Merge remote-tracking branch 'origin/develop' into cpu_avx2

example/01_gemm/gemm_xdl_int8.cpp

@@ -53,9 +53,9 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle
     256,                    // BlockSize
     256,                    // MPerBlock
     128,                    // NPerBlock
-    32,                     // KPerBlock
-    8,                      // AK1
-    8,                      // BK1
+    64,                     // KPerBlock
+    16,                     // AK1
+    16,                     // BK1
     32,                     // MPerXDL
     32,                     // NPerXDL
     4,                      // MXdlPerWave
@@ -64,15 +64,15 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle
     S<1, 0, 2>,             // ABlockTransferThreadClusterArrangeOrder
     S<1, 0, 2>,             // ABlockTransferSrcAccessOrder
     2,                      // ABlockTransferSrcVectorDim
-    8,                      // ABlockTransferSrcScalarPerVector
-    8,                      // ABlockTransferDstScalarPerVector_K1
+    16,                     // ABlockTransferSrcScalarPerVector
+    16,                     // ABlockTransferDstScalarPerVector_K1
     true,                   // ABlockLdsAddExtraM
     S<4, 64, 1>,            // BBlockTransferThreadClusterLengths_K0_N_K1
     S<1, 0, 2>,             // BBlockTransferThreadClusterArrangeOrder
     S<1, 0, 2>,             // BBlockTransferSrcAccessOrder
     2,                      // BBlockTransferSrcVectorDim
-    8,                      // BBlockTransferSrcScalarPerVector
-    8,                      // BBlockTransferDstScalarPerVector_K1
+    16,                     // BBlockTransferSrcScalarPerVector
+    16,                     // BBlockTransferDstScalarPerVector_K1
     true,                   // BBlockLdsAddExtraN
     1,                      // CShuffleMXdlPerWavePerShuffle
     1,                      // CShuffleNXdlPerWavePerShuffle

example/10_conv2d_bwd_data/conv2d_bwd_data_xdl.cpp

@@ -68,6 +68,7 @@ using DeviceConvBwdDataInstance = ck::tensor_operation::device::
 using ReferenceConvBwdInstance = ck::tensor_operation::host::ReferenceConvBwdData<InDataType,
                                                                                   WeiDataType,
                                                                                   OutDataType,
+                                                                                  AccDataType,
                                                                                   InElementOp,
                                                                                   WeiElementOp,
                                                                                   OutElementOp>;

example/14_gemm_xdl_requant_relu_requant/gemm_xdl_requant_relu_requant_int8.cpp

@@ -28,11 +28,11 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
 using PassThrough        = ck::tensor_operation::element_wise::PassThrough;
 using RequantReluRequant = ck::tensor_operation::element_wise::RequantReluRequant;
 
-using ADataType       = int8_t;
-using BDataType       = int8_t;
-using CDataType       = int8_t;
-using AccDataType     = int32_t;
-using ShuffleDataType = int32_t;
+using ADataType        = int8_t;
+using BDataType        = int8_t;
+using CDataType        = int8_t;
+using AccDataType      = int32_t;
+using CShuffleDataType = int32_t;
 
 using ALayout = ck::tensor_layout::gemm::RowMajor;
 using BLayout = ck::tensor_layout::gemm::ColumnMajor;
@@ -44,7 +44,7 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle
     BDataType,              // BDataType
     CDataType,              // CDataType
     AccDataType,            // AccDataType
-    ShuffleDataType,        // ShuffleDataType
+    CShuffleDataType,       // CShuffleDataType
     ALayout,                // ALayout
     BLayout,                // BLayout
     CLayout,                // CLayout
@@ -54,9 +54,9 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle
     256,                    // BlockSize
     256,                    // MPerBlock
     128,                    // NPerBlock
-    32,                     // KPerBlock
-    8,                      // AK1
-    8,                      // BK1
+    64,                     // KPerBlock
+    16,                     // AK1
+    16,                     // BK1
     32,                     // MPerXDL
     32,                     // NPerXDL
     4,                      // MXdlPerWave
@@ -65,20 +65,20 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle
     S<1, 0, 2>,             // ABlockTransferThreadClusterArrangeOrder
     S<1, 0, 2>,             // ABlockTransferSrcAccessOrder
     2,                      // ABlockTransferSrcVectorDim
-    8,                      // ABlockTransferSrcScalarPerVector
-    8,                      // ABlockTransferDstScalarPerVector_K1
+    16,                     // ABlockTransferSrcScalarPerVector
+    16,                     // ABlockTransferDstScalarPerVector_K1
     true,                   // ABlockLdsAddExtraM
     S<4, 64, 1>,            // BBlockTransferThreadClusterLengths_K0_N_K1
     S<1, 0, 2>,             // BBlockTransferThreadClusterArrangeOrder
     S<1, 0, 2>,             // BBlockTransferSrcAccessOrder
     2,                      // BBlockTransferSrcVectorDim
-    8,                      // BBlockTransferSrcScalarPerVector
-    8,                      // BBlockTransferDstScalarPerVector_K1
+    16,                     // BBlockTransferSrcScalarPerVector
+    16,                     // BBlockTransferDstScalarPerVector_K1
     true,                   // BBlockLdsAddExtraN
     1,                      // CShuffleMXdlPerWavePerShuffle
     1,                      // CShuffleNXdlPerWavePerShuffle
-    S<1, 1, 32, 1, 1, 8>,   // CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
-    8>;                     // CBlockTransferScalarPerVector_NWaveNPerXdl
+    S<1, 1, 64, 1, 1, 4>,   // CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
+    16>;                     // CBlockTransferScalarPerVector_NWaveNPerXdl
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::

example/17_convnd_bwd_data_xdl/CMakeLists.txt

+add_example_executable(example_convnd_bwd_data_xdl convnd_bwd_data_xdl.cpp)

example/17_convnd_bwd_data_xdl/README.md

+# Instructions for ```convnd_bwd_data_xdl``` Example
+
+## Docker script
+```bash
+docker run                                                                   \
+-it                                                                          \
+--rm                                                                         \
+--privileged                                                                 \
+--group-add sudo                                                             \
+-w /root/workspace                                                           \
+-v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace                                \
+rocm/tensorflow:rocm4.3.1-tf2.6-dev                                          \
+/bin/bash
+```
+
+## Build ```convnd_bwd_data_xdl```
+```bash
+mkdir build && cd build
+```
+
+```bash
+# Need to specify target ID, example below is gfx908
+cmake                                                                  \
+-D BUILD_DEV=OFF                                                       \
+-D CMAKE_BUILD_TYPE=Release                                            \
+-D CMAKE_CXX_FLAGS="-DCK_AMD_GPU_GFX908 --amdgpu-target=gfx908 -O3 "   \
+-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc                              \
+-D CMAKE_PREFIX_PATH=/opt/rocm                                         \
+..
+```
+
+```bash
+ make -j convnd_bwd_data_xdl
+```
+
+## Run ```example_convnd_bwd_data_xdl```
+```bash
+#arg1: verification (0=no, 1=yes)
+#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
+#arg3: run kernel # of times (>1)
+#arg4: num_dim_spatial(1|2|3)
+#arg5 to ...: N, K, C, [Z,] [Y,] X, [Di,] [Hi,] Wi, S[z,] [Sy,] Sx, [Dz,] [Dy,] Dx, [LeftPz,] [LeftPy,] LeftPx, [RightPy,] [RightPy,] RightPx
+./bin/convnd_bwd_data_xdl 0 1 5 
+```
+
+Result
+```
+in_n_c_hi_wi: dim 4, lengths {128, 128, 71, 71}, strides {645248, 1, 9088, 128}
+wei_k_c_y_x: dim 4, lengths {256, 128, 3, 3}, strides {1152, 1, 384, 128}
+out_n_k_ho_wo: dim 4, lengths {128, 256, 36, 36}, strides {331776, 1, 9216, 256}
+arg.a_grid_desc_k0_m_k1_container_{128, 175232, 8}
+arg.b_grid_desc_k0_n_k1_container_{128, 128, 8}
+arg.c_grid_desc_m_n_container_{ 175232, 128}
+arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 2, 2, 2, 4, 2 ) 
+launch_and_time_kernel: grid_dim {1369, 1, 1}, block_dim {256, 1, 1} 
+Warm up
+Start running 1 times...
+arg.a_grid_desc_k0_m_k1_container_{64, 175232, 8}
+arg.b_grid_desc_k0_n_k1_container_{64, 128, 8}
+arg.c_grid_desc_m_n_container_{ 175232, 128}
+arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 2, 2, 2, 4, 2 ) 
+launch_and_time_kernel: grid_dim {1369, 1, 1}, block_dim {256, 1, 1} 
+Warm up
+Start running 1 times...
+arg.a_grid_desc_k0_m_k1_container_{64, 175232, 8}
+arg.b_grid_desc_k0_n_k1_container_{64, 128, 8}
+arg.c_grid_desc_m_n_container_{ 175232, 128}
+arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 2, 2, 2, 4, 2 ) 
+launch_and_time_kernel: grid_dim {1369, 1, 1}, block_dim {256, 1, 1} 
+Warm up
+Start running 1 times...
+arg.a_grid_desc_k0_m_k1_container_{32, 175232, 8}
+arg.b_grid_desc_k0_n_k1_container_{32, 128, 8}
+arg.c_grid_desc_m_n_container_{ 175232, 128}
+arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 2, 2, 2, 4, 2 ) 
+launch_and_time_kernel: grid_dim {1369, 1, 1}, block_dim {256, 1, 1} 
+Warm up
+Start running 1 times...
+Perf: 1.40031 ms, 69.8734 TFlops, 179.037 GB/s
+```

