <iterator>
+#include <numeric>
+#include <vector>
+
+#include "ck/ck.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_scaleadd_scaleadd_relu.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+using InLayout             = ck::tensor_layout::convolution::NDHWGC;
+using WeiLayout            = ck::tensor_layout::convolution::GKZYXC;
+using OutLayout            = ck::tensor_layout::convolution::NDHWGK;
+using PassThrough          = ck::tensor_operation::element_wise::PassThrough;
+using ScaleAddScaleAddRelu = ck::tensor_operation::element_wise::ScaleAddScaleAddRelu;
+
+static constexpr ck::index_t NumDimSpatial = 3;
+static constexpr ck::index_t G             = 32;
+static constexpr ck::index_t N             = 64; // batch size
+static constexpr ck::index_t K             = 64; // output channel
+static constexpr ck::index_t C             = 32; // input channel (per group)
+static constexpr ck::index_t Z             = 3;  // filter D
+static constexpr ck::index_t Y             = 3;  // filter H
+static constexpr ck::index_t X             = 3;  // filter W
+static constexpr ck::index_t Di            = 14; // input D
+static constexpr ck::index_t Hi            = 14; // input H
+static constexpr ck::index_t Wi            = 14; // input W
+static constexpr ck::index_t Do            = 14; // output D
+static constexpr ck::index_t Ho            = 14; // output H
+static constexpr ck::index_t Wo            = 14; // output W
+
+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 execute_conv_fwd_scaleadd_scaleadd_relu()
+{
+    // We have NHWGC/GKYXC/NHWGK (x, weight, y) in memory space.
+    // However, CK's API only accepts lengths and strides with order of GNCDHW/GKCZYX/GNKDHW.
+    // Hence, we need to adjust the order of strides.
+    std::array<ck::index_t, 6> in_lengths{G, N, C, Di, Hi, Wi};
+    std::array<ck::index_t, 6> in_strides{
+        C, Di * Hi * Wi * G * C, 1, Hi * Wi * G * C, Wi * G * C, G * C};
+    std::array<ck::index_t, 6> wei_lengths{G, K, C, Z, Y, X};
+    std::array<ck::index_t, 6> wei_strides{
+        K * Z * Y * X * C, Z * Y * X * C, 1, Y * X * C, X * C, C};
+    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};
+
+    std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1, 1};
+    std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1, 1};
+    std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1, 1};
+    std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1, 1};
+
+    SimpleDeviceMem in(sizeof(InDataType) * N * Di * Hi * Wi * G * C);
+    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);
+
+    using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<
+        NumDimSpatial,
+        InLayout,
+        WeiLayout,
+        ck::Tuple<OutLayout, OutLayout>,
+        OutLayout,
+        InDataType,
+        WeiDataType,
+        ck::Tuple<std::tuple_element_t<0, DDataTypes>, std::tuple_element_t<1, DDataTypes>>,
+        OutDataType,
+        PassThrough,
+        PassThrough,
+        ScaleAddScaleAddRelu>;
+
+    // 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(),
+                                        {d0.GetDeviceBuffer(), d1.GetDeviceBuffer()},
+                                        out.GetDeviceBuffer(),
+                                        in_lengths,
+                                        in_strides,
+                                        wei_lengths,
+                                        wei_strides,
+                                        {out_lengths, out_lengths},
+                                        {out_strides, out_strides},
+                                        out_lengths,
+                                        out_strides,
+                                        filter_strides,
+                                        filter_dilations,
+                                        input_left_pads,
+                                        input_right_pads,
+                                        PassThrough{},
+                                        PassThrough{},
+                                        ScaleAddScaleAddRelu{2.f, 2.f});
+        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});
+
+            std::size_t flop =
+                std::size_t(2) * G * N * K * C * Ho * Wo * Y * X + 2 * N * Ho * Wo * G * K;
+            std::size_t num_bytes =
+                sizeof(InDataType) * N * Hi * Wi * G * C + sizeof(WeiDataType) * G * K * Y * X * C +
+                (sizeof(OutDataType) + sizeof(std::tuple_element_t<0, DDataTypes>) +
+                 sizeof(std::tuple_element_t<1, DDataTypes>)) *
+                    N * Ho * Wo * G * K;
+
+            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 EXIT_FAILURE;
+    }
+
+    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(),
+                                        {d0.GetDeviceBuffer(), d1.GetDeviceBuffer()},
+                                        out.GetDeviceBuffer(),
+                                        in_lengths,
+                                        in_strides,
+                                        wei_lengths,
+                                        wei_strides,
+                                        {out_lengths, out_lengths},
+                                        {out_strides, out_strides},
+                                        out_lengths,
+                                        out_strides,
+                                        filter_strides,
+                                        filter_dilations,
+                                        input_left_pads,
+                                        input_right_pads,
+                                        PassThrough{},
+                                        PassThrough{},
+                                        ScaleAddScaleAddRelu{2.f, 2.f});
+
+        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 0;
+}

