Merge branch 'develop' into amd-develop

example/ck_tile/02_layernorm2d/layernorm2d_fwd.cpp

@@ -35,7 +35,9 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
                                                                     YDataType,
                                                                     MeanDataType,
                                                                     InvStdDataType,
-                                                                    Shape>;
+                                                                    Shape,
+                                                                    true,
+                                                                    true>;
 
         using Kernel = ck_tile::Layernorm2dFwd<PipelineProblem>;
 

example/ck_tile/04_img2col/CMakeLists.txt

+# not using add_example_executable() to add this target, since we don't want this to have
+# to be included in "make all/install/check"
+add_executable(tile_example_img2col EXCLUDE_FROM_ALL image_to_column.cpp)

example/ck_tile/04_img2col/README.md

+# Image to Column
+
+This folder contains example for Image to Column using ck_tile tile-programming implementation.
+
+## build
+```
+# in the root of ck_tile
+mkdir build && cd build
+sh ../script/cmake-ck-dev.sh  ../ <arch>  # you can replace this <arch> to gfx90a, gfx942...
+make tile_example_img2col -j
+```
+This will result in an executable `build/bin/tile_example_img2col`

example/ck_tile/04_img2col/image_to_column.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <algorithm>
+#include <cstring>
+
+#include "ck_tile/host.hpp"
+#include "image_to_column.hpp"
+
+// Host API implementation
+template <>
+float image_to_column(const image_to_column_traits& traits,
+                      const image_to_column_args<2>& args,
+                      const ck_tile::stream_config& stream_conf)
+{
+    if(traits.data_type.compare("fp16") == 0)
+    {
+        constexpr ck_tile::index_t NDimSpatial = 2;
+        constexpr ck_tile::index_t VectorSize  = 8;
+
+        using thread_tile = ck_tile::sequence<8, 8>;
+        using warp_tile   = ck_tile::sequence<64, 64>;
+        using block_tile  = ck_tile::sequence<128, 128>;
+
+        using Shape = ck_tile::TileImageToColumnShape<thread_tile, warp_tile, block_tile>;
+
+        using InDataType  = ck_tile::half_t;
+        using OutDataType = ck_tile::half_t;
+
+        using PipelineProblem = ck_tile::BlockImageToColumnProblem<InDataType,
+                                                                   OutDataType,
+                                                                   Shape,
+                                                                   NDimSpatial,
+                                                                   VectorSize,
+                                                                   VectorSize>;
+
+        using Kernel = ck_tile::ImageToColumn<PipelineProblem>;
+
+        auto kargs = Kernel::MakeKargs(args.p_in,
+                                       args.p_out,
+                                       args.G,
+                                       args.N,
+                                       args.C,
+                                       args.input_spatial_lengths,
+                                       args.filter_spatial_lengths,
+                                       args.output_spatial_lengths,
+                                       args.image_g_n_c_wis_strides,
+                                       args.gemm_g_m_k_strides,
+                                       args.conv_filter_strides,
+                                       args.conv_filter_dilations,
+                                       args.input_left_pads,
+                                       args.input_right_pads);
+
+        const dim3 grids = Kernel::GridSize(
+            args.N * args.output_spatial_lengths[0] * args.output_spatial_lengths[1],
+            args.filter_spatial_lengths[0] * args.filter_spatial_lengths[1] * args.C,
+            args.G);
+        constexpr dim3 blocks = Kernel::BlockSize();
+
+        constexpr ck_tile::index_t kBlockPerCu = 2;