example/17_convnd_bwd_data_xdl/convnd_bwd_data_xdl.cpp

+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+
+#include "config.hpp"
+#include "conv_utils.hpp"
+#include "print.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "device_tensor.hpp"
+#include "tensor_layout.hpp"
+#include "element_wise_operation.hpp"
+#include "device_convnd_bwd_data_xdl_ndhwc_kzyxc_ndhwk.hpp"
+#include "reference_conv_bwd_data.hpp"
+
+using InDataType  = ck::half_t;
+using WeiDataType = ck::half_t;
+using OutDataType = ck::half_t;
+using AccDataType = float;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using InElementOp  = ck::tensor_operation::element_wise::PassThrough;
+using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
+using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
+static constexpr auto ConvBwdDefault =
+    ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Default;
+
+using DeviceConvBwdDataBasePtr =
+    ck::tensor_operation::device::DeviceConvBwdDataPtr<InElementOp, WeiElementOp, OutElementOp>;
+
+template <ck::index_t NumDimSpatial>
+using DeviceConvNDBwdDataInstance = ck::tensor_operation::device::
+    DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<
+        InDataType,     // InDataType
+        WeiDataType,    // WeiDataType
+        OutDataType,    // OutDataType
+        AccDataType,    // AccDataType
+        InElementOp,    // InElementwiseOperation
+        WeiElementOp,   // WeiElementwiseOperation
+        OutElementOp,   // OutElementwiseOperation
+        ConvBwdDefault, // ConvolutionBackwardDataSpecialization_t
+        NumDimSpatial,  // NumDimSpatial
+        256,            // BlockSize
+        128,            // MPerBlock
+        128,            // NPerBlock
+        4,              // K0PerBlock
+        8,              // K1
+        32,             // MPerXdl
+        32,             // NPerXdl
+        2,              // MXdlPerWave
+        2,              // NXdlPerWave
+        S<4, 64, 1>,    // ABlockTransferThreadClusterLengths_K0_M_K1
+        S<1, 0, 2>,     // ABlockTransferThreadClusterArrangeOrder
+        S<1, 0, 2>,     // ABlockTransferSrcAccessOrder
+        2,              // ABlockTransferSrcVectorDim
+        8,              // ABlockTransferSrcScalarPerVector
+        8,              // ABlockTransferDstScalarPerVector_K1
+        true,           // ABlockLdsAddExtraM
+        S<4, 64, 1>,    // BBlockTransferThreadClusterLengths_K0_N_K1
+        S<2, 0, 1>,     // BBlockTransferThreadClusterArrangeOrder
+        S<0, 2, 1>,     // BBlockTransferSrcAccessOrder
+        1,              // BBlockTransferSrcVectorDim
+        2,              // BBlockTransferSrcScalarPerVector
+        8,              // BBlockTransferDstScalarPerVector_K1
+        true,           // BBlockLdsAddExtraN
+        7,
+        1>; // GemmCThreadTransferDstScalarPerVector
+
+template <ck::index_t NumDimSpatial>
+using ReferenceConvBwdDataInstance =
+    ck::tensor_operation::host::ReferenceConvBwdData<InDataType,
+                                                     WeiDataType,
+                                                     OutDataType,
+                                                     AccDataType,
+                                                     InElementOp,
+                                                     WeiElementOp,
+                                                     OutElementOp,
+                                                     NumDimSpatial>;
+
+void PrintUseMsg()
+{
+    std::cout << "arg1: verification (0=no, 1=yes)\n"
+              << "arg2: initialization (0=no init, 1=random value, 2= init to 1 )\n"
+              << "arg3: run kernel # of times (>1)\n"
+              << "arg4: N spatial dimensions (default 2)\n"
+              << "Following arguments (depending on number of spatial dims):\n"
+              << " N, K, C, \n"
+              << " <filter spatial dimensions>, (ie Y, X for 2D)\n"
+              << " <input image spatial dimensions>, (ie Hi, Wi for 2D)\n"
+              << " <strides>, (ie Sy, Sx for 2D)\n"
+              << " <dilations>, (ie Dy, Dx for 2D)\n"
+              << " <left padding>, (ie LeftPy, LeftPx for 2D)\n"
+              << " <right padding>, (ie RightPy, RightPx for 2D)\n"
+              << std::endl;
+}
+ck::conv_util::ConvParams ParseConvParams(int num_dim_spatial, char* argv[])
+{
+    // (N, K, C) + num_dim_spatial * 6 (filter, input, strides, dilations, pad left, pad right)
+    ck::conv_util::ConvParams params;
+    int arg_idx = 5;
+
+    params.num_dim_spatial = num_dim_spatial;
+    params.N               = std::stoi(argv[arg_idx++]);
+    params.K               = std::stoi(argv[arg_idx++]);
+    params.C               = std::stoi(argv[arg_idx++]);
+
+    params.filter_spatial_lengths.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.filter_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.input_spatial_lengths.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.input_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.conv_filter_strides.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.conv_filter_strides[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.conv_filter_dilations.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.conv_filter_dilations[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.input_left_pads.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.input_left_pads[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.input_right_pads.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.input_right_pads[i] = std::stoi(argv[arg_idx++]);
+    }
+
+    return params;
+}
+
+HostTensorDescriptor GetInputHostTensorDescriptor(const std::vector<std::size_t>& dims,
+                                                  int num_dim_spatial = 2)
+{
+    namespace tl = ck::tensor_layout::convolution;
+
+    switch(num_dim_spatial)
+    {
+    case 3: {
+        return ck::conv_util::GetHostTensorDescriptor(dims, tl::NDHWC{});
+    }
+    case 2: {
+        return ck::conv_util::GetHostTensorDescriptor(dims, tl::NHWC{});
+    }
+    case 1: {
+        return ck::conv_util::GetHostTensorDescriptor(dims, tl::NWC{});
+    }
+    default: {
+        throw std::runtime_error("Unsupported number of spatial dimensions provided!");
+    }
+    }
+}
+HostTensorDescriptor GetFiltersHostTensorDescriptor(const std::vector<std::size_t>& dims,
+                                                    int num_dim_spatial = 2)
+{
+    namespace tl = ck::tensor_layout::convolution;
+
+    switch(num_dim_spatial)
+    {
+    case 3: {
+        return ck::conv_util::GetHostTensorDescriptor(dims, tl::KZYXC{});
+    }
+    case 2: {
+        return ck::conv_util::GetHostTensorDescriptor(dims, tl::KYXC{});
+    }
+    case 1: {
+        return ck::conv_util::GetHostTensorDescriptor(dims, tl::KXC{});
+    }
+    default: {
+        throw std::runtime_error("Unsupported number of spatial dimensions provided!");
+    }
+    }
+}
+
+HostTensorDescriptor GetOutputHostTensorDescriptor(const std::vector<std::size_t>& dims,
+                                                   int num_dim_spatial = 2)
+{
+    namespace tl = ck::tensor_layout::convolution;
+
+    switch(num_dim_spatial)
+    {
+    case 3: {
+        return ck::conv_util::GetHostTensorDescriptor(dims, tl::NDHWK{});
+    }
+    case 2: {
+        return ck::conv_util::GetHostTensorDescriptor(dims, tl::NHWK{});
+    }
+    case 1: {
+        return ck::conv_util::GetHostTensorDescriptor(dims, tl::NWK{});
+    }
+
+    default: {
+        throw std::runtime_error("Unsupported number of spatial dimensions provided!");
+    }
+    }
+}
+
+DeviceConvBwdDataBasePtr GetConvInstance(int num_dim_spatial)
+{
+    switch(num_dim_spatial)
+    {
+    case 3: {
+        return std::make_unique<DeviceConvNDBwdDataInstance<3>>();
+    }
+    case 2: {
+        return std::make_unique<DeviceConvNDBwdDataInstance<2>>();
+    }
+    case 1: {
+        return std::make_unique<DeviceConvNDBwdDataInstance<1>>();
+    }
+    default: {
+        throw std::runtime_error("Unsupported number of spatial dimensions provided!");
+    }
+    }
+}
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = 0;
+    int init_method      = 0;
+    int nrepeat          = 5;
+    int num_dim_spatial  = 2;
+
+    ck::conv_util::ConvParams params;
+    params.C = 128;
+
+    if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        nrepeat         = std::stoi(argv[3]);
+    }
+    else if(argc > 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        nrepeat         = std::stoi(argv[3]);
+        num_dim_spatial = std::stoi(argv[4]);
+        // check args number
+        int conv_args     = 3 + num_dim_spatial * 6;
+        int cmdline_nargs = conv_args + 5;
+        if(cmdline_nargs != argc)
+        {
+            PrintUseMsg();
+            exit(1);
+        }
+
+        params = ParseConvParams(num_dim_spatial, argv);
+    }
+    else if(argc != 1)
+    {
+        PrintUseMsg();
+        exit(1);
+    }
+
+    std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N),
+                                        static_cast<std::size_t>(params.C)};
+    input_dims.insert(std::end(input_dims),
+                      std::begin(params.input_spatial_lengths),
+                      std::end(params.input_spatial_lengths));
+
+    std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K),
+                                         static_cast<std::size_t>(params.C)};
+    filter_dims.insert(std::end(filter_dims),
+                       std::begin(params.filter_spatial_lengths),
+                       std::end(params.filter_spatial_lengths));
+
+    const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
+    std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N),
+                                         static_cast<std::size_t>(params.K)};
+    output_dims.insert(std::end(output_dims),
+                       std::begin(output_spatial_lengths),
+                       std::end(output_spatial_lengths));
+
+    Tensor<InDataType> in_n_c_hi_wi_host_result(
+        GetInputHostTensorDescriptor(input_dims, num_dim_spatial));
+    Tensor<InDataType> in_n_c_hi_wi_device_result(
+        GetInputHostTensorDescriptor(input_dims, num_dim_spatial));
+    Tensor<WeiDataType> wei_k_c_y_x(GetFiltersHostTensorDescriptor(filter_dims, num_dim_spatial));
+    Tensor<OutDataType> out_n_k_ho_wo(GetOutputHostTensorDescriptor(output_dims, num_dim_spatial));
+
+    std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi_host_result.mDesc << std::endl;
+    std::cout << "wei_k_c_y_x: " << wei_k_c_y_x.mDesc << std::endl;
+    std::cout << "out_n_k_ho_wo: " << out_n_k_ho_wo.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_3<OutDataType>{-0.2, 0.2});
+        wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.2, 0.2});
+        break;
+    default:
+        out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_1<OutDataType>{1});
+        wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{1});
+    }
+
+    DeviceMem in_device_buf(sizeof(InDataType) *
+                            in_n_c_hi_wi_device_result.mDesc.GetElementSpace());
+    DeviceMem wei_device_buf(sizeof(WeiDataType) * wei_k_c_y_x.mDesc.GetElementSpace());
+    DeviceMem out_device_buf(sizeof(OutDataType) * out_n_k_ho_wo.mDesc.GetElementSpace());
+
+    out_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
+    wei_device_buf.ToDevice(wei_k_c_y_x.mData.data());
+    // reset input to zero
+    in_n_c_hi_wi_device_result.GenerateTensorValue(GeneratorTensor_1<InDataType>{0});
+    in_device_buf.ToDevice(in_n_c_hi_wi_device_result.mData.data());
+
+    // do GEMM
+    auto conv    = GetConvInstance(num_dim_spatial);
+    auto invoker = conv->MakeInvokerPointer();
+    auto argument =
+        conv->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
+                                  static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
+                                  static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
+                                  params.N,
+                                  params.K,
+                                  params.C,
+                                  params.input_spatial_lengths,
+                                  params.filter_spatial_lengths,
+                                  output_spatial_lengths,
+                                  params.conv_filter_strides,
+                                  params.conv_filter_dilations,
+                                  params.input_left_pads,
+                                  params.input_right_pads,
+                                  InElementOp{},
+                                  WeiElementOp{},
+                                  OutElementOp{});
+
+    if(!conv->IsSupportedArgument(argument.get()))
+    {
+        throw std::runtime_error(
+            "wrong! device_conv with the specified compilation parameters does "
+            "not support this Conv problem");
+    }
+
+    float ave_time = invoker->Run(argument.get(), nrepeat);
+
+    std::size_t flop = ck::conv_util::GetFlops(
+        params.N, params.C, params.K, params.filter_spatial_lengths, output_spatial_lengths);
+    std::size_t num_btype =
+        ck::conv_util::GetBtype<InDataType, WeiDataType, OutDataType>(params.N,
+                                                                      params.C,
+                                                                      params.K,
+                                                                      params.input_spatial_lengths,
+                                                                      params.filter_spatial_lengths,
+                                                                      output_spatial_lengths);
+
+    float tflops     = static_cast<float>(flop) / 1.E9 / ave_time;
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+              << std::endl;
+
+    if(do_verification)
+    {
+        auto verify_f = [&](const auto& ref_conv) {
+            auto ref_invoker = ref_conv.MakeInvoker();
+
+            auto ref_argument = ref_conv.MakeArgument(in_n_c_hi_wi_host_result,
+                                                      wei_k_c_y_x,
+                                                      out_n_k_ho_wo,
+                                                      params.conv_filter_strides,
+                                                      params.conv_filter_dilations,
+                                                      params.input_left_pads,
+                                                      params.input_right_pads,
+                                                      InElementOp{},
+                                                      WeiElementOp{},
+                                                      OutElementOp{});
+
+            ref_invoker.Run(ref_argument);
+
+            in_device_buf.FromDevice(in_n_c_hi_wi_device_result.mData.data());
+
+            check_error(in_n_c_hi_wi_host_result, in_n_c_hi_wi_device_result);
+        };
+
+        switch(num_dim_spatial)
+        {
+        case 3: {
+            auto ref_conv = ReferenceConvBwdDataInstance<3>();
+            verify_f(ref_conv);
+            break;
+        }
+        case 2: {
+            auto ref_conv = ReferenceConvBwdDataInstance<2>();
+            verify_f(ref_conv);
+            break;
+        }
+        case 1: {
+            auto ref_conv = ReferenceConvBwdDataInstance<1>();
+            verify_f(ref_conv);
+            break;
+        }
+        default: {
+            throw std::runtime_error("Unsupported number of spatial dimensions provided!");
+        }
+        }
+    }
+}