client_example/23_grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu_bf16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <tuple>
+
+#include "ck/utility/data_type.hpp"
+#include "ck/utility/tuple.hpp"
+
+using InDataType  = ck::bhalf_t;
+using WeiDataType = ck::bhalf_t;
+using OutDataType = ck::bhalf_t;
+// Use std tuple instead of ck tuple to avoid clang
+// implicit instantiation of undefined template error.
+using DDataTypes = std::tuple<ck::bhalf_t, ck::bhalf_t>;
+
+#include "grouped_conv_fwd_scaleadd_scaleadd_relu.inc"
+
+int main() { return execute_conv_fwd_scaleadd_scaleadd_relu(); }

client_example/23_grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <tuple>
+
+#include "ck/utility/data_type.hpp"
+#include "ck/utility/tuple.hpp"
+
+using InDataType  = ck::half_t;
+using WeiDataType = ck::half_t;
+using OutDataType = ck::half_t;
+// Use std tuple instead of ck tuple to avoid clang
+// implicit instantiation of undefined template error.
+using DDataTypes = std::tuple<ck::half_t, ck::half_t>;
+
+#include "grouped_conv_fwd_scaleadd_scaleadd_relu.inc"
+
+int main() { return execute_conv_fwd_scaleadd_scaleadd_relu(); }

client_example/23_grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu_fp32.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <tuple>
+
+#include "ck/utility/data_type.hpp"
+#include "ck/utility/tuple.hpp"
+
+using InDataType  = float;
+using WeiDataType = float;
+using OutDataType = float;
+// Use std tuple instead of ck tuple to avoid clang
+// implicit instantiation of undefined template error.
+using DDataTypes = std::tuple<float, float>;
+
+#include "grouped_conv_fwd_scaleadd_scaleadd_relu.inc"
+
+int main() { return execute_conv_fwd_scaleadd_scaleadd_relu(); }

client_example/23_grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu_int8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <tuple>
+
+#include "ck/utility/data_type.hpp"
+#include "ck/utility/tuple.hpp"
+
+using InDataType  = int8_t;
+using WeiDataType = int8_t;
+using OutDataType = int8_t;
+// Use std tuple instead of ck tuple to avoid clang
+// implicit instantiation of undefined template error.
+using DDataTypes = std::tuple<float, float>;
+
+#include "grouped_conv_fwd_scaleadd_scaleadd_relu.inc"
+
+int main() { return execute_conv_fwd_scaleadd_scaleadd_relu(); }

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_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_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_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_conv_operations)

client_example/24_grouped_convnd_fwd_scaleadd_ab/grouped_conv_fwd_scaleadd_ab.inc