+
+        float ave_time = ck_tile::launch_kernel(
+            stream_conf,
+            ck_tile::make_kernel<blocks.x, kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
+
+        return ave_time;
+    }
+
+    return 0;
+}
+
+int main(int argc, char* argv[])
+{
+    constexpr ck_tile::index_t NDimSpatial = 2;
+
+    ExecutionConfig config;
+    ck_tile::conv::ConvParam conv_params = DefaultConvParams;
+
+    if(!parse_cmd_args(argc, argv, config, conv_params))
+    {
+        return EXIT_FAILURE;
+    }
+
+    if(conv_params.num_dim_spatial_ != NDimSpatial)
+    {
+        std::cerr << "unsupported # of spatial dimensions" << std::endl;
+        return EXIT_FAILURE;
+    }
+
+    using InDataType  = ck_tile::half_t;
+    using OutDataType = ck_tile::half_t;
+    using ImLayout    = ck_tile::tensor_layout::convolution::NHWGC;
+
+    const auto G = conv_params.G_;
+    const auto N = conv_params.N_;
+    const auto C = conv_params.C_;
+
+    const ck_tile::long_index_t NHoWo =
+        N * std::accumulate(conv_params.output_spatial_lengths_.begin(),
+                            std::next(conv_params.output_spatial_lengths_.begin(), NDimSpatial),
+                            1,
+                            std::multiplies<>());
+
+    const ck_tile::long_index_t CYX =
+        C * std::accumulate(conv_params.filter_spatial_lengths_.begin(),
+                            std::next(conv_params.filter_spatial_lengths_.begin(), NDimSpatial),
+                            1,
+                            std::multiplies<>());
+
+    const auto in_desc =
+        ck_tile::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<ImLayout>(conv_params);
+    const auto out_desc = ck_tile::HostTensorDescriptor({G, NHoWo, CYX});
+
+    // host verify
+    ck_tile::HostTensor<InDataType> in(in_desc);
+    ck_tile::HostTensor<OutDataType> out_device(out_desc);
+    ck_tile::HostTensor<OutDataType> out_host(out_desc);
+
+    switch(config.init_method)
+    {
+    case 0: break;
+    case 1: ck_tile::FillUniformDistributionIntegerValue<InDataType>{-5.f, 5.f}(in); break;
+    default: ck_tile::FillUniformDistribution<InDataType>{-0.5, 0.5}(in); break;
+    }
+
+    ck_tile::DeviceMem in_device_buf(in.get_element_space_size_in_bytes());
+    ck_tile::DeviceMem out_device_buf(out_device.get_element_space_size_in_bytes());
+
+    in_device_buf.ToDevice(in.data());
+
+    image_to_column_traits traits{"fp16"};
+
+    image_to_column_args<NDimSpatial> args{
+        in_device_buf.GetDeviceBuffer(),
+        out_device_buf.GetDeviceBuffer(),
+        G,
+        N,
+        C,
+        ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.input_spatial_lengths_),
+        ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.filter_spatial_lengths_),
+        ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.output_spatial_lengths_),
+        ck_tile::to_array<ck_tile::long_index_t, NDimSpatial + 3>(in_desc.get_strides()),
+        ck_tile::to_array<ck_tile::long_index_t, 3>(out_desc.get_strides()),
+        ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.conv_filter_strides_),
+        ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.conv_filter_dilations_),
+        ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.input_left_pads_),
+        ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.input_right_pads_)};
+
+    float ave_time =
+        image_to_column(traits, args, ck_tile::stream_config{nullptr, config.time_kernel});
+
+    std::size_t num_btype = G * NHoWo * CYX * (sizeof(OutDataType) + sizeof(InDataType));
+    float gb_per_sec      = num_btype / 1.E6 / ave_time;
+    std::cout << "Perf: " << ave_time << " ms, " << gb_per_sec << " GB/s" << std::endl;
+
+    bool pass = true;
+
+    if(config.do_verification)
+    {
+        // reference
+        ck_tile::reference_im2col<InDataType, OutDataType, NDimSpatial>(in, out_host, conv_params);
+
+        out_device_buf.FromDevice(out_device.data());
+        pass = ck_tile::check_err(out_device, out_host);
+
+        std::cout << "valid:" << (pass ? "y" : "n") << std::endl;
+    }
+
+    return !pass;
+}