example/CMakeLists.txt

@@ -39,5 +39,6 @@ add_subdirectory(11_conv2d_bwd_wgt)
 add_subdirectory(12_reduce)
 add_subdirectory(13_pool2d_fwd)
 add_subdirectory(14_gemm_xdl_requant_relu_requant)
+add_subdirectory(17_convnd_bwd_data_xdl)
 add_subdirectory(15_grouped_gemm)
 add_subdirectory(16_gemm_reduce)

include/ck/problem_transform/transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk.hpp

@@ -7,9 +7,9 @@
 
 namespace ck {
 
-// Number of GEMMs = YTilda * XTilda
+// Number of GEMMs = YTilde * XTilde
 // GemmM = C
-// GemmN = N * HTildaSlice * WTildaSlice
+// GemmN = N * HTildeSlice * WTildeSlice
 // GemmK = K * YDotSlice * XDotSlice
 template <typename... Wei,
           typename... In,
@@ -18,8 +18,8 @@ template <typename... Wei,
           typename ConvDilations,
           typename InLeftPads,
           typename InRightPads,
-          index_t IYTildaValue,
-          index_t IXTildaValue,
+          index_t IYTildeValue,
+          index_t IXTildeValue,
           index_t GemmK1Value>
 __host__ __device__ constexpr auto
 transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
@@ -30,8 +30,8 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
     const ConvDilations& conv_dilations,
     const InLeftPads& in_left_pads,
     const InRightPads& in_right_pads,
-    Number<IYTildaValue>,
-    Number<IXTildaValue>,
+    Number<IYTildeValue>,
+    Number<IXTildeValue>,
     Number<GemmK1Value>)
 {
     constexpr auto I0 = Number<0>{};
@@ -40,8 +40,8 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
     constexpr auto I3 = Number<3>{};
 
     constexpr auto GemmK1  = Number<GemmK1Value>{};
-    constexpr auto IYTilda = Number<IYTildaValue>{};
-    constexpr auto IXTilda = Number<IXTildaValue>{};
+    constexpr auto IYTilde = Number<IYTildeValue>{};
+    constexpr auto IXTilde = Number<IXTildeValue>{};
 
     const auto N = in_n_hi_wi_c_grid_desc.GetLength(I0);
     const auto C = in_n_hi_wi_c_grid_desc.GetLength(I3);
@@ -71,55 +71,55 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
     const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
     const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
 
-    const auto YTilda = ConvStrideH / GcdStrideDilationH;
-    const auto XTilda = ConvStrideW / GcdStrideDilationW;
+    const auto YTilde = ConvStrideH / GcdStrideDilationH;
+    const auto XTilde = ConvStrideW / GcdStrideDilationW;
 
-    const auto YDot = math::integer_divide_ceil(Y, YTilda);
-    const auto XDot = math::integer_divide_ceil(X, XTilda);
+    const auto YDot = math::integer_divide_ceil(Y, YTilde);
+    const auto XDot = math::integer_divide_ceil(X, XTilde);
 
-    const auto HTilda = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
-    const auto WTilda = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
+    const auto HTilde = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
+    const auto WTilde = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
 
-    // only work on HTilda and WTilda that contribute to non-padding area of input tensor
-    const auto IHTildaSliceBegin = math::integer_divide_floor(
-        math::max(I0, InLeftPadH - ConvDilationH * (YTilda - I1)), ConvStrideH);
-    const auto IWTildaSliceBegin = math::integer_divide_floor(
-        math::max(I0, InLeftPadW - ConvDilationW * (XTilda - I1)), ConvStrideW);
+    // only work on HTilde and WTilde that contribute to non-padding area of input tensor
+    const auto IHTildeSliceBegin = math::integer_divide_floor(
+        math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
+    const auto IWTildeSliceBegin = math::integer_divide_floor(
+        math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
 
-    const auto IHTildaSliceEnd =
-        math::min(HTilda, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
-    const auto IWTildaSliceEnd =
-        math::min(WTilda, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
+    const auto IHTildeSliceEnd =
+        math::min(HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
+    const auto IWTildeSliceEnd =
+        math::min(WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
 
-    const auto HTildaSlice = IHTildaSliceEnd - IHTildaSliceBegin;
-    const auto WTildaSlice = IWTildaSliceEnd - IWTildaSliceBegin;
+    const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
+    const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
 
     // GemmK is different for each GEMM
-    const auto YDotSlice = math::integer_divide_ceil(Y - IYTilda, YTilda);
-    const auto XDotSlice = math::integer_divide_ceil(X - IXTilda, XTilda);
+    const auto YDotSlice = math::integer_divide_ceil(Y - IYTilde, YTilde);
+    const auto XDotSlice = math::integer_divide_ceil(X - IXTilde, XTilde);
 
     const auto K1 = GemmK1;
     const auto K0 = K / K1;
 
     // weight tensor
-    const auto wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc = transform_tensor_descriptor(
+    const auto wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
         wei_k_y_x_c_grid_desc,
         make_tuple(make_pass_through_transform(K),
-                   make_embed_transform(make_tuple(YDot, YTilda),
+                   make_embed_transform(make_tuple(YDot, YTilde),
                                         make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
-                   make_embed_transform(make_tuple(XDot, XTilda),
+                   make_embed_transform(make_tuple(XDot, XTilde),
                                         make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
     const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
-        transform_tensor_descriptor(wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc,
+        transform_tensor_descriptor(wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
                                     make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
                                                make_slice_transform(YDot, I0, YDotSlice),
                                                make_slice_transform(XDot, I0, XDotSlice),
-                                               make_freeze_transform(IYTilda),
-                                               make_freeze_transform(IXTilda),
+                                               make_freeze_transform(IYTilde),
+                                               make_freeze_transform(IXTilde),
                                                make_pass_through_transform(C)),
                                     make_tuple(Sequence<0>{},
                                                Sequence<1>{},
@@ -163,25 +163,25 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
 