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <cstdlib>
+#include <iomanip>
+#include <iostream>
+#include <iterator>
+#include <numeric>
+#include <vector>
+
+#include "ck/ck.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_scaleadd_ab.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+using InLayout    = ck::tensor_layout::convolution::NDHWGC;
+using WeiLayout   = ck::tensor_layout::convolution::GKZYXC;
+using OutLayout   = ck::tensor_layout::convolution::NDHWGK;
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using ScaleAdd    = ck::tensor_operation::element_wise::ScaleAdd;
+
+static constexpr ck::index_t NumDimSpatial = 3;
+static constexpr ck::index_t G             = 32;
+static constexpr ck::index_t N             = 64; // batch size
+static constexpr ck::index_t K             = 64; // output channel
+static constexpr ck::index_t C             = 32; // input channel (per group)
+static constexpr ck::index_t Z             = 3;  // filter D
+static constexpr ck::index_t Y             = 3;  // filter H
+static constexpr ck::index_t X             = 3;  // filter W
+static constexpr ck::index_t Di            = 14; // input D
+static constexpr ck::index_t Hi            = 14; // input H
+static constexpr ck::index_t Wi            = 14; // input W
+static constexpr ck::index_t Do            = 14; // output D
+static constexpr ck::index_t Ho            = 14; // output H
+static constexpr ck::index_t Wo            = 14; // output W
+
+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 execute_conv_fwd_scaleadd_ab()
+{
+    constexpr ck::index_t NumAs = 2;
+    constexpr ck::index_t NumBs = 2;
+
+    constexpr float scale = 1.5f;
+
+    // We have NHWGC/GKYXC/NHWGK (x, weight, y) in memory space.
+    // However, CK's API only accepts lengths and strides with order of GNCDHW/GKCZYX/GNKDHW.
+    // Hence, we need to adjust the order of strides.
+    std::array<ck::index_t, 6> in_lengths{G, N, C, Di, Hi, Wi};
+    std::array<ck::index_t, 6> in_strides{
+        C, Di * Hi * Wi * G * C, 1, Hi * Wi * G * C, Wi * G * C, G * C};
+    std::array<ck::index_t, 6> wei_lengths{G, K, C, Z, Y, X};
+    std::array<ck::index_t, 6> wei_strides{
+        K * Z * Y * X * C, Z * Y * X * C, 1, Y * X * C, X * C, C};
+    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};
+
+    std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1, 1};
+    std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1, 1};
+    std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1, 1};
+    std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1, 1};
+
+    using InputDtype      = ck::tuple_element_t<0, InDataType>;
+    using InputBiasDtype  = ck::tuple_element_t<1, InDataType>;
+    using WeightDtype     = ck::tuple_element_t<0, WeiDataType>;
+    using WeightBiasDtype = ck::tuple_element_t<1, WeiDataType>;
+
+    SimpleDeviceMem in(sizeof(InputDtype) * N * Di * Hi * Wi * G * C);
+    SimpleDeviceMem in_bias(sizeof(InputBiasDtype) * N * Di * Hi * Wi * G * C);
+    SimpleDeviceMem wei(sizeof(WeightDtype) * G * K * Z * Y * X * C);
+    SimpleDeviceMem wei_bias(sizeof(WeightBiasDtype) * G * K * Z * Y * X * C);
+    SimpleDeviceMem out(sizeof(OutDataType) * N * Do * Ho * Wo * G * K);
+
+    using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
+                                                                                   InLayout,
+                                                                                   WeiLayout,
+                                                                                   ck::Tuple<>,
+                                                                                   OutLayout,
+                                                                                   InDataType,
+                                                                                   WeiDataType,
+                                                                                   ck::Tuple<>,
+                                                                                   OutDataType,
+                                                                                   ScaleAdd,
+                                                                                   ScaleAdd,
+                                                                                   PassThrough>;
+
+    // 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;
+
+    std::array<const void*, NumAs> as = {in.GetDeviceBuffer(), in_bias.GetDeviceBuffer()};
+    std::array<const void*, NumBs> bs = {wei.GetDeviceBuffer(), wei_bias.GetDeviceBuffer()};
+    std::array<const void*, 0> ds{};
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr        = op_ptrs[i];
+        auto argument_ptr   = op_ptr->MakeArgumentPointer(as,
+                                                        bs,
+                                                        ds,
+                                                        out.GetDeviceBuffer(),
+                                                        in_lengths,
+                                                        in_strides,
+                                                        wei_lengths,
+                                                        wei_strides,
+                                                        {},
+                                                        {},
+                                                        out_lengths,
+                                                        out_strides,
+                                                        filter_strides,
+                                                        filter_dilations,
+                                                        input_left_pads,
+                                                        input_right_pads,
+                                                        ScaleAdd{scale},
+                                                        ScaleAdd{scale},
+                                                        PassThrough{});
+        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});
+
+            std::size_t flop = std::size_t(2) * G * N * K * C * Do * Ho * Wo * Z * Y * X +
+                               N * Di * Hi * Wi * G * C + G * K * Z * Y * X * C;
+            std::size_t num_bytes = 2 * sizeof(InDataType) * N * Di * Hi * Wi * G * C +
+                                    2 * sizeof(WeiDataType) * G * K * Z * Y * X * C +
+                                    sizeof(OutDataType) * N * Do * Ho * Wo * G * K;
+
+            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 EXIT_FAILURE;
+    }
+
+    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(as,
+                                                        bs,
+                                                        ds,
+                                                        out.GetDeviceBuffer(),
+                                                        in_lengths,
+                                                        in_strides,
+                                                        wei_lengths,
+                                                        wei_strides,
+                                                        {},
+                                                        {},
+                                                        out_lengths,
+                                                        out_strides,
+                                                        filter_strides,
+                                                        filter_dilations,
+                                                        input_left_pads,
+                                                        input_right_pads,
+                                                        ScaleAdd{scale},
+                                                        ScaleAdd{scale},
+                                                        PassThrough{});
+
+        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 0;
+}

client_example/24_grouped_convnd_fwd_scaleadd_ab/grouped_conv_fwd_scaleadd_ab_bf16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/utility/data_type.hpp"
+#include "ck/utility/tuple.hpp"
+
+using InDataType  = ck::Tuple<ck::bhalf_t, ck::bhalf_t>;
+using WeiDataType = ck::Tuple<ck::bhalf_t, ck::bhalf_t>;
+using OutDataType = ck::bhalf_t;
+
+#include "grouped_conv_fwd_scaleadd_ab.inc"
+
+int main() { return execute_conv_fwd_scaleadd_ab(); }