example/ck_tile/04_img2col/image_to_column.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/host/kernel_launch.hpp"
+#include "ck_tile/ops/image_to_column.hpp"
+#include <string>
+
+#define DefaultConvParams                                                    \
+    ck_tile::conv::ConvParam                                                 \
+    {                                                                        \
+        2, 2, 32, 32, 32, {4, 4}, {64, 64}, {1, 1}, {1, 1}, {0, 0}, { 0, 0 } \
+    }
+
+struct ExecutionConfig final
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+};
+
+inline void print_help_msg()
+{
+    std::cerr << "arg1: verification (0=no, 1=yes)\n"
+              << "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
+              << "arg3: time kernel (0=no, 1=yes)\n"
+              << ck_tile::conv::get_conv_param_parser_helper_msg() << std::endl;
+}
+
+inline bool parse_cmd_args(int argc,
+                           char* argv[],
+                           ExecutionConfig& config,
+                           ck_tile::conv::ConvParam& conv_params)
+{
+    constexpr int num_execution_config_args =
+        3; // arguments for do_verification, init_method, time_kernel
+    constexpr int num_conv_param_leading_args = 5; // arguments for num_dim_spatial_, G_, N_, K_, C_
+
+    constexpr int threshold_to_catch_partial_args = 1 + num_execution_config_args;
+    constexpr int threshold_to_catch_all_args =
+        threshold_to_catch_partial_args + num_conv_param_leading_args;
+
+    if(argc == 1)
+    {
+        // use default
+        config = ExecutionConfig{};
+    }
+    // catch only ExecutionConfig arguments
+    else if(argc == threshold_to_catch_partial_args)
+    {
+        config.do_verification = std::stoi(argv[1]);
+        config.init_method     = std::stoi(argv[2]);
+        config.time_kernel     = std::stoi(argv[3]);
+    }
+    // catch both ExecutionConfig & ConvParam arguments
+    else if(threshold_to_catch_all_args < argc && ((argc - threshold_to_catch_all_args) % 3 == 0))
+    {
+        config.do_verification = std::stoi(argv[1]);
+        config.init_method     = std::stoi(argv[2]);
+        config.time_kernel     = std::stoi(argv[3]);
+
+        const ck_tile::index_t num_dim_spatial = std::stoi(argv[4]);
+        conv_params =
+            ck_tile::conv::parse_conv_param(num_dim_spatial, threshold_to_catch_partial_args, argv);
+    }
+    else
+    {
+        print_help_msg();
+        return false;
+    }
+
+    return true;
+}
+
+struct image_to_column_traits
+{
+    std::string data_type;
+};
+
+template <ck_tile::index_t NDimSpatial>
+struct image_to_column_args
+{
+    const void* p_in;
+    void* p_out;
+    const ck_tile::long_index_t G;
+    const ck_tile::long_index_t N;
+    const ck_tile::long_index_t C;
+    const ck_tile::array<ck_tile::long_index_t, NDimSpatial> input_spatial_lengths;
+    const ck_tile::array<ck_tile::long_index_t, NDimSpatial> filter_spatial_lengths;
+    const ck_tile::array<ck_tile::long_index_t, NDimSpatial> output_spatial_lengths;
+    const ck_tile::array<ck_tile::long_index_t, NDimSpatial + 3> image_g_n_c_wis_strides;
+    const ck_tile::array<ck_tile::long_index_t, 3> gemm_g_m_k_strides;
+    const ck_tile::array<ck_tile::long_index_t, NDimSpatial> conv_filter_strides;
+    const ck_tile::array<ck_tile::long_index_t, NDimSpatial> conv_filter_dilations;
+    const ck_tile::array<ck_tile::long_index_t, NDimSpatial> input_left_pads;
+    const ck_tile::array<ck_tile::long_index_t, NDimSpatial> input_right_pads;
+};
+
+// host API
+template <ck_tile::index_t NDimSpatial>
+float image_to_column(const image_to_column_traits&,
+                      const image_to_column_args<NDimSpatial>&,
+                      const ck_tile::stream_config&);

example/ck_tile/CMakeLists.txt

@@ -5,3 +5,4 @@ include_directories(AFTER
 add_subdirectory(01_fmha)
 add_subdirectory(02_layernorm2d)
 add_subdirectory(03_gemm)
+add_subdirectory(04_img2col)

include/ck/config.h.in

@@ -97,13 +97,6 @@
 #cmakedefine CK_ENABLE_DL_KERNELS @CK_ENABLE_DL_KERNELS@
 #endif
 