-    const auto out_n_ydot_htilda_xdot_wtilda_k_grid_desc = transform_tensor_descriptor(
+    const auto out_n_ydot_htilde_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
         out_n_hop_wop_k_grid_desc,
         make_tuple(make_pass_through_transform(N),
-                   make_embed_transform(make_tuple(YDot, HTilda),
+                   make_embed_transform(make_tuple(YDot, HTilde),
                                         make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
-                   make_embed_transform(make_tuple(XDot, WTilda),
+                   make_embed_transform(make_tuple(XDot, WTilde),
                                         make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
                    make_pass_through_transform(K)),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
-    const auto out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc =
+    const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc =
         transform_tensor_descriptor(
-            out_n_ydot_htilda_xdot_wtilda_k_grid_desc,
+            out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
             make_tuple(make_pass_through_transform(N),
                        make_slice_transform(YDot, I0, YDotSlice),
-                       make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
+                       make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
                        make_slice_transform(XDot, I0, XDotSlice),
-                       make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
+                       make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
                        make_unmerge_transform(make_tuple(K0, K1))),
             make_tuple(Sequence<0>{},
                        Sequence<1>{},
@@ -198,17 +198,17 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
 
 #if 1
     const auto out_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
-        out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
+        out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
         make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
-                   make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
+                   make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
                    make_pass_through_transform(K1)),
         make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
 #else
     const auto out_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
-        out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
+        out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
         make_tuple(make_merge_transform(make_tuple(K0, YDotSlice, XDotSlice)),
-                   make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
+                   make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
                    make_pass_through_transform(K1)),
         make_tuple(Sequence<5, 1, 3>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
@@ -224,24 +224,24 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
 
-    const auto in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc = transform_tensor_descriptor(
+    const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
         in_n_hip_wip_c_grid_desc,
         make_tuple(make_pass_through_transform(N),
-                   make_embed_transform(make_tuple(YTilda, HTilda),
+                   make_embed_transform(make_tuple(YTilde, HTilde),
                                         make_tuple(ConvDilationH, ConvStrideH)),
-                   make_embed_transform(make_tuple(XTilda, WTilda),
+                   make_embed_transform(make_tuple(XTilde, WTilde),
                                         make_tuple(ConvDilationW, ConvStrideW)),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
-    const auto in_n_htildaslice_wtildaslice_c_grid_desc = transform_tensor_descriptor(
-        in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc,
+    const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
+        in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
         make_tuple(make_pass_through_transform(N),
-                   make_freeze_transform(IYTilda),
-                   make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
-                   make_freeze_transform(IXTilda),
-                   make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
+                   make_freeze_transform(IYTilde),
+                   make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
+                   make_freeze_transform(IXTilde),
+                   make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0>{},
                    Sequence<1>{},
@@ -257,9 +257,9 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
                    Sequence<3>{}));
 
     const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
-        in_n_htildaslice_wtildaslice_c_grid_desc,
+        in_n_htildeslice_wtildeslice_c_grid_desc,
         make_tuple(make_pass_through_transform(C),
-                   make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice))),
+                   make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice))),
         make_tuple(Sequence<3>{}, Sequence<0, 1, 2>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}));
 

include/ck/problem_transform/transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk.hpp

@@ -10,8 +10,8 @@ namespace ck {
 // A: out
 // B: wei
 // C: in
-// Number of GEMMs = YTilda * XTilda
-// GemmM = N * HTildaSlice * WTildaSlice
+// Number of GEMMs = YTilde * XTilde
+// GemmM = N * HTildeSlice * WTildeSlice
 // GemmN = C
 // GemmK = K * YDotSlice * XDotSlice
 template <typename... Wei,
@@ -21,8 +21,8 @@ template <typename... Wei,
           typename ConvDilations,
           typename InLeftPads,
           typename InRightPads,
-          typename IYTilda,
-          typename IXTilda,
+          typename IYTilde,
+          typename IXTilde,
           index_t GemmK1Value>
 __host__ __device__ constexpr auto
 transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
@@ -33,8 +33,8 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
     const ConvDilations& conv_dilations,
     const InLeftPads& in_left_pads,
     const InRightPads& in_right_pads,
-    IYTilda i_ytilda,
-    IXTilda i_xtilda,
+    IYTilde i_ytilde,
+    IXTilde i_xtilde,
     Number<GemmK1Value>)
 {
     constexpr auto I0 = Number<0>{};
@@ -72,32 +72,32 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
     const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
     const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
 
-    const auto YTilda = ConvStrideH / GcdStrideDilationH;
-    const auto XTilda = ConvStrideW / GcdStrideDilationW;
+    const auto YTilde = ConvStrideH / GcdStrideDilationH;
+    const auto XTilde = ConvStrideW / GcdStrideDilationW;
 
-    const auto YDot = math::integer_divide_ceil(Y, YTilda);
-    const auto XDot = math::integer_divide_ceil(X, XTilda);
+    const auto YDot = math::integer_divide_ceil(Y, YTilde);
+    const auto XDot = math::integer_divide_ceil(X, XTilde);
 
-    const auto HTilda = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
-    const auto WTilda = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
+    const auto HTilde = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
+    const auto WTilde = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
 
-    // only work on HTilda and WTilda that contribute to non-padding area of input tensor
-    const auto IHTildaSliceBegin = math::integer_divide_floor(
-        math::max(I0, InLeftPadH - ConvDilationH * (YTilda - I1)), ConvStrideH);
-    const auto IWTildaSliceBegin = math::integer_divide_floor(
-        math::max(I0, InLeftPadW - ConvDilationW * (XTilda - I1)), ConvStrideW);
+    // only work on HTilde and WTilde that contribute to non-padding area of input tensor
+    const auto IHTildeSliceBegin = math::integer_divide_floor(
+        math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
+    const auto IWTildeSliceBegin = math::integer_divide_floor(
+        math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
 
-    const auto IHTildaSliceEnd =
-        math::min(HTilda, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
-    const auto IWTildaSliceEnd =
-        math::min(WTilda, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
+    const auto IHTildeSliceEnd =
+        math::min(HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
+    const auto IWTildeSliceEnd =
+        math::min(WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
 
-    const auto HTildaSlice = IHTildaSliceEnd - IHTildaSliceBegin;
-    const auto WTildaSlice = IWTildaSliceEnd - IWTildaSliceBegin;
+    const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
+    const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
 
     // GemmK is different for each GEMM
-    const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilda, YTilda);
-    const auto XDotSlice = math::integer_divide_ceil(X - i_xtilda, XTilda);
+    const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
+    const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
 
     const auto K1 = GemmK1;
     const auto K0 = K / K1;
@@ -113,25 +113,25 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
 
-    const auto out_n_ydot_htilda_xdot_wtilda_k_grid_desc = transform_tensor_descriptor(
+    const auto out_n_ydot_htilde_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
         out_n_hop_wop_k_grid_desc,
         make_tuple(make_pass_through_transform(N),
-                   make_embed_transform(make_tuple(YDot, HTilda),
+                   make_embed_transform(make_tuple(YDot, HTilde),
                                         make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
-                   make_embed_transform(make_tuple(XDot, WTilda),
+                   make_embed_transform(make_tuple(XDot, WTilde),
                                         make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
                    make_pass_through_transform(K)),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
-    const auto out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc =
+    const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc =
         transform_tensor_descriptor(
-            out_n_ydot_htilda_xdot_wtilda_k_grid_desc,
+            out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
             make_tuple(make_pass_through_transform(N),
                        make_slice_transform(YDot, I0, YDotSlice),
-                       make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
+                       make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
                        make_slice_transform(XDot, I0, XDotSlice),
-                       make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
+                       make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
                        make_unmerge_transform(make_tuple(K0, K1))),
             make_tuple(Sequence<0>{},
                        Sequence<1>{},
@@ -148,41 +148,41 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
 
 #if 1
     const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
-        out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
+        out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
         make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
-                   make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
+                   make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
                    make_pass_through_transform(K1)),
         make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
 #else
     const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
-        out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
+        out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
         make_tuple(make_merge_transform(make_tuple(K0, YDotSlice, XDotSlice)),
-                   make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
+                   make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
                    make_pass_through_transform(K1)),
         make_tuple(Sequence<5, 1, 3>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
 #endif
 
     // B: weight tensor
-    const auto wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc = transform_tensor_descriptor(
+    const auto wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
         wei_k_y_x_c_grid_desc,
         make_tuple(make_pass_through_transform(K),
-                   make_embed_transform(make_tuple(YDot, YTilda),
+                   make_embed_transform(make_tuple(YDot, YTilde),
                                         make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
-                   make_embed_transform(make_tuple(XDot, XTilda),
+                   make_embed_transform(make_tuple(XDot, XTilde),
                                         make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
     const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
-        transform_tensor_descriptor(wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc,
+        transform_tensor_descriptor(wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
                                     make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
                                                make_slice_transform(YDot, I0, YDotSlice),
                                                make_slice_transform(XDot, I0, XDotSlice),
-                                               make_freeze_transform(i_ytilda),
-                                               make_freeze_transform(i_xtilda),
+                                               make_freeze_transform(i_ytilde),
+                                               make_freeze_transform(i_xtilde),
                                                make_pass_through_transform(C)),
                                     make_tuple(Sequence<0>{},
                                                Sequence<1>{},
@@ -225,24 +225,24 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
 