client_example/24_grouped_convnd_fwd_scaleadd_ab/grouped_conv_fwd_scaleadd_ab_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/utility/data_type.hpp"
+#include "ck/utility/tuple.hpp"
+
+using InDataType  = ck::Tuple<ck::half_t, ck::half_t>;
+using WeiDataType = ck::Tuple<ck::half_t, ck::half_t>;
+using OutDataType = ck::half_t;
+
+#include "grouped_conv_fwd_scaleadd_ab.inc"
+
+int main() { return execute_conv_fwd_scaleadd_ab(); }

client_example/24_grouped_convnd_fwd_scaleadd_ab/grouped_conv_fwd_scaleadd_ab_fp32.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/utility/data_type.hpp"
+#include "ck/utility/tuple.hpp"
+
+using InDataType  = ck::Tuple<float, float>;
+using WeiDataType = ck::Tuple<float, float>;
+using OutDataType = float;
+
+#include "grouped_conv_fwd_scaleadd_ab.inc"
+
+int main() { return execute_conv_fwd_scaleadd_ab(); }

client_example/24_grouped_convnd_fwd_scaleadd_ab/grouped_conv_fwd_scaleadd_ab_int8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/utility/data_type.hpp"
+#include "ck/utility/tuple.hpp"
+
+using InDataType  = ck::Tuple<int8_t, int8_t>;
+using WeiDataType = ck::Tuple<int8_t, int8_t>;
+using OutDataType = int8_t;
+
+#include "grouped_conv_fwd_scaleadd_ab.inc"
+
+int main() { return execute_conv_fwd_scaleadd_ab(); }

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}")
 

cmake/DoxygenDoc.cmake

@@ -309,6 +309,8 @@ XML_OUTPUT
 XML_PROGRAMLISTING
 )
 
+set(WARN_AS_ERROR YES)
+
 set(DOXYGEN_CONFIG_FILE "${CMAKE_CURRENT_BINARY_DIR}/doxygen/doxygen.conf" CACHE PATH "Path to generated doxygen configuration file")
 
 function(add_doxygen_doc)

cmake/EnableCompilerWarnings.cmake

@@ -70,6 +70,7 @@ else()
             -Wno-option-ignored
             -Wsign-compare
             -Wno-extra-semi-stmt
+            -Wno-unused-template
         )
         if (CMAKE_${COMPILER}_COMPILER_ID MATCHES "Clang")
             list(APPEND CMAKE_COMPILER_WARNINGS

docs/Contributors_Guide.rst

@@ -2,7 +2,101 @@
 Contributor's Guide
 ===================
 
-Pull-request guidelines
-=======================
+This chapter explains how to get started contributing to the Composable Kernel project and what are
+the contributing rules.
 
-[TODO]
+Getting started
+===============
+
+#. **Documentation:** Before contributing to the library, familiarize yourself with the
+   `Composable Kernel User Guide <https://rocm.docs.amd.com/projects/composable_kernel/en/latest/>`_.
+   It provides insight into the core concepts, environment configuration, and steps to obtain or
+   build the library. You can also find some of this information in the
+   `README file <https://github.com/ROCmSoftwarePlatform/composable_kernel/blob/develop/README.md>`_
+   on the project's GitHub page.
+#. **Additional reading:** We also recommend reading a `blog post
+   <https://community.amd.com/t5/instinct-accelerators/amd-composable-kernel-library-efficient-fused-kernels-for-ai/ba-p/553224>`_
+   from the AMD Community portal. It offers a deeper understanding of the library's objectives and
+   showcases its performance capabilities.
+#. **General information:** For broader information about AMD products, consider exploring the
+   `AMD Developer Central portal <https://www.amd.com/en/developer.html>`_.
+
+How do I contribute
+===================
+
+We deeply value contributions from our users. You can make an impact by reporting issues or
+proposing code enhancements through pull requests.
+
+Reporting issues
+----------------
+
+We use `Github issues <https://github.com/ROCmSoftwarePlatform/composable_kernel/issues>`_
+to track public bugs and enhancement requests.
+
+If you encounter an issue with the library, please check if the problem has already been
+reported by searching existing issues on GitHub. If your issue seems unique, please submit a new
+issue. All reported issues must include:
+
+* A comprehensive description of the problem, including:
+
+  * What did you observe?
+  * Why do you think it is a bug (if it seems like one)?
+  * What did you expect to happen? What would indicate the resolution of the problem?
+  * Are there any known workarounds?
+
+* Your configuration details, including:
+
+  * Which GPU are you using?
+  * Which OS version are you on?
+  * Which ROCm version are you using?
+  * Are you using a Docker image? If so, which one?
+
+* Steps to reproduce the issue, including:
+
+  * What actions trigger the issue? What are the reproduction steps?
+
+    * If you build the library from scratch, what CMake command did you use?
+
+  * How frequently does this issue happen? Does it reproduce every time? Or is it a sporadic issue?
+
+Before sumbitting any issue, ensure you have addressed all relevant questions from the checklist.
+
+Creating Pull Requests
+----------------------
+
+You can submit `Pull Requests (PR) on GitHub
+<https://github.com/ROCmSoftwarePlatform/composable_kernel/pulls>`_.
+
+All contributors are required to develop their changes on a separate branch and then create a
+pull requrest to merge their changes into the `develop` branch, which is the default
+development branch in the Composable Kernel project. All external contributors must use their own
+forks of the project to develop their changes.
+
+When submitting a Pull Request you should:
+
+* Describe the change providing information about the motivation for the change and a general
+  description of all code modifications.
+
+* Verify and test the change:
+
+  * Run any relevant existing tests.
+  * Write new tests if added functionality is not covered by current tests.
+
+* Ensure your changes align with the coding style defined in the ``.clang-format`` file located in
+  the project's root directory. We leverage `pre-commit` to run `clang-format` automatically. We
+  highly recommend contributors utilize this method to maintain consistent code formatting.
+  Instructions on setting up `pre-commit` can be found in the project's
+  `README file <https://github.com/ROCmSoftwarePlatform/composable_kernel/blob/develop/README.md>`_
+
+* Link your PR to any related issues:
+
+  * If there is an issue that is resolved by your change, please provide a link to the issue in
+    the description of your pull request.
+
+* For larger contributions, structure your change into a sequence of smaller, focused commits, each
+  addressing a particular aspect or fix.
+
+Following the above guidelines ensures a seamless review process and faster assistance from our
+end.
+
+Thank you for your commitment to enhancing the Composable Kernel project! We look forward to collaborating with you.