-//
-// Instances supports in the current CK build
-//
-#ifndef CK_ENABLE_INSTANCES_ONLY
-#cmakedefine CK_ENABLE_INSTANCES_ONLY @CK_ENABLE_INSTANCES_ONLY@
-#endif
-
 //
 // CK kernels which support XDL (MI series)
 //

include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops.hpp

@@ -406,7 +406,7 @@ struct BlockwiseGemmXdlops_pipeline_v4
     }
 
     template <>
-    __device__ static constexpr auto TailScheduler<1>()
+    __device__ constexpr auto TailScheduler<1>()
     {
         // schedule
         constexpr auto num_ds_read_inst =
@@ -433,7 +433,7 @@ struct BlockwiseGemmXdlops_pipeline_v4
     }
 
     template <>
-    __device__ static constexpr auto TailScheduler<2>()
+    __device__ constexpr auto TailScheduler<2>()
     {
         // schedule
         constexpr auto num_ds_read_inst =

include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v1_ab_scale.hpp

@@ -308,7 +308,7 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
                                 typename vector_type<ComputeDataType,
                                                      xdlops_gemm.K1PerXdlops>::type;
 
-                            xdlops_gemm.template Run(
+                            xdlops_gemm.template Run<>(
                                 a_thread_vec.template AsType<mfma_input_type>(),
                                 b_thread_vec.template AsType<mfma_input_type>(),
                                 c_thread_buf_per_scale.GetVectorTypeReference(I0));
@@ -390,7 +390,8 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
                         using mfma_input_type =
                             typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
 
-                        xdlops_gemm.template Run(a_thread_vec.template AsType<mfma_input_type>(),
+                        xdlops_gemm.template Run<>(
+                            a_thread_vec.template AsType<mfma_input_type>(),
                             b_thread_vec.template AsType<mfma_input_type>(),
                             c_thread_buf_per_scale.GetVectorTypeReference(I0));
                     });

include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v2_ab_scale.hpp

@@ -350,7 +350,7 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
                                     typename vector_type<ComputeDataType,
                                                          xdlops_gemm.K1PerXdlops>::type;
 
-                                xdlops_gemm.template Run(
+                                xdlops_gemm.template Run<>(
                                     a_thread_vec.template AsType<mfma_input_type>(),
                                     b_thread_vec.template AsType<mfma_input_type>(),
                                     c_thread_buf_per_scale.GetVectorTypeReference(I0));
@@ -443,7 +443,7 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
                                 typename vector_type<ComputeDataType,
                                                      xdlops_gemm.K1PerXdlops>::type;
 
-                            xdlops_gemm.template Run(
+                            xdlops_gemm.template Run<>(
                                 a_thread_vec.template AsType<mfma_input_type>(),
                                 b_thread_vec.template AsType<mfma_input_type>(),
                                 c_thread_buf_per_scale.GetVectorTypeReference(I0));
@@ -518,7 +518,8 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
                         using mfma_input_type =
                             typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
 
-                        xdlops_gemm.template Run(a_thread_vec.template AsType<mfma_input_type>(),
+                        xdlops_gemm.template Run<>(
+                            a_thread_vec.template AsType<mfma_input_type>(),
                             b_thread_vec.template AsType<mfma_input_type>(),
                             c_thread_buf_per_scale.GetVectorTypeReference(I0));
                     });
@@ -575,7 +576,8 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
                         using mfma_input_type =
                             typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
 
-                        xdlops_gemm.template Run(a_thread_vec.template AsType<mfma_input_type>(),
+                        xdlops_gemm.template Run<>(
+                            a_thread_vec.template AsType<mfma_input_type>(),
                             b_thread_vec.template AsType<mfma_input_type>(),
                             c_thread_buf_per_scale.GetVectorTypeReference(I0));
                     });

include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v3_ab_scale.hpp

@@ -427,7 +427,7 @@ struct BlockwiseGemmXdlops_pipeline_v3_ab_scale<BlockGemmPipelineScheduler::Intr
                                 typename vector_type<ComputeDataType,
                                                      xdlops_gemm.K1PerXdlops>::type;
 