-    const auto in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc = transform_tensor_descriptor(
+    const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
         in_n_hip_wip_c_grid_desc,
         make_tuple(make_pass_through_transform(N),
-                   make_embed_transform(make_tuple(YTilda, HTilda),
+                   make_embed_transform(make_tuple(YTilde, HTilde),
                                         make_tuple(ConvDilationH, ConvStrideH)),
-                   make_embed_transform(make_tuple(XTilda, WTilda),
+                   make_embed_transform(make_tuple(XTilde, WTilde),
                                         make_tuple(ConvDilationW, ConvStrideW)),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
-    const auto in_n_htildaslice_wtildaslice_c_grid_desc = transform_tensor_descriptor(
-        in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc,
+    const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
+        in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
         make_tuple(make_pass_through_transform(N),
-                   make_freeze_transform(i_ytilda),
-                   make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
-                   make_freeze_transform(i_xtilda),
-                   make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
+                   make_freeze_transform(i_ytilde),
+                   make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
+                   make_freeze_transform(i_xtilde),
+                   make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0>{},
                    Sequence<1>{},
@@ -258,8 +258,8 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
                    Sequence<3>{}));
 
     const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
-        in_n_htildaslice_wtildaslice_c_grid_desc,
-        make_tuple(make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
+        in_n_htildeslice_wtildeslice_c_grid_desc,
+        make_tuple(make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
                    make_pass_through_transform(C)),
         make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
         make_tuple(Sequence<0>{}, Sequence<1>{}));

include/ck/tensor_operation/gpu/device/conv_utils.hpp

@@ -108,6 +108,28 @@ struct ConvParams
           input_right_pads(2, 1)
     {
     }
+    ConvParams(ck::index_t n_dim_spatial,
+               ck::index_t n,
+               ck::index_t k,
+               ck::index_t c,
+               std::vector<ck::index_t> filter_lengths,
+               std::vector<ck::index_t> input_lengths,
+               std::vector<ck::index_t> conv_strides,
+               std::vector<ck::index_t> conv_dilations,
+               std::vector<ck::index_t> left_pads,
+               std::vector<ck::index_t> right_pads)
+        : num_dim_spatial(n_dim_spatial),
+          N(n),
+          K(k),
+          C(c),
+          filter_spatial_lengths(filter_lengths),
+          input_spatial_lengths(input_lengths),
+          conv_filter_strides(conv_strides),
+          conv_filter_dilations(conv_dilations),
+          input_left_pads(left_pads),
+          input_right_pads(right_pads)
+    {
+    }
 
     ck::index_t num_dim_spatial;
     ck::index_t N;
@@ -206,7 +228,7 @@ HostTensorDescriptor GetHostTensorDescriptor(const std::vector<std::size_t>& dim
         return HostTensorDescriptor(
             dims,
             std::vector<std::size_t>{
-                C * dims[2] * dims[3] * dims[4], 1, C * dims[3] * dims[4], C * dims[4], C});
+                C * dims[2] * dims[3] * dims[4], 1, dims[3] * dims[4] * C, dims[4] * C, C});
     }
 
     std::stringstream err_msg;

include/ck/tensor_operation/gpu/device/device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk.hpp

@@ -95,8 +95,8 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
                                                     std::vector<ck::index_t> conv_filter_dilations,
                                                     std::vector<ck::index_t> input_left_pads,
                                                     std::vector<ck::index_t> input_right_pads,
-                                                    index_t i_ytilda,
-                                                    index_t i_xtilda)
+                                                    index_t i_ytilde,
+                                                    index_t i_xtilde)
     {
         using namespace ck;
 
@@ -177,34 +177,34 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
             const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
             const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
 
-            const auto YTilda = ConvStrideH / GcdStrideDilationH;
-            const auto XTilda = ConvStrideW / GcdStrideDilationW;
+            const auto YTilde = ConvStrideH / GcdStrideDilationH;
+            const auto XTilde = ConvStrideW / GcdStrideDilationW;
 
-            const auto YDot = math::integer_divide_ceil(Y, YTilda);
-            const auto XDot = math::integer_divide_ceil(X, XTilda);
+            const auto YDot = math::integer_divide_ceil(Y, YTilde);
+            const auto XDot = math::integer_divide_ceil(X, XTilde);
 
-            const auto HTilda =
+            const auto HTilde =
                 Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
-            const auto WTilda =
+            const auto WTilde =
                 Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
 
-            // only work on HTilda and WTilda that contribute to non-padding area of input tensor
-            const auto IHTildaSliceBegin = math::integer_divide_floor(
-                math::max(I0, InLeftPadH - ConvDilationH * (YTilda - I1)), ConvStrideH);
-            const auto IWTildaSliceBegin = math::integer_divide_floor(
-                math::max(I0, InLeftPadW - ConvDilationW * (XTilda - I1)), ConvStrideW);
+            // only work on HTilde and WTilde that contribute to non-padding area of input tensor
+            const auto IHTildeSliceBegin = math::integer_divide_floor(
+                math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
+            const auto IWTildeSliceBegin = math::integer_divide_floor(
+                math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
 
-            const auto IHTildaSliceEnd = math::min(
-                HTilda, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
-            const auto IWTildaSliceEnd = math::min(
-                WTilda, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
+            const auto IHTildeSliceEnd = math::min(
+                HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
+            const auto IWTildeSliceEnd = math::min(
+                WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
 
-            const auto HTildaSlice = IHTildaSliceEnd - IHTildaSliceBegin;
-            const auto WTildaSlice = IWTildaSliceEnd - IWTildaSliceBegin;
+            const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
+            const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
 
             // GemmK is different for each GEMM
-            const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilda, YTilda);
-            const auto XDotSlice = math::integer_divide_ceil(X - i_xtilda, XTilda);
+            const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
+            const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
 
             // A: output tensor
             const auto out_n_hop_wop_k_grid_desc = transform_tensor_descriptor(
@@ -216,26 +216,26 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
                 make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
                 make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
 
-            const auto out_n_ydot_htilda_xdot_wtilda_k_grid_desc = transform_tensor_descriptor(
+            const auto out_n_ydot_htilde_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
                 out_n_hop_wop_k_grid_desc,
                 make_tuple(
                     make_pass_through_transform(N),
-                    make_embed_transform(make_tuple(YDot, HTilda),
+                    make_embed_transform(make_tuple(YDot, HTilde),
                                          make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
-                    make_embed_transform(make_tuple(XDot, WTilda),
+                    make_embed_transform(make_tuple(XDot, WTilde),
                                          make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
                     make_pass_through_transform(K)),
                 make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
                 make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
-            const auto out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc =
+            const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc =
                 transform_tensor_descriptor(
-                    out_n_ydot_htilda_xdot_wtilda_k_grid_desc,
+                    out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
                     make_tuple(make_pass_through_transform(N),
                                make_slice_transform(YDot, I0, YDotSlice),
-                               make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
+                               make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
                                make_slice_transform(XDot, I0, XDotSlice),
-                               make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
+                               make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
                                make_unmerge_transform(make_tuple(K0, K1))),
                     make_tuple(Sequence<0>{},
                                Sequence<1>{},
@@ -251,32 +251,32 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
                                Sequence<5, 6>{}));
 
             const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
-                out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
+                out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
                 make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
-                           make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
+                           make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
                            make_pass_through_transform(K1)),
                 make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
                 make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
 
             // B weight tensor
-            const auto wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc = transform_tensor_descriptor(
+            const auto wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
                 wei_k_y_x_c_grid_desc,
                 make_tuple(make_pass_through_transform(K),
-                           make_embed_transform(make_tuple(YDot, YTilda),
+                           make_embed_transform(make_tuple(YDot, YTilde),
                                                 make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
-                           make_embed_transform(make_tuple(XDot, XTilda),
+                           make_embed_transform(make_tuple(XDot, XTilde),
                                                 make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
                            make_pass_through_transform(C)),
                 make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
                 make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
             const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
-                transform_tensor_descriptor(wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc,
+                transform_tensor_descriptor(wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
                                             make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
                                                        make_slice_transform(YDot, I0, YDotSlice),
                                                        make_slice_transform(XDot, I0, XDotSlice),
-                                                       make_freeze_transform(i_ytilda),
-                                                       make_freeze_transform(i_xtilda),
+                                                       make_freeze_transform(i_ytilde),
+                                                       make_freeze_transform(i_xtilde),
                                                        make_pass_through_transform(C)),
                                             make_tuple(Sequence<0>{},
                                                        Sequence<1>{},
@@ -309,24 +309,24 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
                 make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
                 make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
 
-            const auto in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc = transform_tensor_descriptor(
+            const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
                 in_n_hip_wip_c_grid_desc,
                 make_tuple(make_pass_through_transform(N),
-                           make_embed_transform(make_tuple(YTilda, HTilda),
+                           make_embed_transform(make_tuple(YTilde, HTilde),
                                                 make_tuple(ConvDilationH, ConvStrideH)),
-                           make_embed_transform(make_tuple(XTilda, WTilda),
+                           make_embed_transform(make_tuple(XTilde, WTilde),
                                                 make_tuple(ConvDilationW, ConvStrideW)),
                            make_pass_through_transform(C)),
                 make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
                 make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
 
-            const auto in_n_htildaslice_wtildaslice_c_grid_desc = transform_tensor_descriptor(
-                in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc,
+            const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
+                in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
                 make_tuple(make_pass_through_transform(N),
-                           make_freeze_transform(i_ytilda),
-                           make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
-                           make_freeze_transform(i_xtilda),
-                           make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
+                           make_freeze_transform(i_ytilde),
+                           make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
+                           make_freeze_transform(i_xtilde),
+                           make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
                            make_pass_through_transform(C)),
                 make_tuple(Sequence<0>{},
                            Sequence<1>{},
@@ -342,8 +342,8 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
                            Sequence<3>{}));
 
             const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
-                in_n_htildaslice_wtildaslice_c_grid_desc,
-                make_tuple(make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
+                in_n_htildeslice_wtildeslice_c_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
                            make_pass_through_transform(C)),
                 make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
                 make_tuple(Sequence<0>{}, Sequence<1>{}));
@@ -452,18 +452,18 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
             const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
             const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
 
-            const auto YTilda = ConvStrideH / GcdStrideDilationH;
-            const auto XTilda = ConvStrideW / GcdStrideDilationW;
+            const auto YTilde = ConvStrideH / GcdStrideDilationH;
+            const auto XTilde = ConvStrideW / GcdStrideDilationW;
 