docs/sphinx/requirements.in

 rocm-docs-core>=0.20.0
-sphinxcontrib-bibtex==2.5.0
+sphinxcontrib-bibtex==2.6.1

docs/sphinx/requirements.txt

@@ -42,12 +42,18 @@ fastjsonschema==2.18.0
     # via rocm-docs-core
 gitdb==4.0.10
     # via gitpython
-gitpython==3.1.31
+gitpython==3.1.35
     # via rocm-docs-core
 idna==3.4
     # via requests
 imagesize==1.4.1
     # via sphinx
+importlib-metadata==6.8.0
+    # via
+    #   sphinx
+    #   sphinxcontrib-bibtex
+importlib-resources==6.1.0
+    # via rocm-docs-core
 jinja2==3.1.2
     # via
     #   myst-parser
@@ -90,9 +96,13 @@ pygments==2.14.0
     #   pydata-sphinx-theme
     #   sphinx
 pyjwt[crypto]==2.6.0
-    # via pygithub
+    # via
+    #   pygithub
+    #   pyjwt
 pynacl==1.5.0
     # via pygithub
+pytz==2023.3.post1
+    # via babel
 pyyaml==6.0
     # via
     #   myst-parser
@@ -103,7 +113,7 @@ requests==2.28.2
     # via
     #   pygithub
     #   sphinx
-rocm-docs-core>=0.20.0
+rocm-docs-core==0.27.0
     # via -r requirements.in
 six==1.16.0
     # via
@@ -139,7 +149,7 @@ sphinx-notfound-page==0.8.3
     # via rocm-docs-core
 sphinxcontrib-applehelp==1.0.4
     # via sphinx
-sphinxcontrib-bibtex==2.5.0
+sphinxcontrib-bibtex==2.6.1
     # via -r requirements.in
 sphinxcontrib-devhelp==1.0.2
     # via sphinx
@@ -157,3 +167,7 @@ urllib3==1.26.15
     # via requests
 wrapt==1.15.0
     # via deprecated
+zipp==3.17.0
+    # via
+    #   importlib-metadata
+    #   importlib-resources

example/01_gemm/CMakeLists.txt

-if(DL_KERNELS)
-  add_custom_target(example_gemm_dl)
-
-  add_example_executable(example_gemm_dl_fp32 gemm_dl_fp32.cpp)
-  add_dependencies(example_gemm_dl example_gemm_dl_fp32)
-  if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
-    add_example_executable(example_gemm_dl_fp16 gemm_dl_fp16.cpp)
-    add_dependencies(example_gemm_dl example_gemm_dl_fp16)
-    add_example_executable(example_gemm_dl_dpp8_fp16 gemm_dl_dpp8_fp16.cpp)
-    add_dependencies(example_gemm_dl example_gemm_dl_dpp8_fp16)
-  endif()
-  if(DTYPES MATCHES "int8" OR NOT DEFINED DTYPES)
-    add_example_executable(example_gemm_dl_int8 gemm_dl_int8.cpp)
-    add_dependencies(example_gemm_dl example_gemm_dl_int8)
-  endif()
-
-  if(USE_BITINT_EXTENSION_INT4)
+add_custom_target(example_gemm_dl)
+
+add_example_executable(example_gemm_dl_fp32 gemm_dl_fp32.cpp)
+add_example_dependencies(example_gemm_dl example_gemm_dl_fp32)
+
+add_example_executable(example_gemm_dl_fp16 gemm_dl_fp16.cpp)
+add_example_dependencies(example_gemm_dl example_gemm_dl_fp16)
+
+add_example_executable(example_gemm_dpp_fp16 gemm_dpp_fp16.cpp)
+
+add_example_executable(example_gemm_dl_int8 gemm_dl_int8.cpp)
+add_example_dependencies(example_gemm_dl example_gemm_dl_int8)
+if(USE_BITINT_EXTENSION_INT4)
     add_example_executable(example_gemm_dl_int4 gemm_dl_int4.cpp)
-    add_dependencies(example_gemm_dl example_gemm_dl_int4)
-  endif(USE_BITINT_EXTENSION_INT4)
-endif()
+    add_example_dependencies(example_gemm_dl example_gemm_dl_int4)
+endif(USE_BITINT_EXTENSION_INT4)
 