-                            xdlops_gemm.template Run(
+                            xdlops_gemm.template Run<>(
                                 a_thread_vec.template AsType<mfma_input_type>(),
                                 b_thread_vec.template AsType<mfma_input_type>(),
                                 c_thread_buf_per_scale.GetVectorTypeReference(I0));
@@ -504,7 +504,8 @@ struct BlockwiseGemmXdlops_pipeline_v3_ab_scale<BlockGemmPipelineScheduler::Intr
                         using mfma_input_type =
                             typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
 
-                        xdlops_gemm.template Run(a_thread_vec.template AsType<mfma_input_type>(),
+                        xdlops_gemm.template Run<>(
+                            a_thread_vec.template AsType<mfma_input_type>(),
                             b_thread_vec.template AsType<mfma_input_type>(),
                             c_thread_buf_per_scale.GetVectorTypeReference(I0));
                     });

include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multi_abd_xdl_fixed_nk.hpp

@@ -64,7 +64,7 @@ __global__ void
     const index_t N = gemm_desc_ptr[group_id].N;
     const index_t K = gemm_desc_ptr[group_id].K;
 
-    if(M * N * K == 0)
+    if(M == 0 || N == 0 || K == 0)
         return;
 
     const auto StrideAs = gemm_desc_ptr[group_id].StrideAs;

include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multiple_d_splitk_xdl_cshuffle_two_stage.hpp

@@ -345,7 +345,7 @@ struct DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage
                 const index_t N = gemm_descs[i].N_;
                 const index_t K = gemm_descs[i].K_;
 
-                if(M * N * K == 0)
+                if(M == 0 || N == 0 || K == 0)
                 {
                     skipped_group_count_++;
                     continue;

include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multiple_d_xdl_cshuffle_tile_loop.hpp

@@ -109,7 +109,7 @@ __global__ void
             N = gemm_desc_ptr[group_id].N;
             K = gemm_desc_ptr[group_id].K;
 
-            if(M * N * K == 0)
+            if(M == 0 || N == 0 || K == 0)
             {
                 grid_size_grp = 0;
                 continue;

include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_fixed_nk.hpp

@@ -68,7 +68,7 @@ __global__ void
     const index_t N = gemm_desc_ptr[group_id].N;
     const index_t K = gemm_desc_ptr[group_id].K;
 
-    if(M * N * K == 0)
+    if(M == 0 || N == 0 || K == 0)
         return;
 
     const auto StrideA  = gemm_desc_ptr[group_id].StrideA;

include/ck/tensor_operation/gpu/warp/dpp_gemm.hpp

@@ -324,55 +324,55 @@ struct DppSelector
     static constexpr auto GetDpp();
 
     template <>
-    static constexpr auto GetDpp<half_t, 8, 32>()
+    constexpr auto GetDpp<half_t, 8, 32>()
     {
         return DppInstr::dpp8_f16_8x32x2;
     }
 
     template <>
-    static constexpr auto GetDpp<half_t, 8, 16>()
+    constexpr auto GetDpp<half_t, 8, 16>()
     {
         return DppInstr::dpp8_f16_8x16x2;
     }
 
     template <>
-    static constexpr auto GetDpp<half_t, 16, 16>()
+    constexpr auto GetDpp<half_t, 16, 16>()
     {
         return DppInstr::dpp8_f16_16x16x2;
     }
 
     template <>
-    static constexpr auto GetDpp<half_t, 32, 8>()
+    constexpr auto GetDpp<half_t, 32, 8>()
     {
         return DppInstr::dpp8_f16_32x8x2;
     }
 