-            for(index_t i_ytilda = 0; i_ytilda < YTilda; ++i_ytilda)
+            for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
             {
-                for(index_t i_xtilda = 0; i_xtilda < XTilda; ++i_xtilda)
+                for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
                 {
                     // check slice is valid
                     const index_t Y      = filter_spatial_lengths_[0];
                     const index_t X      = filter_spatial_lengths_[1];
-                    const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilda, YTilda);
-                    const auto XDotSlice = math::integer_divide_ceil(X - i_xtilda, XTilda);
+                    const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
+                    const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
                     if(YDotSlice * XDotSlice <= 0)
                     {
                         continue;
@@ -480,8 +480,8 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
                         conv_filter_dilations,
                         input_left_pads,
                         input_right_pads,
-                        i_ytilda,
-                        i_xtilda);
+                        i_ytilde,
+                        i_xtilde);
                     a_grid_desc_k0_m_k1_container_.push_back(descs[I0]);
                     b_grid_desc_k0_n_k1_container_.push_back(descs[I1]);
                     c_grid_desc_m_n_container_.push_back(descs[I2]);
@@ -533,7 +533,6 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
 
         float Run(const Argument& arg, int nrepeat = 1)
         {
-            nrepeat        = 1;
             float ave_time = 0;
             for(size_t i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
             {

include/ck/tensor_operation/gpu/device/tensor_layout.hpp

@@ -100,7 +100,6 @@ struct NDHWK : public BaseTensorLayout
 {
     static constexpr const char* name = "NDHWK";
 };
-
 struct NCDHW : public BaseTensorLayout
 {
     static constexpr const char* name = "NCDHW";

library/include/ck/library/obselete_driver_offline/device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk.hpp

@@ -303,14 +303,14 @@ void device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk
         const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
         const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
 
-        const auto YTilda = ConvStrideH / GcdStrideDilationH;
-        const auto XTilda = ConvStrideW / GcdStrideDilationW;
+        const auto YTilde = ConvStrideH / GcdStrideDilationH;
+        const auto XTilde = ConvStrideW / GcdStrideDilationW;
 
         float ave_time = 0;
 
-        for(index_t i_ytilda = 0; i_ytilda < YTilda; ++i_ytilda)
+        for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
         {
-            for(index_t i_xtilda = 0; i_xtilda < XTilda; ++i_xtilda)
+            for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
             {
                 const auto descs =
                     transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
@@ -321,8 +321,8 @@ void device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk
                         conv_dilations,
                         in_left_pads,
                         in_right_pads,
-                        i_ytilda,
-                        i_xtilda,
+                        i_ytilde,
+                        i_xtilde,
                         Number<GemmK1>{});
 
                 const auto out_gemmk0_gemmm_gemmk1_grid_desc = descs[I0];

library/include/ck/library/reference_tensor_operation/cpu/reference_conv_bwd_data.hpp

@@ -14,17 +14,20 @@ namespace host {
 template <typename InDataType,
           typename WeiDataType,
           typename OutDataType,
+          typename AccDataType,
           typename InElementwiseOperation,
           typename WeiElementwiseOperation,
-          typename OutElementwiseOperation>
+          typename OutElementwiseOperation,
+          ck::index_t NumDimSpatial                                                     = 2,
+          typename std::enable_if<NumDimSpatial >= 1 && NumDimSpatial <= 3, bool>::type = false>
 struct ReferenceConvBwdData : public device::BaseOperator
 {
     // Argument
     struct Argument : public device::BaseArgument
     {
-        Argument(Tensor<InDataType>& in_n_c_hi_wi,
-                 const Tensor<WeiDataType>& wei_k_c_y_x,
-                 const Tensor<OutDataType>& out_n_k_ho_wo,
+        Argument(Tensor<InDataType>& input,
+                 const Tensor<WeiDataType>& weight,
+                 const Tensor<OutDataType>& output,
                  std::vector<ck::index_t> conv_filter_strides,
                  std::vector<ck::index_t> conv_filter_dilations,
                  std::vector<ck::index_t> input_left_pads,
@@ -32,9 +35,9 @@ struct ReferenceConvBwdData : public device::BaseOperator
                  InElementwiseOperation in_element_op,
                  WeiElementwiseOperation wei_element_op,
                  OutElementwiseOperation out_element_op)
-            : in_n_c_hi_wi_{in_n_c_hi_wi},
-              wei_k_c_y_x_{wei_k_c_y_x},
-              out_n_k_ho_wo_{out_n_k_ho_wo},
+            : input_{input},
+              weight_{weight},
+              output_{output},
               conv_strides_{conv_filter_strides},
               conv_dilations_{conv_filter_dilations},
               in_left_pads_{input_left_pads},
@@ -45,9 +48,9 @@ struct ReferenceConvBwdData : public device::BaseOperator
         {
         }
 
-        Tensor<InDataType>& in_n_c_hi_wi_;
-        const Tensor<WeiDataType>& wei_k_c_y_x_;
-        const Tensor<OutDataType>& out_n_k_ho_wo_;
+        Tensor<InDataType>& input_;
+        const Tensor<WeiDataType>& weight_;
+        const Tensor<OutDataType>& output_;
 
         std::vector<index_t> conv_strides_;
         std::vector<index_t> conv_dilations_;
@@ -66,67 +69,199 @@ struct ReferenceConvBwdData : public device::BaseOperator
 
         float Run(const Argument& arg)
         {
-            auto f_nchw = [&](auto n, auto c, auto hi, auto wi) {
-                std::size_t K = arg.wei_k_c_y_x_.mDesc.GetLengths()[0];
-                std::size_t Y = arg.wei_k_c_y_x_.mDesc.GetLengths()[2];
-                std::size_t X = arg.wei_k_c_y_x_.mDesc.GetLengths()[3];
+            if constexpr(NumDimSpatial == 1)
+            {
+                auto f_nchw = [&](auto n, auto c, auto wi) {
+                    std::size_t K  = arg.weight_.mDesc.GetLengths()[0];
+                    std::size_t X  = arg.weight_.mDesc.GetLengths()[2];
+                    std::size_t Wo = arg.output_.mDesc.GetLengths()[2];
 
-                std::size_t Ho = arg.out_n_k_ho_wo_.mDesc.GetLengths()[2];
-                std::size_t Wo = arg.out_n_k_ho_wo_.mDesc.GetLengths()[3];
+                    AccDataType v_acc = 0;
 
-                float v_acc = 0;
+                    for(int x = 0; x < X; ++x)
+                    {
+                        int w_tmp = wi + arg.in_left_pads_[0] - x * arg.conv_dilations_[0];
+                        if(w_tmp % arg.conv_strides_[0] == 0)
+                        {
+                            int wo = w_tmp / arg.conv_strides_[0];
+                            if(wo >= 0 && wo < Wo)
+                            {
+                                for(int k = 0; k < K; ++k)
+                                {
+                                    AccDataType v_out = 0;
+                                    AccDataType v_wei = 0;
+
+                                    arg.out_element_op_(
+                                        v_out,
+                                        ck::type_convert<AccDataType>(arg.output_(n, k, wo)));
+                                    arg.wei_element_op_(
+                                        v_wei, ck::type_convert<AccDataType>(arg.weight_(k, c, x)));
+
+                                    v_acc += v_out * v_wei;
+                                }
+                            }
+                        }
+                    }
 
-                for(int y = 0; y < Y; ++y)
-                {
-                    int h_tmp = hi + arg.in_left_pads_[0] - y * arg.conv_dilations_[0];
-                    if(h_tmp % arg.conv_strides_[0] == 0)
+                    float v_in;
+                    arg.in_element_op_(v_in, v_acc);
+                    arg.input_(n, c, wi) = ck::type_convert<InDataType>(v_in);
+                };
+
+                make_ParallelTensorFunctor(f_nchw,
+                                           arg.input_.mDesc.GetLengths()[0],
+                                           arg.input_.mDesc.GetLengths()[1],
+                                           arg.input_.mDesc.GetLengths()[2])(
+                    std::thread::hardware_concurrency());
+
+                return 0;
+            }
+            else if constexpr(NumDimSpatial == 2)
+            {
+                auto f_nchw = [&](auto n, auto c, auto hi, auto wi) {
+                    std::size_t K = arg.weight_.mDesc.GetLengths()[0];
+                    std::size_t Y = arg.weight_.mDesc.GetLengths()[2];
+                    std::size_t X = arg.weight_.mDesc.GetLengths()[3];
+
+                    std::size_t Ho = arg.output_.mDesc.GetLengths()[2];
+                    std::size_t Wo = arg.output_.mDesc.GetLengths()[3];
+
+                    AccDataType v_acc = 0;
+
+                    for(int y = 0; y < Y; ++y)
                     {
-                        int ho = h_tmp / arg.conv_strides_[0];
-                        if(ho >= 0 && ho < Ho)
+                        int h_tmp = hi + arg.in_left_pads_[0] - y * arg.conv_dilations_[0];
+                        if(h_tmp % arg.conv_strides_[0] == 0)
                         {
-                            for(int x = 0; x < X; ++x)
+                            int ho = h_tmp / arg.conv_strides_[0];
+                            if(ho >= 0 && ho < Ho)
                             {
-                                int w_tmp = wi + arg.in_left_pads_[1] - x * arg.conv_dilations_[1];
-                                if(w_tmp % arg.conv_strides_[1] == 0)
+                                for(int x = 0; x < X; ++x)
                                 {
-                                    int wo = w_tmp / arg.conv_strides_[1];
-                                    if(wo >= 0 && wo < Wo)
+                                    int w_tmp =
+                                        wi + arg.in_left_pads_[1] - x * arg.conv_dilations_[1];
+                                    if(w_tmp % arg.conv_strides_[1] == 0)
                                     {
-                                        for(int k = 0; k < K; ++k)
+                                        int wo = w_tmp / arg.conv_strides_[1];
+                                        if(wo >= 0 && wo < Wo)
                                         {
-                                            float v_out = 0;
-                                            float v_wei = 0;
-
-                                            arg.out_element_op_(
-                                                v_out,
-                                                ck::type_convert<float>(
-                                                    arg.out_n_k_ho_wo_(n, k, ho, wo)));
-                                            arg.wei_element_op_(v_wei,
-                                                                ck::type_convert<float>(
-                                                                    arg.wei_k_c_y_x_(k, c, y, x)));
-
-                                            v_acc += v_out * v_wei;
+                                            for(int k = 0; k < K; ++k)
+                                            {
+                                                AccDataType v_out = 0;
+                                                AccDataType v_wei = 0;
+
+                                                arg.out_element_op_(v_out,
+                                                                    ck::type_convert<AccDataType>(
+                                                                        arg.output_(n, k, ho, wo)));
+                                                arg.wei_element_op_(v_wei,
+                                                                    ck::type_convert<AccDataType>(
+                                                                        arg.weight_(k, c, y, x)));
+
+                                                v_acc += v_out * v_wei;
+                                            }
+                                        }
+                                    }
+                                }
+                            }
+                        }
+                    }
+
+                    AccDataType v_in;
+                    arg.in_element_op_(v_in, v_acc);
+                    arg.input_(n, c, hi, wi) = ck::type_convert<InDataType>(v_in);
+                };
+
+                make_ParallelTensorFunctor(f_nchw,
+                                           arg.input_.mDesc.GetLengths()[0],
+                                           arg.input_.mDesc.GetLengths()[1],
+                                           arg.input_.mDesc.GetLengths()[2],
+                                           arg.input_.mDesc.GetLengths()[3])(
+                    std::thread::hardware_concurrency());
+
+                return 0;
+            }
+            else if constexpr(NumDimSpatial == 3)
+            {
+                auto f_nchw = [&](auto n, auto c, auto di, auto hi, auto wi) {
+                    std::size_t K = arg.weight_.mDesc.GetLengths()[0];
+                    std::size_t Z = arg.weight_.mDesc.GetLengths()[2];
+                    std::size_t Y = arg.weight_.mDesc.GetLengths()[3];
+                    std::size_t X = arg.weight_.mDesc.GetLengths()[4];
+
+                    std::size_t Do = arg.output_.mDesc.GetLengths()[2];
+                    std::size_t Ho = arg.output_.mDesc.GetLengths()[3];
+                    std::size_t Wo = arg.output_.mDesc.GetLengths()[4];
+
+                    AccDataType v_acc = 0;
+
+                    for(int z = 0; z < Z; ++z)
+                    {
+                        int d_tmp = di + arg.in_left_pads_[0] - z * arg.conv_dilations_[0];
+                        if(d_tmp % arg.conv_strides_[0] == 0)
+                        {
+                            int do_ = d_tmp / arg.conv_strides_[0];
+                            if(do_ >= 0 && do_ < Do)
+                            {
+                                for(int y = 0; y < Y; ++y)
+                                {
+                                    int h_tmp =
+                                        hi + arg.in_left_pads_[1] - y * arg.conv_dilations_[1];
+                                    if(h_tmp % arg.conv_strides_[1] == 0)
+                                    {
+                                        int ho = h_tmp / arg.conv_strides_[1];
+                                        if(ho >= 0 && ho < Ho)
+                                        {
+                                            for(int x = 0; x < X; ++x)
+                                            {
+                                                int w_tmp = wi + arg.in_left_pads_[2] -
+                                                            x * arg.conv_dilations_[2];
+                                                if(w_tmp % arg.conv_strides_[2] == 0)
+                                                {
+                                                    int wo = w_tmp / arg.conv_strides_[2];
+                                                    if(wo >= 0 && wo < Wo)
+                                                    {
+                                                        for(int k = 0; k < K; ++k)
+                                                        {
+                                                            AccDataType v_out = 0;
+                                                            AccDataType v_wei = 0;
+
+                                                            arg.out_element_op_(
+                                                                v_out,
+                                                                ck::type_convert<AccDataType>(
+                                                                    arg.output_(
+                                                                        n, k, do_, ho, wo)));
+                                                            arg.wei_element_op_(
+                                                                v_wei,
+                                                                ck::type_convert<AccDataType>(
+                                                                    arg.weight_(k, c, z, y, x)));
+
+                                                            v_acc += v_out * v_wei;
+                                                        }
+                                                    }
+                                                }
+                                            }
                                         }
                                     }
                                 }
                             }
                         }
                     }
-                }
 