 add_custom_target(example_gemm_xdl)
-if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
-  add_example_executable(example_gemm_xdl_fp16 gemm_xdl_fp16.cpp)
-  add_example_executable(example_gemm_xdl_wavelet_fp16 gemm_xdl_wavelet_fp16.cpp)
-  add_dependencies(example_gemm_xdl example_gemm_xdl_fp16)
-  add_dependencies(example_gemm_xdl example_gemm_xdl_wavelet_fp16)
-  add_example_executable(example_gemm_xdl_skip_b_lds_fp16 gemm_xdl_skip_b_lds_fp16.cpp)
-  add_dependencies(example_gemm_xdl example_gemm_xdl_skip_b_lds_fp16)
-
-  if(GPU_TARGETS MATCHES "gfx1100" OR GPU_TARGETS MATCHES "gfx1101" OR GPU_TARGETS MATCHES "gfx1102")
+add_example_executable(example_gemm_xdl_fp16 gemm_xdl_fp16.cpp)
+add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16)
+
+add_example_executable(example_gemm_xdl_wavelet_fp16 gemm_xdl_wavelet_fp16.cpp)
+add_example_dependencies(example_gemm_xdl example_gemm_xdl_wavelet_fp16)
+
+add_example_executable(example_gemm_xdl_skip_b_lds_fp16 gemm_xdl_skip_b_lds_fp16.cpp)
+add_example_dependencies(example_gemm_xdl example_gemm_xdl_skip_b_lds_fp16)
+if(GPU_TARGETS MATCHES "gfx1100" OR GPU_TARGETS MATCHES "gfx1101" OR GPU_TARGETS MATCHES "gfx1102")
     add_custom_target(example_gemm_wmma)
     add_example_executable(example_gemm_wmma_fp16 gemm_wmma_fp16.cpp)
-    add_dependencies(example_gemm_wmma example_gemm_wmma_fp16)
-  endif()
-
+    add_example_dependencies(example_gemm_wmma example_gemm_wmma_fp16)
 endif()
 
-if(DTYPES MATCHES "bf16" OR NOT DEFINED DTYPES)
-  add_example_executable(example_gemm_xdl_bf16 gemm_xdl_bf16.cpp)
-  add_dependencies(example_gemm_xdl example_gemm_xdl_bf16)
+add_example_executable(example_gemm_xdl_bf16 gemm_xdl_bf16.cpp)
+add_example_dependencies(example_gemm_xdl example_gemm_xdl_bf16)
 
-  add_example_executable(example_gemm_xdl_bf16_rtn gemm_xdl_bf16_rtn.cpp)
-  add_dependencies(example_gemm_xdl example_gemm_xdl_bf16_rtn)
-endif()
+add_example_executable(example_gemm_xdl_bf16_rtn gemm_xdl_bf16_rtn.cpp)
+add_example_dependencies(example_gemm_xdl example_gemm_xdl_bf16_rtn)
 
-if(DTYPES MATCHES "int8" OR NOT DEFINED DTYPES)
-  add_example_executable(example_gemm_xdl_int8 gemm_xdl_int8.cpp)
-  add_dependencies(example_gemm_xdl example_gemm_xdl_int8)
-endif()
+add_example_executable(example_gemm_xdl_int8 gemm_xdl_int8.cpp)
+add_example_dependencies(example_gemm_xdl example_gemm_xdl_int8)
 
 if(USE_BITINT_EXTENSION_INT4)
-  add_example_executable(example_gemm_xdl_int4 gemm_xdl_int4.cpp)
-  add_dependencies(example_gemm_xdl example_gemm_xdl_int4)
+    add_example_executable(example_gemm_xdl_int4 gemm_xdl_int4.cpp)
+    add_example_dependencies(example_gemm_xdl example_gemm_xdl_int4)
 endif(USE_BITINT_EXTENSION_INT4)
 