     template <>
-    static constexpr auto GetDpp<half_t, 1, 32>()
+    constexpr auto GetDpp<half_t, 1, 32>()
     {
         return DppInstr::dpp8_f16_1x32x2;
     }
 
     template <>
-    static constexpr auto GetDpp<half_t, 2, 32>()
+    constexpr auto GetDpp<half_t, 2, 32>()
     {
         return DppInstr::dpp8_f16_2x32x2;
     }
 
     template <>
-    static constexpr auto GetDpp<half_t, 2, 16>()
+    constexpr auto GetDpp<half_t, 2, 16>()
     {
         return DppInstr::dpp8_f16_2x16x2;
     }
 
     template <>
-    static constexpr auto GetDpp<half_t, 4, 16>()
+    constexpr auto GetDpp<half_t, 4, 16>()
     {
         return DppInstr::dpp8_f16_4x16x2;
     }
 
     template <>
-    static constexpr auto GetDpp<half_t, 4, 32>()
+    constexpr auto GetDpp<half_t, 4, 32>()
     {
         return DppInstr::dpp8_f16_4x32x2;
     }

include/ck/tensor_operation/gpu/warp/wmma_gemm.hpp

@@ -415,7 +415,7 @@ struct WmmaSelector
     static constexpr auto GetWmma();
 
     template <>
-    static constexpr auto GetWmma<half_t, half_t, float, 16, 16>()
+    constexpr auto GetWmma<half_t, half_t, float, 16, 16>()
     {
 #ifdef __gfx12__
         return WmmaInstr::wmma_f32_16x16x16_f16_gfx12;
@@ -425,7 +425,7 @@ struct WmmaSelector
     }
 
     template <>
-    static constexpr auto GetWmma<bhalf_t, bhalf_t, float, 16, 16>()
+    constexpr auto GetWmma<bhalf_t, bhalf_t, float, 16, 16>()
     {
 #ifdef __gfx12__
         return WmmaInstr::wmma_f32_16x16x16_bf16_gfx12;
@@ -435,19 +435,19 @@ struct WmmaSelector
     }
 
     template <>
-    static constexpr auto GetWmma<half_t, half_t, half_t, 16, 16>()
+    constexpr auto GetWmma<half_t, half_t, half_t, 16, 16>()
     {
         return WmmaInstr::wmma_f16_16x16x16_f16;
     }
 
     template <>
-    static constexpr auto GetWmma<bhalf_t, bhalf_t, bhalf_t, 16, 16>()
+    constexpr auto GetWmma<bhalf_t, bhalf_t, bhalf_t, 16, 16>()
     {
         return WmmaInstr::wmma_bf16_16x16x16_bf16;
     }
 
     template <>
-    static constexpr auto GetWmma<int8_t, int8_t, int, 16, 16>()
+    constexpr auto GetWmma<int8_t, int8_t, int, 16, 16>()
     {
 #ifdef __gfx12__
         return WmmaInstr::wmma_i32_16x16x16_iu8_gfx12;
@@ -458,7 +458,7 @@ struct WmmaSelector
 
 #ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
     template <>
-    static constexpr auto GetWmma<int4_t, int4_t, int, 16, 16>()
+    constexpr auto GetWmma<int4_t, int4_t, int, 16, 16>()
     {
         return WmmaInstr::wmma_i32_16x16x16_iu4;
     }

include/ck/tensor_operation/gpu/warp/xdlops_gemm.hpp

@@ -651,97 +651,97 @@ struct MfmaSelector
     static constexpr auto GetMfma();
 
     template <>
-    static constexpr auto GetMfma<double, 16, 16>()
+    constexpr auto GetMfma<double, 16, 16>()
     {
         return MfmaInstr::mfma_f64_16x16x4f64;
     }
 
     template <>
-    static constexpr auto GetMfma<float, 64, 64>()
+    constexpr auto GetMfma<float, 64, 64>()
     {
         return MfmaInstr::mfma_f32_32x32x1xf32;
     }
 
     template <>
-    static constexpr auto GetMfma<float, 32, 64>()
+    constexpr auto GetMfma<float, 32, 64>()
     {
         return MfmaInstr::mfma_f32_32x32x1xf32;
     }
 