-                float v_in;
-                arg.in_element_op_(v_in, v_acc);
-                arg.in_n_c_hi_wi_(n, c, hi, wi) = ck::type_convert<InDataType>(v_in);
-            };
+                    AccDataType v_in;
+                    arg.in_element_op_(v_in, v_acc);
+                    arg.input_(n, c, di, hi, wi) = ck::type_convert<InDataType>(v_in);
+                };
 
-            make_ParallelTensorFunctor(f_nchw,
-                                       arg.in_n_c_hi_wi_.mDesc.GetLengths()[0],
-                                       arg.in_n_c_hi_wi_.mDesc.GetLengths()[1],
-                                       arg.in_n_c_hi_wi_.mDesc.GetLengths()[2],
-                                       arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])(
-                std::thread::hardware_concurrency());
+                make_ParallelTensorFunctor(f_nchw,
+                                           arg.input_.mDesc.GetLengths()[0],
+                                           arg.input_.mDesc.GetLengths()[1],
+                                           arg.input_.mDesc.GetLengths()[2],
+                                           arg.input_.mDesc.GetLengths()[3],
+                                           arg.input_.mDesc.GetLengths()[4])(
+                    std::thread::hardware_concurrency());
 
-            return 0;
+                return 0;
+            }
         }
 
         float Run(const device::BaseArgument* p_arg, int) override
@@ -143,9 +278,9 @@ struct ReferenceConvBwdData : public device::BaseOperator
 
     bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
 
-    static auto MakeArgument(Tensor<InDataType>& in_n_c_hi_wi,
-                             const Tensor<WeiDataType>& wei_k_c_y_x,
-                             const Tensor<OutDataType>& out_n_k_ho_wo,
+    static auto MakeArgument(Tensor<InDataType>& input,
+                             const Tensor<WeiDataType>& weight,
+                             const Tensor<OutDataType>& output,
                              std::vector<ck::index_t> conv_filter_strides,
                              std::vector<ck::index_t> conv_filter_dilations,
                              std::vector<ck::index_t> input_left_pads,
@@ -154,9 +289,9 @@ struct ReferenceConvBwdData : public device::BaseOperator
                              WeiElementwiseOperation wei_element_op,
                              OutElementwiseOperation out_element_op)
     {
-        return Argument{in_n_c_hi_wi,
-                        wei_k_c_y_x,
-                        out_n_k_ho_wo,
+        return Argument{input,
+                        weight,
+                        output,
                         conv_filter_strides,
                         conv_filter_dilations,
                         input_left_pads,

library/src/tensor_operation_instance/gpu/CMakeLists.txt

@@ -37,4 +37,5 @@ add_subdirectory(conv2d_fwd_bias_relu_add)
 add_subdirectory(conv2d_fwd_bias_relu_atomic_add)
 add_subdirectory(conv2d_bwd_data)
 add_subdirectory(reduce)
+add_subdirectory(convnd_bwd_data)
 add_subdirectory(grouped_gemm)

library/src/tensor_operation_instance/gpu/convnd_bwd_data/CMakeLists.txt

+# device_convnd_bwd_data_instance
+set(DEVICE_CONVND_BWD_DATA_INSTANCE_SOURCE 
+   device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instance.cpp;
+   device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instance.cpp;
+   device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instance.cpp;
+   device_conv1d_bwd_data_xdl_nwc_kxc_nwk_int8_instance.cpp;
+   device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f16_instance.cpp;
+   device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f32_instance.cpp;
+   device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_bf16_instance.cpp;
+   device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_int8_instance.cpp;
+   device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f16_instance.cpp;
+   device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f32_instance.cpp;
+   device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_bf16_instance.cpp;
+   device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_int8_instance.cpp;
+) 
+
+add_library(device_convnd_bwd_data_instance SHARED ${DEVICE_CONVND_BWD_DATA_INSTANCE_SOURCE})
+target_compile_features(device_convnd_bwd_data_instance PUBLIC)
+set_target_properties(device_convnd_bwd_data_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
+install(TARGETS device_convnd_bwd_data_instance LIBRARY DESTINATION lib) 
+
+clang_tidy_check(device_convnd_bwd_data_instance)

library/src/tensor_operation_instance/gpu/convnd_bwd_data/device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instance.cpp

+#include <stdlib.h>
+#include "config.hpp"
+#include "device_convnd_bwd_data_xdl_ndhwc_kzyxc_ndhwk.hpp"
+#include "element_wise_operation.hpp"
+#include "device_operation_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace device_conv2d_bwd_data_instance {
+
+using BF16 = ushort;
+using F32  = float;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+static constexpr auto ConvBwdDataDefault =
+    ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Default;
+
+static constexpr auto ConvBwdDataFilter1x1Stride1Pad0 =
+    ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Filter1x1Stride1Pad0;
+
+// Compilation parameters for in[n, hi, wi, c] * wei[k, y, x, c] = out[n, ho, wo, k]
+using device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instances =
+    std::tuple<
+        // clang-format off
+        //#############################################################################| InData| WeiData| OutData| AccData|          In|         Wei|         Out|        ConvBackward|    Num| Block|  MPer|  NPer| K0Per| K1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
+        //#############################################################################|   Type|    Type|    Type|    Type| Elementwise| Elementwise| Elementwise|                Data|    Dim|  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|      Specialization|Spatial|      |      |      |      |   |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |                |       PerVector|
+        //#############################################################################|       |        |        |        |            |            |            |                    |       |      |      |      |      |   |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |                |                |
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     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<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     256,   128,   256,     4,  8,   32,   32,    2,    4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     128,   128,   128,     4,  8,   32,   32,    4,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     256,   128,   128,     4,  8,   32,   32,    2,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     128,   128,    64,     4,  8,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     128,    64,   128,     4,  8,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,      64,    64,    64,     4,  8,   32,   32,    2,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     256,   128,    64,     4,  8,   32,   32,    2,    1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              1,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     256,    64,   128,     4,  8,   32,   32,    1,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     128,   128,    32,     4,  8,   32,   32,    2,    1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              1,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     128,    32,   128,     4,  8,   32,   32,    1,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,      64,    64,    32,     4,  8,   32,   32,    2,    1,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,      64,    32,    64,     4,  8,   32,   32,    1,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>
+        // clang-format on
+        >;
+
+using device_conv1d_bwd_data_xdl_nwc_kxc_nwk_1x1_s1_p0_bf16_instances =
+    std::tuple<
+        // clang-format off
+        //#############################################################################| InData| WeiData| OutData| AccData|          In|         Wei|         Out|                     ConvBackward|    Num| Block|  MPer|  NPer| K0Per| K1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
+        //#############################################################################|   Type|    Type|    Type|    Type| Elementwise| Elementwise| Elementwise|                             Data|    Dim|  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|                   Specialization|Spatial|      |      |      |      |   |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |                |       PerVector|
+        //#############################################################################|       |        |        |        |            |            |            |                                 |       |      |      |      |      |   |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |                |                |
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     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<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     256,   128,   256,     4,  8,   32,   32,    2,    4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,   128,   128,     4,  8,   32,   32,    4,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     256,   128,   128,     4,  8,   32,   32,    2,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,   128,    64,     4,  8,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,    64,   128,     4,  8,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,      64,    64,    64,     4,  8,   32,   32,    2,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     256,   128,    64,     4,  8,   32,   32,    2,    1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              1,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     256,    64,   128,     4,  8,   32,   32,    1,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,   128,    32,     4,  8,   32,   32,    2,    1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              1,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,    32,   128,     4,  8,   32,   32,    1,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,      64,    64,    32,     4,  8,   32,   32,    2,    1,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,      64,    32,    64,     4,  8,   32,   32,    1,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>
+        // clang-format on
+        >;
+
+void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instances(
+    std::vector<DeviceConvBwdDataPtr<PassThrough, PassThrough, PassThrough>>& instances)
+{
+    add_device_operation_instances(instances,
+                                   device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instances{});
+    add_device_operation_instances(
+        instances, device_conv1d_bwd_data_xdl_nwc_kxc_nwk_1x1_s1_p0_bf16_instances{});
+}
+
+} // namespace device_conv2d_bwd_data_instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/convnd_bwd_data/device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instance.cpp