-if(DTYPES MATCHES "fp64" OR NOT DEFINED DTYPES)
-  # FIXME: re-enable this exampe as test when SWDEV-335738 is fixed
-  add_example_executable_no_testing(example_gemm_xdl_fp64 gemm_xdl_fp64.cpp)
-  add_dependencies(example_gemm_xdl example_gemm_xdl_fp64)
-endif()
+# FIXME: re-enable this example as test when SWDEV-335738 is fixed
+add_example_executable_no_testing(example_gemm_xdl_fp64 gemm_xdl_fp64.cpp)
+add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp64)
 
 add_example_executable(example_gemm_xdl_streamk gemm_xdl_streamk.cpp)
 
-if(DTYPES MATCHES "fp8" OR NOT DEFINED DTYPES)
-  if(GPU_TARGETS MATCHES "gfx940" OR GPU_TARGETS MATCHES "gfx941" OR GPU_TARGETS MATCHES "gfx942")
-    add_example_executable(example_gemm_xdl_f8 gemm_xdl_f8.cpp)
-    add_dependencies(example_gemm_xdl example_gemm_xdl_f8)
-  endif()
-endif()
+add_example_executable(example_gemm_xdl_fp8 gemm_xdl_fp8.cpp)
+add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp8)
+
+add_example_executable(example_gemm_xdl_fp8_bf8 gemm_xdl_fp8_bf8.cpp)
+add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp8_bf8)
+
+list(APPEND gpu_list gfx90a gfx940 gfx941 gfx942)
+set(target 0)
+foreach(gpu IN LISTS GPU_TARGETS)
+    if(gpu IN_LIST gpu_list AND target EQUAL 0)
+        add_example_executable(example_gemm_xdl_lds_direct_load_fp32 gemm_xdl_lds_direct_load_fp32.cpp)
+        add_example_dependencies(example_gemm_xdl example_gemm_xdl_lds_direct_load_fp32)
+
+        add_example_executable(example_gemm_xdl_lds_direct_load_fp16 gemm_xdl_lds_direct_load_fp16.cpp)
+        add_example_dependencies(example_gemm_xdl example_gemm_xdl_lds_direct_load_fp16)
+        set(target 1)
+    endif()
+endforeach()
 
-add_example_executable(example_gemm_xdl_fp16_f8 gemm_xdl_fp16_f8.cpp)
-add_dependencies(example_gemm_xdl example_gemm_xdl_fp16_f8)
+add_example_executable(example_gemm_xdl_fp16_fp8 gemm_xdl_fp16_fp8.cpp)
+add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_fp8)

example/01_gemm/gemm_dl_dpp8_fp16.cpp → example/01_gemm/gemm_dpp_fp16.cpp

@@ -3,31 +3,33 @@
 
 #include "common.hpp"
 
-#include "ck/tensor_operation/gpu/device/impl/device_gemm_dl_dpp8.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_dpp.hpp"
 
 using ADataType   = ck::half_t;
 using BDataType   = ck::half_t;
-using CDataType   = ck::half_t;
 using AccDataType = float;
+using CDataType   = ck::half_t;
+
+using F16 = ck::half_t;
 
-using ALayout = Col;
-using BLayout = Row;
+using ALayout = Row;
+using BLayout = Col;
 using CLayout = Row;
 
 using AElementOp = PassThrough;
 using BElementOp = PassThrough;
 using CElementOp = PassThrough;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNPadding;
 
 // clang-format off
-using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmDlDpp8
-// ######|     AData|     BData|     CData|     AccData| ALayout| BLayout| CLayout|           A|           B|           C|           GEMM| Block|  MPer|  NPer| K0Per| K1|      M1Per|      N1Per|   KPer|  M11N11Thread|  M11N11Thread|     ABlockTransfer|       ABlockTransfer| ABlockTransfer| ABlockTransfer|      ABlockTransfer|     ABlockTransfer|      ABlockTransfer|     BBlockTransfer|       BBlockTransfer| BBlockTransfer| BBlockTransfer|      BBlockTransfer|     BBlockTransfer|      BBlockTransfer|     CThreadTransfer| CThreadTransfer|    CThreadTransfer|
-// ######|      Type|      Type|      Type|        Type|        |        |        | Elementwise| Elementwise| Elementwise| Spacialization|  Size| Block| Block| Block|   | ThreadM111| ThreadN111| Thread| ClusterM110Xs| ClusterN110Xs| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster|      SrcAccess|     SrcVectorTensor|    SrcVectorTensor|     DstVectorTensor| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster|      SrcAccess|     SrcVectorTensor|    SrcVectorTensor|     DstVectorTensor|        SrcDstAccess| SrcDstVectorDim| DstScalarPerVector|
-// ######|          |          |          |            |        |        |        |   Operation|   Operation|   Operation|               |      |      |      |      |   |           |           |       |              |              |        K0_M0_M1_K1|          K0_M0_M1_K1|   ArrangeOrder|          Order| Lengths_K0_M0_M1_K1| ContiguousDimOrder| Lengths_K0_M0_M1_K1|        K0_N0_N1_K1|          K0_N0_N1_K1|   ArrangeOrder|          Order| Lengths_K0_N0_N1_K1| ContiguousDimOrder| Lengths_K0_N0_N1_K1|               Order|                |                   |
-// ######|          |          |          |            |        |        |        |            |            |            |               |      |      |      |      |   |           |           |       |              |              |                   |                     |               |               |                    |                   |                    |                   |                     |               |               |                    |                   |                    |                    |                |                   |
-         < ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout,  AElementOp,  BElementOp,  CElementOp,    GemmDefault,   256,   128,   128,    16,  2,          1,          8,      8,       S<8, 8>,       S<4, 1>,      S<2, 1, 4, 2>,      S<8, 1,  32, 1>,  S<0, 3, 1, 2>,  S<0, 3, 1, 2>,       S<1, 1, 4, 1>,      S<0, 3, 1, 2>,       S<1, 1, 4, 2>,      S<2, 1, 4, 2>,       S<8, 1, 32, 1>,  S<0, 3, 1, 2>,  S<0, 3, 1, 2>,       S<1, 1, 4, 1>,      S<0, 3, 1, 2>,       S<1, 1, 4, 2>, S<0, 1, 2, 3, 4, 5>,               5,                  4>;
-// clang-format on
+using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmDpp
+// ######|     AData|     BData|     CData|     AccData| ALayout| BLayout| CLayout|           A|           B|           C|          GEMM| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer|    MDpp|    NDpp|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
+// ######|      Type|      Type|      Type|        Type|        |        |        | Elementwise| Elementwise| Elementwise|Spacialization|  Size| Block| Block| Block|    |    |  Dpp|  Dpp| PerWave| PerWave|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| SrcDstVectorDim|       DstScalar|
+// ######|          |          |          |            |        |        |        |   Operation|   Operation|   Operation|              |      |      |      |      |    |    |     |     |        |        | Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |                |       PerVector|
+// ######|          |          |          |            |        |        |        |            |            |            |              |      |      |      |      |    |    |     |     |        |        |                |               |               |               |               |               |          |                |               |               |              |               |               |          |                |                |
+         < ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout,  AElementOp,  BElementOp,  CElementOp,   GemmDefault,   128,    64,    64,    64,   8,   2,   32,    8,       2,       2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              2,              2,      true,               5,               1>;
+// // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::
     ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;

example/01_gemm/gemm_xdl_fp16.cpp

@@ -9,13 +9,13 @@
 using ADataType        = ck::half_t;
 using BDataType        = ck::half_t;
 using AccDataType      = float;
-using CShuffleDataType = float;
+using CShuffleDataType = ck::half_t;
 using CDataType        = ck::half_t;
 
 using F16 = ck::half_t;
 
 using ALayout = Row;
-using BLayout = Col;
+using BLayout = Row;
 using CLayout = Row;
 
 using AElementOp = PassThrough;
@@ -30,7 +30,7 @@ using DeviceGemmInstance0 = ck::tensor_operation::device::DeviceGemmXdl
 // ######|      Type|      Type|      Type|        Type|        |        |        | Elementwise| Elementwise| Elementwise|Spacialization|  Size| Block| Block| Block|   |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| SrcDstVectorDim|       DstScalar|
 // ######|          |          |          |            |        |        |        |   Operation|   Operation|   Operation|              |      |      |      |      |   |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |                |       PerVector|
 // ######|          |          |          |            |        |        |        |            |            |            |              |      |      |      |      |   |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |                |                |
-         < ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout,  AElementOp,  BElementOp,  CElementOp,   GemmDefault,   256,   256,   128,     4,  8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,      true,               7,               1>;
+         < ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout,  AElementOp,  BElementOp,  CElementOp,   GemmDefault,   256,   256,   128,     4,  8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>;
 // // clang-format on
 
 // clang-format off
@@ -39,9 +39,7 @@ using DeviceGemmInstance1 = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffl
 // ######|        |        |        |      Type|      Type|      Type|        Type|         DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
 // ######|        |        |        |          |          |          |            |                 |   Operation|   Operation|   Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
 // ######|        |        |        |          |          |          |            |                 |            |            |            |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
-         < ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp,  CElementOp,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,               8, 
-         ck::make_default_loop_scheduler(),
-         ck::PipelineVersion::v2>;
+         < ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp,  CElementOp,    GemmDefault,        1,   256,   256,   128,    32,   8,   2,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<8, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              4,              2,         0,           1,           2,              S<1, 16, 1, 16>,               8, ck::LoopScheduler::Interwave, ck::PipelineVersion::v1>;
 // clang-format on
 
 using DeviceGemmInstance = DeviceGemmInstance1;