     template <>
-    static constexpr auto GetMfma<float, 16, 64>()
+    constexpr auto GetMfma<float, 16, 64>()
     {
         return MfmaInstr::mfma_f32_16x16x1xf32;
     }
 
     template <>
-    static constexpr auto GetMfma<float, 8, 64>()
+    constexpr auto GetMfma<float, 8, 64>()
     {
         return MfmaInstr::mfma_f32_4x4x1xf32;
     }
 
     template <>
-    static constexpr auto GetMfma<float, 4, 64>()
+    constexpr auto GetMfma<float, 4, 64>()
     {
         return MfmaInstr::mfma_f32_4x4x1xf32;
     }
 
     template <>
-    static constexpr auto GetMfma<float, 32, 32>()
+    constexpr auto GetMfma<float, 32, 32>()
     {
         return MfmaInstr::mfma_f32_32x32x2xf32;
     }
 
     template <>
-    static constexpr auto GetMfma<float, 16, 16>()
+    constexpr auto GetMfma<float, 16, 16>()
     {
         return MfmaInstr::mfma_f32_16x16x4xf32;
     }
 
     template <>
-    static constexpr auto GetMfma<half_t, 64, 64>()
+    constexpr auto GetMfma<half_t, 64, 64>()
     {
         return MfmaInstr::mfma_f32_32x32x4f16;
     }
 
     template <>
-    static constexpr auto GetMfma<half_t, 32, 64>()
+    constexpr auto GetMfma<half_t, 32, 64>()
     {
         return MfmaInstr::mfma_f32_32x32x4f16;
     }
 
     template <>
-    static constexpr auto GetMfma<half_t, 32, 32>()
+    constexpr auto GetMfma<half_t, 32, 32>()
     {
         return MfmaInstr::mfma_f32_32x32x8f16;
     }
 
     template <>
-    static constexpr auto GetMfma<half_t, 16, 16>()
+    constexpr auto GetMfma<half_t, 16, 16>()
     {
         return MfmaInstr::mfma_f32_16x16x16f16;
     }
 
     template <>
-    static constexpr auto GetMfma<half_t, 16, 64>()
+    constexpr auto GetMfma<half_t, 16, 64>()
     {
         return MfmaInstr::mfma_f32_16x16x4f16;
     }
 
     template <>
-    static constexpr auto GetMfma<half_t, 8, 64>()
+    constexpr auto GetMfma<half_t, 8, 64>()
     {
         return MfmaInstr::mfma_f32_4x4x4f16;
     }
 
     template <>
-    static constexpr auto GetMfma<half_t, 4, 64>()
+    constexpr auto GetMfma<half_t, 4, 64>()
     {
         return MfmaInstr::mfma_f32_4x4x4f16;
     }
 
     template <>
-    static constexpr auto GetMfma<bhalf_t, 32, 32>()
+    constexpr auto GetMfma<bhalf_t, 32, 32>()
     {
 #if defined(CK_USE_AMD_MFMA_BF16_1K_OP)
         return MfmaInstr::mfma_f32_32x32x8bf16_1k;
@@ -751,7 +751,7 @@ struct MfmaSelector
     }
 
     template <>
-    static constexpr auto GetMfma<bhalf_t, 16, 16>()
+    constexpr auto GetMfma<bhalf_t, 16, 16>()
     {
 #if defined(CK_USE_AMD_MFMA_BF16_1K_OP)
         return MfmaInstr::mfma_f32_16x16x16bf16_1k;
@@ -762,72 +762,72 @@ struct MfmaSelector
 
 #if defined(CK_USE_AMD_MFMA_GFX940)
     template <>
-    static constexpr auto GetMfma<int8_t, 32, 32>()
+    constexpr auto GetMfma<int8_t, 32, 32>()
     {
         return MfmaInstr::mfma_i32_32x32x16i8;
     }
     template <>
-    static constexpr auto GetMfma<int8_t, 16, 16>()
+    constexpr auto GetMfma<int8_t, 16, 16>()
     {
         return MfmaInstr::mfma_i32_16x16x32i8;
     }
 #else
     template <>
-    static constexpr auto GetMfma<int8_t, 32, 32>()
+    constexpr auto GetMfma<int8_t, 32, 32>()
     {
         return MfmaInstr::mfma_i32_32x32x8i8;
     }
     template <>
-    static constexpr auto GetMfma<int8_t, 16, 16>()
+    constexpr auto GetMfma<int8_t, 16, 16>()
     {
         return MfmaInstr::mfma_i32_16x16x16i8;
     }
 #endif
 
     template <>
-    static constexpr auto GetMfma<f8_t, 32, 32>()
+    constexpr auto GetMfma<f8_t, 32, 32>()
     {
         return MfmaInstr::mfma_f32_32x32x16f8f8;
     }
 
     template <>
-    static constexpr auto GetMfma<f8_t, 16, 16>()
+    constexpr auto GetMfma<f8_t, 16, 16>()
     {
         return MfmaInstr::mfma_f32_16x16x32f8f8;
     }
 
     template <>
-    static constexpr auto GetMfma<bf8_t, 32, 32>()
+    constexpr auto GetMfma<bf8_t, 32, 32>()
     {
         return MfmaInstr::mfma_f32_32x32x16bf8bf8;
     }
 
     template <>
-    static constexpr auto GetMfma<bf8_t, 16, 16>()
+    constexpr auto GetMfma<bf8_t, 16, 16>()
     {
         return MfmaInstr::mfma_f32_16x16x32bf8bf8;
     }
 
     template <>
-    static constexpr auto GetMfma<f8_t, 32, 32, bf8_t>()
+    constexpr auto GetMfma<f8_t, 32, 32, bf8_t>()
     {
         return MfmaInstr::mfma_f32_32x32x16f8bf8;
     }
 
     template <>
-    static constexpr auto GetMfma<f8_t, 16, 16, bf8_t>()
+    constexpr auto GetMfma<f8_t, 16, 16, bf8_t>()
     {
         return MfmaInstr::mfma_f32_16x16x32f8bf8;
     }
 
     template <>
-    static constexpr auto GetMfma<bf8_t, 32, 32, f8_t>()
+    constexpr auto GetMfma<bf8_t, 32, 32, f8_t>()
     {
         return MfmaInstr::mfma_f32_32x32x16bf8f8;
     }
 
     template <>
-    static constexpr auto GetMfma<bf8_t, 16, 16, f8_t>()
+    constexpr auto GetMfma<bf8_t, 16, 16, f8_t>()
     {
         return MfmaInstr::mfma_f32_16x16x32bf8f8;
     }

include/ck_tile/core/container/array.hpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
 #include <initializer_list>
+#include <vector>
 
 #include "ck_tile/core/config.hpp"
 #include "ck_tile/core/numeric/integer.hpp"
@@ -236,6 +237,16 @@ CK_TILE_HOST_DEVICE constexpr bool operator!=(const array<T, Size>& a, const arr
     return !(a == b);
 }
 
+template <typename T, index_t N, typename X>
+CK_TILE_HOST_DEVICE constexpr auto to_array(const std::vector<X>& x)
+{
+    array<T, N> arr;
+
+    static_for<0, N, 1>{}([&x, &arr](auto i) { arr(i) = x[i]; });
+
+    return arr;
+}
+
 template <typename T, index_t N, typename X>
 CK_TILE_HOST_DEVICE constexpr auto to_array(const X& x)
 {

include/ck_tile/host.hpp

@@ -5,6 +5,8 @@
 
 #include "ck_tile/host/arg_parser.hpp"
 #include "ck_tile/host/check_err.hpp"
+#include "ck_tile/host/convolution_host_tensor_descriptor_helper.hpp"
+#include "ck_tile/host/convolution_parameter.hpp"
 #include "ck_tile/host/device_memory.hpp"
 #include "ck_tile/host/fill.hpp"
 #include "ck_tile/host/hip_check_error.hpp"