+#include <stdlib.h>
+#include "config.hpp"
+#include "device_convnd_bwd_data_xdl_ndhwc_kzyxc_ndhwk.hpp"
+#include "element_wise_operation.hpp"
+#include "device_operation_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace device_conv2d_bwd_data_instance {
+
+using F16 = ck::half_t;
+using F32 = float;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+static constexpr auto ConvBwdDataDefault =
+    ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Default;
+
+static constexpr auto ConvBwdDataFilter1x1Stride1Pad0 =
+    ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Filter1x1Stride1Pad0;
+
+// Compilation parameters for in[n, hi, wi, c] * wei[k, y, x, c] = out[n, ho, wo, k]
+using device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instances =
+    std::tuple<
+        // clang-format off
+        //#############################################################################| InData| WeiData| OutData| AccData|          In|         Wei|         Out|       ConvBackward|    Num| Block|  MPer|  NPer| K0Per| K1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
+        //#############################################################################|   Type|    Type|    Type|    Type| Elementwise| Elementwise| Elementwise|               Data|    Dim|  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|     Specialization|Spatial|      |      |      |      |   |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |                |       PerVector|
+        //#############################################################################|       |        |        |        |            |            |            |                   |       |      |      |      |      |   |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |                |                |
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault,   1,     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<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault,   1,     256,   128,   256,     4,   8,  32,   32,    2,    4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault,   1,     128,   128,   128,     4,   8,  32,   32,    4,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+#if 1
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault,   1,     256,   128,   128,     4,   8,  32,   32,    2,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault,   1,     256,    64,   128,     4,   8,  32,   32,    1,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault,   1,     128,    32,   128,     4,   8,  32,   32,    1,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+#endif
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault,   1,     128,   128,    64,     4,   8,  32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault,   1,     128,    64,   128,     4,   8,  32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault,   1,      64,    64,    64,     4,   8,  32,   32,    2,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault,   1,     256,   128,    64,     4,   8,  32,   32,    2,    1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              1,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault,   1,     128,   128,    32,     4,   8,  32,   32,    2,    1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              1,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault,   1,      64,    64,    32,     4,   8,  32,   32,    2,    1,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault,   1,      64,    32,    64,     4,   8,  32,   32,    1,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>
+        // clang-format on
+        >;
+
+using device_conv1d_bwd_data_xdl_nwc_kxc_nwk_1x1_s1_p0_f16_instances =
+    std::tuple<
+        // clang-format off
+        //#############################################################################| InData| WeiData| OutData| AccData|          In|         Wei|         Out|                     ConvBackward|    Num| Block|  MPer|  NPer| K0Per| K1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
+        //#############################################################################|   Type|    Type|    Type|    Type| Elementwise| Elementwise| Elementwise|                             Data|    Dim|  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|                   Specialization|Spatial|      |      |      |      |   |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |                |       PerVector|
+        //#############################################################################|       |        |        |        |            |            |            |                                 |       |      |      |      |      |   |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |                |                |
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     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<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     256,   128,   256,     4,  8,   32,   32,    2,    4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,   128,   128,     4,  8,   32,   32,    4,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     256,   128,   128,     4,  8,   32,   32,    2,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,   128,    64,     4,  8,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,    64,   128,     4,  8,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,      64,    64,    64,     4,  8,   32,   32,    2,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     256,   128,    64,     4,  8,   32,   32,    2,    1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              1,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     256,    64,   128,     4,  8,   32,   32,    1,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,   128,    32,     4,  8,   32,   32,    2,    1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              1,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,    32,   128,     4,  8,   32,   32,    1,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,      64,    64,    32,     4,  8,   32,   32,    2,    1,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,      64,    32,    64,     4,  8,   32,   32,    1,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              8,      true,               7,               1>
+        // clang-format on
+        >;
+
+void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instances(
+    std::vector<DeviceConvBwdDataPtr<PassThrough, PassThrough, PassThrough>>& instances)
+{
+    add_device_operation_instances(instances,
+                                   device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instances{});
+    add_device_operation_instances(
+        instances, device_conv1d_bwd_data_xdl_nwc_kxc_nwk_1x1_s1_p0_f16_instances{});
+}
+
+} // namespace device_conv2d_bwd_data_instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/convnd_bwd_data/device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instance.cpp

+#include <stdlib.h>
+#include "config.hpp"
+#include "device_convnd_bwd_data_xdl_ndhwc_kzyxc_ndhwk.hpp"
+#include "element_wise_operation.hpp"
+#include "device_operation_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace device_conv2d_bwd_data_instance {
+
+using F32 = float;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+static constexpr auto ConvBwdDataDefault =
+    ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Default;
+
+static constexpr auto ConvBwdDataFilter1x1Stride1Pad0 =
+    ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Filter1x1Stride1Pad0;
+
+// Compilation parameters for in[n, hi, wi, c] * wei[k, y, x, c] = out[n, ho, wo, k]
+using device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances =
+    std::tuple<
+        // clang-format off
+        //#############################################################################| InData| WeiData| OutData| AccData|          In|         Wei|         Out|        ConvBackward|    Num| Block|  MPer|  NPer| K0Per| K1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
+        //#############################################################################|   Type|    Type|    Type|    Type| Elementwise| Elementwise| Elementwise|                Data|    Dim|  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|      Specialization|Spatial|      |      |      |      |   |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |                |       PerVector|
+        //#############################################################################|       |        |        |        |            |            |            |                    |       |      |      |      |      |   |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |                |                |
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     256,   256,   128,     4,  4,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     256,   128,   256,     4,  4,   32,   32,    2,    4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     128,   128,   128,     4,  4,   32,   32,    4,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     256,   128,   128,     4,  4,   32,   32,    2,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     128,   128,    64,     4,  4,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     128,    64,   128,     4,  4,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,      64,    64,    64,     4,  4,   32,   32,    2,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     256,   128,    64,     4,  4,   32,   32,    2,    1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              1,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     256,    64,   128,     4,  4,   32,   32,    1,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     128,   128,    32,     4,  4,   32,   32,    2,    1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              1,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,     128,    32,   128,     4,  4,   32,   32,    1,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,      64,    64,    32,     4,  4,   32,   32,    2,    1,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataDefault,   1,      64,    32,    64,     4,  4,   32,   32,    1,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              4,      true,               7,               1>
+        // clang-format on
+        >;
+
+using device_conv1d_bwd_data_xdl_nwc_kxc_nwk_1x1_s1_p0_f32_instances =
+    std::tuple<
+        // clang-format off
+        //#############################################################################| InData| WeiData| OutData| AccData|          In|         Wei|         Out|                     ConvBackward|    Num| Block|  MPer|  NPer| K0Per| K1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
+        //#############################################################################|   Type|    Type|    Type|    Type| Elementwise| Elementwise| Elementwise|                             Data|    Dim|  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|                   Specialization|Spatial|      |      |      |      |   |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |                |       PerVector|
+        //#############################################################################|       |        |        |        |            |            |            |                                 |       |      |      |      |      |   |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |                |                |
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     256,   256,   128,     4,  4,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     256,   128,   256,     4,  4,   32,   32,    2,    4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,   128,   128,     4,  4,   32,   32,    4,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     256,   128,   128,     4,  4,   32,   32,    2,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,   128,    64,     4,  4,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,    64,   128,     4,  4,   32,   32,    2,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,      64,    64,    64,     4,  4,   32,   32,    2,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     256,   128,    64,     4,  4,   32,   32,    2,    1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              1,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     256,    64,   128,     4,  4,   32,   32,    1,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 64, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,   128,    32,     4,  4,   32,   32,    2,    1,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              1,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,     128,    32,   128,     4,  4,   32,   32,    1,    2,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 32, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,      64,    64,    32,     4,  4,   32,   32,    2,    1,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              2,              4,      true,               7,               1>,
+        DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,  ConvBwdDataFilter1x1Stride1Pad0,   1,      64,    32,    64,     4,  4,   32,   32,    1,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,     S<4, 16, 1>,     S<2, 0, 1>,     S<0, 2, 1>,             1,              4,              4,      true,               7,               1>
+        // clang-format on
+        >;
+
+void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances(
+    std::vector<DeviceConvBwdDataPtr<PassThrough, PassThrough, PassThrough>>& instances)
+{
+    add_device_operation_instances(instances,
+                                   device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances{});
+    add_device_operation_instances(
+        instances, device_conv1d_bwd_data_xdl_nwc_kxc_nwk_1x1_s1_p0_f32_instances{});
+}
+
+} // namespace device_conv2d_bwd_data_instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck